利用Pytorch实现获取特征图的方法详解

目录

• 简单加载官方预训练模型
• 图片预处理
• 提取单个特征图
• 提取多个特征图

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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