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| # This file contains modules common to various models | |
| import math | |
| import numpy as np | |
| import requests | |
| import torch | |
| import torch.nn as nn | |
| from PIL import Image, ImageDraw | |
| from utils.datasets import letterbox | |
| from utils.general import non_max_suppression, make_divisible, scale_coords, xyxy2xywh | |
| from utils.plots import color_list | |
| 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 | |
| def channel_shuffle(x, groups): | |
| batchsize, num_channels, height, width = x.data.size() | |
| channels_per_group = num_channels // groups | |
| # reshape | |
| x = x.view(batchsize, groups, channels_per_group, height, width) | |
| x = torch.transpose(x, 1, 2).contiguous() | |
| # flatten | |
| x = x.view(batchsize, -1, height, width) | |
| return x | |
| def DWConv(c1, c2, k=1, s=1, act=True): | |
| # Depthwise convolution | |
| return Conv(c1, c2, k, s, g=math.gcd(c1, c2), act=act) | |
| 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(Conv, self).__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()) | |
| #self.act = self.act = nn.LeakyReLU(0.1, inplace=True) 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 fuseforward(self, x): | |
| return self.act(self.conv(x)) | |
| class StemBlock(nn.Module): | |
| def __init__(self, c1, c2, k=3, s=2, p=None, g=1, act=True): | |
| super(StemBlock, self).__init__() | |
| self.stem_1 = Conv(c1, c2, k, s, p, g, act) | |
| self.stem_2a = Conv(c2, c2 // 2, 1, 1, 0) | |
| self.stem_2b = Conv(c2 // 2, c2, 3, 2, 1) | |
| self.stem_2p = nn.MaxPool2d(kernel_size=2,stride=2,ceil_mode=True) | |
| self.stem_3 = Conv(c2 * 2, c2, 1, 1, 0) | |
| def forward(self, x): | |
| stem_1_out = self.stem_1(x) | |
| stem_2a_out = self.stem_2a(stem_1_out) | |
| stem_2b_out = self.stem_2b(stem_2a_out) | |
| stem_2p_out = self.stem_2p(stem_1_out) | |
| out = self.stem_3(torch.cat((stem_2b_out,stem_2p_out),1)) | |
| return out | |
| 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(Bottleneck, self).__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(BottleneckCSP, self).__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(C3, self).__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)]) | |
| def forward(self, x): | |
| return self.cv3(torch.cat((self.m(self.cv1(x)), self.cv2(x)), dim=1)) | |
| class ShuffleV2Block(nn.Module): | |
| def __init__(self, inp, oup, stride): | |
| super(ShuffleV2Block, self).__init__() | |
| if not (1 <= stride <= 3): | |
| raise ValueError('illegal stride value') | |
| self.stride = stride | |
| branch_features = oup // 2 | |
| assert (self.stride != 1) or (inp == branch_features << 1) | |
| if self.stride > 1: | |
| self.branch1 = nn.Sequential( | |
| self.depthwise_conv(inp, inp, kernel_size=3, stride=self.stride, padding=1), | |
| nn.BatchNorm2d(inp), | |
| nn.Conv2d(inp, branch_features, kernel_size=1, stride=1, padding=0, bias=False), | |
| nn.BatchNorm2d(branch_features), | |
| nn.SiLU(), | |
| ) | |
| else: | |
| self.branch1 = nn.Sequential() | |
| self.branch2 = nn.Sequential( | |
| nn.Conv2d(inp if (self.stride > 1) else branch_features, branch_features, kernel_size=1, stride=1, padding=0, bias=False), | |
| nn.BatchNorm2d(branch_features), | |
| nn.SiLU(), | |
| self.depthwise_conv(branch_features, branch_features, kernel_size=3, stride=self.stride, padding=1), | |
| nn.BatchNorm2d(branch_features), | |
| nn.Conv2d(branch_features, branch_features, kernel_size=1, stride=1, padding=0, bias=False), | |
| nn.BatchNorm2d(branch_features), | |
| nn.SiLU(), | |
| ) | |
| def depthwise_conv(i, o, kernel_size, stride=1, padding=0, bias=False): | |
| return nn.Conv2d(i, o, kernel_size, stride, padding, bias=bias, groups=i) | |
| def forward(self, x): | |
| if self.stride == 1: | |
| x1, x2 = x.chunk(2, dim=1) | |
| out = torch.cat((x1, self.branch2(x2)), dim=1) | |
| else: | |
| out = torch.cat((self.branch1(x), self.branch2(x)), dim=1) | |
| out = channel_shuffle(out, 2) | |
| return out | |
| class BlazeBlock(nn.Module): | |
| def __init__(self, in_channels,out_channels,mid_channels=None,stride=1): | |
| super(BlazeBlock, self).__init__() | |
| mid_channels = mid_channels or in_channels | |
| assert stride in [1, 2] | |
| if stride>1: | |
| self.use_pool = True | |
| else: | |
| self.use_pool = False | |
| self.branch1 = nn.Sequential( | |
| nn.Conv2d(in_channels=in_channels,out_channels=mid_channels,kernel_size=5,stride=stride,padding=2,groups=in_channels), | |
| nn.BatchNorm2d(mid_channels), | |
| nn.Conv2d(in_channels=mid_channels,out_channels=out_channels,kernel_size=1,stride=1), | |
| nn.BatchNorm2d(out_channels), | |
| ) | |
| if self.use_pool: | |
| self.shortcut = nn.Sequential( | |
| nn.MaxPool2d(kernel_size=stride, stride=stride), | |
| nn.Conv2d(in_channels=in_channels, out_channels=out_channels, kernel_size=1, stride=1), | |
| nn.BatchNorm2d(out_channels), | |
| ) | |
| self.relu = nn.SiLU(inplace=True) | |
| def forward(self, x): | |
| branch1 = self.branch1(x) | |
| out = (branch1+self.shortcut(x)) if self.use_pool else (branch1+x) | |
| return self.relu(out) | |
| class DoubleBlazeBlock(nn.Module): | |
| def __init__(self,in_channels,out_channels,mid_channels=None,stride=1): | |
| super(DoubleBlazeBlock, self).__init__() | |
| mid_channels = mid_channels or in_channels | |
| assert stride in [1, 2] | |
| if stride > 1: | |
| self.use_pool = True | |
| else: | |
| self.use_pool = False | |
| self.branch1 = nn.Sequential( | |
| nn.Conv2d(in_channels=in_channels, out_channels=in_channels, kernel_size=5, stride=stride,padding=2,groups=in_channels), | |
| nn.BatchNorm2d(in_channels), | |
| nn.Conv2d(in_channels=in_channels, out_channels=mid_channels, kernel_size=1, stride=1), | |
| nn.BatchNorm2d(mid_channels), | |
| nn.SiLU(inplace=True), | |
| nn.Conv2d(in_channels=mid_channels, out_channels=mid_channels, kernel_size=5, stride=1,padding=2), | |
| nn.BatchNorm2d(mid_channels), | |
| nn.Conv2d(in_channels=mid_channels, out_channels=out_channels, kernel_size=1, stride=1), | |
| nn.BatchNorm2d(out_channels), | |
| ) | |
| if self.use_pool: | |
| self.shortcut = nn.Sequential( | |
| nn.MaxPool2d(kernel_size=stride, stride=stride), | |
| nn.Conv2d(in_channels=in_channels, out_channels=out_channels, kernel_size=1, stride=1), | |
| nn.BatchNorm2d(out_channels), | |
| ) | |
| self.relu = nn.SiLU(inplace=True) | |
| def forward(self, x): | |
| branch1 = self.branch1(x) | |
| out = (branch1 + self.shortcut(x)) if self.use_pool else (branch1 + x) | |
| return self.relu(out) | |
| class SPP(nn.Module): | |
| # Spatial pyramid pooling layer used in YOLOv3-SPP | |
| def __init__(self, c1, c2, k=(5, 9, 13)): | |
| super(SPP, self).__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) | |
| 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(Focus, self).__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 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): | |
| N, C, H, W = x.size() # assert (H / s == 0) and (W / s == 0), 'Indivisible gain' | |
| s = self.gain | |
| x = x.view(N, 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(N, 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): | |
| N, C, H, W = x.size() # assert C / s ** 2 == 0, 'Indivisible gain' | |
| s = self.gain | |
| x = x.view(N, 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(N, 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(Concat, self).__init__() | |
| self.d = dimension | |
| def forward(self, x): | |
| return torch.cat(x, self.d) | |
| class NMS(nn.Module): | |
| # Non-Maximum Suppression (NMS) module | |
| conf = 0.25 # confidence threshold | |
| iou = 0.45 # IoU threshold | |
| classes = None # (optional list) filter by class | |
| def __init__(self): | |
| super(NMS, self).__init__() | |
| def forward(self, x): | |
| return non_max_suppression(x[0], conf_thres=self.conf, iou_thres=self.iou, classes=self.classes) | |
| class autoShape(nn.Module): | |
| # input-robust model wrapper for passing cv2/np/PIL/torch inputs. Includes preprocessing, inference and NMS | |
| img_size = 640 # inference size (pixels) | |
| conf = 0.25 # NMS confidence threshold | |
| iou = 0.45 # NMS IoU threshold | |
| classes = None # (optional list) filter by class | |
| def __init__(self, model): | |
| super(autoShape, self).__init__() | |
| self.model = model.eval() | |
| def autoshape(self): | |
| print('autoShape already enabled, skipping... ') # model already converted to model.autoshape() | |
| return self | |
| def forward(self, imgs, size=640, augment=False, profile=False): | |
| # Inference from various sources. For height=720, width=1280, RGB images example inputs are: | |
| # filename: imgs = 'data/samples/zidane.jpg' | |
| # URI: = 'https://github.com/ultralytics/yolov5/releases/download/v1.0/zidane.jpg' | |
| # OpenCV: = cv2.imread('image.jpg')[:,:,::-1] # HWC BGR to RGB x(720,1280,3) | |
| # PIL: = Image.open('image.jpg') # HWC x(720,1280,3) | |
| # numpy: = np.zeros((720,1280,3)) # HWC | |
| # torch: = torch.zeros(16,3,720,1280) # BCHW | |
| # multiple: = [Image.open('image1.jpg'), Image.open('image2.jpg'), ...] # list of images | |
| p = next(self.model.parameters()) # for device and type | |
| if isinstance(imgs, torch.Tensor): # torch | |
| 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 = [], [] # image and inference shapes | |
| for i, im in enumerate(imgs): | |
| if isinstance(im, str): # filename or uri | |
| im = Image.open(requests.get(im, stream=True).raw if im.startswith('http') else im) # open | |
| im = np.array(im) # to numpy | |
| 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 # 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 | |
| # Inference | |
| with torch.no_grad(): | |
| y = self.model(x, augment, profile)[0] # forward | |
| y = non_max_suppression(y, conf_thres=self.conf, iou_thres=self.iou, classes=self.classes) # NMS | |
| # Post-process | |
| for i in range(n): | |
| scale_coords(shape1, y[i][:, :4], shape0[i]) | |
| return Detections(imgs, y, self.names) | |
| class Detections: | |
| # detections class for YOLOv5 inference results | |
| def __init__(self, imgs, pred, names=None): | |
| super(Detections, self).__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.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) | |
| def display(self, pprint=False, show=False, save=False, render=False): | |
| colors = color_list() | |
| for i, (img, pred) in enumerate(zip(self.imgs, self.pred)): | |
| str = f'Image {i + 1}/{len(self.pred)}: {img.shape[0]}x{img.shape[1]} ' | |
| if pred is not None: | |
| for c in pred[:, -1].unique(): | |
| n = (pred[:, -1] == c).sum() # detections per class | |
| if len(self.names) > int(c): | |
| str += f'{n} {self.names[int(c)]}s, ' # add to string | |
| if show or save or render: | |
| img = Image.fromarray(img.astype(np.uint8)) if isinstance(img, np.ndarray) else img # from np | |
| for *box, conf, cls in pred: # xyxy, confidence, class | |
| # str += '%s %.2f, ' % (names[int(cls)], conf) # label | |
| ImageDraw.Draw(img).rectangle(box, width=4, outline=colors[int(cls) % 10]) # plot | |
| if pprint: | |
| print(str) | |
| if show: | |
| img.show(f'Image {i}') # show | |
| if save: | |
| f = f'results{i}.jpg' | |
| str += f"saved to '{f}'" | |
| img.save(f) # save | |
| if render: | |
| self.imgs[i] = np.asarray(img) | |
| def print(self): | |
| self.display(pprint=True) # print results | |
| def show(self): | |
| self.display(show=True) # show results | |
| def save(self): | |
| self.display(save=True) # save results | |
| def render(self): | |
| self.display(render=True) # render results | |
| return self.imgs | |
| def __len__(self): | |
| return self.n | |
| 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) 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 | |
| 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(Classify, self).__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) | |