def image_generator_with_top_policies(self,
images,
labels,
batch_size=None):
"""
Args:
images (numpy.array): array with shape (N,dim,dim,channek-size)
labels (numpy.array): array with shape (N), where each eleemnt is an integer from 0 to num_classes-1
batch_size (int): batch size of the generator on demand
Returns:
generator: generator for augmented images
"""
if batch_size is None:
batch_size = self.config["child_batch_size"]
top_policies_list = self.top_policies[[
'A_aug1_type', 'A_aug1_magnitude', 'A_aug2_type',
'A_aug2_magnitude', 'B_aug1_type', 'B_aug1_magnitude',
'B_aug2_type', 'B_aug2_magnitude', 'C_aug1_type',
'C_aug1_magnitude', 'C_aug2_type', 'C_aug2_magnitude',
'D_aug1_type', 'D_aug1_magnitude', 'D_aug2_type',
'D_aug2_magnitude', 'E_aug1_type', 'E_aug1_magnitude',
'E_aug2_type', 'E_aug2_magnitude'
]].to_dict(orient="records")
return deepaugment_image_generator(images,
labels,
top_policies_list,
batch_size=batch_size)
def run_model(dataset_name, num_classes, epochs, batch_size, policies_path):
data, input_shape = DataOp.load(dataset_name)
data = DataOp.preprocess_normal(data)
wrn_28_10 = ChildCNN(
model_name="wrn_28_10",
input_shape=input_shape,
batch_size=batch_size,
num_classes=num_classes,
pre_augmentation_weights_path="initial_model_weights.h5",
logging=logging,
)
if policies_path == "dont_augment":
policy_str = "non_augmented"
else:
policy_str = "augmented"
csv_logger = CSVLogger(
f"{EXPERIMENT_FOLDER_PATH}/wrn_28_10_training_on_{dataset_name}_{policy_str}.csv"
)
if policies_path == "dont_augment":
history = wrn_28_10.fit_normal(data,
epochs=epochs,
csv_logger=csv_logger)
print(f"Reached validation accuracy is {history['val_acc'][-1]}")
else:
datagen = deepaugment_image_generator(
data["X_train"],
data["y_train"],
policies_path,
batch_size=batch_size,
augment_chance=0.8,
)
print("fitting the model")
history = wrn_28_10.fit_with_generator(
datagen,
data["X_val"],
data["y_val"],
train_data_size=len(data["X_train"]),
epochs=epochs,
csv_logger=csv_logger,
)
print(f"Reached validation accuracy is {history['val_acc'][-1]}")
def run_full_model(images,
labels,
test_proportion=0.1,
model="wrn_28_10",
epochs=200,
batch_size=64,
policies_path="dont_augment"):
data = {}
data["X_train"], data["X_val"], data["y_train"], data[
"y_val"] = train_test_split(images,
labels,
test_size=test_proportion,
shuffle=True)
data = DataOp.preprocess_normal(data)
input_shape = data["X_train"][0].shape
num_classes = data["y_train"].shape[1]
cnn_config = {
"model": model,
"weights": "imagenet",
"input_shape": input_shape,
"child_batch_size": batch_size,
"pre_augmentation_weights_path": "initial_model_weights.h5",
"logging": logging
}
full_model = ChildCNN(input_shape=input_shape,
num_classes=num_classes,
config=cnn_config)
if policies_path == "dont_augment":
policy_str = "non_augmented"
else:
policy_str = "augmented"
csv_logger = CSVLogger(
f"{EXPERIMENT_FOLDER_PATH}/wrn_28_10_training_on_{policy_str}.csv")
if policies_path == "dont_augment":
history = full_model.fit_normal(data,
epochs=epochs,
csv_logger=csv_logger)
print(f"Reached validation accuracy is {history['val_acc'][-1]}")
else:
datagen = deepaugment_image_generator(
data["X_train"],
data["y_train"],
policies_path,
batch_size=batch_size,
augment_chance=0.8,
)
file = open('datagen.txt', 'w')
file.write(datagen)
file.close()
print("fitting the model")
history = full_model.fit_with_generator(
datagen,
data["X_val"],
data["y_val"],
train_data_size=len(data["X_train"]),
epochs=epochs,
csv_logger=csv_logger,
)
print(f"Reached validation accuracy is {history['val_acc'][-1]}")