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Reduced parameter size for predict function

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Signed-off-by: Jim Martens <github@2martens.de>
This commit is contained in:
Jim Martens
2019-09-13 11:55:19 +02:00
parent 90d64ae7c7
commit 64300c3842
2 changed files with 29 additions and 61 deletions
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15
src/twomartens/masterthesis/cli.py
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@ -268,22 +268,11 @@ def _ssd_test(args: argparse.Namespace) -> None:
steps_per_epoch = _get_nr_batches(generators.length, conf_obj.parameters.batch_size) steps_per_epoch = _get_nr_batches(generators.length, conf_obj.parameters.batch_size)
ssd.predict(generator=generators.generator, ssd.predict(generator=generators.generator,
model=ssd_model, model=ssd_model,
conf_obj=conf_obj,
steps_per_epoch=steps_per_epoch, steps_per_epoch=steps_per_epoch,
image_size=conf_obj.parameters.ssd_image_size,
batch_size=conf_obj.parameters.batch_size,
forward_passes_per_image=conf_obj.parameters.ssd_forward_passes_per_image,
use_nms=conf_obj.parameters.ssd_use_nms,
use_entropy_threshold=conf_obj.parameters.ssd_use_entropy_threshold,
entropy_threshold_min=conf_obj.parameters.ssd_entropy_threshold_min,
entropy_threshold_max=conf_obj.parameters.ssd_entropy_threshold_max,
confidence_threshold=conf_obj.parameters.ssd_confidence_threshold,
iou_threshold=conf_obj.parameters.ssd_iou_threshold,
top_k=conf_obj.parameters.ssd_top_k,
output_path=paths.output_path,
coco_path=conf_obj.paths.coco,
use_dropout=use_dropout, use_dropout=use_dropout,
nr_digits=nr_digits, nr_digits=nr_digits,
nr_classes=conf_obj.parameters.nr_classes) output_path=paths.output_path)
def _ssd_evaluate(args: argparse.Namespace) -> None: def _ssd_evaluate(args: argparse.Namespace) -> None:

75
src/twomartens/masterthesis/ssd.py
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@ -37,6 +37,7 @@ import math
import numpy as np import numpy as np
import tensorflow as tf import tensorflow as tf
from twomartens.masterthesis import config
from twomartens.masterthesis import debug from twomartens.masterthesis import debug
from twomartens.masterthesis.ssd_keras.bounding_box_utils import bounding_box_utils from twomartens.masterthesis.ssd_keras.bounding_box_utils import bounding_box_utils
from twomartens.masterthesis.ssd_keras.data_generator import object_detection_2d_misc_utils from twomartens.masterthesis.ssd_keras.data_generator import object_detection_2d_misc_utils
@ -146,22 +147,11 @@ def compile_model(model: tf.keras.models.Model, learning_rate: float, loss_func:
def predict(generator: callable, def predict(generator: callable,
model: tf.keras.models.Model, model: tf.keras.models.Model,
conf_obj: config.Config,
steps_per_epoch: int, steps_per_epoch: int,
image_size: int,
batch_size: int,
forward_passes_per_image: int,
use_nms: bool,
use_entropy_threshold: bool,
entropy_threshold_min: float,
entropy_threshold_max: float,
confidence_threshold: float,
iou_threshold: float,
top_k: int,
output_path: str,
coco_path: str,
use_dropout: bool, use_dropout: bool,
nr_digits: int, nr_digits: int,
nr_classes: int) -> None: output_path: str) -> None:
""" """
Run trained SSD on the given data set. Run trained SSD on the given data set.
@ -170,63 +160,52 @@ def predict(generator: callable,
Args: Args:
generator: generator of test data generator: generator of test data
model: compiled and trained Keras model model: compiled and trained Keras model
conf_obj: configuration object
steps_per_epoch: number of batches per epoch steps_per_epoch: number of batches per epoch
image_size: size of input images to model
batch_size: number of items in every batch
forward_passes_per_image: specifies number of forward passes per image
used by DropoutSSD
use_nms: if True non-maximum suppression will be used for Bayesian SSD
use_entropy_threshold: if True entropy thresholding is applied
entropy_threshold_min: specifies the minimum threshold for the entropy
entropy_threshold_max: specifies the maximum threshold for the entropy
confidence_threshold: minimum confidence required for box to count as positive
iou_threshold: all boxes with iou overlap larger than threshold to local maximum box
will be suppressed
top_k: a maximum of top_k boxes remain after NMS
output_path: the path in which the results should be saved
coco_path: the path to the COCO data set
use_dropout: if True, multiple forward passes and observations will be used use_dropout: if True, multiple forward passes and observations will be used
nr_digits: number of digits needed to print largest batch number nr_digits: number of digits needed to print largest batch number
nr_classes: number of classes output_path: the path in which the results should be saved
""" """
output_file, label_output_file = _predict_prepare_paths(output_path, use_dropout) output_file, label_output_file = _predict_prepare_paths(output_path, use_dropout)
_predict_loop(generator, use_dropout, steps_per_epoch, _predict_loop(generator, use_dropout, steps_per_epoch,
dropout_step=functools.partial(_predict_dropout_step, dropout_step=functools.partial(
model=model, _predict_dropout_step,
batch_size=batch_size, model=model,
forward_passes_per_image=forward_passes_per_image), batch_size=conf_obj.parameters.batch_size,
forward_passes_per_image=conf_obj.parameters.ssd_forward_passes_per_image
),
vanilla_step=functools.partial(_predict_vanilla_step, model=model), vanilla_step=functools.partial(_predict_vanilla_step, model=model),
save_images=functools.partial(_predict_save_images, save_images=functools.partial(_predict_save_images,
save_images=debug.save_ssd_train_images, save_images=debug.save_ssd_train_images,
get_coco_cat_maps_func=coco_utils.get_coco_category_maps, get_coco_cat_maps_func=coco_utils.get_coco_category_maps,
output_path=output_path, output_path=output_path,
coco_path=coco_path, coco_path=conf_obj.paths.coco,
image_size=image_size), image_size=conf_obj.parameters.ssd_image_size),
decode_func=functools.partial( decode_func=functools.partial(
_decode_predictions, _decode_predictions,
decode_func=ssd_output_decoder.decode_detections, decode_func=ssd_output_decoder.decode_detections,
image_size=image_size, image_size=conf_obj.parameters.ssd_image_size,
confidence_threshold=confidence_threshold, confidence_threshold=conf_obj.parameters.ssd_confidence_threshold,
iou_threshold=iou_threshold, iou_threshold=conf_obj.parameters.ssd_iou_threshold,
top_k=top_k top_k=conf_obj.parameters.ssd_top_k
), ),
decode_func_dropout=functools.partial( decode_func_dropout=functools.partial(
_decode_predictions_dropout, _decode_predictions_dropout,
decode_func=ssd_output_decoder.decode_detections_dropout, decode_func=ssd_output_decoder.decode_detections_dropout,
image_size=image_size, image_size=conf_obj.parameters.ssd_image_size,
confidence_threshold=confidence_threshold, confidence_threshold=conf_obj.parameters.ssd_confidence_threshold,
), ),
apply_entropy_threshold_func=functools.partial( apply_entropy_threshold_func=functools.partial(
_apply_entropy_filtering, _apply_entropy_filtering,
confidence_threshold=confidence_threshold, confidence_threshold=conf_obj.parameters.ssd_confidence_threshold,
nr_classes=nr_classes, nr_classes=conf_obj.parameters.nr_classes,
iou_threshold=iou_threshold, iou_threshold=conf_obj.parameters.ssd_iou_threshold,
use_nms=use_nms use_nms=conf_obj.parameters.ssd_use_nms
), ),
apply_top_k_func=functools.partial( apply_top_k_func=functools.partial(
_apply_top_k, _apply_top_k,
top_k=top_k top_k=conf_obj.parameters.ssd_top_k
), ),
get_observations_func=_get_observations, get_observations_func=_get_observations,
transform_func=functools.partial( transform_func=functools.partial(
@ -236,9 +215,9 @@ def predict(generator: callable,
output_file=output_file, output_file=output_file,
label_output_file=label_output_file, label_output_file=label_output_file,
nr_digits=nr_digits), nr_digits=nr_digits),
use_entropy_threshold=use_entropy_threshold, use_entropy_threshold=conf_obj.parameters.ssd_use_entropy_threshold,
entropy_threshold_min=entropy_threshold_min, entropy_threshold_min=conf_obj.parameters.ssd_entropy_threshold_min,
entropy_threshold_max=entropy_threshold_max) entropy_threshold_max=conf_obj.parameters.ssd_entropy_threshold_max)
def train(train_generator: callable, def train(train_generator: callable,