model.train() # 确保 model 是在 train model for i, data in enumerate(train_loader): #从dataloader里面一步步取数据 optimizer.zero_grad() # 用 optimizer 将 model 参数的 gradient 清零,防止上一步训练出来的gradient影响这一步的训练 train_pred = model(data[0].cuda()) # 利用 model 得到预测的...
for epoch in range(250): # hidden = (torch.zeros(2, 13, 5), # torch.zeros(2, 13, 5)) # model.hidden = hidden for i, data in enumerate(train_loader): hidden = model.init_hidden(13) inputs = data[0] outputs = data[1] print('inputs', inputs.size()) # print('outputs',...
model.train() epoch_loss = 0 epoch_acc = 0 for i, batch in enumerate(dataloader): # 标签形状为 (batch_size, 1) label = batch["label"] text = batch["text"] # tokenized_text 包括 input_ids, token_type_ids, attention_mask tokenized_text = tokenizer(text, max_length=100, add_speci...
sentiment_train_set = SentimentDataset(data_path + "sentiment.train.data") sentiment_train_loader = DataLoader(sentiment_train_set, batch_size=batch_size, shuffle=True, num_workers=0) # 加载验证集 sentiment_valid_set = SentimentDataset(data_path + "sentiment.valid.data") sentiment_valid_loader...
在我的代码里根本就没有找到任何inplace操作,因此上面这种方法行不通。自己盯着代码,debug,啥也看不出来,好久... 忽然有了新idea。我的训练阶段的代码如下: forepochinrange(1, epochs +1):foridx, (lr, hr)inenumerate(traindata_loader): lrs
epoch_runner(data_loaders[i], **kwargs) File "/home/ding/miniconda3/envs/mmlab/lib/python3.7/site-packages/mmcv/runner/epoch_based_runner.py", line 47, in train for i, data_batch in enumerate(self.data_loader): File "/home/ding/miniconda3/envs/mmlab/lib/python3.7/site-packages/torch...
agg_style_loss = 0. count = 0 for batch_id, (x, _) in enumerate(self.train_loader): n_batch = len(x) count += n_batch self.opt_generator.zero_grad() x = x.to(self.device) y = self.generator(x) y = self.normalize_batch(y.clone()) ...
forepochinrange(2):running_loss=0.0fori,datainenumerate(deeplake_loader):images,labels=data['images'],data['labels']# zero the parameter gradientsoptimizer.zero_grad()# forward + backward + optimizeoutputs=net(images)loss=criterion(outputs,labels.reshape(-1))loss.backward()optimizer.step()# ...
# gradients as None, and larger effective batch sizemodel.train()# Reset the gradients to Noneoptimizer.zero_grad(set_to_none=True)scaler = GradScaler()for i, (features, target) in enumerate(dataloader): # these two calls are nonblocking and overlapping features = features.to('cuda...
train() for batch_idx, (data, target) in enumerate(train_loader): data, target = data.to(device).type(torch.complex64), target.to(device) optimizer.zero_grad() output = model(data) loss = F.nll_loss(output, target) loss.backward() optimizer.step() if batch_idx % 100 == 0: ...