107 lines
3.0 KiB
Python
107 lines
3.0 KiB
Python
|
import torch
|
||
|
|
import torch.nn as nn
|
||
|
|
|
||
|
|
from encoder import OUT_DIM
|
||
|
|
|
||
|
|
|
||
|
|
class PixelDecoder(nn.Module):
|
||
|
|
def __init__(self, obs_shape, feature_dim, num_layers=2, num_filters=32):
|
||
|
|
super().__init__()
|
||
|
|
|
||
|
|
self.num_layers = num_layers
|
||
|
|
self.num_filters = num_filters
|
||
|
|
self.out_dim = OUT_DIM[num_layers]
|
||
|
|
|
||
|
|
self.fc = nn.Linear(
|
||
|
|
feature_dim, num_filters * self.out_dim * self.out_dim
|
||
|
|
)
|
||
|
|
|
||
|
|
self.deconvs = nn.ModuleList()
|
||
|
|
|
||
|
|
for i in range(self.num_layers - 1):
|
||
|
|
self.deconvs.append(
|
||
|
|
nn.ConvTranspose2d(num_filters, num_filters, 3, stride=1)
|
||
|
|
)
|
||
|
|
self.deconvs.append(
|
||
|
|
nn.ConvTranspose2d(
|
||
|
|
num_filters, obs_shape[0], 3, stride=2, output_padding=1
|
||
|
|
)
|
||
|
|
)
|
||
|
|
|
||
|
|
self.outputs = dict()
|
||
|
|
|
||
|
|
def forward(self, h):
|
||
|
|
h = torch.relu(self.fc(h))
|
||
|
|
self.outputs['fc'] = h
|
||
|
|
|
||
|
|
deconv = h.view(-1, self.num_filters, self.out_dim, self.out_dim)
|
||
|
|
self.outputs['deconv1'] = deconv
|
||
|
|
|
||
|
|
for i in range(0, self.num_layers - 1):
|
||
|
|
deconv = torch.relu(self.deconvs[i](deconv))
|
||
|
|
self.outputs['deconv%s' % (i + 1)] = deconv
|
||
|
|
|
||
|
|
obs = self.deconvs[-1](deconv)
|
||
|
|
self.outputs['obs'] = obs
|
||
|
|
|
||
|
|
return obs
|
||
|
|
|
||
|
|
def log(self, L, step, log_freq):
|
||
|
|
if step % log_freq != 0:
|
||
|
|
return
|
||
|
|
|
||
|
|
for k, v in self.outputs.items():
|
||
|
|
L.log_histogram('train_decoder/%s_hist' % k, v, step)
|
||
|
|
if len(v.shape) > 2:
|
||
|
|
L.log_image('train_decoder/%s_i' % k, v[0], step)
|
||
|
|
|
||
|
|
for i in range(self.num_layers):
|
||
|
|
L.log_param(
|
||
|
|
'train_decoder/deconv%s' % (i + 1), self.deconvs[i], step
|
||
|
|
)
|
||
|
|
L.log_param('train_decoder/fc', self.fc, step)
|
||
|
|
|
||
|
|
|
||
|
|
class StateDecoder(nn.Module):
|
||
|
|
def __init__(self, obs_shape, feature_dim):
|
||
|
|
super().__init__()
|
||
|
|
|
||
|
|
assert len(obs_shape) == 1
|
||
|
|
|
||
|
|
self.trunk = nn.Sequential(
|
||
|
|
nn.Linear(feature_dim, 1024), nn.ReLU(), nn.Linear(1024, 1024),
|
||
|
|
nn.ReLU(), nn.Linear(1024, obs_shape[0]), nn.ReLU()
|
||
|
|
)
|
||
|
|
|
||
|
|
self.outputs = dict()
|
||
|
|
|
||
|
|
def forward(self, obs, detach=False):
|
||
|
|
h = self.trunk(obs)
|
||
|
|
if detach:
|
||
|
|
h = h.detach()
|
||
|
|
self.outputs['h'] = h
|
||
|
|
return h
|
||
|
|
|
||
|
|
def log(self, L, step, log_freq):
|
||
|
|
if step % log_freq != 0:
|
||
|
|
return
|
||
|
|
|
||
|
|
L.log_param('train_encoder/fc1', self.trunk[0], step)
|
||
|
|
L.log_param('train_encoder/fc2', self.trunk[2], step)
|
||
|
|
for k, v in self.outputs.items():
|
||
|
|
L.log_histogram('train_encoder/%s_hist' % k, v, step)
|
||
|
|
|
||
|
|
|
||
|
|
_AVAILABLE_DECODERS = {'pixel': PixelDecoder, 'state': StateDecoder}
|
||
|
|
|
||
|
|
|
||
|
|
def make_decoder(
|
||
|
|
decoder_type, obs_shape, feature_dim, num_layers, num_filters
|
||
|
|
):
|
||
|
|
assert decoder_type in _AVAILABLE_DECODERS
|
||
|
|
if decoder_type == 'pixel':
|
||
|
|
return _AVAILABLE_DECODERS[decoder_type](
|
||
|
|
obs_shape, feature_dim, num_layers, num_filters
|
||
|
|
)
|
||
|
|
return _AVAILABLE_DECODERS[decoder_type](obs_shape, feature_dim)
|