# This file contains modules common to various models import math import numpy as np import torch import torch.nn as nn import torch.nn.functional as F 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 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.Hardswish() if act else nn.Identity() self.act = nn.LeakyReLU(0.1, inplace=True) if act else nn.Identity() def forward(self, x): # print('x',x.size()) y = self.act(self.bn(self.conv(x))) # print('y',y.size()) return y def fuseforward(self, x): return self.act(self.conv(x)) 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 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) 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)) 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): # print('Concat x.size()',x.size()) y=torch.cat(x, self.d) # print('Concat y.size()',y.size()) return y 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 forward(self, imgs, size=640, augment=False, profile=False): # supports inference from various sources. For height=720, width=1280, RGB images example inputs are: # opencv: imgs = cv2.imread('image.jpg')[:,:,::-1] # HWC BGR to RGB x(720,1280,3) # PIL: imgs = Image.open('image.jpg') # HWC x(720,1280,3) # numpy: imgs = np.zeros((720,1280,3)) # HWC # torch: imgs = torch.zeros(16,3,720,1280) # BCHW # multiple: imgs = [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 if not isinstance(imgs, list): imgs = [imgs] shape0, shape1 = [], [] # image and inference shapes batch = range(len(imgs)) # batch size for i in batch: imgs[i] = np.array(imgs[i]) # to numpy imgs[i] = imgs[i][:, :, :3] if imgs[i].ndim == 3 else np.tile(imgs[i][:, :, None], 3) # enforce 3ch input s = imgs[i].shape[:2] # HWC shape0.append(s) # image shape g = (size / max(s)) # gain shape1.append([y * g for y in s]) shape1 = [make_divisible(x, int(self.stride.max())) for x in np.stack(shape1, 0).max(0)] # inference shape x = [letterbox(imgs[i], new_shape=shape1, auto=False)[0] for i in batch] # pad x = np.stack(x, 0) if batch[-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 batch: if y[i] is not None: y[i][:, :4] = 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__() 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 gn = [torch.Tensor([*[im.shape[i] for i in [1, 0, 1, 0]], 1., 1.]) for im in imgs] # normalization gains 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 def display(self, pprint=False, show=False, save=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 str += f'{n} {self.names[int(c)]}s, ' # add to string if show or save: 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 save: f = f'results{i}.jpg' str += f"saved to '{f}'" img.save(f) # save if show: img.show(f'Image {i}') # show if pprint: print(str) 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 class Flatten(nn.Module): # Use after nn.AdaptiveAvgPool2d(1) to remove last 2 dimensions @staticmethod def forward(x): return x.view(x.size(0), -1) 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, bias=False) # to x(b,c2,1,1) self.flat = 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) 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))