Fix discriminator
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@ -118,7 +118,7 @@ class Discriminator_VGG_128_GN(nn.Module):
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self.bn5_0 = nn.GroupNorm(8, nf * 8, affine=True)
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self.conv5_1 = nn.Conv2d(nf * 8, nf * 8, 4, 2, 1, bias=False)
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self.bn5_1 = nn.GroupNorm(8, nf * 8, affine=True)
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input_img_factor = input_img_factor // 2
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input_img_factor = input_img_factor / 2
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final_nf = nf * 8
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# activation function
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@ -13,14 +13,14 @@ import torch
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def main():
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split_img = False
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opt = {}
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opt['n_thread'] = 8
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opt['n_thread'] = 16
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opt['compression_level'] = 90 # JPEG compression quality rating.
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# CV_IMWRITE_PNG_COMPRESSION from 0 to 9. A higher value means a smaller size and longer
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# compression time. If read raw images during training, use 0 for faster IO speed.
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opt['dest'] = 'file'
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opt['input_folder'] = 'F:\\4k6k\\datasets\\ns_images\\fkaw\\images'
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opt['save_folder'] = 'F:\\4k6k\\datasets\\ns_images\\vixen\\512_with_ref_and_fkaw'
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opt['input_folder'] = 'F:\\4k6k\\datasets\\ns_images\\imagesets\\imgset2'
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opt['save_folder'] = 'F:\\4k6k\\datasets\\ns_images\\vixen\\USELESS_DELETE_ME'
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opt['crop_sz'] = [1024, 2048] # the size of each sub-image
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opt['step'] = [1024, 2048] # step of the sliding crop window
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opt['thres_sz'] = 512 # size threshold
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