From 7c1aa0d69dd21d7db98b1c46873f3a028e344e95 Mon Sep 17 00:00:00 2001 From: cnn Date: Wed, 21 Oct 2020 17:32:38 +0800 Subject: [PATCH] 2.0rc api rename (#28088) * rename manual_seed to seed * rename xxx1d-->xxx1D, xxx2d-->xxx2D, xxx3d-->xxx3D * rename manual_seed --> seed * do not rename .cc, .cu and .h file * rename manual_seed --> seed * rename manual_seed --> seed * rename manual_seed --> seed * rename manual_seed --> seed * disable_static on doc example code * donot change manual_seed on generator * add enable_static on sample code * convert python/paddle/fluid/layers/nn.py to bak * fix typo * fix code style * fix seed to manual_seed when call functions of Generator() * fix bug --- python/paddle/__init__.py | 2 +- python/paddle/amp/auto_cast.py | 2 +- python/paddle/amp/grad_scaler.py | 6 +- python/paddle/distribution.py | 20 +-- .../contrib/tests/test_weight_decay_extend.py | 4 +- python/paddle/fluid/dygraph/layers.py | 2 +- python/paddle/fluid/dygraph/nn.py | 8 +- python/paddle/fluid/initializer.py | 2 +- python/paddle/fluid/nets.py | 48 ++++--- .../unittests/dygraph_to_static/test_bmn.py | 2 +- .../unittests/dygraph_to_static/test_lac.py | 2 +- .../dygraph_to_static/test_mobile_net.py | 2 +- .../dygraph_to_static/test_ptb_lm.py | 2 +- .../dygraph_to_static/test_ptb_lm_v2.py | 2 +- .../test_reinforcement_learning.py | 2 +- .../dygraph_to_static/test_resnet.py | 2 +- .../dygraph_to_static/test_resnet_v2.py | 4 +- .../dygraph_to_static/test_se_resnet.py | 2 +- .../dygraph_to_static/test_sentiment.py | 2 +- .../dygraph_to_static/test_simnet.py | 2 +- .../dygraph_to_static/test_simnet_v2.py | 2 +- .../dygraph_to_static/test_transformer.py | 8 +- .../unittests/dygraph_to_static/test_tsm.py | 2 +- .../mkldnn/test_conv2d_bf16_mkldnn_op.py | 22 +-- .../mkldnn/test_conv2d_int8_mkldnn_op.py | 30 ++-- .../unittests/mkldnn/test_conv2d_mkldnn_op.py | 48 +++---- .../mkldnn/test_conv2d_transpose_mkldnn_op.py | 36 ++--- .../unittests/mkldnn/test_conv3d_mkldnn_op.py | 10 +- .../mkldnn/test_pool2d_int8_mkldnn_op.py | 10 +- .../parallel_dygraph_sync_batch_norm.py | 6 +- .../unittests/parallel_executor_test_base.py | 2 +- .../tests/unittests/rnn/test_rnn_nets.py | 2 +- .../unittests/test_adaptive_avg_pool1d.py | 4 +- .../unittests/test_adaptive_avg_pool2d.py | 24 ++-- .../unittests/test_adaptive_avg_pool3d.py | 30 ++-- .../unittests/test_adaptive_max_pool1d.py | 4 +- .../unittests/test_adaptive_max_pool2d.py | 24 ++-- .../unittests/test_adaptive_max_pool3d.py | 30 ++-- .../tests/unittests/test_batch_norm_op_v2.py | 32 ++--- .../test_buffer_shared_memory_reuse_pass.py | 2 +- .../tests/unittests/test_compiled_program.py | 6 +- .../tests/unittests/test_conv1d_layer.py | 46 +++--- .../unittests/test_conv1d_transpose_layer.py | 44 +++--- .../tests/unittests/test_conv2d_fusion_op.py | 36 ++--- .../tests/unittests/test_conv2d_layer.py | 2 +- .../fluid/tests/unittests/test_conv2d_op.py | 114 +++++++-------- .../unittests/test_conv2d_transpose_layer.py | 2 +- .../unittests/test_conv2d_transpose_op.py | 60 ++++---- .../tests/unittests/test_conv3d_layer.py | 2 +- .../fluid/tests/unittests/test_conv3d_op.py | 62 ++++---- .../unittests/test_conv3d_transpose_layer.py | 2 +- .../unittests/test_conv3d_transpose_op.py | 22 +-- .../test_conv3d_transpose_part2_op.py | 16 +-- .../tests/unittests/test_conv_nn_grad.py | 2 +- .../tests/unittests/test_cuda_random_seed.py | 14 +- .../unittests/test_decoupled_py_reader.py | 2 +- .../unittests/test_deformable_conv_op.py | 2 +- .../fluid/tests/unittests/test_dropout_op.py | 16 +-- .../unittests/test_dygraph_multi_forward.py | 4 +- .../unittests/test_dygraph_weight_norm.py | 4 +- .../test_eager_deletion_padding_rnn.py | 2 +- .../test_embedding_id_stop_gradient.py | 2 +- .../fluid/tests/unittests/test_fc_op.py | 2 +- .../tests/unittests/test_fuse_bn_act_pass.py | 2 +- .../tests/unittests/test_fused_bn_add_act.py | 2 +- .../unittests/test_gaussian_random_op.py | 2 +- .../fluid/tests/unittests/test_generator.py | 2 - .../unittests/test_generator_dataloader.py | 2 +- .../fluid/tests/unittests/test_hsigmoid_op.py | 2 +- .../test_imperative_auto_mixed_precision.py | 6 +- .../tests/unittests/test_imperative_deepcf.py | 6 +- .../unittests/test_imperative_double_grad.py | 4 +- .../tests/unittests/test_imperative_gan.py | 6 +- .../tests/unittests/test_imperative_gnn.py | 6 +- .../unittests/test_imperative_layer_apply.py | 8 +- .../test_imperative_layer_children.py | 4 +- ..._imperative_lod_tensor_to_selected_rows.py | 4 +- .../test_imperative_ocr_attention_model.py | 4 +- .../unittests/test_imperative_optimizer.py | 6 +- .../unittests/test_imperative_optimizer_v2.py | 6 +- .../unittests/test_imperative_ptb_rnn.py | 4 +- ...test_imperative_ptb_rnn_sorted_gradient.py | 4 +- .../test_imperative_reinforcement.py | 4 +- .../tests/unittests/test_imperative_resnet.py | 4 +- .../test_imperative_resnet_sorted_gradient.py | 4 +- .../unittests/test_imperative_save_load.py | 12 +- .../unittests/test_imperative_save_load_v2.py | 14 +- .../unittests/test_imperative_se_resnext.py | 4 +- ..._imperative_selected_rows_to_lod_tensor.py | 4 +- ...perative_star_gan_with_gradient_penalty.py | 4 +- ..._imperative_transformer_sorted_gradient.py | 4 +- .../unittests/test_inplace_addto_strategy.py | 2 +- .../unittests/test_instance_norm_op_v2.py | 12 +- .../test_ir_memory_optimize_ifelse_op.py | 2 +- .../tests/unittests/test_jit_save_load.py | 10 +- .../fluid/tests/unittests/test_layers.py | 4 +- .../fluid/tests/unittests/test_manual_seed.py | 6 +- .../fluid/tests/unittests/test_normal.py | 8 +- .../tests/unittests/test_paddle_save_load.py | 2 +- .../fluid/tests/unittests/test_pool1d_api.py | 12 +- .../fluid/tests/unittests/test_pool2d_api.py | 24 ++-- .../fluid/tests/unittests/test_pool2d_op.py | 4 +- .../fluid/tests/unittests/test_pool3d_api.py | 22 +-- .../fluid/tests/unittests/test_pool3d_op.py | 54 +++---- .../fluid/tests/unittests/test_py_func_op.py | 2 +- .../fluid/tests/unittests/test_random_seed.py | 36 ++--- .../fluid/tests/unittests/test_regularizer.py | 6 +- .../tests/unittests/test_regularizer_api.py | 6 +- .../tests/unittests/test_retain_graph.py | 6 +- .../tests/unittests/test_rnn_decode_api.py | 2 +- .../unittests/test_sync_batch_norm_op.py | 6 +- .../tests/unittests/test_transformer_api.py | 6 +- .../tests/unittests/test_translated_layer.py | 2 +- .../tests/unittests/test_uniform_random_op.py | 12 +- .../fluid/tests/unittests/test_var_base.py | 2 +- .../fluid/tests/unittests/test_var_conv_2d.py | 18 +-- .../tests/unittests/xpu/test_conv2d_op_xpu.py | 36 ++--- python/paddle/framework/__init__.py | 7 +- python/paddle/framework/random.py | 8 +- python/paddle/hapi/model_summary.py | 8 +- python/paddle/nn/__init__.py | 52 +++---- python/paddle/nn/functional/conv.py | 8 +- python/paddle/nn/functional/norm.py | 4 +- python/paddle/nn/layer/__init__.py | 40 +++--- python/paddle/nn/layer/common.py | 20 +-- python/paddle/nn/layer/conv.py | 80 ++++++----- python/paddle/nn/layer/norm.py | 36 ++--- python/paddle/nn/layer/pooling.py | 136 +++++++++--------- python/paddle/regularizer.py | 8 +- python/paddle/tensor/random.py | 10 +- python/paddle/tensor/to_string.py | 2 +- python/paddle/tests/test_model.py | 12 +- python/paddle/vision/models/lenet.py | 8 +- python/paddle/vision/models/mobilenetv1.py | 6 +- python/paddle/vision/models/mobilenetv2.py | 12 +- python/paddle/vision/models/resnet.py | 24 ++-- python/paddle/vision/models/vgg.py | 8 +- 137 files changed, 929 insertions(+), 906 deletions(-) diff --git a/python/paddle/__init__.py b/python/paddle/__init__.py index 3640dd22bb..54e51200dc 100755 --- a/python/paddle/__init__.py +++ b/python/paddle/__init__.py @@ -222,7 +222,7 @@ from .tensor.search import sort #DEFINE_ALIAS from .tensor.to_string import set_printoptions -from .framework.random import manual_seed #DEFINE_ALIAS +from .framework.random import seed #DEFINE_ALIAS from .framework.random import get_cuda_rng_state #DEFINE_ALIAS from .framework.random import set_cuda_rng_state #DEFINE_ALIAS from .framework import ParamAttr #DEFINE_ALIAS diff --git a/python/paddle/amp/auto_cast.py b/python/paddle/amp/auto_cast.py index e33f6e2afc..63c7d999fd 100644 --- a/python/paddle/amp/auto_cast.py +++ b/python/paddle/amp/auto_cast.py @@ -37,7 +37,7 @@ def auto_cast(enable=True, custom_white_list=None, custom_black_list=None): import paddle - conv2d = paddle.nn.Conv2d(3, 2, 3, bias_attr=False) + conv2d = paddle.nn.Conv2D(3, 2, 3, bias_attr=False) data = paddle.rand([10, 3, 32, 32]) with paddle.amp.auto_cast(): diff --git a/python/paddle/amp/grad_scaler.py b/python/paddle/amp/grad_scaler.py index 0e43e5a6a1..e3cd05dcb3 100644 --- a/python/paddle/amp/grad_scaler.py +++ b/python/paddle/amp/grad_scaler.py @@ -50,7 +50,7 @@ class GradScaler(AmpScaler): import paddle - model = paddle.nn.Conv2d(3, 2, 3, bias_attr=True) + model = paddle.nn.Conv2D(3, 2, 3, bias_attr=True) optimizer = paddle.optimizer.SGD(learning_rate=0.01, parameters=model.parameters()) scaler = paddle.amp.GradScaler(init_loss_scaling=1024) data = paddle.rand([10, 3, 32, 32]) @@ -90,7 +90,7 @@ class GradScaler(AmpScaler): import paddle - model = paddle.nn.Conv2d(3, 2, 3, bias_attr=True) + model = paddle.nn.Conv2D(3, 2, 3, bias_attr=True) optimizer = paddle.optimizer.SGD(learning_rate=0.01, parameters=model.parameters()) scaler = paddle.amp.GradScaler(init_loss_scaling=1024) data = paddle.rand([10, 3, 32, 32]) @@ -122,7 +122,7 @@ class GradScaler(AmpScaler): import paddle - model = paddle.nn.Conv2d(3, 2, 3, bias_attr=True) + model = paddle.nn.Conv2D(3, 2, 3, bias_attr=True) optimizer = paddle.optimizer.SGD(learning_rate=0.01, parameters=model.parameters()) scaler = paddle.amp.GradScaler(init_loss_scaling=1024) data = paddle.rand([10, 3, 32, 32]) diff --git a/python/paddle/distribution.py b/python/paddle/distribution.py index 9133751a53..e9a15feb51 100644 --- a/python/paddle/distribution.py +++ b/python/paddle/distribution.py @@ -670,13 +670,13 @@ class Categorical(Distribution): import paddle from paddle.distribution import Categorical - paddle.manual_seed(100) # on CPU device + paddle.seed(100) # on CPU device x = paddle.rand([6]) print(x.numpy()) # [0.5535528 0.20714243 0.01162981 # 0.51577556 0.36369765 0.2609165 ] - paddle.manual_seed(200) # on CPU device + paddle.seed(200) # on CPU device y = paddle.rand([6]) print(y.numpy()) # [0.77663314 0.90824795 0.15685187 @@ -685,7 +685,7 @@ class Categorical(Distribution): cat = Categorical(x) cat2 = Categorical(y) - paddle.manual_seed(1000) # on CPU device + paddle.seed(1000) # on CPU device cat.sample([2,3]) # [[0, 0, 5], # [3, 4, 5]] @@ -744,7 +744,7 @@ class Categorical(Distribution): import paddle from paddle.distribution import Categorical - paddle.manual_seed(100) # on CPU device + paddle.seed(100) # on CPU device x = paddle.rand([6]) print(x.numpy()) # [0.5535528 0.20714243 0.01162981 @@ -752,7 +752,7 @@ class Categorical(Distribution): cat = Categorical(x) - paddle.manual_seed(1000) # on CPU device + paddle.seed(1000) # on CPU device cat.sample([2,3]) # [[0, 0, 5], # [3, 4, 5]] @@ -791,13 +791,13 @@ class Categorical(Distribution): import paddle from paddle.distribution import Categorical - paddle.manual_seed(100) # on CPU device + paddle.seed(100) # on CPU device x = paddle.rand([6]) print(x.numpy()) # [0.5535528 0.20714243 0.01162981 # 0.51577556 0.36369765 0.2609165 ] - paddle.manual_seed(200) # on CPU device + paddle.seed(200) # on CPU device y = paddle.rand([6]) print(y.numpy()) # [0.77663314 0.90824795 0.15685187 @@ -842,7 +842,7 @@ class Categorical(Distribution): import paddle from paddle.distribution import Categorical - paddle.manual_seed(100) # on CPU device + paddle.seed(100) # on CPU device x = paddle.rand([6]) print(x.numpy()) # [0.5535528 0.20714243 0.01162981 @@ -887,7 +887,7 @@ class Categorical(Distribution): import paddle from paddle.distribution import Categorical - paddle.manual_seed(100) # on CPU device + paddle.seed(100) # on CPU device x = paddle.rand([6]) print(x.numpy()) # [0.5535528 0.20714243 0.01162981 @@ -953,7 +953,7 @@ class Categorical(Distribution): import paddle from paddle.distribution import Categorical - paddle.manual_seed(100) # on CPU device + paddle.seed(100) # on CPU device x = paddle.rand([6]) print(x.numpy()) # [0.5535528 0.20714243 0.01162981 diff --git a/python/paddle/fluid/contrib/tests/test_weight_decay_extend.py b/python/paddle/fluid/contrib/tests/test_weight_decay_extend.py index 6000a44ceb..5ed7fd01a4 100644 --- a/python/paddle/fluid/contrib/tests/test_weight_decay_extend.py +++ b/python/paddle/fluid/contrib/tests/test_weight_decay_extend.py @@ -114,7 +114,7 @@ class TestWeightDecay(unittest.TestCase): return param_sum def check_weight_decay(self, place, model): - paddle.manual_seed(1) + paddle.seed(1) paddle.framework.random._manual_program_seed(1) main_prog = fluid.framework.Program() startup_prog = fluid.framework.Program() @@ -137,7 +137,7 @@ class TestWeightDecay(unittest.TestCase): return param_sum def check_weight_decay2(self, place, model): - paddle.manual_seed(1) + paddle.seed(1) paddle.framework.random._manual_program_seed(1) main_prog = fluid.framework.Program() startup_prog = fluid.framework.Program() diff --git a/python/paddle/fluid/dygraph/layers.py b/python/paddle/fluid/dygraph/layers.py index 3ae6d384be..6fa531c573 100644 --- a/python/paddle/fluid/dygraph/layers.py +++ b/python/paddle/fluid/dygraph/layers.py @@ -1058,7 +1058,7 @@ class Layer(core.Layer): super(Mylayer, self).__init__() self.linear1 = paddle.nn.Linear(10, 10) self.linear2 = paddle.nn.Linear(5, 5) - self.conv2d = paddle.nn.Conv2d(3, 2, 3) + self.conv2d = paddle.nn.Conv2D(3, 2, 3) self.embedding = paddle.nn.Embedding(128, 16) self.h_0 = paddle.to_tensor(np.zeros([10, 10]).astype('float32')) diff --git a/python/paddle/fluid/dygraph/nn.py b/python/paddle/fluid/dygraph/nn.py index 9a23e11b8a..214a7cb802 100644 --- a/python/paddle/fluid/dygraph/nn.py +++ b/python/paddle/fluid/dygraph/nn.py @@ -110,7 +110,7 @@ class Conv2D(layers.Layer): dilation (int or tuple, optional): The dilation size. If dilation is a tuple, it must contain two integers, (dilation_H, dilation_W). Otherwise, the dilation_H = dilation_W = dilation. Default: 1. - groups (int, optional): The groups number of the Conv2d Layer. According to grouped + groups (int, optional): The groups number of the Conv2D Layer. According to grouped convolution in Alex Krizhevsky's Deep CNN paper: when group=2, the first half of the filters is only connected to the first half of the input channels, while the second half of the filters is only @@ -345,7 +345,7 @@ class Conv3D(layers.Layer): dilation (int|tuple, optional): The dilation size. If dilation is a tuple, it must contain three integers, (dilation_D, dilation_H, dilation_W). Otherwise, the dilation_D = dilation_H = dilation_W = dilation. The default value is 1. - groups (int, optional): The groups number of the Conv3d Layer. According to grouped + groups (int, optional): The groups number of the Conv3D Layer. According to grouped convolution in Alex Krizhevsky's Deep CNN paper: when group=2, the first half of the filters is only connected to the first half of the input channels, while the second half of the filters is only @@ -574,7 +574,7 @@ class Conv3DTranspose(layers.Layer): dilation(int|tuple, optional): The dilation size. If dilation is a tuple, it must contain three integers, (dilation_D, dilation_H, dilation_W). Otherwise, the dilation_D = dilation_H = dilation_W = dilation. The default value is 1. - groups(int, optional): The groups number of the Conv3d transpose layer. Inspired by + groups(int, optional): The groups number of the Conv3D transpose layer. Inspired by grouped convolution in Alex Krizhevsky's Deep CNN paper, in which when group=2, the first half of the filters is only connected to the first half of the input channels, while the second half of the @@ -2541,7 +2541,7 @@ class Conv2DTranspose(layers.Layer): dilation(int or tuple, optional): The dilation size. If dilation is a tuple, it must contain two integers, (dilation_H, dilation_W). Otherwise, the dilation_H = dilation_W = dilation. Default: 1. - groups(int, optional): The groups number of the Conv2d transpose layer. Inspired by + groups(int, optional): The groups number of the Conv2D transpose layer. Inspired by grouped convolution in Alex Krizhevsky's Deep CNN paper, in which when group=2, the first half of the filters is only connected to the first half of the input channels, while the second half of the diff --git a/python/paddle/fluid/initializer.py b/python/paddle/fluid/initializer.py index c21a96cb01..46fd932788 100644 --- a/python/paddle/fluid/initializer.py +++ b/python/paddle/fluid/initializer.py @@ -749,7 +749,7 @@ class BilinearInitializer(Initializer): regularizer=L2Decay(0.), initializer=nn.initializer.Bilinear()) data = paddle.rand([B, 3, H, W], dtype='float32') - conv_up = nn.ConvTranspose2d(3, + conv_up = nn.Conv2DTranspose(3, out_channels=C, kernel_size=2 * factor - factor % 2, padding=int( diff --git a/python/paddle/fluid/nets.py b/python/paddle/fluid/nets.py index 8621fc6544..8df8f6b689 100644 --- a/python/paddle/fluid/nets.py +++ b/python/paddle/fluid/nets.py @@ -43,7 +43,7 @@ def simple_img_conv_pool(input, act=None, use_cudnn=True): """ - :api_attr: Static Graph + :api_attr: Static Graph The simple_img_conv_pool api is composed of :ref:`api_fluid_layers_conv2d` and :ref:`api_fluid_layers_pool2d` . @@ -106,6 +106,8 @@ def simple_img_conv_pool(input, .. code-block:: python import paddle.fluid as fluid + import paddle + paddle.enable_static() img = fluid.data(name='img', shape=[100, 1, 28, 28], dtype='float32') conv_pool = fluid.nets.simple_img_conv_pool(input=img, filter_size=5, @@ -151,37 +153,37 @@ def img_conv_group(input, pool_type="max", use_cudnn=True): """ - :api_attr: Static Graph + :api_attr: Static Graph The Image Convolution Group is composed of Convolution2d, BatchNorm, DropOut, - and Pool2d. According to the input arguments, img_conv_group will do serials of + and Pool2D. According to the input arguments, img_conv_group will do serials of computation for Input using Convolution2d, BatchNorm, DropOut, and pass the last - result to Pool2d. + result to Pool2D. Args: input (Variable): The input is 4-D Tensor with shape [N, C, H, W], the data type of input is float32 or float64. conv_num_filter(list|tuple): Indicates the numbers of filter of this group. - pool_size (int|list|tuple): The pooling size of Pool2d Layer. If pool_size + pool_size (int|list|tuple): The pooling size of Pool2D Layer. If pool_size is a list or tuple, it must contain two integers, (pool_size_height, pool_size_width). Otherwise, the pool_size_height = pool_size_width = pool_size. - conv_padding (int|list|tuple): The padding size of the Conv2d Layer. If padding is + conv_padding (int|list|tuple): The padding size of the Conv2D Layer. If padding is a list or tuple, its length must be equal to the length of conv_num_filter. - Otherwise the conv_padding of all Conv2d Layers are the same. Default 1. + Otherwise the conv_padding of all Conv2D Layers are the same. Default 1. conv_filter_size (int|list|tuple): The filter size. If filter_size is a list or tuple, its length must be equal to the length of conv_num_filter. - Otherwise the conv_filter_size of all Conv2d Layers are the same. Default 3. - conv_act (str): Activation type for Conv2d Layer that is not followed by BatchNorm. + Otherwise the conv_filter_size of all Conv2D Layers are the same. Default 3. + conv_act (str): Activation type for Conv2D Layer that is not followed by BatchNorm. Default: None. - param_attr (ParamAttr): The parameters to the Conv2d Layer. Default: None - conv_with_batchnorm (bool|list): Indicates whether to use BatchNorm after Conv2d Layer. + param_attr (ParamAttr): The parameters to the Conv2D Layer. Default: None + conv_with_batchnorm (bool|list): Indicates whether to use BatchNorm after Conv2D Layer. If conv_with_batchnorm is a list, its length must be equal to the length of conv_num_filter. Otherwise, conv_with_batchnorm indicates whether all the - Conv2d Layer follows a BatchNorm. Default False. + Conv2D Layer follows a BatchNorm. Default False. conv_batchnorm_drop_rate (float|list): Indicates the drop_rate of Dropout Layer after BatchNorm. If conv_batchnorm_drop_rate is a list, its length must be equal to the length of conv_num_filter. Otherwise, drop_rate of all Dropout Layers is conv_batchnorm_drop_rate. Default 0.0. - pool_stride (int|list|tuple): The pooling stride of Pool2d layer. If pool_stride + pool_stride (int|list|tuple): The pooling stride of Pool2D layer. If pool_stride is a list or tuple, it must contain two integers, (pooling_stride_H, pooling_stride_W). Otherwise, the pooling_stride_H = pooling_stride_W = pool_stride. Default 1. @@ -192,12 +194,15 @@ def img_conv_group(input, Return: A Variable holding Tensor representing the final result after serial computation using Convolution2d, - BatchNorm, DropOut, and Pool2d, whose data type is the same with input. + BatchNorm, DropOut, and Pool2D, whose data type is the same with input. Examples: .. code-block:: python import paddle.fluid as fluid + import paddle + paddle.enable_static() + img = fluid.data(name='img', shape=[None, 1, 28, 28], dtype='float32') conv_pool = fluid.nets.img_conv_group(input=img, conv_padding=1, @@ -261,7 +266,7 @@ def sequence_conv_pool(input, pool_type="max", bias_attr=None): """ - :api_attr: Static Graph + :api_attr: Static Graph **This api takes input as an LoDTensor. If input is a Tensor, please use** :ref:`api_fluid_nets_simple_img_conv_pool` **instead** @@ -300,6 +305,8 @@ def sequence_conv_pool(input, .. code-block:: python import paddle.fluid as fluid + import paddle + paddle.enable_static() input_dim = 100 #len(word_dict) emb_dim = 128 hid_dim = 512 @@ -327,7 +334,7 @@ def sequence_conv_pool(input, def glu(input, dim=-1): """ - :api_attr: Static Graph + :api_attr: Static Graph The Gated Linear Units(GLU) composed by :ref:`api_fluid_layers_split` , :ref:`api_fluid_layers_sigmoid` and :ref:`api_fluid_layers_elementwise_mul` . @@ -356,6 +363,9 @@ def glu(input, dim=-1): .. code-block:: python import paddle.fluid as fluid + import paddle + paddle.enable_static() + data = fluid.data( name="words", shape=[-1, 6, 3, 9], dtype="float32") # shape of output: [-1, 3, 3, 9] @@ -375,7 +385,7 @@ def scaled_dot_product_attention(queries, num_heads=1, dropout_rate=0.): """ - :api_attr: Static Graph + :api_attr: Static Graph This interface Multi-Head Attention using scaled dot product. Attention mechanism can be seen as mapping a query and a set of key-value @@ -435,7 +445,9 @@ def scaled_dot_product_attention(queries, .. code-block:: python import paddle.fluid as fluid - + import paddle + paddle.enable_static() + queries = fluid.data(name="queries", shape=[3, 5, 9], dtype="float32") keys = fluid.data(name="keys", shape=[3, 6, 9], dtype="float32") values = fluid.data(name="values", shape=[3, 6, 10], dtype="float32") diff --git a/python/paddle/fluid/tests/unittests/dygraph_to_static/test_bmn.py b/python/paddle/fluid/tests/unittests/dygraph_to_static/test_bmn.py index c4f5cc9e2b..f69abb1e37 100644 --- a/python/paddle/fluid/tests/unittests/dygraph_to_static/test_bmn.py +++ b/python/paddle/fluid/tests/unittests/dygraph_to_static/test_bmn.py @@ -564,7 +564,7 @@ def train_bmn(args, place, to_static): loss_data = [] with fluid.dygraph.guard(place): - paddle.manual_seed(SEED) + paddle.seed(SEED) paddle.framework.random._manual_program_seed(SEED) global local_random local_random = np.random.RandomState(SEED) diff --git a/python/paddle/fluid/tests/unittests/dygraph_to_static/test_lac.py b/python/paddle/fluid/tests/unittests/dygraph_to_static/test_lac.py index c9bc8cc647..63da7c2b17 100644 --- a/python/paddle/fluid/tests/unittests/dygraph_to_static/test_lac.py +++ b/python/paddle/fluid/tests/unittests/dygraph_to_static/test_lac.py @@ -450,7 +450,7 @@ def do_train(args, to_static): place = fluid.CUDAPlace(0) if fluid.is_compiled_with_cuda( ) else fluid.CPUPlace() with fluid.dygraph.guard(place): - paddle.manual_seed(SEED) + paddle.seed(SEED) paddle.framework.random._manual_program_seed(SEED) reader = get_random_input_data(args.batch_size, args.vocab_size, diff --git a/python/paddle/fluid/tests/unittests/dygraph_to_static/test_mobile_net.py b/python/paddle/fluid/tests/unittests/dygraph_to_static/test_mobile_net.py index a086bf1455..30c1955adc 100644 --- a/python/paddle/fluid/tests/unittests/dygraph_to_static/test_mobile_net.py +++ b/python/paddle/fluid/tests/unittests/dygraph_to_static/test_mobile_net.py @@ -451,7 +451,7 @@ def train_mobilenet(args, to_static): with fluid.dygraph.guard(args.place): np.random.seed(SEED) - paddle.manual_seed(SEED) + paddle.seed(SEED) paddle.framework.random._manual_program_seed(SEED) if args.model == "MobileNetV1": diff --git a/python/paddle/fluid/tests/unittests/dygraph_to_static/test_ptb_lm.py b/python/paddle/fluid/tests/unittests/dygraph_to_static/test_ptb_lm.py index 61e1614c3a..ea0529ffb2 100644 --- a/python/paddle/fluid/tests/unittests/dygraph_to_static/test_ptb_lm.py +++ b/python/paddle/fluid/tests/unittests/dygraph_to_static/test_ptb_lm.py @@ -218,7 +218,7 @@ def train(place): batch_num = 200 with fluid.dygraph.guard(place): - paddle.manual_seed(SEED) + paddle.seed(SEED) paddle.framework.random._manual_program_seed(SEED) ptb_model = PtbModel( hidden_size=hidden_size, diff --git a/python/paddle/fluid/tests/unittests/dygraph_to_static/test_ptb_lm_v2.py b/python/paddle/fluid/tests/unittests/dygraph_to_static/test_ptb_lm_v2.py index 2c74e5b221..0d45d7edb2 100644 --- a/python/paddle/fluid/tests/unittests/dygraph_to_static/test_ptb_lm_v2.py +++ b/python/paddle/fluid/tests/unittests/dygraph_to_static/test_ptb_lm_v2.py @@ -210,7 +210,7 @@ def train(place): batch_num = 200 paddle.disable_static(place) - paddle.manual_seed(SEED) + paddle.seed(SEED) paddle.framework.random._manual_program_seed(SEED) ptb_model = PtbModel( hidden_size=hidden_size, diff --git a/python/paddle/fluid/tests/unittests/dygraph_to_static/test_reinforcement_learning.py b/python/paddle/fluid/tests/unittests/dygraph_to_static/test_reinforcement_learning.py index 1d211197eb..c127e5882b 100644 --- a/python/paddle/fluid/tests/unittests/dygraph_to_static/test_reinforcement_learning.py +++ b/python/paddle/fluid/tests/unittests/dygraph_to_static/test_reinforcement_learning.py @@ -65,7 +65,7 @@ def train(args, place, to_static): env.seed(SEED) with fluid.dygraph.guard(place): - paddle.manual_seed(SEED) + paddle.seed(SEED) paddle.framework.random._manual_program_seed(SEED) local_random = np.random.RandomState(SEED) diff --git a/python/paddle/fluid/tests/unittests/dygraph_to_static/test_resnet.py b/python/paddle/fluid/tests/unittests/dygraph_to_static/test_resnet.py index 095940d79e..dcc323d064 100644 --- a/python/paddle/fluid/tests/unittests/dygraph_to_static/test_resnet.py +++ b/python/paddle/fluid/tests/unittests/dygraph_to_static/test_resnet.py @@ -219,7 +219,7 @@ def train(to_static): """ with fluid.dygraph.guard(place): np.random.seed(SEED) - paddle.manual_seed(SEED) + paddle.seed(SEED) paddle.framework.random._manual_program_seed(SEED) train_reader = paddle.batch( diff --git a/python/paddle/fluid/tests/unittests/dygraph_to_static/test_resnet_v2.py b/python/paddle/fluid/tests/unittests/dygraph_to_static/test_resnet_v2.py index 88c55f1907..10346ab0cc 100644 --- a/python/paddle/fluid/tests/unittests/dygraph_to_static/test_resnet_v2.py +++ b/python/paddle/fluid/tests/unittests/dygraph_to_static/test_resnet_v2.py @@ -66,7 +66,7 @@ class ConvBNLayer(paddle.nn.Layer): act=None): super(ConvBNLayer, self).__init__() - self._conv = paddle.nn.Conv2d( + self._conv = paddle.nn.Conv2D( in_channels=num_channels, out_channels=num_filters, kernel_size=filter_size, @@ -214,7 +214,7 @@ def train(to_static): """ paddle.disable_static(place) np.random.seed(SEED) - paddle.manual_seed(SEED) + paddle.seed(SEED) paddle.framework.random._manual_program_seed(SEED) train_reader = paddle.batch( diff --git a/python/paddle/fluid/tests/unittests/dygraph_to_static/test_se_resnet.py b/python/paddle/fluid/tests/unittests/dygraph_to_static/test_se_resnet.py index 15cff50183..eb17264977 100644 --- a/python/paddle/fluid/tests/unittests/dygraph_to_static/test_se_resnet.py +++ b/python/paddle/fluid/tests/unittests/dygraph_to_static/test_se_resnet.py @@ -334,7 +334,7 @@ def train(train_reader, to_static): np.random.seed(SEED) with fluid.dygraph.guard(place): - paddle.manual_seed(SEED) + paddle.seed(SEED) paddle.framework.random._manual_program_seed(SEED) se_resnext = SeResNeXt() optimizer = optimizer_setting(train_parameters, se_resnext.parameters()) diff --git a/python/paddle/fluid/tests/unittests/dygraph_to_static/test_sentiment.py b/python/paddle/fluid/tests/unittests/dygraph_to_static/test_sentiment.py index 2aa3396fb7..db03bb9b33 100644 --- a/python/paddle/fluid/tests/unittests/dygraph_to_static/test_sentiment.py +++ b/python/paddle/fluid/tests/unittests/dygraph_to_static/test_sentiment.py @@ -286,7 +286,7 @@ def train(args, to_static): with fluid.dygraph.guard(place): np.random.seed(SEED) - paddle.manual_seed(SEED) + paddle.seed(SEED) paddle.framework.random._manual_program_seed(SEED) train_reader = fake_data_reader(args.class_num, args.vocab_size, diff --git a/python/paddle/fluid/tests/unittests/dygraph_to_static/test_simnet.py b/python/paddle/fluid/tests/unittests/dygraph_to_static/test_simnet.py index 14b9ac2e99..01e9ed07ef 100644 --- a/python/paddle/fluid/tests/unittests/dygraph_to_static/test_simnet.py +++ b/python/paddle/fluid/tests/unittests/dygraph_to_static/test_simnet.py @@ -108,7 +108,7 @@ def train(conf_dict, to_static): place = fluid.CPUPlace() with fluid.dygraph.guard(place): - paddle.manual_seed(SEED) + paddle.seed(SEED) paddle.framework.random._manual_program_seed(SEED) conf_dict['dict_size'] = len(vocab) diff --git a/python/paddle/fluid/tests/unittests/dygraph_to_static/test_simnet_v2.py b/python/paddle/fluid/tests/unittests/dygraph_to_static/test_simnet_v2.py index 284087e61e..872d419ff8 100644 --- a/python/paddle/fluid/tests/unittests/dygraph_to_static/test_simnet_v2.py +++ b/python/paddle/fluid/tests/unittests/dygraph_to_static/test_simnet_v2.py @@ -106,7 +106,7 @@ def train(conf_dict, to_static): place = paddle.CPUPlace() paddle.disable_static(place) - paddle.manual_seed(SEED) + paddle.seed(SEED) paddle.framework.random._manual_program_seed(SEED) conf_dict['dict_size'] = len(vocab) diff --git a/python/paddle/fluid/tests/unittests/dygraph_to_static/test_transformer.py b/python/paddle/fluid/tests/unittests/dygraph_to_static/test_transformer.py index 6721e7a51d..451ceea75c 100644 --- a/python/paddle/fluid/tests/unittests/dygraph_to_static/test_transformer.py +++ b/python/paddle/fluid/tests/unittests/dygraph_to_static/test_transformer.py @@ -33,7 +33,7 @@ STEP_NUM = 10 def train_static(args, batch_generator): paddle.enable_static() - paddle.manual_seed(SEED) + paddle.seed(SEED) paddle.framework.random._manual_program_seed(SEED) train_prog = fluid.Program() startup_prog = fluid.Program() @@ -131,7 +131,7 @@ def train_static(args, batch_generator): def train_dygraph(args, batch_generator): with fluid.dygraph.guard(place): if SEED is not None: - paddle.manual_seed(SEED) + paddle.seed(SEED) paddle.framework.random._manual_program_seed(SEED) # define data loader train_loader = fluid.io.DataLoader.from_generator(capacity=10) @@ -223,7 +223,7 @@ def train_dygraph(args, batch_generator): def predict_dygraph(args, batch_generator): with fluid.dygraph.guard(place): - paddle.manual_seed(SEED) + paddle.seed(SEED) paddle.framework.random._manual_program_seed(SEED) # define data loader @@ -295,7 +295,7 @@ def predict_dygraph(args, batch_generator): def predict_static(args, batch_generator): test_prog = fluid.Program() with fluid.program_guard(test_prog): - paddle.manual_seed(SEED) + paddle.seed(SEED) paddle.framework.random._manual_program_seed(SEED) # define input and reader diff --git a/python/paddle/fluid/tests/unittests/dygraph_to_static/test_tsm.py b/python/paddle/fluid/tests/unittests/dygraph_to_static/test_tsm.py index bedca41215..c9d4bb2e79 100644 --- a/python/paddle/fluid/tests/unittests/dygraph_to_static/test_tsm.py +++ b/python/paddle/fluid/tests/unittests/dygraph_to_static/test_tsm.py @@ -272,7 +272,7 @@ def train(args, fake_data_reader, to_static): random.seed(0) np.random.seed(0) with fluid.dygraph.guard(place): - paddle.manual_seed(1000) + paddle.seed(1000) paddle.framework.random._manual_program_seed(1000) video_model = TSM_ResNet("TSM", train_config, 'Train') diff --git a/python/paddle/fluid/tests/unittests/mkldnn/test_conv2d_bf16_mkldnn_op.py b/python/paddle/fluid/tests/unittests/mkldnn/test_conv2d_bf16_mkldnn_op.py index 0311eb887a..efd0e95dd3 100644 --- a/python/paddle/fluid/tests/unittests/mkldnn/test_conv2d_bf16_mkldnn_op.py +++ b/python/paddle/fluid/tests/unittests/mkldnn/test_conv2d_bf16_mkldnn_op.py @@ -20,7 +20,7 @@ import struct import paddle.fluid.core as core from paddle.fluid.tests.unittests.op_test import OpTest, convert_float_to_uint16 -from paddle.fluid.tests.unittests.test_conv2d_op import conv2d_forward_naive, TestConv2dOp +from paddle.fluid.tests.unittests.test_conv2d_op import conv2d_forward_naive, TestConv2DOp def conv2d_residual_naive(out, residual): @@ -31,7 +31,7 @@ def conv2d_residual_naive(out, residual): @unittest.skipIf(not core.supports_bfloat16(), "place does not support BF16 evaluation") -class TestConv2dBf16Op(TestConv2dOp): +class TestConv2DBf16Op(TestConv2DOp): def setUp(self): self.op_type = "conv2d" self.use_cudnn = False @@ -110,7 +110,7 @@ class TestConv2dBf16Op(TestConv2dOp): pass def init_test_case(self): - TestConv2dOp.init_test_case(self) + TestConv2DOp.init_test_case(self) self.input_size = [1, 1, 5, 5] # NCHW f_c = self.input_size[1] // self.groups self.input_residual_size = [1, 2, 3, 3] @@ -130,7 +130,7 @@ class TestConv2dBf16Op(TestConv2dOp): self.fuse_residual = True -class TestConv2d(TestConv2dBf16Op): +class TestConv2D(TestConv2DBf16Op): def init_test_case(self): self.pad = [0, 0] self.stride = [1, 1] @@ -144,19 +144,19 @@ class TestConv2d(TestConv2dBf16Op): self.input_type = np.uint16 -class TestWithPad(TestConv2d): +class TestWithPad(TestConv2D): def init_test_case(self): - TestConv2d.init_test_case(self) + TestConv2D.init_test_case(self) self.pad = [1, 1] self.input_residual_size = [2, 6, 5, 5] -class TestWithGroup(TestConv2d): +class TestWithGroup(TestConv2D): def init_group(self): self.groups = 3 -class TestWithStride(TestConv2dBf16Op): +class TestWithStride(TestConv2DBf16Op): def init_test_case(self): self.pad = [1, 1] self.stride = [2, 2] @@ -170,7 +170,7 @@ class TestWithStride(TestConv2dBf16Op): self.input_type = np.uint16 -class TestWithDilations(TestConv2dBf16Op): +class TestWithDilations(TestConv2DBf16Op): def init_test_case(self): self.pad = [1, 1] self.stride = [1, 1] @@ -185,7 +185,7 @@ class TestWithDilations(TestConv2dBf16Op): self.input_type = np.uint16 -class TestWith1x1ForceFP32Output(TestConv2dBf16Op): +class TestWith1x1ForceFP32Output(TestConv2DBf16Op): def init_test_case(self): self.pad = [0, 0] self.stride = [1, 1] @@ -201,7 +201,7 @@ class TestWith1x1ForceFP32Output(TestConv2dBf16Op): self.fuse_residual = False -class TestWithInput1x1Filter1x1(TestConv2dBf16Op): +class TestWithInput1x1Filter1x1(TestConv2DBf16Op): def init_test_case(self): self.pad = [0, 0] self.stride = [1, 1] diff --git a/python/paddle/fluid/tests/unittests/mkldnn/test_conv2d_int8_mkldnn_op.py b/python/paddle/fluid/tests/unittests/mkldnn/test_conv2d_int8_mkldnn_op.py index 388eb38fc6..88f1fb7fd2 100644 --- a/python/paddle/fluid/tests/unittests/mkldnn/test_conv2d_int8_mkldnn_op.py +++ b/python/paddle/fluid/tests/unittests/mkldnn/test_conv2d_int8_mkldnn_op.py @@ -19,7 +19,7 @@ import numpy as np import paddle.fluid.core as core from paddle.fluid.tests.unittests.op_test import OpTest -from paddle.fluid.tests.unittests.test_conv2d_op import conv2d_forward_naive, TestConv2dOp +from paddle.fluid.tests.unittests.test_conv2d_op import conv2d_forward_naive, TestConv2DOp def conv2d_forward_refer(input, filter, group, conv_param): @@ -28,7 +28,7 @@ def conv2d_forward_refer(input, filter, group, conv_param): return out -class TestConv2dInt8Op(TestConv2dOp): +class TestConv2DInt8Op(TestConv2DOp): def setUp(self): self.op_type = "conv2d" self.use_cudnn = False @@ -162,7 +162,7 @@ class TestConv2dInt8Op(TestConv2dOp): pass def init_test_case(self): - TestConv2dOp.init_test_case(self) + TestConv2DOp.init_test_case(self) self.input_size = [1, 1, 5, 5] # NCHW f_c = self.input_size[1] // self.groups self.input_residual_size = [1, 2, 3, 3] @@ -186,7 +186,7 @@ class TestConv2dInt8Op(TestConv2dOp): #--------------------test conv2d u8 in and u8 out with residual fuse-------------------- -class TestConv2d(TestConv2dInt8Op): +class TestConv2D(TestConv2DInt8Op): def init_test_case(self): self.pad = [0, 0] self.stride = [1, 1] @@ -201,19 +201,19 @@ class TestConv2d(TestConv2dInt8Op): self.scale_in_eltwise = 0.6 -class TestWithPad(TestConv2d): +class TestWithPad(TestConv2D): def init_test_case(self): - TestConv2d.init_test_case(self) + TestConv2D.init_test_case(self) self.pad = [1, 1] self.input_residual_size = [2, 6, 5, 5] -class TestWithGroup(TestConv2d): +class TestWithGroup(TestConv2D): def init_group(self): self.groups = 3 -class TestWithStride(TestConv2dInt8Op): +class TestWithStride(TestConv2DInt8Op): def init_test_case(self): self.pad = [1, 1] self.stride = [2, 2] @@ -228,7 +228,7 @@ class TestWithStride(TestConv2dInt8Op): self.scale_in_eltwise = 0.5 -class TestWithDilations(TestConv2dInt8Op): +class TestWithDilations(TestConv2DInt8Op): def init_test_case(self): self.pad = [1, 1] self.stride = [1, 1] @@ -244,7 +244,7 @@ class TestWithDilations(TestConv2dInt8Op): self.scale_in_eltwise = 0.5 -class TestWith1x1(TestConv2dInt8Op): +class TestWith1x1(TestConv2DInt8Op): def init_test_case(self): self.pad = [0, 0] self.stride = [1, 1] @@ -259,7 +259,7 @@ class TestWith1x1(TestConv2dInt8Op): self.scale_in_eltwise = 0.5 -class TestWithInput1x1Filter1x1(TestConv2dInt8Op): +class TestWithInput1x1Filter1x1(TestConv2DInt8Op): def init_test_case(self): self.pad = [0, 0] self.stride = [1, 1] @@ -356,7 +356,7 @@ def create_test_int8_class(parent): globals()[cls_name_u8s8_re_1] = TestU8S8ResCase -create_test_int8_class(TestConv2dInt8Op) +create_test_int8_class(TestConv2DInt8Op) create_test_int8_class(TestWithPad) create_test_int8_class(TestWithStride) create_test_int8_class(TestWithDilations) @@ -365,7 +365,7 @@ create_test_int8_class(TestWith1x1) create_test_int8_class(TestWithInput1x1Filter1x1) -class TestConv2dOp_AsyPadding_INT_MKLDNN(TestConv2dInt8Op): +class TestConv2DOp_AsyPadding_INT_MKLDNN(TestConv2DInt8Op): def init_kernel_type(self): self.use_mkldnn = True @@ -374,13 +374,13 @@ class TestConv2dOp_AsyPadding_INT_MKLDNN(TestConv2dInt8Op): self.padding_algorithm = "EXPLICIT" -class TestConv2dOp_Same_INT_MKLDNN(TestConv2dOp_AsyPadding_INT_MKLDNN): +class TestConv2DOp_Same_INT_MKLDNN(TestConv2DOp_AsyPadding_INT_MKLDNN): def init_paddings(self): self.pad = [0, 0] self.padding_algorithm = "SAME" -class TestConv2dOp_Valid_INT_MKLDNN(TestConv2dOp_AsyPadding_INT_MKLDNN): +class TestConv2DOp_Valid_INT_MKLDNN(TestConv2DOp_AsyPadding_INT_MKLDNN): def init_paddings(self): self.pad = [1, 1] self.padding_algorithm = "VALID" diff --git a/python/paddle/fluid/tests/unittests/mkldnn/test_conv2d_mkldnn_op.py b/python/paddle/fluid/tests/unittests/mkldnn/test_conv2d_mkldnn_op.py index 6fad98874e..eb906684f0 100644 --- a/python/paddle/fluid/tests/unittests/mkldnn/test_conv2d_mkldnn_op.py +++ b/python/paddle/fluid/tests/unittests/mkldnn/test_conv2d_mkldnn_op.py @@ -19,7 +19,7 @@ import numpy as np import paddle.fluid.core as core from paddle.fluid.tests.unittests.op_test import OpTest, skip_check_grad_ci -from paddle.fluid.tests.unittests.test_conv2d_op import TestConv2dOp, TestConv2dOp_v2 +from paddle.fluid.tests.unittests.test_conv2d_op import TestConv2DOp, TestConv2DOp_v2 def conv2d_bias_naive(out, bias): @@ -36,7 +36,7 @@ def conv2d_residual_naive(out, residual): return out -class TestConv2dMKLDNNOp(TestConv2dOp): +class TestConv2DMKLDNNOp(TestConv2DOp): def init_group(self): self.groups = 1 @@ -64,7 +64,7 @@ class TestConv2dMKLDNNOp(TestConv2dOp): self.fuse_residual_connection = False self.input_residual_size = None - TestConv2dOp.setUp(self) + TestConv2DOp.setUp(self) output = self.outputs['Output'] @@ -106,9 +106,9 @@ class TestConv2dMKLDNNOp(TestConv2dOp): @skip_check_grad_ci( reason="Fusion is for inference only, check_grad is not required.") -class TestWithbreluFusion(TestConv2dMKLDNNOp): +class TestWithbreluFusion(TestConv2DMKLDNNOp): def init_test_case(self): - TestConv2dMKLDNNOp.init_test_case(self) + TestConv2DMKLDNNOp.init_test_case(self) self.fuse_activation = "relu6" self.fuse_alpha = 6.0 self.dsttype = np.float32 @@ -116,9 +116,9 @@ class TestWithbreluFusion(TestConv2dMKLDNNOp): @skip_check_grad_ci( reason="Fusion is for inference only, check_grad is not required.") -class TestWithFuse(TestConv2dMKLDNNOp): +class TestWithFuse(TestConv2DMKLDNNOp): def init_test_case(self): - TestConv2dMKLDNNOp.init_test_case(self) + TestConv2DMKLDNNOp.init_test_case(self) self.pad = [1, 1] self.fuse_bias = True self.bias_size = [6] @@ -126,22 +126,22 @@ class TestWithFuse(TestConv2dMKLDNNOp): self.input_residual_size = [2, 6, 5, 5] -class TestWithPadWithBias(TestConv2dMKLDNNOp): +class TestWithPadWithBias(TestConv2DMKLDNNOp): def init_test_case(self): - TestConv2dMKLDNNOp.init_test_case(self) + TestConv2DMKLDNNOp.init_test_case(self) self.pad = [1, 1] self.input_size = [2, 3, 6, 6] -class TestWithStride(TestConv2dMKLDNNOp): +class TestWithStride(TestConv2DMKLDNNOp): def init_test_case(self): - TestConv2dMKLDNNOp.init_test_case(self) + TestConv2DMKLDNNOp.init_test_case(self) self.pad = [1, 1] self.stride = [2, 2] self.input_size = [2, 3, 6, 6] -class TestWithGroup(TestConv2dMKLDNNOp): +class TestWithGroup(TestConv2DMKLDNNOp): def init_test_case(self): self.pad = [0, 0] self.stride = [1, 1] @@ -154,15 +154,15 @@ class TestWithGroup(TestConv2dMKLDNNOp): self.groups = 3 -class TestWith1x1(TestConv2dMKLDNNOp): +class TestWith1x1(TestConv2DMKLDNNOp): def init_test_case(self): - TestConv2dMKLDNNOp.init_test_case(self) + TestConv2DMKLDNNOp.init_test_case(self) self.filter_size = [40, 3, 1, 1] -class TestWithInput1x1Filter1x1(TestConv2dMKLDNNOp): +class TestWithInput1x1Filter1x1(TestConv2DMKLDNNOp): def init_test_case(self): - TestConv2dMKLDNNOp.init_test_case(self) + TestConv2DMKLDNNOp.init_test_case(self) self.input_size = [2, 60, 1, 1] # NCHW assert np.mod(self.input_size[1], self.groups) == 0 f_c = self.input_size[1] // self.groups @@ -172,7 +172,7 @@ class TestWithInput1x1Filter1x1(TestConv2dMKLDNNOp): self.groups = 3 -class TestConv2dOp_AsyPadding_MKLDNN(TestConv2dOp_v2): +class TestConv2DOp_AsyPadding_MKLDNN(TestConv2DOp_v2): def init_kernel_type(self): self.use_mkldnn = True self.dtype = np.float32 @@ -182,19 +182,19 @@ class TestConv2dOp_AsyPadding_MKLDNN(TestConv2dOp_v2): self.padding_algorithm = "EXPLICIT" -class TestConv2dOp_Same_MKLDNN(TestConv2dOp_AsyPadding_MKLDNN): +class TestConv2DOp_Same_MKLDNN(TestConv2DOp_AsyPadding_MKLDNN): def init_paddings(self): self.pad = [0, 0] self.padding_algorithm = "SAME" -class TestConv2dOp_Valid_MKLDNN(TestConv2dOp_AsyPadding_MKLDNN): +class TestConv2DOp_Valid_MKLDNN(TestConv2DOp_AsyPadding_MKLDNN): def init_paddings(self): self.pad = [1, 1] self.padding_algorithm = "VALID" -class TestConv2dOp_Valid_NHWC_MKLDNN(TestConv2dOp_Valid_MKLDNN): +class TestConv2DOp_Valid_NHWC_MKLDNN(TestConv2DOp_Valid_MKLDNN): def init_data_format(self): self.data_format = "NHWC" @@ -203,21 +203,21 @@ class TestConv2dOp_Valid_NHWC_MKLDNN(TestConv2dOp_Valid_MKLDNN): self.input_size = [N, H, W, C] -class TestConv2dOp_Same_NHWC_MKLDNN(TestConv2dOp_Valid_NHWC_MKLDNN): +class TestConv2DOp_Same_NHWC_MKLDNN(TestConv2DOp_Valid_NHWC_MKLDNN): def init_paddings(self): self.pad = [0, 0] self.padding_algorithm = "SAME" -class TestConv2dOp_AsyPadding_NHWC_MKLDNN(TestConv2dOp_Valid_NHWC_MKLDNN): +class TestConv2DOp_AsyPadding_NHWC_MKLDNN(TestConv2DOp_Valid_NHWC_MKLDNN): def init_paddings(self): self.pad = [0, 0, 1, 2] self.padding_algorithm = "EXPLICIT" -class TestMKLDNNDilations(TestConv2dMKLDNNOp): +class TestMKLDNNDilations(TestConv2DMKLDNNOp): def init_test_case(self): - TestConv2dMKLDNNOp.init_test_case(self) + TestConv2DMKLDNNOp.init_test_case(self) self.pad = [0, 0] self.stride = [1, 1] self.input_size = [2, 3, 10, 10] # NCHW diff --git a/python/paddle/fluid/tests/unittests/mkldnn/test_conv2d_transpose_mkldnn_op.py b/python/paddle/fluid/tests/unittests/mkldnn/test_conv2d_transpose_mkldnn_op.py index 1f68c35ec2..7da274917a 100644 --- a/python/paddle/fluid/tests/unittests/mkldnn/test_conv2d_transpose_mkldnn_op.py +++ b/python/paddle/fluid/tests/unittests/mkldnn/test_conv2d_transpose_mkldnn_op.py @@ -19,7 +19,7 @@ import numpy as np import paddle.fluid.core as core from paddle.fluid.tests.unittests.op_test import OpTest -from paddle.fluid.tests.unittests.test_conv2d_transpose_op import conv2dtranspose_forward_naive, TestConv2dTransposeOp +from paddle.fluid.tests.unittests.test_conv2d_transpose_op import conv2dtranspose_forward_naive, TestConv2DTransposeOp def conv2d_bias_naive(out, bias): @@ -30,7 +30,7 @@ def conv2d_bias_naive(out, bias): return out -class TestConv2dTransposeMKLDNNOp(TestConv2dTransposeOp): +class TestConv2DTransposeMKLDNNOp(TestConv2DTransposeOp): def test_check_grad(self): return @@ -64,7 +64,7 @@ class TestConv2dTransposeMKLDNNOp(TestConv2dTransposeOp): def setUp(self): - TestConv2dTransposeOp.setUp(self) + TestConv2DTransposeOp.setUp(self) output = self.outputs['Output'] @@ -86,46 +86,46 @@ class TestConv2dTransposeMKLDNNOp(TestConv2dTransposeOp): self.outputs['Output'] = output -class TestMKLDNNFuseBias(TestConv2dTransposeMKLDNNOp): +class TestMKLDNNFuseBias(TestConv2DTransposeMKLDNNOp): def init_test_case(self): - TestConv2dTransposeMKLDNNOp.init_test_case(self) + TestConv2DTransposeMKLDNNOp.init_test_case(self) self.pad = [1, 1] self.fuse_bias = True self.bias_size = [6] -class TestMKLDNNWithPad(TestConv2dTransposeMKLDNNOp): +class TestMKLDNNWithPad(TestConv2DTransposeMKLDNNOp): def init_test_case(self): - TestConv2dTransposeMKLDNNOp.init_test_case(self) + TestConv2DTransposeMKLDNNOp.init_test_case(self) self.pad = [1, 1] self.input_size = [2, 3, 10, 10] -class TestMKLDNNWithStride(TestConv2dTransposeMKLDNNOp): +class TestMKLDNNWithStride(TestConv2DTransposeMKLDNNOp): def init_test_case(self): - TestConv2dTransposeMKLDNNOp.init_test_case(self) + TestConv2DTransposeMKLDNNOp.init_test_case(self) self.pad = [1, 1] self.stride = [2, 2] self.input_size = [2, 3, 6, 6] # NCHW -class TestMKLDNNWithAsymPad(TestConv2dTransposeMKLDNNOp): +class TestMKLDNNWithAsymPad(TestConv2DTransposeMKLDNNOp): def init_test_case(self): - TestConv2dTransposeMKLDNNOp.init_test_case(self) + TestConv2DTransposeMKLDNNOp.init_test_case(self) self.pad = [0, 0, 1, 2] self.padding_algorithm = "EXPLICIT" -class TestMKLDNNWithSamePad(TestConv2dTransposeMKLDNNOp): +class TestMKLDNNWithSamePad(TestConv2DTransposeMKLDNNOp): def init_test_case(self): - TestConv2dTransposeMKLDNNOp.init_test_case(self) + TestConv2DTransposeMKLDNNOp.init_test_case(self) self.pad = [0, 0] self.padding_algorithm = "SAME" -class TestMKLDNNWithValidPad(TestConv2dTransposeMKLDNNOp): +class TestMKLDNNWithValidPad(TestConv2DTransposeMKLDNNOp): def init_test_case(self): - TestConv2dTransposeMKLDNNOp.init_test_case(self) + TestConv2DTransposeMKLDNNOp.init_test_case(self) self.pad = [1, 1] self.padding_algorithm = "VALID" @@ -138,10 +138,10 @@ class TestMKLDNNWithValidPad_NHWC(TestMKLDNNWithValidPad): self.input_size = [N, H, W, C] -class TestConv2dTransposeMKLDNNWithDilationsExplicitPad( - TestConv2dTransposeMKLDNNOp): +class TestConv2DTransposeMKLDNNWithDilationsExplicitPad( + TestConv2DTransposeMKLDNNOp): def init_test_case(self): - TestConv2dTransposeMKLDNNOp.init_test_case(self) + TestConv2DTransposeMKLDNNOp.init_test_case(self) self.stride = [2, 1] self.dilations = [1, 2] self.groups = 1 diff --git a/python/paddle/fluid/tests/unittests/mkldnn/test_conv3d_mkldnn_op.py b/python/paddle/fluid/tests/unittests/mkldnn/test_conv3d_mkldnn_op.py index 8f310946db..ca25b849b4 100644 --- a/python/paddle/fluid/tests/unittests/mkldnn/test_conv3d_mkldnn_op.py +++ b/python/paddle/fluid/tests/unittests/mkldnn/test_conv3d_mkldnn_op.py @@ -16,10 +16,10 @@ from __future__ import print_function import unittest import numpy as np -from paddle.fluid.tests.unittests.test_conv3d_op import TestConv3dOp, TestCase1, TestWithGroup1, TestWithGroup2, TestWith1x1, TestWithInput1x1Filter1x1, TestConv3dOp_2 +from paddle.fluid.tests.unittests.test_conv3d_op import TestConv3DOp, TestCase1, TestWithGroup1, TestWithGroup2, TestWith1x1, TestWithInput1x1Filter1x1, TestConv3DOp_2 -class TestMKLDNN(TestConv3dOp): +class TestMKLDNN(TestConv3DOp): def init_kernel_type(self): self.use_mkldnn = True self.data_format = "NCHW" @@ -61,7 +61,7 @@ class TestMKLDNNWithInput1x1Filter1x1(TestWithInput1x1Filter1x1): self.dtype = np.float32 -class TestConv3dOp_AsyPadding_MKLDNN(TestConv3dOp): +class TestConv3DOp_AsyPadding_MKLDNN(TestConv3DOp): def init_kernel_type(self): self.use_mkldnn = True self.data_format = "NCHW" @@ -72,7 +72,7 @@ class TestConv3dOp_AsyPadding_MKLDNN(TestConv3dOp): self.padding_algorithm = "EXPLICIT" -class TestConv3dOp_Same_MKLDNN(TestConv3dOp_AsyPadding_MKLDNN): +class TestConv3DOp_Same_MKLDNN(TestConv3DOp_AsyPadding_MKLDNN): def init_paddings(self): self.pad = [0, 0, 0] self.padding_algorithm = "SAME" @@ -83,7 +83,7 @@ class TestConv3dOp_Same_MKLDNN(TestConv3dOp_AsyPadding_MKLDNN): self.dtype = np.float32 -class TestConv3dOp_Valid_MKLDNN(TestConv3dOp_AsyPadding_MKLDNN): +class TestConv3DOp_Valid_MKLDNN(TestConv3DOp_AsyPadding_MKLDNN): def init_paddings(self): self.pad = [1, 1, 1] self.padding_algorithm = "VALID" diff --git a/python/paddle/fluid/tests/unittests/mkldnn/test_pool2d_int8_mkldnn_op.py b/python/paddle/fluid/tests/unittests/mkldnn/test_pool2d_int8_mkldnn_op.py index cccc83306b..639cb570a8 100644 --- a/python/paddle/fluid/tests/unittests/mkldnn/test_pool2d_int8_mkldnn_op.py +++ b/python/paddle/fluid/tests/unittests/mkldnn/test_pool2d_int8_mkldnn_op.py @@ -23,7 +23,7 @@ from paddle.fluid.tests.unittests.op_test import OpTest from paddle.fluid.tests.unittests.test_pool2d_op import TestPool2D_Op, avg_pool2D_forward_naive, max_pool2D_forward_naive -class TestPool2dMKLDNNInt8_Op(TestPool2D_Op): +class TestPool2DMKLDNNInt8_Op(TestPool2D_Op): def init_kernel_type(self): self.use_mkldnn = True @@ -51,7 +51,7 @@ class TestPool2dMKLDNNInt8_Op(TestPool2D_Op): pass -class TestCase1Avg(TestPool2dMKLDNNInt8_Op): +class TestCase1Avg(TestPool2DMKLDNNInt8_Op): def init_test_case(self): self.shape = [2, 3, 7, 7] self.ksize = [3, 3] @@ -65,7 +65,7 @@ class TestCase1Avg(TestPool2dMKLDNNInt8_Op): self.exclusive = True -class TestCase2Avg(TestPool2dMKLDNNInt8_Op): +class TestCase2Avg(TestPool2DMKLDNNInt8_Op): def init_test_case(self): self.shape = [2, 3, 7, 7] self.ksize = [3, 3] @@ -79,7 +79,7 @@ class TestCase2Avg(TestPool2dMKLDNNInt8_Op): self.exclusive = False -class TestCase0Max(TestPool2dMKLDNNInt8_Op): +class TestCase0Max(TestPool2DMKLDNNInt8_Op): def init_pool_type(self): self.pool_type = "max" self.pool2D_forward_naive = max_pool2D_forward_naive @@ -114,7 +114,7 @@ def create_test_s8_u8_class(parent): globals()[cls_name_u8] = TestU8Case -create_test_s8_u8_class(TestPool2dMKLDNNInt8_Op) +create_test_s8_u8_class(TestPool2DMKLDNNInt8_Op) create_test_s8_u8_class(TestCase1Avg) create_test_s8_u8_class(TestCase2Avg) create_test_s8_u8_class(TestCase0Max) diff --git a/python/paddle/fluid/tests/unittests/parallel_dygraph_sync_batch_norm.py b/python/paddle/fluid/tests/unittests/parallel_dygraph_sync_batch_norm.py index b7ef54a5c2..dcf5151578 100644 --- a/python/paddle/fluid/tests/unittests/parallel_dygraph_sync_batch_norm.py +++ b/python/paddle/fluid/tests/unittests/parallel_dygraph_sync_batch_norm.py @@ -26,7 +26,7 @@ import paddle.fluid as fluid import paddle.fluid.dygraph as dygraph from paddle.fluid import core from paddle.fluid.optimizer import SGDOptimizer -from paddle.nn import Conv2d, Linear, SyncBatchNorm +from paddle.nn import Conv2D, Linear, SyncBatchNorm from paddle.fluid.dygraph.base import to_variable from test_dist_base import runtime_main, TestParallelDyGraphRunnerBase @@ -42,7 +42,7 @@ class TestLayer(fluid.dygraph.Layer): act=None): super(TestLayer, self).__init__() - self._conv = Conv2d( + self._conv = Conv2D( in_channels=num_channels, out_channels=num_filters, kernel_size=filter_size, @@ -53,7 +53,7 @@ class TestLayer(fluid.dygraph.Layer): self._sync_batch_norm = SyncBatchNorm(num_filters) - self._conv2 = Conv2d( + self._conv2 = Conv2D( in_channels=num_filters, out_channels=num_filters, kernel_size=filter_size, diff --git a/python/paddle/fluid/tests/unittests/parallel_executor_test_base.py b/python/paddle/fluid/tests/unittests/parallel_executor_test_base.py index 9c3ed13cbb..c71e0e3361 100644 --- a/python/paddle/fluid/tests/unittests/parallel_executor_test_base.py +++ b/python/paddle/fluid/tests/unittests/parallel_executor_test_base.py @@ -65,7 +65,7 @@ class TestParallelExecutorBase(unittest.TestCase): feed_data_reader, FeedDataReader ), "feed_data_reader must be type of FeedDataReader" - paddle.manual_seed(1) + paddle.seed(1) paddle.framework.random._manual_program_seed(1) main = fluid.Program() startup = fluid.Program() diff --git a/python/paddle/fluid/tests/unittests/rnn/test_rnn_nets.py b/python/paddle/fluid/tests/unittests/rnn/test_rnn_nets.py index f40065cf8a..2eec265b5d 100644 --- a/python/paddle/fluid/tests/unittests/rnn/test_rnn_nets.py +++ b/python/paddle/fluid/tests/unittests/rnn/test_rnn_nets.py @@ -259,7 +259,7 @@ class TestLSTM(unittest.TestCase): def test_predict(self): place = paddle.set_device(self.place) - paddle.manual_seed(123) + paddle.seed(123) np.random.seed(123) class Net(paddle.nn.Layer): diff --git a/python/paddle/fluid/tests/unittests/test_adaptive_avg_pool1d.py b/python/paddle/fluid/tests/unittests/test_adaptive_avg_pool1d.py index 424406c15b..4765851855 100644 --- a/python/paddle/fluid/tests/unittests/test_adaptive_avg_pool1d.py +++ b/python/paddle/fluid/tests/unittests/test_adaptive_avg_pool1d.py @@ -72,7 +72,7 @@ def avg_pool1D_forward_naive(x, return out -class TestPool1d_API(unittest.TestCase): +class TestPool1D_API(unittest.TestCase): def setUp(self): np.random.seed(123) self.places = [fluid.CPUPlace()] @@ -89,7 +89,7 @@ class TestPool1d_API(unittest.TestCase): self.assertTrue(np.allclose(result.numpy(), result_np)) - ada_max_pool1d_dg = paddle.nn.layer.AdaptiveAvgPool1d( + ada_max_pool1d_dg = paddle.nn.layer.AdaptiveAvgPool1D( output_size=16) result = ada_max_pool1d_dg(input) self.assertTrue(np.allclose(result.numpy(), result_np)) diff --git a/python/paddle/fluid/tests/unittests/test_adaptive_avg_pool2d.py b/python/paddle/fluid/tests/unittests/test_adaptive_avg_pool2d.py index 25692808d0..2b104041f9 100644 --- a/python/paddle/fluid/tests/unittests/test_adaptive_avg_pool2d.py +++ b/python/paddle/fluid/tests/unittests/test_adaptive_avg_pool2d.py @@ -84,7 +84,7 @@ def adaptive_pool2d_forward(x, output_size, data_format='NCHW', return out -class TestAdaptiveAvgPool2dAPI(unittest.TestCase): +class TestAdaptiveAvgPool2DAPI(unittest.TestCase): def setUp(self): self.x_np = np.random.random([2, 3, 7, 7]).astype("float32") self.res_1_np = adaptive_pool2d_forward( @@ -179,7 +179,7 @@ class TestAdaptiveAvgPool2dAPI(unittest.TestCase): assert np.allclose(out_6.numpy(), self.res_3_np) -class TestAdaptiveAvgPool2dClassAPI(unittest.TestCase): +class TestAdaptiveAvgPool2DClassAPI(unittest.TestCase): def setUp(self): self.x_np = np.random.random([2, 3, 7, 7]).astype("float32") self.res_1_np = adaptive_pool2d_forward( @@ -207,20 +207,20 @@ class TestAdaptiveAvgPool2dClassAPI(unittest.TestCase): paddle.enable_static() x = paddle.fluid.data(name="x", shape=[2, 3, 7, 7], dtype="float32") - adaptive_avg_pool = paddle.nn.AdaptiveAvgPool2d(output_size=[3, 3]) + adaptive_avg_pool = paddle.nn.AdaptiveAvgPool2D(output_size=[3, 3]) out_1 = adaptive_avg_pool(x=x) - adaptive_avg_pool = paddle.nn.AdaptiveAvgPool2d(output_size=5) + adaptive_avg_pool = paddle.nn.AdaptiveAvgPool2D(output_size=5) out_2 = adaptive_avg_pool(x=x) - adaptive_avg_pool = paddle.nn.AdaptiveAvgPool2d(output_size=[2, 5]) + adaptive_avg_pool = paddle.nn.AdaptiveAvgPool2D(output_size=[2, 5]) out_3 = adaptive_avg_pool(x=x) - adaptive_avg_pool = paddle.nn.AdaptiveAvgPool2d( + adaptive_avg_pool = paddle.nn.AdaptiveAvgPool2D( output_size=[3, 3], data_format="NHWC") out_4 = adaptive_avg_pool(x=x) - adaptive_avg_pool = paddle.nn.AdaptiveAvgPool2d( + adaptive_avg_pool = paddle.nn.AdaptiveAvgPool2D( output_size=[None, 3]) out_5 = adaptive_avg_pool(x=x) @@ -247,20 +247,20 @@ class TestAdaptiveAvgPool2dClassAPI(unittest.TestCase): paddle.disable_static(place=place) x = paddle.to_tensor(self.x_np) - adaptive_avg_pool = paddle.nn.AdaptiveAvgPool2d(output_size=[3, 3]) + adaptive_avg_pool = paddle.nn.AdaptiveAvgPool2D(output_size=[3, 3]) out_1 = adaptive_avg_pool(x=x) - adaptive_avg_pool = paddle.nn.AdaptiveAvgPool2d(output_size=5) + adaptive_avg_pool = paddle.nn.AdaptiveAvgPool2D(output_size=5) out_2 = adaptive_avg_pool(x=x) - adaptive_avg_pool = paddle.nn.AdaptiveAvgPool2d(output_size=[2, 5]) + adaptive_avg_pool = paddle.nn.AdaptiveAvgPool2D(output_size=[2, 5]) out_3 = adaptive_avg_pool(x=x) - adaptive_avg_pool = paddle.nn.AdaptiveAvgPool2d( + adaptive_avg_pool = paddle.nn.AdaptiveAvgPool2D( output_size=[3, 3], data_format="NHWC") out_4 = adaptive_avg_pool(x=x) - adaptive_avg_pool = paddle.nn.AdaptiveAvgPool2d( + adaptive_avg_pool = paddle.nn.AdaptiveAvgPool2D( output_size=[None, 3]) out_5 = adaptive_avg_pool(x=x) diff --git a/python/paddle/fluid/tests/unittests/test_adaptive_avg_pool3d.py b/python/paddle/fluid/tests/unittests/test_adaptive_avg_pool3d.py index ce85f6bf9f..deb45da8a0 100755 --- a/python/paddle/fluid/tests/unittests/test_adaptive_avg_pool3d.py +++ b/python/paddle/fluid/tests/unittests/test_adaptive_avg_pool3d.py @@ -99,7 +99,7 @@ def adaptive_pool3d_forward(x, return out -class TestAdaptiveAvgPool3dAPI(unittest.TestCase): +class TestAdaptiveAvgPool3DAPI(unittest.TestCase): def setUp(self): self.x_np = np.random.random([2, 3, 5, 7, 7]).astype("float32") self.res_1_np = adaptive_pool3d_forward( @@ -125,7 +125,8 @@ class TestAdaptiveAvgPool3dAPI(unittest.TestCase): if core.is_compiled_with_cuda() else [False]): place = paddle.CUDAPlace(0) if use_cuda else paddle.CPUPlace() paddle.enable_static() - x = paddle.fluid.data(name="x", shape=[2, 3, 5, 7, 7], dtype="float32") + x = paddle.fluid.data( + name="x", shape=[2, 3, 5, 7, 7], dtype="float32") out_1 = paddle.nn.functional.adaptive_avg_pool3d( x=x, output_size=[3, 3, 3]) @@ -194,7 +195,7 @@ class TestAdaptiveAvgPool3dAPI(unittest.TestCase): assert np.allclose(out_6.numpy(), self.res_3_np) -class TestAdaptiveAvgPool3dClassAPI(unittest.TestCase): +class TestAdaptiveAvgPool3DClassAPI(unittest.TestCase): def setUp(self): self.x_np = np.random.random([2, 3, 5, 7, 7]).astype("float32") self.res_1_np = adaptive_pool3d_forward( @@ -220,24 +221,25 @@ class TestAdaptiveAvgPool3dClassAPI(unittest.TestCase): if core.is_compiled_with_cuda() else [False]): place = paddle.CUDAPlace(0) if use_cuda else paddle.CPUPlace() paddle.enable_static() - x = paddle.fluid.data(name="x", shape=[2, 3, 5, 7, 7], dtype="float32") + x = paddle.fluid.data( + name="x", shape=[2, 3, 5, 7, 7], dtype="float32") - adaptive_avg_pool = paddle.nn.AdaptiveAvgPool3d( + adaptive_avg_pool = paddle.nn.AdaptiveAvgPool3D( output_size=[3, 3, 3]) out_1 = adaptive_avg_pool(x=x) - adaptive_avg_pool = paddle.nn.AdaptiveAvgPool3d(output_size=5) + adaptive_avg_pool = paddle.nn.AdaptiveAvgPool3D(output_size=5) out_2 = adaptive_avg_pool(x=x) - adaptive_avg_pool = paddle.nn.AdaptiveAvgPool3d( + adaptive_avg_pool = paddle.nn.AdaptiveAvgPool3D( output_size=[2, 3, 5]) out_3 = adaptive_avg_pool(x=x) - adaptive_avg_pool = paddle.nn.AdaptiveAvgPool3d( + adaptive_avg_pool = paddle.nn.AdaptiveAvgPool3D( output_size=[3, 3, 3], data_format="NDHWC") out_4 = adaptive_avg_pool(x=x) - adaptive_avg_pool = paddle.nn.AdaptiveAvgPool3d( + adaptive_avg_pool = paddle.nn.AdaptiveAvgPool3D( output_size=[None, 3, None]) out_5 = adaptive_avg_pool(x=x) @@ -264,22 +266,22 @@ class TestAdaptiveAvgPool3dClassAPI(unittest.TestCase): paddle.disable_static(place=place) x = paddle.to_tensor(self.x_np) - adaptive_avg_pool = paddle.nn.AdaptiveAvgPool3d( + adaptive_avg_pool = paddle.nn.AdaptiveAvgPool3D( output_size=[3, 3, 3]) out_1 = adaptive_avg_pool(x=x) - adaptive_avg_pool = paddle.nn.AdaptiveAvgPool3d(output_size=5) + adaptive_avg_pool = paddle.nn.AdaptiveAvgPool3D(output_size=5) out_2 = adaptive_avg_pool(x=x) - adaptive_avg_pool = paddle.nn.AdaptiveAvgPool3d( + adaptive_avg_pool = paddle.nn.AdaptiveAvgPool3D( output_size=[2, 3, 5]) out_3 = adaptive_avg_pool(x=x) - adaptive_avg_pool = paddle.nn.AdaptiveAvgPool3d( + adaptive_avg_pool = paddle.nn.AdaptiveAvgPool3D( output_size=[3, 3, 3], data_format="NDHWC") out_4 = adaptive_avg_pool(x=x) - adaptive_avg_pool = paddle.nn.AdaptiveAvgPool3d( + adaptive_avg_pool = paddle.nn.AdaptiveAvgPool3D( output_size=[None, 3, None]) out_5 = adaptive_avg_pool(x=x) diff --git a/python/paddle/fluid/tests/unittests/test_adaptive_max_pool1d.py b/python/paddle/fluid/tests/unittests/test_adaptive_max_pool1d.py index 875fdf9e9c..57fe91a818 100644 --- a/python/paddle/fluid/tests/unittests/test_adaptive_max_pool1d.py +++ b/python/paddle/fluid/tests/unittests/test_adaptive_max_pool1d.py @@ -63,7 +63,7 @@ def max_pool1D_forward_naive(x, return out -class TestPool1d_API(unittest.TestCase): +class TestPool1D_API(unittest.TestCase): def setUp(self): np.random.seed(123) self.places = [fluid.CPUPlace()] @@ -80,7 +80,7 @@ class TestPool1d_API(unittest.TestCase): input_np, ksize=[16], strides=[0], paddings=[0], adaptive=True) self.assertTrue(np.allclose(result.numpy(), result_np)) - ada_max_pool1d_dg = paddle.nn.layer.AdaptiveMaxPool1d( + ada_max_pool1d_dg = paddle.nn.layer.AdaptiveMaxPool1D( output_size=16) result = ada_max_pool1d_dg(input) self.assertTrue(np.allclose(result.numpy(), result_np)) diff --git a/python/paddle/fluid/tests/unittests/test_adaptive_max_pool2d.py b/python/paddle/fluid/tests/unittests/test_adaptive_max_pool2d.py index 14de5aa53a..944725fab6 100644 --- a/python/paddle/fluid/tests/unittests/test_adaptive_max_pool2d.py +++ b/python/paddle/fluid/tests/unittests/test_adaptive_max_pool2d.py @@ -84,7 +84,7 @@ def adaptive_pool2d_forward(x, output_size, data_format='NCHW', return out -class TestAdaptiveMaxPool2dAPI(unittest.TestCase): +class TestAdaptiveMaxPool2DAPI(unittest.TestCase): def setUp(self): self.x_np = np.random.random([2, 3, 7, 7]).astype("float32") self.res_1_np = adaptive_pool2d_forward( @@ -174,7 +174,7 @@ class TestAdaptiveMaxPool2dAPI(unittest.TestCase): assert np.allclose(out_5.numpy(), self.res_5_np) -class TestAdaptiveMaxPool2dClassAPI(unittest.TestCase): +class TestAdaptiveMaxPool2DClassAPI(unittest.TestCase): def setUp(self): self.x_np = np.random.random([2, 3, 7, 7]).astype("float32") self.res_1_np = adaptive_pool2d_forward( @@ -202,20 +202,20 @@ class TestAdaptiveMaxPool2dClassAPI(unittest.TestCase): paddle.enable_static() x = paddle.fluid.data(name="x", shape=[2, 3, 7, 7], dtype="float32") - adaptive_max_pool = paddle.nn.AdaptiveMaxPool2d(output_size=[3, 3]) + adaptive_max_pool = paddle.nn.AdaptiveMaxPool2D(output_size=[3, 3]) out_1 = adaptive_max_pool(x=x) - adaptive_max_pool = paddle.nn.AdaptiveMaxPool2d(output_size=5) + adaptive_max_pool = paddle.nn.AdaptiveMaxPool2D(output_size=5) out_2 = adaptive_max_pool(x=x) - adaptive_max_pool = paddle.nn.AdaptiveMaxPool2d(output_size=[2, 5]) + adaptive_max_pool = paddle.nn.AdaptiveMaxPool2D(output_size=[2, 5]) out_3 = adaptive_max_pool(x=x) - # adaptive_max_pool = paddle.nn.AdaptiveMaxPool2d( + # adaptive_max_pool = paddle.nn.AdaptiveMaxPool2D( # output_size=[3, 3], data_format="NHWC") # out_4 = adaptive_max_pool(x=x) - adaptive_max_pool = paddle.nn.AdaptiveMaxPool2d( + adaptive_max_pool = paddle.nn.AdaptiveMaxPool2D( output_size=[None, 3]) out_5 = adaptive_max_pool(x=x) @@ -242,20 +242,20 @@ class TestAdaptiveMaxPool2dClassAPI(unittest.TestCase): paddle.disable_static(place=place) x = paddle.to_tensor(self.x_np) - adaptive_max_pool = paddle.nn.AdaptiveMaxPool2d(output_size=[3, 3]) + adaptive_max_pool = paddle.nn.AdaptiveMaxPool2D(output_size=[3, 3]) out_1 = adaptive_max_pool(x=x) - adaptive_max_pool = paddle.nn.AdaptiveMaxPool2d(output_size=5) + adaptive_max_pool = paddle.nn.AdaptiveMaxPool2D(output_size=5) out_2 = adaptive_max_pool(x=x) - adaptive_max_pool = paddle.nn.AdaptiveMaxPool2d(output_size=[2, 5]) + adaptive_max_pool = paddle.nn.AdaptiveMaxPool2D(output_size=[2, 5]) out_3 = adaptive_max_pool(x=x) - #adaptive_max_pool = paddle.nn.AdaptiveMaxPool2d( + #adaptive_max_pool = paddle.nn.AdaptiveMaxPool2D( # output_size=[3, 3], data_format="NHWC") #out_4 = adaptive_max_pool(x=x) - adaptive_max_pool = paddle.nn.AdaptiveMaxPool2d( + adaptive_max_pool = paddle.nn.AdaptiveMaxPool2D( output_size=[None, 3]) out_5 = adaptive_max_pool(x=x) diff --git a/python/paddle/fluid/tests/unittests/test_adaptive_max_pool3d.py b/python/paddle/fluid/tests/unittests/test_adaptive_max_pool3d.py index 0aa97bdf1c..65e0738a99 100755 --- a/python/paddle/fluid/tests/unittests/test_adaptive_max_pool3d.py +++ b/python/paddle/fluid/tests/unittests/test_adaptive_max_pool3d.py @@ -99,7 +99,7 @@ def adaptive_pool3d_forward(x, return out -class TestAdaptiveMaxPool3dAPI(unittest.TestCase): +class TestAdaptiveMaxPool3DAPI(unittest.TestCase): def setUp(self): self.x_np = np.random.random([2, 3, 5, 7, 7]).astype("float32") self.res_1_np = adaptive_pool3d_forward( @@ -125,7 +125,8 @@ class TestAdaptiveMaxPool3dAPI(unittest.TestCase): if core.is_compiled_with_cuda() else [False]): place = paddle.CUDAPlace(0) if use_cuda else paddle.CPUPlace() paddle.enable_static() - x = paddle.fluid.data(name="x", shape=[2, 3, 5, 7, 7], dtype="float32") + x = paddle.fluid.data( + name="x", shape=[2, 3, 5, 7, 7], dtype="float32") out_1 = paddle.nn.functional.adaptive_max_pool3d( x=x, output_size=[3, 3, 3]) @@ -189,7 +190,7 @@ class TestAdaptiveMaxPool3dAPI(unittest.TestCase): assert np.allclose(out_5.numpy(), self.res_5_np) -class TestAdaptiveMaxPool3dClassAPI(unittest.TestCase): +class TestAdaptiveMaxPool3DClassAPI(unittest.TestCase): def setUp(self): self.x_np = np.random.random([2, 3, 5, 7, 7]).astype("float32") self.res_1_np = adaptive_pool3d_forward( @@ -215,24 +216,25 @@ class TestAdaptiveMaxPool3dClassAPI(unittest.TestCase): if core.is_compiled_with_cuda() else [False]): place = paddle.CUDAPlace(0) if use_cuda else paddle.CPUPlace() paddle.enable_static() - x = paddle.fluid.data(name="x", shape=[2, 3, 5, 7, 7], dtype="float32") + x = paddle.fluid.data( + name="x", shape=[2, 3, 5, 7, 7], dtype="float32") - adaptive_max_pool = paddle.nn.AdaptiveMaxPool3d( + adaptive_max_pool = paddle.nn.AdaptiveMaxPool3D( output_size=[3, 3, 3]) out_1 = adaptive_max_pool(x=x) - adaptive_max_pool = paddle.nn.AdaptiveMaxPool3d(output_size=5) + adaptive_max_pool = paddle.nn.AdaptiveMaxPool3D(output_size=5) out_2 = adaptive_max_pool(x=x) - adaptive_max_pool = paddle.nn.AdaptiveMaxPool3d( + adaptive_max_pool = paddle.nn.AdaptiveMaxPool3D( output_size=[2, 3, 5]) out_3 = adaptive_max_pool(x=x) - # adaptive_max_pool = paddle.nn.AdaptiveMaxPool3d( + # adaptive_max_pool = paddle.nn.AdaptiveMaxPool3D( # output_size=[3, 3, 3], data_format="NDHWC") # out_4 = adaptive_max_pool(x=x) - adaptive_max_pool = paddle.nn.AdaptiveMaxPool3d( + adaptive_max_pool = paddle.nn.AdaptiveMaxPool3D( output_size=[None, 3, None]) out_5 = adaptive_max_pool(x=x) @@ -259,22 +261,22 @@ class TestAdaptiveMaxPool3dClassAPI(unittest.TestCase): paddle.disable_static(place=place) x = paddle.to_tensor(self.x_np) - adaptive_max_pool = paddle.nn.AdaptiveMaxPool3d( + adaptive_max_pool = paddle.nn.AdaptiveMaxPool3D( output_size=[3, 3, 3]) out_1 = adaptive_max_pool(x=x) - adaptive_max_pool = paddle.nn.AdaptiveMaxPool3d(output_size=5) + adaptive_max_pool = paddle.nn.AdaptiveMaxPool3D(output_size=5) out_2 = adaptive_max_pool(x=x) - adaptive_max_pool = paddle.nn.AdaptiveMaxPool3d( + adaptive_max_pool = paddle.nn.AdaptiveMaxPool3D( output_size=[2, 3, 5]) out_3 = adaptive_max_pool(x=x) - # adaptive_max_pool = paddle.nn.AdaptiveMaxPool3d( + # adaptive_max_pool = paddle.nn.AdaptiveMaxPool3D( # output_size=[3, 3, 3], data_format="NDHWC") # out_4 = adaptive_max_pool(x=x) - adaptive_max_pool = paddle.nn.AdaptiveMaxPool3d( + adaptive_max_pool = paddle.nn.AdaptiveMaxPool3D( output_size=[None, 3, None]) out_5 = adaptive_max_pool(x=x) diff --git a/python/paddle/fluid/tests/unittests/test_batch_norm_op_v2.py b/python/paddle/fluid/tests/unittests/test_batch_norm_op_v2.py index 324d4cf711..8118961919 100644 --- a/python/paddle/fluid/tests/unittests/test_batch_norm_op_v2.py +++ b/python/paddle/fluid/tests/unittests/test_batch_norm_op_v2.py @@ -32,7 +32,7 @@ class TestBatchNorm(unittest.TestCase): places.append(fluid.CUDAPlace(0)) for p in places: with fluid.dygraph.guard(p): - batch_norm1d = paddle.nn.BatchNorm1d(1, name="test") + batch_norm1d = paddle.nn.BatchNorm1D(1, name="test") def test_error(self): places = [fluid.CPUPlace()] @@ -45,32 +45,32 @@ class TestBatchNorm(unittest.TestCase): def error1d_dataformat(): x_data_4 = np.random.random(size=(2, 1, 3, 3)).astype('float32') - batch_norm1d = paddle.nn.BatchNorm1d(1, data_format='NCDHW') + batch_norm1d = paddle.nn.BatchNorm1D(1, data_format='NCDHW') batch_norm1d(fluid.dygraph.to_variable(x_data_4)) def error2d_dataformat(): x_data_3 = np.random.random(size=(2, 1, 3)).astype('float32') - batch_norm2d = paddle.nn.BatchNorm2d(1, data_format='NCDHW') + batch_norm2d = paddle.nn.BatchNorm2D(1, data_format='NCDHW') batch_norm2d(fluid.dygraph.to_variable(x_data_3)) def error3d_dataformat(): x_data_4 = np.random.random(size=(2, 1, 3, 3)).astype('float32') - batch_norm3d = paddle.nn.BatchNorm3d(1, data_format='NCL') + batch_norm3d = paddle.nn.BatchNorm3D(1, data_format='NCL') batch_norm3d(fluid.dygraph.to_variable(x_data_4)) def error1d(): x_data_4 = np.random.random(size=(2, 1, 3, 3)).astype('float32') - batch_norm1d = paddle.nn.BatchNorm1d(1) + batch_norm1d = paddle.nn.BatchNorm1D(1) batch_norm1d(fluid.dygraph.to_variable(x_data_4)) def error2d(): x_data_3 = np.random.random(size=(2, 1, 3)).astype('float32') - batch_norm2d = paddle.nn.BatchNorm2d(1) + batch_norm2d = paddle.nn.BatchNorm2D(1) batch_norm2d(fluid.dygraph.to_variable(x_data_3)) def error3d(): x_data_4 = np.random.random(size=(2, 1, 3, 3)).astype('float32') - batch_norm3d = paddle.nn.BatchNorm3d(1) + batch_norm3d = paddle.nn.BatchNorm3D(1) batch_norm3d(fluid.dygraph.to_variable(x_data_4)) with fluid.dygraph.guard(p): @@ -99,7 +99,7 @@ class TestBatchNorm(unittest.TestCase): def compute_v2(x): with fluid.dygraph.guard(p): - bn = paddle.nn.BatchNorm2d(shape[1]) + bn = paddle.nn.BatchNorm2D(shape[1]) y = bn(fluid.dygraph.to_variable(x)) return y.numpy() @@ -120,7 +120,7 @@ class TestBatchNorm(unittest.TestCase): def compute_v4(x): with fluid.dygraph.guard(p): - bn = paddle.nn.BatchNorm2d( + bn = paddle.nn.BatchNorm2D( shape[1], weight_attr=False, bias_attr=False) y = bn(fluid.dygraph.to_variable(x)) return y.numpy() @@ -155,7 +155,7 @@ class TestBatchNorm(unittest.TestCase): def compute_v2(x_np): with program_guard(Program(), Program()): - bn = paddle.nn.BatchNorm2d(shape[1]) + bn = paddle.nn.BatchNorm2D(shape[1]) x = fluid.data(name='x', shape=x_np.shape, dtype=x_np.dtype) y = bn(x) exe.run(fluid.default_startup_program()) @@ -183,8 +183,8 @@ class TestBatchNormChannelLast(unittest.TestCase): for p in self.places: with fluid.dygraph.guard(p): x = paddle.randn([2, 6, 4]) - net1 = paddle.nn.BatchNorm1d(4, data_format="NLC") - net2 = paddle.nn.BatchNorm1d(4) + net1 = paddle.nn.BatchNorm1D(4, data_format="NLC") + net2 = paddle.nn.BatchNorm1D(4) net2.weight = net1.weight net2.bias = net1.bias y1 = net1(x) @@ -197,8 +197,8 @@ class TestBatchNormChannelLast(unittest.TestCase): for p in self.places: with fluid.dygraph.guard(p): x = paddle.randn([2, 6, 6, 4]) - net1 = paddle.nn.BatchNorm2d(4, data_format="NHWC") - net2 = paddle.nn.BatchNorm2d(4) + net1 = paddle.nn.BatchNorm2D(4, data_format="NHWC") + net2 = paddle.nn.BatchNorm2D(4) net2.weight = net1.weight net2.bias = net1.bias y1 = net1(x) @@ -211,8 +211,8 @@ class TestBatchNormChannelLast(unittest.TestCase): for p in self.places: with fluid.dygraph.guard(p): x = paddle.randn([2, 6, 6, 6, 4]) - net1 = paddle.nn.BatchNorm3d(4, data_format="NDHWC") - net2 = paddle.nn.BatchNorm3d(4) + net1 = paddle.nn.BatchNorm3D(4, data_format="NDHWC") + net2 = paddle.nn.BatchNorm3D(4) net2.weight = net1.weight net2.bias = net1.bias y1 = net1(x) diff --git a/python/paddle/fluid/tests/unittests/test_buffer_shared_memory_reuse_pass.py b/python/paddle/fluid/tests/unittests/test_buffer_shared_memory_reuse_pass.py index 7bdfa3d2df..4b1a54d3c6 100644 --- a/python/paddle/fluid/tests/unittests/test_buffer_shared_memory_reuse_pass.py +++ b/python/paddle/fluid/tests/unittests/test_buffer_shared_memory_reuse_pass.py @@ -47,7 +47,7 @@ class InplaceTestBase(unittest.TestCase): def build_program_and_scope(self): self.place = fluid.CUDAPlace(0) if self.use_cuda else fluid.CPUPlace() - paddle.manual_seed(1) + paddle.seed(1) paddle.framework.random._manual_program_seed(1) startup_program = fluid.Program() main_program = fluid.Program() diff --git a/python/paddle/fluid/tests/unittests/test_compiled_program.py b/python/paddle/fluid/tests/unittests/test_compiled_program.py index 751fed2e56..79ee383f3f 100644 --- a/python/paddle/fluid/tests/unittests/test_compiled_program.py +++ b/python/paddle/fluid/tests/unittests/test_compiled_program.py @@ -30,7 +30,7 @@ class TestCompiledProgram(unittest.TestCase): self.label = np.random.randint( low=0, high=10, size=[16, 1], dtype=np.int64) with new_program_scope(): - paddle.manual_seed(self.seed) + paddle.seed(self.seed) paddle.framework.random._manual_program_seed(self.seed) place = fluid.CUDAPlace(0) if core.is_compiled_with_cuda( ) else fluid.CPUPlace() @@ -47,7 +47,7 @@ class TestCompiledProgram(unittest.TestCase): def test_compiled_program_base(self): with new_program_scope(): - paddle.manual_seed(self.seed) + paddle.seed(self.seed) paddle.framework.random._manual_program_seed(self.seed) place = fluid.CUDAPlace(0) if core.is_compiled_with_cuda( ) else fluid.CPUPlace() @@ -65,7 +65,7 @@ class TestCompiledProgram(unittest.TestCase): def test_compiled_program_with_data_parallel(self): with new_program_scope(): - paddle.manual_seed(self.seed) + paddle.seed(self.seed) paddle.framework.random._manual_program_seed(self.seed) place = fluid.CUDAPlace(0) if core.is_compiled_with_cuda( ) else fluid.CPUPlace() diff --git a/python/paddle/fluid/tests/unittests/test_conv1d_layer.py b/python/paddle/fluid/tests/unittests/test_conv1d_layer.py index 35fce9e9d6..fc0a64b18a 100644 --- a/python/paddle/fluid/tests/unittests/test_conv1d_layer.py +++ b/python/paddle/fluid/tests/unittests/test_conv1d_layer.py @@ -21,7 +21,7 @@ import paddle.fluid.initializer as I import unittest -class Conv1dTestCase(unittest.TestCase): +class Conv1DTestCase(unittest.TestCase): def __init__(self, methodName='runTest', batch_size=4, @@ -37,7 +37,7 @@ class Conv1dTestCase(unittest.TestCase): no_bias=False, dtype="float32", data_format="NCL"): - super(Conv1dTestCase, self).__init__(methodName) + super(Conv1DTestCase, self).__init__(methodName) self.batch_size = batch_size self.num_channels = num_channels self.num_filters = num_filters @@ -107,7 +107,7 @@ class Conv1dTestCase(unittest.TestCase): def paddle_nn_layer(self): x_var = paddle.to_tensor(self.input) - conv = nn.Conv1d( + conv = nn.Conv1D( self.num_channels, self.num_filters, self.filter_size, @@ -139,7 +139,7 @@ class Conv1dTestCase(unittest.TestCase): self._test_equivalence(place) -class Conv1dErrorTestCase(Conv1dTestCase): +class Conv1DErrorTestCase(Conv1DTestCase): def runTest(self): place = fluid.CPUPlace() with dg.guard(place): @@ -147,7 +147,7 @@ class Conv1dErrorTestCase(Conv1dTestCase): self.paddle_nn_layer() -class Conv1dTypeErrorTestCase(Conv1dTestCase): +class Conv1DTypeErrorTestCase(Conv1DTestCase): def runTest(self): place = fluid.CPUPlace() with dg.guard(place): @@ -156,27 +156,27 @@ class Conv1dTypeErrorTestCase(Conv1dTestCase): def add_cases(suite): - suite.addTest(Conv1dTestCase(methodName='runTest')) - suite.addTest(Conv1dTestCase(methodName='runTest', stride=[1], dilation=2)) - suite.addTest(Conv1dTestCase(methodName='runTest', stride=2, dilation=(1))) + suite.addTest(Conv1DTestCase(methodName='runTest')) + suite.addTest(Conv1DTestCase(methodName='runTest', stride=[1], dilation=2)) + suite.addTest(Conv1DTestCase(methodName='runTest', stride=2, dilation=(1))) suite.addTest( - Conv1dTestCase( + Conv1DTestCase( methodName='runTest', padding="same", no_bias=True)) suite.addTest( - Conv1dTestCase( + Conv1DTestCase( methodName='runTest', filter_size=3, padding='valid')) suite.addTest( - Conv1dTestCase( + Conv1DTestCase( methodName='runTest', padding=2, data_format='NLC')) - suite.addTest(Conv1dTestCase(methodName='runTest', padding=[1])) - suite.addTest(Conv1dTestCase(methodName='runTest', padding=[1, 2])) - suite.addTest(Conv1dTestCase(methodName='runTest', padding=2)) - suite.addTest(Conv1dTestCase(methodName='runTest')) + suite.addTest(Conv1DTestCase(methodName='runTest', padding=[1])) + suite.addTest(Conv1DTestCase(methodName='runTest', padding=[1, 2])) + suite.addTest(Conv1DTestCase(methodName='runTest', padding=2)) + suite.addTest(Conv1DTestCase(methodName='runTest')) suite.addTest( - Conv1dTestCase( + Conv1DTestCase( methodName='runTest', groups=2, padding="valid")) suite.addTest( - Conv1dTestCase( + Conv1DTestCase( methodName='runTest', num_filters=6, num_channels=3, @@ -187,22 +187,22 @@ def add_cases(suite): def add_error_cases(suite): suite.addTest( - Conv1dTypeErrorTestCase( + Conv1DTypeErrorTestCase( methodName='runTest', padding_mode="reflect", padding="valid")) suite.addTest( - Conv1dErrorTestCase( + Conv1DErrorTestCase( methodName='runTest', data_format="VALID")) suite.addTest( - Conv1dErrorTestCase( + Conv1DErrorTestCase( methodName='runTest', padding_mode="VALID")) suite.addTest( - Conv1dErrorTestCase( + Conv1DErrorTestCase( methodName='runTest', num_channels=5, groups=2)) suite.addTest( - Conv1dErrorTestCase( + Conv1DErrorTestCase( methodName='runTest', num_filters=8, num_channels=15, groups=3)) suite.addTest( - Conv1dErrorTestCase( + Conv1DErrorTestCase( methodName='runTest', padding=[1, 2, 3, 4, 5])) diff --git a/python/paddle/fluid/tests/unittests/test_conv1d_transpose_layer.py b/python/paddle/fluid/tests/unittests/test_conv1d_transpose_layer.py index 4c98aacd20..9c43e2f3e6 100644 --- a/python/paddle/fluid/tests/unittests/test_conv1d_transpose_layer.py +++ b/python/paddle/fluid/tests/unittests/test_conv1d_transpose_layer.py @@ -21,7 +21,7 @@ import paddle.fluid.initializer as I import unittest -class ConvTranspose1dTestCase(unittest.TestCase): +class Conv1DTransposeTestCase(unittest.TestCase): def __init__(self, methodName='runTest', batch_size=4, @@ -38,7 +38,7 @@ class ConvTranspose1dTestCase(unittest.TestCase): no_bias=False, data_format="NCL", dtype="float32"): - super(ConvTranspose1dTestCase, self).__init__(methodName) + super(Conv1DTransposeTestCase, self).__init__(methodName) self.batch_size = batch_size self.in_channels = in_channels self.out_channels = out_channels @@ -113,7 +113,7 @@ class ConvTranspose1dTestCase(unittest.TestCase): def paddle_nn_layer(self): x_var = paddle.to_tensor(self.input) - conv = nn.ConvTranspose1d( + conv = nn.Conv1DTranspose( self.in_channels, self.out_channels, self.filter_size, @@ -145,7 +145,7 @@ class ConvTranspose1dTestCase(unittest.TestCase): self._test_equivalence(place) -class ConvTranspose1dErrorTestCase(ConvTranspose1dTestCase): +class Conv1DTransposeErrorTestCase(Conv1DTransposeTestCase): def runTest(self): place = fluid.CPUPlace() with dg.guard(place): @@ -154,68 +154,68 @@ class ConvTranspose1dErrorTestCase(ConvTranspose1dTestCase): def add_cases(suite): - suite.addTest(ConvTranspose1dTestCase(methodName='runTest')) + suite.addTest(Conv1DTransposeTestCase(methodName='runTest')) suite.addTest( - ConvTranspose1dTestCase( + Conv1DTransposeTestCase( methodName='runTest', stride=[2], no_bias=True, dilation=2)) suite.addTest( - ConvTranspose1dTestCase( + Conv1DTransposeTestCase( methodName='runTest', filter_size=(3), output_size=[36], stride=[2], dilation=2)) suite.addTest( - ConvTranspose1dTestCase( + Conv1DTransposeTestCase( methodName='runTest', stride=2, dilation=(2))) suite.addTest( - ConvTranspose1dTestCase( + Conv1DTransposeTestCase( methodName='runTest', padding="valid")) suite.addTest( - ConvTranspose1dTestCase( + Conv1DTransposeTestCase( methodName='runTest', padding='valid')) suite.addTest( - ConvTranspose1dTestCase( + Conv1DTransposeTestCase( methodName='runTest', filter_size=1, padding=3)) - suite.addTest(ConvTranspose1dTestCase(methodName='runTest', padding=[2])) + suite.addTest(Conv1DTransposeTestCase(methodName='runTest', padding=[2])) suite.addTest( - ConvTranspose1dTestCase( + Conv1DTransposeTestCase( methodName='runTest', data_format="NLC")) suite.addTest( - ConvTranspose1dTestCase( + Conv1DTransposeTestCase( methodName='runTest', groups=2, padding="valid")) suite.addTest( - ConvTranspose1dTestCase( + Conv1DTransposeTestCase( methodName='runTest', out_channels=6, in_channels=3, groups=3, padding="valid")) suite.addTest( - ConvTranspose1dTestCase( + Conv1DTransposeTestCase( methodName='runTest', data_format="NLC", spartial_shape=16, output_size=18)) suite.addTest( - ConvTranspose1dTestCase( + Conv1DTransposeTestCase( methodName='runTest', data_format="NLC", stride=3, output_padding=2)) - suite.addTest(ConvTranspose1dTestCase(methodName='runTest', padding=[1, 2])) + suite.addTest(Conv1DTransposeTestCase(methodName='runTest', padding=[1, 2])) def add_error_cases(suite): suite.addTest( - ConvTranspose1dErrorTestCase( + Conv1DTransposeErrorTestCase( methodName='runTest', data_format="not_valid")) suite.addTest( - ConvTranspose1dErrorTestCase( + Conv1DTransposeErrorTestCase( methodName='runTest', in_channels=5, groups=2)) suite.addTest( - ConvTranspose1dErrorTestCase( + Conv1DTransposeErrorTestCase( methodName='runTest', stride=2, output_padding=3)) suite.addTest( - ConvTranspose1dErrorTestCase( + Conv1DTransposeErrorTestCase( methodName='runTest', output_size="not_valid")) diff --git a/python/paddle/fluid/tests/unittests/test_conv2d_fusion_op.py b/python/paddle/fluid/tests/unittests/test_conv2d_fusion_op.py index dd1e69f74b..5f3d141a50 100644 --- a/python/paddle/fluid/tests/unittests/test_conv2d_fusion_op.py +++ b/python/paddle/fluid/tests/unittests/test_conv2d_fusion_op.py @@ -45,7 +45,7 @@ def create_test_padding_VALID_class(parent): globals()[cls_name] = TestPaddingVALIDCase -class TestConv2dFusionOp(OpTest): +class TestConv2DFusionOp(OpTest): def setUp(self): self.op_type = "conv2d_fusion" self.exhaustive_search = False @@ -157,28 +157,28 @@ class TestConv2dFusionOp(OpTest): self.padding_algorithm = "EXPLICIT" -class TestWithoutResidual(TestConv2dFusionOp): +class TestWithoutResidual(TestConv2DFusionOp): def init_residual(self): self.add_residual_data = False -class TestIdentityActivation(TestConv2dFusionOp): +class TestIdentityActivation(TestConv2DFusionOp): def init_activation(self): self.activation = 'identity' -class TestIdentityActivation(TestConv2dFusionOp): +class TestIdentityActivation(TestConv2DFusionOp): def init_activation(self): self.activation = 'identity' self.add_residual_data = False -class TestWithGroup(TestConv2dFusionOp): +class TestWithGroup(TestConv2DFusionOp): def init_group(self): self.groups = 3 -class TestWithDilation(TestConv2dFusionOp): +class TestWithDilation(TestConv2DFusionOp): def init_test_case(self): self.pad = [0, 0] self.stride = [1, 1] @@ -194,12 +194,12 @@ class TestWithDilation(TestConv2dFusionOp): self.groups = 3 -class TestCUDNNExhaustiveSearch(TestConv2dFusionOp): +class TestCUDNNExhaustiveSearch(TestConv2DFusionOp): def set_search_method(self): self.exhaustive_search = True -class TestMultipleOutputs(TestConv2dFusionOp): +class TestMultipleOutputs(TestConv2DFusionOp): def init_test_case(self): self.pad = [1, 1] self.stride = [1, 1] @@ -215,13 +215,13 @@ class TestMultipleOutputs(TestConv2dFusionOp): self.outputs['Outputs'] = [('out1', out1), ('out2', out2)] -class TestAsyPadding(TestConv2dFusionOp): +class TestAsyPadding(TestConv2DFusionOp): def init_paddings(self): self.pad = [0, 0, 1, 2] self.padding_algorithm = "EXPLICIT" -class TestWithPad_AsyPadding(TestConv2dFusionOp): +class TestWithPad_AsyPadding(TestConv2DFusionOp): def init_test_case(self): self.stride = [1, 1] self.input_size = [2, 3, 10, 10] # NCHW @@ -234,7 +234,7 @@ class TestWithPad_AsyPadding(TestConv2dFusionOp): self.padding_algorithm = "EXPLICIT" -class TestWithStride_AsyPadding(TestConv2dFusionOp): +class TestWithStride_AsyPadding(TestConv2DFusionOp): def init_test_case(self): self.stride = [2, 2] self.input_size = [2, 3, 6, 6] # NCHW @@ -247,7 +247,7 @@ class TestWithStride_AsyPadding(TestConv2dFusionOp): self.padding_algorithm = "EXPLICIT" -class TestWith1x1_AsyPadding(TestConv2dFusionOp): +class TestWith1x1_AsyPadding(TestConv2DFusionOp): def init_test_case(self): self.stride = [1, 1] self.input_size = [2, 3, 5, 5] # NCHW @@ -263,12 +263,12 @@ class TestWith1x1_AsyPadding(TestConv2dFusionOp): self.padding_algorithm = "EXPLICIT" -class TestWithGroup_AsyPadding(TestConv2dFusionOp): +class TestWithGroup_AsyPadding(TestConv2DFusionOp): def init_group(self): self.groups = 3 -class TestWithDepthWise3x3_AsyPadding(TestConv2dFusionOp): +class TestWithDepthWise3x3_AsyPadding(TestConv2DFusionOp): def init_test_case(self): self.stride = [1, 1] self.input_size = [3, 4, 10, 10] # NCHW @@ -287,7 +287,7 @@ class TestWithDepthWise3x3_AsyPadding(TestConv2dFusionOp): self.padding_algorithm = "EXPLICIT" -class TestWithDepthWise5x5_AsyPadding(TestConv2dFusionOp): +class TestWithDepthWise5x5_AsyPadding(TestConv2DFusionOp): def init_test_case(self): self.stride = [1, 1] self.input_size = [2, 4, 10, 10] # NCHW @@ -303,7 +303,7 @@ class TestWithDepthWise5x5_AsyPadding(TestConv2dFusionOp): self.padding_algorithm = "EXPLICIT" -class TestWithDepthWise7x7_AsyPadding(TestConv2dFusionOp): +class TestWithDepthWise7x7_AsyPadding(TestConv2DFusionOp): def init_test_case(self): self.stride = [2, 2] self.input_size = [2, 8, 10, 10] # NCHW @@ -319,7 +319,7 @@ class TestWithDepthWise7x7_AsyPadding(TestConv2dFusionOp): self.padding_algorithm = "EXPLICIT" -class TestWithDilation_AsyPadding(TestConv2dFusionOp): +class TestWithDilation_AsyPadding(TestConv2DFusionOp): def init_test_case(self): self.stride = [1, 1] self.input_size = [2, 3, 10, 10] # NCHW @@ -338,7 +338,7 @@ class TestWithDilation_AsyPadding(TestConv2dFusionOp): self.padding_algorithm = "EXPLICIT" -class TestWithInput1x1Filter1x1_AsyPadding(TestConv2dFusionOp): +class TestWithInput1x1Filter1x1_AsyPadding(TestConv2DFusionOp): def init_test_case(self): self.stride = [1, 1] self.input_size = [2, 3, 1, 1] # NCHW diff --git a/python/paddle/fluid/tests/unittests/test_conv2d_layer.py b/python/paddle/fluid/tests/unittests/test_conv2d_layer.py index 6bfe2aca53..f92a05158c 100644 --- a/python/paddle/fluid/tests/unittests/test_conv2d_layer.py +++ b/python/paddle/fluid/tests/unittests/test_conv2d_layer.py @@ -166,7 +166,7 @@ class Conv2DTestCase(unittest.TestCase): def paddle_nn_layer(self): x_var = dg.to_variable(self.input) - conv = nn.Conv2d( + conv = nn.Conv2D( self.num_channels, self.num_filters, self.filter_size, diff --git a/python/paddle/fluid/tests/unittests/test_conv2d_op.py b/python/paddle/fluid/tests/unittests/test_conv2d_op.py index 8025a33239..d2c2d2cecd 100644 --- a/python/paddle/fluid/tests/unittests/test_conv2d_op.py +++ b/python/paddle/fluid/tests/unittests/test_conv2d_op.py @@ -289,7 +289,7 @@ def create_test_cudnn_padding_VALID_class(parent): globals()[cls_name] = TestCUDNNPaddingVALIDCase -class TestConv2dOp(OpTest): +class TestConv2DOp(OpTest): def setUp(self): self.op_type = "conv2d" self.use_cudnn = False @@ -412,7 +412,7 @@ class TestConv2dOp(OpTest): pass -class TestWithPad(TestConv2dOp): +class TestWithPad(TestConv2DOp): def init_test_case(self): self.pad = [1, 1] self.stride = [1, 1] @@ -422,7 +422,7 @@ class TestWithPad(TestConv2dOp): self.filter_size = [6, f_c, 3, 3] -class TestWithStride(TestConv2dOp): +class TestWithStride(TestConv2DOp): def init_test_case(self): self.pad = [1, 1] self.stride = [2, 2] @@ -432,7 +432,7 @@ class TestWithStride(TestConv2dOp): self.filter_size = [6, f_c, 3, 3] -class TestWithGroup(TestConv2dOp): +class TestWithGroup(TestConv2DOp): def init_test_case(self): self.pad = [0, 0] self.stride = [1, 1] @@ -443,7 +443,7 @@ class TestWithGroup(TestConv2dOp): self.filter_size = [18, f_c, 3, 3] -class TestWith1x1(TestConv2dOp): +class TestWith1x1(TestConv2DOp): def init_test_case(self): self.pad = [0, 0] self.stride = [1, 1] @@ -456,7 +456,7 @@ class TestWith1x1(TestConv2dOp): self.groups = 3 -class TestWithDepthWise3x3(TestConv2dOp): +class TestWithDepthWise3x3(TestConv2DOp): def init_test_case(self): self.pad = [1, 1] self.stride = [1, 1] @@ -472,7 +472,7 @@ class TestWithDepthWise3x3(TestConv2dOp): self.groups = 4 -class TestWithDepthWise5x5(TestConv2dOp): +class TestWithDepthWise5x5(TestConv2DOp): def init_test_case(self): self.pad = [0, 0] self.stride = [1, 1] @@ -485,7 +485,7 @@ class TestWithDepthWise5x5(TestConv2dOp): self.groups = 4 -class TestWithDepthWise7x7(TestConv2dOp): +class TestWithDepthWise7x7(TestConv2DOp): def init_test_case(self): self.pad = [1, 1] self.stride = [2, 2] @@ -498,7 +498,7 @@ class TestWithDepthWise7x7(TestConv2dOp): self.groups = 8 -class TestWithDilation(TestConv2dOp): +class TestWithDilation(TestConv2DOp): def init_test_case(self): self.pad = [0, 0] self.stride = [1, 1] @@ -514,7 +514,7 @@ class TestWithDilation(TestConv2dOp): self.groups = 3 -class TestWithInput1x1Filter1x1(TestConv2dOp): +class TestWithInput1x1Filter1x1(TestConv2DOp): def init_test_case(self): self.pad = [0, 0] self.stride = [1, 1] @@ -527,18 +527,18 @@ class TestWithInput1x1Filter1x1(TestConv2dOp): self.groups = 3 -#----------------Conv2dCUDNN---------------- +#----------------Conv2DCUDNN---------------- -create_test_cudnn_class(TestConv2dOp) +create_test_cudnn_class(TestConv2DOp) create_test_cudnn_class(TestWithPad) create_test_cudnn_class(TestWithStride) create_test_cudnn_class(TestWithGroup) create_test_cudnn_class(TestWith1x1) create_test_cudnn_class(TestWithInput1x1Filter1x1) -#----------------Conv2dCUDNN fp16---------------- +#----------------Conv2DCUDNN fp16---------------- -create_test_cudnn_fp16_class(TestConv2dOp, grad_check=False) +create_test_cudnn_fp16_class(TestConv2DOp, grad_check=False) create_test_cudnn_fp16_class(TestWithPad, grad_check=False) create_test_cudnn_fp16_class(TestWithStride, grad_check=False) create_test_cudnn_fp16_class(TestWithGroup, grad_check=False) @@ -548,7 +548,7 @@ create_test_cudnn_fp16_class(TestWithInput1x1Filter1x1, grad_check=False) #----------------TestDepthwiseConv ----- -class TestDepthwiseConv(TestConv2dOp): +class TestDepthwiseConv(TestConv2DOp): def init_test_case(self): self.use_cuda = True self.pad = [1, 1] @@ -561,7 +561,7 @@ class TestDepthwiseConv(TestConv2dOp): self.op_type = "depthwise_conv2d" -class TestDepthwiseConv2(TestConv2dOp): +class TestDepthwiseConv2(TestConv2DOp): def init_test_case(self): self.use_cuda = True self.pad = [1, 1] @@ -574,7 +574,7 @@ class TestDepthwiseConv2(TestConv2dOp): self.op_type = "depthwise_conv2d" -class TestDepthwiseConv3(TestConv2dOp): +class TestDepthwiseConv3(TestConv2DOp): def init_test_case(self): self.use_cuda = True self.pad = [1, 1] @@ -587,7 +587,7 @@ class TestDepthwiseConv3(TestConv2dOp): self.op_type = "depthwise_conv2d" -class TestDepthwiseConvWithDilation(TestConv2dOp): +class TestDepthwiseConvWithDilation(TestConv2DOp): def init_test_case(self): self.use_cuda = True self.pad = [1, 1] @@ -601,7 +601,7 @@ class TestDepthwiseConvWithDilation(TestConv2dOp): self.op_type = "depthwise_conv2d" -class TestDepthwiseConvWithDilation2(TestConv2dOp): +class TestDepthwiseConvWithDilation2(TestConv2DOp): def init_test_case(self): self.use_cuda = True self.pad = [1, 1] @@ -615,7 +615,7 @@ class TestDepthwiseConvWithDilation2(TestConv2dOp): self.op_type = "depthwise_conv2d" -class TestDepthwiseConvandFuse(TestConv2dOp): +class TestDepthwiseConvandFuse(TestConv2DOp): def init_test_case(self): self.fuse_relu_before_depthwise_conv = True self.use_cuda = True @@ -629,7 +629,7 @@ class TestDepthwiseConvandFuse(TestConv2dOp): self.op_type = "depthwise_conv2d" -class TestDepthwiseConv2andFuse(TestConv2dOp): +class TestDepthwiseConv2andFuse(TestConv2DOp): def init_test_case(self): self.fuse_relu_before_depthwise_conv = True self.use_cuda = True @@ -643,7 +643,7 @@ class TestDepthwiseConv2andFuse(TestConv2dOp): self.op_type = "depthwise_conv2d" -class TestDepthwiseConv3andFuse(TestConv2dOp): +class TestDepthwiseConv3andFuse(TestConv2DOp): def init_test_case(self): self.fuse_relu_before_depthwise_conv = True self.use_cuda = True @@ -657,7 +657,7 @@ class TestDepthwiseConv3andFuse(TestConv2dOp): self.op_type = "depthwise_conv2d" -class TestDepthwiseConvWithDilationandFuse(TestConv2dOp): +class TestDepthwiseConvWithDilationandFuse(TestConv2DOp): def init_test_case(self): self.fuse_relu_before_depthwise_conv = True self.use_cuda = True @@ -672,7 +672,7 @@ class TestDepthwiseConvWithDilationandFuse(TestConv2dOp): self.op_type = "depthwise_conv2d" -class TestDepthwiseConvWithDilation2andFuse(TestConv2dOp): +class TestDepthwiseConvWithDilation2andFuse(TestConv2DOp): def init_test_case(self): self.fuse_relu_before_depthwise_conv = True self.use_cuda = True @@ -687,13 +687,13 @@ class TestDepthwiseConvWithDilation2andFuse(TestConv2dOp): self.op_type = "depthwise_conv2d" -class TestCUDNNExhaustiveSearch(TestConv2dOp): +class TestCUDNNExhaustiveSearch(TestConv2DOp): def init_kernel_type(self): self.use_cudnn = True self.exhaustive_search = True -class TestConv2dOpError(unittest.TestCase): +class TestConv2DOpError(unittest.TestCase): def test_errors(self): with program_guard(Program(), Program()): @@ -724,7 +724,7 @@ class TestConv2dOpError(unittest.TestCase): # ---- test asymmetric padding ---- -class TestConv2dOp_v2(OpTest): +class TestConv2DOp_v2(OpTest): def setUp(self): self.op_type = "conv2d" self.use_cudnn = False @@ -854,13 +854,13 @@ class TestConv2dOp_v2(OpTest): pass -class TestConv2dOp_AsyPadding(TestConv2dOp_v2): +class TestConv2DOp_AsyPadding(TestConv2DOp_v2): def init_paddings(self): self.pad = [0, 0, 1, 2] self.padding_algorithm = "EXPLICIT" -class TestWithPad_AsyPadding(TestConv2dOp_v2): +class TestWithPad_AsyPadding(TestConv2DOp_v2): def init_test_case(self): self.stride = [1, 1] self.input_size = [2, 3, 5, 5] # NCHW @@ -873,7 +873,7 @@ class TestWithPad_AsyPadding(TestConv2dOp_v2): self.padding_algorithm = "EXPLICIT" -class TestWithStride_AsyPadding(TestConv2dOp_v2): +class TestWithStride_AsyPadding(TestConv2DOp_v2): def init_test_case(self): self.stride = [2, 2] self.input_size = [2, 3, 6, 6] # NCHW @@ -886,7 +886,7 @@ class TestWithStride_AsyPadding(TestConv2dOp_v2): self.padding_algorithm = "EXPLICIT" -class TestWithGroup_AsyPadding(TestConv2dOp_v2): +class TestWithGroup_AsyPadding(TestConv2DOp_v2): def init_test_case(self): self.pad = [0, 0] self.stride = [1, 2] @@ -897,7 +897,7 @@ class TestWithGroup_AsyPadding(TestConv2dOp_v2): self.filter_size = [24, f_c, 4, 3] -class TestWith1x1_AsyPadding(TestConv2dOp_v2): +class TestWith1x1_AsyPadding(TestConv2DOp_v2): def init_test_case(self): self.stride = [1, 1] self.input_size = [2, 3, 5, 5] # NCHW @@ -913,7 +913,7 @@ class TestWith1x1_AsyPadding(TestConv2dOp_v2): self.padding_algorithm = "EXPLICIT" -class TestWithDepthWise3x3_AsyPadding(TestConv2dOp_v2): +class TestWithDepthWise3x3_AsyPadding(TestConv2DOp_v2): def init_test_case(self): self.stride = [1, 1] self.input_size = [3, 4, 10, 10] # NCHW @@ -932,7 +932,7 @@ class TestWithDepthWise3x3_AsyPadding(TestConv2dOp_v2): self.padding_algorithm = "EXPLICIT" -class TestWithDepthWise5x5_AsyPadding(TestConv2dOp_v2): +class TestWithDepthWise5x5_AsyPadding(TestConv2DOp_v2): def init_test_case(self): self.stride = [1, 1] self.input_size = [2, 4, 10, 10] # NCHW @@ -948,7 +948,7 @@ class TestWithDepthWise5x5_AsyPadding(TestConv2dOp_v2): self.padding_algorithm = "EXPLICIT" -class TestWithDepthWise7x7_AsyPadding(TestConv2dOp_v2): +class TestWithDepthWise7x7_AsyPadding(TestConv2DOp_v2): def init_test_case(self): self.stride = [2, 2] self.input_size = [2, 8, 10, 10] # NCHW @@ -964,7 +964,7 @@ class TestWithDepthWise7x7_AsyPadding(TestConv2dOp_v2): self.padding_algorithm = "EXPLICIT" -class TestWithDilation_AsyPadding(TestConv2dOp_v2): +class TestWithDilation_AsyPadding(TestConv2DOp_v2): def init_test_case(self): self.stride = [1, 1] self.input_size = [2, 3, 10, 10] # NCHW @@ -983,7 +983,7 @@ class TestWithDilation_AsyPadding(TestConv2dOp_v2): self.padding_algorithm = "EXPLICIT" -class TestWithInput1x1Filter1x1_AsyPadding(TestConv2dOp_v2): +class TestWithInput1x1Filter1x1_AsyPadding(TestConv2DOp_v2): def init_test_case(self): self.stride = [1, 1] self.input_size = [40, 3, 1, 1] # NCHW @@ -999,7 +999,7 @@ class TestWithInput1x1Filter1x1_AsyPadding(TestConv2dOp_v2): self.padding_algorithm = "EXPLICIT" -create_test_cudnn_class(TestConv2dOp_AsyPadding) +create_test_cudnn_class(TestConv2DOp_AsyPadding) create_test_cudnn_class(TestWithPad_AsyPadding) create_test_cudnn_class(TestWithStride_AsyPadding) create_test_cudnn_class(TestWithGroup_AsyPadding) @@ -1007,7 +1007,7 @@ create_test_cudnn_class(TestWith1x1_AsyPadding) create_test_cudnn_class(TestWithInput1x1Filter1x1_AsyPadding) -class TestDepthwiseConv_AsyPadding(TestConv2dOp_v2): +class TestDepthwiseConv_AsyPadding(TestConv2DOp_v2): def init_test_case(self): self.use_cuda = True self.stride = [2, 2] @@ -1023,7 +1023,7 @@ class TestDepthwiseConv_AsyPadding(TestConv2dOp_v2): self.padding_algorithm = "EXPLICIT" -class TestDepthwiseConv2_AsyPadding(TestConv2dOp_v2): +class TestDepthwiseConv2_AsyPadding(TestConv2DOp_v2): def init_test_case(self): self.use_cuda = True self.stride = [1, 1] @@ -1039,7 +1039,7 @@ class TestDepthwiseConv2_AsyPadding(TestConv2dOp_v2): self.padding_algorithm = "EXPLICIT" -class TestDepthwiseConv3_AsyPadding(TestConv2dOp_v2): +class TestDepthwiseConv3_AsyPadding(TestConv2DOp_v2): def init_test_case(self): self.use_cuda = True self.stride = [1, 1] @@ -1055,7 +1055,7 @@ class TestDepthwiseConv3_AsyPadding(TestConv2dOp_v2): self.padding_algorithm = "EXPLICIT" -class TestDepthwiseConvWithDilation_AsyPadding(TestConv2dOp_v2): +class TestDepthwiseConvWithDilation_AsyPadding(TestConv2DOp_v2): def init_test_case(self): self.use_cuda = True self.pad = [1, 1] @@ -1073,7 +1073,7 @@ class TestDepthwiseConvWithDilation_AsyPadding(TestConv2dOp_v2): self.padding_algorithm = "EXPLICIT" -class TestDepthwiseConvWithDilation2_AsyPadding(TestConv2dOp_v2): +class TestDepthwiseConvWithDilation2_AsyPadding(TestConv2DOp_v2): def init_test_case(self): self.use_cuda = True self.pad = [1, 1] @@ -1091,7 +1091,7 @@ class TestDepthwiseConvWithDilation2_AsyPadding(TestConv2dOp_v2): self.padding_algorithm = "EXPLICIT" -class TestDepthwiseConvandFuse_AsyPadding(TestConv2dOp_v2): +class TestDepthwiseConvandFuse_AsyPadding(TestConv2DOp_v2): def init_test_case(self): self.fuse_relu_before_depthwise_conv = True self.use_cuda = True @@ -1109,7 +1109,7 @@ class TestDepthwiseConvandFuse_AsyPadding(TestConv2dOp_v2): self.padding_algorithm = "EXPLICIT" -class TestDepthwiseConv2andFuse_AsyPadding(TestConv2dOp_v2): +class TestDepthwiseConv2andFuse_AsyPadding(TestConv2DOp_v2): def init_test_case(self): self.fuse_relu_before_depthwise_conv = True self.use_cuda = True @@ -1127,7 +1127,7 @@ class TestDepthwiseConv2andFuse_AsyPadding(TestConv2dOp_v2): self.padding_algorithm = "EXPLICIT" -class TestDepthwiseConv3andFuse_AsyPadding(TestConv2dOp_v2): +class TestDepthwiseConv3andFuse_AsyPadding(TestConv2DOp_v2): def init_test_case(self): self.fuse_relu_before_depthwise_conv = True self.use_cuda = True @@ -1145,7 +1145,7 @@ class TestDepthwiseConv3andFuse_AsyPadding(TestConv2dOp_v2): self.padding_algorithm = "EXPLICIT" -class TestDepthwiseConvWithDilationandFuse_AsyPadding(TestConv2dOp_v2): +class TestDepthwiseConvWithDilationandFuse_AsyPadding(TestConv2DOp_v2): def init_test_case(self): self.fuse_relu_before_depthwise_conv = True self.use_cuda = True @@ -1164,7 +1164,7 @@ class TestDepthwiseConvWithDilationandFuse_AsyPadding(TestConv2dOp_v2): self.padding_algorithm = "EXPLICIT" -class TestDepthwiseConvWithDilation2andFuse_AsyPadding(TestConv2dOp_v2): +class TestDepthwiseConvWithDilation2andFuse_AsyPadding(TestConv2DOp_v2): def init_test_case(self): self.fuse_relu_before_depthwise_conv = True self.use_cuda = True @@ -1184,25 +1184,25 @@ class TestDepthwiseConvWithDilation2andFuse_AsyPadding(TestConv2dOp_v2): #---------- test SAME VALID ----------- -create_test_padding_SAME_class(TestConv2dOp_AsyPadding) +create_test_padding_SAME_class(TestConv2DOp_AsyPadding) create_test_padding_SAME_class(TestWithPad_AsyPadding) create_test_padding_SAME_class(TestWithStride_AsyPadding) create_test_padding_SAME_class(TestWithGroup_AsyPadding) create_test_padding_SAME_class(TestWithInput1x1Filter1x1_AsyPadding) -create_test_padding_VALID_class(TestConv2dOp_AsyPadding) +create_test_padding_VALID_class(TestConv2DOp_AsyPadding) create_test_padding_VALID_class(TestWithPad_AsyPadding) create_test_padding_VALID_class(TestWithStride_AsyPadding) create_test_padding_VALID_class(TestWithGroup_AsyPadding) create_test_padding_VALID_class(TestWithInput1x1Filter1x1_AsyPadding) -create_test_cudnn_padding_SAME_class(TestConv2dOp_AsyPadding) +create_test_cudnn_padding_SAME_class(TestConv2DOp_AsyPadding) create_test_cudnn_padding_SAME_class(TestWithPad_AsyPadding) create_test_cudnn_padding_SAME_class(TestWithStride_AsyPadding) create_test_cudnn_padding_SAME_class(TestWithGroup_AsyPadding) create_test_cudnn_padding_SAME_class(TestWithInput1x1Filter1x1_AsyPadding) -create_test_cudnn_padding_VALID_class(TestConv2dOp_AsyPadding) +create_test_cudnn_padding_VALID_class(TestConv2DOp_AsyPadding) create_test_cudnn_padding_VALID_class(TestWithPad_AsyPadding) create_test_cudnn_padding_VALID_class(TestWithStride_AsyPadding) create_test_cudnn_padding_VALID_class(TestWithGroup_AsyPadding) @@ -1221,7 +1221,7 @@ create_test_padding_VALID_class(TestDepthwiseConvandFuse_AsyPadding) create_test_padding_VALID_class(TestDepthwiseConvWithDilationandFuse_AsyPadding) # ------------ test channel last --------- -create_test_channel_last_class(TestConv2dOp_AsyPadding) +create_test_channel_last_class(TestConv2DOp_AsyPadding) create_test_channel_last_class(TestWithPad_AsyPadding) create_test_channel_last_class(TestWithGroup_AsyPadding) create_test_channel_last_class(TestWith1x1_AsyPadding) @@ -1232,14 +1232,14 @@ create_test_channel_last_class(TestDepthwiseConvWithDilation2_AsyPadding) create_test_channel_last_class(TestDepthwiseConvandFuse_AsyPadding) create_test_channel_last_class(TestDepthwiseConvWithDilationandFuse_AsyPadding) -create_test_cudnn_channel_last_class(TestConv2dOp_AsyPadding) +create_test_cudnn_channel_last_class(TestConv2DOp_AsyPadding) create_test_cudnn_channel_last_class(TestWithPad_AsyPadding) create_test_cudnn_channel_last_class(TestWithStride_AsyPadding) create_test_cudnn_channel_last_class(TestWithGroup_AsyPadding) create_test_cudnn_channel_last_class(TestWithDilation_AsyPadding) create_test_cudnn_channel_last_fp16_class( - TestConv2dOp_AsyPadding, grad_check=False) + TestConv2DOp_AsyPadding, grad_check=False) create_test_cudnn_channel_last_fp16_class( TestWithPad_AsyPadding, grad_check=False) create_test_cudnn_channel_last_fp16_class( @@ -1251,7 +1251,7 @@ create_test_cudnn_channel_last_fp16_class( # --------- test python API --------------- -class TestConv2dAPI(unittest.TestCase): +class TestConv2DAPI(unittest.TestCase): def test_api(self): input_NHWC = fluid.layers.data( @@ -1327,7 +1327,7 @@ class TestConv2dAPI(unittest.TestCase): data_format="NCHW") -class TestConv2dAPI_Error(unittest.TestCase): +class TestConv2DAPI_Error(unittest.TestCase): def test_api(self): input = fluid.layers.data( name="input", diff --git a/python/paddle/fluid/tests/unittests/test_conv2d_transpose_layer.py b/python/paddle/fluid/tests/unittests/test_conv2d_transpose_layer.py index ba450b345b..28c3a466aa 100644 --- a/python/paddle/fluid/tests/unittests/test_conv2d_transpose_layer.py +++ b/python/paddle/fluid/tests/unittests/test_conv2d_transpose_layer.py @@ -155,7 +155,7 @@ class Conv2DTransposeTestCase(unittest.TestCase): else: output_size = self.output_size - conv = nn.ConvTranspose2d( + conv = nn.Conv2DTranspose( self.num_channels, self.num_filters, self.filter_size, diff --git a/python/paddle/fluid/tests/unittests/test_conv2d_transpose_op.py b/python/paddle/fluid/tests/unittests/test_conv2d_transpose_op.py index 913db51da5..bc87e76fd9 100644 --- a/python/paddle/fluid/tests/unittests/test_conv2d_transpose_op.py +++ b/python/paddle/fluid/tests/unittests/test_conv2d_transpose_op.py @@ -111,7 +111,7 @@ def conv2dtranspose_forward_naive(input_, filter_, attrs): return out -class TestConv2dTransposeOp(OpTest): +class TestConv2DTransposeOp(OpTest): def setUp(self): # init as conv transpose self.dtype = np.float64 @@ -211,7 +211,7 @@ class TestConv2dTransposeOp(OpTest): self.op_type = "conv2d_transpose" -class TestWithSymmetricPad(TestConv2dTransposeOp): +class TestWithSymmetricPad(TestConv2DTransposeOp): def init_test_case(self): self.pad = [1, 1] self.stride = [1, 1] @@ -222,7 +222,7 @@ class TestWithSymmetricPad(TestConv2dTransposeOp): self.filter_size = [f_c, 6, 3, 3] -class TestWithAsymmetricPad(TestConv2dTransposeOp): +class TestWithAsymmetricPad(TestConv2DTransposeOp): def init_test_case(self): self.pad = [1, 0, 1, 2] self.stride = [1, 1] @@ -233,7 +233,7 @@ class TestWithAsymmetricPad(TestConv2dTransposeOp): self.filter_size = [f_c, 6, 3, 3] -class TestWithSAMEPad(TestConv2dTransposeOp): +class TestWithSAMEPad(TestConv2DTransposeOp): def init_test_case(self): self.stride = [2, 1] self.dilations = [1, 2] @@ -244,7 +244,7 @@ class TestWithSAMEPad(TestConv2dTransposeOp): self.padding_algorithm = 'SAME' -class TestWithVALIDPad(TestConv2dTransposeOp): +class TestWithVALIDPad(TestConv2DTransposeOp): def init_test_case(self): self.stride = [1, 1] self.dilations = [1, 1] @@ -255,7 +255,7 @@ class TestWithVALIDPad(TestConv2dTransposeOp): self.padding_algorithm = 'VALID' -class TestWithGroups(TestConv2dTransposeOp): +class TestWithGroups(TestConv2DTransposeOp): def init_test_case(self): self.pad = [1, 1] self.stride = [1, 1] @@ -266,7 +266,7 @@ class TestWithGroups(TestConv2dTransposeOp): self.filter_size = [f_c, 3, 3, 3] -class TestWithStride(TestConv2dTransposeOp): +class TestWithStride(TestConv2DTransposeOp): def init_test_case(self): self.pad = [1, 1] self.stride = [2, 2] @@ -277,7 +277,7 @@ class TestWithStride(TestConv2dTransposeOp): self.filter_size = [f_c, 6, 3, 3] -class TestWithDilation(TestConv2dTransposeOp): +class TestWithDilation(TestConv2DTransposeOp): def init_test_case(self): self.pad = [1, 1] self.stride = [1, 1] @@ -288,7 +288,7 @@ class TestWithDilation(TestConv2dTransposeOp): self.filter_size = [f_c, 6, 3, 3] -class TestWithEvenUpsample(TestConv2dTransposeOp): +class TestWithEvenUpsample(TestConv2DTransposeOp): def init_test_case(self): self.pad = [2, 2] self.stride = [2, 2] @@ -300,7 +300,7 @@ class TestWithEvenUpsample(TestConv2dTransposeOp): self.filter_size = [f_c, 6, 5, 5] -class TestWithEvenUpsampleOutputPadding(TestConv2dTransposeOp): +class TestWithEvenUpsampleOutputPadding(TestConv2DTransposeOp): def init_test_case(self): self.pad = [2, 2] self.stride = [2, 2] @@ -312,7 +312,7 @@ class TestWithEvenUpsampleOutputPadding(TestConv2dTransposeOp): self.filter_size = [f_c, 6, 5, 5] -class Test_NHWC(TestConv2dTransposeOp): +class Test_NHWC(TestConv2DTransposeOp): def init_test_case(self): self.pad = [0, 0] self.stride = [1, 1] @@ -324,7 +324,7 @@ class Test_NHWC(TestConv2dTransposeOp): self.data_format = 'NHWC' -class TestWithSymmetricPad_NHWC(TestConv2dTransposeOp): +class TestWithSymmetricPad_NHWC(TestConv2DTransposeOp): def init_test_case(self): self.pad = [1, 1] self.stride = [1, 1] @@ -336,7 +336,7 @@ class TestWithSymmetricPad_NHWC(TestConv2dTransposeOp): self.data_format = 'NHWC' -class TestWithAsymmetricPad_NHWC(TestConv2dTransposeOp): +class TestWithAsymmetricPad_NHWC(TestConv2DTransposeOp): def init_test_case(self): self.pad = [1, 0, 1, 2] self.stride = [1, 1] @@ -348,7 +348,7 @@ class TestWithAsymmetricPad_NHWC(TestConv2dTransposeOp): self.data_format = 'NHWC' -class TestWithGroups_NHWC(TestConv2dTransposeOp): +class TestWithGroups_NHWC(TestConv2DTransposeOp): def init_test_case(self): self.pad = [1, 1] self.stride = [1, 1] @@ -360,7 +360,7 @@ class TestWithGroups_NHWC(TestConv2dTransposeOp): self.data_format = 'NHWC' -class TestWithStride_NHWC(TestConv2dTransposeOp): +class TestWithStride_NHWC(TestConv2DTransposeOp): def init_test_case(self): self.pad = [1, 1] self.stride = [2, 2] @@ -372,7 +372,7 @@ class TestWithStride_NHWC(TestConv2dTransposeOp): self.data_format = 'NHWC' -class TestWithDilation_NHWC(TestConv2dTransposeOp): +class TestWithDilation_NHWC(TestConv2DTransposeOp): def init_test_case(self): self.pad = [1, 1] self.stride = [1, 1] @@ -384,7 +384,7 @@ class TestWithDilation_NHWC(TestConv2dTransposeOp): self.data_format = 'NHWC' -class TestWithEvenUpsample_NHWC(TestConv2dTransposeOp): +class TestWithEvenUpsample_NHWC(TestConv2DTransposeOp): def init_test_case(self): self.pad = [2, 2] self.stride = [2, 2] @@ -397,7 +397,7 @@ class TestWithEvenUpsample_NHWC(TestConv2dTransposeOp): self.data_format = 'NHWC' -class TestWithEvenUpsample_NHWC_output_padding(TestConv2dTransposeOp): +class TestWithEvenUpsample_NHWC_output_padding(TestConv2DTransposeOp): def init_test_case(self): self.pad = [2, 2] self.stride = [2, 2] @@ -413,7 +413,7 @@ class TestWithEvenUpsample_NHWC_output_padding(TestConv2dTransposeOp): # ------------ test_cudnn ------------ @unittest.skipIf(not core.is_compiled_with_cuda(), "core is not compiled with CUDA") -class TestCUDNN(TestConv2dTransposeOp): +class TestCUDNN(TestConv2DTransposeOp): def init_op_type(self): self.use_cudnn = True self.op_type = "conv2d_transpose" @@ -547,7 +547,7 @@ class TestCUDNNWithEvenUpsample(TestWithEvenUpsample): @unittest.skipIf(not core.is_compiled_with_cuda(), "core is not compiled with CUDA") -class TestCUDNN_NHWC(TestConv2dTransposeOp): +class TestCUDNN_NHWC(TestConv2DTransposeOp): def init_test_case(self): self.pad = [0, 0] self.stride = [1, 1] @@ -654,7 +654,7 @@ class TestCUDNNWithEvenUpsample_NHWC(TestWithEvenUpsample): self.op_type = "conv2d_transpose" -class TestDepthwiseConvTranspose(TestConv2dTransposeOp): +class TestDepthwiseConvTranspose(TestConv2DTransposeOp): def init_test_case(self): self.pad = [1, 1] self.stride = [2, 2] @@ -667,7 +667,7 @@ class TestDepthwiseConvTranspose(TestConv2dTransposeOp): self.op_type = "depthwise_conv2d_transpose" -class TestDepthwiseConvTransposeAsymmetricPad(TestConv2dTransposeOp): +class TestDepthwiseConvTransposeAsymmetricPad(TestConv2DTransposeOp): def init_test_case(self): self.pad = [1, 0, 1, 2] self.stride = [2, 2] @@ -681,7 +681,7 @@ class TestDepthwiseConvTransposeAsymmetricPad(TestConv2dTransposeOp): self.data_format = 'NCHW' -class TestDepthwiseConvTransposeSAMEPad(TestConv2dTransposeOp): +class TestDepthwiseConvTransposeSAMEPad(TestConv2DTransposeOp): def init_test_case(self): self.stride = [2, 2] self.dilations = [1, 1] @@ -694,7 +694,7 @@ class TestDepthwiseConvTransposeSAMEPad(TestConv2dTransposeOp): self.padding_algorithm = 'SAME' -class TestDepthwiseConvTransposeVALIDPad(TestConv2dTransposeOp): +class TestDepthwiseConvTransposeVALIDPad(TestConv2DTransposeOp): def init_test_case(self): self.stride = [2, 2] self.dilations = [1, 1] @@ -707,7 +707,7 @@ class TestDepthwiseConvTransposeVALIDPad(TestConv2dTransposeOp): self.padding_algorithm = 'VALID' -class TestDepthwiseConvTranspose_NHWC_4x4kernel(TestConv2dTransposeOp): +class TestDepthwiseConvTranspose_NHWC_4x4kernel(TestConv2DTransposeOp): def init_test_case(self): self.pad = [1, 1] self.stride = [2, 2] @@ -721,7 +721,7 @@ class TestDepthwiseConvTranspose_NHWC_4x4kernel(TestConv2dTransposeOp): self.data_format = 'NHWC' -class TestDepthwiseConvTranspose_NHWC_3x3kernel(TestConv2dTransposeOp): +class TestDepthwiseConvTranspose_NHWC_3x3kernel(TestConv2DTransposeOp): def init_test_case(self): self.pad = [1, 1] self.stride = [2, 2] @@ -735,7 +735,7 @@ class TestDepthwiseConvTranspose_NHWC_3x3kernel(TestConv2dTransposeOp): self.data_format = 'NHWC' -class TestDepthwiseConvTransposeAsymmetricPad_NHWC(TestConv2dTransposeOp): +class TestDepthwiseConvTransposeAsymmetricPad_NHWC(TestConv2DTransposeOp): def init_test_case(self): self.pad = [1, 0, 1, 2] self.stride = [2, 2] @@ -751,7 +751,7 @@ class TestDepthwiseConvTransposeAsymmetricPad_NHWC(TestConv2dTransposeOp): @unittest.skipIf(not core.is_compiled_with_cuda(), "core is not compiled with CUDA") -class TestCUDNN_FP16(TestConv2dTransposeOp): +class TestCUDNN_FP16(TestConv2DTransposeOp): def init_test_case(self): self.dtype = np.float16 self.pad = [1, 1] @@ -867,7 +867,7 @@ class TestCUDNNWithEvenUpsample_NHWC_FP16(TestCUDNN_FP16): self.data_format = 'NHWC' -class TestConv2dTransposeAPI(unittest.TestCase): +class TestConv2DTransposeAPI(unittest.TestCase): def test_case1(self): data1 = fluid.layers.data( name='data1', shape=[3, 5, 5], dtype='float32') @@ -945,7 +945,7 @@ class TestConv2dTransposeAPI(unittest.TestCase): self.assertIsNotNone(results[6]) -class TestConv2dTransposeOpException(unittest.TestCase): +class TestConv2DTransposeOpException(unittest.TestCase): def test_exception(self): data = fluid.layers.data(name='data', shape=[3, 5, 5], dtype="float32") diff --git a/python/paddle/fluid/tests/unittests/test_conv3d_layer.py b/python/paddle/fluid/tests/unittests/test_conv3d_layer.py index 56355a1c95..b45e2d1a6a 100644 --- a/python/paddle/fluid/tests/unittests/test_conv3d_layer.py +++ b/python/paddle/fluid/tests/unittests/test_conv3d_layer.py @@ -135,7 +135,7 @@ class Conv3DTestCase(unittest.TestCase): def paddle_nn_layer(self): x_var = dg.to_variable(self.input) - conv = nn.Conv3d( + conv = nn.Conv3D( self.num_channels, self.num_filters, self.filter_size, diff --git a/python/paddle/fluid/tests/unittests/test_conv3d_op.py b/python/paddle/fluid/tests/unittests/test_conv3d_op.py index 8f1f2094fa..1636019a62 100644 --- a/python/paddle/fluid/tests/unittests/test_conv3d_op.py +++ b/python/paddle/fluid/tests/unittests/test_conv3d_op.py @@ -228,7 +228,7 @@ def create_test_cudnn_channel_last_class(parent): globals()[cls_name] = TestCudnnChannelLastCase -class TestConv3dOp(OpTest): +class TestConv3DOp(OpTest): def setUp(self): self.op_type = "conv3d" self.use_cudnn = False @@ -334,7 +334,7 @@ class TestConv3dOp(OpTest): pass -class TestCase1(TestConv3dOp): +class TestCase1(TestConv3DOp): def init_test_case(self): self.pad = [1, 1, 1] self.stride = [1, 1, 1] @@ -344,7 +344,7 @@ class TestCase1(TestConv3dOp): self.filter_size = [6, f_c, 3, 3, 3] -class TestWithGroup1(TestConv3dOp): +class TestWithGroup1(TestConv3DOp): def init_group(self): self.groups = 3 @@ -354,7 +354,7 @@ class TestWithGroup2(TestCase1): self.groups = 3 -class TestWith1x1(TestConv3dOp): +class TestWith1x1(TestConv3DOp): def init_test_case(self): self.pad = [0, 0, 0] self.stride = [1, 1, 1] @@ -370,7 +370,7 @@ class TestWith1x1(TestConv3dOp): self.groups = 3 -class TestWithInput1x1Filter1x1(TestConv3dOp): +class TestWithInput1x1Filter1x1(TestConv3DOp): def init_test_case(self): self.pad = [0, 0, 0] self.stride = [1, 1, 1] @@ -386,7 +386,7 @@ class TestWithInput1x1Filter1x1(TestConv3dOp): self.groups = 3 -class TestWithDilation(TestConv3dOp): +class TestWithDilation(TestConv3DOp): def init_test_case(self): self.pad = [0, 0, 0] self.stride = [1, 1, 1] @@ -402,19 +402,19 @@ class TestWithDilation(TestConv3dOp): self.groups = 3 -#---------------- Conv3dCUDNN ---------------- +#---------------- Conv3DCUDNN ---------------- @unittest.skipIf(not core.is_compiled_with_cuda(), "core is not compiled with CUDA") -class TestCUDNN(TestConv3dOp): +class TestCUDNN(TestConv3DOp): def init_kernel_type(self): self.use_cudnn = True @unittest.skipIf(not core.is_compiled_with_cuda(), "core is not compiled with CUDA") -class TestFP16CUDNN(TestConv3dOp): +class TestFP16CUDNN(TestConv3DOp): def init_kernel_type(self): self.use_cudnn = True self.dtype = np.float16 @@ -519,7 +519,7 @@ class TestCUDNNExhaustiveSearch(TestCUDNN): # ---- test asymmetric padding ---- -class TestConv3dOp_2(OpTest): +class TestConv3DOp_2(OpTest): def setUp(self): self.op_type = "conv3d" self.use_cudnn = False @@ -624,7 +624,7 @@ class TestConv3dOp_2(OpTest): self.data_format = "NCDHW" -class TestConv3dOp_AsyPadding(TestConv3dOp_2): +class TestConv3DOp_AsyPadding(TestConv3DOp_2): def init_test_case(self): self.stride = [1, 1, 2] self.input_size = [2, 3, 4, 4, 4] # NCDHW @@ -637,7 +637,7 @@ class TestConv3dOp_AsyPadding(TestConv3dOp_2): self.padding_algorithm = "EXPLICIT" -class TestConv3dOp_DiffDataInDiffDim(TestConv3dOp_2): +class TestConv3DOp_DiffDataInDiffDim(TestConv3DOp_2): def init_test_case(self): self.stride = [1, 1, 2] self.input_size = [2, 3, 4, 5, 5] # NCDHW @@ -650,12 +650,12 @@ class TestConv3dOp_DiffDataInDiffDim(TestConv3dOp_2): self.padding_algorithm = "EXPLICIT" -create_test_padding_SAME_class(TestConv3dOp_DiffDataInDiffDim) -create_test_padding_VALID_class(TestConv3dOp_DiffDataInDiffDim) -create_test_channel_last_class(TestConv3dOp_DiffDataInDiffDim) +create_test_padding_SAME_class(TestConv3DOp_DiffDataInDiffDim) +create_test_padding_VALID_class(TestConv3DOp_DiffDataInDiffDim) +create_test_channel_last_class(TestConv3DOp_DiffDataInDiffDim) -class TestCase1_AsyPadding(TestConv3dOp_2): +class TestCase1_AsyPadding(TestConv3DOp_2): def init_test_case(self): self.stride = [1, 1, 1] self.input_size = [2, 3, 4, 4, 4] # NCDHW @@ -668,7 +668,7 @@ class TestCase1_AsyPadding(TestConv3dOp_2): self.padding_algorithm = "EXPLICIT" -class TestWithGroup1_AsyPadding(TestConv3dOp_2): +class TestWithGroup1_AsyPadding(TestConv3DOp_2): def init_group(self): self.groups = 3 @@ -677,7 +677,7 @@ class TestWithGroup1_AsyPadding(TestConv3dOp_2): self.padding_algorithm = "EXPLICIT" -class TestWithGroup2_AsyPadding(TestConv3dOp_2): +class TestWithGroup2_AsyPadding(TestConv3DOp_2): def init_test_case(self): self.stride = [1, 1, 1] self.input_size = [2, 3, 4, 4, 4] # NCDHW @@ -693,7 +693,7 @@ class TestWithGroup2_AsyPadding(TestConv3dOp_2): self.padding_algorithm = "EXPLICIT" -class TestWith1x1_AsyPadding(TestConv3dOp_2): +class TestWith1x1_AsyPadding(TestConv3DOp_2): def init_test_case(self): self.stride = [1, 1, 1] self.input_size = [2, 3, 4, 4, 4] @@ -712,7 +712,7 @@ class TestWith1x1_AsyPadding(TestConv3dOp_2): self.padding_algorithm = "EXPLICIT" -class TestWithDilation_AsyPadding(TestConv3dOp_2): +class TestWithDilation_AsyPadding(TestConv3DOp_2): def init_test_case(self): self.stride = [1, 1, 1] self.input_size = [2, 3, 6, 6, 6] @@ -731,41 +731,41 @@ class TestWithDilation_AsyPadding(TestConv3dOp_2): self.padding_algorithm = "EXPLICIT" -create_test_cudnn_class(TestConv3dOp_AsyPadding) +create_test_cudnn_class(TestConv3DOp_AsyPadding) create_test_cudnn_class(TestWithGroup1_AsyPadding) create_test_cudnn_class(TestWithGroup2_AsyPadding) create_test_cudnn_class(TestWith1x1_AsyPadding) create_test_cudnn_class(TestWithDilation_AsyPadding) -create_test_padding_SAME_class(TestConv3dOp_AsyPadding) +create_test_padding_SAME_class(TestConv3DOp_AsyPadding) create_test_padding_SAME_class(TestWithGroup1_AsyPadding) create_test_padding_SAME_class(TestWith1x1_AsyPadding) -create_test_padding_VALID_class(TestConv3dOp_AsyPadding) +create_test_padding_VALID_class(TestConv3DOp_AsyPadding) create_test_padding_VALID_class(TestWithGroup1_AsyPadding) create_test_padding_VALID_class(TestWith1x1_AsyPadding) -create_test_cudnn_padding_SAME_class(TestConv3dOp_AsyPadding) +create_test_cudnn_padding_SAME_class(TestConv3DOp_AsyPadding) create_test_cudnn_padding_SAME_class(TestWithGroup1_AsyPadding) create_test_cudnn_padding_SAME_class(TestWith1x1_AsyPadding) -create_test_cudnn_padding_VALID_class(TestConv3dOp_AsyPadding) +create_test_cudnn_padding_VALID_class(TestConv3DOp_AsyPadding) create_test_cudnn_padding_VALID_class(TestWithGroup1_AsyPadding) create_test_cudnn_padding_VALID_class(TestWith1x1_AsyPadding) -create_test_channel_last_class(TestConv3dOp_AsyPadding) +create_test_channel_last_class(TestConv3DOp_AsyPadding) create_test_channel_last_class(TestWithGroup1_AsyPadding) create_test_channel_last_class(TestWith1x1_AsyPadding) -create_test_channel_last_class(TestConv3dOp_AsyPadding) +create_test_channel_last_class(TestConv3DOp_AsyPadding) create_test_channel_last_class(TestWithGroup1_AsyPadding) create_test_channel_last_class(TestWith1x1_AsyPadding) -create_test_cudnn_channel_last_class(TestConv3dOp_AsyPadding) +create_test_cudnn_channel_last_class(TestConv3DOp_AsyPadding) create_test_cudnn_channel_last_class(TestWithGroup1_AsyPadding) create_test_cudnn_channel_last_class(TestWith1x1_AsyPadding) -create_test_cudnn_channel_last_class(TestConv3dOp_AsyPadding) +create_test_cudnn_channel_last_class(TestConv3DOp_AsyPadding) create_test_cudnn_channel_last_class(TestWithGroup1_AsyPadding) create_test_cudnn_channel_last_class(TestWith1x1_AsyPadding) @@ -777,7 +777,7 @@ create_test_cudnn_channel_last_class(TestWith1x1_AsyPadding) # --------- test python API --------------- -class TestConv3dAPI(unittest.TestCase): +class TestConv3DAPI(unittest.TestCase): def test_api(self): input_NDHWC = fluid.layers.data( @@ -853,7 +853,7 @@ class TestConv3dAPI(unittest.TestCase): data_format="NCDHW") -class TestConv3dAPI_Error(unittest.TestCase): +class TestConv3DAPI_Error(unittest.TestCase): def test_api(self): input = fluid.layers.data( name="input", diff --git a/python/paddle/fluid/tests/unittests/test_conv3d_transpose_layer.py b/python/paddle/fluid/tests/unittests/test_conv3d_transpose_layer.py index e30f0cd3ec..dac84a8486 100644 --- a/python/paddle/fluid/tests/unittests/test_conv3d_transpose_layer.py +++ b/python/paddle/fluid/tests/unittests/test_conv3d_transpose_layer.py @@ -139,7 +139,7 @@ class Conv3DTransposeTestCase(unittest.TestCase): def paddle_nn_layer(self): x_var = dg.to_variable(self.input) - conv = nn.ConvTranspose3d( + conv = nn.Conv3DTranspose( self.num_channels, self.num_filters, self.filter_size, diff --git a/python/paddle/fluid/tests/unittests/test_conv3d_transpose_op.py b/python/paddle/fluid/tests/unittests/test_conv3d_transpose_op.py index 6570fb8f35..42062b1557 100644 --- a/python/paddle/fluid/tests/unittests/test_conv3d_transpose_op.py +++ b/python/paddle/fluid/tests/unittests/test_conv3d_transpose_op.py @@ -107,7 +107,7 @@ def conv3dtranspose_forward_naive(input_, filter_, attrs): return out -class TestConv3dTransposeOp(OpTest): +class TestConv3DTransposeOp(OpTest): def setUp(self): # init as conv transpose self.use_cudnn = False @@ -200,7 +200,7 @@ class TestConv3dTransposeOp(OpTest): self.op_type = "conv3d_transpose" -class TestWithSymmetricPad(TestConv3dTransposeOp): +class TestWithSymmetricPad(TestConv3DTransposeOp): def init_test_case(self): self.check_no_input = True self.pad = [1, 1, 1] @@ -212,7 +212,7 @@ class TestWithSymmetricPad(TestConv3dTransposeOp): self.filter_size = [f_c, 6, 3, 3, 3] -class TestWithAsymmetricPad(TestConv3dTransposeOp): +class TestWithAsymmetricPad(TestConv3DTransposeOp): def init_test_case(self): self.pad = [1, 0, 1, 0, 1, 2] self.stride = [1, 1, 1] @@ -223,7 +223,7 @@ class TestWithAsymmetricPad(TestConv3dTransposeOp): self.filter_size = [f_c, 6, 3, 3, 3] -class TestWithSAMEPad(TestConv3dTransposeOp): +class TestWithSAMEPad(TestConv3DTransposeOp): def init_test_case(self): self.stride = [1, 1, 2] self.dilations = [1, 2, 1] @@ -234,7 +234,7 @@ class TestWithSAMEPad(TestConv3dTransposeOp): self.padding_algorithm = 'SAME' -class TestWithVALIDPad(TestConv3dTransposeOp): +class TestWithVALIDPad(TestConv3DTransposeOp): def init_test_case(self): self.stride = [2, 1, 1] self.dilations = [1, 1, 1] @@ -245,7 +245,7 @@ class TestWithVALIDPad(TestConv3dTransposeOp): self.padding_algorithm = 'VALID' -class TestWithStride(TestConv3dTransposeOp): +class TestWithStride(TestConv3DTransposeOp): def init_test_case(self): self.check_no_filter = True self.pad = [1, 1, 1] @@ -257,7 +257,7 @@ class TestWithStride(TestConv3dTransposeOp): self.filter_size = [f_c, 6, 3, 3, 3] -class TestWithGroups(TestConv3dTransposeOp): +class TestWithGroups(TestConv3DTransposeOp): def init_test_case(self): self.pad = [1, 1, 1] self.stride = [1, 1, 1] @@ -268,7 +268,7 @@ class TestWithGroups(TestConv3dTransposeOp): self.filter_size = [f_c, 3, 3, 3, 3] -class TestWithDilation(TestConv3dTransposeOp): +class TestWithDilation(TestConv3DTransposeOp): def init_test_case(self): self.pad = [1, 1, 1] self.stride = [1, 1, 1] @@ -279,7 +279,7 @@ class TestWithDilation(TestConv3dTransposeOp): self.filter_size = [f_c, 6, 3, 3, 3] -class Test_NHWC(TestConv3dTransposeOp): +class Test_NHWC(TestConv3DTransposeOp): def init_test_case(self): self.pad = [0, 0, 0] self.stride = [1, 1, 1] @@ -294,7 +294,7 @@ class Test_NHWC(TestConv3dTransposeOp): # ------------ test_cudnn ------------ @unittest.skipIf(not core.is_compiled_with_cuda(), "core is not compiled with CUDA") -class TestCUDNN(TestConv3dTransposeOp): +class TestCUDNN(TestConv3DTransposeOp): def init_op_type(self): self.use_cudnn = True self.op_type = "conv3d_transpose" @@ -419,7 +419,7 @@ class TestCUDNNWithGroups(TestWithGroups): @unittest.skipIf(not core.is_compiled_with_cuda(), "core is not compiled with CUDA") -class TestCUDNN_NHWC(TestConv3dTransposeOp): +class TestCUDNN_NHWC(TestConv3DTransposeOp): def init_test_case(self): self.pad = [0, 0, 0] self.stride = [1, 1, 1] diff --git a/python/paddle/fluid/tests/unittests/test_conv3d_transpose_part2_op.py b/python/paddle/fluid/tests/unittests/test_conv3d_transpose_part2_op.py index 241f6b570f..d597045641 100644 --- a/python/paddle/fluid/tests/unittests/test_conv3d_transpose_part2_op.py +++ b/python/paddle/fluid/tests/unittests/test_conv3d_transpose_part2_op.py @@ -20,10 +20,10 @@ import numpy as np import paddle.fluid.core as core import paddle.fluid as fluid from op_test import OpTest -from test_conv3d_transpose_op import conv3dtranspose_forward_naive, TestConv3dTransposeOp +from test_conv3d_transpose_op import TestConv3DTransposeOp -class TestWithSymmetricPad_NHWC(TestConv3dTransposeOp): +class TestWithSymmetricPad_NHWC(TestConv3DTransposeOp): def init_test_case(self): self.pad = [1, 1, 1] self.stride = [1, 1, 1] @@ -35,7 +35,7 @@ class TestWithSymmetricPad_NHWC(TestConv3dTransposeOp): self.data_format = 'NHWC' -class TestWithAsymmetricPad_NHWC(TestConv3dTransposeOp): +class TestWithAsymmetricPad_NHWC(TestConv3DTransposeOp): def init_test_case(self): self.pad = [1, 0, 1, 0, 1, 2] self.stride = [1, 1, 1] @@ -47,7 +47,7 @@ class TestWithAsymmetricPad_NHWC(TestConv3dTransposeOp): self.data_format = 'NHWC' -class TestWithGroups_NHWC(TestConv3dTransposeOp): +class TestWithGroups_NHWC(TestConv3DTransposeOp): def init_test_case(self): self.check_no_filter = True self.pad = [1, 1, 1] @@ -60,7 +60,7 @@ class TestWithGroups_NHWC(TestConv3dTransposeOp): self.data_format = 'NHWC' -class TestWithStride_NHWC(TestConv3dTransposeOp): +class TestWithStride_NHWC(TestConv3DTransposeOp): def init_test_case(self): self.pad = [1, 1, 1] self.stride = [2, 2, 2] @@ -72,7 +72,7 @@ class TestWithStride_NHWC(TestConv3dTransposeOp): self.data_format = 'NHWC' -class TestWithDilation_NHWC(TestConv3dTransposeOp): +class TestWithDilation_NHWC(TestConv3DTransposeOp): def init_test_case(self): self.check_no_input = True self.pad = [1, 1, 1] @@ -85,7 +85,7 @@ class TestWithDilation_NHWC(TestConv3dTransposeOp): self.data_format = 'NHWC' -class TestConv3dTransposeAPI(unittest.TestCase): +class TestConv3DTransposeAPI(unittest.TestCase): def test_case1(self): data1 = fluid.layers.data( name='data1', shape=[3, 5, 5, 5], dtype='float32') @@ -164,7 +164,7 @@ class TestConv3dTransposeAPI(unittest.TestCase): self.assertIsNotNone(results[6]) -class TestConv3dTransposeOpException(unittest.TestCase): +class TestConv3DTransposeOpException(unittest.TestCase): def test_exception(self): data = fluid.layers.data( name='data', shape=[3, 5, 5, 5], dtype="float32") diff --git a/python/paddle/fluid/tests/unittests/test_conv_nn_grad.py b/python/paddle/fluid/tests/unittests/test_conv_nn_grad.py index c953841be0..31f2000f3a 100644 --- a/python/paddle/fluid/tests/unittests/test_conv_nn_grad.py +++ b/python/paddle/fluid/tests/unittests/test_conv_nn_grad.py @@ -438,7 +438,7 @@ class TestConv3DDoubleGradCheck_ChannelLast(unittest.TestCase): self.func(p) -class TestConv3dDoubleGradCheck_ChannelLast_AsyPadding(unittest.TestCase): +class TestConv3DDoubleGradCheck_ChannelLast_AsyPadding(unittest.TestCase): @prog_scope() def func(self, place): shape = [2, 2, 2, 2, 3] diff --git a/python/paddle/fluid/tests/unittests/test_cuda_random_seed.py b/python/paddle/fluid/tests/unittests/test_cuda_random_seed.py index 0c2520038a..686e738b8e 100644 --- a/python/paddle/fluid/tests/unittests/test_cuda_random_seed.py +++ b/python/paddle/fluid/tests/unittests/test_cuda_random_seed.py @@ -31,7 +31,7 @@ class TestGeneratorSeed(unittest.TestCase): """ def test_gen_dropout_dygraph(self): - gen = paddle.manual_seed(12343) + gen = paddle.seed(12343) fluid.enable_dygraph() @@ -70,13 +70,13 @@ class TestGeneratorSeed(unittest.TestCase): """Test Generator seed.""" fluid.enable_dygraph() - paddle.manual_seed(12312321111) + paddle.seed(12312321111) x = fluid.layers.gaussian_random([120], dtype="float32") st1 = paddle.get_cuda_rng_state() x1 = fluid.layers.gaussian_random([120], dtype="float32") paddle.set_cuda_rng_state(st1) x2 = fluid.layers.gaussian_random([120], dtype="float32") - paddle.manual_seed(12312321111) + paddle.seed(12312321111) x3 = fluid.layers.gaussian_random([120], dtype="float32") x_np = x.numpy() x1_np = x1.numpy() @@ -93,13 +93,13 @@ class TestGeneratorSeed(unittest.TestCase): fluid.enable_dygraph() - gen = paddle.manual_seed(12312321111) + gen = paddle.seed(12312321111) x = paddle.randint(low=10, shape=[10], dtype="int32") st1 = gen.get_state() x1 = paddle.randint(low=10, shape=[10], dtype="int32") gen.set_state(st1) x2 = paddle.randint(low=10, shape=[10], dtype="int32") - paddle.manual_seed(12312321111) + paddle.seed(12312321111) x3 = paddle.randint(low=10, shape=[10], dtype="int32") x_np = x.numpy() x1_np = x1.numpy() @@ -114,7 +114,7 @@ class TestGeneratorSeed(unittest.TestCase): def test_gen_TruncatedNormal_initializer(self): fluid.disable_dygraph() - gen = paddle.manual_seed(123123143) + gen = paddle.seed(123123143) cur_state = paddle.get_cuda_rng_state() startup_program = fluid.Program() @@ -140,7 +140,7 @@ class TestGeneratorSeed(unittest.TestCase): feed={}, fetch_list=[result_1, result_2]) - paddle.manual_seed(123123143) + paddle.seed(123123143) with fluid.program_guard(train_program, startup_program): exe.run(startup_program) out2 = exe.run(train_program, diff --git a/python/paddle/fluid/tests/unittests/test_decoupled_py_reader.py b/python/paddle/fluid/tests/unittests/test_decoupled_py_reader.py index cc0f3745bb..a7c1b14d26 100644 --- a/python/paddle/fluid/tests/unittests/test_decoupled_py_reader.py +++ b/python/paddle/fluid/tests/unittests/test_decoupled_py_reader.py @@ -34,7 +34,7 @@ def random_reader(): def simple_fc_net(places, use_legacy_py_reader, use_double_buffer): - paddle.manual_seed(1) + paddle.seed(1) paddle.framework.random._manual_program_seed(1) startup_prog = fluid.Program() main_prog = fluid.Program() diff --git a/python/paddle/fluid/tests/unittests/test_deformable_conv_op.py b/python/paddle/fluid/tests/unittests/test_deformable_conv_op.py index eed637b1d5..80c1088682 100644 --- a/python/paddle/fluid/tests/unittests/test_deformable_conv_op.py +++ b/python/paddle/fluid/tests/unittests/test_deformable_conv_op.py @@ -286,7 +286,7 @@ class TestModulatedDeformableConvInvalidInput(unittest.TestCase): self.assertRaises(TypeError, test_invalid_offset) -class TestDeformConv2dAPI(unittest.TestCase): +class TestDeformConv2DAPI(unittest.TestCase): def test_api(self): def test_deform_conv2d_v1(): paddle.enable_static() diff --git a/python/paddle/fluid/tests/unittests/test_dropout_op.py b/python/paddle/fluid/tests/unittests/test_dropout_op.py index 7b9e25e1d4..0d0273c167 100644 --- a/python/paddle/fluid/tests/unittests/test_dropout_op.py +++ b/python/paddle/fluid/tests/unittests/test_dropout_op.py @@ -487,7 +487,7 @@ class TestDropoutCAPI(unittest.TestCase): self.assertTrue(np.allclose(result.numpy(), result_np)) -class TestDropout2dFAPI(unittest.TestCase): +class TestDropout2DFAPI(unittest.TestCase): def setUp(self): np.random.seed(123) self.places = [fluid.CPUPlace()] @@ -535,7 +535,7 @@ class TestDropout2dFAPI(unittest.TestCase): self.assertTrue(np.allclose(res.numpy(), res_np)) -class TestDropout2dFAPIError(unittest.TestCase): +class TestDropout2DFAPIError(unittest.TestCase): def test_errors(self): with program_guard(Program(), Program()): @@ -554,7 +554,7 @@ class TestDropout2dFAPIError(unittest.TestCase): self.assertRaises(ValueError, test_dataformat) -class TestDropout2dCAPI(unittest.TestCase): +class TestDropout2DCAPI(unittest.TestCase): def setUp(self): np.random.seed(123) self.places = [fluid.CPUPlace()] @@ -567,13 +567,13 @@ class TestDropout2dCAPI(unittest.TestCase): input_np = np.random.random([2, 3, 4, 5]).astype("float32") result_np = input_np input = fluid.dygraph.to_variable(input_np) - m = paddle.nn.Dropout2d(p=0.) + m = paddle.nn.Dropout2D(p=0.) m.eval() result = m(input) self.assertTrue(np.allclose(result.numpy(), result_np)) -class TestDropout3dFAPI(unittest.TestCase): +class TestDropout3DFAPI(unittest.TestCase): def setUp(self): np.random.seed(123) self.places = [fluid.CPUPlace()] @@ -621,7 +621,7 @@ class TestDropout3dFAPI(unittest.TestCase): self.assertTrue(np.allclose(res.numpy(), res_np)) -class TestDropout3dFAPIError(unittest.TestCase): +class TestDropout3DFAPIError(unittest.TestCase): def test_errors(self): with program_guard(Program(), Program()): @@ -640,7 +640,7 @@ class TestDropout3dFAPIError(unittest.TestCase): self.assertRaises(ValueError, test_dataformat) -class TestDropout3dCAPI(unittest.TestCase): +class TestDropout3DCAPI(unittest.TestCase): def setUp(self): np.random.seed(123) self.places = [fluid.CPUPlace()] @@ -653,7 +653,7 @@ class TestDropout3dCAPI(unittest.TestCase): input_np = np.random.random([2, 3, 4, 5, 6]).astype("float32") result_np = input_np input = fluid.dygraph.to_variable(input_np) - m = paddle.nn.Dropout3d(p=0.) + m = paddle.nn.Dropout3D(p=0.) m.eval() result = m(input) self.assertTrue(np.allclose(result.numpy(), result_np)) diff --git a/python/paddle/fluid/tests/unittests/test_dygraph_multi_forward.py b/python/paddle/fluid/tests/unittests/test_dygraph_multi_forward.py index 88b496c1d8..a1165f3358 100644 --- a/python/paddle/fluid/tests/unittests/test_dygraph_multi_forward.py +++ b/python/paddle/fluid/tests/unittests/test_dygraph_multi_forward.py @@ -110,7 +110,7 @@ class TestDygraphMultiForward(unittest.TestCase): epoch_num = 1 with fluid.dygraph.guard(): - paddle.manual_seed(SEED) + paddle.seed(SEED) paddle.framework.random._manual_program_seed(SEED) mnist = MNIST() sgd = SGDOptimizer( @@ -143,7 +143,7 @@ class TestDygraphMultiForward(unittest.TestCase): dy_param_init_value[param.name] = param.numpy() with new_program_scope(): - paddle.manual_seed(SEED) + paddle.seed(SEED) paddle.framework.random._manual_program_seed(SEED) exe = fluid.Executor(fluid.CPUPlace( ) if not core.is_compiled_with_cuda() else fluid.CUDAPlace(0)) diff --git a/python/paddle/fluid/tests/unittests/test_dygraph_weight_norm.py b/python/paddle/fluid/tests/unittests/test_dygraph_weight_norm.py index a963c2ece0..f95546f15f 100644 --- a/python/paddle/fluid/tests/unittests/test_dygraph_weight_norm.py +++ b/python/paddle/fluid/tests/unittests/test_dygraph_weight_norm.py @@ -117,7 +117,7 @@ class TestDygraphWeightNorm(unittest.TestCase): def test_check_output(self): fluid.enable_imperative() - linear = paddle.nn.Conv2d(2, 3, 3) + linear = paddle.nn.Conv2D(2, 3, 3) before_weight = linear.weight.numpy() if self.dim == None: self.dim = -1 @@ -179,7 +179,7 @@ class TestDygraphRemoveWeightNorm(unittest.TestCase): def test_check_output(self): fluid.enable_imperative() - linear = paddle.nn.Conv2d(2, 3, 3) + linear = paddle.nn.Conv2D(2, 3, 3) before_weight = linear.weight wn = weight_norm(linear, dim=self.dim) rwn = remove_weight_norm(linear) diff --git a/python/paddle/fluid/tests/unittests/test_eager_deletion_padding_rnn.py b/python/paddle/fluid/tests/unittests/test_eager_deletion_padding_rnn.py index e0c0277270..ff99a06e49 100644 --- a/python/paddle/fluid/tests/unittests/test_eager_deletion_padding_rnn.py +++ b/python/paddle/fluid/tests/unittests/test_eager_deletion_padding_rnn.py @@ -466,7 +466,7 @@ class PaddingRNNTestBase(unittest.TestCase): pass def _prepare_program(self, config, parallel=True): - paddle.manual_seed(config.random_seed) + paddle.seed(config.random_seed) self.main_program = fluid.Program() self.startup_program = fluid.Program() with fluid.program_guard(self.main_program, self.startup_program): diff --git a/python/paddle/fluid/tests/unittests/test_embedding_id_stop_gradient.py b/python/paddle/fluid/tests/unittests/test_embedding_id_stop_gradient.py index c18b7c5b04..120880a5fc 100644 --- a/python/paddle/fluid/tests/unittests/test_embedding_id_stop_gradient.py +++ b/python/paddle/fluid/tests/unittests/test_embedding_id_stop_gradient.py @@ -39,7 +39,7 @@ class TestEmbeddingIdStopGradientBase(unittest.TestCase): def run_program(self, place, stop_gradient=False): np.random.seed(1) - paddle.manual_seed(1) + paddle.seed(1) paddle.framework.random._manual_program_seed(1) startup_program = fluid.Program() diff --git a/python/paddle/fluid/tests/unittests/test_fc_op.py b/python/paddle/fluid/tests/unittests/test_fc_op.py index 1272d82dfd..3bbc8df188 100644 --- a/python/paddle/fluid/tests/unittests/test_fc_op.py +++ b/python/paddle/fluid/tests/unittests/test_fc_op.py @@ -137,7 +137,7 @@ class TestFCOpWithPadding(TestFCOp): class TestFcOp_NumFlattenDims_NegOne(unittest.TestCase): def test_api(self): def run_program(num_flatten_dims): - paddle.manual_seed(SEED) + paddle.seed(SEED) startup_program = Program() main_program = Program() diff --git a/python/paddle/fluid/tests/unittests/test_fuse_bn_act_pass.py b/python/paddle/fluid/tests/unittests/test_fuse_bn_act_pass.py index 5bcfc8720d..6a1700e758 100644 --- a/python/paddle/fluid/tests/unittests/test_fuse_bn_act_pass.py +++ b/python/paddle/fluid/tests/unittests/test_fuse_bn_act_pass.py @@ -57,7 +57,7 @@ class TestFuseBatchNormActPass(unittest.TestCase): return x, y, loss def check(self, place, use_cuda): - paddle.manual_seed(1) + paddle.seed(1) paddle.framework.random._manual_program_seed(1) main_program = fluid.Program() startup_program = fluid.Program() diff --git a/python/paddle/fluid/tests/unittests/test_fused_bn_add_act.py b/python/paddle/fluid/tests/unittests/test_fused_bn_add_act.py index 1bc305cd1f..45c2755274 100644 --- a/python/paddle/fluid/tests/unittests/test_fused_bn_add_act.py +++ b/python/paddle/fluid/tests/unittests/test_fused_bn_add_act.py @@ -158,7 +158,7 @@ class TestFusedBnAddActAPI(unittest.TestCase): return x, y, loss def check(self, place, use_cuda): - paddle.manual_seed(1) + paddle.seed(1) paddle.framework.random._manual_program_seed(1) iters = 5 batch_size = 16 diff --git a/python/paddle/fluid/tests/unittests/test_gaussian_random_op.py b/python/paddle/fluid/tests/unittests/test_gaussian_random_op.py index dddc6811ef..121dcbb3cd 100644 --- a/python/paddle/fluid/tests/unittests/test_gaussian_random_op.py +++ b/python/paddle/fluid/tests/unittests/test_gaussian_random_op.py @@ -38,7 +38,7 @@ class TestGaussianRandomOp(OpTest): "seed": 10, "use_mkldnn": self.use_mkldnn } - paddle.manual_seed(10) + paddle.seed(10) self.outputs = {'Out': np.zeros((123, 92), dtype='float32')} diff --git a/python/paddle/fluid/tests/unittests/test_generator.py b/python/paddle/fluid/tests/unittests/test_generator.py index 8b1f420358..ef9a305053 100644 --- a/python/paddle/fluid/tests/unittests/test_generator.py +++ b/python/paddle/fluid/tests/unittests/test_generator.py @@ -30,8 +30,6 @@ class TestGenerator(unittest.TestCase): """Test basic generator.""" gen = generator.Generator() gen.manual_seed(123123143) - s = gen.initial_seed() - s = gen.seed() st = gen.get_state() gen.set_state(st) gen.random() diff --git a/python/paddle/fluid/tests/unittests/test_generator_dataloader.py b/python/paddle/fluid/tests/unittests/test_generator_dataloader.py index 7c1ff41f7e..c36550fca8 100644 --- a/python/paddle/fluid/tests/unittests/test_generator_dataloader.py +++ b/python/paddle/fluid/tests/unittests/test_generator_dataloader.py @@ -35,7 +35,7 @@ def random_reader(): def simple_fc_net(places, use_legacy_py_reader, use_double_buffer): - paddle.manual_seed(1) + paddle.seed(1) paddle.framework.random._manual_program_seed(1) startup_prog = fluid.Program() main_prog = fluid.Program() diff --git a/python/paddle/fluid/tests/unittests/test_hsigmoid_op.py b/python/paddle/fluid/tests/unittests/test_hsigmoid_op.py index 3f8eed08ad..590c3e061f 100644 --- a/python/paddle/fluid/tests/unittests/test_hsigmoid_op.py +++ b/python/paddle/fluid/tests/unittests/test_hsigmoid_op.py @@ -269,7 +269,7 @@ class TestHSigmoidOpWithSparseGrad(unittest.TestCase): def training_test(self, is_sparse): with fluid.program_guard(fluid.Program(), fluid.Program()): - paddle.manual_seed(1) + paddle.seed(1) start_up = fluid.default_startup_program() x = np.arange(6).reshape(6) path_table = np.array([(1, 2, -1), (1, 2, -1)]).astype('int64') diff --git a/python/paddle/fluid/tests/unittests/test_imperative_auto_mixed_precision.py b/python/paddle/fluid/tests/unittests/test_imperative_auto_mixed_precision.py index 71381ecfde..2d1d2949a4 100644 --- a/python/paddle/fluid/tests/unittests/test_imperative_auto_mixed_precision.py +++ b/python/paddle/fluid/tests/unittests/test_imperative_auto_mixed_precision.py @@ -120,7 +120,7 @@ class TestAmpScaler(unittest.TestCase): inp_np = np.random.random(size=[1, 3, 128, 128]).astype(np.float32) def run_simple_conv(inp_np, use_scaler=True): - paddle.manual_seed(10) + paddle.seed(10) paddle.framework.random._manual_program_seed(10) with fluid.dygraph.guard(): model = SimpleConv( @@ -205,7 +205,7 @@ class TestResnet2(unittest.TestCase): paddle.disable_static() - paddle.manual_seed(seed) + paddle.seed(seed) paddle.framework.random._manual_program_seed(seed) resnet = ResNet(use_cudnn=True) @@ -282,7 +282,7 @@ class TestResnet(unittest.TestCase): batch_num = 1 with fluid.dygraph.guard(): - paddle.manual_seed(seed) + paddle.seed(seed) paddle.framework.random._manual_program_seed(seed) resnet = ResNet(use_cudnn=True) diff --git a/python/paddle/fluid/tests/unittests/test_imperative_deepcf.py b/python/paddle/fluid/tests/unittests/test_imperative_deepcf.py index cc6c2f97a9..04a0e5e4cd 100644 --- a/python/paddle/fluid/tests/unittests/test_imperative_deepcf.py +++ b/python/paddle/fluid/tests/unittests/test_imperative_deepcf.py @@ -206,7 +206,7 @@ class TestDygraphDeepCF(unittest.TestCase): else: (users_np, items_np, labels_np, num_users, num_items, matrix) = get_data() - paddle.manual_seed(seed) + paddle.seed(seed) paddle.framework.random._manual_program_seed(seed) startup = fluid.Program() main = fluid.Program() @@ -243,7 +243,7 @@ class TestDygraphDeepCF(unittest.TestCase): sys.stderr.write('static loss %s\n' % static_loss) with fluid.dygraph.guard(): - paddle.manual_seed(seed) + paddle.seed(seed) paddle.framework.random._manual_program_seed(seed) deepcf = DeepCF(num_users, num_items, matrix) @@ -268,7 +268,7 @@ class TestDygraphDeepCF(unittest.TestCase): sys.stderr.write('dynamic loss: %s %s\n' % (slice, dy_loss)) with fluid.dygraph.guard(): - paddle.manual_seed(seed) + paddle.seed(seed) paddle.framework.random._manual_program_seed(seed) deepcf2 = DeepCF(num_users, num_items, matrix) diff --git a/python/paddle/fluid/tests/unittests/test_imperative_double_grad.py b/python/paddle/fluid/tests/unittests/test_imperative_double_grad.py index 39c6fca89c..600ee6d10e 100644 --- a/python/paddle/fluid/tests/unittests/test_imperative_double_grad.py +++ b/python/paddle/fluid/tests/unittests/test_imperative_double_grad.py @@ -311,7 +311,7 @@ class TestDygraphDoubleGradVisitedUniq(TestCase): fluid.set_flags({'FLAGS_sort_sum_gradient': True}) with fluid.dygraph.guard(): - paddle.manual_seed(123) + paddle.seed(123) paddle.framework.random._manual_program_seed(123) a = fluid.dygraph.to_variable(value) a.stop_gradient = False @@ -328,7 +328,7 @@ class TestDygraphDoubleGradVisitedUniq(TestCase): grad_1 = dx[0].numpy() with fluid.dygraph.guard(): - paddle.manual_seed(123) + paddle.seed(123) paddle.framework.random._manual_program_seed(123) a = fluid.dygraph.to_variable(value) a.stop_gradient = False diff --git a/python/paddle/fluid/tests/unittests/test_imperative_gan.py b/python/paddle/fluid/tests/unittests/test_imperative_gan.py index b752b439f0..189745e729 100644 --- a/python/paddle/fluid/tests/unittests/test_imperative_gan.py +++ b/python/paddle/fluid/tests/unittests/test_imperative_gan.py @@ -56,7 +56,7 @@ class Generator(fluid.Layer): class TestDygraphGAN(unittest.TestCase): def test_gan_float32(self): seed = 90 - paddle.manual_seed(1) + paddle.seed(1) paddle.framework.random._manual_program_seed(1) startup = fluid.Program() discriminate_p = fluid.Program() @@ -131,7 +131,7 @@ class TestDygraphGAN(unittest.TestCase): dy_params = dict() with fluid.dygraph.guard(): - paddle.manual_seed(1) + paddle.seed(1) paddle.framework.random._manual_program_seed(1) discriminator = Discriminator() @@ -176,7 +176,7 @@ class TestDygraphGAN(unittest.TestCase): dy_params2 = dict() with fluid.dygraph.guard(): fluid.set_flags({'FLAGS_sort_sum_gradient': True}) - paddle.manual_seed(1) + paddle.seed(1) paddle.framework.random._manual_program_seed(1) discriminator2 = Discriminator() generator2 = Generator() diff --git a/python/paddle/fluid/tests/unittests/test_imperative_gnn.py b/python/paddle/fluid/tests/unittests/test_imperative_gnn.py index 4db6f2d0da..c813aeede6 100644 --- a/python/paddle/fluid/tests/unittests/test_imperative_gnn.py +++ b/python/paddle/fluid/tests/unittests/test_imperative_gnn.py @@ -61,7 +61,7 @@ class GCN(fluid.Layer): class TestDygraphGNN(unittest.TestCase): def test_gnn_float32(self): - paddle.manual_seed(90) + paddle.seed(90) paddle.framework.random._manual_program_seed(90) startup = fluid.Program() main = fluid.Program() @@ -112,7 +112,7 @@ class TestDygraphGNN(unittest.TestCase): scope.find_var(model.gc.weight.name).get_tensor()) with fluid.dygraph.guard(): - paddle.manual_seed(90) + paddle.seed(90) paddle.framework.random._manual_program_seed(90) features = np.ones([1, 100, 50], dtype=np.float32) @@ -138,7 +138,7 @@ class TestDygraphGNN(unittest.TestCase): model_gc_weight_value = model.gc.weight.numpy() with fluid.dygraph.guard(): - paddle.manual_seed(90) + paddle.seed(90) paddle.framework.random._manual_program_seed(90) features2 = np.ones([1, 100, 50], dtype=np.float32) diff --git a/python/paddle/fluid/tests/unittests/test_imperative_layer_apply.py b/python/paddle/fluid/tests/unittests/test_imperative_layer_apply.py index ab9a98588f..c18dab61fc 100644 --- a/python/paddle/fluid/tests/unittests/test_imperative_layer_apply.py +++ b/python/paddle/fluid/tests/unittests/test_imperative_layer_apply.py @@ -28,11 +28,11 @@ class LeNetDygraph(fluid.dygraph.Layer): super(LeNetDygraph, self).__init__() self.num_classes = num_classes self.features = nn.Sequential( - nn.Conv2d( + nn.Conv2D( 1, 6, 3, stride=1, padding=1), nn.ReLU(), paddle.fluid.dygraph.Pool2D(2, 'max', 2), - nn.Conv2d( + nn.Conv2D( 6, 16, 5, stride=1, padding=0), nn.ReLU(), paddle.fluid.dygraph.Pool2D(2, 'max', 2)) @@ -60,7 +60,7 @@ def init_weights(layer): new_bias = paddle.fluid.layers.fill_constant( layer.bias.shape, layer.bias.dtype, value=-0.1) layer.bias.set_value(new_bias) - elif type(layer) == nn.Conv2d: + elif type(layer) == nn.Conv2D: new_weight = paddle.fluid.layers.fill_constant( layer.weight.shape, layer.weight.dtype, value=0.7) layer.weight.set_value(new_weight) @@ -80,7 +80,7 @@ class TestLayerApply(unittest.TestCase): if type(layer) == nn.Linear: np.testing.assert_allclose(layer.weight.numpy(), 0.9) np.testing.assert_allclose(layer.bias.numpy(), -0.1) - elif type(layer) == nn.Conv2d: + elif type(layer) == nn.Conv2D: np.testing.assert_allclose(layer.weight.numpy(), 0.7) np.testing.assert_allclose(layer.bias.numpy(), -0.2) diff --git a/python/paddle/fluid/tests/unittests/test_imperative_layer_children.py b/python/paddle/fluid/tests/unittests/test_imperative_layer_children.py index 95d3b87f0e..870d48f2fb 100644 --- a/python/paddle/fluid/tests/unittests/test_imperative_layer_children.py +++ b/python/paddle/fluid/tests/unittests/test_imperative_layer_children.py @@ -27,11 +27,11 @@ class LeNetDygraph(fluid.dygraph.Layer): def __init__(self): super(LeNetDygraph, self).__init__() self.features = nn.Sequential( - nn.Conv2d( + nn.Conv2D( 1, 6, 3, stride=1, padding=1), nn.ReLU(), paddle.fluid.dygraph.Pool2D(2, 'max', 2), - nn.Conv2d( + nn.Conv2D( 6, 16, 5, stride=1, padding=0), nn.ReLU(), paddle.fluid.dygraph.Pool2D(2, 'max', 2)) diff --git a/python/paddle/fluid/tests/unittests/test_imperative_lod_tensor_to_selected_rows.py b/python/paddle/fluid/tests/unittests/test_imperative_lod_tensor_to_selected_rows.py index f0fea2d7eb..e7af249cf8 100644 --- a/python/paddle/fluid/tests/unittests/test_imperative_lod_tensor_to_selected_rows.py +++ b/python/paddle/fluid/tests/unittests/test_imperative_lod_tensor_to_selected_rows.py @@ -95,7 +95,7 @@ class TestDygraphSimpleNet(unittest.TestCase): for is_sort_sum_gradient in [True, False]: with fluid.dygraph.guard(place): - paddle.manual_seed(seed) + paddle.seed(seed) paddle.framework.random._manual_program_seed(seed) simple_net = SimpleNet( @@ -140,7 +140,7 @@ class TestDygraphSimpleNet(unittest.TestCase): dy_loss_value = dy_loss.numpy() with new_program_scope(): - paddle.manual_seed(seed) + paddle.seed(seed) paddle.framework.random._manual_program_seed(seed) simple_net = SimpleNet( diff --git a/python/paddle/fluid/tests/unittests/test_imperative_ocr_attention_model.py b/python/paddle/fluid/tests/unittests/test_imperative_ocr_attention_model.py index afe50664ef..f256e97e83 100644 --- a/python/paddle/fluid/tests/unittests/test_imperative_ocr_attention_model.py +++ b/python/paddle/fluid/tests/unittests/test_imperative_ocr_attention_model.py @@ -403,7 +403,7 @@ class TestDygraphOCRAttention(unittest.TestCase): with fluid.dygraph.guard(): fluid.set_flags({'FLAGS_sort_sum_gradient': True}) - paddle.manual_seed(seed) + paddle.seed(seed) paddle.framework.random._manual_program_seed(seed) ocr_attention = OCRAttention() @@ -454,7 +454,7 @@ class TestDygraphOCRAttention(unittest.TestCase): dy_param_value[param.name] = param.numpy() with new_program_scope(): - paddle.manual_seed(seed) + paddle.seed(seed) paddle.framework.random._manual_program_seed(seed) exe = fluid.Executor(fluid.CPUPlace( ) if not core.is_compiled_with_cuda() else fluid.CUDAPlace(0)) diff --git a/python/paddle/fluid/tests/unittests/test_imperative_optimizer.py b/python/paddle/fluid/tests/unittests/test_imperative_optimizer.py index 7876675bcc..cd019c9207 100644 --- a/python/paddle/fluid/tests/unittests/test_imperative_optimizer.py +++ b/python/paddle/fluid/tests/unittests/test_imperative_optimizer.py @@ -74,7 +74,7 @@ class TestImperativeOptimizerBase(unittest.TestCase): with fluid.dygraph.guard(place): try: - paddle.manual_seed(seed) + paddle.seed(seed) paddle.framework.random._manual_program_seed(seed) mlp = MLP() optimizer = self.get_optimizer_dygraph( @@ -91,7 +91,7 @@ class TestImperativeOptimizerBase(unittest.TestCase): ) else fluid.CUDAPlace(0) with fluid.dygraph.guard(place): - paddle.manual_seed(seed) + paddle.seed(seed) paddle.framework.random._manual_program_seed(seed) mlp = MLP() @@ -132,7 +132,7 @@ class TestImperativeOptimizerBase(unittest.TestCase): dy_param_value[param.name] = param.numpy() with new_program_scope(): - paddle.manual_seed(seed) + paddle.seed(seed) paddle.framework.random._manual_program_seed(seed) if place == None: diff --git a/python/paddle/fluid/tests/unittests/test_imperative_optimizer_v2.py b/python/paddle/fluid/tests/unittests/test_imperative_optimizer_v2.py index e1b7847a6e..4b1e7ec5e6 100644 --- a/python/paddle/fluid/tests/unittests/test_imperative_optimizer_v2.py +++ b/python/paddle/fluid/tests/unittests/test_imperative_optimizer_v2.py @@ -74,7 +74,7 @@ class TestImperativeOptimizerBase(unittest.TestCase): try: paddle.disable_static() - paddle.manual_seed(seed) + paddle.seed(seed) paddle.framework.random._manual_program_seed(seed) mlp = MLP() optimizer = self.get_optimizer_dygraph( @@ -93,7 +93,7 @@ class TestImperativeOptimizerBase(unittest.TestCase): ) else fluid.CUDAPlace(0) paddle.disable_static(place) - paddle.manual_seed(seed) + paddle.seed(seed) paddle.framework.random._manual_program_seed(seed) mlp = MLP() @@ -142,7 +142,7 @@ class TestImperativeOptimizerBase(unittest.TestCase): paddle.enable_static() with new_program_scope(): - paddle.manual_seed(seed) + paddle.seed(seed) paddle.framework.random._manual_program_seed(seed) if place == None: diff --git a/python/paddle/fluid/tests/unittests/test_imperative_ptb_rnn.py b/python/paddle/fluid/tests/unittests/test_imperative_ptb_rnn.py index fa23ff8e7c..1c183a8c2b 100644 --- a/python/paddle/fluid/tests/unittests/test_imperative_ptb_rnn.py +++ b/python/paddle/fluid/tests/unittests/test_imperative_ptb_rnn.py @@ -226,7 +226,7 @@ class TestDygraphPtbRnn(unittest.TestCase): traced_layer = None with fluid.dygraph.guard(): - paddle.manual_seed(seed) + paddle.seed(seed) paddle.framework.random._manual_program_seed(seed) # TODO: marsyang1993 Change seed to ptb_model = PtbModel( @@ -294,7 +294,7 @@ class TestDygraphPtbRnn(unittest.TestCase): dy_last_hidden_value = last_hidden.numpy() with new_program_scope(): - paddle.manual_seed(seed) + paddle.seed(seed) paddle.framework.random._manual_program_seed(seed) ptb_model = PtbModel( hidden_size=hidden_size, diff --git a/python/paddle/fluid/tests/unittests/test_imperative_ptb_rnn_sorted_gradient.py b/python/paddle/fluid/tests/unittests/test_imperative_ptb_rnn_sorted_gradient.py index 0487f8dd9a..e5453eed13 100644 --- a/python/paddle/fluid/tests/unittests/test_imperative_ptb_rnn_sorted_gradient.py +++ b/python/paddle/fluid/tests/unittests/test_imperative_ptb_rnn_sorted_gradient.py @@ -45,7 +45,7 @@ class TestDygraphPtbRnnSortGradient(unittest.TestCase): with fluid.dygraph.guard(): fluid.set_flags({'FLAGS_sort_sum_gradient': True}) - paddle.manual_seed(seed) + paddle.seed(seed) paddle.framework.random._manual_program_seed(seed) # TODO: marsyang1993 Change seed to @@ -95,7 +95,7 @@ class TestDygraphPtbRnnSortGradient(unittest.TestCase): dy_last_hidden_value = last_hidden.numpy() with new_program_scope(): - paddle.manual_seed(seed) + paddle.seed(seed) paddle.framework.random._manual_program_seed(seed) ptb_model = PtbModel( diff --git a/python/paddle/fluid/tests/unittests/test_imperative_reinforcement.py b/python/paddle/fluid/tests/unittests/test_imperative_reinforcement.py index 0076c61e58..a89628c594 100644 --- a/python/paddle/fluid/tests/unittests/test_imperative_reinforcement.py +++ b/python/paddle/fluid/tests/unittests/test_imperative_reinforcement.py @@ -64,7 +64,7 @@ class TestImperativeMnist(unittest.TestCase): mask = np.array(mask_list).astype("float32") with fluid.dygraph.guard(): - paddle.manual_seed(seed) + paddle.seed(seed) paddle.framework.random._manual_program_seed(seed) policy = Policy(input_size=4) @@ -105,7 +105,7 @@ class TestImperativeMnist(unittest.TestCase): dy_param_value[param.name] = param.numpy() with new_program_scope(): - paddle.manual_seed(seed) + paddle.seed(seed) paddle.framework.random._manual_program_seed(seed) exe = fluid.Executor(fluid.CPUPlace( diff --git a/python/paddle/fluid/tests/unittests/test_imperative_resnet.py b/python/paddle/fluid/tests/unittests/test_imperative_resnet.py index e8a2298c17..2d67af82de 100644 --- a/python/paddle/fluid/tests/unittests/test_imperative_resnet.py +++ b/python/paddle/fluid/tests/unittests/test_imperative_resnet.py @@ -251,7 +251,7 @@ class TestDygraphResnet(unittest.TestCase): traced_layer = None with fluid.dygraph.guard(): - paddle.manual_seed(seed) + paddle.seed(seed) paddle.framework.random._manual_program_seed(seed) resnet = ResNet() @@ -334,7 +334,7 @@ class TestDygraphResnet(unittest.TestCase): dy_param_value[param.name] = param.numpy() with new_program_scope(): - paddle.manual_seed(seed) + paddle.seed(seed) paddle.framework.random._manual_program_seed(seed) exe = fluid.Executor(fluid.CPUPlace( diff --git a/python/paddle/fluid/tests/unittests/test_imperative_resnet_sorted_gradient.py b/python/paddle/fluid/tests/unittests/test_imperative_resnet_sorted_gradient.py index 13b12da331..13570d1bf7 100644 --- a/python/paddle/fluid/tests/unittests/test_imperative_resnet_sorted_gradient.py +++ b/python/paddle/fluid/tests/unittests/test_imperative_resnet_sorted_gradient.py @@ -78,7 +78,7 @@ class TestDygraphResnetSortGradient(unittest.TestCase): batch_num = 10 with fluid.dygraph.guard(): fluid.set_flags({'FLAGS_sort_sum_gradient': True}) - paddle.manual_seed(seed) + paddle.seed(seed) paddle.framework.random._manual_program_seed(seed) resnet = ResNet() @@ -137,7 +137,7 @@ class TestDygraphResnetSortGradient(unittest.TestCase): dy_param_value[param.name] = param.numpy() with new_program_scope(): - paddle.manual_seed(seed) + paddle.seed(seed) paddle.framework.random._manual_program_seed(seed) exe = fluid.Executor(fluid.CPUPlace( diff --git a/python/paddle/fluid/tests/unittests/test_imperative_save_load.py b/python/paddle/fluid/tests/unittests/test_imperative_save_load.py index 45709a3586..6c6b164bde 100644 --- a/python/paddle/fluid/tests/unittests/test_imperative_save_load.py +++ b/python/paddle/fluid/tests/unittests/test_imperative_save_load.py @@ -219,7 +219,7 @@ class TestDygraphPtbRnn(unittest.TestCase): batch_num = 200 with fluid.dygraph.guard(): - paddle.manual_seed(seed) + paddle.seed(seed) paddle.framework.random._manual_program_seed(seed) # TODO: marsyang1993 Change seed to ptb_model = PtbModel( @@ -305,7 +305,7 @@ class TestDygraphPtbRnn(unittest.TestCase): batch_num = 200 with fluid.dygraph.guard(): - paddle.manual_seed(seed) + paddle.seed(seed) paddle.framework.random._manual_program_seed(seed) # TODO: marsyang1993 Change seed to ptb_model = PtbModel( @@ -414,7 +414,7 @@ class TestDygraphPtbRnn(unittest.TestCase): batch_num = 200 with fluid.dygraph.guard(): - paddle.manual_seed(seed) + paddle.seed(seed) paddle.framework.random._manual_program_seed(seed) # TODO: marsyang1993 Change seed to ptb_model = PtbModel( @@ -521,7 +521,7 @@ class TestDygraphPtbRnn(unittest.TestCase): batch_num = 200 with fluid.dygraph.guard(): - paddle.manual_seed(seed) + paddle.seed(seed) paddle.framework.random._manual_program_seed(seed) # TODO: marsyang1993 Change seed to ptb_model = PtbModel( @@ -711,7 +711,7 @@ class TestDygraphPtbRnn(unittest.TestCase): batch_num = 200 with fluid.dygraph.guard(): - paddle.manual_seed(seed) + paddle.seed(seed) paddle.framework.random._manual_program_seed(seed) # TODO: marsyang1993 Change seed to ptb_model = PtbModel( @@ -802,7 +802,7 @@ class TestDygraphPtbRnn(unittest.TestCase): batch_num = 200 with fluid.dygraph.guard(): - paddle.manual_seed(seed) + paddle.seed(seed) paddle.framework.random._manual_program_seed(seed) # TODO: marsyang1993 Change seed to diff --git a/python/paddle/fluid/tests/unittests/test_imperative_save_load_v2.py b/python/paddle/fluid/tests/unittests/test_imperative_save_load_v2.py index 0335fa5476..672ffa9d39 100644 --- a/python/paddle/fluid/tests/unittests/test_imperative_save_load_v2.py +++ b/python/paddle/fluid/tests/unittests/test_imperative_save_load_v2.py @@ -219,7 +219,7 @@ class TestDygraphPtbRnn(unittest.TestCase): batch_num = 200 with fluid.dygraph.guard(): - paddle.manual_seed(seed) + paddle.seed(seed) paddle.framework.random._manual_program_seed(seed) # TODO: marsyang1993 Change seed to ptb_model = PtbModel( @@ -308,7 +308,7 @@ class TestDygraphPtbRnn(unittest.TestCase): batch_num = 200 with fluid.dygraph.guard(): - paddle.manual_seed(seed) + paddle.seed(seed) paddle.framework.random._manual_program_seed(seed) # TODO: marsyang1993 Change seed to ptb_model = PtbModel( @@ -416,7 +416,7 @@ class TestDygraphPtbRnn(unittest.TestCase): batch_num = 200 with fluid.dygraph.guard(): - paddle.manual_seed(seed) + paddle.seed(seed) paddle.framework.random._manual_program_seed(seed) # TODO: marsyang1993 Change seed to ptb_model = PtbModel( @@ -524,7 +524,7 @@ class TestDygraphPtbRnn(unittest.TestCase): batch_num = 200 with fluid.dygraph.guard(): - paddle.manual_seed(seed) + paddle.seed(seed) paddle.framework.random._manual_program_seed(seed) # TODO: marsyang1993 Change seed to ptb_model = PtbModel( @@ -638,7 +638,7 @@ class TestDygraphPtbRnn(unittest.TestCase): batch_num = 200 with fluid.dygraph.guard(): - paddle.manual_seed(seed) + paddle.seed(seed) paddle.framework.random._manual_program_seed(seed) # TODO: marsyang1993 Change seed to ptb_model = PtbModel( @@ -717,7 +717,7 @@ class TestDygraphPtbRnn(unittest.TestCase): batch_num = 200 with fluid.dygraph.guard(): - paddle.manual_seed(seed) + paddle.seed(seed) paddle.framework.random._manual_program_seed(seed) # TODO: marsyang1993 Change seed to ptb_model = PtbModel( @@ -808,7 +808,7 @@ class TestDygraphPtbRnn(unittest.TestCase): batch_num = 200 with fluid.dygraph.guard(): - paddle.manual_seed(seed) + paddle.seed(seed) paddle.framework.random._manual_program_seed(seed) # TODO: marsyang1993 Change seed to ptb_model = PtbModel( diff --git a/python/paddle/fluid/tests/unittests/test_imperative_se_resnext.py b/python/paddle/fluid/tests/unittests/test_imperative_se_resnext.py index e47a70054b..8f8890557a 100644 --- a/python/paddle/fluid/tests/unittests/test_imperative_se_resnext.py +++ b/python/paddle/fluid/tests/unittests/test_imperative_se_resnext.py @@ -311,7 +311,7 @@ class TestImperativeResneXt(unittest.TestCase): batch_num = 1 epoch_num = 1 with fluid.dygraph.guard(): - paddle.manual_seed(seed) + paddle.seed(seed) paddle.framework.random._manual_program_seed(seed) se_resnext = SeResNeXt() @@ -372,7 +372,7 @@ class TestImperativeResneXt(unittest.TestCase): dy_param_value[param.name] = param.numpy() with new_program_scope(): - paddle.manual_seed(seed) + paddle.seed(seed) paddle.framework.random._manual_program_seed(seed) exe = fluid.Executor(fluid.CPUPlace( diff --git a/python/paddle/fluid/tests/unittests/test_imperative_selected_rows_to_lod_tensor.py b/python/paddle/fluid/tests/unittests/test_imperative_selected_rows_to_lod_tensor.py index 794f59e485..2f2a3e5de5 100644 --- a/python/paddle/fluid/tests/unittests/test_imperative_selected_rows_to_lod_tensor.py +++ b/python/paddle/fluid/tests/unittests/test_imperative_selected_rows_to_lod_tensor.py @@ -102,7 +102,7 @@ class TestDygraphSimpleNet(unittest.TestCase): for is_sort_sum_gradient in [True, False]: traced_layer = None with fluid.dygraph.guard(place): - paddle.manual_seed(seed) + paddle.seed(seed) paddle.framework.random._manual_program_seed(seed) simple_net = SimpleNet( @@ -146,7 +146,7 @@ class TestDygraphSimpleNet(unittest.TestCase): dy_loss_value = dy_loss.numpy() with new_program_scope(): - paddle.manual_seed(seed) + paddle.seed(seed) paddle.framework.random._manual_program_seed(seed) simple_net = SimpleNet( diff --git a/python/paddle/fluid/tests/unittests/test_imperative_star_gan_with_gradient_penalty.py b/python/paddle/fluid/tests/unittests/test_imperative_star_gan_with_gradient_penalty.py index 1ab37aaed2..e114961c0c 100644 --- a/python/paddle/fluid/tests/unittests/test_imperative_star_gan_with_gradient_penalty.py +++ b/python/paddle/fluid/tests/unittests/test_imperative_star_gan_with_gradient_penalty.py @@ -468,7 +468,7 @@ def build_optimizer(layer, cfg, loss=None): class DyGraphTrainModel(object): def __init__(self, cfg): - paddle.manual_seed(1) + paddle.seed(1) paddle.framework.random._manual_program_seed(1) self.generator = Generator(cfg) @@ -529,7 +529,7 @@ class StaticGraphTrainModel(object): shape=[None, cfg.c_dim], dtype='float32', name='label_trg') return image_real, label_org, label_trg - paddle.manual_seed(cfg.seed) + paddle.seed(cfg.seed) paddle.framework.random._manual_program_seed(cfg.seed) self.gen_program = fluid.Program() gen_startup_program = fluid.Program() diff --git a/python/paddle/fluid/tests/unittests/test_imperative_transformer_sorted_gradient.py b/python/paddle/fluid/tests/unittests/test_imperative_transformer_sorted_gradient.py index 9f58ef881e..57da838c55 100644 --- a/python/paddle/fluid/tests/unittests/test_imperative_transformer_sorted_gradient.py +++ b/python/paddle/fluid/tests/unittests/test_imperative_transformer_sorted_gradient.py @@ -951,7 +951,7 @@ class TestDygraphTransformerSortGradient(unittest.TestCase): with guard(): fluid.set_flags({'FLAGS_sort_sum_gradient': True}) - paddle.manual_seed(seed) + paddle.seed(seed) paddle.framework.random._manual_program_seed(seed) transformer = TransFormer( ModelHyperParams.src_vocab_size, @@ -1035,7 +1035,7 @@ class TestDygraphTransformerSortGradient(unittest.TestCase): dy_token_num_value = dy_token_num.numpy() with new_program_scope(): - paddle.manual_seed(seed) + paddle.seed(seed) paddle.framework.random._manual_program_seed(seed) transformer = TransFormer( ModelHyperParams.src_vocab_size, diff --git a/python/paddle/fluid/tests/unittests/test_inplace_addto_strategy.py b/python/paddle/fluid/tests/unittests/test_inplace_addto_strategy.py index c75acd7c15..0c43d56934 100644 --- a/python/paddle/fluid/tests/unittests/test_inplace_addto_strategy.py +++ b/python/paddle/fluid/tests/unittests/test_inplace_addto_strategy.py @@ -80,7 +80,7 @@ class TestInplaceAddto(unittest.TestCase): def test_result(self): def run_program(enable_addto): np.random.seed(10) - paddle.manual_seed(10) + paddle.seed(10) paddle.framework.random._manual_program_seed(10) if fluid.core.is_compiled_with_cuda(): fluid.set_flags({"FLAGS_cudnn_deterministic": True}) diff --git a/python/paddle/fluid/tests/unittests/test_instance_norm_op_v2.py b/python/paddle/fluid/tests/unittests/test_instance_norm_op_v2.py index c45c144e3a..19d0b1ea98 100644 --- a/python/paddle/fluid/tests/unittests/test_instance_norm_op_v2.py +++ b/python/paddle/fluid/tests/unittests/test_instance_norm_op_v2.py @@ -35,22 +35,22 @@ class TestInstanceNorm(unittest.TestCase): def error1d(): x_data_4 = np.random.random(size=(2, 1, 3, 3)).astype('float32') - instance_norm1d = paddle.nn.InstanceNorm1d(1) + instance_norm1d = paddle.nn.InstanceNorm1D(1) instance_norm1d(fluid.dygraph.to_variable(x_data_4)) def error2d(): x_data_3 = np.random.random(size=(2, 1, 3)).astype('float32') - instance_norm2d = paddle.nn.InstanceNorm2d(1) + instance_norm2d = paddle.nn.InstanceNorm2D(1) instance_norm2d(fluid.dygraph.to_variable(x_data_3)) def error3d(): x_data_4 = np.random.random(size=(2, 1, 3, 3)).astype('float32') - instance_norm3d = paddle.nn.BatchNorm3d(1) + instance_norm3d = paddle.nn.BatchNorm3D(1) instance_norm3d(fluid.dygraph.to_variable(x_data_4)) def weight_bias_false(): x_data_4 = np.random.random(size=(2, 1, 3, 3)).astype('float32') - instance_norm3d = paddle.nn.BatchNorm3d( + instance_norm3d = paddle.nn.BatchNorm3D( 1, weight_attr=False, bias_attr=False) with fluid.dygraph.guard(p): @@ -75,7 +75,7 @@ class TestInstanceNorm(unittest.TestCase): def compute_v2(x): with fluid.dygraph.guard(p): - bn = paddle.nn.InstanceNorm2d(shape[1]) + bn = paddle.nn.InstanceNorm2D(shape[1]) y = bn(fluid.dygraph.to_variable(x)) return y.numpy() @@ -104,7 +104,7 @@ class TestInstanceNorm(unittest.TestCase): def compute_v2(x_np): with program_guard(Program(), Program()): - ins = paddle.nn.InstanceNorm2d(shape[1]) + ins = paddle.nn.InstanceNorm2D(shape[1]) x = fluid.data(name='x', shape=x_np.shape, dtype=x_np.dtype) y = ins(x) exe.run(fluid.default_startup_program()) diff --git a/python/paddle/fluid/tests/unittests/test_ir_memory_optimize_ifelse_op.py b/python/paddle/fluid/tests/unittests/test_ir_memory_optimize_ifelse_op.py index eaa7e711a2..0ace288d9d 100644 --- a/python/paddle/fluid/tests/unittests/test_ir_memory_optimize_ifelse_op.py +++ b/python/paddle/fluid/tests/unittests/test_ir_memory_optimize_ifelse_op.py @@ -37,7 +37,7 @@ class TestIrMemoryOptimizeIfElseOp(unittest.TestCase): use_cuda=True, use_mem_opt=False, iter_num=5): - paddle.manual_seed(100) + paddle.seed(100) paddle.framework.random._manual_program_seed(100) prog = Program() startup_prog = Program() diff --git a/python/paddle/fluid/tests/unittests/test_jit_save_load.py b/python/paddle/fluid/tests/unittests/test_jit_save_load.py index 71ec1271a0..ac9a3f06f8 100644 --- a/python/paddle/fluid/tests/unittests/test_jit_save_load.py +++ b/python/paddle/fluid/tests/unittests/test_jit_save_load.py @@ -222,7 +222,7 @@ class TestJitSaveLoad(unittest.TestCase): # enable dygraph mode fluid.enable_dygraph() # config seed - paddle.manual_seed(SEED) + paddle.seed(SEED) paddle.framework.random._manual_program_seed(SEED) def train_and_save_model(self, model_path=None): @@ -370,7 +370,7 @@ class TestJitSaveLoadConfig(unittest.TestCase): # enable dygraph mode fluid.enable_dygraph() # config seed - paddle.manual_seed(SEED) + paddle.seed(SEED) paddle.framework.random._manual_program_seed(SEED) def test_output_spec(self): @@ -429,7 +429,7 @@ class TestJitMultipleLoading(unittest.TestCase): # enable dygraph mode fluid.enable_dygraph() # config seed - paddle.manual_seed(SEED) + paddle.seed(SEED) paddle.framework.random._manual_program_seed(SEED) # train and save base model self.train_and_save_orig_model() @@ -457,7 +457,7 @@ class TestJitPruneModelAndLoad(unittest.TestCase): # enable dygraph mode fluid.enable_dygraph() # config seed - paddle.manual_seed(SEED) + paddle.seed(SEED) paddle.framework.random._manual_program_seed(SEED) def train_and_save(self): @@ -512,7 +512,7 @@ class TestJitSaveMultiCases(unittest.TestCase): # enable dygraph mode fluid.enable_dygraph() # config seed - paddle.manual_seed(SEED) + paddle.seed(SEED) paddle.framework.random._manual_program_seed(SEED) def verify_inference_correctness(self, layer, model_path, with_label=False): diff --git a/python/paddle/fluid/tests/unittests/test_layers.py b/python/paddle/fluid/tests/unittests/test_layers.py index e3f477c1d9..3908d65229 100644 --- a/python/paddle/fluid/tests/unittests/test_layers.py +++ b/python/paddle/fluid/tests/unittests/test_layers.py @@ -57,7 +57,7 @@ class LayerTest(unittest.TestCase): @contextlib.contextmanager def static_graph(self): with new_program_scope(): - paddle.manual_seed(self.seed) + paddle.seed(self.seed) paddle.framework.random._manual_program_seed(self.seed) yield @@ -77,7 +77,7 @@ class LayerTest(unittest.TestCase): def dynamic_graph(self, force_to_use_cpu=False): with fluid.dygraph.guard( self._get_place(force_to_use_cpu=force_to_use_cpu)): - paddle.manual_seed(self.seed) + paddle.seed(self.seed) paddle.framework.random._manual_program_seed(self.seed) yield diff --git a/python/paddle/fluid/tests/unittests/test_manual_seed.py b/python/paddle/fluid/tests/unittests/test_manual_seed.py index a1d6eb915c..75753dcd1e 100644 --- a/python/paddle/fluid/tests/unittests/test_manual_seed.py +++ b/python/paddle/fluid/tests/unittests/test_manual_seed.py @@ -17,16 +17,16 @@ import unittest import paddle import paddle.fluid as fluid -from paddle.framework import manual_seed +from paddle.framework import seed from paddle.fluid.framework import Program, default_main_program, default_startup_program import numpy as np class TestManualSeed(unittest.TestCase): - def test_manual_seed(self): + def test_seed(self): fluid.enable_dygraph() - gen = paddle.manual_seed(12312321111) + gen = paddle.seed(12312321111) x = fluid.layers.gaussian_random([10], dtype="float32") st1 = gen.get_state() x1 = fluid.layers.gaussian_random([10], dtype="float32") diff --git a/python/paddle/fluid/tests/unittests/test_normal.py b/python/paddle/fluid/tests/unittests/test_normal.py index 595e0bb480..7963281766 100644 --- a/python/paddle/fluid/tests/unittests/test_normal.py +++ b/python/paddle/fluid/tests/unittests/test_normal.py @@ -18,7 +18,7 @@ import paddle import copy np.random.seed(10) -paddle.manual_seed(10) +paddle.seed(10) class TestNormalAPI(unittest.TestCase): @@ -61,7 +61,8 @@ class TestNormalAPI(unittest.TestCase): if isinstance(self.mean, np.ndarray) \ and isinstance(self.std, np.ndarray): with paddle.static.program_guard(paddle.static.Program()): - mean = paddle.fluid.data('Mean', self.mean.shape, self.mean.dtype) + mean = paddle.fluid.data('Mean', self.mean.shape, + self.mean.dtype) std = paddle.fluid.data('Std', self.std.shape, self.std.dtype) out = paddle.normal(mean, std, self.shape) @@ -76,7 +77,8 @@ class TestNormalAPI(unittest.TestCase): return ret_all elif isinstance(self.mean, np.ndarray): with paddle.static.program_guard(paddle.static.Program()): - mean = paddle.fluid.data('Mean', self.mean.shape, self.mean.dtype) + mean = paddle.fluid.data('Mean', self.mean.shape, + self.mean.dtype) out = paddle.normal(mean, self.std, self.shape) exe = paddle.static.Executor(self.place) diff --git a/python/paddle/fluid/tests/unittests/test_paddle_save_load.py b/python/paddle/fluid/tests/unittests/test_paddle_save_load.py index fee3494558..e211a38e7e 100644 --- a/python/paddle/fluid/tests/unittests/test_paddle_save_load.py +++ b/python/paddle/fluid/tests/unittests/test_paddle_save_load.py @@ -73,7 +73,7 @@ class TestSaveLoad(unittest.TestCase): paddle.disable_static() # config seed - paddle.manual_seed(SEED) + paddle.seed(SEED) paddle.framework.random._manual_program_seed(SEED) def build_and_train_model(self): diff --git a/python/paddle/fluid/tests/unittests/test_pool1d_api.py b/python/paddle/fluid/tests/unittests/test_pool1d_api.py index c1169dfc52..cc2490d1f1 100644 --- a/python/paddle/fluid/tests/unittests/test_pool1d_api.py +++ b/python/paddle/fluid/tests/unittests/test_pool1d_api.py @@ -105,7 +105,7 @@ def avg_pool1D_forward_naive(x, return out -class TestPool1d_API(unittest.TestCase): +class TestPool1D_API(unittest.TestCase): def setUp(self): np.random.seed(123) self.places = [fluid.CPUPlace()] @@ -138,7 +138,7 @@ class TestPool1d_API(unittest.TestCase): self.assertTrue(np.allclose(result.numpy(), result_np)) - avg_pool1d_dg = paddle.nn.layer.AvgPool1d( + avg_pool1d_dg = paddle.nn.layer.AvgPool1D( kernel_size=2, stride=None, padding=0) result = avg_pool1d_dg(input) self.assertTrue(np.allclose(result.numpy(), result_np)) @@ -159,7 +159,7 @@ class TestPool1d_API(unittest.TestCase): self.assertTrue(np.allclose(result.numpy(), result_np)) - avg_pool1d_dg = paddle.nn.AvgPool1d( + avg_pool1d_dg = paddle.nn.AvgPool1D( kernel_size=2, stride=None, padding=1, count_include_pad=True) result = avg_pool1d_dg(input) self.assertTrue(np.allclose(result.numpy(), result_np)) @@ -190,7 +190,7 @@ class TestPool1d_API(unittest.TestCase): self.assertTrue(np.allclose(result.numpy(), result_np)) - max_pool1d_dg = paddle.nn.layer.MaxPool1d( + max_pool1d_dg = paddle.nn.layer.MaxPool1D( kernel_size=2, stride=None, padding=0) result = max_pool1d_dg(input) self.assertTrue(np.allclose(result.numpy(), result_np)) @@ -207,7 +207,7 @@ class TestPool1d_API(unittest.TestCase): self.assertTrue(np.allclose(result.numpy(), result_np)) - max_pool1d_dg = paddle.nn.layer.MaxPool1d( + max_pool1d_dg = paddle.nn.layer.MaxPool1D( kernel_size=2, stride=None, padding=0) result = max_pool1d_dg(input) self.assertTrue(np.allclose(result.numpy(), result_np)) @@ -248,7 +248,7 @@ class TestPool1d_API(unittest.TestCase): self.check_max_dygraph_return_index_results(place) -class TestPool2dError_API(unittest.TestCase): +class TestPool2DError_API(unittest.TestCase): def test_error_api(self): def run1(): with fluid.dygraph.guard(): diff --git a/python/paddle/fluid/tests/unittests/test_pool2d_api.py b/python/paddle/fluid/tests/unittests/test_pool2d_api.py index 91faf78418..66505327c2 100644 --- a/python/paddle/fluid/tests/unittests/test_pool2d_api.py +++ b/python/paddle/fluid/tests/unittests/test_pool2d_api.py @@ -22,7 +22,7 @@ import paddle.fluid as fluid import paddle -class TestPool2d_API(unittest.TestCase): +class TestPool2D_API(unittest.TestCase): def setUp(self): np.random.seed(123) self.places = [fluid.CPUPlace()] @@ -63,7 +63,7 @@ class TestPool2d_API(unittest.TestCase): pool_type='avg') self.assertTrue(np.allclose(result.numpy(), result_np)) - avg_pool2d_dg = paddle.nn.layer.AvgPool2d( + avg_pool2d_dg = paddle.nn.layer.AvgPool2D( kernel_size=2, stride=2, padding=0) result = avg_pool2d_dg(input) self.assertTrue(np.allclose(result.numpy(), result_np)) @@ -84,7 +84,7 @@ class TestPool2d_API(unittest.TestCase): exclusive=False) self.assertTrue(np.allclose(result.numpy(), result_np)) - avg_pool2d_dg = paddle.nn.layer.AvgPool2d( + avg_pool2d_dg = paddle.nn.layer.AvgPool2D( kernel_size=2, stride=2, padding=1, ceil_mode=False) result = avg_pool2d_dg(input) self.assertTrue(np.allclose(result.numpy(), result_np)) @@ -104,7 +104,7 @@ class TestPool2d_API(unittest.TestCase): ceil_mode=True) self.assertTrue(np.allclose(result.numpy(), result_np)) - avg_pool2d_dg = paddle.nn.layer.AvgPool2d( + avg_pool2d_dg = paddle.nn.layer.AvgPool2D( kernel_size=2, stride=2, padding=0, ceil_mode=True) result = avg_pool2d_dg(input) self.assertTrue(np.allclose(result.numpy(), result_np)) @@ -144,7 +144,7 @@ class TestPool2d_API(unittest.TestCase): pool_type='max') self.assertTrue(np.allclose(result.numpy(), result_np)) - max_pool2d_dg = paddle.nn.layer.MaxPool2d( + max_pool2d_dg = paddle.nn.layer.MaxPool2D( kernel_size=2, stride=2, padding=0) result = max_pool2d_dg(input) self.assertTrue(np.allclose(result.numpy(), result_np)) @@ -188,7 +188,7 @@ class TestPool2d_API(unittest.TestCase): exclusive=False) self.assertTrue(np.allclose(result.numpy(), result_np)) - max_pool2d_dg = paddle.nn.layer.MaxPool2d( + max_pool2d_dg = paddle.nn.layer.MaxPool2D( kernel_size=2, stride=2, padding=1, ceil_mode=False) result = max_pool2d_dg(input) self.assertTrue(np.allclose(result.numpy(), result_np)) @@ -208,7 +208,7 @@ class TestPool2d_API(unittest.TestCase): ceil_mode=True) self.assertTrue(np.allclose(result.numpy(), result_np)) - max_pool2d_dg = paddle.nn.layer.MaxPool2d( + max_pool2d_dg = paddle.nn.layer.MaxPool2D( kernel_size=2, stride=2, padding=0, ceil_mode=True) result = max_pool2d_dg(input) self.assertTrue(np.allclose(result.numpy(), result_np)) @@ -233,7 +233,7 @@ class TestPool2d_API(unittest.TestCase): padding_algorithm="SAME") self.assertTrue(np.allclose(result.numpy(), result_np)) - max_pool2d_dg = paddle.nn.layer.MaxPool2d( + max_pool2d_dg = paddle.nn.layer.MaxPool2D( kernel_size=2, stride=2, padding=0) result = max_pool2d_dg(input) self.assertTrue(np.allclose(result.numpy(), result_np)) @@ -254,7 +254,7 @@ class TestPool2d_API(unittest.TestCase): padding_algorithm="SAME") self.assertTrue(np.allclose(result.numpy(), result_np)) - avg_pool2d_dg = paddle.nn.layer.AvgPool2d( + avg_pool2d_dg = paddle.nn.layer.AvgPool2D( kernel_size=2, stride=2, padding=0) result = avg_pool2d_dg(input) self.assertTrue(np.allclose(result.numpy(), result_np)) @@ -279,7 +279,7 @@ class TestPool2d_API(unittest.TestCase): pool_type='max') self.assertTrue(np.allclose(result.numpy(), result_np)) - max_pool2d_dg = paddle.nn.layer.MaxPool2d( + max_pool2d_dg = paddle.nn.layer.MaxPool2D( kernel_size=2, stride=2, padding=0) result = max_pool2d_dg(input) self.assertTrue(np.allclose(result.numpy(), result_np)) @@ -304,7 +304,7 @@ class TestPool2d_API(unittest.TestCase): pool_type='avg') self.assertTrue(np.allclose(result.numpy(), result_np)) - avg_pool2d_dg = paddle.nn.layer.AvgPool2d( + avg_pool2d_dg = paddle.nn.layer.AvgPool2D( kernel_size=2, stride=2, padding=0) result = avg_pool2d_dg(input) self.assertTrue(np.allclose(result.numpy(), result_np)) @@ -325,7 +325,7 @@ class TestPool2d_API(unittest.TestCase): self.check_max_dygraph_nhwc_results(place) -class TestPool2dError_API(unittest.TestCase): +class TestPool2DError_API(unittest.TestCase): def test_error_api(self): def run1(): with fluid.dygraph.guard(): diff --git a/python/paddle/fluid/tests/unittests/test_pool2d_op.py b/python/paddle/fluid/tests/unittests/test_pool2d_op.py index 5e8828c3e9..8553fa8b99 100644 --- a/python/paddle/fluid/tests/unittests/test_pool2d_op.py +++ b/python/paddle/fluid/tests/unittests/test_pool2d_op.py @@ -1018,7 +1018,7 @@ create_test_cudnn_padding_SAME_class(TestCase1_strides) # ----- test API -class TestPool2dAPI(unittest.TestCase): +class TestPool2DAPI(unittest.TestCase): def test_api(self): x_NHWC = np.random.random([2, 5, 5, 3]).astype("float32") x_NCHW = np.random.random([2, 3, 5, 5]).astype("float32") @@ -1237,7 +1237,7 @@ class TestPool2dAPI(unittest.TestCase): data_format="NHWC")) -class TestPool2dAPI_Error(unittest.TestCase): +class TestPool2DAPI_Error(unittest.TestCase): def test_api(self): input_NHWC = fluid.layers.data( name="input_NHWC", diff --git a/python/paddle/fluid/tests/unittests/test_pool3d_api.py b/python/paddle/fluid/tests/unittests/test_pool3d_api.py index 505a1c7383..b2700303ee 100644 --- a/python/paddle/fluid/tests/unittests/test_pool3d_api.py +++ b/python/paddle/fluid/tests/unittests/test_pool3d_api.py @@ -25,7 +25,7 @@ from paddle.nn.functional import avg_pool3d, max_pool3d from test_pool3d_op import adaptive_start_index, adaptive_end_index, pool3D_forward_naive, avg_pool3D_forward_naive, max_pool3D_forward_naive -class TestPool3d_API(unittest.TestCase): +class TestPool3D_API(unittest.TestCase): def setUp(self): np.random.seed(123) self.places = [fluid.CPUPlace()] @@ -68,7 +68,7 @@ class TestPool3d_API(unittest.TestCase): self.assertTrue(np.allclose(result.numpy(), result_np)) - avg_pool3d_dg = paddle.nn.layer.AvgPool3d( + avg_pool3d_dg = paddle.nn.layer.AvgPool3D( kernel_size=2, stride=None, padding="SAME") result = avg_pool3d_dg(input) self.assertTrue(np.allclose(result.numpy(), result_np)) @@ -95,7 +95,7 @@ class TestPool3d_API(unittest.TestCase): self.assertTrue(np.allclose(result.numpy(), result_np)) - avg_pool3d_dg = paddle.nn.layer.AvgPool3d( + avg_pool3d_dg = paddle.nn.layer.AvgPool3D( kernel_size=2, stride=None, padding=1, @@ -120,7 +120,7 @@ class TestPool3d_API(unittest.TestCase): self.assertTrue(np.allclose(result.numpy(), result_np)) - avg_pool3d_dg = paddle.nn.layer.AvgPool3d( + avg_pool3d_dg = paddle.nn.layer.AvgPool3D( kernel_size=2, stride=None, padding=0, ceil_mode=True) result = avg_pool3d_dg(input) self.assertTrue(np.allclose(result.numpy(), result_np)) @@ -159,7 +159,7 @@ class TestPool3d_API(unittest.TestCase): pool_type='max') self.assertTrue(np.allclose(result.numpy(), result_np)) - max_pool3d_dg = paddle.nn.layer.MaxPool3d( + max_pool3d_dg = paddle.nn.layer.MaxPool3D( kernel_size=2, stride=None, padding=0) result = max_pool3d_dg(input) self.assertTrue(np.allclose(result.numpy(), result_np)) @@ -204,7 +204,7 @@ class TestPool3d_API(unittest.TestCase): self.assertTrue(np.allclose(result.numpy(), result_np)) - max_pool3d_dg = paddle.nn.layer.MaxPool3d( + max_pool3d_dg = paddle.nn.layer.MaxPool3D( kernel_size=2, stride=None, padding=0, ceil_mode=True) result = max_pool3d_dg(input) self.assertTrue(np.allclose(result.numpy(), result_np)) @@ -225,7 +225,7 @@ class TestPool3d_API(unittest.TestCase): self.assertTrue(np.allclose(result.numpy(), result_np)) - max_pool3d_dg = paddle.nn.layer.MaxPool3d( + max_pool3d_dg = paddle.nn.layer.MaxPool3D( kernel_size=2, stride=None, padding=1, ceil_mode=False) result = max_pool3d_dg(input) self.assertTrue(np.allclose(result.numpy(), result_np)) @@ -250,7 +250,7 @@ class TestPool3d_API(unittest.TestCase): padding_algorithm="SAME") self.assertTrue(np.allclose(result.numpy(), result_np)) - max_pool3d_dg = paddle.nn.layer.MaxPool3d( + max_pool3d_dg = paddle.nn.layer.MaxPool3D( kernel_size=2, stride=2, padding=0) result = max_pool3d_dg(input) self.assertTrue(np.allclose(result.numpy(), result_np)) @@ -270,7 +270,7 @@ class TestPool3d_API(unittest.TestCase): pool_type='max') self.assertTrue(np.allclose(result.numpy(), result_np)) - max_pool3d_dg = paddle.nn.layer.MaxPool3d( + max_pool3d_dg = paddle.nn.layer.MaxPool3D( kernel_size=2, stride=2, padding=0) result = max_pool3d_dg(input) self.assertTrue(np.allclose(result.numpy(), result_np)) @@ -299,7 +299,7 @@ class TestPool3d_API(unittest.TestCase): pool_type='avg') self.assertTrue(np.allclose(result.numpy(), result_np)) - avg_pool3d_dg = paddle.nn.layer.AvgPool3d( + avg_pool3d_dg = paddle.nn.layer.AvgPool3D( kernel_size=2, stride=2, padding=0) result = avg_pool3d_dg(input) self.assertTrue(np.allclose(result.numpy(), result_np)) @@ -327,7 +327,7 @@ class TestPool3d_API(unittest.TestCase): self.check_max_dygraph_ceilmode_results(place) -class TestPool3dError_API(unittest.TestCase): +class TestPool3DError_API(unittest.TestCase): def test_error_api(self): def run1(): with fluid.dygraph.guard(): diff --git a/python/paddle/fluid/tests/unittests/test_pool3d_op.py b/python/paddle/fluid/tests/unittests/test_pool3d_op.py index eab7126c7a..fade169121 100644 --- a/python/paddle/fluid/tests/unittests/test_pool3d_op.py +++ b/python/paddle/fluid/tests/unittests/test_pool3d_op.py @@ -219,7 +219,7 @@ def avg_pool3D_forward_naive(x, return out -class TestPool3d_Op(OpTest): +class TestPool3D_Op(OpTest): def setUp(self): self.op_type = "pool3d" self.init_kernel_type() @@ -312,7 +312,7 @@ class TestPool3d_Op(OpTest): self.adaptive = False -class TestCase1(TestPool3d_Op): +class TestCase1(TestPool3D_Op): def init_shape(self): self.shape = [2, 3, 7, 7, 7] @@ -330,7 +330,7 @@ class TestCase1(TestPool3d_Op): self.global_pool = False -class TestCase2(TestPool3d_Op): +class TestCase2(TestPool3D_Op): def init_shape(self): self.shape = [2, 3, 6, 7, 7] @@ -348,7 +348,7 @@ class TestCase2(TestPool3d_Op): self.global_pool = False -class TestCase3(TestPool3d_Op): +class TestCase3(TestPool3D_Op): def init_pool_type(self): self.pool_type = "max" @@ -378,7 +378,7 @@ def create_test_cudnn_class(parent): globals()[cls_name] = TestCUDNNCase -create_test_cudnn_class(TestPool3d_Op) +create_test_cudnn_class(TestPool3D_Op) create_test_cudnn_class(TestCase1) create_test_cudnn_class(TestCase2) create_test_cudnn_class(TestCase3) @@ -405,7 +405,7 @@ def create_test_cudnn_fp16_class(parent): globals()[cls_name] = TestCUDNNFp16Case -create_test_cudnn_fp16_class(TestPool3d_Op) +create_test_cudnn_fp16_class(TestPool3D_Op) create_test_cudnn_fp16_class(TestCase1) create_test_cudnn_fp16_class(TestCase2) create_test_cudnn_fp16_class(TestCase3) @@ -429,7 +429,7 @@ def create_test_cudnn_use_ceil_class(parent): globals()[cls_name] = TestPool3DUseCeilCase -create_test_cudnn_use_ceil_class(TestPool3d_Op) +create_test_cudnn_use_ceil_class(TestPool3D_Op) create_test_cudnn_use_ceil_class(TestCase1) @@ -480,7 +480,7 @@ class TestAvgPoolAdaptiveAsyOutSize(TestCase1): #-------test pool3d with asymmetric padding------ -class TestPool3d_Op_AsyPadding(TestPool3d_Op): +class TestPool3D_Op_AsyPadding(TestPool3D_Op): def init_test_case(self): self.ksize = [3, 4, 3] self.strides = [1, 1, 2] @@ -552,21 +552,21 @@ class TestCase5_AsyPadding(TestCase5): self.shape = [2, 3, 7, 7, 7] -create_test_cudnn_class(TestPool3d_Op_AsyPadding) +create_test_cudnn_class(TestPool3D_Op_AsyPadding) create_test_cudnn_class(TestCase1_AsyPadding) create_test_cudnn_class(TestCase2_AsyPadding) create_test_cudnn_class(TestCase3_AsyPadding) create_test_cudnn_class(TestCase4_AsyPadding) create_test_cudnn_class(TestCase5_AsyPadding) -create_test_cudnn_fp16_class(TestPool3d_Op_AsyPadding) +create_test_cudnn_fp16_class(TestPool3D_Op_AsyPadding) create_test_cudnn_fp16_class(TestCase1_AsyPadding) create_test_cudnn_fp16_class(TestCase2_AsyPadding) create_test_cudnn_fp16_class(TestCase3_AsyPadding) create_test_cudnn_fp16_class(TestCase4_AsyPadding) create_test_cudnn_fp16_class(TestCase5_AsyPadding) -create_test_cudnn_use_ceil_class(TestPool3d_Op_AsyPadding) +create_test_cudnn_use_ceil_class(TestPool3D_Op_AsyPadding) create_test_cudnn_use_ceil_class(TestCase1_AsyPadding) create_test_use_ceil_class(TestCase1_AsyPadding) @@ -606,7 +606,7 @@ class TestAvgPoolAdaptive_AsyPadding(TestCase1): # ------------ test channel_last -------------- -class TestPool3d_channel_last(TestPool3d_Op): +class TestPool3D_channel_last(TestPool3D_Op): def init_data_format(self): self.data_format = "NDHWC" @@ -654,14 +654,14 @@ class TestCase5_channel_last(TestCase5): self.shape = [2, 7, 7, 7, 3] -create_test_cudnn_class(TestPool3d_channel_last) +create_test_cudnn_class(TestPool3D_channel_last) create_test_cudnn_class(TestCase1_channel_last) create_test_cudnn_class(TestCase2_channel_last) create_test_cudnn_class(TestCase3_channel_last) create_test_cudnn_class(TestCase4_channel_last) create_test_cudnn_class(TestCase5_channel_last) -create_test_cudnn_use_ceil_class(TestPool3d_channel_last) +create_test_cudnn_use_ceil_class(TestPool3D_channel_last) create_test_cudnn_use_ceil_class(TestCase1_channel_last) create_test_use_ceil_class(TestCase1_channel_last) @@ -716,7 +716,7 @@ class TestAvgPoolAdaptive_channel_last(TestCase1_channel_last): # --- asy padding -class TestPool3d_Op_AsyPadding_channel_last(TestPool3d_Op_AsyPadding): +class TestPool3D_Op_AsyPadding_channel_last(TestPool3D_Op_AsyPadding): def init_data_format(self): self.data_format = "NDHWC" @@ -764,14 +764,14 @@ class TestCase5_AsyPadding_channel_last(TestCase5_AsyPadding): self.shape = [2, 7, 8, 6, 3] -create_test_cudnn_class(TestPool3d_Op_AsyPadding_channel_last) +create_test_cudnn_class(TestPool3D_Op_AsyPadding_channel_last) create_test_cudnn_class(TestCase1_AsyPadding_channel_last) create_test_cudnn_class(TestCase2_AsyPadding_channel_last) create_test_cudnn_class(TestCase3_AsyPadding_channel_last) create_test_cudnn_class(TestCase4_AsyPadding_channel_last) create_test_cudnn_class(TestCase5_AsyPadding_channel_last) -create_test_cudnn_use_ceil_class(TestPool3d_Op_AsyPadding_channel_last) +create_test_cudnn_use_ceil_class(TestPool3D_Op_AsyPadding_channel_last) create_test_cudnn_use_ceil_class(TestCase1_AsyPadding_channel_last) create_test_use_ceil_class(TestCase1_AsyPadding_channel_last) @@ -812,14 +812,14 @@ def create_test_padding_SAME_class(parent): globals()[cls_name] = TestPaddingSMAECase -create_test_padding_SAME_class(TestPool3d_Op) +create_test_padding_SAME_class(TestPool3D_Op) create_test_padding_SAME_class(TestCase1) create_test_padding_SAME_class(TestCase2) create_test_padding_SAME_class(TestCase3) create_test_padding_SAME_class(TestCase4) create_test_padding_SAME_class(TestCase5) -create_test_padding_SAME_class(TestPool3d_channel_last) +create_test_padding_SAME_class(TestPool3D_channel_last) create_test_padding_SAME_class(TestCase1_channel_last) create_test_padding_SAME_class(TestCase2_channel_last) create_test_padding_SAME_class(TestCase3_channel_last) @@ -843,14 +843,14 @@ def create_test_cudnn_padding_SAME_class(parent): globals()[cls_name] = TestCUDNNPaddingSMAECase -create_test_cudnn_padding_SAME_class(TestPool3d_Op) +create_test_cudnn_padding_SAME_class(TestPool3D_Op) create_test_cudnn_padding_SAME_class(TestCase1) create_test_cudnn_padding_SAME_class(TestCase2) create_test_cudnn_padding_SAME_class(TestCase3) create_test_cudnn_padding_SAME_class(TestCase4) create_test_cudnn_padding_SAME_class(TestCase5) -create_test_cudnn_padding_SAME_class(TestPool3d_channel_last) +create_test_cudnn_padding_SAME_class(TestPool3D_channel_last) create_test_cudnn_padding_SAME_class(TestCase1_channel_last) create_test_cudnn_padding_SAME_class(TestCase2_channel_last) create_test_cudnn_padding_SAME_class(TestCase3_channel_last) @@ -869,14 +869,14 @@ def create_test_padding_VALID_class(parent): globals()[cls_name] = TestPaddingVALIDCase -create_test_padding_VALID_class(TestPool3d_Op) +create_test_padding_VALID_class(TestPool3D_Op) create_test_padding_VALID_class(TestCase1) create_test_padding_VALID_class(TestCase2) create_test_padding_VALID_class(TestCase3) create_test_padding_VALID_class(TestCase4) create_test_padding_VALID_class(TestCase5) -create_test_padding_VALID_class(TestPool3d_channel_last) +create_test_padding_VALID_class(TestPool3D_channel_last) create_test_padding_VALID_class(TestCase1_channel_last) create_test_padding_VALID_class(TestCase2_channel_last) create_test_padding_VALID_class(TestCase3_channel_last) @@ -900,14 +900,14 @@ def create_test_cudnn_padding_VALID_class(parent): globals()[cls_name] = TestCUDNNPaddingVALIDCase -create_test_cudnn_padding_VALID_class(TestPool3d_Op) +create_test_cudnn_padding_VALID_class(TestPool3D_Op) create_test_cudnn_padding_VALID_class(TestCase1) create_test_cudnn_padding_VALID_class(TestCase2) create_test_cudnn_padding_VALID_class(TestCase3) create_test_cudnn_padding_VALID_class(TestCase4) create_test_cudnn_padding_VALID_class(TestCase5) -create_test_cudnn_padding_VALID_class(TestPool3d_channel_last) +create_test_cudnn_padding_VALID_class(TestPool3D_channel_last) create_test_cudnn_padding_VALID_class(TestCase1_channel_last) create_test_cudnn_padding_VALID_class(TestCase2_channel_last) create_test_cudnn_padding_VALID_class(TestCase3_channel_last) @@ -916,7 +916,7 @@ create_test_cudnn_padding_VALID_class(TestCase5_channel_last) #test API -class TestPool3dAPI(unittest.TestCase): +class TestPool3DAPI(unittest.TestCase): def test_api(self): x_NDHWC = np.random.random([2, 5, 5, 5, 3]).astype("float32") x_NCDHW = np.random.random([2, 3, 5, 5, 5]).astype("float32") @@ -1101,7 +1101,7 @@ class TestPool3dAPI(unittest.TestCase): atol=1e-05) -class TestPool3dAPI_Error(unittest.TestCase): +class TestPool3DAPI_Error(unittest.TestCase): def test_api(self): input_NDHWC = fluid.layers.data( name="input_NDHWC", diff --git a/python/paddle/fluid/tests/unittests/test_py_func_op.py b/python/paddle/fluid/tests/unittests/test_py_func_op.py index 32d8f73552..14b0eec9cb 100644 --- a/python/paddle/fluid/tests/unittests/test_py_func_op.py +++ b/python/paddle/fluid/tests/unittests/test_py_func_op.py @@ -147,7 +147,7 @@ def test_main(use_cuda, use_py_func_op, use_parallel_executor): with fluid.program_guard(fluid.Program(), fluid.Program()): with fluid.scope_guard(fluid.core.Scope()): - gen = paddle.manual_seed(1) + gen = paddle.seed(1) np.random.seed(1) img = fluid.layers.data(name='image', shape=[784], dtype='float32') label = fluid.layers.data(name='label', shape=[1], dtype='int64') diff --git a/python/paddle/fluid/tests/unittests/test_random_seed.py b/python/paddle/fluid/tests/unittests/test_random_seed.py index 343508bf61..2a759d5b54 100644 --- a/python/paddle/fluid/tests/unittests/test_random_seed.py +++ b/python/paddle/fluid/tests/unittests/test_random_seed.py @@ -35,7 +35,7 @@ class TestGeneratorSeed(unittest.TestCase): fluid.enable_dygraph() - gen = paddle.manual_seed(12312321111) + gen = paddle.seed(12312321111) x = fluid.layers.uniform_random([10], dtype="float32", min=0.0, max=1.0) st1 = gen.get_state() @@ -47,7 +47,7 @@ class TestGeneratorSeed(unittest.TestCase): x2 = fluid.layers.uniform_random( [10], dtype="float32", min=0.0, max=1.0) - paddle.manual_seed(12312321111) + paddle.seed(12312321111) x3 = fluid.layers.uniform_random( [10], dtype="float32", min=0.0, max=1.0) @@ -63,7 +63,7 @@ class TestGeneratorSeed(unittest.TestCase): def test_generator_uniform_random_static(self): fluid.disable_dygraph() - gen = paddle.manual_seed(123123143) + gen = paddle.seed(123123143) startup_program = fluid.Program() train_program = fluid.Program() @@ -97,7 +97,7 @@ class TestGeneratorSeed(unittest.TestCase): def test_gen_dropout_dygraph(self): fluid.enable_dygraph() - gen = paddle.manual_seed(111111111) + gen = paddle.seed(111111111) st = gen.get_state() # x = np.arange(1,101).reshape(2,50).astype("float32") x = fluid.layers.uniform_random( @@ -118,7 +118,7 @@ class TestGeneratorSeed(unittest.TestCase): def test_gen_dropout_static(self): fluid.disable_dygraph() - gen = paddle.manual_seed(123123143) + gen = paddle.seed(123123143) startup_program = fluid.Program() train_program = fluid.Program() @@ -144,7 +144,7 @@ class TestGeneratorSeed(unittest.TestCase): """Test Generator seed.""" fluid.enable_dygraph() - gen = paddle.manual_seed(12312321111) + gen = paddle.seed(12312321111) x = fluid.layers.gaussian_random([10], dtype="float32") st1 = gen.get_state() x1 = fluid.layers.gaussian_random([10], dtype="float32") @@ -165,7 +165,7 @@ class TestGeneratorSeed(unittest.TestCase): def test_generator_gaussian_random_static(self): fluid.disable_dygraph() - gen = paddle.manual_seed(123123143) + gen = paddle.seed(123123143) startup_program = fluid.Program() train_program = fluid.Program() @@ -203,7 +203,7 @@ class TestGeneratorSeed(unittest.TestCase): fluid.enable_dygraph() - gen = paddle.manual_seed(12312321111) + gen = paddle.seed(12312321111) x = paddle.randint(low=10, shape=[10], dtype="int32") st1 = gen.get_state() x1 = paddle.randint(low=10, shape=[10], dtype="int32") @@ -224,7 +224,7 @@ class TestGeneratorSeed(unittest.TestCase): def test_generator_uniform_random_static(self): fluid.disable_dygraph() - gen = paddle.manual_seed(123123143) + gen = paddle.seed(123123143) startup_program = fluid.Program() train_program = fluid.Program() @@ -259,7 +259,7 @@ class TestGeneratorSeed(unittest.TestCase): """Test Generator seed.""" fluid.enable_dygraph() - gen = paddle.manual_seed(12312321111) + gen = paddle.seed(12312321111) x = paddle.randint(low=1) st1 = gen.get_state() x1 = paddle.randint(low=1) @@ -278,7 +278,7 @@ class TestGeneratorSeed(unittest.TestCase): def test_generator_ranint_static(self): fluid.disable_dygraph() - gen = paddle.manual_seed(123123143) + gen = paddle.seed(123123143) startup_program = fluid.Program() train_program = fluid.Program() @@ -315,7 +315,7 @@ class TestGeneratorSeed(unittest.TestCase): fluid.enable_dygraph() - gen = paddle.manual_seed(12312321111) + gen = paddle.seed(12312321111) x = paddle.randperm(10) st1 = gen.get_state() x1 = paddle.randperm(10) @@ -337,7 +337,7 @@ class TestGeneratorSeed(unittest.TestCase): fluid.disable_dygraph() - paddle.manual_seed(123123143) + paddle.seed(123123143) startup_program = fluid.Program() train_program = fluid.Program() @@ -353,7 +353,7 @@ class TestGeneratorSeed(unittest.TestCase): feed={}, fetch_list=[result_1, result_2]) - paddle.manual_seed(123123143) + paddle.seed(123123143) out2 = exe.run(train_program, feed={}, fetch_list=[result_1, result_2]) @@ -371,7 +371,7 @@ class TestGeneratorSeed(unittest.TestCase): def test_generator_sampling_id_dygraph(self): """Test Generator seed.""" - gen = paddle.manual_seed(12312321111) + gen = paddle.seed(12312321111) fluid.enable_dygraph() @@ -409,7 +409,7 @@ class TestGeneratorSeed(unittest.TestCase): fluid.disable_dygraph() - paddle.manual_seed(123123143) + paddle.seed(123123143) startup_program = fluid.Program() train_program = fluid.Program() @@ -426,7 +426,7 @@ class TestGeneratorSeed(unittest.TestCase): feed={}, fetch_list=[result_1, result_2]) - paddle.manual_seed(123123143) + paddle.seed(123123143) out2 = exe.run(train_program, feed={}, fetch_list=[result_1, result_2]) @@ -445,7 +445,7 @@ class TestGeneratorSeed(unittest.TestCase): def test_gen_TruncatedNormal_initializer(self): fluid.disable_dygraph() - gen = paddle.manual_seed(123123143) + gen = paddle.seed(123123143) cur_state = gen.get_state() startup_program = fluid.Program() diff --git a/python/paddle/fluid/tests/unittests/test_regularizer.py b/python/paddle/fluid/tests/unittests/test_regularizer.py index 167a8a017c..04c6e45625 100644 --- a/python/paddle/fluid/tests/unittests/test_regularizer.py +++ b/python/paddle/fluid/tests/unittests/test_regularizer.py @@ -169,7 +169,7 @@ class TestRegularizer(unittest.TestCase): return param_sum def check_l2decay_regularizer(self, place, model): - paddle.manual_seed(1) + paddle.seed(1) paddle.framework.random._manual_program_seed(1) main_prog = fluid.framework.Program() startup_prog = fluid.framework.Program() @@ -189,7 +189,7 @@ class TestRegularizer(unittest.TestCase): return param_sum def check_l2decay(self, place, model): - paddle.manual_seed(1) + paddle.seed(1) paddle.framework.random._manual_program_seed(1) main_prog = fluid.framework.Program() startup_prog = fluid.framework.Program() @@ -246,7 +246,7 @@ class TestRegularizer(unittest.TestCase): with fluid.dygraph.guard(): input = fluid.dygraph.to_variable( np.random.randn(3, 2).astype('float32')) - paddle.manual_seed(1) + paddle.seed(1) paddle.framework.random._manual_program_seed(1) linear1 = fluid.dygraph.Linear( diff --git a/python/paddle/fluid/tests/unittests/test_regularizer_api.py b/python/paddle/fluid/tests/unittests/test_regularizer_api.py index 76186d2e39..e00a97aaa1 100644 --- a/python/paddle/fluid/tests/unittests/test_regularizer_api.py +++ b/python/paddle/fluid/tests/unittests/test_regularizer_api.py @@ -94,7 +94,7 @@ class TestRegularizer(unittest.TestCase): return param_sum def check_l2decay_regularizer(self, place, model): - paddle.manual_seed(1) + paddle.seed(1) paddle.framework.random._manual_program_seed(1) main_prog = fluid.framework.Program() startup_prog = fluid.framework.Program() @@ -114,7 +114,7 @@ class TestRegularizer(unittest.TestCase): return param_sum def check_l2decay(self, place, model): - paddle.manual_seed(1) + paddle.seed(1) paddle.framework.random._manual_program_seed(1) main_prog = fluid.framework.Program() startup_prog = fluid.framework.Program() @@ -171,7 +171,7 @@ class TestRegularizer(unittest.TestCase): with fluid.dygraph.guard(): input = fluid.dygraph.to_variable( np.random.randn(3, 2).astype('float32')) - paddle.manual_seed(1) + paddle.seed(1) paddle.framework.random._manual_program_seed(1) linear1 = fluid.dygraph.Linear( diff --git a/python/paddle/fluid/tests/unittests/test_retain_graph.py b/python/paddle/fluid/tests/unittests/test_retain_graph.py index 3e1dd4ef57..de94e0b0fc 100644 --- a/python/paddle/fluid/tests/unittests/test_retain_graph.py +++ b/python/paddle/fluid/tests/unittests/test_retain_graph.py @@ -20,13 +20,13 @@ import unittest paddle.disable_static() SEED = 2020 np.random.seed(SEED) -paddle.manual_seed(SEED) +paddle.seed(SEED) class Generator(fluid.dygraph.Layer): def __init__(self): super(Generator, self).__init__() - self.conv1 = paddle.nn.Conv2d(3, 3, 3, padding=1) + self.conv1 = paddle.nn.Conv2D(3, 3, 3, padding=1) def forward(self, x): x = self.conv1(x) @@ -37,7 +37,7 @@ class Generator(fluid.dygraph.Layer): class Discriminator(fluid.dygraph.Layer): def __init__(self): super(Discriminator, self).__init__() - self.convd = paddle.nn.Conv2d(6, 3, 1) + self.convd = paddle.nn.Conv2D(6, 3, 1) def forward(self, x): x = self.convd(x) diff --git a/python/paddle/fluid/tests/unittests/test_rnn_decode_api.py b/python/paddle/fluid/tests/unittests/test_rnn_decode_api.py index 066d0a37e1..304e7cd9a5 100644 --- a/python/paddle/fluid/tests/unittests/test_rnn_decode_api.py +++ b/python/paddle/fluid/tests/unittests/test_rnn_decode_api.py @@ -617,7 +617,7 @@ class ModuleApiTest(unittest.TestCase): fluid.enable_dygraph(place) else: fluid.disable_dygraph() - gen = paddle.manual_seed(self._random_seed) + gen = paddle.seed(self._random_seed) gen._is_init_py = False paddle.framework.random._manual_program_seed(self._random_seed) scope = fluid.core.Scope() diff --git a/python/paddle/fluid/tests/unittests/test_sync_batch_norm_op.py b/python/paddle/fluid/tests/unittests/test_sync_batch_norm_op.py index 1c11e831b0..bfd22dbe1c 100644 --- a/python/paddle/fluid/tests/unittests/test_sync_batch_norm_op.py +++ b/python/paddle/fluid/tests/unittests/test_sync_batch_norm_op.py @@ -228,12 +228,12 @@ class TestConvertSyncBatchNorm(unittest.TestCase): with program_guard(Program(), Program()): compare_model = paddle.nn.Sequential( - paddle.nn.Conv2d(3, 5, 3), paddle.nn.BatchNorm2d(5)) + paddle.nn.Conv2D(3, 5, 3), paddle.nn.BatchNorm2D(5)) model = paddle.nn.Sequential( - paddle.nn.Conv2d(3, 5, 3), paddle.nn.BatchNorm2d(5)) + paddle.nn.Conv2D(3, 5, 3), paddle.nn.BatchNorm2D(5)) model = paddle.nn.SyncBatchNorm.convert_sync_batchnorm(model) for idx, sublayer in enumerate(compare_model.sublayers()): - if isinstance(sublayer, paddle.nn.BatchNorm2d): + if isinstance(sublayer, paddle.nn.BatchNorm2D): self.assertEqual( isinstance(model[idx], paddle.nn.SyncBatchNorm), True) diff --git a/python/paddle/fluid/tests/unittests/test_transformer_api.py b/python/paddle/fluid/tests/unittests/test_transformer_api.py index 3133aad0f4..23df03da1e 100644 --- a/python/paddle/fluid/tests/unittests/test_transformer_api.py +++ b/python/paddle/fluid/tests/unittests/test_transformer_api.py @@ -211,7 +211,7 @@ def ffn(src, encoder_layer, ffn_fc1_act="relu"): class TestTransformer(unittest.TestCase): def test_multi_head_attention(self): def multihead_attention_test_helper(self_attention, cache): - paddle.manual_seed(2020) + paddle.seed(2020) paddle.framework.random._manual_program_seed(2020) # self_attention|cross_attention, cache|No cache with fluid.dygraph.guard(fluid.CPUPlace()): @@ -275,7 +275,7 @@ class TestTransformer(unittest.TestCase): def test_transformer_encoder_layer(self): with fluid.dygraph.guard(fluid.CPUPlace()): - paddle.framework.manual_seed(2020) + paddle.framework.seed(2020) paddle.framework.random._manual_program_seed(2020) ffn_fc1_act = "relu" @@ -320,7 +320,7 @@ class TestTransformer(unittest.TestCase): def test_transformer_decoder_layer(self): with fluid.dygraph.guard(fluid.CPUPlace()): - paddle.framework.manual_seed(2020) + paddle.framework.seed(2020) activation = "relu" normalize_before = False batch_size, d_model, n_head, dim_feedforward, dropout, attn_dropout, act_dropout, source_length, target_length = generate_basic_params( diff --git a/python/paddle/fluid/tests/unittests/test_translated_layer.py b/python/paddle/fluid/tests/unittests/test_translated_layer.py index e5dc279750..d0b361d6f2 100644 --- a/python/paddle/fluid/tests/unittests/test_translated_layer.py +++ b/python/paddle/fluid/tests/unittests/test_translated_layer.py @@ -77,7 +77,7 @@ class TestTranslatedLayer(unittest.TestCase): paddle.disable_static(place) # config seed - paddle.manual_seed(SEED) + paddle.seed(SEED) paddle.framework.random._manual_program_seed(SEED) # create network diff --git a/python/paddle/fluid/tests/unittests/test_uniform_random_op.py b/python/paddle/fluid/tests/unittests/test_uniform_random_op.py index 5ecf25c53b..6de36c02be 100644 --- a/python/paddle/fluid/tests/unittests/test_uniform_random_op.py +++ b/python/paddle/fluid/tests/unittests/test_uniform_random_op.py @@ -235,7 +235,7 @@ class TestUniformRandomOpSelectedRows(unittest.TestCase): def check_with_place(self, place): scope = core.Scope() out = scope.var("X").get_selected_rows() - paddle.manual_seed(10) + paddle.seed(10) op = Operator( "uniform_random", Out="X", @@ -256,7 +256,7 @@ class TestUniformRandomOpSelectedRowsWithDiagInit( def check_with_place(self, place): scope = core.Scope() out = scope.var("X").get_selected_rows() - paddle.manual_seed(10) + paddle.seed(10) op = Operator( "uniform_random", Out="X", @@ -277,7 +277,7 @@ class TestUniformRandomOpSelectedRowsWithDiagInit( class TestUniformRandomOpApi(unittest.TestCase): def test_api(self): - paddle.manual_seed(10) + paddle.seed(10) x = fluid.layers.data('x', shape=[16], dtype='float32', lod_level=1) y = fluid.layers.fc(x, size=16, @@ -350,7 +350,7 @@ class TestUniformRandomOp_attr_tensor_API(unittest.TestCase): class TestUniformRandomOp_API_seed(unittest.TestCase): def test_attr_tensor_API(self): _seed = 10 - gen = paddle.manual_seed(_seed) + gen = paddle.seed(_seed) gen._is_init_py = False startup_program = fluid.Program() train_program = fluid.Program() @@ -392,7 +392,7 @@ class TestUniformRandomOpSelectedRowsShapeTensor(unittest.TestCase): out = scope.var("X").get_selected_rows() shape_tensor = scope.var("Shape").get_tensor() shape_tensor.set(np.array([1000, 784]).astype("int64"), place) - paddle.manual_seed(10) + paddle.seed(10) op = Operator( "uniform_random", ShapeTensor="Shape", @@ -426,7 +426,7 @@ class TestUniformRandomOpSelectedRowsShapeTensorList(unittest.TestCase): shape_1.set(np.array([1000]).astype("int64"), place) shape_2 = scope.var("shape2").get_tensor() shape_2.set(np.array([784]).astype("int64"), place) - paddle.manual_seed(10) + paddle.seed(10) op = Operator( "uniform_random", ShapeTensorList=["shape1", "shape2"], diff --git a/python/paddle/fluid/tests/unittests/test_var_base.py b/python/paddle/fluid/tests/unittests/test_var_base.py index 6d4258a426..2df24b0079 100644 --- a/python/paddle/fluid/tests/unittests/test_var_base.py +++ b/python/paddle/fluid/tests/unittests/test_var_base.py @@ -416,7 +416,7 @@ class TestVarBase(unittest.TestCase): def test_tensor_str(self): paddle.enable_static() paddle.disable_static(paddle.CPUPlace()) - paddle.manual_seed(10) + paddle.seed(10) a = paddle.rand([10, 20]) paddle.set_printoptions(4, 100, 3) a_str = str(a) diff --git a/python/paddle/fluid/tests/unittests/test_var_conv_2d.py b/python/paddle/fluid/tests/unittests/test_var_conv_2d.py index db5debdb43..4e23b20581 100644 --- a/python/paddle/fluid/tests/unittests/test_var_conv_2d.py +++ b/python/paddle/fluid/tests/unittests/test_var_conv_2d.py @@ -19,7 +19,7 @@ import numpy as np from op_test import OpTest, skip_check_grad_ci -class TestVarConv2dOp(OpTest): +class TestVarConv2DOp(OpTest): def setUp(self): self.init_op_type() self.set_data() @@ -179,7 +179,7 @@ class TestVarConv2dOp(OpTest): ['X'], 'Out', max_relative_error=0.005, check_dygraph=False) -class TestVarConv2dOpCase1(TestVarConv2dOp): +class TestVarConv2DOpCase1(TestVarConv2DOp): def set_data(self): # set in_ch 1 input_channel = 1 @@ -192,7 +192,7 @@ class TestVarConv2dOpCase1(TestVarConv2dOp): col) -class TestVarConv2dOpCase2(TestVarConv2dOp): +class TestVarConv2DOpCase2(TestVarConv2DOp): def set_data(self): # set out_ch 1 input_channel = 2 @@ -205,7 +205,7 @@ class TestVarConv2dOpCase2(TestVarConv2dOp): col) -class TestVarConv2dOpCase3(TestVarConv2dOp): +class TestVarConv2DOpCase3(TestVarConv2DOp): def set_data(self): # set batch 1 input_channel = 2 @@ -218,7 +218,7 @@ class TestVarConv2dOpCase3(TestVarConv2dOp): col) -class TestVarConv2dOpCase4(TestVarConv2dOp): +class TestVarConv2DOpCase4(TestVarConv2DOp): def set_data(self): # set filter size very large input_channel = 3 @@ -231,7 +231,7 @@ class TestVarConv2dOpCase4(TestVarConv2dOp): col) -class TestVarConv2dOpCase5(TestVarConv2dOp): +class TestVarConv2DOpCase5(TestVarConv2DOp): def set_data(self): # set input very small input_channel = 50 @@ -247,7 +247,7 @@ class TestVarConv2dOpCase5(TestVarConv2dOp): @skip_check_grad_ci( reason="[skip shape check] Use shape of input_channel, row and col all is 1 to test special LoDTensor." ) -class TestVarConv2dOpCase6(TestVarConv2dOp): +class TestVarConv2DOpCase6(TestVarConv2DOp): def set_data(self): input_channel = 1 output_channel = 3 @@ -259,7 +259,7 @@ class TestVarConv2dOpCase6(TestVarConv2dOp): col) -class TestVarConv2dOpCase7(TestVarConv2dOp): +class TestVarConv2DOpCase7(TestVarConv2DOp): def set_data(self): input_channel = 2 output_channel = 3 @@ -271,7 +271,7 @@ class TestVarConv2dOpCase7(TestVarConv2dOp): col) -class TestVarConv2dApi(unittest.TestCase): +class TestVarConv2DApi(unittest.TestCase): def test_api(self): import paddle.fluid as fluid diff --git a/python/paddle/fluid/tests/unittests/xpu/test_conv2d_op_xpu.py b/python/paddle/fluid/tests/unittests/xpu/test_conv2d_op_xpu.py index f826448c59..aaa4f636b0 100644 --- a/python/paddle/fluid/tests/unittests/xpu/test_conv2d_op_xpu.py +++ b/python/paddle/fluid/tests/unittests/xpu/test_conv2d_op_xpu.py @@ -159,7 +159,7 @@ def create_test_padding_VALID_class(parent): globals()[cls_name] = TestPaddingVALIDCase -class TestConv2dOp(OpTest): +class TestConv2DOp(OpTest): def setUp(self): self.op_type = "conv2d" self.use_cudnn = False @@ -274,7 +274,7 @@ class TestConv2dOp(OpTest): pass -class TestWithPad(TestConv2dOp): +class TestWithPad(TestConv2DOp): def init_test_case(self): self.pad = [1, 1] self.stride = [1, 1] @@ -284,7 +284,7 @@ class TestWithPad(TestConv2dOp): self.filter_size = [6, f_c, 3, 3] -class TestWithStride(TestConv2dOp): +class TestWithStride(TestConv2DOp): def init_test_case(self): self.pad = [1, 1] self.stride = [2, 2] @@ -294,7 +294,7 @@ class TestWithStride(TestConv2dOp): self.filter_size = [6, f_c, 3, 3] -class TestWithGroup(TestConv2dOp): +class TestWithGroup(TestConv2DOp): def init_test_case(self): self.pad = [0, 0] self.stride = [1, 1] @@ -305,7 +305,7 @@ class TestWithGroup(TestConv2dOp): self.filter_size = [18, f_c, 3, 3] -class TestWith1x1(TestConv2dOp): +class TestWith1x1(TestConv2DOp): def init_test_case(self): self.pad = [0, 0] self.stride = [1, 1] @@ -318,7 +318,7 @@ class TestWith1x1(TestConv2dOp): self.groups = 3 -class TestWithDilation(TestConv2dOp): +class TestWithDilation(TestConv2DOp): def init_test_case(self): self.pad = [0, 0] self.stride = [1, 1] @@ -334,7 +334,7 @@ class TestWithDilation(TestConv2dOp): self.groups = 3 -class TestWithInput1x1Filter1x1(TestConv2dOp): +class TestWithInput1x1Filter1x1(TestConv2DOp): def init_test_case(self): self.pad = [0, 0] self.stride = [1, 1] @@ -356,7 +356,7 @@ class TestWithInput1x1Filter1x1(TestConv2dOp): # ---- test asymmetric padding ---- -class TestConv2dOp_v2(OpTest): +class TestConv2DOp_v2(OpTest): def setUp(self): self.op_type = "conv2d" self.use_cudnn = False @@ -482,13 +482,13 @@ class TestConv2dOp_v2(OpTest): pass -class TestConv2dOp_AsyPadding(TestConv2dOp_v2): +class TestConv2DOp_AsyPadding(TestConv2DOp_v2): def init_paddings(self): self.pad = [0, 0, 1, 2] self.padding_algorithm = "EXPLICIT" -class TestWithPad_AsyPadding(TestConv2dOp_v2): +class TestWithPad_AsyPadding(TestConv2DOp_v2): def init_test_case(self): self.stride = [1, 1] self.input_size = [2, 3, 5, 5] # NCHW @@ -501,7 +501,7 @@ class TestWithPad_AsyPadding(TestConv2dOp_v2): self.padding_algorithm = "EXPLICIT" -class TestWithStride_AsyPadding(TestConv2dOp_v2): +class TestWithStride_AsyPadding(TestConv2DOp_v2): def init_test_case(self): self.stride = [2, 2] self.input_size = [2, 3, 6, 6] # NCHW @@ -514,7 +514,7 @@ class TestWithStride_AsyPadding(TestConv2dOp_v2): self.padding_algorithm = "EXPLICIT" -class TestWithGroup_AsyPadding(TestConv2dOp_v2): +class TestWithGroup_AsyPadding(TestConv2DOp_v2): def init_test_case(self): self.pad = [0, 0] self.stride = [1, 2] @@ -525,7 +525,7 @@ class TestWithGroup_AsyPadding(TestConv2dOp_v2): self.filter_size = [24, f_c, 4, 3] -class TestWith1x1_AsyPadding(TestConv2dOp_v2): +class TestWith1x1_AsyPadding(TestConv2DOp_v2): def init_test_case(self): self.stride = [1, 1] self.input_size = [2, 3, 5, 5] # NCHW @@ -541,7 +541,7 @@ class TestWith1x1_AsyPadding(TestConv2dOp_v2): self.padding_algorithm = "EXPLICIT" -class TestWithDilation_AsyPadding(TestConv2dOp_v2): +class TestWithDilation_AsyPadding(TestConv2DOp_v2): def init_test_case(self): self.stride = [1, 1] self.input_size = [2, 3, 10, 10] # NCHW @@ -560,7 +560,7 @@ class TestWithDilation_AsyPadding(TestConv2dOp_v2): self.padding_algorithm = "EXPLICIT" -class TestWithInput1x1Filter1x1_AsyPadding(TestConv2dOp_v2): +class TestWithInput1x1Filter1x1_AsyPadding(TestConv2DOp_v2): def init_test_case(self): self.stride = [1, 1] self.input_size = [40, 3, 1, 1] # NCHW @@ -577,20 +577,20 @@ class TestWithInput1x1Filter1x1_AsyPadding(TestConv2dOp_v2): #---------- test SAME VALID ----------- -create_test_padding_SAME_class(TestConv2dOp_AsyPadding) +create_test_padding_SAME_class(TestConv2DOp_AsyPadding) create_test_padding_SAME_class(TestWithPad_AsyPadding) create_test_padding_SAME_class(TestWithStride_AsyPadding) create_test_padding_SAME_class(TestWithGroup_AsyPadding) create_test_padding_SAME_class(TestWithInput1x1Filter1x1_AsyPadding) -create_test_padding_VALID_class(TestConv2dOp_AsyPadding) +create_test_padding_VALID_class(TestConv2DOp_AsyPadding) create_test_padding_VALID_class(TestWithPad_AsyPadding) create_test_padding_VALID_class(TestWithStride_AsyPadding) create_test_padding_VALID_class(TestWithGroup_AsyPadding) create_test_padding_VALID_class(TestWithInput1x1Filter1x1_AsyPadding) # ------------ test channel last --------- -create_test_channel_last_class(TestConv2dOp_AsyPadding) +create_test_channel_last_class(TestConv2DOp_AsyPadding) create_test_channel_last_class(TestWithPad_AsyPadding) create_test_channel_last_class(TestWithGroup_AsyPadding) create_test_channel_last_class(TestWith1x1_AsyPadding) diff --git a/python/paddle/framework/__init__.py b/python/paddle/framework/__init__.py index e52d9da99c..3d06b4ab91 100644 --- a/python/paddle/framework/__init__.py +++ b/python/paddle/framework/__init__.py @@ -14,9 +14,8 @@ # TODO: import framework api under this directory __all__ = [ - 'create_parameter', 'ParamAttr', - 'CPUPlace', 'CUDAPlace', 'CUDAPinnedPlace', 'get_default_dtype', - 'set_default_dtype' + 'create_parameter', 'ParamAttr', 'CPUPlace', 'CUDAPlace', 'CUDAPinnedPlace', + 'get_default_dtype', 'set_default_dtype' ] __all__ += [ @@ -25,7 +24,7 @@ __all__ += [ ] from . import random -from .random import manual_seed +from .random import seed from .framework import get_default_dtype from .framework import set_default_dtype diff --git a/python/paddle/framework/random.py b/python/paddle/framework/random.py index ba2cf603d4..1624a069a5 100644 --- a/python/paddle/framework/random.py +++ b/python/paddle/framework/random.py @@ -16,10 +16,10 @@ import paddle.fluid as fluid from paddle.fluid import core -__all__ = ['manual_seed', 'get_cuda_rng_state', 'set_cuda_rng_state'] +__all__ = ['seed', 'get_cuda_rng_state', 'set_cuda_rng_state'] -def manual_seed(seed): +def seed(seed): """ Sets the seed for global default generator, which manages the random number generation. @@ -34,7 +34,7 @@ def manual_seed(seed): .. code-block:: python import paddle - gen = paddle.manual_seed(102) + gen = paddle.seed(102) """ #TODO(zhiqiu): 1. remove program.random_seed when all random-related op upgrade @@ -109,7 +109,7 @@ def _manual_program_seed(seed): """ Sets global seed for generating random numbers. - NOTE(zhiqiu): This is the original implemention of manual_seed. Keeps it temporally + NOTE(zhiqiu): This is the original implemention of seed. Keeps it temporally since CUDA generator is not developed, so we need it in the unittest. Args: diff --git a/python/paddle/hapi/model_summary.py b/python/paddle/hapi/model_summary.py index 30b22a2f32..c6288ea40c 100644 --- a/python/paddle/hapi/model_summary.py +++ b/python/paddle/hapi/model_summary.py @@ -51,14 +51,14 @@ def summary(net, input_size, dtypes=None): super(LeNet, self).__init__() self.num_classes = num_classes self.features = nn.Sequential( - nn.Conv2d( + nn.Conv2D( 1, 6, 3, stride=1, padding=1), nn.ReLU(), - nn.MaxPool2d(2, 2), - nn.Conv2d( + nn.MaxPool2D(2, 2), + nn.Conv2D( 6, 16, 5, stride=1, padding=0), nn.ReLU(), - nn.MaxPool2d(2, 2)) + nn.MaxPool2D(2, 2)) if num_classes > 0: self.fc = nn.Sequential( diff --git a/python/paddle/nn/__init__.py b/python/paddle/nn/__init__.py index 1d626c38c2..e53ba753a9 100644 --- a/python/paddle/nn/__init__.py +++ b/python/paddle/nn/__init__.py @@ -83,29 +83,29 @@ from .layer.common import Flatten #DEFINE_ALIAS from .layer.common import Upsample #DEFINE_ALIAS from .layer.common import Bilinear #DEFINE_ALIAS from .layer.common import Dropout #DEFINE_ALIAS -from .layer.common import Dropout2d #DEFINE_ALIAS -from .layer.common import Dropout3d #DEFINE_ALIAS +from .layer.common import Dropout2D #DEFINE_ALIAS +from .layer.common import Dropout3D #DEFINE_ALIAS from .layer.common import AlphaDropout #DEFINE_ALIAS -from .layer.pooling import AvgPool1d #DEFINE_ALIAS -from .layer.pooling import AvgPool2d #DEFINE_ALIAS -from .layer.pooling import AvgPool3d #DEFINE_ALIAS -from .layer.pooling import MaxPool1d #DEFINE_ALIAS -from .layer.pooling import MaxPool2d #DEFINE_ALIAS -from .layer.pooling import MaxPool3d #DEFINE_ALIAS -from .layer.pooling import AdaptiveAvgPool1d #DEFINE_ALIAS -from .layer.pooling import AdaptiveAvgPool2d #DEFINE_ALIAS -from .layer.pooling import AdaptiveAvgPool3d #DEFINE_ALIAS +from .layer.pooling import AvgPool1D #DEFINE_ALIAS +from .layer.pooling import AvgPool2D #DEFINE_ALIAS +from .layer.pooling import AvgPool3D #DEFINE_ALIAS +from .layer.pooling import MaxPool1D #DEFINE_ALIAS +from .layer.pooling import MaxPool2D #DEFINE_ALIAS +from .layer.pooling import MaxPool3D #DEFINE_ALIAS +from .layer.pooling import AdaptiveAvgPool1D #DEFINE_ALIAS +from .layer.pooling import AdaptiveAvgPool2D #DEFINE_ALIAS +from .layer.pooling import AdaptiveAvgPool3D #DEFINE_ALIAS -from .layer.pooling import AdaptiveMaxPool1d #DEFINE_ALIAS -from .layer.pooling import AdaptiveMaxPool2d #DEFINE_ALIAS -from .layer.pooling import AdaptiveMaxPool3d #DEFINE_ALIAS -from .layer.conv import Conv1d #DEFINE_ALIAS -from .layer.conv import Conv2d #DEFINE_ALIAS -from .layer.conv import Conv3d #DEFINE_ALIAS -from .layer.conv import ConvTranspose1d #DEFINE_ALIAS -from .layer.conv import ConvTranspose2d #DEFINE_ALIAS -from .layer.conv import ConvTranspose3d #DEFINE_ALIAS +from .layer.pooling import AdaptiveMaxPool1D #DEFINE_ALIAS +from .layer.pooling import AdaptiveMaxPool2D #DEFINE_ALIAS +from .layer.pooling import AdaptiveMaxPool3D #DEFINE_ALIAS +from .layer.conv import Conv1D #DEFINE_ALIAS +from .layer.conv import Conv2D #DEFINE_ALIAS +from .layer.conv import Conv3D #DEFINE_ALIAS +from .layer.conv import Conv1DTranspose #DEFINE_ALIAS +from .layer.conv import Conv2DTranspose #DEFINE_ALIAS +from .layer.conv import Conv3DTranspose #DEFINE_ALIAS # from .layer.conv import TreeConv #DEFINE_ALIAS # from .layer.conv import Conv1D #DEFINE_ALIAS from .layer.extension import RowConv #DEFINE_ALIAS @@ -127,12 +127,12 @@ from .layer.norm import SyncBatchNorm #DEFINE_ALIAS from .layer.norm import GroupNorm #DEFINE_ALIAS from .layer.norm import LayerNorm #DEFINE_ALIAS from .layer.norm import SpectralNorm #DEFINE_ALIAS -from .layer.norm import InstanceNorm1d #DEFINE_ALIAS -from .layer.norm import InstanceNorm2d #DEFINE_ALIAS -from .layer.norm import InstanceNorm3d #DEFINE_ALIAS -from .layer.norm import BatchNorm1d #DEFINE_ALIAS -from .layer.norm import BatchNorm2d #DEFINE_ALIAS -from .layer.norm import BatchNorm3d #DEFINE_ALIAS +from .layer.norm import InstanceNorm1D #DEFINE_ALIAS +from .layer.norm import InstanceNorm2D #DEFINE_ALIAS +from .layer.norm import InstanceNorm3D #DEFINE_ALIAS +from .layer.norm import BatchNorm1D #DEFINE_ALIAS +from .layer.norm import BatchNorm2D #DEFINE_ALIAS +from .layer.norm import BatchNorm3D #DEFINE_ALIAS from .layer.norm import LocalResponseNorm #DEFINE_ALIAS from .layer.rnn import RNNCellBase #DEFINE_ALIAS diff --git a/python/paddle/nn/functional/conv.py b/python/paddle/nn/functional/conv.py index d2e4ee2ac9..03dd40fb14 100644 --- a/python/paddle/nn/functional/conv.py +++ b/python/paddle/nn/functional/conv.py @@ -405,7 +405,7 @@ def conv2d(x, points. If dilation is a tuple, it must contain two integers, (dilation_height, dilation_width). Otherwise, dilation_height = dilation_width = dilation. Default: dilation = 1. - groups (int): The groups number of the Conv2d Layer. According to grouped + groups (int): The groups number of the Conv2D Layer. According to grouped convolution in Alex Krizhevsky's Deep CNN paper: when group=2, the first half of the filters is only connected to the first half of the input channels, while the second half of the filters is only @@ -896,7 +896,7 @@ def conv_transpose2d(x, Default: padding = 0. output_padding(int|list|tuple, optional): Additional size added to one side of each dimension in the output shape. Default: 0. - groups(int, optional): The groups number of the Conv2d transpose layer. Inspired by + groups(int, optional): The groups number of the Conv2D transpose layer. Inspired by grouped convolution in Alex Krizhevsky's Deep CNN paper, in which when group=2, the first half of the filters is only connected to the first half of the input channels, while the second half of the @@ -1122,7 +1122,7 @@ def conv3d(x, If dilation is a tuple, it must contain three integers, (dilation_depth, dilation_height, dilation_width). Otherwise, dilation_depth = dilation_height = dilation_width = dilation. Default: dilation = 1. - groups (int): The groups number of the Conv3d Layer. According to grouped + groups (int): The groups number of the Conv3D Layer. According to grouped convolution in Alex Krizhevsky's Deep CNN paper: when group=2, the first half of the filters is only connected to the first half of the input channels, while the second half of the filters is only @@ -1340,7 +1340,7 @@ def conv_transpose3d(x, Default: padding = 0. output_padding(int|list|tuple, optional): Additional size added to one side of each dimension in the output shape. Default: 0. - groups(int, optional): The groups number of the Conv3d transpose layer. Inspired by + groups(int, optional): The groups number of the Conv3D transpose layer. Inspired by grouped convolution in Alex Krizhevsky's Deep CNN paper, in which when group=2, the first half of the filters is only connected to the first half of the input channels, while the second half of the diff --git a/python/paddle/nn/functional/norm.py b/python/paddle/nn/functional/norm.py index 9b78368259..0a1547bebb 100644 --- a/python/paddle/nn/functional/norm.py +++ b/python/paddle/nn/functional/norm.py @@ -127,7 +127,7 @@ def batch_norm(x, """ Applies Batch Normalization as described in the paper Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift . - nn.functional.batch_norm is uesd for nn.BatchNorm1d, nn.BatchNorm2d, nn.BatchNorm3d. Please use above API for BatchNorm. + nn.functional.batch_norm is uesd for nn.BatchNorm1D, nn.BatchNorm2D, nn.BatchNorm3D. Please use above API for BatchNorm. Parameters: x(Tesnor): input value. It's data type should be float32, float64. @@ -338,7 +338,7 @@ def instance_norm(x, data_format="NCHW", name=None): """ - See more detail in nn.layer.InstanceNorm2d. + See more detail in nn.layer.InstanceNorm2D. Parameters: x(Tensor): Input Tensor. It's data type should be float32, float64. diff --git a/python/paddle/nn/layer/__init__.py b/python/paddle/nn/layer/__init__.py index 1defed3362..801290e995 100644 --- a/python/paddle/nn/layer/__init__.py +++ b/python/paddle/nn/layer/__init__.py @@ -53,27 +53,27 @@ from .common import Linear #DEFINE_ALIAS from .common import Flatten #DEFINE_ALIAS from .common import Upsample #DEFINE_ALIAS from .common import Dropout #DEFINE_ALIAS -from .common import Dropout2d #DEFINE_ALIAS -from .common import Dropout3d #DEFINE_ALIAS +from .common import Dropout2D #DEFINE_ALIAS +from .common import Dropout3D #DEFINE_ALIAS from .common import AlphaDropout #DEFINE_ALIAS -from .pooling import AvgPool1d #DEFINE_ALIAS -from .pooling import AvgPool2d #DEFINE_ALIAS -from .pooling import AvgPool3d #DEFINE_ALIAS -from .pooling import MaxPool1d #DEFINE_ALIAS -from .pooling import MaxPool2d #DEFINE_ALIAS -from .pooling import MaxPool3d #DEFINE_ALIAS -from .pooling import AdaptiveAvgPool1d #DEFINE_ALIAS -from .pooling import AdaptiveAvgPool2d #DEFINE_ALIAS -from .pooling import AdaptiveAvgPool3d #DEFINE_ALIAS -from .pooling import AdaptiveMaxPool1d #DEFINE_ALIAS -from .pooling import AdaptiveMaxPool2d #DEFINE_ALIAS -from .pooling import AdaptiveMaxPool3d #DEFINE_ALIAS -from .conv import Conv1d #DEFINE_ALIAS -from .conv import Conv2d #DEFINE_ALIAS -from .conv import Conv3d #DEFINE_ALIAS -from .conv import ConvTranspose1d #DEFINE_ALIAS -from .conv import ConvTranspose2d #DEFINE_ALIAS -from .conv import ConvTranspose3d #DEFINE_ALIAS +from .pooling import AvgPool1D #DEFINE_ALIAS +from .pooling import AvgPool2D #DEFINE_ALIAS +from .pooling import AvgPool3D #DEFINE_ALIAS +from .pooling import MaxPool1D #DEFINE_ALIAS +from .pooling import MaxPool2D #DEFINE_ALIAS +from .pooling import MaxPool3D #DEFINE_ALIAS +from .pooling import AdaptiveAvgPool1D #DEFINE_ALIAS +from .pooling import AdaptiveAvgPool2D #DEFINE_ALIAS +from .pooling import AdaptiveAvgPool3D #DEFINE_ALIAS +from .pooling import AdaptiveMaxPool1D #DEFINE_ALIAS +from .pooling import AdaptiveMaxPool2D #DEFINE_ALIAS +from .pooling import AdaptiveMaxPool3D #DEFINE_ALIAS +from .conv import Conv1D #DEFINE_ALIAS +from .conv import Conv2D #DEFINE_ALIAS +from .conv import Conv3D #DEFINE_ALIAS +from .conv import Conv1DTranspose #DEFINE_ALIAS +from .conv import Conv2DTranspose #DEFINE_ALIAS +from .conv import Conv3DTranspose #DEFINE_ALIAS # from .conv import TreeConv #DEFINE_ALIAS # from .conv import Conv1D #DEFINE_ALIAS from .extension import RowConv #DEFINE_ALIAS diff --git a/python/paddle/nn/layer/common.py b/python/paddle/nn/layer/common.py index 71bddefdb1..ad8263e483 100644 --- a/python/paddle/nn/layer/common.py +++ b/python/paddle/nn/layer/common.py @@ -32,8 +32,8 @@ __all__ = [ 'Pad3D', 'CosineSimilarity', 'Dropout', - 'Dropout2d', - 'Dropout3d', + 'Dropout2D', + 'Dropout3D', 'Bilinear', 'AlphaDropout', ] @@ -538,12 +538,12 @@ class Dropout(layers.Layer): return out -class Dropout2d(layers.Layer): +class Dropout2D(layers.Layer): """ Randomly zero out entire channels (in the batched input 4d tensor with the shape `NCHW` , a channel is a 2D feature map with the shape `HW`). Each channel will be zeroed out independently on every forward call with probability `p` using samples from a Bernoulli distribution. - Dropout2d will help promote independence between feature maps as described in the paper: + Dropout2D will help promote independence between feature maps as described in the paper: `Efficient Object Localization Using Convolutional Networks `_ See ``paddle.nn.functional.dropout2d`` for more details. @@ -570,7 +570,7 @@ class Dropout2d(layers.Layer): paddle.disable_static() x = np.random.random(size=(2, 3, 4, 5)).astype('float32') x = paddle.to_tensor(x) - m = paddle.nn.Dropout2d(p=0.5) + m = paddle.nn.Dropout2D(p=0.5) y_train = m(x) m.eval() # switch the model to test phase y_test = m(x) @@ -580,7 +580,7 @@ class Dropout2d(layers.Layer): """ def __init__(self, p=0.5, data_format='NCHW', name=None): - super(Dropout2d, self).__init__() + super(Dropout2D, self).__init__() self.p = p self.data_format = data_format @@ -596,12 +596,12 @@ class Dropout2d(layers.Layer): return out -class Dropout3d(layers.Layer): +class Dropout3D(layers.Layer): """ Randomly zero out entire channels (in the batched input 5d tensor with the shape `NCDHW` , a channel is a 3D feature map with the shape `DHW` ). Each channel will be zeroed out independently on every forward call with probability `p` using samples from a Bernoulli distribution. - Dropout3d will help promote independence between feature maps as described in the paper: + Dropout3D will help promote independence between feature maps as described in the paper: `Efficient Object Localization Using Convolutional Networks `_ See ``paddle.nn.functional.dropout3d`` for more details. @@ -628,7 +628,7 @@ class Dropout3d(layers.Layer): paddle.disable_static() x = np.random.random(size=(2, 3, 4, 5, 6)).astype('float32') x = paddle.to_tensor(x) - m = paddle.nn.Dropout3d(p=0.5) + m = paddle.nn.Dropout3D(p=0.5) y_train = m(x) m.eval() # switch the model to test phase y_test = m(x) @@ -638,7 +638,7 @@ class Dropout3d(layers.Layer): """ def __init__(self, p=0.5, data_format='NCDHW', name=None): - super(Dropout3d, self).__init__() + super(Dropout3D, self).__init__() self.p = p self.data_format = data_format diff --git a/python/paddle/nn/layer/conv.py b/python/paddle/nn/layer/conv.py index baa89798b7..51c466d113 100644 --- a/python/paddle/nn/layer/conv.py +++ b/python/paddle/nn/layer/conv.py @@ -15,12 +15,12 @@ # TODO: define classes of convolutional neural network __all__ = [ - 'Conv1d', - 'Conv2d', - 'Conv3d', - 'ConvTranspose1d', - 'ConvTranspose2d', - 'ConvTranspose3d', + 'Conv1D', + 'Conv2D', + 'Conv3D', + 'Conv1DTranspose', + 'Conv2DTranspose', + 'Conv3DTranspose', ] import numpy as np @@ -113,9 +113,9 @@ class _ConvNd(layers.Layer): attr=self._bias_attr, shape=[self._out_channels], is_bias=True) -class Conv1d(_ConvNd): +class Conv1D(_ConvNd): """ - This interface is used to construct a callable object of the ``Conv1d`` class. + This interface is used to construct a callable object of the ``Conv1D`` class. For more details, refer to code examples. The convolution1D layer calculates the output based on the input, filter and stride, padding, dilation, groups parameters. Input and @@ -194,7 +194,7 @@ class Conv1d(_ConvNd): Examples: .. code-block:: python import paddle - from paddle.nn import Conv1d + from paddle.nn import Conv1D import numpy as np x = np.array([[[4, 8, 1, 9], [7, 2, 0, 9], @@ -208,7 +208,7 @@ class Conv1d(_ConvNd): [5, 6, 8]]]).astype(np.float32) paddle.disable_static() x_t = paddle.to_tensor(x) - conv = Conv1d(3, 2, 3) + conv = Conv1D(3, 2, 3) conv.weight.set_value(w) y_t = conv(x_t) y_np = y_t.numpy() @@ -229,7 +229,7 @@ class Conv1d(_ConvNd): weight_attr=None, bias_attr=None, data_format="NCL"): - super(Conv1d, self).__init__( + super(Conv1D, self).__init__( in_channels, out_channels, kernel_size, @@ -266,9 +266,9 @@ class Conv1d(_ConvNd): return out -class ConvTranspose1d(_ConvNd): +class Conv1DTranspose(_ConvNd): """ - This interface is used to construct a callable object of the ``ConvTranspose1d`` class. + This interface is used to construct a callable object of the ``Conv1DTranspose`` class. For more details, refer to code examples. The 1-D convolution transpose layer calculates the output based on the input, filter, and dilation, stride, padding. Input(Input) and output(Output) @@ -340,7 +340,7 @@ class ConvTranspose1d(_ConvNd): `[pad]` or `[pad_left, pad_right]`. Default: padding = 0. output_padding(int|list|tuple, optional): The count of zeros to be added to tail of each dimension. If it is a tuple, it must contain one integer. Default: 0. - groups(int, optional): The groups number of the Conv2d transpose layer. Inspired by + groups(int, optional): The groups number of the Conv2D transpose layer. Inspired by grouped convolution in Alex Krizhevsky's Deep CNN paper, in which when group=2, the first half of the filters is only connected to the first half of the input channels, while the second half of the @@ -379,7 +379,7 @@ class ConvTranspose1d(_ConvNd): .. code-block:: python import paddle - from paddle.nn import ConvTranspose1d + from paddle.nn import Conv1DTranspose import numpy as np paddle.disable_static() @@ -390,7 +390,7 @@ class ConvTranspose1d(_ConvNd): y=np.array([[[7, 0]], [[4, 2]]]).astype(np.float32) x_t = paddle.to_tensor(x) - conv = ConvTranspose1d(2, 1, 2) + conv = Conv1DTranspose(2, 1, 2) conv.weight.set_value(y) y_t = conv(x_t) y_np = y_t.numpy() @@ -411,7 +411,7 @@ class ConvTranspose1d(_ConvNd): weight_attr=None, bias_attr=None, data_format="NCL"): - super(ConvTranspose1d, self).__init__( + super(Conv1DTranspose, self).__init__( in_channels, out_channels, kernel_size, @@ -441,9 +441,9 @@ class ConvTranspose1d(_ConvNd): return out -class Conv2d(_ConvNd): +class Conv2D(_ConvNd): """ - This interface is used to construct a callable object of the ``Conv2d`` class. + This interface is used to construct a callable object of the ``Conv2D`` class. For more details, refer to code examples. The convolution2D layer calculates the output based on the input, filter and strides, paddings, dilations, groups parameters. Input and @@ -491,7 +491,7 @@ class Conv2d(_ConvNd): dilation(int|list|tuple, optional): The dilation size. If dilation is a tuple, it must contain three integers, (dilation_D, dilation_H, dilation_W). Otherwise, the dilation_D = dilation_H = dilation_W = dilation. The default value is 1. - groups(int, optional): The groups number of the Conv3d Layer. According to grouped + groups(int, optional): The groups number of the Conv3D Layer. According to grouped convolution in Alex Krizhevsky's Deep CNN paper: when group=2, the first half of the filters is only connected to the first half of the input channels, while the second half of the filters is only @@ -536,10 +536,12 @@ class Conv2d(_ConvNd): import paddle import paddle.nn as nn - + + paddle.disable_static() + x_var = paddle.uniform((2, 4, 8, 8), dtype='float32', min=-1., max=1.) - conv = nn.Conv2d(4, 6, (3, 3)) + conv = nn.Conv2D(4, 6, (3, 3)) y_var = conv(x_var) y_np = y_var.numpy() print(y_np.shape) @@ -558,7 +560,7 @@ class Conv2d(_ConvNd): weight_attr=None, bias_attr=None, data_format="NCHW"): - super(Conv2d, self).__init__( + super(Conv2D, self).__init__( in_channels, out_channels, kernel_size, @@ -600,9 +602,9 @@ class Conv2d(_ConvNd): return out -class ConvTranspose2d(_ConvNd): +class Conv2DTranspose(_ConvNd): """ - This interface is used to construct a callable object of the ``ConvTranspose2d`` class. + This interface is used to construct a callable object of the ``Conv2DTranspose`` class. For more details, refer to code examples. The convolution2D transpose layer calculates the output based on the input, filter, and dilations, strides, paddings. Input and output @@ -653,7 +655,7 @@ class ConvTranspose2d(_ConvNd): dilation(int|list|tuple, optional): The dilation size. If dilation is a tuple, it must contain two integers, (dilation_H, dilation_W). Otherwise, the dilation_H = dilation_W = dilation. Default: 1. - groups(int, optional): The groups number of the Conv2d transpose layer. Inspired by + groups(int, optional): The groups number of the Conv2D transpose layer. Inspired by grouped convolution in Alex Krizhevsky's Deep CNN paper, in which when group=2, the first half of the filters is only connected to the first half of the input channels, while the second half of the @@ -701,10 +703,12 @@ class ConvTranspose2d(_ConvNd): import paddle import paddle.nn as nn + + paddle.disable_static() x_var = paddle.uniform((2, 4, 8, 8), dtype='float32', min=-1., max=1.) - conv = nn.ConvTranspose2d(4, 6, (3, 3)) + conv = nn.Conv2DTranspose(4, 6, (3, 3)) y_var = conv(x_var) y_np = y_var.numpy() print(y_np.shape) @@ -723,7 +727,7 @@ class ConvTranspose2d(_ConvNd): weight_attr=None, bias_attr=None, data_format="NCHW"): - super(ConvTranspose2d, self).__init__( + super(Conv2DTranspose, self).__init__( in_channels, out_channels, kernel_size, @@ -758,7 +762,7 @@ class ConvTranspose2d(_ConvNd): return out -class Conv3d(_ConvNd): +class Conv3D(_ConvNd): """ **Convlution3d Layer** The convolution3d layer calculates the output based on the input, filter @@ -802,7 +806,7 @@ class Conv3d(_ConvNd): dilation(int|list|tuple, optional): The dilation size. If dilation is a tuple, it must contain three integers, (dilation_D, dilation_H, dilation_W). Otherwise, the dilation_D = dilation_H = dilation_W = dilation. The default value is 1. - groups(int, optional): The groups number of the Conv3d Layer. According to grouped + groups(int, optional): The groups number of the Conv3D Layer. According to grouped convolution in Alex Krizhevsky's Deep CNN paper: when group=2, the first half of the filters is only connected to the first half of the input channels, while the second half of the filters is only @@ -853,10 +857,12 @@ class Conv3d(_ConvNd): import paddle import paddle.nn as nn + + paddle.disable_static() x_var = paddle.uniform((2, 4, 8, 8, 8), dtype='float32', min=-1., max=1.) - conv = nn.Conv3d(4, 6, (3, 3, 3)) + conv = nn.Conv3D(4, 6, (3, 3, 3)) y_var = conv(x_var) y_np = y_var.numpy() print(y_np.shape) @@ -875,7 +881,7 @@ class Conv3d(_ConvNd): weight_attr=None, bias_attr=None, data_format="NCDHW"): - super(Conv3d, self).__init__( + super(Conv3D, self).__init__( in_channels, out_channels, kernel_size, @@ -917,7 +923,7 @@ class Conv3d(_ConvNd): return out -class ConvTranspose3d(_ConvNd): +class Conv3DTranspose(_ConvNd): """ **Convlution3D transpose layer** The convolution3D transpose layer calculates the output based on the input, @@ -981,7 +987,7 @@ class ConvTranspose3d(_ConvNd): dilation(int|list|tuple, optional): The dilation size. If dilation is a tuple, it must contain three integers, (dilation_D, dilation_H, dilation_W). Otherwise, the dilation_D = dilation_H = dilation_W = dilation. The default value is 1. - groups(int, optional): The groups number of the Conv3d transpose layer. Inspired by + groups(int, optional): The groups number of the Conv3D transpose layer. Inspired by grouped convolution in Alex Krizhevsky's Deep CNN paper, in which when group=2, the first half of the filters is only connected to the first half of the input channels, while the second half of the @@ -1035,10 +1041,12 @@ class ConvTranspose3d(_ConvNd): import paddle import paddle.nn as nn + + paddle.disable_static() x_var = paddle.uniform((2, 4, 8, 8, 8), dtype='float32', min=-1., max=1.) - conv = nn.ConvTranspose3d(4, 6, (3, 3, 3)) + conv = nn.Conv3DTranspose(4, 6, (3, 3, 3)) y_var = conv(x_var) y_np = y_var.numpy() print(y_np.shape) @@ -1057,7 +1065,7 @@ class ConvTranspose3d(_ConvNd): weight_attr=None, bias_attr=None, data_format="NCDHW"): - super(ConvTranspose3d, self).__init__( + super(Conv3DTranspose, self).__init__( in_channels, out_channels, kernel_size, diff --git a/python/paddle/nn/layer/norm.py b/python/paddle/nn/layer/norm.py index ad8dc9b64e..a996844c8f 100644 --- a/python/paddle/nn/layer/norm.py +++ b/python/paddle/nn/layer/norm.py @@ -54,17 +54,17 @@ from ...fluid.dygraph.base import no_grad from .. import functional as F __all__ = [ - 'BatchNorm', 'GroupNorm', 'LayerNorm', 'SpectralNorm', 'BatchNorm1d', - 'BatchNorm2d', 'BatchNorm3d', 'InstanceNorm1d', 'InstanceNorm2d', - 'InstanceNorm3d', 'SyncBatchNorm', 'LocalResponseNorm' + 'BatchNorm', 'GroupNorm', 'LayerNorm', 'SpectralNorm', 'BatchNorm1D', + 'BatchNorm2D', 'BatchNorm3D', 'InstanceNorm1D', 'InstanceNorm2D', + 'InstanceNorm3D', 'SyncBatchNorm', 'LocalResponseNorm' ] class _InstanceNormBase(layers.Layer): """ - This class is based class for InstanceNorm1d, 2d, 3d. + This class is based class for InstanceNorm1D, 2d, 3d. - See InstaceNorm1d, InstanceNorm2d or InstanceNorm3d for more details. + See InstaceNorm1D, InstanceNorm2D or InstanceNorm3D for more details. """ def __init__(self, @@ -109,7 +109,7 @@ class _InstanceNormBase(layers.Layer): input, weight=self.scale, bias=self.bias, eps=self._epsilon) -class InstanceNorm1d(_InstanceNormBase): +class InstanceNorm1D(_InstanceNormBase): """ Applies Instance Normalization over a 3D input (a mini-batch of 1D inputs with additional channel dimension) as described in the paper Instance Normalization: The Missing Ingredient for Fast Stylization . @@ -174,7 +174,7 @@ class InstanceNorm1d(_InstanceNormBase): np.random.seed(123) x_data = np.random.random(size=(2, 2, 3)).astype('float32') x = paddle.to_tensor(x_data) - instance_norm = paddle.nn.InstanceNorm1d(2) + instance_norm = paddle.nn.InstanceNorm1D(2) instance_norm_out = instance_norm(x) print(instance_norm_out.numpy()) @@ -187,7 +187,7 @@ class InstanceNorm1d(_InstanceNormBase): len(input.shape))) -class InstanceNorm2d(_InstanceNormBase): +class InstanceNorm2D(_InstanceNormBase): """ Applies Instance Normalization over a 4D input (a mini-batch of 2D inputs with additional channel dimension) as described in the paper Instance Normalization: The Missing Ingredient for Fast Stylization . @@ -251,7 +251,7 @@ class InstanceNorm2d(_InstanceNormBase): np.random.seed(123) x_data = np.random.random(size=(2, 2, 2, 3)).astype('float32') x = paddle.to_tensor(x_data) - instance_norm = paddle.nn.InstanceNorm2d(2) + instance_norm = paddle.nn.InstanceNorm2D(2) instance_norm_out = instance_norm(x) print(instance_norm_out.numpy()) @@ -263,7 +263,7 @@ class InstanceNorm2d(_InstanceNormBase): len(input.shape))) -class InstanceNorm3d(_InstanceNormBase): +class InstanceNorm3D(_InstanceNormBase): """ Applies Instance Normalization over a 5D input (a mini-batch of 3D inputs with additional channel dimension) as described in the paper Instance Normalization: The Missing Ingredient for Fast Stylization . @@ -327,7 +327,7 @@ class InstanceNorm3d(_InstanceNormBase): np.random.seed(123) x_data = np.random.random(size=(2, 2, 2, 2, 3)).astype('float32') x = paddle.to_tensor(x_data) - instance_norm = paddle.nn.InstanceNorm3d(2) + instance_norm = paddle.nn.InstanceNorm3D(2) instance_norm_out = instance_norm(x) print(instance_norm_out.numpy()) @@ -671,7 +671,7 @@ class _BatchNormBase(layers.Layer): data_format=self._data_format) -class BatchNorm1d(_BatchNormBase): +class BatchNorm1D(_BatchNormBase): """ Applies Batch Normalization over a 2D or 3D input (a mini-batch of 1D inputswith additional channel dimension) as described in the paper Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift . @@ -747,7 +747,7 @@ class BatchNorm1d(_BatchNormBase): np.random.seed(123) x_data = np.random.random(size=(2, 1, 3)).astype('float32') x = paddle.to_tensor(x_data) - batch_norm = paddle.nn.BatchNorm1d(1) + batch_norm = paddle.nn.BatchNorm1D(1) batch_norm_out = batch_norm(x) print(batch_norm_out.numpy()) @@ -768,7 +768,7 @@ class BatchNorm1d(_BatchNormBase): len(input.shape))) -class BatchNorm2d(_BatchNormBase): +class BatchNorm2D(_BatchNormBase): """ Applies Batch Normalization over a 4D input (a mini-batch of 2D inputswith additional channel dimension) as described in the paper Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift . @@ -843,7 +843,7 @@ class BatchNorm2d(_BatchNormBase): np.random.seed(123) x_data = np.random.random(size=(2, 1, 2, 3)).astype('float32') x = paddle.to_tensor(x_data) - batch_norm = paddle.nn.BatchNorm2d(1) + batch_norm = paddle.nn.BatchNorm2D(1) batch_norm_out = batch_norm(x) print(batch_norm_out.numpy()) @@ -863,7 +863,7 @@ class BatchNorm2d(_BatchNormBase): len(input.shape))) -class BatchNorm3d(_BatchNormBase): +class BatchNorm3D(_BatchNormBase): """ Applies Batch Normalization over a 5D input (a mini-batch of 3D inputswith additional channel dimension) as described in the paper Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift . @@ -938,7 +938,7 @@ class BatchNorm3d(_BatchNormBase): np.random.seed(123) x_data = np.random.random(size=(2, 1, 2, 2, 3)).astype('float32') x = paddle.to_tensor(x_data) - batch_norm = paddle.nn.BatchNorm3d(1) + batch_norm = paddle.nn.BatchNorm3D(1) batch_norm_out = batch_norm(x) print(batch_norm_out.numpy()) @@ -1141,7 +1141,7 @@ class SyncBatchNorm(_BatchNormBase): import paddle.nn as nn paddle.disable_static() - model = nn.Sequential(nn.Conv2d(3, 5, 3), nn.BatchNorm2d(5)) + model = nn.Sequential(nn.Conv2D(3, 5, 3), nn.BatchNorm2D(5)) sync_model = nn.SyncBatchNorm.convert_sync_batchnorm(model) """ diff --git a/python/paddle/nn/layer/pooling.py b/python/paddle/nn/layer/pooling.py index 129dae93b3..9e544cb02e 100755 --- a/python/paddle/nn/layer/pooling.py +++ b/python/paddle/nn/layer/pooling.py @@ -17,22 +17,22 @@ from ...fluid.layer_helper import LayerHelper from .. import functional as F __all__ = [ - 'AvgPool1d', - 'AvgPool2d', - 'AvgPool3d', - 'MaxPool1d', - 'MaxPool2d', - 'MaxPool3d', - 'AdaptiveAvgPool1d', - 'AdaptiveAvgPool2d', - 'AdaptiveAvgPool3d', - 'AdaptiveMaxPool1d', - 'AdaptiveMaxPool2d', - 'AdaptiveMaxPool3d', + 'AvgPool1D', + 'AvgPool2D', + 'AvgPool3D', + 'MaxPool1D', + 'MaxPool2D', + 'MaxPool3D', + 'AdaptiveAvgPool1D', + 'AdaptiveAvgPool2D', + 'AdaptiveAvgPool3D', + 'AdaptiveMaxPool1D', + 'AdaptiveMaxPool2D', + 'AdaptiveMaxPool3D', ] -class AvgPool1d(layers.Layer): +class AvgPool1D(layers.Layer): """ This operation applies a 1D average pooling over an input signal composed of several input planes, based on the input, output_size, return_indices parameters. @@ -93,8 +93,8 @@ class AvgPool1d(layers.Layer): paddle.disable_static() data = paddle.to_tensor(np.random.uniform(-1, 1, [1, 3, 32]).astype(np.float32)) - AvgPool1d = nn.AvgPool1d(kernel_size=2, stride=2, padding=0) - pool_out = AvgPool1d(data) + AvgPool1D = nn.AvgPool1D(kernel_size=2, stride=2, padding=0) + pool_out = AvgPool1D(data) # pool_out shape: [1, 3, 16] """ @@ -106,7 +106,7 @@ class AvgPool1d(layers.Layer): count_include_pad=True, ceil_mode=False, name=None): - super(AvgPool1d, self).__init__() + super(AvgPool1D, self).__init__() self.kernel_size = kernel_size self.stride = stride self.padding = padding @@ -120,7 +120,7 @@ class AvgPool1d(layers.Layer): return out -class AvgPool2d(layers.Layer): +class AvgPool2D(layers.Layer): """ This operation applies 2D average pooling over input features based on the input, and kernel_size, stride, padding parameters. Input(X) and Output(Out) are @@ -185,7 +185,7 @@ class AvgPool2d(layers.Layer): # max pool2d input = paddle.to_tensor(np.random.uniform(-1, 1, [1, 3, 32, 32]).astype(np.float32)) - AvgPool2d = nn.AvgPool2d(kernel_size=2, + AvgPool2D = nn.AvgPool2D(kernel_size=2, stride=2, padding=0) output = AvgPoo2d(input) # output.shape [1, 3, 16, 16] @@ -201,7 +201,7 @@ class AvgPool2d(layers.Layer): divisor_override=None, data_format="NCHW", name=None): - super(AvgPool2d, self).__init__() + super(AvgPool2D, self).__init__() self.ksize = kernel_size self.stride = stride self.padding = padding @@ -224,7 +224,7 @@ class AvgPool2d(layers.Layer): name=self.name) -class AvgPool3d(layers.Layer): +class AvgPool3D(layers.Layer): """ This operation applies 3D max pooling over input features based on the input, and kernel_size, stride, padding parameters. Input(X) and Output(Out) are @@ -277,9 +277,9 @@ class AvgPool3d(layers.Layer): # avg pool3d input = paddle.to_tensor(np.random.uniform(-1, 1, [1, 2, 3, 32, 32]).astype(np.float32)) - AvgPool3d = nn.AvgPool3d(kernel_size=2, + AvgPool3D = nn.AvgPool3D(kernel_size=2, stride=2, padding=0) - output = AvgPool3d(input) + output = AvgPool3D(input) # output.shape [1, 2, 3, 16, 16] """ @@ -293,7 +293,7 @@ class AvgPool3d(layers.Layer): divisor_override=None, data_format="NCDHW", name=None): - super(AvgPool3d, self).__init__() + super(AvgPool3D, self).__init__() self.ksize = kernel_size self.stride = stride self.padding = padding @@ -316,7 +316,7 @@ class AvgPool3d(layers.Layer): name=self.name) -class MaxPool1d(layers.Layer): +class MaxPool1D(layers.Layer): """ Applies a 1D max pooling over an input signal composed of several input planes based on the input, output_size, return_indices parameters. @@ -373,12 +373,12 @@ class MaxPool1d(layers.Layer): paddle.disable_static() data = paddle.to_tensor(np.random.uniform(-1, 1, [1, 3, 32]).astype(np.float32)) - MaxPool1d = nn.MaxPool1d(kernel_size=2, stride=2, padding=0) - pool_out = MaxPool1d(data) + MaxPool1D = nn.MaxPool1D(kernel_size=2, stride=2, padding=0) + pool_out = MaxPool1D(data) # pool_out shape: [1, 3, 16] - MaxPool1d = nn.MaxPool1d(kernel_size=2, stride=2, padding=0, return_indices=True) - pool_out, indices = MaxPool1d(data) + MaxPool1D = nn.MaxPool1D(kernel_size=2, stride=2, padding=0, return_indices=True) + pool_out, indices = MaxPool1D(data) # pool_out shape: [1, 3, 16], indices shape: [1, 3, 16] """ @@ -390,7 +390,7 @@ class MaxPool1d(layers.Layer): return_indices=False, ceil_mode=False, name=None): - super(MaxPool1d, self).__init__() + super(MaxPool1D, self).__init__() self.kernel_size = kernel_size self.stride = stride self.padding = padding @@ -404,7 +404,7 @@ class MaxPool1d(layers.Layer): return out -class MaxPool2d(layers.Layer): +class MaxPool2D(layers.Layer): """ This operation applies 2D max pooling over input feature based on the input, and kernel_size, stride, padding parameters. Input(X) and Output(Out) are @@ -468,14 +468,14 @@ class MaxPool2d(layers.Layer): # max pool2d input = paddle.to_tensor(np.random.uniform(-1, 1, [1, 3, 32, 32]).astype(np.float32)) - MaxPool2d = nn.MaxPool2d(kernel_size=2, + MaxPool2D = nn.MaxPool2D(kernel_size=2, stride=2, padding=0) - output = MaxPool2d(input) + output = MaxPool2D(input) # output.shape [1, 3, 16, 16] # for return_indices=True - MaxPool2d = nn.MaxPool2d(kernel_size=2,stride=2, padding=0, return_indices=True) - output, max_indices = MaxPool2d(input) + MaxPool2D = nn.MaxPool2D(kernel_size=2,stride=2, padding=0, return_indices=True) + output, max_indices = MaxPool2D(input) # output.shape [1, 3, 16, 16], max_indices.shape [1, 3, 16, 16], """ @@ -487,7 +487,7 @@ class MaxPool2d(layers.Layer): ceil_mode=False, data_format="NCHW", name=None): - super(MaxPool2d, self).__init__() + super(MaxPool2D, self).__init__() self.ksize = kernel_size self.stride = stride self.padding = padding @@ -507,7 +507,7 @@ class MaxPool2d(layers.Layer): name=self.name) -class MaxPool3d(layers.Layer): +class MaxPool3D(layers.Layer): """ This operation applies 3D max pooling over input features based on the input, and kernel_size, stride, padding parameters. Input(X) and Output(Out) are @@ -559,14 +559,14 @@ class MaxPool3d(layers.Layer): # max pool3d input = paddle.to_tensor(np.random.uniform(-1, 1, [1, 2, 3, 32, 32]).astype(np.float32)) - MaxPool3d = nn.MaxPool3d(kernel_size=2, + MaxPool3D = nn.MaxPool3D(kernel_size=2, stride=2, padding=0) - output = MaxPool3d(input) + output = MaxPool3D(input) # output.shape [1, 2, 3, 16, 16] # for return_indices=True - MaxPool3d = nn.MaxPool3d(kernel_size=2,stride=2, padding=0, return_indices=True) - output, max_indices = MaxPool3d(input) + MaxPool3D = nn.MaxPool3D(kernel_size=2,stride=2, padding=0, return_indices=True) + output, max_indices = MaxPool3D(input) # output.shape [1, 2, 3, 16, 16], max_indices.shape [1, 2, 3, 16, 16], """ @@ -578,7 +578,7 @@ class MaxPool3d(layers.Layer): ceil_mode=False, data_format="NCDHW", name=None): - super(MaxPool3d, self).__init__() + super(MaxPool3D, self).__init__() self.ksize = kernel_size self.stride = stride self.padding = padding @@ -598,7 +598,7 @@ class MaxPool3d(layers.Layer): name=self.name) -class AdaptiveAvgPool1d(layers.Layer): +class AdaptiveAvgPool1D(layers.Layer): """ This operation applies a 1D adaptive average pooling over an input signal composed @@ -653,13 +653,13 @@ class AdaptiveAvgPool1d(layers.Layer): paddle.disable_static() data = paddle.to_tensor(np.random.uniform(-1, 1, [1, 3, 32]).astype(np.float32)) - AdaptiveAvgPool1d = nn.AdaptiveAvgPool1d(output_size=16) - pool_out = AdaptiveAvgPool1d(data) + AdaptiveAvgPool1D = nn.AdaptiveAvgPool1D(output_size=16) + pool_out = AdaptiveAvgPool1D(data) # pool_out shape: [1, 3, 16] """ def __init__(self, output_size, name=None): - super(AdaptiveAvgPool1d, self).__init__() + super(AdaptiveAvgPool1D, self).__init__() self.output_size = output_size self.name = name @@ -667,7 +667,7 @@ class AdaptiveAvgPool1d(layers.Layer): return F.adaptive_avg_pool1d(input, self.output_size, self.name) -class AdaptiveAvgPool2d(layers.Layer): +class AdaptiveAvgPool2D(layers.Layer): """ This operation applies 2D adaptive avg pooling on input tensor. The h and w dimensions @@ -704,7 +704,7 @@ class AdaptiveAvgPool2d(layers.Layer): output (Tensor): The output tensor of adaptive avg pool2d operator, which is a 4-D tensor. The data type is same as input x. Returns: - A callable object of AdaptiveAvgPool2d. + A callable object of AdaptiveAvgPool2D. Examples: .. code-block:: python @@ -730,13 +730,13 @@ class AdaptiveAvgPool2d(layers.Layer): input_data = np.random.rand(2, 3, 32, 32) x = paddle.to_tensor(input_data) # x.shape is [2, 3, 32, 32] - adaptive_avg_pool = paddle.nn.AdaptiveAvgPool2d(output_size=3) + adaptive_avg_pool = paddle.nn.AdaptiveAvgPool2D(output_size=3) pool_out = adaptive_avg_pool(x = x) # pool_out.shape is [2, 3, 3, 3] """ def __init__(self, output_size, data_format="NCHW", name=None): - super(AdaptiveAvgPool2d, self).__init__() + super(AdaptiveAvgPool2D, self).__init__() self._output_size = output_size self._data_format = data_format self._name = name @@ -749,7 +749,7 @@ class AdaptiveAvgPool2d(layers.Layer): name=self._name) -class AdaptiveAvgPool3d(layers.Layer): +class AdaptiveAvgPool3D(layers.Layer): """ This operation applies 3D adaptive avg pooling on input tensor. The h and w dimensions @@ -789,7 +789,7 @@ class AdaptiveAvgPool3d(layers.Layer): output (Tensor): The output tensor of adaptive avg pool3d operator, which is a 5-D tensor. The data type is same as input x. Returns: - A callable object of AdaptiveAvgPool3d. + A callable object of AdaptiveAvgPool3D. Examples: .. code-block:: python @@ -818,13 +818,13 @@ class AdaptiveAvgPool3d(layers.Layer): input_data = np.random.rand(2, 3, 8, 32, 32) x = paddle.to_tensor(input_data) # x.shape is [2, 3, 8, 32, 32] - adaptive_avg_pool = paddle.nn.AdaptiveAvgPool3d(output_size=3) + adaptive_avg_pool = paddle.nn.AdaptiveAvgPool3D(output_size=3) pool_out = adaptive_avg_pool(x = x) # pool_out = [2, 3, 3, 3, 3] """ def __init__(self, output_size, data_format="NCDHW", name=None): - super(AdaptiveAvgPool3d, self).__init__() + super(AdaptiveAvgPool3D, self).__init__() self._output_size = output_size self._data_format = data_format self._name = name @@ -837,7 +837,7 @@ class AdaptiveAvgPool3d(layers.Layer): name=self._name) -class AdaptiveMaxPool1d(layers.Layer): +class AdaptiveMaxPool1D(layers.Layer): """ This operation applies a 1D adaptive max pooling over an input signal composed @@ -894,19 +894,19 @@ class AdaptiveMaxPool1d(layers.Layer): paddle.disable_static() data = paddle.to_tensor(np.random.uniform(-1, 1, [1, 3, 32]).astype(np.float32)) - AdaptiveMaxPool1d = nn.AdaptiveMaxPool1d(output_size=16) - pool_out = AdaptiveMaxPool1d(data) + AdaptiveMaxPool1D = nn.AdaptiveMaxPool1D(output_size=16) + pool_out = AdaptiveMaxPool1D(data) # pool_out shape: [1, 3, 16] # for return_indices = true - AdaptiveMaxPool1d = nn.AdaptiveMaxPool1d(output_size=16, return_indices=True) - pool_out, indices = AdaptiveMaxPool1d(data) + AdaptiveMaxPool1D = nn.AdaptiveMaxPool1D(output_size=16, return_indices=True) + pool_out, indices = AdaptiveMaxPool1D(data) # pool_out shape: [1, 3, 16], indices shape: [1, 3, 16] """ def __init__(self, output_size, return_indices=False, name=None): - super(AdaptiveMaxPool1d, self).__init__() + super(AdaptiveMaxPool1D, self).__init__() self.output_size = output_size self.return_indices = return_indices self.name = name @@ -916,7 +916,7 @@ class AdaptiveMaxPool1d(layers.Layer): self.return_indices, self.name) -class AdaptiveMaxPool2d(layers.Layer): +class AdaptiveMaxPool2D(layers.Layer): """ This operation applies 2D adaptive max pooling on input tensor. The h and w dimensions of the output tensor are determined by the parameter output_size. The difference between adaptive pooling and pooling is adaptive one focus on the output size. @@ -941,7 +941,7 @@ class AdaptiveMaxPool2d(layers.Layer): output (Tensor): The output tensor of adaptive max pool2d operator, which is a 4-D tensor. The data type is same as input x. Returns: - A callable object of AdaptiveMaxPool2d. + A callable object of AdaptiveMaxPool2D. Examples: .. code-block:: python @@ -965,12 +965,12 @@ class AdaptiveMaxPool2d(layers.Layer): paddle.disable_static() input_data = np.random.rand(2, 3, 32, 32) x = paddle.to_tensor(input_data) - adaptive_max_pool = paddle.nn.AdaptiveMaxPool2d(output_size=3, return_indices=True) + adaptive_max_pool = paddle.nn.AdaptiveMaxPool2D(output_size=3, return_indices=True) pool_out, indices = adaptive_max_pool(x = x) """ def __init__(self, output_size, return_indices=False, name=None): - super(AdaptiveMaxPool2d, self).__init__() + super(AdaptiveMaxPool2D, self).__init__() self._output_size = output_size self._return_indices = return_indices self._name = name @@ -983,7 +983,7 @@ class AdaptiveMaxPool2d(layers.Layer): name=self._name) -class AdaptiveMaxPool3d(layers.Layer): +class AdaptiveMaxPool3D(layers.Layer): """ This operation applies 3D adaptive max pooling on input tensor. The h and w dimensions of the output tensor are determined by the parameter output_size. The difference between adaptive pooling and pooling is adaptive one focus on the output size. @@ -1010,7 +1010,7 @@ class AdaptiveMaxPool3d(layers.Layer): x (Tensor): The input tensor of adaptive max pool3d operator, which is a 5-D tensor. The data type can be float32, float64. output (Tensor): The output tensor of adaptive max pool3d operator, which is a 5-D tensor. The data type is same as input x. Returns: - A callable object of AdaptiveMaxPool3d. + A callable object of AdaptiveMaxPool3D. Examples: .. code-block:: python @@ -1037,17 +1037,17 @@ class AdaptiveMaxPool3d(layers.Layer): paddle.disable_static() input_data = np.random.rand(2, 3, 8, 32, 32) x = paddle.to_tensor(input_data) - pool = paddle.nn.AdaptiveMaxPool3d(output_size=4) + pool = paddle.nn.AdaptiveMaxPool3D(output_size=4) out = pool(x) # out shape: [2, 3, 4, 4, 4] - pool = paddle.nn.AdaptiveMaxPool3d(output_size=3, return_indices=True) + pool = paddle.nn.AdaptiveMaxPool3D(output_size=3, return_indices=True) out, indices = pool(x) # out shape: [2, 3, 4, 4, 4], indices shape: [2, 3, 4, 4, 4] """ def __init__(self, output_size, return_indices=False, name=None): - super(AdaptiveMaxPool3d, self).__init__() + super(AdaptiveMaxPool3D, self).__init__() self._output_size = output_size self._return_indices = return_indices self._name = name diff --git a/python/paddle/regularizer.py b/python/paddle/regularizer.py index 5cbb86bfef..a1ab329169 100644 --- a/python/paddle/regularizer.py +++ b/python/paddle/regularizer.py @@ -61,11 +61,11 @@ class L1Decay(fluid.regularizer.L1Decay): # Example2: set Regularizer in parameters # Set L1 regularization in parameters. # Global regularizer does not take effect on my_conv2d for this case. - from paddle.nn import Conv2d + from paddle.nn import Conv2D from paddle import ParamAttr from paddle.regularizer import L2Decay - my_conv2d = Conv2d( + my_conv2d = Conv2D( in_channels=10, out_channels=10, kernel_size=1, @@ -123,11 +123,11 @@ class L2Decay(fluid.regularizer.L2Decay): # Example2: set Regularizer in parameters # Set L2 regularization in parameters. # Global regularizer does not take effect on my_conv2d for this case. - from paddle.nn import Conv2d + from paddle.nn import Conv2D from paddle import ParamAttr from paddle.regularizer import L2Decay - my_conv2d = Conv2d( + my_conv2d = Conv2D( in_channels=10, out_channels=10, kernel_size=1, diff --git a/python/paddle/tensor/random.py b/python/paddle/tensor/random.py index 3a0435e776..7e4d3d7bf9 100644 --- a/python/paddle/tensor/random.py +++ b/python/paddle/tensor/random.py @@ -59,13 +59,13 @@ def bernoulli(x, name=None): import paddle - paddle.manual_seed(100) # on CPU device + paddle.seed(100) # on CPU device x = paddle.rand([2,3]) print(x.numpy()) # [[0.5535528 0.20714243 0.01162981] # [0.51577556 0.36369765 0.2609165 ]] - paddle.manual_seed(200) # on CPU device + paddle.seed(200) # on CPU device out = paddle.bernoulli(x) print(out.numpy()) # [[0. 0. 0.] @@ -110,13 +110,13 @@ def multinomial(x, num_samples=1, replacement=False, name=None): import paddle - paddle.manual_seed(100) # on CPU device + paddle.seed(100) # on CPU device x = paddle.rand([2,4]) print(x.numpy()) # [[0.5535528 0.20714243 0.01162981 0.51577556] # [0.36369765 0.2609165 0.18905126 0.5621971 ]] - paddle.manual_seed(200) # on CPU device + paddle.seed(200) # on CPU device out1 = paddle.multinomial(x, num_samples=5, replacement=True) print(out1.numpy()) # [[3 3 0 0 0] @@ -126,7 +126,7 @@ def multinomial(x, num_samples=1, replacement=False, name=None): # InvalidArgumentError: When replacement is False, number of samples # should be less than non-zero categories - paddle.manual_seed(300) # on CPU device + paddle.seed(300) # on CPU device out3 = paddle.multinomial(x, num_samples=3) print(out3.numpy()) # [[3 0 1] diff --git a/python/paddle/tensor/to_string.py b/python/paddle/tensor/to_string.py index 0da110146a..c56c1baa7a 100644 --- a/python/paddle/tensor/to_string.py +++ b/python/paddle/tensor/to_string.py @@ -52,7 +52,7 @@ def set_printoptions(precision=None, import paddle - paddle.manual_seed(10) + paddle.seed(10) a = paddle.rand([10, 20]) paddle.set_printoptions(4, 100, 3) print(a) diff --git a/python/paddle/tests/test_model.py b/python/paddle/tests/test_model.py index bcb910a5ad..3513f06234 100644 --- a/python/paddle/tests/test_model.py +++ b/python/paddle/tests/test_model.py @@ -25,7 +25,7 @@ import tempfile import paddle from paddle import fluid from paddle import to_tensor -from paddle.nn import Conv2d, Linear, ReLU, Sequential, Softmax +from paddle.nn import Conv2D, Linear, ReLU, Sequential, Softmax from paddle import Model from paddle.static import InputSpec @@ -44,11 +44,11 @@ class LeNetDygraph(paddle.nn.Layer): super(LeNetDygraph, self).__init__() self.num_classes = num_classes self.features = Sequential( - Conv2d( + Conv2D( 1, 6, 3, stride=1, padding=1), ReLU(), paddle.fluid.dygraph.Pool2D(2, 'max', 2), - Conv2d( + Conv2D( 6, 16, 5, stride=1, padding=0), ReLU(), paddle.fluid.dygraph.Pool2D(2, 'max', 2)) @@ -142,7 +142,7 @@ class TestModel(unittest.TestCase): cls.test_dataset, places=cls.device, batch_size=64) seed = 333 - paddle.manual_seed(seed) + paddle.seed(seed) paddle.framework.random._manual_program_seed(seed) dy_lenet = LeNetDygraph() @@ -194,7 +194,7 @@ class TestModel(unittest.TestCase): def fit(self, dynamic, num_replicas=None, rank=None): fluid.enable_dygraph(self.device) if dynamic else None seed = 333 - paddle.manual_seed(seed) + paddle.seed(seed) paddle.framework.random._manual_program_seed(seed) net = LeNet() @@ -306,7 +306,7 @@ class MyDataset(Dataset): class TestModelFunction(unittest.TestCase): def set_seed(self, seed=1024): - paddle.manual_seed(seed) + paddle.seed(seed) paddle.framework.random._manual_program_seed(seed) def test_train_batch(self, dynamic=True): diff --git a/python/paddle/vision/models/lenet.py b/python/paddle/vision/models/lenet.py index b30d5992f9..119be85db5 100644 --- a/python/paddle/vision/models/lenet.py +++ b/python/paddle/vision/models/lenet.py @@ -38,14 +38,14 @@ class LeNet(nn.Layer): super(LeNet, self).__init__() self.num_classes = num_classes self.features = nn.Sequential( - nn.Conv2d( + nn.Conv2D( 1, 6, 3, stride=1, padding=1), nn.ReLU(), - nn.MaxPool2d(2, 2), - nn.Conv2d( + nn.MaxPool2D(2, 2), + nn.Conv2D( 6, 16, 5, stride=1, padding=0), nn.ReLU(), - nn.MaxPool2d(2, 2)) + nn.MaxPool2D(2, 2)) if num_classes > 0: self.fc = nn.Sequential( diff --git a/python/paddle/vision/models/mobilenetv1.py b/python/paddle/vision/models/mobilenetv1.py index 4e6030bd14..22d177248e 100644 --- a/python/paddle/vision/models/mobilenetv1.py +++ b/python/paddle/vision/models/mobilenetv1.py @@ -36,7 +36,7 @@ class ConvBNLayer(nn.Layer): num_groups=1): super(ConvBNLayer, self).__init__() - self._conv = nn.Conv2d( + self._conv = nn.Conv2D( in_channels, out_channels, kernel_size, @@ -45,7 +45,7 @@ class ConvBNLayer(nn.Layer): groups=num_groups, bias_attr=False) - self._norm_layer = nn.BatchNorm2d(out_channels) + self._norm_layer = nn.BatchNorm2D(out_channels) self._act = nn.ReLU() def forward(self, x): @@ -214,7 +214,7 @@ class MobileNetV1(nn.Layer): self.dwsl.append(dws6) if with_pool: - self.pool2d_avg = nn.AdaptiveAvgPool2d(1) + self.pool2d_avg = nn.AdaptiveAvgPool2D(1) if num_classes > 0: self.fc = nn.Linear(int(1024 * scale), num_classes) diff --git a/python/paddle/vision/models/mobilenetv2.py b/python/paddle/vision/models/mobilenetv2.py index 0f4dc22f67..f1cbaab1f9 100644 --- a/python/paddle/vision/models/mobilenetv2.py +++ b/python/paddle/vision/models/mobilenetv2.py @@ -46,11 +46,11 @@ class ConvBNReLU(nn.Sequential): kernel_size=3, stride=1, groups=1, - norm_layer=nn.BatchNorm2d): + norm_layer=nn.BatchNorm2D): padding = (kernel_size - 1) // 2 super(ConvBNReLU, self).__init__( - nn.Conv2d( + nn.Conv2D( in_planes, out_planes, kernel_size, @@ -68,7 +68,7 @@ class InvertedResidual(nn.Layer): oup, stride, expand_ratio, - norm_layer=nn.BatchNorm2d): + norm_layer=nn.BatchNorm2D): super(InvertedResidual, self).__init__() self.stride = stride assert stride in [1, 2] @@ -88,7 +88,7 @@ class InvertedResidual(nn.Layer): stride=stride, groups=hidden_dim, norm_layer=norm_layer), - nn.Conv2d( + nn.Conv2D( hidden_dim, oup, 1, 1, 0, bias_attr=False), norm_layer(oup), ]) @@ -127,7 +127,7 @@ class MobileNetV2(nn.Layer): block = InvertedResidual round_nearest = 8 - norm_layer = nn.BatchNorm2d + norm_layer = nn.BatchNorm2D inverted_residual_setting = [ [1, 16, 1, 1], [6, 24, 2, 2], @@ -169,7 +169,7 @@ class MobileNetV2(nn.Layer): self.features = nn.Sequential(*features) if with_pool: - self.pool2d_avg = nn.AdaptiveAvgPool2d(1) + self.pool2d_avg = nn.AdaptiveAvgPool2D(1) if self.num_classes > 0: self.classifier = nn.Sequential( diff --git a/python/paddle/vision/models/resnet.py b/python/paddle/vision/models/resnet.py index 3ae01b6fd7..8cf797f171 100644 --- a/python/paddle/vision/models/resnet.py +++ b/python/paddle/vision/models/resnet.py @@ -52,17 +52,17 @@ class BasicBlock(nn.Layer): norm_layer=None): super(BasicBlock, self).__init__() if norm_layer is None: - norm_layer = nn.BatchNorm2d + norm_layer = nn.BatchNorm2D if dilation > 1: raise NotImplementedError( "Dilation > 1 not supported in BasicBlock") - self.conv1 = nn.Conv2d( + self.conv1 = nn.Conv2D( inplanes, planes, 3, padding=1, stride=stride, bias_attr=False) self.bn1 = norm_layer(planes) self.relu = nn.ReLU() - self.conv2 = nn.Conv2d(planes, planes, 3, padding=1, bias_attr=False) + self.conv2 = nn.Conv2D(planes, planes, 3, padding=1, bias_attr=False) self.bn2 = norm_layer(planes) self.downsample = downsample self.stride = stride @@ -101,13 +101,13 @@ class BottleneckBlock(nn.Layer): norm_layer=None): super(BottleneckBlock, self).__init__() if norm_layer is None: - norm_layer = nn.BatchNorm2d + norm_layer = nn.BatchNorm2D width = int(planes * (base_width / 64.)) * groups - self.conv1 = nn.Conv2d(inplanes, width, 1, bias_attr=False) + self.conv1 = nn.Conv2D(inplanes, width, 1, bias_attr=False) self.bn1 = norm_layer(width) - self.conv2 = nn.Conv2d( + self.conv2 = nn.Conv2D( width, width, 3, @@ -118,7 +118,7 @@ class BottleneckBlock(nn.Layer): bias_attr=False) self.bn2 = norm_layer(width) - self.conv3 = nn.Conv2d( + self.conv3 = nn.Conv2D( width, planes * self.expansion, 1, bias_attr=False) self.bn3 = norm_layer(planes * self.expansion) self.relu = nn.ReLU() @@ -183,12 +183,12 @@ class ResNet(nn.Layer): layers = layer_cfg[depth] self.num_classes = num_classes self.with_pool = with_pool - self._norm_layer = nn.BatchNorm2d + self._norm_layer = nn.BatchNorm2D self.inplanes = 64 self.dilation = 1 - self.conv1 = nn.Conv2d( + self.conv1 = nn.Conv2D( 3, self.inplanes, kernel_size=7, @@ -197,13 +197,13 @@ class ResNet(nn.Layer): bias_attr=False) self.bn1 = self._norm_layer(self.inplanes) self.relu = nn.ReLU() - self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1) + self.maxpool = nn.MaxPool2D(kernel_size=3, stride=2, padding=1) self.layer1 = self._make_layer(block, 64, layers[0]) self.layer2 = self._make_layer(block, 128, layers[1], stride=2) self.layer3 = self._make_layer(block, 256, layers[2], stride=2) self.layer4 = self._make_layer(block, 512, layers[3], stride=2) if with_pool: - self.avgpool = nn.AdaptiveAvgPool2d((1, 1)) + self.avgpool = nn.AdaptiveAvgPool2D((1, 1)) if num_classes > 0: self.fc = nn.Linear(512 * block.expansion, num_classes) @@ -217,7 +217,7 @@ class ResNet(nn.Layer): stride = 1 if stride != 1 or self.inplanes != planes * block.expansion: downsample = nn.Sequential( - nn.Conv2d( + nn.Conv2D( self.inplanes, planes * block.expansion, 1, diff --git a/python/paddle/vision/models/vgg.py b/python/paddle/vision/models/vgg.py index 2d62e1d22d..bb158569d3 100644 --- a/python/paddle/vision/models/vgg.py +++ b/python/paddle/vision/models/vgg.py @@ -57,7 +57,7 @@ class VGG(nn.Layer): def __init__(self, features, num_classes=1000): super(VGG, self).__init__() self.features = features - self.avgpool = nn.AdaptiveAvgPool2d((7, 7)) + self.avgpool = nn.AdaptiveAvgPool2D((7, 7)) self.classifier = nn.Sequential( nn.Linear(512 * 7 * 7, 4096), nn.ReLU(), @@ -80,11 +80,11 @@ def make_layers(cfg, batch_norm=False): in_channels = 3 for v in cfg: if v == 'M': - layers += [nn.MaxPool2d(kernel_size=2, stride=2)] + layers += [nn.MaxPool2D(kernel_size=2, stride=2)] else: - conv2d = nn.Conv2d(in_channels, v, kernel_size=3, padding=1) + conv2d = nn.Conv2D(in_channels, v, kernel_size=3, padding=1) if batch_norm: - layers += [conv2d, nn.BatchNorm2d(v), nn.ReLU()] + layers += [conv2d, nn.BatchNorm2D(v), nn.ReLU()] else: layers += [conv2d, nn.ReLU()] in_channels = v -- GitLab