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chatgpt lock tf in pt 2

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Stupdi Go
2026-01-10 23:41:56 -06:00
parent 7bab57bdcb
commit fa06e4d49e
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training.py
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@@ -14,21 +14,13 @@ import torch.optim as optim
from torch.utils.data import Dataset, DataLoader from torch.utils.data import Dataset, DataLoader
from sklearn.model_selection import train_test_split from sklearn.model_selection import train_test_split
from sklearn.preprocessing import LabelEncoder from sklearn.preprocessing import LabelEncoder
from collections import Counter
from multiprocessing import Pool, cpu_count from multiprocessing import Pool, cpu_count
from functools import partial from functools import partial
from tqdm import tqdm from tqdm import tqdm
from collections import Counter
# =============================== # ===============================
# DEVICE # DATA LOADING
# ===============================
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
print(f"Using device: {device}")
if device.type == "cuda":
print("GPU:", torch.cuda.get_device_name(0))
# ===============================
# DATA LOADING / FEATURES
# =============================== # ===============================
def load_kaggle_asl_data(base_path): def load_kaggle_asl_data(base_path):
train_df = pd.read_csv(os.path.join(base_path, "train.csv")) train_df = pd.read_csv(os.path.join(base_path, "train.csv"))
@@ -36,39 +28,52 @@ def load_kaggle_asl_data(base_path):
sign_to_idx = json.load(f) sign_to_idx = json.load(f)
return train_df, sign_to_idx return train_df, sign_to_idx
def extract_hand_landmarks_from_parquet(path): def extract_hand_landmarks_from_parquet(path):
try: try:
df = pd.read_parquet(path) df = pd.read_parquet(path)
left = df[df["type"] == "left_hand"] left = df[df["type"] == "left_hand"]
right = df[df["type"] == "right_hand"] right = df[df["type"] == "right_hand"]
hand = left if len(left) >= len(right) else right hand = left if len(left) >= len(right) else right
if len(hand) == 0: if len(hand) == 0:
return None return None
frames = sorted(hand['frame'].unique()) frames = sorted(hand['frame'].unique())
landmarks_seq = [] landmarks_seq = []
for f in frames:
lm_frame = hand[hand['frame']==f] for frame in frames:
lm_frame = hand[hand['frame'] == frame]
lm_list = [] lm_list = []
for i in range(21): for i in range(21):
lm = lm_frame[lm_frame['landmark_index'] == i] lm = lm_frame[lm_frame['landmark_index'] == i]
if len(lm) == 0: if len(lm) == 0:
lm_list.append([0.0, 0.0, 0.0]) lm_list.append([0.0, 0.0, 0.0])
else: else:
lm_list.append([float(lm['x'].iloc[0]), float(lm['y'].iloc[0]), float(lm['z'].iloc[0])]) lm_list.append([
float(lm['x'].iloc[0]),
float(lm['y'].iloc[0]),
float(lm['z'].iloc[0])
])
landmarks_seq.append(lm_list) landmarks_seq.append(lm_list)
return np.array(landmarks_seq, dtype=np.float32) return np.array(landmarks_seq, dtype=np.float32)
except: except:
return None return None
def get_features_sequence_augmented(landmarks_seq, max_frames=100):
def get_features_sequence(landmarks_seq, max_frames=100):
if landmarks_seq is None or len(landmarks_seq) == 0: if landmarks_seq is None or len(landmarks_seq) == 0:
return None, None return None, None
# Center on wrist # Center on wrist
landmarks_seq -= landmarks_seq[:,0:1,:] landmarks_seq = landmarks_seq - landmarks_seq[:, 0:1, :]
# Scale using wrist → middle finger MCP
# Scale using wrist → middle finger MCP distance
scale = np.linalg.norm(landmarks_seq[:, 0] - landmarks_seq[:, 9], axis=1, keepdims=True) scale = np.linalg.norm(landmarks_seq[:, 0] - landmarks_seq[:, 9], axis=1, keepdims=True)
scale = np.maximum(scale, 1e-6) scale = np.maximum(scale, 1e-6)
landmarks_seq /= scale[:, np.newaxis, :] landmarks_seq = landmarks_seq / scale[:, np.newaxis, :]
# Finger curl distances # Finger curl distances
tips = [4, 8, 12, 16, 20] tips = [4, 8, 12, 16, 20]
bases = [1, 5, 9, 13, 17] bases = [1, 5, 9, 13, 17]
@@ -76,47 +81,44 @@ def get_features_sequence_augmented(landmarks_seq, max_frames=100):
for b, t in zip(bases, tips): for b, t in zip(bases, tips):
curl_features.append(np.linalg.norm(landmarks_seq[:, t] - landmarks_seq[:, b], axis=1)) curl_features.append(np.linalg.norm(landmarks_seq[:, t] - landmarks_seq[:, b], axis=1))
curl_features = np.stack(curl_features, axis=1) # (T,5) curl_features = np.stack(curl_features, axis=1) # (T,5)
# Temporal deltas # Temporal deltas
deltas = np.zeros_like(landmarks_seq) deltas = np.zeros_like(landmarks_seq)
deltas[1:] = landmarks_seq[1:] - landmarks_seq[:-1] deltas[1:] = landmarks_seq[1:] - landmarks_seq[:-1]
# Flatten
seq = np.concatenate([landmarks_seq, deltas, curl_features], axis=1) # Flatten features along last axis
# Pad / truncate seq = np.concatenate([landmarks_seq, deltas, curl_features[:, :, np.newaxis]], axis=2)
T = seq.shape[0] seq = seq.reshape(seq.shape[0], -1) # (T, feature_dim)
# Pad or truncate to max_frames
T, F = seq.shape
if T < max_frames: if T < max_frames:
pad = np.zeros((max_frames - T, seq.shape[1]), dtype=np.float32) pad = np.zeros((max_frames - T, F), dtype=np.float32)
seq = np.concatenate([seq, pad]) seq = np.concatenate([seq, pad], axis=0)
else: elif T > max_frames:
seq = seq[:max_frames] seq = seq[:max_frames, :]
# Mask # Mask
valid_mask = np.zeros(max_frames, dtype=bool) valid_mask = np.zeros(max_frames, dtype=bool)
valid_mask[:min(T, max_frames)] = True valid_mask[:min(T, max_frames)] = True
return seq, valid_mask
return seq.astype(np.float32), valid_mask
def process_row(row, base_path, max_frames=100): def process_row(row, base_path, max_frames=100):
path = os.path.join(base_path, row["path"]) path = os.path.join(base_path, row["path"])
if not os.path.exists(path): if not os.path.exists(path):
return None, None, None return None, None, None
try:
lm = extract_hand_landmarks_from_parquet(path) lm = extract_hand_landmarks_from_parquet(path)
if lm is None: if lm is None:
return None, None, None return None, None, None
seq, mask = get_features_sequence_augmented(lm, max_frames) feat, mask = get_features_sequence(lm, max_frames)
if seq is None: if feat is None:
return None, None, None
return feat, mask, row["sign"]
except:
return None, None, None return None, None, None
return seq, mask, row["sign"]
# ===============================
# DATASET
# ===============================
class ASLSequenceDataset(Dataset):
def __init__(self, X, masks, y):
self.X = torch.from_numpy(X).float()
self.masks = torch.from_numpy(masks)
self.y = torch.from_numpy(y).long()
def __len__(self):
return len(self.X)
def __getitem__(self, idx):
return self.X[idx], self.masks[idx], self.y[idx]
# =============================== # ===============================
# TRANSFORMER MODEL # TRANSFORMER MODEL
@@ -125,26 +127,40 @@ class PositionalEncoding(nn.Module):
def __init__(self, d_model, max_len=128): def __init__(self, d_model, max_len=128):
super().__init__() super().__init__()
pe = torch.zeros(max_len, d_model) pe = torch.zeros(max_len, d_model)
pos = torch.arange(0,max_len,dtype=torch.float).unsqueeze(1) position = torch.arange(0, max_len, dtype=torch.float).unsqueeze(1)
div_term = torch.exp(torch.arange(0, d_model, 2).float() * (-math.log(10000.0) / d_model)) div_term = torch.exp(torch.arange(0, d_model, 2).float() * (-math.log(10000.0) / d_model))
pe[:,0::2] = torch.sin(pos*div_term) pe[:, 0::2] = torch.sin(position * div_term)
pe[:,1::2] = torch.cos(pos*div_term) pe[:, 1::2] = torch.cos(position * div_term)
self.register_buffer('pe', pe.unsqueeze(0)) self.register_buffer('pe', pe.unsqueeze(0))
def forward(self, x): def forward(self, x):
return x + self.pe[:, :x.size(1)] return x + self.pe[:, :x.size(1)]
class TransformerASL(nn.Module): class TransformerASL(nn.Module):
def __init__(self, input_dim=131, num_classes=250, d_model=192, nhead=6, num_layers=4): def __init__(self, input_dim=63, num_classes=250, d_model=192, nhead=6, num_layers=4):
super().__init__() super().__init__()
self.proj = nn.Linear(input_dim, d_model) self.proj = nn.Linear(input_dim, d_model)
self.norm_in = nn.LayerNorm(d_model) self.norm_in = nn.LayerNorm(d_model)
self.pos = PositionalEncoding(d_model) self.pos = PositionalEncoding(d_model)
enc_layer = nn.TransformerEncoderLayer( enc_layer = nn.TransformerEncoderLayer(
d_model=d_model, nhead=nhead, dim_feedforward=d_model*4, d_model=d_model,
dropout=0.2, activation='gelu', batch_first=True, norm_first=True nhead=nhead,
dim_feedforward=d_model * 4,
dropout=0.15,
activation='gelu',
batch_first=True,
norm_first=True
) )
self.encoder = nn.TransformerEncoder(enc_layer, num_layers=num_layers) self.encoder = nn.TransformerEncoder(enc_layer, num_layers=num_layers)
self.head = nn.Sequential(nn.LayerNorm(d_model), nn.Dropout(0.25), nn.Linear(d_model,num_classes))
self.head = nn.Sequential(
nn.LayerNorm(d_model),
nn.Dropout(0.25),
nn.Linear(d_model, num_classes)
)
def forward(self, x, key_padding_mask=None): def forward(self, x, key_padding_mask=None):
x = self.proj(x) x = self.proj(x)
x = self.norm_in(x) x = self.norm_in(x)
@@ -153,121 +169,156 @@ class TransformerASL(nn.Module):
x = x.mean(dim=1) x = x.mean(dim=1)
return self.head(x) return self.head(x)
def create_padding_mask(lengths, max_len): def create_padding_mask(lengths, max_len):
return torch.arange(max_len, device=lengths.device)[None, :] >= lengths[:, None] return torch.arange(max_len, device=lengths.device)[None, :] >= lengths[:, None]
# =============================== # ===============================
# MAIN # MAIN TRAINING
# =============================== # ===============================
def main(): def main():
base_path = "asl_kaggle" # adjust path device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
print(f"Using device: {device}")
if device.type == "cuda":
print("GPU:", torch.cuda.get_device_name(0))
base_path = "asl_kaggle" # ← set your dataset path
max_frames = 100 max_frames = 100
MIN_SAMPLES_PER_CLASS = 6 MIN_SAMPLES_PER_CLASS = 6
# Load metadata
print("Loading metadata...") print("Loading metadata...")
train_df, sign_to_idx = load_kaggle_asl_data(base_path) train_df, sign_to_idx = load_kaggle_asl_data(base_path)
rows = [row for _, row in train_df.iterrows()] rows = [row for _, row in train_df.iterrows()]
# Extract features print("Processing landmark sequences...")
print("Extracting features...")
with Pool(cpu_count()) as pool: with Pool(cpu_count()) as pool:
results = list(tqdm(pool.imap(partial(process_row,base_path=base_path,max_frames=max_frames),rows), results = list(tqdm(
total=len(rows))) pool.imap(
partial(process_row, base_path=base_path, max_frames=max_frames),
rows
),
total=len(rows),
desc="Extracting landmarks"
))
X_list, masks_list, y_list = [], [], [] X_list, masks_list, y_list = [], [], []
for seq, mask, sign in results: for feat, mask, sign in results:
if seq is not None: if feat is not None and feat.shape[0] == max_frames:
X_list.append(seq) X_list.append(feat)
masks_list.append(mask) masks_list.append(mask)
y_list.append(sign) y_list.append(sign)
if not X_list: if not X_list:
print("No valid sequences found") print("No valid sequences found. Check parquet files / paths.")
return return
X = np.stack(X_list) X = np.stack(X_list)
masks = np.stack(masks_list) masks = np.stack(masks_list)
print(f"{len(X)} sequences | shape: {X.shape}") print(f"Loaded {len(X)} sequences | shape: {X.shape}")
# Normalize # Global normalization
X = np.clip(X, -5.0, 5.0)
mean = X.mean(axis=(0, 1), keepdims=True) mean = X.mean(axis=(0, 1), keepdims=True)
std = X.std(axis=(0, 1), keepdims=True) + 1e-8 std = X.std(axis=(0, 1), keepdims=True) + 1e-8
X = (X - mean) / std X = (X - mean) / std
# Labels
le = LabelEncoder() le = LabelEncoder()
y = le.fit_transform(y_list) y = le.fit_transform(y_list)
# Remove classes with too few samples
counts = Counter(y) counts = Counter(y)
valid_classes = [cls for cls, cnt in counts.items() if cnt >= MIN_SAMPLES_PER_CLASS] valid_classes = [cls for cls, cnt in counts.items() if cnt >= MIN_SAMPLES_PER_CLASS]
mask_valid = np.isin(y, valid_classes) mask_valid = np.isin(y, valid_classes)
X = X[mask_valid]; masks = masks[mask_valid]; y = y[mask_valid] X = X[mask_valid]
le = LabelEncoder(); y = le.fit_transform(y) masks = masks[mask_valid]
print(f"{len(X)} samples | {len(le.classes_)} classes") y = y[mask_valid]
# Split le = LabelEncoder()
y = le.fit_transform(y)
print(f"{len(X)} samples | {len(le.classes_)} classes after filtering")
# Train-test split
X_train, X_test, masks_train, masks_test, y_train, y_test = train_test_split( X_train, X_test, masks_train, masks_test, y_train, y_test = train_test_split(
X, masks, y, test_size=0.15, stratify=y, random_state=42 X, masks, y, test_size=0.15, stratify=y, random_state=42
) )
# Datasets / loaders # Dataset
train_dataset = ASLSequenceDataset(X_train, masks_train, y_train) class ASLSequenceDataset(Dataset):
test_dataset = ASLSequenceDataset(X_test, masks_test, y_test) def __init__(self, X, masks, y):
train_loader = DataLoader(train_dataset, batch_size=64,shuffle=True,num_workers=4,pin_memory=True) self.X = torch.from_numpy(X).float()
test_loader = DataLoader(test_dataset, batch_size=96,shuffle=False,num_workers=4,pin_memory=True) self.masks = torch.from_numpy(masks)
self.y = torch.from_numpy(y).long()
# Model def __len__(self):
model = TransformerASL(input_dim=X.shape[2], num_classes=len(le.classes_)).to(device) return len(self.X)
print(f"Model params: {sum(p.numel() for p in model.parameters()):,}")
# Class weights def __getitem__(self, idx):
counts = Counter(y_train) return self.X[idx], self.masks[idx], self.y[idx]
class_weights = np.array([len(y_train)/ (len(counts)*counts[i]) for i in range(len(counts))],dtype=np.float32)
criterion = nn.CrossEntropyLoss(weight=torch.tensor(class_weights).to(device),label_smoothing=0.05) train_loader = DataLoader(
optimizer = optim.AdamW(model.parameters(), lr=3e-4, weight_decay=1e-4) ASLSequenceDataset(X_train, masks_train, y_train),
batch_size=64, shuffle=True, num_workers=4, pin_memory=True
)
test_loader = DataLoader(
ASLSequenceDataset(X_test, masks_test, y_test),
batch_size=96, shuffle=False, num_workers=4, pin_memory=True
)
model = TransformerASL(
input_dim=X.shape[2],
num_classes=len(le.classes_),
d_model=192,
nhead=6,
num_layers=4
).to(device)
print(f"Model parameters: {sum(p.numel() for p in model.parameters()):,}")
criterion = nn.CrossEntropyLoss(label_smoothing=0.05)
optimizer = optim.AdamW(model.parameters(), lr=5e-4, weight_decay=1e-4)
scheduler = optim.lr_scheduler.CosineAnnealingWarmRestarts(optimizer, T_0=10) scheduler = optim.lr_scheduler.CosineAnnealingWarmRestarts(optimizer, T_0=10)
# Training / eval # Training
def train_epoch(): best_acc = 0.0
patience = 15
wait = 0
epochs = 70
for epoch in range(epochs):
model.train() model.train()
total_loss=0; correct=total=0 total_loss = 0
correct = total = 0
for x, mask, yb in tqdm(train_loader, desc="Train"): for x, mask, yb in tqdm(train_loader, desc="Train"):
x, mask, yb = x.to(device), mask.to(device), yb.to(device) x, mask, yb = x.to(device), mask.to(device), yb.to(device)
lengths = mask.sum(dim=1) key_mask = ~mask # True where padding
pad_mask = create_padding_mask(lengths, x.size(1))
optimizer.zero_grad(set_to_none=True) optimizer.zero_grad(set_to_none=True)
logits = model(x,key_padding_mask=pad_mask) logits = model(x, key_padding_mask=key_mask)
loss = criterion(logits, yb) loss = criterion(logits, yb)
loss.backward() loss.backward()
torch.nn.utils.clip_grad_norm_(model.parameters(),0.8) torch.nn.utils.clip_grad_norm_(model.parameters(), max_norm=0.8)
optimizer.step() optimizer.step()
total_loss += loss.item() total_loss += loss.item()
correct += (logits.argmax(-1) == yb).sum().item() correct += (logits.argmax(-1) == yb).sum().item()
total += yb.size(0) total += yb.size(0)
return total_loss/len(train_loader), correct/total*100 train_acc = correct / total * 100
@torch.no_grad() # Eval
def evaluate():
model.eval() model.eval()
correct = total = 0 correct = total = 0
with torch.no_grad():
for x, mask, yb in test_loader: for x, mask, yb in test_loader:
x, mask, yb = x.to(device), mask.to(device), yb.to(device) x, mask, yb = x.to(device), mask.to(device), yb.to(device)
lengths = mask.sum(dim=1) key_mask = ~mask
pad_mask = create_padding_mask(lengths, x.size(1)) logits = model(x, key_padding_mask=key_mask)
logits = model(x,key_padding_mask=pad_mask)
correct += (logits.argmax(-1) == yb).sum().item() correct += (logits.argmax(-1) == yb).sum().item()
total += yb.size(0) total += yb.size(0)
return correct/total*100 if total>0 else 0.0 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}%")
# Train loop
best_acc=0.0; patience=15; wait=0; epochs=70
for epoch in range(epochs):
loss, train_acc = train_epoch()
test_acc = evaluate()
print(f"[{epoch+1:2d}/{epochs}] loss:{loss:.4f} train:{train_acc:.2f}% test:{test_acc:.2f}%")
scheduler.step() scheduler.step()
if test_acc > best_acc: if test_acc > best_acc:
best_acc=test_acc; wait=0 best_acc = test_acc
wait = 0
torch.save({ torch.save({
'model': model.state_dict(), 'model': model.state_dict(),
'optimizer': optimizer.state_dict(), 'optimizer': optimizer.state_dict(),
@@ -281,7 +332,8 @@ def main():
if wait >= patience: if wait >= patience:
print("Early stopping") print("Early stopping")
break 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() main()