From b7fc9d37386792c33ceaee6b4a634068dfdc0c56 Mon Sep 17 00:00:00 2001 From: yehuitang Date: Wed, 23 Nov 2022 20:34:08 +0800 Subject: [PATCH] Add GhostNetV2 --- ghostnetv2_pytorch/README.md | 39 ++ ghostnetv2_pytorch/fig/dfc.PNG | Bin 0 -> 267640 bytes ghostnetv2_pytorch/fig/ghostnetv2.PNG | Bin 0 -> 132589 bytes ghostnetv2_pytorch/fig/imagenet.PNG | Bin 0 -> 97761 bytes ghostnetv2_pytorch/model/ghostnetv2_torch.py | 261 ++++++++ ghostnetv2_pytorch/train.py | 662 +++++++++++++++++++ 6 files changed, 962 insertions(+) create mode 100644 ghostnetv2_pytorch/README.md create mode 100644 ghostnetv2_pytorch/fig/dfc.PNG create mode 100644 ghostnetv2_pytorch/fig/ghostnetv2.PNG create mode 100644 ghostnetv2_pytorch/fig/imagenet.PNG create mode 100644 ghostnetv2_pytorch/model/ghostnetv2_torch.py create mode 100644 ghostnetv2_pytorch/train.py diff --git a/ghostnetv2_pytorch/README.md b/ghostnetv2_pytorch/README.md new file mode 100644 index 0000000..ff0a319 --- /dev/null +++ b/ghostnetv2_pytorch/README.md @@ -0,0 +1,39 @@ +# GhostNetV2: Enhance Cheap Operation with Long-Range Attention + +Code for our NeurIPS 2022 (Spotlight) paper, [GhostNetV2: Enhance Cheap Operation with Long-Range Attention](https://openreview.net/pdf/6db544c65bbd0fa7d7349508454a433c112470e2.pdf). Light-weight convolutional neural networks (CNNs) are specially designed for applications on mobile devices with faster inference speed. The convolutional operation can only capture local information in a window region, which prevents performance from being further improved. Introducing self-attention into convolution can capture global information well, but it will largely encumber the actual speed. In this paper, we propose a hardware-friendly attention mechanism (dubbed DFC attention) and then present a new GhostNetV2 architecture for mobile applications. The proposed DFC attention is constructed based on fully-connected layers, which can not only execute fast on common hardware but also capture the dependence between long-range pixels. We further revisit the expressiveness bottleneck in previous GhostNet and propose to enhance expanded features produced by cheap operations with DFC attention, so that a GhostNetV2 block can aggregate local and long-range information simultaneously. Extensive experiments demonstrate the superiority of GhostNetV2 over existing architectures. For example, it achieves 75.3% top-1 accuracy on ImageNet with 167M FLOPs, significantly suppressing GhostNetV1 (74.5%) with a similar computational cost. + +The information flow of DFC attention: + +

+ +

+ + +The diagrams of blocks in GhostNetV1 and GhostNetV2: + +

+ +

+ + + +## Requirements + +- python 3 +- pytorch == 1.7.1 +- torchvision == 0.8.2 +- timm==0.3.2 + +## Usage + + +Run ghostnetv2/train.py` to train models. For example, you can run the following code to train GhostNetV2 on ImageNet dataset. + +```shell +python -m torch.distributed.launch --nproc_per_node=8 train.py path_to_imagenet/ --output /cache/models/ --model ghostnetv2 -b 128 --sched step --epochs 450 --decay-epochs 2.4 --decay-rate .973 --opt rmsproptf --opt-eps .001 -j 7 --warmup-lr 1e-6 --weight-decay 1e-5 --drop 0.2 --model-ema --model-ema-decay 0.9999 --aa rand-m9-mstd0.5 --remode pixel --reprob 0.2 --lr .064 --lr-noise 0.42 0.9 --width 1.0 +``` +## Results + +

+ +

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a/ghostnetv2_pytorch/model/ghostnetv2_torch.py b/ghostnetv2_pytorch/model/ghostnetv2_torch.py new file mode 100644 index 0000000..ba58f19 --- /dev/null +++ b/ghostnetv2_pytorch/model/ghostnetv2_torch.py @@ -0,0 +1,261 @@ +# 2020.11.06-Changed for building GhostNetV2 +# Huawei Technologies Co., Ltd. +""" +Creates a GhostNet Model as defined in: +GhostNet: More Features from Cheap Operations By Kai Han, Yunhe Wang, Qi Tian, Jianyuan Guo, Chunjing Xu, Chang Xu. +https://arxiv.org/abs/1911.11907 +Modified from https://github.com/d-li14/mobilenetv3.pytorch and https://github.com/rwightman/pytorch-image-models +""" +import torch +import torch.nn as nn +import torch.nn.functional as F +import math + +from timm.models.registry import register_model + +def _make_divisible(v, divisor, min_value=None): + """ + This function is taken from the original tf repo. + It ensures that all layers have a channel number that is divisible by 8 + It can be seen here: + https://github.com/tensorflow/models/blob/master/research/slim/nets/mobilenet/mobilenet.py + """ + if min_value is None: + min_value = divisor + new_v = max(min_value, int(v + divisor / 2) // divisor * divisor) + # Make sure that round down does not go down by more than 10%. + if new_v < 0.9 * v: + new_v += divisor + return new_v + +def hard_sigmoid(x, inplace: bool = False): + if inplace: + return x.add_(3.).clamp_(0., 6.).div_(6.) + else: + return F.relu6(x + 3.) / 6. + +class SqueezeExcite(nn.Module): + def __init__(self, in_chs, se_ratio=0.25, reduced_base_chs=None, + act_layer=nn.ReLU, gate_fn=hard_sigmoid, divisor=4, **_): + super(SqueezeExcite, self).__init__() + self.gate_fn = gate_fn + reduced_chs = _make_divisible((reduced_base_chs or in_chs) * se_ratio, divisor) + self.avg_pool = nn.AdaptiveAvgPool2d(1) + self.conv_reduce = nn.Conv2d(in_chs, reduced_chs, 1, bias=True) + self.act1 = act_layer(inplace=True) + self.conv_expand = nn.Conv2d(reduced_chs, in_chs, 1, bias=True) + + def forward(self, x): + x_se = self.avg_pool(x) + x_se = self.conv_reduce(x_se) + x_se = self.act1(x_se) + x_se = self.conv_expand(x_se) + x = x * self.gate_fn(x_se) + return x + +class ConvBnAct(nn.Module): + def __init__(self, in_chs, out_chs, kernel_size, + stride=1, act_layer=nn.ReLU): + super(ConvBnAct, self).__init__() + self.conv = nn.Conv2d(in_chs, out_chs, kernel_size, stride, kernel_size//2, bias=False) + self.bn1 = nn.BatchNorm2d(out_chs) + self.act1 = act_layer(inplace=True) + + def forward(self, x): + x = self.conv(x) + x = self.bn1(x) + x = self.act1(x) + return x + +class GhostModuleV2(nn.Module): + def __init__(self, inp, oup, kernel_size=1, ratio=2, dw_size=3, stride=1, relu=True,mode=None,args=None): + super(GhostModuleV2, self).__init__() + self.mode=mode + self.gate_fn=nn.Sigmoid() + + if self.mode in ['original']: + self.oup = oup + init_channels = math.ceil(oup / ratio) + new_channels = init_channels*(ratio-1) + self.primary_conv = nn.Sequential( + nn.Conv2d(inp, init_channels, kernel_size, stride, kernel_size//2, bias=False), + nn.BatchNorm2d(init_channels), + nn.ReLU(inplace=True) if relu else nn.Sequential(), + ) + self.cheap_operation = nn.Sequential( + nn.Conv2d(init_channels, new_channels, dw_size, 1, dw_size//2, groups=init_channels, bias=False), + nn.BatchNorm2d(new_channels), + nn.ReLU(inplace=True) if relu else nn.Sequential(), + ) + elif self.mode in ['attn']: + self.oup = oup + init_channels = math.ceil(oup / ratio) + new_channels = init_channels*(ratio-1) + self.primary_conv = nn.Sequential( + nn.Conv2d(inp, init_channels, kernel_size, stride, kernel_size//2, bias=False), + nn.BatchNorm2d(init_channels), + nn.ReLU(inplace=True) if relu else nn.Sequential(), + ) + self.cheap_operation = nn.Sequential( + nn.Conv2d(init_channels, new_channels, dw_size, 1, dw_size//2, groups=init_channels, bias=False), + nn.BatchNorm2d(new_channels), + nn.ReLU(inplace=True) if relu else nn.Sequential(), + ) + self.short_conv = nn.Sequential( + nn.Conv2d(inp, oup, kernel_size, stride, kernel_size//2, bias=False), + nn.BatchNorm2d(oup), + nn.Conv2d(oup, oup, kernel_size=(1,5), stride=1, padding=(0,2), groups=oup,bias=False), + nn.BatchNorm2d(oup), + nn.Conv2d(oup, oup, kernel_size=(5,1), stride=1, padding=(2,0), groups=oup,bias=False), + nn.BatchNorm2d(oup), + ) + + def forward(self, x): + if self.mode in ['original']: + x1 = self.primary_conv(x) + x2 = self.cheap_operation(x1) + out = torch.cat([x1,x2], dim=1) + return out[:,:self.oup,:,:] + elif self.mode in ['attn']: + res=self.short_conv(F.avg_pool2d(x,kernel_size=2,stride=2)) + x1 = self.primary_conv(x) + x2 = self.cheap_operation(x1) + out = torch.cat([x1,x2], dim=1) + return out[:,:self.oup,:,:]*F.interpolate(self.gate_fn(res),size=out.shape[-1],mode='nearest') + + +class GhostBottleneckV2(nn.Module): + + def __init__(self, in_chs, mid_chs, out_chs, dw_kernel_size=3, + stride=1, act_layer=nn.ReLU, se_ratio=0.,layer_id=None,args=None): + super(GhostBottleneckV2, self).__init__() + has_se = se_ratio is not None and se_ratio > 0. + self.stride = stride + + # Point-wise expansion + if layer_id<=1: + self.ghost1 = GhostModuleV2(in_chs, mid_chs, relu=True,mode='original',args=args) + else: + self.ghost1 = GhostModuleV2(in_chs, mid_chs, relu=True,mode='attn',args=args) + + # Depth-wise convolution + if self.stride > 1: + self.conv_dw = nn.Conv2d(mid_chs, mid_chs, dw_kernel_size, stride=stride, + padding=(dw_kernel_size-1)//2,groups=mid_chs, bias=False) + self.bn_dw = nn.BatchNorm2d(mid_chs) + + # Squeeze-and-excitation + if has_se: + self.se = SqueezeExcite(mid_chs, se_ratio=se_ratio) + else: + self.se = None + + self.ghost2 = GhostModuleV2(mid_chs, out_chs, relu=False,mode='original',args=args) + + # shortcut + if (in_chs == out_chs and self.stride == 1): + self.shortcut = nn.Sequential() + else: + self.shortcut = nn.Sequential( + nn.Conv2d(in_chs, in_chs, dw_kernel_size, stride=stride, + padding=(dw_kernel_size-1)//2, groups=in_chs, bias=False), + nn.BatchNorm2d(in_chs), + nn.Conv2d(in_chs, out_chs, 1, stride=1, padding=0, bias=False), + nn.BatchNorm2d(out_chs), + ) + def forward(self, x): + residual = x + x = self.ghost1(x) + if self.stride > 1: + x = self.conv_dw(x) + x = self.bn_dw(x) + if self.se is not None: + x = self.se(x) + x = self.ghost2(x) + x += self.shortcut(residual) + return x + + +class GhostNetV2(nn.Module): + def __init__(self, cfgs, num_classes=1000, width=1.0, dropout=0.2,block=GhostBottleneckV2,args=None): + super(GhostNetV2, self).__init__() + self.cfgs = cfgs + self.dropout = dropout + + # building first layer + output_channel = _make_divisible(16 * width, 4) + self.conv_stem = nn.Conv2d(3, output_channel, 3, 2, 1, bias=False) + self.bn1 = nn.BatchNorm2d(output_channel) + self.act1 = nn.ReLU(inplace=True) + input_channel = output_channel + + # building inverted residual blocks + stages = [] + #block = block + layer_id=0 + for cfg in self.cfgs: + layers = [] + for k, exp_size, c, se_ratio, s in cfg: + output_channel = _make_divisible(c * width, 4) + hidden_channel = _make_divisible(exp_size * width, 4) + if block==GhostBottleneckV2: + layers.append(block(input_channel, hidden_channel, output_channel, k, s, + se_ratio=se_ratio,layer_id=layer_id,args=args)) + input_channel = output_channel + layer_id+=1 + stages.append(nn.Sequential(*layers)) + + output_channel = _make_divisible(exp_size * width, 4) + stages.append(nn.Sequential(ConvBnAct(input_channel, output_channel, 1))) + input_channel = output_channel + + self.blocks = nn.Sequential(*stages) + + # building last several layers + output_channel = 1280 + self.global_pool = nn.AdaptiveAvgPool2d((1, 1)) + self.conv_head = nn.Conv2d(input_channel, output_channel, 1, 1, 0, bias=True) + self.act2 = nn.ReLU(inplace=True) + self.classifier = nn.Linear(output_channel, num_classes) + + def forward(self, x): + x = self.conv_stem(x) + x = self.bn1(x) + x = self.act1(x) + x = self.blocks(x) + x = self.global_pool(x) + x = self.conv_head(x) + x = self.act2(x) + x = x.view(x.size(0), -1) + if self.dropout > 0.: + x = F.dropout(x, p=self.dropout, training=self.training) + x = self.classifier(x) + return x + +@register_model +def ghostnetv2(**kwargs): + cfgs = [ + # k, t, c, SE, s + [[3, 16, 16, 0, 1]], + [[3, 48, 24, 0, 2]], + [[3, 72, 24, 0, 1]], + [[5, 72, 40, 0.25, 2]], + [[5, 120, 40, 0.25, 1]], + [[3, 240, 80, 0, 2]], + [[3, 200, 80, 0, 1], + [3, 184, 80, 0, 1], + [3, 184, 80, 0, 1], + [3, 480, 112, 0.25, 1], + [3, 672, 112, 0.25, 1] + ], + [[5, 672, 160, 0.25, 2]], + [[5, 960, 160, 0, 1], + [5, 960, 160, 0.25, 1], + [5, 960, 160, 0, 1], + [5, 960, 160, 0.25, 1] + ] + ] + return GhostNetV2(cfgs, num_classes=kwargs['num_classes'], + width=kwargs['width'], + dropout=kwargs['dropout'], + args=kwargs['args']) diff --git a/ghostnetv2_pytorch/train.py b/ghostnetv2_pytorch/train.py new file mode 100644 index 0000000..17d983c --- /dev/null +++ b/ghostnetv2_pytorch/train.py @@ -0,0 +1,662 @@ + + +# 2022.11.06-Changed for main script for ghostnetv2 model +# Huawei Technologies Co., Ltd. + +#!/usr/bin/env python +""" ImageNet Training Script + +This is intended to be a lean and easily modifiable ImageNet training script that reproduces ImageNet +training results with some of the latest networks and training techniques. It favours canonical PyTorch +and standard Python style over trying to be able to 'do it all.' That said, it offers quite a few speed +and training result improvements over the usual PyTorch example scripts. Repurpose as you see fit. + +This script was started from an early version of the PyTorch ImageNet example +(https://github.com/pytorch/examples/tree/master/imagenet) + +NVIDIA CUDA specific speedups adopted from NVIDIA Apex examples +(https://github.com/NVIDIA/apex/tree/master/examples/imagenet) + +Hacked together by Ross Wightman (https://github.com/rwightman) +""" +import os +import argparse +import time +import yaml +from datetime import datetime +import warnings +warnings.filterwarnings('ignore') + +try: + from apex import amp + from apex.parallel import DistributedDataParallel as DDP + from apex.parallel import convert_syncbn_model + has_apex = True +except ImportError: + from torch.nn.parallel import DistributedDataParallel as DDP + has_apex = False + +from timm.data import Dataset, create_loader, resolve_data_config, FastCollateMixup, mixup_batch, AugMixDataset +from timm.models import create_model, resume_checkpoint, convert_splitbn_model +from timm.utils import * +from timm.loss import LabelSmoothingCrossEntropy, SoftTargetCrossEntropy, JsdCrossEntropy +from timm.optim import create_optimizer +from timm.scheduler import create_scheduler + +import torch +import torch.nn as nn +import torchvision.utils + +from model import ghostnetv2_torch + +torch.backends.cudnn.benchmark = True + +config_parser = parser = argparse.ArgumentParser(description='Training Config', add_help=False) +parser.add_argument('-c', '--config', default='', type=str, metavar='FILE', + help='YAML config file specifying default arguments') + + +parser = argparse.ArgumentParser(description='PyTorch ImageNet Training') +# Dataset / Model parameters +parser.add_argument('data', metavar='DIR', + help='path to dataset') +parser.add_argument('--model', default='resnet101', type=str, metavar='MODEL', + help='Name of model to train (default: "countception"') +parser.add_argument('--pretrained', action='store_true', default=False, + help='Start with pretrained version of specified network (if avail)') +parser.add_argument('--initial-checkpoint', default='', type=str, metavar='PATH', + help='Initialize model from this checkpoint (default: none)') +parser.add_argument('--resume', default='', type=str, metavar='PATH', + help='Resume full model and optimizer state from checkpoint (default: none)') +parser.add_argument('--no-resume-opt', action='store_true', default=False, + help='prevent resume of optimizer state when resuming model') +parser.add_argument('--num-classes', type=int, default=1000, metavar='N', + help='number of label classes (default: 1000)') +parser.add_argument('--gp', default='avg', type=str, metavar='POOL', + help='Type of global pool, "avg", "max", "avgmax", "avgmaxc" (default: "avg")') +parser.add_argument('--img-size', type=int, default=None, metavar='N', + help='Image patch size (default: None => model default)') +parser.add_argument('--crop-pct', default=None, type=float, + metavar='N', help='Input image center crop percent (for validation only)') +parser.add_argument('--mean', type=float, nargs='+', default=None, metavar='MEAN', + help='Override mean pixel value of dataset') +parser.add_argument('--std', type=float, nargs='+', default=None, metavar='STD', + help='Override std deviation of of dataset') +parser.add_argument('--interpolation', default='', type=str, metavar='NAME', + help='Image resize interpolation type (overrides model)') +parser.add_argument('-b', '--batch-size', type=int, default=32, metavar='N', + help='input batch size for training (default: 32)') +parser.add_argument('-vb', '--validation-batch-size-multiplier', type=int, default=1, metavar='N', + help='ratio of validation batch size to training batch size (default: 1)') +parser.add_argument('--drop', type=float, default=0.0, metavar='PCT', + help='Dropout rate (default: 0.)') +parser.add_argument('--drop-connect', type=float, default=None, metavar='PCT', + help='Drop connect rate, DEPRECATED, use drop-path (default: None)') +parser.add_argument('--drop-path', type=float, default=None, metavar='PCT', + help='Drop path rate (default: None)') +parser.add_argument('--drop-block', type=float, default=None, metavar='PCT', + help='Drop block rate (default: None)') +parser.add_argument('--jsd', action='store_true', default=False, + help='Enable Jensen-Shannon Divergence + CE loss. Use with `--aug-splits`.') +parser.add_argument('--space_to_depth', type=int, default=1, metavar='N', + help='space_to_depth rate (default: 1)') +parser.add_argument('--depth_to_space', type=int, default=1, metavar='N', + help='depth_to_space rate (default: 1)') +parser.add_argument('--width', type=float, default=1.0, metavar='PCT', + help='width rate (default: 1)') +# Optimizer parameters +parser.add_argument('--opt', default='sgd', type=str, metavar='OPTIMIZER', + help='Optimizer (default: "sgd"') +parser.add_argument('--opt-eps', default=1e-8, type=float, metavar='EPSILON', + help='Optimizer Epsilon (default: 1e-8)') +parser.add_argument('--momentum', type=float, default=0.9, metavar='M', + help='SGD momentum (default: 0.9)') +parser.add_argument('--weight-decay', type=float, default=0.0001, + help='weight decay (default: 0.0001)') + +parser.add_argument('--sched', default='step', type=str, metavar='SCHEDULER', + help='LR scheduler (default: "step"') +parser.add_argument('--lr', type=float, default=0.01, metavar='LR', + help='learning rate (default: 0.01)') +parser.add_argument('--lr-noise', type=float, nargs='+', default=None, metavar='pct, pct', + help='learning rate noise on/off epoch percentages') +parser.add_argument('--lr-noise-pct', type=float, default=0.67, metavar='PERCENT', + help='learning rate noise limit percent (default: 0.67)') +parser.add_argument('--lr-noise-std', type=float, default=1.0, metavar='STDDEV', + help='learning rate noise std-dev (default: 1.0)') +parser.add_argument('--warmup-lr', type=float, default=0.0001, metavar='LR', + help='warmup learning rate (default: 0.0001)') +parser.add_argument('--min-lr', type=float, default=1e-5, metavar='LR', + help='lower lr bound for cyclic schedulers that hit 0 (1e-5)') +parser.add_argument('--epochs', type=int, default=200, metavar='N', + help='number of epochs to train (default: 2)') +parser.add_argument('--start-epoch', default=None, type=int, metavar='N', + help='manual epoch number (useful on restarts)') +parser.add_argument('--decay-epochs', type=float, default=30, metavar='N', + help='epoch interval to decay LR') +parser.add_argument('--warmup-epochs', type=int, default=3, metavar='N', + help='epochs to warmup LR, if scheduler supports') +parser.add_argument('--cooldown-epochs', type=int, default=10, metavar='N', + help='epochs to cooldown LR at min_lr, after cyclic schedule ends') +parser.add_argument('--patience-epochs', type=int, default=10, metavar='N', + help='patience epochs for Plateau LR scheduler (default: 10') +parser.add_argument('--decay-rate', '--dr', type=float, default=0.1, metavar='RATE', + help='LR decay rate (default: 0.1)') +parser.add_argument('--color-jitter', type=float, default=0.4, metavar='PCT', + help='Color jitter factor (default: 0.4)') +parser.add_argument('--aa', type=str, default=None, metavar='NAME', + help='Use AutoAugment policy. "v0" or "original". (default: None)'), +parser.add_argument('--aug-splits', type=int, default=0, + help='Number of augmentation splits (default: 0, valid: 0 or >=2)') +parser.add_argument('--reprob', type=float, default=0., metavar='PCT', + help='Random erase prob (default: 0.)') +parser.add_argument('--remode', type=str, default='const', + help='Random erase mode (default: "const")') +parser.add_argument('--recount', type=int, default=1, + help='Random erase count (default: 1)') +parser.add_argument('--resplit', action='store_true', default=False, + help='Do not random erase first (clean) augmentation split') +parser.add_argument('--mixup', type=float, default=0.0, + help='mixup alpha, mixup enabled if > 0. (default: 0.)') +parser.add_argument('--mixup-off-epoch', default=0, type=int, metavar='N', + help='turn off mixup after this epoch, disabled if 0 (default: 0)') +parser.add_argument('--smoothing', type=float, default=0.1, + help='label smoothing (default: 0.1)') +parser.add_argument('--train-interpolation', type=str, default='random', + help='Training interpolation (random, bilinear, bicubic default: "random")') +# Batch norm parameters (only works with gen_efficientnet based models currently) +parser.add_argument('--bn-tf', action='store_true', default=False, + help='Use Tensorflow BatchNorm defaults for models that support it (default: False)') +parser.add_argument('--bn-momentum', type=float, default=None, + help='BatchNorm momentum override (if not None)') +parser.add_argument('--bn-eps', type=float, default=None, + help='BatchNorm epsilon override (if not None)') +parser.add_argument('--sync-bn', action='store_true', + help='Enable NVIDIA Apex or Torch synchronized BatchNorm.') +parser.add_argument('--dist-bn', type=str, default='', + help='Distribute BatchNorm stats between nodes after each epoch ("broadcast", "reduce", or "")') +parser.add_argument('--split-bn', action='store_true', + help='Enable separate BN layers per augmentation split.') +# Model Exponential Moving Average +parser.add_argument('--model-ema', action='store_true', default=False, + help='Enable tracking moving average of model weights') +parser.add_argument('--model-ema-force-cpu', action='store_true', default=False, + help='Force ema to be tracked on CPU, rank=0 node only. Disables EMA validation.') +parser.add_argument('--model-ema-decay', type=float, default=0.9998, + help='decay factor for model weights moving average (default: 0.9998)') +# Misc +parser.add_argument('--seed', type=int, default=42, metavar='S', + help='random seed (default: 42)') +parser.add_argument('--log-interval', type=int, default=100, metavar='N', + help='how many batches to wait before logging training status') +parser.add_argument('--recovery-interval', type=int, default=0, metavar='N', + help='how many batches to wait before writing recovery checkpoint') +parser.add_argument('-j', '--workers', type=int, default=4, metavar='N', + help='how many training processes to use (default: 1)') +parser.add_argument('--num-gpu', type=int, default=1, + help='Number of GPUS to use') +parser.add_argument('--save-images', action='store_true', default=False, + help='save images of input bathes every log interval for debugging') +parser.add_argument('--amp', action='store_true', default=False, + help='use NVIDIA amp for mixed precision training') +parser.add_argument('--pin-mem', action='store_true', default=False, + help='Pin CPU memory in DataLoader for more efficient (sometimes) transfer to GPU.') +parser.add_argument('--no-prefetcher', action='store_true', default=False, + help='disable fast prefetcher') +parser.add_argument('--output', default='', type=str, metavar='PATH', + help='path to output folder (default: none, current dir)') +parser.add_argument('--eval-metric', default='top1', type=str, metavar='EVAL_METRIC', + help='Best metric (default: "top1"') +parser.add_argument('--tta', type=int, default=0, metavar='N', + help='Test/inference time augmentation (oversampling) factor. 0=None (default: 0)') +parser.add_argument("--local_rank", default=0, type=int) +# added +parser.add_argument("--init_method", default='env://', type=str) +parser.add_argument('--r', type=float, default=1.0) +parser.add_argument('--w', type=float, default=1.0) +parser.add_argument('--d', type=float, default=1.0) + + +def _parse_args(): + # Do we have a config file to parse? + args_config, remaining = config_parser.parse_known_args() + if args_config.config: + with open(args_config.config, 'r') as f: + cfg = yaml.safe_load(f) + parser.set_defaults(**cfg) + + # The main arg parser parses the rest of the args, the usual + # defaults will have been overridden if config file specified. + args = parser.parse_args(remaining) + + # Cache the args as a text string to save them in the output dir later + args_text = yaml.safe_dump(args.__dict__, default_flow_style=False) + return args, args_text + +def main(): + setup_default_logging() + args, args_text = _parse_args() + + args.prefetcher = not args.no_prefetcher + args.distributed = False + if 'WORLD_SIZE' in os.environ: + args.distributed = int(os.environ['WORLD_SIZE']) > 1 + if args.distributed and args.num_gpu > 1: + logging.warning('Using more than one GPU per process in distributed mode is not allowed. Setting num_gpu to 1.') + args.num_gpu = 1 + + args.device = 'cuda:0' + args.world_size = 1 + args.rank = 0 # global rank + if args.distributed: + args.num_gpu = 1 + args.device = 'cuda:%d' % args.local_rank + torch.cuda.set_device(args.local_rank) + args.world_size = int(os.environ['WORLD_SIZE']) + args.rank = int(os.environ['RANK']) + torch.distributed.init_process_group(backend='nccl', init_method=args.init_method, rank=args.rank, world_size=args.world_size) + args.world_size = torch.distributed.get_world_size() + args.rank = torch.distributed.get_rank() + assert args.rank >= 0 + + if args.distributed: + logging.info('Training in distributed mode with multiple processes, 1 GPU per process. Process %d, total %d.' + % (args.rank, args.world_size)) + else: + logging.info('Training with a single process on %d GPUs.' % args.num_gpu) + + torch.manual_seed(args.seed + args.rank) + + model = create_model(args.model,num_classes=args.num_classes,width=args.width,dropout=args.drop,args=args) + + if args.local_rank == 0: + logging.info('Model %s created, param count: %d' % + (args.model, sum([m.numel() for m in model.parameters()]))) + + data_config = resolve_data_config(vars(args), model=model, verbose=args.local_rank == 0) + + num_aug_splits = 0 + if args.aug_splits > 0: + assert args.aug_splits > 1, 'A split of 1 makes no sense' + num_aug_splits = args.aug_splits + + if args.split_bn: + assert num_aug_splits > 1 or args.resplit + model = convert_splitbn_model(model, max(num_aug_splits, 2)) + + if args.num_gpu > 1: + if args.amp: + logging.warning( + 'AMP does not work well with nn.DataParallel, disabling. Use distributed mode for multi-GPU AMP.') + args.amp = False + model = nn.DataParallel(model, device_ids=list(range(args.num_gpu))).cuda() + else: + model.cuda() + + optimizer = create_optimizer(args, model) + + use_amp = False + if has_apex and args.amp: + model, optimizer = amp.initialize(model, optimizer, opt_level='O1') + use_amp = True + if args.local_rank == 0: + logging.info('NVIDIA APEX {}. AMP {}.'.format( + 'installed' if has_apex else 'not installed', 'on' if use_amp else 'off')) + + # optionally resume from a checkpoint + resume_state = {} + resume_epoch = None + if args.resume: + resume_state, resume_epoch = resume_checkpoint(model, args.resume) + if resume_state and not args.no_resume_opt: + if 'optimizer' in resume_state: + if args.local_rank == 0: + logging.info('Restoring Optimizer state from checkpoint') + optimizer.load_state_dict(resume_state['optimizer']) + if use_amp and 'amp' in resume_state and 'load_state_dict' in amp.__dict__: + if args.local_rank == 0: + logging.info('Restoring NVIDIA AMP state from checkpoint') + amp.load_state_dict(resume_state['amp']) + del resume_state + + model_ema = None + if args.model_ema: + # Important to create EMA model after cuda(), DP wrapper, and AMP but before SyncBN and DDP wrapper + model_ema = ModelEma( + model, + decay=args.model_ema_decay, + device='cpu' if args.model_ema_force_cpu else '', + resume=args.resume) + + if args.distributed: + if args.sync_bn: + assert not args.split_bn + try: + if has_apex: + model = convert_syncbn_model(model) + else: + model = torch.nn.SyncBatchNorm.convert_sync_batchnorm(model) + if args.local_rank == 0: + logging.info( + 'Converted model to use Synchronized BatchNorm. WARNING: You may have issues if using ' + 'zero initialized BN layers (enabled by default for ResNets) while sync-bn enabled.') + except Exception as e: + logging.error('Failed to enable Synchronized BatchNorm. Install Apex or Torch >= 1.1') + if has_apex: + model = DDP(model, delay_allreduce=True) + else: + if args.local_rank == 0: + logging.info("Using torch DistributedDataParallel. Install NVIDIA Apex for Apex DDP.") + model = DDP(model, device_ids=[args.local_rank]) # can use device str in Torch >= 1.1 + # NOTE: EMA model does not need to be wrapped by DDP + + lr_scheduler, num_epochs = create_scheduler(args, optimizer) + start_epoch = 0 + if args.start_epoch is not None: + # a specified start_epoch will always override the resume epoch + start_epoch = args.start_epoch + elif resume_epoch is not None: + start_epoch = resume_epoch + if lr_scheduler is not None and start_epoch > 0: + lr_scheduler.step(start_epoch) + + if args.local_rank == 0: + logging.info('Scheduled epochs: {}'.format(num_epochs)) + + train_dir = os.path.join(args.data, 'train') + if not os.path.exists(train_dir): + logging.error('Training folder does not exist at: {}'.format(train_dir)) + exit(1) + dataset_train = Dataset(train_dir) + + collate_fn = None + if args.prefetcher and args.mixup > 0: + assert not num_aug_splits # collate conflict (need to support deinterleaving in collate mixup) + collate_fn = FastCollateMixup(args.mixup, args.smoothing, args.num_classes) + + if num_aug_splits > 1: + dataset_train = AugMixDataset(dataset_train, num_splits=num_aug_splits) + + print('data_config',data_config) + loader_train = create_loader( + dataset_train, + input_size=data_config['input_size'], + batch_size=args.batch_size, + is_training=True, + use_prefetcher=args.prefetcher, + re_prob=args.reprob, + re_mode=args.remode, + re_count=args.recount, + re_split=args.resplit, + color_jitter=args.color_jitter, + auto_augment=args.aa, + num_aug_splits=num_aug_splits, + interpolation=args.train_interpolation, + mean=data_config['mean'], + std=data_config['std'], + num_workers=args.workers, + distributed=args.distributed, + collate_fn=collate_fn, + pin_memory=args.pin_mem, + ) + + eval_dir = os.path.join(args.data, 'val') + if not os.path.isdir(eval_dir): + eval_dir = os.path.join(args.data, 'validation') + if not os.path.isdir(eval_dir): + logging.error('Validation folder does not exist at: {}'.format(eval_dir)) + exit(1) + dataset_eval = Dataset(eval_dir) + + loader_eval = create_loader( + dataset_eval, + input_size=data_config['input_size'], + batch_size=args.validation_batch_size_multiplier * args.batch_size, + is_training=False, + use_prefetcher=args.prefetcher, + interpolation=data_config['interpolation'], + mean=data_config['mean'], + std=data_config['std'], + num_workers=args.workers, + distributed=args.distributed, + crop_pct=data_config['crop_pct'], + pin_memory=args.pin_mem, + ) + + if args.jsd: + assert num_aug_splits > 1 # JSD only valid with aug splits set + train_loss_fn = JsdCrossEntropy(num_splits=num_aug_splits, smoothing=args.smoothing).cuda() + validate_loss_fn = nn.CrossEntropyLoss().cuda() + elif args.mixup > 0.: + # smoothing is handled with mixup label transform + train_loss_fn = SoftTargetCrossEntropy().cuda() + validate_loss_fn = nn.CrossEntropyLoss().cuda() + elif args.smoothing: + train_loss_fn = LabelSmoothingCrossEntropy(smoothing=args.smoothing).cuda() + validate_loss_fn = nn.CrossEntropyLoss().cuda() + else: + train_loss_fn = nn.CrossEntropyLoss().cuda() + validate_loss_fn = train_loss_fn + + eval_metric = args.eval_metric + best_metric = None + best_epoch = None + saver = None + output_dir = '' + if args.local_rank == 0: + output_base = args.output if args.output else './output' + exp_name = '-'.join([ + datetime.now().strftime("%Y%m%d-%H%M%S"), + args.model, + str(data_config['input_size'][-1]) + ]) + output_dir = get_outdir(output_base, 'train', exp_name) + decreasing = True if eval_metric == 'loss' else False + saver = CheckpointSaver(checkpoint_dir=output_dir, decreasing=decreasing) + with open(os.path.join(output_dir, 'args.yaml'), 'w') as f: + f.write(args_text) + + try: + for epoch in range(start_epoch, num_epochs): + if args.distributed: + loader_train.sampler.set_epoch(epoch) + + train_metrics = train_epoch( + epoch, model, loader_train, optimizer, train_loss_fn, args, + lr_scheduler=lr_scheduler, saver=saver, output_dir=output_dir, + use_amp=use_amp, model_ema=model_ema) + + if args.distributed and args.dist_bn in ('broadcast', 'reduce'): + if args.local_rank == 0: + logging.info("Distributing BatchNorm running means and vars") + distribute_bn(model, args.world_size, args.dist_bn == 'reduce') + + eval_metrics = validate(model, loader_eval, validate_loss_fn, args) + + if model_ema is not None and not args.model_ema_force_cpu: + if args.distributed and args.dist_bn in ('broadcast', 'reduce'): + distribute_bn(model_ema, args.world_size, args.dist_bn == 'reduce') + ema_eval_metrics = validate( + model_ema.ema, loader_eval, validate_loss_fn, args, log_suffix=' (EMA)') + eval_metrics = ema_eval_metrics + + if lr_scheduler is not None: + # step LR for next epoch + lr_scheduler.step(epoch + 1, eval_metrics[eval_metric]) + + update_summary( + epoch, train_metrics, eval_metrics, os.path.join(output_dir, 'summary.csv'), + write_header=best_metric is None) + + if saver is not None: + # save proper checkpoint with eval metric + save_metric = eval_metrics[eval_metric] + best_metric, best_epoch = saver.save_checkpoint( + model, optimizer, args, + epoch=epoch, model_ema=model_ema, metric=save_metric, use_amp=use_amp) + + except KeyboardInterrupt: + pass + if best_metric is not None: + logging.info('*** Best metric: {0} (epoch {1})'.format(best_metric, best_epoch)) + +def train_epoch( + epoch, model, loader, optimizer, loss_fn, args, + lr_scheduler=None, saver=None, output_dir='', use_amp=False, model_ema=None): + + if args.prefetcher and args.mixup > 0 and loader.mixup_enabled: + if args.mixup_off_epoch and epoch >= args.mixup_off_epoch: + loader.mixup_enabled = False + + batch_time_m = AverageMeter() + data_time_m = AverageMeter() + losses_m = AverageMeter() + + model.train() + + end = time.time() + last_idx = len(loader) - 1 + num_updates = epoch * len(loader) + for batch_idx, (input, target) in enumerate(loader): + last_batch = batch_idx == last_idx + data_time_m.update(time.time() - end) + if not args.prefetcher: + input, target = input.cuda(), target.cuda() + if args.mixup > 0.: + input, target = mixup_batch( + input, target, + alpha=args.mixup, num_classes=args.num_classes, smoothing=args.smoothing, + disable=args.mixup_off_epoch and epoch >= args.mixup_off_epoch) + + output = model(input) + + loss = loss_fn(output, target) + if not args.distributed: + losses_m.update(loss.item(), input.size(0)) + + optimizer.zero_grad() + if use_amp: + with amp.scale_loss(loss, optimizer) as scaled_loss: + scaled_loss.backward() + else: + loss.backward() + optimizer.step() + + torch.cuda.synchronize() + if model_ema is not None: + model_ema.update(model) + num_updates += 1 + + batch_time_m.update(time.time() - end) + if last_batch or batch_idx % args.log_interval == 0: + lrl = [param_group['lr'] for param_group in optimizer.param_groups] + lr = sum(lrl) / len(lrl) + + if args.distributed: + reduced_loss = reduce_tensor(loss.data, args.world_size) + losses_m.update(reduced_loss.item(), input.size(0)) + + if args.local_rank == 0: + logging.info( + 'Train: {} [{:>4d}/{} ({:>3.0f}%)] ' + 'Loss: {loss.val:>9.6f} ({loss.avg:>6.4f}) ' + 'Time: {batch_time.val:.3f}s, {rate:>7.2f}/s ' + '({batch_time.avg:.3f}s, {rate_avg:>7.2f}/s) ' + 'LR: {lr:.3e} ' + 'Data: {data_time.val:.3f} ({data_time.avg:.3f})'.format( + epoch, + batch_idx, len(loader), + 100. * batch_idx / last_idx, + loss=losses_m, + batch_time=batch_time_m, + rate=input.size(0) * args.world_size / batch_time_m.val, + rate_avg=input.size(0) * args.world_size / batch_time_m.avg, + lr=lr, + data_time=data_time_m)) + + if args.save_images and output_dir: + torchvision.utils.save_image( + input, + os.path.join(output_dir, 'train-batch-%d.jpg' % batch_idx), + padding=0, + normalize=True) + + if saver is not None and args.recovery_interval and ( + last_batch or (batch_idx + 1) % args.recovery_interval == 0): + saver.save_recovery( + model, optimizer, args, epoch, model_ema=model_ema, use_amp=use_amp, batch_idx=batch_idx) + + if lr_scheduler is not None: + lr_scheduler.step_update(num_updates=num_updates, metric=losses_m.avg) + + end = time.time() + # end for + + if hasattr(optimizer, 'sync_lookahead'): + optimizer.sync_lookahead() + + return OrderedDict([('loss', losses_m.avg)]) + + +def validate(model, loader, loss_fn, args, log_suffix=''): + batch_time_m = AverageMeter() + losses_m = AverageMeter() + top1_m = AverageMeter() + top5_m = AverageMeter() + + model.eval() + + end = time.time() + last_idx = len(loader) - 1 + with torch.no_grad(): + for batch_idx, (input, target) in enumerate(loader): + last_batch = batch_idx == last_idx + if not args.prefetcher: + input = input.cuda() + target = target.cuda() + + output = model(input) + if isinstance(output, (tuple, list)): + output = output[0] + + # augmentation reduction + reduce_factor = args.tta + if reduce_factor > 1: + output = output.unfold(0, reduce_factor, reduce_factor).mean(dim=2) + target = target[0:target.size(0):reduce_factor] + + loss = loss_fn(output, target) + acc1, acc5 = accuracy(output, target, topk=(1, 5)) + + if args.distributed: + reduced_loss = reduce_tensor(loss.data, args.world_size) + acc1 = reduce_tensor(acc1, args.world_size) + acc5 = reduce_tensor(acc5, args.world_size) + else: + reduced_loss = loss.data + + torch.cuda.synchronize() + + losses_m.update(reduced_loss.item(), input.size(0)) + top1_m.update(acc1.item(), output.size(0)) + top5_m.update(acc5.item(), output.size(0)) + + batch_time_m.update(time.time() - end) + end = time.time() + if args.local_rank == 0 and (last_batch or batch_idx % args.log_interval == 0): + log_name = 'Test' + log_suffix + logging.info( + '{0}: [{1:>4d}/{2}] ' + 'Time: {batch_time.val:.3f} ({batch_time.avg:.3f}) ' + 'Loss: {loss.val:>7.4f} ({loss.avg:>6.4f}) ' + 'Acc@1: {top1.val:>7.4f} ({top1.avg:>7.4f}) ' + 'Acc@5: {top5.val:>7.4f} ({top5.avg:>7.4f})'.format( + log_name, batch_idx, last_idx, batch_time=batch_time_m, + loss=losses_m, top1=top1_m, top5=top5_m)) + + metrics = OrderedDict([('loss', losses_m.avg), ('top1', top1_m.avg), ('top5', top5_m.avg)]) + + return metrics + +if __name__ == '__main__': + main()