Improved readability of code
Signed-off-by: Jim Martens <github@2martens.de>
This commit is contained in:
parent
01f43651b5
commit
d877da3ef3
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@ -37,6 +37,7 @@ from typing import Union
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import math
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import numpy as np
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import tensorflow as tf
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from attributedict.collections import AttributeDict
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from twomartens.masterthesis import config
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from twomartens.masterthesis import debug
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@ -116,6 +117,7 @@ def get_model(use_bayesian: bool,
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def get_loss_func() -> callable:
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"""Returns loss function for SSD."""
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return keras_ssd_loss.SSDLoss().compute_loss
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@ -164,56 +166,63 @@ def predict(generator: callable,
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"""
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output_file, label_output_file = _predict_prepare_paths(output_path, use_bayesian)
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_predict_loop(generator, use_bayesian, steps_per_epoch,
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dropout_step=functools.partial(
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_predict_dropout_step,
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model=model,
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batch_size=conf_obj.parameters.batch_size,
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forward_passes_per_image=conf_obj.parameters.ssd_forward_passes_per_image
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),
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vanilla_step=functools.partial(_predict_vanilla_step, model=model),
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save_images=functools.partial(_predict_save_images,
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save_images=debug.save_ssd_train_images,
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get_coco_cat_maps_func=coco_utils.get_coco_category_maps,
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output_path=output_path,
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coco_path=conf_obj.paths.coco,
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image_size=conf_obj.parameters.ssd_image_size),
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decode_func=functools.partial(
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_decode_predictions,
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decode_func=ssd_output_decoder.decode_detections,
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image_size=conf_obj.parameters.ssd_image_size,
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confidence_threshold=conf_obj.parameters.ssd_confidence_threshold,
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iou_threshold=conf_obj.parameters.ssd_iou_threshold,
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top_k=conf_obj.parameters.ssd_top_k
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),
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decode_func_dropout=functools.partial(
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_decode_predictions_dropout,
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decode_func=ssd_output_decoder.decode_detections_dropout,
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image_size=conf_obj.parameters.ssd_image_size,
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confidence_threshold=conf_obj.parameters.ssd_confidence_threshold,
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),
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apply_entropy_threshold_func=functools.partial(
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_apply_entropy_filtering,
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confidence_threshold=conf_obj.parameters.ssd_confidence_threshold,
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nr_classes=conf_obj.parameters.nr_classes,
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iou_threshold=conf_obj.parameters.ssd_iou_threshold,
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use_nms=conf_obj.parameters.ssd_use_nms
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),
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apply_top_k_func=functools.partial(
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_apply_top_k,
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top_k=conf_obj.parameters.ssd_top_k
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),
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get_observations_func=_get_observations,
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transform_func=functools.partial(
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_transform_predictions,
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inverse_transform_func=object_detection_2d_misc_utils.apply_inverse_transforms),
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save_func=functools.partial(_save_predictions,
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output_file=output_file,
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label_output_file=label_output_file,
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nr_digits=nr_digits),
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use_entropy_threshold=conf_obj.parameters.ssd_use_entropy_threshold,
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entropy_threshold_min=conf_obj.parameters.ssd_entropy_threshold_min,
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entropy_threshold_max=conf_obj.parameters.ssd_entropy_threshold_max)
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_predict_loop(
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generator=generator,
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use_bayesian=use_bayesian,
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conf_obj=conf_obj,
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steps_per_epoch=steps_per_epoch,
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callables=AttributeDict({
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"dropout_step": functools.partial(
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_predict_dropout_step,
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model=model,
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batch_size=conf_obj.parameters.batch_size,
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forward_passes_per_image=conf_obj.parameters.ssd_forward_passes_per_image
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),
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"vanilla_step": functools.partial(_predict_vanilla_step, model=model),
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"save_images": functools.partial(
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_predict_save_images,
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save_images=debug.save_ssd_train_images,
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get_coco_cat_maps_func=coco_utils.get_coco_category_maps,
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output_path=output_path,
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conf_obj=conf_obj
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),
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"decode_func": functools.partial(
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_decode_predictions,
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decode_func=ssd_output_decoder.decode_detections,
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image_size=conf_obj.parameters.ssd_image_size,
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confidence_threshold=conf_obj.parameters.ssd_confidence_threshold,
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iou_threshold=conf_obj.parameters.ssd_iou_threshold,
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top_k=conf_obj.parameters.ssd_top_k
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),
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"decode_func_dropout": functools.partial(
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_decode_predictions_dropout,
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decode_func=ssd_output_decoder.decode_detections_dropout,
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image_size=conf_obj.parameters.ssd_image_size,
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confidence_threshold=conf_obj.parameters.ssd_confidence_threshold,
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),
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"apply_entropy_threshold_func": functools.partial(
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_apply_entropy_filtering,
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confidence_threshold=conf_obj.parameters.ssd_confidence_threshold,
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nr_classes=conf_obj.parameters.nr_classes,
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iou_threshold=conf_obj.parameters.ssd_iou_threshold,
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use_nms=conf_obj.parameters.ssd_use_nms
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),
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"apply_top_k_func": functools.partial(
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_apply_top_k,
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top_k=conf_obj.parameters.ssd_top_k
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),
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"get_observations_func": _get_observations,
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"transform_func": functools.partial(
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_transform_predictions,
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inverse_transform_func=object_detection_2d_misc_utils.apply_inverse_transforms),
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"save_func": functools.partial(
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_save_predictions,
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output_file=output_file,
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label_output_file=label_output_file,
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nr_digits=nr_digits
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)
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})
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)
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def train(train_generator: callable,
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@ -285,58 +294,58 @@ def _predict_prepare_paths(output_path: str, use_dropout: bool) -> Tuple[str, st
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return output_file, label_output_file
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def _predict_loop(generator: Generator, use_dropout: bool, steps_per_epoch: int,
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dropout_step: callable, vanilla_step: callable,
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save_images: callable, decode_func: callable,
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decode_func_dropout: callable, get_observations_func: callable,
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apply_entropy_threshold_func: callable, apply_top_k_func: callable,
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transform_func: callable, save_func: callable,
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use_entropy_threshold: bool, entropy_threshold_min: float,
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entropy_threshold_max: float) -> None:
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def _predict_loop(generator: Generator,
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use_bayesian: bool,
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conf_obj: config.Config,
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steps_per_epoch: int,
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callables: AttributeDict) -> None:
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batch_counter = 0
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saved_images_prediction = False
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saved_images_decoding = False
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if use_entropy_threshold:
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nr_steps = math.floor((entropy_threshold_max - entropy_threshold_min) * 10)
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entropy_thresholds = [round(i / 10 + entropy_threshold_min, 1) for i in range(nr_steps)]
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if conf_obj.parameters.ssd_use_entropy_threshold:
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nr_steps = math.floor(
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(conf_obj.parameters.ssd_entropy_threshold_max - conf_obj.parameters.ssd_entropy_threshold_min) * 10
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)
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entropy_thresholds = [round(i / 10 + conf_obj.parameters.ssd_entropy_threshold_min, 1) for i in range(nr_steps)]
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else:
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entropy_thresholds = [0]
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for inputs, filenames, inverse_transforms, original_labels in generator:
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if use_dropout:
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predictions = dropout_step(inputs)
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if use_bayesian:
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predictions = callables.dropout_step(inputs)
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else:
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predictions = vanilla_step(inputs)
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predictions = callables.vanilla_step(inputs)
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if not saved_images_prediction:
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save_images(inputs, predictions, custom_string="after-prediction")
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callables.save_images(inputs, predictions, custom_string="after-prediction")
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saved_images_prediction = True
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if use_dropout:
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decoded_predictions = decode_func_dropout(predictions)
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observations = get_observations_func(decoded_predictions)
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if use_bayesian:
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decoded_predictions = callables.decode_func_dropout(predictions)
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observations = callables.get_observations_func(decoded_predictions)
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for entropy_threshold in entropy_thresholds:
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if use_dropout:
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save_func(observations, original_labels, filenames,
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batch_nr=batch_counter, entropy_threshold=entropy_threshold,
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suffix="_prediction")
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decoded_predictions = apply_entropy_threshold_func(observations,
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entropy_threshold=entropy_threshold)
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save_func(decoded_predictions, original_labels, filenames,
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batch_nr=batch_counter, entropy_threshold=entropy_threshold,
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suffix="_entropy")
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decoded_predictions = apply_top_k_func(decoded_predictions)
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if use_bayesian:
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callables.save_func(observations, original_labels, filenames,
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batch_nr=batch_counter, entropy_threshold=entropy_threshold,
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suffix="_prediction")
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decoded_predictions = callables.apply_entropy_threshold_func(observations,
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entropy_threshold=entropy_threshold)
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callables.save_func(decoded_predictions, original_labels, filenames,
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batch_nr=batch_counter, entropy_threshold=entropy_threshold,
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suffix="_entropy")
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decoded_predictions = callables.apply_top_k_func(decoded_predictions)
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else:
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decoded_predictions = decode_func(predictions, entropy_threshold=entropy_threshold)
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decoded_predictions = callables.decode_func(predictions, entropy_threshold=entropy_threshold)
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if not saved_images_decoding:
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custom_string = f"after-decoding-{entropy_threshold}" if use_entropy_threshold else "after-decoding"
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save_images(inputs, decoded_predictions, custom_string=custom_string)
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custom_string = f"after-decoding-{entropy_threshold}" \
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if conf_obj.parameters.ssd_use_entropy_threshold else "after-decoding"
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callables.save_images(inputs, decoded_predictions, custom_string=custom_string)
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transformed_predictions = transform_func(decoded_predictions,
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inverse_transforms)
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save_func(transformed_predictions, original_labels, filenames,
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batch_nr=batch_counter, entropy_threshold=entropy_threshold,
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suffix="_transformed")
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transformed_predictions = callables.transform_func(decoded_predictions,
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inverse_transforms)
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callables.save_func(transformed_predictions, original_labels, filenames,
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batch_nr=batch_counter, entropy_threshold=entropy_threshold,
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suffix="_transformed")
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if not saved_images_decoding:
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saved_images_decoding = True
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@ -387,10 +396,7 @@ def _decode_predictions(predictions: np.ndarray,
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def _decode_predictions_dropout(predictions: np.ndarray,
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decode_func: callable,
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image_size: int,
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# entropy_threshold: float,
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confidence_threshold: float,
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# iou_threshold: float,
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# top_k: int
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) -> List[np.ndarray]:
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return decode_func(
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y_pred=predictions,
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@ -492,10 +498,11 @@ def _save_predictions(transformed_predictions: Union[np.ndarray, Sequence[np.nda
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def _predict_save_images(inputs: np.ndarray, predictions: np.ndarray,
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save_images: callable,
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get_coco_cat_maps_func: callable,
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output_path: str, coco_path: str,
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image_size: int, custom_string: str) -> None:
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output_path: str,
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conf_obj: config.Config,
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custom_string: str) -> None:
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save_images(inputs, predictions,
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output_path, coco_path, image_size,
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output_path, conf_obj.paths.coco, conf_obj.parameters.ssd_image_size,
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get_coco_cat_maps_func, custom_string)
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