wkcn commented on issue #19264:
URL:
https://github.com/apache/incubator-mxnet/issues/19264#issuecomment-702475163
It is a bug.
The temporary solution is to pass multiple NDArrays rather than a list of
NDArray.
For example:
```python
import os
os.environ["DMLC_LOG_STACK_TRACE_DEPTH"]="10"
import mxnet as mx
print(mx.__version__)
import mxnet.gluon as gluon
class nn(object):
@staticmethod
def Sequential(*args):
bl = gluon.nn.HybridSequential()
for a in args:
bl.add(a)
return bl
@staticmethod
def Upsample(scale_factor, mode):
# return BilinearResize2D(scale_factor=scale_factor)
return mx.gluon.nn.HybridLambda(lambda F, x:
F.contrib.BilinearResize2D(x, scale_width=scale_factor,
scale_height=scale_factor, name="fwd"))
class HighResolutionModule(gluon.nn.HybridBlock):
def __init__(self):
super(HighResolutionModule, self).__init__()
self.relu = mx.gluon.nn.Activation("relu")
self.fff = nn.Sequential(
mx.gluon.nn.Conv2D(in_channels=64, channels=32, kernel_size=3,
padding=1),
nn.Upsample(scale_factor=2, mode="nearest")
)
self.fff1 = nn.Sequential(
mx.gluon.nn.Conv2D(in_channels=32, channels=64, kernel_size=3,
padding=1, strides=2),
mx.gluon.nn.BatchNorm(axis=1, momentum=.9, in_channels=32)
)
def hybrid_forward(self, F, *x, **kwargs):
y0 = self.relu(x[0] + self.fff(x[1]))
y1 = self.relu(self.fff1(x[0]) + x[1])
return [y0, y1]
class HighResolutionNet(gluon.nn.HybridBlock):
def __init__(self):
super(HighResolutionNet, self).__init__()
self.stage2 = self._make_stage()
def _make_stage(self):
modules = []
for i in range(2):
modules.append(
HighResolutionModule()
)
return nn.Sequential(*modules)
def hybrid_forward(self, F, *x_list):
y_list = self.stage2(*x_list)
return y_list
def get_cls_net():
model = HighResolutionNet()
return model
if __name__ == '__main__':
import easydict
ctx = mx.cpu()
args = easydict.EasyDict()
model = get_cls_net()
model.initialize()
model.reset_ctx(ctx)
model.hybridize()
y_hat = model(mx.nd.random.randn(1, 32, 56, 56, ctx=ctx),
mx.nd.random.randn(1, 64, 28, 28, ctx=ctx))
```
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