souging/TRELLIS_TextTo3D

1import torch2import torch.nn as nn3from . import SparseTensor4from . import DEBUG5 6__all__ = [7    'SparseGroupNorm',8    'SparseLayerNorm',9    'SparseGroupNorm32',10    'SparseLayerNorm32',11]12 13 14class SparseGroupNorm(nn.GroupNorm):15    def __init__(self, num_groups, num_channels, eps=1e-5, affine=True):16        super(SparseGroupNorm, self).__init__(num_groups, num_channels, eps, affine)17 18    def forward(self, input: SparseTensor) -> SparseTensor:19        nfeats = torch.zeros_like(input.feats)20        for k in range(input.shape[0]):21            if DEBUG:22                assert (input.coords[input.layout[k], 0] == k).all(), f"SparseGroupNorm: batch index mismatch"23            bfeats = input.feats[input.layout[k]]24            bfeats = bfeats.permute(1, 0).reshape(1, input.shape[1], -1)25            bfeats = super().forward(bfeats)26            bfeats = bfeats.reshape(input.shape[1], -1).permute(1, 0)27            nfeats[input.layout[k]] = bfeats28        return input.replace(nfeats)29 30 31class SparseLayerNorm(nn.LayerNorm):32    def __init__(self, normalized_shape, eps=1e-5, elementwise_affine=True):33        super(SparseLayerNorm, self).__init__(normalized_shape, eps, elementwise_affine)34 35    def forward(self, input: SparseTensor) -> SparseTensor:36        nfeats = torch.zeros_like(input.feats)37        for k in range(input.shape[0]):38            bfeats = input.feats[input.layout[k]]39            bfeats = bfeats.permute(1, 0).reshape(1, input.shape[1], -1)40            bfeats = super().forward(bfeats)41            bfeats = bfeats.reshape(input.shape[1], -1).permute(1, 0)42            nfeats[input.layout[k]] = bfeats43        return input.replace(nfeats)44 45 46class SparseGroupNorm32(SparseGroupNorm):47    """48    A GroupNorm layer that converts to float32 before the forward pass.49    """50    def forward(self, x: SparseTensor) -> SparseTensor:51        return super().forward(x.float()).type(x.dtype)52 53class SparseLayerNorm32(SparseLayerNorm):54    """55    A LayerNorm layer that converts to float32 before the forward pass.56    """57    def forward(self, x: SparseTensor) -> SparseTensor:58        return super().forward(x.float()).type(x.dtype)59