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planning_llh_bgc
Commit 2e42fa8c authored by Larkin Heintzman's avatar Larkin Heintzman
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added simple run controls

parent 179fbe0a
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Showing with 110 additions and 92 deletions
test_robotgp.py
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......@@ -169,16 +169,34 @@ def main(iteration = 0, parameters = -1):
if __name__ == "__main__":
kentland_heatmap = 'C:\\Users\\Larkin\\planning_llh_bgc\\LP model\\analysis\\outputs\\kentland_hiker\\ic_2_con_hiker_t8.csv'
hmpark_heatmap = 'C:\\Users\\Larkin\\planning_llh_bgc\\LP model\\analysis\\outputs\\hmpark_hiker\\ic_2_con_hiker_t8.csv'
# self.params.setdefault('lp_filename', 'C:\\Users\\Larkin\\planning_llh_bgc\\LP model\\analysis\\outputs\\kentland_hiker\\ic_2_con_hiker_t8.csv')
# self.params.setdefault('lin_feat_filename', 'C:\\Users\\Larkin\\ags_grabber\\matlab_data_locale\\BW_LFandInac_Zelev_kentland.mat')
n_max = 6
s_max = 2
global_fail_max = 1000
global_fails = 0
avg_runs = 5
start_time = time.time()
params = ({
'save_folder': 'hmpark_n{}_s{}_rc'.format(n, s),
'lp_model': 'custom',
'opt_iterations': 1,
'path_style': 'rc',
'stats_name': 'hmpark',
'anchor_point': [36.891640, -81.524214], # hmpark
'num_searchers': s,
'num_robots': n,
'lp_filename': hmpark_heatmap,
'plot_data': True, # plots data in plotly viewer upon finishing
'save_data': False # saves risk-cost and waypoint related data in json upon finishing
})
params = Default(params).params
torch.cuda.empty_cache()
torch.cuda.ipc_collect()
main(iteration=0, parameters=params)
# kentland_heatmap = 'C:\\Users\\Larkin\\planning_llh_bgc\\LP model\\analysis\\outputs\\kentland_hiker\\ic_2_con_hiker_t8.csv'
# hmpark_heatmap = 'C:\\Users\\Larkin\\planning_llh_bgc\\LP model\\analysis\\outputs\\hmpark_hiker\\ic_2_con_hiker_t8.csv'
# # self.params.setdefault('lp_filename', 'C:\\Users\\Larkin\\planning_llh_bgc\\LP model\\analysis\\outputs\\kentland_hiker\\ic_2_con_hiker_t8.csv')
# # self.params.setdefault('lin_feat_filename', 'C:\\Users\\Larkin\\ags_grabber\\matlab_data_locale\\BW_LFandInac_Zelev_kentland.mat')
# n_max = 6
# s_max = 2
# global_fail_max = 1000
# global_fails = 0
# avg_runs = 5
# start_time = time.time()
# for n in range(1, n_max + 1):
# for s in range(2,s_max + 1):
......@@ -344,85 +362,85 @@ if __name__ == "__main__":
# global_fails += 1
#
# # -----------------------------------------------------------------------------------------------
for n in range(5, n_max + 1):
for s in range(2, s_max + 1):
params = ({
'save_folder': 'hmpark_n{}_s{}_unopt'.format(n, s),
'lp_model': 'custom',
'opt_iterations': 3,
'path_style': 'basic',
'stats_name': 'hmpark',
'anchor_point': [36.891640, -81.524214], # hmpark
'num_searchers': s,
'num_robots': n,
'lp_filename': hmpark_heatmap
})
params = Default(params).params
counter = 0
while counter < avg_runs and global_fails <= global_fail_max: # number of averaging runs
torch.cuda.empty_cache()
torch.cuda.ipc_collect()
try:
main(iteration=counter, parameters=params)
counter += 1
except AttributeError as e:
print("\n\n ------- bad optimization, re trying ---------- \n")
global_fails += 1
# -----------------------------------------------------------------------------------------------
for n in range(1, n_max + 1):
for s in range(2, s_max + 1):
params = ({
'save_folder': 'hmpark_n{}_s{}_sweep'.format(n, s),
'lp_model': 'custom',
'opt_iterations': 1,
'path_style': 'sweep',
'stats_name': 'hmpark',
'anchor_point': [36.891640, -81.524214], # hmpark
'num_searchers': s,
'num_robots': n,
'lp_filename': hmpark_heatmap
})
params = Default(params).params
counter = 0
while counter < avg_runs and global_fails <= global_fail_max: # number of averaging runs
torch.cuda.empty_cache()
torch.cuda.ipc_collect()
try:
main(iteration=counter, parameters=params)
counter += 1
except AttributeError as e:
print("\n\n ------- bad optimization, re trying ---------- \n")
global_fails += 1
# -----------------------------------------------------------------------------------------------
for n in range(1, n_max + 1):
for s in range(2, s_max + 1):
params = ({
'save_folder': 'hmpark_n{}_s{}_rc'.format(n, s),
'lp_model': 'custom',
'opt_iterations': 1,
'path_style': 'rc',
'stats_name': 'hmpark',
'anchor_point': [36.891640, -81.524214], # hmpark
'num_searchers': s,
'num_robots': n,
'lp_filename': hmpark_heatmap
})
params = Default(params).params
counter = 0
while counter < avg_runs and global_fails <= global_fail_max: # number of averaging runs
torch.cuda.empty_cache()
torch.cuda.ipc_collect()
try:
main(iteration=counter, parameters=params)
counter += 1
except AttributeError as e:
print("\n\n ------- bad optimization, re trying ---------- \n")
global_fails += 1
\ No newline at end of file
#
# for n in range(5, n_max + 1):
# for s in range(2, s_max + 1):
# params = ({
# 'save_folder': 'hmpark_n{}_s{}_unopt'.format(n, s),
# 'lp_model': 'custom',
# 'opt_iterations': 3,
# 'path_style': 'basic',
# 'stats_name': 'hmpark',
# 'anchor_point': [36.891640, -81.524214], # hmpark
# 'num_searchers': s,
# 'num_robots': n,
# 'lp_filename': hmpark_heatmap
# })
# params = Default(params).params
#
# counter = 0
# while counter < avg_runs and global_fails <= global_fail_max: # number of averaging runs
# torch.cuda.empty_cache()
# torch.cuda.ipc_collect()
# try:
# main(iteration=counter, parameters=params)
# counter += 1
# except AttributeError as e:
# print("\n\n ------- bad optimization, re trying ---------- \n")
# global_fails += 1
#
# # -----------------------------------------------------------------------------------------------
#
# for n in range(1, n_max + 1):
# for s in range(2, s_max + 1):
# params = ({
# 'save_folder': 'hmpark_n{}_s{}_sweep'.format(n, s),
# 'lp_model': 'custom',
# 'opt_iterations': 1,
# 'path_style': 'sweep',
# 'stats_name': 'hmpark',
# 'anchor_point': [36.891640, -81.524214], # hmpark
# 'num_searchers': s,
# 'num_robots': n,
# 'lp_filename': hmpark_heatmap
# })
# params = Default(params).params
#
# counter = 0
# while counter < avg_runs and global_fails <= global_fail_max: # number of averaging runs
# torch.cuda.empty_cache()
# torch.cuda.ipc_collect()
# try:
# main(iteration=counter, parameters=params)
# counter += 1
# except AttributeError as e:
# print("\n\n ------- bad optimization, re trying ---------- \n")
# global_fails += 1
#
# # -----------------------------------------------------------------------------------------------
#
# for n in range(1, n_max + 1):
# for s in range(2, s_max + 1):
# params = ({
# 'save_folder': 'hmpark_n{}_s{}_rc'.format(n, s),
# 'lp_model': 'custom',
# 'opt_iterations': 1,
# 'path_style': 'rc',
# 'stats_name': 'hmpark',
# 'anchor_point': [36.891640, -81.524214], # hmpark
# 'num_searchers': s,
# 'num_robots': n,
# 'lp_filename': hmpark_heatmap
# })
# params = Default(params).params
#
# counter = 0
# while counter < avg_runs and global_fails <= global_fail_max: # number of averaging runs
# torch.cuda.empty_cache()
# torch.cuda.ipc_collect()
# try:
# main(iteration=counter, parameters=params)
# counter += 1
# except AttributeError as e:
# print("\n\n ------- bad optimization, re trying ---------- \n")
# global_fails += 1
\ No newline at end of file
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