Python tensorflow.contrib.slim.queues.QueueRunners() Examples
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Example #1
Source File: data_provider_test.py From object_detection_kitti with Apache License 2.0 | 5 votes |
def test_optionally_applies_central_crop(self): batch_size = 4 data = data_provider.get_data( dataset=datasets.fsns_test.get_test_split(), batch_size=batch_size, augment=True, central_crop_size=(500, 100)) with self.test_session() as sess, queues.QueueRunners(sess): images_np = sess.run(data.images) self.assertEqual(images_np.shape, (batch_size, 100, 500, 3))
Example #2
Source File: data_provider_test.py From multilabel-image-classification-tensorflow with MIT License | 5 votes |
def test_optionally_applies_central_crop(self): batch_size = 4 data = data_provider.get_data( dataset=datasets.fsns_test.get_test_split(), batch_size=batch_size, augment=True, central_crop_size=(500, 100)) with self.test_session() as sess, queues.QueueRunners(sess): images_np = sess.run(data.images) self.assertEqual(images_np.shape, (batch_size, 100, 500, 3))
Example #3
Source File: data_provider_test.py From multilabel-image-classification-tensorflow with MIT License | 5 votes |
def test_provided_data_has_correct_shape(self): batch_size = 4 data = data_provider.get_data( dataset=datasets.fsns_test.get_test_split(), batch_size=batch_size, augment=True, central_crop_size=None) with self.test_session() as sess, queues.QueueRunners(sess): images_np, labels_np = sess.run([data.images, data.labels_one_hot]) self.assertEqual(images_np.shape, (batch_size, 150, 600, 3)) self.assertEqual(labels_np.shape, (batch_size, 37, 134))
Example #4
Source File: data_provider_test.py From models with Apache License 2.0 | 5 votes |
def test_optionally_applies_central_crop(self): batch_size = 4 data = data_provider.get_data( dataset=datasets.fsns_test.get_test_split(), batch_size=batch_size, augment=True, central_crop_size=(500, 100)) with self.test_session() as sess, queues.QueueRunners(sess): images_np = sess.run(data.images) self.assertEqual(images_np.shape, (batch_size, 100, 500, 3))
Example #5
Source File: data_provider_test.py From models with Apache License 2.0 | 5 votes |
def test_provided_data_has_correct_shape(self): batch_size = 4 data = data_provider.get_data( dataset=datasets.fsns_test.get_test_split(), batch_size=batch_size, augment=True, central_crop_size=None) with self.test_session() as sess, queues.QueueRunners(sess): images_np, labels_np = sess.run([data.images, data.labels_one_hot]) self.assertEqual(images_np.shape, (batch_size, 150, 600, 3)) self.assertEqual(labels_np.shape, (batch_size, 37, 134))
Example #6
Source File: data_provider_test.py From g-tensorflow-models with Apache License 2.0 | 5 votes |
def test_optionally_applies_central_crop(self): batch_size = 4 data = data_provider.get_data( dataset=datasets.fsns_test.get_test_split(), batch_size=batch_size, augment=True, central_crop_size=(500, 100)) with self.test_session() as sess, queues.QueueRunners(sess): images_np = sess.run(data.images) self.assertEqual(images_np.shape, (batch_size, 100, 500, 3))
Example #7
Source File: data_provider_test.py From g-tensorflow-models with Apache License 2.0 | 5 votes |
def test_provided_data_has_correct_shape(self): batch_size = 4 data = data_provider.get_data( dataset=datasets.fsns_test.get_test_split(), batch_size=batch_size, augment=True, central_crop_size=None) with self.test_session() as sess, queues.QueueRunners(sess): images_np, labels_np = sess.run([data.images, data.labels_one_hot]) self.assertEqual(images_np.shape, (batch_size, 150, 600, 3)) self.assertEqual(labels_np.shape, (batch_size, 37, 134))
Example #8
Source File: data_provider_test.py From object_detection_with_tensorflow with MIT License | 5 votes |
def test_optionally_applies_central_crop(self): batch_size = 4 data = data_provider.get_data( dataset=datasets.fsns_test.get_test_split(), batch_size=batch_size, augment=True, central_crop_size=(500, 100)) with self.test_session() as sess, queues.QueueRunners(sess): images_np = sess.run(data.images) self.assertEqual(images_np.shape, (batch_size, 100, 500, 3))
Example #9
Source File: data_provider_test.py From object_detection_with_tensorflow with MIT License | 5 votes |
def test_provided_data_has_correct_shape(self): batch_size = 4 data = data_provider.get_data( dataset=datasets.fsns_test.get_test_split(), batch_size=batch_size, augment=True, central_crop_size=None) with self.test_session() as sess, queues.QueueRunners(sess): images_np, labels_np = sess.run([data.images, data.labels_one_hot]) self.assertEqual(images_np.shape, (batch_size, 150, 600, 3)) self.assertEqual(labels_np.shape, (batch_size, 37, 134))
Example #10
Source File: data_provider_test.py From DOTA_models with Apache License 2.0 | 5 votes |
def test_provided_data_has_correct_shape(self): batch_size = 4 data = data_provider.get_data( dataset=datasets.fsns_test.get_test_split(), batch_size=batch_size, augment=True, central_crop_size=None) with self.test_session() as sess, queues.QueueRunners(sess): images_np, labels_np = sess.run([data.images, data.labels_one_hot]) self.assertEqual(images_np.shape, (batch_size, 150, 600, 3)) self.assertEqual(labels_np.shape, (batch_size, 37, 134))
Example #11
Source File: data_provider_test.py From object_detection_kitti with Apache License 2.0 | 5 votes |
def test_provided_data_has_correct_shape(self): batch_size = 4 data = data_provider.get_data( dataset=datasets.fsns_test.get_test_split(), batch_size=batch_size, augment=True, central_crop_size=None) with self.test_session() as sess, queues.QueueRunners(sess): images_np, labels_np = sess.run([data.images, data.labels_one_hot]) self.assertEqual(images_np.shape, (batch_size, 150, 600, 3)) self.assertEqual(labels_np.shape, (batch_size, 37, 134))
Example #12
Source File: data_provider_test.py From hands-detection with MIT License | 5 votes |
def test_optionally_applies_central_crop(self): batch_size = 4 data = data_provider.get_data( dataset=datasets.fsns_test.get_test_split(), batch_size=batch_size, augment=True, central_crop_size=(500, 100)) with self.test_session() as sess, queues.QueueRunners(sess): images_np = sess.run(data.images) self.assertEqual(images_np.shape, (batch_size, 100, 500, 3))
Example #13
Source File: data_provider_test.py From hands-detection with MIT License | 5 votes |
def test_provided_data_has_correct_shape(self): batch_size = 4 data = data_provider.get_data( dataset=datasets.fsns_test.get_test_split(), batch_size=batch_size, augment=True, central_crop_size=None) with self.test_session() as sess, queues.QueueRunners(sess): images_np, labels_np = sess.run([data.images, data.labels_one_hot]) self.assertEqual(images_np.shape, (batch_size, 150, 600, 3)) self.assertEqual(labels_np.shape, (batch_size, 37, 134))
Example #14
Source File: data_provider_test.py From Gun-Detector with Apache License 2.0 | 5 votes |
def test_optionally_applies_central_crop(self): batch_size = 4 data = data_provider.get_data( dataset=datasets.fsns_test.get_test_split(), batch_size=batch_size, augment=True, central_crop_size=(500, 100)) with self.test_session() as sess, queues.QueueRunners(sess): images_np = sess.run(data.images) self.assertEqual(images_np.shape, (batch_size, 100, 500, 3))
Example #15
Source File: data_provider_test.py From Gun-Detector with Apache License 2.0 | 5 votes |
def test_provided_data_has_correct_shape(self): batch_size = 4 data = data_provider.get_data( dataset=datasets.fsns_test.get_test_split(), batch_size=batch_size, augment=True, central_crop_size=None) with self.test_session() as sess, queues.QueueRunners(sess): images_np, labels_np = sess.run([data.images, data.labels_one_hot]) self.assertEqual(images_np.shape, (batch_size, 150, 600, 3)) self.assertEqual(labels_np.shape, (batch_size, 37, 134))
Example #16
Source File: data_provider_test.py From yolo_v2 with Apache License 2.0 | 5 votes |
def test_optionally_applies_central_crop(self): batch_size = 4 data = data_provider.get_data( dataset=datasets.fsns_test.get_test_split(), batch_size=batch_size, augment=True, central_crop_size=(500, 100)) with self.test_session() as sess, queues.QueueRunners(sess): images_np = sess.run(data.images) self.assertEqual(images_np.shape, (batch_size, 100, 500, 3))
Example #17
Source File: data_provider_test.py From yolo_v2 with Apache License 2.0 | 5 votes |
def test_provided_data_has_correct_shape(self): batch_size = 4 data = data_provider.get_data( dataset=datasets.fsns_test.get_test_split(), batch_size=batch_size, augment=True, central_crop_size=None) with self.test_session() as sess, queues.QueueRunners(sess): images_np, labels_np = sess.run([data.images, data.labels_one_hot]) self.assertEqual(images_np.shape, (batch_size, 150, 600, 3)) self.assertEqual(labels_np.shape, (batch_size, 37, 134))
Example #18
Source File: data_provider_test.py From DOTA_models with Apache License 2.0 | 5 votes |
def test_optionally_applies_central_crop(self): batch_size = 4 data = data_provider.get_data( dataset=datasets.fsns_test.get_test_split(), batch_size=batch_size, augment=True, central_crop_size=(500, 100)) with self.test_session() as sess, queues.QueueRunners(sess): images_np = sess.run(data.images) self.assertEqual(images_np.shape, (batch_size, 100, 500, 3))