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models.py
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models.py
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## TODO: define the convolutional neural network architecture
import torch
from torch.autograd import Variable
import torch.nn as nn
import torch.nn.functional as F
# can use the below import should you choose to initialize the weights of your Net
import torch.nn.init as I
class Net(nn.Module):
def __init__(self):
super(Net, self).__init__()
## TODO: Define all the layers of this CNN, the only requirements are:
## 1. This network takes in a square (same width and height), grayscale image as input
## 2. It ends with a linear layer that represents the keypoints
## it's suggested that you make this last layer output 136 values, 2 for each of the 68 keypoint (x, y) pairs
# As an example, you've been given a convolutional layer, which you may (but don't have to) change:
# 1 input image channel (grayscale), 32 output channels/feature maps, 5x5 square convolution kernel
self.conv1 = nn.Conv2d(1, 32, 5)#(224-5)/1+1=220
self.pool = nn.MaxPool2d(2, 2)#32,110
self.conv2 = nn.Conv2d(32, 64, 5)#64,106 ->64,53
self.fc1 = nn.Linear(64*53*53, 2*68)
## Note that among the layers to add, consider including:
# maxpooling layers, multiple conv layers, fully-connected layers, and other layers (such as dropout or batch normalization) to avoid overfitting
def forward(self, x):
## TODO: Define the feedforward behavior of this model
## x is the input image and, as an example, here you may choose to include a pool/conv step:
x = self.pool(F.relu(self.conv1(x)))
x = self.pool(F.relu(self.conv2(x)))
x = x.view(x.size(0), -1)
# one linear layer
x = self.fc1(x)
# a modified x, having gone through all the layers of your model, should be returned
return x