diff --git a/python/paddle/fluid/evaluator.py b/python/paddle/fluid/evaluator.py index 5ea1d1e5358a2..472bcbd3cac4b 100644 --- a/python/paddle/fluid/evaluator.py +++ b/python/paddle/fluid/evaluator.py @@ -21,7 +21,6 @@ from . import unique_name from .layer_helper import LayerHelper from .initializer import Constant -from .layers import detection def _clone_var_(block, var): diff --git a/python/paddle/fluid/layers/__init__.py b/python/paddle/fluid/layers/__init__.py index 6a49ad01177e3..0e98d907738d5 100644 --- a/python/paddle/fluid/layers/__init__.py +++ b/python/paddle/fluid/layers/__init__.py @@ -24,8 +24,6 @@ from .math_op_patch import * from . import loss from .loss import * -from . import detection -from .detection import * from .learning_rate_scheduler import * from .collective import * from .sequence_lod import * @@ -36,7 +34,6 @@ __all__ += io.__all__ __all__ += tensor.__all__ __all__ += control_flow.__all__ -__all__ += detection.__all__ __all__ += learning_rate_scheduler.__all__ __all__ += sequence_lod.__all__ __all__ += loss.__all__ diff --git a/python/paddle/fluid/layers/detection.py b/python/paddle/fluid/layers/detection.py deleted file mode 100644 index 634665dfede63..0000000000000 --- a/python/paddle/fluid/layers/detection.py +++ /dev/null @@ -1,2179 +0,0 @@ -# Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserve. -# -# 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. -""" -All layers just related to the detection neural network. -""" - -import paddle - -from .layer_function_generator import templatedoc -from ..layer_helper import LayerHelper -from ..framework import Variable, _non_static_mode, static_only, in_dygraph_mode -from .. import core -from paddle.fluid.layers import softmax_with_cross_entropy -from . import tensor -from . import nn -from ..data_feeder import check_variable_and_dtype, check_type, check_dtype -import math -import numpy as np -from functools import reduce -from ..data_feeder import ( - convert_dtype, - check_variable_and_dtype, - check_type, - check_dtype, -) -from paddle.utils import deprecated -from paddle import _C_ops, _legacy_C_ops -from ..framework import in_dygraph_mode - -__all__ = [ - 'density_prior_box', - 'multi_box_head', - 'anchor_generator', - 'roi_perspective_transform', - 'generate_proposal_labels', - 'generate_proposals', - 'generate_mask_labels', - 'box_clip', - 'multiclass_nms', - 'locality_aware_nms', - 'matrix_nms', - 'retinanet_detection_output', - 'distribute_fpn_proposals', - 'box_decoder_and_assign', - 'collect_fpn_proposals', -] - - -def density_prior_box( - input, - image, - densities=None, - fixed_sizes=None, - fixed_ratios=None, - variance=[0.1, 0.1, 0.2, 0.2], - clip=False, - steps=[0.0, 0.0], - offset=0.5, - flatten_to_2d=False, - name=None, -): - r""" - - This op generates density 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 densities, fixed_sizes and fixed_ratios. - Boxes center at grid points around each input position is generated by - this operator, and the grid points is determined by densities and - the count of density prior box is determined by fixed_sizes and fixed_ratios. - Obviously, the number of fixed_sizes is equal to the number of densities. - - For densities_i in densities: - - .. math:: - - N\_density_prior\_box = SUM(N\_fixed\_ratios * densities\_i^2) - - N_density_prior_box is the number of density_prior_box and N_fixed_ratios is the number of fixed_ratios. - - Parameters: - input(Variable): 4-D tensor(NCHW), the data type should be float32 of float64. - image(Variable): 4-D tensor(NCHW), the input image data of PriorBoxOp, the data type should be float32 or float64. - the layout is NCHW. - densities(list|tuple|None): The densities of generated density prior - boxes, this attribute should be a list or tuple of integers. - Default: None. - fixed_sizes(list|tuple|None): The fixed sizes of generated density - prior boxes, this attribute should a list or tuple of same - length with :attr:`densities`. Default: None. - fixed_ratios(list|tuple|None): The fixed ratios of generated density - prior boxes, if this attribute is not set and :attr:`densities` - and :attr:`fix_sizes` is set, :attr:`aspect_ratios` will be used - to generate density prior boxes. - variance(list|tuple): The variances to be encoded in density prior boxes. - Default:[0.1, 0.1, 0.2, 0.2]. - 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 0.0 or step[1] equals 0.0, the density 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 - flatten_to_2d(bool): Whether to flatten output prior boxes and variance - to 2D shape, the second dim is 4. 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: the output density prior boxes of PriorBox. - 4-D tensor, the layout is [H, W, num_priors, 4] when flatten_to_2d is False. - 2-D tensor, the layout is [H * W * num_priors, 4] when flatten_to_2d is True. - H is the height of input, W is the width of input, and num_priors is the total box count of each position of input. - - variances: the expanded variances of PriorBox. - 4-D tensor, the layout is [H, W, num_priors, 4] when flatten_to_2d is False. - 2-D tensor, the layout is [H * W * num_priors, 4] when flatten_to_2d is True. - H is the height of input, W is the width of input, and 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.density_prior_box( - input=input, - image=image, - densities=[4, 2, 1], - fixed_sizes=[32.0, 64.0, 128.0], - fixed_ratios=[1.], - clip=True, - flatten_to_2d=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) - # (1134, 4) - # print(var_out.shape) - # (1134, 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.density_prior_box( - input=input, - image=image, - densities=[4, 2, 1], - fixed_sizes=[32.0, 64.0, 128.0], - fixed_ratios=[1.], - clip=True) - - # print(box.shape) - # [6L, 9L, 21L, 4L] - # print(var.shape) - # [6L, 9L, 21L, 4L] - - """ - helper = LayerHelper("density_prior_box", **locals()) - dtype = helper.input_dtype() - check_variable_and_dtype( - input, 'input', ['float32', 'float64'], 'density_prior_box' - ) - - def _is_list_or_tuple_(data): - return isinstance(data, list) or isinstance(data, tuple) - - check_type(densities, 'densities', (list, tuple), 'density_prior_box') - check_type(fixed_sizes, 'fixed_sizes', (list, tuple), 'density_prior_box') - check_type(fixed_ratios, 'fixed_ratios', (list, tuple), 'density_prior_box') - if len(densities) != len(fixed_sizes): - raise ValueError('densities and fixed_sizes length should be euqal.') - - if not (_is_list_or_tuple_(steps) and len(steps) == 2): - raise ValueError( - 'steps should be a list or tuple ', - 'with length 2, (step_width, step_height).', - ) - - densities = list(map(int, densities)) - fixed_sizes = list(map(float, fixed_sizes)) - fixed_ratios = list(map(float, fixed_ratios)) - steps = list(map(float, steps)) - - attrs = { - 'variances': variance, - 'clip': clip, - 'step_w': steps[0], - 'step_h': steps[1], - 'offset': offset, - 'densities': densities, - 'fixed_sizes': fixed_sizes, - 'fixed_ratios': fixed_ratios, - 'flatten_to_2d': flatten_to_2d, - } - box = helper.create_variable_for_type_inference(dtype) - var = helper.create_variable_for_type_inference(dtype) - helper.append_op( - type="density_prior_box", - inputs={"Input": input, "Image": image}, - outputs={"Boxes": box, "Variances": var}, - attrs=attrs, - ) - box.stop_gradient = True - var.stop_gradient = True - return box, var - - -@static_only -def multi_box_head( - inputs, - image, - base_size, - num_classes, - aspect_ratios, - min_ratio=None, - max_ratio=None, - min_sizes=None, - max_sizes=None, - steps=None, - step_w=None, - step_h=None, - offset=0.5, - variance=[0.1, 0.1, 0.2, 0.2], - flip=True, - clip=False, - kernel_size=1, - pad=0, - stride=1, - name=None, - min_max_aspect_ratios_order=False, -): - """ - :api_attr: Static Graph - - Base on SSD ((Single Shot MultiBox Detector) algorithm, generate prior boxes, - regression location and classification confidence on multiple input feature - maps, then output the concatenate results. The details of this algorithm, - please refer the section 2.2 of SSD paper `SSD: Single Shot MultiBox Detector - `_ . - - Args: - inputs (list(Variable)|tuple(Variable)): The list of input variables, - the format of all Variables are 4-D Tensor, layout is NCHW. - Data type should be float32 or float64. - image (Variable): The input image, layout is NCHW. Data type should be - the same as inputs. - base_size(int): the base_size is input image size. When len(inputs) > 2 - and `min_size` and `max_size` are None, the `min_size` and `max_size` - are calculated by `baze_size`, 'min_ratio' and `max_ratio`. The - formula is as follows: - - .. code-block:: text - - min_sizes = [] - max_sizes = [] - step = int(math.floor(((max_ratio - min_ratio)) / (num_layer - 2))) - for ratio in range(min_ratio, max_ratio + 1, step): - min_sizes.append(base_size * ratio / 100.) - max_sizes.append(base_size * (ratio + step) / 100.) - min_sizes = [base_size * .10] + min_sizes - max_sizes = [base_size * .20] + max_sizes - - num_classes(int): The number of classes. - aspect_ratios(list(float) | tuple(float)): the aspect ratios of generated - prior boxes. The length of input and aspect_ratios must be equal. - min_ratio(int): the min ratio of generated prior boxes. - max_ratio(int): the max ratio of generated prior boxes. - min_sizes(list|tuple|None): If `len(inputs) <=2`, - min_sizes must be set up, and the length of min_sizes - should equal to the length of inputs. Default: None. - max_sizes(list|tuple|None): If `len(inputs) <=2`, - max_sizes must be set up, and the length of min_sizes - should equal to the length of inputs. Default: None. - steps(list|tuple): If step_w and step_h are the same, - step_w and step_h can be replaced by steps. - step_w(list|tuple): Prior boxes step - across width. If step_w[i] == 0.0, the prior boxes step - across width of the inputs[i] will be automatically - calculated. Default: None. - step_h(list|tuple): Prior boxes step across height, If - step_h[i] == 0.0, the prior boxes step across height of - the inputs[i] will be automatically calculated. Default: None. - offset(float): Prior boxes center offset. Default: 0.5 - 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. - kernel_size(int): The kernel size of conv2d. Default: 1. - pad(int|list|tuple): The padding of conv2d. Default:0. - stride(int|list|tuple): The stride of conv2d. Default:1, - name(str): 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`. - 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. - - Returns: - tuple: A tuple with four Variables. (mbox_loc, mbox_conf, boxes, variances) - - mbox_loc (Variable): The predicted boxes' location of the inputs. The - layout is [N, num_priors, 4], where N is batch size, ``num_priors`` - is the number of prior boxes. Data type is the same as input. - - mbox_conf (Variable): The predicted boxes' confidence of the inputs. - The layout is [N, num_priors, C], where ``N`` and ``num_priors`` - has the same meaning as above. C is the number of Classes. - Data type is the same as input. - - boxes (Variable): the output prior boxes. The layout is [num_priors, 4]. - The meaning of num_priors is the same as above. - Data type is the same as input. - - variances (Variable): the expanded variances for prior boxes. - The layout is [num_priors, 4]. Data type is the same as input. - - Examples 1: set min_ratio and max_ratio: - .. code-block:: python - - import paddle - paddle.enable_static() - - images = paddle.static.data(name='data', shape=[None, 3, 300, 300], dtype='float32') - conv1 = paddle.static.data(name='conv1', shape=[None, 512, 19, 19], dtype='float32') - conv2 = paddle.static.data(name='conv2', shape=[None, 1024, 10, 10], dtype='float32') - conv3 = paddle.static.data(name='conv3', shape=[None, 512, 5, 5], dtype='float32') - conv4 = paddle.static.data(name='conv4', shape=[None, 256, 3, 3], dtype='float32') - conv5 = paddle.static.data(name='conv5', shape=[None, 256, 2, 2], dtype='float32') - conv6 = paddle.static.data(name='conv6', shape=[None, 128, 1, 1], dtype='float32') - - mbox_locs, mbox_confs, box, var = paddle.static.nn.multi_box_head( - inputs=[conv1, conv2, conv3, conv4, conv5, conv6], - image=images, - num_classes=21, - min_ratio=20, - max_ratio=90, - aspect_ratios=[[2.], [2., 3.], [2., 3.], [2., 3.], [2.], [2.]], - base_size=300, - offset=0.5, - flip=True, - clip=True) - - Examples 2: set min_sizes and max_sizes: - .. code-block:: python - - import paddle - paddle.enable_static() - - images = paddle.static.data(name='data', shape=[None, 3, 300, 300], dtype='float32') - conv1 = paddle.static.data(name='conv1', shape=[None, 512, 19, 19], dtype='float32') - conv2 = paddle.static.data(name='conv2', shape=[None, 1024, 10, 10], dtype='float32') - conv3 = paddle.static.data(name='conv3', shape=[None, 512, 5, 5], dtype='float32') - conv4 = paddle.static.data(name='conv4', shape=[None, 256, 3, 3], dtype='float32') - conv5 = paddle.static.data(name='conv5', shape=[None, 256, 2, 2], dtype='float32') - conv6 = paddle.static.data(name='conv6', shape=[None, 128, 1, 1], dtype='float32') - - mbox_locs, mbox_confs, box, var = paddle.static.nn.multi_box_head( - inputs=[conv1, conv2, conv3, conv4, conv5, conv6], - image=images, - num_classes=21, - min_sizes=[60.0, 105.0, 150.0, 195.0, 240.0, 285.0], - max_sizes=[[], 150.0, 195.0, 240.0, 285.0, 300.0], - aspect_ratios=[[2.], [2., 3.], [2., 3.], [2., 3.], [2.], [2.]], - base_size=300, - offset=0.5, - flip=True, - clip=True) - - """ - - def _reshape_with_axis_(input, axis=1): - # Note : axis!=0 in current references to this func - # if axis == 0: - # x = paddle.flatten(input, 0, -1) - # x = paddle.unsqueeze(x, 0) - # return x - # else: - x = paddle.flatten(input, axis, -1) - x = paddle.flatten(x, 0, axis - 1) - return x - - def _is_list_or_tuple_(data): - return isinstance(data, list) or isinstance(data, tuple) - - def _is_list_or_tuple_and_equal(data, length, err_info): - if not (_is_list_or_tuple_(data) and len(data) == length): - raise ValueError(err_info) - - if not _is_list_or_tuple_(inputs): - raise ValueError('inputs should be a list or tuple.') - - num_layer = len(inputs) - - if num_layer <= 2: - assert min_sizes is not None and max_sizes is not None - assert len(min_sizes) == num_layer and len(max_sizes) == num_layer - elif min_sizes is None and max_sizes is None: - min_sizes = [] - max_sizes = [] - step = int(math.floor(((max_ratio - min_ratio)) / (num_layer - 2))) - for ratio in range(min_ratio, max_ratio + 1, step): - min_sizes.append(base_size * ratio / 100.0) - max_sizes.append(base_size * (ratio + step) / 100.0) - min_sizes = [base_size * 0.10] + min_sizes - max_sizes = [base_size * 0.20] + max_sizes - - if aspect_ratios: - _is_list_or_tuple_and_equal( - aspect_ratios, - num_layer, - 'aspect_ratios should be list or tuple, and the length of inputs ' - 'and aspect_ratios should be the same.', - ) - if step_h is not None: - _is_list_or_tuple_and_equal( - step_h, - num_layer, - 'step_h should be list or tuple, and the length of inputs and ' - 'step_h should be the same.', - ) - if step_w is not None: - _is_list_or_tuple_and_equal( - step_w, - num_layer, - 'step_w should be list or tuple, and the length of inputs and ' - 'step_w should be the same.', - ) - if steps is not None: - _is_list_or_tuple_and_equal( - steps, - num_layer, - 'steps should be list or tuple, and the length of inputs and ' - 'step_w should be the same.', - ) - step_w = steps - step_h = steps - - mbox_locs = [] - mbox_confs = [] - box_results = [] - var_results = [] - for i, input in enumerate(inputs): - min_size = min_sizes[i] - max_size = max_sizes[i] - - if not _is_list_or_tuple_(min_size): - min_size = [min_size] - if not _is_list_or_tuple_(max_size): - max_size = [max_size] - - aspect_ratio = [] - if aspect_ratios is not None: - aspect_ratio = aspect_ratios[i] - if not _is_list_or_tuple_(aspect_ratio): - 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 = paddle.vision.ops.prior_box( - input, - image, - min_size, - max_size, - aspect_ratio, - variance, - flip, - clip, - step, - offset, - min_max_aspect_ratios_order, - None, - ) - - box_results.append(box) - var_results.append(var) - - num_boxes = box.shape[2] - - # get loc - num_loc_output = num_boxes * 4 - mbox_loc = nn.conv2d( - input=input, - num_filters=num_loc_output, - filter_size=kernel_size, - padding=pad, - stride=stride, - ) - - mbox_loc = paddle.transpose(mbox_loc, perm=[0, 2, 3, 1]) - mbox_loc_flatten = paddle.flatten(mbox_loc, 1, -1) - mbox_locs.append(mbox_loc_flatten) - - # get conf - num_conf_output = num_boxes * num_classes - conf_loc = nn.conv2d( - input=input, - num_filters=num_conf_output, - filter_size=kernel_size, - padding=pad, - stride=stride, - ) - - conf_loc = paddle.transpose(conf_loc, perm=[0, 2, 3, 1]) - conf_loc_flatten = paddle.flatten(conf_loc, 1, -1) - mbox_confs.append(conf_loc_flatten) - - if len(box_results) == 1: - box = box_results[0] - var = var_results[0] - mbox_locs_concat = mbox_locs[0] - mbox_confs_concat = mbox_confs[0] - else: - reshaped_boxes = [] - reshaped_vars = [] - for i in range(len(box_results)): - reshaped_boxes.append(_reshape_with_axis_(box_results[i], axis=3)) - reshaped_vars.append(_reshape_with_axis_(var_results[i], axis=3)) - - box = tensor.concat(reshaped_boxes) - var = tensor.concat(reshaped_vars) - mbox_locs_concat = tensor.concat(mbox_locs, axis=1) - mbox_locs_concat = paddle.reshape(mbox_locs_concat, shape=[0, -1, 4]) - mbox_confs_concat = tensor.concat(mbox_confs, axis=1) - mbox_confs_concat = paddle.reshape( - mbox_confs_concat, shape=[0, -1, num_classes] - ) - - box.stop_gradient = True - var.stop_gradient = True - return mbox_locs_concat, mbox_confs_concat, box, var - - -def anchor_generator( - input, - anchor_sizes=None, - aspect_ratios=None, - variance=[0.1, 0.1, 0.2, 0.2], - stride=None, - offset=0.5, - name=None, -): - """ - - **Anchor generator operator** - - Generate anchors for Faster RCNN algorithm. - Each position of the input produce N anchors, N = - size(anchor_sizes) * size(aspect_ratios). The order of generated anchors - is firstly aspect_ratios loop then anchor_sizes loop. - - Args: - input(Variable): 4-D Tensor with shape [N,C,H,W]. The input feature map. - anchor_sizes(float32|list|tuple, optional): The anchor sizes of generated - anchors, given in absolute pixels e.g. [64., 128., 256., 512.]. - For instance, the anchor size of 64 means the area of this anchor - equals to 64**2. None by default. - aspect_ratios(float32|list|tuple, optional): The height / width ratios - of generated anchors, e.g. [0.5, 1.0, 2.0]. None by default. - variance(list|tuple, optional): The variances to be used in box - regression deltas. The data type is float32, [0.1, 0.1, 0.2, 0.2] by - default. - stride(list|tuple, optional): The anchors stride across width and height. - The data type is float32. e.g. [16.0, 16.0]. None by default. - offset(float32, optional): Prior boxes center offset. 0.5 by default. - name(str, optional): For detailed information, please refer - to :ref:`api_guide_Name`. Usually name is no need to set and None - by default. - - Returns: - Tuple: - - Anchors(Variable): The output anchors with a layout of [H, W, num_anchors, 4]. - H is the height of input, W is the width of input, - num_anchors is the box count of each position. - Each anchor is in (xmin, ymin, xmax, ymax) format an unnormalized. - - Variances(Variable): The expanded variances of anchors - with a layout of [H, W, num_priors, 4]. - H is the height of input, W is the width of input - num_anchors is the box count of each position. - Each variance is in (xcenter, ycenter, w, h) format. - - - Examples: - - .. code-block:: python - - import paddle.fluid as fluid - import paddle - - paddle.enable_static() - conv1 = fluid.data(name='conv1', shape=[None, 48, 16, 16], dtype='float32') - anchor, var = fluid.layers.anchor_generator( - input=conv1, - anchor_sizes=[64, 128, 256, 512], - aspect_ratios=[0.5, 1.0, 2.0], - variance=[0.1, 0.1, 0.2, 0.2], - stride=[16.0, 16.0], - offset=0.5) - """ - helper = LayerHelper("anchor_generator", **locals()) - dtype = helper.input_dtype() - - def _is_list_or_tuple_(data): - return isinstance(data, list) or isinstance(data, tuple) - - if not _is_list_or_tuple_(anchor_sizes): - anchor_sizes = [anchor_sizes] - if not _is_list_or_tuple_(aspect_ratios): - aspect_ratios = [aspect_ratios] - if not (_is_list_or_tuple_(stride) and len(stride) == 2): - raise ValueError( - 'stride should be a list or tuple ', - 'with length 2, (stride_width, stride_height).', - ) - - anchor_sizes = list(map(float, anchor_sizes)) - aspect_ratios = list(map(float, aspect_ratios)) - stride = list(map(float, stride)) - - attrs = { - 'anchor_sizes': anchor_sizes, - 'aspect_ratios': aspect_ratios, - 'variances': variance, - 'stride': stride, - 'offset': offset, - } - - anchor = helper.create_variable_for_type_inference(dtype) - var = helper.create_variable_for_type_inference(dtype) - helper.append_op( - type="anchor_generator", - inputs={"Input": input}, - outputs={"Anchors": anchor, "Variances": var}, - attrs=attrs, - ) - anchor.stop_gradient = True - var.stop_gradient = True - return anchor, var - - -def roi_perspective_transform( - input, - rois, - transformed_height, - transformed_width, - spatial_scale=1.0, - name=None, -): - """ - **The** `rois` **of this op should be a LoDTensor.** - - ROI perspective transform op applies perspective transform to map each roi into an - rectangular region. Perspective transform is a type of transformation in linear algebra. - - Parameters: - input (Variable): 4-D Tensor, input of ROIPerspectiveTransformOp. The format of - input tensor is NCHW. Where N is batch size, C is the - number of input channels, H is the height of the feature, - and W is the width of the feature. The data type is float32. - rois (Variable): 2-D LoDTensor, ROIs (Regions of Interest) to be transformed. - It should be a 2-D LoDTensor of shape (num_rois, 8). Given as - [[x1, y1, x2, y2, x3, y3, x4, y4], ...], (x1, y1) is the - top left coordinates, and (x2, y2) is the top right - coordinates, and (x3, y3) is the bottom right coordinates, - and (x4, y4) is the bottom left coordinates. The data type is the - same as `input` - transformed_height (int): The height of transformed output. - transformed_width (int): The width of transformed output. - spatial_scale (float): Spatial scale factor to scale ROI coords. Default: 1.0 - 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: - A tuple with three Variables. (out, mask, transform_matrix) - - out: The output of ROIPerspectiveTransformOp which is a 4-D tensor with shape - (num_rois, channels, transformed_h, transformed_w). The data type is the same as `input` - - mask: The mask of ROIPerspectiveTransformOp which is a 4-D tensor with shape - (num_rois, 1, transformed_h, transformed_w). The data type is int32 - - transform_matrix: The transform matrix of ROIPerspectiveTransformOp which is - a 2-D tensor with shape (num_rois, 9). The data type is the same as `input` - - Return Type: - tuple - - Examples: - .. code-block:: python - - import paddle.fluid as fluid - - x = fluid.data(name='x', shape=[100, 256, 28, 28], dtype='float32') - rois = fluid.data(name='rois', shape=[None, 8], lod_level=1, dtype='float32') - out, mask, transform_matrix = fluid.layers.roi_perspective_transform(x, rois, 7, 7, 1.0) - """ - check_variable_and_dtype( - input, 'input', ['float32'], 'roi_perspective_transform' - ) - check_variable_and_dtype( - rois, 'rois', ['float32'], 'roi_perspective_transform' - ) - check_type( - transformed_height, - 'transformed_height', - int, - 'roi_perspective_transform', - ) - check_type( - transformed_width, 'transformed_width', int, 'roi_perspective_transform' - ) - check_type( - spatial_scale, 'spatial_scale', float, 'roi_perspective_transform' - ) - - helper = LayerHelper('roi_perspective_transform', **locals()) - dtype = helper.input_dtype() - out = helper.create_variable_for_type_inference(dtype) - mask = helper.create_variable_for_type_inference(dtype="int32") - transform_matrix = helper.create_variable_for_type_inference(dtype) - out2in_idx = helper.create_variable_for_type_inference(dtype="int32") - out2in_w = helper.create_variable_for_type_inference(dtype) - helper.append_op( - type="roi_perspective_transform", - inputs={"X": input, "ROIs": rois}, - outputs={ - "Out": out, - "Out2InIdx": out2in_idx, - "Out2InWeights": out2in_w, - "Mask": mask, - "TransformMatrix": transform_matrix, - }, - attrs={ - "transformed_height": transformed_height, - "transformed_width": transformed_width, - "spatial_scale": spatial_scale, - }, - ) - return out, mask, transform_matrix - - -def generate_proposal_labels( - rpn_rois, - gt_classes, - is_crowd, - gt_boxes, - im_info, - batch_size_per_im=256, - fg_fraction=0.25, - fg_thresh=0.25, - bg_thresh_hi=0.5, - bg_thresh_lo=0.0, - bbox_reg_weights=[0.1, 0.1, 0.2, 0.2], - class_nums=None, - use_random=True, - is_cls_agnostic=False, - is_cascade_rcnn=False, - max_overlap=None, - return_max_overlap=False, -): - """ - - **Generate Proposal Labels of Faster-RCNN** - - This operator can be, for given the GenerateProposalOp output bounding boxes and groundtruth, - to sample foreground boxes and background boxes, and compute loss target. - - RpnRois is the output boxes of RPN and was processed by generate_proposal_op, these boxes - were combined with groundtruth boxes and sampled according to batch_size_per_im and fg_fraction, - If an instance with a groundtruth overlap greater than fg_thresh, then it was considered as a foreground sample. - If an instance with a groundtruth overlap greater than bg_thresh_lo and lower than bg_thresh_hi, - then it was considered as a background sample. - After all foreground and background boxes are chosen (so called Rois), - then we apply random sampling to make sure - the number of foreground boxes is no more than batch_size_per_im * fg_fraction. - - For each box in Rois, we assign the classification (class label) and regression targets (box label) to it. - Finally BboxInsideWeights and BboxOutsideWeights are used to specify whether it would contribute to training loss. - - Args: - rpn_rois(Variable): A 2-D LoDTensor with shape [N, 4]. N is the number of the GenerateProposalOp's output, each element is a bounding box with [xmin, ymin, xmax, ymax] format. The data type can be float32 or float64. - gt_classes(Variable): A 2-D LoDTensor with shape [M, 1]. M is the number of groundtruth, each element is a class label of groundtruth. The data type must be int32. - is_crowd(Variable): A 2-D LoDTensor with shape [M, 1]. M is the number of groundtruth, each element is a flag indicates whether a groundtruth is crowd. The data type must be int32. - gt_boxes(Variable): A 2-D LoDTensor with shape [M, 4]. M is the number of groundtruth, each element is a bounding box with [xmin, ymin, xmax, ymax] format. - im_info(Variable): A 2-D LoDTensor with shape [B, 3]. B is the number of input images, each element consists of im_height, im_width, im_scale. - - batch_size_per_im(int): Batch size of rois per images. The data type must be int32. - fg_fraction(float): Foreground fraction in total batch_size_per_im. The data type must be float32. - fg_thresh(float): Overlap threshold which is used to chose foreground sample. The data type must be float32. - bg_thresh_hi(float): Overlap threshold upper bound which is used to chose background sample. The data type must be float32. - bg_thresh_lo(float): Overlap threshold lower bound which is used to chose background sample. The data type must be float32. - bbox_reg_weights(list|tuple): Box regression weights. The data type must be float32. - class_nums(int): Class number. The data type must be int32. - use_random(bool): Use random sampling to choose foreground and background boxes. - is_cls_agnostic(bool): bbox regression use class agnostic simply which only represent fg and bg boxes. - is_cascade_rcnn(bool): it will filter some bbox crossing the image's boundary when setting True. - max_overlap(Variable): Maximum overlap between each proposal box and ground-truth. - return_max_overlap(bool): Whether return the maximum overlap between each sampled RoI and ground-truth. - - Returns: - tuple: - A tuple with format``(rois, labels_int32, bbox_targets, bbox_inside_weights, bbox_outside_weights, max_overlap)``. - - - **rois**: 2-D LoDTensor with shape ``[batch_size_per_im * batch_size, 4]``. The data type is the same as ``rpn_rois``. - - **labels_int32**: 2-D LoDTensor with shape ``[batch_size_per_im * batch_size, 1]``. The data type must be int32. - - **bbox_targets**: 2-D LoDTensor with shape ``[batch_size_per_im * batch_size, 4 * class_num]``. The regression targets of all RoIs. The data type is the same as ``rpn_rois``. - - **bbox_inside_weights**: 2-D LoDTensor with shape ``[batch_size_per_im * batch_size, 4 * class_num]``. The weights of foreground boxes' regression loss. The data type is the same as ``rpn_rois``. - - **bbox_outside_weights**: 2-D LoDTensor with shape ``[batch_size_per_im * batch_size, 4 * class_num]``. The weights of regression loss. The data type is the same as ``rpn_rois``. - - **max_overlap**: 1-D LoDTensor with shape ``[P]``. P is the number of output ``rois``. The maximum overlap between each sampled RoI and ground-truth. - - Examples: - .. code-block:: python - - import paddle - import paddle.fluid as fluid - paddle.enable_static() - rpn_rois = fluid.data(name='rpn_rois', shape=[None, 4], dtype='float32') - gt_classes = fluid.data(name='gt_classes', shape=[None, 1], dtype='int32') - is_crowd = fluid.data(name='is_crowd', shape=[None, 1], dtype='int32') - gt_boxes = fluid.data(name='gt_boxes', shape=[None, 4], dtype='float32') - im_info = fluid.data(name='im_info', shape=[None, 3], dtype='float32') - rois, labels, bbox, inside_weights, outside_weights = fluid.layers.generate_proposal_labels( - rpn_rois, gt_classes, is_crowd, gt_boxes, im_info, - class_nums=10) - - """ - - helper = LayerHelper('generate_proposal_labels', **locals()) - - check_variable_and_dtype( - rpn_rois, 'rpn_rois', ['float32', 'float64'], 'generate_proposal_labels' - ) - check_variable_and_dtype( - gt_classes, 'gt_classes', ['int32'], 'generate_proposal_labels' - ) - check_variable_and_dtype( - is_crowd, 'is_crowd', ['int32'], 'generate_proposal_labels' - ) - if is_cascade_rcnn: - assert ( - max_overlap is not None - ), "Input max_overlap of generate_proposal_labels should not be None if is_cascade_rcnn is True" - - rois = helper.create_variable_for_type_inference(dtype=rpn_rois.dtype) - labels_int32 = helper.create_variable_for_type_inference( - dtype=gt_classes.dtype - ) - bbox_targets = helper.create_variable_for_type_inference( - dtype=rpn_rois.dtype - ) - bbox_inside_weights = helper.create_variable_for_type_inference( - dtype=rpn_rois.dtype - ) - bbox_outside_weights = helper.create_variable_for_type_inference( - dtype=rpn_rois.dtype - ) - max_overlap_with_gt = helper.create_variable_for_type_inference( - dtype=rpn_rois.dtype - ) - - inputs = { - 'RpnRois': rpn_rois, - 'GtClasses': gt_classes, - 'IsCrowd': is_crowd, - 'GtBoxes': gt_boxes, - 'ImInfo': im_info, - } - if max_overlap is not None: - inputs['MaxOverlap'] = max_overlap - helper.append_op( - type="generate_proposal_labels", - inputs=inputs, - outputs={ - 'Rois': rois, - 'LabelsInt32': labels_int32, - 'BboxTargets': bbox_targets, - 'BboxInsideWeights': bbox_inside_weights, - 'BboxOutsideWeights': bbox_outside_weights, - 'MaxOverlapWithGT': max_overlap_with_gt, - }, - attrs={ - 'batch_size_per_im': batch_size_per_im, - 'fg_fraction': fg_fraction, - 'fg_thresh': fg_thresh, - 'bg_thresh_hi': bg_thresh_hi, - 'bg_thresh_lo': bg_thresh_lo, - 'bbox_reg_weights': bbox_reg_weights, - 'class_nums': class_nums, - 'use_random': use_random, - 'is_cls_agnostic': is_cls_agnostic, - 'is_cascade_rcnn': is_cascade_rcnn, - }, - ) - - rois.stop_gradient = True - labels_int32.stop_gradient = True - bbox_targets.stop_gradient = True - bbox_inside_weights.stop_gradient = True - bbox_outside_weights.stop_gradient = True - max_overlap_with_gt.stop_gradient = True - - if return_max_overlap: - return ( - rois, - labels_int32, - bbox_targets, - bbox_inside_weights, - bbox_outside_weights, - max_overlap_with_gt, - ) - return ( - rois, - labels_int32, - bbox_targets, - bbox_inside_weights, - bbox_outside_weights, - ) - - -def generate_mask_labels( - im_info, - gt_classes, - is_crowd, - gt_segms, - rois, - labels_int32, - num_classes, - resolution, -): - r""" - - **Generate Mask Labels for Mask-RCNN** - - This operator can be, for given the RoIs and corresponding labels, - to sample foreground RoIs. This mask branch also has - a :math: `K \\times M^{2}` dimensional output targets for each foreground - RoI, which encodes K binary masks of resolution M x M, one for each of the - K classes. This mask targets are used to compute loss of mask branch. - - Please note, the data format of groud-truth segmentation, assumed the - segmentations are as follows. The first instance has two gt objects. - The second instance has one gt object, this object has two gt segmentations. - - .. code-block:: python - - #[ - # [[[229.14, 370.9, 229.14, 370.9, ...]], - # [[343.7, 139.85, 349.01, 138.46, ...]]], # 0-th instance - # [[[500.0, 390.62, ...],[115.48, 187.86, ...]]] # 1-th instance - #] - - batch_masks = [] - for semgs in batch_semgs: - gt_masks = [] - for semg in semgs: - gt_segm = [] - for polys in semg: - gt_segm.append(np.array(polys).reshape(-1, 2)) - gt_masks.append(gt_segm) - batch_masks.append(gt_masks) - - - place = fluid.CPUPlace() - feeder = fluid.DataFeeder(place=place, feed_list=feeds) - feeder.feed(batch_masks) - - Args: - im_info (Variable): A 2-D Tensor with shape [N, 3] and float32 - data type. N is the batch size, each element is - [height, width, scale] of image. Image scale is - target_size / original_size, target_size is the size after resize, - original_size is the original image size. - gt_classes (Variable): A 2-D LoDTensor with shape [M, 1]. Data type - should be int. M is the total number of ground-truth, each - element is a class label. - is_crowd (Variable): A 2-D LoDTensor with same shape and same data type - as gt_classes, each element is a flag indicating whether a - groundtruth is crowd. - gt_segms (Variable): This input is a 2D LoDTensor with shape [S, 2] and - float32 data type, it's LoD level is 3. - Usually users do not needs to understand LoD, - The users should return correct data format in reader. - The LoD[0] represents the ground-truth objects number of - each instance. LoD[1] represents the segmentation counts of each - objects. LoD[2] represents the polygons number of each segmentation. - S the total number of polygons coordinate points. Each element is - (x, y) coordinate points. - rois (Variable): A 2-D LoDTensor with shape [R, 4] and float32 data type - float32. R is the total number of RoIs, each element is a bounding - box with (xmin, ymin, xmax, ymax) format in the range of original image. - labels_int32 (Variable): A 2-D LoDTensor in shape of [R, 1] with type - of int32. R is the same as it in `rois`. Each element represents - a class label of a RoI. - num_classes (int): Class number. - resolution (int): Resolution of mask predictions. - - Returns: - mask_rois (Variable): A 2D LoDTensor with shape [P, 4] and same data - type as `rois`. P is the total number of sampled RoIs. Each element - is a bounding box with [xmin, ymin, xmax, ymax] format in range of - original image size. - - mask_rois_has_mask_int32 (Variable): A 2D LoDTensor with shape [P, 1] - and int data type, each element represents the output mask RoI - index with regard to input RoIs. - - mask_int32 (Variable): A 2D LoDTensor with shape [P, K * M * M] and int - data type, K is the classes number and M is the resolution of mask - predictions. Each element represents the binary mask targets. - - Examples: - .. code-block:: python - - import paddle.fluid as fluid - - im_info = fluid.data(name="im_info", shape=[None, 3], - dtype="float32") - gt_classes = fluid.data(name="gt_classes", shape=[None, 1], - dtype="float32", lod_level=1) - is_crowd = fluid.data(name="is_crowd", shape=[None, 1], - dtype="float32", lod_level=1) - gt_masks = fluid.data(name="gt_masks", shape=[None, 2], - dtype="float32", lod_level=3) - # rois, roi_labels can be the output of - # fluid.layers.generate_proposal_labels. - rois = fluid.data(name="rois", shape=[None, 4], - dtype="float32", lod_level=1) - roi_labels = fluid.data(name="roi_labels", shape=[None, 1], - dtype="int32", lod_level=1) - mask_rois, mask_index, mask_int32 = fluid.layers.generate_mask_labels( - im_info=im_info, - gt_classes=gt_classes, - is_crowd=is_crowd, - gt_segms=gt_masks, - rois=rois, - labels_int32=roi_labels, - num_classes=81, - resolution=14) - """ - - helper = LayerHelper('generate_mask_labels', **locals()) - - mask_rois = helper.create_variable_for_type_inference(dtype=rois.dtype) - roi_has_mask_int32 = helper.create_variable_for_type_inference( - dtype=gt_classes.dtype - ) - mask_int32 = helper.create_variable_for_type_inference( - dtype=gt_classes.dtype - ) - - helper.append_op( - type="generate_mask_labels", - inputs={ - 'ImInfo': im_info, - 'GtClasses': gt_classes, - 'IsCrowd': is_crowd, - 'GtSegms': gt_segms, - 'Rois': rois, - 'LabelsInt32': labels_int32, - }, - outputs={ - 'MaskRois': mask_rois, - 'RoiHasMaskInt32': roi_has_mask_int32, - 'MaskInt32': mask_int32, - }, - attrs={'num_classes': num_classes, 'resolution': resolution}, - ) - - mask_rois.stop_gradient = True - roi_has_mask_int32.stop_gradient = True - mask_int32.stop_gradient = True - - return mask_rois, roi_has_mask_int32, mask_int32 - - -def generate_proposals( - scores, - bbox_deltas, - im_info, - anchors, - variances, - pre_nms_top_n=6000, - post_nms_top_n=1000, - nms_thresh=0.5, - min_size=0.1, - eta=1.0, - return_rois_num=False, - name=None, -): - """ - - **Generate proposal Faster-RCNN** - - This operation proposes RoIs according to each box with their - probability to be a foreground object and - the box can be calculated by anchors. Bbox_deltais and scores - to be an object are the output of RPN. Final proposals - could be used to train detection net. - - For generating proposals, this operation performs following steps: - - 1. Transposes and resizes scores and bbox_deltas in size of - (H*W*A, 1) and (H*W*A, 4) - 2. Calculate box locations as proposals candidates. - 3. Clip boxes to image - 4. Remove predicted boxes with small area. - 5. Apply NMS to get final proposals as output. - - Args: - scores(Variable): A 4-D Tensor with shape [N, A, H, W] represents - the probability for each box to be an object. - N is batch size, A is number of anchors, H and W are height and - width of the feature map. The data type must be float32. - bbox_deltas(Variable): A 4-D Tensor with shape [N, 4*A, H, W] - represents the difference between predicted box location and - anchor location. The data type must be float32. - im_info(Variable): A 2-D Tensor with shape [N, 3] represents origin - image information for N batch. Height and width are the input sizes - and scale is the ratio of network input size and original size. - The data type can be float32 or float64. - anchors(Variable): A 4-D Tensor represents the anchors with a layout - of [H, W, A, 4]. H and W are height and width of the feature map, - num_anchors is the box count of each position. Each anchor is - in (xmin, ymin, xmax, ymax) format an unnormalized. The data type must be float32. - variances(Variable): A 4-D Tensor. The expanded variances of anchors with a layout of - [H, W, num_priors, 4]. Each variance is in - (xcenter, ycenter, w, h) format. The data type must be float32. - pre_nms_top_n(float): Number of total bboxes to be kept per - image before NMS. The data type must be float32. `6000` by default. - post_nms_top_n(float): Number of total bboxes to be kept per - image after NMS. The data type must be float32. `1000` by default. - nms_thresh(float): Threshold in NMS. The data type must be float32. `0.5` by default. - min_size(float): Remove predicted boxes with either height or - width < min_size. The data type must be float32. `0.1` by default. - eta(float): Apply in adaptive NMS, if adaptive `threshold > 0.5`, - `adaptive_threshold = adaptive_threshold * eta` in each iteration. - return_rois_num(bool): When setting True, it will return a 1D Tensor with shape [N, ] that includes Rois's - num of each image in one batch. The N is the image's num. For example, the tensor has values [4,5] that represents - the first image has 4 Rois, the second image has 5 Rois. It only used in rcnn model. - 'False' by default. - name(str, optional): For detailed information, please refer - to :ref:`api_guide_Name`. Usually name is no need to set and - None by default. - - Returns: - tuple: - A tuple with format ``(rpn_rois, rpn_roi_probs)``. - - - **rpn_rois**: The generated RoIs. 2-D Tensor with shape ``[N, 4]`` while ``N`` is the number of RoIs. The data type is the same as ``scores``. - - **rpn_roi_probs**: The scores of generated RoIs. 2-D Tensor with shape ``[N, 1]`` while ``N`` is the number of RoIs. The data type is the same as ``scores``. - - Examples: - .. code-block:: python - - import paddle.fluid as fluid - import paddle - paddle.enable_static() - scores = fluid.data(name='scores', shape=[None, 4, 5, 5], dtype='float32') - bbox_deltas = fluid.data(name='bbox_deltas', shape=[None, 16, 5, 5], dtype='float32') - im_info = fluid.data(name='im_info', shape=[None, 3], dtype='float32') - anchors = fluid.data(name='anchors', shape=[None, 5, 4, 4], dtype='float32') - variances = fluid.data(name='variances', shape=[None, 5, 10, 4], dtype='float32') - rois, roi_probs = fluid.layers.generate_proposals(scores, bbox_deltas, - im_info, anchors, variances) - - """ - return paddle.vision.ops.generate_proposals( - scores=scores, - bbox_deltas=bbox_deltas, - img_size=im_info[:2], - anchors=anchors, - variances=variances, - pre_nms_top_n=pre_nms_top_n, - post_nms_top_n=post_nms_top_n, - nms_thresh=nms_thresh, - min_size=min_size, - eta=eta, - return_rois_num=return_rois_num, - name=name, - ) - - -def box_clip(input, im_info, name=None): - """ - - Clip the box into the size given by im_info - For each input box, The formula is given as follows: - - .. code-block:: text - - xmin = max(min(xmin, im_w - 1), 0) - ymin = max(min(ymin, im_h - 1), 0) - xmax = max(min(xmax, im_w - 1), 0) - ymax = max(min(ymax, im_h - 1), 0) - - where im_w and im_h are computed from im_info: - - .. code-block:: text - - im_h = round(height / scale) - im_w = round(weight / scale) - - Args: - input(Variable): The input Tensor with shape :math:`[N_1, N_2, ..., N_k, 4]`, - the last dimension is 4 and data type is float32 or float64. - im_info(Variable): The 2-D Tensor with shape [N, 3] with layout - (height, width, scale) representing the information of image. - Height and width are the input sizes and scale is the ratio of network input - size and original size. The data type is float32 or float64. - name(str, optional): For detailed information, please refer - to :ref:`api_guide_Name`. Usually name is no need to set and - None by default. - - Returns: - Variable: - - output(Variable): The clipped tensor with data type float32 or float64. - The shape is same as input. - - - Examples: - .. code-block:: python - - import paddle.fluid as fluid - import paddle - paddle.enable_static() - boxes = fluid.data( - name='boxes', shape=[None, 8, 4], dtype='float32', lod_level=1) - im_info = fluid.data(name='im_info', shape=[-1 ,3]) - out = fluid.layers.box_clip( - input=boxes, im_info=im_info) - """ - - check_variable_and_dtype(input, 'input', ['float32', 'float64'], 'box_clip') - check_variable_and_dtype( - im_info, 'im_info', ['float32', 'float64'], 'box_clip' - ) - - helper = LayerHelper("box_clip", **locals()) - output = helper.create_variable_for_type_inference(dtype=input.dtype) - inputs = {"Input": input, "ImInfo": im_info} - helper.append_op(type="box_clip", inputs=inputs, outputs={"Output": output}) - - return output - - -def retinanet_detection_output( - bboxes, - scores, - anchors, - im_info, - score_threshold=0.05, - nms_top_k=1000, - keep_top_k=100, - nms_threshold=0.3, - nms_eta=1.0, -): - """ - **Detection Output Layer for the detector RetinaNet.** - - In the detector `RetinaNet `_ , many - `FPN `_ levels output the category - and location predictions, this OP is to get the detection results by - performing following steps: - - 1. For each FPN level, decode box predictions according to the anchor - boxes from at most :attr:`nms_top_k` top-scoring predictions after - thresholding detector confidence at :attr:`score_threshold`. - 2. Merge top predictions from all levels and apply multi-class non - maximum suppression (NMS) on them to get the final detections. - - Args: - bboxes(List): A list of Tensors from multiple FPN levels represents - the location prediction for all anchor boxes. Each element is - a 3-D Tensor with shape :math:`[N, Mi, 4]`, :math:`N` is the - batch size, :math:`Mi` is the number of bounding boxes from - :math:`i`-th FPN level and each bounding box has four coordinate - values and the layout is [xmin, ymin, xmax, ymax]. The data type - of each element is float32 or float64. - scores(List): A list of Tensors from multiple FPN levels represents - the category prediction for all anchor boxes. Each element is a - 3-D Tensor with shape :math:`[N, Mi, C]`, :math:`N` is the batch - size, :math:`C` is the class number (**excluding background**), - :math:`Mi` is the number of bounding boxes from :math:`i`-th FPN - level. The data type of each element is float32 or float64. - anchors(List): A list of Tensors from multiple FPN levels represents - the locations of all anchor boxes. Each element is a 2-D Tensor - with shape :math:`[Mi, 4]`, :math:`Mi` is the number of bounding - boxes from :math:`i`-th FPN level, and each bounding box has four - coordinate values and the layout is [xmin, ymin, xmax, ymax]. - The data type of each element is float32 or float64. - im_info(Variable): A 2-D Tensor with shape :math:`[N, 3]` represents the size - information of input images. :math:`N` is the batch size, the size - information of each image is a 3-vector which are the height and width - of the network input along with the factor scaling the origin image to - the network input. The data type of :attr:`im_info` is float32. - score_threshold(float): Threshold to filter out bounding boxes - with a confidence score before NMS, default value is set to 0.05. - nms_top_k(int): Maximum number of detections per FPN layer to be - kept according to the confidences before NMS, default value is set to - 1000. - keep_top_k(int): Number of total bounding boxes to be kept per image after - NMS step. Default value is set to 100, -1 means keeping all bounding - boxes after NMS step. - nms_threshold(float): The Intersection-over-Union(IoU) threshold used to - filter out boxes in NMS. - nms_eta(float): The parameter for adjusting :attr:`nms_threshold` in NMS. - Default value is set to 1., which represents the value of - :attr:`nms_threshold` keep the same in NMS. If :attr:`nms_eta` is set - to be lower than 1. and the value of :attr:`nms_threshold` is set to - be higher than 0.5, everytime a bounding box is filtered out, - the adjustment for :attr:`nms_threshold` like :attr:`nms_threshold` - = :attr:`nms_threshold` * :attr:`nms_eta` will not be stopped until - the actual value of :attr:`nms_threshold` is lower than or equal to - 0.5. - - **Notice**: In some cases where the image sizes are very small, it's possible - that there is no detection if :attr:`score_threshold` are used at all - levels. Hence, this OP do not filter out anchors from the highest FPN level - before NMS. And the last element in :attr:`bboxes`:, :attr:`scores` and - :attr:`anchors` is required to be from the highest FPN level. - - Returns: - Variable(The data type is float32 or float64): - The detection output is a 1-level LoDTensor with shape :math:`[No, 6]`. - Each row has six values: [label, confidence, xmin, ymin, xmax, ymax]. - :math:`No` is the total number of detections in this mini-batch. - The :math:`i`-th image has `LoD[i + 1] - LoD[i]` detected - results, if `LoD[i + 1] - LoD[i]` is 0, the :math:`i`-th image - has no detected results. If all images have no detected results, - LoD will be set to 0, and the output tensor is empty (None). - - Examples: - .. code-block:: python - - import paddle.fluid as fluid - - bboxes_low = fluid.data( - name='bboxes_low', shape=[1, 44, 4], dtype='float32') - bboxes_high = fluid.data( - name='bboxes_high', shape=[1, 11, 4], dtype='float32') - scores_low = fluid.data( - name='scores_low', shape=[1, 44, 10], dtype='float32') - scores_high = fluid.data( - name='scores_high', shape=[1, 11, 10], dtype='float32') - anchors_low = fluid.data( - name='anchors_low', shape=[44, 4], dtype='float32') - anchors_high = fluid.data( - name='anchors_high', shape=[11, 4], dtype='float32') - im_info = fluid.data( - name="im_info", shape=[1, 3], dtype='float32') - nmsed_outs = fluid.layers.retinanet_detection_output( - bboxes=[bboxes_low, bboxes_high], - scores=[scores_low, scores_high], - anchors=[anchors_low, anchors_high], - im_info=im_info, - score_threshold=0.05, - nms_top_k=1000, - keep_top_k=100, - nms_threshold=0.45, - nms_eta=1.0) - """ - - check_type(bboxes, 'bboxes', (list), 'retinanet_detection_output') - for i, bbox in enumerate(bboxes): - check_variable_and_dtype( - bbox, - 'bbox{}'.format(i), - ['float32', 'float64'], - 'retinanet_detection_output', - ) - check_type(scores, 'scores', (list), 'retinanet_detection_output') - for i, score in enumerate(scores): - check_variable_and_dtype( - score, - 'score{}'.format(i), - ['float32', 'float64'], - 'retinanet_detection_output', - ) - check_type(anchors, 'anchors', (list), 'retinanet_detection_output') - for i, anchor in enumerate(anchors): - check_variable_and_dtype( - anchor, - 'anchor{}'.format(i), - ['float32', 'float64'], - 'retinanet_detection_output', - ) - check_variable_and_dtype( - im_info, 'im_info', ['float32', 'float64'], 'retinanet_detection_output' - ) - - helper = LayerHelper('retinanet_detection_output', **locals()) - output = helper.create_variable_for_type_inference( - dtype=helper.input_dtype('scores') - ) - helper.append_op( - type="retinanet_detection_output", - inputs={ - 'BBoxes': bboxes, - 'Scores': scores, - 'Anchors': anchors, - 'ImInfo': im_info, - }, - attrs={ - 'score_threshold': score_threshold, - 'nms_top_k': nms_top_k, - 'nms_threshold': nms_threshold, - 'keep_top_k': keep_top_k, - 'nms_eta': 1.0, - }, - outputs={'Out': output}, - ) - output.stop_gradient = True - return output - - -def multiclass_nms( - bboxes, - scores, - score_threshold, - nms_top_k, - keep_top_k, - nms_threshold=0.3, - normalized=True, - nms_eta=1.0, - background_label=0, - name=None, -): - """ - - **Multiclass NMS** - - This operator is to do multi-class non maximum suppression (NMS) on - boxes and scores. - - In the NMS step, this operator greedily selects a subset of detection bounding - boxes that have high scores larger than score_threshold, if providing this - threshold, then selects the largest nms_top_k confidences scores if nms_top_k - is larger than -1. Then this operator pruns away boxes that have high IOU - (intersection over union) overlap with already selected boxes by adaptive - threshold NMS based on parameters of nms_threshold and nms_eta. - Aftern NMS step, at most keep_top_k number of total bboxes are to be kept - per image if keep_top_k is larger than -1. - - See below for an example: - - .. code-block:: text - - if: - box1.data = (2.0, 3.0, 7.0, 5.0) format is (xmin, ymin, xmax, ymax) - box1.scores = (0.7, 0.2, 0.4) which is (label0.score=0.7, label1.score=0.2, label2.cores=0.4) - - box2.data = (3.0, 4.0, 8.0, 5.0) - box2.score = (0.3, 0.3, 0.1) - - nms_threshold = 0.3 - background_label = 0 - score_threshold = 0 - - - Then: - iou = 4/11 > 0.3 - out.data = [[1, 0.3, 3.0, 4.0, 8.0, 5.0], - [2, 0.4, 2.0, 3.0, 7.0, 5.0]] - - Out format is (label, confidence, xmin, ymin, xmax, ymax) - Args: - bboxes (Variable): Two types of bboxes are supported: - 1. (Tensor) A 3-D Tensor with shape - [N, M, 4 or 8 16 24 32] represents the - predicted locations of M bounding bboxes, - N is the batch size. Each bounding box has four - coordinate values and the layout is - [xmin, ymin, xmax, ymax], when box size equals to 4. - The data type is float32 or float64. - 2. (LoDTensor) A 3-D Tensor with shape [M, C, 4] - M is the number of bounding boxes, C is the - class number. The data type is float32 or float64. - scores (Variable): Two types of scores are supported: - 1. (Tensor) A 3-D Tensor with shape [N, C, M] - represents the predicted confidence predictions. - N is the batch size, C is the class number, M is - number of bounding boxes. For each category there - are total M scores which corresponding M bounding - boxes. Please note, M is equal to the 2nd dimension - of BBoxes.The data type is float32 or float64. - 2. (LoDTensor) A 2-D LoDTensor with shape [M, C]. - M is the number of bbox, C is the class number. - In this case, input BBoxes should be the second - case with shape [M, C, 4].The data type is float32 or float64. - background_label (int): The index of background label, the background - label will be ignored. If set to -1, then all - categories will be considered. Default: 0 - score_threshold (float): Threshold to filter out bounding boxes with - low confidence score. If not provided, - consider all boxes. - nms_top_k (int): Maximum number of detections to be kept according to - the confidences after the filtering detections based - on score_threshold. - nms_threshold (float): The threshold to be used in NMS. Default: 0.3 - nms_eta (float): The threshold to be used in NMS. Default: 1.0 - keep_top_k (int): Number of total bboxes to be kept per image after NMS - step. -1 means keeping all bboxes after NMS step. - normalized (bool): Whether detections are normalized. Default: True - name(str): Name of the multiclass nms op. Default: None. - - Returns: - Variable: A 2-D LoDTensor with shape [No, 6] represents the detections. - Each row has 6 values: [label, confidence, xmin, ymin, xmax, ymax] - or A 2-D LoDTensor with shape [No, 10] represents the detections. - Each row has 10 values: - [label, confidence, x1, y1, x2, y2, x3, y3, x4, y4]. No is the - total number of detections. If there is no detected boxes for all - images, lod will be set to {1} and Out only contains one value - which is -1. - (After version 1.3, when no boxes detected, the lod is changed - from {0} to {1}) - - - Examples: - .. code-block:: python - - - import paddle.fluid as fluid - import paddle - paddle.enable_static() - boxes = fluid.data(name='bboxes', shape=[None,81, 4], - dtype='float32', lod_level=1) - scores = fluid.data(name='scores', shape=[None,81], - dtype='float32', lod_level=1) - out = fluid.layers.multiclass_nms(bboxes=boxes, - scores=scores, - background_label=0, - score_threshold=0.5, - nms_top_k=400, - nms_threshold=0.3, - keep_top_k=200, - normalized=False) - """ - check_variable_and_dtype( - bboxes, 'BBoxes', ['float32', 'float64'], 'multiclass_nms' - ) - check_variable_and_dtype( - scores, 'Scores', ['float32', 'float64'], 'multiclass_nms' - ) - check_type(score_threshold, 'score_threshold', float, 'multicalss_nms') - check_type(nms_top_k, 'nums_top_k', int, 'multiclass_nms') - check_type(keep_top_k, 'keep_top_k', int, 'mutliclass_nms') - check_type(nms_threshold, 'nms_threshold', float, 'multiclass_nms') - check_type(normalized, 'normalized', bool, 'multiclass_nms') - check_type(nms_eta, 'nms_eta', float, 'multiclass_nms') - check_type(background_label, 'background_label', int, 'multiclass_nms') - - helper = LayerHelper('multiclass_nms', **locals()) - output = helper.create_variable_for_type_inference(dtype=bboxes.dtype) - helper.append_op( - type="multiclass_nms", - inputs={'BBoxes': bboxes, 'Scores': scores}, - attrs={ - 'background_label': background_label, - 'score_threshold': score_threshold, - 'nms_top_k': nms_top_k, - 'nms_threshold': nms_threshold, - 'nms_eta': nms_eta, - 'keep_top_k': keep_top_k, - 'normalized': normalized, - }, - outputs={'Out': output}, - ) - output.stop_gradient = True - - return output - - -def locality_aware_nms( - bboxes, - scores, - score_threshold, - nms_top_k, - keep_top_k, - nms_threshold=0.3, - normalized=True, - nms_eta=1.0, - background_label=-1, - name=None, -): - """ - **Local Aware NMS** - - `Local Aware NMS `_ is to do locality-aware non maximum - suppression (LANMS) on boxes and scores. - - Firstly, this operator merge box and score according their IOU - (intersection over union). In the NMS step, this operator greedily selects a - subset of detection bounding boxes that have high scores larger than score_threshold, - if providing this threshold, then selects the largest nms_top_k confidences scores - if nms_top_k is larger than -1. Then this operator pruns away boxes that have high - IOU overlap with already selected boxes by adaptive threshold NMS based on parameters - of nms_threshold and nms_eta. - - Aftern NMS step, at most keep_top_k number of total bboxes are to be kept - per image if keep_top_k is larger than -1. - - Args: - bboxes (Variable): A 3-D Tensor with shape [N, M, 4 or 8 16 24 32] - represents the predicted locations of M bounding - bboxes, N is the batch size. Each bounding box - has four coordinate values and the layout is - [xmin, ymin, xmax, ymax], when box size equals to 4. - The data type is float32 or float64. - scores (Variable): A 3-D Tensor with shape [N, C, M] represents the - predicted confidence predictions. N is the batch - size, C is the class number, M is number of bounding - boxes. Now only support 1 class. For each category - there are total M scores which corresponding M bounding - boxes. Please note, M is equal to the 2nd dimension of - BBoxes. The data type is float32 or float64. - background_label (int): The index of background label, the background - label will be ignored. If set to -1, then all - categories will be considered. Default: -1 - score_threshold (float): Threshold to filter out bounding boxes with - low confidence score. If not provided, - consider all boxes. - nms_top_k (int): Maximum number of detections to be kept according to - the confidences after the filtering detections based - on score_threshold. - keep_top_k (int): Number of total bboxes to be kept per image after NMS - step. -1 means keeping all bboxes after NMS step. - nms_threshold (float): The threshold to be used in NMS. Default: 0.3 - nms_eta (float): The threshold to be used in NMS. Default: 1.0 - normalized (bool): Whether detections are normalized. Default: True - name(str): Name of the locality aware nms op, please refer to :ref:`api_guide_Name` . - Default: None. - - Returns: - Variable: A 2-D LoDTensor with shape [No, 6] represents the detections. - Each row has 6 values: [label, confidence, xmin, ymin, xmax, ymax] - or A 2-D LoDTensor with shape [No, 10] represents the detections. - Each row has 10 values: - [label, confidence, x1, y1, x2, y2, x3, y3, x4, y4]. No is the - total number of detections. If there is no detected boxes for all - images, lod will be set to {1} and Out only contains one value - which is -1. - (After version 1.3, when no boxes detected, the lod is changed - from {0} to {1}). The data type is float32 or float64. - - - Examples: - .. code-block:: python - - - import paddle.fluid as fluid - boxes = fluid.data(name='bboxes', shape=[None, 81, 8], - dtype='float32') - scores = fluid.data(name='scores', shape=[None, 1, 81], - dtype='float32') - out = fluid.layers.locality_aware_nms(bboxes=boxes, - scores=scores, - score_threshold=0.5, - nms_top_k=400, - nms_threshold=0.3, - keep_top_k=200, - normalized=False) - """ - check_variable_and_dtype( - bboxes, 'bboxes', ['float32', 'float64'], 'locality_aware_nms' - ) - check_variable_and_dtype( - scores, 'scores', ['float32', 'float64'], 'locality_aware_nms' - ) - check_type(background_label, 'background_label', int, 'locality_aware_nms') - check_type(score_threshold, 'score_threshold', float, 'locality_aware_nms') - check_type(nms_top_k, 'nms_top_k', int, 'locality_aware_nms') - check_type(nms_eta, 'nms_eta', float, 'locality_aware_nms') - check_type(nms_threshold, 'nms_threshold', float, 'locality_aware_nms') - check_type(keep_top_k, 'keep_top_k', int, 'locality_aware_nms') - check_type(normalized, 'normalized', bool, 'locality_aware_nms') - - shape = scores.shape - assert len(shape) == 3, "dim size of scores must be 3" - assert ( - shape[1] == 1 - ), "locality_aware_nms only support one class, Tensor score shape must be [N, 1, M]" - - helper = LayerHelper('locality_aware_nms', **locals()) - - output = helper.create_variable_for_type_inference(dtype=bboxes.dtype) - out = {'Out': output} - - helper.append_op( - type="locality_aware_nms", - inputs={'BBoxes': bboxes, 'Scores': scores}, - attrs={ - 'background_label': background_label, - 'score_threshold': score_threshold, - 'nms_top_k': nms_top_k, - 'nms_threshold': nms_threshold, - 'nms_eta': nms_eta, - 'keep_top_k': keep_top_k, - 'nms_eta': nms_eta, - 'normalized': normalized, - }, - outputs={'Out': output}, - ) - output.stop_gradient = True - - return output - - -def matrix_nms( - bboxes, - scores, - score_threshold, - post_threshold, - nms_top_k, - keep_top_k, - use_gaussian=False, - gaussian_sigma=2.0, - background_label=0, - normalized=True, - return_index=False, - name=None, -): - """ - **Matrix NMS** - - This operator does matrix non maximum suppression (NMS). - - First selects a subset of candidate bounding boxes that have higher scores - than score_threshold (if provided), then the top k candidate is selected if - nms_top_k is larger than -1. Score of the remaining candidate are then - decayed according to the Matrix NMS scheme. - Aftern NMS step, at most keep_top_k number of total bboxes are to be kept - per image if keep_top_k is larger than -1. - - Args: - bboxes (Variable): A 3-D Tensor with shape [N, M, 4] represents the - predicted locations of M bounding bboxes, - N is the batch size. Each bounding box has four - coordinate values and the layout is - [xmin, ymin, xmax, ymax], when box size equals to 4. - The data type is float32 or float64. - scores (Variable): A 3-D Tensor with shape [N, C, M] - represents the predicted confidence predictions. - N is the batch size, C is the class number, M is - number of bounding boxes. For each category there - are total M scores which corresponding M bounding - boxes. Please note, M is equal to the 2nd dimension - of BBoxes. The data type is float32 or float64. - score_threshold (float): Threshold to filter out bounding boxes with - low confidence score. - post_threshold (float): Threshold to filter out bounding boxes with - low confidence score AFTER decaying. - nms_top_k (int): Maximum number of detections to be kept according to - the confidences after the filtering detections based - on score_threshold. - keep_top_k (int): Number of total bboxes to be kept per image after NMS - step. -1 means keeping all bboxes after NMS step. - use_gaussian (bool): Use Gaussian as the decay function. Default: False - gaussian_sigma (float): Sigma for Gaussian decay function. Default: 2.0 - background_label (int): The index of background label, the background - label will be ignored. If set to -1, then all - categories will be considered. Default: 0 - normalized (bool): Whether detections are normalized. Default: True - return_index(bool): Whether return selected index. Default: False - name(str): Name of the matrix nms op. Default: None. - - Returns: - A tuple with two Variables: (Out, Index) if return_index is True, - otherwise, one Variable(Out) is returned. - - Out (Variable): A 2-D LoDTensor with shape [No, 6] containing the - detection results. - Each row has 6 values: [label, confidence, xmin, ymin, xmax, ymax] - (After version 1.3, when no boxes detected, the lod is changed - from {0} to {1}) - - Index (Variable): A 2-D LoDTensor with shape [No, 1] containing the - selected indices, which are absolute values cross batches. - - Examples: - .. code-block:: python - - - import paddle.fluid as fluid - boxes = fluid.data(name='bboxes', shape=[None,81, 4], - dtype='float32', lod_level=1) - scores = fluid.data(name='scores', shape=[None,81], - dtype='float32', lod_level=1) - out = fluid.layers.matrix_nms(bboxes=boxes, - scores=scores, - background_label=0, - score_threshold=0.5, - post_threshold=0.1, - nms_top_k=400, - keep_top_k=200, - normalized=False) - """ - if in_dygraph_mode(): - attrs = ( - score_threshold, - nms_top_k, - keep_top_k, - post_threshold, - use_gaussian, - gaussian_sigma, - background_label, - normalized, - ) - - out, index = _C_ops.matrix_nms(bboxes, scores, *attrs) - if return_index: - return out, index - else: - return out - - check_variable_and_dtype( - bboxes, 'BBoxes', ['float32', 'float64'], 'matrix_nms' - ) - check_variable_and_dtype( - scores, 'Scores', ['float32', 'float64'], 'matrix_nms' - ) - check_type(score_threshold, 'score_threshold', float, 'matrix_nms') - check_type(post_threshold, 'post_threshold', float, 'matrix_nms') - check_type(nms_top_k, 'nums_top_k', int, 'matrix_nms') - check_type(keep_top_k, 'keep_top_k', int, 'matrix_nms') - check_type(normalized, 'normalized', bool, 'matrix_nms') - check_type(use_gaussian, 'use_gaussian', bool, 'matrix_nms') - check_type(gaussian_sigma, 'gaussian_sigma', float, 'matrix_nms') - check_type(background_label, 'background_label', int, 'matrix_nms') - - helper = LayerHelper('matrix_nms', **locals()) - output = helper.create_variable_for_type_inference(dtype=bboxes.dtype) - index = helper.create_variable_for_type_inference(dtype='int') - helper.append_op( - type="matrix_nms", - inputs={'BBoxes': bboxes, 'Scores': scores}, - attrs={ - 'score_threshold': score_threshold, - 'post_threshold': post_threshold, - 'nms_top_k': nms_top_k, - 'keep_top_k': keep_top_k, - 'use_gaussian': use_gaussian, - 'gaussian_sigma': gaussian_sigma, - 'background_label': background_label, - 'normalized': normalized, - }, - outputs={'Out': output, 'Index': index}, - ) - output.stop_gradient = True - - if return_index: - return output, index - else: - return output - - -def distribute_fpn_proposals( - fpn_rois, - min_level, - max_level, - refer_level, - refer_scale, - rois_num=None, - name=None, -): - r""" - - **This op only takes LoDTensor as input.** In Feature Pyramid Networks - (FPN) models, it is needed to distribute all proposals into different FPN - level, with respect to scale of the proposals, the referring scale and the - referring level. Besides, to restore the order of proposals, we return an - array which indicates the original index of rois in current proposals. - To compute FPN level for each roi, the formula is given as follows: - - .. math:: - - roi\_scale &= \sqrt{BBoxArea(fpn\_roi)} - - level = floor(&\log(\\frac{roi\_scale}{refer\_scale}) + refer\_level) - - where BBoxArea is a function to compute the area of each roi. - - Args: - - fpn_rois(Variable): 2-D Tensor with shape [N, 4] and data type is - float32 or float64. The input fpn_rois. - min_level(int32): The lowest level of FPN layer where the proposals come - from. - max_level(int32): The highest level of FPN layer where the proposals - come from. - refer_level(int32): The referring level of FPN layer with specified scale. - refer_scale(int32): The referring scale of FPN layer with specified level. - rois_num(Tensor): 1-D Tensor contains the number of RoIs in each image. - The shape is [B] and data type is int32. B is the number of images. - If it is not None then return a list of 1-D Tensor. Each element - is the output RoIs' number of each image on the corresponding level - and the shape is [B]. None by default. - name(str, optional): For detailed information, please refer - to :ref:`api_guide_Name`. Usually name is no need to set and - None by default. - - Returns: - Tuple: - - multi_rois(List) : A list of 2-D LoDTensor with shape [M, 4] - and data type of float32 and float64. The length is - max_level-min_level+1. The proposals in each FPN level. - - restore_ind(Variable): A 2-D Tensor with shape [N, 1], N is - the number of total rois. The data type is int32. It is - used to restore the order of fpn_rois. - - rois_num_per_level(List): A list of 1-D Tensor and each Tensor is - the RoIs' number in each image on the corresponding level. The shape - is [B] and data type of int32. B is the number of images - - - Examples: - .. code-block:: python - - import paddle.fluid as fluid - import paddle - paddle.enable_static() - fpn_rois = fluid.data( - name='data', shape=[None, 4], dtype='float32', lod_level=1) - multi_rois, restore_ind = fluid.layers.distribute_fpn_proposals( - fpn_rois=fpn_rois, - min_level=2, - max_level=5, - refer_level=4, - refer_scale=224) - """ - return paddle.vision.ops.distribute_fpn_proposals( - fpn_rois=fpn_rois, - min_level=min_level, - max_level=max_level, - refer_level=refer_level, - refer_scale=refer_scale, - rois_num=rois_num, - name=name, - ) - - -@templatedoc() -def box_decoder_and_assign( - prior_box, prior_box_var, target_box, box_score, box_clip, name=None -): - """ - - ${comment} - Args: - prior_box(${prior_box_type}): ${prior_box_comment} - prior_box_var(${prior_box_var_type}): ${prior_box_var_comment} - target_box(${target_box_type}): ${target_box_comment} - box_score(${box_score_type}): ${box_score_comment} - box_clip(${box_clip_type}): ${box_clip_comment} - name(str, optional): For detailed information, please refer - to :ref:`api_guide_Name`. Usually name is no need to set and - None by default. - - Returns: - Tuple: - - decode_box(${decode_box_type}): ${decode_box_comment} - - output_assign_box(${output_assign_box_type}): ${output_assign_box_comment} - - - Examples: - .. code-block:: python - - import paddle.fluid as fluid - import paddle - paddle.enable_static() - pb = fluid.data( - name='prior_box', shape=[None, 4], dtype='float32') - pbv = fluid.data( - name='prior_box_var', shape=[4], dtype='float32') - loc = fluid.data( - name='target_box', shape=[None, 4*81], dtype='float32') - scores = fluid.data( - name='scores', shape=[None, 81], dtype='float32') - decoded_box, output_assign_box = fluid.layers.box_decoder_and_assign( - pb, pbv, loc, scores, 4.135) - - """ - check_variable_and_dtype( - prior_box, 'prior_box', ['float32', 'float64'], 'box_decoder_and_assign' - ) - check_variable_and_dtype( - target_box, - 'target_box', - ['float32', 'float64'], - 'box_decoder_and_assign', - ) - check_variable_and_dtype( - box_score, 'box_score', ['float32', 'float64'], 'box_decoder_and_assign' - ) - helper = LayerHelper("box_decoder_and_assign", **locals()) - - decoded_box = helper.create_variable_for_type_inference( - dtype=prior_box.dtype - ) - output_assign_box = helper.create_variable_for_type_inference( - dtype=prior_box.dtype - ) - - helper.append_op( - type="box_decoder_and_assign", - inputs={ - "PriorBox": prior_box, - "PriorBoxVar": prior_box_var, - "TargetBox": target_box, - "BoxScore": box_score, - }, - attrs={"box_clip": box_clip}, - outputs={ - "DecodeBox": decoded_box, - "OutputAssignBox": output_assign_box, - }, - ) - return decoded_box, output_assign_box - - -def collect_fpn_proposals( - multi_rois, - multi_scores, - min_level, - max_level, - post_nms_top_n, - rois_num_per_level=None, - name=None, -): - """ - - **This OP only supports LoDTensor as input**. Concat multi-level RoIs - (Region of Interest) and select N RoIs with respect to multi_scores. - This operation performs the following steps: - - 1. Choose num_level RoIs and scores as input: num_level = max_level - min_level - 2. Concat multi-level RoIs and scores - 3. Sort scores and select post_nms_top_n scores - 4. Gather RoIs by selected indices from scores - 5. Re-sort RoIs by corresponding batch_id - - Args: - multi_rois(list): List of RoIs to collect. Element in list is 2-D - LoDTensor with shape [N, 4] and data type is float32 or float64, - N is the number of RoIs. - multi_scores(list): List of scores of RoIs to collect. Element in list - is 2-D LoDTensor with shape [N, 1] and data type is float32 or - float64, N is the number of RoIs. - min_level(int): The lowest level of FPN layer to collect - max_level(int): The highest level of FPN layer to collect - post_nms_top_n(int): The number of selected RoIs - rois_num_per_level(list, optional): The List of RoIs' numbers. - Each element is 1-D Tensor which contains the RoIs' number of each - image on each level and the shape is [B] and data type is - int32, B is the number of images. If it is not None then return - a 1-D Tensor contains the output RoIs' number of each image and - the shape is [B]. Default: None - name(str, optional): For detailed information, please refer - to :ref:`api_guide_Name`. Usually name is no need to set and - None by default. - - Returns: - Variable: - - fpn_rois(Variable): 2-D LoDTensor with shape [N, 4] and data type is - float32 or float64. Selected RoIs. - - rois_num(Tensor): 1-D Tensor contains the RoIs's number of each - image. The shape is [B] and data type is int32. B is the number of - images. - - Examples: - .. code-block:: python - - import paddle.fluid as fluid - import paddle - paddle.enable_static() - multi_rois = [] - multi_scores = [] - for i in range(4): - multi_rois.append(fluid.data( - name='roi_'+str(i), shape=[None, 4], dtype='float32', lod_level=1)) - for i in range(4): - multi_scores.append(fluid.data( - name='score_'+str(i), shape=[None, 1], dtype='float32', lod_level=1)) - - fpn_rois = fluid.layers.collect_fpn_proposals( - multi_rois=multi_rois, - multi_scores=multi_scores, - min_level=2, - max_level=5, - post_nms_top_n=2000) - """ - num_lvl = max_level - min_level + 1 - input_rois = multi_rois[:num_lvl] - input_scores = multi_scores[:num_lvl] - - if _non_static_mode(): - assert ( - rois_num_per_level is not None - ), "rois_num_per_level should not be None in dygraph mode." - attrs = ('post_nms_topN', post_nms_top_n) - output_rois, rois_num = _legacy_C_ops.collect_fpn_proposals( - input_rois, input_scores, rois_num_per_level, *attrs - ) - - check_type(multi_rois, 'multi_rois', list, 'collect_fpn_proposals') - check_type(multi_scores, 'multi_scores', list, 'collect_fpn_proposals') - helper = LayerHelper('collect_fpn_proposals', **locals()) - dtype = helper.input_dtype('multi_rois') - check_dtype( - dtype, 'multi_rois', ['float32', 'float64'], 'collect_fpn_proposals' - ) - output_rois = helper.create_variable_for_type_inference(dtype) - output_rois.stop_gradient = True - - inputs = { - 'MultiLevelRois': input_rois, - 'MultiLevelScores': input_scores, - } - outputs = {'FpnRois': output_rois} - if rois_num_per_level is not None: - inputs['MultiLevelRoIsNum'] = rois_num_per_level - rois_num = helper.create_variable_for_type_inference(dtype='int32') - rois_num.stop_gradient = True - outputs['RoisNum'] = rois_num - helper.append_op( - type='collect_fpn_proposals', - inputs=inputs, - outputs=outputs, - attrs={'post_nms_topN': post_nms_top_n}, - ) - if rois_num_per_level is not None: - return output_rois, rois_num - return output_rois diff --git a/python/paddle/fluid/metrics.py b/python/paddle/fluid/metrics.py index 82921b9b60877..5d702b8e521bf 100644 --- a/python/paddle/fluid/metrics.py +++ b/python/paddle/fluid/metrics.py @@ -23,7 +23,6 @@ from . import unique_name from .framework import Program, Variable, program_guard from . import layers -from .layers import detection __all__ = [ 'MetricBase', diff --git a/python/paddle/fluid/tests/test_detection.py b/python/paddle/fluid/tests/test_detection.py index 1d4cda6e2f6e2..aa97a32054929 100644 --- a/python/paddle/fluid/tests/test_detection.py +++ b/python/paddle/fluid/tests/test_detection.py @@ -75,230 +75,6 @@ def dynamic_graph(self, force_to_use_cpu=False): yield -class TestDensityPriorBox(unittest.TestCase): - def test_density_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.density_prior_box( - input=conv1, - image=images, - densities=[3, 4], - fixed_sizes=[50.0, 60.0], - fixed_ratios=[1.0], - clip=True, - ) - assert len(box.shape) == 4 - assert box.shape == var.shape - assert box.shape[-1] == 4 - - -class TestAnchorGenerator(unittest.TestCase): - def test_anchor_generator(self): - data_shape = [3, 224, 224] - images = fluid.layers.data( - name='pixel', shape=data_shape, dtype='float32' - ) - conv1 = fluid.layers.conv2d(images, 3, 3, 2) - anchor, var = fluid.layers.anchor_generator( - input=conv1, - anchor_sizes=[64, 128, 256, 512], - aspect_ratios=[0.5, 1.0, 2.0], - variance=[0.1, 0.1, 0.2, 0.2], - stride=[16.0, 16.0], - offset=0.5, - ) - assert len(anchor.shape) == 4 - assert anchor.shape == var.shape - assert anchor.shape[3] == 4 - - -class TestGenerateProposalLabels(unittest.TestCase): - def check_out(self, outs): - rois = outs[0] - labels_int32 = outs[1] - bbox_targets = outs[2] - bbox_inside_weights = outs[3] - bbox_outside_weights = outs[4] - assert rois.shape[1] == 4 - assert rois.shape[0] == labels_int32.shape[0] - assert rois.shape[0] == bbox_targets.shape[0] - assert rois.shape[0] == bbox_inside_weights.shape[0] - assert rois.shape[0] == bbox_outside_weights.shape[0] - assert bbox_targets.shape[1] == 4 * self.class_nums - assert bbox_inside_weights.shape[1] == 4 * self.class_nums - assert bbox_outside_weights.shape[1] == 4 * self.class_nums - if len(outs) == 6: - max_overlap_with_gt = outs[5] - assert max_overlap_with_gt.shape[0] == rois.shape[0] - - def test_generate_proposal_labels(self): - program = Program() - with program_guard(program): - rpn_rois = fluid.data( - name='rpn_rois', shape=[4, 4], dtype='float32', lod_level=1 - ) - gt_classes = fluid.data( - name='gt_classes', shape=[6], dtype='int32', lod_level=1 - ) - is_crowd = fluid.data( - name='is_crowd', shape=[6], dtype='int32', lod_level=1 - ) - gt_boxes = fluid.data( - name='gt_boxes', shape=[6, 4], dtype='float32', lod_level=1 - ) - im_info = fluid.data(name='im_info', shape=[1, 3], dtype='float32') - max_overlap = fluid.data( - name='max_overlap', shape=[4], dtype='float32', lod_level=1 - ) - self.class_nums = 5 - outs = fluid.layers.generate_proposal_labels( - rpn_rois=rpn_rois, - gt_classes=gt_classes, - is_crowd=is_crowd, - gt_boxes=gt_boxes, - im_info=im_info, - batch_size_per_im=2, - fg_fraction=0.5, - fg_thresh=0.5, - bg_thresh_hi=0.5, - bg_thresh_lo=0.0, - bbox_reg_weights=[0.1, 0.1, 0.2, 0.2], - class_nums=self.class_nums, - ) - outs_1 = fluid.layers.generate_proposal_labels( - rpn_rois=rpn_rois, - gt_classes=gt_classes, - is_crowd=is_crowd, - gt_boxes=gt_boxes, - im_info=im_info, - batch_size_per_im=2, - fg_fraction=0.5, - fg_thresh=0.5, - bg_thresh_hi=0.5, - bg_thresh_lo=0.0, - bbox_reg_weights=[0.1, 0.1, 0.2, 0.2], - class_nums=self.class_nums, - is_cascade_rcnn=True, - max_overlap=max_overlap, - return_max_overlap=True, - ) - - self.check_out(outs) - self.check_out(outs_1) - rois = outs[0] - - -class TestGenerateMaskLabels(unittest.TestCase): - def test_generate_mask_labels(self): - program = Program() - with program_guard(program): - im_info = layers.data( - name='im_info', - shape=[1, 3], - dtype='float32', - lod_level=1, - append_batch_size=False, - ) - gt_classes = layers.data( - name='gt_classes', - shape=[2, 1], - dtype='int32', - lod_level=1, - append_batch_size=False, - ) - is_crowd = layers.data( - name='is_crowd', - shape=[2, 1], - dtype='int32', - lod_level=1, - append_batch_size=False, - ) - gt_segms = layers.data( - name='gt_segms', - shape=[20, 2], - dtype='float32', - lod_level=3, - append_batch_size=False, - ) - rois = layers.data( - name='rois', - shape=[4, 4], - dtype='float32', - lod_level=1, - append_batch_size=False, - ) - labels_int32 = layers.data( - name='labels_int32', - shape=[4, 1], - dtype='int32', - lod_level=1, - append_batch_size=False, - ) - num_classes = 5 - resolution = 14 - outs = fluid.layers.generate_mask_labels( - im_info=im_info, - gt_classes=gt_classes, - is_crowd=is_crowd, - gt_segms=gt_segms, - rois=rois, - labels_int32=labels_int32, - num_classes=num_classes, - resolution=resolution, - ) - mask_rois, roi_has_mask_int32, mask_int32 = outs - assert mask_rois.shape[1] == 4 - assert mask_int32.shape[1] == num_classes * resolution * resolution - - -class TestMultiBoxHead(unittest.TestCase): - def test_multi_box_head(self): - data_shape = [3, 224, 224] - mbox_locs, mbox_confs, box, var = self.multi_box_head_output(data_shape) - - assert len(box.shape) == 2 - assert box.shape == var.shape - assert box.shape[1] == 4 - assert mbox_locs.shape[1] == mbox_confs.shape[1] - - def multi_box_head_output(self, data_shape): - images = fluid.layers.data( - name='pixel', shape=data_shape, dtype='float32' - ) - conv1 = fluid.layers.conv2d(images, 3, 3, 2) - conv2 = fluid.layers.conv2d(conv1, 3, 3, 2) - conv3 = fluid.layers.conv2d(conv2, 3, 3, 2) - conv4 = fluid.layers.conv2d(conv3, 3, 3, 2) - conv5 = fluid.layers.conv2d(conv4, 3, 3, 2) - - mbox_locs, mbox_confs, box, var = layers.multi_box_head( - inputs=[conv1, conv2, conv3, conv4, conv5, conv5], - image=images, - num_classes=21, - min_ratio=20, - max_ratio=90, - aspect_ratios=[ - [2.0], - [2.0, 3.0], - [2.0, 3.0], - [2.0, 3.0], - [2.0], - [2.0], - ], - base_size=300, - offset=0.5, - flip=True, - clip=True, - ) - - return mbox_locs, mbox_confs, box, var - - class TestGenerateProposals(LayerTest): def test_generate_proposals(self): scores_np = np.random.rand(2, 3, 4, 4).astype('float32') @@ -323,10 +99,10 @@ def test_generate_proposals(self): variances = fluid.data( name='var', shape=[4, 4, 3, 4], dtype='float32' ) - rois, roi_probs, rois_num = fluid.layers.generate_proposals( + rois, roi_probs, rois_num = paddle.vision.ops.generate_proposals( scores, bbox_deltas, - im_info, + im_info[:2], anchors, variances, pre_nms_top_n=10, @@ -355,10 +131,10 @@ def test_generate_proposals(self): im_info_dy = base.to_variable(im_info_np) anchors_dy = base.to_variable(anchors_np) variances_dy = base.to_variable(variances_np) - rois, roi_probs, rois_num = fluid.layers.generate_proposals( + rois, roi_probs, rois_num = paddle.vision.ops.generate_proposals( scores_dy, bbox_deltas_dy, - im_info_dy, + im_info_dy[:2], anchors_dy, variances_dy, pre_nms_top_n=10, @@ -374,62 +150,6 @@ def test_generate_proposals(self): np.testing.assert_array_equal(np.array(rois_num_stat), rois_num_dy) -class TestBoxClip(unittest.TestCase): - def test_box_clip(self): - program = Program() - with program_guard(program): - input_box = layers.data( - name='input_box', shape=[7, 4], dtype='float32', lod_level=1 - ) - im_info = layers.data(name='im_info', shape=[3], dtype='float32') - out = layers.box_clip(input_box, im_info) - self.assertIsNotNone(out) - - -class TestMulticlassNMS(unittest.TestCase): - def test_multiclass_nms(self): - program = Program() - with program_guard(program): - bboxes = layers.data( - name='bboxes', shape=[-1, 10, 4], dtype='float32' - ) - scores = layers.data(name='scores', shape=[-1, 10], dtype='float32') - output = layers.multiclass_nms(bboxes, scores, 0.3, 400, 200, 0.7) - self.assertIsNotNone(output) - - def test_multiclass_nms_error(self): - program = Program() - with program_guard(program): - bboxes1 = fluid.data( - name='bboxes1', shape=[10, 10, 4], dtype='int32' - ) - scores1 = fluid.data( - name='scores1', shape=[10, 10], dtype='float32' - ) - bboxes2 = fluid.data( - name='bboxes2', shape=[10, 10, 4], dtype='float32' - ) - scores2 = fluid.data(name='scores2', shape=[10, 10], dtype='int32') - self.assertRaises( - TypeError, - layers.multiclass_nms, - bboxes=bboxes1, - scores=scores1, - score_threshold=0.5, - nms_top_k=400, - keep_top_k=200, - ) - self.assertRaises( - TypeError, - layers.multiclass_nms, - bboxes=bboxes2, - scores=scores2, - score_threshold=0.5, - nms_top_k=400, - keep_top_k=200, - ) - - class TestMulticlassNMS2(unittest.TestCase): def test_multiclass_nms2(self): program = Program() @@ -449,138 +169,6 @@ def test_multiclass_nms2(self): self.assertIsNotNone(index) -class TestCollectFpnPropsals(LayerTest): - def test_collect_fpn_proposals(self): - multi_bboxes_np = [] - multi_scores_np = [] - rois_num_per_level_np = [] - for i in range(4): - bboxes_np = np.random.rand(5, 4).astype('float32') - scores_np = np.random.rand(5, 1).astype('float32') - rois_num = np.array([2, 3]).astype('int32') - multi_bboxes_np.append(bboxes_np) - multi_scores_np.append(scores_np) - rois_num_per_level_np.append(rois_num) - - with self.static_graph(): - multi_bboxes = [] - multi_scores = [] - rois_num_per_level = [] - for i in range(4): - bboxes = fluid.data( - name='rois' + str(i), - shape=[5, 4], - dtype='float32', - lod_level=1, - ) - scores = fluid.data( - name='scores' + str(i), - shape=[5, 1], - dtype='float32', - lod_level=1, - ) - rois_num = fluid.data( - name='rois_num' + str(i), shape=[None], dtype='int32' - ) - - multi_bboxes.append(bboxes) - multi_scores.append(scores) - rois_num_per_level.append(rois_num) - - fpn_rois, rois_num = layers.collect_fpn_proposals( - multi_bboxes, - multi_scores, - 2, - 5, - 10, - rois_num_per_level=rois_num_per_level, - ) - feed = {} - for i in range(4): - feed['rois' + str(i)] = multi_bboxes_np[i] - feed['scores' + str(i)] = multi_scores_np[i] - feed['rois_num' + str(i)] = rois_num_per_level_np[i] - fpn_rois_stat, rois_num_stat = self.get_static_graph_result( - feed=feed, fetch_list=[fpn_rois, rois_num], with_lod=True - ) - fpn_rois_stat = np.array(fpn_rois_stat) - rois_num_stat = np.array(rois_num_stat) - - with self.dynamic_graph(): - multi_bboxes_dy = [] - multi_scores_dy = [] - rois_num_per_level_dy = [] - for i in range(4): - bboxes_dy = base.to_variable(multi_bboxes_np[i]) - scores_dy = base.to_variable(multi_scores_np[i]) - rois_num_dy = base.to_variable(rois_num_per_level_np[i]) - multi_bboxes_dy.append(bboxes_dy) - multi_scores_dy.append(scores_dy) - rois_num_per_level_dy.append(rois_num_dy) - fpn_rois_dy, rois_num_dy = fluid.layers.collect_fpn_proposals( - multi_bboxes_dy, - multi_scores_dy, - 2, - 5, - 10, - rois_num_per_level=rois_num_per_level_dy, - ) - fpn_rois_dy = fpn_rois_dy.numpy() - rois_num_dy = rois_num_dy.numpy() - - np.testing.assert_array_equal(fpn_rois_stat, fpn_rois_dy) - np.testing.assert_array_equal(rois_num_stat, rois_num_dy) - - def test_collect_fpn_proposals_error(self): - def generate_input(bbox_type, score_type, name): - multi_bboxes = [] - multi_scores = [] - for i in range(4): - bboxes = fluid.data( - name='rois' + name + str(i), - shape=[10, 4], - dtype=bbox_type, - lod_level=1, - ) - scores = fluid.data( - name='scores' + name + str(i), - shape=[10, 1], - dtype=score_type, - lod_level=1, - ) - multi_bboxes.append(bboxes) - multi_scores.append(scores) - return multi_bboxes, multi_scores - - program = Program() - with program_guard(program): - bbox1 = fluid.data( - name='rois', shape=[5, 10, 4], dtype='float32', lod_level=1 - ) - score1 = fluid.data( - name='scores', shape=[5, 10, 1], dtype='float32', lod_level=1 - ) - bbox2, score2 = generate_input('int32', 'float32', '2') - self.assertRaises( - TypeError, - layers.collect_fpn_proposals, - multi_rois=bbox1, - multi_scores=score1, - min_level=2, - max_level=5, - post_nms_top_n=2000, - ) - self.assertRaises( - TypeError, - layers.collect_fpn_proposals, - multi_rois=bbox2, - multi_scores=score2, - min_level=2, - max_level=5, - post_nms_top_n=2000, - ) - - class TestDistributeFpnProposals(LayerTest): def test_distribute_fpn_proposals(self): rois_np = np.random.rand(10, 4).astype('float32') @@ -592,7 +180,7 @@ def test_distribute_fpn_proposals(self): multi_rois, restore_ind, rois_num_per_level, - ) = layers.distribute_fpn_proposals( + ) = paddle.vision.ops.distribute_fpn_proposals( fpn_rois=rois, min_level=2, max_level=5, @@ -619,7 +207,7 @@ def test_distribute_fpn_proposals(self): multi_rois_dy, restore_ind_dy, rois_num_per_level_dy, - ) = layers.distribute_fpn_proposals( + ) = paddle.vision.ops.distribute_fpn_proposals( fpn_rois=rois_dy, min_level=2, max_level=5, @@ -646,7 +234,7 @@ def test_distribute_fpn_proposals_error(self): ) self.assertRaises( TypeError, - layers.distribute_fpn_proposals, + paddle.vision.ops.distribute_fpn_proposals, fpn_rois=fpn_rois, min_level=2, max_level=5, @@ -655,81 +243,6 @@ def test_distribute_fpn_proposals_error(self): ) -class TestBoxDecoderAndAssign(unittest.TestCase): - def test_box_decoder_and_assign(self): - program = Program() - with program_guard(program): - pb = fluid.data(name='prior_box', shape=[None, 4], dtype='float32') - pbv = fluid.data(name='prior_box_var', shape=[4], dtype='float32') - loc = fluid.data( - name='target_box', shape=[None, 4 * 81], dtype='float32' - ) - scores = fluid.data( - name='scores', shape=[None, 81], dtype='float32' - ) - ( - decoded_box, - output_assign_box, - ) = fluid.layers.box_decoder_and_assign(pb, pbv, loc, scores, 4.135) - self.assertIsNotNone(decoded_box) - self.assertIsNotNone(output_assign_box) - - def test_box_decoder_and_assign_error(self): - def generate_input(pb_type, pbv_type, loc_type, score_type, name): - pb = fluid.data( - name='prior_box' + name, shape=[None, 4], dtype=pb_type - ) - pbv = fluid.data( - name='prior_box_var' + name, shape=[4], dtype=pbv_type - ) - loc = fluid.data( - name='target_box' + name, shape=[None, 4 * 81], dtype=loc_type - ) - scores = fluid.data( - name='scores' + name, shape=[None, 81], dtype=score_type - ) - return pb, pbv, loc, scores - - program = Program() - with program_guard(program): - pb1, pbv1, loc1, scores1 = generate_input( - 'int32', 'float32', 'float32', 'float32', '1' - ) - pb2, pbv2, loc2, scores2 = generate_input( - 'float32', 'float32', 'int32', 'float32', '2' - ) - pb3, pbv3, loc3, scores3 = generate_input( - 'float32', 'float32', 'float32', 'int32', '3' - ) - self.assertRaises( - TypeError, - layers.box_decoder_and_assign, - prior_box=pb1, - prior_box_var=pbv1, - target_box=loc1, - box_score=scores1, - box_clip=4.0, - ) - self.assertRaises( - TypeError, - layers.box_decoder_and_assign, - prior_box=pb2, - prior_box_var=pbv2, - target_box=loc2, - box_score=scores2, - box_clip=4.0, - ) - self.assertRaises( - TypeError, - layers.box_decoder_and_assign, - prior_box=pb3, - prior_box_var=pbv3, - target_box=loc3, - box_score=scores3, - box_clip=4.0, - ) - - if __name__ == '__main__': paddle.enable_static() unittest.main() diff --git a/python/paddle/fluid/tests/unittests/dygraph_to_static/yolov3.py b/python/paddle/fluid/tests/unittests/dygraph_to_static/yolov3.py index 2fe1f652cce48..934c792362909 100644 --- a/python/paddle/fluid/tests/unittests/dygraph_to_static/yolov3.py +++ b/python/paddle/fluid/tests/unittests/dygraph_to_static/yolov3.py @@ -19,6 +19,7 @@ import paddle import paddle.fluid as fluid +from paddle import _legacy_C_ops from paddle.fluid.param_attr import ParamAttr from paddle.fluid.regularizer import L2Decay from paddle.jit.api import declarative @@ -351,7 +352,7 @@ def forward( yolo_boxes = fluid.layers.concat(self.boxes, axis=1) yolo_scores = fluid.layers.concat(self.scores, axis=2) - pred = fluid.layers.multiclass_nms( + pred = _legacy_C_ops.multiclass_nms( bboxes=yolo_boxes, scores=yolo_scores, score_threshold=cfg.valid_thresh, diff --git a/python/paddle/fluid/tests/unittests/ir/inference/test_trt_anchor_generator_op.py b/python/paddle/fluid/tests/unittests/ir/inference/test_trt_anchor_generator_op.py deleted file mode 100644 index 88743ef399740..0000000000000 --- a/python/paddle/fluid/tests/unittests/ir/inference/test_trt_anchor_generator_op.py +++ /dev/null @@ -1,138 +0,0 @@ -# Copyright (c) 2020 PaddlePaddle 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. - -import unittest - -import numpy as np -from inference_pass_test import InferencePassTest - -import paddle -import paddle.fluid as fluid -import paddle.fluid.core as core -import paddle.static.nn as nn -from paddle.fluid.core import AnalysisConfig, PassVersionChecker - - -class TRTAnchorGeneratorBaseTest(InferencePassTest): - def setUp(self): - self.bs = 1 - self.channel = 16 - self.height = 32 - self.width = 32 - self.anchor_sizes = [64.0, 128.0, 256.0, 512.0] - self.aspect_ratios = [0.5, 1.0, 2.0] - self.variance = [0.1, 0.1, 0.2, 0.2] - self.stride = [8.0, 8.0] - self.precision = AnalysisConfig.Precision.Float32 - self.serialize = False - self.enable_trt = True - self.feeds = { - 'data': np.random.random( - [self.bs, self.channel, self.height, self.width] - ).astype('float32'), - } - - def build(self): - min_graph_size = 3 if self.dynamic_shape_params is not None else 2 - self.trt_parameters = InferencePassTest.TensorRTParam( - 1 << 30, - self.bs, - min_graph_size, - self.precision, - self.serialize, - False, - ) - with fluid.program_guard(self.main_program, self.startup_program): - data = fluid.data( - name='data', - shape=[-1, self.channel, self.height, self.width], - dtype='float32', - ) - anchor, var = fluid.layers.detection.anchor_generator( - data, - anchor_sizes=self.anchor_sizes, - aspect_ratios=self.aspect_ratios, - variance=self.variance, - stride=self.stride, - ) - if self.dynamic_shape_params is not None: - anchor = paddle.transpose(anchor, [2, 3, 0, 1]) - out = nn.batch_norm(anchor, is_test=True) - - self.fetch_list = [out, var] - - def run_test(self): - self.build() - self.check_output() - - def set_dynamic(self): - self.dynamic_shape_params = InferencePassTest.DynamicShapeParam( - { - 'data': [ - self.bs, - self.channel, - self.height // 2, - self.width // 2, - ] - }, - {'data': [self.bs, self.channel, self.height, self.width]}, - {'data': [self.bs, self.channel, self.height, self.width]}, - False, - ) - - def test_base(self): - self.run_test() - - def test_fp16(self): - self.precision = AnalysisConfig.Precision.Half - self.run_test() - - def test_serialize(self): - self.serialize = True - self.run_test() - - def test_dynamic(self): - self.set_dynamic() - self.run_test() - - def test_dynamic_fp16(self): - self.precision = AnalysisConfig.Precision.Half - self.set_dynamic() - self.run_test() - - def test_dynamic_serialize(self): - self.serialize = True - self.set_dynamic() - self.run_test() - - def test_dynamic_fp16_serialize(self): - self.serialize = True - self.precision = AnalysisConfig.Precision.Half - self.set_dynamic() - self.run_test() - - def check_output(self): - if core.is_compiled_with_cuda(): - use_gpu = True - atol = 1e-5 - if self.trt_parameters.precision == AnalysisConfig.Precision.Half: - atol = 1e-3 - self.check_output_with_option(use_gpu, atol, flatten=True) - self.assertTrue( - PassVersionChecker.IsCompatible('tensorrt_subgraph_pass') - ) - - -if __name__ == "__main__": - unittest.main() diff --git a/python/paddle/fluid/tests/unittests/ir/inference/test_trt_multiclass_nms_op.py b/python/paddle/fluid/tests/unittests/ir/inference/test_trt_multiclass_nms_op.py deleted file mode 100644 index b5f84dcc9f760..0000000000000 --- a/python/paddle/fluid/tests/unittests/ir/inference/test_trt_multiclass_nms_op.py +++ /dev/null @@ -1,167 +0,0 @@ -# Copyright (c) 2022 PaddlePaddle 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. - -import itertools -import unittest - -import numpy as np -from inference_pass_test import InferencePassTest - -import paddle -import paddle.fluid as fluid -import paddle.fluid.core as core -import paddle.static.nn as nn -from paddle.fluid.core import AnalysisConfig, PassVersionChecker - - -class TensorRTMultiClassNMSTest(InferencePassTest): - def setUp(self): - self.enable_trt = True - self.enable_tensorrt_varseqlen = True - self.precision = AnalysisConfig.Precision.Float32 - self.serialize = False - self.bs = 1 - self.background_label = -1 - self.score_threshold = 0.5 - self.nms_top_k = 8 - self.nms_threshold = 0.3 - self.keep_top_k = 8 - self.normalized = False - self.num_classes = 8 - self.num_boxes = 8 - self.trt_parameters = InferencePassTest.TensorRTParam( - 1 << 30, self.bs, 2, self.precision, self.serialize, False - ) - - def build(self): - with fluid.program_guard(self.main_program, self.startup_program): - boxes = fluid.data( - name='bboxes', shape=[-1, self.num_boxes, 4], dtype='float32' - ) - scores = fluid.data( - name='scores', - shape=[-1, self.num_classes, self.num_boxes], - dtype='float32', - ) - multiclass_nms_out = fluid.layers.multiclass_nms( - bboxes=boxes, - scores=scores, - background_label=self.background_label, - score_threshold=self.score_threshold, - nms_top_k=self.nms_top_k, - nms_threshold=self.nms_threshold, - keep_top_k=self.keep_top_k, - normalized=self.normalized, - ) - mutliclass_nms_out = multiclass_nms_out + 1.0 - multiclass_nms_out = paddle.reshape( - multiclass_nms_out, - [self.bs, 1, self.keep_top_k, 6], - name='reshape', - ) - out = nn.batch_norm(multiclass_nms_out, is_test=True) - - boxes_data = ( - np.arange(self.num_boxes * 4) - .reshape([self.bs, self.num_boxes, 4]) - .astype('float32') - ) - scores_data = ( - np.arange(1 * self.num_classes * self.num_boxes) - .reshape([self.bs, self.num_classes, self.num_boxes]) - .astype('float32') - ) - self.feeds = { - 'bboxes': boxes_data, - 'scores': scores_data, - } - self.fetch_list = [out] - - def run_test(self): - self.build() - self.check_output() - - def run_test_all(self): - precision_opt = [ - AnalysisConfig.Precision.Float32, - AnalysisConfig.Precision.Half, - ] - serialize_opt = [False, True] - max_shape = { - 'bboxes': [self.bs, self.num_boxes, 4], - 'scores': [self.bs, self.num_classes, self.num_boxes], - } - opt_shape = max_shape - dynamic_shape_opt = [ - None, - InferencePassTest.DynamicShapeParam( - {'bboxes': [1, 1, 4], 'scores': [1, 1, 1]}, - max_shape, - opt_shape, - False, - ), - ] - for precision, serialize, dynamic_shape in itertools.product( - precision_opt, serialize_opt, dynamic_shape_opt - ): - self.precision = precision - self.serialize = serialize - self.dynamic_shape_params = dynamic_shape - self.build() - self.check_output() - - def check_output(self): - if core.is_compiled_with_cuda(): - use_gpu = True - self.check_output_with_option(use_gpu) - self.assertTrue( - PassVersionChecker.IsCompatible('tensorrt_subgraph_pass') - ) - - def test_base(self): - self.run_test() - - def test_fp16(self): - self.precision = AnalysisConfig.Precision.Half - self.run_test() - - def test_serialize(self): - self.serialize = True - self.run_test() - - def test_dynamic(self): - max_shape = { - 'bboxes': [self.bs, self.num_boxes, 4], - 'scores': [self.bs, self.num_classes, self.num_boxes], - } - opt_shape = max_shape - self.dynamic_shape_params = InferencePassTest.DynamicShapeParam( - {'bboxes': [1, 1, 4], 'scores': [1, 1, 1]}, - max_shape, - opt_shape, - False, - ) - self.run_test() - - def test_background(self): - self.background = 7 - self.run_test() - - def test_disable_varseqlen(self): - self.diable_tensorrt_varseqlen = False - self.run_test() - - -if __name__ == "__main__": - unittest.main() diff --git a/python/paddle/fluid/tests/unittests/test_directory_migration.py b/python/paddle/fluid/tests/unittests/test_directory_migration.py index 34816d5703814..439104b69a18c 100644 --- a/python/paddle/fluid/tests/unittests/test_directory_migration.py +++ b/python/paddle/fluid/tests/unittests/test_directory_migration.py @@ -96,7 +96,6 @@ def test_new_directory(self): 'paddle.static.nn.group_norm', 'paddle.static.nn.instance_norm', 'paddle.static.nn.layer_norm', - 'paddle.static.nn.multi_box_head', 'paddle.static.nn.nce', 'paddle.static.nn.prelu', 'paddle.static.nn.row_conv', diff --git a/python/paddle/fluid/tests/unittests/test_generate_mask_labels_op.py b/python/paddle/fluid/tests/unittests/test_generate_mask_labels_op.py index 12a1fa082e4bc..f5ccff9db32cb 100644 --- a/python/paddle/fluid/tests/unittests/test_generate_mask_labels_op.py +++ b/python/paddle/fluid/tests/unittests/test_generate_mask_labels_op.py @@ -16,7 +16,6 @@ import unittest import numpy as np -from op_test import OpTest ''' # Equivalent code @@ -314,148 +313,5 @@ def trans_lod(lod): return new_lod -class TestGenerateMaskLabels(OpTest): - def set_data(self): - self.init_test_case() - self.make_generate_proposal_labels_out() - self.generate_gt_polys() - self.generate_groundtruth() - self.init_test_output() - self.inputs = { - 'ImInfo': self.im_info, - 'GtClasses': (self.gt_classes.astype(np.int32), self.gt_lod), - 'IsCrowd': (self.is_crowd.astype(np.int32), self.gt_lod), - 'LabelsInt32': (self.label_int32.astype(np.int32), self.rois_lod), - 'GtSegms': (self.gt_polys.astype(np.float32), self.masks_lod), - 'Rois': (self.rois.astype(np.float32), self.rois_lod), - } - self.attrs = { - 'num_classes': self.num_classes, - 'resolution': self.resolution, - } - self.outputs = { - 'MaskRois': (self.mask_rois, [self.new_lod]), - 'RoiHasMaskInt32': (self.roi_has_mask_int32, [self.new_lod]), - 'MaskInt32': (self.mask_int32, [self.new_lod]), - } - - def init_test_case(self): - self.num_classes = 81 - self.resolution = 14 - self.batch_size = 2 - self.batch_size_per_im = 64 - self.images_shape = [100, 200] - np.random.seed(0) - - def make_generate_proposal_labels_out(self): - rois = [] - self.rois_lod = [[]] - self.label_int32 = [] - for bno in range(self.batch_size): - self.rois_lod[0].append(self.batch_size_per_im) - for i in range(self.batch_size_per_im): - xywh = np.random.rand(4) - xy1 = xywh[0:2] * 2 - wh = xywh[2:4] * (self.images_shape[0] - xy1) - xy2 = xy1 + wh - roi = [xy1[0], xy1[1], xy2[0], xy2[1]] - rois.append(roi) - self.rois = np.array(rois).astype("float32") - for idx, roi_num in enumerate(self.rois_lod[0]): - for roi_id in range(roi_num): - class_id = np.random.random_integers(self.num_classes - 1) - if idx == 0: - # set an image with no foreground, to test the empty case - self.label_int32.append(0) - else: - self.label_int32.append(class_id) - label_np = np.array(self.label_int32) - self.label_int32 = label_np[:, np.newaxis] - - def generate_gt_polys(self): - h, w = self.images_shape[0:2] - self.gt_polys = [] - self.gt_polys_list = [] - max_gt = 4 - max_poly_num = 5 - min_poly_size = 4 - max_poly_size = 16 - lod0 = [] - lod1 = [] - lod2 = [] - for i in range(self.batch_size): - gt_num = np.random.randint(1, high=max_gt, size=1)[0] - lod0.append(gt_num) - ptss = [] - for i in range(gt_num): - poly_num = np.random.randint(1, max_poly_num, size=1)[0] - lod1.append(poly_num) - pts = [] - for j in range(poly_num): - poly_size = np.random.randint( - min_poly_size, max_poly_size, size=1 - )[0] - x = np.random.rand(poly_size, 1) * w - y = np.random.rand(poly_size, 1) * h - xy = np.concatenate((x, y), axis=1) - pts.append(xy.flatten().tolist()) - self.gt_polys.extend(xy.flatten().tolist()) - lod2.append(poly_size) - ptss.append(pts) - self.gt_polys_list.append(ptss) - self.masks_lod = [lod0, lod1, lod2] - self.gt_lod = [lod0] - self.gt_polys = np.array(self.gt_polys).astype('float32').reshape(-1, 2) - - def generate_groundtruth(self): - self.im_info = [] - self.gt_classes = [] - self.is_crowd = [] - for roi_num in self.gt_lod[0]: - self.im_info.append(self.images_shape + [1.0]) - for roi_id in range(roi_num): - class_id = np.random.random_integers(self.num_classes - 1) - self.gt_classes.append(class_id) - self.is_crowd.append(0) - self.im_info = np.array(self.im_info).astype(np.float32) - gt_classes_np = np.array(self.gt_classes) - self.gt_classes = gt_classes_np[:, np.newaxis] - is_crowd_np = np.array(self.is_crowd) - self.is_crowd = is_crowd_np[:, np.newaxis] - - def init_test_output(self): - roi_lod = trans_lod(self.rois_lod[0]) - gt_lod = trans_lod(self.gt_lod[0]) - outs = generate_mask_labels( - self.num_classes, - self.im_info, - self.gt_classes, - self.is_crowd, - self.label_int32, - self.gt_polys_list, - self.resolution, - self.rois, - roi_lod, - gt_lod, - ) - self.mask_rois = outs[0] - self.roi_has_mask_int32 = outs[1] - self.mask_int32 = outs[2] - self.new_lod = outs[3] - - self.mask_rois = np.vstack(self.mask_rois) - self.roi_has_mask_int32 = np.hstack(self.roi_has_mask_int32)[ - :, np.newaxis - ] - self.mask_int32 = np.vstack(self.mask_int32) - - def setUp(self): - self.op_type = "generate_mask_labels" - self.set_data() - - def test_check_output(self): - self.check_output() - - if __name__ == '__main__': unittest.main() diff --git a/python/paddle/fluid/tests/unittests/test_layers.py b/python/paddle/fluid/tests/unittests/test_layers.py index fc05b1cb79566..8640db18aa1ec 100644 --- a/python/paddle/fluid/tests/unittests/test_layers.py +++ b/python/paddle/fluid/tests/unittests/test_layers.py @@ -2806,16 +2806,6 @@ def test_sequence_enumerate(self): x = layers.data(name="input", shape=[1], dtype='int32', lod_level=1) out = layers.sequence_enumerate(input=x, win_size=2, pad_value=0) - def test_roi_perspective_transform(self): - # TODO(minqiyang): dygraph do not support lod now - with self.static_graph(): - x = layers.data(name="x", shape=[256, 30, 30], dtype="float32") - rois = layers.data( - name="rois", shape=[8], dtype="float32", lod_level=1 - ) - output = layers.roi_perspective_transform(x, rois, 7, 7, 0.6) - return output - def test_row_conv(self): # TODO(minqiyang): dygraph do not support lod now with self.static_graph(): @@ -2897,47 +2887,6 @@ def test_addmm(self): out = paddle.addmm(input=input, x=x, y=y) return out - def test_retinanet_detection_output(self): - with program_guard( - fluid.default_main_program(), fluid.default_startup_program() - ): - bboxes = layers.data( - name='bboxes', - shape=[1, 21, 4], - append_batch_size=False, - dtype='float32', - ) - scores = layers.data( - name='scores', - shape=[1, 21, 10], - append_batch_size=False, - dtype='float32', - ) - anchors = layers.data( - name='anchors', - shape=[21, 4], - append_batch_size=False, - dtype='float32', - ) - im_info = layers.data( - name="im_info", - shape=[1, 3], - append_batch_size=False, - dtype='float32', - ) - nmsed_outs = layers.retinanet_detection_output( - bboxes=[bboxes, bboxes], - scores=[scores, scores], - anchors=[anchors, anchors], - im_info=im_info, - score_threshold=0.05, - nms_top_k=1000, - keep_top_k=100, - nms_threshold=0.3, - nms_eta=1.0, - ) - return nmsed_outs - def test_warpctc_with_padding(self): # TODO(minqiyang): dygraph do not support lod now with self.static_graph(): diff --git a/python/paddle/fluid/tests/unittests/test_locality_aware_nms_op.py b/python/paddle/fluid/tests/unittests/test_locality_aware_nms_op.py index e5b71c2d65e78..fd2e64e90c77f 100644 --- a/python/paddle/fluid/tests/unittests/test_locality_aware_nms_op.py +++ b/python/paddle/fluid/tests/unittests/test_locality_aware_nms_op.py @@ -19,8 +19,6 @@ from op_test import OpTest from test_multiclass_nms_op import iou -import paddle.fluid as fluid - def weight_merge(box1, box2, score1, score2): for i in range(len(box1)): @@ -409,153 +407,5 @@ def test_check_output(self): self.check_output() -class TestLocalityAwareNMSAPI(unittest.TestCase): - def test_api(self): - boxes = fluid.data(name='bboxes', shape=[None, 81, 8], dtype='float32') - scores = fluid.data(name='scores', shape=[None, 1, 81], dtype='float32') - fluid.layers.locality_aware_nms( - bboxes=boxes, - scores=scores, - score_threshold=0.5, - nms_top_k=400, - nms_threshold=0.3, - keep_top_k=200, - normalized=False, - ) - - -class TestLocalityAwareNMSError(unittest.TestCase): - def test_error(self): - boxes = fluid.data(name='bboxes', shape=[None, 81, 8], dtype='float32') - scores = fluid.data(name='scores', shape=[None, 1, 81], dtype='float32') - - boxes_int = fluid.data( - name='bboxes_int', shape=[None, 81, 8], dtype='int32' - ) - scores_int = fluid.data( - name='scores_int', shape=[None, 1, 81], dtype='int32' - ) - boxes_tmp = [1, 2] - scores_tmp = [1, 2] - - # type of boxes and scores must be variable - self.assertRaises( - TypeError, - fluid.layers.locality_aware_nms, - boxes_tmp, - scores, - 0.5, - 400, - 200, - ) - self.assertRaises( - TypeError, - fluid.layers.locality_aware_nms, - boxes, - scores_tmp, - 0.5, - 400, - 200, - ) - - # dtype of boxes and scores must in ['float32', 'float64'] - self.assertRaises( - TypeError, - fluid.layers.locality_aware_nms, - boxes_int, - scores, - 0.5, - 400, - 200, - ) - self.assertRaises( - TypeError, - fluid.layers.locality_aware_nms, - boxes, - scores_int, - 0.5, - 400, - 200, - ) - - score_threshold = int(1) - # type of score_threshold must be float - self.assertRaises( - TypeError, - fluid.layers.locality_aware_nms, - boxes, - scores, - score_threshold, - 400, - 200, - ) - - nms_top_k = 400.5 - # type of num_top_k must be int - self.assertRaises( - TypeError, - fluid.layers.locality_aware_nms, - boxes, - scores, - 0.5, - nms_top_k, - 200, - ) - - keep_top_k = 200.5 - # type of keep_top_k must be int - self.assertRaises( - TypeError, - fluid.layers.locality_aware_nms, - boxes, - scores, - 0.5, - 400, - keep_top_k, - ) - - nms_threshold = int(0) - # type of nms_threshold must be int - self.assertRaises( - TypeError, - fluid.layers.locality_aware_nms, - boxes, - scores, - 0.5, - 400, - 200, - nms_threshold, - ) - - nms_eta = int(1) - # type of nms_eta must be float - self.assertRaises( - TypeError, - fluid.layers.locality_aware_nms, - boxes, - scores, - 0.5, - 400, - 200, - 0.5, - nms_eta, - ) - - bg_label = 1.5 - # type of background_label must be int - self.assertRaises( - TypeError, - fluid.layers.locality_aware_nms, - boxes, - scores, - 0.5, - 400, - 200, - 0.5, - 1.0, - bg_label, - ) - - if __name__ == '__main__': unittest.main() diff --git a/python/paddle/fluid/tests/unittests/test_matrix_nms_op.py b/python/paddle/fluid/tests/unittests/test_matrix_nms_op.py index 980c0342facaf..0d60e0edeaabf 100644 --- a/python/paddle/fluid/tests/unittests/test_matrix_nms_op.py +++ b/python/paddle/fluid/tests/unittests/test_matrix_nms_op.py @@ -334,14 +334,6 @@ def test_errors(self): def test_bboxes_Variable(): # the bboxes type must be Variable - fluid.layers.matrix_nms( - bboxes=boxes_np, - scores=scores_data, - score_threshold=score_threshold, - post_threshold=post_threshold, - nms_top_k=nms_top_k, - keep_top_k=keep_top_k, - ) paddle.vision.ops.matrix_nms( bboxes=boxes_np, scores=scores_data, @@ -353,14 +345,6 @@ def test_bboxes_Variable(): def test_scores_Variable(): # the scores type must be Variable - fluid.layers.matrix_nms( - bboxes=boxes_data, - scores=scores_np, - score_threshold=score_threshold, - post_threshold=post_threshold, - nms_top_k=nms_top_k, - keep_top_k=keep_top_k, - ) paddle.vision.ops.matrix_nms( bboxes=boxes_data, scores=scores_np, @@ -372,17 +356,6 @@ def test_scores_Variable(): def test_empty(): # when all score are lower than threshold - try: - fluid.layers.matrix_nms( - bboxes=boxes_data, - scores=scores_data, - score_threshold=score_threshold, - post_threshold=post_threshold, - nms_top_k=nms_top_k, - keep_top_k=keep_top_k, - ) - except Exception as e: - self.fail(e) try: paddle.vision.ops.matrix_nms( bboxes=boxes_data, @@ -397,17 +370,6 @@ def test_empty(): def test_coverage(): # cover correct workflow - try: - fluid.layers.matrix_nms( - bboxes=boxes_data, - scores=scores_data, - score_threshold=score_threshold, - post_threshold=post_threshold, - nms_top_k=nms_top_k, - keep_top_k=keep_top_k, - ) - except Exception as e: - self.fail(e) try: paddle.vision.ops.matrix_nms( bboxes=boxes_data, diff --git a/python/paddle/fluid/tests/unittests/test_multiclass_nms_op.py b/python/paddle/fluid/tests/unittests/test_multiclass_nms_op.py index 2b846c0fed8da..44fe9939688b4 100644 --- a/python/paddle/fluid/tests/unittests/test_multiclass_nms_op.py +++ b/python/paddle/fluid/tests/unittests/test_multiclass_nms_op.py @@ -19,14 +19,8 @@ from op_test import OpTest import paddle -import paddle.fluid as fluid from paddle import _C_ops, _legacy_C_ops -from paddle.fluid import ( - Program, - _non_static_mode, - in_dygraph_mode, - program_guard, -) +from paddle.fluid import _non_static_mode, in_dygraph_mode from paddle.fluid.layer_helper import LayerHelper @@ -738,39 +732,6 @@ def set_argument(self): self.score_threshold = 2.0 -class TestMulticlassNMSError(unittest.TestCase): - def test_errors(self): - with program_guard(Program(), Program()): - M = 1200 - N = 7 - C = 21 - BOX_SIZE = 4 - - boxes_np = np.random.random((M, C, BOX_SIZE)).astype('float32') - scores = np.random.random((N * M, C)).astype('float32') - scores = np.apply_along_axis(softmax, 1, scores) - scores = np.reshape(scores, (N, M, C)) - scores_np = np.transpose(scores, (0, 2, 1)) - - boxes_data = fluid.data( - name='bboxes', shape=[M, C, BOX_SIZE], dtype='float32' - ) - scores_data = fluid.data( - name='scores', shape=[N, C, M], dtype='float32' - ) - - def test_bboxes_Variable(): - # the bboxes type must be Variable - fluid.layers.multiclass_nms(bboxes=boxes_np, scores=scores_data) - - def test_scores_Variable(): - # the bboxes type must be Variable - fluid.layers.multiclass_nms(bboxes=boxes_data, scores=scores_np) - - self.assertRaises(TypeError, test_bboxes_Variable) - self.assertRaises(TypeError, test_scores_Variable) - - class TestMulticlassNMS3Op(TestMulticlassNMS2Op): def setUp(self): self.python_api = multiclass_nms3 diff --git a/python/paddle/fluid/tests/unittests/test_retinanet_detection_output.py b/python/paddle/fluid/tests/unittests/test_retinanet_detection_output.py index 2e43d10129c8f..a120dfd50eefc 100644 --- a/python/paddle/fluid/tests/unittests/test_retinanet_detection_output.py +++ b/python/paddle/fluid/tests/unittests/test_retinanet_detection_output.py @@ -21,8 +21,6 @@ from test_multiclass_nms_op import nms import paddle -import paddle.fluid as fluid -from paddle.fluid import Program, program_guard def multiclass_nms(prediction, class_num, keep_top_k, nms_threshold): @@ -508,132 +506,6 @@ def set_argument(self): self.layer_w.append(2 ** (num_levels - i)) -class TestRetinanetDetectionOutOpError(unittest.TestCase): - def test_errors(self): - with program_guard(Program(), Program()): - bboxes_low1 = fluid.data( - name='bboxes_low1', shape=[1, 44, 4], dtype='float32' - ) - bboxes_high1 = fluid.data( - name='bboxes_high1', shape=[1, 11, 4], dtype='float32' - ) - scores_low1 = fluid.data( - name='scores_low1', shape=[1, 44, 10], dtype='float32' - ) - scores_high1 = fluid.data( - name='scores_high1', shape=[1, 11, 10], dtype='float32' - ) - anchors_low1 = fluid.data( - name='anchors_low1', shape=[44, 4], dtype='float32' - ) - anchors_high1 = fluid.data( - name='anchors_high1', shape=[11, 4], dtype='float32' - ) - im_info1 = fluid.data( - name="im_info1", shape=[1, 3], dtype='float32' - ) - - # The `bboxes` must be list, each element must be Variable and - # its Tensor data type must be one of float32 and float64. - def test_bboxes_type(): - fluid.layers.retinanet_detection_output( - bboxes=bboxes_low1, - scores=[scores_low1, scores_high1], - anchors=[anchors_low1, anchors_high1], - im_info=im_info1, - ) - - self.assertRaises(TypeError, test_bboxes_type) - - def test_bboxes_tensor_dtype(): - bboxes_high2 = fluid.data( - name='bboxes_high2', shape=[1, 11, 4], dtype='int32' - ) - fluid.layers.retinanet_detection_output( - bboxes=[bboxes_high2, 5], - scores=[scores_low1, scores_high1], - anchors=[anchors_low1, anchors_high1], - im_info=im_info1, - ) - - self.assertRaises(TypeError, test_bboxes_tensor_dtype) - - # The `scores` must be list, each element must be Variable and its - # Tensor data type must be one of float32 and float64. - def test_scores_type(): - fluid.layers.retinanet_detection_output( - bboxes=[bboxes_low1, bboxes_high1], - scores=scores_low1, - anchors=[anchors_low1, anchors_high1], - im_info=im_info1, - ) - - self.assertRaises(TypeError, test_scores_type) - - def test_scores_tensor_dtype(): - scores_high2 = fluid.data( - name='scores_high2', shape=[1, 11, 10], dtype='int32' - ) - fluid.layers.retinanet_detection_output( - bboxes=[bboxes_low1, bboxes_high1], - scores=[scores_high2, 5], - anchors=[anchors_low1, anchors_high1], - im_info=im_info1, - ) - - self.assertRaises(TypeError, test_scores_tensor_dtype) - - # The `anchors` must be list, each element must be Variable and its - # Tensor data type must be one of float32 and float64. - def test_anchors_type(): - fluid.layers.retinanet_detection_output( - bboxes=[bboxes_low1, bboxes_high1], - scores=[scores_low1, scores_high1], - anchors=anchors_low1, - im_info=im_info1, - ) - - self.assertRaises(TypeError, test_anchors_type) - - def test_anchors_tensor_dtype(): - anchors_high2 = fluid.data( - name='anchors_high2', shape=[11, 4], dtype='int32' - ) - fluid.layers.retinanet_detection_output( - bboxes=[bboxes_low1, bboxes_high1], - scores=[scores_low1, scores_high1], - anchors=[anchors_high2, 5], - im_info=im_info1, - ) - - self.assertRaises(TypeError, test_anchors_tensor_dtype) - - # The `im_info` must be Variable and the data type of `im_info` - # Tensor must be one of float32 and float64. - def test_iminfo_type(): - fluid.layers.retinanet_detection_output( - bboxes=[bboxes_low1, bboxes_high1], - scores=[scores_low1, scores_high1], - anchors=[anchors_low1, anchors_high1], - im_info=[2, 3, 4], - ) - - self.assertRaises(TypeError, test_iminfo_type) - - def test_iminfo_tensor_dtype(): - im_info2 = fluid.data( - name='im_info2', shape=[1, 3], dtype='int32' - ) - fluid.layers.retinanet_detection_output( - bboxes=[bboxes_low1, bboxes_high1], - scores=[scores_low1, scores_high1], - anchors=[anchors_low1, anchors_high1], - im_info=im_info2, - ) - - self.assertRaises(TypeError, test_iminfo_tensor_dtype) - - if __name__ == '__main__': paddle.enable_static() unittest.main() diff --git a/python/paddle/fluid/tests/unittests/test_roi_perspective_transform_op.py b/python/paddle/fluid/tests/unittests/test_roi_perspective_transform_op.py index 6a82d953bb24c..d3bfac6081095 100644 --- a/python/paddle/fluid/tests/unittests/test_roi_perspective_transform_op.py +++ b/python/paddle/fluid/tests/unittests/test_roi_perspective_transform_op.py @@ -16,9 +16,6 @@ from math import floor, sqrt import numpy as np -from op_test import OpTest - -from paddle import fluid def gt_e(a, b): @@ -261,166 +258,5 @@ def roi_transform( return out.astype("float32"), mask, matrix -class TestROIPoolOp(OpTest): - def set_data(self): - self.init_test_case() - self.make_rois() - - self.inputs = {'X': self.x, 'ROIs': (self.rois, self.rois_lod)} - - self.attrs = { - 'spatial_scale': self.spatial_scale, - 'transformed_height': self.transformed_height, - 'transformed_width': self.transformed_width, - } - out, mask, transform_matrix = roi_transform( - self.x, - self.rois, - self.rois_lod, - self.transformed_height, - self.transformed_width, - self.spatial_scale, - ) - self.outputs = { - 'Out': out, - 'Mask': mask, - 'TransformMatrix': transform_matrix, - } - - def init_test_case(self): - self.batch_size = 2 - self.channels = 2 - self.height = 8 - self.width = 8 - - # n, c, h, w - self.x_dim = (self.batch_size, self.channels, self.height, self.width) - - self.spatial_scale = 1.0 / 2.0 - self.transformed_height = 2 - self.transformed_width = 3 - - self.x = np.random.random(self.x_dim).astype('float32') - - def make_rois(self): - rois = [] - self.rois_lod = [[]] - for bno in range(self.batch_size): - self.rois_lod[0].append(bno + 1) - for i in range(bno + 1): - x1 = np.random.randint( - 0, self.width // self.spatial_scale - self.transformed_width - ) - y1 = np.random.randint( - 0, - self.height // self.spatial_scale - self.transformed_height, - ) - - x2 = np.random.randint( - x1 + self.transformed_width, - self.width // self.spatial_scale, - ) - y2 = np.random.randint( - 0, - self.height // self.spatial_scale - self.transformed_height, - ) - - x3 = np.random.randint( - x1 + self.transformed_width, - self.width // self.spatial_scale, - ) - y3 = np.random.randint( - y1 + self.transformed_height, - self.height // self.spatial_scale, - ) - - x4 = np.random.randint( - 0, self.width // self.spatial_scale - self.transformed_width - ) - y4 = np.random.randint( - y1 + self.transformed_height, - self.height // self.spatial_scale, - ) - - roi = [x1, y1, x2, y2, x3, y3, x4, y4] - rois.append(roi) - self.rois_num = len(rois) - self.rois = np.array(rois).astype("float32") - - def setUp(self): - self.op_type = "roi_perspective_transform" - self.set_data() - - def test_check_output(self): - self.check_output() - - def test_check_grad(self): - self.outputs['Out2InIdx'] = np.zeros( - [np.product(self.outputs['Out'].shape), 4] - ).astype("int32") - self.outputs['Out2InWeights'] = np.zeros( - [np.product(self.outputs['Out'].shape), 4] - ).astype("float32") - self.check_grad(['X'], 'Out') - - def test_errors(self): - x = fluid.data(name='x', shape=[100, 256, 28, 28], dtype='float32') - rois = fluid.data( - name='rois', shape=[None, 8], lod_level=1, dtype='float32' - ) - - x_int = fluid.data( - name='x_int', shape=[100, 256, 28, 28], dtype='int32' - ) - rois_int = fluid.data( - name='rois_int', shape=[None, 8], lod_level=1, dtype='int32' - ) - x_tmp = [1, 2] - rois_tmp = [1, 2] - - # type of intput and rois must be variable - self.assertRaises( - TypeError, fluid.layers.roi_perspective_transform, x_tmp, rois, 7, 7 - ) - self.assertRaises( - TypeError, fluid.layers.roi_perspective_transform, x, rois_tmp, 7, 7 - ) - - # dtype of intput and rois must be float32 - self.assertRaises( - TypeError, fluid.layers.roi_perspective_transform, x_int, rois, 7, 7 - ) - self.assertRaises( - TypeError, fluid.layers.roi_perspective_transform, x, rois_int, 7, 7 - ) - - height = 7.5 - width = 7.5 - # type of transformed_height and transformed_width must be int - self.assertRaises( - TypeError, - fluid.layers.roi_perspective_transform, - x, - rois, - height, - 7, - ) - self.assertRaises( - TypeError, fluid.layers.roi_perspective_transform, x, rois, 7, width - ) - - scale = int(2) - # type of spatial_scale must be float - self.assertRaises( - TypeError, - fluid.layers.roi_perspective_transform, - x, - rois, - 7, - 7, - scale, - ) - - if __name__ == '__main__': unittest.main() diff --git a/python/paddle/fluid/tests/unittests/white_list/op_accuracy_white_list.py b/python/paddle/fluid/tests/unittests/white_list/op_accuracy_white_list.py index ba93c5bb874a3..3478c4aac2647 100644 --- a/python/paddle/fluid/tests/unittests/white_list/op_accuracy_white_list.py +++ b/python/paddle/fluid/tests/unittests/white_list/op_accuracy_white_list.py @@ -54,7 +54,6 @@ 'reduce_max', 'reduce_min', 'reshape2', - 'roi_perspective_transform', 'row_conv', 'scatter', 'sequence_conv', diff --git a/python/paddle/static/nn/__init__.py b/python/paddle/static/nn/__init__.py index 37594c937e5d6..d4b0d921bed68 100755 --- a/python/paddle/static/nn/__init__.py +++ b/python/paddle/static/nn/__init__.py @@ -32,7 +32,6 @@ from ...tensor.creation import create_parameter # noqa: F401 from ...fluid.layers import conv2d # noqa: F401 from ...fluid.layers import layer_norm # noqa: F401 -from ...fluid.layers import multi_box_head # noqa: F401 from .loss import nce # noqa: F401 from .common import prelu # noqa: F401 from ...fluid.layers import row_conv # noqa: F401 @@ -76,8 +75,8 @@ 'group_norm', 'instance_norm', 'layer_norm', - 'multi_box_head', 'nce', + 'prelu', 'py_func', 'row_conv', 'spectral_norm', diff --git a/python/paddle/static/nn/common.py b/python/paddle/static/nn/common.py old mode 100755 new mode 100644 index c94ef112484e8..ee12eb8b295d9 --- a/python/paddle/static/nn/common.py +++ b/python/paddle/static/nn/common.py @@ -799,7 +799,7 @@ def conv3d( `[batch_size, input_channels, input_height, input_width]`. Returns: - A Variable holding Tensor representing the conv3d, whose data type is + A Tensor representing the conv3d, whose data type is the same with input. If act is None, the tensor variable storing the convolution result, and if act is not None, the tensor variable storing convolution and non-linearity activation result. @@ -1190,7 +1190,7 @@ def conv2d_transpose( helper = LayerHelper(op_type, **locals()) if not isinstance(input, Variable): - raise TypeError("Input of conv2d_transpose must be Variable") + raise TypeError("Input of conv2d_transpose must be Tensor") stride = utils.convert_to_list(stride, 2, 'stride') dilation = utils.convert_to_list(dilation, 2, 'dilation') @@ -1280,7 +1280,7 @@ def is_list_or_tuple(ele): output_size ): raise ValueError( - "filter_size should not be None when output_size is Variable or contain Variable in static mode." + "filter_size should not be None when output_size is Tensor or contain Tensor in static mode." ) else: output_size = utils.convert_shape_to_list(output_size) @@ -1497,7 +1497,7 @@ def conv3d_transpose( `[batch_size, input_channels, input_height, input_width]`. Returns: - A Variable holding Tensor representing the conv3d_transpose, whose data + A Tensor representing the conv3d_transpose, whose data type is the same with input and shape is (num_batches, channels, out_d, out_h, out_w) or (num_batches, out_d, out_h, out_w, channels). If act is None, the tensor variable storing the transposed convolution result, and if act is not None, the tensor @@ -1546,7 +1546,7 @@ def conv3d_transpose( l_type = "conv3d_transpose" helper = LayerHelper(l_type, **locals()) if not isinstance(input, Variable): - raise TypeError("Input of conv3d_transpose must be Variable") + raise TypeError("Input of conv3d_transpose must be Tensor") if len(input.shape) != 5: raise ValueError( "Input should be 5D tensor, but received input with the shape of {}".format( @@ -1785,7 +1785,7 @@ def deformable_conv( float32, float64. offset (Tensor): The input coordinate offset of deformable convolution layer. A Tensor with type float32, float64. - Mask (Variable, Optional): The input mask of deformable convolution layer. + Mask (Tensor, Optional): The input mask of deformable convolution layer. A Tensor with type float32, float64. It should be None when you use deformable convolution v1. num_filters(int): The number of filter. It is as same as the output @@ -1876,9 +1876,9 @@ def deformable_conv( dtype = helper.input_dtype() if not isinstance(input, paddle.static.Variable): - raise TypeError("Input of deformable_conv must be Variable") + raise TypeError("Input of deformable_conv must be Tensor") if not isinstance(offset, paddle.static.Variable): - raise TypeError("Input Offset of deformable_conv must be Variable") + raise TypeError("Input Offset of deformable_conv must be Tensor") if groups is None: num_filter_channels = num_channels @@ -2155,9 +2155,9 @@ def bilinear_tensor_product( - :math:`y^\mathrm{T}`: the transpose of :math:`y_{2}`. Args: - x (Variable): 2-D input tensor with shape [batch_size, M]. Data type + x (Tensor): 2-D input tensor with shape [batch_size, M]. Data type is float32 or float64. - y (Variable): 2-D input tensor with shape [batch_size, N]. Data type + y (Tensor): 2-D input tensor with shape [batch_size, N]. Data type should be same as **x**. size (int): The dimension of this layer. act (str|None): Activation to be applied to the output of this layer. Default None. @@ -2832,7 +2832,7 @@ def py_func_demo(): y = paddle.static.data(name='y', shape=[2,3], dtype='int32') # Output of the forward function, name/dtype/shape must be specified output = create_tmp_var('output','int32', [3,1]) - # Multiple Variable should be passed in the form of tuple(Variale) or list[Variale] + # Multiple Tensor should be passed in the form of tuple(Tensor) or list[Tensor] paddle.static.py_func(func=element_wise_add, x=[x,y], out=output) exe=paddle.static.Executor(paddle.CPUPlace()) exe.run(start_program) @@ -2857,7 +2857,7 @@ def py_func_demo(): elif isinstance(x, tuple): x = list(x) elif not isinstance(x, (list, tuple, Variable)): - raise TypeError('Input must be Variable/list(Variable)/tuple(Variable)') + raise TypeError('Input must be Tensor/list(Tensor)/tuple(Tensor)') check_type(out, 'Out', (list, tuple, Variable, type(None)), 'py_func') if out is None: out_list = [] @@ -2868,9 +2868,7 @@ def py_func_demo(): elif isinstance(out, list): out_list = out else: - raise TypeError( - 'Output must be Variable/list(Variable)/tuple(Variable)' - ) + raise TypeError('Output must be Tensor/list(Tensor)/tuple(Tensor)') fwd_func_id = PyFuncRegistry(func).id bwd_func_id = ( @@ -2895,7 +2893,7 @@ def py_func_demo(): for v in skip_vars_in_backward_input: if v.name not in fwd_in_out: raise ValueError( - 'Variable {} is not found in forward inputs and outputs'.format( + 'Tensor {} is not found in forward inputs and outputs'.format( v.name ) ) diff --git a/tools/parallel_UT_rule.py b/tools/parallel_UT_rule.py index 2225ae33021d8..2584dbd1e0ecc 100755 --- a/tools/parallel_UT_rule.py +++ b/tools/parallel_UT_rule.py @@ -702,7 +702,6 @@ 'test_uniform_random_bf16_op', 'test_custom_concat', 'test_weight_quantization_mobilenetv1', - 'test_retinanet_detection_output', 'test_concat_mkldnn_op', 'test_gaussian_random_mkldnn_op', 'test_parallel_executor_seresnext_with_reduce_cpu', @@ -786,7 +785,6 @@ 'test_lr_scheduler', 'test_generate_proposals_op', 'test_masked_select_op', - 'test_trt_anchor_generator_op', 'test_imperative_ocr_attention_model', 'test_sentiment', 'test_chunk_op', @@ -1748,7 +1746,6 @@ 'test_rpn_target_assign_op', 'test_row_conv', 'test_rnn_memory_helper_op', - 'test_retinanet_detection_output', 'test_reshape_transpose_matmul_mkldnn_fuse_pass', 'test_reshape_bf16_op', 'test_require_version', @@ -2506,7 +2503,6 @@ 'test_where_index', 'test_variance_layer', 'test_unsqueeze_op', - 'test_trt_anchor_generator_op', 'test_translated_layer', 'test_tensor_shape', 'test_slice',