wip

src/eynollah/training/metrics.py

@@ -147,6 +147,7 @@ def generalized_dice_loss(y_true, y_pred):
     return 1 - generalized_dice_coeff2(y_true, y_pred)
 
 
+# TODO: document where this is from
 def soft_dice_loss(y_true, y_pred, epsilon=1e-6):
     """
     Soft dice loss calculation for arbitrary batch size, number of classes, and number of spatial dimensions.
@@ -175,6 +176,7 @@ def soft_dice_loss(y_true, y_pred, epsilon=1e-6):
     return 1.00 - K.mean(numerator / (denominator + epsilon))  # average over classes and batch
 
 
+# TODO: document where this is from
 def seg_metrics(y_true, y_pred, metric_name, metric_type='standard', drop_last=True, mean_per_class=False,
                 verbose=False):
     """ 
@@ -267,6 +269,8 @@ def seg_metrics(y_true, y_pred, metric_name, metric_type='standard', drop_last=T
     return K.mean(non_zero_sum / non_zero_count)
 
 
+# TODO: document where this is from
+# TODO: Why a different implementation than IoU from utils?
 def mean_iou(y_true, y_pred, **kwargs):
     """
     Compute mean Intersection over Union of two segmentation masks, via Keras.
@@ -311,6 +315,7 @@ def iou_vahid(y_true, y_pred):
     return K.mean(iou)
 
 
+# TODO: copy from utils?
 def IoU_metric(Yi, y_predi):
     #  mean Intersection over Union
     #  Mean IoU = TP/(FN + TP + FP)
@@ -337,6 +342,7 @@ def IoU_metric_keras(y_true, y_pred):
     return IoU_metric(y_true.eval(session=sess), y_pred.eval(session=sess))
 
 
+# TODO: unused, remove?
 def jaccard_distance_loss(y_true, y_pred, smooth=100):
     """
     Jaccard = (|X & Y|)/ (|X|+ |Y| - |X & Y|)

src/eynollah/training/models.py

@@ -5,6 +5,8 @@ from tensorflow.keras.layers import *
 from tensorflow.keras import layers
 from tensorflow.keras.regularizers import l2
 
+from eynollah.patch_encoder import Patches, PatchEncoder
+
 ##mlp_head_units = [512, 256]#[2048, 1024]
 ###projection_dim = 64
 ##transformer_layers = 2#8
@@ -38,87 +40,6 @@ def mlp(x, hidden_units, dropout_rate):
         x = layers.Dropout(dropout_rate)(x)
     return x
 
-class Patches(layers.Layer):
-    def __init__(self, patch_size_x, patch_size_y):#__init__(self, **kwargs):#:__init__(self, patch_size):#__init__(self, **kwargs):
-        super(Patches, self).__init__()
-        self.patch_size_x = patch_size_x
-        self.patch_size_y = patch_size_y
-
-    def call(self, images):
-        #print(tf.shape(images)[1],'images')
-        #print(self.patch_size,'self.patch_size')
-        batch_size = tf.shape(images)[0]
-        patches = tf.image.extract_patches(
-            images=images,
-            sizes=[1, self.patch_size_y, self.patch_size_x, 1],
-            strides=[1, self.patch_size_y, self.patch_size_x, 1],
-            rates=[1, 1, 1, 1],
-            padding="VALID",
-        )
-        #patch_dims = patches.shape[-1]
-        patch_dims = tf.shape(patches)[-1]
-        patches = tf.reshape(patches, [batch_size, -1, patch_dims])
-        return patches
-    def get_config(self):
-
-        config = super().get_config().copy()
-        config.update({
-            'patch_size_x': self.patch_size_x,
-            'patch_size_y': self.patch_size_y,
-        })
-        return config
-
-class Patches_old(layers.Layer):
-    def __init__(self, patch_size):#__init__(self, **kwargs):#:__init__(self, patch_size):#__init__(self, **kwargs):
-        super(Patches, self).__init__()
-        self.patch_size = patch_size
-
-    def call(self, images):
-        #print(tf.shape(images)[1],'images')
-        #print(self.patch_size,'self.patch_size')
-        batch_size = tf.shape(images)[0]
-        patches = tf.image.extract_patches(
-            images=images,
-            sizes=[1, self.patch_size, self.patch_size, 1],
-            strides=[1, self.patch_size, self.patch_size, 1],
-            rates=[1, 1, 1, 1],
-            padding="VALID",
-        )
-        patch_dims = patches.shape[-1]
-        #print(patches.shape,patch_dims,'patch_dims')
-        patches = tf.reshape(patches, [batch_size, -1, patch_dims])
-        return patches
-    def get_config(self):
-
-        config = super().get_config().copy()
-        config.update({
-            'patch_size': self.patch_size,
-        })
-        return config
-
-    
-class PatchEncoder(layers.Layer):
-    def __init__(self, num_patches, projection_dim):
-        super(PatchEncoder, self).__init__()
-        self.num_patches = num_patches
-        self.projection = layers.Dense(units=projection_dim)
-        self.position_embedding = layers.Embedding(
-            input_dim=num_patches, output_dim=projection_dim
-        )
-
-    def call(self, patch):
-        positions = tf.range(start=0, limit=self.num_patches, delta=1)
-        encoded = self.projection(patch) + self.position_embedding(positions)
-        return encoded
-    def get_config(self):
-
-        config = super().get_config().copy()
-        config.update({
-            'num_patches': self.num_patches,
-            'projection': self.projection,
-            'position_embedding': self.position_embedding,
-        })
-        return config
     
     
 def one_side_pad(x):

src/eynollah/training/train.py

@@ -175,22 +175,94 @@ def config_params():
     characters_txt_file = None # Directory of characters text file needed for cnn_rnn_ocr model training. The file ends with .txt
 
 @ex.automain
-def run(_config, n_classes, n_epochs, input_height,
-        input_width, weight_decay, weighted_loss,
-        index_start, dir_of_start_model, is_loss_soft_dice,
-        n_batch, patches, augmentation, flip_aug,
-        blur_aug, padding_white, padding_black, scaling, shifting, degrading,channels_shuffling,
-        brightening, binarization, adding_rgb_background, adding_rgb_foreground, add_red_textlines, blur_k, scales, degrade_scales,shuffle_indexes,
-        brightness, dir_train, data_is_provided, scaling_bluring,
-        scaling_brightness, scaling_binarization, rotation, rotation_not_90,
-        thetha, thetha_padd, scaling_flip, continue_training, transformer_projection_dim,
-        transformer_mlp_head_units, transformer_layers, transformer_num_heads, transformer_cnn_first,
-        transformer_patchsize_x, transformer_patchsize_y,
-        transformer_num_patches_xy, backbone_type, save_interval, flip_index, dir_eval, dir_output,
-        pretraining, learning_rate, task, f1_threshold_classification, classification_classes_name, dir_img_bin, number_of_backgrounds_per_image,dir_rgb_backgrounds,
-        dir_rgb_foregrounds, characters_txt_file, color_padding_rotation, bin_deg, image_inversion, white_noise_strap, textline_skewing, textline_skewing_bin,
-        textline_left_in_depth, textline_left_in_depth_bin, textline_right_in_depth, textline_right_in_depth_bin, textline_up_in_depth, textline_up_in_depth_bin,
-        textline_down_in_depth, textline_down_in_depth_bin, pepper_bin_aug, pepper_aug, padd_colors, pepper_indexes, white_padds, skewing_amplitudes, max_len):
+def run(
+    _config,
+    n_classes,
+    n_epochs,
+    input_height,
+    input_width,
+    weight_decay,
+    weighted_loss,
+    index_start,
+    dir_of_start_model,
+    is_loss_soft_dice,
+    n_batch,
+    patches,
+    augmentation,
+    flip_aug,
+    blur_aug,
+    padding_white,
+    padding_black,
+    scaling,
+    shifting,
+    degrading,
+    channels_shuffling,
+    brightening,
+    binarization,
+    adding_rgb_background,
+    adding_rgb_foreground,
+    add_red_textlines,
+    blur_k,
+    scales,
+    degrade_scales,
+    shuffle_indexes,
+    brightness,
+    dir_train,
+    data_is_provided,
+    scaling_bluring,
+    scaling_brightness,
+    scaling_binarization,
+    rotation,
+    rotation_not_90,
+    thetha,
+    thetha_padd,
+    scaling_flip,
+    continue_training,
+    transformer_projection_dim,
+    transformer_mlp_head_units,
+    transformer_layers,
+    transformer_num_heads,
+    transformer_cnn_first,
+    transformer_patchsize_x,
+    transformer_patchsize_y,
+    transformer_num_patches_xy,
+    backbone_type,
+    save_interval,
+    flip_index,
+    dir_eval,
+    dir_output,
+    pretraining,
+    learning_rate,
+    task,
+    f1_threshold_classification,
+    classification_classes_name,
+    dir_img_bin,
+    number_of_backgrounds_per_image,
+    dir_rgb_backgrounds,
+    dir_rgb_foregrounds,
+    characters_txt_file,
+    color_padding_rotation,
+    bin_deg,
+    image_inversion,
+    white_noise_strap,
+    textline_skewing,
+    textline_skewing_bin,
+    textline_left_in_depth,
+    textline_left_in_depth_bin,
+    textline_right_in_depth,
+    textline_right_in_depth_bin,
+    textline_up_in_depth,
+    textline_up_in_depth_bin,
+    textline_down_in_depth,
+    textline_down_in_depth_bin,
+    pepper_bin_aug,
+    pepper_aug,
+    padd_colors,
+    pepper_indexes,
+    white_padds,
+    skewing_amplitudes,
+    max_len,
+):
     
     if dir_rgb_backgrounds:
         list_all_possible_background_images = os.listdir(dir_rgb_backgrounds)
@@ -201,6 +273,10 @@ def run(_config, n_classes, n_epochs, input_height,
         list_all_possible_foreground_rgbs = os.listdir(dir_rgb_foregrounds)
     else:
         list_all_possible_foreground_rgbs = None
+
+    dir_seg = None
+    weights = None
+    model = None
         
     if task == "segmentation" or task == "enhancement" or task == "binarization":
         if data_is_provided:
@@ -285,6 +361,7 @@ def run(_config, n_classes, n_epochs, input_height,
                         pass
             else:
 
+                assert dir_seg is not None
                 for obj in os.listdir(dir_seg):
                     try:
                         label_obj = cv2.imread(dir_seg + '/' + obj)
@@ -314,6 +391,8 @@ def run(_config, n_classes, n_epochs, input_height,
                     model = load_model(dir_of_start_model, compile=True, custom_objects={'loss': weighted_categorical_crossentropy(weights)})
                 if not is_loss_soft_dice and not weighted_loss:
                     model = load_model(dir_of_start_model , compile=True,custom_objects = {"PatchEncoder": PatchEncoder, "Patches": Patches})
+            else:
+                raise ValueError("backbone_type must be 'nontransformer' or 'transformer'")
         else:
             index_start = 0
             if backbone_type=='nontransformer':
@@ -348,6 +427,7 @@ def run(_config, n_classes, n_epochs, input_height,
                         sys.exit(1)
                     model = vit_resnet50_unet_transformer_before_cnn(n_classes, transformer_patchsize_x, transformer_patchsize_y, num_patches, transformer_mlp_head_units, transformer_layers, transformer_num_heads, transformer_projection_dim, input_height, input_width, task, weight_decay, pretraining)
         
+        assert model is not None
         #if you want to see the model structure just uncomment model summary.
         model.summary()
 
@@ -377,9 +457,7 @@ def run(_config, n_classes, n_epochs, input_height,
         ##score_best=[]
         ##score_best.append(0)
         
-        if save_interval:
-            save_weights_callback = SaveWeightsAfterSteps(save_interval, dir_output, _config)
-            
+        save_weights_callback = SaveWeightsAfterSteps(save_interval, dir_output, _config) if save_interval else None
             
         for i in tqdm(range(index_start, n_epochs + index_start)):
             if save_interval:
@@ -459,8 +537,7 @@ def run(_config, n_classes, n_epochs, input_height,
         opt = tf.keras.optimizers.Adam(learning_rate=learning_rate)#1e-4)#(lr_schedule)
         model.compile(optimizer=opt)
         
-        if save_interval:
-            save_weights_callback = SaveWeightsAfterSteps(save_interval, dir_output, _config)
+        save_weights_callback = SaveWeightsAfterSteps(save_interval, dir_output, _config) if save_interval else None
         
         for i in tqdm(range(index_start, n_epochs + index_start)):
             if save_interval:
@@ -559,8 +636,7 @@ def run(_config, n_classes, n_epochs, input_height,
         model.compile(loss="binary_crossentropy",
                             optimizer = opt_adam,metrics=['accuracy'])
         
-        if save_interval:
-            save_weights_callback = SaveWeightsAfterSteps(save_interval, dir_output, _config)
+        save_weights_callback = SaveWeightsAfterSteps(save_interval, dir_output, _config) if save_interval else None
             
         for i in range(n_epochs):
             if save_interval: