""" Image to Patch Hybird Embedding Layer
Hacked together by / Copyright 2020 Ross Wightman
"""
import logging
import math
from typing import List, Optional, Tuple, Union
import torch
from torch import nn as nn
import torch.nn.functional as F
from .format import Format, nchw_to
from .helpers import to_2tuple
from .patch_embed import resample_patch_embed
_logger = logging.getLogger(__name__)
class HybridEmbed(nn.Module):
""" CNN Feature Map Embedding
Extract feature map from CNN, flatten, project to embedding dim.
"""
output_fmt: Format
dynamic_img_pad: torch.jit.Final[bool]
def __init__(
self,
backbone: nn.Module,
img_size: Union[int, Tuple[int, int]] = 224,
patch_size: Union[int, Tuple[int, int]] = 1,
feature_size: Optional[Union[int, Tuple[int, int]]] = None,
feature_ratio: Optional[Union[int, Tuple[int, int]]] = None,
in_chans: int = 3,
embed_dim: int = 768,
bias: bool = True,
proj: bool = True,
flatten: bool = True,
output_fmt: Optional[str] = None,
strict_img_size: bool = True,
dynamic_img_pad: bool = False,
):
super().__init__()
assert isinstance(backbone, nn.Module)
self.backbone = backbone
self.in_chans = in_chans
(
self.img_size,
self.patch_size,
self.feature_size,
self.feature_ratio,
self.feature_dim,
self.grid_size,
self.num_patches,
) = self._init_backbone(
img_size=img_size,
patch_size=patch_size,
feature_size=feature_size,
feature_ratio=feature_ratio,
)
if output_fmt is not None:
self.flatten = False
self.output_fmt = Format(output_fmt)
else:
# flatten spatial dim and transpose to channels last, kept for bwd compat
self.flatten = flatten
self.output_fmt = Format.NCHW
self.strict_img_size = strict_img_size
self.dynamic_img_pad = dynamic_img_pad
if not dynamic_img_pad:
assert self.feature_size[0] % self.patch_size[0] == 0 and self.feature_size[1] % self.patch_size[1] == 0
if proj:
self.proj = nn.Conv2d(
self.feature_dim,
embed_dim,
kernel_size=patch_size,
stride=patch_size,
bias=bias,
)
else:
assert self.feature_dim == embed_dim, \
f'The feature dim ({self.feature_dim} must match embed dim ({embed_dim}) when projection disabled.'
self.proj = nn.Identity()
def _init_backbone(
self,
img_size: Union[int, Tuple[int, int]] = 224,
patch_size: Union[int, Tuple[int, int]] = 1,
feature_size: Optional[Union[int, Tuple[int, int]]] = None,
feature_ratio: Optional[Union[int, Tuple[int, int]]] = None,
feature_dim: Optional[int] = None,
):
img_size = to_2tuple(img_size)
patch_size = to_2tuple(patch_size)
if feature_size is None:
with torch.no_grad():
# NOTE Most reliable way of determining output dims is to run forward pass
training = self.backbone.training
if training:
self.backbone.eval()
o = self.backbone(torch.zeros(1, self.in_chans, img_size[0], img_size[1]))
if isinstance(o, (list, tuple)):
o = o[-1] # last feature if backbone outputs list/tuple of features
feature_size = o.shape[-2:]
feature_dim = o.shape[1]
self.backbone.train(training)
feature_ratio = tuple([s // f for s, f in zip(img_size, feature_size)])
else:
feature_size = to_2tuple(feature_size)
feature_ratio = to_2tuple(feature_ratio or 16)
if feature_dim is None:
if hasattr(self.backbone, 'feature_info'):
feature_dim = self.backbone.feature_info.channels()[-1]
else:
feature_dim = self.backbone.num_features
grid_size = tuple([f // p for f, p in zip(feature_size, patch_size)])
num_patches = grid_size[0] * grid_size[1]
return img_size, patch_size, feature_size, feature_ratio, feature_dim, grid_size, num_patches
def set_input_size(
self,
img_size: Optional[Union[int, Tuple[int, int]]] = None,
patch_size: Optional[Union[int, Tuple[int, int]]] = None,
feature_size: Optional[Union[int, Tuple[int, int]]] = None,
feature_ratio: Optional[Union[int, Tuple[int, int]]] = None,
feature_dim: Optional[int] = None,
):
assert img_size is not None or patch_size is not None
img_size = img_size or self.img_size
new_patch_size = None
if patch_size is not None:
new_patch_size = to_2tuple(patch_size)
if new_patch_size is not None and new_patch_size != self.patch_size:
assert isinstance(self.proj, nn.Conv2d), 'HybridEmbed must have a projection layer to change patch size.'
with torch.no_grad():
new_proj = nn.Conv2d(
self.proj.in_channels,
self.proj.out_channels,
kernel_size=new_patch_size,
stride=new_patch_size,
bias=self.proj.bias is not None,
)
new_proj.weight.copy_(resample_patch_embed(self.proj.weight, new_patch_size, verbose=True))
if self.proj.bias is not None:
new_proj.bias.copy_(self.proj.bias)
self.proj = new_proj
patch_size = new_patch_size
patch_size = patch_size or self.patch_size
if img_size != self.img_size or patch_size != self.patch_size:
(
self.img_size,
self.patch_size,
self.feature_size,
self.feature_ratio,
self.feature_dim,
self.grid_size,
self.num_patches,
) = self._init_backbone(
img_size=img_size,
patch_size=patch_size,
feature_size=feature_size,
feature_ratio=feature_ratio,
feature_dim=feature_dim,
)
def feat_ratio(self, as_scalar=True) -> Union[Tuple[int, int], int]:
total_reduction = (
self.feature_ratio[0] * self.patch_size[0],
self.feature_ratio[1] * self.patch_size[1]
)
if as_scalar:
return max(total_reduction)
else:
return total_reduction
def dynamic_feat_size(self, img_size: Tuple[int, int]) -> Tuple[int, int]:
""" Get feature grid size taking account dynamic padding and backbone network feat reduction
"""
feat_size = (img_size[0] // self.feature_ratio[0], img_size[1] // self.feature_ratio[1])
if self.dynamic_img_pad:
return math.ceil(feat_size[0] / self.patch_size[0]), math.ceil(feat_size[1] / self.patch_size[1])
else:
return feat_size[0] // self.patch_size[0], feat_size[1] // self.patch_size[1]
@torch.jit.ignore
def set_grad_checkpointing(self, enable: bool = True):
if hasattr(self.backbone, 'set_grad_checkpointing'):
self.backbone.set_grad_checkpointing(enable=enable)
elif hasattr(self.backbone, 'grad_checkpointing'):
self.backbone.grad_checkpointing = enable
def forward(self, x):
x = self.backbone(x)
if isinstance(x, (list, tuple)):
x = x[-1] # last feature if backbone outputs list/tuple of features
_, _, H, W = x.shape
if self.dynamic_img_pad:
pad_h = (self.patch_size[0] - H % self.patch_size[0]) % self.patch_size[0]
pad_w = (self.patch_size[1] - W % self.patch_size[1]) % self.patch_size[1]
x = F.pad(x, (0, pad_w, 0, pad_h))
x = self.proj(x)
if self.flatten:
x = x.flatten(2).transpose(1, 2) # NCHW -> NLC
elif self.output_fmt != Format.NCHW:
x = nchw_to(x, self.output_fmt)
return x
class HybridEmbedWithSize(HybridEmbed):
""" CNN Feature Map Embedding
Extract feature map from CNN, flatten, project to embedding dim.
"""
def __init__(
self,
backbone: nn.Module,
img_size: Union[int, Tuple[int, int]] = 224,
patch_size: Union[int, Tuple[int, int]] = 1,
feature_size: Optional[Union[int, Tuple[int, int]]] = None,
feature_ratio: Optional[Union[int, Tuple[int, int]]] = None,
in_chans: int = 3,
embed_dim: int = 768,
bias=True,
proj=True,
):
super().__init__(
backbone=backbone,
img_size=img_size,
patch_size=patch_size,
feature_size=feature_size,
feature_ratio=feature_ratio,
in_chans=in_chans,
embed_dim=embed_dim,
bias=bias,
proj=proj,
)
@torch.jit.ignore
def set_grad_checkpointing(self, enable: bool = True):
if hasattr(self.backbone, 'set_grad_checkpointing'):
self.backbone.set_grad_checkpointing(enable=enable)
elif hasattr(self.backbone, 'grad_checkpointing'):
self.backbone.grad_checkpointing = enable
def forward(self, x) -> Tuple[torch.Tensor, List[int]]:
x = self.backbone(x)
if isinstance(x, (list, tuple)):
x = x[-1] # last feature if backbone outputs list/tuple of features
x = self.proj(x)
return x.flatten(2).transpose(1, 2), x.shape[-2:]