def setup(args):
# Prepare model
config = CONFIGS[args.model_type]
config.split = args.split
config.slide_step = args.slide_step
if args.dataset == "CUB_200_2011":
num_classes = 4
elif args.dataset == "car":
num_classes = 196
elif args.dataset == "nabirds":
num_classes = 555
elif args.dataset == "dog":
num_classes = 120
elif args.dataset == "INat2017":
num_classes = 5089
model = VisionTransformer(config,
args.img_size,
zero_head=True,
num_classes=num_classes,
smoothing_value=args.smoothing_value)
model.load_from(np.load(args.pretrained_dir))
if args.pretrained_model is not None:
pretrained_model = torch.load(args.pretrained_model)['model']
model.load_state_dict(pretrained_model)
model.to(args.device)
num_params = count_parameters(model)
logger.info("{}".format(config))
logger.info("Training parameters %s", args)
logger.info("Total Parameter: \t%2.1fM" % num_params)
return args, model
def setup(args):
# Prepare model
config = CONFIGS[args.model_type]
config.mixup = args.mixup
config.mixup_layer = args.mixup_layer
config.mixup_alpha = args.mixup_alpha
num_classes = 10 if args.dataset == "cifar10" else 100
model = VisionTransformer(config, args.img_size, zero_head=True, num_classes=num_classes)
try:
model.load_from(np.load(args.pretrained_dir))
except:
load_model(args.pretrained_dir, model)
model.to(args.device)
num_params = count_parameters(model)
print("{}".format(config))
for key, value in vars(args).items():
logging.info(f"{key}: {value}")
#print("Training parameters %s", arg)
print("#Trainable Parameter: \t%2.2fM" % num_params)
print(num_params)
return args, model
def setup(args, device):
# Prepare model
config = CONFIGS[args.model_type]
output_size = 256
model = VisionTransformer(config, args.img_size, zero_head=True, num_classes=output_size)
model.load_from(np.load(args.pretrained_dir))
model.to(device)
num_params = count_parameters(model)
logger.info("{}".format(config))
logger.info("Training parameters %s", args)
logger.info("Total Parameter: \t%2.1fM" % num_params)
print(num_params)
return args, model
def visualize_attn(args):
"""
Visualization of learned attention map.
"""
config = CONFIGS[args.model_type]
num_classes = 10 if args.dataset == "cifar10" else 100
model = VisionTransformer(config, args.img_size,
norm_type=args.norm_type,
zero_head=True,
num_classes=num_classes,
vis=True)
ckpt_file = os.path.join(args.output_dir, args.name + "_checkpoint.bin")
ckpt = torch.load(ckpt_file)
# use single card for visualize attn map
model.load_state_dict(ckpt)
model.to(args.device)
model.eval()
_, test_loader = get_loader(args)
sample_idx = 0
layer_ids = [0, 3, 6, 9]
head_id = 0
with torch.no_grad():
for step, batch in enumerate(test_loader):
batch = tuple(t.to(args.device) for t in batch)
x, y = batch
select_x = x[sample_idx].unsqueeze(0)
output, attn_weights = model(select_x)
# attn_weights is List[(1, number_of_head, len_h, len_h)]
for layer_id in layer_ids:
vis_attn(args, attn_weights[layer_id].squeeze(0)[head_id], layer_id=layer_id)
break # visualize the first sample in the first batch
print("done.")
exit(0)
def setup(args):
# Prepare model
config = CONFIGS[args.model_type]
num_classes = 10 if args.dataset == "cifar10" else 100
model = VisionTransformer(config,
args.img_size,
zero_head=True,
num_classes=num_classes)
model.load_from(np.load(args.pretrained_dir))
model.to(args.device)
num_params = count_parameters(model)
logger.info("{}".format(config))
logger.info("Training parameters %s", args)
logger.info("Total Parameter: \t%2.1fM" % num_params)
return args, model
## Constructing the training loader
train_loader = MMTDataset_baseline(args.train_set, args.labels_dict, args.img_folder, args.input_dim, transformer = True)
train_loader_T = DataLoader(train_loader, batch_size=args.batch_size, shuffle=True)
train_loader = iter(train_loader_T)
## Constructing the validation loader
val_loader = MMTDataset_baseline(args.train_set, args.labels_dict, args.img_folder, args.input_dim, transformer = True)
val_loader = DataLoader(val_loader, batch_size=args.batch_size, shuffle=False) ## no point in shufflying the validation data
## Initializing the model
config = CONFIGS["ViT-B_16"]
num_classes = 18
model = VisionTransformer(config, args.input_dim, zero_head=True, num_classes=num_classes)
model.epochs = args.epochs
model.session_name = args.session_name
model.load_pretrained(args.pretrained_weights)
model.cuda()
if not os.path.exists(model.session_name):
os.makedirs(model.session_name)
# Prepare optimizer and scheduler
optimizer = torch.optim.SGD(model.parameters(),
lr=args.lr,
momentum=0.9,
weight_decay=args.weight_decay)
if not os.path.isfile("attention_data/ViT-B_16-224.npz"):
urlretrieve(
"https://storage.googleapis.com/vit_models/imagenet21k+imagenet2012/ViT-B_16-224.npz",
"attention_data/ViT-B_16-224.npz")
imagenet_labels = dict(
enumerate(open('attention_data/ilsvrc2012_wordnet_lemmas.txt')))
img_url = "https://images.mypetlife.co.kr/content/uploads/2019/04/09192811/welsh-corgi-1581119_960_720.jpg"
urlretrieve(img_url, "attention_data/img.jpg")
# Prepare Model
config = CONFIGS["ViT-B_16"]
model = VisionTransformer(config,
num_classes=1000,
zero_head=False,
img_size=224,
vis=True)
model.load_from(np.load("attention_data/ViT-B_16-224.npz"))
model.eval()
transform = transforms.Compose([
transforms.Resize((224, 224)),
transforms.ToTensor(),
transforms.Normalize(mean=[0.5, 0.5, 0.5], std=[0.5, 0.5, 0.5]),
])
im = Image.open("attention_data/img.jpg")
x = transform(im)
x.size()
# %%
logits, att_mat = model(x.unsqueeze(0))
def main(args):
savedir = "./saved-outputs/model" + str(args.base_idx) + "/"
print('Preparing directory %s' % savedir)
os.makedirs(savedir, exist_ok=True)
with open(os.path.join(savedir, 'base_command.sh'), 'w') as f:
f.write(' '.join(sys.argv))
f.write('\n')
trainloader, testloader = get_loader(args)
config = CONFIGS['ViT-B_16']
num_classes = 100
model = VisionTransformer(config, args.img_size, zero_head=True, num_classes=num_classes)
modeldir = "./cifar100-100_500_seed_" + str(args.base_idx) + "/"
modelname = "cifar100-100_500_seed_" + str(args.base_idx) + "_checkpoint.bin"
model.load_state_dict(torch.load(modeldir+modelname))
simplex_model = BasicSimplex(model, num_vertices=1, fixed_points=[False]).cuda()
del model
## add a new points and train ##
for vv in range(1, args.n_verts+1):
simplex_model.add_vert()
simplex_model = simplex_model.cuda()
optimizer = torch.optim.SGD(
simplex_model.parameters(),
lr=args.lr_init,
momentum=0.9,
weight_decay=args.wd
)
scheduler = torch.optim.lr_scheduler.CosineAnnealingLR(optimizer,
T_max=args.epochs)
criterion = torch.nn.CrossEntropyLoss()
columns = ['vert', 'ep', 'lr', 'tr_loss',
'tr_acc', 'te_loss', 'te_acc', 'time']
for epoch in range(args.epochs):
time_ep = time.time()
train_res = simp_utils.train_transformer_epoch(
trainloader,
simplex_model,
criterion,
optimizer,
args.n_sample,
vol_reg=1e-4,
gradient_accumulation_steps=args.gradient_accumulation_steps,
)
start_ep = (epoch == 0)
eval_ep = epoch % args.eval_freq == args.eval_freq - 1
end_ep = epoch == args.epochs - 1
# test_res = {'loss': None, 'accuracy': None}
if eval_ep:
test_res = simp_utils.eval(testloader, simplex_model, criterion)
else:
test_res = {'loss': None, 'accuracy': None}
time_ep = time.time() - time_ep
lr = optimizer.param_groups[0]['lr']
scheduler.step()
values = [vv, epoch + 1, lr,
train_res['loss'], train_res['accuracy'],
test_res['loss'], test_res['accuracy'], time_ep]
table = tabulate.tabulate([values], columns,
tablefmt='simple', floatfmt='8.4f')
if epoch % 40 == 0:
table = table.split('\n')
table = '\n'.join([table[1]] + table)
else:
table = table.split('\n')[2]
print(table, flush=True)
checkpoint = simplex_model.state_dict()
fname = "lr_"+str(args.lr_init)+"simplex_vertex" + str(vv) + ".pt"
torch.save(checkpoint, savedir + fname)
from PIL import Image
from torchvision import transforms
from models.modeling import VisionTransformer, CONFIGS
transform = transforms.Compose([
transforms.Resize((224, 224)),
transforms.ToTensor(),
transforms.Normalize(mean=[0.5, 0.5, 0.5], std=[0.5, 0.5, 0.5]),
])
if __name__ == "__main__":
config = CONFIGS["ViT-B_16"]
model = VisionTransformer(config,
num_classes=5,
zero_head=False,
img_size=224,
vis=True)
model.load_state_dict(torch.load("output/cassava_trial1_checkpoint.pth"))
model.eval()
labels = pd.read_csv(
"/home/ligia/Documents/facultate/master-1.1/modelare_numerica/leaf_classification/dataset/train.csv"
)
accuracy = 0.0
count = 0
for path in glob(
"/home/ligia/Documents/facultate/master-1.1/modelare_numerica/leaf_classification/dataset/train/*.jpg"
):
## Constructing the test loader
test_loader = MMTDataset_baseline(args.test_set,
args.labels_dict,
args.img_folder,
args.input_dim,
transform=loader_transform)
test_loader = DataLoader(test_loader,
batch_size=args.batch_size,
shuffle=False)
## Initializing the model
config = CONFIGS["ViT-B_16"]
num_classes = 18
model = VisionTransformer(config,
args.input_dim,
zero_head=True,
num_classes=num_classes)
model.epochs = args.epochs
model.session_name = args.session_name
model.load_pretrained(args.pretrained_weights)
model.cuda()
if not os.path.exists(model.session_name):
os.makedirs(model.session_name)
# Prepare optimizer and scheduler
optimizer = torch.optim.SGD(model.parameters(),
lr=args.lr,
momentum=0.9,
weight_decay=args.weight_decay)