| import torch.nn as nn | |
| from ..modules import sparse as sp | |
| FP16_MODULES = ( | |
| nn.Conv1d, | |
| nn.Conv2d, | |
| nn.Conv3d, | |
| nn.ConvTranspose1d, | |
| nn.ConvTranspose2d, | |
| nn.ConvTranspose3d, | |
| nn.Linear, | |
| sp.SparseConv3d, | |
| sp.SparseInverseConv3d, | |
| sp.SparseLinear, | |
| ) | |
| def convert_module_to_f16(l): | |
| """ | |
| Convert primitive modules to float16. | |
| """ | |
| if isinstance(l, FP16_MODULES): | |
| for p in l.parameters(): | |
| p.data = p.data.half() | |
| def convert_module_to_f32(l): | |
| """ | |
| Convert primitive modules to float32, undoing convert_module_to_f16(). | |
| """ | |
| if isinstance(l, FP16_MODULES): | |
| for p in l.parameters(): | |
| p.data = p.data.float() | |
| def zero_module(module): | |
| """ | |
| Zero out the parameters of a module and return it. | |
| """ | |
| for p in module.parameters(): | |
| p.detach().zero_() | |
| return module | |
| def scale_module(module, scale): | |
| """ | |
| Scale the parameters of a module and return it. | |
| """ | |
| for p in module.parameters(): | |
| p.detach().mul_(scale) | |
| return module | |
| def modulate(x, shift, scale): | |
| return x * (1 + scale.unsqueeze(1)) + shift.unsqueeze(1) | |