- InceptionV4 Description
- Model Architecture
- Dataset
- Features
- Environment Requirements
- Script Description
- Model Description
- Description of Random Situation
- ModelZoo Homepage
Inception-v4 is a convolutional neural network architecture that builds on previous iterations of the Inception family by simplifying the architecture and using more inception modules than Inception-v3. This idea was proposed in the paper Inception-v4, Inception-ResNet and the Impact of Residual Connections on Learning, published in 2016.
Paper Christian Szegedy, Sergey Ioffe, Vincent Vanhoucke, Alex Alemi. Computer Vision and Pattern Recognition[J]. 2016.
The overall network architecture of InceptionV4 is show below:
Dataset used can refer to paper.
- Dataset: ImageNet2012
- Dataset size: 125G, 1250k colorful images in 1000 classes
- Train: 120G, 1200k images
- Test: 5G, 50k images
- Data format: RGB images.
- Note: Data will be processed in src/dataset.py
- Data path: http://www.image-net.org/download-images
The mixed precision training method accelerates the deep learning neural network training process by using both the single-precision and half-precision data formats, and maintains the network precision achieved by the single-precision training at the same time. Mixed precision training can accelerate the computation process, reduce memory usage, and enable a larger model or batch size to be trained on specific hardware.
For FP16 operators, if the input data type is FP32, the backend of MindSpore will automatically handle it with reduced precision. Users could check the reduced-precision operators by enabling INFO log and then searching ‘reduce precision’.
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Hardware(Ascend/GPU)
- Prepare hardware environment with Ascend processor.
- or prepare GPU processor.
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Framework
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For more information, please check the resources below:
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Running on ModelArts
# Train 8p with Ascend # (1) Perform a or b. # a. Set "enable_modelarts=True" on default_config.yaml file. # Set "distribute=True" on default_config.yaml file. # Set "need_modelarts_dataset_unzip=True" on default_config.yaml file. # Set "modelarts_dataset_unzip_name='ImageNet_Original'" on default_config.yaml file. # Set "lr_init=0.00004" on default_config.yaml file. # Set "dataset_path='/cache/data'" on default_config.yaml file. # Set "epoch_size=250" on default_config.yaml file. # (optional)Set "checkpoint_url='s3://dir_to_your_pretrained/'" on default_config.yaml file. # Set other parameters on default_config.yaml file you need. # b. Add "enable_modelarts=True" on the website UI interface. # Add "need_modelarts_dataset_unzip=True" on the website UI interface. # Add "modelarts_dataset_unzip_name='ImageNet_Original'" on the website UI interface. # Add "distribute=True" on the website UI interface. # Add "lr_init=0.00004" on the website UI interface. # Add "dataset_path=/cache/data" on the website UI interface. # Add "epoch_size=250" on the website UI interface. # (optional)Add "checkpoint_url='s3://dir_to_your_pretrained/'" on the website UI interface. # Add other parameters on the website UI interface. # (2) Prepare model code # (3) Upload or copy your pretrained model to S3 bucket if you want to finetune. # (4) Perform a or b. (suggested option a) # a. First, zip MindRecord dataset to one zip file. # Second, upload your zip dataset to S3 bucket.(you could also upload the origin mindrecord dataset, but it can be so slow.) # b. Upload the original coco dataset to S3 bucket. # (Data set conversion occurs during training process and costs a lot of time. it happens every time you train.) # (5) Set the code directory to "/path/inceptionv4" on the website UI interface. # (6) Set the startup file to "train.py" on the website UI interface. # (7) Set the "Dataset path" and "Output file path" and "Job log path" to your path on the website UI interface. # (8) Create your job. # # Train 1p with Ascend # (1) Perform a or b. # a. Set "enable_modelarts=True" on default_config.yaml file. # Set "need_modelarts_dataset_unzip=True" on default_config.yaml file. # Set "modelarts_dataset_unzip_name='ImageNet_Original'" on default_config.yaml file. # Set "dataset_path='/cache/data'" on default_config.yaml file. # Set "epoch_size=250" on default_config.yaml file. # (optional)Set "checkpoint_url='s3://dir_to_your_pretrained/'" on default_config.yaml file. # Set other parameters on default_config.yaml file you need. # b. Add "enable_modelarts=True" on the website UI interface. # Add "need_modelarts_dataset_unzip=True" on the website UI interface. # Add "modelarts_dataset_unzip_name='ImageNet_Original'" on the website UI interface. # Add "dataset_path='/cache/data'" on the website UI interface. # Add "epoch_size=250" on the website UI interface. # (optional)Add "checkpoint_url='s3://dir_to_your_pretrained/'" on the website UI interface. # Add other parameters on the website UI interface. # (2) Prepare model code # (3) Upload or copy your pretrained model to S3 bucket if you want to finetune. # (4) Perform a or b. (suggested option a) # a. zip MindRecord dataset to one zip file. # Second, upload your zip dataset to S3 bucket.(you could also upload the origin mindrecord dataset, but it can be so slow.) # b. Upload the original coco dataset to S3 bucket. # (Data set conversion occurs during training process and costs a lot of time. it happens every time you train.) # (5) Set the code directory to "/path/inceptionv4" on the website UI interface. # (6) Set the startup file to "train.py" on the website UI interface. # (7) Set the "Dataset path" and "Output file path" and "Job log path" to your path on the website UI interface. # (8) Create your job. # # Eval 1p with Ascend # (1) Perform a or b. # a. Set "enable_modelarts=True" on default_config.yaml file. # Set "need_modelarts_dataset_unzip=True" on default_config.yaml file. # Set "modelarts_dataset_unzip_name='ImageNet_Original'" on default_config.yaml file. # Set "checkpoint_url='s3://dir_to_your_trained_model/'" on base_config.yaml file. # Set "checkpoint_path='./inceptionv4/inceptionv4-train-250_1251.ckpt'" on default_config.yaml file. # Set "dataset_path='/cache/data'" on default_config.yaml file. # Set other parameters on default_config.yaml file you need. # b. Add "enable_modelarts=True" on the website UI interface. # Add "need_modelarts_dataset_unzip=True" on the website UI interface. # Add "modelarts_dataset_unzip_name='ImageNet_Original'" on the website UI interface. # Add "checkpoint_url='s3://dir_to_your_trained_model/'" on the website UI interface. # Add "checkpoint_path='./inceptionv4/inceptionv4-train-250_1251.ckpt'" on the website UI interface. # Add "dataset_path='/cache/data'" on the website UI interface. # (2) Prepare model code # Add other parameters on the website UI interface. # (3) Upload or copy your trained model to S3 bucket. # (4) Perform a or b. (suggested option a) # a. First, zip MindRecord dataset to one zip file. # Second, upload your zip dataset to S3 bucket.(you could also upload the origin mindrecord dataset, but it can be so slow.) # b. Upload the original coco dataset to S3 bucket. # (Data set conversion occurs during training process and costs a lot of time. it happens every time you train.) # (5) Set the code directory to "/path/inceptionv4" on the website UI interface. # (6) Set the startup file to "eval.py" on the website UI interface. # (7) Set the "Dataset path" and "Output file path" and "Job log path" to your path on the website UI interface. # (8) Create your job.
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Export on ModelArts (If you want to run in modelarts, please check the official documentation of modelarts, and you can start evaluating as follows)
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Export s8 multiscale and flip with voc val dataset on modelarts, evaluating steps are as follows:
# (1) Perform a or b. # a. Set "enable_modelarts=True" on base_config.yaml file. # Set "file_name='inceptionv4'" on base_config.yaml file. # Set "file_format='MINDIR'" on base_config.yaml file. # Set "checkpoint_url='/The path of checkpoint in S3/'" on beta_config.yaml file. # Set "ckpt_file='/cache/checkpoint_path/model.ckpt'" on base_config.yaml file. # Set other parameters on base_config.yaml file you need. # b. Add "enable_modelarts=True" on the website UI interface. # Add "file_name='inceptionv4'" on the website UI interface. # Add "file_format='MINDIR'" on the website UI interface. # Add "checkpoint_url='/The path of checkpoint in S3/'" on the website UI interface. # Add "ckpt_file='/cache/checkpoint_path/model.ckpt'" on the website UI interface. # Add other parameters on the website UI interface. # (2) Upload or copy your trained model to S3 bucket. # (3) Set the code directory to "/path/inceptionv4" on the website UI interface. # (4) Set the startup file to "export.py" on the website UI interface. # (5) Set the "Dataset path" and "Output file path" and "Job log path" to your path on the website UI interface. # (6) Create your job.
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└─Inception-v4
├─README.md
├─ascend310_infer # application for 310 inference
├─scripts
├─run_distribute_train_gpu.sh # launch distributed training with gpu platform(8p)
├─run_eval_gpu.sh # launch evaluating with gpu platform
├─run_eval_cpu.sh # launch evaluating with cpu platform
├─run_standalone_train_cpu.sh # launch standalone training with cpu platform(1p)
├─run_standalone_train_ascend.sh # launch standalone training with ascend platform(1p)
├─run_distribute_train_ascend.sh # launch distributed training with ascend platform(8p)
├─run_infer_310.sh # shell script for 310 inference
├─run_onnx_eval.sh # shell script for onnx evaluating
└─run_eval_ascend.sh # launch evaluating with ascend platform
├─src
├─dataset.py # data preprocessing
├─inceptionv4.py # network definition
├─callback.py # eval callback function
└─model_utils
├─config.py # Processing configuration parameters
├─device_adapter.py # Get cloud ID
├─local_adapter.py # Get local ID
└─moxing_adapter.py # Parameter processing
├─default_config.yaml # Training parameter profile(ascend)
├─default_config_cpu.yaml # Training parameter profile(cpu)
├─default_config_gpu.yaml # Training parameter profile(gpu)
├─eval.py # eval net
├─eval_onnx.py # onnx eval
├─export.py # export checkpoint, surpport .onnx, .air, .mindir convert
├─postprogress.py # post process for 310 inference
└─train.py # train net
Major parameters in train.py and config.py are:
'is_save_on_master' # save checkpoint only on master device
'batch_size' # input batchsize
'epoch_size' # total epoch numbers
'num_classes' # dataset class numbers
'work_nums' # number of workers to read data
'loss_scale' # loss scale
'smooth_factor' # label smoothing factor
'weight_decay' # weight decay
'momentum' # momentum
'amp_level' # precision training, Supports [O0, O2, O3]
'decay' # decay used in optimize function
'epsilon' # epsilon used in iptimize function
'keep_checkpoint_max' # max numbers to keep checkpoints
'save_checkpoint_epochs' # save checkpoints per n epoch
'lr_init' # init leaning rate
'lr_end' # end of learning rate
'lr_max' # max bound of learning rate
'warmup_epochs' # warmup epoch numbers
'start_epoch' # number of start epoch range[1, epoch_size]
You can start training using python or shell scripts. The usage of shell scripts as follows:
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Ascend:
ds_type:imagenet or ds_type:cifar10 Take training cifar10 as an example, the ds_type parameter is set to cifar10
# distribute training example(8p) bash scripts/run_distribute_train_ascend.sh [RANK_TABLE_FILE] [DATA_DIR] # example: bash scripts/run_distribute_train_ascend.sh ~/hccl_8p.json /home/DataSet/cifar10/ # standalone training bash scripts/run_standalone_train_ascend.sh [DEVICE_ID] [DATA_DIR] # example: bash scripts/run_standalone_train_ascend.sh 0 /home/DataSet/cifar10/
Notes: RANK_TABLE_FILE can refer to Link , and the device_ip can be got as Link. For large models like InceptionV4, it's better to export an external environment variable
export HCCL_CONNECT_TIMEOUT=600
to extend hccl connection checking time from the default 120 seconds to 600 seconds. Otherwise, the connection could be timeout since compiling time increases with the growth of model size.This is processor cores binding operation regarding the
device_num
and total processor numbers. If you are not expect to do it, remove the operationstaskset
inscripts/run_distribute_train.sh
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GPU:
# distribute training example(8p) bash scripts/run_distribute_train_gpu.sh DATA_PATH
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CPU:
# standalone training example with shell bash scripts/run_standalone_train_cpu.sh DATA_PATH
# training example
shell:
Ascend:
# distribute training example(8p)
bash scripts/run_distribute_train_ascend.sh [RANK_TABLE_FILE] [DATA_DIR]
# example: bash scripts/run_distribute_train_ascend.sh ~/hccl_8p.json /home/DataSet/cifar10/
# standalone training
bash scripts/run_standalone_train_ascend.sh [DEVICE_ID] [DATA_DIR]
# example: bash scripts/run_standalone_train_ascend.sh 0 /home/DataSet/cifar10/
GPU:
# distribute training example(8p)
bash scripts/run_distribute_train_gpu.sh DATA_PATH
CPU:
# standalone training example with shell
bash scripts/run_standalone_train_cpu.sh DATA_PATH
Training result will be stored in the example path. Checkpoints will be stored at ckpt_path
by default, and training log will be redirected to ./log.txt
like following.
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Ascend
epoch: 1 step: 1251, loss is 5.4833196 Epoch time: 520274.060, per step time: 415.887 epoch: 2 step: 1251, loss is 4.093194 Epoch time: 288520.628, per step time: 230.632 epoch: 3 step: 1251, loss is 3.6242008 Epoch time: 288507.506, per step time: 230.622
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GPU
epoch: 1 step: 1251, loss is 6.49775 Epoch time: 1487493.604, per step time: 1189.044 epoch: 2 step: 1251, loss is 5.6884665 Epoch time: 1421838.433, per step time: 1136.561 epoch: 3 step: 1251, loss is 5.5168786 Epoch time: 1423009.501, per step time: 1137.498
You can start training using python or shell scripts. The usage of shell scripts as follows:
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Ascend:
bash scripts/run_eval_ascend.sh [DEVICE_ID] [DATA_DIR] [CHECKPOINT_PATH] # example: bash scripts/run_eval_ascend.sh 0 /home/DataSet/cifar10/ /home/model/inceptionv4/ckpt/inceptionv4-train-250_1251
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GPU
bash scripts/run_eval_gpu.sh DATA_DIR CHECKPOINT_PATH
# eval example
shell:
Ascend:
bash scripts/run_eval_ascend.sh [DEVICE_ID] [DATA_DIR] [CHECKPOINT_PATH]
GPU:
bash scripts/run_eval_gpu.sh DATA_DIR CHECKPOINT_PATH
checkpoint can be produced in training process.
Evaluation result will be stored in the example path, you can find result like the following in eval.log
.
- Ascend
metric: {'Loss': 0.9849, 'Top1-Acc':0.7985, 'Top5-Acc':0.9460}
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GPU(8p)
metric: {'Loss': 0.8144, 'Top1-Acc': 0.8009, 'Top5-Acc': 0.9457}
python export.py --config_path [CONFIG_FILE] --ckpt_file [CKPT_PATH] --device_target [DEVICE_TARGET] --file_format[EXPORT_FORMAT]
EXPORT_FORMAT
should be in ["AIR", "MINDIR", "ONNX"]
Before inference, please refer to MindSpore Inference with C++ Deployment Guide to set environment variables.
Before performing inference, the model file must be exported by export script on the Ascend910 environment.
# Ascend310 inference
bash run_infer_310.sh [MINDIR_PATH] [DATA_PATH] [ANN_FILE] [DEVICE_ID]
-NOTE:Ascend310 inference use Imagenet dataset . The label of the image is the number of folder which is started from 0 after sorting. This file can be converted by script from models/utils/cpp_infer/imgid2label.py
Before performing inference, the model file must be exported by export script.
# ONNX inference
bash scripts/run_onnx_eval.sh [DATA_PATH] [DATASET_TYPE] [DEVICE_TYPE] [FILE_TYPE] [ONNX_PATH]
# example: bash scripts/run_onnx_eval.sh /path/to/dataset imagenet GPU ONNX /path/to/inceptionv4.onnx
-NOTE:ONNX inference use Imagenet dataset .
Inference result is saved in current path, you can find result like this in acc.log file.
accuracy:80.044
Parameters | Ascend | GPU |
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Model Version | InceptionV4 | InceptionV4 |
Resource | Ascend 910; cpu 2.60GHz, 192cores; memory 755G; OS Euler2.8 | NV SMX2 V100-32G |
uploaded Date | 11/04/2020 | 03/05/2021 |
MindSpore Version | 1.0.0 | 1.0.0 |
Dataset | 1200k images | 1200K images |
Batch_size | 128 | 128 |
Training Parameters | src/model_utils/default_config.yaml (Ascend) | src/model_utils/default_config.yaml (GPU) |
Optimizer | RMSProp | RMSProp |
Loss Function | SoftmaxCrossEntropyWithLogits | SoftmaxCrossEntropyWithLogits |
Outputs | probability | probability |
Loss | 0.98486 | 0.8144 |
Accuracy (8p) | ACC1[79.85%] ACC5[94.60%] | ACC1[80.09%] ACC5[94.57%] |
Total time (8p) | 20h | 95h |
Params (M) | 153M | 153M |
Checkpoint for Fine tuning | 2135M | 489M |
Scripts | inceptionv4 script | inceptionv4 script |
Parameters | Ascend | GPU |
---|---|---|
Model Version | InceptionV4 | InceptionV4 |
Resource | Ascend 910; cpu 2.60GHz, 192cores; memory 755G; OS Euler2.8 | NV SMX2 V100-32G |
Uploaded Date | 11/04/2020 | 03/05/2021 |
MindSpore Version | 1.0.0 | 1.0.0 |
Dataset | 50k images | 50K images |
Batch_size | 128 | 128 |
Outputs | probability | probability |
Accuracy | ACC1[79.85%] ACC5[94.60%] | ACC1[80.09%] ACC5[94.57%] |
Total time | 2mins | 2mins |
Model for inference | 2135M (.ckpt file) | 489M (.ckpt file) |
Ascend | train performance |
---|---|
1p | 556 img/s |
Ascend | train performance |
---|---|
8p | 4430 img/s |
GPU | train performance |
---|---|
8p | 906 img/s |
In dataset.py, we set the seed inside “create_dataset" function. We also use random seed in train.py.
Please check the official homepage.