def flow_generator_inference(cfg):
# make configuration
cfg = projects.setup_run(cfg)
# turn "models" in your project configuration to "full/path/to/models"
log.info('args: {}'.format(' '.join(sys.argv)))
log.info('configuration used in inference: ')
log.info(OmegaConf.to_yaml(cfg))
if 'sequence' not in cfg.keys() or 'latent_name' not in cfg.sequence.keys(
) or cfg.sequence.latent_name is None:
latent_name = cfg.feature_extractor.arch
else:
latent_name = cfg.sequence.latent_name
log.info('Latent name used in HDF5 file: {}'.format(latent_name))
directory_list = cfg.inference.directory_list
# figure out which videos to run inference on
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')
elif isinstance(directory_list, str):
directory_list = [directory_list]
elif isinstance(directory_list, list):
pass
elif isinstance(directory_list, ListConfig):
directory_list = OmegaConf.to_container(directory_list)
else:
raise ValueError(
'unknown value for directory list: {}'.format(directory_list))
# 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)
rgb = []
for record in records:
rgb.append(record['rgb'])
assert cfg.feature_extractor.n_flows + 1 == cfg.flow_generator.n_rgb, 'Flow generator inputs must be one greater ' \
'than feature extractor num flows '
# set up gpu augmentation
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
cpu_transform = get_cpu_transforms(cfg.augs)['val']
gpu_transform = get_gpu_transforms(cfg.augs, mode)
log.info('gpu_transform: {}'.format(gpu_transform))
flow_generator_weights = projects.get_weightfile_from_cfg(
cfg, 'flow_generator')
assert os.path.isfile(flow_generator_weights)
run_files = get_run_files_from_weights(flow_generator_weights,
'opticalflow')
if cfg.inference.use_loaded_model_cfg:
loaded_config_file = run_files['config_file']
loaded_cfg = OmegaConf.load(loaded_config_file)
loaded_model_cfg = loaded_cfg.flow_generator
current_model_cfg = cfg.flow_generator
model_cfg = OmegaConf.merge(current_model_cfg, loaded_model_cfg)
cfg.flow_generator = model_cfg
# we don't want to use the weights that the trained model was initialized with, but the weights after training
# therefore, overwrite the loaded configuration with the current weights
cfg.flow_generator.weights = flow_generator_weights
# num_classes = len(loaded_cfg.project.class_names)
log.info('model loaded')
# log.warning('Overwriting current project classes with loaded classes! REVERT')
model = build_flow_generator(cfg)
model = utils.load_weights(model, flow_generator_weights, device='cpu')
# _, _, _, _, model = model_components
device = 'cuda:{}'.format(cfg.compute.gpu_id)
model = model.to(device)
movie_format = 'ffmpeg'
maxval = 5
polar = True
save_rgb_side_by_side = True
for movie in tqdm(rgb):
out_video = os.path.splitext(movie)[0] + '_flows'
if movie_format == 'directory':
pass
elif movie_format == 'hdf5':
out_video += '.h5'
elif movie_format == 'ffmpeg':
out_video += '.mp4'
else:
out_video += '.avi'
if os.path.isdir(out_video):
shutil.rmtree(out_video)
elif os.path.isfile(out_video):
os.remove(out_video)
extract_movie(movie,
out_video,
model,
device,
cpu_transform,
gpu_transform,
mean_by_channels=cfg.augs.normalization.mean,
num_workers=1,
num_rgb=input_images,
maxval=maxval,
polar=polar,
movie_format=movie_format,
save_rgb_side_by_side=save_rgb_side_by_side)
def build_model_from_cfg(
cfg: DictConfig,
return_components: bool = False,
pos: np.ndarray = None,
neg: np.ndarray = None) -> Union[Type[nn.Module], tuple]:
""" Builds feature extractor from a configuration object.
Parameters
----------
cfg: DictConfig
configuration, e.g. from Hydra command line
return_components: bool
if True, returns spatial classifier and flow classifier individually
pos: np.ndarray
Number of positive examples in dataset. Used for initializing biases in final layer
neg: np.ndarray
Number of negative examples in dataset. Used for initializing biases in final layer
Returns
-------
if `return_components`:
spatial_classifier, flow_classifier: nn.Module, nn.Module
cnns for classifying rgb images and optic flows
else:
hidden two stream model: nn.Module
hidden two stream CNN
"""
device = torch.device(
"cuda:" +
str(cfg.compute.gpu_id) if torch.cuda.is_available() else "cpu")
feature_extractor_weights = get_weightfile_from_cfg(
cfg, 'feature_extractor')
num_classes = len(cfg.project.class_names)
# if feature_extractor_weights is None:
# # we get the dataloaders here just for the pos and negative example fields of this dictionary. This allows us
# # to build our models with initialization based on the class imbalance of our dataset
# dataloaders = get_dataloaders_from_cfg(cfg, model_type='feature_extractor',
# input_images=cfg.feature_extractor.n_flows + 1)
# else:
# dataloaders = {'pos': None, 'neg': None}
in_channels = cfg.feature_extractor.n_rgb * 3 if '3d' not in cfg.feature_extractor.arch else 3
reload_imagenet = feature_extractor_weights is None
if cfg.feature_extractor.arch == 'resnet3d_34':
assert feature_extractor_weights is not None, 'Must specify path to resnet3d weights!'
spatial_classifier = get_cnn(cfg.feature_extractor.arch,
in_channels=in_channels,
dropout_p=cfg.feature_extractor.dropout_p,
num_classes=num_classes,
reload_imagenet=reload_imagenet,
pos=pos,
neg=neg)
# load this specific component from the weight file
if feature_extractor_weights is not None:
spatial_classifier = utils.load_feature_extractor_components(
spatial_classifier,
feature_extractor_weights,
'spatial',
device=device)
in_channels = cfg.feature_extractor.n_flows * 2 if '3d' not in cfg.feature_extractor.arch else 2
flow_classifier = get_cnn(cfg.feature_extractor.arch,
in_channels=in_channels,
dropout_p=cfg.feature_extractor.dropout_p,
num_classes=num_classes,
reload_imagenet=reload_imagenet,
pos=pos,
neg=neg)
# load this specific component from the weight file
if feature_extractor_weights is not None:
flow_classifier = utils.load_feature_extractor_components(
flow_classifier, feature_extractor_weights, 'flow', device=device)
if return_components:
return spatial_classifier, flow_classifier
flow_generator = build_flow_generator(cfg)
flow_weights = get_weightfile_from_cfg(cfg, 'flow_generator')
assert flow_weights is not None, (
'Must have a valid weightfile for flow generator. Use '
'deepethogram.flow_generator.train or cfg.reload.latest')
flow_generator = utils.load_weights(flow_generator,
flow_weights,
device=device)
model = HiddenTwoStream(flow_generator,
spatial_classifier,
flow_classifier,
cfg.feature_extractor.arch,
fusion_style=cfg.feature_extractor.fusion,
num_classes=num_classes)
model.set_mode('classifier')
return model
def feature_extractor_train(cfg: DictConfig) -> nn.Module:
"""Trains feature extractor models from a configuration.
Parameters
----------
cfg : DictConfig
Configuration, e.g. that returned by deepethogram.configration.make_feature_extractor_train_cfg
Returns
-------
nn.Module
Trained feature extractor
"""
# rundir = os.getcwd()
cfg = projects.setup_run(cfg)
log.info('args: {}'.format(' '.join(sys.argv)))
# change the project paths from relative to absolute
# allow for editing
OmegaConf.set_struct(cfg, False)
# SHOULD NEVER MODIFY / MAKE ASSIGNMENTS TO THE CFG OBJECT AFTER RIGHT HERE!
log.info('configuration used ~~~~~')
log.info(OmegaConf.to_yaml(cfg))
# we build flow generator independently because you might want to load it from a different location
flow_generator = build_flow_generator(cfg)
flow_weights = projects.get_weightfile_from_cfg(cfg, 'flow_generator')
assert flow_weights is not None, (
'Must have a valid weightfile for flow generator. Use '
'deepethogram.flow_generator.train or cfg.reload.latest')
log.info('loading flow generator from file {}'.format(flow_weights))
flow_generator = utils.load_weights(flow_generator, flow_weights)
_, data_info = get_datasets_from_cfg(
cfg,
model_type='feature_extractor',
input_images=cfg.feature_extractor.n_flows + 1)
model_parts = build_model_from_cfg(cfg,
pos=data_info['pos'],
neg=data_info['neg'])
_, spatial_classifier, flow_classifier, fusion, model = model_parts
# log.info('model: {}'.format(model))
num_classes = len(cfg.project.class_names)
utils.save_dict_to_yaml(data_info['split'],
os.path.join(cfg.run.dir, 'split.yaml'))
metrics = get_metrics(
cfg.run.dir,
num_classes=num_classes,
num_parameters=utils.get_num_parameters(spatial_classifier),
key_metric='f1_class_mean_nobg',
num_workers=cfg.compute.metrics_workers)
# cfg.compute.batch_size will be changed by the automatic batch size finder, possibly. store here so that
# with each step of the curriculum, we can auto-tune it
original_batch_size = cfg.compute.batch_size
original_lr = cfg.train.lr
# training in a curriculum goes as follows:
# first, we train the spatial classifier, which takes still images as input
# second, we train the flow classifier, which generates optic flow with the flow_generator model and then classifies
# it. Thirdly, we will train the whole thing end to end
# Without the curriculum we just train end to end from the start
if cfg.feature_extractor.curriculum:
# train spatial model, then flow model, then both end-to-end
# dataloaders = get_dataloaders_from_cfg(cfg, model_type='feature_extractor',
# input_images=cfg.feature_extractor.n_rgb)
datasets, data_info = get_datasets_from_cfg(
cfg,
model_type='feature_extractor',
input_images=cfg.feature_extractor.n_rgb)
stopper = get_stopper(cfg)
criterion = get_criterion(cfg, spatial_classifier, data_info)
lightning_module = HiddenTwoStreamLightning(spatial_classifier, cfg,
datasets, metrics,
criterion)
trainer = get_trainer_from_cfg(cfg, lightning_module, stopper)
# this horrible syntax is because we just changed our configuration's batch size and learning rate, if they are
# set to auto. so we need to re-instantiate our lightning module
# https://pytorch-lightning.readthedocs.io/en/latest/lr_finder.html?highlight=auto%20scale%20learning%20rate
# I tried to do this without re-creating module, but finding the learning rate increments the epoch??
# del lightning_module
# log.info('epoch num: {}'.format(trainer.current_epoch))
# lightning_module = HiddenTwoStreamLightning(spatial_classifier, cfg, datasets, metrics, criterion)
trainer.fit(lightning_module)
# free RAM. note: this doesn't do much
log.info('free ram')
del datasets, lightning_module, trainer, stopper, data_info
torch.cuda.empty_cache()
gc.collect()
# return
datasets, data_info = get_datasets_from_cfg(
cfg,
model_type='feature_extractor',
input_images=cfg.feature_extractor.n_flows + 1)
# re-initialize stopper so that it doesn't think we need to stop due to the previous model
stopper = get_stopper(cfg)
cfg.compute.batch_size = original_batch_size
cfg.train.lr = original_lr
# this class will freeze the flow generator
flow_generator_and_classifier = FlowOnlyClassifier(
flow_generator, flow_classifier)
criterion = get_criterion(cfg, flow_generator_and_classifier,
data_info)
lightning_module = HiddenTwoStreamLightning(
flow_generator_and_classifier, cfg, datasets, metrics, criterion)
trainer = get_trainer_from_cfg(cfg, lightning_module, stopper)
# lightning_module = HiddenTwoStreamLightning(flow_generator_and_classifier, cfg, datasets, metrics, criterion)
trainer.fit(lightning_module)
del datasets, lightning_module, trainer, stopper, data_info
torch.cuda.empty_cache()
gc.collect()
torch.cuda.empty_cache()
gc.collect()
model = HiddenTwoStream(flow_generator, spatial_classifier,
flow_classifier, fusion,
cfg.feature_extractor.arch)
model.set_mode('classifier')
datasets, data_info = get_datasets_from_cfg(
cfg,
model_type='feature_extractor',
input_images=cfg.feature_extractor.n_flows + 1)
criterion = get_criterion(cfg, model, data_info)
stopper = get_stopper(cfg)
cfg.compute.batch_size = original_batch_size
cfg.train.lr = original_lr
# log.warning('SETTING ANAOMALY DETECTION TO TRUE! WILL SLOW DOWN.')
# torch.autograd.set_detect_anomaly(True)
lightning_module = HiddenTwoStreamLightning(model, cfg, datasets, metrics,
criterion)
trainer = get_trainer_from_cfg(cfg, lightning_module, stopper)
# see above for horrible syntax explanation
# lightning_module = HiddenTwoStreamLightning(model, cfg, datasets, metrics, criterion)
trainer.fit(lightning_module)
# trainer.test(model=lightning_module)
return model
def train_from_cfg(cfg: DictConfig) -> Type[nn.Module]:
""" train DeepEthogram feature extractors from a configuration object.
Args:
cfg (DictConfig): configuration object generated by Hydra
Returns:
trained feature extractor
"""
rundir = os.getcwd() # done by hydra
device = torch.device(
"cuda:" +
str(cfg.compute.gpu_id) if torch.cuda.is_available() else "cpu")
if device != 'cpu': torch.cuda.set_device(device)
flow_generator = build_flow_generator(cfg)
flow_weights = get_weightfile_from_cfg(cfg, 'flow_generator')
assert flow_weights is not None, (
'Must have a valid weightfile for flow generator. Use '
'deepethogram.flow_generator.train or cfg.reload.latest')
log.info('loading flow generator from file {}'.format(flow_weights))
flow_generator = utils.load_weights(flow_generator,
flow_weights,
device=device)
flow_generator = flow_generator.to(device)
dataloaders = get_dataloaders_from_cfg(
cfg,
model_type='feature_extractor',
input_images=cfg.feature_extractor.n_flows + 1)
spatial_classifier, flow_classifier = build_model_from_cfg(
cfg,
return_components=True,
pos=dataloaders['pos'],
neg=dataloaders['neg'])
spatial_classifier = spatial_classifier.to(device)
flow_classifier = flow_classifier.to(device)
num_classes = len(cfg.project.class_names)
utils.save_dict_to_yaml(dataloaders['split'],
os.path.join(rundir, 'split.yaml'))
criterion = get_criterion(cfg.feature_extractor.final_activation,
dataloaders, device)
steps_per_epoch = dict(cfg.train.steps_per_epoch)
metrics = get_metrics(
rundir,
num_classes=num_classes,
num_parameters=utils.get_num_parameters(spatial_classifier))
dali = cfg.compute.dali
# training in a curriculum goes as follows:
# first, we train the spatial classifier, which takes still images as input
# second, we train the flow classifier, which generates optic flow with the flow_generator model and then classifies
# it. Thirdly, we will train the whole thing end to end
# Without the curriculum we just train end to end from the start
if cfg.feature_extractor.curriculum:
del dataloaders
# train spatial model, then flow model, then both end-to-end
dataloaders = get_dataloaders_from_cfg(
cfg,
model_type='feature_extractor',
input_images=cfg.feature_extractor.n_rgb)
log.info('Num training batches {}, num val: {}'.format(
len(dataloaders['train']), len(dataloaders['val'])))
# we'll use this to visualize our data, because it is loaded z-scored. we want it to be in the range [0-1] or
# [0-255] for visualization, and for that we need to know mean and std
normalizer = get_normalizer(cfg,
input_images=cfg.feature_extractor.n_rgb)
optimizer = optim.Adam(filter(lambda p: p.requires_grad,
spatial_classifier.parameters()),
lr=cfg.train.lr,
weight_decay=cfg.feature_extractor.weight_decay)
spatialdir = os.path.join(rundir, 'spatial')
if not os.path.isdir(spatialdir):
os.makedirs(spatialdir)
stopper = get_stopper(cfg)
# we're using validation loss as our key metric
scheduler = initialize_scheduler(
optimizer,
cfg,
mode='min',
reduction_factor=cfg.train.reduction_factor)
log.info('key metric: {}'.format(metrics.key_metric))
spatial_classifier = train(
spatial_classifier,
dataloaders,
criterion,
optimizer,
metrics,
scheduler,
spatialdir,
stopper,
device,
steps_per_epoch,
final_activation=cfg.feature_extractor.final_activation,
sequence=False,
normalizer=normalizer,
dali=dali)
log.info('Training flow stream....')
input_images = cfg.feature_extractor.n_flows + 1
del dataloaders
dataloaders = get_dataloaders_from_cfg(cfg,
model_type='feature_extractor',
input_images=input_images)
normalizer = get_normalizer(cfg, input_images=input_images)
log.info('Num training batches {}, num val: {}'.format(
len(dataloaders['train']), len(dataloaders['val'])))
flowdir = os.path.join(rundir, 'flow')
if not os.path.isdir(flowdir):
os.makedirs(flowdir)
flow_generator_and_classifier = FlowOnlyClassifier(
flow_generator, flow_classifier).to(device)
optimizer = optim.Adam(filter(lambda p: p.requires_grad,
flow_classifier.parameters()),
lr=cfg.train.lr,
weight_decay=cfg.feature_extractor.weight_decay)
stopper = get_stopper(cfg)
# we're using validation loss as our key metric
scheduler = initialize_scheduler(
optimizer,
cfg,
mode='min',
reduction_factor=cfg.train.reduction_factor)
flow_generator_and_classifier = train(
flow_generator_and_classifier,
dataloaders,
criterion,
optimizer,
metrics,
scheduler,
flowdir,
stopper,
device,
steps_per_epoch,
final_activation=cfg.feature_extractor.final_activation,
sequence=False,
normalizer=normalizer,
dali=dali)
flow_classifier = flow_generator_and_classifier.flow_classifier
# overwrite checkpoint
utils.checkpoint(flow_classifier, flowdir, stopper.epoch_counter)
model = HiddenTwoStream(flow_generator,
spatial_classifier,
flow_classifier,
cfg.feature_extractor.arch,
fusion_style=cfg.feature_extractor.fusion,
num_classes=num_classes).to(device)
# setting the mode to end-to-end would allow to backprop gradients into the flow generator itself
# the paper does this, but I don't expect that users would have enough data for this to make sense
model.set_mode('classifier')
log.info('Training end to end...')
input_images = cfg.feature_extractor.n_flows + 1
dataloaders = get_dataloaders_from_cfg(cfg,
model_type='feature_extractor',
input_images=input_images)
normalizer = get_normalizer(cfg, input_images=input_images)
optimizer = optim.Adam(filter(lambda p: p.requires_grad,
model.parameters()),
lr=cfg.train.lr,
weight_decay=cfg.feature_extractor.weight_decay)
stopper = get_stopper(cfg)
# we're using validation loss as our key metric
scheduler = initialize_scheduler(
optimizer,
cfg,
mode='min',
reduction_factor=cfg.train.reduction_factor)
log.info('Total trainable params: {:,}'.format(
utils.get_num_parameters(model)))
model = train(model,
dataloaders,
criterion,
optimizer,
metrics,
scheduler,
rundir,
stopper,
device,
steps_per_epoch,
final_activation=cfg.feature_extractor.final_activation,
sequence=False,
normalizer=normalizer,
dali=dali)
utils.save_hidden_two_stream(model, rundir, dict(cfg),
stopper.epoch_counter)
return model
def build_model_from_cfg(cfg: DictConfig,
pos: np.ndarray = None,
neg: np.ndarray = None,
num_classes: int = None) -> tuple:
""" Builds feature extractor from a configuration object.
Parameters
----------
cfg: DictConfig
configuration, e.g. from Hydra command line
return_components: bool
if True, returns spatial classifier and flow classifier individually
pos: np.ndarray
Number of positive examples in dataset. Used for initializing biases in final layer
neg: np.ndarray
Number of negative examples in dataset. Used for initializing biases in final layer
Returns
-------
if `return_components`:
spatial_classifier, flow_classifier: nn.Module, nn.Module
cnns for classifying rgb images and optic flows
else:
hidden two stream model: nn.Module
hidden two stream CNN
"""
# device = torch.device("cuda:" + str(cfg.compute.gpu_id) if torch.cuda.is_available() else "cpu")
device = 'cpu'
feature_extractor_weights = projects.get_weightfile_from_cfg(
cfg, 'feature_extractor')
if num_classes is None:
num_classes = len(cfg.project.class_names)
log.info(
'feature extractor weightfile: {}'.format(feature_extractor_weights))
in_channels = cfg.feature_extractor.n_rgb * 3 if '3d' not in cfg.feature_extractor.arch else 3
reload_imagenet = feature_extractor_weights is None
if cfg.feature_extractor.arch == 'resnet3d_34':
assert feature_extractor_weights is not None, 'Must specify path to resnet3d weights!'
spatial_classifier = get_cnn(cfg.feature_extractor.arch,
in_channels=in_channels,
dropout_p=cfg.feature_extractor.dropout_p,
num_classes=num_classes,
reload_imagenet=reload_imagenet,
pos=pos,
neg=neg,
final_bn=cfg.feature_extractor.final_bn)
# load this specific component from the weight file
if feature_extractor_weights is not None:
spatial_classifier = utils.load_feature_extractor_components(
spatial_classifier,
feature_extractor_weights,
'spatial',
device=device)
in_channels = cfg.feature_extractor.n_flows * 2 if '3d' not in cfg.feature_extractor.arch else 2
flow_classifier = get_cnn(cfg.feature_extractor.arch,
in_channels=in_channels,
dropout_p=cfg.feature_extractor.dropout_p,
num_classes=num_classes,
reload_imagenet=reload_imagenet,
pos=pos,
neg=neg,
final_bn=cfg.feature_extractor.final_bn)
# load this specific component from the weight file
if feature_extractor_weights is not None:
flow_classifier = utils.load_feature_extractor_components(
flow_classifier, feature_extractor_weights, 'flow', device=device)
flow_generator = build_flow_generator(cfg)
flow_weights = projects.get_weightfile_from_cfg(cfg, 'flow_generator')
assert flow_weights is not None, (
'Must have a valid weightfile for flow generator. Use '
'deepethogram.flow_generator.train or cfg.reload.latest')
flow_generator = utils.load_weights(flow_generator,
flow_weights,
device=device)
spatial_classifier, flow_classifier, fusion = build_fusion_layer(
spatial_classifier, flow_classifier, cfg.feature_extractor.fusion,
num_classes)
if feature_extractor_weights is not None:
fusion = utils.load_feature_extractor_components(
fusion, feature_extractor_weights, 'fusion', device=device)
model = HiddenTwoStream(flow_generator, spatial_classifier,
flow_classifier, fusion,
cfg.feature_extractor.arch)
# log.info(model.fusion.flow_weight)
model.set_mode('classifier')
return flow_generator, spatial_classifier, flow_classifier, fusion, model