Skip to content

GitLab

Commit 47bc1813 authored by syiming's avatar syiming
Browse files

Merge remote-tracking branch 'upstream/master' into add_multilevel_crop_and_resize

parents d8611151 b035a227
Hide whitespace changes
Inline Side-by-side
Showing with 906 additions and 1448 deletions
research/object_detection/models/faster_rcnn_inception_resnet_v2_keras_feature_extractor.py
View file @ 47bc1813
......@@ -59,6 +59,7 @@ class FasterRCNNInceptionResnetV2KerasFeatureExtractor(
is_training, first_stage_features_stride, batch_norm_trainable,
weight_decay)
self._variable_dict = {}
self.classification_backbone = None
def preprocess(self, resized_inputs):
"""Faster R-CNN with Inception Resnet v2 preprocessing.
......@@ -95,19 +96,20 @@ class FasterRCNNInceptionResnetV2KerasFeatureExtractor(
And returns rpn_feature_map:
A tensor with shape [batch, height, width, depth]
"""
with tf.name_scope(name):
with tf.name_scope('InceptionResnetV2'):
model = inception_resnet_v2.inception_resnet_v2(
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)
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
keras_model = tf.keras.Model(
inputs=model.inputs,
inputs=self.classification_backbone.inputs,
outputs=proposal_features)
for variable in keras_model.variables:
self._variable_dict[variable.name[:-2]] = variable
......@@ -132,962 +134,26 @@ class FasterRCNNInceptionResnetV2KerasFeatureExtractor(
[batch_size * self.max_num_proposals, height, width, depth]
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('InceptionResnetV2'):
model = inception_resnet_v2.inception_resnet_v2(
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(
proposal_feature_maps = self.classification_backbone.get_layer(
name='block17_20_ac').output
proposal_classifier_features = model.get_layer(
proposal_classifier_features = self.classification_backbone.get_layer(
name='conv_7b_ac').output
keras_model = model_util.extract_submodel(
model=model,
model=self.classification_backbone,
inputs=proposal_feature_maps,
outputs=proposal_classifier_features)
for variable in keras_model.variables:
self._variable_dict[variable.name[:-2]] = variable
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',
'FirstStageFeatureExtractor/InceptionResnetV2/freezable_batch_norm_13/moving_mean': 'InceptionResnetV2/Repeat/block35_1/Branch_1/Conv2d_0a_1x1/BatchNorm/moving_mean',
'FirstStageFeatureExtractor/InceptionResnetV2/freezable_batch_norm_13/moving_variance': 'InceptionResnetV2/Repeat/block35_1/Branch_1/Conv2d_0a_1x1/BatchNorm/moving_variance',
'FirstStageFeatureExtractor/InceptionResnetV2/conv2d_14/kernel': 'InceptionResnetV2/Repeat/block35_1/Branch_1/Conv2d_0b_3x3/weights',
'FirstStageFeatureExtractor/InceptionResnetV2/freezable_batch_norm_14/beta': 'InceptionResnetV2/Repeat/block35_1/Branch_1/Conv2d_0b_3x3/BatchNorm/beta',
'FirstStageFeatureExtractor/InceptionResnetV2/freezable_batch_norm_14/moving_mean': 'InceptionResnetV2/Repeat/block35_1/Branch_1/Conv2d_0b_3x3/BatchNorm/moving_mean',
'FirstStageFeatureExtractor/InceptionResnetV2/freezable_batch_norm_14/moving_variance': 'InceptionResnetV2/Repeat/block35_1/Branch_1/Conv2d_0b_3x3/BatchNorm/moving_variance',
'FirstStageFeatureExtractor/InceptionResnetV2/conv2d_15/kernel': 'InceptionResnetV2/Repeat/block35_1/Branch_2/Conv2d_0a_1x1/weights',
'FirstStageFeatureExtractor/InceptionResnetV2/freezable_batch_norm_15/beta': 'InceptionResnetV2/Repeat/block35_1/Branch_2/Conv2d_0a_1x1/BatchNorm/beta',
'FirstStageFeatureExtractor/InceptionResnetV2/freezable_batch_norm_15/moving_mean': 'InceptionResnetV2/Repeat/block35_1/Branch_2/Conv2d_0a_1x1/BatchNorm/moving_mean',
'FirstStageFeatureExtractor/InceptionResnetV2/freezable_batch_norm_15/moving_variance': 'InceptionResnetV2/Repeat/block35_1/Branch_2/Conv2d_0a_1x1/BatchNorm/moving_variance',
'FirstStageFeatureExtractor/InceptionResnetV2/conv2d_16/kernel': 'InceptionResnetV2/Repeat/block35_1/Branch_2/Conv2d_0b_3x3/weights',
'FirstStageFeatureExtractor/InceptionResnetV2/freezable_batch_norm_16/beta': 'InceptionResnetV2/Repeat/block35_1/Branch_2/Conv2d_0b_3x3/BatchNorm/beta',
'FirstStageFeatureExtractor/InceptionResnetV2/freezable_batch_norm_16/moving_mean': 'InceptionResnetV2/Repeat/block35_1/Branch_2/Conv2d_0b_3x3/BatchNorm/moving_mean',
'FirstStageFeatureExtractor/InceptionResnetV2/freezable_batch_norm_16/moving_variance': 'InceptionResnetV2/Repeat/block35_1/Branch_2/Conv2d_0b_3x3/BatchNorm/moving_variance',
'FirstStageFeatureExtractor/InceptionResnetV2/conv2d_17/kernel': 'InceptionResnetV2/Repeat/block35_1/Branch_2/Conv2d_0c_3x3/weights',
'FirstStageFeatureExtractor/InceptionResnetV2/freezable_batch_norm_17/beta': 'InceptionResnetV2/Repeat/block35_1/Branch_2/Conv2d_0c_3x3/BatchNorm/beta',
'FirstStageFeatureExtractor/InceptionResnetV2/freezable_batch_norm_17/moving_mean': 'InceptionResnetV2/Repeat/block35_1/Branch_2/Conv2d_0c_3x3/BatchNorm/moving_mean',
'FirstStageFeatureExtractor/InceptionResnetV2/freezable_batch_norm_17/moving_variance': 'InceptionResnetV2/Repeat/block35_1/Branch_2/Conv2d_0c_3x3/BatchNorm/moving_variance',
'FirstStageFeatureExtractor/InceptionResnetV2/block35_1_conv/kernel': 'InceptionResnetV2/Repeat/block35_1/Conv2d_1x1/weights',
'FirstStageFeatureExtractor/InceptionResnetV2/block35_1_conv/bias': 'InceptionResnetV2/Repeat/block35_1/Conv2d_1x1/biases',
'FirstStageFeatureExtractor/InceptionResnetV2/conv2d_18/kernel': 'InceptionResnetV2/Repeat/block35_2/Branch_0/Conv2d_1x1/weights',
'FirstStageFeatureExtractor/InceptionResnetV2/freezable_batch_norm_18/beta': 'InceptionResnetV2/Repeat/block35_2/Branch_0/Conv2d_1x1/BatchNorm/beta',
'FirstStageFeatureExtractor/InceptionResnetV2/freezable_batch_norm_18/moving_mean': 'InceptionResnetV2/Repeat/block35_2/Branch_0/Conv2d_1x1/BatchNorm/moving_mean',
'FirstStageFeatureExtractor/InceptionResnetV2/freezable_batch_norm_18/moving_variance': 'InceptionResnetV2/Repeat/block35_2/Branch_0/Conv2d_1x1/BatchNorm/moving_variance',
'FirstStageFeatureExtractor/InceptionResnetV2/conv2d_19/kernel': 'InceptionResnetV2/Repeat/block35_2/Branch_1/Conv2d_0a_1x1/weights',
'FirstStageFeatureExtractor/InceptionResnetV2/freezable_batch_norm_19/beta': 'InceptionResnetV2/Repeat/block35_2/Branch_1/Conv2d_0a_1x1/BatchNorm/beta',
'FirstStageFeatureExtractor/InceptionResnetV2/freezable_batch_norm_19/moving_mean': 'InceptionResnetV2/Repeat/block35_2/Branch_1/Conv2d_0a_1x1/BatchNorm/moving_mean',
'FirstStageFeatureExtractor/InceptionResnetV2/freezable_batch_norm_19/moving_variance': 'InceptionResnetV2/Repeat/block35_2/Branch_1/Conv2d_0a_1x1/BatchNorm/moving_variance',
'FirstStageFeatureExtractor/InceptionResnetV2/conv2d_20/kernel': 'InceptionResnetV2/Repeat/block35_2/Branch_1/Conv2d_0b_3x3/weights',
'FirstStageFeatureExtractor/InceptionResnetV2/freezable_batch_norm_20/beta': 'InceptionResnetV2/Repeat/block35_2/Branch_1/Conv2d_0b_3x3/BatchNorm/beta',
'FirstStageFeatureExtractor/InceptionResnetV2/freezable_batch_norm_20/moving_mean': 'InceptionResnetV2/Repeat/block35_2/Branch_1/Conv2d_0b_3x3/BatchNorm/moving_mean',
'FirstStageFeatureExtractor/InceptionResnetV2/freezable_batch_norm_20/moving_variance': 'InceptionResnetV2/Repeat/block35_2/Branch_1/Conv2d_0b_3x3/BatchNorm/moving_variance',
'FirstStageFeatureExtractor/InceptionResnetV2/conv2d_21/kernel': 'InceptionResnetV2/Repeat/block35_2/Branch_2/Conv2d_0a_1x1/weights',
'FirstStageFeatureExtractor/InceptionResnetV2/freezable_batch_norm_21/beta': 'InceptionResnetV2/Repeat/block35_2/Branch_2/Conv2d_0a_1x1/BatchNorm/beta',
'FirstStageFeatureExtractor/InceptionResnetV2/freezable_batch_norm_21/moving_mean': 'InceptionResnetV2/Repeat/block35_2/Branch_2/Conv2d_0a_1x1/BatchNorm/moving_mean',
'FirstStageFeatureExtractor/InceptionResnetV2/freezable_batch_norm_21/moving_variance': 'InceptionResnetV2/Repeat/block35_2/Branch_2/Conv2d_0a_1x1/BatchNorm/moving_variance',
'FirstStageFeatureExtractor/InceptionResnetV2/conv2d_22/kernel': 'InceptionResnetV2/Repeat/block35_2/Branch_2/Conv2d_0b_3x3/weights',
'FirstStageFeatureExtractor/InceptionResnetV2/freezable_batch_norm_22/beta': 'InceptionResnetV2/Repeat/block35_2/Branch_2/Conv2d_0b_3x3/BatchNorm/beta',
'FirstStageFeatureExtractor/InceptionResnetV2/freezable_batch_norm_22/moving_mean': 'InceptionResnetV2/Repeat/block35_2/Branch_2/Conv2d_0b_3x3/BatchNorm/moving_mean',
'FirstStageFeatureExtractor/InceptionResnetV2/freezable_batch_norm_22/moving_variance': 'InceptionResnetV2/Repeat/block35_2/Branch_2/Conv2d_0b_3x3/BatchNorm/moving_variance',
'FirstStageFeatureExtractor/InceptionResnetV2/conv2d_23/kernel': 'InceptionResnetV2/Repeat/block35_2/Branch_2/Conv2d_0c_3x3/weights',
'FirstStageFeatureExtractor/InceptionResnetV2/freezable_batch_norm_23/beta': 'InceptionResnetV2/Repeat/block35_2/Branch_2/Conv2d_0c_3x3/BatchNorm/beta',
'FirstStageFeatureExtractor/InceptionResnetV2/freezable_batch_norm_23/moving_mean': 'InceptionResnetV2/Repeat/block35_2/Branch_2/Conv2d_0c_3x3/BatchNorm/moving_mean',
'FirstStageFeatureExtractor/InceptionResnetV2/freezable_batch_norm_23/moving_variance': 'InceptionResnetV2/Repeat/block35_2/Branch_2/Conv2d_0c_3x3/BatchNorm/moving_variance',
'FirstStageFeatureExtractor/InceptionResnetV2/block35_2_conv/kernel': 'InceptionResnetV2/Repeat/block35_2/Conv2d_1x1/weights',
'FirstStageFeatureExtractor/InceptionResnetV2/block35_2_conv/bias': 'InceptionResnetV2/Repeat/block35_2/Conv2d_1x1/biases',
'FirstStageFeatureExtractor/InceptionResnetV2/conv2d_24/kernel': 'InceptionResnetV2/Repeat/block35_3/Branch_0/Conv2d_1x1/weights',
'FirstStageFeatureExtractor/InceptionResnetV2/freezable_batch_norm_24/beta': 'InceptionResnetV2/Repeat/block35_3/Branch_0/Conv2d_1x1/BatchNorm/beta',
'FirstStageFeatureExtractor/InceptionResnetV2/freezable_batch_norm_24/moving_mean': 'InceptionResnetV2/Repeat/block35_3/Branch_0/Conv2d_1x1/BatchNorm/moving_mean',
'FirstStageFeatureExtractor/InceptionResnetV2/freezable_batch_norm_24/moving_variance': 'InceptionResnetV2/Repeat/block35_3/Branch_0/Conv2d_1x1/BatchNorm/moving_variance',
'FirstStageFeatureExtractor/InceptionResnetV2/conv2d_25/kernel': 'InceptionResnetV2/Repeat/block35_3/Branch_1/Conv2d_0a_1x1/weights',
'FirstStageFeatureExtractor/InceptionResnetV2/freezable_batch_norm_25/beta': 'InceptionResnetV2/Repeat/block35_3/Branch_1/Conv2d_0a_1x1/BatchNorm/beta',
'FirstStageFeatureExtractor/InceptionResnetV2/freezable_batch_norm_25/moving_mean': 'InceptionResnetV2/Repeat/block35_3/Branch_1/Conv2d_0a_1x1/BatchNorm/moving_mean',
'FirstStageFeatureExtractor/InceptionResnetV2/freezable_batch_norm_25/moving_variance': 'InceptionResnetV2/Repeat/block35_3/Branch_1/Conv2d_0a_1x1/BatchNorm/moving_variance',
'FirstStageFeatureExtractor/InceptionResnetV2/conv2d_26/kernel': 'InceptionResnetV2/Repeat/block35_3/Branch_1/Conv2d_0b_3x3/weights',
'FirstStageFeatureExtractor/InceptionResnetV2/freezable_batch_norm_26/beta': 'InceptionResnetV2/Repeat/block35_3/Branch_1/Conv2d_0b_3x3/BatchNorm/beta',
'FirstStageFeatureExtractor/InceptionResnetV2/freezable_batch_norm_26/moving_mean': 'InceptionResnetV2/Repeat/block35_3/Branch_1/Conv2d_0b_3x3/BatchNorm/moving_mean',
'FirstStageFeatureExtractor/InceptionResnetV2/freezable_batch_norm_26/moving_variance': 'InceptionResnetV2/Repeat/block35_3/Branch_1/Conv2d_0b_3x3/BatchNorm/moving_variance',
'FirstStageFeatureExtractor/InceptionResnetV2/conv2d_27/kernel': 'InceptionResnetV2/Repeat/block35_3/Branch_2/Conv2d_0a_1x1/weights',
'FirstStageFeatureExtractor/InceptionResnetV2/freezable_batch_norm_27/beta': 'InceptionResnetV2/Repeat/block35_3/Branch_2/Conv2d_0a_1x1/BatchNorm/beta',
'FirstStageFeatureExtractor/InceptionResnetV2/freezable_batch_norm_27/moving_mean': 'InceptionResnetV2/Repeat/block35_3/Branch_2/Conv2d_0a_1x1/BatchNorm/moving_mean',
'FirstStageFeatureExtractor/InceptionResnetV2/freezable_batch_norm_27/moving_variance': 'InceptionResnetV2/Repeat/block35_3/Branch_2/Conv2d_0a_1x1/BatchNorm/moving_variance',
'FirstStageFeatureExtractor/InceptionResnetV2/conv2d_28/kernel': 'InceptionResnetV2/Repeat/block35_3/Branch_2/Conv2d_0b_3x3/weights',
'FirstStageFeatureExtractor/InceptionResnetV2/freezable_batch_norm_28/beta': 'InceptionResnetV2/Repeat/block35_3/Branch_2/Conv2d_0b_3x3/BatchNorm/beta',
'FirstStageFeatureExtractor/InceptionResnetV2/freezable_batch_norm_28/moving_mean': 'InceptionResnetV2/Repeat/block35_3/Branch_2/Conv2d_0b_3x3/BatchNorm/moving_mean',
'FirstStageFeatureExtractor/InceptionResnetV2/freezable_batch_norm_28/moving_variance': 'InceptionResnetV2/Repeat/block35_3/Branch_2/Conv2d_0b_3x3/BatchNorm/moving_variance',
'FirstStageFeatureExtractor/InceptionResnetV2/conv2d_29/kernel': 'InceptionResnetV2/Repeat/block35_3/Branch_2/Conv2d_0c_3x3/weights',
'FirstStageFeatureExtractor/InceptionResnetV2/freezable_batch_norm_29/beta': 'InceptionResnetV2/Repeat/block35_3/Branch_2/Conv2d_0c_3x3/BatchNorm/beta',
'FirstStageFeatureExtractor/InceptionResnetV2/freezable_batch_norm_29/moving_mean': 'InceptionResnetV2/Repeat/block35_3/Branch_2/Conv2d_0c_3x3/BatchNorm/moving_mean',
'FirstStageFeatureExtractor/InceptionResnetV2/freezable_batch_norm_29/moving_variance': 'InceptionResnetV2/Repeat/block35_3/Branch_2/Conv2d_0c_3x3/BatchNorm/moving_variance',
'FirstStageFeatureExtractor/InceptionResnetV2/block35_3_conv/kernel': 'InceptionResnetV2/Repeat/block35_3/Conv2d_1x1/weights',
'FirstStageFeatureExtractor/InceptionResnetV2/block35_3_conv/bias': 'InceptionResnetV2/Repeat/block35_3/Conv2d_1x1/biases',
'FirstStageFeatureExtractor/InceptionResnetV2/conv2d_30/kernel': 'InceptionResnetV2/Repeat/block35_4/Branch_0/Conv2d_1x1/weights',
'FirstStageFeatureExtractor/InceptionResnetV2/freezable_batch_norm_30/beta': 'InceptionResnetV2/Repeat/block35_4/Branch_0/Conv2d_1x1/BatchNorm/beta',
'FirstStageFeatureExtractor/InceptionResnetV2/freezable_batch_norm_30/moving_mean': 'InceptionResnetV2/Repeat/block35_4/Branch_0/Conv2d_1x1/BatchNorm/moving_mean',
'FirstStageFeatureExtractor/InceptionResnetV2/freezable_batch_norm_30/moving_variance': 'InceptionResnetV2/Repeat/block35_4/Branch_0/Conv2d_1x1/BatchNorm/moving_variance',
'FirstStageFeatureExtractor/InceptionResnetV2/conv2d_31/kernel': 'InceptionResnetV2/Repeat/block35_4/Branch_1/Conv2d_0a_1x1/weights',
'FirstStageFeatureExtractor/InceptionResnetV2/freezable_batch_norm_31/beta': 'InceptionResnetV2/Repeat/block35_4/Branch_1/Conv2d_0a_1x1/BatchNorm/beta',
'FirstStageFeatureExtractor/InceptionResnetV2/freezable_batch_norm_31/moving_mean': 'InceptionResnetV2/Repeat/block35_4/Branch_1/Conv2d_0a_1x1/BatchNorm/moving_mean',
'FirstStageFeatureExtractor/InceptionResnetV2/freezable_batch_norm_31/moving_variance': 'InceptionResnetV2/Repeat/block35_4/Branch_1/Conv2d_0a_1x1/BatchNorm/moving_variance',
'FirstStageFeatureExtractor/InceptionResnetV2/conv2d_32/kernel': 'InceptionResnetV2/Repeat/block35_4/Branch_1/Conv2d_0b_3x3/weights',
'FirstStageFeatureExtractor/InceptionResnetV2/freezable_batch_norm_32/beta': 'InceptionResnetV2/Repeat/block35_4/Branch_1/Conv2d_0b_3x3/BatchNorm/beta',
'FirstStageFeatureExtractor/InceptionResnetV2/freezable_batch_norm_32/moving_mean': 'InceptionResnetV2/Repeat/block35_4/Branch_1/Conv2d_0b_3x3/BatchNorm/moving_mean',
'FirstStageFeatureExtractor/InceptionResnetV2/freezable_batch_norm_32/moving_variance': 'InceptionResnetV2/Repeat/block35_4/Branch_1/Conv2d_0b_3x3/BatchNorm/moving_variance',
'FirstStageFeatureExtractor/InceptionResnetV2/conv2d_33/kernel': 'InceptionResnetV2/Repeat/block35_4/Branch_2/Conv2d_0a_1x1/weights',
'FirstStageFeatureExtractor/InceptionResnetV2/freezable_batch_norm_33/beta': 'InceptionResnetV2/Repeat/block35_4/Branch_2/Conv2d_0a_1x1/BatchNorm/beta',
'FirstStageFeatureExtractor/InceptionResnetV2/freezable_batch_norm_33/moving_mean': 'InceptionResnetV2/Repeat/block35_4/Branch_2/Conv2d_0a_1x1/BatchNorm/moving_mean',
'FirstStageFeatureExtractor/InceptionResnetV2/freezable_batch_norm_33/moving_variance': 'InceptionResnetV2/Repeat/block35_4/Branch_2/Conv2d_0a_1x1/BatchNorm/moving_variance',
'FirstStageFeatureExtractor/InceptionResnetV2/conv2d_34/kernel': 'InceptionResnetV2/Repeat/block35_4/Branch_2/Conv2d_0b_3x3/weights',
'FirstStageFeatureExtractor/InceptionResnetV2/freezable_batch_norm_34/beta': 'InceptionResnetV2/Repeat/block35_4/Branch_2/Conv2d_0b_3x3/BatchNorm/beta',
'FirstStageFeatureExtractor/InceptionResnetV2/freezable_batch_norm_34/moving_mean': 'InceptionResnetV2/Repeat/block35_4/Branch_2/Conv2d_0b_3x3/BatchNorm/moving_mean',
'FirstStageFeatureExtractor/InceptionResnetV2/freezable_batch_norm_34/moving_variance': 'InceptionResnetV2/Repeat/block35_4/Branch_2/Conv2d_0b_3x3/BatchNorm/moving_variance',
'FirstStageFeatureExtractor/InceptionResnetV2/conv2d_35/kernel': 'InceptionResnetV2/Repeat/block35_4/Branch_2/Conv2d_0c_3x3/weights',
'FirstStageFeatureExtractor/InceptionResnetV2/freezable_batch_norm_35/beta': 'InceptionResnetV2/Repeat/block35_4/Branch_2/Conv2d_0c_3x3/BatchNorm/beta',
'FirstStageFeatureExtractor/InceptionResnetV2/freezable_batch_norm_35/moving_mean': 'InceptionResnetV2/Repeat/block35_4/Branch_2/Conv2d_0c_3x3/BatchNorm/moving_mean',
'FirstStageFeatureExtractor/InceptionResnetV2/freezable_batch_norm_35/moving_variance': 'InceptionResnetV2/Repeat/block35_4/Branch_2/Conv2d_0c_3x3/BatchNorm/moving_variance',
'FirstStageFeatureExtractor/InceptionResnetV2/block35_4_conv/kernel': 'InceptionResnetV2/Repeat/block35_4/Conv2d_1x1/weights',
'FirstStageFeatureExtractor/InceptionResnetV2/block35_4_conv/bias': 'InceptionResnetV2/Repeat/block35_4/Conv2d_1x1/biases',
'FirstStageFeatureExtractor/InceptionResnetV2/conv2d_36/kernel': 'InceptionResnetV2/Repeat/block35_5/Branch_0/Conv2d_1x1/weights',
'FirstStageFeatureExtractor/InceptionResnetV2/freezable_batch_norm_36/beta': 'InceptionResnetV2/Repeat/block35_5/Branch_0/Conv2d_1x1/BatchNorm/beta',
'FirstStageFeatureExtractor/InceptionResnetV2/freezable_batch_norm_36/moving_mean': 'InceptionResnetV2/Repeat/block35_5/Branch_0/Conv2d_1x1/BatchNorm/moving_mean',
'FirstStageFeatureExtractor/InceptionResnetV2/freezable_batch_norm_36/moving_variance': 'InceptionResnetV2/Repeat/block35_5/Branch_0/Conv2d_1x1/BatchNorm/moving_variance',
'FirstStageFeatureExtractor/InceptionResnetV2/conv2d_37/kernel': 'InceptionResnetV2/Repeat/block35_5/Branch_1/Conv2d_0a_1x1/weights',
'FirstStageFeatureExtractor/InceptionResnetV2/freezable_batch_norm_37/beta': 'InceptionResnetV2/Repeat/block35_5/Branch_1/Conv2d_0a_1x1/BatchNorm/beta',
'FirstStageFeatureExtractor/InceptionResnetV2/freezable_batch_norm_37/moving_mean': 'InceptionResnetV2/Repeat/block35_5/Branch_1/Conv2d_0a_1x1/BatchNorm/moving_mean',
'FirstStageFeatureExtractor/InceptionResnetV2/freezable_batch_norm_37/moving_variance': 'InceptionResnetV2/Repeat/block35_5/Branch_1/Conv2d_0a_1x1/BatchNorm/moving_variance',
'FirstStageFeatureExtractor/InceptionResnetV2/conv2d_38/kernel': 'InceptionResnetV2/Repeat/block35_5/Branch_1/Conv2d_0b_3x3/weights',
'FirstStageFeatureExtractor/InceptionResnetV2/freezable_batch_norm_38/beta': 'InceptionResnetV2/Repeat/block35_5/Branch_1/Conv2d_0b_3x3/BatchNorm/beta',
'FirstStageFeatureExtractor/InceptionResnetV2/freezable_batch_norm_38/moving_mean': 'InceptionResnetV2/Repeat/block35_5/Branch_1/Conv2d_0b_3x3/BatchNorm/moving_mean',
'FirstStageFeatureExtractor/InceptionResnetV2/freezable_batch_norm_38/moving_variance': 'InceptionResnetV2/Repeat/block35_5/Branch_1/Conv2d_0b_3x3/BatchNorm/moving_variance',
'FirstStageFeatureExtractor/InceptionResnetV2/conv2d_39/kernel': 'InceptionResnetV2/Repeat/block35_5/Branch_2/Conv2d_0a_1x1/weights',
'FirstStageFeatureExtractor/InceptionResnetV2/freezable_batch_norm_39/beta': 'InceptionResnetV2/Repeat/block35_5/Branch_2/Conv2d_0a_1x1/BatchNorm/beta',
'FirstStageFeatureExtractor/InceptionResnetV2/freezable_batch_norm_39/moving_mean': 'InceptionResnetV2/Repeat/block35_5/Branch_2/Conv2d_0a_1x1/BatchNorm/moving_mean',
'FirstStageFeatureExtractor/InceptionResnetV2/freezable_batch_norm_39/moving_variance': 'InceptionResnetV2/Repeat/block35_5/Branch_2/Conv2d_0a_1x1/BatchNorm/moving_variance',
'FirstStageFeatureExtractor/InceptionResnetV2/conv2d_40/kernel': 'InceptionResnetV2/Repeat/block35_5/Branch_2/Conv2d_0b_3x3/weights',
'FirstStageFeatureExtractor/InceptionResnetV2/freezable_batch_norm_40/beta': 'InceptionResnetV2/Repeat/block35_5/Branch_2/Conv2d_0b_3x3/BatchNorm/beta',
'FirstStageFeatureExtractor/InceptionResnetV2/freezable_batch_norm_40/moving_mean': 'InceptionResnetV2/Repeat/block35_5/Branch_2/Conv2d_0b_3x3/BatchNorm/moving_mean',
'FirstStageFeatureExtractor/InceptionResnetV2/freezable_batch_norm_40/moving_variance': 'InceptionResnetV2/Repeat/block35_5/Branch_2/Conv2d_0b_3x3/BatchNorm/moving_variance',
'FirstStageFeatureExtractor/InceptionResnetV2/conv2d_41/kernel': 'InceptionResnetV2/Repeat/block35_5/Branch_2/Conv2d_0c_3x3/weights',
'FirstStageFeatureExtractor/InceptionResnetV2/freezable_batch_norm_41/beta': 'InceptionResnetV2/Repeat/block35_5/Branch_2/Conv2d_0c_3x3/BatchNorm/beta',
'FirstStageFeatureExtractor/InceptionResnetV2/freezable_batch_norm_41/moving_mean': 'InceptionResnetV2/Repeat/block35_5/Branch_2/Conv2d_0c_3x3/BatchNorm/moving_mean',
'FirstStageFeatureExtractor/InceptionResnetV2/freezable_batch_norm_41/moving_variance': 'InceptionResnetV2/Repeat/block35_5/Branch_2/Conv2d_0c_3x3/BatchNorm/moving_variance',
'FirstStageFeatureExtractor/InceptionResnetV2/block35_5_conv/kernel': 'InceptionResnetV2/Repeat/block35_5/Conv2d_1x1/weights',
'FirstStageFeatureExtractor/InceptionResnetV2/block35_5_conv/bias': 'InceptionResnetV2/Repeat/block35_5/Conv2d_1x1/biases',
'FirstStageFeatureExtractor/InceptionResnetV2/conv2d_42/kernel': 'InceptionResnetV2/Repeat/block35_6/Branch_0/Conv2d_1x1/weights',
'FirstStageFeatureExtractor/InceptionResnetV2/freezable_batch_norm_42/beta': 'InceptionResnetV2/Repeat/block35_6/Branch_0/Conv2d_1x1/BatchNorm/beta',
'FirstStageFeatureExtractor/InceptionResnetV2/freezable_batch_norm_42/moving_mean': 'InceptionResnetV2/Repeat/block35_6/Branch_0/Conv2d_1x1/BatchNorm/moving_mean',
'FirstStageFeatureExtractor/InceptionResnetV2/freezable_batch_norm_42/moving_variance': 'InceptionResnetV2/Repeat/block35_6/Branch_0/Conv2d_1x1/BatchNorm/moving_variance',
'FirstStageFeatureExtractor/InceptionResnetV2/conv2d_43/kernel': 'InceptionResnetV2/Repeat/block35_6/Branch_1/Conv2d_0a_1x1/weights',
'FirstStageFeatureExtractor/InceptionResnetV2/freezable_batch_norm_43/beta': 'InceptionResnetV2/Repeat/block35_6/Branch_1/Conv2d_0a_1x1/BatchNorm/beta',
'FirstStageFeatureExtractor/InceptionResnetV2/freezable_batch_norm_43/moving_mean': 'InceptionResnetV2/Repeat/block35_6/Branch_1/Conv2d_0a_1x1/BatchNorm/moving_mean',
'FirstStageFeatureExtractor/InceptionResnetV2/freezable_batch_norm_43/moving_variance': 'InceptionResnetV2/Repeat/block35_6/Branch_1/Conv2d_0a_1x1/BatchNorm/moving_variance',
'FirstStageFeatureExtractor/InceptionResnetV2/conv2d_44/kernel': 'InceptionResnetV2/Repeat/block35_6/Branch_1/Conv2d_0b_3x3/weights',
'FirstStageFeatureExtractor/InceptionResnetV2/freezable_batch_norm_44/beta': 'InceptionResnetV2/Repeat/block35_6/Branch_1/Conv2d_0b_3x3/BatchNorm/beta',
'FirstStageFeatureExtractor/InceptionResnetV2/freezable_batch_norm_44/moving_mean': 'InceptionResnetV2/Repeat/block35_6/Branch_1/Conv2d_0b_3x3/BatchNorm/moving_mean',
'FirstStageFeatureExtractor/InceptionResnetV2/freezable_batch_norm_44/moving_variance': 'InceptionResnetV2/Repeat/block35_6/Branch_1/Conv2d_0b_3x3/BatchNorm/moving_variance',
'FirstStageFeatureExtractor/InceptionResnetV2/conv2d_45/kernel': 'InceptionResnetV2/Repeat/block35_6/Branch_2/Conv2d_0a_1x1/weights',
'FirstStageFeatureExtractor/InceptionResnetV2/freezable_batch_norm_45/beta': 'InceptionResnetV2/Repeat/block35_6/Branch_2/Conv2d_0a_1x1/BatchNorm/beta',
'FirstStageFeatureExtractor/InceptionResnetV2/freezable_batch_norm_45/moving_mean': 'InceptionResnetV2/Repeat/block35_6/Branch_2/Conv2d_0a_1x1/BatchNorm/moving_mean',
'FirstStageFeatureExtractor/InceptionResnetV2/freezable_batch_norm_45/moving_variance': 'InceptionResnetV2/Repeat/block35_6/Branch_2/Conv2d_0a_1x1/BatchNorm/moving_variance',
'FirstStageFeatureExtractor/InceptionResnetV2/conv2d_46/kernel': 'InceptionResnetV2/Repeat/block35_6/Branch_2/Conv2d_0b_3x3/weights',
'FirstStageFeatureExtractor/InceptionResnetV2/freezable_batch_norm_46/beta': 'InceptionResnetV2/Repeat/block35_6/Branch_2/Conv2d_0b_3x3/BatchNorm/beta',
'FirstStageFeatureExtractor/InceptionResnetV2/freezable_batch_norm_46/moving_mean': 'InceptionResnetV2/Repeat/block35_6/Branch_2/Conv2d_0b_3x3/BatchNorm/moving_mean',
'FirstStageFeatureExtractor/InceptionResnetV2/freezable_batch_norm_46/moving_variance': 'InceptionResnetV2/Repeat/block35_6/Branch_2/Conv2d_0b_3x3/BatchNorm/moving_variance',
'FirstStageFeatureExtractor/InceptionResnetV2/conv2d_47/kernel': 'InceptionResnetV2/Repeat/block35_6/Branch_2/Conv2d_0c_3x3/weights',
'FirstStageFeatureExtractor/InceptionResnetV2/freezable_batch_norm_47/beta': 'InceptionResnetV2/Repeat/block35_6/Branch_2/Conv2d_0c_3x3/BatchNorm/beta',
'FirstStageFeatureExtractor/InceptionResnetV2/freezable_batch_norm_47/moving_mean': 'InceptionResnetV2/Repeat/block35_6/Branch_2/Conv2d_0c_3x3/BatchNorm/moving_mean',
'FirstStageFeatureExtractor/InceptionResnetV2/freezable_batch_norm_47/moving_variance': 'InceptionResnetV2/Repeat/block35_6/Branch_2/Conv2d_0c_3x3/BatchNorm/moving_variance',
'FirstStageFeatureExtractor/InceptionResnetV2/block35_6_conv/kernel': 'InceptionResnetV2/Repeat/block35_6/Conv2d_1x1/weights',
'FirstStageFeatureExtractor/InceptionResnetV2/block35_6_conv/bias': 'InceptionResnetV2/Repeat/block35_6/Conv2d_1x1/biases',
'FirstStageFeatureExtractor/InceptionResnetV2/conv2d_48/kernel': 'InceptionResnetV2/Repeat/block35_7/Branch_0/Conv2d_1x1/weights',
'FirstStageFeatureExtractor/InceptionResnetV2/freezable_batch_norm_48/beta': 'InceptionResnetV2/Repeat/block35_7/Branch_0/Conv2d_1x1/BatchNorm/beta',
'FirstStageFeatureExtractor/InceptionResnetV2/freezable_batch_norm_48/moving_mean': 'InceptionResnetV2/Repeat/block35_7/Branch_0/Conv2d_1x1/BatchNorm/moving_mean',
'FirstStageFeatureExtractor/InceptionResnetV2/freezable_batch_norm_48/moving_variance': 'InceptionResnetV2/Repeat/block35_7/Branch_0/Conv2d_1x1/BatchNorm/moving_variance',
'FirstStageFeatureExtractor/InceptionResnetV2/conv2d_49/kernel': 'InceptionResnetV2/Repeat/block35_7/Branch_1/Conv2d_0a_1x1/weights',
'FirstStageFeatureExtractor/InceptionResnetV2/freezable_batch_norm_49/beta': 'InceptionResnetV2/Repeat/block35_7/Branch_1/Conv2d_0a_1x1/BatchNorm/beta',
'FirstStageFeatureExtractor/InceptionResnetV2/freezable_batch_norm_49/moving_mean': 'InceptionResnetV2/Repeat/block35_7/Branch_1/Conv2d_0a_1x1/BatchNorm/moving_mean',
'FirstStageFeatureExtractor/InceptionResnetV2/freezable_batch_norm_49/moving_variance': 'InceptionResnetV2/Repeat/block35_7/Branch_1/Conv2d_0a_1x1/BatchNorm/moving_variance',
'FirstStageFeatureExtractor/InceptionResnetV2/conv2d_50/kernel': 'InceptionResnetV2/Repeat/block35_7/Branch_1/Conv2d_0b_3x3/weights',
'FirstStageFeatureExtractor/InceptionResnetV2/freezable_batch_norm_50/beta': 'InceptionResnetV2/Repeat/block35_7/Branch_1/Conv2d_0b_3x3/BatchNorm/beta',
'FirstStageFeatureExtractor/InceptionResnetV2/freezable_batch_norm_50/moving_mean': 'InceptionResnetV2/Repeat/block35_7/Branch_1/Conv2d_0b_3x3/BatchNorm/moving_mean',
'FirstStageFeatureExtractor/InceptionResnetV2/freezable_batch_norm_50/moving_variance': 'InceptionResnetV2/Repeat/block35_7/Branch_1/Conv2d_0b_3x3/BatchNorm/moving_variance',
'FirstStageFeatureExtractor/InceptionResnetV2/conv2d_51/kernel': 'InceptionResnetV2/Repeat/block35_7/Branch_2/Conv2d_0a_1x1/weights',
'FirstStageFeatureExtractor/InceptionResnetV2/freezable_batch_norm_51/beta': 'InceptionResnetV2/Repeat/block35_7/Branch_2/Conv2d_0a_1x1/BatchNorm/beta',
'FirstStageFeatureExtractor/InceptionResnetV2/freezable_batch_norm_51/moving_mean': 'InceptionResnetV2/Repeat/block35_7/Branch_2/Conv2d_0a_1x1/BatchNorm/moving_mean',
'FirstStageFeatureExtractor/InceptionResnetV2/freezable_batch_norm_51/moving_variance': 'InceptionResnetV2/Repeat/block35_7/Branch_2/Conv2d_0a_1x1/BatchNorm/moving_variance',
'FirstStageFeatureExtractor/InceptionResnetV2/conv2d_52/kernel': 'InceptionResnetV2/Repeat/block35_7/Branch_2/Conv2d_0b_3x3/weights',
'FirstStageFeatureExtractor/InceptionResnetV2/freezable_batch_norm_52/beta': 'InceptionResnetV2/Repeat/block35_7/Branch_2/Conv2d_0b_3x3/BatchNorm/beta',
'FirstStageFeatureExtractor/InceptionResnetV2/freezable_batch_norm_52/moving_mean': 'InceptionResnetV2/Repeat/block35_7/Branch_2/Conv2d_0b_3x3/BatchNorm/moving_mean',
'FirstStageFeatureExtractor/InceptionResnetV2/freezable_batch_norm_52/moving_variance': 'InceptionResnetV2/Repeat/block35_7/Branch_2/Conv2d_0b_3x3/BatchNorm/moving_variance',
'FirstStageFeatureExtractor/InceptionResnetV2/conv2d_53/kernel': 'InceptionResnetV2/Repeat/block35_7/Branch_2/Conv2d_0c_3x3/weights',
'FirstStageFeatureExtractor/InceptionResnetV2/freezable_batch_norm_53/beta': 'InceptionResnetV2/Repeat/block35_7/Branch_2/Conv2d_0c_3x3/BatchNorm/beta',
'FirstStageFeatureExtractor/InceptionResnetV2/freezable_batch_norm_53/moving_mean': 'InceptionResnetV2/Repeat/block35_7/Branch_2/Conv2d_0c_3x3/BatchNorm/moving_mean',
'FirstStageFeatureExtractor/InceptionResnetV2/freezable_batch_norm_53/moving_variance': 'InceptionResnetV2/Repeat/block35_7/Branch_2/Conv2d_0c_3x3/BatchNorm/moving_variance',
'FirstStageFeatureExtractor/InceptionResnetV2/block35_7_conv/kernel': 'InceptionResnetV2/Repeat/block35_7/Conv2d_1x1/weights',
'FirstStageFeatureExtractor/InceptionResnetV2/block35_7_conv/bias': 'InceptionResnetV2/Repeat/block35_7/Conv2d_1x1/biases',
'FirstStageFeatureExtractor/InceptionResnetV2/conv2d_54/kernel': 'InceptionResnetV2/Repeat/block35_8/Branch_0/Conv2d_1x1/weights',
'FirstStageFeatureExtractor/InceptionResnetV2/freezable_batch_norm_54/beta': 'InceptionResnetV2/Repeat/block35_8/Branch_0/Conv2d_1x1/BatchNorm/beta',
'FirstStageFeatureExtractor/InceptionResnetV2/freezable_batch_norm_54/moving_mean': 'InceptionResnetV2/Repeat/block35_8/Branch_0/Conv2d_1x1/BatchNorm/moving_mean',
'FirstStageFeatureExtractor/InceptionResnetV2/freezable_batch_norm_54/moving_variance': 'InceptionResnetV2/Repeat/block35_8/Branch_0/Conv2d_1x1/BatchNorm/moving_variance',
'FirstStageFeatureExtractor/InceptionResnetV2/conv2d_55/kernel': 'InceptionResnetV2/Repeat/block35_8/Branch_1/Conv2d_0a_1x1/weights',
'FirstStageFeatureExtractor/InceptionResnetV2/freezable_batch_norm_55/beta': 'InceptionResnetV2/Repeat/block35_8/Branch_1/Conv2d_0a_1x1/BatchNorm/beta',
'FirstStageFeatureExtractor/InceptionResnetV2/freezable_batch_norm_55/moving_mean': 'InceptionResnetV2/Repeat/block35_8/Branch_1/Conv2d_0a_1x1/BatchNorm/moving_mean',
'FirstStageFeatureExtractor/InceptionResnetV2/freezable_batch_norm_55/moving_variance': 'InceptionResnetV2/Repeat/block35_8/Branch_1/Conv2d_0a_1x1/BatchNorm/moving_variance',
'FirstStageFeatureExtractor/InceptionResnetV2/conv2d_56/kernel': 'InceptionResnetV2/Repeat/block35_8/Branch_1/Conv2d_0b_3x3/weights',
'FirstStageFeatureExtractor/InceptionResnetV2/freezable_batch_norm_56/beta': 'InceptionResnetV2/Repeat/block35_8/Branch_1/Conv2d_0b_3x3/BatchNorm/beta',
'FirstStageFeatureExtractor/InceptionResnetV2/freezable_batch_norm_56/moving_mean': 'InceptionResnetV2/Repeat/block35_8/Branch_1/Conv2d_0b_3x3/BatchNorm/moving_mean',
'FirstStageFeatureExtractor/InceptionResnetV2/freezable_batch_norm_56/moving_variance': 'InceptionResnetV2/Repeat/block35_8/Branch_1/Conv2d_0b_3x3/BatchNorm/moving_variance',
'FirstStageFeatureExtractor/InceptionResnetV2/conv2d_57/kernel': 'InceptionResnetV2/Repeat/block35_8/Branch_2/Conv2d_0a_1x1/weights',
'FirstStageFeatureExtractor/InceptionResnetV2/freezable_batch_norm_57/beta': 'InceptionResnetV2/Repeat/block35_8/Branch_2/Conv2d_0a_1x1/BatchNorm/beta',
'FirstStageFeatureExtractor/InceptionResnetV2/freezable_batch_norm_57/moving_mean': 'InceptionResnetV2/Repeat/block35_8/Branch_2/Conv2d_0a_1x1/BatchNorm/moving_mean',
'FirstStageFeatureExtractor/InceptionResnetV2/freezable_batch_norm_57/moving_variance': 'InceptionResnetV2/Repeat/block35_8/Branch_2/Conv2d_0a_1x1/BatchNorm/moving_variance',
'FirstStageFeatureExtractor/InceptionResnetV2/conv2d_58/kernel': 'InceptionResnetV2/Repeat/block35_8/Branch_2/Conv2d_0b_3x3/weights',
'FirstStageFeatureExtractor/InceptionResnetV2/freezable_batch_norm_58/beta': 'InceptionResnetV2/Repeat/block35_8/Branch_2/Conv2d_0b_3x3/BatchNorm/beta',
'FirstStageFeatureExtractor/InceptionResnetV2/freezable_batch_norm_58/moving_mean': 'InceptionResnetV2/Repeat/block35_8/Branch_2/Conv2d_0b_3x3/BatchNorm/moving_mean',
'FirstStageFeatureExtractor/InceptionResnetV2/freezable_batch_norm_58/moving_variance': 'InceptionResnetV2/Repeat/block35_8/Branch_2/Conv2d_0b_3x3/BatchNorm/moving_variance',
'FirstStageFeatureExtractor/InceptionResnetV2/conv2d_59/kernel': 'InceptionResnetV2/Repeat/block35_8/Branch_2/Conv2d_0c_3x3/weights',
'FirstStageFeatureExtractor/InceptionResnetV2/freezable_batch_norm_59/beta': 'InceptionResnetV2/Repeat/block35_8/Branch_2/Conv2d_0c_3x3/BatchNorm/beta',
'FirstStageFeatureExtractor/InceptionResnetV2/freezable_batch_norm_59/moving_mean': 'InceptionResnetV2/Repeat/block35_8/Branch_2/Conv2d_0c_3x3/BatchNorm/moving_mean',
'FirstStageFeatureExtractor/InceptionResnetV2/freezable_batch_norm_59/moving_variance': 'InceptionResnetV2/Repeat/block35_8/Branch_2/Conv2d_0c_3x3/BatchNorm/moving_variance',
'FirstStageFeatureExtractor/InceptionResnetV2/block35_8_conv/kernel': 'InceptionResnetV2/Repeat/block35_8/Conv2d_1x1/weights',
'FirstStageFeatureExtractor/InceptionResnetV2/block35_8_conv/bias': 'InceptionResnetV2/Repeat/block35_8/Conv2d_1x1/biases',
'FirstStageFeatureExtractor/InceptionResnetV2/conv2d_60/kernel': 'InceptionResnetV2/Repeat/block35_9/Branch_0/Conv2d_1x1/weights',
'FirstStageFeatureExtractor/InceptionResnetV2/freezable_batch_norm_60/beta': 'InceptionResnetV2/Repeat/block35_9/Branch_0/Conv2d_1x1/BatchNorm/beta',
'FirstStageFeatureExtractor/InceptionResnetV2/freezable_batch_norm_60/moving_mean': 'InceptionResnetV2/Repeat/block35_9/Branch_0/Conv2d_1x1/BatchNorm/moving_mean',
'FirstStageFeatureExtractor/InceptionResnetV2/freezable_batch_norm_60/moving_variance': 'InceptionResnetV2/Repeat/block35_9/Branch_0/Conv2d_1x1/BatchNorm/moving_variance',
'FirstStageFeatureExtractor/InceptionResnetV2/conv2d_61/kernel': 'InceptionResnetV2/Repeat/block35_9/Branch_1/Conv2d_0a_1x1/weights',
'FirstStageFeatureExtractor/InceptionResnetV2/freezable_batch_norm_61/beta': 'InceptionResnetV2/Repeat/block35_9/Branch_1/Conv2d_0a_1x1/BatchNorm/beta',
'FirstStageFeatureExtractor/InceptionResnetV2/freezable_batch_norm_61/moving_mean': 'InceptionResnetV2/Repeat/block35_9/Branch_1/Conv2d_0a_1x1/BatchNorm/moving_mean',
'FirstStageFeatureExtractor/InceptionResnetV2/freezable_batch_norm_61/moving_variance': 'InceptionResnetV2/Repeat/block35_9/Branch_1/Conv2d_0a_1x1/BatchNorm/moving_variance',
'FirstStageFeatureExtractor/InceptionResnetV2/conv2d_62/kernel': 'InceptionResnetV2/Repeat/block35_9/Branch_1/Conv2d_0b_3x3/weights',
'FirstStageFeatureExtractor/InceptionResnetV2/freezable_batch_norm_62/beta': 'InceptionResnetV2/Repeat/block35_9/Branch_1/Conv2d_0b_3x3/BatchNorm/beta',
'FirstStageFeatureExtractor/InceptionResnetV2/freezable_batch_norm_62/moving_mean': 'InceptionResnetV2/Repeat/block35_9/Branch_1/Conv2d_0b_3x3/BatchNorm/moving_mean',
'FirstStageFeatureExtractor/InceptionResnetV2/freezable_batch_norm_62/moving_variance': 'InceptionResnetV2/Repeat/block35_9/Branch_1/Conv2d_0b_3x3/BatchNorm/moving_variance',
'FirstStageFeatureExtractor/InceptionResnetV2/conv2d_63/kernel': 'InceptionResnetV2/Repeat/block35_9/Branch_2/Conv2d_0a_1x1/weights',
'FirstStageFeatureExtractor/InceptionResnetV2/freezable_batch_norm_63/beta': 'InceptionResnetV2/Repeat/block35_9/Branch_2/Conv2d_0a_1x1/BatchNorm/beta',
'FirstStageFeatureExtractor/InceptionResnetV2/freezable_batch_norm_63/moving_mean': 'InceptionResnetV2/Repeat/block35_9/Branch_2/Conv2d_0a_1x1/BatchNorm/moving_mean',
'FirstStageFeatureExtractor/InceptionResnetV2/freezable_batch_norm_63/moving_variance': 'InceptionResnetV2/Repeat/block35_9/Branch_2/Conv2d_0a_1x1/BatchNorm/moving_variance',
'FirstStageFeatureExtractor/InceptionResnetV2/conv2d_64/kernel': 'InceptionResnetV2/Repeat/block35_9/Branch_2/Conv2d_0b_3x3/weights',
'FirstStageFeatureExtractor/InceptionResnetV2/freezable_batch_norm_64/beta': 'InceptionResnetV2/Repeat/block35_9/Branch_2/Conv2d_0b_3x3/BatchNorm/beta',
'FirstStageFeatureExtractor/InceptionResnetV2/freezable_batch_norm_64/moving_mean': 'InceptionResnetV2/Repeat/block35_9/Branch_2/Conv2d_0b_3x3/BatchNorm/moving_mean',
'FirstStageFeatureExtractor/InceptionResnetV2/freezable_batch_norm_64/moving_variance': 'InceptionResnetV2/Repeat/block35_9/Branch_2/Conv2d_0b_3x3/BatchNorm/moving_variance',
'FirstStageFeatureExtractor/InceptionResnetV2/conv2d_65/kernel': 'InceptionResnetV2/Repeat/block35_9/Branch_2/Conv2d_0c_3x3/weights',
'FirstStageFeatureExtractor/InceptionResnetV2/freezable_batch_norm_65/beta': 'InceptionResnetV2/Repeat/block35_9/Branch_2/Conv2d_0c_3x3/BatchNorm/beta',
'FirstStageFeatureExtractor/InceptionResnetV2/freezable_batch_norm_65/moving_mean': 'InceptionResnetV2/Repeat/block35_9/Branch_2/Conv2d_0c_3x3/BatchNorm/moving_mean',
'FirstStageFeatureExtractor/InceptionResnetV2/freezable_batch_norm_65/moving_variance': 'InceptionResnetV2/Repeat/block35_9/Branch_2/Conv2d_0c_3x3/BatchNorm/moving_variance',
'FirstStageFeatureExtractor/InceptionResnetV2/block35_9_conv/kernel': 'InceptionResnetV2/Repeat/block35_9/Conv2d_1x1/weights',
'FirstStageFeatureExtractor/InceptionResnetV2/block35_9_conv/bias': 'InceptionResnetV2/Repeat/block35_9/Conv2d_1x1/biases',
'FirstStageFeatureExtractor/InceptionResnetV2/conv2d_66/kernel': 'InceptionResnetV2/Repeat/block35_10/Branch_0/Conv2d_1x1/weights',
'FirstStageFeatureExtractor/InceptionResnetV2/freezable_batch_norm_66/beta': 'InceptionResnetV2/Repeat/block35_10/Branch_0/Conv2d_1x1/BatchNorm/beta',
'FirstStageFeatureExtractor/InceptionResnetV2/freezable_batch_norm_66/moving_mean': 'InceptionResnetV2/Repeat/block35_10/Branch_0/Conv2d_1x1/BatchNorm/moving_mean',
'FirstStageFeatureExtractor/InceptionResnetV2/freezable_batch_norm_66/moving_variance': 'InceptionResnetV2/Repeat/block35_10/Branch_0/Conv2d_1x1/BatchNorm/moving_variance',
'FirstStageFeatureExtractor/InceptionResnetV2/conv2d_67/kernel': 'InceptionResnetV2/Repeat/block35_10/Branch_1/Conv2d_0a_1x1/weights',
'FirstStageFeatureExtractor/InceptionResnetV2/freezable_batch_norm_67/beta': 'InceptionResnetV2/Repeat/block35_10/Branch_1/Conv2d_0a_1x1/BatchNorm/beta',
'FirstStageFeatureExtractor/InceptionResnetV2/freezable_batch_norm_67/moving_mean': 'InceptionResnetV2/Repeat/block35_10/Branch_1/Conv2d_0a_1x1/BatchNorm/moving_mean',
'FirstStageFeatureExtractor/InceptionResnetV2/freezable_batch_norm_67/moving_variance': 'InceptionResnetV2/Repeat/block35_10/Branch_1/Conv2d_0a_1x1/BatchNorm/moving_variance',
'FirstStageFeatureExtractor/InceptionResnetV2/conv2d_68/kernel': 'InceptionResnetV2/Repeat/block35_10/Branch_1/Conv2d_0b_3x3/weights',
'FirstStageFeatureExtractor/InceptionResnetV2/freezable_batch_norm_68/beta': 'InceptionResnetV2/Repeat/block35_10/Branch_1/Conv2d_0b_3x3/BatchNorm/beta',
'FirstStageFeatureExtractor/InceptionResnetV2/freezable_batch_norm_68/moving_mean': 'InceptionResnetV2/Repeat/block35_10/Branch_1/Conv2d_0b_3x3/BatchNorm/moving_mean',
'FirstStageFeatureExtractor/InceptionResnetV2/freezable_batch_norm_68/moving_variance': 'InceptionResnetV2/Repeat/block35_10/Branch_1/Conv2d_0b_3x3/BatchNorm/moving_variance',
'FirstStageFeatureExtractor/InceptionResnetV2/conv2d_69/kernel': 'InceptionResnetV2/Repeat/block35_10/Branch_2/Conv2d_0a_1x1/weights',
'FirstStageFeatureExtractor/InceptionResnetV2/freezable_batch_norm_69/beta': 'InceptionResnetV2/Repeat/block35_10/Branch_2/Conv2d_0a_1x1/BatchNorm/beta',
'FirstStageFeatureExtractor/InceptionResnetV2/freezable_batch_norm_69/moving_mean': 'InceptionResnetV2/Repeat/block35_10/Branch_2/Conv2d_0a_1x1/BatchNorm/moving_mean',
'FirstStageFeatureExtractor/InceptionResnetV2/freezable_batch_norm_69/moving_variance': 'InceptionResnetV2/Repeat/block35_10/Branch_2/Conv2d_0a_1x1/BatchNorm/moving_variance',
'FirstStageFeatureExtractor/InceptionResnetV2/conv2d_70/kernel': 'InceptionResnetV2/Repeat/block35_10/Branch_2/Conv2d_0b_3x3/weights',
'FirstStageFeatureExtractor/InceptionResnetV2/freezable_batch_norm_70/beta': 'InceptionResnetV2/Repeat/block35_10/Branch_2/Conv2d_0b_3x3/BatchNorm/beta',
'FirstStageFeatureExtractor/InceptionResnetV2/freezable_batch_norm_70/moving_mean': 'InceptionResnetV2/Repeat/block35_10/Branch_2/Conv2d_0b_3x3/BatchNorm/moving_mean',
'FirstStageFeatureExtractor/InceptionResnetV2/freezable_batch_norm_70/moving_variance': 'InceptionResnetV2/Repeat/block35_10/Branch_2/Conv2d_0b_3x3/BatchNorm/moving_variance',
'FirstStageFeatureExtractor/InceptionResnetV2/conv2d_71/kernel': 'InceptionResnetV2/Repeat/block35_10/Branch_2/Conv2d_0c_3x3/weights',
'FirstStageFeatureExtractor/InceptionResnetV2/freezable_batch_norm_71/beta': 'InceptionResnetV2/Repeat/block35_10/Branch_2/Conv2d_0c_3x3/BatchNorm/beta',
'FirstStageFeatureExtractor/InceptionResnetV2/freezable_batch_norm_71/moving_mean': 'InceptionResnetV2/Repeat/block35_10/Branch_2/Conv2d_0c_3x3/BatchNorm/moving_mean',
'FirstStageFeatureExtractor/InceptionResnetV2/freezable_batch_norm_71/moving_variance': 'InceptionResnetV2/Repeat/block35_10/Branch_2/Conv2d_0c_3x3/BatchNorm/moving_variance',
'FirstStageFeatureExtractor/InceptionResnetV2/block35_10_conv/kernel': 'InceptionResnetV2/Repeat/block35_10/Conv2d_1x1/weights',
'FirstStageFeatureExtractor/InceptionResnetV2/block35_10_conv/bias': 'InceptionResnetV2/Repeat/block35_10/Conv2d_1x1/biases',
'FirstStageFeatureExtractor/InceptionResnetV2/conv2d_72/kernel': 'InceptionResnetV2/Mixed_6a/Branch_0/Conv2d_1a_3x3/weights',
'FirstStageFeatureExtractor/InceptionResnetV2/freezable_batch_norm_72/beta': 'InceptionResnetV2/Mixed_6a/Branch_0/Conv2d_1a_3x3/BatchNorm/beta',
'FirstStageFeatureExtractor/InceptionResnetV2/freezable_batch_norm_72/moving_mean': 'InceptionResnetV2/Mixed_6a/Branch_0/Conv2d_1a_3x3/BatchNorm/moving_mean',
'FirstStageFeatureExtractor/InceptionResnetV2/freezable_batch_norm_72/moving_variance': 'InceptionResnetV2/Mixed_6a/Branch_0/Conv2d_1a_3x3/BatchNorm/moving_variance',
'FirstStageFeatureExtractor/InceptionResnetV2/conv2d_73/kernel': 'InceptionResnetV2/Mixed_6a/Branch_1/Conv2d_0a_1x1/weights',
'FirstStageFeatureExtractor/InceptionResnetV2/freezable_batch_norm_73/beta': 'InceptionResnetV2/Mixed_6a/Branch_1/Conv2d_0a_1x1/BatchNorm/beta',
'FirstStageFeatureExtractor/InceptionResnetV2/freezable_batch_norm_73/moving_mean': 'InceptionResnetV2/Mixed_6a/Branch_1/Conv2d_0a_1x1/BatchNorm/moving_mean',
'FirstStageFeatureExtractor/InceptionResnetV2/freezable_batch_norm_73/moving_variance': 'InceptionResnetV2/Mixed_6a/Branch_1/Conv2d_0a_1x1/BatchNorm/moving_variance',
'FirstStageFeatureExtractor/InceptionResnetV2/conv2d_74/kernel': 'InceptionResnetV2/Mixed_6a/Branch_1/Conv2d_0b_3x3/weights',
'FirstStageFeatureExtractor/InceptionResnetV2/freezable_batch_norm_74/beta': 'InceptionResnetV2/Mixed_6a/Branch_1/Conv2d_0b_3x3/BatchNorm/beta',
'FirstStageFeatureExtractor/InceptionResnetV2/freezable_batch_norm_74/moving_mean': 'InceptionResnetV2/Mixed_6a/Branch_1/Conv2d_0b_3x3/BatchNorm/moving_mean',
'FirstStageFeatureExtractor/InceptionResnetV2/freezable_batch_norm_74/moving_variance': 'InceptionResnetV2/Mixed_6a/Branch_1/Conv2d_0b_3x3/BatchNorm/moving_variance',
'FirstStageFeatureExtractor/InceptionResnetV2/conv2d_75/kernel': 'InceptionResnetV2/Mixed_6a/Branch_1/Conv2d_1a_3x3/weights',
'FirstStageFeatureExtractor/InceptionResnetV2/freezable_batch_norm_75/beta': 'InceptionResnetV2/Mixed_6a/Branch_1/Conv2d_1a_3x3/BatchNorm/beta',
'FirstStageFeatureExtractor/InceptionResnetV2/freezable_batch_norm_75/moving_mean': 'InceptionResnetV2/Mixed_6a/Branch_1/Conv2d_1a_3x3/BatchNorm/moving_mean',
'FirstStageFeatureExtractor/InceptionResnetV2/freezable_batch_norm_75/moving_variance': 'InceptionResnetV2/Mixed_6a/Branch_1/Conv2d_1a_3x3/BatchNorm/moving_variance',
'FirstStageFeatureExtractor/InceptionResnetV2/conv2d_76/kernel': 'InceptionResnetV2/Repeat_1/block17_1/Branch_0/Conv2d_1x1/weights',
'FirstStageFeatureExtractor/InceptionResnetV2/freezable_batch_norm_76/beta': 'InceptionResnetV2/Repeat_1/block17_1/Branch_0/Conv2d_1x1/BatchNorm/beta',
'FirstStageFeatureExtractor/InceptionResnetV2/freezable_batch_norm_76/moving_mean': 'InceptionResnetV2/Repeat_1/block17_1/Branch_0/Conv2d_1x1/BatchNorm/moving_mean',
'FirstStageFeatureExtractor/InceptionResnetV2/freezable_batch_norm_76/moving_variance': 'InceptionResnetV2/Repeat_1/block17_1/Branch_0/Conv2d_1x1/BatchNorm/moving_variance',
'FirstStageFeatureExtractor/InceptionResnetV2/conv2d_77/kernel': 'InceptionResnetV2/Repeat_1/block17_1/Branch_1/Conv2d_0a_1x1/weights',
'FirstStageFeatureExtractor/InceptionResnetV2/freezable_batch_norm_77/beta': 'InceptionResnetV2/Repeat_1/block17_1/Branch_1/Conv2d_0a_1x1/BatchNorm/beta',
'FirstStageFeatureExtractor/InceptionResnetV2/freezable_batch_norm_77/moving_mean': 'InceptionResnetV2/Repeat_1/block17_1/Branch_1/Conv2d_0a_1x1/BatchNorm/moving_mean',
'FirstStageFeatureExtractor/InceptionResnetV2/freezable_batch_norm_77/moving_variance': 'InceptionResnetV2/Repeat_1/block17_1/Branch_1/Conv2d_0a_1x1/BatchNorm/moving_variance',
'FirstStageFeatureExtractor/InceptionResnetV2/conv2d_78/kernel': 'InceptionResnetV2/Repeat_1/block17_1/Branch_1/Conv2d_0b_1x7/weights',
'FirstStageFeatureExtractor/InceptionResnetV2/freezable_batch_norm_78/beta': 'InceptionResnetV2/Repeat_1/block17_1/Branch_1/Conv2d_0b_1x7/BatchNorm/beta',
'FirstStageFeatureExtractor/InceptionResnetV2/freezable_batch_norm_78/moving_mean': 'InceptionResnetV2/Repeat_1/block17_1/Branch_1/Conv2d_0b_1x7/BatchNorm/moving_mean',
'FirstStageFeatureExtractor/InceptionResnetV2/freezable_batch_norm_78/moving_variance': 'InceptionResnetV2/Repeat_1/block17_1/Branch_1/Conv2d_0b_1x7/BatchNorm/moving_variance',
'FirstStageFeatureExtractor/InceptionResnetV2/conv2d_79/kernel': 'InceptionResnetV2/Repeat_1/block17_1/Branch_1/Conv2d_0c_7x1/weights',
'FirstStageFeatureExtractor/InceptionResnetV2/freezable_batch_norm_79/beta': 'InceptionResnetV2/Repeat_1/block17_1/Branch_1/Conv2d_0c_7x1/BatchNorm/beta',
'FirstStageFeatureExtractor/InceptionResnetV2/freezable_batch_norm_79/moving_mean': 'InceptionResnetV2/Repeat_1/block17_1/Branch_1/Conv2d_0c_7x1/BatchNorm/moving_mean',
'FirstStageFeatureExtractor/InceptionResnetV2/freezable_batch_norm_79/moving_variance': 'InceptionResnetV2/Repeat_1/block17_1/Branch_1/Conv2d_0c_7x1/BatchNorm/moving_variance',
'FirstStageFeatureExtractor/InceptionResnetV2/block17_1_conv/kernel': 'InceptionResnetV2/Repeat_1/block17_1/Conv2d_1x1/weights',
'FirstStageFeatureExtractor/InceptionResnetV2/block17_1_conv/bias': 'InceptionResnetV2/Repeat_1/block17_1/Conv2d_1x1/biases',
'FirstStageFeatureExtractor/InceptionResnetV2/conv2d_80/kernel': 'InceptionResnetV2/Repeat_1/block17_2/Branch_0/Conv2d_1x1/weights',
'FirstStageFeatureExtractor/InceptionResnetV2/freezable_batch_norm_80/beta': 'InceptionResnetV2/Repeat_1/block17_2/Branch_0/Conv2d_1x1/BatchNorm/beta',
'FirstStageFeatureExtractor/InceptionResnetV2/freezable_batch_norm_80/moving_mean': 'InceptionResnetV2/Repeat_1/block17_2/Branch_0/Conv2d_1x1/BatchNorm/moving_mean',
'FirstStageFeatureExtractor/InceptionResnetV2/freezable_batch_norm_80/moving_variance': 'InceptionResnetV2/Repeat_1/block17_2/Branch_0/Conv2d_1x1/BatchNorm/moving_variance',
'FirstStageFeatureExtractor/InceptionResnetV2/conv2d_81/kernel': 'InceptionResnetV2/Repeat_1/block17_2/Branch_1/Conv2d_0a_1x1/weights',
'FirstStageFeatureExtractor/InceptionResnetV2/freezable_batch_norm_81/beta': 'InceptionResnetV2/Repeat_1/block17_2/Branch_1/Conv2d_0a_1x1/BatchNorm/beta',
'FirstStageFeatureExtractor/InceptionResnetV2/freezable_batch_norm_81/moving_mean': 'InceptionResnetV2/Repeat_1/block17_2/Branch_1/Conv2d_0a_1x1/BatchNorm/moving_mean',
'FirstStageFeatureExtractor/InceptionResnetV2/freezable_batch_norm_81/moving_variance': 'InceptionResnetV2/Repeat_1/block17_2/Branch_1/Conv2d_0a_1x1/BatchNorm/moving_variance',
'FirstStageFeatureExtractor/InceptionResnetV2/conv2d_82/kernel': 'InceptionResnetV2/Repeat_1/block17_2/Branch_1/Conv2d_0b_1x7/weights',
'FirstStageFeatureExtractor/InceptionResnetV2/freezable_batch_norm_82/beta': 'InceptionResnetV2/Repeat_1/block17_2/Branch_1/Conv2d_0b_1x7/BatchNorm/beta',
'FirstStageFeatureExtractor/InceptionResnetV2/freezable_batch_norm_82/moving_mean': 'InceptionResnetV2/Repeat_1/block17_2/Branch_1/Conv2d_0b_1x7/BatchNorm/moving_mean',
'FirstStageFeatureExtractor/InceptionResnetV2/freezable_batch_norm_82/moving_variance': 'InceptionResnetV2/Repeat_1/block17_2/Branch_1/Conv2d_0b_1x7/BatchNorm/moving_variance',
'FirstStageFeatureExtractor/InceptionResnetV2/conv2d_83/kernel': 'InceptionResnetV2/Repeat_1/block17_2/Branch_1/Conv2d_0c_7x1/weights',
'FirstStageFeatureExtractor/InceptionResnetV2/freezable_batch_norm_83/beta': 'InceptionResnetV2/Repeat_1/block17_2/Branch_1/Conv2d_0c_7x1/BatchNorm/beta',
'FirstStageFeatureExtractor/InceptionResnetV2/freezable_batch_norm_83/moving_mean': 'InceptionResnetV2/Repeat_1/block17_2/Branch_1/Conv2d_0c_7x1/BatchNorm/moving_mean',
'FirstStageFeatureExtractor/InceptionResnetV2/freezable_batch_norm_83/moving_variance': 'InceptionResnetV2/Repeat_1/block17_2/Branch_1/Conv2d_0c_7x1/BatchNorm/moving_variance',
'FirstStageFeatureExtractor/InceptionResnetV2/block17_2_conv/kernel': 'InceptionResnetV2/Repeat_1/block17_2/Conv2d_1x1/weights',
'FirstStageFeatureExtractor/InceptionResnetV2/block17_2_conv/bias': 'InceptionResnetV2/Repeat_1/block17_2/Conv2d_1x1/biases',
'FirstStageFeatureExtractor/InceptionResnetV2/conv2d_84/kernel': 'InceptionResnetV2/Repeat_1/block17_3/Branch_0/Conv2d_1x1/weights',
'FirstStageFeatureExtractor/InceptionResnetV2/freezable_batch_norm_84/beta': 'InceptionResnetV2/Repeat_1/block17_3/Branch_0/Conv2d_1x1/BatchNorm/beta',
'FirstStageFeatureExtractor/InceptionResnetV2/freezable_batch_norm_84/moving_mean': 'InceptionResnetV2/Repeat_1/block17_3/Branch_0/Conv2d_1x1/BatchNorm/moving_mean',
'FirstStageFeatureExtractor/InceptionResnetV2/freezable_batch_norm_84/moving_variance': 'InceptionResnetV2/Repeat_1/block17_3/Branch_0/Conv2d_1x1/BatchNorm/moving_variance',
'FirstStageFeatureExtractor/InceptionResnetV2/conv2d_85/kernel': 'InceptionResnetV2/Repeat_1/block17_3/Branch_1/Conv2d_0a_1x1/weights',
'FirstStageFeatureExtractor/InceptionResnetV2/freezable_batch_norm_85/beta': 'InceptionResnetV2/Repeat_1/block17_3/Branch_1/Conv2d_0a_1x1/BatchNorm/beta',
'FirstStageFeatureExtractor/InceptionResnetV2/freezable_batch_norm_85/moving_mean': 'InceptionResnetV2/Repeat_1/block17_3/Branch_1/Conv2d_0a_1x1/BatchNorm/moving_mean',
'FirstStageFeatureExtractor/InceptionResnetV2/freezable_batch_norm_85/moving_variance': 'InceptionResnetV2/Repeat_1/block17_3/Branch_1/Conv2d_0a_1x1/BatchNorm/moving_variance',
'FirstStageFeatureExtractor/InceptionResnetV2/conv2d_86/kernel': 'InceptionResnetV2/Repeat_1/block17_3/Branch_1/Conv2d_0b_1x7/weights',
'FirstStageFeatureExtractor/InceptionResnetV2/freezable_batch_norm_86/beta': 'InceptionResnetV2/Repeat_1/block17_3/Branch_1/Conv2d_0b_1x7/BatchNorm/beta',
'FirstStageFeatureExtractor/InceptionResnetV2/freezable_batch_norm_86/moving_mean': 'InceptionResnetV2/Repeat_1/block17_3/Branch_1/Conv2d_0b_1x7/BatchNorm/moving_mean',
'FirstStageFeatureExtractor/InceptionResnetV2/freezable_batch_norm_86/moving_variance': 'InceptionResnetV2/Repeat_1/block17_3/Branch_1/Conv2d_0b_1x7/BatchNorm/moving_variance',
'FirstStageFeatureExtractor/InceptionResnetV2/conv2d_87/kernel': 'InceptionResnetV2/Repeat_1/block17_3/Branch_1/Conv2d_0c_7x1/weights',
'FirstStageFeatureExtractor/InceptionResnetV2/freezable_batch_norm_87/beta': 'InceptionResnetV2/Repeat_1/block17_3/Branch_1/Conv2d_0c_7x1/BatchNorm/beta',
'FirstStageFeatureExtractor/InceptionResnetV2/freezable_batch_norm_87/moving_mean': 'InceptionResnetV2/Repeat_1/block17_3/Branch_1/Conv2d_0c_7x1/BatchNorm/moving_mean',
'FirstStageFeatureExtractor/InceptionResnetV2/freezable_batch_norm_87/moving_variance': 'InceptionResnetV2/Repeat_1/block17_3/Branch_1/Conv2d_0c_7x1/BatchNorm/moving_variance',
'FirstStageFeatureExtractor/InceptionResnetV2/block17_3_conv/kernel': 'InceptionResnetV2/Repeat_1/block17_3/Conv2d_1x1/weights',
'FirstStageFeatureExtractor/InceptionResnetV2/block17_3_conv/bias': 'InceptionResnetV2/Repeat_1/block17_3/Conv2d_1x1/biases',
'FirstStageFeatureExtractor/InceptionResnetV2/conv2d_88/kernel': 'InceptionResnetV2/Repeat_1/block17_4/Branch_0/Conv2d_1x1/weights',
'FirstStageFeatureExtractor/InceptionResnetV2/freezable_batch_norm_88/beta': 'InceptionResnetV2/Repeat_1/block17_4/Branch_0/Conv2d_1x1/BatchNorm/beta',
'FirstStageFeatureExtractor/InceptionResnetV2/freezable_batch_norm_88/moving_mean': 'InceptionResnetV2/Repeat_1/block17_4/Branch_0/Conv2d_1x1/BatchNorm/moving_mean',
'FirstStageFeatureExtractor/InceptionResnetV2/freezable_batch_norm_88/moving_variance': 'InceptionResnetV2/Repeat_1/block17_4/Branch_0/Conv2d_1x1/BatchNorm/moving_variance',
'FirstStageFeatureExtractor/InceptionResnetV2/conv2d_89/kernel': 'InceptionResnetV2/Repeat_1/block17_4/Branch_1/Conv2d_0a_1x1/weights',
'FirstStageFeatureExtractor/InceptionResnetV2/freezable_batch_norm_89/beta': 'InceptionResnetV2/Repeat_1/block17_4/Branch_1/Conv2d_0a_1x1/BatchNorm/beta',
'FirstStageFeatureExtractor/InceptionResnetV2/freezable_batch_norm_89/moving_mean': 'InceptionResnetV2/Repeat_1/block17_4/Branch_1/Conv2d_0a_1x1/BatchNorm/moving_mean',
'FirstStageFeatureExtractor/InceptionResnetV2/freezable_batch_norm_89/moving_variance': 'InceptionResnetV2/Repeat_1/block17_4/Branch_1/Conv2d_0a_1x1/BatchNorm/moving_variance',
'FirstStageFeatureExtractor/InceptionResnetV2/conv2d_90/kernel': 'InceptionResnetV2/Repeat_1/block17_4/Branch_1/Conv2d_0b_1x7/weights',
'FirstStageFeatureExtractor/InceptionResnetV2/freezable_batch_norm_90/beta': 'InceptionResnetV2/Repeat_1/block17_4/Branch_1/Conv2d_0b_1x7/BatchNorm/beta',
'FirstStageFeatureExtractor/InceptionResnetV2/freezable_batch_norm_90/moving_mean': 'InceptionResnetV2/Repeat_1/block17_4/Branch_1/Conv2d_0b_1x7/BatchNorm/moving_mean',
'FirstStageFeatureExtractor/InceptionResnetV2/freezable_batch_norm_90/moving_variance': 'InceptionResnetV2/Repeat_1/block17_4/Branch_1/Conv2d_0b_1x7/BatchNorm/moving_variance',
'FirstStageFeatureExtractor/InceptionResnetV2/conv2d_91/kernel': 'InceptionResnetV2/Repeat_1/block17_4/Branch_1/Conv2d_0c_7x1/weights',
'FirstStageFeatureExtractor/InceptionResnetV2/freezable_batch_norm_91/beta': 'InceptionResnetV2/Repeat_1/block17_4/Branch_1/Conv2d_0c_7x1/BatchNorm/beta',
'FirstStageFeatureExtractor/InceptionResnetV2/freezable_batch_norm_91/moving_mean': 'InceptionResnetV2/Repeat_1/block17_4/Branch_1/Conv2d_0c_7x1/BatchNorm/moving_mean',
'FirstStageFeatureExtractor/InceptionResnetV2/freezable_batch_norm_91/moving_variance': 'InceptionResnetV2/Repeat_1/block17_4/Branch_1/Conv2d_0c_7x1/BatchNorm/moving_variance',
'FirstStageFeatureExtractor/InceptionResnetV2/block17_4_conv/kernel': 'InceptionResnetV2/Repeat_1/block17_4/Conv2d_1x1/weights',
'FirstStageFeatureExtractor/InceptionResnetV2/block17_4_conv/bias': 'InceptionResnetV2/Repeat_1/block17_4/Conv2d_1x1/biases',
'FirstStageFeatureExtractor/InceptionResnetV2/conv2d_92/kernel': 'InceptionResnetV2/Repeat_1/block17_5/Branch_0/Conv2d_1x1/weights',
'FirstStageFeatureExtractor/InceptionResnetV2/freezable_batch_norm_92/beta': 'InceptionResnetV2/Repeat_1/block17_5/Branch_0/Conv2d_1x1/BatchNorm/beta',
'FirstStageFeatureExtractor/InceptionResnetV2/freezable_batch_norm_92/moving_mean': 'InceptionResnetV2/Repeat_1/block17_5/Branch_0/Conv2d_1x1/BatchNorm/moving_mean',
'FirstStageFeatureExtractor/InceptionResnetV2/freezable_batch_norm_92/moving_variance': 'InceptionResnetV2/Repeat_1/block17_5/Branch_0/Conv2d_1x1/BatchNorm/moving_variance',
'FirstStageFeatureExtractor/InceptionResnetV2/conv2d_93/kernel': 'InceptionResnetV2/Repeat_1/block17_5/Branch_1/Conv2d_0a_1x1/weights',
'FirstStageFeatureExtractor/InceptionResnetV2/freezable_batch_norm_93/beta': 'InceptionResnetV2/Repeat_1/block17_5/Branch_1/Conv2d_0a_1x1/BatchNorm/beta',
'FirstStageFeatureExtractor/InceptionResnetV2/freezable_batch_norm_93/moving_mean': 'InceptionResnetV2/Repeat_1/block17_5/Branch_1/Conv2d_0a_1x1/BatchNorm/moving_mean',
'FirstStageFeatureExtractor/InceptionResnetV2/freezable_batch_norm_93/moving_variance': 'InceptionResnetV2/Repeat_1/block17_5/Branch_1/Conv2d_0a_1x1/BatchNorm/moving_variance',
'FirstStageFeatureExtractor/InceptionResnetV2/conv2d_94/kernel': 'InceptionResnetV2/Repeat_1/block17_5/Branch_1/Conv2d_0b_1x7/weights',
'FirstStageFeatureExtractor/InceptionResnetV2/freezable_batch_norm_94/beta': 'InceptionResnetV2/Repeat_1/block17_5/Branch_1/Conv2d_0b_1x7/BatchNorm/beta',
'FirstStageFeatureExtractor/InceptionResnetV2/freezable_batch_norm_94/moving_mean': 'InceptionResnetV2/Repeat_1/block17_5/Branch_1/Conv2d_0b_1x7/BatchNorm/moving_mean',
'FirstStageFeatureExtractor/InceptionResnetV2/freezable_batch_norm_94/moving_variance': 'InceptionResnetV2/Repeat_1/block17_5/Branch_1/Conv2d_0b_1x7/BatchNorm/moving_variance',
'FirstStageFeatureExtractor/InceptionResnetV2/conv2d_95/kernel': 'InceptionResnetV2/Repeat_1/block17_5/Branch_1/Conv2d_0c_7x1/weights',
'FirstStageFeatureExtractor/InceptionResnetV2/freezable_batch_norm_95/beta': 'InceptionResnetV2/Repeat_1/block17_5/Branch_1/Conv2d_0c_7x1/BatchNorm/beta',
'FirstStageFeatureExtractor/InceptionResnetV2/freezable_batch_norm_95/moving_mean': 'InceptionResnetV2/Repeat_1/block17_5/Branch_1/Conv2d_0c_7x1/BatchNorm/moving_mean',
'FirstStageFeatureExtractor/InceptionResnetV2/freezable_batch_norm_95/moving_variance': 'InceptionResnetV2/Repeat_1/block17_5/Branch_1/Conv2d_0c_7x1/BatchNorm/moving_variance',
'FirstStageFeatureExtractor/InceptionResnetV2/block17_5_conv/kernel': 'InceptionResnetV2/Repeat_1/block17_5/Conv2d_1x1/weights',
'FirstStageFeatureExtractor/InceptionResnetV2/block17_5_conv/bias': 'InceptionResnetV2/Repeat_1/block17_5/Conv2d_1x1/biases',
'FirstStageFeatureExtractor/InceptionResnetV2/conv2d_96/kernel': 'InceptionResnetV2/Repeat_1/block17_6/Branch_0/Conv2d_1x1/weights',
'FirstStageFeatureExtractor/InceptionResnetV2/freezable_batch_norm_96/beta': 'InceptionResnetV2/Repeat_1/block17_6/Branch_0/Conv2d_1x1/BatchNorm/beta',
'FirstStageFeatureExtractor/InceptionResnetV2/freezable_batch_norm_96/moving_mean': 'InceptionResnetV2/Repeat_1/block17_6/Branch_0/Conv2d_1x1/BatchNorm/moving_mean',
'FirstStageFeatureExtractor/InceptionResnetV2/freezable_batch_norm_96/moving_variance': 'InceptionResnetV2/Repeat_1/block17_6/Branch_0/Conv2d_1x1/BatchNorm/moving_variance',
'FirstStageFeatureExtractor/InceptionResnetV2/conv2d_97/kernel': 'InceptionResnetV2/Repeat_1/block17_6/Branch_1/Conv2d_0a_1x1/weights',
'FirstStageFeatureExtractor/InceptionResnetV2/freezable_batch_norm_97/beta': 'InceptionResnetV2/Repeat_1/block17_6/Branch_1/Conv2d_0a_1x1/BatchNorm/beta',
'FirstStageFeatureExtractor/InceptionResnetV2/freezable_batch_norm_97/moving_mean': 'InceptionResnetV2/Repeat_1/block17_6/Branch_1/Conv2d_0a_1x1/BatchNorm/moving_mean',
'FirstStageFeatureExtractor/InceptionResnetV2/freezable_batch_norm_97/moving_variance': 'InceptionResnetV2/Repeat_1/block17_6/Branch_1/Conv2d_0a_1x1/BatchNorm/moving_variance',
'FirstStageFeatureExtractor/InceptionResnetV2/conv2d_98/kernel': 'InceptionResnetV2/Repeat_1/block17_6/Branch_1/Conv2d_0b_1x7/weights',
'FirstStageFeatureExtractor/InceptionResnetV2/freezable_batch_norm_98/beta': 'InceptionResnetV2/Repeat_1/block17_6/Branch_1/Conv2d_0b_1x7/BatchNorm/beta',
'FirstStageFeatureExtractor/InceptionResnetV2/freezable_batch_norm_98/moving_mean': 'InceptionResnetV2/Repeat_1/block17_6/Branch_1/Conv2d_0b_1x7/BatchNorm/moving_mean',
'FirstStageFeatureExtractor/InceptionResnetV2/freezable_batch_norm_98/moving_variance': 'InceptionResnetV2/Repeat_1/block17_6/Branch_1/Conv2d_0b_1x7/BatchNorm/moving_variance',
'FirstStageFeatureExtractor/InceptionResnetV2/conv2d_99/kernel': 'InceptionResnetV2/Repeat_1/block17_6/Branch_1/Conv2d_0c_7x1/weights',
'FirstStageFeatureExtractor/InceptionResnetV2/freezable_batch_norm_99/beta': 'InceptionResnetV2/Repeat_1/block17_6/Branch_1/Conv2d_0c_7x1/BatchNorm/beta',
'FirstStageFeatureExtractor/InceptionResnetV2/freezable_batch_norm_99/moving_mean': 'InceptionResnetV2/Repeat_1/block17_6/Branch_1/Conv2d_0c_7x1/BatchNorm/moving_mean',
'FirstStageFeatureExtractor/InceptionResnetV2/freezable_batch_norm_99/moving_variance': 'InceptionResnetV2/Repeat_1/block17_6/Branch_1/Conv2d_0c_7x1/BatchNorm/moving_variance',
'FirstStageFeatureExtractor/InceptionResnetV2/block17_6_conv/kernel': 'InceptionResnetV2/Repeat_1/block17_6/Conv2d_1x1/weights',
'FirstStageFeatureExtractor/InceptionResnetV2/block17_6_conv/bias': 'InceptionResnetV2/Repeat_1/block17_6/Conv2d_1x1/biases',
'FirstStageFeatureExtractor/InceptionResnetV2/conv2d_100/kernel': 'InceptionResnetV2/Repeat_1/block17_7/Branch_0/Conv2d_1x1/weights',
'FirstStageFeatureExtractor/InceptionResnetV2/freezable_batch_norm_100/beta': 'InceptionResnetV2/Repeat_1/block17_7/Branch_0/Conv2d_1x1/BatchNorm/beta',
'FirstStageFeatureExtractor/InceptionResnetV2/freezable_batch_norm_100/moving_mean': 'InceptionResnetV2/Repeat_1/block17_7/Branch_0/Conv2d_1x1/BatchNorm/moving_mean',
'FirstStageFeatureExtractor/InceptionResnetV2/freezable_batch_norm_100/moving_variance': 'InceptionResnetV2/Repeat_1/block17_7/Branch_0/Conv2d_1x1/BatchNorm/moving_variance',
'FirstStageFeatureExtractor/InceptionResnetV2/conv2d_101/kernel': 'InceptionResnetV2/Repeat_1/block17_7/Branch_1/Conv2d_0a_1x1/weights',
'FirstStageFeatureExtractor/InceptionResnetV2/freezable_batch_norm_101/beta': 'InceptionResnetV2/Repeat_1/block17_7/Branch_1/Conv2d_0a_1x1/BatchNorm/beta',
'FirstStageFeatureExtractor/InceptionResnetV2/freezable_batch_norm_101/moving_mean': 'InceptionResnetV2/Repeat_1/block17_7/Branch_1/Conv2d_0a_1x1/BatchNorm/moving_mean',
'FirstStageFeatureExtractor/InceptionResnetV2/freezable_batch_norm_101/moving_variance': 'InceptionResnetV2/Repeat_1/block17_7/Branch_1/Conv2d_0a_1x1/BatchNorm/moving_variance',
'FirstStageFeatureExtractor/InceptionResnetV2/conv2d_102/kernel': 'InceptionResnetV2/Repeat_1/block17_7/Branch_1/Conv2d_0b_1x7/weights',
'FirstStageFeatureExtractor/InceptionResnetV2/freezable_batch_norm_102/beta': 'InceptionResnetV2/Repeat_1/block17_7/Branch_1/Conv2d_0b_1x7/BatchNorm/beta',
'FirstStageFeatureExtractor/InceptionResnetV2/freezable_batch_norm_102/moving_mean': 'InceptionResnetV2/Repeat_1/block17_7/Branch_1/Conv2d_0b_1x7/BatchNorm/moving_mean',
'FirstStageFeatureExtractor/InceptionResnetV2/freezable_batch_norm_102/moving_variance': 'InceptionResnetV2/Repeat_1/block17_7/Branch_1/Conv2d_0b_1x7/BatchNorm/moving_variance',
'FirstStageFeatureExtractor/InceptionResnetV2/conv2d_103/kernel': 'InceptionResnetV2/Repeat_1/block17_7/Branch_1/Conv2d_0c_7x1/weights',
'FirstStageFeatureExtractor/InceptionResnetV2/freezable_batch_norm_103/beta': 'InceptionResnetV2/Repeat_1/block17_7/Branch_1/Conv2d_0c_7x1/BatchNorm/beta',
'FirstStageFeatureExtractor/InceptionResnetV2/freezable_batch_norm_103/moving_mean': 'InceptionResnetV2/Repeat_1/block17_7/Branch_1/Conv2d_0c_7x1/BatchNorm/moving_mean',
'FirstStageFeatureExtractor/InceptionResnetV2/freezable_batch_norm_103/moving_variance': 'InceptionResnetV2/Repeat_1/block17_7/Branch_1/Conv2d_0c_7x1/BatchNorm/moving_variance',
'FirstStageFeatureExtractor/InceptionResnetV2/block17_7_conv/kernel': 'InceptionResnetV2/Repeat_1/block17_7/Conv2d_1x1/weights',
'FirstStageFeatureExtractor/InceptionResnetV2/block17_7_conv/bias': 'InceptionResnetV2/Repeat_1/block17_7/Conv2d_1x1/biases',
'FirstStageFeatureExtractor/InceptionResnetV2/conv2d_104/kernel': 'InceptionResnetV2/Repeat_1/block17_8/Branch_0/Conv2d_1x1/weights',
'FirstStageFeatureExtractor/InceptionResnetV2/freezable_batch_norm_104/beta': 'InceptionResnetV2/Repeat_1/block17_8/Branch_0/Conv2d_1x1/BatchNorm/beta',
'FirstStageFeatureExtractor/InceptionResnetV2/freezable_batch_norm_104/moving_mean': 'InceptionResnetV2/Repeat_1/block17_8/Branch_0/Conv2d_1x1/BatchNorm/moving_mean',
'FirstStageFeatureExtractor/InceptionResnetV2/freezable_batch_norm_104/moving_variance': 'InceptionResnetV2/Repeat_1/block17_8/Branch_0/Conv2d_1x1/BatchNorm/moving_variance',
'FirstStageFeatureExtractor/InceptionResnetV2/conv2d_105/kernel': 'InceptionResnetV2/Repeat_1/block17_8/Branch_1/Conv2d_0a_1x1/weights',
'FirstStageFeatureExtractor/InceptionResnetV2/freezable_batch_norm_105/beta': 'InceptionResnetV2/Repeat_1/block17_8/Branch_1/Conv2d_0a_1x1/BatchNorm/beta',
'FirstStageFeatureExtractor/InceptionResnetV2/freezable_batch_norm_105/moving_mean': 'InceptionResnetV2/Repeat_1/block17_8/Branch_1/Conv2d_0a_1x1/BatchNorm/moving_mean',
'FirstStageFeatureExtractor/InceptionResnetV2/freezable_batch_norm_105/moving_variance': 'InceptionResnetV2/Repeat_1/block17_8/Branch_1/Conv2d_0a_1x1/BatchNorm/moving_variance',
'FirstStageFeatureExtractor/InceptionResnetV2/conv2d_106/kernel': 'InceptionResnetV2/Repeat_1/block17_8/Branch_1/Conv2d_0b_1x7/weights',
'FirstStageFeatureExtractor/InceptionResnetV2/freezable_batch_norm_106/beta': 'InceptionResnetV2/Repeat_1/block17_8/Branch_1/Conv2d_0b_1x7/BatchNorm/beta',
'FirstStageFeatureExtractor/InceptionResnetV2/freezable_batch_norm_106/moving_mean': 'InceptionResnetV2/Repeat_1/block17_8/Branch_1/Conv2d_0b_1x7/BatchNorm/moving_mean',
'FirstStageFeatureExtractor/InceptionResnetV2/freezable_batch_norm_106/moving_variance': 'InceptionResnetV2/Repeat_1/block17_8/Branch_1/Conv2d_0b_1x7/BatchNorm/moving_variance',
'FirstStageFeatureExtractor/InceptionResnetV2/conv2d_107/kernel': 'InceptionResnetV2/Repeat_1/block17_8/Branch_1/Conv2d_0c_7x1/weights',
'FirstStageFeatureExtractor/InceptionResnetV2/freezable_batch_norm_107/beta': 'InceptionResnetV2/Repeat_1/block17_8/Branch_1/Conv2d_0c_7x1/BatchNorm/beta',
'FirstStageFeatureExtractor/InceptionResnetV2/freezable_batch_norm_107/moving_mean': 'InceptionResnetV2/Repeat_1/block17_8/Branch_1/Conv2d_0c_7x1/BatchNorm/moving_mean',
'FirstStageFeatureExtractor/InceptionResnetV2/freezable_batch_norm_107/moving_variance': 'InceptionResnetV2/Repeat_1/block17_8/Branch_1/Conv2d_0c_7x1/BatchNorm/moving_variance',
'FirstStageFeatureExtractor/InceptionResnetV2/block17_8_conv/kernel': 'InceptionResnetV2/Repeat_1/block17_8/Conv2d_1x1/weights',
'FirstStageFeatureExtractor/InceptionResnetV2/block17_8_conv/bias': 'InceptionResnetV2/Repeat_1/block17_8/Conv2d_1x1/biases',
'FirstStageFeatureExtractor/InceptionResnetV2/conv2d_108/kernel': 'InceptionResnetV2/Repeat_1/block17_9/Branch_0/Conv2d_1x1/weights',
'FirstStageFeatureExtractor/InceptionResnetV2/freezable_batch_norm_108/beta': 'InceptionResnetV2/Repeat_1/block17_9/Branch_0/Conv2d_1x1/BatchNorm/beta',
'FirstStageFeatureExtractor/InceptionResnetV2/freezable_batch_norm_108/moving_mean': 'InceptionResnetV2/Repeat_1/block17_9/Branch_0/Conv2d_1x1/BatchNorm/moving_mean',
'FirstStageFeatureExtractor/InceptionResnetV2/freezable_batch_norm_108/moving_variance': 'InceptionResnetV2/Repeat_1/block17_9/Branch_0/Conv2d_1x1/BatchNorm/moving_variance',
'FirstStageFeatureExtractor/InceptionResnetV2/conv2d_109/kernel': 'InceptionResnetV2/Repeat_1/block17_9/Branch_1/Conv2d_0a_1x1/weights',
'FirstStageFeatureExtractor/InceptionResnetV2/freezable_batch_norm_109/beta': 'InceptionResnetV2/Repeat_1/block17_9/Branch_1/Conv2d_0a_1x1/BatchNorm/beta',
'FirstStageFeatureExtractor/InceptionResnetV2/freezable_batch_norm_109/moving_mean': 'InceptionResnetV2/Repeat_1/block17_9/Branch_1/Conv2d_0a_1x1/BatchNorm/moving_mean',
'FirstStageFeatureExtractor/InceptionResnetV2/freezable_batch_norm_109/moving_variance': 'InceptionResnetV2/Repeat_1/block17_9/Branch_1/Conv2d_0a_1x1/BatchNorm/moving_variance',
'FirstStageFeatureExtractor/InceptionResnetV2/conv2d_110/kernel': 'InceptionResnetV2/Repeat_1/block17_9/Branch_1/Conv2d_0b_1x7/weights',
'FirstStageFeatureExtractor/InceptionResnetV2/freezable_batch_norm_110/beta': 'InceptionResnetV2/Repeat_1/block17_9/Branch_1/Conv2d_0b_1x7/BatchNorm/beta',
'FirstStageFeatureExtractor/InceptionResnetV2/freezable_batch_norm_110/moving_mean': 'InceptionResnetV2/Repeat_1/block17_9/Branch_1/Conv2d_0b_1x7/BatchNorm/moving_mean',
'FirstStageFeatureExtractor/InceptionResnetV2/freezable_batch_norm_110/moving_variance': 'InceptionResnetV2/Repeat_1/block17_9/Branch_1/Conv2d_0b_1x7/BatchNorm/moving_variance',
'FirstStageFeatureExtractor/InceptionResnetV2/conv2d_111/kernel': 'InceptionResnetV2/Repeat_1/block17_9/Branch_1/Conv2d_0c_7x1/weights',
'FirstStageFeatureExtractor/InceptionResnetV2/freezable_batch_norm_111/beta': 'InceptionResnetV2/Repeat_1/block17_9/Branch_1/Conv2d_0c_7x1/BatchNorm/beta',
'FirstStageFeatureExtractor/InceptionResnetV2/freezable_batch_norm_111/moving_mean': 'InceptionResnetV2/Repeat_1/block17_9/Branch_1/Conv2d_0c_7x1/BatchNorm/moving_mean',
'FirstStageFeatureExtractor/InceptionResnetV2/freezable_batch_norm_111/moving_variance': 'InceptionResnetV2/Repeat_1/block17_9/Branch_1/Conv2d_0c_7x1/BatchNorm/moving_variance',
'FirstStageFeatureExtractor/InceptionResnetV2/block17_9_conv/kernel': 'InceptionResnetV2/Repeat_1/block17_9/Conv2d_1x1/weights',
'FirstStageFeatureExtractor/InceptionResnetV2/block17_9_conv/bias': 'InceptionResnetV2/Repeat_1/block17_9/Conv2d_1x1/biases',
'FirstStageFeatureExtractor/InceptionResnetV2/conv2d_112/kernel': 'InceptionResnetV2/Repeat_1/block17_10/Branch_0/Conv2d_1x1/weights',
'FirstStageFeatureExtractor/InceptionResnetV2/freezable_batch_norm_112/beta': 'InceptionResnetV2/Repeat_1/block17_10/Branch_0/Conv2d_1x1/BatchNorm/beta',
'FirstStageFeatureExtractor/InceptionResnetV2/freezable_batch_norm_112/moving_mean': 'InceptionResnetV2/Repeat_1/block17_10/Branch_0/Conv2d_1x1/BatchNorm/moving_mean',
'FirstStageFeatureExtractor/InceptionResnetV2/freezable_batch_norm_112/moving_variance': 'InceptionResnetV2/Repeat_1/block17_10/Branch_0/Conv2d_1x1/BatchNorm/moving_variance',
'FirstStageFeatureExtractor/InceptionResnetV2/conv2d_113/kernel': 'InceptionResnetV2/Repeat_1/block17_10/Branch_1/Conv2d_0a_1x1/weights',
'FirstStageFeatureExtractor/InceptionResnetV2/freezable_batch_norm_113/beta': 'InceptionResnetV2/Repeat_1/block17_10/Branch_1/Conv2d_0a_1x1/BatchNorm/beta',
'FirstStageFeatureExtractor/InceptionResnetV2/freezable_batch_norm_113/moving_mean': 'InceptionResnetV2/Repeat_1/block17_10/Branch_1/Conv2d_0a_1x1/BatchNorm/moving_mean',
'FirstStageFeatureExtractor/InceptionResnetV2/freezable_batch_norm_113/moving_variance': 'InceptionResnetV2/Repeat_1/block17_10/Branch_1/Conv2d_0a_1x1/BatchNorm/moving_variance',
'FirstStageFeatureExtractor/InceptionResnetV2/conv2d_114/kernel': 'InceptionResnetV2/Repeat_1/block17_10/Branch_1/Conv2d_0b_1x7/weights',
'FirstStageFeatureExtractor/InceptionResnetV2/freezable_batch_norm_114/beta': 'InceptionResnetV2/Repeat_1/block17_10/Branch_1/Conv2d_0b_1x7/BatchNorm/beta',
'FirstStageFeatureExtractor/InceptionResnetV2/freezable_batch_norm_114/moving_mean': 'InceptionResnetV2/Repeat_1/block17_10/Branch_1/Conv2d_0b_1x7/BatchNorm/moving_mean',
'FirstStageFeatureExtractor/InceptionResnetV2/freezable_batch_norm_114/moving_variance': 'InceptionResnetV2/Repeat_1/block17_10/Branch_1/Conv2d_0b_1x7/BatchNorm/moving_variance',
'FirstStageFeatureExtractor/InceptionResnetV2/conv2d_115/kernel': 'InceptionResnetV2/Repeat_1/block17_10/Branch_1/Conv2d_0c_7x1/weights',
'FirstStageFeatureExtractor/InceptionResnetV2/freezable_batch_norm_115/beta': 'InceptionResnetV2/Repeat_1/block17_10/Branch_1/Conv2d_0c_7x1/BatchNorm/beta',
'FirstStageFeatureExtractor/InceptionResnetV2/freezable_batch_norm_115/moving_mean': 'InceptionResnetV2/Repeat_1/block17_10/Branch_1/Conv2d_0c_7x1/BatchNorm/moving_mean',
'FirstStageFeatureExtractor/InceptionResnetV2/freezable_batch_norm_115/moving_variance': 'InceptionResnetV2/Repeat_1/block17_10/Branch_1/Conv2d_0c_7x1/BatchNorm/moving_variance',
'FirstStageFeatureExtractor/InceptionResnetV2/block17_10_conv/kernel': 'InceptionResnetV2/Repeat_1/block17_10/Conv2d_1x1/weights',
'FirstStageFeatureExtractor/InceptionResnetV2/block17_10_conv/bias': 'InceptionResnetV2/Repeat_1/block17_10/Conv2d_1x1/biases',
'FirstStageFeatureExtractor/InceptionResnetV2/conv2d_116/kernel': 'InceptionResnetV2/Repeat_1/block17_11/Branch_0/Conv2d_1x1/weights',
'FirstStageFeatureExtractor/InceptionResnetV2/freezable_batch_norm_116/beta': 'InceptionResnetV2/Repeat_1/block17_11/Branch_0/Conv2d_1x1/BatchNorm/beta',
'FirstStageFeatureExtractor/InceptionResnetV2/freezable_batch_norm_116/moving_mean': 'InceptionResnetV2/Repeat_1/block17_11/Branch_0/Conv2d_1x1/BatchNorm/moving_mean',
'FirstStageFeatureExtractor/InceptionResnetV2/freezable_batch_norm_116/moving_variance': 'InceptionResnetV2/Repeat_1/block17_11/Branch_0/Conv2d_1x1/BatchNorm/moving_variance',
'FirstStageFeatureExtractor/InceptionResnetV2/conv2d_117/kernel': 'InceptionResnetV2/Repeat_1/block17_11/Branch_1/Conv2d_0a_1x1/weights',
'FirstStageFeatureExtractor/InceptionResnetV2/freezable_batch_norm_117/beta': 'InceptionResnetV2/Repeat_1/block17_11/Branch_1/Conv2d_0a_1x1/BatchNorm/beta',
'FirstStageFeatureExtractor/InceptionResnetV2/freezable_batch_norm_117/moving_mean': 'InceptionResnetV2/Repeat_1/block17_11/Branch_1/Conv2d_0a_1x1/BatchNorm/moving_mean',
'FirstStageFeatureExtractor/InceptionResnetV2/freezable_batch_norm_117/moving_variance': 'InceptionResnetV2/Repeat_1/block17_11/Branch_1/Conv2d_0a_1x1/BatchNorm/moving_variance',
'FirstStageFeatureExtractor/InceptionResnetV2/conv2d_118/kernel': 'InceptionResnetV2/Repeat_1/block17_11/Branch_1/Conv2d_0b_1x7/weights',
'FirstStageFeatureExtractor/InceptionResnetV2/freezable_batch_norm_118/beta': 'InceptionResnetV2/Repeat_1/block17_11/Branch_1/Conv2d_0b_1x7/BatchNorm/beta',
'FirstStageFeatureExtractor/InceptionResnetV2/freezable_batch_norm_118/moving_mean': 'InceptionResnetV2/Repeat_1/block17_11/Branch_1/Conv2d_0b_1x7/BatchNorm/moving_mean',
'FirstStageFeatureExtractor/InceptionResnetV2/freezable_batch_norm_118/moving_variance': 'InceptionResnetV2/Repeat_1/block17_11/Branch_1/Conv2d_0b_1x7/BatchNorm/moving_variance',
'FirstStageFeatureExtractor/InceptionResnetV2/conv2d_119/kernel': 'InceptionResnetV2/Repeat_1/block17_11/Branch_1/Conv2d_0c_7x1/weights',
'FirstStageFeatureExtractor/InceptionResnetV2/freezable_batch_norm_119/beta': 'InceptionResnetV2/Repeat_1/block17_11/Branch_1/Conv2d_0c_7x1/BatchNorm/beta',
'FirstStageFeatureExtractor/InceptionResnetV2/freezable_batch_norm_119/moving_mean': 'InceptionResnetV2/Repeat_1/block17_11/Branch_1/Conv2d_0c_7x1/BatchNorm/moving_mean',
'FirstStageFeatureExtractor/InceptionResnetV2/freezable_batch_norm_119/moving_variance': 'InceptionResnetV2/Repeat_1/block17_11/Branch_1/Conv2d_0c_7x1/BatchNorm/moving_variance',
'FirstStageFeatureExtractor/InceptionResnetV2/block17_11_conv/kernel': 'InceptionResnetV2/Repeat_1/block17_11/Conv2d_1x1/weights',
'FirstStageFeatureExtractor/InceptionResnetV2/block17_11_conv/bias': 'InceptionResnetV2/Repeat_1/block17_11/Conv2d_1x1/biases',
'FirstStageFeatureExtractor/InceptionResnetV2/conv2d_120/kernel': 'InceptionResnetV2/Repeat_1/block17_12/Branch_0/Conv2d_1x1/weights',
'FirstStageFeatureExtractor/InceptionResnetV2/freezable_batch_norm_120/beta': 'InceptionResnetV2/Repeat_1/block17_12/Branch_0/Conv2d_1x1/BatchNorm/beta',
'FirstStageFeatureExtractor/InceptionResnetV2/freezable_batch_norm_120/moving_mean': 'InceptionResnetV2/Repeat_1/block17_12/Branch_0/Conv2d_1x1/BatchNorm/moving_mean',
'FirstStageFeatureExtractor/InceptionResnetV2/freezable_batch_norm_120/moving_variance': 'InceptionResnetV2/Repeat_1/block17_12/Branch_0/Conv2d_1x1/BatchNorm/moving_variance',
'FirstStageFeatureExtractor/InceptionResnetV2/conv2d_121/kernel': 'InceptionResnetV2/Repeat_1/block17_12/Branch_1/Conv2d_0a_1x1/weights',
'FirstStageFeatureExtractor/InceptionResnetV2/freezable_batch_norm_121/beta': 'InceptionResnetV2/Repeat_1/block17_12/Branch_1/Conv2d_0a_1x1/BatchNorm/beta',
'FirstStageFeatureExtractor/InceptionResnetV2/freezable_batch_norm_121/moving_mean': 'InceptionResnetV2/Repeat_1/block17_12/Branch_1/Conv2d_0a_1x1/BatchNorm/moving_mean',
'FirstStageFeatureExtractor/InceptionResnetV2/freezable_batch_norm_121/moving_variance': 'InceptionResnetV2/Repeat_1/block17_12/Branch_1/Conv2d_0a_1x1/BatchNorm/moving_variance',
'FirstStageFeatureExtractor/InceptionResnetV2/conv2d_122/kernel': 'InceptionResnetV2/Repeat_1/block17_12/Branch_1/Conv2d_0b_1x7/weights',
'FirstStageFeatureExtractor/InceptionResnetV2/freezable_batch_norm_122/beta': 'InceptionResnetV2/Repeat_1/block17_12/Branch_1/Conv2d_0b_1x7/BatchNorm/beta',
'FirstStageFeatureExtractor/InceptionResnetV2/freezable_batch_norm_122/moving_mean': 'InceptionResnetV2/Repeat_1/block17_12/Branch_1/Conv2d_0b_1x7/BatchNorm/moving_mean',
'FirstStageFeatureExtractor/InceptionResnetV2/freezable_batch_norm_122/moving_variance': 'InceptionResnetV2/Repeat_1/block17_12/Branch_1/Conv2d_0b_1x7/BatchNorm/moving_variance',
'FirstStageFeatureExtractor/InceptionResnetV2/conv2d_123/kernel': 'InceptionResnetV2/Repeat_1/block17_12/Branch_1/Conv2d_0c_7x1/weights',
'FirstStageFeatureExtractor/InceptionResnetV2/freezable_batch_norm_123/beta': 'InceptionResnetV2/Repeat_1/block17_12/Branch_1/Conv2d_0c_7x1/BatchNorm/beta',
'FirstStageFeatureExtractor/InceptionResnetV2/freezable_batch_norm_123/moving_mean': 'InceptionResnetV2/Repeat_1/block17_12/Branch_1/Conv2d_0c_7x1/BatchNorm/moving_mean',
'FirstStageFeatureExtractor/InceptionResnetV2/freezable_batch_norm_123/moving_variance': 'InceptionResnetV2/Repeat_1/block17_12/Branch_1/Conv2d_0c_7x1/BatchNorm/moving_variance',
'FirstStageFeatureExtractor/InceptionResnetV2/block17_12_conv/kernel': 'InceptionResnetV2/Repeat_1/block17_12/Conv2d_1x1/weights',
'FirstStageFeatureExtractor/InceptionResnetV2/block17_12_conv/bias': 'InceptionResnetV2/Repeat_1/block17_12/Conv2d_1x1/biases',
'FirstStageFeatureExtractor/InceptionResnetV2/conv2d_124/kernel': 'InceptionResnetV2/Repeat_1/block17_13/Branch_0/Conv2d_1x1/weights',
'FirstStageFeatureExtractor/InceptionResnetV2/freezable_batch_norm_124/beta': 'InceptionResnetV2/Repeat_1/block17_13/Branch_0/Conv2d_1x1/BatchNorm/beta',
'FirstStageFeatureExtractor/InceptionResnetV2/freezable_batch_norm_124/moving_mean': 'InceptionResnetV2/Repeat_1/block17_13/Branch_0/Conv2d_1x1/BatchNorm/moving_mean',
'FirstStageFeatureExtractor/InceptionResnetV2/freezable_batch_norm_124/moving_variance': 'InceptionResnetV2/Repeat_1/block17_13/Branch_0/Conv2d_1x1/BatchNorm/moving_variance',
'FirstStageFeatureExtractor/InceptionResnetV2/conv2d_125/kernel': 'InceptionResnetV2/Repeat_1/block17_13/Branch_1/Conv2d_0a_1x1/weights',
'FirstStageFeatureExtractor/InceptionResnetV2/freezable_batch_norm_125/beta': 'InceptionResnetV2/Repeat_1/block17_13/Branch_1/Conv2d_0a_1x1/BatchNorm/beta',
'FirstStageFeatureExtractor/InceptionResnetV2/freezable_batch_norm_125/moving_mean': 'InceptionResnetV2/Repeat_1/block17_13/Branch_1/Conv2d_0a_1x1/BatchNorm/moving_mean',
'FirstStageFeatureExtractor/InceptionResnetV2/freezable_batch_norm_125/moving_variance': 'InceptionResnetV2/Repeat_1/block17_13/Branch_1/Conv2d_0a_1x1/BatchNorm/moving_variance',
'FirstStageFeatureExtractor/InceptionResnetV2/conv2d_126/kernel': 'InceptionResnetV2/Repeat_1/block17_13/Branch_1/Conv2d_0b_1x7/weights',
'FirstStageFeatureExtractor/InceptionResnetV2/freezable_batch_norm_126/beta': 'InceptionResnetV2/Repeat_1/block17_13/Branch_1/Conv2d_0b_1x7/BatchNorm/beta',
'FirstStageFeatureExtractor/InceptionResnetV2/freezable_batch_norm_126/moving_mean': 'InceptionResnetV2/Repeat_1/block17_13/Branch_1/Conv2d_0b_1x7/BatchNorm/moving_mean',
'FirstStageFeatureExtractor/InceptionResnetV2/freezable_batch_norm_126/moving_variance': 'InceptionResnetV2/Repeat_1/block17_13/Branch_1/Conv2d_0b_1x7/BatchNorm/moving_variance',
'FirstStageFeatureExtractor/InceptionResnetV2/conv2d_127/kernel': 'InceptionResnetV2/Repeat_1/block17_13/Branch_1/Conv2d_0c_7x1/weights',
'FirstStageFeatureExtractor/InceptionResnetV2/freezable_batch_norm_127/beta': 'InceptionResnetV2/Repeat_1/block17_13/Branch_1/Conv2d_0c_7x1/BatchNorm/beta',
'FirstStageFeatureExtractor/InceptionResnetV2/freezable_batch_norm_127/moving_mean': 'InceptionResnetV2/Repeat_1/block17_13/Branch_1/Conv2d_0c_7x1/BatchNorm/moving_mean',
'FirstStageFeatureExtractor/InceptionResnetV2/freezable_batch_norm_127/moving_variance': 'InceptionResnetV2/Repeat_1/block17_13/Branch_1/Conv2d_0c_7x1/BatchNorm/moving_variance',
'FirstStageFeatureExtractor/InceptionResnetV2/block17_13_conv/kernel': 'InceptionResnetV2/Repeat_1/block17_13/Conv2d_1x1/weights',
'FirstStageFeatureExtractor/InceptionResnetV2/block17_13_conv/bias': 'InceptionResnetV2/Repeat_1/block17_13/Conv2d_1x1/biases',
'FirstStageFeatureExtractor/InceptionResnetV2/conv2d_128/kernel': 'InceptionResnetV2/Repeat_1/block17_14/Branch_0/Conv2d_1x1/weights',
'FirstStageFeatureExtractor/InceptionResnetV2/freezable_batch_norm_128/beta': 'InceptionResnetV2/Repeat_1/block17_14/Branch_0/Conv2d_1x1/BatchNorm/beta',
'FirstStageFeatureExtractor/InceptionResnetV2/freezable_batch_norm_128/moving_mean': 'InceptionResnetV2/Repeat_1/block17_14/Branch_0/Conv2d_1x1/BatchNorm/moving_mean',
'FirstStageFeatureExtractor/InceptionResnetV2/freezable_batch_norm_128/moving_variance': 'InceptionResnetV2/Repeat_1/block17_14/Branch_0/Conv2d_1x1/BatchNorm/moving_variance',
'FirstStageFeatureExtractor/InceptionResnetV2/conv2d_129/kernel': 'InceptionResnetV2/Repeat_1/block17_14/Branch_1/Conv2d_0a_1x1/weights',
'FirstStageFeatureExtractor/InceptionResnetV2/freezable_batch_norm_129/beta': 'InceptionResnetV2/Repeat_1/block17_14/Branch_1/Conv2d_0a_1x1/BatchNorm/beta',
'FirstStageFeatureExtractor/InceptionResnetV2/freezable_batch_norm_129/moving_mean': 'InceptionResnetV2/Repeat_1/block17_14/Branch_1/Conv2d_0a_1x1/BatchNorm/moving_mean',
'FirstStageFeatureExtractor/InceptionResnetV2/freezable_batch_norm_129/moving_variance': 'InceptionResnetV2/Repeat_1/block17_14/Branch_1/Conv2d_0a_1x1/BatchNorm/moving_variance',
'FirstStageFeatureExtractor/InceptionResnetV2/conv2d_130/kernel': 'InceptionResnetV2/Repeat_1/block17_14/Branch_1/Conv2d_0b_1x7/weights',
'FirstStageFeatureExtractor/InceptionResnetV2/freezable_batch_norm_130/beta': 'InceptionResnetV2/Repeat_1/block17_14/Branch_1/Conv2d_0b_1x7/BatchNorm/beta',
'FirstStageFeatureExtractor/InceptionResnetV2/freezable_batch_norm_130/moving_mean': 'InceptionResnetV2/Repeat_1/block17_14/Branch_1/Conv2d_0b_1x7/BatchNorm/moving_mean',
'FirstStageFeatureExtractor/InceptionResnetV2/freezable_batch_norm_130/moving_variance': 'InceptionResnetV2/Repeat_1/block17_14/Branch_1/Conv2d_0b_1x7/BatchNorm/moving_variance',
'FirstStageFeatureExtractor/InceptionResnetV2/conv2d_131/kernel': 'InceptionResnetV2/Repeat_1/block17_14/Branch_1/Conv2d_0c_7x1/weights',
'FirstStageFeatureExtractor/InceptionResnetV2/freezable_batch_norm_131/beta': 'InceptionResnetV2/Repeat_1/block17_14/Branch_1/Conv2d_0c_7x1/BatchNorm/beta',
'FirstStageFeatureExtractor/InceptionResnetV2/freezable_batch_norm_131/moving_mean': 'InceptionResnetV2/Repeat_1/block17_14/Branch_1/Conv2d_0c_7x1/BatchNorm/moving_mean',
'FirstStageFeatureExtractor/InceptionResnetV2/freezable_batch_norm_131/moving_variance': 'InceptionResnetV2/Repeat_1/block17_14/Branch_1/Conv2d_0c_7x1/BatchNorm/moving_variance',
'FirstStageFeatureExtractor/InceptionResnetV2/block17_14_conv/kernel': 'InceptionResnetV2/Repeat_1/block17_14/Conv2d_1x1/weights',
'FirstStageFeatureExtractor/InceptionResnetV2/block17_14_conv/bias': 'InceptionResnetV2/Repeat_1/block17_14/Conv2d_1x1/biases',
'FirstStageFeatureExtractor/InceptionResnetV2/conv2d_132/kernel': 'InceptionResnetV2/Repeat_1/block17_15/Branch_0/Conv2d_1x1/weights',
'FirstStageFeatureExtractor/InceptionResnetV2/freezable_batch_norm_132/beta': 'InceptionResnetV2/Repeat_1/block17_15/Branch_0/Conv2d_1x1/BatchNorm/beta',
'FirstStageFeatureExtractor/InceptionResnetV2/freezable_batch_norm_132/moving_mean': 'InceptionResnetV2/Repeat_1/block17_15/Branch_0/Conv2d_1x1/BatchNorm/moving_mean',
'FirstStageFeatureExtractor/InceptionResnetV2/freezable_batch_norm_132/moving_variance': 'InceptionResnetV2/Repeat_1/block17_15/Branch_0/Conv2d_1x1/BatchNorm/moving_variance',
'FirstStageFeatureExtractor/InceptionResnetV2/conv2d_133/kernel': 'InceptionResnetV2/Repeat_1/block17_15/Branch_1/Conv2d_0a_1x1/weights',
'FirstStageFeatureExtractor/InceptionResnetV2/freezable_batch_norm_133/beta': 'InceptionResnetV2/Repeat_1/block17_15/Branch_1/Conv2d_0a_1x1/BatchNorm/beta',
'FirstStageFeatureExtractor/InceptionResnetV2/freezable_batch_norm_133/moving_mean': 'InceptionResnetV2/Repeat_1/block17_15/Branch_1/Conv2d_0a_1x1/BatchNorm/moving_mean',
'FirstStageFeatureExtractor/InceptionResnetV2/freezable_batch_norm_133/moving_variance': 'InceptionResnetV2/Repeat_1/block17_15/Branch_1/Conv2d_0a_1x1/BatchNorm/moving_variance',
'FirstStageFeatureExtractor/InceptionResnetV2/conv2d_134/kernel': 'InceptionResnetV2/Repeat_1/block17_15/Branch_1/Conv2d_0b_1x7/weights',
'FirstStageFeatureExtractor/InceptionResnetV2/freezable_batch_norm_134/beta': 'InceptionResnetV2/Repeat_1/block17_15/Branch_1/Conv2d_0b_1x7/BatchNorm/beta',
'FirstStageFeatureExtractor/InceptionResnetV2/freezable_batch_norm_134/moving_mean': 'InceptionResnetV2/Repeat_1/block17_15/Branch_1/Conv2d_0b_1x7/BatchNorm/moving_mean',
'FirstStageFeatureExtractor/InceptionResnetV2/freezable_batch_norm_134/moving_variance': 'InceptionResnetV2/Repeat_1/block17_15/Branch_1/Conv2d_0b_1x7/BatchNorm/moving_variance',
'FirstStageFeatureExtractor/InceptionResnetV2/conv2d_135/kernel': 'InceptionResnetV2/Repeat_1/block17_15/Branch_1/Conv2d_0c_7x1/weights',
'FirstStageFeatureExtractor/InceptionResnetV2/freezable_batch_norm_135/beta': 'InceptionResnetV2/Repeat_1/block17_15/Branch_1/Conv2d_0c_7x1/BatchNorm/beta',
'FirstStageFeatureExtractor/InceptionResnetV2/freezable_batch_norm_135/moving_mean': 'InceptionResnetV2/Repeat_1/block17_15/Branch_1/Conv2d_0c_7x1/BatchNorm/moving_mean',
'FirstStageFeatureExtractor/InceptionResnetV2/freezable_batch_norm_135/moving_variance': 'InceptionResnetV2/Repeat_1/block17_15/Branch_1/Conv2d_0c_7x1/BatchNorm/moving_variance',
'FirstStageFeatureExtractor/InceptionResnetV2/block17_15_conv/kernel': 'InceptionResnetV2/Repeat_1/block17_15/Conv2d_1x1/weights',
'FirstStageFeatureExtractor/InceptionResnetV2/block17_15_conv/bias': 'InceptionResnetV2/Repeat_1/block17_15/Conv2d_1x1/biases',
'FirstStageFeatureExtractor/InceptionResnetV2/conv2d_136/kernel': 'InceptionResnetV2/Repeat_1/block17_16/Branch_0/Conv2d_1x1/weights',
'FirstStageFeatureExtractor/InceptionResnetV2/freezable_batch_norm_136/beta': 'InceptionResnetV2/Repeat_1/block17_16/Branch_0/Conv2d_1x1/BatchNorm/beta',
'FirstStageFeatureExtractor/InceptionResnetV2/freezable_batch_norm_136/moving_mean': 'InceptionResnetV2/Repeat_1/block17_16/Branch_0/Conv2d_1x1/BatchNorm/moving_mean',
'FirstStageFeatureExtractor/InceptionResnetV2/freezable_batch_norm_136/moving_variance': 'InceptionResnetV2/Repeat_1/block17_16/Branch_0/Conv2d_1x1/BatchNorm/moving_variance',
'FirstStageFeatureExtractor/InceptionResnetV2/conv2d_137/kernel': 'InceptionResnetV2/Repeat_1/block17_16/Branch_1/Conv2d_0a_1x1/weights',
'FirstStageFeatureExtractor/InceptionResnetV2/freezable_batch_norm_137/beta': 'InceptionResnetV2/Repeat_1/block17_16/Branch_1/Conv2d_0a_1x1/BatchNorm/beta',
'FirstStageFeatureExtractor/InceptionResnetV2/freezable_batch_norm_137/moving_mean': 'InceptionResnetV2/Repeat_1/block17_16/Branch_1/Conv2d_0a_1x1/BatchNorm/moving_mean',
'FirstStageFeatureExtractor/InceptionResnetV2/freezable_batch_norm_137/moving_variance': 'InceptionResnetV2/Repeat_1/block17_16/Branch_1/Conv2d_0a_1x1/BatchNorm/moving_variance',
'FirstStageFeatureExtractor/InceptionResnetV2/conv2d_138/kernel': 'InceptionResnetV2/Repeat_1/block17_16/Branch_1/Conv2d_0b_1x7/weights',
'FirstStageFeatureExtractor/InceptionResnetV2/freezable_batch_norm_138/beta': 'InceptionResnetV2/Repeat_1/block17_16/Branch_1/Conv2d_0b_1x7/BatchNorm/beta',
'FirstStageFeatureExtractor/InceptionResnetV2/freezable_batch_norm_138/moving_mean': 'InceptionResnetV2/Repeat_1/block17_16/Branch_1/Conv2d_0b_1x7/BatchNorm/moving_mean',
'FirstStageFeatureExtractor/InceptionResnetV2/freezable_batch_norm_138/moving_variance': 'InceptionResnetV2/Repeat_1/block17_16/Branch_1/Conv2d_0b_1x7/BatchNorm/moving_variance',
'FirstStageFeatureExtractor/InceptionResnetV2/conv2d_139/kernel': 'InceptionResnetV2/Repeat_1/block17_16/Branch_1/Conv2d_0c_7x1/weights',
'FirstStageFeatureExtractor/InceptionResnetV2/freezable_batch_norm_139/beta': 'InceptionResnetV2/Repeat_1/block17_16/Branch_1/Conv2d_0c_7x1/BatchNorm/beta',
'FirstStageFeatureExtractor/InceptionResnetV2/freezable_batch_norm_139/moving_mean': 'InceptionResnetV2/Repeat_1/block17_16/Branch_1/Conv2d_0c_7x1/BatchNorm/moving_mean',
'FirstStageFeatureExtractor/InceptionResnetV2/freezable_batch_norm_139/moving_variance': 'InceptionResnetV2/Repeat_1/block17_16/Branch_1/Conv2d_0c_7x1/BatchNorm/moving_variance',
'FirstStageFeatureExtractor/InceptionResnetV2/block17_16_conv/kernel': 'InceptionResnetV2/Repeat_1/block17_16/Conv2d_1x1/weights',
'FirstStageFeatureExtractor/InceptionResnetV2/block17_16_conv/bias': 'InceptionResnetV2/Repeat_1/block17_16/Conv2d_1x1/biases',
'FirstStageFeatureExtractor/InceptionResnetV2/conv2d_140/kernel': 'InceptionResnetV2/Repeat_1/block17_17/Branch_0/Conv2d_1x1/weights',
'FirstStageFeatureExtractor/InceptionResnetV2/freezable_batch_norm_140/beta': 'InceptionResnetV2/Repeat_1/block17_17/Branch_0/Conv2d_1x1/BatchNorm/beta',
'FirstStageFeatureExtractor/InceptionResnetV2/freezable_batch_norm_140/moving_mean': 'InceptionResnetV2/Repeat_1/block17_17/Branch_0/Conv2d_1x1/BatchNorm/moving_mean',
'FirstStageFeatureExtractor/InceptionResnetV2/freezable_batch_norm_140/moving_variance': 'InceptionResnetV2/Repeat_1/block17_17/Branch_0/Conv2d_1x1/BatchNorm/moving_variance',
'FirstStageFeatureExtractor/InceptionResnetV2/conv2d_141/kernel': 'InceptionResnetV2/Repeat_1/block17_17/Branch_1/Conv2d_0a_1x1/weights',
'FirstStageFeatureExtractor/InceptionResnetV2/freezable_batch_norm_141/beta': 'InceptionResnetV2/Repeat_1/block17_17/Branch_1/Conv2d_0a_1x1/BatchNorm/beta',
'FirstStageFeatureExtractor/InceptionResnetV2/freezable_batch_norm_141/moving_mean': 'InceptionResnetV2/Repeat_1/block17_17/Branch_1/Conv2d_0a_1x1/BatchNorm/moving_mean',
'FirstStageFeatureExtractor/InceptionResnetV2/freezable_batch_norm_141/moving_variance': 'InceptionResnetV2/Repeat_1/block17_17/Branch_1/Conv2d_0a_1x1/BatchNorm/moving_variance',
'FirstStageFeatureExtractor/InceptionResnetV2/conv2d_142/kernel': 'InceptionResnetV2/Repeat_1/block17_17/Branch_1/Conv2d_0b_1x7/weights',
'FirstStageFeatureExtractor/InceptionResnetV2/freezable_batch_norm_142/beta': 'InceptionResnetV2/Repeat_1/block17_17/Branch_1/Conv2d_0b_1x7/BatchNorm/beta',
'FirstStageFeatureExtractor/InceptionResnetV2/freezable_batch_norm_142/moving_mean': 'InceptionResnetV2/Repeat_1/block17_17/Branch_1/Conv2d_0b_1x7/BatchNorm/moving_mean',
'FirstStageFeatureExtractor/InceptionResnetV2/freezable_batch_norm_142/moving_variance': 'InceptionResnetV2/Repeat_1/block17_17/Branch_1/Conv2d_0b_1x7/BatchNorm/moving_variance',
'FirstStageFeatureExtractor/InceptionResnetV2/conv2d_143/kernel': 'InceptionResnetV2/Repeat_1/block17_17/Branch_1/Conv2d_0c_7x1/weights',
'FirstStageFeatureExtractor/InceptionResnetV2/freezable_batch_norm_143/beta': 'InceptionResnetV2/Repeat_1/block17_17/Branch_1/Conv2d_0c_7x1/BatchNorm/beta',
'FirstStageFeatureExtractor/InceptionResnetV2/freezable_batch_norm_143/moving_mean': 'InceptionResnetV2/Repeat_1/block17_17/Branch_1/Conv2d_0c_7x1/BatchNorm/moving_mean',
'FirstStageFeatureExtractor/InceptionResnetV2/freezable_batch_norm_143/moving_variance': 'InceptionResnetV2/Repeat_1/block17_17/Branch_1/Conv2d_0c_7x1/BatchNorm/moving_variance',
'FirstStageFeatureExtractor/InceptionResnetV2/block17_17_conv/kernel': 'InceptionResnetV2/Repeat_1/block17_17/Conv2d_1x1/weights',
'FirstStageFeatureExtractor/InceptionResnetV2/block17_17_conv/bias': 'InceptionResnetV2/Repeat_1/block17_17/Conv2d_1x1/biases',
'FirstStageFeatureExtractor/InceptionResnetV2/conv2d_144/kernel': 'InceptionResnetV2/Repeat_1/block17_18/Branch_0/Conv2d_1x1/weights',
'FirstStageFeatureExtractor/InceptionResnetV2/freezable_batch_norm_144/beta': 'InceptionResnetV2/Repeat_1/block17_18/Branch_0/Conv2d_1x1/BatchNorm/beta',
'FirstStageFeatureExtractor/InceptionResnetV2/freezable_batch_norm_144/moving_mean': 'InceptionResnetV2/Repeat_1/block17_18/Branch_0/Conv2d_1x1/BatchNorm/moving_mean',
'FirstStageFeatureExtractor/InceptionResnetV2/freezable_batch_norm_144/moving_variance': 'InceptionResnetV2/Repeat_1/block17_18/Branch_0/Conv2d_1x1/BatchNorm/moving_variance',
'FirstStageFeatureExtractor/InceptionResnetV2/conv2d_145/kernel': 'InceptionResnetV2/Repeat_1/block17_18/Branch_1/Conv2d_0a_1x1/weights',
'FirstStageFeatureExtractor/InceptionResnetV2/freezable_batch_norm_145/beta': 'InceptionResnetV2/Repeat_1/block17_18/Branch_1/Conv2d_0a_1x1/BatchNorm/beta',
'FirstStageFeatureExtractor/InceptionResnetV2/freezable_batch_norm_145/moving_mean': 'InceptionResnetV2/Repeat_1/block17_18/Branch_1/Conv2d_0a_1x1/BatchNorm/moving_mean',
'FirstStageFeatureExtractor/InceptionResnetV2/freezable_batch_norm_145/moving_variance': 'InceptionResnetV2/Repeat_1/block17_18/Branch_1/Conv2d_0a_1x1/BatchNorm/moving_variance',
'FirstStageFeatureExtractor/InceptionResnetV2/conv2d_146/kernel': 'InceptionResnetV2/Repeat_1/block17_18/Branch_1/Conv2d_0b_1x7/weights',
'FirstStageFeatureExtractor/InceptionResnetV2/freezable_batch_norm_146/beta': 'InceptionResnetV2/Repeat_1/block17_18/Branch_1/Conv2d_0b_1x7/BatchNorm/beta',
'FirstStageFeatureExtractor/InceptionResnetV2/freezable_batch_norm_146/moving_mean': 'InceptionResnetV2/Repeat_1/block17_18/Branch_1/Conv2d_0b_1x7/BatchNorm/moving_mean',
'FirstStageFeatureExtractor/InceptionResnetV2/freezable_batch_norm_146/moving_variance': 'InceptionResnetV2/Repeat_1/block17_18/Branch_1/Conv2d_0b_1x7/BatchNorm/moving_variance',
'FirstStageFeatureExtractor/InceptionResnetV2/conv2d_147/kernel': 'InceptionResnetV2/Repeat_1/block17_18/Branch_1/Conv2d_0c_7x1/weights',
'FirstStageFeatureExtractor/InceptionResnetV2/freezable_batch_norm_147/beta': 'InceptionResnetV2/Repeat_1/block17_18/Branch_1/Conv2d_0c_7x1/BatchNorm/beta',
'FirstStageFeatureExtractor/InceptionResnetV2/freezable_batch_norm_147/moving_mean': 'InceptionResnetV2/Repeat_1/block17_18/Branch_1/Conv2d_0c_7x1/BatchNorm/moving_mean',
'FirstStageFeatureExtractor/InceptionResnetV2/freezable_batch_norm_147/moving_variance': 'InceptionResnetV2/Repeat_1/block17_18/Branch_1/Conv2d_0c_7x1/BatchNorm/moving_variance',
'FirstStageFeatureExtractor/InceptionResnetV2/block17_18_conv/kernel': 'InceptionResnetV2/Repeat_1/block17_18/Conv2d_1x1/weights',
'FirstStageFeatureExtractor/InceptionResnetV2/block17_18_conv/bias': 'InceptionResnetV2/Repeat_1/block17_18/Conv2d_1x1/biases',
'FirstStageFeatureExtractor/InceptionResnetV2/conv2d_148/kernel': 'InceptionResnetV2/Repeat_1/block17_19/Branch_0/Conv2d_1x1/weights',
'FirstStageFeatureExtractor/InceptionResnetV2/freezable_batch_norm_148/beta': 'InceptionResnetV2/Repeat_1/block17_19/Branch_0/Conv2d_1x1/BatchNorm/beta',
'FirstStageFeatureExtractor/InceptionResnetV2/freezable_batch_norm_148/moving_mean': 'InceptionResnetV2/Repeat_1/block17_19/Branch_0/Conv2d_1x1/BatchNorm/moving_mean',
'FirstStageFeatureExtractor/InceptionResnetV2/freezable_batch_norm_148/moving_variance': 'InceptionResnetV2/Repeat_1/block17_19/Branch_0/Conv2d_1x1/BatchNorm/moving_variance',
'FirstStageFeatureExtractor/InceptionResnetV2/conv2d_149/kernel': 'InceptionResnetV2/Repeat_1/block17_19/Branch_1/Conv2d_0a_1x1/weights',
'FirstStageFeatureExtractor/InceptionResnetV2/freezable_batch_norm_149/beta': 'InceptionResnetV2/Repeat_1/block17_19/Branch_1/Conv2d_0a_1x1/BatchNorm/beta',
'FirstStageFeatureExtractor/InceptionResnetV2/freezable_batch_norm_149/moving_mean': 'InceptionResnetV2/Repeat_1/block17_19/Branch_1/Conv2d_0a_1x1/BatchNorm/moving_mean',
'FirstStageFeatureExtractor/InceptionResnetV2/freezable_batch_norm_149/moving_variance': 'InceptionResnetV2/Repeat_1/block17_19/Branch_1/Conv2d_0a_1x1/BatchNorm/moving_variance',
'FirstStageFeatureExtractor/InceptionResnetV2/conv2d_150/kernel': 'InceptionResnetV2/Repeat_1/block17_19/Branch_1/Conv2d_0b_1x7/weights',
'FirstStageFeatureExtractor/InceptionResnetV2/freezable_batch_norm_150/beta': 'InceptionResnetV2/Repeat_1/block17_19/Branch_1/Conv2d_0b_1x7/BatchNorm/beta',
'FirstStageFeatureExtractor/InceptionResnetV2/freezable_batch_norm_150/moving_mean': 'InceptionResnetV2/Repeat_1/block17_19/Branch_1/Conv2d_0b_1x7/BatchNorm/moving_mean',
'FirstStageFeatureExtractor/InceptionResnetV2/freezable_batch_norm_150/moving_variance': 'InceptionResnetV2/Repeat_1/block17_19/Branch_1/Conv2d_0b_1x7/BatchNorm/moving_variance',
'FirstStageFeatureExtractor/InceptionResnetV2/conv2d_151/kernel': 'InceptionResnetV2/Repeat_1/block17_19/Branch_1/Conv2d_0c_7x1/weights',
'FirstStageFeatureExtractor/InceptionResnetV2/freezable_batch_norm_151/beta': 'InceptionResnetV2/Repeat_1/block17_19/Branch_1/Conv2d_0c_7x1/BatchNorm/beta',
'FirstStageFeatureExtractor/InceptionResnetV2/freezable_batch_norm_151/moving_mean': 'InceptionResnetV2/Repeat_1/block17_19/Branch_1/Conv2d_0c_7x1/BatchNorm/moving_mean',
'FirstStageFeatureExtractor/InceptionResnetV2/freezable_batch_norm_151/moving_variance': 'InceptionResnetV2/Repeat_1/block17_19/Branch_1/Conv2d_0c_7x1/BatchNorm/moving_variance',
'FirstStageFeatureExtractor/InceptionResnetV2/block17_19_conv/kernel': 'InceptionResnetV2/Repeat_1/block17_19/Conv2d_1x1/weights',
'FirstStageFeatureExtractor/InceptionResnetV2/block17_19_conv/bias': 'InceptionResnetV2/Repeat_1/block17_19/Conv2d_1x1/biases',
'FirstStageFeatureExtractor/InceptionResnetV2/conv2d_152/kernel': 'InceptionResnetV2/Repeat_1/block17_20/Branch_0/Conv2d_1x1/weights',
'FirstStageFeatureExtractor/InceptionResnetV2/freezable_batch_norm_152/beta': 'InceptionResnetV2/Repeat_1/block17_20/Branch_0/Conv2d_1x1/BatchNorm/beta',
'FirstStageFeatureExtractor/InceptionResnetV2/freezable_batch_norm_152/moving_mean': 'InceptionResnetV2/Repeat_1/block17_20/Branch_0/Conv2d_1x1/BatchNorm/moving_mean',
'FirstStageFeatureExtractor/InceptionResnetV2/freezable_batch_norm_152/moving_variance': 'InceptionResnetV2/Repeat_1/block17_20/Branch_0/Conv2d_1x1/BatchNorm/moving_variance',
'FirstStageFeatureExtractor/InceptionResnetV2/conv2d_153/kernel': 'InceptionResnetV2/Repeat_1/block17_20/Branch_1/Conv2d_0a_1x1/weights',
'FirstStageFeatureExtractor/InceptionResnetV2/freezable_batch_norm_153/beta': 'InceptionResnetV2/Repeat_1/block17_20/Branch_1/Conv2d_0a_1x1/BatchNorm/beta',
'FirstStageFeatureExtractor/InceptionResnetV2/freezable_batch_norm_153/moving_mean': 'InceptionResnetV2/Repeat_1/block17_20/Branch_1/Conv2d_0a_1x1/BatchNorm/moving_mean',
'FirstStageFeatureExtractor/InceptionResnetV2/freezable_batch_norm_153/moving_variance': 'InceptionResnetV2/Repeat_1/block17_20/Branch_1/Conv2d_0a_1x1/BatchNorm/moving_variance',
'FirstStageFeatureExtractor/InceptionResnetV2/conv2d_154/kernel': 'InceptionResnetV2/Repeat_1/block17_20/Branch_1/Conv2d_0b_1x7/weights',
'FirstStageFeatureExtractor/InceptionResnetV2/freezable_batch_norm_154/beta': 'InceptionResnetV2/Repeat_1/block17_20/Branch_1/Conv2d_0b_1x7/BatchNorm/beta',
'FirstStageFeatureExtractor/InceptionResnetV2/freezable_batch_norm_154/moving_mean': 'InceptionResnetV2/Repeat_1/block17_20/Branch_1/Conv2d_0b_1x7/BatchNorm/moving_mean',
'FirstStageFeatureExtractor/InceptionResnetV2/freezable_batch_norm_154/moving_variance': 'InceptionResnetV2/Repeat_1/block17_20/Branch_1/Conv2d_0b_1x7/BatchNorm/moving_variance',
'FirstStageFeatureExtractor/InceptionResnetV2/conv2d_155/kernel': 'InceptionResnetV2/Repeat_1/block17_20/Branch_1/Conv2d_0c_7x1/weights',
'FirstStageFeatureExtractor/InceptionResnetV2/freezable_batch_norm_155/beta': 'InceptionResnetV2/Repeat_1/block17_20/Branch_1/Conv2d_0c_7x1/BatchNorm/beta',
'FirstStageFeatureExtractor/InceptionResnetV2/freezable_batch_norm_155/moving_mean': 'InceptionResnetV2/Repeat_1/block17_20/Branch_1/Conv2d_0c_7x1/BatchNorm/moving_mean',
'FirstStageFeatureExtractor/InceptionResnetV2/freezable_batch_norm_155/moving_variance': 'InceptionResnetV2/Repeat_1/block17_20/Branch_1/Conv2d_0c_7x1/BatchNorm/moving_variance',
'FirstStageFeatureExtractor/InceptionResnetV2/block17_20_conv/kernel': 'InceptionResnetV2/Repeat_1/block17_20/Conv2d_1x1/weights',
'FirstStageFeatureExtractor/InceptionResnetV2/block17_20_conv/bias': 'InceptionResnetV2/Repeat_1/block17_20/Conv2d_1x1/biases',
'SecondStageFeatureExtractor/InceptionResnetV2/conv2d_359/kernel': 'InceptionResnetV2/Mixed_7a/Branch_0/Conv2d_0a_1x1/weights',
'SecondStageFeatureExtractor/InceptionResnetV2/freezable_batch_norm_359/beta': 'InceptionResnetV2/Mixed_7a/Branch_0/Conv2d_0a_1x1/BatchNorm/beta',
'SecondStageFeatureExtractor/InceptionResnetV2/freezable_batch_norm_359/moving_mean': 'InceptionResnetV2/Mixed_7a/Branch_0/Conv2d_0a_1x1/BatchNorm/moving_mean',
'SecondStageFeatureExtractor/InceptionResnetV2/freezable_batch_norm_359/moving_variance': 'InceptionResnetV2/Mixed_7a/Branch_0/Conv2d_0a_1x1/BatchNorm/moving_variance',
'SecondStageFeatureExtractor/InceptionResnetV2/conv2d_360/kernel': 'InceptionResnetV2/Mixed_7a/Branch_0/Conv2d_1a_3x3/weights',
'SecondStageFeatureExtractor/InceptionResnetV2/freezable_batch_norm_360/beta': 'InceptionResnetV2/Mixed_7a/Branch_0/Conv2d_1a_3x3/BatchNorm/beta',
'SecondStageFeatureExtractor/InceptionResnetV2/freezable_batch_norm_360/moving_mean': 'InceptionResnetV2/Mixed_7a/Branch_0/Conv2d_1a_3x3/BatchNorm/moving_mean',
'SecondStageFeatureExtractor/InceptionResnetV2/freezable_batch_norm_360/moving_variance': 'InceptionResnetV2/Mixed_7a/Branch_0/Conv2d_1a_3x3/BatchNorm/moving_variance',
'SecondStageFeatureExtractor/InceptionResnetV2/conv2d_361/kernel': 'InceptionResnetV2/Mixed_7a/Branch_1/Conv2d_0a_1x1/weights',
'SecondStageFeatureExtractor/InceptionResnetV2/freezable_batch_norm_361/beta': 'InceptionResnetV2/Mixed_7a/Branch_1/Conv2d_0a_1x1/BatchNorm/beta',
'SecondStageFeatureExtractor/InceptionResnetV2/freezable_batch_norm_361/moving_mean': 'InceptionResnetV2/Mixed_7a/Branch_1/Conv2d_0a_1x1/BatchNorm/moving_mean',
'SecondStageFeatureExtractor/InceptionResnetV2/freezable_batch_norm_361/moving_variance': 'InceptionResnetV2/Mixed_7a/Branch_1/Conv2d_0a_1x1/BatchNorm/moving_variance',
'SecondStageFeatureExtractor/InceptionResnetV2/conv2d_362/kernel': 'InceptionResnetV2/Mixed_7a/Branch_1/Conv2d_1a_3x3/weights',
'SecondStageFeatureExtractor/InceptionResnetV2/freezable_batch_norm_362/beta': 'InceptionResnetV2/Mixed_7a/Branch_1/Conv2d_1a_3x3/BatchNorm/beta',
'SecondStageFeatureExtractor/InceptionResnetV2/freezable_batch_norm_362/moving_mean': 'InceptionResnetV2/Mixed_7a/Branch_1/Conv2d_1a_3x3/BatchNorm/moving_mean',
'SecondStageFeatureExtractor/InceptionResnetV2/freezable_batch_norm_362/moving_variance': 'InceptionResnetV2/Mixed_7a/Branch_1/Conv2d_1a_3x3/BatchNorm/moving_variance',
'SecondStageFeatureExtractor/InceptionResnetV2/conv2d_363/kernel': 'InceptionResnetV2/Mixed_7a/Branch_2/Conv2d_0a_1x1/weights',
'SecondStageFeatureExtractor/InceptionResnetV2/freezable_batch_norm_363/beta': 'InceptionResnetV2/Mixed_7a/Branch_2/Conv2d_0a_1x1/BatchNorm/beta',
'SecondStageFeatureExtractor/InceptionResnetV2/freezable_batch_norm_363/moving_mean': 'InceptionResnetV2/Mixed_7a/Branch_2/Conv2d_0a_1x1/BatchNorm/moving_mean',
'SecondStageFeatureExtractor/InceptionResnetV2/freezable_batch_norm_363/moving_variance': 'InceptionResnetV2/Mixed_7a/Branch_2/Conv2d_0a_1x1/BatchNorm/moving_variance',
'SecondStageFeatureExtractor/InceptionResnetV2/conv2d_364/kernel': 'InceptionResnetV2/Mixed_7a/Branch_2/Conv2d_0b_3x3/weights',
'SecondStageFeatureExtractor/InceptionResnetV2/freezable_batch_norm_364/beta': 'InceptionResnetV2/Mixed_7a/Branch_2/Conv2d_0b_3x3/BatchNorm/beta',
'SecondStageFeatureExtractor/InceptionResnetV2/freezable_batch_norm_364/moving_mean': 'InceptionResnetV2/Mixed_7a/Branch_2/Conv2d_0b_3x3/BatchNorm/moving_mean',
'SecondStageFeatureExtractor/InceptionResnetV2/freezable_batch_norm_364/moving_variance': 'InceptionResnetV2/Mixed_7a/Branch_2/Conv2d_0b_3x3/BatchNorm/moving_variance',
'SecondStageFeatureExtractor/InceptionResnetV2/conv2d_365/kernel': 'InceptionResnetV2/Mixed_7a/Branch_2/Conv2d_1a_3x3/weights',
'SecondStageFeatureExtractor/InceptionResnetV2/freezable_batch_norm_365/beta': 'InceptionResnetV2/Mixed_7a/Branch_2/Conv2d_1a_3x3/BatchNorm/beta',
'SecondStageFeatureExtractor/InceptionResnetV2/freezable_batch_norm_365/moving_mean': 'InceptionResnetV2/Mixed_7a/Branch_2/Conv2d_1a_3x3/BatchNorm/moving_mean',
'SecondStageFeatureExtractor/InceptionResnetV2/freezable_batch_norm_365/moving_variance': 'InceptionResnetV2/Mixed_7a/Branch_2/Conv2d_1a_3x3/BatchNorm/moving_variance',
'SecondStageFeatureExtractor/InceptionResnetV2/conv2d_366/kernel': 'InceptionResnetV2/Repeat_2/block8_1/Branch_0/Conv2d_1x1/weights',
'SecondStageFeatureExtractor/InceptionResnetV2/freezable_batch_norm_366/beta': 'InceptionResnetV2/Repeat_2/block8_1/Branch_0/Conv2d_1x1/BatchNorm/beta',
'SecondStageFeatureExtractor/InceptionResnetV2/freezable_batch_norm_366/moving_mean': 'InceptionResnetV2/Repeat_2/block8_1/Branch_0/Conv2d_1x1/BatchNorm/moving_mean',
'SecondStageFeatureExtractor/InceptionResnetV2/freezable_batch_norm_366/moving_variance': 'InceptionResnetV2/Repeat_2/block8_1/Branch_0/Conv2d_1x1/BatchNorm/moving_variance',
'SecondStageFeatureExtractor/InceptionResnetV2/conv2d_367/kernel': 'InceptionResnetV2/Repeat_2/block8_1/Branch_1/Conv2d_0a_1x1/weights',
'SecondStageFeatureExtractor/InceptionResnetV2/freezable_batch_norm_367/beta': 'InceptionResnetV2/Repeat_2/block8_1/Branch_1/Conv2d_0a_1x1/BatchNorm/beta',
'SecondStageFeatureExtractor/InceptionResnetV2/freezable_batch_norm_367/moving_mean': 'InceptionResnetV2/Repeat_2/block8_1/Branch_1/Conv2d_0a_1x1/BatchNorm/moving_mean',
'SecondStageFeatureExtractor/InceptionResnetV2/freezable_batch_norm_367/moving_variance': 'InceptionResnetV2/Repeat_2/block8_1/Branch_1/Conv2d_0a_1x1/BatchNorm/moving_variance',
'SecondStageFeatureExtractor/InceptionResnetV2/conv2d_368/kernel': 'InceptionResnetV2/Repeat_2/block8_1/Branch_1/Conv2d_0b_1x3/weights',
'SecondStageFeatureExtractor/InceptionResnetV2/freezable_batch_norm_368/beta': 'InceptionResnetV2/Repeat_2/block8_1/Branch_1/Conv2d_0b_1x3/BatchNorm/beta',
'SecondStageFeatureExtractor/InceptionResnetV2/freezable_batch_norm_368/moving_mean': 'InceptionResnetV2/Repeat_2/block8_1/Branch_1/Conv2d_0b_1x3/BatchNorm/moving_mean',
'SecondStageFeatureExtractor/InceptionResnetV2/freezable_batch_norm_368/moving_variance': 'InceptionResnetV2/Repeat_2/block8_1/Branch_1/Conv2d_0b_1x3/BatchNorm/moving_variance',
'SecondStageFeatureExtractor/InceptionResnetV2/conv2d_369/kernel': 'InceptionResnetV2/Repeat_2/block8_1/Branch_1/Conv2d_0c_3x1/weights',
'SecondStageFeatureExtractor/InceptionResnetV2/freezable_batch_norm_369/beta': 'InceptionResnetV2/Repeat_2/block8_1/Branch_1/Conv2d_0c_3x1/BatchNorm/beta',
'SecondStageFeatureExtractor/InceptionResnetV2/freezable_batch_norm_369/moving_mean': 'InceptionResnetV2/Repeat_2/block8_1/Branch_1/Conv2d_0c_3x1/BatchNorm/moving_mean',
'SecondStageFeatureExtractor/InceptionResnetV2/freezable_batch_norm_369/moving_variance': 'InceptionResnetV2/Repeat_2/block8_1/Branch_1/Conv2d_0c_3x1/BatchNorm/moving_variance',
'SecondStageFeatureExtractor/InceptionResnetV2/block8_1_conv/kernel': 'InceptionResnetV2/Repeat_2/block8_1/Conv2d_1x1/weights',
'SecondStageFeatureExtractor/InceptionResnetV2/block8_1_conv/bias': 'InceptionResnetV2/Repeat_2/block8_1/Conv2d_1x1/biases',
'SecondStageFeatureExtractor/InceptionResnetV2/conv2d_370/kernel': 'InceptionResnetV2/Repeat_2/block8_2/Branch_0/Conv2d_1x1/weights',
'SecondStageFeatureExtractor/InceptionResnetV2/freezable_batch_norm_370/beta': 'InceptionResnetV2/Repeat_2/block8_2/Branch_0/Conv2d_1x1/BatchNorm/beta',
'SecondStageFeatureExtractor/InceptionResnetV2/freezable_batch_norm_370/moving_mean': 'InceptionResnetV2/Repeat_2/block8_2/Branch_0/Conv2d_1x1/BatchNorm/moving_mean',
'SecondStageFeatureExtractor/InceptionResnetV2/freezable_batch_norm_370/moving_variance': 'InceptionResnetV2/Repeat_2/block8_2/Branch_0/Conv2d_1x1/BatchNorm/moving_variance',
'SecondStageFeatureExtractor/InceptionResnetV2/conv2d_371/kernel': 'InceptionResnetV2/Repeat_2/block8_2/Branch_1/Conv2d_0a_1x1/weights',
'SecondStageFeatureExtractor/InceptionResnetV2/freezable_batch_norm_371/beta': 'InceptionResnetV2/Repeat_2/block8_2/Branch_1/Conv2d_0a_1x1/BatchNorm/beta',
'SecondStageFeatureExtractor/InceptionResnetV2/freezable_batch_norm_371/moving_mean': 'InceptionResnetV2/Repeat_2/block8_2/Branch_1/Conv2d_0a_1x1/BatchNorm/moving_mean',
'SecondStageFeatureExtractor/InceptionResnetV2/freezable_batch_norm_371/moving_variance': 'InceptionResnetV2/Repeat_2/block8_2/Branch_1/Conv2d_0a_1x1/BatchNorm/moving_variance',
'SecondStageFeatureExtractor/InceptionResnetV2/conv2d_372/kernel': 'InceptionResnetV2/Repeat_2/block8_2/Branch_1/Conv2d_0b_1x3/weights',
'SecondStageFeatureExtractor/InceptionResnetV2/freezable_batch_norm_372/beta': 'InceptionResnetV2/Repeat_2/block8_2/Branch_1/Conv2d_0b_1x3/BatchNorm/beta',
'SecondStageFeatureExtractor/InceptionResnetV2/freezable_batch_norm_372/moving_mean': 'InceptionResnetV2/Repeat_2/block8_2/Branch_1/Conv2d_0b_1x3/BatchNorm/moving_mean',
'SecondStageFeatureExtractor/InceptionResnetV2/freezable_batch_norm_372/moving_variance': 'InceptionResnetV2/Repeat_2/block8_2/Branch_1/Conv2d_0b_1x3/BatchNorm/moving_variance',
'SecondStageFeatureExtractor/InceptionResnetV2/conv2d_373/kernel': 'InceptionResnetV2/Repeat_2/block8_2/Branch_1/Conv2d_0c_3x1/weights',
'SecondStageFeatureExtractor/InceptionResnetV2/freezable_batch_norm_373/beta': 'InceptionResnetV2/Repeat_2/block8_2/Branch_1/Conv2d_0c_3x1/BatchNorm/beta',
'SecondStageFeatureExtractor/InceptionResnetV2/freezable_batch_norm_373/moving_mean': 'InceptionResnetV2/Repeat_2/block8_2/Branch_1/Conv2d_0c_3x1/BatchNorm/moving_mean',
'SecondStageFeatureExtractor/InceptionResnetV2/freezable_batch_norm_373/moving_variance': 'InceptionResnetV2/Repeat_2/block8_2/Branch_1/Conv2d_0c_3x1/BatchNorm/moving_variance',
'SecondStageFeatureExtractor/InceptionResnetV2/block8_2_conv/kernel': 'InceptionResnetV2/Repeat_2/block8_2/Conv2d_1x1/weights',
'SecondStageFeatureExtractor/InceptionResnetV2/block8_2_conv/bias': 'InceptionResnetV2/Repeat_2/block8_2/Conv2d_1x1/biases',
'SecondStageFeatureExtractor/InceptionResnetV2/conv2d_374/kernel': 'InceptionResnetV2/Repeat_2/block8_3/Branch_0/Conv2d_1x1/weights',
'SecondStageFeatureExtractor/InceptionResnetV2/freezable_batch_norm_374/beta': 'InceptionResnetV2/Repeat_2/block8_3/Branch_0/Conv2d_1x1/BatchNorm/beta',
'SecondStageFeatureExtractor/InceptionResnetV2/freezable_batch_norm_374/moving_mean': 'InceptionResnetV2/Repeat_2/block8_3/Branch_0/Conv2d_1x1/BatchNorm/moving_mean',
'SecondStageFeatureExtractor/InceptionResnetV2/freezable_batch_norm_374/moving_variance': 'InceptionResnetV2/Repeat_2/block8_3/Branch_0/Conv2d_1x1/BatchNorm/moving_variance',
'SecondStageFeatureExtractor/InceptionResnetV2/conv2d_375/kernel': 'InceptionResnetV2/Repeat_2/block8_3/Branch_1/Conv2d_0a_1x1/weights',
'SecondStageFeatureExtractor/InceptionResnetV2/freezable_batch_norm_375/beta': 'InceptionResnetV2/Repeat_2/block8_3/Branch_1/Conv2d_0a_1x1/BatchNorm/beta',
'SecondStageFeatureExtractor/InceptionResnetV2/freezable_batch_norm_375/moving_mean': 'InceptionResnetV2/Repeat_2/block8_3/Branch_1/Conv2d_0a_1x1/BatchNorm/moving_mean',
'SecondStageFeatureExtractor/InceptionResnetV2/freezable_batch_norm_375/moving_variance': 'InceptionResnetV2/Repeat_2/block8_3/Branch_1/Conv2d_0a_1x1/BatchNorm/moving_variance',
'SecondStageFeatureExtractor/InceptionResnetV2/conv2d_376/kernel': 'InceptionResnetV2/Repeat_2/block8_3/Branch_1/Conv2d_0b_1x3/weights',
'SecondStageFeatureExtractor/InceptionResnetV2/freezable_batch_norm_376/beta': 'InceptionResnetV2/Repeat_2/block8_3/Branch_1/Conv2d_0b_1x3/BatchNorm/beta',
'SecondStageFeatureExtractor/InceptionResnetV2/freezable_batch_norm_376/moving_mean': 'InceptionResnetV2/Repeat_2/block8_3/Branch_1/Conv2d_0b_1x3/BatchNorm/moving_mean',
'SecondStageFeatureExtractor/InceptionResnetV2/freezable_batch_norm_376/moving_variance': 'InceptionResnetV2/Repeat_2/block8_3/Branch_1/Conv2d_0b_1x3/BatchNorm/moving_variance',
'SecondStageFeatureExtractor/InceptionResnetV2/conv2d_377/kernel': 'InceptionResnetV2/Repeat_2/block8_3/Branch_1/Conv2d_0c_3x1/weights',
'SecondStageFeatureExtractor/InceptionResnetV2/freezable_batch_norm_377/beta': 'InceptionResnetV2/Repeat_2/block8_3/Branch_1/Conv2d_0c_3x1/BatchNorm/beta',
'SecondStageFeatureExtractor/InceptionResnetV2/freezable_batch_norm_377/moving_mean': 'InceptionResnetV2/Repeat_2/block8_3/Branch_1/Conv2d_0c_3x1/BatchNorm/moving_mean',
'SecondStageFeatureExtractor/InceptionResnetV2/freezable_batch_norm_377/moving_variance': 'InceptionResnetV2/Repeat_2/block8_3/Branch_1/Conv2d_0c_3x1/BatchNorm/moving_variance',
'SecondStageFeatureExtractor/InceptionResnetV2/block8_3_conv/kernel': 'InceptionResnetV2/Repeat_2/block8_3/Conv2d_1x1/weights',
'SecondStageFeatureExtractor/InceptionResnetV2/block8_3_conv/bias': 'InceptionResnetV2/Repeat_2/block8_3/Conv2d_1x1/biases',
'SecondStageFeatureExtractor/InceptionResnetV2/conv2d_378/kernel': 'InceptionResnetV2/Repeat_2/block8_4/Branch_0/Conv2d_1x1/weights',
'SecondStageFeatureExtractor/InceptionResnetV2/freezable_batch_norm_378/beta': 'InceptionResnetV2/Repeat_2/block8_4/Branch_0/Conv2d_1x1/BatchNorm/beta',
'SecondStageFeatureExtractor/InceptionResnetV2/freezable_batch_norm_378/moving_mean': 'InceptionResnetV2/Repeat_2/block8_4/Branch_0/Conv2d_1x1/BatchNorm/moving_mean',
'SecondStageFeatureExtractor/InceptionResnetV2/freezable_batch_norm_378/moving_variance': 'InceptionResnetV2/Repeat_2/block8_4/Branch_0/Conv2d_1x1/BatchNorm/moving_variance',
'SecondStageFeatureExtractor/InceptionResnetV2/conv2d_379/kernel': 'InceptionResnetV2/Repeat_2/block8_4/Branch_1/Conv2d_0a_1x1/weights',
'SecondStageFeatureExtractor/InceptionResnetV2/freezable_batch_norm_379/beta': 'InceptionResnetV2/Repeat_2/block8_4/Branch_1/Conv2d_0a_1x1/BatchNorm/beta',
'SecondStageFeatureExtractor/InceptionResnetV2/freezable_batch_norm_379/moving_mean': 'InceptionResnetV2/Repeat_2/block8_4/Branch_1/Conv2d_0a_1x1/BatchNorm/moving_mean',
'SecondStageFeatureExtractor/InceptionResnetV2/freezable_batch_norm_379/moving_variance': 'InceptionResnetV2/Repeat_2/block8_4/Branch_1/Conv2d_0a_1x1/BatchNorm/moving_variance',
'SecondStageFeatureExtractor/InceptionResnetV2/conv2d_380/kernel': 'InceptionResnetV2/Repeat_2/block8_4/Branch_1/Conv2d_0b_1x3/weights',
'SecondStageFeatureExtractor/InceptionResnetV2/freezable_batch_norm_380/beta': 'InceptionResnetV2/Repeat_2/block8_4/Branch_1/Conv2d_0b_1x3/BatchNorm/beta',
'SecondStageFeatureExtractor/InceptionResnetV2/freezable_batch_norm_380/moving_mean': 'InceptionResnetV2/Repeat_2/block8_4/Branch_1/Conv2d_0b_1x3/BatchNorm/moving_mean',
'SecondStageFeatureExtractor/InceptionResnetV2/freezable_batch_norm_380/moving_variance': 'InceptionResnetV2/Repeat_2/block8_4/Branch_1/Conv2d_0b_1x3/BatchNorm/moving_variance',
'SecondStageFeatureExtractor/InceptionResnetV2/conv2d_381/kernel': 'InceptionResnetV2/Repeat_2/block8_4/Branch_1/Conv2d_0c_3x1/weights',
'SecondStageFeatureExtractor/InceptionResnetV2/freezable_batch_norm_381/beta': 'InceptionResnetV2/Repeat_2/block8_4/Branch_1/Conv2d_0c_3x1/BatchNorm/beta',
'SecondStageFeatureExtractor/InceptionResnetV2/freezable_batch_norm_381/moving_mean': 'InceptionResnetV2/Repeat_2/block8_4/Branch_1/Conv2d_0c_3x1/BatchNorm/moving_mean',
'SecondStageFeatureExtractor/InceptionResnetV2/freezable_batch_norm_381/moving_variance': 'InceptionResnetV2/Repeat_2/block8_4/Branch_1/Conv2d_0c_3x1/BatchNorm/moving_variance',
'SecondStageFeatureExtractor/InceptionResnetV2/block8_4_conv/kernel': 'InceptionResnetV2/Repeat_2/block8_4/Conv2d_1x1/weights',
'SecondStageFeatureExtractor/InceptionResnetV2/block8_4_conv/bias': 'InceptionResnetV2/Repeat_2/block8_4/Conv2d_1x1/biases',
'SecondStageFeatureExtractor/InceptionResnetV2/conv2d_382/kernel': 'InceptionResnetV2/Repeat_2/block8_5/Branch_0/Conv2d_1x1/weights',
'SecondStageFeatureExtractor/InceptionResnetV2/freezable_batch_norm_382/beta': 'InceptionResnetV2/Repeat_2/block8_5/Branch_0/Conv2d_1x1/BatchNorm/beta',
'SecondStageFeatureExtractor/InceptionResnetV2/freezable_batch_norm_382/moving_mean': 'InceptionResnetV2/Repeat_2/block8_5/Branch_0/Conv2d_1x1/BatchNorm/moving_mean',
'SecondStageFeatureExtractor/InceptionResnetV2/freezable_batch_norm_382/moving_variance': 'InceptionResnetV2/Repeat_2/block8_5/Branch_0/Conv2d_1x1/BatchNorm/moving_variance',
'SecondStageFeatureExtractor/InceptionResnetV2/conv2d_383/kernel': 'InceptionResnetV2/Repeat_2/block8_5/Branch_1/Conv2d_0a_1x1/weights',
'SecondStageFeatureExtractor/InceptionResnetV2/freezable_batch_norm_383/beta': 'InceptionResnetV2/Repeat_2/block8_5/Branch_1/Conv2d_0a_1x1/BatchNorm/beta',
'SecondStageFeatureExtractor/InceptionResnetV2/freezable_batch_norm_383/moving_mean': 'InceptionResnetV2/Repeat_2/block8_5/Branch_1/Conv2d_0a_1x1/BatchNorm/moving_mean',
'SecondStageFeatureExtractor/InceptionResnetV2/freezable_batch_norm_383/moving_variance': 'InceptionResnetV2/Repeat_2/block8_5/Branch_1/Conv2d_0a_1x1/BatchNorm/moving_variance',
'SecondStageFeatureExtractor/InceptionResnetV2/conv2d_384/kernel': 'InceptionResnetV2/Repeat_2/block8_5/Branch_1/Conv2d_0b_1x3/weights',
'SecondStageFeatureExtractor/InceptionResnetV2/freezable_batch_norm_384/beta': 'InceptionResnetV2/Repeat_2/block8_5/Branch_1/Conv2d_0b_1x3/BatchNorm/beta',
'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_test.py → research/object_detection/models/faster_rcnn_inception_resnet_v2_keras_feature_extractor_tf2_test.py
View file @ 47bc1813
......@@ -14,12 +14,14 @@
# ==============================================================================
"""Tests for models.faster_rcnn_inception_resnet_v2_keras_feature_extractor."""
import unittest
import tensorflow.compat.v1 as tf
from object_detection.models import faster_rcnn_inception_resnet_v2_keras_feature_extractor as frcnn_inc_res
from object_detection.utils import tf_version
@unittest.skipIf(tf_version.is_tf1(), 'Skipping TF2.X only test.')
class FasterRcnnInceptionResnetV2KerasFeatureExtractorTest(tf.test.TestCase):
def _build_feature_extractor(self, first_stage_features_stride):
......@@ -38,11 +40,7 @@ class FasterRcnnInceptionResnetV2KerasFeatureExtractorTest(tf.test.TestCase):
name='TestScope')(preprocessed_inputs)
features_shape = tf.shape(rpn_feature_map)
init_op = tf.global_variables_initializer()
with self.test_session() as sess:
sess.run(init_op)
features_shape_out = sess.run(features_shape)
self.assertAllEqual(features_shape_out, [1, 19, 19, 1088])
self.assertAllEqual(features_shape.numpy(), [1, 19, 19, 1088])
def test_extract_proposal_features_stride_eight(self):
feature_extractor = self._build_feature_extractor(
......@@ -53,11 +51,7 @@ class FasterRcnnInceptionResnetV2KerasFeatureExtractorTest(tf.test.TestCase):
name='TestScope')(preprocessed_inputs)
features_shape = tf.shape(rpn_feature_map)
init_op = tf.global_variables_initializer()
with self.test_session() as sess:
sess.run(init_op)
features_shape_out = sess.run(features_shape)
self.assertAllEqual(features_shape_out, [1, 28, 28, 1088])
self.assertAllEqual(features_shape.numpy(), [1, 28, 28, 1088])
def test_extract_proposal_features_half_size_input(self):
feature_extractor = self._build_feature_extractor(
......@@ -67,25 +61,7 @@ class FasterRcnnInceptionResnetV2KerasFeatureExtractorTest(tf.test.TestCase):
rpn_feature_map = feature_extractor.get_proposal_feature_extractor_model(
name='TestScope')(preprocessed_inputs)
features_shape = tf.shape(rpn_feature_map)
init_op = tf.global_variables_initializer()
with self.test_session() as sess:
sess.run(init_op)
features_shape_out = sess.run(features_shape)
self.assertAllEqual(features_shape_out, [1, 7, 7, 1088])
def test_extract_proposal_features_dies_on_invalid_stride(self):
with self.assertRaises(ValueError):
self._build_feature_extractor(first_stage_features_stride=99)
def test_extract_proposal_features_dies_with_incorrect_rank_inputs(self):
feature_extractor = self._build_feature_extractor(
first_stage_features_stride=16)
preprocessed_inputs = tf.random_uniform(
[224, 224, 3], maxval=255, dtype=tf.float32)
with self.assertRaises(ValueError):
feature_extractor.get_proposal_feature_extractor_model(
name='TestScope')(preprocessed_inputs)
self.assertAllEqual(features_shape.numpy(), [1, 7, 7, 1088])
def test_extract_box_classifier_features_returns_expected_size(self):
feature_extractor = self._build_feature_extractor(
......@@ -97,12 +73,7 @@ class FasterRcnnInceptionResnetV2KerasFeatureExtractorTest(tf.test.TestCase):
proposal_classifier_features = (
model(proposal_feature_maps))
features_shape = tf.shape(proposal_classifier_features)
init_op = tf.global_variables_initializer()
with self.test_session() as sess:
sess.run(init_op)
features_shape_out = sess.run(features_shape)
self.assertAllEqual(features_shape_out, [2, 8, 8, 1536])
self.assertAllEqual(features_shape.numpy(), [2, 9, 9, 1536])
if __name__ == '__main__':
......
research/object_detection/models/faster_rcnn_inception_v2_feature_extractor_test.py → research/object_detection/models/faster_rcnn_inception_v2_feature_extractor_tf1_test.py
View file @ 47bc1813
......@@ -14,13 +14,15 @@
# ==============================================================================
"""Tests for faster_rcnn_inception_v2_feature_extractor."""
import unittest
import numpy as np
import tensorflow.compat.v1 as tf
from object_detection.models import faster_rcnn_inception_v2_feature_extractor as faster_rcnn_inception_v2
from object_detection.utils import tf_version
@unittest.skipIf(tf_version.is_tf2(), 'Skipping TF1.X only test.')
class FasterRcnnInceptionV2FeatureExtractorTest(tf.test.TestCase):
def _build_feature_extractor(self, first_stage_features_stride):
......
research/object_detection/models/faster_rcnn_mobilenet_v1_feature_extractor_test.py → research/object_detection/models/faster_rcnn_mobilenet_v1_feature_extractor_tf1_test.py
View file @ 47bc1813
......@@ -14,13 +14,15 @@
# ==============================================================================
"""Tests for faster_rcnn_mobilenet_v1_feature_extractor."""
import unittest
import numpy as np
import tensorflow.compat.v1 as tf
from object_detection.models import faster_rcnn_mobilenet_v1_feature_extractor as faster_rcnn_mobilenet_v1
from object_detection.utils import tf_version
@unittest.skipIf(tf_version.is_tf2(), 'Skipping TF1.X only test.')
class FasterRcnnMobilenetV1FeatureExtractorTest(tf.test.TestCase):
def _build_feature_extractor(self, first_stage_features_stride):
......
research/object_detection/models/faster_rcnn_nas_feature_extractor.py
View file @ 47bc1813
......@@ -31,8 +31,14 @@ import tf_slim as slim
from object_detection.meta_architectures import faster_rcnn_meta_arch
from object_detection.utils import variables_helper
from nets.nasnet import nasnet
from nets.nasnet import nasnet_utils
# pylint: disable=g-import-not-at-top
try:
from nets.nasnet import nasnet
from nets.nasnet import nasnet_utils
except: # pylint: disable=bare-except
pass
# pylint: enable=g-import-not-at-top
arg_scope = slim.arg_scope
......
research/object_detection/models/faster_rcnn_nas_feature_extractor_test.py → research/object_detection/models/faster_rcnn_nas_feature_extractor_tf1_test.py
View file @ 47bc1813
......@@ -14,12 +14,14 @@
# ==============================================================================
"""Tests for models.faster_rcnn_nas_feature_extractor."""
import unittest
import tensorflow.compat.v1 as tf
from object_detection.models import faster_rcnn_nas_feature_extractor as frcnn_nas
from object_detection.utils import tf_version
@unittest.skipIf(tf_version.is_tf2(), 'Skipping TF1.X only test.')
class FasterRcnnNASFeatureExtractorTest(tf.test.TestCase):
def _build_feature_extractor(self, first_stage_features_stride):
......
research/object_detection/models/faster_rcnn_pnas_feature_extractor.py
View file @ 47bc1813
......@@ -30,7 +30,11 @@ import tf_slim as slim
from object_detection.meta_architectures import faster_rcnn_meta_arch
from object_detection.utils import variables_helper
from nets.nasnet import nasnet_utils
from nets.nasnet import pnasnet
try:
from nets.nasnet import pnasnet # pylint: disable=g-import-not-at-top
except: # pylint: disable=bare-except
pass
arg_scope = slim.arg_scope
......
research/object_detection/models/faster_rcnn_pnas_feature_extractor_test.py → research/object_detection/models/faster_rcnn_pnas_feature_extractor_tf1_test.py
View file @ 47bc1813
......@@ -14,12 +14,14 @@
# ==============================================================================
"""Tests for models.faster_rcnn_pnas_feature_extractor."""
import unittest
import tensorflow.compat.v1 as tf
from object_detection.models import faster_rcnn_pnas_feature_extractor as frcnn_pnas
from object_detection.utils import tf_version
@unittest.skipIf(tf_version.is_tf2(), 'Skipping TF1.X only test.')
class FasterRcnnPNASFeatureExtractorTest(tf.test.TestCase):
def _build_feature_extractor(self, first_stage_features_stride):
......
research/object_detection/models/faster_rcnn_resnet_keras_feature_extractor.py 0 → 100644
View file @ 47bc1813
# 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.
# ==============================================================================
"""Resnet based Faster R-CNN implementation in Keras.
See Deep Residual Learning for Image Recognition by He et al.
https://arxiv.org/abs/1512.03385
"""
import tensorflow.compat.v1 as tf
from object_detection.meta_architectures import faster_rcnn_meta_arch
from object_detection.models.keras_models import resnet_v1
from object_detection.utils import model_util
_RESNET_MODEL_CONV4_LAST_LAYERS = {
'resnet_v1_50': 'conv4_block6_out',
'resnet_v1_101': 'conv4_block23_out',
'resnet_v1_152': 'conv4_block36_out',
}
class FasterRCNNResnetKerasFeatureExtractor(
faster_rcnn_meta_arch.FasterRCNNKerasFeatureExtractor):
"""Faster R-CNN with Resnet feature extractor implementation."""
def __init__(self,
is_training,
resnet_v1_base_model,
resnet_v1_base_model_name,
first_stage_features_stride=16,
batch_norm_trainable=False,
weight_decay=0.0):
"""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.
batch_norm_trainable: See base class.
weight_decay: See base class.
Raises:
ValueError: If `first_stage_features_stride` is not 8 or 16.
"""
if first_stage_features_stride != 16:
raise ValueError('`first_stage_features_stride` must be 16.')
super(FasterRCNNResnetKerasFeatureExtractor, self).__init__(
is_training, first_stage_features_stride, batch_norm_trainable,
weight_decay)
self.classification_backbone = None
self._variable_dict = {}
self._resnet_v1_base_model = resnet_v1_base_model
self._resnet_v1_base_model_name = resnet_v1_base_model_name
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 first half of the Resnet v1 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 tensor with shape [batch, height, width, depth]
"""
if not self.classification_backbone:
self.classification_backbone = self._resnet_v1_base_model(
batchnorm_training=self._train_batch_norm,
conv_hyperparams=None,
weight_decay=self._weight_decay,
classes=None,
weights=None,
include_top=False
)
with tf.name_scope(name):
with tf.name_scope('ResnetV1'):
conv4_last_layer = _RESNET_MODEL_CONV4_LAST_LAYERS[
self._resnet_v1_base_model_name]
proposal_features = self.classification_backbone.get_layer(
name=conv4_last_layer).output
keras_model = tf.keras.Model(
inputs=self.classification_backbone.inputs,
outputs=proposal_features)
for variable in keras_model.variables:
self._variable_dict[variable.name[:-2]] = variable
return keras_model
def get_box_classifier_feature_extractor_model(self, name=None):
"""Returns a model that extracts second stage box classifier features.
This function reconstructs the "second half" of the ResNet v1
network after the part defined in `get_proposal_feature_extractor_model`.
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, height, width, depth]
representing box classifier features for each proposal.
"""
if not self.classification_backbone:
self.classification_backbone = self._resnet_v1_base_model(
batchnorm_training=self._train_batch_norm,
conv_hyperparams=None,
weight_decay=self._weight_decay,
classes=None,
weights=None,
include_top=False
)
with tf.name_scope(name):
with tf.name_scope('ResnetV1'):
conv4_last_layer = _RESNET_MODEL_CONV4_LAST_LAYERS[
self._resnet_v1_base_model_name]
proposal_feature_maps = self.classification_backbone.get_layer(
name=conv4_last_layer).output
proposal_classifier_features = self.classification_backbone.get_layer(
name='conv5_block3_out').output
keras_model = model_util.extract_submodel(
model=self.classification_backbone,
inputs=proposal_feature_maps,
outputs=proposal_classifier_features)
for variable in keras_model.variables:
self._variable_dict[variable.name[:-2]] = variable
return keras_model
class FasterRCNNResnet50KerasFeatureExtractor(
FasterRCNNResnetKerasFeatureExtractor):
"""Faster R-CNN with Resnet50 feature extractor implementation."""
def __init__(self,
is_training,
first_stage_features_stride=16,
batch_norm_trainable=False,
weight_decay=0.0):
"""Constructor.
Args:
is_training: See base class.
first_stage_features_stride: See base class.
batch_norm_trainable: See base class.
weight_decay: See base class.
"""
super(FasterRCNNResnet50KerasFeatureExtractor, self).__init__(
is_training=is_training,
resnet_v1_base_model=resnet_v1.resnet_v1_50,
resnet_v1_base_model_name='resnet_v1_50',
first_stage_features_stride=first_stage_features_stride,
batch_norm_trainable=batch_norm_trainable,
weight_decay=weight_decay)
class FasterRCNNResnet101KerasFeatureExtractor(
FasterRCNNResnetKerasFeatureExtractor):
"""Faster R-CNN with Resnet101 feature extractor implementation."""
def __init__(self,
is_training,
first_stage_features_stride=16,
batch_norm_trainable=False,
weight_decay=0.0):
"""Constructor.
Args:
is_training: See base class.
first_stage_features_stride: See base class.
batch_norm_trainable: See base class.
weight_decay: See base class.
"""
super(FasterRCNNResnet101KerasFeatureExtractor, self).__init__(
is_training=is_training,
resnet_v1_base_model=resnet_v1.resnet_v1_101,
resnet_v1_base_model_name='resnet_v1_101',
first_stage_features_stride=first_stage_features_stride,
batch_norm_trainable=batch_norm_trainable,
weight_decay=weight_decay)
class FasterRCNNResnet152KerasFeatureExtractor(
FasterRCNNResnetKerasFeatureExtractor):
"""Faster R-CNN with Resnet152 feature extractor implementation."""
def __init__(self,
is_training,
first_stage_features_stride=16,
batch_norm_trainable=False,
weight_decay=0.0):
"""Constructor.
Args:
is_training: See base class.
first_stage_features_stride: See base class.
batch_norm_trainable: See base class.
weight_decay: See base class.
"""
super(FasterRCNNResnet152KerasFeatureExtractor, self).__init__(
is_training=is_training,
resnet_v1_base_model=resnet_v1.resnet_v1_152,
resnet_v1_base_model_name='resnet_v1_152',
first_stage_features_stride=first_stage_features_stride,
batch_norm_trainable=batch_norm_trainable,
weight_decay=weight_decay)
research/object_detection/models/faster_rcnn_resnet_keras_feature_extractor_tf2_test.py 0 → 100644
View file @ 47bc1813
# 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_keras_feature_extractor."""
import unittest
import tensorflow.compat.v1 as tf
from object_detection.models import faster_rcnn_resnet_keras_feature_extractor as frcnn_res
from object_detection.utils import tf_version
@unittest.skipIf(tf_version.is_tf1(), 'Skipping TF2.X only test.')
class FasterRcnnResnetKerasFeatureExtractorTest(tf.test.TestCase):
def _build_feature_extractor(self, architecture='resnet_v1_50'):
return frcnn_res.FasterRCNNResnet50KerasFeatureExtractor(
is_training=False,
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(
[1, 224, 224, 3], maxval=255, dtype=tf.float32)
rpn_feature_map = feature_extractor.get_proposal_feature_extractor_model(
name='TestScope')(preprocessed_inputs)
features_shape = tf.shape(rpn_feature_map)
self.assertAllEqual(features_shape.numpy(), [1, 14, 14, 1024])
def test_extract_proposal_features_half_size_input(self):
feature_extractor = self._build_feature_extractor()
preprocessed_inputs = tf.random_uniform(
[1, 112, 112, 3], maxval=255, dtype=tf.float32)
rpn_feature_map = feature_extractor.get_proposal_feature_extractor_model(
name='TestScope')(preprocessed_inputs)
features_shape = tf.shape(rpn_feature_map)
self.assertAllEqual(features_shape.numpy(), [1, 7, 7, 1024])
def test_extract_proposal_features_dies_with_incorrect_rank_inputs(self):
feature_extractor = self._build_feature_extractor()
preprocessed_inputs = tf.random_uniform(
[224, 224, 3], maxval=255, dtype=tf.float32)
with self.assertRaises(tf.errors.InvalidArgumentError):
feature_extractor.get_proposal_feature_extractor_model(
name='TestScope')(preprocessed_inputs)
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)
# Note: due to a slight mismatch in slim and keras resnet definitions
# the output shape of the box classifier is slightly different compared to
# that of the slim implementation. The keras version is more `canonical`
# in that it more accurately reflects the original authors' implementation.
# TODO(jonathanhuang): make the output shape match that of the slim
# implementation by using atrous convolutions.
self.assertAllEqual(features_shape.numpy(), [3, 4, 4, 2048])
if __name__ == '__main__':
tf.enable_v2_behavior()
tf.test.main()
research/object_detection/models/faster_rcnn_resnet_v1_feature_extractor_test.py → research/object_detection/models/faster_rcnn_resnet_v1_feature_extractor_tf1_test.py
View file @ 47bc1813
......@@ -14,13 +14,15 @@
# ==============================================================================
"""Tests for object_detection.models.faster_rcnn_resnet_v1_feature_extractor."""
import unittest
import numpy as np
import tensorflow.compat.v1 as tf
from object_detection.models import faster_rcnn_resnet_v1_feature_extractor as faster_rcnn_resnet_v1
from object_detection.utils import tf_version
@unittest.skipIf(tf_version.is_tf2(), 'Skipping TF1.X only test.')
class FasterRcnnResnetV1FeatureExtractorTest(tf.test.TestCase):
def _build_feature_extractor(self,
......
research/object_detection/models/feature_map_generators_test.py
View file @ 47bc1813
......@@ -14,7 +14,7 @@
# ==============================================================================
"""Tests for feature map generators."""
import unittest
from absl.testing import parameterized
import numpy as np
......@@ -25,6 +25,9 @@ from google.protobuf import text_format
from object_detection.builders import hyperparams_builder
from object_detection.models import feature_map_generators
from object_detection.protos import hyperparams_pb2
from object_detection.utils import test_case
from object_detection.utils import test_utils
from object_detection.utils import tf_version
INCEPTION_V2_LAYOUT = {
'from_layer': ['Mixed_3c', 'Mixed_4c', 'Mixed_5c', '', '', ''],
......@@ -52,11 +55,7 @@ SSD_MOBILENET_V1_WEIGHT_SHARED_LAYOUT = {
}
@parameterized.parameters(
{'use_keras': False},
{'use_keras': True},
)
class MultiResolutionFeatureMapGeneratorTest(tf.test.TestCase):
class MultiResolutionFeatureMapGeneratorTest(test_case.TestCase):
def _build_conv_hyperparams(self):
conv_hyperparams = hyperparams_pb2.Hyperparams()
......@@ -73,9 +72,9 @@ class MultiResolutionFeatureMapGeneratorTest(tf.test.TestCase):
text_format.Merge(conv_hyperparams_text_proto, conv_hyperparams)
return hyperparams_builder.KerasLayerHyperparams(conv_hyperparams)
def _build_feature_map_generator(self, feature_map_layout, use_keras,
def _build_feature_map_generator(self, feature_map_layout,
pool_residual=False):
if use_keras:
if tf_version.is_tf2():
return feature_map_generators.KerasMultiResolutionFeatureMaps(
feature_map_layout=feature_map_layout,
depth_multiplier=1,
......@@ -97,17 +96,18 @@ class MultiResolutionFeatureMapGeneratorTest(tf.test.TestCase):
pool_residual=pool_residual)
return feature_map_generator
def test_get_expected_feature_map_shapes_with_inception_v2(self, use_keras):
image_features = {
'Mixed_3c': tf.random_uniform([4, 28, 28, 256], dtype=tf.float32),
'Mixed_4c': tf.random_uniform([4, 14, 14, 576], dtype=tf.float32),
'Mixed_5c': tf.random_uniform([4, 7, 7, 1024], dtype=tf.float32)
}
feature_map_generator = self._build_feature_map_generator(
feature_map_layout=INCEPTION_V2_LAYOUT,
use_keras=use_keras
)
feature_maps = feature_map_generator(image_features)
def test_get_expected_feature_map_shapes_with_inception_v2(self):
with test_utils.GraphContextOrNone() as g:
image_features = {
'Mixed_3c': tf.random_uniform([4, 28, 28, 256], dtype=tf.float32),
'Mixed_4c': tf.random_uniform([4, 14, 14, 576], dtype=tf.float32),
'Mixed_5c': tf.random_uniform([4, 7, 7, 1024], dtype=tf.float32)
}
feature_map_generator = self._build_feature_map_generator(
feature_map_layout=INCEPTION_V2_LAYOUT)
def graph_fn():
feature_maps = feature_map_generator(image_features)
return feature_maps
expected_feature_map_shapes = {
'Mixed_3c': (4, 28, 28, 256),
......@@ -116,29 +116,25 @@ class MultiResolutionFeatureMapGeneratorTest(tf.test.TestCase):
'Mixed_5c_2_Conv2d_3_3x3_s2_512': (4, 4, 4, 512),
'Mixed_5c_2_Conv2d_4_3x3_s2_256': (4, 2, 2, 256),
'Mixed_5c_2_Conv2d_5_3x3_s2_256': (4, 1, 1, 256)}
init_op = tf.global_variables_initializer()
with self.test_session() as sess:
sess.run(init_op)
out_feature_maps = sess.run(feature_maps)
out_feature_map_shapes = dict(
(key, value.shape) for key, value in out_feature_maps.items())
self.assertDictEqual(expected_feature_map_shapes, out_feature_map_shapes)
out_feature_maps = self.execute(graph_fn, [], g)
out_feature_map_shapes = dict(
(key, value.shape) for key, value in out_feature_maps.items())
self.assertDictEqual(expected_feature_map_shapes, out_feature_map_shapes)
def test_get_expected_feature_map_shapes_with_inception_v2_use_depthwise(
self, use_keras):
image_features = {
'Mixed_3c': tf.random_uniform([4, 28, 28, 256], dtype=tf.float32),
'Mixed_4c': tf.random_uniform([4, 14, 14, 576], dtype=tf.float32),
'Mixed_5c': tf.random_uniform([4, 7, 7, 1024], dtype=tf.float32)
}
layout_copy = INCEPTION_V2_LAYOUT.copy()
layout_copy['use_depthwise'] = True
feature_map_generator = self._build_feature_map_generator(
feature_map_layout=layout_copy,
use_keras=use_keras
)
feature_maps = feature_map_generator(image_features)
self):
with test_utils.GraphContextOrNone() as g:
image_features = {
'Mixed_3c': tf.random_uniform([4, 28, 28, 256], dtype=tf.float32),
'Mixed_4c': tf.random_uniform([4, 14, 14, 576], dtype=tf.float32),
'Mixed_5c': tf.random_uniform([4, 7, 7, 1024], dtype=tf.float32)
}
layout_copy = INCEPTION_V2_LAYOUT.copy()
layout_copy['use_depthwise'] = True
feature_map_generator = self._build_feature_map_generator(
feature_map_layout=layout_copy)
def graph_fn():
return feature_map_generator(image_features)
expected_feature_map_shapes = {
'Mixed_3c': (4, 28, 28, 256),
......@@ -147,29 +143,25 @@ class MultiResolutionFeatureMapGeneratorTest(tf.test.TestCase):
'Mixed_5c_2_Conv2d_3_3x3_s2_512': (4, 4, 4, 512),
'Mixed_5c_2_Conv2d_4_3x3_s2_256': (4, 2, 2, 256),
'Mixed_5c_2_Conv2d_5_3x3_s2_256': (4, 1, 1, 256)}
init_op = tf.global_variables_initializer()
with self.test_session() as sess:
sess.run(init_op)
out_feature_maps = sess.run(feature_maps)
out_feature_map_shapes = dict(
(key, value.shape) for key, value in out_feature_maps.items())
self.assertDictEqual(expected_feature_map_shapes, out_feature_map_shapes)
def test_get_expected_feature_map_shapes_use_explicit_padding(
self, use_keras):
image_features = {
'Mixed_3c': tf.random_uniform([4, 28, 28, 256], dtype=tf.float32),
'Mixed_4c': tf.random_uniform([4, 14, 14, 576], dtype=tf.float32),
'Mixed_5c': tf.random_uniform([4, 7, 7, 1024], dtype=tf.float32)
}
layout_copy = INCEPTION_V2_LAYOUT.copy()
layout_copy['use_explicit_padding'] = True
feature_map_generator = self._build_feature_map_generator(
feature_map_layout=layout_copy,
use_keras=use_keras
)
feature_maps = feature_map_generator(image_features)
out_feature_maps = self.execute(graph_fn, [], g)
out_feature_map_shapes = dict(
(key, value.shape) for key, value in out_feature_maps.items())
self.assertDictEqual(expected_feature_map_shapes, out_feature_map_shapes)
def test_get_expected_feature_map_shapes_use_explicit_padding(self):
with test_utils.GraphContextOrNone() as g:
image_features = {
'Mixed_3c': tf.random_uniform([4, 28, 28, 256], dtype=tf.float32),
'Mixed_4c': tf.random_uniform([4, 14, 14, 576], dtype=tf.float32),
'Mixed_5c': tf.random_uniform([4, 7, 7, 1024], dtype=tf.float32)
}
layout_copy = INCEPTION_V2_LAYOUT.copy()
layout_copy['use_explicit_padding'] = True
feature_map_generator = self._build_feature_map_generator(
feature_map_layout=layout_copy,
)
def graph_fn():
return feature_map_generator(image_features)
expected_feature_map_shapes = {
'Mixed_3c': (4, 28, 28, 256),
......@@ -178,27 +170,24 @@ class MultiResolutionFeatureMapGeneratorTest(tf.test.TestCase):
'Mixed_5c_2_Conv2d_3_3x3_s2_512': (4, 4, 4, 512),
'Mixed_5c_2_Conv2d_4_3x3_s2_256': (4, 2, 2, 256),
'Mixed_5c_2_Conv2d_5_3x3_s2_256': (4, 1, 1, 256)}
out_feature_maps = self.execute(graph_fn, [], g)
out_feature_map_shapes = dict(
(key, value.shape) for key, value in out_feature_maps.items())
self.assertDictEqual(expected_feature_map_shapes, out_feature_map_shapes)
def test_get_expected_feature_map_shapes_with_inception_v3(self):
with test_utils.GraphContextOrNone() as g:
image_features = {
'Mixed_5d': tf.random_uniform([4, 35, 35, 256], dtype=tf.float32),
'Mixed_6e': tf.random_uniform([4, 17, 17, 576], dtype=tf.float32),
'Mixed_7c': tf.random_uniform([4, 8, 8, 1024], dtype=tf.float32)
}
init_op = tf.global_variables_initializer()
with self.test_session() as sess:
sess.run(init_op)
out_feature_maps = sess.run(feature_maps)
out_feature_map_shapes = dict(
(key, value.shape) for key, value in out_feature_maps.items())
self.assertDictEqual(expected_feature_map_shapes, out_feature_map_shapes)
def test_get_expected_feature_map_shapes_with_inception_v3(self, use_keras):
image_features = {
'Mixed_5d': tf.random_uniform([4, 35, 35, 256], dtype=tf.float32),
'Mixed_6e': tf.random_uniform([4, 17, 17, 576], dtype=tf.float32),
'Mixed_7c': tf.random_uniform([4, 8, 8, 1024], dtype=tf.float32)
}
feature_map_generator = self._build_feature_map_generator(
feature_map_layout=INCEPTION_V3_LAYOUT,
use_keras=use_keras
)
feature_maps = feature_map_generator(image_features)
feature_map_generator = self._build_feature_map_generator(
feature_map_layout=INCEPTION_V3_LAYOUT,
)
def graph_fn():
return feature_map_generator(image_features)
expected_feature_map_shapes = {
'Mixed_5d': (4, 35, 35, 256),
......@@ -207,29 +196,26 @@ class MultiResolutionFeatureMapGeneratorTest(tf.test.TestCase):
'Mixed_7c_2_Conv2d_3_3x3_s2_512': (4, 4, 4, 512),
'Mixed_7c_2_Conv2d_4_3x3_s2_256': (4, 2, 2, 256),
'Mixed_7c_2_Conv2d_5_3x3_s2_128': (4, 1, 1, 128)}
init_op = tf.global_variables_initializer()
with self.test_session() as sess:
sess.run(init_op)
out_feature_maps = sess.run(feature_maps)
out_feature_map_shapes = dict(
(key, value.shape) for key, value in out_feature_maps.items())
self.assertDictEqual(expected_feature_map_shapes, out_feature_map_shapes)
out_feature_maps = self.execute(graph_fn, [], g)
out_feature_map_shapes = dict(
(key, value.shape) for key, value in out_feature_maps.items())
self.assertDictEqual(expected_feature_map_shapes, out_feature_map_shapes)
def test_get_expected_feature_map_shapes_with_embedded_ssd_mobilenet_v1(
self, use_keras):
image_features = {
'Conv2d_11_pointwise': tf.random_uniform([4, 16, 16, 512],
dtype=tf.float32),
'Conv2d_13_pointwise': tf.random_uniform([4, 8, 8, 1024],
dtype=tf.float32),
}
self):
with test_utils.GraphContextOrNone() as g:
image_features = {
'Conv2d_11_pointwise': tf.random_uniform([4, 16, 16, 512],
dtype=tf.float32),
'Conv2d_13_pointwise': tf.random_uniform([4, 8, 8, 1024],
dtype=tf.float32),
}
feature_map_generator = self._build_feature_map_generator(
feature_map_layout=EMBEDDED_SSD_MOBILENET_V1_LAYOUT,
use_keras=use_keras
)
feature_maps = feature_map_generator(image_features)
feature_map_generator = self._build_feature_map_generator(
feature_map_layout=EMBEDDED_SSD_MOBILENET_V1_LAYOUT,
)
def graph_fn():
return feature_map_generator(image_features)
expected_feature_map_shapes = {
'Conv2d_11_pointwise': (4, 16, 16, 512),
......@@ -237,55 +223,50 @@ class MultiResolutionFeatureMapGeneratorTest(tf.test.TestCase):
'Conv2d_13_pointwise_2_Conv2d_2_3x3_s2_512': (4, 4, 4, 512),
'Conv2d_13_pointwise_2_Conv2d_3_3x3_s2_256': (4, 2, 2, 256),
'Conv2d_13_pointwise_2_Conv2d_4_2x2_s2_256': (4, 1, 1, 256)}
init_op = tf.global_variables_initializer()
with self.test_session() as sess:
sess.run(init_op)
out_feature_maps = sess.run(feature_maps)
out_feature_map_shapes = dict(
(key, value.shape) for key, value in out_feature_maps.items())
self.assertDictEqual(expected_feature_map_shapes, out_feature_map_shapes)
out_feature_maps = self.execute(graph_fn, [], g)
out_feature_map_shapes = dict(
(key, value.shape) for key, value in out_feature_maps.items())
self.assertDictEqual(expected_feature_map_shapes, out_feature_map_shapes)
def test_feature_map_shapes_with_pool_residual_ssd_mobilenet_v1(
self, use_keras):
image_features = {
'Conv2d_13_pointwise': tf.random_uniform([4, 8, 8, 1024],
dtype=tf.float32),
}
self):
with test_utils.GraphContextOrNone() as g:
image_features = {
'Conv2d_13_pointwise': tf.random_uniform([4, 8, 8, 1024],
dtype=tf.float32),
}
feature_map_generator = self._build_feature_map_generator(
feature_map_layout=SSD_MOBILENET_V1_WEIGHT_SHARED_LAYOUT,
use_keras=use_keras,
pool_residual=True
)
feature_maps = feature_map_generator(image_features)
feature_map_generator = self._build_feature_map_generator(
feature_map_layout=SSD_MOBILENET_V1_WEIGHT_SHARED_LAYOUT,
pool_residual=True
)
def graph_fn():
return feature_map_generator(image_features)
expected_feature_map_shapes = {
'Conv2d_13_pointwise': (4, 8, 8, 1024),
'Conv2d_13_pointwise_2_Conv2d_1_3x3_s2_256': (4, 4, 4, 256),
'Conv2d_13_pointwise_2_Conv2d_2_3x3_s2_256': (4, 2, 2, 256),
'Conv2d_13_pointwise_2_Conv2d_3_3x3_s2_256': (4, 1, 1, 256)}
out_feature_maps = self.execute(graph_fn, [], g)
out_feature_map_shapes = dict(
(key, value.shape) for key, value in out_feature_maps.items())
self.assertDictEqual(expected_feature_map_shapes, out_feature_map_shapes)
def test_get_expected_variable_names_with_inception_v2(self):
with test_utils.GraphContextOrNone() as g:
image_features = {
'Mixed_3c': tf.random_uniform([4, 28, 28, 256], dtype=tf.float32),
'Mixed_4c': tf.random_uniform([4, 14, 14, 576], dtype=tf.float32),
'Mixed_5c': tf.random_uniform([4, 7, 7, 1024], dtype=tf.float32)
}
feature_map_generator = self._build_feature_map_generator(
feature_map_layout=INCEPTION_V2_LAYOUT,
)
def graph_fn():
return feature_map_generator(image_features)
init_op = tf.global_variables_initializer()
with self.test_session() as sess:
sess.run(init_op)
out_feature_maps = sess.run(feature_maps)
out_feature_map_shapes = dict(
(key, value.shape) for key, value in out_feature_maps.items())
self.assertDictEqual(expected_feature_map_shapes, out_feature_map_shapes)
def test_get_expected_variable_names_with_inception_v2(self, use_keras):
image_features = {
'Mixed_3c': tf.random_uniform([4, 28, 28, 256], dtype=tf.float32),
'Mixed_4c': tf.random_uniform([4, 14, 14, 576], dtype=tf.float32),
'Mixed_5c': tf.random_uniform([4, 7, 7, 1024], dtype=tf.float32)
}
feature_map_generator = self._build_feature_map_generator(
feature_map_layout=INCEPTION_V2_LAYOUT,
use_keras=use_keras
)
feature_maps = feature_map_generator(image_features)
self.execute(graph_fn, [], g)
expected_slim_variables = set([
'Mixed_5c_1_Conv2d_3_1x1_256/weights',
'Mixed_5c_1_Conv2d_3_1x1_256/biases',
......@@ -316,32 +297,32 @@ class MultiResolutionFeatureMapGeneratorTest(tf.test.TestCase):
'FeatureMaps/Mixed_5c_2_Conv2d_5_3x3_s2_256_conv/bias',
])
init_op = tf.global_variables_initializer()
with self.test_session() as sess:
sess.run(init_op)
sess.run(feature_maps)
if tf_version.is_tf2():
actual_variable_set = set(
[var.op.name for var in tf.trainable_variables()])
if use_keras:
self.assertSetEqual(expected_keras_variables, actual_variable_set)
else:
self.assertSetEqual(expected_slim_variables, actual_variable_set)
[var.name.split(':')[0] for var in feature_map_generator.variables])
self.assertSetEqual(expected_keras_variables, actual_variable_set)
else:
with g.as_default():
actual_variable_set = set(
[var.op.name for var in tf.trainable_variables()])
self.assertSetEqual(expected_slim_variables, actual_variable_set)
def test_get_expected_variable_names_with_inception_v2_use_depthwise(
self,
use_keras):
image_features = {
'Mixed_3c': tf.random_uniform([4, 28, 28, 256], dtype=tf.float32),
'Mixed_4c': tf.random_uniform([4, 14, 14, 576], dtype=tf.float32),
'Mixed_5c': tf.random_uniform([4, 7, 7, 1024], dtype=tf.float32)
}
layout_copy = INCEPTION_V2_LAYOUT.copy()
layout_copy['use_depthwise'] = True
feature_map_generator = self._build_feature_map_generator(
feature_map_layout=layout_copy,
use_keras=use_keras
)
feature_maps = feature_map_generator(image_features)
self):
with test_utils.GraphContextOrNone() as g:
image_features = {
'Mixed_3c': tf.random_uniform([4, 28, 28, 256], dtype=tf.float32),
'Mixed_4c': tf.random_uniform([4, 14, 14, 576], dtype=tf.float32),
'Mixed_5c': tf.random_uniform([4, 7, 7, 1024], dtype=tf.float32)
}
layout_copy = INCEPTION_V2_LAYOUT.copy()
layout_copy['use_depthwise'] = True
feature_map_generator = self._build_feature_map_generator(
feature_map_layout=layout_copy,
)
def graph_fn():
return feature_map_generator(image_features)
self.execute(graph_fn, [], g)
expected_slim_variables = set([
'Mixed_5c_1_Conv2d_3_1x1_256/weights',
......@@ -391,23 +372,20 @@ class MultiResolutionFeatureMapGeneratorTest(tf.test.TestCase):
'FeatureMaps/Mixed_5c_2_Conv2d_5_3x3_s2_256_conv/bias',
])
init_op = tf.global_variables_initializer()
with self.test_session() as sess:
sess.run(init_op)
sess.run(feature_maps)
if tf_version.is_tf2():
actual_variable_set = set(
[var.op.name for var in tf.trainable_variables()])
if use_keras:
self.assertSetEqual(expected_keras_variables, actual_variable_set)
else:
self.assertSetEqual(expected_slim_variables, actual_variable_set)
[var.name.split(':')[0] for var in feature_map_generator.variables])
self.assertSetEqual(expected_keras_variables, actual_variable_set)
else:
with g.as_default():
actual_variable_set = set(
[var.op.name for var in tf.trainable_variables()])
self.assertSetEqual(expected_slim_variables, actual_variable_set)
@parameterized.parameters({'use_native_resize_op': True, 'use_keras': False},
{'use_native_resize_op': False, 'use_keras': False},
{'use_native_resize_op': True, 'use_keras': True},
{'use_native_resize_op': False, 'use_keras': True})
class FPNFeatureMapGeneratorTest(tf.test.TestCase, parameterized.TestCase):
@parameterized.parameters({'use_native_resize_op': True},
{'use_native_resize_op': False})
class FPNFeatureMapGeneratorTest(test_case.TestCase, parameterized.TestCase):
def _build_conv_hyperparams(self):
conv_hyperparams = hyperparams_pb2.Hyperparams()
......@@ -425,10 +403,10 @@ class FPNFeatureMapGeneratorTest(tf.test.TestCase, parameterized.TestCase):
return hyperparams_builder.KerasLayerHyperparams(conv_hyperparams)
def _build_feature_map_generator(
self, image_features, depth, use_keras, use_bounded_activations=False,
self, image_features, depth, use_bounded_activations=False,
use_native_resize_op=False, use_explicit_padding=False,
use_depthwise=False):
if use_keras:
if tf_version.is_tf2():
return feature_map_generators.KerasFpnTopDownFeatureMaps(
num_levels=len(image_features),
depth=depth,
......@@ -454,19 +432,20 @@ class FPNFeatureMapGeneratorTest(tf.test.TestCase, parameterized.TestCase):
return feature_map_generator
def test_get_expected_feature_map_shapes(
self, use_native_resize_op, use_keras):
image_features = [
('block2', tf.random_uniform([4, 8, 8, 256], dtype=tf.float32)),
('block3', tf.random_uniform([4, 4, 4, 256], dtype=tf.float32)),
('block4', tf.random_uniform([4, 2, 2, 256], dtype=tf.float32)),
('block5', tf.random_uniform([4, 1, 1, 256], dtype=tf.float32))
]
feature_map_generator = self._build_feature_map_generator(
image_features=image_features,
depth=128,
use_keras=use_keras,
use_native_resize_op=use_native_resize_op)
feature_maps = feature_map_generator(image_features)
self, use_native_resize_op):
with test_utils.GraphContextOrNone() as g:
image_features = [
('block2', tf.random_uniform([4, 8, 8, 256], dtype=tf.float32)),
('block3', tf.random_uniform([4, 4, 4, 256], dtype=tf.float32)),
('block4', tf.random_uniform([4, 2, 2, 256], dtype=tf.float32)),
('block5', tf.random_uniform([4, 1, 1, 256], dtype=tf.float32))
]
feature_map_generator = self._build_feature_map_generator(
image_features=image_features,
depth=128,
use_native_resize_op=use_native_resize_op)
def graph_fn():
return feature_map_generator(image_features)
expected_feature_map_shapes = {
'top_down_block2': (4, 8, 8, 128),
......@@ -474,30 +453,27 @@ class FPNFeatureMapGeneratorTest(tf.test.TestCase, parameterized.TestCase):
'top_down_block4': (4, 2, 2, 128),
'top_down_block5': (4, 1, 1, 128)
}
init_op = tf.global_variables_initializer()
with self.test_session() as sess:
sess.run(init_op)
out_feature_maps = sess.run(feature_maps)
out_feature_map_shapes = {key: value.shape
for key, value in out_feature_maps.items()}
self.assertDictEqual(out_feature_map_shapes, expected_feature_map_shapes)
out_feature_maps = self.execute(graph_fn, [], g)
out_feature_map_shapes = dict(
(key, value.shape) for key, value in out_feature_maps.items())
self.assertDictEqual(expected_feature_map_shapes, out_feature_map_shapes)
def test_get_expected_feature_map_shapes_with_explicit_padding(
self, use_native_resize_op, use_keras):
image_features = [
('block2', tf.random_uniform([4, 8, 8, 256], dtype=tf.float32)),
('block3', tf.random_uniform([4, 4, 4, 256], dtype=tf.float32)),
('block4', tf.random_uniform([4, 2, 2, 256], dtype=tf.float32)),
('block5', tf.random_uniform([4, 1, 1, 256], dtype=tf.float32))
]
feature_map_generator = self._build_feature_map_generator(
image_features=image_features,
depth=128,
use_keras=use_keras,
use_explicit_padding=True,
use_native_resize_op=use_native_resize_op)
feature_maps = feature_map_generator(image_features)
self, use_native_resize_op):
with test_utils.GraphContextOrNone() as g:
image_features = [
('block2', tf.random_uniform([4, 8, 8, 256], dtype=tf.float32)),
('block3', tf.random_uniform([4, 4, 4, 256], dtype=tf.float32)),
('block4', tf.random_uniform([4, 2, 2, 256], dtype=tf.float32)),
('block5', tf.random_uniform([4, 1, 1, 256], dtype=tf.float32))
]
feature_map_generator = self._build_feature_map_generator(
image_features=image_features,
depth=128,
use_explicit_padding=True,
use_native_resize_op=use_native_resize_op)
def graph_fn():
return feature_map_generator(image_features)
expected_feature_map_shapes = {
'top_down_block2': (4, 8, 8, 128),
......@@ -505,19 +481,15 @@ class FPNFeatureMapGeneratorTest(tf.test.TestCase, parameterized.TestCase):
'top_down_block4': (4, 2, 2, 128),
'top_down_block5': (4, 1, 1, 128)
}
out_feature_maps = self.execute(graph_fn, [], g)
out_feature_map_shapes = dict(
(key, value.shape) for key, value in out_feature_maps.items())
self.assertDictEqual(expected_feature_map_shapes, out_feature_map_shapes)
init_op = tf.global_variables_initializer()
with self.test_session() as sess:
sess.run(init_op)
out_feature_maps = sess.run(feature_maps)
out_feature_map_shapes = {key: value.shape
for key, value in out_feature_maps.items()}
self.assertDictEqual(out_feature_map_shapes, expected_feature_map_shapes)
@unittest.skipIf(tf_version.is_tf2(), 'Skipping TF1.X only test.')
def test_use_bounded_activations_add_operations(
self, use_native_resize_op, use_keras):
tf_graph = tf.Graph()
with tf_graph.as_default():
self, use_native_resize_op):
with test_utils.GraphContextOrNone() as g:
image_features = [('block2',
tf.random_uniform([4, 8, 8, 256], dtype=tf.float32)),
('block3',
......@@ -529,34 +501,23 @@ class FPNFeatureMapGeneratorTest(tf.test.TestCase, parameterized.TestCase):
feature_map_generator = self._build_feature_map_generator(
image_features=image_features,
depth=128,
use_keras=use_keras,
use_bounded_activations=True,
use_native_resize_op=use_native_resize_op)
feature_map_generator(image_features)
if use_keras:
expected_added_operations = dict.fromkeys([
'FeatureMaps/top_down/clip_by_value/clip_by_value',
'FeatureMaps/top_down/clip_by_value_1/clip_by_value',
'FeatureMaps/top_down/clip_by_value_2/clip_by_value',
'FeatureMaps/top_down/clip_by_value_3/clip_by_value',
'FeatureMaps/top_down/clip_by_value_4/clip_by_value',
'FeatureMaps/top_down/clip_by_value_5/clip_by_value',
'FeatureMaps/top_down/clip_by_value_6/clip_by_value',
])
else:
expected_added_operations = dict.fromkeys([
'top_down/clip_by_value', 'top_down/clip_by_value_1',
'top_down/clip_by_value_2', 'top_down/clip_by_value_3',
'top_down/clip_by_value_4', 'top_down/clip_by_value_5',
'top_down/clip_by_value_6'
])
op_names = {op.name: None for op in tf_graph.get_operations()}
self.assertDictContainsSubset(expected_added_operations, op_names)
def graph_fn():
return feature_map_generator(image_features)
self.execute(graph_fn, [], g)
expected_added_operations = dict.fromkeys([
'top_down/clip_by_value', 'top_down/clip_by_value_1',
'top_down/clip_by_value_2', 'top_down/clip_by_value_3',
'top_down/clip_by_value_4', 'top_down/clip_by_value_5',
'top_down/clip_by_value_6'
])
op_names = {op.name: None for op in g.get_operations()}
self.assertDictContainsSubset(expected_added_operations, op_names)
@unittest.skipIf(tf_version.is_tf2(), 'Skipping TF1.X only test.')
def test_use_bounded_activations_clip_value(
self, use_native_resize_op, use_keras):
self, use_native_resize_op):
tf_graph = tf.Graph()
with tf_graph.as_default():
image_features = [
......@@ -568,28 +529,16 @@ class FPNFeatureMapGeneratorTest(tf.test.TestCase, parameterized.TestCase):
feature_map_generator = self._build_feature_map_generator(
image_features=image_features,
depth=128,
use_keras=use_keras,
use_bounded_activations=True,
use_native_resize_op=use_native_resize_op)
feature_map_generator(image_features)
if use_keras:
expected_clip_by_value_ops = dict.fromkeys([
'FeatureMaps/top_down/clip_by_value/clip_by_value',
'FeatureMaps/top_down/clip_by_value_1/clip_by_value',
'FeatureMaps/top_down/clip_by_value_2/clip_by_value',
'FeatureMaps/top_down/clip_by_value_3/clip_by_value',
'FeatureMaps/top_down/clip_by_value_4/clip_by_value',
'FeatureMaps/top_down/clip_by_value_5/clip_by_value',
'FeatureMaps/top_down/clip_by_value_6/clip_by_value',
])
else:
expected_clip_by_value_ops = [
'top_down/clip_by_value', 'top_down/clip_by_value_1',
'top_down/clip_by_value_2', 'top_down/clip_by_value_3',
'top_down/clip_by_value_4', 'top_down/clip_by_value_5',
'top_down/clip_by_value_6'
]
expected_clip_by_value_ops = [
'top_down/clip_by_value', 'top_down/clip_by_value_1',
'top_down/clip_by_value_2', 'top_down/clip_by_value_3',
'top_down/clip_by_value_4', 'top_down/clip_by_value_5',
'top_down/clip_by_value_6'
]
# Gathers activation tensors before and after clip_by_value operations.
activations = {}
......@@ -631,20 +580,21 @@ class FPNFeatureMapGeneratorTest(tf.test.TestCase, parameterized.TestCase):
self.assertLessEqual(after_clipping_upper_bound, expected_upper_bound)
def test_get_expected_feature_map_shapes_with_depthwise(
self, use_native_resize_op, use_keras):
image_features = [
('block2', tf.random_uniform([4, 8, 8, 256], dtype=tf.float32)),
('block3', tf.random_uniform([4, 4, 4, 256], dtype=tf.float32)),
('block4', tf.random_uniform([4, 2, 2, 256], dtype=tf.float32)),
('block5', tf.random_uniform([4, 1, 1, 256], dtype=tf.float32))
]
feature_map_generator = self._build_feature_map_generator(
image_features=image_features,
depth=128,
use_keras=use_keras,
use_depthwise=True,
use_native_resize_op=use_native_resize_op)
feature_maps = feature_map_generator(image_features)
self, use_native_resize_op):
with test_utils.GraphContextOrNone() as g:
image_features = [
('block2', tf.random_uniform([4, 8, 8, 256], dtype=tf.float32)),
('block3', tf.random_uniform([4, 4, 4, 256], dtype=tf.float32)),
('block4', tf.random_uniform([4, 2, 2, 256], dtype=tf.float32)),
('block5', tf.random_uniform([4, 1, 1, 256], dtype=tf.float32))
]
feature_map_generator = self._build_feature_map_generator(
image_features=image_features,
depth=128,
use_depthwise=True,
use_native_resize_op=use_native_resize_op)
def graph_fn():
return feature_map_generator(image_features)
expected_feature_map_shapes = {
'top_down_block2': (4, 8, 8, 128),
......@@ -652,30 +602,27 @@ class FPNFeatureMapGeneratorTest(tf.test.TestCase, parameterized.TestCase):
'top_down_block4': (4, 2, 2, 128),
'top_down_block5': (4, 1, 1, 128)
}
init_op = tf.global_variables_initializer()
with self.test_session() as sess:
sess.run(init_op)
out_feature_maps = sess.run(feature_maps)
out_feature_map_shapes = {key: value.shape
for key, value in out_feature_maps.items()}
self.assertDictEqual(out_feature_map_shapes, expected_feature_map_shapes)
out_feature_maps = self.execute(graph_fn, [], g)
out_feature_map_shapes = dict(
(key, value.shape) for key, value in out_feature_maps.items())
self.assertDictEqual(expected_feature_map_shapes, out_feature_map_shapes)
def test_get_expected_variable_names(
self, use_native_resize_op, use_keras):
image_features = [
('block2', tf.random_uniform([4, 8, 8, 256], dtype=tf.float32)),
('block3', tf.random_uniform([4, 4, 4, 256], dtype=tf.float32)),
('block4', tf.random_uniform([4, 2, 2, 256], dtype=tf.float32)),
('block5', tf.random_uniform([4, 1, 1, 256], dtype=tf.float32))
]
feature_map_generator = self._build_feature_map_generator(
image_features=image_features,
depth=128,
use_keras=use_keras,
use_native_resize_op=use_native_resize_op)
feature_maps = feature_map_generator(image_features)
self, use_native_resize_op):
with test_utils.GraphContextOrNone() as g:
image_features = [
('block2', tf.random_uniform([4, 8, 8, 256], dtype=tf.float32)),
('block3', tf.random_uniform([4, 4, 4, 256], dtype=tf.float32)),
('block4', tf.random_uniform([4, 2, 2, 256], dtype=tf.float32)),
('block5', tf.random_uniform([4, 1, 1, 256], dtype=tf.float32))
]
feature_map_generator = self._build_feature_map_generator(
image_features=image_features,
depth=128,
use_native_resize_op=use_native_resize_op)
def graph_fn():
return feature_map_generator(image_features)
self.execute(graph_fn, [], g)
expected_slim_variables = set([
'projection_1/weights',
'projection_1/biases',
......@@ -709,33 +656,34 @@ class FPNFeatureMapGeneratorTest(tf.test.TestCase, parameterized.TestCase):
'FeatureMaps/top_down/smoothing_3_conv/kernel',
'FeatureMaps/top_down/smoothing_3_conv/bias'
])
init_op = tf.global_variables_initializer()
with self.test_session() as sess:
sess.run(init_op)
sess.run(feature_maps)
if tf_version.is_tf2():
actual_variable_set = set(
[var.op.name for var in tf.trainable_variables()])
if use_keras:
self.assertSetEqual(expected_keras_variables, actual_variable_set)
else:
self.assertSetEqual(expected_slim_variables, actual_variable_set)
[var.name.split(':')[0] for var in feature_map_generator.variables])
self.assertSetEqual(expected_keras_variables, actual_variable_set)
else:
with g.as_default():
actual_variable_set = set(
[var.op.name for var in tf.trainable_variables()])
self.assertSetEqual(expected_slim_variables, actual_variable_set)
def test_get_expected_variable_names_with_depthwise(
self, use_native_resize_op, use_keras):
image_features = [
('block2', tf.random_uniform([4, 8, 8, 256], dtype=tf.float32)),
('block3', tf.random_uniform([4, 4, 4, 256], dtype=tf.float32)),
('block4', tf.random_uniform([4, 2, 2, 256], dtype=tf.float32)),
('block5', tf.random_uniform([4, 1, 1, 256], dtype=tf.float32))
]
feature_map_generator = self._build_feature_map_generator(
image_features=image_features,
depth=128,
use_keras=use_keras,
use_depthwise=True,
use_native_resize_op=use_native_resize_op)
feature_maps = feature_map_generator(image_features)
self, use_native_resize_op):
with test_utils.GraphContextOrNone() as g:
image_features = [
('block2', tf.random_uniform([4, 8, 8, 256], dtype=tf.float32)),
('block3', tf.random_uniform([4, 4, 4, 256], dtype=tf.float32)),
('block4', tf.random_uniform([4, 2, 2, 256], dtype=tf.float32)),
('block5', tf.random_uniform([4, 1, 1, 256], dtype=tf.float32))
]
feature_map_generator = self._build_feature_map_generator(
image_features=image_features,
depth=128,
use_depthwise=True,
use_native_resize_op=use_native_resize_op)
def graph_fn():
return feature_map_generator(image_features)
self.execute(graph_fn, [], g)
expected_slim_variables = set([
'projection_1/weights',
'projection_1/biases',
......@@ -775,16 +723,16 @@ class FPNFeatureMapGeneratorTest(tf.test.TestCase, parameterized.TestCase):
'FeatureMaps/top_down/smoothing_3_depthwise_conv/pointwise_kernel',
'FeatureMaps/top_down/smoothing_3_depthwise_conv/bias'
])
init_op = tf.global_variables_initializer()
with self.test_session() as sess:
sess.run(init_op)
sess.run(feature_maps)
if tf_version.is_tf2():
actual_variable_set = set(
[var.op.name for var in tf.trainable_variables()])
if use_keras:
self.assertSetEqual(expected_keras_variables, actual_variable_set)
else:
self.assertSetEqual(expected_slim_variables, actual_variable_set)
[var.name.split(':')[0] for var in feature_map_generator.variables])
self.assertSetEqual(expected_keras_variables, actual_variable_set)
else:
with g.as_default():
actual_variable_set = set(
[var.op.name for var in tf.trainable_variables()])
self.assertSetEqual(expected_slim_variables, actual_variable_set)
class GetDepthFunctionTest(tf.test.TestCase):
......@@ -804,6 +752,7 @@ class GetDepthFunctionTest(tf.test.TestCase):
{'replace_pool_with_conv': False},
{'replace_pool_with_conv': True},
)
@unittest.skipIf(tf_version.is_tf2(), 'Skipping TF1.X only test.')
class PoolingPyramidFeatureMapGeneratorTest(tf.test.TestCase):
def test_get_expected_feature_map_shapes(self, replace_pool_with_conv):
......
research/object_detection/models/keras_models/convert_keras_models.py 0 → 100644
View file @ 47bc1813
# Copyright 2019 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.
# ==============================================================================
"""Write keras weights into a tensorflow checkpoint.
The imagenet weights in `keras.applications` are downloaded from github.
This script converts them into the tensorflow checkpoint format and stores them
on disk where they can be easily accessible during training.
"""
from __future__ import print_function
import os
from absl import app
import numpy as np
import tensorflow.compat.v1 as tf
FLAGS = tf.flags.FLAGS
tf.flags.DEFINE_string('model', 'resnet_v2_101',
'The model to load. The following are supported: '
'"resnet_v1_50", "resnet_v1_101", "resnet_v2_50", '
'"resnet_v2_101"')
tf.flags.DEFINE_string('output_path', None,
'The directory to output weights in.')
tf.flags.DEFINE_boolean('verify_weights', True,
('Verify the weights are loaded correctly by making '
'sure the predictions are the same before and after '
'saving.'))
def init_model(name):
"""Creates a Keras Model with the specific ResNet version."""
if name == 'resnet_v1_50':
model = tf.keras.applications.ResNet50(weights='imagenet')
elif name == 'resnet_v1_101':
model = tf.keras.applications.ResNet101(weights='imagenet')
elif name == 'resnet_v2_50':
model = tf.keras.applications.ResNet50V2(weights='imagenet')
elif name == 'resnet_v2_101':
model = tf.keras.applications.ResNet101V2(weights='imagenet')
else:
raise ValueError('Model {} not supported'.format(FLAGS.model))
return model
def main(_):
model = init_model(FLAGS.model)
path = os.path.join(FLAGS.output_path, FLAGS.model)
tf.gfile.MakeDirs(path)
weights_path = os.path.join(path, 'weights')
ckpt = tf.train.Checkpoint(feature_extractor=model)
saved_path = ckpt.save(weights_path)
if FLAGS.verify_weights:
imgs = np.random.randn(1, 224, 224, 3).astype(np.float32)
keras_preds = model(imgs)
model = init_model(FLAGS.model)
ckpt.restore(saved_path)
loaded_weights_pred = model(imgs).numpy()
if not np.all(np.isclose(keras_preds, loaded_weights_pred)):
raise RuntimeError('The model was not saved correctly.')
if __name__ == '__main__':
tf.enable_v2_behavior()
app.run(main)
research/object_detection/models/keras_models/hourglass_network.py
View file @ 47bc1813
......@@ -43,6 +43,15 @@ def _get_padding_for_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):
"""Block that aggregates Convolution + Norm layer + ReLU."""
......@@ -73,8 +82,7 @@ class ConvolutionalBlock(tf.keras.layers.Layer):
filters=out_channels, kernel_size=kernel_size, use_bias=False,
strides=stride, padding=padding)
self.norm = tf.keras.layers.experimental.SyncBatchNormalization(
name='batchnorm', epsilon=1e-5, momentum=0.1)
self.norm = batchnorm()
if relu:
self.relu = tf.keras.layers.ReLU()
......@@ -124,8 +132,7 @@ class ResidualBlock(tf.keras.layers.Layer):
self.conv = tf.keras.layers.Conv2D(
filters=out_channels, kernel_size=kernel_size, use_bias=False,
strides=1, padding=padding)
self.norm = tf.keras.layers.experimental.SyncBatchNormalization(
name='batchnorm', epsilon=1e-5, momentum=0.1)
self.norm = batchnorm()
if skip_conv:
self.skip = SkipConvolution(out_channels=out_channels,
......
research/object_detection/models/keras_models/hourglass_network_test.py → research/object_detection/models/keras_models/hourglass_network_tf2_test.py
View file @ 47bc1813
......@@ -13,14 +13,16 @@
# limitations under the License.
# ==============================================================================
"""Testing the Hourglass network."""
import unittest
from absl.testing import parameterized
import numpy as np
import tensorflow.compat.v1 as tf
from object_detection.models.keras_models import hourglass_network as hourglass
from object_detection.utils import tf_version
@unittest.skipIf(tf_version.is_tf1(), 'Skipping TF2.X only test.')
class HourglassFeatureExtractorTest(tf.test.TestCase, parameterized.TestCase):
def test_identity_layer(self):
......@@ -95,5 +97,4 @@ class HourglassFeatureExtractorTest(tf.test.TestCase, parameterized.TestCase):
if __name__ == '__main__':
tf.enable_v2_behavior()
tf.test.main()
research/object_detection/models/keras_models/inception_resnet_v2_test.py → research/object_detection/models/keras_models/inception_resnet_v2_tf2_test.py
View file @ 47bc1813
......@@ -30,13 +30,14 @@ consistent.
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import unittest
import numpy as np
from six.moves import zip
import tensorflow.compat.v1 as tf
from object_detection.models.keras_models import inception_resnet_v2
from object_detection.utils import test_case
from object_detection.utils import tf_version
_KERAS_TO_SLIM_ENDPOINT_NAMES = {
'activation': 'Conv2d_1a_3x3',
......@@ -100,6 +101,7 @@ _NUM_CHANNELS = 3
_BATCH_SIZE = 2
@unittest.skipIf(tf_version.is_tf1(), 'Skipping TF2.X only test.')
class InceptionResnetV2Test(test_case.TestCase):
def _create_application_with_layer_outputs(
......@@ -166,8 +168,7 @@ class InceptionResnetV2Test(test_case.TestCase):
model = self._create_application_with_layer_outputs(
layer_names=layer_names,
batchnorm_training=False)
preprocessed_inputs = tf.placeholder(
tf.float32, (4, None, None, _NUM_CHANNELS))
preprocessed_inputs = tf.random.uniform([4, 40, 40, _NUM_CHANNELS])
model(preprocessed_inputs)
return model.variables
......
research/object_detection/models/keras_models/mobilenet_v1_test.py → research/object_detection/models/keras_models/mobilenet_v1_tf2_test.py
View file @ 47bc1813
......@@ -29,7 +29,7 @@ consistent.
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import unittest
import numpy as np
from six.moves import zip
import tensorflow.compat.v1 as tf
......@@ -42,6 +42,7 @@ from object_detection.models.keras_models import model_utils
from object_detection.models.keras_models import test_utils
from object_detection.protos import hyperparams_pb2
from object_detection.utils import test_case
from object_detection.utils import tf_version
_KERAS_LAYERS_TO_CHECK = [
'conv1_relu',
......@@ -64,6 +65,7 @@ _NUM_CHANNELS = 3
_BATCH_SIZE = 2
@unittest.skipIf(tf_version.is_tf1(), 'Skipping TF2.X only test.')
class MobilenetV1Test(test_case.TestCase):
def _build_conv_hyperparams(self):
......@@ -118,19 +120,17 @@ class MobilenetV1Test(test_case.TestCase):
self, image_height, image_width, depth_multiplier,
expected_feature_map_shape, use_explicit_padding=False, min_depth=8,
layer_names=None, conv_defs=None):
def graph_fn(image_tensor):
model = self._create_application_with_layer_outputs(
layer_names=layer_names,
batchnorm_training=False,
use_explicit_padding=use_explicit_padding,
min_depth=min_depth,
alpha=depth_multiplier,
conv_defs=conv_defs)
return model(image_tensor)
model = self._create_application_with_layer_outputs(
layer_names=layer_names,
batchnorm_training=False,
use_explicit_padding=use_explicit_padding,
min_depth=min_depth,
alpha=depth_multiplier,
conv_defs=conv_defs)
image_tensor = np.random.rand(_BATCH_SIZE, image_height, image_width,
_NUM_CHANNELS).astype(np.float32)
feature_maps = self.execute(graph_fn, [image_tensor])
feature_maps = model(image_tensor)
for feature_map, expected_shape in zip(feature_maps,
expected_feature_map_shape):
......@@ -140,36 +140,29 @@ class MobilenetV1Test(test_case.TestCase):
self, image_height, image_width, depth_multiplier,
expected_feature_map_shape, use_explicit_padding=False, min_depth=8,
layer_names=None):
def graph_fn(image_height, image_width):
image_tensor = tf.random_uniform([_BATCH_SIZE, image_height, image_width,
_NUM_CHANNELS], dtype=tf.float32)
model = self._create_application_with_layer_outputs(
layer_names=layer_names,
batchnorm_training=False,
use_explicit_padding=use_explicit_padding,
alpha=depth_multiplier)
return model(image_tensor)
image_tensor = tf.random_uniform([_BATCH_SIZE, image_height, image_width,
_NUM_CHANNELS], dtype=tf.float32)
model = self._create_application_with_layer_outputs(
layer_names=layer_names,
batchnorm_training=False,
use_explicit_padding=use_explicit_padding,
alpha=depth_multiplier)
feature_maps = self.execute_cpu(graph_fn, [
np.array(image_height, dtype=np.int32),
np.array(image_width, dtype=np.int32)
])
feature_maps = model(image_tensor)
for feature_map, expected_shape in zip(feature_maps,
expected_feature_map_shape):
self.assertAllEqual(feature_map.shape, expected_shape)
def _get_variables(self, depth_multiplier, layer_names=None):
g = tf.Graph()
with g.as_default():
preprocessed_inputs = tf.placeholder(
tf.float32, (4, None, None, _NUM_CHANNELS))
model = self._create_application_with_layer_outputs(
layer_names=layer_names,
batchnorm_training=False, use_explicit_padding=False,
alpha=depth_multiplier)
model(preprocessed_inputs)
return g.get_collection(tf.GraphKeys.GLOBAL_VARIABLES)
tf.keras.backend.clear_session()
model = self._create_application_with_layer_outputs(
layer_names=layer_names,
batchnorm_training=False, use_explicit_padding=False,
alpha=depth_multiplier)
preprocessed_inputs = tf.random.uniform([2, 40, 40, 3])
model(preprocessed_inputs)
return model.variables
def test_returns_correct_shapes_128(self):
image_height = 128
......
research/object_detection/models/keras_models/mobilenet_v2_test.py → research/object_detection/models/keras_models/mobilenet_v2_tf2_test.py
View file @ 47bc1813
......@@ -18,7 +18,7 @@
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import unittest
import numpy as np
from six.moves import zip
import tensorflow.compat.v1 as tf
......@@ -31,6 +31,7 @@ from object_detection.models.keras_models import model_utils
from object_detection.models.keras_models import test_utils
from object_detection.protos import hyperparams_pb2
from object_detection.utils import test_case
from object_detection.utils import tf_version
_layers_to_check = [
'Conv1_relu',
......@@ -53,6 +54,7 @@ _layers_to_check = [
'out_relu']
@unittest.skipIf(tf_version.is_tf1(), 'Skipping TF2.X only test.')
class MobilenetV2Test(test_case.TestCase):
def _build_conv_hyperparams(self):
......@@ -86,6 +88,8 @@ class MobilenetV2Test(test_case.TestCase):
min_depth=None,
conv_defs=None):
"""Constructs Keras mobilenetv2 that extracts intermediate layer outputs."""
# Have to clear the Keras backend to ensure isolation in layer naming
tf.keras.backend.clear_session()
if not layer_names:
layer_names = _layers_to_check
full_model = mobilenet_v2.mobilenet_v2(
......@@ -107,19 +111,17 @@ class MobilenetV2Test(test_case.TestCase):
self, batch_size, image_height, image_width, depth_multiplier,
expected_feature_map_shapes, use_explicit_padding=False, min_depth=None,
layer_names=None, conv_defs=None):
def graph_fn(image_tensor):
model = self._create_application_with_layer_outputs(
layer_names=layer_names,
batchnorm_training=False,
use_explicit_padding=use_explicit_padding,
min_depth=min_depth,
alpha=depth_multiplier,
conv_defs=conv_defs)
return model(image_tensor)
model = self._create_application_with_layer_outputs(
layer_names=layer_names,
batchnorm_training=False,
use_explicit_padding=use_explicit_padding,
min_depth=min_depth,
alpha=depth_multiplier,
conv_defs=conv_defs)
image_tensor = np.random.rand(batch_size, image_height, image_width,
3).astype(np.float32)
feature_maps = self.execute(graph_fn, [image_tensor])
feature_maps = model([image_tensor])
for feature_map, expected_shape in zip(feature_maps,
expected_feature_map_shapes):
......@@ -129,34 +131,30 @@ class MobilenetV2Test(test_case.TestCase):
self, batch_size, image_height, image_width, depth_multiplier,
expected_feature_map_shapes, use_explicit_padding=False,
layer_names=None):
def graph_fn(image_height, image_width):
image_tensor = tf.random_uniform([batch_size, image_height, image_width,
3], dtype=tf.float32)
model = self._create_application_with_layer_outputs(
layer_names=layer_names,
batchnorm_training=False, use_explicit_padding=use_explicit_padding,
alpha=depth_multiplier)
return model(image_tensor)
feature_maps = self.execute_cpu(graph_fn, [
np.array(image_height, dtype=np.int32),
np.array(image_width, dtype=np.int32)
])
height = tf.random.uniform([], minval=image_height, maxval=image_height+1,
dtype=tf.int32)
width = tf.random.uniform([], minval=image_width, maxval=image_width+1,
dtype=tf.int32)
image_tensor = tf.random.uniform([batch_size, height, width,
3], dtype=tf.float32)
model = self._create_application_with_layer_outputs(
layer_names=layer_names,
batchnorm_training=False, use_explicit_padding=use_explicit_padding,
alpha=depth_multiplier)
feature_maps = model(image_tensor)
for feature_map, expected_shape in zip(feature_maps,
expected_feature_map_shapes):
self.assertAllEqual(feature_map.shape, expected_shape)
def _get_variables(self, depth_multiplier, layer_names=None):
g = tf.Graph()
with g.as_default():
preprocessed_inputs = tf.placeholder(tf.float32, (4, None, None, 3))
model = self._create_application_with_layer_outputs(
layer_names=layer_names,
batchnorm_training=False, use_explicit_padding=False,
alpha=depth_multiplier)
model(preprocessed_inputs)
return g.get_collection(tf.GraphKeys.GLOBAL_VARIABLES)
tf.keras.backend.clear_session()
model = self._create_application_with_layer_outputs(
layer_names=layer_names,
batchnorm_training=False, use_explicit_padding=False,
alpha=depth_multiplier)
preprocessed_inputs = tf.random.uniform([2, 40, 40, 3])
model(preprocessed_inputs)
return model.variables
def test_returns_correct_shapes_128(self):
image_height = 128
......
research/object_detection/models/keras_models/resnet_v1_test.py → research/object_detection/models/keras_models/resnet_v1_tf2_test.py
View file @ 47bc1813
......@@ -19,7 +19,7 @@ object detection. To verify the consistency of the two models, we compare:
1. Output shape of each layer given different inputs.
2. Number of global variables.
"""
import unittest
import numpy as np
from six.moves import zip
import tensorflow.compat.v1 as tf
......@@ -30,6 +30,7 @@ from object_detection.builders import hyperparams_builder
from object_detection.models.keras_models import resnet_v1
from object_detection.protos import hyperparams_pb2
from object_detection.utils import test_case
from object_detection.utils import tf_version
_EXPECTED_SHAPES_224_RESNET50 = {
'conv2_block3_out': (4, 56, 56, 256),
......@@ -65,6 +66,7 @@ _NUM_CHANNELS = 3
_BATCH_SIZE = 4
@unittest.skipIf(tf_version.is_tf1(), 'Skipping TF2.X only test.')
class ResnetV1Test(test_case.TestCase):
def _build_conv_hyperparams(self):
......@@ -146,8 +148,7 @@ class ResnetV1Test(test_case.TestCase):
tf.keras.backend.clear_session()
model = self._create_application_with_layer_outputs(
model_index, batchnorm_training=False)
preprocessed_inputs = tf.placeholder(tf.float32,
(4, None, None, _NUM_CHANNELS))
preprocessed_inputs = tf.random.uniform([2, 40, 40, _NUM_CHANNELS])
model(preprocessed_inputs)
return model.variables
......
research/object_detection/models/ssd_feature_extractor_test.py
View file @ 47bc1813
......@@ -31,6 +31,7 @@ from google.protobuf import text_format
from object_detection.builders import hyperparams_builder
from object_detection.protos import hyperparams_pb2
from object_detection.utils import test_case
from object_detection.utils import test_utils
class SsdFeatureExtractorTestBase(test_case.TestCase):
......@@ -89,14 +90,13 @@ class SsdFeatureExtractorTestBase(test_case.TestCase):
"""
pass
def _extract_features(self,
image_tensor,
depth_multiplier,
pad_to_multiple,
use_explicit_padding=False,
use_depthwise=False,
num_layers=6,
use_keras=False):
def _create_features(self,
depth_multiplier,
pad_to_multiple,
use_explicit_padding=False,
use_depthwise=False,
num_layers=6,
use_keras=False):
kwargs = {}
if use_explicit_padding:
kwargs.update({'use_explicit_padding': use_explicit_padding})
......@@ -110,6 +110,12 @@ class SsdFeatureExtractorTestBase(test_case.TestCase):
depth_multiplier,
pad_to_multiple,
**kwargs)
return feature_extractor
def _extract_features(self,
image_tensor,
feature_extractor,
use_keras=False):
if use_keras:
feature_maps = feature_extractor(image_tensor)
else:
......@@ -127,10 +133,8 @@ class SsdFeatureExtractorTestBase(test_case.TestCase):
num_layers=6,
use_keras=False,
use_depthwise=False):
def graph_fn(image_tensor):
return self._extract_features(
image_tensor,
with test_utils.GraphContextOrNone() as g:
feature_extractor = self._create_features(
depth_multiplier,
pad_to_multiple,
use_explicit_padding=use_explicit_padding,
......@@ -138,9 +142,15 @@ class SsdFeatureExtractorTestBase(test_case.TestCase):
use_keras=use_keras,
use_depthwise=use_depthwise)
def graph_fn(image_tensor):
return self._extract_features(
image_tensor,
feature_extractor,
use_keras=use_keras)
image_tensor = np.random.rand(batch_size, image_height, image_width,
3).astype(np.float32)
feature_maps = self.execute(graph_fn, [image_tensor])
feature_maps = self.execute(graph_fn, [image_tensor], graph=g)
for feature_map, expected_shape in zip(
feature_maps, expected_feature_map_shapes):
self.assertAllEqual(feature_map.shape, expected_shape)
......@@ -158,11 +168,8 @@ class SsdFeatureExtractorTestBase(test_case.TestCase):
use_keras=False,
use_depthwise=False):
def graph_fn(image_height, image_width):
image_tensor = tf.random_uniform([batch_size, image_height, image_width,
3], dtype=tf.float32)
return self._extract_features(
image_tensor,
with test_utils.GraphContextOrNone() as g:
feature_extractor = self._create_features(
depth_multiplier,
pad_to_multiple,
use_explicit_padding=use_explicit_padding,
......@@ -170,10 +177,18 @@ class SsdFeatureExtractorTestBase(test_case.TestCase):
use_keras=use_keras,
use_depthwise=use_depthwise)
def graph_fn(image_height, image_width):
image_tensor = tf.random_uniform([batch_size, image_height, image_width,
3], dtype=tf.float32)
return self._extract_features(
image_tensor,
feature_extractor,
use_keras=use_keras)
feature_maps = self.execute_cpu(graph_fn, [
np.array(image_height, dtype=np.int32),
np.array(image_width, dtype=np.int32)
])
], graph=g)
for feature_map, expected_shape in zip(
feature_maps, expected_feature_map_shapes):
self.assertAllEqual(feature_map.shape, expected_shape)
......@@ -186,19 +201,33 @@ class SsdFeatureExtractorTestBase(test_case.TestCase):
pad_to_multiple,
use_keras=False,
use_depthwise=False):
preprocessed_inputs = tf.placeholder(tf.float32, (4, None, None, 3))
feature_maps = self._extract_features(
preprocessed_inputs,
depth_multiplier,
pad_to_multiple,
use_keras=use_keras,
use_depthwise=use_depthwise)
test_preprocessed_image = np.random.rand(4, image_height, image_width, 3)
with self.test_session() as sess:
sess.run(tf.global_variables_initializer())
with test_utils.GraphContextOrNone() as g:
batch = 4
width = tf.random.uniform([], minval=image_width, maxval=image_width+1,
dtype=tf.int32)
height = tf.random.uniform([], minval=image_height, maxval=image_height+1,
dtype=tf.int32)
shape = tf.stack([batch, height, width, 3])
preprocessed_inputs = tf.random.uniform(shape)
feature_extractor = self._create_features(
depth_multiplier,
pad_to_multiple,
use_keras=use_keras,
use_depthwise=use_depthwise)
def graph_fn():
feature_maps = self._extract_features(
preprocessed_inputs,
feature_extractor,
use_keras=use_keras)
return feature_maps
if self.is_tf2():
with self.assertRaises(ValueError):
self.execute_cpu(graph_fn, [], graph=g)
else:
with self.assertRaises(tf.errors.InvalidArgumentError):
sess.run(feature_maps,
feed_dict={preprocessed_inputs: test_preprocessed_image})
self.execute_cpu(graph_fn, [], graph=g)
def check_feature_extractor_variables_under_scope(self,
depth_multiplier,
......@@ -221,11 +250,14 @@ class SsdFeatureExtractorTestBase(test_case.TestCase):
use_depthwise=False):
g = tf.Graph()
with g.as_default():
preprocessed_inputs = tf.placeholder(tf.float32, (4, None, None, 3))
self._extract_features(
preprocessed_inputs,
feature_extractor = self._create_features(
depth_multiplier,
pad_to_multiple,
use_keras=use_keras,
use_depthwise=use_depthwise)
preprocessed_inputs = tf.placeholder(tf.float32, (4, None, None, 3))
self._extract_features(
preprocessed_inputs,
feature_extractor,
use_keras=use_keras)
return g.get_collection(tf.GraphKeys.GLOBAL_VARIABLES)
Markdown is supported
0% or .
You are about to add 0 people to the discussion. Proceed with caution.
Finish editing this message first!
Please register or to comment