!56 Synchronization code423 to ms-incubator
Merge pull request !56 from changzherui/syn-code423pull/915/head
mindspore-ci-bot
5 years ago
committed by
Gitee
commit
6844ea633d
@ -1,16 +1,15 @@
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set(incubator_tvm_gpu_CFLAGS "-pipe -Wall -fPIC -fstack-protector-all -D_FORTIFY_SOURCE=2 -O2")
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set(incubator_tvm_gpu_CXXFLAGS "-std=c++11 -pipe -Wall -fPIC -fstack-protector-all -D_FORTIFY_SOURCE=2 -O2")
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set(USE_CUDA "ON")
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set(incubator_tvm_gpu_CXXFLAGS "-D_FORTIFY_SOURCE=2 -O2")
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set(incubator_tvm_gpu_CFLAGS "-D_FORTIFY_SOURCE=2 -O2")
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mindspore_add_pkg(incubator_tvm_gpu
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VER 0.6.0
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LIBS tvm
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URL https://github.com/apache/incubator-tvm/archive/v0.6.0.tar.gz
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MD5 9cbbd32545a776023acabbba270449fe
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CUSTOM_CMAKE ${CMAKE_SOURCE_DIR}/third_party/patch/incubator-tvm/
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SUBMODULES ${dlpack_DIRPATH} ${dmlc-core_DIRPATH} ${rang_DIRPATH}
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SOURCEMODULES topi/python/topi python/tvm
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PATCHES ${CMAKE_SOURCE_DIR}/third_party/patch/incubator-tvm/find_library.patch
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${CMAKE_SOURCE_DIR}/third_party/patch/incubator-tvm/include.patch
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${CMAKE_SOURCE_DIR}/third_party/patch/incubator-tvm/src_pass.patch
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CMAKE_OPTION -DBUILD_TESTING=OFF -DCMAKE_POSITION_INDEPENDENT_CODE=ON -DBUILD_SHARED_LIBS=ON)
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include_directories(${incubator_tvm_gpu_INC})
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add_library(mindspore::tvm ALIAS incubator_tvm_gpu::tvm)
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${CMAKE_SOURCE_DIR}/third_party/patch/incubator-tvm/include.patch
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${CMAKE_SOURCE_DIR}/third_party/patch/incubator-tvm/src_pass.patch
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CMAKE_OPTION " ")
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add_library(mindspore::tvm ALIAS incubator_tvm_gpu::tvm)
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@ -0,0 +1,58 @@
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# AlexNet Example
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## Description
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Training AlexNet with CIFAR-10 dataset in MindSpore.
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This is the simple tutorial for training AlexNet in MindSpore.
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## Requirements
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- Install [MindSpore](https://www.mindspore.cn/install/en).
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- Download the CIFAR-10 dataset at <http://www.cs.toronto.edu/~kriz/cifar-10-binary.tar.gz>. The directory structure is as follows:
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```
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├─cifar-10-batches-bin
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│
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└─cifar-10-verify-bin
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```
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## Running the example
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```python
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# train AlexNet, hyperparameter setting in config.py
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python train.py --data_path cifar-10-batches-bin
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```
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You can get loss with each step similar to this:
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```bash
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epoch: 1 step: 1, loss is 2.2791853
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...
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epoch: 1 step: 1536, loss is 1.9366643
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epoch: 1 step: 1537, loss is 1.6983616
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epoch: 1 step: 1538, loss is 1.0221305
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...
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```
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Then, test AlexNet according to network model
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```python
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# test AlexNet, 1 epoch training accuracy is up to 51.1%; 10 epoch training accuracy is up to 81.2%
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python eval.py --data_path cifar-10-verify-bin --mode test --ckpt_path checkpoint_alexnet-1_1562.ckpt
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```
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## Note
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There are some optional arguments:
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```bash
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-h, --help show this help message and exit
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--device_target {Ascend,GPU}
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device where the code will be implemented (default: Ascend)
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--data_path DATA_PATH
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path where the dataset is saved
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--dataset_sink_mode DATASET_SINK_MODE
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dataset_sink_mode is False or True
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```
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You can run ```python train.py -h``` or ```python eval.py -h``` to get more information.
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@ -0,0 +1,46 @@
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# MindRecord generating guidelines
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<!-- TOC -->
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- [MindRecord generating guidelines](#mindrecord-generating-guidelines)
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- [Create work space](#create-work-space)
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- [Implement data generator](#implement-data-generator)
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- [Run data generator](#run-data-generator)
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<!-- /TOC -->
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## Create work space
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Assume the dataset name is 'xyz'
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* Create work space from template
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```shell
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cd ${your_mindspore_home}/example/convert_to_mindrecord
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cp -r template xyz
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```
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## Implement data generator
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Edit dictionary data generator
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* Edit file
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```shell
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cd ${your_mindspore_home}/example/convert_to_mindrecord
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vi xyz/mr_api.py
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```
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Two API, 'mindrecord_task_number' and 'mindrecord_dict_data', must be implemented
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- 'mindrecord_task_number()' returns number of tasks. Return 1 if data row is generated serially. Return N if generator can be split into N parallel-run tasks.
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- 'mindrecord_dict_data(task_id)' yields dictionary data row by row. 'task_id' is 0..N-1, if N is return value of mindrecord_task_number()
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Tricky for parallel run
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- For imagenet, one directory can be a task.
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- For TFRecord with multiple files, each file can be a task.
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- For TFRecord with 1 file only, it could also be split into N tasks. Task_id=K means: data row is picked only if (count % N == K)
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## Run data generator
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* run python script
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```shell
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cd ${your_mindspore_home}/example/convert_to_mindrecord
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python writer.py --mindrecord_script imagenet [...]
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```
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@ -0,0 +1,122 @@
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# Copyright 2020 Huawei Technologies Co., Ltd
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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# ==============================================================================
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"""
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User-defined API for MindRecord writer.
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Two API must be implemented,
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1. mindrecord_task_number()
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# Return number of parallel tasks. return 1 if no parallel
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2. mindrecord_dict_data(task_id)
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# Yield data for one task
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# task_id is 0..N-1, if N is return value of mindrecord_task_number()
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"""
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import argparse
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import os
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import pickle
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######## mindrecord_schema begin ##########
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mindrecord_schema = {"label": {"type": "int64"},
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"data": {"type": "bytes"},
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"file_name": {"type": "string"}}
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######## mindrecord_schema end ##########
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######## Frozen code begin ##########
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with open('mr_argument.pickle', 'rb') as mindrecord_argument_file_handle:
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ARG_LIST = pickle.load(mindrecord_argument_file_handle)
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######## Frozen code end ##########
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parser = argparse.ArgumentParser(description='Mind record imagenet example')
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parser.add_argument('--label_file', type=str, default="", help='label file')
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parser.add_argument('--image_dir', type=str, default="", help='images directory')
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######## Frozen code begin ##########
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args = parser.parse_args(ARG_LIST)
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print(args)
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######## Frozen code end ##########
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def _user_defined_private_func():
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"""
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Internal function for tasks list
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Return:
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tasks list
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"""
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if not os.path.exists(args.label_file):
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raise IOError("map file {} not exists".format(args.label_file))
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label_dict = {}
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with open(args.label_file) as file_handle:
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line = file_handle.readline()
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while line:
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labels = line.split(" ")
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label_dict[labels[1]] = labels[0]
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line = file_handle.readline()
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# get all the dir which are n02087046, n02094114, n02109525
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dir_paths = {}
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for item in label_dict:
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real_path = os.path.join(args.image_dir, label_dict[item])
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if not os.path.isdir(real_path):
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print("{} dir is not exist".format(real_path))
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continue
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dir_paths[item] = real_path
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if not dir_paths:
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print("not valid image dir in {}".format(args.image_dir))
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return {}, {}
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dir_list = []
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for label in dir_paths:
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dir_list.append(label)
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return dir_list, dir_paths
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dir_list_global, dir_paths_global = _user_defined_private_func()
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def mindrecord_task_number():
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"""
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Get task size.
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Return:
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number of tasks
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"""
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return len(dir_list_global)
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def mindrecord_dict_data(task_id):
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"""
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Get data dict.
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Yields:
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data (dict): data row which is dict.
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"""
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# get the filename, label and image binary as a dict
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label = dir_list_global[task_id]
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for item in os.listdir(dir_paths_global[label]):
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file_name = os.path.join(dir_paths_global[label], item)
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if not item.endswith("JPEG") and not item.endswith(
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"jpg") and not item.endswith("jpeg"):
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print("{} file is not suffix with JPEG/jpg, skip it.".format(file_name))
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continue
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data = {}
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data["file_name"] = str(file_name)
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data["label"] = int(label)
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# get the image data
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image_file = open(file_name, "rb")
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image_bytes = image_file.read()
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image_file.close()
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data["data"] = image_bytes
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yield data
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@ -0,0 +1,8 @@
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#!/bin/bash
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rm /tmp/imagenet/mr/*
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python writer.py --mindrecord_script imagenet \
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--mindrecord_file "/tmp/imagenet/mr/m" \
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--mindrecord_partitions 16 \
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--label_file "/tmp/imagenet/label.txt" \
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--image_dir "/tmp/imagenet/jpeg"
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@ -0,0 +1,6 @@
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#!/bin/bash
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rm /tmp/template/*
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python writer.py --mindrecord_script template \
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--mindrecord_file "/tmp/template/m" \
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--mindrecord_partitions 4
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@ -0,0 +1,73 @@
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# Copyright 2020 Huawei Technologies Co., Ltd
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# 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.
|
||||
# ==============================================================================
|
||||
"""
|
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User-defined API for MindRecord writer.
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Two API must be implemented,
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1. mindrecord_task_number()
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# Return number of parallel tasks. return 1 if no parallel
|
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2. mindrecord_dict_data(task_id)
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# Yield data for one task
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# task_id is 0..N-1, if N is return value of mindrecord_task_number()
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"""
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import argparse
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import pickle
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# ## Parse argument
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with open('mr_argument.pickle', 'rb') as mindrecord_argument_file_handle: # Do NOT change this line
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ARG_LIST = pickle.load(mindrecord_argument_file_handle) # Do NOT change this line
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parser = argparse.ArgumentParser(description='Mind record api template') # Do NOT change this line
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# ## Your arguments below
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# parser.add_argument(...)
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args = parser.parse_args(ARG_LIST) # Do NOT change this line
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print(args) # Do NOT change this line
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# ## Default mindrecord vars. Comment them unless default value has to be changed.
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# mindrecord_index_fields = ['label']
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# mindrecord_header_size = 1 << 24
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# mindrecord_page_size = 1 << 25
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|
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# define global vars here if necessary
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# ####### Your code below ##########
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mindrecord_schema = {"label": {"type": "int32"}}
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|
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def mindrecord_task_number():
|
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"""
|
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Get task size.
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Return:
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number of tasks
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"""
|
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return 1
|
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|
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def mindrecord_dict_data(task_id):
|
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"""
|
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Get data dict.
|
||||
|
||||
Yields:
|
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data (dict): data row which is dict.
|
||||
"""
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||||
print("task is {}".format(task_id))
|
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for i in range(256):
|
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data = {}
|
||||
data['label'] = i
|
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yield data
|
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@ -0,0 +1,152 @@
|
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# Copyright 2020 Huawei Technologies Co., Ltd
|
||||
#
|
||||
# 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.
|
||||
# ==============================================================================
|
||||
"""
|
||||
######################## write mindrecord example ########################
|
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Write mindrecord by data dictionary:
|
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python writer.py --mindrecord_script /YourScriptPath ...
|
||||
"""
|
||||
import argparse
|
||||
import os
|
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import pickle
|
||||
import time
|
||||
from importlib import import_module
|
||||
from multiprocessing import Pool
|
||||
|
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from mindspore.mindrecord import FileWriter
|
||||
|
||||
|
||||
def _exec_task(task_id, parallel_writer=True):
|
||||
"""
|
||||
Execute task with specified task id
|
||||
"""
|
||||
print("exec task {}, parallel: {} ...".format(task_id, parallel_writer))
|
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imagenet_iter = mindrecord_dict_data(task_id)
|
||||
batch_size = 2048
|
||||
transform_count = 0
|
||||
while True:
|
||||
data_list = []
|
||||
try:
|
||||
for _ in range(batch_size):
|
||||
data_list.append(imagenet_iter.__next__())
|
||||
transform_count += 1
|
||||
writer.write_raw_data(data_list, parallel_writer=parallel_writer)
|
||||
print("transformed {} record...".format(transform_count))
|
||||
except StopIteration:
|
||||
if data_list:
|
||||
writer.write_raw_data(data_list, parallel_writer=parallel_writer)
|
||||
print("transformed {} record...".format(transform_count))
|
||||
break
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
parser = argparse.ArgumentParser(description='Mind record writer')
|
||||
parser.add_argument('--mindrecord_script', type=str, default="template",
|
||||
help='path where script is saved')
|
||||
|
||||
parser.add_argument('--mindrecord_file', type=str, default="/tmp/mindrecord",
|
||||
help='written file name prefix')
|
||||
|
||||
parser.add_argument('--mindrecord_partitions', type=int, default=1,
|
||||
help='number of written files')
|
||||
|
||||
parser.add_argument('--mindrecord_workers', type=int, default=8,
|
||||
help='number of parallel workers')
|
||||
|
||||
args = parser.parse_known_args()
|
||||
|
||||
args, other_args = parser.parse_known_args()
|
||||
|
||||
print(args)
|
||||
print(other_args)
|
||||
|
||||
with open('mr_argument.pickle', 'wb') as file_handle:
|
||||
pickle.dump(other_args, file_handle)
|
||||
|
||||
try:
|
||||
mr_api = import_module(args.mindrecord_script + '.mr_api')
|
||||
except ModuleNotFoundError:
|
||||
raise RuntimeError("Unknown module path: {}".format(args.mindrecord_script + '.mr_api'))
|
||||
|
||||
num_tasks = mr_api.mindrecord_task_number()
|
||||
|
||||
print("Write mindrecord ...")
|
||||
|
||||
mindrecord_dict_data = mr_api.mindrecord_dict_data
|
||||
|
||||
# get number of files
|
||||
writer = FileWriter(args.mindrecord_file, args.mindrecord_partitions)
|
||||
|
||||
start_time = time.time()
|
||||
|
||||
# set the header size
|
||||
try:
|
||||
header_size = mr_api.mindrecord_header_size
|
||||
writer.set_header_size(header_size)
|
||||
except AttributeError:
|
||||
print("Default header size: {}".format(1 << 24))
|
||||
|
||||
# set the page size
|
||||
try:
|
||||
page_size = mr_api.mindrecord_page_size
|
||||
writer.set_page_size(page_size)
|
||||
except AttributeError:
|
||||
print("Default page size: {}".format(1 << 25))
|
||||
|
||||
# get schema
|
||||
try:
|
||||
mindrecord_schema = mr_api.mindrecord_schema
|
||||
except AttributeError:
|
||||
raise RuntimeError("mindrecord_schema is not defined in mr_api.py.")
|
||||
|
||||
# create the schema
|
||||
writer.add_schema(mindrecord_schema, "mindrecord_schema")
|
||||
|
||||
# add the index
|
||||
try:
|
||||
index_fields = mr_api.mindrecord_index_fields
|
||||
writer.add_index(index_fields)
|
||||
except AttributeError:
|
||||
print("Default index fields: all simple fields are indexes.")
|
||||
|
||||
writer.open_and_set_header()
|
||||
|
||||
task_list = list(range(num_tasks))
|
||||
|
||||
# set number of workers
|
||||
num_workers = args.mindrecord_workers
|
||||
|
||||
if num_tasks < 1:
|
||||
num_tasks = 1
|
||||
|
||||
if num_workers > num_tasks:
|
||||
num_workers = num_tasks
|
||||
|
||||
if os.name == 'nt':
|
||||
for window_task_id in task_list:
|
||||
_exec_task(window_task_id, False)
|
||||
elif num_tasks > 1:
|
||||
with Pool(num_workers) as p:
|
||||
p.map(_exec_task, task_list)
|
||||
else:
|
||||
_exec_task(0, False)
|
||||
|
||||
ret = writer.commit()
|
||||
|
||||
os.remove("{}".format("mr_argument.pickle"))
|
||||
|
||||
end_time = time.time()
|
||||
print("--------------------------------------------")
|
||||
print("END. Total time: {}".format(end_time - start_time))
|
||||
print("--------------------------------------------")
|
||||
@ -0,0 +1,63 @@
|
||||
# LeNet Example
|
||||
|
||||
## Description
|
||||
|
||||
Training LeNet with MNIST dataset in MindSpore.
|
||||
|
||||
This is the simple and basic tutorial for constructing a network in MindSpore.
|
||||
|
||||
## Requirements
|
||||
|
||||
- Install [MindSpore](https://www.mindspore.cn/install/en).
|
||||
|
||||
- Download the MNIST dataset at <http://yann.lecun.com/exdb/mnist/>. The directory structure is as follows:
|
||||
|
||||
```
|
||||
└─MNIST_Data
|
||||
├─test
|
||||
│ t10k-images.idx3-ubyte
|
||||
│ t10k-labels.idx1-ubyte
|
||||
│
|
||||
└─train
|
||||
train-images.idx3-ubyte
|
||||
train-labels.idx1-ubyte
|
||||
```
|
||||
|
||||
## Running the example
|
||||
|
||||
```python
|
||||
# train LeNet, hyperparameter setting in config.py
|
||||
python train.py --data_path MNIST_Data
|
||||
```
|
||||
|
||||
You can get loss with each step similar to this:
|
||||
|
||||
```bash
|
||||
epoch: 1 step: 1, loss is 2.3040335
|
||||
...
|
||||
epoch: 1 step: 1739, loss is 0.06952668
|
||||
epoch: 1 step: 1740, loss is 0.05038793
|
||||
epoch: 1 step: 1741, loss is 0.05018193
|
||||
...
|
||||
```
|
||||
|
||||
Then, test LeNet according to network model
|
||||
```python
|
||||
# test LeNet, after 1 epoch training, the accuracy is up to 96.5%
|
||||
python eval.py --data_path MNIST_Data --mode test --ckpt_path checkpoint_lenet-1_1875.ckpt
|
||||
```
|
||||
|
||||
## Note
|
||||
There are some optional arguments:
|
||||
|
||||
```bash
|
||||
-h, --help show this help message and exit
|
||||
--device_target {Ascend,GPU,CPU}
|
||||
device where the code will be implemented (default: Ascend)
|
||||
--data_path DATA_PATH
|
||||
path where the dataset is saved
|
||||
--dataset_sink_mode DATASET_SINK_MODE
|
||||
dataset_sink_mode is False or True
|
||||
```
|
||||
|
||||
You can run ```python train.py -h``` or ```python eval.py -h``` to get more information.
|
||||
@ -1 +1 @@
|
||||
Subproject commit 70bb745b459ff9a0e7fc1008d15fe4b510f03da7
|
||||
Subproject commit 43a715bc461fd70b7837051a2f47f0a1b19c5859
|
||||
@ -0,0 +1,40 @@
|
||||
# Copyright 2020 Huawei Technologies Co., Ltd
|
||||
#
|
||||
# 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.
|
||||
"""less_equal"""
|
||||
import _akg.tvm
|
||||
from _akg.ops.math import less_equal
|
||||
from _akg.topi.generic import schedule_elemwise
|
||||
|
||||
def LessEqual(x, y):
|
||||
"""LessEqual."""
|
||||
return less_equal.less_equal(x, y)
|
||||
|
||||
|
||||
def gpu_schedule_LessEqual(outs):
|
||||
"""
|
||||
GPU schedule for LessEqual.
|
||||
|
||||
Args:
|
||||
outs (tvm.tensor.Tensor): Outputs of compute.
|
||||
|
||||
Returns:
|
||||
sch (schedule.Schedule): The created schedule.
|
||||
"""
|
||||
device = 'cuda'
|
||||
ctx = _akg.tvm.context(device, 0)
|
||||
if not ctx.exist:
|
||||
raise SystemError("Skip because %s is not enabled" % device)
|
||||
with _akg.tvm.target.create(device):
|
||||
sch = schedule_elemwise(outs)
|
||||
return sch
|
||||
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