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perception.py
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perception.py
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from torchvision import models
import torch.nn as nn
import torch.nn.functional as F
from torch.autograd import Variable
import torch
from utils import norm_col_init, weights_init, weights_init_mlp
class CNN_net(nn.Module):
def __init__(self, obs_shape, stack_frames):
super(CNN_net, self).__init__()
self.conv1 = nn.Conv2d(obs_shape[0], 32, 5, stride=1, padding=2)
self.maxp1 = nn.MaxPool2d(2, 2)
self.conv2 = nn.Conv2d(32, 32, 5, stride=1, padding=1)
self.maxp2 = nn.MaxPool2d(2, 2)
self.conv3 = nn.Conv2d(32, 64, 4, stride=1, padding=1)
self.maxp3 = nn.MaxPool2d(2, 2)
self.conv4 = nn.Conv2d(64, 64, 3, stride=1, padding=1)
self.maxp4 = nn.MaxPool2d(2, 2)
relu_gain = nn.init.calculate_gain('relu')
self.conv1.weight.data.mul_(relu_gain)
self.conv2.weight.data.mul_(relu_gain)
self.conv3.weight.data.mul_(relu_gain)
self.conv4.weight.data.mul_(relu_gain)
dummy_state = Variable(torch.rand(stack_frames, obs_shape[0], obs_shape[1], obs_shape[2]))
out = self.forward(dummy_state)
self.outdim = out.size(-1)
self.apply(weights_init)
self.train()
def forward(self, x):
x = F.relu(self.maxp1(self.conv1(x)))
x = F.relu(self.maxp2(self.conv2(x)))
x = F.relu(self.maxp3(self.conv3(x)))
x = F.relu(self.maxp4(self.conv4(x)))
x = x.view(x.shape[0], -1)
return x