models/bls2017.py [239:331]: - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - model.compile( optimizer=tf.keras.optimizers.Adam(learning_rate=1e-4), ) 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.TerminateOnNaN(), tf.keras.callbacks.TensorBoard( log_dir=args.train_path, histogram_freq=1, update_freq="epoch"), tf.keras.callbacks.experimental.BackupAndRestore(args.train_path), ], verbose=int(args.verbose), ) model.save(args.model_path) def compress(args): """Compresses an image.""" # Load model and use it to compress the image. model = tf.keras.models.load_model(args.model_path) x = read_png(args.input_file) tensors = model.compress(x) # Write a binary file with the shape information and the compressed string. packed = tfc.PackedTensors() packed.pack(tensors) with open(args.output_file, "wb") as f: f.write(packed.string) # If requested, decompress the image and measure performance. if args.verbose: x_hat = model.decompress(*tensors) # 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 print(f"Mean squared error: {mse:0.4f}") print(f"PSNR (dB): {psnr:0.2f}") print(f"Multiscale SSIM: {msssim:0.4f}") print(f"Multiscale SSIM (dB): {msssim_db:0.2f}") print(f"Bits per pixel: {bpp:0.4f}") def decompress(args): """Decompresses an image.""" # Load the model and determine the dtypes of tensors required to decompress. model = tf.keras.models.load_model(args.model_path) dtypes = [t.dtype for t in model.decompress.input_signature] # Read the shape information and compressed string from the binary file, # and decompress the image using the model. with open(args.input_file, "rb") as f: packed = tfc.PackedTensors(f.read()) tensors = packed.unpack(dtypes) x_hat = model.decompress(*tensors) # Write reconstructed image out as a PNG file. write_png(args.output_file, x_hat) def parse_args(argv): """Parses command line arguments.""" parser = argparse_flags.ArgumentParser( formatter_class=argparse.ArgumentDefaultsHelpFormatter) # High-level options. parser.add_argument( "--verbose", "-V", action="store_true", help="Report progress and metrics when training or compressing.") parser.add_argument( - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - models/ms2020.py [486:578]: - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - model.compile( optimizer=tf.keras.optimizers.Adam(learning_rate=1e-4), ) 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.TerminateOnNaN(), tf.keras.callbacks.TensorBoard( log_dir=args.train_path, histogram_freq=1, update_freq="epoch"), tf.keras.callbacks.experimental.BackupAndRestore(args.train_path), ], verbose=int(args.verbose), ) model.save(args.model_path) def compress(args): """Compresses an image.""" # Load model and use it to compress the image. model = tf.keras.models.load_model(args.model_path) x = read_png(args.input_file) tensors = model.compress(x) # Write a binary file with the shape information and the compressed string. packed = tfc.PackedTensors() packed.pack(tensors) with open(args.output_file, "wb") as f: f.write(packed.string) # If requested, decompress the image and measure performance. if args.verbose: x_hat = model.decompress(*tensors) # 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 print(f"Mean squared error: {mse:0.4f}") print(f"PSNR (dB): {psnr:0.2f}") print(f"Multiscale SSIM: {msssim:0.4f}") print(f"Multiscale SSIM (dB): {msssim_db:0.2f}") print(f"Bits per pixel: {bpp:0.4f}") def decompress(args): """Decompresses an image.""" # Load the model and determine the dtypes of tensors required to decompress. model = tf.keras.models.load_model(args.model_path) dtypes = [t.dtype for t in model.decompress.input_signature] # Read the shape information and compressed string from the binary file, # and decompress the image using the model. with open(args.input_file, "rb") as f: packed = tfc.PackedTensors(f.read()) tensors = packed.unpack(dtypes) x_hat = model.decompress(*tensors) # Write reconstructed image out as a PNG file. write_png(args.output_file, x_hat) def parse_args(argv): """Parses command line arguments.""" parser = argparse_flags.ArgumentParser( formatter_class=argparse.ArgumentDefaultsHelpFormatter) # High-level options. parser.add_argument( "--verbose", "-V", action="store_true", help="Report progress and metrics when training or compressing.") parser.add_argument( - 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