def test(full_dataset, state_name):
# load model and use weights we saved before.
model = MyModel()
model.load_state_dict(
torch.load(f'mymodel_{state_name}.pth', map_location='cpu'))
model.eval()
criterion = RMSLELoss()
# convert dataframe to tensor
inputs = full_dataset.test[full_dataset.col].astype(float).values
inputs = torch.tensor(inputs).float()
# predict
outputs = model(inputs)
# get labels
labels = full_dataset.test_target.values
# RMSLE Loss
loss = criterion(outputs, torch.from_numpy(labels))
test_loss = sqrt(loss / len(full_dataset.test))
print(f'Testing Loss: {test_loss:.6f}')
#save the result
result = full_dataset.test["id"].to_frame()
result.insert(1, "visitors_pred", outputs.detach().numpy())
result.insert(2, "visitors_actual", labels)
result.to_csv(f'result_{state_name}.csv', index=False)
def test():
# load model and use weights we saved before.
model = MyModel()
model.load_state_dict(torch.load('mymodel.pth', map_location='cpu'))
model.eval()
# load testing data
data = pd.read_csv('test.csv', encoding='utf-8')
label_col = [
'Input_A6_024', 'Input_A3_016', 'Input_C_013', 'Input_A2_016',
'Input_A3_017', 'Input_C_050', 'Input_A6_001', 'Input_C_096',
'Input_A3_018', 'Input_A6_019', 'Input_A1_020', 'Input_A6_011',
'Input_A3_015', 'Input_C_046', 'Input_C_049', 'Input_A2_024',
'Input_C_058', 'Input_C_057', 'Input_A3_013', 'Input_A2_017'
]
# ================================================================ #
# if do some operations with training data,
# do the same operations to the testing data in this block
data = data.fillna(0)
# ================================================================ #
# convert dataframe to tensor, no need to rewrite
inputs = data.values
inputs = torch.tensor(inputs)
# predict and save the result
result = pd.DataFrame(columns=label_col)
outputs = model(inputs.float())
for i in range(len(outputs)):
tmp = outputs[i].detach().numpy()
tmp = pd.DataFrame([tmp], columns=label_col)
result = pd.concat([result, tmp], ignore_index=True)
result.to_csv('result.csv', index=False)
def predict(dim,
names,
weight,
batch_size,
pretrain_model_path,
model_types=None):
print('-' * 100)
print('multi-models begin predicting ...')
print('-' * 100)
# read test data
test_file = '/kaggle/input/quora-question-pairs/test.csv.zip'
# data
test_df = pd.read_csv(test_file)
test_ids = test_df['test_id'].values.tolist()
result_prob_tmp = torch.zeros((len(test_ids), 2))
# load model
for i, name in enumerate(names):
# 3.17 add
weight_ = weight[i]
#model_path = '../model/' + name + '.pkl'
output_model_file = os.path.join('output', name + '.pkl')
state = torch.load(output_model_file)
# 3.10 add
model_type = model_types[i]
if model_type == 'mlp':
test_iter = MyDataset(file=test_file,
is_train=False,
pretrain_model_path=pretrain_model_path[i])
test_iter = get_dataloader(test_iter,
batch_size,
shuffle=False,
drop_last=False)
model = MyModel(dim=dim[i],
pretrain_model_path=pretrain_model_path[i])
elif model_type == 'cnn':
test_iter = MyDataset(file=test_file,
is_train=False,
pretrain_model_path=pretrain_model_path[i])
test_iter = get_dataloader(test_iter,
batch_size,
shuffle=False,
drop_last=False)
model = MyTextCNNModel(dim=dim[i],
pretrain_model_path=pretrain_model_path[i])
elif model_type == 'rcnn':
test_iter = MyDataset(file=test_file,
is_train=False,
pretrain_model_path=pretrain_model_path[i])
test_iter = get_dataloader(test_iter,
batch_size,
shuffle=False,
drop_last=False)
model = MyRCNNModel(dim=dim[i],
pretrain_model_path=pretrain_model_path[i])
model.to(device)
model.load_state_dict(state['model_state'])
model.eval()
print('-' * 20, 'model', i, '-' * 20)
print('load model:%s, loss:%.4f, e:%d, lr:%.7f, time:%d' %
(name, state['loss'], state['e'], state['lr'], state['time']))
# predict
with torch.no_grad():
j = 0
for batch in tqdm(test_iter):
batch = [b.cuda() for b in batch]
out = model(batch, task='eval')
out = out.cpu() # gpu -> cpu
if j == 0:
tmp = out # 初始化 tmp
else:
tmp = torch.cat([tmp, out], dim=0) # 将之后的预测结果拼接到 tmp 中
j += 1
# 当前 模型预测完成
print('model', i, 'predict finished!\n')
# 3.17 按权重融合
result_prob_tmp += (weight_ / len(names)) * tmp
# 删除模型
del model
gc.collect()
time.sleep(1)
# 3.10 当前融合策略：prob 简单的取 avg
_, result = torch.max(result_prob_tmp, dim=-1)
result = result.numpy()
# 3.16 update: label 0的prob 大于 3，就认为是 label=0
# with open('tmp.txt', 'w', encoding='utf-8') as f:
# for r in result_prob_tmp:
# f.write(str(r) + '\n')
# save result
df = pd.DataFrame()
df['test_id'] = test_ids
df['is_duplicate'] = result
df.to_csv("submission.csv", encoding='utf-8', index=False)
def train(
batch_size=16,
pretrain_model_path='',
name='',
model_type='mlp',
after_bert_choice='last_cls',
dim=1024,
lr=1e-5,
epoch=12,
smoothing=0.05,
sample=False,
#open_ad='',
dialog_name='xxx'):
if not pretrain_model_path or not name:
assert 1 == -1
print('\n********** model type:', model_type, '**********')
print('batch_size:', batch_size)
# load dataset
train_file = '/kaggle/input/dataset/my_train.csv'
dev_file = '/kaggle/input/dataset/my_dev.csv'
train_num = len(pd.read_csv(train_file).values.tolist())
val_num = len(pd.read_csv(dev_file).values.tolist())
print('train_num: %d, dev_num: %d' % (train_num, val_num))
# 选择模型
if model_type in ['siam', 'esim', 'sbert']:
assert 1 == -1
else:
train_iter = MyDataset(file=train_file,
is_train=True,
sample=sample,
pretrain_model_path=pretrain_model_path)
train_iter = get_dataloader(train_iter,
batch_size,
shuffle=True,
drop_last=True)
dev_iter = MyDataset(file=dev_file,
is_train=True,
sample=sample,
pretrain_model_path=pretrain_model_path)
dev_iter = get_dataloader(dev_iter,
batch_size,
shuffle=False,
drop_last=False)
if model_type == 'mlp':
model = MyModel(dim=dim,
pretrain_model_path=pretrain_model_path,
smoothing=smoothing,
after_bert_choice='last_cls')
elif model_type == 'cnn':
model = MyTextCNNModel(dim=dim,
pretrain_model_path=pretrain_model_path,
smoothing=smoothing)
elif model_type == 'rcnn':
model = MyRCNNModel(dim=dim,
pretrain_model_path=pretrain_model_path,
smoothing=smoothing)
#模型加载到gpu
model.to(device)
model_param_num = 0
##### 3.24 muppti-gpu-training
if n_gpu > 1:
model = torch.nn.DataParallel(model)
for p in model.parameters():
if p.requires_grad:
model_param_num += p.nelement()
print('param_num:%d\n' % model_param_num)
# 加入对抗训练，提升泛化能力；但是训练速度明显变慢 (插件式调用)
# 3.12 change to FGM 更快！
"""
if open_ad == 'fgm':
fgm = FGM(model)
elif open_ad == 'pgd':
pgd = PGD(model)
K = 3
"""
# model-store-path
#model_path = '/kaggle/output/' + name + '.pkl' # 输出模型默认存放在当前路径下
output_dir = 'output'
state = {}
time0 = time.time()
best_loss = 999
early_stop = 0
for e in range(epoch):
print("*" * 100)
print("Epoch:", e)
param_optimizer = list(model.named_parameters())
no_decay = ['bias', 'LayerNorm.bias', 'LayerNorm.weight']
optimizer_grouped_parameters = [{
'params': [
p for n, p in param_optimizer
if not any(nd in n for nd in no_decay)
],
'weight_decay':
0.01
}, {
'params':
[p for n, p in param_optimizer if any(nd in n for nd in no_decay)],
'weight_decay':
0.0
}]
optimizer = BertAdam(optimizer_grouped_parameters,
lr=lr,
warmup=0.05,
t_total=len(train_iter)) # 设置优化器
train_loss = 0
train_c = 0
train_right_num = 0
model.train() # 将模型设置成训练模式（Sets the module in training mode）
print('training..., %s, e:%d, lr:%7f' % (name, e, lr))
for batch in tqdm(train_iter): # 每一次返回 batch_size 条数据
optimizer.zero_grad() # 清空梯度
batch = [b.to(device) for b in batch] # cpu -> GPU
# 正常训练
labels = batch[-1].view(-1).cpu().numpy()
loss, bert_enc = model(batch, task='train',
epoch=epoch) # 进行前向传播，真正开始训练；计算 loss
right_num = count_right_num(bert_enc, labels)
# multi-gpu training!
if n_gpu > 1:
loss = loss.mean()
loss.backward() # 反向传播计算参数的梯度
#"""
if open_ad == 'fgm':
# 对抗训练
fgm.attack() # 在embedding上添加对抗扰动
if model_type == 'multi-task':
loss_adv, _, _ = model(batch, task='train')
else:
loss_adv, _ = model(batch, task='train')
if n_gpu > 1:
loss_adv = loss_adv.mean()
loss_adv.backward() # 反向传播，并在正常的grad基础上，累加对抗训练的梯度
fgm.restore() # 恢复embedding参数
elif open_ad == 'pgd':
pgd.backup_grad()
# 对抗训练
for t in range(K):
pgd.attack(is_first_attack=(
t == 0
)) # 在embedding上添加对抗扰动, first attack时备份param.data
if t != K - 1:
optimizer.zero_grad()
else:
pgd.restore_grad()
if model_type == 'multi-task':
loss_adv, _, _ = model(batch, task='train')
else:
loss_adv, _ = model(batch, task='train')
if n_gpu > 1:
loss_adv = loss_adv.mean()
loss_adv.backward() # 反向传播，并在正常的grad基础上，累加对抗训练的梯度
pgd.restore() # 恢复embedding参数
#"""
optimizer.step() # 更新参数
train_loss += loss.item() # loss 求和
train_c += 1
train_right_num += right_num
val_loss = 0
val_c = 0
val_right_num = 0
model.eval()
print('eval...')
with torch.no_grad(): # 不进行梯度的反向传播
for batch in tqdm(dev_iter): # 每一次返回 batch_size 条数据
batch = [b.to(device) for b in batch]
labels = batch[-1].view(-1).cpu().numpy()
loss, bert_enc = model(batch, task='train',
epoch=epoch) # 进行前向传播，真正开始训练；计算 loss
right_num = count_right_num(bert_enc, labels)
if n_gpu > 1:
loss = loss.mean()
val_c += 1
val_loss += loss.item()
val_right_num += right_num
train_acc = train_right_num / train_num
val_acc = val_right_num / val_num
print('train_acc: %.4f, val_acc: %.4f' % (train_acc, val_acc))
print('train_loss: %.4f, val_loss: %.4f, time: %d' %
(train_loss / train_c, val_loss / val_c, time.time() - time0))
if val_loss / val_c < best_loss:
early_stop = 0
best_loss = val_loss / val_c
best_acc = val_acc
# 3.24 update 多卡训练时模型保存避坑:
if not os.path.exists(output_dir):
os.makedirs(output_dir)
model_to_save = model.module if hasattr(model, 'module') else model
state['model_state'] = model_to_save.state_dict()
state['loss'] = val_loss / val_c
state['acc'] = val_acc
state['e'] = e
state['time'] = time.time() - time0
state['lr'] = lr
output_model_file = os.path.join(output_dir, name + '.pkl')
torch.save(state, output_model_file)
#torch.save(state, model_path)
best_epoch = e
cost_time = time.time() - time0
tmp_train_acc = train_acc
best_model = model
else:
early_stop += 1
if early_stop == 2:
break
model = best_model
lr = lr * 0.5
print("best_loss:", best_loss)
# 3.12 add 打印显示最终的最优结果
print('-' * 30)
print('best_epoch:', best_epoch, 'best_loss:', best_loss, 'best_acc:',
best_acc, 'reach time:', cost_time, '\n')
# model-clean
del model
gc.collect()
# 实验结果写入日志
"""
