def main(cfg: DictConfig):
# turn "models" in your project configuration to "full/path/to/models"
cfg = utils.get_absolute_paths_from_cfg(cfg)
log.info('configuration used: ')
log.info(cfg.pretty())
weights = projects.get_weightfile_from_cfg(cfg, model_type='sequence')
assert weights is not None, 'Must either specify a weightfile or use reload.latest=True'
if cfg.sequence.latent_name is None:
# find the latent name used in the weight file you loaded
rundir = os.path.dirname(weights)
loaded_cfg = utils.load_yaml(os.path.join(rundir, 'config.yaml'))
latent_name = loaded_cfg['sequence']['latent_name']
# if this latent name is also None, use the arch of the feature extractor
# this should never happen
if latent_name is None:
latent_name = loaded_cfg['feature_extractor']['arch']
else:
latent_name = cfg.sequence.latent_name
# the output name will be a group in the output hdf5 dataset containing probabilities, etc
if cfg.sequence.output_name is None:
output_name = cfg.sequence.arch
else:
output_name = cfg.sequence.output_name
directory_list = cfg.inference.directory_list
if directory_list is None or len(directory_list) == 0:
raise ValueError('must pass list of directories from commmand line. '
'Ex: directory_list=[path_to_dir1,path_to_dir2] or directory_list=all')
elif type(directory_list) == str and directory_list == 'all':
basedir = cfg.project.data_path
directory_list = utils.get_subfiles(basedir, 'directory')
outputfiles = []
for directory in directory_list:
assert os.path.isdir(directory), 'Not a directory: {}'.format(directory)
record = projects.get_record_from_subdir(directory)
assert record['output'] is not None
outputfiles.append(record['output'])
model = build_model_from_cfg(cfg, 1024, len(cfg.project.class_names))
log.info('model: {}'.format(model))
model = utils.load_weights(model, weights)
metrics_file = os.path.join(os.path.dirname(weights), 'classification_metrics.h5')
with h5py.File(metrics_file, 'r') as f:
thresholds = f['threshold_curves']['val']['optimum'][:]
log.info('thresholds: {}'.format(thresholds))
device = 'cuda:{}'.format(cfg.compute.gpu_id)
class_names = cfg.project.class_names
class_names = np.array(class_names)
extract(model, outputfiles, thresholds, cfg.feature_extractor.final_activation, latent_name, output_name,
cfg.sequence.sequence_length, None, True, device, cfg.inference.ignore_error,
cfg.inference.overwrite, class_names=class_names)
def main(cfg: DictConfig) -> None:
log.debug('cwd: {}'.format(os.getcwd()))
# only two custom overwrites of the configuration file
# first, change the project paths from relative to absolute
cfg = utils.get_absolute_paths_from_cfg(cfg)
if cfg.sequence.latent_name is None:
cfg.sequence.latent_name = cfg.feature_extractor.arch
# second, use the model directory to find the most recent run of each model type
# cfg = projects.overwrite_cfg_with_latest_weights(cfg, cfg.project.model_path, model_type='flow_generator')
# SHOULD NEVER MODIFY / MAKE ASSIGNMENTS TO THE CFG OBJECT AFTER RIGHT HERE!
log.info('Configuration used: ')
log.info(cfg.pretty())
model = train_from_cfg(cfg)
def main(cfg: DictConfig) -> None:
log.debug('cwd: {}'.format(os.getcwd()))
# only two custom overwrites of the configuration file
# first, change the project paths from relative to absolute
cfg = utils.get_absolute_paths_from_cfg(cfg)
# second, use the model directory to find the most recent run of each model type
# cfg = projects.overwrite_cfg_with_latest_weights(cfg, cfg.project.model_path, model_type='flow_generator')
# SHOULD NEVER MODIFY / MAKE ASSIGNMENTS TO THE CFG OBJECT AFTER RIGHT HERE!
log.info('Configuration used: ')
log.info(cfg.pretty())
try:
model = train_from_cfg(cfg)
except KeyboardInterrupt:
torch.cuda.empty_cache()
raise
def main(cfg: DictConfig):
# turn "models" in your project configuration to "full/path/to/models"
cfg = utils.get_absolute_paths_from_cfg(cfg)
log.info('configuration used in inference: ')
log.info(cfg.pretty())
if cfg.sequence.latent_name is None:
latent_name = cfg.feature_extractor.arch
else:
latent_name = cfg.sequence.latent_name
directory_list = cfg.inference.directory_list
if directory_list is None or len(directory_list) == 0:
raise ValueError('must pass list of directories from commmand line. '
'Ex: directory_list=[path_to_dir1,path_to_dir2]')
elif type(directory_list) == str and directory_list == 'all':
basedir = cfg.project.data_path
directory_list = utils.get_subfiles(basedir, 'directory')
# video files are found in your input list of directories using the records.yaml file that should be present
# in each directory
records = []
for directory in directory_list:
assert os.path.isdir(directory), 'Not a directory: {}'.format(
directory)
record = projects.get_record_from_subdir(directory)
assert record['rgb'] is not None
records.append(record)
assert cfg.feature_extractor.n_flows + 1 == cfg.flow_generator.n_rgb, 'Flow generator inputs must be one greater ' \
'than feature extractor num flows '
input_images = cfg.feature_extractor.n_flows + 1
mode = '3d' if '3d' in cfg.feature_extractor.arch.lower() else '2d'
# get the validation transforms. should have resizing, etc
transform = get_transforms(cfg.augs, input_images, mode)['val']
rgb = []
for record in records:
rgb.append(record['rgb'])
model = build_feature_extractor(cfg)
device = 'cuda:{}'.format(cfg.compute.gpu_id)
feature_extractor_weights = projects.get_weightfile_from_cfg(
cfg, 'feature_extractor')
metrics_file = os.path.join(os.path.dirname(feature_extractor_weights),
'classification_metrics.h5')
assert os.path.isfile(metrics_file)
with h5py.File(metrics_file, 'r') as f:
thresholds = f['threshold_curves']['val']['optimum'][:]
log.info('thresholds: {}'.format(thresholds))
class_names = list(cfg.project.class_names)
# class_names = projects.get_classes_from_project(cfg)
class_names = np.array(class_names)
extract(rgb,
model,
final_activation=cfg.feature_extractor.final_activation,
thresholds=thresholds,
fusion=cfg.feature_extractor.fusion,
num_rgb=input_images,
latent_name=latent_name,
device=device,
transform=transform,
ignore_error=cfg.inference.ignore_error,
overwrite=cfg.inference.overwrite,
class_names=class_names,
conv_2d=mode == '2d')
# update each record file in the subdirectory to add our new output files
projects.write_all_records(cfg.project.data_path)