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train_fvit.py
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train_fvit.py
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import torch
from module.ViT import SharedFViT, TwoFViT
import torch.optim as optim
import torch.nn as nn
import argparse
import yaml
from time import time
import glob
import os
from tqdm import tqdm
from torch.utils.data import DataLoader
from dataset.vins_dataset import VinsDataset
from tensorboardX import SummaryWriter
from losses.loss_functions import Shrinkage_loss
def main(configs):
_debug = False
# log writer
writer = SummaryWriter()
# model and data configuration
m_configs = yaml.safe_load(open(configs.model_cfg, 'r'))
model_cfg = m_configs['model']
data_cfg = m_configs['data']
# hyper parameters
learning_rate = float(model_cfg['learning_rate'])
weight_decay = float(model_cfg['weight_decay'])
intermediate_channels = list(model_cfg['intermediate_channels'])
num_patches = int(model_cfg['num_patches'])
patch_size = int(model_cfg['patch_size'])
pos_dim = int(model_cfg['pos_dim'])
emb_dim = int(model_cfg['emb_dim'])
code_dim = int(model_cfg['code_dim'])
depth = int(model_cfg['depth'])
heads = int(model_cfg['heads'])
mlp_dim = int(model_cfg['mlp_dim'])
pool = model_cfg['pool']
channels = int(model_cfg['channels'])
dim_head = int(model_cfg['dim_head'])
dropout = float(model_cfg['dropout'])
emb_dropout = float(model_cfg['emb_dropout'])
batch_size = int(model_cfg['batch_size'])
max_epochs = int(model_cfg['max_epochs'])
epsilon_w = float(model_cfg['epsilon_w'])
momentum = float(model_cfg['momentum'])
scheduler_gamma = float(model_cfg['scheduler_gamma'])
shrinkage_a = float(model_cfg['shrinkage_a'])
shrinkage_c = float(model_cfg['shrinkage_c'])
# device of model
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
# initialize model
model = SharedFViT(
num_patches=num_patches,
patch_size=patch_size,
pos_dim=pos_dim,
emb_dim=emb_dim,
code_dim=code_dim,
depth=depth,
heads=heads,
mlp_dim=mlp_dim,
pool=pool,
channels=channels, # rgbd
dim_head=dim_head,
dropout=dropout,
emb_dropout=emb_dropout
)
# loss function define
criterion = torch.nn.MSELoss().to(device)
# criterion = Shrinkage_loss(shrinkage_a, shrinkage_c).to(device)
# optimizer define
optimizer = optim.SGD(model.parameters(), lr=learning_rate, momentum=momentum, weight_decay=weight_decay)
scheduler = optim.lr_scheduler.ExponentialLR(optimizer, scheduler_gamma)
model.to(device)
criterion.to(device)
# prepare the data
""" dummy test data
img = torch.randn(1, 128, 16, 16, 4).to(device)
pos = torch.randn(1, 128, 3).to(device)
img2 = torch.randn(1, 128, 16, 16, 4).to(device)
pos2 = torch.randn(1, 128, 3).to(device)
preds = model(img, pos, img2, pos2).to(device) # (1, 1000)
gt_score = torch.tensor([[0.01]]).to(device)
print(preds.shape)
"""
# training dataset and validation dataset
train_data_root = data_cfg['train_dataset']
val_data_root = data_cfg['val_dataset']
ckpt_out_path = data_cfg['ckpt_output']
summary_out_path = data_cfg['summary_output']
train_dataset = VinsDataset(train_data_root, split='train')
val_dataset = VinsDataset(val_data_root, split='val')
train_dataloader = DataLoader(
train_dataset,
batch_size=batch_size,
shuffle=True,
num_workers=8)
val_dataloader = DataLoader(
val_dataset,
batch_size=batch_size,
shuffle=True,
num_workers=8
)
best_model_loss = 1e5
for epoch in range(max_epochs):
train_iter = iter(train_dataloader)
val_iter = iter(val_dataloader)
# Training
if config.continue_training:
checkpoint_fnames = sorted(list(glob.glob(os.path.join(ckpt_out_path, "*.pth"))))
if len(checkpoint_fnames) > 0:
latest_ckpt_path = checkpoint_fnames[-1]
model_dict = torch.load(latest_ckpt_path)
model.load_state_dict(model_dict["state_dict"])
optimizer.load_state_dict(model_dict['optimizer'])
scheduler.load_state_dict(model_dict['scheduler'])
else:
print("path: {} has no checkpoint file".format(ckpt_out_path))
model.train()
bar = tqdm(desc="Training Epoch:{}/{}".format(epoch, max_epochs - 1), initial=0,
total=len(train_dataloader), unit='batches', dynamic_ncols=True, bar_format="{l_bar}{bar:12}{r_bar}")
for i, data in enumerate(train_iter):
if _debug and i > 5:
print('------debug mode on---------')
break
tstart = time()
optimizer.zero_grad()
patch_ref = data['patch_data_ref'].float().to(device)
patch_next = data['patch_data_next'].float().to(device)
pos_ref = data['key_points_xyz_data_ref'].float().to(device)
pos_next = data['key_points_xyz_data_next'].float().to(device)
gt_score = data['iou_data'].float().to(device)
preds = model(patch_ref, pos_ref, patch_next, pos_next).squeeze() # (B, 1)
loss = criterion(preds, gt_score)
loss.backward()
optimizer.step()
scheduler.step()
time_spend = time() - tstart
with torch.no_grad():
bar.update(1)
bar_dict = {}
bar_dict['loss'] = loss.cpu().numpy()
for ll, vv in bar_dict.items():
if isinstance(vv, str):
continue
bar_dict[ll] = round(float(vv), 3)
bar.set_postfix(bar_dict)
writer.add_scalar('train/loss', loss.cpu().numpy(), i)
writer.flush()
# Evaluation
bar.close()
model.eval()
batches = len(val_dataloader)
with torch.no_grad():
valid_bar = tqdm(desc="Validating", initial=0, total=batches, unit='batches', dynamic_ncols=True,
bar_format="{l_bar}{bar:12}{r_bar}")
val_acc_loss = 0
for i, data in enumerate(val_iter):
tstart = time()
patch_ref = data['patch_data_ref'].float().to(device)
patch_next = data['patch_data_next'].float().to(device)
pos_ref = data['key_points_xyz_data_ref'].float().to(device)
pos_next = data['key_points_xyz_data_next'].float().to(device)
gt_score = data['iou_data'].float().to(device)
preds = model(patch_ref, pos_ref, patch_next, pos_next).squeeze() # (B, 1)
loss = criterion(preds, gt_score)
writer.add_scalar('val/loss', loss.cpu().numpy(), i)
val_acc_loss += loss.cpu().numpy()
val_bar_dict = {}
val_bar_dict['loss'] = loss.cpu().numpy()
for ll, vv in val_bar_dict.items():
if isinstance(vv, str):
continue
val_bar_dict[ll] = round(float(vv), 3)
valid_bar.set_postfix(val_bar_dict)
valid_bar.update(i)
val_acc_loss = val_acc_loss / len(val_dataset)
if val_acc_loss < best_model_loss:
torch.save(
{
'state_dict': model.state_dict(),
'optimizer': optimizer.state_dict(),
'scheduler': scheduler.state_dict(),
'curr_epoch': epoch,
}, os.path.join(ckpt_out_path, "weights_epoch_best.pth"))
torch.save(
{
'state_dict': model.state_dict(),
'optimizer': optimizer.state_dict(),
'scheduler': scheduler.state_dict(),
'curr_epoch': epoch,
}, os.path.join(ckpt_out_path, "weights_epoch_{}.pth".format(epoch)))
if __name__ == "__main__":
parser = argparse.ArgumentParser()
parser.add_argument('--weight_name', type=str, default="weights_epoch_3.pth")
parser.add_argument('--continue_training', type=bool, default=False)
parser.add_argument(
'--model_cfg', '-dc',
type=str,
required=False,
default='config/vit_config.yaml',
help='Classification yaml cfg file. See /config/labels for sample. No default!',
)
config = parser.parse_args()
main(config)