Pytorch:获取最后一层的正确尺寸 [英] Pytorch: Getting the correct dimensions for final layer
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问题描述
Pytorch 新手来了！我正在尝试微调 VGG16 模型以预测 3 个不同的类别.我的部分工作涉及将 FC 层转换为 CONV 层.但是，我的预测值不在 0 到 2(3 个类别)之间.
Pytorch newbie here! I am trying to fine-tune a VGG16 model to predict 3 different classes. Part of my work involves converting FC layers to CONV layers. However, the values of my predictions don't fall between 0 to 2 (the 3 classes).
有人能给我指出一个关于如何计算最后一层正确尺寸的好资源吗?
Can someone point me to a good resource on how to compute the correct dimensions for the final layer?
以下是 VGG16 的原始 fC 层:
Here are the original fC layers of VGG16:
(classifier): Sequential(
(0): Linear(in_features=25088, out_features=4096, bias=True)
(1): ReLU(inplace)
(2): Dropout(p=0.5)
(3): Linear(in_features=4096, out_features=4096, bias=True)
(4): ReLU(inplace)
(5): Dropout(p=0.5)
(6): Linear(in_features=4096, out_features=1000, bias=True)
)
我的将 FC 层转换为 CONV 的代码:
My code for converting FC layers to CONV:
def convert_fc_to_conv(self, fc_layers):
# Replace first FC layer with CONV layer
fc = fc_layers[0].state_dict()
in_ch = 512
out_ch = fc["weight"].size(0)
first_conv = nn.Conv2d(512, out_ch, kernel_size=(1, 1), stride=(1, 1))
conv_list = [first_conv]
for idx, layer in enumerate(fc_layers[1:]):
if isinstance(layer, nn.Linear):
fc = layer.state_dict()
in_ch = fc["weight"].size(1)
out_ch = fc["weight"].size(0)
if idx == len(fc_layers)-4:
in_ch = 3
conv = nn.Conv2d(out_ch, in_ch, kernel_size=(1, 1), stride=(1, 1))
conv_list += [conv]
else:
conv_list += [layer]
gc.collect()
avg_pool = nn.AvgPool2d(kernel_size=2, stride=1, ceil_mode=False)
conv_list += [avg_pool, nn.Softmax()]
top_layers = nn.Sequential(*conv_list)
return top_layers
最终模型架构:
Model(
(features): Sequential(
(0): Conv2d(3, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(1): ReLU(inplace)
(2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(3): ReLU(inplace)
(4): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)
(5): Conv2d(64, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(6): ReLU(inplace)
(7): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(8): ReLU(inplace)
(9): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)
(10): Conv2d(128, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(11): ReLU(inplace)
(12): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(13): ReLU(inplace)
(14): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(15): ReLU(inplace)
(16): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)
(17): Conv2d(256, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(18): ReLU(inplace)
(19): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(20): ReLU(inplace)
(21): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(22): ReLU(inplace)
(23): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)
(24): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(25): ReLU(inplace)
(26): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(27): ReLU(inplace)
(28): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(29): ReLU(inplace)
(30): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False))
(classifier): Sequential(
(0): Conv2d(512, 4096, kernel_size=(1, 1), stride=(1, 1))
(1): ReLU(inplace)
(2): Dropout(p=0.5)
(3): Conv2d(4096, 3, kernel_size=(1, 1), stride=(1, 1))
(4): ReLU(inplace)
(5): Dropout(p=0.5)
(6): AvgPool2d(kernel_size=2, stride=1, padding=0)
(7): Softmax()
)
)
模型总结:
Conv2d-1 [-1, 64, 224, 224] 1,792
ReLU-2 [-1, 64, 224, 224] 0
Conv2d-3 [-1, 64, 224, 224] 36,928
ReLU-4 [-1, 64, 224, 224] 0
MaxPool2d-5 [-1, 64, 112, 112] 0
Conv2d-6 [-1, 128, 112, 112] 73,856
ReLU-7 [-1, 128, 112, 112] 0
Conv2d-8 [-1, 128, 112, 112] 147,584
ReLU-9 [-1, 128, 112, 112] 0
MaxPool2d-10 [-1, 128, 56, 56] 0
Conv2d-11 [-1, 256, 56, 56] 295,168
ReLU-12 [-1, 256, 56, 56] 0
Conv2d-13 [-1, 256, 56, 56] 590,080
ReLU-14 [-1, 256, 56, 56] 0
Conv2d-15 [-1, 256, 56, 56] 590,080
ReLU-16 [-1, 256, 56, 56] 0
MaxPool2d-17 [-1, 256, 28, 28] 0
Conv2d-18 [-1, 512, 28, 28] 1,180,160
ReLU-19 [-1, 512, 28, 28] 0
Conv2d-20 [-1, 512, 28, 28] 2,359,808
ReLU-21 [-1, 512, 28, 28] 0
Conv2d-22 [-1, 512, 28, 28] 2,359,808
ReLU-23 [-1, 512, 28, 28] 0
MaxPool2d-24 [-1, 512, 14, 14] 0
Conv2d-25 [-1, 512, 14, 14] 2,359,808
ReLU-26 [-1, 512, 14, 14] 0
Conv2d-27 [-1, 512, 14, 14] 2,359,808
ReLU-28 [-1, 512, 14, 14] 0
Conv2d-29 [-1, 512, 14, 14] 2,359,808
ReLU-30 [-1, 512, 14, 14] 0
MaxPool2d-31 [-1, 512, 7, 7] 0
Conv2d-32 [-1, 4096, 7, 7] 2,101,248
ReLU-33 [-1, 4096, 7, 7] 0
Dropout-34 [-1, 4096, 7, 7] 0
Conv2d-35 [-1, 3, 7, 7] 12,291
ReLU-36 [-1, 3, 7, 7] 0
Dropout-37 [-1, 3, 7, 7] 0
AvgPool2d-38 [-1, 3, 6, 6] 0
Softmax-39 [-1, 3, 6, 6] 0
推荐答案
我写了一个函数，将 Pytorch 模型作为输入，将分类层转换为卷积层.它目前适用于 VGG 和 Alexnet，但您也可以将其扩展到其他模型.
I wrote a function that takes a Pytorch model as input and converts the classification layer to convolution layer. It works for VGG and Alexnet for now, but you can extend it for other models as well.
import torch
import torch.nn as nn
from torchvision.models import alexnet, vgg16
def convolutionize(model, num_classes, input_size=(3, 224, 224)):
'''Converts the classification layers of VGG & Alexnet to convolutions
Input:
model: torch.models
num_classes: number of output classes
input_size: size of input tensor to the model
Returns:
model: converted model with convolutions
'''
features = model.features
classifier = model.classifier
# create a dummy input tensor and add a dim for batch-size
x = torch.zeros(input_size).unsqueeze_(dim=0)
# change the last layer output to the num_classes
classifier[-1] = nn.Linear(in_features=classifier[-1].in_features,
out_features=num_classes)
# pass the dummy input tensor through the features layer to compute the output size
for layer in features:
x = layer(x)
conv_classifier = []
for layer in classifier:
if isinstance(layer, nn.Linear):
# create a convolution equivalent of linear layer
conv_layer = nn.Conv2d(in_channels=x.size(1),
out_channels=layer.weight.size(0),
kernel_size=(x.size(2), x.size(3)))
# transfer the weights
conv_layer.weight.data.view(-1).copy_(layer.weight.data.view(-1))
conv_layer.bias.data.view(-1).copy_(layer.bias.data.view(-1))
layer = conv_layer
x = layer(x)
conv_classifier.append(layer)
# replace the model.classifier with newly created convolution layers
model.classifier = nn.Sequential(*conv_classifier)
return model
def visualize(model, input_size=(3, 224, 224)):
'''Visualize the input size though the layers of the model'''
x = torch.zeros(input_size).unsqueeze_(dim=0)
print(x.size())
for layer in list(model.features) + list(model.classifier):
x = layer(x)
print(x.size())
这是输入通过模型时的样子
This is how the input looks when passed through the model
_vgg = vgg16()
vgg = convolutionize(_vgg, 100)
print('
VGG')
visualize(vgg)
...
VGG
torch.Size([1, 3, 224, 224])
torch.Size([1, 64, 224, 224])
torch.Size([1, 64, 224, 224])
torch.Size([1, 64, 224, 224])
torch.Size([1, 64, 224, 224])
torch.Size([1, 64, 112, 112])
torch.Size([1, 128, 112, 112])
torch.Size([1, 128, 112, 112])
torch.Size([1, 128, 112, 112])
torch.Size([1, 128, 112, 112])
torch.Size([1, 128, 56, 56])
torch.Size([1, 256, 56, 56])
torch.Size([1, 256, 56, 56])
torch.Size([1, 256, 56, 56])
torch.Size([1, 256, 56, 56])
torch.Size([1, 256, 56, 56])
torch.Size([1, 256, 56, 56])
torch.Size([1, 256, 28, 28])
torch.Size([1, 512, 28, 28])
torch.Size([1, 512, 28, 28])
torch.Size([1, 512, 28, 28])
torch.Size([1, 512, 28, 28])
torch.Size([1, 512, 28, 28])
torch.Size([1, 512, 28, 28])
torch.Size([1, 512, 14, 14])
torch.Size([1, 512, 14, 14])
torch.Size([1, 512, 14, 14])
torch.Size([1, 512, 14, 14])
torch.Size([1, 512, 14, 14])
torch.Size([1, 512, 14, 14])
torch.Size([1, 512, 14, 14])
torch.Size([1, 512, 7, 7])
torch.Size([1, 4096, 1, 1])
torch.Size([1, 4096, 1, 1])
torch.Size([1, 4096, 1, 1])
torch.Size([1, 4096, 1, 1])
torch.Size([1, 4096, 1, 1])
torch.Size([1, 4096, 1, 1])
torch.Size([1, 100, 1, 1])
这篇关于Pytorch:获取最后一层的正确尺寸的文章就介绍到这了，希望我们推荐的答案对大家有所帮助，也希望大家多多支持IT屋！
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