Python preprocessing.preprocessing_factory.get_preprocessing() Examples
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code examples of preprocessing.preprocessing_factory.get_preprocessing().
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Example #1
| Source File: style_swap_model.py From style_swap_tensorflow with Apache License 2.0 | 6 votes |
|
def _train_inverse(self, generated, swaped_tensor):
preprocess_fn = preprocessing_factory.get_preprocessing(self.config.net_name, is_training=False)
network_fn = nets_factory.get_network_fn(self.config.net_name, num_classes=1, is_training=False)
with tf.variable_scope("", reuse=True):
preprocessed_image = tf.stack([preprocess_fn(img, self.PREPROCESS_SIZE, self.PREPROCESS_SIZE)
for img in tf.unstack(generated, axis=0)])
_, inversed_endpoints_dict = network_fn(preprocessed_image, spatial_squeeze=False)
layer_names = list(inversed_endpoints_dict.keys())
[layer_name] = [l_name for l_name in layer_names if self.style_layer in l_name]
inversed_style_layer = inversed_endpoints_dict[layer_name]
# print(inversed_style_layer.get_shape())
tf.losses.add_loss(tf.nn.l2_loss(swaped_tensor - inversed_style_layer))
self.loss_op = tf.losses.get_total_loss()
train_vars = [var for var in tf.trainable_variables() if var.name.startswith("inverse_net")]
slim.summarize_tensor(self.loss_op, "loss")
slim.summarize_tensors(train_vars)
# print(train_vars)
self.save_variables = train_vars
learning_rate = tf.train.exponential_decay(self.config.learning_rate, self.global_step, 1000, 0.66,
name="learning_rate")
self.train_op = tf.train.AdamOptimizer(learning_rate).minimize(self.loss_op, self.global_step, train_vars)
Example #2
| Source File: style_swap_model.py From style_swap_tensorflow with Apache License 2.0 | 6 votes |
|
def _build_evaluate_model(self):
self.input_image = tf.placeholder(tf.float32, shape=[None, None, 3])
self.style_image = tf.placeholder(tf.float32, shape=[None, None, 3])
preprocess_fn = preprocessing_factory.get_preprocessing(self.config.net_name, is_training=False)
height = self.evaluate_height if self.evaluate_height else self.PREPROCESS_SIZE
width = self.evaluate_width if self.evaluate_width else self.PREPROCESS_SIZE
preprocessed_image = preprocess_fn(self.input_image, height, width, resize_side_min=min(height, width))
images = tf.expand_dims(preprocessed_image, axis=0)
style_images = tf.expand_dims(preprocess_fn(self.style_image, self.PREPROCESS_SIZE, self.PREPROCESS_SIZE), axis=0)
self.swaped_tensor = self._swap_net(images, style_images)
#
# network_fn = nets_factory.get_network_fn(self.config.net_name, num_classes=1, is_training=False)
# _, endpoints_dict = network_fn(images, spatial_squeeze=False)
# self.swaped_tensor = endpoints_dict[self.config.net_name + self.style_layer]
self.generated = self._inverse_net(self.swaped_tensor)
self.evaluate_op = tf.squeeze(self.generated, axis=0)
self.init_op = self._get_network_init_fn()
self.save_variables = [var for var in tf.trainable_variables() if var.name.startswith("inverse_net")]
Example #3
| Source File: style_swap_model.py From style_swap_tensorflow with Apache License 2.0 | 6 votes |
|
def _build_train_model(self):
preprocess_fn = preprocessing_factory.get_preprocessing(self.config.net_name, is_training=False)
[image] = self.records_loader.get_data()
preprocessed_image = preprocess_fn(image, self.PREPROCESS_SIZE, self.PREPROCESS_SIZE)
images = self.records_loader.batch_data(preprocessed_image)
style_image = self.style_loader.get_data()
preprocessed_style_image = preprocess_fn(style_image, self.PREPROCESS_SIZE, self.PREPROCESS_SIZE)
style_images = self.style_loader.batch_data(preprocessed_style_image)
self.swaped_tensor = self._swap_net(images, style_images)
self.generated = self._inverse_net(self.swaped_tensor)
slim.summary.image("generated", self.generated)
slim.summary.image("origin", images)
slim.summary.image("style", style_images)
self._train_inverse(self.generated, self.swaped_tensor)
self.init_op = self._get_network_init_fn()
Example #4
| Source File: post_training_quantization.py From models with Apache License 2.0 | 6 votes |
|
def _representative_dataset_gen():
"""Gets a python generator of numpy arrays for the given dataset."""
image_size = FLAGS.image_size
dataset = tfds.builder(FLAGS.dataset_name, data_dir=FLAGS.dataset_dir)
dataset.download_and_prepare()
data = dataset.as_dataset()[FLAGS.dataset_split]
iterator = tf.data.make_one_shot_iterator(data)
if FLAGS.use_model_specific_preprocessing:
preprocess_fn = functools.partial(
preprocessing_factory.get_preprocessing(name=FLAGS.model_name),
output_height=image_size,
output_width=image_size)
else:
preprocess_fn = functools.partial(
_preprocess_for_quantization, image_size=image_size)
features = iterator.get_next()
image = features["image"]
image = preprocess_fn(image)
image = tf.reshape(image, [1, image_size, image_size, 3])
for _ in range(FLAGS.num_steps):
yield [image.eval()]
Example #5
| Source File: readfromtfrecords_batch_train.py From SSD_tensorflow_VOC with Apache License 2.0 | 5 votes |
|
def __get_images_labels(self):
dataset = dataset_factory.get_dataset(
self.dataset_name, self.dataset_split_name, self.dataset_dir)
provider = slim.dataset_data_provider.DatasetDataProvider(
dataset,
num_readers=self.num_readers,
common_queue_capacity=20 * self.batch_size,
common_queue_min=10 * self.batch_size)
[image, label] = provider.get(['image', 'label'])
label -= self.labels_offset
network_fn = nets_factory.get_network_fn(
self.model_name,
num_classes=(dataset.num_classes - self.labels_offset),
weight_decay=self.weight_decay,
is_training=True)
train_image_size = self.train_image_size or network_fn.default_image_size
preprocessing_name = self.preprocessing_name or self.model_name
image_preprocessing_fn = preprocessing_factory.get_preprocessing(
preprocessing_name,
is_training=True)
image = image_preprocessing_fn(image, train_image_size, train_image_size)
images, labels = tf.train.batch(
[image, label],
batch_size=self.batch_size,
num_threads=self.num_preprocessing_threads,
capacity=5 * self.batch_size)
labels = slim.one_hot_encoding(
labels, dataset.num_classes - self.labels_offset)
batch_queue = slim.prefetch_queue.prefetch_queue(
[images, labels], capacity=2)
images, labels = batch_queue.dequeue()
return images, labels
Example #6
| Source File: readfromtfrecords_batch_eval.py From SSD_tensorflow_VOC with Apache License 2.0 | 5 votes |
|
def __get_images_labels(self):
dataset = dataset_factory.get_dataset(
self.dataset_name, self.dataset_split_name, self.dataset_dir)
provider = slim.dataset_data_provider.DatasetDataProvider(
dataset,
shuffle=False,
common_queue_capacity=2 * self.batch_size,
common_queue_min=self.batch_size)
[image, label] = provider.get(['image', 'label'])
label -= self.labels_offset
network_fn = nets_factory.get_network_fn(
self.model_name,
num_classes=(dataset.num_classes - self.labels_offset),
is_training=False)
preprocessing_name = self.preprocessing_name or self.model_name
image_preprocessing_fn = preprocessing_factory.get_preprocessing(
preprocessing_name,
is_training=False)
eval_image_size = self.eval_image_size or network_fn.default_image_size
image = image_preprocessing_fn(image, eval_image_size, eval_image_size)
images, labels = tf.train.batch(
[image, label],
batch_size=self.batch_size,
num_threads=self.num_preprocessing_threads,
capacity=5 * self.batch_size)
return images, labels
Example #7
| Source File: slim_eval_test.py From SSD_tensorflow_VOC with Apache License 2.0 | 5 votes |
|
def __get_images_labels(self):
dataset = dataset_factory.get_dataset(
self.dataset_name, self.dataset_split_name, self.dataset_dir)
provider = slim.dataset_data_provider.DatasetDataProvider(
dataset,
shuffle=False,
common_queue_capacity=2 * self.batch_size,
common_queue_min=self.batch_size)
[image_raw, label] = provider.get(['image', 'label'])
label -= self.labels_offset
network_fn = nets_factory.get_network_fn(
self.model_name,
num_classes=(dataset.num_classes - self.labels_offset),
is_training=False)
preprocessing_name = self.preprocessing_name or self.model_name
image_preprocessing_fn = preprocessing_factory.get_preprocessing(
preprocessing_name,
is_training=False)
eval_image_size = self.eval_image_size or network_fn.default_image_size
image = image_preprocessing_fn(image_raw, eval_image_size, eval_image_size)
# Preprocess the image for display purposes.
image_raw = tf.expand_dims(image_raw, 0)
image_raw = tf.image.resize_images(image_raw, [eval_image_size, eval_image_size])
image_raw = tf.squeeze(image_raw)
images, labels, image_raws = tf.train.batch(
[image, label, image_raw],
batch_size=self.batch_size,
num_threads=self.num_preprocessing_threads,
capacity=5 * self.batch_size)
self.network_fn = network_fn
self.dataset = dataset
return images, labels,image_raws
Example #8
| Source File: mobilenet_v1_eval.py From SENet-tensorflow-slim with MIT License | 5 votes |
|
def imagenet_input(is_training):
"""Data reader for imagenet.
Reads in imagenet data and performs pre-processing on the images.
Args:
is_training: bool specifying if train or validation dataset is needed.
Returns:
A batch of images and labels.
"""
if is_training:
dataset = dataset_factory.get_dataset('imagenet', 'train',
FLAGS.dataset_dir)
else:
dataset = dataset_factory.get_dataset('imagenet', 'validation',
FLAGS.dataset_dir)
provider = slim.dataset_data_provider.DatasetDataProvider(
dataset,
shuffle=is_training,
common_queue_capacity=2 * FLAGS.batch_size,
common_queue_min=FLAGS.batch_size)
[image, label] = provider.get(['image', 'label'])
image_preprocessing_fn = preprocessing_factory.get_preprocessing(
'mobilenet_v1', is_training=is_training)
image = image_preprocessing_fn(image, FLAGS.image_size, FLAGS.image_size)
images, labels = tf.train.batch(
tensors=[image, label],
batch_size=FLAGS.batch_size,
num_threads=4,
capacity=5 * FLAGS.batch_size)
return images, labels
Example #9
| Source File: mobilenet_v1_train.py From SENet-tensorflow-slim with MIT License | 5 votes |
|
def imagenet_input(is_training):
"""Data reader for imagenet.
Reads in imagenet data and performs pre-processing on the images.
Args:
is_training: bool specifying if train or validation dataset is needed.
Returns:
A batch of images and labels.
"""
if is_training:
dataset = dataset_factory.get_dataset('imagenet', 'train',
FLAGS.dataset_dir)
else:
dataset = dataset_factory.get_dataset('imagenet', 'validation',
FLAGS.dataset_dir)
provider = slim.dataset_data_provider.DatasetDataProvider(
dataset,
shuffle=is_training,
common_queue_capacity=2 * FLAGS.batch_size,
common_queue_min=FLAGS.batch_size)
[image, label] = provider.get(['image', 'label'])
image_preprocessing_fn = preprocessing_factory.get_preprocessing(
'mobilenet_v1', is_training=is_training)
image = image_preprocessing_fn(image, FLAGS.image_size, FLAGS.image_size)
images, labels = tf.train.batch(
[image, label],
batch_size=FLAGS.batch_size,
num_threads=4,
capacity=5 * FLAGS.batch_size)
labels = slim.one_hot_encoding(labels, FLAGS.num_classes)
return images, labels
Example #10
| Source File: mobilenet_v1_eval.py From MAX-Object-Detector with Apache License 2.0 | 5 votes |
|
def imagenet_input(is_training):
"""Data reader for imagenet.
Reads in imagenet data and performs pre-processing on the images.
Args:
is_training: bool specifying if train or validation dataset is needed.
Returns:
A batch of images and labels.
"""
if is_training:
dataset = dataset_factory.get_dataset('imagenet', 'train',
FLAGS.dataset_dir)
else:
dataset = dataset_factory.get_dataset('imagenet', 'validation',
FLAGS.dataset_dir)
provider = slim.dataset_data_provider.DatasetDataProvider(
dataset,
shuffle=is_training,
common_queue_capacity=2 * FLAGS.batch_size,
common_queue_min=FLAGS.batch_size)
[image, label] = provider.get(['image', 'label'])
image_preprocessing_fn = preprocessing_factory.get_preprocessing(
'mobilenet_v1', is_training=is_training)
image = image_preprocessing_fn(image, FLAGS.image_size, FLAGS.image_size)
images, labels = tf.train.batch(
tensors=[image, label],
batch_size=FLAGS.batch_size,
num_threads=4,
capacity=5 * FLAGS.batch_size)
return images, labels
Example #11
| Source File: mobilenet_v1_train.py From MAX-Object-Detector with Apache License 2.0 | 5 votes |
|
def imagenet_input(is_training):
"""Data reader for imagenet.
Reads in imagenet data and performs pre-processing on the images.
Args:
is_training: bool specifying if train or validation dataset is needed.
Returns:
A batch of images and labels.
"""
if is_training:
dataset = dataset_factory.get_dataset('imagenet', 'train',
FLAGS.dataset_dir)
else:
dataset = dataset_factory.get_dataset('imagenet', 'validation',
FLAGS.dataset_dir)
provider = slim.dataset_data_provider.DatasetDataProvider(
dataset,
shuffle=is_training,
common_queue_capacity=2 * FLAGS.batch_size,
common_queue_min=FLAGS.batch_size)
[image, label] = provider.get(['image', 'label'])
image_preprocessing_fn = preprocessing_factory.get_preprocessing(
'mobilenet_v1', is_training=is_training)
image = image_preprocessing_fn(image, FLAGS.image_size, FLAGS.image_size)
images, labels = tf.train.batch(
[image, label],
batch_size=FLAGS.batch_size,
num_threads=4,
capacity=5 * FLAGS.batch_size)
labels = slim.one_hot_encoding(labels, FLAGS.num_classes)
return images, labels
Example #12
| Source File: mobilenet_v1_eval.py From g-tensorflow-models with Apache License 2.0 | 5 votes |
|
def imagenet_input(is_training):
"""Data reader for imagenet.
Reads in imagenet data and performs pre-processing on the images.
Args:
is_training: bool specifying if train or validation dataset is needed.
Returns:
A batch of images and labels.
"""
if is_training:
dataset = dataset_factory.get_dataset('imagenet', 'train',
FLAGS.dataset_dir)
else:
dataset = dataset_factory.get_dataset('imagenet', 'validation',
FLAGS.dataset_dir)
provider = slim.dataset_data_provider.DatasetDataProvider(
dataset,
shuffle=is_training,
common_queue_capacity=2 * FLAGS.batch_size,
common_queue_min=FLAGS.batch_size)
[image, label] = provider.get(['image', 'label'])
image_preprocessing_fn = preprocessing_factory.get_preprocessing(
'mobilenet_v1', is_training=is_training)
image = image_preprocessing_fn(image, FLAGS.image_size, FLAGS.image_size)
images, labels = tf.train.batch(
tensors=[image, label],
batch_size=FLAGS.batch_size,
num_threads=4,
capacity=5 * FLAGS.batch_size)
return images, labels
Example #13
| Source File: mobilenet_v1_train.py From g-tensorflow-models with Apache License 2.0 | 5 votes |
|
def imagenet_input(is_training):
"""Data reader for imagenet.
Reads in imagenet data and performs pre-processing on the images.
Args:
is_training: bool specifying if train or validation dataset is needed.
Returns:
A batch of images and labels.
"""
if is_training:
dataset = dataset_factory.get_dataset('imagenet', 'train',
FLAGS.dataset_dir)
else:
dataset = dataset_factory.get_dataset('imagenet', 'validation',
FLAGS.dataset_dir)
provider = slim.dataset_data_provider.DatasetDataProvider(
dataset,
shuffle=is_training,
common_queue_capacity=2 * FLAGS.batch_size,
common_queue_min=FLAGS.batch_size)
[image, label] = provider.get(['image', 'label'])
image_preprocessing_fn = preprocessing_factory.get_preprocessing(
'mobilenet_v1', is_training=is_training)
image = image_preprocessing_fn(image, FLAGS.image_size, FLAGS.image_size)
images, labels = tf.train.batch(
[image, label],
batch_size=FLAGS.batch_size,
num_threads=4,
capacity=5 * FLAGS.batch_size)
labels = slim.one_hot_encoding(labels, FLAGS.num_classes)
return images, labels
Example #14
| Source File: mobilenet_v1_eval.py From models with Apache License 2.0 | 5 votes |
|
def imagenet_input(is_training):
"""Data reader for imagenet.
Reads in imagenet data and performs pre-processing on the images.
Args:
is_training: bool specifying if train or validation dataset is needed.
Returns:
A batch of images and labels.
"""
if is_training:
dataset = dataset_factory.get_dataset('imagenet', 'train',
FLAGS.dataset_dir)
else:
dataset = dataset_factory.get_dataset('imagenet', 'validation',
FLAGS.dataset_dir)
provider = slim.dataset_data_provider.DatasetDataProvider(
dataset,
shuffle=is_training,
common_queue_capacity=2 * FLAGS.batch_size,
common_queue_min=FLAGS.batch_size)
[image, label] = provider.get(['image', 'label'])
image_preprocessing_fn = preprocessing_factory.get_preprocessing(
'mobilenet_v1', is_training=is_training)
image = image_preprocessing_fn(image, FLAGS.image_size, FLAGS.image_size)
images, labels = tf.train.batch(
tensors=[image, label],
batch_size=FLAGS.batch_size,
num_threads=4,
capacity=5 * FLAGS.batch_size)
return images, labels
Example #15
| Source File: mobilenet_v1_train.py From models with Apache License 2.0 | 5 votes |
|
def imagenet_input(is_training):
"""Data reader for imagenet.
Reads in imagenet data and performs pre-processing on the images.
Args:
is_training: bool specifying if train or validation dataset is needed.
Returns:
A batch of images and labels.
"""
if is_training:
dataset = dataset_factory.get_dataset('imagenet', 'train',
FLAGS.dataset_dir)
else:
dataset = dataset_factory.get_dataset('imagenet', 'validation',
FLAGS.dataset_dir)
provider = slim.dataset_data_provider.DatasetDataProvider(
dataset,
shuffle=is_training,
common_queue_capacity=2 * FLAGS.batch_size,
common_queue_min=FLAGS.batch_size)
[image, label] = provider.get(['image', 'label'])
image_preprocessing_fn = preprocessing_factory.get_preprocessing(
'mobilenet_v1', is_training=is_training)
image = image_preprocessing_fn(image, FLAGS.image_size, FLAGS.image_size)
images, labels = tf.train.batch([image, label],
batch_size=FLAGS.batch_size,
num_threads=4,
capacity=5 * FLAGS.batch_size)
labels = slim.one_hot_encoding(labels, FLAGS.num_classes)
return images, labels
Example #16
| Source File: mobilenet_v1_eval.py From multilabel-image-classification-tensorflow with MIT License | 5 votes |
|
def imagenet_input(is_training):
"""Data reader for imagenet.
Reads in imagenet data and performs pre-processing on the images.
Args:
is_training: bool specifying if train or validation dataset is needed.
Returns:
A batch of images and labels.
"""
if is_training:
dataset = dataset_factory.get_dataset('imagenet', 'train',
FLAGS.dataset_dir)
else:
dataset = dataset_factory.get_dataset('imagenet', 'validation',
FLAGS.dataset_dir)
provider = slim.dataset_data_provider.DatasetDataProvider(
dataset,
shuffle=is_training,
common_queue_capacity=2 * FLAGS.batch_size,
common_queue_min=FLAGS.batch_size)
[image, label] = provider.get(['image', 'label'])
image_preprocessing_fn = preprocessing_factory.get_preprocessing(
'mobilenet_v1', is_training=is_training)
image = image_preprocessing_fn(image, FLAGS.image_size, FLAGS.image_size)
images, labels = tf.train.batch(
tensors=[image, label],
batch_size=FLAGS.batch_size,
num_threads=4,
capacity=5 * FLAGS.batch_size)
return images, labels
Example #17
| Source File: mobilenet_v1_train.py From multilabel-image-classification-tensorflow with MIT License | 5 votes |
|
def imagenet_input(is_training):
"""Data reader for imagenet.
Reads in imagenet data and performs pre-processing on the images.
Args:
is_training: bool specifying if train or validation dataset is needed.
Returns:
A batch of images and labels.
"""
if is_training:
dataset = dataset_factory.get_dataset('imagenet', 'train',
FLAGS.dataset_dir)
else:
dataset = dataset_factory.get_dataset('imagenet', 'validation',
FLAGS.dataset_dir)
provider = slim.dataset_data_provider.DatasetDataProvider(
dataset,
shuffle=is_training,
common_queue_capacity=2 * FLAGS.batch_size,
common_queue_min=FLAGS.batch_size)
[image, label] = provider.get(['image', 'label'])
image_preprocessing_fn = preprocessing_factory.get_preprocessing(
'mobilenet_v1', is_training=is_training)
image = image_preprocessing_fn(image, FLAGS.image_size, FLAGS.image_size)
images, labels = tf.train.batch(
[image, label],
batch_size=FLAGS.batch_size,
num_threads=4,
capacity=5 * FLAGS.batch_size)
labels = slim.one_hot_encoding(labels, FLAGS.num_classes)
return images, labels
Example #18
| Source File: xdet_v2_resnet_eval.py From X-Detector with Apache License 2.0 | 5 votes |
|
def input_pipeline():
image_preprocessing_fn = lambda image_, shape_, glabels_, gbboxes_ : preprocessing_factory.get_preprocessing(
'xdet_resnet', is_training=False)(image_, glabels_, gbboxes_, out_shape=[FLAGS.train_image_size] * 2, data_format=('NCHW' if FLAGS.data_format=='channels_first' else 'NHWC'))
anchor_creator = anchor_manipulator.AnchorCreator([FLAGS.train_image_size] * 2,
layers_shapes = [(38, 38)],
anchor_scales = [[0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8]],
extra_anchor_scales = [[0.1]],
anchor_ratios = [[1., 2., 3., .5, 0.3333]],
layer_steps = [8])
def input_fn():
all_anchors, num_anchors_list = anchor_creator.get_all_anchors()
anchor_encoder_decoder = anchor_manipulator.AnchorEncoder(all_anchors,
num_classes = FLAGS.num_classes,
allowed_borders = [0.05],
positive_threshold = FLAGS.match_threshold,
ignore_threshold = FLAGS.neg_threshold,
prior_scaling=[0.1, 0.1, 0.2, 0.2])
num_readers_to_use = FLAGS.num_readers if FLAGS.run_on_cloud else 2
num_preprocessing_threads_to_use = FLAGS.num_preprocessing_threads if FLAGS.run_on_cloud else 2
list_from_batch, _ = dataset_factory.get_dataset(FLAGS.dataset_name,
FLAGS.dataset_split_name,
FLAGS.data_dir,
image_preprocessing_fn,
file_pattern = None,
reader = None,
batch_size = 1,
num_readers = num_readers_to_use,
num_preprocessing_threads = num_preprocessing_threads_to_use,
num_epochs = 1,
method = 'eval',
anchor_encoder = anchor_encoder_decoder.encode_all_anchors)
return list_from_batch[-1], {'targets': list_from_batch[:-1],
'decode_fn': lambda pred : anchor_encoder_decoder.decode_all_anchors([pred])[0],
'num_anchors_list': num_anchors_list}
return input_fn
Example #19
| Source File: light_head_rfcn_train.py From X-Detector with Apache License 2.0 | 5 votes |
|
def input_pipeline():
image_preprocessing_fn = lambda image_, shape_, glabels_, gbboxes_ : preprocessing_factory.get_preprocessing(
'xception_lighthead', is_training=True)(image_, glabels_, gbboxes_, out_shape=[FLAGS.train_image_size] * 2, data_format=('NCHW' if FLAGS.data_format=='channels_first' else 'NHWC'))
anchor_creator = anchor_manipulator.AnchorCreator([FLAGS.train_image_size] * 2,
layers_shapes = [(30, 30)],
anchor_scales = [[0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8]],
extra_anchor_scales = [[0.1]],
anchor_ratios = [[1., 2., .5]],
layer_steps = [16])
def input_fn():
all_anchors, num_anchors_list = anchor_creator.get_all_anchors()
anchor_encoder_decoder = anchor_manipulator.AnchorEncoder(all_anchors,
num_classes = FLAGS.num_classes,
allowed_borders = [0.],
positive_threshold = FLAGS.rpn_match_threshold,
ignore_threshold = FLAGS.rpn_neg_threshold,
prior_scaling=[1., 1., 1., 1.],#[0.1, 0.1, 0.2, 0.2],
rpn_fg_thres = FLAGS.match_threshold,
rpn_bg_high_thres = FLAGS.neg_threshold_high,
rpn_bg_low_thres = FLAGS.neg_threshold_low)
list_from_batch, _ = dataset_factory.get_dataset(FLAGS.dataset_name,
FLAGS.dataset_split_name,
FLAGS.data_dir,
image_preprocessing_fn,
file_pattern = None,
reader = None,
batch_size = FLAGS.batch_size,
num_readers = FLAGS.num_readers,
num_preprocessing_threads = FLAGS.num_preprocessing_threads,
num_epochs = FLAGS.train_epochs,
anchor_encoder = anchor_encoder_decoder.encode_all_anchors)
#print(list_from_batch[-4], list_from_batch[-3])
return list_from_batch[-1], {'targets': list_from_batch[:-1],
'rpn_decode_fn': lambda pred : anchor_encoder_decoder.decode_all_anchors([pred], squeeze_inner=True)[0],
'head_decode_fn': lambda rois, pred : anchor_encoder_decoder.ext_decode_rois(rois, pred, head_prior_scaling=[1., 1., 1., 1.]),
'rpn_encode_fn': lambda rois : anchor_encoder_decoder.ext_encode_rois(rois, list_from_batch[-4], list_from_batch[-3], FLAGS.roi_one_image, FLAGS.fg_ratio, 0.1, head_prior_scaling=[1., 1., 1., 1.]),
'num_anchors_list': num_anchors_list}
return input_fn
Example #20
| Source File: xdet_resnet_train.py From X-Detector with Apache License 2.0 | 5 votes |
|
def input_pipeline():
image_preprocessing_fn = lambda image_, shape_, glabels_, gbboxes_ : preprocessing_factory.get_preprocessing(
'xdet_resnet', is_training=True)(image_, glabels_, gbboxes_, out_shape=[FLAGS.train_image_size] * 2, data_format=('NCHW' if FLAGS.data_format=='channels_first' else 'NHWC'))
anchor_creator = anchor_manipulator.AnchorCreator([FLAGS.train_image_size] * 2,
layers_shapes = [(40, 40)],
anchor_scales = [[0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8]],
extra_anchor_scales = [[0.1]],
anchor_ratios = [[1., 2., 3., .5, 0.3333]],
layer_steps = [8])
def input_fn():
all_anchors, num_anchors_list = anchor_creator.get_all_anchors()
anchor_encoder_decoder = anchor_manipulator.AnchorEncoder(all_anchors,
num_classes = FLAGS.num_classes,
allowed_borders = [0.05],
positive_threshold = FLAGS.match_threshold,
ignore_threshold = FLAGS.neg_threshold,
prior_scaling=[0.1, 0.1, 0.2, 0.2])
list_from_batch, _ = dataset_factory.get_dataset(FLAGS.dataset_name,
FLAGS.dataset_split_name,
FLAGS.data_dir,
image_preprocessing_fn,
file_pattern = None,
reader = None,
batch_size = FLAGS.batch_size,
num_readers = FLAGS.num_readers,
num_preprocessing_threads = FLAGS.num_preprocessing_threads,
num_epochs = FLAGS.train_epochs,
anchor_encoder = anchor_encoder_decoder.encode_all_anchors)
return list_from_batch[-1], {'targets': list_from_batch[:-1],
'decode_fn': lambda pred : anchor_encoder_decoder.decode_all_anchors([pred])[0],
'num_anchors_list': num_anchors_list}
return input_fn
Example #21
| Source File: xdet_v2_resnet_train.py From X-Detector with Apache License 2.0 | 5 votes |
|
def input_pipeline():
image_preprocessing_fn = lambda image_, shape_, glabels_, gbboxes_ : preprocessing_factory.get_preprocessing(
'xdet_resnet', is_training=True)(image_, glabels_, gbboxes_, out_shape=[FLAGS.train_image_size] * 2, data_format=('NCHW' if FLAGS.data_format=='channels_first' else 'NHWC'))
anchor_creator = anchor_manipulator.AnchorCreator([FLAGS.train_image_size] * 2,
layers_shapes = [(38, 38)],
anchor_scales = [[0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8]],
extra_anchor_scales = [[0.1]],
anchor_ratios = [[1., 2., 3., .5, 0.3333]],
layer_steps = [8])
def input_fn():
all_anchors, num_anchors_list = anchor_creator.get_all_anchors()
anchor_encoder_decoder = anchor_manipulator.AnchorEncoder(all_anchors,
num_classes = FLAGS.num_classes,
allowed_borders = [0.05],
positive_threshold = FLAGS.match_threshold,
ignore_threshold = FLAGS.neg_threshold,
prior_scaling=[0.1, 0.1, 0.2, 0.2])
list_from_batch, _ = dataset_factory.get_dataset(FLAGS.dataset_name,
FLAGS.dataset_split_name,
FLAGS.data_dir,
image_preprocessing_fn,
file_pattern = None,
reader = None,
batch_size = FLAGS.batch_size,
num_readers = FLAGS.num_readers,
num_preprocessing_threads = FLAGS.num_preprocessing_threads,
num_epochs = FLAGS.train_epochs,
anchor_encoder = anchor_encoder_decoder.encode_all_anchors)
return list_from_batch[-1], {'targets': list_from_batch[:-1],
'decode_fn': lambda pred : anchor_encoder_decoder.decode_all_anchors([pred])[0],
'num_anchors_list': num_anchors_list}
return input_fn
Example #22
| Source File: xdet_resnet_eval.py From X-Detector with Apache License 2.0 | 5 votes |
|
def input_pipeline():
image_preprocessing_fn = lambda image_, shape_, glabels_, gbboxes_ : preprocessing_factory.get_preprocessing(
'xdet_resnet', is_training=False)(image_, glabels_, gbboxes_, out_shape=[FLAGS.train_image_size] * 2, data_format=('NCHW' if FLAGS.data_format=='channels_first' else 'NHWC'))
anchor_creator = anchor_manipulator.AnchorCreator([FLAGS.train_image_size] * 2,
layers_shapes = [(40, 40)],
anchor_scales = [[0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8]],
extra_anchor_scales = [[0.1]],
anchor_ratios = [[1., 2., 3., .5, 0.3333]],
layer_steps = [8])
def input_fn():
all_anchors, num_anchors_list = anchor_creator.get_all_anchors()
anchor_encoder_decoder = anchor_manipulator.AnchorEncoder(all_anchors,
num_classes = FLAGS.num_classes,
allowed_borders = [0.05],
positive_threshold = FLAGS.match_threshold,
ignore_threshold = FLAGS.neg_threshold,
prior_scaling=[0.1, 0.1, 0.2, 0.2])
num_readers_to_use = FLAGS.num_readers if FLAGS.run_on_cloud else 2
num_preprocessing_threads_to_use = FLAGS.num_preprocessing_threads if FLAGS.run_on_cloud else 2
list_from_batch, _ = dataset_factory.get_dataset(FLAGS.dataset_name,
FLAGS.dataset_split_name,
FLAGS.data_dir,
image_preprocessing_fn,
file_pattern = None,
reader = None,
batch_size = 1,
num_readers = num_readers_to_use,
num_preprocessing_threads = num_preprocessing_threads_to_use,
num_epochs = 1,
method = 'eval',
anchor_encoder = anchor_encoder_decoder.encode_all_anchors)
return list_from_batch[-1], {'targets': list_from_batch[:-1],
'decode_fn': lambda pred : anchor_encoder_decoder.decode_all_anchors([pred])[0],
'num_anchors_list': num_anchors_list}
return input_fn
Example #23
| Source File: xdet_v3_resnet_eval.py From X-Detector with Apache License 2.0 | 5 votes |
|
def input_pipeline():
image_preprocessing_fn = lambda image_, shape_, glabels_, gbboxes_ : preprocessing_factory.get_preprocessing(
'xdet_resnet', is_training=False)(image_, glabels_, gbboxes_, out_shape=[FLAGS.train_image_size] * 2, data_format=('NCHW' if FLAGS.data_format=='channels_first' else 'NHWC'))
anchor_creator = anchor_manipulator.AnchorCreator([FLAGS.train_image_size] * 2,
layers_shapes = [(22, 22)],
anchor_scales = [[0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8]],
extra_anchor_scales = [[0.1]],
anchor_ratios = [[1., 2., 3., .5, 0.3333]],
layer_steps = [16])
def input_fn():
all_anchors, num_anchors_list = anchor_creator.get_all_anchors()
anchor_encoder_decoder = anchor_manipulator.AnchorEncoder(all_anchors,
num_classes = FLAGS.num_classes,
allowed_borders = [0.05],
positive_threshold = FLAGS.match_threshold,
ignore_threshold = FLAGS.neg_threshold,
prior_scaling=[0.1, 0.1, 0.2, 0.2])
num_readers_to_use = FLAGS.num_readers if FLAGS.run_on_cloud else 2
num_preprocessing_threads_to_use = FLAGS.num_preprocessing_threads if FLAGS.run_on_cloud else 2
list_from_batch, _ = dataset_factory.get_dataset(FLAGS.dataset_name,
FLAGS.dataset_split_name,
FLAGS.data_dir,
image_preprocessing_fn,
file_pattern = None,
reader = None,
batch_size = 1,
num_readers = num_readers_to_use,
num_preprocessing_threads = num_preprocessing_threads_to_use,
num_epochs = 1,
method = 'eval',
anchor_encoder = anchor_encoder_decoder.encode_all_anchors)
return list_from_batch[-1], {'targets': list_from_batch[:-1],
'decode_fn': lambda pred : anchor_encoder_decoder.decode_all_anchors([pred])[0],
'num_anchors_list': num_anchors_list}
return input_fn
Example #24
| Source File: mobilenet_v1_train.py From Gun-Detector with Apache License 2.0 | 5 votes |
|
def imagenet_input(is_training):
"""Data reader for imagenet.
Reads in imagenet data and performs pre-processing on the images.
Args:
is_training: bool specifying if train or validation dataset is needed.
Returns:
A batch of images and labels.
"""
if is_training:
dataset = dataset_factory.get_dataset('imagenet', 'train',
FLAGS.dataset_dir)
else:
dataset = dataset_factory.get_dataset('imagenet', 'validation',
FLAGS.dataset_dir)
provider = slim.dataset_data_provider.DatasetDataProvider(
dataset,
shuffle=is_training,
common_queue_capacity=2 * FLAGS.batch_size,
common_queue_min=FLAGS.batch_size)
[image, label] = provider.get(['image', 'label'])
image_preprocessing_fn = preprocessing_factory.get_preprocessing(
'mobilenet_v1', is_training=is_training)
image = image_preprocessing_fn(image, FLAGS.image_size, FLAGS.image_size)
images, labels = tf.train.batch(
[image, label],
batch_size=FLAGS.batch_size,
num_threads=4,
capacity=5 * FLAGS.batch_size)
labels = slim.one_hot_encoding(labels, FLAGS.num_classes)
return images, labels
Example #25
| Source File: mobilenet_v1_train.py From DeepLab_v3 with MIT License | 5 votes |
|
def imagenet_input(is_training):
"""Data reader for imagenet.
Reads in imagenet data and performs pre-processing on the images.
Args:
is_training: bool specifying if train or validation dataset is needed.
Returns:
A batch of images and labels.
"""
if is_training:
dataset = dataset_factory.get_dataset('imagenet', 'train',
FLAGS.dataset_dir)
else:
dataset = dataset_factory.get_dataset('imagenet', 'validation',
FLAGS.dataset_dir)
provider = slim.dataset_data_provider.DatasetDataProvider(
dataset,
shuffle=is_training,
common_queue_capacity=2 * FLAGS.batch_size,
common_queue_min=FLAGS.batch_size)
[image, label] = provider.get(['image', 'label'])
image_preprocessing_fn = preprocessing_factory.get_preprocessing(
'mobilenet_v1', is_training=is_training)
image = image_preprocessing_fn(image, FLAGS.image_size, FLAGS.image_size)
images, labels = tf.train.batch(
[image, label],
batch_size=FLAGS.batch_size,
num_threads=4,
capacity=5 * FLAGS.batch_size)
labels = slim.one_hot_encoding(labels, FLAGS.num_classes)
return images, labels
Example #26
| Source File: mobilenet_v1_eval.py From CVTron with Apache License 2.0 | 5 votes |
|
def imagenet_input(is_training):
"""Data reader for imagenet.
Reads in imagenet data and performs pre-processing on the images.
Args:
is_training: bool specifying if train or validation dataset is needed.
Returns:
A batch of images and labels.
"""
if is_training:
dataset = dataset_factory.get_dataset('imagenet', 'train',
FLAGS.dataset_dir)
else:
dataset = dataset_factory.get_dataset('imagenet', 'validation',
FLAGS.dataset_dir)
provider = slim.dataset_data_provider.DatasetDataProvider(
dataset,
shuffle=is_training,
common_queue_capacity=2 * FLAGS.batch_size,
common_queue_min=FLAGS.batch_size)
[image, label] = provider.get(['image', 'label'])
image_preprocessing_fn = preprocessing_factory.get_preprocessing(
'mobilenet_v1', is_training=is_training)
image = image_preprocessing_fn(image, FLAGS.image_size, FLAGS.image_size)
images, labels = tf.train.batch(
tensors=[image, label],
batch_size=FLAGS.batch_size,
num_threads=4,
capacity=5 * FLAGS.batch_size)
return images, labels
Example #27
| Source File: mobilenet_v1_train.py From CVTron with Apache License 2.0 | 5 votes |
|
def imagenet_input(is_training):
"""Data reader for imagenet.
Reads in imagenet data and performs pre-processing on the images.
Args:
is_training: bool specifying if train or validation dataset is needed.
Returns:
A batch of images and labels.
"""
if is_training:
dataset = dataset_factory.get_dataset('imagenet', 'train',
FLAGS.dataset_dir)
else:
dataset = dataset_factory.get_dataset('imagenet', 'validation',
FLAGS.dataset_dir)
provider = slim.dataset_data_provider.DatasetDataProvider(
dataset,
shuffle=is_training,
common_queue_capacity=2 * FLAGS.batch_size,
common_queue_min=FLAGS.batch_size)
[image, label] = provider.get(['image', 'label'])
image_preprocessing_fn = preprocessing_factory.get_preprocessing(
'mobilenet_v1', is_training=is_training)
image = image_preprocessing_fn(image, FLAGS.image_size, FLAGS.image_size)
images, labels = tf.train.batch(
[image, label],
batch_size=FLAGS.batch_size,
num_threads=4,
capacity=5 * FLAGS.batch_size)
labels = slim.one_hot_encoding(labels, FLAGS.num_classes)
return images, labels
Example #28
| Source File: mobilenet_v1_eval.py From edafa with MIT License | 5 votes |
|
def imagenet_input(is_training):
"""Data reader for imagenet.
Reads in imagenet data and performs pre-processing on the images.
Args:
is_training: bool specifying if train or validation dataset is needed.
Returns:
A batch of images and labels.
"""
if is_training:
dataset = dataset_factory.get_dataset('imagenet', 'train',
FLAGS.dataset_dir)
else:
dataset = dataset_factory.get_dataset('imagenet', 'validation',
FLAGS.dataset_dir)
provider = slim.dataset_data_provider.DatasetDataProvider(
dataset,
shuffle=is_training,
common_queue_capacity=2 * FLAGS.batch_size,
common_queue_min=FLAGS.batch_size)
[image, label] = provider.get(['image', 'label'])
image_preprocessing_fn = preprocessing_factory.get_preprocessing(
'mobilenet_v1', is_training=is_training)
image = image_preprocessing_fn(image, FLAGS.image_size, FLAGS.image_size)
images, labels = tf.train.batch(
tensors=[image, label],
batch_size=FLAGS.batch_size,
num_threads=4,
capacity=5 * FLAGS.batch_size)
return images, labels
Example #29
| Source File: mobilenet_v1_train.py From edafa with MIT License | 5 votes |
|
def imagenet_input(is_training):
"""Data reader for imagenet.
Reads in imagenet data and performs pre-processing on the images.
Args:
is_training: bool specifying if train or validation dataset is needed.
Returns:
A batch of images and labels.
"""
if is_training:
dataset = dataset_factory.get_dataset('imagenet', 'train',
FLAGS.dataset_dir)
else:
dataset = dataset_factory.get_dataset('imagenet', 'validation',
FLAGS.dataset_dir)
provider = slim.dataset_data_provider.DatasetDataProvider(
dataset,
shuffle=is_training,
common_queue_capacity=2 * FLAGS.batch_size,
common_queue_min=FLAGS.batch_size)
[image, label] = provider.get(['image', 'label'])
image_preprocessing_fn = preprocessing_factory.get_preprocessing(
'mobilenet_v1', is_training=is_training)
image = image_preprocessing_fn(image, FLAGS.image_size, FLAGS.image_size)
images, labels = tf.train.batch(
[image, label],
batch_size=FLAGS.batch_size,
num_threads=4,
capacity=5 * FLAGS.batch_size)
labels = slim.one_hot_encoding(labels, FLAGS.num_classes)
return images, labels
Example #30
| Source File: mobilenet_v1_eval.py From CBAM-tensorflow-slim with MIT License | 5 votes |
|
def imagenet_input(is_training):
"""Data reader for imagenet.
Reads in imagenet data and performs pre-processing on the images.
Args:
is_training: bool specifying if train or validation dataset is needed.
Returns:
A batch of images and labels.
"""
if is_training:
dataset = dataset_factory.get_dataset('imagenet', 'train',
FLAGS.dataset_dir)
else:
dataset = dataset_factory.get_dataset('imagenet', 'validation',
FLAGS.dataset_dir)
provider = slim.dataset_data_provider.DatasetDataProvider(
dataset,
shuffle=is_training,
common_queue_capacity=2 * FLAGS.batch_size,
common_queue_min=FLAGS.batch_size)
[image, label] = provider.get(['image', 'label'])
image_preprocessing_fn = preprocessing_factory.get_preprocessing(
'mobilenet_v1', is_training=is_training)
image = image_preprocessing_fn(image, FLAGS.image_size, FLAGS.image_size)
images, labels = tf.train.batch(
tensors=[image, label],
batch_size=FLAGS.batch_size,
num_threads=4,
capacity=5 * FLAGS.batch_size)
return images, labels
