如何解决Batch_size> 1的Keras细分模型的形状不兼容问题
我正在尝试使用Unet中的segmentation model对多通道(> 3)图像进行语义分割。 如果batch_size = 1,则该代码有效。但是,如果我将batch_size更改为其他值(例如2),则会发生错误( InvalidArgumentError:形状不兼容):
---------------------------------------------------------------------------
InvalidArgumentError Traceback (most recent call last)
<ipython-input-19-15dc3666afa8> in <module>
22 validation_steps = 1,23 callbacks=build_callbacks(),---> 24 verbose = 1)
25
~/.virtualenvs/sm/lib/python3.6/site-packages/keras/legacy/interfaces.py in wrapper(*args,**kwargs)
89 warnings.warn('Update your `' + object_name +
90 '` call to the Keras 2 API: ' + signature,stacklevel=2)
---> 91 return func(*args,**kwargs)
92 wrapper._original_function = func
93 return wrapper
~/.virtualenvs/sm/lib/python3.6/site-packages/keras/engine/training.py in fit_generator(self,generator,steps_per_epoch,epochs,verbose,callbacks,validation_data,validation_steps,class_weight,max_queue_size,workers,use_multiprocessing,shuffle,initial_epoch)
1424 use_multiprocessing=use_multiprocessing,1425 shuffle=shuffle,-> 1426 initial_epoch=initial_epoch)
1427
1428 @interfaces.legacy_generator_methods_support
~/.virtualenvs/sm/lib/python3.6/site-packages/keras/engine/training_generator.py in fit_generator(model,initial_epoch)
189 outs = model.train_on_batch(x,y,190 sample_weight=sample_weight,--> 191 class_weight=class_weight)
192
193 if not isinstance(outs,list):
~/.virtualenvs/sm/lib/python3.6/site-packages/keras/engine/training.py in train_on_batch(self,x,sample_weight,class_weight)
1218 ins = x + y + sample_weights
1219 self._make_train_function()
-> 1220 outputs = self.train_function(ins)
1221 if len(outputs) == 1:
1222 return outputs[0]
~/.virtualenvs/sm/lib/python3.6/site-packages/keras/backend/tensorflow_backend.py in __call__(self,inputs)
2659 return self._legacy_call(inputs)
2660
-> 2661 return self._call(inputs)
2662 else:
2663 if py_any(is_tensor(x) for x in inputs):
~/.virtualenvs/sm/lib/python3.6/site-packages/keras/backend/tensorflow_backend.py in _call(self,inputs)
2629 symbol_vals,2630 session)
-> 2631 fetched = self._callable_fn(*array_vals)
2632 return fetched[:len(self.outputs)]
2633
~/.virtualenvs/sm/lib/python3.6/site-packages/tensorflow_core/python/client/session.py in __call__(self,*args,**kwargs)
1470 ret = tf_session.TF_Sessionruncallable(self._session._session,1471 self._handle,args,-> 1472 run_Metadata_ptr)
1473 if run_Metadata:
1474 proto_data = tf_session.TF_GetBuffer(run_Metadata_ptr)
InvalidArgumentError: Incompatible shapes: [2,256,1] vs. [2,256]
[[{{node loss_1/model_4_loss/mul}}]]
我试图通过关注论坛中的不同帖子来游玩,但无法解决。这是为batch_size = 1运行的一部分代码。
batch_size = 1 # CHANGING ‘batch_size ‘ value other than 1 gives error
train_image_files = glob(patch_img + "/**/*.tif")
# simple_image_generator() is used to work with multi channel (>3) images (the function is
at the end)
train_image_generator = simple_image_generator(train_image_files,batch_size=batch_size,rotation_range=45,horizontal_flip=True,vertical_flip=True)
train_mask_files = glob(patch_ann + "/**/*.tif")
train_mask_generator = simple_image_generator(train_mask_files,batch_size=batch_size)
test_image_files = glob(test_img + "/**/*.tif")
test_image_generator = simple_image_generator(test_image_files,vertical_flip=True)
test_mask_files = glob(test_ann + "/**/*.tif")
test_mask_generator = simple_image_generator(test_mask_files,batch_size=batch_size)
train_generator = (pair for pair in zip(train_image_generator,train_mask_generator))
test_generator = (pair for pair in zip(test_image_generator,test_mask_generator))
.
.
num_channels = 8 # no. of channel
base_model = sm.Unet(backbone_name='resnet34',encoder_weights='imagenet')
inp = Input(shape=( None,None,num_channels))
layer_1 = Conv2D( 3,(1,1))(inp) # map N channels data to 3 channels
out = base_model(layer_1)
model = Model(inp,out,name=base_model.name)
model.summary()
model.compile(
optimizer = keras.optimizers.Adam(lr=learning_rate),loss = sm.losses.bce_jaccard_loss,metrics = ['accuracy',sm.metrics.IoU_score]
)
model_history = model.fit_generator(train_generator,epochs = 1,steps_per_epoch = 1,validation_data = test_generator,validation_steps = 1,callbacks = build_callbacks(),verbose = 1)
其他信息: 我没有使用keras提供的默认imageGenerator。我正在使用“ simple_image_generator”(稍作修改)
def simple_image_generator(files,batch_size=32,rotation_range=0,horizontal_flip=False,vertical_flip=False):
while True:
# select batch_size number of samples without replacement
batch_files = sample(files,batch_size)
# array for images
batch_X = []
# loop over images of the current batch
for idx,input_path in enumerate(batch_files):
image = np.array(imread(input_path),dtype=float)
# process image
if horizontal_flip:
# randomly flip image up/down
if choice([True,False]):
image = np.flipud(image)
if vertical_flip:
# randomly flip image left/right
if choice([True,False]):
image = np.fliplr(image)
# rotate image by random angle between
# -rotation_range <= angle < rotation_range
if rotation_range is not 0:
angle = np.random.uniform(low=-abs(rotation_range),high=abs(rotation_range))
image = rotate(image,angle,mode='reflect',order=1,preserve_range=True)
# put all together
batch_X += [image]
# convert lists to np.array
X = np.array(batch_X)
yield(X)
解决方法
通过重新定义新的图像生成器而不是simple_image_generator()解决了该错误。 simple_image_generator()可以很好地处理图像的形状(8个波段),但不能很好地应付蒙版的形状(1个波段)。
在执行过程中,image_generator具有4个维度,为[2,256,1](即batch_size,(图像大小),波段),但是mask_generator只有3个维度,而[[2,256](即batch_size,(图像大小))>
因此将[2,256]的掩码重塑为[2,256,1]即可解决此问题。
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