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Commit 31ca3b97 authored by Kaushik Shivakumar's avatar Kaushik Shivakumar
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research/object_detection/models/center_net_mobilenet_v2_feature_extractor.py 0 → 100644
View file @ 31ca3b97
# Copyright 2020 The TensorFlow Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ==============================================================================
"""MobileNet V2[1] feature extractor for CenterNet[2] meta architecture.
[1]: https://arxiv.org/abs/1801.04381
[2]: https://arxiv.org/abs/1904.07850
"""
import tensorflow.compat.v1 as tf
from object_detection.meta_architectures import center_net_meta_arch
from object_detection.models.keras_models import mobilenet_v2 as mobilenetv2
class CenterNetMobileNetV2FeatureExtractor(
center_net_meta_arch.CenterNetFeatureExtractor):
"""The MobileNet V2 feature extractor for CenterNet."""
def __init__(self,
mobilenet_v2_net,
channel_means=(0., 0., 0.),
channel_stds=(1., 1., 1.),
bgr_ordering=False):
"""Intializes the feature extractor.
Args:
mobilenet_v2_net: The underlying mobilenet_v2 network to use.
channel_means: A tuple of floats, denoting the mean of each channel
which will be subtracted from it.
channel_stds: A tuple of floats, denoting the standard deviation of each
channel. Each channel will be divided by its standard deviation value.
bgr_ordering: bool, if set will change the channel ordering to be in the
[blue, red, green] order.
"""
super(CenterNetMobileNetV2FeatureExtractor, self).__init__(
channel_means=channel_means,
channel_stds=channel_stds,
bgr_ordering=bgr_ordering)
self._network = mobilenet_v2_net
output = self._network(self._network.input)
# TODO(nkhadke): Try out MobileNet+FPN next (skip connections are cheap and
# should help with performance).
# MobileNet by itself transforms a 224x224x3 volume into a 7x7x1280, which
# leads to a stride of 32. We perform upsampling to get it to a target
# stride of 4.
for num_filters in [256, 128, 64]:
# 1. We use a simple convolution instead of a deformable convolution
conv = tf.keras.layers.Conv2D(
filters=num_filters, kernel_size=1, strides=1, padding='same')
output = conv(output)
output = tf.keras.layers.BatchNormalization()(output)
output = tf.keras.layers.ReLU()(output)
# 2. We use the default initialization for the convolution layers
# instead of initializing it to do bilinear upsampling.
conv_transpose = tf.keras.layers.Conv2DTranspose(
filters=num_filters, kernel_size=3, strides=2, padding='same')
output = conv_transpose(output)
output = tf.keras.layers.BatchNormalization()(output)
output = tf.keras.layers.ReLU()(output)
self._network = tf.keras.models.Model(
inputs=self._network.input, outputs=output)
def preprocess(self, resized_inputs):
resized_inputs = super(CenterNetMobileNetV2FeatureExtractor,
self).preprocess(resized_inputs)
return tf.keras.applications.mobilenet_v2.preprocess_input(resized_inputs)
def load_feature_extractor_weights(self, path):
self._network.load_weights(path)
def get_base_model(self):
return self._network
def call(self, inputs):
return [self._network(inputs)]
@property
def out_stride(self):
"""The stride in the output image of the network."""
return 4
@property
def num_feature_outputs(self):
"""The number of feature outputs returned by the feature extractor."""
return 1
def get_model(self):
return self._network
def mobilenet_v2(channel_means, channel_stds, bgr_ordering):
"""The MobileNetV2 backbone for CenterNet."""
# We set 'is_training' to True for now.
network = mobilenetv2.mobilenet_v2(True, include_top=False)
return CenterNetMobileNetV2FeatureExtractor(
network,
channel_means=channel_means,
channel_stds=channel_stds,
bgr_ordering=bgr_ordering)
research/object_detection/models/center_net_mobilenet_v2_feature_extractor_tf2_test.py 0 → 100644
View file @ 31ca3b97
# Copyright 2020 The TensorFlow Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ==============================================================================
"""Testing mobilenet_v2 feature extractor for CenterNet."""
import unittest
import numpy as np
import tensorflow.compat.v1 as tf
from object_detection.models import center_net_mobilenet_v2_feature_extractor
from object_detection.models.keras_models import mobilenet_v2
from object_detection.utils import test_case
from object_detection.utils import tf_version
@unittest.skipIf(tf_version.is_tf1(), 'Skipping TF2.X only test.')
class CenterNetMobileNetV2FeatureExtractorTest(test_case.TestCase):
def test_center_net_mobilenet_v2_feature_extractor(self):
net = mobilenet_v2.mobilenet_v2(True, include_top=False)
model = center_net_mobilenet_v2_feature_extractor.CenterNetMobileNetV2FeatureExtractor(
net)
def graph_fn():
img = np.zeros((8, 224, 224, 3), dtype=np.float32)
processed_img = model.preprocess(img)
return model(processed_img)
outputs = self.execute(graph_fn, [])
self.assertEqual(outputs.shape, (8, 56, 56, 64))
if __name__ == '__main__':
tf.test.main()
research/object_detection/models/center_net_resnet_v1_fpn_feature_extractor.py
View file @ 31ca3b97
...@@ -21,9 +21,14 @@ ...@@ -21,9 +21,14 @@
import tensorflow.compat.v1 as tf import tensorflow.compat.v1 as tf
from object_detection.meta_architectures.center_net_meta_arch import CenterNetFeatureExtractor from object_detection.meta_architectures.center_net_meta_arch import CenterNetFeatureExtractor
from object_detection.models.keras_models import resnet_v1
_RESNET_MODEL_OUTPUT_LAYERS = { _RESNET_MODEL_OUTPUT_LAYERS = {
'resnet_v1_18': ['conv2_block2_out', 'conv3_block2_out',
'conv4_block2_out', 'conv5_block2_out'],
'resnet_v1_34': ['conv2_block3_out', 'conv3_block4_out',
'conv4_block6_out', 'conv5_block3_out'],
'resnet_v1_50': ['conv2_block3_out', 'conv3_block4_out', 'resnet_v1_50': ['conv2_block3_out', 'conv3_block4_out',
'conv4_block6_out', 'conv5_block3_out'], 'conv4_block6_out', 'conv5_block3_out'],
'resnet_v1_101': ['conv2_block3_out', 'conv3_block4_out', 'resnet_v1_101': ['conv2_block3_out', 'conv3_block4_out',
...@@ -69,6 +74,10 @@ class CenterNetResnetV1FpnFeatureExtractor(CenterNetFeatureExtractor): ...@@ -69,6 +74,10 @@ class CenterNetResnetV1FpnFeatureExtractor(CenterNetFeatureExtractor):
self._base_model = tf.keras.applications.ResNet50(weights=None) self._base_model = tf.keras.applications.ResNet50(weights=None)
elif resnet_type == 'resnet_v1_101': elif resnet_type == 'resnet_v1_101':
self._base_model = tf.keras.applications.ResNet101(weights=None) self._base_model = tf.keras.applications.ResNet101(weights=None)
elif resnet_type == 'resnet_v1_18':
self._base_model = resnet_v1.resnet_v1_18(weights=None)
elif resnet_type == 'resnet_v1_34':
self._base_model = resnet_v1.resnet_v1_34(weights=None)
else: else:
raise ValueError('Unknown Resnet Model {}'.format(resnet_type)) raise ValueError('Unknown Resnet Model {}'.format(resnet_type))
output_layers = _RESNET_MODEL_OUTPUT_LAYERS[resnet_type] output_layers = _RESNET_MODEL_OUTPUT_LAYERS[resnet_type]
...@@ -174,3 +183,24 @@ def resnet_v1_50_fpn(channel_means, channel_stds, bgr_ordering): ...@@ -174,3 +183,24 @@ def resnet_v1_50_fpn(channel_means, channel_stds, bgr_ordering):
channel_means=channel_means, channel_means=channel_means,
channel_stds=channel_stds, channel_stds=channel_stds,
bgr_ordering=bgr_ordering) bgr_ordering=bgr_ordering)
def resnet_v1_34_fpn(channel_means, channel_stds, bgr_ordering):
"""The ResNet v1 34 FPN feature extractor."""
return CenterNetResnetV1FpnFeatureExtractor(
resnet_type='resnet_v1_34',
channel_means=channel_means,
channel_stds=channel_stds,
bgr_ordering=bgr_ordering
)
def resnet_v1_18_fpn(channel_means, channel_stds, bgr_ordering):
"""The ResNet v1 18 FPN feature extractor."""
return CenterNetResnetV1FpnFeatureExtractor(
resnet_type='resnet_v1_18',
channel_means=channel_means,
channel_stds=channel_stds,
bgr_ordering=bgr_ordering)
research/object_detection/models/center_net_resnet_v1_fpn_feature_extractor_tf2_test.py
View file @ 31ca3b97
...@@ -31,6 +31,8 @@ class CenterNetResnetV1FpnFeatureExtractorTest(test_case.TestCase, ...@@ -31,6 +31,8 @@ class CenterNetResnetV1FpnFeatureExtractorTest(test_case.TestCase,
@parameterized.parameters( @parameterized.parameters(
{'resnet_type': 'resnet_v1_50'}, {'resnet_type': 'resnet_v1_50'},
{'resnet_type': 'resnet_v1_101'}, {'resnet_type': 'resnet_v1_101'},
{'resnet_type': 'resnet_v1_18'},
{'resnet_type': 'resnet_v1_34'},
) )
def test_correct_output_size(self, resnet_type): def test_correct_output_size(self, resnet_type):
"""Verify that shape of features returned by the backbone is correct.""" """Verify that shape of features returned by the backbone is correct."""
......
research/object_detection/models/faster_rcnn_inception_resnet_v2_keras_feature_extractor.py
View file @ 31ca3b97
...@@ -59,6 +59,7 @@ class FasterRCNNInceptionResnetV2KerasFeatureExtractor( ...@@ -59,6 +59,7 @@ class FasterRCNNInceptionResnetV2KerasFeatureExtractor(
is_training, first_stage_features_stride, batch_norm_trainable, is_training, first_stage_features_stride, batch_norm_trainable,
weight_decay) weight_decay)
self._variable_dict = {} self._variable_dict = {}
self.classification_backbone = None
def preprocess(self, resized_inputs): def preprocess(self, resized_inputs):
"""Faster R-CNN with Inception Resnet v2 preprocessing. """Faster R-CNN with Inception Resnet v2 preprocessing.
...@@ -95,19 +96,20 @@ class FasterRCNNInceptionResnetV2KerasFeatureExtractor( ...@@ -95,19 +96,20 @@ class FasterRCNNInceptionResnetV2KerasFeatureExtractor(
And returns rpn_feature_map: And returns rpn_feature_map:
A tensor with shape [batch, height, width, depth] A tensor with shape [batch, height, width, depth]
""" """
with tf.name_scope(name): if not self.classification_backbone:
with tf.name_scope('InceptionResnetV2'): self.classification_backbone = inception_resnet_v2.inception_resnet_v2(
model = inception_resnet_v2.inception_resnet_v2(
self._train_batch_norm, self._train_batch_norm,
output_stride=self._first_stage_features_stride, output_stride=self._first_stage_features_stride,
align_feature_maps=True, align_feature_maps=True,
weight_decay=self._weight_decay, weight_decay=self._weight_decay,
weights=None, weights=None,
include_top=False) include_top=False)
proposal_features = model.get_layer( with tf.name_scope(name):
with tf.name_scope('InceptionResnetV2'):
proposal_features = self.classification_backbone.get_layer(
name='block17_20_ac').output name='block17_20_ac').output
keras_model = tf.keras.Model( keras_model = tf.keras.Model(
inputs=model.inputs, inputs=self.classification_backbone.inputs,
outputs=proposal_features) outputs=proposal_features)
for variable in keras_model.variables: for variable in keras_model.variables:
self._variable_dict[variable.name[:-2]] = variable self._variable_dict[variable.name[:-2]] = variable
...@@ -132,962 +134,26 @@ class FasterRCNNInceptionResnetV2KerasFeatureExtractor( ...@@ -132,962 +134,26 @@ class FasterRCNNInceptionResnetV2KerasFeatureExtractor(
[batch_size * self.max_num_proposals, height, width, depth] [batch_size * self.max_num_proposals, height, width, depth]
representing box classifier features for each proposal. representing box classifier features for each proposal.
""" """
if not self.classification_backbone:
self.classification_backbone = inception_resnet_v2.inception_resnet_v2(
self._train_batch_norm,
output_stride=self._first_stage_features_stride,
align_feature_maps=True,
weight_decay=self._weight_decay,
weights=None,
include_top=False)
with tf.name_scope(name): with tf.name_scope(name):
with tf.name_scope('InceptionResnetV2'): with tf.name_scope('InceptionResnetV2'):
model = inception_resnet_v2.inception_resnet_v2( proposal_feature_maps = self.classification_backbone.get_layer(
self._train_batch_norm,
output_stride=16,
align_feature_maps=False,
weight_decay=self._weight_decay,
weights=None,
include_top=False)
proposal_feature_maps = model.get_layer(
name='block17_20_ac').output name='block17_20_ac').output
proposal_classifier_features = model.get_layer( proposal_classifier_features = self.classification_backbone.get_layer(
name='conv_7b_ac').output name='conv_7b_ac').output
keras_model = model_util.extract_submodel( keras_model = model_util.extract_submodel(
model=model, model=self.classification_backbone,
inputs=proposal_feature_maps, inputs=proposal_feature_maps,
outputs=proposal_classifier_features) outputs=proposal_classifier_features)
for variable in keras_model.variables: for variable in keras_model.variables:
self._variable_dict[variable.name[:-2]] = variable self._variable_dict[variable.name[:-2]] = variable
return keras_model return keras_model
def restore_from_classification_checkpoint_fn(
self,
first_stage_feature_extractor_scope,
second_stage_feature_extractor_scope):
"""Returns a map of variables to load from a foreign checkpoint.
This uses a hard-coded conversion to load into Keras from a slim-trained
inception_resnet_v2 checkpoint.
Note that this overrides the default implementation in
faster_rcnn_meta_arch.FasterRCNNKerasFeatureExtractor which does not work
for InceptionResnetV2 checkpoints.
Args:
first_stage_feature_extractor_scope: A scope name for the first stage
feature extractor.
second_stage_feature_extractor_scope: A scope name for the second stage
feature extractor.
Returns:
A dict mapping variable names (to load from a checkpoint) to variables in
the model graph.
"""
keras_to_slim_name_mapping = {
'FirstStageFeatureExtractor/InceptionResnetV2/conv2d/kernel': 'InceptionResnetV2/Conv2d_1a_3x3/weights',
'FirstStageFeatureExtractor/InceptionResnetV2/freezable_batch_norm/beta': 'InceptionResnetV2/Conv2d_1a_3x3/BatchNorm/beta',
'FirstStageFeatureExtractor/InceptionResnetV2/freezable_batch_norm/moving_mean': 'InceptionResnetV2/Conv2d_1a_3x3/BatchNorm/moving_mean',
'FirstStageFeatureExtractor/InceptionResnetV2/freezable_batch_norm/moving_variance': 'InceptionResnetV2/Conv2d_1a_3x3/BatchNorm/moving_variance',
'FirstStageFeatureExtractor/InceptionResnetV2/conv2d_1/kernel': 'InceptionResnetV2/Conv2d_2a_3x3/weights',
'FirstStageFeatureExtractor/InceptionResnetV2/freezable_batch_norm_1/beta': 'InceptionResnetV2/Conv2d_2a_3x3/BatchNorm/beta',
'FirstStageFeatureExtractor/InceptionResnetV2/freezable_batch_norm_1/moving_mean': 'InceptionResnetV2/Conv2d_2a_3x3/BatchNorm/moving_mean',
'FirstStageFeatureExtractor/InceptionResnetV2/freezable_batch_norm_1/moving_variance': 'InceptionResnetV2/Conv2d_2a_3x3/BatchNorm/moving_variance',
'FirstStageFeatureExtractor/InceptionResnetV2/conv2d_2/kernel': 'InceptionResnetV2/Conv2d_2b_3x3/weights',
'FirstStageFeatureExtractor/InceptionResnetV2/freezable_batch_norm_2/beta': 'InceptionResnetV2/Conv2d_2b_3x3/BatchNorm/beta',
'FirstStageFeatureExtractor/InceptionResnetV2/freezable_batch_norm_2/moving_mean': 'InceptionResnetV2/Conv2d_2b_3x3/BatchNorm/moving_mean',
'FirstStageFeatureExtractor/InceptionResnetV2/freezable_batch_norm_2/moving_variance': 'InceptionResnetV2/Conv2d_2b_3x3/BatchNorm/moving_variance',
'FirstStageFeatureExtractor/InceptionResnetV2/conv2d_3/kernel': 'InceptionResnetV2/Conv2d_3b_1x1/weights',
'FirstStageFeatureExtractor/InceptionResnetV2/freezable_batch_norm_3/beta': 'InceptionResnetV2/Conv2d_3b_1x1/BatchNorm/beta',
'FirstStageFeatureExtractor/InceptionResnetV2/freezable_batch_norm_3/moving_mean': 'InceptionResnetV2/Conv2d_3b_1x1/BatchNorm/moving_mean',
'FirstStageFeatureExtractor/InceptionResnetV2/freezable_batch_norm_3/moving_variance': 'InceptionResnetV2/Conv2d_3b_1x1/BatchNorm/moving_variance',
'FirstStageFeatureExtractor/InceptionResnetV2/conv2d_4/kernel': 'InceptionResnetV2/Conv2d_4a_3x3/weights',
'FirstStageFeatureExtractor/InceptionResnetV2/freezable_batch_norm_4/beta': 'InceptionResnetV2/Conv2d_4a_3x3/BatchNorm/beta',
'FirstStageFeatureExtractor/InceptionResnetV2/freezable_batch_norm_4/moving_mean': 'InceptionResnetV2/Conv2d_4a_3x3/BatchNorm/moving_mean',
'FirstStageFeatureExtractor/InceptionResnetV2/freezable_batch_norm_4/moving_variance': 'InceptionResnetV2/Conv2d_4a_3x3/BatchNorm/moving_variance',
'FirstStageFeatureExtractor/InceptionResnetV2/conv2d_5/kernel': 'InceptionResnetV2/Mixed_5b/Branch_0/Conv2d_1x1/weights',
'FirstStageFeatureExtractor/InceptionResnetV2/freezable_batch_norm_5/beta': 'InceptionResnetV2/Mixed_5b/Branch_0/Conv2d_1x1/BatchNorm/beta',
'FirstStageFeatureExtractor/InceptionResnetV2/freezable_batch_norm_5/moving_mean': 'InceptionResnetV2/Mixed_5b/Branch_0/Conv2d_1x1/BatchNorm/moving_mean',
'FirstStageFeatureExtractor/InceptionResnetV2/freezable_batch_norm_5/moving_variance': 'InceptionResnetV2/Mixed_5b/Branch_0/Conv2d_1x1/BatchNorm/moving_variance',
'FirstStageFeatureExtractor/InceptionResnetV2/conv2d_6/kernel': 'InceptionResnetV2/Mixed_5b/Branch_1/Conv2d_0a_1x1/weights',
'FirstStageFeatureExtractor/InceptionResnetV2/freezable_batch_norm_6/beta': 'InceptionResnetV2/Mixed_5b/Branch_1/Conv2d_0a_1x1/BatchNorm/beta',
'FirstStageFeatureExtractor/InceptionResnetV2/freezable_batch_norm_6/moving_mean': 'InceptionResnetV2/Mixed_5b/Branch_1/Conv2d_0a_1x1/BatchNorm/moving_mean',
'FirstStageFeatureExtractor/InceptionResnetV2/freezable_batch_norm_6/moving_variance': 'InceptionResnetV2/Mixed_5b/Branch_1/Conv2d_0a_1x1/BatchNorm/moving_variance',
'FirstStageFeatureExtractor/InceptionResnetV2/conv2d_7/kernel': 'InceptionResnetV2/Mixed_5b/Branch_1/Conv2d_0b_5x5/weights',
'FirstStageFeatureExtractor/InceptionResnetV2/freezable_batch_norm_7/beta': 'InceptionResnetV2/Mixed_5b/Branch_1/Conv2d_0b_5x5/BatchNorm/beta',
'FirstStageFeatureExtractor/InceptionResnetV2/freezable_batch_norm_7/moving_mean': 'InceptionResnetV2/Mixed_5b/Branch_1/Conv2d_0b_5x5/BatchNorm/moving_mean',
'FirstStageFeatureExtractor/InceptionResnetV2/freezable_batch_norm_7/moving_variance': 'InceptionResnetV2/Mixed_5b/Branch_1/Conv2d_0b_5x5/BatchNorm/moving_variance',
'FirstStageFeatureExtractor/InceptionResnetV2/conv2d_8/kernel': 'InceptionResnetV2/Mixed_5b/Branch_2/Conv2d_0a_1x1/weights',
'FirstStageFeatureExtractor/InceptionResnetV2/freezable_batch_norm_8/beta': 'InceptionResnetV2/Mixed_5b/Branch_2/Conv2d_0a_1x1/BatchNorm/beta',
'FirstStageFeatureExtractor/InceptionResnetV2/freezable_batch_norm_8/moving_mean': 'InceptionResnetV2/Mixed_5b/Branch_2/Conv2d_0a_1x1/BatchNorm/moving_mean',
'FirstStageFeatureExtractor/InceptionResnetV2/freezable_batch_norm_8/moving_variance': 'InceptionResnetV2/Mixed_5b/Branch_2/Conv2d_0a_1x1/BatchNorm/moving_variance',
'FirstStageFeatureExtractor/InceptionResnetV2/conv2d_9/kernel': 'InceptionResnetV2/Mixed_5b/Branch_2/Conv2d_0b_3x3/weights',
'FirstStageFeatureExtractor/InceptionResnetV2/freezable_batch_norm_9/beta': 'InceptionResnetV2/Mixed_5b/Branch_2/Conv2d_0b_3x3/BatchNorm/beta',
'FirstStageFeatureExtractor/InceptionResnetV2/freezable_batch_norm_9/moving_mean': 'InceptionResnetV2/Mixed_5b/Branch_2/Conv2d_0b_3x3/BatchNorm/moving_mean',
'FirstStageFeatureExtractor/InceptionResnetV2/freezable_batch_norm_9/moving_variance': 'InceptionResnetV2/Mixed_5b/Branch_2/Conv2d_0b_3x3/BatchNorm/moving_variance',
'FirstStageFeatureExtractor/InceptionResnetV2/conv2d_10/kernel': 'InceptionResnetV2/Mixed_5b/Branch_2/Conv2d_0c_3x3/weights',
'FirstStageFeatureExtractor/InceptionResnetV2/freezable_batch_norm_10/beta': 'InceptionResnetV2/Mixed_5b/Branch_2/Conv2d_0c_3x3/BatchNorm/beta',
'FirstStageFeatureExtractor/InceptionResnetV2/freezable_batch_norm_10/moving_mean': 'InceptionResnetV2/Mixed_5b/Branch_2/Conv2d_0c_3x3/BatchNorm/moving_mean',
'FirstStageFeatureExtractor/InceptionResnetV2/freezable_batch_norm_10/moving_variance': 'InceptionResnetV2/Mixed_5b/Branch_2/Conv2d_0c_3x3/BatchNorm/moving_variance',
'FirstStageFeatureExtractor/InceptionResnetV2/conv2d_11/kernel': 'InceptionResnetV2/Mixed_5b/Branch_3/Conv2d_0b_1x1/weights',
'FirstStageFeatureExtractor/InceptionResnetV2/freezable_batch_norm_11/beta': 'InceptionResnetV2/Mixed_5b/Branch_3/Conv2d_0b_1x1/BatchNorm/beta',
'FirstStageFeatureExtractor/InceptionResnetV2/freezable_batch_norm_11/moving_mean': 'InceptionResnetV2/Mixed_5b/Branch_3/Conv2d_0b_1x1/BatchNorm/moving_mean',
'FirstStageFeatureExtractor/InceptionResnetV2/freezable_batch_norm_11/moving_variance': 'InceptionResnetV2/Mixed_5b/Branch_3/Conv2d_0b_1x1/BatchNorm/moving_variance',
'FirstStageFeatureExtractor/InceptionResnetV2/conv2d_12/kernel': 'InceptionResnetV2/Repeat/block35_1/Branch_0/Conv2d_1x1/weights',
'FirstStageFeatureExtractor/InceptionResnetV2/freezable_batch_norm_12/beta': 'InceptionResnetV2/Repeat/block35_1/Branch_0/Conv2d_1x1/BatchNorm/beta',
'FirstStageFeatureExtractor/InceptionResnetV2/freezable_batch_norm_12/moving_mean': 'InceptionResnetV2/Repeat/block35_1/Branch_0/Conv2d_1x1/BatchNorm/moving_mean',
'FirstStageFeatureExtractor/InceptionResnetV2/freezable_batch_norm_12/moving_variance': 'InceptionResnetV2/Repeat/block35_1/Branch_0/Conv2d_1x1/BatchNorm/moving_variance',
'FirstStageFeatureExtractor/InceptionResnetV2/conv2d_13/kernel': 'InceptionResnetV2/Repeat/block35_1/Branch_1/Conv2d_0a_1x1/weights',
'FirstStageFeatureExtractor/InceptionResnetV2/freezable_batch_norm_13/beta': 'InceptionResnetV2/Repeat/block35_1/Branch_1/Conv2d_0a_1x1/BatchNorm/beta',
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'SecondStageFeatureExtractor/InceptionResnetV2/freezable_batch_norm_384/moving_mean': 'InceptionResnetV2/Repeat_2/block8_5/Branch_1/Conv2d_0b_1x3/BatchNorm/moving_mean',
'SecondStageFeatureExtractor/InceptionResnetV2/freezable_batch_norm_384/moving_variance': 'InceptionResnetV2/Repeat_2/block8_5/Branch_1/Conv2d_0b_1x3/BatchNorm/moving_variance',
'SecondStageFeatureExtractor/InceptionResnetV2/conv2d_385/kernel': 'InceptionResnetV2/Repeat_2/block8_5/Branch_1/Conv2d_0c_3x1/weights',
'SecondStageFeatureExtractor/InceptionResnetV2/freezable_batch_norm_385/beta': 'InceptionResnetV2/Repeat_2/block8_5/Branch_1/Conv2d_0c_3x1/BatchNorm/beta',
'SecondStageFeatureExtractor/InceptionResnetV2/freezable_batch_norm_385/moving_mean': 'InceptionResnetV2/Repeat_2/block8_5/Branch_1/Conv2d_0c_3x1/BatchNorm/moving_mean',
'SecondStageFeatureExtractor/InceptionResnetV2/freezable_batch_norm_385/moving_variance': 'InceptionResnetV2/Repeat_2/block8_5/Branch_1/Conv2d_0c_3x1/BatchNorm/moving_variance',
'SecondStageFeatureExtractor/InceptionResnetV2/block8_5_conv/kernel': 'InceptionResnetV2/Repeat_2/block8_5/Conv2d_1x1/weights',
'SecondStageFeatureExtractor/InceptionResnetV2/block8_5_conv/bias': 'InceptionResnetV2/Repeat_2/block8_5/Conv2d_1x1/biases',
'SecondStageFeatureExtractor/InceptionResnetV2/conv2d_386/kernel': 'InceptionResnetV2/Repeat_2/block8_6/Branch_0/Conv2d_1x1/weights',
'SecondStageFeatureExtractor/InceptionResnetV2/freezable_batch_norm_386/beta': 'InceptionResnetV2/Repeat_2/block8_6/Branch_0/Conv2d_1x1/BatchNorm/beta',
'SecondStageFeatureExtractor/InceptionResnetV2/freezable_batch_norm_386/moving_mean': 'InceptionResnetV2/Repeat_2/block8_6/Branch_0/Conv2d_1x1/BatchNorm/moving_mean',
'SecondStageFeatureExtractor/InceptionResnetV2/freezable_batch_norm_386/moving_variance': 'InceptionResnetV2/Repeat_2/block8_6/Branch_0/Conv2d_1x1/BatchNorm/moving_variance',
'SecondStageFeatureExtractor/InceptionResnetV2/conv2d_387/kernel': 'InceptionResnetV2/Repeat_2/block8_6/Branch_1/Conv2d_0a_1x1/weights',
'SecondStageFeatureExtractor/InceptionResnetV2/freezable_batch_norm_387/beta': 'InceptionResnetV2/Repeat_2/block8_6/Branch_1/Conv2d_0a_1x1/BatchNorm/beta',
'SecondStageFeatureExtractor/InceptionResnetV2/freezable_batch_norm_387/moving_mean': 'InceptionResnetV2/Repeat_2/block8_6/Branch_1/Conv2d_0a_1x1/BatchNorm/moving_mean',
'SecondStageFeatureExtractor/InceptionResnetV2/freezable_batch_norm_387/moving_variance': 'InceptionResnetV2/Repeat_2/block8_6/Branch_1/Conv2d_0a_1x1/BatchNorm/moving_variance',
'SecondStageFeatureExtractor/InceptionResnetV2/conv2d_388/kernel': 'InceptionResnetV2/Repeat_2/block8_6/Branch_1/Conv2d_0b_1x3/weights',
'SecondStageFeatureExtractor/InceptionResnetV2/freezable_batch_norm_388/beta': 'InceptionResnetV2/Repeat_2/block8_6/Branch_1/Conv2d_0b_1x3/BatchNorm/beta',
'SecondStageFeatureExtractor/InceptionResnetV2/freezable_batch_norm_388/moving_mean': 'InceptionResnetV2/Repeat_2/block8_6/Branch_1/Conv2d_0b_1x3/BatchNorm/moving_mean',
'SecondStageFeatureExtractor/InceptionResnetV2/freezable_batch_norm_388/moving_variance': 'InceptionResnetV2/Repeat_2/block8_6/Branch_1/Conv2d_0b_1x3/BatchNorm/moving_variance',
'SecondStageFeatureExtractor/InceptionResnetV2/conv2d_389/kernel': 'InceptionResnetV2/Repeat_2/block8_6/Branch_1/Conv2d_0c_3x1/weights',
'SecondStageFeatureExtractor/InceptionResnetV2/freezable_batch_norm_389/beta': 'InceptionResnetV2/Repeat_2/block8_6/Branch_1/Conv2d_0c_3x1/BatchNorm/beta',
'SecondStageFeatureExtractor/InceptionResnetV2/freezable_batch_norm_389/moving_mean': 'InceptionResnetV2/Repeat_2/block8_6/Branch_1/Conv2d_0c_3x1/BatchNorm/moving_mean',
'SecondStageFeatureExtractor/InceptionResnetV2/freezable_batch_norm_389/moving_variance': 'InceptionResnetV2/Repeat_2/block8_6/Branch_1/Conv2d_0c_3x1/BatchNorm/moving_variance',
'SecondStageFeatureExtractor/InceptionResnetV2/block8_6_conv/kernel': 'InceptionResnetV2/Repeat_2/block8_6/Conv2d_1x1/weights',
'SecondStageFeatureExtractor/InceptionResnetV2/block8_6_conv/bias': 'InceptionResnetV2/Repeat_2/block8_6/Conv2d_1x1/biases',
'SecondStageFeatureExtractor/InceptionResnetV2/conv2d_390/kernel': 'InceptionResnetV2/Repeat_2/block8_7/Branch_0/Conv2d_1x1/weights',
'SecondStageFeatureExtractor/InceptionResnetV2/freezable_batch_norm_390/beta': 'InceptionResnetV2/Repeat_2/block8_7/Branch_0/Conv2d_1x1/BatchNorm/beta',
'SecondStageFeatureExtractor/InceptionResnetV2/freezable_batch_norm_390/moving_mean': 'InceptionResnetV2/Repeat_2/block8_7/Branch_0/Conv2d_1x1/BatchNorm/moving_mean',
'SecondStageFeatureExtractor/InceptionResnetV2/freezable_batch_norm_390/moving_variance': 'InceptionResnetV2/Repeat_2/block8_7/Branch_0/Conv2d_1x1/BatchNorm/moving_variance',
'SecondStageFeatureExtractor/InceptionResnetV2/conv2d_391/kernel': 'InceptionResnetV2/Repeat_2/block8_7/Branch_1/Conv2d_0a_1x1/weights',
'SecondStageFeatureExtractor/InceptionResnetV2/freezable_batch_norm_391/beta': 'InceptionResnetV2/Repeat_2/block8_7/Branch_1/Conv2d_0a_1x1/BatchNorm/beta',
'SecondStageFeatureExtractor/InceptionResnetV2/freezable_batch_norm_391/moving_mean': 'InceptionResnetV2/Repeat_2/block8_7/Branch_1/Conv2d_0a_1x1/BatchNorm/moving_mean',
'SecondStageFeatureExtractor/InceptionResnetV2/freezable_batch_norm_391/moving_variance': 'InceptionResnetV2/Repeat_2/block8_7/Branch_1/Conv2d_0a_1x1/BatchNorm/moving_variance',
'SecondStageFeatureExtractor/InceptionResnetV2/conv2d_392/kernel': 'InceptionResnetV2/Repeat_2/block8_7/Branch_1/Conv2d_0b_1x3/weights',
'SecondStageFeatureExtractor/InceptionResnetV2/freezable_batch_norm_392/beta': 'InceptionResnetV2/Repeat_2/block8_7/Branch_1/Conv2d_0b_1x3/BatchNorm/beta',
'SecondStageFeatureExtractor/InceptionResnetV2/freezable_batch_norm_392/moving_mean': 'InceptionResnetV2/Repeat_2/block8_7/Branch_1/Conv2d_0b_1x3/BatchNorm/moving_mean',
'SecondStageFeatureExtractor/InceptionResnetV2/freezable_batch_norm_392/moving_variance': 'InceptionResnetV2/Repeat_2/block8_7/Branch_1/Conv2d_0b_1x3/BatchNorm/moving_variance',
'SecondStageFeatureExtractor/InceptionResnetV2/conv2d_393/kernel': 'InceptionResnetV2/Repeat_2/block8_7/Branch_1/Conv2d_0c_3x1/weights',
'SecondStageFeatureExtractor/InceptionResnetV2/freezable_batch_norm_393/beta': 'InceptionResnetV2/Repeat_2/block8_7/Branch_1/Conv2d_0c_3x1/BatchNorm/beta',
'SecondStageFeatureExtractor/InceptionResnetV2/freezable_batch_norm_393/moving_mean': 'InceptionResnetV2/Repeat_2/block8_7/Branch_1/Conv2d_0c_3x1/BatchNorm/moving_mean',
'SecondStageFeatureExtractor/InceptionResnetV2/freezable_batch_norm_393/moving_variance': 'InceptionResnetV2/Repeat_2/block8_7/Branch_1/Conv2d_0c_3x1/BatchNorm/moving_variance',
'SecondStageFeatureExtractor/InceptionResnetV2/block8_7_conv/kernel': 'InceptionResnetV2/Repeat_2/block8_7/Conv2d_1x1/weights',
'SecondStageFeatureExtractor/InceptionResnetV2/block8_7_conv/bias': 'InceptionResnetV2/Repeat_2/block8_7/Conv2d_1x1/biases',
'SecondStageFeatureExtractor/InceptionResnetV2/conv2d_394/kernel': 'InceptionResnetV2/Repeat_2/block8_8/Branch_0/Conv2d_1x1/weights',
'SecondStageFeatureExtractor/InceptionResnetV2/freezable_batch_norm_394/beta': 'InceptionResnetV2/Repeat_2/block8_8/Branch_0/Conv2d_1x1/BatchNorm/beta',
'SecondStageFeatureExtractor/InceptionResnetV2/freezable_batch_norm_394/moving_mean': 'InceptionResnetV2/Repeat_2/block8_8/Branch_0/Conv2d_1x1/BatchNorm/moving_mean',
'SecondStageFeatureExtractor/InceptionResnetV2/freezable_batch_norm_394/moving_variance': 'InceptionResnetV2/Repeat_2/block8_8/Branch_0/Conv2d_1x1/BatchNorm/moving_variance',
'SecondStageFeatureExtractor/InceptionResnetV2/conv2d_395/kernel': 'InceptionResnetV2/Repeat_2/block8_8/Branch_1/Conv2d_0a_1x1/weights',
'SecondStageFeatureExtractor/InceptionResnetV2/freezable_batch_norm_395/beta': 'InceptionResnetV2/Repeat_2/block8_8/Branch_1/Conv2d_0a_1x1/BatchNorm/beta',
'SecondStageFeatureExtractor/InceptionResnetV2/freezable_batch_norm_395/moving_mean': 'InceptionResnetV2/Repeat_2/block8_8/Branch_1/Conv2d_0a_1x1/BatchNorm/moving_mean',
'SecondStageFeatureExtractor/InceptionResnetV2/freezable_batch_norm_395/moving_variance': 'InceptionResnetV2/Repeat_2/block8_8/Branch_1/Conv2d_0a_1x1/BatchNorm/moving_variance',
'SecondStageFeatureExtractor/InceptionResnetV2/conv2d_396/kernel': 'InceptionResnetV2/Repeat_2/block8_8/Branch_1/Conv2d_0b_1x3/weights',
'SecondStageFeatureExtractor/InceptionResnetV2/freezable_batch_norm_396/beta': 'InceptionResnetV2/Repeat_2/block8_8/Branch_1/Conv2d_0b_1x3/BatchNorm/beta',
'SecondStageFeatureExtractor/InceptionResnetV2/freezable_batch_norm_396/moving_mean': 'InceptionResnetV2/Repeat_2/block8_8/Branch_1/Conv2d_0b_1x3/BatchNorm/moving_mean',
'SecondStageFeatureExtractor/InceptionResnetV2/freezable_batch_norm_396/moving_variance': 'InceptionResnetV2/Repeat_2/block8_8/Branch_1/Conv2d_0b_1x3/BatchNorm/moving_variance',
'SecondStageFeatureExtractor/InceptionResnetV2/conv2d_397/kernel': 'InceptionResnetV2/Repeat_2/block8_8/Branch_1/Conv2d_0c_3x1/weights',
'SecondStageFeatureExtractor/InceptionResnetV2/freezable_batch_norm_397/beta': 'InceptionResnetV2/Repeat_2/block8_8/Branch_1/Conv2d_0c_3x1/BatchNorm/beta',
'SecondStageFeatureExtractor/InceptionResnetV2/freezable_batch_norm_397/moving_mean': 'InceptionResnetV2/Repeat_2/block8_8/Branch_1/Conv2d_0c_3x1/BatchNorm/moving_mean',
'SecondStageFeatureExtractor/InceptionResnetV2/freezable_batch_norm_397/moving_variance': 'InceptionResnetV2/Repeat_2/block8_8/Branch_1/Conv2d_0c_3x1/BatchNorm/moving_variance',
'SecondStageFeatureExtractor/InceptionResnetV2/block8_8_conv/kernel': 'InceptionResnetV2/Repeat_2/block8_8/Conv2d_1x1/weights',
'SecondStageFeatureExtractor/InceptionResnetV2/block8_8_conv/bias': 'InceptionResnetV2/Repeat_2/block8_8/Conv2d_1x1/biases',
'SecondStageFeatureExtractor/InceptionResnetV2/conv2d_398/kernel': 'InceptionResnetV2/Repeat_2/block8_9/Branch_0/Conv2d_1x1/weights',
'SecondStageFeatureExtractor/InceptionResnetV2/freezable_batch_norm_398/beta': 'InceptionResnetV2/Repeat_2/block8_9/Branch_0/Conv2d_1x1/BatchNorm/beta',
'SecondStageFeatureExtractor/InceptionResnetV2/freezable_batch_norm_398/moving_mean': 'InceptionResnetV2/Repeat_2/block8_9/Branch_0/Conv2d_1x1/BatchNorm/moving_mean',
'SecondStageFeatureExtractor/InceptionResnetV2/freezable_batch_norm_398/moving_variance': 'InceptionResnetV2/Repeat_2/block8_9/Branch_0/Conv2d_1x1/BatchNorm/moving_variance',
'SecondStageFeatureExtractor/InceptionResnetV2/conv2d_399/kernel': 'InceptionResnetV2/Repeat_2/block8_9/Branch_1/Conv2d_0a_1x1/weights',
'SecondStageFeatureExtractor/InceptionResnetV2/freezable_batch_norm_399/beta': 'InceptionResnetV2/Repeat_2/block8_9/Branch_1/Conv2d_0a_1x1/BatchNorm/beta',
'SecondStageFeatureExtractor/InceptionResnetV2/freezable_batch_norm_399/moving_mean': 'InceptionResnetV2/Repeat_2/block8_9/Branch_1/Conv2d_0a_1x1/BatchNorm/moving_mean',
'SecondStageFeatureExtractor/InceptionResnetV2/freezable_batch_norm_399/moving_variance': 'InceptionResnetV2/Repeat_2/block8_9/Branch_1/Conv2d_0a_1x1/BatchNorm/moving_variance',
'SecondStageFeatureExtractor/InceptionResnetV2/conv2d_400/kernel': 'InceptionResnetV2/Repeat_2/block8_9/Branch_1/Conv2d_0b_1x3/weights',
'SecondStageFeatureExtractor/InceptionResnetV2/freezable_batch_norm_400/beta': 'InceptionResnetV2/Repeat_2/block8_9/Branch_1/Conv2d_0b_1x3/BatchNorm/beta',
'SecondStageFeatureExtractor/InceptionResnetV2/freezable_batch_norm_400/moving_mean': 'InceptionResnetV2/Repeat_2/block8_9/Branch_1/Conv2d_0b_1x3/BatchNorm/moving_mean',
'SecondStageFeatureExtractor/InceptionResnetV2/freezable_batch_norm_400/moving_variance': 'InceptionResnetV2/Repeat_2/block8_9/Branch_1/Conv2d_0b_1x3/BatchNorm/moving_variance',
'SecondStageFeatureExtractor/InceptionResnetV2/conv2d_401/kernel': 'InceptionResnetV2/Repeat_2/block8_9/Branch_1/Conv2d_0c_3x1/weights',
'SecondStageFeatureExtractor/InceptionResnetV2/freezable_batch_norm_401/beta': 'InceptionResnetV2/Repeat_2/block8_9/Branch_1/Conv2d_0c_3x1/BatchNorm/beta',
'SecondStageFeatureExtractor/InceptionResnetV2/freezable_batch_norm_401/moving_mean': 'InceptionResnetV2/Repeat_2/block8_9/Branch_1/Conv2d_0c_3x1/BatchNorm/moving_mean',
'SecondStageFeatureExtractor/InceptionResnetV2/freezable_batch_norm_401/moving_variance': 'InceptionResnetV2/Repeat_2/block8_9/Branch_1/Conv2d_0c_3x1/BatchNorm/moving_variance',
'SecondStageFeatureExtractor/InceptionResnetV2/block8_9_conv/kernel': 'InceptionResnetV2/Repeat_2/block8_9/Conv2d_1x1/weights',
'SecondStageFeatureExtractor/InceptionResnetV2/block8_9_conv/bias': 'InceptionResnetV2/Repeat_2/block8_9/Conv2d_1x1/biases',
'SecondStageFeatureExtractor/InceptionResnetV2/conv2d_402/kernel': 'InceptionResnetV2/Block8/Branch_0/Conv2d_1x1/weights',
'SecondStageFeatureExtractor/InceptionResnetV2/freezable_batch_norm_402/beta': 'InceptionResnetV2/Block8/Branch_0/Conv2d_1x1/BatchNorm/beta',
'SecondStageFeatureExtractor/InceptionResnetV2/freezable_batch_norm_402/moving_mean': 'InceptionResnetV2/Block8/Branch_0/Conv2d_1x1/BatchNorm/moving_mean',
'SecondStageFeatureExtractor/InceptionResnetV2/freezable_batch_norm_402/moving_variance': 'InceptionResnetV2/Block8/Branch_0/Conv2d_1x1/BatchNorm/moving_variance',
'SecondStageFeatureExtractor/InceptionResnetV2/conv2d_403/kernel': 'InceptionResnetV2/Block8/Branch_1/Conv2d_0a_1x1/weights',
'SecondStageFeatureExtractor/InceptionResnetV2/freezable_batch_norm_403/beta': 'InceptionResnetV2/Block8/Branch_1/Conv2d_0a_1x1/BatchNorm/beta',
'SecondStageFeatureExtractor/InceptionResnetV2/freezable_batch_norm_403/moving_mean': 'InceptionResnetV2/Block8/Branch_1/Conv2d_0a_1x1/BatchNorm/moving_mean',
'SecondStageFeatureExtractor/InceptionResnetV2/freezable_batch_norm_403/moving_variance': 'InceptionResnetV2/Block8/Branch_1/Conv2d_0a_1x1/BatchNorm/moving_variance',
'SecondStageFeatureExtractor/InceptionResnetV2/conv2d_404/kernel': 'InceptionResnetV2/Block8/Branch_1/Conv2d_0b_1x3/weights',
'SecondStageFeatureExtractor/InceptionResnetV2/freezable_batch_norm_404/beta': 'InceptionResnetV2/Block8/Branch_1/Conv2d_0b_1x3/BatchNorm/beta',
'SecondStageFeatureExtractor/InceptionResnetV2/freezable_batch_norm_404/moving_mean': 'InceptionResnetV2/Block8/Branch_1/Conv2d_0b_1x3/BatchNorm/moving_mean',
'SecondStageFeatureExtractor/InceptionResnetV2/freezable_batch_norm_404/moving_variance': 'InceptionResnetV2/Block8/Branch_1/Conv2d_0b_1x3/BatchNorm/moving_variance',
'SecondStageFeatureExtractor/InceptionResnetV2/conv2d_405/kernel': 'InceptionResnetV2/Block8/Branch_1/Conv2d_0c_3x1/weights',
'SecondStageFeatureExtractor/InceptionResnetV2/freezable_batch_norm_405/beta': 'InceptionResnetV2/Block8/Branch_1/Conv2d_0c_3x1/BatchNorm/beta',
'SecondStageFeatureExtractor/InceptionResnetV2/freezable_batch_norm_405/moving_mean': 'InceptionResnetV2/Block8/Branch_1/Conv2d_0c_3x1/BatchNorm/moving_mean',
'SecondStageFeatureExtractor/InceptionResnetV2/freezable_batch_norm_405/moving_variance': 'InceptionResnetV2/Block8/Branch_1/Conv2d_0c_3x1/BatchNorm/moving_variance',
'SecondStageFeatureExtractor/InceptionResnetV2/block8_10_conv/kernel': 'InceptionResnetV2/Block8/Conv2d_1x1/weights',
'SecondStageFeatureExtractor/InceptionResnetV2/block8_10_conv/bias': 'InceptionResnetV2/Block8/Conv2d_1x1/biases',
'SecondStageFeatureExtractor/InceptionResnetV2/conv_7b/kernel': 'InceptionResnetV2/Conv2d_7b_1x1/weights',
'SecondStageFeatureExtractor/InceptionResnetV2/conv_7b_bn/beta': 'InceptionResnetV2/Conv2d_7b_1x1/BatchNorm/beta',
'SecondStageFeatureExtractor/InceptionResnetV2/conv_7b_bn/moving_mean': 'InceptionResnetV2/Conv2d_7b_1x1/BatchNorm/moving_mean',
'SecondStageFeatureExtractor/InceptionResnetV2/conv_7b_bn/moving_variance': 'InceptionResnetV2/Conv2d_7b_1x1/BatchNorm/moving_variance',
}
variables_to_restore = {}
if tf.executing_eagerly():
for key in self._variable_dict:
# variable.name includes ":0" at the end, but the names in the
# checkpoint do not have the suffix ":0". So, we strip it here.
var_name = keras_to_slim_name_mapping.get(key)
if var_name:
variables_to_restore[var_name] = self._variable_dict[key]
else:
for variable in variables_helper.get_global_variables_safely():
var_name = keras_to_slim_name_mapping.get(variable.op.name)
if var_name:
variables_to_restore[var_name] = variable
return variables_to_restore
research/object_detection/models/faster_rcnn_inception_resnet_v2_keras_feature_extractor_tf2_test.py
View file @ 31ca3b97
...@@ -73,7 +73,7 @@ class FasterRcnnInceptionResnetV2KerasFeatureExtractorTest(tf.test.TestCase): ...@@ -73,7 +73,7 @@ class FasterRcnnInceptionResnetV2KerasFeatureExtractorTest(tf.test.TestCase):
proposal_classifier_features = ( proposal_classifier_features = (
model(proposal_feature_maps)) model(proposal_feature_maps))
features_shape = tf.shape(proposal_classifier_features) features_shape = tf.shape(proposal_classifier_features)
self.assertAllEqual(features_shape.numpy(), [2, 8, 8, 1536]) self.assertAllEqual(features_shape.numpy(), [2, 9, 9, 1536])
if __name__ == '__main__': if __name__ == '__main__':
......
research/object_detection/models/faster_rcnn_resnet_keras_feature_extractor.py
View file @ 31ca3b97
...@@ -175,23 +175,6 @@ class FasterRCNNResnetKerasFeatureExtractor( ...@@ -175,23 +175,6 @@ class FasterRCNNResnetKerasFeatureExtractor(
self._variable_dict[variable.name[:-2]] = variable self._variable_dict[variable.name[:-2]] = variable
return keras_model return keras_model
def restore_from_classification_checkpoint_fn(
self,
first_stage_feature_extractor_scope,
second_stage_feature_extractor_scope):
"""Returns a map for restoring from an (object-based) checkpoint.
Args:
first_stage_feature_extractor_scope: A scope name for the first stage
feature extractor (unused).
second_stage_feature_extractor_scope: A scope name for the second stage
feature extractor (unused).
Returns:
A dict mapping keys to Keras models
"""
return {'feature_extractor': self.classification_backbone}
class FasterRCNNResnet50KerasFeatureExtractor( class FasterRCNNResnet50KerasFeatureExtractor(
FasterRCNNResnetKerasFeatureExtractor): FasterRCNNResnetKerasFeatureExtractor):
......
research/object_detection/models/faster_rcnn_resnet_v1_fpn_keras_feature_extractor.py 0 → 100644
View file @ 31ca3b97
# Copyright 2020 The TensorFlow Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ==============================================================================
"""Faster RCNN Keras-based Resnet V1 FPN Feature Extractor."""
import tensorflow.compat.v1 as tf
from object_detection.meta_architectures import faster_rcnn_meta_arch
from object_detection.models import feature_map_generators
from object_detection.models.keras_models import resnet_v1
_RESNET_MODEL_OUTPUT_LAYERS = {
'resnet_v1_50': ['conv2_block3_out', 'conv3_block4_out',
'conv4_block6_out', 'conv5_block3_out'],
'resnet_v1_101': ['conv2_block3_out', 'conv3_block4_out',
'conv4_block23_out', 'conv5_block3_out'],
'resnet_v1_152': ['conv2_block3_out', 'conv3_block8_out',
'conv4_block36_out', 'conv5_block3_out'],
}
class FasterRCNNResnetV1FpnKerasFeatureExtractor(
faster_rcnn_meta_arch.FasterRCNNKerasFeatureExtractor):
"""Faster RCNN Feature Extractor using Keras-based Resnet V1 FPN features."""
def __init__(self,
is_training,
resnet_v1_base_model,
resnet_v1_base_model_name,
first_stage_features_stride,
conv_hyperparams,
batch_norm_trainable=False,
weight_decay=0.0,
fpn_min_level=2,
fpn_max_level=6,
additional_layer_depth=256,
override_base_feature_extractor_hyperparams=False):
"""Constructor.
Args:
is_training: See base class.
resnet_v1_base_model: base resnet v1 network to use. One of
the resnet_v1.resnet_v1_{50,101,152} models.
resnet_v1_base_model_name: model name under which to construct resnet v1.
first_stage_features_stride: See base class.
conv_hyperparams: a `hyperparams_builder.KerasLayerHyperparams` object
containing convolution hyperparameters for the layers added on top of
the base feature extractor.
batch_norm_trainable: See base class.
weight_decay: See base class.
fpn_min_level: the highest resolution feature map to use in FPN. The valid
values are {2, 3, 4, 5} which map to Resnet v1 layers.
fpn_max_level: the smallest resolution feature map to construct or use in
FPN. FPN constructions uses features maps starting from fpn_min_level
upto the fpn_max_level. In the case that there are not enough feature
maps in the backbone network, additional feature maps are created by
applying stride 2 convolutions until we get the desired number of fpn
levels.
additional_layer_depth: additional feature map layer channel depth.
override_base_feature_extractor_hyperparams: Whether to override
hyperparameters of the base feature extractor with the one from
`conv_hyperparams`.
Raises:
ValueError: If `first_stage_features_stride` is not 8 or 16.
"""
if first_stage_features_stride != 8 and first_stage_features_stride != 16:
raise ValueError('`first_stage_features_stride` must be 8 or 16.')
super(FasterRCNNResnetV1FpnKerasFeatureExtractor, self).__init__(
is_training=is_training,
first_stage_features_stride=first_stage_features_stride,
batch_norm_trainable=batch_norm_trainable,
weight_decay=weight_decay)
self._resnet_v1_base_model = resnet_v1_base_model
self._resnet_v1_base_model_name = resnet_v1_base_model_name
self._conv_hyperparams = conv_hyperparams
self._fpn_min_level = fpn_min_level
self._fpn_max_level = fpn_max_level
self._additional_layer_depth = additional_layer_depth
self._freeze_batchnorm = (not batch_norm_trainable)
self._override_base_feature_extractor_hyperparams = \
override_base_feature_extractor_hyperparams
self._resnet_block_names = ['block1', 'block2', 'block3', 'block4']
self.classification_backbone = None
self._fpn_features_generator = None
self._coarse_feature_layers = []
def preprocess(self, resized_inputs):
"""Faster R-CNN Resnet V1 preprocessing.
VGG style channel mean subtraction as described here:
https://gist.github.com/ksimonyan/211839e770f7b538e2d8#file-readme-md
Note that if the number of channels is not equal to 3, the mean subtraction
will be skipped and the original resized_inputs will be returned.
Args:
resized_inputs: A [batch, height_in, width_in, channels] float32 tensor
representing a batch of images with values between 0 and 255.0.
Returns:
preprocessed_inputs: A [batch, height_out, width_out, channels] float32
tensor representing a batch of images.
"""
if resized_inputs.shape.as_list()[3] == 3:
channel_means = [123.68, 116.779, 103.939]
return resized_inputs - [[channel_means]]
else:
return resized_inputs
def get_proposal_feature_extractor_model(self, name=None):
"""Returns a model that extracts first stage RPN features.
Extracts features using the Resnet v1 FPN network.
Args:
name: A scope name to construct all variables within.
Returns:
A Keras model that takes preprocessed_inputs:
A [batch, height, width, channels] float32 tensor
representing a batch of images.
And returns rpn_feature_map:
A list of tensors with shape [batch, height, width, depth]
"""
with tf.name_scope(name):
with tf.name_scope('ResnetV1FPN'):
full_resnet_v1_model = self._resnet_v1_base_model(
batchnorm_training=self._train_batch_norm,
conv_hyperparams=(self._conv_hyperparams if
self._override_base_feature_extractor_hyperparams
else None),
classes=None,
weights=None,
include_top=False)
output_layers = _RESNET_MODEL_OUTPUT_LAYERS[
self._resnet_v1_base_model_name]
outputs = [full_resnet_v1_model.get_layer(output_layer_name).output
for output_layer_name in output_layers]
self.classification_backbone = tf.keras.Model(
inputs=full_resnet_v1_model.inputs,
outputs=outputs)
backbone_outputs = self.classification_backbone(
full_resnet_v1_model.inputs)
# construct FPN feature generator
self._base_fpn_max_level = min(self._fpn_max_level, 5)
self._num_levels = self._base_fpn_max_level + 1 - self._fpn_min_level
self._fpn_features_generator = (
feature_map_generators.KerasFpnTopDownFeatureMaps(
num_levels=self._num_levels,
depth=self._additional_layer_depth,
is_training=self._is_training,
conv_hyperparams=self._conv_hyperparams,
freeze_batchnorm=self._freeze_batchnorm,
name='FeatureMaps'))
feature_block_list = []
for level in range(self._fpn_min_level, self._base_fpn_max_level + 1):
feature_block_list.append('block{}'.format(level - 1))
feature_block_map = dict(
list(zip(self._resnet_block_names, backbone_outputs)))
fpn_input_image_features = [
(feature_block, feature_block_map[feature_block])
for feature_block in feature_block_list]
fpn_features = self._fpn_features_generator(fpn_input_image_features)
# Construct coarse feature layers
for i in range(self._base_fpn_max_level, self._fpn_max_level):
layers = []
layer_name = 'bottom_up_block{}'.format(i)
layers.append(
tf.keras.layers.Conv2D(
self._additional_layer_depth,
[3, 3],
padding='SAME',
strides=2,
name=layer_name + '_conv',
**self._conv_hyperparams.params()))
layers.append(
self._conv_hyperparams.build_batch_norm(
training=(self._is_training and not self._freeze_batchnorm),
name=layer_name + '_batchnorm'))
layers.append(
self._conv_hyperparams.build_activation_layer(
name=layer_name))
self._coarse_feature_layers.append(layers)
feature_maps = []
for level in range(self._fpn_min_level, self._base_fpn_max_level + 1):
feature_maps.append(fpn_features['top_down_block{}'.format(level-1)])
last_feature_map = fpn_features['top_down_block{}'.format(
self._base_fpn_max_level - 1)]
for coarse_feature_layers in self._coarse_feature_layers:
for layer in coarse_feature_layers:
last_feature_map = layer(last_feature_map)
feature_maps.append(last_feature_map)
feature_extractor_model = tf.keras.models.Model(
inputs=full_resnet_v1_model.inputs, outputs=feature_maps)
return feature_extractor_model
def get_box_classifier_feature_extractor_model(self, name=None):
"""Returns a model that extracts second stage box classifier features.
Construct two fully connected layer to extract the box classifier features.
Args:
name: A scope name to construct all variables within.
Returns:
A Keras model that takes proposal_feature_maps:
A 4-D float tensor with shape
[batch_size * self.max_num_proposals, crop_height, crop_width, depth]
representing the feature map cropped to each proposal.
And returns proposal_classifier_features:
A 4-D float tensor with shape
[batch_size * self.max_num_proposals, 1024]
representing box classifier features for each proposal.
"""
with tf.name_scope(name):
with tf.name_scope('ResnetV1FPN'):
# TODO(yiming): Add a batchnorm layer between two fc layers.
feature_extractor_model = tf.keras.models.Sequential([
tf.keras.layers.Flatten(),
tf.keras.layers.Dense(units=1024, activation='relu'),
tf.keras.layers.Dense(units=1024, activation='relu')
])
return feature_extractor_model
class FasterRCNNResnet50FpnKerasFeatureExtractor(
FasterRCNNResnetV1FpnKerasFeatureExtractor):
"""Faster RCNN with Resnet50 FPN feature extractor."""
def __init__(self,
is_training,
first_stage_features_stride=16,
conv_hyperparams=None,
batch_norm_trainable=False,
weight_decay=0.0,
fpn_min_level=2,
fpn_max_level=6,
additional_layer_depth=256,
override_base_feature_extractor_hyperparams=False):
"""Constructor.
Args:
is_training: See base class.
first_stage_features_stride: See base class.
conv_hyperparams: See base class.
batch_norm_trainable: See base class.
weight_decay: See base class.
fpn_min_level: See base class.
fpn_max_level: See base class.
additional_layer_depth: See base class.
override_base_feature_extractor_hyperparams: See base class.
"""
super(FasterRCNNResnet50FpnKerasFeatureExtractor, self).__init__(
is_training=is_training,
first_stage_features_stride=first_stage_features_stride,
conv_hyperparams=conv_hyperparams,
resnet_v1_base_model=resnet_v1.resnet_v1_50,
resnet_v1_base_model_name='resnet_v1_50',
batch_norm_trainable=batch_norm_trainable,
weight_decay=weight_decay,
fpn_min_level=fpn_min_level,
fpn_max_level=fpn_max_level,
additional_layer_depth=additional_layer_depth,
override_base_feature_extractor_hyperparams=
override_base_feature_extractor_hyperparams
)
class FasterRCNNResnet101FpnKerasFeatureExtractor(
FasterRCNNResnetV1FpnKerasFeatureExtractor):
"""Faster RCNN with Resnet101 FPN feature extractor."""
def __init__(self,
is_training,
first_stage_features_stride=16,
conv_hyperparams=None,
batch_norm_trainable=False,
weight_decay=0.0,
fpn_min_level=2,
fpn_max_level=6,
additional_layer_depth=256,
override_base_feature_extractor_hyperparams=False):
"""Constructor.
Args:
is_training: See base class.
first_stage_features_stride: See base class.
conv_hyperparams: See base class.
batch_norm_trainable: See base class.
weight_decay: See base class.
fpn_min_level: See base class.
fpn_max_level: See base class.
additional_layer_depth: See base class.
override_base_feature_extractor_hyperparams: See base class.
"""
super(FasterRCNNResnet101FpnKerasFeatureExtractor, self).__init__(
is_training=is_training,
first_stage_features_stride=first_stage_features_stride,
conv_hyperparams=conv_hyperparams,
resnet_v1_base_model=resnet_v1.resnet_v1_101,
resnet_v1_base_model_name='resnet_v1_101',
batch_norm_trainable=batch_norm_trainable,
weight_decay=weight_decay,
fpn_min_level=fpn_min_level,
fpn_max_level=fpn_max_level,
additional_layer_depth=additional_layer_depth,
override_base_feature_extractor_hyperparams=
override_base_feature_extractor_hyperparams)
class FasterRCNNResnet152FpnKerasFeatureExtractor(
FasterRCNNResnetV1FpnKerasFeatureExtractor):
"""Faster RCNN with Resnet152 FPN feature extractor."""
def __init__(self,
is_training,
first_stage_features_stride=16,
conv_hyperparams=None,
batch_norm_trainable=False,
weight_decay=0.0,
fpn_min_level=2,
fpn_max_level=6,
additional_layer_depth=256,
override_base_feature_extractor_hyperparams=False):
"""Constructor.
Args:
is_training: See base class.
first_stage_features_stride: See base class.
conv_hyperparams: See base class.
batch_norm_trainable: See base class.
weight_decay: See base class.
fpn_min_level: See base class.
fpn_max_level: See base class.
additional_layer_depth: See base class.
override_base_feature_extractor_hyperparams: See base class.
"""
super(FasterRCNNResnet152FpnKerasFeatureExtractor, self).__init__(
is_training=is_training,
first_stage_features_stride=first_stage_features_stride,
conv_hyperparams=conv_hyperparams,
resnet_v1_base_model=resnet_v1.resnet_v1_152,
resnet_v1_base_model_name='resnet_v1_152',
batch_norm_trainable=batch_norm_trainable,
weight_decay=weight_decay,
fpn_min_level=fpn_min_level,
fpn_max_level=fpn_max_level,
additional_layer_depth=additional_layer_depth,
override_base_feature_extractor_hyperparams=
override_base_feature_extractor_hyperparams)
research/object_detection/models/faster_rcnn_resnet_v1_fpn_keras_feature_extractor_tf2_test.py 0 → 100644
View file @ 31ca3b97
# Copyright 2020 The TensorFlow Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ==============================================================================
"""Tests for models.faster_rcnn_resnet_v1_fpn_keras_feature_extractor."""
import unittest
import tensorflow.compat.v1 as tf
from google.protobuf import text_format
from object_detection.builders import hyperparams_builder
from object_detection.models import faster_rcnn_resnet_v1_fpn_keras_feature_extractor as frcnn_res_fpn
from object_detection.protos import hyperparams_pb2
from object_detection.utils import tf_version
@unittest.skipIf(tf_version.is_tf1(), 'Skipping TF2.X only test.')
class FasterRCNNResnetV1FpnKerasFeatureExtractorTest(tf.test.TestCase):
def _build_conv_hyperparams(self):
conv_hyperparams = hyperparams_pb2.Hyperparams()
conv_hyperparams_text_proto = """
regularizer {
l2_regularizer {
}
}
initializer {
truncated_normal_initializer {
}
}
"""
text_format.Parse(conv_hyperparams_text_proto, conv_hyperparams)
return hyperparams_builder.KerasLayerHyperparams(conv_hyperparams)
def _build_feature_extractor(self):
return frcnn_res_fpn.FasterRCNNResnet50FpnKerasFeatureExtractor(
is_training=False,
conv_hyperparams=self._build_conv_hyperparams(),
first_stage_features_stride=16,
batch_norm_trainable=False,
weight_decay=0.0)
def test_extract_proposal_features_returns_expected_size(self):
feature_extractor = self._build_feature_extractor()
preprocessed_inputs = tf.random_uniform(
[2, 448, 448, 3], maxval=255, dtype=tf.float32)
rpn_feature_maps = feature_extractor.get_proposal_feature_extractor_model(
name='TestScope')(preprocessed_inputs)
features_shapes = [tf.shape(rpn_feature_map)
for rpn_feature_map in rpn_feature_maps]
self.assertAllEqual(features_shapes[0].numpy(), [2, 112, 112, 256])
self.assertAllEqual(features_shapes[1].numpy(), [2, 56, 56, 256])
self.assertAllEqual(features_shapes[2].numpy(), [2, 28, 28, 256])
self.assertAllEqual(features_shapes[3].numpy(), [2, 14, 14, 256])
self.assertAllEqual(features_shapes[4].numpy(), [2, 7, 7, 256])
def test_extract_proposal_features_half_size_input(self):
feature_extractor = self._build_feature_extractor()
preprocessed_inputs = tf.random_uniform(
[2, 224, 224, 3], maxval=255, dtype=tf.float32)
rpn_feature_maps = feature_extractor.get_proposal_feature_extractor_model(
name='TestScope')(preprocessed_inputs)
features_shapes = [tf.shape(rpn_feature_map)
for rpn_feature_map in rpn_feature_maps]
self.assertAllEqual(features_shapes[0].numpy(), [2, 56, 56, 256])
self.assertAllEqual(features_shapes[1].numpy(), [2, 28, 28, 256])
self.assertAllEqual(features_shapes[2].numpy(), [2, 14, 14, 256])
self.assertAllEqual(features_shapes[3].numpy(), [2, 7, 7, 256])
self.assertAllEqual(features_shapes[4].numpy(), [2, 4, 4, 256])
def test_extract_box_classifier_features_returns_expected_size(self):
feature_extractor = self._build_feature_extractor()
proposal_feature_maps = tf.random_uniform(
[3, 7, 7, 1024], maxval=255, dtype=tf.float32)
model = feature_extractor.get_box_classifier_feature_extractor_model(
name='TestScope')
proposal_classifier_features = (
model(proposal_feature_maps))
features_shape = tf.shape(proposal_classifier_features)
self.assertAllEqual(features_shape.numpy(), [3, 1024])
research/object_detection/models/keras_models/hourglass_network.py
View file @ 31ca3b97
...@@ -43,6 +43,15 @@ def _get_padding_for_kernel_size(kernel_size): ...@@ -43,6 +43,15 @@ def _get_padding_for_kernel_size(kernel_size):
kernel_size)) kernel_size))
def batchnorm():
try:
return tf.keras.layers.experimental.SyncBatchNormalization(
name='batchnorm', epsilon=1e-5, momentum=0.1)
except AttributeError:
return tf.keras.layers.BatchNormalization(
name='batchnorm', epsilon=1e-5, momentum=0.1, fused=BATCH_NORM_FUSED)
class ConvolutionalBlock(tf.keras.layers.Layer): class ConvolutionalBlock(tf.keras.layers.Layer):
"""Block that aggregates Convolution + Norm layer + ReLU.""" """Block that aggregates Convolution + Norm layer + ReLU."""
...@@ -73,8 +82,7 @@ class ConvolutionalBlock(tf.keras.layers.Layer): ...@@ -73,8 +82,7 @@ class ConvolutionalBlock(tf.keras.layers.Layer):
filters=out_channels, kernel_size=kernel_size, use_bias=False, filters=out_channels, kernel_size=kernel_size, use_bias=False,
strides=stride, padding=padding) strides=stride, padding=padding)
self.norm = tf.keras.layers.experimental.SyncBatchNormalization( self.norm = batchnorm()
name='batchnorm', epsilon=1e-5, momentum=0.1)
if relu: if relu:
self.relu = tf.keras.layers.ReLU() self.relu = tf.keras.layers.ReLU()
...@@ -124,8 +132,7 @@ class ResidualBlock(tf.keras.layers.Layer): ...@@ -124,8 +132,7 @@ class ResidualBlock(tf.keras.layers.Layer):
self.conv = tf.keras.layers.Conv2D( self.conv = tf.keras.layers.Conv2D(
filters=out_channels, kernel_size=kernel_size, use_bias=False, filters=out_channels, kernel_size=kernel_size, use_bias=False,
strides=1, padding=padding) strides=1, padding=padding)
self.norm = tf.keras.layers.experimental.SyncBatchNormalization( self.norm = batchnorm()
name='batchnorm', epsilon=1e-5, momentum=0.1)
if skip_conv: if skip_conv:
self.skip = SkipConvolution(out_channels=out_channels, self.skip = SkipConvolution(out_channels=out_channels,
......
research/object_detection/models/keras_models/resnet_v1.py
View file @ 31ca3b97
...@@ -21,6 +21,7 @@ from __future__ import print_function ...@@ -21,6 +21,7 @@ from __future__ import print_function
import tensorflow.compat.v1 as tf import tensorflow.compat.v1 as tf
from tensorflow.python.keras.applications import resnet
from object_detection.core import freezable_batch_norm from object_detection.core import freezable_batch_norm
from object_detection.models.keras_models import model_utils from object_detection.models.keras_models import model_utils
...@@ -95,11 +96,11 @@ class _LayersOverride(object): ...@@ -95,11 +96,11 @@ class _LayersOverride(object):
self.regularizer = tf.keras.regularizers.l2(weight_decay) self.regularizer = tf.keras.regularizers.l2(weight_decay)
self.initializer = tf.variance_scaling_initializer() self.initializer = tf.variance_scaling_initializer()
def _FixedPaddingLayer(self, kernel_size, rate=1): def _FixedPaddingLayer(self, kernel_size, rate=1): # pylint: disable=invalid-name
return tf.keras.layers.Lambda( return tf.keras.layers.Lambda(
lambda x: _fixed_padding(x, kernel_size, rate)) lambda x: _fixed_padding(x, kernel_size, rate))
def Conv2D(self, filters, kernel_size, **kwargs): def Conv2D(self, filters, kernel_size, **kwargs): # pylint: disable=invalid-name
"""Builds a Conv2D layer according to the current Object Detection config. """Builds a Conv2D layer according to the current Object Detection config.
Overrides the Keras Resnet application's convolutions with ones that Overrides the Keras Resnet application's convolutions with ones that
...@@ -141,7 +142,7 @@ class _LayersOverride(object): ...@@ -141,7 +142,7 @@ class _LayersOverride(object):
else: else:
return tf.keras.layers.Conv2D(filters, kernel_size, **kwargs) return tf.keras.layers.Conv2D(filters, kernel_size, **kwargs)
def Activation(self, *args, **kwargs): # pylint: disable=unused-argument def Activation(self, *args, **kwargs): # pylint: disable=unused-argument,invalid-name
"""Builds an activation layer. """Builds an activation layer.
Overrides the Keras application Activation layer specified by the Overrides the Keras application Activation layer specified by the
...@@ -163,7 +164,7 @@ class _LayersOverride(object): ...@@ -163,7 +164,7 @@ class _LayersOverride(object):
else: else:
return tf.keras.layers.Lambda(tf.nn.relu, name=name) return tf.keras.layers.Lambda(tf.nn.relu, name=name)
def BatchNormalization(self, **kwargs): def BatchNormalization(self, **kwargs): # pylint: disable=invalid-name
"""Builds a normalization layer. """Builds a normalization layer.
Overrides the Keras application batch norm with the norm specified by the Overrides the Keras application batch norm with the norm specified by the
...@@ -191,7 +192,7 @@ class _LayersOverride(object): ...@@ -191,7 +192,7 @@ class _LayersOverride(object):
momentum=self._default_batchnorm_momentum, momentum=self._default_batchnorm_momentum,
**kwargs) **kwargs)
def Input(self, shape): def Input(self, shape): # pylint: disable=invalid-name
"""Builds an Input layer. """Builds an Input layer.
Overrides the Keras application Input layer with one that uses a Overrides the Keras application Input layer with one that uses a
...@@ -219,7 +220,7 @@ class _LayersOverride(object): ...@@ -219,7 +220,7 @@ class _LayersOverride(object):
input=input_tensor, shape=[None] + shape) input=input_tensor, shape=[None] + shape)
return model_utils.input_layer(shape, placeholder_with_default) return model_utils.input_layer(shape, placeholder_with_default)
def MaxPooling2D(self, pool_size, **kwargs): def MaxPooling2D(self, pool_size, **kwargs): # pylint: disable=invalid-name
"""Builds a MaxPooling2D layer with default padding as 'SAME'. """Builds a MaxPooling2D layer with default padding as 'SAME'.
This is specified by the default resnet arg_scope in slim. This is specified by the default resnet arg_scope in slim.
...@@ -237,7 +238,7 @@ class _LayersOverride(object): ...@@ -237,7 +238,7 @@ class _LayersOverride(object):
# Add alias as Keras also has it. # Add alias as Keras also has it.
MaxPool2D = MaxPooling2D # pylint: disable=invalid-name MaxPool2D = MaxPooling2D # pylint: disable=invalid-name
def ZeroPadding2D(self, padding, **kwargs): # pylint: disable=unused-argument def ZeroPadding2D(self, padding, **kwargs): # pylint: disable=unused-argument,invalid-name
"""Replaces explicit padding in the Keras application with a no-op. """Replaces explicit padding in the Keras application with a no-op.
Args: Args:
...@@ -395,3 +396,146 @@ def resnet_v1_152(batchnorm_training, ...@@ -395,3 +396,146 @@ def resnet_v1_152(batchnorm_training,
return tf.keras.applications.resnet.ResNet152( return tf.keras.applications.resnet.ResNet152(
layers=layers_override, **kwargs) layers=layers_override, **kwargs)
# pylint: enable=invalid-name # pylint: enable=invalid-name
# The following codes are based on the existing keras ResNet model pattern:
# google3/third_party/tensorflow/python/keras/applications/resnet.py
def block_basic(x,
filters,
kernel_size=3,
stride=1,
conv_shortcut=False,
name=None):
"""A residual block for ResNet18/34.
Arguments:
x: input tensor.
filters: integer, filters of the bottleneck layer.
kernel_size: default 3, kernel size of the bottleneck layer.
stride: default 1, stride of the first layer.
conv_shortcut: default False, use convolution shortcut if True, otherwise
identity shortcut.
name: string, block label.
Returns:
Output tensor for the residual block.
"""
layers = tf.keras.layers
bn_axis = 3 if tf.keras.backend.image_data_format() == 'channels_last' else 1
preact = layers.BatchNormalization(
axis=bn_axis, epsilon=1.001e-5, name=name + '_preact_bn')(
x)
preact = layers.Activation('relu', name=name + '_preact_relu')(preact)
if conv_shortcut:
shortcut = layers.Conv2D(
filters, 1, strides=1, name=name + '_0_conv')(
preact)
else:
shortcut = layers.MaxPooling2D(1, strides=stride)(x) if stride > 1 else x
x = layers.ZeroPadding2D(
padding=((1, 1), (1, 1)), name=name + '_1_pad')(
preact)
x = layers.Conv2D(
filters, kernel_size, strides=1, use_bias=False, name=name + '_1_conv')(
x)
x = layers.BatchNormalization(
axis=bn_axis, epsilon=1.001e-5, name=name + '_1_bn')(
x)
x = layers.Activation('relu', name=name + '_1_relu')(x)
x = layers.ZeroPadding2D(padding=((1, 1), (1, 1)), name=name + '_2_pad')(x)
x = layers.Conv2D(
filters,
kernel_size,
strides=stride,
use_bias=False,
name=name + '_2_conv')(
x)
x = layers.BatchNormalization(
axis=bn_axis, epsilon=1.001e-5, name=name + '_2_bn')(
x)
x = layers.Activation('relu', name=name + '_2_relu')(x)
x = layers.Add(name=name + '_out')([shortcut, x])
return x
def stack_basic(x, filters, blocks, stride1=2, name=None):
"""A set of stacked residual blocks for ResNet18/34.
Arguments:
x: input tensor.
filters: integer, filters of the bottleneck layer in a block.
blocks: integer, blocks in the stacked blocks.
stride1: default 2, stride of the first layer in the first block.
name: string, stack label.
Returns:
Output tensor for the stacked blocks.
"""
x = block_basic(x, filters, conv_shortcut=True, name=name + '_block1')
for i in range(2, blocks):
x = block_basic(x, filters, name=name + '_block' + str(i))
x = block_basic(
x, filters, stride=stride1, name=name + '_block' + str(blocks))
return x
def resnet_v1_18(include_top=True,
weights='imagenet',
input_tensor=None,
input_shape=None,
pooling=None,
classes=1000,
classifier_activation='softmax'):
"""Instantiates the ResNet18 architecture."""
def stack_fn(x):
x = stack_basic(x, 64, 2, stride1=1, name='conv2')
x = stack_basic(x, 128, 2, name='conv3')
x = stack_basic(x, 256, 2, name='conv4')
return stack_basic(x, 512, 2, name='conv5')
return resnet.ResNet(
stack_fn,
True,
True,
'resnet18',
include_top,
weights,
input_tensor,
input_shape,
pooling,
classes,
classifier_activation=classifier_activation)
def resnet_v1_34(include_top=True,
weights='imagenet',
input_tensor=None,
input_shape=None,
pooling=None,
classes=1000,
classifier_activation='softmax'):
"""Instantiates the ResNet34 architecture."""
def stack_fn(x):
x = stack_basic(x, 64, 3, stride1=1, name='conv2')
x = stack_basic(x, 128, 4, name='conv3')
x = stack_basic(x, 256, 6, name='conv4')
return stack_basic(x, 512, 3, name='conv5')
return resnet.ResNet(
stack_fn,
True,
True,
'resnet34',
include_top,
weights,
input_tensor,
input_shape,
pooling,
classes,
classifier_activation=classifier_activation)
research/object_detection/models/keras_models/resnet_v1_tf2_test.py
View file @ 31ca3b97
...@@ -20,12 +20,13 @@ object detection. To verify the consistency of the two models, we compare: ...@@ -20,12 +20,13 @@ object detection. To verify the consistency of the two models, we compare:
2. Number of global variables. 2. Number of global variables.
""" """
import unittest import unittest
from absl.testing import parameterized
import numpy as np import numpy as np
from six.moves import zip from six.moves import zip
import tensorflow.compat.v1 as tf import tensorflow.compat.v1 as tf
from google.protobuf import text_format from google.protobuf import text_format
from object_detection.builders import hyperparams_builder from object_detection.builders import hyperparams_builder
from object_detection.models.keras_models import resnet_v1 from object_detection.models.keras_models import resnet_v1
from object_detection.protos import hyperparams_pb2 from object_detection.protos import hyperparams_pb2
...@@ -180,5 +181,46 @@ class ResnetV1Test(test_case.TestCase): ...@@ -180,5 +181,46 @@ class ResnetV1Test(test_case.TestCase):
self.assertEqual(len(variables), var_num) self.assertEqual(len(variables), var_num)
class ResnetShapeTest(test_case.TestCase, parameterized.TestCase):
@unittest.skipIf(tf_version.is_tf1(), 'Skipping TF2.X only test.')
@parameterized.parameters(
{
'resnet_type':
'resnet_v1_34',
'output_layer_names': [
'conv2_block3_out', 'conv3_block4_out', 'conv4_block6_out',
'conv5_block3_out'
]
}, {
'resnet_type':
'resnet_v1_18',
'output_layer_names': [
'conv2_block2_out', 'conv3_block2_out', 'conv4_block2_out',
'conv5_block2_out'
]
})
def test_output_shapes(self, resnet_type, output_layer_names):
if resnet_type == 'resnet_v1_34':
model = resnet_v1.resnet_v1_34(weights=None)
else:
model = resnet_v1.resnet_v1_18(weights=None)
outputs = [
model.get_layer(output_layer_name).output
for output_layer_name in output_layer_names
]
resnet_model = tf.keras.models.Model(inputs=model.input, outputs=outputs)
outputs = resnet_model(np.zeros((2, 64, 64, 3), dtype=np.float32))
# Check the shape of 'conv2_block3_out':
self.assertEqual(outputs[0].shape, [2, 16, 16, 64])
# Check the shape of 'conv3_block4_out':
self.assertEqual(outputs[1].shape, [2, 8, 8, 128])
# Check the shape of 'conv4_block6_out':
self.assertEqual(outputs[2].shape, [2, 4, 4, 256])
# Check the shape of 'conv5_block3_out':
self.assertEqual(outputs[3].shape, [2, 2, 2, 512])
if __name__ == '__main__': if __name__ == '__main__':
tf.test.main() tf.test.main()
research/object_detection/models/ssd_efficientnet_bifpn_feature_extractor.py 0 → 100644
View file @ 31ca3b97
# Copyright 2020 The TensorFlow Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ==============================================================================
"""SSD Keras-based EfficientNet + BiFPN (EfficientDet) Feature Extractor."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
from absl import logging
from six.moves import range
from six.moves import zip
import tensorflow.compat.v2 as tf
from object_detection.meta_architectures import ssd_meta_arch
from object_detection.models import bidirectional_feature_pyramid_generators as bifpn_generators
from object_detection.utils import ops
from object_detection.utils import shape_utils
from object_detection.utils import tf_version
# pylint: disable=g-import-not-at-top
if tf_version.is_tf2():
from official.vision.image_classification.efficientnet import efficientnet_model
_EFFICIENTNET_LEVEL_ENDPOINTS = {
1: 'stack_0/block_0/project_bn',
2: 'stack_1/block_1/add',
3: 'stack_2/block_1/add',
4: 'stack_4/block_2/add',
5: 'stack_6/block_0/project_bn',
}
class SSDEfficientNetBiFPNKerasFeatureExtractor(
ssd_meta_arch.SSDKerasFeatureExtractor):
"""SSD Keras-based EfficientNetBiFPN (EfficientDet) Feature Extractor."""
def __init__(self,
is_training,
depth_multiplier,
min_depth,
pad_to_multiple,
conv_hyperparams,
freeze_batchnorm,
inplace_batchnorm_update,
bifpn_min_level,
bifpn_max_level,
bifpn_num_iterations,
bifpn_num_filters,
bifpn_combine_method,
efficientnet_version,
use_explicit_padding=None,
use_depthwise=None,
override_base_feature_extractor_hyperparams=None,
name=None):
"""SSD Keras-based EfficientNetBiFPN (EfficientDet) feature extractor.
Args:
is_training: whether the network is in training mode.
depth_multiplier: unsupported by EfficientNetBiFPN. float, depth
multiplier for the 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: a `hyperparams_builder.KerasLayerHyperparams` object
containing convolution hyperparameters for the layers added on top of
the base feature extractor.
freeze_batchnorm: whether to freeze batch norm parameters during training
or not. When training with a small batch size (e.g. 1), it is desirable
to freeze batch norm update and use pretrained batch norm params.
inplace_batchnorm_update: whether to update batch norm moving average
values inplace. When this is false train op must add a control
dependency on tf.graphkeys.UPDATE_OPS collection in order to update
batch norm statistics.
bifpn_min_level: the highest resolution feature map to use in BiFPN. The
valid values are {2, 3, 4, 5} which map to Resnet blocks {1, 2, 3, 4}
respectively.
bifpn_max_level: the smallest resolution feature map to use in the BiFPN.
BiFPN constructions uses features maps starting from bifpn_min_level
upto the bifpn_max_level. In the case that there are not enough feature
maps in the backbone network, additional feature maps are created by
applying stride 2 convolutions until we get the desired number of BiFPN
levels.
bifpn_num_iterations: number of BiFPN iterations. Overrided if
efficientdet_version is provided.
bifpn_num_filters: number of filters (channels) in all BiFPN layers.
Overrided if efficientdet_version is provided.
bifpn_combine_method: the method used to combine BiFPN nodes.
efficientnet_version: the EfficientNet version to use for this feature
extractor's backbone.
use_explicit_padding: unsupported by EfficientNetBiFPN. Whether to use
explicit padding when extracting features.
use_depthwise: unsupported by EfficientNetBiFPN, since BiFPN uses regular
convolutions when inputs to a node have a differing number of channels,
and use separable convolutions after combine operations.
override_base_feature_extractor_hyperparams: unsupported. Whether to
override hyperparameters of the base feature extractor with the one from
`conv_hyperparams`.
name: a string name scope to assign to the model. If 'None', Keras will
auto-generate one from the class name.
"""
super(SSDEfficientNetBiFPNKerasFeatureExtractor, self).__init__(
is_training=is_training,
depth_multiplier=depth_multiplier,
min_depth=min_depth,
pad_to_multiple=pad_to_multiple,
conv_hyperparams=conv_hyperparams,
freeze_batchnorm=freeze_batchnorm,
inplace_batchnorm_update=inplace_batchnorm_update,
use_explicit_padding=None,
use_depthwise=None,
override_base_feature_extractor_hyperparams=
override_base_feature_extractor_hyperparams,
name=name)
if depth_multiplier != 1.0:
raise ValueError('EfficientNetBiFPN does not support a non-default '
'depth_multiplier.')
if use_explicit_padding:
raise ValueError('EfficientNetBiFPN does not support explicit padding.')
if use_depthwise:
raise ValueError('EfficientNetBiFPN does not support use_depthwise.')
if override_base_feature_extractor_hyperparams:
raise ValueError('EfficientNetBiFPN does not support '
'override_base_feature_extractor_hyperparams.')
self._bifpn_min_level = bifpn_min_level
self._bifpn_max_level = bifpn_max_level
self._bifpn_num_iterations = bifpn_num_iterations
self._bifpn_num_filters = max(bifpn_num_filters, min_depth)
self._bifpn_node_params = {'combine_method': bifpn_combine_method}
self._efficientnet_version = efficientnet_version
logging.info('EfficientDet EfficientNet backbone version: %s',
self._efficientnet_version)
logging.info('EfficientDet BiFPN num filters: %d', self._bifpn_num_filters)
logging.info('EfficientDet BiFPN num iterations: %d',
self._bifpn_num_iterations)
self._backbone_max_level = min(
max(_EFFICIENTNET_LEVEL_ENDPOINTS.keys()), bifpn_max_level)
self._output_layer_names = [
_EFFICIENTNET_LEVEL_ENDPOINTS[i]
for i in range(bifpn_min_level, self._backbone_max_level + 1)]
self._output_layer_alias = [
'level_{}'.format(i)
for i in range(bifpn_min_level, self._backbone_max_level + 1)]
# Initialize the EfficientNet backbone.
# Note, this is currently done in the init method rather than in the build
# method, since doing so introduces an error which is not well understood.
efficientnet_base = efficientnet_model.EfficientNet.from_name(
model_name=self._efficientnet_version,
overrides={'rescale_input': False})
outputs = [efficientnet_base.get_layer(output_layer_name).output
for output_layer_name in self._output_layer_names]
self._efficientnet = tf.keras.Model(
inputs=efficientnet_base.inputs, outputs=outputs)
self.classification_backbone = efficientnet_base
self._bifpn_stage = None
def build(self, input_shape):
self._bifpn_stage = bifpn_generators.KerasBiFpnFeatureMaps(
bifpn_num_iterations=self._bifpn_num_iterations,
bifpn_num_filters=self._bifpn_num_filters,
fpn_min_level=self._bifpn_min_level,
fpn_max_level=self._bifpn_max_level,
input_max_level=self._backbone_max_level,
is_training=self._is_training,
conv_hyperparams=self._conv_hyperparams,
freeze_batchnorm=self._freeze_batchnorm,
bifpn_node_params=self._bifpn_node_params,
name='bifpn')
self.built = True
def preprocess(self, inputs):
"""SSD preprocessing.
Channel-wise mean subtraction and scaling.
Args:
inputs: a [batch, height, width, channels] float tensor representing a
batch of images.
Returns:
preprocessed_inputs: a [batch, height, width, channels] float tensor
representing a batch of images.
"""
if inputs.shape.as_list()[3] == 3:
# Input images are expected to be in the range [0, 255].
channel_offset = [0.485, 0.456, 0.406]
channel_scale = [0.229, 0.224, 0.225]
return ((inputs / 255.0) - [[channel_offset]]) / [[channel_scale]]
else:
return inputs
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(
129, preprocessed_inputs)
base_feature_maps = self._efficientnet(
ops.pad_to_multiple(preprocessed_inputs, self._pad_to_multiple))
output_feature_map_dict = self._bifpn_stage(
list(zip(self._output_layer_alias, base_feature_maps)))
return list(output_feature_map_dict.values())
class SSDEfficientNetB0BiFPNKerasFeatureExtractor(
SSDEfficientNetBiFPNKerasFeatureExtractor):
"""SSD Keras EfficientNet-b0 BiFPN (EfficientDet-d0) Feature Extractor."""
def __init__(self,
is_training,
depth_multiplier,
min_depth,
pad_to_multiple,
conv_hyperparams,
freeze_batchnorm,
inplace_batchnorm_update,
bifpn_min_level=3,
bifpn_max_level=7,
bifpn_num_iterations=3,
bifpn_num_filters=64,
bifpn_combine_method='fast_attention',
use_explicit_padding=None,
use_depthwise=None,
override_base_feature_extractor_hyperparams=None,
name='EfficientDet-D0'):
"""SSD Keras EfficientNet-b0 BiFPN (EfficientDet-d0) Feature Extractor.
Args:
is_training: whether the network is in training mode.
depth_multiplier: unsupported by EfficientNetBiFPN. float, depth
multiplier for the 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: a `hyperparams_builder.KerasLayerHyperparams` object
containing convolution hyperparameters for the layers added on top of
the base feature extractor.
freeze_batchnorm: whether to freeze batch norm parameters during training
or not. When training with a small batch size (e.g. 1), it is desirable
to freeze batch norm update and use pretrained batch norm params.
inplace_batchnorm_update: whether to update batch norm moving average
values inplace. When this is false train op must add a control
dependency on tf.graphkeys.UPDATE_OPS collection in order to update
batch norm statistics.
bifpn_min_level: the highest resolution feature map to use in BiFPN. The
valid values are {2, 3, 4, 5} which map to Resnet blocks {1, 2, 3, 4}
respectively.
bifpn_max_level: the smallest resolution feature map to use in the BiFPN.
BiFPN constructions uses features maps starting from bifpn_min_level
upto the bifpn_max_level. In the case that there are not enough feature
maps in the backbone network, additional feature maps are created by
applying stride 2 convolutions until we get the desired number of BiFPN
levels.
bifpn_num_iterations: number of BiFPN iterations. Overrided if
efficientdet_version is provided.
bifpn_num_filters: number of filters (channels) in all BiFPN layers.
Overrided if efficientdet_version is provided.
bifpn_combine_method: the method used to combine BiFPN nodes.
use_explicit_padding: unsupported by EfficientNetBiFPN. Whether to use
explicit padding when extracting features.
use_depthwise: unsupported by EfficientNetBiFPN, since BiFPN uses regular
convolutions when inputs to a node have a differing number of channels,
and use separable convolutions after combine operations.
override_base_feature_extractor_hyperparams: unsupported. Whether to
override hyperparameters of the base feature extractor with the one from
`conv_hyperparams`.
name: a string name scope to assign to the model. If 'None', Keras will
auto-generate one from the class name.
"""
super(SSDEfficientNetB0BiFPNKerasFeatureExtractor, self).__init__(
is_training=is_training,
depth_multiplier=depth_multiplier,
min_depth=min_depth,
pad_to_multiple=pad_to_multiple,
conv_hyperparams=conv_hyperparams,
freeze_batchnorm=freeze_batchnorm,
inplace_batchnorm_update=inplace_batchnorm_update,
bifpn_min_level=bifpn_min_level,
bifpn_max_level=bifpn_max_level,
bifpn_num_iterations=bifpn_num_iterations,
bifpn_num_filters=bifpn_num_filters,
bifpn_combine_method=bifpn_combine_method,
efficientnet_version='efficientnet-b0',
use_explicit_padding=use_explicit_padding,
use_depthwise=use_depthwise,
override_base_feature_extractor_hyperparams=
override_base_feature_extractor_hyperparams,
name=name)
class SSDEfficientNetB1BiFPNKerasFeatureExtractor(
SSDEfficientNetBiFPNKerasFeatureExtractor):
"""SSD Keras EfficientNet-b1 BiFPN (EfficientDet-d1) Feature Extractor."""
def __init__(self,
is_training,
depth_multiplier,
min_depth,
pad_to_multiple,
conv_hyperparams,
freeze_batchnorm,
inplace_batchnorm_update,
bifpn_min_level=3,
bifpn_max_level=7,
bifpn_num_iterations=4,
bifpn_num_filters=88,
bifpn_combine_method='fast_attention',
use_explicit_padding=None,
use_depthwise=None,
override_base_feature_extractor_hyperparams=None,
name='EfficientDet-D1'):
"""SSD Keras EfficientNet-b1 BiFPN (EfficientDet-d1) Feature Extractor.
Args:
is_training: whether the network is in training mode.
depth_multiplier: unsupported by EfficientNetBiFPN. float, depth
multiplier for the 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: a `hyperparams_builder.KerasLayerHyperparams` object
containing convolution hyperparameters for the layers added on top of
the base feature extractor.
freeze_batchnorm: whether to freeze batch norm parameters during training
or not. When training with a small batch size (e.g. 1), it is desirable
to freeze batch norm update and use pretrained batch norm params.
inplace_batchnorm_update: whether to update batch norm moving average
values inplace. When this is false train op must add a control
dependency on tf.graphkeys.UPDATE_OPS collection in order to update
batch norm statistics.
bifpn_min_level: the highest resolution feature map to use in BiFPN. The
valid values are {2, 3, 4, 5} which map to Resnet blocks {1, 2, 3, 4}
respectively.
bifpn_max_level: the smallest resolution feature map to use in the BiFPN.
BiFPN constructions uses features maps starting from bifpn_min_level
upto the bifpn_max_level. In the case that there are not enough feature
maps in the backbone network, additional feature maps are created by
applying stride 2 convolutions until we get the desired number of BiFPN
levels.
bifpn_num_iterations: number of BiFPN iterations. Overrided if
efficientdet_version is provided.
bifpn_num_filters: number of filters (channels) in all BiFPN layers.
Overrided if efficientdet_version is provided.
bifpn_combine_method: the method used to combine BiFPN nodes.
use_explicit_padding: unsupported by EfficientNetBiFPN. Whether to use
explicit padding when extracting features.
use_depthwise: unsupported by EfficientNetBiFPN, since BiFPN uses regular
convolutions when inputs to a node have a differing number of channels,
and use separable convolutions after combine operations.
override_base_feature_extractor_hyperparams: unsupported. Whether to
override hyperparameters of the base feature extractor with the one from
`conv_hyperparams`.
name: a string name scope to assign to the model. If 'None', Keras will
auto-generate one from the class name.
"""
super(SSDEfficientNetB1BiFPNKerasFeatureExtractor, self).__init__(
is_training=is_training,
depth_multiplier=depth_multiplier,
min_depth=min_depth,
pad_to_multiple=pad_to_multiple,
conv_hyperparams=conv_hyperparams,
freeze_batchnorm=freeze_batchnorm,
inplace_batchnorm_update=inplace_batchnorm_update,
bifpn_min_level=bifpn_min_level,
bifpn_max_level=bifpn_max_level,
bifpn_num_iterations=bifpn_num_iterations,
bifpn_num_filters=bifpn_num_filters,
bifpn_combine_method=bifpn_combine_method,
efficientnet_version='efficientnet-b1',
use_explicit_padding=use_explicit_padding,
use_depthwise=use_depthwise,
override_base_feature_extractor_hyperparams=
override_base_feature_extractor_hyperparams,
name=name)
class SSDEfficientNetB2BiFPNKerasFeatureExtractor(
SSDEfficientNetBiFPNKerasFeatureExtractor):
"""SSD Keras EfficientNet-b2 BiFPN (EfficientDet-d2) Feature Extractor."""
def __init__(self,
is_training,
depth_multiplier,
min_depth,
pad_to_multiple,
conv_hyperparams,
freeze_batchnorm,
inplace_batchnorm_update,
bifpn_min_level=3,
bifpn_max_level=7,
bifpn_num_iterations=5,
bifpn_num_filters=112,
bifpn_combine_method='fast_attention',
use_explicit_padding=None,
use_depthwise=None,
override_base_feature_extractor_hyperparams=None,
name='EfficientDet-D2'):
"""SSD Keras EfficientNet-b2 BiFPN (EfficientDet-d2) Feature Extractor.
Args:
is_training: whether the network is in training mode.
depth_multiplier: unsupported by EfficientNetBiFPN. float, depth
multiplier for the 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: a `hyperparams_builder.KerasLayerHyperparams` object
containing convolution hyperparameters for the layers added on top of
the base feature extractor.
freeze_batchnorm: whether to freeze batch norm parameters during training
or not. When training with a small batch size (e.g. 1), it is desirable
to freeze batch norm update and use pretrained batch norm params.
inplace_batchnorm_update: whether to update batch norm moving average
values inplace. When this is false train op must add a control
dependency on tf.graphkeys.UPDATE_OPS collection in order to update
batch norm statistics.
bifpn_min_level: the highest resolution feature map to use in BiFPN. The
valid values are {2, 3, 4, 5} which map to Resnet blocks {1, 2, 3, 4}
respectively.
bifpn_max_level: the smallest resolution feature map to use in the BiFPN.
BiFPN constructions uses features maps starting from bifpn_min_level
upto the bifpn_max_level. In the case that there are not enough feature
maps in the backbone network, additional feature maps are created by
applying stride 2 convolutions until we get the desired number of BiFPN
levels.
bifpn_num_iterations: number of BiFPN iterations. Overrided if
efficientdet_version is provided.
bifpn_num_filters: number of filters (channels) in all BiFPN layers.
Overrided if efficientdet_version is provided.
bifpn_combine_method: the method used to combine BiFPN nodes.
use_explicit_padding: unsupported by EfficientNetBiFPN. Whether to use
explicit padding when extracting features.
use_depthwise: unsupported by EfficientNetBiFPN, since BiFPN uses regular
convolutions when inputs to a node have a differing number of channels,
and use separable convolutions after combine operations.
override_base_feature_extractor_hyperparams: unsupported. Whether to
override hyperparameters of the base feature extractor with the one from
`conv_hyperparams`.
name: a string name scope to assign to the model. If 'None', Keras will
auto-generate one from the class name.
"""
super(SSDEfficientNetB2BiFPNKerasFeatureExtractor, self).__init__(
is_training=is_training,
depth_multiplier=depth_multiplier,
min_depth=min_depth,
pad_to_multiple=pad_to_multiple,
conv_hyperparams=conv_hyperparams,
freeze_batchnorm=freeze_batchnorm,
inplace_batchnorm_update=inplace_batchnorm_update,
bifpn_min_level=bifpn_min_level,
bifpn_max_level=bifpn_max_level,
bifpn_num_iterations=bifpn_num_iterations,
bifpn_num_filters=bifpn_num_filters,
bifpn_combine_method=bifpn_combine_method,
efficientnet_version='efficientnet-b2',
use_explicit_padding=use_explicit_padding,
use_depthwise=use_depthwise,
override_base_feature_extractor_hyperparams=
override_base_feature_extractor_hyperparams,
name=name)
class SSDEfficientNetB3BiFPNKerasFeatureExtractor(
SSDEfficientNetBiFPNKerasFeatureExtractor):
"""SSD Keras EfficientNet-b3 BiFPN (EfficientDet-d3) Feature Extractor."""
def __init__(self,
is_training,
depth_multiplier,
min_depth,
pad_to_multiple,
conv_hyperparams,
freeze_batchnorm,
inplace_batchnorm_update,
bifpn_min_level=3,
bifpn_max_level=7,
bifpn_num_iterations=6,
bifpn_num_filters=160,
bifpn_combine_method='fast_attention',
use_explicit_padding=None,
use_depthwise=None,
override_base_feature_extractor_hyperparams=None,
name='EfficientDet-D3'):
"""SSD Keras EfficientNet-b3 BiFPN (EfficientDet-d3) Feature Extractor.
Args:
is_training: whether the network is in training mode.
depth_multiplier: unsupported by EfficientNetBiFPN. float, depth
multiplier for the 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: a `hyperparams_builder.KerasLayerHyperparams` object
containing convolution hyperparameters for the layers added on top of
the base feature extractor.
freeze_batchnorm: whether to freeze batch norm parameters during training
or not. When training with a small batch size (e.g. 1), it is desirable
to freeze batch norm update and use pretrained batch norm params.
inplace_batchnorm_update: whether to update batch norm moving average
values inplace. When this is false train op must add a control
dependency on tf.graphkeys.UPDATE_OPS collection in order to update
batch norm statistics.
bifpn_min_level: the highest resolution feature map to use in BiFPN. The
valid values are {2, 3, 4, 5} which map to Resnet blocks {1, 2, 3, 4}
respectively.
bifpn_max_level: the smallest resolution feature map to use in the BiFPN.
BiFPN constructions uses features maps starting from bifpn_min_level
upto the bifpn_max_level. In the case that there are not enough feature
maps in the backbone network, additional feature maps are created by
applying stride 2 convolutions until we get the desired number of BiFPN
levels.
bifpn_num_iterations: number of BiFPN iterations. Overrided if
efficientdet_version is provided.
bifpn_num_filters: number of filters (channels) in all BiFPN layers.
Overrided if efficientdet_version is provided.
bifpn_combine_method: the method used to combine BiFPN nodes.
use_explicit_padding: unsupported by EfficientNetBiFPN. Whether to use
explicit padding when extracting features.
use_depthwise: unsupported by EfficientNetBiFPN, since BiFPN uses regular
convolutions when inputs to a node have a differing number of channels,
and use separable convolutions after combine operations.
override_base_feature_extractor_hyperparams: unsupported. Whether to
override hyperparameters of the base feature extractor with the one from
`conv_hyperparams`.
name: a string name scope to assign to the model. If 'None', Keras will
auto-generate one from the class name.
"""
super(SSDEfficientNetB3BiFPNKerasFeatureExtractor, self).__init__(
is_training=is_training,
depth_multiplier=depth_multiplier,
min_depth=min_depth,
pad_to_multiple=pad_to_multiple,
conv_hyperparams=conv_hyperparams,
freeze_batchnorm=freeze_batchnorm,
inplace_batchnorm_update=inplace_batchnorm_update,
bifpn_min_level=bifpn_min_level,
bifpn_max_level=bifpn_max_level,
bifpn_num_iterations=bifpn_num_iterations,
bifpn_num_filters=bifpn_num_filters,
bifpn_combine_method=bifpn_combine_method,
efficientnet_version='efficientnet-b3',
use_explicit_padding=use_explicit_padding,
use_depthwise=use_depthwise,
override_base_feature_extractor_hyperparams=
override_base_feature_extractor_hyperparams,
name=name)
class SSDEfficientNetB4BiFPNKerasFeatureExtractor(
SSDEfficientNetBiFPNKerasFeatureExtractor):
"""SSD Keras EfficientNet-b4 BiFPN (EfficientDet-d4) Feature Extractor."""
def __init__(self,
is_training,
depth_multiplier,
min_depth,
pad_to_multiple,
conv_hyperparams,
freeze_batchnorm,
inplace_batchnorm_update,
bifpn_min_level=3,
bifpn_max_level=7,
bifpn_num_iterations=7,
bifpn_num_filters=224,
bifpn_combine_method='fast_attention',
use_explicit_padding=None,
use_depthwise=None,
override_base_feature_extractor_hyperparams=None,
name='EfficientDet-D4'):
"""SSD Keras EfficientNet-b4 BiFPN (EfficientDet-d4) Feature Extractor.
Args:
is_training: whether the network is in training mode.
depth_multiplier: unsupported by EfficientNetBiFPN. float, depth
multiplier for the 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: a `hyperparams_builder.KerasLayerHyperparams` object
containing convolution hyperparameters for the layers added on top of
the base feature extractor.
freeze_batchnorm: whether to freeze batch norm parameters during training
or not. When training with a small batch size (e.g. 1), it is desirable
to freeze batch norm update and use pretrained batch norm params.
inplace_batchnorm_update: whether to update batch norm moving average
values inplace. When this is false train op must add a control
dependency on tf.graphkeys.UPDATE_OPS collection in order to update
batch norm statistics.
bifpn_min_level: the highest resolution feature map to use in BiFPN. The
valid values are {2, 3, 4, 5} which map to Resnet blocks {1, 2, 3, 4}
respectively.
bifpn_max_level: the smallest resolution feature map to use in the BiFPN.
BiFPN constructions uses features maps starting from bifpn_min_level
upto the bifpn_max_level. In the case that there are not enough feature
maps in the backbone network, additional feature maps are created by
applying stride 2 convolutions until we get the desired number of BiFPN
levels.
bifpn_num_iterations: number of BiFPN iterations. Overrided if
efficientdet_version is provided.
bifpn_num_filters: number of filters (channels) in all BiFPN layers.
Overrided if efficientdet_version is provided.
bifpn_combine_method: the method used to combine BiFPN nodes.
use_explicit_padding: unsupported by EfficientNetBiFPN. Whether to use
explicit padding when extracting features.
use_depthwise: unsupported by EfficientNetBiFPN, since BiFPN uses regular
convolutions when inputs to a node have a differing number of channels,
and use separable convolutions after combine operations.
override_base_feature_extractor_hyperparams: unsupported. Whether to
override hyperparameters of the base feature extractor with the one from
`conv_hyperparams`.
name: a string name scope to assign to the model. If 'None', Keras will
auto-generate one from the class name.
"""
super(SSDEfficientNetB4BiFPNKerasFeatureExtractor, self).__init__(
is_training=is_training,
depth_multiplier=depth_multiplier,
min_depth=min_depth,
pad_to_multiple=pad_to_multiple,
conv_hyperparams=conv_hyperparams,
freeze_batchnorm=freeze_batchnorm,
inplace_batchnorm_update=inplace_batchnorm_update,
bifpn_min_level=bifpn_min_level,
bifpn_max_level=bifpn_max_level,
bifpn_num_iterations=bifpn_num_iterations,
bifpn_num_filters=bifpn_num_filters,
bifpn_combine_method=bifpn_combine_method,
efficientnet_version='efficientnet-b4',
use_explicit_padding=use_explicit_padding,
use_depthwise=use_depthwise,
override_base_feature_extractor_hyperparams=
override_base_feature_extractor_hyperparams,
name=name)
class SSDEfficientNetB5BiFPNKerasFeatureExtractor(
SSDEfficientNetBiFPNKerasFeatureExtractor):
"""SSD Keras EfficientNet-b5 BiFPN (EfficientDet-d5) Feature Extractor."""
def __init__(self,
is_training,
depth_multiplier,
min_depth,
pad_to_multiple,
conv_hyperparams,
freeze_batchnorm,
inplace_batchnorm_update,
bifpn_min_level=3,
bifpn_max_level=7,
bifpn_num_iterations=7,
bifpn_num_filters=288,
bifpn_combine_method='fast_attention',
use_explicit_padding=None,
use_depthwise=None,
override_base_feature_extractor_hyperparams=None,
name='EfficientDet-D5'):
"""SSD Keras EfficientNet-b5 BiFPN (EfficientDet-d5) Feature Extractor.
Args:
is_training: whether the network is in training mode.
depth_multiplier: unsupported by EfficientNetBiFPN. float, depth
multiplier for the 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: a `hyperparams_builder.KerasLayerHyperparams` object
containing convolution hyperparameters for the layers added on top of
the base feature extractor.
freeze_batchnorm: whether to freeze batch norm parameters during training
or not. When training with a small batch size (e.g. 1), it is desirable
to freeze batch norm update and use pretrained batch norm params.
inplace_batchnorm_update: whether to update batch norm moving average
values inplace. When this is false train op must add a control
dependency on tf.graphkeys.UPDATE_OPS collection in order to update
batch norm statistics.
bifpn_min_level: the highest resolution feature map to use in BiFPN. The
valid values are {2, 3, 4, 5} which map to Resnet blocks {1, 2, 3, 4}
respectively.
bifpn_max_level: the smallest resolution feature map to use in the BiFPN.
BiFPN constructions uses features maps starting from bifpn_min_level
upto the bifpn_max_level. In the case that there are not enough feature
maps in the backbone network, additional feature maps are created by
applying stride 2 convolutions until we get the desired number of BiFPN
levels.
bifpn_num_iterations: number of BiFPN iterations. Overrided if
efficientdet_version is provided.
bifpn_num_filters: number of filters (channels) in all BiFPN layers.
Overrided if efficientdet_version is provided.
bifpn_combine_method: the method used to combine BiFPN nodes.
use_explicit_padding: unsupported by EfficientNetBiFPN. Whether to use
explicit padding when extracting features.
use_depthwise: unsupported by EfficientNetBiFPN, since BiFPN uses regular
convolutions when inputs to a node have a differing number of channels,
and use separable convolutions after combine operations.
override_base_feature_extractor_hyperparams: unsupported. Whether to
override hyperparameters of the base feature extractor with the one from
`conv_hyperparams`.
name: a string name scope to assign to the model. If 'None', Keras will
auto-generate one from the class name.
"""
super(SSDEfficientNetB5BiFPNKerasFeatureExtractor, self).__init__(
is_training=is_training,
depth_multiplier=depth_multiplier,
min_depth=min_depth,
pad_to_multiple=pad_to_multiple,
conv_hyperparams=conv_hyperparams,
freeze_batchnorm=freeze_batchnorm,
inplace_batchnorm_update=inplace_batchnorm_update,
bifpn_min_level=bifpn_min_level,
bifpn_max_level=bifpn_max_level,
bifpn_num_iterations=bifpn_num_iterations,
bifpn_num_filters=bifpn_num_filters,
bifpn_combine_method=bifpn_combine_method,
efficientnet_version='efficientnet-b5',
use_explicit_padding=use_explicit_padding,
use_depthwise=use_depthwise,
override_base_feature_extractor_hyperparams=
override_base_feature_extractor_hyperparams,
name=name)
class SSDEfficientNetB6BiFPNKerasFeatureExtractor(
SSDEfficientNetBiFPNKerasFeatureExtractor):
"""SSD Keras EfficientNet-b6 BiFPN (EfficientDet-d[6,7]) Feature Extractor."""
def __init__(self,
is_training,
depth_multiplier,
min_depth,
pad_to_multiple,
conv_hyperparams,
freeze_batchnorm,
inplace_batchnorm_update,
bifpn_min_level=3,
bifpn_max_level=7,
bifpn_num_iterations=8,
bifpn_num_filters=384,
bifpn_combine_method='sum',
use_explicit_padding=None,
use_depthwise=None,
override_base_feature_extractor_hyperparams=None,
name='EfficientDet-D6-D7'):
"""SSD Keras EfficientNet-b6 BiFPN (EfficientDet-d[6,7]) Feature Extractor.
SSD Keras EfficientNet-b6 BiFPN Feature Extractor, a.k.a. EfficientDet-d6
and EfficientDet-d7. The EfficientDet-d[6,7] models use the same backbone
EfficientNet-b6 and the same BiFPN architecture, and therefore have the same
number of parameters. They only differ in their input resolutions.
Args:
is_training: whether the network is in training mode.
depth_multiplier: unsupported by EfficientNetBiFPN. float, depth
multiplier for the 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: a `hyperparams_builder.KerasLayerHyperparams` object
containing convolution hyperparameters for the layers added on top of
the base feature extractor.
freeze_batchnorm: whether to freeze batch norm parameters during training
or not. When training with a small batch size (e.g. 1), it is desirable
to freeze batch norm update and use pretrained batch norm params.
inplace_batchnorm_update: whether to update batch norm moving average
values inplace. When this is false train op must add a control
dependency on tf.graphkeys.UPDATE_OPS collection in order to update
batch norm statistics.
bifpn_min_level: the highest resolution feature map to use in BiFPN. The
valid values are {2, 3, 4, 5} which map to Resnet blocks {1, 2, 3, 4}
respectively.
bifpn_max_level: the smallest resolution feature map to use in the BiFPN.
BiFPN constructions uses features maps starting from bifpn_min_level
upto the bifpn_max_level. In the case that there are not enough feature
maps in the backbone network, additional feature maps are created by
applying stride 2 convolutions until we get the desired number of BiFPN
levels.
bifpn_num_iterations: number of BiFPN iterations. Overrided if
efficientdet_version is provided.
bifpn_num_filters: number of filters (channels) in all BiFPN layers.
Overrided if efficientdet_version is provided.
bifpn_combine_method: the method used to combine BiFPN nodes.
use_explicit_padding: unsupported by EfficientNetBiFPN. Whether to use
explicit padding when extracting features.
use_depthwise: unsupported by EfficientNetBiFPN, since BiFPN uses regular
convolutions when inputs to a node have a differing number of channels,
and use separable convolutions after combine operations.
override_base_feature_extractor_hyperparams: unsupported. Whether to
override hyperparameters of the base feature extractor with the one from
`conv_hyperparams`.
name: a string name scope to assign to the model. If 'None', Keras will
auto-generate one from the class name.
"""
super(SSDEfficientNetB6BiFPNKerasFeatureExtractor, self).__init__(
is_training=is_training,
depth_multiplier=depth_multiplier,
min_depth=min_depth,
pad_to_multiple=pad_to_multiple,
conv_hyperparams=conv_hyperparams,
freeze_batchnorm=freeze_batchnorm,
inplace_batchnorm_update=inplace_batchnorm_update,
bifpn_min_level=bifpn_min_level,
bifpn_max_level=bifpn_max_level,
bifpn_num_iterations=bifpn_num_iterations,
bifpn_num_filters=bifpn_num_filters,
bifpn_combine_method=bifpn_combine_method,
efficientnet_version='efficientnet-b6',
use_explicit_padding=use_explicit_padding,
use_depthwise=use_depthwise,
override_base_feature_extractor_hyperparams=
override_base_feature_extractor_hyperparams,
name=name)
class SSDEfficientNetB7BiFPNKerasFeatureExtractor(
SSDEfficientNetBiFPNKerasFeatureExtractor):
"""SSD Keras EfficientNet-b7 BiFPN Feature Extractor."""
def __init__(self,
is_training,
depth_multiplier,
min_depth,
pad_to_multiple,
conv_hyperparams,
freeze_batchnorm,
inplace_batchnorm_update,
bifpn_min_level=3,
bifpn_max_level=7,
bifpn_num_iterations=8,
bifpn_num_filters=384,
bifpn_combine_method='sum',
use_explicit_padding=None,
use_depthwise=None,
override_base_feature_extractor_hyperparams=None,
name='EfficientNet-B7_BiFPN'):
"""SSD Keras EfficientNet-b7 BiFPN Feature Extractor.
Args:
is_training: whether the network is in training mode.
depth_multiplier: unsupported by EfficientNetBiFPN. float, depth
multiplier for the 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: a `hyperparams_builder.KerasLayerHyperparams` object
containing convolution hyperparameters for the layers added on top of
the base feature extractor.
freeze_batchnorm: whether to freeze batch norm parameters during training
or not. When training with a small batch size (e.g. 1), it is desirable
to freeze batch norm update and use pretrained batch norm params.
inplace_batchnorm_update: whether to update batch norm moving average
values inplace. When this is false train op must add a control
dependency on tf.graphkeys.UPDATE_OPS collection in order to update
batch norm statistics.
bifpn_min_level: the highest resolution feature map to use in BiFPN. The
valid values are {2, 3, 4, 5} which map to Resnet blocks {1, 2, 3, 4}
respectively.
bifpn_max_level: the smallest resolution feature map to use in the BiFPN.
BiFPN constructions uses features maps starting from bifpn_min_level
upto the bifpn_max_level. In the case that there are not enough feature
maps in the backbone network, additional feature maps are created by
applying stride 2 convolutions until we get the desired number of BiFPN
levels.
bifpn_num_iterations: number of BiFPN iterations. Overrided if
efficientdet_version is provided.
bifpn_num_filters: number of filters (channels) in all BiFPN layers.
Overrided if efficientdet_version is provided.
bifpn_combine_method: the method used to combine BiFPN nodes.
use_explicit_padding: unsupported by EfficientNetBiFPN. Whether to use
explicit padding when extracting features.
use_depthwise: unsupported by EfficientNetBiFPN, since BiFPN uses regular
convolutions when inputs to a node have a differing number of channels,
and use separable convolutions after combine operations.
override_base_feature_extractor_hyperparams: unsupported. Whether to
override hyperparameters of the base feature extractor with the one from
`conv_hyperparams`.
name: a string name scope to assign to the model. If 'None', Keras will
auto-generate one from the class name.
"""
super(SSDEfficientNetB7BiFPNKerasFeatureExtractor, self).__init__(
is_training=is_training,
depth_multiplier=depth_multiplier,
min_depth=min_depth,
pad_to_multiple=pad_to_multiple,
conv_hyperparams=conv_hyperparams,
freeze_batchnorm=freeze_batchnorm,
inplace_batchnorm_update=inplace_batchnorm_update,
bifpn_min_level=bifpn_min_level,
bifpn_max_level=bifpn_max_level,
bifpn_num_iterations=bifpn_num_iterations,
bifpn_num_filters=bifpn_num_filters,
bifpn_combine_method=bifpn_combine_method,
efficientnet_version='efficientnet-b7',
use_explicit_padding=use_explicit_padding,
use_depthwise=use_depthwise,
override_base_feature_extractor_hyperparams=
override_base_feature_extractor_hyperparams,
name=name)
research/object_detection/models/ssd_efficientnet_bifpn_feature_extractor_tf2_test.py 0 → 100644
View file @ 31ca3b97
# Copyright 2020 The TensorFlow Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ==============================================================================
"""Tests for the ssd_efficientnet_bifpn_feature_extractor."""
import unittest
from absl.testing import parameterized
import numpy as np
import tensorflow.compat.v2 as tf
from google.protobuf import text_format
from object_detection.builders import hyperparams_builder
from object_detection.models import ssd_efficientnet_bifpn_feature_extractor
from object_detection.protos import hyperparams_pb2
from object_detection.utils import test_case
from object_detection.utils import tf_version
def _count_params(model, trainable_only=True):
"""Returns the count of all model parameters, or just trainable ones."""
if not trainable_only:
return model.count_params()
else:
return int(np.sum([
tf.keras.backend.count_params(p) for p in model.trainable_weights]))
@parameterized.parameters(
{'efficientdet_version': 'efficientdet-d0',
'efficientnet_version': 'efficientnet-b0',
'bifpn_num_iterations': 3,
'bifpn_num_filters': 64,
'bifpn_combine_method': 'fast_attention'},
{'efficientdet_version': 'efficientdet-d1',
'efficientnet_version': 'efficientnet-b1',
'bifpn_num_iterations': 4,
'bifpn_num_filters': 88,
'bifpn_combine_method': 'fast_attention'},
{'efficientdet_version': 'efficientdet-d2',
'efficientnet_version': 'efficientnet-b2',
'bifpn_num_iterations': 5,
'bifpn_num_filters': 112,
'bifpn_combine_method': 'fast_attention'},
{'efficientdet_version': 'efficientdet-d3',
'efficientnet_version': 'efficientnet-b3',
'bifpn_num_iterations': 6,
'bifpn_num_filters': 160,
'bifpn_combine_method': 'fast_attention'},
{'efficientdet_version': 'efficientdet-d4',
'efficientnet_version': 'efficientnet-b4',
'bifpn_num_iterations': 7,
'bifpn_num_filters': 224,
'bifpn_combine_method': 'fast_attention'},
{'efficientdet_version': 'efficientdet-d5',
'efficientnet_version': 'efficientnet-b5',
'bifpn_num_iterations': 7,
'bifpn_num_filters': 288,
'bifpn_combine_method': 'fast_attention'},
# efficientdet-d6 and efficientdet-d7 only differ in input size.
{'efficientdet_version': 'efficientdet-d6-d7',
'efficientnet_version': 'efficientnet-b6',
'bifpn_num_iterations': 8,
'bifpn_num_filters': 384,
'bifpn_combine_method': 'sum'})
@unittest.skipIf(tf_version.is_tf1(), 'Skipping TF2.X only test.')
class SSDEfficientNetBiFPNFeatureExtractorTest(
test_case.TestCase, parameterized.TestCase):
def _build_conv_hyperparams(self, add_batch_norm=True):
conv_hyperparams = hyperparams_pb2.Hyperparams()
conv_hyperparams_text_proto = """
force_use_bias: true
activation: SWISH
regularizer {
l2_regularizer {
weight: 0.0004
}
}
initializer {
truncated_normal_initializer {
stddev: 0.03
mean: 0.0
}
}
"""
if add_batch_norm:
batch_norm_proto = """
batch_norm {
scale: true,
decay: 0.99,
epsilon: 0.001,
}
"""
conv_hyperparams_text_proto += batch_norm_proto
text_format.Merge(conv_hyperparams_text_proto, conv_hyperparams)
return hyperparams_builder.KerasLayerHyperparams(conv_hyperparams)
def _create_feature_extractor(self,
efficientnet_version='efficientnet-b0',
bifpn_num_iterations=3,
bifpn_num_filters=64,
bifpn_combine_method='fast_attention'):
"""Constructs a new EfficientNetBiFPN feature extractor."""
depth_multiplier = 1.0
pad_to_multiple = 1
min_depth = 16
return (ssd_efficientnet_bifpn_feature_extractor
.SSDEfficientNetBiFPNKerasFeatureExtractor(
is_training=True,
depth_multiplier=depth_multiplier,
min_depth=min_depth,
pad_to_multiple=pad_to_multiple,
conv_hyperparams=self._build_conv_hyperparams(),
freeze_batchnorm=False,
inplace_batchnorm_update=False,
bifpn_min_level=3,
bifpn_max_level=7,
bifpn_num_iterations=bifpn_num_iterations,
bifpn_num_filters=bifpn_num_filters,
bifpn_combine_method=bifpn_combine_method,
efficientnet_version=efficientnet_version))
def test_efficientdet_feature_extractor_shapes(self,
efficientdet_version,
efficientnet_version,
bifpn_num_iterations,
bifpn_num_filters,
bifpn_combine_method):
feature_extractor = self._create_feature_extractor(
efficientnet_version=efficientnet_version,
bifpn_num_iterations=bifpn_num_iterations,
bifpn_num_filters=bifpn_num_filters,
bifpn_combine_method=bifpn_combine_method)
outputs = feature_extractor(np.zeros((2, 256, 256, 3), dtype=np.float32))
self.assertEqual(outputs[0].shape, (2, 32, 32, bifpn_num_filters))
self.assertEqual(outputs[1].shape, (2, 16, 16, bifpn_num_filters))
self.assertEqual(outputs[2].shape, (2, 8, 8, bifpn_num_filters))
self.assertEqual(outputs[3].shape, (2, 4, 4, bifpn_num_filters))
self.assertEqual(outputs[4].shape, (2, 2, 2, bifpn_num_filters))
def test_efficientdet_feature_extractor_params(self,
efficientdet_version,
efficientnet_version,
bifpn_num_iterations,
bifpn_num_filters,
bifpn_combine_method):
feature_extractor = self._create_feature_extractor(
efficientnet_version=efficientnet_version,
bifpn_num_iterations=bifpn_num_iterations,
bifpn_num_filters=bifpn_num_filters,
bifpn_combine_method=bifpn_combine_method)
_ = feature_extractor(np.zeros((2, 256, 256, 3), dtype=np.float32))
expected_params = {
'efficientdet-d0': 5484829,
'efficientdet-d1': 8185156,
'efficientdet-d2': 9818153,
'efficientdet-d3': 13792706,
'efficientdet-d4': 22691445,
'efficientdet-d5': 35795677,
'efficientdet-d6-d7': 53624512,
}
num_params = _count_params(feature_extractor)
self.assertEqual(expected_params[efficientdet_version], num_params)
if __name__ == '__main__':
tf.test.main()
research/object_detection/models/ssd_mobilenet_v1_keras_feature_extractor.py
View file @ 31ca3b97
...@@ -163,14 +163,3 @@ class SSDMobileNetV1KerasFeatureExtractor( ...@@ -163,14 +163,3 @@ class SSDMobileNetV1KerasFeatureExtractor(
'Conv2d_13_pointwise': image_features[1]}) 'Conv2d_13_pointwise': image_features[1]})
return list(feature_maps.values()) return list(feature_maps.values())
def restore_from_classification_checkpoint_fn(self, feature_extractor_scope):
"""Returns a map for restoring from an (object-based) checkpoint.
Args:
feature_extractor_scope: A scope name for the feature extractor (unused).
Returns:
A dict mapping keys to Keras models
"""
return {'feature_extractor': self.classification_backbone}
research/object_detection/models/ssd_mobilenet_v2_fpn_keras_feature_extractor.py
View file @ 31ca3b97
...@@ -241,14 +241,3 @@ class SSDMobileNetV2FpnKerasFeatureExtractor( ...@@ -241,14 +241,3 @@ class SSDMobileNetV2FpnKerasFeatureExtractor(
last_feature_map = layer(last_feature_map) last_feature_map = layer(last_feature_map)
feature_maps.append(last_feature_map) feature_maps.append(last_feature_map)
return feature_maps return feature_maps
def restore_from_classification_checkpoint_fn(self, feature_extractor_scope):
"""Returns a map for restoring from an (object-based) checkpoint.
Args:
feature_extractor_scope: A scope name for the feature extractor (unused).
Returns:
A dict mapping keys to Keras models
"""
return {'feature_extractor': self.classification_backbone}
research/object_detection/models/ssd_mobilenet_v2_keras_feature_extractor.py
View file @ 31ca3b97
...@@ -166,14 +166,3 @@ class SSDMobileNetV2KerasFeatureExtractor( ...@@ -166,14 +166,3 @@ class SSDMobileNetV2KerasFeatureExtractor(
'layer_19': image_features[1]}) 'layer_19': image_features[1]})
return list(feature_maps.values()) return list(feature_maps.values())
def restore_from_classification_checkpoint_fn(self, feature_extractor_scope):
"""Returns a map for restoring from an (object-based) checkpoint.
Args:
feature_extractor_scope: A scope name for the feature extractor (unused).
Returns:
A dict mapping keys to Keras models
"""
return {'feature_extractor': self.classification_backbone}
research/object_detection/models/ssd_resnet_v1_fpn_keras_feature_extractor.py
View file @ 31ca3b97
...@@ -246,17 +246,6 @@ class SSDResNetV1FpnKerasFeatureExtractor( ...@@ -246,17 +246,6 @@ class SSDResNetV1FpnKerasFeatureExtractor(
feature_maps.append(last_feature_map) feature_maps.append(last_feature_map)
return feature_maps return feature_maps
def restore_from_classification_checkpoint_fn(self, feature_extractor_scope):
"""Returns a map for restoring from an (object-based) checkpoint.
Args:
feature_extractor_scope: A scope name for the feature extractor (unused).
Returns:
A dict mapping keys to Keras models
"""
return {'feature_extractor': self.classification_backbone}
class SSDResNet50V1FpnKerasFeatureExtractor( class SSDResNet50V1FpnKerasFeatureExtractor(
SSDResNetV1FpnKerasFeatureExtractor): SSDResNetV1FpnKerasFeatureExtractor):
......
research/object_detection/packages/tf1/setup.py 0 → 100644
View file @ 31ca3b97
"""Setup script for object_detection with TF1.0."""
import os
from setuptools import find_packages
from setuptools import setup
REQUIRED_PACKAGES = ['apache-beam', 'pillow', 'lxml', 'matplotlib', 'Cython',
'contextlib2', 'tf-slim', 'six', 'pycocotools', 'scipy',
'pandas']
setup(
name='object_detection',
version='0.1',
install_requires=REQUIRED_PACKAGES,
include_package_data=True,
packages=(
[p for p in find_packages() if p.startswith('object_detection')] +
find_packages(where=os.path.join('.', 'slim'))),
package_dir={
'datasets': os.path.join('slim', 'datasets'),
'nets': os.path.join('slim', 'nets'),
'preprocessing': os.path.join('slim', 'preprocessing'),
'deployment': os.path.join('slim', 'deployment'),
'scripts': os.path.join('slim', 'scripts'),
},
description='Tensorflow Object Detection Library with TF1.0',
python_requires='>3.6',
)
research/object_detection/packages/tf2/setup.py 0 → 100644
View file @ 31ca3b97
"""Setup script for object_detection with TF2.0."""
import os
from setuptools import find_packages
from setuptools import setup
# Note: adding apache-beam to required packages causes conflict with
# tf-models-offical requirements. These packages request for incompatible
# oauth2client package.
REQUIRED_PACKAGES = ['pillow', 'lxml', 'matplotlib', 'Cython', 'contextlib2',
'tf-slim', 'six', 'pycocotools', 'scipy', 'pandas',
'tf-models-official']
setup(
name='object_detection',
version='0.1',
install_requires=REQUIRED_PACKAGES,
include_package_data=True,
packages=(
[p for p in find_packages() if p.startswith('object_detection')] +
find_packages(where=os.path.join('.', 'slim'))),
package_dir={
'datasets': os.path.join('slim', 'datasets'),
'nets': os.path.join('slim', 'nets'),
'preprocessing': os.path.join('slim', 'preprocessing'),
'deployment': os.path.join('slim', 'deployment'),
'scripts': os.path.join('slim', 'scripts'),
},
description='Tensorflow Object Detection Library',
python_requires='>3.6',
)
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