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Unverified Commit 3f78f4cf authored by derekjchow's avatar derekjchow Committed by GitHub
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Merge pull request #3494 from pkulzc/master 

Update object detection with internal changes and remove unused BUILD files.
parents 73748d01 0319908c
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research/object_detection/BUILD deleted 100644 → 0
View file @ 73748d01
# Tensorflow Object Detection API: main runnables.
package(
default_visibility = ["//visibility:public"],
)
load("//learning/brain/contrib/learn/tpu:tpu.bzl", "cloud_tpu_py_binaries")
licenses(["notice"])
# Apache 2.0
exports_files(["LICENSE"])
py_library(
name = "inputs",
srcs = [
"inputs.py",
],
deps = [
"//tensorflow",
"//tensorflow/models/research/object_detection/builders:dataset_builder",
"//tensorflow/models/research/object_detection/builders:image_resizer_builder",
"//tensorflow/models/research/object_detection/builders:model_builder",
"//tensorflow/models/research/object_detection/builders:preprocessor_builder",
"//tensorflow/models/research/object_detection/protos:input_reader_py_pb2",
"//tensorflow/models/research/object_detection/protos:model_py_pb2",
"//tensorflow/models/research/object_detection/protos:train_py_pb2",
"//tensorflow/models/research/object_detection/utils:config_util",
"//tensorflow/models/research/object_detection/utils:dataset_util",
"//tensorflow/models/research/object_detection/utils:ops",
],
)
py_test(
name = "inputs_test",
srcs = [
"inputs_test.py",
],
data = [
"//tensorflow/models/research/object_detection/data:pet_label_map.pbtxt",
"//tensorflow/models/research/object_detection/samples/configs:faster_rcnn_resnet50_pets.config",
"//tensorflow/models/research/object_detection/samples/configs:ssd_inception_v2_pets.config",
"//tensorflow/models/research/object_detection/test_data:pets_examples.record",
],
deps = [
":inputs",
"//tensorflow",
"//tensorflow/models/research/object_detection/core:standard_fields",
"//tensorflow/models/research/object_detection/utils:config_util",
],
)
py_binary(
name = "model",
srcs = [
"model.py",
],
deps = [
":inputs",
":model_hparams",
"//tensorflow",
"//tensorflow/models/research/object_detection:eval_util",
"//tensorflow/models/research/object_detection/builders:model_builder",
"//tensorflow/models/research/object_detection/builders:optimizer_builder",
"//tensorflow/models/research/object_detection/metrics:coco_evaluation",
"//tensorflow/models/research/object_detection/utils:config_util",
"//tensorflow/models/research/object_detection/utils:label_map_util",
"//tensorflow/models/research/object_detection/utils:ops",
"//tensorflow/models/research/object_detection/utils:shape_utils",
"//tensorflow/models/research/object_detection/utils:variables_helper",
"//tensorflow/models/research/object_detection/utils:visualization_utils",
],
)
py_library(
name = "model_hparams",
srcs = [
"model_hparams.py",
],
deps = [
"//tensorflow",
],
)
py_test(
name = "model_test",
timeout = "long",
srcs = [
"model_test.py",
],
data = [
"//tensorflow/models/research/object_detection/data:pet_label_map.pbtxt",
"//tensorflow/models/research/object_detection/samples/configs:faster_rcnn_resnet50_pets.config",
"//tensorflow/models/research/object_detection/samples/configs:ssd_inception_v2_pets.config",
"//tensorflow/models/research/object_detection/test_data:pets_examples.record",
],
deps = [
":inputs",
":model",
":model_hparams",
":model_test_util",
"//mock",
"//tensorflow",
"//tensorflow/models/research/object_detection/core:standard_fields",
"//tensorflow/models/research/object_detection/data_decoders:tf_example_decoder",
"//tensorflow/models/research/object_detection/utils:config_util",
"//tensorflow/models/research/object_detection/utils:ops",
],
)
MODEL_TPU_DEPS = [
":inputs",
":model",
":model_hparams",
"//tensorflow",
"//tensorflow/models/research/object_detection:eval_util",
"//tensorflow/models/research/object_detection/builders:model_builder",
"//tensorflow/models/research/object_detection/builders:optimizer_builder",
"//tensorflow/models/research/object_detection/metrics:coco_evaluation",
"//tensorflow/models/research/object_detection/utils:config_util",
"//tensorflow/models/research/object_detection/utils:label_map_util",
"//tensorflow/models/research/object_detection/utils:ops",
"//tensorflow/models/research/object_detection/utils:variables_helper",
"//tensorflow/models/research/object_detection/utils:visualization_utils",
]
cloud_tpu_py_binaries(
name = "model_tpu",
srcs = [
"model_tpu.py",
],
main = "model_tpu.py",
deps = MODEL_TPU_DEPS,
)
py_library(
name = "model_tpu_lib",
srcs = [
"model_tpu.py",
],
deps = MODEL_TPU_DEPS,
)
py_library(
name = "model_test_util",
srcs = [
"model_test_util.py",
],
deps = [
":model",
":model_hparams",
"//tensorflow",
],
)
py_binary(
name = "train",
srcs = [
"train.py",
],
deps = [
":trainer",
"//tensorflow",
"//tensorflow/models/research/object_detection/builders:dataset_builder",
"//tensorflow/models/research/object_detection/builders:model_builder",
"//tensorflow/models/research/object_detection/utils:config_util",
"//tensorflow/models/research/object_detection/utils:dataset_util",
],
)
py_library(
name = "trainer",
srcs = ["trainer.py"],
deps = [
"//tensorflow",
"//tensorflow/models/research/object_detection/builders:optimizer_builder",
"//tensorflow/models/research/object_detection/builders:preprocessor_builder",
"//tensorflow/models/research/object_detection/core:batcher",
"//tensorflow/models/research/object_detection/core:preprocessor",
"//tensorflow/models/research/object_detection/core:standard_fields",
"//tensorflow/models/research/object_detection/utils:ops",
"//tensorflow/models/research/object_detection/utils:variables_helper",
"//third_party/tensorflow_models/slim:model_deploy",
],
)
py_test(
name = "trainer_test",
srcs = ["trainer_test.py"],
deps = [
":trainer",
"//tensorflow",
"//tensorflow/models/research/object_detection/core:losses",
"//tensorflow/models/research/object_detection/core:model",
"//tensorflow/models/research/object_detection/core:standard_fields",
"//tensorflow/models/research/object_detection/protos:train_py_pb2",
],
)
py_library(
name = "eval_util",
srcs = [
"eval_util.py",
],
deps = [
"//tensorflow",
"//tensorflow/models/research/object_detection/core:box_list",
"//tensorflow/models/research/object_detection/core:box_list_ops",
"//tensorflow/models/research/object_detection/core:keypoint_ops",
"//tensorflow/models/research/object_detection/core:standard_fields",
"//tensorflow/models/research/object_detection/utils:label_map_util",
"//tensorflow/models/research/object_detection/utils:ops",
"//tensorflow/models/research/object_detection/utils:visualization_utils",
],
)
py_library(
name = "evaluator",
srcs = ["evaluator.py"],
deps = [
"//tensorflow",
"//tensorflow/models/research/object_detection:eval_util",
"//tensorflow/models/research/object_detection/core:prefetcher",
"//tensorflow/models/research/object_detection/core:standard_fields",
"//tensorflow/models/research/object_detection/metrics:coco_evaluation",
"//tensorflow/models/research/object_detection/protos:eval_py_pb2",
"//tensorflow/models/research/object_detection/utils:object_detection_evaluation",
],
)
py_binary(
name = "eval",
srcs = [
"eval.py",
],
deps = [
":evaluator",
"//tensorflow",
"//tensorflow/models/research/object_detection/builders:dataset_builder",
"//tensorflow/models/research/object_detection/builders:model_builder",
"//tensorflow/models/research/object_detection/utils:config_util",
"//tensorflow/models/research/object_detection/utils:dataset_util",
"//tensorflow/models/research/object_detection/utils:label_map_util",
],
)
py_library(
name = "exporter",
srcs = [
"exporter.py",
],
deps = [
"//tensorflow",
"//tensorflow/python/tools:freeze_graph_lib",
"//tensorflow/models/research/object_detection/builders:model_builder",
"//tensorflow/models/research/object_detection/core:standard_fields",
"//tensorflow/models/research/object_detection/data_decoders:tf_example_decoder",
],
)
py_test(
name = "exporter_test",
srcs = [
"exporter_test.py",
],
deps = [
":exporter",
"//tensorflow",
"//tensorflow/models/research/object_detection/builders:model_builder",
"//tensorflow/models/research/object_detection/core:model",
"//tensorflow/models/research/object_detection/protos:pipeline_py_pb2",
],
)
py_binary(
name = "export_inference_graph",
srcs = [
"export_inference_graph.py",
],
deps = [
":exporter",
"//tensorflow",
"//tensorflow/models/research/object_detection/protos:pipeline_py_pb2",
],
)
research/object_detection/README.md
View file @ 3f78f4cf
......@@ -70,7 +70,7 @@ Extras:
* <a href='g3doc/oid_inference_and_evaluation.md'>
Inference and evaluation on the Open Images dataset</a><br>
* <a href='g3doc/instance_segmentation.md'>
Run an instance segmentation model
Run an instance segmentation model</a><br>
## Getting Help
......@@ -83,6 +83,8 @@ tensorflow/models GitHub
[issue tracker](https://github.com/tensorflow/models/issues), prefixing the
issue name with "object_detection".
Please check [FAQ](g3doc/faq.md) for frequently asked questions before
reporting an issue.
## Release information
......
research/object_detection/anchor_generators/BUILD deleted 100644 → 0
View file @ 73748d01
# Tensorflow Object Detection API: Anchor Generator implementations.
package(
default_visibility = ["//visibility:public"],
)
licenses(["notice"])
# Apache 2.0
py_library(
name = "grid_anchor_generator",
srcs = [
"grid_anchor_generator.py",
],
deps = [
"//tensorflow",
"//tensorflow/models/research/object_detection/core:anchor_generator",
"//tensorflow/models/research/object_detection/core:box_list",
"//tensorflow/models/research/object_detection/utils:ops",
],
)
py_test(
name = "grid_anchor_generator_test",
srcs = [
"grid_anchor_generator_test.py",
],
deps = [
":grid_anchor_generator",
"//tensorflow",
"//tensorflow/models/research/object_detection/utils:test_case",
],
)
py_library(
name = "multiple_grid_anchor_generator",
srcs = [
"multiple_grid_anchor_generator.py",
],
deps = [
":grid_anchor_generator",
"//tensorflow",
"//tensorflow/models/research/object_detection/core:anchor_generator",
"//tensorflow/models/research/object_detection/core:box_list_ops",
],
)
py_test(
name = "multiple_grid_anchor_generator_test",
srcs = [
"multiple_grid_anchor_generator_test.py",
],
deps = [
":multiple_grid_anchor_generator",
"//numpy",
"//tensorflow/models/research/object_detection/utils:test_case",
],
)
py_library(
name = "multiscale_grid_anchor_generator",
srcs = [
"multiscale_grid_anchor_generator.py",
],
deps = [
":grid_anchor_generator",
"//tensorflow",
"//tensorflow/models/research/object_detection/core:anchor_generator",
"//tensorflow/models/research/object_detection/core:box_list_ops",
],
)
py_test(
name = "multiscale_grid_anchor_generator_test",
srcs = [
"multiscale_grid_anchor_generator_test.py",
],
deps = [
":multiscale_grid_anchor_generator",
"//numpy",
"//tensorflow/models/research/object_detection/utils:test_case",
],
)
research/object_detection/anchor_generators/grid_anchor_generator.py
View file @ 3f78f4cf
......@@ -93,11 +93,9 @@ class GridAnchorGenerator(anchor_generator.AnchorGenerator):
allowed.
Returns:
boxes: a BoxList holding a collection of N anchor boxes. Additionally
this BoxList also holds a `feature_map_index` field which is set to 0
for each anchor; this field exists for interchangeability reasons with
the MultipleGridAnchorGenerator (see the docstring for the corresponding
`_generate` function in multiple_grid_anchor_generator.py)
boxes_list: a list of BoxLists each holding anchor boxes corresponding to
the input feature map shapes.
Raises:
ValueError: if feature_map_shape_list, box_specs_list do not have the same
length.
......@@ -128,7 +126,7 @@ class GridAnchorGenerator(anchor_generator.AnchorGenerator):
num_anchors = anchors.num_boxes()
anchor_indices = tf.zeros([num_anchors])
anchors.add_field('feature_map_index', anchor_indices)
return anchors
return [anchors]
def tile_anchors(grid_height,
......
research/object_detection/anchor_generators/grid_anchor_generator_test.py
View file @ 3f78f4cf
......@@ -31,8 +31,8 @@ class GridAnchorGeneratorTest(test_case.TestCase):
anchor_offset = [7, -3]
anchor_generator = grid_anchor_generator.GridAnchorGenerator(
scales, aspect_ratios, anchor_offset=anchor_offset)
anchors = anchor_generator.generate(feature_map_shape_list=[(1, 1)])
anchor_corners = anchors.get()
anchors_list = anchor_generator.generate(feature_map_shape_list=[(1, 1)])
anchor_corners = anchors_list[0].get()
return (anchor_corners,)
exp_anchor_corners = [[-121, -35, 135, 29], [-249, -67, 263, 61],
[-505, -131, 519, 125], [-57, -67, 71, 61],
......@@ -57,8 +57,8 @@ class GridAnchorGeneratorTest(test_case.TestCase):
anchor_stride=anchor_stride,
anchor_offset=anchor_offset)
anchors = anchor_generator.generate(feature_map_shape_list=[(2, 2)])
anchor_corners = anchors.get()
anchors_list = anchor_generator.generate(feature_map_shape_list=[(2, 2)])
anchor_corners = anchors_list[0].get()
return (anchor_corners,)
exp_anchor_corners = [[-2.5, -2.5, 2.5, 2.5], [-5., -5., 5., 5.],
[-10., -10., 10., 10.], [-2.5, 16.5, 2.5, 21.5],
......@@ -83,9 +83,9 @@ class GridAnchorGeneratorTest(test_case.TestCase):
anchor_stride=anchor_stride,
anchor_offset=anchor_offset)
anchors = anchor_generator.generate(
anchors_list = anchor_generator.generate(
feature_map_shape_list=[(feature_map_height, feature_map_width)])
anchor_corners = anchors.get()
anchor_corners = anchors_list[0].get()
return (anchor_corners,)
exp_anchor_corners = [[-2.5, -2.5, 2.5, 2.5], [-5., -5., 5., 5.],
......
research/object_detection/anchor_generators/multiple_grid_anchor_generator.py
View file @ 3f78f4cf
......@@ -165,10 +165,9 @@ class MultipleGridAnchorGenerator(anchor_generator.AnchorGenerator):
grid.
Returns:
boxes: a BoxList holding a collection of N anchor boxes. Additionally
this BoxList also holds a `feature_map_index` field which, for each
anchor, stores the index of the corresponding feature map which was used
to generate it.
boxes_list: a list of BoxLists each holding anchor boxes corresponding to
the input feature map shapes.
Raises:
ValueError: if feature_map_shape_list, box_specs_list do not have the same
length.
......@@ -211,7 +210,6 @@ class MultipleGridAnchorGenerator(anchor_generator.AnchorGenerator):
raise ValueError('%s must be a list of pairs.' % arg_name)
anchor_grid_list = []
anchor_indices_list = []
min_im_shape = tf.minimum(im_height, im_width)
scale_height = min_im_shape / im_height
scale_width = min_im_shape / im_width
......@@ -219,10 +217,11 @@ class MultipleGridAnchorGenerator(anchor_generator.AnchorGenerator):
scale_height * self._base_anchor_size[0],
scale_width * self._base_anchor_size[1]
]
for feature_map_index, (
grid_size, scales, aspect_ratios, stride, offset) in enumerate(
zip(feature_map_shape_list, self._scales, self._aspect_ratios,
anchor_strides, anchor_offsets)):
for feature_map_index, (grid_size, scales, aspect_ratios, stride,
offset) in enumerate(
zip(feature_map_shape_list, self._scales,
self._aspect_ratios, anchor_strides,
anchor_offsets)):
tiled_anchors = grid_anchor_generator.tile_anchors(
grid_height=grid_size[0],
grid_width=grid_size[1],
......@@ -231,30 +230,17 @@ class MultipleGridAnchorGenerator(anchor_generator.AnchorGenerator):
base_anchor_size=base_anchor_size,
anchor_stride=stride,
anchor_offset=offset)
anchor_grid_list.append(tiled_anchors)
if self._clip_window is not None:
tiled_anchors = box_list_ops.clip_to_window(
tiled_anchors, self._clip_window, filter_nonoverlapping=False)
num_anchors_in_layer = tiled_anchors.num_boxes_static()
if num_anchors_in_layer is None:
num_anchors_in_layer = tiled_anchors.num_boxes()
anchor_indices_list.append(
feature_map_index * tf.ones([num_anchors_in_layer]))
concatenated_anchors = box_list_ops.concatenate(anchor_grid_list)
anchor_indices = tf.concat(anchor_indices_list, 0)
num_anchors = concatenated_anchors.num_boxes_static()
if num_anchors is None:
num_anchors = concatenated_anchors.num_boxes()
if self._clip_window is not None:
concatenated_anchors = box_list_ops.clip_to_window(
concatenated_anchors, self._clip_window, filter_nonoverlapping=False)
# TODO: make reshape an option for the clip_to_window op
concatenated_anchors.set(
tf.reshape(concatenated_anchors.get(), [num_anchors, 4]))
stddevs_tensor = 0.01 * tf.ones(
[num_anchors, 4], dtype=tf.float32, name='stddevs')
concatenated_anchors.add_field('stddev', stddevs_tensor)
concatenated_anchors.add_field('feature_map_index', anchor_indices)
anchor_indices = feature_map_index * tf.ones([num_anchors_in_layer])
tiled_anchors.add_field('feature_map_index', anchor_indices)
anchor_grid_list.append(tiled_anchors)
return concatenated_anchors
return anchor_grid_list
def create_ssd_anchors(num_layers=6,
......@@ -285,7 +271,7 @@ def create_ssd_anchors(num_layers=6,
grid sizes passed in at generation time)
min_scale: scale of anchors corresponding to finest resolution (float)
max_scale: scale of anchors corresponding to coarsest resolution (float)
scales: As list of anchor scales to use. When not None and not emtpy,
scales: As list of anchor scales to use. When not None and not empty,
min_scale and max_scale are not used.
aspect_ratios: list or tuple of (float) aspect ratios to place on each
grid point.
......
research/object_detection/anchor_generators/multiple_grid_anchor_generator_test.py
View file @ 3f78f4cf
......@@ -37,8 +37,8 @@ class MultipleGridAnchorGeneratorTest(test_case.TestCase):
base_anchor_size=tf.constant([256, 256], dtype=tf.float32),
anchor_strides=[(16, 16)],
anchor_offsets=[(7, -3)])
anchors = anchor_generator.generate(feature_map_shape_list=[(1, 1)])
return anchors.get()
anchors_list = anchor_generator.generate(feature_map_shape_list=[(1, 1)])
return anchors_list[0].get()
exp_anchor_corners = [[-121, -35, 135, 29], [-249, -67, 263, 61],
[-505, -131, 519, 125], [-57, -67, 71, 61],
[-121, -131, 135, 125], [-249, -259, 263, 253],
......@@ -57,8 +57,8 @@ class MultipleGridAnchorGeneratorTest(test_case.TestCase):
base_anchor_size=tf.constant([10, 10], dtype=tf.float32),
anchor_strides=[(19, 19)],
anchor_offsets=[(0, 0)])
anchors = anchor_generator.generate(feature_map_shape_list=[(2, 2)])
return anchors.get()
anchors_list = anchor_generator.generate(feature_map_shape_list=[(2, 2)])
return anchors_list[0].get()
exp_anchor_corners = [[-2.5, -2.5, 2.5, 2.5], [-5., -5., 5., 5.],
[-10., -10., 10., 10.], [-2.5, 16.5, 2.5, 21.5],
[-5., 14., 5, 24], [-10., 9., 10, 29],
......@@ -76,9 +76,9 @@ class MultipleGridAnchorGeneratorTest(test_case.TestCase):
anchor_generator = ag.MultipleGridAnchorGenerator(
box_specs_list, base_anchor_size=tf.constant([1, 1],
dtype=tf.float32))
anchors = anchor_generator.generate(feature_map_shape_list=[(tf.constant(
1, dtype=tf.int32), tf.constant(2, dtype=tf.int32))])
return anchors.get()
anchors_list = anchor_generator.generate(feature_map_shape_list=[(
tf.constant(1, dtype=tf.int32), tf.constant(2, dtype=tf.int32))])
return anchors_list[0].get()
exp_anchor_corners = [[0., -0.25, 1., 0.75], [0., 0.25, 1., 1.25]]
anchor_corners_out = self.execute(graph_fn, [])
......@@ -91,9 +91,9 @@ class MultipleGridAnchorGeneratorTest(test_case.TestCase):
anchor_generator = ag.MultipleGridAnchorGenerator(
box_specs_list, base_anchor_size=tf.constant([1, 1],
dtype=tf.float32))
anchors = anchor_generator.generate(feature_map_shape_list=[(height,
anchors_list = anchor_generator.generate(feature_map_shape_list=[(height,
width)])
return anchors.get()
return anchors_list[0].get()
exp_anchor_corners = [[0., -0.25, 1., 0.75], [0., 0.25, 1., 1.25]]
......@@ -109,12 +109,12 @@ class MultipleGridAnchorGeneratorTest(test_case.TestCase):
anchor_generator = ag.MultipleGridAnchorGenerator(
box_specs_list, base_anchor_size=tf.constant([1, 1],
dtype=tf.float32))
anchors = anchor_generator.generate(
anchors_list = anchor_generator.generate(
feature_map_shape_list=[(tf.constant(1, dtype=tf.int32), tf.constant(
2, dtype=tf.int32))],
im_height=320,
im_width=640)
return anchors.get()
return anchors_list[0].get()
exp_anchor_corners = [[0., 0., 1., 0.5], [0., 0.5, 1., 1.]]
anchor_corners_out = self.execute(graph_fn, [])
......@@ -131,9 +131,9 @@ class MultipleGridAnchorGeneratorTest(test_case.TestCase):
base_anchor_size=tf.constant([1.0, 1.0], dtype=tf.float32),
anchor_strides=[(.25, .25), (.5, .5)],
anchor_offsets=[(.125, .125), (.25, .25)])
anchors = anchor_generator.generate(feature_map_shape_list=[(4, 4),
(2, 2)])
return anchors.get()
anchors_list = anchor_generator.generate(feature_map_shape_list=[(4, 4), (
2, 2)])
return [anchors.get() for anchors in anchors_list]
# height and width of box with .5 aspect ratio
h = np.sqrt(2)
w = 1.0/np.sqrt(2)
......@@ -150,7 +150,7 @@ class MultipleGridAnchorGeneratorTest(test_case.TestCase):
[.125-1.0, .125-1.0, .125+1.0, .125+1.0],
[.125-.5*h, .125-.5*w, .125+.5*h, .125+.5*w],]
anchor_corners_out = self.execute(graph_fn, [])
anchor_corners_out = np.concatenate(self.execute(graph_fn, []), axis=0)
self.assertEquals(anchor_corners_out.shape, (56, 4))
big_grid_corners = anchor_corners_out[0:3, :]
small_grid_corners = anchor_corners_out[48:, :]
......@@ -168,9 +168,9 @@ class MultipleGridAnchorGeneratorTest(test_case.TestCase):
box_specs_list,
base_anchor_size=tf.constant([1.0, 1.0], dtype=tf.float32),
clip_window=clip_window)
anchors = anchor_generator.generate(feature_map_shape_list=[(4, 4),
(2, 2)])
return anchors.get()
anchors_list = anchor_generator.generate(feature_map_shape_list=[(4, 4), (
2, 2)])
return [anchors.get() for anchors in anchors_list]
# height and width of box with .5 aspect ratio
h = np.sqrt(2)
w = 1.0/np.sqrt(2)
......@@ -183,7 +183,7 @@ class MultipleGridAnchorGeneratorTest(test_case.TestCase):
[.25, .25, 1, 1],
[.75-.5*h, .75-.5*w, 1, 1]]
anchor_corners_out = self.execute(graph_fn, [])
anchor_corners_out = np.concatenate(self.execute(graph_fn, []), axis=0)
small_grid_corners = anchor_corners_out[48:, :]
self.assertAllClose(small_grid_corners, exp_small_grid_corners)
......@@ -264,10 +264,10 @@ class CreateSSDAnchorsTest(test_case.TestCase):
feature_map_shape_list = [(38, 38), (19, 19), (10, 10),
(5, 5), (3, 3), (1, 1)]
anchors = anchor_generator.generate(
anchors_list = anchor_generator.generate(
feature_map_shape_list=feature_map_shape_list)
return anchors.get()
anchor_corners_out = self.execute(graph_fn1, [])
return [anchors.get() for anchors in anchors_list]
anchor_corners_out = np.concatenate(self.execute(graph_fn1, []), axis=0)
self.assertEquals(anchor_corners_out.shape, (7308, 4))
def graph_fn2():
......@@ -278,10 +278,10 @@ class CreateSSDAnchorsTest(test_case.TestCase):
feature_map_shape_list = [(38, 38), (19, 19), (10, 10),
(5, 5), (3, 3), (1, 1)]
anchors = anchor_generator.generate(
anchors_list = anchor_generator.generate(
feature_map_shape_list=feature_map_shape_list)
return anchors.get()
anchor_corners_out = self.execute(graph_fn2, [])
return [anchors.get() for anchors in anchors_list]
anchor_corners_out = np.concatenate(self.execute(graph_fn2, []), axis=0)
self.assertEquals(anchor_corners_out.shape, (11640, 4))
......
research/object_detection/anchor_generators/multiscale_grid_anchor_generator.py
View file @ 3f78f4cf
......@@ -21,14 +21,15 @@ T.-Y. Lin, P. Goyal, R. Girshick, K. He, P. Dollar
"""
from object_detection.anchor_generators import grid_anchor_generator
from object_detection.core import anchor_generator
from object_detection.core import box_list_ops
class MultiscaleGridAnchorGenerator(object):
class MultiscaleGridAnchorGenerator(anchor_generator.AnchorGenerator):
"""Generate a grid of anchors for multiple CNN layers of different scale."""
def __init__(self, min_level, max_level, anchor_scale, aspect_ratios,
scales_per_octave):
scales_per_octave, normalize_coordinates=True):
"""Constructs a MultiscaleGridAnchorGenerator.
To construct anchors, at multiple scale resolutions, one must provide a
......@@ -48,10 +49,13 @@ class MultiscaleGridAnchorGenerator(object):
aspect_ratios: list or tuple of (float) aspect ratios to place on each
grid point.
scales_per_octave: integer number of intermediate scales per scale octave.
normalize_coordinates: whether to produce anchors in normalized
coordinates. (defaults to True).
"""
self._anchor_grid_info = []
self._aspect_ratios = aspect_ratios
self._scales_per_octave = scales_per_octave
self._normalize_coordinates = normalize_coordinates
for level in range(min_level, max_level + 1):
anchor_stride = [2**level, 2**level]
......@@ -80,7 +84,7 @@ class MultiscaleGridAnchorGenerator(object):
return len(self._anchor_grid_info) * [
len(self._aspect_ratios) * self._scales_per_octave]
def generate(self, feature_map_shape_list, im_height, im_width):
def _generate(self, feature_map_shape_list, im_height, im_width):
"""Generates a collection of bounding boxes to be used as anchors.
Currently we require the input image shape to be statically defined. That
......@@ -95,7 +99,8 @@ class MultiscaleGridAnchorGenerator(object):
im_width: the width of the image to generate the grid for.
Returns:
boxes: a BoxList holding a collection of N anchor boxes
boxes_list: a list of BoxLists each holding anchor boxes corresponding to
the input feature map shapes.
Raises:
ValueError: if im_height and im_width are not integers.
"""
......@@ -105,7 +110,7 @@ class MultiscaleGridAnchorGenerator(object):
anchor_grid_list = []
for feat_shape, grid_info in zip(feature_map_shape_list,
self._anchor_grid_info):
# TODO check the feature_map_shape_list is consistent with
# TODO(rathodv) check the feature_map_shape_list is consistent with
# self._anchor_grid_info
level = grid_info['level']
stride = 2**level
......@@ -123,9 +128,11 @@ class MultiscaleGridAnchorGenerator(object):
base_anchor_size=base_anchor_size,
anchor_stride=anchor_stride,
anchor_offset=anchor_offset)
anchor_grid_list.append(
ag.generate(feature_map_shape_list=[(feat_h, feat_w)]))
(anchor_grid,) = ag.generate(feature_map_shape_list=[(feat_h, feat_w)])
concatenated_anchors = box_list_ops.concatenate(anchor_grid_list)
if self._normalize_coordinates:
anchor_grid = box_list_ops.to_normalized_coordinates(
anchor_grid, im_height, im_width, check_range=False)
anchor_grid_list.append(anchor_grid)
return concatenated_anchors
return anchor_grid_list
research/object_detection/anchor_generators/multiscale_grid_anchor_generator_test.py
View file @ 3f78f4cf
......@@ -37,10 +37,35 @@ class MultiscaleGridAnchorGeneratorTest(test_case.TestCase):
[-16, -48, 112, 80],
[-16, -16, 112, 112]]
anchor_generator = mg.MultiscaleGridAnchorGenerator(
min_level, max_level, anchor_scale, aspect_ratios, scales_per_octave)
anchors = anchor_generator.generate(feature_map_shape_list,
im_height, im_width)
anchor_corners = anchors.get()
min_level, max_level, anchor_scale, aspect_ratios, scales_per_octave,
normalize_coordinates=False)
anchors_list = anchor_generator.generate(
feature_map_shape_list, im_height=im_height, im_width=im_width)
anchor_corners = anchors_list[0].get()
with self.test_session():
anchor_corners_out = anchor_corners.eval()
self.assertAllClose(anchor_corners_out, exp_anchor_corners)
def test_construct_single_anchor_in_normalized_coordinates(self):
min_level = 5
max_level = 5
anchor_scale = 4.0
aspect_ratios = [1.0]
scales_per_octave = 1
im_height = 64
im_width = 128
feature_map_shape_list = [(2, 2)]
exp_anchor_corners = [[-48./64, -48./128, 80./64, 80./128],
[-48./64, -16./128, 80./64, 112./128],
[-16./64, -48./128, 112./64, 80./128],
[-16./64, -16./128, 112./64, 112./128]]
anchor_generator = mg.MultiscaleGridAnchorGenerator(
min_level, max_level, anchor_scale, aspect_ratios, scales_per_octave,
normalize_coordinates=True)
anchors_list = anchor_generator.generate(
feature_map_shape_list, im_height=im_height, im_width=im_width)
anchor_corners = anchors_list[0].get()
with self.test_session():
anchor_corners_out = anchor_corners.eval()
......@@ -53,7 +78,8 @@ class MultiscaleGridAnchorGeneratorTest(test_case.TestCase):
aspect_ratios = [1.0, 2.0]
scales_per_octave = 3
anchor_generator = mg.MultiscaleGridAnchorGenerator(
min_level, max_level, anchor_scale, aspect_ratios, scales_per_octave)
min_level, max_level, anchor_scale, aspect_ratios, scales_per_octave,
normalize_coordinates=False)
self.assertEqual(anchor_generator.num_anchors_per_location(), [6, 6])
def test_construct_single_anchor_fails_with_tensor_image_size(self):
......@@ -66,9 +92,11 @@ class MultiscaleGridAnchorGeneratorTest(test_case.TestCase):
im_width = tf.constant(64)
feature_map_shape_list = [(2, 2)]
anchor_generator = mg.MultiscaleGridAnchorGenerator(
min_level, max_level, anchor_scale, aspect_ratios, scales_per_octave)
min_level, max_level, anchor_scale, aspect_ratios, scales_per_octave,
normalize_coordinates=False)
with self.assertRaises(ValueError):
anchor_generator.generate(feature_map_shape_list, im_height, im_width)
anchor_generator.generate(
feature_map_shape_list, im_height=im_height, im_width=im_width)
def test_construct_single_anchor_with_odd_input_dimension(self):
......@@ -82,10 +110,11 @@ class MultiscaleGridAnchorGeneratorTest(test_case.TestCase):
im_width = 65
feature_map_shape_list = [(3, 3)]
anchor_generator = mg.MultiscaleGridAnchorGenerator(
min_level, max_level, anchor_scale, aspect_ratios, scales_per_octave)
anchors = anchor_generator.generate(feature_map_shape_list, im_height,
im_width)
anchor_corners = anchors.get()
min_level, max_level, anchor_scale, aspect_ratios, scales_per_octave,
normalize_coordinates=False)
anchors_list = anchor_generator.generate(
feature_map_shape_list, im_height=im_height, im_width=im_width)
anchor_corners = anchors_list[0].get()
return (anchor_corners,)
anchor_corners_out = self.execute(graph_fn, [])
exp_anchor_corners = [[-64, -64, 64, 64],
......@@ -111,13 +140,15 @@ class MultiscaleGridAnchorGeneratorTest(test_case.TestCase):
im_width = 64
feature_map_shape_list = [(2, 2), (1, 1)]
anchor_generator = mg.MultiscaleGridAnchorGenerator(
min_level, max_level, anchor_scale, aspect_ratios, scales_per_octave)
anchors = anchor_generator.generate(feature_map_shape_list, im_height,
im_width)
anchor_corners = anchors.get()
return (anchor_corners,)
min_level, max_level, anchor_scale, aspect_ratios, scales_per_octave,
normalize_coordinates=False)
anchors_list = anchor_generator.generate(feature_map_shape_list,
im_height=im_height,
im_width=im_width)
anchor_corners = [anchors.get() for anchors in anchors_list]
return anchor_corners
anchor_corners_out = self.execute(graph_fn, [])
anchor_corners_out = np.concatenate(self.execute(graph_fn, []), axis=0)
exp_anchor_corners = [[-48, -48, 80, 80],
[-48, -16, 80, 112],
[-16, -48, 112, 80],
......@@ -135,19 +166,22 @@ class MultiscaleGridAnchorGeneratorTest(test_case.TestCase):
scales_per_octave = 2
im_height = 64
im_width = 64
feature_map_shape_list = [(1, 1), (1, 1)]
feature_map_shape_list = [(1, 1)]
anchor_generator = mg.MultiscaleGridAnchorGenerator(
min_level, max_level, anchor_scale, aspect_ratios, scales_per_octave)
anchors = anchor_generator.generate(feature_map_shape_list, im_height,
im_width)
anchor_corners = anchors.get()
return (anchor_corners,)
min_level, max_level, anchor_scale, aspect_ratios, scales_per_octave,
normalize_coordinates=False)
anchors_list = anchor_generator.generate(feature_map_shape_list,
im_height=im_height,
im_width=im_width)
anchor_corners = [anchors.get() for anchors in anchors_list]
return anchor_corners
# There are 4 set of anchors in this configuration. The order is:
# [[2**0.0 intermediate scale + 1.0 aspect],
# [2**0.5 intermediate scale + 1.0 aspect]]
exp_anchor_corners = [[-96., -96., 160., 160.],
[-149.0193, -149.0193, 213.0193, 213.0193]]
anchor_corners_out = self.execute(graph_fn, [])
self.assertAllClose(anchor_corners_out, exp_anchor_corners)
......@@ -160,18 +194,21 @@ class MultiscaleGridAnchorGeneratorTest(test_case.TestCase):
scales_per_octave = 2
im_height = 64
im_width = 64
feature_map_shape_list = [(1, 1), (1, 1), (1, 1), (1, 1)]
feature_map_shape_list = [(1, 1)]
anchor_generator = mg.MultiscaleGridAnchorGenerator(
min_level, max_level, anchor_scale, aspect_ratios, scales_per_octave)
anchors = anchor_generator.generate(feature_map_shape_list, im_height,
im_width)
anchor_corners = anchors.get()
min_level, max_level, anchor_scale, aspect_ratios, scales_per_octave,
normalize_coordinates=False)
anchors_list = anchor_generator.generate(feature_map_shape_list,
im_height=im_height,
im_width=im_width)
anchor_corners = [anchors.get() for anchors in anchors_list]
return anchor_corners
# There are 4 set of anchors in this configuration. The order is:
# [[2**0.0 intermediate scale + 1.0 aspect],
# [2**0.5 intermediate scale + 1.0 aspect],
# [2**0.0 intermediate scale + 2.0 aspect],
# [2**0.5 intermediate scale + 2.0 aspect]]
exp_anchor_corners = [[-96., -96., 160., 160.],
[-149.0193, -149.0193, 213.0193, 213.0193],
[-58.50967, -149.0193, 122.50967, 213.0193],
......@@ -193,18 +230,22 @@ class MultiscaleGridAnchorGeneratorTest(test_case.TestCase):
feature_map_shape_list = [(feature_map1_height, feature_map1_width),
(feature_map2_height, feature_map2_width)]
anchor_generator = mg.MultiscaleGridAnchorGenerator(
min_level, max_level, anchor_scale, aspect_ratios, scales_per_octave)
anchors = anchor_generator.generate(feature_map_shape_list, im_height,
im_width)
anchor_corners = anchors.get()
return (anchor_corners,)
min_level, max_level, anchor_scale, aspect_ratios, scales_per_octave,
normalize_coordinates=False)
anchors_list = anchor_generator.generate(feature_map_shape_list,
im_height=im_height,
im_width=im_width)
anchor_corners = [anchors.get() for anchors in anchors_list]
return anchor_corners
anchor_corners_out = self.execute_cpu(graph_fn, [
anchor_corners_out = np.concatenate(
self.execute_cpu(graph_fn, [
np.array(2, dtype=np.int32),
np.array(2, dtype=np.int32),
np.array(1, dtype=np.int32),
np.array(1, dtype=np.int32)
])
]),
axis=0)
exp_anchor_corners = [[-48, -48, 80, 80],
[-48, -16, 80, 112],
[-16, -48, 112, 80],
......
research/object_detection/box_coders/BUILD deleted 100644 → 0
View file @ 73748d01
# Tensorflow Object Detection API: Box Coder implementations.
package(
default_visibility = ["//visibility:public"],
)
licenses(["notice"])
# Apache 2.0
py_library(
name = "faster_rcnn_box_coder",
srcs = [
"faster_rcnn_box_coder.py",
],
deps = [
"//tensorflow/models/research/object_detection/core:box_coder",
"//tensorflow/models/research/object_detection/core:box_list",
],
)
py_test(
name = "faster_rcnn_box_coder_test",
srcs = [
"faster_rcnn_box_coder_test.py",
],
deps = [
":faster_rcnn_box_coder",
"//tensorflow",
"//tensorflow/models/research/object_detection/core:box_list",
],
)
py_library(
name = "keypoint_box_coder",
srcs = [
"keypoint_box_coder.py",
],
deps = [
"//tensorflow/models/research/object_detection/core:box_coder",
"//tensorflow/models/research/object_detection/core:box_list",
"//tensorflow/models/research/object_detection/core:standard_fields",
],
)
py_test(
name = "keypoint_box_coder_test",
srcs = [
"keypoint_box_coder_test.py",
],
deps = [
":keypoint_box_coder",
"//tensorflow",
"//tensorflow/models/research/object_detection/core:box_list",
"//tensorflow/models/research/object_detection/core:standard_fields",
],
)
py_library(
name = "mean_stddev_box_coder",
srcs = [
"mean_stddev_box_coder.py",
],
deps = [
"//tensorflow/models/research/object_detection/core:box_coder",
"//tensorflow/models/research/object_detection/core:box_list",
],
)
py_test(
name = "mean_stddev_box_coder_test",
srcs = [
"mean_stddev_box_coder_test.py",
],
deps = [
":mean_stddev_box_coder",
"//tensorflow",
"//tensorflow/models/research/object_detection/core:box_list",
],
)
py_library(
name = "square_box_coder",
srcs = [
"square_box_coder.py",
],
deps = [
"//tensorflow/models/research/object_detection/core:box_coder",
"//tensorflow/models/research/object_detection/core:box_list",
],
)
py_test(
name = "square_box_coder_test",
srcs = [
"square_box_coder_test.py",
],
deps = [
":square_box_coder",
"//tensorflow",
"//tensorflow/models/research/object_detection/core:box_list",
],
)
research/object_detection/builders/BUILD deleted 100644 → 0
View file @ 73748d01
# Tensorflow Object Detection API: component builders.
package(
default_visibility = ["//visibility:public"],
)
licenses(["notice"])
# Apache 2.0
py_library(
name = "model_builder",
srcs = ["model_builder.py"],
deps = [
":anchor_generator_builder",
":box_coder_builder",
":box_predictor_builder",
":hyperparams_builder",
":image_resizer_builder",
":losses_builder",
":matcher_builder",
":post_processing_builder",
":region_similarity_calculator_builder",
"//tensorflow/models/research/object_detection/core:box_predictor",
"//tensorflow/models/research/object_detection/meta_architectures:faster_rcnn_meta_arch",
"//tensorflow/models/research/object_detection/meta_architectures:rfcn_meta_arch",
"//tensorflow/models/research/object_detection/meta_architectures:ssd_meta_arch",
"//tensorflow/models/research/object_detection/models:embedded_ssd_mobilenet_v1_feature_extractor",
"//tensorflow/models/research/object_detection/models:faster_rcnn_inception_resnet_v2_feature_extractor",
"//tensorflow/models/research/object_detection/models:faster_rcnn_inception_v2_feature_extractor",
"//tensorflow/models/research/object_detection/models:faster_rcnn_nas_feature_extractor",
"//tensorflow/models/research/object_detection/models:faster_rcnn_resnet_v1_feature_extractor",
"//tensorflow/models/research/object_detection/models:ssd_inception_v2_feature_extractor",
"//tensorflow/models/research/object_detection/models:ssd_inception_v3_feature_extractor",
"//tensorflow/models/research/object_detection/models:ssd_mobilenet_v1_feature_extractor",
"//tensorflow/models/research/object_detection/models:ssd_resnet_v1_fpn_feature_extractor",
"//tensorflow/models/research/object_detection/protos:model_py_pb2",
],
)
py_test(
name = "model_builder_test",
srcs = ["model_builder_test.py"],
deps = [
":model_builder",
"//tensorflow",
"//tensorflow/models/research/object_detection/meta_architectures:faster_rcnn_meta_arch",
"//tensorflow/models/research/object_detection/meta_architectures:ssd_meta_arch",
"//tensorflow/models/research/object_detection/models:embedded_ssd_mobilenet_v1_feature_extractor",
"//tensorflow/models/research/object_detection/models:faster_rcnn_inception_resnet_v2_feature_extractor",
"//tensorflow/models/research/object_detection/models:faster_rcnn_inception_v2_feature_extractor",
"//tensorflow/models/research/object_detection/models:faster_rcnn_nas_feature_extractor",
"//tensorflow/models/research/object_detection/models:faster_rcnn_resnet_v1_feature_extractor",
"//tensorflow/models/research/object_detection/models:ssd_inception_v2_feature_extractor",
"//tensorflow/models/research/object_detection/models:ssd_inception_v3_feature_extractor",
"//tensorflow/models/research/object_detection/models:ssd_mobilenet_v1_feature_extractor",
"//tensorflow/models/research/object_detection/models:ssd_resnet_v1_fpn_feature_extractor",
"//tensorflow/models/research/object_detection/protos:model_py_pb2",
],
)
py_library(
name = "matcher_builder",
srcs = ["matcher_builder.py"],
deps = [
"//tensorflow/models/research/object_detection/matchers:argmax_matcher",
"//tensorflow/models/research/object_detection/matchers:bipartite_matcher",
"//tensorflow/models/research/object_detection/protos:matcher_py_pb2",
],
)
py_test(
name = "matcher_builder_test",
srcs = ["matcher_builder_test.py"],
deps = [
":matcher_builder",
"//tensorflow/models/research/object_detection/matchers:argmax_matcher",
"//tensorflow/models/research/object_detection/matchers:bipartite_matcher",
"//tensorflow/models/research/object_detection/protos:matcher_py_pb2",
],
)
py_library(
name = "box_coder_builder",
srcs = ["box_coder_builder.py"],
deps = [
"//tensorflow/models/research/object_detection/box_coders:faster_rcnn_box_coder",
"//tensorflow/models/research/object_detection/box_coders:keypoint_box_coder",
"//tensorflow/models/research/object_detection/box_coders:mean_stddev_box_coder",
"//tensorflow/models/research/object_detection/box_coders:square_box_coder",
"//tensorflow/models/research/object_detection/protos:box_coder_py_pb2",
],
)
py_test(
name = "box_coder_builder_test",
srcs = ["box_coder_builder_test.py"],
deps = [
":box_coder_builder",
"//tensorflow",
"//tensorflow/models/research/object_detection/box_coders:faster_rcnn_box_coder",
"//tensorflow/models/research/object_detection/box_coders:keypoint_box_coder",
"//tensorflow/models/research/object_detection/box_coders:mean_stddev_box_coder",
"//tensorflow/models/research/object_detection/box_coders:square_box_coder",
"//tensorflow/models/research/object_detection/protos:box_coder_py_pb2",
],
)
py_library(
name = "anchor_generator_builder",
srcs = ["anchor_generator_builder.py"],
deps = [
"//tensorflow/models/research/object_detection/anchor_generators:grid_anchor_generator",
"//tensorflow/models/research/object_detection/anchor_generators:multiple_grid_anchor_generator",
"//tensorflow/models/research/object_detection/anchor_generators:multiscale_grid_anchor_generator",
"//tensorflow/models/research/object_detection/protos:anchor_generator_py_pb2",
],
)
py_test(
name = "anchor_generator_builder_test",
srcs = ["anchor_generator_builder_test.py"],
deps = [
":anchor_generator_builder",
"//tensorflow",
"//tensorflow/models/research/object_detection/anchor_generators:grid_anchor_generator",
"//tensorflow/models/research/object_detection/anchor_generators:multiple_grid_anchor_generator",
"//tensorflow/models/research/object_detection/anchor_generators:multiscale_grid_anchor_generator",
"//tensorflow/models/research/object_detection/protos:anchor_generator_py_pb2",
],
)
py_library(
name = "dataset_builder",
srcs = ["dataset_builder.py"],
deps = [
"//tensorflow",
"//tensorflow/models/research/object_detection/data_decoders:tf_example_decoder",
"//tensorflow/models/research/object_detection/protos:input_reader_py_pb2",
"//tensorflow/models/research/object_detection/utils:dataset_util",
],
)
py_test(
name = "dataset_builder_test",
srcs = [
"dataset_builder_test.py",
],
deps = [
":dataset_builder",
"//tensorflow",
"//tensorflow/models/research/object_detection/core:standard_fields",
"//tensorflow/models/research/object_detection/protos:input_reader_py_pb2",
"//tensorflow/models/research/object_detection/utils:dataset_util",
],
)
py_library(
name = "input_reader_builder",
srcs = ["input_reader_builder.py"],
deps = [
"//tensorflow",
"//tensorflow/models/research/object_detection/data_decoders:tf_example_decoder",
"//tensorflow/models/research/object_detection/protos:input_reader_py_pb2",
],
)
py_test(
name = "input_reader_builder_test",
srcs = [
"input_reader_builder_test.py",
],
deps = [
":input_reader_builder",
"//tensorflow",
"//tensorflow/models/research/object_detection/core:standard_fields",
"//tensorflow/models/research/object_detection/protos:input_reader_py_pb2",
],
)
py_library(
name = "losses_builder",
srcs = ["losses_builder.py"],
deps = [
"//tensorflow/models/research/object_detection/core:losses",
"//tensorflow/models/research/object_detection/protos:losses_py_pb2",
],
)
py_test(
name = "losses_builder_test",
srcs = ["losses_builder_test.py"],
deps = [
":losses_builder",
"//tensorflow/models/research/object_detection/core:losses",
"//tensorflow/models/research/object_detection/protos:losses_py_pb2",
],
)
py_library(
name = "optimizer_builder",
srcs = ["optimizer_builder.py"],
deps = [
"//tensorflow",
"//tensorflow/models/research/object_detection/utils:learning_schedules",
],
)
py_test(
name = "optimizer_builder_test",
srcs = ["optimizer_builder_test.py"],
deps = [
":optimizer_builder",
"//tensorflow",
"//tensorflow/models/research/object_detection/protos:optimizer_py_pb2",
],
)
py_library(
name = "post_processing_builder",
srcs = ["post_processing_builder.py"],
deps = [
"//tensorflow",
"//tensorflow/models/research/object_detection/core:post_processing",
"//tensorflow/models/research/object_detection/protos:post_processing_py_pb2",
],
)
py_test(
name = "post_processing_builder_test",
srcs = ["post_processing_builder_test.py"],
deps = [
":post_processing_builder",
"//tensorflow",
"//tensorflow/models/research/object_detection/protos:post_processing_py_pb2",
],
)
py_library(
name = "hyperparams_builder",
srcs = ["hyperparams_builder.py"],
deps = [
"//tensorflow/models/research/object_detection/protos:hyperparams_py_pb2",
],
)
py_test(
name = "hyperparams_builder_test",
srcs = ["hyperparams_builder_test.py"],
deps = [
":hyperparams_builder",
"//tensorflow",
"//tensorflow/models/research/object_detection/protos:hyperparams_py_pb2",
],
)
py_library(
name = "box_predictor_builder",
srcs = ["box_predictor_builder.py"],
deps = [
":hyperparams_builder",
"//tensorflow/models/research/object_detection/core:box_predictor",
"//tensorflow/models/research/object_detection/protos:box_predictor_py_pb2",
],
)
py_test(
name = "box_predictor_builder_test",
srcs = ["box_predictor_builder_test.py"],
deps = [
":box_predictor_builder",
":hyperparams_builder",
"//tensorflow",
"//tensorflow/models/research/object_detection/protos:box_predictor_py_pb2",
"//tensorflow/models/research/object_detection/protos:hyperparams_py_pb2",
],
)
py_library(
name = "region_similarity_calculator_builder",
srcs = ["region_similarity_calculator_builder.py"],
deps = [
"//tensorflow/models/research/object_detection/core:region_similarity_calculator",
"//tensorflow/models/research/object_detection/protos:region_similarity_calculator_py_pb2",
],
)
py_test(
name = "region_similarity_calculator_builder_test",
srcs = ["region_similarity_calculator_builder_test.py"],
deps = [
":region_similarity_calculator_builder",
"//tensorflow",
],
)
py_library(
name = "preprocessor_builder",
srcs = ["preprocessor_builder.py"],
deps = [
"//tensorflow",
"//tensorflow/models/research/object_detection/core:preprocessor",
"//tensorflow/models/research/object_detection/protos:preprocessor_py_pb2",
],
)
py_test(
name = "preprocessor_builder_test",
srcs = [
"preprocessor_builder_test.py",
],
deps = [
":preprocessor_builder",
"//tensorflow",
"//tensorflow/models/research/object_detection/core:preprocessor",
"//tensorflow/models/research/object_detection/protos:preprocessor_py_pb2",
],
)
py_library(
name = "image_resizer_builder",
srcs = ["image_resizer_builder.py"],
deps = [
"//tensorflow",
"//tensorflow/models/research/object_detection/core:preprocessor",
"//tensorflow/models/research/object_detection/protos:image_resizer_py_pb2",
],
)
py_test(
name = "image_resizer_builder_test",
srcs = ["image_resizer_builder_test.py"],
deps = [
":image_resizer_builder",
"//tensorflow",
"//tensorflow/models/research/object_detection/protos:image_resizer_py_pb2",
],
)
research/object_detection/builders/anchor_generator_builder.py
View file @ 3f78f4cf
......@@ -87,7 +87,8 @@ def build(anchor_generator_config):
cfg.max_level,
cfg.anchor_scale,
[float(aspect_ratio) for aspect_ratio in cfg.aspect_ratios],
cfg.scales_per_octave
cfg.scales_per_octave,
cfg.normalize_coordinates
)
else:
raise ValueError('Empty anchor generator.')
research/object_detection/builders/anchor_generator_builder_test.py
View file @ 3f78f4cf
......@@ -276,6 +276,24 @@ class AnchorGeneratorBuilderTest(tf.test.TestCase):
self.assertAllClose(anchor_grid_info['info'][2],
[4.0 * 2**level, 4.0 * 2**level])
self.assertAllClose(anchor_grid_info['info'][3], [2**level, 2**level])
self.assertTrue(anchor_generator_object._normalize_coordinates)
def test_build_multiscale_anchor_generator_with_anchors_in_pixel_coordinates(
self):
anchor_generator_text_proto = """
multiscale_anchor_generator {
aspect_ratios: [1.0]
normalize_coordinates: false
}
"""
anchor_generator_proto = anchor_generator_pb2.AnchorGenerator()
text_format.Merge(anchor_generator_text_proto, anchor_generator_proto)
anchor_generator_object = anchor_generator_builder.build(
anchor_generator_proto)
self.assertTrue(isinstance(anchor_generator_object,
multiscale_grid_anchor_generator.
MultiscaleGridAnchorGenerator))
self.assertFalse(anchor_generator_object._normalize_coordinates)
if __name__ == '__main__':
......
research/object_detection/builders/hyperparams_builder.py
View file @ 3f78f4cf
......@@ -134,6 +134,10 @@ def _build_initializer(initializer):
return tf.truncated_normal_initializer(
mean=initializer.truncated_normal_initializer.mean,
stddev=initializer.truncated_normal_initializer.stddev)
if initializer_oneof == 'random_normal_initializer':
return tf.random_normal_initializer(
mean=initializer.random_normal_initializer.mean,
stddev=initializer.random_normal_initializer.stddev)
if initializer_oneof == 'variance_scaling_initializer':
enum_descriptor = (hyperparams_pb2.VarianceScalingInitializer.
DESCRIPTOR.enum_types_by_name['Mode'])
......
research/object_detection/builders/hyperparams_builder_test.py
View file @ 3f78f4cf
......@@ -28,7 +28,7 @@ slim = tf.contrib.slim
class HyperparamsBuilderTest(tf.test.TestCase):
# TODO: Make this a public api in slim arg_scope.py.
# TODO(rathodv): Make this a public api in slim arg_scope.py.
def _get_scope_key(self, op):
return getattr(op, '_key_op', str(op))
......@@ -444,6 +444,27 @@ class HyperparamsBuilderTest(tf.test.TestCase):
self._assert_variance_in_range(initializer, shape=[100, 40],
variance=0.49, tol=1e-1)
def test_variance_in_range_with_random_normal_initializer(self):
conv_hyperparams_text_proto = """
regularizer {
l2_regularizer {
}
}
initializer {
random_normal_initializer {
mean: 0.0
stddev: 0.8
}
}
"""
conv_hyperparams_proto = hyperparams_pb2.Hyperparams()
text_format.Merge(conv_hyperparams_text_proto, conv_hyperparams_proto)
scope = hyperparams_builder.build(conv_hyperparams_proto, is_training=True)
conv_scope_arguments = scope.values()[0]
initializer = conv_scope_arguments['weights_initializer']
self._assert_variance_in_range(initializer, shape=[100, 40],
variance=0.64, tol=1e-1)
if __name__ == '__main__':
tf.test.main()
research/object_detection/builders/image_resizer_builder.py
View file @ 3f78f4cf
......@@ -72,8 +72,8 @@ def build(image_resizer_config):
raise ValueError('image_resizer_config not of type '
'image_resizer_pb2.ImageResizer.')
if image_resizer_config.WhichOneof(
'image_resizer_oneof') == 'keep_aspect_ratio_resizer':
image_resizer_oneof = image_resizer_config.WhichOneof('image_resizer_oneof')
if image_resizer_oneof == 'keep_aspect_ratio_resizer':
keep_aspect_ratio_config = image_resizer_config.keep_aspect_ratio_resizer
if not (keep_aspect_ratio_config.min_dimension <=
keep_aspect_ratio_config.max_dimension):
......@@ -87,8 +87,7 @@ def build(image_resizer_config):
pad_to_max_dimension=keep_aspect_ratio_config.pad_to_max_dimension)
if not keep_aspect_ratio_config.convert_to_grayscale:
return image_resizer_fn
elif image_resizer_config.WhichOneof(
'image_resizer_oneof') == 'fixed_shape_resizer':
elif image_resizer_oneof == 'fixed_shape_resizer':
fixed_shape_resizer_config = image_resizer_config.fixed_shape_resizer
method = _tf_resize_method(fixed_shape_resizer_config.resize_method)
image_resizer_fn = functools.partial(
......@@ -99,7 +98,8 @@ def build(image_resizer_config):
if not fixed_shape_resizer_config.convert_to_grayscale:
return image_resizer_fn
else:
raise ValueError('Invalid image resizer option.')
raise ValueError(
'Invalid image resizer option: \'%s\'.' % image_resizer_oneof)
def grayscale_image_resizer(image):
[resized_image, resized_image_shape] = image_resizer_fn(image)
......
research/object_detection/builders/losses_builder.py
View file @ 3f78f4cf
......@@ -150,7 +150,8 @@ def _build_localization_loss(loss_config):
return losses.WeightedL2LocalizationLoss()
if loss_type == 'weighted_smooth_l1':
return losses.WeightedSmoothL1LocalizationLoss()
return losses.WeightedSmoothL1LocalizationLoss(
loss_config.weighted_smooth_l1.delta)
if loss_type == 'weighted_iou':
return losses.WeightedIOULocalizationLoss()
......
research/object_detection/builders/losses_builder_test.py
View file @ 3f78f4cf
......@@ -42,7 +42,7 @@ class LocalizationLossBuilderTest(tf.test.TestCase):
self.assertTrue(isinstance(localization_loss,
losses.WeightedL2LocalizationLoss))
def test_build_weighted_smooth_l1_localization_loss(self):
def test_build_weighted_smooth_l1_localization_loss_default_delta(self):
losses_text_proto = """
localization_loss {
weighted_smooth_l1 {
......@@ -58,6 +58,26 @@ class LocalizationLossBuilderTest(tf.test.TestCase):
_, localization_loss, _, _, _ = losses_builder.build(losses_proto)
self.assertTrue(isinstance(localization_loss,
losses.WeightedSmoothL1LocalizationLoss))
self.assertAlmostEqual(localization_loss._delta, 1.0)
def test_build_weighted_smooth_l1_localization_loss_non_default_delta(self):
losses_text_proto = """
localization_loss {
weighted_smooth_l1 {
delta: 0.1
}
}
classification_loss {
weighted_softmax {
}
}
"""
losses_proto = losses_pb2.Loss()
text_format.Merge(losses_text_proto, losses_proto)
_, localization_loss, _, _, _ = losses_builder.build(losses_proto)
self.assertTrue(isinstance(localization_loss,
losses.WeightedSmoothL1LocalizationLoss))
self.assertAlmostEqual(localization_loss._delta, 0.1)
def test_build_weighted_iou_localization_loss(self):
losses_text_proto = """
......
research/object_detection/builders/model_builder.py
View file @ 3f78f4cf
......@@ -153,6 +153,7 @@ def _build_ssd_model(ssd_config, is_training, add_summaries):
region_similarity_calculator = sim_calc.build(
ssd_config.similarity_calculator)
encode_background_as_zeros = ssd_config.encode_background_as_zeros
negative_class_weight = ssd_config.negative_class_weight
ssd_box_predictor = box_predictor_builder.build(hyperparams_builder.build,
ssd_config.box_predictor,
is_training, num_classes)
......@@ -165,6 +166,7 @@ def _build_ssd_model(ssd_config, is_training, add_summaries):
localization_weight,
hard_example_miner) = losses_builder.build(ssd_config.loss)
normalize_loss_by_num_matches = ssd_config.normalize_loss_by_num_matches
normalize_loc_loss_by_codesize = ssd_config.normalize_loc_loss_by_codesize
return ssd_meta_arch.SSDMetaArch(
is_training,
......@@ -175,6 +177,7 @@ def _build_ssd_model(ssd_config, is_training, add_summaries):
matcher,
region_similarity_calculator,
encode_background_as_zeros,
negative_class_weight,
image_resizer_fn,
non_max_suppression_fn,
score_conversion_fn,
......@@ -184,7 +187,8 @@ def _build_ssd_model(ssd_config, is_training, add_summaries):
localization_weight,
normalize_loss_by_num_matches,
hard_example_miner,
add_summaries=add_summaries)
add_summaries=add_summaries,
normalize_loc_loss_by_codesize=normalize_loc_loss_by_codesize)
def _build_faster_rcnn_feature_extractor(
......
research/object_detection/builders/model_builder_test.py
View file @ 3f78f4cf
......@@ -259,13 +259,15 @@ class ModelBuilderTest(tf.test.TestCase):
}
}
initializer {
truncated_normal_initializer {
random_normal_initializer {
}
}
}
num_layers_before_predictor: 1
}
}
normalize_loss_by_num_matches: true
normalize_loc_loss_by_codesize: true
loss {
classification_loss {
weighted_sigmoid_focal {
......@@ -275,6 +277,7 @@ class ModelBuilderTest(tf.test.TestCase):
}
localization_loss {
weighted_smooth_l1 {
delta: 0.1
}
}
classification_weight: 1.0
......@@ -344,6 +347,7 @@ class ModelBuilderTest(tf.test.TestCase):
}
}
}
normalize_loc_loss_by_codesize: true
loss {
classification_loss {
weighted_softmax {
......@@ -362,6 +366,7 @@ class ModelBuilderTest(tf.test.TestCase):
self.assertIsInstance(model._feature_extractor,
SSDMobileNetV1FeatureExtractor)
self.assertTrue(model._feature_extractor._batch_norm_trainable)
self.assertTrue(model._normalize_loc_loss_by_codesize)
def test_create_embedded_ssd_mobilenet_v1_model_from_config(self):
model_text_proto = """
......
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