From 1ed6f3e463cd10109c9c350128d607a001bc3487 Mon Sep 17 00:00:00 2001 From: Jay Joshi Date: Tue, 2 Oct 2018 12:03:12 +0530 Subject: [PATCH] remove unused parameter "bn_mode" "bn_mode" was passed in almost every function but it never got used. If it is not used at all it shouldn't be passed everywhere and it also creates confusion. --- pix2pix/src/model/models.py | 38 ++++++++++++++++++------------------- 1 file changed, 19 insertions(+), 19 deletions(-) diff --git a/pix2pix/src/model/models.py b/pix2pix/src/model/models.py index 7bb576c..02a1fe1 100644 --- a/pix2pix/src/model/models.py +++ b/pix2pix/src/model/models.py @@ -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: @@ -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) @@ -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) @@ -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) @@ -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) @@ -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 @@ -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 @@ -119,7 +119,7 @@ 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 @@ -127,7 +127,7 @@ def generator_unet_upsampling(img_dim, bn_mode, model_name="generator_unet_upsam 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]) @@ -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" @@ -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 @@ -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) @@ -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 @@ -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 @@ -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) @@ -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)