ValueError: 层“sequential_2”的输入 0 与层不兼容：预期形状=(None, 256, 256, 3)，发现形状=(None, 1, 256, 256, 3)

Question

I have the following code, and I want to train the U-Net model with 1000 images, and the corresponding masks. When I try to train the model with just one image everything goes smooth, but when I try to train it with the whole data, I receive the following error:

ValueError: Input 0 of layer "sequential_2" is incompatible with the layer: expected shape=(None, 256, 256, 3), found shape=(None, 1, 256, 256, 3)

dataset_dir='/content/drive/MyDrive/training'
# Path to mhd images (not gt)
image_ids = []
mascaras=[]
imagens=[]
# r=root, d=directories, f = files
for r, d, f in os.walk(dataset_dir):
    for file in f:    
        if ('ED.mhd' in file) or ('ES.mhd' in file):
            image_ids.append(os.path.join(r, file))
            #print(image_ids)
            image_path=os.path.join(r, file)
            image1 = sitk.GetArrayFromImage(sitk.ReadImage(image_path,sitk.sitkFloat32))
            image1 = image1[0,:,:] #2D grayscale image
            image2=cv2.resize(image1,(256,256))
            image3=image2/255
            
            #mask0
            mask0_path=os.path.join(r,file)[:-4]+"_gt0.mhd"
            mask0_1=sitk.GetArrayFromImage(sitk.ReadImage(mask0_path,sitk.sitkFloat32))
            mask0_2=cv2.resize(mask0_1,(32,32))
            mask0_2= np.expand_dims(mask0_2, axis=0)
            #mask1
            mask1_path = os.path.join(r,file)[:-4]+"_gt1.mhd"
            mask1_1=sitk.GetArrayFromImage(sitk.ReadImage(mask1_path,sitk.sitkFloat32))
            mask1_2=cv2.resize(mask1_1,(32,32))
            mask1_2= np.expand_dims(mask1_2, axis=0)
            #mask2
            mask2_path = os.path.join(r,file)[:-4]+"_gt2.mhd"
            mask2_1=sitk.GetArrayFromImage(sitk.ReadImage(mask2_path,sitk.sitkFloat32))
            mask2_2=cv2.resize(mask2_1,(32,32))
            mask2_2= np.expand_dims(mask2_2, axis=0)
            #mask3
            mask3_path = os.path.join(r,file)[:-4]+"_gt3.mhd"
            mask3_1=sitk.GetArrayFromImage(sitk.ReadImage(mask3_path,sitk.sitkFloat32))
            mask3_2=cv2.resize(mask3_1,(32,32))
            mask3_2= np.expand_dims(mask3_2, axis=0)

            #mask total
            mask_f=np.stack((mask0_2, mask1_2,mask2_2,mask3_2),axis=3)
            mascaras.append(mask_f)
            mascaras=np.array(mascaras)
            #imagem total
            image_f=np.stack((image3,image3,image3),axis=2)
            image_f=np.expand_dims(image_f, axis=0)
            imagens.append(image_f)
            imagens=np.array(imagens)

history=model1.fit(imagens,mascaras,epochs=20)

Answer 1

您可以通过发出来reshape与模型输入形状匹配的输入数据；

imagengs = imagens.reshape((256, 256, 3))