added dinov2 weights

checkpoints/dinov2.bin

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:78971dc00a0c488f2b2dff17d6dcb7ebe787af70a703d8212b38fc6a33dbcdd4
+size 1217608166

script.py

@@ -4,12 +4,13 @@ import pandas as pd
 import timm
 import torch
 import torch.nn as nn
-import torch.nn.functional as F
-import torchvision.transforms as T
 from PIL import Image
 from timm.models.metaformer import MlpHead
 from torch.utils.data import DataLoader, Dataset
 from tqdm import tqdm
+from albumentations import Compose, Normalize, Resize
+from albumentations.pytorch import ToTensorV2
+import cv2
 
 DIM = 518
 DATE_SIZE = 4
@@ -99,11 +100,11 @@ SUBSTRATE = [
 class ImageDataset(Dataset):
     def __init__(self, df, local_filepath):
         self.df = df
-        self.transform = T.Compose(
+        self.transform = Compose(
             [
-                T.Resize((DIM, DIM)),
-                T.ToTensor(),
-                T.Normalize([0.5, 0.5, 0.5], [0.5, 0.5, 0.5]),
+                Resize(DIM, DIM),
+                Normalize(mean=(0.5, 0.5, 0.5), std=(0.5, 0.5, 0.5)),
+                ToTensorV2(),
             ]
         )
 
@@ -117,9 +118,10 @@ class ImageDataset(Dataset):
     def __getitem__(self, idx):
         image_path = os.path.join(self.local_filepath, self.filepaths[idx])
 
-        image = Image.open(image_path).convert("RGB")
+        image = cv2.imread(image_path)
+        image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
 
-        return self.transform(image)
+        return self.transform(image=image)['image']
 
 
 class EmbeddingMetadataDataset(Dataset):
@@ -270,11 +272,10 @@ class FungiMEEModel(nn.Module):
 
 class FungiEnsembleModel(nn.Module):
 
-    def __init__(self, models, softmax=True) -> None:
+    def __init__(self, models) -> None:
         super().__init__()
 
         self.models = nn.ModuleList()
-        self.softmax = softmax
         self.device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
 
         for model in models:
@@ -291,12 +292,7 @@ class FungiEnsembleModel(nn.Module):
         for model in self.models:
             logits = model.forward(img_emb, metadata)
 
-            p = (
-                logits.softmax(dim=1).detach().cpu()
-                if self.softmax
-                else logits.detach().cpu()
-            )
-
+            p = logits.softmax(dim=1).detach().cpu()
             probs.append(p)
 
         return torch.stack(probs).mean(dim=0)
@@ -314,25 +310,32 @@ def make_submission(metadata_df):
     OUTPUT_CSV_PATH = "./submission.csv"
     BASE_CKPT_PATH = "./checkpoints"
 
-    model_names = os.listdir(BASE_CKPT_PATH)
+    ckpt_path = os.path.join(BASE_CKPT_PATH, "dino_2_optuna_05242055.ckpt")
+    # model_names = os.listdir(BASE_CKPT_PATH)
 
-    models = []
+    # models = []
 
-    for model_path in model_names:
-        print("loading ", model_path)
-        ckpt_path = os.path.join(BASE_CKPT_PATH, model_path)
+    # for model_path in model_names:
+    #     print("loading ", model_path)
+    #     ckpt_path = os.path.join(BASE_CKPT_PATH, model_path)
 
-        ckpt = torch.load(ckpt_path)
-        model = FungiMEEModel()
-        model.load_state_dict(
-            {w: ckpt["model." + w] for w in model.state_dict().keys()}
-        )
-        model.eval()
-        model.cuda()
+    #     ckpt = torch.load(ckpt_path)
+    #     model = FungiMEEModel()
+    #     model.load_state_dict(
+    #         {w: ckpt["model." + w] for w in model.state_dict().keys()}
+    #     )
+    #     model.eval()
+    #     model.cuda()
 
-        models.append(model)
+    #     models.append(model)
 
-    ensemble_model = FungiEnsembleModel(models)
+    # fungi_model = FungiEnsembleModel(models)
+
+    fungi_model = FungiMEEModel()
+    ckpt = torch.load(ckpt_path)
+    fungi_model.load_state_dict(
+        {w: ckpt["model." + w] for w in fungi_model.state_dict().keys()}
+    )
 
     embedding_dataset = EmbeddingMetadataDataset(metadata_df)
     loader = DataLoader(embedding_dataset, batch_size=128, shuffle=False)
@@ -340,7 +343,7 @@ def make_submission(metadata_df):
     preds = []
     for data in tqdm(loader):
         emb, metadata = data
-        pred = ensemble_model.forward(emb, metadata)
+        pred = fungi_model.forward(emb, metadata)
         preds.append(pred)
 
     all_preds = torch.vstack(preds).numpy()
@@ -363,18 +366,21 @@ def make_submission(metadata_df):
 
 if __name__ == "__main__":
 
-    # # # Real submission
-    import zipfile
+    MODEL_PATH = "metaformer-s-224.pth"
+    MODEL_NAME = "timm/vit_base_patch14_reg4_dinov2.lvd142m"
+
+    # # # # Real submission
+    # import zipfile
 
-    with zipfile.ZipFile("/tmp/data/private_testset.zip", "r") as zip_ref:
-        zip_ref.extractall("/tmp/data")
+    # with zipfile.ZipFile("/tmp/data/private_testset.zip", "r") as zip_ref:
+    #     zip_ref.extractall("/tmp/data")
 
-    metadata_file_path = "./_test_preprocessed.csv"
-    root_dir = "/tmp/data"
+    # metadata_file_path = "./_test_preprocessed.csv"
+    # root_dir = "/tmp/data"
 
     # Test submission
-    # metadata_file_path = "../trial_submission.csv"
-    # root_dir = "../data/DF_FULL"
+    metadata_file_path = "../trial_submission.csv"
+    root_dir = "../data/DF_FULL"
 
     ##############