Upload burn_scar_batch_inference_script.py

burn_scar_batch_inference_script.py

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+import argparse
+from mmcv import Config
+from mmcv.runner import (get_dist_info, init_dist, load_checkpoint,wrap_fp16_model)
+from mmseg.models import build_segmentor
+
+import matplotlib.pyplot as plt
+import mmcv
+import torch
+from mmcv.parallel import collate, scatter
+from mmcv.runner import load_checkpoint
+
+from mmseg.datasets.pipelines import Compose
+from mmseg.models import build_segmentor
+
+from mmseg.datasets import build_dataloader, build_dataset, load_flood_test_data
+import rasterio
+import torch
+import torch.nn.functional as F
+
+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
+import pdb
+
+import numpy as np
+import glob
+import os
+
+import time
+
+def parse_args():
+
+    parser = argparse.ArgumentParser(description="Inference on burn scar fine-tuned model")
+    parser.add_argument('-config', help='path to model configuration file')
+    parser.add_argument('-ckpt', help='path to model checkpoint')
+    parser.add_argument('-input', help='path to input images folder for inference')
+    parser.add_argument('-output', help='directory path to save output images')
+    parser.add_argument('-input_type', help='file type of input images',default="tif")
+
+    args = parser.parse_args()
+
+    return args
+
+def open_tiff(fname):
+
+    with rasterio.open(fname, "r") as src:
+
+        data = src.read()
+
+    return data
+
+def write_tiff(img_wrt, filename, metadata):
+
+    """
+    It writes a raster image to file.
+
+    :param img_wrt: numpy array containing the data (can be 2D for single band or 3D for multiple bands)
+    :param filename: file path to the output file
+    :param metadata: metadata to use to write the raster to disk
+    :return:
+    """
+
+    with rasterio.open(filename, "w", **metadata) as dest:
+
+        if len(img_wrt.shape) == 2:
+
+            img_wrt = img_wrt[None]
+
+        for i in range(img_wrt.shape[0]):
+            dest.write(img_wrt[i, :, :], i + 1)
+
+
+def get_meta(fname):
+
+    with rasterio.open(fname, "r") as src:
+
+        meta = src.meta
+
+    return meta
+
+def preprocess_image(data, means, stds, nodata=-9999):
+
+    data=np.where(data == nodata, 0, data)
+    data = data.astype(np.float32)
+
+    if len(data)==2:
+        (x, y) = data
+    else:
+        x=data
+        y=np.full((x.shape[-2], x.shape[-1]), -1)
+
+    im, label = x.copy(), y.copy()
+    label = label.astype(np.float64)
+
+    im1 = im[0]  # red
+    im2 = im[1]  # green
+    im3 = im[2]  # blue
+    im4 = im[3]  # NIR narrow
+    im5 = im[4]  # swir 1
+    im6 = im[5]  # swir 2
+
+    dim = x.shape[-1]
+    label = label.squeeze()
+    norm = transforms.Normalize(means, stds)
+    ims = [torch.stack((transforms.ToTensor()(im1).squeeze(),
+                        transforms.ToTensor()(im2).squeeze(),
+                        transforms.ToTensor()(im3).squeeze(),
+                        transforms.ToTensor()(im4).squeeze(),
+                        transforms.ToTensor()(im5).squeeze(),
+                        transforms.ToTensor()(im6).squeeze()))]
+    ims = [norm(im) for im in ims]
+    ims = torch.stack(ims)
+
+    label = transforms.ToTensor()(label).squeeze()
+
+    _img_metas = {
+        'ori_shape': (dim, dim),
+        'img_shape': (dim, dim),
+        'pad_shape': (dim, dim),
+        'scale_factor': [1., 1., 1., 1.],
+        'flip': False, # needs flip direction specified
+    }
+
+    img_metas = [_img_metas] * 1
+    return {"img": ims,
+            "img_metas": img_metas,
+            "gt_semantic_seg": label}
+
+
+def load_model(config, ckpt):
+
+    print('Loading configuration...')
+    cfg = Config.fromfile(config)
+    print('Building model...')
+    model = build_segmentor(cfg.model, test_cfg=cfg.get('test_cfg'))
+    print('Loading checkpoint...')
+    checkpoint = load_checkpoint(model,ckpt, map_location='cpu')
+    print('Evaluating model...')
+    model = MMDataParallel(model, device_ids=[0])
+    model.eval()
+
+    return model
+
+
+def inference_on_file(model, target_image, output_image, means, stds):
+
+    try:
+        st = time.time()
+        data_orig = open_tiff(target_image)
+        meta = get_meta(target_image)
+        nodata = meta['nodata'] if meta['nodata'] is not None else -9999
+
+        data = preprocess_image(data_orig, means, stds, nodata)
+
+        small_fixed_size_arrs =  custom.split_and_pad(data['img'][:,:,None,:,:], (1, 6, 1, 224, 224))
+        single_chip_batch = [torch.vstack([torch.tensor(t) for t in small_fixed_size_arrs])]
+        print('Running inference...')
+        with torch.no_grad():
+            result = model(single_chip_batch, data['img_metas'], return_loss=False, rescale=False)
+            print("Result: Unique Values: ",np.unique(result))
+
+        print("Output has shape: " + str(result[0].shape))
+        #### TO DO: Post process (e.g. morphological operations)
+
+        result = custom.merge_and_unpad(result, (data_orig.shape[-2],data_orig.shape[-1]), (224, 224))
+
+        print("Result: Unique Values: ",np.unique(result))
+
+        ##### Save file to disk
+        meta["count"] = 1
+        meta["dtype"] = "int16"
+        meta["compress"] = "lzw"
+        meta["nodata"] = -1
+        meta["nodata"] = nodata
+        print('Saving output...')
+        # pdb.set_trace()
+        result = np.where(data_orig[0] == nodata, nodata, result)
+
+        write_tiff(result, output_image, meta)
+        et = time.time()
+        print(f'Inference completed in {str(np.round(et - st, 1))} seconds. Output available at: ' + output_image)
+
+    except:
+        print(f'Error on image {target_image} \nContinue to next input')
+
+def main():
+
+    args = parse_args()
+
+    model = load_model(args.config, args.ckpt)
+    image_pattern = "*merged"
+    target_images = glob.glob(os.path.join(args.input, image_pattern + "." + args.input_type))
+
+    print('Identified images to predict on: ' + str(len(target_images)))
+
+    if not os.path.isdir(args.output):
+        os.mkdir(args.output)
+
+    means, stds = custom.calculate_band_statistics(args.input, image_pattern, bands=[0, 1, 2, 3, 4, 5])
+
+    for i, target_image in enumerate(target_images):
+
+        print(f'Working on Image {i}')
+        output_image = os.path.join(args.output,target_image.split("/")[-1].split(f"_{image_pattern[1:]}.")[0]+'_pred.'+args.input_type)
+
+        inference_on_file(model, target_image, output_image, means, stds)
+
+    print("Running metric eval")
+
+    gt_dir = "/home/workdir/hls-foundation/data/burn_scars/validation"
+    pred_dir = args.output
+    avg_dice_score = custom.compute_metrics(gt_dir, pred_dir)
+    print("Average Dice score:", avg_dice_score)
+
+
+if __name__ == "__main__":
+    main()