def tuning(mode, n_iter, n_gpu, devices, save_interval, n_blocks, block_id):
if n_gpu == -1:
n_gpu = len(devices.split(','))
space = [
{
'loss': ['arcface'],
# 'verifythresh': [20, 30, 40, 50],
# 'freqthresh': [1, 2, 3, 4, 5, 6, 7, 8, 9, 10],
},
]
if mode == 'grid':
candidate_list = list(ParameterGrid(space))
elif mode == 'random':
candidate_list = list(
ParameterSampler(space, n_iter, random_state=params['seed']))
else:
raise ValueError
n_per_block = math.ceil(len(candidate_list) / n_blocks)
candidate_chunk = candidate_list[block_id * n_per_block:(block_id + 1) *
n_per_block]
utils.launch_tuning(mode=mode,
n_iter=n_iter,
n_gpu=n_gpu,
devices=devices,
params=params,
root=ROOT,
save_interval=save_interval,
candidate_list=candidate_chunk)
def tuning(mode, n_iter, n_gpu, devices, save_interval, n_blocks, block_id):
if n_gpu == -1:
n_gpu = len(devices.split(','))
space = [
{
'loss': ['arcface', 'adacos', 'cosface'],
'pooling': ['G,G,G,G'],
},
{
'loss': ['arcface'],
'pooling': ['SMG,MG,G,G'],
},
]
if mode == 'grid':
candidate_list = list(ParameterGrid(space))
elif mode == 'random':
candidate_list = list(ParameterSampler(space, n_iter, random_state=params['seed']))
else:
raise ValueError
n_per_block = math.ceil(len(candidate_list) / n_blocks)
candidate_chunk = candidate_list[block_id * n_per_block: (block_id + 1) * n_per_block]
utils.launch_tuning(mode=mode, n_iter=n_iter, n_gpu=n_gpu, devices=devices,
params=params, root=ROOT, save_interval=save_interval,
candidate_list=candidate_chunk)
def tuning(mode, n_iter, n_gpu, devices, save_interval, n_blocks, block_id):
if n_gpu == -1:
n_gpu = len(devices.split(','))
space = [
{
# 'fc_dim': [512, 1024],
'epochs': [7],
'augmentation': ['middle'],
'verifythresh': [30],
'freqthresh': [2, 3],
},
]
if mode == 'grid':
candidate_list = list(ParameterGrid(space))
elif mode == 'random':
candidate_list = list(
ParameterSampler(space, n_iter, random_state=params['seed']))
else:
raise ValueError
n_per_block = math.ceil(len(candidate_list) / n_blocks)
candidate_chunk = candidate_list[block_id * n_per_block:(block_id + 1) *
n_per_block]
utils.launch_tuning(mode=mode,
n_iter=n_iter,
n_gpu=n_gpu,
devices=devices,
params=params,
root=ROOT,
save_interval=save_interval,
candidate_list=candidate_chunk)
def tuning(mode, n_iter, n_gpu, devices, save_interval, n_blocks, block_id):
if n_gpu == -1:
n_gpu = len(devices.split(','))
space = [{
'pooling': ['G,G,G,G'],
'epochs': [5],
'batch_size': [32],
# 'fc_dim': [128, 256, 512],
}]
if mode == 'grid':
candidate_list = list(ParameterGrid(space))
elif mode == 'random':
candidate_list = list(
ParameterSampler(space, n_iter, random_state=params['seed']))
else:
raise ValueError
n_per_block = math.ceil(len(candidate_list) / n_blocks)
candidate_chunk = candidate_list[block_id * n_per_block:(block_id + 1) *
n_per_block]
utils.launch_tuning(mode=mode,
n_iter=n_iter,
n_gpu=n_gpu,
devices=devices,
params=params,
root=ROOT,
save_interval=save_interval,
candidate_list=candidate_chunk)
def tuning(mode, n_iter, n_gpu, devices, save_interval, n_blocks, block_id):
"""
Example:
python v12.py tuning --devices 0,1,2,3 --n-gpu 2
"""
if n_gpu == -1:
n_gpu = len(devices.split(','))
space = [
{
'loss': ['arcface'],
'freqthresh': [2, 5],
'margin': [0.3, 0.4],
},
]
if mode == 'grid':
candidate_list = list(ParameterGrid(space))
elif mode == 'random':
candidate_list = list(ParameterSampler(space, n_iter, random_state=params['seed']))
else:
raise ValueError
n_per_block = math.ceil(len(candidate_list) / n_blocks)
candidate_chunk = candidate_list[block_id * n_per_block: (block_id + 1) * n_per_block]
utils.launch_tuning(mode=mode, n_iter=n_iter, n_gpu=n_gpu, devices=devices,
params=params, root=ROOT, save_interval=save_interval,
candidate_list=candidate_chunk)
def tuning(mode, n_iter, n_gpu, devices, save_interval, n_blocks, block_id):
"""
Example:
python v12.py tuning --devices 0,1,2,3 --n-gpu 2
"""
if n_gpu == -1:
n_gpu = len(devices.split(','))
space = [
{
'batch_size': [32],
# 'epochs': [1],
# 'scaleup_epochs': [0],
'loss': ['arcface'],
'verifythresh': [30],
'freqthresh': [3],
'margin': [0.3, 0.2],
's': [30],
# 'augmentation': ['soft'],
},
# for reproduce
# {
# 'batch_size': [32],
# 'loss': ['arcface'],
# 'verifythresh': [30, 40, 50],
# 'freqthresh': [2, 3, 5],
# 'margin': [0.3],
# 's': [30],
# },
# {
# 'batch_size': [32],
# 'loss': ['arcface'],
# 'verifythresh': [30],
# 'freqthresh': [3],
# 'margin': [0.2],
# 's': [30],
# },
]
if mode == 'grid':
candidate_list = list(ParameterGrid(space))
elif mode == 'random':
candidate_list = list(ParameterSampler(space, n_iter, random_state=params['seed']))
else:
raise ValueError
n_per_block = math.ceil(len(candidate_list) / n_blocks)
candidate_chunk = candidate_list[block_id * n_per_block: (block_id + 1) * n_per_block]
utils.launch_tuning(mode=mode, n_iter=n_iter, n_gpu=n_gpu, devices=devices,
params=params, root=ROOT, save_interval=save_interval,
candidate_list=candidate_chunk)
def tuning(mode, n_iter, n_gpu, devices):
"""
Example:
python exp0.py tuning --devices 0,1 --n-gpu 1
python exp0.py tuning --devices 0,1 --n-gpu 1 --mode 'random' --n-iter 4
"""
if n_gpu == -1:
n_gpu = len(devices.split(','))
space = {
'lr': [1e-5, 1e-4, 1e-3],
'batch_size': [16, 8],
}
utils.launch_tuning(mode, n_iter, n_gpu, devices, params, space, ROOT)
