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Commit 7a9934df authored by Zhichao Lu's avatar Zhichao Lu Committed by lzc5123016
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Merged commit includes the following changes: 

184048729  by Zhichao Lu:

    Modify target_assigner so that it creates regression targets taking keypoints into account.

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184027183  by Zhichao Lu:

    Resnet V1 FPN based feature extractors for SSD meta architecture in Object Detection V2 API.

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184004730  by Zhichao Lu:

    Expose a lever to override the configured mask_type.

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183933113  by Zhichao Lu:

    Weight shared convolutional box predictor as described in https://arxiv.org/abs/1708.02002

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183929669  by Zhichao Lu:

    Expanding box list operations for future data augmentations.

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183916792  by Zhichao Lu:

    Fix unrecognized assertion function in tests.

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183906851  by Zhichao Lu:

    - Change ssd meta architecture to use regression weights to compute loss normalizer.

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183871003  by Zhichao Lu:

    Fix config_util_test wrong dependency.

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183782120  by Zhichao Lu:

    Add __init__ file to third_party directories.

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183779109  by Zhichao Lu:

    Setup regular version sync.

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183768772  by Zhichao Lu:

    Make test compatible with numpy 1.12 and higher

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183767893  by Zhichao Lu:

    Make test compatible with numpy 1.12 and higher

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183719318  by Zhichao Lu:

    Use the new test interface in ssd feature extractor.

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183714671  by Zhichao Lu:

    Use the new test_case interface for all anchor generators.

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183708155  by Zhichao Lu:

    Change variable scopes in ConvolutionalBoxPredictor such that previously trained checkpoints are still compatible after the change in BoxPredictor interface

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183705798  by Zhichao Lu:

    Internal change.

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183636023  by Zhichao Lu:

    Fixing argument name for np_box_list_ops.concatenate() function.

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183490404  by Zhichao Lu:

    Make sure code that relies in SSD older code still works.

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175743440  by Zhichao Lu:

    Use Toggle instead of bool to make the layout optimizer name and usage consistent with other optimizers.

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174249903  by Zhichao Lu:

    Fix nasnet image classification and object detection by moving the option to turn ON or OFF batch norm training into it's own arg_scope used only by detection

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174048035  by Zhichao Lu:

    Fix the pointer for downloading the NAS Faster-RCNN model.

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172815204  by Zhichao Lu:

    Allow for label maps in tf.Example decoding.

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172509113  by Zhichao Lu:

    Allow for label maps in tf.Example decoding.

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172151758  by Zhichao Lu:

    Minor updates to some README files.

    As a result of these friendly issues:
    https://github.com/tensorflow/models/issues/2530
    https://github.com/tensorflow/models/issues/2534

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172147420  by Zhichao Lu:

    Fix illegal summary name and move from slim's get_or_create_global_step deprecated use of tf.contrib.framework* to tf.train*.

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169763373  by Zhichao Lu:

    Fix broken GitHub links in tensorflow and tensorflow_models resulting from The Great Models Move (a.k.a. the research subfolder)

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    Move display strings to below the bounding box if they would otherwise be outside the image.

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PiperOrigin-RevId: 184048729
parent 7ef602be
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Showing with 1193 additions and 322 deletions
research/object_detection/BUILD
View file @ 7a9934df
...@@ -7,6 +7,43 @@ package( ...@@ -7,6 +7,43 @@ package(
licenses(["notice"]) licenses(["notice"])
# Apache 2.0 # Apache 2.0
py_library(
name = "inputs",
srcs = [
"inputs.py",
],
deps = [
"//tensorflow",
"//tensorflow/models/research/object_detection:trainer",
"//tensorflow/models/research/object_detection/builders:dataset_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:train_py_pb2",
"//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( py_binary(
name = "train", name = "train",
srcs = [ srcs = [
...@@ -15,9 +52,10 @@ py_binary( ...@@ -15,9 +52,10 @@ py_binary(
deps = [ deps = [
":trainer", ":trainer",
"//tensorflow", "//tensorflow",
"//tensorflow_models/object_detection/builders:input_reader_builder", "//tensorflow/models/research/object_detection/builders:dataset_builder",
"//tensorflow_models/object_detection/builders:model_builder", "//tensorflow/models/research/object_detection/builders:model_builder",
"//tensorflow_models/object_detection/utils:config_util", "//tensorflow/models/research/object_detection/utils:config_util",
"//tensorflow/models/research/object_detection/utils:dataset_util",
], ],
) )
...@@ -26,14 +64,14 @@ py_library( ...@@ -26,14 +64,14 @@ py_library(
srcs = ["trainer.py"], srcs = ["trainer.py"],
deps = [ deps = [
"//tensorflow", "//tensorflow",
"//tensorflow_models/object_detection/builders:optimizer_builder", "//tensorflow/models/research/object_detection/builders:optimizer_builder",
"//tensorflow_models/object_detection/builders:preprocessor_builder", "//tensorflow/models/research/object_detection/builders:preprocessor_builder",
"//tensorflow_models/object_detection/core:batcher", "//tensorflow/models/research/object_detection/core:batcher",
"//tensorflow_models/object_detection/core:preprocessor", "//tensorflow/models/research/object_detection/core:preprocessor",
"//tensorflow_models/object_detection/core:standard_fields", "//tensorflow/models/research/object_detection/core:standard_fields",
"//tensorflow_models/object_detection/utils:ops", "//tensorflow/models/research/object_detection/utils:ops",
"//tensorflow_models/object_detection/utils:variables_helper", "//tensorflow/models/research/object_detection/utils:variables_helper",
"//tensorflow_models/slim:model_deploy", "//third_party/tensorflow_models/slim:model_deploy",
], ],
) )
...@@ -43,10 +81,10 @@ py_test( ...@@ -43,10 +81,10 @@ py_test(
deps = [ deps = [
":trainer", ":trainer",
"//tensorflow", "//tensorflow",
"//tensorflow_models/object_detection/core:losses", "//tensorflow/models/research/object_detection/core:losses",
"//tensorflow_models/object_detection/core:model", "//tensorflow/models/research/object_detection/core:model",
"//tensorflow_models/object_detection/core:standard_fields", "//tensorflow/models/research/object_detection/core:standard_fields",
"//tensorflow_models/object_detection/protos:train_py_pb2", "//tensorflow/models/research/object_detection/protos:train_py_pb2",
], ],
) )
...@@ -57,13 +95,13 @@ py_library( ...@@ -57,13 +95,13 @@ py_library(
], ],
deps = [ deps = [
"//tensorflow", "//tensorflow",
"//tensorflow_models/object_detection/core:box_list", "//tensorflow/models/research/object_detection/core:box_list",
"//tensorflow_models/object_detection/core:box_list_ops", "//tensorflow/models/research/object_detection/core:box_list_ops",
"//tensorflow_models/object_detection/core:keypoint_ops", "//tensorflow/models/research/object_detection/core:keypoint_ops",
"//tensorflow_models/object_detection/core:standard_fields", "//tensorflow/models/research/object_detection/core:standard_fields",
"//tensorflow_models/object_detection/utils:label_map_util", "//tensorflow/models/research/object_detection/utils:label_map_util",
"//tensorflow_models/object_detection/utils:ops", "//tensorflow/models/research/object_detection/utils:ops",
"//tensorflow_models/object_detection/utils:visualization_utils", "//tensorflow/models/research/object_detection/utils:visualization_utils",
], ],
) )
...@@ -72,11 +110,11 @@ py_library( ...@@ -72,11 +110,11 @@ py_library(
srcs = ["evaluator.py"], srcs = ["evaluator.py"],
deps = [ deps = [
"//tensorflow", "//tensorflow",
"//tensorflow_models/object_detection:eval_util", "//tensorflow/models/research/object_detection:eval_util",
"//tensorflow_models/object_detection/core:prefetcher", "//tensorflow/models/research/object_detection/core:prefetcher",
"//tensorflow_models/object_detection/core:standard_fields", "//tensorflow/models/research/object_detection/core:standard_fields",
"//tensorflow_models/object_detection/protos:eval_py_pb2", "//tensorflow/models/research/object_detection/protos:eval_py_pb2",
"//tensorflow_models/object_detection/utils:object_detection_evaluation", "//tensorflow/models/research/object_detection/utils:object_detection_evaluation",
], ],
) )
...@@ -88,10 +126,11 @@ py_binary( ...@@ -88,10 +126,11 @@ py_binary(
deps = [ deps = [
":evaluator", ":evaluator",
"//tensorflow", "//tensorflow",
"//tensorflow_models/object_detection/builders:input_reader_builder", "//tensorflow/models/research/object_detection/builders:dataset_builder",
"//tensorflow_models/object_detection/builders:model_builder", "//tensorflow/models/research/object_detection/builders:model_builder",
"//tensorflow_models/object_detection/utils:config_util", "//tensorflow/models/research/object_detection/utils:config_util",
"//tensorflow_models/object_detection/utils:label_map_util", "//tensorflow/models/research/object_detection/utils:dataset_util",
"//tensorflow/models/research/object_detection/utils:label_map_util",
], ],
) )
...@@ -103,9 +142,9 @@ py_library( ...@@ -103,9 +142,9 @@ py_library(
deps = [ deps = [
"//tensorflow", "//tensorflow",
"//tensorflow/python/tools:freeze_graph_lib", "//tensorflow/python/tools:freeze_graph_lib",
"//tensorflow_models/object_detection/builders:model_builder", "//tensorflow/models/research/object_detection/builders:model_builder",
"//tensorflow_models/object_detection/core:standard_fields", "//tensorflow/models/research/object_detection/core:standard_fields",
"//tensorflow_models/object_detection/data_decoders:tf_example_decoder", "//tensorflow/models/research/object_detection/data_decoders:tf_example_decoder",
], ],
) )
...@@ -117,9 +156,9 @@ py_test( ...@@ -117,9 +156,9 @@ py_test(
deps = [ deps = [
":exporter", ":exporter",
"//tensorflow", "//tensorflow",
"//tensorflow_models/object_detection/builders:model_builder", "//tensorflow/models/research/object_detection/builders:model_builder",
"//tensorflow_models/object_detection/core:model", "//tensorflow/models/research/object_detection/core:model",
"//tensorflow_models/object_detection/protos:pipeline_py_pb2", "//tensorflow/models/research/object_detection/protos:pipeline_py_pb2",
], ],
) )
...@@ -131,6 +170,6 @@ py_binary( ...@@ -131,6 +170,6 @@ py_binary(
deps = [ deps = [
":exporter", ":exporter",
"//tensorflow", "//tensorflow",
"//tensorflow_models/object_detection/protos:pipeline_py_pb2", "//tensorflow/models/research/object_detection/protos:pipeline_py_pb2",
], ],
) )
research/object_detection/README.md
View file @ 7a9934df
...@@ -32,6 +32,7 @@ https://scholar.googleusercontent.com/scholar.bib?q=info:l291WsrB-hQJ:scholar.go ...@@ -32,6 +32,7 @@ https://scholar.googleusercontent.com/scholar.bib?q=info:l291WsrB-hQJ:scholar.go
* Derek Chow, github: [derekjchow](https://github.com/derekjchow) * Derek Chow, github: [derekjchow](https://github.com/derekjchow)
* Chen Sun, github: [jesu9](https://github.com/jesu9) * Chen Sun, github: [jesu9](https://github.com/jesu9)
* Menglong Zhu, github: [dreamdragon](https://github.com/dreamdragon) * Menglong Zhu, github: [dreamdragon](https://github.com/dreamdragon)
* Alireza Fathi, github: [afathi3](https://github.com/afathi3)
## Table of contents ## Table of contents
......
research/object_detection/anchor_generators/BUILD
View file @ 7a9934df
...@@ -14,9 +14,9 @@ py_library( ...@@ -14,9 +14,9 @@ py_library(
], ],
deps = [ deps = [
"//tensorflow", "//tensorflow",
"//tensorflow_models/object_detection/core:anchor_generator", "//tensorflow/models/research/object_detection/core:anchor_generator",
"//tensorflow_models/object_detection/core:box_list", "//tensorflow/models/research/object_detection/core:box_list",
"//tensorflow_models/object_detection/utils:ops", "//tensorflow/models/research/object_detection/utils:ops",
], ],
) )
...@@ -28,6 +28,7 @@ py_test( ...@@ -28,6 +28,7 @@ py_test(
deps = [ deps = [
":grid_anchor_generator", ":grid_anchor_generator",
"//tensorflow", "//tensorflow",
"//tensorflow/models/research/object_detection/utils:test_case",
], ],
) )
...@@ -39,8 +40,8 @@ py_library( ...@@ -39,8 +40,8 @@ py_library(
deps = [ deps = [
":grid_anchor_generator", ":grid_anchor_generator",
"//tensorflow", "//tensorflow",
"//tensorflow_models/object_detection/core:anchor_generator", "//tensorflow/models/research/object_detection/core:anchor_generator",
"//tensorflow_models/object_detection/core:box_list_ops", "//tensorflow/models/research/object_detection/core:box_list_ops",
], ],
) )
...@@ -51,6 +52,32 @@ py_test( ...@@ -51,6 +52,32 @@ py_test(
], ],
deps = [ deps = [
":multiple_grid_anchor_generator", ":multiple_grid_anchor_generator",
"//third_party/py/numpy", "//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 @ 7a9934df
...@@ -42,25 +42,27 @@ class GridAnchorGenerator(anchor_generator.AnchorGenerator): ...@@ -42,25 +42,27 @@ class GridAnchorGenerator(anchor_generator.AnchorGenerator):
scales: a list of (float) scales, default=(0.5, 1.0, 2.0) scales: a list of (float) scales, default=(0.5, 1.0, 2.0)
aspect_ratios: a list of (float) aspect ratios, default=(0.5, 1.0, 2.0) aspect_ratios: a list of (float) aspect ratios, default=(0.5, 1.0, 2.0)
base_anchor_size: base anchor size as height, width ( base_anchor_size: base anchor size as height, width (
(length-2 float32 list, default=[256, 256]) (length-2 float32 list or tensor, default=[256, 256])
anchor_stride: difference in centers between base anchors for adjacent anchor_stride: difference in centers between base anchors for adjacent
grid positions (length-2 float32 list, default=[16, 16]) grid positions (length-2 float32 list or tensor,
default=[16, 16])
anchor_offset: center of the anchor with scale and aspect ratio 1 for the anchor_offset: center of the anchor with scale and aspect ratio 1 for the
upper left element of the grid, this should be zero for upper left element of the grid, this should be zero for
feature networks with only VALID padding and even receptive feature networks with only VALID padding and even receptive
field size, but may need additional calculation if other field size, but may need additional calculation if other
padding is used (length-2 float32 tensor, default=[0, 0]) padding is used (length-2 float32 list or tensor,
default=[0, 0])
""" """
# Handle argument defaults # Handle argument defaults
if base_anchor_size is None: if base_anchor_size is None:
base_anchor_size = [256, 256] base_anchor_size = [256, 256]
base_anchor_size = tf.constant(base_anchor_size, tf.float32) base_anchor_size = tf.to_float(tf.convert_to_tensor(base_anchor_size))
if anchor_stride is None: if anchor_stride is None:
anchor_stride = [16, 16] anchor_stride = [16, 16]
anchor_stride = tf.constant(anchor_stride, dtype=tf.float32) anchor_stride = tf.to_float(tf.convert_to_tensor(anchor_stride))
if anchor_offset is None: if anchor_offset is None:
anchor_offset = [0, 0] anchor_offset = [0, 0]
anchor_offset = tf.constant(anchor_offset, dtype=tf.float32) anchor_offset = tf.to_float(tf.convert_to_tensor(anchor_offset))
self._scales = scales self._scales = scales
self._aspect_ratios = aspect_ratios self._aspect_ratios = aspect_ratios
...@@ -91,7 +93,11 @@ class GridAnchorGenerator(anchor_generator.AnchorGenerator): ...@@ -91,7 +93,11 @@ class GridAnchorGenerator(anchor_generator.AnchorGenerator):
allowed. allowed.
Returns: Returns:
boxes: a BoxList holding a collection of N anchor boxes 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)
Raises: Raises:
ValueError: if feature_map_shape_list, box_specs_list do not have the same ValueError: if feature_map_shape_list, box_specs_list do not have the same
length. length.
...@@ -109,13 +115,20 @@ class GridAnchorGenerator(anchor_generator.AnchorGenerator): ...@@ -109,13 +115,20 @@ class GridAnchorGenerator(anchor_generator.AnchorGenerator):
self._aspect_ratios) self._aspect_ratios)
scales_grid = tf.reshape(scales_grid, [-1]) scales_grid = tf.reshape(scales_grid, [-1])
aspect_ratios_grid = tf.reshape(aspect_ratios_grid, [-1]) aspect_ratios_grid = tf.reshape(aspect_ratios_grid, [-1])
return tile_anchors(grid_height, anchors = tile_anchors(grid_height,
grid_width, grid_width,
scales_grid, scales_grid,
aspect_ratios_grid, aspect_ratios_grid,
self._base_anchor_size, self._base_anchor_size,
self._anchor_stride, self._anchor_stride,
self._anchor_offset) self._anchor_offset)
num_anchors = anchors.num_boxes_static()
if num_anchors is None:
num_anchors = anchors.num_boxes()
anchor_indices = tf.zeros([num_anchors])
anchors.add_field('feature_map_index', anchor_indices)
return anchors
def tile_anchors(grid_height, def tile_anchors(grid_height,
......
research/object_detection/anchor_generators/grid_anchor_generator_test.py
View file @ 7a9934df
...@@ -14,62 +14,90 @@ ...@@ -14,62 +14,90 @@
# ============================================================================== # ==============================================================================
"""Tests for object_detection.grid_anchor_generator.""" """Tests for object_detection.grid_anchor_generator."""
import numpy as np
import tensorflow as tf import tensorflow as tf
from object_detection.anchor_generators import grid_anchor_generator from object_detection.anchor_generators import grid_anchor_generator
from object_detection.utils import test_case
class GridAnchorGeneratorTest(tf.test.TestCase): class GridAnchorGeneratorTest(test_case.TestCase):
def test_construct_single_anchor(self): def test_construct_single_anchor(self):
"""Builds a 1x1 anchor grid to test the size of the output boxes.""" """Builds a 1x1 anchor grid to test the size of the output boxes."""
scales = [0.5, 1.0, 2.0] def graph_fn():
aspect_ratios = [0.25, 1.0, 4.0] scales = [0.5, 1.0, 2.0]
anchor_offset = [7, -3] aspect_ratios = [0.25, 1.0, 4.0]
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()
return (anchor_corners,)
exp_anchor_corners = [[-121, -35, 135, 29], [-249, -67, 263, 61], exp_anchor_corners = [[-121, -35, 135, 29], [-249, -67, 263, 61],
[-505, -131, 519, 125], [-57, -67, 71, 61], [-505, -131, 519, 125], [-57, -67, 71, 61],
[-121, -131, 135, 125], [-249, -259, 263, 253], [-121, -131, 135, 125], [-249, -259, 263, 253],
[-25, -131, 39, 125], [-57, -259, 71, 253], [-25, -131, 39, 125], [-57, -259, 71, 253],
[-121, -515, 135, 509]] [-121, -515, 135, 509]]
anchor_corners_out = self.execute(graph_fn, [])
anchor_generator = grid_anchor_generator.GridAnchorGenerator( self.assertAllClose(anchor_corners_out, exp_anchor_corners)
scales, aspect_ratios,
anchor_offset=anchor_offset)
anchors = anchor_generator.generate(feature_map_shape_list=[(1, 1)])
anchor_corners = anchors.get()
with self.test_session():
anchor_corners_out = anchor_corners.eval()
self.assertAllClose(anchor_corners_out, exp_anchor_corners)
def test_construct_anchor_grid(self): def test_construct_anchor_grid(self):
base_anchor_size = [10, 10] def graph_fn():
anchor_stride = [19, 19] base_anchor_size = [10, 10]
anchor_offset = [0, 0] anchor_stride = [19, 19]
scales = [0.5, 1.0, 2.0] anchor_offset = [0, 0]
aspect_ratios = [1.0] scales = [0.5, 1.0, 2.0]
aspect_ratios = [1.0]
anchor_generator = grid_anchor_generator.GridAnchorGenerator(
scales,
aspect_ratios,
base_anchor_size=base_anchor_size,
anchor_stride=anchor_stride,
anchor_offset=anchor_offset)
anchors = anchor_generator.generate(feature_map_shape_list=[(2, 2)])
anchor_corners = anchors.get()
return (anchor_corners,)
exp_anchor_corners = [[-2.5, -2.5, 2.5, 2.5], [-5., -5., 5., 5.], 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], [-10., -10., 10., 10.], [-2.5, 16.5, 2.5, 21.5],
[-5., 14., 5, 24], [-10., 9., 10, 29], [-5., 14., 5, 24], [-10., 9., 10, 29],
[16.5, -2.5, 21.5, 2.5], [14., -5., 24, 5], [16.5, -2.5, 21.5, 2.5], [14., -5., 24, 5],
[9., -10., 29, 10], [16.5, 16.5, 21.5, 21.5], [9., -10., 29, 10], [16.5, 16.5, 21.5, 21.5],
[14., 14., 24, 24], [9., 9., 29, 29]] [14., 14., 24, 24], [9., 9., 29, 29]]
anchor_corners_out = self.execute(graph_fn, [])
self.assertAllClose(anchor_corners_out, exp_anchor_corners)
anchor_generator = grid_anchor_generator.GridAnchorGenerator( def test_construct_anchor_grid_with_dynamic_feature_map_shapes(self):
scales, def graph_fn(feature_map_height, feature_map_width):
aspect_ratios, base_anchor_size = [10, 10]
base_anchor_size=base_anchor_size, anchor_stride = [19, 19]
anchor_stride=anchor_stride, anchor_offset = [0, 0]
anchor_offset=anchor_offset) scales = [0.5, 1.0, 2.0]
aspect_ratios = [1.0]
anchor_generator = grid_anchor_generator.GridAnchorGenerator(
scales,
aspect_ratios,
base_anchor_size=base_anchor_size,
anchor_stride=anchor_stride,
anchor_offset=anchor_offset)
anchors = anchor_generator.generate(feature_map_shape_list=[(2, 2)]) anchors = anchor_generator.generate(
anchor_corners = anchors.get() feature_map_shape_list=[(feature_map_height, feature_map_width)])
anchor_corners = anchors.get()
return (anchor_corners,)
with self.test_session(): exp_anchor_corners = [[-2.5, -2.5, 2.5, 2.5], [-5., -5., 5., 5.],
anchor_corners_out = anchor_corners.eval() [-10., -10., 10., 10.], [-2.5, 16.5, 2.5, 21.5],
self.assertAllClose(anchor_corners_out, exp_anchor_corners) [-5., 14., 5, 24], [-10., 9., 10, 29],
[16.5, -2.5, 21.5, 2.5], [14., -5., 24, 5],
[9., -10., 29, 10], [16.5, 16.5, 21.5, 21.5],
[14., 14., 24, 24], [9., 9., 29, 29]]
anchor_corners_out = self.execute_cpu(graph_fn,
[np.array(2, dtype=np.int32),
np.array(2, dtype=np.int32)])
self.assertAllClose(anchor_corners_out, exp_anchor_corners)
if __name__ == '__main__': if __name__ == '__main__':
......
research/object_detection/anchor_generators/multiple_grid_anchor_generator.py
View file @ 7a9934df
...@@ -165,7 +165,10 @@ class MultipleGridAnchorGenerator(anchor_generator.AnchorGenerator): ...@@ -165,7 +165,10 @@ class MultipleGridAnchorGenerator(anchor_generator.AnchorGenerator):
grid. grid.
Returns: Returns:
boxes: a BoxList holding a collection of N anchor boxes 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.
Raises: Raises:
ValueError: if feature_map_shape_list, box_specs_list do not have the same ValueError: if feature_map_shape_list, box_specs_list do not have the same
length. length.
...@@ -208,6 +211,7 @@ class MultipleGridAnchorGenerator(anchor_generator.AnchorGenerator): ...@@ -208,6 +211,7 @@ class MultipleGridAnchorGenerator(anchor_generator.AnchorGenerator):
raise ValueError('%s must be a list of pairs.' % arg_name) raise ValueError('%s must be a list of pairs.' % arg_name)
anchor_grid_list = [] anchor_grid_list = []
anchor_indices_list = []
min_im_shape = tf.minimum(im_height, im_width) min_im_shape = tf.minimum(im_height, im_width)
scale_height = min_im_shape / im_height scale_height = min_im_shape / im_height
scale_width = min_im_shape / im_width scale_width = min_im_shape / im_width
...@@ -215,32 +219,40 @@ class MultipleGridAnchorGenerator(anchor_generator.AnchorGenerator): ...@@ -215,32 +219,40 @@ class MultipleGridAnchorGenerator(anchor_generator.AnchorGenerator):
scale_height * self._base_anchor_size[0], scale_height * self._base_anchor_size[0],
scale_width * self._base_anchor_size[1] scale_width * self._base_anchor_size[1]
] ]
for grid_size, scales, aspect_ratios, stride, offset in zip( for feature_map_index, (
feature_map_shape_list, self._scales, self._aspect_ratios, grid_size, scales, aspect_ratios, stride, offset) in enumerate(
anchor_strides, anchor_offsets): zip(feature_map_shape_list, self._scales, self._aspect_ratios,
anchor_grid_list.append( anchor_strides, anchor_offsets)):
grid_anchor_generator.tile_anchors( tiled_anchors = grid_anchor_generator.tile_anchors(
grid_height=grid_size[0], grid_height=grid_size[0],
grid_width=grid_size[1], grid_width=grid_size[1],
scales=scales, scales=scales,
aspect_ratios=aspect_ratios, aspect_ratios=aspect_ratios,
base_anchor_size=base_anchor_size, base_anchor_size=base_anchor_size,
anchor_stride=stride, anchor_stride=stride,
anchor_offset=offset)) anchor_offset=offset)
anchor_grid_list.append(tiled_anchors)
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) concatenated_anchors = box_list_ops.concatenate(anchor_grid_list)
anchor_indices = tf.concat(anchor_indices_list, 0)
num_anchors = concatenated_anchors.num_boxes_static() num_anchors = concatenated_anchors.num_boxes_static()
if num_anchors is None: if num_anchors is None:
num_anchors = concatenated_anchors.num_boxes() num_anchors = concatenated_anchors.num_boxes()
if self._clip_window is not None: if self._clip_window is not None:
concatenated_anchors = box_list_ops.clip_to_window( concatenated_anchors = box_list_ops.clip_to_window(
concatenated_anchors, self._clip_window, filter_nonoverlapping=False) concatenated_anchors, self._clip_window, filter_nonoverlapping=False)
# TODO(jonathanhuang): make reshape an option for the clip_to_window op # TODO: make reshape an option for the clip_to_window op
concatenated_anchors.set( concatenated_anchors.set(
tf.reshape(concatenated_anchors.get(), [num_anchors, 4])) tf.reshape(concatenated_anchors.get(), [num_anchors, 4]))
stddevs_tensor = 0.01 * tf.ones( stddevs_tensor = 0.01 * tf.ones(
[num_anchors, 4], dtype=tf.float32, name='stddevs') [num_anchors, 4], dtype=tf.float32, name='stddevs')
concatenated_anchors.add_field('stddev', stddevs_tensor) concatenated_anchors.add_field('stddev', stddevs_tensor)
concatenated_anchors.add_field('feature_map_index', anchor_indices)
return concatenated_anchors return concatenated_anchors
......
research/object_detection/anchor_generators/multiple_grid_anchor_generator_test.py
View file @ 7a9934df
...@@ -20,35 +20,45 @@ import numpy as np ...@@ -20,35 +20,45 @@ import numpy as np
import tensorflow as tf import tensorflow as tf
from object_detection.anchor_generators import multiple_grid_anchor_generator as ag from object_detection.anchor_generators import multiple_grid_anchor_generator as ag
from object_detection.utils import test_case
class MultipleGridAnchorGeneratorTest(tf.test.TestCase): class MultipleGridAnchorGeneratorTest(test_case.TestCase):
def test_construct_single_anchor_grid(self): def test_construct_single_anchor_grid(self):
"""Builds a 1x1 anchor grid to test the size of the output boxes.""" """Builds a 1x1 anchor grid to test the size of the output boxes."""
def graph_fn():
box_specs_list = [[(.5, .25), (1.0, .25), (2.0, .25),
(.5, 1.0), (1.0, 1.0), (2.0, 1.0),
(.5, 4.0), (1.0, 4.0), (2.0, 4.0)]]
anchor_generator = ag.MultipleGridAnchorGenerator(
box_specs_list,
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()
exp_anchor_corners = [[-121, -35, 135, 29], [-249, -67, 263, 61], exp_anchor_corners = [[-121, -35, 135, 29], [-249, -67, 263, 61],
[-505, -131, 519, 125], [-57, -67, 71, 61], [-505, -131, 519, 125], [-57, -67, 71, 61],
[-121, -131, 135, 125], [-249, -259, 263, 253], [-121, -131, 135, 125], [-249, -259, 263, 253],
[-25, -131, 39, 125], [-57, -259, 71, 253], [-25, -131, 39, 125], [-57, -259, 71, 253],
[-121, -515, 135, 509]] [-121, -515, 135, 509]]
box_specs_list = [[(.5, .25), (1.0, .25), (2.0, .25), anchor_corners_out = self.execute(graph_fn, [])
(.5, 1.0), (1.0, 1.0), (2.0, 1.0), self.assertAllClose(anchor_corners_out, exp_anchor_corners)
(.5, 4.0), (1.0, 4.0), (2.0, 4.0)]]
anchor_generator = ag.MultipleGridAnchorGenerator(
box_specs_list,
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)])
anchor_corners = anchors.get()
with self.test_session():
anchor_corners_out = anchor_corners.eval()
self.assertAllClose(anchor_corners_out, exp_anchor_corners)
def test_construct_anchor_grid(self): def test_construct_anchor_grid(self):
box_specs_list = [[(0.5, 1.0), (1.0, 1.0), (2.0, 1.0)]] def graph_fn():
box_specs_list = [[(0.5, 1.0), (1.0, 1.0), (2.0, 1.0)]]
anchor_generator = ag.MultipleGridAnchorGenerator(
box_specs_list,
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()
exp_anchor_corners = [[-2.5, -2.5, 2.5, 2.5], [-5., -5., 5., 5.], 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], [-10., -10., 10., 10.], [-2.5, 16.5, 2.5, 21.5],
[-5., 14., 5, 24], [-10., 9., 10, 29], [-5., 14., 5, 24], [-10., 9., 10, 29],
...@@ -56,55 +66,74 @@ class MultipleGridAnchorGeneratorTest(tf.test.TestCase): ...@@ -56,55 +66,74 @@ class MultipleGridAnchorGeneratorTest(tf.test.TestCase):
[9., -10., 29, 10], [16.5, 16.5, 21.5, 21.5], [9., -10., 29, 10], [16.5, 16.5, 21.5, 21.5],
[14., 14., 24, 24], [9., 9., 29, 29]] [14., 14., 24, 24], [9., 9., 29, 29]]
anchor_generator = ag.MultipleGridAnchorGenerator( anchor_corners_out = self.execute(graph_fn, [])
box_specs_list, self.assertAllClose(anchor_corners_out, exp_anchor_corners)
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)])
anchor_corners = anchors.get()
with self.test_session():
anchor_corners_out = anchor_corners.eval()
self.assertAllClose(anchor_corners_out, exp_anchor_corners)
def test_construct_anchor_grid_non_square(self): def test_construct_anchor_grid_non_square(self):
box_specs_list = [[(1.0, 1.0)]]
def graph_fn():
box_specs_list = [[(1.0, 1.0)]]
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()
exp_anchor_corners = [[0., -0.25, 1., 0.75], [0., 0.25, 1., 1.25]] exp_anchor_corners = [[0., -0.25, 1., 0.75], [0., 0.25, 1., 1.25]]
anchor_corners_out = self.execute(graph_fn, [])
self.assertAllClose(anchor_corners_out, exp_anchor_corners)
anchor_generator = ag.MultipleGridAnchorGenerator( def test_construct_dynamic_size_anchor_grid(self):
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))])
anchor_corners = anchors.get()
with self.test_session(): def graph_fn(height, width):
anchor_corners_out = anchor_corners.eval() box_specs_list = [[(1.0, 1.0)]]
self.assertAllClose(anchor_corners_out, exp_anchor_corners) 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,
width)])
return anchors.get()
def test_construct_anchor_grid_normalized(self): exp_anchor_corners = [[0., -0.25, 1., 0.75], [0., 0.25, 1., 1.25]]
box_specs_list = [[(1.0, 1.0)]]
exp_anchor_corners = [[0., 0., 1., 0.5], [0., 0.5, 1., 1.]] anchor_corners_out = self.execute_cpu(graph_fn,
[np.array(1, dtype=np.int32),
np.array(2, dtype=np.int32)])
self.assertAllClose(anchor_corners_out, exp_anchor_corners)
anchor_generator = ag.MultipleGridAnchorGenerator( def test_construct_anchor_grid_normalized(self):
box_specs_list, base_anchor_size=tf.constant([1, 1], dtype=tf.float32)) def graph_fn():
anchors = anchor_generator.generate( box_specs_list = [[(1.0, 1.0)]]
feature_map_shape_list=[(tf.constant(1, dtype=tf.int32), tf.constant(
2, dtype=tf.int32))], anchor_generator = ag.MultipleGridAnchorGenerator(
im_height=320, box_specs_list, base_anchor_size=tf.constant([1, 1],
im_width=640) dtype=tf.float32))
anchor_corners = anchors.get() anchors = 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()
with self.test_session(): exp_anchor_corners = [[0., 0., 1., 0.5], [0., 0.5, 1., 1.]]
anchor_corners_out = anchor_corners.eval() anchor_corners_out = self.execute(graph_fn, [])
self.assertAllClose(anchor_corners_out, exp_anchor_corners) self.assertAllClose(anchor_corners_out, exp_anchor_corners)
def test_construct_multiple_grids(self): def test_construct_multiple_grids(self):
box_specs_list = [[(1.0, 1.0), (2.0, 1.0), (1.0, 0.5)],
[(1.0, 1.0), (1.0, 0.5)]]
def graph_fn():
box_specs_list = [[(1.0, 1.0), (2.0, 1.0), (1.0, 0.5)],
[(1.0, 1.0), (1.0, 0.5)]]
anchor_generator = ag.MultipleGridAnchorGenerator(
box_specs_list,
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()
# height and width of box with .5 aspect ratio # height and width of box with .5 aspect ratio
h = np.sqrt(2) h = np.sqrt(2)
w = 1.0/np.sqrt(2) w = 1.0/np.sqrt(2)
...@@ -121,26 +150,27 @@ class MultipleGridAnchorGeneratorTest(tf.test.TestCase): ...@@ -121,26 +150,27 @@ class MultipleGridAnchorGeneratorTest(tf.test.TestCase):
[.125-1.0, .125-1.0, .125+1.0, .125+1.0], [.125-1.0, .125-1.0, .125+1.0, .125+1.0],
[.125-.5*h, .125-.5*w, .125+.5*h, .125+.5*w],] [.125-.5*h, .125-.5*w, .125+.5*h, .125+.5*w],]
anchor_generator = ag.MultipleGridAnchorGenerator( anchor_corners_out = self.execute(graph_fn, [])
box_specs_list, self.assertEquals(anchor_corners_out.shape, (56, 4))
base_anchor_size=tf.constant([1.0, 1.0], dtype=tf.float32), big_grid_corners = anchor_corners_out[0:3, :]
anchor_strides=[(.25, .25), (.5, .5)], small_grid_corners = anchor_corners_out[48:, :]
anchor_offsets=[(.125, .125), (.25, .25)]) self.assertAllClose(small_grid_corners, exp_small_grid_corners)
anchors = anchor_generator.generate(feature_map_shape_list=[(4, 4), (2, 2)]) self.assertAllClose(big_grid_corners, exp_big_grid_corners)
anchor_corners = anchors.get()
with self.test_session():
anchor_corners_out = anchor_corners.eval()
self.assertEquals(anchor_corners_out.shape, (56, 4))
big_grid_corners = anchor_corners_out[0:3, :]
small_grid_corners = anchor_corners_out[48:, :]
self.assertAllClose(small_grid_corners, exp_small_grid_corners)
self.assertAllClose(big_grid_corners, exp_big_grid_corners)
def test_construct_multiple_grids_with_clipping(self): def test_construct_multiple_grids_with_clipping(self):
box_specs_list = [[(1.0, 1.0), (2.0, 1.0), (1.0, 0.5)],
[(1.0, 1.0), (1.0, 0.5)]]
def graph_fn():
box_specs_list = [[(1.0, 1.0), (2.0, 1.0), (1.0, 0.5)],
[(1.0, 1.0), (1.0, 0.5)]]
clip_window = tf.constant([0, 0, 1, 1], dtype=tf.float32)
anchor_generator = ag.MultipleGridAnchorGenerator(
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()
# height and width of box with .5 aspect ratio # height and width of box with .5 aspect ratio
h = np.sqrt(2) h = np.sqrt(2)
w = 1.0/np.sqrt(2) w = 1.0/np.sqrt(2)
...@@ -153,18 +183,9 @@ class MultipleGridAnchorGeneratorTest(tf.test.TestCase): ...@@ -153,18 +183,9 @@ class MultipleGridAnchorGeneratorTest(tf.test.TestCase):
[.25, .25, 1, 1], [.25, .25, 1, 1],
[.75-.5*h, .75-.5*w, 1, 1]] [.75-.5*h, .75-.5*w, 1, 1]]
clip_window = tf.constant([0, 0, 1, 1], dtype=tf.float32) anchor_corners_out = self.execute(graph_fn, [])
anchor_generator = ag.MultipleGridAnchorGenerator( small_grid_corners = anchor_corners_out[48:, :]
box_specs_list, self.assertAllClose(small_grid_corners, exp_small_grid_corners)
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)])
anchor_corners = anchors.get()
with self.test_session():
anchor_corners_out = anchor_corners.eval()
small_grid_corners = anchor_corners_out[48:, :]
self.assertAllClose(small_grid_corners, exp_small_grid_corners)
def test_invalid_box_specs(self): def test_invalid_box_specs(self):
# not all box specs are pairs # not all box specs are pairs
...@@ -229,38 +250,39 @@ class MultipleGridAnchorGeneratorTest(tf.test.TestCase): ...@@ -229,38 +250,39 @@ class MultipleGridAnchorGeneratorTest(tf.test.TestCase):
anchor_generator.generate(feature_map_shape_list=[(4), (2, 2)]) anchor_generator.generate(feature_map_shape_list=[(4), (2, 2)])
class CreateSSDAnchorsTest(tf.test.TestCase): class CreateSSDAnchorsTest(test_case.TestCase):
def test_create_ssd_anchors_returns_correct_shape(self): def test_create_ssd_anchors_returns_correct_shape(self):
anchor_generator = ag.create_ssd_anchors(
num_layers=6, def graph_fn1():
min_scale=0.2, anchor_generator = ag.create_ssd_anchors(
max_scale=0.95, num_layers=6,
aspect_ratios=(1.0, 2.0, 3.0, 1.0 / 2, 1.0 / 3), min_scale=0.2,
reduce_boxes_in_lowest_layer=True) max_scale=0.95,
aspect_ratios=(1.0, 2.0, 3.0, 1.0 / 2, 1.0 / 3),
feature_map_shape_list = [(38, 38), (19, 19), (10, 10), reduce_boxes_in_lowest_layer=True)
(5, 5), (3, 3), (1, 1)]
anchors = anchor_generator.generate( feature_map_shape_list = [(38, 38), (19, 19), (10, 10),
feature_map_shape_list=feature_map_shape_list) (5, 5), (3, 3), (1, 1)]
anchor_corners = anchors.get() anchors = anchor_generator.generate(
with self.test_session(): feature_map_shape_list=feature_map_shape_list)
anchor_corners_out = anchor_corners.eval() return anchors.get()
self.assertEquals(anchor_corners_out.shape, (7308, 4)) anchor_corners_out = self.execute(graph_fn1, [])
self.assertEquals(anchor_corners_out.shape, (7308, 4))
anchor_generator = ag.create_ssd_anchors(
num_layers=6, min_scale=0.2, max_scale=0.95, def graph_fn2():
aspect_ratios=(1.0, 2.0, 3.0, 1.0/2, 1.0/3), anchor_generator = ag.create_ssd_anchors(
reduce_boxes_in_lowest_layer=False) num_layers=6, min_scale=0.2, max_scale=0.95,
aspect_ratios=(1.0, 2.0, 3.0, 1.0/2, 1.0/3),
feature_map_shape_list = [(38, 38), (19, 19), (10, 10), reduce_boxes_in_lowest_layer=False)
(5, 5), (3, 3), (1, 1)]
anchors = anchor_generator.generate( feature_map_shape_list = [(38, 38), (19, 19), (10, 10),
feature_map_shape_list=feature_map_shape_list) (5, 5), (3, 3), (1, 1)]
anchor_corners = anchors.get() anchors = anchor_generator.generate(
with self.test_session(): feature_map_shape_list=feature_map_shape_list)
anchor_corners_out = anchor_corners.eval() return anchors.get()
self.assertEquals(anchor_corners_out.shape, (11640, 4)) anchor_corners_out = self.execute(graph_fn2, [])
self.assertEquals(anchor_corners_out.shape, (11640, 4))
if __name__ == '__main__': if __name__ == '__main__':
......
research/object_detection/anchor_generators/multiscale_grid_anchor_generator.py 0 → 100644
View file @ 7a9934df
# 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.
# ==============================================================================
"""Generates grid anchors on the fly corresponding to multiple CNN layers.
Generates grid anchors on the fly corresponding to multiple CNN layers as
described in:
"Focal Loss for Dense Object Detection"
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 box_list_ops
class MultiscaleGridAnchorGenerator(object):
"""Generate a grid of anchors for multiple CNN layers."""
def __init__(self, min_level, max_level, anchor_scale, aspect_ratios,
scales_per_octave):
"""Constructs a MultiscaleGridAnchorGenerator.
To construct anchors, at multiple scale resolutions, one must provide a
the minimum level and maximum levels on a scale pyramid. To define the size
of anchor, the anchor scale is provided to decide the size relatively to the
stride of the corresponding feature map. The generator allows one pixel
location on feature map maps to multiple anchors, that have different aspect
ratios and intermediate scales.
Args:
min_level: minimum level in feature pyramid.
max_level: maximum level in feature pyramid.
anchor_scale: anchor scale and feature stride define the size of the base
anchor on an image. For example, given a feature pyramid with strides
[2^3, ..., 2^7] and anchor scale 4. The base anchor size is
4 * [2^3, ..., 2^7].
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.
"""
self._anchor_grid_info = []
self._aspect_ratios = aspect_ratios
self._scales_per_octave = scales_per_octave
for level in range(min_level, max_level + 1):
anchor_stride = [2**level, 2**level]
scales = []
aspects = []
for scale in range(scales_per_octave):
scales.append(2**(float(scale) / scales_per_octave))
for aspect_ratio in aspect_ratios:
aspects.append(aspect_ratio)
base_anchor_size = [2**level * anchor_scale, 2**level * anchor_scale]
self._anchor_grid_info.append({
'level': level,
'info': [scales, aspects, base_anchor_size, anchor_stride]
})
def name_scope(self):
return 'MultiscaleGridAnchorGenerator'
def num_anchors_per_location(self):
"""Returns the number of anchors per spatial location.
Returns:
a list of integers, one for each expected feature map to be passed to
the Generate function.
"""
return self._aspect_ratios * self._scales_per_octave
def generate(self, feature_map_shape_list, im_height, im_width):
"""Generates a collection of bounding boxes to be used as anchors.
Args:
feature_map_shape_list: list of pairs of convnet layer resolutions in the
format [(height_0, width_0), (height_1, width_1), ...]. For example,
setting feature_map_shape_list=[(8, 8), (7, 7)] asks for anchors that
correspond to an 8x8 layer followed by a 7x7 layer.
im_height: the height of the image to generate the grid for.
im_width: the width of the image to generate the grid for.
Returns:
boxes: a BoxList holding a collection of N anchor boxes
"""
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
# self._anchor_grid_info
level = grid_info['level']
stride = 2**level
scales, aspect_ratios, base_anchor_size, anchor_stride = grid_info['info']
feat_h = feat_shape[0]
feat_w = feat_shape[1]
anchor_offset = [0, 0]
if im_height % 2.0**level == 0:
anchor_offset[0] = stride / 2.0
if im_width % 2.0**level == 0:
anchor_offset[1] = stride / 2.0
ag = grid_anchor_generator.GridAnchorGenerator(
scales,
aspect_ratios,
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)]))
concatenated_anchors = box_list_ops.concatenate(anchor_grid_list)
return concatenated_anchors
research/object_detection/anchor_generators/multiscale_grid_anchor_generator_test.py 0 → 100644
View file @ 7a9934df
# 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 anchor_generators.multiscale_grid_anchor_generator_test.py."""
import numpy as np
import tensorflow as tf
from object_detection.anchor_generators import multiscale_grid_anchor_generator as mg
from object_detection.utils import test_case
class MultiscaleGridAnchorGeneratorTest(test_case.TestCase):
def test_construct_single_anchor(self):
min_level = 5
max_level = 5
anchor_scale = 4.0
aspect_ratios = [1.0]
scales_per_octave = 1
im_height = 64
im_width = 64
feature_map_shape_list = [(2, 2)]
exp_anchor_corners = [[-48, -48, 80, 80],
[-48, -16, 80, 112],
[-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()
with self.test_session():
anchor_corners_out = anchor_corners.eval()
self.assertAllClose(anchor_corners_out, exp_anchor_corners)
def test_construct_single_anchor_with_odd_input_dimension(self):
def graph_fn():
min_level = 5
max_level = 5
anchor_scale = 4.0
aspect_ratios = [1.0]
scales_per_octave = 1
im_height = 65
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()
return (anchor_corners,)
anchor_corners_out = self.execute(graph_fn, [])
exp_anchor_corners = [[-64, -64, 64, 64],
[-64, -32, 64, 96],
[-64, 0, 64, 128],
[-32, -64, 96, 64],
[-32, -32, 96, 96],
[-32, 0, 96, 128],
[0, -64, 128, 64],
[0, -32, 128, 96],
[0, 0, 128, 128]]
self.assertAllClose(anchor_corners_out, exp_anchor_corners)
def test_construct_single_anchor_on_two_feature_maps(self):
def graph_fn():
min_level = 5
max_level = 6
anchor_scale = 4.0
aspect_ratios = [1.0]
scales_per_octave = 1
im_height = 64
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,)
anchor_corners_out = self.execute(graph_fn, [])
exp_anchor_corners = [[-48, -48, 80, 80],
[-48, -16, 80, 112],
[-16, -48, 112, 80],
[-16, -16, 112, 112],
[-96, -96, 160, 160]]
self.assertAllClose(anchor_corners_out, exp_anchor_corners)
def test_construct_single_anchor_with_two_scales_per_octave(self):
def graph_fn():
min_level = 6
max_level = 6
anchor_scale = 4.0
aspect_ratios = [1.0]
scales_per_octave = 2
im_height = 64
im_width = 64
feature_map_shape_list = [(1, 1), (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,)
# 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)
def test_construct_single_anchor_with_two_scales_per_octave_and_aspect(self):
def graph_fn():
min_level = 6
max_level = 6
anchor_scale = 4.0
aspect_ratios = [1.0, 2.0]
scales_per_octave = 2
im_height = 64
im_width = 64
feature_map_shape_list = [(1, 1), (1, 1), (1, 1), (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
# 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],
[-96., -224., 160., 288.]]
anchor_corners_out = self.execute(graph_fn, [])
self.assertAllClose(anchor_corners_out, exp_anchor_corners)
def test_construct_single_anchors_on_feature_maps_with_dynamic_shape(self):
def graph_fn(feature_map1_height, feature_map1_width, feature_map2_height,
feature_map2_width):
min_level = 5
max_level = 6
anchor_scale = 4.0
aspect_ratios = [1.0]
scales_per_octave = 1
im_height = 64
im_width = 64
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,)
anchor_corners_out = 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)
])
exp_anchor_corners = [[-48, -48, 80, 80],
[-48, -16, 80, 112],
[-16, -48, 112, 80],
[-16, -16, 112, 112],
[-96, -96, 160, 160]]
self.assertAllClose(anchor_corners_out, exp_anchor_corners)
if __name__ == '__main__':
tf.test.main()
research/object_detection/box_coders/BUILD
View file @ 7a9934df
...@@ -13,8 +13,8 @@ py_library( ...@@ -13,8 +13,8 @@ py_library(
"faster_rcnn_box_coder.py", "faster_rcnn_box_coder.py",
], ],
deps = [ deps = [
"//tensorflow_models/object_detection/core:box_coder", "//tensorflow/models/research/object_detection/core:box_coder",
"//tensorflow_models/object_detection/core:box_list", "//tensorflow/models/research/object_detection/core:box_list",
], ],
) )
...@@ -26,7 +26,7 @@ py_test( ...@@ -26,7 +26,7 @@ py_test(
deps = [ deps = [
":faster_rcnn_box_coder", ":faster_rcnn_box_coder",
"//tensorflow", "//tensorflow",
"//tensorflow_models/object_detection/core:box_list", "//tensorflow/models/research/object_detection/core:box_list",
], ],
) )
...@@ -36,9 +36,9 @@ py_library( ...@@ -36,9 +36,9 @@ py_library(
"keypoint_box_coder.py", "keypoint_box_coder.py",
], ],
deps = [ deps = [
"//tensorflow_models/object_detection/core:box_coder", "//tensorflow/models/research/object_detection/core:box_coder",
"//tensorflow_models/object_detection/core:box_list", "//tensorflow/models/research/object_detection/core:box_list",
"//tensorflow_models/object_detection/core:standard_fields", "//tensorflow/models/research/object_detection/core:standard_fields",
], ],
) )
...@@ -50,8 +50,8 @@ py_test( ...@@ -50,8 +50,8 @@ py_test(
deps = [ deps = [
":keypoint_box_coder", ":keypoint_box_coder",
"//tensorflow", "//tensorflow",
"//tensorflow_models/object_detection/core:box_list", "//tensorflow/models/research/object_detection/core:box_list",
"//tensorflow_models/object_detection/core:standard_fields", "//tensorflow/models/research/object_detection/core:standard_fields",
], ],
) )
...@@ -61,8 +61,8 @@ py_library( ...@@ -61,8 +61,8 @@ py_library(
"mean_stddev_box_coder.py", "mean_stddev_box_coder.py",
], ],
deps = [ deps = [
"//tensorflow_models/object_detection/core:box_coder", "//tensorflow/models/research/object_detection/core:box_coder",
"//tensorflow_models/object_detection/core:box_list", "//tensorflow/models/research/object_detection/core:box_list",
], ],
) )
...@@ -74,7 +74,7 @@ py_test( ...@@ -74,7 +74,7 @@ py_test(
deps = [ deps = [
":mean_stddev_box_coder", ":mean_stddev_box_coder",
"//tensorflow", "//tensorflow",
"//tensorflow_models/object_detection/core:box_list", "//tensorflow/models/research/object_detection/core:box_list",
], ],
) )
...@@ -84,8 +84,8 @@ py_library( ...@@ -84,8 +84,8 @@ py_library(
"square_box_coder.py", "square_box_coder.py",
], ],
deps = [ deps = [
"//tensorflow_models/object_detection/core:box_coder", "//tensorflow/models/research/object_detection/core:box_coder",
"//tensorflow_models/object_detection/core:box_list", "//tensorflow/models/research/object_detection/core:box_list",
], ],
) )
...@@ -97,6 +97,6 @@ py_test( ...@@ -97,6 +97,6 @@ py_test(
deps = [ deps = [
":square_box_coder", ":square_box_coder",
"//tensorflow", "//tensorflow",
"//tensorflow_models/object_detection/core:box_list", "//tensorflow/models/research/object_detection/core:box_list",
], ],
) )
research/object_detection/builders/BUILD
View file @ 7a9934df
...@@ -20,18 +20,19 @@ py_library( ...@@ -20,18 +20,19 @@ py_library(
":matcher_builder", ":matcher_builder",
":post_processing_builder", ":post_processing_builder",
":region_similarity_calculator_builder", ":region_similarity_calculator_builder",
"//tensorflow_models/object_detection/core:box_predictor", "//tensorflow/models/research/object_detection/core:box_predictor",
"//tensorflow_models/object_detection/meta_architectures:faster_rcnn_meta_arch", "//tensorflow/models/research/object_detection/meta_architectures:faster_rcnn_meta_arch",
"//tensorflow_models/object_detection/meta_architectures:rfcn_meta_arch", "//tensorflow/models/research/object_detection/meta_architectures:rfcn_meta_arch",
"//tensorflow_models/object_detection/meta_architectures:ssd_meta_arch", "//tensorflow/models/research/object_detection/meta_architectures:ssd_meta_arch",
"//tensorflow_models/object_detection/models:embedded_ssd_mobilenet_v1_feature_extractor", "//tensorflow/models/research/object_detection/models:embedded_ssd_mobilenet_v1_feature_extractor",
"//tensorflow_models/object_detection/models:faster_rcnn_inception_resnet_v2_feature_extractor", "//tensorflow/models/research/object_detection/models:faster_rcnn_inception_resnet_v2_feature_extractor",
"//tensorflow_models/object_detection/models:faster_rcnn_inception_v2_feature_extractor", "//tensorflow/models/research/object_detection/models:faster_rcnn_inception_v2_feature_extractor",
"//tensorflow_models/object_detection/models:faster_rcnn_resnet_v1_feature_extractor", "//tensorflow/models/research/object_detection/models:faster_rcnn_nas_feature_extractor",
"//tensorflow_models/object_detection/models:ssd_inception_v2_feature_extractor", "//tensorflow/models/research/object_detection/models:faster_rcnn_resnet_v1_feature_extractor",
"//tensorflow_models/object_detection/models:ssd_inception_v3_feature_extractor", "//tensorflow/models/research/object_detection/models:ssd_inception_v2_feature_extractor",
"//tensorflow_models/object_detection/models:ssd_mobilenet_v1_feature_extractor", "//tensorflow/models/research/object_detection/models:ssd_inception_v3_feature_extractor",
"//tensorflow_models/object_detection/protos:model_py_pb2", "//tensorflow/models/research/object_detection/models:ssd_mobilenet_v1_feature_extractor",
"//tensorflow/models/research/object_detection/protos:model_py_pb2",
], ],
) )
...@@ -41,15 +42,16 @@ py_test( ...@@ -41,15 +42,16 @@ py_test(
deps = [ deps = [
":model_builder", ":model_builder",
"//tensorflow", "//tensorflow",
"//tensorflow_models/object_detection/meta_architectures:faster_rcnn_meta_arch", "//tensorflow/models/research/object_detection/meta_architectures:faster_rcnn_meta_arch",
"//tensorflow_models/object_detection/meta_architectures:ssd_meta_arch", "//tensorflow/models/research/object_detection/meta_architectures:ssd_meta_arch",
"//tensorflow_models/object_detection/models:faster_rcnn_inception_resnet_v2_feature_extractor", "//tensorflow/models/research/object_detection/models:faster_rcnn_inception_resnet_v2_feature_extractor",
"//tensorflow_models/object_detection/models:faster_rcnn_inception_v2_feature_extractor", "//tensorflow/models/research/object_detection/models:faster_rcnn_inception_v2_feature_extractor",
"//tensorflow_models/object_detection/models:faster_rcnn_resnet_v1_feature_extractor", "//tensorflow/models/research/object_detection/models:faster_rcnn_nas_feature_extractor",
"//tensorflow_models/object_detection/models:ssd_inception_v2_feature_extractor", "//tensorflow/models/research/object_detection/models:faster_rcnn_resnet_v1_feature_extractor",
"//tensorflow_models/object_detection/models:ssd_inception_v3_feature_extractor", "//tensorflow/models/research/object_detection/models:ssd_inception_v2_feature_extractor",
"//tensorflow_models/object_detection/models:ssd_mobilenet_v1_feature_extractor", "//tensorflow/models/research/object_detection/models:ssd_inception_v3_feature_extractor",
"//tensorflow_models/object_detection/protos:model_py_pb2", "//tensorflow/models/research/object_detection/models:ssd_mobilenet_v1_feature_extractor",
"//tensorflow/models/research/object_detection/protos:model_py_pb2",
], ],
) )
...@@ -57,9 +59,9 @@ py_library( ...@@ -57,9 +59,9 @@ py_library(
name = "matcher_builder", name = "matcher_builder",
srcs = ["matcher_builder.py"], srcs = ["matcher_builder.py"],
deps = [ deps = [
"//tensorflow_models/object_detection/matchers:argmax_matcher", "//tensorflow/models/research/object_detection/matchers:argmax_matcher",
"//tensorflow_models/object_detection/matchers:bipartite_matcher", "//tensorflow/models/research/object_detection/matchers:bipartite_matcher",
"//tensorflow_models/object_detection/protos:matcher_py_pb2", "//tensorflow/models/research/object_detection/protos:matcher_py_pb2",
], ],
) )
...@@ -68,9 +70,9 @@ py_test( ...@@ -68,9 +70,9 @@ py_test(
srcs = ["matcher_builder_test.py"], srcs = ["matcher_builder_test.py"],
deps = [ deps = [
":matcher_builder", ":matcher_builder",
"//tensorflow_models/object_detection/matchers:argmax_matcher", "//tensorflow/models/research/object_detection/matchers:argmax_matcher",
"//tensorflow_models/object_detection/matchers:bipartite_matcher", "//tensorflow/models/research/object_detection/matchers:bipartite_matcher",
"//tensorflow_models/object_detection/protos:matcher_py_pb2", "//tensorflow/models/research/object_detection/protos:matcher_py_pb2",
], ],
) )
...@@ -78,11 +80,11 @@ py_library( ...@@ -78,11 +80,11 @@ py_library(
name = "box_coder_builder", name = "box_coder_builder",
srcs = ["box_coder_builder.py"], srcs = ["box_coder_builder.py"],
deps = [ deps = [
"//tensorflow_models/object_detection/box_coders:faster_rcnn_box_coder", "//tensorflow/models/research/object_detection/box_coders:faster_rcnn_box_coder",
"//tensorflow_models/object_detection/box_coders:keypoint_box_coder", "//tensorflow/models/research/object_detection/box_coders:keypoint_box_coder",
"//tensorflow_models/object_detection/box_coders:mean_stddev_box_coder", "//tensorflow/models/research/object_detection/box_coders:mean_stddev_box_coder",
"//tensorflow_models/object_detection/box_coders:square_box_coder", "//tensorflow/models/research/object_detection/box_coders:square_box_coder",
"//tensorflow_models/object_detection/protos:box_coder_py_pb2", "//tensorflow/models/research/object_detection/protos:box_coder_py_pb2",
], ],
) )
...@@ -92,11 +94,11 @@ py_test( ...@@ -92,11 +94,11 @@ py_test(
deps = [ deps = [
":box_coder_builder", ":box_coder_builder",
"//tensorflow", "//tensorflow",
"//tensorflow_models/object_detection/box_coders:faster_rcnn_box_coder", "//tensorflow/models/research/object_detection/box_coders:faster_rcnn_box_coder",
"//tensorflow_models/object_detection/box_coders:keypoint_box_coder", "//tensorflow/models/research/object_detection/box_coders:keypoint_box_coder",
"//tensorflow_models/object_detection/box_coders:mean_stddev_box_coder", "//tensorflow/models/research/object_detection/box_coders:mean_stddev_box_coder",
"//tensorflow_models/object_detection/box_coders:square_box_coder", "//tensorflow/models/research/object_detection/box_coders:square_box_coder",
"//tensorflow_models/object_detection/protos:box_coder_py_pb2", "//tensorflow/models/research/object_detection/protos:box_coder_py_pb2",
], ],
) )
...@@ -104,9 +106,10 @@ py_library( ...@@ -104,9 +106,10 @@ py_library(
name = "anchor_generator_builder", name = "anchor_generator_builder",
srcs = ["anchor_generator_builder.py"], srcs = ["anchor_generator_builder.py"],
deps = [ deps = [
"//tensorflow_models/object_detection/anchor_generators:grid_anchor_generator", "//tensorflow/models/research/object_detection/anchor_generators:grid_anchor_generator",
"//tensorflow_models/object_detection/anchor_generators:multiple_grid_anchor_generator", "//tensorflow/models/research/object_detection/anchor_generators:multiple_grid_anchor_generator",
"//tensorflow_models/object_detection/protos:anchor_generator_py_pb2", "//tensorflow/models/research/object_detection/anchor_generators:multiscale_grid_anchor_generator",
"//tensorflow/models/research/object_detection/protos:anchor_generator_py_pb2",
], ],
) )
...@@ -116,9 +119,35 @@ py_test( ...@@ -116,9 +119,35 @@ py_test(
deps = [ deps = [
":anchor_generator_builder", ":anchor_generator_builder",
"//tensorflow", "//tensorflow",
"//tensorflow_models/object_detection/anchor_generators:grid_anchor_generator", "//tensorflow/models/research/object_detection/anchor_generators:grid_anchor_generator",
"//tensorflow_models/object_detection/anchor_generators:multiple_grid_anchor_generator", "//tensorflow/models/research/object_detection/anchor_generators:multiple_grid_anchor_generator",
"//tensorflow_models/object_detection/protos:anchor_generator_py_pb2", "//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",
], ],
) )
...@@ -127,8 +156,8 @@ py_library( ...@@ -127,8 +156,8 @@ py_library(
srcs = ["input_reader_builder.py"], srcs = ["input_reader_builder.py"],
deps = [ deps = [
"//tensorflow", "//tensorflow",
"//tensorflow_models/object_detection/data_decoders:tf_example_decoder", "//tensorflow/models/research/object_detection/data_decoders:tf_example_decoder",
"//tensorflow_models/object_detection/protos:input_reader_py_pb2", "//tensorflow/models/research/object_detection/protos:input_reader_py_pb2",
], ],
) )
...@@ -140,8 +169,8 @@ py_test( ...@@ -140,8 +169,8 @@ py_test(
deps = [ deps = [
":input_reader_builder", ":input_reader_builder",
"//tensorflow", "//tensorflow",
"//tensorflow_models/object_detection/core:standard_fields", "//tensorflow/models/research/object_detection/core:standard_fields",
"//tensorflow_models/object_detection/protos:input_reader_py_pb2", "//tensorflow/models/research/object_detection/protos:input_reader_py_pb2",
], ],
) )
...@@ -149,8 +178,8 @@ py_library( ...@@ -149,8 +178,8 @@ py_library(
name = "losses_builder", name = "losses_builder",
srcs = ["losses_builder.py"], srcs = ["losses_builder.py"],
deps = [ deps = [
"//tensorflow_models/object_detection/core:losses", "//tensorflow/models/research/object_detection/core:losses",
"//tensorflow_models/object_detection/protos:losses_py_pb2", "//tensorflow/models/research/object_detection/protos:losses_py_pb2",
], ],
) )
...@@ -159,8 +188,8 @@ py_test( ...@@ -159,8 +188,8 @@ py_test(
srcs = ["losses_builder_test.py"], srcs = ["losses_builder_test.py"],
deps = [ deps = [
":losses_builder", ":losses_builder",
"//tensorflow_models/object_detection/core:losses", "//tensorflow/models/research/object_detection/core:losses",
"//tensorflow_models/object_detection/protos:losses_py_pb2", "//tensorflow/models/research/object_detection/protos:losses_py_pb2",
], ],
) )
...@@ -169,7 +198,7 @@ py_library( ...@@ -169,7 +198,7 @@ py_library(
srcs = ["optimizer_builder.py"], srcs = ["optimizer_builder.py"],
deps = [ deps = [
"//tensorflow", "//tensorflow",
"//tensorflow_models/object_detection/utils:learning_schedules", "//tensorflow/models/research/object_detection/utils:learning_schedules",
], ],
) )
...@@ -179,7 +208,7 @@ py_test( ...@@ -179,7 +208,7 @@ py_test(
deps = [ deps = [
":optimizer_builder", ":optimizer_builder",
"//tensorflow", "//tensorflow",
"//tensorflow_models/object_detection/protos:optimizer_py_pb2", "//tensorflow/models/research/object_detection/protos:optimizer_py_pb2",
], ],
) )
...@@ -188,8 +217,8 @@ py_library( ...@@ -188,8 +217,8 @@ py_library(
srcs = ["post_processing_builder.py"], srcs = ["post_processing_builder.py"],
deps = [ deps = [
"//tensorflow", "//tensorflow",
"//tensorflow_models/object_detection/core:post_processing", "//tensorflow/models/research/object_detection/core:post_processing",
"//tensorflow_models/object_detection/protos:post_processing_py_pb2", "//tensorflow/models/research/object_detection/protos:post_processing_py_pb2",
], ],
) )
...@@ -199,7 +228,7 @@ py_test( ...@@ -199,7 +228,7 @@ py_test(
deps = [ deps = [
":post_processing_builder", ":post_processing_builder",
"//tensorflow", "//tensorflow",
"//tensorflow_models/object_detection/protos:post_processing_py_pb2", "//tensorflow/models/research/object_detection/protos:post_processing_py_pb2",
], ],
) )
...@@ -207,7 +236,7 @@ py_library( ...@@ -207,7 +236,7 @@ py_library(
name = "hyperparams_builder", name = "hyperparams_builder",
srcs = ["hyperparams_builder.py"], srcs = ["hyperparams_builder.py"],
deps = [ deps = [
"//tensorflow_models/object_detection/protos:hyperparams_py_pb2", "//tensorflow/models/research/object_detection/protos:hyperparams_py_pb2",
], ],
) )
...@@ -217,7 +246,7 @@ py_test( ...@@ -217,7 +246,7 @@ py_test(
deps = [ deps = [
":hyperparams_builder", ":hyperparams_builder",
"//tensorflow", "//tensorflow",
"//tensorflow_models/object_detection/protos:hyperparams_py_pb2", "//tensorflow/models/research/object_detection/protos:hyperparams_py_pb2",
], ],
) )
...@@ -226,8 +255,8 @@ py_library( ...@@ -226,8 +255,8 @@ py_library(
srcs = ["box_predictor_builder.py"], srcs = ["box_predictor_builder.py"],
deps = [ deps = [
":hyperparams_builder", ":hyperparams_builder",
"//tensorflow_models/object_detection/core:box_predictor", "//tensorflow/models/research/object_detection/core:box_predictor",
"//tensorflow_models/object_detection/protos:box_predictor_py_pb2", "//tensorflow/models/research/object_detection/protos:box_predictor_py_pb2",
], ],
) )
...@@ -238,8 +267,8 @@ py_test( ...@@ -238,8 +267,8 @@ py_test(
":box_predictor_builder", ":box_predictor_builder",
":hyperparams_builder", ":hyperparams_builder",
"//tensorflow", "//tensorflow",
"//tensorflow_models/object_detection/protos:box_predictor_py_pb2", "//tensorflow/models/research/object_detection/protos:box_predictor_py_pb2",
"//tensorflow_models/object_detection/protos:hyperparams_py_pb2", "//tensorflow/models/research/object_detection/protos:hyperparams_py_pb2",
], ],
) )
...@@ -247,8 +276,8 @@ py_library( ...@@ -247,8 +276,8 @@ py_library(
name = "region_similarity_calculator_builder", name = "region_similarity_calculator_builder",
srcs = ["region_similarity_calculator_builder.py"], srcs = ["region_similarity_calculator_builder.py"],
deps = [ deps = [
"//tensorflow_models/object_detection/core:region_similarity_calculator", "//tensorflow/models/research/object_detection/core:region_similarity_calculator",
"//tensorflow_models/object_detection/protos:region_similarity_calculator_py_pb2", "//tensorflow/models/research/object_detection/protos:region_similarity_calculator_py_pb2",
], ],
) )
...@@ -266,8 +295,8 @@ py_library( ...@@ -266,8 +295,8 @@ py_library(
srcs = ["preprocessor_builder.py"], srcs = ["preprocessor_builder.py"],
deps = [ deps = [
"//tensorflow", "//tensorflow",
"//tensorflow_models/object_detection/core:preprocessor", "//tensorflow/models/research/object_detection/core:preprocessor",
"//tensorflow_models/object_detection/protos:preprocessor_py_pb2", "//tensorflow/models/research/object_detection/protos:preprocessor_py_pb2",
], ],
) )
...@@ -279,8 +308,8 @@ py_test( ...@@ -279,8 +308,8 @@ py_test(
deps = [ deps = [
":preprocessor_builder", ":preprocessor_builder",
"//tensorflow", "//tensorflow",
"//tensorflow_models/object_detection/core:preprocessor", "//tensorflow/models/research/object_detection/core:preprocessor",
"//tensorflow_models/object_detection/protos:preprocessor_py_pb2", "//tensorflow/models/research/object_detection/protos:preprocessor_py_pb2",
], ],
) )
...@@ -289,8 +318,8 @@ py_library( ...@@ -289,8 +318,8 @@ py_library(
srcs = ["image_resizer_builder.py"], srcs = ["image_resizer_builder.py"],
deps = [ deps = [
"//tensorflow", "//tensorflow",
"//tensorflow_models/object_detection/core:preprocessor", "//tensorflow/models/research/object_detection/core:preprocessor",
"//tensorflow_models/object_detection/protos:image_resizer_py_pb2", "//tensorflow/models/research/object_detection/protos:image_resizer_py_pb2",
], ],
) )
...@@ -300,6 +329,6 @@ py_test( ...@@ -300,6 +329,6 @@ py_test(
deps = [ deps = [
":image_resizer_builder", ":image_resizer_builder",
"//tensorflow", "//tensorflow",
"//tensorflow_models/object_detection/protos:image_resizer_py_pb2", "//tensorflow/models/research/object_detection/protos:image_resizer_py_pb2",
], ],
) )
research/object_detection/builders/anchor_generator_builder.py
View file @ 7a9934df
...@@ -17,6 +17,7 @@ ...@@ -17,6 +17,7 @@
from object_detection.anchor_generators import grid_anchor_generator from object_detection.anchor_generators import grid_anchor_generator
from object_detection.anchor_generators import multiple_grid_anchor_generator from object_detection.anchor_generators import multiple_grid_anchor_generator
from object_detection.anchor_generators import multiscale_grid_anchor_generator
from object_detection.protos import anchor_generator_pb2 from object_detection.protos import anchor_generator_pb2
...@@ -78,5 +79,15 @@ def build(anchor_generator_config): ...@@ -78,5 +79,15 @@ def build(anchor_generator_config):
anchor_offsets=anchor_offsets, anchor_offsets=anchor_offsets,
reduce_boxes_in_lowest_layer=( reduce_boxes_in_lowest_layer=(
ssd_anchor_generator_config.reduce_boxes_in_lowest_layer)) ssd_anchor_generator_config.reduce_boxes_in_lowest_layer))
elif anchor_generator_config.WhichOneof(
'anchor_generator_oneof') == 'multiscale_anchor_generator':
cfg = anchor_generator_config.multiscale_anchor_generator
return multiscale_grid_anchor_generator.MultiscaleGridAnchorGenerator(
cfg.min_lvl,
cfg.max_lvl,
cfg.anchor_scale,
cfg.aspect_ratios,
cfg.scales_per_octave
)
else: else:
raise ValueError('Empty anchor generator.') raise ValueError('Empty anchor generator.')
research/object_detection/builders/box_predictor_builder.py
View file @ 7a9934df
...@@ -68,6 +68,24 @@ def build(argscope_fn, box_predictor_config, is_training, num_classes): ...@@ -68,6 +68,24 @@ def build(argscope_fn, box_predictor_config, is_training, num_classes):
) )
return box_predictor_object return box_predictor_object
if box_predictor_oneof == 'weight_shared_convolutional_box_predictor':
conv_box_predictor = (box_predictor_config.
weight_shared_convolutional_box_predictor)
conv_hyperparams = argscope_fn(conv_box_predictor.conv_hyperparams,
is_training)
box_predictor_object = box_predictor.WeightSharedConvolutionalBoxPredictor(
is_training=is_training,
num_classes=num_classes,
conv_hyperparams=conv_hyperparams,
depth=conv_box_predictor.depth,
num_layers_before_predictor=(conv_box_predictor.
num_layers_before_predictor),
kernel_size=conv_box_predictor.kernel_size,
box_code_size=conv_box_predictor.box_code_size,
class_prediction_bias_init=conv_box_predictor.class_prediction_bias_init
)
return box_predictor_object
if box_predictor_oneof == 'mask_rcnn_box_predictor': if box_predictor_oneof == 'mask_rcnn_box_predictor':
mask_rcnn_box_predictor = box_predictor_config.mask_rcnn_box_predictor mask_rcnn_box_predictor = box_predictor_config.mask_rcnn_box_predictor
fc_hyperparams = argscope_fn(mask_rcnn_box_predictor.fc_hyperparams, fc_hyperparams = argscope_fn(mask_rcnn_box_predictor.fc_hyperparams,
...@@ -85,8 +103,12 @@ def build(argscope_fn, box_predictor_config, is_training, num_classes): ...@@ -85,8 +103,12 @@ def build(argscope_fn, box_predictor_config, is_training, num_classes):
box_code_size=mask_rcnn_box_predictor.box_code_size, box_code_size=mask_rcnn_box_predictor.box_code_size,
conv_hyperparams=conv_hyperparams, conv_hyperparams=conv_hyperparams,
predict_instance_masks=mask_rcnn_box_predictor.predict_instance_masks, predict_instance_masks=mask_rcnn_box_predictor.predict_instance_masks,
mask_prediction_conv_depth=(mask_rcnn_box_predictor. mask_height=mask_rcnn_box_predictor.mask_height,
mask_prediction_conv_depth), mask_width=mask_rcnn_box_predictor.mask_width,
mask_prediction_num_conv_layers=(
mask_rcnn_box_predictor.mask_prediction_num_conv_layers),
mask_prediction_conv_depth=(
mask_rcnn_box_predictor.mask_prediction_conv_depth),
predict_keypoints=mask_rcnn_box_predictor.predict_keypoints) predict_keypoints=mask_rcnn_box_predictor.predict_keypoints)
return box_predictor_object return box_predictor_object
......
research/object_detection/builders/box_predictor_builder_test.py
View file @ 7a9934df
...@@ -150,6 +150,120 @@ class ConvolutionalBoxPredictorBuilderTest(tf.test.TestCase): ...@@ -150,6 +150,120 @@ class ConvolutionalBoxPredictorBuilderTest(tf.test.TestCase):
self.assertTrue(box_predictor._is_training) self.assertTrue(box_predictor._is_training)
class WeightSharedConvolutionalBoxPredictorBuilderTest(tf.test.TestCase):
def test_box_predictor_calls_conv_argscope_fn(self):
conv_hyperparams_text_proto = """
regularizer {
l1_regularizer {
weight: 0.0003
}
}
initializer {
truncated_normal_initializer {
mean: 0.0
stddev: 0.3
}
}
activation: RELU_6
"""
hyperparams_proto = hyperparams_pb2.Hyperparams()
text_format.Merge(conv_hyperparams_text_proto, hyperparams_proto)
def mock_conv_argscope_builder(conv_hyperparams_arg, is_training):
return (conv_hyperparams_arg, is_training)
box_predictor_proto = box_predictor_pb2.BoxPredictor()
(box_predictor_proto.weight_shared_convolutional_box_predictor
.conv_hyperparams.CopyFrom(hyperparams_proto))
box_predictor = box_predictor_builder.build(
argscope_fn=mock_conv_argscope_builder,
box_predictor_config=box_predictor_proto,
is_training=False,
num_classes=10)
(conv_hyperparams_actual, is_training) = box_predictor._conv_hyperparams
self.assertAlmostEqual((hyperparams_proto.regularizer.
l1_regularizer.weight),
(conv_hyperparams_actual.regularizer.l1_regularizer.
weight))
self.assertAlmostEqual((hyperparams_proto.initializer.
truncated_normal_initializer.stddev),
(conv_hyperparams_actual.initializer.
truncated_normal_initializer.stddev))
self.assertAlmostEqual((hyperparams_proto.initializer.
truncated_normal_initializer.mean),
(conv_hyperparams_actual.initializer.
truncated_normal_initializer.mean))
self.assertEqual(hyperparams_proto.activation,
conv_hyperparams_actual.activation)
self.assertFalse(is_training)
def test_construct_non_default_conv_box_predictor(self):
box_predictor_text_proto = """
weight_shared_convolutional_box_predictor {
depth: 2
num_layers_before_predictor: 2
kernel_size: 7
box_code_size: 3
class_prediction_bias_init: 4.0
}
"""
conv_hyperparams_text_proto = """
regularizer {
l1_regularizer {
}
}
initializer {
truncated_normal_initializer {
}
}
"""
hyperparams_proto = hyperparams_pb2.Hyperparams()
text_format.Merge(conv_hyperparams_text_proto, hyperparams_proto)
def mock_conv_argscope_builder(conv_hyperparams_arg, is_training):
return (conv_hyperparams_arg, is_training)
box_predictor_proto = box_predictor_pb2.BoxPredictor()
text_format.Merge(box_predictor_text_proto, box_predictor_proto)
(box_predictor_proto.weight_shared_convolutional_box_predictor.
conv_hyperparams.CopyFrom(hyperparams_proto))
box_predictor = box_predictor_builder.build(
argscope_fn=mock_conv_argscope_builder,
box_predictor_config=box_predictor_proto,
is_training=False,
num_classes=10)
self.assertEqual(box_predictor._depth, 2)
self.assertEqual(box_predictor._num_layers_before_predictor, 2)
self.assertAlmostEqual(box_predictor._class_prediction_bias_init, 4.0)
self.assertEqual(box_predictor.num_classes, 10)
self.assertFalse(box_predictor._is_training)
def test_construct_default_conv_box_predictor(self):
box_predictor_text_proto = """
weight_shared_convolutional_box_predictor {
conv_hyperparams {
regularizer {
l1_regularizer {
}
}
initializer {
truncated_normal_initializer {
}
}
}
}"""
box_predictor_proto = box_predictor_pb2.BoxPredictor()
text_format.Merge(box_predictor_text_proto, box_predictor_proto)
box_predictor = box_predictor_builder.build(
argscope_fn=hyperparams_builder.build,
box_predictor_config=box_predictor_proto,
is_training=True,
num_classes=90)
self.assertEqual(box_predictor._depth, 0)
self.assertEqual(box_predictor._num_layers_before_predictor, 0)
self.assertEqual(box_predictor.num_classes, 90)
self.assertTrue(box_predictor._is_training)
class MaskRCNNBoxPredictorBuilderTest(tf.test.TestCase): class MaskRCNNBoxPredictorBuilderTest(tf.test.TestCase):
def test_box_predictor_builder_calls_fc_argscope_fn(self): def test_box_predictor_builder_calls_fc_argscope_fn(self):
...@@ -247,6 +361,8 @@ class MaskRCNNBoxPredictorBuilderTest(tf.test.TestCase): ...@@ -247,6 +361,8 @@ class MaskRCNNBoxPredictorBuilderTest(tf.test.TestCase):
hyperparams_pb2.Hyperparams.CONV) hyperparams_pb2.Hyperparams.CONV)
box_predictor_proto.mask_rcnn_box_predictor.predict_instance_masks = True box_predictor_proto.mask_rcnn_box_predictor.predict_instance_masks = True
box_predictor_proto.mask_rcnn_box_predictor.mask_prediction_conv_depth = 512 box_predictor_proto.mask_rcnn_box_predictor.mask_prediction_conv_depth = 512
box_predictor_proto.mask_rcnn_box_predictor.mask_height = 16
box_predictor_proto.mask_rcnn_box_predictor.mask_width = 16
mock_argscope_fn = mock.Mock(return_value='arg_scope') mock_argscope_fn = mock.Mock(return_value='arg_scope')
box_predictor = box_predictor_builder.build( box_predictor = box_predictor_builder.build(
argscope_fn=mock_argscope_fn, argscope_fn=mock_argscope_fn,
......
research/object_detection/builders/dataset_builder.py 0 → 100644
View file @ 7a9934df
# 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.
# ==============================================================================
"""tf.data.Dataset builder.
Creates data sources for DetectionModels from an InputReader config. See
input_reader.proto for options.
Note: If users wishes to also use their own InputReaders with the Object
Detection configuration framework, they should define their own builder function
that wraps the build function.
"""
import tensorflow as tf
from object_detection.data_decoders import tf_example_decoder
from object_detection.protos import input_reader_pb2
from object_detection.utils import dataset_util
def build(input_reader_config, num_workers=1, worker_index=0):
"""Builds a tf.data.Dataset based on the InputReader config.
Args:
input_reader_config: A input_reader_pb2.InputReader object.
num_workers: Number of workers / shards.
worker_index: Id for the current worker.
Returns:
A tf.data.Dataset based on the input_reader_config.
Raises:
ValueError: On invalid input reader proto.
ValueError: If no input paths are specified.
"""
if not isinstance(input_reader_config, input_reader_pb2.InputReader):
raise ValueError('input_reader_config not of type '
'input_reader_pb2.InputReader.')
if input_reader_config.WhichOneof('input_reader') == 'tf_record_input_reader':
config = input_reader_config.tf_record_input_reader
if not config.input_path:
raise ValueError('At least one input path must be specified in '
'`input_reader_config`.')
label_map_proto_file = None
if input_reader_config.HasField('label_map_path'):
label_map_proto_file = input_reader_config.label_map_path
decoder = tf_example_decoder.TfExampleDecoder(
load_instance_masks=input_reader_config.load_instance_masks,
instance_mask_type=input_reader_config.mask_type,
label_map_proto_file=label_map_proto_file)
return dataset_util.read_dataset(
tf.data.TFRecordDataset, decoder.decode, config.input_path[:],
input_reader_config, num_workers, worker_index)
raise ValueError('Unsupported input_reader_config.')
research/object_detection/builders/dataset_builder_test.py 0 → 100644
View file @ 7a9934df
# 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 dataset_builder."""
import os
import numpy as np
import tensorflow as tf
from google.protobuf import text_format
from tensorflow.core.example import example_pb2
from tensorflow.core.example import feature_pb2
from object_detection.builders import dataset_builder
from object_detection.core import standard_fields as fields
from object_detection.protos import input_reader_pb2
from object_detection.utils import dataset_util
class DatasetBuilderTest(tf.test.TestCase):
def create_tf_record(self):
path = os.path.join(self.get_temp_dir(), 'tfrecord')
writer = tf.python_io.TFRecordWriter(path)
image_tensor = np.random.randint(255, size=(4, 5, 3)).astype(np.uint8)
flat_mask = (4 * 5) * [1.0]
with self.test_session():
encoded_jpeg = tf.image.encode_jpeg(tf.constant(image_tensor)).eval()
example = example_pb2.Example(
features=feature_pb2.Features(
feature={
'image/encoded':
feature_pb2.Feature(
bytes_list=feature_pb2.BytesList(value=[encoded_jpeg])),
'image/format':
feature_pb2.Feature(
bytes_list=feature_pb2.BytesList(
value=['jpeg'.encode('utf-8')])),
'image/height':
feature_pb2.Feature(
int64_list=feature_pb2.Int64List(value=[4])),
'image/width':
feature_pb2.Feature(
int64_list=feature_pb2.Int64List(value=[5])),
'image/object/bbox/xmin':
feature_pb2.Feature(
float_list=feature_pb2.FloatList(value=[0.0])),
'image/object/bbox/xmax':
feature_pb2.Feature(
float_list=feature_pb2.FloatList(value=[1.0])),
'image/object/bbox/ymin':
feature_pb2.Feature(
float_list=feature_pb2.FloatList(value=[0.0])),
'image/object/bbox/ymax':
feature_pb2.Feature(
float_list=feature_pb2.FloatList(value=[1.0])),
'image/object/class/label':
feature_pb2.Feature(
int64_list=feature_pb2.Int64List(value=[2])),
'image/object/mask':
feature_pb2.Feature(
float_list=feature_pb2.FloatList(value=flat_mask)),
}))
writer.write(example.SerializeToString())
writer.close()
return path
def test_build_tf_record_input_reader(self):
tf_record_path = self.create_tf_record()
input_reader_text_proto = """
shuffle: false
num_readers: 1
tf_record_input_reader {{
input_path: '{0}'
}}
""".format(tf_record_path)
input_reader_proto = input_reader_pb2.InputReader()
text_format.Merge(input_reader_text_proto, input_reader_proto)
tensor_dict = dataset_util.make_initializable_iterator(
dataset_builder.build(input_reader_proto)).get_next()
sv = tf.train.Supervisor(logdir=self.get_temp_dir())
with sv.prepare_or_wait_for_session() as sess:
sv.start_queue_runners(sess)
output_dict = sess.run(tensor_dict)
self.assertTrue(
fields.InputDataFields.groundtruth_instance_masks not in output_dict)
self.assertEquals((4, 5, 3),
output_dict[fields.InputDataFields.image].shape)
self.assertEquals([2],
output_dict[fields.InputDataFields.groundtruth_classes])
self.assertEquals(
(1, 4), output_dict[fields.InputDataFields.groundtruth_boxes].shape)
self.assertAllEqual(
[0.0, 0.0, 1.0, 1.0],
output_dict[fields.InputDataFields.groundtruth_boxes][0])
def test_build_tf_record_input_reader_and_load_instance_masks(self):
tf_record_path = self.create_tf_record()
input_reader_text_proto = """
shuffle: false
num_readers: 1
load_instance_masks: true
tf_record_input_reader {{
input_path: '{0}'
}}
""".format(tf_record_path)
input_reader_proto = input_reader_pb2.InputReader()
text_format.Merge(input_reader_text_proto, input_reader_proto)
tensor_dict = dataset_util.make_initializable_iterator(
dataset_builder.build(input_reader_proto)).get_next()
sv = tf.train.Supervisor(logdir=self.get_temp_dir())
with sv.prepare_or_wait_for_session() as sess:
sv.start_queue_runners(sess)
output_dict = sess.run(tensor_dict)
self.assertEquals((4, 5, 3),
output_dict[fields.InputDataFields.image].shape)
self.assertEquals([2],
output_dict[fields.InputDataFields.groundtruth_classes])
self.assertEquals(
(1, 4), output_dict[fields.InputDataFields.groundtruth_boxes].shape)
self.assertAllEqual(
[0.0, 0.0, 1.0, 1.0],
output_dict[fields.InputDataFields.groundtruth_boxes][0])
self.assertAllEqual(
(1, 4, 5),
output_dict[fields.InputDataFields.groundtruth_instance_masks].shape)
def test_raises_error_with_no_input_paths(self):
input_reader_text_proto = """
shuffle: false
num_readers: 1
load_instance_masks: true
"""
input_reader_proto = input_reader_pb2.InputReader()
text_format.Merge(input_reader_text_proto, input_reader_proto)
with self.assertRaises(ValueError):
dataset_builder.build(input_reader_proto)
if __name__ == '__main__':
tf.test.main()
research/object_detection/builders/image_resizer_builder.py
View file @ 7a9934df
...@@ -83,7 +83,8 @@ def build(image_resizer_config): ...@@ -83,7 +83,8 @@ def build(image_resizer_config):
preprocessor.resize_to_range, preprocessor.resize_to_range,
min_dimension=keep_aspect_ratio_config.min_dimension, min_dimension=keep_aspect_ratio_config.min_dimension,
max_dimension=keep_aspect_ratio_config.max_dimension, max_dimension=keep_aspect_ratio_config.max_dimension,
method=method) method=method,
pad_to_max_dimension=keep_aspect_ratio_config.pad_to_max_dimension)
if image_resizer_config.WhichOneof( if image_resizer_config.WhichOneof(
'image_resizer_oneof') == 'fixed_shape_resizer': 'image_resizer_oneof') == 'fixed_shape_resizer':
fixed_shape_resizer_config = image_resizer_config.fixed_shape_resizer fixed_shape_resizer_config = image_resizer_config.fixed_shape_resizer
......
research/object_detection/builders/image_resizer_builder_test.py
View file @ 7a9934df
...@@ -29,11 +29,11 @@ class ImageResizerBuilderTest(tf.test.TestCase): ...@@ -29,11 +29,11 @@ class ImageResizerBuilderTest(tf.test.TestCase):
image_resizer_fn = image_resizer_builder.build(image_resizer_config) image_resizer_fn = image_resizer_builder.build(image_resizer_config)
images = tf.to_float( images = tf.to_float(
tf.random_uniform(input_shape, minval=0, maxval=255, dtype=tf.int32)) tf.random_uniform(input_shape, minval=0, maxval=255, dtype=tf.int32))
resized_images = image_resizer_fn(images) resized_images, _ = image_resizer_fn(images)
with self.test_session() as sess: with self.test_session() as sess:
return sess.run(resized_images).shape return sess.run(resized_images).shape
def test_built_keep_aspect_ratio_resizer_returns_expected_shape(self): def test_build_keep_aspect_ratio_resizer_returns_expected_shape(self):
image_resizer_text_proto = """ image_resizer_text_proto = """
keep_aspect_ratio_resizer { keep_aspect_ratio_resizer {
min_dimension: 10 min_dimension: 10
...@@ -46,6 +46,20 @@ class ImageResizerBuilderTest(tf.test.TestCase): ...@@ -46,6 +46,20 @@ class ImageResizerBuilderTest(tf.test.TestCase):
input_shape, image_resizer_text_proto) input_shape, image_resizer_text_proto)
self.assertEqual(output_shape, expected_output_shape) self.assertEqual(output_shape, expected_output_shape)
def test_build_keep_aspect_ratio_resizer_with_padding(self):
image_resizer_text_proto = """
keep_aspect_ratio_resizer {
min_dimension: 10
max_dimension: 20
pad_to_max_dimension: true
}
"""
input_shape = (50, 25, 3)
expected_output_shape = (20, 20, 3)
output_shape = self._shape_of_resized_random_image_given_text_proto(
input_shape, image_resizer_text_proto)
self.assertEqual(output_shape, expected_output_shape)
def test_built_fixed_shape_resizer_returns_expected_shape(self): def test_built_fixed_shape_resizer_returns_expected_shape(self):
image_resizer_text_proto = """ image_resizer_text_proto = """
fixed_shape_resizer { fixed_shape_resizer {
...@@ -69,7 +83,7 @@ class ImageResizerBuilderTest(tf.test.TestCase): ...@@ -69,7 +83,7 @@ class ImageResizerBuilderTest(tf.test.TestCase):
text_format.Merge(text_proto, image_resizer_config) text_format.Merge(text_proto, image_resizer_config)
image_resizer_fn = image_resizer_builder.build(image_resizer_config) image_resizer_fn = image_resizer_builder.build(image_resizer_config)
image_placeholder = tf.placeholder(tf.uint8, [1, None, None, 3]) image_placeholder = tf.placeholder(tf.uint8, [1, None, None, 3])
resized_image = image_resizer_fn(image_placeholder) resized_image, _ = image_resizer_fn(image_placeholder)
with self.test_session() as sess: with self.test_session() as sess:
return sess.run(resized_image, feed_dict={image_placeholder: image}) return sess.run(resized_image, feed_dict={image_placeholder: image})
......
research/object_detection/builders/input_reader_builder.py
View file @ 7a9934df
...@@ -69,6 +69,7 @@ def build(input_reader_config): ...@@ -69,6 +69,7 @@ def build(input_reader_config):
label_map_proto_file = input_reader_config.label_map_path label_map_proto_file = input_reader_config.label_map_path
decoder = tf_example_decoder.TfExampleDecoder( decoder = tf_example_decoder.TfExampleDecoder(
load_instance_masks=input_reader_config.load_instance_masks, load_instance_masks=input_reader_config.load_instance_masks,
instance_mask_type=input_reader_config.mask_type,
label_map_proto_file=label_map_proto_file) label_map_proto_file=label_map_proto_file)
return decoder.decode(string_tensor) return decoder.decode(string_tensor)
......
research/object_detection/builders/losses_builder.py
View file @ 7a9934df
...@@ -116,18 +116,17 @@ def build_faster_rcnn_classification_loss(loss_config): ...@@ -116,18 +116,17 @@ def build_faster_rcnn_classification_loss(loss_config):
loss_type = loss_config.WhichOneof('classification_loss') loss_type = loss_config.WhichOneof('classification_loss')
if loss_type == 'weighted_sigmoid': if loss_type == 'weighted_sigmoid':
config = loss_config.weighted_sigmoid return losses.WeightedSigmoidClassificationLoss()
return losses.WeightedSigmoidClassificationLoss(
anchorwise_output=config.anchorwise_output)
if loss_type == 'weighted_softmax': if loss_type == 'weighted_softmax':
config = loss_config.weighted_softmax config = loss_config.weighted_softmax
return losses.WeightedSoftmaxClassificationLoss( return losses.WeightedSoftmaxClassificationLoss(
anchorwise_output=config.anchorwise_output) logit_scale=config.logit_scale)
# By default, Faster RCNN second stage classifier uses Softmax loss # By default, Faster RCNN second stage classifier uses Softmax loss
# with anchor-wise outputs. # with anchor-wise outputs.
config = loss_config.weighted_softmax
return losses.WeightedSoftmaxClassificationLoss( return losses.WeightedSoftmaxClassificationLoss(
anchorwise_output=True) logit_scale=config.logit_scale)
def _build_localization_loss(loss_config): def _build_localization_loss(loss_config):
...@@ -148,14 +147,10 @@ def _build_localization_loss(loss_config): ...@@ -148,14 +147,10 @@ def _build_localization_loss(loss_config):
loss_type = loss_config.WhichOneof('localization_loss') loss_type = loss_config.WhichOneof('localization_loss')
if loss_type == 'weighted_l2': if loss_type == 'weighted_l2':
config = loss_config.weighted_l2 return losses.WeightedL2LocalizationLoss()
return losses.WeightedL2LocalizationLoss(
anchorwise_output=config.anchorwise_output)
if loss_type == 'weighted_smooth_l1': if loss_type == 'weighted_smooth_l1':
config = loss_config.weighted_smooth_l1 return losses.WeightedSmoothL1LocalizationLoss()
return losses.WeightedSmoothL1LocalizationLoss(
anchorwise_output=config.anchorwise_output)
if loss_type == 'weighted_iou': if loss_type == 'weighted_iou':
return losses.WeightedIOULocalizationLoss() return losses.WeightedIOULocalizationLoss()
...@@ -181,9 +176,7 @@ def _build_classification_loss(loss_config): ...@@ -181,9 +176,7 @@ def _build_classification_loss(loss_config):
loss_type = loss_config.WhichOneof('classification_loss') loss_type = loss_config.WhichOneof('classification_loss')
if loss_type == 'weighted_sigmoid': if loss_type == 'weighted_sigmoid':
config = loss_config.weighted_sigmoid return losses.WeightedSigmoidClassificationLoss()
return losses.WeightedSigmoidClassificationLoss(
anchorwise_output=config.anchorwise_output)
if loss_type == 'weighted_sigmoid_focal': if loss_type == 'weighted_sigmoid_focal':
config = loss_config.weighted_sigmoid_focal config = loss_config.weighted_sigmoid_focal
...@@ -191,21 +184,18 @@ def _build_classification_loss(loss_config): ...@@ -191,21 +184,18 @@ def _build_classification_loss(loss_config):
if config.HasField('alpha'): if config.HasField('alpha'):
alpha = config.alpha alpha = config.alpha
return losses.SigmoidFocalClassificationLoss( return losses.SigmoidFocalClassificationLoss(
anchorwise_output=config.anchorwise_output,
gamma=config.gamma, gamma=config.gamma,
alpha=alpha) alpha=alpha)
if loss_type == 'weighted_softmax': if loss_type == 'weighted_softmax':
config = loss_config.weighted_softmax config = loss_config.weighted_softmax
return losses.WeightedSoftmaxClassificationLoss( return losses.WeightedSoftmaxClassificationLoss(
anchorwise_output=config.anchorwise_output,
logit_scale=config.logit_scale) logit_scale=config.logit_scale)
if loss_type == 'bootstrapped_sigmoid': if loss_type == 'bootstrapped_sigmoid':
config = loss_config.bootstrapped_sigmoid config = loss_config.bootstrapped_sigmoid
return losses.BootstrappedSigmoidClassificationLoss( return losses.BootstrappedSigmoidClassificationLoss(
alpha=config.alpha, alpha=config.alpha,
bootstrap_type=('hard' if config.hard_bootstrap else 'soft'), bootstrap_type=('hard' if config.hard_bootstrap else 'soft'))
anchorwise_output=config.anchorwise_output)
raise ValueError('Empty loss config.') raise ValueError('Empty loss config.')
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