def _create_mapping_loader(config, dataset_class, tf3, partition,
truncate=False, truncate_pc=None,
tencrop=False,
shuffle=False):
if truncate:
print("Note: creating mapping loader with truncate == True")
if tencrop:
assert (tf3 is None)
imgs_list = []
if config.test_on_all_frame and partition == "test":
for i in xrange(10):
imgs_curr = dataset_class(root=config.dataset_root,
transform=tf3,
frame=i,
crop=config.crop_by_bb,
partition=partition)
if truncate:
print("shrinking dataset from %d" % len(imgs_curr))
imgs_curr = TruncatedDataset(imgs_curr, pc=truncate_pc)
print("... to %d" % len(imgs_curr))
if tencrop:
imgs_curr = TenCropAndFinish(imgs_curr, input_sz=config.input_sz,
include_rgb=config.include_rgb)
imgs_list.append(imgs_curr)
else:
for i in xrange(config.base_num):
imgs_curr = dataset_class(root=config.dataset_root,
transform=tf3,
frame=config.base_frame + config.base_interval * i,
crop=config.crop_by_bb,
partition=partition)
if truncate:
print("shrinking dataset from %d" % len(imgs_curr))
imgs_curr = TruncatedDataset(imgs_curr, pc=truncate_pc)
print("... to %d" % len(imgs_curr))
if tencrop:
imgs_curr = TenCropAndFinish(imgs_curr, input_sz=config.input_sz,
include_rgb=config.include_rgb)
imgs_list.append(imgs_curr)
imgs = ConcatDataset(imgs_list)
dataloader = torch.utils.data.DataLoader(imgs,
batch_size=config.batch_sz,
# full batch
shuffle=shuffle,
num_workers=0,
drop_last=False)
if not shuffle:
assert (isinstance(dataloader.sampler,
torch.utils.data.sampler.SequentialSampler))
return dataloader
def _create_mapping_loader(config,
dataset_class,
tf3,
partitions,
target_transform=None,
truncate=False,
truncate_pc=None,
tencrop=False,
shuffle=False):
if truncate:
print("Note: creating mapping loader with truncate == True")
if tencrop:
assert (tf3 is None)
imgs_list = []
for partition in partitions:
if "STL10" == config.dataset:
imgs_curr = dataset_class(root=config.dataset_root,
transform=tf3,
split=partition,
target_transform=target_transform)
elif config.dataset == "MNIST-adv":
imgs_curr = dataset_class(
root=config.dataset_root,
transform=tf3,
train=partition,
target_transform=target_transform) + AdversarialDataset(
config.adv_path, config.adv_n)
else:
imgs_curr = dataset_class(root=config.dataset_root,
transform=tf3,
train=partition,
target_transform=target_transform)
if truncate:
print("shrinking dataset from %d" % len(imgs_curr))
imgs_curr = TruncatedDataset(imgs_curr, pc=truncate_pc)
print("... to %d" % len(imgs_curr))
if tencrop:
imgs_curr = TenCropAndFinish(imgs_curr,
input_sz=config.input_sz,
include_rgb=config.include_rgb)
imgs_list.append(imgs_curr)
imgs = ConcatDataset(imgs_list)
dataloader = torch.utils.data.DataLoader(
imgs,
batch_size=config.batch_sz,
# full batch
shuffle=shuffle,
num_workers=0,
drop_last=False)
if not shuffle:
assert (isinstance(dataloader.sampler,
torch.utils.data.sampler.SequentialSampler))
return dataloader
def main():
parser = argparse.ArgumentParser()
parser.add_argument("--model_ind", type=int, required=True)
parser.add_argument("--arch", type=str, required=True)
parser.add_argument("--head_lr", type=float, required=True)
parser.add_argument("--trunk_lr", type=float, required=True)
parser.add_argument("--num_epochs", type=int, default=3200)
parser.add_argument("--new_batch_sz", type=int, default=-1)
parser.add_argument("--old_model_ind", type=int, required=True)
parser.add_argument("--penultimate_features",
default=False,
action="store_true")
parser.add_argument("--random_affine", default=False, action="store_true")
parser.add_argument("--affine_p", type=float, default=0.5)
parser.add_argument("--cutout", default=False, action="store_true")
parser.add_argument("--cutout_p", type=float, default=0.5)
parser.add_argument("--cutout_max_box", type=float, default=0.5)
parser.add_argument("--restart", default=False, action="store_true")
parser.add_argument("--lr_schedule", type=int, nargs="+", default=[])
parser.add_argument("--lr_mult", type=float, default=0.5)
parser.add_argument("--restart_new_model_ind",
default=False,
action="store_true")
parser.add_argument("--new_model_ind", type=int, default=0)
parser.add_argument("--out_root",
type=str,
default="/scratch/shared/slow/xuji/iid_private")
config = parser.parse_args() # new config
# Setup ----------------------------------------------------------------------
config.contiguous_sz = 10 # Tencrop
config.out_dir = os.path.join(config.out_root, str(config.model_ind))
if not os.path.exists(config.out_dir):
os.makedirs(config.out_dir)
if config.restart:
given_config = config
reloaded_config_path = os.path.join(given_config.out_dir,
"config.pickle")
print("Loading restarting config from: %s" % reloaded_config_path)
with open(reloaded_config_path, "rb") as config_f:
config = pickle.load(config_f)
assert (config.model_ind == given_config.model_ind)
config.restart = True
config.num_epochs = given_config.num_epochs # train for longer
config.restart_new_model_ind = given_config.restart_new_model_ind
config.new_model_ind = given_config.new_model_ind
start_epoch = config.last_epoch + 1
print("...restarting from epoch %d" % start_epoch)
# in case we overshot without saving
config.epoch_acc = config.epoch_acc[:start_epoch]
config.epoch_loss = config.epoch_loss[:start_epoch]
else:
config.epoch_acc = []
config.epoch_loss = []
start_epoch = 0
# old config only used retrospectively for setting up model at start
reloaded_config_path = os.path.join(
os.path.join(config.out_root, str(config.old_model_ind)),
"config.pickle")
print("Loading old features config from: %s" % reloaded_config_path)
with open(reloaded_config_path, "rb") as config_f:
old_config = pickle.load(config_f)
assert (old_config.model_ind == config.old_model_ind)
if config.new_batch_sz == -1:
config.new_batch_sz = old_config.batch_sz
fig, axarr = plt.subplots(2, sharex=False, figsize=(20, 20))
# Data -----------------------------------------------------------------------
assert (old_config.dataset == "STL10")
# make supervised data: train on train, test on test, unlabelled is unused
tf1, tf2, tf3 = sobel_make_transforms(old_config,
random_affine=config.random_affine,
cutout=config.cutout,
cutout_p=config.cutout_p,
cutout_max_box=config.cutout_max_box,
affine_p=config.affine_p)
dataset_class = torchvision.datasets.STL10
train_data = dataset_class(
root=old_config.dataset_root,
transform=tf2, # also could use tf1
split="train")
train_loader = torch.utils.data.DataLoader(train_data,
batch_size=config.new_batch_sz,
shuffle=True,
num_workers=0,
drop_last=False)
test_data = dataset_class(root=old_config.dataset_root,
transform=None,
split="test")
test_data = TenCropAndFinish(test_data,
input_sz=old_config.input_sz,
include_rgb=old_config.include_rgb)
test_loader = torch.utils.data.DataLoader(
test_data,
batch_size=config.new_batch_sz,
# full batch
shuffle=False,
num_workers=0,
drop_last=False)
# Model ----------------------------------------------------------------------
net_features = archs.__dict__[old_config.arch](old_config)
if not config.restart:
model_path = os.path.join(old_config.out_dir, "best_net.pytorch")
net_features.load_state_dict(
torch.load(model_path, map_location=lambda storage, loc: storage))
dlen = get_dlen(net_features,
train_loader,
include_rgb=old_config.include_rgb,
penultimate_features=config.penultimate_features)
print("dlen: %d" % dlen)
assert (config.arch == "SupHead5")
net = SupHead5(net_features, dlen=dlen, gt_k=old_config.gt_k)
if config.restart:
print("restarting from latest net")
model_path = os.path.join(config.out_dir, "latest_net.pytorch")
net.load_state_dict(
torch.load(model_path, map_location=lambda storage, loc: storage))
net.cuda()
net = torch.nn.DataParallel(net)
opt_trunk = torch.optim.Adam(net.module.trunk.parameters(),
lr=config.trunk_lr)
opt_head = torch.optim.Adam(net.module.head.parameters(),
lr=(config.head_lr))
if config.restart:
print("restarting from latest optimiser")
optimiser_states = torch.load(
os.path.join(config.out_dir, "latest_optimiser.pytorch"))
opt_trunk.load_state_dict(optimiser_states["opt_trunk"])
opt_head.load_state_dict(optimiser_states["opt_head"])
else:
print("using new optimiser state")
criterion = nn.CrossEntropyLoss().cuda()
if not config.restart:
net.eval()
acc = assess_acc_block(
net,
test_loader,
gt_k=old_config.gt_k,
include_rgb=old_config.include_rgb,
penultimate_features=config.penultimate_features,
contiguous_sz=config.contiguous_sz)
print("pre: model %d old model %d, acc %f time %s" %
(config.model_ind, config.old_model_ind, acc, datetime.now()))
sys.stdout.flush()
config.epoch_acc.append(acc)
if config.restart_new_model_ind:
assert (config.restart)
config.model_ind = config.new_model_ind # old_model_ind stays same
config.out_dir = os.path.join(config.out_root, str(config.model_ind))
print("restarting as model %d" % config.model_ind)
if not os.path.exists(config.out_dir):
os.makedirs(config.out_dir)
# Train ----------------------------------------------------------------------
for e_i in xrange(start_epoch, config.num_epochs):
net.train()
if e_i in config.lr_schedule:
print("e_i %d, multiplying lr for opt trunk and head by %f" %
(e_i, config.lr_mult))
opt_trunk = update_lr(opt_trunk, lr_mult=config.lr_mult)
opt_head = update_lr(opt_head, lr_mult=config.lr_mult)
if not hasattr(config, "lr_changes"):
config.lr_changes = []
config.lr_changes.append((e_i, config.lr_mult))
avg_loss = 0.
num_batches = len(train_loader)
for i, (imgs, targets) in enumerate(train_loader):
imgs = sobel_process(imgs.cuda(), old_config.include_rgb)
targets = targets.cuda()
x_out = net(imgs, penultimate_features=config.penultimate_features)
loss = criterion(x_out, targets)
avg_loss += float(loss.data)
opt_trunk.zero_grad()
opt_head.zero_grad()
loss.backward()
opt_trunk.step()
opt_head.step()
if (i % 100 == 0) or (e_i == start_epoch):
print("batch %d of %d, loss %f, time %s" %
(i, num_batches, float(loss.data), datetime.now()))
sys.stdout.flush()
avg_loss /= num_batches
net.eval()
acc = assess_acc_block(
net,
test_loader,
gt_k=old_config.gt_k,
include_rgb=old_config.include_rgb,
penultimate_features=config.penultimate_features,
contiguous_sz=config.contiguous_sz)
print(
"model %d old model %d epoch %d acc %f time %s" %
(config.model_ind, config.old_model_ind, e_i, acc, datetime.now()))
sys.stdout.flush()
is_best = False
if acc > max(config.epoch_acc):
is_best = True
config.epoch_acc.append(acc)
config.epoch_loss.append(avg_loss)
axarr[0].clear()
axarr[0].plot(config.epoch_acc)
axarr[0].set_title("Acc")
axarr[1].clear()
axarr[1].plot(config.epoch_loss)
axarr[1].set_title("Loss")
fig.canvas.draw_idle()
fig.savefig(os.path.join(config.out_dir, "plots.png"))
if is_best or (e_i % 10 == 0):
net.module.cpu()
if is_best:
torch.save(net.module.state_dict(),
os.path.join(config.out_dir, "best_net.pytorch"))
torch.save(
{
"opt_head": opt_head.state_dict(),
"opt_trunk": opt_trunk.state_dict()
}, os.path.join(config.out_dir, "best_optimiser.pytorch"))
# save model sparingly for this script
if e_i % 10 == 0:
torch.save(net.module.state_dict(),
os.path.join(config.out_dir, "latest_net.pytorch"))
torch.save(
{
"opt_head": opt_head.state_dict(),
"opt_trunk": opt_trunk.state_dict()
}, os.path.join(config.out_dir,
"latest_optimiser.pytorch"))
net.module.cuda()
config.last_epoch = e_i # for last saved version
with open(os.path.join(config.out_dir, "config.pickle"),
'w') as outfile:
pickle.dump(config, outfile)
with open(os.path.join(config.out_dir, "config.txt"),
"w") as text_file:
text_file.write("%s" % config)