chatgpt lock tf in
This commit is contained in:
248
training.py
248
training.py
@@ -10,34 +10,31 @@ import torch.optim as optim
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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 sklearn.preprocessing import LabelEncoder, StandardScaler
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from sklearn.preprocessing import LabelEncoder
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from multiprocessing import Pool, cpu_count
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from functools import partial
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from tqdm import tqdm
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from collections import Counter
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# ===============================
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# DATA LOADING
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# ===============================
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def load_kaggle_asl_data(base_path):
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train_df = pd.read_csv(os.path.join(base_path, "train.csv"))
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with open(os.path.join(base_path, "sign_to_prediction_index_map.json")) as f:
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sign_to_idx = json.load(f)
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return train_df, sign_to_idx
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def extract_hand_landmarks_from_parquet(path):
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try:
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df = pd.read_parquet(path)
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left = df[df["type"] == "left_hand"]
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right = df[df["type"] == "right_hand"]
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hand = left if len(left) >= len(right) else right
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if len(hand) == 0:
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return None
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frames = sorted(hand['frame'].unique())
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landmarks_seq = []
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for frame in frames:
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lm_frame = hand[hand['frame'] == frame]
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lm_list = []
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@@ -52,12 +49,10 @@ def extract_hand_landmarks_from_parquet(path):
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float(lm['z'].iloc[0])
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])
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landmarks_seq.append(lm_list)
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return np.array(landmarks_seq, dtype=np.float32)
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except:
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return None
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def get_features_sequence(landmarks_seq, max_frames=100):
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if landmarks_seq is None or len(landmarks_seq) == 0:
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return None
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@@ -65,45 +60,51 @@ def get_features_sequence(landmarks_seq, max_frames=100):
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# Center on wrist
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landmarks_seq -= landmarks_seq[:, 0:1, :]
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# Scale using index → middle finger tip distance (more stable than single point)
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scale = np.linalg.norm(landmarks_seq[:, 8] - landmarks_seq[:, 12], axis=1, keepdims=True)
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# Robust scale: wrist → middle finger MCP
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scale = np.linalg.norm(landmarks_seq[:,0] - landmarks_seq[:,9], axis=1, keepdims=True)
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scale = np.maximum(scale, 1e-6)
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landmarks_seq /= scale
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landmarks_seq /= scale[:, np.newaxis, :]
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# Flatten
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seq = landmarks_seq.reshape(landmarks_seq.shape[0], -1)
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# Pad / truncate
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if len(seq) < max_frames:
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pad = np.zeros((max_frames - len(seq), seq.shape[1]), dtype=np.float32)
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T = seq.shape[0]
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if T < max_frames:
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pad = np.zeros((max_frames - T, seq.shape[1]), dtype=np.float32)
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seq = np.concatenate([seq, pad])
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else:
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seq = seq[:max_frames]
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return seq
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# Mask for valid frames
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valid_mask = np.zeros(max_frames, dtype=bool)
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valid_mask[:min(T, max_frames)] = True
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return seq, valid_mask
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def process_row(row, base_path, max_frames=100):
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path = os.path.join(base_path, row["path"])
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if not os.path.exists(path):
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return None, None
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return None, None, None
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try:
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lm = extract_hand_landmarks_from_parquet(path)
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if lm is None:
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return None, None
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feat = get_features_sequence(lm, max_frames)
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return None, None, None
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feat, mask = get_features_sequence(lm, max_frames)
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if feat is None:
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return None, None
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return feat, row["sign"]
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return None, None, None
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return feat, mask, row["sign"]
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except:
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return None, None
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return None, None, None
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# ===============================
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# TRANSFORMER MODEL
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# ===============================
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class PositionalEncoding(nn.Module):
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def __init__(self, d_model, max_len=128):
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super().__init__()
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pe = torch.zeros(max_len, d_model)
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position = torch.arange(0, max_len, dtype=torch.float).unsqueeze(1)
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position = torch.arange(0, max_len, dtype=torch.float32).unsqueeze(1)
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div_term = torch.exp(torch.arange(0, d_model, 2).float() * (-math.log(10000.0) / d_model))
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pe[:, 0::2] = torch.sin(position * div_term)
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pe[:, 1::2] = torch.cos(position * div_term)
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@@ -112,28 +113,25 @@ class PositionalEncoding(nn.Module):
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def forward(self, x):
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return x + self.pe[:, :x.size(1)]
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class TransformerASL(nn.Module):
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def __init__(self, input_dim=63, num_classes=250, d_model=192, nhead=6, num_layers=4):
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def __init__(self, input_dim=63, num_classes=250, d_model=128, nhead=4, num_layers=2):
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super().__init__()
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self.proj = nn.Linear(input_dim, d_model)
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self.norm_in = nn.LayerNorm(d_model)
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self.pos = PositionalEncoding(d_model)
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enc_layer = nn.TransformerEncoderLayer(
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d_model=d_model,
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nhead=nhead,
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dim_feedforward=d_model * 4,
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dropout=0.15,
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dim_feedforward=d_model*4,
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dropout=0.1,
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activation='gelu',
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batch_first=True,
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norm_first=True
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)
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self.encoder = nn.TransformerEncoder(enc_layer, num_layers=num_layers)
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self.head = nn.Sequential(
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nn.LayerNorm(d_model),
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nn.Dropout(0.25),
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nn.Dropout(0.2),
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nn.Linear(d_model, num_classes)
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)
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@@ -142,209 +140,144 @@ class TransformerASL(nn.Module):
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x = self.norm_in(x)
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x = self.pos(x)
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x = self.encoder(x, src_key_padding_mask=key_padding_mask)
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x = x.mean(dim=1) # global average pooling
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x = x.mean(dim=1)
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return self.head(x)
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def create_padding_mask(valid_masks):
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# valid_masks: (B,T) bool, True for valid
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return ~valid_masks # True in mask = positions to ignore
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def create_padding_mask(lengths, max_len):
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return torch.arange(max_len, device=lengths.device)[None, :] >= lengths[:, None]
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# ===============================
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# MAIN
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# ===============================
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def main():
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# ===============================
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# DEVICE
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# ===============================
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device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
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print(f"Using device: {device}")
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if device.type == "cuda":
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print("GPU:", torch.cuda.get_device_name(0))
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# ===============================
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# CONFIG
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# ===============================
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base_path = "asl_kaggle" # ← CHANGE THIS TO YOUR ACTUAL PATH
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base_path = "asl_kaggle"
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max_frames = 100
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MIN_SAMPLES_PER_CLASS = 6 # ← important! prevents stratified split crash
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MIN_SAMPLES_PER_CLASS = 6
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# ===============================
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# DATA LOADING & PROCESSING
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# ===============================
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print("Loading metadata...")
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# --- LOAD DATA ---
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train_df, sign_to_idx = load_kaggle_asl_data(base_path)
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print(f"Processing {len(train_df)} videos...")
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rows = [row for _, row in train_df.iterrows()]
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with Pool(cpu_count()) as pool:
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results = list(tqdm(
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pool.imap(
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partial(process_row, base_path=base_path, max_frames=max_frames),
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rows
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),
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pool.imap(partial(process_row, base_path=base_path, max_frames=max_frames), rows),
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total=len(rows),
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desc="Extracting landmarks"
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desc="Processing"
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))
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X_list, y_list = [], []
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for feat, sign in results:
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X_list, mask_list, y_list = [], [], []
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for feat, mask, sign in results:
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if feat is not None:
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X_list.append(feat)
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mask_list.append(mask)
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y_list.append(sign)
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if not X_list:
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print("No valid sequences found. Check parquet files / paths.")
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print("No valid sequences found.")
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return
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X = np.stack(X_list)
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print(f"Loaded {len(X)} valid sequences | shape: {X.shape}")
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masks = np.stack(mask_list)
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print(f"Loaded {len(X)} sequences | shape: {X.shape}")
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# Global normalization (very important for stability)
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print("Before global norm → mean:", X.mean(), "std:", X.std())
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X = np.clip(X, -5.0, 5.0)
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mean = X.mean(axis=(0, 1), keepdims=True)
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std = X.std(axis=(0, 1), keepdims=True) + 1e-8
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X = (X - mean) / std
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print("After global norm → mean:", X.mean(), "std:", X.std())
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# --- NORMALIZE only valid frames ---
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for i in range(X.shape[0]):
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valid_idx = masks[i]
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X[i, valid_idx] = (X[i, valid_idx] - X[i, valid_idx].mean(0)) / (X[i, valid_idx].std(0) + 1e-8)
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# ===============================
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# LABELS
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# ===============================
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# --- LABELS ---
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le = LabelEncoder()
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y = le.fit_transform(y_list)
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# Remove classes with too few samples (prevents stratify error)
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# Remove rare classes
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counts = Counter(y)
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valid_classes = [cls for cls, cnt in counts.items() if cnt >= MIN_SAMPLES_PER_CLASS]
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mask = np.isin(y, valid_classes)
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X = X[mask]
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y = y[mask]
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# Re-encode labels consecutively (0,1,2,... no gaps)
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mask_keep = np.isin(y, valid_classes)
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X, masks, y = X[mask_keep], masks[mask_keep], y[mask_keep]
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le = LabelEncoder()
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y = le.fit_transform(y)
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print(f"{len(X)} samples remain | {len(le.classes_)} classes")
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print(f"After filtering: {len(X)} samples remain | {len(le.classes_)} classes")
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# ===============================
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# SPLIT
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# ===============================
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X_train, X_test, y_train, y_test = train_test_split(
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X, y,
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test_size=0.15,
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stratify=y, # should be safe now
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random_state=42
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# --- SPLIT ---
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X_train, X_test, masks_train, masks_test, y_train, y_test = train_test_split(
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X, masks, y, test_size=0.15, stratify=y, random_state=42
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)
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# ===============================
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# DATASET & LOADERS
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# ===============================
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# --- DATASETS ---
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class ASLSequenceDataset(Dataset):
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def __init__(self, X, y):
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def __init__(self, X, masks, y):
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self.X = torch.from_numpy(X).float()
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self.masks = torch.from_numpy(masks)
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self.y = torch.from_numpy(y).long()
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def __len__(self):
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return len(self.X)
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def __getitem__(self, idx):
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return self.X[idx], self.y[idx]
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return self.X[idx], self.masks[idx], self.y[idx]
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train_loader = DataLoader(
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ASLSequenceDataset(X_train, y_train),
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batch_size=64,
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shuffle=True,
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num_workers=4,
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pin_memory=True
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)
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train_loader = DataLoader(ASLSequenceDataset(X_train, masks_train, y_train),
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batch_size=64, shuffle=True, num_workers=4, pin_memory=True)
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test_loader = DataLoader(ASLSequenceDataset(X_test, masks_test, y_test),
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batch_size=96, shuffle=False, num_workers=4, pin_memory=True)
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test_loader = DataLoader(
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ASLSequenceDataset(X_test, y_test),
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batch_size=96,
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shuffle=False,
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num_workers=4,
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pin_memory=True
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)
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# --- MODEL ---
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model = TransformerASL(input_dim=X.shape[2], num_classes=len(le.classes_)).to(device)
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print(f"Model params: {sum(p.numel() for p in model.parameters()):,}")
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# ===============================
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# MODEL
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# ===============================
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model = TransformerASL(
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input_dim=63,
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num_classes=len(le.classes_),
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d_model=192,
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nhead=6,
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num_layers=4
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).to(device)
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print(f"Model parameters: {sum(p.numel() for p in model.parameters()):,}")
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# ===============================
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# TRAINING SETUP
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# ===============================
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criterion = nn.CrossEntropyLoss(label_smoothing=0.05)
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optimizer = optim.AdamW(model.parameters(), lr=5e-4, weight_decay=1e-4)
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optimizer = optim.AdamW(model.parameters(), lr=1e-4, weight_decay=1e-4)
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scheduler = optim.lr_scheduler.CosineAnnealingWarmRestarts(optimizer, T_0=10)
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# ===============================
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# TRAIN / EVAL
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# ===============================
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# --- TRAINING ---
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best_acc = 0.0
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patience = 15
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wait = 0
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epochs = 70
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def train_epoch():
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model.train()
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total_loss = 0
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correct = total = 0
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for x, y in tqdm(train_loader, desc="Train"):
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x, y = x.to(device), y.to(device)
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lengths = (x.abs().sum(dim=2) > 1e-5).sum(dim=1)
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mask = create_padding_mask(lengths, x.size(1))
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for x, m, yb in tqdm(train_loader, desc="Train"):
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x, m, yb = x.to(device), m.to(device), yb.to(device)
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mask = create_padding_mask(m)
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optimizer.zero_grad(set_to_none=True)
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logits = model(x, key_padding_mask=mask)
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loss = criterion(logits, y)
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loss = criterion(logits, yb)
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loss.backward()
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grad_norm = torch.nn.utils.clip_grad_norm_(model.parameters(), max_norm=0.8)
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torch.nn.utils.clip_grad_norm_(model.parameters(), max_norm=0.8)
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optimizer.step()
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total_loss += loss.item()
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correct += (logits.argmax(-1) == y).sum().item()
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total += y.size(0)
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correct += (logits.argmax(-1) == yb).sum().item()
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total += yb.size(0)
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return total_loss / len(train_loader), correct / total * 100
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@torch.no_grad()
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def evaluate():
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model.eval()
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correct = total = 0
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for x, y in test_loader:
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x, y = x.to(device), y.to(device)
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lengths = (x.abs().sum(dim=2) > 1e-5).sum(dim=1)
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mask = create_padding_mask(lengths, x.size(1))
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for x, m, yb in test_loader:
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x, m, yb = x.to(device), m.to(device), yb.to(device)
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mask = create_padding_mask(m)
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logits = model(x, key_padding_mask=mask)
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correct += (logits.argmax(-1) == y).sum().item()
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total += y.size(0)
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correct += (logits.argmax(-1) == yb).sum().item()
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total += yb.size(0)
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return correct / total * 100 if total > 0 else 0.0
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# ===============================
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# TRAINING LOOP
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# ===============================
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best_acc = 0.0
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patience = 15
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wait = 0
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epochs = 70
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for epoch in range(epochs):
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loss, train_acc = train_epoch()
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test_acc = evaluate()
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print(f"[{epoch + 1:2d}/{epochs}] loss: {loss:.4f} | train: {train_acc:.2f}% | test: {test_acc:.2f}%")
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print(f"[{epoch+1}/{epochs}] loss: {loss:.4f} | train: {train_acc:.2f}% | test: {test_acc:.2f}%")
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scheduler.step()
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if test_acc > best_acc:
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best_acc = test_acc
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wait = 0
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@@ -362,8 +295,7 @@ def main():
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print("Early stopping")
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break
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print(f"\nBest test accuracy reached: {best_acc:.2f}%")
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print(f"\nBest test accuracy: {best_acc:.2f}%")
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if __name__ == '__main__':
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main()
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