( dataset, batch_size=1, shuffle=False, collate_fn=LazyDataset.ignore_none_collate, ) prediction=[] for page_num,page_as_tensor in tqdm(enumerate(dataloader)): model_output = model.inference(image_tensors=page_as_tensor[0]) output = markdown_compatible(model_output["predictions"][0]) ...
for epoch in range(1, epochs + 1): # 学习率自我衰减。 if epoch > 2: trainer.set_learning_rate(trainer.learning_rate * 0.1) for batch_i, (data, label) in enumerate(data_iter): with autograd.record(): output = net(data) loss = square_loss(output, label) loss.backward() trainer....
cuda() from tqdm import tqdm for epoch in range(num_epochs): model.train() total_loss = 0 t = tqdm(train_dataloader) for step, batch in enumerate(t): for k, v in batch.items(): batch[k] = v.cuda() outputs = model( input_ids=batch["input_ids"], token_type_ids=batch["...
test_loader = DataLoader(test_set, batch_size=batch_size, shuffle=False) 1. 2. 3. 代码解读 model_best.eval() #使用刚刚训练好的model_best来进行预测 prediction = [] with torch.no_grad(): for i, data in enumerate(test_loader): test_pred = model_best(data.cuda()) test_label = np....
model.cuda()fromtqdmimporttqdmforepochinrange(num_epochs): model.train() total_loss =0t = tqdm(train_dataloader)forstep, batchinenumerate(t):fork, vinbatch.items(): batch[k] = v.cuda() outputs = model( input_ids=batch["input_ids"], ...
1defcalc_logit(self, dataloader):2self.model.eval()3n_stage =self.args.nBlocks4logits = [[]for_inrange(n_stage)]5targets =[]6fori, (input, target)inenumerate(dataloader):7targets.append(target)8with torch.no_grad():9input_var =torch.autograd.Variable(input)10#模型生成每个分类器的预...
for data in DataLoader(dataset, batch_size=1): optimizer.zero_grad() # 获取图数据和边索引 x, edge_index = data.x, data.edge_index # 正样本对和负样本对的获取略过 # pos_data, neg_data = generate_positive_negative_pairs(data)
self.bn1 = nn.BatchNorm2d(16) self.z_dim = z_dim self.hope = HyperNetwork(z_dim=self.z_dim) self.zs_size = [[1, 1], [1, 1], [1, 1], [1, 1], [1, 1], [1, 1], [1, 1], [1, 1], [1, 1], [1, 1], [1, 1], [1, 1], ...
in range(num_epochs): model.train() total_loss = 0 t = tqdm(train_dataloader) for step, batch in enumerate(t): for k, v in batch.items(): batch[k] = v.cuda() outputs = model( input_ids=batch["input_ids"], token_type_ids=batch["token_type_ids"], attention_mask=batch["...
发现Residual TSM融合了时间信息,效果好于In-place TSM,In-place损失了空间特征学习的能力。 1.3 整体模型机理 通过上图就很容易理解模型在对视频分类的原理了。首先通过对每一帧进行上述的shift操作,在进行卷积块操作即可(后面代码会清晰梳理原理),这里需要注意的是最终输出我们采用的是全局平均池化,得到特征在经过fc...