Added 4x upscale on basic super resolution model and other minor changes

README.md

@@ -191,9 +191,10 @@ optional arguments:
 ## Installation Instructions
 
 ### Setup Virtual Environment
-
+python3.9 or higher
 ```bash
 python3 -m venv venv
+source venv/bin/activate
 ```
 
 ## Installing Required Packages

superresolution/SuperResolution.py

@@ -299,7 +299,7 @@ def run_train_model(args: dict, training_dataset: Dataset, validation_dataset: D
 
 		# TODO: improve
 		if os.path.exists(checkpoint_root_path):
-			custom_objects = {'PSNRMetric' : PSNRMetric(), 'VGG16Error' : VGG16Error()}
+			custom_objects = {'PSNRMetric': PSNRMetric(), 'VGG16Error': VGG16Error()}
 			training_model = tf.keras.models.load_model(checkpoint_root_path, custom_objects=custom_objects)
 
 		# Create a callback that saves the model weights
@@ -320,7 +320,7 @@ def run_train_model(args: dict, training_dataset: Dataset, validation_dataset: D
 		training_callbacks.append(example_result_call_back)
 
 		# Debug output of the trained augmented data.
-		#training_callbacks.append(SaveExampleResultImageCallBack(
+		# training_callbacks.append(SaveExampleResultImageCallBack(
 		#	args.output_dir,
 		#	training_dataset, args.color_space, fileprefix="trainSuperResolution",
 		#	nth_batch_sample=args.example_nth_batch, grid_size=args.example_nth_batch_grid_size))
@@ -429,7 +429,7 @@ def dcsuperresolution_program(vargs=None):
 		#
 		parser.add_argument('--model', dest='model',
 							default='dcsr',
-							choices=['cnnsr', 'dcsr', 'dscr-post', 'dscr-pre', 'edsr', 'dcsr-ae', 'dcsr-resnet',
+							choices=['dcsr', 'dscr-post', 'dscr-pre', 'edsr', 'dcsr-ae', 'dcsr-resnet',
 									 'vdsr'],
 							help='Set which model type to use.', type=str)
 		#
@@ -482,7 +482,7 @@ def dcsuperresolution_program(vargs=None):
 			args.model_filepath = os.path.join(args.output_dir, args.model_filepath)
 
 		# Allow override to enable cropping for increase details in the dataset.
-		override_size: tuple = (512, 512)  # TODO fix.
+		override_size: tuple = (768, 768)  # TODO fix.
 
 		# Setup Dataset
 		training_dataset = None

superresolution/models/DCSuperResolution.py

@@ -9,6 +9,7 @@
 
 class DCSuperResolutionModel(ModelBase):
 	def __init__(self):
+		self.possible_upscale = [2, 4]
 		self.parser = argparse.ArgumentParser(add_help=False, prog="Basic SuperResolution",
 											  description="Basic Deep Convolutional Super Resolution")
 		group = self.parser.add_argument_group(self.get_name())
@@ -31,9 +32,15 @@ def load_argument(self) -> argparse.ArgumentParser:
 		return self.parser
 
 	def create_model(self, input_shape, output_shape, **kwargs) -> keras.Model:
+		scale_factor: int = int(output_shape[0] / input_shape[0])
+		scale_factor: int = int(output_shape[1] / input_shape[1])
+
+		if scale_factor not in self.possible_upscale and scale_factor not in self.possible_upscale:
+			raise ValueError("Invalid upscale")
+
 		# Model Construct Parameters.
 		regularization: float = kwargs.get("regularization", 0.000001)  #
-		upscale_mode: int = kwargs.get("upscale_mode", 2)  #
+		upscale_mode: int = scale_factor
 		nr_filters: int = kwargs.get("filters", 64)
 
 		#

superresolution/models/SuperResolutionCNN.py

@@ -9,9 +9,9 @@
 
 class SuperResolutionModelCNN(ModelBase):
 	def __init__(self):
-		self.parser = argparse.ArgumentParser(add_help=False)  # , prog="Basic SuperResolution",
-		#											  description="Basic Deep Convolutional Super Resolution")
-		#
+		self.possible_upscale = [2, 4]
+		self.parser = argparse.ArgumentParser(add_help=False)
+
 		self.parser.add_argument('--regularization', dest='regularization',
 								 type=float,
 								 default=0.000001,
@@ -30,13 +30,20 @@ def load_argument(self) -> argparse.ArgumentParser:
 		return self.parser
 
 	def create_model(self, input_shape, output_shape, **kwargs) -> keras.Model:
+		scale_factor: int = int(output_shape[0] / input_shape[0])
+		scale_factor: int = int(output_shape[1] / input_shape[1])
+
+		if scale_factor not in self.possible_upscale and scale_factor not in self.possible_upscale:
+			raise ValueError("Invalid upscale")
+
 		# Model Construct Parameters.
 		regularization: float = kwargs.get("regularization", 0.000001)  #
-		upscale_mode: int = kwargs.get("upscale_mode", 2)  #
+		upscale_mode: int = scale_factor  #
+		num_input_filters: int = kwargs.get("input_filters", 64)  #
 
 		#
 		return create_cnn_model(input_shape=input_shape,
-								output_shape=output_shape, input_filter_size=64, regularization=regularization,
+								output_shape=output_shape, input_filter_size=num_input_filters, regularization=regularization,
 								upscale_mode=upscale_mode,
 								kernel_activation='relu')
 
@@ -51,6 +58,7 @@ def get_model_interface() -> ModelBase:
 def create_cnn_model(input_shape: tuple, output_shape: tuple, input_filter_size: int, regularization: float,
 					 upscale_mode: int,
 					 kernel_activation: str):
+
 	use_batch_norm: bool = True
 	use_bias: bool = True
 	num_conv_block: int = 3
@@ -63,19 +71,18 @@ def create_cnn_model(input_shape: tuple, output_shape: tuple, input_filter_size:
 		x = layers.UpSampling2D(size=(2, 2), interpolation='bilinear')(x)
 
 	# Convolutional block
-	for i in range(0, int(upscale_mode / 2)):
-		for _ in range(0, num_conv_block):
-			filter_size = input_filter_size << i
-			x = layers.Conv2D(filters=filter_size, kernel_size=(3, 3), strides=1, padding='same', use_bias=use_bias,
-							  kernel_initializer=tf.keras.initializers.HeNormal())(x)
-			if use_batch_norm:
-				x = layers.BatchNormalization(dtype='float32')(x)
-			x = create_activation(kernel_activation)(x)
+	for _ in range(0, num_conv_block):
+		filter_size = input_filter_size << i
+		x = layers.Conv2D(filters=filter_size, kernel_size=(3, 3), strides=1, padding='same', use_bias=use_bias,
+						  kernel_initializer=tf.keras.initializers.HeNormal())(x)
+		if use_batch_norm:
+			x = layers.BatchNormalization(dtype='float32')(x)
+		x = create_activation(kernel_activation)(x)
 
 	# Output to 3 channel output.
 	x = layers.Conv2DTranspose(filters=output_channels, kernel_size=(9, 9), strides=(
 		1, 1), padding='same', use_bias=use_bias, kernel_initializer=tf.keras.initializers.HeNormal(),
-							   bias_initializer=tf.keras.initializers.HeNormal())(x)
+		bias_initializer=tf.keras.initializers.HeNormal())(x)
 	x = layers.Activation('tanh')(x)
 	x = layers.ActivityRegularization(l1=regularization, l2=0)(x)
 

superresolution/models/SuperResolutionResNet.py

@@ -19,12 +19,6 @@ def __init__(self):
 								 default=0.001,
 								 help='Set the L1 Regularization applied.')
 
-		self.parser.add_argument('--upscale-mode', dest='upscale_mode',
-								 type=str,
-								 choices=[''],
-								 default='',
-								 help='Set the L1 Regularization applied.')
-
 	def load_argument(self) -> argparse.ArgumentParser:
 		"""Load in the file for extracting text."""
 

superresolution/models/SuperResolutionVDSR.py

@@ -12,7 +12,6 @@ class VDSRSuperResolutionModel(ModelBase):
 	def __init__(self):
 		self.possible_upscale = [2, 4]
 
-
 		self.parser = argparse.ArgumentParser(add_help=False)
 		#
 		self.parser.add_argument('--regularization', dest='regularization',
@@ -35,9 +34,6 @@ def create_model(self, input_shape, output_shape, **kwargs) -> keras.Model:
 
 		if scale_factor not in self.possible_upscale and scale_factor not in self.possible_upscale:
 			raise ValueError("Invalid upscale")
-
-		# parser_result = self.parser.parse_known_args(sys.argv[1:])
-		# Model constructor parameters.
 
 		regularization: float = kwargs.get("regularization", 0.00001)  #
 		upscale_mode: int = scale_factor  #

superresolution/models/__init__.py

@@ -1,7 +1,7 @@
 from tensorflow.keras import layers
 
 
-def create_activation(activation):
+def create_activation(activation: str):
 	if activation == "leaky_relu":
 		return layers.LeakyReLU(alpha=0.2, dtype='float32')
 	elif activation == "relu":

superresolution/util/convert_model.py

@@ -1,7 +1,7 @@
 import tensorflow as tf
 
-def convert_model(model, dataset=None):
 
+def convert_model(model, dataset=None):
 	converter = tf.lite.TFLiteConverter.from_keras_model(model)
 	converter.optimizations = [tf.lite.Optimize.DEFAULT]
 	converter.inference_input_type = tf.float32
@@ -14,7 +14,7 @@ def convert_model(model, dataset=None):
 							]
 
 	converter.post_training_quantize = True
-	
+
 	if dataset:
 		converter.representative_dataset = tf.lite.RepresentativeDataset(
 			dataset)

superresolution/util/dataProcessing.py

@@ -150,7 +150,7 @@ def DownScaleLayer(data):
 				interpolation='bilinear',
 				crop_to_aspect_ratio=False
 			)])
-		
+
 		expectedScale = tf.keras.Sequential([
 			layers.Resizing(
 				output_size[0],
@@ -181,7 +181,7 @@ def resize_data(images):
 
 	if crop:
 		dataset = dataset.map(resize_data)
-		
+
 	DownScaledDataSet = (
 		dataset
 		.map(DownScaleLayer,

superresolution/util/trainingcallback.py

@@ -13,7 +13,8 @@ def compute_normalized_PSNR(orignal, data):
 
 class SaveExampleResultImageCallBack(tf.keras.callbacks.Callback):
 
-	def __init__(self, dir_path, train_data_subset, color_space: str, nth_batch_sample: int = 512, grid_size: int = 6, fileprefix: str = "SuperResolution",
+	def __init__(self, dir_path, train_data_subset, color_space: str, nth_batch_sample: int = 512, grid_size: int = 6,
+				 fileprefix: str = "SuperResolution",
 				 **kwargs):
 		super(tf.keras.callbacks.Callback, self).__init__(**kwargs)
 
@@ -37,15 +38,16 @@ def on_epoch_begin(self, epoch, logs=None):
 	def on_epoch_end(self, epoch, logs=None):
 		fig = show_expect_predicted_result(model=self.model, image_batch_dataset=self.trainSet,
 										   color_space=self.color_space, nr_col=self.grid_size)
-		fig.savefig(os.path.join(self.dir_path, "{0}{1}.png".format(self.fileprefix,epoch)))
+		fig.savefig(os.path.join(self.dir_path, "{0}{1}.png".format(self.fileprefix, epoch)))
 		fig.clf()
 		plt.close(fig)
 
 	def on_train_batch_end(self, batch, logs=None):
 		if batch % self.nth_batch_sample == 0:
 			fig = show_expect_predicted_result(model=self.model, image_batch_dataset=self.trainSet,
 											   color_space=self.color_space, nr_col=self.grid_size)
-			fig.savefig(os.path.join(self.dir_path, "{0}_{1}_{2}.png".format(self.fileprefix, self.current_epoch, batch)))
+			fig.savefig(
+				os.path.join(self.dir_path, "{0}_{1}_{2}.png".format(self.fileprefix, self.current_epoch, batch)))
 			fig.clf()
 			plt.close(fig)
 
@@ -154,7 +156,7 @@ def on_train_batch_end(self, batch, logs=None):
 	def on_epoch_end(self, epoch, logs=None):
 		super().on_epoch_end(epoch=epoch, logs=logs)
 
-		#TODO: add file output.
+		# TODO: add file output.
 
 		# Plot detailed
 		fig = plotTrainingHistory(self.batch_history, x_label="Batches", y_label="value")