From df3025c3ee6a3948fe2f659bc9f01f52f6a81b6f Mon Sep 17 00:00:00 2001 From: qingqing01 Date: Fri, 11 Oct 2019 15:05:27 +0800 Subject: [PATCH] Polish En doc for some APIs. (#20418) * Polish En doc for some APIs * Update some comments and API.spec --- paddle/fluid/API.spec | 10 +- python/paddle/fluid/layers/detection.py | 315 ++++++++++++++---------- python/paddle/fluid/layers/nn.py | 16 +- 3 files changed, 199 insertions(+), 142 deletions(-) diff --git a/paddle/fluid/API.spec b/paddle/fluid/API.spec index 03104e2cc87..8dbddfb487e 100755 --- a/paddle/fluid/API.spec +++ b/paddle/fluid/API.spec @@ -214,7 +214,7 @@ paddle.fluid.layers.scatter (ArgSpec(args=['input', 'index', 'updates', 'name', paddle.fluid.layers.scatter_nd_add (ArgSpec(args=['ref', 'index', 'updates', 'name'], varargs=None, keywords=None, defaults=(None,)), ('document', '2607b5c9369fbc52f208de066a80fc25')) paddle.fluid.layers.scatter_nd (ArgSpec(args=['index', 'updates', 'shape', 'name'], varargs=None, keywords=None, defaults=(None,)), ('document', 'e43f1d3a938b35da246aea3e72a020ec')) paddle.fluid.layers.sequence_scatter (ArgSpec(args=['input', 'index', 'updates', 'name'], varargs=None, keywords=None, defaults=(None,)), ('document', 'abe3f714120117a5a3d3e639853932bf')) -paddle.fluid.layers.random_crop (ArgSpec(args=['x', 'shape', 'seed'], varargs=None, keywords=None, defaults=(None,)), ('document', '042af0b8abea96b40c22f6e70d99e042')) +paddle.fluid.layers.random_crop (ArgSpec(args=['x', 'shape', 'seed'], varargs=None, keywords=None, defaults=(None,)), ('document', '44f35002962cf24e14dd2958f6584e3d')) paddle.fluid.layers.mean_iou (ArgSpec(args=['input', 'label', 'num_classes'], varargs=None, keywords=None, defaults=None), ('document', 'dea29c0c3cdbd5b498afef60e58c9d7c')) paddle.fluid.layers.relu (ArgSpec(args=['x', 'name'], varargs=None, keywords=None, defaults=(None,)), ('document', '0942c174f4f6fb274976d4357356f6a2')) paddle.fluid.layers.selu (ArgSpec(args=['x', 'scale', 'alpha', 'name'], varargs=None, keywords=None, defaults=(None, None, None)), ('document', '3ee40bc474b4bccdaf112d3f0d847318')) @@ -408,10 +408,10 @@ paddle.fluid.layers.cumsum (ArgSpec(args=['x', 'axis', 'exclusive', 'reverse'], 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.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', 'fd58078fdfffd899b91f992ba224628f')) +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', 'e9685f32d21bec8c013626c0254502c5')) -paddle.fluid.layers.detection_output (ArgSpec(args=['loc', 'scores', 'prior_box', 'prior_box_var', 'background_label', 'nms_threshold', 'nms_top_k', 'keep_top_k', 'score_threshold', 'nms_eta', 'return_index'], varargs=None, keywords=None, defaults=(0, 0.3, 400, 200, 0.01, 1.0, False)), ('document', '5485bcaceb0cde2695565a2ffd5bbd40')) +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')) +paddle.fluid.layers.detection_output (ArgSpec(args=['loc', 'scores', 'prior_box', 'prior_box_var', 'background_label', 'nms_threshold', 'nms_top_k', 'keep_top_k', 'score_threshold', 'nms_eta', 'return_index'], varargs=None, keywords=None, defaults=(0, 0.3, 400, 200, 0.01, 1.0, False)), ('document', 'ed19f55b366e68ed686318ef7aff120d')) paddle.fluid.layers.ssd_loss (ArgSpec(args=['location', 'confidence', 'gt_box', 'gt_label', 'prior_box', 'prior_box_var', 'background_label', 'overlap_threshold', 'neg_pos_ratio', 'neg_overlap', 'loc_loss_weight', 'conf_loss_weight', 'match_type', 'mining_type', 'normalize', 'sample_size'], varargs=None, keywords=None, defaults=(None, 0, 0.5, 3.0, 0.5, 1.0, 1.0, 'per_prediction', 'max_negative', True, None)), ('document', '1f1ab4f908ceddef1d99a8363e6826af')) paddle.fluid.layers.rpn_target_assign (ArgSpec(args=['bbox_pred', 'cls_logits', 'anchor_box', 'anchor_var', 'gt_boxes', 'is_crowd', 'im_info', 'rpn_batch_size_per_im', 'rpn_straddle_thresh', 'rpn_fg_fraction', 'rpn_positive_overlap', 'rpn_negative_overlap', 'use_random'], varargs=None, keywords=None, defaults=(256, 0.0, 0.5, 0.7, 0.3, True)), ('document', 'd46629656b4ce9b07809e32c0482cbef')) paddle.fluid.layers.retinanet_target_assign (ArgSpec(args=['bbox_pred', 'cls_logits', 'anchor_box', 'anchor_var', 'gt_boxes', 'gt_labels', 'is_crowd', 'im_info', 'num_classes', 'positive_overlap', 'negative_overlap'], varargs=None, keywords=None, defaults=(1, 0.5, 0.4)), ('document', '543b2a40641260e745a76b1f7a25fb2a')) @@ -420,7 +420,7 @@ paddle.fluid.layers.anchor_generator (ArgSpec(args=['input', 'anchor_sizes', 'as paddle.fluid.layers.roi_perspective_transform (ArgSpec(args=['input', 'rois', 'transformed_height', 'transformed_width', 'spatial_scale', 'name'], varargs=None, keywords=None, defaults=(1.0, None)), ('document', 'b007f545ad41e66b814203bdb76516c6')) paddle.fluid.layers.generate_proposal_labels (ArgSpec(args=['rpn_rois', 'gt_classes', 'is_crowd', 'gt_boxes', 'im_info', 'batch_size_per_im', 'fg_fraction', 'fg_thresh', 'bg_thresh_hi', 'bg_thresh_lo', 'bbox_reg_weights', 'class_nums', 'use_random', 'is_cls_agnostic', 'is_cascade_rcnn'], varargs=None, keywords=None, defaults=(256, 0.25, 0.25, 0.5, 0.0, [0.1, 0.1, 0.2, 0.2], None, True, False, False)), ('document', 'f2342042127b536a0a16390f149f1bba')) paddle.fluid.layers.generate_proposals (ArgSpec(args=['scores', 'bbox_deltas', 'im_info', 'anchors', 'variances', 'pre_nms_top_n', 'post_nms_top_n', 'nms_thresh', 'min_size', 'eta', 'name'], varargs=None, keywords=None, defaults=(6000, 1000, 0.5, 0.1, 1.0, None)), ('document', '5cba014b41610431f8949e2d7336f1cc')) -paddle.fluid.layers.generate_mask_labels (ArgSpec(args=['im_info', 'gt_classes', 'is_crowd', 'gt_segms', 'rois', 'labels_int32', 'num_classes', 'resolution'], varargs=None, keywords=None, defaults=None), ('document', 'b319b10ddaf17fb4ddf03518685a17ef')) +paddle.fluid.layers.generate_mask_labels (ArgSpec(args=['im_info', 'gt_classes', 'is_crowd', 'gt_segms', 'rois', 'labels_int32', 'num_classes', 'resolution'], varargs=None, keywords=None, defaults=None), ('document', '2bacc35429f4fffe72a30c5a49a61eb7')) paddle.fluid.layers.iou_similarity (ArgSpec(args=['x', 'y', 'name'], varargs=None, keywords=None, defaults=(None,)), ('document', 'e24478fd1fcf1727d4947fe14356b3d4')) paddle.fluid.layers.box_coder (ArgSpec(args=['prior_box', 'prior_box_var', 'target_box', 'code_type', 'box_normalized', 'name', 'axis'], varargs=None, keywords=None, defaults=('encode_center_size', True, None, 0)), ('document', '511d7033c0cfce1a5b88c04ad6e7ed5b')) paddle.fluid.layers.polygon_box_transform (ArgSpec(args=['input', 'name'], varargs=None, keywords=None, defaults=(None,)), ('document', '2183f03c4f16712dcef6a474dbcefa24')) diff --git a/python/paddle/fluid/layers/detection.py b/python/paddle/fluid/layers/detection.py index 58ccc1b3b17..c09d5720bd0 100644 --- a/python/paddle/fluid/layers/detection.py +++ b/python/paddle/fluid/layers/detection.py @@ -525,12 +525,11 @@ def detection_output(loc, nms_eta=1.0, return_index=False): """ - **Detection Output Layer for Single Shot Multibox Detector (SSD).** + Given the regression locations, classification confidences and prior boxes, + calculate the detection outputs by performing following steps: - This operation is to get the detection results by performing following - two steps: - - 1. Decode input bounding box predictions according to the prior boxes. + 1. Decode input bounding box predictions according to the prior boxes and + regression locations. 2. Get the final detection results by applying multi-class non maximum suppression (NMS). @@ -539,33 +538,33 @@ def detection_output(loc, Args: loc(Variable): A 3-D Tensor with shape [N, M, 4] represents the - predicted locations of M bounding bboxes. N is the batch size, + predicted locations of M bounding bboxes. Data type should be + float32 or float64. N is the batch size, and each bounding box has four coordinate values and the layout is [xmin, ymin, xmax, ymax]. scores(Variable): A 3-D Tensor with shape [N, M, C] 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. + predicted confidence predictions. Data type should be float32 + or float64. N is the batch size, C is the + class number, M is number of bounding boxes. prior_box(Variable): A 2-D Tensor with shape [M, 4] holds M boxes, - each box is represented as [xmin, ymin, xmax, ymax], - [xmin, ymin] is the left top coordinate of the anchor box, - if the input is image feature map, they are close to the origin - of the coordinate system. [xmax, ymax] is the right bottom - coordinate of the anchor box. + each box is represented as [xmin, ymin, xmax, ymax]. Data type + should be float32 or float64. prior_box_var(Variable): A 2-D Tensor with shape [M, 4] holds M group - of variance. - background_label(float): The index of background label, + of variance. Data type should be 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. - nms_threshold(float): The threshold to be used in NMS. + categories will be considered. Default: 0. + nms_threshold(float): The threshold to be used in NMS. Default: 0.3. nms_top_k(int): Maximum number of detections to be kept according - to the confidences aftern the filtering detections based on - score_threshold. + to the confidences aftern filtering detections based on + score_threshold and before NMS. Default: 400. 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 step. -1 means keeping all bboxes after NMS step. Default: 200. score_threshold(float): Threshold to filter out bounding boxes with low confidence score. If not provided, consider all boxes. - nms_eta(float): The parameter for adaptive NMS. + Default: 0.01. + nms_eta(float): The parameter for adaptive NMS. It works only when the + value is less than 1.0. Default: 1.0. return_index(bool): Whether return selected index. Default: False Returns: @@ -573,22 +572,18 @@ def detection_output(loc, A tuple with two Variables: (Out, Index) if return_index is True, otherwise, a tuple with one Variable(Out) is returned. - Out: The detection outputs is a LoDTensor with shape [No, 6]. Each row - has six values: [label, confidence, xmin, ymin, xmax, ymax]. `No` is - the total number of detections in this mini-batch. For each instance, - the offsets in first dimension are called LoD, the offset number is - N + 1, N is the batch size. The i-th image has `LoD[i + 1] - LoD[i]` - detected results, if it is 0, the i-th image has no detected results. - - If all images have not detected results, LoD will be set to {1}, and - output tensor only contains one value, which is -1. - (After version 1.3, when no boxes detected, the lod is changed - from {0} to {1}.) - - Index: Only return when return_index is True. A 2-D LoDTensor with - shape [No, 1] represents the selected index which type is Integer. - The index is the absolute value cross batches. No is the same number - as Out. If the index is used to gather other attribute such as age, + Out (Variable): The detection outputs is a LoDTensor with shape [No, 6]. + Data type is the same as input (loc). Each row has six values: + [label, confidence, xmin, ymin, xmax, ymax]. `No` is + the total number of detections in this mini-batch. For each instance, + the offsets in first dimension are called LoD, the offset number is + N + 1, N is the batch size. The i-th image has `LoD[i + 1] - LoD[i]` + detected results, if it is 0, the i-th image has no detected results. + + Index (Variable): Only return when return_index is True. A 2-D LoDTensor + with shape [No, 1] represents the selected index which type is Integer. + The index is the absolute value cross batches. No is the same number + as Out. If the index is used to gather other attribute such as age, one needs to reshape the input(N, M, 1) to (N * M, 1) as first, where N is the batch size and M is the number of boxes. @@ -598,14 +593,10 @@ def detection_output(loc, import paddle.fluid as fluid - pb = fluid.layers.data(name='prior_box', shape=[10, 4], - append_batch_size=False, dtype='float32') - pbv = fluid.layers.data(name='prior_box_var', shape=[10, 4], - append_batch_size=False, dtype='float32') - loc = fluid.layers.data(name='target_box', shape=[2, 21, 4], - append_batch_size=False, dtype='float32') - scores = fluid.layers.data(name='scores', shape=[2, 21, 10], - append_batch_size=False, dtype='float32') + pb = fluid.data(name='prior_box', shape=[10, 4], dtype='float32') + pbv = fluid.data(name='prior_box_var', shape=[10, 4], dtype='float32') + loc = fluid.data(name='target_box', shape=[2, 21, 4], dtype='float32') + scores = fluid.data(name='scores', shape=[2, 21, 10], dtype='float32') nmsed_outs, index = fluid.layers.detection_output(scores=scores, loc=loc, prior_box=pb, @@ -1318,51 +1309,57 @@ def target_assign(input, out[j][j][0 : K] = {mismatch_value, mismatch_value, ...} out_weight[i][j] = 0. - 2. Assigning out_weight based on `neg_indices` if `neg_indices` is provided: + 2. Assigning outputs based on `neg_indices` if `neg_indices` is provided: - Assumed that the row offset for each instance in `neg_indices` is called neg_lod, - for i-th instance and each `id` of neg_indices in this instance: + Assumed that i-th instance in `neg_indices` is called `neg_indice`, + for i-th instance: .. code-block:: text - out[i][id][0 : K] = {mismatch_value, mismatch_value, ...} - out_weight[i][id] = 1.0 + for id in neg_indice: + out[i][id][0 : K] = {mismatch_value, mismatch_value, ...} + out_weight[i][id] = 1.0 Args: - inputs (Variable): This input is a 3D LoDTensor with shape [M, P, K]. - matched_indices (Variable): Tensor), The input matched indices + input (Variable): This input is a 3D LoDTensor with shape [M, P, K]. + Data type should be int32 or float32. + matched_indices (Variable): The input matched indices is 2D Tenosr with shape [N, P], If MatchIndices[i][j] is -1, the j-th entity of column is not matched to any entity of row in i-th instance. - negative_indices (Variable): The input negative example indices are - an optional input with shape [Neg, 1] and int32 type, where Neg is + negative_indices (Variable, optional): The input negative example indices + are an optional input with shape [Neg, 1] and int32 type, where Neg is the total number of negative example indices. - mismatch_value (float32): Fill this value to the mismatched location. + mismatch_value (float32, optional): Fill this value to the mismatched + location. + name (string): 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(out, out_weight) is returned. out is a 3D Tensor with - shape [N, P, K], N and P is the same as they are in - `neg_indices`, K is the same as it in input of X. If - `match_indices[i][j]`. out_weight is the weight for output with - the shape of [N, P, 1]. + tuple: A tuple(out, out_weight) is returned. + + out (Variable): a 3D Tensor with shape [N, P, K] and same data type + with `input`, N and P is the same as they are in `matched_indices`, + K is the same as it in input of X. + + out_weight (Variable): the weight for output with the shape of [N, P, 1]. + Data type is float32. Examples: .. code-block:: python import paddle.fluid as fluid - x = fluid.layers.data( + x = fluid.data( name='x', shape=[4, 20, 4], dtype='float', - lod_level=1, - append_batch_size=False) - matched_id = fluid.layers.data( + lod_level=1) + matched_id = fluid.data( name='indices', shape=[8, 20], - dtype='int32', - append_batch_size=False) + dtype='int32') trg, trg_weight = fluid.layers.target_assign( x, matched_id, @@ -1905,21 +1902,37 @@ def multi_box_head(inputs, name=None, min_max_aspect_ratios_order=False): """ - Generate prior boxes for SSD(Single Shot MultiBox Detector) - algorithm. The details of this algorithm, please refer the - section 2.2 of SSD paper `SSD: Single Shot MultiBox Detector + 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|tuple): The list of input Variables, the format - of all Variables is NCHW. - image(Variable): The input image data of PriorBoxOp, - the layout is NCHW. - base_size(int): the base_size is used to get min_size - and max_size according to min_ratio and max_ratio. + 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 six.moves.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|tuple): the aspect ratios of generated prior - boxes. The length of input and aspect_ratios must be equal. + 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`, @@ -1945,7 +1958,9 @@ def multi_box_head(inputs, 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): Name of the prior box layer. Default: None. + 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 @@ -1955,33 +1970,34 @@ def multi_box_head(inputs, Returns: tuple: A tuple with four Variables. (mbox_loc, mbox_conf, boxes, variances) - mbox_loc: The predicted boxes' location of the inputs. The layout - is [N, H*W*Priors, 4]. where Priors is the number of predicted - boxes each position of each input. - - mbox_conf: The predicted boxes' confidence of the inputs. The layout - is [N, H*W*Priors, C]. where Priors is the number of predicted boxes - each position of each input and C is the number of Classes. + 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. - boxes: the output prior boxes of PriorBox. The layout is [num_priors, 4]. - num_priors is the total box count of each position of inputs. + 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. - variances: the expanded variances of PriorBox. The layout is - [num_priors, 4]. num_priors is the total box count of each position of inputs + 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: + Examples 1: set min_ratio and max_ratio: .. code-block:: python import paddle.fluid as fluid - images = fluid.layers.data(name='data', shape=[3, 300, 300], dtype='float32') - conv1 = fluid.layers.data(name='conv1', shape=[512, 19, 19], dtype='float32') - conv2 = fluid.layers.data(name='conv2', shape=[1024, 10, 10], dtype='float32') - conv3 = fluid.layers.data(name='conv3', shape=[512, 5, 5], dtype='float32') - conv4 = fluid.layers.data(name='conv4', shape=[256, 3, 3], dtype='float32') - conv5 = fluid.layers.data(name='conv5', shape=[256, 2, 2], dtype='float32') - conv6 = fluid.layers.data(name='conv6', shape=[128, 1, 1], dtype='float32') + images = fluid.data(name='data', shape=[None, 3, 300, 300], dtype='float32') + conv1 = fluid.data(name='conv1', shape=[None, 512, 19, 19], dtype='float32') + conv2 = fluid.data(name='conv2', shape=[None, 1024, 10, 10], dtype='float32') + conv3 = fluid.data(name='conv3', shape=[None, 512, 5, 5], dtype='float32') + conv4 = fluid.data(name='conv4', shape=[None, 256, 3, 3], dtype='float32') + conv5 = fluid.data(name='conv5', shape=[None, 256, 2, 2], dtype='float32') + conv6 = fluid.data(name='conv6', shape=[None, 128, 1, 1], dtype='float32') mbox_locs, mbox_confs, box, var = fluid.layers.multi_box_head( inputs=[conv1, conv2, conv3, conv4, conv5, conv6], @@ -1994,6 +2010,32 @@ def multi_box_head(inputs, offset=0.5, flip=True, clip=True) + + Examples 2: set min_sizes and max_sizes: + .. code-block:: python + + import paddle.fluid as fluid + + images = fluid.data(name='data', shape=[None, 3, 300, 300], dtype='float32') + conv1 = fluid.data(name='conv1', shape=[None, 512, 19, 19], dtype='float32') + conv2 = fluid.data(name='conv2', shape=[None, 1024, 10, 10], dtype='float32') + conv3 = fluid.data(name='conv3', shape=[None, 512, 5, 5], dtype='float32') + conv4 = fluid.data(name='conv4', shape=[None, 256, 3, 3], dtype='float32') + conv5 = fluid.data(name='conv5', shape=[None, 256, 2, 2], dtype='float32') + conv6 = fluid.data(name='conv6', shape=[None, 128, 1, 1], dtype='float32') + + mbox_locs, mbox_confs, box, var = fluid.layers.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): @@ -2439,7 +2481,7 @@ def generate_proposal_labels(rpn_rois, def generate_mask_labels(im_info, gt_classes, is_crowd, gt_segms, rois, labels_int32, num_classes, resolution): """ - ** Generate Mask Labels for Mask-RCNN ** + **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 @@ -2475,62 +2517,67 @@ def generate_mask_labels(im_info, gt_classes, is_crowd, gt_segms, rois, feeder.feed(batch_masks) Args: - im_info(Variable): A 2-D Tensor with shape [N, 3]. N is the batch size, - each element is [height, width, scale] of image. Image scale is - target_size) / original_size. - gt_classes(Variable): A 2-D LoDTensor with shape [M, 1]. M is the total - number of ground-truth, each element is a class label. - is_crowd(Variable): A 2-D LoDTensor with shape 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], - it's LoD level is 3. Usually users do not needs to understand LoD, + 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 + shoule 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 gt objects number of + 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]. 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 + 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 repersents a class label of a RoI. - num_classes(int): Class number. - resolution(int): Resolution of mask predictions. + num_classes (int): Class number. + resolution (int): Resolution of mask predictions. Returns: - mask_rois (Variable): A 2D LoDTensor with shape [P, 4]. P is the total - number of sampled RoIs. Each element is a bounding box with - [xmin, ymin, xmax, ymax] format in range of orignal image size. - mask_rois_has_mask_int32 (Variable): A 2D LoDTensor with shape [P, 1], - each element repersents the output mask RoI index with regard to - to input RoIs. - mask_int32 (Variable): A 2D LoDTensor with shape [P, K * M * M], - K is the classes number and M is the resolution of mask predictions. - Each element repersents the binary mask targets. + 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 + orignal image size. + + mask_rois_has_mask_int32 (Variable): A 2D LoDTensor with shape [P, 1] + and int data type, each element repersents 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 repersents the binary mask targets. Examples: .. code-block:: python import paddle.fluid as fluid - im_info = fluid.layers.data(name="im_info", shape=[3], + im_info = fluid.data(name="im_info", shape=[None, 3], dtype="float32") - gt_classes = fluid.layers.data(name="gt_classes", shape=[1], + gt_classes = fluid.data(name="gt_classes", shape=[None, 1], dtype="float32", lod_level=1) - is_crowd = fluid.layers.data(name="is_crowd", shape=[1], + is_crowd = fluid.data(name="is_crowd", shape=[None, 1], dtype="float32", lod_level=1) - gt_masks = fluid.layers.data(name="gt_masks", shape=[2], + 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.layers.data(name="rois", shape=[4], + rois = fluid.data(name="rois", shape=[None, 4], dtype="float32", lod_level=1) - roi_labels = fluid.layers.data(name="roi_labels", shape=[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, diff --git a/python/paddle/fluid/layers/nn.py b/python/paddle/fluid/layers/nn.py index 4b8038d47c6..092b39111aa 100755 --- a/python/paddle/fluid/layers/nn.py +++ b/python/paddle/fluid/layers/nn.py @@ -10510,9 +10510,19 @@ def random_crop(x, shape, seed=None): ${out_comment} Examples: - >>> import paddle.fluid as fluid - >>> img = fluid.layers.data("img", [3, 256, 256]) - >>> cropped_img = fluid.layers.random_crop(img, shape=[3, 224, 224]) + .. code-block:: python + + import paddle.fluid as fluid + img = fluid.data("img", [None, 3, 256, 256]) + # cropped_img is [-1, 3, 224, 224] + cropped_img = fluid.layers.random_crop(img, shape=[3, 224, 224]) + + # cropped_img2 shape: [-1, 2, 224, 224] + # cropped_img2 = fluid.layers.random_crop(img, shape=[2, 224, 224]) + + # cropped_img3 shape: [-1, 3, 128, 224] + # cropped_img3 = fluid.layers.random_crop(img, shape=[128, 224]) + """ helper = LayerHelper("random_crop", **locals()) dtype = x.dtype -- GitLab