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update dataloader

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chenxuanhong
2022-02-15 02:08:57 +08:00
parent 4a6197a685
commit 9c1e6bb77d
3 changed files with 11 additions and 6 deletions
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components/Generator_modulation_depthwise.py
+8 -3
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@@ -5,7 +5,7 @@
# Created Date: Sunday January 16th 2022 # Created Date: Sunday January 16th 2022
# Author: Chen Xuanhong # Author: Chen Xuanhong
# Email: chenxuanhongzju@outlook.com # Email: chenxuanhongzju@outlook.com
# Last Modified: Monday, 14th February 2022 11:35:32 pm # Last Modified: Tuesday, 15th February 2022 1:54:50 am
# Modified By: Chen Xuanhong # Modified By: Chen Xuanhong
# Copyright (c) 2022 Shanghai Jiao Tong University # Copyright (c) 2022 Shanghai Jiao Tong University
############################################################# #############################################################
@@ -119,8 +119,11 @@ class Generator(nn.Module):
# self.first_layer = nn.Sequential(nn.ReflectionPad2d(3), nn.Conv2d(3, 64, kernel_size=7, padding=0, bias=False), # self.first_layer = nn.Sequential(nn.ReflectionPad2d(3), nn.Conv2d(3, 64, kernel_size=7, padding=0, bias=False),
# nn.BatchNorm2d(64), activation) # nn.BatchNorm2d(64), activation)
self.first_layer = nn.Sequential(nn.Conv2d(3, 64, kernel_size=3, padding=1, bias=False), self.first_layer = nn.Sequential(nn.ReflectionPad2d(1),
nn.Conv2d(3, 64, kernel_size=3, padding=0, bias=False),
nn.BatchNorm2d(64), activation) nn.BatchNorm2d(64), activation)
# self.first_layer = nn.Sequential(nn.Conv2d(3, 64, kernel_size=3, padding=1, bias=False),
# nn.BatchNorm2d(64), activation)
### downsample ### downsample
self.down1 = nn.Sequential(nn.Conv2d(64, 64, kernel_size=3, groups=64, padding=1, stride=2), self.down1 = nn.Sequential(nn.Conv2d(64, 64, kernel_size=3, groups=64, padding=1, stride=2),
nn.Conv2d(64, 128, kernel_size=1, bias=False), nn.Conv2d(64, 128, kernel_size=1, bias=False),
@@ -164,7 +167,9 @@ class Generator(nn.Module):
DeConv(128,64,3), DeConv(128,64,3),
nn.BatchNorm2d(64), activation nn.BatchNorm2d(64), activation
) )
self.last_layer = nn.Sequential(nn.Conv2d(64, 3, kernel_size=3, padding=1)) # self.last_layer = nn.Sequential(nn.Conv2d(64, 3, kernel_size=3, padding=1))
self.last_layer = nn.Sequential(nn.ReflectionPad2d(1),
nn.Conv2d(64, 3, kernel_size=3, padding=0))
# self.last_layer = nn.Sequential(nn.ReflectionPad2d(3), # self.last_layer = nn.Sequential(nn.ReflectionPad2d(3),
# nn.Conv2d(64, 3, kernel_size=7, padding=0)) # nn.Conv2d(64, 3, kernel_size=7, padding=0))
data_tools/data_loader_VGGFace2HQ_multigpu.py
+2 -2
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@@ -5,7 +5,7 @@
# Created Date: Sunday February 6th 2022 # Created Date: Sunday February 6th 2022
# Author: Chen Xuanhong # Author: Chen Xuanhong
# Email: chenxuanhongzju@outlook.com # Email: chenxuanhongzju@outlook.com
# Last Modified: Tuesday, 15th February 2022 1:35:41 am # Last Modified: Tuesday, 15th February 2022 1:50:19 am
# Modified By: Chen Xuanhong # Modified By: Chen Xuanhong
# Copyright (c) 2022 Shanghai Jiao Tong University # Copyright (c) 2022 Shanghai Jiao Tong University
############################################################# #############################################################
@@ -56,7 +56,7 @@ class InfiniteSampler(torch.utils.data.Sampler):
class data_prefetcher(): class data_prefetcher():
def __init__(self, loader, cur_gpu): def __init__(self, loader, cur_gpu):
torch.cuda.set_device(cur_gpu) torch.cuda.set_device(cur_gpu) # must add this line to avoid excessive use of GPU 0 by the prefetcher
self.loader = loader self.loader = loader
self.dataiter = iter(loader) self.dataiter = iter(loader)
self.stream = torch.cuda.Stream(device=cur_gpu) self.stream = torch.cuda.Stream(device=cur_gpu)
train_yamls/train_Depthwise.yaml
+1 -1
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@@ -29,7 +29,7 @@ dataloader: VGGFace2HQ_multigpu
dataset_name: vggface2_hq dataset_name: vggface2_hq
dataset_params: dataset_params:
random_seed: 1234 random_seed: 1234
dataloader_workers: 4 dataloader_workers: 6
eval_dataloader: DIV2K_hdf5 eval_dataloader: DIV2K_hdf5
eval_dataset_name: DF2K_H5_Eval eval_dataset_name: DF2K_H5_Eval