chatgpt lock tf in pt 2
This commit is contained in:
300
training.py
300
training.py
@@ -1,3 +1,6 @@
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# ===============================
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# IMPORTS
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# ===============================
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import os
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import json
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import math
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@@ -11,13 +14,21 @@ 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 collections import Counter
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from multiprocessing import Pool, cpu_count
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from functools import partial
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from tqdm import tqdm
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from collections import Counter
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# ===============================
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# DATA LOADING
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# DEVICE
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# ===============================
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device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
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print(f"Using device: {device}")
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if device.type == "cuda":
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print("GPU:", torch.cuda.get_device_name(0))
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# ===============================
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# DATA LOADING / FEATURES
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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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@@ -28,46 +39,48 @@ def load_kaggle_asl_data(base_path):
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def extract_hand_landmarks_from_parquet(path):
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try:
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df = pd.read_parquet(path)
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left = df[df["type"] == "left_hand"]
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right = df[df["type"] == "right_hand"]
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left = df[df["type"]=="left_hand"]
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right = df[df["type"]=="right_hand"]
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hand = left if len(left) >= len(right) else right
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if len(hand) == 0:
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return None
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frames = sorted(hand['frame'].unique())
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landmarks_seq = []
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for frame in frames:
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lm_frame = hand[hand['frame'] == frame]
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for f in frames:
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lm_frame = hand[hand['frame']==f]
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lm_list = []
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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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lm = lm_frame[lm_frame['landmark_index']==i]
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if len(lm) == 0:
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lm_list.append([0.0, 0.0, 0.0])
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lm_list.append([0.0,0.0,0.0])
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else:
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lm_list.append([
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float(lm['x'].iloc[0]),
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float(lm['y'].iloc[0]),
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float(lm['z'].iloc[0])
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])
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lm_list.append([float(lm['x'].iloc[0]), float(lm['y'].iloc[0]), float(lm['z'].iloc[0])])
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landmarks_seq.append(lm_list)
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return np.array(landmarks_seq, dtype=np.float32)
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except:
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return None
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def get_features_sequence(landmarks_seq, max_frames=100):
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if landmarks_seq is None or len(landmarks_seq) == 0:
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return None
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def get_features_sequence_augmented(landmarks_seq, max_frames=100):
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if landmarks_seq is None or len(landmarks_seq)==0:
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return None, None
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# Center on wrist
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landmarks_seq -= landmarks_seq[:, 0:1, :]
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# Robust scale: wrist → middle finger MCP
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landmarks_seq -= landmarks_seq[:,0:1,:]
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# Scale using wrist → middle finger MCP
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scale = np.linalg.norm(landmarks_seq[:,0] - landmarks_seq[:,9], axis=1, keepdims=True)
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scale = np.maximum(scale, 1e-6)
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landmarks_seq /= scale[:, np.newaxis, :]
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# Finger curl distances
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tips = [4,8,12,16,20]
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bases = [1,5,9,13,17]
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curl_features = []
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for b,t in zip(bases,tips):
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curl_features.append(np.linalg.norm(landmarks_seq[:,t]-landmarks_seq[:,b], axis=1))
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curl_features = np.stack(curl_features, axis=1) # (T,5)
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# Temporal deltas
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deltas = np.zeros_like(landmarks_seq)
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deltas[1:] = landmarks_seq[1:] - landmarks_seq[:-1]
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# Flatten
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seq = landmarks_seq.reshape(landmarks_seq.shape[0], -1)
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seq = np.concatenate([landmarks_seq, deltas, curl_features], axis=1)
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# Pad / truncate
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T = seq.shape[0]
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if T < max_frames:
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@@ -75,67 +88,64 @@ def get_features_sequence(landmarks_seq, max_frames=100):
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seq = np.concatenate([seq, pad])
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else:
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seq = seq[:max_frames]
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# Mask for valid frames
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# Mask
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valid_mask = np.zeros(max_frames, dtype=bool)
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valid_mask[:min(T, max_frames)] = True
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valid_mask[:min(T,max_frames)] = True
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return seq, valid_mask
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def process_row(row, base_path, max_frames=100):
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path = os.path.join(base_path, row["path"])
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if not os.path.exists(path):
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return None, None, None
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try:
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lm = extract_hand_landmarks_from_parquet(path)
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if lm is None:
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return None, None, None
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feat, mask = get_features_sequence(lm, max_frames)
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if feat is None:
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return None, None, None
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return feat, mask, row["sign"]
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except:
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seq, mask = get_features_sequence_augmented(lm, max_frames)
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if seq is None:
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return None, None, None
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return seq, mask, row["sign"]
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# ===============================
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# DATASET
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# ===============================
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class ASLSequenceDataset(Dataset):
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def __init__(self, X, masks, y):
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self.X = torch.from_numpy(X).float()
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self.masks = torch.from_numpy(masks)
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self.y = torch.from_numpy(y).long()
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def __len__(self):
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return len(self.X)
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def __getitem__(self, idx):
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return self.X[idx], self.masks[idx], self.y[idx]
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# ===============================
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# TRANSFORMER MODEL
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# ===============================
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class PositionalEncoding(nn.Module):
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def __init__(self, d_model, max_len=128):
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def __init__(self,d_model,max_len=128):
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super().__init__()
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pe = torch.zeros(max_len, d_model)
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position = torch.arange(0, max_len, dtype=torch.float32).unsqueeze(1)
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div_term = torch.exp(torch.arange(0, d_model, 2).float() * (-math.log(10000.0) / d_model))
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pe[:, 0::2] = torch.sin(position * div_term)
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pe[:, 1::2] = torch.cos(position * div_term)
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pe = torch.zeros(max_len,d_model)
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pos = 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(pos*div_term)
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pe[:,1::2] = torch.cos(pos*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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return x + self.pe[:, :x.size(1)]
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def forward(self,x):
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return x + self.pe[:,:x.size(1)]
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class TransformerASL(nn.Module):
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def __init__(self, input_dim=63, num_classes=250, d_model=128, nhead=4, num_layers=2):
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def __init__(self, input_dim=131, num_classes=250, d_model=192, nhead=6, 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.proj = nn.Linear(input_dim,d_model)
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self.norm_in = nn.LayerNorm(d_model)
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self.pos = PositionalEncoding(d_model)
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enc_layer = nn.TransformerEncoderLayer(
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d_model=d_model,
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nhead=nhead,
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dim_feedforward=d_model*4,
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dropout=0.1,
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activation='gelu',
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batch_first=True,
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norm_first=True
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d_model=d_model, nhead=nhead, dim_feedforward=d_model*4,
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dropout=0.2, activation='gelu', batch_first=True, norm_first=True
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)
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self.encoder = nn.TransformerEncoder(enc_layer, num_layers=num_layers)
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self.head = nn.Sequential(
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nn.LayerNorm(d_model),
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nn.Dropout(0.2),
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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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self.encoder = nn.TransformerEncoder(enc_layer,num_layers=num_layers)
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self.head = nn.Sequential(nn.LayerNorm(d_model), nn.Dropout(0.25), nn.Linear(d_model,num_classes))
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def forward(self,x,key_padding_mask=None):
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x = self.proj(x)
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x = self.norm_in(x)
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x = self.pos(x)
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@@ -143,159 +153,135 @@ class TransformerASL(nn.Module):
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x = x.mean(dim=1)
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return self.head(x)
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def create_padding_mask(valid_masks):
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# valid_masks: (B,T) bool, True for valid
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return ~valid_masks # True in mask = positions to ignore
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def create_padding_mask(lengths,max_len):
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return torch.arange(max_len,device=lengths.device)[None,:] >= lengths[:,None]
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# ===============================
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# MAIN
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# ===============================
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def main():
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device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
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print(f"Using device: {device}")
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if device.type == "cuda":
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print("GPU:", torch.cuda.get_device_name(0))
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base_path = "asl_kaggle"
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base_path = "asl_kaggle" # adjust path
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max_frames = 100
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MIN_SAMPLES_PER_CLASS = 6
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# --- LOAD DATA ---
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# Load metadata
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print("Loading metadata...")
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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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# Extract features
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print("Extracting features...")
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with Pool(cpu_count()) as pool:
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results = list(tqdm(
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pool.imap(partial(process_row, base_path=base_path, max_frames=max_frames), rows),
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total=len(rows),
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desc="Processing"
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))
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X_list, mask_list, y_list = [], [], []
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for feat, mask, sign in results:
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if feat is not None:
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X_list.append(feat)
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mask_list.append(mask)
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results = list(tqdm(pool.imap(partial(process_row,base_path=base_path,max_frames=max_frames),rows),
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total=len(rows)))
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X_list, masks_list, y_list = [], [], []
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for seq, mask, sign in results:
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if seq is not None:
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X_list.append(seq)
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masks_list.append(mask)
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y_list.append(sign)
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if not X_list:
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print("No valid sequences found.")
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print("No valid sequences found")
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return
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X = np.stack(X_list)
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masks = np.stack(mask_list)
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print(f"Loaded {len(X)} sequences | shape: {X.shape}")
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masks = np.stack(masks_list)
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print(f"{len(X)} sequences | shape: {X.shape}")
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# --- NORMALIZE only valid frames ---
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for i in range(X.shape[0]):
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valid_idx = masks[i]
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X[i, valid_idx] = (X[i, valid_idx] - X[i, valid_idx].mean(0)) / (X[i, valid_idx].std(0) + 1e-8)
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# Normalize
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mean = X.mean(axis=(0,1),keepdims=True)
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std = X.std(axis=(0,1),keepdims=True)+1e-8
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X = (X - mean)/std
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# --- LABELS ---
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# Labels
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le = LabelEncoder()
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y = le.fit_transform(y_list)
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# Remove rare classes
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# Remove classes with too few samples
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counts = Counter(y)
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valid_classes = [cls for cls, cnt in counts.items() if cnt >= MIN_SAMPLES_PER_CLASS]
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mask_keep = np.isin(y, valid_classes)
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X, masks, y = X[mask_keep], masks[mask_keep], y[mask_keep]
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le = LabelEncoder()
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y = le.fit_transform(y)
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print(f"{len(X)} samples remain | {len(le.classes_)} classes")
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valid_classes = [cls for cls,cnt in counts.items() if cnt>=MIN_SAMPLES_PER_CLASS]
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mask_valid = np.isin(y, valid_classes)
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X = X[mask_valid]; masks = masks[mask_valid]; y = y[mask_valid]
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le = LabelEncoder(); y = le.fit_transform(y)
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print(f"{len(X)} samples | {len(le.classes_)} classes")
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# --- SPLIT ---
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# Split
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X_train, X_test, masks_train, masks_test, y_train, y_test = train_test_split(
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X, masks, y, test_size=0.15, stratify=y, random_state=42
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)
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# --- DATASETS ---
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class ASLSequenceDataset(Dataset):
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def __init__(self, X, masks, y):
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self.X = torch.from_numpy(X).float()
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self.masks = torch.from_numpy(masks)
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self.y = torch.from_numpy(y).long()
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# Datasets / loaders
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train_dataset = ASLSequenceDataset(X_train, masks_train, y_train)
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test_dataset = ASLSequenceDataset(X_test, masks_test, y_test)
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train_loader = DataLoader(train_dataset, batch_size=64,shuffle=True,num_workers=4,pin_memory=True)
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test_loader = DataLoader(test_dataset, batch_size=96,shuffle=False,num_workers=4,pin_memory=True)
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def __len__(self):
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return len(self.X)
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def __getitem__(self, idx):
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return self.X[idx], self.masks[idx], self.y[idx]
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train_loader = DataLoader(ASLSequenceDataset(X_train, masks_train, y_train),
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batch_size=64, shuffle=True, num_workers=4, pin_memory=True)
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test_loader = DataLoader(ASLSequenceDataset(X_test, masks_test, y_test),
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batch_size=96, shuffle=False, num_workers=4, pin_memory=True)
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# --- MODEL ---
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# Model
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model = TransformerASL(input_dim=X.shape[2], num_classes=len(le.classes_)).to(device)
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print(f"Model params: {sum(p.numel() for p in model.parameters()):,}")
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criterion = nn.CrossEntropyLoss(label_smoothing=0.05)
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optimizer = optim.AdamW(model.parameters(), lr=1e-4, weight_decay=1e-4)
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# Class weights
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counts = Counter(y_train)
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class_weights = np.array([len(y_train)/ (len(counts)*counts[i]) for i in range(len(counts))],dtype=np.float32)
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criterion = nn.CrossEntropyLoss(weight=torch.tensor(class_weights).to(device),label_smoothing=0.05)
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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, T_0=10)
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# --- TRAINING ---
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best_acc = 0.0
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patience = 15
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wait = 0
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epochs = 70
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# Training / eval
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def train_epoch():
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model.train()
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total_loss = 0
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correct = total = 0
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for x, m, yb in tqdm(train_loader, desc="Train"):
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x, m, yb = x.to(device), m.to(device), yb.to(device)
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mask = create_padding_mask(m)
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total_loss=0; correct=total=0
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for x, mask, yb in tqdm(train_loader, desc="Train"):
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x, mask, yb = x.to(device), mask.to(device), yb.to(device)
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lengths = mask.sum(dim=1)
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pad_mask = create_padding_mask(lengths, x.size(1))
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optimizer.zero_grad(set_to_none=True)
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logits = model(x, key_padding_mask=mask)
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loss = criterion(logits, yb)
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logits = model(x,key_padding_mask=pad_mask)
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loss = criterion(logits,yb)
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loss.backward()
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torch.nn.utils.clip_grad_norm_(model.parameters(), max_norm=0.8)
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torch.nn.utils.clip_grad_norm_(model.parameters(),0.8)
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optimizer.step()
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total_loss += loss.item()
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correct += (logits.argmax(-1) == yb).sum().item()
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correct += (logits.argmax(-1)==yb).sum().item()
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total += yb.size(0)
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return total_loss / len(train_loader), correct / total * 100
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return total_loss/len(train_loader), correct/total*100
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@torch.no_grad()
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def evaluate():
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model.eval()
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correct = total = 0
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for x, m, yb in test_loader:
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x, m, yb = x.to(device), m.to(device), yb.to(device)
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mask = create_padding_mask(m)
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logits = model(x, key_padding_mask=mask)
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correct += (logits.argmax(-1) == yb).sum().item()
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correct=total=0
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for x, mask, yb in test_loader:
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x, mask, yb = x.to(device), mask.to(device), yb.to(device)
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lengths = mask.sum(dim=1)
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pad_mask = create_padding_mask(lengths, x.size(1))
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logits = model(x,key_padding_mask=pad_mask)
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correct += (logits.argmax(-1)==yb).sum().item()
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total += yb.size(0)
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return correct / total * 100 if total > 0 else 0.0
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return correct/total*100 if total>0 else 0.0
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# Train loop
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best_acc=0.0; patience=15; wait=0; epochs=70
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for epoch in range(epochs):
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loss, train_acc = train_epoch()
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test_acc = evaluate()
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print(f"[{epoch+1}/{epochs}] loss: {loss:.4f} | train: {train_acc:.2f}% | test: {test_acc:.2f}%")
|
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print(f"[{epoch+1:2d}/{epochs}] loss:{loss:.4f} train:{train_acc:.2f}% test:{test_acc:.2f}%")
|
||||
scheduler.step()
|
||||
if test_acc > best_acc:
|
||||
best_acc = test_acc
|
||||
wait = 0
|
||||
if test_acc>best_acc:
|
||||
best_acc=test_acc; wait=0
|
||||
torch.save({
|
||||
'model': model.state_dict(),
|
||||
'optimizer': optimizer.state_dict(),
|
||||
'label_encoder_classes': le.classes_,
|
||||
'acc': best_acc,
|
||||
'epoch': epoch
|
||||
}, "best_asl_transformer.pth")
|
||||
'model':model.state_dict(),
|
||||
'optimizer':optimizer.state_dict(),
|
||||
'label_encoder_classes':le.classes_,
|
||||
'acc':best_acc,
|
||||
'epoch':epoch
|
||||
},"best_asl_transformer.pth")
|
||||
print(" → New best saved")
|
||||
else:
|
||||
wait += 1
|
||||
if wait >= patience:
|
||||
wait+=1
|
||||
if wait>=patience:
|
||||
print("Early stopping")
|
||||
break
|
||||
print(f"\nBest test accuracy reached: {best_acc:.2f}%")
|
||||
|
||||
print(f"\nBest test accuracy: {best_acc:.2f}%")
|
||||
|
||||
if __name__ == '__main__':
|
||||
if __name__=="__main__":
|
||||
main()
|
||||
|
||||
Reference in New Issue
Block a user