forked from mindspore-Ecosystem/mindspore
support xception lr slice and fix usability problems
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@ -81,9 +81,10 @@ For FP16 operators, if the input data type is FP32, the backend of MindSpore wil
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├─config.py # parameter configuration
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├─dataset.py # data preprocessing
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├─Xception.py # network definition
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├─CrossEntropySmooth.py # Customized CrossEntropy loss function
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├─loss.py # Customized CrossEntropy loss function
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└─lr_generator.py # learning rate generator
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├─train.py # train net
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├─export.py # export net
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└─eval.py # eval net
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```
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@ -110,7 +111,6 @@ Major parameters in train.py and config.py are:
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'lr_init': 0.00004 # initiate learning rate
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'lr_max': 0.4 # max bound of learning rate
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'lr_end': 0.00004 # min bound of learning rate
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"weight_init": 'xavier_uniform' # Weight initialization mode
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```
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## [Training process](#contents)
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@ -149,13 +149,13 @@ sh scripts/run_standalone_train.sh DEVICE_ID DATA_PATH
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### Result
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Training result will be stored in the example path. Checkpoints will be stored at `. /model_0` by default, and training log will be redirected to `log.txt` like followings.
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Training result will be stored in the example path. Checkpoints will be stored at `. /ckpt_0` by default, and training log will be redirected to `log.txt` like followings.
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``` shell
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epoch: [ 0/250], step:[ 1250/ 1251], loss:[4.761/5.613], time:[529.305], lr:[0.400]
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epoch time: 1128662.862, per step time: 902.209, avg loss: 5.609
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epoch: [ 1/250], step:[ 1250/ 1251], loss:[4.164/4.318], time:[503.708], lr:[0.398]
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epoch time: 889163.081, per step time: 710.762, avg loss: 4.312
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epoch: 1 step: 1251, loss is 4.8427444
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epoch time: 701242.350 ms, per step time: 560.545 ms
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epoch: 2 step: 1251, loss is 4.0637593
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epoch time: 598591.422 ms, per step time: 478.490ms
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```
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## [Eval process](#contents)
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@ -199,18 +199,19 @@ result: {'Loss': 1.7797744848789312, 'Top_1_Acc': 0.7985777243589743, 'Top_5_Acc
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| -------------------------- | ---------------------------------------------- |
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| Model Version | Xception |
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| Resource | HUAWEI CLOUD Modelarts |
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| uploaded Date | 11/15/2020 |
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| MindSpore Version | 1.0.0 |
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| uploaded Date | 12/10/2020 |
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| MindSpore Version | 1.1.0 |
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| Dataset | 1200k images |
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| Batch_size | 128 |
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| Training Parameters | src/config.py |
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| Optimizer | Momentum |
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| Loss Function | CrossEntropySmooth |
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| Loss | 1.78 |
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| Accuracy (8p) | Top1[79.9%] Top5[94.9%] |
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| Total time (8p) | 63h |
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| Accuracy (8p) | Top1[79.8%] Top5[94.8%] |
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| Per step time (8p) | 479 ms/step |
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| Total time (8p) | 42h |
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| Params (M) | 180M |
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| Scripts | [Xception script](https://gitee.com/mindspore/mindspore/tree/master/model_zoo/official/cv/Xception) |
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| Scripts | [Xception script](https://gitee.com/mindspore/mindspore/tree/master/model_zoo/official/cv/xception) |
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#### Inference Performance
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@ -231,4 +232,4 @@ In `dataset.py`, we set the seed inside `create_dataset` function. We also use r
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# [ModelZoo Homepage](#contents)
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Please check the official [homepage](https://gitee.com/mindspore/mindspore/tree/master/model_zoo).
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Please check the official [homepage](https://gitee.com/mindspore/mindspore/tree/master/model_zoo).
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@ -27,13 +27,10 @@ echo "avg_core_per_rank" $avg_core_per_rank
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echo "core_gap" $core_gap
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for((i=0;i<RANK_SIZE;i++))
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do
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start=`expr $i \* $avg_core_per_rank`
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export DEVICE_ID=$i
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export RANK_ID=$i
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export DEPLOY_MODE=0
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export GE_USE_STATIC_MEMORY=1
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end=`expr $start \+ $core_gap`
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cmdopt=$start"-"$end
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rm -rf train_parallel$i
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mkdir ./train_parallel$i
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@ -42,7 +39,7 @@ do
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echo "start training for rank $i, device $DEVICE_ID"
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env > env.log
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taskset -c $cmdopt python ../train.py \
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python ../train.py \
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--is_distributed \
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--device_target=Ascend \
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--dataset_path=$DATA_DIR > log.txt 2>&1 &
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@ -18,8 +18,14 @@ export DEVICE_ID=$1
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DATA_DIR=$2
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PATH_CHECKPOINT=$3
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python ./eval.py \
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rm -rf eval_output
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mkdir ./eval_output
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cd ./eval_output || exit
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echo "start evaluating model..."
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python ../eval.py \
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--device_target=Ascend \
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--device_id=$DEVICE_ID \
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--checkpoint_path=$PATH_CHECKPOINT \
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--dataset_path=$DATA_DIR > eval.log 2>&1 &
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cd ../
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@ -16,7 +16,13 @@
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export DEVICE_ID=$1
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DATA_DIR=$2
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python ./train.py \
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rm -rf train_standalone
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mkdir ./train_standalone
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cd ./train_standalone || exit
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echo "start training standalone on device $DEVICE_ID"
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python ../train.py \
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--device_target=Ascend \
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--dataset_path=$DATA_DIR > log.txt 2>&1 &
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cd ../
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@ -15,15 +15,14 @@
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"""Xception."""
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import mindspore.nn as nn
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import mindspore.ops.operations as P
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from src.config import config
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class SeparableConv2d(nn.Cell):
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def __init__(self, in_channels, out_channels, kernel_size=1, stride=1, padding=0):
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super(SeparableConv2d, self).__init__()
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self.conv1 = nn.Conv2d(in_channels, in_channels, kernel_size, stride, group=in_channels, pad_mode='pad',
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padding=padding, weight_init=config.weight_init)
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padding=padding, weight_init='xavier_uniform')
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self.pointwise = nn.Conv2d(in_channels, out_channels, kernel_size=1, stride=1, pad_mode='valid',
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weight_init=config.weight_init)
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weight_init='xavier_uniform')
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def construct(self, x):
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x = self.conv1(x)
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@ -37,7 +36,7 @@ class Block(nn.Cell):
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if out_filters != in_filters or strides != 1:
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self.skip = nn.Conv2d(in_filters, out_filters, 1, stride=strides, pad_mode='valid', has_bias=False,
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weight_init=config.weight_init)
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weight_init='xavier_uniform')
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self.skipbn = nn.BatchNorm2d(out_filters, momentum=0.9)
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else:
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self.skip = None
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@ -96,10 +95,10 @@ class Xception(nn.Cell):
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"""
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super(Xception, self).__init__()
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self.num_classes = num_classes
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self.conv1 = nn.Conv2d(3, 32, 3, 2, pad_mode='valid', weight_init=config.weight_init)
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self.conv1 = nn.Conv2d(3, 32, 3, 2, pad_mode='valid', weight_init='xavier_uniform')
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self.bn1 = nn.BatchNorm2d(32, momentum=0.9)
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self.relu = nn.ReLU()
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self.conv2 = nn.Conv2d(32, 64, 3, pad_mode='valid', weight_init=config.weight_init)
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self.conv2 = nn.Conv2d(32, 64, 3, pad_mode='valid', weight_init='xavier_uniform')
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self.bn2 = nn.BatchNorm2d(64, momentum=0.9)
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# Entry flow
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@ -36,6 +36,5 @@ config = ed({
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"label_smooth_factor": 0.1,
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"lr_init": 0.00004,
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"lr_max": 0.4,
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"lr_end": 0.00004,
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"weight_init": 'xavier_uniform'
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"lr_end": 0.00004
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})
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@ -17,7 +17,7 @@ import math
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import numpy as np
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def get_lr(lr_init, lr_end, lr_max, warmup_epochs, total_epochs, steps_per_epoch, lr_decay_mode):
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def get_lr(lr_init, lr_end, lr_max, warmup_epochs, total_epochs, steps_per_epoch, lr_decay_mode, global_step=0):
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"""
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generate learning rate array
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@ -82,6 +82,6 @@ def get_lr(lr_init, lr_end, lr_max, warmup_epochs, total_epochs, steps_per_epoch
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lr = lr_max - (lr_max - lr_end) * (i - warmup_steps) / (total_steps - warmup_steps)
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lr_each_step.append(lr)
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lr_each_step = np.array(lr_each_step).astype(np.float32)
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lr_each_step = np.array(lr_each_step[global_step:]).astype(np.float32)
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return lr_each_step
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@ -59,7 +59,8 @@ if __name__ == '__main__':
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else:
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rank = 0
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group_size = 1
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context.set_context(device_id=0)
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if os.getenv('DEVICE_ID', "not_set").isdigit():
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context.set_context(device_id=int(os.getenv('DEVICE_ID')))
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# define network
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net = xception(class_num=config.class_num)
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@ -88,7 +89,8 @@ if __name__ == '__main__':
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warmup_epochs=config.warmup_epochs,
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total_epochs=config.epoch_size,
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steps_per_epoch=step_size,
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lr_decay_mode=config.lr_decay_mode))
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lr_decay_mode=config.lr_decay_mode,
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global_step=config.finish_epoch * step_size))
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# define optimization
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opt = Momentum(net.trainable_params(), lr, config.momentum, config.weight_decay, config.loss_scale)
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@ -100,7 +102,7 @@ if __name__ == '__main__':
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# define callbacks
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cb = [TimeMonitor(), LossMonitor()]
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if config.save_checkpoint:
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save_ckpt_path = os.path.join(config.save_checkpoint_path, 'model_' + str(rank) + '/')
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save_ckpt_path = os.path.join(config.save_checkpoint_path, 'ckpt_' + str(rank) + '/')
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config_ck = CheckpointConfig(save_checkpoint_steps=config.save_checkpoint_epochs * step_size,
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keep_checkpoint_max=config.keep_checkpoint_max)
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ckpt_cb = ModelCheckpoint(f"Xception-rank{rank}", directory=save_ckpt_path, config=config_ck)
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