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from collections import defaultdict
from contextlib import contextmanager
from logging import getLogger
import math
import sys
from typing import List, Union, Iterable
import numpy as np
import torch
from torch import nn
from timm.models import VisionTransformer
from einops import rearrange
DEFAULT_NUM_WINDOWED = 5
class VitDetArgs:
def __init__(self,
window_size: int,
num_summary_tokens: int,
num_windowed: int = DEFAULT_NUM_WINDOWED,
):
self.window_size = window_size
self.num_summary_tokens = num_summary_tokens
self.num_windowed = num_windowed
def apply_vitdet_arch(model: VisionTransformer, args: VitDetArgs):
if isinstance(model, VisionTransformer):
patch_embed = getattr(model, 'patch_generator', model.patch_embed)
return ViTDetHook(patch_embed, model.blocks, args)
else:
print(f'Warning: Unable to apply VitDet aug!', file=sys.stderr)
class ViTDetHook:
def __init__(self,
embedder: nn.Module,
blocks: nn.Sequential,
args: VitDetArgs,
):
self.blocks = blocks
self.num_summary_tokens = args.num_summary_tokens
self.window_size = args.window_size
self._input_resolution = None
self._num_windows = None
self._cls_patch = None
self._order_cache = dict()
embedder.register_forward_pre_hook(self._enter_model)
# This will decide if we window-fy the patches
# and enable vit-det for this iteration, and if so,
# rearrange the patches for efficient mode switching
blocks.register_forward_pre_hook(self._enter_blocks)
is_global = True
period = args.num_windowed + 1
for i, layer in enumerate(blocks[:-1]):
ctr = i % period
if ctr == 0:
layer.register_forward_pre_hook(self._to_windows)
is_global = False
elif ctr == args.num_windowed:
layer.register_forward_pre_hook(self._to_global)
is_global = True
# Always ensure the final layer is a global layer
if not is_global:
blocks[-1].register_forward_pre_hook(self._to_global)
blocks.register_forward_hook(self._exit_model)
def _enter_model(self, _, input: List[torch.Tensor]):
self._input_resolution = input[0].shape[-2:]
def _enter_blocks(self, _, input: List[torch.Tensor]):
# print(f'{get_rank()} - ViTDet Window Size: {self._window_size}', file=sys.stderr)
patches = input[0]
patches = self._rearrange_patches(patches)
return (patches,) + input[1:]
def _to_windows(self, _, input: List[torch.Tensor]):
patches = input[0]
if self.num_summary_tokens:
self._cls_patch = patches[:, :self.num_summary_tokens]
patches = patches[:, self.num_summary_tokens:]
patches = rearrange(
patches, 'b (p t) c -> (b p) t c',
p=self._num_windows, t=self.window_size ** 2,
)
return (patches,) + input[1:]
def _to_global(self, _, input: List[torch.Tensor]):
patches = input[0]
patches = rearrange(
patches, '(b p) t c -> b (p t) c',
p=self._num_windows, t=self.window_size ** 2,
b=patches.shape[0] // self._num_windows,
)
if self.num_summary_tokens:
patches = torch.cat([
self._cls_patch,
patches,
], dim=1)
return (patches,) + input[1:]
def _exit_model(self, _, inputs: List[torch.Tensor], patches: torch.Tensor):
# Return patches to their original order
patch_order = self._order_cache[self._input_resolution][0]
patch_order = patch_order.reshape(1, -1, 1).expand_as(patches)
ret_patches = torch.empty_like(patches)
ret_patches = torch.scatter(
ret_patches,
dim=1,
index=patch_order,
src=patches,
)
return ret_patches
def _rearrange_patches(self, patches: torch.Tensor):
# We rearrange the patches so that we can efficiently
# switch between windowed and global mode by just
# reshaping the tensor
patch_order, self._num_windows = self._order_cache.get(self._input_resolution, (None, None))
if patch_order is None:
num_feat_patches = patches.shape[1] - self.num_summary_tokens
num_pixels = self._input_resolution[0] * self._input_resolution[1]
patch_size = int(round(math.sqrt(num_pixels / num_feat_patches)))
rows = self._input_resolution[-2] // patch_size
cols = self._input_resolution[-1] // patch_size
w_rows = rows // self.window_size
w_cols = cols // self.window_size
patch_order = torch.arange(0, num_feat_patches, device=patches.device)
patch_order = rearrange(
patch_order, '(wy py wx px) -> (wy wx py px)',
wy=w_rows, wx=w_cols,
py=self.window_size, px=self.window_size,
)
if self.num_summary_tokens:
patch_order = torch.cat([
torch.arange(self.num_summary_tokens, dtype=patch_order.dtype, device=patch_order.device),
patch_order + self.num_summary_tokens,
])
self._num_windows = w_rows * w_cols
self._order_cache[self._input_resolution] = (
patch_order,
self._num_windows,
)
patch_order = patch_order.reshape(1, -1, 1).expand_as(patches)
patches = torch.gather(patches, dim=1, index=patch_order)
return patches
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