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fix spelling errors (#17941)

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* fix spelling errors; test=develop

* Update API.spec

update md5

* Update API.spec

* change the order of api;test=develop
revert-18229-add_multi_gpu_install_check
翟飞跃 6 years ago committed by Tao Luo
parent 0569ff78fa
commit 802ea50956
30 changed files with 86 additions and 86 deletions
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2
benchmark/fluid/README.md
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@ -59,7 +59,7 @@ python -c 'from recordio_converter import *; prepare_mnist("data", 1)'
## Run Distributed Benchmark on Kubernetes Cluster
You may need to build a Docker image before submitting a cluster job onto Kubernetes, or you will
have to start all those processes mannually on each node, which is not recommended.
have to start all those processes manually on each node, which is not recommended.
To build the Docker image, you need to choose a paddle "whl" package to run with, you may either
download it from

2
paddle/.common_test_util.sh
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@ -26,7 +26,7 @@ chmod a+rw $PORT_FILE $PORT_LOCK_FILE 2>/dev/null
#
# There are two parameter of this method
# param 1: the begin of port range
# param 2: the lenght of port range.
# param 2: the length of port range.
# so, the port range is [param1, param1+param2)
acquire_ports(){
(

18
paddle/fluid/API.spec
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@ -74,8 +74,8 @@ paddle.fluid.initializer.init_on_cpu (ArgSpec(args=[], varargs=None, keywords=No
paddle.fluid.initializer.NumpyArrayInitializer.__init__ (ArgSpec(args=['self', 'value'], varargs=None, keywords=None, defaults=None), ('document', '6adf97f83acf6453d4a6a4b1070f3754'))
paddle.fluid.layers.fc (ArgSpec(args=['input', 'size', 'num_flatten_dims', 'param_attr', 'bias_attr', 'act', 'is_test', 'name'], varargs=None, keywords=None, defaults=(1, None, None, None, False, None)), ('document', '424e898365195e3ccbc2e7dc8b63605e'))
paddle.fluid.layers.embedding (ArgSpec(args=['input', 'size', 'is_sparse', 'is_distributed', 'padding_idx', 'param_attr', 'dtype'], varargs=None, keywords=None, defaults=(False, False, None, None, 'float32')), ('document', '6f9f96d2a1517cd1affebc960c3526f7'))
paddle.fluid.layers.dynamic_lstm (ArgSpec(args=['input', 'size', 'h_0', 'c_0', 'param_attr', 'bias_attr', 'use_peepholes', 'is_reverse', 'gate_activation', 'cell_activation', 'candidate_activation', 'dtype', 'name'], varargs=None, keywords=None, defaults=(None, None, None, None, True, False, 'sigmoid', 'tanh', 'tanh', 'float32', None)), ('document', '8e35ca26adbe44eb631d71045c8d64d5'))
paddle.fluid.layers.dynamic_lstmp (ArgSpec(args=['input', 'size', 'proj_size', 'param_attr', 'bias_attr', 'use_peepholes', 'is_reverse', 'gate_activation', 'cell_activation', 'candidate_activation', 'proj_activation', 'dtype', 'name', 'h_0', 'c_0', 'cell_clip', 'proj_clip'], varargs=None, keywords=None, defaults=(None, None, True, False, 'sigmoid', 'tanh', 'tanh', 'tanh', 'float32', None, None, None, None, None)), ('document', 'b4b608b986eb9617aa0525e1be21d32d'))
paddle.fluid.layers.dynamic_lstm (ArgSpec(args=['input', 'size', 'h_0', 'c_0', 'param_attr', 'bias_attr', 'use_peepholes', 'is_reverse', 'gate_activation', 'cell_activation', 'candidate_activation', 'dtype', 'name'], varargs=None, keywords=None, defaults=(None, None, None, None, True, False, 'sigmoid', 'tanh', 'tanh', 'float32', None)), ('document', '246ff18abc877dd576653006991918e9'))
paddle.fluid.layers.dynamic_lstmp (ArgSpec(args=['input', 'size', 'proj_size', 'param_attr', 'bias_attr', 'use_peepholes', 'is_reverse', 'gate_activation', 'cell_activation', 'candidate_activation', 'proj_activation', 'dtype', 'name', 'h_0', 'c_0', 'cell_clip', 'proj_clip'], varargs=None, keywords=None, defaults=(None, None, True, False, 'sigmoid', 'tanh', 'tanh', 'tanh', 'float32', None, None, None, None, None)), ('document', '4f63053354bcc6c743b4d2f4e7104e25'))
paddle.fluid.layers.dynamic_gru (ArgSpec(args=['input', 'size', 'param_attr', 'bias_attr', 'is_reverse', 'gate_activation', 'candidate_activation', 'h_0', 'origin_mode'], varargs=None, keywords=None, defaults=(None, None, False, 'sigmoid', 'tanh', None, False)), ('document', '83617c165827e030636c80486d5de6f3'))
paddle.fluid.layers.gru_unit (ArgSpec(args=['input', 'hidden', 'size', 'param_attr', 'bias_attr', 'activation', 'gate_activation', 'origin_mode'], varargs=None, keywords=None, defaults=(None, None, 'tanh', 'sigmoid', False)), ('document', '33974b9bfa69f2f1eb85e6f956dff04e'))
paddle.fluid.layers.linear_chain_crf (ArgSpec(args=['input', 'label', 'param_attr'], varargs=None, keywords=None, defaults=(None,)), ('document', '34f96be41684b0959897a9e735997e20'))
@ -119,7 +119,7 @@ paddle.fluid.layers.dropout (ArgSpec(args=['x', 'dropout_prob', 'is_test', 'seed
paddle.fluid.layers.split (ArgSpec(args=['input', 'num_or_sections', 'dim', 'name'], varargs=None, keywords=None, defaults=(-1, None)), ('document', '59b28903ce8fb6a7e3861ff355592eb4'))
paddle.fluid.layers.ctc_greedy_decoder (ArgSpec(args=['input', 'blank', 'name'], varargs=None, keywords=None, defaults=(None,)), ('document', '2bc3a59efa9d52b628a6255422d9f0e8'))
paddle.fluid.layers.edit_distance (ArgSpec(args=['input', 'label', 'normalized', 'ignored_tokens'], varargs=None, keywords=None, defaults=(True, None)), ('document', 'f2c252aa2f83f8e503ffaf79668eaa28'))
paddle.fluid.layers.l2_normalize (ArgSpec(args=['x', 'axis', 'epsilon', 'name'], varargs=None, keywords=None, defaults=(1e-12, None)), ('document', '35c6a241bcc1a1fc89508860d82ad62b'))
paddle.fluid.layers.l2_normalize (ArgSpec(args=['x', 'axis', 'epsilon', 'name'], varargs=None, keywords=None, defaults=(1e-12, None)), ('document', 'd0484a1f85b40009a794d45a1a298c12'))
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', 'aa27ca4405e70c6a733cb9806a76af30'))
paddle.fluid.layers.topk (ArgSpec(args=['input', 'k', 'name'], varargs=None, keywords=None, defaults=(None,)), ('document', '2a1e9ea041ff4d6a9948bb8d03b743ea'))
paddle.fluid.layers.warpctc (ArgSpec(args=['input', 'label', 'blank', 'norm_by_times', 'use_cudnn'], varargs=None, keywords=None, defaults=(0, False, False)), ('document', '4aa9df890b47eb67d5442f04aaf9eeec'))
@ -196,7 +196,7 @@ paddle.fluid.layers.elementwise_floordiv (ArgSpec(args=['x', 'y', 'axis', 'act',
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', 'c8c7518358cfbb3822a019e6b5fbea52'))
paddle.fluid.layers.gaussian_random (ArgSpec(args=['shape', 'mean', 'std', 'seed', 'dtype'], varargs=None, keywords=None, defaults=(0.0, 1.0, 0, 'float32')), ('document', '8c78ccb77e291e4a0f0673d34823ce4b'))
paddle.fluid.layers.sampling_id (ArgSpec(args=['x', 'min', 'max', 'seed', 'dtype'], varargs=None, keywords=None, defaults=(0.0, 1.0, 0, 'float32')), ('document', '35428949368cad5121dd37f8522ef8b0'))
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', '9e520987168f8ddb7dd71ffd68aa352c'))
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', '7536418f4cf0360a1a897c265f06e77e'))
paddle.fluid.layers.sum (ArgSpec(args=['x'], varargs=None, keywords=None, defaults=None), ('document', '4527fd90e222f67b5f7451fb0cf7c845'))
paddle.fluid.layers.slice (ArgSpec(args=['input', 'axes', 'starts', 'ends'], varargs=None, keywords=None, defaults=None), ('document', '3ca6a761570d86e303e473afba99bb49'))
paddle.fluid.layers.shape (ArgSpec(args=['input'], varargs=None, keywords=None, defaults=None), ('document', 'bf61c8f79d795a8371bdb3b5468aa82b'))
@ -341,17 +341,17 @@ paddle.fluid.layers.uniform_random (ArgSpec(args=['shape', 'dtype', 'min', 'max'
paddle.fluid.layers.hard_shrink (ArgSpec(args=['x', 'threshold'], varargs=None, keywords=None, defaults=(None,)), ('document', 'c142f5884f3255e0d6075c286bbd531e'))
paddle.fluid.layers.cumsum (ArgSpec(args=['x', 'axis', 'exclusive', 'reverse'], varargs=None, keywords=None, defaults=(None, None, None)), ('document', '944d7c03057f5fc88bc78acd4d82f926'))
paddle.fluid.layers.thresholded_relu (ArgSpec(args=['x', 'threshold'], varargs=None, keywords=None, defaults=(None,)), ('document', '90566ea449ea4c681435546e2f70610a'))
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', 'a00d43a08ec664454e8e685bc54e9e78'))
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', '7e62e12ce8b127f2c7ce8db79299c3c3'))
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', 'b351a05b758f7e5370898cc7d7d40dca'))
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', '05c43e8fd25efe34f75e35a2c045ded3'))
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', '3ddb9b966f193900193a95a3df77c3c1'))
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'], varargs=None, keywords=None, defaults=(0, 0.3, 400, 200, 0.01, 1.0)), ('document', 'efae414c1137c7944d6174dd08c5347a'))
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', '6d5028fd09d01ab82d296adc0ea95aee'))
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', '1e164a56fe9376e18a56d22563d9f801'))
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', '055bd5070ad72dccc0949b4ed036f39c'))
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', '70d0109c864bced99b6b0aca4574af5e'))
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', '82b2aefeeb1b706bc4afec70928a259a'))
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', 'acc23232f4c8c03791598500b5bf7790'))
paddle.fluid.layers.roi_perspective_transform (ArgSpec(args=['input', 'rois', 'transformed_height', 'transformed_width', 'spatial_scale'], varargs=None, keywords=None, defaults=(1.0,)), ('document', 'd1ddc75629fedee46f82e631e22c79dc'))
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', 'c0d00acf724691ff3480d4207036a722'))
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'))

2
paddle/fluid/inference/api/demo_ci/run.sh
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@ -4,7 +4,7 @@ PADDLE_ROOT=$1
TURN_ON_MKL=$2 # use MKL or Openblas
TEST_GPU_CPU=$3 # test both GPU/CPU mode or only CPU mode
DATA_DIR=$4 # dataset
TENSORRT_INCLUDE_DIR=$5 # TensorRT header file dir, defalut to /usr/local/TensorRT/include
TENSORRT_INCLUDE_DIR=$5 # TensorRT header file dir, default to /usr/local/TensorRT/include
TENSORRT_LIB_DIR=$6 # TensorRT lib file dir, default to /usr/local/TensorRT/lib
inference_install_dir=${PADDLE_ROOT}/build/fluid_inference_install_dir

2
paddle/fluid/operators/attention_lstm_op.cc
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@ -207,7 +207,7 @@ void AttentionLSTMOpMaker::Make() {
.InEnum({"sigmoid", "tanh", "relu", "identity"});
AddAttr<std::string>("cell_activation",
"(string, default: tanh)"
"The activation for cell output, `tanh` by defalut.")
"The activation for cell output, `tanh` by default.")
.SetDefault("tanh")
.InEnum({"sigmoid", "tanh", "relu", "identity"});
AddAttr<std::string>("candidate_activation",

2
paddle/fluid/operators/batch_norm_op.cu
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@ -31,7 +31,7 @@ limitations under the License. */
// input data range.
DEFINE_bool(cudnn_batchnorm_spatial_persistent, false,
"Whether enable CUDNN_BATCHNORM_SPATIAL_PERSISTENT mode for cudnn "
"batch_norm, defalut is False.");
"batch_norm, default is False.");
namespace paddle {
namespace operators {

10
paddle/fluid/operators/conv_cudnn_op.cu.cc
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@ -33,7 +33,7 @@ DEFINE_uint64(conv_workspace_size_limit,
"cuDNN convolution workspace limit in MB unit.");
DEFINE_bool(cudnn_exhaustive_search, false,
"Whether enable exhaustive search for cuDNN convolution or "
"not, defalut is False.");
"not, default is False.");
namespace paddle {
namespace operators {
@ -102,7 +102,7 @@ class CUDNNConvOpKernel : public framework::OpKernel<T> {
conv_desc.descriptor<T>(paddings, strides, dilations);
#if CUDNN_VERSION_MIN(7, 0, 1)
// cudnn 7 can support groups, no need to do it mannually
// cudnn 7 can support groups, no need to do it manually
// FIXME(typhoonzero): find a better way to disable groups
// rather than setting it to 1.
CUDNN_ENFORCE(platform::dynload::cudnnSetConvolutionGroupCount(
@ -300,7 +300,7 @@ class CUDNNConvGradOpKernel : public framework::OpKernel<T> {
FLAGS_cudnn_exhaustive_search || ctx.Attr<bool>("exhaustive_search");
if (exhaustive_search && FLAGS_cudnn_deterministic) {
PADDLE_THROW(
"Cann't set exhaustive_search True and "
"Can't set exhaustive_search True and "
"FLAGS_cudnn_deterministic True at same time.");
}
@ -320,7 +320,7 @@ class CUDNNConvGradOpKernel : public framework::OpKernel<T> {
conv_desc.descriptor<T>(paddings, strides, dilations);
#if CUDNN_VERSION_MIN(7, 0, 1)
// cudnn 7 can support groups, no need to do it mannually
// cudnn 7 can support groups, no need to do it manually
// FIXME(typhoonzero): find a better way to disable groups
// rather than setting it to 1.
CUDNN_ENFORCE(platform::dynload::cudnnSetConvolutionGroupCount(
@ -665,7 +665,7 @@ class CUDNNConvDoubleGradOpKernel : public framework::OpKernel<T> {
bool deterministic = FLAGS_cudnn_deterministic;
if (exhaustive_search && deterministic) {
PADDLE_THROW(
"Cann't set exhaustive_search True and "
"Can't set exhaustive_search True and "
"FLAGS_cudnn_deterministic True at same time.");
}

2
paddle/fluid/operators/conv_fusion_op.cu.cc
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@ -18,7 +18,7 @@ limitations under the License. */
DEFINE_int64(cudnn_exhaustive_search_times, -1,
"Exhaustive search times for cuDNN convolution, "
"defalut is -1, not exhaustive search");
"default is -1, not exhaustive search");
namespace paddle {
namespace operators {

4
paddle/fluid/operators/conv_op.cc
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@ -259,7 +259,7 @@ void Conv2DOpMaker::Make() {
AddAttr<bool>("exhaustive_search",
"(bool, default false) cuDNN has many algorithm to calculation "
"convolution, whether enable exhaustive search "
"for cuDNN convolution or not, defalut is False.")
"for cuDNN convolution or not, default is False.")
.SetDefault(false);
AddComment(R"DOC(
Convolution Operator.
@ -378,7 +378,7 @@ void Conv3DOpMaker::Make() {
AddAttr<bool>("exhaustive_search",
"(bool, default false) cuDNN has many algorithm to calculation "
"convolution, whether enable exhaustive search "
"for cuDNN convolution or not, defalut is False.")
"for cuDNN convolution or not, default is False.")
.SetDefault(false);
AddComment(R"DOC(
Convolution3D Operator.

6
paddle/fluid/operators/detection/bipartite_match_op.cc
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@ -231,14 +231,14 @@ class BipartiteMatchOpMaker : public framework::OpProtoAndCheckerMaker {
"entities.");
AddAttr<std::string>(
"match_type",
"(string, defalut: per_prediction) "
"(string, default: per_prediction) "
"The type of matching method, should be 'bipartite' or "
"'per_prediction', 'bipartite' by defalut.")
"'per_prediction', 'bipartite' by default.")
.SetDefault("bipartite")
.InEnum({"bipartite", "per_prediction"});
AddAttr<float>(
"dist_threshold",
"(float, defalut: 0.5) "
"(float, default: 0.5) "
"If `match_type` is 'per_prediction', this threshold is to determine "
"the extra matching bboxes based on the maximum distance.")
.SetDefault(0.5);

4
paddle/fluid/operators/detection/multiclass_nms_op.cc
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@ -463,7 +463,7 @@ class MultiClassNMSOpMaker : public framework::OpProtoAndCheckerMaker {
"Input BBoxes should be the second case with shape [M, C, 4].");
AddAttr<int>(
"background_label",
"(int, defalut: 0) "
"(int, default: 0) "
"The index of background label, the background label will be ignored. "
"If set to -1, then all categories will be considered.")
.SetDefault(0);
@ -477,7 +477,7 @@ class MultiClassNMSOpMaker : public framework::OpProtoAndCheckerMaker {
"confidences aftern the filtering detections based on "
"score_threshold");
AddAttr<float>("nms_threshold",
"(float, defalut: 0.3) "
"(float, default: 0.3) "
"The threshold to be used in NMS.")
.SetDefault(0.3);
AddAttr<float>("nms_eta",

2
paddle/fluid/operators/detection_map_op.cc
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@ -150,7 +150,7 @@ class DetectionMAPOpMaker : public framework::OpProtoAndCheckerMaker {
"The class number.");
AddAttr<int>(
"background_label",
"(int, defalut: 0) "
"(int, default: 0) "
"The index of background label, the background label will be ignored. "
"If set to -1, then all categories will be considered.")
.SetDefault(0);

8
paddle/fluid/operators/fused/fused_embedding_fc_lstm_op.cc
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@ -174,15 +174,15 @@ void FusedEmbeddingFCLSTMOpMaker::Make() {
AddOutput("ReorderedH0", "(LoDTensor) (N x D).").AsIntermediate();
AddOutput("ReorderedC0", "(LoDTensor) (N x D).").AsIntermediate();
AddAttr<bool>("use_peepholes",
"(bool, defalut: True) "
"(bool, default: True) "
"whether to enable diagonal/peephole connections.")
.SetDefault(true);
AddAttr<bool>("is_reverse",
"(bool, defalut: False) "
"(bool, default: False) "
"whether to compute reversed LSTM.")
.SetDefault(false);
AddAttr<bool>("use_seq",
"(bool, defalut: True) "
"(bool, default: True) "
"whether to use seq mode to compute.")
.SetDefault(true);
AddAttr<std::string>("gate_activation",
@ -193,7 +193,7 @@ void FusedEmbeddingFCLSTMOpMaker::Make() {
.InEnum({"sigmoid", "tanh", "relu", "identity"});
AddAttr<std::string>("cell_activation",
"(string, default: tanh)"
"The activation for cell output, `tanh` by defalut.")
"The activation for cell output, `tanh` by default.")
.SetDefault("tanh")
.InEnum({"sigmoid", "tanh", "relu", "identity"});
AddAttr<std::string>("candidate_activation",

4
paddle/fluid/operators/fused/fusion_conv_inception_op.cc
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@ -67,7 +67,7 @@ class ConvInceptionFusionOpMaker : public framework::OpProtoAndCheckerMaker {
void Make() override {
AddInput("Input", "(Tensor) NCHW layout.");
AddInput("Filter", "(vector<Tensor>) 4 aggregated filters").AsDuplicable();
AddInput("Bias", "(vector<Tensor>) it's lenght is equal to Filter")
AddInput("Bias", "(vector<Tensor>) it's length is equal to Filter")
.AsDuplicable();
AddOutput("Output",
"(Tensor) The output tensor of convolution operator. "
@ -82,7 +82,7 @@ class ConvInceptionFusionOpMaker : public framework::OpProtoAndCheckerMaker {
"exclusive",
"(bool, default True) When true, will exclude the zero-padding in the "
"averaging calculating, otherwise, include the zero-padding. Note, it "
"is only used when pooling_type is avg. The defalut is True.")
"is only used when pooling_type is avg. The default is True.")
.SetDefault(true);
AddAttr<std::string>(
"activation",

4
paddle/fluid/operators/fused/fusion_gru_op.cc
Unescape View File

@ -147,11 +147,11 @@ void FusionGRUOpMaker::Make() {
"The activation type used in update gate and reset gate.")
.SetDefault("sigmoid");
AddAttr<bool>("is_reverse",
"(bool, defalut: False) "
"(bool, default: False) "
"whether to compute reversed GRU.")
.SetDefault(false);
AddAttr<bool>("use_seq",
"(bool, defalut: True) "
"(bool, default: True) "
"whether to use seq mode to compute GRU.")
.SetDefault(true);
AddComment(R"DOC(

8
paddle/fluid/operators/fused/fusion_lstm_op.cc
Unescape View File

@ -179,15 +179,15 @@ void FusionLSTMOpMaker::Make() {
AddOutput("CheckedCell", "(Tensor) (2 x D) only for peephole.")
.AsIntermediate();
AddAttr<bool>("use_peepholes",
"(bool, defalut: True) "
"(bool, default: True) "
"whether to enable diagonal/peephole connections.")
.SetDefault(true);
AddAttr<bool>("is_reverse",
"(bool, defalut: False) "
"(bool, default: False) "
"whether to compute reversed LSTM.")
.SetDefault(false);
AddAttr<bool>("use_seq",
"(bool, defalut: True) "
"(bool, default: True) "
"whether to use seq mode to compute.")
.SetDefault(true);
AddAttr<std::string>("gate_activation",
@ -198,7 +198,7 @@ void FusionLSTMOpMaker::Make() {
.InEnum({"sigmoid", "tanh", "relu", "identity"});
AddAttr<std::string>("cell_activation",
"(string, default: tanh)"
"The activation for cell output, `tanh` by defalut.")
"The activation for cell output, `tanh` by default.")
.SetDefault("tanh")
.InEnum({"sigmoid", "tanh", "relu", "identity"});
AddAttr<std::string>("candidate_activation",

2
paddle/fluid/operators/gaussian_random_batch_size_like_op.cc
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@ -58,7 +58,7 @@ class GaussianRandomBatchSizeLikeOpMaker : public BatchSizeLikeOpMaker {
AddComment(R"DOC(
Used to initialize tensors with gaussian random generator.
The defalut mean of the distribution is 0. and defalut standard
The default mean of the distribution is 0. and default standard
deviation (std) of the distribution is 1.. Uers can set mean and std
by input arguments.
)DOC");

2
paddle/fluid/operators/gru_op.cc
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@ -137,7 +137,7 @@ class GRUOpMaker : public framework::OpProtoAndCheckerMaker {
"The activation type used in update gate and reset gate.")
.SetDefault("sigmoid");
AddAttr<bool>("is_reverse",
"(bool, defalut: False) "
"(bool, default: False) "
"whether to compute reversed GRU.")
.SetDefault(false);
AddAttr<bool>("origin_mode",

10
paddle/fluid/operators/lstm_op.cc
Unescape View File

@ -153,11 +153,11 @@ class LSTMOpMaker : public framework::OpProtoAndCheckerMaker {
"in the backward.")
.AsIntermediate();
AddAttr<bool>("use_peepholes",
"(bool, defalut: True) "
"(bool, default: True) "
"whether to enable diagonal/peephole connections.")
.SetDefault(true);
AddAttr<bool>("is_reverse",
"(bool, defalut: False) "
"(bool, default: False) "
"whether to compute reversed LSTM.")
.SetDefault(false);
AddAttr<std::string>(
@ -169,7 +169,7 @@ class LSTMOpMaker : public framework::OpProtoAndCheckerMaker {
.InEnum({"sigmoid", "tanh", "relu", "identity"});
AddAttr<std::string>("cell_activation",
"(string, default: tanh)"
"The activation for cell output, `tanh` by defalut.")
"The activation for cell output, `tanh` by default.")
.SetDefault("tanh")
.InEnum({"sigmoid", "tanh", "relu", "identity"});
AddAttr<std::string>("candidate_activation",
@ -181,7 +181,7 @@ class LSTMOpMaker : public framework::OpProtoAndCheckerMaker {
AddComment(R"DOC(
Long-Short Term Memory (LSTM) Operator.
The defalut implementation is diagonal/peephole connection
The default implementation is diagonal/peephole connection
(https://arxiv.org/pdf/1402.1128.pdf), the formula is as follows:
$$ i_t = \\sigma(W_{ix}x_{t} + W_{ih}h_{t-1} + W_{ic}c_{t-1} + b_i) $$
@ -199,7 +199,7 @@ $$ h_t = o_t \\odot act_h(c_t) $$
- W terms denote weight matrices (e.g. $W_{xi}$ is the matrix
of weights from the input gate to the input), $W_{ic}, W_{fc}, W_{oc}$
are diagonal weight matrices for peephole connections. In our implementation,
we use vectors to reprenset these diagonal weight matrices.
we use vectors to represent these diagonal weight matrices.
- The b terms denote bias vectors ($b_i$ is the input gate bias vector).
- $\sigma$ is the non-line activations, such as logistic sigmoid function.
- $i, f, o$ and $c$ are the input gate, forget gate, output gate,

14
paddle/fluid/operators/lstmp_op.cc
Unescape View File

@ -177,20 +177,20 @@ class LSTMPOpMaker : public framework::OpProtoAndCheckerMaker {
"backward.")
.AsIntermediate();
AddAttr<bool>("use_peepholes",
"(bool, defalut: True) "
"(bool, default: True) "
"whether to enable diagonal/peephole connections.")
.SetDefault(true);
AddAttr<bool>("is_reverse",
"(bool, defalut: False) "
"(bool, default: False) "
"whether to compute reversed LSTMP.")
.SetDefault(false);
AddAttr<float>("cell_clip",
"(float, defalut: 0.0) "
"(float, default: 0.0) "
"Clip for Tensor for cell state tensor when clip value is "
"greater than 0.0")
.SetDefault(0.0);
AddAttr<float>("proj_clip",
"(float, defalut: 0.0) "
"(float, default: 0.0) "
"Clip for Tensor for projection tensor when clip value is "
"greater than 0.0")
.SetDefault(0.0);
@ -203,7 +203,7 @@ class LSTMPOpMaker : public framework::OpProtoAndCheckerMaker {
.InEnum({"sigmoid", "tanh", "relu", "identity"});
AddAttr<std::string>("cell_activation",
"(string, default: tanh)"
"The activation for cell output, `tanh` by defalut.")
"The activation for cell output, `tanh` by default.")
.SetDefault("tanh")
.InEnum({"sigmoid", "tanh", "relu", "identity"});
AddAttr<std::string>("candidate_activation",
@ -215,7 +215,7 @@ class LSTMPOpMaker : public framework::OpProtoAndCheckerMaker {
AddAttr<std::string>("proj_activation",
"(string, default: tanh)"
"The activation for projection output, "
"`tanh` by defalut.")
"`tanh` by default.")
.SetDefault("tanh")
.InEnum({"sigmoid", "tanh", "relu", "identity"});
AddComment(R"DOC(
@ -248,7 +248,7 @@ $$
where the W terms denote weight matrices (e.g. $W_{xi}$ is the matrix
of weights from the input gate to the input), $W_{ic}, W_{fc}, W_{oc}$
are diagonal weight matrices for peephole connections. In our implementation,
we use vectors to reprenset these diagonal weight matrices. The b terms
we use vectors to represent these diagonal weight matrices. The b terms
denote bias vectors ($b_i$ is the input gate bias vector), $\sigma$
is the activation, such as logistic sigmoid function, and
$i, f, o$ and $c$ are the input gate, forget gate, output gate,

4
paddle/fluid/operators/pool_op.cc
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@ -190,7 +190,7 @@ void Pool2dOpMaker::Make() {
"exclusive",
"(bool, default True) When true, will exclude the zero-padding in the "
"averaging calculating, otherwise, include the zero-padding. Note, it "
"is only used when pooling_type is avg. The defalut is True.")
"is only used when pooling_type is avg. The default is True.")
.SetDefault(true);
AddAttr<bool>(
"adaptive",
@ -360,7 +360,7 @@ void Pool3dOpMaker::Make() {
"exclusive",
"(bool, default True) When true, will exclude the zero-padding in the "
"averaging calculating, otherwise, include the zero-padding. Note, it "
"is only used when pooling_type is avg. The defalut is True.")
"is only used when pooling_type is avg. The default is True.")
.SetDefault(true);
AddAttr<bool>(
"adaptive",

2
paddle/fluid/operators/unpool_op.cc
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@ -46,7 +46,7 @@ class Unpool2dOpMaker : public framework::OpProtoAndCheckerMaker {
"strides (height, width) of unpooling operator.")
.SetDefault({1, 1});
AddAttr<std::vector<int>>("paddings",
"(vector defalut:{0,0}), "
"(vector default:{0,0}), "
"paddings (height, width) of unpooling operator.")
.SetDefault({0, 0});
AddAttr<std::string>(

12
python/paddle/fluid/contrib/slim/quantization/quantization_strategy.py
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@ -53,11 +53,11 @@ class QuantizationStrategy(Strategy):
start_epoch(int): The 'on_epoch_begin' function will be called in start_epoch. default: 0
end_epoch(int): The 'on_epoch_end' function will be called in end_epoch. default: 0
float_model_save_path(str): The path to save model with float weights.
None means it doesn't save float model. defalut: None.
None means it doesn't save float model. default: None.
mobile_model_save_path(str): The path to save model for paddle-mobile execution.
None means it doesn't save mobile model. defalut: None.
None means it doesn't save mobile model. default: None.
int8_model_save_path(str): The path to save model with int8_t weight.
None means it doesn't save int8 model. defalut: None.
None means it doesn't save int8 model. default: None.
activation_bits(int): quantization bit number for activation. default: 8.
weight_bits(int): quantization bit number for weights. The bias is not quantized.
default: 8.
@ -90,7 +90,7 @@ class QuantizationStrategy(Strategy):
def restore_from_checkpoint(self, context):
"""
Restore graph when the compressoin task is inited from checkpoint.
Restore graph when the compression task is inited from checkpoint.
"""
# It is inited from checkpoint and has missed start epoch.
if context.epoch_id != 0 and context.epoch_id > self.start_epoch:
@ -100,7 +100,7 @@ class QuantizationStrategy(Strategy):
def _modify_graph_for_quantization(self, context):
"""
Insert fake_quantize_op and fake_dequantize_op before trainging and testing.
Insert fake_quantize_op and fake_dequantize_op before training and testing.
"""
train_ir_graph = IrGraph(
core.Graph(context.optimize_graph.program.clone().desc),
@ -151,7 +151,7 @@ class QuantizationStrategy(Strategy):
def on_epoch_begin(self, context):
"""
Insert fake_quantize_op and fake_dequantize_op before trainging and testing.
Insert fake_quantize_op and fake_dequantize_op before training and testing.
"""
super(QuantizationStrategy, self).on_epoch_begin(context)
if self.start_epoch == context.epoch_id:

8
python/paddle/fluid/contrib/slim/tests/quantization/compress.yaml
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@ -1,15 +1,15 @@
#start_epoch(int): The epoch to insert quantization operators. default: 0
#
#end_epoch(int): The epoch to save inferecne model. default: 0
#end_epoch(int): The epoch to save inference model. default: 0
#
#float_model_save_path(str): The path to save model with float weights.
# None means it doesn't save float model. defalut: None.
# None means it doesn't save float model. default: None.
#
#mobile_model_save_path(str): The path to save model for paddle-mobile execution.
# None means it doesn't save mobile model. defalut: None.
# None means it doesn't save mobile model. default: None.
#
#int8_model_save_path(str): The path to save model with int8_t weight.
# None means it doesn't save int8 model. defalut: None.
# None means it doesn't save int8 model. default: None.
#
#activation_bits(int): quantization bit number for activation. default: 8.
#

6
python/paddle/fluid/evaluator.py
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@ -324,11 +324,11 @@ class DetectionMAP(Evaluator):
class_num (int): The class number.
background_label (int): The index of background label, the background
label will be ignored. If set to -1, then all categories will be
considered, 0 by defalut.
considered, 0 by default.
overlap_threshold (float): The threshold for deciding true/false
positive, 0.5 by defalut.
positive, 0.5 by default.
evaluate_difficult (bool): Whether to consider difficult ground truth
for evaluation, True by defalut. This argument does not work when
for evaluation, True by default. This argument does not work when
gt_difficult is None.
ap_version (string): The average precision calculation ways, it must be
'integral' or '11point'. Please check

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