1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
274
275
276
277
278
279
280
281
282
283
284
285
286
287
288
289
290
291
292
293
294
295
296
297
298
299
300
301
302
303
304
305
306
307
308
309
310
311
312
313
314
315
316
317
318
319
320
321
322
323
324
325
326
327
328
329
330
331
332
333
334
335
336
337
338
339
340
341
342
343
344
345
346
347
348
349
350
351
352
353
354
355
356
357
358
359
360
361
362
363
364
365
366
367
368
369
370
371
372
373
374
375
376
377
378
379
380
381
382
383
384
385
386
387
388
389
390
391
392
393
394
395
396
397
398
399
400
401
402
403
404
405
406
407
408
409
410
411
412
413
414
415
416
417
418
419
420
421
422
423
424
425
426
427
428
429
430
431
432
433
434
435
436
437
438
439
440
441
442
443
444
445
446
447
448
449
450
451
452
453
454
455
456
457
458
459
460
|
- # YOLOv5 🚀 by Ultralytics, GPL-3.0 license
- """
- Common modules
- """
- import logging
- import math
- import warnings
- from copy import copy
- from pathlib import Path
- 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.datasets import exif_transpose, letterbox
- from utils.general import colorstr, increment_path, is_ascii, make_divisible, non_max_suppression, save_one_box, \
- scale_coords, xyxy2xywh
- from utils.plots import Annotator, colors
- from utils.torch_utils import time_sync
- LOGGER = logging.getLogger(__name__)
- 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).unsqueeze(0).transpose(0, 3).squeeze(3)
- return self.tr(p + self.linear(p)).unsqueeze(3).transpose(0, 3).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.LeakyReLU(0.1, inplace=True)
- 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), dim=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)), dim=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 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
- classes = None # (optional list) filter by class
- multi_label = False # NMS multiple labels per box
- max_det = 1000 # maximum number of detections per image
- def __init__(self, model):
- super().__init__()
- self.model = model.eval()
- def autoshape(self):
- LOGGER.info('AutoShape already enabled, skipping... ') # model already converted to model.autoshape()
- return self
- @torch.no_grad()
- 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()) # for device and type
- if isinstance(imgs, torch.Tensor): # torch
- with amp.autocast(enabled=p.device.type != 'cpu'):
- 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, int(self.stride.max())) for x in np.stack(shape1, 0).max(0)] # inference shape
- x = [letterbox(im, new_shape=shape1, auto=False)[0] for im in imgs] # pad
- x = np.stack(x, 0) if n > 1 else x[0][None] # stack
- x = np.ascontiguousarray(x.transpose((0, 3, 1, 2))) # 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(enabled=p.device.type != 'cpu'):
- # Inference
- y = self.model(x, augment, profile)[0] # forward
- t.append(time_sync())
- # Post-process
- y = non_max_suppression(y, self.conf, iou_thres=self.iou, classes=self.classes,
- multi_label=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=None, 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.ascii = is_ascii(names) # names are ascii (use PIL for UTF-8)
- self.files = files # image filenames
- 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)):
- str = f'image {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() # detections per class
- str += f"{n} {self.names[int(c)]}{'s' * (n > 1)}, " # add to string
- if show or save or render or crop:
- annotator = Annotator(im, pil=not self.ascii)
- 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:
- str += '(no detections)'
- im = Image.fromarray(im.astype(np.uint8)) if isinstance(im, np.ndarray) else im # from np
- if pprint:
- LOGGER.info(str.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():'
- x = [Detections([self.imgs[i]], [self.pred[i]], self.names, self.s) for i in range(self.n)]
- 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)
|