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auto_color_main.py
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auto_color_main.py
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'''
By danczs (https://github.com/danczs)
References:
https://github.com/facebookresearch/mae
https://github.com/openai/CLIP
https://github.com/Lednik7/CLIP-ONNX
https://github.com/rwightman/pytorch-image-models
'''
import argparse
import os
import numpy as np
import torch
import torch.backends.cudnn as cudnn
import util.misc as misc
from util.dataset_autocolor import build_dataset
import timm.optim.optim_factory as optim_factory
from util.misc import NativeScalerWithGradNormCount as NativeScaler
import util.lr_sched as lr_sched
from color_decoder import mae_color_decoder_base
from super_color import SuperColor
import torch.nn.functional as F
#
def get_args_parser():
parser = argparse.ArgumentParser('MAE fine-tuning for image classification', add_help=False)
parser.add_argument('--batch_size', default=64, type=int,
help='Batch size per GPU (effective batch size is batch_size * accum_iter * # gpus')
parser.add_argument('--epochs', default=10, type=int)
parser.add_argument('--input_size', default=224, type=int,
help='images input size')
parser.add_argument('--input_size_supercolor', default=448, type=int,
help='images input size')
parser.add_argument('--data_path', default='E://data//carton_subset//train', type=str,
help='dataset path')
parser.add_argument('--nb_classes', default=1000, type=int,
help='number of the classification types')
parser.add_argument('--drop_path', type=float, default=0.1, metavar='PCT',
help='Drop path rate (default: 0.1)')
parser.add_argument('--output_dir', default=None, type=str,
help='the output dir of models and logs')
parser.add_argument('--eval', action='store_true', help='evaluete the model')
parser.add_argument('--colormask_prob', type=float, default=0.1, metavar='PCT',
help='the hyper-paramter of generating a colormask')
parser.add_argument('--mae_feature_path', default=None, type=str,
help='the mae feature path')
parser.add_argument('--clip_feature_path', default=None, type=str,
help='the clip feature path')
parser.add_argument('--mae_model_path', default=None, type=str,
help='the clip model')
parser.add_argument('--clip_model_path', default=None, type=str,
help='the clip feature path')
parser.add_argument('--colordecoder_model_path', default=None, type=str,
help='the initialization of color decoder weights. \
If not specified, it will use the pre-trained mae decoder weights.')
parser.add_argument('--supercolor_model_path', default=None, type=str,
help='the initialization of the super color weights')
parser.add_argument('--grad_state', default='010', type=str,
help=' whether or not to train mae encoder, mae color decoder and super color. \
e.g. 010 indicates only training the color decoder')
parser.add_argument('--supercolor_only',action='store_true',
help='only train or eval the supercolor model')
# Optimizer parameters
parser.add_argument('--clip_grad', type=float, default=None, metavar='NORM',
help='Clip gradient norm (default: None, no clipping)')
parser.add_argument('--weight_decay', type=float, default=0.05,
help='weight decay (default: 0.05)')
parser.add_argument('--blr_cd', type=float, default=5e-3, metavar='LR',
help='base learning rate color decoder: absolute_lr = base_lr * total_batch_size / 256')
parser.add_argument('--blr_sc', type=float, default=1e-1, metavar='LR',
help='base learning rate of super color: absolute_lr = base_lr * total_batch_size / 256')
parser.add_argument('--layer_decay', type=float, default=0.75,
help='layer-wise lr decay from ELECTRA/BEiT')
parser.add_argument('--min_lr', type=float, default=1e-6, metavar='LR',
help='lower lr bound for cyclic schedulers that hit 0')
parser.add_argument('--warmup_epochs', type=int, default=1, metavar='N',
help='epochs to warmup LR')
parser.add_argument('--alpha', type=float, default=0.5,
help='hyper-parameter to balance L1 loss and L2 loss')
parser.add_argument('--accum_iter', default=1, type=int,
help='Accumulate gradient iterations (for increasing the effective batch size under memory constraints)')
parser.add_argument('--seed', default=0, type=int)
return parser
def main(args):
# fix the seed for reproducibility
seed = args.seed + misc.get_rank()
torch.manual_seed(seed)
np.random.seed(seed)
cudnn.benchmark = True
#dataset anda data loader
dataset = build_dataset(args=args)
if args.eval:
sampler = torch.utils.data.SequentialSampler(dataset)
else:
sampler = torch.utils.data.RandomSampler(dataset)
data_loader = torch.utils.data.DataLoader(
dataset, sampler=sampler,
batch_size=args.batch_size,
num_workers=2,
pin_memory=True,
drop_last= not args.eval
)
device = "cuda" if torch.cuda.is_available() else "cpu"
# mae model
assert len(args.grad_state) == 3
mae_eval, color_decoder_eval, super_color_eval = [i=='0' for i in args.grad_state]
#mae encoder model
if args.mae_feature_path is None:
from mae_encoder import mae_vit_base_patch16_dec512d8b
mae_model = mae_vit_base_patch16_dec512d8b()
mae_weights = torch.load(args.mae_model_path, map_location='cpu')['model']
msg = mae_model.load_state_dict(mae_weights, strict=False)
print(msg)
if mae_eval or args.eval:
mae_model.eval()
mae_model = mae_model.to(device)
# build color decoder model
if not args.supercolor_only:
color_decoder = mae_color_decoder_base()
if args.colordecoder_model_path is None:
mae_weights = torch.load(args.mae_model_path, map_location='cpu')['model']
del mae_weights['decoder_pos_embed']
msg = color_decoder.load_state_dict(mae_weights, strict=False)
print(msg)
else:
color_decoder_weight = torch.load(args.colordecoder_model_path, map_location='cpu')
msg = color_decoder.load_state_dict(color_decoder_weight, strict=False)
print(msg)
if color_decoder_eval or args.eval:
color_decoder.eval()
color_decoder = color_decoder.to(device)
#build supercolor model
if not super_color_eval or args.eval:
super_color = SuperColor(kernel_size=5, group=4)
if args.supercolor_model_path:
super_color_checkpoint = torch.load(args.supercolor_model_path, map_location='cpu')
msg = super_color.load_state_dict(super_color_checkpoint, strict=False)
print(msg)
if args.eval:
super_color.eval()
super_color = super_color.to(device)
if color_decoder_eval is False:
lr_cd = args.blr_cd * args.batch_size / 256
param_groups_cd = optim_factory.param_groups_weight_decay(color_decoder,weight_decay=args.weight_decay)
optimizer_cd = torch.optim.AdamW(param_groups_cd, lr=lr_cd, betas=(0.9, 0.95))
else:
optimizer_cd = None
if super_color_eval is False:
lr_sc = args.blr_sc * args.batch_size / 256
param_groups_sc = optim_factory.param_groups_weight_decay(super_color, weight_decay=args.weight_decay)
optimizer_sc = torch.optim.AdamW(param_groups_sc, lr=lr_sc, betas=(0.9, 0.95))
else:
optimizer_sc = None
loss_scaler = NativeScaler()
print(f"Start training for {args.epochs} epochs")
for epoch in range(args.epochs):
avg_loss =0
for iter_step, (mae_feature, clip_feature, color_mask, img_l, img_l_gray, img_h, img_h_gray, target) in enumerate(data_loader):
color_mask = color_mask.to(device, non_blocking=True)
img_l = img_l.to(device, non_blocking=True)
img_h = img_h.to(device, non_blocking=True)
img_l_gray = img_l_gray.to(device, non_blocking=True)
img_h_gray = img_h_gray.to(device, non_blocking=True)
clip_feature = clip_feature.to(device, non_blocking=True)
if iter_step % args.accum_iter == 0:
if optimizer_cd is not None:
lr_sched.adjust_learning_rate(optimizer_cd, iter_step / len(data_loader) + epoch, lr_cd, args)
if optimizer_sc is not None:
lr_sched.adjust_learning_rate(optimizer_sc, iter_step / len(data_loader) + epoch, lr_sc, args)
if args.mae_feature_path is not None:
mae_feature = mae_feature.to(device,non_blocking=True)
else:
with torch.cuda.amp.autocast():
mae_feature = mae_model(img_l_gray)
with torch.cuda.amp.autocast():
if not args.supercolor_only:
pred = color_decoder(mae_feature, clip_feature, color_mask=color_mask)
pred_upsampling = F.interpolate(pred + img_l_gray, size=(img_h.size()[2:]))
if not color_decoder_eval or args.eval:
loss_decoder = color_decoder.forward_loss(pred, img_l_gray, img_l,alpha=args.alpha)
else:
pred_upsampling = F.interpolate(img_l, size=(img_h.size()[2:]))
if not super_color_eval or args.eval:
color_mask_sc = F.interpolate(color_mask, size=(img_h.size()[2:]))
sc_pred = super_color(pred_upsampling.detach(), img_h_gray, color_mask_sc) #detach cd and sc
loss_sc = super_color.forward_loss(sc_pred, img_h_gray, img_h,alpha=args.alpha)
if args.eval:
if loss_decoder is not None:
avg_loss += loss_decoder
if loss_sc is not None:
avg_loss += loss_sc
continue
if optimizer_cd is not None:
loss_decoder /= args.accum_iter
loss_scaler(loss_decoder, optimizer_cd, parameters=color_decoder.parameters(),
update_grad=(iter_step + 1) % args.accum_iter == 0)
if (iter_step + 1) % args.accum_iter == 0:
optimizer_cd.zero_grad()
avg_loss += loss_decoder.detach().item()
lr = optimizer_cd.param_groups[0]["lr"]
if iter_step % 20 == 0:
print('epoch:{} iter:{} color deocder loss:{} lr:{}'.format(epoch, iter_step, loss_decoder, lr))
if optimizer_sc is not None:
loss_sc /= args.accum_iter
loss_scaler(loss_sc, optimizer_sc, parameters=super_color.parameters(),
update_grad=(iter_step + 1) % args.accum_iter == 0)
if (iter_step + 1) % args.accum_iter == 0:
optimizer_sc.zero_grad()
avg_loss += loss_sc.detach().item()
lr = optimizer_sc.param_groups[0]["lr"]
if iter_step % 20 == 0:
print('epoch:{} iter:{} super color loss:{} lr:{}'.format(epoch, iter_step, loss_sc, lr))
torch.cuda.synchronize()
print('epoch:{} avg loss:{}'.format(epoch,avg_loss/len(data_loader)))
if args.eval:
break
if optimizer_cd:
torch.save(color_decoder.state_dict(),
os.path.join(args.output_dir,'colordecoder_alpha{}_lr{}_p{}.pth'.format(args.alpha,args.blr_cd,args.colormask_prob)))
if optimizer_sc:
torch.save(super_color.state_dict(),
os.path.join(args.output_dir,'supercolor_alpha{}_lr{}_p{}.pth'.format(args.alpha, args.blr_sc,args.colormask_prob)))
if __name__=='__main__':
args = get_args_parser()
args = args.parse_args()
main(args)