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未验证 提交 851c8c52 编写于 作者: 王 王肖 提交者： GitHub
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fix bugs of PaddleNLP/similarity_net & dygraph/similarity_net (#4362) 

* Update README.md (#4267)

* test=develop (#4269)

* 3d use new api (#4275)

* PointNet++ and PointRCNN use new API

* Update Readme of Dygraph BERT (#4277)

Fix some typos.

* Update run_classifier_multi_gpu.sh (#4279)

remove the CUDA_VISIBLE_DEVICES

* Update README.md (#4280)

* add similarity_net dygraph

* fix similarity_net dygraph

* fix bugs of dygraph/similarity_net

* Fix some bugs running on the GPU of dygraph/similarity_net

* fix a bug in pointwise mode of dygraph/similarity_net

* fix a bug of paddleNLP/similarity_net

* fix a bug and remove unuse files of dygraph/similarity_net
Co-authored-by: Npkpk <xiyzhouang@gmail.com>
Co-authored-by: NKaipeng Deng <dengkaipeng@baidu.com>
上级 53ea4b36
展开全部 隐藏空白更改
内联 并排
Showing with 22 addition and 1308 deletion
PaddleNLP/similarity_net/run_classifier.py
浏览文件 @ 851c8c52
......@@ -53,12 +53,12 @@ def create_model(args, pyreader_name, is_inference=False, is_pointwise=False):
"""
if is_inference:
inf_pyreader = fluid.layers.py_reader(
capacity=16,
shapes=([-1, 1], [-1, 1]),
dtypes=('int64', 'int64'),
lod_levels=(1, 1),
name=pyreader_name,
use_double_buffer=False)
capacity=16,
shapes=([-1], [-1]),
dtypes=('int64', 'int64'),
lod_levels=(1, 1),
name=pyreader_name,
use_double_buffer=False)
left, pos_right = fluid.layers.read_file(inf_pyreader)
return inf_pyreader, left, pos_right
......@@ -66,27 +66,26 @@ def create_model(args, pyreader_name, is_inference=False, is_pointwise=False):
else:
if is_pointwise:
pointwise_pyreader = fluid.layers.py_reader(
capacity=16,
shapes=([-1, 1], [-1, 1], [-1, 1]),
dtypes=('int64', 'int64', 'int64'),
lod_levels=(1, 1, 0),
name=pyreader_name,
use_double_buffer=False)
capacity=16,
shapes=([-1], [-1], [-1]),
dtypes=('int64', 'int64', 'int64'),
lod_levels=(1, 1, 0),
name=pyreader_name,
use_double_buffer=False)
left, right, label = fluid.layers.read_file(pointwise_pyreader)
return pointwise_pyreader, left, right, label
else:
pairwise_pyreader = fluid.layers.py_reader(
capacity=16,
shapes=([-1, 1], [-1, 1], [-1, 1]),
dtypes=('int64', 'int64', 'int64'),
lod_levels=(1, 1, 1),
name=pyreader_name,
use_double_buffer=False)
left, pos_right, neg_right = fluid.layers.read_file(
pairwise_pyreader)
capacity=16,
shapes=([-1], [-1], [-1]),
dtypes=('int64', 'int64', 'int64'),
lod_levels=(1, 1, 1),
name=pyreader_name,
use_double_buffer=False)
left, pos_right, neg_right = fluid.layers.read_file(pairwise_pyreader)
return pairwise_pyreader, left, pos_right, neg_right
......
dygraph/similarity_net/nets/base_layers.py 已删除 100644 → 0
浏览文件 @ 53ea4b36
# Copyright (c) 2019 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""
base layers
"""
from paddle.fluid import layers
from paddle.fluid.dygraph import Layer
from paddle.fluid.dygraph import GRUUnit
from paddle.fluid.dygraph.base import to_variable
# import numpy as np
# import logging
class DynamicGRU(Layer):
def __init__(self,
size,
param_attr=None,
bias_attr=None,
is_reverse=False,
gate_activation='sigmoid',
candidate_activation='tanh',
h_0=None,
origin_mode=False,
init_size = None):
super(DynamicGRU, self).__init__()
self.gru_unit = GRUUnit(
size * 3,
param_attr=param_attr,
bias_attr=bias_attr,
activation=candidate_activation,
gate_activation=gate_activation,
origin_mode=origin_mode)
self.size = size
self.h_0 = h_0
self.is_reverse = is_reverse
def forward(self, inputs):
hidden = self.h_0
res = []
for i in range(inputs.shape[1]):
if self.is_reverse:
i = inputs.shape[1] - 1 - i
input_ = inputs[ :, i:i+1, :]
input_ = fluid.layers.reshape(input_, [-1, input_.shape[2]], inplace=False)
hidden, reset, gate = self.gru_unit(input_, hidden)
hidden_ = fluid.layers.reshape(hidden, [-1, 1, hidden.shape[1]], inplace=False)
res.append(hidden_)
if self.is_reverse:
res = res[::-1]
res = fluid.layers.concat(res, axis=1)
return res
\ No newline at end of file
dygraph/similarity_net/nets/copy.py 已删除 100644 → 0
浏览文件 @ 53ea4b36
此差异已折叠。
dygraph/similarity_net/nets/mm_dnn.py
浏览文件 @ 851c8c52
......@@ -42,7 +42,7 @@ class MMDNN(Layer):
self.dpool_size1 = int(config['net']['dpool_size_left'])
self.dpool_size2 = int(config['net']['dpool_size_right'])
self.hidden_size = int(config['net']['hidden_size'])
self.seq_len = int(conf_dict["seq_len"])
self.seq_len = int(config["seq_len"])
self.seq_len1 = self.seq_len
#int(config['max_len_left'])
self.seq_len2 = self.seq_len
......@@ -157,7 +157,7 @@ class MMDNN(Layer):
conv = self.conv(emb_expand)
if mask is not None:
cross_mask = fluid.layers.stack(x=[mask] * self.kernel_size, axis=0)
cross_mask = fluid.layers.stack(x=[cross] * conv.shape[1], axis=1)
cross_mask = fluid.layers.stack(x=[cross_mask] * conv.shape[0], axis=0)
conv = cross_mask * conv + (1 - cross_mask) * (-2**self.seq_len + 1)
pool = self.pool_layer(conv)
......
dygraph/similarity_net/nets/paddle_layers.1.py 已删除 100644 → 0
浏览文件 @ 53ea4b36
# Copyright (c) 2019 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""
network layers
"""
import paddle.fluid as fluid
import paddle.fluid.param_attr as attr
class EmbeddingLayer(object):
"""
Embedding Layer class
"""
def __init__(self, dict_size, emb_dim, name="emb"):
"""
initialize
"""
self.dict_size = dict_size
self.emb_dim = emb_dim
self.name = name
def ops(self, input):
"""
operation
"""
emb = fluid.dygraph.Embedding(
input=input,
size=[self.dict_size, self.emb_dim],
is_sparse=True,
param_attr=attr.ParamAttr(name=self.name))
return emb
class SequencePoolLayer(object):
"""
Sequence Pool Layer class
"""
def __init__(self, pool_type):
"""
initialize
"""
self.pool_type = pool_type
def ops(self, input):
"""
operation
"""
pool = fluid.dygraph.Pool2D(input=input, pool_type=self.pool_type)
return pool
class FCLayer(object):
"""
Fully Connect Layer class
"""
def __init__(self, fc_dim, act, name="fc"):
"""
initialize
"""
self.fc_dim = fc_dim
self.act = act
self.name = name
def ops(self, input):
"""
operation
"""
fc = fluid.dygraph.FC(input=input,
size=self.fc_dim,
param_attr=attr.ParamAttr(name="%s.w" % self.name),
bias_attr=attr.ParamAttr(name="%s.b" % self.name),
act=self.act,
name=self.name)
return fc
class DynamicGRULayer(object):
"""
Dynamic GRU Layer class
"""
def __init__(self, gru_dim, name="dyn_gru"):
"""
initialize
"""
self.gru_dim = gru_dim
self.name = name
def ops(self, input):
"""
operation
"""
proj = fluid.dygraph.FC(
input=input,
size=self.gru_dim * 3,
param_attr=attr.ParamAttr(name="%s_fc.w" % self.name),
bias_attr=attr.ParamAttr(name="%s_fc.b" % self.name))
gru = fluid.layers.dynamic_gru(
input=proj,
size=self.gru_dim,
param_attr=attr.ParamAttr(name="%s.w" % self.name),
bias_attr=attr.ParamAttr(name="%s.b" % self.name))
return gru
class DynamicLSTMLayer(object):
"""
Dynamic LSTM Layer class
"""
def __init__(self, lstm_dim, name="dyn_lstm"):
"""
initialize
"""
self.lstm_dim = lstm_dim
self.name = name
def ops(self, input):
"""
operation
"""
proj = fluid.dygraph.FC(
input=input,
size=self.lstm_dim * 4,
param_attr=attr.ParamAttr(name="%s_fc.w" % self.name),
bias_attr=attr.ParamAttr(name="%s_fc.b" % self.name))
lstm, _ = fluid.layers.dynamic_lstm(
input=proj,
size=self.lstm_dim * 4,
param_attr=attr.ParamAttr(name="%s.w" % self.name),
bias_attr=attr.ParamAttr(name="%s.b" % self.name))
return lstm
class SequenceLastStepLayer(object):
"""
Get Last Step Sequence Layer class
"""
def __init__(self):
"""
initialize
"""
pass
def ops(self, input):
"""
operation
"""
last = fluid.layers.sequence_last_step(input)
return last
class SequenceConvPoolLayer(object):
"""
Sequence convolution and pooling Layer class
"""
def __init__(self, filter_size, num_filters, name):
"""
initialize
Args:
filter_size:Convolution kernel size
num_filters:Convolution kernel number
"""
self.filter_size = filter_size
self.num_filters = num_filters
self.name = name
def ops(self, input):
"""
operation
"""
conv = fluid.nets.sequence_conv_pool(
input=input,
filter_size=self.filter_size,
num_filters=self.num_filters,
param_attr=attr.ParamAttr(name=self.name),
act="relu")
return conv
class DataLayer(object):
"""
Data Layer class
"""
def __init__(self):
"""
initialize
"""
pass
def ops(self, name, shape, dtype, lod_level=0):
"""
operation
"""
data = fluid.layers.data( #不用改
name=name, shape=shape, dtype=dtype, lod_level=lod_level)
return data
class ConcatLayer(object):
"""
Connection Layer class
"""
def __init__(self, axis):
"""
initialize
"""
self.axis = axis
def ops(self, inputs):
"""
operation
"""
concat = fluid.layers.concat(inputs, axis=self.axis)
return concat
class ReduceMeanLayer(object):
"""
Reduce Mean Layer class
"""
def __init__(self):
"""
initialize
"""
pass
def ops(self, input):
"""
operation
"""
mean = fluid.layers.reduce_mean(input)
return mean
class CrossEntropyLayer(object):
"""
Cross Entropy Calculate Layer
"""
def __init__(self, name="cross_entropy"):
"""
initialize
"""
pass
def ops(self, input, label):
"""
operation
"""
loss = fluid.layers.cross_entropy(input=input, label=label) # 不用改
return loss
class SoftmaxWithCrossEntropyLayer(object):
"""
Softmax with Cross Entropy Calculate Layer
"""
def __init__(self, name="softmax_with_cross_entropy"):
"""
initialize
"""
pass
def ops(self, input, label):
"""
operation
"""
loss = fluid.layers.softmax_with_cross_entropy( # 不用改
logits=input, label=label)
return loss
class CosSimLayer(object):
"""
Cos Similarly Calculate Layer
"""
def __init__(self):
"""
initialize
"""
pass
def ops(self, x, y):
"""
operation
"""
sim = fluid.layers.cos_sim(x, y)
return sim
class ElementwiseMaxLayer(object):
"""
Elementwise Max Layer class
"""
def __init__(self):
"""
initialize
"""
pass
def ops(self, x, y):
"""
operation
"""
max = fluid.layers.elementwise_max(x, y)
return max
class ElementwiseAddLayer(object):
"""
Elementwise Add Layer class
"""
def __init__(self):
"""
initialize
"""
pass
def ops(self, x, y):
"""
operation
"""
add = fluid.layers.elementwise_add(x, y)
return add
class ElementwiseSubLayer(object):
"""
Elementwise Add Layer class
"""
def __init__(self):
"""
initialize
"""
pass
def ops(self, x, y):
"""
operation
"""
sub = fluid.layers.elementwise_sub(x, y)
return sub
class ConstantLayer(object):
"""
Generate A Constant Layer class
"""
def __init__(self):
"""
initialize
"""
pass
def ops(self, input, shape, dtype, value):
"""
operation
"""
constant = fluid.layers.fill_constant_batch_size_like(input, shape,
dtype, value)
return constant
class SigmoidLayer(object):
"""
Sigmoid Layer class
"""
def __init__(self):
"""
initialize
"""
pass
def ops(self, input):
"""
operation
"""
sigmoid = fluid.layers.sigmoid(input)
return sigmoid
class SoftsignLayer(object):
"""
Softsign Layer class
"""
def __init__(self):
"""
initialize
"""
pass
def ops(self, input):
"""
operation
"""
softsign = fluid.layers.softsign(input)
return softsign
# class MatmulLayer(object):
# def __init__(self, transpose_x, transpose_y):
# self.transpose_x = transpose_x
# self.transpose_y = transpose_y
# def ops(self, x, y):
# matmul = fluid.layers.matmul(x, y, self.transpose_x, self.transpose_y)
# return matmul
# class Conv2dLayer(object):
# def __init__(self, num_filters, filter_size, act, name):
# self.num_filters = num_filters
# self.filter_size = filter_size
# self.act = act
# self.name = name
# def ops(self, input):
# conv = fluid.layers.conv2d(input, self.num_filters, self.filter_size, param_attr=attr.ParamAttr(name="%s.w" % self.name), bias_attr=attr.ParamAttr(name="%s.b" % self.name), act=self.act)
# return conv
# class Pool2dLayer(object):
# def __init__(self, pool_size, pool_type):
# self.pool_size = pool_size
# self.pool_type = pool_type
# def ops(self, input):
# pool = fluid.layers.pool2d(input, self.pool_size, self.pool_type)
# return pool
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