75 lines
2.0 KiB
Python
75 lines
2.0 KiB
Python
import torch
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import torch.nn as nn
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from encoder import OUT_DIM
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class PixelDecoder(nn.Module):
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def __init__(self, obs_shape, feature_dim, num_layers=2, num_filters=32):
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super().__init__()
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self.num_layers = num_layers
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self.num_filters = num_filters
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self.out_dim = OUT_DIM[num_layers]
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self.fc = nn.Linear(
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feature_dim, num_filters * self.out_dim * self.out_dim
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)
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self.deconvs = nn.ModuleList()
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for i in range(self.num_layers - 1):
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self.deconvs.append(
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nn.ConvTranspose2d(num_filters, num_filters, 3, stride=1)
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)
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self.deconvs.append(
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nn.ConvTranspose2d(
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num_filters, obs_shape[0], 3, stride=2, output_padding=1
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)
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)
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self.outputs = dict()
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def forward(self, h):
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h = torch.relu(self.fc(h))
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self.outputs['fc'] = h
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deconv = h.view(-1, self.num_filters, self.out_dim, self.out_dim)
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self.outputs['deconv1'] = deconv
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for i in range(0, self.num_layers - 1):
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deconv = torch.relu(self.deconvs[i](deconv))
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self.outputs['deconv%s' % (i + 1)] = deconv
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obs = self.deconvs[-1](deconv)
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self.outputs['obs'] = obs
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return obs
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def log(self, L, step, log_freq):
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if step % log_freq != 0:
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return
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for k, v in self.outputs.items():
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L.log_histogram('train_decoder/%s_hist' % k, v, step)
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if len(v.shape) > 2:
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L.log_image('train_decoder/%s_i' % k, v[0], step)
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for i in range(self.num_layers):
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L.log_param(
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'train_decoder/deconv%s' % (i + 1), self.deconvs[i], step
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)
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L.log_param('train_decoder/fc', self.fc, step)
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_AVAILABLE_DECODERS = {'pixel': PixelDecoder}
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def make_decoder(
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decoder_type, obs_shape, feature_dim, num_layers, num_filters
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):
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assert decoder_type in _AVAILABLE_DECODERS
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return _AVAILABLE_DECODERS[decoder_type](
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obs_shape, feature_dim, num_layers, num_filters
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)
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