Feature map up-sampling.

Crops to center of image with padding then scales image_size.

Flattens predictions in the batch (binary case) Remove labels equal to 'ignore'

Flattens predictions in the batch

Freeze or unfreeze parameters of the specified modules and those of all their hierarchical descendants. This is done in place. Args:

Creates a MixNet Small model. Ref impl: https://github.com/tensorflow/tpu/tree/master/models/official/mnasnet/mixnet Paper: https://arxiv.org

ViT weight initialization * When called without n, head_bias, jax_impl args it will behave exactly the same as my original init for compati

Lazily initialize the dataset. This is necessary to init the Tensorflow dataset pipeline in the (dataloader) process that will be us

Binary Lovasz hinge loss Args: logits: [P] Logits at each prediction (between -infinity and +infinity) labels: [P] Tensor, bina

Multi-class Lovasz-Softmax loss Args: @param probas: [P, C] Class probabilities at each prediction (between 0 and 1) @param lab

Gets attributes of a node which is polymorphic over return type. From https://github.com/pytorch/pytorch/pull/82628

Per-stage depth scaling Scales the block repeats in each stage. This depth scaling impl maintains compatibility with the EfficientNet scaling

Aggregation Type: Default 'depth', ['all', 'sector', 'depth']

https://github.com/VCL3D/SphericalViewSynthesis/blob/master/supervision/splatting.py

image to blocks Args: x (Tensor): with shape (B, H, W, C) block_size (int): edge length of a single square block in units of H, W

Returns list of utf-8 byte and a corresponding list of unicode strings. The reversible bpe codes work on unicode strings. This means you

blocks to image Args: x (Tensor): with shape (B, T, N, C) where T is number of blocks and N is sequence size per block block_size

Dispatch to gradient clipping method Args: parameters (Iterable): model parameters to clip value (float): clipping value/factor/

生成当前LiDAR场景中出现类型的数量文本描述 Example: Two cars, three trucks, one tree, ... , zero barrier.

生成当前LiDAR场景中出现类型的数量文本描述 Example: Two cars, three trucks, one tree, ... , zero barrier.

Returns the location of the directory where models are cached (and creates it if necessary).

NOTE: shape arg should include spatial dim only

Return set of symbol pairs in a word. Word is represented as tuple of symbols (symbols being variable-length strings).