AI, we don't believe that you can fly bro
This commit is contained in:
172
training.py
172
training.py
@@ -1,6 +1,3 @@
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# ===============================
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# IMPORTS
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# ===============================
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import os
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import json
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import math
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@@ -20,7 +17,26 @@ 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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# GPU CONFIGURATION
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# ===============================
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print("=" * 60)
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print("GPU CONFIGURATION")
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print("=" * 60)
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if torch.cuda.is_available():
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print(f"✓ CUDA available!")
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print(f"✓ GPU: {torch.cuda.get_device_name(0)}")
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device = torch.device('cuda:0')
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torch.backends.cudnn.benchmark = True
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else:
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print("✗ CUDA not available, using CPU")
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device = torch.device('cpu')
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print("=" * 60)
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# ===============================
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# DATA LOADING WITH NaN HANDLING
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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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@@ -30,39 +46,70 @@ def load_kaggle_asl_data(base_path):
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def extract_hand_landmarks_from_parquet(path):
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"""Extract hand landmarks, handling NaN values properly"""
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try:
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df = pd.read_parquet(path)
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# Get hand data
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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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# Choose hand with more non-NaN data
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left_valid = left[['x', 'y', 'z']].notna().all(axis=1).sum()
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right_valid = right[['x', 'y', 'z']].notna().all(axis=1).sum()
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if left_valid == 0 and right_valid == 0:
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return None # No valid hand data
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hand = left if left_valid >= right_valid else right
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if len(hand) == 0:
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return None
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# Get frames with valid data
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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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# Check if this frame has valid data
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valid_rows = lm_frame[['x', 'y', 'z']].notna().all(axis=1)
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if valid_rows.sum() < 10: # Need at least 10 valid landmarks
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continue
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lm_list = []
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frame_has_data = False
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for i in range(21):
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lm = lm_frame[lm_frame['landmark_index'] == i]
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if len(lm) == 0:
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lm_list.append([0.0, 0.0, 0.0])
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else:
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lm_list.append([
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float(lm['x'].iloc[0]),
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float(lm['y'].iloc[0]),
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float(lm['z'].iloc[0])
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])
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x = lm['x'].iloc[0]
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y = lm['y'].iloc[0]
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z = lm['z'].iloc[0]
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# Check for NaN
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if pd.isna(x) or pd.isna(y) or pd.isna(z):
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lm_list.append([0.0, 0.0, 0.0])
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else:
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lm_list.append([float(x), float(y), float(z)])
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frame_has_data = True
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if frame_has_data:
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landmarks_seq.append(lm_list)
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if len(landmarks_seq) == 0:
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return None
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return np.array(landmarks_seq, dtype=np.float32)
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except:
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except Exception as e:
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return None
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def get_features_sequence(landmarks_seq, max_frames=100):
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"""Extract features from landmark sequence"""
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if landmarks_seq is None or len(landmarks_seq) == 0:
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return None, None
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@@ -74,23 +121,27 @@ def get_features_sequence(landmarks_seq, max_frames=100):
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scale = np.maximum(scale, 1e-6)
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landmarks_seq = landmarks_seq / scale[:, np.newaxis, :]
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# Replace any remaining NaN/Inf with 0
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landmarks_seq = np.nan_to_num(landmarks_seq, nan=0.0, posinf=0.0, neginf=0.0)
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# Finger curl distances
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tips = [4, 8, 12, 16, 20]
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bases = [1, 5, 9, 13, 17]
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curl_features = []
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for b, t in zip(bases, tips):
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curl_features.append(np.linalg.norm(landmarks_seq[:, t] - landmarks_seq[:, b], axis=1))
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curl_features = np.stack(curl_features, axis=1) # (T,5)
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curl = np.linalg.norm(landmarks_seq[:, t] - landmarks_seq[:, b], axis=1)
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curl_features.append(curl)
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curl_features = np.stack(curl_features, axis=1)
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# Temporal deltas
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deltas = np.zeros_like(landmarks_seq)
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deltas[1:] = landmarks_seq[1:] - landmarks_seq[:-1]
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# Flatten features along last axis
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# Flatten features
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seq = np.concatenate([landmarks_seq, deltas, curl_features[:, :, np.newaxis]], axis=2)
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seq = seq.reshape(seq.shape[0], -1) # (T, feature_dim)
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seq = seq.reshape(seq.shape[0], -1)
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# Pad or truncate to max_frames
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# Pad or truncate
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T, F = seq.shape
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if T < max_frames:
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pad = np.zeros((max_frames - T, F), dtype=np.float32)
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@@ -98,7 +149,7 @@ def get_features_sequence(landmarks_seq, max_frames=100):
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elif T > max_frames:
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seq = seq[:max_frames, :]
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# Mask
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# Create mask
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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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@@ -106,20 +157,29 @@ def get_features_sequence(landmarks_seq, max_frames=100):
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def process_row(row, base_path, max_frames=100):
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"""Process a single row"""
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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, 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, 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, None
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# Final NaN check
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if np.isnan(feat).any() or np.isinf(feat).any():
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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, None
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# ===============================
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# TRANSFORMER MODEL
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# ===============================
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@@ -138,7 +198,7 @@ class PositionalEncoding(nn.Module):
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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=68, num_classes=250, d_model=256, nhead=8, num_layers=4):
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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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@@ -170,37 +230,33 @@ class TransformerASL(nn.Module):
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return self.head(x)
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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 TRAINING
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# ===============================
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def main():
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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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base_path = "asl_kaggle" # ← set your dataset 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
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print("Loading metadata...")
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print("\nLoading metadata...")
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train_df, sign_to_idx = load_kaggle_asl_data(base_path)
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print(f"Total sequences: {len(train_df)}")
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rows = [row for _, row in train_df.iterrows()]
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print("Processing landmark sequences...")
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print("\nProcessing sequences with NaN handling...")
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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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rows,
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chunksize=100
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),
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total=len(rows),
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desc="Extracting landmarks"
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))
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# Filter valid results
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X_list, masks_list, y_list = [], [], []
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for feat, mask, sign in results:
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if feat is not None and feat.shape[0] == max_frames:
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@@ -208,13 +264,19 @@ def main():
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masks_list.append(mask)
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y_list.append(sign)
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print(f"\n✓ Valid sequences: {len(X_list)} out of {len(train_df)}")
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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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print("\nPossible issues:")
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print(" 1. Most files contain only NaN hand landmarks")
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print(" 2. Hand detection failed in most videos")
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print(" 3. Dataset might be corrupted")
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return
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X = np.stack(X_list)
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masks = np.stack(masks_list)
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print(f"Loaded {len(X)} sequences | shape: {X.shape}")
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print(f"Data shape: {X.shape}")
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# Global normalization
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X = np.clip(X, -5.0, 5.0)
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@@ -222,9 +284,11 @@ def main():
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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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# Encode labels
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le = LabelEncoder()
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y = le.fit_transform(y_list)
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# Filter classes with too few samples
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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_valid = np.isin(y, valid_classes)
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@@ -232,9 +296,11 @@ def main():
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masks = masks[mask_valid]
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y = y[mask_valid]
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# Re-encode
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le = LabelEncoder()
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y = le.fit_transform(y)
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print(f"{len(X)} samples | {len(le.classes_)} classes after filtering")
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print(f"Final dataset: {len(X)} samples | {len(le.classes_)} classes")
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# Train-test split
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X_train, X_test, masks_train, masks_test, y_train, y_test = train_test_split(
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@@ -254,23 +320,28 @@ def main():
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def __getitem__(self, idx):
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return self.X[idx], self.masks[idx], self.y[idx]
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batch_size = 128 if device.type == 'cuda' else 64
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train_loader = DataLoader(
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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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batch_size=batch_size, shuffle=True, num_workers=4, pin_memory=True
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)
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test_loader = DataLoader(
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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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batch_size=batch_size * 2, shuffle=False, num_workers=4, pin_memory=True
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)
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# Model
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model = TransformerASL(
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input_dim=X.shape[2],
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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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d_model=256,
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nhead=8,
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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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total_params = sum(p.numel() for p in model.parameters())
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print(f"\nModel parameters: {total_params:,}")
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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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@@ -282,22 +353,29 @@ def main():
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wait = 0
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epochs = 70
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print("\nStarting training...")
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print("=" * 60)
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for epoch in range(epochs):
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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, mask, yb in tqdm(train_loader, desc="Train"):
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for x, mask, yb in tqdm(train_loader, desc=f"Epoch {epoch + 1}/{epochs}"):
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x, mask, yb = x.to(device), mask.to(device), yb.to(device)
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key_mask = ~mask # True where padding
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key_mask = ~mask
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optimizer.zero_grad(set_to_none=True)
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logits = model(x, key_padding_mask=key_mask)
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loss = criterion(logits, yb)
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loss.backward()
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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) == yb).sum().item()
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total += yb.size(0)
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train_acc = correct / total * 100
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# Eval
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@@ -310,12 +388,14 @@ def main():
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logits = model(x, key_padding_mask=key_mask)
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correct += (logits.argmax(-1) == yb).sum().item()
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total += yb.size(0)
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test_acc = correct / total * 100
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print(f"[{epoch + 1:2d}/{epochs}] Loss: {total_loss / len(train_loader):.4f} | "
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f"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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@@ -324,16 +404,22 @@ def main():
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'optimizer': optimizer.state_dict(),
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'label_encoder_classes': le.classes_,
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'acc': best_acc,
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'epoch': epoch
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'epoch': epoch,
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'input_dim': X.shape[2],
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'num_classes': len(le.classes_)
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}, "best_asl_transformer.pth")
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print(" → New best saved")
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print(f" → New best: {best_acc:.2f}%")
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else:
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wait += 1
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if wait >= patience:
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print("Early stopping")
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break
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print(f"\nBest test accuracy: {best_acc:.2f}%")
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print("=" * 60)
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print(f"\n✓ Training complete!")
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print(f"✓ Best test accuracy: {best_acc:.2f}%")
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print(f"✓ Model saved: best_asl_transformer.pth")
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if __name__ == "__main__":
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main()
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