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>
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Glenn Jocherandpre-commit-ci[bot] authored and GitHub committed
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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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