update inf script to use correct import, isntructions for dependencies and data

README.md

@@ -33,6 +33,37 @@ Code for Finetuning is available through [github](https://github.com/NASA-IMPACT
 Configuration used for finetuning is available through [config](https://github.com/NASA-IMPACT/hls-foundation-os/blob/main/fine-tuning-examples/configs/firescars_config.py
 )
 
+To run inference, first install dependencies 
+
+```
+mamba create -n prithvi-burn-scar python=3.10 torchvision numpy matplotlib rasterio torchmetrics openmim
+mamba activate prithvi-burn-scar
+mim install mmcv-full==1.5
+```
+
+#### Instructions for downloading from [HuggingFace datasets](https://huggingface.co/datasets)
+
+1. Create account on https://huggingface.co/join
+2. Install `git` following https://git-scm.com/downloads
+3. Install git-lfs with `sudo apt install git-lfs` and `git lfs install`
+4. Run the following command to download the HLS datasets. You may need to
+   enter your HuggingFace username/password to do the `git clone`.
+
+   ```
+   mkdir data
+   cd data/
+   git clone https://huggingface.co/datasets/ibm-nasa-geospatial/hls_burn_scars burn_scars
+   tar -xzvf burn_scars/hls_burn_scars.tar.gz -C data/
+   ls -lh data/
+   ```
+
+
+With the datasets and the environment, you can now run the inference script.
+
+```
+
+
+```
 
 ### Results
 

burn_scar_batch_inference_script.py

@@ -21,7 +21,7 @@ from torchvision import transforms
 from mmcv.parallel import MMDataParallel, MMDistributedDataParallel
 
 from mmseg.apis import multi_gpu_test, single_gpu_test, init_segmentor
-from mmseg.utils import custom # custom preprocessing for hls
+from . import custom # custom preprocessing for hls
 import pdb
 
 import numpy as np

custom.py

@@ -0,0 +1,191 @@
+# utils.py
+
+import numpy as np
+import glob
+import rasterio
+from torchvision import transforms
+import torch
+import re
+from torchmetrics import Dice
+import os
+
+def calculate_band_statistics(image_directory, image_pattern, bands=[0, 1, 2, 3, 4, 5]):
+    """
+    Calculate the mean and standard deviation of each band in a folder of GeoTIFF files.
+
+    Args:
+        image_directory (str): Directory where the source GeoTIFF files are stored that are passed to model for training.
+        image_pattern (str): Pattern of the GeoTIFF file names that globs files for computing stats.
+        bands (list, optional): List of bands to calculate statistics for. Defaults to [0, 1, 2, 3, 4, 5].
+
+    Raises:
+        Exception: If no images are found in the given directory.
+
+    Returns:
+        tuple: Two lists containing the means and standard deviations of each band.
+    """
+    # Initialize lists to store the means and standard deviations
+    all_means = []
+    all_stds = []
+
+    # Use glob to get a list of all .tif images in the directory
+    all_images = glob.glob(f"{image_directory}/{image_pattern}.tif")
+
+    # Make sure there are images to process
+    if not all_images:
+        raise Exception("No images found")
+
+    # Get the number of bands
+    num_bands = len(bands)
+
+    # Initialize arrays to hold sums and sum of squares for each band
+    band_sums = np.zeros(num_bands)
+    band_sq_sums = np.zeros(num_bands)
+    pixel_counts = np.zeros(num_bands)
+
+    # Iterate over each image
+    for image_file in all_images:
+        with rasterio.open(image_file) as src:
+            # For each band, calculate the sum, square sum, and pixel count
+            for band in bands:
+                data = src.read(band + 1)  # rasterio band index starts from 1
+                band_sums[band] += np.nansum(data)
+                band_sq_sums[band] += np.nansum(data**2)
+                pixel_counts[band] += np.count_nonzero(~np.isnan(data))
+
+    # Calculate means and standard deviations for each band
+    for i in bands:
+        mean = band_sums[i] / pixel_counts[i]
+        std = np.sqrt((band_sq_sums[i] / pixel_counts[i]) - (mean**2))
+        all_means.append(mean)
+        all_stds.append(std)
+
+    return all_means, all_stds
+
+
+def split_and_pad(array, target_shape):
+    """
+    Splits the input array into smaller arrays of the target shape, padding if necessary.
+
+    Args:
+        array (numpy.ndarray): The input array. Must be shape (batch, band, time, height, width)
+        target_shape (tuple): The target shape of the smaller arrays. Must be of shape
+        (batch, band, time, height, width)
+
+    Raises:
+        ValueError: If target shape is larger than the array shape.
+
+    Returns:
+        list[numpy.ndarray]: A list of the smaller arrays.
+    """
+    # Check if the target shape is smaller or equal to the array shape
+    if target_shape[-2:] > array.shape[-2:]:
+        raise ValueError('Target shape must be smaller or equal to the array shape.')
+
+    # Calculate how much padding is needed
+    pad_h = (target_shape[-2] - array.shape[-2] % target_shape[-2]) % target_shape[-2]
+    pad_w = (target_shape[-1] - array.shape[-1] % target_shape[-1]) % target_shape[-1]
+
+    # Apply padding to the array
+    padded_array = np.pad(array, ((0, 0), (0, 0), (0, 0), (0, pad_h), (0, pad_w)))
+
+    # Split the array into smaller arrays of the target shape
+    result = []
+    for i in range(0, padded_array.shape[-2], target_shape[-2]):
+        for j in range(0, padded_array.shape[-1], target_shape[-1]):
+            result.append(padded_array[..., i:i+target_shape[-2], j:j+target_shape[-1]])
+
+    return result
+
+def merge_and_unpad(np_array_list, original_shape, target_shape):
+    """
+    Assembles smaller numpy arrays back into the original larger numpy array, removing padding if necessary.
+
+    Args:
+        np_array_list (list[numpy.ndarray]): The list of smaller numpy arrays derived from split_and_pad.
+        original_shape (tuple): The original shape of the larger numpy array. Must be shape (Height, Width).
+        target_shape (tuple): The target shape of the smaller numpy arrays. Must be shape (Height, Width).
+
+    Returns:
+        numpy.ndarray: The original larger numpy array.
+    """
+    # Calculate how much padding was added
+    pad_h = (target_shape[0] - original_shape[0] % target_shape[0]) % target_shape[0]
+    pad_w = (target_shape[1] - original_shape[1] % target_shape[1]) % target_shape[1]
+
+    # Calculate the shape of the padded larger array
+    padded_shape = (original_shape[0] + pad_h, original_shape[1] + pad_w)
+
+    # Calculate the number of smaller arrays in each dimension
+    num_arrays_h = padded_shape[0] // target_shape[0]
+    num_arrays_w = padded_shape[1] // target_shape[1]
+
+    # Reshape the list of smaller arrays back into the shape of the padded larger array
+    merged_array = np.stack(np_array_list).reshape(num_arrays_h, num_arrays_w, *target_shape)
+
+    # Rearrange the array dimensions
+    merged_array = merged_array.transpose(0, 2, 1, 3).reshape(*padded_shape)
+
+    # Remove the padding
+    unpadded_array = merged_array[:original_shape[0], :original_shape[1]]
+
+    return unpadded_array
+
+def compute_metrics(gt_dir, pred_dir):
+    """
+    Compute the Dice similarity coefficient between the predicted and ground truth images.
+
+    Args:
+        gt_dir (str): Directory where the ground truth images are stored.
+        pred_dir (str): Directory where the predicted images are stored.
+
+    Returns:
+        Tensor: Dice similarity coefficient score.
+    """
+    dice_metric = Dice()
+
+    # find all .tif files in the prediction directory
+    pred_files = glob.glob(os.path.join(pred_dir, "*.tif"))
+
+    # iterate over each prediction file
+    for pred_file in pred_files:
+        # extract the unique_id from the file name
+        unique_id = re.search('HLS\..*\.v1\.4', os.path.basename(pred_file))
+
+        if unique_id is not None:
+            unique_id = unique_id.group()
+
+            # create the unique pattern for the gt directory
+            gt_file_pattern = os.path.join(gt_dir, f"*{unique_id}*mask.tif")
+
+            # glob the file pattern
+            gt_files = glob.glob(gt_file_pattern)
+
+            # if we found a matching gt file
+            if len(gt_files) == 1:
+                gt_file = gt_files[0]
+
+                # read the .tif files
+                with rasterio.open(gt_file) as src:
+                    gt_img = src.read(1) # ground truth image
+
+                with rasterio.open(pred_file) as src:
+                    pred_img = src.read(1) # predicted image
+
+                # make sure the images are binary (values are 0 or 1)
+                gt_img = (gt_img > 0).astype(np.uint8)
+                pred_img = (pred_img > 0).astype(np.uint8)
+
+                # convert numpy arrays to PyTorch tensors
+                gt_img_tensor = torch.from_numpy(gt_img).long().flatten()
+                pred_img_tensor = torch.from_numpy(pred_img).long().flatten()
+
+                # update dice_metric
+                dice_metric.update(pred_img_tensor, gt_img_tensor)
+
+            else:
+                print(f"No matching ground truth file for prediction file {pred_file}.")
+
+    # compute the dice score
+    dice_score = dice_metric.compute()
+    return dice_score