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a10ba7f | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 | import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
from torch.utils.data import DataLoader, ConcatDataset
import os
import time
import numpy as np
from tqdm import tqdm
import sys
from pathlib import Path
# Disable HDF5 file locking for Windows compatibility
os.environ["HDF5_USE_FILE_LOCKING"] = "FALSE"
# Add project root to path
sys.path.append(str(Path(__file__).parent.parent))
from src.models.student import LIPEV2Student
from src.models.loss import GazeDistillationLoss
from src.data.dataset import GazeDataset
def get_dann_alpha(epoch, max_epochs):
"""
Alpha schedule for DANN: starts from 0 and grows to 1.
"""
p = float(epoch) / max_epochs
alpha = 2. / (1. + np.exp(-10 * p)) - 1
return alpha
def train_dann_epoch(model, loader, criterion, domain_criterion, optimizer, device, epoch, max_epochs, w_aw, w_kd, w_domain=0.1, w_consist=0.5):
model.train()
running_loss = 0.0
running_aw = 0.0
running_kd = 0.0
running_domain = 0.0
running_consist = 0.0
alpha = get_dann_alpha(epoch, max_epochs)
pbar = tqdm(loader, desc=f"Epoch {epoch} [alpha={alpha:.2f}]")
for batch in pbar:
# GazeDataset now returns (patch, landmarks, gaze, t_pitch, t_yaw, domain_id)
patches, landmarks, gaze, t_p_logits, t_yaw_logits, domains = [b.to(device) for b in batch]
# Check if we have valid teacher labels
if torch.abs(t_p_logits).sum() > 0:
teacher_logits = (t_p_logits, t_yaw_logits)
else:
teacher_logits = None
optimizer.zero_grad()
# 1. Standard Forward Pass
s_p_logits, s_y_logits, domain_logits = model(patches, landmarks, state='A', alpha=alpha, domain_id=domains)
# 2. Gaze Loss
criterion.w_aw = w_aw
criterion.w_kd = w_kd
gaze_loss, l_aw, l_kd = criterion((s_p_logits, s_y_logits), gaze, teacher_logits)
# 3. Domain Loss
d_loss = domain_criterion(domain_logits, domains)
# 4. Flip Consistency Loss
patches_flipped = torch.flip(patches, dims=[3])
s_p_flip, s_y_flip, _ = model(patches_flipped, landmarks, state='A', alpha=alpha, domain_id=domains)
def get_deg(logits):
idx = torch.arange(90).float().to(device)
prob = torch.softmax(logits, dim=1)
return torch.sum(prob * idx, dim=1) * 2 - 90
p_deg = get_deg(s_p_logits)
y_deg = get_deg(s_y_logits)
p_deg_f = get_deg(s_p_flip)
y_deg_f = get_deg(s_y_flip)
loss_consist = F.mse_loss(p_deg, p_deg_f) + F.mse_loss(y_deg, -y_deg_f)
total_loss = gaze_loss + w_domain * d_loss + w_consist * loss_consist
total_loss.backward()
optimizer.step()
running_loss += total_loss.item()
running_aw += l_aw.item() if isinstance(l_aw, torch.Tensor) else l_aw
running_kd += l_kd.item() if isinstance(l_kd, torch.Tensor) else l_kd
running_domain += d_loss.item()
running_consist += loss_consist.item()
pbar.set_postfix({'loss': f"{total_loss.item():.4f}", 'aw': f"{l_aw:.4f}", 'con': f"{loss_consist.item():.4f}"})
return running_loss / len(loader), running_aw / len(loader), running_kd / len(loader), running_domain / len(loader), running_consist / len(loader)
def train_dann(h5_dir, target_file='gaze360_robust_v16_train_A.h5', num_epochs=100, batch_size=32, baseline_model='checkpoints/baseline_v16/best_student_p11.pt', test_participant=None):
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
print(f"DANN + AdaLN + Consistency Training on {device}")
all_files = os.listdir(h5_dir)
source_files = []
for f in all_files:
if f.endswith('_v16_new.h5') and f.startswith('p'):
if test_participant and f.startswith(test_participant):
print(f"LOPO: Skipping {f} for training.")
continue
f_path = os.path.join(h5_dir, f)
if os.path.getsize(f_path) > 1024 * 1024:
source_files.append(f_path)
target_path = os.path.join(h5_dir, target_file)
if not source_files:
print("No robust source files found! Using available _v16.h5 files as fallback.")
for f in all_files:
if f.endswith('_v16.h5') and f.startswith('p'):
if test_participant and f.startswith(test_participant): continue
f_path = os.path.join(h5_dir, f)
if os.path.getsize(f_path) > 1024 * 1024:
source_files.append(f_path)
print(f"Source files: {len(source_files)}")
print(f"Target file: {target_path}")
num_cpus = os.cpu_count() or 2
source_ds = GazeDataset(source_files, transform=True, domain_id=0)
target_ds = GazeDataset([target_path], transform=True, domain_id=1)
train_ds = ConcatDataset([source_ds, target_ds])
train_loader = DataLoader(train_ds, batch_size=batch_size, shuffle=True, num_workers=min(num_cpus, 4))
model = LIPEV2Student().to(device)
if baseline_model and os.path.exists(baseline_model):
print(f"Initializing with baseline weights: {baseline_model}")
state_dict = torch.load(baseline_model, map_location=device)
model.load_state_dict(state_dict, strict=False)
criterion = GazeDistillationLoss()
domain_criterion = torch.nn.CrossEntropyLoss()
optimizer = optim.Adam(model.parameters(), lr=5e-5)
out_dir = f'checkpoints/dann_lopo/{test_participant}' if test_participant else 'checkpoints/dann'
os.makedirs(out_dir, exist_ok=True)
for epoch in range(1, num_epochs + 1):
metrics = train_dann_epoch(model, train_loader, criterion, domain_criterion, optimizer, device, epoch, num_epochs, 1.0, 0.5)
t_loss, l_aw, l_kd, d_loss, c_loss = metrics
print(f"Epoch {epoch}: Total {t_loss:.4f}, AW {l_aw:.4f}, KD {l_kd:.4f}, Dom {d_loss:.4f}, Consist {c_loss:.4f}")
if epoch % 20 == 0 or epoch == num_epochs:
save_path = os.path.join(out_dir, f'student_e{epoch}.pt')
torch.save(model.state_dict(), save_path)
if __name__ == '__main__':
import argparse
parser = argparse.ArgumentParser()
parser.add_argument('--test_participant', type=str, default=None)
parser.add_argument('--num_epochs', type=int, default=100)
parser.add_argument('--batch_size', type=int, default=32)
parser.add_argument('--h5_dir', type=str, default='data/processed')
args = parser.parse_args()
train_dann(args.h5_dir, num_epochs=args.num_epochs, batch_size=args.batch_size, test_participant=args.test_participant)
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