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未验证 提交 b04c55ef 编写于 作者: C cnn 提交者： GitHub
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2.0rc api rename (#28088) (#28179) 

* 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
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python/paddle/__init__.py
浏览文件 @ b04c55ef
......@@ -221,7 +221,7 @@ 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
......
python/paddle/amp/auto_cast.py
浏览文件 @ b04c55ef
......@@ -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():
......
python/paddle/amp/grad_scaler.py
浏览文件 @ b04c55ef
......@@ -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])
......
python/paddle/distribution.py
浏览文件 @ b04c55ef
......@@ -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
......
python/paddle/fluid/contrib/tests/test_weight_decay_extend.py
浏览文件 @ b04c55ef
......@@ -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()
......
python/paddle/fluid/dygraph/layers.py
浏览文件 @ b04c55ef
......@@ -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'))
......
python/paddle/fluid/dygraph/nn.py
浏览文件 @ b04c55ef
......@@ -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
......
python/paddle/fluid/initializer.py
浏览文件 @ b04c55ef
......@@ -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(
......
python/paddle/fluid/nets.py
浏览文件 @ b04c55ef
......@@ -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")
......
python/paddle/fluid/tests/unittests/dygraph_to_static/test_bmn.py
浏览文件 @ b04c55ef
......@@ -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)
......
python/paddle/fluid/tests/unittests/dygraph_to_static/test_lac.py
浏览文件 @ b04c55ef
......@@ -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,
......
python/paddle/fluid/tests/unittests/dygraph_to_static/test_mobile_net.py
浏览文件 @ b04c55ef
......@@ -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":
......
python/paddle/fluid/tests/unittests/dygraph_to_static/test_ptb_lm.py
浏览文件 @ b04c55ef
......@@ -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,
......
python/paddle/fluid/tests/unittests/dygraph_to_static/test_ptb_lm_v2.py
浏览文件 @ b04c55ef
......@@ -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,
......
python/paddle/fluid/tests/unittests/dygraph_to_static/test_reinforcement_learning.py
浏览文件 @ b04c55ef
......@@ -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)
......
python/paddle/fluid/tests/unittests/dygraph_to_static/test_resnet.py
浏览文件 @ b04c55ef
......@@ -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(
......
python/paddle/fluid/tests/unittests/dygraph_to_static/test_resnet_v2.py
浏览文件 @ b04c55ef
......@@ -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(
......
python/paddle/fluid/tests/unittests/dygraph_to_static/test_se_resnet.py
浏览文件 @ b04c55ef
......@@ -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())
......
python/paddle/fluid/tests/unittests/dygraph_to_static/test_sentiment.py
浏览文件 @ b04c55ef
......@@ -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,
......
python/paddle/fluid/tests/unittests/dygraph_to_static/test_simnet.py
浏览文件 @ b04c55ef
......@@ -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)
......
python/paddle/fluid/tests/unittests/dygraph_to_static/test_simnet_v2.py
浏览文件 @ b04c55ef
......@@ -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)
......
python/paddle/fluid/tests/unittests/dygraph_to_static/test_transformer.py
浏览文件 @ b04c55ef
......@@ -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
......
python/paddle/fluid/tests/unittests/dygraph_to_static/test_tsm.py
浏览文件 @ b04c55ef
......@@ -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')
......
python/paddle/fluid/tests/unittests/mkldnn/test_conv2d_bf16_mkldnn_op.py
浏览文件 @ b04c55ef
......@@ -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]
......
python/paddle/fluid/tests/unittests/mkldnn/test_conv2d_int8_mkldnn_op.py
浏览文件 @ b04c55ef
......@@ -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"
......
python/paddle/fluid/tests/unittests/mkldnn/test_conv2d_mkldnn_op.py
浏览文件 @ b04c55ef
......@@ -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
......
python/paddle/fluid/tests/unittests/mkldnn/test_conv2d_transpose_mkldnn_op.py
浏览文件 @ b04c55ef
......@@ -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
......
python/paddle/fluid/tests/unittests/mkldnn/test_conv3d_mkldnn_op.py
浏览文件 @ b04c55ef
......@@ -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"
......
python/paddle/fluid/tests/unittests/mkldnn/test_pool2d_int8_mkldnn_op.py
浏览文件 @ b04c55ef
......@@ -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)
......
python/paddle/fluid/tests/unittests/parallel_dygraph_sync_batch_norm.py
浏览文件 @ b04c55ef
......@@ -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,
......
python/paddle/fluid/tests/unittests/parallel_executor_test_base.py
浏览文件 @ b04c55ef
......@@ -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()
......
python/paddle/fluid/tests/unittests/rnn/test_rnn_nets.py
浏览文件 @ b04c55ef
......@@ -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):
......
python/paddle/fluid/tests/unittests/test_adaptive_avg_pool1d.py
浏览文件 @ b04c55ef
......@@ -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))
......
python/paddle/fluid/tests/unittests/test_adaptive_avg_pool2d.py
浏览文件 @ b04c55ef
......@@ -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)
......
python/paddle/fluid/tests/unittests/test_adaptive_avg_pool3d.py
浏览文件 @ b04c55ef
......@@ -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)
......
python/paddle/fluid/tests/unittests/test_adaptive_max_pool1d.py
浏览文件 @ b04c55ef
......@@ -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))
......
python/paddle/fluid/tests/unittests/test_adaptive_max_pool2d.py
浏览文件 @ b04c55ef
......@@ -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)
......
python/paddle/fluid/tests/unittests/test_adaptive_max_pool3d.py
浏览文件 @ b04c55ef
......@@ -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)
......
python/paddle/fluid/tests/unittests/test_batch_norm_op_v2.py
浏览文件 @ b04c55ef
......@@ -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)
......
python/paddle/fluid/tests/unittests/test_buffer_shared_memory_reuse_pass.py
浏览文件 @ b04c55ef
......@@ -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()
......
python/paddle/fluid/tests/unittests/test_compiled_program.py
浏览文件 @ b04c55ef
......@@ -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()
......
python/paddle/fluid/tests/unittests/test_conv1d_layer.py
浏览文件 @ b04c55ef
......@@ -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]))
......
python/paddle/fluid/tests/unittests/test_conv1d_transpose_layer.py
浏览文件 @ b04c55ef
......@@ -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"))
......
python/paddle/fluid/tests/unittests/test_conv2d_fusion_op.py
浏览文件 @ b04c55ef
......@@ -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
......
python/paddle/fluid/tests/unittests/test_conv2d_layer.py
浏览文件 @ b04c55ef
......@@ -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,
......
python/paddle/fluid/tests/unittests/test_conv2d_op.py
浏览文件 @ b04c55ef
......@@ -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",
......
python/paddle/fluid/tests/unittests/test_conv2d_transpose_layer.py
浏览文件 @ b04c55ef
......@@ -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,
......
python/paddle/fluid/tests/unittests/test_conv2d_transpose_op.py
浏览文件 @ b04c55ef
......@@ -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")
......
python/paddle/fluid/tests/unittests/test_conv3d_layer.py
浏览文件 @ b04c55ef
......@@ -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,
......
python/paddle/fluid/tests/unittests/test_conv3d_op.py
浏览文件 @ b04c55ef
......@@ -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",
......
python/paddle/fluid/tests/unittests/test_conv3d_transpose_layer.py
浏览文件 @ b04c55ef
......@@ -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,
......
python/paddle/fluid/tests/unittests/test_conv3d_transpose_op.py
浏览文件 @ b04c55ef
......@@ -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]
......
python/paddle/fluid/tests/unittests/test_conv3d_transpose_part2_op.py
浏览文件 @ b04c55ef
......@@ -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")
......
python/paddle/fluid/tests/unittests/test_conv_nn_grad.py
浏览文件 @ b04c55ef
......@@ -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]
......
python/paddle/fluid/tests/unittests/test_cuda_random_seed.py
浏览文件 @ b04c55ef
......@@ -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,
......
python/paddle/fluid/tests/unittests/test_decoupled_py_reader.py
浏览文件 @ b04c55ef
......@@ -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()
......
python/paddle/fluid/tests/unittests/test_deformable_conv_op.py
浏览文件 @ b04c55ef
......@@ -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()
......
python/paddle/fluid/tests/unittests/test_dropout_op.py
浏览文件 @ b04c55ef
......@@ -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))
......
python/paddle/fluid/tests/unittests/test_dygraph_multi_forward.py
浏览文件 @ b04c55ef
......@@ -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))
......
python/paddle/fluid/tests/unittests/test_dygraph_weight_norm.py
浏览文件 @ b04c55ef
......@@ -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)
......
python/paddle/fluid/tests/unittests/test_eager_deletion_padding_rnn.py
浏览文件 @ b04c55ef
......@@ -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):
......
python/paddle/fluid/tests/unittests/test_embedding_id_stop_gradient.py
浏览文件 @ b04c55ef
......@@ -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()
......
python/paddle/fluid/tests/unittests/test_fc_op.py
浏览文件 @ b04c55ef
......@@ -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()
......
python/paddle/fluid/tests/unittests/test_fuse_bn_act_pass.py
浏览文件 @ b04c55ef
......@@ -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()
......
python/paddle/fluid/tests/unittests/test_fused_bn_add_act.py
浏览文件 @ b04c55ef
......@@ -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
......
python/paddle/fluid/tests/unittests/test_gaussian_random_op.py
浏览文件 @ b04c55ef
......@@ -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')}
......
python/paddle/fluid/tests/unittests/test_generator.py
浏览文件 @ b04c55ef
......@@ -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()
......
python/paddle/fluid/tests/unittests/test_generator_dataloader.py
浏览文件 @ b04c55ef
......@@ -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()
......
python/paddle/fluid/tests/unittests/test_hsigmoid_op.py
浏览文件 @ b04c55ef
......@@ -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')
......
python/paddle/fluid/tests/unittests/test_imperative_auto_mixed_precision.py
浏览文件 @ b04c55ef
......@@ -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)
......
python/paddle/fluid/tests/unittests/test_imperative_deepcf.py
浏览文件 @ b04c55ef
......@@ -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)
......
python/paddle/fluid/tests/unittests/test_imperative_double_grad.py
浏览文件 @ b04c55ef
......@@ -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
......
python/paddle/fluid/tests/unittests/test_imperative_gan.py
浏览文件 @ b04c55ef
......@@ -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()
......
python/paddle/fluid/tests/unittests/test_imperative_gnn.py
浏览文件 @ b04c55ef
......@@ -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)
......
python/paddle/fluid/tests/unittests/test_imperative_layer_apply.py
浏览文件 @ b04c55ef
......@@ -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)
......
python/paddle/fluid/tests/unittests/test_imperative_layer_children.py
浏览文件 @ b04c55ef
......@@ -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))
......
python/paddle/fluid/tests/unittests/test_imperative_lod_tensor_to_selected_rows.py
浏览文件 @ b04c55ef
......@@ -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(
......
python/paddle/fluid/tests/unittests/test_imperative_ocr_attention_model.py
浏览文件 @ b04c55ef
......@@ -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))
......
python/paddle/fluid/tests/unittests/test_imperative_optimizer.py
浏览文件 @ b04c55ef
......@@ -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:
......
python/paddle/fluid/tests/unittests/test_imperative_optimizer_v2.py
浏览文件 @ b04c55ef
......@@ -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:
......
python/paddle/fluid/tests/unittests/test_imperative_ptb_rnn.py
浏览文件 @ b04c55ef
......@@ -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,
......
python/paddle/fluid/tests/unittests/test_imperative_ptb_rnn_sorted_gradient.py
浏览文件 @ b04c55ef
......@@ -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(
......
python/paddle/fluid/tests/unittests/test_imperative_reinforcement.py
浏览文件 @ b04c55ef
......@@ -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(
......
python/paddle/fluid/tests/unittests/test_imperative_resnet.py
浏览文件 @ b04c55ef
......@@ -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(
......
python/paddle/fluid/tests/unittests/test_imperative_resnet_sorted_gradient.py
浏览文件 @ b04c55ef
......@@ -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(
......
python/paddle/fluid/tests/unittests/test_imperative_save_load.py
浏览文件 @ b04c55ef
......@@ -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
......
python/paddle/fluid/tests/unittests/test_imperative_save_load_v2.py
浏览文件 @ b04c55ef
......@@ -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(
......
python/paddle/fluid/tests/unittests/test_imperative_se_resnext.py
浏览文件 @ b04c55ef
......@@ -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(
......
python/paddle/fluid/tests/unittests/test_imperative_selected_rows_to_lod_tensor.py
浏览文件 @ b04c55ef
......@@ -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(
......
python/paddle/fluid/tests/unittests/test_imperative_star_gan_with_gradient_penalty.py
浏览文件 @ b04c55ef
......@@ -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()
......
python/paddle/fluid/tests/unittests/test_imperative_transformer_sorted_gradient.py
浏览文件 @ b04c55ef
......@@ -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,
......
python/paddle/fluid/tests/unittests/test_inplace_addto_strategy.py
浏览文件 @ b04c55ef
......@@ -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})
......
python/paddle/fluid/tests/unittests/test_instance_norm_op_v2.py
浏览文件 @ b04c55ef
......@@ -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())
......
python/paddle/fluid/tests/unittests/test_ir_memory_optimize_ifelse_op.py
浏览文件 @ b04c55ef
......@@ -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()
......
python/paddle/fluid/tests/unittests/test_jit_save_load.py
浏览文件 @ b04c55ef
......@@ -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):
......
python/paddle/fluid/tests/unittests/test_layers.py
浏览文件 @ b04c55ef
......@@ -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
......
python/paddle/fluid/tests/unittests/test_manual_seed.py
浏览文件 @ b04c55ef
......@@ -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")
......
python/paddle/fluid/tests/unittests/test_normal.py
浏览文件 @ b04c55ef
......@@ -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)
......
python/paddle/fluid/tests/unittests/test_paddle_save_load.py
浏览文件 @ b04c55ef
......@@ -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):
......
python/paddle/fluid/tests/unittests/test_pool1d_api.py
浏览文件 @ b04c55ef
......@@ -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():
......
python/paddle/fluid/tests/unittests/test_pool2d_api.py
浏览文件 @ b04c55ef
......@@ -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():
......
python/paddle/fluid/tests/unittests/test_pool2d_op.py
浏览文件 @ b04c55ef
......@@ -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",
......
python/paddle/fluid/tests/unittests/test_pool3d_api.py
浏览文件 @ b04c55ef
......@@ -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():
......
python/paddle/fluid/tests/unittests/test_pool3d_op.py
浏览文件 @ b04c55ef
......@@ -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",
......
python/paddle/fluid/tests/unittests/test_py_func_op.py
浏览文件 @ b04c55ef
......@@ -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')
......
python/paddle/fluid/tests/unittests/test_random_seed.py
浏览文件 @ b04c55ef
......@@ -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()
......
python/paddle/fluid/tests/unittests/test_regularizer.py
浏览文件 @ b04c55ef
......@@ -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(
......
python/paddle/fluid/tests/unittests/test_regularizer_api.py
浏览文件 @ b04c55ef
......@@ -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(
......
python/paddle/fluid/tests/unittests/test_retain_graph.py
浏览文件 @ b04c55ef
......@@ -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)
......
python/paddle/fluid/tests/unittests/test_rnn_decode_api.py
浏览文件 @ b04c55ef
......@@ -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()
......
python/paddle/fluid/tests/unittests/test_sync_batch_norm_op.py
浏览文件 @ b04c55ef
......@@ -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)
......
python/paddle/fluid/tests/unittests/test_transformer_api.py
浏览文件 @ b04c55ef
......@@ -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(
......
python/paddle/fluid/tests/unittests/test_translated_layer.py
浏览文件 @ b04c55ef
......@@ -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
......
python/paddle/fluid/tests/unittests/test_uniform_random_op.py
浏览文件 @ b04c55ef
......@@ -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"],
......
python/paddle/fluid/tests/unittests/test_var_base.py
浏览文件 @ b04c55ef
......@@ -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)
......
python/paddle/fluid/tests/unittests/test_var_conv_2d.py
浏览文件 @ b04c55ef
......@@ -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
......
python/paddle/fluid/tests/unittests/xpu/test_conv2d_op_xpu.py
浏览文件 @ b04c55ef
......@@ -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)
......
python/paddle/framework/__init__.py
浏览文件 @ b04c55ef
......@@ -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
......
python/paddle/framework/random.py
浏览文件 @ b04c55ef
......@@ -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:
......
python/paddle/hapi/model_summary.py
浏览文件 @ b04c55ef
......@@ -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(
......
python/paddle/nn/__init__.py
浏览文件 @ b04c55ef
......@@ -81,29 +81,29 @@ 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
......@@ -125,12 +125,12 @@ 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
......
python/paddle/nn/functional/conv.py
浏览文件 @ b04c55ef
......@@ -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
......
python/paddle/nn/functional/norm.py
浏览文件 @ b04c55ef
......@@ -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.
......
python/paddle/nn/layer/__init__.py
浏览文件 @ b04c55ef
......@@ -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
......
python/paddle/nn/layer/common.py
浏览文件 @ b04c55ef
......@@ -32,8 +32,8 @@ __all__ = [
'Pad3D',
'CosineSimilarity',
'Dropout',
'Dropout2d',
'Dropout3d',
'Dropout2D',
'Dropout3D',
'Bilinear',
'AlphaDropout',
]
......@@ -708,12 +708,12 @@ 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 <https://arxiv.org/abs/1411.4280>`_
See ``paddle.nn.functional.dropout2d`` for more details.
......@@ -740,7 +740,7 @@ 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)
......@@ -750,7 +750,7 @@ 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
......@@ -766,12 +766,12 @@ 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 <https://arxiv.org/abs/1411.4280>`_
See ``paddle.nn.functional.dropout3d`` for more details.
......@@ -798,7 +798,7 @@ 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)
......@@ -808,7 +808,7 @@ 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
......
python/paddle/nn/layer/conv.py
浏览文件 @ b04c55ef
......@@ -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,
......
python/paddle/nn/layer/norm.py
浏览文件 @ b04c55ef
......@@ -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)
"""
......
python/paddle/nn/layer/pooling.py
浏览文件 @ b04c55ef
......@@ -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
......
python/paddle/regularizer.py
浏览文件 @ b04c55ef
......@@ -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,
......
python/paddle/tensor/random.py
浏览文件 @ b04c55ef
......@@ -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]
......
python/paddle/tensor/to_string.py
浏览文件 @ b04c55ef
......@@ -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)
......
python/paddle/tests/test_model.py
浏览文件 @ b04c55ef
......@@ -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):
......
python/paddle/vision/models/lenet.py
浏览文件 @ b04c55ef
......@@ -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(
......
python/paddle/vision/models/mobilenetv1.py
浏览文件 @ b04c55ef
......@@ -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)
......
python/paddle/vision/models/mobilenetv2.py
浏览文件 @ b04c55ef
......@@ -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(
......
python/paddle/vision/models/resnet.py
浏览文件 @ b04c55ef
......@@ -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,
......
python/paddle/vision/models/vgg.py
浏览文件 @ b04c55ef
......@@ -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
......
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