这篇文章主要为大家详细介绍了如何利用Pytorch实现获取特征图，包括提取单个特征图和提取多个特征图，文中的示例代码讲解详细，需要的可以参考一下
简单加载官方预训练模型
torchvision.models预定义了很多公开的模型结构
如果pretrained参数设置为False，那么仅仅设定模型结构；如果设置为True，那么会启动一个下载流程，下载预训练参数
如果只想调用模型，不想训练，那么设置model.eval()和model.requires_grad_(False)
想查看模型参数可以使用modules和named_modules，其中named_modules是一个长度为2的tuple，第一个变量是name，第二个变量是module本身。
# -*- coding: utf-8 -*- from torch import nn from torchvision import models # load model. If pretrained is True, there will be a downloading process model = models.vgg19(pretrained=True) model.eval() model.requires_grad_(False) # get model component features = model.features modules = features.modules() named_modules = features.named_modules() # print modules for module in modules: if isinstance(module, nn.Conv2d): weight = module.weight bias = module.bias print(module, weight.shape, bias.shape, weight.requires_grad, bias.requires_grad) elif isinstance(module, nn.ReLU): print(module) print() for named_module in named_modules: name = named_module[0] module = named_module[1] if isinstance(module, nn.Conv2d): weight = module.weight bias = module.bias print(name, module, weight.shape, bias.shape, weight.requires_grad, bias.requires_grad) elif isinstance(module, nn.ReLU): print(name, module)
图片预处理
使用opencv和pil读图都可以使用transforms.ToTensor()把原本[H, W, 3]的数据转成[3, H, W]的tensor。但opencv要注意把数据改成RGB顺序。
vgg系列模型需要做normalization，建议配合torchvision.transforms来实现。
mini-batches of 3-channel RGB images of shape (3 x H x W), where H and W are expected to be at least 224. The images have to be loaded in to a range of [0, 1] and then normalized using mean = [0.485, 0.456, 0.406] and std = [0.229, 0.224, 0.225].
参考：https://pytorch.org/hub/pytorch_vision_vgg/
# -*- coding: utf-8 -*- from PIL import Image import cv2 import torch from torchvision import transforms # transforms for preprocess preprocess = transforms.Compose([ transforms.ToTensor(), transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]) ]) # load image using cv2 image_cv2 = cv2.imread('lena_std.bmp') image_cv2 = cv2.cvtColor(image_cv2, cv2.COLOR_BGR2RGB) image_cv2 = preprocess(image_cv2) # load image using pil image_pil = Image.open('lena_std.bmp') image_pil = preprocess(image_pil) # check whether image_cv2 and image_pil are same print(torch.all(image_cv2 == image_pil)) print(image_cv2.shape, image_pil.shape)提取单个特征图
如果只提取单层特征图，可以把模型截断，以节省算力和显存消耗。
下面索引之所以有+1是因为pytorch预训练模型里面第一个索引的module总是完整模块结构，第二个才开始子模块。
# -*- coding: utf-8 -*- from PIL import Image from torchvision import models from torchvision import transforms # load model. If pretrained is True, there will be a downloading process model = models.vgg19(pretrained=True) model = model.features[:16 + 1] # 16 = conv3_4 model.eval() model.requires_grad_(False) model.to('cuda') print(model) # load and preprocess image preprocess = transforms.Compose([ transforms.ToTensor(), transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]), transforms.Resize(size=(224, 224)) ]) image = Image.open('lena_std.bmp') image = preprocess(image) inputs = image.unsqueeze(0) # add batch dimension inputs = inputs.cuda() # forward output = model(inputs) print(output.shape)提取多个特征图
第一种方式：逐层运行model，如果碰到了需要保存的feature map就存下来。
第二种方式：使用register_forward_hook，使用这种方式需要用一个类把feature map以成员变量的形式缓存下来。
两种方式的运行效率差不多
第一种方式简单直观，但是只能处理类似VGG这种没有跨层连接的网络；第二种方式更加通用。
# -*- coding: utf-8 -*- from PIL import Image import torch from torchvision import models from torchvision import transforms # load model. If pretrained is True, there will be a downloading process model = models.vgg19(pretrained=True) model = model.features[:16 + 1] # 16 = conv3_4 model.eval() model.requires_grad_(False) model.to('cuda') # check module name for named_module in model.named_modules(): name = named_module[0] module = named_module[1] print('-------- %s --------' % name) print(module) print() # load and preprocess image preprocess = transforms.Compose([ transforms.ToTensor(), transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]), transforms.Resize(size=(224, 224)) ]) image = Image.open('lena_std.bmp') image = preprocess(image) inputs = image.unsqueeze(0) # add batch dimension inputs = inputs.cuda() # forward - 1 layers = [2, 7, 8, 9, 16] layers = sorted(set(layers)) feature_maps = {} feature = inputs for i in range(max(layers) + 1): feature = model[i](feature) if i in layers: feature_maps[i] = feature for key in feature_maps: print(key, feature_maps.get(key).shape) # forward - 2 class FeatureHook: def __init__(self, module): self.inputs = None self.output = None self.hook = module.register_forward_hook(self.get_features) def get_features(self, module, inputs, output): self.inputs = inputs self.output = output layer_names = ['2', '7', '8', '9', '16'] hook_modules = [] for named_module in model.named_modules(): name = named_module[0] module = named_module[1] if name in layer_names: hook_modules.append(module) hooks = [FeatureHook(module) for module in hook_modules] output = model(inputs) features = [hook.output for hook in hooks] for feature in features: print(feature.shape) # check correctness for i, layer in enumerate(layers): feature1 = feature_maps.get(layer) feature2 = features[i] print(torch.all(feature1 == feature2))使用第二种方式（register_forward_hook），resnet特征图也可以顺利拿到。
而由于resnet的model已经不可以用model[i]的形式索引，所以无法使用第一种方式。
# -*- coding: utf-8 -*- from PIL import Image from torchvision import models from torchvision import transforms # load model. If pretrained is True, there will be a downloading process model = models.resnet18(pretrained=True) model.eval() model.requires_grad_(False) model.to('cuda') # check module name for named_module in model.named_modules(): name = named_module[0] module = named_module[1] print('-------- %s --------' % name) print(module) print() # load and preprocess image preprocess = transforms.Compose([ transforms.ToTensor(), transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]), transforms.Resize(size=(224, 224)) ]) image = Image.open('lena_std.bmp') image = preprocess(image) inputs = image.unsqueeze(0) # add batch dimension inputs = inputs.cuda() class FeatureHook: def __init__(self, module): self.inputs = None self.output = None self.hook = module.register_forward_hook(self.get_features) def get_features(self, module, inputs, output): self.inputs = inputs self.output = output layer_names = [ 'conv1', 'layer1.0.relu', 'layer2.0.conv1' ] hook_modules = [] for named_module in model.named_modules(): name = named_module[0] module = named_module[1] if name in layer_names: hook_modules.append(module) hooks = [FeatureHook(module) for module in hook_modules] output = model(inputs) features = [hook.output for hook in hooks] for feature in features: print(feature.shape)问题来了，resnet这种类型的网络结构怎么截断？
使用如下命令就可以，print查看需要截断到哪里，然后用nn.Sequential重组即可。
需注意重组后网络的module_name会发生变化。
print(list(model.children()) model = torch.nn.Sequential(*list(model.children())[:6])
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