From a43862fe78957461656b0d78d802d069466ab622 Mon Sep 17 00:00:00 2001 From: zq19 <50872563+zq19@users.noreply.github.com> Date: Thu, 1 Aug 2019 14:45:03 +0800 Subject: [PATCH] Release/1.5 synchronize api_en and api_cn (#1068) * synchronize api_en and api_cn * synchronize api_en and api_cn * synchronize api_en and api_cn * synchronize api_en and api_cn * synchronize api_en and api_cn * synchronize api_en and api_cn * synchronize api_en and api_cn * synchronize api_en and api_cn * synchronize api_en and api_cn * synchronize api_en and api_cn * synchronize api_en and api_cn * synchronize api_en and api_cn * synchronize api_en and api_cn * synchronize api_en and api_cn * synchronize api_en and api_cn * synchronize api_en and api_cn * synchronize api_en and api_cn * synchronize api_en and api_cn * synchronize api_en and api_cn * synchronize api_en and api_cn * synchronize api_en and api_cn --- doc/fluid/api_cn/clip_cn.rst | 2 +- doc/fluid/api_cn/dataset_cn.rst | 4 +- doc/fluid/api_cn/fluid_cn.rst | 13 ++++-- doc/fluid/api_cn/initializer_cn.rst | 2 +- doc/fluid/api_cn/layers_cn/detection_cn.rst | 28 +++++------ doc/fluid/api_cn/layers_cn/io_cn.rst | 4 +- doc/fluid/api_cn/layers_cn/metric_op_cn.rst | 2 +- doc/fluid/api_cn/layers_cn/nn_cn.rst | 52 ++++++++++----------- doc/fluid/api_cn/layers_cn/tensor_cn.rst | 12 ++--- doc/fluid/api_cn/metrics_cn.rst | 3 +- 10 files changed, 64 insertions(+), 58 deletions(-) diff --git a/doc/fluid/api_cn/clip_cn.rst b/doc/fluid/api_cn/clip_cn.rst index a4004ba99..4fd795379 100644 --- a/doc/fluid/api_cn/clip_cn.rst +++ b/doc/fluid/api_cn/clip_cn.rst @@ -183,7 +183,7 @@ GradientClipByValue w_param_attrs = fluid.ParamAttr(name=None, initializer=fluid.initializer.UniformInitializer(low=-1.0, high=1.0, seed=0), learning_rate=1.0, - regularizer=fluid.regualrizer.L1Decay(1.0), + regularizer=fluid.regularizer.L1Decay(1.0), trainable=True, gradient_clip=fluid.clip.GradientClipByValue(-1.0, 1.0)) x = fluid.layers.data(name='x', shape=[10], dtype='float32') diff --git a/doc/fluid/api_cn/dataset_cn.rst b/doc/fluid/api_cn/dataset_cn.rst index 55fd8363e..218a620e5 100644 --- a/doc/fluid/api_cn/dataset_cn.rst +++ b/doc/fluid/api_cn/dataset_cn.rst @@ -22,7 +22,7 @@ DatasetFactory是一个按数据集名称创建数据集的 "工厂"，可以创 .. code-block:: python import paddle.fluid as fluid - dataset = paddle.fluid.DatasetFactory().create_dataset("InMemoryDataset") + dataset = fluid.DatasetFactory().create_dataset("InMemoryDataset") .. py:method:: create_dataset(datafeed_class='QueueDataset') @@ -127,7 +127,7 @@ InMemoryDataset会向内存中加载数据并在训练前缓冲数据。此类 dataset.global_shuffle(fleet) exe = fluid.Executor(fluid.CPUPlace()) exe.run(fluid.default_startup_program()) - exe.train_from_dataset(fluid.default_main_program(), dataset)dataset.release_memory() + exe.train_from_dataset(fluid.default_main_program(), dataset) dataset.release_memory() .. py:method:: get_memory_data_size(fleet=None) diff --git a/doc/fluid/api_cn/fluid_cn.rst b/doc/fluid/api_cn/fluid_cn.rst index 8d405e6ae..ee8d09071 100644 --- a/doc/fluid/api_cn/fluid_cn.rst +++ b/doc/fluid/api_cn/fluid_cn.rst @@ -1041,8 +1041,9 @@ DistributeTranspiler t = fluid.DistributeTranspiler(config=config) t.transpile(trainer_id=trainer_id, trainers=trainer_endpoints, current_endpoint="192.168.0.1:6174") exe = fluid.ParallelExecutor( + use_cuda=True, loss_name=avg_loss.name, - num_trainers=len(trainer_num, + num_trainers=trainer_num, trainer_id=trainer_id ) @@ -1273,11 +1274,11 @@ ExecutionStrategy int型成员。它表明了清空执行时产生的临时变量需要的程序执行迭代次数。因为临时变量的形状可能在两次重复过程中保持一致，所以它会使整体执行过程更快。默认值为1。 .. note:: - 1. 如果在调用 ``run`` 方法时获取结果数据，``ParallelExecutor`` 会在当前程序重复执行尾部清空临时变量 + 1. 如果在调用 ``run`` 方法时获取结果数据，``llelExecutor`` 会在当前程序重复执行尾部清空临时变量 2. 在一些NLP模型里，该成员会致使GPU内存不足。此时，你应减少 ``num_iteration_per_drop_scope`` 的值 -.. py:attribute:: num_iteration_per_run +.. py:attribute:: num_iteration_per_runPara 它配置了当用户在python脚本中调用pe.run()时执行器会执行的迭代次数。 .. py:attribute:: num_threads @@ -1508,7 +1509,7 @@ infer_from_dataset的文档与train_from_dataset几乎完全相同，只是在 filelist = [] # 您可以设置您自己的filelist，如filelist = ["dataA.txt"] dataset.set_filelist(filelist) exe.run(fluid.default_startup_program()) - exe.infer_from_dataset(program=fluid.default_main_program(), + exe.train_from_dataset(program=fluid.default_main_program(), dataset=dataset) @@ -2100,6 +2101,7 @@ ParallelExecutor loss = fluid.layers.mean(hidden) place = fluid.CUDAPlace(0) if use_cuda else fluid.CPUPlace() + exe = fluid.Executor(place) exe.run(startup_program) parallel_exe = fluid.ParallelExecutor(use_cuda=use_cuda, @@ -2143,8 +2145,9 @@ ParamAttr w_param_attrs = fluid.ParamAttr(name="fc_weight", learning_rate=0.5, - regularizer=fluid.L2Decay(1.0), + regularizer=fluid.regularizer.L2Decay(1.0), trainable=True) + x = fluid.layers.data(name='X', shape=[1], dtype='float32') y_predict = fluid.layers.fc(input=x, size=10, param_attr=w_param_attrs) diff --git a/doc/fluid/api_cn/initializer_cn.rst b/doc/fluid/api_cn/initializer_cn.rst index 05a34812e..925864c6c 100644 --- a/doc/fluid/api_cn/initializer_cn.rst +++ b/doc/fluid/api_cn/initializer_cn.rst @@ -227,7 +227,7 @@ NormalInitializer import paddle.fluid as fluid x = fluid.layers.data(name="data", shape=[32, 32], dtype="float32") fc = fluid.layers.fc(input=x, size=10, - param_attr=fluid.initializer.Normal(loc=0.0, scale=2.0) + param_attr=fluid.initializer.Normal(loc=0.0, scale=2.0)) .. _cn_api_fluid_initializer_NumpyArrayInitializer: diff --git a/doc/fluid/api_cn/layers_cn/detection_cn.rst b/doc/fluid/api_cn/layers_cn/detection_cn.rst index 3de5941a3..3cfa7789e 100644 --- a/doc/fluid/api_cn/layers_cn/detection_cn.rst +++ b/doc/fluid/api_cn/layers_cn/detection_cn.rst @@ -138,7 +138,7 @@ box_clip name='boxes', shape=[8, 4], dtype='float32', lod_level=1) im_info = fluid.layers.data(name='im_info', shape=[3]) out = fluid.layers.box_clip( - input=boxes, im_info=im_info, inplace=True) + input=boxes, im_info=im_info) @@ -286,7 +286,7 @@ box decode过程得出decode_box，然后分配方案如下所述： 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)) + 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( @@ -1288,14 +1288,16 @@ rpn_target_assign .. code-block:: python import paddle.fluid as fluid - bbox_pred = fluid.layers.data(name=’bbox_pred’, shape=[100, 4], - append_batch_size=False, dtype=’float32’) - cls_logits = fluid.layers.data(name=’cls_logits’, shape=[100, 1], - append_batch_size=False, dtype=’float32’) - anchor_box = fluid.layers.data(name=’anchor_box’, shape=[20, 4], - append_batch_size=False, dtype=’float32’) - gt_boxes = fluid.layers.data(name=’gt_boxes’, shape=[10, 4], - append_batch_size=False, dtype=’float32’) + bbox_pred = fluid.layers.data(name='bbox_pred', shape=[100, 4], + append_batch_size=False, dtype='float32') + cls_logits = fluid.layers.data(name='cls_logits', shape=[100, 1], + append_batch_size=False, dtype='float32') + anchor_box = fluid.layers.data(name='anchor_box', shape=[20, 4], + append_batch_size=False, dtype='float32') + anchor_var = fluid.layers.data(name='anchor_var', shape=[20, 4], + append_batch_size=False, dtype='float32') + gt_boxes = fluid.layers.data(name='gt_boxes', shape=[10, 4], + append_batch_size=False, dtype='float32') is_crowd = fluid.layers.data(name='is_crowd', shape=[1], append_batch_size=False, dtype='float32') im_info = fluid.layers.data(name='im_infoss', shape=[1, 3], @@ -1684,9 +1686,9 @@ yolov3_loss import paddle.fluid as fluid x = fluid.layers.data(name='x', shape=[255, 13, 13], dtype='float32') - gt_box = fluid.layers.data(name='gtbox', shape=[6, 4], dtype='float32') - gt_label = fluid.layers.data(name='gtlabel', shape=[6], dtype='int32') - gt_score = fluid.layers.data(name='gtscore', shape=[6], dtype='float32') + gt_box = fluid.layers.data(name='gt_box', shape=[6, 4], dtype='float32') + gt_label = fluid.layers.data(name='gt_label', shape=[6], dtype='int32') + gt_score = fluid.layers.data(name='gt_score', shape=[6], dtype='float32') anchors = [10, 13, 16, 30, 33, 23, 30, 61, 62, 45, 59, 119, 116, 90, 156, 198, 373, 326] anchor_mask = [0, 1, 2] loss = fluid.layers.yolov3_loss(x=x, gt_box=gt_box, gt_label=gt_label, diff --git a/doc/fluid/api_cn/layers_cn/io_cn.rst b/doc/fluid/api_cn/layers_cn/io_cn.rst index bf8132f3b..a07ff3a83 100644 --- a/doc/fluid/api_cn/layers_cn/io_cn.rst +++ b/doc/fluid/api_cn/layers_cn/io_cn.rst @@ -85,8 +85,8 @@ create_py_reader_by_data loss = fluid.layers.cross_entropy(input=predict, label=label) return fluid.layers.mean(loss) - image = fluid.layers.data(name='image', shape=[1, 28, 28], dtypes='float32') - label = fluid.layers.data(name='label', shape=[1], dtypes='int64') + image = fluid.layers.data(name='image', shape=[1, 28, 28], dtype='float32') + label = fluid.layers.data(name='label', shape=[1], dtype='int64') reader = fluid.layers.create_py_reader_by_data(capacity=64, feed_list=[image, label]) reader.decorate_paddle_reader( diff --git a/doc/fluid/api_cn/layers_cn/metric_op_cn.rst b/doc/fluid/api_cn/layers_cn/metric_op_cn.rst index 65621ffe4..72d63bbe7 100644 --- a/doc/fluid/api_cn/layers_cn/metric_op_cn.rst +++ b/doc/fluid/api_cn/layers_cn/metric_op_cn.rst @@ -85,7 +85,7 @@ auc data = fluid.layers.data(name="data", shape=[32, 32], dtype="float32") label = fluid.layers.data(name="label", shape=[1], dtype="int32") predict = fluid.layers.fc(input=data, size=2) - auc_out=fluid.layers.auc(input=prediction, label=label) + auc_out=fluid.layers.auc(input=predict, label=label) diff --git a/doc/fluid/api_cn/layers_cn/nn_cn.rst b/doc/fluid/api_cn/layers_cn/nn_cn.rst index 7e4b495ef..5d803401d 100644 --- a/doc/fluid/api_cn/layers_cn/nn_cn.rst +++ b/doc/fluid/api_cn/layers_cn/nn_cn.rst @@ -144,7 +144,7 @@ pooling3d操作根据输入 ``input`` ，``pool_size`` ， ``pool_type`` 参数 data = fluid.layers.data( name='data', shape=[3, 32, 32, 32], dtype='float32') - pool_out, mask = fluid.layers.adaptive_pool3d( + pool_out = fluid.layers.adaptive_pool3d( input=data, pool_size=[3, 3, 3], pool_type='avg') @@ -520,7 +520,7 @@ beam_search name='probs', shape=[10000], dtype='float32') topk_scores, topk_indices = fluid.layers.topk(probs, k=beam_size) accu_scores = fluid.layers.elementwise_add( - x=fluid.layers.log(x=topk_scores)), + x=fluid.layers.log(x=topk_scores), y=fluid.layers.reshape( pre_scores, shape=[-1]), axis=0) @@ -698,7 +698,7 @@ BRelu 激活函数 .. code-block:: python import paddle.fluid as fluid - x = fluid.layers.data(name="x", shape=[2,3,16,16], dtype=”float32”) + x = fluid.layers.data(name="x", shape=[2,3,16,16], dtype="float32") y = fluid.layers.brelu(x, t_min=1.0, t_max=20.0) @@ -1456,7 +1456,7 @@ crop ## or z = fluid.layers.data(name="z", shape=[3, 5], dtype="float32") - crop = fluid.layers.crop(z, shape=[2, 3]) + crop = fluid.layers.crop(z, shape=[-1, 2, 3]) @@ -3481,7 +3481,7 @@ gaussian_random算子。 import paddle.fluid as fluid import paddle.fluid.layers as layers - out = fluid.layers.gaussian_random(shape=[20, 30]) + out = layers.gaussian_random(shape=[20, 30]) @@ -3629,9 +3629,9 @@ step 2： import paddle.fluid as fluid - x = fluid.layers.data(name='x', shape=[3, 10, 32, 32], dtype='float32') - theta = fluid.layers.data(name='theta', shape=[3, 2, 3], dtype='float32') - grid = fluid.layers.affine_grid(theta=theta, out_shape=[3, 10, 32, 32]}) + x = fluid.layers.data(name='x', shape=[10, 32, 32], dtype='float32') + theta = fluid.layers.data(name='theta', shape=[2, 3], dtype='float32') + grid = fluid.layers.affine_grid(theta=theta, out_shape=[3, 10, 32, 32]) out = fluid.layers.grid_sampler(x=x, grid=grid) @@ -4383,9 +4383,9 @@ label_smooth import paddle.fluid as fluid import paddle.fluid.layers as layers - label = fluid.layers.data(name="label", shape=[1], dtype="float32") - one_hot_label = fluid.layers.one_hot(input=label, depth=10) - smooth_label = fluid.layers.label_smooth( + label = layers.data(name="label", shape=[1], dtype="float32") + one_hot_label = layers.one_hot(input=label, depth=10) + smooth_label = layers.label_smooth( label=one_hot_label, epsilon=0.1, dtype="float32") @@ -5043,7 +5043,7 @@ sigmoid的计算公式为： :math:`sigmoid(x) = 1 / (1 + e^{-x})` 。 init_h = layers.fill_constant( [num_layers, batch_size, hidden_size], 'float32', 0.0 ) init_c = layers.fill_constant( [num_layers, batch_size, hidden_size], 'float32', 0.0 ) - rnn_out, last_h, last_c = fluid.layers.lstm(emb, init_h, init_c, max_len, hidden_size, num_layers, dropout_prob=dropout_prob) + rnn_out, last_h, last_c = layers.lstm(emb, init_h, init_c, max_len, hidden_size, num_layers, dropout_prob=dropout_prob) @@ -5118,8 +5118,8 @@ lstm单元的输入包括 :math:`x_{t}` ， :math:`h_{t-1}` 和 :math:`c_{t-1}` pre_cell = fluid.layers.data(name='pre_cell', shape=[hidden_dim], dtype='float32') hidden = fluid.layers.lstm_unit( x_t=x, - hidden_t_prev=prev_hidden, - cell_t_prev=prev_cell) + hidden_t_prev=pre_hidden, + cell_t_prev=pre_cell) @@ -5373,7 +5373,7 @@ mean_iou import paddle.fluid as fluid predict = fluid.layers.data(name='predict', shape=[3, 32, 32]) label = fluid.layers.data(name='label', shape=[1]) - iou, wrongs, corrects = fluid.layers.mean_iou(predict, label, num_classes) + iou, wrongs, corrects = fluid.layers.mean_iou(predict, label, num_classes=5) @@ -7652,7 +7652,7 @@ sequence_expand x = fluid.layers.data(name='x', shape=[10], dtype='float32') y = fluid.layers.data(name='y', shape=[10, 20], dtype='float32', lod_level=1) - out = fluid.layers.sequence_expand(x=x, y=y, ref_level=0) + out = layers.sequence_expand(x=x, y=y, ref_level=0) @@ -8806,7 +8806,7 @@ SoftRelu 激活函数 import paddle.fluid as fluid - x = fluid.layers.data(name=”x”, shape=[2,3,16,16], dtype=”float32”) + x = fluid.layers.data(name="x", shape=[3,16,16], dtype="float32") y = fluid.layers.soft_relu(x, threshold=20.0) @@ -9078,7 +9078,7 @@ split # x1.shape [-1, 3, 3, 5] # x2.shape [-1, 3, 3, 5] - x0, x1, x2 = fluid.layers.split(input, num_or_sections=[2, 3, 4], dim=2) + x0, x1, x2 = fluid.layers.split(input, num_or_sections=3, dim=2) # x0.shape [-1, 3, 2, 5] # x1.shape [-1, 3, 3, 5] # x2.shape [-1, 3, 4, 5] @@ -9180,8 +9180,8 @@ squeeze import paddle.fluid as fluid import paddle.fluid.layers as layers - x = fluid.layers.data(name='x', shape=[5, 1, 10]) - y = fluid.layers.sequeeze(input=x, axes=[1]) + x = layers.data(name='x', shape=[5, 1, 10]) + y = layers.squeeze(input=x, axes=[1]) @@ -9331,9 +9331,9 @@ sum算子。 import paddle.fluid as fluid import paddle.fluid.layers as layers - input0 = fluid.layers.data(name="input0", shape=[13, 11], dtype='float32') + input0 = layers.data(name="input0", shape=[13, 11], dtype='float32') input1 = layers.data(name="input1", shape=[13, 11], dtype='float32') - out = fluid.layers.sum([input0,input1]) + out = layers.sum([input0,input1]) @@ -9518,7 +9518,7 @@ topk import paddle.fluid as fluid import paddle.fluid.layers as layers input = layers.data(name="input", shape=[13, 11], dtype='float32') - top5_values, top5_indices = fluid.layers.topk(input, k=5) + top5_values, top5_indices = layers.topk(input, k=5) @@ -9651,8 +9651,8 @@ uniform_random_batch_size_like算子。 import paddle.fluid as fluid import paddle.fluid.layers as layers - input = fluid.layers.data(name="input", shape=[13, 11], dtype='float32') - out = fluid.layers.uniform_random_batch_size_like(input, [-1, 11]) + input = layers.data(name="input", shape=[13, 11], dtype='float32') + out = layers.uniform_random_batch_size_like(input, [-1, 11]) @@ -9685,7 +9685,7 @@ unsqueeze import paddle.fluid as fluid x = fluid.layers.data(name='x', shape=[5, 10]) - y = fluid.layers.unsequeeze(input=x, axes=[1]) + y = fluid.layers.unsqueeze(input=x, axes=[1]) diff --git a/doc/fluid/api_cn/layers_cn/tensor_cn.rst b/doc/fluid/api_cn/layers_cn/tensor_cn.rst index fd58b14db..53b118c47 100644 --- a/doc/fluid/api_cn/layers_cn/tensor_cn.rst +++ b/doc/fluid/api_cn/layers_cn/tensor_cn.rst @@ -28,8 +28,8 @@ argmax import paddle.fluid as fluid x = fluid.layers.data(name="x", shape=[3, 4], dtype="float32") - out = fluid.layers.argmax(x=in, axis=0) - out = fluid.layers.argmax(x=in, axis=-1) + out = fluid.layers.argmax(x, axis=0) + out = fluid.layers.argmax(x, axis=-1) @@ -64,8 +64,8 @@ argmin import paddle.fluid as fluid x = fluid.layers.data(name="x", shape=[3, 4], dtype="float32") - out = fluid.layers.argmin(x=in, axis=0) - out = fluid.layers.argmin(x=in, axis=-1) + out = fluid.layers.argmin(x, axis=0) + out = fluid.layers.argmin(x, axis=-1) @@ -224,7 +224,7 @@ concat b = fluid.layers.data(name='b', shape=[2, 3], dtype='float32') c = fluid.layers.data(name='c', shape=[2, 2], dtype='float32') d = fluid.layers.data(name='d', shape=[2, 5], dtype='float32') - out = fluid.layers.concat(input=[Efirst, Esecond, Ethird, Efourth]) + out = fluid.layers.concat(input=[a, b, c, d], axis=2) @@ -298,7 +298,7 @@ create_parameter import paddle.fluid as fluid import paddle.fluid.layers as layers - W = fluid.layers.create_parameter(shape=[784, 200], dtype='float32') + W = layers.create_parameter(shape=[784, 200], dtype='float32') diff --git a/doc/fluid/api_cn/metrics_cn.rst b/doc/fluid/api_cn/metrics_cn.rst index c76502a71..6326a93cc 100644 --- a/doc/fluid/api_cn/metrics_cn.rst +++ b/doc/fluid/api_cn/metrics_cn.rst @@ -357,7 +357,6 @@ EditDistance seq_num_batch0 = batch_size distance_evaluator.update(edit_distances_batch0, seq_num_batch0) - distance, instance_error = distance_evaluator.eval() avg_distance, wrong_instance_ratio = distance_evaluator.eval() print("the average edit distance for batch0 is %.2f and the wrong instance ratio is %.2f " % (avg_distance, wrong_instance_ratio)) edit_distances_batch1 = np.random.randint(low = 0, high = 10, size = (batch_size, 1)) @@ -367,6 +366,8 @@ EditDistance avg_distance, wrong_instance_ratio = distance_evaluator.eval() print("the average edit distance for batch0 and batch1 is %.2f and the wrong instance ratio is %.2f " % (avg_distance, wrong_instance_ratio)) + distance_evaluator.reset() + .. py:method:: distance_evaluator.reset() -- GitLab