everything works just perfectly in the first loop.
but in the second loop, memory keeps raising up and finally blows up.
any idea?
by the way, the size of b_x is not always identical in the second for loop.
ps: I am working with Pytorch 1.3, my friend told me that this could be version issue, not conformed yet.
Hi,
How big is the output of your model? You do store all the outputs and labels in lists. So it is expected to use more and more memory.
Hi
the output of my model is just float64, and there are no more than 2m float64 in the list.
actually，I did try the following code, and the memory is okay which may imply that there is something wrong in the model.
the source code of my naive baby model.
class InceptionV1(nn.Module):
def __init__(self, in_channels, channels_7x7, out_channels):
super(InceptionV1, self).__init__()
self.features = nn.Sequential(
OrderedDict([
# the output is (4*out_channels, H, W)
('inceptionc', InceptionC(in_channels, channels_7x7, out_channels=out_channels)),
# (128, H, W)
('basicConv2d_1x1', BasicConv2d(out_channels*4, 128, kernel_size=1, )),
# (128, 20, 24)
('maxpool', nn.MaxPool2d(kernel_size=2, stride=2)),
# (256, 10, 12)
('basicConv2d_2x2_0', BasicConv2d(128, out_channels=256, kernel_size=2, stride=2)),
# (512, 5, 6)
('basicConv2d_2x2_1', BasicConv2d(256, out_channels=512, kernel_size=2, stride=2)),
# (512, 1, 1)
('avg_pool', nn.AvgPool2d(kernel_size=(5, 6)))
])
)
self.linear = nn.Sequential(
nn.Linear(512, 128),
nn.ReLU(),
nn.Linear(128, 1),
nn.Tanh()
)
def forward(self, *input):
features = self.features(input[0].view(-1, 3, 40, 48))
label = 3 * self.linear(features.view(features.size(0), -1))
return label
class InceptionC(nn.Module):
def __init__(self, in_channels, channels_7x7, out_channels):
super(InceptionC, self).__init__()
self.branch1x1 = BasicConv2d(in_channels, out_channels, kernel_size=1)
c7 = channels_7x7
self.branch7x7 = nn.Sequential(
BasicConv2d(in_channels, c7, kernel_size=1),
BasicConv2d(c7, c7, kernel_size=(1, 7), padding=(0,3)),
BasicConv2d(c7, out_channels, kernel_size=(7,1), padding=(3, 0)))
self.branch7x7dbl = nn.Sequential(
BasicConv2d(in_channels, c7, kernel_size=1),
BasicConv2d(c7, c7, kernel_size=(7,1), padding=(3,0)),
BasicConv2d(c7, c7, kernel_size=(1,7), padding=(0,3)),
BasicConv2d(c7, c7, kernel_size=(7,1), padding=(3,0)),
BasicConv2d(c7, out_channels, kernel_size=(1,7), padding=(0,3))
)
self.branch_pool = BasicConv2d(in_channels, out_channels, kernel_size=1)
def forward(self, x):
branch1x1 = self.branch1x1(x)
branch7x7 = self.branch7x7(x)
branch7x7dbl = self.branch7x7dbl(x)
branch_pool = F.avg_pool2d(x, kernel_size=3, stride=1, padding=1)
branch_pool = self.branch_pool(branch_pool)
outputs = [branch1x1, branch7x7, branch7x7dbl, branch_pool]
return torch.cat(outputs, 1)
class BasicConv2d(nn.Module):
def __init__(self, in_channels, out_channels, **kwargs):
super(BasicConv2d, self).__init__()
self.conv = nn.Conv2d(in_channels, out_channels, bias=False, **kwargs)
self.bn = nn.BatchNorm2d(out_channels, eps=0.001)
def forward(self, x):
x = self.conv(x)
x = self.bn(x)
return F.relu(x, inplace=True)
Did you ever find a solution?