Upload 693 files

mlruns/481616812065881245/10cda43e9e42477388168fb0c51964bb/artifacts/best_accuracy/MLmodel

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+artifact_path: best_accuracy
+flavors:
+  python_function:
+    config:
+      device: null
+    data: data
+    env:
+      conda: conda.yaml
+      virtualenv: python_env.yaml
+    loader_module: mlflow.pytorch
+    pickle_module_name: mlflow.pytorch.pickle_module
+    python_version: 3.12.2
+  pytorch:
+    code: null
+    model_data: data
+    pytorch_version: 2.3.0+cu121
+mlflow_version: 2.12.2
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+model_uuid: 553bd7634b7b4386a4ad9f80f7e66a32
+run_id: 10cda43e9e42477388168fb0c51964bb
+utc_time_created: '2024-05-19 22:24:47.347724'

mlruns/481616812065881245/10cda43e9e42477388168fb0c51964bb/artifacts/best_accuracy/conda.yaml

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+channels:
+- conda-forge
+dependencies:
+- python=3.12.2
+- pip<=24.0
+- pip:
+  - mlflow==2.12.2
+  - cloudpickle==3.0.0
+  - numpy==1.26.4
+  - packaging==24.0
+  - pandas==2.2.2
+  - pynvml==11.5.0
+  - pyyaml==6.0.1
+  - torch==2.3.0
+  - torchvision==0.18.0
+  - tqdm==4.66.4
+name: mlflow-env

mlruns/481616812065881245/10cda43e9e42477388168fb0c51964bb/artifacts/best_accuracy/data/model.pth

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mlruns/481616812065881245/10cda43e9e42477388168fb0c51964bb/artifacts/best_accuracy/data/pickle_module_info.txt

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mlruns/481616812065881245/10cda43e9e42477388168fb0c51964bb/artifacts/best_accuracy/metadata/MLmodel

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+artifact_path: best_accuracy
+flavors:
+  python_function:
+    config:
+      device: null
+    data: data
+    env:
+      conda: conda.yaml
+      virtualenv: python_env.yaml
+    loader_module: mlflow.pytorch
+    pickle_module_name: mlflow.pytorch.pickle_module
+    python_version: 3.12.2
+  pytorch:
+    code: null
+    model_data: data
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+mlflow_version: 2.12.2
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mlruns/481616812065881245/10cda43e9e42477388168fb0c51964bb/artifacts/best_accuracy/metadata/conda.yaml

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+channels:
+- conda-forge
+dependencies:
+- python=3.12.2
+- pip<=24.0
+- pip:
+  - mlflow==2.12.2
+  - cloudpickle==3.0.0
+  - numpy==1.26.4
+  - packaging==24.0
+  - pandas==2.2.2
+  - pynvml==11.5.0
+  - pyyaml==6.0.1
+  - torch==2.3.0
+  - torchvision==0.18.0
+  - tqdm==4.66.4
+name: mlflow-env

mlruns/481616812065881245/10cda43e9e42477388168fb0c51964bb/artifacts/best_accuracy/metadata/python_env.yaml

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+python: 3.12.2
+build_dependencies:
+- pip==24.0
+- setuptools==69.5.1
+- wheel
+dependencies:
+- -r requirements.txt

mlruns/481616812065881245/10cda43e9e42477388168fb0c51964bb/artifacts/best_accuracy/metadata/requirements.txt

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+pyyaml==6.0.1
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mlruns/481616812065881245/10cda43e9e42477388168fb0c51964bb/artifacts/best_accuracy/python_env.yaml

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+python: 3.12.2
+build_dependencies:
+- pip==24.0
+- setuptools==69.5.1
+- wheel
+dependencies:
+- -r requirements.txt

mlruns/481616812065881245/10cda43e9e42477388168fb0c51964bb/artifacts/best_accuracy/requirements.txt

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+packaging==24.0
+pandas==2.2.2
+pynvml==11.5.0
+pyyaml==6.0.1
+torch==2.3.0
+torchvision==0.18.0
+tqdm==4.66.4

mlruns/481616812065881245/10cda43e9e42477388168fb0c51964bb/artifacts/best_f1/MLmodel

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mlruns/481616812065881245/10cda43e9e42477388168fb0c51964bb/artifacts/best_f1/conda.yaml

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+channels:
+- conda-forge
+dependencies:
+- python=3.12.2
+- pip<=24.0
+- pip:
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+  - cloudpickle==3.0.0
+  - numpy==1.26.4
+  - packaging==24.0
+  - pandas==2.2.2
+  - pynvml==11.5.0
+  - pyyaml==6.0.1
+  - torch==2.3.0
+  - torchvision==0.18.0
+  - tqdm==4.66.4
+name: mlflow-env

mlruns/481616812065881245/10cda43e9e42477388168fb0c51964bb/artifacts/best_f1/data/model.pth

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mlruns/481616812065881245/10cda43e9e42477388168fb0c51964bb/artifacts/best_f1/data/pickle_module_info.txt

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mlruns/481616812065881245/10cda43e9e42477388168fb0c51964bb/artifacts/best_f1/metadata/MLmodel

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mlruns/481616812065881245/10cda43e9e42477388168fb0c51964bb/artifacts/best_f1/metadata/conda.yaml

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+channels:
+- conda-forge
+dependencies:
+- python=3.12.2
+- pip<=24.0
+- pip:
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+  - cloudpickle==3.0.0
+  - numpy==1.26.4
+  - packaging==24.0
+  - pandas==2.2.2
+  - pynvml==11.5.0
+  - pyyaml==6.0.1
+  - torch==2.3.0
+  - torchvision==0.18.0
+  - tqdm==4.66.4
+name: mlflow-env

mlruns/481616812065881245/10cda43e9e42477388168fb0c51964bb/artifacts/best_f1/metadata/python_env.yaml

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+- wheel
+dependencies:
+- -r requirements.txt

mlruns/481616812065881245/10cda43e9e42477388168fb0c51964bb/artifacts/best_f1/metadata/requirements.txt

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mlruns/481616812065881245/10cda43e9e42477388168fb0c51964bb/artifacts/best_f1/python_env.yaml

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+- wheel
+dependencies:
+- -r requirements.txt

mlruns/481616812065881245/10cda43e9e42477388168fb0c51964bb/artifacts/best_f1/requirements.txt

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mlruns/481616812065881245/10cda43e9e42477388168fb0c51964bb/artifacts/best_loss/MLmodel

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mlruns/481616812065881245/10cda43e9e42477388168fb0c51964bb/artifacts/best_loss/conda.yaml

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+- conda-forge
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+- python=3.12.2
+- pip<=24.0
+- pip:
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+  - cloudpickle==3.0.0
+  - numpy==1.26.4
+  - packaging==24.0
+  - pandas==2.2.2
+  - pynvml==11.5.0
+  - pyyaml==6.0.1
+  - torch==2.3.0
+  - torchvision==0.18.0
+  - tqdm==4.66.4
+name: mlflow-env

mlruns/481616812065881245/10cda43e9e42477388168fb0c51964bb/artifacts/best_loss/data/model.pth

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mlruns/481616812065881245/10cda43e9e42477388168fb0c51964bb/artifacts/best_loss/data/pickle_module_info.txt

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mlruns/481616812065881245/10cda43e9e42477388168fb0c51964bb/artifacts/best_loss/metadata/MLmodel

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mlruns/481616812065881245/10cda43e9e42477388168fb0c51964bb/artifacts/best_loss/metadata/conda.yaml

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+channels:
+- conda-forge
+dependencies:
+- python=3.12.2
+- pip<=24.0
+- pip:
+  - mlflow==2.12.2
+  - cloudpickle==3.0.0
+  - numpy==1.26.4
+  - packaging==24.0
+  - pandas==2.2.2
+  - pynvml==11.5.0
+  - pyyaml==6.0.1
+  - torch==2.3.0
+  - torchvision==0.18.0
+  - tqdm==4.66.4
+name: mlflow-env

mlruns/481616812065881245/10cda43e9e42477388168fb0c51964bb/artifacts/best_loss/metadata/python_env.yaml

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+build_dependencies:
+- pip==24.0
+- setuptools==69.5.1
+- wheel
+dependencies:
+- -r requirements.txt

mlruns/481616812065881245/10cda43e9e42477388168fb0c51964bb/artifacts/best_loss/metadata/requirements.txt

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mlruns/481616812065881245/10cda43e9e42477388168fb0c51964bb/artifacts/best_loss/python_env.yaml

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+build_dependencies:
+- pip==24.0
+- setuptools==69.5.1
+- wheel
+dependencies:
+- -r requirements.txt

mlruns/481616812065881245/10cda43e9e42477388168fb0c51964bb/artifacts/best_loss/requirements.txt

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+cloudpickle==3.0.0
+numpy==1.26.4
+packaging==24.0
+pandas==2.2.2
+pynvml==11.5.0
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+torch==2.3.0
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mlruns/481616812065881245/10cda43e9e42477388168fb0c51964bb/artifacts/history.csv

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+train_loss,valid_loss,train_accuracy,valid_accuracy,train_f1,valid_f1
+0.21376326996621423,0.1515542075037956,tensor(0.9118),tensor(0.9392),tensor(0.9117),tensor(0.9392)
+0.14085557462054898,0.3056702448055148,tensor(0.9418),tensor(0.8924),tensor(0.9421),tensor(0.8803)

mlruns/481616812065881245/10cda43e9e42477388168fb0c51964bb/artifacts/train_model.py

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+import torch
+from torch import nn
+import torchvision.models as models
+from tqdm import tqdm
+import numpy as np
+import pandas as pd
+from PIL import Image
+import seaborn as sns
+import matplotlib.pyplot as plt
+from sklearn.model_selection import train_test_split
+from torchvision import transforms
+from mlflow import log_metric, log_param, log_artifacts
+from torcheval.metrics import BinaryF1Score, BinaryAccuracy, BinaryConfusionMatrix
+import mlflow
+import warnings
+warnings.filterwarnings("ignore", category=UserWarning)
+
+
+class CatsDogsDataset(torch.utils.data.Dataset):
+    def __init__(self, df, transform=None):
+        self.df = df
+        self.transform = transform
+        self.label2int = {"Cat":0, "Dog":1}
+        self.image_cache = {}
+    
+    def __len__(self):
+        return len(self.df)
+
+    def __getitem__(self, idx):
+        image_path = self.df.iloc[idx]['image_path']
+
+        if image_path not in self.image_cache:
+            image = Image.open(image_path)
+            image = image.convert('RGB')
+            image = np.array(image)
+            image = torch.from_numpy(image)
+            image = image.float() / 255
+            image = image.permute(2, 0, 1)
+            self.image_cache[image_path] = image
+        else:
+            image = self.image_cache[image_path]
+
+        label = self.df.iloc[idx]['image_class']
+        if self.transform is not None:
+            image = self.transform(image)
+        
+        return image, torch.tensor([self.label2int[label]], dtype=torch.float32)
+
+class CatsDogsDatasetNoCache(torch.utils.data.Dataset):
+    def __init__(self, df, transform=None):
+        self.df = df
+        self.transform = transform
+        self.label2int = {"Cat":0, "Dog":1}
+    
+    def __len__(self):
+        return len(self.df)
+
+    def __getitem__(self, idx):
+        image_path = self.df.iloc[idx]['image_path']
+
+        image = Image.open(image_path)
+        image = image.convert('RGB')
+        image = np.array(image)
+        image = torch.from_numpy(image)
+        image = image.float() / 255
+        image = image.permute(2, 0, 1)
+
+        label = self.df.iloc[idx]['image_class']
+        if self.transform is not None:
+            image = self.transform(image)
+        
+        return image, torch.tensor([self.label2int[label]], dtype=torch.float32)
+
+def train_model(
+    seed=42,
+    num_epochs=10,
+    batch_size=32,
+    final_size=224,
+    color_jitter=0.2,
+    test_size=0.1,
+    rotation=20, 
+):
+    df = pd.read_csv("data/data.csv")
+
+    np.random.seed(seed)
+    torch.manual_seed(seed)
+
+    mlflow.enable_system_metrics_logging()
+
+    train_df, valid_df = train_test_split(
+        df,
+        test_size=test_size,
+        random_state=seed,
+        stratify=df["image_class"]
+    )
+    train_transform = transforms.Compose([
+        transforms.ToPILImage(),                # Convert the image to a PIL Image
+        transforms.Resize((final_size, final_size)), # Resize the image to final_size x final_size
+        # transforms.RandomResizedCrop(final_size), # Crop the image to a random size and aspect ratio
+        transforms.RandomHorizontalFlip(),     # Randomly flip the image horizontally
+        transforms.ColorJitter(color_jitter, color_jitter, color_jitter), # Randomly adjust brightness, contrast, saturation, and hue
+        transforms.RandomRotation(rotation),         # Randomly rotate the image by up to 20 degrees
+        transforms.ToTensor(),                 # Convert the image to a PyTorch tensor
+        transforms.Normalize(                  # Normalize the image
+            mean=[0.485, 0.456, 0.406],
+            std=[0.229, 0.224, 0.225]
+        )
+    ])
+
+    valid_transform = transforms.Compose([
+        transforms.ToPILImage(),                # Convert the image to a PIL Image
+        transforms.Resize((final_size, final_size)), # Resize the image to final_size x final_size
+        transforms.ToTensor(),                 # Convert the image to a PyTorch tensor
+        transforms.Normalize(                  # Normalize the image
+            mean=[0.485, 0.456, 0.406],
+            std=[0.229, 0.224, 0.225]
+        )
+    ])
+
+    train_ds = CatsDogsDatasetNoCache(train_df, transform=train_transform)
+    valid_ds = CatsDogsDatasetNoCache(valid_df, transform=valid_transform)
+    train_loader = torch.utils.data.DataLoader(
+        train_ds,
+        batch_size=batch_size,
+        shuffle=True,
+    )
+
+    valid_loader = torch.utils.data.DataLoader(
+        valid_ds,
+        batch_size=batch_size,
+        shuffle=False,
+    )
+
+    device = torch.device("cuda")
+
+
+    model = models.resnet50(pretrained=True)
+    num_ftrs = model.fc.in_features
+    model.fc = nn.Linear(num_ftrs, 1)
+    model.to(device)
+
+    criterion = nn.BCELoss()
+    optimizer = torch.optim.Adam(model.parameters())
+
+    f1_score = BinaryF1Score()
+    accuracy_score = BinaryAccuracy()
+    confusion_matrix = BinaryConfusionMatrix()
+
+    history = {
+        "train_loss": [],
+        "valid_loss": [],
+        "train_accuracy": [],
+        "valid_accuracy": [],
+        "train_f1": [],
+        "valid_f1": []
+    }
+
+    best_loss = float("inf")
+    best_f1 = 0
+    best_accuracy = 0
+    mlflow.set_experiment("PyTorch_cats_dogs")
+    with mlflow.start_run():
+        log_param("num_epochs", num_epochs)
+        log_param("batch_size", batch_size)
+        log_param("seed", seed)
+        log_param("final_size", final_size)
+        log_param("model", "resnet50")
+        log_param("optimizer", "Adam")
+        log_param("criterion", "BCELoss")
+
+        mlflow.log_artifact("data/data.csv")
+        mlflow.log_artifact(__file__)
+
+        for epoch_idx in range(num_epochs):
+            train_loss = 0
+            valid_loss = 0
+            train_accuracy = 0
+            valid_accuracy = 0
+            train_f1 = 0
+            valid_f1 = 0
+
+            model.train()
+            for x, y in tqdm(train_loader):
+                x, y = x.to(device), y.to(device)
+                output = model(x)
+                loss = criterion(torch.sigmoid(output), y)
+                train_loss += loss.item()
+                optimizer.zero_grad()
+                loss.backward()
+                optimizer.step()
+
+                f1_score.update(torch.sigmoid(output).squeeze(), y.squeeze())
+                accuracy_score.update(torch.sigmoid(output).squeeze(), y.squeeze())
+
+            history["train_loss"].append(train_loss / len(train_loader))
+            history["train_accuracy"].append(accuracy_score.compute())
+            history["train_f1"].append(f1_score.compute())
+
+            accuracy_score.reset()
+            f1_score.reset()
+
+            mlflow.log_metric("train_loss", history["train_loss"][-1], step=epoch_idx)
+            mlflow.log_metric("train_accuracy", history["train_accuracy"][-1], step=epoch_idx)
+            mlflow.log_metric("train_f1", history["train_f1"][-1], step=epoch_idx)
+            model.eval()
+            with torch.no_grad():
+                for x, y in tqdm(valid_loader):
+                    x, y = x.to(device), y.to(device)
+
+                    output = model(x)
+                    loss = criterion(torch.sigmoid(output), y)
+                    valid_loss += loss.item()
+
+                    f1_score.update(torch.sigmoid(output).squeeze(), y.squeeze())
+                    accuracy_score.update(torch.sigmoid(output).squeeze(), y.squeeze())
+                    confusion_matrix.update(torch.sigmoid(output).squeeze(), y.squeeze().long())
+
+            history["valid_loss"].append(valid_loss / len(valid_loader))
+            history["valid_accuracy"].append(accuracy_score.compute())
+            history["valid_f1"].append(f1_score.compute())
+
+            confusion_matrix_values = confusion_matrix.compute()
+            confusion_matrix.reset()
+            print(confusion_matrix_values)
+
+            cm_df = pd.DataFrame(confusion_matrix_values, index=["True 0", "True 1"], columns=["Predicted 0", "Predicted 1"])
+            plt.figure(figsize=(10, 7))
+            cm_df = cm_df.astype(int)
+            sns.heatmap(cm_df, annot=True, fmt="d", cmap="Blues")
+            plt.title("Confusion Matrix")
+            plt.xlabel("Predicted")
+            plt.ylabel("True")
+            plt.savefig("confusion_matrix.png")
+            mlflow.log_artifact("confusion_matrix.png")
+
+
+            accuracy_score.reset()
+            f1_score.reset()
+
+            if history["valid_loss"][-1] < best_loss:
+                best_loss = history["valid_loss"][-1]
+                print(f"Found better loss: {best_loss}")
+                torch.save(model.state_dict(), "best_loss.pth")
+                mlflow.pytorch.log_model(model, "best_loss")
+
+            if history["valid_f1"][-1] > best_f1:
+                best_f1 = history["valid_f1"][-1]
+                print(f"Found better f1: {best_f1}")
+                torch.save(model.state_dict(), "best_f1.pth")
+                mlflow.pytorch.log_model(model, "best_f1")
+
+            if history["valid_accuracy"][-1] > best_accuracy:
+                best_accuracy = history["valid_accuracy"][-1]
+                print(f"Found better accuracy: {best_accuracy}")
+                torch.save(model.state_dict(), "best_accuracy.pth")
+                mlflow.pytorch.log_model(model, "best_accuracy")
+            
+            mlflow.log_metric("valid_loss", history["valid_loss"][-1], step=epoch_idx)
+            mlflow.log_metric("valid_accuracy", history["valid_accuracy"][-1], step=epoch_idx)
+            mlflow.log_metric("valid_f1", history["valid_f1"][-1], step=epoch_idx)
+
+            print(
+                f"Epoch {epoch_idx + 1}/{num_epochs} "
+                f"Loss: {history['train_loss'][-1]:.4f}/{history['valid_loss'][-1]:.4f} "
+                f"Accuracy: {history['train_accuracy'][-1]:.4f}/{history['valid_accuracy'][-1]:.4f} "
+                f"F1: {history['train_f1'][-1]:.4f}/{history['valid_f1'][-1]:.4f}"
+            )
+
+        pd.DataFrame(history).to_csv("history.csv", index=False)
+        mlflow.log_artifact("history.csv")
+        return history["valid_f1"][-1]
+
+
+if __name__ == "__main__":
+    train_model()

mlruns/481616812065881245/10cda43e9e42477388168fb0c51964bb/meta.yaml

@@ -0,0 +1,15 @@
+artifact_uri: file:///home/osbm/Documents/github/ain3009-project/mlruns/481616812065881245/10cda43e9e42477388168fb0c51964bb/artifacts
+end_time: 1716157597240
+entry_point_name: ''
+experiment_id: '481616812065881245'
+lifecycle_stage: active
+run_id: 10cda43e9e42477388168fb0c51964bb
+run_name: able-wren-338
+run_uuid: 10cda43e9e42477388168fb0c51964bb
+source_name: ''
+source_type: 4
+source_version: ''
+start_time: 1716157377160
+status: 3
+tags: []
+user_id: osbm

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