chatgpt, i hate u
This commit is contained in:
222
training.py
222
training.py
@@ -5,7 +5,6 @@ import os
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import json
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import math
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import time
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import pickle
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import numpy as np
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import pandas as pd
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@@ -25,13 +24,12 @@ from functools import partial
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# ===============================
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device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
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print(f"Using device: {device}")
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if device.type == "cuda":
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print("GPU:", torch.cuda.get_device_name(0))
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torch.backends.cudnn.benchmark = True
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# ===============================
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# DATA LOADING
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# DATA LOADING & FEATURE 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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@@ -39,13 +37,12 @@ def load_kaggle_asl_data(base_path):
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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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df = pd.read_parquet(path)
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left = df[df["type"] == "left_hand"]
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right = df[df["type"] == "right_hand"]
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hand = None
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if len(left) > 0:
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hand = left
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elif len(right) > 0:
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@@ -53,67 +50,56 @@ def extract_hand_landmarks_from_parquet(path):
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else:
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return None
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landmarks = []
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# Keep all 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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lm_list = []
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for i in range(21):
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lm = hand[hand["landmark_index"] == i]
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lm = lm_frame[lm_frame['landmark_index'] == i]
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if len(lm) == 0:
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landmarks.append([0.0, 0.0, 0.0])
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lm_list.append([0.0, 0.0, 0.0])
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else:
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landmarks.append([
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lm["x"].mean(),
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lm["y"].mean(),
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lm["z"].mean()
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lm_list.append([
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lm['x'].mean(),
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lm['y'].mean(),
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lm['z'].mean()
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])
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landmarks_seq.append(lm_list)
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return np.array(landmarks, dtype=np.float32)
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return np.array(landmarks_seq, dtype=np.float32) # (T, 21, 3)
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def get_features(landmarks):
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if landmarks is None:
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def get_features_sequence(landmarks_seq, max_frames=100):
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if landmarks_seq is None:
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return None
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# Center on wrist
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points = landmarks_seq - landmarks_seq[:, 0:1, :]
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scale = np.linalg.norm(points[:, 9, :], axis=1, keepdims=True)
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scale[scale < 1e-6] = 1.0
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points /= scale[:, np.newaxis, :]
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# Flatten per frame
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frames = points.reshape(points.shape[0], -1)
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# Pad or truncate
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if frames.shape[0] < max_frames:
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pad = np.zeros((max_frames - frames.shape[0], frames.shape[1]), dtype=np.float32)
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frames = np.vstack([frames, pad])
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else:
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frames = frames[:max_frames]
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return frames # (max_frames, 63)
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wrist = landmarks[0]
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points = landmarks - wrist
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scale = np.linalg.norm(points[9])
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if scale < 1e-6:
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scale = 1.0
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points /= scale
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mean = points.mean(axis=0)
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std = points.std(axis=0) + 1e-6
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points = (points - mean) / std
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features = points.flatten()
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tips = [4, 8, 12, 16, 20]
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bases = [1, 5, 9, 13, 17]
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tip_dist = []
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curl = []
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for b, t in zip(bases, tips):
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curl.append(np.linalg.norm(points[t] - points[b]))
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for i in range(len(tips) - 1):
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tip_dist.append(np.linalg.norm(points[tips[i]] - points[tips[i+1]]))
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return np.concatenate([features, tip_dist, curl]).astype(np.float32)
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def process_row(row, base_path):
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path = os.path.join(base_path, row["path"])
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def process_row(row, base_path, max_frames=100):
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path = os.path.join(base_path, row['path'])
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if not os.path.exists(path):
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return None, None
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try:
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lm = extract_hand_landmarks_from_parquet(path)
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feat = get_features(lm)
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return feat, row["sign"]
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lm_seq = extract_hand_landmarks_from_parquet(path)
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feat_seq = get_features_sequence(lm_seq, max_frames)
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return feat_seq, row['sign']
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except:
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return None, None
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# ===============================
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# LOAD + PROCESS DATA
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# ===============================
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@@ -123,18 +109,17 @@ train_df, sign_to_idx = load_kaggle_asl_data(base_path)
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rows = [row for _, row in train_df.iterrows()]
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X, y = [], []
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func = partial(process_row, base_path=base_path, max_frames=100)
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with Pool(cpu_count()) as pool:
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func = partial(process_row, base_path=base_path)
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for feat, sign in pool.map(func, rows):
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if feat is not None:
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X.append(feat)
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for feat_seq, sign in pool.map(func, rows):
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if feat_seq is not None:
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X.append(feat_seq)
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y.append(sign)
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X = np.array(X, dtype=np.float32)
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X = np.stack(X) # (N, T, 63)
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y = np.array(y)
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print("Samples:", len(X))
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print("Feature dim:", X.shape[1])
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print("Sequence shape:", X.shape[1:])
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# ===============================
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# LABEL ENCODING
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@@ -142,6 +127,7 @@ print("Feature dim:", X.shape[1])
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le = LabelEncoder()
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y = le.fit_transform(y)
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num_classes = len(le.classes_)
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print("Num classes:", num_classes)
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# ===============================
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# SPLIT
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@@ -153,7 +139,7 @@ X_train, X_test, y_train, y_test = train_test_split(
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# ===============================
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# DATASET
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# ===============================
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class ASLDataset(Dataset):
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class ASLSequenceDataset(Dataset):
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def __init__(self, X, y):
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self.X = torch.tensor(X, dtype=torch.float32)
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self.y = torch.tensor(y, dtype=torch.long)
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@@ -164,119 +150,93 @@ class ASLDataset(Dataset):
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def __getitem__(self, idx):
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return self.X[idx], self.y[idx]
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train_loader = DataLoader(
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ASLDataset(X_train, y_train),
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batch_size=256,
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shuffle=True,
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pin_memory=True
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)
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test_loader = DataLoader(
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ASLDataset(X_test, y_test),
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batch_size=256,
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shuffle=False,
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pin_memory=True
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)
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train_loader = DataLoader(ASLSequenceDataset(X_train, y_train), batch_size=64, shuffle=True, pin_memory=True)
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test_loader = DataLoader(ASLSequenceDataset(X_test, y_test), batch_size=64, shuffle=False, pin_memory=True)
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# ===============================
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# MODEL (FIXED)
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# TRANSFORMER MODEL
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# ===============================
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class TransformerASL(nn.Module):
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def __init__(self, input_dim, num_classes):
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class PositionalEncoding(nn.Module):
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def __init__(self, d_model, max_len=100):
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super().__init__()
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pe = torch.zeros(max_len, d_model)
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position = torch.arange(0, max_len, dtype=torch.float).unsqueeze(1)
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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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self.proj = nn.Linear(input_dim, 256)
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self.norm = nn.LayerNorm(256)
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def forward(self, x):
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return x + self.pe[:, :x.size(1), :]
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encoder_layer = nn.TransformerEncoderLayer(
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d_model=256,
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nhead=8,
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dim_feedforward=1024,
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dropout=0.1,
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activation="gelu",
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batch_first=True,
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norm_first=True
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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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super().__init__()
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self.proj = nn.Linear(input_dim, d_model)
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self.norm = nn.LayerNorm(d_model)
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self.pos = PositionalEncoding(d_model)
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encoder_layer = nn.TransformerEncoderLayer(d_model=d_model, nhead=nhead, dim_feedforward=1024,
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dropout=0.1, activation='gelu', batch_first=True, norm_first=True)
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self.encoder = nn.TransformerEncoder(encoder_layer, num_layers=num_layers)
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self.fc = nn.Sequential(
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nn.Linear(d_model, 512),
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nn.BatchNorm1d(512),
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nn.GELU(),
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nn.Dropout(0.3),
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nn.Linear(512, num_classes)
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)
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self.encoder = nn.TransformerEncoder(encoder_layer, num_layers=4)
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self.fc1 = nn.Linear(256, 512)
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self.bn1 = nn.BatchNorm1d(512)
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self.drop1 = nn.Dropout(0.4)
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self.fc2 = nn.Linear(512, 256)
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self.bn2 = nn.BatchNorm1d(256)
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self.drop2 = nn.Dropout(0.3)
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self.out = nn.Linear(256, num_classes)
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def forward(self, x):
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x = self.proj(x)
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x = self.norm(x)
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x = self.pos(x)
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x = self.encoder(x) # (B, T, d_model)
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x = x.mean(dim=1) # temporal average
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x = self.fc(x)
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return x
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x = x.unsqueeze(1) # (B, 1, 256)
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x = self.encoder(x)
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x = x.squeeze(1)
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x = F.gelu(self.bn1(self.fc1(x)))
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x = self.drop1(x)
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x = F.gelu(self.bn2(self.fc2(x)))
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x = self.drop2(x)
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return self.out(x)
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model = TransformerASL(X.shape[1], num_classes).to(device)
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model = TransformerASL(input_dim=X.shape[2], num_classes=num_classes).to(device)
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print("Parameters:", sum(p.numel() for p in model.parameters()))
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# ===============================
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# TRAINING SETUP
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# TRAIN SETUP
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# ===============================
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criterion = nn.CrossEntropyLoss(label_smoothing=0.1)
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optimizer = optim.AdamW(model.parameters(), lr=3e-4, weight_decay=1e-4)
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scheduler = optim.lr_scheduler.CosineAnnealingWarmRestarts(optimizer, 10)
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scheduler = optim.lr_scheduler.CosineAnnealingWarmRestarts(optimizer, T_0=10)
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# ===============================
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# TRAIN / EVAL
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# TRAIN / EVAL FUNCTIONS
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# ===============================
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def train_epoch():
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model.train()
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total, correct, loss_sum = 0, 0, 0
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for x, y in train_loader:
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x, y = x.to(device), y.to(device)
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optimizer.zero_grad(set_to_none=True)
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logits = model(x)
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loss = criterion(logits, y)
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loss.backward()
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torch.nn.utils.clip_grad_norm_(model.parameters(), 1.0)
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optimizer.step()
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loss_sum += loss.item()
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correct += (logits.argmax(1) == y).sum().item()
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total += y.size(0)
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return loss_sum/len(train_loader), 100*correct/total
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@torch.no_grad()
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def evaluate():
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model.eval()
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total, correct = 0, 0
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for x, y in test_loader:
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x, y = x.to(device), y.to(device)
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logits = model(x)
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correct += (logits.argmax(1) == y).sum().item()
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total += y.size(0)
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return 100*correct/total
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# ===============================
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# TRAIN LOOP
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# ===============================
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@@ -289,22 +249,14 @@ for epoch in range(epochs):
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loss, train_acc = train_epoch()
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test_acc = evaluate()
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scheduler.step()
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print(f"Epoch {epoch+1}/{epochs} | "
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f"Loss {loss:.4f} | "
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f"Train {train_acc:.2f}% | "
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f"Test {test_acc:.2f}%")
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print(f"Epoch {epoch+1}/{epochs} | Loss {loss:.4f} | Train {train_acc:.2f}% | Test {test_acc:.2f}%")
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if test_acc > best_acc:
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best_acc = test_acc
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wait = 0
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torch.save({
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"model": model.state_dict(),
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"label_encoder": le
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}, "asl_transformer_fixed.pth")
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torch.save({"model": model.state_dict(), "label_encoder": le}, "asl_transformer_full.pth")
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else:
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wait += 1
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if wait >= patience:
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print("Early stopping")
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break
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