[ADD] use_datagenerator

facades.py

@@ -15,24 +15,30 @@
 from keras.utils import multi_gpu_model
 
 from ml import residual_model
+from utils import load_images_from_folder
 
 keras.backend.set_image_data_format('channels_last')
 
 
 class Facades:  # TODO: Predict  # TODO: Comments
-    def __init__(self, dim=(720, 1080), n_channels=3):
+    def __init__(self, dim=(720, 1080), n_channels=3, use_datagenerator=True):
         """Initialize main parameters of Facades class
         :param dim: Images resolution in format (height, width)
         :param n_channels: Number of channels in image (1 or 3)
         """
+        self.batch_size = None
         self.dim = dim
-        self.n_channels = n_channels
         self.shape = dim + (n_channels,)
+        self.n_channels = n_channels
 
         self.model = None
-        self.train_generator, self.valid_generator = None, None
-        self.batch_size = None
         self.training_init_check = False
+        self.use_datagenerator = use_datagenerator
+
+        if use_datagenerator:
+            self.train_generator, self.valid_generator = None, None
+        else:
+            self.train_data, self.valid_data = None, None
 
     # noinspection PyAttributeOutsideInit
     def training_init(self, dataset_dir="dataset", batch_size=8):
@@ -46,47 +52,60 @@ def training_init(self, dataset_dir="dataset", batch_size=8):
                 > masks
         :param batch_size: Number of images in one training iteration
         """
+        # TODO: os.path.join
         self.training_init_check = True
         self.batch_size = batch_size
 
-        # Initialize ImageDataGenerator classes
-        data_datagen = ImageDataGenerator(
-            rescale=1. / 255,
-
-            height_shift_range=0.2,
-            horizontal_flip=True,
-            rotation_range=45,
-            width_shift_range=0.2,
-            zoom_range=[0.5, 1.0]
-        )
-
-        masks_datagen = ImageDataGenerator(
-            height_shift_range=0.2,
-            horizontal_flip=True,
-            rotation_range=45,
-            width_shift_range=0.2,
-            zoom_range=[0.5, 1.0]
-        )
-
-        # Initialize data generators
-        seed = 909  # to transform image and masks with same augmentation parameter.
-        image_generator = data_datagen.flow_from_directory(dataset_dir + "train/data/", class_mode=None, seed=seed,
-                                                           batch_size=batch_size, target_size=self.dim)
-        masks_generator = masks_datagen.flow_from_directory(dataset_dir + "train/masks/", class_mode=None, seed=seed,
-                                                            batch_size=batch_size, target_size=self.dim,
-                                                            color_mode="grayscale")
-
-        self.train_generator = zip(image_generator, masks_generator)
-
-        if os.path.exists(dataset_dir + "valid"):
-            valid_datagen = ImageDataGenerator(rescale=1. / 255)
-
-            valid_image_generator = valid_datagen.flow_from_directory(dataset_dir + "valid/masks/",
-                                                                      class_mode=None, seed=seed)
-            valid_masks_generator = valid_datagen.flow_from_directory(dataset_dir + "valid/masks/",
-                                                                      class_mode=None, seed=seed)
-
-            self.valid_generator = zip(valid_image_generator, valid_masks_generator)
+        if self.use_datagenerator:
+            # Initialize ImageDataGenerator classes
+            data_datagen = ImageDataGenerator(
+                rescale=1. / 255,
+
+                height_shift_range=0.2,
+                horizontal_flip=True,
+                rotation_range=45,
+                width_shift_range=0.2,
+                zoom_range=[0.5, 1.0]
+            )
+
+            masks_datagen = ImageDataGenerator(
+                height_shift_range=0.2,
+                horizontal_flip=True,
+                rotation_range=45,
+                width_shift_range=0.2,
+                zoom_range=[0.5, 1.0]
+            )
+
+            # Initialize data generators
+            seed = 909  # to transform image and masks with same augmentation parameter.
+            image_generator = data_datagen.flow_from_directory(dataset_dir + "train/data/", class_mode=None, seed=seed,
+                                                               batch_size=batch_size, target_size=self.dim)
+            masks_generator = masks_datagen.flow_from_directory(dataset_dir + "train/masks/", class_mode=None, seed=seed,
+                                                                batch_size=batch_size, target_size=self.dim,
+                                                                color_mode="grayscale")
+
+            self.train_generator = zip(image_generator, masks_generator)
+
+            if os.path.exists(dataset_dir + "/valid"):
+                valid_datagen = ImageDataGenerator(rescale=1. / 255)
+
+                valid_image_generator = valid_datagen.flow_from_directory(dataset_dir + "/valid/masks/",
+                                                                          class_mode=None, seed=seed)
+                valid_masks_generator = valid_datagen.flow_from_directory(dataset_dir + "/valid/masks/",
+                                                                          class_mode=None, seed=seed)
+
+                self.valid_generator = zip(valid_image_generator, valid_masks_generator)
+        else:
+            x_train = load_images_from_folder(dataset_dir + "/train/data/img", self.shape)
+            y_train = load_images_from_folder(dataset_dir + "/train/masks/img", self.dim + (1,))
+
+            self.train_data = (x_train, y_train)
+
+            if os.path.exists(dataset_dir + "/valid/img"):
+                x_valid = load_images_from_folder(dataset_dir + "/valid/data/img", self.shape)
+                y_valid = load_images_from_folder(dataset_dir + "/valid/masks/img", self.dim + (1,))  # TODO: Add Y num_channels variable to init
+
+                self.valid_data = (x_valid, y_valid)
 
         # Initialize model
         base_model = keras.applications.xception.Xception(weights='imagenet',
@@ -98,9 +117,10 @@ def training_init(self, dataset_dir="dataset", batch_size=8):
         self.model = Model(base_model.input, output_layer)
         # keras.utils.plot_model(self.model, 'model.png', show_shapes=True)
 
-    def train(self, epochs=1, remote_monitor_ip="localhost:4959"):
+    def train(self, epochs=1, number_of_GPUs=0, remote_monitor_ip="localhost:4959"):
         """Train model
         :param epochs: Number of epochs to train model
+        :param number_of_GPUs: Number of devices to train on multiple GPUs
         :param remote_monitor_ip: IP where to run Keras RemoteMonitor (default: localhost:4959)  # TODO
         """
         # Check training init before train
@@ -111,17 +131,23 @@ def train(self, epochs=1, remote_monitor_ip="localhost:4959"):
         # Initialize callbacks
         callbacks = [
             EarlyStopping(monitor='accuracy', patience=10, verbose=1),
-            ModelCheckpoint('checkpoints/weights.{epoch:02d}-{accuracy:.2f}.hdf5', verbose=1,
-                            save_best_only=True, save_weights_only=True),
+            ModelCheckpoint('checkpoints/weights.{epoch:02d}-{accuracy:.2f}.hdf5', monitor="accuracy",
+                            verbose=1, save_best_only=True, save_weights_only=True),
             ReduceLROnPlateau(monitor='accuracy', factor=0.2, patience=5, min_lr=0.001, verbose=1)
         ]
 
         # Run on GPU if needed
+        if number_of_GPUs:
+            self.model = multi_gpu_model(self.model, gpus=number_of_GPUs)
 
         # Run model training
         self.model.compile(optimizer=Adam(), loss="binary_crossentropy", metrics=["accuracy"])
-        self.model.fit_generator(self.train_generator, steps_per_epoch=np.floor(160 / self.batch_size),
-                                 epochs=epochs, callbacks=callbacks, verbose=2)
+
+        if self.use_datagenerator:
+            self.model.fit_generator(self.train_generator, steps_per_epoch=np.floor(160 / self.batch_size),
+                                     epochs=epochs, callbacks=callbacks, verbose=2)
+        else:
+            self.model.fit(*self.train_data, self.batch_size, epochs=epochs, callbacks=callbacks, verbose=2)
 
 
 if __name__ == "__main__":