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[draft] Add q_ViT implementation #2

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[draft] Add q_ViT implementation #2

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114 changes: 5 additions & 109 deletions quant_train.py
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Original file line number Diff line number Diff line change
Expand Up @@ -174,8 +174,8 @@
help='path to latest fp32 checkpoint (default: none)')
best_acc1 = 0

arch_dict = {'q_resmlp': resmlp_24, 'q_resmlp_norm': resmlp_24_norm, 'q_resmlp_v2': resmlp_24, 'q_resmlp_v3': resmlp_24, 'q_resmlp_v4': resmlp_24_v4}
quantize_arch_dict = {'q_resmlp': q_resmlp, 'q_resmlp_norm': q_resmlp_norm, 'q_resmlp_v2': q_resmlp_v2, 'q_resmlp_v3': q_resmlp_v3, 'q_resmlp_v4': q_resmlp_v4}
arch_dict = {'q_resmlp': resmlp_24, 'q_resmlp_norm': resmlp_24_norm, 'q_resmlp_v2': resmlp_24, 'q_resmlp_v3': resmlp_24, 'q_resmlp_v4': resmlp_24_v4, 'q_deit_s' : deit_small_patch16_224}
quantize_arch_dict = {'q_resmlp': q_resmlp, 'q_resmlp_norm': q_resmlp_norm, 'q_resmlp_v2': q_resmlp_v2, 'q_resmlp_v3': q_resmlp_v3, 'q_resmlp_v4': q_resmlp_v4, 'q_deit_s' : q_vit}

args = parser.parse_args()
if not os.path.exists(args.save_path):
Expand Down Expand Up @@ -234,12 +234,12 @@ def main_worker(gpu, ngpus_per_node, args):
logging.info("=> using pre-trained model '{}'".format(args.arch))
arch = arch_dict[args.arch]
model = arch(pretrained=True)

print(model)
else:
logging.info("=> creating model '{}'".format(args.arch))
arch = arch_dict[args.arch]
model = arch(pretrained=False)

if args.load_pretrain:
logging.info("=> loading fp32 checkpoint '{}'".format(args.load_pretrain))
model.load_state_dict(torch.load(args.load_pretrain)['model'])
Expand Down Expand Up @@ -284,6 +284,7 @@ def main_worker(gpu, ngpus_per_node, args):
quantize_arch = quantize_arch_dict[args.arch]
model = quantize_arch(model)

print(model)
# if args.arch == "q_resmlp_v4":
# for i in range(0, 24):
# model.blocks[i].inner.weight.requires_grad = False
Expand Down Expand Up @@ -533,111 +534,6 @@ def train(train_loader, model, criterion, optimizer, epoch, args):



# def train_kd(train_loader, model, teacher, criterion, optimizer, epoch, val_loader, args, ngpus_per_node,
# dataset_length):
# batch_time = AverageMeter('Time', ':6.3f')
# data_time = AverageMeter('Data', ':6.3f')
# losses = AverageMeter('Loss', ':.4e')
# top1 = AverageMeter('Acc@1', ':6.2f')
# top5 = AverageMeter('Acc@5', ':6.2f')
# progress = ProgressMeter(
# len(train_loader),
# [batch_time, data_time, losses, top1, top5],
# prefix="Epoch: [{}]".format(epoch))

# # switch to train mode
# if args.fix_BN == True:
# model.eval()
# else:
# model.train()
# teacher.eval()

# end = time.time()

# for i, (images, target) in enumerate(train_loader):
# # measure data loading time
# data_time.update(time.time() - end)

# if args.gpu is not None:
# images = images.cuda(args.gpu, non_blocking=True)
# target = target.cuda(args.gpu, non_blocking=True)

# # compute output
# output = model(images)
# if args.distill_method != 'None':
# with torch.no_grad():
# teacher_output = teacher(images)

# if args.distill_method == 'None':
# loss = criterion(output, target)
# elif args.distill_method == 'KD_naive':
# loss = loss_kd(output, target, teacher_output, args)
# else:
# raise NotImplementedError

# # measure accuracy and record loss
# acc1, acc5 = accuracy(output, target, topk=(1, 5))
# losses.update(loss.item(), images.size(0))
# top1.update(acc1[0], images.size(0))
# top5.update(acc5[0], images.size(0))

# # compute gradient and do SGD step
# optimizer.zero_grad()
# loss.backward()
# optimizer.step()

# # measure elapsed time
# batch_time.update(time.time() - end)
# end = time.time()

# if i % args.print_freq == 0:
# progress.display(i)
# if i % args.print_freq == 0 and args.rank == 0:
# print('Epoch {epoch_} [{iters}] Acc@1 {top1.avg:.3f} Acc@5 {top5.avg:.3f}'.format(epoch_=epoch, iters=i,
# top1=top1, top5=top5))
# if args.wandb:
# to_log = {
# "train_loss": loss.item(),
# "train_acc1": acc1[0],
# "train_acc5": acc5[0]
# }

# scales = model.get_scales()
# # for i, scale in enumerate(scales):
# # to_log[f"train_quant/scale_{i}"] = scale
# # wandb.log(to_log)

# if i % ((dataset_length // (
# args.batch_size * args.evaluate_times)) + 2) == 0 and i > 0 and args.evaluate_times > 0:
# acc1 = validate(val_loader, model, criterion, args)

# # switch to train mode
# if args.fix_BN == True:
# model.eval()
# else:
# model.train()

# # remember best acc@1 and save checkpoint
# global best_acc1
# is_best = acc1 > best_acc1
# best_acc1 = max(acc1, best_acc1)

# if not args.multiprocessing_distributed or (args.multiprocessing_distributed
# and args.rank % ngpus_per_node == 0):
# if not os.path.exists(args.save_path):
# os.makedirs(args.save_path)

# save_checkpoint({
# 'epoch': epoch + 1,
# 'arch': args.arch,
# 'state_dict': model.state_dict(),
# 'best_acc1': best_acc1,
# 'optimizer': optimizer.state_dict(),
# }, is_best, args.save_path)
# # print(model.state_dict())
# print("Saved checkpoint.")


def validate(val_loader, model, criterion, args):
batch_time = AverageMeter('Time', ':6.3f')
losses = AverageMeter('Loss', ':.4e')
Expand Down
5 changes: 4 additions & 1 deletion src/models/__init__.py
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Original file line number Diff line number Diff line change
Expand Up @@ -12,4 +12,7 @@
from .q_resmlp_v2_5 import *

from .resmlp_model_v4 import *
from .q_resmlp_v4 import *
from .q_resmlp_v4 import *

from .vit import *
from .q_vit import *
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