New `DetectMultiBackend()` class (#5549)
* New `DetectMultiBackend()` class * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * pb to pt fix * Cleanup * explicit apply_classifier path * Cleanup2 * Cleanup3 * Cleanup4 * Cleanup5 * Cleanup6 * val.py MultiBackend inference * warmup fix * to device fix * pt fix * device fix * Val cleanup * COCO128 URL to assets * half fix * detect fix * detect fix 2 * remove half from DetectMultiBackend * training half handling * training half handling 2 * training half handling 3 * Cleanup * Fix CI error * Add torchscript _extra_files * Add TorchScript * Add CoreML * CoreML cleanup * New `DetectMultiBackend()` class * pb to pt fix * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * Cleanup * explicit apply_classifier path * Cleanup2 * Cleanup3 * Cleanup4 * Cleanup5 * Cleanup6 * val.py MultiBackend inference * warmup fix * to device fix * pt fix * device fix * Val cleanup * COCO128 URL to assets * half fix * detect fix * detect fix 2 * remove half from DetectMultiBackend * training half handling * training half handling 2 * training half handling 3 * Cleanup * Fix CI error * Add torchscript _extra_files * Add TorchScript * Add CoreML * CoreML cleanup * revert default to pt * Add Usage examples * Cleanup val Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>pull/5592/head
Glenn Jocher
GitHub
parent
79bca2bf64
commit
3883261143
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@ -27,4 +27,4 @@ names: ['person', 'bicycle', 'car', 'motorcycle', 'airplane', 'bus', 'train', 't
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# Download script/URL (optional)
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download: https://github.com/ultralytics/yolov5/releases/download/v1.0/coco128.zip
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download: https://ultralytics.com/assets/coco128.zip
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135
detect.py
135
detect.py
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@ -14,12 +14,10 @@ Usage:
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import argparse
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import os
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import platform
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import sys
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from pathlib import Path
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import cv2
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import numpy as np
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import torch
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import torch.backends.cudnn as cudnn
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@ -29,13 +27,12 @@ if str(ROOT) not in sys.path:
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sys.path.append(str(ROOT)) # add ROOT to PATH
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ROOT = Path(os.path.relpath(ROOT, Path.cwd())) # relative
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from models.experimental import attempt_load
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from models.common import DetectMultiBackend
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from utils.datasets import IMG_FORMATS, VID_FORMATS, LoadImages, LoadStreams
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from utils.general import (LOGGER, apply_classifier, check_file, check_img_size, check_imshow, check_requirements,
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check_suffix, colorstr, increment_path, non_max_suppression, print_args, scale_coords,
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strip_optimizer, xyxy2xywh)
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from utils.general import (LOGGER, check_file, check_img_size, check_imshow, check_requirements, colorstr,
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increment_path, non_max_suppression, print_args, scale_coords, strip_optimizer, xyxy2xywh)
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from utils.plots import Annotator, colors, save_one_box
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from utils.torch_utils import load_classifier, select_device, time_sync
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from utils.torch_utils import select_device, time_sync
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@torch.no_grad()
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@ -77,120 +74,45 @@ def run(weights=ROOT / 'yolov5s.pt', # model.pt path(s)
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save_dir = increment_path(Path(project) / name, exist_ok=exist_ok) # increment run
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(save_dir / 'labels' if save_txt else save_dir).mkdir(parents=True, exist_ok=True) # make dir
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# Initialize
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device = select_device(device)
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half &= device.type != 'cpu' # half precision only supported on CUDA
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# Load model
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w = str(weights[0] if isinstance(weights, list) else weights)
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classify, suffix, suffixes = False, Path(w).suffix.lower(), ['.pt', '.onnx', '.tflite', '.pb', '']
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check_suffix(w, suffixes) # check weights have acceptable suffix
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pt, onnx, tflite, pb, saved_model = (suffix == x for x in suffixes) # backend booleans
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stride, names = 64, [f'class{i}' for i in range(1000)] # assign defaults
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if pt:
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model = torch.jit.load(w) if 'torchscript' in w else attempt_load(weights, map_location=device)
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stride = int(model.stride.max()) # model stride
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names = model.module.names if hasattr(model, 'module') else model.names # get class names
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if half:
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model.half() # to FP16
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if classify: # second-stage classifier
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modelc = load_classifier(name='resnet50', n=2) # initialize
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modelc.load_state_dict(torch.load('resnet50.pt', map_location=device)['model']).to(device).eval()
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elif onnx:
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if dnn:
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check_requirements(('opencv-python>=4.5.4',))
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net = cv2.dnn.readNetFromONNX(w)
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else:
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check_requirements(('onnx', 'onnxruntime-gpu' if torch.has_cuda else 'onnxruntime'))
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import onnxruntime
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session = onnxruntime.InferenceSession(w, None)
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else: # TensorFlow models
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import tensorflow as tf
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if pb: # https://www.tensorflow.org/guide/migrate#a_graphpb_or_graphpbtxt
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def wrap_frozen_graph(gd, inputs, outputs):
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x = tf.compat.v1.wrap_function(lambda: tf.compat.v1.import_graph_def(gd, name=""), []) # wrapped import
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return x.prune(tf.nest.map_structure(x.graph.as_graph_element, inputs),
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tf.nest.map_structure(x.graph.as_graph_element, outputs))
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graph_def = tf.Graph().as_graph_def()
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graph_def.ParseFromString(open(w, 'rb').read())
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frozen_func = wrap_frozen_graph(gd=graph_def, inputs="x:0", outputs="Identity:0")
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elif saved_model:
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model = tf.keras.models.load_model(w)
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elif tflite:
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if "edgetpu" in w: # https://www.tensorflow.org/lite/guide/python#install_tensorflow_lite_for_python
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import tflite_runtime.interpreter as tflri
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delegate = {'Linux': 'libedgetpu.so.1', # install libedgetpu https://coral.ai/software/#edgetpu-runtime
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'Darwin': 'libedgetpu.1.dylib',
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'Windows': 'edgetpu.dll'}[platform.system()]
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interpreter = tflri.Interpreter(model_path=w, experimental_delegates=[tflri.load_delegate(delegate)])
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else:
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interpreter = tf.lite.Interpreter(model_path=w) # load TFLite model
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interpreter.allocate_tensors() # allocate
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input_details = interpreter.get_input_details() # inputs
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output_details = interpreter.get_output_details() # outputs
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int8 = input_details[0]['dtype'] == np.uint8 # is TFLite quantized uint8 model
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device = select_device(device)
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model = DetectMultiBackend(weights, device=device, dnn=dnn)
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stride, names, pt, jit, onnx = model.stride, model.names, model.pt, model.jit, model.onnx
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imgsz = check_img_size(imgsz, s=stride) # check image size
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# Half
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half &= pt and device.type != 'cpu' # half precision only supported by PyTorch on CUDA
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if pt:
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model.model.half() if half else model.model.float()
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# Dataloader
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if webcam:
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view_img = check_imshow()
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cudnn.benchmark = True # set True to speed up constant image size inference
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dataset = LoadStreams(source, img_size=imgsz, stride=stride, auto=pt)
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dataset = LoadStreams(source, img_size=imgsz, stride=stride, auto=pt and not jit)
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bs = len(dataset) # batch_size
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else:
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dataset = LoadImages(source, img_size=imgsz, stride=stride, auto=pt)
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dataset = LoadImages(source, img_size=imgsz, stride=stride, auto=pt and not jit)
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bs = 1 # batch_size
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vid_path, vid_writer = [None] * bs, [None] * bs
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# Run inference
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if pt and device.type != 'cpu':
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model(torch.zeros(1, 3, *imgsz).to(device).type_as(next(model.parameters()))) # run once
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model(torch.zeros(1, 3, *imgsz).to(device).type_as(next(model.model.parameters()))) # warmup
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dt, seen = [0.0, 0.0, 0.0], 0
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for path, img, im0s, vid_cap, s in dataset:
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for path, im, im0s, vid_cap, s in dataset:
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t1 = time_sync()
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if onnx:
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img = img.astype('float32')
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else:
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img = torch.from_numpy(img).to(device)
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img = img.half() if half else img.float() # uint8 to fp16/32
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img /= 255 # 0 - 255 to 0.0 - 1.0
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if len(img.shape) == 3:
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img = img[None] # expand for batch dim
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im = torch.from_numpy(im).to(device)
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im = im.half() if half else im.float() # uint8 to fp16/32
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im /= 255 # 0 - 255 to 0.0 - 1.0
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if len(im.shape) == 3:
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im = im[None] # expand for batch dim
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t2 = time_sync()
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dt[0] += t2 - t1
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# Inference
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if pt:
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visualize = increment_path(save_dir / Path(path).stem, mkdir=True) if visualize else False
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pred = model(img, augment=augment, visualize=visualize)[0]
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elif onnx:
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if dnn:
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net.setInput(img)
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pred = torch.tensor(net.forward())
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else:
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pred = torch.tensor(session.run([session.get_outputs()[0].name], {session.get_inputs()[0].name: img}))
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else: # tensorflow model (tflite, pb, saved_model)
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imn = img.permute(0, 2, 3, 1).cpu().numpy() # image in numpy
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if pb:
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pred = frozen_func(x=tf.constant(imn)).numpy()
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elif saved_model:
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pred = model(imn, training=False).numpy()
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elif tflite:
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if int8:
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scale, zero_point = input_details[0]['quantization']
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imn = (imn / scale + zero_point).astype(np.uint8) # de-scale
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interpreter.set_tensor(input_details[0]['index'], imn)
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interpreter.invoke()
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pred = interpreter.get_tensor(output_details[0]['index'])
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if int8:
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scale, zero_point = output_details[0]['quantization']
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pred = (pred.astype(np.float32) - zero_point) * scale # re-scale
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pred[..., 0] *= imgsz[1] # x
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pred[..., 1] *= imgsz[0] # y
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pred[..., 2] *= imgsz[1] # w
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pred[..., 3] *= imgsz[0] # h
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pred = torch.tensor(pred)
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visualize = increment_path(save_dir / Path(path).stem, mkdir=True) if visualize else False
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pred = model(im, augment=augment, visualize=visualize)
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t3 = time_sync()
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dt[1] += t3 - t2
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@ -199,8 +121,7 @@ def run(weights=ROOT / 'yolov5s.pt', # model.pt path(s)
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dt[2] += time_sync() - t3
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# Second-stage classifier (optional)
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if classify:
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pred = apply_classifier(pred, modelc, img, im0s)
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# pred = utils.general.apply_classifier(pred, classifier_model, im, im0s)
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# Process predictions
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for i, det in enumerate(pred): # per image
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@ -212,15 +133,15 @@ def run(weights=ROOT / 'yolov5s.pt', # model.pt path(s)
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p, im0, frame = path, im0s.copy(), getattr(dataset, 'frame', 0)
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p = Path(p) # to Path
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save_path = str(save_dir / p.name) # img.jpg
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txt_path = str(save_dir / 'labels' / p.stem) + ('' if dataset.mode == 'image' else f'_{frame}') # img.txt
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s += '%gx%g ' % img.shape[2:] # print string
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save_path = str(save_dir / p.name) # im.jpg
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txt_path = str(save_dir / 'labels' / p.stem) + ('' if dataset.mode == 'image' else f'_{frame}') # im.txt
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s += '%gx%g ' % im.shape[2:] # print string
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gn = torch.tensor(im0.shape)[[1, 0, 1, 0]] # normalization gain whwh
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imc = im0.copy() if save_crop else im0 # for save_crop
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annotator = Annotator(im0, line_width=line_thickness, example=str(names))
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if len(det):
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# Rescale boxes from img_size to im0 size
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det[:, :4] = scale_coords(img.shape[2:], det[:, :4], im0.shape).round()
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det[:, :4] = scale_coords(im.shape[2:], det[:, :4], im0.shape).round()
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# Print results
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for c in det[:, -1].unique():
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@ -21,6 +21,7 @@ TensorFlow.js:
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"""
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import argparse
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import json
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import os
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import subprocess
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import sys
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@ -54,7 +55,9 @@ def export_torchscript(model, im, file, optimize, prefix=colorstr('TorchScript:'
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f = file.with_suffix('.torchscript.pt')
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ts = torch.jit.trace(model, im, strict=False)
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(optimize_for_mobile(ts) if optimize else ts).save(f)
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d = {"shape": im.shape, "stride": int(max(model.stride)), "names": model.names}
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extra_files = {'config.txt': json.dumps(d)} # torch._C.ExtraFilesMap()
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(optimize_for_mobile(ts) if optimize else ts).save(f, _extra_files=extra_files)
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LOGGER.info(f'{prefix} export success, saved as {f} ({file_size(f):.1f} MB)')
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except Exception as e:
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128
models/common.py
128
models/common.py
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@ -3,11 +3,14 @@
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Common modules
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"""
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import json
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import math
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import platform
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import warnings
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from copy import copy
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from pathlib import Path
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import cv2
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import numpy as np
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import pandas as pd
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import requests
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@ -17,7 +20,8 @@ from PIL import Image
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from torch.cuda import amp
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from utils.datasets import exif_transpose, letterbox
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from utils.general import LOGGER, colorstr, increment_path, make_divisible, non_max_suppression, scale_coords, xyxy2xywh
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from utils.general import (LOGGER, check_requirements, check_suffix, colorstr, increment_path, make_divisible,
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non_max_suppression, scale_coords, xywh2xyxy, xyxy2xywh)
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from utils.plots import Annotator, colors, save_one_box
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from utils.torch_utils import time_sync
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@ -269,6 +273,128 @@ class Concat(nn.Module):
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return torch.cat(x, self.d)
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class DetectMultiBackend(nn.Module):
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# YOLOv5 MultiBackend class for python inference on various backends
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def __init__(self, weights='yolov5s.pt', device=None, dnn=True):
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# Usage:
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# PyTorch: weights = *.pt
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# TorchScript: *.torchscript.pt
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# CoreML: *.mlmodel
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# TensorFlow: *_saved_model
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# TensorFlow: *.pb
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# TensorFlow Lite: *.tflite
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# ONNX Runtime: *.onnx
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# OpenCV DNN: *.onnx with dnn=True
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super().__init__()
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w = str(weights[0] if isinstance(weights, list) else weights)
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suffix, suffixes = Path(w).suffix.lower(), ['.pt', '.onnx', '.tflite', '.pb', '', '.mlmodel']
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check_suffix(w, suffixes) # check weights have acceptable suffix
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pt, onnx, tflite, pb, saved_model, coreml = (suffix == x for x in suffixes) # backend booleans
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jit = pt and 'torchscript' in w.lower()
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stride, names = 64, [f'class{i}' for i in range(1000)] # assign defaults
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if jit: # TorchScript
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LOGGER.info(f'Loading {w} for TorchScript inference...')
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extra_files = {'config.txt': ''} # model metadata
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model = torch.jit.load(w, _extra_files=extra_files)
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if extra_files['config.txt']:
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d = json.loads(extra_files['config.txt']) # extra_files dict
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stride, names = int(d['stride']), d['names']
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elif pt: # PyTorch
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from models.experimental import attempt_load # scoped to avoid circular import
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model = torch.jit.load(w) if 'torchscript' in w else attempt_load(weights, map_location=device)
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stride = int(model.stride.max()) # model stride
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names = model.module.names if hasattr(model, 'module') else model.names # get class names
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elif coreml: # CoreML *.mlmodel
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import coremltools as ct
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model = ct.models.MLModel(w)
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elif dnn: # ONNX OpenCV DNN
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LOGGER.info(f'Loading {w} for ONNX OpenCV DNN inference...')
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check_requirements(('opencv-python>=4.5.4',))
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net = cv2.dnn.readNetFromONNX(w)
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elif onnx: # ONNX Runtime
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LOGGER.info(f'Loading {w} for ONNX Runtime inference...')
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check_requirements(('onnx', 'onnxruntime-gpu' if torch.has_cuda else 'onnxruntime'))
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import onnxruntime
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session = onnxruntime.InferenceSession(w, None)
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else: # TensorFlow model (TFLite, pb, saved_model)
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import tensorflow as tf
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if pb: # https://www.tensorflow.org/guide/migrate#a_graphpb_or_graphpbtxt
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def wrap_frozen_graph(gd, inputs, outputs):
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x = tf.compat.v1.wrap_function(lambda: tf.compat.v1.import_graph_def(gd, name=""), []) # wrapped
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return x.prune(tf.nest.map_structure(x.graph.as_graph_element, inputs),
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tf.nest.map_structure(x.graph.as_graph_element, outputs))
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LOGGER.info(f'Loading {w} for TensorFlow *.pb inference...')
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graph_def = tf.Graph().as_graph_def()
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graph_def.ParseFromString(open(w, 'rb').read())
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frozen_func = wrap_frozen_graph(gd=graph_def, inputs="x:0", outputs="Identity:0")
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elif saved_model:
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LOGGER.info(f'Loading {w} for TensorFlow saved_model inference...')
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model = tf.keras.models.load_model(w)
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elif tflite: # https://www.tensorflow.org/lite/guide/python#install_tensorflow_lite_for_python
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if 'edgetpu' in w.lower():
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LOGGER.info(f'Loading {w} for TensorFlow Edge TPU inference...')
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import tflite_runtime.interpreter as tfli
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delegate = {'Linux': 'libedgetpu.so.1', # install https://coral.ai/software/#edgetpu-runtime
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'Darwin': 'libedgetpu.1.dylib',
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'Windows': 'edgetpu.dll'}[platform.system()]
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interpreter = tfli.Interpreter(model_path=w, experimental_delegates=[tfli.load_delegate(delegate)])
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else:
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LOGGER.info(f'Loading {w} for TensorFlow Lite inference...')
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interpreter = tf.lite.Interpreter(model_path=w) # load TFLite model
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interpreter.allocate_tensors() # allocate
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input_details = interpreter.get_input_details() # inputs
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output_details = interpreter.get_output_details() # outputs
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self.__dict__.update(locals()) # assign all variables to self
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def forward(self, im, augment=False, visualize=False, val=False):
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# YOLOv5 MultiBackend inference
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b, ch, h, w = im.shape # batch, channel, height, width
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if self.pt: # PyTorch
|
||||
y = self.model(im) if self.jit else self.model(im, augment=augment, visualize=visualize)
|
||||
return y if val else y[0]
|
||||
elif self.coreml: # CoreML *.mlmodel
|
||||
im = im.permute(0, 2, 3, 1).cpu().numpy() # torch BCHW to numpy BHWC shape(1,320,192,3)
|
||||
im = Image.fromarray((im[0] * 255).astype('uint8'))
|
||||
# im = im.resize((192, 320), Image.ANTIALIAS)
|
||||
y = self.model.predict({'image': im}) # coordinates are xywh normalized
|
||||
box = xywh2xyxy(y['coordinates'] * [[w, h, w, h]]) # xyxy pixels
|
||||
conf, cls = y['confidence'].max(1), y['confidence'].argmax(1).astype(np.float)
|
||||
y = np.concatenate((box, conf.reshape(-1, 1), cls.reshape(-1, 1)), 1)
|
||||
elif self.onnx: # ONNX
|
||||
im = im.cpu().numpy() # torch to numpy
|
||||
if self.dnn: # ONNX OpenCV DNN
|
||||
self.net.setInput(im)
|
||||
y = self.net.forward()
|
||||
else: # ONNX Runtime
|
||||
y = self.session.run([self.session.get_outputs()[0].name], {self.session.get_inputs()[0].name: im})[0]
|
||||
else: # TensorFlow model (TFLite, pb, saved_model)
|
||||
im = im.permute(0, 2, 3, 1).cpu().numpy() # torch BCHW to numpy BHWC shape(1,320,192,3)
|
||||
if self.pb:
|
||||
y = self.frozen_func(x=self.tf.constant(im)).numpy()
|
||||
elif self.saved_model:
|
||||
y = self.model(im, training=False).numpy()
|
||||
elif self.tflite:
|
||||
input, output = self.input_details[0], self.output_details[0]
|
||||
int8 = input['dtype'] == np.uint8 # is TFLite quantized uint8 model
|
||||
if int8:
|
||||
scale, zero_point = input['quantization']
|
||||
im = (im / scale + zero_point).astype(np.uint8) # de-scale
|
||||
self.interpreter.set_tensor(input['index'], im)
|
||||
self.interpreter.invoke()
|
||||
y = self.interpreter.get_tensor(output['index'])
|
||||
if int8:
|
||||
scale, zero_point = output['quantization']
|
||||
y = (y.astype(np.float32) - zero_point) * scale # re-scale
|
||||
y[..., 0] *= w # x
|
||||
y[..., 1] *= h # y
|
||||
y[..., 2] *= w # w
|
||||
y[..., 3] *= h # h
|
||||
y = torch.tensor(y)
|
||||
return (y, []) if val else y
|
||||
|
||||
|
||||
class AutoShape(nn.Module):
|
||||
# YOLOv5 input-robust model wrapper for passing cv2/np/PIL/torch inputs. Includes preprocessing, inference and NMS
|
||||
conf = 0.25 # NMS confidence threshold
|
||||
|
|
|
|||
|
|
@ -785,7 +785,8 @@ def print_mutation(results, hyp, save_dir, bucket):
|
|||
|
||||
|
||||
def apply_classifier(x, model, img, im0):
|
||||
# Apply a second stage classifier to yolo outputs
|
||||
# Apply a second stage classifier to YOLO outputs
|
||||
# Example model = torchvision.models.__dict__['efficientnet_b0'](pretrained=True).to(device).eval()
|
||||
im0 = [im0] if isinstance(im0, np.ndarray) else im0
|
||||
for i, d in enumerate(x): # per image
|
||||
if d is not None and len(d):
|
||||
|
|
|
|||
|
|
@ -17,7 +17,6 @@ import torch
|
|||
import torch.distributed as dist
|
||||
import torch.nn as nn
|
||||
import torch.nn.functional as F
|
||||
import torchvision
|
||||
|
||||
from utils.general import LOGGER
|
||||
|
||||
|
|
@ -235,25 +234,6 @@ def model_info(model, verbose=False, img_size=640):
|
|||
LOGGER.info(f"Model Summary: {len(list(model.modules()))} layers, {n_p} parameters, {n_g} gradients{fs}")
|
||||
|
||||
|
||||
def load_classifier(name='resnet101', n=2):
|
||||
# Loads a pretrained model reshaped to n-class output
|
||||
model = torchvision.models.__dict__[name](pretrained=True)
|
||||
|
||||
# ResNet model properties
|
||||
# input_size = [3, 224, 224]
|
||||
# input_space = 'RGB'
|
||||
# input_range = [0, 1]
|
||||
# mean = [0.485, 0.456, 0.406]
|
||||
# std = [0.229, 0.224, 0.225]
|
||||
|
||||
# Reshape output to n classes
|
||||
filters = model.fc.weight.shape[1]
|
||||
model.fc.bias = nn.Parameter(torch.zeros(n), requires_grad=True)
|
||||
model.fc.weight = nn.Parameter(torch.zeros(n, filters), requires_grad=True)
|
||||
model.fc.out_features = n
|
||||
return model
|
||||
|
||||
|
||||
def scale_img(img, ratio=1.0, same_shape=False, gs=32): # img(16,3,256,416)
|
||||
# scales img(bs,3,y,x) by ratio constrained to gs-multiple
|
||||
if ratio == 1.0:
|
||||
|
|
|
|||
74
val.py
74
val.py
|
|
@ -23,10 +23,10 @@ if str(ROOT) not in sys.path:
|
|||
sys.path.append(str(ROOT)) # add ROOT to PATH
|
||||
ROOT = Path(os.path.relpath(ROOT, Path.cwd())) # relative
|
||||
|
||||
from models.experimental import attempt_load
|
||||
from models.common import DetectMultiBackend
|
||||
from utils.callbacks import Callbacks
|
||||
from utils.datasets import create_dataloader
|
||||
from utils.general import (LOGGER, box_iou, check_dataset, check_img_size, check_requirements, check_suffix, check_yaml,
|
||||
from utils.general import (LOGGER, box_iou, check_dataset, check_img_size, check_requirements, check_yaml,
|
||||
coco80_to_coco91_class, colorstr, increment_path, non_max_suppression, print_args,
|
||||
scale_coords, xywh2xyxy, xyxy2xywh)
|
||||
from utils.metrics import ConfusionMatrix, ap_per_class
|
||||
|
|
@ -100,6 +100,7 @@ def run(data,
|
|||
name='exp', # save to project/name
|
||||
exist_ok=False, # existing project/name ok, do not increment
|
||||
half=True, # use FP16 half-precision inference
|
||||
dnn=False, # use OpenCV DNN for ONNX inference
|
||||
model=None,
|
||||
dataloader=None,
|
||||
save_dir=Path(''),
|
||||
|
|
@ -110,8 +111,10 @@ def run(data,
|
|||
# Initialize/load model and set device
|
||||
training = model is not None
|
||||
if training: # called by train.py
|
||||
device = next(model.parameters()).device # get model device
|
||||
device, pt = next(model.parameters()).device, True # get model device, PyTorch model
|
||||
|
||||
half &= device.type != 'cpu' # half precision only supported on CUDA
|
||||
model.half() if half else model.float()
|
||||
else: # called directly
|
||||
device = select_device(device, batch_size=batch_size)
|
||||
|
||||
|
|
@ -120,22 +123,21 @@ def run(data,
|
|||
(save_dir / 'labels' if save_txt else save_dir).mkdir(parents=True, exist_ok=True) # make dir
|
||||
|
||||
# Load model
|
||||
check_suffix(weights, '.pt')
|
||||
model = attempt_load(weights, map_location=device) # load FP32 model
|
||||
gs = max(int(model.stride.max()), 32) # grid size (max stride)
|
||||
imgsz = check_img_size(imgsz, s=gs) # check image size
|
||||
|
||||
# Multi-GPU disabled, incompatible with .half() https://github.com/ultralytics/yolov5/issues/99
|
||||
# if device.type != 'cpu' and torch.cuda.device_count() > 1:
|
||||
# model = nn.DataParallel(model)
|
||||
model = DetectMultiBackend(weights, device=device, dnn=dnn)
|
||||
stride, pt = model.stride, model.pt
|
||||
imgsz = check_img_size(imgsz, s=stride) # check image size
|
||||
half &= pt and device.type != 'cpu' # half precision only supported by PyTorch on CUDA
|
||||
if pt:
|
||||
model.model.half() if half else model.model.float()
|
||||
else:
|
||||
half = False
|
||||
batch_size = 1 # export.py models default to batch-size 1
|
||||
device = torch.device('cpu')
|
||||
LOGGER.info(f'Forcing --batch-size 1 square inference shape(1,3,{imgsz},{imgsz}) for non-PyTorch backends')
|
||||
|
||||
# Data
|
||||
data = check_dataset(data) # check
|
||||
|
||||
# Half
|
||||
half &= device.type != 'cpu' # half precision only supported on CUDA
|
||||
model.half() if half else model.float()
|
||||
|
||||
# Configure
|
||||
model.eval()
|
||||
is_coco = isinstance(data.get('val'), str) and data['val'].endswith('coco/val2017.txt') # COCO dataset
|
||||
|
|
@ -145,11 +147,11 @@ def run(data,
|
|||
|
||||
# Dataloader
|
||||
if not training:
|
||||
if device.type != 'cpu':
|
||||
model(torch.zeros(1, 3, imgsz, imgsz).to(device).type_as(next(model.parameters()))) # run once
|
||||
if pt and device.type != 'cpu':
|
||||
model(torch.zeros(1, 3, imgsz, imgsz).to(device).type_as(next(model.model.parameters()))) # warmup
|
||||
pad = 0.0 if task == 'speed' else 0.5
|
||||
task = task if task in ('train', 'val', 'test') else 'val' # path to train/val/test images
|
||||
dataloader = create_dataloader(data[task], imgsz, batch_size, gs, single_cls, pad=pad, rect=True,
|
||||
dataloader = create_dataloader(data[task], imgsz, batch_size, stride, single_cls, pad=pad, rect=pt,
|
||||
prefix=colorstr(f'{task}: '))[0]
|
||||
|
||||
seen = 0
|
||||
|
|
@ -160,32 +162,33 @@ def run(data,
|
|||
dt, p, r, f1, mp, mr, map50, map = [0.0, 0.0, 0.0], 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0
|
||||
loss = torch.zeros(3, device=device)
|
||||
jdict, stats, ap, ap_class = [], [], [], []
|
||||
for batch_i, (img, targets, paths, shapes) in enumerate(tqdm(dataloader, desc=s)):
|
||||
for batch_i, (im, targets, paths, shapes) in enumerate(tqdm(dataloader, desc=s)):
|
||||
t1 = time_sync()
|
||||
img = img.to(device, non_blocking=True)
|
||||
img = img.half() if half else img.float() # uint8 to fp16/32
|
||||
img /= 255 # 0 - 255 to 0.0 - 1.0
|
||||
targets = targets.to(device)
|
||||
nb, _, height, width = img.shape # batch size, channels, height, width
|
||||
if pt:
|
||||
im = im.to(device, non_blocking=True)
|
||||
targets = targets.to(device)
|
||||
im = im.half() if half else im.float() # uint8 to fp16/32
|
||||
im /= 255 # 0 - 255 to 0.0 - 1.0
|
||||
nb, _, height, width = im.shape # batch size, channels, height, width
|
||||
t2 = time_sync()
|
||||
dt[0] += t2 - t1
|
||||
|
||||
# Run model
|
||||
out, train_out = model(img, augment=augment) # inference and training outputs
|
||||
# Inference
|
||||
out, train_out = model(im) if training else model(im, augment=augment, val=True) # inference, loss outputs
|
||||
dt[1] += time_sync() - t2
|
||||
|
||||
# Compute loss
|
||||
# Loss
|
||||
if compute_loss:
|
||||
loss += compute_loss([x.float() for x in train_out], targets)[1] # box, obj, cls
|
||||
|
||||
# Run NMS
|
||||
# NMS
|
||||
targets[:, 2:] *= torch.Tensor([width, height, width, height]).to(device) # to pixels
|
||||
lb = [targets[targets[:, 0] == i, 1:] for i in range(nb)] if save_hybrid else [] # for autolabelling
|
||||
t3 = time_sync()
|
||||
out = non_max_suppression(out, conf_thres, iou_thres, labels=lb, multi_label=True, agnostic=single_cls)
|
||||
dt[2] += time_sync() - t3
|
||||
|
||||
# Statistics per image
|
||||
# Metrics
|
||||
for si, pred in enumerate(out):
|
||||
labels = targets[targets[:, 0] == si, 1:]
|
||||
nl = len(labels)
|
||||
|
|
@ -202,12 +205,12 @@ def run(data,
|
|||
if single_cls:
|
||||
pred[:, 5] = 0
|
||||
predn = pred.clone()
|
||||
scale_coords(img[si].shape[1:], predn[:, :4], shape, shapes[si][1]) # native-space pred
|
||||
scale_coords(im[si].shape[1:], predn[:, :4], shape, shapes[si][1]) # native-space pred
|
||||
|
||||
# Evaluate
|
||||
if nl:
|
||||
tbox = xywh2xyxy(labels[:, 1:5]) # target boxes
|
||||
scale_coords(img[si].shape[1:], tbox, shape, shapes[si][1]) # native-space labels
|
||||
scale_coords(im[si].shape[1:], tbox, shape, shapes[si][1]) # native-space labels
|
||||
labelsn = torch.cat((labels[:, 0:1], tbox), 1) # native-space labels
|
||||
correct = process_batch(predn, labelsn, iouv)
|
||||
if plots:
|
||||
|
|
@ -221,16 +224,16 @@ def run(data,
|
|||
save_one_txt(predn, save_conf, shape, file=save_dir / 'labels' / (path.stem + '.txt'))
|
||||
if save_json:
|
||||
save_one_json(predn, jdict, path, class_map) # append to COCO-JSON dictionary
|
||||
callbacks.run('on_val_image_end', pred, predn, path, names, img[si])
|
||||
callbacks.run('on_val_image_end', pred, predn, path, names, im[si])
|
||||
|
||||
# Plot images
|
||||
if plots and batch_i < 3:
|
||||
f = save_dir / f'val_batch{batch_i}_labels.jpg' # labels
|
||||
Thread(target=plot_images, args=(img, targets, paths, f, names), daemon=True).start()
|
||||
Thread(target=plot_images, args=(im, targets, paths, f, names), daemon=True).start()
|
||||
f = save_dir / f'val_batch{batch_i}_pred.jpg' # predictions
|
||||
Thread(target=plot_images, args=(img, output_to_target(out), paths, f, names), daemon=True).start()
|
||||
Thread(target=plot_images, args=(im, output_to_target(out), paths, f, names), daemon=True).start()
|
||||
|
||||
# Compute statistics
|
||||
# Compute metrics
|
||||
stats = [np.concatenate(x, 0) for x in zip(*stats)] # to numpy
|
||||
if len(stats) and stats[0].any():
|
||||
p, r, ap, f1, ap_class = ap_per_class(*stats, plot=plots, save_dir=save_dir, names=names)
|
||||
|
|
@ -318,6 +321,7 @@ def parse_opt():
|
|||
parser.add_argument('--name', default='exp', help='save to project/name')
|
||||
parser.add_argument('--exist-ok', action='store_true', help='existing project/name ok, do not increment')
|
||||
parser.add_argument('--half', action='store_true', help='use FP16 half-precision inference')
|
||||
parser.add_argument('--dnn', action='store_true', help='use OpenCV DNN for ONNX inference')
|
||||
opt = parser.parse_args()
|
||||
opt.data = check_yaml(opt.data) # check YAML
|
||||
opt.save_json |= opt.data.endswith('coco.yaml')
|
||||
|
|
|
|||
Loading…
Reference in New Issue