StyleTransferTrilogy
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CortexFoundation
风格迁移三部曲
Hi, I'd like to leverage your package on MRI data set. My images have a common 3D shape of (width=170, height=170, depth=124). I'd like to transfer of the image style...
Traceback (most recent call last): File "Situation3.py", line 187, in print("random.choice(style_dataset)",random.choice(style_dataset)) File "/home/guest/cwy/miniconda3/lib/python3.8/random.py", line 291, in choice return seq[i] File "/home/guest/cwy/miniconda3/lib/python3.8/site-packages/torchvision/datasets/folder.py", line 153, in __getitem__ sample = self.transform(sample) File "/home/guest/cwy/miniconda3/lib/python3.8/site-packages/torchvision/transforms/transforms.py",...
To achieve good visual resits, it needs 20 iterations of updates but during tesing speed the updates are not included
plt.imshow() will add something others to the image. How should I pre-process the generated image by the third algorithm before I can save it with scipy.misc.imsave. I tried minus the...
Traceback (most recent call last): File "keras_version/3_situation.py", line 328, in g_model,loss_model,train_model,meta_model,loss_model_debug = get_Net(style_weight = style_weight,content_weight = content_weight,tv_weight =tv_weight) File "keras_version/3_situation.py", line 213, in get_Net content_feature_model = Model(vgg.input,layer_features) File "D:\installFile\Anaconda3\lib\site-packages\keras\legacy\interfaces.py", line...
util.py line 130 .132 reverse1 = K.reverse(inputs,-1) inputs = K.concatenate([reverse1[:,:,:,-self.padding_length:],inputs,reverse1[:,:,:,:self.padding_length]],axis = -1) reverse2 = K.reverse(inputs,-2) inputs = K.concatenate([reverse2[:,:,-self.padding_length:,:],inputs,reverse2[:,:,:self.padding_length,:]],axis = -2) maybe it is K.concatenate([reverse1[:,:,:,:self.padding_length],inputs,reverse1[:,:,:,-self.padding_length:]],axis = -1) @ypwhs