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AI, we don't believe that you can fly bro pt 2

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This commit is contained in:
Stupdi Go
2026-01-11 15:30:49 -06:00
parent ad33523ae2
commit 10880809a7
2 changed files with 794 additions and 89 deletions
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257
training.py
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@@ -28,6 +28,7 @@ if torch.cuda.is_available():
print(f"✓ GPU: {torch.cuda.get_device_name(0)}")
device = torch.device('cuda:0')
torch.backends.cudnn.benchmark = True
torch.backends.cudnn.enabled = True
else:
print("✗ CUDA not available, using CPU")
device = torch.device('cpu')
@@ -36,7 +37,7 @@ print("=" * 60)
# ===============================
# DATA LOADING WITH NaN HANDLING
# DATA LOADING - HANDLES PARTIAL NaN
# ===============================
def load_kaggle_asl_data(base_path):
train_df = pd.read_csv(os.path.join(base_path, "train.csv"))
@@ -46,7 +47,7 @@ def load_kaggle_asl_data(base_path):
def extract_hand_landmarks_from_parquet(path):
"""Extract hand landmarks, handling NaN values properly"""
"""Extract hand landmarks - ONLY uses frames with valid (non-NaN) data"""
try:
df = pd.read_parquet(path)
@@ -54,56 +55,70 @@ def extract_hand_landmarks_from_parquet(path):
left = df[df["type"] == "left_hand"]
right = df[df["type"] == "right_hand"]
# Choose hand with more non-NaN data
left_valid = left[['x', 'y', 'z']].notna().all(axis=1).sum()
right_valid = right[['x', 'y', 'z']].notna().all(axis=1).sum()
if left_valid == 0 and right_valid == 0:
return None # No valid hand data
hand = left if left_valid >= right_valid else right
if len(hand) == 0:
if len(left) == 0 and len(right) == 0:
return None
# Get frames with valid data
# Count valid (non-NaN) rows for each hand
left_valid = 0
right_valid = 0
if len(left) > 0:
left_valid = left[['x', 'y', 'z']].notna().all(axis=1).sum()
if len(right) > 0:
right_valid = right[['x', 'y', 'z']].notna().all(axis=1).sum()
# No valid data at all
if left_valid == 0 and right_valid == 0:
return None
# Choose hand with more valid data
hand = left if left_valid >= right_valid else right
# Get unique frames
frames = sorted(hand['frame'].unique())
landmarks_seq = []
for frame in frames:
lm_frame = hand[hand['frame'] == frame]
# Check if this frame has valid data
valid_rows = lm_frame[['x', 'y', 'z']].notna().all(axis=1)
if valid_rows.sum() < 10: # Need at least 10 valid landmarks
# Count how many valid landmarks this frame has
valid_count = lm_frame[['x', 'y', 'z']].notna().all(axis=1).sum()
# Skip frames with too few valid landmarks
if valid_count < 10:
continue
lm_list = []
frame_has_data = False
# Extract landmarks for this frame
frame_landmarks = []
valid_landmarks_in_frame = 0
for i in range(21):
lm = lm_frame[lm_frame['landmark_index'] == i]
if len(lm) == 0:
lm_list.append([0.0, 0.0, 0.0])
frame_landmarks.append([0.0, 0.0, 0.0])
else:
x = lm['x'].iloc[0]
y = lm['y'].iloc[0]
z = lm['z'].iloc[0]
x = float(lm['x'].iloc[0])
y = float(lm['y'].iloc[0])
z = float(lm['z'].iloc[0])
# Check for NaN
if pd.isna(x) or pd.isna(y) or pd.isna(z):
lm_list.append([0.0, 0.0, 0.0])
# Check if valid
if pd.notna(x) and pd.notna(y) and pd.notna(z):
frame_landmarks.append([x, y, z])
valid_landmarks_in_frame += 1
else:
lm_list.append([float(x), float(y), float(z)])
frame_has_data = True
frame_landmarks.append([0.0, 0.0, 0.0])
if frame_has_data:
landmarks_seq.append(lm_list)
# Only add frame if it has enough valid landmarks
if valid_landmarks_in_frame >= 10:
landmarks_seq.append(frame_landmarks)
if len(landmarks_seq) == 0:
# Need at least 3 valid frames
if len(landmarks_seq) < 3:
return None
return np.array(landmarks_seq, dtype=np.float32)
except Exception as e:
return None
@@ -113,43 +128,57 @@ def get_features_sequence(landmarks_seq, max_frames=100):
if landmarks_seq is None or len(landmarks_seq) == 0:
return None, None
# Center on wrist
landmarks_seq = landmarks_seq - landmarks_seq[:, 0:1, :]
# Center on wrist (landmark 0)
wrist = landmarks_seq[:, 0:1, :].copy()
landmarks_seq = landmarks_seq - wrist
# Scale using wrist → middle finger MCP distance
scale = np.linalg.norm(landmarks_seq[:, 0] - landmarks_seq[:, 9], axis=1, keepdims=True)
scale = np.maximum(scale, 1e-6)
# Scale normalization using wrist to middle finger MCP (landmark 9)
scale = np.linalg.norm(landmarks_seq[:, 9, :] - np.zeros(3), axis=1, keepdims=True)
scale = np.maximum(scale, 1e-6) # Avoid division by zero
landmarks_seq = landmarks_seq / scale[:, np.newaxis, :]
# Replace any remaining NaN/Inf with 0
# Clean up any remaining NaN/Inf
landmarks_seq = np.nan_to_num(landmarks_seq, nan=0.0, posinf=0.0, neginf=0.0)
# Finger curl distances
tips = [4, 8, 12, 16, 20]
bases = [1, 5, 9, 13, 17]
# Clip extreme values
landmarks_seq = np.clip(landmarks_seq, -10, 10)
# Calculate finger curl features
tips = [4, 8, 12, 16, 20] # Thumb, index, middle, ring, pinky tips
bases = [1, 5, 9, 13, 17] # Corresponding base joints
curl_features = []
for b, t in zip(bases, tips):
curl = np.linalg.norm(landmarks_seq[:, t] - landmarks_seq[:, b], axis=1)
curl_features.append(curl)
curl_features = np.stack(curl_features, axis=1)
curl_features = np.stack(curl_features, axis=1) # (T, 5)
# Temporal deltas
# Temporal deltas (motion)
deltas = np.zeros_like(landmarks_seq)
deltas[1:] = landmarks_seq[1:] - landmarks_seq[:-1]
if len(landmarks_seq) > 1:
deltas[1:] = landmarks_seq[1:] - landmarks_seq[:-1]
# Flatten features
seq = np.concatenate([landmarks_seq, deltas, curl_features[:, :, np.newaxis]], axis=2)
# Combine all features
seq = np.concatenate([
landmarks_seq, # (T, 21, 3)
deltas, # (T, 21, 3)
curl_features[:, :, np.newaxis] # (T, 5, 1)
], axis=2)
# Flatten spatial dimensions: (T, 21*3 + 21*3 + 5) = (T, 131)
seq = seq.reshape(seq.shape[0], -1)
# Pad or truncate
# Pad or truncate to max_frames
T, F = seq.shape
if T < max_frames:
# Pad with zeros
pad = np.zeros((max_frames - T, F), dtype=np.float32)
seq = np.concatenate([seq, pad], axis=0)
elif T > max_frames:
# Truncate
seq = seq[:max_frames, :]
# Create mask
# Create attention mask (True for valid positions)
valid_mask = np.zeros(max_frames, dtype=bool)
valid_mask[:min(T, max_frames)] = True
@@ -157,26 +186,30 @@ def get_features_sequence(landmarks_seq, max_frames=100):
def process_row(row, base_path, max_frames=100):
"""Process a single row"""
"""Process a single row - worker function for multiprocessing"""
path = os.path.join(base_path, row["path"])
if not os.path.exists(path):
return None, None, None
try:
# Extract landmarks
lm = extract_hand_landmarks_from_parquet(path)
if lm is None:
return None, None, None
# Get features
feat, mask = get_features_sequence(lm, max_frames)
if feat is None:
return None, None, None
# Final NaN check
# Final safety check
if np.isnan(feat).any() or np.isinf(feat).any():
return None, None, None
return feat, mask, row["sign"]
except:
except Exception as e:
return None, None, None
@@ -198,12 +231,15 @@ class PositionalEncoding(nn.Module):
class TransformerASL(nn.Module):
def __init__(self, input_dim=68, num_classes=250, d_model=256, nhead=8, num_layers=4):
def __init__(self, input_dim, num_classes, d_model=256, nhead=8, num_layers=4):
super().__init__()
# Input projection
self.proj = nn.Linear(input_dim, d_model)
self.norm_in = nn.LayerNorm(d_model)
self.pos = PositionalEncoding(d_model)
self.pos = PositionalEncoding(d_model, max_len=128)
# Transformer encoder
enc_layer = nn.TransformerEncoderLayer(
d_model=d_model,
nhead=nhead,
@@ -215,6 +251,7 @@ class TransformerASL(nn.Module):
)
self.encoder = nn.TransformerEncoder(enc_layer, num_layers=num_layers)
# Classification head
self.head = nn.Sequential(
nn.LayerNorm(d_model),
nn.Dropout(0.25),
@@ -222,11 +259,17 @@ class TransformerASL(nn.Module):
)
def forward(self, x, key_padding_mask=None):
# x: (batch, seq_len, input_dim)
# key_padding_mask: (batch, seq_len) - True for padding positions
x = self.proj(x)
x = self.norm_in(x)
x = self.pos(x)
x = self.encoder(x, src_key_padding_mask=key_padding_mask)
# Global average pooling over valid positions
x = x.mean(dim=1)
return self.head(x)
@@ -236,7 +279,7 @@ class TransformerASL(nn.Module):
def main():
base_path = "asl_kaggle"
max_frames = 100
MIN_SAMPLES_PER_CLASS = 6
MIN_SAMPLES_PER_CLASS = 5
print("\nLoading metadata...")
train_df, sign_to_idx = load_kaggle_asl_data(base_path)
@@ -244,7 +287,10 @@ def main():
rows = [row for _, row in train_df.iterrows()]
print("\nProcessing sequences with NaN handling...")
print("\nProcessing sequences (this will take a few minutes)...")
print("Expected: ~36,000 valid sequences based on diagnostic")
# Process with multiprocessing
with Pool(cpu_count()) as pool:
results = list(tqdm(
pool.imap(
@@ -259,27 +305,30 @@ def main():
# Filter valid results
X_list, masks_list, y_list = [], [], []
for feat, mask, sign in results:
if feat is not None and feat.shape[0] == max_frames:
X_list.append(feat)
masks_list.append(mask)
y_list.append(sign)
if feat is not None and mask is not None and sign is not None:
if feat.shape[0] == max_frames:
X_list.append(feat)
masks_list.append(mask)
y_list.append(sign)
print(f"\n✓ Valid sequences: {len(X_list)} out of {len(train_df)}")
print(f"\n✓ Successfully extracted: {len(X_list)} valid sequences")
print(f" Success rate: {len(X_list) / len(train_df) * 100:.1f}%")
if not X_list:
print("❌ No valid sequences found!")
print("\nPossible issues:")
print(" 1. Most files contain only NaN hand landmarks")
print(" 2. Hand detection failed in most videos")
print(" 3. Dataset might be corrupted")
if len(X_list) < 100:
print("❌ Too few valid sequences found!")
print(" This shouldn't happen - please share this output for debugging")
return
# Stack into arrays
X = np.stack(X_list)
masks = np.stack(masks_list)
print(f"Data shape: {X.shape}")
print(f"\nData shape: {X.shape}")
print(f"Feature dimension: {X.shape[2]}")
# Global normalization
X = np.clip(X, -5.0, 5.0)
print("Normalizing features...")
X = np.clip(X, -10.0, 10.0)
mean = X.mean(axis=(0, 1), keepdims=True)
std = X.std(axis=(0, 1), keepdims=True) + 1e-8
X = (X - mean) / std
@@ -292,22 +341,29 @@ def main():
counts = Counter(y)
valid_classes = [cls for cls, cnt in counts.items() if cnt >= MIN_SAMPLES_PER_CLASS]
mask_valid = np.isin(y, valid_classes)
X = X[mask_valid]
masks = masks[mask_valid]
y = y[mask_valid]
# Re-encode
# Re-encode after filtering
le = LabelEncoder()
y = le.fit_transform(y)
print(f"Final dataset: {len(X)} samples | {len(le.classes_)} classes")
print(f"\nFinal dataset after filtering:")
print(f" Samples: {len(X):,}")
print(f" Classes: {len(le.classes_)}")
print(f" Sign examples: {list(le.classes_[:10])}")
# 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
)
# Dataset
print(f"\nTrain set: {len(X_train):,} samples")
print(f"Test set: {len(X_test):,} samples")
# Dataset wrapper
class ASLSequenceDataset(Dataset):
def __init__(self, X, masks, y):
self.X = torch.from_numpy(X).float()
@@ -320,18 +376,27 @@ def main():
def __getitem__(self, idx):
return self.X[idx], self.masks[idx], self.y[idx]
# DataLoaders
batch_size = 128 if device.type == 'cuda' else 64
train_loader = DataLoader(
ASLSequenceDataset(X_train, masks_train, y_train),
batch_size=batch_size, shuffle=True, num_workers=4, pin_memory=True
)
test_loader = DataLoader(
ASLSequenceDataset(X_test, masks_test, y_test),
batch_size=batch_size * 2, shuffle=False, num_workers=4, pin_memory=True
batch_size=batch_size,
shuffle=True,
num_workers=4,
pin_memory=True if device.type == 'cuda' else False
)
# Model
test_loader = DataLoader(
ASLSequenceDataset(X_test, masks_test, y_test),
batch_size=batch_size * 2,
shuffle=False,
num_workers=4,
pin_memory=True if device.type == 'cuda' else False
)
# Initialize model
print("\nInitializing model...")
model = TransformerASL(
input_dim=X.shape[2],
num_classes=len(le.classes_),
@@ -341,35 +406,41 @@ def main():
).to(device)
total_params = sum(p.numel() for p in model.parameters())
print(f"\nModel parameters: {total_params:,}")
print(f"Model parameters: {total_params:,}")
# Training setup
criterion = nn.CrossEntropyLoss(label_smoothing=0.05)
optimizer = optim.AdamW(model.parameters(), lr=5e-4, weight_decay=1e-4)
scheduler = optim.lr_scheduler.CosineAnnealingWarmRestarts(optimizer, T_0=10)
scheduler = optim.lr_scheduler.CosineAnnealingWarmRestarts(optimizer, T_0=10, T_mult=2)
# Training
# Training loop
best_acc = 0.0
patience = 15
wait = 0
epochs = 70
epochs = 60
print("\nStarting training...")
print("\n" + "=" * 60)
print("STARTING TRAINING")
print("=" * 60)
for epoch in range(epochs):
# Train
model.train()
total_loss = 0
correct = total = 0
for x, mask, yb in tqdm(train_loader, desc=f"Epoch {epoch + 1}/{epochs}"):
for x, mask, yb in tqdm(train_loader, desc=f"Epoch {epoch + 1}/{epochs}", leave=False):
x, mask, yb = x.to(device), mask.to(device), yb.to(device)
# Invert mask: True for padding positions
key_mask = ~mask
optimizer.zero_grad(set_to_none=True)
logits = model(x, key_padding_mask=key_mask)
loss = criterion(logits, yb)
loss.backward()
torch.nn.utils.clip_grad_norm_(model.parameters(), max_norm=0.8)
torch.nn.utils.clip_grad_norm_(model.parameters(), max_norm=1.0)
optimizer.step()
total_loss += loss.item()
@@ -378,7 +449,7 @@ def main():
train_acc = correct / total * 100
# Eval
# Evaluate
model.eval()
correct = total = 0
with torch.no_grad():
@@ -391,11 +462,13 @@ def main():
test_acc = correct / total * 100
# Print progress
print(f"[{epoch + 1:2d}/{epochs}] Loss: {total_loss / len(train_loader):.4f} | "
f"Train: {train_acc:.2f}% | Test: {test_acc:.2f}%")
f"Train: {train_acc:.2f}% | Test: {test_acc:.2f}%", end="")
scheduler.step()
# Save best model
if test_acc > best_acc:
best_acc = test_acc
wait = 0
@@ -406,19 +479,25 @@ def main():
'acc': best_acc,
'epoch': epoch,
'input_dim': X.shape[2],
'num_classes': len(le.classes_)
'num_classes': len(le.classes_),
'd_model': 256,
'nhead': 8,
'num_layers': 4
}, "best_asl_transformer.pth")
print(f" → New best: {best_acc:.2f}%")
print(f" → New best: {best_acc:.2f}% ✓")
else:
wait += 1
print()
if wait >= patience:
print("Early stopping")
print(f"\nEarly stopping triggered at epoch {epoch + 1}")
break
print("=" * 60)
print(f"\n✓ Training complete!")
print("\n" + "=" * 60)
print(f"✓ Training complete!")
print(f"✓ Best test accuracy: {best_acc:.2f}%")
print(f"✓ Model saved: best_asl_transformer.pth")
print("=" * 60)
if __name__ == "__main__":