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from collections import OrderedDict
from typing import Callable, Optional, Union
from einops import rearrange
import torch
import torch.nn as nn
from torch.utils.checkpoint import checkpoint
from timm.models.layers import to_2tuple
from timm.models.layers import trunc_normal_
from timm.models.layers import DropPath
from .attention_mask import get_attention_mask
class LayerScale(nn.Module):
def __init__(self, dim, init_values=1e-5, inplace=False):
super().__init__()
self.inplace = inplace
self.gamma = nn.Parameter(init_values * torch.ones(dim))
def forward(self, x):
return x.mul_(self.gamma) if self.inplace else x * self.gamma
class ResidualAttentionBlock(nn.Module):
def __init__(
self,
d_model: int,
n_head: int,
mlp_ratio: float = 4.0,
ls_init_value: float = None,
drop: float = 0.,
attn_drop: float = 0.,
drop_path: float = 0.,
act_layer: Callable = nn.GELU,
norm_layer: Callable = nn.LayerNorm,
use_preln: bool = True,
):
super().__init__()
self.ln_1 = norm_layer(d_model)
self.attn = nn.MultiheadAttention(d_model, n_head, dropout=attn_drop)
self.ls_1 = LayerScale(d_model, ls_init_value) if ls_init_value is not None else nn.Identity()
self.ln_2 = norm_layer(d_model)
mlp_width = int(d_model * mlp_ratio)
self.mlp = nn.Sequential(OrderedDict([
("c_fc", nn.Linear(d_model, mlp_width)),
("gelu", act_layer()),
# disable this following JAX implementation.
# Reference: https://github.com/google-research/magvit/blob/main/videogvt/models/simplified_bert.py#L112
# ("drop1", nn.Dropout(drop)),
("c_proj", nn.Linear(mlp_width, d_model)),
("drop2", nn.Dropout(drop)),
]))
self.ls_2 = LayerScale(d_model, ls_init_value) if ls_init_value is not None else nn.Identity()
self.drop_path = DropPath(drop_path) if drop_path > 0. else nn.Identity()
self.use_preln = use_preln
def attention(self, x: torch.Tensor, attn_mask: Optional[torch.Tensor] = None, is_causal: bool = False):
attn_mask = attn_mask.to(x.dtype) if attn_mask is not None else None
return self.attn(x, x, x, need_weights=False, attn_mask=attn_mask, is_causal=is_causal)[0]
def checkpoint_forward(self, x: torch.Tensor,
attn_mask: Optional[torch.Tensor] = None,
is_causal: bool = False):
state = x
if self.use_preln:
x = checkpoint(self.ln_1, x, use_reentrant=False)
x = self.attention(x, attn_mask, is_causal)
x = checkpoint(self.ls_1, x, use_reentrant=False)
state = state + self.drop_path(x)
x = checkpoint(self.ln_2, state, use_reentrant=False)
x = self.mlp(x)
x = checkpoint(self.ls_2, x, use_reentrant=False)
state = state + self.drop_path(x)
else:
x = self.attention(x, attn_mask, is_causal)
x = state + self.drop_path(x)
state = checkpoint(self.ln_1, x, use_reentrant=False)
x = self.mlp(state)
state = state + self.drop_path(x)
state = checkpoint(self.ln_2, state, use_reentrant=False)
return state
def forward(self, x: torch.Tensor,
attn_mask: Optional[torch.Tensor] = None, is_causal: bool =False,
selective_checkpointing: bool = False):
if selective_checkpointing:
return self.checkpoint_forward(x, attn_mask, is_causal=is_causal)
if self.use_preln:
x = x + self.drop_path(self.ls_1(self.attention(self.ln_1(x), attn_mask=attn_mask, is_causal=is_causal)))
x = x + self.drop_path(self.ls_2(self.mlp(self.ln_2(x))))
else:
x = x + self.drop_path(self.attention(x, attn_mask=attn_mask, is_causal=is_causal))
x = self.ln_1(x)
x = x + self.drop_path(self.mlp(x))
x = self.ln_2(x)
return x
class Transformer(nn.Module):
def __init__(self,
width: int,
layers: int,
heads: int,
mlp_ratio: float = 4.0,
ls_init_value: float = None,
drop: float = 0.,
attn_drop: float = 0.,
drop_path: float = 0.,
act_layer: nn.Module = nn.GELU,
norm_layer: nn.Module = nn.LayerNorm,
use_preln: bool = True,
):
super().__init__()
self.width = width
self.layers = layers
self.grad_checkpointing = False
self.selective_checkpointing = False
self.grad_checkpointing_params = {'use_reentrant': False}
if attn_drop == 0 and drop_path == 0 and drop_path == 0:
self.grad_checkpointing_params.update({'preserve_rng_state': False})
else:
self.grad_checkpointing_params.update({'preserve_rng_state': True})
self.resblocks = nn.ModuleList([
ResidualAttentionBlock(
width, heads, mlp_ratio, ls_init_value=ls_init_value,
drop=drop, attn_drop=attn_drop, drop_path=drop_path,
act_layer=act_layer, norm_layer=norm_layer,
use_preln=use_preln)
for _ in range(layers)
])
def forward(self, x: torch.Tensor,
attn_mask: Optional[torch.Tensor] = None,
is_causal: bool =False):
for r in self.resblocks:
if self.training and self.grad_checkpointing and not torch.jit.is_scripting():
if not self.selective_checkpointing:
x = checkpoint(r, x, attn_mask, is_causal=is_causal, **self.grad_checkpointing_params)
else:
x = r(x, attn_mask=attn_mask, is_causal=is_causal, selective_checkpointing=True)
else:
x = r(x, attn_mask=attn_mask)
return x
class TransformerEncoder(nn.Module):
def __init__(self,
image_size: int,
patch_size: int,
width: int,
layers: int,
heads: int,
mlp_ratio: float,
num_frames: int = 1,
cross_frames: bool = True,
ls_init_value: float = None,
drop_rate: float = 0.,
attn_drop_rate: float = 0.,
drop_path_rate: float = 0.,
ln_pre: bool = True,
ln_post: bool = True,
act_layer: str = 'gelu',
norm_layer: str = 'layer_norm',
mask_type: Union[str, None] = 'none',
mask_block_size: int = -1
):
super().__init__()
self.image_size = to_2tuple(image_size)
self.patch_size = to_2tuple(patch_size)
self.grid_size = (self.image_size[0] // self.patch_size[0], self.image_size[1] // self.patch_size[1])
self.patches_per_frame = self.grid_size[0] * self.grid_size[1]
self.mask_type = mask_type
self.mask_block_size = mask_block_size
if act_layer.lower() == 'gelu':
self.act_layer = nn.GELU
else:
raise ValueError(f"Unsupported activation function: {act_layer}")
if norm_layer.lower() == 'layer_norm':
self.norm_layer = nn.LayerNorm
else:
raise ValueError(f"Unsupported normalization: {norm_layer}")
self.conv1 = nn.Linear(
in_features=3 * self.patch_size[0] * self.patch_size[1],
out_features=width,
bias=not ln_pre
)
scale = width ** -0.5
self.positional_embedding = nn.Parameter(scale * torch.randn(self.grid_size[0] * self.grid_size[1], width))
assert num_frames >= 1
self.num_frames = num_frames
self.cross_frames = cross_frames
if num_frames > 1 and cross_frames:
self.temporal_positional_embedding = nn.Parameter(torch.zeros(num_frames, width))
else:
self.temporal_positional_embedding = None
self.ln_pre = self.norm_layer(width) if ln_pre else nn.Identity()
self.transformer = Transformer(
width, layers, heads, mlp_ratio, ls_init_value=ls_init_value,
drop=drop_rate, attn_drop=attn_drop_rate, drop_path=drop_path_rate,
act_layer=self.act_layer, norm_layer=self.norm_layer,
)
self.ln_post = self.norm_layer(width)
self.init_parameters()
def init_parameters(self):
if self.positional_embedding is not None:
nn.init.normal_(self.positional_embedding, std=0.02)
trunc_normal_(self.conv1.weight, std=0.02)
for block in self.transformer.resblocks:
for n, p in block.named_parameters():
if 'weight' in n:
if 'ln' not in n:
trunc_normal_(p, std=0.02)
elif 'bias' in n:
nn.init.zeros_(p)
else:
raise NotImplementedError(f'Unknown parameters named {n}')
@torch.jit.ignore
def set_grad_checkpointing(self, enable=True, selective=False):
self.transformer.grad_checkpointing = enable
self.transformer.selective_checkpointing = selective
def forward(self, x):
if self.num_frames == 1:
x = rearrange(
x, "b c (hh sh) (ww sw) -> b (hh ww) (c sh sw)",
sh=self.patch_size[0], sw=self.patch_size[1]
)
x = self.conv1(x)
x = x + self.positional_embedding.to(x.dtype)
elif self.cross_frames:
num_frames = x.shape[2]
assert num_frames <= self.num_frames, 'Number of frames should be less or equal to the model setting'
x = rearrange(
x, "b c t (hh sh) (ww sw) -> b (t hh ww) (c sh sw)",
sh=self.patch_size[0], sw=self.patch_size[1]
)
x = self.conv1(x)
tile_pos_embed = self.positional_embedding.repeat(num_frames, 1)
tile_tem_embed = self.temporal_positional_embedding[:num_frames].repeat_interleave(self.patches_per_frame, 0)
total_pos_embed = tile_pos_embed + tile_tem_embed
x = x + total_pos_embed.to(x.dtype).squeeze(0)
else:
x = rearrange(
x, "b c t (hh sh) (ww sw) -> (b t) (hh ww) (c sh sw)",
sh=self.patch_size[0], sw=self.patch_size[1]
)
x = self.conv1(x)
x = x + self.positional_embedding.to(x.dtype)
x = self.ln_pre(x)
x = x.permute(1, 0, 2)
block_size = self.grid_size[0] * self.grid_size[1] if self.mask_block_size <= 0 else self.mask_block_size
attn_mask = get_attention_mask(x.size(0), x.device, mask_type=self.mask_type, block_size=block_size)
x = self.transformer(x, attn_mask, is_causal=self.mask_type == 'causal')
x = x.permute(1, 0, 2)
x = self.ln_post(x)
return x
class TransformerDecoder(nn.Module):
def __init__(self,
image_size: int,
patch_size: int,
width: int,
layers: int,
heads: int,
mlp_ratio: float,
num_frames: int = 1,
cross_frames: bool = True,
ls_init_value: float = None,
drop_rate: float = 0.,
attn_drop_rate: float = 0.,
drop_path_rate: float = 0.,
ln_pre: bool = True,
ln_post: bool = True,
act_layer: str = 'gelu',
norm_layer: str = 'layer_norm',
use_ffn_output: bool = True,
dim_ffn_output: int = 3072,
logit_laplace: bool = False,
mask_type: Union[str, None] = 'none',
mask_block_size: int = -1
):
super().__init__()
self.image_size = to_2tuple(image_size)
self.patch_size = to_2tuple(patch_size)
self.grid_size = (self.image_size[0] // self.patch_size[0], self.image_size[1] // self.patch_size[1])
self.patches_per_frame = self.grid_size[0] * self.grid_size[1]
self.mask_type = mask_type
self.mask_block_size = mask_block_size
if act_layer.lower() == 'gelu':
self.act_layer = nn.GELU
else:
raise ValueError(f"Unsupported activation function: {act_layer}")
if norm_layer.lower() == 'layer_norm':
self.norm_layer = nn.LayerNorm
else:
raise ValueError(f"Unsupported normalization: {norm_layer}")
self.use_ffn_output = use_ffn_output
if use_ffn_output:
self.ffn = nn.Sequential(
nn.Linear(width, dim_ffn_output),
nn.Tanh(),
)
self.conv_out = nn.Linear(
in_features=dim_ffn_output,
out_features=3 * self.patch_size[0] * self.patch_size[1] * (1 + logit_laplace)
)
else:
self.ffn = nn.Identity()
self.conv_out = nn.Linear(
in_features=width,
out_features=3 * self.patch_size[0] * self.patch_size[1] * (1 + logit_laplace)
)
scale = width ** -0.5
self.positional_embedding = nn.Parameter(scale * torch.randn(self.grid_size[0] * self.grid_size[1], width))
assert num_frames >= 1
self.num_frames = num_frames
self.cross_frames = cross_frames
if num_frames > 1 and cross_frames:
self.temporal_positional_embedding = nn.Parameter(torch.zeros(num_frames, width))
else:
self.temporal_positional_embedding = None
self.ln_pre = self.norm_layer(width) if ln_pre else nn.Identity()
self.transformer = Transformer(
width, layers, heads, mlp_ratio, ls_init_value=ls_init_value,
drop=drop_rate, attn_drop=attn_drop_rate, drop_path=drop_path_rate,
act_layer=self.act_layer, norm_layer=self.norm_layer,
)
self.ln_post = self.norm_layer(width) if ln_post else nn.Identity()
self.init_parameters()
def init_parameters(self):
if self.positional_embedding is not None:
nn.init.normal_(self.positional_embedding, std=0.02)
for block in self.transformer.resblocks:
for n, p in block.named_parameters():
if 'weight' in n:
if 'ln' not in n:
trunc_normal_(p, std=0.02)
elif 'bias' in n:
nn.init.zeros_(p)
else:
raise NotImplementedError(f'Unknown parameters named {n}')
if self.use_ffn_output:
trunc_normal_(self.ffn[0].weight, std=0.02)
trunc_normal_(self.conv_out.weight, std=0.02)
@torch.jit.ignore
def set_grad_checkpointing(self, enable=True, selective=False):
self.transformer.grad_checkpointing = enable
self.transformer.selective_checkpointing = selective
def forward(self, x):
if self.num_frames == 1 or not self.cross_frames:
x = x + self.positional_embedding.to(x.dtype)
else:
num_frames = x.shape[1] // self.patches_per_frame
assert num_frames <= self.num_frames, 'Number of frames should be less or equal to the model setting'
tile_pos_embed = self.positional_embedding.repeat(num_frames, 1)
tile_tem_embed = self.temporal_positional_embedding[:num_frames].repeat_interleave(self.patches_per_frame, 0)
total_pos_embed = tile_pos_embed + tile_tem_embed
x = x + total_pos_embed.to(x.dtype).squeeze(0)
x = self.ln_pre(x)
x = x.permute(1, 0, 2)
block_size = self.grid_size[0] * self.grid_size[1] if self.mask_block_size <= 0 else self.mask_block_size
attn_mask = get_attention_mask(x.size(0), x.device, mask_type=self.mask_type, block_size=block_size)
x = self.transformer(x, attn_mask, is_causal=self.mask_type == 'causal')
x = x.permute(1, 0, 2)
x = self.ln_post(x)
x = self.ffn(x)
x = self.conv_out(x)
if self.num_frames == 1:
x = rearrange(
x, "b (hh ww) (c sh sw) -> b c (hh sh) (ww sw)",
hh = self.grid_size[0], ww=self.grid_size[1],
sh=self.patch_size[0], sw=self.patch_size[1]
)
elif self.cross_frames:
x = rearrange(
x, "b (t hh ww) (c sh sw) -> b c t (hh sh) (ww sw)",
t = num_frames, hh = self.grid_size[0], ww=self.grid_size[1],
sh=self.patch_size[0], sw=self.patch_size[1]
)
else:
x = rearrange(
x, "(b t) (hh ww) (c sh sw) -> b c t (hh sh) (ww sw)",
t = num_frames, hh = self.grid_size[0], ww=self.grid_size[1],
sh=self.patch_size[0], sw=self.patch_size[1]
)
return x
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