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trainer.py
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trainer.py
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import argparse
import ast
from datetime import datetime
import pandas as pd
import torch
import torchbearer
from torch import nn, optim
from torchbearer import Trial
from torchbearer.callbacks import MultiStepLR, CosineAnnealingLR
from torchbearer.callbacks import TensorBoard, TensorBoardText, Cutout, CutMix, RandomErase, on_forward_validation
from datasets.datasets import ds, dsmeta, nlp_data
from implementations.torchbearer_implementation import FMix, PointNetFMix
from models.models import get_model
from utils import RMixup, MSDAAlternator, WarmupLR
from datasets.toxic import ToxicHelper
# Setup
parser = argparse.ArgumentParser(description='PyTorch CIFAR10 Training')
parser.add_argument('--dataset', type=str, default='cifar10',
choices=['cifar10', 'cifar100', 'reduced_cifar', 'fashion', 'imagenet', 'imagenet_hdf5', 'imagenet_a', 'tinyimagenet',
'commands', 'modelnet', 'toxic', 'toxic_bert', 'bengali_r', 'bengali_c', 'bengali_v', 'imdb', 'yelp_2', 'yelp_5'])
parser.add_argument('--dataset-path', type=str, default=None, help='Optional dataset path')
parser.add_argument('--split-fraction', type=float, default=1., help='Fraction of total data to train on for reduced_cifar dataset')
parser.add_argument('--pointcloud-resolution', default=128, type=int, help='Resolution of pointclouds in modelnet dataset')
parser.add_argument('--model', default="ResNet18", type=str, help='model type')
parser.add_argument('--epoch', default=200, type=int, help='total epochs to run')
parser.add_argument('--train-steps', type=int, default=None, help='Number of training steps to run per "epoch"')
parser.add_argument('--lr', default=0.1, type=float, help='learning rate')
parser.add_argument('--lr-warmup', type=ast.literal_eval, default=False, help='Use lr warmup')
parser.add_argument('--batch-size', default=128, type=int, help='batch size')
parser.add_argument('--device', default='cuda', type=str, help='Device on which to run')
parser.add_argument('--num-workers', default=7, type=int, help='Number of dataloader workers')
parser.add_argument('--auto-augment', type=ast.literal_eval, default=False, help='Use auto augment with cifar10/100')
parser.add_argument('--augment', type=ast.literal_eval, default=True, help='use standard augmentation (default: True)')
parser.add_argument('--parallel', type=ast.literal_eval, default=False, help='Use DataParallel')
parser.add_argument('--reload', type=ast.literal_eval, default=False, help='Set to resume training from model path')
parser.add_argument('--verbose', type=int, default=2, choices=[0, 1, 2])
parser.add_argument('--seed', default=0, type=int, help='random seed')
# Augs
parser.add_argument('--random-erase', default=False, type=ast.literal_eval, help='Apply random erase')
parser.add_argument('--cutout', default=False, type=ast.literal_eval, help='Apply Cutout')
parser.add_argument('--msda-mode', default=None, type=str, choices=['fmix', 'cutmix', 'mixup', 'alt_mixup_fmix',
'alt_mixup_cutmix', 'alt_fmix_cutmix', 'None'])
# Aug Params
parser.add_argument('--alpha', default=1., type=float, help='mixup/fmix interpolation coefficient')
parser.add_argument('--f-decay', default=3.0, type=float, help='decay power')
parser.add_argument('--cutout_l', default=16, type=int, help='cutout/erase length')
parser.add_argument('--reformulate', default=False, type=ast.literal_eval, help='Use reformulated fmix/mixup')
# Scheduling
parser.add_argument('--schedule', type=int, nargs='+', default=[100, 150], help='Decrease learning rate at these epochs.')
parser.add_argument('--cosine-scheduler', type=ast.literal_eval, default=False, help='Set to use a cosine scheduler instead of step scheduler')
# Cross validation
parser.add_argument('--fold-path', type=str, default='./data/folds.npz', help='Path to object storing fold ids. Run-id 0 will regen this if not existing')
parser.add_argument('--n-folds', type=int, default=6, help='Number of cross val folds')
parser.add_argument('--fold', type=str, default='test', help='One of [1, ..., n] or "test"')
# Logs
parser.add_argument('--run-id', type=int, default=0, help='Run id')
parser.add_argument('--log-dir', default='./logs/testing', help='Tensorboard log dir')
parser.add_argument('--model-file', default='./saved_models/model.pt', help='Path under which to save model. eg ./model.py')
args = parser.parse_args()
if args.seed != 0:
torch.manual_seed(args.seed)
print('==> Preparing data..')
data = ds[args.dataset]
meta = dsmeta[args.dataset]
classes, nc, size = meta['classes'], meta['nc'], meta['size']
trainset, valset, testset = data(args)
# Toxic comments uses its own data loaders
trainloader = torch.utils.data.DataLoader(trainset, batch_size=args.batch_size, shuffle=True, num_workers=args.num_workers) if (trainset is not None) and (args.dataset not in nlp_data) else trainset
valloader = torch.utils.data.DataLoader(valset, batch_size=args.batch_size, shuffle=True, num_workers=args.num_workers) if (valset is not None) and (args.dataset not in nlp_data) else valset
testloader = torch.utils.data.DataLoader(testset, batch_size=args.batch_size, shuffle=True, num_workers=args.num_workers) if (args.dataset not in nlp_data) else testset
print('==> Building model..')
net = get_model(args, classes, nc)
net = nn.DataParallel(net) if args.parallel else net
optimizer = optim.SGD(net.parameters(), lr=args.lr, momentum=0.9, weight_decay=1e-4)
if (args.dataset in nlp_data) or ('modelnet' in args.dataset):
optimizer = optim.Adam(net.parameters(), lr=args.lr)
print('==> Setting up callbacks..')
current_time = datetime.now().strftime('%b%d_%H-%M-%S') + "-run-" + str(args.run_id)
tboard = TensorBoard(write_graph=False, comment=current_time, log_dir=args.log_dir)
tboardtext = TensorBoardText(write_epoch_metrics=False, comment=current_time, log_dir=args.log_dir)
@torchbearer.callbacks.on_start
def write_params(_):
params = vars(args)
params['schedule'] = str(params['schedule'])
df = pd.DataFrame(params, index=[0]).transpose()
tboardtext.get_writer(tboardtext.log_dir).add_text('params', df.to_html(), 1)
modes = {
'fmix': FMix(decay_power=args.f_decay, alpha=args.alpha, size=size, max_soft=0, reformulate=args.reformulate),
'mixup': RMixup(args.alpha, reformulate=args.reformulate),
'cutmix': CutMix(args.alpha, classes, True),
'pointcloud_fmix': PointNetFMix(args.pointcloud_resolution, decay_power=args.f_decay, alpha=args.alpha, max_soft=0,
reformulate=args.reformulate)
}
modes.update({
'alt_mixup_fmix': MSDAAlternator(modes['fmix'], modes['mixup']),
'alt_mixup_cutmix': MSDAAlternator(modes['mixup'], modes['cutmix']),
'alt_fmix_cutmix': MSDAAlternator(modes['fmix'], modes['cutmix']),
})
# Pointcloud fmix converts voxel grids back into point clouds after mixing
mode = 'pointcloud_fmix' if (args.msda_mode == 'fmix' and args.dataset == 'modelnet') else args.msda_mode
# CutMix callback returns mixed and original targets. We mix in the loss function instead
@torchbearer.callbacks.on_sample
def cutmix_reformat(state):
state[torchbearer.Y_TRUE] = state[torchbearer.Y_TRUE][0]
cb = [tboard, tboardtext, write_params, torchbearer.callbacks.MostRecent(args.model_file)]
# Toxic helper needs to go before the msda to reshape the input
cb.append(ToxicHelper(to_float=args.dataset != 'yelp_5')) if (args.dataset in ['toxic', 'imdb', 'yelp_2', 'yelp_5']) else []
cb.append(modes[mode]) if args.msda_mode not in [None, 'None'] else []
cb.append(Cutout(1, args.cutout_l)) if args.cutout else []
cb.append(RandomErase(1, args.cutout_l)) if args.random_erase else []
# WARNING: Schedulers appear to be broken (wrong lr output) in some versions of PyTorch, including 1.4. We used 1.3.1
cb.append(MultiStepLR(args.schedule)) if not args.cosine_scheduler else cb.append(CosineAnnealingLR(args.epoch, eta_min=0.))
cb.append(WarmupLR(0.1, args.lr)) if args.lr_warmup else []
cb.append(cutmix_reformat) if args.msda_mode == 'cutmix' else []
# FMix loss is equivalent to mixup loss and works for all msda in torchbearer
if args.msda_mode not in [None, 'None']:
bce = True if (args.dataset in ['toxic', 'toxic_bert', 'imdb', 'yelp_2']) else False
criterion = modes['fmix'].loss(bce)
elif args.dataset in ['toxic', 'toxic_bert', 'imdb', 'yelp_2']:
criterion = nn.BCEWithLogitsLoss()
else:
criterion = nn.CrossEntropyLoss()
metrics_append = []
if 'bengali' in args.dataset:
from utils.macro_recall import MacroRecall
metrics_append = [MacroRecall()]
elif 'imagenet' in args.dataset:
metrics_append = ['top_5_acc']
elif 'toxic' in args.dataset:
from torchbearer.metrics import to_dict, EpochLambda
@to_dict
class RocAucScore(EpochLambda):
def __init__(self):
import sklearn.metrics
super().__init__('roc_auc_score',
lambda y_pred, y_true: sklearn.metrics.roc_auc_score(y_true.cpu().numpy(), y_pred.detach().sigmoid().cpu().numpy()),
running=False)
metrics_append = [RocAucScore()]
if args.dataset == 'imagenet_a':
from datasets.imagenet_a import indices_in_1k
@on_forward_validation
def map_preds(state):
state[torchbearer.PREDICTION] = state[torchbearer.PREDICTION][:, indices_in_1k]
cb.append(map_preds)
print('==> Training model..')
acc = 'acc'
if args.dataset in ['toxic', 'toxic_bert', 'imdb', 'yelp_2']:
acc = 'binary_acc'
trial = Trial(net, optimizer, criterion, metrics=[acc, 'loss', 'lr'] + metrics_append, callbacks=cb)
trial.with_generators(train_generator=trainloader, val_generator=valloader, train_steps=args.train_steps, test_generator=testloader).to(args.device)
if args.reload:
state = torch.load(args.model_file)
trial.load_state_dict(state, resume=args.dataset != 'imagenet_a')
trial.replay()
if trainloader is not None:
trial.run(args.epoch, verbose=args.verbose)
trial.evaluate(data_key=torchbearer.TEST_DATA)