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662 lines
30 KiB
662 lines
30 KiB
#!/usr/bin/env python
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""" ImageNet Training Script
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This is intended to be a lean and easily modifiable ImageNet training script that reproduces ImageNet
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training results with some of the latest networks and training techniques. It favours canonical PyTorch
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and standard Python style over trying to be able to 'do it all.' That said, it offers quite a few speed
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and training result improvements over the usual PyTorch example scripts. Repurpose as you see fit.
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This script was started from an early version of the PyTorch ImageNet example
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(https://github.com/pytorch/examples/tree/master/imagenet)
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NVIDIA CUDA specific speedups adopted from NVIDIA Apex examples
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(https://github.com/NVIDIA/apex/tree/master/examples/imagenet)
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Hacked together by Ross Wightman (https://github.com/rwightman)
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"""
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import argparse
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import time
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import yaml
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from datetime import datetime
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try:
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from apex import amp
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from apex.parallel import DistributedDataParallel as DDP
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from apex.parallel import convert_syncbn_model
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has_apex = True
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except ImportError:
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from torch.nn.parallel import DistributedDataParallel as DDP
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has_apex = False
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from timm.data import Dataset, create_loader, resolve_data_config, FastCollateMixup, mixup_batch, AugMixDataset
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from timm.models import create_model, resume_checkpoint, convert_splitbn_model
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from timm.utils import *
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from timm.loss import LabelSmoothingCrossEntropy, SoftTargetCrossEntropy, JsdCrossEntropy
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from timm.optim import create_optimizer
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from timm.scheduler import create_scheduler
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import torch
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import torch.nn as nn
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import torchvision.utils
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torch.backends.cudnn.benchmark = True
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# The first arg parser parses out only the --config argument, this argument is used to
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# load a yaml file containing key-values that override the defaults for the main parser below
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config_parser = parser = argparse.ArgumentParser(description='Training Config', add_help=False)
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parser.add_argument('-c', '--config', default='', type=str, metavar='FILE',
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help='YAML config file specifying default arguments')
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parser = argparse.ArgumentParser(description='PyTorch ImageNet Training')
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# Dataset / Model parameters
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parser.add_argument('data', metavar='DIR',
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help='path to dataset')
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parser.add_argument('--model', default='resnet101', type=str, metavar='MODEL',
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help='Name of model to train (default: "countception"')
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parser.add_argument('--pretrained', action='store_true', default=False,
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help='Start with pretrained version of specified network (if avail)')
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parser.add_argument('--initial-checkpoint', default='', type=str, metavar='PATH',
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help='Initialize model from this checkpoint (default: none)')
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parser.add_argument('--resume', default='', type=str, metavar='PATH',
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help='Resume full model and optimizer state from checkpoint (default: none)')
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parser.add_argument('--no-resume-opt', action='store_true', default=False,
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help='prevent resume of optimizer state when resuming model')
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parser.add_argument('--num-classes', type=int, default=1000, metavar='N',
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help='number of label classes (default: 1000)')
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parser.add_argument('--gp', default='avg', type=str, metavar='POOL',
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help='Type of global pool, "avg", "max", "avgmax", "avgmaxc" (default: "avg")')
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parser.add_argument('--img-size', type=int, default=None, metavar='N',
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help='Image patch size (default: None => model default)')
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parser.add_argument('--crop-pct', default=None, type=float,
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metavar='N', help='Input image center crop percent (for validation only)')
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parser.add_argument('--mean', type=float, nargs='+', default=None, metavar='MEAN',
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help='Override mean pixel value of dataset')
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parser.add_argument('--std', type=float, nargs='+', default=None, metavar='STD',
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help='Override std deviation of of dataset')
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parser.add_argument('--interpolation', default='', type=str, metavar='NAME',
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help='Image resize interpolation type (overrides model)')
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parser.add_argument('-b', '--batch-size', type=int, default=32, metavar='N',
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help='input batch size for training (default: 32)')
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parser.add_argument('-vb', '--validation-batch-size-multiplier', type=int, default=1, metavar='N',
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help='ratio of validation batch size to training batch size (default: 1)')
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parser.add_argument('--drop', type=float, default=0.0, metavar='PCT',
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help='Dropout rate (default: 0.)')
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parser.add_argument('--drop-connect', type=float, default=None, metavar='PCT',
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help='Drop connect rate, DEPRECATED, use drop-path (default: None)')
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parser.add_argument('--drop-path', type=float, default=None, metavar='PCT',
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help='Drop path rate (default: None)')
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parser.add_argument('--drop-block', type=float, default=None, metavar='PCT',
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help='Drop block rate (default: None)')
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parser.add_argument('--jsd', action='store_true', default=False,
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help='Enable Jensen-Shannon Divergence + CE loss. Use with `--aug-splits`.')
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# Optimizer parameters
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parser.add_argument('--opt', default='sgd', type=str, metavar='OPTIMIZER',
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help='Optimizer (default: "sgd"')
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parser.add_argument('--opt-eps', default=1e-8, type=float, metavar='EPSILON',
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help='Optimizer Epsilon (default: 1e-8)')
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parser.add_argument('--momentum', type=float, default=0.9, metavar='M',
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help='SGD momentum (default: 0.9)')
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parser.add_argument('--weight-decay', type=float, default=0.0001,
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help='weight decay (default: 0.0001)')
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# Learning rate schedule parameters
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parser.add_argument('--sched', default='step', type=str, metavar='SCHEDULER',
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help='LR scheduler (default: "step"')
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parser.add_argument('--lr', type=float, default=0.01, metavar='LR',
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help='learning rate (default: 0.01)')
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parser.add_argument('--lr-noise', type=float, nargs='+', default=None, metavar='pct, pct',
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help='learning rate noise on/off epoch percentages')
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parser.add_argument('--lr-noise-pct', type=float, default=0.67, metavar='PERCENT',
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help='learning rate noise limit percent (default: 0.67)')
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parser.add_argument('--lr-noise-std', type=float, default=1.0, metavar='STDDEV',
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help='learning rate noise std-dev (default: 1.0)')
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parser.add_argument('--warmup-lr', type=float, default=0.0001, metavar='LR',
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help='warmup learning rate (default: 0.0001)')
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parser.add_argument('--min-lr', type=float, default=1e-5, metavar='LR',
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help='lower lr bound for cyclic schedulers that hit 0 (1e-5)')
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parser.add_argument('--epochs', type=int, default=200, metavar='N',
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help='number of epochs to train (default: 2)')
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parser.add_argument('--start-epoch', default=None, type=int, metavar='N',
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help='manual epoch number (useful on restarts)')
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parser.add_argument('--decay-epochs', type=float, default=30, metavar='N',
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help='epoch interval to decay LR')
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parser.add_argument('--warmup-epochs', type=int, default=3, metavar='N',
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help='epochs to warmup LR, if scheduler supports')
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parser.add_argument('--cooldown-epochs', type=int, default=10, metavar='N',
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help='epochs to cooldown LR at min_lr, after cyclic schedule ends')
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parser.add_argument('--patience-epochs', type=int, default=10, metavar='N',
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help='patience epochs for Plateau LR scheduler (default: 10')
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parser.add_argument('--decay-rate', '--dr', type=float, default=0.1, metavar='RATE',
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help='LR decay rate (default: 0.1)')
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# Augmentation parameters
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parser.add_argument('--color-jitter', type=float, default=0.4, metavar='PCT',
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help='Color jitter factor (default: 0.4)')
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parser.add_argument('--aa', type=str, default=None, metavar='NAME',
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help='Use AutoAugment policy. "v0" or "original". (default: None)'),
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parser.add_argument('--aug-splits', type=int, default=0,
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help='Number of augmentation splits (default: 0, valid: 0 or >=2)')
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parser.add_argument('--reprob', type=float, default=0., metavar='PCT',
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help='Random erase prob (default: 0.)')
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parser.add_argument('--remode', type=str, default='const',
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help='Random erase mode (default: "const")')
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parser.add_argument('--recount', type=int, default=1,
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help='Random erase count (default: 1)')
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parser.add_argument('--resplit', action='store_true', default=False,
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help='Do not random erase first (clean) augmentation split')
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parser.add_argument('--mixup', type=float, default=0.0,
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help='mixup alpha, mixup enabled if > 0. (default: 0.)')
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parser.add_argument('--mixup-off-epoch', default=0, type=int, metavar='N',
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help='turn off mixup after this epoch, disabled if 0 (default: 0)')
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parser.add_argument('--smoothing', type=float, default=0.1,
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help='label smoothing (default: 0.1)')
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parser.add_argument('--train-interpolation', type=str, default='random',
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help='Training interpolation (random, bilinear, bicubic default: "random")')
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# Batch norm parameters (only works with gen_efficientnet based models currently)
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parser.add_argument('--bn-tf', action='store_true', default=False,
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help='Use Tensorflow BatchNorm defaults for models that support it (default: False)')
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parser.add_argument('--bn-momentum', type=float, default=None,
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help='BatchNorm momentum override (if not None)')
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parser.add_argument('--bn-eps', type=float, default=None,
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help='BatchNorm epsilon override (if not None)')
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parser.add_argument('--sync-bn', action='store_true',
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help='Enable NVIDIA Apex or Torch synchronized BatchNorm.')
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parser.add_argument('--dist-bn', type=str, default='',
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help='Distribute BatchNorm stats between nodes after each epoch ("broadcast", "reduce", or "")')
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parser.add_argument('--split-bn', action='store_true',
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help='Enable separate BN layers per augmentation split.')
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# Model Exponential Moving Average
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parser.add_argument('--model-ema', action='store_true', default=False,
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help='Enable tracking moving average of model weights')
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parser.add_argument('--model-ema-force-cpu', action='store_true', default=False,
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help='Force ema to be tracked on CPU, rank=0 node only. Disables EMA validation.')
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parser.add_argument('--model-ema-decay', type=float, default=0.9998,
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help='decay factor for model weights moving average (default: 0.9998)')
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# Misc
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parser.add_argument('--seed', type=int, default=42, metavar='S',
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help='random seed (default: 42)')
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parser.add_argument('--log-interval', type=int, default=50, metavar='N',
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help='how many batches to wait before logging training status')
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parser.add_argument('--recovery-interval', type=int, default=0, metavar='N',
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help='how many batches to wait before writing recovery checkpoint')
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parser.add_argument('-j', '--workers', type=int, default=4, metavar='N',
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help='how many training processes to use (default: 1)')
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parser.add_argument('--num-gpu', type=int, default=1,
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help='Number of GPUS to use')
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parser.add_argument('--save-images', action='store_true', default=False,
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help='save images of input bathes every log interval for debugging')
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parser.add_argument('--amp', action='store_true', default=False,
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help='use NVIDIA amp for mixed precision training')
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parser.add_argument('--pin-mem', action='store_true', default=False,
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help='Pin CPU memory in DataLoader for more efficient (sometimes) transfer to GPU.')
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parser.add_argument('--no-prefetcher', action='store_true', default=False,
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help='disable fast prefetcher')
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parser.add_argument('--output', default='', type=str, metavar='PATH',
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help='path to output folder (default: none, current dir)')
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parser.add_argument('--eval-metric', default='top1', type=str, metavar='EVAL_METRIC',
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help='Best metric (default: "top1"')
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parser.add_argument('--tta', type=int, default=0, metavar='N',
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help='Test/inference time augmentation (oversampling) factor. 0=None (default: 0)')
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parser.add_argument("--local_rank", default=0, type=int)
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parser.add_argument('--use-multi-epochs-loader', action='store_true', default=False,
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help='use the multi-epochs-loader to save time at the beginning of every epoch')
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def _parse_args():
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# Do we have a config file to parse?
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args_config, remaining = config_parser.parse_known_args()
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if args_config.config:
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with open(args_config.config, 'r') as f:
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cfg = yaml.safe_load(f)
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parser.set_defaults(**cfg)
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# The main arg parser parses the rest of the args, the usual
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# defaults will have been overridden if config file specified.
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args = parser.parse_args(remaining)
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# Cache the args as a text string to save them in the output dir later
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args_text = yaml.safe_dump(args.__dict__, default_flow_style=False)
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return args, args_text
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def main():
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setup_default_logging()
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args, args_text = _parse_args()
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args.prefetcher = not args.no_prefetcher
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args.distributed = False
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if 'WORLD_SIZE' in os.environ:
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args.distributed = int(os.environ['WORLD_SIZE']) > 1
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if args.distributed and args.num_gpu > 1:
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logging.warning('Using more than one GPU per process in distributed mode is not allowed. Setting num_gpu to 1.')
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args.num_gpu = 1
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args.device = 'cuda:0'
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args.world_size = 1
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args.rank = 0 # global rank
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if args.distributed:
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args.num_gpu = 1
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args.device = 'cuda:%d' % args.local_rank
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torch.cuda.set_device(args.local_rank)
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torch.distributed.init_process_group(backend='nccl', init_method='env://')
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args.world_size = torch.distributed.get_world_size()
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args.rank = torch.distributed.get_rank()
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assert args.rank >= 0
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if args.distributed:
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logging.info('Training in distributed mode with multiple processes, 1 GPU per process. Process %d, total %d.'
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% (args.rank, args.world_size))
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else:
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logging.info('Training with a single process on %d GPUs.' % args.num_gpu)
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torch.manual_seed(args.seed + args.rank)
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model = create_model(
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args.model,
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pretrained=args.pretrained,
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num_classes=args.num_classes,
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drop_rate=args.drop,
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drop_connect_rate=args.drop_connect, # DEPRECATED, use drop_path
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drop_path_rate=args.drop_path,
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drop_block_rate=args.drop_block,
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global_pool=args.gp,
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bn_tf=args.bn_tf,
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bn_momentum=args.bn_momentum,
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bn_eps=args.bn_eps,
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checkpoint_path=args.initial_checkpoint)
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if args.local_rank == 0:
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logging.info('Model %s created, param count: %d' %
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(args.model, sum([m.numel() for m in model.parameters()])))
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data_config = resolve_data_config(vars(args), model=model, verbose=args.local_rank == 0)
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num_aug_splits = 0
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if args.aug_splits > 0:
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assert args.aug_splits > 1, 'A split of 1 makes no sense'
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num_aug_splits = args.aug_splits
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if args.split_bn:
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assert num_aug_splits > 1 or args.resplit
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model = convert_splitbn_model(model, max(num_aug_splits, 2))
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if args.num_gpu > 1:
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if args.amp:
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logging.warning(
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'AMP does not work well with nn.DataParallel, disabling. Use distributed mode for multi-GPU AMP.')
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args.amp = False
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model = nn.DataParallel(model, device_ids=list(range(args.num_gpu))).cuda()
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else:
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model.cuda()
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optimizer = create_optimizer(args, model)
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use_amp = False
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if has_apex and args.amp:
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model, optimizer = amp.initialize(model, optimizer, opt_level='O1')
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use_amp = True
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if args.local_rank == 0:
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logging.info('NVIDIA APEX {}. AMP {}.'.format(
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'installed' if has_apex else 'not installed', 'on' if use_amp else 'off'))
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# optionally resume from a checkpoint
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resume_state = {}
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resume_epoch = None
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if args.resume:
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resume_state, resume_epoch = resume_checkpoint(model, args.resume)
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if resume_state and not args.no_resume_opt:
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if 'optimizer' in resume_state:
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if args.local_rank == 0:
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logging.info('Restoring Optimizer state from checkpoint')
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optimizer.load_state_dict(resume_state['optimizer'])
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if use_amp and 'amp' in resume_state and 'load_state_dict' in amp.__dict__:
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if args.local_rank == 0:
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logging.info('Restoring NVIDIA AMP state from checkpoint')
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amp.load_state_dict(resume_state['amp'])
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del resume_state
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model_ema = None
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if args.model_ema:
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# Important to create EMA model after cuda(), DP wrapper, and AMP but before SyncBN and DDP wrapper
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model_ema = ModelEma(
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model,
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decay=args.model_ema_decay,
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device='cpu' if args.model_ema_force_cpu else '',
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resume=args.resume)
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if args.distributed:
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if args.sync_bn:
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assert not args.split_bn
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try:
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if has_apex:
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model = convert_syncbn_model(model)
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else:
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model = torch.nn.SyncBatchNorm.convert_sync_batchnorm(model)
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if args.local_rank == 0:
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logging.info(
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'Converted model to use Synchronized BatchNorm. WARNING: You may have issues if using '
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'zero initialized BN layers (enabled by default for ResNets) while sync-bn enabled.')
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except Exception as e:
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logging.error('Failed to enable Synchronized BatchNorm. Install Apex or Torch >= 1.1')
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if has_apex:
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model = DDP(model, delay_allreduce=True)
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else:
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if args.local_rank == 0:
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logging.info("Using torch DistributedDataParallel. Install NVIDIA Apex for Apex DDP.")
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model = DDP(model, device_ids=[args.local_rank]) # can use device str in Torch >= 1.1
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# NOTE: EMA model does not need to be wrapped by DDP
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lr_scheduler, num_epochs = create_scheduler(args, optimizer)
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start_epoch = 0
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if args.start_epoch is not None:
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# a specified start_epoch will always override the resume epoch
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start_epoch = args.start_epoch
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elif resume_epoch is not None:
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start_epoch = resume_epoch
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if lr_scheduler is not None and start_epoch > 0:
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lr_scheduler.step(start_epoch)
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if args.local_rank == 0:
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logging.info('Scheduled epochs: {}'.format(num_epochs))
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train_dir = os.path.join(args.data, 'train')
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if not os.path.exists(train_dir):
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logging.error('Training folder does not exist at: {}'.format(train_dir))
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exit(1)
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dataset_train = Dataset(train_dir)
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collate_fn = None
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if args.prefetcher and args.mixup > 0:
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assert not num_aug_splits # collate conflict (need to support deinterleaving in collate mixup)
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collate_fn = FastCollateMixup(args.mixup, args.smoothing, args.num_classes)
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if num_aug_splits > 1:
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dataset_train = AugMixDataset(dataset_train, num_splits=num_aug_splits)
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loader_train = create_loader(
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dataset_train,
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input_size=data_config['input_size'],
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batch_size=args.batch_size,
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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,
|
|
use_multi_epochs_loader=args.use_multi_epochs_loader
|
|
)
|
|
|
|
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()
|