Update other docs with dumb config options

This commit is contained in:
James Betker 2020-12-18 16:21:28 -07:00
parent 81256d553a
commit e82f4552db
4 changed files with 2 additions and 8 deletions

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@ -293,7 +293,7 @@ class Trainer:
if __name__ == '__main__': if __name__ == '__main__':
parser = argparse.ArgumentParser() parser = argparse.ArgumentParser()
parser.add_argument('-opt', type=str, help='Path to option YAML file.', default='../options/train_exd_mi1_rrdb_bigboi_pretrain.yml') parser.add_argument('-opt', type=str, help='Path to option YAML file.', default='../options/train_faces_glean.yml')
parser.add_argument('--launcher', choices=['none', 'pytorch'], default='none', help='job launcher') parser.add_argument('--launcher', choices=['none', 'pytorch'], default='none', help='job launcher')
parser.add_argument('--local_rank', type=int, default=0) parser.add_argument('--local_rank', type=int, default=0)
args = parser.parse_args() args = parser.parse_args()

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@ -15,8 +15,6 @@ def parse(opt_path, is_train=True):
print('export CUDA_VISIBLE_DEVICES=' + gpu_list) print('export CUDA_VISIBLE_DEVICES=' + gpu_list)
opt['is_train'] = is_train opt['is_train'] = is_train
if opt['distortion'] == 'sr' or opt['distortion'] == 'downsample':
scale = opt['scale']
# datasets # datasets
if 'datasets' in opt.keys(): if 'datasets' in opt.keys():

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@ -2,7 +2,6 @@
name: train_div2k_byol name: train_div2k_byol
use_tb_logger: true use_tb_logger: true
model: extensibletrainer model: extensibletrainer
distortion: sr
scale: 1 scale: 1
gpu_ids: [0] gpu_ids: [0]
fp16: false fp16: false

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@ -2,7 +2,6 @@
name: train_div2k_srflow name: train_div2k_srflow
use_tb_logger: true use_tb_logger: true
model: extensibletrainer model: extensibletrainer
distortion: sr
scale: 4 scale: 4
gpu_ids: [0] gpu_ids: [0]
fp16: false fp16: false
@ -17,12 +16,10 @@ datasets:
name: div2k name: div2k
mode: single_image_extensible mode: single_image_extensible
paths: /content/div2k # <-- Put your path here. paths: /content/div2k # <-- Put your path here.
target_size: 160 # <-- SRFlow trains better with factors of 160 for some reason. target_size: 160
force_multiple: 1 force_multiple: 1
scale: 4 scale: 4
eval: False
num_corrupts_per_image: 0 num_corrupts_per_image: 0
strict: false
networks: networks:
generator: generator: