def evualate_model(model, data, test_paths, settings):
"""
"""
# predict on testing data
pred_mtx = temp_model.predict_generator(
data_generator(data, test_paths, settings),
steps=ceil(len(test_paths) / settings["BATCH_SIZE"]),
)
# write out images
write_images(data, test_paths, pred_mtx, settings, iter_path)
# return evualation data frame
self.eval_metrics = self.eval_metrics.append(
calculate_metrics(data, full_testing_paths, pred_mtx, self.settings,
self.curr_iter),
ignore_index=True,
)
def main(): data_generator() train()
total_flops, total_params = get_flops(FLOPS_PATH, MODEL)
print(total_flops, total_params)
exit()
# define where to save the model
model_name = f"{P_NAME}_{MODEL}_{BACKBONE}_{IN_HEIGHT}_{IN_WIDTH}_weights_{datetime.now().strftime('%d_%m_%y-%H_%M_%p')}"
callbacks = get_callbacks(weights_path=WEIGHTS_OUT_PATH, model_name=model_name)
# Load data using generator
if generator:
train_generator = data_generator(frames_path=FRAMES_TRAIN_PATH,
masks_path=MASKS_TRAIN_PATH,
fnames=os.listdir(FRAMES_TRAIN_PATH),
n_classes=CLASSES,
input_h=IN_HEIGHT,
input_w=IN_WIDTH,
batch_size=TRAIN_BATCH_SIZE,
is_resizable=False,
training=True)
val_generator = data_generator(frames_path=FRAMES_VAL_PATH,
masks_path=MASKS_VAL_PATH,
fnames=os.listdir(FRAMES_VAL_PATH),
n_classes=CLASSES,
input_h=IN_HEIGHT,
input_w=IN_WIDTH,
batch_size=VAL_BATCH_SIZE,
is_resizable=False,
training=True)
test_masks = None
if not os.path.isfile(UNET_MODEL_PATH):
quit(f"Model file not found {UNET_MODEL_PATH}")
if not os.path.isdir(FRAMES_TEST_IN_PATH) or not os.path.isdir(MASKS_TEST_IN_PATH):
quit(f"Directory not found")
# Create model
model = UNet.build(pre_trained=True, model_path=UNET_MODEL_PATH, n_classes=3, input_h=320, input_w=320)
# If images are loaded from generator
if is_generator:
test_data_generated = data_generator(frames_path=FRAMES_TEST_IN_PATH,
masks_path=MASKS_TEST_IN_PATH,
fnames=os.listdir(MASKS_TEST_IN_PATH),
input_h=320,
input_w=320,
n_classes=3,
batch_size=10,
is_resizable=True)
predicted_masks = model.predict_generator(test_data_generated, steps=15)
# If images are loaded from builder(no generator)
else:
test_frames, test_masks = data_loader(frames_path=FRAMES_TEST_IN_PATH,
masks_path=MASKS_TEST_IN_PATH,
input_h=320,
input_w=320,
n_classes=3,
fnames=os.listdir(MASKS_TEST_IN_PATH),
is_resizable=True)
# Predictive probs
if not os.path.isfile(WEIGHTS_IN_PATH):
quit(f"Model file not found {WEIGHTS_IN_PATH}")
if not os.path.isdir(FRAMES_TEST_IN_PATH) or not os.path.isdir(MASKS_TEST_IN_PATH):
quit(f"Directory not found")
# Load model from dispatcher and build
network = MODELS[MODEL]
model = network.build()
#model.summary()
# If images are loaded from generator
if is_generator:
test_data_generated = data_generator(frames_path=FRAMES_TEST_IN_PATH,
masks_path=MASKS_TEST_IN_PATH,
fnames=os.listdir(MASKS_TEST_IN_PATH),
input_h=IN_HEIGHT,
input_w=IN_WIDTH,
n_classes=NO_CLASSES,
batch_size=1,
is_resizable=True)
predicted_masks = model.predict_generator(test_data_generated, steps=6)
# If images are loaded from builder(no generator)
else:
test_frames, test_masks = data_loader(frames_path=FRAMES_TEST_IN_PATH,
masks_path=MASKS_TEST_IN_PATH,
input_h=IN_HEIGHT,
input_w=IN_WIDTH,
n_classes=NO_CLASSES,
fnames=os.listdir(MASKS_TEST_IN_PATH),
is_resizable=False)
# Predictive probs
# Load Env. variables
FRAMES_TRAIN_PATH = os.environ.get("FRAMES_TRAIN_PATH")
MASKS_TRAIN_PATH = os.environ.get("MASKS_TRAIN_PATH")
FRAMES_VAL_PATH = os.environ.get("FRAMES_VAL_PATH")
MASKS_VAL_PATH = os.environ.get("MASKS_VAL_PATH")
#MODEL = os.environ.get("MODEL")
MODELS_OUT_PATH = os.environ.get("MODELS_OUT_PATH")
if __name__ == '__main__':
# Image generator
train_generator = data_generator(frames_path=FRAMES_TRAIN_PATH,
masks_path=MASKS_TRAIN_PATH,
fnames=os.listdir(FRAMES_TRAIN_PATH),
n_classes=3,
input_h=320,
input_w=320,
batch_size=25,
is_resizable=True)
val_generator = data_generator(frames_path=FRAMES_VAL_PATH,
masks_path=MASKS_VAL_PATH,
fnames=os.listdir(FRAMES_VAL_PATH),
n_classes=3,
input_h=320,
input_w=320,
batch_size=10,
is_resizable=True)
# Save model
model_checkpoint_path = os.path.join(