add new model

run_pretraining.sh

+#!/usr/bin/env bash
+export HIP_VISIBLE_DEVICES=0,1,2,3
+
+python -m torch.distributed.run --nproc_per_node 4 train.py --batch-size=128 --mode=small --print-freq=1 --dataset=CIFAR10 --ema-decay=0 --label-smoothing=0 --lr=0.2 --save-epoch-freq=10 --lr-decay=cos --lr-min=0 --warmup-epochs=5 --weight-decay=6e-5 --num-epochs=400 --num-workers=2 --width-multiplier=1  --data-dir /data/ --save-path /data/mobilenetv3out/ $1 $2

statistics.py

+# -*- coding: UTF-8 -*-
+
+'''
+statistical information and display
+Ref: https://github.com/pytorch/examples/blob/master/imagenet/main.py
+'''
+
+import torch
+
+def accuracy(output, target, topk=(1,)):
+    '''
+    Computes the accuracy over the k top predictions for the specified values of k
+    '''
+    with torch.no_grad():
+        maxk = max(topk)
+        batch_size = target.size(0)
+
+        _, pred = output.topk(maxk, 1, True, True)
+        pred = pred.t()
+        correct = pred.eq(target.view(1, -1).expand_as(pred))
+
+        res = []
+        for k in topk:
+            correct_k = correct[:k].contiguous().view(-1).float().sum(0, keepdim=True)
+            res.append(correct_k.mul_(100.0 / batch_size))
+        return res
+
+class AverageMeter(object):
+    '''
+    Computes and stores the average and current value
+    '''
+    def __init__(self, name, fmt=':f'):
+        self.name = name
+        self.fmt = fmt
+        self.reset()
+
+    def reset(self):
+        self.val = 0
+        self.avg = 0
+        self.sum = 0
+        self.count = 0
+        
+    def update(self, val, n=1):
+        self.val = val
+        self.sum += val * n
+        self.count += n
+        self.avg = self.sum / self.count
+
+    def __str__(self):
+        # fmtstr = '{name}:{val' + self.fmt + '}(avg:{avg' + self.fmt + '})'
+        fmtstr = 'Avg {name}:{avg' + self.fmt + '}'
+        return fmtstr.format(**self.__dict__)
+
+class ProgressMeter(object):
+    def __init__(self, num_batches, meters, prefix=""):
+        self.batch_fmtstr = self._get_batch_fmtstr(num_batches)
+        self.meters = meters
+        self.prefix = prefix
+
+    def display(self, batch):
+        entries = [self.prefix + self.batch_fmtstr.format(batch)]
+        entries += [str(meter) for meter in self.meters]
+        print('    '.join(entries))
+
+    def _get_batch_fmtstr(self, num_batches):
+        num_digits = len(str(num_batches // 1))
+        fmt = '{:' + str(num_digits) + 'd}'
+        return '[' + fmt + '/' + fmt.format(num_batches) + ']'
\ No newline at end of file

train.sh

+python -m torch.distributed.run --nproc_per_node 4 train.py --batch-size=128 --mode=small --print-freq=1 --dataset=CIFAR10 --ema-decay=0 --label-smoothing=0 --lr=0.2 --save-epoch-freq=10 --lr-decay=cos --lr-min=0 --warmup-epochs=5 --weight-decay=6e-5 --num-epochs=400 --num-workers=2 --width-multiplier=1  --data-dir /data/ --save-path /data/mobilenetv3out/ --log_dir /data/pid.txt 2>&1 | tee mobilenetv3_dcu_`date +%Y%m%d%H%M%S`.log