本文整理汇总了Python中object_detection.model_lib.unstack_batch方法的典型用法代码示例。如果您正苦于以下问题：Python model_lib.unstack_batch方法的具体用法？Python model_lib.unstack_batch怎么用？Python model_lib.unstack_batch使用的例子？那么恭喜您, 这里精选的方法代码示例或许可以为您提供帮助。您也可以进一步了解该方法所在类object_detection.model_lib的用法示例。
在下文中一共展示了model_lib.unstack_batch方法的2个代码示例，这些例子默认根据受欢迎程度排序。您可以为喜欢或者感觉有用的代码点赞，您的评价将有助于系统推荐出更棒的Python代码示例。
示例1: test_unbatch_without_unpadding
# 需要导入模块: from object_detection import model_lib [as 别名]
# 或者: from object_detection.model_lib import unstack_batch [as 别名]
def test_unbatch_without_unpadding(self):
image_placeholder = tf.placeholder(tf.float32, [2, None, None, None])
groundtruth_boxes_placeholder = tf.placeholder(tf.float32, [2, None, None])
groundtruth_classes_placeholder = tf.placeholder(tf.float32,
[2, None, None])
groundtruth_weights_placeholder = tf.placeholder(tf.float32, [2, None])
tensor_dict = {
fields.InputDataFields.image:
image_placeholder,
fields.InputDataFields.groundtruth_boxes:
groundtruth_boxes_placeholder,
fields.InputDataFields.groundtruth_classes:
groundtruth_classes_placeholder,
fields.InputDataFields.groundtruth_weights:
groundtruth_weights_placeholder
}
unbatched_tensor_dict = model_lib.unstack_batch(
tensor_dict, unpad_groundtruth_tensors=False)
with self.test_session() as sess:
unbatched_tensor_dict_out = sess.run(
unbatched_tensor_dict,
feed_dict={
image_placeholder:
np.random.rand(2, 4, 4, 3).astype(np.float32),
groundtruth_boxes_placeholder:
np.random.rand(2, 5, 4).astype(np.float32),
groundtruth_classes_placeholder:
np.random.rand(2, 5, 6).astype(np.float32),
groundtruth_weights_placeholder:
np.random.rand(2, 5).astype(np.float32)
})
for image_out in unbatched_tensor_dict_out[fields.InputDataFields.image]:
self.assertAllEqual(image_out.shape, [4, 4, 3])
for groundtruth_boxes_out in unbatched_tensor_dict_out[
fields.InputDataFields.groundtruth_boxes]:
self.assertAllEqual(groundtruth_boxes_out.shape, [5, 4])
for groundtruth_classes_out in unbatched_tensor_dict_out[
fields.InputDataFields.groundtruth_classes]:
self.assertAllEqual(groundtruth_classes_out.shape, [5, 6])
for groundtruth_weights_out in unbatched_tensor_dict_out[
fields.InputDataFields.groundtruth_weights]:
self.assertAllEqual(groundtruth_weights_out.shape, [5]) 示例2: test_unbatch_and_unpad_groundtruth_tensors
# 需要导入模块: from object_detection import model_lib [as 别名]
# 或者: from object_detection.model_lib import unstack_batch [as 别名]
def test_unbatch_and_unpad_groundtruth_tensors(self):
image_placeholder = tf.placeholder(tf.float32, [2, None, None, None])
groundtruth_boxes_placeholder = tf.placeholder(tf.float32, [2, 5, None])
groundtruth_classes_placeholder = tf.placeholder(tf.float32, [2, 5, None])
groundtruth_weights_placeholder = tf.placeholder(tf.float32, [2, 5])
num_groundtruth_placeholder = tf.placeholder(tf.int32, [2])
tensor_dict = {
fields.InputDataFields.image:
image_placeholder,
fields.InputDataFields.groundtruth_boxes:
groundtruth_boxes_placeholder,
fields.InputDataFields.groundtruth_classes:
groundtruth_classes_placeholder,
fields.InputDataFields.groundtruth_weights:
groundtruth_weights_placeholder,
fields.InputDataFields.num_groundtruth_boxes:
num_groundtruth_placeholder
}
unbatched_tensor_dict = model_lib.unstack_batch(
tensor_dict, unpad_groundtruth_tensors=True)
with self.test_session() as sess:
unbatched_tensor_dict_out = sess.run(
unbatched_tensor_dict,
feed_dict={
image_placeholder:
np.random.rand(2, 4, 4, 3).astype(np.float32),
groundtruth_boxes_placeholder:
np.random.rand(2, 5, 4).astype(np.float32),
groundtruth_classes_placeholder:
np.random.rand(2, 5, 6).astype(np.float32),
groundtruth_weights_placeholder:
np.random.rand(2, 5).astype(np.float32),
num_groundtruth_placeholder:
np.array([3, 3], np.int32)
})
for image_out in unbatched_tensor_dict_out[fields.InputDataFields.image]:
self.assertAllEqual(image_out.shape, [4, 4, 3])
for groundtruth_boxes_out in unbatched_tensor_dict_out[
fields.InputDataFields.groundtruth_boxes]:
self.assertAllEqual(groundtruth_boxes_out.shape, [3, 4])
for groundtruth_classes_out in unbatched_tensor_dict_out[
fields.InputDataFields.groundtruth_classes]:
self.assertAllEqual(groundtruth_classes_out.shape, [3, 6])
for groundtruth_weights_out in unbatched_tensor_dict_out[
fields.InputDataFields.groundtruth_weights]:
self.assertAllEqual(groundtruth_weights_out.shape, [3])