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Added function to save images directly after prediction 

Signed-off-by: Jim Martens <github@2martens.de>
This commit is contained in:
Jim Martens 2019-07-15 10:34:48 +02:00
parent d77c27727f
commit 5827d92b8c
2 changed files with 25 additions and 1 deletions
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1
src/twomartens/masterthesis/cli.py
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@ -296,6 +296,7 @@ def _ssd_test(args: argparse.Namespace) -> None:
batch_size,
forward_passes_per_image,
output_path,
coco_path,
use_dropout,
nr_digits)

25
src/twomartens/masterthesis/ssd.py
View File

@ -34,8 +34,10 @@ from typing import Optional
import numpy as np
import tensorflow as tf
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
from twomartens.masterthesis.ssd_keras.eval_utils import coco_utils
from twomartens.masterthesis.ssd_keras.keras_loss_function import keras_ssd_loss
from twomartens.masterthesis.ssd_keras.ssd_encoder_decoder import ssd_output_decoder
@ -144,6 +146,7 @@ def predict(generator: callable,
batch_size: int,
forward_passes_per_image: int,
output_path: str,
coco_path: str,
use_dropout: bool,
nr_digits: int) -> None:
"""
@ -160,6 +163,7 @@ def predict(generator: callable,
forward_passes_per_image: specifies number of forward passes per image
used by DropoutSSD
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
"""
@ -179,7 +183,14 @@ def predict(generator: callable,
functools.partial(_save_predictions,
output_file=output_file,
label_output_file=label_output_file,
nr_digits=nr_digits))
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)
)
def train(train_generator: callable,
@ -253,6 +264,7 @@ 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,
transform_func: callable, save_func: callable) -> None:
batch_counter = 0
@ -262,6 +274,7 @@ def _predict_loop(generator: Generator, use_dropout: bool, steps_per_epoch: int,
else:
predictions = vanilla_step(inputs)
save_images(inputs, predictions)
transformed_predictions = transform_func(predictions, inverse_transforms)
save_func(transformed_predictions, original_labels, filenames,
batch_nr=batch_counter)
@ -322,6 +335,16 @@ def _save_predictions(transformed_predictions: np.ndarray, original_labels: np.n
pickle.dump({"labels": original_labels, "filenames": filenames}, label_file)
def _predict_save_images(inputs: np.ndarray, predictions: np.ndarray,
save_images: callable,
get_coco_cat_maps_func: callable,
output_path: str, coco_path: str,
image_size: int) -> None:
save_images(inputs, predictions,
output_path, coco_path, image_size,
get_coco_cat_maps_func, "after-prediction")
def _get_observations(detections: Sequence[Sequence[np.ndarray]]) -> List[List[np.ndarray]]:
batch_size = len(detections)
observations = [[] for _ in range(batch_size)]