Battle against a True Hero pt 2
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@@ -4,166 +4,173 @@ 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 Dataset, DataLoader
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from sklearn.model_selection import train_test_split
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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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# --- CONFIG ---
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BASE_PATH = "asl_kaggle"
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TARGET_FRAMES = 22
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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
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device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
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# --- DATASET ENGINE ---
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# --- DATA LOADING WITH RELATIVE FEATURES ---
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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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def load_file_to_memory(path, base_path=BASE_PATH):
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try:
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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
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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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# 1. Global Anchor (Nose)
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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([
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pl.col("frame"),
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pl.col("x").alias("nx"),
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pl.col("y").alias("ny"),
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pl.col("z").alias("nz")
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])
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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. Local Anchors (Wrists)
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# Left: 468, Right: 522
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wrists = (
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df.filter(pl.col("landmark_index").is_in([468, 522]))
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.select([
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pl.col("frame"),
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pl.col("landmark_index"),
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pl.col("x").alias("wx"),
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pl.col("y").alias("wy")
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])
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)
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# 2. Slice and Reshape
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raw_tensor = processed.select(["x", "y", "z"]).to_numpy().reshape(-1, 543, 3)
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reduced_tensor = raw_tensor[:, SELECTED_INDICES, :]
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processed = df.join(anchors, on="frame", how="left")
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# 3. Temporal Resampling
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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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# Join wrist data to the main frame
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# We use a left join on frame and landmark_index to align wrist coords with their rows
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processed = (
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processed.join(wrists, on=["frame", "landmark_index"], how="left")
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.with_columns([
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# Global (Nose-relative)
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(pl.col("x") - pl.col("nx")).alias("x_g"),
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(pl.col("y") - pl.col("ny")).alias("y_g"),
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(pl.col("z") - pl.col("nz")).alias("z_g"),
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# Local (Wrist-relative - defaults to global if not a hand point)
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(pl.col("x") - pl.col("wx")).fill_null(pl.col("x") - pl.col("nx")).alias("x_l"),
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(pl.col("y") - pl.col("wy")).fill_null(pl.col("y") - pl.col("ny")).alias("y_l"),
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])
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.sort(["frame", "type", "landmark_index"])
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)
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# We now have 5 channels: (x_g, y_g, z_g, x_l, y_l)
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n_frames = processed["frame"].n_unique()
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# Reshape to (Frames, 543 landmarks, 5 features)
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tensor = processed.select(["x_g", "y_g", "z_g", "x_l", "y_l"]).to_numpy().reshape(n_frames, 543, 5)
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# Temporal Resampling
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indices = np.linspace(0, n_frames - 1, num=TARGET_FRAMES).round().astype(int)
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return tensor[indices]
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except Exception:
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return np.zeros((TARGET_FRAMES, 543, 5))
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class ASLDataset(Dataset):
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def __init__(self, paths, labels):
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self.paths = paths
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self.labels = labels
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def __len__(self):
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return len(self.paths)
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def __getitem__(self, idx):
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try:
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x = load_and_preprocess(self.paths[idx])
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y = self.labels[idx]
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return torch.tensor(x, dtype=torch.float32), torch.tensor(y, dtype=torch.long)
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except Exception as e:
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# Return a zero tensor if a file is corrupted to prevent crash
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return torch.zeros((TARGET_FRAMES, len(SELECTED_INDICES), 3)), torch.tensor(self.labels[idx],
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dtype=torch.long)
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# --- MODEL ---
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# --- DUAL-STREAM MODEL ---
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class ASLClassifier(nn.Module):
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def __init__(self, num_classes):
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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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# 543 landmarks * 5 features per landmark = 2715
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self.feat_dim = 543 * 5
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self.conv1 = nn.Conv1d(self.feat_dim, 512, kernel_size=3, padding=1)
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self.bn1 = nn.BatchNorm1d(512)
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self.conv2 = nn.Conv1d(512, 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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self.dropout = nn.Dropout(0.4)
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self.fc = nn.Sequential(
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nn.Linear(512, 1024),
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nn.ReLU(),
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nn.Dropout(0.2),
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nn.Linear(1024, num_classes)
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)
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def forward(self, x):
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# x shape: (Batch, 22, 96, 3)
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b, t, l, c = x.shape
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x = x.view(b, t, -1).transpose(1, 2) # (Batch, Features, Time)
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# x shape: (Batch, 22, 543, 5)
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b, t, l, f = x.shape
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# Flatten landmarks and features into one vector, then transpose for Conv1d
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x = x.view(b, t, -1).transpose(1, 2) # (Batch, 2715, 22)
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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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# Global Average Pool across the time dimension
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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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return self.fc(self.dropout(x))
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# --- TRAINING LOOP ---
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# --- EXECUTION ---
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def run_training():
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# 1. Prepare Metadata
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train_df = pl.read_csv(os.path.join(BASE_PATH, "train.csv"))
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unique_signs = sorted(train_df["sign"].unique().to_list())
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if __name__ == "__main__":
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# 1. Setup Data
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metadata = pl.read_csv(os.path.join(BASE_PATH, "train.csv"))
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unique_signs = sorted(metadata["sign"].unique().to_list())
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sign_to_idx = {sign: i for i, sign in enumerate(unique_signs)}
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labels = [sign_to_idx[s] for s in metadata["sign"].to_list()]
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paths = train_df["path"].to_list()
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labels = [sign_to_idx[s] for s in train_df["sign"].to_list()]
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paths = metadata["path"].to_list()
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# 2. Split
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p_train, p_val, l_train, l_val = train_test_split(paths, labels, test_size=0.15, stratify=labels)
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# 2. Load to RAM (Parallelized)
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print(f"Loading {len(paths)} files into RAM with 5-channel features...")
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with ProcessPoolExecutor() as executor:
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data_list = list(tqdm(executor.map(load_file_to_memory, paths), total=len(paths)))
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# 3. Loaders
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train_ds = ASLDataset(p_train, l_train)
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val_ds = ASLDataset(p_val, l_val)
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X = torch.tensor(np.array(data_list), dtype=torch.float32)
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y = torch.tensor(labels, dtype=torch.long)
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# num_workers=4 allows the CPU to preprocess the next batch while GPU trains
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train_loader = DataLoader(train_ds, batch_size=64, shuffle=True, num_workers=4, pin_memory=True)
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val_loader = DataLoader(val_ds, batch_size=64, shuffle=False, num_workers=4, pin_memory=True)
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# 3. Split
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X_train, X_val, y_train, y_val = train_test_split(X, y, test_size=0.1, stratify=y, random_state=42)
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# 4. Init Model & Optim
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train_loader = DataLoader(TensorDataset(X_train, y_train), batch_size=64, shuffle=True)
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val_loader = DataLoader(TensorDataset(X_val, y_val), batch_size=64)
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# 4. Train
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model = ASLClassifier(len(unique_signs)).to(device)
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optimizer = torch.optim.Adam(model.parameters(), lr=1e-3)
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criterion = nn.CrossEntropyLoss(label_smoothing=0.1)
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scaler = torch.amp.GradScaler(enabled=(device.type == 'cuda'))
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optimizer = torch.optim.Adam(model.parameters(), lr=0.001)
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criterion = nn.CrossEntropyLoss(label_smoothing=0.1) # Helps prevent over-confidence
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# 5. Loop
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for epoch in range(30):
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print(f"Starting training on {device}...")
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for epoch in range(25):
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model.train()
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t_correct, t_total = 0, 0
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pbar = tqdm(train_loader, desc=f"Epoch {epoch + 1}")
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for x, y in pbar:
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x, y = x.to(device), y.to(device)
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train_loss = 0
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for batch_x, batch_y in tqdm(train_loader, desc=f"Epoch {epoch + 1}"):
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batch_x, batch_y = batch_x.to(device), batch_y.to(device)
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optimizer.zero_grad()
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# Use Mixed Precision for speed
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with torch.amp.autocast(device_type=device.type, enabled=(device.type == 'cuda')):
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outputs = model(x)
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loss = criterion(outputs, y)
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scaler.scale(loss).backward()
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scaler.step(optimizer)
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scaler.update()
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_, pred = torch.max(outputs, 1)
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t_total += y.size(0)
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t_correct += (pred == y).sum().item()
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pbar.set_postfix(acc=f"{(t_correct / t_total) * 100:.1f}%")
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output = model(batch_x)
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loss = criterion(output, batch_y)
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loss.backward()
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optimizer.step()
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train_loss += loss.item()
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# Validation
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model.eval()
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v_correct, v_total = 0, 0
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correct, total = 0, 0
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with torch.no_grad():
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for x, y in val_loader:
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x, y = x.to(device), y.to(device)
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outputs = model(x)
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_, pred = torch.max(outputs, 1)
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v_total += y.size(0)
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v_correct += (pred == y).sum().item()
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for vx, vy in val_loader:
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vx, vy = vx.to(device), vy.to(device)
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pred = model(vx).argmax(1)
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correct += (pred == vy).sum().item()
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total += vy.size(0)
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print(f"Validation Accuracy: {(v_correct / v_total) * 100:.2f}%")
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torch.save(model.state_dict(), f"asl_model_epoch_{epoch}.pth")
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print(f"Epoch {epoch + 1} | Loss: {train_loss / len(train_loader):.4f} | Val Acc: {100 * correct / total:.2f}%")
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if __name__ == "__main__":
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run_training()
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if (epoch + 1) % 5 == 0:
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torch.save(model.state_dict(), f"asl_model_v2_e{epoch + 1}.pth")
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