def _main():
torch.set_grad_enabled(False)
torch.backends.cudnn.benchmark = True
test_dir = "../input/deepfake-detection-challenge/test_videos"
csv_path = "../input/deepfake-detection-challenge/sample_submission.csv"
face_detector = FaceDetector()
face_detector.load_checkpoint("../input/pretrained/RetinaFace-Resnet50-fixed.pth")
loader = DFDCLoader(test_dir, face_detector, T.ToTensor())
model1 = xception(num_classes=2, pretrained=False)
ckpt = torch.load("../input/pretrained/xception.pth")
model1.load_state_dict(ckpt["state_dict"])
model1 = model1.cuda()
model1.eval()
model2 = WSDAN(num_classes=2, M=8, net="xception", pretrained=False).cuda()
ckpt = torch.load("../input/pretrained/wsdan.pth")
model2.load_state_dict(ckpt["state_dict"])
model2.eval()
zhq_nm_avg = torch.Tensor([.4479, .3744, .3473]).view(1, 3, 1, 1).cuda()
zhq_nm_std = torch.Tensor([.2537, .2502, .2424]).view(1, 3, 1, 1).cuda()
for batch in loader:
batch = batch.cuda(non_blocking=True)
m1 = F.interpolate(batch, size=299, mode="bilinear")
m1.sub_(0.5).mul_(2.0)
m1 = model1(m1).softmax(-1)[:, 1].cpu().numpy()
m2 = (batch - zhq_nm_avg) / zhq_nm_std
m2, _, _ = model2(m2)
m2 = m2.softmax(-1)[:, 1].cpu().numpy()
prediction = 0.25 * m1 + 0.75 * m2
loader.feedback(prediction)
with open(csv_path) as fin, open("submission.csv", "w") as fout:
fout.write(next(fin))
for line in fin:
fname = line.split(",", 1)[0]
pred = loader.score[fname]
print("%s,%.6f" % (fname, pred), file=fout)
def main():
parser = OptionParser()
parser.add_option('-j', '--workers', dest='workers', default=16, type='int',
help='number of data loading workers (default: 16)')
parser.add_option('-e', '--epochs', dest='epochs', default=80, type='int',
help='number of epochs (default: 80)')
parser.add_option('-b', '--batch-size', dest='batch_size', default=16, type='int',
help='batch size (default: 16)')
parser.add_option('-c', '--ckpt', dest='ckpt', default=False,
help='load checkpoint model (default: False)')
parser.add_option('-v', '--verbose', dest='verbose', default=100, type='int',
help='show information for each <verbose> iterations (default: 100)')
parser.add_option('--lr', '--learning-rate', dest='lr', default=1e-3, type='float',
help='learning rate (default: 1e-3)')
parser.add_option('--sf', '--save-freq', dest='save_freq', default=1, type='int',
help='saving frequency of .ckpt models (default: 1)')
parser.add_option('--sd', '--save-dir', dest='save_dir', default='./models/wsdan/',
help='saving directory of .ckpt models (default: ./models/wsdan)')
parser.add_option('--ln', '--log-name', dest='log_name', default='train.log',
help='log name (default: train.log)')
parser.add_option('--mn', '--model-name', dest='model_name', default='model.ckpt',
help='model name (default:model.ckpt)')
parser.add_option('--init', '--initial-training', dest='initial_training', default=1, type='int',
help='train from 1-beginning or 0-resume training (default: 1)')
(options, args) = parser.parse_args()
##################################
# Initialize saving directory
##################################
if not os.path.exists(options.save_dir):
os.makedirs(options.save_dir)
##################################
# Logging setting
##################################
logging.basicConfig(
filename=os.path.join( options.save_dir, options.log_name),
filemode='w',
format='%(asctime)s: %(levelname)s: [%(filename)s:%(lineno)d]: %(message)s',
level=logging.INFO)
warnings.filterwarnings("ignore")
##################################
# Load dataset
##################################
image_size = (256,256)
num_classes = 4
transform = transforms.Compose([transforms.Resize(size=image_size),
transforms.ToTensor(),
transforms.Normalize(mean=[0.485, 0.456, 0.406],
std=[0.229, 0.224, 0.225])])
train_dataset = CustomDataset(data_root='/mnt/HDD/RFW/train/data/',csv_file='data/RFW_Train40k_Images_Metada.csv',transform=transform)
val_dataset = CustomDataset(data_root='/mnt/HDD/RFW/train/data/',csv_file='data/RFW_Val4k_Images_Metadata.csv',transform=transform)
test_dataset = CustomDataset(data_root='/mnt/HDD/RFW/test/data/',csv_file='data/RFW_Test_Images_Metadata.csv',transform=transform)
train_loader = DataLoader(train_dataset, batch_size=options.batch_size, shuffle=True,num_workers=options.workers, pin_memory=True)
validate_loader = DataLoader(val_dataset, batch_size=options.batch_size * 4, shuffle=False,num_workers=options.workers, pin_memory=True)
test_loader = DataLoader(test_dataset, batch_size=options.batch_size * 4, shuffle=False,num_workers=options.workers, pin_memory=True)
##################################
# Initialize model
##################################
logs = {}
start_epoch = 0
num_attentions = 32
feature_net = inception_v3(pretrained=True)
net = WSDAN(num_classes=num_classes, M=num_attentions, net='inception_mixed_6e', pretrained=True)
# feature_center: size of (#classes, #attention_maps * #channel_features)
feature_center = torch.zeros(num_classes, num_attentions * net.num_features).to(device)
if options.ckpt:
# Load ckpt and get state_dict
checkpoint = torch.load(options.ckpt)
# Get epoch and some logs
logs = checkpoint['logs']
start_epoch = int(logs['epoch'])
# Load weights
state_dict = checkpoint['state_dict']
net.load_state_dict(state_dict)
logging.info('Network loaded from {}'.format(options.ckpt))
# load feature center
if 'feature_center' in checkpoint:
feature_center = checkpoint['feature_center'].to(device)
logging.info('feature_center loaded from {}'.format(options.ckpt))
logging.info('Network weights save to {}'.format(options.save_dir))
feature_net = inception_v3(pretrained=True)
if options.ckpt:
ckpt = options.ckpt
if options.initial_training == 0:
# Get Name (epoch)
epoch_name = (ckpt.split('/')[-1]).split('.')[0]
start_epoch = int(epoch_name)
# Load ckpt and get state_dict
checkpoint = torch.load(ckpt)
state_dict = checkpoint['state_dict']
# Load weights
net.load_state_dict(state_dict)
logging.info('Network loaded from {}'.format(options.ckpt))
# load feature center
if 'feature_center' in checkpoint:
feature_center = checkpoint['feature_center'].to(torch.device("cuda"))
logging.info('feature_center loaded from {}'.format(options.ckpt))
##################################
# Use cuda
##################################
net.to(device)
if torch.cuda.device_count() > 1:
net = nn.DataParallel(net)
##################################
# Optimizer, LR Scheduler
##################################
learning_rate = logs['lr'] if 'lr' in logs else options.lr
optimizer = torch.optim.SGD(net.parameters(), lr=learning_rate, momentum=0.9, weight_decay=1e-5)
# scheduler = torch.optim.lr_scheduler.ReduceLROnPlateau(optimizer, factor=0.9, patience=2)
scheduler = torch.optim.lr_scheduler.StepLR(optimizer, step_size=2, gamma=0.9)
##################################
# ModelCheckpoint
##################################
callback_monitor = 'val_{}'.format(raw_metric.name)
callback = ModelCheckpoint(savepath=os.path.join(options.save_dir, options.model_name),
monitor=callback_monitor,
mode='max')
if callback_monitor in logs:
callback.set_best_score(logs[callback_monitor])
else:
callback.reset()
##################################
# TRAINING
##################################
logging.info('')
logging.info('Start training: Total epochs: {}, Batch size: {}, Training size: {}, Validation size: {}'.
format(options.epochs, options.batch_size, len(train_dataset), len(val_dataset)))
for epoch in range(start_epoch, options.epochs):
callback.on_epoch_begin()
logs['epoch'] = epoch + 1
logs['lr'] = optimizer.param_groups[0]['lr']
logging.info('Epoch {:03d}, Learning Rate {:g}'.format(epoch + 1, optimizer.param_groups[0]['lr']))
pbar = tqdm(total=len(train_loader), unit=' batches')
pbar.set_description('Epoch {}/{}'.format(epoch + 1, options.epochs))
train(logs=logs,
data_loader=train_loader,
net=net,
feature_center=feature_center,
optimizer=optimizer,
pbar=pbar)
validate(logs=logs,
data_loader=validate_loader,
net=net,
pbar=pbar)
if isinstance(scheduler, torch.optim.lr_scheduler.ReduceLROnPlateau):
scheduler.step(logs['val_loss'])
else:
scheduler.step()
callback.on_epoch_end(logs, net, feature_center=feature_center)
pbar.close()
def predict(image_path,
model_param_path,
save_path,
img_save_name,
resize=(224, 224),
gen_hm=False):
image = Image.open(image_path).convert('RGB')
transform = transforms.Compose([
# transforms.Resize(size=(int(resize[0] / 0.875), int(resize[1] / 0.875))),
transforms.Resize(size=(int(resize[0]), int(resize[1]))),
# transforms.CenterCrop(resize),
transforms.ToTensor(),
transforms.Normalize(mean=[0.485, 0.456, 0.406],
std=[0.229, 0.224, 0.225])
])
image = transform(image)
image = image.unsqueeze(0)
net = WSDAN(num_classes=4)
net.load_state_dict(torch.load(model_param_path))
net.eval()
if 'gpu' in model_param_path:
print("please make sure your computer has a GPU")
device = torch.device("cuda")
try:
net.to(device)
except:
print("No GPU in the environment")
else:
device = torch.device("cpu")
X = image
X = X.to(device)
# WS-DAN
y_pred_raw, _, attention_maps = net(X)
attention_maps = torch.mean(attention_maps, dim=1, keepdim=True)
# Augmentation with crop_mask
crop_image = batch_augment(X,
attention_maps,
mode='crop',
theta=0.1,
padding_ratio=0.05)
y_pred_crop, _, _ = net(crop_image)
y_pred = (y_pred_raw + y_pred_crop) / 2.
y_pred = F.softmax(y_pred)
if gen_hm:
attention_maps = F.upsample_bilinear(attention_maps,
size=(X.size(2), X.size(3)))
attention_maps = torch.sqrt(attention_maps.cpu() /
attention_maps.max().item())
# get heat attention maps
heat_attention_maps = generate_heatmap(attention_maps)
# raw_image, heat_attention, raw_attention
raw_image = X.cpu() * STD + MEAN
heat_attention_image = raw_image * 0.4 + heat_attention_maps * 0.6
raw_attention_image = raw_image * attention_maps
for batch_idx in range(X.size(0)):
rimg = ToPILImage(raw_image[batch_idx])
raimg = ToPILImage(raw_attention_image[batch_idx])
haimg = ToPILImage(heat_attention_image[batch_idx])
rimg.save(
os.path.join(save_path, '{}_raw.jpg'.format(img_save_name)))
raimg.save(
os.path.join(save_path,
'{}_raw_atten.jpg'.format(img_save_name)))
haimg.save(
os.path.join(save_path,
'{}_heat_atten.jpg'.format(img_save_name)))
df = pd.read_csv("../data/train.csv")
for i in range(len(df)):
# if df.loc[i, 'image_id'] in image_path:
head, tail = os.path.split(image_path)
if df.loc[i, 'image_id'] == tail[:-4]:
label = torch.tensor(
df.loc[i, ['healthy', 'multiple_diseases', 'rust', 'scab']])
break
return y_pred, label
def main():
logging.basicConfig(
format=
'%(asctime)s: %(levelname)s: [%(filename)s:%(lineno)d]: %(message)s',
level=logging.INFO)
warnings.filterwarnings("ignore")
try:
ckpt = config.eval_ckpt
except:
logging.info('Set ckpt for evaluation in config.py')
return
##################################
# Dataset for testing
##################################
# _, test_dataset = get_trainval_datasets(config.tag, resize=config.image_size)
test_dataset = CarDataset('test')
test_loader = DataLoader(test_dataset,
batch_size=config.batch_size,
shuffle=False,
num_workers=2,
pin_memory=True)
name2label, label2name = mapping('../training_labels.csv')
##################################
# Initialize model
##################################
net = WSDAN(num_classes=test_dataset.num_classes,
M=config.num_attentions,
net=config.net)
# Load ckpt and get state_dict
checkpoint = torch.load(ckpt)
state_dict = checkpoint['state_dict']
# Load weights
net.load_state_dict(state_dict)
logging.info('Network loaded from {}'.format(ckpt))
##################################
# use cuda
##################################
net.to(device)
if torch.cuda.device_count() > 1:
net = nn.DataParallel(net)
##################################
# Prediction
##################################
raw_accuracy = TopKAccuracyMetric(topk=(1, 5))
ref_accuracy = TopKAccuracyMetric(topk=(1, 5))
raw_accuracy.reset()
ref_accuracy.reset()
net.eval()
logits = []
ids = []
with torch.no_grad():
pbar = tqdm(total=len(test_loader), unit=' batches')
pbar.set_description('Validation')
for i, (X, y, id) in enumerate(test_loader):
X = X.to(device)
y = y.to(device)
ids.extend(id)
# WS-DAN
y_pred_raw, _, attention_maps = net(X)
# Augmentation with crop_mask
crop_image = batch_augment(X,
attention_maps,
mode='crop',
theta=0.1,
padding_ratio=0.05)
y_pred_crop, _, _ = net(crop_image)
y_pred = (y_pred_raw + y_pred_crop) / 2.
# Save the predictions
logits.append(y_pred.cpu())
prediction = torch.argmax(torch.cat(logits, dim=0), dim=1)
submission = pd.DataFrame(
[ids,
[label2name[x] for x in prediction.numpy()]]).transpose()
submission.columns = ['id', 'label']
submission.to_csv(savepath + 'predictions.csv', index=False)
if visualize:
# reshape attention maps
attention_maps = F.upsample_bilinear(attention_maps,
size=(X.size(2),
X.size(3)))
attention_maps = torch.sqrt(attention_maps.cpu() /
attention_maps.max().item())
# get heat attention maps
heat_attention_maps = generate_heatmap(attention_maps)
# raw_image, heat_attention, raw_attention
raw_image = X.cpu() * STD + MEAN
heat_attention_image = raw_image * 0.5 + heat_attention_maps * 0.5
raw_attention_image = raw_image * attention_maps
for batch_idx in range(X.size(0)):
rimg = ToPILImage(raw_image[batch_idx])
raimg = ToPILImage(raw_attention_image[batch_idx])
haimg = ToPILImage(heat_attention_image[batch_idx])
rimg.save(
os.path.join(
savepath, '%03d_raw.jpg' %
(i * config.batch_size + batch_idx)))
raimg.save(
os.path.join(
savepath, '%03d_raw_atten.jpg' %
(i * config.batch_size + batch_idx)))
haimg.save(
os.path.join(
savepath, '%03d_heat_atten.jpg' %
(i * config.batch_size + batch_idx)))
# Top K
epoch_raw_acc = raw_accuracy(y_pred_raw, y)
epoch_ref_acc = ref_accuracy(y_pred, y)
# end of this batch
batch_info = 'Val Acc: Raw ({:.2f}, {:.2f}), Refine ({:.2f}, {:.2f})'.format(
epoch_raw_acc[0], epoch_raw_acc[1], epoch_ref_acc[0],
epoch_ref_acc[1])
pbar.update()
pbar.set_postfix_str(batch_info)
pbar.close()
val_loader_crop = torch.utils.data.DataLoader(
datasets.ImageFolder(args.data_crop + VALID_IMAGES,
transform=data_transforms_val),
batch_size=2, shuffle=False, num_workers=1)
device = torch.device("cuda")
print("define wsdan")
model = WSDAN(num_classes=args.num_classes, M=num_attentions, net=NET, pretrained=True)
feature_center = torch.zeros(args.num_classes, num_attentions * model.num_features).to(device)
center_loss = CenterLoss()
cross_entropy_loss = nn.CrossEntropyLoss()
if args.model:
print("loading pretrained model")
checkpoint = torch.load(args.model)
model.load_state_dict(checkpoint)
if use_cuda:
print('Using GPU')
model.cuda()
else:
print('Using CPU')
# optimizer = optim.Adam(model.parameters(), lr=args.lr) #momentum=args.momentum
# optimizer = optim.SGD(model.parameters(), lr=args.lr, momentum=args.momentum) #momentum=args.momentum
# lr_scheduler = torch.optim.lr_scheduler.CosineAnnealingLR(optimizer, args.epochs)
# optimizer = AdaBelief(model.parameters(), lr=args.lr, eps=1e-16, betas=(0.9,0.999), weight_decouple = True, rectify = False)
optimizer = RangerAdaBelief(model.parameters(), lr=args.lr, eps=1e-12, betas=(0.9,0.999))
def main():
logging.basicConfig(
format=
'%(asctime)s: %(levelname)s: [%(filename)s:%(lineno)d]: %(message)s',
level=logging.INFO)
warnings.filterwarnings("ignore")
try:
ckpt = sys.argv[1]
except:
logging.info('Usage: python3 eval.py <model.ckpt>')
return
##################################
# Dataset for testing
##################################
test_dataset = CarDataset(phase='test', resize=448)
test_loader = DataLoader(test_dataset,
batch_size=1,
shuffle=False,
num_workers=1,
pin_memory=True)
##################################
# Initialize model
##################################
net = WSDAN(num_classes=test_dataset.num_classes,
M=32,
net='inception_mixed_6e')
# Load ckpt and get state_dict
checkpoint = torch.load(ckpt)
state_dict = checkpoint['state_dict']
# Load weights
net.load_state_dict(state_dict)
logging.info('Network loaded from {}'.format(ckpt))
##################################
# use cuda
##################################
cudnn.benchmark = True
net.to(device)
net = nn.DataParallel(net)
net.eval()
##################################
# Prediction
##################################
accuracy = TopKAccuracyMetric(topk=(1, 5))
accuracy.reset()
with torch.no_grad():
pbar = tqdm(total=len(test_loader), unit=' batches')
pbar.set_description('Validation')
for i, (X, y) in enumerate(test_loader):
X = X.to(device)
y = y.to(device)
# WS-DAN
y_pred_raw, feature_matrix, attention_maps = net(X)
# Augmentation with crop_mask
crop_image = batch_augment(X,
attention_maps,
mode='crop',
theta=0.1)
y_pred_crop, _, _ = net(crop_image)
pred = (y_pred_raw + y_pred_crop) / 2.
if visualize:
# reshape attention maps
attention_maps = F.upsample_bilinear(attention_maps,
size=(X.size(2),
X.size(3)))
attention_maps = torch.sqrt(attention_maps.cpu() /
attention_maps.max().item())
# get heat attention maps
heat_attention_maps = generate_heatmap(attention_maps)
# raw_image, heat_attention, raw_attention
raw_image = X.cpu() * STD + MEAN
heat_attention_image = raw_image * 0.5 + heat_attention_maps * 0.5
raw_attention_image = raw_image * attention_maps
for batch_idx in range(X.size(0)):
rimg = ToPILImage(raw_image[batch_idx])
raimg = ToPILImage(raw_attention_image[batch_idx])
haimg = ToPILImage(heat_attention_image[batch_idx])
rimg.save(
os.path.join(savepath,
'%03d_raw.jpg' % (i + batch_idx)))
raimg.save(
os.path.join(savepath,
'%03d_raw_atten.jpg' % (i + batch_idx)))
haimg.save(
os.path.join(savepath,
'%03d_heat_atten.jpg' % (i + batch_idx)))
# Top K
epoch_acc = accuracy(pred, y)
# end of this batch
batch_info = 'Val Acc ({:.2f}, {:.2f})'.format(
epoch_acc[0], epoch_acc[1])
pbar.update()
pbar.set_postfix_str(batch_info)
pbar.close()
# show information for this epoch
logging.info('Accuracy: %.2f, %.2f' % (epoch_acc[0], epoch_acc[1]))
def main():
##################################
# Initialize saving directory
##################################
if not os.path.exists(config.save_dir):
os.makedirs(config.save_dir)
##################################
# Logging setting
##################################
logging.basicConfig(
filename=os.path.join(config.save_dir, config.log_name),
filemode='w',
format=
'%(asctime)s: %(levelname)s: [%(filename)s:%(lineno)d]: %(message)s',
level=logging.INFO)
warnings.filterwarnings("ignore")
##################################
# Load dataset
##################################
# train_dataset, validate_dataset = get_trainval_datasets(config.tag, config.image_size)
full_train_dataset = CarDataset('train')
n = len(full_train_dataset)
# train_dataset, validate_dataset = torch.utils.data.random_split(full_train_dataset, [int(n*0.8), n-int(n*0.8)])
train_dataset = full_train_dataset
validate_dataset = full_train_dataset
train_loader, validate_loader = DataLoader(train_dataset, batch_size=config.batch_size, shuffle=True,
num_workers=config.workers, pin_memory=True), \
DataLoader(validate_dataset, batch_size=config.batch_size * 4, shuffle=False,
num_workers=config.workers, pin_memory=True)
num_classes = full_train_dataset.num_classes
##################################
# Initialize model
##################################
logs = {}
start_epoch = 0
net = WSDAN(num_classes=num_classes,
M=config.num_attentions,
net=config.net,
pretrained=True)
# feature_center: size of (#classes, #attention_maps * #channel_features)
feature_center = torch.zeros(num_classes, config.num_attentions *
net.num_features).to(device)
if config.ckpt:
# Load ckpt and get state_dict
checkpoint = torch.load(config.ckpt)
# Get epoch and some logs
logs = checkpoint['logs']
start_epoch = int(logs['epoch'])
# Load weights
state_dict = checkpoint['state_dict']
net.load_state_dict(state_dict)
logging.info('Network loaded from {}'.format(config.ckpt))
# load feature center
if 'feature_center' in checkpoint:
feature_center = checkpoint['feature_center'].to(device)
logging.info('feature_center loaded from {}'.format(config.ckpt))
logging.info('Network weights save to {}'.format(config.save_dir))
##################################
# Use cuda
##################################
net.to(device)
if torch.cuda.device_count() > 1:
net = nn.DataParallel(net)
##################################
# Optimizer, LR Scheduler
##################################
learning_rate = logs['lr'] if 'lr' in logs else config.learning_rate
optimizer = torch.optim.SGD(net.parameters(),
lr=learning_rate,
momentum=0.9,
weight_decay=1e-5)
# scheduler = torch.optim.lr_scheduler.ReduceLROnPlateau(optimizer, factor=0.9, patience=2)
scheduler = torch.optim.lr_scheduler.StepLR(optimizer,
step_size=2,
gamma=0.9)
##################################
# ModelCheckpoint
##################################
callback_monitor = 'val_{}'.format(raw_metric.name)
callback = ModelCheckpoint(savepath=os.path.join(config.save_dir,
config.model_name),
monitor=callback_monitor,
mode='max')
if callback_monitor in logs:
callback.set_best_score(logs[callback_monitor])
else:
callback.reset()
##################################
# TRAINING
##################################
logging.info(
'Start training: Total epochs: {}, Batch size: {}, Training size: {}, Validation size: {}'
.format(config.epochs, config.batch_size, len(train_dataset),
len(validate_dataset)))
logging.info('')
for epoch in range(start_epoch, config.epochs):
callback.on_epoch_begin()
logs['epoch'] = epoch + 1
logs['lr'] = optimizer.param_groups[0]['lr']
logging.info('Epoch {:03d}, Learning Rate {:g}'.format(
epoch + 1, optimizer.param_groups[0]['lr']))
pbar = tqdm(total=len(train_loader), unit=' batches')
pbar.set_description('Epoch {}/{}'.format(epoch + 1, config.epochs))
train(logs=logs,
data_loader=train_loader,
net=net,
feature_center=feature_center,
optimizer=optimizer,
pbar=pbar)
validate(logs=logs, data_loader=validate_loader, net=net, pbar=pbar)
if isinstance(scheduler, torch.optim.lr_scheduler.ReduceLROnPlateau):
scheduler.step(logs['val_loss'])
else:
scheduler.step()
callback.on_epoch_end(logs, net, feature_center=feature_center)
pbar.close()
def main(result_arr):
logging.basicConfig(
format='%(asctime)s: %(levelname)s: [%(filename)s:%(lineno)d]: %(message)s',
level=logging.INFO)
warnings.filterwarnings("ignore")
try:
ckpt = config.eval_ckpt
except:
logging.info('Set ckpt for evaluation in config.py')
return
##################################
# Dataset for testing
##################################
_, test_dataset = get_trainval_datasets(config.tag, resize=config.image_size)
test_loader = DataLoader(test_dataset, batch_size=config.batch_size, shuffle=False,
num_workers=2, pin_memory=True)
##################################
# Initialize model
##################################
net = WSDAN(num_classes=test_dataset.num_classes, M=config.num_attentions, net=config.net)
# Load ckpt and get state_dict
checkpoint = torch.load(ckpt)
state_dict = checkpoint['state_dict']
# Load weights
net.load_state_dict(state_dict)
logging.info('Network loaded from {}'.format(ckpt))
##################################
# use cuda
##################################
net.to(device)
if torch.cuda.device_count() > 1:
net = nn.DataParallel(net)
##################################
# Prediction
##################################
raw_accuracy = TopKAccuracyMetric(topk=(1, 5))
ref_accuracy = TopKAccuracyMetric(topk=(1, 5))
raw_accuracy.reset()
ref_accuracy.reset()
net.eval()
with torch.no_grad():
pbar = tqdm(total=len(test_loader), unit=' batches')
pbar.set_description('Validation')
for i, (X, y) in enumerate(test_loader):
X = X.to(device)
y = y.to(device)
# WS-DAN
y_pred_raw, _, attention_maps = net(X)
# Augmentation with crop_mask
crop_image = batch_augment(X, attention_maps, mode='crop', theta=0.1, padding_ratio=0.05)
y_pred_crop, _, _ = net(crop_image)
y_pred = (y_pred_raw + y_pred_crop) / 2.
d = {}
reader = csv.reader(open('/home/naman/Documents/Assignment_Job/out_dict.csv', 'r'))
for row in reader:
k, v = row
d[v] = k
result.append(y_pred, d[y_pred)]
if visualize:
# reshape attention maps
attention_maps = F.upsample_bilinear(attention_maps, size=(X.size(2), X.size(3)))
attention_maps = torch.sqrt(attention_maps.cpu() / attention_maps.max().item())
# get heat attention maps
heat_attention_maps = generate_heatmap(attention_maps)
# raw_image, heat_attention, raw_attention
raw_image = X.cpu() * STD + MEAN
heat_attention_image = raw_image * 0.5 + heat_attention_maps * 0.5
raw_attention_image = raw_image * attention_maps
for batch_idx in range(X.size(0)):
rimg = ToPILImage(raw_image[batch_idx])
raimg = ToPILImage(raw_attention_image[batch_idx])
haimg = ToPILImage(heat_attention_image[batch_idx])
rimg.save(os.path.join(savepath, '%03d_raw.jpg' % (i * config.batch_size + batch_idx)))
raimg.save(os.path.join(savepath, '%03d_raw_atten.jpg' % (i * config.batch_size + batch_idx)))
haimg.save(os.path.join(savepath, '%03d_heat_atten.jpg' % (i * config.batch_size + batch_idx)))
# Top K
epoch_raw_acc = raw_accuracy(y_pred_raw, y)
epoch_ref_acc = ref_accuracy(y_pred, y)
# end of this batch
batch_info = 'Val Acc: Raw ({:.2f}, {:.2f}), Refine ({:.2f}, {:.2f})'.format(
epoch_raw_acc[0], epoch_raw_acc[1], epoch_ref_acc[0], epoch_ref_acc[1])
pbar.update()
pbar.set_postfix_str(batch_info)
pbar.close()
def main():
parser = OptionParser()
parser.add_option('--gpu',
'--gpu',
dest='GPU',
default=0,
type='int',
help='GPU Id (default: 0)')
parser.add_option('--evalckpt',
'--eval-ckpt',
dest='eval_ckpt',
default='models/wsdan/003.ckpt',
help='saved models are in ckpt directory')
parser.add_option('-b',
'--batch-size',
dest='batch_size',
default=64,
type='int',
help='batch size (default: 16)')
parser.add_option('-j',
'--workers',
dest='workers',
default=4,
type='int',
help='number of data loading workers (default: 16)')
parser.add_option('--na',
'--num-attentions',
dest='num_attentions',
default=32,
type='int',
help='number of attentions')
parser.add_option('--cm',
'--confusion_matrix',
dest='confusion_matrix',
default=True,
help='if you want to create confusion matrix')
(options, args) = parser.parse_args()
logging.basicConfig(
format=
'%(asctime)s: %(levelname)s: [%(filename)s:%(lineno)d]: %(message)s',
level=logging.INFO)
warnings.filterwarnings("ignore")
try:
ckpt = options.eval_ckpt
except:
logging.info('Set ckpt for evaluation options')
return
# Dataset for testing
transform = transforms.Compose([
transforms.Resize(size=(256, 256)),
transforms.ToTensor(),
transforms.Normalize(mean=[0.485, 0.456, 0.406],
std=[0.229, 0.224, 0.225])
])
test_dataset = CustomDataset(
data_root='/mnt/HDD/DatasetOriginals/RFW/test/data/',
csv_file='data/RFW_Test_Images_Metadata.csv',
transform=transform)
test_loader = DataLoader(test_dataset,
batch_size=options.batch_size * 4,
shuffle=False,
num_workers=options.workers,
pin_memory=True)
##################################
# Initialize model
##################################
net = WSDAN(num_classes=4, M=32, net='inception_mixed_6e')
# Load ckpt and get state_dict
checkpoint = torch.load(ckpt)
state_dict = checkpoint['state_dict']
# Load weights
net.load_state_dict(state_dict)
logging.info('Network loaded from {}'.format(ckpt))
##################################
# use cuda
##################################
net.to(device)
if torch.cuda.device_count() > 1:
net = nn.DataParallel(net)
##################################
# Prediction
##################################
raw_accuracy = TopKAccuracyMetric(topk=(1, 3))
ref_accuracy = TopKAccuracyMetric(topk=(1, 3))
raw_accuracy.reset()
ref_accuracy.reset()
top1 = AverageMeter('Acc@1', ':6.2f')
top_refined = AverageMeter('Acc@1', ':6.2f')
net.eval()
y_pred, y_true = [], []
with torch.no_grad():
pbar = tqdm(total=len(test_loader), unit=' batches')
pbar.set_description('Validation')
for i, (X, y) in enumerate(test_loader):
y_true += list(y.numpy())
X = X.to(device)
y = y.to(device)
# WS-DAN
y_pred_raw, _, attention_maps = net(X)
# Augmentation with crop_mask
crop_image = batch_augment(X,
attention_maps,
mode='crop',
theta=0.1,
padding_ratio=0.05)
y_pred_crop, _, _ = net(crop_image)
y_predicted = (y_pred_raw + y_pred_crop) / 2.
_, pred = y_predicted.topk(1, 1, True, True)
y_pred += list(pred.cpu().numpy())
if visualize:
# reshape attention maps
attention_maps = F.upsample_bilinear(attention_maps,
size=(X.size(2),
X.size(3)))
attention_maps = torch.sqrt(attention_maps.cpu() /
attention_maps.max().item())
# get heat attention maps
heat_attention_maps = generate_heatmap(attention_maps)
# raw_image, heat_attention, raw_attention
raw_image = X.cpu() * STD + MEAN
heat_attention_image = raw_image * 0.5 + heat_attention_maps * 0.5
raw_attention_image = raw_image * attention_maps
for batch_idx in range(X.size(0)):
rimg = ToPILImage(raw_image[batch_idx])
raimg = ToPILImage(raw_attention_image[batch_idx])
haimg = ToPILImage(heat_attention_image[batch_idx])
rimg.save(
os.path.join(
savepath, '%03d_raw.jpg' %
(i * options.batch_size + batch_idx)))
raimg.save(
os.path.join(
savepath, '%03d_raw_atten.jpg' %
(i * options.batch_size + batch_idx)))
haimg.save(
os.path.join(
savepath, '%03d_heat_atten.jpg' %
(i * options.batch_size + batch_idx)))
# Top K
epoch_raw_acc = raw_accuracy(y_pred_raw, y)
epoch_ref_acc = ref_accuracy(y_predicted, y)
top1.update(epoch_raw_acc[0], X.size(0))
top_refined.update(epoch_ref_acc[0], X.size(0))
# end of this batch
batch_info = 'Val Acc: Raw ({:.2f}, {:.2f}), Refine ({:.2f}, {:.2f})'.format(
epoch_raw_acc[0], epoch_raw_acc[1], epoch_ref_acc[0],
epoch_ref_acc[1])
pbar.update()
pbar.set_postfix_str(batch_info)
pbar.close()
print(' * Raw Accuracy {top1.avg:.3f}'.format(top1=top1))
print(' * Refined Accuracy {top1.avg:.3f}'.format(top1=top_refined))
print(len(y_pred), len(y_true))
if options.confusion_matrix:
file_name = 'source/wsdan_confusion_matrix.svg'
draw_confusion_matrix(np.asarray(y_true), np.asarray(y_pred),
file_name)