torch_terminal.py

#%%
import torch
from torch.autograd import Variable
import torch.nn as nn
import torch.nn.functional as F
import numpy as np

"""
input:[batch_size,in_channel,height,width]
kernel:[out_channel,in_channel,kh,kw]
"""
class Net(nn.Module):

    def __init__(self):
        super(Net, self).__init__()
        # 1 input image channel, 6 output channels, 5x5 square convolution
        # kernel
        self.conv1 = nn.Conv2d(1, 6, 5,padding=(2,2))
        #(28-5+1)/2=12
        self.conv2 = nn.Conv2d(6, 16, 5,padding=(2,2))
        #(12-5+1)/2=4
        # an affine operation: y = Wx + b
        self.fc1 = nn.Linear(16 * 8 * 8, 120)
        self.fc2 = nn.Linear(120, 84)
        self.fc3 = nn.Linear(84, 10)

    def forward(self, x):
        # Max pooling over a (2, 2) window
        x = F.max_pool2d(F.relu(self.conv1(x)), (2, 2))
        # If the size is a square you can only specify a single number
        print "after conv1 size is {}".format(x.size())
        x = F.max_pool2d(F.relu(self.conv2(x)), 2)
        print "after conv2 size is {}".format(x.size())
        x = x.view(-1, self.num_flat_features(x))
        x = F.relu(self.fc1(x))
        x = F.relu(self.fc2(x))
        x = self.fc3(x)
        return x

    def num_flat_features(self, x):
        size = x.size()[1:]  # all dimensions except the batch dimension
        num_features = 1
        for s in size:
            num_features *= s
        return num_features

net = Net()
print(net)

print type(net.parameters())
print "hello world"
input = Variable(torch.Tensor(np.random.randint(1,10,size=(1,1,32,32))))
net.forward(input)