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main.py
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main.py
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import os
os.environ["CUDA_DEVICE_ORDER"] = "PCI_BUS_ID"
import pdb
import sys
import cv2
import yaml
import torch
import random
import importlib
import faulthandler
import numpy as np
import torch.nn as nn
from collections import OrderedDict
faulthandler.enable()
import utils
from modules.sync_batchnorm import convert_model
from seq_scripts import seq_train, seq_eval, seq_feature_generation
class Processor():
def __init__(self, arg):
self.arg = arg
self.save_arg()
if self.arg.random_fix:
self.rng = utils.RandomState(seed=self.arg.random_seed)
self.device = utils.GpuDataParallel()
self.recoder = utils.Recorder(self.arg.work_dir, self.arg.print_log, self.arg.log_interval)
self.dataset = {}
self.data_loader = {}
self.gloss_dict = np.load(self.arg.dataset_info['dict_path'], allow_pickle=True).item()
self.arg.model_args['num_classes'] = len(self.gloss_dict) + 1
self.model, self.optimizer = self.loading()
def start(self):
if self.arg.phase == 'train':
self.recoder.print_log('Parameters:\n{}\n'.format(str(vars(self.arg))))
seq_model_list = []
for epoch in range(self.arg.optimizer_args['start_epoch'], self.arg.num_epoch):
save_model = epoch % self.arg.save_interval == 0
eval_model = epoch % self.arg.eval_interval == 0
# train end2end model
seq_train(self.data_loader['train'], self.model, self.optimizer,
self.device, epoch, self.recoder)
if eval_model:
dev_wer = seq_eval(self.arg, self.data_loader['dev'], self.model, self.device,
'dev', epoch, self.arg.work_dir, self.recoder, self.arg.evaluate_tool)
self.recoder.print_log("Dev WER: {:05.2f}%".format(dev_wer))
if save_model:
model_path = "{}dev_{:05.2f}_epoch{}_model.pt".format(self.arg.work_dir, dev_wer, epoch)
seq_model_list.append(model_path)
print("seq_model_list", seq_model_list)
self.save_model(epoch, model_path)
elif self.arg.phase == 'test':
if self.arg.load_weights is None and self.arg.load_checkpoints is None:
raise ValueError('Please appoint --load-weights.')
self.recoder.print_log('Model: {}.'.format(self.arg.model))
self.recoder.print_log('Weights: {}.'.format(self.arg.load_weights))
# train_wer = seq_eval(self.arg, self.data_loader["train_eval"], self.model, self.device,
# "train", 6667, self.arg.work_dir, self.recoder, self.arg.evaluate_tool)
dev_wer = seq_eval(self.arg, self.data_loader["dev"], self.model, self.device,
"dev", 6667, self.arg.work_dir, self.recoder, self.arg.evaluate_tool)
test_wer = seq_eval(self.arg, self.data_loader["test"], self.model, self.device,
"test", 6667, self.arg.work_dir, self.recoder, self.arg.evaluate_tool)
self.recoder.print_log('Evaluation Done.\n')
elif self.arg.phase == "features":
for mode in ["train", "dev", "test"]:
seq_feature_generation(
self.data_loader[mode + "_eval" if mode == "train" else mode],
self.model, self.device, mode, self.arg.work_dir, self.recoder
)
def save_arg(self):
arg_dict = vars(self.arg)
if not os.path.exists(self.arg.work_dir):
os.makedirs(self.arg.work_dir)
with open('{}/config.yaml'.format(self.arg.work_dir), 'w') as f:
yaml.dump(arg_dict, f)
def save_model(self, epoch, save_path):
torch.save({
'epoch': epoch,
'model_state_dict': self.model.state_dict(),
'optimizer_state_dict': self.optimizer.state_dict(),
'scheduler_state_dict': self.optimizer.scheduler.state_dict(),
'rng_state': self.rng.save_rng_state(),
}, save_path)
def loading(self):
self.device.set_device(self.arg.device)
print("Loading model")
model_class = import_class(self.arg.model)
model = model_class(
**self.arg.model_args,
gloss_dict=self.gloss_dict,
loss_weights=self.arg.loss_weights,
)
optimizer = utils.Optimizer(model, self.arg.optimizer_args)
if self.arg.load_weights:
self.load_model_weights(model, self.arg.load_weights)
elif self.arg.load_checkpoints:
self.load_checkpoint_weights(model, optimizer)
model = self.model_to_device(model)
print("Loading model finished.")
self.load_data()
return model, optimizer
def model_to_device(self, model):
model = model.to(self.device.output_device)
if len(self.device.gpu_list) > 1:
model.conv2d = nn.DataParallel(
model.conv2d,
device_ids=self.device.gpu_list,
output_device=self.device.output_device)
model = convert_model(model)
model.cuda()
return model
def load_model_weights(self, model, weight_path):
state_dict = torch.load(weight_path)
if len(self.arg.ignore_weights):
for w in self.arg.ignore_weights:
if state_dict.pop(w, None) is not None:
print('Successfully Remove Weights: {}.'.format(w))
else:
print('Can Not Remove Weights: {}.'.format(w))
weights = self.modified_weights(state_dict['model_state_dict'], False)
# weights = self.modified_weights(state_dict['model_state_dict'])
model.load_state_dict(weights, strict=True)
@staticmethod
def modified_weights(state_dict, modified=False):
state_dict = OrderedDict([(k.replace('.module', ''), v) for k, v in state_dict.items()])
if not modified:
return state_dict
modified_dict = dict()
return modified_dict
def load_checkpoint_weights(self, model, optimizer):
self.load_model_weights(model, self.arg.load_checkpoints)
state_dict = torch.load(self.arg.load_checkpoints)
if len(torch.cuda.get_rng_state_all()) == len(state_dict['rng_state']['cuda']):
print("Loading random seeds...")
self.rng.set_rng_state(state_dict['rng_state'])
if "optimizer_state_dict" in state_dict.keys():
print("Loading optimizer parameters...")
optimizer.load_state_dict(state_dict["optimizer_state_dict"])
optimizer.to(self.device.output_device)
if "scheduler_state_dict" in state_dict.keys():
print("Loading scheduler parameters...")
optimizer.scheduler.load_state_dict(state_dict["scheduler_state_dict"])
self.arg.optimizer_args['start_epoch'] = state_dict["epoch"] + 1
self.recoder.print_log("Resuming from checkpoint: epoch {self.arg.optimizer_args['start_epoch']}")
def load_data(self):
print("Loading data")
self.feeder = import_class(self.arg.feeder)
dataset_list = zip(["train", "train_eval", "dev", "test"], [True, False, False, False])
for idx, (mode, train_flag) in enumerate(dataset_list):
arg = self.arg.feeder_args
arg["prefix"] = self.arg.dataset_info['dataset_root']
arg["mode"] = mode.split("_")[0]
arg["transform_mode"] = train_flag
self.dataset[mode] = self.feeder(gloss_dict=self.gloss_dict, **arg)
self.data_loader[mode] = self.build_dataloader(self.dataset[mode], mode, train_flag)
print("Loading data finished.")
def build_dataloader(self, dataset, mode, train_flag):
return torch.utils.data.DataLoader(
dataset,
batch_size=self.arg.batch_size if mode == "train" else self.arg.test_batch_size,
shuffle=train_flag,
drop_last=train_flag,
num_workers=self.arg.num_worker, # if train_flag else 0
collate_fn=self.feeder.collate_fn,
)
def import_class(name):
components = name.rsplit('.', 1)
mod = importlib.import_module(components[0])
mod = getattr(mod, components[1])
return mod
if __name__ == '__main__':
sparser = utils.get_parser()
p = sparser.parse_args()
# p.config = "baseline_iter.yaml"
if p.config is not None:
with open(p.config, 'r') as f:
try:
default_arg = yaml.load(f, Loader=yaml.FullLoader)
except AttributeError:
default_arg = yaml.load(f)
key = vars(p).keys()
for k in default_arg.keys():
if k not in key:
print('WRONG ARG: {}'.format(k))
assert (k in key)
sparser.set_defaults(**default_arg)
args = sparser.parse_args()
with open(f"./configs/{args.dataset}.yaml", 'r') as f:
args.dataset_info = yaml.load(f, Loader=yaml.FullLoader)
processor = Processor(args)
utils.pack_code("./", args.work_dir)
processor.start()