209 lines
7.2 KiB
Python
209 lines
7.2 KiB
Python
import os
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import polars as pl
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import numpy as np
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import torch
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import torch.nn as nn
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import torch.nn.functional as F
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from torch.utils.data import TensorDataset, DataLoader
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from concurrent.futures import ProcessPoolExecutor
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from tqdm import tqdm
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from sklearn.model_selection import train_test_split
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# --- CONFIGURATION ---
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BASE_PATH = "asl_kaggle"
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TARGET_FRAMES = 22
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# Hand landmarks + Lip landmarks (approximate indices for high-value face points)
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LIPS = [61, 146, 91, 181, 84, 17, 314, 405, 321, 375, 291, 308, 324, 318, 402, 317, 14, 87, 178, 88, 95]
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HANDS = list(range(468, 543))
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SELECTED_INDICES = LIPS + HANDS
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NUM_FEATS = len(SELECTED_INDICES) * 3 # X, Y, Z for each selected point
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device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
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# --- DATA PROCESSING ---
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def load_kaggle_metadata(base_path):
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return pl.read_csv(os.path.join(base_path, "train.csv"))
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def load_and_preprocess(path, base_path=BASE_PATH, target_frames=TARGET_FRAMES):
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parquet_path = os.path.join(base_path, path)
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df = pl.read_parquet(parquet_path)
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# 1. Spatial Normalization (Nose Anchor)
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anchors = (
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df.filter((pl.col("type") == "face") & (pl.col("landmark_index") == 0))
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.select([pl.col("frame"), pl.col("x").alias("nx"), pl.col("y").alias("ny"), pl.col("z").alias("nz")])
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)
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processed = (
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df.join(anchors, on="frame", how="left")
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.with_columns([
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(pl.col("x") - pl.col("nx")).fill_null(0.0),
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(pl.col("y") - pl.col("ny")).fill_null(0.0),
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(pl.col("z") - pl.col("nz")).fill_null(0.0),
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])
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.sort(["frame", "type", "landmark_index"])
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)
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# 2. Reshape & Feature Selection
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# Get unique frames and total landmarks (543)
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raw_tensor = processed.select(["x", "y", "z"]).to_numpy().reshape(-1, 543, 3)
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# Slice to keep only Hands and Lips
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reduced_tensor = raw_tensor[:, SELECTED_INDICES, :]
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# 3. Temporal Normalization (Resample to fixed frame count)
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curr_len = reduced_tensor.shape[0]
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indices = np.linspace(0, curr_len - 1, num=target_frames).round().astype(int)
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return reduced_tensor[indices]
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# --- MODEL ARCHITECTURE ---
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class ASLClassifier(nn.Module):
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def __init__(self, num_classes, target_frames=TARGET_FRAMES, num_feats=NUM_FEATS):
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super().__init__()
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self.conv1 = nn.Conv1d(num_feats, 256, kernel_size=3, padding=1)
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self.bn1 = nn.BatchNorm1d(256)
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self.conv2 = nn.Conv1d(256, 512, kernel_size=3, padding=1)
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self.bn2 = nn.BatchNorm1d(512)
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self.pool = nn.MaxPool1d(2)
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self.dropout = nn.Dropout(0.5)
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self.fc = nn.Linear(512, num_classes)
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def forward(self, x):
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# x: (Batch, Frames, Selected_Landmarks, 3)
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x = x.view(x.shape[0], x.shape[1], -1) # Flatten landmarks/coords
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x = x.transpose(1, 2) # (Batch, Features, Time)
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x = F.relu(self.bn1(self.conv1(x)))
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x = self.pool(x)
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x = F.relu(self.bn2(self.conv2(x)))
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x = self.pool(x)
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x = F.adaptive_avg_pool1d(x, 1).squeeze(-1)
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x = self.dropout(x)
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return self.fc(x)
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# --- TRAINING FUNCTION ---
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def train_model(model, train_loader, val_loader, epochs=20, lr=0.001):
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criterion = nn.CrossEntropyLoss()
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optimizer = torch.optim.Adam(model.parameters(), lr=lr)
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scheduler = torch.optim.lr_scheduler.ReduceLROnPlateau(optimizer, mode='min', patience=3, factor=0.5)
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best_val_acc = 0.0
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for epoch in range(epochs):
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# Training phase
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model.train()
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train_loss = 0.0
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train_correct = 0
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train_total = 0
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pbar = tqdm(train_loader, desc=f"Epoch {epoch + 1}/{epochs} [Train]")
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for inputs, labels in pbar:
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inputs, labels = inputs.to(device), labels.to(device)
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optimizer.zero_grad()
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outputs = model(inputs)
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loss = criterion(outputs, labels)
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loss.backward()
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optimizer.step()
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train_loss += loss.item()
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_, predicted = torch.max(outputs, 1)
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train_total += labels.size(0)
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train_correct += (predicted == labels).sum().item()
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pbar.set_postfix({'loss': f'{loss.item():.4f}', 'acc': f'{100 * train_correct / train_total:.2f}%'})
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train_acc = 100 * train_correct / train_total
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avg_train_loss = train_loss / len(train_loader)
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# Validation phase
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model.eval()
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val_loss = 0.0
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val_correct = 0
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val_total = 0
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with torch.no_grad():
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for inputs, labels in tqdm(val_loader, desc=f"Epoch {epoch + 1}/{epochs} [Val]"):
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inputs, labels = inputs.to(device), labels.to(device)
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outputs = model(inputs)
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loss = criterion(outputs, labels)
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val_loss += loss.item()
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_, predicted = torch.max(outputs, 1)
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val_total += labels.size(0)
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val_correct += (predicted == labels).sum().item()
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val_acc = 100 * val_correct / val_total
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avg_val_loss = val_loss / len(val_loader)
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scheduler.step(avg_val_loss)
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print(f"\nEpoch {epoch + 1}/{epochs}:")
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print(f" Train Loss: {avg_train_loss:.4f} | Train Acc: {train_acc:.2f}%")
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print(f" Val Loss: {avg_val_loss:.4f} | Val Acc: {val_acc:.2f}%")
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# Save best model
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if val_acc > best_val_acc:
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best_val_acc = val_acc
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torch.save(model.state_dict(), 'best_asl_model.pth')
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print(f" ✓ New best model saved! (Val Acc: {val_acc:.2f}%)")
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print()
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print(f"Training complete! Best validation accuracy: {best_val_acc:.2f}%")
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# --- EXECUTION ---
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if __name__ == "__main__":
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asl_data = load_kaggle_metadata(BASE_PATH)
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# Process all files
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paths = asl_data["path"].to_list()
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labels = asl_data["sign"].to_list()
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# Create label mapping
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unique_signs = sorted(set(labels))
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sign_to_idx = {sign: idx for idx, sign in enumerate(unique_signs)}
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label_indices = [sign_to_idx[sign] for sign in labels]
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print(f"Processing {len(paths)} files in parallel...")
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with ProcessPoolExecutor() as executor:
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results = list(tqdm(executor.map(load_and_preprocess, paths), total=len(paths)))
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# Create tensors
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X = torch.tensor(np.array(results), dtype=torch.float32)
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y = torch.tensor(label_indices, dtype=torch.long)
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print(f"Dataset Tensor Shape: {X.shape}")
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print(f"Labels Tensor Shape: {y.shape}")
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print(f"Number of unique signs: {len(unique_signs)}")
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# Train/Val split
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X_train, X_val, y_train, y_val = train_test_split(X, y, test_size=0.2, random_state=42, stratify=y)
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# Create DataLoaders
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train_dataset = TensorDataset(X_train, y_train)
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val_dataset = TensorDataset(X_val, y_val)
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train_loader = DataLoader(train_dataset, batch_size=32, shuffle=True)
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val_loader = DataLoader(val_dataset, batch_size=32, shuffle=False)
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print(f"Train samples: {len(train_dataset)}")
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print(f"Val samples: {len(val_dataset)}")
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# Initialize and train model
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model = ASLClassifier(num_classes=len(unique_signs))
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model.to(device)
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print(f"\nModel initialized with {sum(p.numel() for p in model.parameters()):,} parameters")
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print("Starting training...\n")
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train_model(model, train_loader, val_loader, epochs=20, lr=0.002) |