vit_patch_generator.py

# 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: Optional[int] = None,
                 num_registers: Optional[int] = None,
                 patch_bias: bool = False,
                 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, bias=patch_bias, **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,
            num_registers=num_registers,
        )

        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, bias: bool = False, **factory):
        super().__init__(
            3 * (patch_size ** 2),
            embed_dim,
            bias=bias,
            **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)