Python object_detection.utils.ops.pad_to_multiple() Examples
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Example #1
Source File: ssd_mobilenet_v2_keras_feature_extractor.py From vehicle_counting_tensorflow with MIT License | 6 votes |
def _extract_features(self, preprocessed_inputs): """Extract features from preprocessed inputs. Args: preprocessed_inputs: a [batch, height, width, channels] float tensor representing a batch of images. Returns: feature_maps: a list of tensors where the ith tensor has shape [batch, height_i, width_i, depth_i] """ preprocessed_inputs = shape_utils.check_min_image_dim( 33, preprocessed_inputs) image_features = self.mobilenet_v2( ops.pad_to_multiple(preprocessed_inputs, self._pad_to_multiple)) feature_maps = self.feature_map_generator({ 'layer_15/expansion_output': image_features[0], 'layer_19': image_features[1]}) return feature_maps.values()
Example #2
Source File: ssd_resnet_v1_fpn_feature_extractor.py From Person-Detection-and-Tracking with MIT License | 5 votes |
def __init__(self, is_training, depth_multiplier, min_depth, pad_to_multiple, conv_hyperparams_fn, reuse_weights=None, use_explicit_padding=False, use_depthwise=False, override_base_feature_extractor_hyperparams=False): """SSD Resnet152 V1 FPN feature extractor based on Resnet v1 architecture. Args: is_training: whether the network is in training mode. depth_multiplier: float depth multiplier for feature extractor. UNUSED currently. min_depth: minimum feature extractor depth. UNUSED Currently. pad_to_multiple: the nearest multiple to zero pad the input height and width dimensions to. conv_hyperparams_fn: A function to construct tf slim arg_scope for conv2d and separable_conv2d ops in the layers that are added on top of the base feature extractor. reuse_weights: Whether to reuse variables. Default is None. use_explicit_padding: Whether to use explicit padding when extracting features. Default is False. UNUSED currently. use_depthwise: Whether to use depthwise convolutions. UNUSED currently. override_base_feature_extractor_hyperparams: Whether to override hyperparameters of the base feature extractor with the one from `conv_hyperparams_fn`. """ super(SSDResnet152V1FpnFeatureExtractor, self).__init__( is_training, depth_multiplier, min_depth, pad_to_multiple, conv_hyperparams_fn, resnet_v1.resnet_v1_152, 'resnet_v1_152', 'fpn', reuse_weights, use_explicit_padding, override_base_feature_extractor_hyperparams)
Example #3
Source File: ops_test.py From HereIsWally with MIT License | 5 votes |
def test_padding(self): tensor = tf.constant([[[[0.], [0.]], [[0.], [0.]]]]) padded_tensor = ops.pad_to_multiple(tensor, 4) with self.test_session() as sess: padded_tensor_out = sess.run(padded_tensor) self.assertEqual((1, 4, 4, 1), padded_tensor_out.shape)
Example #4
Source File: ops_test.py From HereIsWally with MIT License | 5 votes |
def test_no_padding(self): tensor = tf.constant([[[[0.], [0.]], [[0.], [0.]]]]) padded_tensor = ops.pad_to_multiple(tensor, 2) with self.test_session() as sess: padded_tensor_out = sess.run(padded_tensor) self.assertEqual((1, 2, 2, 1), padded_tensor_out.shape)
Example #5
Source File: ops_test.py From HereIsWally with MIT License | 5 votes |
def test_zero_padding(self): tensor = tf.constant([[[[0.], [0.]], [[0.], [0.]]]]) padded_tensor = ops.pad_to_multiple(tensor, 1) with self.test_session() as sess: padded_tensor_out = sess.run(padded_tensor) self.assertEqual((1, 2, 2, 1), padded_tensor_out.shape)
Example #6
Source File: ssd_inception_v2_feature_extractor.py From yolo_v2 with Apache License 2.0 | 5 votes |
def __init__(self, is_training, depth_multiplier, min_depth, pad_to_multiple, conv_hyperparams, batch_norm_trainable=True, reuse_weights=None): """InceptionV2 Feature Extractor for SSD Models. Args: is_training: whether the network is in training mode. depth_multiplier: float depth multiplier for feature extractor. min_depth: minimum feature extractor depth. pad_to_multiple: the nearest multiple to zero pad the input height and width dimensions to. conv_hyperparams: tf slim arg_scope for conv2d and separable_conv2d ops. batch_norm_trainable: Whether to update batch norm parameters during training or not. When training with a small batch size (e.g. 1), it is desirable to disable batch norm update and use pretrained batch norm params. reuse_weights: Whether to reuse variables. Default is None. """ super(SSDInceptionV2FeatureExtractor, self).__init__( is_training, depth_multiplier, min_depth, pad_to_multiple, conv_hyperparams, batch_norm_trainable, reuse_weights)
Example #7
Source File: ssd_inception_v2_feature_extractor.py From yolo_v2 with Apache License 2.0 | 5 votes |
def extract_features(self, preprocessed_inputs): """Extract features from preprocessed inputs. Args: preprocessed_inputs: a [batch, height, width, channels] float tensor representing a batch of images. Returns: feature_maps: a list of tensors where the ith tensor has shape [batch, height_i, width_i, depth_i] """ preprocessed_inputs.get_shape().assert_has_rank(4) shape_assert = tf.Assert( tf.logical_and(tf.greater_equal(tf.shape(preprocessed_inputs)[1], 33), tf.greater_equal(tf.shape(preprocessed_inputs)[2], 33)), ['image size must at least be 33 in both height and width.']) feature_map_layout = { 'from_layer': ['Mixed_4c', 'Mixed_5c', '', '', '', ''], 'layer_depth': [-1, -1, 512, 256, 256, 128], } with tf.control_dependencies([shape_assert]): with slim.arg_scope(self._conv_hyperparams): with tf.variable_scope('InceptionV2', reuse=self._reuse_weights) as scope: _, image_features = inception_v2.inception_v2_base( ops.pad_to_multiple(preprocessed_inputs, self._pad_to_multiple), final_endpoint='Mixed_5c', min_depth=self._min_depth, depth_multiplier=self._depth_multiplier, scope=scope) feature_maps = feature_map_generators.multi_resolution_feature_maps( feature_map_layout=feature_map_layout, depth_multiplier=self._depth_multiplier, min_depth=self._min_depth, insert_1x1_conv=True, image_features=image_features) return feature_maps.values()
Example #8
Source File: ssd_inception_v3_feature_extractor.py From yolo_v2 with Apache License 2.0 | 5 votes |
def __init__(self, is_training, depth_multiplier, min_depth, pad_to_multiple, conv_hyperparams, batch_norm_trainable=True, reuse_weights=None): """InceptionV3 Feature Extractor for SSD Models. Args: is_training: whether the network is in training mode. depth_multiplier: float depth multiplier for feature extractor. min_depth: minimum feature extractor depth. pad_to_multiple: the nearest multiple to zero pad the input height and width dimensions to. conv_hyperparams: tf slim arg_scope for conv2d and separable_conv2d ops. batch_norm_trainable: Whether to update batch norm parameters during training or not. When training with a small batch size (e.g. 1), it is desirable to disable batch norm update and use pretrained batch norm params. reuse_weights: Whether to reuse variables. Default is None. """ super(SSDInceptionV3FeatureExtractor, self).__init__( is_training, depth_multiplier, min_depth, pad_to_multiple, conv_hyperparams, batch_norm_trainable, reuse_weights)
Example #9
Source File: ssd_inception_v3_feature_extractor.py From yolo_v2 with Apache License 2.0 | 5 votes |
def extract_features(self, preprocessed_inputs): """Extract features from preprocessed inputs. Args: preprocessed_inputs: a [batch, height, width, channels] float tensor representing a batch of images. Returns: feature_maps: a list of tensors where the ith tensor has shape [batch, height_i, width_i, depth_i] """ preprocessed_inputs.get_shape().assert_has_rank(4) shape_assert = tf.Assert( tf.logical_and(tf.greater_equal(tf.shape(preprocessed_inputs)[1], 33), tf.greater_equal(tf.shape(preprocessed_inputs)[2], 33)), ['image size must at least be 33 in both height and width.']) feature_map_layout = { 'from_layer': ['Mixed_5d', 'Mixed_6e', 'Mixed_7c', '', '', ''], 'layer_depth': [-1, -1, -1, 512, 256, 128], } with tf.control_dependencies([shape_assert]): with slim.arg_scope(self._conv_hyperparams): with tf.variable_scope('InceptionV3', reuse=self._reuse_weights) as scope: _, image_features = inception_v3.inception_v3_base( ops.pad_to_multiple(preprocessed_inputs, self._pad_to_multiple), final_endpoint='Mixed_7c', min_depth=self._min_depth, depth_multiplier=self._depth_multiplier, scope=scope) feature_maps = feature_map_generators.multi_resolution_feature_maps( feature_map_layout=feature_map_layout, depth_multiplier=self._depth_multiplier, min_depth=self._min_depth, insert_1x1_conv=True, image_features=image_features) return feature_maps.values()
Example #10
Source File: ops_test.py From Traffic-Rule-Violation-Detection-System with MIT License | 5 votes |
def test_zero_padding(self): tensor = tf.constant([[[[0.], [0.]], [[0.], [0.]]]]) padded_tensor = ops.pad_to_multiple(tensor, 1) with self.test_session() as sess: padded_tensor_out = sess.run(padded_tensor) self.assertEqual((1, 2, 2, 1), padded_tensor_out.shape)
Example #11
Source File: ops_test.py From Traffic-Rule-Violation-Detection-System with MIT License | 5 votes |
def test_no_padding(self): tensor = tf.constant([[[[0.], [0.]], [[0.], [0.]]]]) padded_tensor = ops.pad_to_multiple(tensor, 2) with self.test_session() as sess: padded_tensor_out = sess.run(padded_tensor) self.assertEqual((1, 2, 2, 1), padded_tensor_out.shape)
Example #12
Source File: ops_test.py From Traffic-Rule-Violation-Detection-System with MIT License | 5 votes |
def test_padding(self): tensor = tf.constant([[[[0.], [0.]], [[0.], [0.]]]]) padded_tensor = ops.pad_to_multiple(tensor, 4) with self.test_session() as sess: padded_tensor_out = sess.run(padded_tensor) self.assertEqual((1, 4, 4, 1), padded_tensor_out.shape)
Example #13
Source File: embedded_ssd_mobilenet_v1_feature_extractor.py From Traffic-Rule-Violation-Detection-System with MIT License | 5 votes |
def __init__(self, is_training, depth_multiplier, min_depth, pad_to_multiple, conv_hyperparams, batch_norm_trainable=True, reuse_weights=None, use_explicit_padding=False, use_depthwise=False): """MobileNetV1 Feature Extractor for Embedded-friendly SSD Models. Args: is_training: whether the network is in training mode. depth_multiplier: float depth multiplier for feature extractor. min_depth: minimum feature extractor depth. pad_to_multiple: the nearest multiple to zero pad the input height and width dimensions to. For EmbeddedSSD it must be set to 1. conv_hyperparams: tf slim arg_scope for conv2d and separable_conv2d ops. batch_norm_trainable: Whether to update batch norm parameters during training or not. When training with a small batch size (e.g. 1), it is desirable to disable batch norm update and use pretrained batch norm params. reuse_weights: Whether to reuse variables. Default is None. use_explicit_padding: Whether to use explicit padding when extracting features. Default is False. use_depthwise: Whether to use depthwise convolutions. Default is False. Raises: ValueError: upon invalid `pad_to_multiple` values. """ if pad_to_multiple != 1: raise ValueError('Embedded-specific SSD only supports `pad_to_multiple` ' 'of 1.') super(EmbeddedSSDMobileNetV1FeatureExtractor, self).__init__( is_training, depth_multiplier, min_depth, pad_to_multiple, conv_hyperparams, batch_norm_trainable, reuse_weights, use_explicit_padding, use_depthwise)
Example #14
Source File: embedded_ssd_mobilenet_v1_feature_extractor.py From yolo_v2 with Apache License 2.0 | 5 votes |
def __init__(self, is_training, depth_multiplier, min_depth, pad_to_multiple, conv_hyperparams, batch_norm_trainable=True, reuse_weights=None): """MobileNetV1 Feature Extractor for Embedded-friendly SSD Models. Args: is_training: whether the network is in training mode. depth_multiplier: float depth multiplier for feature extractor. min_depth: minimum feature extractor depth. pad_to_multiple: the nearest multiple to zero pad the input height and width dimensions to. For EmbeddedSSD it must be set to 1. conv_hyperparams: tf slim arg_scope for conv2d and separable_conv2d ops. batch_norm_trainable: Whether to update batch norm parameters during training or not. When training with a small batch size (e.g. 1), it is desirable to disable batch norm update and use pretrained batch norm params. reuse_weights: Whether to reuse variables. Default is None. Raises: ValueError: upon invalid `pad_to_multiple` values. """ if pad_to_multiple != 1: raise ValueError('Embedded-specific SSD only supports `pad_to_multiple` ' 'of 1.') super(EmbeddedSSDMobileNetV1FeatureExtractor, self).__init__( is_training, depth_multiplier, min_depth, pad_to_multiple, conv_hyperparams, batch_norm_trainable, reuse_weights)
Example #15
Source File: ops_test.py From garbage-object-detection-tensorflow with MIT License | 5 votes |
def test_zero_padding(self): tensor = tf.constant([[[[0.], [0.]], [[0.], [0.]]]]) padded_tensor = ops.pad_to_multiple(tensor, 1) with self.test_session() as sess: padded_tensor_out = sess.run(padded_tensor) self.assertEqual((1, 2, 2, 1), padded_tensor_out.shape)
Example #16
Source File: ssd_inception_v2_feature_extractor.py From Person-Detection-and-Tracking with MIT License | 5 votes |
def extract_features(self, preprocessed_inputs): """Extract features from preprocessed inputs. Args: preprocessed_inputs: a [batch, height, width, channels] float tensor representing a batch of images. Returns: feature_maps: a list of tensors where the ith tensor has shape [batch, height_i, width_i, depth_i] """ preprocessed_inputs = shape_utils.check_min_image_dim( 33, preprocessed_inputs) feature_map_layout = { 'from_layer': ['Mixed_4c', 'Mixed_5c', '', '', '', ''], 'layer_depth': [-1, -1, 512, 256, 256, 128], 'use_explicit_padding': self._use_explicit_padding, 'use_depthwise': self._use_depthwise, } with slim.arg_scope(self._conv_hyperparams_fn()): with tf.variable_scope('InceptionV2', reuse=self._reuse_weights) as scope: _, image_features = inception_v2.inception_v2_base( ops.pad_to_multiple(preprocessed_inputs, self._pad_to_multiple), final_endpoint='Mixed_5c', min_depth=self._min_depth, depth_multiplier=self._depth_multiplier, scope=scope) feature_maps = feature_map_generators.multi_resolution_feature_maps( feature_map_layout=feature_map_layout, depth_multiplier=self._depth_multiplier, min_depth=self._min_depth, insert_1x1_conv=True, image_features=image_features) return feature_maps.values()
Example #17
Source File: ops_test.py From garbage-object-detection-tensorflow with MIT License | 5 votes |
def test_no_padding(self): tensor = tf.constant([[[[0.], [0.]], [[0.], [0.]]]]) padded_tensor = ops.pad_to_multiple(tensor, 2) with self.test_session() as sess: padded_tensor_out = sess.run(padded_tensor) self.assertEqual((1, 2, 2, 1), padded_tensor_out.shape)
Example #18
Source File: ssd_resnet_v1_fpn_feature_extractor.py From Person-Detection-and-Tracking with MIT License | 5 votes |
def __init__(self, is_training, depth_multiplier, min_depth, pad_to_multiple, conv_hyperparams_fn, reuse_weights=None, use_explicit_padding=False, use_depthwise=False, override_base_feature_extractor_hyperparams=False): """SSD Resnet101 V1 FPN feature extractor based on Resnet v1 architecture. Args: is_training: whether the network is in training mode. depth_multiplier: float depth multiplier for feature extractor. UNUSED currently. min_depth: minimum feature extractor depth. UNUSED Currently. pad_to_multiple: the nearest multiple to zero pad the input height and width dimensions to. conv_hyperparams_fn: A function to construct tf slim arg_scope for conv2d and separable_conv2d ops in the layers that are added on top of the base feature extractor. reuse_weights: Whether to reuse variables. Default is None. use_explicit_padding: Whether to use explicit padding when extracting features. Default is False. UNUSED currently. use_depthwise: Whether to use depthwise convolutions. UNUSED currently. override_base_feature_extractor_hyperparams: Whether to override hyperparameters of the base feature extractor with the one from `conv_hyperparams_fn`. """ super(SSDResnet101V1FpnFeatureExtractor, self).__init__( is_training, depth_multiplier, min_depth, pad_to_multiple, conv_hyperparams_fn, resnet_v1.resnet_v1_101, 'resnet_v1_101', 'fpn', reuse_weights, use_explicit_padding, override_base_feature_extractor_hyperparams)
Example #19
Source File: ssd_mobilenet_v1_feature_extractor.py From Person-Detection-and-Tracking with MIT License | 5 votes |
def __init__(self, is_training, depth_multiplier, min_depth, pad_to_multiple, conv_hyperparams_fn, reuse_weights=None, use_explicit_padding=False, use_depthwise=False, override_base_feature_extractor_hyperparams=False): """MobileNetV1 Feature Extractor for SSD Models. Args: is_training: whether the network is in training mode. depth_multiplier: float depth multiplier for feature extractor. min_depth: minimum feature extractor depth. pad_to_multiple: the nearest multiple to zero pad the input height and width dimensions to. conv_hyperparams_fn: A function to construct tf slim arg_scope for conv2d and separable_conv2d ops in the layers that are added on top of the base feature extractor. reuse_weights: Whether to reuse variables. Default is None. use_explicit_padding: Use 'VALID' padding for convolutions, but prepad inputs so that the output dimensions are the same as if 'SAME' padding were used. use_depthwise: Whether to use depthwise convolutions. Default is False. override_base_feature_extractor_hyperparams: Whether to override hyperparameters of the base feature extractor with the one from `conv_hyperparams_fn`. """ super(SSDMobileNetV1FeatureExtractor, self).__init__( is_training, depth_multiplier, min_depth, pad_to_multiple, conv_hyperparams_fn, reuse_weights, use_explicit_padding, use_depthwise, override_base_feature_extractor_hyperparams)
Example #20
Source File: embedded_ssd_mobilenet_v1_feature_extractor.py From Person-Detection-and-Tracking with MIT License | 5 votes |
def __init__(self, is_training, depth_multiplier, min_depth, pad_to_multiple, conv_hyperparams_fn, reuse_weights=None, use_explicit_padding=False, use_depthwise=False, override_base_feature_extractor_hyperparams=False): """MobileNetV1 Feature Extractor for Embedded-friendly SSD Models. Args: is_training: whether the network is in training mode. depth_multiplier: float depth multiplier for feature extractor. min_depth: minimum feature extractor depth. pad_to_multiple: the nearest multiple to zero pad the input height and width dimensions to. For EmbeddedSSD it must be set to 1. conv_hyperparams_fn: A function to construct tf slim arg_scope for conv2d and separable_conv2d ops in the layers that are added on top of the base feature extractor. reuse_weights: Whether to reuse variables. Default is None. use_explicit_padding: Whether to use explicit padding when extracting features. Default is False. use_depthwise: Whether to use depthwise convolutions. Default is False. override_base_feature_extractor_hyperparams: Whether to override hyperparameters of the base feature extractor with the one from `conv_hyperparams_fn`. Raises: ValueError: upon invalid `pad_to_multiple` values. """ if pad_to_multiple != 1: raise ValueError('Embedded-specific SSD only supports `pad_to_multiple` ' 'of 1.') super(EmbeddedSSDMobileNetV1FeatureExtractor, self).__init__( is_training, depth_multiplier, min_depth, pad_to_multiple, conv_hyperparams_fn, reuse_weights, use_explicit_padding, use_depthwise, override_base_feature_extractor_hyperparams)
Example #21
Source File: ops_test.py From Person-Detection-and-Tracking with MIT License | 5 votes |
def test_padding(self): tensor = tf.constant([[[[0.], [0.]], [[0.], [0.]]]]) padded_tensor = ops.pad_to_multiple(tensor, 4) with self.test_session() as sess: padded_tensor_out = sess.run(padded_tensor) self.assertEqual((1, 4, 4, 1), padded_tensor_out.shape)
Example #22
Source File: ops_test.py From Person-Detection-and-Tracking with MIT License | 5 votes |
def test_non_square_padding(self): tensor = tf.constant([[[[0.], [0.]]]]) padded_tensor = ops.pad_to_multiple(tensor, 2) with self.test_session() as sess: padded_tensor_out = sess.run(padded_tensor) self.assertEqual((1, 2, 2, 1), padded_tensor_out.shape)
Example #23
Source File: ops_test.py From Person-Detection-and-Tracking with MIT License | 5 votes |
def test_no_padding(self): tensor = tf.constant([[[[0.], [0.]], [[0.], [0.]]]]) padded_tensor = ops.pad_to_multiple(tensor, 2) with self.test_session() as sess: padded_tensor_out = sess.run(padded_tensor) self.assertEqual((1, 2, 2, 1), padded_tensor_out.shape)
Example #24
Source File: ops_test.py From Person-Detection-and-Tracking with MIT License | 5 votes |
def test_zero_padding(self): tensor = tf.constant([[[[0.], [0.]], [[0.], [0.]]]]) padded_tensor = ops.pad_to_multiple(tensor, 1) with self.test_session() as sess: padded_tensor_out = sess.run(padded_tensor) self.assertEqual((1, 2, 2, 1), padded_tensor_out.shape)
Example #25
Source File: ssd_inception_v3_feature_extractor.py From ros_people_object_detection_tensorflow with Apache License 2.0 | 5 votes |
def __init__(self, is_training, depth_multiplier, min_depth, pad_to_multiple, conv_hyperparams, batch_norm_trainable=True, reuse_weights=None, use_explicit_padding=False, use_depthwise=False): """InceptionV3 Feature Extractor for SSD Models. Args: is_training: whether the network is in training mode. depth_multiplier: float depth multiplier for feature extractor. min_depth: minimum feature extractor depth. pad_to_multiple: the nearest multiple to zero pad the input height and width dimensions to. conv_hyperparams: tf slim arg_scope for conv2d and separable_conv2d ops. batch_norm_trainable: Whether to update batch norm parameters during training or not. When training with a small batch size (e.g. 1), it is desirable to disable batch norm update and use pretrained batch norm params. reuse_weights: Whether to reuse variables. Default is None. use_explicit_padding: Whether to use explicit padding when extracting features. Default is False. use_depthwise: Whether to use depthwise convolutions. Default is False. """ super(SSDInceptionV3FeatureExtractor, self).__init__( is_training, depth_multiplier, min_depth, pad_to_multiple, conv_hyperparams, batch_norm_trainable, reuse_weights, use_explicit_padding, use_depthwise)
Example #26
Source File: ssd_inception_v2_feature_extractor.py From ros_people_object_detection_tensorflow with Apache License 2.0 | 5 votes |
def extract_features(self, preprocessed_inputs): """Extract features from preprocessed inputs. Args: preprocessed_inputs: a [batch, height, width, channels] float tensor representing a batch of images. Returns: feature_maps: a list of tensors where the ith tensor has shape [batch, height_i, width_i, depth_i] """ preprocessed_inputs = shape_utils.check_min_image_dim( 33, preprocessed_inputs) feature_map_layout = { 'from_layer': ['Mixed_4c', 'Mixed_5c', '', '', '', ''], 'layer_depth': [-1, -1, 512, 256, 256, 128], 'use_explicit_padding': self._use_explicit_padding, 'use_depthwise': self._use_depthwise, } with slim.arg_scope(self._conv_hyperparams): with tf.variable_scope('InceptionV2', reuse=self._reuse_weights) as scope: _, image_features = inception_v2.inception_v2_base( ops.pad_to_multiple(preprocessed_inputs, self._pad_to_multiple), final_endpoint='Mixed_5c', min_depth=self._min_depth, depth_multiplier=self._depth_multiplier, scope=scope) feature_maps = feature_map_generators.multi_resolution_feature_maps( feature_map_layout=feature_map_layout, depth_multiplier=self._depth_multiplier, min_depth=self._min_depth, insert_1x1_conv=True, image_features=image_features) return feature_maps.values()
Example #27
Source File: ssd_inception_v2_feature_extractor.py From ros_people_object_detection_tensorflow with Apache License 2.0 | 5 votes |
def __init__(self, is_training, depth_multiplier, min_depth, pad_to_multiple, conv_hyperparams, batch_norm_trainable=True, reuse_weights=None, use_explicit_padding=False, use_depthwise=False): """InceptionV2 Feature Extractor for SSD Models. Args: is_training: whether the network is in training mode. depth_multiplier: float depth multiplier for feature extractor. min_depth: minimum feature extractor depth. pad_to_multiple: the nearest multiple to zero pad the input height and width dimensions to. conv_hyperparams: tf slim arg_scope for conv2d and separable_conv2d ops. batch_norm_trainable: Whether to update batch norm parameters during training or not. When training with a small batch size (e.g. 1), it is desirable to disable batch norm update and use pretrained batch norm params. reuse_weights: Whether to reuse variables. Default is None. use_explicit_padding: Whether to use explicit padding when extracting features. Default is False. use_depthwise: Whether to use depthwise convolutions. Default is False. """ super(SSDInceptionV2FeatureExtractor, self).__init__( is_training, depth_multiplier, min_depth, pad_to_multiple, conv_hyperparams, batch_norm_trainable, reuse_weights, use_explicit_padding, use_depthwise)
Example #28
Source File: ssd_resnet_v1_fpn_feature_extractor.py From ros_people_object_detection_tensorflow with Apache License 2.0 | 5 votes |
def __init__(self, is_training, depth_multiplier, min_depth, pad_to_multiple, conv_hyperparams, batch_norm_trainable=True, reuse_weights=None, use_explicit_padding=False, use_depthwise=False): """Resnet152 v1 FPN Feature Extractor for SSD Models. Args: is_training: whether the network is in training mode. depth_multiplier: float depth multiplier for feature extractor. min_depth: minimum feature extractor depth. pad_to_multiple: the nearest multiple to zero pad the input height and width dimensions to. conv_hyperparams: tf slim arg_scope for conv2d and separable_conv2d ops. batch_norm_trainable: Whether to update batch norm parameters during training or not. When training with a small batch size (e.g. 1), it is desirable to disable batch norm update and use pretrained batch norm params. reuse_weights: Whether to reuse variables. Default is None. use_explicit_padding: Whether to use explicit padding when extracting features. Default is False. UNUSED currently. use_depthwise: Whether to use depthwise convolutions. UNUSED currently. """ super(SSDResnet152V1FpnFeatureExtractor, self).__init__( is_training, depth_multiplier, min_depth, pad_to_multiple, conv_hyperparams, resnet_v1.resnet_v1_152, 'resnet_v1_152', 'fpn', batch_norm_trainable, reuse_weights, use_explicit_padding)
Example #29
Source File: ssd_resnet_v1_fpn_feature_extractor.py From ros_people_object_detection_tensorflow with Apache License 2.0 | 5 votes |
def __init__(self, is_training, depth_multiplier, min_depth, pad_to_multiple, conv_hyperparams, batch_norm_trainable=True, reuse_weights=None, use_explicit_padding=False, use_depthwise=False): """Resnet101 v1 FPN Feature Extractor for SSD Models. Args: is_training: whether the network is in training mode. depth_multiplier: float depth multiplier for feature extractor. min_depth: minimum feature extractor depth. pad_to_multiple: the nearest multiple to zero pad the input height and width dimensions to. conv_hyperparams: tf slim arg_scope for conv2d and separable_conv2d ops. batch_norm_trainable: Whether to update batch norm parameters during training or not. When training with a small batch size (e.g. 1), it is desirable to disable batch norm update and use pretrained batch norm params. reuse_weights: Whether to reuse variables. Default is None. use_explicit_padding: Whether to use explicit padding when extracting features. Default is False. UNUSED currently. use_depthwise: Whether to use depthwise convolutions. UNUSED currently. """ super(SSDResnet101V1FpnFeatureExtractor, self).__init__( is_training, depth_multiplier, min_depth, pad_to_multiple, conv_hyperparams, resnet_v1.resnet_v1_101, 'resnet_v1_101', 'fpn', batch_norm_trainable, reuse_weights, use_explicit_padding)
Example #30
Source File: ssd_mobilenet_v1_feature_extractor.py From ros_people_object_detection_tensorflow with Apache License 2.0 | 5 votes |
def __init__(self, is_training, depth_multiplier, min_depth, pad_to_multiple, conv_hyperparams, batch_norm_trainable=True, reuse_weights=None, use_explicit_padding=False, use_depthwise=False): """MobileNetV1 Feature Extractor for SSD Models. Args: is_training: whether the network is in training mode. depth_multiplier: float depth multiplier for feature extractor. min_depth: minimum feature extractor depth. pad_to_multiple: the nearest multiple to zero pad the input height and width dimensions to. conv_hyperparams: tf slim arg_scope for conv2d and separable_conv2d ops. batch_norm_trainable: Whether to update batch norm parameters during training or not. When training with a small batch size (e.g. 1), it is desirable to disable batch norm update and use pretrained batch norm params. reuse_weights: Whether to reuse variables. Default is None. use_explicit_padding: Use 'VALID' padding for convolutions, but prepad inputs so that the output dimensions are the same as if 'SAME' padding were used. use_depthwise: Whether to use depthwise convolutions. Default is False. """ super(SSDMobileNetV1FeatureExtractor, self).__init__( is_training, depth_multiplier, min_depth, pad_to_multiple, conv_hyperparams, batch_norm_trainable, reuse_weights, use_explicit_padding, use_depthwise)