def check_model(model_name, weights, fold):
"""
Helper test method to do a simple sanity check of model predictions
:param model_name: name of the model
:param weights: model weights file path
:param fold: fold to check
"""
model = MODELS[model_name].factory(lock_base_model=True)
model.load_weights(str(weights), by_name=False)
dataset = SingleFrameCNNDataset(
preprocess_input_func=MODELS[model_name].preprocess_input,
batch_size=1,
validation_batch_size=1,
fold=fold)
batch_id = 0
for X, y in dataset.generate_test():
pred = model.predict_on_batch(X)
print()
for i, cls in enumerate(CLASSES):
print(f'gt: {y[0, i]} pred: {pred[0, i]:.03f} {cls}')
batch_id += 1
for batch_frame in range(dataset.batch_size):
plt.imshow(utils.preprocessed_input_to_img_resnet(X[batch_frame]))
# plt.imshow(X[batch_frame]/2.0+0.5)
plt.show()
def check_generator(use_test):
"""
Helper function used to visually check X and y vectors generated by SingleFrameCNNDataset
:param use_test: true if validation samples are checked, false for training samples.
"""
dataset = SingleFrameCNNDataset(
preprocess_input_func=preprocess_input_resnet50,
batch_size=2,
validation_batch_size=2,
fold=1)
batch_id = 0
startTime = time.time()
if use_test:
gen = dataset.generate_test()
else:
gen = dataset.generate()
for X, y in gen:
batch_id += 1
elapsedTime = time.time() - startTime
startTime = time.time()
print(f'{batch_id} {elapsedTime:.3}')
for batch_frame in range(dataset.batch_size):
print(y[batch_frame])
plt.imshow(utils.preprocessed_input_to_img_resnet(X[batch_frame]))
plt.show()