Embeddings from Prithvi 100M NASA/IBM

inference.py

import yaml
import numpy as np
import rasterio as rio
import torch
from prithvi import MaskedAutoencoderViT
from huggingface_hub import hf_hub_download

# Constants
REPO_ID = "ibm-nasa-geospatial/Prithvi-100M"
CONFIG = "Prithvi_100M_config.yaml"
CHECKPOINT = "Prithvi_100M.pt"
DEVICE = torch.device("cuda") if torch.cuda.is_available() else torch.device("cpu")
NO_DATA = -9999
NO_DATA_FLOAT = 0.0001

def get_model(config, checkpoint):
    """
    Initializes and returns the MaskedAutoencoderViT model with the provided configuration and checkpoint.

    Args:
        config (dict): The configuration dictionary for the model.
        checkpoint (str): Path to the model checkpoint.

    Returns:
        MaskedAutoencoderViT: The initialized model.
    """
    model_args = config["model_args"]
    model = MaskedAutoencoderViT(**model_args)
    model = model.to(DEVICE)
    state_dict = torch.load(checkpoint, map_location=DEVICE)
    model.load_state_dict(state_dict)
    model.eval()
    return model

def get_data(chips, mean, std):
    """
    Preprocesses the input data chips and returns them as a tensor.

    Args:
        chips (list of np.ndarray): List of chips where each chip is a numpy array of shape (channels, height, width).
        mean (float): The mean value used for normalization.
        std (float): The standard deviation value used for normalization.

    Returns:
        torch.Tensor: The preprocessed data tensor of shape (batch, channels, timesteps, height, width).
    """
    for i in range(len(chips)):
        chips[i] = np.moveaxis(chips[i], 0, -1)  # Rearrange axes to (height, width, channels)
        chips[i] = np.where(chips[i] == NO_DATA, NO_DATA_FLOAT, (chips[i] - mean) / std)  # Normalize and replace NO_DATA values

    chips = np.stack(chips, axis=0)  # Stack chips along a new axis to form (num_frames, height, width, channels)
    chips = np.moveaxis(chips, -1, 0).astype('float32')  # Rearrange axes to (channels, num_frames, height, width)
    chips = np.expand_dims(chips, axis=0)  # Add batch dimension

    chips = torch.tensor(chips, device=DEVICE)
    return chips

if __name__ == "__main__":
    # Download and load configuration and checkpoint
    config_path = hf_hub_download(repo_id=REPO_ID, filename=CONFIG)
    config = yaml.safe_load(open(config_path, "r"))
    checkpoint_path = hf_hub_download(repo_id=REPO_ID, filename=CHECKPOINT)
    
    # Initialize the model
    model = get_model(config, checkpoint_path)

    # Generate random data chips for testing
    chips = [np.random.randn(6, 224, 224) for _ in range(3)]
    mean = config["train_params"]["data_mean"]
    std = config["train_params"]["data_std"]
    data = get_data(chips, mean, std)

    # Obtain feature embeddings from the model
    embedding = model.forward_encoder(data, mask_ratio=0.0)

    # Extract CLS token embedding
    cls_embedding = embedding[:, 0, :]

    # Print shapes of the embeddings
    print(embedding.shape, cls_embedding.shape)

prithvi.py

# Code from: https://huggingface.co/ibm-nasa-geospatial/Prithvi-100M

# Copyright (c) Meta Platforms, Inc. and affiliates.
# All rights reserved.

# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
# --------------------------------------------------------
# References:
# timm: https://github.com/rwightman/pytorch-image-models/tree/master/timm
# DeiT: https://github.com/facebookresearch/deit
# --------------------------------------------------------

from functools import partial

import torch
import torch.nn as nn

from timm.models.vision_transformer import Block
from timm.models.layers import to_2tuple

import numpy as np

from einops import rearrange

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.
    omega = 1. / 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_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_3d_sincos_pos_embed(embed_dim, grid_size, cls_token=False):
    """
    grid_size: 3d tuple of grid size: t, h, w
    return:
    pos_embed: L, D
    """

    assert embed_dim % 16 == 0

    t_size, h_size, w_size = grid_size

    w_embed_dim = embed_dim // 16 * 6
    h_embed_dim = embed_dim // 16 * 6
    t_embed_dim = embed_dim // 16 * 4

    w_pos_embed = get_1d_sincos_pos_embed_from_grid(w_embed_dim, np.arange(w_size))
    h_pos_embed = get_1d_sincos_pos_embed_from_grid(h_embed_dim, np.arange(h_size))
    t_pos_embed = get_1d_sincos_pos_embed_from_grid(t_embed_dim, np.arange(t_size))

    w_pos_embed = np.tile(w_pos_embed, (t_size * h_size, 1))
    h_pos_embed = np.tile(np.repeat(h_pos_embed, w_size, axis=0), (t_size, 1))
    t_pos_embed = np.repeat(t_pos_embed, h_size * w_size, axis=0)

    pos_embed = np.concatenate((w_pos_embed, h_pos_embed, t_pos_embed), axis=1)

    if cls_token:
        pos_embed = np.concatenate([np.zeros([1, embed_dim]), pos_embed], axis=0)
    return pos_embed


class PatchEmbed(nn.Module):
    """ Frames of 2D Images to Patch Embedding
    The 3D version of timm.models.vision_transformer.PatchEmbed
    """
    def __init__(
            self,
            img_size=224,
            patch_size=16,
            num_frames=3,
            tubelet_size=1,
            in_chans=3,
            embed_dim=768,
            norm_layer=None,
            flatten=True,
            bias=True,
    ):
        super().__init__()
        img_size = to_2tuple(img_size)
        patch_size = to_2tuple(patch_size)
        self.img_size = img_size
        self.patch_size = patch_size
        self.num_frames = num_frames
        self.tubelet_size = tubelet_size
        self.grid_size = (num_frames // tubelet_size, img_size[0] // patch_size[0], img_size[1] // patch_size[1])
        self.num_patches = self.grid_size[0] * self.grid_size[1] * self.grid_size[2]
        self.flatten = flatten

        self.proj = nn.Conv3d(in_chans, embed_dim,
                              kernel_size=(tubelet_size, patch_size[0], patch_size[1]),
                              stride=(tubelet_size, patch_size[0], patch_size[1]), bias=bias)
        self.norm = norm_layer(embed_dim) if norm_layer else nn.Identity()

    def forward(self, x):
        B, C, T, H, W = x.shape
        x = self.proj(x)
        if self.flatten:
            x = x.flatten(2).transpose(1, 2)  # B,C,T,H,W -> B,C,L -> B,L,C
        x = self.norm(x)
        return x


class MaskedAutoencoderViT(nn.Module):
    """ Masked Autoencoder with VisionTransformer backbone
    """
    def __init__(self, img_size=224, patch_size=16,
                 num_frames=3, tubelet_size=1,
                 in_chans=3, embed_dim=1024, depth=24, num_heads=16,
                 decoder_embed_dim=512, decoder_depth=8, decoder_num_heads=16,
                 mlp_ratio=4., norm_layer=nn.LayerNorm, norm_pix_loss=False):
        super().__init__()

        # --------------------------------------------------------------------------
        # MAE encoder specifics
        self.patch_embed = PatchEmbed(img_size, patch_size,num_frames, tubelet_size, in_chans, embed_dim)
        num_patches = self.patch_embed.num_patches

        self.cls_token = nn.Parameter(torch.zeros(1, 1, embed_dim))
        self.pos_embed = nn.Parameter(torch.zeros(1, num_patches + 1, embed_dim), requires_grad=False)  # fixed sin-cos embedding

        self.blocks = nn.ModuleList([
            Block(embed_dim, num_heads, mlp_ratio, qkv_bias=True, norm_layer=norm_layer)
            for i in range(depth)])
        self.norm = norm_layer(embed_dim)
        # --------------------------------------------------------------------------

        # --------------------------------------------------------------------------
        # MAE decoder specifics
        self.decoder_embed = nn.Linear(embed_dim, decoder_embed_dim, bias=True)

        self.mask_token = nn.Parameter(torch.zeros(1, 1, decoder_embed_dim))

        self.decoder_pos_embed = nn.Parameter(torch.zeros(1, num_patches + 1, decoder_embed_dim), requires_grad=False)  # fixed sin-cos embedding

        self.decoder_blocks = nn.ModuleList([
            Block(decoder_embed_dim, decoder_num_heads, mlp_ratio, qkv_bias=True, norm_layer=norm_layer)
            for i in range(decoder_depth)])

        self.decoder_norm = norm_layer(decoder_embed_dim)
        self.decoder_pred = nn.Linear(decoder_embed_dim, tubelet_size * patch_size * patch_size * in_chans, bias=True) # decoder to patch
        # --------------------------------------------------------------------------

        self.norm_pix_loss = norm_pix_loss

        self.initialize_weights()

    def initialize_weights(self):
        # initialization
        # initialize (and freeze) pos_embed by sin-cos embedding
        pos_embed = get_3d_sincos_pos_embed(self.pos_embed.shape[-1], self.patch_embed.grid_size, cls_token=True)
        self.pos_embed.data.copy_(torch.from_numpy(pos_embed).float().unsqueeze(0))

        decoder_pos_embed = get_3d_sincos_pos_embed(self.decoder_pos_embed.shape[-1], self.patch_embed.grid_size, cls_token=True)
        self.decoder_pos_embed.data.copy_(torch.from_numpy(decoder_pos_embed).float().unsqueeze(0))

        # initialize patch_embed like nn.Linear (instead of nn.Conv2d)
        w = self.patch_embed.proj.weight.data
        torch.nn.init.xavier_uniform_(w.view([w.shape[0], -1]))

        # timm's trunc_normal_(std=.02) is effectively normal_(std=0.02) as cutoff is too big (2.)
        torch.nn.init.normal_(self.cls_token, std=.02)
        torch.nn.init.normal_(self.mask_token, std=.02)

        # initialize nn.Linear and nn.LayerNorm
        self.apply(self._init_weights)

    def _init_weights(self, m):
        if isinstance(m, nn.Linear):
            # we use xavier_uniform following official JAX ViT:
            torch.nn.init.xavier_uniform_(m.weight)
            if isinstance(m, nn.Linear) and m.bias is not None:
                nn.init.constant_(m.bias, 0)
        elif isinstance(m, nn.LayerNorm):
            nn.init.constant_(m.bias, 0)
            nn.init.constant_(m.weight, 1.0)

    def patchify(self, imgs):
        """
        imgs: B, C, T, H, W
        x: B, L, D
        """
        p = self.patch_embed.patch_size[0]
        tub = self.patch_embed.tubelet_size
        x = rearrange(imgs, 'b c (t tub) (h p) (w q) -> b (t h w) (tub p q c)', tub=tub, p=p, q=p)

        return x

    def unpatchify(self, x):
        """
        x: B, L, D
        imgs: B, C, T, H, W
        """
        p = self.patch_embed.patch_size[0]
        num_p = self.patch_embed.img_size[0] // p
        tub = self.patch_embed.tubelet_size
        imgs = rearrange(x, 'b (t h w) (tub p q c) -> b c (t tub) (h p) (w q)', h=num_p, w=num_p, tub=tub, p=p, q=p)
        return imgs

    def random_masking(self, x, mask_ratio):
        """
        Perform per-sample random masking by per-sample shuffling.
        Per-sample shuffling is done by argsort random noise.
        x: [N, L, D], sequence
        """
        N, L, D = x.shape  # batch, length, dim
        len_keep = int(L * (1 - mask_ratio))

        noise = torch.rand(N, L, device=x.device)  # noise in [0, 1]

        # sort noise for each sample
        ids_shuffle = torch.argsort(noise, dim=1)  # ascend: small is keep, large is remove
        ids_restore = torch.argsort(ids_shuffle, dim=1)

        # keep the first subset
        ids_keep = ids_shuffle[:, :len_keep]
        x_masked = torch.gather(x, dim=1, index=ids_keep.unsqueeze(-1).repeat(1, 1, D))

        # generate the binary mask: 0 is keep, 1 is remove
        mask = torch.ones([N, L], device=x.device)
        mask[:, :len_keep] = 0
        # unshuffle to get the binary mask
        mask = torch.gather(mask, dim=1, index=ids_restore)

        return x_masked, mask, ids_restore

    def forward_encoder(self, x, mask_ratio):
        # embed patches
        x = self.patch_embed(x)

        # add pos embed w/o cls token
        x = x + self.pos_embed[:, 1:, :]

        if mask_ratio:
            # Apply random masking to the input
            x, mask, ids_restore = self.random_masking(x, mask_ratio)

        # Add the cls token to the input
        cls_token = self.cls_token + self.pos_embed[:, :1, :]
        cls_tokens = cls_token.expand(x.shape[0], -1, -1)
        x = torch.cat((cls_tokens, x), dim=1)

        # Pass the input through Transformer blocks
        for blk in self.blocks:
            x = blk(x)
        x = self.norm(x)

        if mask_ratio:
            return x, mask, ids_restore
        else:
            return x


    def forward_decoder(self, x, ids_restore):
        # embed tokens
        x = self.decoder_embed(x)

        # append mask tokens to sequence
        mask_tokens = self.mask_token.repeat(x.shape[0], ids_restore.shape[1] + 1 - x.shape[1], 1)
        x_ = torch.cat([x[:, 1:, :], mask_tokens], dim=1)  # no cls token
        x_ = torch.gather(x_, dim=1, index=ids_restore.unsqueeze(-1).repeat(1, 1, x.shape[2]))  # unshuffle
        x = torch.cat([x[:, :1, :], x_], dim=1)  # append cls token

        # add pos embed
        x = x + self.decoder_pos_embed

        # apply Transformer blocks
        for blk in self.decoder_blocks:
            x = blk(x)
        x = self.decoder_norm(x)

        # predictor projection
        x = self.decoder_pred(x)

        # remove cls token
        x = x[:, 1:, :]

        return x

    def forward_loss(self, imgs, pred, mask):
        """
        imgs: B, C, T, H, W
        target: B, L, D
        pred: B, L, D
        mask: B, L. 0 is keep, 1 is remove,
        """
        target = self.patchify(imgs)
        if self.norm_pix_loss:
            mean = target.mean(dim=-1, keepdim=True)
            var = target.var(dim=-1, keepdim=True)
            target = (target - mean) / (var + 1.e-6)**.5

        loss = (pred - target) ** 2
        loss = loss.mean(dim=-1)  # [N, L], mean loss per patch

        loss = (loss * mask).sum() / mask.sum()  # mean loss on removed patches
        return loss

    def forward(self, imgs, mask_ratio=0.75):
        latent, mask, ids_restore = self.forward_encoder(imgs, mask_ratio)
        pred = self.forward_decoder(latent, ids_restore)
        loss = self.forward_loss(imgs, pred, mask)
        return loss, pred, mask