File size: 7,919 Bytes
b072050 |
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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 |
import torch
import torch.nn as nn
from torch.utils.data import DataLoader
import os
from dataset import MyDataset
import numpy as np
import time
from model import LipCoordNet
import torch.optim as optim
from tensorboardX import SummaryWriter
import options as opt
from tqdm import tqdm
def dataset2dataloader(dataset, num_workers=opt.num_workers, shuffle=True):
return DataLoader(
dataset,
batch_size=opt.batch_size,
shuffle=shuffle,
num_workers=num_workers,
drop_last=False,
pin_memory=opt.pin_memory,
)
def show_lr(optimizer):
lr = []
for param_group in optimizer.param_groups:
lr += [param_group["lr"]]
return np.array(lr).mean()
def ctc_decode(y):
y = y.argmax(-1)
return [MyDataset.ctc_arr2txt(y[_], start=1) for _ in range(y.size(0))]
def test(model, net):
with torch.no_grad():
dataset = MyDataset(
opt.video_path,
opt.anno_path,
opt.coords_path,
opt.val_list,
opt.vid_padding,
opt.txt_padding,
"test",
)
print("num_test_data:{}".format(len(dataset.data)))
model.eval()
loader = dataset2dataloader(dataset, shuffle=False)
loss_list = []
wer = []
cer = []
crit = nn.CTCLoss()
tic = time.time()
print("RUNNING VALIDATION")
pbar = tqdm(loader)
for i_iter, input in enumerate(pbar):
vid = input.get("vid").cuda(non_blocking=opt.pin_memory)
txt = input.get("txt").cuda(non_blocking=opt.pin_memory)
vid_len = input.get("vid_len").cuda(non_blocking=opt.pin_memory)
txt_len = input.get("txt_len").cuda(non_blocking=opt.pin_memory)
coord = input.get("coord").cuda(non_blocking=opt.pin_memory)
y = net(vid, coord)
loss = (
crit(
y.transpose(0, 1).log_softmax(-1),
txt,
vid_len.view(-1),
txt_len.view(-1),
)
.detach()
.cpu()
.numpy()
)
loss_list.append(loss)
pred_txt = ctc_decode(y)
truth_txt = [MyDataset.arr2txt(txt[_], start=1) for _ in range(txt.size(0))]
wer.extend(MyDataset.wer(pred_txt, truth_txt))
cer.extend(MyDataset.cer(pred_txt, truth_txt))
if i_iter % opt.display == 0:
v = 1.0 * (time.time() - tic) / (i_iter + 1)
eta = v * (len(loader) - i_iter) / 3600.0
print("".join(101 * "-"))
print("{:<50}|{:>50}".format("predict", "truth"))
print("".join(101 * "-"))
for predict, truth in list(zip(pred_txt, truth_txt))[:10]:
print("{:<50}|{:>50}".format(predict, truth))
print("".join(101 * "-"))
print(
"test_iter={},eta={},wer={},cer={}".format(
i_iter, eta, np.array(wer).mean(), np.array(cer).mean()
)
)
print("".join(101 * "-"))
return (np.array(loss_list).mean(), np.array(wer).mean(), np.array(cer).mean())
def train(model, net):
dataset = MyDataset(
opt.video_path,
opt.anno_path,
opt.coords_path,
opt.train_list,
opt.vid_padding,
opt.txt_padding,
"train",
)
loader = dataset2dataloader(dataset)
optimizer = optim.Adam(
model.parameters(), lr=opt.base_lr, weight_decay=0.0, amsgrad=True
)
print("num_train_data:{}".format(len(dataset.data)))
crit = nn.CTCLoss()
tic = time.time()
train_wer = []
for epoch in range(opt.max_epoch):
print(f"RUNNING EPOCH {epoch}")
pbar = tqdm(loader)
for i_iter, input in enumerate(pbar):
model.train()
vid = input.get("vid").cuda(non_blocking=opt.pin_memory)
txt = input.get("txt").cuda(non_blocking=opt.pin_memory)
vid_len = input.get("vid_len").cuda(non_blocking=opt.pin_memory)
txt_len = input.get("txt_len").cuda(non_blocking=opt.pin_memory)
coord = input.get("coord").cuda(non_blocking=opt.pin_memory)
optimizer.zero_grad()
y = net(vid, coord)
loss = crit(
y.transpose(0, 1).log_softmax(-1),
txt,
vid_len.view(-1),
txt_len.view(-1),
)
loss.backward()
if opt.is_optimize:
optimizer.step()
tot_iter = i_iter + epoch * len(loader)
pred_txt = ctc_decode(y)
truth_txt = [MyDataset.arr2txt(txt[_], start=1) for _ in range(txt.size(0))]
train_wer.extend(MyDataset.wer(pred_txt, truth_txt))
if tot_iter % opt.display == 0:
v = 1.0 * (time.time() - tic) / (tot_iter + 1)
eta = (len(loader) - i_iter) * v / 3600.0
writer.add_scalar("train loss", loss, tot_iter)
writer.add_scalar("train wer", np.array(train_wer).mean(), tot_iter)
print("".join(101 * "-"))
print("{:<50}|{:>50}".format("predict", "truth"))
print("".join(101 * "-"))
for predict, truth in list(zip(pred_txt, truth_txt))[:3]:
print("{:<50}|{:>50}".format(predict, truth))
print("".join(101 * "-"))
print(
"epoch={},tot_iter={},eta={},loss={},train_wer={}".format(
epoch, tot_iter, eta, loss, np.array(train_wer).mean()
)
)
print("".join(101 * "-"))
if tot_iter % opt.test_step == 0:
(loss, wer, cer) = test(model, net)
print(
"i_iter={},lr={},loss={},wer={},cer={}".format(
tot_iter, show_lr(optimizer), loss, wer, cer
)
)
writer.add_scalar("val loss", loss, tot_iter)
writer.add_scalar("wer", wer, tot_iter)
writer.add_scalar("cer", cer, tot_iter)
savename = "{}_loss_{}_wer_{}_cer_{}.pt".format(
opt.save_prefix, loss, wer, cer
)
(path, name) = os.path.split(savename)
if not os.path.exists(path):
os.makedirs(path)
torch.save(model.state_dict(), savename)
if not opt.is_optimize:
exit()
if __name__ == "__main__":
print("Loading options...")
os.environ["CUDA_VISIBLE_DEVICES"] = opt.gpu
writer = SummaryWriter()
model = LipCoordNet()
model = model.cuda()
net = nn.DataParallel(model).cuda()
if hasattr(opt, "weights"):
pretrained_dict = torch.load(opt.weights)
model_dict = model.state_dict()
pretrained_dict = {
k: v
for k, v in pretrained_dict.items()
if k in model_dict.keys() and v.size() == model_dict[k].size()
}
# freeze the pretrained layers
for k, param in pretrained_dict.items():
param.requires_grad = False
missed_params = [
k for k, v in model_dict.items() if not k in pretrained_dict.keys()
]
print(
"loaded params/tot params:{}/{}".format(
len(pretrained_dict), len(model_dict)
)
)
print("miss matched params:{}".format(missed_params))
model_dict.update(pretrained_dict)
model.load_state_dict(model_dict)
torch.manual_seed(opt.random_seed)
torch.cuda.manual_seed_all(opt.random_seed)
torch.backends.cudnn.benchmark = True
train(model, net)
|