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import os
from typing import Union, Optional
import torch
import torch.nn as nn
from transformers.image_processing_utils import BaseImageProcessor
class SAFEReducerBlock(nn.Module):
"""
This is the block that reduces the size of an vactor w and h be half. It is designed to be iterative
So it is run multiple times to reduce an image to a desired dimension while carrying a shrinking residual
along for the ride. This is done to preserve information.
"""
def __init__(self, channels=512):
super(SAFEReducerBlock, self).__init__()
self.channels = channels
activation = nn.GELU
self.reducer = nn.Sequential(
nn.Conv2d(channels, channels, kernel_size=3, padding=1),
activation(),
nn.BatchNorm2d(channels),
nn.Conv2d(channels, channels, kernel_size=3, padding=1),
activation(),
nn.BatchNorm2d(channels),
nn.AvgPool2d(kernel_size=2, stride=2),
)
self.residual_shrink = nn.AvgPool2d(kernel_size=2, stride=2)
def forward(self, x):
res = self.residual_shrink(x)
reduced = self.reducer(x)
return reduced + res
class SizeAgnosticFeatureEncoder(nn.Module):
def __init__(
self,
in_channels=3,
num_tokens=8,
num_vectors=768,
reducer_channels=512,
channels=2048,
downscale_factor: int = 8,
):
super(SizeAgnosticFeatureEncoder, self).__init__()
self.num_tokens = num_tokens
self.num_vectors = num_vectors
self.channels = channels
self.reducer_channels = reducer_channels
self.gradient_checkpointing = False
# input is minimum of (bs, 3, 256, 256)
subpixel_channels = in_channels * downscale_factor ** 2
# PixelUnshuffle(8 = # (bs, 3, 32, 32) -> (bs, 192, 32, 32)
# PixelUnshuffle(16 = # (bs, 3, 16, 16) -> (bs, 48, 16, 16)
self.unshuffle = nn.PixelUnshuffle(downscale_factor) # (bs, 3, 256, 256) -> (bs, 192, 32, 32)
self.conv_in = nn.Conv2d(subpixel_channels, reducer_channels, kernel_size=3, padding=1) # (bs, 192, 32, 32) -> (bs, 512, 32, 32)
# run as many times as needed to get to min feature of 8 on the smallest dimension
self.reducer = SAFEReducerBlock(reducer_channels) # (bs, 512, 32, 32) -> (bs, 512, 8, 8)
self.reduced_out = nn.Conv2d(
reducer_channels, self.channels, kernel_size=3, padding=1
) # (bs, 512, 8, 8) -> (bs, 2048, 8, 8)
# (bs, 2048, 8, 8)
self.block1 = SAFEReducerBlock(self.channels) # (bs, 2048, 8, 8) -> (bs, 2048, 4, 4)
self.block2 = SAFEReducerBlock(self.channels) # (bs, 2048, 8, 8) -> (bs, 2048, 2, 2)
# reduce mean of dims 2 and 3
self.adaptive_pool = nn.Sequential(
nn.AdaptiveAvgPool2d((1, 1)),
nn.Flatten(),
)
# (bs, 2048)
# linear layer to (bs, self.num_vectors * self.num_tokens)
self.fc1 = nn.Linear(self.channels, self.num_vectors * self.num_tokens)
# (bs, self.num_vectors * self.num_tokens) = (bs, 8 * 768) = (bs, 6144)
def forward(self, x):
x = self.unshuffle(x)
x = self.conv_in(x)
while True:
# reduce until we get as close to 8x8 as possible without going under
x = self.reducer(x)
if x.shape[2] // 2 < 8 or x.shape[3] // 2 < 8:
break
x = self.reduced_out(x)
x = self.block1(x)
x = self.block2(x)
x = self.adaptive_pool(x)
x = self.fc1(x)
# reshape
x = x.view(-1, self.num_tokens, self.num_vectors)
return x
class SAFEIPReturn:
def __init__(self, pixel_values):
self.pixel_values = pixel_values
class SAFEImageProcessor(BaseImageProcessor):
def __init__(
self,
max_size=1024,
min_size=256,
**kwargs
):
super().__init__(**kwargs)
self.max_size = max_size
self.min_size = min_size
@classmethod
def from_pretrained(
cls,
pretrained_model_name_or_path: Union[str, os.PathLike],
cache_dir: Optional[Union[str, os.PathLike]] = None,
force_download: bool = False,
local_files_only: bool = False,
token: Optional[Union[str, bool]] = None,
revision: str = "main",
**kwargs,
):
# not needed
return cls(**kwargs)
def __call__(
self,
images,
**kwargs
):
# TODO allow for random resizing
# comes in 0 - 1 range
# if any size is smaller than 256, resize to 256
# if any size is larger than max_size, resize to max_size
if images.min() < -0.3 or images.max() > 1.3:
raise ValueError(
"images fed into SAFEImageProcessor values must be between 0 and 1. Got min: {}, max: {}".format(
images.min(), images.max()
))
# make sure we have (bs, 3, h, w)
while len(images.shape) < 4:
images = images.unsqueeze(0)
# expand to 3 channels if we only have 1 channel
if images.shape[1] == 1:
images = torch.cat([images, images, images], dim=1)
width = images.shape[3]
height = images.shape[2]
if width < self.min_size or height < self.min_size:
# scale up so that the smallest size is 256
if width < height:
new_width = self.min_size
new_height = int(height * (self.min_size / width))
else:
new_height = self.min_size
new_width = int(width * (self.min_size / height))
images = nn.functional.interpolate(images, size=(new_height, new_width), mode='bilinear',
align_corners=False)
elif width > self.max_size or height > self.max_size:
# scale down so that the largest size is max_size but do not shrink the other size below 256
if width > height:
new_width = self.max_size
new_height = int(height * (self.max_size / width))
else:
new_height = self.max_size
new_width = int(width * (self.max_size / height))
if new_width < self.min_size:
new_width = self.min_size
new_height = int(height * (self.min_size / width))
if new_height < self.min_size:
new_height = self.min_size
new_width = int(width * (self.min_size / height))
images = nn.functional.interpolate(images, size=(new_height, new_width), mode='bilinear',
align_corners=False)
# if wither side is not divisible by 16, mirror pad to make it so
if images.shape[2] % 16 != 0:
pad = 16 - (images.shape[2] % 16)
pad1 = pad // 2
pad2 = pad - pad1
images = nn.functional.pad(images, (0, 0, pad1, pad2), mode='reflect')
if images.shape[3] % 16 != 0:
pad = 16 - (images.shape[3] % 16)
pad1 = pad // 2
pad2 = pad - pad1
images = nn.functional.pad(images, (pad1, pad2, 0, 0), mode='reflect')
return SAFEIPReturn(images)
class SAFEVMConfig:
def __init__(
self,
in_channels=3,
num_tokens=8,
num_vectors=768,
reducer_channels=512,
channels=2048,
downscale_factor: int = 8,
**kwargs
):
self.in_channels = in_channels
self.num_tokens = num_tokens
self.num_vectors = num_vectors
self.reducer_channels = reducer_channels
self.channels = channels
self.downscale_factor = downscale_factor
self.image_size = 224
self.hidden_size = num_vectors
self.projection_dim = num_vectors
class SAFEVMReturn:
def __init__(self, output):
self.output = output
# todo actually do hidden states. This is just for code compatability for now
self.hidden_states = [output for _ in range(13)]
class SAFEVisionModel(SizeAgnosticFeatureEncoder):
def __init__(self, **kwargs):
self.config = SAFEVMConfig(**kwargs)
self.image_size = None
# super().__init__(**kwargs)
super(SAFEVisionModel, self).__init__(**kwargs)
@classmethod
def from_pretrained(cls, *args, **kwargs):
# not needed
return SAFEVisionModel(**kwargs)
def forward(self, x, **kwargs):
return SAFEVMReturn(super().forward(x))
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