506 lines
15 KiB
Python
506 lines
15 KiB
Python
import os
|
|
import json
|
|
import math
|
|
import numpy as np
|
|
import pandas as pd
|
|
import torch
|
|
import torch.nn as nn
|
|
import torch.nn.functional as F
|
|
import torch.optim as optim
|
|
|
|
from torch.utils.data import Dataset, DataLoader
|
|
from sklearn.model_selection import train_test_split
|
|
from sklearn.preprocessing import LabelEncoder
|
|
from multiprocessing import Pool, cpu_count
|
|
from functools import partial
|
|
from tqdm import tqdm
|
|
from collections import Counter
|
|
|
|
# ===============================
|
|
# GPU CONFIGURATION
|
|
# ===============================
|
|
print("=" * 60)
|
|
print("GPU CONFIGURATION")
|
|
print("=" * 60)
|
|
|
|
if torch.cuda.is_available():
|
|
print(f"✓ CUDA 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')
|
|
|
|
print("=" * 60)
|
|
|
|
|
|
# ===============================
|
|
# DATA LOADING - HANDLES PARTIAL NaN
|
|
# ===============================
|
|
def load_kaggle_asl_data(base_path):
|
|
train_df = pd.read_csv(os.path.join(base_path, "train.csv"))
|
|
with open(os.path.join(base_path, "sign_to_prediction_index_map.json")) as f:
|
|
sign_to_idx = json.load(f)
|
|
return train_df, sign_to_idx
|
|
|
|
|
|
def extract_hand_landmarks_from_parquet(path):
|
|
"""Extract hand landmarks - ONLY uses frames with valid (non-NaN) data"""
|
|
try:
|
|
df = pd.read_parquet(path)
|
|
|
|
# Get hand data
|
|
left = df[df["type"] == "left_hand"]
|
|
right = df[df["type"] == "right_hand"]
|
|
|
|
if len(left) == 0 and len(right) == 0:
|
|
return None
|
|
|
|
# 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]
|
|
|
|
# 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
|
|
|
|
# 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:
|
|
frame_landmarks.append([0.0, 0.0, 0.0])
|
|
else:
|
|
x = float(lm['x'].iloc[0])
|
|
y = float(lm['y'].iloc[0])
|
|
z = float(lm['z'].iloc[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:
|
|
frame_landmarks.append([0.0, 0.0, 0.0])
|
|
|
|
# Only add frame if it has enough valid landmarks
|
|
if valid_landmarks_in_frame >= 10:
|
|
landmarks_seq.append(frame_landmarks)
|
|
|
|
# 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
|
|
|
|
|
|
def get_features_sequence(landmarks_seq, max_frames=100):
|
|
"""Extract features from landmark sequence"""
|
|
if landmarks_seq is None or len(landmarks_seq) == 0:
|
|
return None, None
|
|
|
|
# Center on wrist (landmark 0)
|
|
wrist = landmarks_seq[:, 0:1, :].copy()
|
|
landmarks_seq = landmarks_seq - wrist
|
|
|
|
# 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, :]
|
|
|
|
# Clean up any remaining NaN/Inf
|
|
landmarks_seq = np.nan_to_num(landmarks_seq, nan=0.0, posinf=0.0, neginf=0.0)
|
|
|
|
# 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) # (T, 5)
|
|
|
|
# Temporal deltas (motion)
|
|
deltas = np.zeros_like(landmarks_seq)
|
|
if len(landmarks_seq) > 1:
|
|
deltas[1:] = landmarks_seq[1:] - landmarks_seq[:-1]
|
|
|
|
# Flatten each component separately, then concatenate
|
|
landmarks_flat = landmarks_seq.reshape(landmarks_seq.shape[0], -1) # (T, 63)
|
|
deltas_flat = deltas.reshape(deltas.shape[0], -1) # (T, 63)
|
|
# curl_features is already (T, 5)
|
|
|
|
# Combine: 63 + 63 + 5 = 131 features per frame
|
|
seq = np.concatenate([
|
|
landmarks_flat,
|
|
deltas_flat,
|
|
curl_features
|
|
], axis=1)
|
|
|
|
# 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 attention mask (True for valid positions)
|
|
valid_mask = np.zeros(max_frames, dtype=bool)
|
|
valid_mask[:min(T, max_frames)] = True
|
|
|
|
return seq.astype(np.float32), valid_mask
|
|
|
|
|
|
def process_row(row, base_path, max_frames=100):
|
|
"""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 safety check
|
|
if np.isnan(feat).any() or np.isinf(feat).any():
|
|
return None, None, None
|
|
|
|
return feat, mask, row["sign"]
|
|
|
|
except Exception as e:
|
|
return None, None, None
|
|
|
|
|
|
# ===============================
|
|
# TRANSFORMER MODEL
|
|
# ===============================
|
|
class PositionalEncoding(nn.Module):
|
|
def __init__(self, d_model, max_len=128):
|
|
super().__init__()
|
|
pe = torch.zeros(max_len, d_model)
|
|
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))
|
|
pe[:, 0::2] = torch.sin(position * div_term)
|
|
pe[:, 1::2] = torch.cos(position * div_term)
|
|
self.register_buffer('pe', pe.unsqueeze(0))
|
|
|
|
def forward(self, x):
|
|
return x + self.pe[:, :x.size(1)]
|
|
|
|
|
|
class TransformerASL(nn.Module):
|
|
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, max_len=128)
|
|
|
|
# Transformer encoder
|
|
enc_layer = nn.TransformerEncoderLayer(
|
|
d_model=d_model,
|
|
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)
|
|
|
|
# Classification head
|
|
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):
|
|
# 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)
|
|
|
|
|
|
# ===============================
|
|
# MAIN TRAINING
|
|
# ===============================
|
|
def main():
|
|
base_path = "asl_kaggle"
|
|
max_frames = 100
|
|
MIN_SAMPLES_PER_CLASS = 5
|
|
|
|
print("\nLoading metadata...")
|
|
train_df, sign_to_idx = load_kaggle_asl_data(base_path)
|
|
print(f"Total sequences: {len(train_df)}")
|
|
|
|
rows = [row for _, row in train_df.iterrows()]
|
|
|
|
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(
|
|
partial(process_row, base_path=base_path, max_frames=max_frames),
|
|
rows,
|
|
chunksize=100
|
|
),
|
|
total=len(rows),
|
|
desc="Extracting landmarks"
|
|
))
|
|
|
|
# Filter valid results
|
|
X_list, masks_list, y_list = [], [], []
|
|
for feat, mask, sign in results:
|
|
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✓ Successfully extracted: {len(X_list)} valid sequences")
|
|
print(f" Success rate: {len(X_list) / len(train_df) * 100:.1f}%")
|
|
|
|
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"\nData shape: {X.shape}")
|
|
print(f"Feature dimension: {X.shape[2]}")
|
|
|
|
# Global normalization
|
|
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
|
|
|
|
# Encode labels
|
|
le = LabelEncoder()
|
|
y = le.fit_transform(y_list)
|
|
|
|
# Filter classes with too few samples
|
|
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 after filtering
|
|
le = LabelEncoder()
|
|
y = le.fit_transform(y)
|
|
|
|
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
|
|
)
|
|
|
|
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()
|
|
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]
|
|
|
|
# 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 if device.type == 'cuda' else False
|
|
)
|
|
|
|
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_),
|
|
d_model=256,
|
|
nhead=8,
|
|
num_layers=4
|
|
).to(device)
|
|
|
|
total_params = sum(p.numel() for p in model.parameters())
|
|
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, T_mult=2)
|
|
|
|
# Training loop
|
|
best_acc = 0.0
|
|
patience = 15
|
|
wait = 0
|
|
epochs = 60
|
|
|
|
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}", 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=1.0)
|
|
optimizer.step()
|
|
|
|
total_loss += loss.item()
|
|
correct += (logits.argmax(-1) == yb).sum().item()
|
|
total += yb.size(0)
|
|
|
|
train_acc = correct / total * 100
|
|
|
|
# Evaluate
|
|
model.eval()
|
|
correct = total = 0
|
|
with torch.no_grad():
|
|
for x, mask, yb in test_loader:
|
|
x, mask, yb = x.to(device), mask.to(device), yb.to(device)
|
|
key_mask = ~mask
|
|
logits = model(x, key_padding_mask=key_mask)
|
|
correct += (logits.argmax(-1) == yb).sum().item()
|
|
total += yb.size(0)
|
|
|
|
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}%", end="")
|
|
|
|
scheduler.step()
|
|
|
|
# Save best model
|
|
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,
|
|
'input_dim': X.shape[2],
|
|
'num_classes': len(le.classes_),
|
|
'd_model': 256,
|
|
'nhead': 8,
|
|
'num_layers': 4
|
|
}, "best_asl_transformer.pth")
|
|
print(f" → New best: {best_acc:.2f}% ✓")
|
|
else:
|
|
wait += 1
|
|
print()
|
|
|
|
if wait >= patience:
|
|
print(f"\nEarly stopping triggered at epoch {epoch + 1}")
|
|
break
|
|
|
|
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__":
|
|
main() |