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train_ALL_CNN.py
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import os
import sys
import torch
import torch.autograd as autograd
import torch.nn.functional as F
import torch.nn.utils as utils
import torch.optim.lr_scheduler as lr_scheduler
import shutil
import re
import random
import hyperparams
torch.manual_seed(hyperparams.seed_num)
random.seed(hyperparams.seed_num)
def train(train_iter, dev_iter, test_iter, model, args):
if args.cuda:
model.cuda()
# optimizer = torch.optim.Adam(model.parameters(), lr=args.lr, weight_decay=1e-8)
# optimizer = torch.optim.Adam(model.parameters(), lr=args.lr, weight_decay=args.init_weight_decay)
# optimizer = torch.optim.SGD(model.parameters(), lr=args.lr,momentum=)
# optimizer = torch.optim.Adam(filter(lambda p: p.requires_grad, model.parameters()), lr=args.lr)
if args.Adam is True:
print("Adam Training......")
optimizer = torch.optim.Adam(model.parameters(), lr=args.lr, weight_decay=args.init_weight_decay)
elif args.SGD is True:
print("SGD Training.......")
optimizer = torch.optim.SGD(model.parameters(), lr=args.lr, weight_decay=args.init_weight_decay,
momentum=args.momentum_value)
elif args.Adadelta is True:
print("Adadelta Training.......")
optimizer = torch.optim.Adadelta(model.parameters(), lr=args.lr, weight_decay=args.init_weight_decay)
# lambda1 = lambda epoch: epoch // 30
# lambda2 = lambda epoch: 0.99 ** epoch
# print("lambda1 {} lambda2 {} ".format(lambda1, lambda2))
# scheduler = lr_scheduler.LambdaLR(optimizer, lr_lambda=[lambda2])
# scheduler = lr_scheduler.StepLR(optimizer, step_size=3, gamma=0.9)
scheduler = lr_scheduler.ReduceLROnPlateau(optimizer, 'min')
steps = 0
epoch_step = 0
model_count = 0
model.train()
for epoch in range(1, args.epochs+1):
print("\n## 第{} 轮迭代，共计迭代 {} 次 ！##\n".format(epoch, args.epochs))
# scheduler.step()
# print("now lr is {} \n".format(scheduler.get_lr()))
print("now lr is {} \n".format(optimizer.param_groups[0].get("lr")))
for batch in train_iter:
feature, target = batch.text, batch.label
feature.data.t_(), target.data.sub_(1) # batch first, index align
if args.cuda:
feature, target = feature.cuda(), target.cuda()
optimizer.zero_grad()
logit = model(feature)
# print(target)
loss = F.cross_entropy(logit, target)
loss.backward()
if args.init_clip_max_norm is not None:
utils.clip_grad_norm(model.parameters(), max_norm=args.init_clip_max_norm)
optimizer.step()
steps += 1
if steps % args.log_interval == 0:
train_size = len(train_iter.dataset)
corrects = (torch.max(logit, 1)[1].view(target.size()).data == target.data).sum()
accuracy = float(corrects)/batch.batch_size * 100.0
sys.stdout.write(
'\rBatch[{}/{}] - loss: {:.6f} acc: {:.4f}%({}/{})'.format(steps,
train_size,
loss.data[0],
accuracy,
corrects,
batch.batch_size))
if steps % args.test_interval == 0:
eval(dev_iter, model, args, scheduler)
if steps % args.save_interval == 0:
if not os.path.isdir(args.save_dir):
os.makedirs(args.save_dir)
save_prefix = os.path.join(args.save_dir, 'snapshot')
save_path = '{}_steps{}.pt'.format(save_prefix, steps)
torch.save(model, save_path)
print("\n", save_path, end=" ")
test_model = torch.load(save_path)
model_count += 1
test_eval(test_iter, test_model, save_path, args, model_count)
return model_count
def eval(data_iter, model, args, scheduler):
model.eval()
corrects, avg_loss = 0, 0
for batch in data_iter:
feature, target = batch.text, batch.label
feature.data.t_(), target.data.sub_(1) # batch first, index align
if args.cuda:
feature, target = feature.cuda(), target.cuda()
logit = model(feature)
loss = F.cross_entropy(logit, target, size_average=False)
avg_loss += loss.data[0]
corrects += (torch.max(logit, 1)[1].view(target.size()).data == target.data).sum()
size = len(data_iter.dataset)
avg_loss = loss.data[0]/size
# accuracy = float(corrects)/size * 100.0
accuracy = 100.0 * corrects/size
model.train()
print('\nEvaluation - loss: {:.6f} acc: {:.4f}%({}/{}) \n'.format(avg_loss,
accuracy,
corrects,
size))
def test_eval(data_iter, model, save_path, args, model_count):
model.eval()
corrects, avg_loss = 0, 0
for batch in data_iter:
feature, target = batch.text, batch.label
feature.data.t_(), target.data.sub_(1) # batch first, index align
if args.cuda:
feature, target = feature.cuda(), target.cuda()
logit = model(feature)
loss = F.cross_entropy(logit, target, size_average=False)
avg_loss += loss.data[0]
corrects += (torch.max(logit, 1)
[1].view(target.size()).data == target.data).sum()
size = len(data_iter.dataset)
avg_loss = loss.data[0]/size
# accuracy = float(corrects)/size * 100.0
accuracy = 100.0 * corrects/size
model.train()
print('\nEvaluation - loss: {:.6f} acc: {:.4f}%({}/{}) \n'.format(avg_loss,
accuracy,
corrects,
size))
print("model_count {}".format(model_count))
# test result
if os.path.exists("./Test_Result.txt"):
file = open("./Test_Result.txt", "a")
else:
file = open("./Test_Result.txt", "w")
file.write("model " + save_path + "\n")
file.write("Evaluation - loss: {:.6f} acc: {:.4f}%({}/{}) \n".format(avg_loss, accuracy, corrects, size))
file.write("model_count {} \n".format(model_count))
file.write("\n")
file.close()
# calculate the best score in current file
resultlist = []
if os.path.exists("./Test_Result.txt"):
file = open("./Test_Result.txt")
for line in file.readlines():
if line[:10] == "Evaluation":
resultlist.append(float(line[34:41]))
result = sorted(resultlist)
file.close()
file = open("./Test_Result.txt", "a")
file.write("\nThe Current Best Result is : " + str(result[len(result) - 1]))
file.write("\n\n")
file.close()
shutil.copy("./Test_Result.txt", "./snapshot/" + args.mulu + "/Test_Result.txt")
# whether to delete the model after test acc so that to save space
if os.path.isfile(save_path) and args.rm_model is True:
os.remove(save_path)