Issue Description

Hello, when I read your code, I have some questions about the vgg_prepro functions in utils.py This function is defined as follows：

def vgg_prepro(x):
    x = imresize(x, [244, 244], interp='bilinear', mode=None)
    x = np.tile(x, 3)
    x = x / 127.5 - 1
    return x

I know this function is to preprocess an image, such as to Changing an image from [256,256,1] to [244,244,3], but I don't understand why do we need to use x/127.5 - 1 , here we have already to scale the image to [-1,1].

In addition, I would also like to ask why we cut the size of 244 instead of 224 which is the size of the initial VGG paper.

I was hoping you could help me with this problem, I can't get it right.

Hi, @nebulaV, I run your code and reconstruct one image, I use the following code during evaluate time,

    start_time = time.time()
    evaluate_restore_img = sess.run(net.outputs, {evaluate_image: evaluate_samples_bad})
    print("took: %4.4fs" % (time.time() - start_time)) 

I run on the GPU platform, but the time is about 16s, but your code is 5.4ms , I don not understand how to calculate that results.

Hello, May I ask a problem? Thank you for your code, I have been learn AI from about 3 weeks ago. During training, It occur the error above like title. Help me, please.

up7 = {DeConv2d} Last layer is: DeConv2d (u_net/deconv7) [25, 2, 2, 512] all_drop = {dict} {} all_layers = {list} <class 'list'>: [<tf.Tensor 'u_net/conv1/Identity:0' shape=(25, 1, 1, 64) dtype=float32>, <tf.Tensor 'u_net/conv2/Identity:0' shape=(25, 1, 1, 128) dtype=float32>, <tf.Tensor 'u_net/bn2/lrelu:0' shape=(25, 1, 1, 128) dtype=float32>, <tf.Tensor 'u_net/conv3/Identity:0' shape=(25, 1, 1, 256) dtype=float32>, <tf.Tensor 'u_net/bn3/lrelu:0' shape=(25, 1, 1, 256) dtype=float32>, <tf.Tensor 'u_net/conv4/Identity:0' shape=(25, 1, 1, 512) dtype=float32>, <tf.Tensor 'u_net/bn4/lrelu:0' shape=(25, 1, 1, 512) dtype=float32>, <tf.Tensor 'u_net/conv5/Identity:0' shape=(25, 1, 1, 512) dtype=float32>, <tf.Tensor 'u_net/bn5/lrelu:0' shape=(25, 1, 1, 512) dtype=float32>, <tf.Tensor 'u_net/conv6/Identity:0' shape=(25, 1, 1, 512) dtype=float32>, <tf.Tensor 'u_net/bn6/lrelu:0' shape=(25, 1, 1, 512) dtype=float32>, <tf.Tensor 'u_net/conv7/Identity:0' shape=(25, 1, 1, 512) dtype=float32>, <tf.Tensor 'u_net/bn7/lrelu:0' shape=(25, 1, 1, 512) dtype=float32>, <tf.Tensor 'u_net/conv8/lrelu:0' shape=(25, 1, 1, 51... all_params = {list} <class 'list'>: [<tf.Variable 'u_net/conv1/kernel:0' shape=(4, 4, 1, 64) dtype=float32_ref>, <tf.Variable 'u_net/conv1/bias:0' shape=(64,) dtype=float32_ref>, <tf.Variable 'u_net/conv2/kernel:0' shape=(4, 4, 64, 128) dtype=float32_ref>, <tf.Variable 'u_net/conv2/bias:0' shape=(128,) dtype=float32_ref>, <tf.Variable 'u_net/bn2/beta:0' shape=(128,) dtype=float32_ref>, <tf.Variable 'u_net/bn2/gamma:0' shape=(128,) dtype=float32_ref>, <tf.Variable 'u_net/bn2/moving_mean:0' shape=(128,) dtype=float32_ref>, <tf.Variable 'u_net/bn2/moving_variance:0' shape=(128,) dtype=float32_ref>, <tf.Variable 'u_net/conv3/kernel:0' shape=(4, 4, 128, 256) dtype=float32_ref>, <tf.Variable 'u_net/conv3/bias:0' shape=(256,) dtype=float32_ref>, <tf.Variable 'u_net/bn3/beta:0' shape=(256,) dtype=float32_ref>, <tf.Variable 'u_net/bn3/gamma:0' shape=(256,) dtype=float32_ref>, <tf.Variable 'u_net/bn3/moving_mean:0' shape=(256,) dtype=float32_ref>, <tf.Variable 'u_net/bn3/moving_variance:0' shape=(256,) dtype=float3...

• inputs = {Tensor} Tensor("u_net/conv8/lrelu:0", shape=(25, 1, 1, 512), dtype=float32)
• name = {str} 'u_net/deconv7'
• outputs = {Tensor} Tensor("u_net/deconv7/Identity:0", shape=(25, 2, 2, 512), dtype=float32) w_init = {TruncatedNormal} <tensorflow.python.ops.init_ops.TruncatedNormal object at 0x7fd754f26358> x = {Tensor} Tensor("bad_image:0", shape=(25, 1, 1, 1), dtype=float32)

Hello. Your paper is very helpful and thankful. However, I want to learn a bigger size image. I need advice on how to do it.

Can you tell me how to configure the network?

For reference, I am studying to eliminate aliasing of Aliasing images with Aliasing and Ground truth images.

Hello,

I had come accross your work and was very excited to run it. Unfortunately I keep getting an error message "name 'tl' is not defined", this occurs when I run train.py here https://github.com/tensorlayer/DAGAN/blob/master/train.py#L475, I ran this according to the README. It seems that this t1 object has many inbuilt functions so doesnt look like I can get away with commenting all the t1 lines. Can you please help ?

Hello,

I noticed that you use 15 as the weight for pixel loss, which is much larger than other weights such as for perceptual loss, frequency loss and also generator loss. If such a large coefficient is used, the influence of the discriminator will be reduced and the generator might become a direct estimator approximately. I would like to know if mode collapse can happen when the large weight for pixel loss is used.

BTW, I implemented conditional WGAN for MRI without pixel loss, but the image quality is not good enough.

Thanks!

hello, my question is that can we apply this model on generating the new dataset which related to other applications? such as Phase contrast microscopy images such as live cells dataset?

while loading data_loader.py it showing up dimension error in " img_2d = np.transpose(img_2d, (1, 0))" . Also while trying to do after reducing number of data set for fast training it shows up error "Traceback (most recent call last): File "sef_data_loader.py", line 94, in X_train = X_train[:, :, :, np.newaxis]
IndexError: too many indices for array "

After reading your paper and codes, i'm strange about the calculation of PSNR. I use some mri data to test the ZF reconstruction and try to calculate the NMSE in your codes, it seems right. But when I calculate the PSNR based on your NMSE or RMSE defined by myself, it seems wrong. What's more, don't you think the PSNR is too high via ZF reconstruction with 20% data kept, because when PSNR reaches 34dB, pictures may seem very similar.