Training on greyscale dataset

Hi, is it possible to train the model on a grayscale dataset without transforming it to rgb? I tried the example code with c_dim=1 in the RRDN model but I have an error with the dimensions of the next layers:

WARNING:tensorflow:Model was constructed with shape (None, 80, 80, 3) for input KerasTensor(type_spec=TensorSpec(shape=(None, 80, 80, 3), dtype=tf.float32, name='input_2'), name='input_2', description="created by layer 'input_2'"), but it was called on an input with incompatible shape (None, 80, 80, 1). Traceback (most recent call last): File "/home/dolabok/Documents/[...]/super_resolution.py", line 76, in trainer = Trainer( File "/home/dolabok/anaconda3/lib/python3.9/site-packages/ISR-2.2.0-py3.9.egg/ISR/train/trainer.py", line 105, in init File "/home/dolabok/anaconda3/lib/python3.9/site-packages/ISR-2.2.0-py3.9.egg/ISR/train/trainer.py", line 185, in _combine_networks File "/home/dolabok/anaconda3/lib/python3.9/site-packages/keras/utils/traceback_utils.py", line 70, in error_handler raise e.with_traceback(filtered_tb) from None File "/home/dolabok/anaconda3/lib/python3.9/site-packages/keras/engine/input_spec.py", line 277, in assert_input_compatibility raise ValueError( ValueError: Exception encountered when calling layer "discriminator" (type Functional).

Input 0 of layer "Conv_1" is incompatible with the layer: expected axis -1 of input shape to have value 3, but received input with shape (None, 80, 80, 1)

Call arguments received by layer "discriminator" (type Functional): • inputs=tf.Tensor(shape=(None, 80, 80, 1), dtype=float32) • training=False • mask=None

it seems that the discriminator is hard-coded with channel = 3

My code :

# Initialize the models
lr_train_patch_size = 40
layers_to_extract = [5, 9]
scale = 2
hr_train_patch_size = lr_train_patch_size * scale

rrdn = RRDN(arch_params={'C':4, 'D':1, 'G':64, 'G0':64, 'T':10, 'x':scale}, c_dim=1, patch_size=lr_train_patch_size)
f_ext = Cut_VGG19(patch_size=hr_train_patch_size, layers_to_extract=layers_to_extract)
discr = Discriminator(patch_size=hr_train_patch_size, kernel_size=3)

# Training settings
loss_weights = {
  'generator': 0.0,
  'feature_extractor': 0.0833,
  'discriminator': 0.01
}
losses = {
  'generator': 'mae',
  'feature_extractor': 'mse',
  'discriminator': 'binary_crossentropy'
}

learning_rate = {'initial_value': 0.0004, 'decay_factor': 0.5, 'decay_frequency': 30}
flatness = {'min': 0.0, 'max': 0.15, 'increase': 0.01, 'increase_frequency': 5}
log_dirs = {'logs': './logs', 'weights': './weights'}

# Initialize the trainer
trainer = Trainer(
    generator=rrdn,
    discriminator=discr,
    feature_extractor=f_ext,
    lr_train_dir='low_res_train',
    hr_train_dir='high_res_train',
    lr_valid_dir='low_res_train',
    hr_valid_dir='high_res_train',
    loss_weights=loss_weights,
    learning_rate=learning_rate,
    flatness=flatness,
    dataname='image_dataset',
    log_dirs=log_dirs,
    weights_generator=None,
    weights_discriminator=None,
    n_validation=40,
)

# Train the model
trainer.train(
    epochs=100,
    steps_per_epoch=500,
    batch_size=16,
    monitored_metrics={'val_PSNR_Y': 'max'}
)

idealo / image-super-resolution

Training on greyscale dataset #243