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- """
- Common modules
- """
- import ast
- import contextlib
- import json
- import math
- import platform
- import warnings
- import zipfile
- from collections import OrderedDict, namedtuple
- from copy import copy
- from pathlib import Path
- from urllib.parse import urlparse
- import cv2
- import numpy as np
- import pandas as pd
- import requests
- import torch
- import torch.nn as nn
- from PIL import Image
- from torch.cuda import amp
- from utils import TryExcept
- from utils.dataloaders import exif_transpose, letterbox
- from utils.general import (LOGGER, ROOT, Profile, check_requirements, check_suffix, check_version, colorstr,
- increment_path, is_jupyter, make_divisible, non_max_suppression, scale_boxes, xywh2xyxy,
- xyxy2xywh, yaml_load)
- from utils.plots import Annotator, colors, save_one_box
- from utils.torch_utils import copy_attr, smart_inference_mode
- def autopad(k, p=None, d=1):
-
- if d > 1:
- k = d * (k - 1) + 1 if isinstance(k, int) else [d * (x - 1) + 1 for x in k]
- if p is None:
- p = k // 2 if isinstance(k, int) else [x // 2 for x in k]
- return p
- class Conv(nn.Module):
-
- default_act = nn.SiLU()
- def __init__(self, c1, c2, k=1, s=1, p=None, g=1, d=1, act=True):
- super().__init__()
- self.conv = nn.Conv2d(c1, c2, k, s, autopad(k, p, d), groups=g, dilation=d, bias=False)
- self.bn = nn.BatchNorm2d(c2)
- self.act = self.default_act 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):
-
- def __init__(self, c1, c2, k=1, s=1, d=1, act=True):
- super().__init__(c1, c2, k, s, g=math.gcd(c1, c2), d=d, act=act)
- class DWConvTranspose2d(nn.ConvTranspose2d):
-
- def __init__(self, c1, c2, k=1, s=1, p1=0, p2=0):
- super().__init__(c1, c2, k, s, p1, p2, groups=math.gcd(c1, c2))
- class TransformerLayer(nn.Module):
-
- 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):
-
- 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)
- 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):
-
- def __init__(self, c1, c2, shortcut=True, g=1, e=0.5):
- super().__init__()
- c_ = int(c2 * e)
- 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):
-
- def __init__(self, c1, c2, n=1, shortcut=True, g=1, e=0.5):
- super().__init__()
- c_ = int(c2 * e)
- 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_)
- 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 CrossConv(nn.Module):
-
- def __init__(self, c1, c2, k=3, s=1, g=1, e=1.0, shortcut=False):
-
- super().__init__()
- c_ = int(c2 * e)
- self.cv1 = Conv(c1, c_, (1, k), (1, s))
- self.cv2 = Conv(c_, c2, (k, 1), (s, 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 C3(nn.Module):
-
- def __init__(self, c1, c2, n=1, shortcut=True, g=1, e=0.5):
- super().__init__()
- c_ = int(c2 * e)
- self.cv1 = Conv(c1, c_, 1, 1)
- self.cv2 = Conv(c1, c_, 1, 1)
- self.cv3 = Conv(2 * c_, c2, 1)
- self.m = nn.Sequential(*(Bottleneck(c_, c_, shortcut, g, e=1.0) for _ in range(n)))
- def forward(self, x):
- return self.cv3(torch.cat((self.m(self.cv1(x)), self.cv2(x)), 1))
- class C3x(C3):
-
- 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 = nn.Sequential(*(CrossConv(c_, c_, 3, 1, g, 1.0, shortcut) for _ in range(n)))
- class C3TR(C3):
-
- 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):
-
- 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):
-
- 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 = nn.Sequential(*(GhostBottleneck(c_, c_) for _ in range(n)))
- class SPP(nn.Module):
-
- def __init__(self, c1, c2, k=(5, 9, 13)):
- super().__init__()
- c_ = c1 // 2
- 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')
- return self.cv2(torch.cat([x] + [m(x) for m in self.m], 1))
- class SPPF(nn.Module):
-
- def __init__(self, c1, c2, k=5):
- super().__init__()
- c_ = c1 // 2
- 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')
- y1 = self.m(x)
- y2 = self.m(y1)
- return self.cv2(torch.cat((x, y1, y2, self.m(y2)), 1))
- class Focus(nn.Module):
-
- def __init__(self, c1, c2, k=1, s=1, p=None, g=1, act=True):
- super().__init__()
- self.conv = Conv(c1 * 4, c2, k, s, p, g, act=act)
-
- def forward(self, x):
- return self.conv(torch.cat((x[..., ::2, ::2], x[..., 1::2, ::2], x[..., ::2, 1::2], x[..., 1::2, 1::2]), 1))
-
- class GhostConv(nn.Module):
-
- def __init__(self, c1, c2, k=1, s=1, g=1, act=True):
- super().__init__()
- c_ = c2 // 2
- self.cv1 = Conv(c1, c_, k, s, None, g, act=act)
- self.cv2 = Conv(c_, c_, 5, 1, None, c_, act=act)
- def forward(self, x):
- y = self.cv1(x)
- return torch.cat((y, self.cv2(y)), 1)
- class GhostBottleneck(nn.Module):
-
- def __init__(self, c1, c2, k=3, s=1):
- super().__init__()
- c_ = c2 // 2
- self.conv = nn.Sequential(
- GhostConv(c1, c_, 1, 1),
- DWConv(c_, c_, k, s, act=False) if s == 2 else nn.Identity(),
- GhostConv(c_, c2, 1, 1, act=False))
- 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):
-
- def __init__(self, gain=2):
- super().__init__()
- self.gain = gain
- def forward(self, x):
- b, c, h, w = x.size()
- s = self.gain
- x = x.view(b, c, h // s, s, w // s, s)
- x = x.permute(0, 3, 5, 1, 2, 4).contiguous()
- return x.view(b, c * s * s, h // s, w // s)
- class Expand(nn.Module):
-
- def __init__(self, gain=2):
- super().__init__()
- self.gain = gain
- def forward(self, x):
- b, c, h, w = x.size()
- s = self.gain
- x = x.view(b, s, s, c // s ** 2, h, w)
- x = x.permute(0, 3, 4, 1, 5, 2).contiguous()
- return x.view(b, c // s ** 2, h * s, w * s)
- class Concat(nn.Module):
-
- def __init__(self, dimension=1):
- super().__init__()
- self.d = dimension
- def forward(self, x):
- return torch.cat(x, self.d)
- class DetectMultiBackend(nn.Module):
-
- def __init__(self, weights='yolov5s.pt', device=torch.device('cpu'), dnn=False, data=None, fp16=False, fuse=True):
-
-
-
-
-
-
-
-
-
-
-
-
-
- from models.experimental import attempt_download, attempt_load
- super().__init__()
- w = str(weights[0] if isinstance(weights, list) else weights)
- pt, jit, onnx, xml, engine, coreml, saved_model, pb, tflite, edgetpu, tfjs, paddle, triton = self._model_type(w)
- fp16 &= pt or jit or onnx or engine or triton
- nhwc = coreml or saved_model or pb or tflite or edgetpu
- stride = 32
- cuda = torch.cuda.is_available() and device.type != 'cpu'
- if not (pt or triton):
- w = attempt_download(w)
- if pt:
- model = attempt_load(weights if isinstance(weights, list) else w, device=device, inplace=True, fuse=fuse)
- stride = max(int(model.stride.max()), 32)
- names = model.module.names if hasattr(model, 'module') else model.names
- model.half() if fp16 else model.float()
- self.model = model
- elif jit:
- LOGGER.info(f'Loading {w} for TorchScript inference...')
- extra_files = {'config.txt': ''}
- model = torch.jit.load(w, _extra_files=extra_files, map_location=device)
- model.half() if fp16 else model.float()
- if extra_files['config.txt']:
- d = json.loads(extra_files['config.txt'],
- object_hook=lambda d: {
- int(k) if k.isdigit() else k: v
- for k, v in d.items()})
- stride, names = int(d['stride']), d['names']
- elif 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:
- LOGGER.info(f'Loading {w} for ONNX Runtime inference...')
- check_requirements(('onnx', 'onnxruntime-gpu' if cuda else 'onnxruntime'))
- import onnxruntime
- providers = ['CUDAExecutionProvider', 'CPUExecutionProvider'] if cuda else ['CPUExecutionProvider']
- session = onnxruntime.InferenceSession(w, providers=providers)
- output_names = [x.name for x in session.get_outputs()]
- meta = session.get_modelmeta().custom_metadata_map
- if 'stride' in meta:
- stride, names = int(meta['stride']), eval(meta['names'])
- elif xml:
- LOGGER.info(f'Loading {w} for OpenVINO inference...')
- check_requirements('openvino')
- from openvino.runtime import Core, Layout, get_batch
- ie = Core()
- if not Path(w).is_file():
- w = next(Path(w).glob('*.xml'))
- network = ie.read_model(model=w, weights=Path(w).with_suffix('.bin'))
- if network.get_parameters()[0].get_layout().empty:
- network.get_parameters()[0].set_layout(Layout('NCHW'))
- batch_dim = get_batch(network)
- if batch_dim.is_static:
- batch_size = batch_dim.get_length()
- executable_network = ie.compile_model(network, device_name='CPU')
- stride, names = self._load_metadata(Path(w).with_suffix('.yaml'))
- elif engine:
- LOGGER.info(f'Loading {w} for TensorRT inference...')
- import tensorrt as trt
- check_version(trt.__version__, '7.0.0', hard=True)
- if device.type == 'cpu':
- device = torch.device('cuda: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())
- context = model.create_execution_context()
- bindings = OrderedDict()
- output_names = []
- fp16 = False
- dynamic = False
- for i in range(model.num_bindings):
- name = model.get_binding_name(i)
- dtype = trt.nptype(model.get_binding_dtype(i))
- if model.binding_is_input(i):
- if -1 in tuple(model.get_binding_shape(i)):
- dynamic = True
- context.set_binding_shape(i, tuple(model.get_profile_shape(0, i)[2]))
- if dtype == np.float16:
- fp16 = True
- else:
- output_names.append(name)
- shape = tuple(context.get_binding_shape(i))
- im = torch.from_numpy(np.empty(shape, dtype=dtype)).to(device)
- bindings[name] = Binding(name, dtype, shape, im, int(im.data_ptr()))
- binding_addrs = OrderedDict((n, d.ptr) for n, d in bindings.items())
- batch_size = bindings['images'].shape[0]
- elif coreml:
- LOGGER.info(f'Loading {w} for CoreML inference...')
- import coremltools as ct
- model = ct.models.MLModel(w)
- elif saved_model:
- LOGGER.info(f'Loading {w} for TensorFlow SavedModel inference...')
- import tensorflow as tf
- keras = False
- model = tf.keras.models.load_model(w) if keras else tf.saved_model.load(w)
- elif pb:
- 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=''), [])
- ge = x.graph.as_graph_element
- return x.prune(tf.nest.map_structure(ge, inputs), tf.nest.map_structure(ge, outputs))
- def gd_outputs(gd):
- name_list, input_list = [], []
- for node in gd.node:
- name_list.append(node.name)
- input_list.extend(node.input)
- return sorted(f'{x}:0' for x in list(set(name_list) - set(input_list)) if not x.startswith('NoOp'))
- gd = tf.Graph().as_graph_def()
- with open(w, 'rb') as f:
- gd.ParseFromString(f.read())
- frozen_func = wrap_frozen_graph(gd, inputs='x:0', outputs=gd_outputs(gd))
- elif tflite or edgetpu:
- try:
- 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:
- 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:
- LOGGER.info(f'Loading {w} for TensorFlow Lite inference...')
- interpreter = Interpreter(model_path=w)
- interpreter.allocate_tensors()
- input_details = interpreter.get_input_details()
- output_details = interpreter.get_output_details()
-
- with contextlib.suppress(zipfile.BadZipFile):
- with zipfile.ZipFile(w, 'r') as model:
- meta_file = model.namelist()[0]
- meta = ast.literal_eval(model.read(meta_file).decode('utf-8'))
- stride, names = int(meta['stride']), meta['names']
- elif tfjs:
- raise NotImplementedError('ERROR: YOLOv5 TF.js inference is not supported')
- elif paddle:
- LOGGER.info(f'Loading {w} for PaddlePaddle inference...')
- check_requirements('paddlepaddle-gpu' if cuda else 'paddlepaddle')
- import paddle.inference as pdi
- if not Path(w).is_file():
- w = next(Path(w).rglob('*.pdmodel'))
- weights = Path(w).with_suffix('.pdiparams')
- config = pdi.Config(str(w), str(weights))
- if cuda:
- config.enable_use_gpu(memory_pool_init_size_mb=2048, device_id=0)
- predictor = pdi.create_predictor(config)
- input_handle = predictor.get_input_handle(predictor.get_input_names()[0])
- output_names = predictor.get_output_names()
- elif triton:
- LOGGER.info(f'Using {w} as Triton Inference Server...')
- check_requirements('tritonclient[all]')
- from utils.triton import TritonRemoteModel
- model = TritonRemoteModel(url=w)
- nhwc = model.runtime.startswith('tensorflow')
- else:
- raise NotImplementedError(f'ERROR: {w} is not a supported format')
-
- if 'names' not in locals():
- names = yaml_load(data)['names'] if data else {i: f'class{i}' for i in range(999)}
- if names[0] == 'n01440764' and len(names) == 1000:
- names = yaml_load(ROOT / 'data/ImageNet.yaml')['names']
- self.__dict__.update(locals())
- def forward(self, im, augment=False, visualize=False):
-
- b, ch, h, w = im.shape
- if self.fp16 and im.dtype != torch.float16:
- im = im.half()
- if self.nhwc:
- im = im.permute(0, 2, 3, 1)
- if self.pt:
- y = self.model(im, augment=augment, visualize=visualize) if augment or visualize else self.model(im)
- elif self.jit:
- y = self.model(im)
- elif self.dnn:
- im = im.cpu().numpy()
- self.net.setInput(im)
- y = self.net.forward()
- elif self.onnx:
- im = im.cpu().numpy()
- y = self.session.run(self.output_names, {self.session.get_inputs()[0].name: im})
- elif self.xml:
- im = im.cpu().numpy()
- y = list(self.executable_network([im]).values())
- elif self.engine:
- if self.dynamic and im.shape != self.bindings['images'].shape:
- i = self.model.get_binding_index('images')
- self.context.set_binding_shape(i, im.shape)
- self.bindings['images'] = self.bindings['images']._replace(shape=im.shape)
- for name in self.output_names:
- i = self.model.get_binding_index(name)
- self.bindings[name].data.resize_(tuple(self.context.get_binding_shape(i)))
- s = self.bindings['images'].shape
- assert im.shape == s, f"input size {im.shape} {'>' if self.dynamic else 'not equal to'} max model size {s}"
- self.binding_addrs['images'] = int(im.data_ptr())
- self.context.execute_v2(list(self.binding_addrs.values()))
- y = [self.bindings[x].data for x in sorted(self.output_names)]
- elif self.coreml:
- im = im.cpu().numpy()
- im = Image.fromarray((im[0] * 255).astype('uint8'))
-
- y = self.model.predict({'image': im})
- if 'confidence' in y:
- box = xywh2xyxy(y['coordinates'] * [[w, h, w, h]])
- 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:
- y = list(reversed(y.values()))
- elif self.paddle:
- im = im.cpu().numpy().astype(np.float32)
- self.input_handle.copy_from_cpu(im)
- self.predictor.run()
- y = [self.predictor.get_output_handle(x).copy_to_cpu() for x in self.output_names]
- elif self.triton:
- y = self.model(im)
- else:
- im = im.cpu().numpy()
- if self.saved_model:
- y = self.model(im, training=False) if self.keras else self.model(im)
- elif self.pb:
- y = self.frozen_func(x=self.tf.constant(im))
- else:
- input = self.input_details[0]
- int8 = input['dtype'] == np.uint8
- if int8:
- scale, zero_point = input['quantization']
- im = (im / scale + zero_point).astype(np.uint8)
- self.interpreter.set_tensor(input['index'], im)
- self.interpreter.invoke()
- y = []
- for output in self.output_details:
- x = self.interpreter.get_tensor(output['index'])
- if int8:
- scale, zero_point = output['quantization']
- x = (x.astype(np.float32) - zero_point) * scale
- y.append(x)
- y = [x if isinstance(x, np.ndarray) else x.numpy() for x in y]
- y[0][..., :4] *= [w, h, w, h]
- if isinstance(y, (list, tuple)):
- return self.from_numpy(y[0]) if len(y) == 1 else [self.from_numpy(x) for x in y]
- else:
- return self.from_numpy(y)
- def from_numpy(self, x):
- return torch.from_numpy(x).to(self.device) if isinstance(x, np.ndarray) else x
- def warmup(self, imgsz=(1, 3, 640, 640)):
-
- warmup_types = self.pt, self.jit, self.onnx, self.engine, self.saved_model, self.pb, self.triton
- if any(warmup_types) and (self.device.type != 'cpu' or self.triton):
- im = torch.empty(*imgsz, dtype=torch.half if self.fp16 else torch.float, device=self.device)
- for _ in range(2 if self.jit else 1):
- self.forward(im)
- @staticmethod
- def _model_type(p='path/to/model.pt'):
-
-
- from export import export_formats
- from utils.downloads import is_url
- sf = list(export_formats().Suffix)
- if not is_url(p, check=False):
- check_suffix(p, sf)
- url = urlparse(p)
- types = [s in Path(p).name for s in sf]
- types[8] &= not types[9]
- triton = not any(types) and all([any(s in url.scheme for s in ['http', 'grpc']), url.netloc])
- return types + [triton]
- @staticmethod
- def _load_metadata(f=Path('path/to/meta.yaml')):
-
- if f.exists():
- d = yaml_load(f)
- return d['stride'], d['names']
- return None, None
- class AutoShape(nn.Module):
-
- conf = 0.25
- iou = 0.45
- agnostic = False
- multi_label = False
- classes = None
- max_det = 1000
- amp = False
- def __init__(self, model, verbose=True):
- super().__init__()
- if verbose:
- LOGGER.info('Adding AutoShape... ')
- copy_attr(self, model, include=('yaml', 'nc', 'hyp', 'names', 'stride', 'abc'), exclude=())
- self.dmb = isinstance(model, DetectMultiBackend)
- self.pt = not self.dmb or model.pt
- self.model = model.eval()
- if self.pt:
- m = self.model.model.model[-1] if self.dmb else self.model.model[-1]
- m.inplace = False
- m.export = True
- def _apply(self, fn):
-
- self = super()._apply(fn)
- if self.pt:
- m = self.model.model.model[-1] if self.dmb else self.model.model[-1]
- 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
- @smart_inference_mode()
- def forward(self, ims, size=640, augment=False, profile=False):
-
-
-
-
-
-
-
-
- dt = (Profile(), Profile(), Profile())
- with dt[0]:
- if isinstance(size, int):
- size = (size, size)
- p = next(self.model.parameters()) if self.pt else torch.empty(1, device=self.model.device)
- autocast = self.amp and (p.device.type != 'cpu')
- if isinstance(ims, torch.Tensor):
- with amp.autocast(autocast):
- return self.model(ims.to(p.device).type_as(p), augment=augment)
-
- n, ims = (len(ims), list(ims)) if isinstance(ims, (list, tuple)) else (1, [ims])
- shape0, shape1, files = [], [], []
- for i, im in enumerate(ims):
- f = f'image{i}'
- if isinstance(im, (str, Path)):
- 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):
- 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:
- im = im.transpose((1, 2, 0))
- im = im[..., :3] if im.ndim == 3 else cv2.cvtColor(im, cv2.COLOR_GRAY2BGR)
- s = im.shape[:2]
- shape0.append(s)
- g = max(size) / max(s)
- shape1.append([int(y * g) for y in s])
- ims[i] = im if im.data.contiguous else np.ascontiguousarray(im)
- shape1 = [make_divisible(x, self.stride) for x in np.array(shape1).max(0)]
- x = [letterbox(im, shape1, auto=False)[0] for im in ims]
- x = np.ascontiguousarray(np.array(x).transpose((0, 3, 1, 2)))
- x = torch.from_numpy(x).to(p.device).type_as(p) / 255
- with amp.autocast(autocast):
-
- with dt[1]:
- y = self.model(x, augment=augment)
-
- with dt[2]:
- 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)
- for i in range(n):
- scale_boxes(shape1, y[i][:, :4], shape0[i])
- return Detections(ims, y, files, dt, self.names, x.shape)
- class Detections:
-
- def __init__(self, ims, pred, files, times=(0, 0, 0), names=None, shape=None):
- super().__init__()
- d = pred[0].device
- gn = [torch.tensor([*(im.shape[i] for i in [1, 0, 1, 0]), 1, 1], device=d) for im in ims]
- self.ims = ims
- self.pred = pred
- self.names = names
- self.files = files
- self.times = times
- self.xyxy = pred
- self.xywh = [xyxy2xywh(x) for x in pred]
- self.xyxyn = [x / g for x, g in zip(self.xyxy, gn)]
- self.xywhn = [x / g for x, g in zip(self.xywh, gn)]
- self.n = len(self.pred)
- self.t = tuple(x.t / self.n * 1E3 for x in times)
- self.s = tuple(shape)
- def _run(self, pprint=False, show=False, save=False, crop=False, render=False, labels=True, save_dir=Path('')):
- s, crops = '', []
- for i, (im, pred) in enumerate(zip(self.ims, self.pred)):
- s += f'\nimage {i + 1}/{len(self.pred)}: {im.shape[0]}x{im.shape[1]} '
- if pred.shape[0]:
- for c in pred[:, -1].unique():
- n = (pred[:, -1] == c).sum()
- s += f"{n} {self.names[int(c)]}{'s' * (n > 1)}, "
- s = s.rstrip(', ')
- if show or save or render or crop:
- annotator = Annotator(im, example=str(self.names))
- for *box, conf, cls in reversed(pred):
- 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:
- annotator.box_label(box, label if labels else '', color=colors(cls))
- im = annotator.im
- else:
- s += '(no detections)'
- im = Image.fromarray(im.astype(np.uint8)) if isinstance(im, np.ndarray) else im
- if show:
- if is_jupyter():
- from IPython.display import display
- display(im)
- else:
- im.show(self.files[i])
- if save:
- f = self.files[i]
- im.save(save_dir / f)
- if i == self.n - 1:
- LOGGER.info(f"Saved {self.n} image{'s' * (self.n > 1)} to {colorstr('bold', save_dir)}")
- if render:
- self.ims[i] = np.asarray(im)
- if pprint:
- s = s.lstrip('\n')
- return f'{s}\nSpeed: %.1fms pre-process, %.1fms inference, %.1fms NMS per image at shape {self.s}' % self.t
- if crop:
- if save:
- LOGGER.info(f'Saved results to {save_dir}\n')
- return crops
- @TryExcept('Showing images is not supported in this environment')
- def show(self, labels=True):
- self._run(show=True, labels=labels)
- def save(self, labels=True, save_dir='runs/detect/exp', exist_ok=False):
- save_dir = increment_path(save_dir, exist_ok, mkdir=True)
- self._run(save=True, labels=labels, save_dir=save_dir)
- def crop(self, save=True, save_dir='runs/detect/exp', exist_ok=False):
- save_dir = increment_path(save_dir, exist_ok, mkdir=True) if save else None
- return self._run(crop=True, save=save, save_dir=save_dir)
- def render(self, labels=True):
- self._run(render=True, labels=labels)
- return self.ims
- def pandas(self):
-
- new = copy(self)
- ca = 'xmin', 'ymin', 'xmax', 'ymax', 'confidence', 'class', 'name'
- cb = 'xcenter', 'ycenter', 'width', 'height', 'confidence', 'class', 'name'
- 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)]
- setattr(new, k, [pd.DataFrame(x, columns=c) for x in a])
- return new
- def tolist(self):
-
- r = range(self.n)
- x = [Detections([self.ims[i]], [self.pred[i]], [self.files[i]], self.times, self.names, self.s) for i in r]
-
-
-
- return x
- def print(self):
- LOGGER.info(self.__str__())
- def __len__(self):
- return self.n
- def __str__(self):
- return self._run(pprint=True)
- def __repr__(self):
- return f'YOLOv5 {self.__class__} instance\n' + self.__str__()
- class Proto(nn.Module):
-
- def __init__(self, c1, c_=256, c2=32):
- super().__init__()
- self.cv1 = Conv(c1, c_, k=3)
- self.upsample = nn.Upsample(scale_factor=2, mode='nearest')
- self.cv2 = Conv(c_, c_, k=3)
- self.cv3 = Conv(c_, c2)
- def forward(self, x):
- return self.cv3(self.cv2(self.upsample(self.cv1(x))))
- class Classify(nn.Module):
-
- def __init__(self,
- c1,
- c2,
- k=1,
- s=1,
- p=None,
- g=1,
- dropout_p=0.0):
- super().__init__()
- c_ = 1280
- self.conv = Conv(c1, c_, k, s, autopad(k, p), g)
- self.pool = nn.AdaptiveAvgPool2d(1)
- self.drop = nn.Dropout(p=dropout_p, inplace=True)
- self.linear = nn.Linear(c_, c2)
- def forward(self, x):
- if isinstance(x, list):
- x = torch.cat(x, 1)
- return self.linear(self.drop(self.pool(self.conv(x)).flatten(1)))
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