def main(noeval, **args):
#args should be the info you need to specify the params
# for a given experiment, but only params should be used below
params = fill_params(**args)
utils.set_gpus(params["gpus"])
net = utils.create_network(**params)
if not noeval:
net.eval()
utils.log_tagged_modules(params["modules_used"], params["log_dir"],
params["log_tag"], params["chkpt_num"])
for dset in params["dsets"]:
print(dset)
fs = make_forward_scanner(dset, **params)
output = forward.forward(net,
fs,
params["scan_spec"],
activation=params["activation"])
save_output(output, dset, **params)
def main_fwd(noeval, **args):
#args should be the info you need to specify the params
# for a given experiment, but only params should be used below
params = fill_params_fwd(**args)
utils.set_gpus(params["gpus"])
net = utils.create_network(**params)
if not noeval:
net.eval()
utils.log_tagged_modules(params["modules_used"], params["log_dir"],
params["log_tag"], params["chkpt_num"])
#lightsheet mods - input folder contains list of our "big" patches
input_fld = os.path.join(params["data_dir"],
"input_patches") #set directory
output_fld = os.path.join(params["data_dir"],
"cnn_output") #set patches directory
if not os.path.exists(output_fld): os.mkdir(output_fld)
jobid = 0 #for demo only
#find files that need to be processed
fls = [os.path.join(input_fld, xx) for xx in os.listdir(input_fld)]
fls.sort()
#select the file to process for this batch job
if jobid > len(fls):
#essentially kill job if too high - doing this to hopefully help with karma score although might not make a difference
sys.stdout.write("\njobid {} > number of files {}\n".format(
jobid, len(fls)))
sys.stdout.flush()
else:
dset = fls[jobid]
start = time.time()
fs = make_forward_scanner(dset, **params)
sys.stdout.write("\striding by: {}".format(fs.stride))
sys.stdout.flush()
output = forward.forward(
net,
fs,
params["scan_spec"], #runs forward pass
activation=params["activation"])
save_output(output, dset, output_fld, jobid, params["output_tag"],
params["chkpt_num"]) #saves tif
fs._init() #clear out scanner
sys.stdout.write("\patch {}: {} min\n".format(
jobid + 1, round((time.time() - start) / 60, 1)))
sys.stdout.flush()
def main(noeval, **args):
#args should be the info you need to specify the params
# for a given experiment, but only params should be used below
params = fill_params(**args)
utils.set_gpus(params["gpus"])
net = utils.create_network(**params)
if not noeval:
net.eval()
utils.log_tagged_modules(params["modules_used"], params["log_dir"],
params["log_tag"], params["chkpt_num"])
#lightsheet mods - input folder contains list of our "big" patches
input_fld = os.path.join(params["data_dir"],
"input_chnks") #set patches directory
sys.stdout.write("running inference on: \n{}\n".format(
os.path.basename(params["data_dir"])))
sys.stdout.flush()
output_fld = os.path.join(params["data_dir"],
"output_chnks") #set output directory
jobid = int(params["jobid"]) #set patch no. to run through cnn
#find files that need to be processed
fls = [os.path.join(input_fld, xx) for xx in os.listdir(input_fld)]
fls.sort()
#select the file to process for this array job
if jobid > len(fls) - 1:
sys.stdout.write("\njobid {} > number of files {}".format(
jobid, len(fls)))
sys.stdout.flush()
else:
start = time.time()
dset = fls[jobid]
fs = make_forward_scanner(dset, **params)
output = forward.forward(
net,
fs,
params["scan_spec"], #runs forward pass
activation=params["activation"])
save_output(output, dset, output_fld, **params) #saves tif
fs._init() #clear out scanner
sys.stdout.write("patch {}: {} min\n".format(
jobid + 1, round((time.time() - start) / 60, 1)))
sys.stdout.flush()
def main(**args):
#args should be the info you need to specify the params
# for a given experiment, but only params should be used below
params = fill_params(**args)
utils.set_gpus(params["gpus"])
utils.make_required_dirs(**params)
utils.log_tagged_modules(params["modules_used"],
params["log_dir"], "train",
chkpt_num=params["chkpt_num"])
start_training(**params)
