r"""Fills the input Tensor with values drawn from a truncated normal distribution. The values are effectively drawn from the normal distributi

r"""Fills the input Tensor with values drawn from a truncated normal distribution. The values are effectively drawn from the normal distributi

3x3 convolution with padding

Keep reading image until succeed. This can avoid IOError incurred by heavy IO process.

Check if all files are available before going deeper

Return a process group based on gloo backend, containing all the ranks The result is cached.

Returns: list[int]: size of the tensor, on each rank Tensor: padded tensor that has the max size

Run all_gather on arbitrary picklable data (not necessarily tensors). Args: data: any picklable object group: a torch process

Drop paths (Stochastic Depth) per sample (when applied in main path of residual blocks). This is the same as the DropConnect impl I created for E

Drop paths (Stochastic Depth) per sample (when applied in main path of residual blocks). This is the same as the DropConnect impl I created for E

Args: x: pytorch Variable, with shape [m, d] y: pytorch Variable, with shape [n, d] Returns: dist: pytorch Variable, with s

Evaluation with market1501 metric Key: for each query identity, its gallery images from the same camera view are discarded.

For each anchor, find the hardest positive and negative sample. Args: dist_mat: pytorch Variable, pair wise distance between samples, shape

Normalizing to unit length along the specified dimension. Args: x: pytorch Variable Returns: x: pytorch Variable, same shape as in

Returns: int: a random number that is the same across all workers. If workers need a shared RNG, they can use this shared see