forked from mindspore-Ecosystem/mindspore
!21501 bug fix for protonet
Merge pull request !21501 from wukesong/wl_master_protonet
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Gitee
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7c2fcfe1ee
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@ -29,7 +29,12 @@ Proto-Net contains 2 parts named Encoder and Relation. The former one has 4 conv
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Note that you can run the scripts based on the dataset mentioned in original paper or widely used in relevant domain/network architecture. In the following sections, we will introduce how to run the scripts using the related dataset below.
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Dataset used: [omniglot](https://github.com/brendenlake/omniglot)
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The dataset omniglot can be obtained from (https://github.com/orobix/Prototypical-Networks-for-Few-shot-Learning-PyTorch/blob/master/). You can obtain the dataset after running the scripts.
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```bash
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cd src
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python train.py
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```
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- Dataset size 4.02M，32462 28*28 in 1622 classes
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- Train 1,200 classes
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@ -39,7 +44,7 @@ Dataset used: [omniglot](https://github.com/brendenlake/omniglot)
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- The directory structure is as follows:
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```text
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```shell
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└─Data
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├─raw
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├─spilts
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@ -67,13 +72,13 @@ Dataset used: [omniglot](https://github.com/brendenlake/omniglot)
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After installing MindSpore via the official website, you can start training and evaluation as follows:
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```shell
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# enter script dir, train ProtoNet in standalone
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sh run_standalone_train_ascend.sh dataset 1 20 20
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# enter script dir, train ProtoNet in distribution
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sh run_distribution_ascend.sh dataset rank_table dataset 20
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```python
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# enter script dir, train ProtoNet
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sh run_standalone_train_ascend.sh "../dataset" 1 60 500
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# enter script dir, evaluate ProtoNet
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sh run_standalone_eval_ascend.sh dataset best.ckpt 1 20
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sh run_standalone_eval_ascend.sh "../dataset" "./output/best_ck.ckpt" 1 5
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# enter script dir, train ProtoNet distributed
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sh run_distribution_ascend.sh "./rank_table.json" "../dataset" 60 500
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```
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## [Script and Sample Code](#contents)
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@ -120,8 +125,7 @@ Major parameters in train.py and config.py as follows:
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### Training
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```bash
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# enter script dir, train ProtoNet in standalone
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sh run_standalone_train_ascend.sh dataset 1 20 20
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sh run_standalone_train_ascend.sh "../dataset" 1 60 500
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```
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The model checkpoint will be saved in the current directory.
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@ -133,11 +137,11 @@ The model checkpoint will be saved in the current directory.
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Before running the command below, please check the checkpoint path used for evaluation.
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```bash
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# enter script dir, evaluate ProtoNet
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sh run_standalone_eval_ascend.sh dataset best.ckpt 1 20
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sh run_standalone_eval_ascend.sh "../dataset" "./output/best_ck.ckpt" 1 5
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```
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```text
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```shell
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Test Acc: 0.9954400658607483 Loss: 0.02102319709956646
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```
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@ -149,9 +153,9 @@ Test Acc: 0.9954400658607483 Loss: 0.02102319709956646
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| Parameters | ProtoNet |
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| -------------------------- | ---------------------------------------------------------- |
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| Resource | CentOs 8.2; Ascend 910; CPU 2.60GHz; 192cores; Memory 755G |
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| Resource | CentOs 8.2; Ascend 910 ; CPU 2.60GHz，192cores；Memory 755G |
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| uploaded Date | 03/26/2021 (month/day/year) |
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| MindSpore Version | 1.2.0 |
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| MindSpore Version | 1.1.1 |
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| Dataset | OMNIGLOT |
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| Training Parameters | episode=500, class_num = 5, lr=0.001, classes_per_it_tr=60, num_support_tr=5, num_query_tr=5, classes_per_it_val=20, num_support_val=5, num_query_val=15 |
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| Optimizer | Adam |
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@ -161,7 +165,7 @@ Test Acc: 0.9954400658607483 Loss: 0.02102319709956646
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| Speed | 215 ms/step |
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| Total time | 3 h 23m (8p) |
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| Checkpoint for Fine tuning | 440 KB (.ckpt file) |
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| Scripts | https://gitee.com/mindspore/mindspore/tree/master/model_zoo/research/cv/ProtoNet |
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| Scripts | https://gitee.com/mindspore/mindspore/tree/r1.1/model_zoo/research/cv/protonet |
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# [ModelZoo Homepage](#contents)
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@ -15,14 +15,13 @@
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"""
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ProtoNet evaluation script.
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"""
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import os
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import numpy as np
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from mindspore import dataset as ds
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from mindspore import load_checkpoint
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import mindspore.context as context
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from src.protonet import ProtoNet
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from src.parser_util import get_parser
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from src.PrototypicalLoss import PrototypicalLoss
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import numpy as np
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from model_init import init_dataloader
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from train import WithLossCell
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@ -67,5 +66,5 @@ if __name__ == '__main__':
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options.classes_per_it_val, is_train=False)
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Net = WithLossCell(Net, loss_fn)
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val_dataloader = init_dataloader(options, 'val', datapath)
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load_checkpoint(os.path.join(ckptpath, 'best_ck.ckpt'), net=Net)
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load_checkpoint(ckptpath, net=Net)
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test(val_dataloader, Net)
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@ -16,7 +16,7 @@
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# an simple tutorial as follows, more parameters can be setting
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if [ $# != 4 ]
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then
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echo "Usage: sh run_distribution_ascend.sh [RANK_TABLE_FILE] [DATA_PATH] [TRAIN_CLASS]"
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echo "Usage: sh run_distribution_ascend.sh [RANK_TABLE_FILE] [DATA_PATH] [TRAIN_CLASS] [EPOCHS]"
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exit 1
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fi
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@ -33,6 +33,7 @@ RANK_TABLE_FILE=$(realpath $1)
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export RANK_TABLE_FILE
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export DATA_PATH=$2
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export TRAIN_CLASS=$3
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export EPOCHS=$4
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echo "RANK_TABLE_FILE=${RANK_TABLE_FILE}"
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export SERVER_ID=0
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@ -43,13 +44,16 @@ do
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export RANK_ID=$((rank_start + i))
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rm -rf ./train_parallel$i
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mkdir ./train_parallel$i
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cp -r ./src ./train_parallel$i
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cp ./train.py ./train_parallel$i
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cp -r ../src ./train_parallel$i
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cp ../train.py ./train_parallel$i
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cp ../model_init.py ./train_parallel$i
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echo "start training for rank $RANK_ID, device $DEVICE_ID"
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cd ./train_parallel$i ||exit
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env > env.log
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python train.py --data_path=$DATA_PATH \
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python train.py --dataset_root=$DATA_PATH \
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--device_id=$DEVICE_ID --device_target="Ascend" \
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--classes_per_it_tr=$TRAIN_CLASS > log 2>&1 &
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--classes_per_it_tr=$TRAIN_CLASS\
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--experiment_root=./output\
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--epochs=$EPOCHS > log 2>&1 &
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cd ..
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done
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@ -49,7 +49,7 @@ def get_parser():
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parser.add_argument('-exp', '--experiment_root',
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type=str,
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help='root where to store models, losses and accuracies',
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default='..' + os.sep + 'output')
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default='.' + os.sep + 'output')
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parser.add_argument('-nep', '--epochs',
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type=int,
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