app.py

import logging
import os
from huggingface_hub import hf_hub_download

# pull files from hub
yaml_file_path = hf_hub_download(repo_id="ibm-nasa-geospatial/Prithvi-EO-2.0-300M-TL",
                                 filename="Prithvi_EO_V2_300M_TL_config.yaml", token=os.environ.get("token"))
checkpoint = hf_hub_download(repo_id="ibm-nasa-geospatial/Prithvi-EO-2.0-300M-TL",
                             filename='Prithvi_EO_V2_300M_TL.pt', token=os.environ.get("token"))
model_def = hf_hub_download(repo_id="ibm-nasa-geospatial/Prithvi-EO-2.0-300M-TL",
                            filename='prithvi_mae.py', token=os.environ.get("token"))
model_inference = hf_hub_download(repo_id="ibm-nasa-geospatial/Prithvi-EO-2.0-300M-TL",
                                  filename='inference.py', token=os.environ.get("token"))
os.system(f'cp {model_def} .')
os.system(f'cp {model_inference} .')

import os
import torch
import yaml
import numpy as np
import gradio as gr
from einops import rearrange
from functools import partial
from prithvi_mae import PrithviMAE
from inference import process_channel_group, read_geotiff, save_geotiff, _convert_np_uint8, load_example, run_model


NO_DATA = -9999
NO_DATA_FLOAT = 0.0001
PERCENTILES = (0.1, 99.9)


# def process_channel_group(orig_img, new_img, channels, data_mean, data_std):
#     """ Process *orig_img* and *new_img* for RGB visualization. Each band is rescaled back to the
#         original range using *data_mean* and *data_std* and then lowest and highest percentiles are
#         removed to enhance contrast. Data is rescaled to (0, 1) range and stacked channels_first.
#     Args:
#         orig_img: torch.Tensor representing original image (reference) with shape = (bands, H, W).
#         new_img: torch.Tensor representing image with shape = (bands, H, W).
#         channels: list of indices representing RGB channels.
#         data_mean: list of mean values for each band.
#         data_std: list of std values for each band.
#     Returns:
#         torch.Tensor with shape (num_channels, height, width) for original image
#         torch.Tensor with shape (num_channels, height, width) for the other image
#     """
# 
#     stack_c = [], []
# 
#     for c in channels:
#         orig_ch = orig_img[c, ...]
#         valid_mask = torch.ones_like(orig_ch, dtype=torch.bool)
#         valid_mask[orig_ch == NO_DATA_FLOAT] = False
# 
#         # Back to original data range
#         orig_ch = (orig_ch * data_std[c]) + data_mean[c]
#         new_ch = (new_img[c, ...] * data_std[c]) + data_mean[c]
# 
#         # Rescale (enhancing contrast)
#         min_value, max_value = np.percentile(orig_ch[valid_mask], PERCENTILES)
# 
#         orig_ch = torch.clamp((orig_ch - min_value) / (max_value - min_value), 0, 1)
#         new_ch = torch.clamp((new_ch - min_value) / (max_value - min_value), 0, 1)
# 
#         # No data as zeros
#         orig_ch[~valid_mask] = 0
#         new_ch[~valid_mask] = 0
# 
#         stack_c[0].append(orig_ch)
#         stack_c[1].append(new_ch)
# 
#     # Channels first
#     stack_orig = torch.stack(stack_c[0], dim=0)
#     stack_rec = torch.stack(stack_c[1], dim=0)
# 
#     return stack_orig, stack_rec
# 
# 
# def read_geotiff(file_path: str):
#     """ Read all bands from *file_path* and returns image + meta info.
#     Args:
#         file_path: path to image file.
#     Returns:
#         np.ndarray with shape (bands, height, width)
#         meta info dict
#     """
# 
#     with rasterio.open(file_path) as src:
#         img = src.read()
#         meta = src.meta
#         coords = src.lnglat()
# 
#     return img, meta, coords
# 
# 
# def save_geotiff(image, output_path: str, meta: dict):
#     """ Save multi-band image in Geotiff file.
#     Args:
#         image: np.ndarray with shape (bands, height, width)
#         output_path: path where to save the image
#         meta: dict with meta info.
#     """
# 
#     with rasterio.open(output_path, "w", **meta) as dest:
#         for i in range(image.shape[0]):
#             dest.write(image[i, :, :], i + 1)
# 
#     return
# 
# 
# def _convert_np_uint8(float_image: torch.Tensor):
# 
#     image = float_image.numpy() * 255.0
#     image = image.astype(dtype=np.uint8)
#     image = image.transpose((1, 2, 0))
# 
#     return image
# 
# 
# def load_example(file_paths: List[str], mean: List[float], std: List[float]):
#     """ Build an input example by loading images in *file_paths*.
#     Args:
#         file_paths: list of file paths .
#         mean: list containing mean values for each band in the images in *file_paths*.
#         std: list containing std values for each band in the images in *file_paths*.
#     Returns:
#         np.array containing created example
#         list of meta info for each image in *file_paths*
#     """
# 
#     imgs = []
#     metas = []
# 
#     for file in file_paths:
#         img, meta = read_geotiff(file)
#         img = img[:6]*10000 if img[:6].mean() <= 2 else img[:6]
# 
#         # Rescaling (don't normalize on nodata)
#         img = np.moveaxis(img, 0, -1)   # channels last for rescaling
#         img = np.where(img == NO_DATA, NO_DATA_FLOAT, (img - mean) / std)
# 
#         imgs.append(img)
#         metas.append(meta)
# 
#     imgs = np.stack(imgs, axis=0)    # num_frames, H, W, C
#     imgs = np.moveaxis(imgs, -1, 0).astype('float32')  # C, num_frames, H, W
#     imgs = np.expand_dims(imgs, axis=0)  # add batch dim
# 
#     return imgs, metas
# 
# 
# def run_model(model: torch.nn.Module, input_data: torch.Tensor, mask_ratio: float, device: torch.device):
#     """ Run *model* with *input_data* and create images from output tokens (mask, reconstructed + visible).
#     Args:
#         model: MAE model to run.
#         input_data: torch.Tensor with shape (B, C, T, H, W).
#         mask_ratio: mask ratio to use.
#         device: device where model should run.
#     Returns:
#         3 torch.Tensor with shape (B, C, T, H, W).
#     """
# 
#     with torch.no_grad():
#         x = input_data.to(device)
# 
#         _, pred, mask = model(x, mask_ratio)
# 
#     # Create mask and prediction images (un-patchify)
#     mask_img = model.unpatchify(mask.unsqueeze(-1).repeat(1, 1, pred.shape[-1])).detach().cpu()
#     pred_img = model.unpatchify(pred).detach().cpu()
# 
#     # Mix visible and predicted patches
#     rec_img = input_data.clone()
#     rec_img[mask_img == 1] = pred_img[mask_img == 1]  # binary mask: 0 is keep, 1 is remove
# 
#     # Switch zeros/ones in mask images so masked patches appear darker in plots (better visualization)
#     mask_img = (~(mask_img.to(torch.bool))).to(torch.float)
# 
#     return rec_img, mask_img
# 
# 
# def save_rgb_imgs(input_img, rec_img, mask_img, channels, mean, std, output_dir, meta_data):
#     """ Wrapper function to save Geotiff images (original, reconstructed, masked) per timestamp.
#     Args:
#         input_img: input torch.Tensor with shape (C, T, H, W).
#         rec_img: reconstructed torch.Tensor with shape (C, T, H, W).
#         mask_img: mask torch.Tensor with shape (C, T, H, W).
#         channels: list of indices representing RGB channels.
#         mean: list of mean values for each band.
#         std: list of std values for each band.
#         output_dir: directory where to save outputs.
#         meta_data: list of dicts with geotiff meta info.
#     """
# 
#     for t in range(input_img.shape[1]):
#         rgb_orig, rgb_pred = process_channel_group(orig_img=input_img[:, t, :, :],
#                                                    new_img=rec_img[:, t, :, :],
#                                                    channels=channels, data_mean=mean,
#                                                    data_std=std)
# 
#         rgb_mask = mask_img[channels, t, :, :] * rgb_orig
# 
#         # Saving images
# 
#         save_geotiff(image=_convert_np_uint8(rgb_orig),
#                      output_path=os.path.join(output_dir, f"original_rgb_t{t}.tiff"),
#                      meta=meta_data[t])
# 
#         save_geotiff(image=_convert_np_uint8(rgb_pred),
#                      output_path=os.path.join(output_dir, f"predicted_rgb_t{t}.tiff"),
#                      meta=meta_data[t])
# 
#         save_geotiff(image=_convert_np_uint8(rgb_mask),
#                      output_path=os.path.join(output_dir, f"masked_rgb_t{t}.tiff"),
#                      meta=meta_data[t])


def extract_rgb_imgs(input_img, rec_img, mask_img, channels, mean, std):
    """ Wrapper function to save Geotiff images (original, reconstructed, masked) per timestamp.
    Args:
        input_img: input torch.Tensor with shape (C, T, H, W).
        rec_img: reconstructed torch.Tensor with shape (C, T, H, W).
        mask_img: mask torch.Tensor with shape (C, T, H, W).
        channels: list of indices representing RGB channels.
        mean: list of mean values for each band.
        std: list of std values for each band.
        output_dir: directory where to save outputs.
        meta_data: list of dicts with geotiff meta info.
    """
    rgb_orig_list = []
    rgb_mask_list = []
    rgb_pred_list = []
    
    for t in range(input_img.shape[1]):
        rgb_orig, rgb_pred = process_channel_group(orig_img=input_img[:, t, :, :],
                                                   new_img=rec_img[:, t, :, :],
                                                   channels=channels,
                                                   mean=mean,
                                                   std=std)

        rgb_mask = mask_img[channels, t, :, :] * rgb_orig

        # extract images
        rgb_orig_list.append(_convert_np_uint8(rgb_orig).transpose(1, 2, 0))
        rgb_mask_list.append(_convert_np_uint8(rgb_mask).transpose(1, 2, 0))
        rgb_pred_list.append(_convert_np_uint8(rgb_pred).transpose(1, 2, 0))
        
    outputs = rgb_orig_list + rgb_mask_list + rgb_pred_list

    return outputs


def predict_on_images(data_files: list, mask_ratio: float, yaml_file_path: str, checkpoint: str):

    
    try:
        data_files = [x.name for x in data_files]
        print('Path extracted from example')
    except:
        print('Files submitted through UI')

    # Get parameters --------
    print('This is the printout', data_files)

    with open(yaml_file_path, 'r') as f:
        config = yaml.safe_load(f)

    batch_size = 8
    bands = config['DATA']['BANDS']
    num_frames = len(data_files)
    mean = config['DATA']['MEAN']
    std = config['DATA']['STD']
    coords_encoding = config['MODEL']['COORDS_ENCODING']
    img_size = config['DATA']['INPUT_SIZE'][-1]

    mask_ratio = mask_ratio or config['DATA']['MASK_RATIO']

    if num_frames > 4:
        # TODO: Check if we can limit this via UI
        logging.warning("Model was only trained with only four timestamps.")

    if torch.cuda.is_available():
        device = torch.device('cuda')
    else:
        device = torch.device('cpu')

    print(f"Using {device} device.\n")

    # Loading data ---------------------------------------------------------------------------------

    input_data, temporal_coords, location_coords, meta_data = load_example(file_paths=data_files, mean=mean, std=std)

    if len(temporal_coords) != num_frames and 'time' in coords_encoding:
        coords_encoding.pop('time')
    if not len(location_coords) and 'location' in coords_encoding:
        coords_encoding.pop('location')

    # Create model and load checkpoint -------------------------------------------------------------

    model = PrithviMAE(img_size=config['DATA']['INPUT_SIZE'][-2:],
                       patch_size=config['MODEL']['PATCH_SIZE'],
                       num_frames=num_frames,
                       in_chans=len(bands),
                       embed_dim=config['MODEL']['EMBED_DIM'],
                       depth=config['MODEL']['DEPTH'],
                       num_heads=config['MODEL']['NUM_HEADS'],
                       decoder_embed_dim=config['MODEL']['DECODER_EMBED_DIM'],
                       decoder_depth=config['MODEL']['DECODER_DEPTH'],
                       decoder_num_heads=config['MODEL']['DECODER_NUM_HEADS'],
                       mlp_ratio=config['MODEL']['MLP_RATIO'],
                       norm_layer=partial(torch.nn.LayerNorm, eps=1e-6),
                       norm_pix_loss=config['MODEL']['NORM_PIX_LOSS'],
                       coords_encoding=coords_encoding,
                       coords_scale_learn=config['MODEL']['COORDS_SCALE_LEARN'])

    total_params = sum(p.numel() for p in model.parameters() if p.requires_grad)
    print(f"\n--> Model has {total_params:,} parameters.\n")

    model.to(device)

    state_dict = torch.load(checkpoint, map_location=device, weights_only=False)
    # discard fixed pos_embedding weight
    for k in list(state_dict.keys()):
        if 'pos_embed' in k:
            del state_dict[k]
    model.load_state_dict(state_dict, strict=False)
    print(f"Loaded checkpoint from {checkpoint}")

    # Running model --------------------------------------------------------------------------------

    model.eval()
    channels = [bands.index(b) for b in ['B04', 'B03', 'B02']]  # BGR -> RGB
    
    # Reflect pad if not divisible by img_size
    original_h, original_w = input_data.shape[-2:]
    pad_h = img_size - (original_h % img_size)
    pad_w = img_size - (original_w % img_size)
    input_data = np.pad(input_data, ((0, 0), (0, 0), (0, 0), (0, pad_h), (0, pad_w)), mode='reflect')

    # Build sliding window
    batch = torch.tensor(input_data, device='cpu')
    windows = batch.unfold(3, img_size, img_size).unfold(4, img_size, img_size)
    h1, w1 = windows.shape[3:5]
    windows = rearrange(windows, 'b c t h1 w1 h w -> (b h1 w1) c t h w', h=img_size, w=img_size)

    # Split into batches if number of windows > batch_size
    num_batches = windows.shape[0] // batch_size if windows.shape[0] > batch_size else 1
    windows = torch.tensor_split(windows, num_batches, dim=0)

    # Run model
    rec_imgs = []
    mask_imgs = []
    for x in windows:
        temp_coords = torch.Tensor([temporal_coords] * len(x))
        loc_coords = torch.Tensor([location_coords[0]] * len(x))
        rec_img, mask_img = run_model(model, x, temp_coords, loc_coords, mask_ratio, device)
        rec_imgs.append(rec_img)
        mask_imgs.append(mask_img)

    rec_imgs = torch.concat(rec_imgs, dim=0)
    mask_imgs = torch.concat(mask_imgs, dim=0)

    # Build images from patches
    rec_imgs = rearrange(rec_imgs, '(b h1 w1) c t h w -> b c t (h1 h) (w1 w)',
                         h=img_size, w=img_size, b=1, c=len(bands), t=num_frames, h1=h1, w1=w1)
    mask_imgs = rearrange(mask_imgs, '(b h1 w1) c t h w -> b c t (h1 h) (w1 w)',
                          h=img_size, w=img_size, b=1, c=len(bands), t=num_frames, h1=h1, w1=w1)

    # Cut padded images back to original size
    rec_imgs_full = rec_imgs[..., :original_h, :original_w]
    mask_imgs_full = mask_imgs[..., :original_h, :original_w]
    batch_full = batch[..., :original_h, :original_w]

    # Build RGB images
    for d in meta_data:
        d.update(count=3, dtype='uint8', compress='lzw', nodata=0)

    outputs = extract_rgb_imgs(batch_full[0, ...], rec_imgs_full[0, ...], mask_imgs_full[0, ...],
                  channels, mean, std)

    print("Done!")

    return outputs


func = partial(predict_on_images, yaml_file_path=yaml_file_path,checkpoint=checkpoint)

with gr.Blocks() as demo:

    gr.Markdown(value='# Prithvi-EO-2.0 image reconstruction demo')
    gr.Markdown(value='''
Prithvi-EO-2.0 is the second generation EO foundation model developed by the IBM and NASA team. 
The temporal ViT is train on 4.2M Harmonised Landsat Sentinel 2 (HLS) samples with four timestamps each, using the Masked AutoEncoder learning strategy. 
The model includes spatial and temporal attention across multiple patches and timestamps. 
Additionally, temporal and location information is added to the model input via embeddings. 
More info about the model are available [here](https://huggingface.co/ibm-nasa-geospatial/Prithvi-EO-2.0-300M-TL).\n

This demo showcases the image reconstruction over one to four timestamps. 
The model randomly masks out some proportion of the images and then reconstructing them based on the not masked portion of the images.\n
The user needs to provide the HLS geotiff images, including the following channels in reflectance units: Blue, Green, Red, Narrow NIR, SWIR, SWIR 2. 
Optionally, the location information is extracted from the tif files while the temporal information can be provided in the filename in the format `<date>T<time>` or `<year><julian day>T<time>` (HLS format). 
We recommend submitting images of size 224 to 1000 pixels for faster processing time. Some example images are provided at the end of this page.
''')
    with gr.Row():
        with gr.Column():
            inp_files = gr.Files(elem_id='files')
            # inp_slider = gr.Slider(0, 100, value=50, label="Mask ratio", info="Choose ratio of masking between 0 and 100", elem_id='slider'),
            btn = gr.Button("Submit")
    with gr.Row():
        gr.Markdown(value='## Original images')
    with gr.Row():
        gr.Markdown(value='T1')
        gr.Markdown(value='T2')
        gr.Markdown(value='T3')
    with gr.Row():
        out1_orig_t1 = gr.Image(image_mode='RGB')
        out2_orig_t2 = gr.Image(image_mode='RGB')
        out3_orig_t3 = gr.Image(image_mode='RGB')
    with gr.Row():
        gr.Markdown(value='## Masked images')
    with gr.Row():
        gr.Markdown(value='T1')
        gr.Markdown(value='T2')
        gr.Markdown(value='T3')
    with gr.Row():
        out4_masked_t1 = gr.Image(image_mode='RGB')
        out5_masked_t2 = gr.Image(image_mode='RGB')
        out6_masked_t3 = gr.Image(image_mode='RGB')
    with gr.Row():
        gr.Markdown(value='## Reonstructed images')
    with gr.Row():
        gr.Markdown(value='T1')
        gr.Markdown(value='T2')
        gr.Markdown(value='T3')
    with gr.Row():
        out7_pred_t1 = gr.Image(image_mode='RGB')
        out8_pred_t2 = gr.Image(image_mode='RGB')
        out9_pred_t3 = gr.Image(image_mode='RGB')


    btn.click(fn=func, 
              # inputs=[inp_files, inp_slider], 
              inputs=inp_files,
              outputs=[out1_orig_t1, 
                       out2_orig_t2, 
                       out3_orig_t3, 
                       out4_masked_t1, 
                       out5_masked_t2, 
                       out6_masked_t3,
                       out7_pred_t1,
                       out8_pred_t2,
                       out9_pred_t3])

    with gr.Row():
        gr.Examples(examples=[[[os.path.join(os.path.dirname(__file__), "examples/HLS.L30.T13REN.2018013T172747.v2.0.B02.B03.B04.B05.B06.B07_cropped.tif"),
                      os.path.join(os.path.dirname(__file__), "examples/HLS.L30.T13REN.2018029T172738.v2.0.B02.B03.B04.B05.B06.B07_cropped.tif"),
                      os.path.join(os.path.dirname(__file__), "examples/HLS.L30.T13REN.2018061T172724.v2.0.B02.B03.B04.B05.B06.B07_cropped.tif")]],
                     [[os.path.join(os.path.dirname(__file__), "examples/HLS.L30.T17RMP.2018004T155509.v2.0.B02.B03.B04.B05.B06.B07_cropped.tif"),
                      os.path.join(os.path.dirname(__file__), "examples/HLS.L30.T17RMP.2018036T155452.v2.0.B02.B03.B04.B05.B06.B07_cropped.tif"),
                      os.path.join(os.path.dirname(__file__), "examples/HLS.L30.T17RMP.2018068T155438.v2.0.B02.B03.B04.B05.B06.B07_cropped.tif")]],
                     [[os.path.join(os.path.dirname(__file__), "examples/HLS.L30.T18TVL.2018029T154533.v2.0.B02.B03.B04.B05.B06.B07_cropped.tif"),
                      os.path.join(os.path.dirname(__file__), "examples/HLS.L30.T18TVL.2018141T154435.v2.0.B02.B03.B04.B05.B06.B07_cropped.tif"),
                      os.path.join(os.path.dirname(__file__), "examples/HLS.L30.T18TVL.2018189T154446.v2.0.B02.B03.B04.B05.B06.B07_cropped.tif")]]],
                    inputs=inp_files,
                    outputs=[out1_orig_t1,
                           out2_orig_t2,
                           out3_orig_t3,
                           out4_masked_t1,
                           out5_masked_t2,
                           out6_masked_t3,
                           out7_pred_t1,
                           out8_pred_t2,
                           out9_pred_t3],
                    fn=func,
                    cache_examples=True
    )

demo.launch()