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"""
Encoder class for Patch Embedder
"""
import math
from functools import partial
from typing import Callable, Optional, Tuple, Union
import torch
import torch.nn as nn
from torch.nn.init import trunc_normal_
from uniception.models.encoders.base import (
UniCeptionViTEncoderBase,
ViTEncoderInput,
ViTEncoderNonImageInput,
ViTEncoderOutput,
)
def make_2tuple(x):
if isinstance(x, tuple):
assert len(x) == 2
return x
assert isinstance(x, int)
return (x, x)
class PatchEmbedder(UniCeptionViTEncoderBase):
"UniCeption Patch Embedder"
def __init__(
self,
name: str,
data_norm_type: str = "patch_embedder",
input_size: Union[int, Tuple[int, int]] = 518,
patch_size: int = 14,
in_chans: int = 3,
enc_embed_dim: int = 1024,
norm_layer: Optional[Callable] = None,
post_pe_norm_layer: Optional[Callable] = partial(nn.LayerNorm, eps=1e-6),
interpolate_antialias: bool = False,
interpolate_offset: float = 0.1,
pretrained_checkpoint_path: str = None,
*args,
**kwargs,
):
"""
Patch Encoder for extracting patch-wise features from a spatial input of size (B, C, H, W).
Learnable positional encoding is also applied to the patch-wise features.
"""
# Init the base class
super().__init__(
name=name,
data_norm_type=data_norm_type,
patch_size=patch_size,
*args,
**kwargs,
)
# Init the Patch Embedder specific attributes
patch_HW = make_2tuple(patch_size)
self.input_size = make_2tuple(input_size)
self.patches_resolution = (self.input_size[0] // patch_HW[0], self.input_size[1] // patch_HW[1])
self.num_patches = self.patches_resolution[0] * self.patches_resolution[1]
self.in_chans = in_chans
self.enc_embed_dim = enc_embed_dim
# Init the Patch Embedder layers
self.proj = nn.Conv2d(in_chans, enc_embed_dim, kernel_size=patch_HW, stride=patch_HW)
self.norm = norm_layer(enc_embed_dim) if norm_layer else nn.Identity()
# Init the learnable positional encodings
self.pos_embed = nn.Parameter(torch.zeros(1, self.num_patches, self.enc_embed_dim))
trunc_normal_(self.pos_embed, std=0.02)
self.interpolate_antialias = interpolate_antialias
self.interpolate_offset = interpolate_offset
# Init the norm layer after positional encoding
self.post_pe_norm = post_pe_norm_layer(enc_embed_dim) if post_pe_norm_layer else nn.Identity()
# Load the pretrained checkpoint if provided
self.pretrained_checkpoint_path = pretrained_checkpoint_path
if self.pretrained_checkpoint_path:
print(f"Loading custom pretrained Patch Embedder checkpoint from {self.pretrained_checkpoint_path} ...")
ckpt = torch.load(self.pretrained_checkpoint_path, weights_only=False)
print(self.load_state_dict(ckpt["model"]))
def interpolate_pos_encoding(self, features, height, width):
"""
Interpolate the positional encoding to the expected size.
Args:
features (torch.Tensor): Input tensor of shape (B, N, C).
height (int, float): Height of the input tensor.
width (int, float): Width of the input tensor.
Returns:
torch.Tensor: Interpolated positional encoding tensor of shape (1, N, C).
"""
previous_dtype = features.dtype
npatch = features.shape[1]
N = self.pos_embed.shape[1]
if npatch == N and height == width:
return self.pos_embed
patch_pos_embed = self.pos_embed.float()
dim = features.shape[-1]
height0 = height // self.patch_size
width0 = width // self.patch_size
M = int(math.sqrt(N)) # Recover the number of patches in each dimension
assert N == M * M
kwargs = {}
if self.interpolate_offset:
# Historical kludge: add a small number to avoid floating point error in the interpolation, see https://github.com/facebookresearch/dino/issues/8
# Note: still needed for backward-compatibility, the underlying operators are using both output size and scale factors
sh = float(height0 + self.interpolate_offset) / M
sw = float(width0 + self.interpolate_offset) / M
kwargs["scale_factor"] = (sh, sw)
else:
# Simply specify an output size instead of a scale factor
kwargs["size"] = (height0, width0)
patch_pos_embed = nn.functional.interpolate(
patch_pos_embed.reshape(1, M, M, dim).permute(0, 3, 1, 2),
mode="bicubic",
antialias=self.interpolate_antialias,
**kwargs,
)
assert (height0, width0) == patch_pos_embed.shape[-2:]
patch_pos_embed = patch_pos_embed.permute(0, 2, 3, 1).view(1, -1, dim)
return patch_pos_embed.to(previous_dtype)
def forward(self, encoder_input: Union[ViTEncoderInput, ViTEncoderNonImageInput]) -> ViTEncoderOutput:
"""
Patch Embedder Forward Pass
Args:
encoder_input (Union[ViTEncoderInput, ViTEncoderNonImageInput]): Input data for the encoder.
If input type is ViTEncoderInput, input data must contain image normalization type and normalized image tensor.
If input type is ViTEncoderNonImageInput, input data must contain a tensor of size (B, C, H, W).
Returns:
ViTEncoderOutput: Output data from the encoder.
"""
# Get the input data and verify normalization if the input type is ViTEncoderInput
if isinstance(encoder_input, ViTEncoderInput):
self._check_data_normalization_type(encoder_input.data_norm_type)
input_data = encoder_input.image
elif isinstance(encoder_input, ViTEncoderNonImageInput):
input_data = encoder_input.data
else:
raise ValueError("Unsupported input type for Patch Embedder.")
# Check the dtype and shape of the input
assert isinstance(input_data, torch.Tensor), "Input must be a torch.Tensor"
assert input_data.ndim == 4, "Input must be of shape (B, C, H, W)"
batch_size, channels, height, width = input_data.shape
assert (
height % self.patch_size == 0 and width % self.patch_size == 0
), f"Input shape must be divisible by patch size: {self.patch_size}"
# Patchify the input data and project into expected latent space
features = self.proj(input_data) # (B, C, H, W) -> (B, E, H / Patch_Size, W / Patch_Size)
features = features.flatten(2).transpose(
1, 2
) # (B, E, H / Patch_Size, W / Patch_Size) -> (B, H / Patch_Size * W / Patch_Size, E)
features = self.norm(features) # Normalize the features after patch embedding
features = features + self.interpolate_pos_encoding(
features, height, width
) # (B, H / Patch_Size * W / Patch_Size, E)
features = self.post_pe_norm(features) # Normalize the features after positional encoding
# Resize the features to the expected shape
# (B x Num_patches x Embed_dim) -> (B x Embed_dim x H / Patch_Size x W / Patch_Size)
features = features.permute(0, 2, 1)
features = features.reshape(
-1, self.enc_embed_dim, height // self.patch_size, width // self.patch_size
).contiguous()
return ViTEncoderOutput(features=features)
if __name__ == "__main__":
# Init Patch Embedder for images as input
patch_embedder = PatchEmbedder(
name="patch_embedder",
data_norm_type="patch_embedder",
input_size=518,
patch_size=14,
in_chans=3,
enc_embed_dim=1024,
)
# Test dummy image input
dummy_image = torch.randn(1, 3, 518, 518)
patch_embedder_output = patch_embedder(ViTEncoderInput(data_norm_type="patch_embedder", image=dummy_image))
assert patch_embedder_output.features.shape == (
1,
1024,
37,
37,
), "Output features must have shape (1, 1024, 37, 37)"
# Init Patch Embedder for non-image data as input
patch_embedder = PatchEmbedder(
name="patch_embedder",
data_norm_type="patch_embedder",
input_size=518,
patch_size=14,
in_chans=6,
enc_embed_dim=1024,
)
# Init Patch Embedder for single channel input
patch_embedder = PatchEmbedder(
name="patch_embedder",
data_norm_type="patch_embedder",
input_size=518,
patch_size=14,
in_chans=1,
enc_embed_dim=1024,
)
# Test dummy non-image input
dummy_image = torch.randn(1, 1, 518, 518)
patch_embedder_output = patch_embedder(ViTEncoderNonImageInput(data=dummy_image))
assert patch_embedder_output.features.shape == (
1,
1024,
37,
37,
), "Output features must have shape (1, 1024, 37, 37)"
print("All variants of Patch Embedder have been initialized successfully!")
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