def build(self, input_shape):
'''
input: concat of z_a and z_p -> 16 x 16 x 2048
output: reconstructed image 256 x 256 x 3
'''
concat = Input(shape=input_shape)
up = layers.up(concat) # 32 x 32
up = layers.conv_bn_act(up, 1024, (3, 3))
up = layers.conv_bn_act(up, 512, (3, 3))
up = layers.conv_bn_act(up, 256, (3, 3))
up = layers.up(up) # 64 x 64
up = layers.conv_bn_act(up, 256, (3, 3))
up = layers.conv_bn_act(up, 256, (3, 3))
up = layers.conv_bn_act(up, 128, (3, 3))
up = layers.up(up) # 128 x 128
up = layers.conv_bn_act(up, 128, (3, 3))
up = layers.conv_bn_act(up, 64, (3, 3))
up = layers.up(up) # 256 x 256
up = layers.conv_bn_act(up, 3, (3, 3))
up = layers.conv_bn(
up, 3, (1, 1)
) # 3 channels, output shape of this should be (None, 3, 256, 256)
# TODO: should we permute here or have the input formatted with channels first?
# perm = Permute((1, 2))(up)
# i_hat = Permute((2, 3))(perm)
i_hat = up
self._model = Model(inputs=concat, outputs=i_hat, name='decoder')
def build(self, input_shape):
concat = Input(shape=input_shape)
# z_a = Input(shape=inp.get_shape().as_list()[1:]) # for now, only the z_a part (8 x 8 x 2048)
up = layers.up(concat) # 16 x 16
up = layers.conv_bn_act(up, 512, (3, 3))
up = layers.conv_bn_act(up, 512, (3, 3))
up = layers.conv_bn_act(up, 512, (3, 3))
up = layers.up(up) # 32 x 32
up = layers.conv_bn_act(up, 512, (3, 3))
up = layers.conv_bn_act(up, 512, (3, 3))
up = layers.conv_bn_act(up, 256, (3, 3))
up = layers.up(up) # 64 x 64
up = layers.conv_bn_act(up, 256, (3, 3))
up = layers.conv_bn_act(up, 256, (3, 3))
up = layers.conv_bn_act(up, 128, (3, 3))
up = layers.up(up) # 128 x 128
up = layers.conv_bn_act(up, 128, (3, 3))
up = layers.conv_bn_act(up, 64, (3, 3))
up = layers.up(up) # 256 x 256
up = layers.conv_bn_act(up, 3, (3, 3))
up = layers.conv_bn(up, 3, (1, 1)) # 3 channels, output shape of this should be (None, 3, 256, 256)
# TODO: should we permute here or have the input formatted with channels first?
# perm = Permute((1, 2))(up)
# i_hat = Permute((2, 3))(perm)
i_hat = up
self._model = Model(inputs=concat, outputs=i_hat, name='old_decoder')
def build(self):
inp = Input(shape=self.input_shape)
enc_model = ResNet50(include_top=False,
weights='imagenet',
input_tensor=inp)
z_a = enc_model.output # 8 x 8 x 2048
# decoder part
up = layers.up(z_a) # 16 x 16
up = layers.conv_bn_act(up, 512, (3, 3))
up = layers.conv_bn_act(up, 512, (3, 3))
up = layers.conv_bn_act(up, 512, (3, 3))
up = layers.up(up) # 32 x 32
up = layers.conv_bn_act(up, 512, (3, 3))
up = layers.conv_bn_act(up, 512, (3, 3))
up = layers.conv_bn_act(up, 256, (3, 3))
up = layers.up(up) # 64 x 64
up = layers.conv_bn_act(up, 256, (3, 3))
up = layers.conv_bn_act(up, 256, (3, 3))
up = layers.conv_bn_act(up, 128, (3, 3))
up = layers.up(up) # 128 x 128
up = layers.conv_bn_act(up, 128, (3, 3))
up = layers.conv_bn_act(up, 64, (3, 3))
up = layers.up(up) # 256 x 256
up = layers.conv_bn_act(up, 3, (3, 3))
up = layers.conv_bn(
up, 3, (1, 1)
) # 3 channels, output shape of this should be (None, 3, 256, 256)
# TODO: should we permute here or have the input formatted with channels first?
# perm = Permute((1, 2))(up)
# i_hat = Permute((2, 3))(perm)
i_hat = up
self.model = Model(inputs=inp, outputs=i_hat)
# loss = losses.combined_loss()
loss = mean_squared_error
# run_opts = tf.RunOptions(report_tensor_allocations_upon_oom = True)
# self.model.compile(loss=loss, optimizer=RMSprop(lr=self.start_lr), options=run_opts)
self.model.compile(loss=loss, optimizer=RMSprop(lr=self.start_lr))
self.model.summary()