def copy_conv_weights_from(self, source):
"""Tie convolutional layers"""
for i in range(len(self.convs)):
if 'stride' not in self.convs[i].__constants__:
pass
else:
utils.tie_weights(src=source.convs[i], trg=self.convs[i])
def copy_conv_weights_from(self, source):
assert isinstance(source, DqnEncoder), \
"Source and target encoder must be the same type!"
for trg_m, src_m in zip(self.convs, source.convs):
if type(trg_m) == type(src_m) == nn.Conv2d:
tie_weights(src=src_m, trg=trg_m)
def copy_conv_weights_from(self, source):
"""Tie convolutional layers"""
for i in range(self.num_layers):
utils.tie_weights(src=source.convs[i], trg=self.convs[i])
def copy_conv_weights_from(self, source):
for i in range(self.num_layers):
tie_weights(src=source.convs[i], trg=self.convs[i])
