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体验新版 GitCode,发现更多精彩内容 >>
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4437d7b4
编写于
10月 26, 2020
作者:
J
Jonathan Huang
提交者:
TF Object Detection Team
10月 26, 2020
浏览文件
操作
浏览文件
下载
电子邮件补丁
差异文件
Add support for LVIS metrics.
PiperOrigin-RevId: 339190667
上级
b1809d94
变更
10
展开全部
隐藏空白更改
内联
并排
Showing
10 changed file
with
1085 addition
and
9 deletion
+1085
-9
research/object_detection/core/standard_fields.py
research/object_detection/core/standard_fields.py
+5
-0
research/object_detection/eval_util_test.py
research/object_detection/eval_util_test.py
+8
-1
research/object_detection/metrics/coco_evaluation.py
research/object_detection/metrics/coco_evaluation.py
+12
-6
research/object_detection/metrics/coco_evaluation_test.py
research/object_detection/metrics/coco_evaluation_test.py
+15
-0
research/object_detection/metrics/lvis_evaluation.py
research/object_detection/metrics/lvis_evaluation.py
+443
-0
research/object_detection/metrics/lvis_evaluation_test.py
research/object_detection/metrics/lvis_evaluation_test.py
+182
-0
research/object_detection/metrics/lvis_tools.py
research/object_detection/metrics/lvis_tools.py
+259
-0
research/object_detection/metrics/lvis_tools_test.py
research/object_detection/metrics/lvis_tools_test.py
+158
-0
research/object_detection/packages/tf1/setup.py
research/object_detection/packages/tf1/setup.py
+2
-2
research/object_detection/packages/tf2/setup.py
research/object_detection/packages/tf2/setup.py
+1
-0
未找到文件。
research/object_detection/core/standard_fields.py
浏览文件 @
4437d7b4
...
@@ -70,6 +70,9 @@ class InputDataFields(object):
...
@@ -70,6 +70,9 @@ class InputDataFields(object):
groundtruth_keypoint_visibilities: ground truth keypoint visibilities.
groundtruth_keypoint_visibilities: ground truth keypoint visibilities.
groundtruth_keypoint_weights: groundtruth weight factor for keypoints.
groundtruth_keypoint_weights: groundtruth weight factor for keypoints.
groundtruth_label_weights: groundtruth label weights.
groundtruth_label_weights: groundtruth label weights.
groundtruth_verified_negative_classes: groundtruth verified negative classes
groundtruth_not_exhaustive_classes: groundtruth not-exhaustively labeled
classes.
groundtruth_weights: groundtruth weight factor for bounding boxes.
groundtruth_weights: groundtruth weight factor for bounding boxes.
groundtruth_dp_num_points: The number of DensePose sampled points for each
groundtruth_dp_num_points: The number of DensePose sampled points for each
instance.
instance.
...
@@ -120,6 +123,8 @@ class InputDataFields(object):
...
@@ -120,6 +123,8 @@ class InputDataFields(object):
groundtruth_keypoint_visibilities
=
'groundtruth_keypoint_visibilities'
groundtruth_keypoint_visibilities
=
'groundtruth_keypoint_visibilities'
groundtruth_keypoint_weights
=
'groundtruth_keypoint_weights'
groundtruth_keypoint_weights
=
'groundtruth_keypoint_weights'
groundtruth_label_weights
=
'groundtruth_label_weights'
groundtruth_label_weights
=
'groundtruth_label_weights'
groundtruth_verified_neg_classes
=
'groundtruth_verified_neg_classes'
groundtruth_not_exhaustive_classes
=
'groundtruth_not_exhaustive_classes'
groundtruth_weights
=
'groundtruth_weights'
groundtruth_weights
=
'groundtruth_weights'
groundtruth_dp_num_points
=
'groundtruth_dp_num_points'
groundtruth_dp_num_points
=
'groundtruth_dp_num_points'
groundtruth_dp_part_ids
=
'groundtruth_dp_part_ids'
groundtruth_dp_part_ids
=
'groundtruth_dp_part_ids'
...
...
research/object_detection/eval_util_test.py
浏览文件 @
4437d7b4
...
@@ -85,6 +85,8 @@ class EvalUtilTest(test_case.TestCase, parameterized.TestCase):
...
@@ -85,6 +85,8 @@ class EvalUtilTest(test_case.TestCase, parameterized.TestCase):
groundtruth_boxes
=
tf
.
constant
([[
0.
,
0.
,
1.
,
1.
]])
groundtruth_boxes
=
tf
.
constant
([[
0.
,
0.
,
1.
,
1.
]])
groundtruth_classes
=
tf
.
constant
([
1
])
groundtruth_classes
=
tf
.
constant
([
1
])
groundtruth_instance_masks
=
tf
.
ones
(
shape
=
[
1
,
20
,
20
],
dtype
=
tf
.
uint8
)
groundtruth_instance_masks
=
tf
.
ones
(
shape
=
[
1
,
20
,
20
],
dtype
=
tf
.
uint8
)
original_image_spatial_shapes
=
tf
.
constant
([[
20
,
20
]],
dtype
=
tf
.
int32
)
groundtruth_keypoints
=
tf
.
constant
([[
0.0
,
0.0
],
[
0.5
,
0.5
],
[
1.0
,
1.0
]])
groundtruth_keypoints
=
tf
.
constant
([[
0.0
,
0.0
],
[
0.5
,
0.5
],
[
1.0
,
1.0
]])
if
resized_groundtruth_masks
:
if
resized_groundtruth_masks
:
groundtruth_instance_masks
=
tf
.
ones
(
shape
=
[
1
,
10
,
10
],
dtype
=
tf
.
uint8
)
groundtruth_instance_masks
=
tf
.
ones
(
shape
=
[
1
,
10
,
10
],
dtype
=
tf
.
uint8
)
...
@@ -100,6 +102,8 @@ class EvalUtilTest(test_case.TestCase, parameterized.TestCase):
...
@@ -100,6 +102,8 @@ class EvalUtilTest(test_case.TestCase, parameterized.TestCase):
groundtruth_keypoints
=
tf
.
tile
(
groundtruth_keypoints
=
tf
.
tile
(
tf
.
expand_dims
(
groundtruth_keypoints
,
0
),
tf
.
expand_dims
(
groundtruth_keypoints
,
0
),
multiples
=
[
batch_size
,
1
,
1
])
multiples
=
[
batch_size
,
1
,
1
])
original_image_spatial_shapes
=
tf
.
tile
(
original_image_spatial_shapes
,
multiples
=
[
batch_size
,
1
])
detections
=
{
detections
=
{
detection_fields
.
detection_boxes
:
detection_boxes
,
detection_fields
.
detection_boxes
:
detection_boxes
,
...
@@ -112,7 +116,10 @@ class EvalUtilTest(test_case.TestCase, parameterized.TestCase):
...
@@ -112,7 +116,10 @@ class EvalUtilTest(test_case.TestCase, parameterized.TestCase):
input_data_fields
.
groundtruth_boxes
:
groundtruth_boxes
,
input_data_fields
.
groundtruth_boxes
:
groundtruth_boxes
,
input_data_fields
.
groundtruth_classes
:
groundtruth_classes
,
input_data_fields
.
groundtruth_classes
:
groundtruth_classes
,
input_data_fields
.
groundtruth_keypoints
:
groundtruth_keypoints
,
input_data_fields
.
groundtruth_keypoints
:
groundtruth_keypoints
,
input_data_fields
.
groundtruth_instance_masks
:
groundtruth_instance_masks
input_data_fields
.
groundtruth_instance_masks
:
groundtruth_instance_masks
,
input_data_fields
.
original_image_spatial_shape
:
original_image_spatial_shapes
}
}
if
batch_size
>
1
:
if
batch_size
>
1
:
return
eval_util
.
result_dict_for_batched_example
(
return
eval_util
.
result_dict_for_batched_example
(
...
...
research/object_detection/metrics/coco_evaluation.py
浏览文件 @
4437d7b4
...
@@ -1191,18 +1191,20 @@ class CocoMaskEvaluator(object_detection_evaluation.DetectionEvaluator):
...
@@ -1191,18 +1191,20 @@ class CocoMaskEvaluator(object_detection_evaluation.DetectionEvaluator):
groundtruth_instance_masks_batched
,
groundtruth_instance_masks_batched
,
groundtruth_is_crowd_batched
,
num_gt_boxes_per_image
,
groundtruth_is_crowd_batched
,
num_gt_boxes_per_image
,
detection_scores_batched
,
detection_classes_batched
,
detection_scores_batched
,
detection_classes_batched
,
detection_masks_batched
,
num_det_boxes_per_image
):
detection_masks_batched
,
num_det_boxes_per_image
,
original_image_spatial_shape
):
"""Update op for metrics."""
"""Update op for metrics."""
for
(
image_id
,
groundtruth_boxes
,
groundtruth_classes
,
for
(
image_id
,
groundtruth_boxes
,
groundtruth_classes
,
groundtruth_instance_masks
,
groundtruth_is_crowd
,
num_gt_box
,
groundtruth_instance_masks
,
groundtruth_is_crowd
,
num_gt_box
,
detection_scores
,
detection_classes
,
detection_scores
,
detection_classes
,
detection_masks
,
num_det_box
)
in
zip
(
detection_masks
,
num_det_box
,
original_image_shape
)
in
zip
(
image_id_batched
,
groundtruth_boxes_batched
,
image_id_batched
,
groundtruth_boxes_batched
,
groundtruth_classes_batched
,
groundtruth_instance_masks_batched
,
groundtruth_classes_batched
,
groundtruth_instance_masks_batched
,
groundtruth_is_crowd_batched
,
num_gt_boxes_per_image
,
groundtruth_is_crowd_batched
,
num_gt_boxes_per_image
,
detection_scores_batched
,
detection_classes_batched
,
detection_scores_batched
,
detection_classes_batched
,
detection_masks_batched
,
num_det_boxes_per_image
):
detection_masks_batched
,
num_det_boxes_per_image
,
original_image_spatial_shape
):
self
.
add_single_ground_truth_image_info
(
self
.
add_single_ground_truth_image_info
(
image_id
,
{
image_id
,
{
'groundtruth_boxes'
:
'groundtruth_boxes'
:
...
@@ -1210,7 +1212,8 @@ class CocoMaskEvaluator(object_detection_evaluation.DetectionEvaluator):
...
@@ -1210,7 +1212,8 @@ class CocoMaskEvaluator(object_detection_evaluation.DetectionEvaluator):
'groundtruth_classes'
:
'groundtruth_classes'
:
groundtruth_classes
[:
num_gt_box
],
groundtruth_classes
[:
num_gt_box
],
'groundtruth_instance_masks'
:
'groundtruth_instance_masks'
:
groundtruth_instance_masks
[:
num_gt_box
],
groundtruth_instance_masks
[:
num_gt_box
][
:
original_image_shape
[
0
],
:
original_image_shape
[
1
]],
'groundtruth_is_crowd'
:
'groundtruth_is_crowd'
:
groundtruth_is_crowd
[:
num_gt_box
]
groundtruth_is_crowd
[:
num_gt_box
]
})
})
...
@@ -1218,13 +1221,16 @@ class CocoMaskEvaluator(object_detection_evaluation.DetectionEvaluator):
...
@@ -1218,13 +1221,16 @@ class CocoMaskEvaluator(object_detection_evaluation.DetectionEvaluator):
image_id
,
{
image_id
,
{
'detection_scores'
:
detection_scores
[:
num_det_box
],
'detection_scores'
:
detection_scores
[:
num_det_box
],
'detection_classes'
:
detection_classes
[:
num_det_box
],
'detection_classes'
:
detection_classes
[:
num_det_box
],
'detection_masks'
:
detection_masks
[:
num_det_box
]
'detection_masks'
:
detection_masks
[:
num_det_box
][
:
original_image_shape
[
0
],
:
original_image_shape
[
1
]]
})
})
# Unpack items from the evaluation dictionary.
# Unpack items from the evaluation dictionary.
input_data_fields
=
standard_fields
.
InputDataFields
input_data_fields
=
standard_fields
.
InputDataFields
detection_fields
=
standard_fields
.
DetectionResultFields
detection_fields
=
standard_fields
.
DetectionResultFields
image_id
=
eval_dict
[
input_data_fields
.
key
]
image_id
=
eval_dict
[
input_data_fields
.
key
]
original_image_spatial_shape
=
eval_dict
[
input_data_fields
.
original_image_spatial_shape
]
groundtruth_boxes
=
eval_dict
[
input_data_fields
.
groundtruth_boxes
]
groundtruth_boxes
=
eval_dict
[
input_data_fields
.
groundtruth_boxes
]
groundtruth_classes
=
eval_dict
[
input_data_fields
.
groundtruth_classes
]
groundtruth_classes
=
eval_dict
[
input_data_fields
.
groundtruth_classes
]
groundtruth_instance_masks
=
eval_dict
[
groundtruth_instance_masks
=
eval_dict
[
...
@@ -1276,7 +1282,7 @@ class CocoMaskEvaluator(object_detection_evaluation.DetectionEvaluator):
...
@@ -1276,7 +1282,7 @@ class CocoMaskEvaluator(object_detection_evaluation.DetectionEvaluator):
image_id
,
groundtruth_boxes
,
groundtruth_classes
,
image_id
,
groundtruth_boxes
,
groundtruth_classes
,
groundtruth_instance_masks
,
groundtruth_is_crowd
,
groundtruth_instance_masks
,
groundtruth_is_crowd
,
num_gt_boxes_per_image
,
detection_scores
,
detection_classes
,
num_gt_boxes_per_image
,
detection_scores
,
detection_classes
,
detection_masks
,
num_det_boxes_per_image
detection_masks
,
num_det_boxes_per_image
,
original_image_spatial_shape
],
[])
],
[])
def
get_estimator_eval_metric_ops
(
self
,
eval_dict
):
def
get_estimator_eval_metric_ops
(
self
,
eval_dict
):
...
...
research/object_detection/metrics/coco_evaluation_test.py
浏览文件 @
4437d7b4
...
@@ -1601,6 +1601,7 @@ class CocoMaskEvaluationPyFuncTest(tf.test.TestCase):
...
@@ -1601,6 +1601,7 @@ class CocoMaskEvaluationPyFuncTest(tf.test.TestCase):
groundtruth_boxes
=
tf
.
placeholder
(
tf
.
float32
,
shape
=
(
None
,
4
))
groundtruth_boxes
=
tf
.
placeholder
(
tf
.
float32
,
shape
=
(
None
,
4
))
groundtruth_classes
=
tf
.
placeholder
(
tf
.
float32
,
shape
=
(
None
))
groundtruth_classes
=
tf
.
placeholder
(
tf
.
float32
,
shape
=
(
None
))
groundtruth_masks
=
tf
.
placeholder
(
tf
.
uint8
,
shape
=
(
None
,
None
,
None
))
groundtruth_masks
=
tf
.
placeholder
(
tf
.
uint8
,
shape
=
(
None
,
None
,
None
))
original_image_spatial_shape
=
tf
.
placeholder
(
tf
.
int32
,
shape
=
(
None
,
2
))
detection_scores
=
tf
.
placeholder
(
tf
.
float32
,
shape
=
(
None
))
detection_scores
=
tf
.
placeholder
(
tf
.
float32
,
shape
=
(
None
))
detection_classes
=
tf
.
placeholder
(
tf
.
float32
,
shape
=
(
None
))
detection_classes
=
tf
.
placeholder
(
tf
.
float32
,
shape
=
(
None
))
detection_masks
=
tf
.
placeholder
(
tf
.
uint8
,
shape
=
(
None
,
None
,
None
))
detection_masks
=
tf
.
placeholder
(
tf
.
uint8
,
shape
=
(
None
,
None
,
None
))
...
@@ -1612,6 +1613,8 @@ class CocoMaskEvaluationPyFuncTest(tf.test.TestCase):
...
@@ -1612,6 +1613,8 @@ class CocoMaskEvaluationPyFuncTest(tf.test.TestCase):
input_data_fields
.
groundtruth_boxes
:
groundtruth_boxes
,
input_data_fields
.
groundtruth_boxes
:
groundtruth_boxes
,
input_data_fields
.
groundtruth_classes
:
groundtruth_classes
,
input_data_fields
.
groundtruth_classes
:
groundtruth_classes
,
input_data_fields
.
groundtruth_instance_masks
:
groundtruth_masks
,
input_data_fields
.
groundtruth_instance_masks
:
groundtruth_masks
,
input_data_fields
.
original_image_spatial_shape
:
original_image_spatial_shape
,
detection_fields
.
detection_scores
:
detection_scores
,
detection_fields
.
detection_scores
:
detection_scores
,
detection_fields
.
detection_classes
:
detection_classes
,
detection_fields
.
detection_classes
:
detection_classes
,
detection_fields
.
detection_masks
:
detection_masks
,
detection_fields
.
detection_masks
:
detection_masks
,
...
@@ -1637,6 +1640,7 @@ class CocoMaskEvaluationPyFuncTest(tf.test.TestCase):
...
@@ -1637,6 +1640,7 @@ class CocoMaskEvaluationPyFuncTest(tf.test.TestCase):
np
.
ones
([
50
,
50
],
dtype
=
np
.
uint8
),
((
0
,
70
),
(
0
,
70
)),
np
.
ones
([
50
,
50
],
dtype
=
np
.
uint8
),
((
0
,
70
),
(
0
,
70
)),
mode
=
'constant'
)
mode
=
'constant'
)
]),
]),
original_image_spatial_shape
:
np
.
array
([[
120
,
120
]]),
detection_scores
:
detection_scores
:
np
.
array
([.
9
,
.
8
]),
np
.
array
([.
9
,
.
8
]),
detection_classes
:
detection_classes
:
...
@@ -1661,6 +1665,7 @@ class CocoMaskEvaluationPyFuncTest(tf.test.TestCase):
...
@@ -1661,6 +1665,7 @@ class CocoMaskEvaluationPyFuncTest(tf.test.TestCase):
groundtruth_boxes
=
tf
.
placeholder
(
tf
.
float32
,
shape
=
(
None
,
4
))
groundtruth_boxes
=
tf
.
placeholder
(
tf
.
float32
,
shape
=
(
None
,
4
))
groundtruth_classes
=
tf
.
placeholder
(
tf
.
float32
,
shape
=
(
None
))
groundtruth_classes
=
tf
.
placeholder
(
tf
.
float32
,
shape
=
(
None
))
groundtruth_masks
=
tf
.
placeholder
(
tf
.
uint8
,
shape
=
(
None
,
None
,
None
))
groundtruth_masks
=
tf
.
placeholder
(
tf
.
uint8
,
shape
=
(
None
,
None
,
None
))
original_image_spatial_shape
=
tf
.
placeholder
(
tf
.
int32
,
shape
=
(
None
,
2
))
detection_scores
=
tf
.
placeholder
(
tf
.
float32
,
shape
=
(
None
))
detection_scores
=
tf
.
placeholder
(
tf
.
float32
,
shape
=
(
None
))
detection_classes
=
tf
.
placeholder
(
tf
.
float32
,
shape
=
(
None
))
detection_classes
=
tf
.
placeholder
(
tf
.
float32
,
shape
=
(
None
))
detection_masks
=
tf
.
placeholder
(
tf
.
uint8
,
shape
=
(
None
,
None
,
None
))
detection_masks
=
tf
.
placeholder
(
tf
.
uint8
,
shape
=
(
None
,
None
,
None
))
...
@@ -1672,6 +1677,8 @@ class CocoMaskEvaluationPyFuncTest(tf.test.TestCase):
...
@@ -1672,6 +1677,8 @@ class CocoMaskEvaluationPyFuncTest(tf.test.TestCase):
input_data_fields
.
groundtruth_boxes
:
groundtruth_boxes
,
input_data_fields
.
groundtruth_boxes
:
groundtruth_boxes
,
input_data_fields
.
groundtruth_classes
:
groundtruth_classes
,
input_data_fields
.
groundtruth_classes
:
groundtruth_classes
,
input_data_fields
.
groundtruth_instance_masks
:
groundtruth_masks
,
input_data_fields
.
groundtruth_instance_masks
:
groundtruth_masks
,
input_data_fields
.
original_image_spatial_shape
:
original_image_spatial_shape
,
detection_fields
.
detection_scores
:
detection_scores
,
detection_fields
.
detection_scores
:
detection_scores
,
detection_fields
.
detection_classes
:
detection_classes
,
detection_fields
.
detection_classes
:
detection_classes
,
detection_fields
.
detection_masks
:
detection_masks
,
detection_fields
.
detection_masks
:
detection_masks
,
...
@@ -1701,6 +1708,7 @@ class CocoMaskEvaluationPyFuncTest(tf.test.TestCase):
...
@@ -1701,6 +1708,7 @@ class CocoMaskEvaluationPyFuncTest(tf.test.TestCase):
np
.
ones
([
50
,
50
],
dtype
=
np
.
uint8
),
((
0
,
70
),
(
0
,
70
)),
np
.
ones
([
50
,
50
],
dtype
=
np
.
uint8
),
((
0
,
70
),
(
0
,
70
)),
mode
=
'constant'
)
mode
=
'constant'
)
]),
]),
original_image_spatial_shape
:
np
.
array
([[
120
,
120
],
[
120
,
120
]]),
detection_scores
:
detection_scores
:
np
.
array
([.
9
,
.
8
]),
np
.
array
([.
9
,
.
8
]),
detection_classes
:
detection_classes
:
...
@@ -1725,6 +1733,7 @@ class CocoMaskEvaluationPyFuncTest(tf.test.TestCase):
...
@@ -1725,6 +1733,7 @@ class CocoMaskEvaluationPyFuncTest(tf.test.TestCase):
dtype
=
np
.
uint8
),
dtype
=
np
.
uint8
),
((
0
,
0
),
(
10
,
10
),
(
10
,
10
)),
((
0
,
0
),
(
10
,
10
),
(
10
,
10
)),
mode
=
'constant'
),
mode
=
'constant'
),
original_image_spatial_shape
:
np
.
array
([[
70
,
70
]]),
detection_scores
:
np
.
array
([.
8
]),
detection_scores
:
np
.
array
([.
8
]),
detection_classes
:
np
.
array
([
1
]),
detection_classes
:
np
.
array
([
1
]),
detection_masks
:
np
.
pad
(
np
.
ones
([
1
,
50
,
50
],
dtype
=
np
.
uint8
),
detection_masks
:
np
.
pad
(
np
.
ones
([
1
,
50
,
50
],
dtype
=
np
.
uint8
),
...
@@ -1740,6 +1749,7 @@ class CocoMaskEvaluationPyFuncTest(tf.test.TestCase):
...
@@ -1740,6 +1749,7 @@ class CocoMaskEvaluationPyFuncTest(tf.test.TestCase):
dtype
=
np
.
uint8
),
dtype
=
np
.
uint8
),
((
0
,
0
),
(
10
,
10
),
(
10
,
10
)),
((
0
,
0
),
(
10
,
10
),
(
10
,
10
)),
mode
=
'constant'
),
mode
=
'constant'
),
original_image_spatial_shape
:
np
.
array
([[
45
,
45
]]),
detection_scores
:
np
.
array
([.
8
]),
detection_scores
:
np
.
array
([.
8
]),
detection_classes
:
np
.
array
([
1
]),
detection_classes
:
np
.
array
([
1
]),
detection_masks
:
np
.
pad
(
np
.
ones
([
1
,
25
,
25
],
detection_masks
:
np
.
pad
(
np
.
ones
([
1
,
25
,
25
],
...
@@ -1778,6 +1788,7 @@ class CocoMaskEvaluationPyFuncTest(tf.test.TestCase):
...
@@ -1778,6 +1788,7 @@ class CocoMaskEvaluationPyFuncTest(tf.test.TestCase):
groundtruth_classes
=
tf
.
placeholder
(
tf
.
float32
,
shape
=
(
batch_size
,
None
))
groundtruth_classes
=
tf
.
placeholder
(
tf
.
float32
,
shape
=
(
batch_size
,
None
))
groundtruth_masks
=
tf
.
placeholder
(
groundtruth_masks
=
tf
.
placeholder
(
tf
.
uint8
,
shape
=
(
batch_size
,
None
,
None
,
None
))
tf
.
uint8
,
shape
=
(
batch_size
,
None
,
None
,
None
))
original_image_spatial_shape
=
tf
.
placeholder
(
tf
.
int32
,
shape
=
(
None
,
2
))
detection_scores
=
tf
.
placeholder
(
tf
.
float32
,
shape
=
(
batch_size
,
None
))
detection_scores
=
tf
.
placeholder
(
tf
.
float32
,
shape
=
(
batch_size
,
None
))
detection_classes
=
tf
.
placeholder
(
tf
.
float32
,
shape
=
(
batch_size
,
None
))
detection_classes
=
tf
.
placeholder
(
tf
.
float32
,
shape
=
(
batch_size
,
None
))
detection_masks
=
tf
.
placeholder
(
detection_masks
=
tf
.
placeholder
(
...
@@ -1790,6 +1801,8 @@ class CocoMaskEvaluationPyFuncTest(tf.test.TestCase):
...
@@ -1790,6 +1801,8 @@ class CocoMaskEvaluationPyFuncTest(tf.test.TestCase):
input_data_fields
.
groundtruth_boxes
:
groundtruth_boxes
,
input_data_fields
.
groundtruth_boxes
:
groundtruth_boxes
,
input_data_fields
.
groundtruth_classes
:
groundtruth_classes
,
input_data_fields
.
groundtruth_classes
:
groundtruth_classes
,
input_data_fields
.
groundtruth_instance_masks
:
groundtruth_masks
,
input_data_fields
.
groundtruth_instance_masks
:
groundtruth_masks
,
input_data_fields
.
original_image_spatial_shape
:
original_image_spatial_shape
,
detection_fields
.
detection_scores
:
detection_scores
,
detection_fields
.
detection_scores
:
detection_scores
,
detection_fields
.
detection_classes
:
detection_classes
,
detection_fields
.
detection_classes
:
detection_classes
,
detection_fields
.
detection_masks
:
detection_masks
,
detection_fields
.
detection_masks
:
detection_masks
,
...
@@ -1826,6 +1839,8 @@ class CocoMaskEvaluationPyFuncTest(tf.test.TestCase):
...
@@ -1826,6 +1839,8 @@ class CocoMaskEvaluationPyFuncTest(tf.test.TestCase):
mode
=
'constant'
)
mode
=
'constant'
)
],
],
axis
=
0
),
axis
=
0
),
original_image_spatial_shape
:
np
.
array
(
[[
100
,
100
],
[
100
,
100
],
[
100
,
100
]]),
detection_scores
:
detection_scores
:
np
.
array
([[.
8
],
[.
8
],
[.
8
]]),
np
.
array
([[.
8
],
[.
8
],
[.
8
]]),
detection_classes
:
detection_classes
:
...
...
research/object_detection/metrics/lvis_evaluation.py
0 → 100644
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此差异已折叠。
点击以展开。
research/object_detection/metrics/lvis_evaluation_test.py
0 → 100644
浏览文件 @
4437d7b4
# Copyright 2020 The TensorFlow Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ==============================================================================
"""Tests for tensorflow_models.object_detection.metrics.coco_evaluation."""
from
__future__
import
absolute_import
from
__future__
import
division
from
__future__
import
print_function
import
unittest
import
numpy
as
np
import
tensorflow.compat.v1
as
tf
from
object_detection.core
import
standard_fields
as
fields
from
object_detection.metrics
import
lvis_evaluation
from
object_detection.utils
import
tf_version
def
_get_categories_list
():
return
[{
'id'
:
1
,
'name'
:
'person'
,
'frequency'
:
'f'
},
{
'id'
:
2
,
'name'
:
'dog'
,
'frequency'
:
'c'
},
{
'id'
:
3
,
'name'
:
'cat'
,
'frequency'
:
'r'
}]
class
LvisMaskEvaluationTest
(
tf
.
test
.
TestCase
):
def
testGetOneMAPWithMatchingGroundtruthAndDetections
(
self
):
"""Tests that mAP is calculated correctly on GT and Detections."""
masks1
=
np
.
expand_dims
(
np
.
pad
(
np
.
ones
([
100
,
100
],
dtype
=
np
.
uint8
),
((
100
,
56
),
(
100
,
56
)),
mode
=
'constant'
),
axis
=
0
)
masks2
=
np
.
expand_dims
(
np
.
pad
(
np
.
ones
([
50
,
50
],
dtype
=
np
.
uint8
),
((
50
,
156
),
(
50
,
156
)),
mode
=
'constant'
),
axis
=
0
)
masks3
=
np
.
expand_dims
(
np
.
pad
(
np
.
ones
([
25
,
25
],
dtype
=
np
.
uint8
),
((
25
,
206
),
(
25
,
206
)),
mode
=
'constant'
),
axis
=
0
)
lvis_evaluator
=
lvis_evaluation
.
LVISMaskEvaluator
(
_get_categories_list
())
lvis_evaluator
.
add_single_ground_truth_image_info
(
image_id
=
'image1'
,
groundtruth_dict
=
{
fields
.
InputDataFields
.
groundtruth_boxes
:
np
.
array
([[
100.
,
100.
,
200.
,
200.
]]),
fields
.
InputDataFields
.
groundtruth_classes
:
np
.
array
([
1
]),
fields
.
InputDataFields
.
groundtruth_instance_masks
:
masks1
,
fields
.
InputDataFields
.
groundtruth_verified_neg_classes
:
np
.
array
([
0
,
0
,
0
]),
fields
.
InputDataFields
.
groundtruth_not_exhaustive_classes
:
np
.
array
([
0
,
0
,
0
])
})
lvis_evaluator
.
add_single_detected_image_info
(
image_id
=
'image1'
,
detections_dict
=
{
fields
.
DetectionResultFields
.
detection_masks
:
masks1
,
fields
.
DetectionResultFields
.
detection_scores
:
np
.
array
([.
8
]),
fields
.
DetectionResultFields
.
detection_classes
:
np
.
array
([
1
])
})
lvis_evaluator
.
add_single_ground_truth_image_info
(
image_id
=
'image2'
,
groundtruth_dict
=
{
fields
.
InputDataFields
.
groundtruth_boxes
:
np
.
array
([[
50.
,
50.
,
100.
,
100.
]]),
fields
.
InputDataFields
.
groundtruth_classes
:
np
.
array
([
1
]),
fields
.
InputDataFields
.
groundtruth_instance_masks
:
masks2
,
fields
.
InputDataFields
.
groundtruth_verified_neg_classes
:
np
.
array
([
0
,
0
,
0
]),
fields
.
InputDataFields
.
groundtruth_not_exhaustive_classes
:
np
.
array
([
0
,
0
,
0
])
})
lvis_evaluator
.
add_single_detected_image_info
(
image_id
=
'image2'
,
detections_dict
=
{
fields
.
DetectionResultFields
.
detection_masks
:
masks2
,
fields
.
DetectionResultFields
.
detection_scores
:
np
.
array
([.
8
]),
fields
.
DetectionResultFields
.
detection_classes
:
np
.
array
([
1
])
})
lvis_evaluator
.
add_single_ground_truth_image_info
(
image_id
=
'image3'
,
groundtruth_dict
=
{
fields
.
InputDataFields
.
groundtruth_boxes
:
np
.
array
([[
25.
,
25.
,
50.
,
50.
]]),
fields
.
InputDataFields
.
groundtruth_classes
:
np
.
array
([
1
]),
fields
.
InputDataFields
.
groundtruth_instance_masks
:
masks3
,
fields
.
InputDataFields
.
groundtruth_verified_neg_classes
:
np
.
array
([
0
,
0
,
0
]),
fields
.
InputDataFields
.
groundtruth_not_exhaustive_classes
:
np
.
array
([
0
,
0
,
0
])
})
lvis_evaluator
.
add_single_detected_image_info
(
image_id
=
'image3'
,
detections_dict
=
{
fields
.
DetectionResultFields
.
detection_masks
:
masks3
,
fields
.
DetectionResultFields
.
detection_scores
:
np
.
array
([.
8
]),
fields
.
DetectionResultFields
.
detection_classes
:
np
.
array
([
1
])
})
metrics
=
lvis_evaluator
.
evaluate
()
self
.
assertAlmostEqual
(
metrics
[
'DetectionMasks_AP'
],
1.0
)
@
unittest
.
skipIf
(
tf_version
.
is_tf1
(),
'Only Supported in TF2.X'
)
class
LVISMaskEvaluationPyFuncTest
(
tf
.
test
.
TestCase
):
def
testAddEvalDict
(
self
):
lvis_evaluator
=
lvis_evaluation
.
LVISMaskEvaluator
(
_get_categories_list
())
image_id
=
tf
.
constant
(
'image1'
,
dtype
=
tf
.
string
)
groundtruth_boxes
=
tf
.
constant
(
np
.
array
([[
100.
,
100.
,
200.
,
200.
],
[
50.
,
50.
,
100.
,
100.
]]),
dtype
=
tf
.
float32
)
groundtruth_classes
=
tf
.
constant
(
np
.
array
([
1
,
2
]),
dtype
=
tf
.
float32
)
groundtruth_masks
=
tf
.
constant
(
np
.
stack
([
np
.
pad
(
np
.
ones
([
100
,
100
],
dtype
=
np
.
uint8
),
((
10
,
10
),
(
10
,
10
)),
mode
=
'constant'
),
np
.
pad
(
np
.
ones
([
50
,
50
],
dtype
=
np
.
uint8
),
((
0
,
70
),
(
0
,
70
)),
mode
=
'constant'
)
]),
dtype
=
tf
.
uint8
)
original_image_spatial_shapes
=
tf
.
constant
([[
120
,
120
],
[
120
,
120
]],
dtype
=
tf
.
int32
)
groundtruth_verified_neg_classes
=
tf
.
constant
(
np
.
array
([
0
,
0
,
0
]),
dtype
=
tf
.
float32
)
groundtruth_not_exhaustive_classes
=
tf
.
constant
(
np
.
array
([
0
,
0
,
0
]),
dtype
=
tf
.
float32
)
detection_scores
=
tf
.
constant
(
np
.
array
([.
9
,
.
8
]),
dtype
=
tf
.
float32
)
detection_classes
=
tf
.
constant
(
np
.
array
([
2
,
1
]),
dtype
=
tf
.
float32
)
detection_masks
=
tf
.
constant
(
np
.
stack
([
np
.
pad
(
np
.
ones
([
50
,
50
],
dtype
=
np
.
uint8
),
((
0
,
70
),
(
0
,
70
)),
mode
=
'constant'
),
np
.
pad
(
np
.
ones
([
100
,
100
],
dtype
=
np
.
uint8
),
((
10
,
10
),
(
10
,
10
)),
mode
=
'constant'
),
]),
dtype
=
tf
.
uint8
)
input_data_fields
=
fields
.
InputDataFields
detection_fields
=
fields
.
DetectionResultFields
eval_dict
=
{
input_data_fields
.
key
:
image_id
,
input_data_fields
.
groundtruth_boxes
:
groundtruth_boxes
,
input_data_fields
.
groundtruth_classes
:
groundtruth_classes
,
input_data_fields
.
groundtruth_instance_masks
:
groundtruth_masks
,
input_data_fields
.
groundtruth_verified_neg_classes
:
groundtruth_verified_neg_classes
,
input_data_fields
.
groundtruth_not_exhaustive_classes
:
groundtruth_not_exhaustive_classes
,
input_data_fields
.
original_image_spatial_shape
:
original_image_spatial_shapes
,
detection_fields
.
detection_scores
:
detection_scores
,
detection_fields
.
detection_classes
:
detection_classes
,
detection_fields
.
detection_masks
:
detection_masks
}
lvis_evaluator
.
add_eval_dict
(
eval_dict
)
self
.
assertLen
(
lvis_evaluator
.
_groundtruth_list
,
2
)
self
.
assertLen
(
lvis_evaluator
.
_detection_masks_list
,
2
)
if
__name__
==
'__main__'
:
tf
.
test
.
main
()
research/object_detection/metrics/lvis_tools.py
0 → 100644
浏览文件 @
4437d7b4
# Copyright 2020 The TensorFlow Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ==============================================================================
"""Wrappers for third party lvis to be used within object_detection.
Usage example: given a set of images with ids in the list image_ids
and corresponding lists of numpy arrays encoding groundtruth (boxes,
masks and classes) and detections (masks, scores and classes), where
elements of each list correspond to detections/annotations of a single image,
then evaluation can be invoked as follows:
groundtruth = lvis_tools.LVISWrapper(groundtruth_dict)
detections = lvis_results.LVISResults(groundtruth, detections_list)
evaluator = lvis_tools.LVISEvalWrapper(groundtruth, detections,
iou_type='segm')
summary_metrics = evaluator.ComputeMetrics()
TODO(jonathanhuang): Add support for exporting to JSON.
"""
from
__future__
import
absolute_import
from
__future__
import
division
from
__future__
import
print_function
import
logging
from
lvis
import
eval
as
lvis_eval
from
lvis
import
lvis
import
numpy
as
np
from
pycocotools
import
mask
import
six
from
six.moves
import
range
def
RleCompress
(
masks
):
"""Compresses mask using Run-length encoding provided by pycocotools.
Args:
masks: uint8 numpy array of shape [mask_height, mask_width] with values in
{0, 1}.
Returns:
A pycocotools Run-length encoding of the mask.
"""
rle
=
mask
.
encode
(
np
.
asfortranarray
(
masks
))
rle
[
'counts'
]
=
six
.
ensure_str
(
rle
[
'counts'
])
return
rle
def
_ConvertBoxToCOCOFormat
(
box
):
"""Converts a box in [ymin, xmin, ymax, xmax] format to COCO format.
This is a utility function for converting from our internal
[ymin, xmin, ymax, xmax] convention to the convention used by the COCO API
i.e., [xmin, ymin, width, height].
Args:
box: a [ymin, xmin, ymax, xmax] numpy array
Returns:
a list of floats representing [xmin, ymin, width, height]
"""
return
[
float
(
box
[
1
]),
float
(
box
[
0
]),
float
(
box
[
3
]
-
box
[
1
]),
float
(
box
[
2
]
-
box
[
0
])]
class
LVISWrapper
(
lvis
.
LVIS
):
"""Wrapper for the lvis.LVIS class."""
def
__init__
(
self
,
dataset
,
detection_type
=
'bbox'
):
"""LVISWrapper constructor.
See https://www.lvisdataset.org/dataset for a description of the format.
By default, the coco.COCO class constructor reads from a JSON file.
This function duplicates the same behavior but loads from a dictionary,
allowing us to perform evaluation without writing to external storage.
Args:
dataset: a dictionary holding bounding box annotations in the COCO format.
detection_type: type of detections being wrapped. Can be one of ['bbox',
'segmentation']
Raises:
ValueError: if detection_type is unsupported.
"""
self
.
logger
=
logging
.
getLogger
(
__name__
)
self
.
logger
.
info
(
'Loading annotations.'
)
self
.
dataset
=
dataset
self
.
_create_index
()
class
LVISEvalWrapper
(
lvis_eval
.
LVISEval
):
"""LVISEval wrapper."""
def
__init__
(
self
,
groundtruth
=
None
,
detections
=
None
,
iou_type
=
'bbox'
):
lvis_eval
.
LVISEval
.
__init__
(
self
,
groundtruth
,
detections
,
iou_type
=
iou_type
)
self
.
_iou_type
=
iou_type
def
ComputeMetrics
(
self
):
self
.
run
()
summary_metrics
=
{}
summary_metrics
=
self
.
results
return
summary_metrics
def
ExportSingleImageGroundtruthToLVIS
(
image_id
,
next_annotation_id
,
category_id_set
,
groundtruth_boxes
,
groundtruth_classes
,
groundtruth_masks
=
None
,
groundtruth_area
=
None
):
"""Export groundtruth of a single image to LVIS format.
This function converts groundtruth detection annotations represented as numpy
arrays to dictionaries that can be ingested by the LVIS evaluation API. Note
that the image_ids provided here must match the ones given to
ExportSingleImageDetectionMasksToLVIS. We assume that boxes, classes and masks
are in correspondence - that is, e.g., groundtruth_boxes[i, :], and
groundtruth_classes[i] are associated with the same groundtruth annotation.
In the exported result, "area" fields are always set to the area of the
groundtruth bounding box.
Args:
image_id: a unique image identifier either of type integer or string.
next_annotation_id: integer specifying the first id to use for the
groundtruth annotations. All annotations are assigned a continuous integer
id starting from this value.
category_id_set: A set of valid class ids. Groundtruth with classes not in
category_id_set are dropped.
groundtruth_boxes: numpy array (float32) with shape [num_gt_boxes, 4]
groundtruth_classes: numpy array (int) with shape [num_gt_boxes]
groundtruth_masks: optional uint8 numpy array of shape [num_detections,
image_height, image_width] containing detection_masks.
groundtruth_area: numpy array (float32) with shape [num_gt_boxes]. If
provided, then the area values (in the original absolute coordinates) will
be populated instead of calculated from bounding box coordinates.
Returns:
a list of groundtruth annotations for a single image in the COCO format.
Raises:
ValueError: if (1) groundtruth_boxes and groundtruth_classes do not have the
right lengths or (2) if each of the elements inside these lists do not
have the correct shapes or (3) if image_ids are not integers
"""
if
len
(
groundtruth_classes
.
shape
)
!=
1
:
raise
ValueError
(
'groundtruth_classes is '
'expected to be of rank 1.'
)
if
len
(
groundtruth_boxes
.
shape
)
!=
2
:
raise
ValueError
(
'groundtruth_boxes is expected to be of '
'rank 2.'
)
if
groundtruth_boxes
.
shape
[
1
]
!=
4
:
raise
ValueError
(
'groundtruth_boxes should have '
'shape[1] == 4.'
)
num_boxes
=
groundtruth_classes
.
shape
[
0
]
if
num_boxes
!=
groundtruth_boxes
.
shape
[
0
]:
raise
ValueError
(
'Corresponding entries in groundtruth_classes, '
'and groundtruth_boxes should have '
'compatible shapes (i.e., agree on the 0th dimension).'
'Classes shape: %d. Boxes shape: %d. Image ID: %s'
%
(
groundtruth_classes
.
shape
[
0
],
groundtruth_boxes
.
shape
[
0
],
image_id
))
groundtruth_list
=
[]
for
i
in
range
(
num_boxes
):
if
groundtruth_classes
[
i
]
in
category_id_set
:
if
groundtruth_area
is
not
None
and
groundtruth_area
[
i
]
>
0
:
area
=
float
(
groundtruth_area
[
i
])
else
:
area
=
float
((
groundtruth_boxes
[
i
,
2
]
-
groundtruth_boxes
[
i
,
0
])
*
(
groundtruth_boxes
[
i
,
3
]
-
groundtruth_boxes
[
i
,
1
]))
export_dict
=
{
'id'
:
next_annotation_id
+
i
,
'image_id'
:
image_id
,
'category_id'
:
int
(
groundtruth_classes
[
i
]),
'bbox'
:
list
(
_ConvertBoxToCOCOFormat
(
groundtruth_boxes
[
i
,
:])),
'area'
:
area
,
}
if
groundtruth_masks
is
not
None
:
export_dict
[
'segmentation'
]
=
RleCompress
(
groundtruth_masks
[
i
])
groundtruth_list
.
append
(
export_dict
)
return
groundtruth_list
def
ExportSingleImageDetectionMasksToLVIS
(
image_id
,
category_id_set
,
detection_masks
,
detection_scores
,
detection_classes
):
"""Export detection masks of a single image to LVIS format.
This function converts detections represented as numpy arrays to dictionaries
that can be ingested by the LVIS evaluation API. We assume that
detection_masks, detection_scores, and detection_classes are in correspondence
- that is: detection_masks[i, :], detection_classes[i] and detection_scores[i]
are associated with the same annotation.
Args:
image_id: unique image identifier either of type integer or string.
category_id_set: A set of valid class ids. Detections with classes not in
category_id_set are dropped.
detection_masks: uint8 numpy array of shape [num_detections, image_height,
image_width] containing detection_masks.
detection_scores: float numpy array of shape [num_detections] containing
scores for detection masks.
detection_classes: integer numpy array of shape [num_detections] containing
the classes for detection masks.
Returns:
a list of detection mask annotations for a single image in the COCO format.
Raises:
ValueError: if (1) detection_masks, detection_scores and detection_classes
do not have the right lengths or (2) if each of the elements inside these
lists do not have the correct shapes or (3) if image_ids are not integers.
"""
if
len
(
detection_classes
.
shape
)
!=
1
or
len
(
detection_scores
.
shape
)
!=
1
:
raise
ValueError
(
'All entries in detection_classes and detection_scores'
'expected to be of rank 1.'
)
num_boxes
=
detection_classes
.
shape
[
0
]
if
not
num_boxes
==
len
(
detection_masks
)
==
detection_scores
.
shape
[
0
]:
raise
ValueError
(
'Corresponding entries in detection_classes, '
'detection_scores and detection_masks should have '
'compatible lengths and shapes '
'Classes length: %d. Masks length: %d. '
'Scores length: %d'
%
(
detection_classes
.
shape
[
0
],
len
(
detection_masks
),
detection_scores
.
shape
[
0
]
))
detections_list
=
[]
for
i
in
range
(
num_boxes
):
if
detection_classes
[
i
]
in
category_id_set
:
detections_list
.
append
({
'image_id'
:
image_id
,
'category_id'
:
int
(
detection_classes
[
i
]),
'segmentation'
:
RleCompress
(
detection_masks
[
i
]),
'score'
:
float
(
detection_scores
[
i
])
})
return
detections_list
research/object_detection/metrics/lvis_tools_test.py
0 → 100644
浏览文件 @
4437d7b4
# Copyright 2020 The TensorFlow Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ==============================================================================
"""Tests for tensorflow_model.object_detection.metrics.lvis_tools."""
from
lvis
import
results
as
lvis_results
import
numpy
as
np
from
pycocotools
import
mask
import
tensorflow.compat.v1
as
tf
from
object_detection.metrics
import
lvis_tools
class
LVISToolsTest
(
tf
.
test
.
TestCase
):
def
setUp
(
self
):
super
(
LVISToolsTest
,
self
).
setUp
()
mask1
=
np
.
pad
(
np
.
ones
([
100
,
100
],
dtype
=
np
.
uint8
),
((
100
,
56
),
(
100
,
56
)),
mode
=
'constant'
)
mask2
=
np
.
pad
(
np
.
ones
([
50
,
50
],
dtype
=
np
.
uint8
),
((
50
,
156
),
(
50
,
156
)),
mode
=
'constant'
)
mask1_rle
=
lvis_tools
.
RleCompress
(
mask1
)
mask2_rle
=
lvis_tools
.
RleCompress
(
mask2
)
groundtruth_annotations_list
=
[
{
'id'
:
1
,
'image_id'
:
'first'
,
'category_id'
:
1
,
'bbox'
:
[
100.
,
100.
,
100.
,
100.
],
'area'
:
100.
**
2
,
'segmentation'
:
mask1_rle
},
{
'id'
:
2
,
'image_id'
:
'second'
,
'category_id'
:
1
,
'bbox'
:
[
50.
,
50.
,
50.
,
50.
],
'area'
:
50.
**
2
,
'segmentation'
:
mask2_rle
},
]
image_list
=
[
{
'id'
:
'first'
,
'neg_category_ids'
:
[],
'not_exhaustive_category_ids'
:
[],
'height'
:
256
,
'width'
:
256
},
{
'id'
:
'second'
,
'neg_category_ids'
:
[],
'not_exhaustive_category_ids'
:
[],
'height'
:
256
,
'width'
:
256
}
]
category_list
=
[{
'id'
:
0
,
'name'
:
'person'
,
'frequency'
:
'f'
},
{
'id'
:
1
,
'name'
:
'cat'
,
'frequency'
:
'c'
},
{
'id'
:
2
,
'name'
:
'dog'
,
'frequency'
:
'r'
}]
self
.
_groundtruth_dict
=
{
'annotations'
:
groundtruth_annotations_list
,
'images'
:
image_list
,
'categories'
:
category_list
}
self
.
_detections_list
=
[
{
'image_id'
:
'first'
,
'category_id'
:
1
,
'segmentation'
:
mask1_rle
,
'score'
:
.
8
},
{
'image_id'
:
'second'
,
'category_id'
:
1
,
'segmentation'
:
mask2_rle
,
'score'
:
.
7
},
]
def
testLVISWrappers
(
self
):
groundtruth
=
lvis_tools
.
LVISWrapper
(
self
.
_groundtruth_dict
)
detections
=
lvis_results
.
LVISResults
(
groundtruth
,
self
.
_detections_list
)
evaluator
=
lvis_tools
.
LVISEvalWrapper
(
groundtruth
,
detections
,
iou_type
=
'segm'
)
summary_metrics
=
evaluator
.
ComputeMetrics
()
self
.
assertAlmostEqual
(
1.0
,
summary_metrics
[
'AP'
])
def
testSingleImageDetectionMaskExport
(
self
):
masks
=
np
.
array
(
[[[
1
,
1
,],
[
1
,
1
]],
[[
0
,
0
],
[
0
,
1
]],
[[
0
,
0
],
[
0
,
0
]]],
dtype
=
np
.
uint8
)
classes
=
np
.
array
([
1
,
2
,
3
],
dtype
=
np
.
int32
)
scores
=
np
.
array
([
0.8
,
0.2
,
0.7
],
dtype
=
np
.
float32
)
lvis_annotations
=
lvis_tools
.
ExportSingleImageDetectionMasksToLVIS
(
image_id
=
'first_image'
,
category_id_set
=
set
([
1
,
2
,
3
]),
detection_classes
=
classes
,
detection_scores
=
scores
,
detection_masks
=
masks
)
expected_counts
=
[
'04'
,
'31'
,
'4'
]
for
i
,
mask_annotation
in
enumerate
(
lvis_annotations
):
self
.
assertEqual
(
mask_annotation
[
'segmentation'
][
'counts'
],
expected_counts
[
i
])
self
.
assertTrue
(
np
.
all
(
np
.
equal
(
mask
.
decode
(
mask_annotation
[
'segmentation'
]),
masks
[
i
])))
self
.
assertEqual
(
mask_annotation
[
'image_id'
],
'first_image'
)
self
.
assertEqual
(
mask_annotation
[
'category_id'
],
classes
[
i
])
self
.
assertAlmostEqual
(
mask_annotation
[
'score'
],
scores
[
i
])
def
testSingleImageGroundtruthExport
(
self
):
masks
=
np
.
array
(
[[[
1
,
1
,],
[
1
,
1
]],
[[
0
,
0
],
[
0
,
1
]],
[[
0
,
0
],
[
0
,
0
]]],
dtype
=
np
.
uint8
)
boxes
=
np
.
array
([[
0
,
0
,
1
,
1
],
[
0
,
0
,
.
5
,
.
5
],
[.
5
,
.
5
,
1
,
1
]],
dtype
=
np
.
float32
)
lvis_boxes
=
np
.
array
([[
0
,
0
,
1
,
1
],
[
0
,
0
,
.
5
,
.
5
],
[.
5
,
.
5
,
.
5
,
.
5
]],
dtype
=
np
.
float32
)
classes
=
np
.
array
([
1
,
2
,
3
],
dtype
=
np
.
int32
)
next_annotation_id
=
1
expected_counts
=
[
'04'
,
'31'
,
'4'
]
lvis_annotations
=
lvis_tools
.
ExportSingleImageGroundtruthToLVIS
(
image_id
=
'first_image'
,
category_id_set
=
set
([
1
,
2
,
3
]),
next_annotation_id
=
next_annotation_id
,
groundtruth_boxes
=
boxes
,
groundtruth_classes
=
classes
,
groundtruth_masks
=
masks
)
for
i
,
annotation
in
enumerate
(
lvis_annotations
):
self
.
assertEqual
(
annotation
[
'segmentation'
][
'counts'
],
expected_counts
[
i
])
self
.
assertTrue
(
np
.
all
(
np
.
equal
(
mask
.
decode
(
annotation
[
'segmentation'
]),
masks
[
i
])))
self
.
assertTrue
(
np
.
all
(
np
.
isclose
(
annotation
[
'bbox'
],
lvis_boxes
[
i
])))
self
.
assertEqual
(
annotation
[
'image_id'
],
'first_image'
)
self
.
assertEqual
(
annotation
[
'category_id'
],
classes
[
i
])
self
.
assertEqual
(
annotation
[
'id'
],
i
+
next_annotation_id
)
if
__name__
==
'__main__'
:
tf
.
test
.
main
()
research/object_detection/packages/tf1/setup.py
浏览文件 @
4437d7b4
...
@@ -4,8 +4,8 @@ from setuptools import find_packages
...
@@ -4,8 +4,8 @@ from setuptools import find_packages
from
setuptools
import
setup
from
setuptools
import
setup
REQUIRED_PACKAGES
=
[
'pillow'
,
'lxml'
,
'matplotlib'
,
'Cython'
,
REQUIRED_PACKAGES
=
[
'pillow'
,
'lxml'
,
'matplotlib'
,
'Cython'
,
'contextlib2'
,
'tf-slim'
,
'six'
,
'pycocotools'
,
'
scipy
'
,
'contextlib2'
,
'tf-slim'
,
'six'
,
'pycocotools'
,
'
lvis
'
,
'pandas'
]
'
scipy'
,
'
pandas'
]
setup
(
setup
(
name
=
'object_detection'
,
name
=
'object_detection'
,
...
...
research/object_detection/packages/tf2/setup.py
浏览文件 @
4437d7b4
...
@@ -18,6 +18,7 @@ REQUIRED_PACKAGES = [
...
@@ -18,6 +18,7 @@ REQUIRED_PACKAGES = [
'tf-slim'
,
'tf-slim'
,
'six'
,
'six'
,
'pycocotools'
,
'pycocotools'
,
'lvis'
,
'scipy'
,
'scipy'
,
'pandas'
,
'pandas'
,
'tf-models-official'
'tf-models-official'
...
...
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