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Posted to commits@mxnet.apache.org by GitBox <gi...@apache.org> on 2018/06/06 23:49:18 UTC
[GitHub] RoacherM commented on issue #11177: mxnet.base.MXNetError
RoacherM commented on issue #11177: mxnet.base.MXNetError
URL: https://github.com/apache/incubator-mxnet/issues/11177#issuecomment-395248719
hello,@marcoabreu .My network design is here:
class ConcatNet(nn.HybridBlock):
def __init__(self,net1,net2,**kwargs):
super(ConcatNet,self).__init__(**kwargs)
self.net1 = nn.HybridSequential()
self.net1.add(net1)
self.net1.add(nn.GlobalAvgPool2D())
self.net2 = nn.HybridSequential()
self.net2.add(net2)
self.net2.add(nn.GlobalAvgPool2D())
def hybrid_forward(self,F,x1,x2):
return F.concat(*[self.net1(x1),self.net2(x2)])
class OneNet(nn.HybridBlock):
def __init__(self,features,output,**kwargs):
super(OneNet,self).__init__(**kwargs)
self.features = features
self.output = output
def hybrid_forward(self,F,x1,x2):
return self.output(self.features(x1,x2))
class Net():
def __init__(self,ctx,nameparams=None):
#(255,299,299)
inception = vision.inception_v3(pretrained=True,ctx=ctx).features
#(255,224,224)
resnet = vision.densnet121(pretrained=True,ctx=ctx).features
self.features = ConcatNet(resnet,inception)
self.output = self.__get_output(ctx,nameparams)
self.net = OneNet(self.features,self.output)
def __get_output(self,ctx,ParamsName=None):
net = nn.HybridSequential("output")
with net.name_scope():
#可以添加batchnorm等层
net.add(nn.Dense(256,activation='relu'))
net.add(nn.Dropout(.5))
#分类
net.add(nn.Dense(2))
if ParamsName is not None:
net.collect_params().load(ParamsName,ctx)
else:
net.initialize(init = init.Xavier(),ctx=ctx)
return net
class Pre():
def __init__(self,nameparams,idx,ctx=0):
self.idx = idx
if ctx == 0:
self.ctx = mx.cpu()
if ctx == 1:
self.ctx = mx.gpu()
self.net = Net(self.ctx,nameparams=nameparams).net
self.Timg = transform_test
def PreImg(self,img):
imgs = self.Timg(img,None)
out = nd.softmax(self.net(nd.reshape(imgs[0],(1,3,224,224)).as_in_context(self.ctx),nd.reshape(imgs[1],(1,3,299,299)).as_in_context(self.ctx))).asnumpy()
return self.idx[np.where(out == out.max())[1][0]]
def PreName(self,Name):
img = image.imread(Name)
return self.PreImg(img)
def transform_train(data, label):
im1 = image.imresize(data,224,224).astype('float32')
im2 = image.imresize(data,299,299).astype('float32')
# im1 = image.imresize(data.astype('float32') / 255, 224, 224)
# im2 = image.imresize(data.astype('float32')/255,299,299)
auglist1 = image.CreateAugmenter(data_shape=(3, 224, 224), resize=0,
rand_crop=True, rand_resize=True, rand_mirror=True,
mean=np.array([0.485, 0.456, 0.406]), std=np.array([0.229, 0.224, 0.225]),
brightness=0.125, contrast=0.5,
saturation=0, hue=0,
pca_noise=0, rand_gray=1, inter_method=2)
auglist2 = image.CreateAugmenter(data_shape=(3,299,299), resize=0,
rand_crop=True, rand_resize=True, rand_mirror=True,
mean=np.array([0.485, 0.456, 0.406]), std=np.array([0.229, 0.224, 0.225]),
brightness=0.125, contrast=0.5,
saturation=0, hue=0,
pca_noise=0, rand_gray=1, inter_method=2)
for aug in auglist1:
im1 = aug(im1)
for aug in auglist2:
im2 = aug(im2)
# （0,1,2）-（2,0,1）
im1 = nd.transpose(im1, (2,0,1))
im2 = nd.transpose(im2, (2,0,1))
return (im1,im2,nd.array([label]).asscalar().astype('float32'))
def transform_test(data, label):
im1 = image.imresize(data,224,224).astype('float32')
im2 = image.imresize(data,299,299).astype('float32')
# im1 = image.imresize(data.astype('float32') / 255, 224, 224)
# im2 = image.imresize(data.astype('float32') / 255, 299, 299)
auglist1 = image.CreateAugmenter(data_shape=(3,224,224),
mean=np.array([0.485, 0.456, 0.406]),
std=np.array([0.229, 0.224, 0.225]))
auglist2 = image.CreateAugmenter(data_shape=(3,299,299),
mean=np.array([0.485, 0.456, 0.406]),
std=np.array([0.229, 0.224, 0.225]))
for aug in auglist1:
im1 = aug(im1)
for aug in auglist2:
im2 = aug(im2)
im1 = nd.transpose(im1, (2,0,1))
im2 = nd.transpose(im2, (2,0,1))
return (im1,im2,nd.array([label]).asscalar().astype('float32'))
and when i run my code:
def SaveNd(data,net,name):
x =[]
y =[]
for fear1,fear2,label in tqdm(data):
fear1 = fear1.as_in_context(mx.gpu())
fear2 = fear2.as_in_context(mx.gpu())
# fear3 = fear3.as_in_context(mx.gpu())
out = net(fear1,fear2).as_in_context(mx.cpu())
x.append(out)
y.append(label)
x = nd.concat(*x,dim=0)
y = nd.concat(*y,dim=0)
nd.save(name,[x,y])
SaveNd(train_data,net,'train.nd')
SaveNd(valid_data,net,'valid.nd')
SaveNd(train_valid_data,net,'input.nd')
i come across the error:
File "C:\Python_3.6.2\lib\site-packages\mxnet\base.py", line 210, in check_call
raise MXNetError(py_str(_LIB.MXGetLastError()))
mxnet.base.MXNetError: Error in operator pool0_fwd: [02:02:07] C:\Jenkins\workspace\mxnet\mxnet\src\operator\nn\pooling.cc:99: Check failed: dshape.ndim() >= 3U (2 vs. 3) Pooling: Input data should be 3D in (batch, channel, x) Or 4D in (batch, channel, y, x) Or 5D in (batch, channel, d, y, x)
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