Pytorch:为最终层获取正确的尺寸 [英] Pytorch: Getting the correct dimensions for final layer
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问题描述
Pytorch新手在这里！我正在尝试微调VGG16模型以预测3个不同的类.我的部分工作涉及将FC层转换为CONV层.但是，我的预测值不介于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).
有人可以为我提供有关如何计算最终层的正确尺寸的良好资源吗?
Can someone point me to a good resource on how to compute the correct dimensions for the final layer?
这是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)
)
我将FC图层转换为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()
)
)
模型摘要:
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
我编写了一个函数，该函数采用Pytorch模型作为输入，并将分类层转换为卷积层.目前，它适用于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())
这是通过模型传递输入时的样子
This is how the input looks when passed through the model
_vgg = vgg16()
vgg = convolutionize(_vgg, 100)
print('\n\nVGG')
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])
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