update dataloader
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@@ -5,7 +5,7 @@
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# Created Date: Sunday January 16th 2022
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# Author: Chen Xuanhong
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# Email: chenxuanhongzju@outlook.com
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# Last Modified: Monday, 14th February 2022 11:35:32 pm
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# Last Modified: Tuesday, 15th February 2022 1:54:50 am
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# Modified By: Chen Xuanhong
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# Copyright (c) 2022 Shanghai Jiao Tong University
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#############################################################
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@@ -119,8 +119,11 @@ class Generator(nn.Module):
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# self.first_layer = nn.Sequential(nn.ReflectionPad2d(3), nn.Conv2d(3, 64, kernel_size=7, padding=0, bias=False),
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# nn.BatchNorm2d(64), activation)
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self.first_layer = nn.Sequential(nn.Conv2d(3, 64, kernel_size=3, padding=1, bias=False),
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self.first_layer = nn.Sequential(nn.ReflectionPad2d(1),
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nn.Conv2d(3, 64, kernel_size=3, padding=0, bias=False),
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nn.BatchNorm2d(64), activation)
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# self.first_layer = nn.Sequential(nn.Conv2d(3, 64, kernel_size=3, padding=1, bias=False),
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# nn.BatchNorm2d(64), activation)
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### downsample
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self.down1 = nn.Sequential(nn.Conv2d(64, 64, kernel_size=3, groups=64, padding=1, stride=2),
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nn.Conv2d(64, 128, kernel_size=1, bias=False),
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@@ -164,7 +167,9 @@ class Generator(nn.Module):
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DeConv(128,64,3),
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nn.BatchNorm2d(64), activation
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)
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self.last_layer = nn.Sequential(nn.Conv2d(64, 3, kernel_size=3, padding=1))
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# self.last_layer = nn.Sequential(nn.Conv2d(64, 3, kernel_size=3, padding=1))
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self.last_layer = nn.Sequential(nn.ReflectionPad2d(1),
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nn.Conv2d(64, 3, kernel_size=3, padding=0))
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# self.last_layer = nn.Sequential(nn.ReflectionPad2d(3),
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# nn.Conv2d(64, 3, kernel_size=7, padding=0))
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@@ -5,7 +5,7 @@
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# Created Date: Sunday February 6th 2022
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# Author: Chen Xuanhong
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# Email: chenxuanhongzju@outlook.com
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# Last Modified: Tuesday, 15th February 2022 1:35:41 am
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# Last Modified: Tuesday, 15th February 2022 1:50:19 am
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# Modified By: Chen Xuanhong
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# Copyright (c) 2022 Shanghai Jiao Tong University
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#############################################################
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@@ -56,7 +56,7 @@ class InfiniteSampler(torch.utils.data.Sampler):
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class data_prefetcher():
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def __init__(self, loader, cur_gpu):
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torch.cuda.set_device(cur_gpu)
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torch.cuda.set_device(cur_gpu) # must add this line to avoid excessive use of GPU 0 by the prefetcher
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self.loader = loader
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self.dataiter = iter(loader)
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self.stream = torch.cuda.Stream(device=cur_gpu)
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@@ -29,7 +29,7 @@ dataloader: VGGFace2HQ_multigpu
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dataset_name: vggface2_hq
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dataset_params:
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random_seed: 1234
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dataloader_workers: 4
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dataloader_workers: 6
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eval_dataloader: DIV2K_hdf5
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eval_dataset_name: DF2K_H5_Eval
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