class DatasetAll(TensorDataset):
def __init__(self,
X,
y,
perturbations,
k=20,
return_index=False,
joint_transform=None):
if perturbations.size(0) < k:
raise ValueError
perturbations = perturbations[:, :, :, :, :k]
perturbations = perturbations.permute(0, 4, 1, 2,
3).reshape(-1,
*X.size()[1:])
super(DatasetAll, self).__init__(perturbations)
self.Xy = TensorDataset(X, y)
self.k = k
self.return_index = return_index
self.joint_transform = joint_transform
def __getitem__(self, index):
t3, = super(DatasetAll, self).__getitem__(index)
# print(index, self.k, index//self.k)
t1, t2 = self.Xy.__getitem__(int(index // self.k))
if self.joint_transform is not None:
t1, t3 = self.joint_transform(t1, t3)
if self.return_index:
return t1, t2, t3, index
else:
return t1, t2, t3
class RawBinaryDataset(Dataset):
"""Binary features dataset."""
def __init__(self, json_file):
"""
Args:
json_file (string): Path to the json file the 1 Kb samples.
"""
with open(json_file, "r") as f:
data = f.read()
self.data_frame = json.loads(data)
data_frame_X, data_frame_Y = self._from_json_to_tensor(self.data_frame)
self.dataset = TensorDataset(data_frame_X, data_frame_Y)
def _from_json_to_tensor(self, json_data: dict):
x = []
y = []
for key, value in json_data.items():
y.extend([ARCHITECTURES.index(key)] * len(value))
x.extend(value)
return torch.Tensor(x), torch.Tensor(y)
def __len__(self):
return len(self.dataset)
def __getitem__(self, idx):
return self.dataset.__getitem__(idx)
def predict(self, image_url):
inp = self.process_raw(image_url)
test_img = TensorDataset(torch.from_numpy(inp).float())
output = self.convnet(test_img.__getitem__())
print(output)
_, predicted = torch.max(output.data, 1)
return self.labels[predicted.item()]
class FeatureDataset(Dataset):
"""Binary features dataset."""
def __init__(self, csv_file):
"""
Args:
csv_file (string): Path to the csv file with annotations.
"""
self.data_frame = np.genfromtxt(csv_file,
delimiter=',',
skip_header=True,
filling_values=0)
self.data_frame = torch.from_numpy(self.data_frame)
data_frame_X = self.data_frame[:, 0:-1]
data_frame_Y = self.data_frame[:, -1]
self.dataset = TensorDataset(data_frame_X, data_frame_Y)
def __len__(self):
return len(self.data_frame)
def __getitem__(self, idx):
return self.dataset.__getitem__(idx)
