def init_repo(args={},debug=False,init=False):
'''
initilize a repo as coolkit repo with default configuration
if any parent repo is already initilized then we will just copy the config from there
'''
cwd = abs_path(os.getcwd())
home_loc = abs_path('~')
root_loc = abs_path('/')
now = cwd
while(now != home_loc and now != root_loc):
if('.coolkit' in os.listdir(now) and os.path.isdir(os.path.join(now,'.coolkit'))):
if(debug): print('got .coolkit at ',now)
break
now = abs_path(os.path.join(now,os.pardir))
if(now == root_loc):
Colour.print('Coolkit should be run in path under home directory',Colour.RED)
sys.exit(1)
if(now == home_loc):
SrbJson(cwd+'/.coolkit/config',srbjson.local_template)
Colour.print('initialized empty CoolKit repository in '+cwd+'/.coolkit/',Colour.GREEN)
elif(now != cwd):
verify_folder(cwd+'/.coolkit/')
shutil.copy(now+'/.coolkit/config',cwd+'/.coolkit/config')
Colour.print('initialized empty CoolKit repository in '+cwd+'/.coolkit/',Colour.GREEN)
else:
if(init): Colour.print('Already a coolkit repo',Colour.YELLOW)
if(not 'c_name' in args or not args['c_name']):
contest_name = get_contest_name(cwd.split('/')[-1])
if(not contest_name):
args['c_name'] = None
else:
args['c_name'] = contest_name
SrbJson.dump_data(args,cwd+'/.coolkit/config',srbjson.local_template)
def __init__(self,
code1,
code2,
tester_script,
maxlim=10000,
idd="0",
timeout="10"):
self.idd = '_' + str(idd)
if os_name == 'windows':
self.timeout = ''
else:
self.timeout = 'timeout ' + str(timeout) + 's '
code1 = abs_path(code1)
code2 = abs_path(code2)
tester_script = abs_path(tester_script)
self.code1 = code1
self.code2 = code2
self.base1 = code1.split('/')[-1].split('\\')[-1].split('.')[0]
self.base2 = code2.split('/')[-1].split('\\')[-1].split('.')[0]
self.out1 = '.' + '-'.join(
relative_path(code1).split('/')[1:]) + self.idd + '.tester'
self.out2 = '.' + '-'.join(
relative_path(code2).split('/')[1:]) + self.idd + '.tester'
self.tester_script = tester_script
self.maxlim = int(maxlim)
if not Code_tester.check_exception(code1, code2, tester_script):
sys.exit(0)
self.exec1 = self.compile_code(self.code1)
self.exec2 = self.compile_code(self.code2)
self.exec3 = self.compile_code(self.tester_script)
def install_tester():
os_name = get_os_name()
pwd = str(os.getcwd()) #always return without / at end;
pwd = abs_path(pwd)
binary_path = pwd + '/even_tester/binaries/' + os_name + '/even_validator'
if (os_name == 'windows'): binary_path += '.exe'
if (os_name == 'windows'):
install_path = abs_path('~/programs', '\\')
binary_path = abs_path(binary_path, '\\')
user_path = os.environ['path'] + ';'
if not install_path + ';' in user_path:
print('adding to userpath')
print(install_path)
print(user_path)
os.system('setx path "%path%;' + install_path + '"')
else:
install_path = abs_path('~/.local/bin')
verify_folder(install_path)
dependency_map = {
'even_validator': {
'ubuntu': 'cp ' + binary_path + ' ' + install_path,
'windows': 'copy "' + binary_path + '" "' + install_path + '"',
},
}
install_dependencies(dependency_map, verbose=True)
def get_true_bbox(img_path, base_xml_dir=abs_path(settings.imagenet_val_xml_path)):
# parse the xml for bounding box coordinates
temp_img = Image.open(img_path)
sz = temp_img.size # width x height
im_name = img_path.split('/')[-1].split('.')[0]
tree = ET.parse(os.path.join(abs_path(base_xml_dir), f'{im_name}.xml'))
root = tree.getroot()
# Get Ground Truth ImageNet masks
# temp_area = 0
gt_mask = np.zeros((sz[1], sz[0])) # because we want rox x col
for iIdx, type_tag in enumerate(root.findall('object/bndbox')):
xmin = int(type_tag[0].text)
ymin = int(type_tag[1].text)
xmax = int(type_tag[2].text)
ymax = int(type_tag[3].text)
# if (ymax - ymin)*(xmax - xmin) > temp_area:
# temp_area = (ymax - ymin)*(xmax - xmin)
gt_mask[ymin:ymax, xmin:xmax] = 1
gt = preprocess_gt_bb(gt_mask, 224)
gt = (gt >= 0.5).astype(float) #binarize after resize
return gt
def fetch_data_from_local_config():
Args._verify_init()
cwd = abs_path(os.getcwd())
home_loc = abs_path('~')
root_loc = abs_path('/')
now = cwd
while(now != home_loc and now!= root_loc):
if('.coolkit' in os.listdir(now) and os.path.isdir(os.path.join(now,'.coolkit'))):
break
now = abs_path(os.path.join(now,os.pardir))
if(now == root_loc):
Colour.print('Coolkit should be run in path under home directory',Colour.RED)
sys.exit(1)
return SrbJson(now+'/.coolkit/config',srbjson.local_template).data
def check_init():
'''
check if directory is initilized or not
'''
cwd = abs_path(os.getcwd())
home_loc = abs_path('~')
root_loc = abs_path('/')
now = cwd
while(now != home_loc and now != root_loc):
if('.coolkit' in os.listdir(now) and os.path.isdir(os.path.join(now,'.coolkit'))):
return now # path of .coolkit
now = abs_path(os.path.join(now,os.pardir))
if(now == root_loc):
Colour.print('Coolkit should be run in path under home directory',Colour.RED)
sys.exit(1)
return False
def __init__(self,
data_path,
transform,
img_idxs=[0, 1],
idx_flag=1,
args=None):
self.data_path = data_path
self.transform = transform
self.args = args
if data_path == abs_path(settings.imagenet_val_path):
aa = img_name_list[img_idxs[0]:img_idxs[1]]
self.img_filenames = [
os.path.join(data_path, f'{ii}.JPEG') for ii in aa
]
# self.img_filenames.sort()
else:
self.img_filenames = []
for file in glob.glob(os.path.join(data_path, "*.JPEG")):
self.img_filenames.append(file)
self.img_filenames.sort()
if idx_flag == 1:
print('Only prodicing results for 1 image')
img_idxs = [0]
self.img_filenames = [self.img_filenames[i] for i in img_idxs]
def get_init_path():
'''
set config to global config file.
'''
cwd = abs_path(os.getcwd())
home_loc = abs_path('~')
root_loc = abs_path('/')
now = cwd
while(now != home_loc and now != root_loc):
if('.coolkit' in os.listdir(now) and os.path.isdir(os.path.join(now,'.coolkit'))):
return now+'/.coolkit'
now = abs_path(os.path.join(now,os.pardir))
if(now == root_loc):
Colour.print('Coolkit should be run in path under home directory',Colour.RED)
sys.exit(1)
return None
def get_image_class(self, filepath):
base_dir = abs_path('~/CS231n/heatmap_tests/')
# ipdb.set_trace()
# ImageNet 2012 validation set images?
with open(os.path.join(settings.imagenet_class_mappings, "ground_truth_val2012")) as f:
# with open(os.path.join(base_dir, "imagenet_class_mappings", "ground_truth_val2012")) as f:
ground_truth_val2012 = {x.split()[0]: int(x.split()[1])
for x in f.readlines() if len(x.strip()) > 0}
with open(os.path.join(settings.imagenet_class_mappings, "synset_id_to_class")) as f:
# with open(os.path.join(base_dir, "imagenet_class_mappings", "synset_id_to_class")) as f:
synset_to_class = {x.split()[1]: int(x.split()[0])
for x in f.readlines() if len(x.strip()) > 0}
def get_class(f):
# ipdb.set_trace()
# File from ImageNet 2012 validation set
ret = ground_truth_val2012.get(f, None)
if ret is None:
# File from ImageNet training sets
ret = synset_to_class.get(f.split("_")[0], None)
if ret is None:
# Random JPEG file
ret = 1000
return ret
image_class = get_class(filepath.split('/')[-1])
return image_class
def export(self, path):
from srblib import abs_path
path = abs_path(path)
writer = PyPDF2.PdfFileWriter()
for doc, pnum in self._outpages:
writer.addPage(doc.get_page(pnum))
with open(path, 'wb') as f:
writer.write(f)
def _verified_file(file_path):
if not file_path:
return None
file_path = abs_path(file_path)
if not os.path.isfile(file_path):
Colour.print('No such file : ' + Colour.END + file_path,
Colour.RED)
sys.exit()
return file_path
class Const:
cache_dir = abs_path('~/.config/coolkit')
mult_soln_words = [
'multiple possible', 'multiple answers', 'output any', 'any',
'multiple'
]
def __init__():
pass
def write_data(data,fout):
dump_data(data,'result/json/'+fout+'.json')
out = ""
rank = 1
for item in data:
out+=str(rank)+" "+item.get_result()+"\n\n"
rank+=1
fout = abs_path('result/text/' + fout + '.txt')
verify_file(fout)
open(fout,'w').write(out)
def data_to_excel(data, out_path):
out_path = abs_path(out_path)
book = xlwt.Workbook(encoding="utf-8")
sheet = book.add_sheet("Sheet1")
for i in range(len(data)):
row = data[i]
for j in range(len(row)):
sheet.write(i, j, row[j])
book.save(out_path)
def os_cd(path):
"""
A short-cut for _`cd` like_ commands
**Throws**
- `Throws/Raises` if path does not exist
"""
abspath = srblib.abs_path(path)
if os.path.isdir(abspath) is False:
raise TypeError("No directory at {abspath}".format(abspath = abspath))
os.chdir(abspath)
def main(args):
## Debugging Mode
if sys.gettrace() is not None:
print('In debug mode')
args.profiler = 'simple'
args.num_workers = 0
args.pin_memory = False
args.default_root_dir = mkdir_p(abs_path('./tmp_dir'))
args.stochastic_weight_avg = True
args.limit_train_batches = 0.001
args.limit_val_batches = 0.001
args.num_sanity_val_steps = 0
args.terminate_on_nan = True
args.deterministic = True
args.auto_select_gpus = False
args.fast_dev_run = False # Quick Check Working
args.progress_bar_refresh_rate = 0
args.gpus = 1
args.precision = 16
args.train_batch_size = 32
# args.val_batch_size=4
args.freeze_encoder = True
args.verbose = True
args.max_epochs = 2
## Model
dict_vars = vars(args)
model = BertForRegression(**dict_vars)
print(f'Model Init Done')
## TODO: Early Stopping Callback
## Callbacks
checkpoint_callback = pl.callbacks.ModelCheckpoint(
filename='{epoch:02d}-{val_loss:.5f}-{step_count}',
dirpath=None,
prefix="best_model_checkpoint",
monitor="val_loss",
mode="min",
save_top_k=args.save_top_k,
)
# lr_monitor = pl.callbacks.LearningRateMonitor(logging_interval='step')
trainer = pl.Trainer.from_argparse_args(
args,
callbacks=[LoggingCallback(), checkpoint_callback],
)
trainer.fit(model)
best_model_path = trainer.checkpoint_callback.best_model_path
print(f'\nBEST MODEL PATH IS {best_model_path}')
def set_local_config(args={},debug=False):
'''
set config to config file.
parent config if found
creates init if not
'''
if(not Args.check_init()):
Args.init_repo()
cwd = abs_path(os.getcwd())
home_loc = abs_path('~')
root_loc = abs_path('/')
now = cwd
while(now != home_loc and now != root_loc):
if('.coolkit' in os.listdir(now) and os.path.isdir(os.path.join(now,'.coolkit'))):
if(debug): print('got .coolkit at ',now)
break
now = abs_path(os.path.join(now,os.pardir))
if(now == root_loc):
Colour.print('Coolkit should be run in path under home directory',Colour.RED)
sys.exit(1)
SrbJson.dump_data(args,now+'/.coolkit/config',srbjson.local_template)
def mkdir_p(mypath):
"""Creates a directory. equivalent to using mkdir -p on the command line"""
from errno import EEXIST
from os import makedirs, path
from srblib import abs_path
mypath = abs_path(mypath)
try:
makedirs(mypath)
except OSError as exc: # Python >2.5
if exc.errno == EEXIST and path.isdir(mypath):
pass
else:
raise
return mypath
def __init__(self, data_path, img_idxs=[0, 1], idx_flag=1):
self.data_path = data_path
if data_path == abs_path(settings.imagenet_val_path):
aa = img_name_list[img_idxs[0]:img_idxs[1]]
self.img_filenames = [
os.path.join(data_path, f'{ii}.JPEG') for ii in aa
]
else:
self.img_filenames = []
for file in glob.glob(os.path.join(data_path, "*.JPEG")):
self.img_filenames.append(file)
self.img_filenames.sort()
self.img_filenames = self.img_filenames[:50]
print(
f'\nNo. of images to be analyzed are {len(self.img_filenames)}\n')
if idx_flag == 1:
print('Only prodicing results for 1 image')
img_idxs = [0]
self.img_filenames = [self.img_filenames[i] for i in img_idxs]
def excel_to_data(inp_path):
inp_path = abs_path(inp_path)
raw_data = pandas.read_excel(inp_path)
header = list(raw_data.columns)
if (len(header) == 0):
return []
temp_data = []
for head in header:
col = list(raw_data[head])
temp_data.append(col)
data = [header]
for i in range(len(temp_data[0])):
row = []
for j in range(len(header)):
row.append(temp_data[j][i])
data.append(row)
return data
def test_view():
if not on_travis:
return
print()
cwd = abs_path(os.getcwd())
try:
loc = cwd+'/test/contests/837'
os.chdir(loc)
remove(loc+'/.coolkit')
assert(os.system('coolkit init')==0)
assert(os.system('coolkit view user srbcheema1')==0)
assert(os.system('coolkit view contest')==0)
assert(os.system('coolkit view prob A')==0)
assert(os.system('coolkit view upcoming')==0)
if(utils.do_online_test()):
assert(os.system('coolkit view friends')==0)
assert(os.system('coolkit view standings 935')==0)
os.chdir(cwd)
except Exception as e:
remove(loc+'/.coolkit')
os.chdir(cwd)
raise e
import os
import shutil
from importlib.util import spec_from_file_location, module_from_spec
from srblib import abs_path, verify_file
from srblib import Module
from .Constants import Const
_path_of_default_global_config = '/'.join(
abs_path(__file__).split('/')[:-2]) + '/extra/global_config.py'
_global_config = Module(Const.cache_dir + '/global_config.py',
_path_of_default_global_config)
def get_contest_name(folder):
return _global_config.get_contest_name(folder)
def get_problem_name(file_name):
return _global_config.get_problem_name(file_name)
# from robustness import model_utils, datasets
from user_constants import DATA_PATH_DICT
import settings
import warnings
warnings.filterwarnings("ignore")
import utils as eutils
use_cuda = torch.cuda.is_available()
FloatTensor = torch.cuda.FloatTensor if use_cuda else torch.FloatTensor
LongTensor = torch.cuda.LongTensor if use_cuda else torch.LongTensor
Tensor = FloatTensor
# os.environ.set("MAX_LEN_IDENTIFIER", 300)
text_file = abs_path(settings.paper_img_txt_file)
# text_file = f'/home/naman/CS231n/heatmap_tests/' \
# f'Madri/Madri_New/robustness_applications/img_name_files/' \
# f'time_15669152608009198_seed_0_' \
# f'common_correct_imgs_model_names_madry_ressnet50_googlenet.txt'
img_name_list = []
with open(text_file, 'r') as f:
for line in f:
img_name_list.append(line.split('\n')[0])
## For reproducebility
torch.manual_seed(0)
torch.backends.cudnn.deterministic = True
torch.backends.cudnn.benchmark = False
def test_files_verifiers():
if(not on_travis):
return
verify_folder(abs_path('./hello/world'))
assert(os.path.exists(abs_path('./hello')) == True)
assert(os.path.exists(abs_path('./hello/world')) == True)
remove(abs_path('./hello/world'))
assert(os.path.exists(abs_path('./hello')) == True)
assert(os.path.exists(abs_path('./hello/world')) == False)
verify_file(abs_path('./hello/world/helloworld.py'))
assert(os.path.exists(abs_path('./hello')) == True)
assert(os.path.exists(abs_path('./hello/world')) == True)
assert(os.path.exists(abs_path('./hello/world/helloworld.py')) == True)
remove(abs_path('./hello'))
assert(os.path.exists(abs_path('./hello')) == False)
assert(os.path.exists(abs_path('./hello/world')) == False)
assert(os.path.exists(abs_path('./hello/world/helloworld.py')) == False)
from srblib import abs_path
import os
data_dir = abs_path('./datasets')
file_name = os.path.join(data_dir, 'Eluvio_DS_Challenge.csv')
n_train_samples = 254618
n_val_samples = 127309
n_test_samples = 127309
# file_name = abs_path('./datasets/temp_data.csv')
# n_train_samples = 14
# n_val_samples = 7
# n_test_samples = 7
import os
from srblib import abs_path
from srblib import SrbJson
config_file_path = abs_path('~/.config/exam_scheduler/config.json')
default_output_xlsx_path = abs_path('./output.xls')
_default_room_list = None
_default_teachers_list = None
_default_schedule_list = None
if os.path.exists(abs_path('input/')):
for fname in os.listdir(abs_path('input/')):
fname = 'input/' + fname
if not _default_room_list and 'room_list' in fname:
_default_room_list = abs_path(fname)
elif not _default_teachers_list and 'teachers_list' in fname:
_default_teachers_list = abs_path(fname)
elif not _default_schedule_list and 'schedule_list' in fname:
_default_schedule_list = abs_path(fname)
for fname in os.listdir(abs_path('./')):
if not _default_room_list and 'room_list' in fname:
_default_room_list = abs_path(fname)
elif not _default_teachers_list and 'teachers_list' in fname:
_default_teachers_list = abs_path(fname)
elif not _default_schedule_list and 'schedule_list' in fname:
_default_schedule_list = abs_path(fname)
_config_template = \
hog2 = hog(h2, pixels_per_cell=(16, 16))
out, _ = pearsonr(hog1, hog2)
elif metric_name.lower() == 'spearman':
out, _ = spearmanr(h1, h2, axis=None)
else:
print(f'Still not implemented.\nExiting')
sys.exit(1)
return out
########################################################################################################################
if __name__ == '__main__':
base_img_dir = abs_path(settings.imagenet_val_path)
# base_img_dir = '/home/naman/CS231n/heatmap_tests/images/ILSVRC2012_img_val'
# text_file = f'/home/naman/CS231n/heatmap_tests/' \
# f'Madri/Madri_New/robustness_applications/img_name_files/' \
# f'time_15669152608009198_seed_0_' \
# f'common_correct_imgs_model_names_madry_ressnet50_googlenet.txt'
s_time = time.time()
f_time = ''.join(str(s_time).split('.'))
args = get_arguments()
im_label_map = eutils.imagenet_label_mappings()
eutils.mkdir_p(args.out_path)
img_filenames = os.listdir(args.input_dir_path)
img_filenames = [
i for i in img_filenames if 'ILSVRC2012_val_000' in i
and int(i.split('_')[-1]) in range(1, 50001)
def test_Args():
if not on_travis:
return
print()
cwd = abs_path(os.getcwd())
try:
loc = cwd+'/test/contests/837'
os.chdir(loc)
remove(loc+'/.coolkit')
args = {}
args['c_name'] = '837'
args['c_type'] = 'contest'
args['p_name'] = None
args['c_site'] = 'codeforces'
Args.init_repo(args)
print(Colour.CYAN+'Content of confing file'+Colour.END)
os.system('cat '+loc+'/.coolkit/config')
print(Colour.CYAN+'trying to init it again'+Colour.END)
Args.init_repo(args)
print(Colour.CYAN+'setting problem name to A'+Colour.END)
Args.set_local_config({'p_name':'A'})
print(Colour.CYAN+'Content of confing file'+Colour.END)
os.system('cat '+loc+'/.coolkit/config')
print(Colour.CYAN+'Deinitialize the folder'+Colour.END)
remove(loc+'/.coolkit')
print(Colour.CYAN+'Try to run the file'+Colour.END)
os.system('coolkit run one.cpp')
if(os.environ['USER'] == 'travis'):
os.system('coolkit config --user coolkit --pswd coolkit')
print(Colour.CYAN+'Try to submit wrong file'+Colour.END)
os.system('coolkit submit three.cpp')
if(utils.do_online_test()):
print(Colour.CYAN+'Try to submit right file'+Colour.END)
make_unique(loc)
os.system('coolkit submit hidden_one.cpp')
remove(loc+'/hidden_one.cpp')
print(Colour.CYAN+'Deinitialize the folder'+Colour.END)
remove(loc+'/.coolkit')
remove('~/.config/coolkit/contest/222')
print(Colour.CYAN+'Fetching some contest'+Colour.END)
os.system('coolkit fetch -c 222 ')
print(Colour.CYAN+'Fetching contest without force'+Colour.END)
os.system('coolkit fetch -c 222')
print(Colour.CYAN+'After making files defected'+Colour.END)
os.remove(abs_path('~/.config/coolkit/contest/222/prob/A/io/Input1'))
os.system('coolkit fetch -c 222')
remove('~/.config/coolkit/contest/222')
os.chdir(cwd)
except Exception as e:
# do clean up even in case of exception
loc = cwd+'/test/contests/837'
remove('~/.config/coolkit/contest/222')
remove(loc+'/.coolkit')
remove(loc+'/hidden_one.cpp')
os.chdir(cwd)
raise e
def get_arguments():
# Initialize the parser
parser = argparse.ArgumentParser(
description='Paramters for sensitivity analysis of heatmaps')
parser.add_argument('-idp',
'--input_dir_path',
help='Path of the input directory',
metavar='DIR')
parser.add_argument(
'-op',
'--out_path',
help=
'Path of the output directory where you want to save the text files (Default is ./)'
)
parser.add_argument(
'-mn',
'--method_name',
choices=['sg'],
#['occlusion', 'ig', 'sg', 'grad', 'lime', 'mp', 'inpgrad'],
help='Method you are analysing')
parser.add_argument('--exp_num',
choices=[
'a30',
'a31',
],
help='Which experiment of SmoothGrad')
parser.add_argument('--metric_name',
choices=['ssim', 'hog', 'spearman'],
help='Metric to be computed')
# parser.add_argument('--num_variations', type=int,
# help='Number of variations for a particular method.')
# parser.add_argument('--no_img_name_dir_flag', action='store_false', default=True,
# help=f'Flag to say that image name is stored as seperate directory in the input path.'
# f'Default=True')
#
# parser.add_argument('--no_model_name_dir_flag', action='store_false', default=True,
# help=f'Flag to say that model name is stored as seperate directory in the input path. '
# f'Default=True')
parser.add_argument(
'--idx_flag',
type=int,
help=f'Flag whether to use some images in the folder (1) or all (0). '
f'This is just for testing purposes. '
f'Default=0',
default=0,
)
parser.add_argument(
'-s_idx',
'--start_idx',
type=int,
help='Start index for selecting images. Default: 0',
default=0,
)
parser.add_argument(
'-e_idx',
'--end_idx',
type=int,
help='End index for selecting images. Default: 2K',
default=1735,
)
# parser.add_argument('--if_random', action='store_true', default=False,
# help=f'Flag to say you want to compute results for baseline'
# f'Default=False')
# Parse the arguments
args = parser.parse_args()
args.no_model_name_dir_flag = False
args.if_random = False
args.no_img_name_dir_flag = True
# if args.if_random:
# np.random.seed(0)
# if args.num_variations is None:
# print('Please provide this number.\nExiting')
# sys.exit(0)
# elif args.num_variations < 2:
# print('This number cant be less than 2.\nExiting')
# sys.exit(0)
if args.method_name is None:
print('Please provide the name of the method.\nExiting')
sys.exit(0)
if args.exp_num is None:
print('Please provide the experiment number.\nExiting')
sys.exit(0)
if args.metric_name is None:
print('Please provide the name of the metric.\nExiting')
sys.exit(0)
if args.input_dir_path is None:
print('Please provide image dir path. Exiting')
sys.exit(1)
args.input_dir_path = abs_path(args.input_dir_path)
if args.out_path is None:
args.out_path = './'
args.out_path = os.path.abspath(args.out_path)
return args
## Testing loop
tot_loss = 0
for l_idx, test_batch in enumerate(test_loader):
## Put data on device
for key in test_batch.keys():
test_batch[key] = test_batch[key].to(torch_device)
## Compute the loss
loss = model._step(test_batch).item()
tot_loss += loss
if verbose:
print(f'Type: Test, '
f'BatchIdx: {l_idx+1: 3d}/{len(test_loader)}, '
f'Per_Step_Test_Loss: {loss: 8.3f}')
avg_loss = tot_loss / len(test_loader)
print(f'Avergae test loss is {avg_loss}')
if __name__ == '__main__':
# model_path = '/home/nzb0040/eluvio/lightning_logs/best_model_checkpoint-epoch=29-val_loss=290297.96875-step_count=0.ckpt'
args = get_arguments()
# import ipdb
# ipdb.set_trace()
main(model_path=abs_path(args.model_path),
batch_sz=args.test_batch_size,
num_workers=args.num_workers,
pin_memory=args.pin_memory,
verbose=args.verbose)
