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# Copyright (c) 2023-2024, NVIDIA CORPORATION. All rights reserved.
#
# NVIDIA CORPORATION and its licensors retain all intellectual property
# and proprietary rights in and to this software, related documentation
# and any modifications thereto. Any use, reproduction, disclosure or
# distribution of this software and related documentation without an express
# license agreement from NVIDIA CORPORATION is strictly prohibited.
import math
from typing import Union, Tuple, Optional
import torch
import torch.nn.functional as F
from torch import nn
from einops import rearrange
from .cls_token import ClsToken
input_dim_t = Union[int, Tuple[int, int]]
try:
# raise ImportError()
from indirect_grid_sample import indirect_grid_sample
except ImportError:
indirect_grid_sample = None
class ViTPatchGenerator(nn.Module):
def __init__(self,
patch_size: int,
embed_dim: int,
input_dims: input_dim_t,
abs_pos: bool = True,
normalize_patches: bool = False,
cls_token: bool = False,
max_input_dims: Optional[input_dim_t] = None,
pos_dropout: float = 0.0,
return_pos_enc: bool = False,
num_cls_tokens: int = 1,
register_multiple: int = 0,
device=None, dtype=None,
):
super().__init__()
if isinstance(input_dims, int):
input_dims = (input_dims, input_dims)
if max_input_dims is None:
max_input_dims = input_dims
if isinstance(max_input_dims, int):
max_input_dims = (max_input_dims, max_input_dims)
max_input_dims = tuple(
int(math.ceil(d / patch_size) * patch_size)
for d in max_input_dims
)
self.cpe_mode = max_input_dims != input_dims
self.pos_dropout = pos_dropout
self.return_pos_enc = return_pos_enc
factory = dict(device=device, dtype=dtype)
self.patch_size = patch_size
self.abs_pos = abs_pos
self.embed_dim = embed_dim
self.num_rows = max_input_dims[0] // patch_size
self.num_cols = max_input_dims[1] // patch_size
self.input_dims = tuple(d // patch_size for d in input_dims)
self.num_patches = self.num_rows * self.num_cols
self.max_input_dims = max_input_dims
self.im_to_patches = Im2Patches(patch_size)
self.embedder = ViTPatchLinear(patch_size, embed_dim, **factory)
if abs_pos:
scale = embed_dim ** -0.5
self.pos_embed = nn.Parameter(torch.randn(1, self.num_patches, embed_dim, **factory) * scale)
self.cls_token = ClsToken(
embed_dim,
num_tokens=num_cls_tokens,
enabled=cls_token,
register_multiple=register_multiple,
)
self.patch_normalizer = nn.LayerNorm(embed_dim) if normalize_patches else nn.Identity()
def forward(self, x: torch.Tensor) -> torch.Tensor:
patches = self.embed_patches(x)
patches, pos_enc = self.apply_pos_enc(patches, input_size=x.shape[2:])
patches = self.cls_token(patches)
patches = self.patch_normalizer(patches)
if self.return_pos_enc:
return patches, pos_enc
return patches
@property
def apply_cls_token(self):
return self.cls_token.enabled
@property
def num_cls_tokens(self):
return self.cls_token.num_tokens
@property
def num_registers(self):
return self.cls_token.num_registers
@property
def num_skip(self):
return self.num_cls_tokens + self.num_registers
def no_weight_decay(self):
return [
'pos_embed',
]
def _load_from_state_dict(self, state_dict, prefix, local_metadata, strict, missing_keys, unexpected_keys, error_msgs):
if self.abs_pos:
self._load_embed(state_dict[f'{prefix}pos_embed'], self.pos_embed)
def _load_embed(self, src_embed: torch.Tensor, targ_embed: nn.Parameter):
if src_embed.shape != targ_embed.shape:
src_size = int(math.sqrt(src_embed.shape[1]))
assert src_size ** 2 == src_embed.shape[1], 'Unable to interpolate non-square embedding'
src_embed = rearrange(src_embed, 'b (h w) c -> b c h w', h=src_size, w=src_size)
src_embed = F.interpolate(src_embed, size=(self.num_rows, self.num_cols), mode='bicubic', align_corners=True, antialias=False)
src_embed = rearrange(src_embed, 'b c h w -> b (h w) c')
targ_embed.data.copy_(src_embed)
def _load_projection(self, src_proj_weight: torch.Tensor, targ_proj_weight: torch.Tensor):
if src_proj_weight.shape != targ_proj_weight.shape:
src_patch_size = int(math.sqrt(src_proj_weight.shape[1] // 3))
assert (src_patch_size ** 2) * 3 == src_proj_weight.shape[1], 'Unable to interpolate non-square patch size'
src_proj_weight = rearrange(src_proj_weight, 'b (c h w) -> b c h w', c=3, h=src_patch_size, w=src_patch_size)
src_proj_weight = F.interpolate(src_proj_weight, size=(self.patch_size, self.patch_size), mode='bicubic', align_corners=True, antialias=False)
src_proj_weight = rearrange(src_proj_weight, 'b c h w -> b (c h w)')
targ_proj_weight.data.copy_(src_proj_weight)
def embed_patches(self, x: torch.Tensor) -> torch.Tensor:
patches = self.im_to_patches(x)
patches = self.embedder(patches)
return patches
def apply_pos_enc(self,
patches: torch.Tensor,
patch_idxs: Optional[torch.Tensor] = None,
input_size: Optional[Tuple[int, int]] = None,
) -> torch.Tensor:
if not self.abs_pos:
return patches
pos_enc = self.get_pos_enc(patches.shape[0], patch_idxs, input_size)
if self.training and self.pos_dropout > 0:
keeps = torch.rand(patches.shape[0], 1, 1, dtype=pos_enc.dtype, device=pos_enc.device) > self.pos_dropout
pos_enc_drop = torch.where(keeps, pos_enc, 0)
else:
pos_enc_drop = pos_enc
return patches + pos_enc_drop, pos_enc
def get_pos_enc(self,
batch_size: int,
patch_idxs: Optional[torch.Tensor] = None,
input_size: Optional[Tuple[int, int]] = None,
) -> torch.Tensor:
if input_size is None:
input_dims = self.input_dims
else:
input_dims = tuple(d // self.patch_size for d in input_size)
pos_embed = self._get_pos_embeddings(batch_size, input_dims)
if patch_idxs is None:
return pos_embed
exp_patch_idxs = patch_idxs.unsqueeze(-1).expand(-1, -1, pos_embed.shape[-1])
pos_embed = torch.gather(pos_embed.expand(patch_idxs.shape[0], -1, -1), dim=1, index=exp_patch_idxs)
return pos_embed
def _get_pos_embeddings(self, batch_size: int, input_dims: Tuple[int, int]):
if (self.num_rows, self.num_cols) == input_dims:
return self.pos_embed
pos_embed = self.pos_embed.reshape(1, self.num_rows, self.num_cols, -1).permute(0, 3, 1, 2)
def window_select(pos_embed):
if input_dims[0] < pos_embed.shape[-2]:
pos_embed = pos_embed[..., :input_dims[0], :]
if input_dims[1] < pos_embed.shape[-1]:
pos_embed = pos_embed[..., :, :input_dims[1]]
return pos_embed
if self.cpe_mode:
if self.training:
min_scale = math.sqrt(0.1)
scale = torch.rand(batch_size, 1, 1, device=pos_embed.device) * (1 - min_scale) + min_scale
aspect_min = math.log(3 / 4)
aspect_max = -aspect_min
aspect = torch.exp(torch.rand(batch_size, 1, 1, device=pos_embed.device) * (aspect_max - aspect_min) + aspect_min)
scale_x = scale * aspect
scale_y = scale * (1 / aspect)
scale_xy = torch.stack([scale_x, scale_y], dim=-1).clamp_(0, 1)
pos_xy = torch.rand(batch_size, 1, 1, 2, device=pos_embed.device) * (1 - scale_xy)
lin_x = torch.linspace(0, 1, steps=input_dims[1], device=pos_embed.device)[None, None].expand(batch_size, input_dims[0], -1)
lin_y = torch.linspace(0, 1, steps=input_dims[0], device=pos_embed.device)[None, :, None].expand(batch_size, -1, input_dims[1])
lin_xy = torch.stack([lin_x, lin_y], dim=-1)
grid_xy = lin_xy * scale_xy + pos_xy
# Convert to [-1, 1] range
grid_xy.mul_(2).sub_(1)
pos_embed = F.grid_sample(
pos_embed.float().expand(batch_size, -1, -1, -1),
grid=grid_xy,
mode='bilinear',
padding_mode='zeros',
align_corners=True,
).to(pos_embed.dtype)
else:
# i_rows, i_cols = input_dims
# p_rows, p_cols = pos_embed.shape[2:]
# if i_rows <= p_rows and i_cols <= p_cols:
# left = (p_cols - i_cols) // 2
# top = (p_rows - i_rows) // 2
# pos_embed = pos_embed[..., top:top+i_rows, left:left+i_cols]
# else:
max_dim = max(input_dims)
pos_embed = F.interpolate(pos_embed.float(), size=(max_dim, max_dim), align_corners=True, mode='bilinear').to(pos_embed.dtype)
pos_embed = window_select(pos_embed)
else:
pos_embed = window_select(pos_embed)
if pos_embed.shape[-2:] != input_dims:
pos_embed = F.interpolate(pos_embed.float(), size=input_dims, align_corners=True, mode='bilinear').to(pos_embed.dtype)
pos_embed = pos_embed.flatten(2).permute(0, 2, 1)
return pos_embed
class Im2Patches(nn.Module):
def __init__(self, patch_size: int):
super().__init__()
self.patch_size = patch_size
def forward(self, x: torch.Tensor) -> torch.Tensor:
if self.patch_size == 1:
patches = x.flatten(2)
patches = patches.permute(0, 2, 1)
return patches
py = x.shape[-2] // self.patch_size
px = x.shape[-1] // self.patch_size
patches = rearrange(x, 'b c (py yy) (px xx) -> b (py px) (c yy xx)',
py=py, yy=self.patch_size,
px=px, xx=self.patch_size,
)
return patches
class ViTPatchLinear(nn.Linear):
def __init__(self, patch_size: int, embed_dim: int, **factory):
super().__init__(
3 * (patch_size ** 2),
embed_dim,
bias=False,
**factory
)
self.patch_size = patch_size
def _load_from_state_dict(self, state_dict, prefix, local_metadata, strict, missing_keys, unexpected_keys, error_msgs):
if self.bias is not None:
self.bias.data.copy_(state_dict[f'{prefix}bias'])
chk_weight = state_dict[f'{prefix}weight']
if chk_weight.shape != self.weight.shape:
src_patch_size = int(math.sqrt(chk_weight.shape[1] // 3))
assert (src_patch_size ** 2) * 3 == chk_weight.shape[1], 'Unable to interpolate non-square patch size'
chk_weight = rearrange(chk_weight, 'b (c h w) -> b c h w', c=3, h=src_patch_size, w=src_patch_size)
chk_weight = F.interpolate(chk_weight, size=(self.patch_size, self.patch_size), mode='bicubic', align_corners=True, antialias=False)
chk_weight = rearrange(chk_weight, 'b c h w -> b (c h w)')
self.weight.data.copy_(chk_weight)
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