def main(exp_const, data_const, model_const):
io.mkdir_if_not_exists(exp_const.vis_dir)
print('Creating network ...')
model = Model()
model.const = model_const
model.net = ResnetModel(model.const.net)
if model.const.model_num is not None:
model.net.load_state_dict(torch.load(model.const.net_path))
model.net.cuda()
if exp_const.feedforward == False:
model.embed2class = Embed2Class(model.const.embed2class)
if model.const.model_num is not None:
model.embed2class.load_state_dict(
torch.load(model.const.embed2class_path))
model.embed2class.cuda()
model.img_mean = np.array([0.485, 0.456, 0.406])
model.img_std = np.array([0.229, 0.224, 0.225])
print('Creating dataloader ...')
dataset = Cifar100Dataset(data_const)
dataloader = DataLoader(dataset,
batch_size=exp_const.batch_size,
shuffle=True,
num_workers=exp_const.num_workers)
eval_results = eval_model(model, dataloader, exp_const)
confmat_npy = os.path.join(exp_const.exp_dir, 'confmat.npy')
np.save(confmat_npy, eval_results['Conf Mat'])
results = {
'Avg Loss': eval_results['Avg Loss'],
'Acc': eval_results['Acc']
}
print(results)
results_json = os.path.join(exp_const.exp_dir, 'results.json')
io.dump_json_object(results, results_json)
embeddings_npy = os.path.join(exp_const.exp_dir, 'embeddings.npy')
if exp_const.feedforward == True:
np.save(embeddings_npy,
model.net.resnet_layers.fc.weight.data.cpu().numpy())
else:
np.save(embeddings_npy,
model.embed2class.embed.weight.data.cpu().numpy())
labels_npy = os.path.join(exp_const.exp_dir, 'labels.npy')
np.save(labels_npy, dataset.labels)
def main(exp_const, data_const, model_const):
io.mkdir_if_not_exists(exp_const.exp_dir, recursive=True)
io.mkdir_if_not_exists(exp_const.log_dir)
io.mkdir_if_not_exists(exp_const.model_dir)
io.mkdir_if_not_exists(exp_const.vis_dir)
configure(exp_const.log_dir)
save_constants({
'exp': exp_const,
'data': data_const,
'model': model_const
}, exp_const.exp_dir)
print('Creating network ...')
model = Model()
model.const = model_const
model.net = ResnetModel(model.const.net)
model.embed2class = Embed2Class(model.const.embed2class)
if model.const.model_num is not None:
model.net.load_state_dict(torch.load(model.const.net_path))
model.embed2class.load_state_dict(
torch.load(model.const.embed2class_path))
model.net.cuda()
model.embed2class.cuda()
model.img_mean = np.array([0.485, 0.456, 0.406])
model.img_std = np.array([0.229, 0.224, 0.225])
model.to_file(os.path.join(exp_const.exp_dir, 'model.txt'))
print('Creating dataloader ...')
dataloaders = {}
for mode, subset in exp_const.subset.items():
data_const = copy.deepcopy(data_const)
if subset == 'train':
data_const.train = True
else:
data_const.train = False
dataset = Cifar100Dataset(data_const)
collate_fn = dataset.get_collate_fn()
dataloaders[mode] = DataLoader(dataset,
batch_size=exp_const.batch_size,
shuffle=True,
num_workers=exp_const.num_workers,
collate_fn=collate_fn)
train_model(model, dataloaders, exp_const)