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Unverified Commit 420a7253 authored by pkulzc's avatar pkulzc Committed by GitHub
Browse files

Refactor tests for Object Detection API. (#8688) 

Internal changes

--

PiperOrigin-RevId: 316837667
parent d0ef3913
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Showing with 872 additions and 543 deletions
research/object_detection/models/ssd_mobilenet_v3_feature_extractor_test.py → research/object_detection/models/ssd_mobilenet_v3_feature_extractor_tf1_test.py
View file @ 420a7253
......@@ -13,17 +13,15 @@
# limitations under the License.
# ==============================================================================
"""Tests for ssd_mobilenet_v3_feature_extractor."""
import unittest
import tensorflow.compat.v1 as tf
import tf_slim as slim
from object_detection.models import ssd_mobilenet_v3_feature_extractor
from object_detection.models import ssd_mobilenet_v3_feature_extractor_testbase
from object_detection.utils import tf_version
slim = slim
@unittest.skipIf(tf_version.is_tf2(), 'Skipping TF1.X only test.')
class SsdMobilenetV3LargeFeatureExtractorTest(
ssd_mobilenet_v3_feature_extractor_testbase
._SsdMobilenetV3FeatureExtractorTestBase):
......@@ -63,6 +61,7 @@ class SsdMobilenetV3LargeFeatureExtractorTest(
use_explicit_padding=use_explicit_padding))
@unittest.skipIf(tf_version.is_tf2(), 'Skipping TF1.X only test.')
class SsdMobilenetV3SmallFeatureExtractorTest(
ssd_mobilenet_v3_feature_extractor_testbase
._SsdMobilenetV3FeatureExtractorTestBase):
......
research/object_detection/models/ssd_pnasnet_feature_extractor.py
View file @ 420a7253
......@@ -27,7 +27,10 @@ from object_detection.models import feature_map_generators
from object_detection.utils import context_manager
from object_detection.utils import ops
from object_detection.utils import variables_helper
from nets.nasnet import pnasnet
try:
from nets.nasnet import pnasnet # pylint: disable=g-import-not-at-top
except: # pylint: disable=bare-except
pass
def pnasnet_large_arg_scope_for_detection(is_batch_norm_training=False):
......
research/object_detection/models/ssd_pnasnet_feature_extractor_test.py → research/object_detection/models/ssd_pnasnet_feature_extractor_tf1_test.py
View file @ 420a7253
......@@ -14,13 +14,16 @@
# ==============================================================================
"""Tests for ssd_pnas_feature_extractor."""
import unittest
import numpy as np
import tensorflow.compat.v1 as tf
from object_detection.models import ssd_feature_extractor_test
from object_detection.models import ssd_pnasnet_feature_extractor
from object_detection.utils import tf_version
@unittest.skipIf(tf_version.is_tf2(), 'Skipping TF1.X only test.')
class SsdPnasNetFeatureExtractorTest(
ssd_feature_extractor_test.SsdFeatureExtractorTestBase):
......
research/object_detection/models/ssd_resnet_v1_fpn_feature_extractor_testbase.py
View file @ 420a7253
......@@ -19,24 +19,20 @@ from __future__ import division
from __future__ import print_function
import abc
from absl.testing import parameterized
import numpy as np
from six.moves import zip
import tensorflow.compat.v1 as tf
from object_detection.models import ssd_feature_extractor_test
from object_detection.utils import test_utils
@parameterized.parameters(
{'use_keras': False},
{'use_keras': True},
)
class SSDResnetFPNFeatureExtractorTestBase(
ssd_feature_extractor_test.SsdFeatureExtractorTestBase):
"""Helper test class for SSD Resnet v1 FPN feature extractors."""
@abc.abstractmethod
def _resnet_scope_name(self, use_keras):
def _resnet_scope_name(self):
pass
@abc.abstractmethod
......@@ -52,7 +48,7 @@ class SSDResnetFPNFeatureExtractorTestBase(
use_keras=False):
pass
def test_extract_features_returns_correct_shapes_256(self, use_keras):
def test_extract_features_returns_correct_shapes_256(self):
image_height = 256
image_width = 256
depth_multiplier = 1.0
......@@ -62,10 +58,10 @@ class SSDResnetFPNFeatureExtractorTestBase(
(2, 2, 2, 256)]
self.check_extract_features_returns_correct_shape(
2, image_height, image_width, depth_multiplier, pad_to_multiple,
expected_feature_map_shape)
expected_feature_map_shape, use_keras=self.is_tf2())
def test_extract_features_returns_correct_shapes_with_dynamic_inputs(
self, use_keras):
self):
image_height = 256
image_width = 256
depth_multiplier = 1.0
......@@ -75,10 +71,10 @@ class SSDResnetFPNFeatureExtractorTestBase(
(2, 2, 2, 256)]
self.check_extract_features_returns_correct_shapes_with_dynamic_inputs(
2, image_height, image_width, depth_multiplier, pad_to_multiple,
expected_feature_map_shape, use_keras=use_keras)
expected_feature_map_shape, use_keras=self.is_tf2())
def test_extract_features_returns_correct_shapes_with_depth_multiplier(
self, use_keras):
self):
image_height = 256
image_width = 256
depth_multiplier = 0.5
......@@ -91,10 +87,10 @@ class SSDResnetFPNFeatureExtractorTestBase(
(2, 2, 2, expected_num_channels)]
self.check_extract_features_returns_correct_shape(
2, image_height, image_width, depth_multiplier, pad_to_multiple,
expected_feature_map_shape, use_keras=use_keras)
expected_feature_map_shape, use_keras=self.is_tf2())
def test_extract_features_returns_correct_shapes_with_min_depth(
self, use_keras):
self):
image_height = 256
image_width = 256
depth_multiplier = 1.0
......@@ -106,23 +102,24 @@ class SSDResnetFPNFeatureExtractorTestBase(
(2, 4, 4, min_depth),
(2, 2, 2, min_depth)]
def graph_fn(image_tensor):
with test_utils.GraphContextOrNone() as g:
image_tensor = tf.random.uniform([2, image_height, image_width, 3])
feature_extractor = self._create_feature_extractor(
depth_multiplier, pad_to_multiple, min_depth=min_depth,
use_keras=use_keras)
if use_keras:
use_keras=self.is_tf2())
def graph_fn():
if self.is_tf2():
return feature_extractor(image_tensor)
return feature_extractor.extract_features(image_tensor)
image_tensor = np.random.rand(2, image_height, image_width,
3).astype(np.float32)
feature_maps = self.execute(graph_fn, [image_tensor])
feature_maps = self.execute(graph_fn, [], graph=g)
for feature_map, expected_shape in zip(feature_maps,
expected_feature_map_shape):
self.assertAllEqual(feature_map.shape, expected_shape)
def test_extract_features_returns_correct_shapes_with_pad_to_multiple(
self, use_keras):
self):
image_height = 254
image_width = 254
depth_multiplier = 1.0
......@@ -133,55 +130,62 @@ class SSDResnetFPNFeatureExtractorTestBase(
self.check_extract_features_returns_correct_shape(
2, image_height, image_width, depth_multiplier, pad_to_multiple,
expected_feature_map_shape, use_keras=use_keras)
expected_feature_map_shape, use_keras=self.is_tf2())
def test_extract_features_raises_error_with_invalid_image_size(
self, use_keras):
self):
image_height = 32
image_width = 32
depth_multiplier = 1.0
pad_to_multiple = 1
self.check_extract_features_raises_error_with_invalid_image_size(
image_height, image_width, depth_multiplier, pad_to_multiple,
use_keras=use_keras)
use_keras=self.is_tf2())
def test_preprocess_returns_correct_value_range(self, use_keras):
def test_preprocess_returns_correct_value_range(self):
image_height = 128
image_width = 128
depth_multiplier = 1
pad_to_multiple = 1
test_image = tf.constant(np.random.rand(4, image_height, image_width, 3))
feature_extractor = self._create_feature_extractor(depth_multiplier,
pad_to_multiple,
use_keras=use_keras)
preprocessed_image = feature_extractor.preprocess(test_image)
with self.test_session() as sess:
test_image_out, preprocessed_image_out = sess.run(
[test_image, preprocessed_image])
self.assertAllClose(preprocessed_image_out,
test_image_out - [[123.68, 116.779, 103.939]])
def test_variables_only_created_in_scope(self, use_keras):
test_image_np = np.random.rand(4, image_height, image_width, 3)
with test_utils.GraphContextOrNone() as g:
test_image = tf.constant(test_image_np)
feature_extractor = self._create_feature_extractor(
depth_multiplier, pad_to_multiple, use_keras=self.is_tf2())
def graph_fn():
preprocessed_image = feature_extractor.preprocess(test_image)
return preprocessed_image
preprocessed_image_out = self.execute(graph_fn, [], graph=g)
self.assertAllClose(preprocessed_image_out,
test_image_np - [[123.68, 116.779, 103.939]])
def test_variables_only_created_in_scope(self):
if self.is_tf2():
self.skipTest('test_variables_only_created_in_scope is only tf1')
depth_multiplier = 1
pad_to_multiple = 1
scope_name = self._resnet_scope_name(use_keras)
scope_name = self._resnet_scope_name()
self.check_feature_extractor_variables_under_scope(
depth_multiplier,
pad_to_multiple,
scope_name,
use_keras=use_keras)
use_keras=self.is_tf2())
def test_variable_count(self, use_keras):
def test_variable_count(self):
if self.is_tf2():
self.skipTest('test_variable_count is only tf1')
depth_multiplier = 1
pad_to_multiple = 1
variables = self.get_feature_extractor_variables(
depth_multiplier,
pad_to_multiple,
use_keras=use_keras)
use_keras=self.is_tf2())
# The number of expected variables in resnet_v1_50, resnet_v1_101,
# and resnet_v1_152 is 279, 534, and 789 respectively.
expected_variables_len = 279
scope_name = self._resnet_scope_name(use_keras)
scope_name = self._resnet_scope_name()
if scope_name in ('ResNet101V1_FPN', 'resnet_v1_101'):
expected_variables_len = 534
elif scope_name in ('ResNet152V1_FPN', 'resnet_v1_152'):
......
research/object_detection/models/ssd_resnet_v1_fpn_feature_extractor_test.py → research/object_detection/models/ssd_resnet_v1_fpn_feature_extractor_tf1_test.py
View file @ 420a7253
......@@ -13,13 +13,15 @@
# limitations under the License.
# ==============================================================================
"""Tests for ssd resnet v1 FPN feature extractors."""
import unittest
import tensorflow.compat.v1 as tf
from object_detection.models import ssd_resnet_v1_fpn_feature_extractor
from object_detection.models import ssd_resnet_v1_fpn_feature_extractor_testbase
from object_detection.models import ssd_resnet_v1_fpn_keras_feature_extractor
from object_detection.utils import tf_version
@unittest.skipIf(tf_version.is_tf2(), 'Skipping TF1.X only test.')
class SSDResnet50V1FeatureExtractorTest(
ssd_resnet_v1_fpn_feature_extractor_testbase.
SSDResnetFPNFeatureExtractorTestBase):
......@@ -29,31 +31,17 @@ class SSDResnet50V1FeatureExtractorTest(
use_explicit_padding=False, min_depth=32,
use_keras=False):
is_training = True
if use_keras:
return (ssd_resnet_v1_fpn_keras_feature_extractor.
SSDResNet50V1FpnKerasFeatureExtractor(
is_training=is_training,
depth_multiplier=depth_multiplier,
min_depth=min_depth,
pad_to_multiple=pad_to_multiple,
conv_hyperparams=self._build_conv_hyperparams(
add_batch_norm=False),
freeze_batchnorm=False,
inplace_batchnorm_update=False,
name='ResNet50V1_FPN'))
else:
return (
ssd_resnet_v1_fpn_feature_extractor.SSDResnet50V1FpnFeatureExtractor(
is_training, depth_multiplier, min_depth, pad_to_multiple,
self.conv_hyperparams_fn,
use_explicit_padding=use_explicit_padding))
return (
ssd_resnet_v1_fpn_feature_extractor.SSDResnet50V1FpnFeatureExtractor(
is_training, depth_multiplier, min_depth, pad_to_multiple,
self.conv_hyperparams_fn,
use_explicit_padding=use_explicit_padding))
def _resnet_scope_name(self, use_keras=False):
if use_keras:
return 'ResNet50V1_FPN'
def _resnet_scope_name(self):
return 'resnet_v1_50'
@unittest.skipIf(tf_version.is_tf2(), 'Skipping TF1.X only test.')
class SSDResnet101V1FeatureExtractorTest(
ssd_resnet_v1_fpn_feature_extractor_testbase.
SSDResnetFPNFeatureExtractorTestBase):
......@@ -63,31 +51,17 @@ class SSDResnet101V1FeatureExtractorTest(
use_explicit_padding=False, min_depth=32,
use_keras=False):
is_training = True
if use_keras:
return (ssd_resnet_v1_fpn_keras_feature_extractor.
SSDResNet101V1FpnKerasFeatureExtractor(
is_training=is_training,
depth_multiplier=depth_multiplier,
min_depth=min_depth,
pad_to_multiple=pad_to_multiple,
conv_hyperparams=self._build_conv_hyperparams(
add_batch_norm=False),
freeze_batchnorm=False,
inplace_batchnorm_update=False,
name='ResNet101V1_FPN'))
else:
return (
ssd_resnet_v1_fpn_feature_extractor.SSDResnet101V1FpnFeatureExtractor(
is_training, depth_multiplier, min_depth, pad_to_multiple,
self.conv_hyperparams_fn,
use_explicit_padding=use_explicit_padding))
return (
ssd_resnet_v1_fpn_feature_extractor.SSDResnet101V1FpnFeatureExtractor(
is_training, depth_multiplier, min_depth, pad_to_multiple,
self.conv_hyperparams_fn,
use_explicit_padding=use_explicit_padding))
def _resnet_scope_name(self, use_keras):
if use_keras:
return 'ResNet101V1_FPN'
def _resnet_scope_name(self):
return 'resnet_v1_101'
@unittest.skipIf(tf_version.is_tf2(), 'Skipping TF1.X only test.')
class SSDResnet152V1FeatureExtractorTest(
ssd_resnet_v1_fpn_feature_extractor_testbase.
SSDResnetFPNFeatureExtractorTestBase):
......@@ -97,28 +71,13 @@ class SSDResnet152V1FeatureExtractorTest(
use_explicit_padding=False, min_depth=32,
use_keras=False):
is_training = True
if use_keras:
return (ssd_resnet_v1_fpn_keras_feature_extractor.
SSDResNet152V1FpnKerasFeatureExtractor(
is_training=is_training,
depth_multiplier=depth_multiplier,
min_depth=min_depth,
pad_to_multiple=pad_to_multiple,
conv_hyperparams=self._build_conv_hyperparams(
add_batch_norm=False),
freeze_batchnorm=False,
inplace_batchnorm_update=False,
name='ResNet152V1_FPN'))
else:
return (
ssd_resnet_v1_fpn_feature_extractor.SSDResnet152V1FpnFeatureExtractor(
is_training, depth_multiplier, min_depth, pad_to_multiple,
self.conv_hyperparams_fn,
use_explicit_padding=use_explicit_padding))
return (
ssd_resnet_v1_fpn_feature_extractor.SSDResnet152V1FpnFeatureExtractor(
is_training, depth_multiplier, min_depth, pad_to_multiple,
self.conv_hyperparams_fn,
use_explicit_padding=use_explicit_padding))
def _resnet_scope_name(self, use_keras):
if use_keras:
return 'ResNet152V1_FPN'
def _resnet_scope_name(self):
return 'resnet_v1_152'
......
research/object_detection/models/ssd_resnet_v1_fpn_feature_extractor_tf2_test.py 0 → 100644
View file @ 420a7253
# Copyright 2017 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 ssd resnet v1 FPN feature extractors."""
import unittest
import tensorflow.compat.v1 as tf
from object_detection.models import ssd_resnet_v1_fpn_feature_extractor_testbase
from object_detection.models import ssd_resnet_v1_fpn_keras_feature_extractor
from object_detection.utils import tf_version
@unittest.skipIf(tf_version.is_tf1(), 'Skipping TF2.X only test.')
class SSDResnet50V1FeatureExtractorTest(
ssd_resnet_v1_fpn_feature_extractor_testbase.
SSDResnetFPNFeatureExtractorTestBase):
"""SSDResnet50v1Fpn feature extractor test."""
def _create_feature_extractor(self, depth_multiplier, pad_to_multiple,
use_explicit_padding=False, min_depth=32,
use_keras=True):
is_training = True
return (ssd_resnet_v1_fpn_keras_feature_extractor.
SSDResNet50V1FpnKerasFeatureExtractor(
is_training=is_training,
depth_multiplier=depth_multiplier,
min_depth=min_depth,
pad_to_multiple=pad_to_multiple,
conv_hyperparams=self._build_conv_hyperparams(
add_batch_norm=False),
freeze_batchnorm=False,
inplace_batchnorm_update=False,
name='ResNet50V1_FPN'))
def _resnet_scope_name(self):
return 'ResNet50V1_FPN'
@unittest.skipIf(tf_version.is_tf1(), 'Skipping TF2.X only test.')
class SSDResnet101V1FeatureExtractorTest(
ssd_resnet_v1_fpn_feature_extractor_testbase.
SSDResnetFPNFeatureExtractorTestBase):
"""SSDResnet101v1Fpn feature extractor test."""
def _create_feature_extractor(self, depth_multiplier, pad_to_multiple,
use_explicit_padding=False, min_depth=32,
use_keras=False):
is_training = True
return (ssd_resnet_v1_fpn_keras_feature_extractor.
SSDResNet101V1FpnKerasFeatureExtractor(
is_training=is_training,
depth_multiplier=depth_multiplier,
min_depth=min_depth,
pad_to_multiple=pad_to_multiple,
conv_hyperparams=self._build_conv_hyperparams(
add_batch_norm=False),
freeze_batchnorm=False,
inplace_batchnorm_update=False,
name='ResNet101V1_FPN'))
def _resnet_scope_name(self):
return 'ResNet101V1_FPN'
@unittest.skipIf(tf_version.is_tf1(), 'Skipping TF2.X only test.')
class SSDResnet152V1FeatureExtractorTest(
ssd_resnet_v1_fpn_feature_extractor_testbase.
SSDResnetFPNFeatureExtractorTestBase):
"""SSDResnet152v1Fpn feature extractor test."""
def _create_feature_extractor(self, depth_multiplier, pad_to_multiple,
use_explicit_padding=False, min_depth=32,
use_keras=False):
is_training = True
return (ssd_resnet_v1_fpn_keras_feature_extractor.
SSDResNet152V1FpnKerasFeatureExtractor(
is_training=is_training,
depth_multiplier=depth_multiplier,
min_depth=min_depth,
pad_to_multiple=pad_to_multiple,
conv_hyperparams=self._build_conv_hyperparams(
add_batch_norm=False),
freeze_batchnorm=False,
inplace_batchnorm_update=False,
name='ResNet152V1_FPN'))
def _resnet_scope_name(self):
return 'ResNet152V1_FPN'
if __name__ == '__main__':
tf.test.main()
research/object_detection/models/ssd_resnet_v1_ppn_feature_extractor_test.py → research/object_detection/models/ssd_resnet_v1_ppn_feature_extractor_tf1_test.py
View file @ 420a7253
......@@ -13,12 +13,15 @@
# limitations under the License.
# ==============================================================================
"""Tests for ssd resnet v1 feature extractors."""
import unittest
import tensorflow.compat.v1 as tf
from object_detection.models import ssd_resnet_v1_ppn_feature_extractor
from object_detection.models import ssd_resnet_v1_ppn_feature_extractor_testbase
from object_detection.utils import tf_version
@unittest.skipIf(tf_version.is_tf2(), 'Skipping TF1.X only test.')
class SSDResnet50V1PpnFeatureExtractorTest(
ssd_resnet_v1_ppn_feature_extractor_testbase.
SSDResnetPpnFeatureExtractorTestBase):
......@@ -40,6 +43,7 @@ class SSDResnet50V1PpnFeatureExtractorTest(
return 'resnet_v1_50'
@unittest.skipIf(tf_version.is_tf2(), 'Skipping TF1.X only test.')
class SSDResnet101V1PpnFeatureExtractorTest(
ssd_resnet_v1_ppn_feature_extractor_testbase.
SSDResnetPpnFeatureExtractorTestBase):
......@@ -62,6 +66,7 @@ class SSDResnet101V1PpnFeatureExtractorTest(
return 'resnet_v1_101'
@unittest.skipIf(tf_version.is_tf2(), 'Skipping TF1.X only test.')
class SSDResnet152V1PpnFeatureExtractorTest(
ssd_resnet_v1_ppn_feature_extractor_testbase.
SSDResnetPpnFeatureExtractorTestBase):
......
research/object_detection/predictors/convolutional_box_predictor_test.py → research/object_detection/predictors/convolutional_box_predictor_tf1_test.py
View file @ 420a7253
......@@ -19,7 +19,7 @@
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import unittest
from absl.testing import parameterized
import numpy as np
from six.moves import range
......@@ -35,8 +35,10 @@ from object_detection.predictors.heads import class_head
from object_detection.predictors.heads import mask_head
from object_detection.protos import hyperparams_pb2
from object_detection.utils import test_case
from object_detection.utils import tf_version
@unittest.skipIf(tf_version.is_tf2(), 'Skipping TF1.X only test.')
class ConvolutionalBoxPredictorTest(test_case.TestCase):
def _build_arg_scope_with_conv_hyperparams(self):
......@@ -281,6 +283,7 @@ class ConvolutionalBoxPredictorTest(test_case.TestCase):
self.assertEqual(bad_dangling_ops, [])
@unittest.skipIf(tf_version.is_tf2(), 'Skipping TF1.X only test.')
class WeightSharedConvolutionalBoxPredictorTest(test_case.TestCase):
def _build_arg_scope_with_conv_hyperparams(self):
......
research/object_detection/predictors/convolutional_keras_box_predictor_test.py → research/object_detection/predictors/convolutional_keras_box_predictor_tf2_test.py
View file @ 420a7253
......@@ -14,6 +14,7 @@
# ==============================================================================
"""Tests for object_detection.predictors.convolutional_keras_box_predictor."""
import unittest
import numpy as np
import tensorflow.compat.v1 as tf
......@@ -26,8 +27,10 @@ from object_detection.predictors.heads import keras_class_head
from object_detection.predictors.heads import keras_mask_head
from object_detection.protos import hyperparams_pb2
from object_detection.utils import test_case
from object_detection.utils import tf_version
@unittest.skipIf(tf_version.is_tf1(), 'Skipping TF2.X only test.')
class ConvolutionalKerasBoxPredictorTest(test_case.TestCase):
def _build_conv_hyperparams(self):
......@@ -47,23 +50,23 @@ class ConvolutionalKerasBoxPredictorTest(test_case.TestCase):
return hyperparams_builder.KerasLayerHyperparams(conv_hyperparams)
def test_get_boxes_for_five_aspect_ratios_per_location(self):
conv_box_predictor = (
box_predictor_builder.build_convolutional_keras_box_predictor(
is_training=False,
num_classes=0,
conv_hyperparams=self._build_conv_hyperparams(),
freeze_batchnorm=False,
inplace_batchnorm_update=False,
num_predictions_per_location_list=[5],
min_depth=0,
max_depth=32,
num_layers_before_predictor=1,
use_dropout=True,
dropout_keep_prob=0.8,
kernel_size=1,
box_code_size=4
))
def graph_fn(image_features):
conv_box_predictor = (
box_predictor_builder.build_convolutional_keras_box_predictor(
is_training=False,
num_classes=0,
conv_hyperparams=self._build_conv_hyperparams(),
freeze_batchnorm=False,
inplace_batchnorm_update=False,
num_predictions_per_location_list=[5],
min_depth=0,
max_depth=32,
num_layers_before_predictor=1,
use_dropout=True,
dropout_keep_prob=0.8,
kernel_size=1,
box_code_size=4
))
box_predictions = conv_box_predictor([image_features])
box_encodings = tf.concat(
box_predictions[box_predictor.BOX_ENCODINGS], axis=1)
......@@ -78,23 +81,23 @@ class ConvolutionalKerasBoxPredictorTest(test_case.TestCase):
self.assertAllEqual(objectness_predictions.shape, [4, 320, 1])
def test_get_boxes_for_one_aspect_ratio_per_location(self):
conv_box_predictor = (
box_predictor_builder.build_convolutional_keras_box_predictor(
is_training=False,
num_classes=0,
conv_hyperparams=self._build_conv_hyperparams(),
freeze_batchnorm=False,
inplace_batchnorm_update=False,
num_predictions_per_location_list=[1],
min_depth=0,
max_depth=32,
num_layers_before_predictor=1,
use_dropout=True,
dropout_keep_prob=0.8,
kernel_size=1,
box_code_size=4
))
def graph_fn(image_features):
conv_box_predictor = (
box_predictor_builder.build_convolutional_keras_box_predictor(
is_training=False,
num_classes=0,
conv_hyperparams=self._build_conv_hyperparams(),
freeze_batchnorm=False,
inplace_batchnorm_update=False,
num_predictions_per_location_list=[1],
min_depth=0,
max_depth=32,
num_layers_before_predictor=1,
use_dropout=True,
dropout_keep_prob=0.8,
kernel_size=1,
box_code_size=4
))
box_predictions = conv_box_predictor([image_features])
box_encodings = tf.concat(
box_predictions[box_predictor.BOX_ENCODINGS], axis=1)
......@@ -111,23 +114,23 @@ class ConvolutionalKerasBoxPredictorTest(test_case.TestCase):
self):
num_classes_without_background = 6
image_features = np.random.rand(4, 8, 8, 64).astype(np.float32)
conv_box_predictor = (
box_predictor_builder.build_convolutional_keras_box_predictor(
is_training=False,
num_classes=num_classes_without_background,
conv_hyperparams=self._build_conv_hyperparams(),
freeze_batchnorm=False,
inplace_batchnorm_update=False,
num_predictions_per_location_list=[5],
min_depth=0,
max_depth=32,
num_layers_before_predictor=1,
use_dropout=True,
dropout_keep_prob=0.8,
kernel_size=1,
box_code_size=4
))
def graph_fn(image_features):
conv_box_predictor = (
box_predictor_builder.build_convolutional_keras_box_predictor(
is_training=False,
num_classes=num_classes_without_background,
conv_hyperparams=self._build_conv_hyperparams(),
freeze_batchnorm=False,
inplace_batchnorm_update=False,
num_predictions_per_location_list=[5],
min_depth=0,
max_depth=32,
num_layers_before_predictor=1,
use_dropout=True,
dropout_keep_prob=0.8,
kernel_size=1,
box_code_size=4
))
box_predictions = conv_box_predictor([image_features])
box_encodings = tf.concat(
box_predictions[box_predictor.BOX_ENCODINGS], axis=1)
......@@ -144,7 +147,7 @@ class ConvolutionalKerasBoxPredictorTest(test_case.TestCase):
def test_get_predictions_with_feature_maps_of_dynamic_shape(
self):
image_features = tf.placeholder(dtype=tf.float32, shape=[4, None, None, 64])
tf.keras.backend.clear_session()
conv_box_predictor = (
box_predictor_builder.build_convolutional_keras_box_predictor(
is_training=False,
......@@ -161,28 +164,25 @@ class ConvolutionalKerasBoxPredictorTest(test_case.TestCase):
kernel_size=1,
box_code_size=4
))
box_predictions = conv_box_predictor([image_features])
box_encodings = tf.concat(
box_predictions[box_predictor.BOX_ENCODINGS], axis=1)
objectness_predictions = tf.concat(
box_predictions[box_predictor.CLASS_PREDICTIONS_WITH_BACKGROUND],
axis=1)
init_op = tf.global_variables_initializer()
variables = []
def graph_fn(image_features):
box_predictions = conv_box_predictor([image_features])
variables.extend(list(conv_box_predictor.variables))
box_encodings = tf.concat(
box_predictions[box_predictor.BOX_ENCODINGS], axis=1)
objectness_predictions = tf.concat(
box_predictions[box_predictor.CLASS_PREDICTIONS_WITH_BACKGROUND],
axis=1)
return box_encodings, objectness_predictions
resolution = 32
expected_num_anchors = resolution*resolution*5
with self.test_session() as sess:
sess.run(init_op)
(box_encodings_shape,
objectness_predictions_shape) = sess.run(
[tf.shape(box_encodings), tf.shape(objectness_predictions)],
feed_dict={image_features:
np.random.rand(4, resolution, resolution, 64)})
actual_variable_set = set(
[var.op.name for var in tf.trainable_variables()])
self.assertAllEqual(box_encodings_shape, [4, expected_num_anchors, 1, 4])
self.assertAllEqual(objectness_predictions_shape,
[4, expected_num_anchors, 1])
box_encodings, objectness_predictions = self.execute(
graph_fn, [np.random.rand(4, resolution, resolution, 64)])
actual_variable_set = set([var.name.split(':')[0] for var in variables])
self.assertAllEqual(box_encodings.shape, [4, expected_num_anchors, 1, 4])
self.assertAllEqual(objectness_predictions.shape,
[4, expected_num_anchors, 1])
expected_variable_set = set([
'BoxPredictor/SharedConvolutions_0/Conv2d_0_1x1_32/bias',
'BoxPredictor/SharedConvolutions_0/Conv2d_0_1x1_32/kernel',
......@@ -195,7 +195,7 @@ class ConvolutionalKerasBoxPredictorTest(test_case.TestCase):
['box_encodings', 'class_predictions_with_background'])
def test_use_depthwise_convolution(self):
image_features = tf.placeholder(dtype=tf.float32, shape=[4, None, None, 64])
tf.keras.backend.clear_session()
conv_box_predictor = (
box_predictor_builder.build_convolutional_keras_box_predictor(
is_training=False,
......@@ -213,27 +213,25 @@ class ConvolutionalKerasBoxPredictorTest(test_case.TestCase):
box_code_size=4,
use_depthwise=True
))
box_predictions = conv_box_predictor([image_features])
box_encodings = tf.concat(
box_predictions[box_predictor.BOX_ENCODINGS], axis=1)
objectness_predictions = tf.concat(
box_predictions[box_predictor.CLASS_PREDICTIONS_WITH_BACKGROUND],
axis=1)
init_op = tf.global_variables_initializer()
variables = []
def graph_fn(image_features):
box_predictions = conv_box_predictor([image_features])
variables.extend(list(conv_box_predictor.variables))
box_encodings = tf.concat(
box_predictions[box_predictor.BOX_ENCODINGS], axis=1)
objectness_predictions = tf.concat(
box_predictions[box_predictor.CLASS_PREDICTIONS_WITH_BACKGROUND],
axis=1)
return box_encodings, objectness_predictions
resolution = 32
expected_num_anchors = resolution*resolution*5
with self.test_session() as sess:
sess.run(init_op)
(box_encodings_shape,
objectness_predictions_shape) = sess.run(
[tf.shape(box_encodings), tf.shape(objectness_predictions)],
feed_dict={image_features:
np.random.rand(4, resolution, resolution, 64)})
actual_variable_set = set(
[var.op.name for var in tf.trainable_variables()])
self.assertAllEqual(box_encodings_shape, [4, expected_num_anchors, 1, 4])
self.assertAllEqual(objectness_predictions_shape,
box_encodings, objectness_predictions = self.execute(
graph_fn, [np.random.rand(4, resolution, resolution, 64)])
actual_variable_set = set([var.name.split(':')[0] for var in variables])
self.assertAllEqual(box_encodings.shape, [4, expected_num_anchors, 1, 4])
self.assertAllEqual(objectness_predictions.shape,
[4, expected_num_anchors, 1])
expected_variable_set = set([
'BoxPredictor/SharedConvolutions_0/Conv2d_0_1x1_32/bias',
......@@ -259,6 +257,7 @@ class ConvolutionalKerasBoxPredictorTest(test_case.TestCase):
['box_encodings', 'class_predictions_with_background'])
@unittest.skipIf(tf_version.is_tf1(), 'Skipping TF2.X only test.')
class WeightSharedConvolutionalKerasBoxPredictorTest(test_case.TestCase):
def _build_conv_hyperparams(self, add_batch_norm=True):
......@@ -288,19 +287,20 @@ class WeightSharedConvolutionalKerasBoxPredictorTest(test_case.TestCase):
# pylint: disable=line-too-long
def test_get_boxes_for_five_aspect_ratios_per_location(self):
conv_box_predictor = (
box_predictor_builder
.build_weight_shared_convolutional_keras_box_predictor(
is_training=False,
num_classes=0,
conv_hyperparams=self._build_conv_hyperparams(),
freeze_batchnorm=False,
inplace_batchnorm_update=False,
num_predictions_per_location_list=[5],
depth=32,
num_layers_before_predictor=1,
box_code_size=4))
def graph_fn(image_features):
conv_box_predictor = (
box_predictor_builder.build_weight_shared_convolutional_keras_box_predictor(
is_training=False,
num_classes=0,
conv_hyperparams=self._build_conv_hyperparams(),
freeze_batchnorm=False,
inplace_batchnorm_update=False,
num_predictions_per_location_list=[5],
depth=32,
num_layers_before_predictor=1,
box_code_size=4))
box_predictions = conv_box_predictor([image_features])
box_encodings = tf.concat(
box_predictions[box_predictor.BOX_ENCODINGS], axis=1)
......@@ -314,20 +314,21 @@ class WeightSharedConvolutionalKerasBoxPredictorTest(test_case.TestCase):
self.assertAllEqual(objectness_predictions.shape, [4, 320, 1])
def test_bias_predictions_to_background_with_sigmoid_score_conversion(self):
conv_box_predictor = (
box_predictor_builder
.build_weight_shared_convolutional_keras_box_predictor(
is_training=True,
num_classes=2,
conv_hyperparams=self._build_conv_hyperparams(),
freeze_batchnorm=False,
inplace_batchnorm_update=False,
num_predictions_per_location_list=[5],
depth=32,
num_layers_before_predictor=1,
class_prediction_bias_init=-4.6,
box_code_size=4))
def graph_fn(image_features):
conv_box_predictor = (
box_predictor_builder.build_weight_shared_convolutional_keras_box_predictor(
is_training=True,
num_classes=2,
conv_hyperparams=self._build_conv_hyperparams(),
freeze_batchnorm=False,
inplace_batchnorm_update=False,
num_predictions_per_location_list=[5],
depth=32,
num_layers_before_predictor=1,
class_prediction_bias_init=-4.6,
box_code_size=4))
box_predictions = conv_box_predictor([image_features])
class_predictions = tf.concat(box_predictions[
box_predictor.CLASS_PREDICTIONS_WITH_BACKGROUND], axis=1)
......@@ -339,20 +340,21 @@ class WeightSharedConvolutionalKerasBoxPredictorTest(test_case.TestCase):
def test_get_multi_class_predictions_for_five_aspect_ratios_per_location(
self):
num_classes_without_background = 6
conv_box_predictor = (
box_predictor_builder
.build_weight_shared_convolutional_keras_box_predictor(
is_training=False,
num_classes=num_classes_without_background,
conv_hyperparams=self._build_conv_hyperparams(),
freeze_batchnorm=False,
inplace_batchnorm_update=False,
num_predictions_per_location_list=[5],
depth=32,
num_layers_before_predictor=1,
box_code_size=4))
def graph_fn(image_features):
conv_box_predictor = (
box_predictor_builder.build_weight_shared_convolutional_keras_box_predictor(
is_training=False,
num_classes=num_classes_without_background,
conv_hyperparams=self._build_conv_hyperparams(),
freeze_batchnorm=False,
inplace_batchnorm_update=False,
num_predictions_per_location_list=[5],
depth=32,
num_layers_before_predictor=1,
box_code_size=4))
box_predictions = conv_box_predictor([image_features])
box_encodings = tf.concat(
box_predictions[box_predictor.BOX_ENCODINGS], axis=1)
......@@ -369,20 +371,21 @@ class WeightSharedConvolutionalKerasBoxPredictorTest(test_case.TestCase):
def test_get_multi_class_predictions_from_two_feature_maps(
self):
num_classes_without_background = 6
conv_box_predictor = (
box_predictor_builder
.build_weight_shared_convolutional_keras_box_predictor(
is_training=False,
num_classes=num_classes_without_background,
conv_hyperparams=self._build_conv_hyperparams(),
freeze_batchnorm=False,
inplace_batchnorm_update=False,
num_predictions_per_location_list=[5, 5],
depth=32,
num_layers_before_predictor=1,
box_code_size=4))
def graph_fn(image_features1, image_features2):
conv_box_predictor = (
box_predictor_builder.build_weight_shared_convolutional_keras_box_predictor(
is_training=False,
num_classes=num_classes_without_background,
conv_hyperparams=self._build_conv_hyperparams(),
freeze_batchnorm=False,
inplace_batchnorm_update=False,
num_predictions_per_location_list=[5, 5],
depth=32,
num_layers_before_predictor=1,
box_code_size=4))
box_predictions = conv_box_predictor([image_features1, image_features2])
box_encodings = tf.concat(
box_predictions[box_predictor.BOX_ENCODINGS], axis=1)
......@@ -401,20 +404,21 @@ class WeightSharedConvolutionalKerasBoxPredictorTest(test_case.TestCase):
def test_get_multi_class_predictions_from_feature_maps_of_different_depth(
self):
num_classes_without_background = 6
conv_box_predictor = (
box_predictor_builder
.build_weight_shared_convolutional_keras_box_predictor(
is_training=False,
num_classes=num_classes_without_background,
conv_hyperparams=self._build_conv_hyperparams(),
freeze_batchnorm=False,
inplace_batchnorm_update=False,
num_predictions_per_location_list=[5, 5, 5],
depth=32,
num_layers_before_predictor=1,
box_code_size=4))
def graph_fn(image_features1, image_features2, image_features3):
conv_box_predictor = (
box_predictor_builder.build_weight_shared_convolutional_keras_box_predictor(
is_training=False,
num_classes=num_classes_without_background,
conv_hyperparams=self._build_conv_hyperparams(),
freeze_batchnorm=False,
inplace_batchnorm_update=False,
num_predictions_per_location_list=[5, 5, 5],
depth=32,
num_layers_before_predictor=1,
box_code_size=4))
box_predictions = conv_box_predictor(
[image_features1, image_features2, image_features3])
box_encodings = tf.concat(
......@@ -435,20 +439,25 @@ class WeightSharedConvolutionalKerasBoxPredictorTest(test_case.TestCase):
def test_predictions_multiple_feature_maps_share_weights_separate_batchnorm(
self):
tf.keras.backend.clear_session()
num_classes_without_background = 6
conv_box_predictor = (
box_predictor_builder
.build_weight_shared_convolutional_keras_box_predictor(
is_training=False,
num_classes=num_classes_without_background,
conv_hyperparams=self._build_conv_hyperparams(),
freeze_batchnorm=False,
inplace_batchnorm_update=False,
num_predictions_per_location_list=[5, 5],
depth=32,
num_layers_before_predictor=2,
box_code_size=4))
variables = []
def graph_fn(image_features1, image_features2):
conv_box_predictor = (
box_predictor_builder.build_weight_shared_convolutional_keras_box_predictor(
is_training=False,
num_classes=num_classes_without_background,
conv_hyperparams=self._build_conv_hyperparams(),
freeze_batchnorm=False,
inplace_batchnorm_update=False,
num_predictions_per_location_list=[5, 5],
depth=32,
num_layers_before_predictor=2,
box_code_size=4))
box_predictions = conv_box_predictor([image_features1, image_features2])
variables.extend(list(conv_box_predictor.variables))
box_encodings = tf.concat(
box_predictions[box_predictor.BOX_ENCODINGS], axis=1)
class_predictions_with_background = tf.concat(
......@@ -456,25 +465,41 @@ class WeightSharedConvolutionalKerasBoxPredictorTest(test_case.TestCase):
axis=1)
return (box_encodings, class_predictions_with_background)
with self.test_session(graph=tf.Graph()):
graph_fn(tf.random_uniform([4, 32, 32, 3], dtype=tf.float32),
tf.random_uniform([4, 16, 16, 3], dtype=tf.float32))
actual_variable_set = set(
[var.op.name for var in tf.trainable_variables()])
self.execute(graph_fn, [
np.random.rand(4, 32, 32, 3).astype(np.float32),
np.random.rand(4, 16, 16, 3).astype(np.float32)
])
actual_variable_set = set([var.name.split(':')[0] for var in variables])
expected_variable_set = set([
# Box prediction tower
('WeightSharedConvolutionalBoxPredictor/'
'BoxPredictionTower/conv2d_0/kernel'),
('WeightSharedConvolutionalBoxPredictor/'
'BoxPredictionTower/conv2d_0/BatchNorm/feature_0/beta'),
('WeightSharedConvolutionalBoxPredictor/'
'BoxPredictionTower/conv2d_0/BatchNorm/feature_0/moving_mean'),
('WeightSharedConvolutionalBoxPredictor/'
'BoxPredictionTower/conv2d_0/BatchNorm/feature_0/moving_variance'),
('WeightSharedConvolutionalBoxPredictor/'
'BoxPredictionTower/conv2d_0/BatchNorm/feature_1/beta'),
('WeightSharedConvolutionalBoxPredictor/'
'BoxPredictionTower/conv2d_0/BatchNorm/feature_1/moving_mean'),
('WeightSharedConvolutionalBoxPredictor/'
'BoxPredictionTower/conv2d_0/BatchNorm/feature_1/moving_variance'),
('WeightSharedConvolutionalBoxPredictor/'
'BoxPredictionTower/conv2d_1/kernel'),
('WeightSharedConvolutionalBoxPredictor/'
'BoxPredictionTower/conv2d_1/BatchNorm/feature_0/beta'),
('WeightSharedConvolutionalBoxPredictor/'
'BoxPredictionTower/conv2d_1/BatchNorm/feature_0/moving_mean'),
('WeightSharedConvolutionalBoxPredictor/'
'BoxPredictionTower/conv2d_1/BatchNorm/feature_0/moving_variance'),
('WeightSharedConvolutionalBoxPredictor/'
'BoxPredictionTower/conv2d_1/BatchNorm/feature_1/beta'),
('WeightSharedConvolutionalBoxPredictor/'
'BoxPredictionTower/conv2d_1/BatchNorm/feature_1/moving_mean'),
('WeightSharedConvolutionalBoxPredictor/'
'BoxPredictionTower/conv2d_1/BatchNorm/feature_1/moving_variance'),
# Box prediction head
('WeightSharedConvolutionalBoxPredictor/'
'WeightSharedConvolutionalBoxHead/BoxPredictor/kernel'),
......@@ -485,14 +510,30 @@ class WeightSharedConvolutionalKerasBoxPredictorTest(test_case.TestCase):
'ClassPredictionTower/conv2d_0/kernel'),
('WeightSharedConvolutionalBoxPredictor/'
'ClassPredictionTower/conv2d_0/BatchNorm/feature_0/beta'),
('WeightSharedConvolutionalBoxPredictor/'
'ClassPredictionTower/conv2d_0/BatchNorm/feature_0/moving_mean'),
('WeightSharedConvolutionalBoxPredictor/'
'ClassPredictionTower/conv2d_0/BatchNorm/feature_0/moving_variance'),
('WeightSharedConvolutionalBoxPredictor/'
'ClassPredictionTower/conv2d_0/BatchNorm/feature_1/beta'),
('WeightSharedConvolutionalBoxPredictor/'
'ClassPredictionTower/conv2d_0/BatchNorm/feature_1/moving_mean'),
('WeightSharedConvolutionalBoxPredictor/'
'ClassPredictionTower/conv2d_0/BatchNorm/feature_1/moving_variance'),
('WeightSharedConvolutionalBoxPredictor/'
'ClassPredictionTower/conv2d_1/kernel'),
('WeightSharedConvolutionalBoxPredictor/'
'ClassPredictionTower/conv2d_1/BatchNorm/feature_0/beta'),
('WeightSharedConvolutionalBoxPredictor/'
'ClassPredictionTower/conv2d_1/BatchNorm/feature_0/moving_mean'),
('WeightSharedConvolutionalBoxPredictor/'
'ClassPredictionTower/conv2d_1/BatchNorm/feature_0/moving_variance'),
('WeightSharedConvolutionalBoxPredictor/'
'ClassPredictionTower/conv2d_1/BatchNorm/feature_1/beta'),
('WeightSharedConvolutionalBoxPredictor/'
'ClassPredictionTower/conv2d_1/BatchNorm/feature_1/moving_mean'),
('WeightSharedConvolutionalBoxPredictor/'
'ClassPredictionTower/conv2d_1/BatchNorm/feature_1/moving_variance'),
# Class prediction head
('WeightSharedConvolutionalBoxPredictor/'
'WeightSharedConvolutionalClassHead/ClassPredictor/kernel'),
......@@ -502,21 +543,26 @@ class WeightSharedConvolutionalKerasBoxPredictorTest(test_case.TestCase):
def test_predictions_multiple_feature_maps_share_weights_without_batchnorm(
self):
tf.keras.backend.clear_session()
num_classes_without_background = 6
conv_box_predictor = (
box_predictor_builder
.build_weight_shared_convolutional_keras_box_predictor(
is_training=False,
num_classes=num_classes_without_background,
conv_hyperparams=self._build_conv_hyperparams(),
freeze_batchnorm=False,
inplace_batchnorm_update=False,
num_predictions_per_location_list=[5, 5],
depth=32,
num_layers_before_predictor=2,
box_code_size=4,
apply_batch_norm=False))
variables = []
def graph_fn(image_features1, image_features2):
conv_box_predictor = (
box_predictor_builder.build_weight_shared_convolutional_keras_box_predictor(
is_training=False,
num_classes=num_classes_without_background,
conv_hyperparams=self._build_conv_hyperparams(),
freeze_batchnorm=False,
inplace_batchnorm_update=False,
num_predictions_per_location_list=[5, 5],
depth=32,
num_layers_before_predictor=2,
box_code_size=4,
apply_batch_norm=False))
box_predictions = conv_box_predictor([image_features1, image_features2])
variables.extend(list(conv_box_predictor.variables))
box_encodings = tf.concat(
box_predictions[box_predictor.BOX_ENCODINGS], axis=1)
class_predictions_with_background = tf.concat(
......@@ -524,11 +570,11 @@ class WeightSharedConvolutionalKerasBoxPredictorTest(test_case.TestCase):
axis=1)
return (box_encodings, class_predictions_with_background)
with self.test_session(graph=tf.Graph()):
graph_fn(tf.random_uniform([4, 32, 32, 3], dtype=tf.float32),
tf.random_uniform([4, 16, 16, 3], dtype=tf.float32))
actual_variable_set = set(
[var.op.name for var in tf.trainable_variables()])
self.execute(graph_fn, [
np.random.rand(4, 32, 32, 3).astype(np.float32),
np.random.rand(4, 16, 16, 3).astype(np.float32)
])
actual_variable_set = set([var.name.split(':')[0] for var in variables])
expected_variable_set = set([
# Box prediction tower
('WeightSharedConvolutionalBoxPredictor/'
......@@ -562,23 +608,27 @@ class WeightSharedConvolutionalKerasBoxPredictorTest(test_case.TestCase):
def test_predictions_multiple_feature_maps_share_weights_with_depthwise(
self):
tf.keras.backend.clear_session()
num_classes_without_background = 6
conv_box_predictor = (
box_predictor_builder
.build_weight_shared_convolutional_keras_box_predictor(
is_training=False,
num_classes=num_classes_without_background,
conv_hyperparams=self._build_conv_hyperparams(add_batch_norm=False),
freeze_batchnorm=False,
inplace_batchnorm_update=False,
num_predictions_per_location_list=[5, 5],
depth=32,
num_layers_before_predictor=2,
box_code_size=4,
apply_batch_norm=False,
use_depthwise=True))
variables = []
def graph_fn(image_features1, image_features2):
conv_box_predictor = (
box_predictor_builder.build_weight_shared_convolutional_keras_box_predictor(
is_training=False,
num_classes=num_classes_without_background,
conv_hyperparams=self._build_conv_hyperparams(
add_batch_norm=False),
freeze_batchnorm=False,
inplace_batchnorm_update=False,
num_predictions_per_location_list=[5, 5],
depth=32,
num_layers_before_predictor=2,
box_code_size=4,
apply_batch_norm=False,
use_depthwise=True))
box_predictions = conv_box_predictor([image_features1, image_features2])
variables.extend(list(conv_box_predictor.variables))
box_encodings = tf.concat(
box_predictions[box_predictor.BOX_ENCODINGS], axis=1)
class_predictions_with_background = tf.concat(
......@@ -586,11 +636,11 @@ class WeightSharedConvolutionalKerasBoxPredictorTest(test_case.TestCase):
axis=1)
return (box_encodings, class_predictions_with_background)
with self.test_session(graph=tf.Graph()):
graph_fn(tf.random_uniform([4, 32, 32, 3], dtype=tf.float32),
tf.random_uniform([4, 16, 16, 3], dtype=tf.float32))
actual_variable_set = set(
[var.op.name for var in tf.trainable_variables()])
self.execute(graph_fn, [
np.random.rand(4, 32, 32, 3).astype(np.float32),
np.random.rand(4, 16, 16, 3).astype(np.float32)
])
actual_variable_set = set([var.name.split(':')[0] for var in variables])
expected_variable_set = set([
# Box prediction tower
('WeightSharedConvolutionalBoxPredictor/'
......@@ -635,23 +685,27 @@ class WeightSharedConvolutionalKerasBoxPredictorTest(test_case.TestCase):
self.assertEqual(expected_variable_set, actual_variable_set)
def test_no_batchnorm_params_when_batchnorm_is_not_configured(self):
tf.keras.backend.clear_session()
num_classes_without_background = 6
conv_box_predictor = (
box_predictor_builder
.build_weight_shared_convolutional_keras_box_predictor(
is_training=False,
num_classes=num_classes_without_background,
conv_hyperparams=self._build_conv_hyperparams(add_batch_norm=False),
freeze_batchnorm=False,
inplace_batchnorm_update=False,
num_predictions_per_location_list=[5, 5],
depth=32,
num_layers_before_predictor=2,
box_code_size=4,
apply_batch_norm=False))
variables = []
def graph_fn(image_features1, image_features2):
conv_box_predictor = (
box_predictor_builder.build_weight_shared_convolutional_keras_box_predictor(
is_training=False,
num_classes=num_classes_without_background,
conv_hyperparams=self._build_conv_hyperparams(
add_batch_norm=False),
freeze_batchnorm=False,
inplace_batchnorm_update=False,
num_predictions_per_location_list=[5, 5],
depth=32,
num_layers_before_predictor=2,
box_code_size=4,
apply_batch_norm=False))
box_predictions = conv_box_predictor(
[image_features1, image_features2])
variables.extend(list(conv_box_predictor.variables))
box_encodings = tf.concat(
box_predictions[box_predictor.BOX_ENCODINGS], axis=1)
class_predictions_with_background = tf.concat(
......@@ -659,11 +713,11 @@ class WeightSharedConvolutionalKerasBoxPredictorTest(test_case.TestCase):
axis=1)
return (box_encodings, class_predictions_with_background)
with self.test_session(graph=tf.Graph()):
graph_fn(tf.random_uniform([4, 32, 32, 3], dtype=tf.float32),
tf.random_uniform([4, 16, 16, 3], dtype=tf.float32))
actual_variable_set = set(
[var.op.name for var in tf.trainable_variables()])
self.execute(graph_fn, [
np.random.rand(4, 32, 32, 3).astype(np.float32),
np.random.rand(4, 16, 16, 3).astype(np.float32)
])
actual_variable_set = set([var.name.split(':')[0] for var in variables])
expected_variable_set = set([
# Box prediction tower
('WeightSharedConvolutionalBoxPredictor/'
......@@ -697,22 +751,27 @@ class WeightSharedConvolutionalKerasBoxPredictorTest(test_case.TestCase):
def test_predictions_share_weights_share_tower_separate_batchnorm(
self):
tf.keras.backend.clear_session()
num_classes_without_background = 6
conv_box_predictor = (
box_predictor_builder
.build_weight_shared_convolutional_keras_box_predictor(
is_training=False,
num_classes=num_classes_without_background,
conv_hyperparams=self._build_conv_hyperparams(),
freeze_batchnorm=False,
inplace_batchnorm_update=False,
num_predictions_per_location_list=[5, 5],
depth=32,
num_layers_before_predictor=2,
box_code_size=4,
share_prediction_tower=True))
variables = []
def graph_fn(image_features1, image_features2):
conv_box_predictor = (
box_predictor_builder.build_weight_shared_convolutional_keras_box_predictor(
is_training=False,
num_classes=num_classes_without_background,
conv_hyperparams=self._build_conv_hyperparams(),
freeze_batchnorm=False,
inplace_batchnorm_update=False,
num_predictions_per_location_list=[5, 5],
depth=32,
num_layers_before_predictor=2,
box_code_size=4,
share_prediction_tower=True))
box_predictions = conv_box_predictor(
[image_features1, image_features2])
variables.extend(list(conv_box_predictor.variables))
box_encodings = tf.concat(
box_predictions[box_predictor.BOX_ENCODINGS], axis=1)
class_predictions_with_background = tf.concat(
......@@ -720,11 +779,11 @@ class WeightSharedConvolutionalKerasBoxPredictorTest(test_case.TestCase):
axis=1)
return (box_encodings, class_predictions_with_background)
with self.test_session(graph=tf.Graph()):
graph_fn(tf.random_uniform([4, 32, 32, 3], dtype=tf.float32),
tf.random_uniform([4, 16, 16, 3], dtype=tf.float32))
actual_variable_set = set(
[var.op.name for var in tf.trainable_variables()])
self.execute(graph_fn, [
np.random.rand(4, 32, 32, 3).astype(np.float32),
np.random.rand(4, 16, 16, 3).astype(np.float32)
])
actual_variable_set = set([var.name.split(':')[0] for var in variables])
expected_variable_set = set([
# Shared prediction tower
('WeightSharedConvolutionalBoxPredictor/'
......@@ -733,12 +792,28 @@ class WeightSharedConvolutionalKerasBoxPredictorTest(test_case.TestCase):
'PredictionTower/conv2d_0/BatchNorm/feature_0/beta'),
('WeightSharedConvolutionalBoxPredictor/'
'PredictionTower/conv2d_0/BatchNorm/feature_1/beta'),
('WeightSharedConvolutionalBoxPredictor/'
'PredictionTower/conv2d_0/BatchNorm/feature_0/moving_mean'),
('WeightSharedConvolutionalBoxPredictor/'
'PredictionTower/conv2d_0/BatchNorm/feature_1/moving_mean'),
('WeightSharedConvolutionalBoxPredictor/'
'PredictionTower/conv2d_0/BatchNorm/feature_0/moving_variance'),
('WeightSharedConvolutionalBoxPredictor/'
'PredictionTower/conv2d_0/BatchNorm/feature_1/moving_variance'),
('WeightSharedConvolutionalBoxPredictor/'
'PredictionTower/conv2d_1/kernel'),
('WeightSharedConvolutionalBoxPredictor/'
'PredictionTower/conv2d_1/BatchNorm/feature_0/beta'),
('WeightSharedConvolutionalBoxPredictor/'
'PredictionTower/conv2d_1/BatchNorm/feature_1/beta'),
('WeightSharedConvolutionalBoxPredictor/'
'PredictionTower/conv2d_1/BatchNorm/feature_0/moving_mean'),
('WeightSharedConvolutionalBoxPredictor/'
'PredictionTower/conv2d_1/BatchNorm/feature_1/moving_mean'),
('WeightSharedConvolutionalBoxPredictor/'
'PredictionTower/conv2d_1/BatchNorm/feature_0/moving_variance'),
('WeightSharedConvolutionalBoxPredictor/'
'PredictionTower/conv2d_1/BatchNorm/feature_1/moving_variance'),
# Box prediction head
('WeightSharedConvolutionalBoxPredictor/'
'WeightSharedConvolutionalBoxHead/BoxPredictor/kernel'),
......@@ -753,24 +828,28 @@ class WeightSharedConvolutionalKerasBoxPredictorTest(test_case.TestCase):
def test_predictions_share_weights_share_tower_without_batchnorm(
self):
tf.keras.backend.clear_session()
num_classes_without_background = 6
conv_box_predictor = (
box_predictor_builder
.build_weight_shared_convolutional_keras_box_predictor(
is_training=False,
num_classes=num_classes_without_background,
conv_hyperparams=self._build_conv_hyperparams(add_batch_norm=False),
freeze_batchnorm=False,
inplace_batchnorm_update=False,
num_predictions_per_location_list=[5, 5],
depth=32,
num_layers_before_predictor=2,
box_code_size=4,
share_prediction_tower=True,
apply_batch_norm=False))
variables = []
def graph_fn(image_features1, image_features2):
conv_box_predictor = (
box_predictor_builder.build_weight_shared_convolutional_keras_box_predictor(
is_training=False,
num_classes=num_classes_without_background,
conv_hyperparams=self._build_conv_hyperparams(
add_batch_norm=False),
freeze_batchnorm=False,
inplace_batchnorm_update=False,
num_predictions_per_location_list=[5, 5],
depth=32,
num_layers_before_predictor=2,
box_code_size=4,
share_prediction_tower=True,
apply_batch_norm=False))
box_predictions = conv_box_predictor(
[image_features1, image_features2])
variables.extend(list(conv_box_predictor.variables))
box_encodings = tf.concat(
box_predictions[box_predictor.BOX_ENCODINGS], axis=1)
class_predictions_with_background = tf.concat(
......@@ -778,11 +857,11 @@ class WeightSharedConvolutionalKerasBoxPredictorTest(test_case.TestCase):
axis=1)
return (box_encodings, class_predictions_with_background)
with self.test_session(graph=tf.Graph()):
graph_fn(tf.random_uniform([4, 32, 32, 3], dtype=tf.float32),
tf.random_uniform([4, 16, 16, 3], dtype=tf.float32))
actual_variable_set = set(
[var.op.name for var in tf.trainable_variables()])
self.execute(graph_fn, [
np.random.rand(4, 32, 32, 3).astype(np.float32),
np.random.rand(4, 16, 16, 3).astype(np.float32)
])
actual_variable_set = set([var.name.split(':')[0] for var in variables])
expected_variable_set = set([
# Shared prediction tower
('WeightSharedConvolutionalBoxPredictor/'
......@@ -806,40 +885,6 @@ class WeightSharedConvolutionalKerasBoxPredictorTest(test_case.TestCase):
self.assertEqual(expected_variable_set, actual_variable_set)
def test_get_predictions_with_feature_maps_of_dynamic_shape(
self):
image_features = tf.placeholder(dtype=tf.float32, shape=[4, None, None, 64])
conv_box_predictor = (
box_predictor_builder.build_weight_shared_convolutional_keras_box_predictor(
is_training=False,
num_classes=0,
conv_hyperparams=self._build_conv_hyperparams(),
freeze_batchnorm=False,
inplace_batchnorm_update=False,
num_predictions_per_location_list=[5],
depth=32,
num_layers_before_predictor=1,
box_code_size=4))
box_predictions = conv_box_predictor([image_features])
box_encodings = tf.concat(box_predictions[box_predictor.BOX_ENCODINGS],
axis=1)
objectness_predictions = tf.concat(box_predictions[
box_predictor.CLASS_PREDICTIONS_WITH_BACKGROUND], axis=1)
init_op = tf.global_variables_initializer()
resolution = 32
expected_num_anchors = resolution*resolution*5
with self.test_session() as sess:
sess.run(init_op)
(box_encodings_shape,
objectness_predictions_shape) = sess.run(
[tf.shape(box_encodings), tf.shape(objectness_predictions)],
feed_dict={image_features:
np.random.rand(4, resolution, resolution, 64)})
self.assertAllEqual(box_encodings_shape, [4, expected_num_anchors, 4])
self.assertAllEqual(objectness_predictions_shape,
[4, expected_num_anchors, 1])
def test_other_heads_predictions(self):
box_code_size = 4
num_classes_without_background = 3
......@@ -847,37 +892,36 @@ class WeightSharedConvolutionalKerasBoxPredictorTest(test_case.TestCase):
mask_height = 5
mask_width = 5
num_predictions_per_location = 5
box_prediction_head = keras_box_head.WeightSharedConvolutionalBoxHead(
box_code_size=box_code_size,
conv_hyperparams=self._build_conv_hyperparams(),
num_predictions_per_location=num_predictions_per_location)
class_prediction_head = keras_class_head.WeightSharedConvolutionalClassHead(
num_class_slots=num_classes_without_background + 1,
conv_hyperparams=self._build_conv_hyperparams(),
num_predictions_per_location=num_predictions_per_location)
other_heads = {
other_head_name:
keras_mask_head.WeightSharedConvolutionalMaskHead(
num_classes=num_classes_without_background,
conv_hyperparams=self._build_conv_hyperparams(),
num_predictions_per_location=num_predictions_per_location,
mask_height=mask_height,
mask_width=mask_width)
}
conv_box_predictor = box_predictor.WeightSharedConvolutionalBoxPredictor(
is_training=False,
num_classes=num_classes_without_background,
box_prediction_head=box_prediction_head,
class_prediction_head=class_prediction_head,
other_heads=other_heads,
conv_hyperparams=self._build_conv_hyperparams(),
freeze_batchnorm=False,
inplace_batchnorm_update=False,
depth=32,
num_layers_before_predictor=2)
def graph_fn(image_features):
box_prediction_head = keras_box_head.WeightSharedConvolutionalBoxHead(
box_code_size=box_code_size,
conv_hyperparams=self._build_conv_hyperparams(),
num_predictions_per_location=num_predictions_per_location)
class_prediction_head = keras_class_head.WeightSharedConvolutionalClassHead(
num_class_slots=num_classes_without_background + 1,
conv_hyperparams=self._build_conv_hyperparams(),
num_predictions_per_location=num_predictions_per_location)
other_heads = {
other_head_name:
keras_mask_head.WeightSharedConvolutionalMaskHead(
num_classes=num_classes_without_background,
conv_hyperparams=self._build_conv_hyperparams(),
num_predictions_per_location=num_predictions_per_location,
mask_height=mask_height,
mask_width=mask_width)
}
conv_box_predictor = box_predictor.WeightSharedConvolutionalBoxPredictor(
is_training=False,
num_classes=num_classes_without_background,
box_prediction_head=box_prediction_head,
class_prediction_head=class_prediction_head,
other_heads=other_heads,
conv_hyperparams=self._build_conv_hyperparams(),
freeze_batchnorm=False,
inplace_batchnorm_update=False,
depth=32,
num_layers_before_predictor=2)
box_predictions = conv_box_predictor([image_features])
for key, value in box_predictions.items():
box_predictions[key] = tf.concat(value, axis=1)
......
research/object_detection/predictors/heads/box_head_test.py → research/object_detection/predictors/heads/box_head_tf1_test.py
View file @ 420a7253
......@@ -14,6 +14,7 @@
# ==============================================================================
"""Tests for object_detection.predictors.heads.box_head."""
import unittest
import tensorflow.compat.v1 as tf
from google.protobuf import text_format
......@@ -21,8 +22,10 @@ from object_detection.builders import hyperparams_builder
from object_detection.predictors.heads import box_head
from object_detection.protos import hyperparams_pb2
from object_detection.utils import test_case
from object_detection.utils import tf_version
@unittest.skipIf(tf_version.is_tf2(), 'Skipping TF1.X only test.')
class MaskRCNNBoxHeadTest(test_case.TestCase):
def _build_arg_scope_with_hyperparams(self,
......@@ -59,6 +62,7 @@ class MaskRCNNBoxHeadTest(test_case.TestCase):
self.assertAllEqual([64, 1, 20, 4], prediction.get_shape().as_list())
@unittest.skipIf(tf_version.is_tf2(), 'Skipping TF1.X only test.')
class ConvolutionalBoxPredictorTest(test_case.TestCase):
def _build_arg_scope_with_hyperparams(
......@@ -92,6 +96,7 @@ class ConvolutionalBoxPredictorTest(test_case.TestCase):
self.assertAllEqual([64, 323, 1, 4], box_encodings.get_shape().as_list())
@unittest.skipIf(tf_version.is_tf2(), 'Skipping TF1.X only test.')
class WeightSharedConvolutionalBoxPredictorTest(test_case.TestCase):
def _build_arg_scope_with_hyperparams(
......
research/object_detection/predictors/heads/class_head_test.py → research/object_detection/predictors/heads/class_head_tf1_test.py
View file @ 420a7253
......@@ -14,6 +14,7 @@
# ==============================================================================
"""Tests for object_detection.predictors.heads.class_head."""
import unittest
import tensorflow.compat.v1 as tf
from google.protobuf import text_format
......@@ -21,8 +22,10 @@ from object_detection.builders import hyperparams_builder
from object_detection.predictors.heads import class_head
from object_detection.protos import hyperparams_pb2
from object_detection.utils import test_case
from object_detection.utils import tf_version
@unittest.skipIf(tf_version.is_tf2(), 'Skipping TF1.X only test.')
class MaskRCNNClassHeadTest(test_case.TestCase):
def _build_arg_scope_with_hyperparams(self,
......@@ -81,6 +84,7 @@ class MaskRCNNClassHeadTest(test_case.TestCase):
self.assertSetEqual(expected_var_names, actual_variable_set)
@unittest.skipIf(tf_version.is_tf2(), 'Skipping TF1.X only test.')
class ConvolutionalClassPredictorTest(test_case.TestCase):
def _build_arg_scope_with_hyperparams(
......@@ -140,6 +144,7 @@ class ConvolutionalClassPredictorTest(test_case.TestCase):
self.assertSetEqual(expected_var_names, actual_variable_set)
@unittest.skipIf(tf_version.is_tf2(), 'Skipping TF1.X only test.')
class WeightSharedConvolutionalClassPredictorTest(test_case.TestCase):
def _build_arg_scope_with_hyperparams(
......
research/object_detection/predictors/heads/keras_box_head_test.py → research/object_detection/predictors/heads/keras_box_head_tf2_test.py
View file @ 420a7253
......@@ -14,6 +14,7 @@
# ==============================================================================
"""Tests for object_detection.predictors.heads.box_head."""
import unittest
import tensorflow.compat.v1 as tf
from google.protobuf import text_format
......@@ -21,8 +22,10 @@ from object_detection.builders import hyperparams_builder
from object_detection.predictors.heads import keras_box_head
from object_detection.protos import hyperparams_pb2
from object_detection.utils import test_case
from object_detection.utils import tf_version
@unittest.skipIf(tf_version.is_tf1(), 'Skipping TF2.X only test.')
class ConvolutionalKerasBoxHeadTest(test_case.TestCase):
def _build_conv_hyperparams(self):
......@@ -51,10 +54,13 @@ class ConvolutionalKerasBoxHeadTest(test_case.TestCase):
freeze_batchnorm=False,
num_predictions_per_location=1,
use_depthwise=False)
image_feature = tf.random_uniform(
[64, 17, 19, 1024], minval=-10.0, maxval=10.0, dtype=tf.float32)
box_encodings = box_prediction_head(image_feature)
self.assertAllEqual([64, 323, 1, 4], box_encodings.get_shape().as_list())
def graph_fn():
image_feature = tf.random_uniform(
[64, 17, 19, 1024], minval=-10.0, maxval=10.0, dtype=tf.float32)
box_encodings = box_prediction_head(image_feature)
return box_encodings
box_encodings = self.execute(graph_fn, [])
self.assertAllEqual([64, 323, 1, 4], box_encodings.shape)
def test_prediction_size_depthwise_true(self):
conv_hyperparams = self._build_conv_hyperparams()
......@@ -66,12 +72,16 @@ class ConvolutionalKerasBoxHeadTest(test_case.TestCase):
freeze_batchnorm=False,
num_predictions_per_location=1,
use_depthwise=True)
image_feature = tf.random_uniform(
[64, 17, 19, 1024], minval=-10.0, maxval=10.0, dtype=tf.float32)
box_encodings = box_prediction_head(image_feature)
self.assertAllEqual([64, 323, 1, 4], box_encodings.get_shape().as_list())
def graph_fn():
image_feature = tf.random_uniform(
[64, 17, 19, 1024], minval=-10.0, maxval=10.0, dtype=tf.float32)
box_encodings = box_prediction_head(image_feature)
return box_encodings
box_encodings = self.execute(graph_fn, [])
self.assertAllEqual([64, 323, 1, 4], box_encodings.shape)
@unittest.skipIf(tf_version.is_tf1(), 'Skipping TF2.X only test.')
class MaskRCNNKerasBoxHeadTest(test_case.TestCase):
def _build_fc_hyperparams(
......@@ -102,12 +112,16 @@ class MaskRCNNKerasBoxHeadTest(test_case.TestCase):
dropout_keep_prob=0.5,
box_code_size=4,
share_box_across_classes=False)
roi_pooled_features = tf.random_uniform(
[64, 7, 7, 1024], minval=-10.0, maxval=10.0, dtype=tf.float32)
prediction = box_prediction_head(roi_pooled_features)
self.assertAllEqual([64, 1, 20, 4], prediction.get_shape().as_list())
def graph_fn():
roi_pooled_features = tf.random_uniform(
[64, 7, 7, 1024], minval=-10.0, maxval=10.0, dtype=tf.float32)
prediction = box_prediction_head(roi_pooled_features)
return prediction
prediction = self.execute(graph_fn, [])
self.assertAllEqual([64, 1, 20, 4], prediction.shape)
@unittest.skipIf(tf_version.is_tf1(), 'Skipping TF2.X only test.')
class WeightSharedConvolutionalKerasBoxHead(test_case.TestCase):
def _build_conv_hyperparams(self):
......@@ -133,10 +147,13 @@ class WeightSharedConvolutionalKerasBoxHead(test_case.TestCase):
conv_hyperparams=conv_hyperparams,
num_predictions_per_location=1,
use_depthwise=False)
image_feature = tf.random_uniform(
[64, 17, 19, 1024], minval=-10.0, maxval=10.0, dtype=tf.float32)
box_encodings = box_prediction_head(image_feature)
self.assertAllEqual([64, 323, 4], box_encodings.get_shape().as_list())
def graph_fn():
image_feature = tf.random_uniform(
[64, 17, 19, 1024], minval=-10.0, maxval=10.0, dtype=tf.float32)
box_encodings = box_prediction_head(image_feature)
return box_encodings
box_encodings = self.execute(graph_fn, [])
self.assertAllEqual([64, 323, 4], box_encodings.shape)
def test_prediction_size_depthwise_true(self):
conv_hyperparams = self._build_conv_hyperparams()
......@@ -145,40 +162,38 @@ class WeightSharedConvolutionalKerasBoxHead(test_case.TestCase):
conv_hyperparams=conv_hyperparams,
num_predictions_per_location=1,
use_depthwise=True)
image_feature = tf.random_uniform(
[64, 17, 19, 1024], minval=-10.0, maxval=10.0, dtype=tf.float32)
box_encodings = box_prediction_head(image_feature)
self.assertAllEqual([64, 323, 4], box_encodings.get_shape().as_list())
def test_variable_count_depth_wise_true(self):
g = tf.Graph()
with g.as_default():
conv_hyperparams = self._build_conv_hyperparams()
box_prediction_head = keras_box_head.WeightSharedConvolutionalBoxHead(
box_code_size=4,
conv_hyperparams=conv_hyperparams,
num_predictions_per_location=1,
use_depthwise=True)
def graph_fn():
image_feature = tf.random_uniform(
[64, 17, 19, 1024], minval=-10.0, maxval=10.0, dtype=tf.float32)
_ = box_prediction_head(image_feature)
variables = g.get_collection(tf.GraphKeys.GLOBAL_VARIABLES)
self.assertEqual(len(variables), 3)
box_encodings = box_prediction_head(image_feature)
return box_encodings
box_encodings = self.execute(graph_fn, [])
self.assertAllEqual([64, 323, 4], box_encodings.shape)
def test_variable_count_depth_wise_true(self):
conv_hyperparams = self._build_conv_hyperparams()
box_prediction_head = keras_box_head.WeightSharedConvolutionalBoxHead(
box_code_size=4,
conv_hyperparams=conv_hyperparams,
num_predictions_per_location=1,
use_depthwise=True)
image_feature = tf.random_uniform(
[64, 17, 19, 1024], minval=-10.0, maxval=10.0, dtype=tf.float32)
box_prediction_head(image_feature)
self.assertEqual(len(box_prediction_head.variables), 3)
def test_variable_count_depth_wise_False(self):
g = tf.Graph()
with g.as_default():
conv_hyperparams = self._build_conv_hyperparams()
box_prediction_head = keras_box_head.WeightSharedConvolutionalBoxHead(
box_code_size=4,
conv_hyperparams=conv_hyperparams,
num_predictions_per_location=1,
use_depthwise=False)
image_feature = tf.random_uniform(
[64, 17, 19, 1024], minval=-10.0, maxval=10.0, dtype=tf.float32)
_ = box_prediction_head(image_feature)
variables = g.get_collection(tf.GraphKeys.GLOBAL_VARIABLES)
self.assertEqual(len(variables), 2)
conv_hyperparams = self._build_conv_hyperparams()
box_prediction_head = keras_box_head.WeightSharedConvolutionalBoxHead(
box_code_size=4,
conv_hyperparams=conv_hyperparams,
num_predictions_per_location=1,
use_depthwise=False)
image_feature = tf.random_uniform(
[64, 17, 19, 1024], minval=-10.0, maxval=10.0, dtype=tf.float32)
box_prediction_head(image_feature)
self.assertEqual(len(box_prediction_head.variables), 2)
if __name__ == '__main__':
tf.test.main()
research/object_detection/predictors/heads/keras_class_head_test.py → research/object_detection/predictors/heads/keras_class_head_tf2_test.py
View file @ 420a7253
......@@ -14,6 +14,7 @@
# ==============================================================================
"""Tests for object_detection.predictors.heads.class_head."""
import unittest
import tensorflow.compat.v1 as tf
from google.protobuf import text_format
......@@ -21,8 +22,10 @@ from object_detection.builders import hyperparams_builder
from object_detection.predictors.heads import keras_class_head
from object_detection.protos import hyperparams_pb2
from object_detection.utils import test_case
from object_detection.utils import tf_version
@unittest.skipIf(tf_version.is_tf1(), 'Skipping TF2.X only test.')
class ConvolutionalKerasClassPredictorTest(test_case.TestCase):
def _build_conv_hyperparams(self):
......@@ -53,11 +56,13 @@ class ConvolutionalKerasClassPredictorTest(test_case.TestCase):
freeze_batchnorm=False,
num_predictions_per_location=1,
use_depthwise=False)
image_feature = tf.random_uniform(
[64, 17, 19, 1024], minval=-10.0, maxval=10.0, dtype=tf.float32)
class_predictions = class_prediction_head(image_feature,)
self.assertAllEqual([64, 323, 20],
class_predictions.get_shape().as_list())
def graph_fn():
image_feature = tf.random_uniform(
[64, 17, 19, 1024], minval=-10.0, maxval=10.0, dtype=tf.float32)
class_predictions = class_prediction_head(image_feature,)
return class_predictions
class_predictions = self.execute(graph_fn, [])
self.assertAllEqual([64, 323, 20], class_predictions.shape)
def test_prediction_size_depthwise_true(self):
conv_hyperparams = self._build_conv_hyperparams()
......@@ -71,13 +76,16 @@ class ConvolutionalKerasClassPredictorTest(test_case.TestCase):
freeze_batchnorm=False,
num_predictions_per_location=1,
use_depthwise=True)
image_feature = tf.random_uniform(
[64, 17, 19, 1024], minval=-10.0, maxval=10.0, dtype=tf.float32)
class_predictions = class_prediction_head(image_feature,)
self.assertAllEqual([64, 323, 20],
class_predictions.get_shape().as_list())
def graph_fn():
image_feature = tf.random_uniform(
[64, 17, 19, 1024], minval=-10.0, maxval=10.0, dtype=tf.float32)
class_predictions = class_prediction_head(image_feature,)
return class_predictions
class_predictions = self.execute(graph_fn, [])
self.assertAllEqual([64, 323, 20], class_predictions.shape)
@unittest.skipIf(tf_version.is_tf1(), 'Skipping TF2.X only test.')
class MaskRCNNClassHeadTest(test_case.TestCase):
def _build_fc_hyperparams(self,
......@@ -106,12 +114,16 @@ class MaskRCNNClassHeadTest(test_case.TestCase):
freeze_batchnorm=False,
use_dropout=True,
dropout_keep_prob=0.5)
roi_pooled_features = tf.random_uniform(
[64, 7, 7, 1024], minval=-10.0, maxval=10.0, dtype=tf.float32)
prediction = class_prediction_head(roi_pooled_features)
self.assertAllEqual([64, 1, 20], prediction.get_shape().as_list())
def graph_fn():
roi_pooled_features = tf.random_uniform(
[64, 7, 7, 1024], minval=-10.0, maxval=10.0, dtype=tf.float32)
prediction = class_prediction_head(roi_pooled_features)
return prediction
prediction = self.execute(graph_fn, [])
self.assertAllEqual([64, 1, 20], prediction.shape)
@unittest.skipIf(tf_version.is_tf1(), 'Skipping TF2.X only test.')
class WeightSharedConvolutionalKerasClassPredictorTest(test_case.TestCase):
def _build_conv_hyperparams(self):
......@@ -137,10 +149,13 @@ class WeightSharedConvolutionalKerasClassPredictorTest(test_case.TestCase):
conv_hyperparams=conv_hyperparams,
num_predictions_per_location=1,
use_depthwise=False)
image_feature = tf.random_uniform(
[64, 17, 19, 1024], minval=-10.0, maxval=10.0, dtype=tf.float32)
class_predictions = class_prediction_head(image_feature)
self.assertAllEqual([64, 323, 20], class_predictions.get_shape().as_list())
def graph_fn():
image_feature = tf.random_uniform(
[64, 17, 19, 1024], minval=-10.0, maxval=10.0, dtype=tf.float32)
class_predictions = class_prediction_head(image_feature)
return class_predictions
class_predictions = self.execute(graph_fn, [])
self.assertAllEqual([64, 323, 20], class_predictions.shape)
def test_prediction_size_depthwise_true(self):
conv_hyperparams = self._build_conv_hyperparams()
......@@ -149,42 +164,39 @@ class WeightSharedConvolutionalKerasClassPredictorTest(test_case.TestCase):
conv_hyperparams=conv_hyperparams,
num_predictions_per_location=1,
use_depthwise=True)
image_feature = tf.random_uniform(
[64, 17, 19, 1024], minval=-10.0, maxval=10.0, dtype=tf.float32)
class_predictions = class_prediction_head(image_feature)
self.assertAllEqual([64, 323, 20], class_predictions.get_shape().as_list())
def test_variable_count_depth_wise_true(self):
g = tf.Graph()
with g.as_default():
conv_hyperparams = self._build_conv_hyperparams()
class_prediction_head = (
keras_class_head.WeightSharedConvolutionalClassHead(
num_class_slots=20,
conv_hyperparams=conv_hyperparams,
num_predictions_per_location=1,
use_depthwise=True))
def graph_fn():
image_feature = tf.random_uniform(
[64, 17, 19, 1024], minval=-10.0, maxval=10.0, dtype=tf.float32)
_ = class_prediction_head(image_feature)
variables = g.get_collection(tf.GraphKeys.GLOBAL_VARIABLES)
self.assertEqual(len(variables), 3)
class_predictions = class_prediction_head(image_feature)
return class_predictions
class_predictions = self.execute(graph_fn, [])
self.assertAllEqual([64, 323, 20], class_predictions.shape)
def test_variable_count_depth_wise_true(self):
conv_hyperparams = self._build_conv_hyperparams()
class_prediction_head = (
keras_class_head.WeightSharedConvolutionalClassHead(
num_class_slots=20,
conv_hyperparams=conv_hyperparams,
num_predictions_per_location=1,
use_depthwise=True))
image_feature = tf.random_uniform(
[64, 17, 19, 1024], minval=-10.0, maxval=10.0, dtype=tf.float32)
class_prediction_head(image_feature)
self.assertEqual(len(class_prediction_head.variables), 3)
def test_variable_count_depth_wise_False(self):
g = tf.Graph()
with g.as_default():
conv_hyperparams = self._build_conv_hyperparams()
class_prediction_head = (
keras_class_head.WeightSharedConvolutionalClassHead(
num_class_slots=20,
conv_hyperparams=conv_hyperparams,
num_predictions_per_location=1,
use_depthwise=False))
image_feature = tf.random_uniform(
[64, 17, 19, 1024], minval=-10.0, maxval=10.0, dtype=tf.float32)
_ = class_prediction_head(image_feature)
variables = g.get_collection(tf.GraphKeys.GLOBAL_VARIABLES)
self.assertEqual(len(variables), 2)
conv_hyperparams = self._build_conv_hyperparams()
class_prediction_head = (
keras_class_head.WeightSharedConvolutionalClassHead(
num_class_slots=20,
conv_hyperparams=conv_hyperparams,
num_predictions_per_location=1,
use_depthwise=False))
image_feature = tf.random_uniform(
[64, 17, 19, 1024], minval=-10.0, maxval=10.0, dtype=tf.float32)
class_prediction_head(image_feature)
self.assertEqual(len(class_prediction_head.variables), 2)
if __name__ == '__main__':
......
research/object_detection/predictors/heads/keras_mask_head_test.py → research/object_detection/predictors/heads/keras_mask_head_tf2_test.py
View file @ 420a7253
......@@ -14,6 +14,7 @@
# ==============================================================================
"""Tests for object_detection.predictors.heads.mask_head."""
import unittest
import tensorflow.compat.v1 as tf
from google.protobuf import text_format
......@@ -21,8 +22,10 @@ from object_detection.builders import hyperparams_builder
from object_detection.predictors.heads import keras_mask_head
from object_detection.protos import hyperparams_pb2
from object_detection.utils import test_case
from object_detection.utils import tf_version
@unittest.skipIf(tf_version.is_tf1(), 'Skipping TF2.X only test.')
class ConvolutionalMaskPredictorTest(test_case.TestCase):
def _build_conv_hyperparams(self):
......@@ -55,11 +58,13 @@ class ConvolutionalMaskPredictorTest(test_case.TestCase):
use_depthwise=False,
mask_height=7,
mask_width=7)
image_feature = tf.random_uniform(
[64, 17, 19, 1024], minval=-10.0, maxval=10.0, dtype=tf.float32)
mask_predictions = mask_prediction_head(image_feature)
self.assertAllEqual([64, 323, 20, 7, 7],
mask_predictions.get_shape().as_list())
def graph_fn():
image_feature = tf.random_uniform(
[64, 17, 19, 1024], minval=-10.0, maxval=10.0, dtype=tf.float32)
mask_predictions = mask_prediction_head(image_feature)
return mask_predictions
mask_predictions = self.execute(graph_fn, [])
self.assertAllEqual([64, 323, 20, 7, 7], mask_predictions.shape)
def test_prediction_size_use_depthwise_true(self):
conv_hyperparams = self._build_conv_hyperparams()
......@@ -75,11 +80,13 @@ class ConvolutionalMaskPredictorTest(test_case.TestCase):
use_depthwise=True,
mask_height=7,
mask_width=7)
image_feature = tf.random_uniform(
[64, 17, 19, 1024], minval=-10.0, maxval=10.0, dtype=tf.float32)
mask_predictions = mask_prediction_head(image_feature)
self.assertAllEqual([64, 323, 20, 7, 7],
mask_predictions.get_shape().as_list())
def graph_fn():
image_feature = tf.random_uniform(
[64, 17, 19, 1024], minval=-10.0, maxval=10.0, dtype=tf.float32)
mask_predictions = mask_prediction_head(image_feature)
return mask_predictions
mask_predictions = self.execute(graph_fn, [])
self.assertAllEqual([64, 323, 20, 7, 7], mask_predictions.shape)
def test_class_agnostic_prediction_size_use_depthwise_false(self):
conv_hyperparams = self._build_conv_hyperparams()
......@@ -96,11 +103,13 @@ class ConvolutionalMaskPredictorTest(test_case.TestCase):
mask_height=7,
mask_width=7,
masks_are_class_agnostic=True)
image_feature = tf.random_uniform(
[64, 17, 19, 1024], minval=-10.0, maxval=10.0, dtype=tf.float32)
mask_predictions = mask_prediction_head(image_feature)
self.assertAllEqual([64, 323, 1, 7, 7],
mask_predictions.get_shape().as_list())
def graph_fn():
image_feature = tf.random_uniform(
[64, 17, 19, 1024], minval=-10.0, maxval=10.0, dtype=tf.float32)
mask_predictions = mask_prediction_head(image_feature)
return mask_predictions
mask_predictions = self.execute(graph_fn, [])
self.assertAllEqual([64, 323, 1, 7, 7], mask_predictions.shape)
def test_class_agnostic_prediction_size_use_depthwise_true(self):
conv_hyperparams = self._build_conv_hyperparams()
......@@ -117,13 +126,16 @@ class ConvolutionalMaskPredictorTest(test_case.TestCase):
mask_height=7,
mask_width=7,
masks_are_class_agnostic=True)
image_feature = tf.random_uniform(
[64, 17, 19, 1024], minval=-10.0, maxval=10.0, dtype=tf.float32)
mask_predictions = mask_prediction_head(image_feature)
self.assertAllEqual([64, 323, 1, 7, 7],
mask_predictions.get_shape().as_list())
def graph_fn():
image_feature = tf.random_uniform(
[64, 17, 19, 1024], minval=-10.0, maxval=10.0, dtype=tf.float32)
mask_predictions = mask_prediction_head(image_feature)
return mask_predictions
mask_predictions = self.execute(graph_fn, [])
self.assertAllEqual([64, 323, 1, 7, 7], mask_predictions.shape)
@unittest.skipIf(tf_version.is_tf1(), 'Skipping TF2.X only test.')
class MaskRCNNMaskHeadTest(test_case.TestCase):
def _build_conv_hyperparams(self,
......@@ -155,10 +167,13 @@ class MaskRCNNMaskHeadTest(test_case.TestCase):
mask_prediction_num_conv_layers=2,
mask_prediction_conv_depth=256,
masks_are_class_agnostic=False)
roi_pooled_features = tf.random_uniform(
[64, 7, 7, 1024], minval=-10.0, maxval=10.0, dtype=tf.float32)
prediction = mask_prediction_head(roi_pooled_features)
self.assertAllEqual([64, 1, 20, 14, 14], prediction.get_shape().as_list())
def graph_fn():
roi_pooled_features = tf.random_uniform(
[64, 7, 7, 1024], minval=-10.0, maxval=10.0, dtype=tf.float32)
prediction = mask_prediction_head(roi_pooled_features)
return prediction
prediction = self.execute(graph_fn, [])
self.assertAllEqual([64, 1, 20, 14, 14], prediction.shape)
def test_prediction_size_with_convolve_then_upsample(self):
mask_prediction_head = keras_mask_head.MaskRCNNMaskHead(
......@@ -172,12 +187,16 @@ class MaskRCNNMaskHeadTest(test_case.TestCase):
mask_prediction_conv_depth=256,
masks_are_class_agnostic=True,
convolve_then_upsample=True)
roi_pooled_features = tf.random_uniform(
[64, 14, 14, 1024], minval=-10.0, maxval=10.0, dtype=tf.float32)
prediction = mask_prediction_head(roi_pooled_features)
self.assertAllEqual([64, 1, 1, 28, 28], prediction.get_shape().as_list())
def graph_fn():
roi_pooled_features = tf.random_uniform(
[64, 14, 14, 1024], minval=-10.0, maxval=10.0, dtype=tf.float32)
prediction = mask_prediction_head(roi_pooled_features)
return prediction
prediction = self.execute(graph_fn, [])
self.assertAllEqual([64, 1, 1, 28, 28], prediction.shape)
@unittest.skipIf(tf_version.is_tf1(), 'Skipping TF2.X only test.')
class WeightSharedConvolutionalMaskPredictorTest(test_case.TestCase):
def _build_conv_hyperparams(self):
......@@ -204,11 +223,13 @@ class WeightSharedConvolutionalMaskPredictorTest(test_case.TestCase):
conv_hyperparams=self._build_conv_hyperparams(),
mask_height=7,
mask_width=7))
image_feature = tf.random_uniform(
[64, 17, 19, 1024], minval=-10.0, maxval=10.0, dtype=tf.float32)
mask_predictions = mask_prediction_head(image_feature)
self.assertAllEqual([64, 323, 20, 7, 7],
mask_predictions.get_shape().as_list())
def graph_fn():
image_feature = tf.random_uniform(
[64, 17, 19, 1024], minval=-10.0, maxval=10.0, dtype=tf.float32)
mask_predictions = mask_prediction_head(image_feature)
return mask_predictions
mask_predictions = self.execute(graph_fn, [])
self.assertAllEqual([64, 323, 20, 7, 7], mask_predictions.shape)
def test_class_agnostic_prediction_size(self):
mask_prediction_head = (
......@@ -219,11 +240,13 @@ class WeightSharedConvolutionalMaskPredictorTest(test_case.TestCase):
mask_height=7,
mask_width=7,
masks_are_class_agnostic=True))
image_feature = tf.random_uniform(
[64, 17, 19, 1024], minval=-10.0, maxval=10.0, dtype=tf.float32)
mask_predictions = mask_prediction_head(image_feature)
self.assertAllEqual([64, 323, 1, 7, 7],
mask_predictions.get_shape().as_list())
def graph_fn():
image_feature = tf.random_uniform(
[64, 17, 19, 1024], minval=-10.0, maxval=10.0, dtype=tf.float32)
mask_predictions = mask_prediction_head(image_feature)
return mask_predictions
mask_predictions = self.execute(graph_fn, [])
self.assertAllEqual([64, 323, 1, 7, 7], mask_predictions.shape)
if __name__ == '__main__':
tf.test.main()
research/object_detection/predictors/heads/keypoint_head_test.py → research/object_detection/predictors/heads/keypoint_head_tf1_test.py
View file @ 420a7253
......@@ -14,6 +14,7 @@
# ==============================================================================
"""Tests for object_detection.predictors.heads.keypoint_head."""
import unittest
import tensorflow.compat.v1 as tf
from google.protobuf import text_format
......@@ -21,8 +22,10 @@ from object_detection.builders import hyperparams_builder
from object_detection.predictors.heads import keypoint_head
from object_detection.protos import hyperparams_pb2
from object_detection.utils import test_case
from object_detection.utils import tf_version
@unittest.skipIf(tf_version.is_tf2(), 'Skipping TF1.X only test.')
class MaskRCNNKeypointHeadTest(test_case.TestCase):
def _build_arg_scope_with_hyperparams(self,
......
research/object_detection/predictors/heads/mask_head_test.py → research/object_detection/predictors/heads/mask_head_tf1_test.py
View file @ 420a7253
......@@ -14,6 +14,7 @@
# ==============================================================================
"""Tests for object_detection.predictors.heads.mask_head."""
import unittest
import tensorflow.compat.v1 as tf
from google.protobuf import text_format
......@@ -21,8 +22,10 @@ from object_detection.builders import hyperparams_builder
from object_detection.predictors.heads import mask_head
from object_detection.protos import hyperparams_pb2
from object_detection.utils import test_case
from object_detection.utils import tf_version
@unittest.skipIf(tf_version.is_tf2(), 'Skipping TF1.X only test.')
class MaskRCNNMaskHeadTest(test_case.TestCase):
def _build_arg_scope_with_hyperparams(self,
......@@ -75,6 +78,7 @@ class MaskRCNNMaskHeadTest(test_case.TestCase):
self.assertAllEqual([64, 1, 1, 28, 28], prediction.get_shape().as_list())
@unittest.skipIf(tf_version.is_tf2(), 'Skipping TF1.X only test.')
class ConvolutionalMaskPredictorTest(test_case.TestCase):
def _build_arg_scope_with_hyperparams(
......@@ -131,6 +135,7 @@ class ConvolutionalMaskPredictorTest(test_case.TestCase):
mask_predictions.get_shape().as_list())
@unittest.skipIf(tf_version.is_tf2(), 'Skipping TF1.X only test.')
class WeightSharedConvolutionalMaskPredictorTest(test_case.TestCase):
def _build_arg_scope_with_hyperparams(
......
research/object_detection/predictors/mask_rcnn_box_predictor_test.py → research/object_detection/predictors/mask_rcnn_box_predictor_tf1_test.py
View file @ 420a7253
......@@ -14,6 +14,7 @@
# ==============================================================================
"""Tests for object_detection.predictors.mask_rcnn_box_predictor."""
import unittest
import numpy as np
import tensorflow.compat.v1 as tf
......@@ -23,8 +24,10 @@ from object_detection.builders import hyperparams_builder
from object_detection.predictors import mask_rcnn_box_predictor as box_predictor
from object_detection.protos import hyperparams_pb2
from object_detection.utils import test_case
from object_detection.utils import tf_version
@unittest.skipIf(tf_version.is_tf2(), 'Skipping TF1.X only test.')
class MaskRCNNBoxPredictorTest(test_case.TestCase):
def _build_arg_scope_with_hyperparams(self,
......
research/object_detection/predictors/mask_rcnn_keras_box_predictor_test.py → research/object_detection/predictors/mask_rcnn_keras_box_predictor_tf2_test.py
View file @ 420a7253
......@@ -14,6 +14,7 @@
# ==============================================================================
"""Tests for object_detection.predictors.mask_rcnn_box_predictor."""
import unittest
import numpy as np
import tensorflow.compat.v1 as tf
......@@ -23,8 +24,10 @@ from object_detection.builders import hyperparams_builder
from object_detection.predictors import mask_rcnn_keras_box_predictor as box_predictor
from object_detection.protos import hyperparams_pb2
from object_detection.utils import test_case
from object_detection.utils import tf_version
@unittest.skipIf(tf_version.is_tf1(), 'Skipping TF2.X only test.')
class MaskRCNNKerasBoxPredictorTest(test_case.TestCase):
def _build_hyperparams(self,
......@@ -46,17 +49,17 @@ class MaskRCNNKerasBoxPredictorTest(test_case.TestCase):
return hyperparams_builder.KerasLayerHyperparams(hyperparams)
def test_get_boxes_with_five_classes(self):
mask_box_predictor = (
box_predictor_builder.build_mask_rcnn_keras_box_predictor(
is_training=False,
num_classes=5,
fc_hyperparams=self._build_hyperparams(),
freeze_batchnorm=False,
use_dropout=False,
dropout_keep_prob=0.5,
box_code_size=4,
))
def graph_fn(image_features):
mask_box_predictor = (
box_predictor_builder.build_mask_rcnn_keras_box_predictor(
is_training=False,
num_classes=5,
fc_hyperparams=self._build_hyperparams(),
freeze_batchnorm=False,
use_dropout=False,
dropout_keep_prob=0.5,
box_code_size=4,
))
box_predictions = mask_box_predictor(
[image_features],
prediction_stage=2)
......@@ -70,18 +73,19 @@ class MaskRCNNKerasBoxPredictorTest(test_case.TestCase):
self.assertAllEqual(class_predictions_with_background.shape, [2, 1, 6])
def test_get_boxes_with_five_classes_share_box_across_classes(self):
mask_box_predictor = (
box_predictor_builder.build_mask_rcnn_keras_box_predictor(
is_training=False,
num_classes=5,
fc_hyperparams=self._build_hyperparams(),
freeze_batchnorm=False,
use_dropout=False,
dropout_keep_prob=0.5,
box_code_size=4,
share_box_across_classes=True
))
def graph_fn(image_features):
mask_box_predictor = (
box_predictor_builder.build_mask_rcnn_keras_box_predictor(
is_training=False,
num_classes=5,
fc_hyperparams=self._build_hyperparams(),
freeze_batchnorm=False,
use_dropout=False,
dropout_keep_prob=0.5,
box_code_size=4,
share_box_across_classes=True
))
box_predictions = mask_box_predictor(
[image_features],
prediction_stage=2)
......@@ -95,19 +99,19 @@ class MaskRCNNKerasBoxPredictorTest(test_case.TestCase):
self.assertAllEqual(class_predictions_with_background.shape, [2, 1, 6])
def test_get_instance_masks(self):
mask_box_predictor = (
box_predictor_builder.build_mask_rcnn_keras_box_predictor(
is_training=False,
num_classes=5,
fc_hyperparams=self._build_hyperparams(),
freeze_batchnorm=False,
use_dropout=False,
dropout_keep_prob=0.5,
box_code_size=4,
conv_hyperparams=self._build_hyperparams(
op_type=hyperparams_pb2.Hyperparams.CONV),
predict_instance_masks=True))
def graph_fn(image_features):
mask_box_predictor = (
box_predictor_builder.build_mask_rcnn_keras_box_predictor(
is_training=False,
num_classes=5,
fc_hyperparams=self._build_hyperparams(),
freeze_batchnorm=False,
use_dropout=False,
dropout_keep_prob=0.5,
box_code_size=4,
conv_hyperparams=self._build_hyperparams(
op_type=hyperparams_pb2.Hyperparams.CONV),
predict_instance_masks=True))
box_predictions = mask_box_predictor(
[image_features],
prediction_stage=3)
......
research/object_detection/predictors/rfcn_box_predictor_test.py → research/object_detection/predictors/rfcn_box_predictor_tf1_test.py
View file @ 420a7253
......@@ -14,6 +14,7 @@
# ==============================================================================
"""Tests for object_detection.predictors.rfcn_box_predictor."""
import unittest
import numpy as np
import tensorflow.compat.v1 as tf
......@@ -22,8 +23,10 @@ from object_detection.builders import hyperparams_builder
from object_detection.predictors import rfcn_box_predictor as box_predictor
from object_detection.protos import hyperparams_pb2
from object_detection.utils import test_case
from object_detection.utils import tf_version
@unittest.skipIf(tf_version.is_tf2(), 'Skipping TF1.X only test.')
class RfcnBoxPredictorTest(test_case.TestCase):
def _build_arg_scope_with_conv_hyperparams(self):
......
research/object_detection/predictors/rfcn_keras_box_predictor_test.py → research/object_detection/predictors/rfcn_keras_box_predictor_tf2_test.py
View file @ 420a7253
......@@ -14,6 +14,7 @@
# ==============================================================================
"""Tests for object_detection.predictors.rfcn_box_predictor."""
import unittest
import numpy as np
import tensorflow.compat.v1 as tf
......@@ -22,8 +23,10 @@ from object_detection.builders import hyperparams_builder
from object_detection.predictors import rfcn_keras_box_predictor as box_predictor
from object_detection.protos import hyperparams_pb2
from object_detection.utils import test_case
from object_detection.utils import tf_version
@unittest.skipIf(tf_version.is_tf1(), 'Skipping TF2.X only test.')
class RfcnKerasBoxPredictorTest(test_case.TestCase):
def _build_conv_hyperparams(self):
......@@ -42,18 +45,17 @@ class RfcnKerasBoxPredictorTest(test_case.TestCase):
return hyperparams_builder.KerasLayerHyperparams(conv_hyperparams)
def test_get_correct_box_encoding_and_class_prediction_shapes(self):
rfcn_box_predictor = box_predictor.RfcnKerasBoxPredictor(
is_training=False,
num_classes=2,
conv_hyperparams=self._build_conv_hyperparams(),
freeze_batchnorm=False,
num_spatial_bins=[3, 3],
depth=4,
crop_size=[12, 12],
box_code_size=4)
def graph_fn(image_features, proposal_boxes):
rfcn_box_predictor = box_predictor.RfcnKerasBoxPredictor(
is_training=False,
num_classes=2,
conv_hyperparams=self._build_conv_hyperparams(),
freeze_batchnorm=False,
num_spatial_bins=[3, 3],
depth=4,
crop_size=[12, 12],
box_code_size=4
)
box_predictions = rfcn_box_predictor(
[image_features],
proposal_boxes=proposal_boxes)
......
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