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Implemented support for predicting with range of entropy thresholds 

Signed-off-by: Jim Martens <github@2martens.de>
This commit is contained in:
Jim Martens 2019-07-17 12:38:22 +02:00
parent f270e0add1
commit 28a0d35d36
3 changed files with 58 additions and 23 deletions
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21
src/twomartens/masterthesis/cli.py
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@ -264,7 +264,8 @@ def _ssd_test(args: argparse.Namespace) -> None:
batch_size, image_size, learning_rate, \
forward_passes_per_image, nr_classes, iou_threshold, dropout_rate, \
entropy_threshold, top_k, nr_trajectories, \
use_entropy_threshold, entropy_threshold_min, entropy_threshold_max, \
top_k, nr_trajectories, \
coco_path, output_path, weights_path, ground_truth_path = _ssd_test_get_config_values(args, conf.get_property)
use_dropout = _ssd_is_dropout(args)
@ -305,7 +306,9 @@ def _ssd_test(args: argparse.Namespace) -> None:
image_size,
batch_size,
forward_passes_per_image,
entropy_threshold,
use_entropy_threshold,
entropy_threshold_min,
entropy_threshold_max,
output_path,
coco_path,
use_dropout,
@ -502,7 +505,9 @@ def _ssd_train_get_config_values(config_get: Callable[[str], Union[str, float, i
def _ssd_test_get_config_values(args: argparse.Namespace,
config_get: Callable[[str], Union[str, float, int, bool]]
) -> Tuple[int, int, float, int, int, float, float, float, int, int,
) -> Tuple[int, int, float, int, int, float, float,
bool, float, float,
int, int,
str, str, str, str]:
batch_size = config_get("Parameters.batch_size")
@ -512,7 +517,9 @@ def _ssd_test_get_config_values(args: argparse.Namespace,
nr_classes = config_get("Parameters.nr_classes")
iou_threshold = config_get("Parameters.ssd_iou_threshold")
dropout_rate = config_get("Parameters.ssd_dropout_rate")
entropy_threshold = config_get("Parameters.ssd_entropy_threshold")
use_entropy_threshold = config_get("Parameters.ssd_use_entropy_threshold")
entropy_threshold_min = config_get("Parameters.ssd_entropy_threshold_min")
entropy_threshold_max = config_get("Parameters.ssd_entropy_threshold_max")
top_k = config_get("Parameters.ssd_top_k")
nr_trajectories = config_get("Parameters.nr_trajectories")
@ -532,7 +539,11 @@ def _ssd_test_get_config_values(args: argparse.Namespace,
nr_classes,
iou_threshold,
dropout_rate,
entropy_threshold,
#
use_entropy_threshold,
entropy_threshold_min,
entropy_threshold_max,
#
top_k,
nr_trajectories,
#

4
src/twomartens/masterthesis/config.py
View File

@ -59,7 +59,9 @@ _CONFIG_PROPS = {
"ssd_iou_threshold": (float, "0.45"),
"ssd_top_k": (int, "200"),
"ssd_dropout_rate": (float, "0.5"),
"ssd_entropy_threshold": (float, "2.5"),
"ssd_use_entropy_threshold": (bool, False),
"ssd_entropy_threshold_min": (float, "0.1"),
"ssd_entropy_threshold_max": (float, "2.5"),
"nr_trajectories": (int, "-1")
}
}

56
src/twomartens/masterthesis/ssd.py
View File

@ -31,6 +31,7 @@ import pickle
from typing import List, Sequence, Tuple, Generator
from typing import Optional
import math
import numpy as np
import tensorflow as tf
@ -145,7 +146,9 @@ def predict(generator: callable,
image_size: int,
batch_size: int,
forward_passes_per_image: int,
entropy_threshold: float,
use_entropy_threshold: bool,
entropy_threshold_min: float,
entropy_threshold_max: float,
output_path: str,
coco_path: str,
use_dropout: bool,
@ -163,7 +166,9 @@ def predict(generator: callable,
batch_size: number of items in every batch
forward_passes_per_image: specifies number of forward passes per image
used by DropoutSSD
entropy_threshold: specifies the threshold for the entropy
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
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
@ -188,7 +193,6 @@ def predict(generator: callable,
_decode_predictions,
decode_func=ssd_output_decoder.decode_detections_fast,
image_size=image_size,
entropy_threshold=entropy_threshold
),
transform_func=functools.partial(
_transform_predictions,
@ -196,7 +200,10 @@ def predict(generator: callable,
save_func=functools.partial(_save_predictions,
output_file=output_file,
label_output_file=label_output_file,
nr_digits=nr_digits))
nr_digits=nr_digits),
use_entropy_threshold=use_entropy_threshold,
entropy_threshold_min=entropy_threshold_min,
entropy_threshold_max=entropy_threshold_max)
def train(train_generator: callable,
@ -271,26 +278,40 @@ def _predict_prepare_paths(output_path: str, use_dropout: bool) -> Tuple[str, st
def _predict_loop(generator: Generator, use_dropout: bool, steps_per_epoch: int,
dropout_step: callable, vanilla_step: callable,
save_images: callable, decode_func: callable,
transform_func: callable, save_func: callable) -> None:
transform_func: callable, save_func: callable,
use_entropy_threshold: bool, entropy_threshold_min: float,
entropy_threshold_max: float) -> None:
batch_counter = 0
saved_images = False
saved_images_prediction = False
saved_images_decoding = False
if use_entropy_threshold:
nr_steps = math.floor((entropy_threshold_max - entropy_threshold_min) * 10)
entropy_thresholds = [i / 10 + entropy_threshold_min for i in range(nr_steps)]
else:
entropy_thresholds = [0]
for inputs, filenames, inverse_transforms, original_labels in generator:
if use_dropout:
predictions = dropout_step(inputs)
else:
predictions = vanilla_step(inputs)
if not saved_images:
if not saved_images_prediction:
save_images(inputs, predictions, custom_string="after-prediction")
decoded_predictions = decode_func(predictions)
if not saved_images:
save_images(inputs, decoded_predictions, custom_string="after-decoding")
saved_images = True
transformed_predictions = transform_func(decoded_predictions,
inverse_transforms)
save_func(transformed_predictions, original_labels, filenames,
batch_nr=batch_counter)
for entropy_threshold in entropy_thresholds:
decoded_predictions = decode_func(predictions, entropy_threshold=entropy_threshold)
if not saved_images_decoding:
custom_string = f"after-decoding-{entropy_threshold}" if use_entropy_threshold else "after-decoding"
save_images(inputs, decoded_predictions, custom_string=custom_string)
transformed_predictions = transform_func(decoded_predictions,
inverse_transforms)
save_func(transformed_predictions, original_labels, filenames,
batch_nr=batch_counter, entropy_threshold=entropy_threshold)
if not saved_images_decoding:
saved_images_decoding = True
batch_counter += 1
@ -341,10 +362,11 @@ def _transform_predictions(decoded_predictions: np.ndarray, inverse_transforms:
def _save_predictions(transformed_predictions: np.ndarray, original_labels: np.ndarray, filenames: Sequence[str],
output_file: str, label_output_file: str,
batch_nr: int, nr_digits: int) -> None:
batch_nr: int, nr_digits: int, entropy_threshold: float) -> None:
counter_str = str(batch_nr).zfill(nr_digits)
filename = f"{output_file}-{counter_str}.bin"
filename = f"{output_file}-{counter_str}"
filename = f"{filename}-{entropy_threshold}" if entropy_threshold else filename
label_filename = f"{label_output_file}-{counter_str}.bin"
with open(filename, "wb") as file, open(label_filename, "wb") as label_file: