Add competition inference script.

script.py

@@ -0,0 +1,52 @@
+import pandas as pd
+import numpy as np
+import os
+import subprocess
+import sys
+from tqdm import tqdm
+import timm
+import torchvision.transforms as T
+from PIL import Image
+import torch
+
+
+# custom script arguments
+CONFIG_PATH = 'models/swinv2_base_w24_b16x4-fp16_fungi+val_res_384_cb_epochs_6.py'
+CHECKPOINT_PATH = "models/swinv2_base_w24_b16x4-fp16_fungi+val_res_384_cb_epochs_6_epoch_6_20240514-de00365e.pth"
+SCORE_THRESHOLD = 0.2
+
+def run_inference(input_csv, output_csv, data_root_path):
+    """Load model and dataloader and run inference."""
+
+    if not data_root_path.endswith('/'):
+        data_root_path += '/'
+    data_cfg_opts = [
+        f'test_dataloader.dataset.data_root=',
+        f'test_dataloader.dataset.ann_file={input_csv}',
+        f'test_dataloader.dataset.data_prefix={data_root_path}']
+
+    inference = subprocess.Popen([
+        'python', '-m',
+        'tools.test_generate_result_pre-consensus',
+        CONFIG_PATH, CHECKPOINT_PATH,
+        output_csv,
+        '--threshold', str(SCORE_THRESHOLD),
+        '--no-scores',
+        '--cfg-options'] + data_cfg_opts)
+    return_code = inference.wait()
+    if return_code != 0:
+        print(f'Inference crashed with exit code {return_code}')
+        sys.exit(return_code)
+    print(f'Written {output_csv}')
+
+
+if __name__ == "__main__":
+
+    # import zipfile
+
+    # with zipfile.ZipFile("/tmp/data/private_testset.zip", 'r') as zip_ref:
+    #     zip_ref.extractall("/tmp/data")
+
+    metadata_file_path = "./data/fungi2024/FungiCLEF2023_val_metadata_PRODUCTION.csv" #"./FungiCLEF2024_TestMetadata.csv"
+
+    run_inference(metadata_file_path, "./submission.csv", "./data/fungi2024/DF21/")#"/tmp/data/private_testset")

tools/test_generate_result_pre-consensus.py

@@ -95,7 +95,7 @@ def single_gpu_test(model,
     observation_ids = []
     for i, data in enumerate(data_loader):
         with torch.no_grad():
-            #data = model.module.data_preprocessor(data, training=False)
+            data = model.module.data_preprocessor(data, training=False)
             imgs = data['inputs'].cuda()
             result = model.module.extract_feat(imgs)
 
@@ -209,7 +209,7 @@ def main():
     dropped = 0
     total = 0
     with open(args.out, 'w') as f:
-        f.write('observationID,class_id\n')
+        f.write('observation_id,class_id\n')
         for obs_id, result in results.items():
             avg_feats = torch.mean(torch.stack(result, dim=0), dim=0, keepdim=True)
             scores = model.module.head(avg_feats)