hand and face again
This commit is contained in:
577
training.py
577
training.py
@@ -35,188 +35,222 @@ else:
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print("=" * 60)
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# ===============================
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# SELECTED LANDMARK INDICES
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# ===============================
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IMPORTANT_FACE_INDICES = sorted(list(set([
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0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10,
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55, 65, 66, 105, 107, 336, 296, 334,
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33, 133, 160, 159, 158, 144, 145, 153,
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362, 263, 387, 386, 385, 373, 374, 380,
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1, 2, 98, 327,
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61, 185, 40, 39, 37, 0, 267, 269, 270, 409,
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291, 146, 91, 181, 84, 17, 314, 405, 321, 375,
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78, 191, 80, 81, 82, 13, 312, 311, 310, 415,
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308, 324, 318, 402, 317, 14, 87, 178, 88, 95
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])))
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NUM_FACE_POINTS = len(IMPORTANT_FACE_INDICES)
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NUM_HAND_POINTS = 21 * 2
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TOTAL_POINTS_PER_FRAME = NUM_HAND_POINTS + NUM_FACE_POINTS
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# ===============================
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# DATA LOADING - HANDLES PARTIAL NaN
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# ENHANCED DATA EXTRACTION
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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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"""Extract hand landmarks - ONLY uses frames with valid (non-NaN) data"""
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def extract_multi_landmarks(path, min_valid_frames=5):
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"""
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Extract both hands + selected face landmarks with modality flags
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Returns: dict with 'landmarks', 'left_hand_valid', 'right_hand_valid', 'face_valid'
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"""
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try:
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df = pd.read_parquet(path)
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seq = []
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left_valid_frames = []
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right_valid_frames = []
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face_valid_frames = []
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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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if len(left) == 0 and len(right) == 0:
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all_types = df["type"].unique()
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if "left_hand" in all_types or "right_hand" in all_types or "face" in all_types:
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frames = sorted(df["frame"].unique())
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else:
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return None
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# Count valid (non-NaN) rows for each hand
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left_valid = 0
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right_valid = 0
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if len(left) > 0:
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left_valid = left[['x', 'y', 'z']].notna().all(axis=1).sum()
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if len(right) > 0:
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right_valid = right[['x', 'y', 'z']].notna().all(axis=1).sum()
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# No valid data at all
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if left_valid == 0 and right_valid == 0:
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if frames is None or len(frames) < min_valid_frames:
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return None
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# Choose hand with more valid data
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hand = left if left_valid >= right_valid else right
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# Get unique frames
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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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frame_df = df[df["frame"] == frame]
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frame_points = np.full((TOTAL_POINTS_PER_FRAME, 3), np.nan, dtype=np.float32)
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# Count how many valid landmarks this frame has
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valid_count = lm_frame[['x', 'y', 'z']].notna().all(axis=1).sum()
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pos = 0
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left_valid = False
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right_valid = False
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face_valid = False
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# Skip frames with too few valid landmarks
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if valid_count < 10:
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continue
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# Left hand
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left = frame_df[frame_df["type"] == "left_hand"]
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if len(left) >= 15:
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valid_count = 0
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for i in range(21):
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row = left[left["landmark_index"] == i]
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if len(row) > 0 and row[['x', 'y', 'z']].notna().all().all():
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frame_points[pos] = row[['x', 'y', 'z']].values[0]
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valid_count += 1
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pos += 1
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left_valid = (valid_count >= 15)
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else:
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pos += 21
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# Extract landmarks for this frame
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frame_landmarks = []
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valid_landmarks_in_frame = 0
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# Right hand
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right = frame_df[frame_df["type"] == "right_hand"]
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if len(right) >= 15:
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valid_count = 0
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for i in range(21):
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row = right[right["landmark_index"] == i]
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if len(row) > 0 and row[['x', 'y', 'z']].notna().all().all():
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frame_points[pos] = row[['x', 'y', 'z']].values[0]
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valid_count += 1
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pos += 1
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right_valid = (valid_count >= 15)
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else:
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pos += 21
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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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# Face
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face = frame_df[frame_df["type"] == "face"]
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if len(face) > 0:
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valid_count = 0
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for idx in IMPORTANT_FACE_INDICES:
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row = face[face["landmark_index"] == idx]
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if len(row) > 0 and row[['x', 'y', 'z']].notna().all().all():
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frame_points[pos] = row[['x', 'y', 'z']].values[0]
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valid_count += 1
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pos += 1
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face_valid = (valid_count >= len(IMPORTANT_FACE_INDICES) * 0.5)
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if len(lm) == 0:
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frame_landmarks.append([0.0, 0.0, 0.0])
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else:
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x = float(lm['x'].iloc[0])
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y = float(lm['y'].iloc[0])
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z = float(lm['z'].iloc[0])
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valid_ratio = 1 - np.isnan(frame_points).mean()
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if valid_ratio >= 0.40:
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frame_points = np.nan_to_num(frame_points, nan=0.0)
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seq.append(frame_points)
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left_valid_frames.append(left_valid)
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right_valid_frames.append(right_valid)
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face_valid_frames.append(face_valid)
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# Check if valid
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if pd.notna(x) and pd.notna(y) and pd.notna(z):
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frame_landmarks.append([x, y, z])
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valid_landmarks_in_frame += 1
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else:
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frame_landmarks.append([0.0, 0.0, 0.0])
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# Only add frame if it has enough valid landmarks
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if valid_landmarks_in_frame >= 10:
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landmarks_seq.append(frame_landmarks)
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# Need at least 3 valid frames
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if len(landmarks_seq) < 3:
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if len(seq) < min_valid_frames:
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return None
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return np.array(landmarks_seq, dtype=np.float32)
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return {
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'landmarks': np.stack(seq),
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'left_hand_valid': np.array(left_valid_frames),
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'right_hand_valid': np.array(right_valid_frames),
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'face_valid': np.array(face_valid_frames)
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}
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except Exception as e:
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except Exception:
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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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# Center on wrist (landmark 0)
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wrist = landmarks_seq[:, 0:1, :].copy()
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landmarks_seq = landmarks_seq - wrist
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# Scale normalization using wrist to middle finger MCP (landmark 9)
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scale = np.linalg.norm(landmarks_seq[:, 9, :] - np.zeros(3), axis=1, keepdims=True)
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scale = np.maximum(scale, 1e-6) # Avoid division by zero
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landmarks_seq = landmarks_seq / scale[:, np.newaxis, :]
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# Clean up any remaining NaN/Inf
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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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# Clip extreme values
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landmarks_seq = np.clip(landmarks_seq, -10, 10)
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# Calculate finger curl features
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tips = [4, 8, 12, 16, 20] # Thumb, index, middle, ring, pinky tips
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bases = [1, 5, 9, 13, 17] # Corresponding base joints
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curl_features = []
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for b, t in zip(bases, tips):
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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) # (T, 5)
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# Temporal deltas (motion)
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deltas = np.zeros_like(landmarks_seq)
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if len(landmarks_seq) > 1:
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deltas[1:] = landmarks_seq[1:] - landmarks_seq[:-1]
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# Flatten each component separately, then concatenate
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landmarks_flat = landmarks_seq.reshape(landmarks_seq.shape[0], -1) # (T, 63)
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deltas_flat = deltas.reshape(deltas.shape[0], -1) # (T, 63)
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# curl_features is already (T, 5)
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# Combine: 63 + 63 + 5 = 131 features per frame
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seq = np.concatenate([
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landmarks_flat,
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deltas_flat,
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curl_features
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], axis=1)
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# Pad or truncate to max_frames
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T, F = seq.shape
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if T < max_frames:
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# Pad with zeros
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pad = np.zeros((max_frames - T, F), dtype=np.float32)
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seq = np.concatenate([seq, pad], axis=0)
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elif T > max_frames:
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# Truncate
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seq = seq[:max_frames, :]
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# Create attention mask (True for valid positions)
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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.astype(np.float32), valid_mask
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def process_row(row, base_path, max_frames=100):
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"""Process a single row - worker function for multiprocessing"""
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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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def get_features_sequence(landmarks_data, max_frames=100):
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"""
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Enhanced feature extraction with separate modality processing
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"""
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if landmarks_data is None:
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return None, None, None
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landmarks_3d = landmarks_data['landmarks']
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if len(landmarks_3d) == 0:
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return None, None, None
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T, N, _ = landmarks_3d.shape
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# Separate modalities for independent normalization
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left_hand = landmarks_3d[:, :21, :]
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right_hand = landmarks_3d[:, 21:42, :]
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face = landmarks_3d[:, 42:, :]
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# Independent centering per modality
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features_list = []
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for modality, valid_mask in [
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(left_hand, landmarks_data['left_hand_valid']),
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(right_hand, landmarks_data['right_hand_valid']),
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(face, landmarks_data['face_valid'])
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]:
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# Center on modality-specific mean
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valid_frames = modality[valid_mask] if valid_mask.any() else modality
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if len(valid_frames) > 0:
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center = np.mean(valid_frames, axis=(0, 1), keepdims=True)
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spread = np.std(valid_frames, axis=(0, 1), keepdims=True).max()
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else:
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center = 0
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spread = 1
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modality_norm = (modality - center) / max(spread, 1e-6)
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flat = modality_norm.reshape(T, -1)
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# Deltas
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deltas = np.zeros_like(flat)
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if T > 1:
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deltas[1:] = flat[1:] - flat[:-1]
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features_list.append(flat)
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features_list.append(deltas)
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# Combine all modalities
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features = np.concatenate(features_list, axis=1)
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# Create modality availability mask (which frames have which modalities)
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modality_mask = np.stack([
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landmarks_data['left_hand_valid'],
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landmarks_data['right_hand_valid'],
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landmarks_data['face_valid']
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], axis=1).astype(np.float32) # (T, 3)
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# Pad/truncate
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if T < max_frames:
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pad = np.zeros((max_frames - T, features.shape[1]), dtype=np.float32)
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features = np.concatenate([features, pad], axis=0)
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mask_pad = np.zeros((max_frames - T, 3), dtype=np.float32)
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modality_mask = np.concatenate([modality_mask, mask_pad], axis=0)
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frame_mask = np.zeros(max_frames, dtype=bool)
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frame_mask[:T] = True
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else:
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features = features[:max_frames]
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modality_mask = modality_mask[:max_frames]
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frame_mask = np.ones(max_frames, dtype=bool)
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features = np.nan_to_num(features, nan=0.0, posinf=0.0, neginf=0.0)
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features = np.clip(features, -30, 30)
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return features.astype(np.float32), frame_mask, modality_mask
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def process_row(row_data, base_path, max_frames=100):
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"""Process a single row - expects tuple of (path, sign)"""
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path_rel, sign = row_data
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path = os.path.join(base_path, path_rel)
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if not os.path.exists(path):
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return None, None, None, None
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try:
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# Extract landmarks
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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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lm_data = extract_multi_landmarks(path)
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if lm_data is None:
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return None, None, None, None
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# Get features
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feat, mask = get_features_sequence(lm, max_frames)
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feat, frame_mask, modality_mask = get_features_sequence(lm_data, max_frames)
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if feat is None:
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return None, None, None
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return None, None, None, None
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# Final safety 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, frame_mask, modality_mask, sign
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return feat, mask, row["sign"]
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except Exception as e:
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return None, None, None
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except Exception:
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return None, None, None, None
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# ===============================
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# TRANSFORMER MODEL
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# ENHANCED MODEL WITH MODALITY AWARENESS
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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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@@ -232,16 +266,19 @@ class PositionalEncoding(nn.Module):
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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, num_classes, d_model=256, nhead=8, num_layers=4):
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class ModalityAwareTransformer(nn.Module):
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def __init__(self, input_dim, num_classes, d_model=384, nhead=8, num_layers=5):
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super().__init__()
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# Input projection
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# Main projection
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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, max_len=128)
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# Transformer encoder
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# Modality embedding (3 modalities: left_hand, right_hand, face)
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self.modality_embed = nn.Linear(3, 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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@@ -253,30 +290,45 @@ class TransformerASL(nn.Module):
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)
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self.encoder = nn.TransformerEncoder(enc_layer, num_layers=num_layers)
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# Classification head
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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.Linear(d_model, num_classes)
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)
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def forward(self, x, key_padding_mask=None):
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# x: (batch, seq_len, input_dim)
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# key_padding_mask: (batch, seq_len) - True for padding positions
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def forward(self, x, modality_mask=None, key_padding_mask=None):
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# Project features
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x = self.proj(x)
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# Add modality information if available
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if modality_mask is not None:
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mod_embed = self.modality_embed(modality_mask)
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x = x + mod_embed
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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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# Global average pooling over valid positions
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x = x.mean(dim=1)
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# Weighted average (giving more weight to frames with more valid modalities)
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if modality_mask is not None:
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weights = modality_mask.sum(dim=-1, keepdim=True) + 1e-6 # (B, T, 1)
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weights = weights / weights.sum(dim=1, keepdim=True)
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x = (x * weights).sum(dim=1)
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else:
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x = x.mean(dim=1)
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return self.head(x)
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def load_kaggle_asl_data(base_path):
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"""Load training metadata"""
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train_path = os.path.join(base_path, "train.csv")
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train_df = pd.read_csv(train_path)
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return train_df, None
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# ===============================
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# MAIN TRAINING
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# MAIN
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# ===============================
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def main():
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base_path = "asl_kaggle"
|
||||
@@ -284,163 +336,142 @@ def main():
|
||||
MIN_SAMPLES_PER_CLASS = 5
|
||||
|
||||
print("\nLoading metadata...")
|
||||
train_df, sign_to_idx = load_kaggle_asl_data(base_path)
|
||||
print(f"Total sequences: {len(train_df)}")
|
||||
train_df, _ = load_kaggle_asl_data(base_path)
|
||||
|
||||
rows = [row for _, row in train_df.iterrows()]
|
||||
# Convert to simple tuples for multiprocessing compatibility
|
||||
rows = [(row['path'], row['sign']) for _, row in train_df.iterrows()]
|
||||
|
||||
print("\nProcessing sequences (this will take a few minutes)...")
|
||||
print("Expected: ~36,000 valid sequences based on diagnostic")
|
||||
|
||||
# Process with multiprocessing
|
||||
print("\nProcessing sequences with BOTH hands + FACE (enhanced)...")
|
||||
with Pool(cpu_count()) as pool:
|
||||
results = list(tqdm(
|
||||
pool.imap(
|
||||
partial(process_row, base_path=base_path, max_frames=max_frames),
|
||||
rows,
|
||||
chunksize=100
|
||||
chunksize=80
|
||||
),
|
||||
total=len(rows),
|
||||
desc="Extracting landmarks"
|
||||
desc="Landmarks extraction"
|
||||
))
|
||||
|
||||
# Filter valid results
|
||||
X_list, masks_list, y_list = [], [], []
|
||||
for feat, mask, sign in results:
|
||||
if feat is not None and mask is not None and sign is not None:
|
||||
if feat.shape[0] == max_frames:
|
||||
X_list.append(feat)
|
||||
masks_list.append(mask)
|
||||
y_list.append(sign)
|
||||
X_list, frame_masks_list, modality_masks_list, y_list = [], [], [], []
|
||||
for feat, frame_mask, modality_mask, sign in results:
|
||||
if feat is not None and frame_mask is not None:
|
||||
X_list.append(feat)
|
||||
frame_masks_list.append(frame_mask)
|
||||
modality_masks_list.append(modality_mask)
|
||||
y_list.append(sign)
|
||||
|
||||
print(f"\n✓ Successfully extracted: {len(X_list)} valid sequences")
|
||||
print(f" Success rate: {len(X_list) / len(train_df) * 100:.1f}%")
|
||||
|
||||
if len(X_list) < 100:
|
||||
print("❌ Too few valid sequences found!")
|
||||
print(" This shouldn't happen - please share this output for debugging")
|
||||
if not X_list:
|
||||
print("No valid sequences extracted!")
|
||||
return
|
||||
|
||||
# Stack into arrays
|
||||
X = np.stack(X_list)
|
||||
masks = np.stack(masks_list)
|
||||
frame_masks = np.stack(frame_masks_list)
|
||||
modality_masks = np.stack(modality_masks_list)
|
||||
|
||||
print(f"\nData shape: {X.shape}")
|
||||
print(f"Feature dimension: {X.shape[2]}")
|
||||
print(f"\nExtracted {len(X):,} sequences")
|
||||
print(f"Feature shape: {X.shape[1:]} (input_dim = {X.shape[2]})")
|
||||
print(f"Modality mask shape: {modality_masks.shape}")
|
||||
|
||||
# Global normalization
|
||||
print("Normalizing features...")
|
||||
X = np.clip(X, -10.0, 10.0)
|
||||
X = np.clip(X, -30, 30)
|
||||
mean = X.mean(axis=(0, 1), keepdims=True)
|
||||
std = X.std(axis=(0, 1), keepdims=True) + 1e-8
|
||||
X = (X - mean) / std
|
||||
|
||||
# Encode labels
|
||||
# Labels
|
||||
le = LabelEncoder()
|
||||
y = le.fit_transform(y_list)
|
||||
|
||||
# Filter classes with too few samples
|
||||
# Filter rare classes
|
||||
counts = Counter(y)
|
||||
valid_classes = [cls for cls, cnt in counts.items() if cnt >= MIN_SAMPLES_PER_CLASS]
|
||||
mask_valid = np.isin(y, valid_classes)
|
||||
valid = [k for k, v in counts.items() if v >= MIN_SAMPLES_PER_CLASS]
|
||||
mask = np.isin(y, valid)
|
||||
|
||||
X = X[mask_valid]
|
||||
masks = masks[mask_valid]
|
||||
y = y[mask_valid]
|
||||
X = X[mask]
|
||||
frame_masks = frame_masks[mask]
|
||||
modality_masks = modality_masks[mask]
|
||||
y = y[mask]
|
||||
|
||||
# Re-encode after filtering
|
||||
le = LabelEncoder()
|
||||
y = le.fit_transform(y)
|
||||
|
||||
print(f"\nFinal dataset after filtering:")
|
||||
print(f" Samples: {len(X):,}")
|
||||
print(f" Classes: {len(le.classes_)}")
|
||||
print(f" Sign examples: {list(le.classes_[:10])}")
|
||||
print(f"After filtering: {len(X):,} samples | {len(le.classes_)} classes")
|
||||
|
||||
# Train-test split
|
||||
X_train, X_test, masks_train, masks_test, y_train, y_test = train_test_split(
|
||||
X, masks, y, test_size=0.15, stratify=y, random_state=42
|
||||
# Analyze modality usage
|
||||
print("\nModality statistics:")
|
||||
print(f" Sequences with left hand: {(modality_masks[:, :, 0].sum(axis=1) > 0).mean() * 100:.1f}%")
|
||||
print(f" Sequences with right hand: {(modality_masks[:, :, 1].sum(axis=1) > 0).mean() * 100:.1f}%")
|
||||
print(f" Sequences with face: {(modality_masks[:, :, 2].sum(axis=1) > 0).mean() * 100:.1f}%")
|
||||
|
||||
# Split
|
||||
X_tr, X_te, fm_tr, fm_te, mm_tr, mm_te, y_tr, y_te = train_test_split(
|
||||
X, frame_masks, modality_masks, y, test_size=0.15, stratify=y, random_state=42
|
||||
)
|
||||
|
||||
print(f"\nTrain set: {len(X_train):,} samples")
|
||||
print(f"Test set: {len(X_test):,} samples")
|
||||
|
||||
# Dataset wrapper
|
||||
class ASLSequenceDataset(Dataset):
|
||||
def __init__(self, X, masks, y):
|
||||
# Dataset
|
||||
class ASLMultiDataset(Dataset):
|
||||
def __init__(self, X, frame_masks, modality_masks, y):
|
||||
self.X = torch.from_numpy(X).float()
|
||||
self.masks = torch.from_numpy(masks)
|
||||
self.frame_masks = torch.from_numpy(frame_masks).bool()
|
||||
self.modality_masks = torch.from_numpy(modality_masks).float()
|
||||
self.y = torch.from_numpy(y).long()
|
||||
|
||||
def __len__(self):
|
||||
return len(self.X)
|
||||
|
||||
def __getitem__(self, idx):
|
||||
return self.X[idx], self.masks[idx], self.y[idx]
|
||||
return self.X[idx], self.frame_masks[idx], self.modality_masks[idx], self.y[idx]
|
||||
|
||||
# DataLoaders
|
||||
batch_size = 128 if device.type == 'cuda' else 64
|
||||
batch_size = 64 if device.type == 'cuda' else 32
|
||||
|
||||
train_loader = DataLoader(
|
||||
ASLSequenceDataset(X_train, masks_train, y_train),
|
||||
batch_size=batch_size,
|
||||
shuffle=True,
|
||||
num_workers=4,
|
||||
pin_memory=True if device.type == 'cuda' else False
|
||||
ASLMultiDataset(X_tr, fm_tr, mm_tr, y_tr),
|
||||
batch_size=batch_size, shuffle=True,
|
||||
num_workers=4, pin_memory=device.type == 'cuda'
|
||||
)
|
||||
|
||||
test_loader = DataLoader(
|
||||
ASLSequenceDataset(X_test, masks_test, y_test),
|
||||
batch_size=batch_size * 2,
|
||||
shuffle=False,
|
||||
num_workers=4,
|
||||
pin_memory=True if device.type == 'cuda' else False
|
||||
ASLMultiDataset(X_te, fm_te, mm_te, y_te),
|
||||
batch_size=batch_size * 2, shuffle=False,
|
||||
num_workers=4, pin_memory=device.type == 'cuda'
|
||||
)
|
||||
|
||||
# Initialize model
|
||||
print("\nInitializing model...")
|
||||
model = TransformerASL(
|
||||
# Enhanced model
|
||||
model = ModalityAwareTransformer(
|
||||
input_dim=X.shape[2],
|
||||
num_classes=len(le.classes_),
|
||||
d_model=256,
|
||||
d_model=384,
|
||||
nhead=8,
|
||||
num_layers=4
|
||||
num_layers=5
|
||||
).to(device)
|
||||
|
||||
total_params = sum(p.numel() for p in model.parameters())
|
||||
print(f"Model parameters: {total_params:,}")
|
||||
print(f"\nModel parameters: {sum(p.numel() for p in model.parameters()):,}")
|
||||
|
||||
# Training setup
|
||||
criterion = nn.CrossEntropyLoss(label_smoothing=0.05)
|
||||
optimizer = optim.AdamW(model.parameters(), lr=5e-4, weight_decay=1e-4)
|
||||
scheduler = optim.lr_scheduler.CosineAnnealingWarmRestarts(optimizer, T_0=10, T_mult=2)
|
||||
optimizer = optim.AdamW(model.parameters(), lr=4e-4, weight_decay=1e-4)
|
||||
scheduler = optim.lr_scheduler.CosineAnnealingWarmRestarts(optimizer, T_0=10)
|
||||
|
||||
# Training loop
|
||||
best_acc = 0.0
|
||||
epochs = 60
|
||||
|
||||
print("\n" + "=" * 60)
|
||||
print("STARTING TRAINING")
|
||||
print("=" * 60)
|
||||
epochs = 70
|
||||
|
||||
for epoch in range(epochs):
|
||||
# Train
|
||||
model.train()
|
||||
total_loss = 0
|
||||
correct = total = 0
|
||||
total_loss = correct = total = 0
|
||||
|
||||
for x, mask, yb in tqdm(train_loader, desc=f"Epoch {epoch + 1}/{epochs}", leave=False):
|
||||
x, mask, yb = x.to(device), mask.to(device), yb.to(device)
|
||||
for x, frame_mask, modality_mask, yb in tqdm(train_loader, desc=f"Epoch {epoch + 1}", leave=False):
|
||||
x = x.to(device)
|
||||
frame_mask = frame_mask.to(device)
|
||||
modality_mask = modality_mask.to(device)
|
||||
yb = yb.to(device)
|
||||
|
||||
# Invert mask: True for padding positions
|
||||
key_mask = ~mask
|
||||
key_padding_mask = ~frame_mask
|
||||
|
||||
optimizer.zero_grad(set_to_none=True)
|
||||
logits = model(x, key_padding_mask=key_mask)
|
||||
logits = model(x, modality_mask=modality_mask, key_padding_mask=key_padding_mask)
|
||||
loss = criterion(logits, yb)
|
||||
loss.backward()
|
||||
|
||||
torch.nn.utils.clip_grad_norm_(model.parameters(), max_norm=1.0)
|
||||
torch.nn.utils.clip_grad_norm_(model.parameters(), 1.0)
|
||||
optimizer.step()
|
||||
|
||||
total_loss += loss.item()
|
||||
@@ -449,49 +480,35 @@ def main():
|
||||
|
||||
train_acc = correct / total * 100
|
||||
|
||||
# Evaluate
|
||||
# Eval
|
||||
model.eval()
|
||||
correct = total = 0
|
||||
with torch.no_grad():
|
||||
for x, mask, yb in test_loader:
|
||||
x, mask, yb = x.to(device), mask.to(device), yb.to(device)
|
||||
key_mask = ~mask
|
||||
logits = model(x, key_padding_mask=key_mask)
|
||||
for x, frame_mask, modality_mask, yb in test_loader:
|
||||
x = x.to(device)
|
||||
frame_mask = frame_mask.to(device)
|
||||
modality_mask = modality_mask.to(device)
|
||||
yb = yb.to(device)
|
||||
|
||||
key_padding_mask = ~frame_mask
|
||||
logits = model(x, modality_mask=modality_mask, key_padding_mask=key_padding_mask)
|
||||
correct += (logits.argmax(-1) == yb).sum().item()
|
||||
total += yb.size(0)
|
||||
|
||||
test_acc = correct / total * 100
|
||||
scheduler.step()
|
||||
|
||||
# Print progress
|
||||
print(f"[{epoch + 1:2d}/{epochs}] Loss: {total_loss / len(train_loader):.4f} | "
|
||||
f"Train: {train_acc:.2f}% | Test: {test_acc:.2f}%", end="")
|
||||
|
||||
scheduler.step()
|
||||
|
||||
# Save best model
|
||||
if test_acc > best_acc:
|
||||
best_acc = test_acc
|
||||
torch.save({
|
||||
'model': model.state_dict(),
|
||||
'optimizer': optimizer.state_dict(),
|
||||
'label_encoder_classes': le.classes_,
|
||||
'acc': best_acc,
|
||||
'epoch': epoch,
|
||||
'input_dim': X.shape[2],
|
||||
'num_classes': len(le.classes_),
|
||||
'd_model': 256,
|
||||
'nhead': 8,
|
||||
'num_layers': 4
|
||||
}, "best_asl_transformer.pth")
|
||||
print(f" → New best: {best_acc:.2f}% ✓")
|
||||
torch.save(model.state_dict(), "best_asl_modality_aware.pth")
|
||||
print(" → saved")
|
||||
else:
|
||||
print()
|
||||
|
||||
print("\n" + "=" * 60)
|
||||
print(f"✓ Training complete!")
|
||||
print(f"✓ Best test accuracy: {best_acc:.2f}%")
|
||||
print(f"✓ Model saved: best_asl_transformer.pth")
|
||||
print("=" * 60)
|
||||
print(f"\nBest test accuracy: {best_acc:.2f}%")
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
|
||||
Reference in New Issue
Block a user