I tried my best to make a minimal reproducible example: there's an issue in my train() function where on the line output = model(data); I get the error IndexError: Dimension out of range (expected to be in range of [-1, 0], but got 1). I also get an error before that but I cannot decipher the meaning. I've included the full traceback in this message.
I've seen other users post about the same error message, but each one has a different solution; I used the debugger to look into data.location but I'm still unable to resolve the problem. I'm using PySyft v0.2.9.
import torch
import syft as sy
import pandas as pd
from torch.utils.data import Dataset, DataLoader
from sklearn.preprocessing import LabelEncoder
from sklearn.preprocessing import StandardScaler
from auto_esn.esn.esn import ESNBase
import torch.nn.functional as F
import torch.optim as optim
from auto_esn.esn.esn import GroupedDeepESN
from auto_esn.esn.reservoir.util import NRMSELoss
class CarHackingDataset(Dataset):
"""
Loading the Car-Hacking Dataset from
https://ocslab.hksecurity.net/Datasets/car-hacking-dataset
Args:
csv_file: A path to the dataset file which has the extension CSV.
root_dir: The directory of the parent folder of the dataset.
transform (callable, optional): Optional tansform to be applied on a sample.
"""
def __init__(self, csv_file: str, root_dir: str, transform=None):
self.car_hacking_frame = pd.read_csv(csv_file)[:10000]
self.root_dir = root_dir
self.transform = transform
def __getitem__(self,idx):
'''Grabs relevant features from the dataset.'''
if torch.is_tensor(idx):
idx = idx.tolist()
features = ['Timestamp', 'DLC', 'CAN_ID', 'Data']
X_train = self.car_hacking_frame.loc[:, features].values
X_train_scaled = StandardScaler().fit_transform(X_train)
X_train_scaled = torch.as_tensor(X_train_scaled)
# It looks like it's a bad idea to encode features.
# https://stackoverflow.com/questions/61217713/labelencoder-for-categorical-features
class_le = LabelEncoder()
target = class_le.fit_transform(self.car_hacking_frame['Flag'].values)
target = torch.as_tensor(target)
return X_train_scaled[idx], target[idx]
def __len__(self):
return len(self.car_hacking_frame)
train_dataset = CarHackingDataset(csv_file='/content/car_hacking_data/clean_fuzzy_dataset.csv',
root_dir='/content/car_hacking_data')
device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
hook = sy.TorchHook(torch)
car1 = sy.VirtualWorker(hook, id="car1")
car2 = sy.VirtualWorker(hook, id="car2")
args = {
'batch_size' : 32,
'epochs' : 1
}
federated_train_loader = sy.FederatedDataLoader(train_dataset.federate((car1, car2)),
batch_size=args['batch_size'], shuffle=True)
# Intializing the loss function which is probably a variation of mean squared error.
nrmse = NRMSELoss()
def train(model, device, federated_train_loader, optimizer, epoch):
model.train()
for batch_idx, (data, target) in enumerate(federated_train_loader):
model = model.send(data.location)
data, target = data.to(device), target.to(device)
optimizer.zero_grad()
output = model(data)
loss = nrmse(output, target)
loss.backward()
optimizer.step()
model.get()
if batch_idx % 10 == 0:
loss = loss.get()
print(f'''Train Epoch: {epoch} [{(batch_idx * args['batch_size'])}/{(len(federated_train_loader) * args['batch_size'])}'''
+ f'''({100. * batch_idx / len(federated_train_loader):.0f}%)]\tLoss: {loss.item():.6f}''')
model = GroupedDeepESN().to(device)
optimizer = optim.SGD(model.parameters(), lr=0.01)
for epoch in range(1, args['batch_size'] + 1):
train(model, device, federated_train_loader, optimizer, epoch)
Traceback of the error message:
---------------------------------------------------------------------------
PureFrameworkTensorFoundError Traceback (most recent call last)
/usr/local/lib/python3.7/dist-packages/syft/frameworks/torch/tensors/interpreters/native.py in handle_func_command(cls, command)
336 new_args, new_kwargs, new_type, args_type = hook_args.unwrap_args_from_function(
--> 337 cmd, args_, kwargs_, return_args_type=True
338 )
20 frames
/usr/local/lib/python3.7/dist-packages/syft/generic/frameworks/hook/hook_args.py in unwrap_args_from_function(attr, args_, kwargs_, return_args_type)
166 # Run it
--> 167 new_args = args_hook_function(args_)
168
/usr/local/lib/python3.7/dist-packages/syft/generic/frameworks/hook/hook_args.py in <lambda>(x)
355
--> 356 return lambda x: f(lambdas, x)
357
/usr/local/lib/python3.7/dist-packages/syft/generic/frameworks/hook/hook_args.py in three_fold(lambdas, args_, **kwargs)
534 lambdas[0](args_[0], **kwargs),
--> 535 lambdas[1](args_[1], **kwargs),
536 lambdas[2](args_[2], **kwargs),
/usr/local/lib/python3.7/dist-packages/syft/generic/frameworks/hook/hook_args.py in <lambda>(i)
330 # Last if not, rule is probably == 1 so use type to return the right transformation.
--> 331 else lambda i: forward_func[type(i)](i)
332 for a, r in zip(args_, rules) # And do this for all the args / rules provided
/usr/local/lib/python3.7/dist-packages/syft/frameworks/torch/hook/hook_args.py in <lambda>(i)
23 if hasattr(i, "child")
---> 24 else (_ for _ in ()).throw(PureFrameworkTensorFoundError),
25 torch.nn.Parameter: lambda i: i.child
/usr/local/lib/python3.7/dist-packages/syft/frameworks/torch/hook/hook_args.py in <genexpr>(.0)
23 if hasattr(i, "child")
---> 24 else (_ for _ in ()).throw(PureFrameworkTensorFoundError),
25 torch.nn.Parameter: lambda i: i.child
PureFrameworkTensorFoundError:
During handling of the above exception, another exception occurred:
IndexError Traceback (most recent call last)
<ipython-input-6-c9ac87b98598> in <module>
24
25 for epoch in range(1, args['batch_size'] + 1):
---> 26 train(model, device, federated_train_loader, optimizer, epoch)
<ipython-input-6-c9ac87b98598> in train(model, device, federated_train_loader, optimizer, epoch)
7 data, target = data.to(device), target.to(device)
8 optimizer.zero_grad()
----> 9 output = model(data)
10
11 loss = nrmse(output, target)
/usr/local/lib/python3.7/dist-packages/torch/nn/modules/module.py in __call__(self, *input, **kwargs)
530 result = self._slow_forward(*input, **kwargs)
531 else:
--> 532 result = self.forward(*input, **kwargs)
533 for hook in self._forward_hooks.values():
534 hook_result = hook(self, input, result)
/usr/local/lib/python3.7/dist-packages/auto_esn/esn/esn.py in forward(self, input)
31 mapped_input = self.reservoir(input)
32
---> 33 return self.readout(mapped_input)
34
35 def reset_hidden(self):
/usr/local/lib/python3.7/dist-packages/torch/nn/modules/module.py in __call__(self, *input, **kwargs)
530 result = self._slow_forward(*input, **kwargs)
531 else:
--> 532 result = self.forward(*input, **kwargs)
533 for hook in self._forward_hooks.values():
534 hook_result = hook(self, input, result)
/usr/local/lib/python3.7/dist-packages/auto_esn/esn/readout/svr_readout.py in forward(self, input)
10
11 def forward(self, input: Tensor) -> Tensor:
---> 12 return self.readout(input)
13
14 def fit(self, input: Tensor, target: Tensor):
/usr/local/lib/python3.7/dist-packages/torch/nn/modules/module.py in __call__(self, *input, **kwargs)
530 result = self._slow_forward(*input, **kwargs)
531 else:
--> 532 result = self.forward(*input, **kwargs)
533 for hook in self._forward_hooks.values():
534 hook_result = hook(self, input, result)
/usr/local/lib/python3.7/dist-packages/torch/nn/modules/linear.py in forward(self, input)
85
86 def forward(self, input):
---> 87 return F.linear(input, self.weight, self.bias)
88
89 def extra_repr(self):
/usr/local/lib/python3.7/dist-packages/syft/generic/frameworks/hook/hook.py in overloaded_func(*args, **kwargs)
333 handle_func_command = syft.framework.Tensor.handle_func_command
334
--> 335 response = handle_func_command(command)
336
337 return response
/usr/local/lib/python3.7/dist-packages/syft/frameworks/torch/tensors/interpreters/native.py in handle_func_command(cls, command)
378 # in the execute_command function
379 try:
--> 380 response = cls._get_response(cmd, args_, kwargs_)
381 except AttributeError:
382 # Change the library path to avoid errors on layers like AvgPooling
/usr/local/lib/python3.7/dist-packages/syft/frameworks/torch/tensors/interpreters/native.py in _get_response(cmd, args_, kwargs_)
412
413 if isinstance(args_, tuple):
--> 414 response = command_method(*args_, **kwargs_)
415 else:
416 response = command_method(args_, **kwargs_)
/usr/local/lib/python3.7/dist-packages/torch/nn/functional.py in linear(input, weight, bias)
1368 if input.dim() == 2 and bias is not None:
1369 # fused op is marginally faster
-> 1370 ret = torch.addmm(bias, input, weight.t())
1371 else:
1372 output = input.matmul(weight.t())
/usr/local/lib/python3.7/dist-packages/syft/generic/frameworks/hook/hook.py in overloaded_func(*args, **kwargs)
333 handle_func_command = syft.framework.Tensor.handle_func_command
334
--> 335 response = handle_func_command(command)
336
337 return response
/usr/local/lib/python3.7/dist-packages/syft/frameworks/torch/tensors/interpreters/native.py in handle_func_command(cls, command)
378 # in the execute_command function
379 try:
--> 380 response = cls._get_response(cmd, args_, kwargs_)
381 except AttributeError:
382 # Change the library path to avoid errors on layers like AvgPooling
/usr/local/lib/python3.7/dist-packages/syft/frameworks/torch/tensors/interpreters/native.py in _get_response(cmd, args_, kwargs_)
412
413 if isinstance(args_, tuple):
--> 414 response = command_method(*args_, **kwargs_)
415 else:
416 response = command_method(args_, **kwargs_)
IndexError: Dimension out of range (expected to be in range of [-1, 0], but got 1)
I thought it might also be important to include the shape of my data inside the train function's loop over the federated data: Data shape: torch.Size([32, 4]), Target shape: torch.Size([32]).
