众所周知，我们一般是将神经网络理解成一个黑匣子，因此我们往往不知道神经网络特征提取提取的具体是图片的那部分，因此Grad-CAM诞生了，我们只需要少量的代码，Grad-CAM，就可以识别对神经网络模型特征提取图实现可视化，然后使我们清楚地看到神经网络究竟是根据图像的那部分特征进行识别的。
CAM我们就不讲了，挺麻烦的还得重新训练网络才可以绘制自己的热力图，因此为了解决CAM的问题，Grad-CAM于2017年诞生，他通过对某一层卷积层输出进行一系列的处理，可以得到我们网络提取的特征图，进行可视化。
如果我们绘制一系列的热力图我们就可以清楚的看到神经网络如何对我们的网络进行学习的。（详细的可以看上面的论文）这里我们介绍如何使用简单的使用自己的数据集和自己的模型绘制自己的热力图
如图 我这里是（随便拿了个网络的玉米病斑数据，然后用resnet跑了测试了一下效果）要提取做的是识别这里面的病斑，然后我训练好我的模型，打入图片进行预测，之后对于其中最后一个卷积层的输出打入到Grad-CAM进行可视化。
可以清楚的看到，我的神经网络对于病斑的识别，完全是根据病斑本身来确定的。这说明我们的网络训练的非常好。
这里强调一下，BN层，激活函数层的输出是无法可视化的，选择输出那一层会报错，自己跑自己的时候多加调试。
咱们的代码是参考Grad-CAM简介_太阳花的小绿豆的博客-CSDN博客_grad-cam这位大佬的，进行了一部分更细化的修改和对于一些细节的问题进行了优化，使我们更简单的就就可以实现自己网络特征层的可视化。
很方便的替换自己的模型和数据集绘制热力图，有问题可以在评论区指出，看到会回复
import os import numpy as np from PIL import Image from torchvision import transforms from utils import GradCAM, show_cam_on_image, center_crop_img import torch from matplotlib import pyplot as plt from torch import nn from torchvision.transforms import transforms def main(): #这个下面放置你网络的代码，因为载入权重的时候需要读取网络代码，这里我建议直接从自己的训练代码中原封不动的复制过来即可，我这里因为跑代码使用的是Resnet，所以这里将resent的网络复制到这里即可 class BasicBlock(nn.Module): expansion = 1 def __init__(self, in_channel, out_channel, stride=1, downsample=None, **kwargs): super(BasicBlock, self).__init__() self.conv1 = nn.Conv2d(in_channels=in_channel, out_channels=out_channel, kernel_size=3, stride=stride, padding=1, bias=False) self.bn1 = nn.BatchNorm2d(out_channel) self.relu = nn.ReLU() self.conv2 = nn.Conv2d(in_channels=out_channel, out_channels=out_channel, kernel_size=3, stride=1, padding=1, bias=False) self.bn2 = nn.BatchNorm2d(out_channel) self.downsample = downsample def forward(self, x): identity = x if self.downsample is not None: identity = self.downsample(x) out = self.conv1(x) out = self.bn1(out) out = self.relu(out) out = self.conv2(out) out = self.bn2(out) out += identity out = self.relu(out) return out class Bottleneck(nn.Module): """ 注意：原论文中，在虚线残差结构的主分支上，第一个1x1卷积层的步距是2，第二个3x3卷积层步距是1。 但在pytorch官方实现过程中是第一个1x1卷积层的步距是1，第二个3x3卷积层步距是2， 这么做的好处是能够在top1上提升大概0.5%的准确率。 可参考Resnet v1.5 https://ngc.nvidia.com/catalog/model-scripts/nvidia:resnet_50_v1_5_for_pytorch """ expansion = 4 def __init__(self, in_channel, out_channel, stride=1, downsample=None, groups=1, width_per_group=64): super(Bottleneck, self).__init__() width = int(out_channel * (width_per_group / 64.)) * groups self.conv1 = nn.Conv2d(in_channels=in_channel, out_channels=width, kernel_size=1, stride=1, bias=False) # squeeze channels self.bn1 = nn.BatchNorm2d(width) # ----------------------------------------- self.conv2 = nn.Conv2d(in_channels=width, out_channels=width, groups=groups, kernel_size=3, stride=stride, bias=False, padding=1) self.bn2 = nn.BatchNorm2d(width) # ----------------------------------------- self.conv3 = nn.Conv2d(in_channels=width, out_channels=out_channel * self.expansion, kernel_size=1, stride=1, bias=False) # unsqueeze channels self.bn3 = nn.BatchNorm2d(out_channel * self.expansion) self.relu = nn.ReLU(inplace=True) self.downsample = downsample def forward(self, x): identity = x if self.downsample is not None: identity = self.downsample(x) out = self.conv1(x) out = self.bn1(out) out = self.relu(out) out = self.conv2(out) out = self.bn2(out) out = self.relu(out) out = self.conv3(out) out = self.bn3(out) out += identity out = self.relu(out) return out class ResNet(nn.Module): def __init__(self, block, blocks_num, num_classes=5, include_top=True, groups=1, width_per_group=64): super(ResNet, self).__init__() self.include_top = include_top self.in_channel = 64 self.groups = groups self.width_per_group = width_per_group self.conv1 = nn.Conv2d(3, self.in_channel, kernel_size=7, stride=2, padding=3, bias=False) self.bn1 = nn.BatchNorm2d(self.in_channel) self.relu = nn.ReLU(inplace=True) self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1) self.layer1 = self._make_layer(block, 64, blocks_num[0]) self.layer2 = self._make_layer(block, 128, blocks_num[1], stride=2) self.layer3 = self._make_layer(block, 256, blocks_num[2], stride=2) self.layer4 = self._make_layer(block, 512, blocks_num[3], stride=2) if self.include_top: self.avgpool = nn.AdaptiveAvgPool2d((1, 1)) # output size = (1, 1) self.fc = nn.Linear(512 * block.expansion, num_classes) for m in self.modules(): if isinstance(m, nn.Conv2d): nn.init.kaiming_normal_(m.weight, mode='fan_out', nonlinearity='relu') def _make_layer(self, block, channel, block_num, stride=1): downsample = None if stride != 1 or self.in_channel != channel * block.expansion: downsample = nn.Sequential( nn.Conv2d(self.in_channel, channel * block.expansion, kernel_size=1, stride=stride, bias=False), nn.BatchNorm2d(channel * block.expansion)) layers = [] layers.append(block(self.in_channel, channel, downsample=downsample, stride=stride, groups=self.groups, width_per_group=self.width_per_group)) self.in_channel = channel * block.expansion for _ in range(1, block_num): layers.append(block(self.in_channel, channel, groups=self.groups, width_per_group=self.width_per_group)) return nn.Sequential(*layers) def forward(self, x): x = self.conv1(x) x = self.bn1(x) x = self.relu(x) x = self.maxpool(x) x = self.layer1(x) x = self.layer2(x) x = self.layer3(x) x = self.layer4(x) if self.include_top: x = self.avgpool(x) x = torch.flatten(x, 1) x = self.fc(x) return x def resnet34(num_classes=1000, include_top=True): # https://download.pytorch.org/models/resnet34-333f7ec4.pth return ResNet(BasicBlock, [3, 4, 6, 3], num_classes=num_classes, include_top=include_top) def resnet50(num_classes=1000, include_top=True): # https://download.pytorch.org/models/resnet50-19c8e357.pth return ResNet(Bottleneck, [3, 4, 6, 3], num_classes=num_classes, include_top=include_top) def resnet101(num_classes=1000, include_top=True): # https://download.pytorch.org/models/resnet101-5d3b4d8f.pth return ResNet(Bottleneck, [3, 4, 23, 3], num_classes=num_classes, include_top=include_top) def resnext50_32x4d(num_classes=1000, include_top=True): # https://download.pytorch.org/models/resnext50_32x4d-7cdf4587.pth groups = 32 width_per_group = 4 return ResNet(Bottleneck, [3, 4, 6, 3], num_classes=num_classes, include_top=include_top, groups=groups, width_per_group=width_per_group) def resnext101_32x8d(num_classes=1000, include_top=True): # https://download.pytorch.org/models/resnext101_32x8d-8ba56ff5.pth groups = 32 width_per_group = 8 return ResNet(Bottleneck, [3, 4, 23, 3], num_classes=num_classes, include_top=include_top, groups=groups, width_per_group=width_per_group) net = resnet34() device = torch.device("cpu") net.load_state_dict(torch.load("./transfer-learning-resnet.pth", map_location=device)) # 载入训练的resnet模型权重，你将训练的模型权重放到当前文件夹下即可 target_layers = [net.layer4[-1]] #这里是 看你是想看那一层的输出，我这里是打印的resnet最后一层的输出，你也可以根据需要修改成自己的 print(target_layers) data_transform = transforms.Compose([ transforms.ToTensor(), transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])]) # 导入图片 img_path = "./38.jpg"#这里是导入你需要测试图片 image_size = 500#训练图像的尺寸，在你训练图像的时候图像尺寸是多少这里就填多少 assert os.path.exists(img_path), "file: '{}' dose not exist.".format(img_path) img = Image.open(img_path).convert('RGB')#将图片转成RGB格式的 img = np.array(img, dtype=np.uint8) #转成np格式 img = center_crop_img(img, image_size) #将测试图像裁剪成跟训练图片尺寸相同大小的 # [C, H, W] img_tensor = data_transform(img)#简单预处理将图片转化为张量 # expand batch dimension # [C, H, W] -> [N, C, H, W] input_tensor = torch.unsqueeze(img_tensor, dim=0) #增加一个batch维度 cam = GradCAM(model=net, target_layers=target_layers, use_cuda=False) grayscale_cam = cam(input_tensor=input_tensor) grayscale_cam = grayscale_cam[0, :] visualization = show_cam_on_image(img.astype(dtype=np.float32) / 255., grayscale_cam, use_rgb=True) plt.imshow(visualization) plt.savefig('./result.png')#将热力图的结果保存到本地当前文件夹 plt.show() if __name__ == '__main__': main()
util.py
这是需要调用的库文件的代码，你新建一个.py文件与上述的同放在一个文件夹下即可
import cv2 import numpy as np class ActivationsAndGradients: """ Class for extracting activations and registering gradients from targeted intermediate layers """ def __init__(self, model, target_layers, reshape_transform): self.model = model self.gradients = [] self.activations = [] self.reshape_transform = reshape_transform self.handles = [] for target_layer in target_layers: self.handles.append( target_layer.register_forward_hook( self.save_activation)) # Backward compatibility with older pytorch versions: if hasattr(target_layer, 'register_full_backward_hook'): self.handles.append( target_layer.register_full_backward_hook( self.save_gradient)) else: self.handles.append( target_layer.register_backward_hook( self.save_gradient)) def save_activation(self, module, input, output): activation = output if self.reshape_transform is not None: activation = self.reshape_transform(activation) self.activations.append(activation.cpu().detach()) def save_gradient(self, module, grad_input, grad_output): # Gradients are computed in reverse order grad = grad_output[0] if self.reshape_transform is not None: grad = self.reshape_transform(grad) self.gradients = [grad.cpu().detach()] + self.gradients def __call__(self, x): self.gradients = [] self.activations = [] return self.model(x) def release(self): for handle in self.handles: handle.remove() class GradCAM: def __init__(self, model, target_layers, reshape_transform=None, use_cuda=False): self.model = model.eval() self.target_layers = target_layers self.reshape_transform = reshape_transform self.cuda = use_cuda if self.cuda: self.model = model.cuda() self.activations_and_grads = ActivationsAndGradients( self.model, target_layers, reshape_transform) """ Get a vector of weights for every channel in the target layer. Methods that return weights channels, will typically need to only implement this function. """ @staticmethod def get_cam_weights(grads): return np.mean(grads, axis=(2, 3), keepdims=True) @staticmethod def get_loss(output, target_category): loss = 0 for i in range(len(target_category)): loss = loss + output[i, target_category[i]] return loss def get_cam_image(self, activations, grads): weights = self.get_cam_weights(grads) weighted_activations = weights * activations cam = weighted_activations.sum(axis=1) return cam @staticmethod def get_target_width_height(input_tensor): width, height = input_tensor.size(-1), input_tensor.size(-2) return width, height def compute_cam_per_layer(self, input_tensor): activations_list = [a.cpu().data.numpy() for a in self.activations_and_grads.activations] grads_list = [g.cpu().data.numpy() for g in self.activations_and_grads.gradients] target_size = self.get_target_width_height(input_tensor) cam_per_target_layer = [] # Loop over the saliency image from every layer for layer_activations, layer_grads in zip(activations_list, grads_list): cam = self.get_cam_image(layer_activations, layer_grads) cam[cam < 0] = 0 # works like mute the min-max scale in the function of scale_cam_image scaled = self.scale_cam_image(cam, target_size) cam_per_target_layer.append(scaled[:, None, :]) return cam_per_target_layer def aggregate_multi_layers(self, cam_per_target_layer): cam_per_target_layer = np.concatenate(cam_per_target_layer, axis=1) cam_per_target_layer = np.maximum(cam_per_target_layer, 0) result = np.mean(cam_per_target_layer, axis=1) return self.scale_cam_image(result) @staticmethod def scale_cam_image(cam, target_size=None): result = [] for img in cam: img = img - np.min(img) img = img / (1e-7 + np.max(img)) if target_size is not None: img = cv2.resize(img, target_size) result.append(img) result = np.float32(result) return result def __call__(self, input_tensor, target_category=None): if self.cuda: input_tensor = input_tensor.cuda() # 正向传播得到网络输出logits(未经过softmax) output = self.activations_and_grads(input_tensor) if isinstance(target_category, int): target_category = [target_category] * input_tensor.size(0) if target_category is None: target_category = np.argmax(output.cpu().data.numpy(), axis=-1) print(f"category id: {target_category}") else: assert (len(target_category) == input_tensor.size(0)) self.model.zero_grad() loss = self.get_loss(output, target_category) loss.backward(retain_graph=True) # In most of the saliency attribution papers, the saliency is # computed with a single target layer. # Commonly it is the last convolutional layer. # Here we support passing a list with multiple target layers. # It will compute the saliency image for every image, # and then aggregate them (with a default mean aggregation). # This gives you more flexibility in case you just want to # use all conv layers for example, all Batchnorm layers, # or something else. cam_per_layer = self.compute_cam_per_layer(input_tensor) return self.aggregate_multi_layers(cam_per_layer) def __del__(self): self.activations_and_grads.release() def __enter__(self): return self def __exit__(self, exc_type, exc_value, exc_tb): self.activations_and_grads.release() if isinstance(exc_value, IndexError): # Handle IndexError here... print( f"An exception occurred in CAM with block: {exc_type}. Message: {exc_value}") return True def show_cam_on_image(img: np.ndarray, mask: np.ndarray, use_rgb: bool = False, colormap: int = cv2.COLORMAP_JET) -> np.ndarray: """ This function overlays the cam mask on the image as an heatmap. By default the heatmap is in BGR format. :param img: The base image in RGB or BGR format. :param mask: The cam mask. :param use_rgb: Whether to use an RGB or BGR heatmap, this should be set to True if 'img' is in RGB format. :param colormap: The OpenCV colormap to be used. :returns: The default image with the cam overlay. """ heatmap = cv2.applyColorMap(np.uint8(255 * mask), colormap) if use_rgb: heatmap = cv2.cvtColor(heatmap, cv2.COLOR_BGR2RGB) heatmap = np.float32(heatmap) / 255 if np.max(img) > 1: raise Exception( "The input image should np.float32 in the range [0, 1]") cam = heatmap + img cam = cam / np.max(cam) return np.uint8(255 * cam) def center_crop_img(img: np.ndarray, size: int): h, w, c = img.shape if w == h == size: return img if w < h: ratio = size / w new_w = size new_h = int(h * ratio) else: ratio = size / h new_h = size new_w = int(w * ratio) img = cv2.resize(img, dsize=(new_w, new_h)) if new_w == size: h = (new_h - size) // 2 img = img[h: h+size] else: w = (new_w - size) // 2 img = img[:, w: w+size] return img
