环境要求

我的版本是YOLOV5 7.0

先看结果：

结果仅供参考

具体步骤一：

首先配置好YOLO V5环境

这个采用pip install requirements即可

具体配置环境可以看我其他的博客有详细介绍

GPU环境自己配置

步骤二：

运行YOLO 没问题 
  
  
  
  
  
  ，输出结果：

步骤三

在项目文件夹下添加main_gradcam.py文件

main_gradcam.py import os import random import time import argparse import numpy as np from models.gradcam import YOLOV5GradCAM, YOLOV5GradCAMPP from models.yolov5_object_detector import YOLOV5TorchObjectDetector import cv2 # 数据集类别名 names = [person, bicycle, car, motorcycle, airplane, bus, train, truck, boat, traffic light, fire hydrant, stop sign, parking meter, bench, bird, cat, dog, horse, sheep, cow, elephant, bear, zebra, giraffe, backpack, umbrella, handbag, tie, suitcase, frisbee, skis, snowboard, sports ball, kite, baseball bat, baseball glove, skateboard, surfboard, tennis racket, bottle, wine glass, cup, fork, knife, spoon, bowl, banana, apple, sandwich, orange, broccoli, carrot, hot dog, pizza, donut, cake, chair, couch, potted plant, bed, dining table, toilet, tv, laptop, mouse, remote, keyboard, cell phone, microwave, oven, toaster, sink, refrigerator, book, clock, vase, scissors, teddy bear, hair drier, toothbrush] # class names # yolov5s网络中的三个detect层 target_layers = [model_17_cv3_act, model_20_cv3_act, model_23_cv3_act] # Arguments parser = argparse.ArgumentParser() parser.add_argument(--model-path, type=str, default="yolov5s.pt", help=Path to the model) parser.add_argument(--img-path, type=str, default=data/images/bus.jpg, help=input image path) parser.add_argument(--output-dir, type=str, default=runs/result17, help=output dir) parser.add_argument(--img-size, type=int, default=640, help="input image size") parser.add_argument(--target-layer, type=str, default=model_17_cv3_act, help=The layer hierarchical address to which gradcam will applied, the names should be separated by underline) parser.add_argument(--method, type=str, default=gradcam, help=gradcam method) parser.add_argument(--device, type=str, default=cuda, help=cuda or cpu) parser.add_argument(--no_text_box, action=store_true, help=do not show label and box on the heatmap) args = parser.parse_args() def get_res_img(bbox, mask, res_img): mask = mask.squeeze(0).mul(255).add_(0.5).clamp_(0, 255).permute(1, 2, 0).detach().cpu().numpy().astype( np.uint8) heatmap = cv2.applyColorMap(mask, cv2.COLORMAP_JET) # n_heatmat = (Box.fill_outer_box(heatmap, bbox) / 255).astype(np.float32) n_heatmat = (heatmap / 255).astype(np.float32) res_img = res_img / 255 res_img = cv2.add(res_img, n_heatmat) res_img = (res_img / res_img.max()) return res_img, n_heatmat def plot_one_box(x, img, color=None, label=None, line_thickness=3): # this is a bug in cv2. It does not put box on a converted image from torch unless its buffered and read again! cv2.imwrite(temp.jpg, (img * 255).astype(np.uint8)) img = cv2.imread(temp.jpg) # Plots one bounding box on image img tl = line_thickness or round(0.002 * (img.shape[0] + img.shape[1]) / 2) + 1 # line/font thickness color = color or [random.randint(0, 255) for _ in range(3)] c1, c2 = (int(x[0]), int(x[1])), (int(x[2]), int(x[3])) cv2.rectangle(img, c1, c2, color, thickness=tl, lineType=cv2.LINE_AA) if label: tf = max(tl - 1, 1) # font thickness t_size = cv2.getTextSize(label, 0, fontScale=tl / 3, thickness=tf)[0] outside = c1[1] - t_size[1] - 3 >= 0 # label fits outside box up c2 = c1[0] + t_size[0], c1[1] - t_size[1] - 3 if outside else c1[1] + t_size[1] + 3 outsize_right = c2[0] - img.shape[:2][1] > 0 # label fits outside box right c1 = c1[0] - (c2[0] - img.shape[:2][1]) if outsize_right else c1[0], c1[1] c2 = c2[0] - (c2[0] - img.shape[:2][1]) if outsize_right else c2[0], c2[1] cv2.rectangle(img, c1, c2, color, -1, cv2.LINE_AA) # filled cv2.putText(img, label, (c1[0], c1[1] - 2 if outside else c2[1] - 2), 0, tl / 3, [225, 255, 255], thickness=tf, lineType=cv2.LINE_AA) return img # 检测单个图片 def main(img_path): colors = [[random.randint(0, 255) for _ in range(3)] for _ in names] device = args.device input_size = (args.img_size, args.img_size) # 读入图片 img = cv2.imread(img_path) # 读取图像格式：BGR print([INFO] Loading the model) # 实例化YOLOv5模型 
  
  
  
  
  
  
 ，得到检测结果 model = YOLOV5TorchObjectDetector(args.model_path, device, img_size=input_size, names=names) # img[..., ::-1]: BGR --> RGB # (480, 640, 3) --> (1, 3, 480, 640) torch_img = model.preprocessing(img[..., ::-1]) tic = time.time() # 遍历三层检测层 for target_layer in target_layers: # 获取grad-cam方法 if args.method == gradcam: saliency_method = YOLOV5GradCAM(model=model, layer_name=target_layer, img_size=input_size) elif args.method == gradcampp: saliency_method = YOLOV5GradCAMPP(model=model, layer_name=target_layer, img_size=input_size) masks, logits, [boxes, _, class_names, conf] = saliency_method(torch_img) # 得到预测结果 result = torch_img.squeeze(0).mul(255).add_(0.5).clamp_(0, 255).permute(1, 2, 0).detach().cpu().numpy() result = result[..., ::-1] # convert to bgr # 保存设置 imgae_name = os.path.basename(img_path) # 获取图片名 save_path = f{args.output_dir}{imgae_name[:-4]}/{args.method} if not os.path.exists(save_path): os.makedirs(save_path) print(f[INFO] Saving the final image at {save_path}) # 遍历每张图片中的每个目标 for i, mask in enumerate(masks): # 遍历图片中的每个目标 res_img = result.copy() # 获取目标的位置和类别信息 bbox, cls_name = boxes[0][i], class_names[0][i] label = f{cls_name}{conf[0][i]} # 类别+置信分数 # 获取目标的热力图 res_img, heat_map = get_res_img(bbox, mask, res_img) res_img = plot_one_box(bbox, res_img, label=label, color=colors[int(names.index(cls_name))], line_thickness=3) # 缩放到原图片大小 res_img = cv2.resize(res_img, dsize=(img.shape[:-1][::-1])) output_path = f{save_path}/{target_layer[6:8]}_{i}.jpg cv2.imwrite(output_path, res_img) print(f{target_layer[6:8]}_{i}.jpg done!!) print(fTotal time : {round(time.time() - tic, 4)} s) if __name__ == __main__: # 图片路径为文件夹 if os.path.isdir(args.img_path): img_list = os.listdir(args.img_path) print(img_list) for item in img_list: # 依次获取文件夹中的图片名

 


 


 


 


 


 


，组合成图片的路径 main(os.path.join(args.img_path, item)) # 单个图片 else: main(args.img_path)

步骤四

在model文件夹下添加如下两个py文件 
 
 
 
 
 
 ，分别是gradcam.py和yolov5_object_detector.py

gradcam.py代码如下： import time import torch import torch.nn.functional as F def find_yolo_layer(model, layer_name): """Find yolov5 layer to calculate GradCAM and GradCAM++ Args: model: yolov5 model. layer_name (str): the name of layer with its hierarchical information. Return: target_layer: found layer """ hierarchy = layer_name.split(_) target_layer = model.model._modules[hierarchy[0]] for h in hierarchy[1:]: target_layer = target_layer._modules[h] return target_layer class YOLOV5GradCAM: # 初始化 
   
   
   
   
   
   
 ，得到target_layer层 def __init__(self, model, layer_name, img_size=(640, 640)): self.model = model self.gradients = dict() self.activations = dict() def backward_hook(module, grad_input, grad_output): self.gradients[value] = grad_output[0] return None def forward_hook(module, input, output): self.activations[value] = output return None target_layer = find_yolo_layer(self.model, layer_name) # 获取forward过程中每层的输入和输出
 

 

 

 

 

 

，用于对比hook是不是正确记录 target_layer.register_forward_hook(forward_hook) target_layer.register_full_backward_hook(backward_hook) device = cuda if next(self.model.model.parameters()).is_cuda else cpu self.model(torch.zeros(1, 3, *img_size, device=device)) def forward(self, input_img, class_idx=True): """ Args: input_img: input image with shape of (1, 3, H, W) Return: mask: saliency map of the same spatial dimension with input logit: model output preds: The object predictions """ saliency_maps = [] b, c, h, w = input_img.size() preds, logits = self.model(input_img) for logit, cls, cls_name in zip(logits[0], preds[1][0], preds[2][0]): if class_idx: score = logit[cls] else: score = logit.max() self.model.zero_grad() tic = time.time() # 获取梯度 score.backward(retain_graph=True) print(f"[INFO] {cls_name}, model-backward took: ", round(time.time() - tic, 4), seconds) gradients = self.gradients[value] activations = self.activations[value] b, k, u, v = gradients.size() alpha = gradients.view(b, k, -1).mean(2) weights = alpha.view(b, k, 1, 1) saliency_map = (weights * activations).sum(1, keepdim=True) saliency_map = F.relu(saliency_map) saliency_map = F.interpolate(saliency_map, size=(h, w), mode=bilinear, align_corners=False) saliency_map_min, saliency_map_max = saliency_map.min(), saliency_map.max() saliency_map = (saliency_map - saliency_map_min).div(saliency_map_max - saliency_map_min).data saliency_maps.append(saliency_map) return saliency_maps, logits, preds def __call__(self, input_img): return self.forward(input_img) class YOLOV5GradCAMPP(YOLOV5GradCAM): def __init__(self, model, layer_name, img_size=(640, 640)): super(YOLOV5GradCAMPP, self).__init__(model, layer_name, img_size) def forward(self, input_img, class_idx=True): saliency_maps = [] b, c, h, w = input_img.size() tic = time.time() preds, logits = self.model(input_img) print("[INFO] model-forward took: ", round(time.time() - tic, 4), seconds) for logit, cls, cls_name in zip(logits[0], preds[1][0], preds[2][0]): if class_idx: score = logit[cls] else: score = logit.max() self.model.zero_grad() tic = time.time() # 获取梯度 score.backward(retain_graph=True) print(f"[INFO] {cls_name}, model-backward took: ", round(time.time() - tic, 4), seconds) gradients = self.gradients[value] # dS/dA activations = self.activations[value] # A b, k, u, v = gradients.size() alpha_num = gradients.pow(2) alpha_denom = gradients.pow(2).mul(2) + \ activations.mul(gradients.pow(3)).view(b, k, u * v).sum(-1, keepdim=True).view(b, k, 1, 1) # torch.where(condition, x, y) condition是条件
 

 

 

 

 

 

 ，满足条件就返回x             ，不满足就返回y alpha_denom = torch.where(alpha_denom != 0.0, alpha_denom, torch.ones_like(alpha_denom)) alpha = alpha_num.div(alpha_denom + 1e-7) positive_gradients = F.relu(score.exp() * gradients) # ReLU(dY/dA) == ReLU(exp(S)*dS/dA)) weights = (alpha * positive_gradients).view(b, k, u * v).sum(-1).view(b, k, 1, 1) saliency_map = (weights * activations).sum(1, keepdim=True) saliency_map = F.relu(saliency_map) saliency_map = F.interpolate(saliency_map, size=(h, w), mode=bilinear, align_corners=False) saliency_map_min, saliency_map_max = saliency_map.min(), saliency_map.max() saliency_map = (saliency_map - saliency_map_min).div(saliency_map_max - saliency_map_min).data saliency_maps.append(saliency_map) return saliency_maps, logits, preds

yolov5_object_detector.py的代码如下：

import numpy as np import torch from models.experimental import attempt_load from utils.general import xywh2xyxy from utils.dataloaders import letterbox import cv2 import time import torchvision import torch.nn as nn from utils.metrics import box_iou class YOLOV5TorchObjectDetector(nn.Module): def __init__(self, model_weight, device, img_size, names=None, mode=eval, confidence=0.45, iou_thresh=0.45, agnostic_nms=False): super(YOLOV5TorchObjectDetector, self).__init__() self.device = device self.model = None self.img_size = img_size self.mode = mode self.confidence = confidence self.iou_thresh = iou_thresh self.agnostic = agnostic_nms self.model = attempt_load(model_weight, inplace=False, fuse=False) self.model.requires_grad_(True) self.model.to(device) if self.mode == train: self.model.train() else: self.model.eval() # fetch the names if names is None: self.names = [your dataset classname] else: self.names = names # preventing cold start img = torch.zeros((1, 3, *self.img_size), device=device) self.model(img) @staticmethod def non_max_suppression(prediction, logits, conf_thres=0.3, iou_thres=0.45, classes=None, agnostic=False, multi_label=False, labels=(), max_det=300): """Runs Non-Maximum Suppression (NMS) on inference and logits results Returns: list of detections, on (n,6) tensor per image [xyxy, conf, cls] and pruned input logits (n, number-classes) """ nc = prediction.shape[2] - 5 # number of classes xc = prediction[..., 4] > conf_thres # candidates # Checks assert 0 <= conf_thres <= 1, fInvalid Confidence threshold {conf_thres}, valid values are between 0.0 and 1.0 assert 0 <= iou_thres <= 1, fInvalid IoU {iou_thres}, valid values are between 0.0 and 1.0 # Settings min_wh, max_wh = 2, 4096 # (pixels) minimum and maximum box width and height max_nms = 30000 # maximum number of boxes into torchvision.ops.nms() time_limit = 10.0 # seconds to quit after redundant = True # require redundant detections multi_label &= nc > 1 # multiple labels per box (adds 0.5ms/img) merge = False # use merge-NMS t = time.time() output = [torch.zeros((0, 6), device=prediction.device)] * prediction.shape[0] logits_output = [torch.zeros((0, nc), device=logits.device)] * logits.shape[0] # logits_output = [torch.zeros((0, 80), device=logits.device)] * logits.shape[0] for xi, (x, log_) in enumerate(zip(prediction, logits)): # image index, image inference # Apply constraints # x[((x[..., 2:4] < min_wh) | (x[..., 2:4] > max_wh)).any(1), 4] = 0 # width-height x = x[xc[xi]] # confidence log_ = log_[xc[xi]] # Cat apriori labels if autolabelling if labels and len(labels[xi]): l = labels[xi] v = torch.zeros((len(l), nc + 5), device=x.device) v[:, :4] = l[:, 1:5] # box v[:, 4] = 1.0 # conf v[range(len(l)), l[:, 0].long() + 5] = 1.0 # cls x = torch.cat((x, v), 0) # If none remain process next image if not x.shape[0]: continue # Compute conf x[:, 5:] *= x[:, 4:5] # conf = obj_conf * cls_conf # Box (center x, center y, width, height) to (x1, y1, x2, y2) box = xywh2xyxy(x[:, :4]) # Detections matrix nx6 (xyxy, conf, cls) if multi_label: i, j = (x[:, 5:] > conf_thres).nonzero(as_tuple=False).T x = torch.cat((box[i], x[i, j + 5, None], j[:, None].float()), 1) else: # best class only conf, j = x[:, 5:].max(1, keepdim=True) x = torch.cat((box, conf, j.float()), 1)[conf.view(-1) > conf_thres] log_ = log_[conf.view(-1) > conf_thres] # Filter by class if classes is not None: x = x[(x[:, 5:6] == torch.tensor(classes, device=x.device)).any(1)] # Check shape n = x.shape[0] # number of boxes if not n: # no boxes continue elif n > max_nms: # excess boxes x = x[x[:, 4].argsort(descending=True)[:max_nms]] # sort by confidence # Batched NMS c = x[:, 5:6] * (0 if agnostic else max_wh) # classes boxes, scores = x[:, :4] + c, x[:, 4] # boxes (offset by class), scores i = torchvision.ops.nms(boxes, scores, iou_thres) # NMS if i.shape[0] > max_det: # limit detections i = i[:max_det] if merge and (1 < n < 3E3): # Merge NMS (boxes merged using weighted mean) # update boxes as boxes(i,4) = weights(i,n) * boxes(n,4) iou = box_iou(boxes[i], boxes) > iou_thres # iou matrix weights = iou * scores[None] # box weights x[i, :4] = torch.mm(weights, x[:, :4]).float() / weights.sum(1, keepdim=True) # merged boxes if redundant: i = i[iou.sum(1) > 1] # require redundancy output[xi] = x[i] logits_output[xi] = log_[i] assert log_[i].shape[0] == x[i].shape[0] if (time.time() - t) > time_limit: print(fWARNING: NMS time limit {time_limit}s exceeded) break # time limit exceeded return output, logits_output @staticmethod def yolo_resize(img, new_shape=(640, 640), color=(114, 114, 114), auto=True, scaleFill=False, scaleup=True): return letterbox(img, new_shape=new_shape, color=color, auto=auto, scaleFill=scaleFill, scaleup=scaleup) def forward(self, img): prediction, logits, _ = self.model(img, augment=False) prediction, logits = self.non_max_suppression(prediction, logits, self.confidence, self.iou_thresh, classes=None, agnostic=self.agnostic) self.boxes, self.class_names, self.classes, self.confidences = [[[] for _ in range(img.shape[0])] for _ in range(4)] for i, det in enumerate(prediction): # detections per image if len(det): for *xyxy, conf, cls in det: # 返回整数 bbox = [int(b) for b in xyxy] self.boxes[i].append(bbox) self.confidences[i].append(round(conf.item(), 2)) cls = int(cls.item()) self.classes[i].append(cls) if self.names is not None: self.class_names[i].append(self.names[cls]) else: self.class_names[i].append(cls) return [self.boxes, self.classes, self.class_names, self.confidences], logits def preprocessing(self, img): if len(img.shape) != 4: img = np.expand_dims(img, axis=0) im0 = img.astype(np.uint8) img = np.array([self.yolo_resize(im, new_shape=self.img_size)[0] for im in im0]) img = img.transpose((0, 3, 1, 2)) img = np.ascontiguousarray(img) img = torch.from_numpy(img).to(self.device) img = img / 255.0 return img

步骤五

更改model/yolo.py

具体而言

Detect类中的forward函数 def forward(self, x): z = [] # inference output logits_ = [] # 修改---1 for i in range(self.nl): x[i] = self.m[i](x[i]) # conv bs, _, ny, nx = x[i].shape # x(bs,255,20,20) to x(bs,3,20,20,85) x[i] = x[i].view(bs, self.na, self.no, ny, nx).permute(0, 1, 3, 4, 2).contiguous() if not self.training: # inference if self.dynamic or self.grid[i].shape[2:4] != x[i].shape[2:4]: self.grid[i], self.anchor_grid[i] = self._make_grid(nx, ny, i) logits = x[i][..., 5:] # 修改---2 if isinstance(self, Segment): # (boxes + masks) xy, wh, conf, mask = x[i].split((2, 2, self.nc + 1, self.no - self.nc - 5), 4) xy = (xy.sigmoid() * 2 + self.grid[i]) * self.stride[i] # xy wh = (wh.sigmoid() * 2) ** 2 * self.anchor_grid[i] # wh y = torch.cat((xy, wh, conf.sigmoid(), mask), 4) else: # Detect (boxes only) xy, wh, conf = x[i].sigmoid().split((2, 2, self.nc + 1), 4) xy = (xy * 2 + self.grid[i]) * self.stride[i] # xy wh = (wh * 2) ** 2 * self.anchor_grid[i] # wh y = torch.cat((xy, wh, conf), 4) z.append(y.view(bs, self.na * nx * ny, self.no)) logits_.append(logits.view(bs, -1, self.no - 5)) # 修改---3 # return x if self.training else (torch.cat(z, 1),) if self.export else (torch.cat(z, 1), x) return x if self.training else (torch.cat(z, 1), torch.cat(logits_, 1), x) # 修改---4

为了防止大家不知道怎么修改yolo.py文件 

 
 

 
 

 
 

 
 

 
 

 
 

，我将修改后的yolo.py文件放在下方

yolo.py # YOLOv5 🚀 by Ultralytics, GPL-3.0 license """ YOLO-specific modules Usage: $ python models/yolo.py --cfg yolov5s.yaml """ import argparse import contextlib import os import platform import sys from copy import deepcopy from pathlib import Path FILE = Path(__file__).resolve() ROOT = FILE.parents[1] # YOLOv5 root directory if str(ROOT) not in sys.path: sys.path.append(str(ROOT)) # add ROOT to PATH if platform.system() != Windows: ROOT = Path(os.path.relpath(ROOT, Path.cwd())) # relative from models.common import * from models.experimental import * from utils.autoanchor import check_anchor_order from utils.general import LOGGER, check_version, check_yaml, make_divisible, print_args from utils.plots import feature_visualization from utils.torch_utils import (fuse_conv_and_bn, initialize_weights, model_info, profile, scale_img, select_device, time_sync) try: import thop # for FLOPs computation except ImportError: thop = None class Detect(nn.Module): # YOLOv5 Detect head for detection models stride = None # strides computed during build dynamic = False # force grid reconstruction export = False # export mode def __init__(self, nc=80, anchors=(), ch=(), inplace=True): # detection layer super().__init__() self.nc = nc # number of classes self.no = nc + 5 # number of outputs per anchor self.nl = len(anchors) # number of detection layers self.na = len(anchors[0]) // 2 # number of anchors self.grid = [torch.empty(0) for _ in range(self.nl)] # init grid self.anchor_grid = [torch.empty(0) for _ in range(self.nl)] # init anchor grid self.register_buffer(anchors, torch.tensor(anchors).float().view(self.nl, -1, 2)) # shape(nl,na,2) self.m = nn.ModuleList(nn.Conv2d(x, self.no * self.na, 1) for x in ch) # output conv self.inplace = inplace # use inplace ops (e.g. slice assignment) def forward(self, x): z = [] # inference output logits_ = [] # 修改---1 for i in range(self.nl): x[i] = self.m[i](x[i]) # conv bs, _, ny, nx = x[i].shape # x(bs,255,20,20) to x(bs,3,20,20,85) x[i] = x[i].view(bs, self.na, self.no, ny, nx).permute(0, 1, 3, 4, 2).contiguous() if not self.training: # inference if self.dynamic or self.grid[i].shape[2:4] != x[i].shape[2:4]: self.grid[i], self.anchor_grid[i] = self._make_grid(nx, ny, i) logits = x[i][..., 5:] # 修改---2 if isinstance(self, Segment): # (boxes + masks) xy, wh, conf, mask = x[i].split((2, 2, self.nc + 1, self.no - self.nc - 5), 4) xy = (xy.sigmoid() * 2 + self.grid[i]) * self.stride[i] # xy wh = (wh.sigmoid() * 2) ** 2 * self.anchor_grid[i] # wh y = torch.cat((xy, wh, conf.sigmoid(), mask), 4) else: # Detect (boxes only) xy, wh, conf = x[i].sigmoid().split((2, 2, self.nc + 1), 4) xy = (xy * 2 + self.grid[i]) * self.stride[i] # xy wh = (wh * 2) ** 2 * self.anchor_grid[i] # wh y = torch.cat((xy, wh, conf), 4) z.append(y.view(bs, self.na * nx * ny, self.no)) logits_.append(logits.view(bs, -1, self.no - 5)) # 修改---3 # return x if self.training else (torch.cat(z, 1),) if self.export else (torch.cat(z, 1), x) return x if self.training else (torch.cat(z, 1), torch.cat(logits_, 1), x) # 修改---4 def _make_grid(self, nx=20, ny=20, i=0, torch_1_10=check_version(torch.__version__, 1.10.0)): d = self.anchors[i].device t = self.anchors[i].dtype shape = 1, self.na, ny, nx, 2 # grid shape y, x = torch.arange(ny, device=d, dtype=t), torch.arange(nx, device=d, dtype=t) yv, xv = torch.meshgrid(y, x, indexing=ij) if torch_1_10 else torch.meshgrid(y, x) # torch>=0.7 compatibility grid = torch.stack((xv, yv), 2).expand(shape) - 0.5 # add grid offset, i.e. y = 2.0 * x - 0.5 anchor_grid = (self.anchors[i] * self.stride[i]).view((1, self.na, 1, 1, 2)).expand(shape) return grid, anchor_grid class Segment(Detect): # YOLOv5 Segment head for segmentation models def __init__(self, nc=80, anchors=(), nm=32, npr=256, ch=(), inplace=True): super().__init__(nc, anchors, ch, inplace) self.nm = nm # number of masks self.npr = npr # number of protos self.no = 5 + nc + self.nm # number of outputs per anchor self.m = nn.ModuleList(nn.Conv2d(x, self.no * self.na, 1) for x in ch) # output conv self.proto = Proto(ch[0], self.npr, self.nm) # protos self.detect = Detect.forward def forward(self, x): p = self.proto(x[0]) x = self.detect(self, x) return (x, p) if self.training else (x[0], p) if self.export else (x[0], p, x[1]) class BaseModel(nn.Module): # YOLOv5 base model def forward(self, x, profile=False, visualize=False): return self._forward_once(x, profile, visualize) # single-scale inference, train def _forward_once(self, x, profile=False, visualize=False): y, dt = [], [] # outputs for m in self.model: if m.f != -1: # if not from previous layer x = y[m.f] if isinstance(m.f, int) else [x if j == -1 else y[j] for j in m.f] # from earlier layers if profile: self._profile_one_layer(m, x, dt) x = m(x) # run y.append(x if m.i in self.save else None) # save output if visualize: feature_visualization(x, m.type, m.i, save_dir=visualize) return x def _profile_one_layer(self, m, x, dt): c = m == self.model[-1] # is final layer, copy input as inplace fix o = thop.profile(m, inputs=(x.copy() if c else x,), verbose=False)[0] / 1E9 * 2 if thop else 0 # FLOPs t = time_sync() for _ in range(10): m(x.copy() if c else x) dt.append((time_sync() - t) * 100) if m == self.model[0]: LOGGER.info(f"{time (ms):>10s}{GFLOPs:>10s}{params:>10s} module") LOGGER.info(f{dt[-1]:10.2f}{o:10.2f}{m.np:10.0f}{m.type}) if c: LOGGER.info(f"{sum(dt):10.2f}{-:>10s}{-:>10s} Total") def fuse(self): # fuse model Conv2d() + BatchNorm2d() layers LOGGER.info(Fusing layers... ) for m in self.model.modules(): if isinstance(m, (Conv, DWConv)) and hasattr(m, bn): m.conv = fuse_conv_and_bn(m.conv, m.bn) # update conv delattr(m, bn) # remove batchnorm m.forward = m.forward_fuse # update forward self.info() return self def info(self, verbose=False, img_size=640): # print model information model_info(self, verbose, img_size) def _apply(self, fn): # Apply to(), cpu(), cuda(), half() to model tensors that are not parameters or registered buffers self = super()._apply(fn) m = self.model[-1] # Detect() if isinstance(m, (Detect, Segment)): 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 class DetectionModel(BaseModel): # YOLOv5 detection model def __init__(self, cfg=yolov5s.yaml, ch=3, nc=None, anchors=None): # model, input channels, number of classes super().__init__() if isinstance(cfg, dict): self.yaml = cfg # model dict else: # is *.yaml import yaml # for torch hub self.yaml_file = Path(cfg).name with open(cfg, encoding=ascii, errors=ignore) as f: self.yaml = yaml.safe_load(f) # model dict # Define model ch = self.yaml[ch] = self.yaml.get(ch, ch) # input channels if nc and nc != self.yaml[nc]: LOGGER.info(f"Overriding model.yaml nc={self.yaml[nc]} with nc={nc}") self.yaml[nc] = nc # override yaml value if anchors: LOGGER.info(fOverriding model.yaml anchors with anchors={anchors}) self.yaml[anchors] = round(anchors) # override yaml value self.model, self.save = parse_model(deepcopy(self.yaml), ch=[ch]) # model, savelist self.names = [str(i) for i in range(self.yaml[nc])] # default names self.inplace = self.yaml.get(inplace, True) # Build strides, anchors m = self.model[-1] # Detect() if isinstance(m, (Detect, Segment)): s = 256 # 2x min stride m.inplace = self.inplace forward = lambda x: self.forward(x)[0] if isinstance(m, Segment) else self.forward(x) m.stride = torch.tensor([s / x.shape[-2] for x in forward(torch.zeros(1, ch, s, s))]) # forward check_anchor_order(m) m.anchors /= m.stride.view(-1, 1, 1) self.stride = m.stride self._initialize_biases() # only run once # Init weights, biases initialize_weights(self) self.info() LOGGER.info() def forward(self, x, augment=False, profile=False, visualize=False): if augment: return self._forward_augment(x) # augmented inference, None return self._forward_once(x, profile, visualize) # single-scale inference, train def _forward_augment(self, x): img_size = x.shape[-2:] # height, width s = [1, 0.83, 0.67] # scales f = [None, 3, None] # flips (2-ud, 3-lr) y = [] # outputs for si, fi in zip(s, f): xi = scale_img(x.flip(fi) if fi else x, si, gs=int(self.stride.max())) yi = self._forward_once(xi)[0] # forward # cv2.imwrite(fimg_{si}.jpg, 255 * xi[0].cpu().numpy().transpose((1, 2, 0))[:, :, ::-1]) # save yi = self._descale_pred(yi, fi, si, img_size) y.append(yi) y = self._clip_augmented(y) # clip augmented tails return torch.cat(y, 1), None # augmented inference, train def _descale_pred(self, p, flips, scale, img_size): # de-scale predictions following augmented inference (inverse operation) if self.inplace: p[..., :4] /= scale # de-scale if flips == 2: p[..., 1] = img_size[0] - p[..., 1] # de-flip ud elif flips == 3: p[..., 0] = img_size[1] - p[..., 0] # de-flip lr else: x, y, wh = p[..., 0:1] / scale, p[..., 1:2] / scale, p[..., 2:4] / scale # de-scale if flips == 2: y = img_size[0] - y # de-flip ud elif flips == 3: x = img_size[1] - x # de-flip lr p = torch.cat((x, y, wh, p[..., 4:]), -1) return p def _clip_augmented(self, y): # Clip YOLOv5 augmented inference tails nl = self.model[-1].nl # number of detection layers (P3-P5) g = sum(4 ** x for x in range(nl)) # grid points e = 1 # exclude layer count i = (y[0].shape[1] // g) * sum(4 ** x for x in range(e)) # indices y[0] = y[0][:, :-i] # large i = (y[-1].shape[1] // g) * sum(4 ** (nl - 1 - x) for x in range(e)) # indices y[-1] = y[-1][:, i:] # small return y def _initialize_biases(self, cf=None): # initialize biases into Detect(), cf is class frequency # https://arxiv.org/abs/1708.02002 section 3.3 # cf = torch.bincount(torch.tensor(np.concatenate(dataset.labels, 0)[:, 0]).long(), minlength=nc) + 1. m = self.model[-1] # Detect() module for mi, s in zip(m.m, m.stride): # from b = mi.bias.view(m.na, -1) # conv.bias(255) to (3,85) b.data[:, 4] += math.log(8 / (640 / s) ** 2) # obj (8 objects per 640 image) b.data[:, 5:5 + m.nc] += math.log(0.6 / (m.nc - 0.99999)) if cf is None else torch.log(cf / cf.sum()) # cls mi.bias = torch.nn.Parameter(b.view(-1), requires_grad=True) Model = DetectionModel # retain YOLOv5 Model class for backwards compatibility class SegmentationModel(DetectionModel): # YOLOv5 segmentation model def __init__(self, cfg=yolov5s-seg.yaml, ch=3, nc=None, anchors=None): super().__init__(cfg, ch, nc, anchors) class ClassificationModel(BaseModel): # YOLOv5 classification model def __init__(self, cfg=None, model=None, nc=1000, cutoff=10): # yaml, model, number of classes, cutoff index super().__init__() self._from_detection_model(model, nc, cutoff) if model is not None else self._from_yaml(cfg) def _from_detection_model(self, model, nc=1000, cutoff=10): # Create a YOLOv5 classification model from a YOLOv5 detection model if isinstance(model, DetectMultiBackend): model = model.model # unwrap DetectMultiBackend model.model = model.model[:cutoff] # backbone m = model.model[-1] # last layer ch = m.conv.in_channels if hasattr(m, conv) else m.cv1.conv.in_channels # ch into module c = Classify(ch, nc) # Classify() c.i, c.f, c.type = m.i, m.f, models.common.Classify # index, from, type model.model[-1] = c # replace self.model = model.model self.stride = model.stride self.save = [] self.nc = nc def _from_yaml(self, cfg): # Create a YOLOv5 classification model from a *.yaml file self.model = None def parse_model(d, ch): # model_dict, input_channels(3) # Parse a YOLOv5 model.yaml dictionary LOGGER.info(f"\n{:>3}{from:>18}{n:>3}{params:>10}{module:<40}{arguments:<30}") anchors, nc, gd, gw, act = d[anchors], d[nc], d[depth_multiple], d[width_multiple], d.get(activation) if act: Conv.default_act = eval(act) # redefine default activation, i.e. Conv.default_act = nn.SiLU() LOGGER.info(f"{colorstr(activation:)}{act}") # print na = (len(anchors[0]) // 2) if isinstance(anchors, list) else anchors # number of anchors no = na * (nc + 5) # number of outputs = anchors * (classes + 5) layers, save, c2 = [], [], ch[-1] # layers, savelist, ch out for i, (f, n, m, args) in enumerate(d[backbone] + d[head]): # from, number, module, args m = eval(m) if isinstance(m, str) else m # eval strings for j, a in enumerate(args): with contextlib.suppress(NameError): args[j] = eval(a) if isinstance(a, str) else a # eval strings n = n_ = max(round(n * gd), 1) if n > 1 else n # depth gain if m in { Conv, GhostConv, Bottleneck, GhostBottleneck, SPP, SPPF, DWConv, MixConv2d, Focus, CrossConv, BottleneckCSP, C3, C3TR, C3SPP, C3Ghost, nn.ConvTranspose2d, DWConvTranspose2d, C3x}: c1, c2 = ch[f], args[0] if c2 != no: # if not output c2 = make_divisible(c2 * gw, 8) args = [c1, c2, *args[1:]] if m in {BottleneckCSP, C3, C3TR, C3Ghost, C3x}: args.insert(2, n) # number of repeats n = 1 elif m is nn.BatchNorm2d: args = [ch[f]] elif m is Concat: c2 = sum(ch[x] for x in f) # TODO: channel, gw, gd elif m in {Detect, Segment}: args.append([ch[x] for x in f]) if isinstance(args[1], int): # number of anchors args[1] = [list(range(args[1] * 2))] * len(f) if m is Segment: args[3] = make_divisible(args[3] * gw, 8) elif m is Contract: c2 = ch[f] * args[0] ** 2 elif m is Expand: c2 = ch[f] // args[0] ** 2 else: c2 = ch[f] m_ = nn.Sequential(*(m(*args) for _ in range(n))) if n > 1 else m(*args) # module t = str(m)[8:-2].replace(__main__., ) # module type np = sum(x.numel() for x in m_.parameters()) # number params m_.i, m_.f, m_.type, m_.np = i, f, t, np # attach index, from index, type, number params LOGGER.info(f{i:>3}{str(f):>18}{n_:>3}{np:10.0f}{t:<40}{str(args):<30}) # print save.extend(x % i for x in ([f] if isinstance(f, int) else f) if x != -1) # append to savelist layers.append(m_) if i == 0: ch = [] ch.append(c2) return nn.Sequential(*layers), sorted(save) if __name__ == __main__: parser = argparse.ArgumentParser() parser.add_argument(--cfg, type=str, default=yolov5s.yaml, help=model.yaml) parser.add_argument(--batch-size, type=int, default=1, help=total batch size for all GPUs) parser.add_argument(--device, default=, help=cuda device, i.e. 0 or 0,1,2,3 or cpu) parser.add_argument(--profile, action=store_true, help=profile model speed) parser.add_argument(--line-profile, action=store_true, help=profile model speed layer by layer) parser.add_argument(--test, action=store_true, help=test all yolo*.yaml) opt = parser.parse_args() opt.cfg = check_yaml(opt.cfg) # check YAML print_args(vars(opt)) device = select_device(opt.device) # Create model im = torch.rand(opt.batch_size, 3, 640, 640).to(device) model = Model(opt.cfg).to(device) # Options if opt.line_profile: # profile layer by layer model(im, profile=True) elif opt.profile: # profile forward-backward results = profile(input=im, ops=[model], n=3) elif opt.test: # test all models for cfg in Path(ROOT / models).rglob(yolo*.yaml): try: _ = Model(cfg) except Exception as e: print(fError in {cfg}: {e}) else: # report fused model summary model.fuse()

步骤六：

运行main_gradcam.py

参数列表可以自己进行修改              。 # Arguments parser = argparse.ArgumentParser() parser.add_argument(--model-path, type=str, default="yolov5s.pt", help=Path to the model) parser.add_argument(--img-path, type=str, default=data/images/bus.jpg, help=input image path) parser.add_argument(--output-dir, type=str, default=runs/result17, help=output dir) parser.add_argument(--img-size, type=int, default=640, help="input image size") parser.add_argument(--target-layer, type=str, default=model_17_cv3_act, help=The layer hierarchical address to which gradcam will applied, the names should be separated by underline) parser.add_argument(--method, type=str, default=gradcam, help=gradcam method) parser.add_argument(--device, type=str, default=cuda, help=cuda or cpu) parser.add_argument(--no_text_box, action=store_true, help=do not show label and box on the heatmap) args = parser.parse_args()

完成