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Running
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Zero
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import torch
import torch.nn as nn
import torch.nn.functional as F
from einops import repeat
import math
from .udit import UDiT
from .utils.span_mask import compute_mask_indices
class EmbeddingCFG(nn.Module):
"""
Handles label dropout for classifier-free guidance.
"""
# todo: support 2D input
def __init__(self, in_channels):
super().__init__()
self.cfg_embedding = nn.Parameter(
torch.randn(in_channels) / in_channels ** 0.5)
def token_drop(self, condition, condition_mask, cfg_prob):
"""
Drops labels to enable classifier-free guidance.
"""
b, t, device = condition.shape[0], condition.shape[1], condition.device
drop_ids = torch.rand(b, device=device) < cfg_prob
uncond = repeat(self.cfg_embedding, "c -> b t c", b=b, t=t)
condition = torch.where(drop_ids[:, None, None], uncond, condition)
if condition_mask is not None:
condition_mask[drop_ids] = False
condition_mask[drop_ids, 0] = True
return condition, condition_mask
def forward(self, condition, condition_mask, cfg_prob=0.0):
if condition_mask is not None:
condition_mask = condition_mask.clone()
if cfg_prob > 0:
condition, condition_mask = self.token_drop(condition,
condition_mask,
cfg_prob)
return condition, condition_mask
class DiscreteCFG(nn.Module):
def __init__(self, replace_id=2):
super(DiscreteCFG, self).__init__()
self.replace_id = replace_id
def forward(self, context, context_mask, cfg_prob):
context = context.clone()
if context_mask is not None:
context_mask = context_mask.clone()
if cfg_prob > 0:
cfg_mask = torch.rand(len(context)) < cfg_prob
if torch.any(cfg_mask):
context[cfg_mask] = 0
context[cfg_mask, 0] = self.replace_id
if context_mask is not None:
context_mask[cfg_mask] = False
context_mask[cfg_mask, 0] = True
return context, context_mask
class CFGModel(nn.Module):
def __init__(self, context_dim, backbone):
super().__init__()
self.model = backbone
self.context_cfg = EmbeddingCFG(context_dim)
def forward(self, x, timesteps,
context, x_mask=None, context_mask=None,
cfg_prob=0.0):
context = self.context_cfg(context, cfg_prob)
x = self.model(x=x, timesteps=timesteps,
context=context,
x_mask=x_mask, context_mask=context_mask)
return x
class ConcatModel(nn.Module):
def __init__(self, backbone, in_dim, stride=[]):
super().__init__()
self.model = backbone
self.downsample_layers = nn.ModuleList()
for i, s in enumerate(stride):
downsample_layer = nn.Conv1d(
in_dim,
in_dim * 2,
kernel_size=2 * s,
stride=s,
padding=math.ceil(s / 2),
)
self.downsample_layers.append(downsample_layer)
in_dim = in_dim * 2
self.context_cfg = EmbeddingCFG(in_dim)
def forward(self, x, timesteps,
context, x_mask=None,
cfg=False, cfg_prob=0.0):
# todo: support 2D input
# x: B, C, L
# context: B, C, L
for downsample_layer in self.downsample_layers:
context = downsample_layer(context)
context = context.transpose(1, 2)
context = self.context_cfg(caption=context,
cfg=cfg, cfg_prob=cfg_prob)
context = context.transpose(1, 2)
assert context.shape[-1] == x.shape[-1]
x = torch.cat([context, x], dim=1)
x = self.model(x=x, timesteps=timesteps,
context=None, x_mask=x_mask, context_mask=None)
return x
class MaskDiT(nn.Module):
def __init__(self, mae=False, mae_prob=0.5, mask_ratio=[0.25, 1.0], mask_span=10, **kwargs):
super().__init__()
self.model = UDiT(**kwargs)
self.mae = mae
if self.mae:
out_channel = kwargs.pop('out_chans', None)
self.mask_embed = nn.Parameter(torch.zeros((out_channel)))
self.mae_prob = mae_prob
self.mask_ratio = mask_ratio
self.mask_span = mask_span
def random_masking(self, gt, mask_ratios, mae_mask_infer=None):
B, D, L = gt.shape
if mae_mask_infer is None:
# mask = torch.rand(B, L).to(gt.device) < mask_ratios.unsqueeze(1)
mask_ratios = mask_ratios.cpu().numpy()
mask = compute_mask_indices(shape=[B, L],
padding_mask=None,
mask_prob=mask_ratios,
mask_length=self.mask_span,
mask_type="static",
mask_other=0.0,
min_masks=1,
no_overlap=False,
min_space=0,)
mask = mask.unsqueeze(1).expand_as(gt)
else:
mask = mae_mask_infer
mask = mask.expand_as(gt)
gt[mask] = self.mask_embed.view(1, D, 1).expand_as(gt)[mask]
return gt, mask.type_as(gt)
def forward(self, x, timesteps, context,
x_mask=None, context_mask=None, cls_token=None,
gt=None, mae_mask_infer=None,
forward_model=True):
# todo: handle controlnet inside
mae_mask = torch.ones_like(x)
if self.mae:
if gt is not None:
B, D, L = gt.shape
mask_ratios = torch.FloatTensor(B).uniform_(*self.mask_ratio).to(gt.device)
gt, mae_mask = self.random_masking(gt, mask_ratios, mae_mask_infer)
# apply mae only to the selected batches
if mae_mask_infer is None:
# determine mae batch
mae_batch = torch.rand(B) < self.mae_prob
gt[~mae_batch] = self.mask_embed.view(1, D, 1).expand_as(gt)[~mae_batch]
mae_mask[~mae_batch] = 1.0
else:
B, D, L = x.shape
gt = self.mask_embed.view(1, D, 1).expand_as(x)
x = torch.cat([x, gt, mae_mask[:, 0:1, :]], dim=1)
if forward_model:
x = self.model(x=x, timesteps=timesteps, context=context,
x_mask=x_mask, context_mask=context_mask,
cls_token=cls_token)
# print(mae_mask[:, 0, :].sum(dim=-1))
return x, mae_mask
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