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remove unused parameter "bn_mode" #79

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remove unused parameter "bn_mode" #79

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38 changes: 19 additions & 19 deletions pix2pix/src/model/models.py
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Original file line number Diff line number Diff line change
Expand Up @@ -21,7 +21,7 @@ def lambda_output(input_shape):
return input_shape[:2]


# def conv_block_unet(x, f, name, bn_mode, bn_axis, bn=True, dropout=False, strides=(2,2)):
# def conv_block_unet(x, f, name, bn_axis, bn=True, dropout=False, strides=(2,2)):

# x = Conv2D(f, (3, 3), strides=strides, name=name, padding="same")(x)
# if bn:
Expand All @@ -33,7 +33,7 @@ def lambda_output(input_shape):
# return x


# def up_conv_block_unet(x1, x2, f, name, bn_mode, bn_axis, bn=True, dropout=False):
# def up_conv_block_unet(x1, x2, f, name, bn_axis, bn=True, dropout=False):

# x1 = UpSampling2D(size=(2, 2))(x1)
# x = merge([x1, x2], mode="concat", concat_axis=bn_axis)
Expand All @@ -47,7 +47,7 @@ def lambda_output(input_shape):

# return x

def conv_block_unet(x, f, name, bn_mode, bn_axis, bn=True, strides=(2,2)):
def conv_block_unet(x, f, name, bn_axis, bn=True, strides=(2,2)):

x = LeakyReLU(0.2)(x)
x = Conv2D(f, (3, 3), strides=strides, name=name, padding="same")(x)
Expand All @@ -57,7 +57,7 @@ def conv_block_unet(x, f, name, bn_mode, bn_axis, bn=True, strides=(2,2)):
return x


def up_conv_block_unet(x, x2, f, name, bn_mode, bn_axis, bn=True, dropout=False):
def up_conv_block_unet(x, x2, f, name, bn_axis, bn=True, dropout=False):

x = Activation("relu")(x)
x = UpSampling2D(size=(2, 2))(x)
Expand All @@ -71,7 +71,7 @@ def up_conv_block_unet(x, x2, f, name, bn_mode, bn_axis, bn=True, dropout=False)
return x


def deconv_block_unet(x, x2, f, h, w, batch_size, name, bn_mode, bn_axis, bn=True, dropout=False):
def deconv_block_unet(x, x2, f, h, w, batch_size, name, bn_axis, bn=True, dropout=False):

o_shape = (batch_size, h * 2, w * 2, f)
x = Activation("relu")(x)
Expand All @@ -85,7 +85,7 @@ def deconv_block_unet(x, x2, f, h, w, batch_size, name, bn_mode, bn_axis, bn=Tru
return x


def generator_unet_upsampling(img_dim, bn_mode, model_name="generator_unet_upsampling"):
def generator_unet_upsampling(img_dim, model_name="generator_unet_upsampling"):

nb_filters = 64

Expand All @@ -109,7 +109,7 @@ def generator_unet_upsampling(img_dim, bn_mode, model_name="generator_unet_upsam
strides=(2, 2), name="unet_conv2D_1", padding="same")(unet_input)]
for i, f in enumerate(list_nb_filters[1:]):
name = "unet_conv2D_%s" % (i + 2)
conv = conv_block_unet(list_encoder[-1], f, name, bn_mode, bn_axis)
conv = conv_block_unet(list_encoder[-1], f, name, bn_axis)
list_encoder.append(conv)

# Prepare decoder filters
Expand All @@ -119,15 +119,15 @@ def generator_unet_upsampling(img_dim, bn_mode, model_name="generator_unet_upsam

# Decoder
list_decoder = [up_conv_block_unet(list_encoder[-1], list_encoder[-2],
list_nb_filters[0], "unet_upconv2D_1", bn_mode, bn_axis, dropout=True)]
list_nb_filters[0], "unet_upconv2D_1", bn_axis, dropout=True)]
for i, f in enumerate(list_nb_filters[1:]):
name = "unet_upconv2D_%s" % (i + 2)
# Dropout only on first few layers
if i < 2:
d = True
else:
d = False
conv = up_conv_block_unet(list_decoder[-1], list_encoder[-(i + 3)], f, name, bn_mode, bn_axis, dropout=d)
conv = up_conv_block_unet(list_decoder[-1], list_encoder[-(i + 3)], f, name, bn_axis, dropout=d)
list_decoder.append(conv)

x = Activation("relu")(list_decoder[-1])
Expand All @@ -140,7 +140,7 @@ def generator_unet_upsampling(img_dim, bn_mode, model_name="generator_unet_upsam
return generator_unet


def generator_unet_deconv(img_dim, bn_mode, batch_size, model_name="generator_unet_deconv"):
def generator_unet_deconv(img_dim, batch_size, model_name="generator_unet_deconv"):

assert K.backend() == "tensorflow", "Not implemented with theano backend"

Expand All @@ -162,7 +162,7 @@ def generator_unet_deconv(img_dim, bn_mode, batch_size, model_name="generator_un
h, w = h / 2, w / 2
for i, f in enumerate(list_nb_filters[1:]):
name = "unet_conv2D_%s" % (i + 2)
conv = conv_block_unet(list_encoder[-1], f, name, bn_mode, bn_axis)
conv = conv_block_unet(list_encoder[-1], f, name, bn_axis)
list_encoder.append(conv)
h, w = h / 2, w / 2

Expand All @@ -174,7 +174,7 @@ def generator_unet_deconv(img_dim, bn_mode, batch_size, model_name="generator_un
# Decoder
list_decoder = [deconv_block_unet(list_encoder[-1], list_encoder[-2],
list_nb_filters[0], h, w, batch_size,
"unet_upconv2D_1", bn_mode, bn_axis, dropout=True)]
"unet_upconv2D_1", bn_axis, dropout=True)]
h, w = h * 2, w * 2
for i, f in enumerate(list_nb_filters[1:]):
name = "unet_upconv2D_%s" % (i + 2)
Expand All @@ -184,7 +184,7 @@ def generator_unet_deconv(img_dim, bn_mode, batch_size, model_name="generator_un
else:
d = False
conv = deconv_block_unet(list_decoder[-1], list_encoder[-(i + 3)], f, h,
w, batch_size, name, bn_mode, bn_axis, dropout=d)
w, batch_size, name, bn_axis, dropout=d)
list_decoder.append(conv)
h, w = h * 2, w * 2

Expand All @@ -198,7 +198,7 @@ def generator_unet_deconv(img_dim, bn_mode, batch_size, model_name="generator_un
return generator_unet


def DCGAN_discriminator(img_dim, nb_patch, bn_mode, model_name="DCGAN_discriminator", use_mbd=True):
def DCGAN_discriminator(img_dim, nb_patch, model_name="DCGAN_discriminator", use_mbd=True):
"""
Discriminator model of the DCGAN

Expand Down Expand Up @@ -304,24 +304,24 @@ def DCGAN(generator, discriminator_model, img_dim, patch_size, image_dim_orderin
return DCGAN


def load(model_name, img_dim, nb_patch, bn_mode, use_mbd, batch_size):
def load(model_name, img_dim, nb_patch, use_mbd, batch_size):

if model_name == "generator_unet_upsampling":
model = generator_unet_upsampling(img_dim, bn_mode, model_name=model_name)
model = generator_unet_upsampling(img_dim, model_name=model_name)
model.summary()
from keras.utils import plot_model
plot_model(model, to_file="../../figures/%s.png" % model_name, show_shapes=True, show_layer_names=True)
return model

if model_name == "generator_unet_deconv":
model = generator_unet_deconv(img_dim, bn_mode, batch_size, model_name=model_name)
model = generator_unet_deconv(img_dim, batch_size, model_name=model_name)
model.summary()
from keras.utils import plot_model
plot_model(model, to_file="../../figures/%s.png" % model_name, show_shapes=True, show_layer_names=True)
return model

if model_name == "DCGAN_discriminator":
model = DCGAN_discriminator(img_dim, nb_patch, bn_mode, model_name=model_name, use_mbd=use_mbd)
model = DCGAN_discriminator(img_dim, nb_patch, model_name=model_name, use_mbd=use_mbd)
model.summary()
from keras.utils import plot_model
plot_model(model, to_file="../../figures/%s.png" % model_name, show_shapes=True, show_layer_names=True)
Expand All @@ -331,4 +331,4 @@ def load(model_name, img_dim, nb_patch, bn_mode, use_mbd, batch_size):
if __name__ == "__main__":

# load("generator_unet_deconv", (256, 256, 3), 16, 2, False, 32)
load("generator_unet_upsampling", (256, 256, 3), 16, 2, False, 32)
load("generator_unet_upsampling", (256, 256, 3), 16, False, 32)