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# Adapted from: https://github.com/bair-climate-initiative/scale-mae/blob/main/mae/main_finetune.py
import torch
from timm.models.layers import trunc_normal_
from functools import partial
import timm.models.vision_transformer
import torch.nn as nn
from timm.models.vision_transformer import Block, PatchEmbed
import os
from torchvision.io import read_image
import numpy as np
import sys
import random
import pytorch_lightning as pl
import torch.nn.functional as F
from huggingface_hub import PyTorchModelHubMixin
def get_2d_sincos_pos_embed(embed_dim, grid_size, cls_token=False):
"""
grid_size: int of the grid height and width
return:
pos_embed: [grid_size*grid_size, embed_dim] or [1+grid_size*grid_size, embed_dim] (w/ or w/o cls_token)
"""
grid_h = np.arange(grid_size, dtype=np.float32)
grid_w = np.arange(grid_size, dtype=np.float32)
grid = np.meshgrid(grid_w, grid_h) # here w goes first
grid = np.stack(grid, axis=0)
grid = grid.reshape([2, 1, grid_size, grid_size])
pos_embed = get_2d_sincos_pos_embed_from_grid(embed_dim, grid)
if cls_token:
pos_embed = np.concatenate([np.zeros([1, embed_dim]), pos_embed], axis=0)
return pos_embed
def get_2d_sincos_pos_embed_with_resolution(
embed_dim, grid_size, res, cls_token=False, device="cpu"
):
"""
grid_size: int of the grid height and width
res: array of size n, representing the resolution of a pixel (say, in meters),
return:
pos_embed: [n,grid_size*grid_size, embed_dim] or [n,1+grid_size*grid_size, embed_dim] (w/ or w/o cls_token)
"""
# res = torch.FloatTensor(res).to(device)
res = res.to(device)
grid_h = torch.arange(grid_size, dtype=torch.float32, device=device)
grid_w = torch.arange(grid_size, dtype=torch.float32, device=device)
grid = torch.meshgrid(
grid_w, grid_h, indexing="xy"
) # here h goes first,direction reversed for numpy
grid = torch.stack(grid, dim=0) # 2 x h x w
# grid = grid.reshape([2, 1, grid_size, grid_size])
grid = torch.einsum("chw,n->cnhw", grid, res) # 2 x n x h x w
_, n, h, w = grid.shape
pos_embed = get_2d_sincos_pos_embed_from_grid_torch(
embed_dim, grid
) # # (nxH*W, D/2)
pos_embed = pos_embed.reshape(n, h * w, embed_dim)
if cls_token:
pos_embed = torch.cat(
[
torch.zeros(
[n, 1, embed_dim], dtype=torch.float32, device=pos_embed.device
),
pos_embed,
],
dim=1,
)
return pos_embed
def get_2d_sincos_pos_embed_from_grid(embed_dim, grid):
assert embed_dim % 2 == 0
# use half of dimensions to encode grid_h
emb_h = get_1d_sincos_pos_embed_from_grid(embed_dim // 2, grid[0]) # (H*W, D/2)
emb_w = get_1d_sincos_pos_embed_from_grid(embed_dim // 2, grid[1]) # (H*W, D/2)
emb = np.concatenate([emb_h, emb_w], axis=1) # (H*W, D)
return emb
def get_2d_sincos_pos_embed_from_grid_torch(embed_dim, grid):
assert embed_dim % 2 == 0
# use half of dimensions to encode grid_h
emb_h = get_1d_sincos_pos_embed_from_grid_torch(
embed_dim // 2, grid[0]
) # (H*W, D/2)
emb_w = get_1d_sincos_pos_embed_from_grid_torch(
embed_dim // 2, grid[1]
) # (H*W, D/2)
emb = torch.cat([emb_h, emb_w], dim=1) # (H*W, D)
return emb
def get_1d_sincos_pos_embed_from_grid_torch(embed_dim, pos):
"""
embed_dim: output dimension for each position
pos: a list of positions to be encoded: size (M,)
out: (M, D)
"""
assert embed_dim % 2 == 0
old_shape = pos
omega = torch.arange(embed_dim // 2, dtype=torch.float32, device=pos.device)
omega /= embed_dim / 2.0
omega = 1.0 / 10000**omega # (D/2,)
pos = pos.reshape(-1) # (M,)
out = torch.einsum("m,d->md", pos, omega) # (M, D/2), outer product
emb_sin = torch.sin(out) # (M, D/2)
emb_cos = torch.cos(out) # (M, D/2)
emb = torch.cat([emb_sin, emb_cos], dim=1) # (M, D)
return emb
def get_1d_sincos_pos_embed_from_grid(embed_dim, pos):
"""
embed_dim: output dimension for each position
pos: a list of positions to be encoded: size (M,)
out: (M, D)
"""
assert embed_dim % 2 == 0
omega = np.arange(embed_dim // 2, dtype=np.float32)
omega /= embed_dim / 2.0
omega = 1.0 / 10000**omega # (D/2,)
pos = pos.reshape(-1) # (M,)
out = np.einsum("m,d->md", pos, omega) # (M, D/2), outer product
emb_sin = np.sin(out) # (M, D/2)
emb_cos = np.cos(out) # (M, D/2)
emb = np.concatenate([emb_sin, emb_cos], axis=1) # (M, D)
return emb
# --------------------------------------------------------
# Interpolate position embeddings for high-resolution
# References:
# DeiT: https://github.com/facebookresearch/deit
# --------------------------------------------------------
def interpolate_pos_embed(model, checkpoint_model):
if "pos_embed" in checkpoint_model:
pos_embed_checkpoint = checkpoint_model["pos_embed"]
embedding_size = pos_embed_checkpoint.shape[-1]
num_patches = model.patch_embed.num_patches
num_extra_tokens = model.pos_embed.shape[-2] - num_patches
# height (== width) for the checkpoint position embedding
orig_size = int((pos_embed_checkpoint.shape[-2] - num_extra_tokens) ** 0.5)
# height (== width) for the new position embedding
new_size = int(num_patches**0.5)
# class_token and dist_token are kept unchanged
if orig_size != new_size:
print(
"Position interpolate from %dx%d to %dx%d"
% (orig_size, orig_size, new_size, new_size)
)
extra_tokens = pos_embed_checkpoint[:, :num_extra_tokens]
# only the position tokens are interpolated
pos_tokens = pos_embed_checkpoint[:, num_extra_tokens:]
pos_tokens = pos_tokens.reshape(
-1, orig_size, orig_size, embedding_size
).permute(0, 3, 1, 2)
pos_tokens = torch.nn.functional.interpolate(
pos_tokens,
size=(new_size, new_size),
mode="bicubic",
align_corners=False,
)
pos_tokens = pos_tokens.permute(0, 2, 3, 1).flatten(1, 2)
new_pos_embed = torch.cat((extra_tokens, pos_tokens), dim=1)
checkpoint_model["pos_embed"] = new_pos_embed
class PatchEmbedUnSafe(PatchEmbed):
"""Image to Patch Embedding"""
def forward(self, x):
B, C, H, W = x.shape
# Dropped size check in timm
# assert H == self.img_size[0] and W == self.img_size[1], \
# f"Input image size ({H}*{W}) doesn't match model ({self.img_size[0]}*{self.img_size[1]})."
x = self.proj(x).flatten(2).transpose(1, 2)
return x
class VisionTransformer(timm.models.vision_transformer.VisionTransformer):
"""Vision Transformer with support for global average pooling"""
def __init__(
self, cls_token_flag=False, global_pool=False, patch_size=16, in_chans=3, embed_dim=1024, **kwargs
):
super().__init__(embed_dim=embed_dim, **kwargs)
self.cls_token_flag = cls_token_flag
self.patch_embed = PatchEmbedUnSafe(
img_size=224,
patch_size=patch_size,
in_chans=in_chans,
embed_dim=embed_dim,
)
self.global_pool = global_pool
if self.global_pool:
norm_layer = kwargs["norm_layer"]
embed_dim = embed_dim
self.fc_norm = norm_layer(embed_dim)
del self.norm # remove the original norm
del self.head
if self.cls_token_flag == False:
del self.cls_token
del self.pos_embed
def forward_features(self, x, input_res=None):
B, _, h, w = x.shape
x = self.patch_embed(x)
input_res = input_res.cpu()
num_patches = int(
(h * w) / (self.patch_embed.patch_size[0] * self.patch_embed.patch_size[1])
)
pos_embed = get_2d_sincos_pos_embed_with_resolution(
x.shape[-1],
int(num_patches**0.5),
input_res,
cls_token=self.cls_token_flag,
device=x.device,
)
if self.cls_token_flag:
cls_tokens = self.cls_token.expand(
B, -1, -1
) # stole cls_tokens impl from Phil Wang, thanks
x = torch.cat((cls_tokens, x), dim=1)
x = x + pos_embed
x = self.pos_drop(x)
for blk in self.blocks:
x = blk(x)
#x = x[:, 1:, :].mean(dim=1) # global pool without cls token
outcome = self.fc_norm(x)
return outcome
def forward(self, x, input_res=None):
x = self.forward_features(x, input_res=input_res)
return x
def vit_large_patch16(**kwargs):
model = VisionTransformer(
patch_size=16,
embed_dim=1024,
depth=24,
num_heads=16,
mlp_ratio=4,
qkv_bias=True,
norm_layer=partial(nn.LayerNorm, eps=1e-6),
**kwargs
)
return model
def get_ScaleMAE_model(global_pool=True, cls_token=True):
model = vit_large_patch16(
num_classes=1000,
drop_path_rate=0.1,
global_pool=global_pool,
cls_token_flag = cls_token
)
if global_pool:
assert set(msg.missing_keys) == {
"head.weight",
"head.bias",
"fc_norm.weight",
"fc_norm.bias",
}
else:
pass
return model
class ScaleMAE_baseline(pl.LightningModule, PyTorchModelHubMixin):
def __init__(self, feat_dim=1024, fc_dim=1024, global_pool=False, cls_token_flag=True):
super().__init__()
self.model = get_ScaleMAE_model(global_pool= global_pool,cls_token = cls_token_flag)
def forward(self,x,patch_size,input_res=10.0):
input_res = torch.tensor([10.0]).to(x.device)
x = self.model(x,input_res=input_res)
return x |