forked from hzwer/ECCV2022-RIFE
-
Notifications
You must be signed in to change notification settings - Fork 0
/
train.py
156 lines (147 loc) · 7.44 KB
/
train.py
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
import os
import cv2
import math
import time
import torch
import numpy as np
import random
import argparse
import torch.distributed as dist
from model.RIFE import Model
from dataset import *
from torch.utils.data import DataLoader, Dataset
from torch.utils.tensorboard import SummaryWriter
from torch.utils.data.distributed import DistributedSampler
def get_learning_rate(step):
if step < 2000:
mul = step / 2000.
else:
mul = np.cos((step - 2000) / (args.epoch * args.step_per_epoch - 2000.) * math.pi) * 0.5 + 0.5
return 5e-4 * mul
def flow2rgb(flow_map_np):
h, w, _ = flow_map_np.shape
rgb_map = np.ones((h, w, 3)).astype(np.float32)
normalized_flow_map = flow_map_np / (np.abs(flow_map_np).max())
rgb_map[:, :, 0] += normalized_flow_map[:, :, 0]
rgb_map[:, :, 1] -= 0.5 * (normalized_flow_map[:, :, 0] + normalized_flow_map[:, :, 1])
rgb_map[:, :, 2] += normalized_flow_map[:, :, 1]
return rgb_map.clip(0, 1)
def train(model, local_rank):
log_path = 'train_log'
if local_rank == 0:
writer = SummaryWriter(log_path + '/train')
writer_val = SummaryWriter(log_path + '/validate')
else:
writer, writer_val = None, None
step = 0
nr_eval = 0
dataset = VimeoDataset('train')
sampler = DistributedSampler(dataset)
train_data = DataLoader(dataset, batch_size=args.batch_size, num_workers=8, pin_memory=True, drop_last=True, sampler=sampler)
args.step_per_epoch = train_data.__len__()
dataset_val = VimeoDataset('validation')
val_data = DataLoader(dataset_val, batch_size=16, pin_memory=True, num_workers=8)
evaluate(model, val_data, nr_eval, local_rank, writer_val)
model.save_model(log_path, local_rank)
print('training...')
time_stamp = time.time()
for epoch in range(args.epoch):
sampler.set_epoch(epoch)
for i, data in enumerate(train_data):
data_time_interval = time.time() - time_stamp
time_stamp = time.time()
data_gpu, flow_gt = data
data_gpu = data_gpu.to(device, non_blocking=True) / 255.
flow_gt = flow_gt.to(device, non_blocking=True)
imgs = data_gpu[:, :6]
gt = data_gpu[:, 6:9]
mul = np.cos(step / (args.epoch * args.step_per_epoch) * math.pi) * 0.5 + 0.5
learning_rate = get_learning_rate(step)
pred, merged_img, flow, loss_l1, loss_flow, loss_cons, loss_ter, flow_mask = model.update(imgs, gt, learning_rate, mul, True, flow_gt)
train_time_interval = time.time() - time_stamp
time_stamp = time.time()
if step % 100 == 1 and local_rank == 0:
writer.add_scalar('learning_rate', learning_rate, step)
writer.add_scalar('loss_l1', loss_l1, step)
writer.add_scalar('loss_flow', loss_flow, step)
writer.add_scalar('loss_cons', loss_cons, step)
writer.add_scalar('loss_ter', loss_ter, step)
if step % 1000 == 1 and local_rank == 0:
gt = (gt.permute(0, 2, 3, 1).detach().cpu().numpy() * 255).astype('uint8')
pred = (pred.permute(0, 2, 3, 1).detach().cpu().numpy() * 255).astype('uint8')
merged_img = (merged_img.permute(0, 2, 3, 1).detach().cpu().numpy() * 255).astype('uint8')
flow = flow.permute(0, 2, 3, 1).detach().cpu().numpy()
flow_mask = flow_mask.permute(0, 2, 3, 1).detach().cpu().numpy()
flow_gt = flow_gt.permute(0, 2, 3, 1).detach().cpu().numpy()
for i in range(5):
imgs = np.concatenate((merged_img[i], pred[i], gt[i]), 1)[:, :, ::-1]
writer.add_image(str(i) + '/img', imgs, step, dataformats='HWC')
writer.add_image(str(i) + '/flow', flow2rgb(flow[i]), step, dataformats='HWC')
writer.add_image(str(i) + '/flow_gt', flow2rgb(flow_gt[i]), step, dataformats='HWC')
writer.add_image(str(i) + '/flow_mask', flow2rgb(flow[i] * flow_mask[i]), step, dataformats='HWC')
writer.flush()
if local_rank == 0:
print('epoch:{} {}/{} time:{:.2f}+{:.2f} loss_l1:{:.4e}'.format(epoch, i, args.step_per_epoch, data_time_interval, train_time_interval, loss_l1))
step += 1
nr_eval += 1
if nr_eval % 5 == 0:
evaluate(model, val_data, step, local_rank, writer_val)
model.save_model(log_path, local_rank)
dist.barrier()
def evaluate(model, val_data, nr_eval, local_rank, writer_val):
loss_l1_list = []
loss_cons_list = []
loss_ter_list = []
loss_flow_list = []
psnr_list = []
time_stamp = time.time()
for i, data in enumerate(val_data):
data_gpu, flow_gt = data
data_gpu = data_gpu.to(device, non_blocking=True) / 255.
flow_gt = flow_gt.to(device, non_blocking=True)
imgs = data_gpu[:, :6]
gt = data_gpu[:, 6:9]
with torch.no_grad():
pred, merged_img, flow, loss_l1, loss_flow, loss_cons, loss_ter, flow_mask = model.update(imgs, gt, training=False)
loss_l1_list.append(loss_l1.cpu().numpy())
loss_flow_list.append(loss_flow.cpu().numpy())
loss_ter_list.append(loss_ter.cpu().numpy())
loss_cons_list.append(loss_cons.cpu().numpy())
for j in range(gt.shape[0]):
psnr = -10 * math.log10(torch.mean((gt[j] - pred[j]) * (gt[j] - pred[j])).cpu().data)
psnr_list.append(psnr)
gt = (gt.permute(0, 2, 3, 1).cpu().numpy() * 255).astype('uint8')
pred = (pred.permute(0, 2, 3, 1).cpu().numpy() * 255).astype('uint8')
merged_img = (merged_img.permute(0, 2, 3, 1).cpu().numpy() * 255).astype('uint8')
flow = flow.permute(0, 2, 3, 1).cpu().numpy()
if i == 0 and local_rank == 0:
for j in range(5):
imgs = np.concatenate((merged_img[i], pred[i], gt[i]), 1)[:, :, ::-1]
writer_val.add_image(str(i) + '/img', imgs.copy(), nr_eval, dataformats='HWC')
writer_val.add_image(str(i) + '/flow', flow2rgb(flow[i][:, :, ::-1]), nr_eval, dataformats='HWC')
eval_time_interval = time.time() - time_stamp
if local_rank == 0:
print('eval time: {}'.format(eval_time_interval))
writer_val.add_scalar('loss_l1', np.array(loss_l1_list).mean(), nr_eval)
writer_val.add_scalar('loss_flow', np.array(loss_flow_list).mean(), nr_eval)
writer_val.add_scalar('loss_cons', np.array(loss_cons_list).mean(), nr_eval)
writer_val.add_scalar('loss_ter', np.array(loss_ter_list).mean(), nr_eval)
writer_val.add_scalar('psnr', np.array(psnr_list).mean(), nr_eval)
if __name__ == "__main__":
parser = argparse.ArgumentParser(description='slomo')
parser.add_argument('--epoch', default=300, type=int)
parser.add_argument('--batch_size', default=16, type=int, help='minibatch size')
parser.add_argument('--local_rank', default=0, type=int, help='local rank')
parser.add_argument('--world_size', default=4, type=int, help='world size')
args = parser.parse_args()
torch.distributed.init_process_group(backend="nccl", world_size=args.world_size)
torch.cuda.set_device(args.local_rank)
device = torch.device("cuda", args.local_rank)
seed = 1234
random.seed(seed)
np.random.seed(seed)
torch.manual_seed(seed)
torch.cuda.manual_seed_all(seed)
torch.backends.cudnn.benchmark = True
model = Model(args.local_rank)
train(model, args.local_rank)