未验证 提交 e705fede 编写于 作者: R ruri 提交者: GitHub

refine 13 en docs (#20312)

* refine 13 en doc
上级 f0dabe68
......@@ -134,7 +134,7 @@ paddle.fluid.layers.crf_decoding (ArgSpec(args=['input', 'param_attr', 'label',
paddle.fluid.layers.cos_sim (ArgSpec(args=['X', 'Y'], varargs=None, keywords=None, defaults=None), ('document', '48ec1ba2d75c4e2faf8d9a47350462ae'))
paddle.fluid.layers.cross_entropy (ArgSpec(args=['input', 'label', 'soft_label', 'ignore_index'], varargs=None, keywords=None, defaults=(False, -100)), ('document', 'd1985a930a59c3bd41a7c1d72594f5b9'))
paddle.fluid.layers.bpr_loss (ArgSpec(args=['input', 'label', 'name'], varargs=None, keywords=None, defaults=(None,)), ('document', 'ae57e6e5136dade436f0df1f11770afa'))
paddle.fluid.layers.square_error_cost (ArgSpec(args=['input', 'label'], varargs=None, keywords=None, defaults=None), ('document', 'bbb9e708bab250359864fefbdf48e9d9'))
paddle.fluid.layers.square_error_cost (ArgSpec(args=['input', 'label'], varargs=None, keywords=None, defaults=None), ('document', '4ed09e115b50ec7393674c4c09d223a2'))
paddle.fluid.layers.chunk_eval (ArgSpec(args=['input', 'label', 'chunk_scheme', 'num_chunk_types', 'excluded_chunk_types', 'seq_length'], varargs=None, keywords=None, defaults=(None, None)), ('document', 'b02844e0ad4bd713c5fe6802aa13219c'))
paddle.fluid.layers.sequence_conv (ArgSpec(args=['input', 'num_filters', 'filter_size', 'filter_stride', 'padding', 'padding_start', 'bias_attr', 'param_attr', 'act', 'name'], varargs=None, keywords=None, defaults=(3, 1, True, None, None, None, None, None)), ('document', 'ebddcc5a1073ef065d22b4673e36b1d2'))
paddle.fluid.layers.conv2d (ArgSpec(args=['input', 'num_filters', 'filter_size', 'stride', 'padding', 'dilation', 'groups', 'param_attr', 'bias_attr', 'use_cudnn', 'act', 'name', 'data_format'], varargs=None, keywords=None, defaults=(1, 0, 1, None, None, None, True, None, None, 'NCHW')), ('document', 'e91c63b8ac8c35982c0ac518537e44bf'))
......@@ -170,8 +170,8 @@ paddle.fluid.layers.sequence_slice (ArgSpec(args=['input', 'offset', 'length', '
paddle.fluid.layers.dropout (ArgSpec(args=['x', 'dropout_prob', 'is_test', 'seed', 'name', 'dropout_implementation'], varargs=None, keywords=None, defaults=(False, None, None, 'downgrade_in_infer')), ('document', '392dd4bad607fd853f71fec71801044f'))
paddle.fluid.layers.split (ArgSpec(args=['input', 'num_or_sections', 'dim', 'name'], varargs=None, keywords=None, defaults=(-1, None)), ('document', '64073050d3f172d71ace73d7bbb4168e'))
paddle.fluid.layers.ctc_greedy_decoder (ArgSpec(args=['input', 'blank', 'input_length', 'padding_value', 'name'], varargs=None, keywords=None, defaults=(None, 0, None)), ('document', '31e0cbec2898efae95853034adadfe2b'))
paddle.fluid.layers.edit_distance (ArgSpec(args=['input', 'label', 'normalized', 'ignored_tokens', 'input_length', 'label_length'], varargs=None, keywords=None, defaults=(True, None, None, None)), ('document', '77cbfb28cd2fc589f589c7013c5086cd'))
paddle.fluid.layers.l2_normalize (ArgSpec(args=['x', 'axis', 'epsilon', 'name'], varargs=None, keywords=None, defaults=(1e-12, None)), ('document', 'c1df110ea65998984f564c5c10abc54a'))
paddle.fluid.layers.edit_distance (ArgSpec(args=['input', 'label', 'normalized', 'ignored_tokens', 'input_length', 'label_length'], varargs=None, keywords=None, defaults=(True, None, None, None)), ('document', '25f0dd786a98aac31490020725604fe1'))
paddle.fluid.layers.l2_normalize (ArgSpec(args=['x', 'axis', 'epsilon', 'name'], varargs=None, keywords=None, defaults=(1e-12, None)), ('document', '30eeab67154ef09ab3e884117a8d4aee'))
paddle.fluid.layers.matmul (ArgSpec(args=['x', 'y', 'transpose_x', 'transpose_y', 'alpha', 'name'], varargs=None, keywords=None, defaults=(False, False, 1.0, None)), ('document', '3720b4a386585094435993deb028b592'))
paddle.fluid.layers.topk (ArgSpec(args=['input', 'k', 'name'], varargs=None, keywords=None, defaults=(None,)), ('document', 'e50940f3ce5a08cc477b72f517491bf3'))
paddle.fluid.layers.warpctc (ArgSpec(args=['input', 'label', 'blank', 'norm_by_times', 'input_length', 'label_length'], varargs=None, keywords=None, defaults=(0, False, None, None)), ('document', '79aaea078ddea57a82ed7906d71dedc7'))
......@@ -203,11 +203,11 @@ paddle.fluid.layers.label_smooth (ArgSpec(args=['label', 'prior_dist', 'epsilon'
paddle.fluid.layers.roi_pool (ArgSpec(args=['input', 'rois', 'pooled_height', 'pooled_width', 'spatial_scale'], varargs=None, keywords=None, defaults=(1, 1, 1.0)), ('document', '6fc9bae94518bbf3e1a9e479f38f6537'))
paddle.fluid.layers.roi_align (ArgSpec(args=['input', 'rois', 'pooled_height', 'pooled_width', 'spatial_scale', 'sampling_ratio', 'name'], varargs=None, keywords=None, defaults=(1, 1, 1.0, -1, None)), ('document', '3885fd76e122ac0563fa8369bcab7363'))
paddle.fluid.layers.dice_loss (ArgSpec(args=['input', 'label', 'epsilon', 'name'], varargs=None, keywords=None, defaults=(1e-05, None)), ('document', '08d94daffbea3935178810bdc1633f07'))
paddle.fluid.layers.image_resize (ArgSpec(args=['input', 'out_shape', 'scale', 'name', 'resample', 'actual_shape', 'align_corners', 'align_mode', 'data_format'], varargs=None, keywords=None, defaults=(None, None, None, 'BILINEAR', None, True, 1, 'NCHW')), ('document', 'd29d829607b5ff12924197a3ba296c89'))
paddle.fluid.layers.image_resize_short (ArgSpec(args=['input', 'out_short_len', 'resample'], varargs=None, keywords=None, defaults=('BILINEAR',)), ('document', 'bd97ebfe4bdf5110a5fcb8ecb626a447'))
paddle.fluid.layers.resize_bilinear (ArgSpec(args=['input', 'out_shape', 'scale', 'name', 'actual_shape', 'align_corners', 'align_mode', 'data_format'], varargs=None, keywords=None, defaults=(None, None, None, None, True, 1, 'NCHW')), ('document', '44da7890c8a362a83a1c0902a1dc1e4d'))
paddle.fluid.layers.resize_trilinear (ArgSpec(args=['input', 'out_shape', 'scale', 'name', 'actual_shape', 'align_corners', 'align_mode', 'data_format'], varargs=None, keywords=None, defaults=(None, None, None, None, True, 1, 'NCDHW')), ('document', '5b4d0f823f94c260fe5e6f7eec60a797'))
paddle.fluid.layers.resize_nearest (ArgSpec(args=['input', 'out_shape', 'scale', 'name', 'actual_shape', 'align_corners', 'data_format'], varargs=None, keywords=None, defaults=(None, None, None, None, True, 'NCHW')), ('document', '0107a5cbae1aef3f381d3d769a6068eb'))
paddle.fluid.layers.image_resize (ArgSpec(args=['input', 'out_shape', 'scale', 'name', 'resample', 'actual_shape', 'align_corners', 'align_mode', 'data_format'], varargs=None, keywords=None, defaults=(None, None, None, 'BILINEAR', None, True, 1, 'NCHW')), ('document', 'e75264335c94b921712ec3a05d4854b1'))
paddle.fluid.layers.image_resize_short (ArgSpec(args=['input', 'out_short_len', 'resample'], varargs=None, keywords=None, defaults=('BILINEAR',)), ('document', 'd810787ba7ff7f8f64b68779c64a30f8'))
paddle.fluid.layers.resize_bilinear (ArgSpec(args=['input', 'out_shape', 'scale', 'name', 'actual_shape', 'align_corners', 'align_mode', 'data_format'], varargs=None, keywords=None, defaults=(None, None, None, None, True, 1, 'NCHW')), ('document', '06f0e873b64bf993895fb83d1dba882a'))
paddle.fluid.layers.resize_trilinear (ArgSpec(args=['input', 'out_shape', 'scale', 'name', 'actual_shape', 'align_corners', 'align_mode', 'data_format'], varargs=None, keywords=None, defaults=(None, None, None, None, True, 1, 'NCDHW')), ('document', 'f754e5f8b402b83b8bd83275571b2082'))
paddle.fluid.layers.resize_nearest (ArgSpec(args=['input', 'out_shape', 'scale', 'name', 'actual_shape', 'align_corners', 'data_format'], varargs=None, keywords=None, defaults=(None, None, None, None, True, 'NCHW')), ('document', '7be7d9f5a514e9224591b96562adb00f'))
paddle.fluid.layers.gather (ArgSpec(args=['input', 'index', 'overwrite'], varargs=None, keywords=None, defaults=(True,)), ('document', '5c52e8512f97a84608bc3b8b3250fc70'))
paddle.fluid.layers.gather_nd (ArgSpec(args=['input', 'index', 'name'], varargs=None, keywords=None, defaults=(None,)), ('document', 'a7d625028525167b138106f574dffdf9'))
paddle.fluid.layers.scatter (ArgSpec(args=['input', 'index', 'updates', 'name', 'overwrite'], varargs=None, keywords=None, defaults=(None, True)), ('document', '3f94c3348dc79b7b40a839d31a3eaa84'))
......@@ -255,7 +255,7 @@ paddle.fluid.layers.elementwise_mod (ArgSpec(args=['x', 'y', 'axis', 'act', 'nam
paddle.fluid.layers.elementwise_floordiv (ArgSpec(args=['x', 'y', 'axis', 'act', 'name'], varargs=None, keywords=None, defaults=(-1, None, None)), ('document', '67e6101c31314d4082621e8e443cfb68'))
paddle.fluid.layers.uniform_random_batch_size_like (ArgSpec(args=['input', 'shape', 'dtype', 'input_dim_idx', 'output_dim_idx', 'min', 'max', 'seed'], varargs=None, keywords=None, defaults=('float32', 0, 0, -1.0, 1.0, 0)), ('document', 'cfa120e583cd4a5bfa120c8a26f98a28'))
paddle.fluid.layers.gaussian_random (ArgSpec(args=['shape', 'mean', 'std', 'seed', 'dtype'], varargs=None, keywords=None, defaults=(0.0, 1.0, 0, 'float32')), ('document', 'dd4ddb66c78a2564e5d1e0e345d8286f'))
paddle.fluid.layers.sampling_id (ArgSpec(args=['x', 'min', 'max', 'seed', 'dtype'], varargs=None, keywords=None, defaults=(0.0, 1.0, 0, 'float32')), ('document', '2490492db3b41af9144bb1539e4e9116'))
paddle.fluid.layers.sampling_id (ArgSpec(args=['x', 'min', 'max', 'seed', 'dtype'], varargs=None, keywords=None, defaults=(0.0, 1.0, 0, 'float32')), ('document', '9ac9bdc45be94494d8543b8cec5c26e0'))
paddle.fluid.layers.gaussian_random_batch_size_like (ArgSpec(args=['input', 'shape', 'input_dim_idx', 'output_dim_idx', 'mean', 'std', 'seed', 'dtype'], varargs=None, keywords=None, defaults=(0, 0, 0.0, 1.0, 0, 'float32')), ('document', '2aed0f546f220364fb1da724a3176f74'))
paddle.fluid.layers.sum (ArgSpec(args=['x'], varargs=None, keywords=None, defaults=None), ('document', '42c43fc74347bfe9528850aa7f59b2b2'))
paddle.fluid.layers.slice (ArgSpec(args=['input', 'axes', 'starts', 'ends'], varargs=None, keywords=None, defaults=None), ('document', '8c622791994a0d657d8c6c9cefa5bf34'))
......@@ -286,7 +286,7 @@ paddle.fluid.layers.bilinear_tensor_product (ArgSpec(args=['x', 'y', 'size', 'ac
paddle.fluid.layers.merge_selected_rows (ArgSpec(args=['x', 'name'], varargs=None, keywords=None, defaults=(None,)), ('document', 'b2b0e5d5c155ce24bafc38b78cd0b164'))
paddle.fluid.layers.get_tensor_from_selected_rows (ArgSpec(args=['x', 'name'], varargs=None, keywords=None, defaults=(None,)), ('document', '2c568321feb4d16c41a83df43f95089d'))
paddle.fluid.layers.lstm (ArgSpec(args=['input', 'init_h', 'init_c', 'max_len', 'hidden_size', 'num_layers', 'dropout_prob', 'is_bidirec', 'is_test', 'name', 'default_initializer', 'seed'], varargs=None, keywords=None, defaults=(0.0, False, False, None, None, -1)), ('document', '5193cf1113f9d8d8f682ee5a5fc8b391'))
paddle.fluid.layers.shuffle_channel (ArgSpec(args=['x', 'group', 'name'], varargs=None, keywords=None, defaults=(None,)), ('document', '276a1213dd431228cefa33c3146df34a'))
paddle.fluid.layers.shuffle_channel (ArgSpec(args=['x', 'group', 'name'], varargs=None, keywords=None, defaults=(None,)), ('document', '50c06087a53aee4c466afe6fca057d2b'))
paddle.fluid.layers.temporal_shift (ArgSpec(args=['x', 'seg_num', 'shift_ratio', 'name'], varargs=None, keywords=None, defaults=(0.25, None)), ('document', 'd5945431cdcae3cda21914db5bbf383e'))
paddle.fluid.layers.py_func (ArgSpec(args=['func', 'x', 'out', 'backward_func', 'skip_vars_in_backward_input'], varargs=None, keywords=None, defaults=(None, None)), ('document', '231f91231430f5dae2b757df22317c67'))
paddle.fluid.layers.psroi_pool (ArgSpec(args=['input', 'rois', 'output_channels', 'spatial_scale', 'pooled_height', 'pooled_width', 'name'], varargs=None, keywords=None, defaults=(None,)), ('document', '9bf0cc6b0717010b8ceec5dc2541d566'))
......@@ -295,7 +295,7 @@ paddle.fluid.layers.teacher_student_sigmoid_loss (ArgSpec(args=['input', 'label'
paddle.fluid.layers.huber_loss (ArgSpec(args=['input', 'label', 'delta'], varargs=None, keywords=None, defaults=None), ('document', '9d93ee81f7a3e526d68bb280bc695d6c'))
paddle.fluid.layers.kldiv_loss (ArgSpec(args=['x', 'target', 'reduction', 'name'], varargs=None, keywords=None, defaults=('mean', None)), ('document', '45f3ebbcb766fca84cb2fe6307086573'))
paddle.fluid.layers.npair_loss (ArgSpec(args=['anchor', 'positive', 'labels', 'l2_reg'], varargs=None, keywords=None, defaults=(0.002,)), ('document', '3828c4bd81c25af0ab955f52d453c587'))
paddle.fluid.layers.pixel_shuffle (ArgSpec(args=['x', 'upscale_factor'], varargs=None, keywords=None, defaults=None), ('document', '7e5cac851fd9bad344230e1044b6a565'))
paddle.fluid.layers.pixel_shuffle (ArgSpec(args=['x', 'upscale_factor'], varargs=None, keywords=None, defaults=None), ('document', '577a8daeca4a1f7f85cc63fa009ce971'))
paddle.fluid.layers.fsp_matrix (ArgSpec(args=['x', 'y'], varargs=None, keywords=None, defaults=None), ('document', '3a4eb7cce366f5fd8bc38b42b6af5ba1'))
paddle.fluid.layers.continuous_value_model (ArgSpec(args=['input', 'cvm', 'use_cvm'], varargs=None, keywords=None, defaults=(True,)), ('document', 'b335b531931cc8b2d19c65980eadfc1e'))
paddle.fluid.layers.where (ArgSpec(args=['condition'], varargs=None, keywords=None, defaults=None), ('document', '68810eedf448f2cb3abd46518dd46c39'))
......@@ -306,7 +306,7 @@ paddle.fluid.layers.deformable_roi_pooling (ArgSpec(args=['input', 'rois', 'tran
paddle.fluid.layers.filter_by_instag (ArgSpec(args=['ins', 'ins_tag', 'filter_tag', 'is_lod'], varargs=None, keywords=None, defaults=None), ('document', '7703a2088af8de4128b143ff1164ca4a'))
paddle.fluid.layers.shard_index (ArgSpec(args=['input', 'index_num', 'nshards', 'shard_id', 'ignore_value'], varargs=None, keywords=None, defaults=(-1,)), ('document', '3c6b30e9cd57b38d4a5fa1ade887f779'))
paddle.fluid.layers.hard_swish (ArgSpec(args=['x', 'threshold', 'scale', 'offset', 'name'], varargs=None, keywords=None, defaults=(6.0, 6.0, 3.0, None)), ('document', 'bd763b9ca99239d624c3cb4626e3627a'))
paddle.fluid.layers.mse_loss (ArgSpec(args=['input', 'label'], varargs=None, keywords=None, defaults=None), ('document', 'd9ede6469288636e1b3233b461a165c9'))
paddle.fluid.layers.mse_loss (ArgSpec(args=['input', 'label'], varargs=None, keywords=None, defaults=None), ('document', '88b967ef5132567396062d5d654b3064'))
paddle.fluid.layers.uniform_random (ArgSpec(args=['shape', 'dtype', 'min', 'max', 'seed'], varargs=None, keywords=None, defaults=('float32', -1.0, 1.0, 0)), ('document', '126ede8ce0e751244b1b54cd359c89d7'))
paddle.fluid.layers.data (ArgSpec(args=['name', 'shape', 'append_batch_size', 'dtype', 'lod_level', 'type', 'stop_gradient'], varargs=None, keywords=None, defaults=(True, 'float32', 0, VarType.LOD_TENSOR, True)), ('document', '9d7806e31bdf727c1a23b8782a09b545'))
paddle.fluid.layers.read_file (ArgSpec(args=['reader'], varargs=None, keywords=None, defaults=None), ('document', 'd5b41c7b2df1b064fbd42dcf435268cd'))
......@@ -406,8 +406,8 @@ paddle.fluid.layers.softshrink (ArgSpec(args=['x', 'alpha'], varargs=None, keywo
paddle.fluid.layers.hard_shrink (ArgSpec(args=['x', 'threshold'], varargs=None, keywords=None, defaults=(None,)), ('document', '386a4103d2884b2f1312ebc1e8ee6486'))
paddle.fluid.layers.cumsum (ArgSpec(args=['x', 'axis', 'exclusive', 'reverse'], varargs=None, keywords=None, defaults=(None, None, None)), ('document', 'c1f2e4c4511da09d5d89c556ea802bd1'))
paddle.fluid.layers.thresholded_relu (ArgSpec(args=['x', 'threshold'], varargs=None, keywords=None, defaults=(None,)), ('document', '94c71025bf11ab8172fd455350274138'))
paddle.fluid.layers.prior_box (ArgSpec(args=['input', 'image', 'min_sizes', 'max_sizes', 'aspect_ratios', 'variance', 'flip', 'clip', 'steps', 'offset', 'name', 'min_max_aspect_ratios_order'], varargs=None, keywords=None, defaults=(None, [1.0], [0.1, 0.1, 0.2, 0.2], False, False, [0.0, 0.0], 0.5, None, False)), ('document', '0fdf82762fd0a5acb2578a72771b5b44'))
paddle.fluid.layers.density_prior_box (ArgSpec(args=['input', 'image', 'densities', 'fixed_sizes', 'fixed_ratios', 'variance', 'clip', 'steps', 'offset', 'flatten_to_2d', 'name'], varargs=None, keywords=None, defaults=(None, None, None, [0.1, 0.1, 0.2, 0.2], False, [0.0, 0.0], 0.5, False, None)), ('document', '7a484a0da5e993a7734867a3dfa86571'))
paddle.fluid.layers.prior_box (ArgSpec(args=['input', 'image', 'min_sizes', 'max_sizes', 'aspect_ratios', 'variance', 'flip', 'clip', 'steps', 'offset', 'name', 'min_max_aspect_ratios_order'], varargs=None, keywords=None, defaults=(None, [1.0], [0.1, 0.1, 0.2, 0.2], False, False, [0.0, 0.0], 0.5, None, False)), ('document', '7ac39852eaabb48080183b904a4afeee'))
paddle.fluid.layers.density_prior_box (ArgSpec(args=['input', 'image', 'densities', 'fixed_sizes', 'fixed_ratios', 'variance', 'clip', 'steps', 'offset', 'flatten_to_2d', 'name'], varargs=None, keywords=None, defaults=(None, None, None, [0.1, 0.1, 0.2, 0.2], False, [0.0, 0.0], 0.5, False, None)), ('document', 'e6c33478b467a6a768aee956a83cd774'))
paddle.fluid.layers.multi_box_head (ArgSpec(args=['inputs', 'image', 'base_size', 'num_classes', 'aspect_ratios', 'min_ratio', 'max_ratio', 'min_sizes', 'max_sizes', 'steps', 'step_w', 'step_h', 'offset', 'variance', 'flip', 'clip', 'kernel_size', 'pad', 'stride', 'name', 'min_max_aspect_ratios_order'], varargs=None, keywords=None, defaults=(None, None, None, None, None, None, None, 0.5, [0.1, 0.1, 0.2, 0.2], True, False, 1, 0, 1, None, False)), ('document', '61360150b911fa4097f1a221f5d49877'))
paddle.fluid.layers.bipartite_match (ArgSpec(args=['dist_matrix', 'match_type', 'dist_threshold', 'name'], varargs=None, keywords=None, defaults=(None, None, None)), ('document', '6f795f407a8e3a3ec3da52726c73405a'))
paddle.fluid.layers.target_assign (ArgSpec(args=['input', 'matched_indices', 'negative_indices', 'mismatch_value', 'name'], varargs=None, keywords=None, defaults=(None, None, None)), ('document', '4670c1be208835fc8edd61025c21d0e4'))
......
......@@ -1641,67 +1641,100 @@ def prior_box(input,
name=None,
min_max_aspect_ratios_order=False):
"""
**Prior Box Operator**
Generate prior boxes for SSD(Single Shot MultiBox Detector) algorithm.
This op generates prior boxes for SSD(Single Shot MultiBox Detector) algorithm.
Each position of the input produce N prior boxes, N is determined by
the count of min_sizes, max_sizes and aspect_ratios, The size of the
box is in range(min_size, max_size) interval, which is generated in
sequence according to the aspect_ratios.
Args:
input(Variable): The Input Variables, the format is NCHW.
image(Variable): The input image data of PriorBoxOp,
the layout is NCHW.
min_sizes(list|tuple|float value): min sizes of generated prior boxes.
max_sizes(list|tuple|None): max sizes of generated prior boxes.
Parameters:
input(Variable): 4-D tenosr(NCHW), the data type should be float32 or float64.
image(Variable): 4-D tensor(NCHW), the input image data of PriorBoxOp,
the data type should be float32 or float64.
min_sizes(list|tuple|float): the min sizes of generated prior boxes.
max_sizes(list|tuple|None): the max sizes of generated prior boxes.
Default: None.
aspect_ratios(list|tuple|float value): the aspect ratios of generated
aspect_ratios(list|tuple|float): the aspect ratios of generated
prior boxes. Default: [1.].
variance(list|tuple): the variances to be encoded in prior boxes.
Default:[0.1, 0.1, 0.2, 0.2].
flip(bool): Whether to flip aspect ratios. Default:False.
clip(bool): Whether to clip out-of-boundary boxes. Default: False.
step(list|tuple): Prior boxes step across width and height, If
step[0] == 0.0/step[1] == 0.0, the prior boxes step across
height/weight of the input will be automatically calculated.
step[0] equals to 0.0 or step[1] equals to 0.0, the prior boxes step across
height or weight of the input will be automatically calculated.
Default: [0., 0.]
offset(float): Prior boxes center offset. Default: 0.5
name(str): Name of the prior box op. Default: None.
min_max_aspect_ratios_order(bool): If set True, the output prior box is
in order of [min, max, aspect_ratios], which is consistent with
Caffe. Please note, this order affects the weights order of
convolution layer followed by and does not affect the final
detection results. Default: False.
name(str, optional): The default value is None. Normally there is no need for user to set this property. For more information, please refer to :ref:`api_guide_Name`
Returns:
tuple: A tuple with two Variable (boxes, variances)
Tuple: A tuple with two Variable (boxes, variances)
boxes: the output prior boxes of PriorBox.
The layout is [H, W, num_priors, 4].
boxes(Variable): the output prior boxes of PriorBox.
4-D tensor, the layout is [H, W, num_priors, 4].
H is the height of input, W is the width of input,
num_priors is the total
box count of each position of input.
num_priors is the total box count of each position of input.
variances: the expanded variances of PriorBox.
The layout is [H, W, num_priors, 4].
variances(Variable): the expanded variances of PriorBox.
4-D tensor, the layput is [H, W, num_priors, 4].
H is the height of input, W is the width of input
num_priors is the total
box count of each position of input
num_priors is the total box count of each position of input
Examples:
.. code-block:: python
import paddle.fluid as fluid
input = fluid.layers.data(name="input", shape=[3,6,9])
images = fluid.layers.data(name="images", shape=[3,9,12])
box, var = fluid.layers.prior_box(
input=input,
image=images,
min_sizes=[100.],
flip=True,
clip=True)
#declarative mode
import paddle.fluid as fluid
import numpy as np
input = fluid.data(name="input", shape=[None,3,6,9])
image = fluid.data(name="image", shape=[None,3,9,12])
box, var = fluid.layers.prior_box(
input=input,
image=image,
min_sizes=[100.],
clip=True,
flip=True)
place = fluid.CPUPlace()
exe = fluid.Executor(place)
exe.run(fluid.default_startup_program())
# prepare a batch of data
input_data = np.random.rand(1,3,6,9).astype("float32")
image_data = np.random.rand(1,3,9,12).astype("float32")
box_out, var_out = exe.run(fluid.default_main_program(),
feed={"input":input_data,"image":image_data},
fetch_list=[box,var],
return_numpy=True)
# print(box_out.shape)
# (6, 9, 1, 4)
# print(var_out.shape)
# (6, 9, 1, 4)
# imperative mode
import paddle.fluid.dygraph as dg
with dg.guard(place) as g:
input = dg.to_variable(input_data)
image = dg.to_variable(image_data)
box, var = fluid.layers.prior_box(
input=input,
image=image,
min_sizes=[100.],
clip=True,
flip=True)
# print(box.shape)
# [6L, 9L, 1L, 4L]
# print(var.shape)
# [6L, 9L, 1L, 4L]
"""
helper = LayerHelper("prior_box", **locals())
dtype = helper.input_dtype()
......@@ -1763,74 +1796,123 @@ def density_prior_box(input,
flatten_to_2d=False,
name=None):
"""
**Density Prior Box Operator**
Generate density prior boxes for SSD(Single Shot MultiBox Detector)
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:
N_density_prior_box =sum(N_fixed_ratios * densities_i^2),
.. math::
Args:
input(Variable): The Input Variables, the format is NCHW.
image(Variable): The input image data of PriorBoxOp,
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
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
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
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.
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.
clip(bool): Whether to clip out of boundary boxes. Default: False.
step(list|tuple): Prior boxes step across width and height, If
step[0] == 0.0/step[1] == 0.0, the density prior boxes step across
height/weight of the input will be automatically calculated.
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): Name of the density prior box op. Default: None.
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)
Tuple: A tuple with two Variable (boxes, variances)
boxes: the output density prior boxes of PriorBox.
The layout is [H, W, num_priors, 4] when flatten_to_2d is False.
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,
num_priors is the total box count of each position of input.
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.
The layout is [H, W, num_priors, 4] when flatten_to_2d is False.
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
num_priors is the total box count of each position of input.
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
import paddle.fluid as fluid
input = fluid.layers.data(name="input", shape=[3,6,9])
images = fluid.layers.data(name="images", shape=[3,9,12])
box, var = fluid.layers.density_prior_box(
input=input,
image=images,
densities=[4, 2, 1],
fixed_sizes=[32.0, 64.0, 128.0],
fixed_ratios=[1.],
clip=True,
flatten_to_2d=True)
#declarative mode
import paddle.fluid as fluid
import numpy as np
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(mask_out.shape)
# (1134, 4)
print(z_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()
......
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