diff --git a/paddle/fluid/API.spec b/paddle/fluid/API.spec index c3def52d539ce58bf5bc57efedcc00f45066b4de..6bce001a98ba2a41a64b04203bc7e79a6a8d7492 100755 --- a/paddle/fluid/API.spec +++ b/paddle/fluid/API.spec @@ -204,7 +204,7 @@ paddle.fluid.layers.pad (ArgSpec(args=['x', 'paddings', 'pad_value', 'name'], va paddle.fluid.layers.pad_constant_like (ArgSpec(args=['x', 'y', 'pad_value', 'name'], varargs=None, keywords=None, defaults=(0.0, None)), ('document', '95aa1972983f30fe9b5a3713e523e20f')) paddle.fluid.layers.label_smooth (ArgSpec(args=['label', 'prior_dist', 'epsilon', 'dtype', 'name'], varargs=None, keywords=None, defaults=(None, 0.1, 'float32', None)), ('document', '214f1dfbe95a628600bbe99e836319cf')) 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', '49368d724023a66b41b0071be41c0ba5')) -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', 'dc2e2fa3d6e3d30de0a81e8ee70de733')) +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'], varargs=None, keywords=None, defaults=(1e-05,)), ('document', '7e8e4bf1f0f8612961ed113e8af8f0c5')) 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')) @@ -232,7 +232,7 @@ paddle.fluid.layers.pow (ArgSpec(args=['x', 'factor', 'name'], varargs=None, key paddle.fluid.layers.stanh (ArgSpec(args=['x', 'scale_a', 'scale_b', 'name'], varargs=None, keywords=None, defaults=(0.67, 1.7159, None)), ('document', 'd3f742178a7263adf5929153d104883d')) paddle.fluid.layers.hard_sigmoid (ArgSpec(args=['x', 'slope', 'offset', 'name'], varargs=None, keywords=None, defaults=(0.2, 0.5, None)), ('document', '607d79ca873bee40eed1c79a96611591')) paddle.fluid.layers.swish (ArgSpec(args=['x', 'beta', 'name'], varargs=None, keywords=None, defaults=(1.0, None)), ('document', 'e0dc7bc66cba939033bc028d7a62c5f4')) -paddle.fluid.layers.prelu (ArgSpec(args=['x', 'mode', 'param_attr', 'name'], varargs=None, keywords=None, defaults=(None, None)), ('document', '1fadca6622c70bd33cc260817f4ff191')) +paddle.fluid.layers.prelu (ArgSpec(args=['x', 'mode', 'param_attr', 'name'], varargs=None, keywords=None, defaults=(None, None)), ('document', 'cb417a61f701c937f33d057fe85203ab')) paddle.fluid.layers.brelu (ArgSpec(args=['x', 't_min', 't_max', 'name'], varargs=None, keywords=None, defaults=(0.0, 24.0, None)), ('document', '49580538249a52c857fce75c94ad8af7')) paddle.fluid.layers.leaky_relu (ArgSpec(args=['x', 'alpha', 'name'], varargs=None, keywords=None, defaults=(0.02, None)), ('document', '1eb3009c69060299ec87949ee0d4b9ae')) paddle.fluid.layers.soft_relu (ArgSpec(args=['x', 'threshold', 'name'], varargs=None, keywords=None, defaults=(40.0, None)), ('document', '6455afd2498b00198f53f83d63d6c6a4')) @@ -271,11 +271,11 @@ paddle.fluid.layers.logical_or (ArgSpec(args=['x', 'y', 'out', 'name'], varargs= paddle.fluid.layers.logical_xor (ArgSpec(args=['x', 'y', 'out', 'name'], varargs=None, keywords=None, defaults=(None, None)), ('document', '77ccf37b710c507dd97e03f08ce8bb29')) paddle.fluid.layers.logical_not (ArgSpec(args=['x', 'out', 'name'], varargs=None, keywords=None, defaults=(None, None)), ('document', '6e2fe8a322ec69811f6507d22acf8f9f')) paddle.fluid.layers.clip (ArgSpec(args=['x', 'min', 'max', 'name'], varargs=None, keywords=None, defaults=(None,)), ('document', '0ce33756573c572da67302499455dbcd')) -paddle.fluid.layers.clip_by_norm (ArgSpec(args=['x', 'max_norm', 'name'], varargs=None, keywords=None, defaults=(None,)), ('document', '1fc6e217c7a6128df31b806c1a8067ff')) +paddle.fluid.layers.clip_by_norm (ArgSpec(args=['x', 'max_norm', 'name'], varargs=None, keywords=None, defaults=(None,)), ('document', 'a5f4917fda557ceb834168cdbec6d51b')) paddle.fluid.layers.mean (ArgSpec(args=['x', 'name'], varargs=None, keywords=None, defaults=(None,)), ('document', '597257fb94d0597c404a6a5c91ab5258')) paddle.fluid.layers.mul (ArgSpec(args=['x', 'y', 'x_num_col_dims', 'y_num_col_dims', 'name'], varargs=None, keywords=None, defaults=(1, 1, None)), ('document', '784b7e36cea88493f9e37a41b10fbf4d')) paddle.fluid.layers.sigmoid_cross_entropy_with_logits (ArgSpec(args=['x', 'label', 'ignore_index', 'name', 'normalize'], varargs=None, keywords=None, defaults=(-100, None, False)), ('document', '7637c974f2d749d359acae9062c4d96f')) -paddle.fluid.layers.maxout (ArgSpec(args=['x', 'groups', 'name'], varargs=None, keywords=None, defaults=(None,)), ('document', '169882eb87fb693198e0153629134c22')) +paddle.fluid.layers.maxout (ArgSpec(args=['x', 'groups', 'name'], varargs=None, keywords=None, defaults=(None,)), ('document', '406eee439e41988c8a0304186626a0dd')) paddle.fluid.layers.space_to_depth (ArgSpec(args=['x', 'blocksize', 'name'], varargs=None, keywords=None, defaults=(None,)), ('document', '26decdea9376b6b9a0d3432d82ca207b')) paddle.fluid.layers.affine_grid (ArgSpec(args=['theta', 'out_shape', 'name'], varargs=None, keywords=None, defaults=(None,)), ('document', 'f85b263b7b6698d000977529a28f202b')) paddle.fluid.layers.sequence_reverse (ArgSpec(args=['x', 'name'], varargs=None, keywords=None, defaults=(None,)), ('document', '65c8362e48810b8226e311c5d046db51')) @@ -412,30 +412,30 @@ paddle.fluid.layers.thresholded_relu (ArgSpec(args=['x', 'threshold'], varargs=N 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.bipartite_match (ArgSpec(args=['dist_matrix', 'match_type', 'dist_threshold', 'name'], varargs=None, keywords=None, defaults=(None, None, None)), ('document', '409c351dee8a4a4ea02771dc691b49cb')) +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.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', '14d1eeae0f41b6792be43c1c0be0589b')) 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', '651d98d51879dfa1bc1cd40391786a41')) 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', 'fa1d1c9d5e0111684c0db705f86a2595')) paddle.fluid.layers.sigmoid_focal_loss (ArgSpec(args=['x', 'label', 'fg_num', 'gamma', 'alpha'], varargs=None, keywords=None, defaults=(2, 0.25)), ('document', 'aeac6aae100173b3fc7f102cf3023a3d')) -paddle.fluid.layers.anchor_generator (ArgSpec(args=['input', 'anchor_sizes', 'aspect_ratios', 'variance', 'stride', 'offset', 'name'], varargs=None, keywords=None, defaults=(None, None, [0.1, 0.1, 0.2, 0.2], None, 0.5, None)), ('document', 'd25e5e90f9a342764f32b5cd48657148')) +paddle.fluid.layers.anchor_generator (ArgSpec(args=['input', 'anchor_sizes', 'aspect_ratios', 'variance', 'stride', 'offset', 'name'], varargs=None, keywords=None, defaults=(None, None, [0.1, 0.1, 0.2, 0.2], None, 0.5, None)), ('document', 'a7778d4f557c60dca52321673667690d')) paddle.fluid.layers.roi_perspective_transform (ArgSpec(args=['input', 'rois', 'transformed_height', 'transformed_width', 'spatial_scale'], varargs=None, keywords=None, defaults=(1.0,)), ('document', 'a82016342789ba9d85737e405f824ff1')) 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', '69def376b42ef0681d0cc7f53a2dac4b')) 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', 'b7d707822b6af2a586bce608040235b1')) 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.iou_similarity (ArgSpec(args=['x', 'y', 'name'], varargs=None, keywords=None, defaults=(None,)), ('document', '72fca4a39ccf82d5c746ae62d1868a99')) -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', '1d5144c3856673d05c29c752c7c8f821')) +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', 'e308ce1661cb722b220a6f482f85b9e4')) paddle.fluid.layers.yolov3_loss (ArgSpec(args=['x', 'gt_box', 'gt_label', 'anchors', 'anchor_mask', 'class_num', 'ignore_thresh', 'downsample_ratio', 'gt_score', 'use_label_smooth', 'name'], varargs=None, keywords=None, defaults=(None, True, None)), ('document', '400403175718d5a632402cdae88b01b8')) paddle.fluid.layers.yolo_box (ArgSpec(args=['x', 'img_size', 'anchors', 'class_num', 'conf_thresh', 'downsample_ratio', 'name'], varargs=None, keywords=None, defaults=(None,)), ('document', 'ed56ff21536ca5c8ad418d0cfaf6a7b9')) -paddle.fluid.layers.box_clip (ArgSpec(args=['input', 'im_info', 'name'], varargs=None, keywords=None, defaults=(None,)), ('document', '882c99ed2adad54f612a40275b881850')) +paddle.fluid.layers.box_clip (ArgSpec(args=['input', 'im_info', 'name'], varargs=None, keywords=None, defaults=(None,)), ('document', 'ce2bfbd685f2a36eda400e00569908cb')) paddle.fluid.layers.multiclass_nms (ArgSpec(args=['bboxes', 'scores', 'score_threshold', 'nms_top_k', 'keep_top_k', 'nms_threshold', 'normalized', 'nms_eta', 'background_label', 'name'], varargs=None, keywords=None, defaults=(0.3, True, 1.0, 0, None)), ('document', 'f6e333d76922c6e564413b4d216c245c')) paddle.fluid.layers.multiclass_nms2 (ArgSpec(args=['bboxes', 'scores', 'score_threshold', 'nms_top_k', 'keep_top_k', 'nms_threshold', 'normalized', 'nms_eta', 'background_label', 'return_index', 'name'], varargs=None, keywords=None, defaults=(0.3, True, 1.0, 0, False, None)), ('document', 'be156186ee7a2ee56ab30b964acb15e5')) paddle.fluid.layers.retinanet_detection_output (ArgSpec(args=['bboxes', 'scores', 'anchors', 'im_info', 'score_threshold', 'nms_top_k', 'keep_top_k', 'nms_threshold', 'nms_eta'], varargs=None, keywords=None, defaults=(0.05, 1000, 100, 0.3, 1.0)), ('document', '078d28607ce261a0cba2b965a79f6bb8')) -paddle.fluid.layers.distribute_fpn_proposals (ArgSpec(args=['fpn_rois', 'min_level', 'max_level', 'refer_level', 'refer_scale', 'name'], varargs=None, keywords=None, defaults=(None,)), ('document', 'be432c9b5f19ccba7aca38789ead29e4')) -paddle.fluid.layers.box_decoder_and_assign (ArgSpec(args=['prior_box', 'prior_box_var', 'target_box', 'box_score', 'box_clip', 'name'], varargs=None, keywords=None, defaults=(None,)), ('document', '5203935538d06a6d47b8630ad80cb2b0')) -paddle.fluid.layers.collect_fpn_proposals (ArgSpec(args=['multi_rois', 'multi_scores', 'min_level', 'max_level', 'post_nms_top_n', 'name'], varargs=None, keywords=None, defaults=(None,)), ('document', '808fcca082e0040e2b77dbc53a0cf9d5')) +paddle.fluid.layers.distribute_fpn_proposals (ArgSpec(args=['fpn_rois', 'min_level', 'max_level', 'refer_level', 'refer_scale', 'name'], varargs=None, keywords=None, defaults=(None,)), ('document', '1f2b6bfb3027ea63ab86859391f45b03')) +paddle.fluid.layers.box_decoder_and_assign (ArgSpec(args=['prior_box', 'prior_box_var', 'target_box', 'box_score', 'box_clip', 'name'], varargs=None, keywords=None, defaults=(None,)), ('document', '8874f917b4da34541efe427841a8f205')) +paddle.fluid.layers.collect_fpn_proposals (ArgSpec(args=['multi_rois', 'multi_scores', 'min_level', 'max_level', 'post_nms_top_n', 'name'], varargs=None, keywords=None, defaults=(None,)), ('document', 'ff4a651d65a9a9f9da71349ba6a2dc1f')) paddle.fluid.layers.accuracy (ArgSpec(args=['input', 'label', 'k', 'correct', 'total'], varargs=None, keywords=None, defaults=(1, None, None)), ('document', 'b691b7be425e281bd36897b514b2b064')) paddle.fluid.layers.auc (ArgSpec(args=['input', 'label', 'curve', 'num_thresholds', 'topk', 'slide_steps'], varargs=None, keywords=None, defaults=('ROC', 4095, 1, 1)), ('document', 'c36ac7125da977c2bd1b192bee301f75')) paddle.fluid.layers.exponential_decay (ArgSpec(args=['learning_rate', 'decay_steps', 'decay_rate', 'staircase'], varargs=None, keywords=None, defaults=(False,)), ('document', 'eaf430c5a0380fb11bfe9a8922cd6295')) diff --git a/python/paddle/fluid/layers/detection.py b/python/paddle/fluid/layers/detection.py index 1b606da006765cc393db7a88ad2243267d05fe8c..984e0f0674a465e55cb74a12122771cf1cfb5091 100644 --- a/python/paddle/fluid/layers/detection.py +++ b/python/paddle/fluid/layers/detection.py @@ -717,27 +717,23 @@ def box_coder(prior_box, import paddle.fluid as fluid # For encode - prior_box_encode = fluid.layers.data(name='prior_box_encode', + prior_box_encode = fluid.data(name='prior_box_encode', shape=[512, 4], - dtype='float32', - append_batch_size=False) - target_box_encode = fluid.layers.data(name='target_box_encode', - shape=[81,4], - dtype='float32', - append_batch_size=False) + dtype='float32') + target_box_encode = fluid.data(name='target_box_encode', + shape=[81, 4], + dtype='float32') output_encode = fluid.layers.box_coder(prior_box=prior_box_encode, prior_box_var=[0.1,0.1,0.2,0.2], target_box=target_box_encode, code_type="encode_center_size") # For decode - prior_box_decode = fluid.layers.data(name='prior_box_decode', + prior_box_decode = fluid.data(name='prior_box_decode', shape=[512, 4], - dtype='float32', - append_batch_size=False) - target_box_decode = fluid.layers.data(name='target_box_decode', - shape=[512,81,4], - dtype='float32', - append_batch_size=False) + dtype='float32') + target_box_decode = fluid.data(name='target_box_decode', + shape=[512, 81, 4], + dtype='float32') output_decode = fluid.layers.box_coder(prior_box=prior_box_decode, prior_box_var=[0.1,0.1,0.2,0.2], target_box=target_box_decode, @@ -1173,8 +1169,8 @@ def bipartite_match(dist_matrix, Examples: >>> import paddle.fluid as fluid - >>> x = fluid.layers.data(name='x', shape=[4], dtype='float32') - >>> y = fluid.layers.data(name='y', shape=[4], dtype='float32') + >>> x = fluid.data(name='x', shape=[None, 4], dtype='float32') + >>> y = fluid.data(name='y', shape=[None, 4], dtype='float32') >>> iou = fluid.layers.iou_similarity(x=x, y=y) >>> matched_indices, matched_dist = fluid.layers.bipartite_match(iou) """ @@ -2081,7 +2077,7 @@ def anchor_generator(input, .. code-block:: python import paddle.fluid as fluid - conv1 = fluid.layers.data(name='conv1', shape=[48, 16, 16], dtype='float32') + 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], @@ -2607,9 +2603,9 @@ def box_clip(input, im_info, name=None): .. code-block:: python import paddle.fluid as fluid - boxes = fluid.layers.data( - name='boxes', shape=[8, 4], dtype='float32', lod_level=1) - im_info = fluid.layers.data(name='im_info', shape=[3]) + 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) """ @@ -3056,8 +3052,8 @@ def distribute_fpn_proposals(fpn_rois, .. code-block:: python import paddle.fluid as fluid - fpn_rois = fluid.layers.data( - name='data', shape=[4], dtype='float32', lod_level=1) + 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, @@ -3118,15 +3114,14 @@ def box_decoder_and_assign(prior_box, .. code-block:: python import paddle.fluid as fluid - pb = fluid.layers.data( - name='prior_box', shape=[4], dtype='float32') - pbv = fluid.layers.data( - name='prior_box_var', shape=[4], - dtype='float32', append_batch_size=False) - loc = fluid.layers.data( - name='target_box', shape=[4*81], dtype='float32') - scores = fluid.layers.data( - name='scores', shape=[81], dtype='float32') + 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) @@ -3199,11 +3194,11 @@ def collect_fpn_proposals(multi_rois, multi_rois = [] multi_scores = [] for i in range(4): - multi_rois.append(fluid.layers.data( - name='roi_'+str(i), shape=[4], dtype='float32', lod_level=1)) + 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.layers.data( - name='score_'+str(i), shape=[1], dtype='float32', lod_level=1)) + 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, diff --git a/python/paddle/fluid/layers/nn.py b/python/paddle/fluid/layers/nn.py index 0fb8a9f02150d4c948be0616c3bb0a5dc434ae10..c36931083ad234dcc8e6ac0b61fc211d42260d70 100755 --- a/python/paddle/fluid/layers/nn.py +++ b/python/paddle/fluid/layers/nn.py @@ -8613,10 +8613,10 @@ def roi_align(input, .. code-block:: python import paddle.fluid as fluid - x = fluid.layers.data( - name='data', shape=[256, 32, 32], dtype='float32') - rois = fluid.layers.data( - name='rois', shape=[4], dtype='float32') + x = fluid.data( + name='data', shape=[None, 256, 32, 32], dtype='float32') + rois = fluid.data( + name='rois', shape=[None, 4], dtype='float32') align_out = fluid.layers.roi_align(input=x, rois=rois, pooled_height=7, @@ -10980,7 +10980,7 @@ def prelu(x, mode, param_attr=None, name=None): import paddle.fluid as fluid from paddle.fluid.param_attr import ParamAttr - x = fluid.layers.data(name="x", shape=[5,10,10], dtype="float32") + x = fluid.data(name="x", shape=[None,5,10,10], dtype="float32") mode = 'channel' output = fluid.layers.prelu( x,mode,param_attr=ParamAttr(name='alpha')) @@ -13041,8 +13041,8 @@ def clip_by_norm(x, max_norm, name=None): .. code-block:: python import paddle.fluid as fluid - input = fluid.layers.data( - name='data', shape=[1], dtype='float32') + input = fluid.data( + name='data', shape=[None, 1], dtype='float32') reward = fluid.layers.clip_by_norm(x=input, max_norm=1.0) """ @@ -13256,9 +13256,9 @@ def maxout(x, groups, name=None): .. code-block:: python import paddle.fluid as fluid - input = fluid.layers.data( + input = fluid.data( name='data', - shape=[256, 32, 32], + shape=[None, 256, 32, 32], dtype='float32') out = fluid.layers.maxout(input, groups=2) """