无法将数据拟合到 3d 卷积 U-net Keras
Can't fit data to 3d convolutional U-net Keras
我有问题。我想制作 3D 卷积 U-net。为此，我正在使用 Keras。
我的数据是 Data Science Bowl 2017 Competition 的 MRI 图像。所有 MRI 都保存在 numpy 数组中（所有像素从 0 缩放到 1），形状为：
data_ch.shape
(94, 50, 50, 50, 1)
94 - 患者，50 个 50x50 图像的 MRI 切片，1 个通道：
我想制作 3D 卷积 U-net，因此该网络的输入和输出是相同的 3d 数组。
3D U-net：
input_img= Input(shape=(data_ch.shape[1], data_ch.shape[2], data_ch.shape[3], data_ch.shape[4]))
x=Conv3D(filters=8, kernel_size=(3, 3, 3), activation='relu', padding='same')(input_img)
x=MaxPooling3D(pool_size=(2, 2, 2), padding='same')(x)
x=Conv3D(filters=8, kernel_size=(3, 3, 3), activation='relu', padding='same')(x)
x=MaxPooling3D(pool_size=(2, 2, 2), padding='same')(x)
x=UpSampling3D(size=(2, 2, 2))(x)
x=Conv3D(filters=8, kernel_size=(3, 3, 3), activation='relu', padding='same')(x) # PADDING IS NOT THE SAME!!!!!
x=UpSampling3D(size=(2, 2, 2))(x)
x=Conv3D(filters=1, kernel_size=(3, 3, 3), activation='sigmoid')(x)
model=Model(input_img, x)
model.compile(optimizer='adadelta', loss='binary_crossentropy')
model.summary()
Layer (type) Output Shape Param #
=================================================================
input_5 (InputLayer) (None, 50, 50, 50, 1) 0
_________________________________________________________________
conv3d_27 (Conv3D) (None, 50, 50, 50, 8) 224
_________________________________________________________________
max_pooling3d_12 (MaxPooling (None, 25, 25, 25, 8) 0
_________________________________________________________________
conv3d_28 (Conv3D) (None, 25, 25, 25, 8) 1736
_________________________________________________________________
max_pooling3d_13 (MaxPooling (None, 13, 13, 13, 8) 0
_________________________________________________________________
up_sampling3d_12 (UpSampling (None, 26, 26, 26, 8) 0
_________________________________________________________________
conv3d_29 (Conv3D) (None, 26, 26, 26, 8) 1736
_________________________________________________________________
up_sampling3d_13 (UpSampling (None, 52, 52, 52, 8) 0
_________________________________________________________________
conv3d_30 (Conv3D) (None, 50, 50, 50, 1) 217
=================================================================
Total params: 3,913
Trainable params: 3,913
Non-trainable params: 0
但是，当我尝试将数据拟合到该网络时：
model.fit(data_ch, data_ch, epochs=1, batch_size=10, shuffle=True, verbose=1)
程序显示错误：
ValueError Traceback (most recent call last)
C:\Users\Taranov\Anaconda3\lib\site-packages\theano\compile\function_module.py in __call__(self, *args, **kwargs)
883 outputs =\
--> 884 self.fn() if output_subset is None else\
885 self.fn(output_subset=output_subset)
ValueError: CudaNdarray_CopyFromCudaNdarray: need same dimensions for dim 1, destination=13, source=14
During handling of the above exception, another exception occurred:
ValueError Traceback (most recent call last)
<ipython-input-26-b334d38d9608> in <module>()
----> 1 model.fit(data_ch, data_ch, epochs=1, batch_size=10, shuffle=True, verbose=1)
C:\Users\Taranov\Anaconda3\lib\site-packages\keras\engine\training.py in fit(self, x, y, batch_size, epochs, verbose, callbacks, validation_split, validation_data, shuffle, class_weight, sample_weight, initial_epoch, **kwargs)
1496 val_f=val_f, val_ins=val_ins, shuffle=shuffle,
1497 callback_metrics=callback_metrics,
-> 1498 initial_epoch=initial_epoch)
1499
1500 def evaluate(self, x, y, batch_size=32, verbose=1, sample_weight=None):
C:\Users\Taranov\Anaconda3\lib\site-packages\keras\engine\training.py in _fit_loop(self, f, ins, out_labels, batch_size, epochs, verbose, callbacks, val_f, val_ins, shuffle, callback_metrics, initial_epoch)
1150 batch_logs['size'] = len(batch_ids)
1151 callbacks.on_batch_begin(batch_index, batch_logs)
-> 1152 outs = f(ins_batch)
1153 if not isinstance(outs, list):
1154 outs = [outs]
C:\Users\Taranov\Anaconda3\lib\site-packages\keras\backend\theano_backend.py in __call__(self, inputs)
1156 def __call__(self, inputs):
1157 assert isinstance(inputs, (list, tuple))
-> 1158 return self.function(*inputs)
1159
1160
C:\Users\Taranov\Anaconda3\lib\site-packages\theano\compile\function_module.py in __call__(self, *args, **kwargs)
896 node=self.fn.nodes[self.fn.position_of_error],
897 thunk=thunk,
--> 898 storage_map=getattr(self.fn, 'storage_map', None))
899 else:
900 # old-style linkers raise their own exceptions
C:\Users\Taranov\Anaconda3\lib\site-packages\theano\gof\link.py in raise_with_op(node, thunk, exc_info, storage_map)
323 # extra long error message in that case.
324 pass
--> 325 reraise(exc_type, exc_value, exc_trace)
326
327
C:\Users\Taranov\Anaconda3\lib\site-packages\six.py in reraise(tp, value, tb)
683 value = tp()
684 if value.__traceback__ is not tb:
--> 685 raise value.with_traceback(tb)
686 raise value
687
C:\Users\Taranov\Anaconda3\lib\site-packages\theano\compile\function_module.py in __call__(self, *args, **kwargs)
882 try:
883 outputs =\
--> 884 self.fn() if output_subset is None else\
885 self.fn(output_subset=output_subset)
886 except Exception:
ValueError: CudaNdarray_CopyFromCudaNdarray: need same dimensions for dim 1, destination=13, source=14
Apply node that caused the error: GpuAlloc(GpuDimShuffle{0,2,x,3,4,1}.0, Shape_i{0}.0, TensorConstant{13}, TensorConstant{2}, TensorConstant{13}, TensorConstant{13}, TensorConstant{8})
Toposort index: 163
Inputs types: [CudaNdarrayType(float32, (False, False, True, False, False, False)), TensorType(int64, scalar), TensorType(int64, scalar), TensorType(int8, scalar), TensorType(int64, scalar), TensorType(int64, scalar), TensorType(int64, scalar)]
Inputs shapes: [(10, 14, 1, 14, 14, 8), (), (), (), (), (), ()]
Inputs strides: [(21952, 196, 0, 14, 1, 2744), (), (), (), (), (), ()]
Inputs values: ['not shown', array(10, dtype=int64), array(13, dtype=int64), array(2, dtype=int8), array(13, dtype=int64), array(13, dtype=int64), array(8, dtype=int64)]
Outputs clients: [[GpuReshape{5}(GpuAlloc.0, MakeVector{dtype='int64'}.0)]]
HINT: Re-running with most Theano optimization disabled could give you a back-trace of when this node was created. This can be done with by setting the Theano flag 'optimizer=fast_compile'. If that does not work, Theano optimizations can be disabled with 'optimizer=None'.
HINT: Use the Theano flag 'exception_verbosity=high' for a debugprint and storage map footprint of this apply node.
我尝试遵循建议并使用 theano 标志：
import theano
import os
os.environ["THEANO_FLAGS"] = "mode=FAST_RUN,device=gpu,floatX=float32, optimizer='None',exception_verbosity=high"
但是还是不行
你能帮帮我吗？
非常感谢！
好吧....这听起来很奇怪，但是 MaxPooling3D 与 padding='same' 有某种错误。所以我在没有它的情况下编写了您的代码，并添加了一个初始填充只是为了使您的尺寸兼容：
import keras.backend as K
inputShape = (data_ch.shape[1], data_ch.shape[2], data_ch.shape[3], data_ch.shape[4])
paddedShape = (data_ch.shape[1]+2, data_ch.shape[2]+2, data_ch.shape[3]+2, data_ch.shape[4])
#initial padding
input_img= Input(shape=inputShape)
x = Lambda(lambda x: K.spatial_3d_padding(x, padding=((1, 1), (1, 1), (1, 1))),
output_shape=paddedShape)(input_img) #Lambda layers require output_shape
#your original code without padding for MaxPooling layers (replace input_img with x)
x=Conv3D(filters=8, kernel_size=3, activation='relu', padding='same')(x)
x=MaxPooling3D(pool_size=2)(x)
x=Conv3D(filters=8, kernel_size=3, activation='relu', padding='same')(x)
x=MaxPooling3D(pool_size=2)(x)
x=UpSampling3D(size=2)(x)
x=Conv3D(filters=8, kernel_size=3, activation='relu', padding='same')(x) # PADDING IS NOT THE SAME!!!!!
x=UpSampling3D(size=2)(x)
x=Conv3D(filters=1, kernel_size=3, activation='sigmoid')(x)
model=Model(input_img, x)
model.compile(optimizer='adadelta', loss='binary_crossentropy')
model.summary()
print(model.predict(data_ch)[1])
model.fit(data_ch,data_ch,epochs=1,verbose=2,batch_size=10)
尝试将批量大小减少到 2 左右，如果您看到，您的网络需要更多 GPU，因此也请尝试升级它。
我有问题。我想制作 3D 卷积 U-net。为此，我正在使用 Keras。
我的数据是 Data Science Bowl 2017 Competition 的 MRI 图像。所有 MRI 都保存在 numpy 数组中（所有像素从 0 缩放到 1），形状为：
data_ch.shape
(94, 50, 50, 50, 1)
94 - 患者，50 个 50x50 图像的 MRI 切片，1 个通道：
我想制作 3D 卷积 U-net，因此该网络的输入和输出是相同的 3d 数组。 3D U-net：
input_img= Input(shape=(data_ch.shape[1], data_ch.shape[2], data_ch.shape[3], data_ch.shape[4]))
x=Conv3D(filters=8, kernel_size=(3, 3, 3), activation='relu', padding='same')(input_img)
x=MaxPooling3D(pool_size=(2, 2, 2), padding='same')(x)
x=Conv3D(filters=8, kernel_size=(3, 3, 3), activation='relu', padding='same')(x)
x=MaxPooling3D(pool_size=(2, 2, 2), padding='same')(x)
x=UpSampling3D(size=(2, 2, 2))(x)
x=Conv3D(filters=8, kernel_size=(3, 3, 3), activation='relu', padding='same')(x) # PADDING IS NOT THE SAME!!!!!
x=UpSampling3D(size=(2, 2, 2))(x)
x=Conv3D(filters=1, kernel_size=(3, 3, 3), activation='sigmoid')(x)
model=Model(input_img, x)
model.compile(optimizer='adadelta', loss='binary_crossentropy')
model.summary()
Layer (type) Output Shape Param #
=================================================================
input_5 (InputLayer) (None, 50, 50, 50, 1) 0
_________________________________________________________________
conv3d_27 (Conv3D) (None, 50, 50, 50, 8) 224
_________________________________________________________________
max_pooling3d_12 (MaxPooling (None, 25, 25, 25, 8) 0
_________________________________________________________________
conv3d_28 (Conv3D) (None, 25, 25, 25, 8) 1736
_________________________________________________________________
max_pooling3d_13 (MaxPooling (None, 13, 13, 13, 8) 0
_________________________________________________________________
up_sampling3d_12 (UpSampling (None, 26, 26, 26, 8) 0
_________________________________________________________________
conv3d_29 (Conv3D) (None, 26, 26, 26, 8) 1736
_________________________________________________________________
up_sampling3d_13 (UpSampling (None, 52, 52, 52, 8) 0
_________________________________________________________________
conv3d_30 (Conv3D) (None, 50, 50, 50, 1) 217
=================================================================
Total params: 3,913
Trainable params: 3,913
Non-trainable params: 0
但是，当我尝试将数据拟合到该网络时：
model.fit(data_ch, data_ch, epochs=1, batch_size=10, shuffle=True, verbose=1)
程序显示错误：
ValueError Traceback (most recent call last)
C:\Users\Taranov\Anaconda3\lib\site-packages\theano\compile\function_module.py in __call__(self, *args, **kwargs)
883 outputs =\
--> 884 self.fn() if output_subset is None else\
885 self.fn(output_subset=output_subset)
ValueError: CudaNdarray_CopyFromCudaNdarray: need same dimensions for dim 1, destination=13, source=14
During handling of the above exception, another exception occurred:
ValueError Traceback (most recent call last)
<ipython-input-26-b334d38d9608> in <module>()
----> 1 model.fit(data_ch, data_ch, epochs=1, batch_size=10, shuffle=True, verbose=1)
C:\Users\Taranov\Anaconda3\lib\site-packages\keras\engine\training.py in fit(self, x, y, batch_size, epochs, verbose, callbacks, validation_split, validation_data, shuffle, class_weight, sample_weight, initial_epoch, **kwargs)
1496 val_f=val_f, val_ins=val_ins, shuffle=shuffle,
1497 callback_metrics=callback_metrics,
-> 1498 initial_epoch=initial_epoch)
1499
1500 def evaluate(self, x, y, batch_size=32, verbose=1, sample_weight=None):
C:\Users\Taranov\Anaconda3\lib\site-packages\keras\engine\training.py in _fit_loop(self, f, ins, out_labels, batch_size, epochs, verbose, callbacks, val_f, val_ins, shuffle, callback_metrics, initial_epoch)
1150 batch_logs['size'] = len(batch_ids)
1151 callbacks.on_batch_begin(batch_index, batch_logs)
-> 1152 outs = f(ins_batch)
1153 if not isinstance(outs, list):
1154 outs = [outs]
C:\Users\Taranov\Anaconda3\lib\site-packages\keras\backend\theano_backend.py in __call__(self, inputs)
1156 def __call__(self, inputs):
1157 assert isinstance(inputs, (list, tuple))
-> 1158 return self.function(*inputs)
1159
1160
C:\Users\Taranov\Anaconda3\lib\site-packages\theano\compile\function_module.py in __call__(self, *args, **kwargs)
896 node=self.fn.nodes[self.fn.position_of_error],
897 thunk=thunk,
--> 898 storage_map=getattr(self.fn, 'storage_map', None))
899 else:
900 # old-style linkers raise their own exceptions
C:\Users\Taranov\Anaconda3\lib\site-packages\theano\gof\link.py in raise_with_op(node, thunk, exc_info, storage_map)
323 # extra long error message in that case.
324 pass
--> 325 reraise(exc_type, exc_value, exc_trace)
326
327
C:\Users\Taranov\Anaconda3\lib\site-packages\six.py in reraise(tp, value, tb)
683 value = tp()
684 if value.__traceback__ is not tb:
--> 685 raise value.with_traceback(tb)
686 raise value
687
C:\Users\Taranov\Anaconda3\lib\site-packages\theano\compile\function_module.py in __call__(self, *args, **kwargs)
882 try:
883 outputs =\
--> 884 self.fn() if output_subset is None else\
885 self.fn(output_subset=output_subset)
886 except Exception:
ValueError: CudaNdarray_CopyFromCudaNdarray: need same dimensions for dim 1, destination=13, source=14
Apply node that caused the error: GpuAlloc(GpuDimShuffle{0,2,x,3,4,1}.0, Shape_i{0}.0, TensorConstant{13}, TensorConstant{2}, TensorConstant{13}, TensorConstant{13}, TensorConstant{8})
Toposort index: 163
Inputs types: [CudaNdarrayType(float32, (False, False, True, False, False, False)), TensorType(int64, scalar), TensorType(int64, scalar), TensorType(int8, scalar), TensorType(int64, scalar), TensorType(int64, scalar), TensorType(int64, scalar)]
Inputs shapes: [(10, 14, 1, 14, 14, 8), (), (), (), (), (), ()]
Inputs strides: [(21952, 196, 0, 14, 1, 2744), (), (), (), (), (), ()]
Inputs values: ['not shown', array(10, dtype=int64), array(13, dtype=int64), array(2, dtype=int8), array(13, dtype=int64), array(13, dtype=int64), array(8, dtype=int64)]
Outputs clients: [[GpuReshape{5}(GpuAlloc.0, MakeVector{dtype='int64'}.0)]]
HINT: Re-running with most Theano optimization disabled could give you a back-trace of when this node was created. This can be done with by setting the Theano flag 'optimizer=fast_compile'. If that does not work, Theano optimizations can be disabled with 'optimizer=None'.
HINT: Use the Theano flag 'exception_verbosity=high' for a debugprint and storage map footprint of this apply node.
我尝试遵循建议并使用 theano 标志：
import theano
import os
os.environ["THEANO_FLAGS"] = "mode=FAST_RUN,device=gpu,floatX=float32, optimizer='None',exception_verbosity=high"
但是还是不行
你能帮帮我吗？ 非常感谢！
好吧....这听起来很奇怪，但是 MaxPooling3D 与 padding='same' 有某种错误。所以我在没有它的情况下编写了您的代码，并添加了一个初始填充只是为了使您的尺寸兼容：
import keras.backend as K
inputShape = (data_ch.shape[1], data_ch.shape[2], data_ch.shape[3], data_ch.shape[4])
paddedShape = (data_ch.shape[1]+2, data_ch.shape[2]+2, data_ch.shape[3]+2, data_ch.shape[4])
#initial padding
input_img= Input(shape=inputShape)
x = Lambda(lambda x: K.spatial_3d_padding(x, padding=((1, 1), (1, 1), (1, 1))),
output_shape=paddedShape)(input_img) #Lambda layers require output_shape
#your original code without padding for MaxPooling layers (replace input_img with x)
x=Conv3D(filters=8, kernel_size=3, activation='relu', padding='same')(x)
x=MaxPooling3D(pool_size=2)(x)
x=Conv3D(filters=8, kernel_size=3, activation='relu', padding='same')(x)
x=MaxPooling3D(pool_size=2)(x)
x=UpSampling3D(size=2)(x)
x=Conv3D(filters=8, kernel_size=3, activation='relu', padding='same')(x) # PADDING IS NOT THE SAME!!!!!
x=UpSampling3D(size=2)(x)
x=Conv3D(filters=1, kernel_size=3, activation='sigmoid')(x)
model=Model(input_img, x)
model.compile(optimizer='adadelta', loss='binary_crossentropy')
model.summary()
print(model.predict(data_ch)[1])
model.fit(data_ch,data_ch,epochs=1,verbose=2,batch_size=10)
尝试将批量大小减少到 2 左右，如果您看到，您的网络需要更多 GPU，因此也请尝试升级它。