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from functools import partial
from typing import List
import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
from einops import rearrange
from timm.models.vision_transformer import PatchEmbed, VisionTransformer
class SatMAEWrapper(nn.Module):
def __init__(
self,
pretrained_path,
size="large",
img_size=96,
do_pool=True,
temporal_pooling: str = "mean",
):
super().__init__()
if size == "large":
self.encoder = vit_large(img_size=img_size, patch_size=8, in_chans=10)
self.dim = 1024
elif size == "base":
self.encoder = vit_base(img_size=img_size, patch_size=8, in_chans=10)
self.dim = 768
checkpoint = torch.load(pretrained_path, map_location="cpu")["model"]
if img_size != 96:
checkpoint = interpolate_pos_embed(self.encoder, checkpoint)
self.encoder.load_state_dict(checkpoint, strict=False)
self.image_resolution = img_size
self.do_pool = do_pool
self.patch_size = 8
self.grid_size = int(self.image_resolution / self.patch_size)
if temporal_pooling not in ["mean", "max"]:
raise ValueError(
f"Expected temporal_pooling to be in ['mean', 'max'], got {temporal_pooling}"
)
self.temporal_pooling = temporal_pooling
def resize(self, images):
images = F.interpolate(
images,
size=(self.image_resolution, self.image_resolution),
mode="bilinear",
align_corners=False,
)
return images
def preproccess(self, images):
if len(images.shape) == 5:
# take the mean along the temporal dimension
images = torch.mean(images, dim=2)
images = rearrange(images, "b h w c -> b c h w")
assert images.shape[1] == 13
return self.resize(images) # (bsz, C, H, W)
def forward(self, s2=None, s1=None, months=None):
if s2 is None:
raise ValueError("S2 can't be None for SatMAE")
if len(s2.shape) == 5:
outputs_l: List[torch.Tensor] = []
for timestep in range(s2.shape[3]):
image = self.preproccess(s2[:, :, :, timestep])
output = self.encoder.forward_features(image)
# output shape for atto: (bsz, 320, 7, 7)
# output shape for tiny: (bsz, 768, 6, 6)
if self.do_pool:
output = output.mean(dim=1)
else:
output = rearrange(output, "b (c_g l) d -> b l c_g d", c_g=3).mean(dim=-2)
outputs_l.append(output)
outputs_t = torch.stack(outputs_l, dim=-1) # b h w d t
if self.temporal_pooling == "mean":
return outputs_t.mean(dim=-1)
else:
return torch.amax(outputs_t, dim=-1)
else:
s2 = self.preproccess(s2)
output = self.encoder.forward_features(s2)
if self.do_pool:
return output.mean(dim=1)
else:
return rearrange(output, "b (c_g l) d -> b l c_g d", c_g=3).mean(dim=-2)
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=float)
grid_w = np.arange(grid_size, dtype=float)
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_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_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=float)
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
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
omega = torch.arange(embed_dim // 2, dtype=float, 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.double()
# --------------------------------------------------------
# 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]
try:
num_patches = model.patch_embed.num_patches
except AttributeError:
num_patches = model.patch_embed[0].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
return checkpoint_model
class GroupChannelsVisionTransformer(VisionTransformer):
"""Vision Transformer with support for global average pooling"""
def __init__(
self,
global_pool=False,
channel_embed=256,
channel_groups=((0, 1, 2, 6), (3, 4, 5, 7), (8, 9)),
**kwargs,
):
super().__init__(**kwargs)
img_size = kwargs["img_size"]
patch_size = kwargs["patch_size"]
embed_dim = kwargs["embed_dim"]
self.channel_groups = channel_groups
self.patch_embed = nn.ModuleList(
[PatchEmbed(img_size, patch_size, len(group), embed_dim) for group in channel_groups]
)
num_patches = self.patch_embed[0].num_patches
# Positional and channel embed
self.pos_embed = nn.Parameter(torch.zeros(1, num_patches + 1, embed_dim - channel_embed))
pos_embed = get_2d_sincos_pos_embed(
self.pos_embed.shape[-1], int(num_patches**0.5), cls_token=True
)
self.pos_embed.data.copy_(torch.from_numpy(pos_embed).float().unsqueeze(0))
num_groups = len(channel_groups)
self.channel_embed = nn.Parameter(torch.zeros(1, num_groups, channel_embed))
chan_embed = get_1d_sincos_pos_embed_from_grid(
self.channel_embed.shape[-1], torch.arange(num_groups).numpy()
)
self.channel_embed.data.copy_(torch.from_numpy(chan_embed).float().unsqueeze(0))
# Extra embedding for cls to fill embed_dim
self.channel_cls_embed = nn.Parameter(torch.zeros(1, 1, channel_embed))
channel_cls_embed = torch.zeros((1, channel_embed))
self.channel_cls_embed.data.copy_(channel_cls_embed.float().unsqueeze(0))
self.global_pool = global_pool
if self.global_pool:
norm_layer = kwargs["norm_layer"]
embed_dim = kwargs["embed_dim"]
self.fc_norm = norm_layer(embed_dim)
del self.norm # remove the original norm
def forward_features(self, x):
b, c, h, w = x.shape
x_c_embed = []
for i, group in enumerate(self.channel_groups):
x_c = x[:, group, :, :]
x_c_embed.append(self.patch_embed[i](x_c)) # (N, L, D)
x = torch.stack(x_c_embed, dim=1) # (N, G, L, D)
_, G, L, D = x.shape
# add channel embed
channel_embed = self.channel_embed.unsqueeze(2) # (1, c, 1, cD)
pos_embed = self.pos_embed[:, 1:, :].unsqueeze(1) # (1, 1, L, pD)
# Channel embed same across (x,y) position, and pos embed same across channel (c)
channel_embed = channel_embed.expand(-1, -1, pos_embed.shape[2], -1) # (1, c, L, cD)
pos_embed = pos_embed.expand(-1, channel_embed.shape[1], -1, -1) # (1, c, L, pD)
pos_channel = torch.cat((pos_embed, channel_embed), dim=-1) # (1, c, L, D)
# add pos embed w/o cls token
x = x + pos_channel # (N, G, L, D)
x = x.view(b, -1, D) # (N, G*L, D)
cls_pos_channel = torch.cat(
(self.pos_embed[:, :1, :], self.channel_cls_embed), dim=-1
) # (1, 1, D)
cls_tokens = cls_pos_channel + self.cls_token.expand(b, -1, -1)
x = torch.cat((cls_tokens, x), dim=1) # (N, 1 + c*L, D)
x = self.pos_drop(x)
for blk in self.blocks:
x = blk(x)
return x[:, 1:, :] # remove cls token
def vit_base(**kwargs):
model = GroupChannelsVisionTransformer(
channel_embed=256,
embed_dim=768,
depth=12,
num_heads=12,
mlp_ratio=4,
qkv_bias=True,
norm_layer=partial(nn.LayerNorm, eps=1e-6),
**kwargs,
)
return model
def vit_large(**kwargs):
model = GroupChannelsVisionTransformer(
channel_embed=256,
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
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