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| # YOLOv5 🚀 by Ultralytics, GPL-3.0 license | |
| """ | |
| Common modules | |
| """ | |
| import json | |
| import math | |
| import platform | |
| import warnings | |
| from collections import OrderedDict, namedtuple | |
| from copy import copy | |
| from pathlib import Path | |
| import cv2 | |
| import numpy as np | |
| import pandas as pd | |
| import requests | |
| import torch | |
| import torch.nn as nn | |
| import yaml | |
| from PIL import Image | |
| from torch.cuda import amp | |
| from utils.datasets import exif_transpose, letterbox | |
| from utils.general import (LOGGER, check_requirements, check_suffix, check_version, colorstr, increment_path, | |
| make_divisible, non_max_suppression, scale_coords, xywh2xyxy, xyxy2xywh) | |
| from utils.plots import Annotator, colors, save_one_box | |
| from utils.torch_utils import copy_attr, time_sync | |
| def autopad(k, p=None): # kernel, padding | |
| # Pad to 'same' | |
| if p is None: | |
| p = k // 2 if isinstance(k, int) else (x // 2 for x in k) # auto-pad | |
| return p | |
| class Conv(nn.Module): | |
| # Standard convolution | |
| def __init__(self, c1, c2, k=1, s=1, p=None, g=1, act=True): # ch_in, ch_out, kernel, stride, padding, groups | |
| super().__init__() | |
| self.conv = nn.Conv2d(c1, c2, k, s, autopad(k, p), groups=g, bias=False) | |
| self.bn = nn.BatchNorm2d(c2) | |
| self.act = nn.SiLU() if act is True else (act if isinstance(act, nn.Module) else nn.Identity()) | |
| def forward(self, x): | |
| return self.act(self.bn(self.conv(x))) | |
| def forward_fuse(self, x): | |
| return self.act(self.conv(x)) | |
| class DWConv(Conv): | |
| # Depth-wise convolution class | |
| def __init__(self, c1, c2, k=1, s=1, act=True): # ch_in, ch_out, kernel, stride, padding, groups | |
| super().__init__(c1, c2, k, s, g=math.gcd(c1, c2), act=act) | |
| class TransformerLayer(nn.Module): | |
| # Transformer layer https://arxiv.org/abs/2010.11929 (LayerNorm layers removed for better performance) | |
| def __init__(self, c, num_heads): | |
| super().__init__() | |
| self.q = nn.Linear(c, c, bias=False) | |
| self.k = nn.Linear(c, c, bias=False) | |
| self.v = nn.Linear(c, c, bias=False) | |
| self.ma = nn.MultiheadAttention(embed_dim=c, num_heads=num_heads) | |
| self.fc1 = nn.Linear(c, c, bias=False) | |
| self.fc2 = nn.Linear(c, c, bias=False) | |
| def forward(self, x): | |
| x = self.ma(self.q(x), self.k(x), self.v(x))[0] + x | |
| x = self.fc2(self.fc1(x)) + x | |
| return x | |
| class TransformerBlock(nn.Module): | |
| # Vision Transformer https://arxiv.org/abs/2010.11929 | |
| def __init__(self, c1, c2, num_heads, num_layers): | |
| super().__init__() | |
| self.conv = None | |
| if c1 != c2: | |
| self.conv = Conv(c1, c2) | |
| self.linear = nn.Linear(c2, c2) # learnable position embedding | |
| self.tr = nn.Sequential(*(TransformerLayer(c2, num_heads) for _ in range(num_layers))) | |
| self.c2 = c2 | |
| def forward(self, x): | |
| if self.conv is not None: | |
| x = self.conv(x) | |
| b, _, w, h = x.shape | |
| p = x.flatten(2).permute(2, 0, 1) | |
| return self.tr(p + self.linear(p)).permute(1, 2, 0).reshape(b, self.c2, w, h) | |
| class Bottleneck(nn.Module): | |
| # Standard bottleneck | |
| def __init__(self, c1, c2, shortcut=True, g=1, e=0.5): # ch_in, ch_out, shortcut, groups, expansion | |
| super().__init__() | |
| c_ = int(c2 * e) # hidden channels | |
| self.cv1 = Conv(c1, c_, 1, 1) | |
| self.cv2 = Conv(c_, c2, 3, 1, g=g) | |
| self.add = shortcut and c1 == c2 | |
| def forward(self, x): | |
| return x + self.cv2(self.cv1(x)) if self.add else self.cv2(self.cv1(x)) | |
| class BottleneckCSP(nn.Module): | |
| # CSP Bottleneck https://github.com/WongKinYiu/CrossStagePartialNetworks | |
| def __init__(self, c1, c2, n=1, shortcut=True, g=1, e=0.5): # ch_in, ch_out, number, shortcut, groups, expansion | |
| super().__init__() | |
| c_ = int(c2 * e) # hidden channels | |
| self.cv1 = Conv(c1, c_, 1, 1) | |
| self.cv2 = nn.Conv2d(c1, c_, 1, 1, bias=False) | |
| self.cv3 = nn.Conv2d(c_, c_, 1, 1, bias=False) | |
| self.cv4 = Conv(2 * c_, c2, 1, 1) | |
| self.bn = nn.BatchNorm2d(2 * c_) # applied to cat(cv2, cv3) | |
| self.act = nn.SiLU() | |
| self.m = nn.Sequential(*(Bottleneck(c_, c_, shortcut, g, e=1.0) for _ in range(n))) | |
| def forward(self, x): | |
| y1 = self.cv3(self.m(self.cv1(x))) | |
| y2 = self.cv2(x) | |
| return self.cv4(self.act(self.bn(torch.cat((y1, y2), 1)))) | |
| class C3(nn.Module): | |
| # CSP Bottleneck with 3 convolutions | |
| def __init__(self, c1, c2, n=1, shortcut=True, g=1, e=0.5): # ch_in, ch_out, number, shortcut, groups, expansion | |
| super().__init__() | |
| c_ = int(c2 * e) # hidden channels | |
| self.cv1 = Conv(c1, c_, 1, 1) | |
| self.cv2 = Conv(c1, c_, 1, 1) | |
| self.cv3 = Conv(2 * c_, c2, 1) # act=FReLU(c2) | |
| self.m = nn.Sequential(*(Bottleneck(c_, c_, shortcut, g, e=1.0) for _ in range(n))) | |
| # self.m = nn.Sequential(*(CrossConv(c_, c_, 3, 1, g, 1.0, shortcut) for _ in range(n))) | |
| def forward(self, x): | |
| return self.cv3(torch.cat((self.m(self.cv1(x)), self.cv2(x)), 1)) | |
| class C3TR(C3): | |
| # C3 module with TransformerBlock() | |
| def __init__(self, c1, c2, n=1, shortcut=True, g=1, e=0.5): | |
| super().__init__(c1, c2, n, shortcut, g, e) | |
| c_ = int(c2 * e) | |
| self.m = TransformerBlock(c_, c_, 4, n) | |
| class C3SPP(C3): | |
| # C3 module with SPP() | |
| def __init__(self, c1, c2, k=(5, 9, 13), n=1, shortcut=True, g=1, e=0.5): | |
| super().__init__(c1, c2, n, shortcut, g, e) | |
| c_ = int(c2 * e) | |
| self.m = SPP(c_, c_, k) | |
| class C3Ghost(C3): | |
| # C3 module with GhostBottleneck() | |
| def __init__(self, c1, c2, n=1, shortcut=True, g=1, e=0.5): | |
| super().__init__(c1, c2, n, shortcut, g, e) | |
| c_ = int(c2 * e) # hidden channels | |
| self.m = nn.Sequential(*(GhostBottleneck(c_, c_) for _ in range(n))) | |
| class SPP(nn.Module): | |
| # Spatial Pyramid Pooling (SPP) layer https://arxiv.org/abs/1406.4729 | |
| def __init__(self, c1, c2, k=(5, 9, 13)): | |
| super().__init__() | |
| c_ = c1 // 2 # hidden channels | |
| self.cv1 = Conv(c1, c_, 1, 1) | |
| self.cv2 = Conv(c_ * (len(k) + 1), c2, 1, 1) | |
| self.m = nn.ModuleList([nn.MaxPool2d(kernel_size=x, stride=1, padding=x // 2) for x in k]) | |
| def forward(self, x): | |
| x = self.cv1(x) | |
| with warnings.catch_warnings(): | |
| warnings.simplefilter('ignore') # suppress torch 1.9.0 max_pool2d() warning | |
| return self.cv2(torch.cat([x] + [m(x) for m in self.m], 1)) | |
| class SPPF(nn.Module): | |
| # Spatial Pyramid Pooling - Fast (SPPF) layer for YOLOv5 by Glenn Jocher | |
| def __init__(self, c1, c2, k=5): # equivalent to SPP(k=(5, 9, 13)) | |
| super().__init__() | |
| c_ = c1 // 2 # hidden channels | |
| self.cv1 = Conv(c1, c_, 1, 1) | |
| self.cv2 = Conv(c_ * 4, c2, 1, 1) | |
| self.m = nn.MaxPool2d(kernel_size=k, stride=1, padding=k // 2) | |
| def forward(self, x): | |
| x = self.cv1(x) | |
| with warnings.catch_warnings(): | |
| warnings.simplefilter('ignore') # suppress torch 1.9.0 max_pool2d() warning | |
| y1 = self.m(x) | |
| y2 = self.m(y1) | |
| return self.cv2(torch.cat((x, y1, y2, self.m(y2)), 1)) | |
| class Focus(nn.Module): | |
| # Focus wh information into c-space | |
| def __init__(self, c1, c2, k=1, s=1, p=None, g=1, act=True): # ch_in, ch_out, kernel, stride, padding, groups | |
| super().__init__() | |
| self.conv = Conv(c1 * 4, c2, k, s, p, g, act) | |
| # self.contract = Contract(gain=2) | |
| def forward(self, x): # x(b,c,w,h) -> y(b,4c,w/2,h/2) | |
| return self.conv(torch.cat((x[..., ::2, ::2], x[..., 1::2, ::2], x[..., ::2, 1::2], x[..., 1::2, 1::2]), 1)) | |
| # return self.conv(self.contract(x)) | |
| class GhostConv(nn.Module): | |
| # Ghost Convolution https://github.com/huawei-noah/ghostnet | |
| def __init__(self, c1, c2, k=1, s=1, g=1, act=True): # ch_in, ch_out, kernel, stride, groups | |
| super().__init__() | |
| c_ = c2 // 2 # hidden channels | |
| self.cv1 = Conv(c1, c_, k, s, None, g, act) | |
| self.cv2 = Conv(c_, c_, 5, 1, None, c_, act) | |
| def forward(self, x): | |
| y = self.cv1(x) | |
| return torch.cat((y, self.cv2(y)), 1) | |
| class GhostBottleneck(nn.Module): | |
| # Ghost Bottleneck https://github.com/huawei-noah/ghostnet | |
| def __init__(self, c1, c2, k=3, s=1): # ch_in, ch_out, kernel, stride | |
| super().__init__() | |
| c_ = c2 // 2 | |
| self.conv = nn.Sequential(GhostConv(c1, c_, 1, 1), # pw | |
| DWConv(c_, c_, k, s, act=False) if s == 2 else nn.Identity(), # dw | |
| GhostConv(c_, c2, 1, 1, act=False)) # pw-linear | |
| self.shortcut = nn.Sequential(DWConv(c1, c1, k, s, act=False), | |
| Conv(c1, c2, 1, 1, act=False)) if s == 2 else nn.Identity() | |
| def forward(self, x): | |
| return self.conv(x) + self.shortcut(x) | |
| class Contract(nn.Module): | |
| # Contract width-height into channels, i.e. x(1,64,80,80) to x(1,256,40,40) | |
| def __init__(self, gain=2): | |
| super().__init__() | |
| self.gain = gain | |
| def forward(self, x): | |
| b, c, h, w = x.size() # assert (h / s == 0) and (W / s == 0), 'Indivisible gain' | |
| s = self.gain | |
| x = x.view(b, c, h // s, s, w // s, s) # x(1,64,40,2,40,2) | |
| x = x.permute(0, 3, 5, 1, 2, 4).contiguous() # x(1,2,2,64,40,40) | |
| return x.view(b, c * s * s, h // s, w // s) # x(1,256,40,40) | |
| class Expand(nn.Module): | |
| # Expand channels into width-height, i.e. x(1,64,80,80) to x(1,16,160,160) | |
| def __init__(self, gain=2): | |
| super().__init__() | |
| self.gain = gain | |
| def forward(self, x): | |
| b, c, h, w = x.size() # assert C / s ** 2 == 0, 'Indivisible gain' | |
| s = self.gain | |
| x = x.view(b, s, s, c // s ** 2, h, w) # x(1,2,2,16,80,80) | |
| x = x.permute(0, 3, 4, 1, 5, 2).contiguous() # x(1,16,80,2,80,2) | |
| return x.view(b, c // s ** 2, h * s, w * s) # x(1,16,160,160) | |
| class Concat(nn.Module): | |
| # Concatenate a list of tensors along dimension | |
| def __init__(self, dimension=1): | |
| super().__init__() | |
| self.d = dimension | |
| def forward(self, x): | |
| return torch.cat(x, self.d) | |
| class DetectMultiBackend(nn.Module): | |
| # YOLOv5 MultiBackend class for python inference on various backends | |
| def __init__(self, weights='yolov5s.pt', device=torch.device('cpu'), dnn=False, data=None, fp16=False): | |
| # Usage: | |
| # PyTorch: weights = *.pt | |
| # TorchScript: *.torchscript | |
| # ONNX Runtime: *.onnx | |
| # ONNX OpenCV DNN: *.onnx with --dnn | |
| # OpenVINO: *.xml | |
| # CoreML: *.mlmodel | |
| # TensorRT: *.engine | |
| # TensorFlow SavedModel: *_saved_model | |
| # TensorFlow GraphDef: *.pb | |
| # TensorFlow Lite: *.tflite | |
| # TensorFlow Edge TPU: *_edgetpu.tflite | |
| from models.experimental import attempt_download, attempt_load # scoped to avoid circular import | |
| super().__init__() | |
| w = str(weights[0] if isinstance(weights, list) else weights) | |
| pt, jit, onnx, xml, engine, coreml, saved_model, pb, tflite, edgetpu, tfjs = self.model_type(w) # get backend | |
| stride, names = 64, [f'class{i}' for i in range(1000)] # assign defaults | |
| w = attempt_download(w) # download if not local | |
| fp16 &= (pt or jit or onnx or engine) and device.type != 'cpu' # FP16 | |
| if data: # data.yaml path (optional) | |
| with open(data, errors='ignore') as f: | |
| names = yaml.safe_load(f)['names'] # class names | |
| if pt: # PyTorch | |
| model = attempt_load(weights if isinstance(weights, list) else w, map_location=device) | |
| stride = max(int(model.stride.max()), 32) # model stride | |
| names = model.module.names if hasattr(model, 'module') else model.names # get class names | |
| model.half() if fp16 else model.float() | |
| self.model = model # explicitly assign for to(), cpu(), cuda(), half() | |
| elif jit: # TorchScript | |
| LOGGER.info(f'Loading {w} for TorchScript inference...') | |
| extra_files = {'config.txt': ''} # model metadata | |
| model = torch.jit.load(w, _extra_files=extra_files) | |
| model.half() if fp16 else model.float() | |
| if extra_files['config.txt']: | |
| d = json.loads(extra_files['config.txt']) # extra_files dict | |
| stride, names = int(d['stride']), d['names'] | |
| elif dnn: # ONNX OpenCV DNN | |
| LOGGER.info(f'Loading {w} for ONNX OpenCV DNN inference...') | |
| check_requirements(('opencv-python>=4.5.4',)) | |
| net = cv2.dnn.readNetFromONNX(w) | |
| elif onnx: # ONNX Runtime | |
| LOGGER.info(f'Loading {w} for ONNX Runtime inference...') | |
| cuda = torch.cuda.is_available() | |
| check_requirements(('onnx', 'onnxruntime-gpu' if cuda else 'onnxruntime')) | |
| import onnxruntime | |
| providers = ['CUDAExecutionProvider', 'CPUExecutionProvider'] if cuda else ['CPUExecutionProvider'] | |
| session = onnxruntime.InferenceSession(w, providers=providers) | |
| elif xml: # OpenVINO | |
| LOGGER.info(f'Loading {w} for OpenVINO inference...') | |
| check_requirements(('openvino-dev',)) # requires openvino-dev: https://pypi.org/project/openvino-dev/ | |
| import openvino.inference_engine as ie | |
| core = ie.IECore() | |
| if not Path(w).is_file(): # if not *.xml | |
| w = next(Path(w).glob('*.xml')) # get *.xml file from *_openvino_model dir | |
| network = core.read_network(model=w, weights=Path(w).with_suffix('.bin')) # *.xml, *.bin paths | |
| executable_network = core.load_network(network, device_name='CPU', num_requests=1) | |
| elif engine: # TensorRT | |
| LOGGER.info(f'Loading {w} for TensorRT inference...') | |
| import tensorrt as trt # https://developer.nvidia.com/nvidia-tensorrt-download | |
| check_version(trt.__version__, '7.0.0', hard=True) # require tensorrt>=7.0.0 | |
| Binding = namedtuple('Binding', ('name', 'dtype', 'shape', 'data', 'ptr')) | |
| logger = trt.Logger(trt.Logger.INFO) | |
| with open(w, 'rb') as f, trt.Runtime(logger) as runtime: | |
| model = runtime.deserialize_cuda_engine(f.read()) | |
| bindings = OrderedDict() | |
| fp16 = False # default updated below | |
| for index in range(model.num_bindings): | |
| name = model.get_binding_name(index) | |
| dtype = trt.nptype(model.get_binding_dtype(index)) | |
| shape = tuple(model.get_binding_shape(index)) | |
| data = torch.from_numpy(np.empty(shape, dtype=np.dtype(dtype))).to(device) | |
| bindings[name] = Binding(name, dtype, shape, data, int(data.data_ptr())) | |
| if model.binding_is_input(index) and dtype == np.float16: | |
| fp16 = True | |
| binding_addrs = OrderedDict((n, d.ptr) for n, d in bindings.items()) | |
| context = model.create_execution_context() | |
| batch_size = bindings['images'].shape[0] | |
| elif coreml: # CoreML | |
| LOGGER.info(f'Loading {w} for CoreML inference...') | |
| import coremltools as ct | |
| model = ct.models.MLModel(w) | |
| else: # TensorFlow (SavedModel, GraphDef, Lite, Edge TPU) | |
| if saved_model: # SavedModel | |
| LOGGER.info(f'Loading {w} for TensorFlow SavedModel inference...') | |
| import tensorflow as tf | |
| keras = False # assume TF1 saved_model | |
| model = tf.keras.models.load_model(w) if keras else tf.saved_model.load(w) | |
| elif pb: # GraphDef https://www.tensorflow.org/guide/migrate#a_graphpb_or_graphpbtxt | |
| LOGGER.info(f'Loading {w} for TensorFlow GraphDef inference...') | |
| import tensorflow as tf | |
| def wrap_frozen_graph(gd, inputs, outputs): | |
| x = tf.compat.v1.wrap_function(lambda: tf.compat.v1.import_graph_def(gd, name=""), []) # wrapped | |
| ge = x.graph.as_graph_element | |
| return x.prune(tf.nest.map_structure(ge, inputs), tf.nest.map_structure(ge, outputs)) | |
| gd = tf.Graph().as_graph_def() # graph_def | |
| gd.ParseFromString(open(w, 'rb').read()) | |
| frozen_func = wrap_frozen_graph(gd, inputs="x:0", outputs="Identity:0") | |
| elif tflite or edgetpu: # https://www.tensorflow.org/lite/guide/python#install_tensorflow_lite_for_python | |
| try: # https://coral.ai/docs/edgetpu/tflite-python/#update-existing-tf-lite-code-for-the-edge-tpu | |
| from tflite_runtime.interpreter import Interpreter, load_delegate | |
| except ImportError: | |
| import tensorflow as tf | |
| Interpreter, load_delegate = tf.lite.Interpreter, tf.lite.experimental.load_delegate, | |
| if edgetpu: # Edge TPU https://coral.ai/software/#edgetpu-runtime | |
| LOGGER.info(f'Loading {w} for TensorFlow Lite Edge TPU inference...') | |
| delegate = {'Linux': 'libedgetpu.so.1', | |
| 'Darwin': 'libedgetpu.1.dylib', | |
| 'Windows': 'edgetpu.dll'}[platform.system()] | |
| interpreter = Interpreter(model_path=w, experimental_delegates=[load_delegate(delegate)]) | |
| else: # Lite | |
| LOGGER.info(f'Loading {w} for TensorFlow Lite inference...') | |
| interpreter = Interpreter(model_path=w) # load TFLite model | |
| interpreter.allocate_tensors() # allocate | |
| input_details = interpreter.get_input_details() # inputs | |
| output_details = interpreter.get_output_details() # outputs | |
| elif tfjs: | |
| raise Exception('ERROR: YOLOv5 TF.js inference is not supported') | |
| self.__dict__.update(locals()) # assign all variables to self | |
| def forward(self, im, augment=False, visualize=False, val=False): | |
| # YOLOv5 MultiBackend inference | |
| b, ch, h, w = im.shape # batch, channel, height, width | |
| if self.pt or self.jit: # 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.dnn: # ONNX OpenCV DNN | |
| im = im.cpu().numpy() # torch to numpy | |
| self.net.setInput(im) | |
| y = self.net.forward() | |
| elif self.onnx: # ONNX Runtime | |
| im = im.cpu().numpy() # torch to numpy | |
| y = self.session.run([self.session.get_outputs()[0].name], {self.session.get_inputs()[0].name: im})[0] | |
| elif self.xml: # OpenVINO | |
| im = im.cpu().numpy() # FP32 | |
| desc = self.ie.TensorDesc(precision='FP32', dims=im.shape, layout='NCHW') # Tensor Description | |
| request = self.executable_network.requests[0] # inference request | |
| request.set_blob(blob_name='images', blob=self.ie.Blob(desc, im)) # name=next(iter(request.input_blobs)) | |
| request.infer() | |
| y = request.output_blobs['output'].buffer # name=next(iter(request.output_blobs)) | |
| elif self.engine: # TensorRT | |
| assert im.shape == self.bindings['images'].shape, (im.shape, self.bindings['images'].shape) | |
| self.binding_addrs['images'] = int(im.data_ptr()) | |
| self.context.execute_v2(list(self.binding_addrs.values())) | |
| y = self.bindings['output'].data | |
| elif self.coreml: # CoreML | |
| 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 | |
| if 'confidence' in y: | |
| 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) | |
| else: | |
| k = 'var_' + str(sorted(int(k.replace('var_', '')) for k in y)[-1]) # output key | |
| y = y[k] # output | |
| else: # TensorFlow (SavedModel, GraphDef, Lite, Edge TPU) | |
| im = im.permute(0, 2, 3, 1).cpu().numpy() # torch BCHW to numpy BHWC shape(1,320,192,3) | |
| if self.saved_model: # SavedModel | |
| y = (self.model(im, training=False) if self.keras else self.model(im)).numpy() | |
| elif self.pb: # GraphDef | |
| y = self.frozen_func(x=self.tf.constant(im)).numpy() | |
| else: # Lite or Edge TPU | |
| 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[..., :4] *= [w, h, w, h] # xywh normalized to pixels | |
| if isinstance(y, np.ndarray): | |
| y = torch.tensor(y, device=self.device) | |
| return (y, []) if val else y | |
| def warmup(self, imgsz=(1, 3, 640, 640)): | |
| # Warmup model by running inference once | |
| if any((self.pt, self.jit, self.onnx, self.engine, self.saved_model, self.pb)): # warmup types | |
| if self.device.type != 'cpu': # only warmup GPU models | |
| im = torch.zeros(*imgsz, dtype=torch.half if self.fp16 else torch.float, device=self.device) # input | |
| for _ in range(2 if self.jit else 1): # | |
| self.forward(im) # warmup | |
| def model_type(p='path/to/model.pt'): | |
| # Return model type from model path, i.e. path='path/to/model.onnx' -> type=onnx | |
| from export import export_formats | |
| suffixes = list(export_formats().Suffix) + ['.xml'] # export suffixes | |
| check_suffix(p, suffixes) # checks | |
| p = Path(p).name # eliminate trailing separators | |
| pt, jit, onnx, xml, engine, coreml, saved_model, pb, tflite, edgetpu, tfjs, xml2 = (s in p for s in suffixes) | |
| xml |= xml2 # *_openvino_model or *.xml | |
| tflite &= not edgetpu # *.tflite | |
| return pt, jit, onnx, xml, engine, coreml, saved_model, pb, tflite, edgetpu, tfjs | |
| 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 | |
| iou = 0.45 # NMS IoU threshold | |
| agnostic = False # NMS class-agnostic | |
| multi_label = False # NMS multiple labels per box | |
| classes = None # (optional list) filter by class, i.e. = [0, 15, 16] for COCO persons, cats and dogs | |
| max_det = 1000 # maximum number of detections per image | |
| amp = False # Automatic Mixed Precision (AMP) inference | |
| def __init__(self, model): | |
| super().__init__() | |
| LOGGER.info('Adding AutoShape... ') | |
| copy_attr(self, model, include=('yaml', 'nc', 'hyp', 'names', 'stride', 'abc'), exclude=()) # copy attributes | |
| self.dmb = isinstance(model, DetectMultiBackend) # DetectMultiBackend() instance | |
| self.pt = not self.dmb or model.pt # PyTorch model | |
| self.model = model.eval() | |
| def _apply(self, fn): | |
| # Apply to(), cpu(), cuda(), half() to model tensors that are not parameters or registered buffers | |
| self = super()._apply(fn) | |
| if self.pt: | |
| m = self.model.model.model[-1] if self.dmb else self.model.model[-1] # Detect() | |
| m.stride = fn(m.stride) | |
| m.grid = list(map(fn, m.grid)) | |
| if isinstance(m.anchor_grid, list): | |
| m.anchor_grid = list(map(fn, m.anchor_grid)) | |
| return self | |
| def forward(self, imgs, size=640, augment=False, profile=False): | |
| # Inference from various sources. For height=640, width=1280, RGB images example inputs are: | |
| # file: imgs = 'data/images/zidane.jpg' # str or PosixPath | |
| # URI: = 'https://ultralytics.com/images/zidane.jpg' | |
| # OpenCV: = cv2.imread('image.jpg')[:,:,::-1] # HWC BGR to RGB x(640,1280,3) | |
| # PIL: = Image.open('image.jpg') or ImageGrab.grab() # HWC x(640,1280,3) | |
| # numpy: = np.zeros((640,1280,3)) # HWC | |
| # torch: = torch.zeros(16,3,320,640) # BCHW (scaled to size=640, 0-1 values) | |
| # multiple: = [Image.open('image1.jpg'), Image.open('image2.jpg'), ...] # list of images | |
| t = [time_sync()] | |
| p = next(self.model.parameters()) if self.pt else torch.zeros(1) # for device and type | |
| autocast = self.amp and (p.device.type != 'cpu') # Automatic Mixed Precision (AMP) inference | |
| if isinstance(imgs, torch.Tensor): # torch | |
| with amp.autocast(autocast): | |
| return self.model(imgs.to(p.device).type_as(p), augment, profile) # inference | |
| # Pre-process | |
| n, imgs = (len(imgs), imgs) if isinstance(imgs, list) else (1, [imgs]) # number of images, list of images | |
| shape0, shape1, files = [], [], [] # image and inference shapes, filenames | |
| for i, im in enumerate(imgs): | |
| f = f'image{i}' # filename | |
| if isinstance(im, (str, Path)): # filename or uri | |
| im, f = Image.open(requests.get(im, stream=True).raw if str(im).startswith('http') else im), im | |
| im = np.asarray(exif_transpose(im)) | |
| elif isinstance(im, Image.Image): # PIL Image | |
| im, f = np.asarray(exif_transpose(im)), getattr(im, 'filename', f) or f | |
| files.append(Path(f).with_suffix('.jpg').name) | |
| if im.shape[0] < 5: # image in CHW | |
| im = im.transpose((1, 2, 0)) # reverse dataloader .transpose(2, 0, 1) | |
| im = im[..., :3] if im.ndim == 3 else np.tile(im[..., None], 3) # enforce 3ch input | |
| s = im.shape[:2] # HWC | |
| shape0.append(s) # image shape | |
| g = (size / max(s)) # gain | |
| shape1.append([y * g for y in s]) | |
| imgs[i] = im if im.data.contiguous else np.ascontiguousarray(im) # update | |
| shape1 = [make_divisible(x, self.stride) if self.pt else size for x in np.array(shape1).max(0)] # inf shape | |
| x = [letterbox(im, shape1, auto=False)[0] for im in imgs] # pad | |
| x = np.ascontiguousarray(np.array(x).transpose((0, 3, 1, 2))) # stack and BHWC to BCHW | |
| x = torch.from_numpy(x).to(p.device).type_as(p) / 255 # uint8 to fp16/32 | |
| t.append(time_sync()) | |
| with amp.autocast(autocast): | |
| # Inference | |
| y = self.model(x, augment, profile) # forward | |
| t.append(time_sync()) | |
| # Post-process | |
| y = non_max_suppression(y if self.dmb else y[0], self.conf, self.iou, self.classes, self.agnostic, | |
| self.multi_label, max_det=self.max_det) # NMS | |
| for i in range(n): | |
| scale_coords(shape1, y[i][:, :4], shape0[i]) | |
| t.append(time_sync()) | |
| return Detections(imgs, y, files, t, self.names, x.shape) | |
| class Detections: | |
| # YOLOv5 detections class for inference results | |
| def __init__(self, imgs, pred, files, times=(0, 0, 0, 0), names=None, shape=None): | |
| super().__init__() | |
| d = pred[0].device # device | |
| gn = [torch.tensor([*(im.shape[i] for i in [1, 0, 1, 0]), 1, 1], device=d) for im in imgs] # normalizations | |
| self.imgs = imgs # list of images as numpy arrays | |
| self.pred = pred # list of tensors pred[0] = (xyxy, conf, cls) | |
| self.names = names # class names | |
| self.files = files # image filenames | |
| self.times = times # profiling times | |
| self.xyxy = pred # xyxy pixels | |
| self.xywh = [xyxy2xywh(x) for x in pred] # xywh pixels | |
| self.xyxyn = [x / g for x, g in zip(self.xyxy, gn)] # xyxy normalized | |
| self.xywhn = [x / g for x, g in zip(self.xywh, gn)] # xywh normalized | |
| self.n = len(self.pred) # number of images (batch size) | |
| self.t = tuple((times[i + 1] - times[i]) * 1000 / self.n for i in range(3)) # timestamps (ms) | |
| self.s = shape # inference BCHW shape | |
| def display(self, pprint=False, show=False, save=False, crop=False, render=False, save_dir=Path('')): | |
| crops = [] | |
| for i, (im, pred) in enumerate(zip(self.imgs, self.pred)): | |
| s = f'image {i + 1}/{len(self.pred)}: {im.shape[0]}x{im.shape[1]} ' # string | |
| if pred.shape[0]: | |
| for c in pred[:, -1].unique(): | |
| n = (pred[:, -1] == c).sum() # detections per class | |
| s += f"{n} {self.names[int(c)]}{'s' * (n > 1)}, " # add to string | |
| if show or save or render or crop: | |
| annotator = Annotator(im, example=str(self.names)) | |
| for *box, conf, cls in reversed(pred): # xyxy, confidence, class | |
| label = f'{self.names[int(cls)]} {conf:.2f}' | |
| if crop: | |
| file = save_dir / 'crops' / self.names[int(cls)] / self.files[i] if save else None | |
| crops.append({'box': box, 'conf': conf, 'cls': cls, 'label': label, | |
| 'im': save_one_box(box, im, file=file, save=save)}) | |
| else: # all others | |
| annotator.box_label(box, label, color=colors(cls)) | |
| im = annotator.im | |
| else: | |
| s += '(no detections)' | |
| im = Image.fromarray(im.astype(np.uint8)) if isinstance(im, np.ndarray) else im # from np | |
| if pprint: | |
| LOGGER.info(s.rstrip(', ')) | |
| if show: | |
| im.show(self.files[i]) # show | |
| if save: | |
| f = self.files[i] | |
| im.save(save_dir / f) # save | |
| if i == self.n - 1: | |
| LOGGER.info(f"Saved {self.n} image{'s' * (self.n > 1)} to {colorstr('bold', save_dir)}") | |
| if render: | |
| self.imgs[i] = np.asarray(im) | |
| if crop: | |
| if save: | |
| LOGGER.info(f'Saved results to {save_dir}\n') | |
| return crops | |
| def print(self): | |
| self.display(pprint=True) # print results | |
| LOGGER.info(f'Speed: %.1fms pre-process, %.1fms inference, %.1fms NMS per image at shape {tuple(self.s)}' % | |
| self.t) | |
| def show(self): | |
| self.display(show=True) # show results | |
| def save(self, save_dir='runs/detect/exp'): | |
| save_dir = increment_path(save_dir, exist_ok=save_dir != 'runs/detect/exp', mkdir=True) # increment save_dir | |
| self.display(save=True, save_dir=save_dir) # save results | |
| def crop(self, save=True, save_dir='runs/detect/exp'): | |
| save_dir = increment_path(save_dir, exist_ok=save_dir != 'runs/detect/exp', mkdir=True) if save else None | |
| return self.display(crop=True, save=save, save_dir=save_dir) # crop results | |
| def render(self): | |
| self.display(render=True) # render results | |
| return self.imgs | |
| def pandas(self): | |
| # return detections as pandas DataFrames, i.e. print(results.pandas().xyxy[0]) | |
| new = copy(self) # return copy | |
| ca = 'xmin', 'ymin', 'xmax', 'ymax', 'confidence', 'class', 'name' # xyxy columns | |
| cb = 'xcenter', 'ycenter', 'width', 'height', 'confidence', 'class', 'name' # xywh columns | |
| for k, c in zip(['xyxy', 'xyxyn', 'xywh', 'xywhn'], [ca, ca, cb, cb]): | |
| a = [[x[:5] + [int(x[5]), self.names[int(x[5])]] for x in x.tolist()] for x in getattr(self, k)] # update | |
| setattr(new, k, [pd.DataFrame(x, columns=c) for x in a]) | |
| return new | |
| def tolist(self): | |
| # return a list of Detections objects, i.e. 'for result in results.tolist():' | |
| r = range(self.n) # iterable | |
| x = [Detections([self.imgs[i]], [self.pred[i]], [self.files[i]], self.times, self.names, self.s) for i in r] | |
| # for d in x: | |
| # for k in ['imgs', 'pred', 'xyxy', 'xyxyn', 'xywh', 'xywhn']: | |
| # setattr(d, k, getattr(d, k)[0]) # pop out of list | |
| return x | |
| def __len__(self): | |
| return self.n | |
| class Classify(nn.Module): | |
| # Classification head, i.e. x(b,c1,20,20) to x(b,c2) | |
| def __init__(self, c1, c2, k=1, s=1, p=None, g=1): # ch_in, ch_out, kernel, stride, padding, groups | |
| super().__init__() | |
| self.aap = nn.AdaptiveAvgPool2d(1) # to x(b,c1,1,1) | |
| self.conv = nn.Conv2d(c1, c2, k, s, autopad(k, p), groups=g) # to x(b,c2,1,1) | |
| self.flat = nn.Flatten() | |
| def forward(self, x): | |
| z = torch.cat([self.aap(y) for y in (x if isinstance(x, list) else [x])], 1) # cat if list | |
| return self.flat(self.conv(z)) # flatten to x(b,c2) | |