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提交 f6144d84 编写于 作者: Y Youwei Song 提交者： hong
浏览文件
操作

remove build_once & name_scope (#21131) 

* remove build_once & name_scope (Conv2D)
test=develop

* fix unittest
test=develop

* Conv2DTranspose

* Conv3D & Conv3DTranspose
test=develop

* Pool2D & BatchNorm

* Embedding

* LayerNorm

* GRUUnit & NCE

* PRelu

* BilinearTensorProduct

* GroupNorm & SpectralNorm

* TreeConv
test=develop

* fix LayerNorm in transformer unnittest
test=develop

* disable LayerNorm or BatchNorm in multicard
test=develop

* refine Layer.create_parameter api
test=develop

* refine LayerNorm, remove begin_norm_axis param, add normed shape check
test=develop

* LayerNorm bug fix
test=develop
上级 0fe16539
展开全部 隐藏空白更改
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Showing with 399 addition and 527 deletion
python/paddle/fluid/contrib/layers/rnn_impl.py
浏览文件 @ f6144d84
......@@ -104,12 +104,12 @@ class BasicGRUUnit(Layer):
dtype=self._dtype)
self._gate_bias = self.create_parameter(
self._bias_attr,
attr=self._bias_attr,
shape=[2 * self._hiden_size],
dtype=self._dtype,
is_bias=True)
self._candidate_bias = self.create_parameter(
self._bias_attr,
attr=self._bias_attr,
shape=[self._hiden_size],
dtype=self._dtype,
is_bias=True)
......
python/paddle/fluid/dygraph/checkpoint.py
浏览文件 @ f6144d84
......@@ -48,7 +48,7 @@ def save_dygraph(state_dict, model_path):
import paddle.fluid as fluid
with fluid.dygraph.guard():
emb = fluid.dygraph.Embedding( "emb", [10, 10])
emb = fluid.dygraph.Embedding([10, 10])
state_dict = emb.state_dict()
fluid.save_dygraph( state_dict, "paddle_dy")
......@@ -91,7 +91,7 @@ def load_dygraph(model_path):
import paddle.fluid as fluid
with fluid.dygraph.guard():
emb = fluid.dygraph.Embedding( "emb", [10, 10])
emb = fluid.dygraph.Embedding([10, 10])
state_dict = emb.state_dict()
fluid.save_dygraph( state_dict, "paddle_dy")
......
python/paddle/fluid/dygraph/layers.py
浏览文件 @ f6144d84
......@@ -33,10 +33,11 @@ class Layer(core.Layer):
"""Dynamic graph Layer based on OOD, includes the parameters of the layer, the structure of the forward graph and so on.
Parameters:
name_scope (str): prefix name used by the layer to name parameters.
If prefix is "my_model/layer_1", parameter name in MyLayer
can be "my_model/layer_1/MyLayer/w_n", where w is the parameter
name_scope (str, optional): prefix name used by the layer to name parameters.
If prefix is "my_layer", parameter name in MyLayer
can be "mylayer_0.w_n", where w is the parameter
base name and n is an unique suffix auto-generated.
If None, prefix name will be lower cased class name. Default: None.
dtype(str or core.VarDesc.VarType, optional): data type of this parameter.
If set str, it can be "bool", "float16", "float32", "float64",
"int8", "int16", "int32", "int64", "uint8" or "uint16".
......@@ -46,17 +47,22 @@ class Layer(core.Layer):
None
"""
def __init__(self, name_scope, dtype=core.VarDesc.VarType.FP32):
self._full_name = unique_name.generate(name_scope + "/" +
self.__class__.__name__)
def __init__(self, name_scope=None, dtype=core.VarDesc.VarType.FP32):
if name_scope is None:
name_scope = self.__class__.__name__.lower()
self._full_name = unique_name.generate(name_scope)
else:
# TODO: remove name_scope parameter and all hard-coded usages
self._full_name = unique_name.generate(name_scope + "/" +
self.__class__.__name__)
self._helper = LayerObjectHelper(self._full_name)
self._built = False
self._dtype = dtype
self._parameters = collections.OrderedDict()
self._sub_layers = collections.OrderedDict()
self._loaddict_holder = collections.OrderedDict()
self._helper = LayerObjectHelper(self._full_name)
def train(self):
framework._dygraph_tracer().train_mode()
......@@ -72,23 +78,23 @@ class Layer(core.Layer):
return self._full_name
def create_parameter(self,
attr,
shape,
dtype,
attr=None,
dtype='float32',
is_bias=False,
default_initializer=None):
"""Create parameters for this layer.
Parameters:
attr(ParamAttr): Parameter attribute of weight. Please refer to :ref:`api_fluid_ParamAttr`
shape(list): shape of the parameter
dtype(str or core.VarDesc.VarType): data type of this parameter.
shape(list): Shape of the parameter.
attr(ParamAttr, optional): Parameter attribute of weight. Please refer to :ref:`api_fluid_ParamAttr`. Default: None.
dtype(str or core.VarDesc.VarType or str, optional): Data type of this parameter.
If set str, it can be "bool", "float16", "float32", "float64",
"int8", "int16", "int32", "int64", "uint8" or "uint16".
is_bias(bool, optional): if this is a bias parameter. Default: False
"int8", "int16", "int32", "int64", "uint8" or "uint16". Default: "float32".
is_bias(bool, optional): if this is a bias parameter. Default: False.
default_initializer(Initializer, optional): the default initializer for this parameter.
If set None, default initializer will be set to :ref:`api_fluid_initializer_XavierInitializer` and :ref:`api_fluid_initializer_ConstantInitializer`
for non-bias and bias parameter, respectively. Default: None
for non-bias and bias parameter, respectively. Default: None.
Returns:
:ref:`api_guide_Variable_en` : created parameter.
......@@ -294,7 +300,7 @@ class Layer(core.Layer):
import paddle.fluid as fluid
with fluid.dygraph.guard():
emb = fluid.dygraph.Embedding( "emb", [10, 10])
emb = fluid.dygraph.Embedding([10, 10])
state_dict = emb.state_dict()
fluid.save_dygraph( state_dict, "paddle_dy")
......@@ -332,7 +338,7 @@ class Layer(core.Layer):
import paddle.fluid as fluid
with fluid.dygraph.guard():
emb = fluid.dygraph.Embedding( "emb", [10, 10])
emb = fluid.dygraph.Embedding([10, 10])
state_dict = emb.state_dict()
fluid.save_dygraph( state_dict, "paddle_dy")
......@@ -361,7 +367,7 @@ class Layer(core.Layer):
import paddle.fluid as fluid
with fluid.dygraph.guard():
emb = fluid.dygraph.Embedding( "emb", [10, 10])
emb = fluid.dygraph.Embedding([10, 10])
state_dict = emb.state_dict()
fluid.save_dygraph( state_dict, "paddle_dy")
......
python/paddle/fluid/dygraph/nn.py
浏览文件 @ f6144d84
此差异已折叠。
python/paddle/fluid/tests/unittests/parallel_dygraph_mnist.py
浏览文件 @ f6144d84
......@@ -34,7 +34,6 @@ from test_dist_base import runtime_main, TestParallelDyGraphRunnerBase
class SimpleImgConvPool(fluid.dygraph.Layer):
def __init__(self,
name_scope,
num_channels,
num_filters,
filter_size,
......@@ -51,10 +50,10 @@ class SimpleImgConvPool(fluid.dygraph.Layer):
use_cudnn=False,
param_attr=None,
bias_attr=None):
super(SimpleImgConvPool, self).__init__(name_scope)
super(SimpleImgConvPool, self).__init__()
self._conv2d = Conv2D(
self.full_name(),
num_channels=num_channels,
num_filters=num_filters,
filter_size=filter_size,
stride=conv_stride,
......@@ -66,7 +65,6 @@ class SimpleImgConvPool(fluid.dygraph.Layer):
use_cudnn=use_cudnn)
self._pool2d = Pool2D(
self.full_name(),
pool_size=pool_size,
pool_type=pool_type,
pool_stride=pool_stride,
......@@ -85,10 +83,10 @@ class MNIST(fluid.dygraph.Layer):
super(MNIST, self).__init__(name_scope)
self._simple_img_conv_pool_1 = SimpleImgConvPool(
self.full_name(), 1, 20, 5, 2, 2, act="relu")
1, 20, 5, 2, 2, act="relu")
self._simple_img_conv_pool_2 = SimpleImgConvPool(
self.full_name(), 20, 50, 5, 2, 2, act="relu")
20, 50, 5, 2, 2, act="relu")
pool_2_shape = 50 * 4 * 4
SIZE = 10
......
python/paddle/fluid/tests/unittests/parallel_dygraph_se_resnext.py
浏览文件 @ f6144d84
......@@ -27,7 +27,7 @@ import paddle.fluid as fluid
import paddle.fluid.dygraph as dygraph
from paddle.fluid import core
from paddle.fluid.optimizer import SGDOptimizer
from paddle.fluid.dygraph.nn import Conv2D, Pool2D, FC, LayerNorm
from paddle.fluid.dygraph.nn import Conv2D, Pool2D, FC, BatchNorm
from paddle.fluid.dygraph.base import to_variable
from paddle.fluid.layer_helper import LayerHelper
import math
......@@ -77,16 +77,16 @@ def optimizer_setting(params):
class ConvBNLayer(fluid.dygraph.Layer):
def __init__(self,
name_scope,
num_channels,
num_filters,
filter_size,
stride=1,
groups=1,
act=None):
super(ConvBNLayer, self).__init__(name_scope)
super(ConvBNLayer, self).__init__()
self._conv = Conv2D(
"conv2d",
num_channels=num_channels,
num_filters=num_filters,
filter_size=filter_size,
stride=stride,
......@@ -96,11 +96,12 @@ class ConvBNLayer(fluid.dygraph.Layer):
bias_attr=False,
param_attr=fluid.ParamAttr(name="weights"))
self._layer_norm = LayerNorm(self.full_name(), begin_norm_axis=1)
# disable BatchNorm in multi-card. disable LayerNorm because of complex input_shape
# self._batch_norm = BatchNorm(num_filters, act=act)
def forward(self, inputs):
y = self._conv(inputs)
y = self._layer_norm(y)
# y = self._batch_norm(y)
return y
......@@ -109,8 +110,7 @@ class SqueezeExcitation(fluid.dygraph.Layer):
def __init__(self, name_scope, num_channels, reduction_ratio):
super(SqueezeExcitation, self).__init__(name_scope)
self._pool = Pool2D(
self.full_name(), pool_size=0, pool_type='avg', global_pooling=True)
self._pool = Pool2D(pool_size=0, pool_type='avg', global_pooling=True)
stdv = 1.0 / math.sqrt(num_channels * 1.0)
self._squeeze = FC(
self.full_name(),
......@@ -136,29 +136,28 @@ class SqueezeExcitation(fluid.dygraph.Layer):
class BottleneckBlock(fluid.dygraph.Layer):
def __init__(self,
name_scope,
num_channels,
num_filters,
stride,
cardinality,
reduction_ratio,
shortcut=True):
super(BottleneckBlock, self).__init__(name_scope)
super(BottleneckBlock, self).__init__()
self.conv0 = ConvBNLayer(
self.full_name(),
num_channels=num_channels,
num_filters=num_filters,
filter_size=1,
act="relu")
self.conv1 = ConvBNLayer(
self.full_name(),
num_channels=num_filters,
num_filters=num_filters,
filter_size=3,
stride=stride,
groups=cardinality,
act="relu")
self.conv2 = ConvBNLayer(
self.full_name(),
num_channels=num_filters,
num_filters=num_filters * 2,
filter_size=1,
act=None)
......@@ -170,7 +169,7 @@ class BottleneckBlock(fluid.dygraph.Layer):
if not shortcut:
self.short = ConvBNLayer(
self.full_name(),
num_channels=num_channels,
num_filters=num_filters * 2,
filter_size=1,
stride=stride)
......@@ -209,63 +208,51 @@ class SeResNeXt(fluid.dygraph.Layer):
depth = [3, 4, 6, 3]
num_filters = [128, 256, 512, 1024]
self.conv0 = ConvBNLayer(
self.full_name(),
num_channels=3,
num_filters=64,
filter_size=7,
stride=2,
act='relu')
self.pool = Pool2D(
self.full_name(),
pool_size=3,
pool_stride=2,
pool_padding=1,
pool_type='max')
pool_size=3, pool_stride=2, pool_padding=1, pool_type='max')
elif layers == 101:
cardinality = 32
reduction_ratio = 16
depth = [3, 4, 23, 3]
num_filters = [128, 256, 512, 1024]
self.conv0 = ConvBNLayer(
self.full_name(),
num_channels=3,
num_filters=64,
filter_size=7,
stride=2,
act='relu')
self.pool = Pool2D(
self.full_name(),
pool_size=3,
pool_stride=2,
pool_padding=1,
pool_type='max')
pool_size=3, pool_stride=2, pool_padding=1, pool_type='max')
elif layers == 152:
cardinality = 64
reduction_ratio = 16
depth = [3, 8, 36, 3]
num_filters = [128, 256, 512, 1024]
self.conv0 = ConvBNLayer(
self.full_name(),
num_channels=3,
num_filters=64,
filter_size=3,
stride=2,
act='relu')
self.conv1 = ConvBNLayer(
self.full_name(),
num_channels=64,
num_filters=64,
filter_size=3,
stride=1,
act='relu')
self.conv2 = ConvBNLayer(
self.full_name(),
num_channels=64,
num_filters=128,
filter_size=3,
stride=1,
act='relu')
self.pool = Pool2D(
self.full_name(),
pool_size=3,
pool_stride=2,
pool_padding=1,
pool_type='max')
pool_size=3, pool_stride=2, pool_padding=1, pool_type='max')
self.bottleneck_block_list = []
num_channels = 64
......@@ -275,7 +262,6 @@ class SeResNeXt(fluid.dygraph.Layer):
bottleneck_block = self.add_sublayer(
'bb_%d_%d' % (block, i),
BottleneckBlock(
self.full_name(),
num_channels=num_channels,
num_filters=num_filters[block],
stride=2 if i == 0 and block != 0 else 1,
......@@ -287,7 +273,7 @@ class SeResNeXt(fluid.dygraph.Layer):
shortcut = True
self.pool2d_avg = Pool2D(
self.full_name(), pool_size=7, pool_type='avg', global_pooling=True)
pool_size=7, pool_type='avg', global_pooling=True)
stdv = 1.0 / math.sqrt(2048 * 1.0)
self.out = FC(self.full_name(),
......
python/paddle/fluid/tests/unittests/test_dygraph_mnist_fp16.py
浏览文件 @ f6144d84
......@@ -23,7 +23,7 @@ from paddle.fluid.dygraph.nn import Conv2D, Pool2D, FC
class SimpleImgConvPool(fluid.dygraph.Layer):
def __init__(self,
name_scope,
num_channels,
num_filters,
filter_size,
pool_size,
......@@ -40,10 +40,10 @@ class SimpleImgConvPool(fluid.dygraph.Layer):
dtype='float32',
param_attr=None,
bias_attr=None):
super(SimpleImgConvPool, self).__init__(name_scope)
super(SimpleImgConvPool, self).__init__()
self._conv2d = Conv2D(
self.full_name(),
num_channels=num_channels,
num_filters=num_filters,
filter_size=filter_size,
stride=conv_stride,
......@@ -57,7 +57,6 @@ class SimpleImgConvPool(fluid.dygraph.Layer):
act=act)
self._pool2d = Pool2D(
self.full_name(),
pool_size=pool_size,
pool_type=pool_type,
pool_stride=pool_stride,
......@@ -76,7 +75,7 @@ class MNIST(fluid.dygraph.Layer):
super(MNIST, self).__init__(name_scope)
self._simple_img_conv_pool_1 = SimpleImgConvPool(
self.full_name(),
num_channels=3,
num_filters=20,
filter_size=5,
pool_size=2,
......@@ -86,7 +85,7 @@ class MNIST(fluid.dygraph.Layer):
use_cudnn=True)
self._simple_img_conv_pool_2 = SimpleImgConvPool(
self.full_name(),
num_channels=20,
num_filters=50,
filter_size=5,
pool_size=2,
......
python/paddle/fluid/tests/unittests/test_dygraph_multi_forward.py
浏览文件 @ f6144d84
......@@ -30,7 +30,6 @@ from test_imperative_base import new_program_scope
class SimpleImgConvPool(fluid.dygraph.Layer):
def __init__(self,
name_scope,
num_channels,
num_filters,
filter_size,
......@@ -47,10 +46,10 @@ class SimpleImgConvPool(fluid.dygraph.Layer):
use_cudnn=False,
param_attr=None,
bias_attr=None):
super(SimpleImgConvPool, self).__init__(name_scope)
super(SimpleImgConvPool, self).__init__()
self._conv2d = Conv2D(
self.full_name(),
num_channels=num_channels,
num_filters=num_filters,
filter_size=filter_size,
stride=conv_stride,
......@@ -62,7 +61,6 @@ class SimpleImgConvPool(fluid.dygraph.Layer):
use_cudnn=use_cudnn)
self._pool2d = Pool2D(
self.full_name(),
pool_size=pool_size,
pool_type=pool_type,
pool_stride=pool_stride,
......@@ -81,10 +79,10 @@ class MNIST(fluid.dygraph.Layer):
super(MNIST, self).__init__(name_scope)
self._simple_img_conv_pool_1 = SimpleImgConvPool(
self.full_name(), 1, 20, 5, 2, 2, act="relu")
1, 20, 5, 2, 2, act="relu")
self._simple_img_conv_pool_2 = SimpleImgConvPool(
self.full_name(), 20, 50, 5, 2, 2, act="relu")
20, 50, 5, 2, 2, act="relu")
pool_2_shape = 50 * 4 * 4
SIZE = 10
......
python/paddle/fluid/tests/unittests/test_imperative_auto_prune.py
浏览文件 @ f6144d84
......@@ -100,8 +100,8 @@ class AutoPruneLayer3(fluid.Layer):
class MyLayer(fluid.Layer):
def __init__(self, name_scope, vocab_size, size, dtype="float32"):
super(MyLayer, self).__init__(name_scope, dtype)
self.embed0 = fluid.Embedding(self.full_name(), size=(vocab_size, size))
self.embed1 = fluid.Embedding(self.full_name(), size=(vocab_size, size))
self.embed0 = fluid.Embedding(size=(vocab_size, size))
self.embed1 = fluid.Embedding(size=(vocab_size, size))
self.fc0 = fluid.FC(self.full_name(), size=size, dtype=dtype)
self.fc1 = fluid.FC(self.full_name(), size=size, dtype=dtype)
......@@ -122,8 +122,8 @@ class MyLayer(fluid.Layer):
class MyLayer2(fluid.Layer):
def __init__(self, name_scope, vocab_size, size, dtype="float32"):
super(MyLayer2, self).__init__(name_scope, dtype)
self.embed0 = fluid.Embedding(self.full_name(), size=(vocab_size, size))
self.embed1 = fluid.Embedding(self.full_name(), size=(vocab_size, size))
self.embed0 = fluid.Embedding(size=(vocab_size, size))
self.embed1 = fluid.Embedding(size=(vocab_size, size))
self.fc0 = fluid.FC(self.full_name(), size=size, dtype=dtype)
self.fc1 = fluid.FC(self.full_name(), size=size, dtype=dtype)
......
python/paddle/fluid/tests/unittests/test_imperative_deepcf.py
浏览文件 @ f6144d84
......@@ -90,9 +90,9 @@ class DeepCF(fluid.Layer):
self._num_users = num_users
self._num_items = num_items
self._rating_matrix = self.create_parameter(
fluid.ParamAttr(trainable=False),
matrix.shape,
matrix.dtype,
attr=fluid.ParamAttr(trainable=False),
shape=matrix.shape,
dtype=matrix.dtype,
is_bias=False,
default_initializer=fluid.initializer.NumpyArrayInitializer(matrix))
self._rating_matrix.stop_gradient = True
......
python/paddle/fluid/tests/unittests/test_imperative_mnist.py
浏览文件 @ f6144d84
......@@ -32,7 +32,7 @@ from paddle.fluid.dygraph.jit import TracedLayer
class SimpleImgConvPool(fluid.dygraph.Layer):
def __init__(self,
name_scope,
num_channels,
num_filters,
filter_size,
pool_size,
......@@ -48,10 +48,10 @@ class SimpleImgConvPool(fluid.dygraph.Layer):
use_cudnn=False,
param_attr=None,
bias_attr=None):
super(SimpleImgConvPool, self).__init__(name_scope)
super(SimpleImgConvPool, self).__init__()
self._conv2d = Conv2D(
self.full_name(),
num_channels=num_channels,
num_filters=num_filters,
filter_size=filter_size,
stride=conv_stride,
......@@ -63,7 +63,6 @@ class SimpleImgConvPool(fluid.dygraph.Layer):
use_cudnn=use_cudnn)
self._pool2d = Pool2D(
self.full_name(),
pool_size=pool_size,
pool_type=pool_type,
pool_stride=pool_stride,
......@@ -82,10 +81,10 @@ class MNIST(fluid.dygraph.Layer):
super(MNIST, self).__init__(name_scope)
self._simple_img_conv_pool_1 = SimpleImgConvPool(
self.full_name(), 20, 5, 2, 2, act="relu")
1, 20, 5, 2, 2, act="relu")
self._simple_img_conv_pool_2 = SimpleImgConvPool(
self.full_name(), 50, 5, 2, 2, act="relu")
20, 50, 5, 2, 2, act="relu")
pool_2_shape = 50 * 4 * 4
SIZE = 10
......
python/paddle/fluid/tests/unittests/test_imperative_ocr_attention_model.py
浏览文件 @ f6144d84
......@@ -57,7 +57,6 @@ class Config(object):
class ConvBNPool(fluid.dygraph.Layer):
def __init__(self,
name_scope,
group,
out_ch,
channels,
......@@ -65,7 +64,7 @@ class ConvBNPool(fluid.dygraph.Layer):
is_test=False,
pool=True,
use_cudnn=True):
super(ConvBNPool, self).__init__(name_scope)
super(ConvBNPool, self).__init__()
self.group = group
self.pool = pool
......@@ -79,7 +78,7 @@ class ConvBNPool(fluid.dygraph.Layer):
initializer=fluid.initializer.Normal(0.0, conv_std_1))
self.conv_0_layer = Conv2D(
self.full_name(),
channels[0],
out_ch[0],
3,
padding=1,
......@@ -87,10 +86,9 @@ class ConvBNPool(fluid.dygraph.Layer):
bias_attr=False,
act=None,
use_cudnn=use_cudnn)
self.bn_0_layer = BatchNorm(
self.full_name(), out_ch[0], act=act, is_test=is_test)
self.bn_0_layer = BatchNorm(out_ch[0], act=act, is_test=is_test)
self.conv_1_layer = Conv2D(
self.full_name(),
out_ch[0],
num_filters=out_ch[1],
filter_size=3,
padding=1,
......@@ -98,12 +96,10 @@ class ConvBNPool(fluid.dygraph.Layer):
bias_attr=False,
act=None,
use_cudnn=use_cudnn)
self.bn_1_layer = BatchNorm(
self.full_name(), out_ch[1], act=act, is_test=is_test)
self.bn_1_layer = BatchNorm(out_ch[1], act=act, is_test=is_test)
if self.pool:
self.pool_layer = Pool2D(
self.full_name(),
pool_size=2,
pool_type='max',
pool_stride=2,
......@@ -125,22 +121,12 @@ class OCRConv(fluid.dygraph.Layer):
def __init__(self, name_scope, is_test=False, use_cudnn=True):
super(OCRConv, self).__init__(name_scope)
self.conv_bn_pool_1 = ConvBNPool(
self.full_name(),
2, [16, 16], [1, 16],
is_test=is_test,
use_cudnn=use_cudnn)
2, [16, 16], [1, 16], is_test=is_test, use_cudnn=use_cudnn)
self.conv_bn_pool_2 = ConvBNPool(
self.full_name(),
2, [32, 32], [16, 32],
is_test=is_test,
use_cudnn=use_cudnn)
2, [32, 32], [16, 32], is_test=is_test, use_cudnn=use_cudnn)
self.conv_bn_pool_3 = ConvBNPool(
self.full_name(),
2, [64, 64], [32, 64],
is_test=is_test,
use_cudnn=use_cudnn)
2, [64, 64], [32, 64], is_test=is_test, use_cudnn=use_cudnn)
self.conv_bn_pool_4 = ConvBNPool(
self.full_name(),
2, [128, 128], [64, 128],
is_test=is_test,
pool=False,
......@@ -169,7 +155,6 @@ class DynamicGRU(fluid.dygraph.Layer):
super(DynamicGRU, self).__init__(scope_name)
self.gru_unit = GRUUnit(
self.full_name(),
size * 3,
param_attr=param_attr,
bias_attr=bias_attr,
......@@ -337,10 +322,7 @@ class GRUDecoderWithAttention(fluid.dygraph.Layer):
size=decoder_size * 3,
bias_attr=False)
self.gru_unit = GRUUnit(
self.full_name(),
size=decoder_size * 3,
param_attr=None,
bias_attr=None)
size=decoder_size * 3, param_attr=None, bias_attr=None)
self.out_layer = FC(self.full_name(),
size=num_classes + 2,
bias_attr=None,
......@@ -383,8 +365,7 @@ class OCRAttention(fluid.dygraph.Layer):
bias_attr=False,
act='relu')
self.embedding = Embedding(
self.full_name(), [Config.num_classes + 2, Config.word_vector_dim],
dtype='float32')
[Config.num_classes + 2, Config.word_vector_dim], dtype='float32')
self.gru_decoder_with_attention = GRUDecoderWithAttention(
self.full_name(), Config.decoder_size, Config.num_classes)
......
python/paddle/fluid/tests/unittests/test_imperative_ptb_rnn.py
浏览文件 @ f6144d84
......@@ -158,7 +158,6 @@ class PtbModel(fluid.Layer):
init_scale=init_scale,
dropout=dropout)
self.embedding = Embedding(
self.full_name(),
size=[vocab_size, hidden_size],
dtype='float32',
is_sparse=is_sparse,
......
python/paddle/fluid/tests/unittests/test_imperative_resnet.py
浏览文件 @ f6144d84
......@@ -72,16 +72,16 @@ def optimizer_setting(params):
class ConvBNLayer(fluid.Layer):
def __init__(self,
name_scope,
num_channels,
num_filters,
filter_size,
stride=1,
groups=1,
act=None):
super(ConvBNLayer, self).__init__(name_scope)
super(ConvBNLayer, self).__init__()
self._conv = Conv2D(
self.full_name(),
num_channels=num_channels,
num_filters=num_filters,
filter_size=filter_size,
stride=stride,
......@@ -91,7 +91,7 @@ class ConvBNLayer(fluid.Layer):
bias_attr=None,
use_cudnn=False)
self._batch_norm = BatchNorm(self.full_name(), num_filters, act=act)
self._batch_norm = BatchNorm(num_filters, act=act)
def forward(self, inputs):
y = self._conv(inputs)
......@@ -101,29 +101,29 @@ class ConvBNLayer(fluid.Layer):
class BottleneckBlock(fluid.Layer):
def __init__(self, name_scope, num_filters, stride, shortcut=True):
super(BottleneckBlock, self).__init__(name_scope)
def __init__(self, num_channels, num_filters, stride, shortcut=True):
super(BottleneckBlock, self).__init__()
self.conv0 = ConvBNLayer(
self.full_name(),
num_channels=num_channels,
num_filters=num_filters,
filter_size=1,
act='relu')
self.conv1 = ConvBNLayer(
self.full_name(),
num_channels=num_filters,
num_filters=num_filters,
filter_size=3,
stride=stride,
act='relu')
self.conv2 = ConvBNLayer(
self.full_name(),
num_channels=num_filters,
num_filters=num_filters * 4,
filter_size=1,
act=None)
if not shortcut:
self.short = ConvBNLayer(
self.full_name(),
num_channels=num_channels,
num_filters=num_filters * 4,
filter_size=1,
stride=stride)
......@@ -161,20 +161,13 @@ class ResNet(fluid.Layer):
depth = [3, 4, 23, 3]
elif layers == 152:
depth = [3, 8, 36, 3]
num_channels = [64, 256, 512, 1024]
num_filters = [64, 128, 256, 512]
self.conv = ConvBNLayer(
self.full_name(),
num_filters=64,
filter_size=7,
stride=2,
act='relu')
num_channels=3, num_filters=64, filter_size=7, stride=2, act='relu')
self.pool2d_max = Pool2D(
self.full_name(),
pool_size=3,
pool_stride=2,
pool_padding=1,
pool_type='max')
pool_size=3, pool_stride=2, pool_padding=1, pool_type='max')
self.bottleneck_block_list = []
for block in range(len(depth)):
......@@ -183,7 +176,8 @@ class ResNet(fluid.Layer):
bottleneck_block = self.add_sublayer(
'bb_%d_%d' % (block, i),
BottleneckBlock(
self.full_name(),
num_channels=num_channels[block]
if i == 0 else num_filters[block] * 4,
num_filters=num_filters[block],
stride=2 if i == 0 and block != 0 else 1,
shortcut=shortcut))
......@@ -191,7 +185,7 @@ class ResNet(fluid.Layer):
shortcut = True
self.pool2d_avg = Pool2D(
self.full_name(), pool_size=7, pool_type='avg', global_pooling=True)
pool_size=7, pool_type='avg', global_pooling=True)
import math
stdv = 1.0 / math.sqrt(2048 * 1.0)
......
python/paddle/fluid/tests/unittests/test_imperative_save_load.py
浏览文件 @ f6144d84
......@@ -156,7 +156,6 @@ class PtbModel(fluid.Layer):
init_scale=init_scale,
dropout=dropout)
self.embedding = Embedding(
self.full_name(),
size=[vocab_size, hidden_size],
dtype='float32',
is_sparse=False,
......@@ -882,7 +881,7 @@ class TestDygraphPtbRnn(unittest.TestCase):
def testOnlyLoadParams(self):
with fluid.dygraph.guard():
emb = fluid.dygraph.Embedding("emb", [10, 10])
emb = fluid.dygraph.Embedding([10, 10])
state_dict = emb.state_dict()
fluid.save_dygraph(state_dict, "emb_dy")
......
python/paddle/fluid/tests/unittests/test_imperative_se_resnext.py
浏览文件 @ f6144d84
......@@ -63,16 +63,16 @@ def optimizer_setting(params):
class ConvBNLayer(fluid.dygraph.Layer):
def __init__(self,
name_scope,
num_channels,
num_filters,
filter_size,
stride=1,
groups=1,
act=None):
super(ConvBNLayer, self).__init__(name_scope)
super(ConvBNLayer, self).__init__()
self._conv = Conv2D(
self.full_name(),
num_channels=num_channels,
num_filters=num_filters,
filter_size=filter_size,
stride=stride,
......@@ -81,7 +81,7 @@ class ConvBNLayer(fluid.dygraph.Layer):
act=None,
bias_attr=None)
self._batch_norm = BatchNorm(self.full_name(), num_filters, act=act)
self._batch_norm = BatchNorm(num_filters, act=act)
def forward(self, inputs):
y = self._conv(inputs)
......@@ -94,8 +94,7 @@ class SqueezeExcitation(fluid.dygraph.Layer):
def __init__(self, name_scope, num_channels, reduction_ratio):
super(SqueezeExcitation, self).__init__(name_scope)
self._pool = Pool2D(
self.full_name(), pool_size=0, pool_type='avg', global_pooling=True)
self._pool = Pool2D(pool_size=0, pool_type='avg', global_pooling=True)
self._squeeze = FC(
self.full_name(),
size=num_channels // reduction_ratio,
......@@ -119,25 +118,24 @@ class SqueezeExcitation(fluid.dygraph.Layer):
class BottleneckBlock(fluid.dygraph.Layer):
def __init__(self,
name_scope,
num_channels,
num_filters,
stride,
cardinality,
reduction_ratio,
shortcut=True):
super(BottleneckBlock, self).__init__(name_scope)
super(BottleneckBlock, self).__init__()
self.conv0 = ConvBNLayer(
self.full_name(), num_filters=num_filters, filter_size=1)
num_channels=num_channels, num_filters=num_filters, filter_size=1)
self.conv1 = ConvBNLayer(
self.full_name(),
num_channels=num_filters,
num_filters=num_filters,
filter_size=3,
stride=stride,
groups=cardinality)
self.conv2 = ConvBNLayer(
self.full_name(),
num_channels=num_filters,
num_filters=num_filters * 4,
filter_size=1,
act='relu')
......@@ -149,7 +147,7 @@ class BottleneckBlock(fluid.dygraph.Layer):
if not shortcut:
self.short = ConvBNLayer(
self.full_name(),
num_channels=num_channels,
num_filters=num_filters * 4,
filter_size=1,
stride=stride)
......@@ -191,63 +189,51 @@ class SeResNeXt(fluid.dygraph.Layer):
depth = [3, 4, 6, 3]
num_filters = [128, 256, 512, 1024]
self.conv0 = ConvBNLayer(
self.full_name(),
num_channels=3,
num_filters=64,
filter_size=7,
stride=2,
act='relu')
self.pool = Pool2D(
self.full_name(),
pool_size=3,
pool_stride=2,
pool_padding=1,
pool_type='max')
pool_size=3, pool_stride=2, pool_padding=1, pool_type='max')
elif layers == 101:
cardinality = 32
reduction_ratio = 16
depth = [3, 4, 23, 3]
num_filters = [128, 256, 512, 1024]
self.conv0 = ConvBNLayer(
self.full_name(),
num_channels=3,
num_filters=3,
filter_size=7,
stride=2,
act='relu')
self.pool = Pool2D(
self.full_name(),
pool_size=3,
pool_stride=2,
pool_padding=1,
pool_type='max')
pool_size=3, pool_stride=2, pool_padding=1, pool_type='max')
elif layers == 152:
cardinality = 64
reduction_ratio = 16
depth = [3, 8, 36, 3]
num_filters = [128, 256, 512, 1024]
self.conv0 = ConvBNLayer(
self.full_name(),
num_channels=3,
num_filters=3,
filter_size=7,
stride=2,
act='relu')
self.conv1 = ConvBNLayer(
self.full_name(),
num_channels=3,
num_filters=3,
filter_size=7,
stride=2,
act='relu')
self.conv2 = ConvBNLayer(
self.full_name(),
num_channels=7,
num_filters=3,
filter_size=7,
stride=2,
act='relu')
self.pool = Pool2D(
self.full_name(),
pool_size=3,
pool_stride=2,
pool_padding=1,
pool_type='max')
pool_size=3, pool_stride=2, pool_padding=1, pool_type='max')
self.bottleneck_block_list = []
num_channels = 64
......@@ -257,7 +243,6 @@ class SeResNeXt(fluid.dygraph.Layer):
bottleneck_block = self.add_sublayer(
'bb_%d_%d' % (block, i),
BottleneckBlock(
self.full_name(),
num_channels=num_channels,
num_filters=num_filters[block],
stride=2 if i == 0 and block != 0 else 1,
......@@ -269,7 +254,7 @@ class SeResNeXt(fluid.dygraph.Layer):
shortcut = True
self.pool2d_avg = Pool2D(
self.full_name(), pool_size=7, pool_type='avg', global_pooling=True)
pool_size=7, pool_type='avg', global_pooling=True)
import math
stdv = 1.0 / math.sqrt(2048 * 1.0)
......
python/paddle/fluid/tests/unittests/test_imperative_transformer_sorted_gradient.py
浏览文件 @ f6144d84
......@@ -350,13 +350,12 @@ pos_inp2 = position_encoding_init(ModelHyperParams.max_length,
class PrePostProcessLayer(Layer):
def __init__(self, name_scope, process_cmd, shape_len=None):
super(PrePostProcessLayer, self).__init__(name_scope)
def __init__(self, d_model, process_cmd, shape_len=None):
super(PrePostProcessLayer, self).__init__()
for cmd in process_cmd:
if cmd == "n":
self._layer_norm = LayerNorm(
name_scope=self.full_name(),
begin_norm_axis=shape_len - 1,
normalized_shape=d_model,
param_attr=fluid.ParamAttr(
initializer=fluid.initializer.Constant(1.)),
bias_attr=fluid.ParamAttr(
......@@ -508,19 +507,19 @@ class EncoderSubLayer(Layer):
self._postprocess_cmd = postprocess_cmd
self._prepostprocess_dropout = prepostprocess_dropout
self._preprocess_layer = PrePostProcessLayer(self.full_name(),
self._preprocess_layer = PrePostProcessLayer(d_model,
self._preprocess_cmd, 3)
self._multihead_attention_layer = MultiHeadAttentionLayer(
self.full_name(), d_key, d_value, d_model, n_head,
attention_dropout)
self._postprocess_layer = PrePostProcessLayer(
self.full_name(), self._postprocess_cmd, None)
self._preprocess_layer2 = PrePostProcessLayer(self.full_name(),
d_model, self._postprocess_cmd, None)
self._preprocess_layer2 = PrePostProcessLayer(d_model,
self._preprocess_cmd, 3)
self._positionwise_feed_forward = PositionwiseFeedForwardLayer(
self.full_name(), d_inner_hid, d_model, relu_dropout)
self._postprocess_layer2 = PrePostProcessLayer(
self.full_name(), self._postprocess_cmd, None)
d_model, self._postprocess_cmd, None)
def forward(self, enc_input, attn_bias):
pre_process_multihead = self._preprocess_layer(
......@@ -559,7 +558,7 @@ class EncoderLayer(Layer):
self._encoder_sublayers = list()
self._prepostprocess_dropout = prepostprocess_dropout
self._n_layer = n_layer
self._preprocess_layer = PrePostProcessLayer(self.full_name(),
self._preprocess_layer = PrePostProcessLayer(d_model,
self._preprocess_cmd, 3)
for i in range(n_layer):
self._encoder_sublayers.append(
......@@ -595,7 +594,6 @@ class PrepareEncoderDecoderLayer(Layer):
self._src_vocab_size = src_vocab_size
self._dropout_rate = dropout_rate
self._input_emb = Embedding(
name_scope=self.full_name(),
size=[src_vocab_size, src_emb_dim],
is_sparse=is_sparse,
padding_idx=0,
......@@ -608,7 +606,6 @@ class PrepareEncoderDecoderLayer(Layer):
else:
pos_inp = pos_inp2
self._pos_emb = Embedding(
name_scope=self.full_name(),
size=[self._src_max_len, src_emb_dim],
is_sparse=is_sparse,
param_attr=fluid.ParamAttr(
......@@ -698,8 +695,8 @@ class DecoderSubLayer(Layer):
self._postprocess_cmd = postprocess_cmd
self._preprocess_cmd = preprocess_cmd
self._prepostprcess_dropout = prepostprocess_dropout
self._pre_process_layer = PrePostProcessLayer(self.full_name(),
preprocess_cmd, 3)
self._pre_process_layer = PrePostProcessLayer(d_model, preprocess_cmd,
3)
self._multihead_attention_layer = MultiHeadAttentionLayer(
self.full_name(),
d_key,
......@@ -709,10 +706,10 @@ class DecoderSubLayer(Layer):
attention_dropout,
cache=cache,
gather_idx=gather_idx)
self._post_process_layer = PrePostProcessLayer(self.full_name(),
postprocess_cmd, None)
self._pre_process_layer2 = PrePostProcessLayer(self.full_name(),
preprocess_cmd, 3)
self._post_process_layer = PrePostProcessLayer(d_model, postprocess_cmd,
None)
self._pre_process_layer2 = PrePostProcessLayer(d_model, preprocess_cmd,
3)
self._multihead_attention_layer2 = MultiHeadAttentionLayer(
self.full_name(),
d_key,
......@@ -723,13 +720,13 @@ class DecoderSubLayer(Layer):
cache=cache,
gather_idx=gather_idx,
static_kv=True)
self._post_process_layer2 = PrePostProcessLayer(self.full_name(),
self._post_process_layer2 = PrePostProcessLayer(d_model,
postprocess_cmd, None)
self._pre_process_layer3 = PrePostProcessLayer(self.full_name(),
preprocess_cmd, 3)
self._pre_process_layer3 = PrePostProcessLayer(d_model, preprocess_cmd,
3)
self._positionwise_feed_forward_layer = PositionwiseFeedForwardLayer(
self.full_name(), d_inner_hid, d_model, relu_dropout)
self._post_process_layer3 = PrePostProcessLayer(self.full_name(),
self._post_process_layer3 = PrePostProcessLayer(d_model,
postprocess_cmd, None)
def forward(self, dec_input, enc_output, slf_attn_bias, dec_enc_attn_bias):
......@@ -775,8 +772,8 @@ class DecoderLayer(Layer):
caches=None,
gather_idx=None):
super(DecoderLayer, self).__init__(name_scope)
self._pre_process_layer = PrePostProcessLayer(self.full_name(),
preprocess_cmd, 3)
self._pre_process_layer = PrePostProcessLayer(d_model, preprocess_cmd,
3)
self._decoder_sub_layers = list()
self._n_layer = n_layer
self._preprocess_cmd = preprocess_cmd
......
python/paddle/fluid/tests/unittests/test_static_save_load.py
浏览文件 @ f6144d84
......@@ -158,7 +158,6 @@ class PtbModel(fluid.Layer):
init_scale=init_scale,
dropout=dropout)
self.embedding = Embedding(
self.full_name(),
size=[vocab_size, hidden_size],
dtype='float32',
is_sparse=False,
......
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