The pytorch package that you’re installing comes with both cpu and gpu support, thus has a large size. It seems you’re using the free version of heroku, and only require the cpu support. The solution is to install the pytorch package for cpu only i.e. In requirements.txt, write the wheel file path corresponding to the … Read more
unfold imagines a tensor as a longer tensor with repeated columns/rows of values ‘folded’ on top of each other, which is then “unfolded”: size determines how large the folds are step determines how often it is folded E.g. for a 2×5 tensor, unfolding it with step=1, and patch size=2 across dim=1: x = torch.tensor([[1,2,3,4,5], [6,7,8,9,10]]) … Read more
model.train() tells your model that you are training the model. This helps inform layers such as Dropout and BatchNorm, which are designed to behave differently during training and evaluation. For instance, in training mode, BatchNorm updates a moving average on each new batch; whereas, for evaluation mode, these updates are frozen. More details: model.train() sets … Read more
In PyTorch, for every mini-batch during the training phase, we typically want to explicitly set the gradients to zero before starting to do backpropragation (i.e., updating the Weights and biases) because PyTorch accumulates the gradients on subsequent backward passes. This accumulating behaviour is convenient while training RNNs or when we want to compute the gradient … Read more
Single layer To initialize the weights of a single layer, use a function from torch.nn.init. For instance: conv1 = torch.nn.Conv2d(…) torch.nn.init.xavier_uniform(conv1.weight) Alternatively, you can modify the parameters by writing to conv1.weight.data (which is a torch.Tensor). Example: conv1.weight.data.fill_(0.01) The same applies for biases: conv1.bias.data.fill_(0.01) nn.Sequential or custom nn.Module Pass an initialization function to torch.nn.Module.apply. It will … Read more
Single layer To initialize the weights of a single layer, use a function from torch.nn.init. For instance: conv1 = torch.nn.Conv2d(…) torch.nn.init.xavier_uniform(conv1.weight) Alternatively, you can modify the parameters by writing to conv1.weight.data (which is a torch.Tensor). Example: conv1.weight.data.fill_(0.01) The same applies for biases: conv1.bias.data.fill_(0.01) nn.Sequential or custom nn.Module Pass an initialization function to torch.nn.Module.apply. It will … Read more
Found this page on their github repo: Recommended approach for saving a model There are two main approaches for serializing and restoring a model. The first (recommended) saves and loads only the model parameters: torch.save(the_model.state_dict(), PATH) Then later: the_model = TheModelClass(*args, **kwargs) the_model.load_state_dict(torch.load(PATH)) The second saves and loads the entire model: torch.save(the_model, PATH) Then later: … Read more
Your graphics card does not support CUDA 9.0. Since I’ve seen a lot of questions that refer to issues like this I’m writing a broad answer on how to check if your system is compatible with CUDA, specifically targeted at using PyTorch with CUDA support. Various circumstance-dependent options for resolving issues are described in the … Read more
Found this page on their github repo, I’ll just copy-paste the content here. Recommended approach for saving a model There are two main approaches for serializing and restoring a model. The first (recommended) saves and loads only the model parameters: torch.save(the_model.state_dict(), PATH) Then later: the_model = TheModelClass(*args, **kwargs) the_model.load_state_dict(torch.load(PATH)) The second saves and loads the … Read more