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#### pull files from hub
from huggingface_hub import hf_hub_download
import os
yaml_file_path=hf_hub_download(repo_id="ibm-nasa-geospatial/Prithvi-100M", filename="Prithvi_100M_config.yaml", token=os.environ.get("token"))
checkpoint=hf_hub_download(repo_id="ibm-nasa-geospatial/Prithvi-100M", filename='Prithvi_100M.pt', token=os.environ.get("token"))
model_def=hf_hub_download(repo_id="ibm-nasa-geospatial/Prithvi-100M", filename='Prithvi.py', token=os.environ.get("token"))
os.system(f'cp {model_def} .')
#####
import argparse
import functools
import os
from typing import List
import numpy as np
import rasterio
import torch
import yaml
from einops import rearrange
from Prithvi import MaskedAutoencoderViT
import gradio as gr
from functools import partial
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
return img, meta
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, data_mean=mean,
data_std=std)
rgb_mask = mask_img[channels, t, :, :] * rgb_orig
# extract images
rgb_orig_list.append(_convert_np_uint8(rgb_orig))
rgb_mask_list.append(_convert_np_uint8(rgb_mask))
rgb_pred_list.append(_convert_np_uint8(rgb_pred))
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:
params = yaml.safe_load(f)
# data related
num_frames = params['num_frames']
img_size = params['img_size']
bands = params['bands']
mean = params['data_mean']
std = params['data_std']
# model related
depth = params['depth']
patch_size = params['patch_size']
embed_dim = params['embed_dim']
num_heads = params['num_heads']
tubelet_size = params['tubelet_size']
decoder_embed_dim = params['decoder_embed_dim']
decoder_num_heads = params['decoder_num_heads']
decoder_depth = params['decoder_depth']
batch_size = params['batch_size']
mask_ratio = params['mask_ratio'] if mask_ratio is None else mask_ratio
# We must have *num_frames* files to build one example!
assert len(data_files) == num_frames, "File list must be equal to expected number of frames."
if torch.cuda.is_available():
device = torch.device('cuda')
else:
device = torch.device('cpu')
print(f"Using {device} device.\n")
# Loading data ---------------------------------------------------------------------------------
input_data, meta_data = load_example(file_paths=data_files, mean=mean, std=std)
# Create model and load checkpoint -------------------------------------------------------------
model = MaskedAutoencoderViT(
img_size=img_size,
patch_size=patch_size,
num_frames=num_frames,
tubelet_size=tubelet_size,
in_chans=len(bands),
embed_dim=embed_dim,
depth=depth,
num_heads=num_heads,
decoder_embed_dim=decoder_embed_dim,
decoder_depth=decoder_depth,
decoder_num_heads=decoder_num_heads,
mlp_ratio=4.,
norm_layer=functools.partial(torch.nn.LayerNorm, eps=1e-6),
norm_pix_loss=False)
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)
model.load_state_dict(state_dict)
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:
rec_img, mask_img = run_model(model, x, 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)
# save_rgb_imgs(batch[0, ...], rec_imgs_full[0, ...], mask_imgs_full[0, ...],
# channels, mean, std, output_dir, meta_data)
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)
def preprocess_example(example_list):
print('######## preprocessing here ##########')
example_list = [os.path.join(os.path.abspath(''), x) for x in example_list]
return example_list
with gr.Blocks() as demo:
gr.Markdown(value='# Prithvi image reconstruction demo')
gr.Markdown(value='''Prithvi is a first-of-its-kind temporal Vision transformer pretrained by the IBM and NASA team on continental US Harmonised Landsat Sentinel 2 (HLS) data. Particularly, the model adopts a self-supervised encoder developed with a ViT architecture and Masked AutoEncoder learning strategy, with a MSE as a loss function. The model includes spatial attention across multiple patchies and also temporal attention for each patch. More info about the model and its weights are available [here](https://huggingface.co/ibm-nasa-geospatial/Prithvi-100M).\n
This demo showcases the image reconstracting over three timestamps, with the user providing a set of three HLS images and the model randomly masking 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 three HLS geotiff images, including the following channels in reflectance units: Blue, Green, Red, NIRa, SWIR, SWIR 2.
''')
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__), "HLS.L30.T13REN.2018013T172747.v2.0.B02.B03.B04.B05.B06.B07_cropped.tif"),
os.path.join(os.path.dirname(__file__), "HLS.L30.T13REN.2018029T172738.v2.0.B02.B03.B04.B05.B06.B07_cropped.tif"),
os.path.join(os.path.dirname(__file__), "HLS.L30.T13REN.2018061T172724.v2.0.B02.B03.B04.B05.B06.B07_cropped.tif")]],
[[os.path.join(os.path.dirname(__file__), "HLS.L30.T17RMP.2018004T155509.v2.0.B02.B03.B04.B05.B06.B07_cropped.tif"),
os.path.join(os.path.dirname(__file__), "HLS.L30.T17RMP.2018036T155452.v2.0.B02.B03.B04.B05.B06.B07_cropped.tif"),
os.path.join(os.path.dirname(__file__), "HLS.L30.T17RMP.2018068T155438.v2.0.B02.B03.B04.B05.B06.B07_cropped.tif")]],
[[os.path.join(os.path.dirname(__file__), "HLS.L30.T18TVL.2018029T154533.v2.0.B02.B03.B04.B05.B06.B07_cropped.tif"),
os.path.join(os.path.dirname(__file__), "HLS.L30.T18TVL.2018141T154435.v2.0.B02.B03.B04.B05.B06.B07_cropped.tif"),
os.path.join(os.path.dirname(__file__), "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],
# preprocess=preprocess_example,
fn=func,
cache_examples=True
)
demo.launch() |