After loading CIFAR 100, I try to train my neural network. But I don't know why I get the out of bounds error shown below
Optimizing the network with batch size 25
Epoch: 0 of 30 Average loss: -
/mnt_home/klee/LBSBGenGapSharpnessResearch/vgg.py:43: UserWarning: Implicit dimension choice for log_softmax has been deprecated. Change the call to include dim=X as an argument.
return F.log_softmax(x)
Traceback (most recent call last):
File "plot_parametric_pytorch_cifar100.py", line 130, in <module>
loss_fn = F.nll_loss(ops, tgts)
File "/home/klee/anaconda3/envs/sharpenv/lib/python3.7/site-packages/torch/nn/functional.py", line 2115, in nll_loss
ret = torch._C._nn.nll_loss(input, target, weight, _Reduction.get_enum(reduction), ignore_index)
IndexError: Target 42 is out of bounds.
Here is the script I'm running:
cudnn.benchmark = True
(X_train, y_train), (X_test, y_test) = cifar10.load_data()
X_train = X_train.astype('float32')
X_train = np.transpose(X_train, axes=(0, 3, 1, 2))
X_test = X_test.astype('float32')
X_test = np.transpose(X_test, axes=(0, 3, 1, 2))
X_train /= 255
X_test /= 255
device = torch.device('cuda:0')
# This is where you can load any model of your choice.
# I stole PyTorch Vision's VGG network and modified it to work on CIFAR-10.
# You can take this line out and add any other network and the code
# should run just fine.
model = vgg.vgg11_bn()
#model.to(device)
# Forward pass
opfun = lambda X: model.forward(Variable(torch.from_numpy(X)))
# Forward pass through the network given the input
predsfun = lambda op: np.argmax(op.data.numpy(), 1)
# Do the forward pass, then compute the accuracy
accfun = lambda op, y: np.mean(np.equal(predsfun(op), y.squeeze()))*100
# Initial point
x0 = deepcopy(model.state_dict())
# Number of epochs to train for
# Choose a large value since LB training needs higher values
# Changed from 150 to 30
nb_epochs = 30
batch_range = [25, 40, 50, 64, 80, 128, 256, 512, 625, 1024, 1250, 1750, 2048, 2500, 3125, 4096, 4500, 5000]
# parametric plot (i.e., don't train the network)
hotstart = False
if not hotstart:
for batch_size in batch_range:
optimizer = torch.optim.Adam(model.parameters())
model.load_state_dict(x0)
#model.to(device)
average_loss_over_epoch = '-'
print('Optimizing the network with batch size %d' % batch_size)
np.random.seed(1337) #So that both networks see same sequence of batches
for e in range(nb_epochs):
model.eval()
print('Epoch:', e, ' of ', nb_epochs, 'Average loss:', average_loss_over_epoch)
average_loss_over_epoch = 0
# Checkpoint the model every epoch
torch.save(model.state_dict(), "./models/30EpochCIFAR100ExperimentBatchSize" + str(batch_size) + ".pth")
array = np.random.permutation(range(X_train.shape[0]))
slices = X_train.shape[0] // batch_size
beginning = 0
end = 1
# Training loop!
for _ in range(slices):
start_index = batch_size * beginning
end_index = batch_size * end
smpl = array[start_index:end_index]
model.train()
optimizer.zero_grad()
ops = opfun(X_train[smpl])
tgts = Variable(torch.from_numpy(y_train[smpl]).long().squeeze())
loss_fn = F.nll_loss(ops, tgts) <--- errorring linne
average_loss_over_epoch += loss_fn.data.numpy() / (X_train.shape[0] // batch_size)
loss_fn.backward()
optimizer.step()
beginning += 1
end += 1
and here's the VGG model:
__all__ = [
'VGG', 'vgg11', 'vgg11_bn', 'vgg13', 'vgg13_bn', 'vgg16', 'vgg16_bn',
'vgg19_bn', 'vgg19',
]
model_urls = {
'vgg11': 'https://s3.amazonaws.com/pytorch/models/vgg11-fb7e83b2.pth',
'vgg13': 'https://s3.amazonaws.com/pytorch/models/vgg13-58758d87.pth',
'vgg16': 'https://s3.amazonaws.com/pytorch/models/vgg16-82412952.pth',
'vgg19': 'https://s3.amazonaws.com/pytorch/models/vgg19-341d7465.pth',
}
class VGG(nn.Module):
def __init__(self, features):
super(VGG, self).__init__()
self.features = features
self.classifier = nn.Sequential(
nn.Dropout(),
nn.Linear(512, 4096),
nn.ReLU(True),
nn.Dropout(),
nn.Linear(4096, 4096),
nn.ReLU(True),
nn.Linear(4096, 10),
)
self._initialize_weights()
def forward(self, x):
x = self.features(x)
x = x.view(x.size(0), -1)
x = self.classifier(x)
return F.log_softmax(x)
def _initialize_weights(self):
for m in self.modules():
if isinstance(m, nn.Conv2d):
n = m.kernel_size[0] * m.kernel_size[1] * m.out_channels
m.weight.data.normal_(0, math.sqrt(2. / n))
if m.bias is not None:
m.bias.data.zero_()
elif isinstance(m, nn.BatchNorm2d):
m.weight.data.fill_(1)
m.bias.data.zero_()
elif isinstance(m, nn.Linear):
n = m.weight.size(1)
m.weight.data.normal_(0, 0.01)
m.bias.data.zero_()
def make_layers(cfg, batch_norm=False):
layers = []
in_channels = 3
for v in cfg:
if v == 'M':
layers += [nn.MaxPool2d(kernel_size=2, stride=2)]
else:
conv2d = nn.Conv2d(in_channels, v, kernel_size=3, padding=1)
if batch_norm:
layers += [conv2d, nn.BatchNorm2d(v), nn.ReLU(inplace=True)]
else:
layers += [conv2d, nn.ReLU(inplace=True)]
in_channels = v
return nn.Sequential(*layers)
cfg = {
'A': [64, 'M', 128, 'M', 256, 256, 'M', 512, 512, 'M', 512, 512, 'M'],
'B': [64, 64, 'M', 128, 128, 'M', 256, 256, 'M', 512, 512, 'M', 512, 512, 'M'],
'D': [64, 64, 'M', 128, 128, 'M', 256, 256, 256, 'M', 512, 512, 512, 'M', 512, 512, 512, 'M'],
'E': [64, 64, 'M', 128, 128, 'M', 256, 256, 256, 256, 'M', 512, 512, 512, 512, 'M', 512, 512, 512, 512, 'M'],
}
def vgg11(pretrained=False, **kwargs):
"""VGG 11-layer model (configuration "A")
Args:
pretrained (bool): If True, returns a model pre-trained on ImageNet
"""
model = VGG(make_layers(cfg['A']), **kwargs)
if pretrained:
model.load_state_dict(model_zoo.load_url(model_urls['vgg11']))
return model
def vgg11_bn(**kwargs):
"""VGG 11-layer model (configuration "A") with batch normalization"""
return VGG(make_layers(cfg['A'], batch_norm=True), **kwargs)
I'm not sure how to fix the index error. I think it has to do with the number of classes, but I'm not sure where to fix that (in the above code): I read https://discuss.pytorch.org/t/indexerror-target-2-is-out-of-bounds/69614 but not sure where to go from here.