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Sourcery Starbot ⭐ refactored RocketFlash/EmbeddingNet #13

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122 changes: 76 additions & 46 deletions embedding_net/augmentations.py
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Original file line number Diff line number Diff line change
Expand Up @@ -3,53 +3,83 @@

def get_aug(name='default', input_shape=[48, 48, 3]):
if name == 'default':
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
Comment on lines -6 to +85
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@SourceryAI SourceryAI Oct 26, 2022

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Function get_aug refactored with the following changes:

This removes the following comments ( why? ):

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

26 changes: 8 additions & 18 deletions embedding_net/datagenerators.py
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Original file line number Diff line number Diff line change
Expand Up @@ -26,12 +26,12 @@ def __init__(self, dataset_path,
self.dataset_path = dataset_path
self.class_files_paths = {}
self.class_names = []

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()

Comment on lines -29 to +34
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Found the following improvement in Function ENDataLoader.__init__:

self.n_classes = len(self.class_names)
self.n_samples = {k: len(v) for k, v in self.class_files_paths.items()}

Expand Down Expand Up @@ -94,7 +94,7 @@ def _load_from_directory(self):
for class_name, class_dir_path in tqdm.tqdm(zip(self.class_names, class_dir_paths)):
subdirs = [f.path for f in os.scandir(class_dir_path) if f.is_dir()]
temp_list = []
if len(subdirs)>0:
if subdirs:
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Function ENDataLoader._load_from_directory refactored with the following changes:

for subdir in subdirs:
class_image_paths = [f.path for f in os.scandir(subdir) if f.is_file() and
(f.name.endswith('.jpg') or
Expand Down Expand Up @@ -134,10 +134,7 @@ def __init__(self, class_files_paths,
self.n_samples = {k: len(v) for k, v in self.class_files_paths.items()}

def __len__(self):
if self.val_gen:
return self.n_batches_val
else:
return self.n_batches
return self.n_batches_val if self.val_gen else self.n_batches
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Function ENDataGenerator.__len__ refactored with the following changes:


def __getitem__(self, index):
pass
Expand Down Expand Up @@ -207,7 +204,7 @@ def get_batch_triplets_mining(self):
all_embeddings_list = []
all_images_list = []


Comment on lines -210 to +207
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Function TripletsDataGenerator.get_batch_triplets_mining refactored with the following changes:

for idx, cl_img_idxs in enumerate(selected_images):
images = self._get_images_set(selected_classes[idx], cl_img_idxs, with_aug=self.augmentations)
all_images_list.append(images)
Expand Down Expand Up @@ -243,7 +240,7 @@ def get_batch_triplets_mining(self):
triplet_negatives.append(all_images[hard_negative])
targets.append(1)

if len(triplet_anchors) == 0:
if not triplet_anchors:
triplet_anchors.append(all_images[anchor_positive[0]])
triplet_positives.append(all_images[anchor_positive[1]])
triplet_negatives.append(all_images[negative_indices[0]])
Expand Down Expand Up @@ -282,9 +279,7 @@ def get_batch_triplets(self):
np.zeros((self.batch_size, self.input_shape[0], self.input_shape[1], 3))]
targets = np.zeros((self.batch_size,))

count = 0

for i in range(self.batch_size):
for count, i in enumerate(range(self.batch_size)):
Comment on lines -285 to +282
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Function SimpleTripletsDataGenerator.get_batch_triplets refactored with the following changes:

selected_class_idx = random.randrange(0, self.n_classes)
selected_class = self.class_names[selected_class_idx]
selected_class_n_elements = self.n_samples[selected_class]
Expand All @@ -306,8 +301,6 @@ def get_batch_triplets(self):
triplets[1][count, :, :, :] = imgs[1]
triplets[2][count, :, :, :] = imgs[2]
targets[i] = 1
count += 1

return triplets, targets

def __getitem__(self, index):
Expand Down Expand Up @@ -398,9 +391,8 @@ def get_batch(self):
np.zeros((self.batch_size, self.input_shape[0], self.input_shape[1], 3))]
targets = np.zeros((self.batch_size, self.n_classes))

count = 0
with_aug = self.augmentations
for i in range(self.batch_size):
for count, i in enumerate(range(self.batch_size)):
Comment on lines -401 to +395
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Function SimpleDataGenerator.get_batch refactored with the following changes:

selected_class_idx = random.randrange(0, self.n_classes)
selected_class = self.class_names[selected_class_idx]
selected_class_n_elements = len(self.class_files_paths[selected_class])
Expand All @@ -410,8 +402,6 @@ def get_batch(self):
img = self._get_images_set([selected_class], [indx], with_aug=with_aug)
images[0][count, :, :, :] = img[0]
targets[i][selected_class_idx] = 1
count += 1

return images, targets

def __getitem__(self, index):
Expand Down
40 changes: 14 additions & 26 deletions embedding_net/models.py
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Original file line number Diff line number Diff line change
Expand Up @@ -45,8 +45,7 @@ def _create_base_model(self):
self.classification_model = Model(inputs=[self.base_model.layers[0].input],outputs=[output])

def _generate_encodings(self, imgs):
encodings = self.base_model.predict(imgs)
return encodings
return self.base_model.predict(imgs)
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Function EmbeddingNet._generate_encodings refactored with the following changes:



def train_embeddings_classifier(self, data_loader,
Expand All @@ -61,27 +60,25 @@ def train_embeddings_classifier(self, data_loader,
def generate_encodings(self, data_loader, max_n_samples=10,
shuffle=True):
data_paths, data_labels, data_encodings = [], [], []
encoded_training_data = {}

for class_name in data_loader.class_names:
data_list = data_loader.train_data[class_name]
if len(data_list)>max_n_samples:
if shuffle:
random.shuffle(data_list)
data_list = data_list[:max_n_samples]

Comment on lines -64 to +69
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Function EmbeddingNet.generate_encodings refactored with the following changes:

data_paths += data_list
imgs = get_images(data_list, self.params_model['input_shape'])
encods = self._generate_encodings(imgs)
for encod in encods:
data_encodings.append(encod)
data_labels.append(class_name)

encoded_training_data['paths'] = data_paths
encoded_training_data['labels'] = data_labels
encoded_training_data['encodings'] = np.squeeze(np.array(data_encodings))

return encoded_training_data
return {
'paths': data_paths,
'labels': data_labels,
'encodings': np.squeeze(np.array(data_encodings)),
}

def save_encodings(self, encoded_training_data,
save_folder='./',
Expand Down Expand Up @@ -113,23 +110,16 @@ def save_onnx(self, save_folder, save_name='base_model.onnx'):
keras2onnx.save_model(onnx_model, os.path.join(save_folder, save_name))

def predict(self, image):
if type(image) is str:
img = cv2.imread(image)
else:
img = image
img = cv2.imread(image) if type(image) is str else image
img = cv2.resize(img, (self.params_model['input_shape'][0],
self.params_model['input_shape'][1]))
encoding = self.base_model.predict(np.expand_dims(img, axis=0))
distances = self.calculate_distances(encoding)
max_element = np.argmin(distances)
predicted_label = self.encoded_training_data['labels'][max_element]
return predicted_label
return self.encoded_training_data['labels'][max_element]
Comment on lines -116 to +119
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Function EmbeddingNet.predict refactored with the following changes:


def predict_knn(self, image, with_top5=False):
if type(image) is str:
img = cv2.imread(image)
else:
img = image
img = cv2.imread(image) if type(image) is str else image
Comment on lines -129 to +122
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Function EmbeddingNet.predict_knn refactored with the following changes:

img = cv2.resize(img, (self.input_shape[0], self.input_shape[1]))

encoding = self.base_model.predict(np.expand_dims(img, axis=0))
Expand All @@ -145,8 +135,6 @@ def calculate_prediction_accuracy(self, data_loader):
correct_top1 = 0
correct_top5 = 0

accuracies = {'top1':0,
'top5':0 }
Comment on lines -148 to -149
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Function EmbeddingNet.calculate_prediction_accuracy refactored with the following changes:

total_n_of_images = len(data_loader.images_paths['val'])
for img_path, img_label in zip(data_loader.images_paths['val'],
data_loader.images_labels['val']):
Expand All @@ -155,10 +143,10 @@ def calculate_prediction_accuracy(self, data_loader):
correct_top1 += 1
if img_label in prediction_top5:
correct_top5 += 1
accuracies['top1'] = correct_top1/total_n_of_images
accuracies['top5'] = correct_top5/total_n_of_images

return accuracies
return {
'top1': correct_top1 / total_n_of_images,
'top5': correct_top5 / total_n_of_images,
}


class TripletNet(EmbeddingNet):
Expand Down
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