Merge pull request #4 from AICradle/feature/safex-reid-training

CLean up scripts

.gitignore

@@ -137,6 +137,7 @@ dmypy.json
 
 # ReID
 reid-data/
+
 log/
 saved-models/
 model-zoo/

scripts/extract_features.py

@@ -3,11 +3,9 @@ import logging
 import numpy as np
 
 from argparse import ArgumentParser
-
 from tqdm import tqdm
 
 from torchreid.utils import FeatureExtractor
-
 from torchreid.utils.tools import read_jsonl
 
 logging.basicConfig(level=logging.INFO)
@@ -28,12 +26,13 @@ class NumpyEncoder(json.JSONEncoder):
 
 def main(in_fp: str, model_fp: str, out_fp: str, device: str, max_objects: int):
     extractor = FeatureExtractor(
-        model_name="resnet50",
+        model_name="resnet18",
         model_path=model_fp,
         device=device
     )
 
     manifest_entries = read_jsonl(in_fp)
+    # Todo (Josh) speed this up my batching
     with open(out_fp, "w") as f:
         for ix, manifest_entry in tqdm(enumerate(manifest_entries), desc="objects"):
 

scripts/test.py

@@ -17,34 +17,22 @@ logging.basicConfig(level=logging.DEBUG)
 LOGGER = logging.getLogger(__name__)
 
 
-def main(data_dir, save_dir):
+def main(data_dir, save_dir, model_fp):
-    """
-    datamanager = torchreid.data.ImageDataManager(
-        root='reid-data',
-        sources='market1501',
-        targets='market1501',
-        height=256,
-        width=128,
-        batch_size_train=32,
-        batch_size_test=100,
-        transforms=['random_flip', 'random_crop']
-    )
-    """
     datamanager = torchreid.data.ImageDataManager(
         root=data_dir,
-        sources=['safex_carla_simulation'],
+        sources="market1501",
+        targets="market1501",
         height=256,
         width=128,
         batch_size_train=32,
         batch_size_test=100,
-        transforms=['random_flip'],
+        transforms=["random_flip", "random_crop"]
-        split_id=1
     )
 
     model = torchreid.models.build_model(
-        name='resnet50',
+        name="resnet18",
         num_classes=datamanager.num_train_pids,
-        loss='triplet',
+        loss="triplet",
         pretrained=True
     )
 
@@ -52,13 +40,13 @@ def main(data_dir, save_dir):
 
     optimizer = torchreid.optim.build_optimizer(
         model,
-        optim='adam',
+        optim="adam",
         lr=0.0003
     )
 
     scheduler = torchreid.optim.build_lr_scheduler(
         optimizer,
-        lr_scheduler='single_step',
+        lr_scheduler="single_step",
         stepsize=20
     )
 
@@ -71,7 +59,7 @@ def main(data_dir, save_dir):
     )
 
     start_epoch = torchreid.utils.resume_from_checkpoint(
-        '/home/ubuntu/deep-person-reid/reid_out/resnet/model/model.pth.tar-15',
+        model_fp,
         model,
         optimizer
     )
@@ -89,11 +77,13 @@ def main(data_dir, save_dir):
 
 if __name__ == "__main__":
     parser = ArgumentParser()
+    parser.add_argument("--model_fp", required=True)
     parser.add_argument("--data_dir", default="./reid_out/", required=False)
     parser.add_argument("--save_dir", default="./reid_out/", required=False)
 
     args = parser.parse_args()
     main(
+        model_fp=args.model_path,
         data_dir=args.data_dir,
         save_dir=args.save_dir
     )

scripts/train.py

@@ -18,9 +18,8 @@ LOGGER = logging.getLogger(__name__)
 
 
 def main(data_dir, save_dir):
-    """
     datamanager = torchreid.data.ImageDataManager(
-        root='reid-data',
+        root=data_dir,
         sources='market1501',
         targets='market1501',
         height=256,
@@ -29,19 +28,6 @@ def main(data_dir, save_dir):
         batch_size_test=100,
         transforms=['random_flip', 'random_crop']
     )
-    """
-    datamanager = torchreid.data.ImageDataManager(
-        root=data_dir,
-        sources=['safex_carla_simulation'],
-        height=256,
-        width=128,
-        split_id=0,
-        batch_size_train=32,
-        batch_size_test=100,
-        transforms=['random_flip'],
-
-    )
-
     model = torchreid.models.build_model(
         name='osnet_ain_x1_0',
         num_classes=datamanager.num_train_pids,

scripts/train_resnet.py

@@ -18,9 +18,8 @@ LOGGER = logging.getLogger(__name__)
 
 
 def main(data_dir, save_dir):
-    """
     datamanager = torchreid.data.ImageDataManager(
-        root='reid-data',
+        root=data_dir,
         sources='market1501',
         targets='market1501',
         height=256,
@@ -29,20 +28,9 @@ def main(data_dir, save_dir):
         batch_size_test=100,
         transforms=['random_flip', 'random_crop']
     )
-    """
-    datamanager = torchreid.data.ImageDataManager(
-        root=data_dir,
-        sources=['safex_carla_simulation'],
-        height=256,
-        width=128,
-        batch_size_train=32,
-        batch_size_test=100,
-        transforms=['random_flip'],
-
-    )
 
     model = torchreid.models.build_model(
-        name='resnet50',
+        name='resnet18',
         num_classes=datamanager.num_train_pids,
         loss='triplet',
         pretrained=True

torchreid/utils/feature_extractor.py

@@ -40,7 +40,7 @@ class FeatureExtractor(object):
 
         extractor = FeatureExtractor(
             model_name='osnet_x1_0',
-            model_path='a/b/c/model.pth.tar',
+            model_fp='a/b/c/model.pth.tar',
             device='cuda'
         )