Python object_detection.utils.ops.dense_to_sparse_boxes() Examples
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code examples of object_detection.utils.ops.dense_to_sparse_boxes().
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Example #1
| Source File: ops_test.py From g-tensorflow-models with Apache License 2.0 | 6 votes |
|
def test_return_all_boxes_when_all_input_boxes_are_valid(self):
num_classes = 4
num_valid_boxes = 3
code_size = 4
dense_location_placeholder = tf.placeholder(tf.float32,
shape=(num_valid_boxes,
code_size))
dense_num_boxes_placeholder = tf.placeholder(tf.int32, shape=(num_classes))
box_locations, box_classes = ops.dense_to_sparse_boxes(
dense_location_placeholder, dense_num_boxes_placeholder, num_classes)
feed_dict = {dense_location_placeholder: np.random.uniform(
size=[num_valid_boxes, code_size]),
dense_num_boxes_placeholder: np.array([1, 0, 0, 2],
dtype=np.int32)}
expected_box_locations = feed_dict[dense_location_placeholder]
expected_box_classses = np.array([0, 3, 3])
with self.test_session() as sess:
box_locations, box_classes = sess.run([box_locations, box_classes],
feed_dict=feed_dict)
self.assertAllClose(box_locations, expected_box_locations, rtol=1e-6,
atol=1e-6)
self.assertAllEqual(box_classes, expected_box_classses)
Example #2
| Source File: ops_test.py From Traffic-Rule-Violation-Detection-System with MIT License | 6 votes |
|
def test_return_all_boxes_when_all_input_boxes_are_valid(self):
num_classes = 4
num_valid_boxes = 3
code_size = 4
dense_location_placeholder = tf.placeholder(tf.float32,
shape=(num_valid_boxes,
code_size))
dense_num_boxes_placeholder = tf.placeholder(tf.int32, shape=(num_classes))
box_locations, box_classes = ops.dense_to_sparse_boxes(
dense_location_placeholder, dense_num_boxes_placeholder, num_classes)
feed_dict = {dense_location_placeholder: np.random.uniform(
size=[num_valid_boxes, code_size]),
dense_num_boxes_placeholder: np.array([1, 0, 0, 2],
dtype=np.int32)}
expected_box_locations = feed_dict[dense_location_placeholder]
expected_box_classses = np.array([0, 3, 3])
with self.test_session() as sess:
box_locations, box_classes = sess.run([box_locations, box_classes],
feed_dict=feed_dict)
self.assertAllClose(box_locations, expected_box_locations, rtol=1e-6,
atol=1e-6)
self.assertAllEqual(box_classes, expected_box_classses)
Example #3
| Source File: ops_test.py From HereIsWally with MIT License | 6 votes |
|
def test_return_all_boxes_when_all_input_boxes_are_valid(self):
num_classes = 4
num_valid_boxes = 3
code_size = 4
dense_location_placeholder = tf.placeholder(tf.float32,
shape=(num_valid_boxes,
code_size))
dense_num_boxes_placeholder = tf.placeholder(tf.int32, shape=(num_classes))
box_locations, box_classes = ops.dense_to_sparse_boxes(
dense_location_placeholder, dense_num_boxes_placeholder, num_classes)
feed_dict = {dense_location_placeholder: np.random.uniform(
size=[num_valid_boxes, code_size]),
dense_num_boxes_placeholder: np.array([1, 0, 0, 2],
dtype=np.int32)}
expected_box_locations = feed_dict[dense_location_placeholder]
expected_box_classses = np.array([0, 3, 3])
with self.test_session() as sess:
box_locations, box_classes = sess.run([box_locations, box_classes],
feed_dict=feed_dict)
self.assertAllClose(box_locations, expected_box_locations, rtol=1e-6,
atol=1e-6)
self.assertAllEqual(box_classes, expected_box_classses)
Example #4
| Source File: ops_test.py From yolo_v2 with Apache License 2.0 | 6 votes |
|
def test_return_all_boxes_when_all_input_boxes_are_valid(self):
num_classes = 4
num_valid_boxes = 3
code_size = 4
dense_location_placeholder = tf.placeholder(tf.float32,
shape=(num_valid_boxes,
code_size))
dense_num_boxes_placeholder = tf.placeholder(tf.int32, shape=(num_classes))
box_locations, box_classes = ops.dense_to_sparse_boxes(
dense_location_placeholder, dense_num_boxes_placeholder, num_classes)
feed_dict = {dense_location_placeholder: np.random.uniform(
size=[num_valid_boxes, code_size]),
dense_num_boxes_placeholder: np.array([1, 0, 0, 2],
dtype=np.int32)}
expected_box_locations = feed_dict[dense_location_placeholder]
expected_box_classses = np.array([0, 3, 3])
with self.test_session() as sess:
box_locations, box_classes = sess.run([box_locations, box_classes],
feed_dict=feed_dict)
self.assertAllClose(box_locations, expected_box_locations, rtol=1e-6,
atol=1e-6)
self.assertAllEqual(box_classes, expected_box_classses)
Example #5
| Source File: ops_test.py From models with Apache License 2.0 | 6 votes |
|
def test_return_only_valid_boxes_when_input_contains_invalid_boxes(self):
num_classes = 4
num_valid_boxes = 3
num_boxes = 10
code_size = 4
def graph_fn(dense_location, dense_num_boxes):
box_locations, box_classes = ops.dense_to_sparse_boxes(
dense_location, dense_num_boxes, num_classes)
return box_locations, box_classes
dense_location_np = np.random.uniform(size=[num_boxes, code_size])
dense_num_boxes_np = np.array([1, 0, 0, 2], dtype=np.int32)
expected_box_locations = dense_location_np[:num_valid_boxes]
expected_box_classses = np.array([0, 3, 3])
# Executing on CPU only since output shape is not constant.
box_locations, box_classes = self.execute_cpu(
graph_fn, [dense_location_np, dense_num_boxes_np])
self.assertAllClose(box_locations, expected_box_locations, rtol=1e-6,
atol=1e-6)
self.assertAllEqual(box_classes, expected_box_classses)
Example #6
| Source File: ops_test.py From models with Apache License 2.0 | 6 votes |
|
def test_return_all_boxes_when_all_input_boxes_are_valid(self):
num_classes = 4
num_valid_boxes = 3
code_size = 4
def graph_fn(dense_location, dense_num_boxes):
box_locations, box_classes = ops.dense_to_sparse_boxes(
dense_location, dense_num_boxes, num_classes)
return box_locations, box_classes
dense_location_np = np.random.uniform(size=[num_valid_boxes, code_size])
dense_num_boxes_np = np.array([1, 0, 0, 2], dtype=np.int32)
expected_box_locations = dense_location_np
expected_box_classses = np.array([0, 3, 3])
# Executing on CPU only since output shape is not constant.
box_locations, box_classes = self.execute_cpu(
graph_fn, [dense_location_np, dense_num_boxes_np])
self.assertAllClose(box_locations, expected_box_locations, rtol=1e-6,
atol=1e-6)
self.assertAllEqual(box_classes, expected_box_classses)
Example #7
| Source File: ops_test.py From mtl-ssl with Apache License 2.0 | 6 votes |
|
def test_return_all_boxes_when_all_input_boxes_are_valid(self):
num_classes = 4
num_valid_boxes = 3
code_size = 4
dense_location_placeholder = tf.placeholder(tf.float32,
shape=(num_valid_boxes,
code_size))
dense_num_boxes_placeholder = tf.placeholder(tf.int32, shape=(num_classes))
box_locations, box_classes = ops.dense_to_sparse_boxes(
dense_location_placeholder, dense_num_boxes_placeholder, num_classes)
feed_dict = {dense_location_placeholder: np.random.uniform(
size=[num_valid_boxes, code_size]),
dense_num_boxes_placeholder: np.array([1, 0, 0, 2],
dtype=np.int32)}
expected_box_locations = feed_dict[dense_location_placeholder]
expected_box_classses = np.array([0, 3, 3])
with self.test_session() as sess:
box_locations, box_classes = sess.run([box_locations, box_classes],
feed_dict=feed_dict)
self.assertAllClose(box_locations, expected_box_locations, rtol=1e-6,
atol=1e-6)
self.assertAllEqual(box_classes, expected_box_classses)
Example #8
| Source File: ops_test.py From ros_people_object_detection_tensorflow with Apache License 2.0 | 6 votes |
|
def test_return_all_boxes_when_all_input_boxes_are_valid(self):
num_classes = 4
num_valid_boxes = 3
code_size = 4
dense_location_placeholder = tf.placeholder(tf.float32,
shape=(num_valid_boxes,
code_size))
dense_num_boxes_placeholder = tf.placeholder(tf.int32, shape=(num_classes))
box_locations, box_classes = ops.dense_to_sparse_boxes(
dense_location_placeholder, dense_num_boxes_placeholder, num_classes)
feed_dict = {dense_location_placeholder: np.random.uniform(
size=[num_valid_boxes, code_size]),
dense_num_boxes_placeholder: np.array([1, 0, 0, 2],
dtype=np.int32)}
expected_box_locations = feed_dict[dense_location_placeholder]
expected_box_classses = np.array([0, 3, 3])
with self.test_session() as sess:
box_locations, box_classes = sess.run([box_locations, box_classes],
feed_dict=feed_dict)
self.assertAllClose(box_locations, expected_box_locations, rtol=1e-6,
atol=1e-6)
self.assertAllEqual(box_classes, expected_box_classses)
Example #9
| Source File: ops_test.py From object_detection_with_tensorflow with MIT License | 6 votes |
|
def test_return_all_boxes_when_all_input_boxes_are_valid(self):
num_classes = 4
num_valid_boxes = 3
code_size = 4
dense_location_placeholder = tf.placeholder(tf.float32,
shape=(num_valid_boxes,
code_size))
dense_num_boxes_placeholder = tf.placeholder(tf.int32, shape=(num_classes))
box_locations, box_classes = ops.dense_to_sparse_boxes(
dense_location_placeholder, dense_num_boxes_placeholder, num_classes)
feed_dict = {dense_location_placeholder: np.random.uniform(
size=[num_valid_boxes, code_size]),
dense_num_boxes_placeholder: np.array([1, 0, 0, 2],
dtype=np.int32)}
expected_box_locations = feed_dict[dense_location_placeholder]
expected_box_classses = np.array([0, 3, 3])
with self.test_session() as sess:
box_locations, box_classes = sess.run([box_locations, box_classes],
feed_dict=feed_dict)
self.assertAllClose(box_locations, expected_box_locations, rtol=1e-6,
atol=1e-6)
self.assertAllEqual(box_classes, expected_box_classses)
Example #10
| Source File: ops_test.py From vehicle_counting_tensorflow with MIT License | 6 votes |
|
def test_return_all_boxes_when_all_input_boxes_are_valid(self):
num_classes = 4
num_valid_boxes = 3
code_size = 4
dense_location_placeholder = tf.placeholder(tf.float32,
shape=(num_valid_boxes,
code_size))
dense_num_boxes_placeholder = tf.placeholder(tf.int32, shape=(num_classes))
box_locations, box_classes = ops.dense_to_sparse_boxes(
dense_location_placeholder, dense_num_boxes_placeholder, num_classes)
feed_dict = {dense_location_placeholder: np.random.uniform(
size=[num_valid_boxes, code_size]),
dense_num_boxes_placeholder: np.array([1, 0, 0, 2],
dtype=np.int32)}
expected_box_locations = feed_dict[dense_location_placeholder]
expected_box_classses = np.array([0, 3, 3])
with self.test_session() as sess:
box_locations, box_classes = sess.run([box_locations, box_classes],
feed_dict=feed_dict)
self.assertAllClose(box_locations, expected_box_locations, rtol=1e-6,
atol=1e-6)
self.assertAllEqual(box_classes, expected_box_classses)
Example #11
| Source File: ops_test.py From multilabel-image-classification-tensorflow with MIT License | 6 votes |
|
def test_return_all_boxes_when_all_input_boxes_are_valid(self):
num_classes = 4
num_valid_boxes = 3
code_size = 4
dense_location_placeholder = tf.placeholder(tf.float32,
shape=(num_valid_boxes,
code_size))
dense_num_boxes_placeholder = tf.placeholder(tf.int32, shape=(num_classes))
box_locations, box_classes = ops.dense_to_sparse_boxes(
dense_location_placeholder, dense_num_boxes_placeholder, num_classes)
feed_dict = {dense_location_placeholder: np.random.uniform(
size=[num_valid_boxes, code_size]),
dense_num_boxes_placeholder: np.array([1, 0, 0, 2],
dtype=np.int32)}
expected_box_locations = feed_dict[dense_location_placeholder]
expected_box_classses = np.array([0, 3, 3])
with self.test_session() as sess:
box_locations, box_classes = sess.run([box_locations, box_classes],
feed_dict=feed_dict)
self.assertAllClose(box_locations, expected_box_locations, rtol=1e-6,
atol=1e-6)
self.assertAllEqual(box_classes, expected_box_classses)
Example #12
| Source File: ops_test.py From object_detector_app with MIT License | 6 votes |
|
def test_return_all_boxes_when_all_input_boxes_are_valid(self):
num_classes = 4
num_valid_boxes = 3
code_size = 4
dense_location_placeholder = tf.placeholder(tf.float32,
shape=(num_valid_boxes,
code_size))
dense_num_boxes_placeholder = tf.placeholder(tf.int32, shape=(num_classes))
box_locations, box_classes = ops.dense_to_sparse_boxes(
dense_location_placeholder, dense_num_boxes_placeholder, num_classes)
feed_dict = {dense_location_placeholder: np.random.uniform(
size=[num_valid_boxes, code_size]),
dense_num_boxes_placeholder: np.array([1, 0, 0, 2],
dtype=np.int32)}
expected_box_locations = feed_dict[dense_location_placeholder]
expected_box_classses = np.array([0, 3, 3])
with self.test_session() as sess:
box_locations, box_classes = sess.run([box_locations, box_classes],
feed_dict=feed_dict)
self.assertAllClose(box_locations, expected_box_locations, rtol=1e-6,
atol=1e-6)
self.assertAllEqual(box_classes, expected_box_classses)
Example #13
| Source File: ops_test.py From object_detection_with_tensorflow with MIT License | 6 votes |
|
def test_return_all_boxes_when_all_input_boxes_are_valid(self):
num_classes = 4
num_valid_boxes = 3
code_size = 4
dense_location_placeholder = tf.placeholder(tf.float32,
shape=(num_valid_boxes,
code_size))
dense_num_boxes_placeholder = tf.placeholder(tf.int32, shape=(num_classes))
box_locations, box_classes = ops.dense_to_sparse_boxes(
dense_location_placeholder, dense_num_boxes_placeholder, num_classes)
feed_dict = {dense_location_placeholder: np.random.uniform(
size=[num_valid_boxes, code_size]),
dense_num_boxes_placeholder: np.array([1, 0, 0, 2],
dtype=np.int32)}
expected_box_locations = feed_dict[dense_location_placeholder]
expected_box_classses = np.array([0, 3, 3])
with self.test_session() as sess:
box_locations, box_classes = sess.run([box_locations, box_classes],
feed_dict=feed_dict)
self.assertAllClose(box_locations, expected_box_locations, rtol=1e-6,
atol=1e-6)
self.assertAllEqual(box_classes, expected_box_classses)
Example #14
| Source File: ops_test.py From Person-Detection-and-Tracking with MIT License | 6 votes |
|
def test_return_all_boxes_when_all_input_boxes_are_valid(self):
num_classes = 4
num_valid_boxes = 3
code_size = 4
dense_location_placeholder = tf.placeholder(tf.float32,
shape=(num_valid_boxes,
code_size))
dense_num_boxes_placeholder = tf.placeholder(tf.int32, shape=(num_classes))
box_locations, box_classes = ops.dense_to_sparse_boxes(
dense_location_placeholder, dense_num_boxes_placeholder, num_classes)
feed_dict = {dense_location_placeholder: np.random.uniform(
size=[num_valid_boxes, code_size]),
dense_num_boxes_placeholder: np.array([1, 0, 0, 2],
dtype=np.int32)}
expected_box_locations = feed_dict[dense_location_placeholder]
expected_box_classses = np.array([0, 3, 3])
with self.test_session() as sess:
box_locations, box_classes = sess.run([box_locations, box_classes],
feed_dict=feed_dict)
self.assertAllClose(box_locations, expected_box_locations, rtol=1e-6,
atol=1e-6)
self.assertAllEqual(box_classes, expected_box_classses)
Example #15
| Source File: ops_test.py From Hands-On-Machine-Learning-with-OpenCV-4 with MIT License | 6 votes |
|
def test_return_all_boxes_when_all_input_boxes_are_valid(self):
num_classes = 4
num_valid_boxes = 3
code_size = 4
dense_location_placeholder = tf.placeholder(tf.float32,
shape=(num_valid_boxes,
code_size))
dense_num_boxes_placeholder = tf.placeholder(tf.int32, shape=(num_classes))
box_locations, box_classes = ops.dense_to_sparse_boxes(
dense_location_placeholder, dense_num_boxes_placeholder, num_classes)
feed_dict = {dense_location_placeholder: np.random.uniform(
size=[num_valid_boxes, code_size]),
dense_num_boxes_placeholder: np.array([1, 0, 0, 2],
dtype=np.int32)}
expected_box_locations = feed_dict[dense_location_placeholder]
expected_box_classses = np.array([0, 3, 3])
with self.test_session() as sess:
box_locations, box_classes = sess.run([box_locations, box_classes],
feed_dict=feed_dict)
self.assertAllClose(box_locations, expected_box_locations, rtol=1e-6,
atol=1e-6)
self.assertAllEqual(box_classes, expected_box_classses)
Example #16
| Source File: ops_test.py From Elphas with Apache License 2.0 | 6 votes |
|
def test_return_all_boxes_when_all_input_boxes_are_valid(self):
num_classes = 4
num_valid_boxes = 3
code_size = 4
dense_location_placeholder = tf.placeholder(tf.float32,
shape=(num_valid_boxes,
code_size))
dense_num_boxes_placeholder = tf.placeholder(tf.int32, shape=(num_classes))
box_locations, box_classes = ops.dense_to_sparse_boxes(
dense_location_placeholder, dense_num_boxes_placeholder, num_classes)
feed_dict = {dense_location_placeholder: np.random.uniform(
size=[num_valid_boxes, code_size]),
dense_num_boxes_placeholder: np.array([1, 0, 0, 2],
dtype=np.int32)}
expected_box_locations = feed_dict[dense_location_placeholder]
expected_box_classses = np.array([0, 3, 3])
with self.test_session() as sess:
box_locations, box_classes = sess.run([box_locations, box_classes],
feed_dict=feed_dict)
self.assertAllClose(box_locations, expected_box_locations, rtol=1e-6,
atol=1e-6)
self.assertAllEqual(box_classes, expected_box_classses)
Example #17
| Source File: ops_test.py From tensorflow with BSD 2-Clause "Simplified" License | 6 votes |
|
def test_return_all_boxes_when_all_input_boxes_are_valid(self):
num_classes = 4
num_valid_boxes = 3
code_size = 4
dense_location_placeholder = tf.placeholder(tf.float32,
shape=(num_valid_boxes,
code_size))
dense_num_boxes_placeholder = tf.placeholder(tf.int32, shape=(num_classes))
box_locations, box_classes = ops.dense_to_sparse_boxes(
dense_location_placeholder, dense_num_boxes_placeholder, num_classes)
feed_dict = {dense_location_placeholder: np.random.uniform(
size=[num_valid_boxes, code_size]),
dense_num_boxes_placeholder: np.array([1, 0, 0, 2],
dtype=np.int32)}
expected_box_locations = feed_dict[dense_location_placeholder]
expected_box_classses = np.array([0, 3, 3])
with self.test_session() as sess:
box_locations, box_classes = sess.run([box_locations, box_classes],
feed_dict=feed_dict)
self.assertAllClose(box_locations, expected_box_locations, rtol=1e-6,
atol=1e-6)
self.assertAllEqual(box_classes, expected_box_classses)
Example #18
| Source File: ops_test.py From monopsr with MIT License | 6 votes |
|
def test_return_all_boxes_when_all_input_boxes_are_valid(self):
num_classes = 4
num_valid_boxes = 3
code_size = 4
dense_location_placeholder = tf.placeholder(tf.float32,
shape=(num_valid_boxes,
code_size))
dense_num_boxes_placeholder = tf.placeholder(tf.int32, shape=(num_classes))
box_locations, box_classes = ops.dense_to_sparse_boxes(
dense_location_placeholder, dense_num_boxes_placeholder, num_classes)
feed_dict = {dense_location_placeholder: np.random.uniform(
size=[num_valid_boxes, code_size]),
dense_num_boxes_placeholder: np.array([1, 0, 0, 2],
dtype=np.int32)}
expected_box_locations = feed_dict[dense_location_placeholder]
expected_box_classses = np.array([0, 3, 3])
with self.test_session() as sess:
box_locations, box_classes = sess.run([box_locations, box_classes],
feed_dict=feed_dict)
self.assertAllClose(box_locations, expected_box_locations, rtol=1e-6,
atol=1e-6)
self.assertAllEqual(box_classes, expected_box_classses)
Example #19
| Source File: ops_test.py From Gun-Detector with Apache License 2.0 | 6 votes |
|
def test_return_all_boxes_when_all_input_boxes_are_valid(self):
num_classes = 4
num_valid_boxes = 3
code_size = 4
dense_location_placeholder = tf.placeholder(tf.float32,
shape=(num_valid_boxes,
code_size))
dense_num_boxes_placeholder = tf.placeholder(tf.int32, shape=(num_classes))
box_locations, box_classes = ops.dense_to_sparse_boxes(
dense_location_placeholder, dense_num_boxes_placeholder, num_classes)
feed_dict = {dense_location_placeholder: np.random.uniform(
size=[num_valid_boxes, code_size]),
dense_num_boxes_placeholder: np.array([1, 0, 0, 2],
dtype=np.int32)}
expected_box_locations = feed_dict[dense_location_placeholder]
expected_box_classses = np.array([0, 3, 3])
with self.test_session() as sess:
box_locations, box_classes = sess.run([box_locations, box_classes],
feed_dict=feed_dict)
self.assertAllClose(box_locations, expected_box_locations, rtol=1e-6,
atol=1e-6)
self.assertAllEqual(box_classes, expected_box_classses)
Example #20
| Source File: ops_test.py From open-solution-googleai-object-detection with MIT License | 6 votes |
|
def test_return_all_boxes_when_all_input_boxes_are_valid(self):
num_classes = 4
num_valid_boxes = 3
code_size = 4
dense_location_placeholder = tf.placeholder(tf.float32,
shape=(num_valid_boxes,
code_size))
dense_num_boxes_placeholder = tf.placeholder(tf.int32, shape=(num_classes))
box_locations, box_classes = ops.dense_to_sparse_boxes(
dense_location_placeholder, dense_num_boxes_placeholder, num_classes)
feed_dict = {dense_location_placeholder: np.random.uniform(
size=[num_valid_boxes, code_size]),
dense_num_boxes_placeholder: np.array([1, 0, 0, 2],
dtype=np.int32)}
expected_box_locations = feed_dict[dense_location_placeholder]
expected_box_classses = np.array([0, 3, 3])
with self.test_session() as sess:
box_locations, box_classes = sess.run([box_locations, box_classes],
feed_dict=feed_dict)
self.assertAllClose(box_locations, expected_box_locations, rtol=1e-6,
atol=1e-6)
self.assertAllEqual(box_classes, expected_box_classses)
Example #21
| Source File: ops_test.py From ros_tensorflow with Apache License 2.0 | 6 votes |
|
def test_return_all_boxes_when_all_input_boxes_are_valid(self):
num_classes = 4
num_valid_boxes = 3
code_size = 4
dense_location_placeholder = tf.placeholder(tf.float32,
shape=(num_valid_boxes,
code_size))
dense_num_boxes_placeholder = tf.placeholder(tf.int32, shape=(num_classes))
box_locations, box_classes = ops.dense_to_sparse_boxes(
dense_location_placeholder, dense_num_boxes_placeholder, num_classes)
feed_dict = {dense_location_placeholder: np.random.uniform(
size=[num_valid_boxes, code_size]),
dense_num_boxes_placeholder: np.array([1, 0, 0, 2],
dtype=np.int32)}
expected_box_locations = feed_dict[dense_location_placeholder]
expected_box_classses = np.array([0, 3, 3])
with self.test_session() as sess:
box_locations, box_classes = sess.run([box_locations, box_classes],
feed_dict=feed_dict)
self.assertAllClose(box_locations, expected_box_locations, rtol=1e-6,
atol=1e-6)
self.assertAllEqual(box_classes, expected_box_classses)
Example #22
| Source File: ops_test.py From MBMD with MIT License | 6 votes |
|
def test_return_all_boxes_when_all_input_boxes_are_valid(self):
num_classes = 4
num_valid_boxes = 3
code_size = 4
dense_location_placeholder = tf.placeholder(tf.float32,
shape=(num_valid_boxes,
code_size))
dense_num_boxes_placeholder = tf.placeholder(tf.int32, shape=(num_classes))
box_locations, box_classes = ops.dense_to_sparse_boxes(
dense_location_placeholder, dense_num_boxes_placeholder, num_classes)
feed_dict = {dense_location_placeholder: np.random.uniform(
size=[num_valid_boxes, code_size]),
dense_num_boxes_placeholder: np.array([1, 0, 0, 2],
dtype=np.int32)}
expected_box_locations = feed_dict[dense_location_placeholder]
expected_box_classses = np.array([0, 3, 3])
with self.test_session() as sess:
box_locations, box_classes = sess.run([box_locations, box_classes],
feed_dict=feed_dict)
self.assertAllClose(box_locations, expected_box_locations, rtol=1e-6,
atol=1e-6)
self.assertAllEqual(box_classes, expected_box_classses)
Example #23
| Source File: ops_test.py From BMW-TensorFlow-Training-GUI with Apache License 2.0 | 6 votes |
|
def test_return_all_boxes_when_all_input_boxes_are_valid(self):
num_classes = 4
num_valid_boxes = 3
code_size = 4
dense_location_placeholder = tf.placeholder(tf.float32,
shape=(num_valid_boxes,
code_size))
dense_num_boxes_placeholder = tf.placeholder(tf.int32, shape=(num_classes))
box_locations, box_classes = ops.dense_to_sparse_boxes(
dense_location_placeholder, dense_num_boxes_placeholder, num_classes)
feed_dict = {dense_location_placeholder: np.random.uniform(
size=[num_valid_boxes, code_size]),
dense_num_boxes_placeholder: np.array([1, 0, 0, 2],
dtype=np.int32)}
expected_box_locations = feed_dict[dense_location_placeholder]
expected_box_classses = np.array([0, 3, 3])
with self.test_session() as sess:
box_locations, box_classes = sess.run([box_locations, box_classes],
feed_dict=feed_dict)
self.assertAllClose(box_locations, expected_box_locations, rtol=1e-6,
atol=1e-6)
self.assertAllEqual(box_classes, expected_box_classses)
Example #24
| Source File: ops_test.py From MAX-Object-Detector with Apache License 2.0 | 6 votes |
|
def test_return_all_boxes_when_all_input_boxes_are_valid(self):
num_classes = 4
num_valid_boxes = 3
code_size = 4
dense_location_placeholder = tf.placeholder(tf.float32,
shape=(num_valid_boxes,
code_size))
dense_num_boxes_placeholder = tf.placeholder(tf.int32, shape=(num_classes))
box_locations, box_classes = ops.dense_to_sparse_boxes(
dense_location_placeholder, dense_num_boxes_placeholder, num_classes)
feed_dict = {dense_location_placeholder: np.random.uniform(
size=[num_valid_boxes, code_size]),
dense_num_boxes_placeholder: np.array([1, 0, 0, 2],
dtype=np.int32)}
expected_box_locations = feed_dict[dense_location_placeholder]
expected_box_classses = np.array([0, 3, 3])
with self.test_session() as sess:
box_locations, box_classes = sess.run([box_locations, box_classes],
feed_dict=feed_dict)
self.assertAllClose(box_locations, expected_box_locations, rtol=1e-6,
atol=1e-6)
self.assertAllEqual(box_classes, expected_box_classses)
Example #25
| Source File: ops_test.py From moveo_ros with MIT License | 6 votes |
|
def test_return_all_boxes_when_all_input_boxes_are_valid(self):
num_classes = 4
num_valid_boxes = 3
code_size = 4
dense_location_placeholder = tf.placeholder(tf.float32,
shape=(num_valid_boxes,
code_size))
dense_num_boxes_placeholder = tf.placeholder(tf.int32, shape=(num_classes))
box_locations, box_classes = ops.dense_to_sparse_boxes(
dense_location_placeholder, dense_num_boxes_placeholder, num_classes)
feed_dict = {dense_location_placeholder: np.random.uniform(
size=[num_valid_boxes, code_size]),
dense_num_boxes_placeholder: np.array([1, 0, 0, 2],
dtype=np.int32)}
expected_box_locations = feed_dict[dense_location_placeholder]
expected_box_classses = np.array([0, 3, 3])
with self.test_session() as sess:
box_locations, box_classes = sess.run([box_locations, box_classes],
feed_dict=feed_dict)
self.assertAllClose(box_locations, expected_box_locations, rtol=1e-6,
atol=1e-6)
self.assertAllEqual(box_classes, expected_box_classses)
Example #26
| Source File: ops_test.py From hands-detection with MIT License | 6 votes |
|
def test_return_all_boxes_when_all_input_boxes_are_valid(self):
num_classes = 4
num_valid_boxes = 3
code_size = 4
dense_location_placeholder = tf.placeholder(tf.float32,
shape=(num_valid_boxes,
code_size))
dense_num_boxes_placeholder = tf.placeholder(tf.int32, shape=(num_classes))
box_locations, box_classes = ops.dense_to_sparse_boxes(
dense_location_placeholder, dense_num_boxes_placeholder, num_classes)
feed_dict = {dense_location_placeholder: np.random.uniform(
size=[num_valid_boxes, code_size]),
dense_num_boxes_placeholder: np.array([1, 0, 0, 2],
dtype=np.int32)}
expected_box_locations = feed_dict[dense_location_placeholder]
expected_box_classses = np.array([0, 3, 3])
with self.test_session() as sess:
box_locations, box_classes = sess.run([box_locations, box_classes],
feed_dict=feed_dict)
self.assertAllClose(box_locations, expected_box_locations, rtol=1e-6,
atol=1e-6)
self.assertAllEqual(box_classes, expected_box_classses)
Example #27
| Source File: ops_test.py From DOTA_models with Apache License 2.0 | 6 votes |
|
def test_return_all_boxes_when_all_input_boxes_are_valid(self):
num_classes = 4
num_valid_boxes = 3
code_size = 4
dense_location_placeholder = tf.placeholder(tf.float32,
shape=(num_valid_boxes,
code_size))
dense_num_boxes_placeholder = tf.placeholder(tf.int32, shape=(num_classes))
box_locations, box_classes = ops.dense_to_sparse_boxes(
dense_location_placeholder, dense_num_boxes_placeholder, num_classes)
feed_dict = {dense_location_placeholder: np.random.uniform(
size=[num_valid_boxes, code_size]),
dense_num_boxes_placeholder: np.array([1, 0, 0, 2],
dtype=np.int32)}
expected_box_locations = feed_dict[dense_location_placeholder]
expected_box_classses = np.array([0, 3, 3])
with self.test_session() as sess:
box_locations, box_classes = sess.run([box_locations, box_classes],
feed_dict=feed_dict)
self.assertAllClose(box_locations, expected_box_locations, rtol=1e-6,
atol=1e-6)
self.assertAllEqual(box_classes, expected_box_classses)
Example #28
| Source File: ops_test.py From Live-feed-object-device-identification-using-Tensorflow-and-OpenCV with Apache License 2.0 | 6 votes |
|
def test_return_all_boxes_when_all_input_boxes_are_valid(self):
num_classes = 4
num_valid_boxes = 3
code_size = 4
dense_location_placeholder = tf.placeholder(tf.float32,
shape=(num_valid_boxes,
code_size))
dense_num_boxes_placeholder = tf.placeholder(tf.int32, shape=(num_classes))
box_locations, box_classes = ops.dense_to_sparse_boxes(
dense_location_placeholder, dense_num_boxes_placeholder, num_classes)
feed_dict = {dense_location_placeholder: np.random.uniform(
size=[num_valid_boxes, code_size]),
dense_num_boxes_placeholder: np.array([1, 0, 0, 2],
dtype=np.int32)}
expected_box_locations = feed_dict[dense_location_placeholder]
expected_box_classses = np.array([0, 3, 3])
with self.test_session() as sess:
box_locations, box_classes = sess.run([box_locations, box_classes],
feed_dict=feed_dict)
self.assertAllClose(box_locations, expected_box_locations, rtol=1e-6,
atol=1e-6)
self.assertAllEqual(box_classes, expected_box_classses)
Example #29
| Source File: ops_test.py From AniSeg with Apache License 2.0 | 6 votes |
|
def test_return_all_boxes_when_all_input_boxes_are_valid(self):
num_classes = 4
num_valid_boxes = 3
code_size = 4
dense_location_placeholder = tf.placeholder(tf.float32,
shape=(num_valid_boxes,
code_size))
dense_num_boxes_placeholder = tf.placeholder(tf.int32, shape=(num_classes))
box_locations, box_classes = ops.dense_to_sparse_boxes(
dense_location_placeholder, dense_num_boxes_placeholder, num_classes)
feed_dict = {dense_location_placeholder: np.random.uniform(
size=[num_valid_boxes, code_size]),
dense_num_boxes_placeholder: np.array([1, 0, 0, 2],
dtype=np.int32)}
expected_box_locations = feed_dict[dense_location_placeholder]
expected_box_classses = np.array([0, 3, 3])
with self.test_session() as sess:
box_locations, box_classes = sess.run([box_locations, box_classes],
feed_dict=feed_dict)
self.assertAllClose(box_locations, expected_box_locations, rtol=1e-6,
atol=1e-6)
self.assertAllEqual(box_classes, expected_box_classses)
Example #30
| Source File: ops_test.py From object_detection_kitti with Apache License 2.0 | 6 votes |
|
def test_return_all_boxes_when_all_input_boxes_are_valid(self):
num_classes = 4
num_valid_boxes = 3
code_size = 4
dense_location_placeholder = tf.placeholder(tf.float32,
shape=(num_valid_boxes,
code_size))
dense_num_boxes_placeholder = tf.placeholder(tf.int32, shape=(num_classes))
box_locations, box_classes = ops.dense_to_sparse_boxes(
dense_location_placeholder, dense_num_boxes_placeholder, num_classes)
feed_dict = {dense_location_placeholder: np.random.uniform(
size=[num_valid_boxes, code_size]),
dense_num_boxes_placeholder: np.array([1, 0, 0, 2],
dtype=np.int32)}
expected_box_locations = feed_dict[dense_location_placeholder]
expected_box_classses = np.array([0, 3, 3])
with self.test_session() as sess:
box_locations, box_classes = sess.run([box_locations, box_classes],
feed_dict=feed_dict)
self.assertAllClose(box_locations, expected_box_locations, rtol=1e-6,
atol=1e-6)
self.assertAllEqual(box_classes, expected_box_classses)
