From 3a5d6e5e64140a1b84363010a9f077c1fd8fb6e1 Mon Sep 17 00:00:00 2001
From: Yan Chunwei <yanchunwei@outlook.com>
Date: Thu, 14 Feb 2019 15:38:15 +0800
Subject: [PATCH 1/8] move passes to src to avoid different behavior in
deployment (#15705)
---
.../inference/api/paddle_pass_builder.cc | 46 ++++++++++++++++++
.../fluid/inference/api/paddle_pass_builder.h | 47 +------------------
2 files changed, 48 insertions(+), 45 deletions(-)
diff --git a/paddle/fluid/inference/api/paddle_pass_builder.cc b/paddle/fluid/inference/api/paddle_pass_builder.cc
index 039389a4cf..f9c13c2fa8 100644
--- a/paddle/fluid/inference/api/paddle_pass_builder.cc
+++ b/paddle/fluid/inference/api/paddle_pass_builder.cc
@@ -66,8 +66,54 @@ void GpuPassStrategy::EnableMKLDNN() {
LOG(ERROR) << "GPU not support MKLDNN yet";
}
+GpuPassStrategy::GpuPassStrategy() : PassStrategy({}) {
+ passes_.assign({
+ "infer_clean_graph_pass", //
+ "identity_scale_op_clean_pass", //
+ "conv_affine_channel_fuse_pass", //
+ "conv_eltwiseadd_affine_channel_fuse_pass", //
+ "conv_bn_fuse_pass", //
+#if CUDNN_VERSION >= 7100 // To run conv_fusion, the version of cudnn must be
+ // guaranteed at least v7
+ "conv_elementwise_add_act_fuse_pass", //
+ "conv_elementwise_add2_act_fuse_pass", //
+ "conv_elementwise_add_fuse_pass", //
+#endif
+ });
+
+ for (int i = 6; i >= 3; i--) {
+ passes_.push_back("transpose_flatten" + std::to_string(i) +
+ "_concat_fuse_pass");
+ }
+ use_gpu_ = true;
+}
+
void PaddlePassBuilder::AppendAnalysisPass(const std::string &pass) {
analysis_passes_.push_back(pass);
}
+CpuPassStrategy::CpuPassStrategy() : PassStrategy({}) {
+ // NOTE the large fusions should be located in the front, so that they will
+ // not be damaged by smaller ones.
+ passes_.assign({
+ "infer_clean_graph_pass", //
+ "attention_lstm_fuse_pass", //
+ "seqpool_concat_fuse_pass", //
+ "seqconv_eltadd_relu_fuse_pass", //
+ // "embedding_fc_lstm_fuse_pass", //
+ "fc_lstm_fuse_pass", //
+ "mul_lstm_fuse_pass", //
+ "fc_gru_fuse_pass", //
+ "mul_gru_fuse_pass", //
+ "seq_concat_fc_fuse_pass", //
+ "fc_fuse_pass", //
+ "repeated_fc_relu_fuse_pass", //
+ "squared_mat_sub_fuse_pass", //
+ "conv_bn_fuse_pass", //
+ "conv_eltwiseadd_bn_fuse_pass", //
+ "is_test_pass", //
+ "identity_scale_op_clean_pass", //
+ });
+ use_gpu_ = false;
+}
} // namespace paddle
diff --git a/paddle/fluid/inference/api/paddle_pass_builder.h b/paddle/fluid/inference/api/paddle_pass_builder.h
index aa353f12ca..2524d89fcd 100644
--- a/paddle/fluid/inference/api/paddle_pass_builder.h
+++ b/paddle/fluid/inference/api/paddle_pass_builder.h
@@ -97,30 +97,7 @@ class PassStrategy : public PaddlePassBuilder {
*/
class CpuPassStrategy : public PassStrategy {
public:
- CpuPassStrategy() : PassStrategy({}) {
- // NOTE the large fusions should be located in the front, so that they will
- // not be damaged by smaller ones.
- passes_.assign({
- "infer_clean_graph_pass", //
- "attention_lstm_fuse_pass", //
- "seqpool_concat_fuse_pass", //
- "seqconv_eltadd_relu_fuse_pass", //
- // "embedding_fc_lstm_fuse_pass", //
- "fc_lstm_fuse_pass", //
- "mul_lstm_fuse_pass", //
- "fc_gru_fuse_pass", //
- "mul_gru_fuse_pass", //
- "seq_concat_fc_fuse_pass", //
- "fc_fuse_pass", //
- "repeated_fc_relu_fuse_pass", //
- "squared_mat_sub_fuse_pass", //
- "conv_bn_fuse_pass", //
- "conv_eltwiseadd_bn_fuse_pass", //
- "is_test_pass", //
- "identity_scale_op_clean_pass", //
- });
- use_gpu_ = false;
- }
+ CpuPassStrategy();
explicit CpuPassStrategy(const CpuPassStrategy &other)
: PassStrategy(other.AllPasses()) {}
@@ -153,27 +130,7 @@ class CpuPassStrategy : public PassStrategy {
*/
class GpuPassStrategy : public PassStrategy {
public:
- GpuPassStrategy() : PassStrategy({}) {
- passes_.assign({
- "infer_clean_graph_pass", //
- "identity_scale_op_clean_pass", //
- "conv_affine_channel_fuse_pass", //
- "conv_eltwiseadd_affine_channel_fuse_pass", //
- "conv_bn_fuse_pass", //
-#if CUDNN_VERSION >= 7100 // To run conv_fusion, the version of cudnn must be
- // guaranteed at least v7
- "conv_elementwise_add_act_fuse_pass", //
- "conv_elementwise_add2_act_fuse_pass", //
- "conv_elementwise_add_fuse_pass", //
-#endif
- });
-
- for (int i = 6; i >= 3; i--) {
- passes_.push_back("transpose_flatten" + std::to_string(i) +
- "_concat_fuse_pass");
- }
- use_gpu_ = true;
- }
+ GpuPassStrategy();
explicit GpuPassStrategy(const GpuPassStrategy &other)
: PassStrategy(other.AllPasses()) {
From d453b0dcf72d50501bc59309a971c172ef148e31 Mon Sep 17 00:00:00 2001
From: dzhwinter <dzhwinter@gmail.com>
Date: Thu, 14 Feb 2019 16:02:38 +0800
Subject: [PATCH 2/8] add details. test=develop
---
.../paddle/fluid/transpiler/memory_optimization_transpiler.py | 4 ++++
1 file changed, 4 insertions(+)
diff --git a/python/paddle/fluid/transpiler/memory_optimization_transpiler.py b/python/paddle/fluid/transpiler/memory_optimization_transpiler.py
index 52c1aea288..047e0832bc 100755
--- a/python/paddle/fluid/transpiler/memory_optimization_transpiler.py
+++ b/python/paddle/fluid/transpiler/memory_optimization_transpiler.py
@@ -355,6 +355,10 @@ class ControlFlowGraph(object):
is_forward).dtype()
cache_dtype = self._find_var(block_desc, cache_var,
is_forward).dtype()
+ if x_dtype != cache_dtype:
+ if PRINT_LOG:
+ print("x_dtype and cache_dtyp are different")
+ continue
if not compare_shape(x_shape, cache_shape, level):
continue
From 84f067be9405d45436a4326b474f3984ce44d021 Mon Sep 17 00:00:00 2001
From: dzhwinter <dongzhihong01@baidu.com>
Date: Thu, 14 Feb 2019 17:15:00 +0800
Subject: [PATCH 3/8] update. test=develop
test=develop
---
.../paddle/fluid/transpiler/memory_optimization_transpiler.py | 2 +-
1 file changed, 1 insertion(+), 1 deletion(-)
diff --git a/python/paddle/fluid/transpiler/memory_optimization_transpiler.py b/python/paddle/fluid/transpiler/memory_optimization_transpiler.py
index 047e0832bc..ee8cde441f 100755
--- a/python/paddle/fluid/transpiler/memory_optimization_transpiler.py
+++ b/python/paddle/fluid/transpiler/memory_optimization_transpiler.py
@@ -357,7 +357,7 @@ class ControlFlowGraph(object):
is_forward).dtype()
if x_dtype != cache_dtype:
if PRINT_LOG:
- print("x_dtype and cache_dtyp are different")
+ print("x_dtype and cache_dtype are different")
continue
if not compare_shape(x_shape, cache_shape, level):
From 31287cdb4351a7896a6836a868d159c2b29935c2 Mon Sep 17 00:00:00 2001
From: Tao Luo <luotao02@baidu.com>
Date: Thu, 14 Feb 2019 21:04:25 +0800
Subject: [PATCH 5/8] remove legace v2 code in python/paddle/utils
---
python/paddle/utils/dump_config.py | 45 ---
python/paddle/utils/dump_v2_config.py | 62 ----
python/paddle/utils/image_multiproc.py | 278 ----------------
python/paddle/utils/make_model_diagram.py | 140 --------
python/paddle/utils/merge_model.py | 73 -----
python/paddle/utils/predefined_net.py | 381 ----------------------
6 files changed, 979 deletions(-)
delete mode 100644 python/paddle/utils/dump_config.py
delete mode 100644 python/paddle/utils/dump_v2_config.py
delete mode 100644 python/paddle/utils/image_multiproc.py
delete mode 100644 python/paddle/utils/make_model_diagram.py
delete mode 100644 python/paddle/utils/merge_model.py
delete mode 100644 python/paddle/utils/predefined_net.py
diff --git a/python/paddle/utils/dump_config.py b/python/paddle/utils/dump_config.py
deleted file mode 100644
index 6a96a0a78f..0000000000
--- a/python/paddle/utils/dump_config.py
+++ /dev/null
@@ -1,45 +0,0 @@
-# Copyright (c) 2016 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.
-
-from paddle.trainer.config_parser import parse_config
-from paddle.proto import TrainerConfig_pb2
-import sys
-
-__all__ = []
-
-if __name__ == '__main__':
- whole_conf = False
- binary = False
- if len(sys.argv) == 2:
- conf = parse_config(sys.argv[1], '')
- elif len(sys.argv) == 3:
- conf = parse_config(sys.argv[1], sys.argv[2])
- elif len(sys.argv) == 4:
- conf = parse_config(sys.argv[1], sys.argv[2])
- if sys.argv[3] == '--whole':
- whole_conf = True
- elif sys.argv[3] == '--binary':
- binary = True
- else:
- raise RuntimeError()
-
- assert isinstance(conf, TrainerConfig_pb2.TrainerConfig)
-
- if whole_conf:
- print(conf)
- else:
- if binary:
- sys.stdout.write(conf.model_config.SerializeToString())
- else:
- print(conf.model_config)
diff --git a/python/paddle/utils/dump_v2_config.py b/python/paddle/utils/dump_v2_config.py
deleted file mode 100644
index 5dc2111e37..0000000000
--- a/python/paddle/utils/dump_v2_config.py
+++ /dev/null
@@ -1,62 +0,0 @@
-# Copyright (c) 2016 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.
-import collections
-
-from paddle.trainer_config_helpers.layers import LayerOutput
-from paddle.v2.layer import parse_network
-from paddle.proto import TrainerConfig_pb2
-
-__all__ = ["dump_v2_config"]
-
-
-def dump_v2_config(topology, save_path, binary=False):
- """ Dump the network topology to a specified file.
-
- This function is only used to dump network defined by using PaddlePaddle V2
- APIs. This function will NOT dump configurations related to PaddlePaddle
- optimizer.
-
- :param topology: The output layers (can be more than one layers given in a
- Python List or Tuple) of the entire network. Using the
- specified layers (if more than one layer is given) as root,
- traversing back to the data layer(s), all the layers
- connected to the specified output layers will be dumped.
- Layers not connceted to the specified will not be dumped.
- :type topology: LayerOutput|List|Tuple
- :param save_path: The path to save the dumped network topology.
- :type save_path: str
- :param binary: Whether to dump the serialized network topology or not.
- The default value is false. NOTE that, if you call this
- function to generate network topology for PaddlePaddle C-API,
- a serialized version of network topology is required. When
- using PaddlePaddle C-API, this flag MUST be set to True.
- :type binary: bool
- """
-
- if isinstance(topology, LayerOutput):
- topology = [topology]
- elif isinstance(topology, collections.Sequence):
- for out_layer in topology:
- assert isinstance(out_layer, LayerOutput), (
- "The type of each element in the parameter topology "
- "should be LayerOutput.")
- else:
- raise RuntimeError("Error input type for parameter topology.")
-
- model_str = parse_network(topology)
- with open(save_path, "w") as fout:
- if binary:
- fout.write(model_str.SerializeToString())
- else:
- fout.write(str(model_str))
diff --git a/python/paddle/utils/image_multiproc.py b/python/paddle/utils/image_multiproc.py
deleted file mode 100644
index d1bbda3fd3..0000000000
--- a/python/paddle/utils/image_multiproc.py
+++ /dev/null
@@ -1,278 +0,0 @@
-# Copyright (c) 2018 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.
-
-import os, sys
-import numpy as np
-from PIL import Image
-import six
-from six.moves import cStringIO as StringIO
-import multiprocessing
-import functools
-import itertools
-
-from paddle.utils.image_util import *
-from paddle.trainer.config_parser import logger
-
-try:
- import cv2
-except ImportError:
- logger.warning("OpenCV2 is not installed, using PIL to process")
- cv2 = None
-
-__all__ = ["CvTransformer", "PILTransformer", "MultiProcessImageTransformer"]
-
-
-class CvTransformer(ImageTransformer):
- """
- CvTransformer used python-opencv to process image.
- """
-
- def __init__(
- self,
- min_size=None,
- crop_size=None,
- transpose=(2, 0, 1), # transpose to C * H * W
- channel_swap=None,
- mean=None,
- is_train=True,
- is_color=True):
- ImageTransformer.__init__(self, transpose, channel_swap, mean, is_color)
- self.min_size = min_size
- self.crop_size = crop_size
- self.is_train = is_train
-
- def resize(self, im, min_size):
- row, col = im.shape[:2]
- new_row, new_col = min_size, min_size
- if row > col:
- new_row = min_size * row / col
- else:
- new_col = min_size * col / row
- im = cv2.resize(im, (new_row, new_col), interpolation=cv2.INTER_CUBIC)
- return im
-
- def crop_and_flip(self, im):
- """
- Return cropped image.
- The size of the cropped image is inner_size * inner_size.
- im: (H x W x K) ndarrays
- """
- row, col = im.shape[:2]
- start_h, start_w = 0, 0
- if self.is_train:
- start_h = np.random.randint(0, row - self.crop_size + 1)
- start_w = np.random.randint(0, col - self.crop_size + 1)
- else:
- start_h = (row - self.crop_size) / 2
- start_w = (col - self.crop_size) / 2
- end_h, end_w = start_h + self.crop_size, start_w + self.crop_size
- if self.is_color:
- im = im[start_h:end_h, start_w:end_w, :]
- else:
- im = im[start_h:end_h, start_w:end_w]
- if (self.is_train) and (np.random.randint(2) == 0):
- if self.is_color:
- im = im[:, ::-1, :]
- else:
- im = im[:, ::-1]
- return im
-
- def transform(self, im):
- im = self.resize(im, self.min_size)
- im = self.crop_and_flip(im)
- # transpose, swap channel, sub mean
- im = im.astype('float32')
- ImageTransformer.transformer(self, im)
- return im
-
- def load_image_from_string(self, data):
- flag = cv2.CV_LOAD_IMAGE_COLOR if self.is_color else cv2.CV_LOAD_IMAGE_GRAYSCALE
- im = cv2.imdecode(np.fromstring(data, np.uint8), flag)
- return im
-
- def transform_from_string(self, data):
- im = self.load_image_from_string(data)
- return self.transform(im)
-
- def load_image_from_file(self, file):
- flag = cv2.CV_LOAD_IMAGE_COLOR if self.is_color else cv2.CV_LOAD_IMAGE_GRAYSCALE
- im = cv2.imread(file, flag)
- return im
-
- def transform_from_file(self, file):
- im = self.load_image_from_file(file)
- return self.transform(im)
-
-
-class PILTransformer(ImageTransformer):
- """
- PILTransformer used PIL to process image.
- """
-
- def __init__(
- self,
- min_size=None,
- crop_size=None,
- transpose=(2, 0, 1), # transpose to C * H * W
- channel_swap=None,
- mean=None,
- is_train=True,
- is_color=True):
- ImageTransformer.__init__(self, transpose, channel_swap, mean, is_color)
- self.min_size = min_size
- self.crop_size = crop_size
- self.is_train = is_train
-
- def resize(self, im, min_size):
- row, col = im.size[:2]
- new_row, new_col = min_size, min_size
- if row > col:
- new_row = min_size * row / col
- else:
- new_col = min_size * col / row
- im = im.resize((new_row, new_col), Image.ANTIALIAS)
- return im
-
- def crop_and_flip(self, im):
- """
- Return cropped image.
- The size of the cropped image is inner_size * inner_size.
- """
- row, col = im.size[:2]
- start_h, start_w = 0, 0
- if self.is_train:
- start_h = np.random.randint(0, row - self.crop_size + 1)
- start_w = np.random.randint(0, col - self.crop_size + 1)
- else:
- start_h = (row - self.crop_size) / 2
- start_w = (col - self.crop_size) / 2
- end_h, end_w = start_h + self.crop_size, start_w + self.crop_size
- im = im.crop((start_h, start_w, end_h, end_w))
- if (self.is_train) and (np.random.randint(2) == 0):
- im = im.transpose(Image.FLIP_LEFT_RIGHT)
- return im
-
- def transform(self, im):
- im = self.resize(im, self.min_size)
- im = self.crop_and_flip(im)
- im = np.array(im, dtype=np.float32) # convert to numpy.array
- # transpose, swap channel, sub mean
- ImageTransformer.transformer(self, im)
- return im
-
- def load_image_from_string(self, data):
- im = Image.open(StringIO(data))
- return im
-
- def transform_from_string(self, data):
- im = self.load_image_from_string(data)
- return self.transform(im)
-
- def load_image_from_file(self, file):
- im = Image.open(file)
- return im
-
- def transform_from_file(self, file):
- im = self.load_image_from_file(file)
- return self.transform(im)
-
-
-def job(is_img_string, transformer, data_label_pack):
- (data, label) = data_label_pack
- if is_img_string:
- return transformer.transform_from_string(data), label
- else:
- return transformer.transform_from_file(data), label
-
-
-class MultiProcessImageTransformer(object):
- def __init__(self,
- procnum=10,
- resize_size=None,
- crop_size=None,
- transpose=(2, 0, 1),
- channel_swap=None,
- mean=None,
- is_train=True,
- is_color=True,
- is_img_string=True):
- """
- Processing image with multi-process. If it is used in PyDataProvider,
- the simple usage for CNN is as follows:
-
- .. code-block:: python
-
- def hool(settings, is_train, **kwargs):
- settings.is_train = is_train
- settings.mean_value = np.array([103.939,116.779,123.68], dtype=np.float32)
- settings.input_types = [
- dense_vector(3 * 224 * 224),
- integer_value(1)]
- settings.transformer = MultiProcessImageTransformer(
- procnum=10,
- resize_size=256,
- crop_size=224,
- transpose=(2, 0, 1),
- mean=settings.mean_values,
- is_train=settings.is_train)
-
-
- @provider(init_hook=hook, pool_size=20480)
- def process(settings, file_list):
- with open(file_list, 'r') as fdata:
- for line in fdata:
- data_dic = np.load(line.strip()) # load the data batch pickled by Pickle.
- data = data_dic['data']
- labels = data_dic['label']
- labels = np.array(labels, dtype=np.float32)
- for im, lab in settings.dp.run(data, labels):
- yield [im.astype('float32'), int(lab)]
-
- :param procnum: processor number.
- :type procnum: int
- :param resize_size: the shorter edge size of image after resizing.
- :type resize_size: int
- :param crop_size: the croping size.
- :type crop_size: int
- :param transpose: the transpose order, Paddle only allow C * H * W order.
- :type transpose: tuple or list
- :param channel_swap: the channel swap order, RGB or BRG.
- :type channel_swap: tuple or list
- :param mean: the mean values of image, per-channel mean or element-wise mean.
- :type mean: array, The dimension is 1 for per-channel mean.
- The dimension is 3 for element-wise mean.
- :param is_train: training peroid or testing peroid.
- :type is_train: bool.
- :param is_color: the image is color or gray.
- :type is_color: bool.
- :param is_img_string: The input can be the file name of image or image string.
- :type is_img_string: bool.
- """
-
- self.procnum = procnum
- self.pool = multiprocessing.Pool(procnum)
- self.is_img_string = is_img_string
- if cv2 is not None:
- self.transformer = CvTransformer(resize_size, crop_size, transpose,
- channel_swap, mean, is_train,
- is_color)
- else:
- self.transformer = PILTransformer(resize_size, crop_size, transpose,
- channel_swap, mean, is_train,
- is_color)
-
- def run(self, data, label):
- fun = functools.partial(job, self.is_img_string, self.transformer)
- return self.pool.imap_unordered(
- fun, six.moves.zip(data, label), chunksize=100 * self.procnum)
diff --git a/python/paddle/utils/make_model_diagram.py b/python/paddle/utils/make_model_diagram.py
deleted file mode 100644
index 52759d3ad2..0000000000
--- a/python/paddle/utils/make_model_diagram.py
+++ /dev/null
@@ -1,140 +0,0 @@
-# Copyright (c) 2016 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.
-
-# Generate dot diagram file for the given paddle model config
-# The generated file can be viewed using Graphviz (http://graphviz.org)
-
-from __future__ import print_function
-
-import six
-import sys
-import traceback
-
-from paddle.trainer.config_parser import parse_config
-
-
-def make_layer_label(layer_config):
- label = '%s type=%s' % (layer_config.name, layer_config.type)
- if layer_config.reversed:
- label += ' <=='
-
- label2 = ''
- if layer_config.active_type:
- label2 += 'act=%s ' % layer_config.active_type
- if layer_config.bias_parameter_name:
- label2 += 'bias=%s ' % layer_config.bias_parameter_name
-
- if label2:
- label += '\l' + label2
- return label
-
-
-def make_diagram(config_file, dot_file, config_arg_str):
- config = parse_config(config_file, config_arg_str)
- make_diagram_from_proto(config.model_config, dot_file)
-
-
-def make_diagram_from_proto(model_config, dot_file):
- # print >> sys.stderr, config
- name2id = {}
- f = open(dot_file, 'w')
- submodel_layers = set()
-
- def make_link(link):
- return 'l%s -> l%s;' % (name2id[link.layer_name],
- name2id[link.link_name])
-
- def make_mem(mem):
- s = ''
- if mem.boot_layer_name:
- s += 'l%s -> l%s;\n' % (name2id[mem.boot_layer_name],
- name2id[mem.layer_name])
- s += 'l%s -> l%s [style=dashed];' % (name2id[mem.layer_name],
- name2id[mem.link_name])
- return s
-
- print('digraph graphname {', file=f)
- print('node [width=0.375,height=0.25];', file=f)
- for i in six.moves.xrange(len(model_config.layers)):
- l = model_config.layers[i]
- name2id[l.name] = i
-
- i = 0
- for sub_model in model_config.sub_models:
- if sub_model.name == 'root':
- continue
- print('subgraph cluster_%s {' % i, file=f)
- print('style=dashed;', file=f)
- label = '%s ' % sub_model.name
- if sub_model.reversed:
- label += '<=='
- print('label = "%s";' % label, file=f)
- i += 1
- submodel_layers.add(sub_model.name)
- for layer_name in sub_model.layer_names:
- submodel_layers.add(layer_name)
- lid = name2id[layer_name]
- layer_config = model_config.layers[lid]
- label = make_layer_label(layer_config)
- print('l%s [label="%s", shape=box];' % (lid, label), file=f)
- print('}', file=f)
-
- for i in six.moves.xrange(len(model_config.layers)):
- l = model_config.layers[i]
- if l.name not in submodel_layers:
- label = make_layer_label(l)
- print('l%s [label="%s", shape=box];' % (i, label), file=f)
-
- for sub_model in model_config.sub_models:
- if sub_model.name == 'root':
- continue
- for link in sub_model.in_links:
- print(make_link(link), file=f)
- for link in sub_model.out_links:
- print(make_link(link), file=f)
- for mem in sub_model.memories:
- print(make_mem(mem), file=f)
-
- for i in six.moves.xrange(len(model_config.layers)):
- for l in model_config.layers[i].inputs:
- print(
- 'l%s -> l%s [label="%s"];' % (name2id[l.input_layer_name], i,
- l.input_parameter_name),
- file=f)
-
- print('}', file=f)
- f.close()
-
-
-def usage():
- print(
- ("Usage: python show_model_diagram.py" +
- " CONFIG_FILE DOT_FILE [config_str]"),
- file=sys.stderr)
- exit(1)
-
-
-if __name__ == '__main__':
- if len(sys.argv) < 3 or len(sys.argv) > 4:
- usage()
-
- config_file = sys.argv[1]
- dot_file = sys.argv[2]
- config_arg_str = sys.argv[3] if len(sys.argv) == 4 else ''
-
- try:
- make_diagram(config_file, dot_file, config_arg_str)
- except:
- traceback.print_exc()
- raise
diff --git a/python/paddle/utils/merge_model.py b/python/paddle/utils/merge_model.py
deleted file mode 100644
index b74649e936..0000000000
--- a/python/paddle/utils/merge_model.py
+++ /dev/null
@@ -1,73 +0,0 @@
-# Copyright (c) 2016 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.
-
-import gzip
-import struct
-import os
-
-from paddle.trainer_config_helpers.layers import LayerOutput
-from paddle.v2.parameters import Parameters
-from paddle.proto import ModelConfig_pb2
-from paddle.v2.topology import Topology
-
-
-def merge_v2_model(net, param_file, output_file):
- '''Merge the model config and parameters into one file.
-
- The model configuration file describes the model structure which
- ends with .py. The parameters file stores the parameters of the model
- which ends with .tar.gz.
-
- @param net The output layer of the network for inference.
- @param param_file Path of the parameters (.tar.gz) which is stored by
- v2 api.
- @param output_file Path of the merged file which will be generated.
-
- Usage:
-
- from paddle.utils.merge_model import merge_v2_model
- # import your network configuration
- from example_net import net_conf
-
- net = net_conf(is_predict=True)
- param_file = './param_pass_00000.tar.gz'
- output_file = './output.paddle'
-
- merge_v2_model(net, param_file, output_file)
-
- '''
-
- assert isinstance(net, LayerOutput), \
- "The net should be the output of the network for inference"
- assert os.path.exists(param_file), \
- "The model parameters file %s does not exists " % (param_file)
-
- model_proto = Topology(net).proto()
- assert isinstance(model_proto, ModelConfig_pb2.ModelConfig)
-
- with gzip.open(param_file) as f:
- params = Parameters.from_tar(f)
-
- if os.path.exists(output_file):
- os.remove(output_file)
-
- with open(output_file, 'w') as f:
- param_names = [param.name for param in model_proto.parameters]
- conf_str = model_proto.SerializeToString()
- f.write(struct.pack('q', len(conf_str)))
- f.write(conf_str)
- for pname in param_names:
- params.serialize(pname, f)
-
- print('Generate %s success!' % (output_file))
diff --git a/python/paddle/utils/predefined_net.py b/python/paddle/utils/predefined_net.py
deleted file mode 100644
index 2801f4877c..0000000000
--- a/python/paddle/utils/predefined_net.py
+++ /dev/null
@@ -1,381 +0,0 @@
-# Copyright (c) 2016 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.
-
-import numpy as np
-import six
-import os
-from paddle.trainer.config_parser import *
-from paddle.utils.preprocess_img import \
- ImageClassificationDatasetCreater
-from paddle.trainer_config_helpers import *
-
-
-def image_data(data_dir,
- processed_image_size,
- overwrite=False,
- color=True,
- train_list="batches/train.list",
- test_list="batches/test.list",
- meta_file="batches/batches.meta",
- use_jpeg=1):
- """
- Predefined image data provider for image classification.
- train_list: a text file containing a list of training batches.
- test_list: a text file containing a list of test batches.
- processed_image_size: all the input images will be resized into this size.
- If the image is not square. Then the shorter edge will be resized into
- this size, and the aspect ratio is kept the same.
- color: whether the images are color or gray.
- meta_path: the path of the meta file that stores the mean image file and
- other dataset information, such as the size of images,
- the size of the mean image, the number of classes.
- async_load_data: whether to load image data asynchronuously.
- """
- data_creator = ImageClassificationDatasetCreater(
- data_dir, processed_image_size, color)
- batch_data_dir = data_dir
- train_list = os.path.join(batch_data_dir, train_list)
- test_list = os.path.join(batch_data_dir, test_list)
- meta_path = os.path.join(batch_data_dir, meta_file)
- image_size = processed_image_size
- conf = np.load(meta_path)
- mean_image_size = conf["mean_image_size"]
- is_color = conf["color"]
- num_classes = conf["num_classes"]
- color_string = "color" if is_color else "gray"
-
- args = {
- 'meta': meta_path,
- 'mean_img_size': mean_image_size,
- 'img_size': image_size,
- 'num_classes': num_classes,
- 'use_jpeg': use_jpeg != 0,
- 'color': color_string
- }
-
- define_py_data_sources2(
- train_list,
- test_list,
- module='image_provider',
- obj='processData',
- args=args)
- return {
- "image_size": image_size,
- "num_classes": num_classes,
- "is_color": is_color
- }
-
-
-def get_extra_layer_attr(drop_rate):
- if drop_rate == 0:
- return None
- else:
- return ExtraLayerAttribute(drop_rate=drop_rate)
-
-
-def image_data_layers(image_size, num_classes, is_color=False,
- is_predict=False):
- """
- Data layers for image classification.
- image_size: image size.
- num_classes: num of classes.
- is_color: whether the input images are color.
- is_predict: whether the network is used for prediction.
- """
- num_image_channels = 3 if is_color else 1
- data_input = data_layer("input",
- image_size * image_size * num_image_channels)
- if is_predict:
- return data_input, None, num_image_channels
- else:
- label_input = data_layer("label", 1)
- return data_input, label_input, num_image_channels
-
-
-def simple_conv_net(data_conf, is_color=False):
- """
- A Wrapper for a simple network for MNIST digit recognition.
- It contains two convolutional layers, one fully conencted layer, and
- one softmax layer.
- data_conf is a dictionary with the following keys:
- image_size: image size.
- num_classes: num of classes.
- is_color: whether the input images are color.
- """
- for k, v in six.iteritems(data_conf):
- globals()[k] = v
- data_input, label_input, num_image_channels = \
- image_data_layers(image_size, num_classes, is_color, is_predict)
- filter_sizes = [5, 5]
- num_channels = [32, 64]
- strides = [1, 1]
- fc_dims = [500]
- conv_bn_pool1 = img_conv_bn_pool(
- name="g1",
- input=data_input,
- filter_size=filter_sizes[0],
- num_channel=num_image_channels,
- num_filters=num_channels[0],
- conv_stride=1,
- conv_padding=0,
- pool_size=3,
- pool_stride=2,
- act=ReluActivation())
- conv_bn_pool2 = img_conv_bn_pool(
- name="g2",
- input=conv_bn_pool1,
- filter_size=filter_sizes[1],
- num_channel=num_channels[0],
- num_filters=num_channels[1],
- conv_stride=1,
- conv_padding=0,
- pool_size=3,
- pool_stride=2,
- act=ReluActivation())
- fc3 = fc_layer(
- name="fc3", input=conv_bn_pool2, dim=fc_dims[0], act=ReluActivation())
- fc3_dropped = dropout_layer(name="fc3_dropped", input=fc3, dropout_rate=0.5)
- output = fc_layer(
- name="output",
- input=fc3_dropped,
- dim=fc_dims[0],
- act=SoftmaxActivation())
- if is_predict:
- end_of_network(output)
- else:
- cost = classify(name="cost", input=output, label=label_input)
- end_of_network(cost)
-
-
-def conv_layer_group(prefix_num,
- num_layers,
- input,
- input_channels,
- output_channels,
- drop_rates=[],
- strides=[],
- with_bn=[]):
- """
- A set of convolution layers, and batch normalization layers,
- followed by one pooling layer.
- It is utilized in VGG network for image classifcation.
- prefix_num: the prefix number of the layer names.
- For example, if prefix_num = 1, the first convolutioal layer's
- name will be conv_1_1.
- num_layers: number of the convolutional layers.
- input: the name of the input layer.
- input_channels: the number of channels of the input feature map.
- output_channels: the number of channels of the output feature map.
- drop_rates: the drop rates of the BN layers. It will be all zero by default.
- strides: the stride of the convolution for the layers.
- It will be all 1 by default.
- with_bn: whether to use Batch Normalization for Conv layers.
- By default, it is all false.
- """
- if len(drop_rates) == 0: drop_rates = [0] * num_layers
- if len(strides) == 0: strides = [1] * num_layers
- if len(with_bn) == 0: with_bn = [False] * num_layers
- assert (len(drop_rates) == num_layers)
- assert (len(strides) == num_layers)
-
- for i in range(1, num_layers + 1):
- if i == 1:
- i_conv_in = input
- else:
- i_conv_in = group_output
- i_channels_conv = input_channels if i == 1 else output_channels
- conv_act = LinearActivation() if with_bn[i - 1] else ReluActivation()
- conv_output = img_conv_layer(
- name="conv%d_%d" % (prefix_num, i),
- input=i_conv_in,
- filter_size=3,
- num_channels=i_channels_conv,
- num_filters=output_channels,
- stride=strides[i - 1],
- padding=1,
- act=conv_act)
- if with_bn[i - 1]:
- bn = batch_norm_layer(
- name="conv%d_%d_bn" % (prefix_num, i),
- input=conv_output,
- num_channels=output_channels,
- act=ReluActivation(),
- layer_attr=get_extra_layer_attr(drop_rate=drop_rates[i - 1]))
- group_output = bn
- else:
- group_output = conv_output
- pool = img_pool_layer(
- name="pool%d" % prefix_num,
- input=group_output,
- pool_size=2,
- num_channels=output_channels,
- stride=2)
- return pool
-
-
-def vgg_conv_net(image_size,
- num_classes,
- num_layers,
- channels,
- strides,
- with_bn,
- fc_dims,
- drop_rates,
- drop_rates_fc=[],
- is_color=True,
- is_predict=False):
- """
- A Wrapper for a VGG network for image classification.
- It is a set of convolutional groups followed by several fully
- connected layers, and a cross-entropy classifiation loss.
- The detailed architecture of the paper can be found here:
- Very Deep Convolutional Networks for Large-Scale Visual Recognition
- http://www.robots.ox.ac.uk/~vgg/research/very_deep/
- image_size: image size.
- num_classes: num of classes.
- num_layers: the number of layers for all the convolution groups.
- channels: the number of output filters for all the convolution groups.
- with_bn: whether each layer of a convolution group is followed by a
- batch normalization.
- drop_rates: the dropout rates for all the convolutional layers.
- fc_dims: the dimension for all the fully connected layers.
- is_color: whether the input images are color.
- """
- data_input, label_input, num_image_channels = \
- image_data_layers(image_size, num_classes, is_color, is_predict)
- assert (len(num_layers) == len(channels))
- assert (len(num_layers) == len(strides))
- assert (len(num_layers) == len(with_bn))
- num_fc_layers = len(fc_dims)
- assert (num_fc_layers + 1 == len(drop_rates_fc))
-
- for i in range(len(num_layers)):
- input_layer = data_input if i == 0 else group_output
- input_channels = 3 if i == 0 else channels[i - 1]
- group_output = conv_layer_group(
- prefix_num=i + 1,
- num_layers=num_layers[i],
- input=input_layer,
- input_channels=input_channels,
- output_channels=channels[i],
- drop_rates=drop_rates[i],
- strides=strides[i],
- with_bn=with_bn[i])
- conv_output_name = group_output
- if drop_rates_fc[0] != 0.0:
- dropped_pool_name = "pool_dropped"
- conv_output_name = dropout_layer(
- name=dropped_pool_name,
- input=conv_output_name,
- dropout_rate=drop_rates_fc[0])
- for i in range(len(fc_dims)):
- input_layer_name = conv_output_name if i == 0 else fc_output
- active_type = LinearActivation() if i == len(
- fc_dims) - 1 else ReluActivation()
- drop_rate = 0.0 if i == len(fc_dims) - 1 else drop_rates_fc[i + 1]
- fc_output = fc_layer(
- name="fc%d" % (i + 1),
- input=input_layer_name,
- size=fc_dims[i],
- act=active_type,
- layer_attr=get_extra_layer_attr(drop_rate))
- bn = batch_norm_layer(
- name="fc_bn",
- input=fc_output,
- num_channels=fc_dims[len(fc_dims) - 1],
- act=ReluActivation(),
- layer_attr=get_extra_layer_attr(drop_rate=drop_rates_fc[-1]))
- output = fc_layer(
- name="output", input=bn, size=num_classes, act=SoftmaxActivation())
- if is_predict:
- outputs(output)
- else:
- cost = classification_cost(name="cost", input=output, label=label_input)
- outputs(cost)
-
-
-def vgg16_conv_net(image_size, num_classes, is_color=True, is_predict=False):
- """
- A Wrapper for a 16 layers VGG network for image classification.
- The detailed architecture of the paper can be found here:
- Very Deep Convolutional Networks for Large-Scale Visual Recognition
- http://www.robots.ox.ac.uk/~vgg/research/very_deep/
- image_size: image size.
- num_classes: num of classes.
- is_color: whether the input images are color.
- """
- vgg_conv_net(image_size, num_classes,
- num_layers=[2, 2, 3, 3, 3],
- channels=[64, 128, 256, 512, 512],
- strides=[[], [], [], [], []],
- with_bn=[[False, True], [False, True], [False, False, True], \
- [False, False, True], [False, False, True]],
- drop_rates=[[]] * 5,
- drop_rates_fc=[0.0, 0.5, 0.5],
- fc_dims=[4096, 4096],
- is_predict=is_predict)
-
-
-def small_vgg(data_conf, is_predict=False):
- """
- A Wrapper for a small VGG network for CIFAR-10 image classification.
- The detailed architecture of the paper can be found here:
- 92.45% on CIFAR-10 in Torch
- http://torch.ch/blog/2015/07/30/cifar.html
- Due to the constraints of CuDNN, it only has four convolutional groups
- rather than five.
- Thus, it only achieves 91.2% test accuracy and 98.1% training accuracy.
- data_conf is a dictionary with the following keys:
- image_size: image size.
- num_classes: num of classes.
- is_color: whether the input images are color.
- """
- for k, v in six.iteritems(data_conf):
- globals()[k] = v
- vgg_conv_net(image_size, num_classes,
- num_layers=[2, 2, 3, 3],
- channels=[64, 128, 256, 512],
- strides=[[], [], [], []],
- with_bn=[[True, True], [True, True], [True, True, True], \
- [True, True, True]],
- drop_rates=[[0.3, 0.0], [0.4, 0.0],
- [0.4, 0.4, 0.0], [0.4, 0.4, 0.0]],
- drop_rates_fc=[0.5, 0.5],
- fc_dims=[512],
- is_predict=is_predict)
-
-
-def training_settings(learning_rate=0.1,
- batch_size=128,
- algorithm="sgd",
- momentum=0.9,
- decay_rate=0.001):
- """
- Training settings.
- learning_rate: learning rate of the training.
- batch_size: the size of each training batch.
- algorithm: training algorithm, can be
- - sgd
- - adagrad
- - adadelta
- - rmsprop
- momentum: momentum of the training algorithm.
- decay_rate: weight decay rate.
- """
- Settings(
- algorithm=algorithm,
- batch_size=batch_size,
- learning_rate=learning_rate / float(batch_size))
- default_momentum(momentum)
- default_decay_rate(decay_rate * batch_size)
From 031d995080ae40b65dfa3f548c0387f7000fb2a4 Mon Sep 17 00:00:00 2001
From: Tao Luo <luotao02@baidu.com>
Date: Thu, 14 Feb 2019 21:09:07 +0800
Subject: [PATCH 6/8] remove legacy v2 codes in benchmark
---
benchmark/IntelOptimizedPaddle.md | 112 --------
benchmark/README.md | 168 ------------
benchmark/fluid/Dockerfile | 3 -
.../{paddle/image => fluid}/check_env.sh | 0
benchmark/paddle/image/alexnet.py | 93 -------
benchmark/paddle/image/googlenet.py | 245 ------------------
benchmark/paddle/image/plotlog.py | 114 --------
benchmark/paddle/image/provider.py | 47 ----
benchmark/paddle/image/resnet.py | 230 ----------------
benchmark/paddle/image/run.sh | 53 ----
benchmark/paddle/image/run_mkl_infer.sh | 89 -------
benchmark/paddle/image/run_mkl_train.sh | 54 ----
benchmark/paddle/image/run_openblas_infer.sh | 71 -----
benchmark/paddle/image/run_openblas_train.sh | 43 ---
.../paddle/image/smallnet_mnist_cifar.py | 49 ----
benchmark/paddle/image/vgg.py | 119 ---------
benchmark/paddle/rnn/imdb.py | 60 -----
benchmark/paddle/rnn/provider.py | 86 ------
benchmark/paddle/rnn/rnn.py | 38 ---
benchmark/paddle/rnn/run.sh | 52 ----
20 files changed, 1726 deletions(-)
delete mode 100644 benchmark/IntelOptimizedPaddle.md
delete mode 100644 benchmark/README.md
rename benchmark/{paddle/image => fluid}/check_env.sh (100%)
delete mode 100644 benchmark/paddle/image/alexnet.py
delete mode 100644 benchmark/paddle/image/googlenet.py
delete mode 100644 benchmark/paddle/image/plotlog.py
delete mode 100644 benchmark/paddle/image/provider.py
delete mode 100644 benchmark/paddle/image/resnet.py
delete mode 100755 benchmark/paddle/image/run.sh
delete mode 100755 benchmark/paddle/image/run_mkl_infer.sh
delete mode 100755 benchmark/paddle/image/run_mkl_train.sh
delete mode 100755 benchmark/paddle/image/run_openblas_infer.sh
delete mode 100755 benchmark/paddle/image/run_openblas_train.sh
delete mode 100644 benchmark/paddle/image/smallnet_mnist_cifar.py
delete mode 100644 benchmark/paddle/image/vgg.py
delete mode 100755 benchmark/paddle/rnn/imdb.py
delete mode 100644 benchmark/paddle/rnn/provider.py
delete mode 100755 benchmark/paddle/rnn/rnn.py
delete mode 100755 benchmark/paddle/rnn/run.sh
diff --git a/benchmark/IntelOptimizedPaddle.md b/benchmark/IntelOptimizedPaddle.md
deleted file mode 100644
index 8b7dc5b7db..0000000000
--- a/benchmark/IntelOptimizedPaddle.md
+++ /dev/null
@@ -1,112 +0,0 @@
-# Benchmark
-
-Machine:
-
-- Server: Intel(R) Xeon(R) Gold 6148 CPU @ 2.40GHz, 2 Sockets, 20 Cores per socket
-- Laptop: TBD
-
-System: CentOS release 6.3 (Final), Docker 1.12.1.
-
-PaddlePaddle:
-- paddlepaddle/paddle:0.11.0 (for MKLML and MKL-DNN)
- - MKL-DNN tag v0.11
- - MKLML 2018.0.1.20171007
-- paddlepaddle/paddle:0.11.0-openblas (for OpenBLAS)
- - OpenBLAS v0.2.20
-
-On each machine, we will test and compare the performance of training on single node using MKL-DNN / MKLML / OpenBLAS respectively.
-
-## Benchmark Model
-
-### Server
-
-#### Training
-Test on batch size 64, 128, 256 on Intel(R) Xeon(R) Gold 6148 CPU @ 2.40GHz
-Pay attetion that the speed below includes forward, backward and parameter update time. So we can not directly compare the data with the benchmark of caffe `time` [command](https://github.com/PaddlePaddle/Paddle/blob/develop/benchmark/caffe/image/run.sh#L9), which only contain forward and backward. The updating time of parameter would become very heavy when the weight size are large, especially on alexnet.
-
-Input image size - 3 * 224 * 224, Time: images/second
-
-- VGG-19
-
-| BatchSize | 64 | 128 | 256 |
-|--------------|-------| -----| --------|
-| OpenBLAS | 7.80 | 9.00 | 10.80 |
-| MKLML | 12.12 | 13.70 | 16.18 |
-| MKL-DNN | 28.46 | 29.83 | 30.44 |
-
-<img src="figs/vgg-cpu-train.png" width="500">
-
- - ResNet-50
-
-| BatchSize | 64 | 128 | 256 |
-|--------------|-------| ------| -------|
-| OpenBLAS | 25.22 | 25.68 | 27.12 |
-| MKLML | 32.52 | 31.89 | 33.12 |
-| MKL-DNN | 81.69 | 82.35 | 84.08 |
-
-<img src="figs/resnet-cpu-train.png" width="500">
-
- - GoogLeNet
-
-| BatchSize | 64 | 128 | 256 |
-|--------------|-------| ------| -------|
-| OpenBLAS | 89.52 | 96.97 | 108.25 |
-| MKLML | 128.46| 137.89| 158.63 |
-| MKL-DNN | 250.46| 264.83| 269.50 |
-
-<img src="figs/googlenet-cpu-train.png" width="500">
-
-- AlexNet
-
-| BatchSize | 64 | 128 | 256 |
-|--------------|--------| ------ | -------|
-| OpenBLAS | 45.62 | 72.79 | 107.22 |
-| MKLML | 66.37 | 105.60 | 144.04 |
-| MKL-DNN | 399.00 | 498.94 | 626.53 |
-
-<img src="figs/alexnet-cpu-train.png" width="500">
-
-#### Inference
-Test on batch size 1, 2, 4, 8, 16 on Intel(R) Xeon(R) Gold 6148 CPU @ 2.40GHz
-- VGG-19
-
-| BatchSize | 1 | 2 | 4 | 8 | 16 |
-|-----------|-------|-------|-------|-------|-------|
-| OpenBLAS | 1.10 | 1.96 | 3.62 | 3.63 | 2.25 |
-| MKLML | 5.58 | 9.80 | 15.15 | 21.21 | 28.67 |
-| MKL-DNN | 75.07 | 88.64 | 82.58 | 92.29 | 96.75 |
-
-<img src="figs/vgg-cpu-infer.png" width="500">
-
-- ResNet-50
-
-| BatchSize | 1 | 2 | 4 | 8 | 16 |
-|-----------|-------|--------|--------|--------|--------|
-| OpenBLAS | 3.31 | 6.72 | 11.59 | 13.17 | 9.27 |
-| MKLML | 6.33 | 12.02 | 22.88 | 40.53 | 63.09 |
-| MKL-DNN | 107.83| 148.84 | 177.78 | 189.35 | 217.69 |
-
-<img src="figs/resnet-cpu-infer.png" width="500">
-
-- GoogLeNet
-
-| BatchSize | 1 | 2 | 4 | 8 | 16 |
-|-----------|--------|--------|--------|--------|--------|
-| OpenBLAS | 12.06 | 23.56 | 34.48 | 36.45 | 23.12 |
-| MKLML | 22.74 | 41.56 | 81.22 | 133.47 | 210.53 |
-| MKL-DNN | 175.10 | 272.92 | 450.70 | 512.00 | 600.94 |
-
-<img src="figs/googlenet-cpu-infer.png" width="500">
-
-- AlexNet
-
-| BatchSize | 1 | 2 | 4 | 8 | 16 |
-|-----------|--------|--------|--------|--------|--------|
-| OpenBLAS | 3.53 | 6.23 | 15.04 | 26.06 | 31.62 |
-| MKLML | 21.32 | 36.55 | 73.06 | 131.15 | 192.77 |
-| MKL-DNN | 442.91 | 656.41 | 719.10 | 847.68 | 850.51 |
-
-<img src="figs/alexnet-cpu-infer.png" width="500">
-
-### Laptop
-TBD
diff --git a/benchmark/README.md b/benchmark/README.md
deleted file mode 100644
index 367013f045..0000000000
--- a/benchmark/README.md
+++ /dev/null
@@ -1,168 +0,0 @@
-# Benchmark
-
-Machine:
-
-- CPU: 12-core Intel(R) Xeon(R) CPU E5-2620 v2 @2.10GHz
-- GPU: Tesla K40m
-- cuDNN: v5.1
-- system: Docker 1.12.1, all platforms are tested in docker environment.
-
-Platforms:
-
-- PaddlePaddle: paddledev/paddle:gpu-devel-v0.9.0a0
-- Tensorflow: gcr.io/tensorflow/tensorflow:0.11.0rc0-gpu
-- Caffe: kaixhin/cuda-caffe
-
-Several convolutional neural networks and recurrent neural networks are used to test.
-
-## Image
-
-### Benchmark Model
-
-AlexNet, GoogleNet and a small network used in Caffe.
-
-- [AlexNet](https://github.com/BVLC/caffe/tree/master/models/bvlc_alexnet): but the group size is one.
-
-- [GoogleNet](https://github.com/BVLC/caffe/tree/master/models/bvlc_googlenet): but remove loss1 and loss2 when testing benchmark.
-
-- [SmallNet](https://github.com/BVLC/caffe/blob/master/examples/cifar10/cifar10\_quick\_train\_test.prototxt)
-
-
-### Single-GPU
-
-- AlexNet: input - 3 * 227 * 227, Time: ms/batch
-
-| BatchSize | 64 | 128 | 256 | 512 |
-|--------------|-----| -----| ------| -----|
-| PaddlePaddle | 195 | 334 | 602 | 1629 |
-| TensorFlow | 223 | 364 | 645 | 1235 |
-| Caffe | 324 | 627 | 1232 | 2513 |
-
-**Notation**
-
-All platforms use cuDNN-v5.1. We see that caffe is slower in this experiment, because its workspace limit size of cuDNN-conv interface is 8 * 1024 * 1024, which is smaller in PaddlePaddle and TensorFlow. Note that Caffe will be faster if increasing the workspace limit size.
-
-- GoogletNet: input - 3 * 224 * 224, Time: ms/batch
-
-
-| BatchSize | 64 | 128 | 256 |
-|--------------|-------| -------| --------|
-| PaddlePaddle | 613 | 1149 | 2348 |
-| TensorFlow | 644 | 1176 | 2219 |
-| Caffe | 694 | 1364 | out of memory |
-
-- SmallNet: input - 3 * 32 * 32, Time ms/batch
-
-| BatchSize | 64 | 128 | 256 | 512 |
-|--------------|--------| -------- | --------|---------|
-| PaddlePaddle | 10.463 | 18.184 | 33.113 | 63.039 |
-| TensorFlow | 9 | 15 | 28 | 59 |
-| Caffe | 9.373 | 16.6606 | 31.4797 | 59.719 |
-
-**Notation**
-
-All the single-GPU experiments in caffe use `caffe time` to calculate elapsed time, which does not include parameter updating time. However, both PaddlePaddle and TensorFlow experiments contain the parameter updating time. As compared with the total time, this part is relatively little on single machine, we can ignore it.
-
-In Tensorflow, they implement algorithm searching method instead of using the algorithm searching interface in cuDNN.
-
-### Multi-GPU: 4 GPUs
-
-- AlexNet, ms / batch
-
-| total-BatchSize | 128 * 4 | 256 * 4 |
-|------------------|----------| -----------|
-| PaddlePaddle | 347 | 622 |
-| TensorFlow | 377 | 675 |
-| Caffe | 1229 | 2435 |
-
-For example, if `total-BatchSize = 128 * 4`, the speedup ratio is calculated by
-
-```
- time_at_1gpu_batch_128 * 4 / time_at_4gpu_total_batch_512
-= (334 * 4)/347
-= 3.85
-```
-
-<img src="figs/alexnet-4gpu.png" width="420">
-
-
-- GoogleNet, ms / batch
-
-| total-BatchSize | 128 * 4 | 256 * 4 |
-|-------------------|--------------| ----------- |
-| PaddlePaddle | 1178 | 2367 |
-| TensorFlow | 1210 | 2292 |
-| Caffe | 2007 | out of memory |
-
-<img src="figs/googlenet-4gpu.png" width="420">
-
-
-## RNN
-We use lstm network for text classfication to test benchmark.
-
-### Dataset
-- [IMDB](http://www.iro.umontreal.ca/~lisa/deep/data/imdb.pkl)
-- Sequence length is 100. In fact, PaddlePaddle supports training with variable-length sequence, but TensorFlow needs to pad. Thus, we also pad sequence length to 100 in PaddlePaddle in order to compare.
-- Dictionary size=30000
-- Peephole connection is used in `lstmemory` by default in PaddlePaddle. It is also configured in TensorFlow.
-
-### Single-GPU
-
-#### LSTM in Text Classification
-
-Testing `2 lstm layer + fc` network with different hidden size and batch size.
-
-- Batch size = 64, ms / batch
-
-| hidden_size | 256 | 512 | 1280 |
-|--------------|-------| -------| --------|
-| PaddlePaddle | 83 | 184 | 641 |
-| TensorFlow | 175 | 280 | 818 |
-
-- Batch size = 128, ms / batch
-
-| hidden_size | 256 | 512 | 1280 |
-|--------------|------- | -------| --------|
-| PaddlePaddle | 110 | 261 | 1007 |
-| TensorFlow | 181 | 361 | 1237 |
-
-
-- Batch size = 256, ms / batch
-
-| hidden_size | 256 | 512 | 1280 |
-|--------------|-------| -------| --------|
-| PaddlePaddle | 170 | 414 | 1655 |
-| TensorFlow | 238 | 536 | 1905 |
-
-<img src="figs/rnn_lstm_cls.png" width="600">
-
-#### Seq2Seq
-
-The benchmark of sequence-to-sequence network will be added later.
-
-
-### Multi GPU: 4 GPUs
-
-#### LSTM in Text Classification
-
-- hidden_size = 256, ms / batch
-
-| batch_size | 256 | 512 |
-|--------------| -------| --------|
-| PaddlePaddle | 90 | 118 |
-| TensorFlow | 226 | 118 |
-
-
-- hidden_size = 512, ms / batch
-
-| batch_size | 256 | 512 |
-|--------------| -------| --------|
-| PaddlePaddle | 189 | 268 |
-| TensorFlow | 297 | 383 |
-
-
-<img src="figs/rnn_lstm_4gpus.png" width="420">
-
-#### Seq2Seq
-
-The benchmark of sequence-to-sequence network will be added later.
diff --git a/benchmark/fluid/Dockerfile b/benchmark/fluid/Dockerfile
index 2e1e0d3768..81ea870050 100644
--- a/benchmark/fluid/Dockerfile
+++ b/benchmark/fluid/Dockerfile
@@ -15,9 +15,6 @@ RUN ln -s /usr/lib/x86_64-linux-gnu/libcudnn.so.7 /usr/lib/libcudnn.so && ln -s
RUN pip install -U pip
RUN pip install -U kubernetes paddlepaddle
-RUN sh -c 'echo "import paddle.v2 as paddle\npaddle.dataset.cifar.train10()\npaddle.dataset.flowers.fetch()" | python'
-RUN sh -c 'echo "import paddle.v2 as paddle\npaddle.dataset.mnist.train()\npaddle.dataset.mnist.test()\npaddle.dataset.imdb.fetch()" | python'
-RUN sh -c 'echo "import paddle.v2 as paddle\npaddle.dataset.imikolov.fetch()" | python'
RUN pip uninstall -y paddlepaddle && mkdir /workspace
ADD https://raw.githubusercontent.com/PaddlePaddle/cloud/develop/docker/paddle_k8s /usr/bin
diff --git a/benchmark/paddle/image/check_env.sh b/benchmark/fluid/check_env.sh
similarity index 100%
rename from benchmark/paddle/image/check_env.sh
rename to benchmark/fluid/check_env.sh
diff --git a/benchmark/paddle/image/alexnet.py b/benchmark/paddle/image/alexnet.py
deleted file mode 100644
index 9efc3f0494..0000000000
--- a/benchmark/paddle/image/alexnet.py
+++ /dev/null
@@ -1,93 +0,0 @@
-# Copyright (c) 2018 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.
-
-from paddle.trainer_config_helpers import *
-
-height = 227
-width = 227
-num_class = 1000
-batch_size = get_config_arg('batch_size', int, 128)
-gp = get_config_arg('layer_num', int, 1)
-is_infer = get_config_arg("is_infer", bool, False)
-num_samples = get_config_arg('num_samples', int, 2560)
-
-args = {
- 'height': height,
- 'width': width,
- 'color': True,
- 'num_class': num_class,
- 'is_infer': is_infer,
- 'num_samples': num_samples
-}
-define_py_data_sources2(
- "train.list" if not is_infer else None,
- "test.list" if is_infer else None,
- module="provider",
- obj="process",
- args=args)
-
-settings(
- batch_size=batch_size,
- learning_rate=0.01 / batch_size,
- learning_method=MomentumOptimizer(0.9),
- regularization=L2Regularization(0.0005 * batch_size))
-
-# conv1
-net = data_layer('data', size=height * width * 3)
-net = img_conv_layer(
- input=net,
- filter_size=11,
- num_channels=3,
- num_filters=96,
- stride=4,
- padding=1)
-net = img_cmrnorm_layer(input=net, size=5, scale=0.0001, power=0.75)
-net = img_pool_layer(input=net, pool_size=3, stride=2)
-
-# conv2
-net = img_conv_layer(
- input=net, filter_size=5, num_filters=256, stride=1, padding=2, groups=gp)
-net = img_cmrnorm_layer(input=net, size=5, scale=0.0001, power=0.75)
-net = img_pool_layer(input=net, pool_size=3, stride=2)
-
-# conv3
-net = img_conv_layer(
- input=net, filter_size=3, num_filters=384, stride=1, padding=1)
-# conv4
-net = img_conv_layer(
- input=net, filter_size=3, num_filters=384, stride=1, padding=1, groups=gp)
-
-# conv5
-net = img_conv_layer(
- input=net, filter_size=3, num_filters=256, stride=1, padding=1, groups=gp)
-net = img_pool_layer(input=net, pool_size=3, stride=2)
-
-net = fc_layer(
- input=net,
- size=4096,
- act=ReluActivation(),
- layer_attr=ExtraAttr(drop_rate=0.5))
-net = fc_layer(
- input=net,
- size=4096,
- act=ReluActivation(),
- layer_attr=ExtraAttr(drop_rate=0.5))
-net = fc_layer(input=net, size=1000, act=SoftmaxActivation())
-
-if is_infer:
- outputs(net)
-else:
- lab = data_layer('label', num_class)
- loss = cross_entropy(input=net, label=lab)
- outputs(loss)
diff --git a/benchmark/paddle/image/googlenet.py b/benchmark/paddle/image/googlenet.py
deleted file mode 100644
index 2a850ccb7f..0000000000
--- a/benchmark/paddle/image/googlenet.py
+++ /dev/null
@@ -1,245 +0,0 @@
-#!/usr/bin/env python
-from paddle.trainer_config_helpers import *
-
-height = 224
-width = 224
-num_class = 1000
-batch_size = get_config_arg('batch_size', int, 128)
-use_gpu = get_config_arg('use_gpu', bool, True)
-is_infer = get_config_arg("is_infer", bool, False)
-num_samples = get_config_arg('num_samples', int, 2560)
-
-args = {
- 'height': height,
- 'width': width,
- 'color': True,
- 'num_class': num_class,
- 'is_infer': is_infer,
- 'num_samples': num_samples
-}
-define_py_data_sources2(
- "train.list" if not is_infer else None,
- "test.list" if is_infer else None,
- module="provider",
- obj="process",
- args=args)
-
-settings(
- batch_size=batch_size,
- learning_rate=0.01 / batch_size,
- learning_method=MomentumOptimizer(0.9),
- regularization=L2Regularization(0.0005 * batch_size))
-
-conv_projection = conv_projection if use_gpu else img_conv_layer
-
-def inception2(name, input, channels, \
- filter1,
- filter3R, filter3,
- filter5R, filter5,
- proj):
-
- conv1 = name + '_1'
- conv3r = name + '_3r'
- conv3 = name + '_3'
- conv5r = name + '_5r'
- conv5 = name + '_5'
- maxpool = name + '_max'
- convproj = name + '_proj'
-
- cov1 = img_conv_layer(
- name=conv1,
- input=input,
- filter_size=1,
- num_channels=channels,
- num_filters=filter1,
- stride=1,
- padding=0)
-
- cov3r = img_conv_layer(
- name=conv3r,
- input=input,
- filter_size=1,
- num_channels=channels,
- num_filters=filter3R,
- stride=1,
- padding=0)
- cov3 = img_conv_layer(
- name=conv3,
- input=cov3r,
- filter_size=3,
- num_filters=filter3,
- stride=1,
- padding=1)
-
- cov5r = img_conv_layer(
- name=conv5r,
- input=input,
- filter_size=1,
- num_channels=channels,
- num_filters=filter5R,
- stride=1,
- padding=0)
- cov5 = img_conv_layer(
- name=conv5,
- input=cov5r,
- filter_size=5,
- num_filters=filter5,
- stride=1,
- padding=2)
-
- pool1 = img_pool_layer(
- name=maxpool,
- input=input,
- pool_size=3,
- num_channels=channels,
- stride=1,
- padding=1)
- covprj = img_conv_layer(
- name=convproj,
- input=pool1,
- filter_size=1,
- num_filters=proj,
- stride=1,
- padding=0)
-
- cat = concat_layer(name=name, input=[cov1, cov3, cov5, covprj])
- return cat
-
-def inception(name, input, channels, \
- filter1,
- filter3R, filter3,
- filter5R, filter5,
- proj):
-
- cov1 = conv_projection(
- input=input,
- filter_size=1,
- num_channels=channels,
- num_filters=filter1,
- stride=1,
- padding=0)
-
- cov3r = img_conv_layer(
- name=name + '_3r',
- input=input,
- filter_size=1,
- num_channels=channels,
- num_filters=filter3R,
- stride=1,
- padding=0)
- cov3 = conv_projection(
- input=cov3r, filter_size=3, num_filters=filter3, stride=1, padding=1)
-
- cov5r = img_conv_layer(
- name=name + '_5r',
- input=input,
- filter_size=1,
- num_channels=channels,
- num_filters=filter5R,
- stride=1,
- padding=0)
- cov5 = conv_projection(
- input=cov5r, filter_size=5, num_filters=filter5, stride=1, padding=2)
-
- pool1 = img_pool_layer(
- name=name + '_max',
- input=input,
- pool_size=3,
- num_channels=channels,
- stride=1,
- padding=1)
- covprj = conv_projection(
- input=pool1, filter_size=1, num_filters=proj, stride=1, padding=0)
-
- cat = concat_layer(
- name=name,
- input=[cov1, cov3, cov5, covprj],
- bias_attr=True if use_gpu else False,
- act=ReluActivation())
- return cat
-
-
-data = data_layer(name="input", size=3 * height * width)
-
-# stage 1
-conv1 = img_conv_layer(
- name="conv1",
- input=data,
- filter_size=7,
- num_channels=3,
- num_filters=64,
- stride=2,
- padding=3)
-pool1 = img_pool_layer(
- name="pool1", input=conv1, pool_size=3, num_channels=64, stride=2)
-
-# stage 2
-conv2_1 = img_conv_layer(
- name="conv2_1",
- input=pool1,
- filter_size=1,
- num_filters=64,
- stride=1,
- padding=0)
-conv2_2 = img_conv_layer(
- name="conv2_2",
- input=conv2_1,
- filter_size=3,
- num_filters=192,
- stride=1,
- padding=1)
-pool2 = img_pool_layer(
- name="pool2", input=conv2_2, pool_size=3, num_channels=192, stride=2)
-
-# stage 3
-ince3a = inception("ince3a", pool2, 192, 64, 96, 128, 16, 32, 32)
-ince3b = inception("ince3b", ince3a, 256, 128, 128, 192, 32, 96, 64)
-pool3 = img_pool_layer(
- name="pool3", input=ince3b, num_channels=480, pool_size=3, stride=2)
-
-# stage 4
-ince4a = inception("ince4a", pool3, 480, 192, 96, 208, 16, 48, 64)
-ince4b = inception("ince4b", ince4a, 512, 160, 112, 224, 24, 64, 64)
-ince4c = inception("ince4c", ince4b, 512, 128, 128, 256, 24, 64, 64)
-ince4d = inception("ince4d", ince4c, 512, 112, 144, 288, 32, 64, 64)
-ince4e = inception("ince4e", ince4d, 528, 256, 160, 320, 32, 128, 128)
-pool4 = img_pool_layer(
- name="pool4", input=ince4e, num_channels=832, pool_size=3, stride=2)
-
-# stage 5
-ince5a = inception("ince5a", pool4, 832, 256, 160, 320, 32, 128, 128)
-ince5b = inception("ince5b", ince5a, 832, 384, 192, 384, 48, 128, 128)
-pool5 = img_pool_layer(
- name="pool5",
- input=ince5b,
- num_channels=1024,
- pool_size=7,
- stride=7,
- pool_type=AvgPooling())
-
-# We remove loss1 and loss2 for all system when testing benchmark
-# output 1
-# pool_o1 = img_pool_layer(name="pool_o1", input=ince4a, num_channels=512, pool_size=5, stride=3, pool_type=AvgPooling())
-# conv_o1 = img_conv_layer(name="conv_o1", input=pool_o1, filter_size=1, num_filters=128, stride=1, padding=0)
-# fc_o1 = fc_layer(name="fc_o1", input=conv_o1, size=1024, layer_attr=ExtraAttr(drop_rate=0.7), act=ReluActivation())
-# out1 = fc_layer(name="output1", input=fc_o1, size=1000, act=SoftmaxActivation())
-# loss1 = cross_entropy(name='loss1', input=out1, label=lab, coeff=0.3)
-
-# output 2
-#pool_o2 = img_pool_layer(name="pool_o2", input=ince4d, num_channels=528, pool_size=5, stride=3, pool_type=AvgPooling())
-#conv_o2 = img_conv_layer(name="conv_o2", input=pool_o2, filter_size=1, num_filters=128, stride=1, padding=0)
-#fc_o2 = fc_layer(name="fc_o2", input=conv_o2, size=1024, layer_attr=ExtraAttr(drop_rate=0.7), act=ReluActivation())
-#out2 = fc_layer(name="output2", input=fc_o2, size=1000, act=SoftmaxActivation())
-#loss2 = cross_entropy(name='loss2', input=out2, label=lab, coeff=0.3)
-
-# output 3
-dropout = dropout_layer(name="dropout", input=pool5, dropout_rate=0.4)
-out3 = fc_layer(
- name="output3", input=dropout, size=1000, act=SoftmaxActivation())
-
-if is_infer:
- outputs(out3)
-else:
- lab = data_layer(name="label", size=num_class)
- loss3 = cross_entropy(name='loss3', input=out3, label=lab)
- outputs(loss3)
diff --git a/benchmark/paddle/image/plotlog.py b/benchmark/paddle/image/plotlog.py
deleted file mode 100644
index 8679d4f272..0000000000
--- a/benchmark/paddle/image/plotlog.py
+++ /dev/null
@@ -1,114 +0,0 @@
-# Copyright (c) 2016 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.
-
-import sys
-import argparse
-import matplotlib.pyplot as plt
-
-
-def parse_args():
- parser = argparse.ArgumentParser('Parse Log')
- parser.add_argument(
- '--file_path', '-f', type=str, help='the path of the log file')
- parser.add_argument(
- '--sample_rate',
- '-s',
- type=float,
- default=1.0,
- help='the rate to take samples from log')
- parser.add_argument(
- '--log_period', '-p', type=int, default=1, help='the period of log')
-
- args = parser.parse_args()
- return args
-
-
-def parse_file(file_name):
- loss = []
- error = []
- with open(file_name) as f:
- for i, line in enumerate(f):
- line = line.strip()
- if not line.startswith('pass'):
- continue
- line_split = line.split(' ')
- if len(line_split) != 5:
- continue
-
- loss_str = line_split[2][:-1]
- cur_loss = float(loss_str.split('=')[-1])
- loss.append(cur_loss)
-
- err_str = line_split[3][:-1]
- cur_err = float(err_str.split('=')[-1])
- error.append(cur_err)
-
- accuracy = [1.0 - err for err in error]
-
- return loss, accuracy
-
-
-def sample(metric, sample_rate):
- interval = int(1.0 / sample_rate)
- if interval > len(metric):
- return metric[:1]
-
- num = len(metric) / interval
- idx = [interval * i for i in range(num)]
- metric_sample = [metric[id] for id in idx]
- return metric_sample
-
-
-def plot_metric(metric,
- batch_id,
- graph_title,
- line_style='b-',
- line_label='y',
- line_num=1):
- plt.figure()
- plt.title(graph_title)
- if line_num == 1:
- plt.plot(batch_id, metric, line_style, label=line_label)
- else:
- for i in range(line_num):
- plt.plot(batch_id, metric[i], line_style[i], label=line_label[i])
- plt.xlabel('batch')
- plt.ylabel(graph_title)
- plt.legend()
- plt.savefig(graph_title + '.jpg')
- plt.close()
-
-
-def main():
- args = parse_args()
- assert args.sample_rate > 0. and args.sample_rate <= 1.0, "The sample rate should in the range (0, 1]."
-
- loss, accuracy = parse_file(args.file_path)
- batch = [args.log_period * i for i in range(len(loss))]
-
- batch_sample = sample(batch, args.sample_rate)
- loss_sample = sample(loss, args.sample_rate)
- accuracy_sample = sample(accuracy, args.sample_rate)
-
- plot_metric(loss_sample, batch_sample, 'loss', line_label='loss')
- plot_metric(
- accuracy_sample,
- batch_sample,
- 'accuracy',
- line_style='g-',
- line_label='accuracy')
-
-
-if __name__ == '__main__':
- main()
diff --git a/benchmark/paddle/image/provider.py b/benchmark/paddle/image/provider.py
deleted file mode 100644
index 6ad817ccef..0000000000
--- a/benchmark/paddle/image/provider.py
+++ /dev/null
@@ -1,47 +0,0 @@
-# Copyright (c) 2018 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.
-
-import io, os
-import random
-import numpy as np
-from paddle.trainer.PyDataProvider2 import *
-
-
-def initHook(settings, height, width, color, num_class, **kwargs):
- settings.height = height
- settings.width = width
- settings.color = color
- settings.num_class = num_class
- if settings.color:
- settings.data_size = settings.height * settings.width * 3
- else:
- settings.data_size = settings.height * settings.width
- settings.is_infer = kwargs.get('is_infer', False)
- settings.num_samples = kwargs.get('num_samples', 2560)
- if settings.is_infer:
- settings.slots = [dense_vector(settings.data_size)]
- else:
- settings.slots = [dense_vector(settings.data_size), integer_value(1)]
-
-
-@provider(
- init_hook=initHook, min_pool_size=-1, cache=CacheType.CACHE_PASS_IN_MEM)
-def process(settings, file_list):
- for i in xrange(settings.num_samples):
- img = np.random.rand(1, settings.data_size).reshape(-1, 1).flatten()
- if settings.is_infer:
- yield img.astype('float32')
- else:
- lab = random.randint(0, settings.num_class - 1)
- yield img.astype('float32'), int(lab)
diff --git a/benchmark/paddle/image/resnet.py b/benchmark/paddle/image/resnet.py
deleted file mode 100644
index 2846e4763f..0000000000
--- a/benchmark/paddle/image/resnet.py
+++ /dev/null
@@ -1,230 +0,0 @@
-#!/usr/bin/env python
-from paddle.trainer_config_helpers import *
-
-height = 224
-width = 224
-num_class = 1000
-batch_size = get_config_arg('batch_size', int, 64)
-layer_num = get_config_arg("layer_num", int, 50)
-is_infer = get_config_arg("is_infer", bool, False)
-num_samples = get_config_arg('num_samples', int, 2560)
-
-args = {
- 'height': height,
- 'width': width,
- 'color': True,
- 'num_class': num_class,
- 'is_infer': is_infer,
- 'num_samples': num_samples
-}
-define_py_data_sources2(
- "train.list" if not is_infer else None,
- "test.list" if is_infer else None,
- module="provider",
- obj="process",
- args=args)
-
-settings(
- batch_size=batch_size,
- learning_rate=0.01 / batch_size,
- learning_method=MomentumOptimizer(0.9),
- regularization=L2Regularization(0.0005 * batch_size))
-
-
-#######################Network Configuration #############
-def conv_bn_layer(name,
- input,
- filter_size,
- num_filters,
- stride,
- padding,
- channels=None,
- active_type=ReluActivation()):
- """
- A wrapper for conv layer with batch normalization layers.
- Note:
- conv layer has no activation.
- """
-
- tmp = img_conv_layer(
- name=name + "_conv",
- input=input,
- filter_size=filter_size,
- num_channels=channels,
- num_filters=num_filters,
- stride=stride,
- padding=padding,
- act=LinearActivation(),
- bias_attr=False)
- return batch_norm_layer(
- name=name + "_bn",
- input=tmp,
- act=active_type,
- use_global_stats=is_infer)
-
-
-def bottleneck_block(name, input, num_filters1, num_filters2):
- """
- A wrapper for bottlenect building block in ResNet.
- Last conv_bn_layer has no activation.
- Addto layer has activation of relu.
- """
- last_name = conv_bn_layer(
- name=name + '_branch2a',
- input=input,
- filter_size=1,
- num_filters=num_filters1,
- stride=1,
- padding=0)
- last_name = conv_bn_layer(
- name=name + '_branch2b',
- input=last_name,
- filter_size=3,
- num_filters=num_filters1,
- stride=1,
- padding=1)
- last_name = conv_bn_layer(
- name=name + '_branch2c',
- input=last_name,
- filter_size=1,
- num_filters=num_filters2,
- stride=1,
- padding=0,
- active_type=LinearActivation())
-
- return addto_layer(
- name=name + "_addto", input=[input, last_name], act=ReluActivation())
-
-
-def mid_projection(name, input, num_filters1, num_filters2, stride=2):
- """
- A wrapper for middile projection in ResNet.
- projection shortcuts are used for increasing dimensions,
- and other shortcuts are identity
- branch1: projection shortcuts are used for increasing
- dimensions, has no activation.
- branch2x: bottleneck building block, shortcuts are identity.
- """
- # stride = 2
- branch1 = conv_bn_layer(
- name=name + '_branch1',
- input=input,
- filter_size=1,
- num_filters=num_filters2,
- stride=stride,
- padding=0,
- active_type=LinearActivation())
-
- last_name = conv_bn_layer(
- name=name + '_branch2a',
- input=input,
- filter_size=1,
- num_filters=num_filters1,
- stride=stride,
- padding=0)
- last_name = conv_bn_layer(
- name=name + '_branch2b',
- input=last_name,
- filter_size=3,
- num_filters=num_filters1,
- stride=1,
- padding=1)
-
- last_name = conv_bn_layer(
- name=name + '_branch2c',
- input=last_name,
- filter_size=1,
- num_filters=num_filters2,
- stride=1,
- padding=0,
- active_type=LinearActivation())
-
- return addto_layer(
- name=name + "_addto", input=[branch1, last_name], act=ReluActivation())
-
-
-img = data_layer(name='image', size=height * width * 3)
-
-
-def deep_res_net(res2_num=3, res3_num=4, res4_num=6, res5_num=3):
- """
- A wrapper for 50,101,152 layers of ResNet.
- res2_num: number of blocks stacked in conv2_x
- res3_num: number of blocks stacked in conv3_x
- res4_num: number of blocks stacked in conv4_x
- res5_num: number of blocks stacked in conv5_x
- """
- # For ImageNet
- # conv1: 112x112
- tmp = conv_bn_layer(
- "conv1",
- input=img,
- filter_size=7,
- channels=3,
- num_filters=64,
- stride=2,
- padding=3)
- tmp = img_pool_layer(name="pool1", input=tmp, pool_size=3, stride=2)
-
- # conv2_x: 56x56
- tmp = mid_projection(
- name="res2_1", input=tmp, num_filters1=64, num_filters2=256, stride=1)
- for i in xrange(2, res2_num + 1, 1):
- tmp = bottleneck_block(
- name="res2_" + str(i), input=tmp, num_filters1=64, num_filters2=256)
-
- # conv3_x: 28x28
- tmp = mid_projection(
- name="res3_1", input=tmp, num_filters1=128, num_filters2=512)
- for i in xrange(2, res3_num + 1, 1):
- tmp = bottleneck_block(
- name="res3_" + str(i),
- input=tmp,
- num_filters1=128,
- num_filters2=512)
-
- # conv4_x: 14x14
- tmp = mid_projection(
- name="res4_1", input=tmp, num_filters1=256, num_filters2=1024)
- for i in xrange(2, res4_num + 1, 1):
- tmp = bottleneck_block(
- name="res4_" + str(i),
- input=tmp,
- num_filters1=256,
- num_filters2=1024)
-
- # conv5_x: 7x7
- tmp = mid_projection(
- name="res5_1", input=tmp, num_filters1=512, num_filters2=2048)
- for i in xrange(2, res5_num + 1, 1):
- tmp = bottleneck_block(
- name="res5_" + str(i),
- input=tmp,
- num_filters1=512,
- num_filters2=2048)
-
- tmp = img_pool_layer(
- name='avgpool',
- input=tmp,
- pool_size=7,
- stride=1,
- pool_type=AvgPooling())
-
- return fc_layer(input=tmp, size=num_class, act=SoftmaxActivation())
-
-
-if layer_num == 50:
- resnet = deep_res_net(3, 4, 6, 3)
-elif layer_num == 101:
- resnet = deep_res_net(3, 4, 23, 3)
-elif layer_num == 152:
- resnet = deep_res_net(3, 8, 36, 3)
-else:
- print("Wrong layer number.")
-
-if is_infer:
- outputs(resnet)
-else:
- lbl = data_layer(name="label", size=num_class)
- loss = cross_entropy(name='loss', input=resnet, label=lbl)
- outputs(loss)
diff --git a/benchmark/paddle/image/run.sh b/benchmark/paddle/image/run.sh
deleted file mode 100755
index 5b58a8d773..0000000000
--- a/benchmark/paddle/image/run.sh
+++ /dev/null
@@ -1,53 +0,0 @@
-#!/bin/bash
-
-set -e
-
-function train() {
- cfg=$1
- thread=$2
- bz=$3
- args="batch_size=$3"
- prefix=$4
- paddle train --job=time \
- --config=$cfg \
- --use_gpu=True \
- --trainer_count=$thread \
- --log_period=10 \
- --test_period=100 \
- --config_args=$args \
- > logs/$prefix-${thread}gpu-$bz.log 2>&1
-}
-
-if [ ! -d "train.list" ]; then
- echo " " > train.list
-fi
-if [ ! -d "logs" ]; then
- mkdir logs
-fi
-
-#========single-gpu=========#
-# alexnet
-train alexnet.py 1 64 alexnet
-train alexnet.py 1 128 alexnet
-train alexnet.py 1 256 alexnet
-train alexnet.py 1 512 alexnet
-
-# googlenet
-train googlenet.py 1 64 googlenet
-train googlenet.py 1 128 googlenet
-train googlenet.py 1 256 googlenet
-
-# smallnet
-train smallnet_mnist_cifar.py 1 64 smallnet
-train smallnet_mnist_cifar.py 1 128 smallnet
-train smallnet_mnist_cifar.py 1 256 smallnet
-train smallnet_mnist_cifar.py 1 512 smallnet
-
-
-############################
-#========multi-gpus=========#
-train alexnet.py 4 512 alexnet
-train alexnet.py 4 1024 alexnet
-
-train googlenet.py 4 512 googlenet
-train googlenet.py 4 1024 googlenet
diff --git a/benchmark/paddle/image/run_mkl_infer.sh b/benchmark/paddle/image/run_mkl_infer.sh
deleted file mode 100755
index 0fad5e04cc..0000000000
--- a/benchmark/paddle/image/run_mkl_infer.sh
+++ /dev/null
@@ -1,89 +0,0 @@
-#!/bin/bash
-
-set -e
-
-function clock_to_seconds() {
- hours=`echo $1 | awk -F ':' '{print $1}'`
- mins=`echo $1 | awk -F ':' '{print $2}'`
- secs=`echo $1 | awk -F ':' '{print $3}'`
- echo `awk 'BEGIN{printf "%.2f",('$secs' + '$mins' * 60 + '$hours' * 3600)}'`
-}
-
-function infer() {
- unset OMP_NUM_THREADS MKL_NUM_THREADS OMP_DYNAMIC KMP_AFFINITY
- topology=$1
- layer_num=$2
- bs=$3
- use_mkldnn=$4
- if [ $4 == "True" ]; then
- thread=1
- log="logs/infer-${topology}-${layer_num}-mkldnn-${bs}.log"
- elif [ $4 == "False" ]; then
- thread=`nproc`
- if [ $thread -gt $bs ]; then
- thread=$bs
- fi
- log="logs/infer-${topology}-${layer_num}-${thread}mklml-${bs}.log"
- else
- echo "Wrong input $4, use True or False."
- exit 0
- fi
-
- models_in="models/${topology}-${layer_num}/pass-00000/"
- if [ ! -d $models_in ]; then
- echo "Training model ${topology}_${layer_num}"
- paddle train --job=train \
- --config="${topology}.py" \
- --use_mkldnn=True \
- --use_gpu=False \
- --trainer_count=1 \
- --num_passes=1 \
- --save_dir="models/${topology}-${layer_num}" \
- --config_args="batch_size=128,layer_num=${layer_num},num_samples=256" \
- > /dev/null 2>&1
- echo "Done"
- fi
- log_period=$((256 / bs))
- paddle train --job=test \
- --config="${topology}.py" \
- --use_mkldnn=$use_mkldnn \
- --use_gpu=False \
- --trainer_count=$thread \
- --log_period=$log_period \
- --config_args="batch_size=${bs},layer_num=${layer_num},is_infer=True" \
- --init_model_path=$models_in \
- 2>&1 | tee ${log}
-
- # calculate the last 5 logs period time of 1280 samples,
- # the time before are burning time.
- start=`tail ${log} -n 7 | head -n 1 | awk -F ' ' '{print $2}' | xargs`
- end=`tail ${log} -n 2 | head -n 1 | awk -F ' ' '{print $2}' | xargs`
- start_sec=`clock_to_seconds $start`
- end_sec=`clock_to_seconds $end`
- fps=`awk 'BEGIN{printf "%.2f",(1280 / ('$end_sec' - '$start_sec'))}'`
- echo "Last 1280 samples start: ${start}(${start_sec} sec), end: ${end}(${end_sec} sec;" >> ${log}
- echo "FPS: $fps images/sec" 2>&1 | tee -a ${log}
-}
-
-if [ ! -f "train.list" ]; then
- echo " " > train.list
-fi
-if [ ! -f "test.list" ]; then
- echo " " > test.list
-fi
-if [ ! -d "logs" ]; then
- mkdir logs
-fi
-if [ ! -d "models" ]; then
- mkdir -p models
-fi
-
-# inference benchmark
-for use_mkldnn in True False; do
- for batchsize in 1 2 4 8 16; do
- infer vgg 19 $batchsize $use_mkldnn
- infer resnet 50 $batchsize $use_mkldnn
- infer googlenet v1 $batchsize $use_mkldnn
- infer alexnet 2 $batchsize $use_mkldnn
- done
-done
diff --git a/benchmark/paddle/image/run_mkl_train.sh b/benchmark/paddle/image/run_mkl_train.sh
deleted file mode 100755
index 1583bf134a..0000000000
--- a/benchmark/paddle/image/run_mkl_train.sh
+++ /dev/null
@@ -1,54 +0,0 @@
-#!/bin/bash
-
-set -e
-
-function train() {
- unset OMP_NUM_THREADS MKL_NUM_THREADS OMP_DYNAMIC KMP_AFFINITY
- topology=$1
- layer_num=$2
- bs=$3
- use_mkldnn=$4
- if [ $4 == "True" ]; then
- thread=1
- log="logs/train-${topology}-${layer_num}-mkldnn-${bs}.log"
- elif [ $4 == "False" ]; then
- thread=`nproc`
- # each trainer_count use only 1 core to avoid conflict
- log="logs/train-${topology}-${layer_num}-${thread}mklml-${bs}.log"
- else
- echo "Wrong input $4, use True or False."
- exit 0
- fi
- args="batch_size=${bs},layer_num=${layer_num}"
- config="${topology}.py"
- paddle train --job=time \
- --config=$config \
- --use_mkldnn=$use_mkldnn \
- --use_gpu=False \
- --trainer_count=$thread \
- --log_period=10 \
- --test_period=100 \
- --config_args=$args \
- 2>&1 | tee ${log}
-
- avg_time=`tail ${log} -n 1 | awk -F ' ' '{print $8}' | sed 's/avg=//'`
- fps=`awk 'BEGIN{printf "%.2f",('$bs' / '$avg_time' * 1000)}'`
- echo "FPS: $fps images/sec" 2>&1 | tee -a ${log}
-}
-
-if [ ! -f "train.list" ]; then
- echo " " > train.list
-fi
-if [ ! -d "logs" ]; then
- mkdir logs
-fi
-
-# training benchmark
-for use_mkldnn in True False; do
- for batchsize in 64 128 256; do
- train vgg 19 $batchsize $use_mkldnn
- train resnet 50 $batchsize $use_mkldnn
- train googlenet v1 $batchsize $use_mkldnn
- train alexnet 2 $batchsize $use_mkldnn
- done
-done
diff --git a/benchmark/paddle/image/run_openblas_infer.sh b/benchmark/paddle/image/run_openblas_infer.sh
deleted file mode 100755
index 987381cabc..0000000000
--- a/benchmark/paddle/image/run_openblas_infer.sh
+++ /dev/null
@@ -1,71 +0,0 @@
-#!/bin/bash
-
-set -e
-
-function clock_to_seconds() {
- hours=`echo $1 | awk -F ':' '{print $1}'`
- mins=`echo $1 | awk -F ':' '{print $2}'`
- secs=`echo $1 | awk -F ':' '{print $3}'`
- echo `awk 'BEGIN{printf "%.2f",('$secs' + '$mins' * 60 + '$hours' * 3600)}'`
-}
-
-function infer() {
- export OPENBLAS_MAIN_FREE=1
- topology=$1
- layer_num=$2
- bs=$3
- trainers=`nproc`
- if [ $trainers -gt $bs ]; then
- trainers=$bs
- fi
- log="logs/infer-${topology}-${layer_num}-${trainers}openblas-${bs}.log"
- threads=$((`nproc` / trainers))
- if [ $threads -eq 0 ]; then
- threads=1
- fi
- export OPENBLAS_NUM_THREADS=$threads
-
- models_in="models/${topology}-${layer_num}/pass-00000/"
- if [ ! -d $models_in ]; then
- echo "./run_mkl_infer.sh to save the model first"
- exit 0
- fi
- log_period=$((32 / bs))
- paddle train --job=test \
- --config="${topology}.py" \
- --use_mkldnn=False \
- --use_gpu=False \
- --trainer_count=$trainers \
- --log_period=$log_period \
- --config_args="batch_size=${bs},layer_num=${layer_num},is_infer=True,num_samples=256" \
- --init_model_path=$models_in \
- 2>&1 | tee ${log}
-
- # calculate the last 5 logs period time of 160(=32*5) samples,
- # the time before are burning time.
- start=`tail ${log} -n 7 | head -n 1 | awk -F ' ' '{print $2}' | xargs`
- end=`tail ${log} -n 2 | head -n 1 | awk -F ' ' '{print $2}' | xargs`
- start_sec=`clock_to_seconds $start`
- end_sec=`clock_to_seconds $end`
- fps=`awk 'BEGIN{printf "%.2f",(160 / ('$end_sec' - '$start_sec'))}'`
- echo "Last 160 samples start: ${start}(${start_sec} sec), end: ${end}(${end_sec} sec;" >> ${log}
- echo "FPS: $fps images/sec" 2>&1 | tee -a ${log}
-}
-
-if [ ! -f "train.list" ]; then
- echo " " > train.list
-fi
-if [ ! -f "test.list" ]; then
- echo " " > test.list
-fi
-if [ ! -d "logs" ]; then
- mkdir logs
-fi
-
-# inference benchmark
-for batchsize in 1 2 4 8 16; do
- infer vgg 19 $batchsize
- infer resnet 50 $batchsize
- infer googlenet v1 $batchsize
- infer alexnet 2 $batchsize
-done
diff --git a/benchmark/paddle/image/run_openblas_train.sh b/benchmark/paddle/image/run_openblas_train.sh
deleted file mode 100755
index cc64e1d09d..0000000000
--- a/benchmark/paddle/image/run_openblas_train.sh
+++ /dev/null
@@ -1,43 +0,0 @@
-#!/bin/bash
-
-set -e
-
-function train() {
- export OPENBLAS_NUM_THREADS=1
- topology=$1
- layer_num=$2
- bs=$3
- thread=`nproc`
- # each trainer_count use only 1 core to avoid conflict
- log="logs/train-${topology}-${layer_num}-${thread}openblas-${bs}.log"
- args="batch_size=${bs},layer_num=${layer_num}"
- config="${topology}.py"
- paddle train --job=time \
- --config=$config \
- --use_mkldnn=False \
- --use_gpu=False \
- --trainer_count=$thread \
- --log_period=3 \
- --test_period=30 \
- --config_args=$args \
- 2>&1 | tee ${log}
-
- avg_time=`tail ${log} -n 1 | awk -F ' ' '{print $8}' | sed 's/avg=//'`
- fps=`awk 'BEGIN{printf "%.2f",('$bs' / '$avg_time' * 1000)}'`
- echo "FPS: $fps images/sec" 2>&1 | tee -a ${log}
-}
-
-if [ ! -f "train.list" ]; then
- echo " " > train.list
-fi
-if [ ! -d "logs" ]; then
- mkdir logs
-fi
-
-# training benchmark
-for batchsize in 64 128 256; do
- train vgg 19 $batchsize
- train resnet 50 $batchsize
- train googlenet v1 $batchsize
- train alexnet 2 $batchsize
-done
diff --git a/benchmark/paddle/image/smallnet_mnist_cifar.py b/benchmark/paddle/image/smallnet_mnist_cifar.py
deleted file mode 100644
index 58879c454f..0000000000
--- a/benchmark/paddle/image/smallnet_mnist_cifar.py
+++ /dev/null
@@ -1,49 +0,0 @@
-#!/usr/bin/env python
-
-from paddle.trainer_config_helpers import *
-
-height = 32
-width = 32
-num_class = 10
-
-batch_size = get_config_arg('batch_size', int, 128)
-
-args = {'height': height, 'width': width, 'color': True, 'num_class': num_class}
-define_py_data_sources2(
- "train.list", None, module="provider", obj="process", args=args)
-
-settings(
- batch_size=batch_size,
- learning_rate=0.01 / batch_size,
- learning_method=MomentumOptimizer(0.9),
- regularization=L2Regularization(0.0005 * batch_size))
-
-# conv1
-net = data_layer('data', size=height * width * 3)
-net = img_conv_layer(
- input=net,
- filter_size=5,
- num_channels=3,
- num_filters=32,
- stride=1,
- padding=2)
-net = img_pool_layer(input=net, pool_size=3, stride=2, padding=1)
-
-# conv2
-net = img_conv_layer(
- input=net, filter_size=5, num_filters=32, stride=1, padding=2)
-net = img_pool_layer(
- input=net, pool_size=3, stride=2, padding=1, pool_type=AvgPooling())
-
-# conv3
-net = img_conv_layer(
- input=net, filter_size=3, num_filters=64, stride=1, padding=1)
-net = img_pool_layer(
- input=net, pool_size=3, stride=2, padding=1, pool_type=AvgPooling())
-
-net = fc_layer(input=net, size=64, act=ReluActivation())
-net = fc_layer(input=net, size=10, act=SoftmaxActivation())
-
-lab = data_layer('label', num_class)
-loss = classification_cost(input=net, label=lab)
-outputs(loss)
diff --git a/benchmark/paddle/image/vgg.py b/benchmark/paddle/image/vgg.py
deleted file mode 100644
index ca0a6798fb..0000000000
--- a/benchmark/paddle/image/vgg.py
+++ /dev/null
@@ -1,119 +0,0 @@
-#!/usr/bin/env python
-from paddle.trainer_config_helpers import *
-
-height = 224
-width = 224
-num_class = 1000
-batch_size = get_config_arg('batch_size', int, 64)
-layer_num = get_config_arg('layer_num', int, 19)
-is_infer = get_config_arg("is_infer", bool, False)
-num_samples = get_config_arg('num_samples', int, 2560)
-
-args = {
- 'height': height,
- 'width': width,
- 'color': True,
- 'num_class': num_class,
- 'is_infer': is_infer,
- 'num_samples': num_samples
-}
-define_py_data_sources2(
- "train.list" if not is_infer else None,
- "test.list" if is_infer else None,
- module="provider",
- obj="process",
- args=args)
-
-settings(
- batch_size=batch_size,
- learning_rate=0.001 / batch_size,
- learning_method=MomentumOptimizer(0.9),
- regularization=L2Regularization(0.0005 * batch_size))
-
-img = data_layer(name='image', size=height * width * 3)
-
-
-def vgg_network(vgg_num=3):
- tmp = img_conv_group(
- input=img,
- num_channels=3,
- conv_padding=1,
- conv_num_filter=[64, 64],
- conv_filter_size=3,
- conv_act=ReluActivation(),
- pool_size=2,
- pool_stride=2,
- pool_type=MaxPooling())
-
- tmp = img_conv_group(
- input=tmp,
- conv_num_filter=[128, 128],
- conv_padding=1,
- conv_filter_size=3,
- conv_act=ReluActivation(),
- pool_stride=2,
- pool_type=MaxPooling(),
- pool_size=2)
-
- channels = []
- for i in range(vgg_num):
- channels.append(256)
- tmp = img_conv_group(
- input=tmp,
- conv_num_filter=channels,
- conv_padding=1,
- conv_filter_size=3,
- conv_act=ReluActivation(),
- pool_stride=2,
- pool_type=MaxPooling(),
- pool_size=2)
- channels = []
- for i in range(vgg_num):
- channels.append(512)
- tmp = img_conv_group(
- input=tmp,
- conv_num_filter=channels,
- conv_padding=1,
- conv_filter_size=3,
- conv_act=ReluActivation(),
- pool_stride=2,
- pool_type=MaxPooling(),
- pool_size=2)
- tmp = img_conv_group(
- input=tmp,
- conv_num_filter=channels,
- conv_padding=1,
- conv_filter_size=3,
- conv_act=ReluActivation(),
- pool_stride=2,
- pool_type=MaxPooling(),
- pool_size=2)
-
- tmp = fc_layer(
- input=tmp,
- size=4096,
- act=ReluActivation(),
- layer_attr=ExtraAttr(drop_rate=0.5))
-
- tmp = fc_layer(
- input=tmp,
- size=4096,
- act=ReluActivation(),
- layer_attr=ExtraAttr(drop_rate=0.5))
-
- return fc_layer(input=tmp, size=num_class, act=SoftmaxActivation())
-
-
-if layer_num == 16:
- vgg = vgg_network(3)
-elif layer_num == 19:
- vgg = vgg_network(4)
-else:
- print("Wrong layer number.")
-
-if is_infer:
- outputs(vgg)
-else:
- lab = data_layer('label', num_class)
- loss = cross_entropy(input=vgg, label=lab)
- outputs(loss)
diff --git a/benchmark/paddle/rnn/imdb.py b/benchmark/paddle/rnn/imdb.py
deleted file mode 100755
index 2a67f9b0cf..0000000000
--- a/benchmark/paddle/rnn/imdb.py
+++ /dev/null
@@ -1,60 +0,0 @@
-# Copyright (c) 2018 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.
-
-from __future__ import print_function
-import six.moves.cPickle as pickle
-import gzip
-import os
-import numpy
-
-
-def get_dataset_file(dataset, default_dataset, origin):
- data_dir, data_file = os.path.split(dataset)
- if (not os.path.isfile(dataset)) and data_file == default_dataset:
- from six.moves import urllib
- print('Downloading data from %s' % origin)
- urllib.request.urlretrieve(origin, dataset)
-
- return dataset
-
-
-def create_data(path="imdb.pkl"):
-
- if (not os.path.isfile('imdb.train.pkl')):
- path = get_dataset_file(
- path, "imdb.pkl",
- "http://www.iro.umontreal.ca/~lisa/deep/data/imdb.pkl")
-
- if path.endswith(".gz"):
- f = gzip.open(path, 'rb')
- else:
- f = open(path, 'rb')
-
- train_set = pickle.load(f)
- test_set = pickle.load(f)
- f.close()
-
- pickle.dump(train_set, open('imdb.train.pkl', 'wb'))
- pickle.dump(test_set, open('imdb.test.pkl', 'wb'))
-
- if (not os.path.isfile('train.list')):
- file('train.list', 'w').write('imdb.train.pkl\n')
-
-
-def main():
- create_data('imdb.pkl')
-
-
-if __name__ == "__main__":
- main()
diff --git a/benchmark/paddle/rnn/provider.py b/benchmark/paddle/rnn/provider.py
deleted file mode 100644
index 23cc0c44a9..0000000000
--- a/benchmark/paddle/rnn/provider.py
+++ /dev/null
@@ -1,86 +0,0 @@
-# Copyright (c) 2018 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.
-
-import io, os
-import random
-import numpy as np
-import six.moves.cPickle as pickle
-from paddle.trainer.PyDataProvider2 import *
-
-
-def remove_unk(x, n_words):
- return [[1 if w >= n_words else w for w in sen] for sen in x]
-
-
-# ==============================================================
-# tensorflow uses fixed length, but PaddlePaddle can process
-# variable-length. Padding is used in benchmark in order to
-# compare with other platform.
-# ==============================================================
-def pad_sequences(sequences,
- maxlen=None,
- dtype='int32',
- padding='post',
- truncating='post',
- value=0.):
- lengths = [len(s) for s in sequences]
-
- nb_samples = len(sequences)
- if maxlen is None:
- maxlen = np.max(lengths)
-
- x = (np.ones((nb_samples, maxlen)) * value).astype(dtype)
- for idx, s in enumerate(sequences):
- if len(s) == 0:
- continue # empty list was found
- if truncating == 'pre':
- trunc = s[-maxlen:]
- elif truncating == 'post':
- trunc = s[:maxlen]
- else:
- raise ValueError("Truncating type '%s' not understood" % padding)
-
- if padding == 'post':
- x[idx, :len(trunc)] = trunc
- elif padding == 'pre':
- x[idx, -len(trunc):] = trunc
- else:
- raise ValueError("Padding type '%s' not understood" % padding)
- return x
-
-
-def initHook(settings, vocab_size, pad_seq, maxlen, **kwargs):
- settings.vocab_size = vocab_size
- settings.pad_seq = pad_seq
- settings.maxlen = maxlen
- settings.input_types = [
- integer_value_sequence(vocab_size), integer_value(2)
- ]
-
-
-@provider(
- init_hook=initHook, min_pool_size=-1, cache=CacheType.CACHE_PASS_IN_MEM)
-def process(settings, file):
- f = open(file, 'rb')
- train_set = pickle.load(f)
- f.close()
- x, y = train_set
-
- # remove unk, namely remove the words out of dictionary
- x = remove_unk(x, settings.vocab_size)
- if settings.pad_seq:
- x = pad_sequences(x, maxlen=settings.maxlen, value=0.)
-
- for i in range(len(y)):
- yield map(int, x[i]), int(y[i])
diff --git a/benchmark/paddle/rnn/rnn.py b/benchmark/paddle/rnn/rnn.py
deleted file mode 100755
index 83eb3e5654..0000000000
--- a/benchmark/paddle/rnn/rnn.py
+++ /dev/null
@@ -1,38 +0,0 @@
-#!/usr/bin/env python
-
-from paddle.trainer_config_helpers import *
-import imdb
-
-num_class = 2
-vocab_size = 30000
-fixedlen = 100
-batch_size = get_config_arg('batch_size', int, 128)
-lstm_num = get_config_arg('lstm_num', int, 1)
-hidden_size = get_config_arg('hidden_size', int, 128)
-# whether to pad sequence into fixed length
-pad_seq = get_config_arg('pad_seq', bool, True)
-imdb.create_data('imdb.pkl')
-
-args = {'vocab_size': vocab_size, 'pad_seq': pad_seq, 'maxlen': fixedlen}
-define_py_data_sources2(
- "train.list", None, module="provider", obj="process", args=args)
-
-settings(
- batch_size=batch_size,
- learning_rate=2e-3,
- learning_method=AdamOptimizer(),
- regularization=L2Regularization(8e-4),
- gradient_clipping_threshold=25)
-
-net = data_layer('data', size=vocab_size)
-net = embedding_layer(input=net, size=128)
-
-for i in xrange(lstm_num):
- net = simple_lstm(input=net, size=hidden_size)
-
-net = last_seq(input=net)
-net = fc_layer(input=net, size=2, act=SoftmaxActivation())
-
-lab = data_layer('label', num_class)
-loss = classification_cost(input=net, label=lab)
-outputs(loss)
diff --git a/benchmark/paddle/rnn/run.sh b/benchmark/paddle/rnn/run.sh
deleted file mode 100755
index f99a562b3f..0000000000
--- a/benchmark/paddle/rnn/run.sh
+++ /dev/null
@@ -1,52 +0,0 @@
-#!/bin/bash
-
-set -e
-
-function train() {
- cfg=$1
- thread=$2
- args="lstm_num=${3},seq_pad=${4},hidden_size=${5},batch_size=${6}"
- paddle train --job=time \
- --config=$cfg \
- --use_gpu=1 \
- --trainer_count=$thread \
- --log_period=10 \
- --test_period=100 \
- --num_passes=1 \
- --feed_data=1 \
- --config_args=$args \
- >logs/rnn-pad${4}-${thread}gpu-lstm${3}-batch${6}-hid${5}.log 2>&1
-}
-
-if [ ! -d "logs" ]; then
- mkdir logs
-fi
-
-## padding, single gpu
-#-----config--gpu--lstm_num--padding--hidden_size--batch_size
-## lstm_num=2, batch_size=64
-train rnn.py 1 2 1 256 64
-train rnn.py 1 2 1 512 64
-train rnn.py 1 2 1 1280 64
-
-## lstm_num=2, batch_size=128
-train rnn.py 1 2 1 256 128
-train rnn.py 1 2 1 512 128
-train rnn.py 1 2 1 1280 128
-
-## lstm_num=4, batch_size=256
-train rnn.py 1 2 1 256 256
-train rnn.py 1 2 1 512 256
-train rnn.py 1 2 1 1280 256
-
-
-#==================multi gpus=====================#
-# hidden_size=256, lstm_num=2, different batch size
-train rnn.py 4 2 1 256 128
-train rnn.py 4 2 1 256 256
-train rnn.py 4 2 1 256 512
-
-# hidden_size=512, lstm_num=4, different batch size
-train rnn.py 4 2 1 512 128
-train rnn.py 4 2 1 512 256
-train rnn.py 4 2 1 512 512
From b9999435b7244f0477c3c1902ca8793b5f6c2ace Mon Sep 17 00:00:00 2001
From: Tao Luo <luotao02@baidu.com>
Date: Thu, 14 Feb 2019 21:29:01 +0800
Subject: [PATCH 7/8] remove 'import paddle.v2' in read data
test=develop
---
benchmark/tensorflow/machine_translation.py | 2 --
benchmark/tensorflow/mnist.py | 1 -
benchmark/tensorflow/resnet.py | 1 -
benchmark/tensorflow/stacked_dynamic_lstm.py | 2 --
benchmark/tensorflow/vgg.py | 1 -
.../fluid/tests/demo/file_reader/convert_data_to_recordio.py | 1 -
python/paddle/fluid/tests/demo/pyreader.py | 3 +--
7 files changed, 1 insertion(+), 10 deletions(-)
diff --git a/benchmark/tensorflow/machine_translation.py b/benchmark/tensorflow/machine_translation.py
index 8f77dce983..7837669edc 100644
--- a/benchmark/tensorflow/machine_translation.py
+++ b/benchmark/tensorflow/machine_translation.py
@@ -35,8 +35,6 @@ import os
import argparse
import time
-import paddle.v2 as paddle
-
parser = argparse.ArgumentParser(description=__doc__)
parser.add_argument(
"--embedding_dim",
diff --git a/benchmark/tensorflow/mnist.py b/benchmark/tensorflow/mnist.py
index 7140eed6ea..03d533fecf 100644
--- a/benchmark/tensorflow/mnist.py
+++ b/benchmark/tensorflow/mnist.py
@@ -21,7 +21,6 @@ import time
import numpy as np
import tensorflow as tf
-import paddle.v2 as paddle
DTYPE = tf.float32
diff --git a/benchmark/tensorflow/resnet.py b/benchmark/tensorflow/resnet.py
index c432fa8d59..fdb0441957 100644
--- a/benchmark/tensorflow/resnet.py
+++ b/benchmark/tensorflow/resnet.py
@@ -27,7 +27,6 @@ import argparse
import time
import numpy as np
-import paddle.v2 as paddle
import tensorflow as tf
DTYPE = tf.float32
diff --git a/benchmark/tensorflow/stacked_dynamic_lstm.py b/benchmark/tensorflow/stacked_dynamic_lstm.py
index 5285033005..1f532dc2fa 100644
--- a/benchmark/tensorflow/stacked_dynamic_lstm.py
+++ b/benchmark/tensorflow/stacked_dynamic_lstm.py
@@ -21,8 +21,6 @@ import argparse
import time
import tensorflow as tf
-import paddle.v2 as paddle
-
def parse_args():
parser = argparse.ArgumentParser("LSTM model benchmark.")
diff --git a/benchmark/tensorflow/vgg.py b/benchmark/tensorflow/vgg.py
index fba5ec71a4..d32c835bd7 100644
--- a/benchmark/tensorflow/vgg.py
+++ b/benchmark/tensorflow/vgg.py
@@ -13,7 +13,6 @@
# limitations under the License.
"""VGG16 benchmark in TensorFlow"""
import tensorflow as tf
-import paddle.v2 as paddle
import numpy as np
import argparse
import time
diff --git a/python/paddle/fluid/tests/demo/file_reader/convert_data_to_recordio.py b/python/paddle/fluid/tests/demo/file_reader/convert_data_to_recordio.py
index 45a104ec96..b00af91a9d 100644
--- a/python/paddle/fluid/tests/demo/file_reader/convert_data_to_recordio.py
+++ b/python/paddle/fluid/tests/demo/file_reader/convert_data_to_recordio.py
@@ -16,7 +16,6 @@ from __future__ import print_function
import sys
import paddle.fluid as fluid
-import paddle.v2 as paddle
def load_vocab(filename):
diff --git a/python/paddle/fluid/tests/demo/pyreader.py b/python/paddle/fluid/tests/demo/pyreader.py
index ec61e0ebae..bbcef4c3ff 100644
--- a/python/paddle/fluid/tests/demo/pyreader.py
+++ b/python/paddle/fluid/tests/demo/pyreader.py
@@ -20,7 +20,6 @@ import six
import paddle
import paddle.dataset.mnist as mnist
import paddle.fluid as fluid
-import paddle.v2
def network(is_train):
@@ -72,7 +71,7 @@ def main():
use_cuda=use_cuda, share_vars_from=trainer, main_program=test_prog)
train_reader.decorate_paddle_reader(
- paddle.v2.reader.shuffle(
+ paddle.reader.shuffle(
paddle.batch(mnist.train(), 512), buf_size=8192))
test_reader.decorate_paddle_reader(paddle.batch(mnist.test(), 512))
From abcefe721117010277fbffb1e159acf2228f08dc Mon Sep 17 00:00:00 2001
From: qingqing01 <dangqingqing@baidu.com>
Date: Thu, 14 Feb 2019 22:39:55 +0800
Subject: [PATCH 8/8] Fix debug mode in fake_quantize_op (#15693)
* Fix debug mode in fake_quantize_op
* Remove template specialization
---
paddle/fluid/operators/fake_quantize_op.cc | 21 ++++++---------------
1 file changed, 6 insertions(+), 15 deletions(-)
diff --git a/paddle/fluid/operators/fake_quantize_op.cc b/paddle/fluid/operators/fake_quantize_op.cc
index 8aff911141..d51eb054a9 100644
--- a/paddle/fluid/operators/fake_quantize_op.cc
+++ b/paddle/fluid/operators/fake_quantize_op.cc
@@ -21,26 +21,17 @@ limitations under the License. */
namespace paddle {
namespace operators {
-template <typename T, int MajorType = Eigen::RowMajor,
- typename IndexType = Eigen::DenseIndex>
-using EigenVectorArrayMap =
- Eigen::TensorMap<Eigen::Tensor<T, 1, MajorType, IndexType>>;
-
-template <typename T, int MajorType = Eigen::RowMajor,
- typename IndexType = Eigen::DenseIndex>
-using ConstEigenVectorArrayMap =
- Eigen::TensorMap<const Eigen::Tensor<T, 1, MajorType, IndexType>>;
+template <typename T>
+struct Compare {
+ public:
+ bool operator()(const T a, const T b) { return (std::abs(a) < std::abs(b)); }
+};
template <typename T>
struct FindAbsMaxFunctor<platform::CPUDeviceContext, T> {
void operator()(const platform::CPUDeviceContext& ctx, const T* in,
const int num, T* out) {
- Eigen::DSizes<Eigen::DenseIndex, 1> idim(num);
- Eigen::DSizes<Eigen::DenseIndex, 1> odim(1);
- Eigen::TensorMap<Eigen::Tensor<const T, 1, Eigen::RowMajor>> in_e(in, idim);
- Eigen::TensorMap<Eigen::Tensor<T, 1, Eigen::RowMajor>> out_e(out, odim);
-
- out_e = in_e.abs().maximum();
+ *out = *(std::max_element(in + 0, in + num, Compare<T>()));
}
};