Underdog
This commit is contained in:
@@ -1,237 +1,759 @@
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import os
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import polars as pl
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import json
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import math
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import numpy as np
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import polars as pl
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import torch
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import torch.nn as nn
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import torch.nn.functional as F
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from torch.utils.data import Dataset, DataLoader, random_split
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from concurrent.futures import ProcessPoolExecutor
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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
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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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# --- CONFIGURATION ---
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BASE_PATH = "asl_kaggle"
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TARGET_FRAMES = 22
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LIPS = [61, 146, 91, 181, 84, 17, 314, 405, 321, 375, 291, 308, 324, 318, 402, 317, 14, 87, 178, 88, 95]
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HANDS = list(range(468, 543))
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SELECTED_INDICES = LIPS + HANDS
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NUM_FEATS = len(SELECTED_INDICES) * 3
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# ===============================
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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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# Training hyperparameters
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BATCH_SIZE = 32
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EPOCHS = 50
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LEARNING_RATE = 0.001
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TRAIN_SPLIT = 0.8
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CHECKPOINT_DIR = "checkpoints"
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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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torch.backends.cudnn.enabled = 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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device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
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print(f"Using device: {device}")
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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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# --- DATA PROCESSING ---
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def load_kaggle_metadata(base_path):
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return pl.read_csv(os.path.join(base_path, "train.csv"))
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# ===============================
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# DATA AUGMENTATION
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# ===============================
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def augment_sequence(x, modality_mask):
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"""Apply random augmentations to training data"""
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x = x.copy()
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# Random temporal cropping (simulate different signing speeds)
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if np.random.rand() < 0.3 and len(x) > 20:
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start = np.random.randint(0, max(1, len(x) // 4))
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x = x[start:]
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modality_mask = modality_mask[start:]
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# Random spatial scaling
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if np.random.rand() < 0.5:
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scale = np.random.uniform(0.85, 1.15)
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x = x * scale
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# Random rotation (around z-axis for x,y coordinates)
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if np.random.rand() < 0.5:
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angle = np.random.uniform(-0.3, 0.3)
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cos_a, sin_a = np.cos(angle), np.sin(angle)
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# Reshape to get xyz coordinates
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x_reshaped = x.reshape(len(x), -1, 3)
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x_rot = x_reshaped.copy()
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x_rot[..., 0] = x_reshaped[..., 0] * cos_a - x_reshaped[..., 1] * sin_a
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x_rot[..., 1] = x_reshaped[..., 0] * sin_a + x_reshaped[..., 1] * cos_a
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x = x_rot.reshape(x.shape)
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# Random masking (simulate occlusion) - only for some frames
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if np.random.rand() < 0.3:
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n_mask = int(len(x) * 0.15) # mask 15% of frames
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mask_indices = np.random.choice(len(x), n_mask, replace=False)
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x[mask_indices] *= 0.1 # dim but don't completely zero
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# Random noise
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if np.random.rand() < 0.4:
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noise = np.random.normal(0, 0.02, x.shape)
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x = x + noise
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# Random time warping (speed up or slow down)
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if np.random.rand() < 0.3 and len(x) > 20:
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speed = np.random.uniform(0.8, 1.2)
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new_len = int(len(x) * speed)
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new_len = min(new_len, len(x))
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indices = np.linspace(0, len(x) - 1, new_len).astype(int)
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x = x[indices]
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modality_mask = modality_mask[indices]
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return x, modality_mask
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def load_and_preprocess(path, base_path=BASE_PATH, target_frames=TARGET_FRAMES):
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parquet_path = os.path.join(base_path, path)
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df = pl.read_parquet(parquet_path)
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# ===============================
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# ENHANCED DATA EXTRACTION (POLARS)
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# ===============================
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def extract_multi_landmarks(path, min_valid_frames=3):
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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 = pl.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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anchors = (
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df.filter((pl.col("type") == "face") & (pl.col("landmark_index") == 0))
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.select([pl.col("frame"), pl.col("x").alias("nx"), pl.col("y").alias("ny"), pl.col("z").alias("nz")])
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)
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all_types = df.select("type").unique().to_series().to_list()
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has_data = any(t in all_types for t in ["left_hand", "right_hand", "face"])
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processed = (
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df.join(anchors, on="frame", how="left")
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.with_columns([
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(pl.col("x") - pl.col("nx")).fill_null(0.0),
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(pl.col("y") - pl.col("ny")).fill_null(0.0),
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(pl.col("z") - pl.col("nz")).fill_null(0.0),
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])
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.sort(["frame", "type", "landmark_index"])
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)
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if not has_data:
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return None
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raw_tensor = processed.select(["x", "y", "z"]).to_numpy().reshape(-1, 543, 3)
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reduced_tensor = raw_tensor[:, SELECTED_INDICES, :]
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frames = sorted(df.select("frame").unique().to_series().to_list())
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curr_len = reduced_tensor.shape[0]
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indices = np.linspace(0, curr_len - 1, num=target_frames).round().astype(int)
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return reduced_tensor[indices]
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if len(frames) < min_valid_frames:
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return None
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for frame in frames:
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frame_df = df.filter(pl.col("frame") == frame)
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frame_points = np.full((TOTAL_POINTS_PER_FRAME, 3), np.nan, dtype=np.float32)
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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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# Left hand
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left = frame_df.filter(pl.col("type") == "left_hand")
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if left.height > 0:
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valid_count = 0
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for i in range(21):
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row = left.filter(pl.col("landmark_index") == i)
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if row.height > 0:
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coords = row.select(["x", "y", "z"]).row(0)
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if all(c is not None for c in coords):
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frame_points[pos] = coords
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valid_count += 1
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pos += 1
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left_valid = (valid_count >= 10)
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else:
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pos += 21
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# Right hand
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right = frame_df.filter(pl.col("type") == "right_hand")
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if right.height > 0:
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valid_count = 0
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for i in range(21):
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row = right.filter(pl.col("landmark_index") == i)
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if row.height > 0:
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coords = row.select(["x", "y", "z"]).row(0)
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if all(c is not None for c in coords):
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frame_points[pos] = coords
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valid_count += 1
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pos += 1
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right_valid = (valid_count >= 10)
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else:
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pos += 21
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# Face
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face = frame_df.filter(pl.col("type") == "face")
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if face.height > 0:
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valid_count = 0
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for idx in IMPORTANT_FACE_INDICES:
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row = face.filter(pl.col("landmark_index") == idx)
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if row.height > 0:
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coords = row.select(["x", "y", "z"]).row(0)
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if all(c is not None for c in coords):
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frame_points[pos] = coords
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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) * 0.3)
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valid_ratio = 1 - np.isnan(frame_points).mean()
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if valid_ratio >= 0.20:
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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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if len(seq) < min_valid_frames:
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return None
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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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return None
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# --- DATASET CLASS ---
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class ASLDataset(Dataset):
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def __init__(self, tensors, labels):
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self.tensors = tensors
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self.labels = labels
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def get_features_sequence(landmarks_data, max_frames=100):
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"""Enhanced feature extraction with separate modality processing"""
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if landmarks_data is None:
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return None, None, None
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def __len__(self):
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return len(self.tensors)
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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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def __getitem__(self, idx):
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return self.tensors[idx], self.labels[idx]
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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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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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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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features = np.concatenate(features_list, axis=1)
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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)
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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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# --- MODEL ARCHITECTURE ---
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class ASLClassifier(nn.Module):
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def __init__(self, num_classes, target_frames=TARGET_FRAMES, num_feats=NUM_FEATS):
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def process_row(row_data, base_path, max_frames=100):
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"""Process a single row"""
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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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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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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, None
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return feat, frame_mask, modality_mask, sign
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except Exception:
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return None, None, None, None
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# ===============================
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# MIXUP AUGMENTATION
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# ===============================
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def mixup_data(x, frame_mask, modality_mask, y, alpha=0.2):
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"""Mixup augmentation"""
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if alpha > 0:
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lam = np.random.beta(alpha, alpha)
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else:
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lam = 1
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batch_size = x.size(0)
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index = torch.randperm(batch_size).to(x.device)
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mixed_x = lam * x + (1 - lam) * x[index]
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mixed_frame_mask = frame_mask | frame_mask[index] # Union of valid frames
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mixed_modality_mask = torch.max(modality_mask, modality_mask[index])
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y_a, y_b = y, y[index]
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return mixed_x, mixed_frame_mask, mixed_modality_mask, y_a, y_b, lam
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# ===============================
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# ENHANCED MODEL WITH ATTENTION POOLING
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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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self.conv1 = nn.Conv1d(num_feats, 256, kernel_size=3, padding=1)
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self.bn1 = nn.BatchNorm1d(256)
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self.conv2 = nn.Conv1d(256, 512, kernel_size=3, padding=1)
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self.bn2 = nn.BatchNorm1d(512)
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self.pool = nn.MaxPool1d(2)
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self.dropout = nn.Dropout(0.5)
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self.fc = nn.Linear(512, num_classes)
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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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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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self.register_buffer('pe', pe.unsqueeze(0))
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def forward(self, x):
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x = x.view(x.shape[0], x.shape[1], -1)
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x = x.transpose(1, 2)
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x = F.relu(self.bn1(self.conv1(x)))
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x = self.pool(x)
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x = F.relu(self.bn2(self.conv2(x)))
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x = self.pool(x)
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x = F.adaptive_avg_pool1d(x, 1).squeeze(-1)
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x = self.dropout(x)
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return self.fc(x)
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return x + self.pe[:, :x.size(1)]
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# --- TRAINING FUNCTIONS ---
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def train_epoch(model, dataloader, criterion, optimizer, device):
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class ModalityAwareTransformer(nn.Module):
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def __init__(self, input_dim, num_classes, d_model=512, nhead=8, num_layers=6, dropout=0.15):
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super().__init__()
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# Main projection
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self.proj = nn.Linear(input_dim, d_model)
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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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dim_feedforward=d_model * 4,
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dropout=dropout,
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activation='gelu',
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batch_first=True,
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norm_first=True
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)
|
||||
self.encoder = nn.TransformerEncoder(enc_layer, num_layers=num_layers)
|
||||
|
||||
# Attention pooling
|
||||
self.attention_pool = nn.Linear(d_model, 1)
|
||||
|
||||
self.head = nn.Sequential(
|
||||
nn.LayerNorm(d_model),
|
||||
nn.Dropout(0.3),
|
||||
nn.Linear(d_model, d_model // 2),
|
||||
nn.GELU(),
|
||||
nn.Dropout(0.2),
|
||||
nn.Linear(d_model // 2, num_classes)
|
||||
)
|
||||
|
||||
def forward(self, x, modality_mask=None, key_padding_mask=None):
|
||||
# Project features
|
||||
x = self.proj(x)
|
||||
|
||||
# Add modality information
|
||||
if modality_mask is not None:
|
||||
mod_embed = self.modality_embed(modality_mask)
|
||||
x = x + mod_embed
|
||||
|
||||
x = self.norm_in(x)
|
||||
x = self.pos(x)
|
||||
x = self.encoder(x, src_key_padding_mask=key_padding_mask)
|
||||
|
||||
# Attention-based pooling
|
||||
attn_weights = self.attention_pool(x) # (B, T, 1)
|
||||
if key_padding_mask is not None:
|
||||
attn_weights = attn_weights.masked_fill(key_padding_mask.unsqueeze(-1), -1e9)
|
||||
attn_weights = F.softmax(attn_weights, dim=1)
|
||||
x = (x * attn_weights).sum(dim=1)
|
||||
|
||||
return self.head(x)
|
||||
|
||||
|
||||
def load_kaggle_asl_data(base_path):
|
||||
"""Load training metadata using Polars"""
|
||||
train_path = os.path.join(base_path, "train.csv")
|
||||
train_df = pl.read_csv(train_path)
|
||||
return train_df, None
|
||||
|
||||
|
||||
# ===============================
|
||||
# DATASET WITH AUGMENTATION
|
||||
# ===============================
|
||||
class ASLMultiDataset(Dataset):
|
||||
def __init__(self, X, frame_masks, modality_masks, y, training=False, max_frames=100):
|
||||
self.X = X
|
||||
self.frame_masks = frame_masks
|
||||
self.modality_masks = modality_masks
|
||||
self.y = y
|
||||
self.training = training
|
||||
self.max_frames = max_frames
|
||||
|
||||
def __len__(self):
|
||||
return len(self.X)
|
||||
|
||||
def __getitem__(self, idx):
|
||||
x = self.X[idx].copy()
|
||||
frame_mask = self.frame_masks[idx].copy()
|
||||
modality_mask = self.modality_masks[idx].copy()
|
||||
y = self.y[idx]
|
||||
|
||||
if self.training:
|
||||
# Apply augmentation
|
||||
x, modality_mask = augment_sequence(x, modality_mask)
|
||||
|
||||
# Re-pad if needed after augmentation
|
||||
if len(x) < self.max_frames:
|
||||
pad = np.zeros((self.max_frames - len(x), x.shape[1]), dtype=np.float32)
|
||||
x = np.concatenate([x, pad], axis=0)
|
||||
|
||||
mask_pad = np.zeros((self.max_frames - len(x), 3), dtype=np.float32)
|
||||
modality_mask = np.concatenate([modality_mask, mask_pad], axis=0)
|
||||
|
||||
frame_mask = np.zeros(self.max_frames, dtype=bool)
|
||||
frame_mask[:len(x)] = True
|
||||
else:
|
||||
x = x[:self.max_frames]
|
||||
modality_mask = modality_mask[:self.max_frames]
|
||||
frame_mask = np.ones(self.max_frames, dtype=bool)
|
||||
|
||||
return (
|
||||
torch.from_numpy(x).float(),
|
||||
torch.from_numpy(frame_mask).bool(),
|
||||
torch.from_numpy(modality_mask).float(),
|
||||
torch.tensor(y, dtype=torch.long)
|
||||
)
|
||||
|
||||
|
||||
# ===============================
|
||||
# TRAINING SINGLE MODEL
|
||||
# ===============================
|
||||
def train_model(X_tr, fm_tr, mm_tr, y_tr, X_te, fm_te, mm_te, y_te,
|
||||
num_classes, input_dim, model_idx=0, epochs=80):
|
||||
"""Train a single model"""
|
||||
|
||||
# Set different seed for each model
|
||||
torch.manual_seed(42 + model_idx)
|
||||
np.random.seed(42 + model_idx)
|
||||
|
||||
batch_size = 64 if device.type == 'cuda' else 32
|
||||
|
||||
train_dataset = ASLMultiDataset(X_tr, fm_tr, mm_tr, y_tr, training=True, max_frames=100)
|
||||
test_dataset = ASLMultiDataset(X_te, fm_te, mm_te, y_te, training=False, max_frames=100)
|
||||
|
||||
train_loader = DataLoader(
|
||||
train_dataset,
|
||||
batch_size=batch_size, shuffle=True,
|
||||
num_workers=4, pin_memory=device.type == 'cuda'
|
||||
)
|
||||
|
||||
test_loader = DataLoader(
|
||||
test_dataset,
|
||||
batch_size=batch_size * 2, shuffle=False,
|
||||
num_workers=4, pin_memory=device.type == 'cuda'
|
||||
)
|
||||
|
||||
# Enhanced model
|
||||
model = ModalityAwareTransformer(
|
||||
input_dim=input_dim,
|
||||
num_classes=num_classes,
|
||||
d_model=512,
|
||||
nhead=8,
|
||||
num_layers=6,
|
||||
dropout=0.15
|
||||
).to(device)
|
||||
|
||||
print(f"\n[Model {model_idx + 1}] Parameters: {sum(p.numel() for p in model.parameters()):,}")
|
||||
|
||||
criterion = nn.CrossEntropyLoss(label_smoothing=0.1)
|
||||
optimizer = optim.AdamW(model.parameters(), lr=1e-3, weight_decay=1e-4)
|
||||
|
||||
# OneCycleLR scheduler
|
||||
scheduler = optim.lr_scheduler.OneCycleLR(
|
||||
optimizer,
|
||||
max_lr=1e-3,
|
||||
steps_per_epoch=len(train_loader),
|
||||
epochs=epochs,
|
||||
pct_start=0.1,
|
||||
anneal_strategy='cos'
|
||||
)
|
||||
|
||||
best_acc = 0.0
|
||||
save_path = f"best_asl_model_{model_idx}.pth"
|
||||
|
||||
print(f"\n{'=' * 60}")
|
||||
print(f"TRAINING MODEL {model_idx + 1}")
|
||||
print(f"{'=' * 60}")
|
||||
|
||||
for epoch in range(epochs):
|
||||
model.train()
|
||||
running_loss = 0.0
|
||||
correct = 0
|
||||
total = 0
|
||||
total_loss = correct = total = 0
|
||||
|
||||
progress_bar = tqdm(dataloader, desc="Training")
|
||||
for inputs, labels in progress_bar:
|
||||
inputs, labels = inputs.to(device), labels.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)
|
||||
|
||||
# Apply mixup
|
||||
if np.random.rand() < 0.5:
|
||||
x, frame_mask, modality_mask, y_a, y_b, lam = mixup_data(
|
||||
x, frame_mask, modality_mask, yb, alpha=0.2
|
||||
)
|
||||
|
||||
key_padding_mask = ~frame_mask
|
||||
optimizer.zero_grad(set_to_none=True)
|
||||
logits = model(x, modality_mask=modality_mask, key_padding_mask=key_padding_mask)
|
||||
loss = lam * criterion(logits, y_a) + (1 - lam) * criterion(logits, y_b)
|
||||
|
||||
# Use original labels for accuracy
|
||||
correct += (logits.argmax(-1) == yb).sum().item()
|
||||
else:
|
||||
key_padding_mask = ~frame_mask
|
||||
optimizer.zero_grad(set_to_none=True)
|
||||
logits = model(x, modality_mask=modality_mask, key_padding_mask=key_padding_mask)
|
||||
loss = criterion(logits, yb)
|
||||
correct += (logits.argmax(-1) == yb).sum().item()
|
||||
|
||||
optimizer.zero_grad()
|
||||
outputs = model(inputs)
|
||||
loss = criterion(outputs, labels)
|
||||
loss.backward()
|
||||
torch.nn.utils.clip_grad_norm_(model.parameters(), 1.0)
|
||||
optimizer.step()
|
||||
scheduler.step()
|
||||
|
||||
running_loss += loss.item()
|
||||
_, predicted = torch.max(outputs.data, 1)
|
||||
total += labels.size(0)
|
||||
correct += (predicted == labels).sum().item()
|
||||
total_loss += loss.item()
|
||||
total += yb.size(0)
|
||||
|
||||
progress_bar.set_postfix({
|
||||
'loss': running_loss / (progress_bar.n + 1),
|
||||
'acc': 100 * correct / total
|
||||
})
|
||||
train_acc = correct / total * 100
|
||||
|
||||
epoch_loss = running_loss / len(dataloader)
|
||||
epoch_acc = 100 * correct / total
|
||||
return epoch_loss, epoch_acc
|
||||
|
||||
|
||||
def validate(model, dataloader, criterion, device):
|
||||
# Eval
|
||||
model.eval()
|
||||
running_loss = 0.0
|
||||
correct = 0
|
||||
total = 0
|
||||
|
||||
correct = total = 0
|
||||
with torch.no_grad():
|
||||
for inputs, labels in tqdm(dataloader, desc="Validation"):
|
||||
inputs, labels = inputs.to(device), labels.to(device)
|
||||
outputs = model(inputs)
|
||||
loss = criterion(outputs, labels)
|
||||
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)
|
||||
|
||||
running_loss += loss.item()
|
||||
_, predicted = torch.max(outputs.data, 1)
|
||||
total += labels.size(0)
|
||||
correct += (predicted == labels).sum().item()
|
||||
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)
|
||||
|
||||
val_loss = running_loss / len(dataloader)
|
||||
val_acc = 100 * correct / total
|
||||
return val_loss, val_acc
|
||||
test_acc = correct / total * 100
|
||||
|
||||
print(f"[{epoch + 1:2d}/{epochs}] Loss: {total_loss / len(train_loader):.4f} | "
|
||||
f"Train: {train_acc:.2f}% | Test: {test_acc:.2f}%", end="")
|
||||
|
||||
if test_acc > best_acc:
|
||||
best_acc = test_acc
|
||||
torch.save(model.state_dict(), save_path)
|
||||
print(" → saved")
|
||||
else:
|
||||
print()
|
||||
|
||||
print(f"\nModel {model_idx + 1} - Best test accuracy: {best_acc:.2f}%")
|
||||
return save_path, best_acc
|
||||
|
||||
|
||||
def save_checkpoint(model, optimizer, epoch, train_loss, val_loss, val_acc, checkpoint_dir):
|
||||
os.makedirs(checkpoint_dir, exist_ok=True)
|
||||
checkpoint = {
|
||||
'epoch': epoch,
|
||||
'model_state_dict': model.state_dict(),
|
||||
'optimizer_state_dict': optimizer.state_dict(),
|
||||
'train_loss': train_loss,
|
||||
'val_loss': val_loss,
|
||||
'val_acc': val_acc,
|
||||
}
|
||||
path = os.path.join(checkpoint_dir, f'checkpoint_epoch_{epoch}.pt')
|
||||
torch.save(checkpoint, path)
|
||||
print(f"Checkpoint saved: {path}")
|
||||
# ===============================
|
||||
# ENSEMBLE PREDICTION
|
||||
# ===============================
|
||||
def ensemble_predict(model_paths, test_loader, num_classes, input_dim):
|
||||
"""Make predictions using ensemble of models"""
|
||||
all_preds = []
|
||||
|
||||
for model_path in model_paths:
|
||||
model = ModalityAwareTransformer(
|
||||
input_dim=input_dim,
|
||||
num_classes=num_classes,
|
||||
d_model=512,
|
||||
nhead=8,
|
||||
num_layers=6
|
||||
).to(device)
|
||||
|
||||
model.load_state_dict(torch.load(model_path))
|
||||
model.eval()
|
||||
|
||||
preds = []
|
||||
with torch.no_grad():
|
||||
for x, frame_mask, modality_mask, _ in test_loader:
|
||||
x = x.to(device)
|
||||
frame_mask = frame_mask.to(device)
|
||||
modality_mask = modality_mask.to(device)
|
||||
|
||||
key_padding_mask = ~frame_mask
|
||||
logits = model(x, modality_mask=modality_mask, key_padding_mask=key_padding_mask)
|
||||
preds.append(F.softmax(logits, dim=-1))
|
||||
|
||||
all_preds.append(torch.cat(preds, dim=0))
|
||||
|
||||
# Average predictions
|
||||
ensemble_pred = torch.stack(all_preds).mean(0)
|
||||
return ensemble_pred.argmax(-1).cpu().numpy()
|
||||
|
||||
|
||||
# ===============================
|
||||
# MAIN
|
||||
# ===============================
|
||||
def main():
|
||||
base_path = "asl_kaggle"
|
||||
max_frames = 100
|
||||
MIN_SAMPLES_PER_CLASS = 3 # Relaxed from 5
|
||||
NUM_ENSEMBLE_MODELS = 3
|
||||
EPOCHS = 80
|
||||
|
||||
print("\nLoading metadata...")
|
||||
train_df, _ = load_kaggle_asl_data(base_path)
|
||||
print(f"Total samples in train.csv: {train_df.height}")
|
||||
|
||||
rows = [(row[0], row[1]) for row in train_df.select(["path", "sign"]).iter_rows()]
|
||||
|
||||
print("\nProcessing sequences with BOTH hands + FACE (enhanced)...")
|
||||
print("This may take a few minutes...")
|
||||
|
||||
with Pool(cpu_count()) as pool:
|
||||
results = list(tqdm(
|
||||
pool.imap(
|
||||
partial(process_row, base_path=base_path, max_frames=max_frames),
|
||||
rows,
|
||||
chunksize=80
|
||||
),
|
||||
total=len(rows),
|
||||
desc="Landmarks extraction"
|
||||
))
|
||||
|
||||
X_list, frame_masks_list, modality_masks_list, y_list = [], [], [], []
|
||||
failed_count = 0
|
||||
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)
|
||||
else:
|
||||
failed_count += 1
|
||||
|
||||
if not X_list:
|
||||
print(f"\n❌ No valid sequences extracted!")
|
||||
print(f"Failed to process: {failed_count}/{len(results)} files")
|
||||
return
|
||||
|
||||
print(f"\n✓ Successfully processed: {len(X_list)}/{len(results)} files")
|
||||
print(f"✗ Failed: {failed_count}/{len(results)} files")
|
||||
|
||||
X = np.stack(X_list)
|
||||
frame_masks = np.stack(frame_masks_list)
|
||||
modality_masks = np.stack(modality_masks_list)
|
||||
|
||||
print(f"\nExtracted {len(X):,} sequences")
|
||||
print(f"Feature shape: {X.shape[1:]} (input_dim = {X.shape[2]})")
|
||||
|
||||
# Global normalization
|
||||
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
|
||||
|
||||
# Labels
|
||||
le = LabelEncoder()
|
||||
y = le.fit_transform(y_list)
|
||||
|
||||
# Filter rare classes
|
||||
counts = Counter(y)
|
||||
valid = [k for k, v in counts.items() if v >= MIN_SAMPLES_PER_CLASS]
|
||||
mask = np.isin(y, valid)
|
||||
|
||||
X = X[mask]
|
||||
frame_masks = frame_masks[mask]
|
||||
modality_masks = modality_masks[mask]
|
||||
y = y[mask]
|
||||
|
||||
le = LabelEncoder()
|
||||
y = le.fit_transform(y)
|
||||
|
||||
print(f"After filtering: {len(X):,} samples | {len(le.classes_)} classes")
|
||||
|
||||
# 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
|
||||
)
|
||||
|
||||
# Train ensemble of models
|
||||
model_paths = []
|
||||
best_accs = []
|
||||
|
||||
for i in range(NUM_ENSEMBLE_MODELS):
|
||||
model_path, best_acc = train_model(
|
||||
X_tr, fm_tr, mm_tr, y_tr,
|
||||
X_te, fm_te, mm_te, y_te,
|
||||
num_classes=len(le.classes_),
|
||||
input_dim=X.shape[2],
|
||||
model_idx=i,
|
||||
epochs=EPOCHS
|
||||
)
|
||||
model_paths.append(model_path)
|
||||
best_accs.append(best_acc)
|
||||
|
||||
# Ensemble evaluation
|
||||
print("\n" + "=" * 60)
|
||||
print("ENSEMBLE EVALUATION")
|
||||
print("=" * 60)
|
||||
|
||||
test_dataset = ASLMultiDataset(X_te, fm_te, mm_te, y_te, training=False)
|
||||
test_loader = DataLoader(
|
||||
test_dataset,
|
||||
batch_size=128,
|
||||
shuffle=False,
|
||||
num_workers=4,
|
||||
pin_memory=device.type == 'cuda'
|
||||
)
|
||||
|
||||
ensemble_preds = ensemble_predict(model_paths, test_loader, len(le.classes_), X.shape[2])
|
||||
ensemble_acc = (ensemble_preds == y_te).mean() * 100
|
||||
|
||||
print(f"\nIndividual model accuracies:")
|
||||
for i, acc in enumerate(best_accs):
|
||||
print(f" Model {i + 1}: {acc:.2f}%")
|
||||
|
||||
print(f"\n🎯 Ensemble accuracy: {ensemble_acc:.2f}%")
|
||||
print(f" Improvement: +{ensemble_acc - max(best_accs):.2f}% over best single model")
|
||||
|
||||
print("\n" + "=" * 60)
|
||||
print(f"TRAINING COMPLETE")
|
||||
print("=" * 60)
|
||||
|
||||
|
||||
# --- EXECUTION ---
|
||||
if __name__ == "__main__":
|
||||
# Load metadata
|
||||
asl_data = load_kaggle_metadata(BASE_PATH)
|
||||
|
||||
# Create label mapping
|
||||
unique_signs = sorted(asl_data["sign"].unique().to_list())
|
||||
label_to_idx = {sign: idx for idx, sign in enumerate(unique_signs)}
|
||||
labels = torch.tensor([label_to_idx[sign] for sign in asl_data["sign"].to_list()])
|
||||
|
||||
print(f"Number of classes: {len(unique_signs)}")
|
||||
|
||||
# Process data in parallel
|
||||
paths = asl_data["path"].to_list()
|
||||
print(f"Processing {len(paths)} files in parallel...")
|
||||
|
||||
with ProcessPoolExecutor() as executor:
|
||||
results = list(tqdm(executor.map(load_and_preprocess, paths), total=len(paths)))
|
||||
|
||||
dataset_tensor = torch.tensor(np.array(results), dtype=torch.float32)
|
||||
print(f"Final Tensor Shape: {dataset_tensor.shape}")
|
||||
|
||||
# Create dataset and split
|
||||
full_dataset = ASLDataset(dataset_tensor, labels)
|
||||
train_size = int(TRAIN_SPLIT * len(full_dataset))
|
||||
val_size = len(full_dataset) - train_size
|
||||
train_dataset, val_dataset = random_split(full_dataset, [train_size, val_size])
|
||||
|
||||
train_loader = DataLoader(train_dataset, batch_size=BATCH_SIZE, shuffle=True, num_workers=0)
|
||||
val_loader = DataLoader(val_dataset, batch_size=BATCH_SIZE, shuffle=False, num_workers=0)
|
||||
|
||||
print(f"Train samples: {train_size}, Validation samples: {val_size}")
|
||||
|
||||
# Initialize model, loss, optimizer
|
||||
model = ASLClassifier(num_classes=len(unique_signs)).to(device)
|
||||
criterion = nn.CrossEntropyLoss()
|
||||
optimizer = torch.optim.Adam(model.parameters(), lr=LEARNING_RATE)
|
||||
scheduler = torch.optim.lr_scheduler.ReduceLROnPlateau(optimizer, mode='min', factor=0.5, patience=5)
|
||||
|
||||
# Training loop
|
||||
best_val_acc = 0.0
|
||||
|
||||
print("\n" + "=" * 50)
|
||||
print("Starting Training")
|
||||
print("=" * 50 + "\n")
|
||||
|
||||
for epoch in range(EPOCHS):
|
||||
print(f"\nEpoch [{epoch + 1}/{EPOCHS}]")
|
||||
print("-" * 50)
|
||||
|
||||
train_loss, train_acc = train_epoch(model, train_loader, criterion, optimizer, device)
|
||||
val_loss, val_acc = validate(model, val_loader, criterion, device)
|
||||
|
||||
scheduler.step(val_loss)
|
||||
|
||||
print(f"\nEpoch {epoch + 1} Summary:")
|
||||
print(f" Train Loss: {train_loss:.4f} | Train Acc: {train_acc:.2f}%")
|
||||
print(f" Val Loss: {val_loss:.4f} | Val Acc: {val_acc:.2f}%")
|
||||
print(f" Learning Rate: {optimizer.param_groups[0]['lr']:.6f}")
|
||||
|
||||
# Save checkpoint if validation accuracy improved
|
||||
if val_acc > best_val_acc:
|
||||
best_val_acc = val_acc
|
||||
save_checkpoint(model, optimizer, epoch + 1, train_loss, val_loss, val_acc, CHECKPOINT_DIR)
|
||||
print(f" ✓ New best validation accuracy: {best_val_acc:.2f}%")
|
||||
|
||||
print("\n" + "=" * 50)
|
||||
print("Training Complete!")
|
||||
print(f"Best Validation Accuracy: {best_val_acc:.2f}%")
|
||||
print("=" * 50)
|
||||
main()
|
||||
Reference in New Issue
Block a user