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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)
ssd.predict(generator=generators.generator,
model=ssd_model,
conf_obj=conf_obj,
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,
nr_digits=nr_digits,
nr_classes=conf_obj.parameters.nr_classes)
output_path=paths.output_path)
def _ssd_evaluate(args: argparse.Namespace) -> None:

73
src/twomartens/masterthesis/ssd.py
View File

@ -37,6 +37,7 @@ import math
import numpy as np
import tensorflow as tf
from twomartens.masterthesis import config
from twomartens.masterthesis import debug
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
@ -146,22 +147,11 @@ def compile_model(model: tf.keras.models.Model, learning_rate: float, loss_func:
def predict(generator: callable,
model: tf.keras.models.Model,
conf_obj: config.Config,
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,
nr_digits: int,
nr_classes: int) -> None:
output_path: str) -> None:
"""
Run trained SSD on the given data set.
@ -170,63 +160,52 @@ def predict(generator: callable,
Args:
generator: generator of test data
model: compiled and trained Keras model
conf_obj: configuration object
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
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)
_predict_loop(generator, use_dropout, steps_per_epoch,
dropout_step=functools.partial(_predict_dropout_step,
dropout_step=functools.partial(
_predict_dropout_step,
model=model,
batch_size=batch_size,
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),
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),
coco_path=conf_obj.paths.coco,
image_size=conf_obj.parameters.ssd_image_size),
decode_func=functools.partial(
_decode_predictions,
decode_func=ssd_output_decoder.decode_detections,
image_size=image_size,
confidence_threshold=confidence_threshold,
iou_threshold=iou_threshold,
top_k=top_k
image_size=conf_obj.parameters.ssd_image_size,
confidence_threshold=conf_obj.parameters.ssd_confidence_threshold,
iou_threshold=conf_obj.parameters.ssd_iou_threshold,
top_k=conf_obj.parameters.ssd_top_k
),
decode_func_dropout=functools.partial(
_decode_predictions_dropout,
decode_func=ssd_output_decoder.decode_detections_dropout,
image_size=image_size,
confidence_threshold=confidence_threshold,
image_size=conf_obj.parameters.ssd_image_size,
confidence_threshold=conf_obj.parameters.ssd_confidence_threshold,
),
apply_entropy_threshold_func=functools.partial(
_apply_entropy_filtering,
confidence_threshold=confidence_threshold,
nr_classes=nr_classes,
iou_threshold=iou_threshold,
use_nms=use_nms
confidence_threshold=conf_obj.parameters.ssd_confidence_threshold,
nr_classes=conf_obj.parameters.nr_classes,
iou_threshold=conf_obj.parameters.ssd_iou_threshold,
use_nms=conf_obj.parameters.ssd_use_nms
),
apply_top_k_func=functools.partial(
_apply_top_k,
top_k=top_k
top_k=conf_obj.parameters.ssd_top_k
),
get_observations_func=_get_observations,
transform_func=functools.partial(
@ -236,9 +215,9 @@ def predict(generator: callable,
output_file=output_file,
label_output_file=label_output_file,
nr_digits=nr_digits),
use_entropy_threshold=use_entropy_threshold,
entropy_threshold_min=entropy_threshold_min,
entropy_threshold_max=entropy_threshold_max)
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)
def train(train_generator: callable,