CODEWITHSUNDEEP
pythonkerasresize
Jose Ramon
Jose Ramon

Reputation: 5444

Keras model output a model with a specific size by using resize

In a generator model of GANs, I am trying to generate image of a specific size. My target size is 28x280x3. I actually, so far I was creating a generator output of 28x28x3. Therefore, I am trying by using UpSampling2D to increase the size of the model. I am able to make the model output of size 28x224x3 after three UpSampling2D layers. However, my target is 28x280x3. How can I gap that divergence dimensions? I noticed that there is this approach that targets at resizing layers. How can it work in my case? My code is looking like the following:

def build_generator_face(latent_dim, channels, face_sequence):

  model = Sequential()
  model.add(Dense(128 * 7 * 7, activation="relu", input_shape=(None, latent_dim))) 
  model.add(Reshape((7, 7, 128)))
  model.add(UpSampling2D())
  model.add(Conv2D(128, kernel_size=4, padding="same"))
  model.add(BatchNormalization(momentum=0.8))
  model.add(Activation("relu"))
  model.add(UpSampling2D())
  model.add(Conv2D(64, kernel_size=4, padding="same"))
  model.add(BatchNormalization(momentum=0.8))
  model.add(Activation("relu"))

  if face_sequence == False:
    #model.add(UpSampling2D(size=(2, 2)))
    model.add(Conv2D(64, kernel_size=4, padding="same"))
    model.add(BatchNormalization(momentum=0.8))
    model.add(Activation("relu"))

    #model.add(UpSampling2D(size=(2, 2)))
    model.add(Conv2D(64, kernel_size=4, padding="same"))
    model.add(BatchNormalization(momentum=0.8))
    model.add(Activation("relu"))

  else:
    model.add(UpSampling2D(size=(1, 2)))
    model.add(Conv2D(64, kernel_size=4, padding="same"))
    model.add(BatchNormalization(momentum=0.8))
    model.add(Activation("relu"))

    model.add(UpSampling2D(size=(1, 2)))
    model.add(Conv2D(64, kernel_size=4, padding="same"))
    model.add(BatchNormalization(momentum=0.8))
    model.add(Activation("relu"))

    model.add(UpSampling2D(size=(1, 2)))
    model.add(Conv2D(64, kernel_size=4, padding="same"))
    model.add(BatchNormalization(momentum=0.8))
    model.add(Activation("relu"))

    pdb.set_trace()
    model.add(Reshape((-1,3), input_shape=(28,224,3)))
    model.add(Lambda(lambda x: x[:7840])) # throw away some, so that #data = 224^2
    model.add(Reshape(28,280,3)) # this line gives me an error but am not sure if it is necessary or not the code is found in here: https://stackoverflow.com/questions/41903928/add-a-resizing-layer-to-a-keras-sequential-model

  model.add(Conv2D(channels, kernel_size=4, padding="same"))
  model.add(Activation("tanh"))

  model.summary()
  noise = Input(shape=(latent_dim,))
  img = model(noise)

  mdl = Model(noise, output = img)

  return mdl

If face_sequence is False the model is generating an output of 28x28x3. I want when the boolean variable is True to generate an output of size 28x280x3. How this can be done?

Upvotes: 0

Views: 565

Answers (1)

Lowry
Lowry

Reputation: 448

You were using only the first channel with 7840 and then trying to reshape into the wanted shape. For that you would need 23520 elements (28*280*3), but you only had 18816 (28*224*3).

This code resizes earlier in the process and uses one more UpSampling->Conv2D resulting in the wanted shape.

def build_generator_face(latent_dim, channels, face_sequence):

  model = Sequential()

  model.add(Dense(128 * 7 * 7, activation="relu", input_shape=(None, latent_dim))) 
  model.add(Reshape((7, 7, 128)))
  model.add(UpSampling2D())
  model.add(Conv2D(128, kernel_size=4, padding="same"))
  model.add(BatchNormalization(momentum=0.8))
  model.add(Activation("relu"))

  model.add(UpSampling2D())
  model.add(Conv2D(64, kernel_size=4, padding="same"))
  model.add(BatchNormalization(momentum=0.8))
  model.add(Activation("relu"))

  if face_sequence == False:
    model.add(Conv2D(64, kernel_size=4, padding="same"))
    model.add(BatchNormalization(momentum=0.8))
    model.add(Activation("relu"))

    model.add(Conv2D(64, kernel_size=4, padding="same"))
    model.add(BatchNormalization(momentum=0.8))
    model.add(Activation("relu"))

  else:
    model.add(UpSampling2D(size=(1, 2)))
    model.add(Conv2D(64, kernel_size=4, padding="same"))
    model.add(BatchNormalization(momentum=0.8))
    model.add(Activation("relu"))

    # go from 56 to 35 and continue upsampling

    model.add(Lambda(lambda x: x[:,:,:35,:]))



    model.add(UpSampling2D(size=(1, 2)))
    model.add(Conv2D(64, kernel_size=4, padding="same"))
    model.add(BatchNormalization(momentum=0.8))
    model.add(Activation("relu"))

    model.add(UpSampling2D(size=(1, 2)))
    model.add(Conv2D(64, kernel_size=4, padding="same"))
    model.add(BatchNormalization(momentum=0.8))
    model.add(Activation("relu"))

    model.add(UpSampling2D(size=(1, 2)))
    model.add(Conv2D(64, kernel_size=4, padding="same"))
    model.add(BatchNormalization(momentum=0.8))
    model.add(Activation("relu"))

    pdb.set_trace()


  model.add(Conv2D(channels, kernel_size=4, padding="same"))
  model.add(Activation("tanh"))

  model.summary()


  return model

Upvotes: 2

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