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seq_scripts.py
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seq_scripts.py
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import os
import pdb
import sys
import copy
import torch
import numpy as np
import torch.nn as nn
from tqdm import tqdm
import torch.nn.functional as F
from evaluation.slr_eval.wer_calculation import evaluate
from torch.cuda.amp import autocast as autocast
from torch.cuda.amp import GradScaler
def seq_train(loader, model, optimizer, device, epoch_idx, recoder):
model.train()
loss_value = []
clr = [group['lr'] for group in optimizer.optimizer.param_groups]
scaler = GradScaler()
tqdm_loader = tqdm(loader, ncols=100)
nan = 0
for batch_idx, data in enumerate(tqdm_loader):
vid = device.data_to_device(data[0])
vid_lgt = device.data_to_device(data[1])
label = device.data_to_device(data[2])
label_lgt = device.data_to_device(data[3])
optimizer.zero_grad()
with autocast():
ret_dict = model(vid, vid_lgt, label=label, label_lgt=label_lgt)
loss = model.criterion_calculation(ret_dict, label, label_lgt)
if np.isinf(loss.item()) or np.isnan(loss.item()):
print('loss is nan')
print(str(data[1])+' frames')
print(str(data[3])+' glosses')
del ret_dict
del loss
nan+=1
if nan == 30:
exit()
continue
scaler.scale(loss).backward()
scaler.step(optimizer.optimizer)
scaler.update()
# nn.utils.clip_grad_norm_(model.rnn.parameters(), 5)
loss_value.append(loss.item())
if batch_idx % recoder.log_interval == 0:
recoder.print_log(
'\tEpoch: {}, Batch({}/{}) done. Loss: {:.8f} lr:{:.6f}'
.format(epoch_idx, batch_idx, len(loader), loss.item(), clr[0]))
tqdm_loader.set_postfix({'Loss' : loss.item()})
del ret_dict
del loss
optimizer.scheduler.step()
recoder.print_log('\tMean training loss: {:.10f}.'.format(np.mean(loss_value)))
return
def seq_eval(cfg, loader, model, device, mode, epoch, work_dir, recoder,
evaluate_tool="python"):
model.eval()
total_sent = []
total_info = []
total_conv_sent = []
stat = {i: [0, 0] for i in range(len(loader.dataset.dict))}
for batch_idx, data in enumerate(tqdm(loader, ncols=100)):
recoder.record_timer("device")
vid = device.data_to_device(data[0])
vid_lgt = device.data_to_device(data[1])
label = device.data_to_device(data[2])
label_lgt = device.data_to_device(data[3])
with torch.no_grad():
ret_dict = model(vid, vid_lgt, label=label, label_lgt=label_lgt)
total_info += [file_name.split("|")[0] for file_name in data[-1]]
total_sent += ret_dict['recognized_sents']
total_conv_sent += ret_dict['conv_sents']
try:
python_eval = True if evaluate_tool == "python" else False
write2file(work_dir + "output-hypothesis-{}.ctm".format(mode), total_info, total_sent)
write2file(work_dir + "output-hypothesis-{}-conv.ctm".format(mode), total_info,
total_conv_sent)
conv_ret = evaluate(
prefix=work_dir, mode=mode, output_file="output-hypothesis-{}-conv.ctm".format(mode),
evaluate_dir=cfg.dataset_info['evaluation_dir'],
evaluate_prefix=cfg.dataset_info['evaluation_prefix'],
output_dir="epoch_{}_result/".format(epoch),
python_evaluate=python_eval,
)
lstm_ret = evaluate(
prefix=work_dir, mode=mode, output_file="output-hypothesis-{}.ctm".format(mode),
evaluate_dir=cfg.dataset_info['evaluation_dir'],
evaluate_prefix=cfg.dataset_info['evaluation_prefix'],
output_dir="epoch_{}_result/".format(epoch),
python_evaluate=python_eval,
triplet=True,
)
except:
print("Unexpected error:", sys.exc_info()[0])
lstm_ret = 100.0
finally:
pass
del conv_ret
del total_sent
del total_info
del total_conv_sent
del vid
del vid_lgt
del label
del label_lgt
recoder.print_log(f"Epoch {epoch}, {mode} {lstm_ret: 2.2f}%", f"{work_dir}/{mode}.txt")
return lstm_ret
def seq_feature_generation(loader, model, device, mode, work_dir, recoder):
model.eval()
src_path = os.path.abspath(f"{work_dir}{mode}")
tgt_path = os.path.abspath(f"./features/{mode}")
if not os.path.exists("./features/"):
os.makedirs("./features/")
if os.path.islink(tgt_path):
curr_path = os.readlink(tgt_path)
if work_dir[1:] in curr_path and os.path.isabs(curr_path):
return
else:
os.unlink(tgt_path)
else:
if os.path.exists(src_path) and len(loader.dataset) == len(os.listdir(src_path)):
os.symlink(src_path, tgt_path)
return
for batch_idx, data in tqdm(enumerate(loader)):
recoder.record_timer("device")
vid = device.data_to_device(data[0])
vid_lgt = device.data_to_device(data[1])
with torch.no_grad():
ret_dict = model(vid, vid_lgt)
if not os.path.exists(src_path):
os.makedirs(src_path)
start = 0
for sample_idx in range(len(vid)):
end = start + data[3][sample_idx]
filename = f"{src_path}/{data[-1][sample_idx].split('|')[0]}_features.npy"
save_file = {
"label": data[2][start:end],
"features": ret_dict['framewise_features'][sample_idx][:, :vid_lgt[sample_idx]].T.cpu().detach(),
}
np.save(filename, save_file)
start = end
assert end == len(data[2])
os.symlink(src_path, tgt_path)
def write2file(path, info, output):
filereader = open(path, "w")
for sample_idx, sample in enumerate(output):
for word_idx, word in enumerate(sample):
filereader.writelines(
"{} 1 {:.2f} {:.2f} {}\n".format(info[sample_idx],
word_idx * 1.0 / 100,
(word_idx + 1) * 1.0 / 100,
word[0]))