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adding cpu mode for crnn

This commit is contained in:
huangbo77 2021-08-25 11:50:05 +08:00
parent b58f8efa54
commit 032d2626cb
8 changed files with 197 additions and 17 deletions
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37
model_zoo/official/cv/crnn/README.md
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@ -64,7 +64,7 @@ We provide `convert_ic03.py`, `convert_iiit5k.py`, `convert_svt.py` as exmples f
## [Environment Requirements](#contents)
- Hardware
- Prepare hardware environment with Ascend processor or GPU.
- Prepare hardware environment with Ascend, GPU or CPU processor.
- Framework
- [MindSpore](https://gitee.com/mindspore/mindspore)
- For more information, please check the resources below：
@ -105,6 +105,16 @@ We provide `convert_ic03.py`, `convert_iiit5k.py`, `convert_svt.py` as exmples f
$ bash scripts/run_standalone_train.sh [DATASET_NAME] [DATASET_PATH] GPU
```
- Running on CPU
```shell
# standalone training example in CPU
$ bash scripts/run_standalone_train_cpu.sh [DATASET_NAME] [DATASET_PATH]
# evaluation example in CPU
$ bash scripts/run_eval_cpu.sh [DATASET_NAME] [DATASET_PATH] [CHECKPOINT_PATH]
```
DATASET_NAME is one of `ic03`, `ic13`, `svt`, `iiit5k`, `synth`.
For distributed training, a hccl configuration file with JSON format needs to be created in advance.
@ -142,9 +152,11 @@ crnn
├── convert_svt.py # Convert the original SVT dataset
├── requirements.txt # Requirements for this dataset
├── scripts
│ ├── run_distribute_train.sh # Launch distributed training in Ascend(8 pcs)
│ ├── run_eval.sh # Launch evaluation
│ └── run_standalone_train.sh # Launch standalone training(1 pcs)
│ ├── run_standalone_train_cpu.sh # Launch standalone training in CPU
│ ├── run_eval_cpu.sh # Launch evaluation in CPU
│ ├── run_distribute_train.sh # Launch distributed training in Ascend or GPU(8 pcs)
│ ├── run_eval.sh # Launch evaluation in Ascend or GPU
│ └── run_standalone_train.sh # Launch standalone training in Ascend or GPU(1 pcs)
├── src
│ ├── model_utils
│ ├── config.py # Parameter config
@ -172,11 +184,14 @@ crnn
#### Training Script Parameters
```shell
# distributed training
# distributed training in Ascend or GPU
Usage: bash scripts/run_distribute_train.sh [DATASET_NAME] [DATASET_PATH] [PLATFORM] [RANK_TABLE_FILE](if Ascend)
# standalone training
# standalone training in Ascend or GPU
Usage: bash scripts/run_standalone_train.sh [DATASET_NAME] [DATASET_PATH] [PLATFORM]
# standalone training in CPU
Usage: bash scripts/run_standalone_train_cpu.sh [DATASET_NAME] [DATASET_PATH]
```
#### Parameters Configuration
@ -220,6 +235,12 @@ max_text_length": 23, # max number of digits in each
bash scripts/run_standalone_train.sh [DATASET_NAME] [DATASET_PATH] [PLATFORM](optional)
```
- Or run `run_standalone_train_cpu.sh` for non-distributed training of CRNN model in CPU.
``` bash
bash scripts/run_standalone_train_cpu.sh [DATASET_NAME] [DATASET_PATH]
```
#### [Distributed Training](#contents)
- Run `run_distribute_train.sh` for distributed training of CRNN model on Ascend or GPU
@ -296,6 +317,8 @@ Epoch time: 2743.688s, per step time: 0.097s
``` bash
bash scripts/run_eval.sh [DATASET_NAME] [DATASET_PATH] [CHECKPOINT_PATH] [PLATFORM](optional)
bash scripts/run_eval_cpu.sh [DATASET_NAME] [DATASET_PATH] [CHECKPOINT_PATH]
```
Check the `eval/log.txt` and you will get outputs as following:
@ -313,7 +336,7 @@ You can add `run_eval` to start shell and set it True.You need also add `eval_da
### [Export MindIR](#contents)
```shell
python export.py --ckpt_file [CKPT_PATH] --file_name [FILE_NAME] --file_format [FILE_FORMAT]
python export.py --ckpt_file [CKPT_PATH] --file_name [FILE_NAME] --file_format [FILE_FORMAT] --device_target [DEVICE_TARGET] --model_version [MODEL_VERSION](required for cpu)
```
The ckpt_file parameter is required,

4
model_zoo/official/cv/crnn/convert_iiit5k.py
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@ -1,4 +1,4 @@
# Copyright 2020 Huawei Technologies Co., Ltd
# Copyright 2020-21 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.
@ -46,7 +46,7 @@ def mat_to_list(mat_file):
for elem in testdata:
img_name = elem[0]
label = elem[1]
ann = img_name+',' +label
ann = img_name[0] + ',' + label[0]
ann_output.append(ann)
return ann_output

5
model_zoo/official/cv/crnn/eval.py
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@ -1,4 +1,4 @@
# Copyright 2020 Huawei Technologies Co., Ltd
# Copyright 2020-21 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.
@ -51,7 +51,7 @@ def crnn_eval():
loss = CTCLoss(max_sequence_length=config.num_step,
max_label_length=max_text_length,
batch_size=config.batch_size)
net = crnn(config, full_precision=config.device_target == 'GPU')
net = crnn(config, full_precision=config.device_target != 'Ascend')
# load checkpoint
param_dict = load_checkpoint(config.checkpoint_path)
load_param_into_net(net, param_dict)
@ -62,6 +62,5 @@ def crnn_eval():
res = model.eval(dataset, dataset_sink_mode=config.device_target == 'Ascend')
print("result:", res, flush=True)
if __name__ == '__main__':
crnn_eval()

1
model_zoo/official/cv/crnn/export.py
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@ -23,6 +23,7 @@ from src.model_utils.moxing_adapter import moxing_wrapper
from src.model_utils.config import config
from src.model_utils.device_adapter import get_device_id
context.set_context(mode=context.GRAPH_MODE, device_target=config.device_target, save_graphs=False)
def modelarts_pre_process():
config.file_name = os.path.join(config.output_path, config.file_name)

58
model_zoo/official/cv/crnn/scripts/run_eval_cpu.sh Normal file
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@ -0,0 +1,58 @@
#!/bin/bash
# Copyright 2021 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.
# ============================================================================
if [ $# != 3 ]; then
echo "Usage: bash run_eval_cpu.sh [DATASET_NAME] [DATASET_PATH] [CHECKPOINT_PATH]"
exit 1
fi
get_real_path() {
if [ "${1:0:1}" == "/" ]; then
echo "$1"
else
echo "$(realpath -m $PWD/$1)"
fi
}
DATASET_NAME=$1
PATH1=$(get_real_path $2)
PATH2=$(get_real_path $3)
if [ ! -d $PATH1 ]; then
echo "error: DATASET_PATH=$PATH1 is not a directory"
exit 1
fi
if [ ! -f $PATH2 ]; then
echo "error: CHECKPOINT_PATH=$PATH2 is not a file"
exit 1
fi
if [ -d "eval" ]; then
rm -rf ./eval
fi
mkdir ./eval
cp ./*.py ./eval
cp -r ./src ./eval
cp ./*yaml ./eval
cd ./eval || exit
env >env.log
python eval.py --eval_dataset=$DATASET_NAME \
--eval_dataset_path=$PATH1 \
--checkpoint_path=$PATH2 \
--model_version="V2" \
--device_target=CPU > log.txt 2>&1 &
cd ..

54
model_zoo/official/cv/crnn/scripts/run_standalone_train_cpu.sh Normal file
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@ -0,0 +1,54 @@
#!/bin/bash
# Copyright 2021 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.
# ============================================================================
if [ $# != 2 ]; then
echo "Usage: bash scripts/run_standalone_train_cpu.sh [DATASET_NAME] [DATASET_PATH]"
exit 1
fi
get_real_path() {
if [ "${1:0:1}" == "/" ]; then
echo "$1"
else
echo "$(realpath -m $PWD/$1)"
fi
}
DATASET_NAME=$1
PATH1=$(get_real_path $2)
if [ ! -d $PATH1 ]; then
echo "error: DATASET_PATH=$PATH1 is not a directory"
exit 1
fi
if [ -d "train" ]; then
rm -rf ./train
fi
WORKDIR=./train_cpu
rm -rf $WORKDIR
mkdir $WORKDIR
cp ./*.py $WORKDIR
cp -r ./src $WORKDIR
cp ./*yaml $WORKDIR
cd $WORKDIR || exit
env >env.log
python train.py --train_dataset=$DATASET_NAME \
--train_dataset_path=$PATH1 \
--device_target=CPU \
--model_version="V2" > log.txt 2>&1 &
cd ..

49
model_zoo/official/cv/crnn/src/crnn.py
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@ -1,4 +1,4 @@
# Copyright 2020 Huawei Technologies Co., Ltd
# Copyright 2020-21 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.
@ -14,14 +14,17 @@
# ============================================================================
"""Warpctc network definition."""
import math
import numpy as np
import mindspore.nn as nn
from mindspore import Tensor
from mindspore import context
from mindspore.common import dtype as mstype
from mindspore.ops import operations as P
from mindspore.ops import functional as F
from mindspore.common import Parameter
from mindspore.common.initializer import TruncatedNormal
from mindspore.common.initializer import initializer
def _bn(channel):
return nn.BatchNorm2d(channel, eps=1e-4, momentum=0.9, gamma_init=1, beta_init=0, moving_mean_init=0,
@ -42,6 +45,46 @@ class Conv(nn.Cell):
out = self.Relu(out)
return out
class LSTMCPU(nn.Cell):
"""Stacked LSTM (Long Short-Term Memory) layers for CPU"""
def __init__(self,
input_size,
hidden_size,
num_layers=1,
has_bias=True,
dropout=0,
bidirectional=False):
super(LSTMCPU, self).__init__()
self.transpose = P.Transpose()
self.num_layers = num_layers
self.bidirectional = bidirectional
self.dropout = dropout
self.lstm = P.LSTM(input_size=input_size,
hidden_size=hidden_size,
num_layers=num_layers,
has_bias=has_bias,
bidirectional=bidirectional,
dropout=float(dropout))
weight_size = 0
gate_size = 4 * hidden_size
stdv = 1 / math.sqrt(hidden_size)
num_directions = 2 if bidirectional else 1
for layer in range(num_layers):
input_layer_size = input_size if layer == 0 else hidden_size * num_directions
increment_size = gate_size * input_layer_size
increment_size += gate_size * hidden_size
if has_bias:
increment_size += gate_size
weight_size += increment_size * num_directions
w_np = np.random.uniform(-stdv, stdv, (weight_size, 1, 1)).astype(np.float32)
self.weight = Parameter(initializer(Tensor(w_np), [weight_size, 1, 1]), name='weight')
def construct(self, x, hx):
h, c = hx
x, h, c, _, _ = self.lstm(x, h, c, self.weight)
return x, (h, c)
class VGG(nn.Cell):
"""VGG Network structure"""
def __init__(self, is_training=True):
@ -76,7 +119,9 @@ class BidirectionalLSTM(nn.Cell):
def __init__(self, nIn, nHidden, nOut, batch_size):
super(BidirectionalLSTM, self).__init__()
if context.get_context("device_target") == "CPU":
self.rnn = LSTMCPU(nIn, nHidden, bidirectional=True)
else:
self.rnn = nn.LSTM(nIn, nHidden, bidirectional=True)
self.embedding = nn.Dense(in_channels=nHidden * 2, out_channels=nOut)
self.h0 = Tensor(np.zeros([1 * 2, batch_size, nHidden]).astype(np.float32))

4
model_zoo/official/cv/crnn/train.py
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@ -1,4 +1,4 @@
# Copyright 2020 Huawei Technologies Co., Ltd
# Copyright 2020-21 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.
@ -88,7 +88,7 @@ def train():
loss = CTCLoss(max_sequence_length=config.num_step,
max_label_length=max_text_length,
batch_size=config.batch_size)
net = crnn(config, full_precision=config.device_target == 'GPU')
net = crnn(config, full_precision=config.device_target != 'Ascend')
opt = nn.SGD(params=net.trainable_params(), learning_rate=lr, momentum=config.momentum, nesterov=config.nesterov)
net_with_loss = WithLossCell(net, loss)