def setup_output_space(opts):
# create the output directory
flags = opts["flags"]
# Wasn't able to save the GFLAGS object in a pickle easily. So clear the
# GFLAGS, save the dictionary and on re-load just regen the flags.
# create the output directories
out_dir = flags.out_dir
create_dir(out_dir)
create_dir(out_dir + "/predictions")
# create_dir(out_dir + "/predictions/valid")
# create_dir(out_dir + "/predictions/train")
# create_dir(out_dir + "/predictions/test")
create_dir(out_dir + "/plots")
create_dir(out_dir + "/opts")
# create_dir(out_dir + "/grads")
create_dir(out_dir + "/networks")
# save the opts dict (without the gflags obj).
opts["flags"] = None
joblib.dump(opts, os.path.join(flags.out_dir, "opts", "opts.npy"))
opts["flags"] = flags
# save the command
# save_command(opts, flags.out_dir)
save_command2(flags.out_dir, opts["argv"])
git_helper.log_git_status(
os.path.join(opts["flags"].out_dir, "git_status.txt"))
return opts
def create_train_test(opts):
"""Create the training and testing splits."""
# first setup the output space. The output space will be in the same folder
# as the original data.hdf file, but with different names and a seperate
# sub folder for the setup information.
base_out = os.path.dirname(opts["flags"].data)
log_output_path = os.path.join(base_out, opts["flags"].name)
# out_data_name = os.path.join(base_out, opts["flags"].name + ".hdf5")
base_out_name = os.path.join(base_out, opts["flags"].name)
exp_path = os.path.join(base_out, "exps")
paths.create_dir(log_output_path)
# add the initial logging information to the output path.
git_helper.log_git_status(
os.path.join(log_output_path, "00_git_status.txt"))
paths.save_command2(log_output_path, opts["argv"])
# now to do the actual splitting.
# first open the base data.hdf
with h5py.File(opts["flags"].data, "r") as org_data:
exp_list = org_data["exp_names"].value
exp_mask = numpy.ones((exp_list.shape[0], ), dtype="bool")
if opts["flags"].split_type == 2:
train_idx, test_idx, valid_idx = hantman_mouse.setup_full_split2(
opts,
org_data,
mask=exp_mask,
test_mouse=opts["flags"].test_mouse)
else:
# else split type 3... no split type 1
train_idx, test_idx, valid_idx = hantman_mouse.setup_full_split3(
opts, org_data, mask=exp_mask)
split_name = base_out_name + "_train.hdf5"
save_experiments(opts,
org_data,
exp_path,
train_idx,
split_name,
mask=exp_mask)
split_name = base_out_name + "_test.hdf5"
save_experiments(opts,
org_data,
exp_path,
test_idx,
split_name,
mask=exp_mask)
split_name = base_out_name + "_valid.hdf5"
save_experiments(opts,
org_data,
exp_path,
valid_idx,
split_name,
mask=exp_mask)
return
def main(argv):
opts = _setup_opts(sys.argv)
# paths.setup_output_space(opts)
out_dir = os.path.join(opts["flags"].out_dir, 'proc_info')
paths.create_dir(out_dir)
paths.save_command(opts, out_dir)
git_helper.log_git_status(os.path.join(out_dir, "git_status.txt"))
if opts["flags"].cuda_device != -1:
torch.cuda.set_device(opts["flags"].cuda_device)
with h5py.File(opts["flags"].train_file, "r") as train_data:
with h5py.File(opts["flags"].test_file, "r") as test_data:
with h5py.File(opts["flags"].valid_file, "r") as valid_data:
sampler = HantmanVideoFrameSampler(
opts["rng"],
train_data,
opts["flags"].video_dir,
opts["flags"].hantman_mini_batch,
frames=opts["flags"].frames,
use_pool=True,
gpu_id=opts["flags"].cuda_device)
label_weight = _get_label_weight(opts, train_data)
# import pdb; pdb.set_trace()
train_eval = HantmanVideoSampler(
None,
train_data,
opts["flags"].video_dir,
use_pool=True,
gpu_id=opts["flags"].cuda_device)
test_eval = HantmanVideoSampler(
None,
test_data,
opts["flags"].video_dir,
use_pool=True,
gpu_id=opts["flags"].cuda_device)
valid_eval = HantmanVideoSampler(
None,
valid_data,
opts["flags"].video_dir,
use_pool=True,
gpu_id=opts["flags"].cuda_device)
network, optimizer, criterion = _init_network(
opts, label_weight)
# import pdb; pdb.set_trace()
_proc_network(opts, network, optimizer, criterion, sampler,
train_eval, test_eval, valid_eval)
def main(opts):
# create the output directory
paths.create_dir(opts["flags"].out_dir)
paths.save_command2(opts["flags"].out_dir, opts["argv"])
# log the git information
git_helper.log_git_status(
os.path.join(opts["flags"].out_dir, "00_git_status.txt"))
exp_dir = os.path.join(opts["flags"].out_dir, "exps")
paths.create_dir(exp_dir)
outname = os.path.join(opts["flags"].out_dir, "data.hdf5")
logname = os.path.join(opts["flags"].out_dir, "00_log.txt")
skipname = os.path.join(opts["flags"].out_dir, "00_skipped.txt")
with open(logname, "w") as log:
with open(skipname, "w") as skip_log:
with h5py.File(outname, "w") as out_data:
preprocess_features(opts, log, skip_log, out_data, exp_dir)
# import pdb; pdb.set_trace()
out_data["features"] = all_feat
out_data["experiments"] = all_exps
out_data["mice"] = all_mice
out_data["date"] = all_dates
out_data["labels"] = all_labels
# import pdb; pdb.set_trace()
return
if __name__ == "__main__":
opts = create_opts()
opts = setup_opts(opts)
# create the output directory
# paths.setup_output_space(opts["out_dir"])
paths.create_dir(opts["out_dir"])
paths.save_command(opts["out_dir"])
# log the git information
git_helper.log_git_status(
os.path.join(opts["out_dir"], "00_git_status.txt"))
outname = os.path.join(opts["out_dir"], "data.hdf5")
logname = os.path.join(opts["out_dir"], "00_log.txt")
skipname = os.path.join(opts["out_dir"], "00_skipped.txt")
with open(logname, "w") as log:
with open(skipname, "w") as skip_log:
with h5py.File(outname, "w") as out_data:
preprocess_features(opts, log, skip_log, out_data)
def create_train_test(opts):
"""Create the training and testing splits."""
# first setup the output space. The output space will be in the same folder
# as the original data.hdf file, but with different names and a seperate
# sub folder for the setup information.
base_out = os.path.dirname(opts["flags"].data)
log_output_path = os.path.join(base_out, opts["flags"].name)
# out_data_name = os.path.join(base_out, opts["flags"].name + ".hdf5")
base_out_name = os.path.join(base_out, opts["flags"].name)
exp_path = os.path.join(base_out, "exps")
paths.create_dir(log_output_path)
# add the initial logging information to the output path.
git_helper.log_git_status(
os.path.join(log_output_path, "00_git_status.txt"))
paths.save_command2(log_output_path, opts["argv"])
# now to do the actual splitting.
# first open the base data.hdf
with h5py.File(opts["flags"].data, "a") as org_data:
exp_list = org_data["experiments"].value
# get ride of long videos.
import pdb
pdb.set_trace()
exp_mask = hantman_mouse.mask_long_vids(org_data, exp_list)
# prune lists further to make an easier dataset.
exp_mask = prune_mice_dates(opts, org_data, mask=exp_mask)
if opts["flags"].one_mouse is True and opts["flags"].one_day is True:
# If one mouse and one date, then just split randomly.
num_vids = exp_mask.sum()
rand_idx = opts["rng"].permutation(num_vids)
# split percentage is 80% (should this be changeable?)
split_idx = int(np.floor(num_vids * 0.8))
train_idx = rand_idx[:split_idx]
test_idx = rand_idx[split_idx:]
elif opts["flags"].one_mouse is False and opts["flags"].one_day is True:
print("Not defined.")
import pdb
pdb.set_trace()
else:
train_idx, test_idx = hantman_mouse.setup_train_test_samples(
opts, org_data, mask=exp_mask)
split_name = base_out_name + "_train.hdf5"
save_experiments(opts,
org_data,
exp_path,
train_idx,
split_name,
mask=exp_mask)
split_name = base_out_name + "_test.hdf5"
save_experiments(opts,
org_data,
exp_path,
test_idx,
split_name,
mask=exp_mask)
print("hi")
return
data['label_names'] = label_names
data['features'] = all_feats
data['labels'] = all_labels
# data['crop_first'] = all_crop_first
# data['crop_last'] = all_crop_last
data['crops'] = all_crops
data['num_frames'] = all_num_frames
data['frame_idx'] = all_frame_idx
# data['org_frames'] = all_org_frames
return data
if __name__ == "__main__":
opts = create_opts()
opts = setup_opts(opts)
# create the output directory
# paths.setup_output_space(opts['out_dir'])
paths.create_dir(opts['out_dir'])
paths.save_command(opts['out_dir'])
# log the git information
git_helper.log_git_status(
os.path.join(opts['out_dir'], '00_git_status.txt'))
data = parse_matfile(opts)
outname = os.path.join(opts['out_dir'], 'data.npy')
joblib.dump(data, outname)
# create the hdf5 verson of the data
