updated

gallery_features/rn50_stage3_v3_gallery.pt

@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:f923693bf05e01d8c47f6384e0be38924d70bf9426cdf539be7a29097d46058c
-size 52369628

gallery_features/rn50_zeroshot_gallery.pt

@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:df12a27314cf75e99c3c3ade1ecb36f2c673f4a882d3d340bd3435fbd543b4ed
-size 26540252

gallery_features/vitb16_stage3_v4_gallery.pt

@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:10f7b4aa80258ec32724d66d8942713e2d0fc2976034f175084da8b5a29d30df
-size 26540252

gallery_features/vitb16_zeroshot_gallery.pt

@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:a8af24ee16c51086d3397930ee56efd80cd51db4ce1afbd7f8edb926b86eecd2
-size 13625564

inference.py

@@ -1,6 +0,0 @@
-def load_gallery_features(arch="vitb16", stage="stage3"):
-    filename = f"{arch}_{stage}_gallery.pt"
-    path = os.path.join("gallery_features", filename)
-    if not os.path.exists(path):
-        raise FileNotFoundError(f"Gallery file not found: {path}")
-    return torch.load(path)

model_loader.py

@@ -1,49 +0,0 @@
-import os
-import torch
-import clip
-from stylefinder import CLIPModel  # Make sure your model class is here
-
-
-def load_model(arch="vitb16", stage="stage3", device=None):
-    """
-    Loads the appropriate StyleFinder model.
-
-    Args:
-        arch (str): "vitb16" or "rn50"
-        stage (str): "stage3" or "zeroshot"
-        device (str): torch device (e.g., "cuda" or "cpu")
-
-    Returns:
-        model, preprocess
-    """
-    arch = arch.lower()
-    stage = stage.lower()
-    device = device or ("cuda" if torch.cuda.is_available() else "cpu")
-
-    if stage == "zeroshot":
-        model, preprocess = clip.load("ViT-B/16" if arch == "vitb16" else "RN50", device=device)
-        return model, preprocess
-
-    # Load fine-tuned model from local checkpoint
-    checkpoint_paths = {
-        "vitb16": "vitb16_stage3_v4.pth",
-        "rn50": "rn50_stage3_v3.pth"
-    }
-
-    if arch not in checkpoint_paths:
-        raise ValueError(f"Unsupported architecture: {arch}")
-
-    ckpt_path = checkpoint_paths[arch]
-    if not os.path.exists(ckpt_path):
-        raise FileNotFoundError(f"Checkpoint not found: {ckpt_path}")
-
-    # Instantiate and load the fine-tuned model
-    model = CLIPModel(arch=arch)
-    state_dict = torch.load(ckpt_path, map_location=device)
-    model.load_state_dict(state_dict)
-    model.eval()
-    model.to(device)
-
-    from preprocess import build_preprocess
-    preprocess = build_preprocess()
-    return model, preprocess

preprocess.py

@@ -1,10 +0,0 @@
-# preprocess.py
-from torchvision import transforms
-
-def build_preprocess():
-    return transforms.Compose([
-        transforms.Resize((224, 224)),
-        transforms.ToTensor(),
-        transforms.Normalize(mean=[0.4815, 0.4578, 0.4082],
-                             std=[0.2686, 0.2613, 0.2758]),
-    ])