diff --git a/python/paddle/fluid/layers/detection.py b/python/paddle/fluid/layers/detection.py index 1bd377b711ecfa090c26a6c7f29317e565f4ee5b..634665dfede63f592af2203537111799d58a4481 100644 --- a/python/paddle/fluid/layers/detection.py +++ b/python/paddle/fluid/layers/detection.py @@ -39,7 +39,6 @@ from paddle import _C_ops, _legacy_C_ops from ..framework import in_dygraph_mode __all__ = [ - 'prior_box', 'density_prior_box', 'multi_box_head', 'anchor_generator', @@ -58,135 +57,6 @@ __all__ = [ ] -def prior_box( - input, - image, - min_sizes, - max_sizes=None, - aspect_ratios=[1.0], - variance=[0.1, 0.1, 0.2, 0.2], - flip=False, - clip=False, - steps=[0.0, 0.0], - offset=0.5, - name=None, - min_max_aspect_ratios_order=False, -): - """ - - This op generates prior boxes for SSD(Single Shot MultiBox Detector) algorithm. - Each position of the input produce N prior boxes, N is determined by - the count of min_sizes, max_sizes and aspect_ratios, The size of the - box is in range(min_size, max_size) interval, which is generated in - sequence according to the aspect_ratios. - - Parameters: - input(Variable): 4-D tensor(NCHW), the data type should be float32 or float64. - image(Variable): 4-D tensor(NCHW), the input image data of PriorBoxOp, - the data type should be float32 or float64. - min_sizes(list|tuple|float): the min sizes of generated prior boxes. - max_sizes(list|tuple|None): the max sizes of generated prior boxes. - Default: None. - aspect_ratios(list|tuple|float): the aspect ratios of generated - prior boxes. Default: [1.]. - variance(list|tuple): the variances to be encoded in prior boxes. - Default:[0.1, 0.1, 0.2, 0.2]. - flip(bool): Whether to flip aspect ratios. Default:False. - clip(bool): Whether to clip out-of-boundary boxes. Default: False. - step(list|tuple): Prior boxes step across width and height, If - step[0] equals to 0.0 or step[1] equals to 0.0, the prior boxes step across - height or weight of the input will be automatically calculated. - Default: [0., 0.] - offset(float): Prior boxes center offset. Default: 0.5 - min_max_aspect_ratios_order(bool): If set True, the output prior box is - in order of [min, max, aspect_ratios], which is consistent with - Caffe. Please note, this order affects the weights order of - convolution layer followed by and does not affect the final - detection results. Default: False. - name(str, optional): The default value is None. Normally there is no need for user to set this property. For more information, please refer to :ref:`api_guide_Name` - - Returns: - Tuple: A tuple with two Variable (boxes, variances) - - boxes(Variable): the output prior boxes of PriorBox. - 4-D tensor, the layout is [H, W, num_priors, 4]. - H is the height of input, W is the width of input, - num_priors is the total box count of each position of input. - - variances(Variable): the expanded variances of PriorBox. - 4-D tensor, the layput is [H, W, num_priors, 4]. - H is the height of input, W is the width of input - num_priors is the total box count of each position of input - - Examples: - .. code-block:: python - - #declarative mode - import paddle.fluid as fluid - import numpy as np - import paddle - paddle.enable_static() - input = fluid.data(name="input", shape=[None,3,6,9]) - image = fluid.data(name="image", shape=[None,3,9,12]) - box, var = fluid.layers.prior_box( - input=input, - image=image, - min_sizes=[100.], - clip=True, - flip=True) - - place = fluid.CPUPlace() - exe = fluid.Executor(place) - exe.run(fluid.default_startup_program()) - - # prepare a batch of data - input_data = np.random.rand(1,3,6,9).astype("float32") - image_data = np.random.rand(1,3,9,12).astype("float32") - - box_out, var_out = exe.run(fluid.default_main_program(), - feed={"input":input_data,"image":image_data}, - fetch_list=[box,var], - return_numpy=True) - - # print(box_out.shape) - # (6, 9, 1, 4) - # print(var_out.shape) - # (6, 9, 1, 4) - - # imperative mode - import paddle.fluid.dygraph as dg - - with dg.guard(place) as g: - input = dg.to_variable(input_data) - image = dg.to_variable(image_data) - box, var = fluid.layers.prior_box( - input=input, - image=image, - min_sizes=[100.], - clip=True, - flip=True) - # print(box.shape) - # [6L, 9L, 1L, 4L] - # print(var.shape) - # [6L, 9L, 1L, 4L] - - """ - return paddle.vision.ops.prior_box( - input=input, - image=image, - min_sizes=min_sizes, - max_sizes=max_sizes, - aspect_ratios=aspect_ratios, - variance=variance, - flip=flip, - clip=clip, - steps=steps, - offset=offset, - min_max_aspect_ratios_order=min_max_aspect_ratios_order, - name=name, - ) - - def density_prior_box( input, image, @@ -623,7 +493,7 @@ def multi_box_head( aspect_ratio = [aspect_ratio] step = [step_w[i] if step_w else 0.0, step_h[i] if step_w else 0.0] - box, var = prior_box( + box, var = paddle.vision.ops.prior_box( input, image, min_size, @@ -634,8 +504,8 @@ def multi_box_head( clip, step, offset, - None, min_max_aspect_ratios_order, + None, ) box_results.append(box) diff --git a/python/paddle/fluid/tests/test_detection.py b/python/paddle/fluid/tests/test_detection.py index f5eac477d6f4421b4c463c44d04a044668611875..1d4cda6e2f6e2a30801f9ec4f4494152a0e7176d 100644 --- a/python/paddle/fluid/tests/test_detection.py +++ b/python/paddle/fluid/tests/test_detection.py @@ -75,48 +75,6 @@ class LayerTest(unittest.TestCase): yield -class TestPriorBox(unittest.TestCase): - def test_prior_box(self): - program = Program() - with program_guard(program): - data_shape = [3, 224, 224] - images = fluid.layers.data( - name='pixel', shape=data_shape, dtype='float32' - ) - conv1 = fluid.layers.conv2d(images, 3, 3, 2) - box, var = layers.prior_box( - input=conv1, - image=images, - min_sizes=[100.0], - aspect_ratios=[1.0], - flip=True, - clip=True, - ) - assert len(box.shape) == 4 - assert box.shape == var.shape - assert box.shape[3] == 4 - - -class TestPriorBox2(unittest.TestCase): - def test_prior_box(self): - program = Program() - with program_guard(program): - data_shape = [None, 3, None, None] - images = fluid.data(name='pixel', shape=data_shape, dtype='float32') - conv1 = fluid.layers.conv2d(images, 3, 3, 2) - box, var = layers.prior_box( - input=conv1, - image=images, - min_sizes=[100.0], - aspect_ratios=[1.0], - flip=True, - clip=True, - ) - assert len(box.shape) == 4 - assert box.shape == var.shape - assert box.shape[3] == 4 - - class TestDensityPriorBox(unittest.TestCase): def test_density_prior_box(self): program = Program() diff --git a/python/paddle/fluid/tests/unittests/test_prior_box_op.py b/python/paddle/fluid/tests/unittests/test_prior_box_op.py index 493b952d57bf30b49f54455d33b47934fc6c0e2d..fa381b1cc895e05d47482cab4f595760b1c22b3d 100644 --- a/python/paddle/fluid/tests/unittests/test_prior_box_op.py +++ b/python/paddle/fluid/tests/unittests/test_prior_box_op.py @@ -36,7 +36,7 @@ def python_prior_box( min_max_aspect_ratios_order=False, name=None, ): - return paddle.fluid.layers.detection.prior_box( + return paddle.vision.ops.prior_box( input, image, min_sizes=min_sizes,