Shilong Liu
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63
README.md
63
README.md
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@ -22,6 +22,65 @@ Description
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<img src=".asset/hero_figure.png" alt="ODinW" width="100%">
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</details>
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## TODO List
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<div>
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<input type="checkbox" name="uchk" checked>
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<label for="uchk">Release inference code and demo.</label>
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</div>
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<div>
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<input type="checkbox" name="uchk" checked>
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<label for="uchk">Release checkpoints.</label>
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</div>
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<div>
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<input type="checkbox" name="uchk">
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<label for="uchk">Grounding DINO with Stable Diffusion and GLIGEN demos.</label>
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</div>
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## Usage
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### 1. Install
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If you have a CUDA environment, please make sure the environment variable `CUDA_HOME` is set.
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```bash
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pip install -e .
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```
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### 2. Run an inference demo
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See the `demo/inference_on_a_image.py` for more details.
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```bash
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CUDA_VISIBLE_DEVICES=6 python demo/inference_on_a_image.py \
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-c /path/to/config \
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-p /path/to/checkpoint \
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-i .asset/cats.png \
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-o "outputs/0" \
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-t "cat ear."
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```
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### Checkpoints
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<!-- insert a table -->
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<table>
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<thead>
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<tr style="text-align: right;">
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<th></th>
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<th>name</th>
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<th>backbone</th>
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<th>Data</th>
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<th>box AP on COCO</th>
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<th>Checkpoint</th>
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</tr>
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</thead>
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<tbody>
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<tr>
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<th>1</th>
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<td>GroundingDINO-T</td>
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<td>Swin-T</td>
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<td>O365,GoldG,Cap4M</td>
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<td>48.4 (zero-shot) / 57.2 (fine-tune)</td>
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<td><a href="https://github.com/IDEA-Research/GroundingDINO/releases/download/v0.1.0-alpha/groundingdino_swint_ogc.pth">link</a></td>
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</tr>
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</tbody>
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</table>
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## Results
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<details open>
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<summary><font size="4">
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@ -52,6 +111,10 @@ Marrying Grounding DINO with <a href="https://github.com/gligen/GLIGEN">GLIGEN</
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</details>
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## Model
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Includes: a text backbone, an image backbone, a feature enhancer, a language-guided query selection, and a cross-modality decoder.
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@ -0,0 +1,163 @@
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import argparse
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import os
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import sys
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import numpy as np
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import torch
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from PIL import Image, ImageDraw, ImageFont
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import groundingdino.datasets.transforms as T
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from groundingdino.models import build_model
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from groundingdino.util import box_ops
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from groundingdino.util.slconfig import SLConfig
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from groundingdino.util.utils import clean_state_dict, get_phrases_from_posmap
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def plot_boxes_to_image(image_pil, tgt):
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H, W = tgt["size"]
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boxes = tgt["boxes"]
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labels = tgt["labels"]
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assert len(boxes) == len(labels), "boxes and labels must have same length"
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draw = ImageDraw.Draw(image_pil)
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mask = Image.new("L", image_pil.size, 0)
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mask_draw = ImageDraw.Draw(mask)
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# draw boxes and masks
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for box, label in zip(boxes, labels):
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# from 0..1 to 0..W, 0..H
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box = box * torch.Tensor([W, H, W, H])
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# from xywh to xyxy
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box[:2] -= box[2:] / 2
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box[2:] += box[:2]
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# random color
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color = tuple(np.random.randint(0, 255, size=3).tolist())
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# draw
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x0, y0, x1, y1 = box
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x0, y0, x1, y1 = int(x0), int(y0), int(x1), int(y1)
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draw.rectangle([x0, y0, x1, y1], outline=color, width=6)
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# draw.text((x0, y0), str(label), fill=color)
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bbox = draw.textbbox((x0, y0), str(label))
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draw.rectangle(bbox, fill=color)
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draw.text((x0, y0), str(label), fill="white")
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mask_draw.rectangle([x0, y0, x1, y1], fill=255, width=6)
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return image_pil, mask
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def load_image(image_path):
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# load image
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image_pil = Image.open(image_path).convert("RGB") # load image
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transform = T.Compose(
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[
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T.RandomResize([800], max_size=1333),
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T.ToTensor(),
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T.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225]),
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]
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)
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image, _ = transform(image_pil, None) # 3, h, w
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return image_pil, image
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def load_model(model_config_path, model_checkpoint_path):
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args = SLConfig.fromfile(model_config_path)
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args.device = "cuda"
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model = build_model(args)
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checkpoint = torch.load(model_checkpoint_path, map_location="cpu")
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load_res = model.load_state_dict(clean_state_dict(checkpoint["model"]), strict=False)
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print(load_res)
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_ = model.eval()
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return model
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def get_grounding_output(model, image, caption, box_threshold, text_threshold, with_logits=True):
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caption = caption.lower()
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caption = caption.strip()
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if not caption.endswith("."):
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caption = caption + "."
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model = model.cuda()
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image = image.cuda()
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with torch.no_grad():
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outputs = model(image[None], captions=[caption])
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logits = outputs["pred_logits"].cpu().sigmoid()[0] # (nq, 256)
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boxes = outputs["pred_boxes"].cpu()[0] # (nq, 4)
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logits.shape[0]
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# filter output
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logits_filt = logits.clone()
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boxes_filt = boxes.clone()
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filt_mask = logits_filt.max(dim=1)[0] > box_threshold
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logits_filt = logits_filt[filt_mask] # num_filt, 256
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boxes_filt = boxes_filt[filt_mask] # num_filt, 4
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logits_filt.shape[0]
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# get phrase
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tokenlizer = model.tokenizer
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tokenized = tokenlizer(caption)
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# build pred
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pred_phrases = []
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for logit, box in zip(logits_filt, boxes_filt):
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pred_phrase = get_phrases_from_posmap(logit > text_threshold, tokenized, caption)
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if with_logits:
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pred_phrases.append(pred_phrase + f"({str(logit.max().item())[:4]})")
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else:
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pred_phrases.append(pred_phrase)
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return boxes_filt, pred_phrases
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if __name__ == "__main__":
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parser = argparse.ArgumentParser("Grounding DINO example", add_help=True)
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parser.add_argument("--config_file", "-c", type=str, required=True, help="path to config file")
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parser.add_argument(
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"--checkpoint_path", "-p", type=str, required=True, help="path to checkpoint file"
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)
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parser.add_argument("--image_path", "-i", type=str, required=True, help="path to image file")
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parser.add_argument("--text_prompt", "-t", type=str, required=True, help="text prompt")
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parser.add_argument(
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"--output_dir", "-o", type=str, default="outputs", required=True, help="output directory"
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)
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parser.add_argument("--box_threshold", type=float, default=0.3, help="box threshold")
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parser.add_argument("--text_threshold", type=float, default=0.25, help="text threshold")
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args = parser.parse_args()
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# cfg
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config_file = args.config_file # change the path of the model config file
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checkpoint_path = args.checkpoint_path # change the path of the model
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image_path = args.image_path
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text_prompt = args.text_prompt
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output_dir = args.output_dir
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box_threshold = args.box_threshold
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text_threshold = args.box_threshold
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# make dir
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os.makedirs(output_dir, exist_ok=True)
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# load image
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image_pil, image = load_image(image_path)
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# load model
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model = load_model(config_file, checkpoint_path)
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# visualize raw image
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image_pil.save(os.path.join(output_dir, "raw_image.jpg"))
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# run model
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boxes_filt, pred_phrases = get_grounding_output(
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model, image, text_prompt, box_threshold, text_threshold
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)
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# visualize pred
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size = image_pil.size
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pred_dict = {
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"boxes": boxes_filt,
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"size": [size[1], size[0]], # H,W
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"labels": pred_phrases,
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}
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# import ipdb; ipdb.set_trace()
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image_with_box = plot_boxes_to_image(image_pil, pred_dict)[0]
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image_with_box.save(os.path.join(output_dir, "pred.jpg"))
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@ -0,0 +1,43 @@
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batch_size = 1
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modelname = "groundingdino"
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backbone = "swin_T_224_1k"
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position_embedding = "sine"
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pe_temperatureH = 20
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pe_temperatureW = 20
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return_interm_indices = [1, 2, 3]
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backbone_freeze_keywords = None
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enc_layers = 6
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dec_layers = 6
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pre_norm = False
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dim_feedforward = 2048
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hidden_dim = 256
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dropout = 0.0
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nheads = 8
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num_queries = 900
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query_dim = 4
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num_patterns = 0
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num_feature_levels = 4
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enc_n_points = 4
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dec_n_points = 4
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two_stage_type = "standard"
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two_stage_bbox_embed_share = False
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two_stage_class_embed_share = False
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transformer_activation = "relu"
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dec_pred_bbox_embed_share = True
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dn_box_noise_scale = 1.0
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dn_label_noise_ratio = 0.5
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dn_label_coef = 1.0
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dn_bbox_coef = 1.0
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embed_init_tgt = True
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dn_labelbook_size = 2000
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max_text_len = 256
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text_encoder_type = "bert-base-uncased"
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use_text_enhancer = True
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use_fusion_layer = True
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use_checkpoint = True
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use_transformer_ckpt = True
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use_text_cross_attention = True
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text_dropout = 0.0
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fusion_dropout = 0.0
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fusion_droppath = 0.1
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sub_sentence_present = True
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@ -0,0 +1,311 @@
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# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved
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"""
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Transforms and data augmentation for both image + bbox.
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"""
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import os
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import random
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import PIL
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import torch
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import torchvision.transforms as T
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import torchvision.transforms.functional as F
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from groundingdino.util.box_ops import box_xyxy_to_cxcywh
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from groundingdino.util.misc import interpolate
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def crop(image, target, region):
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cropped_image = F.crop(image, *region)
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target = target.copy()
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i, j, h, w = region
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# should we do something wrt the original size?
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target["size"] = torch.tensor([h, w])
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fields = ["labels", "area", "iscrowd", "positive_map"]
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if "boxes" in target:
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boxes = target["boxes"]
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max_size = torch.as_tensor([w, h], dtype=torch.float32)
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cropped_boxes = boxes - torch.as_tensor([j, i, j, i])
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cropped_boxes = torch.min(cropped_boxes.reshape(-1, 2, 2), max_size)
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cropped_boxes = cropped_boxes.clamp(min=0)
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area = (cropped_boxes[:, 1, :] - cropped_boxes[:, 0, :]).prod(dim=1)
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target["boxes"] = cropped_boxes.reshape(-1, 4)
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target["area"] = area
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fields.append("boxes")
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if "masks" in target:
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# FIXME should we update the area here if there are no boxes?
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target["masks"] = target["masks"][:, i : i + h, j : j + w]
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fields.append("masks")
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# remove elements for which the boxes or masks that have zero area
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if "boxes" in target or "masks" in target:
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# favor boxes selection when defining which elements to keep
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# this is compatible with previous implementation
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if "boxes" in target:
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cropped_boxes = target["boxes"].reshape(-1, 2, 2)
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keep = torch.all(cropped_boxes[:, 1, :] > cropped_boxes[:, 0, :], dim=1)
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else:
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keep = target["masks"].flatten(1).any(1)
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for field in fields:
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if field in target:
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target[field] = target[field][keep]
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if os.environ.get("IPDB_SHILONG_DEBUG", None) == "INFO":
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# for debug and visualization only.
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if "strings_positive" in target:
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target["strings_positive"] = [
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_i for _i, _j in zip(target["strings_positive"], keep) if _j
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]
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return cropped_image, target
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||||
def hflip(image, target):
|
||||
flipped_image = F.hflip(image)
|
||||
|
||||
w, h = image.size
|
||||
|
||||
target = target.copy()
|
||||
if "boxes" in target:
|
||||
boxes = target["boxes"]
|
||||
boxes = boxes[:, [2, 1, 0, 3]] * torch.as_tensor([-1, 1, -1, 1]) + torch.as_tensor(
|
||||
[w, 0, w, 0]
|
||||
)
|
||||
target["boxes"] = boxes
|
||||
|
||||
if "masks" in target:
|
||||
target["masks"] = target["masks"].flip(-1)
|
||||
|
||||
return flipped_image, target
|
||||
|
||||
|
||||
def resize(image, target, size, max_size=None):
|
||||
# size can be min_size (scalar) or (w, h) tuple
|
||||
|
||||
def get_size_with_aspect_ratio(image_size, size, max_size=None):
|
||||
w, h = image_size
|
||||
if max_size is not None:
|
||||
min_original_size = float(min((w, h)))
|
||||
max_original_size = float(max((w, h)))
|
||||
if max_original_size / min_original_size * size > max_size:
|
||||
size = int(round(max_size * min_original_size / max_original_size))
|
||||
|
||||
if (w <= h and w == size) or (h <= w and h == size):
|
||||
return (h, w)
|
||||
|
||||
if w < h:
|
||||
ow = size
|
||||
oh = int(size * h / w)
|
||||
else:
|
||||
oh = size
|
||||
ow = int(size * w / h)
|
||||
|
||||
return (oh, ow)
|
||||
|
||||
def get_size(image_size, size, max_size=None):
|
||||
if isinstance(size, (list, tuple)):
|
||||
return size[::-1]
|
||||
else:
|
||||
return get_size_with_aspect_ratio(image_size, size, max_size)
|
||||
|
||||
size = get_size(image.size, size, max_size)
|
||||
rescaled_image = F.resize(image, size)
|
||||
|
||||
if target is None:
|
||||
return rescaled_image, None
|
||||
|
||||
ratios = tuple(float(s) / float(s_orig) for s, s_orig in zip(rescaled_image.size, image.size))
|
||||
ratio_width, ratio_height = ratios
|
||||
|
||||
target = target.copy()
|
||||
if "boxes" in target:
|
||||
boxes = target["boxes"]
|
||||
scaled_boxes = boxes * torch.as_tensor(
|
||||
[ratio_width, ratio_height, ratio_width, ratio_height]
|
||||
)
|
||||
target["boxes"] = scaled_boxes
|
||||
|
||||
if "area" in target:
|
||||
area = target["area"]
|
||||
scaled_area = area * (ratio_width * ratio_height)
|
||||
target["area"] = scaled_area
|
||||
|
||||
h, w = size
|
||||
target["size"] = torch.tensor([h, w])
|
||||
|
||||
if "masks" in target:
|
||||
target["masks"] = (
|
||||
interpolate(target["masks"][:, None].float(), size, mode="nearest")[:, 0] > 0.5
|
||||
)
|
||||
|
||||
return rescaled_image, target
|
||||
|
||||
|
||||
def pad(image, target, padding):
|
||||
# assumes that we only pad on the bottom right corners
|
||||
padded_image = F.pad(image, (0, 0, padding[0], padding[1]))
|
||||
if target is None:
|
||||
return padded_image, None
|
||||
target = target.copy()
|
||||
# should we do something wrt the original size?
|
||||
target["size"] = torch.tensor(padded_image.size[::-1])
|
||||
if "masks" in target:
|
||||
target["masks"] = torch.nn.functional.pad(target["masks"], (0, padding[0], 0, padding[1]))
|
||||
return padded_image, target
|
||||
|
||||
|
||||
class ResizeDebug(object):
|
||||
def __init__(self, size):
|
||||
self.size = size
|
||||
|
||||
def __call__(self, img, target):
|
||||
return resize(img, target, self.size)
|
||||
|
||||
|
||||
class RandomCrop(object):
|
||||
def __init__(self, size):
|
||||
self.size = size
|
||||
|
||||
def __call__(self, img, target):
|
||||
region = T.RandomCrop.get_params(img, self.size)
|
||||
return crop(img, target, region)
|
||||
|
||||
|
||||
class RandomSizeCrop(object):
|
||||
def __init__(self, min_size: int, max_size: int, respect_boxes: bool = False):
|
||||
# respect_boxes: True to keep all boxes
|
||||
# False to tolerence box filter
|
||||
self.min_size = min_size
|
||||
self.max_size = max_size
|
||||
self.respect_boxes = respect_boxes
|
||||
|
||||
def __call__(self, img: PIL.Image.Image, target: dict):
|
||||
init_boxes = len(target["boxes"])
|
||||
max_patience = 10
|
||||
for i in range(max_patience):
|
||||
w = random.randint(self.min_size, min(img.width, self.max_size))
|
||||
h = random.randint(self.min_size, min(img.height, self.max_size))
|
||||
region = T.RandomCrop.get_params(img, [h, w])
|
||||
result_img, result_target = crop(img, target, region)
|
||||
if (
|
||||
not self.respect_boxes
|
||||
or len(result_target["boxes"]) == init_boxes
|
||||
or i == max_patience - 1
|
||||
):
|
||||
return result_img, result_target
|
||||
return result_img, result_target
|
||||
|
||||
|
||||
class CenterCrop(object):
|
||||
def __init__(self, size):
|
||||
self.size = size
|
||||
|
||||
def __call__(self, img, target):
|
||||
image_width, image_height = img.size
|
||||
crop_height, crop_width = self.size
|
||||
crop_top = int(round((image_height - crop_height) / 2.0))
|
||||
crop_left = int(round((image_width - crop_width) / 2.0))
|
||||
return crop(img, target, (crop_top, crop_left, crop_height, crop_width))
|
||||
|
||||
|
||||
class RandomHorizontalFlip(object):
|
||||
def __init__(self, p=0.5):
|
||||
self.p = p
|
||||
|
||||
def __call__(self, img, target):
|
||||
if random.random() < self.p:
|
||||
return hflip(img, target)
|
||||
return img, target
|
||||
|
||||
|
||||
class RandomResize(object):
|
||||
def __init__(self, sizes, max_size=None):
|
||||
assert isinstance(sizes, (list, tuple))
|
||||
self.sizes = sizes
|
||||
self.max_size = max_size
|
||||
|
||||
def __call__(self, img, target=None):
|
||||
size = random.choice(self.sizes)
|
||||
return resize(img, target, size, self.max_size)
|
||||
|
||||
|
||||
class RandomPad(object):
|
||||
def __init__(self, max_pad):
|
||||
self.max_pad = max_pad
|
||||
|
||||
def __call__(self, img, target):
|
||||
pad_x = random.randint(0, self.max_pad)
|
||||
pad_y = random.randint(0, self.max_pad)
|
||||
return pad(img, target, (pad_x, pad_y))
|
||||
|
||||
|
||||
class RandomSelect(object):
|
||||
"""
|
||||
Randomly selects between transforms1 and transforms2,
|
||||
with probability p for transforms1 and (1 - p) for transforms2
|
||||
"""
|
||||
|
||||
def __init__(self, transforms1, transforms2, p=0.5):
|
||||
self.transforms1 = transforms1
|
||||
self.transforms2 = transforms2
|
||||
self.p = p
|
||||
|
||||
def __call__(self, img, target):
|
||||
if random.random() < self.p:
|
||||
return self.transforms1(img, target)
|
||||
return self.transforms2(img, target)
|
||||
|
||||
|
||||
class ToTensor(object):
|
||||
def __call__(self, img, target):
|
||||
return F.to_tensor(img), target
|
||||
|
||||
|
||||
class RandomErasing(object):
|
||||
def __init__(self, *args, **kwargs):
|
||||
self.eraser = T.RandomErasing(*args, **kwargs)
|
||||
|
||||
def __call__(self, img, target):
|
||||
return self.eraser(img), target
|
||||
|
||||
|
||||
class Normalize(object):
|
||||
def __init__(self, mean, std):
|
||||
self.mean = mean
|
||||
self.std = std
|
||||
|
||||
def __call__(self, image, target=None):
|
||||
image = F.normalize(image, mean=self.mean, std=self.std)
|
||||
if target is None:
|
||||
return image, None
|
||||
target = target.copy()
|
||||
h, w = image.shape[-2:]
|
||||
if "boxes" in target:
|
||||
boxes = target["boxes"]
|
||||
boxes = box_xyxy_to_cxcywh(boxes)
|
||||
boxes = boxes / torch.tensor([w, h, w, h], dtype=torch.float32)
|
||||
target["boxes"] = boxes
|
||||
return image, target
|
||||
|
||||
|
||||
class Compose(object):
|
||||
def __init__(self, transforms):
|
||||
self.transforms = transforms
|
||||
|
||||
def __call__(self, image, target):
|
||||
for t in self.transforms:
|
||||
image, target = t(image, target)
|
||||
return image, target
|
||||
|
||||
def __repr__(self):
|
||||
format_string = self.__class__.__name__ + "("
|
||||
for t in self.transforms:
|
||||
format_string += "\n"
|
||||
format_string += " {0}".format(t)
|
||||
format_string += "\n)"
|
||||
return format_string
|
||||
|
|
@ -0,0 +1,15 @@
|
|||
# ------------------------------------------------------------------------
|
||||
# Grounding DINO
|
||||
# url: https://github.com/IDEA-Research/GroundingDINO
|
||||
# Copyright (c) 2023 IDEA. All Rights Reserved.
|
||||
# Licensed under the Apache License, Version 2.0 [see LICENSE for details]
|
||||
# ------------------------------------------------------------------------
|
||||
# Conditional DETR
|
||||
# Copyright (c) 2021 Microsoft. All Rights Reserved.
|
||||
# Licensed under the Apache License, Version 2.0 [see LICENSE for details]
|
||||
# ------------------------------------------------------------------------
|
||||
# Copied from DETR (https://github.com/facebookresearch/detr)
|
||||
# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
|
||||
# ------------------------------------------------------------------------
|
||||
|
||||
from .groundingdino import build_groundingdino
|
||||
|
|
@ -0,0 +1 @@
|
|||
from .backbone import build_backbone
|
||||
|
|
@ -0,0 +1,221 @@
|
|||
# ------------------------------------------------------------------------
|
||||
# Grounding DINO
|
||||
# url: https://github.com/IDEA-Research/GroundingDINO
|
||||
# Copyright (c) 2023 IDEA. All Rights Reserved.
|
||||
# Licensed under the Apache License, Version 2.0 [see LICENSE for details]
|
||||
# ------------------------------------------------------------------------
|
||||
# Conditional DETR
|
||||
# Copyright (c) 2021 Microsoft. All Rights Reserved.
|
||||
# Licensed under the Apache License, Version 2.0 [see LICENSE for details]
|
||||
# ------------------------------------------------------------------------
|
||||
# Copied from DETR (https://github.com/facebookresearch/detr)
|
||||
# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
|
||||
# ------------------------------------------------------------------------
|
||||
|
||||
"""
|
||||
Backbone modules.
|
||||
"""
|
||||
|
||||
from typing import Dict, List
|
||||
|
||||
import torch
|
||||
import torch.nn.functional as F
|
||||
import torchvision
|
||||
from torch import nn
|
||||
from torchvision.models._utils import IntermediateLayerGetter
|
||||
|
||||
from groundingdino.util.misc import NestedTensor, clean_state_dict, is_main_process
|
||||
|
||||
from .position_encoding import build_position_encoding
|
||||
from .swin_transformer import build_swin_transformer
|
||||
|
||||
|
||||
class FrozenBatchNorm2d(torch.nn.Module):
|
||||
"""
|
||||
BatchNorm2d where the batch statistics and the affine parameters are fixed.
|
||||
|
||||
Copy-paste from torchvision.misc.ops with added eps before rqsrt,
|
||||
without which any other models than torchvision.models.resnet[18,34,50,101]
|
||||
produce nans.
|
||||
"""
|
||||
|
||||
def __init__(self, n):
|
||||
super(FrozenBatchNorm2d, self).__init__()
|
||||
self.register_buffer("weight", torch.ones(n))
|
||||
self.register_buffer("bias", torch.zeros(n))
|
||||
self.register_buffer("running_mean", torch.zeros(n))
|
||||
self.register_buffer("running_var", torch.ones(n))
|
||||
|
||||
def _load_from_state_dict(
|
||||
self, state_dict, prefix, local_metadata, strict, missing_keys, unexpected_keys, error_msgs
|
||||
):
|
||||
num_batches_tracked_key = prefix + "num_batches_tracked"
|
||||
if num_batches_tracked_key in state_dict:
|
||||
del state_dict[num_batches_tracked_key]
|
||||
|
||||
super(FrozenBatchNorm2d, self)._load_from_state_dict(
|
||||
state_dict, prefix, local_metadata, strict, missing_keys, unexpected_keys, error_msgs
|
||||
)
|
||||
|
||||
def forward(self, x):
|
||||
# move reshapes to the beginning
|
||||
# to make it fuser-friendly
|
||||
w = self.weight.reshape(1, -1, 1, 1)
|
||||
b = self.bias.reshape(1, -1, 1, 1)
|
||||
rv = self.running_var.reshape(1, -1, 1, 1)
|
||||
rm = self.running_mean.reshape(1, -1, 1, 1)
|
||||
eps = 1e-5
|
||||
scale = w * (rv + eps).rsqrt()
|
||||
bias = b - rm * scale
|
||||
return x * scale + bias
|
||||
|
||||
|
||||
class BackboneBase(nn.Module):
|
||||
def __init__(
|
||||
self,
|
||||
backbone: nn.Module,
|
||||
train_backbone: bool,
|
||||
num_channels: int,
|
||||
return_interm_indices: list,
|
||||
):
|
||||
super().__init__()
|
||||
for name, parameter in backbone.named_parameters():
|
||||
if (
|
||||
not train_backbone
|
||||
or "layer2" not in name
|
||||
and "layer3" not in name
|
||||
and "layer4" not in name
|
||||
):
|
||||
parameter.requires_grad_(False)
|
||||
|
||||
return_layers = {}
|
||||
for idx, layer_index in enumerate(return_interm_indices):
|
||||
return_layers.update(
|
||||
{"layer{}".format(5 - len(return_interm_indices) + idx): "{}".format(layer_index)}
|
||||
)
|
||||
|
||||
# if len:
|
||||
# if use_stage1_feature:
|
||||
# return_layers = {"layer1": "0", "layer2": "1", "layer3": "2", "layer4": "3"}
|
||||
# else:
|
||||
# return_layers = {"layer2": "0", "layer3": "1", "layer4": "2"}
|
||||
# else:
|
||||
# return_layers = {'layer4': "0"}
|
||||
self.body = IntermediateLayerGetter(backbone, return_layers=return_layers)
|
||||
self.num_channels = num_channels
|
||||
|
||||
def forward(self, tensor_list: NestedTensor):
|
||||
xs = self.body(tensor_list.tensors)
|
||||
out: Dict[str, NestedTensor] = {}
|
||||
for name, x in xs.items():
|
||||
m = tensor_list.mask
|
||||
assert m is not None
|
||||
mask = F.interpolate(m[None].float(), size=x.shape[-2:]).to(torch.bool)[0]
|
||||
out[name] = NestedTensor(x, mask)
|
||||
# import ipdb; ipdb.set_trace()
|
||||
return out
|
||||
|
||||
|
||||
class Backbone(BackboneBase):
|
||||
"""ResNet backbone with frozen BatchNorm."""
|
||||
|
||||
def __init__(
|
||||
self,
|
||||
name: str,
|
||||
train_backbone: bool,
|
||||
dilation: bool,
|
||||
return_interm_indices: list,
|
||||
batch_norm=FrozenBatchNorm2d,
|
||||
):
|
||||
if name in ["resnet18", "resnet34", "resnet50", "resnet101"]:
|
||||
backbone = getattr(torchvision.models, name)(
|
||||
replace_stride_with_dilation=[False, False, dilation],
|
||||
pretrained=is_main_process(),
|
||||
norm_layer=batch_norm,
|
||||
)
|
||||
else:
|
||||
raise NotImplementedError("Why you can get here with name {}".format(name))
|
||||
# num_channels = 512 if name in ('resnet18', 'resnet34') else 2048
|
||||
assert name not in ("resnet18", "resnet34"), "Only resnet50 and resnet101 are available."
|
||||
assert return_interm_indices in [[0, 1, 2, 3], [1, 2, 3], [3]]
|
||||
num_channels_all = [256, 512, 1024, 2048]
|
||||
num_channels = num_channels_all[4 - len(return_interm_indices) :]
|
||||
super().__init__(backbone, train_backbone, num_channels, return_interm_indices)
|
||||
|
||||
|
||||
class Joiner(nn.Sequential):
|
||||
def __init__(self, backbone, position_embedding):
|
||||
super().__init__(backbone, position_embedding)
|
||||
|
||||
def forward(self, tensor_list: NestedTensor):
|
||||
xs = self[0](tensor_list)
|
||||
out: List[NestedTensor] = []
|
||||
pos = []
|
||||
for name, x in xs.items():
|
||||
out.append(x)
|
||||
# position encoding
|
||||
pos.append(self[1](x).to(x.tensors.dtype))
|
||||
|
||||
return out, pos
|
||||
|
||||
|
||||
def build_backbone(args):
|
||||
"""
|
||||
Useful args:
|
||||
- backbone: backbone name
|
||||
- lr_backbone:
|
||||
- dilation
|
||||
- return_interm_indices: available: [0,1,2,3], [1,2,3], [3]
|
||||
- backbone_freeze_keywords:
|
||||
- use_checkpoint: for swin only for now
|
||||
|
||||
"""
|
||||
position_embedding = build_position_encoding(args)
|
||||
train_backbone = True
|
||||
if not train_backbone:
|
||||
raise ValueError("Please set lr_backbone > 0")
|
||||
return_interm_indices = args.return_interm_indices
|
||||
assert return_interm_indices in [[0, 1, 2, 3], [1, 2, 3], [3]]
|
||||
args.backbone_freeze_keywords
|
||||
use_checkpoint = getattr(args, "use_checkpoint", False)
|
||||
|
||||
if args.backbone in ["resnet50", "resnet101"]:
|
||||
backbone = Backbone(
|
||||
args.backbone,
|
||||
train_backbone,
|
||||
args.dilation,
|
||||
return_interm_indices,
|
||||
batch_norm=FrozenBatchNorm2d,
|
||||
)
|
||||
bb_num_channels = backbone.num_channels
|
||||
elif args.backbone in [
|
||||
"swin_T_224_1k",
|
||||
"swin_B_224_22k",
|
||||
"swin_B_384_22k",
|
||||
"swin_L_224_22k",
|
||||
"swin_L_384_22k",
|
||||
]:
|
||||
pretrain_img_size = int(args.backbone.split("_")[-2])
|
||||
backbone = build_swin_transformer(
|
||||
args.backbone,
|
||||
pretrain_img_size=pretrain_img_size,
|
||||
out_indices=tuple(return_interm_indices),
|
||||
dilation=False,
|
||||
use_checkpoint=use_checkpoint,
|
||||
)
|
||||
|
||||
bb_num_channels = backbone.num_features[4 - len(return_interm_indices) :]
|
||||
else:
|
||||
raise NotImplementedError("Unknown backbone {}".format(args.backbone))
|
||||
|
||||
assert len(bb_num_channels) == len(
|
||||
return_interm_indices
|
||||
), f"len(bb_num_channels) {len(bb_num_channels)} != len(return_interm_indices) {len(return_interm_indices)}"
|
||||
|
||||
model = Joiner(backbone, position_embedding)
|
||||
model.num_channels = bb_num_channels
|
||||
assert isinstance(
|
||||
bb_num_channels, List
|
||||
), "bb_num_channels is expected to be a List but {}".format(type(bb_num_channels))
|
||||
# import ipdb; ipdb.set_trace()
|
||||
return model
|
||||
|
|
@ -0,0 +1,186 @@
|
|||
# ------------------------------------------------------------------------
|
||||
# Grounding DINO
|
||||
# url: https://github.com/IDEA-Research/GroundingDINO
|
||||
# Copyright (c) 2023 IDEA. All Rights Reserved.
|
||||
# Licensed under the Apache License, Version 2.0 [see LICENSE for details]
|
||||
# ------------------------------------------------------------------------
|
||||
# DINO
|
||||
# Copyright (c) 2022 IDEA. All Rights Reserved.
|
||||
# Licensed under the Apache License, Version 2.0 [see LICENSE for details]
|
||||
# ------------------------------------------------------------------------
|
||||
# Conditional DETR
|
||||
# Copyright (c) 2021 Microsoft. All Rights Reserved.
|
||||
# Licensed under the Apache License, Version 2.0 [see LICENSE for details]
|
||||
# ------------------------------------------------------------------------
|
||||
# Copied from DETR (https://github.com/facebookresearch/detr)
|
||||
# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
|
||||
# ------------------------------------------------------------------------
|
||||
|
||||
"""
|
||||
Various positional encodings for the transformer.
|
||||
"""
|
||||
import math
|
||||
|
||||
import torch
|
||||
from torch import nn
|
||||
|
||||
from groundingdino.util.misc import NestedTensor
|
||||
|
||||
|
||||
class PositionEmbeddingSine(nn.Module):
|
||||
"""
|
||||
This is a more standard version of the position embedding, very similar to the one
|
||||
used by the Attention is all you need paper, generalized to work on images.
|
||||
"""
|
||||
|
||||
def __init__(self, num_pos_feats=64, temperature=10000, normalize=False, scale=None):
|
||||
super().__init__()
|
||||
self.num_pos_feats = num_pos_feats
|
||||
self.temperature = temperature
|
||||
self.normalize = normalize
|
||||
if scale is not None and normalize is False:
|
||||
raise ValueError("normalize should be True if scale is passed")
|
||||
if scale is None:
|
||||
scale = 2 * math.pi
|
||||
self.scale = scale
|
||||
|
||||
def forward(self, tensor_list: NestedTensor):
|
||||
x = tensor_list.tensors
|
||||
mask = tensor_list.mask
|
||||
assert mask is not None
|
||||
not_mask = ~mask
|
||||
y_embed = not_mask.cumsum(1, dtype=torch.float32)
|
||||
x_embed = not_mask.cumsum(2, dtype=torch.float32)
|
||||
if self.normalize:
|
||||
eps = 1e-6
|
||||
# if os.environ.get("SHILONG_AMP", None) == '1':
|
||||
# eps = 1e-4
|
||||
# else:
|
||||
# eps = 1e-6
|
||||
y_embed = y_embed / (y_embed[:, -1:, :] + eps) * self.scale
|
||||
x_embed = x_embed / (x_embed[:, :, -1:] + eps) * self.scale
|
||||
|
||||
dim_t = torch.arange(self.num_pos_feats, dtype=torch.float32, device=x.device)
|
||||
dim_t = self.temperature ** (2 * (dim_t // 2) / self.num_pos_feats)
|
||||
|
||||
pos_x = x_embed[:, :, :, None] / dim_t
|
||||
pos_y = y_embed[:, :, :, None] / dim_t
|
||||
pos_x = torch.stack(
|
||||
(pos_x[:, :, :, 0::2].sin(), pos_x[:, :, :, 1::2].cos()), dim=4
|
||||
).flatten(3)
|
||||
pos_y = torch.stack(
|
||||
(pos_y[:, :, :, 0::2].sin(), pos_y[:, :, :, 1::2].cos()), dim=4
|
||||
).flatten(3)
|
||||
pos = torch.cat((pos_y, pos_x), dim=3).permute(0, 3, 1, 2)
|
||||
return pos
|
||||
|
||||
|
||||
class PositionEmbeddingSineHW(nn.Module):
|
||||
"""
|
||||
This is a more standard version of the position embedding, very similar to the one
|
||||
used by the Attention is all you need paper, generalized to work on images.
|
||||
"""
|
||||
|
||||
def __init__(
|
||||
self, num_pos_feats=64, temperatureH=10000, temperatureW=10000, normalize=False, scale=None
|
||||
):
|
||||
super().__init__()
|
||||
self.num_pos_feats = num_pos_feats
|
||||
self.temperatureH = temperatureH
|
||||
self.temperatureW = temperatureW
|
||||
self.normalize = normalize
|
||||
if scale is not None and normalize is False:
|
||||
raise ValueError("normalize should be True if scale is passed")
|
||||
if scale is None:
|
||||
scale = 2 * math.pi
|
||||
self.scale = scale
|
||||
|
||||
def forward(self, tensor_list: NestedTensor):
|
||||
x = tensor_list.tensors
|
||||
mask = tensor_list.mask
|
||||
assert mask is not None
|
||||
not_mask = ~mask
|
||||
y_embed = not_mask.cumsum(1, dtype=torch.float32)
|
||||
x_embed = not_mask.cumsum(2, dtype=torch.float32)
|
||||
|
||||
# import ipdb; ipdb.set_trace()
|
||||
|
||||
if self.normalize:
|
||||
eps = 1e-6
|
||||
y_embed = y_embed / (y_embed[:, -1:, :] + eps) * self.scale
|
||||
x_embed = x_embed / (x_embed[:, :, -1:] + eps) * self.scale
|
||||
|
||||
dim_tx = torch.arange(self.num_pos_feats, dtype=torch.float32, device=x.device)
|
||||
dim_tx = self.temperatureW ** (2 * (dim_tx // 2) / self.num_pos_feats)
|
||||
pos_x = x_embed[:, :, :, None] / dim_tx
|
||||
|
||||
dim_ty = torch.arange(self.num_pos_feats, dtype=torch.float32, device=x.device)
|
||||
dim_ty = self.temperatureH ** (2 * (dim_ty // 2) / self.num_pos_feats)
|
||||
pos_y = y_embed[:, :, :, None] / dim_ty
|
||||
|
||||
pos_x = torch.stack(
|
||||
(pos_x[:, :, :, 0::2].sin(), pos_x[:, :, :, 1::2].cos()), dim=4
|
||||
).flatten(3)
|
||||
pos_y = torch.stack(
|
||||
(pos_y[:, :, :, 0::2].sin(), pos_y[:, :, :, 1::2].cos()), dim=4
|
||||
).flatten(3)
|
||||
pos = torch.cat((pos_y, pos_x), dim=3).permute(0, 3, 1, 2)
|
||||
|
||||
# import ipdb; ipdb.set_trace()
|
||||
|
||||
return pos
|
||||
|
||||
|
||||
class PositionEmbeddingLearned(nn.Module):
|
||||
"""
|
||||
Absolute pos embedding, learned.
|
||||
"""
|
||||
|
||||
def __init__(self, num_pos_feats=256):
|
||||
super().__init__()
|
||||
self.row_embed = nn.Embedding(50, num_pos_feats)
|
||||
self.col_embed = nn.Embedding(50, num_pos_feats)
|
||||
self.reset_parameters()
|
||||
|
||||
def reset_parameters(self):
|
||||
nn.init.uniform_(self.row_embed.weight)
|
||||
nn.init.uniform_(self.col_embed.weight)
|
||||
|
||||
def forward(self, tensor_list: NestedTensor):
|
||||
x = tensor_list.tensors
|
||||
h, w = x.shape[-2:]
|
||||
i = torch.arange(w, device=x.device)
|
||||
j = torch.arange(h, device=x.device)
|
||||
x_emb = self.col_embed(i)
|
||||
y_emb = self.row_embed(j)
|
||||
pos = (
|
||||
torch.cat(
|
||||
[
|
||||
x_emb.unsqueeze(0).repeat(h, 1, 1),
|
||||
y_emb.unsqueeze(1).repeat(1, w, 1),
|
||||
],
|
||||
dim=-1,
|
||||
)
|
||||
.permute(2, 0, 1)
|
||||
.unsqueeze(0)
|
||||
.repeat(x.shape[0], 1, 1, 1)
|
||||
)
|
||||
return pos
|
||||
|
||||
|
||||
def build_position_encoding(args):
|
||||
N_steps = args.hidden_dim // 2
|
||||
if args.position_embedding in ("v2", "sine"):
|
||||
# TODO find a better way of exposing other arguments
|
||||
position_embedding = PositionEmbeddingSineHW(
|
||||
N_steps,
|
||||
temperatureH=args.pe_temperatureH,
|
||||
temperatureW=args.pe_temperatureW,
|
||||
normalize=True,
|
||||
)
|
||||
elif args.position_embedding in ("v3", "learned"):
|
||||
position_embedding = PositionEmbeddingLearned(N_steps)
|
||||
else:
|
||||
raise ValueError(f"not supported {args.position_embedding}")
|
||||
|
||||
return position_embedding
|
||||
|
|
@ -0,0 +1,802 @@
|
|||
# ------------------------------------------------------------------------
|
||||
# Grounding DINO
|
||||
# url: https://github.com/IDEA-Research/GroundingDINO
|
||||
# Copyright (c) 2023 IDEA. All Rights Reserved.
|
||||
# Licensed under the Apache License, Version 2.0 [see LICENSE for details]
|
||||
# ------------------------------------------------------------------------
|
||||
# DINO
|
||||
# Copyright (c) 2022 IDEA. All Rights Reserved.
|
||||
# Licensed under the Apache License, Version 2.0 [see LICENSE for details]
|
||||
# --------------------------------------------------------
|
||||
# modified from https://github.com/SwinTransformer/Swin-Transformer-Object-Detection/blob/master/mmdet/models/backbones/swin_transformer.py
|
||||
# --------------------------------------------------------
|
||||
|
||||
import numpy as np
|
||||
import torch
|
||||
import torch.nn as nn
|
||||
import torch.nn.functional as F
|
||||
import torch.utils.checkpoint as checkpoint
|
||||
from timm.models.layers import DropPath, to_2tuple, trunc_normal_
|
||||
|
||||
from groundingdino.util.misc import NestedTensor
|
||||
|
||||
|
||||
class Mlp(nn.Module):
|
||||
"""Multilayer perceptron."""
|
||||
|
||||
def __init__(
|
||||
self, in_features, hidden_features=None, out_features=None, act_layer=nn.GELU, drop=0.0
|
||||
):
|
||||
super().__init__()
|
||||
out_features = out_features or in_features
|
||||
hidden_features = hidden_features or in_features
|
||||
self.fc1 = nn.Linear(in_features, hidden_features)
|
||||
self.act = act_layer()
|
||||
self.fc2 = nn.Linear(hidden_features, out_features)
|
||||
self.drop = nn.Dropout(drop)
|
||||
|
||||
def forward(self, x):
|
||||
x = self.fc1(x)
|
||||
x = self.act(x)
|
||||
x = self.drop(x)
|
||||
x = self.fc2(x)
|
||||
x = self.drop(x)
|
||||
return x
|
||||
|
||||
|
||||
def window_partition(x, window_size):
|
||||
"""
|
||||
Args:
|
||||
x: (B, H, W, C)
|
||||
window_size (int): window size
|
||||
Returns:
|
||||
windows: (num_windows*B, window_size, window_size, C)
|
||||
"""
|
||||
B, H, W, C = x.shape
|
||||
x = x.view(B, H // window_size, window_size, W // window_size, window_size, C)
|
||||
windows = x.permute(0, 1, 3, 2, 4, 5).contiguous().view(-1, window_size, window_size, C)
|
||||
return windows
|
||||
|
||||
|
||||
def window_reverse(windows, window_size, H, W):
|
||||
"""
|
||||
Args:
|
||||
windows: (num_windows*B, window_size, window_size, C)
|
||||
window_size (int): Window size
|
||||
H (int): Height of image
|
||||
W (int): Width of image
|
||||
Returns:
|
||||
x: (B, H, W, C)
|
||||
"""
|
||||
B = int(windows.shape[0] / (H * W / window_size / window_size))
|
||||
x = windows.view(B, H // window_size, W // window_size, window_size, window_size, -1)
|
||||
x = x.permute(0, 1, 3, 2, 4, 5).contiguous().view(B, H, W, -1)
|
||||
return x
|
||||
|
||||
|
||||
class WindowAttention(nn.Module):
|
||||
"""Window based multi-head self attention (W-MSA) module with relative position bias.
|
||||
It supports both of shifted and non-shifted window.
|
||||
Args:
|
||||
dim (int): Number of input channels.
|
||||
window_size (tuple[int]): The height and width of the window.
|
||||
num_heads (int): Number of attention heads.
|
||||
qkv_bias (bool, optional): If True, add a learnable bias to query, key, value. Default: True
|
||||
qk_scale (float | None, optional): Override default qk scale of head_dim ** -0.5 if set
|
||||
attn_drop (float, optional): Dropout ratio of attention weight. Default: 0.0
|
||||
proj_drop (float, optional): Dropout ratio of output. Default: 0.0
|
||||
"""
|
||||
|
||||
def __init__(
|
||||
self,
|
||||
dim,
|
||||
window_size,
|
||||
num_heads,
|
||||
qkv_bias=True,
|
||||
qk_scale=None,
|
||||
attn_drop=0.0,
|
||||
proj_drop=0.0,
|
||||
):
|
||||
|
||||
super().__init__()
|
||||
self.dim = dim
|
||||
self.window_size = window_size # Wh, Ww
|
||||
self.num_heads = num_heads
|
||||
head_dim = dim // num_heads
|
||||
self.scale = qk_scale or head_dim**-0.5
|
||||
|
||||
# define a parameter table of relative position bias
|
||||
self.relative_position_bias_table = nn.Parameter(
|
||||
torch.zeros((2 * window_size[0] - 1) * (2 * window_size[1] - 1), num_heads)
|
||||
) # 2*Wh-1 * 2*Ww-1, nH
|
||||
|
||||
# get pair-wise relative position index for each token inside the window
|
||||
coords_h = torch.arange(self.window_size[0])
|
||||
coords_w = torch.arange(self.window_size[1])
|
||||
coords = torch.stack(torch.meshgrid([coords_h, coords_w])) # 2, Wh, Ww
|
||||
coords_flatten = torch.flatten(coords, 1) # 2, Wh*Ww
|
||||
relative_coords = coords_flatten[:, :, None] - coords_flatten[:, None, :] # 2, Wh*Ww, Wh*Ww
|
||||
relative_coords = relative_coords.permute(1, 2, 0).contiguous() # Wh*Ww, Wh*Ww, 2
|
||||
relative_coords[:, :, 0] += self.window_size[0] - 1 # shift to start from 0
|
||||
relative_coords[:, :, 1] += self.window_size[1] - 1
|
||||
relative_coords[:, :, 0] *= 2 * self.window_size[1] - 1
|
||||
relative_position_index = relative_coords.sum(-1) # Wh*Ww, Wh*Ww
|
||||
self.register_buffer("relative_position_index", relative_position_index)
|
||||
|
||||
self.qkv = nn.Linear(dim, dim * 3, bias=qkv_bias)
|
||||
self.attn_drop = nn.Dropout(attn_drop)
|
||||
self.proj = nn.Linear(dim, dim)
|
||||
self.proj_drop = nn.Dropout(proj_drop)
|
||||
|
||||
trunc_normal_(self.relative_position_bias_table, std=0.02)
|
||||
self.softmax = nn.Softmax(dim=-1)
|
||||
|
||||
def forward(self, x, mask=None):
|
||||
"""Forward function.
|
||||
Args:
|
||||
x: input features with shape of (num_windows*B, N, C)
|
||||
mask: (0/-inf) mask with shape of (num_windows, Wh*Ww, Wh*Ww) or None
|
||||
"""
|
||||
B_, N, C = x.shape
|
||||
qkv = (
|
||||
self.qkv(x)
|
||||
.reshape(B_, N, 3, self.num_heads, C // self.num_heads)
|
||||
.permute(2, 0, 3, 1, 4)
|
||||
)
|
||||
q, k, v = qkv[0], qkv[1], qkv[2] # make torchscript happy (cannot use tensor as tuple)
|
||||
|
||||
q = q * self.scale
|
||||
attn = q @ k.transpose(-2, -1)
|
||||
|
||||
relative_position_bias = self.relative_position_bias_table[
|
||||
self.relative_position_index.view(-1)
|
||||
].view(
|
||||
self.window_size[0] * self.window_size[1], self.window_size[0] * self.window_size[1], -1
|
||||
) # Wh*Ww,Wh*Ww,nH
|
||||
relative_position_bias = relative_position_bias.permute(
|
||||
2, 0, 1
|
||||
).contiguous() # nH, Wh*Ww, Wh*Ww
|
||||
attn = attn + relative_position_bias.unsqueeze(0)
|
||||
|
||||
if mask is not None:
|
||||
nW = mask.shape[0]
|
||||
attn = attn.view(B_ // nW, nW, self.num_heads, N, N) + mask.unsqueeze(1).unsqueeze(0)
|
||||
attn = attn.view(-1, self.num_heads, N, N)
|
||||
attn = self.softmax(attn)
|
||||
else:
|
||||
attn = self.softmax(attn)
|
||||
|
||||
attn = self.attn_drop(attn)
|
||||
|
||||
x = (attn @ v).transpose(1, 2).reshape(B_, N, C)
|
||||
x = self.proj(x)
|
||||
x = self.proj_drop(x)
|
||||
return x
|
||||
|
||||
|
||||
class SwinTransformerBlock(nn.Module):
|
||||
"""Swin Transformer Block.
|
||||
Args:
|
||||
dim (int): Number of input channels.
|
||||
num_heads (int): Number of attention heads.
|
||||
window_size (int): Window size.
|
||||
shift_size (int): Shift size for SW-MSA.
|
||||
mlp_ratio (float): Ratio of mlp hidden dim to embedding dim.
|
||||
qkv_bias (bool, optional): If True, add a learnable bias to query, key, value. Default: True
|
||||
qk_scale (float | None, optional): Override default qk scale of head_dim ** -0.5 if set.
|
||||
drop (float, optional): Dropout rate. Default: 0.0
|
||||
attn_drop (float, optional): Attention dropout rate. Default: 0.0
|
||||
drop_path (float, optional): Stochastic depth rate. Default: 0.0
|
||||
act_layer (nn.Module, optional): Activation layer. Default: nn.GELU
|
||||
norm_layer (nn.Module, optional): Normalization layer. Default: nn.LayerNorm
|
||||
"""
|
||||
|
||||
def __init__(
|
||||
self,
|
||||
dim,
|
||||
num_heads,
|
||||
window_size=7,
|
||||
shift_size=0,
|
||||
mlp_ratio=4.0,
|
||||
qkv_bias=True,
|
||||
qk_scale=None,
|
||||
drop=0.0,
|
||||
attn_drop=0.0,
|
||||
drop_path=0.0,
|
||||
act_layer=nn.GELU,
|
||||
norm_layer=nn.LayerNorm,
|
||||
):
|
||||
super().__init__()
|
||||
self.dim = dim
|
||||
self.num_heads = num_heads
|
||||
self.window_size = window_size
|
||||
self.shift_size = shift_size
|
||||
self.mlp_ratio = mlp_ratio
|
||||
assert 0 <= self.shift_size < self.window_size, "shift_size must in 0-window_size"
|
||||
|
||||
self.norm1 = norm_layer(dim)
|
||||
self.attn = WindowAttention(
|
||||
dim,
|
||||
window_size=to_2tuple(self.window_size),
|
||||
num_heads=num_heads,
|
||||
qkv_bias=qkv_bias,
|
||||
qk_scale=qk_scale,
|
||||
attn_drop=attn_drop,
|
||||
proj_drop=drop,
|
||||
)
|
||||
|
||||
self.drop_path = DropPath(drop_path) if drop_path > 0.0 else nn.Identity()
|
||||
self.norm2 = norm_layer(dim)
|
||||
mlp_hidden_dim = int(dim * mlp_ratio)
|
||||
self.mlp = Mlp(
|
||||
in_features=dim, hidden_features=mlp_hidden_dim, act_layer=act_layer, drop=drop
|
||||
)
|
||||
|
||||
self.H = None
|
||||
self.W = None
|
||||
|
||||
def forward(self, x, mask_matrix):
|
||||
"""Forward function.
|
||||
Args:
|
||||
x: Input feature, tensor size (B, H*W, C).
|
||||
H, W: Spatial resolution of the input feature.
|
||||
mask_matrix: Attention mask for cyclic shift.
|
||||
"""
|
||||
B, L, C = x.shape
|
||||
H, W = self.H, self.W
|
||||
assert L == H * W, "input feature has wrong size"
|
||||
|
||||
shortcut = x
|
||||
x = self.norm1(x)
|
||||
x = x.view(B, H, W, C)
|
||||
|
||||
# pad feature maps to multiples of window size
|
||||
pad_l = pad_t = 0
|
||||
pad_r = (self.window_size - W % self.window_size) % self.window_size
|
||||
pad_b = (self.window_size - H % self.window_size) % self.window_size
|
||||
x = F.pad(x, (0, 0, pad_l, pad_r, pad_t, pad_b))
|
||||
_, Hp, Wp, _ = x.shape
|
||||
|
||||
# cyclic shift
|
||||
if self.shift_size > 0:
|
||||
shifted_x = torch.roll(x, shifts=(-self.shift_size, -self.shift_size), dims=(1, 2))
|
||||
attn_mask = mask_matrix
|
||||
else:
|
||||
shifted_x = x
|
||||
attn_mask = None
|
||||
|
||||
# partition windows
|
||||
x_windows = window_partition(
|
||||
shifted_x, self.window_size
|
||||
) # nW*B, window_size, window_size, C
|
||||
x_windows = x_windows.view(
|
||||
-1, self.window_size * self.window_size, C
|
||||
) # nW*B, window_size*window_size, C
|
||||
|
||||
# W-MSA/SW-MSA
|
||||
attn_windows = self.attn(x_windows, mask=attn_mask) # nW*B, window_size*window_size, C
|
||||
|
||||
# merge windows
|
||||
attn_windows = attn_windows.view(-1, self.window_size, self.window_size, C)
|
||||
shifted_x = window_reverse(attn_windows, self.window_size, Hp, Wp) # B H' W' C
|
||||
|
||||
# reverse cyclic shift
|
||||
if self.shift_size > 0:
|
||||
x = torch.roll(shifted_x, shifts=(self.shift_size, self.shift_size), dims=(1, 2))
|
||||
else:
|
||||
x = shifted_x
|
||||
|
||||
if pad_r > 0 or pad_b > 0:
|
||||
x = x[:, :H, :W, :].contiguous()
|
||||
|
||||
x = x.view(B, H * W, C)
|
||||
|
||||
# FFN
|
||||
x = shortcut + self.drop_path(x)
|
||||
x = x + self.drop_path(self.mlp(self.norm2(x)))
|
||||
|
||||
return x
|
||||
|
||||
|
||||
class PatchMerging(nn.Module):
|
||||
"""Patch Merging Layer
|
||||
Args:
|
||||
dim (int): Number of input channels.
|
||||
norm_layer (nn.Module, optional): Normalization layer. Default: nn.LayerNorm
|
||||
"""
|
||||
|
||||
def __init__(self, dim, norm_layer=nn.LayerNorm):
|
||||
super().__init__()
|
||||
self.dim = dim
|
||||
self.reduction = nn.Linear(4 * dim, 2 * dim, bias=False)
|
||||
self.norm = norm_layer(4 * dim)
|
||||
|
||||
def forward(self, x, H, W):
|
||||
"""Forward function.
|
||||
Args:
|
||||
x: Input feature, tensor size (B, H*W, C).
|
||||
H, W: Spatial resolution of the input feature.
|
||||
"""
|
||||
B, L, C = x.shape
|
||||
assert L == H * W, "input feature has wrong size"
|
||||
|
||||
x = x.view(B, H, W, C)
|
||||
|
||||
# padding
|
||||
pad_input = (H % 2 == 1) or (W % 2 == 1)
|
||||
if pad_input:
|
||||
x = F.pad(x, (0, 0, 0, W % 2, 0, H % 2))
|
||||
|
||||
x0 = x[:, 0::2, 0::2, :] # B H/2 W/2 C
|
||||
x1 = x[:, 1::2, 0::2, :] # B H/2 W/2 C
|
||||
x2 = x[:, 0::2, 1::2, :] # B H/2 W/2 C
|
||||
x3 = x[:, 1::2, 1::2, :] # B H/2 W/2 C
|
||||
x = torch.cat([x0, x1, x2, x3], -1) # B H/2 W/2 4*C
|
||||
x = x.view(B, -1, 4 * C) # B H/2*W/2 4*C
|
||||
|
||||
x = self.norm(x)
|
||||
x = self.reduction(x)
|
||||
|
||||
return x
|
||||
|
||||
|
||||
class BasicLayer(nn.Module):
|
||||
"""A basic Swin Transformer layer for one stage.
|
||||
Args:
|
||||
dim (int): Number of feature channels
|
||||
depth (int): Depths of this stage.
|
||||
num_heads (int): Number of attention head.
|
||||
window_size (int): Local window size. Default: 7.
|
||||
mlp_ratio (float): Ratio of mlp hidden dim to embedding dim. Default: 4.
|
||||
qkv_bias (bool, optional): If True, add a learnable bias to query, key, value. Default: True
|
||||
qk_scale (float | None, optional): Override default qk scale of head_dim ** -0.5 if set.
|
||||
drop (float, optional): Dropout rate. Default: 0.0
|
||||
attn_drop (float, optional): Attention dropout rate. Default: 0.0
|
||||
drop_path (float | tuple[float], optional): Stochastic depth rate. Default: 0.0
|
||||
norm_layer (nn.Module, optional): Normalization layer. Default: nn.LayerNorm
|
||||
downsample (nn.Module | None, optional): Downsample layer at the end of the layer. Default: None
|
||||
use_checkpoint (bool): Whether to use checkpointing to save memory. Default: False.
|
||||
"""
|
||||
|
||||
def __init__(
|
||||
self,
|
||||
dim,
|
||||
depth,
|
||||
num_heads,
|
||||
window_size=7,
|
||||
mlp_ratio=4.0,
|
||||
qkv_bias=True,
|
||||
qk_scale=None,
|
||||
drop=0.0,
|
||||
attn_drop=0.0,
|
||||
drop_path=0.0,
|
||||
norm_layer=nn.LayerNorm,
|
||||
downsample=None,
|
||||
use_checkpoint=False,
|
||||
):
|
||||
super().__init__()
|
||||
self.window_size = window_size
|
||||
self.shift_size = window_size // 2
|
||||
self.depth = depth
|
||||
self.use_checkpoint = use_checkpoint
|
||||
|
||||
# build blocks
|
||||
self.blocks = nn.ModuleList(
|
||||
[
|
||||
SwinTransformerBlock(
|
||||
dim=dim,
|
||||
num_heads=num_heads,
|
||||
window_size=window_size,
|
||||
shift_size=0 if (i % 2 == 0) else window_size // 2,
|
||||
mlp_ratio=mlp_ratio,
|
||||
qkv_bias=qkv_bias,
|
||||
qk_scale=qk_scale,
|
||||
drop=drop,
|
||||
attn_drop=attn_drop,
|
||||
drop_path=drop_path[i] if isinstance(drop_path, list) else drop_path,
|
||||
norm_layer=norm_layer,
|
||||
)
|
||||
for i in range(depth)
|
||||
]
|
||||
)
|
||||
|
||||
# patch merging layer
|
||||
if downsample is not None:
|
||||
self.downsample = downsample(dim=dim, norm_layer=norm_layer)
|
||||
else:
|
||||
self.downsample = None
|
||||
|
||||
def forward(self, x, H, W):
|
||||
"""Forward function.
|
||||
Args:
|
||||
x: Input feature, tensor size (B, H*W, C).
|
||||
H, W: Spatial resolution of the input feature.
|
||||
"""
|
||||
|
||||
# calculate attention mask for SW-MSA
|
||||
Hp = int(np.ceil(H / self.window_size)) * self.window_size
|
||||
Wp = int(np.ceil(W / self.window_size)) * self.window_size
|
||||
img_mask = torch.zeros((1, Hp, Wp, 1), device=x.device) # 1 Hp Wp 1
|
||||
h_slices = (
|
||||
slice(0, -self.window_size),
|
||||
slice(-self.window_size, -self.shift_size),
|
||||
slice(-self.shift_size, None),
|
||||
)
|
||||
w_slices = (
|
||||
slice(0, -self.window_size),
|
||||
slice(-self.window_size, -self.shift_size),
|
||||
slice(-self.shift_size, None),
|
||||
)
|
||||
cnt = 0
|
||||
for h in h_slices:
|
||||
for w in w_slices:
|
||||
img_mask[:, h, w, :] = cnt
|
||||
cnt += 1
|
||||
|
||||
mask_windows = window_partition(
|
||||
img_mask, self.window_size
|
||||
) # nW, window_size, window_size, 1
|
||||
mask_windows = mask_windows.view(-1, self.window_size * self.window_size)
|
||||
attn_mask = mask_windows.unsqueeze(1) - mask_windows.unsqueeze(2)
|
||||
attn_mask = attn_mask.masked_fill(attn_mask != 0, float(-100.0)).masked_fill(
|
||||
attn_mask == 0, float(0.0)
|
||||
)
|
||||
|
||||
for blk in self.blocks:
|
||||
blk.H, blk.W = H, W
|
||||
if self.use_checkpoint:
|
||||
x = checkpoint.checkpoint(blk, x, attn_mask)
|
||||
else:
|
||||
x = blk(x, attn_mask)
|
||||
if self.downsample is not None:
|
||||
x_down = self.downsample(x, H, W)
|
||||
Wh, Ww = (H + 1) // 2, (W + 1) // 2
|
||||
return x, H, W, x_down, Wh, Ww
|
||||
else:
|
||||
return x, H, W, x, H, W
|
||||
|
||||
|
||||
class PatchEmbed(nn.Module):
|
||||
"""Image to Patch Embedding
|
||||
Args:
|
||||
patch_size (int): Patch token size. Default: 4.
|
||||
in_chans (int): Number of input image channels. Default: 3.
|
||||
embed_dim (int): Number of linear projection output channels. Default: 96.
|
||||
norm_layer (nn.Module, optional): Normalization layer. Default: None
|
||||
"""
|
||||
|
||||
def __init__(self, patch_size=4, in_chans=3, embed_dim=96, norm_layer=None):
|
||||
super().__init__()
|
||||
patch_size = to_2tuple(patch_size)
|
||||
self.patch_size = patch_size
|
||||
|
||||
self.in_chans = in_chans
|
||||
self.embed_dim = embed_dim
|
||||
|
||||
self.proj = nn.Conv2d(in_chans, embed_dim, kernel_size=patch_size, stride=patch_size)
|
||||
if norm_layer is not None:
|
||||
self.norm = norm_layer(embed_dim)
|
||||
else:
|
||||
self.norm = None
|
||||
|
||||
def forward(self, x):
|
||||
"""Forward function."""
|
||||
# padding
|
||||
_, _, H, W = x.size()
|
||||
if W % self.patch_size[1] != 0:
|
||||
x = F.pad(x, (0, self.patch_size[1] - W % self.patch_size[1]))
|
||||
if H % self.patch_size[0] != 0:
|
||||
x = F.pad(x, (0, 0, 0, self.patch_size[0] - H % self.patch_size[0]))
|
||||
|
||||
x = self.proj(x) # B C Wh Ww
|
||||
if self.norm is not None:
|
||||
Wh, Ww = x.size(2), x.size(3)
|
||||
x = x.flatten(2).transpose(1, 2)
|
||||
x = self.norm(x)
|
||||
x = x.transpose(1, 2).view(-1, self.embed_dim, Wh, Ww)
|
||||
|
||||
return x
|
||||
|
||||
|
||||
class SwinTransformer(nn.Module):
|
||||
"""Swin Transformer backbone.
|
||||
A PyTorch impl of : `Swin Transformer: Hierarchical Vision Transformer using Shifted Windows` -
|
||||
https://arxiv.org/pdf/2103.14030
|
||||
Args:
|
||||
pretrain_img_size (int): Input image size for training the pretrained model,
|
||||
used in absolute postion embedding. Default 224.
|
||||
patch_size (int | tuple(int)): Patch size. Default: 4.
|
||||
in_chans (int): Number of input image channels. Default: 3.
|
||||
embed_dim (int): Number of linear projection output channels. Default: 96.
|
||||
depths (tuple[int]): Depths of each Swin Transformer stage.
|
||||
num_heads (tuple[int]): Number of attention head of each stage.
|
||||
window_size (int): Window size. Default: 7.
|
||||
mlp_ratio (float): Ratio of mlp hidden dim to embedding dim. Default: 4.
|
||||
qkv_bias (bool): If True, add a learnable bias to query, key, value. Default: True
|
||||
qk_scale (float): Override default qk scale of head_dim ** -0.5 if set.
|
||||
drop_rate (float): Dropout rate.
|
||||
attn_drop_rate (float): Attention dropout rate. Default: 0.
|
||||
drop_path_rate (float): Stochastic depth rate. Default: 0.2.
|
||||
norm_layer (nn.Module): Normalization layer. Default: nn.LayerNorm.
|
||||
ape (bool): If True, add absolute position embedding to the patch embedding. Default: False.
|
||||
patch_norm (bool): If True, add normalization after patch embedding. Default: True.
|
||||
out_indices (Sequence[int]): Output from which stages.
|
||||
frozen_stages (int): Stages to be frozen (stop grad and set eval mode).
|
||||
-1 means not freezing any parameters.
|
||||
use_checkpoint (bool): Whether to use checkpointing to save memory. Default: False.
|
||||
dilation (bool): if True, the output size if 16x downsample, ow 32x downsample.
|
||||
"""
|
||||
|
||||
def __init__(
|
||||
self,
|
||||
pretrain_img_size=224,
|
||||
patch_size=4,
|
||||
in_chans=3,
|
||||
embed_dim=96,
|
||||
depths=[2, 2, 6, 2],
|
||||
num_heads=[3, 6, 12, 24],
|
||||
window_size=7,
|
||||
mlp_ratio=4.0,
|
||||
qkv_bias=True,
|
||||
qk_scale=None,
|
||||
drop_rate=0.0,
|
||||
attn_drop_rate=0.0,
|
||||
drop_path_rate=0.2,
|
||||
norm_layer=nn.LayerNorm,
|
||||
ape=False,
|
||||
patch_norm=True,
|
||||
out_indices=(0, 1, 2, 3),
|
||||
frozen_stages=-1,
|
||||
dilation=False,
|
||||
use_checkpoint=False,
|
||||
):
|
||||
super().__init__()
|
||||
|
||||
self.pretrain_img_size = pretrain_img_size
|
||||
self.num_layers = len(depths)
|
||||
self.embed_dim = embed_dim
|
||||
self.ape = ape
|
||||
self.patch_norm = patch_norm
|
||||
self.out_indices = out_indices
|
||||
self.frozen_stages = frozen_stages
|
||||
self.dilation = dilation
|
||||
|
||||
# if use_checkpoint:
|
||||
# print("use_checkpoint!!!!!!!!!!!!!!!!!!!!!!!!")
|
||||
|
||||
# split image into non-overlapping patches
|
||||
self.patch_embed = PatchEmbed(
|
||||
patch_size=patch_size,
|
||||
in_chans=in_chans,
|
||||
embed_dim=embed_dim,
|
||||
norm_layer=norm_layer if self.patch_norm else None,
|
||||
)
|
||||
|
||||
# absolute position embedding
|
||||
if self.ape:
|
||||
pretrain_img_size = to_2tuple(pretrain_img_size)
|
||||
patch_size = to_2tuple(patch_size)
|
||||
patches_resolution = [
|
||||
pretrain_img_size[0] // patch_size[0],
|
||||
pretrain_img_size[1] // patch_size[1],
|
||||
]
|
||||
|
||||
self.absolute_pos_embed = nn.Parameter(
|
||||
torch.zeros(1, embed_dim, patches_resolution[0], patches_resolution[1])
|
||||
)
|
||||
trunc_normal_(self.absolute_pos_embed, std=0.02)
|
||||
|
||||
self.pos_drop = nn.Dropout(p=drop_rate)
|
||||
|
||||
# stochastic depth
|
||||
dpr = [
|
||||
x.item() for x in torch.linspace(0, drop_path_rate, sum(depths))
|
||||
] # stochastic depth decay rule
|
||||
|
||||
# build layers
|
||||
self.layers = nn.ModuleList()
|
||||
# prepare downsample list
|
||||
downsamplelist = [PatchMerging for i in range(self.num_layers)]
|
||||
downsamplelist[-1] = None
|
||||
num_features = [int(embed_dim * 2**i) for i in range(self.num_layers)]
|
||||
if self.dilation:
|
||||
downsamplelist[-2] = None
|
||||
num_features[-1] = int(embed_dim * 2 ** (self.num_layers - 1)) // 2
|
||||
for i_layer in range(self.num_layers):
|
||||
layer = BasicLayer(
|
||||
# dim=int(embed_dim * 2 ** i_layer),
|
||||
dim=num_features[i_layer],
|
||||
depth=depths[i_layer],
|
||||
num_heads=num_heads[i_layer],
|
||||
window_size=window_size,
|
||||
mlp_ratio=mlp_ratio,
|
||||
qkv_bias=qkv_bias,
|
||||
qk_scale=qk_scale,
|
||||
drop=drop_rate,
|
||||
attn_drop=attn_drop_rate,
|
||||
drop_path=dpr[sum(depths[:i_layer]) : sum(depths[: i_layer + 1])],
|
||||
norm_layer=norm_layer,
|
||||
# downsample=PatchMerging if (i_layer < self.num_layers - 1) else None,
|
||||
downsample=downsamplelist[i_layer],
|
||||
use_checkpoint=use_checkpoint,
|
||||
)
|
||||
self.layers.append(layer)
|
||||
|
||||
# num_features = [int(embed_dim * 2 ** i) for i in range(self.num_layers)]
|
||||
self.num_features = num_features
|
||||
|
||||
# add a norm layer for each output
|
||||
for i_layer in out_indices:
|
||||
layer = norm_layer(num_features[i_layer])
|
||||
layer_name = f"norm{i_layer}"
|
||||
self.add_module(layer_name, layer)
|
||||
|
||||
self._freeze_stages()
|
||||
|
||||
def _freeze_stages(self):
|
||||
if self.frozen_stages >= 0:
|
||||
self.patch_embed.eval()
|
||||
for param in self.patch_embed.parameters():
|
||||
param.requires_grad = False
|
||||
|
||||
if self.frozen_stages >= 1 and self.ape:
|
||||
self.absolute_pos_embed.requires_grad = False
|
||||
|
||||
if self.frozen_stages >= 2:
|
||||
self.pos_drop.eval()
|
||||
for i in range(0, self.frozen_stages - 1):
|
||||
m = self.layers[i]
|
||||
m.eval()
|
||||
for param in m.parameters():
|
||||
param.requires_grad = False
|
||||
|
||||
# def init_weights(self, pretrained=None):
|
||||
# """Initialize the weights in backbone.
|
||||
# Args:
|
||||
# pretrained (str, optional): Path to pre-trained weights.
|
||||
# Defaults to None.
|
||||
# """
|
||||
|
||||
# def _init_weights(m):
|
||||
# if isinstance(m, nn.Linear):
|
||||
# trunc_normal_(m.weight, std=.02)
|
||||
# if isinstance(m, nn.Linear) and m.bias is not None:
|
||||
# nn.init.constant_(m.bias, 0)
|
||||
# elif isinstance(m, nn.LayerNorm):
|
||||
# nn.init.constant_(m.bias, 0)
|
||||
# nn.init.constant_(m.weight, 1.0)
|
||||
|
||||
# if isinstance(pretrained, str):
|
||||
# self.apply(_init_weights)
|
||||
# logger = get_root_logger()
|
||||
# load_checkpoint(self, pretrained, strict=False, logger=logger)
|
||||
# elif pretrained is None:
|
||||
# self.apply(_init_weights)
|
||||
# else:
|
||||
# raise TypeError('pretrained must be a str or None')
|
||||
|
||||
def forward_raw(self, x):
|
||||
"""Forward function."""
|
||||
x = self.patch_embed(x)
|
||||
|
||||
Wh, Ww = x.size(2), x.size(3)
|
||||
if self.ape:
|
||||
# interpolate the position embedding to the corresponding size
|
||||
absolute_pos_embed = F.interpolate(
|
||||
self.absolute_pos_embed, size=(Wh, Ww), mode="bicubic"
|
||||
)
|
||||
x = (x + absolute_pos_embed).flatten(2).transpose(1, 2) # B Wh*Ww C
|
||||
else:
|
||||
x = x.flatten(2).transpose(1, 2)
|
||||
x = self.pos_drop(x)
|
||||
|
||||
outs = []
|
||||
for i in range(self.num_layers):
|
||||
layer = self.layers[i]
|
||||
x_out, H, W, x, Wh, Ww = layer(x, Wh, Ww)
|
||||
# import ipdb; ipdb.set_trace()
|
||||
|
||||
if i in self.out_indices:
|
||||
norm_layer = getattr(self, f"norm{i}")
|
||||
x_out = norm_layer(x_out)
|
||||
|
||||
out = x_out.view(-1, H, W, self.num_features[i]).permute(0, 3, 1, 2).contiguous()
|
||||
outs.append(out)
|
||||
# in:
|
||||
# torch.Size([2, 3, 1024, 1024])
|
||||
# outs:
|
||||
# [torch.Size([2, 192, 256, 256]), torch.Size([2, 384, 128, 128]), \
|
||||
# torch.Size([2, 768, 64, 64]), torch.Size([2, 1536, 32, 32])]
|
||||
return tuple(outs)
|
||||
|
||||
def forward(self, tensor_list: NestedTensor):
|
||||
x = tensor_list.tensors
|
||||
|
||||
"""Forward function."""
|
||||
x = self.patch_embed(x)
|
||||
|
||||
Wh, Ww = x.size(2), x.size(3)
|
||||
if self.ape:
|
||||
# interpolate the position embedding to the corresponding size
|
||||
absolute_pos_embed = F.interpolate(
|
||||
self.absolute_pos_embed, size=(Wh, Ww), mode="bicubic"
|
||||
)
|
||||
x = (x + absolute_pos_embed).flatten(2).transpose(1, 2) # B Wh*Ww C
|
||||
else:
|
||||
x = x.flatten(2).transpose(1, 2)
|
||||
x = self.pos_drop(x)
|
||||
|
||||
outs = []
|
||||
for i in range(self.num_layers):
|
||||
layer = self.layers[i]
|
||||
x_out, H, W, x, Wh, Ww = layer(x, Wh, Ww)
|
||||
|
||||
if i in self.out_indices:
|
||||
norm_layer = getattr(self, f"norm{i}")
|
||||
x_out = norm_layer(x_out)
|
||||
|
||||
out = x_out.view(-1, H, W, self.num_features[i]).permute(0, 3, 1, 2).contiguous()
|
||||
outs.append(out)
|
||||
# in:
|
||||
# torch.Size([2, 3, 1024, 1024])
|
||||
# out:
|
||||
# [torch.Size([2, 192, 256, 256]), torch.Size([2, 384, 128, 128]), \
|
||||
# torch.Size([2, 768, 64, 64]), torch.Size([2, 1536, 32, 32])]
|
||||
|
||||
# collect for nesttensors
|
||||
outs_dict = {}
|
||||
for idx, out_i in enumerate(outs):
|
||||
m = tensor_list.mask
|
||||
assert m is not None
|
||||
mask = F.interpolate(m[None].float(), size=out_i.shape[-2:]).to(torch.bool)[0]
|
||||
outs_dict[idx] = NestedTensor(out_i, mask)
|
||||
|
||||
return outs_dict
|
||||
|
||||
def train(self, mode=True):
|
||||
"""Convert the model into training mode while keep layers freezed."""
|
||||
super(SwinTransformer, self).train(mode)
|
||||
self._freeze_stages()
|
||||
|
||||
|
||||
def build_swin_transformer(modelname, pretrain_img_size, **kw):
|
||||
assert modelname in [
|
||||
"swin_T_224_1k",
|
||||
"swin_B_224_22k",
|
||||
"swin_B_384_22k",
|
||||
"swin_L_224_22k",
|
||||
"swin_L_384_22k",
|
||||
]
|
||||
|
||||
model_para_dict = {
|
||||
"swin_T_224_1k": dict(
|
||||
embed_dim=96, depths=[2, 2, 6, 2], num_heads=[3, 6, 12, 24], window_size=7
|
||||
),
|
||||
"swin_B_224_22k": dict(
|
||||
embed_dim=128, depths=[2, 2, 18, 2], num_heads=[4, 8, 16, 32], window_size=7
|
||||
),
|
||||
"swin_B_384_22k": dict(
|
||||
embed_dim=128, depths=[2, 2, 18, 2], num_heads=[4, 8, 16, 32], window_size=12
|
||||
),
|
||||
"swin_L_224_22k": dict(
|
||||
embed_dim=192, depths=[2, 2, 18, 2], num_heads=[6, 12, 24, 48], window_size=7
|
||||
),
|
||||
"swin_L_384_22k": dict(
|
||||
embed_dim=192, depths=[2, 2, 18, 2], num_heads=[6, 12, 24, 48], window_size=12
|
||||
),
|
||||
}
|
||||
kw_cgf = model_para_dict[modelname]
|
||||
kw_cgf.update(kw)
|
||||
model = SwinTransformer(pretrain_img_size=pretrain_img_size, **kw_cgf)
|
||||
return model
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
model = build_swin_transformer("swin_L_384_22k", 384, dilation=True)
|
||||
x = torch.rand(2, 3, 1024, 1024)
|
||||
y = model.forward_raw(x)
|
||||
import ipdb
|
||||
|
||||
ipdb.set_trace()
|
||||
x = torch.rand(2, 3, 384, 384)
|
||||
y = model.forward_raw(x)
|
||||
|
|
@ -0,0 +1,273 @@
|
|||
# ------------------------------------------------------------------------
|
||||
# Grounding DINO
|
||||
# url: https://github.com/IDEA-Research/GroundingDINO
|
||||
# Copyright (c) 2023 IDEA. All Rights Reserved.
|
||||
# Licensed under the Apache License, Version 2.0 [see LICENSE for details]
|
||||
# ------------------------------------------------------------------------
|
||||
|
||||
import torch
|
||||
import torch.nn.functional as F
|
||||
import torch.utils.checkpoint as checkpoint
|
||||
from torch import Tensor, nn
|
||||
from torchvision.ops.boxes import nms
|
||||
from transformers import BertConfig, BertModel, BertPreTrainedModel
|
||||
from transformers.modeling_outputs import BaseModelOutputWithPoolingAndCrossAttentions
|
||||
|
||||
|
||||
class BertModelWarper(nn.Module):
|
||||
def __init__(self, bert_model):
|
||||
super().__init__()
|
||||
# self.bert = bert_modelc
|
||||
|
||||
self.config = bert_model.config
|
||||
self.embeddings = bert_model.embeddings
|
||||
self.encoder = bert_model.encoder
|
||||
self.pooler = bert_model.pooler
|
||||
|
||||
self.get_extended_attention_mask = bert_model.get_extended_attention_mask
|
||||
self.invert_attention_mask = bert_model.invert_attention_mask
|
||||
self.get_head_mask = bert_model.get_head_mask
|
||||
|
||||
def forward(
|
||||
self,
|
||||
input_ids=None,
|
||||
attention_mask=None,
|
||||
token_type_ids=None,
|
||||
position_ids=None,
|
||||
head_mask=None,
|
||||
inputs_embeds=None,
|
||||
encoder_hidden_states=None,
|
||||
encoder_attention_mask=None,
|
||||
past_key_values=None,
|
||||
use_cache=None,
|
||||
output_attentions=None,
|
||||
output_hidden_states=None,
|
||||
return_dict=None,
|
||||
):
|
||||
r"""
|
||||
encoder_hidden_states (:obj:`torch.FloatTensor` of shape :obj:`(batch_size, sequence_length, hidden_size)`, `optional`):
|
||||
Sequence of hidden-states at the output of the last layer of the encoder. Used in the cross-attention if
|
||||
the model is configured as a decoder.
|
||||
encoder_attention_mask (:obj:`torch.FloatTensor` of shape :obj:`(batch_size, sequence_length)`, `optional`):
|
||||
Mask to avoid performing attention on the padding token indices of the encoder input. This mask is used in
|
||||
the cross-attention if the model is configured as a decoder. Mask values selected in ``[0, 1]``:
|
||||
|
||||
- 1 for tokens that are **not masked**,
|
||||
- 0 for tokens that are **masked**.
|
||||
past_key_values (:obj:`tuple(tuple(torch.FloatTensor))` of length :obj:`config.n_layers` with each tuple having 4 tensors of shape :obj:`(batch_size, num_heads, sequence_length - 1, embed_size_per_head)`):
|
||||
Contains precomputed key and value hidden states of the attention blocks. Can be used to speed up decoding.
|
||||
|
||||
If :obj:`past_key_values` are used, the user can optionally input only the last :obj:`decoder_input_ids`
|
||||
(those that don't have their past key value states given to this model) of shape :obj:`(batch_size, 1)`
|
||||
instead of all :obj:`decoder_input_ids` of shape :obj:`(batch_size, sequence_length)`.
|
||||
use_cache (:obj:`bool`, `optional`):
|
||||
If set to :obj:`True`, :obj:`past_key_values` key value states are returned and can be used to speed up
|
||||
decoding (see :obj:`past_key_values`).
|
||||
"""
|
||||
output_attentions = (
|
||||
output_attentions if output_attentions is not None else self.config.output_attentions
|
||||
)
|
||||
output_hidden_states = (
|
||||
output_hidden_states
|
||||
if output_hidden_states is not None
|
||||
else self.config.output_hidden_states
|
||||
)
|
||||
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
|
||||
|
||||
if self.config.is_decoder:
|
||||
use_cache = use_cache if use_cache is not None else self.config.use_cache
|
||||
else:
|
||||
use_cache = False
|
||||
|
||||
if input_ids is not None and inputs_embeds is not None:
|
||||
raise ValueError("You cannot specify both input_ids and inputs_embeds at the same time")
|
||||
elif input_ids is not None:
|
||||
input_shape = input_ids.size()
|
||||
batch_size, seq_length = input_shape
|
||||
elif inputs_embeds is not None:
|
||||
input_shape = inputs_embeds.size()[:-1]
|
||||
batch_size, seq_length = input_shape
|
||||
else:
|
||||
raise ValueError("You have to specify either input_ids or inputs_embeds")
|
||||
|
||||
device = input_ids.device if input_ids is not None else inputs_embeds.device
|
||||
|
||||
# past_key_values_length
|
||||
past_key_values_length = (
|
||||
past_key_values[0][0].shape[2] if past_key_values is not None else 0
|
||||
)
|
||||
|
||||
if attention_mask is None:
|
||||
attention_mask = torch.ones(
|
||||
((batch_size, seq_length + past_key_values_length)), device=device
|
||||
)
|
||||
if token_type_ids is None:
|
||||
token_type_ids = torch.zeros(input_shape, dtype=torch.long, device=device)
|
||||
|
||||
# We can provide a self-attention mask of dimensions [batch_size, from_seq_length, to_seq_length]
|
||||
# ourselves in which case we just need to make it broadcastable to all heads.
|
||||
extended_attention_mask: torch.Tensor = self.get_extended_attention_mask(
|
||||
attention_mask, input_shape, device
|
||||
)
|
||||
|
||||
# If a 2D or 3D attention mask is provided for the cross-attention
|
||||
# we need to make broadcastable to [batch_size, num_heads, seq_length, seq_length]
|
||||
if self.config.is_decoder and encoder_hidden_states is not None:
|
||||
encoder_batch_size, encoder_sequence_length, _ = encoder_hidden_states.size()
|
||||
encoder_hidden_shape = (encoder_batch_size, encoder_sequence_length)
|
||||
if encoder_attention_mask is None:
|
||||
encoder_attention_mask = torch.ones(encoder_hidden_shape, device=device)
|
||||
encoder_extended_attention_mask = self.invert_attention_mask(encoder_attention_mask)
|
||||
else:
|
||||
encoder_extended_attention_mask = None
|
||||
# if os.environ.get('IPDB_SHILONG_DEBUG', None) == 'INFO':
|
||||
# import ipdb; ipdb.set_trace()
|
||||
|
||||
# Prepare head mask if needed
|
||||
# 1.0 in head_mask indicate we keep the head
|
||||
# attention_probs has shape bsz x n_heads x N x N
|
||||
# input head_mask has shape [num_heads] or [num_hidden_layers x num_heads]
|
||||
# and head_mask is converted to shape [num_hidden_layers x batch x num_heads x seq_length x seq_length]
|
||||
head_mask = self.get_head_mask(head_mask, self.config.num_hidden_layers)
|
||||
|
||||
embedding_output = self.embeddings(
|
||||
input_ids=input_ids,
|
||||
position_ids=position_ids,
|
||||
token_type_ids=token_type_ids,
|
||||
inputs_embeds=inputs_embeds,
|
||||
past_key_values_length=past_key_values_length,
|
||||
)
|
||||
|
||||
encoder_outputs = self.encoder(
|
||||
embedding_output,
|
||||
attention_mask=extended_attention_mask,
|
||||
head_mask=head_mask,
|
||||
encoder_hidden_states=encoder_hidden_states,
|
||||
encoder_attention_mask=encoder_extended_attention_mask,
|
||||
past_key_values=past_key_values,
|
||||
use_cache=use_cache,
|
||||
output_attentions=output_attentions,
|
||||
output_hidden_states=output_hidden_states,
|
||||
return_dict=return_dict,
|
||||
)
|
||||
sequence_output = encoder_outputs[0]
|
||||
pooled_output = self.pooler(sequence_output) if self.pooler is not None else None
|
||||
|
||||
if not return_dict:
|
||||
return (sequence_output, pooled_output) + encoder_outputs[1:]
|
||||
|
||||
return BaseModelOutputWithPoolingAndCrossAttentions(
|
||||
last_hidden_state=sequence_output,
|
||||
pooler_output=pooled_output,
|
||||
past_key_values=encoder_outputs.past_key_values,
|
||||
hidden_states=encoder_outputs.hidden_states,
|
||||
attentions=encoder_outputs.attentions,
|
||||
cross_attentions=encoder_outputs.cross_attentions,
|
||||
)
|
||||
|
||||
|
||||
class TextEncoderShell(nn.Module):
|
||||
def __init__(self, text_encoder):
|
||||
super().__init__()
|
||||
self.text_encoder = text_encoder
|
||||
self.config = self.text_encoder.config
|
||||
|
||||
def forward(self, **kw):
|
||||
# feed into text encoder
|
||||
return self.text_encoder(**kw)
|
||||
|
||||
|
||||
def generate_masks_with_special_tokens(tokenized, special_tokens_list, tokenizer):
|
||||
"""Generate attention mask between each pair of special tokens
|
||||
Args:
|
||||
input_ids (torch.Tensor): input ids. Shape: [bs, num_token]
|
||||
special_tokens_mask (list): special tokens mask.
|
||||
Returns:
|
||||
torch.Tensor: attention mask between each special tokens.
|
||||
"""
|
||||
input_ids = tokenized["input_ids"]
|
||||
bs, num_token = input_ids.shape
|
||||
# special_tokens_mask: bs, num_token. 1 for special tokens. 0 for normal tokens
|
||||
special_tokens_mask = torch.zeros((bs, num_token), device=input_ids.device).bool()
|
||||
for special_token in special_tokens_list:
|
||||
special_tokens_mask |= input_ids == special_token
|
||||
|
||||
# idxs: each row is a list of indices of special tokens
|
||||
idxs = torch.nonzero(special_tokens_mask)
|
||||
|
||||
# generate attention mask and positional ids
|
||||
attention_mask = (
|
||||
torch.eye(num_token, device=input_ids.device).bool().unsqueeze(0).repeat(bs, 1, 1)
|
||||
)
|
||||
position_ids = torch.zeros((bs, num_token), device=input_ids.device)
|
||||
previous_col = 0
|
||||
for i in range(idxs.shape[0]):
|
||||
row, col = idxs[i]
|
||||
if (col == 0) or (col == num_token - 1):
|
||||
attention_mask[row, col, col] = True
|
||||
position_ids[row, col] = 0
|
||||
else:
|
||||
attention_mask[row, previous_col + 1 : col + 1, previous_col + 1 : col + 1] = True
|
||||
position_ids[row, previous_col + 1 : col + 1] = torch.arange(
|
||||
0, col - previous_col, device=input_ids.device
|
||||
)
|
||||
|
||||
previous_col = col
|
||||
|
||||
# # padding mask
|
||||
# padding_mask = tokenized['attention_mask']
|
||||
# attention_mask = attention_mask & padding_mask.unsqueeze(1).bool() & padding_mask.unsqueeze(2).bool()
|
||||
|
||||
return attention_mask, position_ids.to(torch.long)
|
||||
|
||||
|
||||
def generate_masks_with_special_tokens_and_transfer_map(tokenized, special_tokens_list, tokenizer):
|
||||
"""Generate attention mask between each pair of special tokens
|
||||
Args:
|
||||
input_ids (torch.Tensor): input ids. Shape: [bs, num_token]
|
||||
special_tokens_mask (list): special tokens mask.
|
||||
Returns:
|
||||
torch.Tensor: attention mask between each special tokens.
|
||||
"""
|
||||
input_ids = tokenized["input_ids"]
|
||||
bs, num_token = input_ids.shape
|
||||
# special_tokens_mask: bs, num_token. 1 for special tokens. 0 for normal tokens
|
||||
special_tokens_mask = torch.zeros((bs, num_token), device=input_ids.device).bool()
|
||||
for special_token in special_tokens_list:
|
||||
special_tokens_mask |= input_ids == special_token
|
||||
|
||||
# idxs: each row is a list of indices of special tokens
|
||||
idxs = torch.nonzero(special_tokens_mask)
|
||||
|
||||
# generate attention mask and positional ids
|
||||
attention_mask = (
|
||||
torch.eye(num_token, device=input_ids.device).bool().unsqueeze(0).repeat(bs, 1, 1)
|
||||
)
|
||||
position_ids = torch.zeros((bs, num_token), device=input_ids.device)
|
||||
cate_to_token_mask_list = [[] for _ in range(bs)]
|
||||
previous_col = 0
|
||||
for i in range(idxs.shape[0]):
|
||||
row, col = idxs[i]
|
||||
if (col == 0) or (col == num_token - 1):
|
||||
attention_mask[row, col, col] = True
|
||||
position_ids[row, col] = 0
|
||||
else:
|
||||
attention_mask[row, previous_col + 1 : col + 1, previous_col + 1 : col + 1] = True
|
||||
position_ids[row, previous_col + 1 : col + 1] = torch.arange(
|
||||
0, col - previous_col, device=input_ids.device
|
||||
)
|
||||
c2t_maski = torch.zeros((num_token), device=input_ids.device).bool()
|
||||
c2t_maski[previous_col + 1 : col] = True
|
||||
cate_to_token_mask_list[row].append(c2t_maski)
|
||||
previous_col = col
|
||||
|
||||
cate_to_token_mask_list = [
|
||||
torch.stack(cate_to_token_mask_listi, dim=0)
|
||||
for cate_to_token_mask_listi in cate_to_token_mask_list
|
||||
]
|
||||
|
||||
# # padding mask
|
||||
# padding_mask = tokenized['attention_mask']
|
||||
# attention_mask = attention_mask & padding_mask.unsqueeze(1).bool() & padding_mask.unsqueeze(2).bool()
|
||||
|
||||
return attention_mask, position_ids.to(torch.long), cate_to_token_mask_list
|
||||
|
|
@ -0,0 +1,64 @@
|
|||
/*!
|
||||
**************************************************************************************************
|
||||
* Deformable DETR
|
||||
* Copyright (c) 2020 SenseTime. All Rights Reserved.
|
||||
* Licensed under the Apache License, Version 2.0 [see LICENSE for details]
|
||||
**************************************************************************************************
|
||||
* Modified from https://github.com/chengdazhi/Deformable-Convolution-V2-PyTorch/tree/pytorch_1.0.0
|
||||
**************************************************************************************************
|
||||
*/
|
||||
|
||||
#pragma once
|
||||
|
||||
#include "ms_deform_attn_cpu.h"
|
||||
|
||||
#ifdef WITH_CUDA
|
||||
#include "ms_deform_attn_cuda.h"
|
||||
#endif
|
||||
|
||||
namespace groundingdino {
|
||||
|
||||
at::Tensor
|
||||
ms_deform_attn_forward(
|
||||
const at::Tensor &value,
|
||||
const at::Tensor &spatial_shapes,
|
||||
const at::Tensor &level_start_index,
|
||||
const at::Tensor &sampling_loc,
|
||||
const at::Tensor &attn_weight,
|
||||
const int im2col_step)
|
||||
{
|
||||
if (value.type().is_cuda())
|
||||
{
|
||||
#ifdef WITH_CUDA
|
||||
return ms_deform_attn_cuda_forward(
|
||||
value, spatial_shapes, level_start_index, sampling_loc, attn_weight, im2col_step);
|
||||
#else
|
||||
AT_ERROR("Not compiled with GPU support");
|
||||
#endif
|
||||
}
|
||||
AT_ERROR("Not implemented on the CPU");
|
||||
}
|
||||
|
||||
std::vector<at::Tensor>
|
||||
ms_deform_attn_backward(
|
||||
const at::Tensor &value,
|
||||
const at::Tensor &spatial_shapes,
|
||||
const at::Tensor &level_start_index,
|
||||
const at::Tensor &sampling_loc,
|
||||
const at::Tensor &attn_weight,
|
||||
const at::Tensor &grad_output,
|
||||
const int im2col_step)
|
||||
{
|
||||
if (value.type().is_cuda())
|
||||
{
|
||||
#ifdef WITH_CUDA
|
||||
return ms_deform_attn_cuda_backward(
|
||||
value, spatial_shapes, level_start_index, sampling_loc, attn_weight, grad_output, im2col_step);
|
||||
#else
|
||||
AT_ERROR("Not compiled with GPU support");
|
||||
#endif
|
||||
}
|
||||
AT_ERROR("Not implemented on the CPU");
|
||||
}
|
||||
|
||||
} // namespace groundingdino
|
||||
|
|
@ -0,0 +1,43 @@
|
|||
/*!
|
||||
**************************************************************************************************
|
||||
* Deformable DETR
|
||||
* Copyright (c) 2020 SenseTime. All Rights Reserved.
|
||||
* Licensed under the Apache License, Version 2.0 [see LICENSE for details]
|
||||
**************************************************************************************************
|
||||
* Modified from https://github.com/chengdazhi/Deformable-Convolution-V2-PyTorch/tree/pytorch_1.0.0
|
||||
**************************************************************************************************
|
||||
*/
|
||||
|
||||
#include <vector>
|
||||
|
||||
#include <ATen/ATen.h>
|
||||
#include <ATen/cuda/CUDAContext.h>
|
||||
|
||||
namespace groundingdino {
|
||||
|
||||
at::Tensor
|
||||
ms_deform_attn_cpu_forward(
|
||||
const at::Tensor &value,
|
||||
const at::Tensor &spatial_shapes,
|
||||
const at::Tensor &level_start_index,
|
||||
const at::Tensor &sampling_loc,
|
||||
const at::Tensor &attn_weight,
|
||||
const int im2col_step)
|
||||
{
|
||||
AT_ERROR("Not implement on cpu");
|
||||
}
|
||||
|
||||
std::vector<at::Tensor>
|
||||
ms_deform_attn_cpu_backward(
|
||||
const at::Tensor &value,
|
||||
const at::Tensor &spatial_shapes,
|
||||
const at::Tensor &level_start_index,
|
||||
const at::Tensor &sampling_loc,
|
||||
const at::Tensor &attn_weight,
|
||||
const at::Tensor &grad_output,
|
||||
const int im2col_step)
|
||||
{
|
||||
AT_ERROR("Not implement on cpu");
|
||||
}
|
||||
|
||||
} // namespace groundingdino
|
||||
|
|
@ -0,0 +1,35 @@
|
|||
/*!
|
||||
**************************************************************************************************
|
||||
* Deformable DETR
|
||||
* Copyright (c) 2020 SenseTime. All Rights Reserved.
|
||||
* Licensed under the Apache License, Version 2.0 [see LICENSE for details]
|
||||
**************************************************************************************************
|
||||
* Modified from https://github.com/chengdazhi/Deformable-Convolution-V2-PyTorch/tree/pytorch_1.0.0
|
||||
**************************************************************************************************
|
||||
*/
|
||||
|
||||
#pragma once
|
||||
#include <torch/extension.h>
|
||||
|
||||
namespace groundingdino {
|
||||
|
||||
at::Tensor
|
||||
ms_deform_attn_cpu_forward(
|
||||
const at::Tensor &value,
|
||||
const at::Tensor &spatial_shapes,
|
||||
const at::Tensor &level_start_index,
|
||||
const at::Tensor &sampling_loc,
|
||||
const at::Tensor &attn_weight,
|
||||
const int im2col_step);
|
||||
|
||||
std::vector<at::Tensor>
|
||||
ms_deform_attn_cpu_backward(
|
||||
const at::Tensor &value,
|
||||
const at::Tensor &spatial_shapes,
|
||||
const at::Tensor &level_start_index,
|
||||
const at::Tensor &sampling_loc,
|
||||
const at::Tensor &attn_weight,
|
||||
const at::Tensor &grad_output,
|
||||
const int im2col_step);
|
||||
|
||||
} // namespace groundingdino
|
||||
|
|
@ -0,0 +1,156 @@
|
|||
/*!
|
||||
**************************************************************************************************
|
||||
* Deformable DETR
|
||||
* Copyright (c) 2020 SenseTime. All Rights Reserved.
|
||||
* Licensed under the Apache License, Version 2.0 [see LICENSE for details]
|
||||
**************************************************************************************************
|
||||
* Modified from https://github.com/chengdazhi/Deformable-Convolution-V2-PyTorch/tree/pytorch_1.0.0
|
||||
**************************************************************************************************
|
||||
*/
|
||||
|
||||
#include <vector>
|
||||
#include "ms_deform_im2col_cuda.cuh"
|
||||
|
||||
#include <ATen/ATen.h>
|
||||
#include <ATen/cuda/CUDAContext.h>
|
||||
#include <cuda.h>
|
||||
#include <cuda_runtime.h>
|
||||
|
||||
namespace groundingdino {
|
||||
|
||||
at::Tensor ms_deform_attn_cuda_forward(
|
||||
const at::Tensor &value,
|
||||
const at::Tensor &spatial_shapes,
|
||||
const at::Tensor &level_start_index,
|
||||
const at::Tensor &sampling_loc,
|
||||
const at::Tensor &attn_weight,
|
||||
const int im2col_step)
|
||||
{
|
||||
AT_ASSERTM(value.is_contiguous(), "value tensor has to be contiguous");
|
||||
AT_ASSERTM(spatial_shapes.is_contiguous(), "spatial_shapes tensor has to be contiguous");
|
||||
AT_ASSERTM(level_start_index.is_contiguous(), "level_start_index tensor has to be contiguous");
|
||||
AT_ASSERTM(sampling_loc.is_contiguous(), "sampling_loc tensor has to be contiguous");
|
||||
AT_ASSERTM(attn_weight.is_contiguous(), "attn_weight tensor has to be contiguous");
|
||||
|
||||
AT_ASSERTM(value.type().is_cuda(), "value must be a CUDA tensor");
|
||||
AT_ASSERTM(spatial_shapes.type().is_cuda(), "spatial_shapes must be a CUDA tensor");
|
||||
AT_ASSERTM(level_start_index.type().is_cuda(), "level_start_index must be a CUDA tensor");
|
||||
AT_ASSERTM(sampling_loc.type().is_cuda(), "sampling_loc must be a CUDA tensor");
|
||||
AT_ASSERTM(attn_weight.type().is_cuda(), "attn_weight must be a CUDA tensor");
|
||||
|
||||
const int batch = value.size(0);
|
||||
const int spatial_size = value.size(1);
|
||||
const int num_heads = value.size(2);
|
||||
const int channels = value.size(3);
|
||||
|
||||
const int num_levels = spatial_shapes.size(0);
|
||||
|
||||
const int num_query = sampling_loc.size(1);
|
||||
const int num_point = sampling_loc.size(4);
|
||||
|
||||
const int im2col_step_ = std::min(batch, im2col_step);
|
||||
|
||||
AT_ASSERTM(batch % im2col_step_ == 0, "batch(%d) must divide im2col_step(%d)", batch, im2col_step_);
|
||||
|
||||
auto output = at::zeros({batch, num_query, num_heads, channels}, value.options());
|
||||
|
||||
const int batch_n = im2col_step_;
|
||||
auto output_n = output.view({batch/im2col_step_, batch_n, num_query, num_heads, channels});
|
||||
auto per_value_size = spatial_size * num_heads * channels;
|
||||
auto per_sample_loc_size = num_query * num_heads * num_levels * num_point * 2;
|
||||
auto per_attn_weight_size = num_query * num_heads * num_levels * num_point;
|
||||
for (int n = 0; n < batch/im2col_step_; ++n)
|
||||
{
|
||||
auto columns = output_n.select(0, n);
|
||||
AT_DISPATCH_FLOATING_TYPES(value.type(), "ms_deform_attn_forward_cuda", ([&] {
|
||||
ms_deformable_im2col_cuda(at::cuda::getCurrentCUDAStream(),
|
||||
value.data<scalar_t>() + n * im2col_step_ * per_value_size,
|
||||
spatial_shapes.data<int64_t>(),
|
||||
level_start_index.data<int64_t>(),
|
||||
sampling_loc.data<scalar_t>() + n * im2col_step_ * per_sample_loc_size,
|
||||
attn_weight.data<scalar_t>() + n * im2col_step_ * per_attn_weight_size,
|
||||
batch_n, spatial_size, num_heads, channels, num_levels, num_query, num_point,
|
||||
columns.data<scalar_t>());
|
||||
|
||||
}));
|
||||
}
|
||||
|
||||
output = output.view({batch, num_query, num_heads*channels});
|
||||
|
||||
return output;
|
||||
}
|
||||
|
||||
|
||||
std::vector<at::Tensor> ms_deform_attn_cuda_backward(
|
||||
const at::Tensor &value,
|
||||
const at::Tensor &spatial_shapes,
|
||||
const at::Tensor &level_start_index,
|
||||
const at::Tensor &sampling_loc,
|
||||
const at::Tensor &attn_weight,
|
||||
const at::Tensor &grad_output,
|
||||
const int im2col_step)
|
||||
{
|
||||
|
||||
AT_ASSERTM(value.is_contiguous(), "value tensor has to be contiguous");
|
||||
AT_ASSERTM(spatial_shapes.is_contiguous(), "spatial_shapes tensor has to be contiguous");
|
||||
AT_ASSERTM(level_start_index.is_contiguous(), "level_start_index tensor has to be contiguous");
|
||||
AT_ASSERTM(sampling_loc.is_contiguous(), "sampling_loc tensor has to be contiguous");
|
||||
AT_ASSERTM(attn_weight.is_contiguous(), "attn_weight tensor has to be contiguous");
|
||||
AT_ASSERTM(grad_output.is_contiguous(), "grad_output tensor has to be contiguous");
|
||||
|
||||
AT_ASSERTM(value.type().is_cuda(), "value must be a CUDA tensor");
|
||||
AT_ASSERTM(spatial_shapes.type().is_cuda(), "spatial_shapes must be a CUDA tensor");
|
||||
AT_ASSERTM(level_start_index.type().is_cuda(), "level_start_index must be a CUDA tensor");
|
||||
AT_ASSERTM(sampling_loc.type().is_cuda(), "sampling_loc must be a CUDA tensor");
|
||||
AT_ASSERTM(attn_weight.type().is_cuda(), "attn_weight must be a CUDA tensor");
|
||||
AT_ASSERTM(grad_output.type().is_cuda(), "grad_output must be a CUDA tensor");
|
||||
|
||||
const int batch = value.size(0);
|
||||
const int spatial_size = value.size(1);
|
||||
const int num_heads = value.size(2);
|
||||
const int channels = value.size(3);
|
||||
|
||||
const int num_levels = spatial_shapes.size(0);
|
||||
|
||||
const int num_query = sampling_loc.size(1);
|
||||
const int num_point = sampling_loc.size(4);
|
||||
|
||||
const int im2col_step_ = std::min(batch, im2col_step);
|
||||
|
||||
AT_ASSERTM(batch % im2col_step_ == 0, "batch(%d) must divide im2col_step(%d)", batch, im2col_step_);
|
||||
|
||||
auto grad_value = at::zeros_like(value);
|
||||
auto grad_sampling_loc = at::zeros_like(sampling_loc);
|
||||
auto grad_attn_weight = at::zeros_like(attn_weight);
|
||||
|
||||
const int batch_n = im2col_step_;
|
||||
auto per_value_size = spatial_size * num_heads * channels;
|
||||
auto per_sample_loc_size = num_query * num_heads * num_levels * num_point * 2;
|
||||
auto per_attn_weight_size = num_query * num_heads * num_levels * num_point;
|
||||
auto grad_output_n = grad_output.view({batch/im2col_step_, batch_n, num_query, num_heads, channels});
|
||||
|
||||
for (int n = 0; n < batch/im2col_step_; ++n)
|
||||
{
|
||||
auto grad_output_g = grad_output_n.select(0, n);
|
||||
AT_DISPATCH_FLOATING_TYPES(value.type(), "ms_deform_attn_backward_cuda", ([&] {
|
||||
ms_deformable_col2im_cuda(at::cuda::getCurrentCUDAStream(),
|
||||
grad_output_g.data<scalar_t>(),
|
||||
value.data<scalar_t>() + n * im2col_step_ * per_value_size,
|
||||
spatial_shapes.data<int64_t>(),
|
||||
level_start_index.data<int64_t>(),
|
||||
sampling_loc.data<scalar_t>() + n * im2col_step_ * per_sample_loc_size,
|
||||
attn_weight.data<scalar_t>() + n * im2col_step_ * per_attn_weight_size,
|
||||
batch_n, spatial_size, num_heads, channels, num_levels, num_query, num_point,
|
||||
grad_value.data<scalar_t>() + n * im2col_step_ * per_value_size,
|
||||
grad_sampling_loc.data<scalar_t>() + n * im2col_step_ * per_sample_loc_size,
|
||||
grad_attn_weight.data<scalar_t>() + n * im2col_step_ * per_attn_weight_size);
|
||||
|
||||
}));
|
||||
}
|
||||
|
||||
return {
|
||||
grad_value, grad_sampling_loc, grad_attn_weight
|
||||
};
|
||||
}
|
||||
|
||||
} // namespace groundingdino
|
||||
|
|
@ -0,0 +1,33 @@
|
|||
/*!
|
||||
**************************************************************************************************
|
||||
* Deformable DETR
|
||||
* Copyright (c) 2020 SenseTime. All Rights Reserved.
|
||||
* Licensed under the Apache License, Version 2.0 [see LICENSE for details]
|
||||
**************************************************************************************************
|
||||
* Modified from https://github.com/chengdazhi/Deformable-Convolution-V2-PyTorch/tree/pytorch_1.0.0
|
||||
**************************************************************************************************
|
||||
*/
|
||||
|
||||
#pragma once
|
||||
#include <torch/extension.h>
|
||||
|
||||
namespace groundingdino {
|
||||
|
||||
at::Tensor ms_deform_attn_cuda_forward(
|
||||
const at::Tensor &value,
|
||||
const at::Tensor &spatial_shapes,
|
||||
const at::Tensor &level_start_index,
|
||||
const at::Tensor &sampling_loc,
|
||||
const at::Tensor &attn_weight,
|
||||
const int im2col_step);
|
||||
|
||||
std::vector<at::Tensor> ms_deform_attn_cuda_backward(
|
||||
const at::Tensor &value,
|
||||
const at::Tensor &spatial_shapes,
|
||||
const at::Tensor &level_start_index,
|
||||
const at::Tensor &sampling_loc,
|
||||
const at::Tensor &attn_weight,
|
||||
const at::Tensor &grad_output,
|
||||
const int im2col_step);
|
||||
|
||||
} // namespace groundingdino
|
||||
File diff suppressed because it is too large
Load Diff
|
|
@ -0,0 +1,7 @@
|
|||
#include <cuda_runtime_api.h>
|
||||
|
||||
namespace groundingdino {
|
||||
int get_cudart_version() {
|
||||
return CUDART_VERSION;
|
||||
}
|
||||
} // namespace groundingdino
|
||||
|
|
@ -0,0 +1,58 @@
|
|||
// Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved
|
||||
|
||||
#include "MsDeformAttn/ms_deform_attn.h"
|
||||
|
||||
namespace groundingdino {
|
||||
|
||||
#ifdef WITH_CUDA
|
||||
extern int get_cudart_version();
|
||||
#endif
|
||||
|
||||
std::string get_cuda_version() {
|
||||
#ifdef WITH_CUDA
|
||||
std::ostringstream oss;
|
||||
|
||||
// copied from
|
||||
// https://github.com/pytorch/pytorch/blob/master/aten/src/ATen/cuda/detail/CUDAHooks.cpp#L231
|
||||
auto printCudaStyleVersion = [&](int v) {
|
||||
oss << (v / 1000) << "." << (v / 10 % 100);
|
||||
if (v % 10 != 0) {
|
||||
oss << "." << (v % 10);
|
||||
}
|
||||
};
|
||||
printCudaStyleVersion(get_cudart_version());
|
||||
return oss.str();
|
||||
#else
|
||||
return std::string("not available");
|
||||
#endif
|
||||
}
|
||||
|
||||
// similar to
|
||||
// https://github.com/pytorch/pytorch/blob/master/aten/src/ATen/Version.cpp
|
||||
std::string get_compiler_version() {
|
||||
std::ostringstream ss;
|
||||
#if defined(__GNUC__)
|
||||
#ifndef __clang__
|
||||
{ ss << "GCC " << __GNUC__ << "." << __GNUC_MINOR__; }
|
||||
#endif
|
||||
#endif
|
||||
|
||||
#if defined(__clang_major__)
|
||||
{
|
||||
ss << "clang " << __clang_major__ << "." << __clang_minor__ << "."
|
||||
<< __clang_patchlevel__;
|
||||
}
|
||||
#endif
|
||||
|
||||
#if defined(_MSC_VER)
|
||||
{ ss << "MSVC " << _MSC_FULL_VER; }
|
||||
#endif
|
||||
return ss.str();
|
||||
}
|
||||
|
||||
PYBIND11_MODULE(TORCH_EXTENSION_NAME, m) {
|
||||
m.def("ms_deform_attn_forward", &ms_deform_attn_forward, "ms_deform_attn_forward");
|
||||
m.def("ms_deform_attn_backward", &ms_deform_attn_backward, "ms_deform_attn_backward");
|
||||
}
|
||||
|
||||
} // namespace groundingdino
|
||||
|
|
@ -0,0 +1,297 @@
|
|||
# ------------------------------------------------------------------------
|
||||
# Grounding DINO
|
||||
# url: https://github.com/IDEA-Research/GroundingDINO
|
||||
# Copyright (c) 2023 IDEA. All Rights Reserved.
|
||||
# Licensed under the Apache License, Version 2.0 [see LICENSE for details]
|
||||
# ------------------------------------------------------------------------
|
||||
|
||||
import torch
|
||||
import torch.nn as nn
|
||||
import torch.nn.functional as F
|
||||
from timm.models.layers import DropPath
|
||||
|
||||
|
||||
class FeatureResizer(nn.Module):
|
||||
"""
|
||||
This class takes as input a set of embeddings of dimension C1 and outputs a set of
|
||||
embedding of dimension C2, after a linear transformation, dropout and normalization (LN).
|
||||
"""
|
||||
|
||||
def __init__(self, input_feat_size, output_feat_size, dropout, do_ln=True):
|
||||
super().__init__()
|
||||
self.do_ln = do_ln
|
||||
# Object feature encoding
|
||||
self.fc = nn.Linear(input_feat_size, output_feat_size, bias=True)
|
||||
self.layer_norm = nn.LayerNorm(output_feat_size, eps=1e-12)
|
||||
self.dropout = nn.Dropout(dropout)
|
||||
|
||||
def forward(self, encoder_features):
|
||||
x = self.fc(encoder_features)
|
||||
if self.do_ln:
|
||||
x = self.layer_norm(x)
|
||||
output = self.dropout(x)
|
||||
return output
|
||||
|
||||
|
||||
def l1norm(X, dim, eps=1e-8):
|
||||
"""L1-normalize columns of X"""
|
||||
norm = torch.abs(X).sum(dim=dim, keepdim=True) + eps
|
||||
X = torch.div(X, norm)
|
||||
return X
|
||||
|
||||
|
||||
def l2norm(X, dim, eps=1e-8):
|
||||
"""L2-normalize columns of X"""
|
||||
norm = torch.pow(X, 2).sum(dim=dim, keepdim=True).sqrt() + eps
|
||||
X = torch.div(X, norm)
|
||||
return X
|
||||
|
||||
|
||||
def func_attention(query, context, smooth=1, raw_feature_norm="softmax", eps=1e-8):
|
||||
"""
|
||||
query: (n_context, queryL, d)
|
||||
context: (n_context, sourceL, d)
|
||||
"""
|
||||
batch_size_q, queryL = query.size(0), query.size(1)
|
||||
batch_size, sourceL = context.size(0), context.size(1)
|
||||
|
||||
# Get attention
|
||||
# --> (batch, d, queryL)
|
||||
queryT = torch.transpose(query, 1, 2)
|
||||
|
||||
# (batch, sourceL, d)(batch, d, queryL)
|
||||
# --> (batch, sourceL, queryL)
|
||||
attn = torch.bmm(context, queryT)
|
||||
if raw_feature_norm == "softmax":
|
||||
# --> (batch*sourceL, queryL)
|
||||
attn = attn.view(batch_size * sourceL, queryL)
|
||||
attn = nn.Softmax()(attn)
|
||||
# --> (batch, sourceL, queryL)
|
||||
attn = attn.view(batch_size, sourceL, queryL)
|
||||
elif raw_feature_norm == "l2norm":
|
||||
attn = l2norm(attn, 2)
|
||||
elif raw_feature_norm == "clipped_l2norm":
|
||||
attn = nn.LeakyReLU(0.1)(attn)
|
||||
attn = l2norm(attn, 2)
|
||||
else:
|
||||
raise ValueError("unknown first norm type:", raw_feature_norm)
|
||||
# --> (batch, queryL, sourceL)
|
||||
attn = torch.transpose(attn, 1, 2).contiguous()
|
||||
# --> (batch*queryL, sourceL)
|
||||
attn = attn.view(batch_size * queryL, sourceL)
|
||||
attn = nn.Softmax()(attn * smooth)
|
||||
# --> (batch, queryL, sourceL)
|
||||
attn = attn.view(batch_size, queryL, sourceL)
|
||||
# --> (batch, sourceL, queryL)
|
||||
attnT = torch.transpose(attn, 1, 2).contiguous()
|
||||
|
||||
# --> (batch, d, sourceL)
|
||||
contextT = torch.transpose(context, 1, 2)
|
||||
# (batch x d x sourceL)(batch x sourceL x queryL)
|
||||
# --> (batch, d, queryL)
|
||||
weightedContext = torch.bmm(contextT, attnT)
|
||||
# --> (batch, queryL, d)
|
||||
weightedContext = torch.transpose(weightedContext, 1, 2)
|
||||
|
||||
return weightedContext, attnT
|
||||
|
||||
|
||||
class BiMultiHeadAttention(nn.Module):
|
||||
def __init__(self, v_dim, l_dim, embed_dim, num_heads, dropout=0.1, cfg=None):
|
||||
super(BiMultiHeadAttention, self).__init__()
|
||||
|
||||
self.embed_dim = embed_dim
|
||||
self.num_heads = num_heads
|
||||
self.head_dim = embed_dim // num_heads
|
||||
self.v_dim = v_dim
|
||||
self.l_dim = l_dim
|
||||
|
||||
assert (
|
||||
self.head_dim * self.num_heads == self.embed_dim
|
||||
), f"embed_dim must be divisible by num_heads (got `embed_dim`: {self.embed_dim} and `num_heads`: {self.num_heads})."
|
||||
self.scale = self.head_dim ** (-0.5)
|
||||
self.dropout = dropout
|
||||
|
||||
self.v_proj = nn.Linear(self.v_dim, self.embed_dim)
|
||||
self.l_proj = nn.Linear(self.l_dim, self.embed_dim)
|
||||
self.values_v_proj = nn.Linear(self.v_dim, self.embed_dim)
|
||||
self.values_l_proj = nn.Linear(self.l_dim, self.embed_dim)
|
||||
|
||||
self.out_v_proj = nn.Linear(self.embed_dim, self.v_dim)
|
||||
self.out_l_proj = nn.Linear(self.embed_dim, self.l_dim)
|
||||
|
||||
self.stable_softmax_2d = True
|
||||
self.clamp_min_for_underflow = True
|
||||
self.clamp_max_for_overflow = True
|
||||
|
||||
self._reset_parameters()
|
||||
|
||||
def _shape(self, tensor: torch.Tensor, seq_len: int, bsz: int):
|
||||
return tensor.view(bsz, seq_len, self.num_heads, self.head_dim).transpose(1, 2).contiguous()
|
||||
|
||||
def _reset_parameters(self):
|
||||
nn.init.xavier_uniform_(self.v_proj.weight)
|
||||
self.v_proj.bias.data.fill_(0)
|
||||
nn.init.xavier_uniform_(self.l_proj.weight)
|
||||
self.l_proj.bias.data.fill_(0)
|
||||
nn.init.xavier_uniform_(self.values_v_proj.weight)
|
||||
self.values_v_proj.bias.data.fill_(0)
|
||||
nn.init.xavier_uniform_(self.values_l_proj.weight)
|
||||
self.values_l_proj.bias.data.fill_(0)
|
||||
nn.init.xavier_uniform_(self.out_v_proj.weight)
|
||||
self.out_v_proj.bias.data.fill_(0)
|
||||
nn.init.xavier_uniform_(self.out_l_proj.weight)
|
||||
self.out_l_proj.bias.data.fill_(0)
|
||||
|
||||
def forward(self, v, l, attention_mask_v=None, attention_mask_l=None):
|
||||
"""_summary_
|
||||
|
||||
Args:
|
||||
v (_type_): bs, n_img, dim
|
||||
l (_type_): bs, n_text, dim
|
||||
attention_mask_v (_type_, optional): _description_. bs, n_img
|
||||
attention_mask_l (_type_, optional): _description_. bs, n_text
|
||||
|
||||
Returns:
|
||||
_type_: _description_
|
||||
"""
|
||||
# if os.environ.get('IPDB_SHILONG_DEBUG', None) == 'INFO':
|
||||
# import ipdb; ipdb.set_trace()
|
||||
bsz, tgt_len, _ = v.size()
|
||||
|
||||
query_states = self.v_proj(v) * self.scale
|
||||
key_states = self._shape(self.l_proj(l), -1, bsz)
|
||||
value_v_states = self._shape(self.values_v_proj(v), -1, bsz)
|
||||
value_l_states = self._shape(self.values_l_proj(l), -1, bsz)
|
||||
|
||||
proj_shape = (bsz * self.num_heads, -1, self.head_dim)
|
||||
query_states = self._shape(query_states, tgt_len, bsz).view(*proj_shape)
|
||||
key_states = key_states.view(*proj_shape)
|
||||
value_v_states = value_v_states.view(*proj_shape)
|
||||
value_l_states = value_l_states.view(*proj_shape)
|
||||
|
||||
src_len = key_states.size(1)
|
||||
attn_weights = torch.bmm(query_states, key_states.transpose(1, 2)) # bs*nhead, nimg, ntxt
|
||||
|
||||
if attn_weights.size() != (bsz * self.num_heads, tgt_len, src_len):
|
||||
raise ValueError(
|
||||
f"Attention weights should be of size {(bsz * self.num_heads, tgt_len, src_len)}, but is {attn_weights.size()}"
|
||||
)
|
||||
|
||||
if self.stable_softmax_2d:
|
||||
attn_weights = attn_weights - attn_weights.max()
|
||||
|
||||
if self.clamp_min_for_underflow:
|
||||
attn_weights = torch.clamp(
|
||||
attn_weights, min=-50000
|
||||
) # Do not increase -50000, data type half has quite limited range
|
||||
if self.clamp_max_for_overflow:
|
||||
attn_weights = torch.clamp(
|
||||
attn_weights, max=50000
|
||||
) # Do not increase 50000, data type half has quite limited range
|
||||
|
||||
attn_weights_T = attn_weights.transpose(1, 2)
|
||||
attn_weights_l = attn_weights_T - torch.max(attn_weights_T, dim=-1, keepdim=True)[0]
|
||||
if self.clamp_min_for_underflow:
|
||||
attn_weights_l = torch.clamp(
|
||||
attn_weights_l, min=-50000
|
||||
) # Do not increase -50000, data type half has quite limited range
|
||||
if self.clamp_max_for_overflow:
|
||||
attn_weights_l = torch.clamp(
|
||||
attn_weights_l, max=50000
|
||||
) # Do not increase 50000, data type half has quite limited range
|
||||
|
||||
# mask vison for language
|
||||
if attention_mask_v is not None:
|
||||
attention_mask_v = (
|
||||
attention_mask_v[:, None, None, :].repeat(1, self.num_heads, 1, 1).flatten(0, 1)
|
||||
)
|
||||
attn_weights_l.masked_fill_(attention_mask_v, float("-inf"))
|
||||
|
||||
attn_weights_l = attn_weights_l.softmax(dim=-1)
|
||||
|
||||
# mask language for vision
|
||||
if attention_mask_l is not None:
|
||||
attention_mask_l = (
|
||||
attention_mask_l[:, None, None, :].repeat(1, self.num_heads, 1, 1).flatten(0, 1)
|
||||
)
|
||||
attn_weights.masked_fill_(attention_mask_l, float("-inf"))
|
||||
attn_weights_v = attn_weights.softmax(dim=-1)
|
||||
|
||||
attn_probs_v = F.dropout(attn_weights_v, p=self.dropout, training=self.training)
|
||||
attn_probs_l = F.dropout(attn_weights_l, p=self.dropout, training=self.training)
|
||||
|
||||
attn_output_v = torch.bmm(attn_probs_v, value_l_states)
|
||||
attn_output_l = torch.bmm(attn_probs_l, value_v_states)
|
||||
|
||||
if attn_output_v.size() != (bsz * self.num_heads, tgt_len, self.head_dim):
|
||||
raise ValueError(
|
||||
f"`attn_output_v` should be of size {(bsz, self.num_heads, tgt_len, self.head_dim)}, but is {attn_output_v.size()}"
|
||||
)
|
||||
|
||||
if attn_output_l.size() != (bsz * self.num_heads, src_len, self.head_dim):
|
||||
raise ValueError(
|
||||
f"`attn_output_l` should be of size {(bsz, self.num_heads, src_len, self.head_dim)}, but is {attn_output_l.size()}"
|
||||
)
|
||||
|
||||
attn_output_v = attn_output_v.view(bsz, self.num_heads, tgt_len, self.head_dim)
|
||||
attn_output_v = attn_output_v.transpose(1, 2)
|
||||
attn_output_v = attn_output_v.reshape(bsz, tgt_len, self.embed_dim)
|
||||
|
||||
attn_output_l = attn_output_l.view(bsz, self.num_heads, src_len, self.head_dim)
|
||||
attn_output_l = attn_output_l.transpose(1, 2)
|
||||
attn_output_l = attn_output_l.reshape(bsz, src_len, self.embed_dim)
|
||||
|
||||
attn_output_v = self.out_v_proj(attn_output_v)
|
||||
attn_output_l = self.out_l_proj(attn_output_l)
|
||||
|
||||
return attn_output_v, attn_output_l
|
||||
|
||||
|
||||
# Bi-Direction MHA (text->image, image->text)
|
||||
class BiAttentionBlock(nn.Module):
|
||||
def __init__(
|
||||
self,
|
||||
v_dim,
|
||||
l_dim,
|
||||
embed_dim,
|
||||
num_heads,
|
||||
dropout=0.1,
|
||||
drop_path=0.0,
|
||||
init_values=1e-4,
|
||||
cfg=None,
|
||||
):
|
||||
"""
|
||||
Inputs:
|
||||
embed_dim - Dimensionality of input and attention feature vectors
|
||||
hidden_dim - Dimensionality of hidden layer in feed-forward network
|
||||
(usually 2-4x larger than embed_dim)
|
||||
num_heads - Number of heads to use in the Multi-Head Attention block
|
||||
dropout - Amount of dropout to apply in the feed-forward network
|
||||
"""
|
||||
super(BiAttentionBlock, self).__init__()
|
||||
|
||||
# pre layer norm
|
||||
self.layer_norm_v = nn.LayerNorm(v_dim)
|
||||
self.layer_norm_l = nn.LayerNorm(l_dim)
|
||||
self.attn = BiMultiHeadAttention(
|
||||
v_dim=v_dim, l_dim=l_dim, embed_dim=embed_dim, num_heads=num_heads, dropout=dropout
|
||||
)
|
||||
|
||||
# add layer scale for training stability
|
||||
self.drop_path = DropPath(drop_path) if drop_path > 0.0 else nn.Identity()
|
||||
self.gamma_v = nn.Parameter(init_values * torch.ones((v_dim)), requires_grad=True)
|
||||
self.gamma_l = nn.Parameter(init_values * torch.ones((l_dim)), requires_grad=True)
|
||||
|
||||
def forward(self, v, l, attention_mask_v=None, attention_mask_l=None):
|
||||
v = self.layer_norm_v(v)
|
||||
l = self.layer_norm_l(l)
|
||||
delta_v, delta_l = self.attn(
|
||||
v, l, attention_mask_v=attention_mask_v, attention_mask_l=attention_mask_l
|
||||
)
|
||||
# v, l = v + delta_v, l + delta_l
|
||||
v = v + self.drop_path(self.gamma_v * delta_v)
|
||||
l = l + self.drop_path(self.gamma_l * delta_l)
|
||||
return v, l
|
||||
|
||||
# def forward(self, v:List[torch.Tensor], l, attention_mask_v=None, attention_mask_l=None)
|
||||
|
|
@ -0,0 +1,395 @@
|
|||
# ------------------------------------------------------------------------
|
||||
# Grounding DINO
|
||||
# url: https://github.com/IDEA-Research/GroundingDINO
|
||||
# Copyright (c) 2023 IDEA. All Rights Reserved.
|
||||
# Licensed under the Apache License, Version 2.0 [see LICENSE for details]
|
||||
# ------------------------------------------------------------------------
|
||||
# Conditional DETR model and criterion classes.
|
||||
# Copyright (c) 2021 Microsoft. All Rights Reserved.
|
||||
# Licensed under the Apache License, Version 2.0 [see LICENSE for details]
|
||||
# ------------------------------------------------------------------------
|
||||
# Modified from DETR (https://github.com/facebookresearch/detr)
|
||||
# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
|
||||
# ------------------------------------------------------------------------
|
||||
# Modified from Deformable DETR (https://github.com/fundamentalvision/Deformable-DETR)
|
||||
# Copyright (c) 2020 SenseTime. All Rights Reserved.
|
||||
# ------------------------------------------------------------------------
|
||||
import copy
|
||||
from typing import List
|
||||
|
||||
import torch
|
||||
import torch.nn.functional as F
|
||||
from torch import nn
|
||||
from torchvision.ops.boxes import nms
|
||||
from transformers import AutoTokenizer, BertModel, BertTokenizer, RobertaModel, RobertaTokenizerFast
|
||||
|
||||
from groundingdino.util import box_ops, get_tokenlizer
|
||||
from groundingdino.util.misc import (
|
||||
NestedTensor,
|
||||
accuracy,
|
||||
get_world_size,
|
||||
interpolate,
|
||||
inverse_sigmoid,
|
||||
is_dist_avail_and_initialized,
|
||||
nested_tensor_from_tensor_list,
|
||||
)
|
||||
from groundingdino.util.utils import get_phrases_from_posmap
|
||||
from groundingdino.util.visualizer import COCOVisualizer
|
||||
from groundingdino.util.vl_utils import create_positive_map_from_span
|
||||
|
||||
from ..registry import MODULE_BUILD_FUNCS
|
||||
from .backbone import build_backbone
|
||||
from .bertwarper import (
|
||||
BertModelWarper,
|
||||
generate_masks_with_special_tokens,
|
||||
generate_masks_with_special_tokens_and_transfer_map,
|
||||
)
|
||||
from .transformer import build_transformer
|
||||
from .utils import MLP, ContrastiveEmbed, sigmoid_focal_loss
|
||||
|
||||
|
||||
class GroundingDINO(nn.Module):
|
||||
"""This is the Cross-Attention Detector module that performs object detection"""
|
||||
|
||||
def __init__(
|
||||
self,
|
||||
backbone,
|
||||
transformer,
|
||||
num_queries,
|
||||
aux_loss=False,
|
||||
iter_update=False,
|
||||
query_dim=2,
|
||||
num_feature_levels=1,
|
||||
nheads=8,
|
||||
# two stage
|
||||
two_stage_type="no", # ['no', 'standard']
|
||||
dec_pred_bbox_embed_share=True,
|
||||
two_stage_class_embed_share=True,
|
||||
two_stage_bbox_embed_share=True,
|
||||
num_patterns=0,
|
||||
dn_number=100,
|
||||
dn_box_noise_scale=0.4,
|
||||
dn_label_noise_ratio=0.5,
|
||||
dn_labelbook_size=100,
|
||||
text_encoder_type="bert-base-uncased",
|
||||
sub_sentence_present=True,
|
||||
max_text_len=256,
|
||||
):
|
||||
"""Initializes the model.
|
||||
Parameters:
|
||||
backbone: torch module of the backbone to be used. See backbone.py
|
||||
transformer: torch module of the transformer architecture. See transformer.py
|
||||
num_queries: number of object queries, ie detection slot. This is the maximal number of objects
|
||||
Conditional DETR can detect in a single image. For COCO, we recommend 100 queries.
|
||||
aux_loss: True if auxiliary decoding losses (loss at each decoder layer) are to be used.
|
||||
"""
|
||||
super().__init__()
|
||||
self.num_queries = num_queries
|
||||
self.transformer = transformer
|
||||
self.hidden_dim = hidden_dim = transformer.d_model
|
||||
self.num_feature_levels = num_feature_levels
|
||||
self.nheads = nheads
|
||||
self.max_text_len = 256
|
||||
self.sub_sentence_present = sub_sentence_present
|
||||
|
||||
# setting query dim
|
||||
self.query_dim = query_dim
|
||||
assert query_dim == 4
|
||||
|
||||
# for dn training
|
||||
self.num_patterns = num_patterns
|
||||
self.dn_number = dn_number
|
||||
self.dn_box_noise_scale = dn_box_noise_scale
|
||||
self.dn_label_noise_ratio = dn_label_noise_ratio
|
||||
self.dn_labelbook_size = dn_labelbook_size
|
||||
|
||||
# bert
|
||||
self.tokenizer = get_tokenlizer.get_tokenlizer(text_encoder_type)
|
||||
self.bert = get_tokenlizer.get_pretrained_language_model(text_encoder_type)
|
||||
self.bert.pooler.dense.weight.requires_grad_(False)
|
||||
self.bert.pooler.dense.bias.requires_grad_(False)
|
||||
self.bert = BertModelWarper(bert_model=self.bert)
|
||||
|
||||
self.feat_map = nn.Linear(self.bert.config.hidden_size, self.hidden_dim, bias=True)
|
||||
nn.init.constant_(self.feat_map.bias.data, 0)
|
||||
nn.init.xavier_uniform_(self.feat_map.weight.data)
|
||||
# freeze
|
||||
|
||||
# special tokens
|
||||
self.specical_tokens = self.tokenizer.convert_tokens_to_ids(["[CLS]", "[SEP]", ".", "?"])
|
||||
|
||||
# prepare input projection layers
|
||||
if num_feature_levels > 1:
|
||||
num_backbone_outs = len(backbone.num_channels)
|
||||
input_proj_list = []
|
||||
for _ in range(num_backbone_outs):
|
||||
in_channels = backbone.num_channels[_]
|
||||
input_proj_list.append(
|
||||
nn.Sequential(
|
||||
nn.Conv2d(in_channels, hidden_dim, kernel_size=1),
|
||||
nn.GroupNorm(32, hidden_dim),
|
||||
)
|
||||
)
|
||||
for _ in range(num_feature_levels - num_backbone_outs):
|
||||
input_proj_list.append(
|
||||
nn.Sequential(
|
||||
nn.Conv2d(in_channels, hidden_dim, kernel_size=3, stride=2, padding=1),
|
||||
nn.GroupNorm(32, hidden_dim),
|
||||
)
|
||||
)
|
||||
in_channels = hidden_dim
|
||||
self.input_proj = nn.ModuleList(input_proj_list)
|
||||
else:
|
||||
assert two_stage_type == "no", "two_stage_type should be no if num_feature_levels=1 !!!"
|
||||
self.input_proj = nn.ModuleList(
|
||||
[
|
||||
nn.Sequential(
|
||||
nn.Conv2d(backbone.num_channels[-1], hidden_dim, kernel_size=1),
|
||||
nn.GroupNorm(32, hidden_dim),
|
||||
)
|
||||
]
|
||||
)
|
||||
|
||||
self.backbone = backbone
|
||||
self.aux_loss = aux_loss
|
||||
self.box_pred_damping = box_pred_damping = None
|
||||
|
||||
self.iter_update = iter_update
|
||||
assert iter_update, "Why not iter_update?"
|
||||
|
||||
# prepare pred layers
|
||||
self.dec_pred_bbox_embed_share = dec_pred_bbox_embed_share
|
||||
# prepare class & box embed
|
||||
_class_embed = ContrastiveEmbed()
|
||||
|
||||
_bbox_embed = MLP(hidden_dim, hidden_dim, 4, 3)
|
||||
nn.init.constant_(_bbox_embed.layers[-1].weight.data, 0)
|
||||
nn.init.constant_(_bbox_embed.layers[-1].bias.data, 0)
|
||||
|
||||
if dec_pred_bbox_embed_share:
|
||||
box_embed_layerlist = [_bbox_embed for i in range(transformer.num_decoder_layers)]
|
||||
else:
|
||||
box_embed_layerlist = [
|
||||
copy.deepcopy(_bbox_embed) for i in range(transformer.num_decoder_layers)
|
||||
]
|
||||
class_embed_layerlist = [_class_embed for i in range(transformer.num_decoder_layers)]
|
||||
self.bbox_embed = nn.ModuleList(box_embed_layerlist)
|
||||
self.class_embed = nn.ModuleList(class_embed_layerlist)
|
||||
self.transformer.decoder.bbox_embed = self.bbox_embed
|
||||
self.transformer.decoder.class_embed = self.class_embed
|
||||
|
||||
# two stage
|
||||
self.two_stage_type = two_stage_type
|
||||
assert two_stage_type in ["no", "standard"], "unknown param {} of two_stage_type".format(
|
||||
two_stage_type
|
||||
)
|
||||
if two_stage_type != "no":
|
||||
if two_stage_bbox_embed_share:
|
||||
assert dec_pred_bbox_embed_share
|
||||
self.transformer.enc_out_bbox_embed = _bbox_embed
|
||||
else:
|
||||
self.transformer.enc_out_bbox_embed = copy.deepcopy(_bbox_embed)
|
||||
|
||||
if two_stage_class_embed_share:
|
||||
assert dec_pred_bbox_embed_share
|
||||
self.transformer.enc_out_class_embed = _class_embed
|
||||
else:
|
||||
self.transformer.enc_out_class_embed = copy.deepcopy(_class_embed)
|
||||
|
||||
self.refpoint_embed = None
|
||||
|
||||
self._reset_parameters()
|
||||
|
||||
def _reset_parameters(self):
|
||||
# init input_proj
|
||||
for proj in self.input_proj:
|
||||
nn.init.xavier_uniform_(proj[0].weight, gain=1)
|
||||
nn.init.constant_(proj[0].bias, 0)
|
||||
|
||||
def init_ref_points(self, use_num_queries):
|
||||
self.refpoint_embed = nn.Embedding(use_num_queries, self.query_dim)
|
||||
|
||||
def forward(self, samples: NestedTensor, targets: List = None, **kw):
|
||||
"""The forward expects a NestedTensor, which consists of:
|
||||
- samples.tensor: batched images, of shape [batch_size x 3 x H x W]
|
||||
- samples.mask: a binary mask of shape [batch_size x H x W], containing 1 on padded pixels
|
||||
|
||||
It returns a dict with the following elements:
|
||||
- "pred_logits": the classification logits (including no-object) for all queries.
|
||||
Shape= [batch_size x num_queries x num_classes]
|
||||
- "pred_boxes": The normalized boxes coordinates for all queries, represented as
|
||||
(center_x, center_y, width, height). These values are normalized in [0, 1],
|
||||
relative to the size of each individual image (disregarding possible padding).
|
||||
See PostProcess for information on how to retrieve the unnormalized bounding box.
|
||||
- "aux_outputs": Optional, only returned when auxilary losses are activated. It is a list of
|
||||
dictionnaries containing the two above keys for each decoder layer.
|
||||
"""
|
||||
if targets is None:
|
||||
captions = kw["captions"]
|
||||
else:
|
||||
captions = [t["caption"] for t in targets]
|
||||
len(captions)
|
||||
|
||||
# encoder texts
|
||||
tokenized = self.tokenizer(captions, padding="longest", return_tensors="pt").to(
|
||||
samples.device
|
||||
)
|
||||
(
|
||||
text_self_attention_masks,
|
||||
position_ids,
|
||||
cate_to_token_mask_list,
|
||||
) = generate_masks_with_special_tokens_and_transfer_map(
|
||||
tokenized, self.specical_tokens, self.tokenizer
|
||||
)
|
||||
|
||||
if text_self_attention_masks.shape[1] > self.max_text_len:
|
||||
text_self_attention_masks = text_self_attention_masks[
|
||||
:, : self.max_text_len, : self.max_text_len
|
||||
]
|
||||
position_ids = position_ids[:, : self.max_text_len]
|
||||
tokenized["input_ids"] = tokenized["input_ids"][:, : self.max_text_len]
|
||||
tokenized["attention_mask"] = tokenized["attention_mask"][:, : self.max_text_len]
|
||||
tokenized["token_type_ids"] = tokenized["token_type_ids"][:, : self.max_text_len]
|
||||
|
||||
# extract text embeddings
|
||||
if self.sub_sentence_present:
|
||||
tokenized_for_encoder = {k: v for k, v in tokenized.items() if k != "attention_mask"}
|
||||
tokenized_for_encoder["attention_mask"] = text_self_attention_masks
|
||||
tokenized_for_encoder["position_ids"] = position_ids
|
||||
else:
|
||||
# import ipdb; ipdb.set_trace()
|
||||
tokenized_for_encoder = tokenized
|
||||
|
||||
bert_output = self.bert(**tokenized_for_encoder) # bs, 195, 768
|
||||
|
||||
encoded_text = self.feat_map(bert_output["last_hidden_state"]) # bs, 195, d_model
|
||||
text_token_mask = tokenized.attention_mask.bool() # bs, 195
|
||||
# text_token_mask: True for nomask, False for mask
|
||||
# text_self_attention_masks: True for nomask, False for mask
|
||||
|
||||
if encoded_text.shape[1] > self.max_text_len:
|
||||
encoded_text = encoded_text[:, : self.max_text_len, :]
|
||||
text_token_mask = text_token_mask[:, : self.max_text_len]
|
||||
position_ids = position_ids[:, : self.max_text_len]
|
||||
text_self_attention_masks = text_self_attention_masks[
|
||||
:, : self.max_text_len, : self.max_text_len
|
||||
]
|
||||
|
||||
text_dict = {
|
||||
"encoded_text": encoded_text, # bs, 195, d_model
|
||||
"text_token_mask": text_token_mask, # bs, 195
|
||||
"position_ids": position_ids, # bs, 195
|
||||
"text_self_attention_masks": text_self_attention_masks, # bs, 195,195
|
||||
}
|
||||
|
||||
# import ipdb; ipdb.set_trace()
|
||||
|
||||
if isinstance(samples, (list, torch.Tensor)):
|
||||
samples = nested_tensor_from_tensor_list(samples)
|
||||
features, poss = self.backbone(samples)
|
||||
|
||||
srcs = []
|
||||
masks = []
|
||||
for l, feat in enumerate(features):
|
||||
src, mask = feat.decompose()
|
||||
srcs.append(self.input_proj[l](src))
|
||||
masks.append(mask)
|
||||
assert mask is not None
|
||||
if self.num_feature_levels > len(srcs):
|
||||
_len_srcs = len(srcs)
|
||||
for l in range(_len_srcs, self.num_feature_levels):
|
||||
if l == _len_srcs:
|
||||
src = self.input_proj[l](features[-1].tensors)
|
||||
else:
|
||||
src = self.input_proj[l](srcs[-1])
|
||||
m = samples.mask
|
||||
mask = F.interpolate(m[None].float(), size=src.shape[-2:]).to(torch.bool)[0]
|
||||
pos_l = self.backbone[1](NestedTensor(src, mask)).to(src.dtype)
|
||||
srcs.append(src)
|
||||
masks.append(mask)
|
||||
poss.append(pos_l)
|
||||
|
||||
input_query_bbox = input_query_label = attn_mask = dn_meta = None
|
||||
hs, reference, hs_enc, ref_enc, init_box_proposal = self.transformer(
|
||||
srcs, masks, input_query_bbox, poss, input_query_label, attn_mask, text_dict
|
||||
)
|
||||
|
||||
# deformable-detr-like anchor update
|
||||
outputs_coord_list = []
|
||||
for dec_lid, (layer_ref_sig, layer_bbox_embed, layer_hs) in enumerate(
|
||||
zip(reference[:-1], self.bbox_embed, hs)
|
||||
):
|
||||
layer_delta_unsig = layer_bbox_embed(layer_hs)
|
||||
layer_outputs_unsig = layer_delta_unsig + inverse_sigmoid(layer_ref_sig)
|
||||
layer_outputs_unsig = layer_outputs_unsig.sigmoid()
|
||||
outputs_coord_list.append(layer_outputs_unsig)
|
||||
outputs_coord_list = torch.stack(outputs_coord_list)
|
||||
|
||||
# output
|
||||
outputs_class = torch.stack(
|
||||
[
|
||||
layer_cls_embed(layer_hs, text_dict)
|
||||
for layer_cls_embed, layer_hs in zip(self.class_embed, hs)
|
||||
]
|
||||
)
|
||||
out = {"pred_logits": outputs_class[-1], "pred_boxes": outputs_coord_list[-1]}
|
||||
|
||||
# # for intermediate outputs
|
||||
# if self.aux_loss:
|
||||
# out['aux_outputs'] = self._set_aux_loss(outputs_class, outputs_coord_list)
|
||||
|
||||
# # for encoder output
|
||||
# if hs_enc is not None:
|
||||
# # prepare intermediate outputs
|
||||
# interm_coord = ref_enc[-1]
|
||||
# interm_class = self.transformer.enc_out_class_embed(hs_enc[-1], text_dict)
|
||||
# out['interm_outputs'] = {'pred_logits': interm_class, 'pred_boxes': interm_coord}
|
||||
# out['interm_outputs_for_matching_pre'] = {'pred_logits': interm_class, 'pred_boxes': init_box_proposal}
|
||||
|
||||
return out
|
||||
|
||||
@torch.jit.unused
|
||||
def _set_aux_loss(self, outputs_class, outputs_coord):
|
||||
# this is a workaround to make torchscript happy, as torchscript
|
||||
# doesn't support dictionary with non-homogeneous values, such
|
||||
# as a dict having both a Tensor and a list.
|
||||
return [
|
||||
{"pred_logits": a, "pred_boxes": b}
|
||||
for a, b in zip(outputs_class[:-1], outputs_coord[:-1])
|
||||
]
|
||||
|
||||
|
||||
@MODULE_BUILD_FUNCS.registe_with_name(module_name="groundingdino")
|
||||
def build_groundingdino(args):
|
||||
|
||||
backbone = build_backbone(args)
|
||||
transformer = build_transformer(args)
|
||||
|
||||
dn_labelbook_size = args.dn_labelbook_size
|
||||
dec_pred_bbox_embed_share = args.dec_pred_bbox_embed_share
|
||||
sub_sentence_present = args.sub_sentence_present
|
||||
|
||||
model = GroundingDINO(
|
||||
backbone,
|
||||
transformer,
|
||||
num_queries=args.num_queries,
|
||||
aux_loss=True,
|
||||
iter_update=True,
|
||||
query_dim=4,
|
||||
num_feature_levels=args.num_feature_levels,
|
||||
nheads=args.nheads,
|
||||
dec_pred_bbox_embed_share=dec_pred_bbox_embed_share,
|
||||
two_stage_type=args.two_stage_type,
|
||||
two_stage_bbox_embed_share=args.two_stage_bbox_embed_share,
|
||||
two_stage_class_embed_share=args.two_stage_class_embed_share,
|
||||
num_patterns=args.num_patterns,
|
||||
dn_number=0,
|
||||
dn_box_noise_scale=args.dn_box_noise_scale,
|
||||
dn_label_noise_ratio=args.dn_label_noise_ratio,
|
||||
dn_labelbook_size=dn_labelbook_size,
|
||||
text_encoder_type=args.text_encoder_type,
|
||||
sub_sentence_present=sub_sentence_present,
|
||||
max_text_len=args.max_text_len,
|
||||
)
|
||||
|
||||
return model
|
||||
|
|
@ -0,0 +1,409 @@
|
|||
# ------------------------------------------------------------------------
|
||||
# Grounding DINO
|
||||
# url: https://github.com/IDEA-Research/GroundingDINO
|
||||
# Copyright (c) 2023 IDEA. All Rights Reserved.
|
||||
# Licensed under the Apache License, Version 2.0 [see LICENSE for details]
|
||||
# ------------------------------------------------------------------------
|
||||
# Deformable DETR
|
||||
# Copyright (c) 2020 SenseTime. All Rights Reserved.
|
||||
# Licensed under the Apache License, Version 2.0 [see LICENSE for details]
|
||||
# ------------------------------------------------------------------------------------------------
|
||||
# Modified from:
|
||||
# https://github.com/fundamentalvision/Deformable-DETR/blob/main/models/ops/functions/ms_deform_attn_func.py
|
||||
# https://github.com/fundamentalvision/Deformable-DETR/blob/main/models/ops/modules/ms_deform_attn.py
|
||||
# https://github.com/open-mmlab/mmcv/blob/master/mmcv/ops/multi_scale_deform_attn.py
|
||||
# ------------------------------------------------------------------------------------------------
|
||||
|
||||
import math
|
||||
import warnings
|
||||
from typing import Optional
|
||||
|
||||
import torch
|
||||
import torch.nn as nn
|
||||
import torch.nn.functional as F
|
||||
from torch.autograd import Function
|
||||
from torch.autograd.function import once_differentiable
|
||||
from torch.nn.init import constant_, xavier_uniform_
|
||||
|
||||
from groundingdino import _C
|
||||
|
||||
|
||||
# helpers
|
||||
def _is_power_of_2(n):
|
||||
if (not isinstance(n, int)) or (n < 0):
|
||||
raise ValueError("invalid input for _is_power_of_2: {} (type: {})".format(n, type(n)))
|
||||
return (n & (n - 1) == 0) and n != 0
|
||||
|
||||
|
||||
class MultiScaleDeformableAttnFunction(Function):
|
||||
@staticmethod
|
||||
def forward(
|
||||
ctx,
|
||||
value,
|
||||
value_spatial_shapes,
|
||||
value_level_start_index,
|
||||
sampling_locations,
|
||||
attention_weights,
|
||||
im2col_step,
|
||||
):
|
||||
ctx.im2col_step = im2col_step
|
||||
output = _C.ms_deform_attn_forward(
|
||||
value,
|
||||
value_spatial_shapes,
|
||||
value_level_start_index,
|
||||
sampling_locations,
|
||||
attention_weights,
|
||||
ctx.im2col_step,
|
||||
)
|
||||
ctx.save_for_backward(
|
||||
value,
|
||||
value_spatial_shapes,
|
||||
value_level_start_index,
|
||||
sampling_locations,
|
||||
attention_weights,
|
||||
)
|
||||
return output
|
||||
|
||||
@staticmethod
|
||||
@once_differentiable
|
||||
def backward(ctx, grad_output):
|
||||
(
|
||||
value,
|
||||
value_spatial_shapes,
|
||||
value_level_start_index,
|
||||
sampling_locations,
|
||||
attention_weights,
|
||||
) = ctx.saved_tensors
|
||||
grad_value, grad_sampling_loc, grad_attn_weight = _C.ms_deform_attn_backward(
|
||||
value,
|
||||
value_spatial_shapes,
|
||||
value_level_start_index,
|
||||
sampling_locations,
|
||||
attention_weights,
|
||||
grad_output,
|
||||
ctx.im2col_step,
|
||||
)
|
||||
|
||||
return grad_value, None, None, grad_sampling_loc, grad_attn_weight, None
|
||||
|
||||
|
||||
def multi_scale_deformable_attn_pytorch(
|
||||
value: torch.Tensor,
|
||||
value_spatial_shapes: torch.Tensor,
|
||||
sampling_locations: torch.Tensor,
|
||||
attention_weights: torch.Tensor,
|
||||
) -> torch.Tensor:
|
||||
|
||||
bs, _, num_heads, embed_dims = value.shape
|
||||
_, num_queries, num_heads, num_levels, num_points, _ = sampling_locations.shape
|
||||
value_list = value.split([H_ * W_ for H_, W_ in value_spatial_shapes], dim=1)
|
||||
sampling_grids = 2 * sampling_locations - 1
|
||||
sampling_value_list = []
|
||||
for level, (H_, W_) in enumerate(value_spatial_shapes):
|
||||
# bs, H_*W_, num_heads, embed_dims ->
|
||||
# bs, H_*W_, num_heads*embed_dims ->
|
||||
# bs, num_heads*embed_dims, H_*W_ ->
|
||||
# bs*num_heads, embed_dims, H_, W_
|
||||
value_l_ = (
|
||||
value_list[level].flatten(2).transpose(1, 2).reshape(bs * num_heads, embed_dims, H_, W_)
|
||||
)
|
||||
# bs, num_queries, num_heads, num_points, 2 ->
|
||||
# bs, num_heads, num_queries, num_points, 2 ->
|
||||
# bs*num_heads, num_queries, num_points, 2
|
||||
sampling_grid_l_ = sampling_grids[:, :, :, level].transpose(1, 2).flatten(0, 1)
|
||||
# bs*num_heads, embed_dims, num_queries, num_points
|
||||
sampling_value_l_ = F.grid_sample(
|
||||
value_l_, sampling_grid_l_, mode="bilinear", padding_mode="zeros", align_corners=False
|
||||
)
|
||||
sampling_value_list.append(sampling_value_l_)
|
||||
# (bs, num_queries, num_heads, num_levels, num_points) ->
|
||||
# (bs, num_heads, num_queries, num_levels, num_points) ->
|
||||
# (bs, num_heads, 1, num_queries, num_levels*num_points)
|
||||
attention_weights = attention_weights.transpose(1, 2).reshape(
|
||||
bs * num_heads, 1, num_queries, num_levels * num_points
|
||||
)
|
||||
output = (
|
||||
(torch.stack(sampling_value_list, dim=-2).flatten(-2) * attention_weights)
|
||||
.sum(-1)
|
||||
.view(bs, num_heads * embed_dims, num_queries)
|
||||
)
|
||||
return output.transpose(1, 2).contiguous()
|
||||
|
||||
|
||||
class MultiScaleDeformableAttention(nn.Module):
|
||||
"""Multi-Scale Deformable Attention Module used in Deformable-DETR
|
||||
|
||||
`Deformable DETR: Deformable Transformers for End-to-End Object Detection.
|
||||
<https://arxiv.org/pdf/2010.04159.pdf>`_.
|
||||
|
||||
Args:
|
||||
embed_dim (int): The embedding dimension of Attention. Default: 256.
|
||||
num_heads (int): The number of attention heads. Default: 8.
|
||||
num_levels (int): The number of feature map used in Attention. Default: 4.
|
||||
num_points (int): The number of sampling points for each query
|
||||
in each head. Default: 4.
|
||||
img2col_steps (int): The step used in image_to_column. Defualt: 64.
|
||||
dropout (float): Dropout layer used in output. Default: 0.1.
|
||||
batch_first (bool): if ``True``, then the input and output tensor will be
|
||||
provided as `(bs, n, embed_dim)`. Default: False. `(n, bs, embed_dim)`
|
||||
"""
|
||||
|
||||
def __init__(
|
||||
self,
|
||||
embed_dim: int = 256,
|
||||
num_heads: int = 8,
|
||||
num_levels: int = 4,
|
||||
num_points: int = 4,
|
||||
img2col_step: int = 64,
|
||||
batch_first: bool = False,
|
||||
):
|
||||
super().__init__()
|
||||
if embed_dim % num_heads != 0:
|
||||
raise ValueError(
|
||||
"embed_dim must be divisible by num_heads, but got {} and {}".format(
|
||||
embed_dim, num_heads
|
||||
)
|
||||
)
|
||||
head_dim = embed_dim // num_heads
|
||||
|
||||
self.batch_first = batch_first
|
||||
|
||||
if not _is_power_of_2(head_dim):
|
||||
warnings.warn(
|
||||
"""
|
||||
You'd better set d_model in MSDeformAttn to make sure that
|
||||
each dim of the attention head a power of 2, which is more efficient.
|
||||
"""
|
||||
)
|
||||
|
||||
self.im2col_step = img2col_step
|
||||
self.embed_dim = embed_dim
|
||||
self.num_heads = num_heads
|
||||
self.num_levels = num_levels
|
||||
self.num_points = num_points
|
||||
self.sampling_offsets = nn.Linear(embed_dim, num_heads * num_levels * num_points * 2)
|
||||
self.attention_weights = nn.Linear(embed_dim, num_heads * num_levels * num_points)
|
||||
self.value_proj = nn.Linear(embed_dim, embed_dim)
|
||||
self.output_proj = nn.Linear(embed_dim, embed_dim)
|
||||
|
||||
self.init_weights()
|
||||
|
||||
def _reset_parameters(self):
|
||||
return self.init_weights()
|
||||
|
||||
def init_weights(self):
|
||||
"""
|
||||
Default initialization for Parameters of Module.
|
||||
"""
|
||||
constant_(self.sampling_offsets.weight.data, 0.0)
|
||||
thetas = torch.arange(self.num_heads, dtype=torch.float32) * (
|
||||
2.0 * math.pi / self.num_heads
|
||||
)
|
||||
grid_init = torch.stack([thetas.cos(), thetas.sin()], -1)
|
||||
grid_init = (
|
||||
(grid_init / grid_init.abs().max(-1, keepdim=True)[0])
|
||||
.view(self.num_heads, 1, 1, 2)
|
||||
.repeat(1, self.num_levels, self.num_points, 1)
|
||||
)
|
||||
for i in range(self.num_points):
|
||||
grid_init[:, :, i, :] *= i + 1
|
||||
with torch.no_grad():
|
||||
self.sampling_offsets.bias = nn.Parameter(grid_init.view(-1))
|
||||
constant_(self.attention_weights.weight.data, 0.0)
|
||||
constant_(self.attention_weights.bias.data, 0.0)
|
||||
xavier_uniform_(self.value_proj.weight.data)
|
||||
constant_(self.value_proj.bias.data, 0.0)
|
||||
xavier_uniform_(self.output_proj.weight.data)
|
||||
constant_(self.output_proj.bias.data, 0.0)
|
||||
|
||||
def freeze_sampling_offsets(self):
|
||||
print("Freeze sampling offsets")
|
||||
self.sampling_offsets.weight.requires_grad = False
|
||||
self.sampling_offsets.bias.requires_grad = False
|
||||
|
||||
def freeze_attention_weights(self):
|
||||
print("Freeze attention weights")
|
||||
self.attention_weights.weight.requires_grad = False
|
||||
self.attention_weights.bias.requires_grad = False
|
||||
|
||||
def forward(
|
||||
self,
|
||||
query: torch.Tensor,
|
||||
key: Optional[torch.Tensor] = None,
|
||||
value: Optional[torch.Tensor] = None,
|
||||
query_pos: Optional[torch.Tensor] = None,
|
||||
key_padding_mask: Optional[torch.Tensor] = None,
|
||||
reference_points: Optional[torch.Tensor] = None,
|
||||
spatial_shapes: Optional[torch.Tensor] = None,
|
||||
level_start_index: Optional[torch.Tensor] = None,
|
||||
**kwargs
|
||||
) -> torch.Tensor:
|
||||
|
||||
"""Forward Function of MultiScaleDeformableAttention
|
||||
|
||||
Args:
|
||||
query (torch.Tensor): Query embeddings with shape
|
||||
`(num_query, bs, embed_dim)`
|
||||
key (torch.Tensor): Key embeddings with shape
|
||||
`(num_key, bs, embed_dim)`
|
||||
value (torch.Tensor): Value embeddings with shape
|
||||
`(num_key, bs, embed_dim)`
|
||||
query_pos (torch.Tensor): The position embedding for `query`. Default: None.
|
||||
key_padding_mask (torch.Tensor): ByteTensor for `query`, with shape `(bs, num_key)`,
|
||||
indicating which elements within `key` to be ignored in attention.
|
||||
reference_points (torch.Tensor): The normalized reference points
|
||||
with shape `(bs, num_query, num_levels, 2)`,
|
||||
all elements is range in [0, 1], top-left (0, 0),
|
||||
bottom-right (1, 1), including padding are.
|
||||
or `(N, Length_{query}, num_levels, 4)`, add additional
|
||||
two dimensions `(h, w)` to form reference boxes.
|
||||
spatial_shapes (torch.Tensor): Spatial shape of features in different levels.
|
||||
With shape `(num_levels, 2)`, last dimension represents `(h, w)`.
|
||||
level_start_index (torch.Tensor): The start index of each level. A tensor with
|
||||
shape `(num_levels, )` which can be represented as
|
||||
`[0, h_0 * w_0, h_0 * w_0 + h_1 * w_1, ...]`.
|
||||
|
||||
Returns:
|
||||
torch.Tensor: forward results with shape `(num_query, bs, embed_dim)`
|
||||
"""
|
||||
|
||||
if value is None:
|
||||
value = query
|
||||
|
||||
if query_pos is not None:
|
||||
query = query + query_pos
|
||||
|
||||
if not self.batch_first:
|
||||
# change to (bs, num_query ,embed_dims)
|
||||
query = query.permute(1, 0, 2)
|
||||
value = value.permute(1, 0, 2)
|
||||
|
||||
bs, num_query, _ = query.shape
|
||||
bs, num_value, _ = value.shape
|
||||
|
||||
assert (spatial_shapes[:, 0] * spatial_shapes[:, 1]).sum() == num_value
|
||||
|
||||
value = self.value_proj(value)
|
||||
if key_padding_mask is not None:
|
||||
value = value.masked_fill(key_padding_mask[..., None], float(0))
|
||||
value = value.view(bs, num_value, self.num_heads, -1)
|
||||
sampling_offsets = self.sampling_offsets(query).view(
|
||||
bs, num_query, self.num_heads, self.num_levels, self.num_points, 2
|
||||
)
|
||||
attention_weights = self.attention_weights(query).view(
|
||||
bs, num_query, self.num_heads, self.num_levels * self.num_points
|
||||
)
|
||||
attention_weights = attention_weights.softmax(-1)
|
||||
attention_weights = attention_weights.view(
|
||||
bs,
|
||||
num_query,
|
||||
self.num_heads,
|
||||
self.num_levels,
|
||||
self.num_points,
|
||||
)
|
||||
|
||||
# bs, num_query, num_heads, num_levels, num_points, 2
|
||||
if reference_points.shape[-1] == 2:
|
||||
offset_normalizer = torch.stack([spatial_shapes[..., 1], spatial_shapes[..., 0]], -1)
|
||||
sampling_locations = (
|
||||
reference_points[:, :, None, :, None, :]
|
||||
+ sampling_offsets / offset_normalizer[None, None, None, :, None, :]
|
||||
)
|
||||
elif reference_points.shape[-1] == 4:
|
||||
sampling_locations = (
|
||||
reference_points[:, :, None, :, None, :2]
|
||||
+ sampling_offsets
|
||||
/ self.num_points
|
||||
* reference_points[:, :, None, :, None, 2:]
|
||||
* 0.5
|
||||
)
|
||||
else:
|
||||
raise ValueError(
|
||||
"Last dim of reference_points must be 2 or 4, but get {} instead.".format(
|
||||
reference_points.shape[-1]
|
||||
)
|
||||
)
|
||||
if torch.cuda.is_available() and value.is_cuda:
|
||||
halffloat = False
|
||||
if value.dtype == torch.float16:
|
||||
halffloat = True
|
||||
value = value.float()
|
||||
sampling_locations = sampling_locations.float()
|
||||
attention_weights = attention_weights.float()
|
||||
|
||||
output = MultiScaleDeformableAttnFunction.apply(
|
||||
value,
|
||||
spatial_shapes,
|
||||
level_start_index,
|
||||
sampling_locations,
|
||||
attention_weights,
|
||||
self.im2col_step,
|
||||
)
|
||||
|
||||
if halffloat:
|
||||
output = output.half()
|
||||
else:
|
||||
output = multi_scale_deformable_attn_pytorch(
|
||||
value, spatial_shapes, sampling_locations, attention_weights
|
||||
)
|
||||
|
||||
output = self.output_proj(output)
|
||||
|
||||
if not self.batch_first:
|
||||
output = output.permute(1, 0, 2)
|
||||
|
||||
return output
|
||||
|
||||
|
||||
def create_dummy_class(klass, dependency, message=""):
|
||||
"""
|
||||
When a dependency of a class is not available, create a dummy class which throws ImportError
|
||||
when used.
|
||||
|
||||
Args:
|
||||
klass (str): name of the class.
|
||||
dependency (str): name of the dependency.
|
||||
message: extra message to print
|
||||
Returns:
|
||||
class: a class object
|
||||
"""
|
||||
err = "Cannot import '{}', therefore '{}' is not available.".format(dependency, klass)
|
||||
if message:
|
||||
err = err + " " + message
|
||||
|
||||
class _DummyMetaClass(type):
|
||||
# throw error on class attribute access
|
||||
def __getattr__(_, __): # noqa: B902
|
||||
raise ImportError(err)
|
||||
|
||||
class _Dummy(object, metaclass=_DummyMetaClass):
|
||||
# throw error on constructor
|
||||
def __init__(self, *args, **kwargs):
|
||||
raise ImportError(err)
|
||||
|
||||
return _Dummy
|
||||
|
||||
|
||||
def create_dummy_func(func, dependency, message=""):
|
||||
"""
|
||||
When a dependency of a function is not available, create a dummy function which throws
|
||||
ImportError when used.
|
||||
|
||||
Args:
|
||||
func (str): name of the function.
|
||||
dependency (str or list[str]): name(s) of the dependency.
|
||||
message: extra message to print
|
||||
Returns:
|
||||
function: a function object
|
||||
"""
|
||||
err = "Cannot import '{}', therefore '{}' is not available.".format(dependency, func)
|
||||
if message:
|
||||
err = err + " " + message
|
||||
|
||||
if isinstance(dependency, (list, tuple)):
|
||||
dependency = ",".join(dependency)
|
||||
|
||||
def _dummy(*args, **kwargs):
|
||||
raise ImportError(err)
|
||||
|
||||
return _dummy
|
||||
|
|
@ -0,0 +1,959 @@
|
|||
# ------------------------------------------------------------------------
|
||||
# Grounding DINO
|
||||
# url: https://github.com/IDEA-Research/GroundingDINO
|
||||
# Copyright (c) 2023 IDEA. All Rights Reserved.
|
||||
# Licensed under the Apache License, Version 2.0 [see LICENSE for details]
|
||||
# ------------------------------------------------------------------------
|
||||
# DINO
|
||||
# Copyright (c) 2022 IDEA. All Rights Reserved.
|
||||
# Licensed under the Apache License, Version 2.0 [see LICENSE for details]
|
||||
# ------------------------------------------------------------------------
|
||||
# Conditional DETR Transformer class.
|
||||
# Copyright (c) 2021 Microsoft. All Rights Reserved.
|
||||
# Licensed under the Apache License, Version 2.0 [see LICENSE for details]
|
||||
# ------------------------------------------------------------------------
|
||||
# Modified from DETR (https://github.com/facebookresearch/detr)
|
||||
# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
|
||||
# ------------------------------------------------------------------------
|
||||
|
||||
from typing import Optional
|
||||
|
||||
import torch
|
||||
import torch.utils.checkpoint as checkpoint
|
||||
from torch import Tensor, nn
|
||||
|
||||
from groundingdino.util.misc import inverse_sigmoid
|
||||
|
||||
from .fuse_modules import BiAttentionBlock
|
||||
from .ms_deform_attn import MultiScaleDeformableAttention as MSDeformAttn
|
||||
from .transformer_vanilla import TransformerEncoderLayer
|
||||
from .utils import (
|
||||
MLP,
|
||||
_get_activation_fn,
|
||||
_get_clones,
|
||||
gen_encoder_output_proposals,
|
||||
gen_sineembed_for_position,
|
||||
get_sine_pos_embed,
|
||||
)
|
||||
|
||||
|
||||
class Transformer(nn.Module):
|
||||
def __init__(
|
||||
self,
|
||||
d_model=256,
|
||||
nhead=8,
|
||||
num_queries=300,
|
||||
num_encoder_layers=6,
|
||||
num_unicoder_layers=0,
|
||||
num_decoder_layers=6,
|
||||
dim_feedforward=2048,
|
||||
dropout=0.0,
|
||||
activation="relu",
|
||||
normalize_before=False,
|
||||
return_intermediate_dec=False,
|
||||
query_dim=4,
|
||||
num_patterns=0,
|
||||
# for deformable encoder
|
||||
num_feature_levels=1,
|
||||
enc_n_points=4,
|
||||
dec_n_points=4,
|
||||
# init query
|
||||
learnable_tgt_init=False,
|
||||
# two stage
|
||||
two_stage_type="no", # ['no', 'standard', 'early', 'combine', 'enceachlayer', 'enclayer1']
|
||||
embed_init_tgt=False,
|
||||
# for text
|
||||
use_text_enhancer=False,
|
||||
use_fusion_layer=False,
|
||||
use_checkpoint=False,
|
||||
use_transformer_ckpt=False,
|
||||
use_text_cross_attention=False,
|
||||
text_dropout=0.1,
|
||||
fusion_dropout=0.1,
|
||||
fusion_droppath=0.0,
|
||||
):
|
||||
super().__init__()
|
||||
self.num_feature_levels = num_feature_levels
|
||||
self.num_encoder_layers = num_encoder_layers
|
||||
self.num_unicoder_layers = num_unicoder_layers
|
||||
self.num_decoder_layers = num_decoder_layers
|
||||
self.num_queries = num_queries
|
||||
assert query_dim == 4
|
||||
|
||||
# choose encoder layer type
|
||||
encoder_layer = DeformableTransformerEncoderLayer(
|
||||
d_model, dim_feedforward, dropout, activation, num_feature_levels, nhead, enc_n_points
|
||||
)
|
||||
|
||||
if use_text_enhancer:
|
||||
text_enhance_layer = TransformerEncoderLayer(
|
||||
d_model=d_model,
|
||||
nhead=nhead // 2,
|
||||
dim_feedforward=dim_feedforward // 2,
|
||||
dropout=text_dropout,
|
||||
)
|
||||
else:
|
||||
text_enhance_layer = None
|
||||
|
||||
if use_fusion_layer:
|
||||
feature_fusion_layer = BiAttentionBlock(
|
||||
v_dim=d_model,
|
||||
l_dim=d_model,
|
||||
embed_dim=dim_feedforward // 2,
|
||||
num_heads=nhead // 2,
|
||||
dropout=fusion_dropout,
|
||||
drop_path=fusion_droppath,
|
||||
)
|
||||
else:
|
||||
feature_fusion_layer = None
|
||||
|
||||
encoder_norm = nn.LayerNorm(d_model) if normalize_before else None
|
||||
assert encoder_norm is None
|
||||
self.encoder = TransformerEncoder(
|
||||
encoder_layer,
|
||||
num_encoder_layers,
|
||||
d_model=d_model,
|
||||
num_queries=num_queries,
|
||||
text_enhance_layer=text_enhance_layer,
|
||||
feature_fusion_layer=feature_fusion_layer,
|
||||
use_checkpoint=use_checkpoint,
|
||||
use_transformer_ckpt=use_transformer_ckpt,
|
||||
)
|
||||
|
||||
# choose decoder layer type
|
||||
decoder_layer = DeformableTransformerDecoderLayer(
|
||||
d_model,
|
||||
dim_feedforward,
|
||||
dropout,
|
||||
activation,
|
||||
num_feature_levels,
|
||||
nhead,
|
||||
dec_n_points,
|
||||
use_text_cross_attention=use_text_cross_attention,
|
||||
)
|
||||
|
||||
decoder_norm = nn.LayerNorm(d_model)
|
||||
self.decoder = TransformerDecoder(
|
||||
decoder_layer,
|
||||
num_decoder_layers,
|
||||
decoder_norm,
|
||||
return_intermediate=return_intermediate_dec,
|
||||
d_model=d_model,
|
||||
query_dim=query_dim,
|
||||
num_feature_levels=num_feature_levels,
|
||||
)
|
||||
|
||||
self.d_model = d_model
|
||||
self.nhead = nhead
|
||||
self.dec_layers = num_decoder_layers
|
||||
self.num_queries = num_queries # useful for single stage model only
|
||||
self.num_patterns = num_patterns
|
||||
if not isinstance(num_patterns, int):
|
||||
Warning("num_patterns should be int but {}".format(type(num_patterns)))
|
||||
self.num_patterns = 0
|
||||
|
||||
if num_feature_levels > 1:
|
||||
if self.num_encoder_layers > 0:
|
||||
self.level_embed = nn.Parameter(torch.Tensor(num_feature_levels, d_model))
|
||||
else:
|
||||
self.level_embed = None
|
||||
|
||||
self.learnable_tgt_init = learnable_tgt_init
|
||||
assert learnable_tgt_init, "why not learnable_tgt_init"
|
||||
self.embed_init_tgt = embed_init_tgt
|
||||
if (two_stage_type != "no" and embed_init_tgt) or (two_stage_type == "no"):
|
||||
self.tgt_embed = nn.Embedding(self.num_queries, d_model)
|
||||
nn.init.normal_(self.tgt_embed.weight.data)
|
||||
else:
|
||||
self.tgt_embed = None
|
||||
|
||||
# for two stage
|
||||
self.two_stage_type = two_stage_type
|
||||
assert two_stage_type in ["no", "standard"], "unknown param {} of two_stage_type".format(
|
||||
two_stage_type
|
||||
)
|
||||
if two_stage_type == "standard":
|
||||
# anchor selection at the output of encoder
|
||||
self.enc_output = nn.Linear(d_model, d_model)
|
||||
self.enc_output_norm = nn.LayerNorm(d_model)
|
||||
self.two_stage_wh_embedding = None
|
||||
|
||||
if two_stage_type == "no":
|
||||
self.init_ref_points(num_queries) # init self.refpoint_embed
|
||||
|
||||
self.enc_out_class_embed = None
|
||||
self.enc_out_bbox_embed = None
|
||||
|
||||
self._reset_parameters()
|
||||
|
||||
def _reset_parameters(self):
|
||||
for p in self.parameters():
|
||||
if p.dim() > 1:
|
||||
nn.init.xavier_uniform_(p)
|
||||
for m in self.modules():
|
||||
if isinstance(m, MSDeformAttn):
|
||||
m._reset_parameters()
|
||||
if self.num_feature_levels > 1 and self.level_embed is not None:
|
||||
nn.init.normal_(self.level_embed)
|
||||
|
||||
def get_valid_ratio(self, mask):
|
||||
_, H, W = mask.shape
|
||||
valid_H = torch.sum(~mask[:, :, 0], 1)
|
||||
valid_W = torch.sum(~mask[:, 0, :], 1)
|
||||
valid_ratio_h = valid_H.float() / H
|
||||
valid_ratio_w = valid_W.float() / W
|
||||
valid_ratio = torch.stack([valid_ratio_w, valid_ratio_h], -1)
|
||||
return valid_ratio
|
||||
|
||||
def init_ref_points(self, use_num_queries):
|
||||
self.refpoint_embed = nn.Embedding(use_num_queries, 4)
|
||||
|
||||
def forward(self, srcs, masks, refpoint_embed, pos_embeds, tgt, attn_mask=None, text_dict=None):
|
||||
"""
|
||||
Input:
|
||||
- srcs: List of multi features [bs, ci, hi, wi]
|
||||
- masks: List of multi masks [bs, hi, wi]
|
||||
- refpoint_embed: [bs, num_dn, 4]. None in infer
|
||||
- pos_embeds: List of multi pos embeds [bs, ci, hi, wi]
|
||||
- tgt: [bs, num_dn, d_model]. None in infer
|
||||
|
||||
"""
|
||||
# prepare input for encoder
|
||||
src_flatten = []
|
||||
mask_flatten = []
|
||||
lvl_pos_embed_flatten = []
|
||||
spatial_shapes = []
|
||||
for lvl, (src, mask, pos_embed) in enumerate(zip(srcs, masks, pos_embeds)):
|
||||
bs, c, h, w = src.shape
|
||||
spatial_shape = (h, w)
|
||||
spatial_shapes.append(spatial_shape)
|
||||
|
||||
src = src.flatten(2).transpose(1, 2) # bs, hw, c
|
||||
mask = mask.flatten(1) # bs, hw
|
||||
pos_embed = pos_embed.flatten(2).transpose(1, 2) # bs, hw, c
|
||||
if self.num_feature_levels > 1 and self.level_embed is not None:
|
||||
lvl_pos_embed = pos_embed + self.level_embed[lvl].view(1, 1, -1)
|
||||
else:
|
||||
lvl_pos_embed = pos_embed
|
||||
lvl_pos_embed_flatten.append(lvl_pos_embed)
|
||||
src_flatten.append(src)
|
||||
mask_flatten.append(mask)
|
||||
src_flatten = torch.cat(src_flatten, 1) # bs, \sum{hxw}, c
|
||||
mask_flatten = torch.cat(mask_flatten, 1) # bs, \sum{hxw}
|
||||
lvl_pos_embed_flatten = torch.cat(lvl_pos_embed_flatten, 1) # bs, \sum{hxw}, c
|
||||
spatial_shapes = torch.as_tensor(
|
||||
spatial_shapes, dtype=torch.long, device=src_flatten.device
|
||||
)
|
||||
level_start_index = torch.cat(
|
||||
(spatial_shapes.new_zeros((1,)), spatial_shapes.prod(1).cumsum(0)[:-1])
|
||||
)
|
||||
valid_ratios = torch.stack([self.get_valid_ratio(m) for m in masks], 1)
|
||||
|
||||
# two stage
|
||||
enc_topk_proposals = enc_refpoint_embed = None
|
||||
|
||||
#########################################################
|
||||
# Begin Encoder
|
||||
#########################################################
|
||||
memory, memory_text = self.encoder(
|
||||
src_flatten,
|
||||
pos=lvl_pos_embed_flatten,
|
||||
level_start_index=level_start_index,
|
||||
spatial_shapes=spatial_shapes,
|
||||
valid_ratios=valid_ratios,
|
||||
key_padding_mask=mask_flatten,
|
||||
memory_text=text_dict["encoded_text"],
|
||||
text_attention_mask=~text_dict["text_token_mask"],
|
||||
# we ~ the mask . False means use the token; True means pad the token
|
||||
position_ids=text_dict["position_ids"],
|
||||
text_self_attention_masks=text_dict["text_self_attention_masks"],
|
||||
)
|
||||
#########################################################
|
||||
# End Encoder
|
||||
# - memory: bs, \sum{hw}, c
|
||||
# - mask_flatten: bs, \sum{hw}
|
||||
# - lvl_pos_embed_flatten: bs, \sum{hw}, c
|
||||
# - enc_intermediate_output: None or (nenc+1, bs, nq, c) or (nenc, bs, nq, c)
|
||||
# - enc_intermediate_refpoints: None or (nenc+1, bs, nq, c) or (nenc, bs, nq, c)
|
||||
#########################################################
|
||||
text_dict["encoded_text"] = memory_text
|
||||
# if os.environ.get("SHILONG_AMP_INFNAN_DEBUG") == '1':
|
||||
# if memory.isnan().any() | memory.isinf().any():
|
||||
# import ipdb; ipdb.set_trace()
|
||||
|
||||
if self.two_stage_type == "standard":
|
||||
output_memory, output_proposals = gen_encoder_output_proposals(
|
||||
memory, mask_flatten, spatial_shapes
|
||||
)
|
||||
output_memory = self.enc_output_norm(self.enc_output(output_memory))
|
||||
|
||||
if text_dict is not None:
|
||||
enc_outputs_class_unselected = self.enc_out_class_embed(output_memory, text_dict)
|
||||
else:
|
||||
enc_outputs_class_unselected = self.enc_out_class_embed(output_memory)
|
||||
|
||||
topk_logits = enc_outputs_class_unselected.max(-1)[0]
|
||||
enc_outputs_coord_unselected = (
|
||||
self.enc_out_bbox_embed(output_memory) + output_proposals
|
||||
) # (bs, \sum{hw}, 4) unsigmoid
|
||||
topk = self.num_queries
|
||||
|
||||
topk_proposals = torch.topk(topk_logits, topk, dim=1)[1] # bs, nq
|
||||
|
||||
# gather boxes
|
||||
refpoint_embed_undetach = torch.gather(
|
||||
enc_outputs_coord_unselected, 1, topk_proposals.unsqueeze(-1).repeat(1, 1, 4)
|
||||
) # unsigmoid
|
||||
refpoint_embed_ = refpoint_embed_undetach.detach()
|
||||
init_box_proposal = torch.gather(
|
||||
output_proposals, 1, topk_proposals.unsqueeze(-1).repeat(1, 1, 4)
|
||||
).sigmoid() # sigmoid
|
||||
|
||||
# gather tgt
|
||||
tgt_undetach = torch.gather(
|
||||
output_memory, 1, topk_proposals.unsqueeze(-1).repeat(1, 1, self.d_model)
|
||||
)
|
||||
if self.embed_init_tgt:
|
||||
tgt_ = (
|
||||
self.tgt_embed.weight[:, None, :].repeat(1, bs, 1).transpose(0, 1)
|
||||
) # nq, bs, d_model
|
||||
else:
|
||||
tgt_ = tgt_undetach.detach()
|
||||
|
||||
if refpoint_embed is not None:
|
||||
refpoint_embed = torch.cat([refpoint_embed, refpoint_embed_], dim=1)
|
||||
tgt = torch.cat([tgt, tgt_], dim=1)
|
||||
else:
|
||||
refpoint_embed, tgt = refpoint_embed_, tgt_
|
||||
|
||||
elif self.two_stage_type == "no":
|
||||
tgt_ = (
|
||||
self.tgt_embed.weight[:, None, :].repeat(1, bs, 1).transpose(0, 1)
|
||||
) # nq, bs, d_model
|
||||
refpoint_embed_ = (
|
||||
self.refpoint_embed.weight[:, None, :].repeat(1, bs, 1).transpose(0, 1)
|
||||
) # nq, bs, 4
|
||||
|
||||
if refpoint_embed is not None:
|
||||
refpoint_embed = torch.cat([refpoint_embed, refpoint_embed_], dim=1)
|
||||
tgt = torch.cat([tgt, tgt_], dim=1)
|
||||
else:
|
||||
refpoint_embed, tgt = refpoint_embed_, tgt_
|
||||
|
||||
if self.num_patterns > 0:
|
||||
tgt_embed = tgt.repeat(1, self.num_patterns, 1)
|
||||
refpoint_embed = refpoint_embed.repeat(1, self.num_patterns, 1)
|
||||
tgt_pat = self.patterns.weight[None, :, :].repeat_interleave(
|
||||
self.num_queries, 1
|
||||
) # 1, n_q*n_pat, d_model
|
||||
tgt = tgt_embed + tgt_pat
|
||||
|
||||
init_box_proposal = refpoint_embed_.sigmoid()
|
||||
|
||||
else:
|
||||
raise NotImplementedError("unknown two_stage_type {}".format(self.two_stage_type))
|
||||
#########################################################
|
||||
# End preparing tgt
|
||||
# - tgt: bs, NQ, d_model
|
||||
# - refpoint_embed(unsigmoid): bs, NQ, d_model
|
||||
#########################################################
|
||||
|
||||
#########################################################
|
||||
# Begin Decoder
|
||||
#########################################################
|
||||
hs, references = self.decoder(
|
||||
tgt=tgt.transpose(0, 1),
|
||||
memory=memory.transpose(0, 1),
|
||||
memory_key_padding_mask=mask_flatten,
|
||||
pos=lvl_pos_embed_flatten.transpose(0, 1),
|
||||
refpoints_unsigmoid=refpoint_embed.transpose(0, 1),
|
||||
level_start_index=level_start_index,
|
||||
spatial_shapes=spatial_shapes,
|
||||
valid_ratios=valid_ratios,
|
||||
tgt_mask=attn_mask,
|
||||
memory_text=text_dict["encoded_text"],
|
||||
text_attention_mask=~text_dict["text_token_mask"],
|
||||
# we ~ the mask . False means use the token; True means pad the token
|
||||
)
|
||||
#########################################################
|
||||
# End Decoder
|
||||
# hs: n_dec, bs, nq, d_model
|
||||
# references: n_dec+1, bs, nq, query_dim
|
||||
#########################################################
|
||||
|
||||
#########################################################
|
||||
# Begin postprocess
|
||||
#########################################################
|
||||
if self.two_stage_type == "standard":
|
||||
hs_enc = tgt_undetach.unsqueeze(0)
|
||||
ref_enc = refpoint_embed_undetach.sigmoid().unsqueeze(0)
|
||||
else:
|
||||
hs_enc = ref_enc = None
|
||||
#########################################################
|
||||
# End postprocess
|
||||
# hs_enc: (n_enc+1, bs, nq, d_model) or (1, bs, nq, d_model) or (n_enc, bs, nq, d_model) or None
|
||||
# ref_enc: (n_enc+1, bs, nq, query_dim) or (1, bs, nq, query_dim) or (n_enc, bs, nq, d_model) or None
|
||||
#########################################################
|
||||
|
||||
return hs, references, hs_enc, ref_enc, init_box_proposal
|
||||
# hs: (n_dec, bs, nq, d_model)
|
||||
# references: sigmoid coordinates. (n_dec+1, bs, bq, 4)
|
||||
# hs_enc: (n_enc+1, bs, nq, d_model) or (1, bs, nq, d_model) or None
|
||||
# ref_enc: sigmoid coordinates. \
|
||||
# (n_enc+1, bs, nq, query_dim) or (1, bs, nq, query_dim) or None
|
||||
|
||||
|
||||
class TransformerEncoder(nn.Module):
|
||||
def __init__(
|
||||
self,
|
||||
encoder_layer,
|
||||
num_layers,
|
||||
d_model=256,
|
||||
num_queries=300,
|
||||
enc_layer_share=False,
|
||||
text_enhance_layer=None,
|
||||
feature_fusion_layer=None,
|
||||
use_checkpoint=False,
|
||||
use_transformer_ckpt=False,
|
||||
):
|
||||
"""_summary_
|
||||
|
||||
Args:
|
||||
encoder_layer (_type_): _description_
|
||||
num_layers (_type_): _description_
|
||||
norm (_type_, optional): _description_. Defaults to None.
|
||||
d_model (int, optional): _description_. Defaults to 256.
|
||||
num_queries (int, optional): _description_. Defaults to 300.
|
||||
enc_layer_share (bool, optional): _description_. Defaults to False.
|
||||
|
||||
"""
|
||||
super().__init__()
|
||||
# prepare layers
|
||||
self.layers = []
|
||||
self.text_layers = []
|
||||
self.fusion_layers = []
|
||||
if num_layers > 0:
|
||||
self.layers = _get_clones(encoder_layer, num_layers, layer_share=enc_layer_share)
|
||||
|
||||
if text_enhance_layer is not None:
|
||||
self.text_layers = _get_clones(
|
||||
text_enhance_layer, num_layers, layer_share=enc_layer_share
|
||||
)
|
||||
if feature_fusion_layer is not None:
|
||||
self.fusion_layers = _get_clones(
|
||||
feature_fusion_layer, num_layers, layer_share=enc_layer_share
|
||||
)
|
||||
else:
|
||||
self.layers = []
|
||||
del encoder_layer
|
||||
|
||||
if text_enhance_layer is not None:
|
||||
self.text_layers = []
|
||||
del text_enhance_layer
|
||||
if feature_fusion_layer is not None:
|
||||
self.fusion_layers = []
|
||||
del feature_fusion_layer
|
||||
|
||||
self.query_scale = None
|
||||
self.num_queries = num_queries
|
||||
self.num_layers = num_layers
|
||||
self.d_model = d_model
|
||||
|
||||
self.use_checkpoint = use_checkpoint
|
||||
self.use_transformer_ckpt = use_transformer_ckpt
|
||||
|
||||
@staticmethod
|
||||
def get_reference_points(spatial_shapes, valid_ratios, device):
|
||||
reference_points_list = []
|
||||
for lvl, (H_, W_) in enumerate(spatial_shapes):
|
||||
|
||||
ref_y, ref_x = torch.meshgrid(
|
||||
torch.linspace(0.5, H_ - 0.5, H_, dtype=torch.float32, device=device),
|
||||
torch.linspace(0.5, W_ - 0.5, W_, dtype=torch.float32, device=device),
|
||||
)
|
||||
ref_y = ref_y.reshape(-1)[None] / (valid_ratios[:, None, lvl, 1] * H_)
|
||||
ref_x = ref_x.reshape(-1)[None] / (valid_ratios[:, None, lvl, 0] * W_)
|
||||
ref = torch.stack((ref_x, ref_y), -1)
|
||||
reference_points_list.append(ref)
|
||||
reference_points = torch.cat(reference_points_list, 1)
|
||||
reference_points = reference_points[:, :, None] * valid_ratios[:, None]
|
||||
return reference_points
|
||||
|
||||
def forward(
|
||||
self,
|
||||
# for images
|
||||
src: Tensor,
|
||||
pos: Tensor,
|
||||
spatial_shapes: Tensor,
|
||||
level_start_index: Tensor,
|
||||
valid_ratios: Tensor,
|
||||
key_padding_mask: Tensor,
|
||||
# for texts
|
||||
memory_text: Tensor = None,
|
||||
text_attention_mask: Tensor = None,
|
||||
pos_text: Tensor = None,
|
||||
text_self_attention_masks: Tensor = None,
|
||||
position_ids: Tensor = None,
|
||||
):
|
||||
"""
|
||||
Input:
|
||||
- src: [bs, sum(hi*wi), 256]
|
||||
- pos: pos embed for src. [bs, sum(hi*wi), 256]
|
||||
- spatial_shapes: h,w of each level [num_level, 2]
|
||||
- level_start_index: [num_level] start point of level in sum(hi*wi).
|
||||
- valid_ratios: [bs, num_level, 2]
|
||||
- key_padding_mask: [bs, sum(hi*wi)]
|
||||
|
||||
- memory_text: bs, n_text, 256
|
||||
- text_attention_mask: bs, n_text
|
||||
False for no padding; True for padding
|
||||
- pos_text: bs, n_text, 256
|
||||
|
||||
- position_ids: bs, n_text
|
||||
Intermedia:
|
||||
- reference_points: [bs, sum(hi*wi), num_level, 2]
|
||||
Outpus:
|
||||
- output: [bs, sum(hi*wi), 256]
|
||||
"""
|
||||
|
||||
output = src
|
||||
|
||||
# preparation and reshape
|
||||
if self.num_layers > 0:
|
||||
reference_points = self.get_reference_points(
|
||||
spatial_shapes, valid_ratios, device=src.device
|
||||
)
|
||||
|
||||
if self.text_layers:
|
||||
# generate pos_text
|
||||
bs, n_text, text_dim = memory_text.shape
|
||||
if pos_text is None and position_ids is None:
|
||||
pos_text = (
|
||||
torch.arange(n_text, device=memory_text.device)
|
||||
.float()
|
||||
.unsqueeze(0)
|
||||
.unsqueeze(-1)
|
||||
.repeat(bs, 1, 1)
|
||||
)
|
||||
pos_text = get_sine_pos_embed(pos_text, num_pos_feats=256, exchange_xy=False)
|
||||
if position_ids is not None:
|
||||
pos_text = get_sine_pos_embed(
|
||||
position_ids[..., None], num_pos_feats=256, exchange_xy=False
|
||||
)
|
||||
|
||||
# main process
|
||||
for layer_id, layer in enumerate(self.layers):
|
||||
# if output.isnan().any() or memory_text.isnan().any():
|
||||
# if os.environ.get('IPDB_SHILONG_DEBUG', None) == 'INFO':
|
||||
# import ipdb; ipdb.set_trace()
|
||||
if self.fusion_layers:
|
||||
if self.use_checkpoint:
|
||||
output, memory_text = checkpoint.checkpoint(
|
||||
self.fusion_layers[layer_id],
|
||||
output,
|
||||
memory_text,
|
||||
key_padding_mask,
|
||||
text_attention_mask,
|
||||
)
|
||||
else:
|
||||
output, memory_text = self.fusion_layers[layer_id](
|
||||
v=output,
|
||||
l=memory_text,
|
||||
attention_mask_v=key_padding_mask,
|
||||
attention_mask_l=text_attention_mask,
|
||||
)
|
||||
|
||||
if self.text_layers:
|
||||
memory_text = self.text_layers[layer_id](
|
||||
src=memory_text.transpose(0, 1),
|
||||
src_mask=~text_self_attention_masks, # note we use ~ for mask here
|
||||
src_key_padding_mask=text_attention_mask,
|
||||
pos=(pos_text.transpose(0, 1) if pos_text is not None else None),
|
||||
).transpose(0, 1)
|
||||
|
||||
# main process
|
||||
if self.use_transformer_ckpt:
|
||||
output = checkpoint.checkpoint(
|
||||
layer,
|
||||
output,
|
||||
pos,
|
||||
reference_points,
|
||||
spatial_shapes,
|
||||
level_start_index,
|
||||
key_padding_mask,
|
||||
)
|
||||
else:
|
||||
output = layer(
|
||||
src=output,
|
||||
pos=pos,
|
||||
reference_points=reference_points,
|
||||
spatial_shapes=spatial_shapes,
|
||||
level_start_index=level_start_index,
|
||||
key_padding_mask=key_padding_mask,
|
||||
)
|
||||
|
||||
return output, memory_text
|
||||
|
||||
|
||||
class TransformerDecoder(nn.Module):
|
||||
def __init__(
|
||||
self,
|
||||
decoder_layer,
|
||||
num_layers,
|
||||
norm=None,
|
||||
return_intermediate=False,
|
||||
d_model=256,
|
||||
query_dim=4,
|
||||
num_feature_levels=1,
|
||||
):
|
||||
super().__init__()
|
||||
if num_layers > 0:
|
||||
self.layers = _get_clones(decoder_layer, num_layers)
|
||||
else:
|
||||
self.layers = []
|
||||
self.num_layers = num_layers
|
||||
self.norm = norm
|
||||
self.return_intermediate = return_intermediate
|
||||
assert return_intermediate, "support return_intermediate only"
|
||||
self.query_dim = query_dim
|
||||
assert query_dim in [2, 4], "query_dim should be 2/4 but {}".format(query_dim)
|
||||
self.num_feature_levels = num_feature_levels
|
||||
|
||||
self.ref_point_head = MLP(query_dim // 2 * d_model, d_model, d_model, 2)
|
||||
self.query_pos_sine_scale = None
|
||||
|
||||
self.query_scale = None
|
||||
self.bbox_embed = None
|
||||
self.class_embed = None
|
||||
|
||||
self.d_model = d_model
|
||||
|
||||
self.ref_anchor_head = None
|
||||
|
||||
def forward(
|
||||
self,
|
||||
tgt,
|
||||
memory,
|
||||
tgt_mask: Optional[Tensor] = None,
|
||||
memory_mask: Optional[Tensor] = None,
|
||||
tgt_key_padding_mask: Optional[Tensor] = None,
|
||||
memory_key_padding_mask: Optional[Tensor] = None,
|
||||
pos: Optional[Tensor] = None,
|
||||
refpoints_unsigmoid: Optional[Tensor] = None, # num_queries, bs, 2
|
||||
# for memory
|
||||
level_start_index: Optional[Tensor] = None, # num_levels
|
||||
spatial_shapes: Optional[Tensor] = None, # bs, num_levels, 2
|
||||
valid_ratios: Optional[Tensor] = None,
|
||||
# for text
|
||||
memory_text: Optional[Tensor] = None,
|
||||
text_attention_mask: Optional[Tensor] = None,
|
||||
):
|
||||
"""
|
||||
Input:
|
||||
- tgt: nq, bs, d_model
|
||||
- memory: hw, bs, d_model
|
||||
- pos: hw, bs, d_model
|
||||
- refpoints_unsigmoid: nq, bs, 2/4
|
||||
- valid_ratios/spatial_shapes: bs, nlevel, 2
|
||||
"""
|
||||
output = tgt
|
||||
|
||||
intermediate = []
|
||||
reference_points = refpoints_unsigmoid.sigmoid()
|
||||
ref_points = [reference_points]
|
||||
|
||||
for layer_id, layer in enumerate(self.layers):
|
||||
|
||||
if reference_points.shape[-1] == 4:
|
||||
reference_points_input = (
|
||||
reference_points[:, :, None]
|
||||
* torch.cat([valid_ratios, valid_ratios], -1)[None, :]
|
||||
) # nq, bs, nlevel, 4
|
||||
else:
|
||||
assert reference_points.shape[-1] == 2
|
||||
reference_points_input = reference_points[:, :, None] * valid_ratios[None, :]
|
||||
query_sine_embed = gen_sineembed_for_position(
|
||||
reference_points_input[:, :, 0, :]
|
||||
) # nq, bs, 256*2
|
||||
|
||||
# conditional query
|
||||
raw_query_pos = self.ref_point_head(query_sine_embed) # nq, bs, 256
|
||||
pos_scale = self.query_scale(output) if self.query_scale is not None else 1
|
||||
query_pos = pos_scale * raw_query_pos
|
||||
# if os.environ.get("SHILONG_AMP_INFNAN_DEBUG") == '1':
|
||||
# if query_pos.isnan().any() | query_pos.isinf().any():
|
||||
# import ipdb; ipdb.set_trace()
|
||||
|
||||
# main process
|
||||
output = layer(
|
||||
tgt=output,
|
||||
tgt_query_pos=query_pos,
|
||||
tgt_query_sine_embed=query_sine_embed,
|
||||
tgt_key_padding_mask=tgt_key_padding_mask,
|
||||
tgt_reference_points=reference_points_input,
|
||||
memory_text=memory_text,
|
||||
text_attention_mask=text_attention_mask,
|
||||
memory=memory,
|
||||
memory_key_padding_mask=memory_key_padding_mask,
|
||||
memory_level_start_index=level_start_index,
|
||||
memory_spatial_shapes=spatial_shapes,
|
||||
memory_pos=pos,
|
||||
self_attn_mask=tgt_mask,
|
||||
cross_attn_mask=memory_mask,
|
||||
)
|
||||
if output.isnan().any() | output.isinf().any():
|
||||
print(f"output layer_id {layer_id} is nan")
|
||||
try:
|
||||
num_nan = output.isnan().sum().item()
|
||||
num_inf = output.isinf().sum().item()
|
||||
print(f"num_nan {num_nan}, num_inf {num_inf}")
|
||||
except Exception as e:
|
||||
print(e)
|
||||
# if os.environ.get("SHILONG_AMP_INFNAN_DEBUG") == '1':
|
||||
# import ipdb; ipdb.set_trace()
|
||||
|
||||
# iter update
|
||||
if self.bbox_embed is not None:
|
||||
# box_holder = self.bbox_embed(output)
|
||||
# box_holder[..., :self.query_dim] += inverse_sigmoid(reference_points)
|
||||
# new_reference_points = box_holder[..., :self.query_dim].sigmoid()
|
||||
|
||||
reference_before_sigmoid = inverse_sigmoid(reference_points)
|
||||
delta_unsig = self.bbox_embed[layer_id](output)
|
||||
outputs_unsig = delta_unsig + reference_before_sigmoid
|
||||
new_reference_points = outputs_unsig.sigmoid()
|
||||
|
||||
reference_points = new_reference_points.detach()
|
||||
# if layer_id != self.num_layers - 1:
|
||||
ref_points.append(new_reference_points)
|
||||
|
||||
intermediate.append(self.norm(output))
|
||||
|
||||
return [
|
||||
[itm_out.transpose(0, 1) for itm_out in intermediate],
|
||||
[itm_refpoint.transpose(0, 1) for itm_refpoint in ref_points],
|
||||
]
|
||||
|
||||
|
||||
class DeformableTransformerEncoderLayer(nn.Module):
|
||||
def __init__(
|
||||
self,
|
||||
d_model=256,
|
||||
d_ffn=1024,
|
||||
dropout=0.1,
|
||||
activation="relu",
|
||||
n_levels=4,
|
||||
n_heads=8,
|
||||
n_points=4,
|
||||
):
|
||||
super().__init__()
|
||||
|
||||
# self attention
|
||||
self.self_attn = MSDeformAttn(
|
||||
embed_dim=d_model,
|
||||
num_levels=n_levels,
|
||||
num_heads=n_heads,
|
||||
num_points=n_points,
|
||||
batch_first=True,
|
||||
)
|
||||
self.dropout1 = nn.Dropout(dropout)
|
||||
self.norm1 = nn.LayerNorm(d_model)
|
||||
|
||||
# ffn
|
||||
self.linear1 = nn.Linear(d_model, d_ffn)
|
||||
self.activation = _get_activation_fn(activation, d_model=d_ffn)
|
||||
self.dropout2 = nn.Dropout(dropout)
|
||||
self.linear2 = nn.Linear(d_ffn, d_model)
|
||||
self.dropout3 = nn.Dropout(dropout)
|
||||
self.norm2 = nn.LayerNorm(d_model)
|
||||
|
||||
@staticmethod
|
||||
def with_pos_embed(tensor, pos):
|
||||
return tensor if pos is None else tensor + pos
|
||||
|
||||
def forward_ffn(self, src):
|
||||
src2 = self.linear2(self.dropout2(self.activation(self.linear1(src))))
|
||||
src = src + self.dropout3(src2)
|
||||
src = self.norm2(src)
|
||||
return src
|
||||
|
||||
def forward(
|
||||
self, src, pos, reference_points, spatial_shapes, level_start_index, key_padding_mask=None
|
||||
):
|
||||
# self attention
|
||||
# import ipdb; ipdb.set_trace()
|
||||
src2 = self.self_attn(
|
||||
query=self.with_pos_embed(src, pos),
|
||||
reference_points=reference_points,
|
||||
value=src,
|
||||
spatial_shapes=spatial_shapes,
|
||||
level_start_index=level_start_index,
|
||||
key_padding_mask=key_padding_mask,
|
||||
)
|
||||
src = src + self.dropout1(src2)
|
||||
src = self.norm1(src)
|
||||
|
||||
# ffn
|
||||
src = self.forward_ffn(src)
|
||||
|
||||
return src
|
||||
|
||||
|
||||
class DeformableTransformerDecoderLayer(nn.Module):
|
||||
def __init__(
|
||||
self,
|
||||
d_model=256,
|
||||
d_ffn=1024,
|
||||
dropout=0.1,
|
||||
activation="relu",
|
||||
n_levels=4,
|
||||
n_heads=8,
|
||||
n_points=4,
|
||||
use_text_feat_guide=False,
|
||||
use_text_cross_attention=False,
|
||||
):
|
||||
super().__init__()
|
||||
|
||||
# cross attention
|
||||
self.cross_attn = MSDeformAttn(
|
||||
embed_dim=d_model,
|
||||
num_levels=n_levels,
|
||||
num_heads=n_heads,
|
||||
num_points=n_points,
|
||||
batch_first=True,
|
||||
)
|
||||
self.dropout1 = nn.Dropout(dropout) if dropout > 0 else nn.Identity()
|
||||
self.norm1 = nn.LayerNorm(d_model)
|
||||
|
||||
# cross attention text
|
||||
if use_text_cross_attention:
|
||||
self.ca_text = nn.MultiheadAttention(d_model, n_heads, dropout=dropout)
|
||||
self.catext_dropout = nn.Dropout(dropout) if dropout > 0 else nn.Identity()
|
||||
self.catext_norm = nn.LayerNorm(d_model)
|
||||
|
||||
# self attention
|
||||
self.self_attn = nn.MultiheadAttention(d_model, n_heads, dropout=dropout)
|
||||
self.dropout2 = nn.Dropout(dropout) if dropout > 0 else nn.Identity()
|
||||
self.norm2 = nn.LayerNorm(d_model)
|
||||
|
||||
# ffn
|
||||
self.linear1 = nn.Linear(d_model, d_ffn)
|
||||
self.activation = _get_activation_fn(activation, d_model=d_ffn, batch_dim=1)
|
||||
self.dropout3 = nn.Dropout(dropout) if dropout > 0 else nn.Identity()
|
||||
self.linear2 = nn.Linear(d_ffn, d_model)
|
||||
self.dropout4 = nn.Dropout(dropout) if dropout > 0 else nn.Identity()
|
||||
self.norm3 = nn.LayerNorm(d_model)
|
||||
|
||||
self.key_aware_proj = None
|
||||
self.use_text_feat_guide = use_text_feat_guide
|
||||
assert not use_text_feat_guide
|
||||
self.use_text_cross_attention = use_text_cross_attention
|
||||
|
||||
def rm_self_attn_modules(self):
|
||||
self.self_attn = None
|
||||
self.dropout2 = None
|
||||
self.norm2 = None
|
||||
|
||||
@staticmethod
|
||||
def with_pos_embed(tensor, pos):
|
||||
return tensor if pos is None else tensor + pos
|
||||
|
||||
def forward_ffn(self, tgt):
|
||||
with torch.cuda.amp.autocast(enabled=False):
|
||||
tgt2 = self.linear2(self.dropout3(self.activation(self.linear1(tgt))))
|
||||
tgt = tgt + self.dropout4(tgt2)
|
||||
tgt = self.norm3(tgt)
|
||||
return tgt
|
||||
|
||||
def forward(
|
||||
self,
|
||||
# for tgt
|
||||
tgt: Optional[Tensor], # nq, bs, d_model
|
||||
tgt_query_pos: Optional[Tensor] = None, # pos for query. MLP(Sine(pos))
|
||||
tgt_query_sine_embed: Optional[Tensor] = None, # pos for query. Sine(pos)
|
||||
tgt_key_padding_mask: Optional[Tensor] = None,
|
||||
tgt_reference_points: Optional[Tensor] = None, # nq, bs, 4
|
||||
memory_text: Optional[Tensor] = None, # bs, num_token, d_model
|
||||
text_attention_mask: Optional[Tensor] = None, # bs, num_token
|
||||
# for memory
|
||||
memory: Optional[Tensor] = None, # hw, bs, d_model
|
||||
memory_key_padding_mask: Optional[Tensor] = None,
|
||||
memory_level_start_index: Optional[Tensor] = None, # num_levels
|
||||
memory_spatial_shapes: Optional[Tensor] = None, # bs, num_levels, 2
|
||||
memory_pos: Optional[Tensor] = None, # pos for memory
|
||||
# sa
|
||||
self_attn_mask: Optional[Tensor] = None, # mask used for self-attention
|
||||
cross_attn_mask: Optional[Tensor] = None, # mask used for cross-attention
|
||||
):
|
||||
"""
|
||||
Input:
|
||||
- tgt/tgt_query_pos: nq, bs, d_model
|
||||
-
|
||||
"""
|
||||
assert cross_attn_mask is None
|
||||
|
||||
# self attention
|
||||
if self.self_attn is not None:
|
||||
# import ipdb; ipdb.set_trace()
|
||||
q = k = self.with_pos_embed(tgt, tgt_query_pos)
|
||||
tgt2 = self.self_attn(q, k, tgt, attn_mask=self_attn_mask)[0]
|
||||
tgt = tgt + self.dropout2(tgt2)
|
||||
tgt = self.norm2(tgt)
|
||||
|
||||
if self.use_text_cross_attention:
|
||||
tgt2 = self.ca_text(
|
||||
self.with_pos_embed(tgt, tgt_query_pos),
|
||||
memory_text.transpose(0, 1),
|
||||
memory_text.transpose(0, 1),
|
||||
key_padding_mask=text_attention_mask,
|
||||
)[0]
|
||||
tgt = tgt + self.catext_dropout(tgt2)
|
||||
tgt = self.catext_norm(tgt)
|
||||
|
||||
tgt2 = self.cross_attn(
|
||||
query=self.with_pos_embed(tgt, tgt_query_pos).transpose(0, 1),
|
||||
reference_points=tgt_reference_points.transpose(0, 1).contiguous(),
|
||||
value=memory.transpose(0, 1),
|
||||
spatial_shapes=memory_spatial_shapes,
|
||||
level_start_index=memory_level_start_index,
|
||||
key_padding_mask=memory_key_padding_mask,
|
||||
).transpose(0, 1)
|
||||
tgt = tgt + self.dropout1(tgt2)
|
||||
tgt = self.norm1(tgt)
|
||||
|
||||
# ffn
|
||||
tgt = self.forward_ffn(tgt)
|
||||
|
||||
return tgt
|
||||
|
||||
|
||||
def build_transformer(args):
|
||||
return Transformer(
|
||||
d_model=args.hidden_dim,
|
||||
dropout=args.dropout,
|
||||
nhead=args.nheads,
|
||||
num_queries=args.num_queries,
|
||||
dim_feedforward=args.dim_feedforward,
|
||||
num_encoder_layers=args.enc_layers,
|
||||
num_decoder_layers=args.dec_layers,
|
||||
normalize_before=args.pre_norm,
|
||||
return_intermediate_dec=True,
|
||||
query_dim=args.query_dim,
|
||||
activation=args.transformer_activation,
|
||||
num_patterns=args.num_patterns,
|
||||
num_feature_levels=args.num_feature_levels,
|
||||
enc_n_points=args.enc_n_points,
|
||||
dec_n_points=args.dec_n_points,
|
||||
learnable_tgt_init=True,
|
||||
# two stage
|
||||
two_stage_type=args.two_stage_type, # ['no', 'standard', 'early']
|
||||
embed_init_tgt=args.embed_init_tgt,
|
||||
use_text_enhancer=args.use_text_enhancer,
|
||||
use_fusion_layer=args.use_fusion_layer,
|
||||
use_checkpoint=args.use_checkpoint,
|
||||
use_transformer_ckpt=args.use_transformer_ckpt,
|
||||
use_text_cross_attention=args.use_text_cross_attention,
|
||||
text_dropout=args.text_dropout,
|
||||
fusion_dropout=args.fusion_dropout,
|
||||
fusion_droppath=args.fusion_droppath,
|
||||
)
|
||||
|
|
@ -0,0 +1,123 @@
|
|||
# ------------------------------------------------------------------------
|
||||
# Grounding DINO
|
||||
# url: https://github.com/IDEA-Research/GroundingDINO
|
||||
# Copyright (c) 2023 IDEA. All Rights Reserved.
|
||||
# Licensed under the Apache License, Version 2.0 [see LICENSE for details]
|
||||
# ------------------------------------------------------------------------
|
||||
# Copyright (c) Aishwarya Kamath & Nicolas Carion. Licensed under the Apache License 2.0. All Rights Reserved
|
||||
# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved
|
||||
"""
|
||||
DETR Transformer class.
|
||||
|
||||
Copy-paste from torch.nn.Transformer with modifications:
|
||||
* positional encodings are passed in MHattention
|
||||
* extra LN at the end of encoder is removed
|
||||
* decoder returns a stack of activations from all decoding layers
|
||||
"""
|
||||
from typing import Optional
|
||||
|
||||
import torch
|
||||
import torch.nn.functional as F
|
||||
from torch import Tensor, nn
|
||||
|
||||
from .utils import (
|
||||
MLP,
|
||||
_get_activation_fn,
|
||||
_get_clones,
|
||||
gen_encoder_output_proposals,
|
||||
gen_sineembed_for_position,
|
||||
sigmoid_focal_loss,
|
||||
)
|
||||
|
||||
|
||||
class TextTransformer(nn.Module):
|
||||
def __init__(self, num_layers, d_model=256, nheads=8, dim_feedforward=2048, dropout=0.1):
|
||||
super().__init__()
|
||||
self.num_layers = num_layers
|
||||
self.d_model = d_model
|
||||
self.nheads = nheads
|
||||
self.dim_feedforward = dim_feedforward
|
||||
self.norm = None
|
||||
|
||||
single_encoder_layer = TransformerEncoderLayer(
|
||||
d_model=d_model, nhead=nheads, dim_feedforward=dim_feedforward, dropout=dropout
|
||||
)
|
||||
self.layers = _get_clones(single_encoder_layer, num_layers)
|
||||
|
||||
def forward(self, memory_text: torch.Tensor, text_attention_mask: torch.Tensor):
|
||||
"""
|
||||
|
||||
Args:
|
||||
text_attention_mask: bs, num_token
|
||||
memory_text: bs, num_token, d_model
|
||||
|
||||
Raises:
|
||||
RuntimeError: _description_
|
||||
|
||||
Returns:
|
||||
output: bs, num_token, d_model
|
||||
"""
|
||||
|
||||
output = memory_text.transpose(0, 1)
|
||||
|
||||
for layer in self.layers:
|
||||
output = layer(output, src_key_padding_mask=text_attention_mask)
|
||||
|
||||
if self.norm is not None:
|
||||
output = self.norm(output)
|
||||
|
||||
return output.transpose(0, 1)
|
||||
|
||||
|
||||
class TransformerEncoderLayer(nn.Module):
|
||||
def __init__(
|
||||
self,
|
||||
d_model,
|
||||
nhead,
|
||||
dim_feedforward=2048,
|
||||
dropout=0.1,
|
||||
activation="relu",
|
||||
normalize_before=False,
|
||||
):
|
||||
super().__init__()
|
||||
self.self_attn = nn.MultiheadAttention(d_model, nhead, dropout=dropout)
|
||||
# Implementation of Feedforward model
|
||||
self.linear1 = nn.Linear(d_model, dim_feedforward)
|
||||
self.dropout = nn.Dropout(dropout)
|
||||
self.linear2 = nn.Linear(dim_feedforward, d_model)
|
||||
|
||||
self.norm1 = nn.LayerNorm(d_model)
|
||||
self.norm2 = nn.LayerNorm(d_model)
|
||||
self.dropout1 = nn.Dropout(dropout)
|
||||
self.dropout2 = nn.Dropout(dropout)
|
||||
|
||||
self.activation = _get_activation_fn(activation)
|
||||
self.normalize_before = normalize_before
|
||||
self.nhead = nhead
|
||||
|
||||
def with_pos_embed(self, tensor, pos: Optional[Tensor]):
|
||||
return tensor if pos is None else tensor + pos
|
||||
|
||||
def forward(
|
||||
self,
|
||||
src,
|
||||
src_mask: Optional[Tensor] = None,
|
||||
src_key_padding_mask: Optional[Tensor] = None,
|
||||
pos: Optional[Tensor] = None,
|
||||
):
|
||||
# repeat attn mask
|
||||
if src_mask.dim() == 3 and src_mask.shape[0] == src.shape[1]:
|
||||
# bs, num_q, num_k
|
||||
src_mask = src_mask.repeat(self.nhead, 1, 1)
|
||||
|
||||
q = k = self.with_pos_embed(src, pos)
|
||||
|
||||
src2 = self.self_attn(q, k, value=src, attn_mask=src_mask)[0]
|
||||
|
||||
# src2 = self.self_attn(q, k, value=src, attn_mask=src_mask, key_padding_mask=src_key_padding_mask)[0]
|
||||
src = src + self.dropout1(src2)
|
||||
src = self.norm1(src)
|
||||
src2 = self.linear2(self.dropout(self.activation(self.linear1(src))))
|
||||
src = src + self.dropout2(src2)
|
||||
src = self.norm2(src)
|
||||
return src
|
||||
|
|
@ -0,0 +1,268 @@
|
|||
# ------------------------------------------------------------------------
|
||||
# Grounding DINO
|
||||
# url: https://github.com/IDEA-Research/GroundingDINO
|
||||
# Copyright (c) 2023 IDEA. All Rights Reserved.
|
||||
# Licensed under the Apache License, Version 2.0 [see LICENSE for details]
|
||||
# ------------------------------------------------------------------------
|
||||
|
||||
import copy
|
||||
import math
|
||||
|
||||
import torch
|
||||
import torch.nn.functional as F
|
||||
from torch import Tensor, nn
|
||||
|
||||
|
||||
def _get_clones(module, N, layer_share=False):
|
||||
# import ipdb; ipdb.set_trace()
|
||||
if layer_share:
|
||||
return nn.ModuleList([module for i in range(N)])
|
||||
else:
|
||||
return nn.ModuleList([copy.deepcopy(module) for i in range(N)])
|
||||
|
||||
|
||||
def get_sine_pos_embed(
|
||||
pos_tensor: torch.Tensor,
|
||||
num_pos_feats: int = 128,
|
||||
temperature: int = 10000,
|
||||
exchange_xy: bool = True,
|
||||
):
|
||||
"""generate sine position embedding from a position tensor
|
||||
Args:
|
||||
pos_tensor (torch.Tensor): shape: [..., n].
|
||||
num_pos_feats (int): projected shape for each float in the tensor.
|
||||
temperature (int): temperature in the sine/cosine function.
|
||||
exchange_xy (bool, optional): exchange pos x and pos y. \
|
||||
For example, input tensor is [x,y], the results will be [pos(y), pos(x)]. Defaults to True.
|
||||
Returns:
|
||||
pos_embed (torch.Tensor): shape: [..., n*num_pos_feats].
|
||||
"""
|
||||
scale = 2 * math.pi
|
||||
dim_t = torch.arange(num_pos_feats, dtype=torch.float32, device=pos_tensor.device)
|
||||
dim_t = temperature ** (2 * torch.div(dim_t, 2, rounding_mode="floor") / num_pos_feats)
|
||||
|
||||
def sine_func(x: torch.Tensor):
|
||||
sin_x = x * scale / dim_t
|
||||
sin_x = torch.stack((sin_x[..., 0::2].sin(), sin_x[..., 1::2].cos()), dim=3).flatten(2)
|
||||
return sin_x
|
||||
|
||||
pos_res = [sine_func(x) for x in pos_tensor.split([1] * pos_tensor.shape[-1], dim=-1)]
|
||||
if exchange_xy:
|
||||
pos_res[0], pos_res[1] = pos_res[1], pos_res[0]
|
||||
pos_res = torch.cat(pos_res, dim=-1)
|
||||
return pos_res
|
||||
|
||||
|
||||
def gen_encoder_output_proposals(
|
||||
memory: Tensor, memory_padding_mask: Tensor, spatial_shapes: Tensor, learnedwh=None
|
||||
):
|
||||
"""
|
||||
Input:
|
||||
- memory: bs, \sum{hw}, d_model
|
||||
- memory_padding_mask: bs, \sum{hw}
|
||||
- spatial_shapes: nlevel, 2
|
||||
- learnedwh: 2
|
||||
Output:
|
||||
- output_memory: bs, \sum{hw}, d_model
|
||||
- output_proposals: bs, \sum{hw}, 4
|
||||
"""
|
||||
N_, S_, C_ = memory.shape
|
||||
proposals = []
|
||||
_cur = 0
|
||||
for lvl, (H_, W_) in enumerate(spatial_shapes):
|
||||
mask_flatten_ = memory_padding_mask[:, _cur : (_cur + H_ * W_)].view(N_, H_, W_, 1)
|
||||
valid_H = torch.sum(~mask_flatten_[:, :, 0, 0], 1)
|
||||
valid_W = torch.sum(~mask_flatten_[:, 0, :, 0], 1)
|
||||
|
||||
# import ipdb; ipdb.set_trace()
|
||||
|
||||
grid_y, grid_x = torch.meshgrid(
|
||||
torch.linspace(0, H_ - 1, H_, dtype=torch.float32, device=memory.device),
|
||||
torch.linspace(0, W_ - 1, W_, dtype=torch.float32, device=memory.device),
|
||||
)
|
||||
grid = torch.cat([grid_x.unsqueeze(-1), grid_y.unsqueeze(-1)], -1) # H_, W_, 2
|
||||
|
||||
scale = torch.cat([valid_W.unsqueeze(-1), valid_H.unsqueeze(-1)], 1).view(N_, 1, 1, 2)
|
||||
grid = (grid.unsqueeze(0).expand(N_, -1, -1, -1) + 0.5) / scale
|
||||
|
||||
if learnedwh is not None:
|
||||
# import ipdb; ipdb.set_trace()
|
||||
wh = torch.ones_like(grid) * learnedwh.sigmoid() * (2.0**lvl)
|
||||
else:
|
||||
wh = torch.ones_like(grid) * 0.05 * (2.0**lvl)
|
||||
|
||||
# scale = torch.cat([W_[None].unsqueeze(-1), H_[None].unsqueeze(-1)], 1).view(1, 1, 1, 2).repeat(N_, 1, 1, 1)
|
||||
# grid = (grid.unsqueeze(0).expand(N_, -1, -1, -1) + 0.5) / scale
|
||||
# wh = torch.ones_like(grid) / scale
|
||||
proposal = torch.cat((grid, wh), -1).view(N_, -1, 4)
|
||||
proposals.append(proposal)
|
||||
_cur += H_ * W_
|
||||
# import ipdb; ipdb.set_trace()
|
||||
output_proposals = torch.cat(proposals, 1)
|
||||
output_proposals_valid = ((output_proposals > 0.01) & (output_proposals < 0.99)).all(
|
||||
-1, keepdim=True
|
||||
)
|
||||
output_proposals = torch.log(output_proposals / (1 - output_proposals)) # unsigmoid
|
||||
output_proposals = output_proposals.masked_fill(memory_padding_mask.unsqueeze(-1), float("inf"))
|
||||
output_proposals = output_proposals.masked_fill(~output_proposals_valid, float("inf"))
|
||||
|
||||
output_memory = memory
|
||||
output_memory = output_memory.masked_fill(memory_padding_mask.unsqueeze(-1), float(0))
|
||||
output_memory = output_memory.masked_fill(~output_proposals_valid, float(0))
|
||||
|
||||
# output_memory = output_memory.masked_fill(memory_padding_mask.unsqueeze(-1), float('inf'))
|
||||
# output_memory = output_memory.masked_fill(~output_proposals_valid, float('inf'))
|
||||
|
||||
return output_memory, output_proposals
|
||||
|
||||
|
||||
class RandomBoxPerturber:
|
||||
def __init__(
|
||||
self, x_noise_scale=0.2, y_noise_scale=0.2, w_noise_scale=0.2, h_noise_scale=0.2
|
||||
) -> None:
|
||||
self.noise_scale = torch.Tensor(
|
||||
[x_noise_scale, y_noise_scale, w_noise_scale, h_noise_scale]
|
||||
)
|
||||
|
||||
def __call__(self, refanchors: Tensor) -> Tensor:
|
||||
nq, bs, query_dim = refanchors.shape
|
||||
device = refanchors.device
|
||||
|
||||
noise_raw = torch.rand_like(refanchors)
|
||||
noise_scale = self.noise_scale.to(device)[:query_dim]
|
||||
|
||||
new_refanchors = refanchors * (1 + (noise_raw - 0.5) * noise_scale)
|
||||
return new_refanchors.clamp_(0, 1)
|
||||
|
||||
|
||||
def sigmoid_focal_loss(
|
||||
inputs, targets, num_boxes, alpha: float = 0.25, gamma: float = 2, no_reduction=False
|
||||
):
|
||||
"""
|
||||
Loss used in RetinaNet for dense detection: https://arxiv.org/abs/1708.02002.
|
||||
Args:
|
||||
inputs: A float tensor of arbitrary shape.
|
||||
The predictions for each example.
|
||||
targets: A float tensor with the same shape as inputs. Stores the binary
|
||||
classification label for each element in inputs
|
||||
(0 for the negative class and 1 for the positive class).
|
||||
alpha: (optional) Weighting factor in range (0,1) to balance
|
||||
positive vs negative examples. Default = -1 (no weighting).
|
||||
gamma: Exponent of the modulating factor (1 - p_t) to
|
||||
balance easy vs hard examples.
|
||||
Returns:
|
||||
Loss tensor
|
||||
"""
|
||||
prob = inputs.sigmoid()
|
||||
ce_loss = F.binary_cross_entropy_with_logits(inputs, targets, reduction="none")
|
||||
p_t = prob * targets + (1 - prob) * (1 - targets)
|
||||
loss = ce_loss * ((1 - p_t) ** gamma)
|
||||
|
||||
if alpha >= 0:
|
||||
alpha_t = alpha * targets + (1 - alpha) * (1 - targets)
|
||||
loss = alpha_t * loss
|
||||
|
||||
if no_reduction:
|
||||
return loss
|
||||
|
||||
return loss.mean(1).sum() / num_boxes
|
||||
|
||||
|
||||
class MLP(nn.Module):
|
||||
"""Very simple multi-layer perceptron (also called FFN)"""
|
||||
|
||||
def __init__(self, input_dim, hidden_dim, output_dim, num_layers):
|
||||
super().__init__()
|
||||
self.num_layers = num_layers
|
||||
h = [hidden_dim] * (num_layers - 1)
|
||||
self.layers = nn.ModuleList(
|
||||
nn.Linear(n, k) for n, k in zip([input_dim] + h, h + [output_dim])
|
||||
)
|
||||
|
||||
def forward(self, x):
|
||||
for i, layer in enumerate(self.layers):
|
||||
x = F.relu(layer(x)) if i < self.num_layers - 1 else layer(x)
|
||||
return x
|
||||
|
||||
|
||||
def _get_activation_fn(activation, d_model=256, batch_dim=0):
|
||||
"""Return an activation function given a string"""
|
||||
if activation == "relu":
|
||||
return F.relu
|
||||
if activation == "gelu":
|
||||
return F.gelu
|
||||
if activation == "glu":
|
||||
return F.glu
|
||||
if activation == "prelu":
|
||||
return nn.PReLU()
|
||||
if activation == "selu":
|
||||
return F.selu
|
||||
|
||||
raise RuntimeError(f"activation should be relu/gelu, not {activation}.")
|
||||
|
||||
|
||||
def gen_sineembed_for_position(pos_tensor):
|
||||
# n_query, bs, _ = pos_tensor.size()
|
||||
# sineembed_tensor = torch.zeros(n_query, bs, 256)
|
||||
scale = 2 * math.pi
|
||||
dim_t = torch.arange(128, dtype=torch.float32, device=pos_tensor.device)
|
||||
dim_t = 10000 ** (2 * (dim_t // 2) / 128)
|
||||
x_embed = pos_tensor[:, :, 0] * scale
|
||||
y_embed = pos_tensor[:, :, 1] * scale
|
||||
pos_x = x_embed[:, :, None] / dim_t
|
||||
pos_y = y_embed[:, :, None] / dim_t
|
||||
pos_x = torch.stack((pos_x[:, :, 0::2].sin(), pos_x[:, :, 1::2].cos()), dim=3).flatten(2)
|
||||
pos_y = torch.stack((pos_y[:, :, 0::2].sin(), pos_y[:, :, 1::2].cos()), dim=3).flatten(2)
|
||||
if pos_tensor.size(-1) == 2:
|
||||
pos = torch.cat((pos_y, pos_x), dim=2)
|
||||
elif pos_tensor.size(-1) == 4:
|
||||
w_embed = pos_tensor[:, :, 2] * scale
|
||||
pos_w = w_embed[:, :, None] / dim_t
|
||||
pos_w = torch.stack((pos_w[:, :, 0::2].sin(), pos_w[:, :, 1::2].cos()), dim=3).flatten(2)
|
||||
|
||||
h_embed = pos_tensor[:, :, 3] * scale
|
||||
pos_h = h_embed[:, :, None] / dim_t
|
||||
pos_h = torch.stack((pos_h[:, :, 0::2].sin(), pos_h[:, :, 1::2].cos()), dim=3).flatten(2)
|
||||
|
||||
pos = torch.cat((pos_y, pos_x, pos_w, pos_h), dim=2)
|
||||
else:
|
||||
raise ValueError("Unknown pos_tensor shape(-1):{}".format(pos_tensor.size(-1)))
|
||||
return pos
|
||||
|
||||
|
||||
class ContrastiveEmbed(nn.Module):
|
||||
def __init__(self, max_text_len=256):
|
||||
"""
|
||||
Args:
|
||||
max_text_len: max length of text.
|
||||
"""
|
||||
super().__init__()
|
||||
self.max_text_len = max_text_len
|
||||
|
||||
def forward(self, x, text_dict):
|
||||
"""_summary_
|
||||
|
||||
Args:
|
||||
x (_type_): _description_
|
||||
text_dict (_type_): _description_
|
||||
{
|
||||
'encoded_text': encoded_text, # bs, 195, d_model
|
||||
'text_token_mask': text_token_mask, # bs, 195
|
||||
# True for used tokens. False for padding tokens
|
||||
}
|
||||
Returns:
|
||||
_type_: _description_
|
||||
"""
|
||||
assert isinstance(text_dict, dict)
|
||||
|
||||
y = text_dict["encoded_text"]
|
||||
text_token_mask = text_dict["text_token_mask"]
|
||||
|
||||
res = x @ y.transpose(-1, -2)
|
||||
res.masked_fill_(~text_token_mask[:, None, :], float("-inf"))
|
||||
|
||||
# padding to max_text_len
|
||||
new_res = torch.full((*res.shape[:-1], self.max_text_len), float("-inf"), device=res.device)
|
||||
new_res[..., : res.shape[-1]] = res
|
||||
|
||||
return new_res
|
||||
|
|
@ -0,0 +1,18 @@
|
|||
# ------------------------------------------------------------------------
|
||||
# Grounding DINO
|
||||
# url: https://github.com/IDEA-Research/GroundingDINO
|
||||
# Copyright (c) 2023 IDEA. All Rights Reserved.
|
||||
# Licensed under the Apache License, Version 2.0 [see LICENSE for details]
|
||||
# ------------------------------------------------------------------------
|
||||
# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved
|
||||
from .GroundingDINO import build_groundingdino
|
||||
|
||||
|
||||
def build_model(args):
|
||||
# we use register to maintain models from catdet6 on.
|
||||
from .registry import MODULE_BUILD_FUNCS
|
||||
|
||||
assert args.modelname in MODULE_BUILD_FUNCS._module_dict
|
||||
build_func = MODULE_BUILD_FUNCS.get(args.modelname)
|
||||
model = build_func(args)
|
||||
return model
|
||||
|
|
@ -0,0 +1,66 @@
|
|||
# ------------------------------------------------------------------------
|
||||
# Grounding DINO
|
||||
# url: https://github.com/IDEA-Research/GroundingDINO
|
||||
# Copyright (c) 2023 IDEA. All Rights Reserved.
|
||||
# Licensed under the Apache License, Version 2.0 [see LICENSE for details]
|
||||
# ------------------------------------------------------------------------
|
||||
# -*- coding: utf-8 -*-
|
||||
# @Author: Yihao Chen
|
||||
# @Date: 2021-08-16 16:03:17
|
||||
# @Last Modified by: Shilong Liu
|
||||
# @Last Modified time: 2022-01-23 15:26
|
||||
# modified from mmcv
|
||||
|
||||
import inspect
|
||||
from functools import partial
|
||||
|
||||
|
||||
class Registry(object):
|
||||
def __init__(self, name):
|
||||
self._name = name
|
||||
self._module_dict = dict()
|
||||
|
||||
def __repr__(self):
|
||||
format_str = self.__class__.__name__ + "(name={}, items={})".format(
|
||||
self._name, list(self._module_dict.keys())
|
||||
)
|
||||
return format_str
|
||||
|
||||
def __len__(self):
|
||||
return len(self._module_dict)
|
||||
|
||||
@property
|
||||
def name(self):
|
||||
return self._name
|
||||
|
||||
@property
|
||||
def module_dict(self):
|
||||
return self._module_dict
|
||||
|
||||
def get(self, key):
|
||||
return self._module_dict.get(key, None)
|
||||
|
||||
def registe_with_name(self, module_name=None, force=False):
|
||||
return partial(self.register, module_name=module_name, force=force)
|
||||
|
||||
def register(self, module_build_function, module_name=None, force=False):
|
||||
"""Register a module build function.
|
||||
Args:
|
||||
module (:obj:`nn.Module`): Module to be registered.
|
||||
"""
|
||||
if not inspect.isfunction(module_build_function):
|
||||
raise TypeError(
|
||||
"module_build_function must be a function, but got {}".format(
|
||||
type(module_build_function)
|
||||
)
|
||||
)
|
||||
if module_name is None:
|
||||
module_name = module_build_function.__name__
|
||||
if not force and module_name in self._module_dict:
|
||||
raise KeyError("{} is already registered in {}".format(module_name, self.name))
|
||||
self._module_dict[module_name] = module_build_function
|
||||
|
||||
return module_build_function
|
||||
|
||||
|
||||
MODULE_BUILD_FUNCS = Registry("model build functions")
|
||||
|
|
@ -0,0 +1 @@
|
|||
# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved
|
||||
|
|
@ -0,0 +1,140 @@
|
|||
# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved
|
||||
"""
|
||||
Utilities for bounding box manipulation and GIoU.
|
||||
"""
|
||||
import torch
|
||||
from torchvision.ops.boxes import box_area
|
||||
|
||||
|
||||
def box_cxcywh_to_xyxy(x):
|
||||
x_c, y_c, w, h = x.unbind(-1)
|
||||
b = [(x_c - 0.5 * w), (y_c - 0.5 * h), (x_c + 0.5 * w), (y_c + 0.5 * h)]
|
||||
return torch.stack(b, dim=-1)
|
||||
|
||||
|
||||
def box_xyxy_to_cxcywh(x):
|
||||
x0, y0, x1, y1 = x.unbind(-1)
|
||||
b = [(x0 + x1) / 2, (y0 + y1) / 2, (x1 - x0), (y1 - y0)]
|
||||
return torch.stack(b, dim=-1)
|
||||
|
||||
|
||||
# modified from torchvision to also return the union
|
||||
def box_iou(boxes1, boxes2):
|
||||
area1 = box_area(boxes1)
|
||||
area2 = box_area(boxes2)
|
||||
|
||||
# import ipdb; ipdb.set_trace()
|
||||
lt = torch.max(boxes1[:, None, :2], boxes2[:, :2]) # [N,M,2]
|
||||
rb = torch.min(boxes1[:, None, 2:], boxes2[:, 2:]) # [N,M,2]
|
||||
|
||||
wh = (rb - lt).clamp(min=0) # [N,M,2]
|
||||
inter = wh[:, :, 0] * wh[:, :, 1] # [N,M]
|
||||
|
||||
union = area1[:, None] + area2 - inter
|
||||
|
||||
iou = inter / (union + 1e-6)
|
||||
return iou, union
|
||||
|
||||
|
||||
def generalized_box_iou(boxes1, boxes2):
|
||||
"""
|
||||
Generalized IoU from https://giou.stanford.edu/
|
||||
|
||||
The boxes should be in [x0, y0, x1, y1] format
|
||||
|
||||
Returns a [N, M] pairwise matrix, where N = len(boxes1)
|
||||
and M = len(boxes2)
|
||||
"""
|
||||
# degenerate boxes gives inf / nan results
|
||||
# so do an early check
|
||||
assert (boxes1[:, 2:] >= boxes1[:, :2]).all()
|
||||
assert (boxes2[:, 2:] >= boxes2[:, :2]).all()
|
||||
# except:
|
||||
# import ipdb; ipdb.set_trace()
|
||||
iou, union = box_iou(boxes1, boxes2)
|
||||
|
||||
lt = torch.min(boxes1[:, None, :2], boxes2[:, :2])
|
||||
rb = torch.max(boxes1[:, None, 2:], boxes2[:, 2:])
|
||||
|
||||
wh = (rb - lt).clamp(min=0) # [N,M,2]
|
||||
area = wh[:, :, 0] * wh[:, :, 1]
|
||||
|
||||
return iou - (area - union) / (area + 1e-6)
|
||||
|
||||
|
||||
# modified from torchvision to also return the union
|
||||
def box_iou_pairwise(boxes1, boxes2):
|
||||
area1 = box_area(boxes1)
|
||||
area2 = box_area(boxes2)
|
||||
|
||||
lt = torch.max(boxes1[:, :2], boxes2[:, :2]) # [N,2]
|
||||
rb = torch.min(boxes1[:, 2:], boxes2[:, 2:]) # [N,2]
|
||||
|
||||
wh = (rb - lt).clamp(min=0) # [N,2]
|
||||
inter = wh[:, 0] * wh[:, 1] # [N]
|
||||
|
||||
union = area1 + area2 - inter
|
||||
|
||||
iou = inter / union
|
||||
return iou, union
|
||||
|
||||
|
||||
def generalized_box_iou_pairwise(boxes1, boxes2):
|
||||
"""
|
||||
Generalized IoU from https://giou.stanford.edu/
|
||||
|
||||
Input:
|
||||
- boxes1, boxes2: N,4
|
||||
Output:
|
||||
- giou: N, 4
|
||||
"""
|
||||
# degenerate boxes gives inf / nan results
|
||||
# so do an early check
|
||||
assert (boxes1[:, 2:] >= boxes1[:, :2]).all()
|
||||
assert (boxes2[:, 2:] >= boxes2[:, :2]).all()
|
||||
assert boxes1.shape == boxes2.shape
|
||||
iou, union = box_iou_pairwise(boxes1, boxes2) # N, 4
|
||||
|
||||
lt = torch.min(boxes1[:, :2], boxes2[:, :2])
|
||||
rb = torch.max(boxes1[:, 2:], boxes2[:, 2:])
|
||||
|
||||
wh = (rb - lt).clamp(min=0) # [N,2]
|
||||
area = wh[:, 0] * wh[:, 1]
|
||||
|
||||
return iou - (area - union) / area
|
||||
|
||||
|
||||
def masks_to_boxes(masks):
|
||||
"""Compute the bounding boxes around the provided masks
|
||||
|
||||
The masks should be in format [N, H, W] where N is the number of masks, (H, W) are the spatial dimensions.
|
||||
|
||||
Returns a [N, 4] tensors, with the boxes in xyxy format
|
||||
"""
|
||||
if masks.numel() == 0:
|
||||
return torch.zeros((0, 4), device=masks.device)
|
||||
|
||||
h, w = masks.shape[-2:]
|
||||
|
||||
y = torch.arange(0, h, dtype=torch.float)
|
||||
x = torch.arange(0, w, dtype=torch.float)
|
||||
y, x = torch.meshgrid(y, x)
|
||||
|
||||
x_mask = masks * x.unsqueeze(0)
|
||||
x_max = x_mask.flatten(1).max(-1)[0]
|
||||
x_min = x_mask.masked_fill(~(masks.bool()), 1e8).flatten(1).min(-1)[0]
|
||||
|
||||
y_mask = masks * y.unsqueeze(0)
|
||||
y_max = y_mask.flatten(1).max(-1)[0]
|
||||
y_min = y_mask.masked_fill(~(masks.bool()), 1e8).flatten(1).min(-1)[0]
|
||||
|
||||
return torch.stack([x_min, y_min, x_max, y_max], 1)
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
x = torch.rand(5, 4)
|
||||
y = torch.rand(3, 4)
|
||||
iou, union = box_iou(x, y)
|
||||
import ipdb
|
||||
|
||||
ipdb.set_trace()
|
||||
|
|
@ -0,0 +1,26 @@
|
|||
from transformers import AutoTokenizer, BertModel, BertTokenizer, RobertaModel, RobertaTokenizerFast
|
||||
|
||||
|
||||
def get_tokenlizer(text_encoder_type):
|
||||
if not isinstance(text_encoder_type, str):
|
||||
# print("text_encoder_type is not a str")
|
||||
if hasattr(text_encoder_type, "text_encoder_type"):
|
||||
text_encoder_type = text_encoder_type.text_encoder_type
|
||||
elif text_encoder_type.get("text_encoder_type", False):
|
||||
text_encoder_type = text_encoder_type.get("text_encoder_type")
|
||||
else:
|
||||
raise ValueError(
|
||||
"Unknown type of text_encoder_type: {}".format(type(text_encoder_type))
|
||||
)
|
||||
print("final text_encoder_type: {}".format(text_encoder_type))
|
||||
|
||||
tokenizer = AutoTokenizer.from_pretrained(text_encoder_type)
|
||||
return tokenizer
|
||||
|
||||
|
||||
def get_pretrained_language_model(text_encoder_type):
|
||||
if text_encoder_type == "bert-base-uncased":
|
||||
return BertModel.from_pretrained(text_encoder_type)
|
||||
if text_encoder_type == "roberta-base":
|
||||
return RobertaModel.from_pretrained(text_encoder_type)
|
||||
raise ValueError("Unknown text_encoder_type {}".format(text_encoder_type))
|
||||
|
|
@ -0,0 +1,93 @@
|
|||
# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved
|
||||
import functools
|
||||
import logging
|
||||
import os
|
||||
import sys
|
||||
|
||||
from termcolor import colored
|
||||
|
||||
|
||||
class _ColorfulFormatter(logging.Formatter):
|
||||
def __init__(self, *args, **kwargs):
|
||||
self._root_name = kwargs.pop("root_name") + "."
|
||||
self._abbrev_name = kwargs.pop("abbrev_name", "")
|
||||
if len(self._abbrev_name):
|
||||
self._abbrev_name = self._abbrev_name + "."
|
||||
super(_ColorfulFormatter, self).__init__(*args, **kwargs)
|
||||
|
||||
def formatMessage(self, record):
|
||||
record.name = record.name.replace(self._root_name, self._abbrev_name)
|
||||
log = super(_ColorfulFormatter, self).formatMessage(record)
|
||||
if record.levelno == logging.WARNING:
|
||||
prefix = colored("WARNING", "red", attrs=["blink"])
|
||||
elif record.levelno == logging.ERROR or record.levelno == logging.CRITICAL:
|
||||
prefix = colored("ERROR", "red", attrs=["blink", "underline"])
|
||||
else:
|
||||
return log
|
||||
return prefix + " " + log
|
||||
|
||||
|
||||
# so that calling setup_logger multiple times won't add many handlers
|
||||
@functools.lru_cache()
|
||||
def setup_logger(output=None, distributed_rank=0, *, color=True, name="imagenet", abbrev_name=None):
|
||||
"""
|
||||
Initialize the detectron2 logger and set its verbosity level to "INFO".
|
||||
|
||||
Args:
|
||||
output (str): a file name or a directory to save log. If None, will not save log file.
|
||||
If ends with ".txt" or ".log", assumed to be a file name.
|
||||
Otherwise, logs will be saved to `output/log.txt`.
|
||||
name (str): the root module name of this logger
|
||||
|
||||
Returns:
|
||||
logging.Logger: a logger
|
||||
"""
|
||||
logger = logging.getLogger(name)
|
||||
logger.setLevel(logging.DEBUG)
|
||||
logger.propagate = False
|
||||
|
||||
if abbrev_name is None:
|
||||
abbrev_name = name
|
||||
|
||||
plain_formatter = logging.Formatter(
|
||||
"[%(asctime)s.%(msecs)03d]: %(message)s", datefmt="%m/%d %H:%M:%S"
|
||||
)
|
||||
# stdout logging: master only
|
||||
if distributed_rank == 0:
|
||||
ch = logging.StreamHandler(stream=sys.stdout)
|
||||
ch.setLevel(logging.DEBUG)
|
||||
if color:
|
||||
formatter = _ColorfulFormatter(
|
||||
colored("[%(asctime)s.%(msecs)03d]: ", "green") + "%(message)s",
|
||||
datefmt="%m/%d %H:%M:%S",
|
||||
root_name=name,
|
||||
abbrev_name=str(abbrev_name),
|
||||
)
|
||||
else:
|
||||
formatter = plain_formatter
|
||||
ch.setFormatter(formatter)
|
||||
logger.addHandler(ch)
|
||||
|
||||
# file logging: all workers
|
||||
if output is not None:
|
||||
if output.endswith(".txt") or output.endswith(".log"):
|
||||
filename = output
|
||||
else:
|
||||
filename = os.path.join(output, "log.txt")
|
||||
if distributed_rank > 0:
|
||||
filename = filename + f".rank{distributed_rank}"
|
||||
os.makedirs(os.path.dirname(filename), exist_ok=True)
|
||||
|
||||
fh = logging.StreamHandler(_cached_log_stream(filename))
|
||||
fh.setLevel(logging.DEBUG)
|
||||
fh.setFormatter(plain_formatter)
|
||||
logger.addHandler(fh)
|
||||
|
||||
return logger
|
||||
|
||||
|
||||
# cache the opened file object, so that different calls to `setup_logger`
|
||||
# with the same file name can safely write to the same file.
|
||||
@functools.lru_cache(maxsize=None)
|
||||
def _cached_log_stream(filename):
|
||||
return open(filename, "a")
|
||||
|
|
@ -0,0 +1,717 @@
|
|||
# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved
|
||||
"""
|
||||
Misc functions, including distributed helpers.
|
||||
|
||||
Mostly copy-paste from torchvision references.
|
||||
"""
|
||||
import colorsys
|
||||
import datetime
|
||||
import functools
|
||||
import io
|
||||
import json
|
||||
import os
|
||||
import pickle
|
||||
import subprocess
|
||||
import time
|
||||
from collections import OrderedDict, defaultdict, deque
|
||||
from typing import List, Optional
|
||||
|
||||
import numpy as np
|
||||
import torch
|
||||
import torch.distributed as dist
|
||||
|
||||
# needed due to empty tensor bug in pytorch and torchvision 0.5
|
||||
import torchvision
|
||||
from torch import Tensor
|
||||
|
||||
__torchvision_need_compat_flag = float(torchvision.__version__.split(".")[1]) < 7
|
||||
if __torchvision_need_compat_flag:
|
||||
from torchvision.ops import _new_empty_tensor
|
||||
from torchvision.ops.misc import _output_size
|
||||
|
||||
|
||||
class SmoothedValue(object):
|
||||
"""Track a series of values and provide access to smoothed values over a
|
||||
window or the global series average.
|
||||
"""
|
||||
|
||||
def __init__(self, window_size=20, fmt=None):
|
||||
if fmt is None:
|
||||
fmt = "{median:.4f} ({global_avg:.4f})"
|
||||
self.deque = deque(maxlen=window_size)
|
||||
self.total = 0.0
|
||||
self.count = 0
|
||||
self.fmt = fmt
|
||||
|
||||
def update(self, value, n=1):
|
||||
self.deque.append(value)
|
||||
self.count += n
|
||||
self.total += value * n
|
||||
|
||||
def synchronize_between_processes(self):
|
||||
"""
|
||||
Warning: does not synchronize the deque!
|
||||
"""
|
||||
if not is_dist_avail_and_initialized():
|
||||
return
|
||||
t = torch.tensor([self.count, self.total], dtype=torch.float64, device="cuda")
|
||||
dist.barrier()
|
||||
dist.all_reduce(t)
|
||||
t = t.tolist()
|
||||
self.count = int(t[0])
|
||||
self.total = t[1]
|
||||
|
||||
@property
|
||||
def median(self):
|
||||
d = torch.tensor(list(self.deque))
|
||||
if d.shape[0] == 0:
|
||||
return 0
|
||||
return d.median().item()
|
||||
|
||||
@property
|
||||
def avg(self):
|
||||
d = torch.tensor(list(self.deque), dtype=torch.float32)
|
||||
return d.mean().item()
|
||||
|
||||
@property
|
||||
def global_avg(self):
|
||||
if os.environ.get("SHILONG_AMP", None) == "1":
|
||||
eps = 1e-4
|
||||
else:
|
||||
eps = 1e-6
|
||||
return self.total / (self.count + eps)
|
||||
|
||||
@property
|
||||
def max(self):
|
||||
return max(self.deque)
|
||||
|
||||
@property
|
||||
def value(self):
|
||||
return self.deque[-1]
|
||||
|
||||
def __str__(self):
|
||||
return self.fmt.format(
|
||||
median=self.median,
|
||||
avg=self.avg,
|
||||
global_avg=self.global_avg,
|
||||
max=self.max,
|
||||
value=self.value,
|
||||
)
|
||||
|
||||
|
||||
@functools.lru_cache()
|
||||
def _get_global_gloo_group():
|
||||
"""
|
||||
Return a process group based on gloo backend, containing all the ranks
|
||||
The result is cached.
|
||||
"""
|
||||
|
||||
if dist.get_backend() == "nccl":
|
||||
return dist.new_group(backend="gloo")
|
||||
|
||||
return dist.group.WORLD
|
||||
|
||||
|
||||
def all_gather_cpu(data):
|
||||
"""
|
||||
Run all_gather on arbitrary picklable data (not necessarily tensors)
|
||||
Args:
|
||||
data: any picklable object
|
||||
Returns:
|
||||
list[data]: list of data gathered from each rank
|
||||
"""
|
||||
|
||||
world_size = get_world_size()
|
||||
if world_size == 1:
|
||||
return [data]
|
||||
|
||||
cpu_group = _get_global_gloo_group()
|
||||
|
||||
buffer = io.BytesIO()
|
||||
torch.save(data, buffer)
|
||||
data_view = buffer.getbuffer()
|
||||
device = "cuda" if cpu_group is None else "cpu"
|
||||
tensor = torch.ByteTensor(data_view).to(device)
|
||||
|
||||
# obtain Tensor size of each rank
|
||||
local_size = torch.tensor([tensor.numel()], device=device, dtype=torch.long)
|
||||
size_list = [torch.tensor([0], device=device, dtype=torch.long) for _ in range(world_size)]
|
||||
if cpu_group is None:
|
||||
dist.all_gather(size_list, local_size)
|
||||
else:
|
||||
print("gathering on cpu")
|
||||
dist.all_gather(size_list, local_size, group=cpu_group)
|
||||
size_list = [int(size.item()) for size in size_list]
|
||||
max_size = max(size_list)
|
||||
assert isinstance(local_size.item(), int)
|
||||
local_size = int(local_size.item())
|
||||
|
||||
# receiving Tensor from all ranks
|
||||
# we pad the tensor because torch all_gather does not support
|
||||
# gathering tensors of different shapes
|
||||
tensor_list = []
|
||||
for _ in size_list:
|
||||
tensor_list.append(torch.empty((max_size,), dtype=torch.uint8, device=device))
|
||||
if local_size != max_size:
|
||||
padding = torch.empty(size=(max_size - local_size,), dtype=torch.uint8, device=device)
|
||||
tensor = torch.cat((tensor, padding), dim=0)
|
||||
if cpu_group is None:
|
||||
dist.all_gather(tensor_list, tensor)
|
||||
else:
|
||||
dist.all_gather(tensor_list, tensor, group=cpu_group)
|
||||
|
||||
data_list = []
|
||||
for size, tensor in zip(size_list, tensor_list):
|
||||
tensor = torch.split(tensor, [size, max_size - size], dim=0)[0]
|
||||
buffer = io.BytesIO(tensor.cpu().numpy())
|
||||
obj = torch.load(buffer)
|
||||
data_list.append(obj)
|
||||
|
||||
return data_list
|
||||
|
||||
|
||||
def all_gather(data):
|
||||
"""
|
||||
Run all_gather on arbitrary picklable data (not necessarily tensors)
|
||||
Args:
|
||||
data: any picklable object
|
||||
Returns:
|
||||
list[data]: list of data gathered from each rank
|
||||
"""
|
||||
|
||||
if os.getenv("CPU_REDUCE") == "1":
|
||||
return all_gather_cpu(data)
|
||||
|
||||
world_size = get_world_size()
|
||||
if world_size == 1:
|
||||
return [data]
|
||||
|
||||
# serialized to a Tensor
|
||||
buffer = pickle.dumps(data)
|
||||
storage = torch.ByteStorage.from_buffer(buffer)
|
||||
tensor = torch.ByteTensor(storage).to("cuda")
|
||||
|
||||
# obtain Tensor size of each rank
|
||||
local_size = torch.tensor([tensor.numel()], device="cuda")
|
||||
size_list = [torch.tensor([0], device="cuda") for _ in range(world_size)]
|
||||
dist.all_gather(size_list, local_size)
|
||||
size_list = [int(size.item()) for size in size_list]
|
||||
max_size = max(size_list)
|
||||
|
||||
# receiving Tensor from all ranks
|
||||
# we pad the tensor because torch all_gather does not support
|
||||
# gathering tensors of different shapes
|
||||
tensor_list = []
|
||||
for _ in size_list:
|
||||
tensor_list.append(torch.empty((max_size,), dtype=torch.uint8, device="cuda"))
|
||||
if local_size != max_size:
|
||||
padding = torch.empty(size=(max_size - local_size,), dtype=torch.uint8, device="cuda")
|
||||
tensor = torch.cat((tensor, padding), dim=0)
|
||||
dist.all_gather(tensor_list, tensor)
|
||||
|
||||
data_list = []
|
||||
for size, tensor in zip(size_list, tensor_list):
|
||||
buffer = tensor.cpu().numpy().tobytes()[:size]
|
||||
data_list.append(pickle.loads(buffer))
|
||||
|
||||
return data_list
|
||||
|
||||
|
||||
def reduce_dict(input_dict, average=True):
|
||||
"""
|
||||
Args:
|
||||
input_dict (dict): all the values will be reduced
|
||||
average (bool): whether to do average or sum
|
||||
Reduce the values in the dictionary from all processes so that all processes
|
||||
have the averaged results. Returns a dict with the same fields as
|
||||
input_dict, after reduction.
|
||||
"""
|
||||
world_size = get_world_size()
|
||||
if world_size < 2:
|
||||
return input_dict
|
||||
with torch.no_grad():
|
||||
names = []
|
||||
values = []
|
||||
# sort the keys so that they are consistent across processes
|
||||
for k in sorted(input_dict.keys()):
|
||||
names.append(k)
|
||||
values.append(input_dict[k])
|
||||
values = torch.stack(values, dim=0)
|
||||
dist.all_reduce(values)
|
||||
if average:
|
||||
values /= world_size
|
||||
reduced_dict = {k: v for k, v in zip(names, values)}
|
||||
return reduced_dict
|
||||
|
||||
|
||||
class MetricLogger(object):
|
||||
def __init__(self, delimiter="\t"):
|
||||
self.meters = defaultdict(SmoothedValue)
|
||||
self.delimiter = delimiter
|
||||
|
||||
def update(self, **kwargs):
|
||||
for k, v in kwargs.items():
|
||||
if isinstance(v, torch.Tensor):
|
||||
v = v.item()
|
||||
assert isinstance(v, (float, int))
|
||||
self.meters[k].update(v)
|
||||
|
||||
def __getattr__(self, attr):
|
||||
if attr in self.meters:
|
||||
return self.meters[attr]
|
||||
if attr in self.__dict__:
|
||||
return self.__dict__[attr]
|
||||
raise AttributeError("'{}' object has no attribute '{}'".format(type(self).__name__, attr))
|
||||
|
||||
def __str__(self):
|
||||
loss_str = []
|
||||
for name, meter in self.meters.items():
|
||||
# print(name, str(meter))
|
||||
# import ipdb;ipdb.set_trace()
|
||||
if meter.count > 0:
|
||||
loss_str.append("{}: {}".format(name, str(meter)))
|
||||
return self.delimiter.join(loss_str)
|
||||
|
||||
def synchronize_between_processes(self):
|
||||
for meter in self.meters.values():
|
||||
meter.synchronize_between_processes()
|
||||
|
||||
def add_meter(self, name, meter):
|
||||
self.meters[name] = meter
|
||||
|
||||
def log_every(self, iterable, print_freq, header=None, logger=None):
|
||||
if logger is None:
|
||||
print_func = print
|
||||
else:
|
||||
print_func = logger.info
|
||||
|
||||
i = 0
|
||||
if not header:
|
||||
header = ""
|
||||
start_time = time.time()
|
||||
end = time.time()
|
||||
iter_time = SmoothedValue(fmt="{avg:.4f}")
|
||||
data_time = SmoothedValue(fmt="{avg:.4f}")
|
||||
space_fmt = ":" + str(len(str(len(iterable)))) + "d"
|
||||
if torch.cuda.is_available():
|
||||
log_msg = self.delimiter.join(
|
||||
[
|
||||
header,
|
||||
"[{0" + space_fmt + "}/{1}]",
|
||||
"eta: {eta}",
|
||||
"{meters}",
|
||||
"time: {time}",
|
||||
"data: {data}",
|
||||
"max mem: {memory:.0f}",
|
||||
]
|
||||
)
|
||||
else:
|
||||
log_msg = self.delimiter.join(
|
||||
[
|
||||
header,
|
||||
"[{0" + space_fmt + "}/{1}]",
|
||||
"eta: {eta}",
|
||||
"{meters}",
|
||||
"time: {time}",
|
||||
"data: {data}",
|
||||
]
|
||||
)
|
||||
MB = 1024.0 * 1024.0
|
||||
for obj in iterable:
|
||||
data_time.update(time.time() - end)
|
||||
yield obj
|
||||
# import ipdb; ipdb.set_trace()
|
||||
iter_time.update(time.time() - end)
|
||||
if i % print_freq == 0 or i == len(iterable) - 1:
|
||||
eta_seconds = iter_time.global_avg * (len(iterable) - i)
|
||||
eta_string = str(datetime.timedelta(seconds=int(eta_seconds)))
|
||||
if torch.cuda.is_available():
|
||||
print_func(
|
||||
log_msg.format(
|
||||
i,
|
||||
len(iterable),
|
||||
eta=eta_string,
|
||||
meters=str(self),
|
||||
time=str(iter_time),
|
||||
data=str(data_time),
|
||||
memory=torch.cuda.max_memory_allocated() / MB,
|
||||
)
|
||||
)
|
||||
else:
|
||||
print_func(
|
||||
log_msg.format(
|
||||
i,
|
||||
len(iterable),
|
||||
eta=eta_string,
|
||||
meters=str(self),
|
||||
time=str(iter_time),
|
||||
data=str(data_time),
|
||||
)
|
||||
)
|
||||
i += 1
|
||||
end = time.time()
|
||||
total_time = time.time() - start_time
|
||||
total_time_str = str(datetime.timedelta(seconds=int(total_time)))
|
||||
print_func(
|
||||
"{} Total time: {} ({:.4f} s / it)".format(
|
||||
header, total_time_str, total_time / len(iterable)
|
||||
)
|
||||
)
|
||||
|
||||
|
||||
def get_sha():
|
||||
cwd = os.path.dirname(os.path.abspath(__file__))
|
||||
|
||||
def _run(command):
|
||||
return subprocess.check_output(command, cwd=cwd).decode("ascii").strip()
|
||||
|
||||
sha = "N/A"
|
||||
diff = "clean"
|
||||
branch = "N/A"
|
||||
try:
|
||||
sha = _run(["git", "rev-parse", "HEAD"])
|
||||
subprocess.check_output(["git", "diff"], cwd=cwd)
|
||||
diff = _run(["git", "diff-index", "HEAD"])
|
||||
diff = "has uncommited changes" if diff else "clean"
|
||||
branch = _run(["git", "rev-parse", "--abbrev-ref", "HEAD"])
|
||||
except Exception:
|
||||
pass
|
||||
message = f"sha: {sha}, status: {diff}, branch: {branch}"
|
||||
return message
|
||||
|
||||
|
||||
def collate_fn(batch):
|
||||
# import ipdb; ipdb.set_trace()
|
||||
batch = list(zip(*batch))
|
||||
batch[0] = nested_tensor_from_tensor_list(batch[0])
|
||||
return tuple(batch)
|
||||
|
||||
|
||||
def _max_by_axis(the_list):
|
||||
# type: (List[List[int]]) -> List[int]
|
||||
maxes = the_list[0]
|
||||
for sublist in the_list[1:]:
|
||||
for index, item in enumerate(sublist):
|
||||
maxes[index] = max(maxes[index], item)
|
||||
return maxes
|
||||
|
||||
|
||||
class NestedTensor(object):
|
||||
def __init__(self, tensors, mask: Optional[Tensor]):
|
||||
self.tensors = tensors
|
||||
self.mask = mask
|
||||
if mask == "auto":
|
||||
self.mask = torch.zeros_like(tensors).to(tensors.device)
|
||||
if self.mask.dim() == 3:
|
||||
self.mask = self.mask.sum(0).to(bool)
|
||||
elif self.mask.dim() == 4:
|
||||
self.mask = self.mask.sum(1).to(bool)
|
||||
else:
|
||||
raise ValueError(
|
||||
"tensors dim must be 3 or 4 but {}({})".format(
|
||||
self.tensors.dim(), self.tensors.shape
|
||||
)
|
||||
)
|
||||
|
||||
def imgsize(self):
|
||||
res = []
|
||||
for i in range(self.tensors.shape[0]):
|
||||
mask = self.mask[i]
|
||||
maxH = (~mask).sum(0).max()
|
||||
maxW = (~mask).sum(1).max()
|
||||
res.append(torch.Tensor([maxH, maxW]))
|
||||
return res
|
||||
|
||||
def to(self, device):
|
||||
# type: (Device) -> NestedTensor # noqa
|
||||
cast_tensor = self.tensors.to(device)
|
||||
mask = self.mask
|
||||
if mask is not None:
|
||||
assert mask is not None
|
||||
cast_mask = mask.to(device)
|
||||
else:
|
||||
cast_mask = None
|
||||
return NestedTensor(cast_tensor, cast_mask)
|
||||
|
||||
def to_img_list_single(self, tensor, mask):
|
||||
assert tensor.dim() == 3, "dim of tensor should be 3 but {}".format(tensor.dim())
|
||||
maxH = (~mask).sum(0).max()
|
||||
maxW = (~mask).sum(1).max()
|
||||
img = tensor[:, :maxH, :maxW]
|
||||
return img
|
||||
|
||||
def to_img_list(self):
|
||||
"""remove the padding and convert to img list
|
||||
|
||||
Returns:
|
||||
[type]: [description]
|
||||
"""
|
||||
if self.tensors.dim() == 3:
|
||||
return self.to_img_list_single(self.tensors, self.mask)
|
||||
else:
|
||||
res = []
|
||||
for i in range(self.tensors.shape[0]):
|
||||
tensor_i = self.tensors[i]
|
||||
mask_i = self.mask[i]
|
||||
res.append(self.to_img_list_single(tensor_i, mask_i))
|
||||
return res
|
||||
|
||||
@property
|
||||
def device(self):
|
||||
return self.tensors.device
|
||||
|
||||
def decompose(self):
|
||||
return self.tensors, self.mask
|
||||
|
||||
def __repr__(self):
|
||||
return str(self.tensors)
|
||||
|
||||
@property
|
||||
def shape(self):
|
||||
return {"tensors.shape": self.tensors.shape, "mask.shape": self.mask.shape}
|
||||
|
||||
|
||||
def nested_tensor_from_tensor_list(tensor_list: List[Tensor]):
|
||||
# TODO make this more general
|
||||
if tensor_list[0].ndim == 3:
|
||||
if torchvision._is_tracing():
|
||||
# nested_tensor_from_tensor_list() does not export well to ONNX
|
||||
# call _onnx_nested_tensor_from_tensor_list() instead
|
||||
return _onnx_nested_tensor_from_tensor_list(tensor_list)
|
||||
|
||||
# TODO make it support different-sized images
|
||||
max_size = _max_by_axis([list(img.shape) for img in tensor_list])
|
||||
# min_size = tuple(min(s) for s in zip(*[img.shape for img in tensor_list]))
|
||||
batch_shape = [len(tensor_list)] + max_size
|
||||
b, c, h, w = batch_shape
|
||||
dtype = tensor_list[0].dtype
|
||||
device = tensor_list[0].device
|
||||
tensor = torch.zeros(batch_shape, dtype=dtype, device=device)
|
||||
mask = torch.ones((b, h, w), dtype=torch.bool, device=device)
|
||||
for img, pad_img, m in zip(tensor_list, tensor, mask):
|
||||
pad_img[: img.shape[0], : img.shape[1], : img.shape[2]].copy_(img)
|
||||
m[: img.shape[1], : img.shape[2]] = False
|
||||
else:
|
||||
raise ValueError("not supported")
|
||||
return NestedTensor(tensor, mask)
|
||||
|
||||
|
||||
# _onnx_nested_tensor_from_tensor_list() is an implementation of
|
||||
# nested_tensor_from_tensor_list() that is supported by ONNX tracing.
|
||||
@torch.jit.unused
|
||||
def _onnx_nested_tensor_from_tensor_list(tensor_list: List[Tensor]) -> NestedTensor:
|
||||
max_size = []
|
||||
for i in range(tensor_list[0].dim()):
|
||||
max_size_i = torch.max(
|
||||
torch.stack([img.shape[i] for img in tensor_list]).to(torch.float32)
|
||||
).to(torch.int64)
|
||||
max_size.append(max_size_i)
|
||||
max_size = tuple(max_size)
|
||||
|
||||
# work around for
|
||||
# pad_img[: img.shape[0], : img.shape[1], : img.shape[2]].copy_(img)
|
||||
# m[: img.shape[1], :img.shape[2]] = False
|
||||
# which is not yet supported in onnx
|
||||
padded_imgs = []
|
||||
padded_masks = []
|
||||
for img in tensor_list:
|
||||
padding = [(s1 - s2) for s1, s2 in zip(max_size, tuple(img.shape))]
|
||||
padded_img = torch.nn.functional.pad(img, (0, padding[2], 0, padding[1], 0, padding[0]))
|
||||
padded_imgs.append(padded_img)
|
||||
|
||||
m = torch.zeros_like(img[0], dtype=torch.int, device=img.device)
|
||||
padded_mask = torch.nn.functional.pad(m, (0, padding[2], 0, padding[1]), "constant", 1)
|
||||
padded_masks.append(padded_mask.to(torch.bool))
|
||||
|
||||
tensor = torch.stack(padded_imgs)
|
||||
mask = torch.stack(padded_masks)
|
||||
|
||||
return NestedTensor(tensor, mask=mask)
|
||||
|
||||
|
||||
def setup_for_distributed(is_master):
|
||||
"""
|
||||
This function disables printing when not in master process
|
||||
"""
|
||||
import builtins as __builtin__
|
||||
|
||||
builtin_print = __builtin__.print
|
||||
|
||||
def print(*args, **kwargs):
|
||||
force = kwargs.pop("force", False)
|
||||
if is_master or force:
|
||||
builtin_print(*args, **kwargs)
|
||||
|
||||
__builtin__.print = print
|
||||
|
||||
|
||||
def is_dist_avail_and_initialized():
|
||||
if not dist.is_available():
|
||||
return False
|
||||
if not dist.is_initialized():
|
||||
return False
|
||||
return True
|
||||
|
||||
|
||||
def get_world_size():
|
||||
if not is_dist_avail_and_initialized():
|
||||
return 1
|
||||
return dist.get_world_size()
|
||||
|
||||
|
||||
def get_rank():
|
||||
if not is_dist_avail_and_initialized():
|
||||
return 0
|
||||
return dist.get_rank()
|
||||
|
||||
|
||||
def is_main_process():
|
||||
return get_rank() == 0
|
||||
|
||||
|
||||
def save_on_master(*args, **kwargs):
|
||||
if is_main_process():
|
||||
torch.save(*args, **kwargs)
|
||||
|
||||
|
||||
def init_distributed_mode(args):
|
||||
if "WORLD_SIZE" in os.environ and os.environ["WORLD_SIZE"] != "": # 'RANK' in os.environ and
|
||||
args.rank = int(os.environ["RANK"])
|
||||
args.world_size = int(os.environ["WORLD_SIZE"])
|
||||
args.gpu = args.local_rank = int(os.environ["LOCAL_RANK"])
|
||||
|
||||
# launch by torch.distributed.launch
|
||||
# Single node
|
||||
# python -m torch.distributed.launch --nproc_per_node=8 main.py --world-size 1 --rank 0 ...
|
||||
# Multi nodes
|
||||
# python -m torch.distributed.launch --nproc_per_node=8 main.py --world-size 2 --rank 0 --dist-url 'tcp://IP_OF_NODE0:FREEPORT' ...
|
||||
# python -m torch.distributed.launch --nproc_per_node=8 main.py --world-size 2 --rank 1 --dist-url 'tcp://IP_OF_NODE0:FREEPORT' ...
|
||||
# args.rank = int(os.environ.get('OMPI_COMM_WORLD_RANK'))
|
||||
# local_world_size = int(os.environ['GPU_PER_NODE_COUNT'])
|
||||
# args.world_size = args.world_size * local_world_size
|
||||
# args.gpu = args.local_rank = int(os.environ['LOCAL_RANK'])
|
||||
# args.rank = args.rank * local_world_size + args.local_rank
|
||||
print(
|
||||
"world size: {}, rank: {}, local rank: {}".format(
|
||||
args.world_size, args.rank, args.local_rank
|
||||
)
|
||||
)
|
||||
print(json.dumps(dict(os.environ), indent=2))
|
||||
elif "SLURM_PROCID" in os.environ:
|
||||
args.rank = int(os.environ["SLURM_PROCID"])
|
||||
args.gpu = args.local_rank = int(os.environ["SLURM_LOCALID"])
|
||||
args.world_size = int(os.environ["SLURM_NPROCS"])
|
||||
|
||||
print(
|
||||
"world size: {}, world rank: {}, local rank: {}, device_count: {}".format(
|
||||
args.world_size, args.rank, args.local_rank, torch.cuda.device_count()
|
||||
)
|
||||
)
|
||||
else:
|
||||
print("Not using distributed mode")
|
||||
args.distributed = False
|
||||
args.world_size = 1
|
||||
args.rank = 0
|
||||
args.local_rank = 0
|
||||
return
|
||||
|
||||
print("world_size:{} rank:{} local_rank:{}".format(args.world_size, args.rank, args.local_rank))
|
||||
args.distributed = True
|
||||
torch.cuda.set_device(args.local_rank)
|
||||
args.dist_backend = "nccl"
|
||||
print("| distributed init (rank {}): {}".format(args.rank, args.dist_url), flush=True)
|
||||
|
||||
torch.distributed.init_process_group(
|
||||
backend=args.dist_backend,
|
||||
world_size=args.world_size,
|
||||
rank=args.rank,
|
||||
init_method=args.dist_url,
|
||||
)
|
||||
|
||||
print("Before torch.distributed.barrier()")
|
||||
torch.distributed.barrier()
|
||||
print("End torch.distributed.barrier()")
|
||||
setup_for_distributed(args.rank == 0)
|
||||
|
||||
|
||||
@torch.no_grad()
|
||||
def accuracy(output, target, topk=(1,)):
|
||||
"""Computes the precision@k for the specified values of k"""
|
||||
if target.numel() == 0:
|
||||
return [torch.zeros([], device=output.device)]
|
||||
maxk = max(topk)
|
||||
batch_size = target.size(0)
|
||||
|
||||
_, pred = output.topk(maxk, 1, True, True)
|
||||
pred = pred.t()
|
||||
correct = pred.eq(target.view(1, -1).expand_as(pred))
|
||||
|
||||
res = []
|
||||
for k in topk:
|
||||
correct_k = correct[:k].view(-1).float().sum(0)
|
||||
res.append(correct_k.mul_(100.0 / batch_size))
|
||||
return res
|
||||
|
||||
|
||||
@torch.no_grad()
|
||||
def accuracy_onehot(pred, gt):
|
||||
"""_summary_
|
||||
|
||||
Args:
|
||||
pred (_type_): n, c
|
||||
gt (_type_): n, c
|
||||
"""
|
||||
tp = ((pred - gt).abs().sum(-1) < 1e-4).float().sum()
|
||||
acc = tp / gt.shape[0] * 100
|
||||
return acc
|
||||
|
||||
|
||||
def interpolate(input, size=None, scale_factor=None, mode="nearest", align_corners=None):
|
||||
# type: (Tensor, Optional[List[int]], Optional[float], str, Optional[bool]) -> Tensor
|
||||
"""
|
||||
Equivalent to nn.functional.interpolate, but with support for empty batch sizes.
|
||||
This will eventually be supported natively by PyTorch, and this
|
||||
class can go away.
|
||||
"""
|
||||
if __torchvision_need_compat_flag < 0.7:
|
||||
if input.numel() > 0:
|
||||
return torch.nn.functional.interpolate(input, size, scale_factor, mode, align_corners)
|
||||
|
||||
output_shape = _output_size(2, input, size, scale_factor)
|
||||
output_shape = list(input.shape[:-2]) + list(output_shape)
|
||||
return _new_empty_tensor(input, output_shape)
|
||||
else:
|
||||
return torchvision.ops.misc.interpolate(input, size, scale_factor, mode, align_corners)
|
||||
|
||||
|
||||
class color_sys:
|
||||
def __init__(self, num_colors) -> None:
|
||||
self.num_colors = num_colors
|
||||
colors = []
|
||||
for i in np.arange(0.0, 360.0, 360.0 / num_colors):
|
||||
hue = i / 360.0
|
||||
lightness = (50 + np.random.rand() * 10) / 100.0
|
||||
saturation = (90 + np.random.rand() * 10) / 100.0
|
||||
colors.append(
|
||||
tuple([int(j * 255) for j in colorsys.hls_to_rgb(hue, lightness, saturation)])
|
||||
)
|
||||
self.colors = colors
|
||||
|
||||
def __call__(self, idx):
|
||||
return self.colors[idx]
|
||||
|
||||
|
||||
def inverse_sigmoid(x, eps=1e-3):
|
||||
x = x.clamp(min=0, max=1)
|
||||
x1 = x.clamp(min=eps)
|
||||
x2 = (1 - x).clamp(min=eps)
|
||||
return torch.log(x1 / x2)
|
||||
|
||||
|
||||
def clean_state_dict(state_dict):
|
||||
new_state_dict = OrderedDict()
|
||||
for k, v in state_dict.items():
|
||||
if k[:7] == "module.":
|
||||
k = k[7:] # remove `module.`
|
||||
new_state_dict[k] = v
|
||||
return new_state_dict
|
||||
|
|
@ -0,0 +1,424 @@
|
|||
# ==========================================================
|
||||
# Modified from mmcv
|
||||
# ==========================================================
|
||||
import ast
|
||||
import os.path as osp
|
||||
import shutil
|
||||
import sys
|
||||
import tempfile
|
||||
from argparse import Action
|
||||
from importlib import import_module
|
||||
|
||||
from addict import Dict
|
||||
from yapf.yapflib.yapf_api import FormatCode
|
||||
|
||||
BASE_KEY = "_base_"
|
||||
DELETE_KEY = "_delete_"
|
||||
RESERVED_KEYS = ["filename", "text", "pretty_text", "get", "dump", "merge_from_dict"]
|
||||
|
||||
|
||||
def check_file_exist(filename, msg_tmpl='file "{}" does not exist'):
|
||||
if not osp.isfile(filename):
|
||||
raise FileNotFoundError(msg_tmpl.format(filename))
|
||||
|
||||
|
||||
class ConfigDict(Dict):
|
||||
def __missing__(self, name):
|
||||
raise KeyError(name)
|
||||
|
||||
def __getattr__(self, name):
|
||||
try:
|
||||
value = super(ConfigDict, self).__getattr__(name)
|
||||
except KeyError:
|
||||
ex = AttributeError(f"'{self.__class__.__name__}' object has no " f"attribute '{name}'")
|
||||
except Exception as e:
|
||||
ex = e
|
||||
else:
|
||||
return value
|
||||
raise ex
|
||||
|
||||
|
||||
class SLConfig(object):
|
||||
"""
|
||||
config files.
|
||||
only support .py file as config now.
|
||||
|
||||
ref: mmcv.utils.config
|
||||
|
||||
Example:
|
||||
>>> cfg = Config(dict(a=1, b=dict(b1=[0, 1])))
|
||||
>>> cfg.a
|
||||
1
|
||||
>>> cfg.b
|
||||
{'b1': [0, 1]}
|
||||
>>> cfg.b.b1
|
||||
[0, 1]
|
||||
>>> cfg = Config.fromfile('tests/data/config/a.py')
|
||||
>>> cfg.filename
|
||||
"/home/kchen/projects/mmcv/tests/data/config/a.py"
|
||||
>>> cfg.item4
|
||||
'test'
|
||||
>>> cfg
|
||||
"Config [path: /home/kchen/projects/mmcv/tests/data/config/a.py]: "
|
||||
"{'item1': [1, 2], 'item2': {'a': 0}, 'item3': True, 'item4': 'test'}"
|
||||
"""
|
||||
|
||||
@staticmethod
|
||||
def _validate_py_syntax(filename):
|
||||
with open(filename) as f:
|
||||
content = f.read()
|
||||
try:
|
||||
ast.parse(content)
|
||||
except SyntaxError:
|
||||
raise SyntaxError("There are syntax errors in config " f"file {filename}")
|
||||
|
||||
@staticmethod
|
||||
def _file2dict(filename):
|
||||
filename = osp.abspath(osp.expanduser(filename))
|
||||
check_file_exist(filename)
|
||||
if filename.lower().endswith(".py"):
|
||||
with tempfile.TemporaryDirectory() as temp_config_dir:
|
||||
temp_config_file = tempfile.NamedTemporaryFile(dir=temp_config_dir, suffix=".py")
|
||||
temp_config_name = osp.basename(temp_config_file.name)
|
||||
shutil.copyfile(filename, osp.join(temp_config_dir, temp_config_name))
|
||||
temp_module_name = osp.splitext(temp_config_name)[0]
|
||||
sys.path.insert(0, temp_config_dir)
|
||||
SLConfig._validate_py_syntax(filename)
|
||||
mod = import_module(temp_module_name)
|
||||
sys.path.pop(0)
|
||||
cfg_dict = {
|
||||
name: value for name, value in mod.__dict__.items() if not name.startswith("__")
|
||||
}
|
||||
# delete imported module
|
||||
del sys.modules[temp_module_name]
|
||||
# close temp file
|
||||
temp_config_file.close()
|
||||
elif filename.lower().endswith((".yml", ".yaml", ".json")):
|
||||
from .slio import slload
|
||||
|
||||
cfg_dict = slload(filename)
|
||||
else:
|
||||
raise IOError("Only py/yml/yaml/json type are supported now!")
|
||||
|
||||
cfg_text = filename + "\n"
|
||||
with open(filename, "r") as f:
|
||||
cfg_text += f.read()
|
||||
|
||||
# parse the base file
|
||||
if BASE_KEY in cfg_dict:
|
||||
cfg_dir = osp.dirname(filename)
|
||||
base_filename = cfg_dict.pop(BASE_KEY)
|
||||
base_filename = base_filename if isinstance(base_filename, list) else [base_filename]
|
||||
|
||||
cfg_dict_list = list()
|
||||
cfg_text_list = list()
|
||||
for f in base_filename:
|
||||
_cfg_dict, _cfg_text = SLConfig._file2dict(osp.join(cfg_dir, f))
|
||||
cfg_dict_list.append(_cfg_dict)
|
||||
cfg_text_list.append(_cfg_text)
|
||||
|
||||
base_cfg_dict = dict()
|
||||
for c in cfg_dict_list:
|
||||
if len(base_cfg_dict.keys() & c.keys()) > 0:
|
||||
raise KeyError("Duplicate key is not allowed among bases")
|
||||
# TODO Allow the duplicate key while warnning user
|
||||
base_cfg_dict.update(c)
|
||||
|
||||
base_cfg_dict = SLConfig._merge_a_into_b(cfg_dict, base_cfg_dict)
|
||||
cfg_dict = base_cfg_dict
|
||||
|
||||
# merge cfg_text
|
||||
cfg_text_list.append(cfg_text)
|
||||
cfg_text = "\n".join(cfg_text_list)
|
||||
|
||||
return cfg_dict, cfg_text
|
||||
|
||||
@staticmethod
|
||||
def _merge_a_into_b(a, b):
|
||||
"""merge dict `a` into dict `b` (non-inplace).
|
||||
values in `a` will overwrite `b`.
|
||||
copy first to avoid inplace modification
|
||||
|
||||
Args:
|
||||
a ([type]): [description]
|
||||
b ([type]): [description]
|
||||
|
||||
Returns:
|
||||
[dict]: [description]
|
||||
"""
|
||||
# import ipdb; ipdb.set_trace()
|
||||
if not isinstance(a, dict):
|
||||
return a
|
||||
|
||||
b = b.copy()
|
||||
for k, v in a.items():
|
||||
if isinstance(v, dict) and k in b and not v.pop(DELETE_KEY, False):
|
||||
|
||||
if not isinstance(b[k], dict) and not isinstance(b[k], list):
|
||||
# if :
|
||||
# import ipdb; ipdb.set_trace()
|
||||
raise TypeError(
|
||||
f"{k}={v} in child config cannot inherit from base "
|
||||
f"because {k} is a dict in the child config but is of "
|
||||
f"type {type(b[k])} in base config. You may set "
|
||||
f"`{DELETE_KEY}=True` to ignore the base config"
|
||||
)
|
||||
b[k] = SLConfig._merge_a_into_b(v, b[k])
|
||||
elif isinstance(b, list):
|
||||
try:
|
||||
_ = int(k)
|
||||
except:
|
||||
raise TypeError(
|
||||
f"b is a list, " f"index {k} should be an int when input but {type(k)}"
|
||||
)
|
||||
b[int(k)] = SLConfig._merge_a_into_b(v, b[int(k)])
|
||||
else:
|
||||
b[k] = v
|
||||
|
||||
return b
|
||||
|
||||
@staticmethod
|
||||
def fromfile(filename):
|
||||
cfg_dict, cfg_text = SLConfig._file2dict(filename)
|
||||
return SLConfig(cfg_dict, cfg_text=cfg_text, filename=filename)
|
||||
|
||||
def __init__(self, cfg_dict=None, cfg_text=None, filename=None):
|
||||
if cfg_dict is None:
|
||||
cfg_dict = dict()
|
||||
elif not isinstance(cfg_dict, dict):
|
||||
raise TypeError("cfg_dict must be a dict, but " f"got {type(cfg_dict)}")
|
||||
for key in cfg_dict:
|
||||
if key in RESERVED_KEYS:
|
||||
raise KeyError(f"{key} is reserved for config file")
|
||||
|
||||
super(SLConfig, self).__setattr__("_cfg_dict", ConfigDict(cfg_dict))
|
||||
super(SLConfig, self).__setattr__("_filename", filename)
|
||||
if cfg_text:
|
||||
text = cfg_text
|
||||
elif filename:
|
||||
with open(filename, "r") as f:
|
||||
text = f.read()
|
||||
else:
|
||||
text = ""
|
||||
super(SLConfig, self).__setattr__("_text", text)
|
||||
|
||||
@property
|
||||
def filename(self):
|
||||
return self._filename
|
||||
|
||||
@property
|
||||
def text(self):
|
||||
return self._text
|
||||
|
||||
@property
|
||||
def pretty_text(self):
|
||||
|
||||
indent = 4
|
||||
|
||||
def _indent(s_, num_spaces):
|
||||
s = s_.split("\n")
|
||||
if len(s) == 1:
|
||||
return s_
|
||||
first = s.pop(0)
|
||||
s = [(num_spaces * " ") + line for line in s]
|
||||
s = "\n".join(s)
|
||||
s = first + "\n" + s
|
||||
return s
|
||||
|
||||
def _format_basic_types(k, v, use_mapping=False):
|
||||
if isinstance(v, str):
|
||||
v_str = f"'{v}'"
|
||||
else:
|
||||
v_str = str(v)
|
||||
|
||||
if use_mapping:
|
||||
k_str = f"'{k}'" if isinstance(k, str) else str(k)
|
||||
attr_str = f"{k_str}: {v_str}"
|
||||
else:
|
||||
attr_str = f"{str(k)}={v_str}"
|
||||
attr_str = _indent(attr_str, indent)
|
||||
|
||||
return attr_str
|
||||
|
||||
def _format_list(k, v, use_mapping=False):
|
||||
# check if all items in the list are dict
|
||||
if all(isinstance(_, dict) for _ in v):
|
||||
v_str = "[\n"
|
||||
v_str += "\n".join(
|
||||
f"dict({_indent(_format_dict(v_), indent)})," for v_ in v
|
||||
).rstrip(",")
|
||||
if use_mapping:
|
||||
k_str = f"'{k}'" if isinstance(k, str) else str(k)
|
||||
attr_str = f"{k_str}: {v_str}"
|
||||
else:
|
||||
attr_str = f"{str(k)}={v_str}"
|
||||
attr_str = _indent(attr_str, indent) + "]"
|
||||
else:
|
||||
attr_str = _format_basic_types(k, v, use_mapping)
|
||||
return attr_str
|
||||
|
||||
def _contain_invalid_identifier(dict_str):
|
||||
contain_invalid_identifier = False
|
||||
for key_name in dict_str:
|
||||
contain_invalid_identifier |= not str(key_name).isidentifier()
|
||||
return contain_invalid_identifier
|
||||
|
||||
def _format_dict(input_dict, outest_level=False):
|
||||
r = ""
|
||||
s = []
|
||||
|
||||
use_mapping = _contain_invalid_identifier(input_dict)
|
||||
if use_mapping:
|
||||
r += "{"
|
||||
for idx, (k, v) in enumerate(input_dict.items()):
|
||||
is_last = idx >= len(input_dict) - 1
|
||||
end = "" if outest_level or is_last else ","
|
||||
if isinstance(v, dict):
|
||||
v_str = "\n" + _format_dict(v)
|
||||
if use_mapping:
|
||||
k_str = f"'{k}'" if isinstance(k, str) else str(k)
|
||||
attr_str = f"{k_str}: dict({v_str}"
|
||||
else:
|
||||
attr_str = f"{str(k)}=dict({v_str}"
|
||||
attr_str = _indent(attr_str, indent) + ")" + end
|
||||
elif isinstance(v, list):
|
||||
attr_str = _format_list(k, v, use_mapping) + end
|
||||
else:
|
||||
attr_str = _format_basic_types(k, v, use_mapping) + end
|
||||
|
||||
s.append(attr_str)
|
||||
r += "\n".join(s)
|
||||
if use_mapping:
|
||||
r += "}"
|
||||
return r
|
||||
|
||||
cfg_dict = self._cfg_dict.to_dict()
|
||||
text = _format_dict(cfg_dict, outest_level=True)
|
||||
# copied from setup.cfg
|
||||
yapf_style = dict(
|
||||
based_on_style="pep8",
|
||||
blank_line_before_nested_class_or_def=True,
|
||||
split_before_expression_after_opening_paren=True,
|
||||
)
|
||||
text, _ = FormatCode(text, style_config=yapf_style, verify=True)
|
||||
|
||||
return text
|
||||
|
||||
def __repr__(self):
|
||||
return f"Config (path: {self.filename}): {self._cfg_dict.__repr__()}"
|
||||
|
||||
def __len__(self):
|
||||
return len(self._cfg_dict)
|
||||
|
||||
def __getattr__(self, name):
|
||||
# # debug
|
||||
# print('+'*15)
|
||||
# print('name=%s' % name)
|
||||
# print("addr:", id(self))
|
||||
# # print('type(self):', type(self))
|
||||
# print(self.__dict__)
|
||||
# print('+'*15)
|
||||
# if self.__dict__ == {}:
|
||||
# raise ValueError
|
||||
|
||||
return getattr(self._cfg_dict, name)
|
||||
|
||||
def __getitem__(self, name):
|
||||
return self._cfg_dict.__getitem__(name)
|
||||
|
||||
def __setattr__(self, name, value):
|
||||
if isinstance(value, dict):
|
||||
value = ConfigDict(value)
|
||||
self._cfg_dict.__setattr__(name, value)
|
||||
|
||||
def __setitem__(self, name, value):
|
||||
if isinstance(value, dict):
|
||||
value = ConfigDict(value)
|
||||
self._cfg_dict.__setitem__(name, value)
|
||||
|
||||
def __iter__(self):
|
||||
return iter(self._cfg_dict)
|
||||
|
||||
def dump(self, file=None):
|
||||
# import ipdb; ipdb.set_trace()
|
||||
if file is None:
|
||||
return self.pretty_text
|
||||
else:
|
||||
with open(file, "w") as f:
|
||||
f.write(self.pretty_text)
|
||||
|
||||
def merge_from_dict(self, options):
|
||||
"""Merge list into cfg_dict
|
||||
|
||||
Merge the dict parsed by MultipleKVAction into this cfg.
|
||||
|
||||
Examples:
|
||||
>>> options = {'model.backbone.depth': 50,
|
||||
... 'model.backbone.with_cp':True}
|
||||
>>> cfg = Config(dict(model=dict(backbone=dict(type='ResNet'))))
|
||||
>>> cfg.merge_from_dict(options)
|
||||
>>> cfg_dict = super(Config, self).__getattribute__('_cfg_dict')
|
||||
>>> assert cfg_dict == dict(
|
||||
... model=dict(backbone=dict(depth=50, with_cp=True)))
|
||||
|
||||
Args:
|
||||
options (dict): dict of configs to merge from.
|
||||
"""
|
||||
option_cfg_dict = {}
|
||||
for full_key, v in options.items():
|
||||
d = option_cfg_dict
|
||||
key_list = full_key.split(".")
|
||||
for subkey in key_list[:-1]:
|
||||
d.setdefault(subkey, ConfigDict())
|
||||
d = d[subkey]
|
||||
subkey = key_list[-1]
|
||||
d[subkey] = v
|
||||
|
||||
cfg_dict = super(SLConfig, self).__getattribute__("_cfg_dict")
|
||||
super(SLConfig, self).__setattr__(
|
||||
"_cfg_dict", SLConfig._merge_a_into_b(option_cfg_dict, cfg_dict)
|
||||
)
|
||||
|
||||
# for multiprocess
|
||||
def __setstate__(self, state):
|
||||
self.__init__(state)
|
||||
|
||||
def copy(self):
|
||||
return SLConfig(self._cfg_dict.copy())
|
||||
|
||||
def deepcopy(self):
|
||||
return SLConfig(self._cfg_dict.deepcopy())
|
||||
|
||||
|
||||
class DictAction(Action):
|
||||
"""
|
||||
argparse action to split an argument into KEY=VALUE form
|
||||
on the first = and append to a dictionary. List options should
|
||||
be passed as comma separated values, i.e KEY=V1,V2,V3
|
||||
"""
|
||||
|
||||
@staticmethod
|
||||
def _parse_int_float_bool(val):
|
||||
try:
|
||||
return int(val)
|
||||
except ValueError:
|
||||
pass
|
||||
try:
|
||||
return float(val)
|
||||
except ValueError:
|
||||
pass
|
||||
if val.lower() in ["true", "false"]:
|
||||
return True if val.lower() == "true" else False
|
||||
if val.lower() in ["none", "null"]:
|
||||
return None
|
||||
return val
|
||||
|
||||
def __call__(self, parser, namespace, values, option_string=None):
|
||||
options = {}
|
||||
for kv in values:
|
||||
key, val = kv.split("=", maxsplit=1)
|
||||
val = [self._parse_int_float_bool(v) for v in val.split(",")]
|
||||
if len(val) == 1:
|
||||
val = val[0]
|
||||
options[key] = val
|
||||
setattr(namespace, self.dest, options)
|
||||
|
|
@ -0,0 +1,177 @@
|
|||
# ==========================================================
|
||||
# Modified from mmcv
|
||||
# ==========================================================
|
||||
|
||||
import json
|
||||
import pickle
|
||||
from abc import ABCMeta, abstractmethod
|
||||
from pathlib import Path
|
||||
|
||||
import yaml
|
||||
|
||||
try:
|
||||
from yaml import CLoader as Loader, CDumper as Dumper
|
||||
except ImportError:
|
||||
from yaml import Loader, Dumper
|
||||
|
||||
|
||||
# ===========================
|
||||
# Rigister handler
|
||||
# ===========================
|
||||
|
||||
|
||||
class BaseFileHandler(metaclass=ABCMeta):
|
||||
@abstractmethod
|
||||
def load_from_fileobj(self, file, **kwargs):
|
||||
pass
|
||||
|
||||
@abstractmethod
|
||||
def dump_to_fileobj(self, obj, file, **kwargs):
|
||||
pass
|
||||
|
||||
@abstractmethod
|
||||
def dump_to_str(self, obj, **kwargs):
|
||||
pass
|
||||
|
||||
def load_from_path(self, filepath, mode="r", **kwargs):
|
||||
with open(filepath, mode) as f:
|
||||
return self.load_from_fileobj(f, **kwargs)
|
||||
|
||||
def dump_to_path(self, obj, filepath, mode="w", **kwargs):
|
||||
with open(filepath, mode) as f:
|
||||
self.dump_to_fileobj(obj, f, **kwargs)
|
||||
|
||||
|
||||
class JsonHandler(BaseFileHandler):
|
||||
def load_from_fileobj(self, file):
|
||||
return json.load(file)
|
||||
|
||||
def dump_to_fileobj(self, obj, file, **kwargs):
|
||||
json.dump(obj, file, **kwargs)
|
||||
|
||||
def dump_to_str(self, obj, **kwargs):
|
||||
return json.dumps(obj, **kwargs)
|
||||
|
||||
|
||||
class PickleHandler(BaseFileHandler):
|
||||
def load_from_fileobj(self, file, **kwargs):
|
||||
return pickle.load(file, **kwargs)
|
||||
|
||||
def load_from_path(self, filepath, **kwargs):
|
||||
return super(PickleHandler, self).load_from_path(filepath, mode="rb", **kwargs)
|
||||
|
||||
def dump_to_str(self, obj, **kwargs):
|
||||
kwargs.setdefault("protocol", 2)
|
||||
return pickle.dumps(obj, **kwargs)
|
||||
|
||||
def dump_to_fileobj(self, obj, file, **kwargs):
|
||||
kwargs.setdefault("protocol", 2)
|
||||
pickle.dump(obj, file, **kwargs)
|
||||
|
||||
def dump_to_path(self, obj, filepath, **kwargs):
|
||||
super(PickleHandler, self).dump_to_path(obj, filepath, mode="wb", **kwargs)
|
||||
|
||||
|
||||
class YamlHandler(BaseFileHandler):
|
||||
def load_from_fileobj(self, file, **kwargs):
|
||||
kwargs.setdefault("Loader", Loader)
|
||||
return yaml.load(file, **kwargs)
|
||||
|
||||
def dump_to_fileobj(self, obj, file, **kwargs):
|
||||
kwargs.setdefault("Dumper", Dumper)
|
||||
yaml.dump(obj, file, **kwargs)
|
||||
|
||||
def dump_to_str(self, obj, **kwargs):
|
||||
kwargs.setdefault("Dumper", Dumper)
|
||||
return yaml.dump(obj, **kwargs)
|
||||
|
||||
|
||||
file_handlers = {
|
||||
"json": JsonHandler(),
|
||||
"yaml": YamlHandler(),
|
||||
"yml": YamlHandler(),
|
||||
"pickle": PickleHandler(),
|
||||
"pkl": PickleHandler(),
|
||||
}
|
||||
|
||||
# ===========================
|
||||
# load and dump
|
||||
# ===========================
|
||||
|
||||
|
||||
def is_str(x):
|
||||
"""Whether the input is an string instance.
|
||||
|
||||
Note: This method is deprecated since python 2 is no longer supported.
|
||||
"""
|
||||
return isinstance(x, str)
|
||||
|
||||
|
||||
def slload(file, file_format=None, **kwargs):
|
||||
"""Load data from json/yaml/pickle files.
|
||||
|
||||
This method provides a unified api for loading data from serialized files.
|
||||
|
||||
Args:
|
||||
file (str or :obj:`Path` or file-like object): Filename or a file-like
|
||||
object.
|
||||
file_format (str, optional): If not specified, the file format will be
|
||||
inferred from the file extension, otherwise use the specified one.
|
||||
Currently supported formats include "json", "yaml/yml" and
|
||||
"pickle/pkl".
|
||||
|
||||
Returns:
|
||||
The content from the file.
|
||||
"""
|
||||
if isinstance(file, Path):
|
||||
file = str(file)
|
||||
if file_format is None and is_str(file):
|
||||
file_format = file.split(".")[-1]
|
||||
if file_format not in file_handlers:
|
||||
raise TypeError(f"Unsupported format: {file_format}")
|
||||
|
||||
handler = file_handlers[file_format]
|
||||
if is_str(file):
|
||||
obj = handler.load_from_path(file, **kwargs)
|
||||
elif hasattr(file, "read"):
|
||||
obj = handler.load_from_fileobj(file, **kwargs)
|
||||
else:
|
||||
raise TypeError('"file" must be a filepath str or a file-object')
|
||||
return obj
|
||||
|
||||
|
||||
def sldump(obj, file=None, file_format=None, **kwargs):
|
||||
"""Dump data to json/yaml/pickle strings or files.
|
||||
|
||||
This method provides a unified api for dumping data as strings or to files,
|
||||
and also supports custom arguments for each file format.
|
||||
|
||||
Args:
|
||||
obj (any): The python object to be dumped.
|
||||
file (str or :obj:`Path` or file-like object, optional): If not
|
||||
specified, then the object is dump to a str, otherwise to a file
|
||||
specified by the filename or file-like object.
|
||||
file_format (str, optional): Same as :func:`load`.
|
||||
|
||||
Returns:
|
||||
bool: True for success, False otherwise.
|
||||
"""
|
||||
if isinstance(file, Path):
|
||||
file = str(file)
|
||||
if file_format is None:
|
||||
if is_str(file):
|
||||
file_format = file.split(".")[-1]
|
||||
elif file is None:
|
||||
raise ValueError("file_format must be specified since file is None")
|
||||
if file_format not in file_handlers:
|
||||
raise TypeError(f"Unsupported format: {file_format}")
|
||||
|
||||
handler = file_handlers[file_format]
|
||||
if file is None:
|
||||
return handler.dump_to_str(obj, **kwargs)
|
||||
elif is_str(file):
|
||||
handler.dump_to_path(obj, file, **kwargs)
|
||||
elif hasattr(file, "write"):
|
||||
handler.dump_to_fileobj(obj, file, **kwargs)
|
||||
else:
|
||||
raise TypeError('"file" must be a filename str or a file-object')
|
||||
|
|
@ -0,0 +1,62 @@
|
|||
import json
|
||||
import time
|
||||
|
||||
|
||||
class TimeCounter:
|
||||
def __init__(self) -> None:
|
||||
pass
|
||||
|
||||
def clear(self):
|
||||
self.timedict = {}
|
||||
self.basetime = time.perf_counter()
|
||||
|
||||
def timeit(self, name):
|
||||
nowtime = time.perf_counter() - self.basetime
|
||||
self.timedict[name] = nowtime
|
||||
self.basetime = time.perf_counter()
|
||||
|
||||
|
||||
class TimeHolder:
|
||||
def __init__(self) -> None:
|
||||
self.timedict = {}
|
||||
|
||||
def update(self, _timedict: dict):
|
||||
for k, v in _timedict.items():
|
||||
if k not in self.timedict:
|
||||
self.timedict[k] = AverageMeter(name=k, val_only=True)
|
||||
self.timedict[k].update(val=v)
|
||||
|
||||
def final_res(self):
|
||||
return {k: v.avg for k, v in self.timedict.items()}
|
||||
|
||||
def __str__(self):
|
||||
return json.dumps(self.final_res(), indent=2)
|
||||
|
||||
|
||||
class AverageMeter(object):
|
||||
"""Computes and stores the average and current value"""
|
||||
|
||||
def __init__(self, name, fmt=":f", val_only=False):
|
||||
self.name = name
|
||||
self.fmt = fmt
|
||||
self.val_only = val_only
|
||||
self.reset()
|
||||
|
||||
def reset(self):
|
||||
self.val = 0
|
||||
self.avg = 0
|
||||
self.sum = 0
|
||||
self.count = 0
|
||||
|
||||
def update(self, val, n=1):
|
||||
self.val = val
|
||||
self.sum += val * n
|
||||
self.count += n
|
||||
self.avg = self.sum / self.count
|
||||
|
||||
def __str__(self):
|
||||
if self.val_only:
|
||||
fmtstr = "{name} {val" + self.fmt + "}"
|
||||
else:
|
||||
fmtstr = "{name} {val" + self.fmt + "} ({avg" + self.fmt + "})"
|
||||
return fmtstr.format(**self.__dict__)
|
||||
|
|
@ -0,0 +1,621 @@
|
|||
import argparse
|
||||
import json
|
||||
import warnings
|
||||
from collections import OrderedDict
|
||||
from copy import deepcopy
|
||||
from typing import Any, Dict, List
|
||||
|
||||
import numpy as np
|
||||
import torch
|
||||
|
||||
from groundingdino.util.slconfig import SLConfig
|
||||
|
||||
|
||||
def slprint(x, name="x"):
|
||||
if isinstance(x, (torch.Tensor, np.ndarray)):
|
||||
print(f"{name}.shape:", x.shape)
|
||||
elif isinstance(x, (tuple, list)):
|
||||
print("type x:", type(x))
|
||||
for i in range(min(10, len(x))):
|
||||
slprint(x[i], f"{name}[{i}]")
|
||||
elif isinstance(x, dict):
|
||||
for k, v in x.items():
|
||||
slprint(v, f"{name}[{k}]")
|
||||
else:
|
||||
print(f"{name}.type:", type(x))
|
||||
|
||||
|
||||
def clean_state_dict(state_dict):
|
||||
new_state_dict = OrderedDict()
|
||||
for k, v in state_dict.items():
|
||||
if k[:7] == "module.":
|
||||
k = k[7:] # remove `module.`
|
||||
new_state_dict[k] = v
|
||||
return new_state_dict
|
||||
|
||||
|
||||
def renorm(
|
||||
img: torch.FloatTensor, mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]
|
||||
) -> torch.FloatTensor:
|
||||
# img: tensor(3,H,W) or tensor(B,3,H,W)
|
||||
# return: same as img
|
||||
assert img.dim() == 3 or img.dim() == 4, "img.dim() should be 3 or 4 but %d" % img.dim()
|
||||
if img.dim() == 3:
|
||||
assert img.size(0) == 3, 'img.size(0) shoule be 3 but "%d". (%s)' % (
|
||||
img.size(0),
|
||||
str(img.size()),
|
||||
)
|
||||
img_perm = img.permute(1, 2, 0)
|
||||
mean = torch.Tensor(mean)
|
||||
std = torch.Tensor(std)
|
||||
img_res = img_perm * std + mean
|
||||
return img_res.permute(2, 0, 1)
|
||||
else: # img.dim() == 4
|
||||
assert img.size(1) == 3, 'img.size(1) shoule be 3 but "%d". (%s)' % (
|
||||
img.size(1),
|
||||
str(img.size()),
|
||||
)
|
||||
img_perm = img.permute(0, 2, 3, 1)
|
||||
mean = torch.Tensor(mean)
|
||||
std = torch.Tensor(std)
|
||||
img_res = img_perm * std + mean
|
||||
return img_res.permute(0, 3, 1, 2)
|
||||
|
||||
|
||||
class CocoClassMapper:
|
||||
def __init__(self) -> None:
|
||||
self.category_map_str = {
|
||||
"1": 1,
|
||||
"2": 2,
|
||||
"3": 3,
|
||||
"4": 4,
|
||||
"5": 5,
|
||||
"6": 6,
|
||||
"7": 7,
|
||||
"8": 8,
|
||||
"9": 9,
|
||||
"10": 10,
|
||||
"11": 11,
|
||||
"13": 12,
|
||||
"14": 13,
|
||||
"15": 14,
|
||||
"16": 15,
|
||||
"17": 16,
|
||||
"18": 17,
|
||||
"19": 18,
|
||||
"20": 19,
|
||||
"21": 20,
|
||||
"22": 21,
|
||||
"23": 22,
|
||||
"24": 23,
|
||||
"25": 24,
|
||||
"27": 25,
|
||||
"28": 26,
|
||||
"31": 27,
|
||||
"32": 28,
|
||||
"33": 29,
|
||||
"34": 30,
|
||||
"35": 31,
|
||||
"36": 32,
|
||||
"37": 33,
|
||||
"38": 34,
|
||||
"39": 35,
|
||||
"40": 36,
|
||||
"41": 37,
|
||||
"42": 38,
|
||||
"43": 39,
|
||||
"44": 40,
|
||||
"46": 41,
|
||||
"47": 42,
|
||||
"48": 43,
|
||||
"49": 44,
|
||||
"50": 45,
|
||||
"51": 46,
|
||||
"52": 47,
|
||||
"53": 48,
|
||||
"54": 49,
|
||||
"55": 50,
|
||||
"56": 51,
|
||||
"57": 52,
|
||||
"58": 53,
|
||||
"59": 54,
|
||||
"60": 55,
|
||||
"61": 56,
|
||||
"62": 57,
|
||||
"63": 58,
|
||||
"64": 59,
|
||||
"65": 60,
|
||||
"67": 61,
|
||||
"70": 62,
|
||||
"72": 63,
|
||||
"73": 64,
|
||||
"74": 65,
|
||||
"75": 66,
|
||||
"76": 67,
|
||||
"77": 68,
|
||||
"78": 69,
|
||||
"79": 70,
|
||||
"80": 71,
|
||||
"81": 72,
|
||||
"82": 73,
|
||||
"84": 74,
|
||||
"85": 75,
|
||||
"86": 76,
|
||||
"87": 77,
|
||||
"88": 78,
|
||||
"89": 79,
|
||||
"90": 80,
|
||||
}
|
||||
self.origin2compact_mapper = {int(k): v - 1 for k, v in self.category_map_str.items()}
|
||||
self.compact2origin_mapper = {int(v - 1): int(k) for k, v in self.category_map_str.items()}
|
||||
|
||||
def origin2compact(self, idx):
|
||||
return self.origin2compact_mapper[int(idx)]
|
||||
|
||||
def compact2origin(self, idx):
|
||||
return self.compact2origin_mapper[int(idx)]
|
||||
|
||||
|
||||
def to_device(item, device):
|
||||
if isinstance(item, torch.Tensor):
|
||||
return item.to(device)
|
||||
elif isinstance(item, list):
|
||||
return [to_device(i, device) for i in item]
|
||||
elif isinstance(item, dict):
|
||||
return {k: to_device(v, device) for k, v in item.items()}
|
||||
else:
|
||||
raise NotImplementedError(
|
||||
"Call Shilong if you use other containers! type: {}".format(type(item))
|
||||
)
|
||||
|
||||
|
||||
#
|
||||
def get_gaussian_mean(x, axis, other_axis, softmax=True):
|
||||
"""
|
||||
|
||||
Args:
|
||||
x (float): Input images(BxCxHxW)
|
||||
axis (int): The index for weighted mean
|
||||
other_axis (int): The other index
|
||||
|
||||
Returns: weighted index for axis, BxC
|
||||
|
||||
"""
|
||||
mat2line = torch.sum(x, axis=other_axis)
|
||||
# mat2line = mat2line / mat2line.mean() * 10
|
||||
if softmax:
|
||||
u = torch.softmax(mat2line, axis=2)
|
||||
else:
|
||||
u = mat2line / (mat2line.sum(2, keepdim=True) + 1e-6)
|
||||
size = x.shape[axis]
|
||||
ind = torch.linspace(0, 1, size).to(x.device)
|
||||
batch = x.shape[0]
|
||||
channel = x.shape[1]
|
||||
index = ind.repeat([batch, channel, 1])
|
||||
mean_position = torch.sum(index * u, dim=2)
|
||||
return mean_position
|
||||
|
||||
|
||||
def get_expected_points_from_map(hm, softmax=True):
|
||||
"""get_gaussian_map_from_points
|
||||
B,C,H,W -> B,N,2 float(0, 1) float(0, 1)
|
||||
softargmax function
|
||||
|
||||
Args:
|
||||
hm (float): Input images(BxCxHxW)
|
||||
|
||||
Returns:
|
||||
weighted index for axis, BxCx2. float between 0 and 1.
|
||||
|
||||
"""
|
||||
# hm = 10*hm
|
||||
B, C, H, W = hm.shape
|
||||
y_mean = get_gaussian_mean(hm, 2, 3, softmax=softmax) # B,C
|
||||
x_mean = get_gaussian_mean(hm, 3, 2, softmax=softmax) # B,C
|
||||
# return torch.cat((x_mean.unsqueeze(-1), y_mean.unsqueeze(-1)), 2)
|
||||
return torch.stack([x_mean, y_mean], dim=2)
|
||||
|
||||
|
||||
# Positional encoding (section 5.1)
|
||||
# borrow from nerf
|
||||
class Embedder:
|
||||
def __init__(self, **kwargs):
|
||||
self.kwargs = kwargs
|
||||
self.create_embedding_fn()
|
||||
|
||||
def create_embedding_fn(self):
|
||||
embed_fns = []
|
||||
d = self.kwargs["input_dims"]
|
||||
out_dim = 0
|
||||
if self.kwargs["include_input"]:
|
||||
embed_fns.append(lambda x: x)
|
||||
out_dim += d
|
||||
|
||||
max_freq = self.kwargs["max_freq_log2"]
|
||||
N_freqs = self.kwargs["num_freqs"]
|
||||
|
||||
if self.kwargs["log_sampling"]:
|
||||
freq_bands = 2.0 ** torch.linspace(0.0, max_freq, steps=N_freqs)
|
||||
else:
|
||||
freq_bands = torch.linspace(2.0**0.0, 2.0**max_freq, steps=N_freqs)
|
||||
|
||||
for freq in freq_bands:
|
||||
for p_fn in self.kwargs["periodic_fns"]:
|
||||
embed_fns.append(lambda x, p_fn=p_fn, freq=freq: p_fn(x * freq))
|
||||
out_dim += d
|
||||
|
||||
self.embed_fns = embed_fns
|
||||
self.out_dim = out_dim
|
||||
|
||||
def embed(self, inputs):
|
||||
return torch.cat([fn(inputs) for fn in self.embed_fns], -1)
|
||||
|
||||
|
||||
def get_embedder(multires, i=0):
|
||||
import torch.nn as nn
|
||||
|
||||
if i == -1:
|
||||
return nn.Identity(), 3
|
||||
|
||||
embed_kwargs = {
|
||||
"include_input": True,
|
||||
"input_dims": 3,
|
||||
"max_freq_log2": multires - 1,
|
||||
"num_freqs": multires,
|
||||
"log_sampling": True,
|
||||
"periodic_fns": [torch.sin, torch.cos],
|
||||
}
|
||||
|
||||
embedder_obj = Embedder(**embed_kwargs)
|
||||
embed = lambda x, eo=embedder_obj: eo.embed(x)
|
||||
return embed, embedder_obj.out_dim
|
||||
|
||||
|
||||
class APOPMeter:
|
||||
def __init__(self) -> None:
|
||||
self.tp = 0
|
||||
self.fp = 0
|
||||
self.tn = 0
|
||||
self.fn = 0
|
||||
|
||||
def update(self, pred, gt):
|
||||
"""
|
||||
Input:
|
||||
pred, gt: Tensor()
|
||||
"""
|
||||
assert pred.shape == gt.shape
|
||||
self.tp += torch.logical_and(pred == 1, gt == 1).sum().item()
|
||||
self.fp += torch.logical_and(pred == 1, gt == 0).sum().item()
|
||||
self.tn += torch.logical_and(pred == 0, gt == 0).sum().item()
|
||||
self.tn += torch.logical_and(pred == 1, gt == 0).sum().item()
|
||||
|
||||
def update_cm(self, tp, fp, tn, fn):
|
||||
self.tp += tp
|
||||
self.fp += fp
|
||||
self.tn += tn
|
||||
self.tn += fn
|
||||
|
||||
|
||||
def inverse_sigmoid(x, eps=1e-5):
|
||||
x = x.clamp(min=0, max=1)
|
||||
x1 = x.clamp(min=eps)
|
||||
x2 = (1 - x).clamp(min=eps)
|
||||
return torch.log(x1 / x2)
|
||||
|
||||
|
||||
def get_raw_dict(args):
|
||||
"""
|
||||
return the dicf contained in args.
|
||||
|
||||
e.g:
|
||||
>>> with open(path, 'w') as f:
|
||||
json.dump(get_raw_dict(args), f, indent=2)
|
||||
"""
|
||||
if isinstance(args, argparse.Namespace):
|
||||
return vars(args)
|
||||
elif isinstance(args, dict):
|
||||
return args
|
||||
elif isinstance(args, SLConfig):
|
||||
return args._cfg_dict
|
||||
else:
|
||||
raise NotImplementedError("Unknown type {}".format(type(args)))
|
||||
|
||||
|
||||
def stat_tensors(tensor):
|
||||
assert tensor.dim() == 1
|
||||
tensor_sm = tensor.softmax(0)
|
||||
entropy = (tensor_sm * torch.log(tensor_sm + 1e-9)).sum()
|
||||
|
||||
return {
|
||||
"max": tensor.max(),
|
||||
"min": tensor.min(),
|
||||
"mean": tensor.mean(),
|
||||
"var": tensor.var(),
|
||||
"std": tensor.var() ** 0.5,
|
||||
"entropy": entropy,
|
||||
}
|
||||
|
||||
|
||||
class NiceRepr:
|
||||
"""Inherit from this class and define ``__nice__`` to "nicely" print your
|
||||
objects.
|
||||
|
||||
Defines ``__str__`` and ``__repr__`` in terms of ``__nice__`` function
|
||||
Classes that inherit from :class:`NiceRepr` should redefine ``__nice__``.
|
||||
If the inheriting class has a ``__len__``, method then the default
|
||||
``__nice__`` method will return its length.
|
||||
|
||||
Example:
|
||||
>>> class Foo(NiceRepr):
|
||||
... def __nice__(self):
|
||||
... return 'info'
|
||||
>>> foo = Foo()
|
||||
>>> assert str(foo) == '<Foo(info)>'
|
||||
>>> assert repr(foo).startswith('<Foo(info) at ')
|
||||
|
||||
Example:
|
||||
>>> class Bar(NiceRepr):
|
||||
... pass
|
||||
>>> bar = Bar()
|
||||
>>> import pytest
|
||||
>>> with pytest.warns(None) as record:
|
||||
>>> assert 'object at' in str(bar)
|
||||
>>> assert 'object at' in repr(bar)
|
||||
|
||||
Example:
|
||||
>>> class Baz(NiceRepr):
|
||||
... def __len__(self):
|
||||
... return 5
|
||||
>>> baz = Baz()
|
||||
>>> assert str(baz) == '<Baz(5)>'
|
||||
"""
|
||||
|
||||
def __nice__(self):
|
||||
"""str: a "nice" summary string describing this module"""
|
||||
if hasattr(self, "__len__"):
|
||||
# It is a common pattern for objects to use __len__ in __nice__
|
||||
# As a convenience we define a default __nice__ for these objects
|
||||
return str(len(self))
|
||||
else:
|
||||
# In all other cases force the subclass to overload __nice__
|
||||
raise NotImplementedError(f"Define the __nice__ method for {self.__class__!r}")
|
||||
|
||||
def __repr__(self):
|
||||
"""str: the string of the module"""
|
||||
try:
|
||||
nice = self.__nice__()
|
||||
classname = self.__class__.__name__
|
||||
return f"<{classname}({nice}) at {hex(id(self))}>"
|
||||
except NotImplementedError as ex:
|
||||
warnings.warn(str(ex), category=RuntimeWarning)
|
||||
return object.__repr__(self)
|
||||
|
||||
def __str__(self):
|
||||
"""str: the string of the module"""
|
||||
try:
|
||||
classname = self.__class__.__name__
|
||||
nice = self.__nice__()
|
||||
return f"<{classname}({nice})>"
|
||||
except NotImplementedError as ex:
|
||||
warnings.warn(str(ex), category=RuntimeWarning)
|
||||
return object.__repr__(self)
|
||||
|
||||
|
||||
def ensure_rng(rng=None):
|
||||
"""Coerces input into a random number generator.
|
||||
|
||||
If the input is None, then a global random state is returned.
|
||||
|
||||
If the input is a numeric value, then that is used as a seed to construct a
|
||||
random state. Otherwise the input is returned as-is.
|
||||
|
||||
Adapted from [1]_.
|
||||
|
||||
Args:
|
||||
rng (int | numpy.random.RandomState | None):
|
||||
if None, then defaults to the global rng. Otherwise this can be an
|
||||
integer or a RandomState class
|
||||
Returns:
|
||||
(numpy.random.RandomState) : rng -
|
||||
a numpy random number generator
|
||||
|
||||
References:
|
||||
.. [1] https://gitlab.kitware.com/computer-vision/kwarray/blob/master/kwarray/util_random.py#L270 # noqa: E501
|
||||
"""
|
||||
|
||||
if rng is None:
|
||||
rng = np.random.mtrand._rand
|
||||
elif isinstance(rng, int):
|
||||
rng = np.random.RandomState(rng)
|
||||
else:
|
||||
rng = rng
|
||||
return rng
|
||||
|
||||
|
||||
def random_boxes(num=1, scale=1, rng=None):
|
||||
"""Simple version of ``kwimage.Boxes.random``
|
||||
|
||||
Returns:
|
||||
Tensor: shape (n, 4) in x1, y1, x2, y2 format.
|
||||
|
||||
References:
|
||||
https://gitlab.kitware.com/computer-vision/kwimage/blob/master/kwimage/structs/boxes.py#L1390
|
||||
|
||||
Example:
|
||||
>>> num = 3
|
||||
>>> scale = 512
|
||||
>>> rng = 0
|
||||
>>> boxes = random_boxes(num, scale, rng)
|
||||
>>> print(boxes)
|
||||
tensor([[280.9925, 278.9802, 308.6148, 366.1769],
|
||||
[216.9113, 330.6978, 224.0446, 456.5878],
|
||||
[405.3632, 196.3221, 493.3953, 270.7942]])
|
||||
"""
|
||||
rng = ensure_rng(rng)
|
||||
|
||||
tlbr = rng.rand(num, 4).astype(np.float32)
|
||||
|
||||
tl_x = np.minimum(tlbr[:, 0], tlbr[:, 2])
|
||||
tl_y = np.minimum(tlbr[:, 1], tlbr[:, 3])
|
||||
br_x = np.maximum(tlbr[:, 0], tlbr[:, 2])
|
||||
br_y = np.maximum(tlbr[:, 1], tlbr[:, 3])
|
||||
|
||||
tlbr[:, 0] = tl_x * scale
|
||||
tlbr[:, 1] = tl_y * scale
|
||||
tlbr[:, 2] = br_x * scale
|
||||
tlbr[:, 3] = br_y * scale
|
||||
|
||||
boxes = torch.from_numpy(tlbr)
|
||||
return boxes
|
||||
|
||||
|
||||
class ModelEma(torch.nn.Module):
|
||||
def __init__(self, model, decay=0.9997, device=None):
|
||||
super(ModelEma, self).__init__()
|
||||
# make a copy of the model for accumulating moving average of weights
|
||||
self.module = deepcopy(model)
|
||||
self.module.eval()
|
||||
|
||||
# import ipdb; ipdb.set_trace()
|
||||
|
||||
self.decay = decay
|
||||
self.device = device # perform ema on different device from model if set
|
||||
if self.device is not None:
|
||||
self.module.to(device=device)
|
||||
|
||||
def _update(self, model, update_fn):
|
||||
with torch.no_grad():
|
||||
for ema_v, model_v in zip(
|
||||
self.module.state_dict().values(), model.state_dict().values()
|
||||
):
|
||||
if self.device is not None:
|
||||
model_v = model_v.to(device=self.device)
|
||||
ema_v.copy_(update_fn(ema_v, model_v))
|
||||
|
||||
def update(self, model):
|
||||
self._update(model, update_fn=lambda e, m: self.decay * e + (1.0 - self.decay) * m)
|
||||
|
||||
def set(self, model):
|
||||
self._update(model, update_fn=lambda e, m: m)
|
||||
|
||||
|
||||
class BestMetricSingle:
|
||||
def __init__(self, init_res=0.0, better="large") -> None:
|
||||
self.init_res = init_res
|
||||
self.best_res = init_res
|
||||
self.best_ep = -1
|
||||
|
||||
self.better = better
|
||||
assert better in ["large", "small"]
|
||||
|
||||
def isbetter(self, new_res, old_res):
|
||||
if self.better == "large":
|
||||
return new_res > old_res
|
||||
if self.better == "small":
|
||||
return new_res < old_res
|
||||
|
||||
def update(self, new_res, ep):
|
||||
if self.isbetter(new_res, self.best_res):
|
||||
self.best_res = new_res
|
||||
self.best_ep = ep
|
||||
return True
|
||||
return False
|
||||
|
||||
def __str__(self) -> str:
|
||||
return "best_res: {}\t best_ep: {}".format(self.best_res, self.best_ep)
|
||||
|
||||
def __repr__(self) -> str:
|
||||
return self.__str__()
|
||||
|
||||
def summary(self) -> dict:
|
||||
return {
|
||||
"best_res": self.best_res,
|
||||
"best_ep": self.best_ep,
|
||||
}
|
||||
|
||||
|
||||
class BestMetricHolder:
|
||||
def __init__(self, init_res=0.0, better="large", use_ema=False) -> None:
|
||||
self.best_all = BestMetricSingle(init_res, better)
|
||||
self.use_ema = use_ema
|
||||
if use_ema:
|
||||
self.best_ema = BestMetricSingle(init_res, better)
|
||||
self.best_regular = BestMetricSingle(init_res, better)
|
||||
|
||||
def update(self, new_res, epoch, is_ema=False):
|
||||
"""
|
||||
return if the results is the best.
|
||||
"""
|
||||
if not self.use_ema:
|
||||
return self.best_all.update(new_res, epoch)
|
||||
else:
|
||||
if is_ema:
|
||||
self.best_ema.update(new_res, epoch)
|
||||
return self.best_all.update(new_res, epoch)
|
||||
else:
|
||||
self.best_regular.update(new_res, epoch)
|
||||
return self.best_all.update(new_res, epoch)
|
||||
|
||||
def summary(self):
|
||||
if not self.use_ema:
|
||||
return self.best_all.summary()
|
||||
|
||||
res = {}
|
||||
res.update({f"all_{k}": v for k, v in self.best_all.summary().items()})
|
||||
res.update({f"regular_{k}": v for k, v in self.best_regular.summary().items()})
|
||||
res.update({f"ema_{k}": v for k, v in self.best_ema.summary().items()})
|
||||
return res
|
||||
|
||||
def __repr__(self) -> str:
|
||||
return json.dumps(self.summary(), indent=2)
|
||||
|
||||
def __str__(self) -> str:
|
||||
return self.__repr__()
|
||||
|
||||
|
||||
def targets_to(targets: List[Dict[str, Any]], device):
|
||||
"""Moves the target dicts to the given device."""
|
||||
excluded_keys = [
|
||||
"questionId",
|
||||
"tokens_positive",
|
||||
"strings_positive",
|
||||
"tokens",
|
||||
"dataset_name",
|
||||
"sentence_id",
|
||||
"original_img_id",
|
||||
"nb_eval",
|
||||
"task_id",
|
||||
"original_id",
|
||||
"token_span",
|
||||
"caption",
|
||||
"dataset_type",
|
||||
]
|
||||
return [
|
||||
{k: v.to(device) if k not in excluded_keys else v for k, v in t.items()} for t in targets
|
||||
]
|
||||
|
||||
|
||||
def get_phrases_from_posmap(posmap: torch.BoolTensor, tokenlized, caption: str):
|
||||
assert isinstance(posmap, torch.Tensor), "posmap must be torch.Tensor"
|
||||
if posmap.dim() == 1:
|
||||
non_zero_idx = posmap.nonzero(as_tuple=True)[0].tolist()
|
||||
words_list = caption.split()
|
||||
|
||||
# build word idx list
|
||||
words_idx_used_list = []
|
||||
for idx in non_zero_idx:
|
||||
word_idx = tokenlized.token_to_word(idx)
|
||||
if word_idx is not None:
|
||||
words_idx_used_list.append(word_idx)
|
||||
words_idx_used_list = set(words_idx_used_list)
|
||||
|
||||
# build phrase
|
||||
words_used_list = []
|
||||
for idx, word in enumerate(words_list):
|
||||
if idx in words_idx_used_list:
|
||||
words_used_list.append(word)
|
||||
|
||||
sentence_res = " ".join(words_used_list)
|
||||
return sentence_res
|
||||
else:
|
||||
raise NotImplementedError("posmap must be 1-dim")
|
||||
|
|
@ -0,0 +1,318 @@
|
|||
# -*- coding: utf-8 -*-
|
||||
"""
|
||||
@File : visualizer.py
|
||||
@Time : 2022/04/05 11:39:33
|
||||
@Author : Shilong Liu
|
||||
@Contact : slongliu86@gmail.com
|
||||
"""
|
||||
|
||||
import datetime
|
||||
import os
|
||||
|
||||
import cv2
|
||||
import matplotlib.pyplot as plt
|
||||
import numpy as np
|
||||
import torch
|
||||
from matplotlib import transforms
|
||||
from matplotlib.collections import PatchCollection
|
||||
from matplotlib.patches import Polygon
|
||||
from pycocotools import mask as maskUtils
|
||||
|
||||
|
||||
def renorm(
|
||||
img: torch.FloatTensor, mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]
|
||||
) -> torch.FloatTensor:
|
||||
# img: tensor(3,H,W) or tensor(B,3,H,W)
|
||||
# return: same as img
|
||||
assert img.dim() == 3 or img.dim() == 4, "img.dim() should be 3 or 4 but %d" % img.dim()
|
||||
if img.dim() == 3:
|
||||
assert img.size(0) == 3, 'img.size(0) shoule be 3 but "%d". (%s)' % (
|
||||
img.size(0),
|
||||
str(img.size()),
|
||||
)
|
||||
img_perm = img.permute(1, 2, 0)
|
||||
mean = torch.Tensor(mean)
|
||||
std = torch.Tensor(std)
|
||||
img_res = img_perm * std + mean
|
||||
return img_res.permute(2, 0, 1)
|
||||
else: # img.dim() == 4
|
||||
assert img.size(1) == 3, 'img.size(1) shoule be 3 but "%d". (%s)' % (
|
||||
img.size(1),
|
||||
str(img.size()),
|
||||
)
|
||||
img_perm = img.permute(0, 2, 3, 1)
|
||||
mean = torch.Tensor(mean)
|
||||
std = torch.Tensor(std)
|
||||
img_res = img_perm * std + mean
|
||||
return img_res.permute(0, 3, 1, 2)
|
||||
|
||||
|
||||
class ColorMap:
|
||||
def __init__(self, basergb=[255, 255, 0]):
|
||||
self.basergb = np.array(basergb)
|
||||
|
||||
def __call__(self, attnmap):
|
||||
# attnmap: h, w. np.uint8.
|
||||
# return: h, w, 4. np.uint8.
|
||||
assert attnmap.dtype == np.uint8
|
||||
h, w = attnmap.shape
|
||||
res = self.basergb.copy()
|
||||
res = res[None][None].repeat(h, 0).repeat(w, 1) # h, w, 3
|
||||
attn1 = attnmap.copy()[..., None] # h, w, 1
|
||||
res = np.concatenate((res, attn1), axis=-1).astype(np.uint8)
|
||||
return res
|
||||
|
||||
|
||||
def rainbow_text(x, y, ls, lc, **kw):
|
||||
"""
|
||||
Take a list of strings ``ls`` and colors ``lc`` and place them next to each
|
||||
other, with text ls[i] being shown in color lc[i].
|
||||
|
||||
This example shows how to do both vertical and horizontal text, and will
|
||||
pass all keyword arguments to plt.text, so you can set the font size,
|
||||
family, etc.
|
||||
"""
|
||||
t = plt.gca().transData
|
||||
fig = plt.gcf()
|
||||
plt.show()
|
||||
|
||||
# horizontal version
|
||||
for s, c in zip(ls, lc):
|
||||
text = plt.text(x, y, " " + s + " ", color=c, transform=t, **kw)
|
||||
text.draw(fig.canvas.get_renderer())
|
||||
ex = text.get_window_extent()
|
||||
t = transforms.offset_copy(text._transform, x=ex.width, units="dots")
|
||||
|
||||
# #vertical version
|
||||
# for s,c in zip(ls,lc):
|
||||
# text = plt.text(x,y," "+s+" ",color=c, transform=t,
|
||||
# rotation=90,va='bottom',ha='center',**kw)
|
||||
# text.draw(fig.canvas.get_renderer())
|
||||
# ex = text.get_window_extent()
|
||||
# t = transforms.offset_copy(text._transform, y=ex.height, units='dots')
|
||||
|
||||
|
||||
class COCOVisualizer:
|
||||
def __init__(self, coco=None, tokenlizer=None) -> None:
|
||||
self.coco = coco
|
||||
|
||||
def visualize(self, img, tgt, caption=None, dpi=180, savedir="vis"):
|
||||
"""
|
||||
img: tensor(3, H, W)
|
||||
tgt: make sure they are all on cpu.
|
||||
must have items: 'image_id', 'boxes', 'size'
|
||||
"""
|
||||
plt.figure(dpi=dpi)
|
||||
plt.rcParams["font.size"] = "5"
|
||||
ax = plt.gca()
|
||||
img = renorm(img).permute(1, 2, 0)
|
||||
# if os.environ.get('IPDB_SHILONG_DEBUG', None) == 'INFO':
|
||||
# import ipdb; ipdb.set_trace()
|
||||
ax.imshow(img)
|
||||
|
||||
self.addtgt(tgt)
|
||||
|
||||
if tgt is None:
|
||||
image_id = 0
|
||||
elif "image_id" not in tgt:
|
||||
image_id = 0
|
||||
else:
|
||||
image_id = tgt["image_id"]
|
||||
|
||||
if caption is None:
|
||||
savename = "{}/{}-{}.png".format(
|
||||
savedir, int(image_id), str(datetime.datetime.now()).replace(" ", "-")
|
||||
)
|
||||
else:
|
||||
savename = "{}/{}-{}-{}.png".format(
|
||||
savedir, caption, int(image_id), str(datetime.datetime.now()).replace(" ", "-")
|
||||
)
|
||||
print("savename: {}".format(savename))
|
||||
os.makedirs(os.path.dirname(savename), exist_ok=True)
|
||||
plt.savefig(savename)
|
||||
plt.close()
|
||||
|
||||
def addtgt(self, tgt):
|
||||
""" """
|
||||
if tgt is None or not "boxes" in tgt:
|
||||
ax = plt.gca()
|
||||
|
||||
if "caption" in tgt:
|
||||
ax.set_title(tgt["caption"], wrap=True)
|
||||
|
||||
ax.set_axis_off()
|
||||
return
|
||||
|
||||
ax = plt.gca()
|
||||
H, W = tgt["size"]
|
||||
numbox = tgt["boxes"].shape[0]
|
||||
|
||||
color = []
|
||||
polygons = []
|
||||
boxes = []
|
||||
for box in tgt["boxes"].cpu():
|
||||
unnormbbox = box * torch.Tensor([W, H, W, H])
|
||||
unnormbbox[:2] -= unnormbbox[2:] / 2
|
||||
[bbox_x, bbox_y, bbox_w, bbox_h] = unnormbbox.tolist()
|
||||
boxes.append([bbox_x, bbox_y, bbox_w, bbox_h])
|
||||
poly = [
|
||||
[bbox_x, bbox_y],
|
||||
[bbox_x, bbox_y + bbox_h],
|
||||
[bbox_x + bbox_w, bbox_y + bbox_h],
|
||||
[bbox_x + bbox_w, bbox_y],
|
||||
]
|
||||
np_poly = np.array(poly).reshape((4, 2))
|
||||
polygons.append(Polygon(np_poly))
|
||||
c = (np.random.random((1, 3)) * 0.6 + 0.4).tolist()[0]
|
||||
color.append(c)
|
||||
|
||||
p = PatchCollection(polygons, facecolor=color, linewidths=0, alpha=0.1)
|
||||
ax.add_collection(p)
|
||||
p = PatchCollection(polygons, facecolor="none", edgecolors=color, linewidths=2)
|
||||
ax.add_collection(p)
|
||||
|
||||
if "strings_positive" in tgt and len(tgt["strings_positive"]) > 0:
|
||||
assert (
|
||||
len(tgt["strings_positive"]) == numbox
|
||||
), f"{len(tgt['strings_positive'])} = {numbox}, "
|
||||
for idx, strlist in enumerate(tgt["strings_positive"]):
|
||||
cate_id = int(tgt["labels"][idx])
|
||||
_string = str(cate_id) + ":" + " ".join(strlist)
|
||||
bbox_x, bbox_y, bbox_w, bbox_h = boxes[idx]
|
||||
# ax.text(bbox_x, bbox_y, _string, color='black', bbox={'facecolor': 'yellow', 'alpha': 1.0, 'pad': 1})
|
||||
ax.text(
|
||||
bbox_x,
|
||||
bbox_y,
|
||||
_string,
|
||||
color="black",
|
||||
bbox={"facecolor": color[idx], "alpha": 0.6, "pad": 1},
|
||||
)
|
||||
|
||||
if "box_label" in tgt:
|
||||
assert len(tgt["box_label"]) == numbox, f"{len(tgt['box_label'])} = {numbox}, "
|
||||
for idx, bl in enumerate(tgt["box_label"]):
|
||||
_string = str(bl)
|
||||
bbox_x, bbox_y, bbox_w, bbox_h = boxes[idx]
|
||||
# ax.text(bbox_x, bbox_y, _string, color='black', bbox={'facecolor': 'yellow', 'alpha': 1.0, 'pad': 1})
|
||||
ax.text(
|
||||
bbox_x,
|
||||
bbox_y,
|
||||
_string,
|
||||
color="black",
|
||||
bbox={"facecolor": color[idx], "alpha": 0.6, "pad": 1},
|
||||
)
|
||||
|
||||
if "caption" in tgt:
|
||||
ax.set_title(tgt["caption"], wrap=True)
|
||||
# plt.figure()
|
||||
# rainbow_text(0.0,0.0,"all unicorns poop rainbows ! ! !".split(),
|
||||
# ['red', 'orange', 'brown', 'green', 'blue', 'purple', 'black'])
|
||||
|
||||
if "attn" in tgt:
|
||||
# if os.environ.get('IPDB_SHILONG_DEBUG', None) == 'INFO':
|
||||
# import ipdb; ipdb.set_trace()
|
||||
if isinstance(tgt["attn"], tuple):
|
||||
tgt["attn"] = [tgt["attn"]]
|
||||
for item in tgt["attn"]:
|
||||
attn_map, basergb = item
|
||||
attn_map = (attn_map - attn_map.min()) / (attn_map.max() - attn_map.min() + 1e-3)
|
||||
attn_map = (attn_map * 255).astype(np.uint8)
|
||||
cm = ColorMap(basergb)
|
||||
heatmap = cm(attn_map)
|
||||
ax.imshow(heatmap)
|
||||
ax.set_axis_off()
|
||||
|
||||
def showAnns(self, anns, draw_bbox=False):
|
||||
"""
|
||||
Display the specified annotations.
|
||||
:param anns (array of object): annotations to display
|
||||
:return: None
|
||||
"""
|
||||
if len(anns) == 0:
|
||||
return 0
|
||||
if "segmentation" in anns[0] or "keypoints" in anns[0]:
|
||||
datasetType = "instances"
|
||||
elif "caption" in anns[0]:
|
||||
datasetType = "captions"
|
||||
else:
|
||||
raise Exception("datasetType not supported")
|
||||
if datasetType == "instances":
|
||||
ax = plt.gca()
|
||||
ax.set_autoscale_on(False)
|
||||
polygons = []
|
||||
color = []
|
||||
for ann in anns:
|
||||
c = (np.random.random((1, 3)) * 0.6 + 0.4).tolist()[0]
|
||||
if "segmentation" in ann:
|
||||
if type(ann["segmentation"]) == list:
|
||||
# polygon
|
||||
for seg in ann["segmentation"]:
|
||||
poly = np.array(seg).reshape((int(len(seg) / 2), 2))
|
||||
polygons.append(Polygon(poly))
|
||||
color.append(c)
|
||||
else:
|
||||
# mask
|
||||
t = self.imgs[ann["image_id"]]
|
||||
if type(ann["segmentation"]["counts"]) == list:
|
||||
rle = maskUtils.frPyObjects(
|
||||
[ann["segmentation"]], t["height"], t["width"]
|
||||
)
|
||||
else:
|
||||
rle = [ann["segmentation"]]
|
||||
m = maskUtils.decode(rle)
|
||||
img = np.ones((m.shape[0], m.shape[1], 3))
|
||||
if ann["iscrowd"] == 1:
|
||||
color_mask = np.array([2.0, 166.0, 101.0]) / 255
|
||||
if ann["iscrowd"] == 0:
|
||||
color_mask = np.random.random((1, 3)).tolist()[0]
|
||||
for i in range(3):
|
||||
img[:, :, i] = color_mask[i]
|
||||
ax.imshow(np.dstack((img, m * 0.5)))
|
||||
if "keypoints" in ann and type(ann["keypoints"]) == list:
|
||||
# turn skeleton into zero-based index
|
||||
sks = np.array(self.loadCats(ann["category_id"])[0]["skeleton"]) - 1
|
||||
kp = np.array(ann["keypoints"])
|
||||
x = kp[0::3]
|
||||
y = kp[1::3]
|
||||
v = kp[2::3]
|
||||
for sk in sks:
|
||||
if np.all(v[sk] > 0):
|
||||
plt.plot(x[sk], y[sk], linewidth=3, color=c)
|
||||
plt.plot(
|
||||
x[v > 0],
|
||||
y[v > 0],
|
||||
"o",
|
||||
markersize=8,
|
||||
markerfacecolor=c,
|
||||
markeredgecolor="k",
|
||||
markeredgewidth=2,
|
||||
)
|
||||
plt.plot(
|
||||
x[v > 1],
|
||||
y[v > 1],
|
||||
"o",
|
||||
markersize=8,
|
||||
markerfacecolor=c,
|
||||
markeredgecolor=c,
|
||||
markeredgewidth=2,
|
||||
)
|
||||
|
||||
if draw_bbox:
|
||||
[bbox_x, bbox_y, bbox_w, bbox_h] = ann["bbox"]
|
||||
poly = [
|
||||
[bbox_x, bbox_y],
|
||||
[bbox_x, bbox_y + bbox_h],
|
||||
[bbox_x + bbox_w, bbox_y + bbox_h],
|
||||
[bbox_x + bbox_w, bbox_y],
|
||||
]
|
||||
np_poly = np.array(poly).reshape((4, 2))
|
||||
polygons.append(Polygon(np_poly))
|
||||
color.append(c)
|
||||
|
||||
# p = PatchCollection(polygons, facecolor=color, linewidths=0, alpha=0.4)
|
||||
# ax.add_collection(p)
|
||||
p = PatchCollection(polygons, facecolor="none", edgecolors=color, linewidths=2)
|
||||
ax.add_collection(p)
|
||||
elif datasetType == "captions":
|
||||
for ann in anns:
|
||||
print(ann["caption"])
|
||||
|
|
@ -0,0 +1,100 @@
|
|||
import os
|
||||
import random
|
||||
from typing import List
|
||||
|
||||
import torch
|
||||
|
||||
|
||||
def create_positive_map_from_span(tokenized, token_span, max_text_len=256):
|
||||
"""construct a map such that positive_map[i,j] = True iff box i is associated to token j
|
||||
Input:
|
||||
- tokenized:
|
||||
- input_ids: Tensor[1, ntokens]
|
||||
- attention_mask: Tensor[1, ntokens]
|
||||
- token_span: list with length num_boxes.
|
||||
- each item: [start_idx, end_idx]
|
||||
"""
|
||||
positive_map = torch.zeros((len(token_span), max_text_len), dtype=torch.float)
|
||||
for j, tok_list in enumerate(token_span):
|
||||
for (beg, end) in tok_list:
|
||||
beg_pos = tokenized.char_to_token(beg)
|
||||
end_pos = tokenized.char_to_token(end - 1)
|
||||
if beg_pos is None:
|
||||
try:
|
||||
beg_pos = tokenized.char_to_token(beg + 1)
|
||||
if beg_pos is None:
|
||||
beg_pos = tokenized.char_to_token(beg + 2)
|
||||
except:
|
||||
beg_pos = None
|
||||
if end_pos is None:
|
||||
try:
|
||||
end_pos = tokenized.char_to_token(end - 2)
|
||||
if end_pos is None:
|
||||
end_pos = tokenized.char_to_token(end - 3)
|
||||
except:
|
||||
end_pos = None
|
||||
if beg_pos is None or end_pos is None:
|
||||
continue
|
||||
|
||||
assert beg_pos is not None and end_pos is not None
|
||||
if os.environ.get("SHILONG_DEBUG_ONLY_ONE_POS", None) == "TRUE":
|
||||
positive_map[j, beg_pos] = 1
|
||||
break
|
||||
else:
|
||||
positive_map[j, beg_pos : end_pos + 1].fill_(1)
|
||||
|
||||
return positive_map / (positive_map.sum(-1)[:, None] + 1e-6)
|
||||
|
||||
|
||||
def build_captions_and_token_span(cat_list, force_lowercase):
|
||||
"""
|
||||
Return:
|
||||
captions: str
|
||||
cat2tokenspan: dict
|
||||
{
|
||||
'dog': [[0, 2]],
|
||||
...
|
||||
}
|
||||
"""
|
||||
|
||||
cat2tokenspan = {}
|
||||
captions = ""
|
||||
for catname in cat_list:
|
||||
class_name = catname
|
||||
if force_lowercase:
|
||||
class_name = class_name.lower()
|
||||
if "/" in class_name:
|
||||
class_name_list: List = class_name.strip().split("/")
|
||||
class_name_list.append(class_name)
|
||||
class_name: str = random.choice(class_name_list)
|
||||
|
||||
tokens_positive_i = []
|
||||
subnamelist = [i.strip() for i in class_name.strip().split(" ")]
|
||||
for subname in subnamelist:
|
||||
if len(subname) == 0:
|
||||
continue
|
||||
if len(captions) > 0:
|
||||
captions = captions + " "
|
||||
strat_idx = len(captions)
|
||||
end_idx = strat_idx + len(subname)
|
||||
tokens_positive_i.append([strat_idx, end_idx])
|
||||
captions = captions + subname
|
||||
|
||||
if len(tokens_positive_i) > 0:
|
||||
captions = captions + " ."
|
||||
cat2tokenspan[class_name] = tokens_positive_i
|
||||
|
||||
return captions, cat2tokenspan
|
||||
|
||||
|
||||
def build_id2posspan_and_caption(category_dict: dict):
|
||||
"""Build id2pos_span and caption from category_dict
|
||||
|
||||
Args:
|
||||
category_dict (dict): category_dict
|
||||
"""
|
||||
cat_list = [item["name"].lower() for item in category_dict]
|
||||
id2catname = {item["id"]: item["name"].lower() for item in category_dict}
|
||||
caption, cat2posspan = build_captions_and_token_span(cat_list, force_lowercase=True)
|
||||
id2posspan = {catid: cat2posspan[catname] for catid, catname in id2catname.items()}
|
||||
return id2posspan, caption
|
||||
|
|
@ -0,0 +1 @@
|
|||
__version__ = "0.1.0"
|
||||
|
|
@ -0,0 +1 @@
|
|||
transformers==4.5.1
|
||||
|
|
@ -0,0 +1,208 @@
|
|||
# coding=utf-8
|
||||
# Copyright 2022 The IDEA Authors. All rights reserved.
|
||||
#
|
||||
# Licensed under the Apache License, Version 2.0 (the "License");
|
||||
# you may not use this file except in compliance with the License.
|
||||
# You may obtain a copy of the License at
|
||||
#
|
||||
# http://www.apache.org/licenses/LICENSE-2.0
|
||||
#
|
||||
# Unless required by applicable law or agreed to in writing, software
|
||||
# distributed under the License is distributed on an "AS IS" BASIS,
|
||||
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
||||
# See the License for the specific language governing permissions and
|
||||
# limitations under the License.
|
||||
# ------------------------------------------------------------------------------------------------
|
||||
# Modified from
|
||||
# https://github.com/fundamentalvision/Deformable-DETR/blob/main/models/ops/setup.py
|
||||
# https://github.com/facebookresearch/detectron2/blob/main/setup.py
|
||||
# https://github.com/open-mmlab/mmdetection/blob/master/setup.py
|
||||
# https://github.com/Oneflow-Inc/libai/blob/main/setup.py
|
||||
# ------------------------------------------------------------------------------------------------
|
||||
|
||||
import glob
|
||||
import os
|
||||
import subprocess
|
||||
|
||||
import torch
|
||||
from setuptools import find_packages, setup
|
||||
from torch.utils.cpp_extension import CUDA_HOME, CppExtension, CUDAExtension
|
||||
|
||||
# groundingdino version info
|
||||
version = "0.1.0"
|
||||
package_name = "groundingdino"
|
||||
cwd = os.path.dirname(os.path.abspath(__file__))
|
||||
|
||||
|
||||
sha = "Unknown"
|
||||
try:
|
||||
sha = subprocess.check_output(["git", "rev-parse", "HEAD"], cwd=cwd).decode("ascii").strip()
|
||||
except Exception:
|
||||
pass
|
||||
|
||||
|
||||
def write_version_file():
|
||||
version_path = os.path.join(cwd, "groundingdino", "version.py")
|
||||
with open(version_path, "w") as f:
|
||||
f.write(f"__version__ = '{version}'\n")
|
||||
# f.write(f"git_version = {repr(sha)}\n")
|
||||
|
||||
|
||||
requirements = ["torch", "torchvision"]
|
||||
|
||||
torch_ver = [int(x) for x in torch.__version__.split(".")[:2]]
|
||||
|
||||
|
||||
def get_extensions():
|
||||
this_dir = os.path.dirname(os.path.abspath(__file__))
|
||||
extensions_dir = os.path.join(this_dir, "groundingdino", "models", "GroundingDINO", "csrc")
|
||||
|
||||
main_source = os.path.join(extensions_dir, "vision.cpp")
|
||||
sources = glob.glob(os.path.join(extensions_dir, "**", "*.cpp"))
|
||||
source_cuda = glob.glob(os.path.join(extensions_dir, "**", "*.cu")) + glob.glob(
|
||||
os.path.join(extensions_dir, "*.cu")
|
||||
)
|
||||
|
||||
sources = [main_source] + sources
|
||||
|
||||
extension = CppExtension
|
||||
|
||||
extra_compile_args = {"cxx": []}
|
||||
define_macros = []
|
||||
|
||||
if torch.cuda.is_available() and CUDA_HOME is not None:
|
||||
print("Compiling with CUDA")
|
||||
extension = CUDAExtension
|
||||
sources += source_cuda
|
||||
define_macros += [("WITH_CUDA", None)]
|
||||
extra_compile_args["nvcc"] = [
|
||||
"-DCUDA_HAS_FP16=1",
|
||||
"-D__CUDA_NO_HALF_OPERATORS__",
|
||||
"-D__CUDA_NO_HALF_CONVERSIONS__",
|
||||
"-D__CUDA_NO_HALF2_OPERATORS__",
|
||||
]
|
||||
else:
|
||||
print("Compiling without CUDA")
|
||||
define_macros += [("WITH_HIP", None)]
|
||||
extra_compile_args["nvcc"] = []
|
||||
return None
|
||||
|
||||
sources = [os.path.join(extensions_dir, s) for s in sources]
|
||||
include_dirs = [extensions_dir]
|
||||
|
||||
ext_modules = [
|
||||
extension(
|
||||
"groundingdino._C",
|
||||
sources,
|
||||
include_dirs=include_dirs,
|
||||
define_macros=define_macros,
|
||||
extra_compile_args=extra_compile_args,
|
||||
)
|
||||
]
|
||||
|
||||
return ext_modules
|
||||
|
||||
|
||||
def parse_requirements(fname="requirements.txt", with_version=True):
|
||||
"""Parse the package dependencies listed in a requirements file but strips
|
||||
specific versioning information.
|
||||
|
||||
Args:
|
||||
fname (str): path to requirements file
|
||||
with_version (bool, default=False): if True include version specs
|
||||
|
||||
Returns:
|
||||
List[str]: list of requirements items
|
||||
|
||||
CommandLine:
|
||||
python -c "import setup; print(setup.parse_requirements())"
|
||||
"""
|
||||
import re
|
||||
import sys
|
||||
from os.path import exists
|
||||
|
||||
require_fpath = fname
|
||||
|
||||
def parse_line(line):
|
||||
"""Parse information from a line in a requirements text file."""
|
||||
if line.startswith("-r "):
|
||||
# Allow specifying requirements in other files
|
||||
target = line.split(" ")[1]
|
||||
for info in parse_require_file(target):
|
||||
yield info
|
||||
else:
|
||||
info = {"line": line}
|
||||
if line.startswith("-e "):
|
||||
info["package"] = line.split("#egg=")[1]
|
||||
elif "@git+" in line:
|
||||
info["package"] = line
|
||||
else:
|
||||
# Remove versioning from the package
|
||||
pat = "(" + "|".join([">=", "==", ">"]) + ")"
|
||||
parts = re.split(pat, line, maxsplit=1)
|
||||
parts = [p.strip() for p in parts]
|
||||
|
||||
info["package"] = parts[0]
|
||||
if len(parts) > 1:
|
||||
op, rest = parts[1:]
|
||||
if ";" in rest:
|
||||
# Handle platform specific dependencies
|
||||
# http://setuptools.readthedocs.io/en/latest/setuptools.html#declaring-platform-specific-dependencies
|
||||
version, platform_deps = map(str.strip, rest.split(";"))
|
||||
info["platform_deps"] = platform_deps
|
||||
else:
|
||||
version = rest # NOQA
|
||||
info["version"] = (op, version)
|
||||
yield info
|
||||
|
||||
def parse_require_file(fpath):
|
||||
with open(fpath, "r") as f:
|
||||
for line in f.readlines():
|
||||
line = line.strip()
|
||||
if line and not line.startswith("#"):
|
||||
for info in parse_line(line):
|
||||
yield info
|
||||
|
||||
def gen_packages_items():
|
||||
if exists(require_fpath):
|
||||
for info in parse_require_file(require_fpath):
|
||||
parts = [info["package"]]
|
||||
if with_version and "version" in info:
|
||||
parts.extend(info["version"])
|
||||
if not sys.version.startswith("3.4"):
|
||||
# apparently package_deps are broken in 3.4
|
||||
platform_deps = info.get("platform_deps")
|
||||
if platform_deps is not None:
|
||||
parts.append(";" + platform_deps)
|
||||
item = "".join(parts)
|
||||
yield item
|
||||
|
||||
packages = list(gen_packages_items())
|
||||
return packages
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
print(f"Building wheel {package_name}-{version}")
|
||||
|
||||
with open("LICENSE", "r", encoding="utf-8") as f:
|
||||
license = f.read()
|
||||
|
||||
write_version_file()
|
||||
|
||||
setup(
|
||||
name="groundingdino",
|
||||
version="0.1.0",
|
||||
author="International Digital Economy Academy, Shilong Liu",
|
||||
url="https://github.com/IDEA-Research/GroundingDINO",
|
||||
description="open-set object detector",
|
||||
license=license,
|
||||
install_requires=parse_requirements("requirements.txt"),
|
||||
packages=find_packages(
|
||||
exclude=(
|
||||
"configs",
|
||||
"tests",
|
||||
)
|
||||
),
|
||||
ext_modules=get_extensions(),
|
||||
cmdclass={"build_ext": torch.utils.cpp_extension.BuildExtension},
|
||||
)
|
||||
Loading…
Reference in New Issue