conv-charm.py

# ==============================================================================
# MIT License

# Copyright (c) 2022 Ahmed Ghorbel

# Permission is hereby granted, free of charge, to any person obtaining a copy
# of this software and associated documentation files (the "Software"), to deal
# in the Software without restriction, including without limitation the rights
# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
# copies of the Software, and to permit persons to whom the Software is
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# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
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# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
# SOFTWARE.
# ==============================================================================

"""
Nonlinear transform coder with hyperprior for RGB images.
This work is based on the image compression model published in:
D. Minnen and S. Singh:
"Channel-wise autoregressive entropy models for learned image compression"
Int. Conf. on Image Compression (ICIP), 2020
https://arxiv.org/abs/2007.08739
This is meant as 'educational' code - you can use this to get started with your
own experiments. To reproduce the exact results from the paper, tuning of hyper-
parameters may be necessary. To compress images with published models, see
`tfci.py`.
This script requires TFC v2 (`pip install tensorflow-compression==2.*`).
"""

import sys
import os
import argparse
import functools
import glob

from absl import app
from absl.flags import argparse_flags
from pathlib import Path
from timeit import default_timer as timer

import tensorflow as tf
import tensorflow_compression as tfc
import tensorflow_datasets as tfds
 
from utils import *


class AnalysisTransform(tf.keras.Sequential):
  def __init__(self, latent_depth, **kwargs):
    super().__init__(name='Analysis_Transform')
    conv = functools.partial(tfc.SignalConv2D, corr=True, strides_down=2,
                             padding="same_zeros", use_bias=True)
    layers = [
        tf.keras.layers.Lambda(lambda x: x / 255.),
        conv(latent_depth, (5, 5), name="layer_0", activation=tfc.GDN(name="gdn_0")),
        conv(latent_depth, (5, 5), name="layer_1", activation=tfc.GDN(name="gdn_1")),
        conv(latent_depth, (5, 5), name="layer_2", activation=tfc.GDN(name="gdn_2")),
        conv(latent_depth, (5, 5), name="layer_3", activation=None),
    ]
    for layer in layers:
      self.add(layer)


class SynthesisTransform(tf.keras.Sequential):
  def __init__(self, latent_depth, **kwargs):
    super().__init__(name='Synthesis_Transform')
    conv = functools.partial(tfc.SignalConv2D, corr=False, strides_up=2,
                             padding="same_zeros", use_bias=True)
    layers = [
        conv(latent_depth, (5, 5), name="layer_0",
             activation=tfc.GDN(name="igdn_0", inverse=True)),
        conv(latent_depth, (5, 5), name="layer_1",
             activation=tfc.GDN(name="igdn_1", inverse=True)),
        conv(latent_depth, (5, 5), name="layer_2",
             activation=tfc.GDN(name="igdn_2", inverse=True)),
        conv(3, (5, 5), name="layer_3",
             activation=None),
        tf.keras.layers.Lambda(lambda x: x * 255.),
    ]
    for layer in layers:
      self.add(layer)


class HyperAnalysisTransform(tf.keras.Sequential):
  def __init__(self, hyperprior_depth, **kwargs):
    super().__init__(name='Hyper_Analysis_Transform')
    conv = functools.partial(tfc.SignalConv2D, corr=True, padding="same_zeros")

    # See Appendix C.2 for more information on using a small hyperprior.
    layers = [
        conv(hyperprior_depth, (3, 3), name="layer_0", strides_down=1, use_bias=True,
             activation=tf.nn.relu),
        conv(hyperprior_depth, (5, 5), name="layer_1", strides_down=2, use_bias=True,
             activation=tf.nn.relu),
        conv(hyperprior_depth, (5, 5), name="layer_2", strides_down=2,
             use_bias=False, activation=None),
    ]
    for layer in layers:
      self.add(layer)


class HyperSynthesisTransform(tf.keras.Sequential):
  def __init__(self, hyperprior_depth, latent_depth, **kwargs):
    super().__init__(name='Hyper_Synthesis_Transform')
    conv = functools.partial(
        tfc.SignalConv2D, corr=False, padding="same_zeros", use_bias=True,
        kernel_parameter="variable", activation=tf.nn.relu)

    # Note that the output tensor is still latent (it represents means and
    # scales but it does NOT hold mean or scale values explicitly). Therefore,
    # the final activation is ReLU rather than None or Exp). For the same
    # reason, it is not a requirement that the final depth of this transform
    # matches the depth of `y`.
    layers = [
        conv(hyperprior_depth, (5, 5), name="layer_0", strides_up=2),
        conv(hyperprior_depth, (5, 5), name="layer_1", strides_up=2),
        conv(latent_depth, (3, 3), name="layer_2", strides_up=1),
    ]
    for layer in layers:
      self.add(layer)  


class SliceTransform(tf.keras.Sequential):
  def __init__(self, latent_depth, num_slices, **kwargs):
    super().__init__(name='Slice_Transform')
    conv = functools.partial(
        tfc.SignalConv2D, corr=False, strides_up=1, padding="same_zeros",
        use_bias=True, kernel_parameter="variable")

    # Note that the number of channels in the output tensor must match the
    # size of the corresponding slice. If we have 10 slices and a bottleneck
    # with 320 channels, the output is 320 / 10 = 32 channels.
    slice_depth = latent_depth // num_slices
    if slice_depth * num_slices != latent_depth:
      raise ValueError("Slices do not evenly divide latent depth (%d / %d)" % (
          latent_depth, num_slices))

    self.add(tf.keras.Sequential([
        conv(224, (5, 5), name="layer_0", activation=tf.nn.relu),
        conv(128, (5, 5), name="layer_1", activation=tf.nn.relu),
        conv(slice_depth, (3, 3), name="layer_2", activation=None),
    ]))


class ConvChARM(tf.keras.Model):
  """Main model class."""
  def __init__(self, lmbda,
               num_filters, latent_depth, hyperprior_depth,
               num_slices, max_support_slices,
               num_scales, scale_min, scale_max, **kwargs):
    super().__init__(name='ConvChARM_Model')
    self.lmbda = lmbda
    self.num_scales = num_scales
    self.num_slices = num_slices
    self.slice_size = latent_depth//self.num_slices
    self.max_support_slices = max_support_slices
    offset = tf.math.log(scale_min)
    factor = (tf.math.log(scale_max) - tf.math.log(scale_min)) / (
            num_scales - 1.)
    self.scale_fn = lambda i: tf.math.exp(offset + factor * i)
    
    self.analysis_transform = AnalysisTransform(latent_depth)
    self.synthesis_transform = SynthesisTransform(latent_depth)
    self.hyper_analysis_transform = HyperAnalysisTransform(hyperprior_depth)
    self.hyper_synthesis_mean_scale_transform = HyperSynthesisTransform(hyperprior_depth, latent_depth)
    
    self.cc_mean_transforms = [
      SliceTransform(latent_depth, num_slices) for _ in range(num_slices)]
    self.cc_scale_transforms = [
      SliceTransform(latent_depth, num_slices) for _ in range(num_slices)]  
    self.hyperprior = tfc.NoisyDeepFactorized(batch_shape=[hyperprior_depth])
    
    self.build((None, None, None, 3))
    # The call signature of decompress() depends on the number of slices, so we
    # need to compile the function dynamically.
    self.decompress = tf.function(
      input_signature=3 * [tf.TensorSpec(shape=(2,), dtype=tf.int32)] +
                      (num_slices + 1) * [tf.TensorSpec(shape=(1,), dtype=tf.string)]
    )(self.decompress)
        

  def call(self, x, training):
    """Computes rate and distortion losses."""
    
    x = tf.cast(x, self.compute_dtype)
    
    # Build the encoder (analysis) half of the hierarchical autoencoder.
    y = self.analysis_transform(x)
    y_shape = tf.shape(y)[1:-1]

    z = self.hyper_analysis_transform(y)
    num_pixels = tf.cast(tf.reduce_prod(tf.shape(x)[1:-1]), tf.float32)

    # Build the entropy model for the hyperprior (z).
    em_z = tfc.ContinuousBatchedEntropyModel(
      self.hyperprior, coding_rank=3, compression=False,
      offset_heuristic=False)

    # When training, z_bpp is based on the noisy version of z (z_tilde).
    _, z_bits = em_z(z, training=training)
    z_bpp = tf.reduce_mean(z_bits) / num_pixels

    # Use rounding (instead of uniform noise) to modify z before passing it
    # to the hyper-synthesis transforms. Note that quantize() overrides the
    # gradient to create a straight-through estimator.
    z_hat = em_z.quantize(z)

    # Build the decoder (synthesis) half of the hierarchical autoencoder.
    latent_means_scales = self.hyper_synthesis_mean_scale_transform(z_hat)
    latent_means, latent_scales = tf.split(latent_means_scales, 2, axis=-1)

    # Build a conditional entropy model for the slices.
    em_y = tfc.LocationScaleIndexedEntropyModel(
      tfc.NoisyNormal, num_scales=self.num_scales, scale_fn=self.scale_fn,
      coding_rank=3, compression=False)

    # En/Decode each slice conditioned on hyperprior and previous slices.
    y_slices = tf.split(y, self.num_slices, axis=-1)
    y_hat_slices = []
    y_bpps = []

    for slice_index, y_slice in enumerate(y_slices):
      # Model may condition on only a subset of previous slices.
      support_slices = (y_hat_slices if self.max_support_slices < 0 else
                        y_hat_slices[:self.max_support_slices])

      start_index = slice_index * self.slice_size
      end_index = slice_index * self.slice_size + self.slice_size
      latent_means_slice = latent_means[:, :, :, start_index:end_index]
      latent_scales_slice = latent_scales[:, :, :, start_index:end_index]

      # Predict mu and sigma for the current slice.
      mean_support = tf.concat([latent_means_slice] + support_slices, axis=-1)
      mu = self.cc_mean_transforms[slice_index](mean_support)
      mu = mu[:, :y_shape[0], :y_shape[1], :]

      # Note that in this implementation, `sigma` represents scale indices,
      # not actual scale values.
      scale_support = tf.concat([latent_scales_slice] + support_slices, axis=-1)
      sigma = self.cc_scale_transforms[slice_index](scale_support)
      sigma = sigma[:, :y_shape[0], :y_shape[1], :]

      _, slice_bits = em_y(y_slice, sigma, loc=mu, training=training)
      slice_bpp = tf.reduce_mean(slice_bits) / num_pixels
      y_bpps.append(slice_bpp)

      # For the synthesis transform, use rounding. Note that quantize()
      # overrides the gradient to create a straight-through estimator.
      y_hat_slice = em_y.quantize(y_slice, loc=mu)      
      y_hat_slices.append(y_hat_slice)

    # Merge slices and generate the image reconstruction.
    y_hat = tf.concat(y_hat_slices, axis=-1)
    x_hat = self.synthesis_transform(y_hat)   
    
    # Total bpp is sum of bpp from hyperprior and all slices.
    total_bpp = tf.add_n(y_bpps + [z_bpp])

    # Mean squared error across pixels.
    # Don't clip or round pixel values while training.   
    mse = tf.reduce_mean(tf.math.squared_difference(x, x_hat))
    mse = tf.cast(mse, total_bpp.dtype)

    # Calculate and return the rate-distortion loss: D + lambda * R.
    loss = total_bpp + self.lmbda * mse 

    return loss, total_bpp, mse

  def train_step(self, x):
    with tf.GradientTape() as tape:
      loss, bpp, mse = self(x, training=True)
    variables = self.trainable_variables
    gradients = tape.gradient(loss, variables)
    self.optimizer.apply_gradients(zip(gradients, variables))
    self.loss.update_state(loss)
    self.bpp.update_state(bpp)
    self.mse.update_state(mse)
    return {m.name: m.result() for m in [self.loss, self.bpp, self.mse]}

  def test_step(self, x):
    loss, bpp, mse = self(x, training=False)
    self.loss.update_state(loss)
    self.bpp.update_state(bpp)
    self.mse.update_state(mse)
    return {m.name: m.result() for m in [self.loss, self.bpp, self.mse]}

  def predict_step(self, x):
    raise NotImplementedError("Prediction API is not supported.")

  def compile(self, **kwargs):
    super().compile(
      loss=None,
      metrics=None,
      loss_weights=None,
      weighted_metrics=None,
      **kwargs,
    )
    self.loss = tf.keras.metrics.Mean(name="loss")
    self.bpp = tf.keras.metrics.Mean(name="bpp")
    self.mse = tf.keras.metrics.Mean(name="mse")

  def fit(self, *args, **kwargs):
    retval = super().fit(*args, **kwargs)
    # After training, fix range coding tables.
    self.em_z = tfc.ContinuousBatchedEntropyModel(
      self.hyperprior, coding_rank=3, compression=True,
      offset_heuristic=False)
    self.em_y = tfc.LocationScaleIndexedEntropyModel(
      tfc.NoisyNormal, num_scales=self.num_scales, scale_fn=self.scale_fn,
      coding_rank=3, compression=True)
    return retval

  @tf.function(input_signature=[
    tf.TensorSpec(shape=(None, None, 3), dtype=tf.uint8),
  ])
  def compress(self, x):
    """Compresses an image."""
    
    # Add batch dimension and cast to float.
    x = tf.expand_dims(x, 0)
    x = tf.cast(x, dtype=self.compute_dtype)
    y_strings = []
    x_shape = tf.shape(x)[1:-1]
    
    # Build the encoder (analysis) half of the hierarchical autoencoder.
    y = self.analysis_transform(x)
    y_shape = tf.shape(y)[1:-1]

    z = self.hyper_analysis_transform(y)
    z_shape = tf.shape(z)[1:-1]

    z_string = self.em_z.compress(z)
    z_hat = self.em_z.decompress(z_string, z_shape)

    # Build the decoder (synthesis) half of the hierarchical autoencoder.
    latent_means_scales = self.hyper_synthesis_mean_scale_transform(z_hat)
    latent_means, latent_scales = tf.split(latent_means_scales, 2, axis=-1)

    # En/Decode each slice conditioned on hyperprior and previous slices.
    y_slices = tf.split(y, self.num_slices, axis=-1)
    y_hat_slices = []
    for slice_index, y_slice in enumerate(y_slices):
      # Model may condition on only a subset of previous slices.
      support_slices = (y_hat_slices if self.max_support_slices < 0 else
                        y_hat_slices[:self.max_support_slices])

      start_index = slice_index * self.slice_size
      end_index = slice_index * self.slice_size + self.slice_size
      latent_means_slice = latent_means[:, :, :, start_index:end_index]
      latent_scales_slice = latent_scales[:, :, :, start_index:end_index]

      # Predict mu and sigma for the current slice.
      mean_support = tf.concat([latent_means_slice] + support_slices, axis=-1)
      mu = self.cc_mean_transforms[slice_index](mean_support)
      mu = mu[:, :y_shape[0], :y_shape[1], :]

      # Note that in this implementation, `sigma` represents scale indices,
      # not actual scale values.
      scale_support = tf.concat([latent_scales_slice] + support_slices, axis=-1)
      sigma = self.cc_scale_transforms[slice_index](scale_support)
      sigma = sigma[:, :y_shape[0], :y_shape[1], :]

      slice_string = self.em_y.compress(y_slice, sigma, mu)
      y_strings.append(slice_string)
      
      y_hat_slice = self.em_y.decompress(slice_string, sigma, mu)      
      y_hat_slices.append(y_hat_slice)

    return (x_shape, y_shape, z_shape, z_string) + tuple(y_strings)

  def decompress(self, x_shape, y_shape, z_shape, z_string, *y_strings):
    """Decompresses an image."""
    
    assert len(y_strings) == self.num_slices
    z_hat = self.em_z.decompress(z_string, z_shape)

    # Build the decoder (synthesis) half of the hierarchical autoencoder.
    latent_means_scales = self.hyper_synthesis_mean_scale_transform(z_hat)
    latent_means, latent_scales = tf.split(latent_means_scales, 2, axis=-1)

    # En/Decode each slice conditioned on hyperprior and previous slices.
    y_hat_slices = []
    for slice_index, y_string in enumerate(y_strings):
      # Model may condition on only a subset of previous slices.
      support_slices = (y_hat_slices if self.max_support_slices < 0 else
                        y_hat_slices[:self.max_support_slices])

      start_index = slice_index * self.slice_size
      end_index = slice_index * self.slice_size + self.slice_size
      latent_means_slice = latent_means[:, :, :, start_index:end_index]
      latent_scales_slice = latent_scales[:, :, :, start_index:end_index]

      # Predict mu and sigma for the current slice.
      mean_support = tf.concat([latent_means_slice] + support_slices, axis=-1)
      mu = self.cc_mean_transforms[slice_index](mean_support)
      mu = mu[:, :y_shape[0], :y_shape[1], :]

      # Note that in this implementation, `sigma` represents scale indices,
      # not actual scale values.
      scale_support = tf.concat([latent_scales_slice] + support_slices, axis=-1)
      sigma = self.cc_scale_transforms[slice_index](scale_support)
      sigma = sigma[:, :y_shape[0], :y_shape[1], :]

      y_hat_slice = self.em_y.decompress(y_string, sigma, loc=mu)            
      y_hat_slices.append(y_hat_slice)

    # Merge slices and generate the image reconstruction.
    y_hat = tf.concat(y_hat_slices, axis=-1)
    x_hat = self.synthesis_transform(y_hat)
    
    # Remove batch dimension, and crop away any extraneous padding.
    x_hat = x_hat[0, :x_shape[0], :x_shape[1], :]
    # Then cast back to 8-bit integer.
    return tf.saturate_cast(tf.round(x_hat), tf.uint8), y_hat

def scheduler(epoch, lr):
  if epoch == 3400:
    return lr * 1e-1
  else:
    return lr


def train(args):
  """Instantiates and trains the model."""
  if args.precision_policy:
    tf.keras.mixed_precision.set_global_policy(args.precision_policy)
  if args.check_numerics:
    tf.debugging.enable_check_numerics()

  model = ConvChARM(
    args.lmbda, args.num_filters, args.latent_depth,
    args.hyperprior_depth, args.num_slices, args.max_support_slices,
    args.num_scales, args.scale_min, args.scale_max)
  model.compile(
    optimizer=tf.keras.optimizers.Adam(learning_rate=1e-4),
  )
  model.summary()
  
  if args.train_glob:
    train_dataset = get_custom_dataset("train", args)
    validation_dataset = get_custom_dataset("validation", args)
  else:
    train_dataset = get_dataset("clic", "train", args)
    validation_dataset = get_dataset("clic", "validation", args)
  validation_dataset = validation_dataset.take(args.max_validation_steps)

  model.fit(
    train_dataset.prefetch(8),
    epochs=args.epochs,
    steps_per_epoch=args.steps_per_epoch,
    validation_data=validation_dataset.cache(),
    validation_freq=1,
    callbacks=[
      tf.keras.callbacks.LearningRateScheduler(scheduler, verbose=1),
      tf.keras.callbacks.TerminateOnNaN(),
      tf.keras.callbacks.TensorBoard(
        log_dir=args.train_path,
        histogram_freq=1, update_freq="epoch"),
      tf.keras.callbacks.BackupAndRestore(args.train_path),
    ],
  )
  model.save(args.model_path)
  

def evaluate(args):
  """Testing."""
  #-- Load model.
  model = tf.keras.models.load_model(args.model_path)
  
  #-- Inits
  mse_list = []
  psnr_list = []
  msssim_list = []
  msssim_db_list = []
  bpp_list = []
  mean_encTime = 0.
  mean_decTime = 0.
  len_testset = len(list_of_paths(args.test_dir))
  
  for l in list_of_paths(args.test_dir):
    x = read_png(l)
    start = timer()
    tensors = model.compress(x)
    mean_encTime += (timer()-start) / len_testset

    #-- Write a binary file with the shape information and the compressed string.
    packed = tfc.PackedTensors()
    packed.pack(tensors)
    
    output_file_tfci = os.path.join(args.tfci_output_dir, Path(l).stem + '.tfci')
    with open(output_file_tfci, "wb") as f:
      f.write(packed.string)
    
    #-- Decompress the image and its latent and measure performance.
    start = timer()
    x_hat, y_hat = model.decompress(*tensors)
    mean_decTime += (timer()-start) / len_testset

    #-- Cast to float in order to compute metrics.
    x = tf.cast(x, tf.float32)
    x_hat = tf.cast(x_hat, tf.float32)
    mse = tf.reduce_mean(tf.math.squared_difference(x, x_hat))
    psnr = tf.squeeze(tf.image.psnr(x, x_hat, 255))
    msssim = tf.squeeze(tf.image.ssim_multiscale(x, x_hat, 255))
    msssim_db = -10. * tf.math.log(1 - msssim) / tf.math.log(10.)

    #-- The actual bits per pixel including entropy coding overhead.
    num_pixels = tf.reduce_prod(tf.shape(x)[:-1])
    bpp = len(packed.string) * 8 / num_pixels
    
    #-- Write a PNG file of the reconstructed image       
    output_file_png = os.path.join(args.png_output_dir, Path(l).stem + '.png')
    write_png(output_file_png, tf.cast(x_hat, tf.uint8))   
    
    mse_list.append(mse)
    psnr_list.append(psnr)
    msssim_list.append(msssim)
    msssim_db_list.append(msssim_db)
    bpp_list.append(bpp)
    
    print('Tested!: ', l)
  
  #-- save results on a text file 
  output_file = 'results/CLIC22/results.txt'
  with open(output_file, "a") as f:         
    f.write('\n\n\n#-- ConvChARM trained with MSE distorsion:')
    f.write(f"\n\nMean encoding time: {mean_encTime:0.4f}s")   
    f.write(f"\nMean decoding time: {mean_decTime:0.4f}s") 
    mean_mse = tf.reduce_mean(mse_list)  
    f.write(f"\nMean squared error: {mean_mse:0.4f}")
    mean_psnr = tf.reduce_mean(psnr_list)
    f.write(f"\nPSNR (dB): {mean_psnr:0.2f}")
    mean_msssim = tf.reduce_mean(msssim_list)
    f.write(f"\nMultiscale SSIM: {mean_msssim:0.4f}")
    mean_msssim_db = tf.reduce_mean(msssim_db_list)
    f.write(f"\nMultiscale SSIM (dB): {mean_msssim_db:0.2f}")
    mean_bpp = tf.reduce_mean(bpp_list)
    f.write(f"\nBits per pixel: {mean_bpp:0.4f}")
        

def parse_args(argv):
  """Parses command line arguments."""
  parser = argparse_flags.ArgumentParser(
    formatter_class=argparse.ArgumentDefaultsHelpFormatter)

  # High-level options.
  parser.add_argument(
    "--model_path", default="ckpts/conv-charm/conv-charm_02/checkpoint",
    help="Path where to save/load the trained model.")
  subparsers = parser.add_subparsers(
    title="commands", dest="command",
    help="What to do: 'train' loads training data and trains (or continues "
         "to train) a new model. 'compress' reads an image file (lossless "
         "PNG format) and writes a compressed binary file. 'decompress' "
         "reads a binary file and reconstructs the image (in PNG format). "
         "input and output filenames need to be provided for the latter "
         "two options. Invoke '<command> -h' for more information.")

  # 'train' subcommand.
  train_cmd = subparsers.add_parser(
    "train",
    formatter_class=argparse.ArgumentDefaultsHelpFormatter,
    description="Trains (or continues to train) a new model. Note that this "
                "model trains on a continuous stream of patches drawn from "
                "the training image dataset. An epoch is always defined as "
                "the same number of batches given by --steps_per_epoch. "
                "The purpose of validation is mostly to evaluate the "
                "rate-distortion performance of the model using actual "
                "quantization rather than the differentiable proxy loss. "
                "Note that when using custom training images, the validation "
                "set is simply a random sampling of patches from the "
                "training set.")
  train_cmd.add_argument(
    "--lambda", type=float, default=0.02, dest="lmbda",
    help="Lambda for rate-distortion tradeoff.")
  train_cmd.add_argument(
    "--train_glob", type=str, default=None,
    help="Glob pattern identifying custom training data. This pattern must "
         "expand to a list of RGB images in PNG format. If unspecified, the "
         "CLIC dataset from TensorFlow Datasets is used.")
  train_cmd.add_argument(
    "--num_filters", type=int, default=192,
    help="Number of filters per layer.")
  train_cmd.add_argument(
    "--latent_depth", type=int, default=320,
    help="Number of filters of the last layer of the analysis transform.")
  train_cmd.add_argument(
    "--hyperprior_depth", type=int, default=192,
    help="Number of filters of the last layer of the hyper-analysis "
         "transform.")
  train_cmd.add_argument(
    "--num_slices", type=int, default=10,
    help="Number of channel slices for conditional entropy modeling.")
  train_cmd.add_argument(
    "--max_support_slices", type=int, default=5,
    help="Maximum number of preceding slices to condition the current slice "
         "on. See Appendix C.1 of the paper for details.")
  train_cmd.add_argument(
    "--num_scales", type=int, default=64,
    help="Number of Gaussian scales to prepare range coding tables for.")
  train_cmd.add_argument(
    "--scale_min", type=float, default=.11,
    help="Minimum value of standard deviation of Gaussians.")
  train_cmd.add_argument(
    "--scale_max", type=float, default=256.,
    help="Maximum value of standard deviation of Gaussians.")
  train_cmd.add_argument(
    "--train_path", default="ckpts/conv-charm/conv-charm_02/backupAndRestore",
    help="Path where to log training metrics for TensorBoard and back up "
         "intermediate model checkpoints.")
  train_cmd.add_argument(
    "--batchsize", type=int, default=8,
    help="Batch size for training and validation.")
  train_cmd.add_argument(
    "--patchsize", type=int, default=256,
    help="Size of image patches for training and validation.")
  train_cmd.add_argument(
    "--epochs", type=int, default=3500,
    help="Train up to this number of epochs. (One epoch is here defined as "
         "the number of steps given by --steps_per_epoch, not iterations "
         "over the full training dataset.)")
  train_cmd.add_argument(
    "--steps_per_epoch", type=int, default=1000,
    help="Perform validation and produce logs after this many batches.")
  train_cmd.add_argument(
    "--max_validation_steps", type=int, default=16,
    help="Maximum number of batches to use for validation. If -1, use one "
         "patch from each image in the training set.")
  train_cmd.add_argument(
    "--preprocess_threads", type=int, default=16,
    help="Number of CPU threads to use for parallel decoding of training "
         "images.")
  train_cmd.add_argument(
    "--precision_policy", type=str, default=None,
    help="Policy for `tf.keras.mixed_precision` training.")
  train_cmd.add_argument(
    "--check_numerics", action="store_true",
    help="Enable TF support for catching NaN and Inf in tensors.")

  # 'evaluate' subcommand.
  evaluate_cmd = subparsers.add_parser(
      "evaluate",
      formatter_class=argparse.ArgumentDefaultsHelpFormatter,
      description="Read a PNG file, compress it and write a TFCI file."
                  "Then, decompress the TFCI file and write a PNG file.")
  evaluate_cmd.add_argument(
      "--test_dir", "-I", type=str, default="testsets/CLIC22", 
      help="Input evaluation directory.")
  evaluate_cmd.add_argument(
      "--tfci_output_dir", "-O", type=str, default="results/CLIC22/conv-charm/conv-charm_02/bitstreams", 
      help="Output directory to store compressed files.")
  evaluate_cmd.add_argument(
      "--png_output_dir", "-P", type=str, default="results/CLIC22/conv-charm/conv-charm_02/reconstructions",
      help="Output directory to store decompressed files.")  

  # Parse arguments.
  args = parser.parse_args(argv[1:])
  if args.command is None:
    parser.print_usage()
    sys.exit(2)
  return args


def main(args):
  # Invoke subcommand.
  if args.command == "train":
    print("\n\n===========> Train ================>")
    train(args)
  elif args.command == "evaluate":
    print("\n\n===========> evaluate =============>")
    evaluate(args)
    
if __name__ == "__main__":
  app.run(main, flags_parser=parse_args)