train and inference base scripts

requirements.txt

@@ -18,7 +18,9 @@ huggingface-hub==0.12.0
 idna==3.4
 joblib==1.2.0
 kiwisolver==1.4.4
+markdown-it-py==2.1.0
 matplotlib==3.6.3
+mdurl==0.1.2
 multidict==6.0.4
 multiprocess==0.70.14
 mypy-extensions==0.4.3
@@ -31,6 +33,7 @@ Pillow==9.4.0
 platformdirs==2.6.2
 pyarrow==11.0.0
 pycodestyle==2.10.0
+Pygments==2.14.0
 pyparsing==3.0.9
 python-dateutil==2.8.2
 pytz==2022.7.1
@@ -38,6 +41,7 @@ PyYAML==6.0
 regex==2022.10.31
 requests==2.28.2
 responses==0.18.0
+rich==13.3.1
 scikit-learn==1.2.1
 scipy==1.10.0
 six==1.16.0

src/acclogloss.py

@@ -0,0 +1,21 @@
+import numpy as np
+
+def BinaryCrossEntropy(y_true, y_pred):
+    y_pred = np.clip(y_pred, 1e-7, 1 - 1e-7)
+    term_0 = (1-y_true) * np.log(1-y_pred)
+    term_1 = y_true * np.log(y_pred)
+    return -np.mean(term_0+term_1, axis=0)
+
+nb_tests = 43444
+
+acc = 0.977
+
+labels = np.ones(nb_tests)
+
+nb_true = int(acc * nb_tests)
+predicitions = np.concatenate((np.ones(nb_true), np.zeros(nb_tests - nb_true)))
+
+logloss = BinaryCrossEntropy(labels, predicitions)
+
+print(f"Accuracy: {acc}")
+print(f"logloss: {logloss}")

src/inference.py

@@ -0,0 +1,83 @@
+import torch
+import pandas as pd
+from transformers import (
+    AutoModelForImageClassification,
+    AutoFeatureExtractor,
+)
+from torchvision.transforms import (
+    CenterCrop,
+    Compose,
+    Resize,
+    ToTensor,
+    Normalize,
+)
+from rich.progress import track
+from datetime import datetime
+
+from dataset import test_ds
+
+feature_extractor = AutoFeatureExtractor.from_pretrained(
+    'test-aiornot/checkpoint-500')
+
+normalize = Normalize(mean=feature_extractor.image_mean,
+                      std=feature_extractor.image_std)
+
+_val_transforms = Compose(
+    [
+        Resize((256, 256)),
+        CenterCrop((256, 256)),
+        ToTensor(),
+        normalize,
+    ]
+)
+def val_transforms(examples):
+    examples['pixel_values'] = [_val_transforms(
+        image.convert("RGB")) for image in examples['image']]
+    return examples
+
+test_ds.set_transform(val_transforms)
+
+def collate_fn(examples):
+    pixel_values = torch.stack([example["pixel_values"] for example in examples])
+    labels = torch.tensor([example["label"] for example in examples])
+    image_paths = [example["image_path"] for example in examples]
+
+    return {
+        "pixel_values": pixel_values,
+        "labels": labels,
+        "image_path": image_paths,
+    }
+
+model = AutoModelForImageClassification.from_pretrained(
+    'test-aiornot/checkpoint-1000',
+)
+
+test_loader = torch.utils.data.DataLoader(
+    test_ds, batch_size=128, collate_fn=collate_fn, pin_memory=True, num_workers=4
+)
+device = "cuda" if torch.cuda.is_available() else "cpu"
+_ = model.to(device)
+
+file_paths = []
+pred_ids = []
+
+for batch in track(test_loader):
+    image_paths = batch["image_path"]
+    image_paths = [x.split("/")[-1] for x in image_paths]
+    file_paths.extend(image_paths)
+    
+    images = batch["pixel_values"].to(device)
+    inputs = {"pixel_values": images}
+    
+    with torch.no_grad(): 
+        logits = model(**inputs).logits
+
+    predictions = logits.argmax(-1).cpu().numpy().tolist()
+    pred_ids.extend(predictions)
+
+
+submission_df = pd.DataFrame({"id": file_paths, "label": pred_ids})
+submission_df.head()
+
+TIMESTAMP = datetime.now().strftime("%Y%m%d%H%M%S")
+submission_df.to_csv(f"{TIMESTAMP}.csv", index=False)

src/main.py

@@ -22,7 +22,7 @@ from datasets import load_metric
 from dataset import train_ds, val_ds, test_ds, labels, id2label, label2id
 
 feature_extractor = AutoFeatureExtractor.from_pretrained(
-    "microsoft/swinv2-base-patch4-window16-256")
+    "facebook/convnext-xlarge-384-22k-1k")
 
 normalize = Normalize(mean=feature_extractor.image_mean,
                       std=feature_extractor.image_std)
@@ -88,7 +88,7 @@ def collate_fn(examples):
 
 
 model = AutoModelForImageClassification.from_pretrained(
-    'microsoft/swinv2-base-patch4-window16-256',
+    'facebook/convnext-xlarge-384-22k-1k',
     num_labels=len(labels),
     id2label=id2label,
     label2id=label2id,
@@ -102,8 +102,8 @@ args = TrainingArguments(
     save_strategy="steps",
     evaluation_strategy="steps",
     learning_rate=2e-5,
-    per_device_train_batch_size=8,
-    per_device_eval_batch_size=8,
+    per_device_train_batch_size=24,
+    per_device_eval_batch_size=24,
     num_train_epochs=3,
     weight_decay=0.01,
     load_best_model_at_end=True,