AntoineH/smart_augmentation
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Harle, Antoine (Contracteur) 2019-11-08 11:28:06 -05:00
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import math
import torch
import torch.nn as nn
import torch.nn.functional as F
class LeNet(nn.Module):
def __init__(self, num_inp, num_out):
super(LeNet, self).__init__()
self._params = nn.ParameterDict({
'w1': nn.Parameter(torch.zeros(20, num_inp, 5, 5)),
'b1': nn.Parameter(torch.zeros(20)),
'w2': nn.Parameter(torch.zeros(50, 20, 5, 5)),
'b2': nn.Parameter(torch.zeros(50)),
#'w3': nn.Parameter(torch.zeros(500,4*4*50)), #num_imp=1
'w3': nn.Parameter(torch.zeros(500,5*5*50)), #num_imp=3
'b3': nn.Parameter(torch.zeros(500)),
'w4': nn.Parameter(torch.zeros(num_out, 500)),
'b4': nn.Parameter(torch.zeros(num_out))
})
self.initialize()
def initialize(self):
nn.init.kaiming_uniform_(self._params["w1"], a=math.sqrt(5))
nn.init.kaiming_uniform_(self._params["w2"], a=math.sqrt(5))
nn.init.kaiming_uniform_(self._params["w3"], a=math.sqrt(5))
nn.init.kaiming_uniform_(self._params["w4"], a=math.sqrt(5))
def forward(self, x):
#print("Start Shape ", x.shape)
out = F.relu(F.conv2d(input=x, weight=self._params["w1"], bias=self._params["b1"]))
#print("Shape ", out.shape)
out = F.max_pool2d(out, 2)
#print("Shape ", out.shape)
out = F.relu(F.conv2d(input=out, weight=self._params["w2"], bias=self._params["b2"]))
#print("Shape ", out.shape)
out = F.max_pool2d(out, 2)
#print("Shape ", out.shape)
out = out.view(out.size(0), -1)
#print("Shape ", out.shape)
out = F.relu(F.linear(out, self._params["w3"], self._params["b3"]))
#print("Shape ", out.shape)
out = F.linear(out, self._params["w4"], self._params["b4"])
#print("Shape ", out.shape)
return F.log_softmax(out, dim=1)
def __getitem__(self, key):
return self._params[key]
def __str__(self):
return "LeNet"