logger = initialize_log(params)
# load Dataset
train_dataset = Dataset(params, 'train')
val_dataset = Dataset(params, 'val')
train_data = DataLoader(train_dataset,
batch_size=params.batch_size,
shuffle=True,
num_workers=1) # num_workers 几个线程参与读数据
valid_data = DataLoader(val_dataset,
batch_size=params.batch_size,
shuffle=False,
num_workers=1)
# build the model
ae = AutoEncoder(params.n_attr).cuda()
lat_dis = LatentDiscriminator(params.n_attr).cuda()
ptc_dis = PatchDiscriminator().cuda()
clf_dis = Classifier(params.n_attr).cuda()
# trainer / evaluator
trainer = Trainer(ae, lat_dis, ptc_dis, clf_dis, train_data, params)
evaluator = Evaluator(ae, lat_dis, ptc_dis, clf_dis, valid_data, params)
for n_epoch in range(params.n_epochs):
logger.info('Starting epoch %i...' % n_epoch)
for n_iter in range(0, params.epoch_size, params.batch_size):
# latent discriminator training
def setUp(self):
self.df = pd.read_csv(os.path.join('tests', './data_for_testing.csv'))
self.device = torch.device(
"cuda:0" if torch.cuda.is_available() else "cpu")
self.model = AutoEncoder(self.df.shape[1])
assert not params.clf_dis_reload or os.path.isfile(params.clf_dis_reload)
#print params.eval_clf
#print "==="
assert os.path.isfile(params.eval_clf)
assert params.lambda_lat_dis == 0 or params.n_lat_dis > 0
assert params.lambda_ptc_dis == 0 or params.n_ptc_dis > 0
assert params.lambda_clf_dis == 0 or params.n_clf_dis > 0
# initialize experiment / load dataset
logger = initialize_exp(params)
data, attributes, data2, attributes2 = load_images(params)
train_data = DataSampler(data[0], attributes[0], data2, attributes2, params)
valid_data = DataSampler(data[1], attributes[1], None, None, params)
# build the model
ae = AutoEncoder(params).cuda()
lat_dis = LatentDiscriminator(params).cuda() if params.n_lat_dis else None
ptc_dis = PatchDiscriminator(params).cuda() if params.n_ptc_dis else None
clf_dis = Classifier(params).cuda() if params.n_clf_dis else None
eval_clf = torch.load(params.eval_clf).cuda().eval()
# trainer / evaluator
trainer = Trainer(ae, lat_dis, ptc_dis, clf_dis, train_data, params)
evaluator = Evaluator(ae, lat_dis, ptc_dis, clf_dis, eval_clf, valid_data, params)
for n_epoch in range(params.n_epochs):
logger.info('Starting epoch %i...' % n_epoch)
for n_iter in range(0, params.epoch_size, params.batch_size):
params.max_fm = params.max_fm + params.n_attr
params.max_fm_orig = params.max_fm
if not params.ae_teacher_reload:
params.ae_teacher_reload = params.ae_reload # typically teacher weights are used for initializating the student (when possible), so these two are the same
# initialize experiment / load dataset
DATAROOT = '/data/tmp'
logger = initialize_exp(params)
data, attributes = load_images(params)
train_data = DataSampler(data[0], attributes[0], params)
valid_data = DataSampler(data[1], attributes[1], params)
# build the trainable model
ae = AutoEncoder(params).cuda()
# build the Teacher model (if required)
if params.lambda_jacobian > 0:
params2 = params
params2.max_fm = 512 + params.n_attr
ae_teacher = AutoEncoder(params2).cuda()
params.max_fm = params.max_fm_orig
else:
ae_teacher = None
# trainer / evaluator
trainer = Trainer(ae, ae_teacher, train_data, params)
evaluator = Evaluator(ae, ae_teacher, valid_data, params)