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train_bth_exps_2.py
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train_bth_exps_2.py
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from __future__ import print_function, absolute_import
from utils import batch_experiments
import os.path as osp
import os
import torch
def bth_train_softmax():
from softmax_loss import main as softmaxfunc
from Param_Config import param_base
dir_name = "logs/Market1501-ResNet50/softmax_baseline"
header = "with-initial-ckpt-"
hyper_param_keys = ('lr', 'features')
hyper_param_values = [(0.1, 512),
# (0.1, 1024),
# (0.1, 2048),
# (0.1, 4096),
# (0.1, 8192),
]
results = batch_experiments(
hyper_param_keys, hyper_param_values, param_base,
header, dir_name, softmaxfunc
)
def bth_train_softmax_mod():
from softmax_loss_modified import main as softmaxfunc
from Param_Config import param_base
dir_name = "logs/Market1501-ResNet50/softmax_baseline"
header = "with-initial-ckpt-"
hyper_param_keys = ('lr', 'features', 'gamma', 'batch_size')
hyper_param_values = [
#(0.01, 1024, 1, 256),
#(0.1, 1024, 10, 256),
(0.1, 512, 0.01, 512),
(0.1, 512, 0.1, 512),
(0.1, 512, 1.0, 512),
#(0.1, 512, 5, 512),
#(0.1, 512, 10, 512),
#(0.1, 512, 20, 512),
#(0.1, 256, 15, 128),
#(0.1, 16284, 15.0, 264 ),
#(0.1, 16284, 15.0, 264),
#(0.1, 65136, 15.0, 512),
#(0.1, 32568, 15.0, 512),
#(0.1, 16284, 15.0, 512),
#(0.1, 8192, 15.0, 512),
#(0.1, 4096, 15.0, 512),
#(0.1, 2048, 15.0, 512),
#(0.1, 1024, 15.0, 512),
#(0.1, 512, 15.0, 512),
# (0.1, 1024),
# (0.1, 2048),
# (0.1, 4096),
# (0.1, 8192),
]
results = batch_experiments(
hyper_param_keys, hyper_param_values, param_base,
header, dir_name, softmaxfunc
)
def bth_train_metric_triplet():
from metric_triplet_loss import main as softmaxfunc
from Param_Config import param_metric_triplet
# dir_name = "logs/Market1501-ResNet50/metric_triplet"
dir_name = "logs/Market1501-ResNet50/triplet"
header = "try-"
# resume = osp.join(osp.dirname(osp.abspath(__file__)), dir_name)
# resume = resume + "/try-lr_0.001_features_128_/model_best.pth.tar"
param_metric_triplet['dist_metric'] = 'euclidean'
param_metric_triplet['epochs'] = 100
param_metric_triplet['batch_size'] = 512
param_metric_triplet['norm_train'] = True
param_metric_triplet['norm_test'] = True
hyper_param_keys = ('lr', 'features', 'margin')
hyper_param_values = [(0.001, 512, 1.1),
(0.001, 512, 1.2),
(0.001, 512, 1.3),
(0.001, 512, 1.4),
(0.001, 512, 1.5),
(0.001, 1024, 1.1),
(0.001, 1024, 1.2),
(0.001, 1024, 1.3),
(0.001, 1024, 1.4),
(0.001, 1024, 1.5),
# (0.01, 128),
# (0.1, 128),
# (0.001, 512),
# (0.01, 512),
]
results = batch_experiments(
hyper_param_keys, hyper_param_values, param_metric_triplet,
header, dir_name, softmaxfunc
)
# def bth_train_triplet():
if __name__ == '__main__':
os.environ["CUDA_VISIBLE_DEVICES"] = "0"
#bth_train_softmax()
bth_train_softmax_mod()
#bth_train_metric_triplet()