def forward(self, inputs):
# sanity check: actor network is deterministic, and receives only states as input
assert len(
inputs) == 1, 'ERROR> this network expects only one input (states)'
# grab the actual input to the model (states)
_states = inputs[0]
if self._config.useBatchnorm:
x = self._bn0(_states)
x = F.relu(self._bn1(self._fc1(x)))
x = F.relu(self._bn2(self._fc2(x)))
x = F.tanh(self._fc3(x))
else:
x = F.relu(self._fc1(_states))
x = F.relu(self._fc2(x))
x = F.tanh(self._fc3(x))
return x
def forward( self, state ) :
r"""Forward pass for this deterministic policy, used for the max Q evaluation
Args:
state (torch.tensor): state used to decide the action
"""
x = F.relu( self.fc1( state ) )
x = F.relu( self.fc2( x ) )
x = F.tanh( self.fc3( x ) )
return x
def forward( self, observation ) :
r"""Forward pass for this deterministic policy, used for the max Q evaluation
Args:
observation (torch.tensor): observation used to decide the action
"""
x = self.bn0( observation )
x = F.relu( self.bn1( self.fc1( x ) ) )
x = F.relu( self.bn2( self.fc2( x ) ) )
x = F.tanh( self.fc3( x ) )
return x
def forward(self, x1, x2, z=None):
conv1_1 = self.conv1(x1)
conv1_2 = self.conv1(x2)
conv2_1 = self.conv2(conv1_1)
conv2_2 = self.conv2(conv1_2)
conv3_1 = self.conv3(conv2_1)
conv3_2 = self.conv3(conv2_2)
conv4_1 = self.conv4(conv3_1)
conv4_2 = self.conv4(conv3_2)
conv5_1 = self.conv5(conv4_1)
conv5_2 = self.conv5(conv4_2)
# center_1 = self.center(self.pool(conv5_1))
# center_2 = self.center(self.pool(conv5_2))
center_1 = self.center(conv5_1)
center_2 = self.center(conv5_2)
feature_1 = self.global_avg_pool(center_1)
feature_2 = self.global_avg_pool(center_2)
feature_12 = torch.cat((feature_1, feature_2), dim=1)
# pdb.set_trace()
attention_12 = F.sigmoid(
self.mlp2(F.tanh(self.mlp1(feature_12.view(-1, 512))))).view(
-1, 256, 1, 1)
center_1 = center_1 * attention_12
center_2 = center_2 * attention_12
dec5_1 = self.dec5(torch.cat([center_1, conv5_1], 1))
dec5_2 = self.dec5(torch.cat([center_2, conv5_2], 1))
dec4_1 = self.dec4(torch.cat([dec5_1, conv4_1], 1))
dec4_2 = self.dec4(torch.cat([dec5_2, conv4_2], 1))
dec3_1 = self.dec3(torch.cat([dec4_1, conv3_1], 1))
dec3_2 = self.dec3(torch.cat([dec4_2, conv3_2], 1))
dec2_1 = self.dec2(torch.cat([dec3_1, conv2_1], 1))
dec2_2 = self.dec2(torch.cat([dec3_2, conv2_2], 1))
dec1_1 = self.dec1(dec2_1)
dec1_2 = self.dec1(dec2_2)
dec0_1 = self.dec0(dec1_1)
dec0_2 = self.dec0(dec1_2)
return self.final(dec0_1), self.final(dec0_2)
def forward(self, x):
l1 = F.tanh(self.l1(x))
l2 = F.tanh(self.l2(l1))
pred = F.softmax(self.l3(l2))
return pred
