lfz / guided-denoise Goto Github PK

Can you provide the extracted images from Imagenet? The original dataset is too big to download.

Hi,
I wonder how many images do you select per class as training dataset? In the paper, you select 30 images per class but in prepare_data n_per_class is set to 4.

Thanks

When i tried to run this code, i encountered the following errors in the 'attack_iter.py' file.

W tensorflow/core/framework/op_kernel.cc:1192] Invalid argument: Shape mismatch in tuple component 0. Expected [299,299,3], got [2038,1500,3]

OutOfRangeError (see above for traceback): FIFOQueue '_1_batch/fifo_queue' is closed and has insufficient elements (requested 3, current size 0)
         [[Node: batch = QueueDequeueUpToV2[component_types=[DT_UINT8, DT_STRING], timeout_ms=-1, _device="/job:localhost/replica:0/task:0/device:CPU:0"](batch/fifo_queue, batch/n)]]

I just changed the image format from '.png' to '.jpg', and changed the following code:

    image = tf.image.decode_png(image_file)

to

    image = tf.image.decode_jpeg(image_file, channels=3)

I have tried some methods to solve it, but nothing worked. So i guess maybe the version of my tensorflow(1.4) is different from yours.

Wish to receive your reply, thank you!

where is your model define file?

Hello!
I'm reading your code,but I can't understand the function of 'requires_control' flags in your code.
Could you please tell me?
Thank you!

https://github.com/lfz/Guided-Denoise/blob/master/Exps/sample/inception.py#L329
https://github.com/lfz/Guided-Denoise/blob/master/Exps/sample/inception.py#L413
But actually you have 1000 classes, why set to 1001 and then outputs.append(x[:,1:])

Hello,

The Models checkpoint link is not working correctly.
Could you please provide them in the Repo ?

Thanks

in prepare_data.ipynb, dose the imagenet_path means the whole imagenet2012 datasets? it seems so big for this dataset

Hello, I have met some problem with loading the dictionary of the model.

The code of mine is like this. I choose InceptionV3 model. I want to load the "inception_v3.ckpt" dictionary provided by you.
However, it failed and return an error. The error is shown below.

    input_size = [299, 299]
    block = Conv
    fwd_out = [64, 128, 256, 256, 256]
    num_fwd = [2, 3, 3, 3, 3]
    back_out = [64, 128, 256, 256]
    num_back = [2, 3, 3, 3]
    n = 1
    hard_mining = 0
    loss_norm = False
    denoise = Denoise(input_size[0], input_size[1], block, 3, fwd_out, num_fwd, back_out, num_back)
    net = Inception3(denoise)

    model_path = "inception_v3.ckpt"
    pretrain_dict = torch.load(model_path)
    state_dict = net.state_dict()
    for key in pretrain_dict.keys():
        assert state_dict.has_key(key)
        value = pretrain_dict[key]
        if not isinstance(value, torch.FloatTensor):
            value = value.data
        state_dict[key] = value

    net.load_state_dict(state_dict)

Could you please tell me where is the bug? The pytorch version used by me is 1.2.0.
Thank you very much!