# batch of size 1 mode="bilinear", scale_factor=2.0, align_corners=False).squeeze(0) # remove batch dimension ) print('==new_img.shape:', np.array(new_img).shape)
除了Parameter,我们在这个视频中讨论的类都是torch.nn.Module的子类。这是 PyTorch 的基类,旨在封装特定于 PyTorch 模型及其组件的行为。 torch.nn.Module的一个重要行为是注册参数。如果特定的Module子类具有学习权重,这些权重被表示为torch.nn.Parameter的实例。Parameter类是torch.Tensor的子类,具有特殊行为,当它们被分...
PyTorch 的特点/亮点 对Python 的原生支持及其库的使用 深度结合于 Facebook 的开发,以满足平台中的所有深度学习要求 PyTorch 确保提供易于使用的API,这有助于更换的使用和理解 API 动态图计算是 PyTorch 的一个主要亮点,可以确保在代码执行的每个点动态构建图形,并且可以在运行时进行操作 PyTorch 速度快,因此可以确保...
1) c = (predicted == labels).squeeze() for i in range(4): label = labels[i] class_correct[label] += c[i].item() class_total[label] += 1 for i in range(10): print('Accuracy of %5s : %2d %%' % (classes[i], 100*class_correct[i]/class_total[i])) 1. 2. 3. ...
Update torch.squeeze to allow squeezing multiple dimensions at once (#89017) Add support for int32 indices in index/index_put ops (#86309) Enable where to have cpu scalar args (#87022) Add support for NumPy scalars to torch.tensor.asarray (#90914) Update opt_einsum to have more reasona...
3.expand3x3.weight','features.3.expand3x3.bias','features.4.squeeze.weight','features.4.squeeze...
max(op, 1) c = (pred == ground_truth).squeeze() for i in range(10000): ground_truth_curr = ground_truth[i] class_sucess[ground_truth_curr] += c[i].item() class_counter[ground_truth_curr] += 1 for i in range(10): print('Model accuracy for class %5s : %2d %%' % ( ...
Linear/Embedding: This is a helper function for loading the weights of a linear/embedding layer usingVarBuilderfrom a checkpoint file. We create these 2 helper functions because we will use them multiple times.: fnembedding(vocab_size:usize,hidden_size:usize,vb:VarBuilder)->Result<Embedding>{le...
[3,4]])>>>torch.matmul(arg1, arg2)tensor([5, 6])>>>arg1 = torch.unsqueeze(arg1,0)# 在一维张量前增加一个维度>>>arg1.shapetorch.Size([1, 2])>>>ans = torch.mm(arg1, arg2)# 进行矩阵乘法>>>anstensor([[5, 6]])>>>ans = torch.squeeze(ans,0)# 移除增加的维度>>>anstensor...
autocast(device.type if device.type != 'mps' else 'cpu', enabled=amp): masks_pred = model(images) if model.n_classes == 1: loss = criterion(masks_pred.squeeze(1), true_masks.float()) loss += dice_loss(F.sigmoid(masks_pred.squeeze(1)), true_masks.float(), multiclass=False) ...