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Unverified Commit 0f0c7745 authored by vivek rathod's avatar vivek rathod Committed by GitHub
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Merged commit includes the following changes: (#8739) 

318417714  by jonathanhuang:

    Internal change.

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318367213  by sbeery:

    Pointing users to more documentation for beam

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318358685  by sbeery:

    Context R-CNN sample config for GPU

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318309800  by rathodv:

    Internal

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318303364  by ronnyvotel:

    Adding the option for parsing and including DensePose annotations. http://densepose.org/

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318291319  by aom:

    Adds conv_bn_act conv_block option, and naming convention changes for BiFPN utils.

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318200598  by ronnyvotel:

    Updating the TF Example Decoder to parse DensePose annotations.

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318174065  by jonathanhuang:

    Internal change.

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318167805  by rathodv:

    Add use_tpu flag to TF2 binary.

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318145285  by aom:

    Adds option for convolutional keras box predictor to force use_bias.

--

PiperOrigin-RevId: 318417714
parent 1e4fd825
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Showing with 220 additions and 1039 deletions
research/object_detection/model_main_tf2.py
View file @ 0f0c7745
......@@ -16,14 +16,6 @@
r"""Creates and runs TF2 object detection models.
##################################
NOTE: This module has not been fully tested; please bear with us while we iron
out the kinks.
##################################
When a TPU device is available, this binary uses TPUStrategy. Otherwise, it uses
GPUS with MirroredStrategy/MultiWorkerMirroredStrategy.
For local training/evaluation run:
PIPELINE_CONFIG_PATH=path/to/pipeline.config
MODEL_DIR=/tmp/model_outputs
......@@ -60,6 +52,8 @@ flags.DEFINE_string(
flags.DEFINE_integer('eval_timeout', 3600, 'Number of seconds to wait for an'
'evaluation checkpoint before exiting.')
flags.DEFINE_bool('use_tpu', False, 'Whether the job is executing on a TPU.')
flags.DEFINE_integer(
'num_workers', 1, 'When num_workers > 1, training uses '
'MultiWorkerMirroredStrategy. When num_workers = 1 it uses '
......@@ -84,7 +78,7 @@ def main(unused_argv):
checkpoint_dir=FLAGS.checkpoint_dir,
wait_interval=300, timeout=FLAGS.eval_timeout)
else:
if tf.config.get_visible_devices('TPU'):
if FLAGS.use_tpu:
resolver = tf.distribute.cluster_resolver.TPUClusterResolver()
tf.config.experimental_connect_to_cluster(resolver)
tf.tpu.experimental.initialize_tpu_system(resolver)
......
research/object_detection/models/faster_rcnn_inception_resnet_v2_keras_feature_extractor.py
View file @ 0f0c7745
......@@ -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',
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'SecondStageFeatureExtractor/InceptionResnetV2/conv2d_394/kernel': 'InceptionResnetV2/Repeat_2/block8_8/Branch_0/Conv2d_1x1/weights',
'SecondStageFeatureExtractor/InceptionResnetV2/freezable_batch_norm_394/beta': 'InceptionResnetV2/Repeat_2/block8_8/Branch_0/Conv2d_1x1/BatchNorm/beta',
'SecondStageFeatureExtractor/InceptionResnetV2/freezable_batch_norm_394/moving_mean': 'InceptionResnetV2/Repeat_2/block8_8/Branch_0/Conv2d_1x1/BatchNorm/moving_mean',
'SecondStageFeatureExtractor/InceptionResnetV2/freezable_batch_norm_394/moving_variance': 'InceptionResnetV2/Repeat_2/block8_8/Branch_0/Conv2d_1x1/BatchNorm/moving_variance',
'SecondStageFeatureExtractor/InceptionResnetV2/conv2d_395/kernel': 'InceptionResnetV2/Repeat_2/block8_8/Branch_1/Conv2d_0a_1x1/weights',
'SecondStageFeatureExtractor/InceptionResnetV2/freezable_batch_norm_395/beta': 'InceptionResnetV2/Repeat_2/block8_8/Branch_1/Conv2d_0a_1x1/BatchNorm/beta',
'SecondStageFeatureExtractor/InceptionResnetV2/freezable_batch_norm_395/moving_mean': 'InceptionResnetV2/Repeat_2/block8_8/Branch_1/Conv2d_0a_1x1/BatchNorm/moving_mean',
'SecondStageFeatureExtractor/InceptionResnetV2/freezable_batch_norm_395/moving_variance': 'InceptionResnetV2/Repeat_2/block8_8/Branch_1/Conv2d_0a_1x1/BatchNorm/moving_variance',
'SecondStageFeatureExtractor/InceptionResnetV2/conv2d_396/kernel': 'InceptionResnetV2/Repeat_2/block8_8/Branch_1/Conv2d_0b_1x3/weights',
'SecondStageFeatureExtractor/InceptionResnetV2/freezable_batch_norm_396/beta': 'InceptionResnetV2/Repeat_2/block8_8/Branch_1/Conv2d_0b_1x3/BatchNorm/beta',
'SecondStageFeatureExtractor/InceptionResnetV2/freezable_batch_norm_396/moving_mean': 'InceptionResnetV2/Repeat_2/block8_8/Branch_1/Conv2d_0b_1x3/BatchNorm/moving_mean',
'SecondStageFeatureExtractor/InceptionResnetV2/freezable_batch_norm_396/moving_variance': 'InceptionResnetV2/Repeat_2/block8_8/Branch_1/Conv2d_0b_1x3/BatchNorm/moving_variance',
'SecondStageFeatureExtractor/InceptionResnetV2/conv2d_397/kernel': 'InceptionResnetV2/Repeat_2/block8_8/Branch_1/Conv2d_0c_3x1/weights',
'SecondStageFeatureExtractor/InceptionResnetV2/freezable_batch_norm_397/beta': 'InceptionResnetV2/Repeat_2/block8_8/Branch_1/Conv2d_0c_3x1/BatchNorm/beta',
'SecondStageFeatureExtractor/InceptionResnetV2/freezable_batch_norm_397/moving_mean': 'InceptionResnetV2/Repeat_2/block8_8/Branch_1/Conv2d_0c_3x1/BatchNorm/moving_mean',
'SecondStageFeatureExtractor/InceptionResnetV2/freezable_batch_norm_397/moving_variance': 'InceptionResnetV2/Repeat_2/block8_8/Branch_1/Conv2d_0c_3x1/BatchNorm/moving_variance',
'SecondStageFeatureExtractor/InceptionResnetV2/block8_8_conv/kernel': 'InceptionResnetV2/Repeat_2/block8_8/Conv2d_1x1/weights',
'SecondStageFeatureExtractor/InceptionResnetV2/block8_8_conv/bias': 'InceptionResnetV2/Repeat_2/block8_8/Conv2d_1x1/biases',
'SecondStageFeatureExtractor/InceptionResnetV2/conv2d_398/kernel': 'InceptionResnetV2/Repeat_2/block8_9/Branch_0/Conv2d_1x1/weights',
'SecondStageFeatureExtractor/InceptionResnetV2/freezable_batch_norm_398/beta': 'InceptionResnetV2/Repeat_2/block8_9/Branch_0/Conv2d_1x1/BatchNorm/beta',
'SecondStageFeatureExtractor/InceptionResnetV2/freezable_batch_norm_398/moving_mean': 'InceptionResnetV2/Repeat_2/block8_9/Branch_0/Conv2d_1x1/BatchNorm/moving_mean',
'SecondStageFeatureExtractor/InceptionResnetV2/freezable_batch_norm_398/moving_variance': 'InceptionResnetV2/Repeat_2/block8_9/Branch_0/Conv2d_1x1/BatchNorm/moving_variance',
'SecondStageFeatureExtractor/InceptionResnetV2/conv2d_399/kernel': 'InceptionResnetV2/Repeat_2/block8_9/Branch_1/Conv2d_0a_1x1/weights',
'SecondStageFeatureExtractor/InceptionResnetV2/freezable_batch_norm_399/beta': 'InceptionResnetV2/Repeat_2/block8_9/Branch_1/Conv2d_0a_1x1/BatchNorm/beta',
'SecondStageFeatureExtractor/InceptionResnetV2/freezable_batch_norm_399/moving_mean': 'InceptionResnetV2/Repeat_2/block8_9/Branch_1/Conv2d_0a_1x1/BatchNorm/moving_mean',
'SecondStageFeatureExtractor/InceptionResnetV2/freezable_batch_norm_399/moving_variance': 'InceptionResnetV2/Repeat_2/block8_9/Branch_1/Conv2d_0a_1x1/BatchNorm/moving_variance',
'SecondStageFeatureExtractor/InceptionResnetV2/conv2d_400/kernel': 'InceptionResnetV2/Repeat_2/block8_9/Branch_1/Conv2d_0b_1x3/weights',
'SecondStageFeatureExtractor/InceptionResnetV2/freezable_batch_norm_400/beta': 'InceptionResnetV2/Repeat_2/block8_9/Branch_1/Conv2d_0b_1x3/BatchNorm/beta',
'SecondStageFeatureExtractor/InceptionResnetV2/freezable_batch_norm_400/moving_mean': 'InceptionResnetV2/Repeat_2/block8_9/Branch_1/Conv2d_0b_1x3/BatchNorm/moving_mean',
'SecondStageFeatureExtractor/InceptionResnetV2/freezable_batch_norm_400/moving_variance': 'InceptionResnetV2/Repeat_2/block8_9/Branch_1/Conv2d_0b_1x3/BatchNorm/moving_variance',
'SecondStageFeatureExtractor/InceptionResnetV2/conv2d_401/kernel': 'InceptionResnetV2/Repeat_2/block8_9/Branch_1/Conv2d_0c_3x1/weights',
'SecondStageFeatureExtractor/InceptionResnetV2/freezable_batch_norm_401/beta': 'InceptionResnetV2/Repeat_2/block8_9/Branch_1/Conv2d_0c_3x1/BatchNorm/beta',
'SecondStageFeatureExtractor/InceptionResnetV2/freezable_batch_norm_401/moving_mean': 'InceptionResnetV2/Repeat_2/block8_9/Branch_1/Conv2d_0c_3x1/BatchNorm/moving_mean',
'SecondStageFeatureExtractor/InceptionResnetV2/freezable_batch_norm_401/moving_variance': 'InceptionResnetV2/Repeat_2/block8_9/Branch_1/Conv2d_0c_3x1/BatchNorm/moving_variance',
'SecondStageFeatureExtractor/InceptionResnetV2/block8_9_conv/kernel': 'InceptionResnetV2/Repeat_2/block8_9/Conv2d_1x1/weights',
'SecondStageFeatureExtractor/InceptionResnetV2/block8_9_conv/bias': 'InceptionResnetV2/Repeat_2/block8_9/Conv2d_1x1/biases',
'SecondStageFeatureExtractor/InceptionResnetV2/conv2d_402/kernel': 'InceptionResnetV2/Block8/Branch_0/Conv2d_1x1/weights',
'SecondStageFeatureExtractor/InceptionResnetV2/freezable_batch_norm_402/beta': 'InceptionResnetV2/Block8/Branch_0/Conv2d_1x1/BatchNorm/beta',
'SecondStageFeatureExtractor/InceptionResnetV2/freezable_batch_norm_402/moving_mean': 'InceptionResnetV2/Block8/Branch_0/Conv2d_1x1/BatchNorm/moving_mean',
'SecondStageFeatureExtractor/InceptionResnetV2/freezable_batch_norm_402/moving_variance': 'InceptionResnetV2/Block8/Branch_0/Conv2d_1x1/BatchNorm/moving_variance',
'SecondStageFeatureExtractor/InceptionResnetV2/conv2d_403/kernel': 'InceptionResnetV2/Block8/Branch_1/Conv2d_0a_1x1/weights',
'SecondStageFeatureExtractor/InceptionResnetV2/freezable_batch_norm_403/beta': 'InceptionResnetV2/Block8/Branch_1/Conv2d_0a_1x1/BatchNorm/beta',
'SecondStageFeatureExtractor/InceptionResnetV2/freezable_batch_norm_403/moving_mean': 'InceptionResnetV2/Block8/Branch_1/Conv2d_0a_1x1/BatchNorm/moving_mean',
'SecondStageFeatureExtractor/InceptionResnetV2/freezable_batch_norm_403/moving_variance': 'InceptionResnetV2/Block8/Branch_1/Conv2d_0a_1x1/BatchNorm/moving_variance',
'SecondStageFeatureExtractor/InceptionResnetV2/conv2d_404/kernel': 'InceptionResnetV2/Block8/Branch_1/Conv2d_0b_1x3/weights',
'SecondStageFeatureExtractor/InceptionResnetV2/freezable_batch_norm_404/beta': 'InceptionResnetV2/Block8/Branch_1/Conv2d_0b_1x3/BatchNorm/beta',
'SecondStageFeatureExtractor/InceptionResnetV2/freezable_batch_norm_404/moving_mean': 'InceptionResnetV2/Block8/Branch_1/Conv2d_0b_1x3/BatchNorm/moving_mean',
'SecondStageFeatureExtractor/InceptionResnetV2/freezable_batch_norm_404/moving_variance': 'InceptionResnetV2/Block8/Branch_1/Conv2d_0b_1x3/BatchNorm/moving_variance',
'SecondStageFeatureExtractor/InceptionResnetV2/conv2d_405/kernel': 'InceptionResnetV2/Block8/Branch_1/Conv2d_0c_3x1/weights',
'SecondStageFeatureExtractor/InceptionResnetV2/freezable_batch_norm_405/beta': 'InceptionResnetV2/Block8/Branch_1/Conv2d_0c_3x1/BatchNorm/beta',
'SecondStageFeatureExtractor/InceptionResnetV2/freezable_batch_norm_405/moving_mean': 'InceptionResnetV2/Block8/Branch_1/Conv2d_0c_3x1/BatchNorm/moving_mean',
'SecondStageFeatureExtractor/InceptionResnetV2/freezable_batch_norm_405/moving_variance': 'InceptionResnetV2/Block8/Branch_1/Conv2d_0c_3x1/BatchNorm/moving_variance',
'SecondStageFeatureExtractor/InceptionResnetV2/block8_10_conv/kernel': 'InceptionResnetV2/Block8/Conv2d_1x1/weights',
'SecondStageFeatureExtractor/InceptionResnetV2/block8_10_conv/bias': 'InceptionResnetV2/Block8/Conv2d_1x1/biases',
'SecondStageFeatureExtractor/InceptionResnetV2/conv_7b/kernel': 'InceptionResnetV2/Conv2d_7b_1x1/weights',
'SecondStageFeatureExtractor/InceptionResnetV2/conv_7b_bn/beta': 'InceptionResnetV2/Conv2d_7b_1x1/BatchNorm/beta',
'SecondStageFeatureExtractor/InceptionResnetV2/conv_7b_bn/moving_mean': 'InceptionResnetV2/Conv2d_7b_1x1/BatchNorm/moving_mean',
'SecondStageFeatureExtractor/InceptionResnetV2/conv_7b_bn/moving_variance': 'InceptionResnetV2/Conv2d_7b_1x1/BatchNorm/moving_variance',
}
variables_to_restore = {}
if tf.executing_eagerly():
for key in self._variable_dict:
# variable.name includes ":0" at the end, but the names in the
# checkpoint do not have the suffix ":0". So, we strip it here.
var_name = keras_to_slim_name_mapping.get(key)
if var_name:
variables_to_restore[var_name] = self._variable_dict[key]
else:
for variable in variables_helper.get_global_variables_safely():
var_name = keras_to_slim_name_mapping.get(variable.op.name)
if var_name:
variables_to_restore[var_name] = variable
return variables_to_restore
research/object_detection/models/faster_rcnn_inception_resnet_v2_keras_feature_extractor_tf2_test.py
View file @ 0f0c7745
......@@ -73,7 +73,7 @@ class FasterRcnnInceptionResnetV2KerasFeatureExtractorTest(tf.test.TestCase):
proposal_classifier_features = (
model(proposal_feature_maps))
features_shape = tf.shape(proposal_classifier_features)
self.assertAllEqual(features_shape.numpy(), [2, 8, 8, 1536])
self.assertAllEqual(features_shape.numpy(), [2, 9, 9, 1536])
if __name__ == '__main__':
......
research/object_detection/models/faster_rcnn_resnet_keras_feature_extractor.py
View file @ 0f0c7745
......@@ -175,23 +175,6 @@ class FasterRCNNResnetKerasFeatureExtractor(
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 for restoring from an (object-based) checkpoint.
Args:
first_stage_feature_extractor_scope: A scope name for the first stage
feature extractor (unused).
second_stage_feature_extractor_scope: A scope name for the second stage
feature extractor (unused).
Returns:
A dict mapping keys to Keras models
"""
return {'feature_extractor': self.classification_backbone}
class FasterRCNNResnet50KerasFeatureExtractor(
FasterRCNNResnetKerasFeatureExtractor):
......
research/object_detection/models/ssd_mobilenet_v1_keras_feature_extractor.py
View file @ 0f0c7745
......@@ -163,14 +163,3 @@ class SSDMobileNetV1KerasFeatureExtractor(
'Conv2d_13_pointwise': image_features[1]})
return list(feature_maps.values())
def restore_from_classification_checkpoint_fn(self, feature_extractor_scope):
"""Returns a map for restoring from an (object-based) checkpoint.
Args:
feature_extractor_scope: A scope name for the feature extractor (unused).
Returns:
A dict mapping keys to Keras models
"""
return {'feature_extractor': self.classification_backbone}
research/object_detection/models/ssd_mobilenet_v2_fpn_keras_feature_extractor.py
View file @ 0f0c7745
......@@ -241,14 +241,3 @@ class SSDMobileNetV2FpnKerasFeatureExtractor(
last_feature_map = layer(last_feature_map)
feature_maps.append(last_feature_map)
return feature_maps
def restore_from_classification_checkpoint_fn(self, feature_extractor_scope):
"""Returns a map for restoring from an (object-based) checkpoint.
Args:
feature_extractor_scope: A scope name for the feature extractor (unused).
Returns:
A dict mapping keys to Keras models
"""
return {'feature_extractor': self.classification_backbone}
research/object_detection/models/ssd_mobilenet_v2_keras_feature_extractor.py
View file @ 0f0c7745
......@@ -166,14 +166,3 @@ class SSDMobileNetV2KerasFeatureExtractor(
'layer_19': image_features[1]})
return list(feature_maps.values())
def restore_from_classification_checkpoint_fn(self, feature_extractor_scope):
"""Returns a map for restoring from an (object-based) checkpoint.
Args:
feature_extractor_scope: A scope name for the feature extractor (unused).
Returns:
A dict mapping keys to Keras models
"""
return {'feature_extractor': self.classification_backbone}
research/object_detection/models/ssd_resnet_v1_fpn_keras_feature_extractor.py
View file @ 0f0c7745
......@@ -246,17 +246,6 @@ class SSDResNetV1FpnKerasFeatureExtractor(
feature_maps.append(last_feature_map)
return feature_maps
def restore_from_classification_checkpoint_fn(self, feature_extractor_scope):
"""Returns a map for restoring from an (object-based) checkpoint.
Args:
feature_extractor_scope: A scope name for the feature extractor (unused).
Returns:
A dict mapping keys to Keras models
"""
return {'feature_extractor': self.classification_backbone}
class SSDResNet50V1FpnKerasFeatureExtractor(
SSDResNetV1FpnKerasFeatureExtractor):
......
research/object_detection/predictors/convolutional_keras_box_predictor.py
View file @ 0f0c7745
......@@ -314,7 +314,8 @@ class WeightSharedConvolutionalBoxPredictor(box_predictor.KerasBoxPredictor):
self, inserted_layer_counter, target_channel):
projection_layers = []
if inserted_layer_counter >= 0:
use_bias = False if self._apply_batch_norm else True
use_bias = False if (self._apply_batch_norm and not
self._conv_hyperparams.force_use_bias()) else True
projection_layers.append(keras.Conv2D(
target_channel, [1, 1], strides=1, padding='SAME',
name='ProjectionLayer/conv2d_{}'.format(inserted_layer_counter),
......@@ -331,7 +332,8 @@ class WeightSharedConvolutionalBoxPredictor(box_predictor.KerasBoxPredictor):
conv_layers = []
batch_norm_layers = []
activation_layers = []
use_bias = False if self._apply_batch_norm else True
use_bias = False if (self._apply_batch_norm and not
self._conv_hyperparams.force_use_bias()) else True
for additional_conv_layer_idx in range(self._num_layers_before_predictor):
layer_name = '{}/conv2d_{}'.format(
tower_name_scope, additional_conv_layer_idx)
......@@ -363,7 +365,9 @@ class WeightSharedConvolutionalBoxPredictor(box_predictor.KerasBoxPredictor):
training=(self._is_training and not self._freeze_batchnorm),
name='{}/conv2d_{}/BatchNorm/feature_{}'.format(
tower_name_scope, additional_conv_layer_idx, feature_index)))
activation_layers.append(tf.keras.layers.Lambda(tf.nn.relu6))
activation_layers.append(self._conv_hyperparams.build_activation_layer(
name='{}/conv2d_{}/activation_{}'.format(
tower_name_scope, additional_conv_layer_idx, feature_index)))
# Set conv layers as the shared conv layers for different feature maps with
# the same tower_name_scope.
......
research/object_detection/protos/input_reader.proto
View file @ 0f0c7745
......@@ -31,7 +31,7 @@ enum InputType {
TF_SEQUENCE_EXAMPLE = 2; // TfSequenceExample Input
}
// Next id: 31
// Next id: 32
message InputReader {
// Name of input reader. Typically used to describe the dataset that is read
// by this input reader.
......@@ -119,6 +119,10 @@ message InputReader {
// Type of instance mask.
optional InstanceMaskType mask_type = 10 [default = NUMERICAL_MASKS];
// Whether to load DensePose data. If set, must also set load_instance_masks
// to true.
optional bool load_dense_pose = 31 [default = false];
// Whether to use the display name when decoding examples. This is only used
// when mapping class text strings to integers.
optional bool use_display_name = 17 [default = false];
......
research/object_detection/protos/train.proto
View file @ 0f0c7745
......@@ -59,7 +59,8 @@ message TrainConfig {
// Whether to load all checkpoint vars that match model variable names and
// sizes. This option is only available if `from_detection_checkpoint` is
// True.
// True. This option is *not* supported for TF2 --- setting it to true
// will raise an error.
optional bool load_all_detection_checkpoint_vars = 19 [default = false];
// Number of steps to train the DetectionModel for. If 0, will train the model
......
research/object_detection/samples/configs/context_rcnn_resnet101_snapshot_serengeti.config 0 → 100644
View file @ 0f0c7745
# Context R-CNN configuration for Snapshot Serengeti Dataset, with sequence
# example input data with context_features.
# This model uses attention into contextual features within the Faster R-CNN
# object detection framework to improve object detection performance.
# See https://arxiv.org/abs/1912.03538 for more information.
# Search for "PATH_TO_BE_CONFIGURED" to find the fields that should be
# configured.
model {
faster_rcnn {
num_classes: 48
image_resizer {
fixed_shape_resizer {
height: 640
width: 640
}
}
feature_extractor {
type: "faster_rcnn_resnet101"
first_stage_features_stride: 16
batch_norm_trainable: true
}
first_stage_anchor_generator {
grid_anchor_generator {
height_stride: 16
width_stride: 16
scales: 0.25
scales: 0.5
scales: 1.0
scales: 2.0
aspect_ratios: 0.5
aspect_ratios: 1.0
aspect_ratios: 2.0
}
}
first_stage_box_predictor_conv_hyperparams {
op: CONV
regularizer {
l2_regularizer {
weight: 0.0
}
}
initializer {
truncated_normal_initializer {
stddev: 0.00999999977648
}
}
}
first_stage_nms_score_threshold: 0.0
first_stage_nms_iou_threshold: 0.699999988079
first_stage_max_proposals: 300
first_stage_localization_loss_weight: 2.0
first_stage_objectness_loss_weight: 1.0
initial_crop_size: 14
maxpool_kernel_size: 2
maxpool_stride: 2
second_stage_box_predictor {
mask_rcnn_box_predictor {
fc_hyperparams {
op: FC
regularizer {
l2_regularizer {
weight: 0.0
}
}
initializer {
variance_scaling_initializer {
factor: 1.0
uniform: true
mode: FAN_AVG
}
}
}
use_dropout: false
dropout_keep_probability: 1.0
share_box_across_classes: true
}
}
second_stage_post_processing {
batch_non_max_suppression {
score_threshold: 0.0
iou_threshold: 0.600000023842
max_detections_per_class: 100
max_total_detections: 300
}
score_converter: SOFTMAX
}
second_stage_localization_loss_weight: 2.0
second_stage_classification_loss_weight: 1.0
use_matmul_crop_and_resize: true
clip_anchors_to_image: true
use_matmul_gather_in_matcher: true
use_static_balanced_label_sampler: true
use_static_shapes: true
context_config {
max_num_context_features: 2000
context_feature_length: 2057
}
}
}
train_config {
batch_size: 8
data_augmentation_options {
random_horizontal_flip {
}
}
sync_replicas: true
optimizer {
momentum_optimizer {
learning_rate {
manual_step_learning_rate {
initial_learning_rate: 0.0
schedule {
step: 400000
learning_rate: 0.002
}
schedule {
step: 500000
learning_rate: 0.0002
}
schedule {
step: 600000
learning_rate: 0.00002
}
warmup: true
}
}
momentum_optimizer_value: 0.9
}
use_moving_average: false
}
gradient_clipping_by_norm: 10.0
fine_tune_checkpoint: "PATH_TO_BE_CONFIGURED/faster_rcnn_resnet101_coco_2018_08_14/model.ckpt"
from_detection_checkpoint: true
num_steps: 5000000
replicas_to_aggregate: 8
max_number_of_boxes: 100
unpad_groundtruth_tensors: false
use_bfloat16: true
}
train_input_reader {
label_map_path: "PATH_TO_BE_CONFIGURED/ss_label_map.pbtxt"
tf_record_input_reader {
input_path: "PATH_TO_BE_CONFIGURED/snapshot_serengeti_train-?????-of-?????"
}
load_context_features: true
input_type: TF_SEQUENCE_EXAMPLE
}
eval_config {
max_evals: 50
metrics_set: "coco_detection_metrics"
use_moving_averages: false
batch_size: 1
}
eval_input_reader {
label_map_path: "PATH_TO_BE_CONFIGURED/ss_label_map.pbtxt"
shuffle: false
num_epochs: 1
tf_record_input_reader {
input_path: "PATH_TO_BE_CONFIGURED/snapshot_serengeti_val-?????-of-?????"
}
load_context_features: true
input_type: TF_SEQUENCE_EXAMPLE
}
research/object_detection/utils/bifpn_utils.py
View file @ 0f0c7745
......@@ -26,7 +26,8 @@ from object_detection.utils import shape_utils
def create_conv_block(name, num_filters, kernel_size, strides, padding,
use_separable, apply_batchnorm, apply_activation,
conv_hyperparams, is_training, freeze_batchnorm):
conv_hyperparams, is_training, freeze_batchnorm,
conv_bn_act_pattern=True):
"""Create Keras layers for regular or separable convolutions.
Args:
......@@ -50,6 +51,9 @@ def create_conv_block(name, num_filters, kernel_size, strides, padding,
training or not. When training with a small batch size (e.g. 1), it is
desirable to freeze batch norm update and use pretrained batch norm
params.
conv_bn_act_pattern: Bool. By default, when True, the layers returned by
this function are in the order [conv, batchnorm, activation]. Otherwise,
when False, the order of the layers is [activation, conv, batchnorm].
Returns:
A list of keras layers, including (regular or seperable) convolution, and
......@@ -73,7 +77,7 @@ def create_conv_block(name, num_filters, kernel_size, strides, padding,
depth_multiplier=1,
padding=padding,
strides=strides,
name=name + '_separable_conv',
name=name + 'separable_conv',
**kwargs))
else:
layers.append(
......@@ -82,18 +86,22 @@ def create_conv_block(name, num_filters, kernel_size, strides, padding,
kernel_size=kernel_size,
padding=padding,
strides=strides,
name=name + '_conv',
name=name + 'conv',
**conv_hyperparams.params()))
if apply_batchnorm:
layers.append(
conv_hyperparams.build_batch_norm(
training=(is_training and not freeze_batchnorm),
name=name + '_batchnorm'))
name=name + 'batchnorm'))
if apply_activation:
layers.append(
conv_hyperparams.build_activation_layer(name=name + '_activation'))
activation_layer = conv_hyperparams.build_activation_layer(
name=name + 'activation')
if conv_bn_act_pattern:
layers.append(activation_layer)
else:
layers = [activation_layer] + layers
return layers
......@@ -133,28 +141,28 @@ def create_downsample_feature_map_ops(scale, downsample_method,
pool_size=kernel_size,
strides=stride,
padding=padding,
name=name + '_downsample_max_x{}'.format(stride)))
name=name + 'downsample_max_x{}'.format(stride)))
elif downsample_method == 'avg_pooling':
layers.append(
tf.keras.layers.AveragePooling2D(
pool_size=kernel_size,
strides=stride,
padding=padding,
name=name + '_downsample_avg_x{}'.format(stride)))
name=name + 'downsample_avg_x{}'.format(stride)))
elif downsample_method == 'depthwise_conv':
layers.append(
tf.keras.layers.DepthwiseConv2D(
kernel_size=kernel_size,
strides=stride,
padding=padding,
name=name + '_downsample_depthwise_x{}'.format(stride)))
name=name + 'downsample_depthwise_x{}'.format(stride)))
layers.append(
conv_hyperparams.build_batch_norm(
training=(is_training and not freeze_batchnorm),
name=name + '_downsample_batchnorm'))
name=name + 'downsample_batchnorm'))
layers.append(
conv_hyperparams.build_activation_layer(name=name +
'_downsample_activation'))
'downsample_activation'))
else:
raise ValueError('Unknown downsample method: {}'.format(downsample_method))
......
research/object_detection/utils/config_util.py
View file @ 0f0c7745
......@@ -147,6 +147,7 @@ def clear_fine_tune_checkpoint(pipeline_config_path,
"""Clears fine_tune_checkpoint and writes a new pipeline config file."""
configs = get_configs_from_pipeline_file(pipeline_config_path)
configs["train_config"].fine_tune_checkpoint = ""
configs["train_config"].load_all_detection_checkpoint_vars = False
pipeline_proto = create_pipeline_proto_from_configs(configs)
with tf.gfile.Open(new_pipeline_config_path, "wb") as f:
f.write(text_format.MessageToString(pipeline_proto))
......
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