How can I change this code
train_0.append(0)
train_1.append(1)
train_2.append(2)
train_3.append(3)
using loop like under?
for i in range(4):
train_i.append(i)
My code occurs this error.
NameError: name 'train_i' is not defined
Thank you.
There is some methods how to do this, but usually such as problems defines bad code... Better to do something with code and covert big amount of variables to something iterable.
There is some methods:
for i in range(4):
train = globals().get("train_{}".format(i), None)
if train:
train.append(i)
for i in range(4):
try:
eval("train_{0}.append({0})".format(i))
except:
pass
In the class, to define self.variance, how I can adjust your solution?
for i in range(4):
globals()["test_{}".format(i)].append(ToTensor(vectors[i]))
Becuase upper code works with your help.
But under case (in class) it doesn't work.
class MyDataset():
def __init__(self, cropped_img_vectors, targets):
self.data_0 = cropped_img_vectors[0]
self.data_1 = cropped_img_vectors[1]
self.data_2 = cropped_img_vectors[2]
self.data_3 = cropped_img_vectors[3]
self.targets = targets
def __getitem__(self, index):
data_0 = self.data_0[index]
data_1 = self.data_1[index]
data_2 = self.data_2[index]
data_3 = self.data_3[index]
y = self.targets[index]
dataset = []
for i in range(4):
dataset.append(["data_{}".format(i)])
return dataset, y
def __len__(self):
return len(self.data_0)
I changed uppder to under.
class MyDataset():
def __init__(self, cropped_1pixel_dataset, targets):
for i in range(4):
globals()["self.data_{}".format(i)] = cropped_1pixel_dataset[i]
self.targets = targets
def __getitem__(self, index):
for i in range(4):
globals()["data_{}".format(i)] = cropped_1pixel_dataset[i][index]
y = self.targets[index]
return [globals()["data_{}".format(i)] for i in range(4)], y
def __len__(self):
return len(self.data_0)
And after run this cell,
MyDataset(train_cropped_1pixel_dataset, train_dataset.targets)
it occur this error.
---------------------------------------------------------------------------
AttributeError Traceback (most recent call last)
<ipython-input-11-960ee70394c1> in <module>
3 train_loader = torch.utils.data.DataLoader(dataset = train_dataset,
4 batch_size = batch_size,
----> 5 shuffle = True)
~/.local/lib/python3.5/site-packages/torch/utils/data/dataloader.py in __init__(self, dataset, batch_size, shuffle, sampler, batch_sampler, num_workers, collate_fn, pin_memory, drop_last, timeout, worker_init_fn)
800 if sampler is None:
801 if shuffle:
--> 802 sampler = RandomSampler(dataset)
803 else:
804 sampler = SequentialSampler(dataset)
~/.local/lib/python3.5/site-packages/torch/utils/data/sampler.py in __init__(self, data_source, replacement, num_samples)
58
59 if self.num_samples is None:
---> 60 self.num_samples = len(self.data_source)
61
62 if not isinstance(self.num_samples, int) or self.num_samples <= 0:
<ipython-input-10-293dc919d173> in __len__(self)
12
13 def __len__(self):
---> 14 return len(self.data_0)
AttributeError: 'MyDataset' object has no attribute 'data_0'
I really need helps.. Thank you.
Provided that you have all train_<i> variables defined in global scope you could access them via globals(). Demo
train_0 = []
train_1 = []
train_2 = []
train_3 = []
for i in range(4):
globals()[f'train_{i}'].append(i)
print(train_0, train_1, train_2, train_3)
