`process_mask_native()` cleanup (#10366)
* process_mask_native() cleanup * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * fix arg name * cleanup anno_json * Remove scale_image * Remove scale_image * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * update to native Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>pull/10059/head
Glenn Jocher
GitHub
parent
a1b6e79ccf
commit
9722e6ffe5
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@ -44,7 +44,7 @@ from models.common import DetectMultiBackend
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from utils.dataloaders import IMG_FORMATS, VID_FORMATS, LoadImages, LoadScreenshots, LoadStreams
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from utils.general import (LOGGER, Profile, check_file, check_img_size, check_imshow, check_requirements, colorstr, cv2,
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increment_path, non_max_suppression, print_args, scale_boxes, scale_segments,
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strip_optimizer, xyxy2xywh)
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strip_optimizer)
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from utils.plots import Annotator, colors, save_one_box
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from utils.segment.general import masks2segments, process_mask, process_mask_native
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from utils.torch_utils import select_device, smart_inference_mode
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@ -161,10 +161,9 @@ def run(
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# Segments
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if save_txt:
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segments = reversed(masks2segments(masks))
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segments = [
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scale_segments(im0.shape if retina_masks else im.shape[2:], x, im0.shape, normalize=True)
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for x in segments]
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for x in reversed(masks2segments(masks))]
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# Print results
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for c in det[:, 5].unique():
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@ -172,15 +171,17 @@ def run(
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s += f"{n} {names[int(c)]}{'s' * (n > 1)}, " # add to string
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# Mask plotting
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plot_img = torch.as_tensor(im0, dtype=torch.float16).to(device).permute(2, 0, 1).flip(0).contiguous() / 255. \
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if retina_masks else im[i]
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annotator.masks(masks, colors=[colors(x, True) for x in det[:, 5]], im_gpu=plot_img)
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annotator.masks(
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masks,
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colors=[colors(x, True) for x in det[:, 5]],
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im_gpu=torch.as_tensor(im0, dtype=torch.float16).to(device).permute(2, 0, 1).flip(0).contiguous() /
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255 if retina_masks else im[i])
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# Write results
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for j, (*xyxy, conf, cls) in enumerate(reversed(det[:, :6])):
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if save_txt: # Write to file
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segj = segments[j].reshape(-1) # (n,2) to (n*2)
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line = (cls, *segj, conf) if save_conf else (cls, *segj) # label format
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seg = segments[j].reshape(-1) # (n,2) to (n*2)
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line = (cls, *seg, conf) if save_conf else (cls, *seg) # label format
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with open(f'{txt_path}.txt', 'a') as f:
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f.write(('%g ' * len(line)).rstrip() % line + '\n')
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@ -48,7 +48,7 @@ from utils.general import (LOGGER, NUM_THREADS, TQDM_BAR_FORMAT, Profile, check_
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from utils.metrics import ConfusionMatrix, box_iou
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from utils.plots import output_to_target, plot_val_study
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from utils.segment.dataloaders import create_dataloader
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from utils.segment.general import mask_iou, process_mask, process_mask_upsample, scale_image
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from utils.segment.general import mask_iou, process_mask, process_mask_native, scale_image
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from utils.segment.metrics import Metrics, ap_per_class_box_and_mask
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from utils.segment.plots import plot_images_and_masks
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from utils.torch_utils import de_parallel, select_device, smart_inference_mode
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@ -160,7 +160,7 @@ def run(
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):
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if save_json:
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check_requirements(['pycocotools'])
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process = process_mask_upsample # more accurate
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process = process_mask_native # more accurate
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else:
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process = process_mask # faster
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@ -312,7 +312,7 @@ def run(
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pred_masks = torch.as_tensor(pred_masks, dtype=torch.uint8)
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if plots and batch_i < 3:
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plot_masks.append(pred_masks[:15].cpu()) # filter top 15 to plot
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plot_masks.append(pred_masks[:15]) # filter top 15 to plot
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# Save/log
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if save_txt:
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@ -367,8 +367,8 @@ def run(
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# Save JSON
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if save_json and len(jdict):
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w = Path(weights[0] if isinstance(weights, list) else weights).stem if weights is not None else '' # weights
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anno_json = str(Path(data.get('path', '../coco')) / 'annotations/instances_val2017.json') # annotations json
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pred_json = str(save_dir / f"{w}_predictions.json") # predictions json
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anno_json = str(Path('../datasets/coco/annotations/instances_val2017.json')) # annotations
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pred_json = str(save_dir / f"{w}_predictions.json") # predictions
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LOGGER.info(f'\nEvaluating pycocotools mAP... saving {pred_json}...')
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with open(pred_json, 'w') as f:
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json.dump(jdict, f)
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@ -25,10 +25,10 @@ def crop_mask(masks, boxes):
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def process_mask_upsample(protos, masks_in, bboxes, shape):
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"""
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Crop after upsample.
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proto_out: [mask_dim, mask_h, mask_w]
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out_masks: [n, mask_dim], n is number of masks after nms
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protos: [mask_dim, mask_h, mask_w]
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masks_in: [n, mask_dim], n is number of masks after nms
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bboxes: [n, 4], n is number of masks after nms
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shape:input_image_size, (h, w)
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shape: input_image_size, (h, w)
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return: h, w, n
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"""
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@ -67,25 +67,25 @@ def process_mask(protos, masks_in, bboxes, shape, upsample=False):
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return masks.gt_(0.5)
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def process_mask_native(protos, masks_in, bboxes, dst_shape):
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def process_mask_native(protos, masks_in, bboxes, shape):
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"""
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Crop after upsample.
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proto_out: [mask_dim, mask_h, mask_w]
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out_masks: [n, mask_dim], n is number of masks after nms
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protos: [mask_dim, mask_h, mask_w]
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masks_in: [n, mask_dim], n is number of masks after nms
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bboxes: [n, 4], n is number of masks after nms
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shape:input_image_size, (h, w)
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shape: input_image_size, (h, w)
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return: h, w, n
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"""
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c, mh, mw = protos.shape # CHW
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masks = (masks_in @ protos.float().view(c, -1)).sigmoid().view(-1, mh, mw)
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gain = min(mh / dst_shape[0], mw / dst_shape[1]) # gain = old / new
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pad = (mw - dst_shape[1] * gain) / 2, (mh - dst_shape[0] * gain) / 2 # wh padding
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gain = min(mh / shape[0], mw / shape[1]) # gain = old / new
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pad = (mw - shape[1] * gain) / 2, (mh - shape[0] * gain) / 2 # wh padding
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top, left = int(pad[1]), int(pad[0]) # y, x
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bottom, right = int(mh - pad[1]), int(mw - pad[0])
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masks = masks[:, top:bottom, left:right]
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masks = F.interpolate(masks[None], dst_shape, mode='bilinear', align_corners=False)[0] # CHW
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masks = F.interpolate(masks[None], shape, mode='bilinear', align_corners=False)[0] # CHW
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masks = crop_mask(masks, bboxes) # CHW
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return masks.gt_(0.5)
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4
val.py
4
val.py
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@ -302,8 +302,8 @@ def run(
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# Save JSON
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if save_json and len(jdict):
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w = Path(weights[0] if isinstance(weights, list) else weights).stem if weights is not None else '' # weights
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anno_json = str(Path(data.get('path', '../coco')) / 'annotations/instances_val2017.json') # annotations json
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pred_json = str(save_dir / f"{w}_predictions.json") # predictions json
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anno_json = str(Path('../datasets/coco/annotations/instances_val2017.json')) # annotations
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pred_json = str(save_dir / f"{w}_predictions.json") # predictions
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LOGGER.info(f'\nEvaluating pycocotools mAP... saving {pred_json}...')
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with open(pred_json, 'w') as f:
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json.dump(jdict, f)
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