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main.py
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import os
import glob
import torch
import random
import logging
import numpy as np
from tqdm import tqdm
import torch.utils.data
import torch.optim as optim
from common.opt import opts
from common.utils import *
from common.load_data_hm36 import Fusion
from common.h36m_dataset import Human36mDataset
from model.dc_gct import DC_GCT
opt = opts().parse()
os.environ["CUDA_VISIBLE_DEVICES"] = opt.gpu
from tensorboardX import SummaryWriter
writer = SummaryWriter(log_dir='./runs/' + opt.model_name)
def train(opt, actions, train_loader, model, optimizer, epoch):
return step('train', opt, actions, train_loader, model, optimizer, epoch)
def val(opt, actions, val_loader, model):
with torch.no_grad():
return step('test', opt, actions, val_loader, model)
def step(split, opt, actions, dataLoader, model, optimizer=None, epoch=None):
loss_all = {'loss': AccumLoss()}
action_error_sum = define_error_list(actions)
if split == 'train':
model.train()
else:
model.eval()
for i, data in enumerate(tqdm(dataLoader, 0)):
batch_cam, gt_3D, input_2D, action, subject, scale, bb_box, cam_ind = data
[input_2D, gt_3D, batch_cam, scale, bb_box] = get_varialbe(split, [input_2D, gt_3D, batch_cam, scale, bb_box])
if split =='train':
output_3D = model(input_2D)
else:
input_2D, output_3D = input_augmentation(input_2D, model)
out_target = gt_3D.clone()
out_target[:, :, 0] = 0
if split == 'train':
# loss = mpjpe_cal(output_3D, out_target)
w_mpjpe = torch.tensor([1, 1, 2.5, 2.5, 1, 2.5, 2.5, 1, 1, 1, 1.5, 1.5, 4, 4, 1.5, 4, 4]).cuda()
loss = weighted_mpjpe(output_3D, out_target, w_mpjpe)
N = input_2D.size(0)
loss_all['loss'].update(loss.detach().cpu().numpy() * N, N)
optimizer.zero_grad()
loss.backward()
optimizer.step()
elif split == 'test':
output_3D = output_3D[:, opt.pad].unsqueeze(1)
output_3D[:, :, 0, :] = 0
action_error_sum = test_calculation(output_3D, out_target, action, action_error_sum, opt.dataset, subject)
if split == 'train':
return loss_all['loss'].avg
elif split == 'test':
p1, p2 = print_error(opt.dataset, action_error_sum, opt.train)
return p1, p2
def input_augmentation(input_2D, model):
joints_left = [4, 5, 6, 11, 12, 13]
joints_right = [1, 2, 3, 14, 15, 16]
input_2D_non_flip = input_2D[:, 0]
input_2D_flip = input_2D[:, 1]
output_3D_non_flip = model(input_2D_non_flip)
output_3D_flip = model(input_2D_flip)
output_3D_flip[:, :, :, 0] *= -1
output_3D_flip[:, :, joints_left + joints_right, :] = output_3D_flip[:, :, joints_right + joints_left, :]
output_3D = (output_3D_non_flip + output_3D_flip) / 2
input_2D = input_2D_non_flip
return input_2D, output_3D
if __name__ == '__main__':
manualSeed = opt.seed
random.seed(manualSeed)
torch.manual_seed(manualSeed)
np.random.seed(manualSeed)
torch.cuda.manual_seed_all(manualSeed)
torch.backends.cudnn.benchmark = False
torch.backends.cudnn.deterministic = True
print("lr: ", opt.lr)
print("batch_size: ", opt.batch_size)
print("channel: ", opt.channel)
print("GPU: ", opt.gpu)
if opt.train:
logging.basicConfig(format='%(asctime)s %(message)s', datefmt='%Y/%m/%d %H:%M:%S', \
filename=os.path.join(opt.checkpoint, 'train.log'), level=logging.INFO)
root_path = opt.root_path
dataset_path = root_path + 'data_3d_' + opt.dataset + '.npz'
dataset = Human36mDataset(dataset_path, opt)
actions = define_actions(opt.actions)
if opt.train:
train_data = Fusion(opt=opt, train=True, dataset=dataset, root_path=root_path)
train_dataloader = torch.utils.data.DataLoader(train_data, batch_size=opt.batch_size,
shuffle=True, num_workers=int(opt.workers), pin_memory=True)
test_data = Fusion(opt=opt, train=False, dataset=dataset, root_path =root_path)
test_dataloader = torch.utils.data.DataLoader(test_data, batch_size=opt.batch_size,
shuffle=False, num_workers=int(opt.workers), pin_memory=True)
model = DC_GCT(opt).cuda()
if opt.reload:
model_dict = model.state_dict()
model_path = sorted(glob.glob(os.path.join(opt.previous_dir, '*.pth')))[0]
print(model_path)
pre_dict = torch.load(model_path)
pre_key = pre_dict.keys()
for name, key in model_dict.items():
model_dict[name] = pre_dict[name]
model.load_state_dict(model_dict)
model_params = 0
for parameter in model.parameters():
model_params += parameter.numel()
print('INFO: Trainable parameter count:', model_params / 1000000)
all_param = []
lr = opt.lr
all_param += list(model.parameters())
optimizer = optim.Adam(all_param, lr=opt.lr, amsgrad=True)
scheduler = torch.optim.lr_scheduler.ReduceLROnPlateau(optimizer, 'min', factor=0.317, patience=5, verbose=True)
for epoch in range(1, opt.nepoch):
if opt.train:
loss = train(opt, actions, train_dataloader, model, optimizer, epoch)
p1, p2 = val(opt, actions, test_dataloader, model)
writer.add_scalar('mpjpe',p1,epoch)
writer.add_scalar('p2',p1,epoch)
if opt.train and p1 < opt.previous_best_threshold:
opt.previous_name = save_model(opt.previous_name, opt.checkpoint, epoch, p1, model)
opt.previous_best_threshold = p1
if opt.train == 0:
print('p1: %.2f, p2: %.2f' % (p1, p2))
break
else:
logging.info('epoch: %d, lr: %.7f, loss: %.4f, p1: %.2f, p2: %.2f' % (epoch, lr, loss, p1, p2))
print('e: %d, lr: %.7f, loss: %.4f, p1: %.2f, p2: %.2f' % (epoch, lr, loss, p1, p2))
if epoch % opt.large_decay_epoch == 0:
for param_group in optimizer.param_groups:
param_group['lr'] *= opt.lr_decay_large
lr *= opt.lr_decay_large
else:
for param_group in optimizer.param_groups:
param_group['lr'] *= opt.lr_decay
lr *= opt.lr_decay