Prithvi-100M / Prithvi_run_inference.py
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import argparse
import functools
import os
from typing import List, Union
import numpy as np
import rasterio
import torch
import yaml
from einops import rearrange
from Prithvi import MaskedAutoencoderViT
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 return 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)
return image
def load_example(
file_paths: List[str],
mean: List[float],
std: List[float],
indices: Union[list[int], None] = None,
):
"""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)
# Rescaling (don't normalize on nodata)
img = np.moveaxis(img, 0, -1) # channels last for rescaling
if indices is not None:
img = img[..., indices]
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 save_imgs(rec_img, mask_img, mean, std, output_dir, meta_data):
"""Wrapper function to save Geotiff images (reconstructed, mask) per timestamp.
Args:
rec_img: reconstructed torch.Tensor with shape (C, T, H, W).
mask_img: mask torch.Tensor with shape (C, T, H, W).
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.
"""
mean = torch.tensor(np.asarray(mean)[:, None, None]) # C H W
std = torch.tensor(np.asarray(std)[:, None, None])
for t in range(rec_img.shape[1]):
# Back to original data range
rec_img_t = ((rec_img[:, t, :, :] * std) + mean).to(torch.int16)
mask_img_t = mask_img[:, t, :, :].to(torch.int16)
# Saving images
save_geotiff(
image=rec_img_t,
output_path=os.path.join(output_dir, f"predicted_t{t}.tiff"),
meta=meta_data[t],
)
save_geotiff(
image=mask_img_t,
output_path=os.path.join(output_dir, f"mask_t{t}.tiff"),
meta=meta_data[t],
)
def main(
data_files: List[str],
yaml_file_path: str,
checkpoint: str,
output_dir: str,
rgb_outputs: bool,
img_size: int,
mask_ratio: float = None,
input_indices: list[int] = None,
):
os.makedirs(output_dir, exist_ok=True)
# Get parameters --------
with open(yaml_file_path, "r") as f:
params = yaml.safe_load(f)
# data related
train_params = params["train_params"]
num_frames = len(data_files)
bands = train_params["bands"]
mean = train_params["data_mean"]
std = train_params["data_std"]
# model related
model_params = params["model_args"]
img_size = model_params["img_size"] if img_size is None else img_size
depth = model_params["depth"]
patch_size = model_params["patch_size"]
embed_dim = model_params["embed_dim"]
num_heads = model_params["num_heads"]
tubelet_size = model_params["tubelet_size"]
decoder_embed_dim = model_params["decoder_embed_dim"]
decoder_num_heads = model_params["decoder_num_heads"]
decoder_depth = model_params["decoder_depth"]
batch_size = 1
mask_ratio = train_params["mask_ratio"] if mask_ratio is None else mask_ratio
print(
f"\nTreating {len(data_files)} files as {len(data_files)} time steps from the same location\n"
)
if len(data_files) != 3:
print(
"The original model was trained for 3 time steps (expecting 3 files). \nResults with different numbers of timesteps may vary"
)
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, indices=input_indices, 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=4.0,
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)
# discard fixed pos_embedding weight
del state_dict["pos_embed"]
del state_dict["decoder_pos_embed"]
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:
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 output images
if rgb_outputs:
for d in meta_data:
d.update(count=3, dtype="uint8", compress="lzw", nodata=0)
save_rgb_imgs(
batch_full[0, ...],
rec_imgs_full[0, ...],
mask_imgs_full[0, ...],
channels,
mean,
std,
output_dir,
meta_data,
)
else:
for d in meta_data:
d.update(compress="lzw", nodata=0)
save_imgs(
rec_imgs_full[0, ...],
mask_imgs_full[0, ...],
mean,
std,
output_dir,
meta_data,
)
print("Done!")
if __name__ == "__main__":
parser = argparse.ArgumentParser("MAE run inference", add_help=False)
parser.add_argument(
"--data_files",
required=True,
type=str,
nargs="+",
help="Path to the data files. Assumes multi-band files.",
)
parser.add_argument(
"--yaml_file_path",
type=str,
required=True,
help="Path to yaml file containing model training parameters.",
)
parser.add_argument(
"--checkpoint",
required=True,
type=str,
help="Path to a checkpoint file to load from.",
)
parser.add_argument(
"--output_dir",
required=True,
type=str,
help="Path to the directory where to save outputs.",
)
parser.add_argument(
"--mask_ratio",
default=None,
type=float,
help="Masking ratio (percentage of removed patches). "
"If None (default) use same value used for pretraining.",
)
parser.add_argument(
"--img_size",
default=224,
type=int,
help="Image size to be used with model. Defaults to 224",
)
parser.add_argument(
"--input_indices",
default=None,
type=int,
nargs="+",
help="0-based indices of channels to be selected from the input. By default takes all.",
)
parser.add_argument(
"--rgb_outputs",
action="store_true",
help="If present, output files will only contain RGB channels. "
"Otherwise, all bands will be saved.",
)
args = parser.parse_args()
main(**vars(args))