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Fixed function calls 

Signed-off-by: Jim Martens <github@2martens.de>
This commit is contained in:
Jim Martens 2019-07-15 11:03:09 +02:00
parent f66bf31785
commit 5c9eaab6e4
1 changed files with 22 additions and 26 deletions
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48
src/twomartens/masterthesis/ssd.py
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@ -170,27 +170,27 @@ def predict(generator: callable,
output_file, label_output_file = _predict_prepare_paths(output_path, use_dropout)
_predict_loop(generator, use_dropout, steps_per_epoch,
functools.partial(_predict_dropout_step,
model=model,
get_observations_func=_get_observations,
batch_size=batch_size,
forward_passes_per_image=forward_passes_per_image),
functools.partial(_predict_vanilla_step, model=model),
functools.partial(_transform_predictions,
decode_func=ssd_output_decoder.decode_detections_fast,
inverse_transform_func=object_detection_2d_misc_utils.apply_inverse_transforms,
image_size=image_size),
functools.partial(_save_predictions,
output_file=output_file,
label_output_file=label_output_file,
nr_digits=nr_digits),
functools.partial(_predict_save_images,
save_images=debug.save_ssd_train_images,
get_coco_cat_maps_func=coco_utils.get_coco_category_maps,
output_path=output_path,
coco_path=coco_path,
image_size=image_size)
)
dropout_step=functools.partial(_predict_dropout_step,
model=model,
get_observations_func=_get_observations,
batch_size=batch_size,
forward_passes_per_image=forward_passes_per_image),
vanilla_step=functools.partial(_predict_vanilla_step, model=model),
save_images=functools.partial(_predict_save_images,
save_images=debug.save_ssd_train_images,
get_coco_cat_maps_func=coco_utils.get_coco_category_maps,
output_path=output_path,
coco_path=coco_path,
image_size=image_size),
transform_func=functools.partial(
_transform_predictions,
decode_func=ssd_output_decoder.decode_detections_fast,
inverse_transform_func=object_detection_2d_misc_utils.apply_inverse_transforms,
image_size=image_size),
save_func=functools.partial(_save_predictions,
output_file=output_file,
label_output_file=label_output_file,
nr_digits=nr_digits))
def train(train_generator: callable,
@ -274,11 +274,7 @@ def _predict_loop(generator: Generator, use_dropout: bool, steps_per_epoch: int,
else:
predictions = vanilla_step(inputs)
# save_images(inputs, predictions)
print((
f"Input shape: {inputs.shape}"
f"Predictions shape: {predictions.shape}"
))
save_images(inputs, predictions)
transformed_predictions = transform_func(predictions, inverse_transforms)
save_func(transformed_predictions, original_labels, filenames,
batch_nr=batch_counter)