# Tensor Utils¶

Utils for generating stats from torch tensors.

opacus.utils.tensor_utils.calc_sample_norms(named_params, flat=True)[source]

Calculates the norm of the given tensors for each sample.

This function calculates the overall norm of the given tensors for each sample, assuming the each batch’s dim is zero.

Parameters

Example

>>> t1 = torch.rand((2, 5))
>>> t2 = torch.rand((2, 5))
>>> calc_sample_norms([("1", t1), ("2", t2)])
[tensor([1.5117, 1.0618])]

Return type
Returns

A list of tensor norms where length of the list is the number of layers

opacus.utils.tensor_utils.sum_over_all_but_batch_and_last_n(tensor, n_dims)[source]

Calculates the sum over all dimensions, except the first (batch dimension), and excluding the last n_dims.

This function will ignore the first dimension and it will not aggregate over the last n_dims dimensions.

Parameters

Example

>>> tensor = torch.ones(1, 2, 3, 4, 5)
>>> sum_over_all_but_batch_and_last_n(tensor, n_dims=2).shape
torch.Size([1, 4, 5])

Return type

Tensor

Returns

A tensor of shape (B, ..., X[n_dims-1])

Extracts sliding local blocks from an batched input tensor.

torch.nn.Unfold only supports 4D inputs (batched image-like tensors). This method implements the same action for 5D inputs

Parameters

Example

>>> B, C, D, H, W = 3, 4, 5, 6, 7
>>> tensor = torch.arange(1,B*C*D*H*W+1.).view(B,C,D,H,W)

A tensor of shape (B, C * np.product(kernel_size), L), where L - output spatial dimensions. See torch.nn.Unfold for more details