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eval_linprobe.py
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eval_linprobe.py
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import sys
import math
from typing import Iterable
import torch
import torch.nn as nn
import datasets as myDBs
from torchvision import transforms as vT
from util import audio_transforms as aT
from models.deepavfusion import DeepAVFusion
from models.classifier import AVClassifier
from util import distributed as dist_utils
from util import misc as misc_utils
from util import data as data_utils
from util import meters, lars, lr_sched
from timm.utils import accuracy
def main_worker(local_rank, args):
# Setup environment
job_dir = f"{args.output_dir}/{args.job_name}"
dist_utils.init_distributed_mode(local_rank, args, log_fn=f"{job_dir}/train.log")
device = torch.device('cpu') if not torch.cuda.is_available() else torch.device('cuda')
print(f'job dir: {job_dir}')
misc_utils.print_args(args)
# Adjust learning rate to batch size
num_tasks = dist_utils.get_world_size()
num_tasks_per_node = max(1, torch.cuda.device_count())
args.env.workers = args.env.workers // num_tasks_per_node
eff_batch_size = args.opt.batch_size * args.opt.accum_iter * num_tasks
if args.opt.lr is None: # only base_lr is specified
args.opt.lr = args.opt.blr * eff_batch_size / 256
print("base lr: %.2e" % args.opt.blr)
print("actual lr: %.2e" % args.opt.lr)
print("accumulate grad iterations: %d" % args.opt.accum_iter)
print("effective batch size: %d" % eff_batch_size)
# Dataloaders
dataset_train = myDBs.load_dataset(
args.data.dataset,
args.data.data_path,
dataset_type='simple',
visual_transform=vT.Compose([
vT.RandomResizedCrop(args.data.image_size, scale=(args.data.crop_min, 1.)),
vT.RandomHorizontalFlip(),
vT.ToTensor(),
vT.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])]),
audio_transform=aT.Compose([
aT.Pad(rate=args.data.audio_rate, dur=args.data.audio_dur),
aT.RandomVol(),
aT.MelSpectrogram(sample_rate=args.data.audio_rate, n_fft=int(args.data.audio_rate * 0.05), hop_length=int(args.data.audio_rate / 64), n_mels=args.data.audio_mels),
aT.Log()]),
train=True,
audio_dur=args.data.audio_dur,
audio_rate=args.data.audio_rate,
temporal_jitter=True
)
loader_train = data_utils.get_dataloader(
dataset_train, args.env.distributed, args.opt.batch_size, args.env.workers, shuffle=True, drop_last=True)
print(dataset_train)
dataset_val = myDBs.load_dataset(
args.data.dataset,
args.data.data_path,
dataset_type='simple',
visual_transform=vT.Compose([
vT.Resize(int(args.data.image_size/0.875)),
vT.CenterCrop(args.data.image_size),
vT.ToTensor(),
vT.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])]),
audio_transform=aT.Compose([
aT.Pad(rate=args.data.audio_rate, dur=args.data.audio_dur),
aT.MelSpectrogram(sample_rate=args.data.audio_rate, n_fft=int(args.data.audio_rate * 0.05), hop_length=int(args.data.audio_rate / 64), n_mels=args.data.audio_mels),
aT.Log()]),
train=False,
audio_dur=args.data.audio_dur,
audio_rate=args.data.audio_rate,
temporal_jitter=False
)
loader_val = data_utils.get_dataloader(
dataset_val, args.env.distributed, args.opt.batch_size, args.env.workers, shuffle=False, drop_last=False)
print(dataset_val)
# Create model
image_size, audio_size = (args.data.image_size, args.data.image_size), (args.data.audio_mels, int(args.data.audio_dur*64))
encoder = DeepAVFusion(
image_arch=args.model.image.backbone, image_pretrained=args.model.image.pretrained, image_size=image_size,
audio_arch=args.model.audio.backbone, audio_pretrained=args.model.audio.pretrained, audio_size=audio_size,
fusion_arch=args.model.fusion.arch,
fusion_layers=args.model.fusion.layers,
num_fusion_tkns=(args.model.fusion.num_fusion_tkns,
args.model.fusion.num_aggr_image_tkns,
args.model.fusion.num_aggr_audio_tkns),
fusion_mlp_ratio=args.model.fusion.mlp_ratio,
fusion_attn_ratio=args.model.fusion.attn_ratio,
fusion_num_heads=args.model.fusion.num_heads
)
model = AVClassifier(encoder, myDBs.NUM_CLASSES[args.data.dataset], freeze_encoder=True, input_norm=True)
model.to(device)
print("Model = %s" % str(model))
if args.checkpoint or args.pretrain_job_name:
pretrain_ckpt = args.checkpoint or f"{args.output_dir}/checkpoints/checkpoint_{args.pretrain_resume_epoch}.pth"
encoder.load_checkpoint(pretrain_ckpt, args.encoder_prefix)
# criterion
criterion = nn.BCEWithLogitsLoss() if myDBs.MULTI_CLASS_DBS[args.data.dataset] else nn.CrossEntropyLoss()
print("criterion = %s" % str(criterion))
# Optimizer
parameters = list(filter(lambda p: p.requires_grad, model.parameters()))
assert len(parameters) == 6
optimizer = lars.LARS(parameters, lr=args.opt.lr, weight_decay=args.opt.weight_decay)
# Trainer
trainer = misc_utils.Trainer(
model,
criterion=criterion,
optimizer=optimizer,
use_amp=args.opt.use_amp,
accum_iter=args.opt.accum_iter,
distributed=args.env.distributed
)
# Checkpointing
ckpt_manager = misc_utils.CheckpointManager(
modules=trainer.module_dict(),
ckpt_dir=f"{job_dir}/checkpoints",
epochs=args.opt.epochs,
save_freq=args.log.save_freq)
start_epoch = ckpt_manager.resume()[0] if args.opt.resume else 0
wb_logger = misc_utils.WBLogger(
f"{job_dir}/wandb", args.log.wandb_entity, args.log.wandb_project+'-linprobe', args.job_name,
model, args)
if args.eval:
evaluate(trainer.eval_model, loader_val, start_epoch, device, args)
exit(0)
# =============================================================== #
# Training loop
print(f"Start training for {args.opt.epochs} epochs")
for epoch in range(start_epoch, args.opt.epochs):
if args.env.distributed:
loader_train.sampler.set_epoch(epoch)
# train for one epoch
train_one_epoch(trainer, loader_train, epoch,
device=device, wb_logger=wb_logger, args=args)
# evaluate
if epoch % args.log.eval_freq == 0 or epoch == args.opt.epochs - 1 or epoch == start_epoch:
global_step = (len(loader_train) // trainer.accum_iter) * (epoch + 1)
test_stats = evaluate(trainer.eval_model, loader_val, epoch, device, args)
wb_logger.log(test_stats, step=global_step, force=True)
# save checkpoint
ckpt_manager.checkpoint(epoch+1, {'epoch': epoch+1})
def train_one_epoch(trainer: misc_utils.Trainer,
loader: Iterable,
epoch: int = 0,
wb_logger: misc_utils.WBLogger = None,
device: torch.device = torch.device('cpu'),
args=None):
trainer.model.train(True)
header = f'[Train][Ep-{epoch}/{args.opt.epochs}]'
metric_logger = meters.MetricLogger(delimiter=" ")
metric_logger.add_meter('lr', meters.SmoothedValue(window_size=1, fmt='{value:.6f}'))
trainer.zero_grad()
for step, (image, audio, anno) in enumerate(metric_logger.log_every(loader, args.log.print_freq, header)):
sys.stdout.flush()
global_step = (len(loader) // trainer.accum_iter) * epoch + step // trainer.accum_iter
if step % args.opt.accum_iter == 0:
lr = lr_sched.adjust_learning_rate(trainer.optimizer, epoch + step / len(loader), args)
metric_logger.update(lr=lr)
# Prepare data
image = image.to(device, non_blocking=True).float()
audio = audio.to(device, non_blocking=True).float()
targets = anno['class'].to(device, non_blocking=True)
targets = targets.float() if myDBs.MULTI_CLASS_DBS[args.data.dataset] else targets.long()
# Forward pass
with trainer.autocast(), trainer.autosync():
preds_image, preds_audio, preds_fusion = trainer.model(image, audio)
preds = (preds_image + preds_audio + preds_fusion) / 3.
loss = trainer.criterion(preds, targets)
if not math.isfinite(loss.item()):
raise f"Loss is {loss.item()}, stopping training"
# Backward pass and model update
grad_norm, amp_scale = trainer.step(loss)
# Log
if trainer.accums == 0:
metric_logger.update(loss=loss.item(), grad_norm=grad_norm, amp_scale=amp_scale, n=image.shape[0])
wb_logger.log(metric_logger.latest(), step=global_step)
if args.debug and step == 100:
break
# gather the stats from all processes
print("Syncing meters...")
metric_logger.synchronize_between_processes()
print("Averaged stats:", metric_logger)
trainer.zero_grad()
return metric_logger.averages()
@torch.no_grad()
def evaluate(model: nn.Module,
loader: Iterable,
epoch: int = 0,
device: torch.device = torch.device('cpu'),
args=None):
model.train(False)
metric_logger = meters.MetricLogger(delimiter=" ")
header = f'[Eval][Ep-{epoch}/{args.opt.epochs}]'
preds_all, preds_image_all, preds_audio_all, preds_fusion_all, labels_all = [], [], [], [], []
for step, (image, audio, anno) in enumerate(metric_logger.log_every(loader, args.log.print_freq, header)):
image = image.to(device, non_blocking=True).float()
audio = audio.to(device, non_blocking=True).float()
label = anno['class'].to(device, non_blocking=True).long()
preds_image, preds_audio, preds_fusion = model(image, audio)
preds = (preds_image + preds_audio + preds_fusion) / 3.
preds_image_all.append(preds_image), preds_audio_all.append(preds_audio), preds_fusion_all.append(preds_fusion)
preds_all.append(preds), labels_all.append(label)
if args.debug and step == 8:
break
# Synchronize across gpus
preds_image_all = dist_utils.concat_all_gather(torch.cat(preds_image_all))
preds_audio_all = dist_utils.concat_all_gather(torch.cat(preds_audio_all))
preds_fusion_all = dist_utils.concat_all_gather(torch.cat(preds_fusion_all))
preds_all = dist_utils.concat_all_gather(torch.cat(preds_all))
labels_all = dist_utils.concat_all_gather(torch.cat(labels_all))
# measure performance
stats = dict()
if myDBs.MULTI_CLASS_DBS[args.data.dataset]:
labels_all = labels_all.cpu().numpy()
for mod, preds in [('image', preds_image_all), ('audio', preds_audio_all), ('fusion', preds_fusion_all), ('all', preds_all)]:
preds = preds.cpu().numpy()
stats_mod = misc_utils.calc_multi_class_stats(labels_all, preds)
stats.update({f'{k}_{mod}': v for k, v in stats_mod.items()})
else:
stats.update(
val_acc1_image=accuracy(preds_image_all, labels_all)[0].item(),
val_acc1_audio=accuracy(preds_audio_all, labels_all)[0].item(),
val_acc1_fusion=accuracy(preds_fusion_all, labels_all)[0].item(),
val_acc1_all=accuracy(preds_all, labels_all)[0].item(),
)
prefix = 'val_'
stats = {f"{prefix}{k}": v for k, v in stats.items()}
msg = ' | '.join([f'{k}={v:.2f}' for k, v in stats.items()])
print(f"{header} {msg}")
return stats