Sourcery Starbot ⭐ refactored RocketFlash/EmbeddingNet #13

SourceryAI · 2022-10-26T08:53:09Z

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SourceryAI · 2022-10-26T08:53:11Z

embedding_net/augmentations.py

-        augmentations = A.Compose([
-            A.RandomBrightnessContrast(p=0.4),
-            A.RandomGamma(p=0.4),
-            A.HueSaturationValue(hue_shift_limit=20,
-                                 sat_shift_limit=30, val_shift_limit=30, p=0.4),
-            A.CLAHE(p=0.4),
-            A.Blur(blur_limit=1, p=0.3),
-            A.GaussNoise(var_limit=(50, 80), p=0.3)
-        ], p=1)
+        return A.Compose(
+            [
+                A.RandomBrightnessContrast(p=0.4),
+                A.RandomGamma(p=0.4),
+                A.HueSaturationValue(
+                    hue_shift_limit=20,
+                    sat_shift_limit=30,
+                    val_shift_limit=30,
+                    p=0.4,
+                ),
+                A.CLAHE(p=0.4),
+                A.Blur(blur_limit=1, p=0.3),
+                A.GaussNoise(var_limit=(50, 80), p=0.3),
+            ],
+            p=1,
+        )
+
    elif name == 'plates':
-        augmentations = A.Compose([
-            A.RandomBrightnessContrast(p=0.4),
-            A.RandomGamma(p=0.4),
-            A.HueSaturationValue(hue_shift_limit=20,
-                                 sat_shift_limit=30, 
-                                 val_shift_limit=30, 
-                                 p=0.4),
-            A.CLAHE(p=0.4),
-            A.HorizontalFlip(p=0.5),
-            A.VerticalFlip(p=0.5),
-            A.Blur(blur_limit=1, p=0.3),
-            A.GaussNoise(var_limit=(50, 80), p=0.3),
-            A.RandomCrop(p=0.8, height=2*input_shape[1]/3, width=2*input_shape[0]/3)
-        ], p=1)
+        return A.Compose(
+            [
+                A.RandomBrightnessContrast(p=0.4),
+                A.RandomGamma(p=0.4),
+                A.HueSaturationValue(
+                    hue_shift_limit=20,
+                    sat_shift_limit=30,
+                    val_shift_limit=30,
+                    p=0.4,
+                ),
+                A.CLAHE(p=0.4),
+                A.HorizontalFlip(p=0.5),
+                A.VerticalFlip(p=0.5),
+                A.Blur(blur_limit=1, p=0.3),
+                A.GaussNoise(var_limit=(50, 80), p=0.3),
+                A.RandomCrop(
+                    p=0.8,
+                    height=2 * input_shape[1] / 3,
+                    width=2 * input_shape[0] / 3,
+                ),
+            ],
+            p=1,
+        )
+
    elif name == 'deepfake':
-        augmentations = A.Compose([
-            A.HorizontalFlip(p=0.5),
-        ], p=1)
+        return A.Compose(
+            [
+                A.HorizontalFlip(p=0.5),
+            ],
+            p=1,
+        )
+
    elif name == 'plates2':
-        augmentations = A.Compose([
-            A.CLAHE(clip_limit=(1,4),p=0.3),
-            A.HorizontalFlip(p=0.5),
-            A.VerticalFlip(p=0.5),
-            A.RandomBrightness(limit=0.2, p=0.3),
-            A.RandomContrast(limit=0.2, p=0.3),
-            # A.Rotate(limit=360, p=0.9),
-            A.RandomRotate90(p=0.3),
-            A.HueSaturationValue(hue_shift_limit=(-50,50), 
-                                 sat_shift_limit=(-15,15), 
-                                 val_shift_limit=(-15,15), 
-                                 p=0.5),
-#             A.Blur(blur_limit=(5,7), p=0.3),
-            A.GaussNoise(var_limit=(10, 50), p=0.3),
-            A.CenterCrop(p=1, height=2*input_shape[1]//3, width=2*input_shape[0]//3),
-            A.Resize(p=1, height=input_shape[1], width=input_shape[0])
-        ], p=1)
-    else:
-        augmentations = None
+        return A.Compose(
+            [
+                A.CLAHE(clip_limit=(1, 4), p=0.3),
+                A.HorizontalFlip(p=0.5),
+                A.VerticalFlip(p=0.5),
+                A.RandomBrightness(limit=0.2, p=0.3),
+                A.RandomContrast(limit=0.2, p=0.3),
+                # A.Rotate(limit=360, p=0.9),
+                A.RandomRotate90(p=0.3),
+                A.HueSaturationValue(
+                    hue_shift_limit=(-50, 50),
+                    sat_shift_limit=(-15, 15),
+                    val_shift_limit=(-15, 15),
+                    p=0.5,
+                ),
+                # #             A.Blur(blur_limit=(5,7), p=0.3),
+                A.GaussNoise(var_limit=(10, 50), p=0.3),
+                A.CenterCrop(
+                    p=1,
+                    height=2 * input_shape[1] // 3,
+                    width=2 * input_shape[0] // 3,
+                ),
+                A.Resize(p=1, height=input_shape[1], width=input_shape[0]),
+            ],
+            p=1,
+        )

-    return augmentations
+    else:
+        return None


Function get_aug refactored with the following changes:

Lift return into if (lift-return-into-if)

This removes the following comments ( why? ):

# A.Blur(blur_limit=(5,7), p=0.3),

SourceryAI · 2022-10-26T08:53:11Z

embedding_net/datagenerators.py

-        
+
        if train_csv_file is not None:
            self.class_files_paths = self._load_from_dataframe(train_csv_file, image_id_column, label_column, is_google)
        else:
            self.class_files_paths = self._load_from_directory()
-        
+


Found the following improvement in Function ENDataLoader.__init__:

SourceryAI · 2022-10-26T08:53:11Z

embedding_net/datagenerators.py

            subdirs = [f.path for f in os.scandir(class_dir_path) if f.is_dir()]
            temp_list = []
-            if len(subdirs)>0:
+            if subdirs:


Function ENDataLoader._load_from_directory refactored with the following changes:

Simplify sequence length comparison (simplify-len-comparison)

SourceryAI · 2022-10-26T08:53:11Z

embedding_net/datagenerators.py

-            return self.n_batches_val
-        else:
-            return self.n_batches
+        return self.n_batches_val if self.val_gen else self.n_batches


Function ENDataGenerator.__len__ refactored with the following changes:

Replace if statement with if expression (assign-if-exp)

SourceryAI · 2022-10-26T08:53:11Z

embedding_net/datagenerators.py

-        
+


Function TripletsDataGenerator.get_batch_triplets_mining refactored with the following changes:

Simplify sequence length comparison (simplify-len-comparison)

SourceryAI · 2022-10-26T08:53:13Z

embedding_net/utils.py

-        ax.set(xlabel='epoch', ylabel='{}'.format(k),
-               title='{}'.format(k))
+        ax.set(xlabel='epoch', ylabel=f'{k}', title=f'{k}')
        ax.grid()

-        fig.savefig("{}{}.png".format(save_path, k))
+        fig.savefig(f"{save_path}{k}.png")


Function plot_grapths refactored with the following changes:

Replace call to format with f-string [×3] (use-fstring-for-formatting)

SourceryAI · 2022-10-26T08:53:13Z

embedding_net/utils.py

-        
+


Found the following improvement in Function plot_batch_simple:

SourceryAI · 2022-10-26T08:53:13Z

embedding_net/utils.py

-        optimizer = optimizers.Adam(lr=learning_rate)
+        return optimizers.Adam(lr=learning_rate)
    elif name == 'rms_prop':
-        optimizer = optimizers.RMSprop(lr=learning_rate)
+        return optimizers.RMSprop(lr=learning_rate)
    elif name == 'radam':
        from keras_radam import RAdam
-        optimizer = RAdam(learning_rate)
+        return RAdam(learning_rate)
    else:
-        optimizer = optimizers.SGD(lr=learning_rate)
-    return optimizer
+        return optimizers.SGD(lr=learning_rate)


Function get_optimizer refactored with the following changes:

Lift return into if (lift-return-into-if)

SourceryAI · 2022-10-26T08:53:14Z

embedding_net/utils.py

-        
+


Found the following improvement in Function parse_params:

SourceryAI · 2022-10-26T08:53:14Z

tools/test.py


    model_prediction = model.predict(image_path)
-    print('Model prediction: {}'.format(model_prediction))
+    print(f'Model prediction: {model_prediction}')


Lines 25-25 refactored with the following changes:

Replace call to format with f-string (use-fstring-for-formatting)

SourceryAI · 2022-10-26T08:53:17Z

tools/train.py

-    args = parser.parse_args()
-
-    return args
+    return parser.parse_args()


Function parse_args refactored with the following changes:

Inline variable that is immediately returned (inline-immediately-returned-variable)

SourceryAI · 2022-10-26T08:53:17Z

tools/train.py

-    
+

    initial_lr = params_train['learning_rate']
    decay_factor = params_train['decay_factor']
    step_size = params_train['step_size']

-    if params_dataloader['validate']:
-        callback_monitor = 'val_loss'
-    else:
-        callback_monitor = 'loss'
-
+    callback_monitor = 'val_loss' if params_dataloader['validate'] else 'loss'


Function main refactored with the following changes:

Replace if statement with if expression (assign-if-exp)

'Refactored by Sourcery'

813475d

SourceryAI commented Oct 26, 2022

View reviewed changes

Sourcery Starbot ⭐ refactored RocketFlash/EmbeddingNet #13

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Sourcery Starbot ⭐ refactored RocketFlash/EmbeddingNet #13

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SourceryAI commented Oct 26, 2022

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