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from .tmux_launcher import Options, TmuxLauncher
class Launcher(TmuxLauncher):
# List of training commands
def commands(self):
opt = Options()
# common options for all training sessions defined in this launcher
opt.set(dataroot="~/datasets/cityscapes/", # specify --dataroot option here
model="contrastive_cycle_gan",
pool_size=0,
no_dropout="",
init_type="xavier",
batch_size=1,
display_freq=400,
evaluation_metrics="fid,cityscapes",
evaluation_freq=10000,
direction="BtoA",
use_recommended_options="",
nce_idt_freq=0.1,
)
# Specify individual options here
commands = [
# first command.
# This command can be run using python -m experiments placeholder run 0
# It will output python train.py [OPTIONS], where OPTIONS are everything defined in the variable opt
"python train.py " + str(opt.clone().set(
name="cityscapes_placeholder_noidt", # name of experiments
nce_idt=False,
)),
# second command.
# This command can be run using python -m experiments placeholder run 1
# It removes the option --nce_idt_freq 0.1 that was defined by our common options
"python train.py " + str(opt.clone().set(
name="cityscapes_placeholder_singlelayer",
nce_layers="16",
).remove("nce_idt_freq")),
# third command that performs multigpu training
# This command can be run using python -m experiments placeholder run 2
"python train.py " + str(opt.clone().set(
name="cityscapes_placeholder_multigpu",
nce_layers="16",
batch_size=4,
gpu_ids="0,1",
)),
]
return commands
# This is the command used for testing.
# They can be run using python -m experiments placeholder run_test $i
def test_commands(self):
opt = Options()
opt.set(dataroot="~/datasets/cityscapes_unaligned/cityscapes",
model="contrastive_cycle_gan",
no_dropout="",
init_type="xavier",
batch_size=1,
direction="BtoA",
epoch=40,
phase='train',
evaluation_metrics="fid",
)
commands = [
"python test.py " + str(opt.clone().set(
name="cityscapes_nce",
nce_layers="0,8,16",
direction="BtoA",
)),
]
return commands
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