def pred_with_flip(learn, ds_type=DatasetType.Valid, probs=None):
# get prediction
preds, acts = learn.get_preds(ds_type)
preds = preds[:, 1, ...]
# add flip to dataset and get prediction
learn.data.dl(ds_type).dl.dataset.tfms.append(flip_lr())
preds_lr, acts = learn.get_preds(ds_type)
del learn.data.dl(ds_type).dl.dataset.tfms[-1]
preds_lr = preds_lr[:, 1, ...]
acts = acts.squeeze()
preds = 0.5 * (preds + torch.flip(preds_lr, [-1]))
del preds_lr
gc.collect()
torch.cuda.empty_cache()
if probs is not None:
for i in range(len(probs)):
p = torch.tensor(probs[i],
dtype=torch.float).unsqueeze(1).unsqueeze(2)
preds[i] = preds[i] * p
return preds, acts
def prepare_cifar(config):
dataset = build_dataset('cifar_10', val_size=config['val_size'])
x_set, y_set = dataset.dataset('train')
x_val, y_val = dataset.dataset('val')
shape = (-1, 3, 32, 32)
x_set = ((x_set - 128) / 128).reshape(shape)
x_val = ((x_val - 128) / 128).reshape(shape)
train_tfms = [*rand_pad(4, 32), flip_lr(p=0.5)] # Transformation to augment images
return x_set, y_set, x_val, y_val, train_tfms
def load_data():
dataset = build_dataset('cifar_10', val_size=val_size)
x_train, y_train = dataset.dataset('train')
x_val, y_val = dataset.dataset('val')
shape = (-1, 3, 32, 32)
x_train = ((x_train - 128) / 128).reshape(shape)
x_val = ((x_val - 128) / 128).reshape(shape)
train_tfms = [*rand_pad(4, 32), flip_lr(p=0.5)]
train_ds = ImageArrayDS(x_train, y_train, train_tfms)
val_ds = ImageArrayDS(x_val, y_val)
data = ImageDataBunch.create(train_ds, val_ds, bs=256)
return data, x_train, y_train, x_val, y_val
def prepare_svhn(config):
dataset = build_dataset('svhn', val_size=config['val_size'])
x_set, y_set = dataset.dataset('train')
x_val, y_val = dataset.dataset('val')
y_set[y_set == 10] = 0
y_val[y_val == 10] = 0
shape = (-1, 32, 32, 3)
x_set = ((x_set - 128) / 128).reshape(shape)
x_val = ((x_val - 128) / 128).reshape(shape)
x_set = np.rollaxis(x_set, 3, 1)
x_val = np.rollaxis(x_val, 3, 1)
train_tfms = [*rand_pad(4, 32),
flip_lr(p=0.5)] # Transformation to augment images
return x_set, y_set, x_val, y_val, train_tfms
def pred_with_flip(
learn: fastai.basic_train.Learner,
ds_type: fastai.basic_data.DatasetType = DatasetType.Valid,
):
# get prediction
preds, ys = learn.get_preds(ds_type)
preds = preds[:, 1, ...]
# add fiip to dataset and get prediction
learn.data.dl(ds_type).dl.dataset.tfms.append(flip_lr())
preds_lr, ys = learn.get_preds(ds_type)
del learn.data.dl(ds_type).dl.dataset.tfms[-1]
preds_lr = preds_lr[:, 1, ...]
ys = ys.squeeze()
preds = 0.5 * (preds + torch.flip(preds_lr, [-1]))
del preds_lr
gc.collect()
torch.cuda.empty_cache()
return preds, ys
pool_size = 200
# Load data
dataset = build_dataset('cifar_10', val_size=val_size)
x_set, y_set = dataset.dataset('train')
x_val, y_val = dataset.dataset('val')
shape = (-1, 3, 32, 32)
x_set = ((x_set - 128) / 128).reshape(shape)
x_val = ((x_val - 128) / 128).reshape(shape)
# x_pool, x_train, y_pool, y_train = train_test_split(x_set, y_set, test_size=start_size, stratify=y_set)
x_train, y_train = x_set, y_set
train_tfms = [*rand_pad(4, 32), flip_lr(p=0.5)]
train_ds = ImageArrayDS(x_train, y_train, train_tfms)
val_ds = ImageArrayDS(x_val, y_val)
data = ImageDataBunch.create(train_ds, val_ds, bs=256)
loss_func = torch.nn.CrossEntropyLoss()
np.set_printoptions(threshold=sys.maxsize, suppress=True)
model = AnotherConv()
# model = resnet_masked(pretrained=True)
# model = resnet_linear(pretrained=True, dropout_rate=0.5, freeze=False)
# learner = Learner(data, model, metrics=accuracy, loss_func=loss_func)
#
# model_path = "experiments/data/model.pt"