def setup_logging(output_dir=None):
"""
Sets up the logging for multiple processes. Only enable the logging for the
master process, and suppress logging for the non-master processes.
"""
# Set up logging format.
_FORMAT = "[%(levelname)s: %(filename)s: %(lineno)4d]: %(message)s"
if du.is_master_proc():
# Enable logging for the master process.
logging.root.handlers = []
else:
# Suppress logging for non-master processes.
_suppress_print()
logger = logging.getLogger()
logger.setLevel(logging.INFO)
logger.propagate = False
plain_formatter = logging.Formatter(
"[%(asctime)s][%(levelname)s] %(filename)s: %(lineno)3d: %(message)s",
datefmt="%m/%d %H:%M:%S",
)
if du.is_master_proc():
ch = logging.StreamHandler(stream=sys.stdout)
ch.setLevel(logging.DEBUG)
ch.setFormatter(plain_formatter)
logger.addHandler(ch)
if output_dir is not None and du.is_master_proc(du.get_world_size()):
filename = os.path.join(output_dir, "stdout.log")
fh = logging.StreamHandler(_cached_log_stream(filename))
fh.setLevel(logging.DEBUG)
fh.setFormatter(plain_formatter)
logger.addHandler(fh)
def test(cfg):
"""
Perform multi-view testing on the pretrained video model.
Args:
cfg (CfgNode): configs. Details can be found in
slowfast/config/defaults.py
"""
# Set up environment.
du.init_distributed_training(cfg)
# Set random seed from configs.
np.random.seed(cfg.RNG_SEED)
torch.manual_seed(cfg.RNG_SEED)
# Setup logging format.
logging.setup_logging(cfg.OUTPUT_DIR)
# Print config.
logger.info("Test with config:")
logger.info(cfg)
# Build the video model and print model statistics.
model = build_model(cfg)
if du.is_master_proc() and cfg.LOG_MODEL_INFO:
misc.log_model_info(model, cfg, use_train_input=False)
cu.load_test_checkpoint(cfg, model)
# Create video testing loaders.
test_loader = loader.construct_loader(cfg, "test")
logger.info("Testing model for {} iterations".format(len(test_loader)))
if cfg.DETECTION.ENABLE:
assert cfg.NUM_GPUS == cfg.TEST.BATCH_SIZE or cfg.NUM_GPUS == 0
test_meter = AVAMeter(len(test_loader), cfg, mode="test")
else:
assert (
test_loader.dataset.num_videos %
(cfg.TEST.NUM_ENSEMBLE_VIEWS * cfg.TEST.NUM_SPATIAL_CROPS) == 0)
# Create meters for multi-view testing.
test_meter = TestMeter(
test_loader.dataset.num_videos //
(cfg.TEST.NUM_ENSEMBLE_VIEWS * cfg.TEST.NUM_SPATIAL_CROPS),
cfg.TEST.NUM_ENSEMBLE_VIEWS * cfg.TEST.NUM_SPATIAL_CROPS,
cfg.MODEL.NUM_CLASSES,
len(test_loader),
cfg.DATA.MULTI_LABEL,
cfg.DATA.ENSEMBLE_METHOD,
)
# Set up writer for logging to Tensorboard format.
if cfg.TENSORBOARD.ENABLE and du.is_master_proc(
cfg.NUM_GPUS * cfg.NUM_SHARDS):
writer = tb.TensorboardWriter(cfg)
else:
writer = None
# # Perform multi-view test on the entire dataset.
test_meter = perform_test(test_loader, model, test_meter, cfg, writer)
if writer is not None:
writer.close()
def visualize(cfg):
"""
Perform layer weights and activations visualization on the model.
Args:
cfg (CfgNode): configs. Details can be found in
slowfast/config/defaults.py
"""
if cfg.TENSORBOARD.ENABLE and cfg.TENSORBOARD.MODEL_VIS.ENABLE:
# Set up environment.
du.init_distributed_training(cfg)
# Set random seed from configs.
np.random.seed(cfg.RNG_SEED)
torch.manual_seed(cfg.RNG_SEED)
# Setup logging format.
logging.setup_logging(cfg.OUTPUT_DIR)
# Print config.
logger.info("Model Visualization with config:")
logger.info(cfg)
# Build the video model and print model statistics.
model = build_model(cfg)
if du.is_master_proc() and cfg.LOG_MODEL_INFO:
misc.log_model_info(model, cfg, is_train=False)
cu.load_test_checkpoint(cfg, model)
# Create video testing loaders.
vis_loader = loader.construct_loader(cfg, "test")
logger.info(
"Visualize model for {} data points".format(len(vis_loader))
)
if cfg.DETECTION.ENABLE:
assert cfg.NUM_GPUS == cfg.TEST.BATCH_SIZE
# Set up writer for logging to Tensorboard format.
if du.is_master_proc(cfg.NUM_GPUS * cfg.NUM_SHARDS):
writer = tb.TensorboardWriter(cfg)
else:
writer = None
# Run visualization on the model
run_visualization(vis_loader, model, cfg, writer)
if writer is not None:
writer.close()
def __init__(self, max_iter, cfg, tblogger):
"""
Args:
max_iter (int): the max number of iteration of the current epoch.
cfg (CfgNode): configs.
tblogger (SummaryWriter): training tblogger FIXME
"""
self._cfg = cfg
self.max_iter = max_iter
self.iter_timer = Timer()
# Current minibatch errors (smoothed over a window).
self.mb_top1_err = ScalarMeter(cfg.LOGS.PERIOD)
self.mb_top5_err = ScalarMeter(cfg.LOGS.PERIOD)
# Min errors (over the full val set).
self.min_top1_err = 100.0
self.min_top5_err = 100.0
# Number of misclassified examples.
self.num_top1_mis = 0
self.num_top5_mis = 0
self.num_samples = 0
# tblogger
if du.is_master_proc():
self.tblogger = tblogger
self.all_preds = []
self.all_labels = []
def save_checkpoint(path_to_job,
model,
optimizer,
epoch,
cfg,
is_best_epoch=False):
"""
Save a checkpoint.
Args:
model (model): model to save the weight to the checkpoint.
optimizer (optim): optimizer to save the historical state.
epoch (int): current number of epoch of the model.
cfg (CfgNode): configs to save.
"""
# Save checkpoints only from the master process.
if not du.is_master_proc(cfg.NUM_GPUS * cfg.NUM_SHARDS):
return
# Ensure that the checkpoint dir exists.
os.makedirs(get_checkpoint_dir(path_to_job), exist_ok=True)
# Omit the DDP wrapper in the multi-gpu setting.
sd = model.module.state_dict() if cfg.NUM_GPUS > 1 else model.state_dict()
# Record the state.
checkpoint = {
"epoch": epoch,
"model_state": sd,
"optimizer_state": optimizer.state_dict(),
"cfg": cfg.dump(),
}
# Write the checkpoint.
path_to_checkpoint = get_path_to_checkpoint(path_to_job, epoch + 1,
is_best_epoch)
torch.save(checkpoint, path_to_checkpoint)
return path_to_checkpoint
def __init__(self, epoch_iters, cfg):
"""
Args:
epoch_iters (int): the overall number of iterations of one epoch.
cfg (CfgNode): configs.
"""
self._cfg = cfg
self.epoch_iters = epoch_iters
self.MAX_EPOCH = cfg.SOLVER.MAX_EPOCH * epoch_iters
self.iter_timer = Timer()
self.loss = ScalarMeter(cfg.LOGS.PERIOD)
self.loss_total = 0.0
self.lr = None
# Current minibatch errors (smoothed over a window).
self.mb_top1_err = ScalarMeter(cfg.LOGS.PERIOD)
self.mb_top5_err = ScalarMeter(cfg.LOGS.PERIOD)
# Number of misclassified examples.
self.num_top1_mis = 0
self.num_top5_mis = 0
self.num_samples = 0
# tblogger
if du.is_master_proc():
self.tblogger = SummaryWriter(self._cfg.LOGS.DIR)
else:
self.tblogger = None
def inference(cfg):
# # Set up environment.
du.init_distributed_training(cfg)
# Set random seed from configs.
np.random.seed(cfg.RNG_SEED)
torch.manual_seed(cfg.RNG_SEED)
# Build the video model and print model statistics.
model = build_model(cfg)
if du.is_master_proc() and cfg.LOG_MODEL_INFO:
misc.log_model_info(model, cfg, use_train_input=False)
cu.load_test_checkpoint(cfg, model)
# Create video loaders.
video_loader = loader.construct_loader(cfg, "test")
# Create saver
saver = Saver(cfg.DATA.PATH_TO_DATA_DIR, video_loader.dataset)
model.eval()
for i, (inputs, index) in tqdm(enumerate(video_loader),
total=len(video_loader)):
for i in range(len(inputs)):
inputs[i] = inputs[i].cuda(non_blocking=True)
index = index.cuda()
feats = model(inputs)
# Gather all the predictions across all the devices to perform ensemble.
if cfg.NUM_GPUS > 1:
feats, index = du.all_gather([feats, index])
saver.save(feats, index)
saver.merge()
def log_epoch_stats(self, cur_epoch):
"""
Log the stats of the current epoch.
Args:
cur_epoch (int): the number of current epoch.
"""
stats = {
"_type": "val_epoch",
"epoch": "{}/{}".format(cur_epoch + 1, self._cfg.SOLVER.MAX_EPOCH),
"time_diff": self.iter_timer.seconds(),
"gpu_mem": "{:.2f} GB".format(misc.gpu_mem_usage()),
"RAM": "{:.2f}/{:.2f} GB".format(*misc.cpu_mem_usage()),
}
if self._cfg.DATA.MULTI_LABEL:
stats["map"] = get_map(
torch.cat(self.all_preds).cpu().numpy(),
torch.cat(self.all_labels).cpu().numpy(),
)
else:
top1_err = self.num_top1_mis / self.num_samples
top5_err = self.num_top5_mis / self.num_samples
self.min_top1_err = min(self.min_top1_err, top1_err)
self.min_top5_err = min(self.min_top5_err, top5_err)
stats["top1_err"] = top1_err
stats["top5_err"] = top5_err
stats["min_top1_err"] = self.min_top1_err
stats["min_top5_err"] = self.min_top5_err
logging.log_json_stats(stats)
if du.is_master_proc():
for k, v in stats.items():
if 'err' in k or 'map' in k:
self.tblogger.add_scalar('val/{}'.format(k), v,
cur_epoch + 1)
def save_checkpoint(path_to_job, model, optimizer, epoch, cfg):
"""
Save a checkpoint.
Args:
model (model): model to save the weight to the checkpoint.
optimizer (optim): optimizer to save the historical state.
epoch (int): current number of epoch of the model.
cfg (CfgNode): configs to save.
"""
# Save checkpoints only from the master process.
if not du.is_master_proc(cfg.NUM_GPUS * cfg.NUM_SHARDS):
return
# Ensure that the checkpoint dir exists.
PathManager.mkdirs(get_checkpoint_dir(path_to_job))
# Omit the DDP wrapper in the multi-gpu setting.
sd = model.module.state_dict() if cfg.NUM_GPUS > 1 else model.state_dict()
normalized_sd = sub_to_normal_bn(sd)
# Record the state.
checkpoint = {
"epoch": epoch,
"model_state": normalized_sd,
"optimizer_state": optimizer.state_dict(),
"cfg": cfg.dump(),
}
# Write the checkpoint.
path_to_checkpoint = get_path_to_checkpoint(path_to_job, epoch + 1)
with PathManager.open(path_to_checkpoint, "wb") as f:
torch.save(checkpoint, f)
return path_to_checkpoint
def test_from_train(model, cfg, cnt=-1):
test_loader = loader.construct_loader(cfg, "test")
logger.info("Testing model for {} iterations".format(len(test_loader)))
if cfg.DETECTION.ENABLE:
assert cfg.NUM_GPUS == cfg.TEST.BATCH_SIZE
test_meter = AVAMeter(len(test_loader), cfg, mode="test")
else:
assert (
len(test_loader.dataset)
% (cfg.TEST.NUM_ENSEMBLE_VIEWS * cfg.TEST.NUM_SPATIAL_CROPS)
== 0
)
# Create meters for multi-view testing.
if cfg.TEST.DATASET == 'epickitchens':
test_meter = EPICTestMeter(
len(test_loader.dataset)
// (cfg.TEST.NUM_ENSEMBLE_VIEWS * cfg.TEST.NUM_SPATIAL_CROPS),
cfg.TEST.NUM_ENSEMBLE_VIEWS * cfg.TEST.NUM_SPATIAL_CROPS,
cfg.MODEL.NUM_CLASSES,
len(test_loader),
)
else:
test_meter = TestMeter(
len(test_loader.dataset)
// (cfg.TEST.NUM_ENSEMBLE_VIEWS * cfg.TEST.NUM_SPATIAL_CROPS),
cfg.TEST.NUM_ENSEMBLE_VIEWS * cfg.TEST.NUM_SPATIAL_CROPS,
cfg.MODEL.NUM_CLASSES,
len(test_loader),
)
# # Perform multi-view test on the entire dataset.
scores_path = os.path.join(cfg.OUTPUT_DIR, 'scores')
if not os.path.exists(scores_path):
os.makedirs(scores_path)
filename_root = cfg.EPICKITCHENS.TEST_LIST.split('.')[0]
if cnt >= 0:
file_name = '{}_{}_{}.pkl'.format(filename_root, cnt, cfg.MODEL.MODEL_NAME)
else:
file_name = '{}_{}_{}.pkl'.format(filename_root, 'test_only', cfg.MODEL.MODEL_NAME)
file_path = os.path.join(scores_path, file_name)
logger.info(file_path)
pickle.dump([], open(file_path, 'wb+'))
preds, labels, metadata = perform_test(test_loader, model, test_meter, cfg)
if du.is_master_proc():
if cfg.TEST.DATASET == 'epickitchens':
results = {'verb_output': preds[0],
'noun_output': preds[1],
'verb_gt': labels[0],
'noun_gt': labels[1],
'narration_id': metadata}
scores_path = os.path.join(cfg.OUTPUT_DIR, 'scores')
if not os.path.exists(scores_path):
os.makedirs(scores_path)
pickle.dump(results, open(file_path, 'wb'))
def make_checkpoint_dir(path_to_job):
"""
Creates the checkpoint directory (if not present already).
Args:
path_to_job (string): the path to the folder of the current job.
"""
checkpoint_dir = os.path.join(path_to_job, "checkpoints")
# Create the checkpoint dir from the master process
if du.is_master_proc() and not os.path.exists(checkpoint_dir):
try:
os.makedirs(checkpoint_dir)
except Exception:
pass
return checkpoint_dir
def log_json_stats(stats):
"""
Logs json stats.
Args:
stats (dict): a dictionary of statistical information to log.
"""
stats = {
k: decimal.Decimal("{:.6f}".format(v)) if isinstance(v, float) else v
for k, v in stats.items()
}
# json_stats = simplejson.dumps(stats, sort_keys=True, use_decimal=True)
logstr = "; ".join(["{}: {}".format(k, v) for k, v in stats.items()])
if du.is_master_proc():
logger = get_logger(__name__)
logger.info(logstr)
def run_evaluation(categories, groundtruth, detections, verbose=True):
"""AVA evaluation main logic."""
pascal_evaluator = object_detection_evaluation.PascalDetectionEvaluator(
categories)
boxes, labels, _ = groundtruth
gt_keys = []
pred_keys = []
for image_key in boxes:
pascal_evaluator.add_single_ground_truth_image_info(
image_key,
{
standard_fields.InputDataFields.groundtruth_boxes:
np.array(boxes[image_key], dtype=float),
standard_fields.InputDataFields.groundtruth_classes:
np.array(labels[image_key], dtype=int),
standard_fields.InputDataFields.groundtruth_difficult:
np.zeros(len(boxes[image_key]), dtype=bool),
},
)
gt_keys.append(image_key)
boxes, labels, scores = detections
for image_key in boxes:
pascal_evaluator.add_single_detected_image_info(
image_key,
{
standard_fields.DetectionResultFields.detection_boxes:
np.array(boxes[image_key], dtype=float),
standard_fields.DetectionResultFields.detection_classes:
np.array(labels[image_key], dtype=int),
standard_fields.DetectionResultFields.detection_scores:
np.array(scores[image_key], dtype=float),
},
)
pred_keys.append(image_key)
metrics = pascal_evaluator.evaluate()
if du.is_master_proc():
pprint.pprint(metrics, indent=2)
return metrics
def setup_logging():
"""
Sets up the logging for multiple processes. Only enable the logging for the
master process, and suppress logging for the non-master processes.
"""
# Set up logging format.
_FORMAT = "[%(levelname)s: %(filename)s: %(lineno)4d]: %(message)s"
if du.is_master_proc():
# Enable logging for the master process.
logging.root.handlers = []
logging.basicConfig(level=logging.INFO,
format=_FORMAT,
stream=sys.stdout)
else:
# Suppress logging for non-master processes.
_suppress_print()
def plotStats(self, stats, ite, typ):
if du.is_master_proc() and stats is not None:
for k, v in stats.items():
try:
val = float(v)
nme = "{}_{}".format(typ, k)
maxx = self.cfg.METRICS.PLOT_MAX_LIMITS.get(k, None)
val = min(val, maxx) if maxx is not None else val
minn = self.cfg.METRICS.PLOT_MIN_LIMITS.get(k, None)
val = max(val, minn) if minn is not None else val
self.logger.log_row(
name=nme,
iter=ite,
val=val,
description="{} master proc".format(nme))
except ValueError:
pass
def build_trainer(cfg):
"""
Build training model and its associated tools, including optimizer,
dataloaders and meters.
Args:
cfg (CfgNode): configs. Details can be found in
slowfast/config/defaults.py
Returns:
model (nn.Module): training model.
optimizer (Optimizer): optimizer.
train_loader (DataLoader): training data loader.
val_loader (DataLoader): validatoin data loader.
precise_bn_loader (DataLoader): training data loader for computing
precise BN.
train_meter (TrainMeter): tool for measuring training stats.
val_meter (ValMeter): tool for measuring validation stats.
"""
# Build the video model and print model statistics.
model = build_model(cfg)
if du.is_master_proc() and cfg.LOG_MODEL_INFO:
misc.log_model_info(model, cfg, use_train_input=True)
# Construct the optimizer.
optimizer = optim.construct_optimizer(model, cfg)
# Create the video train and val loaders.
train_loader = loader.construct_loader(cfg, "train")
val_loader = loader.construct_loader(cfg, "val")
precise_bn_loader = loader.construct_loader(cfg,
"train",
is_precise_bn=True)
# Create meters.
train_meter = TrainMeter(len(train_loader), cfg)
val_meter = ValMeter(len(val_loader), cfg)
return (
model,
optimizer,
train_loader,
val_loader,
precise_bn_loader,
train_meter,
val_meter,
)
def test(cfg):
"""
Perform multi-view testing on the pretrained video model.
Args:
cfg (CfgNode): configs. Details can be found in
slowfast/config/defaults.py
"""
# Set up environment.
du.init_distributed_training(cfg)
# Set random seed from configs.
np.random.seed(cfg.RNG_SEED)
torch.manual_seed(cfg.RNG_SEED)
# Setup logging format.
logging.setup_logging(cfg.OUTPUT_DIR)
# Print config.
logger.info("Test with config:")
logger.info(cfg)
# Build the video model and print model statistics.
model = build_model(cfg)
cu.load_test_checkpoint(cfg, model)
# Create video testing loaders.
test_loader = loader.construct_loader(cfg, "test")
logger.info("Testing model for {} iterations".format(len(test_loader)))
# Create meters for loss tracking
test_meter = TrainMeter(test_loader.dataset.num_videos, cfg)
# Set up writer for logging to Tensorboard format.
if cfg.TENSORBOARD.ENABLE and du.is_master_proc(
cfg.NUM_GPUS * cfg.NUM_SHARDS
):
writer = tb.TensorboardWriter(cfg)
else:
writer = None
# # Perform multi-view test on the entire dataset.
test_meter = perform_test(test_loader, model, test_meter, cfg, writer)
if writer is not None:
writer.close()
def experiment(cfg):
# Setup logging format.
logging.setup_logging()
# Print config.
logger.info("Infer with config:")
logger.info(cfg)
# Build the SlowFast model and print its statistics
model = build_model(cfg)
if du.is_master_proc():
misc.log_model_info(model, cfg, is_train=False)
# load weights
if cfg.INFERENCE.WEIGHTS_FILE_PATH != "":
cu.load_checkpoint(cfg.INFERENCE.WEIGHTS_FILE_PATH, model, cfg.NUM_GPUS > 1, None,
inflation=False, convert_from_caffe2=cfg.INFERENCE.WEIGHTS_TYPE == "caffe2")
else:
raise FileNotFoundError("Model weights file could not be found")
perform_inference(model, cfg)
def log_iter_stats(self, cur_epoch, cur_iter):
"""
log the stats of the current iteration.
Args:
cur_epoch (int): the number of current epoch.
cur_iter (int): the number of current iteration.
"""
if (cur_iter + 1) % self._cfg.LOGS.PERIOD != 0:
return
eta_sec = self.iter_timer.seconds() * (
self.MAX_EPOCH - (cur_epoch * self.epoch_iters + cur_iter + 1))
eta = str(datetime.timedelta(seconds=int(eta_sec)))
stats = {
"_type": "train_iter",
"epoch": "{}/{}".format(cur_epoch + 1, self._cfg.SOLVER.MAX_EPOCH),
"iter": "{}/{}".format(cur_iter + 1, self.epoch_iters),
"time_diff": self.iter_timer.seconds(),
"eta": eta,
"loss": self.loss.get_win_median(),
"lr": self.lr,
"gpu_mem": "{:.2f} GB".format(misc.gpu_mem_usage()),
}
if not self._cfg.DATA.MULTI_LABEL:
stats["top1_err"] = self.mb_top1_err.get_win_median()
stats["top5_err"] = self.mb_top5_err.get_win_median()
logging.log_json_stats(stats)
if du.is_master_proc():
iters = cur_iter + 1 + self.epoch_iters * cur_epoch
for k, v in stats.items():
if 'err' in k or 'loss' in k:
self.tblogger.add_scalar('train/{}'.format(k), v, iters)
elif k == 'eta':
self.tblogger.add_scalar('other/eta', eta_sec, iters)
elif k == 'epoch':
self.tblogger.add_scalar('other/epoch', cur_epoch + 1,
iters)
elif k == 'lr':
self.tblogger.add_scalar('other/lr', v, iters)
else:
continue
def setup_logger(save_dir):
logger = logging.getLogger('slowfast')
logger.setLevel(logging.DEBUG)
logger.propogate = False
# don't log results for the non-master process
if not du.is_master_proc():
_suppress_print()
return None
formatter = logging.Formatter(
"%(asctime)s [%(levelname)s: %(filename)s: %(lineno)4d]: %(message)s")
ch = logging.StreamHandler(stream=sys.stdout)
ch.setLevel(logging.INFO)
ch.setFormatter(formatter)
logger.addHandler(ch)
filename = os.path.join(save_dir, '{}.log'.format(datetime.now()))
fh = logging.FileHandler(filename)
fh.setLevel(logging.DEBUG)
fh.setFormatter(formatter)
logger.addHandler(fh)
return logger
def test(cfg, cnt=-1):
"""
Perform multi-view testing on the pretrained video model.
Args:
cfg (CfgNode): configs. Details can be found in
slowfast/config/defaults.py
"""
# Set random seed from configs.
np.random.seed(cfg.RNG_SEED)
torch.manual_seed(cfg.RNG_SEED)
# Setup logging format.
logging.setup_logging()
# # Perform multi-view test on the entire dataset.
scores_path = os.path.join(cfg.OUTPUT_DIR, 'scores')
if not os.path.exists(scores_path):
os.makedirs(scores_path)
filename_root = cfg.EPICKITCHENS.TEST_LIST.split('.')[0]
if cnt >= 0:
file_name = '{}_{}_{}.pkl'.format(filename_root, cnt, cfg.MODEL.MODEL_NAME)
else:
file_name = '{}_{}_{}.pkl'.format(filename_root, 'test_only', cfg.MODEL.MODEL_NAME)
file_path = os.path.join(scores_path, file_name)
logger.info(file_path)
# Print config.
# if cnt < 0:
# logger.info("Test with config:")
# logger.info(cfg)
# Build the video model and print model statistics.
model = build_model(cfg)
if cfg.EPICKITCHENS.USE_BBOX:
model.module.load_weight_slowfast()
# if du.is_master_proc():
# misc.log_model_info(model, cfg, is_train=False)
# Load a checkpoint to test if applicable.
if cfg.TEST.CHECKPOINT_FILE_PATH != "":
logger.info("Load from given checkpoint file.")
cu.load_checkpoint(
cfg.TEST.CHECKPOINT_FILE_PATH,
model,
cfg.NUM_GPUS > 1,
None,
inflation=False,
convert_from_caffe2=cfg.TEST.CHECKPOINT_TYPE == "caffe2",
)
elif cu.has_checkpoint(cfg.OUTPUT_DIR):
logger.info("Load from last checkpoint.")
last_checkpoint = cu.get_last_checkpoint(cfg.OUTPUT_DIR)
cu.load_checkpoint(last_checkpoint, model, cfg.NUM_GPUS > 1)
elif cfg.TRAIN.CHECKPOINT_FILE_PATH != "":
# If no checkpoint found in TEST.CHECKPOINT_FILE_PATH or in the current
# checkpoint folder, try to load checkpint from
# TRAIN.CHECKPOINT_FILE_PATH and test it.
cu.load_checkpoint(
cfg.TRAIN.CHECKPOINT_FILE_PATH,
model,
cfg.NUM_GPUS > 1,
None,
inflation=False,
convert_from_caffe2=cfg.TRAIN.CHECKPOINT_TYPE == "caffe2",
)
else:
# raise NotImplementedError("Unknown way to load checkpoint.")
logger.info("Testing with random initialization. Only for debugging.")
# Create video testing loaders.
if cfg.TEST.EXTRACT_FEATURES_MODE != "" and cfg.TEST.EXTRACT_FEATURES_MODE in ["test","train","val"]:
test_loader = loader.construct_loader(cfg, cfg.TEST.EXTRACT_FEATURES_MODE)
else:
test_loader = loader.construct_loader(cfg, "test")
logger.info("Testing model for {} iterations".format(len(test_loader)))
if cfg.DETECTION.ENABLE:
assert cfg.NUM_GPUS == cfg.TEST.BATCH_SIZE
test_meter = AVAMeter(len(test_loader), cfg, mode="test")
else:
assert (
len(test_loader.dataset)
% (cfg.TEST.NUM_ENSEMBLE_VIEWS * cfg.TEST.NUM_SPATIAL_CROPS)
== 0
)
# Create meters for multi-view testing.
if cfg.TEST.DATASET == 'epickitchens':
test_meter = EPICTestMeter(
len(test_loader.dataset)
// (cfg.TEST.NUM_ENSEMBLE_VIEWS * cfg.TEST.NUM_SPATIAL_CROPS),
cfg.TEST.NUM_ENSEMBLE_VIEWS * cfg.TEST.NUM_SPATIAL_CROPS,
cfg.MODEL.NUM_CLASSES,
len(test_loader),
)
else:
test_meter = TestMeter(
len(test_loader.dataset)
// (cfg.TEST.NUM_ENSEMBLE_VIEWS * cfg.TEST.NUM_SPATIAL_CROPS),
cfg.TEST.NUM_ENSEMBLE_VIEWS * cfg.TEST.NUM_SPATIAL_CROPS,
cfg.MODEL.NUM_CLASSES,
len(test_loader),
)
pickle.dump([], open(file_path, 'wb+'))
if cfg.TEST.EXTRACT_FEATURES:
preds, labels, metadata, x_feat_list = perform_test(test_loader, model, test_meter, cfg)
else:
preds, labels, metadata = perform_test(test_loader, model, test_meter, cfg)
if du.is_master_proc():
if cfg.TEST.DATASET == 'epickitchens':
results = {'verb_output': preds[0],
'noun_output': preds[1],
'verb_gt': labels[0],
'noun_gt': labels[1],
'narration_id': metadata}
scores_path = os.path.join(cfg.OUTPUT_DIR, 'scores')
if not os.path.exists(scores_path):
os.makedirs(scores_path)
pickle.dump(results, open(file_path, 'wb'))
if cfg.TEST.EXTRACT_FEATURES:
pid = cfg.EPICKITCHENS.FEATURE_VID.split("_")[0]
if not os.path.exists(os.path.join(cfg.TEST.EXTRACT_FEATURES_PATH, pid)):
os.mkdir(os.path.join(cfg.TEST.EXTRACT_FEATURES_PATH, pid))
if not cfg.TEST.EXTRACT_MSTCN_FEATURES and cfg.TEST.EXTRACT_FEATURES:
arr_slow = torch.cat(x_feat_list[0], dim=0).numpy()
arr_fast = torch.cat(x_feat_list[1], dim=0).numpy()
print(arr_slow.shape, arr_fast.shape)
fpath_feat = os.path.join(cfg.TEST.EXTRACT_FEATURES_PATH, pid, '{}.pkl'.format(cfg.EPICKITCHENS.FEATURE_VID))
with open(fpath_feat,'wb+') as f:
pickle.dump([arr_slow, arr_fast], f)
elif cfg.TEST.EXTRACT_MSTCN_FEATURES and cfg.TEST.EXTRACT_FEATURES:
fpath_feat = os.path.join(cfg.TEST.EXTRACT_FEATURES_PATH, pid, '{}.npy'.format(cfg.EPICKITCHENS.FEATURE_VID))
with open(fpath_feat,'wb+') as f:
arr = torch.cat(x_feat_list, dim=0).numpy()
print(arr.shape)
np.save(f, arr)
def visualize(cfg):
"""
Perform layer weights and activations visualization on the model.
Args:
cfg (CfgNode): configs. Details can be found in
slowfast/config/defaults.py
"""
if cfg.TENSORBOARD.ENABLE and (cfg.TENSORBOARD.MODEL_VIS.ENABLE
or cfg.TENSORBOARD.WRONG_PRED_VIS.ENABLE):
# Set up environment.
du.init_distributed_training(cfg)
# Set random seed from configs.
np.random.seed(cfg.RNG_SEED)
torch.manual_seed(cfg.RNG_SEED)
# Setup logging format.
logging.setup_logging(cfg.OUTPUT_DIR)
# Print config.
logger.info("Model Visualization with config:")
logger.info(cfg)
# Build the video model and print model statistics.
model = build_model(cfg)
model.eval()
if du.is_master_proc() and cfg.LOG_MODEL_INFO:
misc.log_model_info(model, cfg, use_train_input=False)
cu.load_test_checkpoint(cfg, model)
# Create video testing loaders.
vis_loader = loader.construct_loader(cfg, "test")
if cfg.DETECTION.ENABLE:
assert cfg.NUM_GPUS == cfg.TEST.BATCH_SIZE or cfg.NUM_GPUS == 0
# Set up writer for logging to Tensorboard format.
if du.is_master_proc(cfg.NUM_GPUS * cfg.NUM_SHARDS):
writer = tb.TensorboardWriter(cfg)
else:
writer = None
if cfg.TENSORBOARD.PREDICTIONS_PATH != "":
assert not cfg.DETECTION.ENABLE, "Detection is not supported."
logger.info(
"Visualizing class-level performance from saved results...")
if writer is not None:
with g_pathmgr.open(cfg.TENSORBOARD.PREDICTIONS_PATH,
"rb") as f:
preds, labels = pickle.load(f, encoding="latin1")
writer.plot_eval(preds, labels)
if cfg.TENSORBOARD.MODEL_VIS.ENABLE:
if cfg.TENSORBOARD.MODEL_VIS.GRAD_CAM.ENABLE:
assert (
not cfg.DETECTION.ENABLE
), "Detection task is currently not supported for Grad-CAM visualization."
if cfg.MODEL.ARCH in cfg.MODEL.SINGLE_PATHWAY_ARCH:
assert (
len(cfg.TENSORBOARD.MODEL_VIS.GRAD_CAM.LAYER_LIST) == 1
), "The number of chosen CNN layers must be equal to the number of pathway(s), given {} layer(s).".format(
len(cfg.TENSORBOARD.MODEL_VIS.GRAD_CAM.LAYER_LIST))
elif cfg.MODEL.ARCH in cfg.MODEL.MULTI_PATHWAY_ARCH:
assert (
len(cfg.TENSORBOARD.MODEL_VIS.GRAD_CAM.LAYER_LIST) == 2
), "The number of chosen CNN layers must be equal to the number of pathway(s), given {} layer(s).".format(
len(cfg.TENSORBOARD.MODEL_VIS.GRAD_CAM.LAYER_LIST))
else:
raise NotImplementedError(
"Model arch {} is not in {}".format(
cfg.MODEL.ARCH,
cfg.MODEL.SINGLE_PATHWAY_ARCH +
cfg.MODEL.MULTI_PATHWAY_ARCH,
))
logger.info("Visualize model analysis for {} iterations".format(
len(vis_loader)))
# Run visualization on the model
run_visualization(vis_loader, model, cfg, writer)
if cfg.TENSORBOARD.WRONG_PRED_VIS.ENABLE:
logger.info("Visualize Wrong Predictions for {} iterations".format(
len(vis_loader)))
perform_wrong_prediction_vis(vis_loader, model, cfg)
if writer is not None:
writer.close()
def train(cfg):
"""
Train a video model for many epochs on train set and evaluate it on val set.
Args:
cfg (CfgNode): configs. Details can be found in
slowfast/config/defaults.py
"""
# Set random seed from configs.
np.random.seed(cfg.RNG_SEED)
torch.manual_seed(cfg.RNG_SEED)
# Setup logging format.
logging.setup_logging()
if du.is_master_proc():
if cfg.ENABLE_WANDB:
wandb.login()
wandb.init(project='bbox', entity='slowfast')
# Print config.
# logger.info("Train with config:")
# logger.info(pprint.pformat(cfg))
# Build the video model and print model statistics.
model = build_model(cfg)
if cfg.EPICKITCHENS.USE_BBOX and not cu.has_checkpoint(cfg.OUTPUT_DIR):
slow_fast_model = SlowFast(cfg)
if cfg.EPICKITCHENS.USE_BBOX and cfg.EPICKITCHENS.LOAD_SLOWFAST_PRETRAIN:
if cfg.EPICKITCHENS.SLOWFAST_PRETRAIN_CHECKPOINT_FILE_PATH != "":
_ = cu.load_checkpoint(
cfg.EPICKITCHENS.SLOWFAST_PRETRAIN_CHECKPOINT_FILE_PATH,
slow_fast_model,
False,
optimizer=None,
inflation=cfg.TRAIN.CHECKPOINT_INFLATE,
convert_from_caffe2=False,
)
# cfg.TRAIN.CHECKPOINT_TYPE == "caffe2"
logger.info("Load from slowfast.")
if cfg.NUM_GPUS > 1:
model.module.load_weight_slowfast(slow_fast_model)
else:
model.load_weight_slowfast(slow_fast_model)
# if du.is_master_proc():
# misc.log_model_info(model, cfg, is_train=True)
if cfg.BN.FREEZE:
model.freeze_fn('bn_parameters')
if cfg.EPICKITCHENS.USE_BBOX and cfg.EPICKITCHENS.FREEZE_BACKBONE:
model.freeze_fn('slowfast_bbox')
# Construct the optimizer.
optimizer = optim.construct_optimizer(model, cfg)
# Load a checkpoint to resume training if applicable.
if cfg.TRAIN.AUTO_RESUME and cu.has_checkpoint(cfg.OUTPUT_DIR):
logger.info("Load from last checkpoint.")
last_checkpoint = cu.get_last_checkpoint(cfg.OUTPUT_DIR)
checkpoint_epoch = cu.load_checkpoint(last_checkpoint, model,
cfg.NUM_GPUS > 1, optimizer)
start_epoch = checkpoint_epoch + 1
elif cfg.TRAIN.CHECKPOINT_FILE_PATH != "" and not cfg.TRAIN.FINETUNE:
logger.info("Load from given checkpoint file.")
checkpoint_epoch = cu.load_checkpoint(
cfg.TRAIN.CHECKPOINT_FILE_PATH,
model,
cfg.NUM_GPUS > 1,
optimizer,
inflation=cfg.TRAIN.CHECKPOINT_INFLATE,
convert_from_caffe2=cfg.TRAIN.CHECKPOINT_TYPE == "caffe2",
)
start_epoch = checkpoint_epoch + 1
elif cfg.TRAIN.CHECKPOINT_FILE_PATH != "" and cfg.TRAIN.FINETUNE:
logger.info("Load from given checkpoint file. Finetuning.")
_ = cu.load_checkpoint(
cfg.TRAIN.CHECKPOINT_FILE_PATH,
model,
cfg.NUM_GPUS > 1,
None,
inflation=cfg.TRAIN.CHECKPOINT_INFLATE,
convert_from_caffe2=cfg.TRAIN.CHECKPOINT_TYPE == "caffe2",
)
start_epoch = 0
else:
start_epoch = 0
# Create the video train and val loaders.
if cfg.TRAIN.DATASET != 'epickitchens' or not cfg.EPICKITCHENS.TRAIN_PLUS_VAL:
train_loader = loader.construct_loader(cfg, "train")
val_loader = loader.construct_loader(cfg, "val")
logger.info("Train loader size: {}".format(len(train_loader)))
logger.info("Val loader size: {}".format(len(val_loader)))
else:
train_loader = loader.construct_loader(cfg, "train+val")
val_loader = loader.construct_loader(cfg, "val")
# Create meters.
if cfg.DETECTION.ENABLE:
train_meter = AVAMeter(len(train_loader), cfg, mode="train")
val_meter = AVAMeter(len(val_loader), cfg, mode="val")
else:
if cfg.TRAIN.DATASET == 'epickitchens':
train_meter = EPICTrainMeterSimple(len(train_loader), cfg)
val_meter = EPICValMeterSimple(len(val_loader), cfg)
else:
train_meter = TrainMeter(len(train_loader), cfg)
val_meter = ValMeter(len(val_loader), cfg)
# Perform the training loop.
logger.info("Start epoch: {}".format(start_epoch + 1))
cnt = 0
# eval_epoch(val_loader, model, val_meter, 0, cfg, cnt)
# test_from_train(model, cfg, cnt=cnt)
for cur_epoch in range(start_epoch, cfg.SOLVER.MAX_EPOCH):
# Shuffle the dataset.
loader.shuffle_dataset(train_loader, cur_epoch)
# Train for one epoch.
cnt = train_epoch(train_loader, model, optimizer, train_meter,
cur_epoch, cfg, cnt)
# Compute precise BN stats.
if cfg.BN.USE_PRECISE_STATS and len(get_bn_modules(model)) > 0:
calculate_and_update_precise_bn(train_loader, model,
cfg.BN.NUM_BATCHES_PRECISE)
# Save a checkpoint.
if cu.is_checkpoint_epoch(cur_epoch, cfg.TRAIN.CHECKPOINT_PERIOD):
cu.save_checkpoint(cfg.OUTPUT_DIR, model, optimizer, cur_epoch,
cfg)
# Evaluate the model on validation set.
if misc.is_eval_epoch(cfg, cur_epoch):
is_best_epoch = eval_epoch(val_loader, model, val_meter, cur_epoch,
cfg, cnt)
if is_best_epoch:
cu.save_checkpoint(cfg.OUTPUT_DIR,
model,
optimizer,
cur_epoch,
cfg,
is_best_epoch=is_best_epoch)
def train(cfg):
"""
Train a video model for many epochs on train set and evaluate it on val set.
Args:
cfg (CfgNode): configs. Details can be found in
slowfast/config/defaults.py
"""
# Set random seed from configs.
np.random.seed(cfg.RNG_SEED)
torch.manual_seed(cfg.RNG_SEED)
# Setup logging format.
logging.setup_logging()
# Print config.
logger.info("Train with config:")
logger.info(pprint.pformat(cfg))
# Build the video model and print model statistics.
model = model_builder.build_model(cfg)
if du.is_master_proc():
misc.log_model_info(model)
# Construct the optimizer.
optimizer = optim.construct_optimizer(model, cfg)
# Load a checkpoint to resume training if applicable.
if cfg.TRAIN.AUTO_RESUME and cu.has_checkpoint(cfg.OUTPUT_DIR):
logger.info("Load from last checkpoint.")
last_checkpoint = cu.get_last_checkpoint(cfg.OUTPUT_DIR)
checkpoint_epoch = cu.load_checkpoint(last_checkpoint, model,
cfg.NUM_GPUS > 1, optimizer)
start_epoch = checkpoint_epoch + 1
elif cfg.TRAIN.CHECKPOINT_FILE_PATH != "":
logger.info("Load from given checkpoint file.")
checkpoint_epoch = cu.load_checkpoint(
cfg.TRAIN.CHECKPOINT_FILE_PATH,
model,
cfg.NUM_GPUS > 1,
optimizer,
inflation=cfg.TRAIN.CHECKPOINT_INFLATE,
convert_from_caffe2=cfg.TRAIN.CHECKPOINT_TYPE == "caffe2",
)
start_epoch = checkpoint_epoch + 1
else:
start_epoch = 0
# Create the video train and val loaders.
train_loader = loader.construct_loader(cfg, "train")
val_loader = loader.construct_loader(cfg, "val")
# Create meters.
if cfg.DETECTION.ENABLE:
train_meter = AVAMeter(len(train_loader), cfg, mode="train")
val_meter = AVAMeter(len(val_loader), cfg, mode="val")
else:
train_meter = TrainMeter(len(train_loader), cfg)
val_meter = ValMeter(len(val_loader), cfg)
# Perform the training loop.
logger.info("Start epoch: {}".format(start_epoch + 1))
for cur_epoch in range(start_epoch, cfg.SOLVER.MAX_EPOCH):
# Shuffle the dataset.
loader.shuffle_dataset(train_loader, cur_epoch)
# Train for one epoch.
train_epoch(train_loader, model, optimizer, train_meter, cur_epoch,
cfg)
# Compute precise BN stats.
if cfg.BN.USE_PRECISE_STATS and len(get_bn_modules(model)) > 0:
calculate_and_update_precise_bn(train_loader, model,
cfg.BN.NUM_BATCHES_PRECISE)
# Save a checkpoint.
if cu.is_checkpoint_epoch(cur_epoch, cfg.TRAIN.CHECKPOINT_PERIOD):
cu.save_checkpoint(cfg.OUTPUT_DIR, model, optimizer, cur_epoch,
cfg)
# Evaluate the model on validation set.
if misc.is_eval_epoch(cfg, cur_epoch):
eval_epoch(val_loader, model, val_meter, cur_epoch, cfg)
def train(cfg):
"""
Train a video model for many epochs on train set and evaluate it on val set.
Args:
cfg (CfgNode): configs. Details can be found in
slowfast/config/defaults.py
"""
# Set random seed from configs.
np.random.seed(cfg.RNG_SEED)
torch.manual_seed(cfg.RNG_SEED)
# Setup logging format.
logging.setup_logging(cfg)
# Print config.
logger.info("Train with config:")
logger.info(pprint.pformat(cfg))
if du.get_rank()==0 and du.is_master_proc(num_gpus=cfg.NUM_GPUS):
writer = SummaryWriter(log_dir=cfg.OUTPUT_DIR)
else:
writer = None
if du.get_rank()==0 and du.is_master_proc(num_gpus=cfg.NUM_GPUS) and not cfg.DEBUG:
tags = []
if 'TAGS' in cfg and cfg.TAGS !=[]:
tags=list(cfg.TAGS)
neptune.set_project('Serre-Lab/motion')
######################
overrides = sys.argv[1:]
overrides_dict = {}
for i in range(len(overrides)//2):
overrides_dict[overrides[2*i]] = overrides[2*i+1]
overrides_dict['dir'] = cfg.OUTPUT_DIR
######################
if 'NEP_ID' in cfg and cfg.NEP_ID != "":
session = Session()
project = session.get_project(project_qualified_name='Serre-Lab/motion')
nep_experiment = project.get_experiments(id=cfg.NEP_ID)[0]
else:
nep_experiment = neptune.create_experiment (name=cfg.NAME,
params=overrides_dict,
tags=tags)
else:
nep_experiment=None
# Build the video model and print model statistics.
model = build_model(cfg)
if du.is_master_proc(num_gpus=cfg.NUM_GPUS):
misc.log_model_info(model, cfg, is_train=True)
# Construct the optimizer.
optimizer = optim.construct_optimizer(model, cfg)
# Load a checkpoint to resume training if applicable.
if cfg.TRAIN.AUTO_RESUME and cu.has_checkpoint(cfg.OUTPUT_DIR):
logger.info("Load from last checkpoint.")
last_checkpoint = cu.get_last_checkpoint(cfg.OUTPUT_DIR)
checkpoint_epoch = cu.load_checkpoint(
last_checkpoint, model, cfg.NUM_GPUS > 1, optimizer
)
start_epoch = checkpoint_epoch + 1
elif cfg.TRAIN.CHECKPOINT_FILE_PATH != "":
logger.info("Load from given checkpoint file.")
checkpoint_epoch = cu.load_checkpoint(
cfg.TRAIN.CHECKPOINT_FILE_PATH,
model,
cfg.NUM_GPUS > 1,
optimizer,
inflation=cfg.TRAIN.CHECKPOINT_INFLATE,
convert_from_caffe2=cfg.TRAIN.CHECKPOINT_TYPE == "caffe2",
)
start_epoch = checkpoint_epoch + 1
else:
start_epoch = 0
# Create the video train and val loaders.
train_loader = loader.construct_loader(cfg, "train")
val_loader = loader.construct_loader(cfg, "val")
# Create meters.
if cfg.DETECTION.ENABLE:
train_meter = AVAMeter(len(train_loader), cfg, mode="train")
val_meter = AVAMeter(len(val_loader), cfg, mode="val")
else:
train_meter = TrainMeter(len(train_loader), cfg)
val_meter = ValMeter(len(val_loader), cfg)
# Perform the training loop.
logger.info("Start epoch: {}".format(start_epoch + 1))
for cur_epoch in range(start_epoch, cfg.SOLVER.MAX_EPOCH):
# Shuffle the dataset.
loader.shuffle_dataset(train_loader, cur_epoch)
# Train for one epoch.
train_epoch(train_loader, model, optimizer, train_meter, cur_epoch, writer, nep_experiment, cfg)
# Compute precise BN stats.
# if cfg.BN.USE_PRECISE_STATS and len(get_bn_modules(model)) > 0:
# calculate_and_update_precise_bn(
# train_loader, model, cfg.BN.NUM_BATCHES_PRECISE
# )
# Save a checkpoint.
if cu.is_checkpoint_epoch(cur_epoch, cfg.TRAIN.CHECKPOINT_PERIOD):
cu.save_checkpoint(cfg.OUTPUT_DIR, model, optimizer, cur_epoch, cfg)
# Evaluate the model on validation set.
if misc.is_eval_epoch(cfg, cur_epoch):
eval_epoch(val_loader, model, val_meter, cur_epoch, nep_experiment, cfg)
if du.get_rank()==0 and du.is_master_proc(num_gpus=cfg.NUM_GPUS) and not cfg.DEBUG:
nep_experiment.log_metric('epoch', cur_epoch)
def train_epoch(train_loader, model, optimizer, train_meter, cur_epoch, writer, nep, cfg):
"""
Perform the video training for one epoch.
Args:
train_loader (loader): video training loader.
model (model): the video model to train.
optimizer (optim): the optimizer to perform optimization on the model's
parameters.
train_meter (TrainMeter): training meters to log the training performance.
cur_epoch (int): current epoch of training.
cfg (CfgNode): configs. Details can be found in
slowfast/config/defaults.py
"""
# Enable train mode.
model.train()
train_meter.iter_tic()
data_size = len(train_loader)
global_iters = data_size*cur_epoch
for cur_iter, (inputs, labels, _, meta) in enumerate(train_loader):
# Transfer the data to the current GPU device.
inputs = inputs.cuda(non_blocking=True)
# labels = torch.repeat_interleave(labels,inputs[i].size(1),0)
# for i in range(len(inputs)):
# if len(inputs[i].shape) > 5:
# inputs[i] = inputs[i].view((-1,)+inputs[i].shape[2:])
# labels = labels.cuda()
# for key, val in meta.items():
# if isinstance(val, (list,)):
# for i in range(len(val)):
# val[i] = val[i].cuda(non_blocking=True)
# else:
# meta[key] = val.cuda(non_blocking=True)
# Update the learning rate.
lr = optim.get_epoch_lr(cur_epoch + float(cur_iter) / data_size, global_iters, cfg)
optim.set_lr(optimizer, lr)
preds = model(inputs)
out_keys = list(preds.keys())
total_loss = preds['total_loss']
# check Nan Loss.
misc.check_nan_losses(total_loss)
# Perform the backward pass.
optimizer.zero_grad()
total_loss.backward()
####################################################################################################################################
# check gradients
# if writer is not None and global_iters%cfg.SUMMARY_PERIOD==0:
# n_p = model.module.named_parameters() if hasattr(model,'module') else model.named_parameters()
# fig = viz_helpers.plot_grad_flow_v2(n_p)
# writer.add_figure('grad_flow/grad_flow', fig, global_iters)
####################################################################################################################################
# Update the parameters.
optimizer.step()
losses = [preds[k] for k in out_keys]
# Gather all the predictions across all the devices.
if cfg.NUM_GPUS > 1:
losses = du.all_reduce(losses)
losses = [l.item() for l in losses]
loss_logs = {}
for i in range(len(losses)):
loss_logs[out_keys[i]] = losses[i]
train_meter.iter_toc()
# Update and log stats.
train_meter.update_stats(
lr, inputs[0].size(0) * cfg.NUM_GPUS, **loss_logs
)
if writer is not None and global_iters%cfg.LOG_PERIOD==0:
logger.info(model.conv0[2].weight)
logger.info(model.conv0[2].bias)
for k,v in loss_logs.items():
writer.add_scalar('loss/'+k.strip('loss_'), train_meter.stats[k].get_win_median(), global_iters)
if nep is not None and global_iters%cfg.LOG_PERIOD==0:
for k,v in loss_logs.items():
nep.log_metric(k.strip('loss_'), train_meter.stats[k].get_win_median())
nep.log_metric('global_iters', global_iters)
if global_iters%cfg.SUMMARY_PERIOD==0 and du.get_rank()==0 and du.is_master_proc(num_gpus=cfg.NUM_GPUS):
with torch.no_grad():
# logger.info(inputs[i].shape)
# sys.stdout.flush()
inputs = inputs[:min(3,len(inputs))]
if 'masks' in meta:
frames = model((inputs, meta['masks'][:min(3,len(inputs))]), extra=['frames'])['frames']
else:
frames = model(inputs, extra=['frames'])['frames']
n_rows = inputs.size(2)-1
inputs = inputs.transpose(1,2)[:, -n_rows:]
frames = frames.transpose(1,2)[:, -n_rows:]
frames = torch.cat([(frames!=inputs)*frames, (frames==inputs)*inputs, torch.zeros_like(frames)], 2)
inputs = torch.cat([inputs]*3, 2)
# inputs = inputs*inputs.new(cfg.DATA.STD)[None,None,:,None,None]+inputs.new(cfg.DATA.MEAN)[None,None,:,None,None]
# frames = frames*frames.new(cfg.DATA.STD)[None,None,:,None,None]+frames.new(cfg.DATA.MEAN)[None,None,:,None,None]
images = torch.cat([inputs, frames], 1).reshape((-1,) + inputs.shape[2:])
# grid = tv.utils.make_grid(images, nrow=8, normalize=True)
# writer.add_image('predictions', images, global_iters)
tv.utils.save_image(images, os.path.join(cfg.OUTPUT_DIR, 'preds_%d.jpg'%global_iters), nrow=n_rows, normalize=True)
# del images
# del frames
# del inputs
train_meter.log_iter_stats(cur_epoch, cur_iter)
train_meter.iter_tic()
global_iters+=1
# Log epoch stats.
train_meter.log_epoch_stats(cur_epoch)
train_meter.reset()
def test(cfg):
"""
Perform multi-view testing on the pretrained video model.
Args:
cfg (CfgNode): configs. Details can be found in
slowfast/config/defaults.py
"""
# Set random seed from configs.
np.random.seed(cfg.RNG_SEED)
torch.manual_seed(cfg.RNG_SEED)
# Setup logging format.
logging.setup_logging()
# Print config.
logger.info("Test with config:")
logger.info(cfg)
# Build the video model and print model statistics.
model = model_builder.build_model(cfg)
if du.is_master_proc():
misc.log_model_info(model)
# Load a checkpoint to test if applicable.
if cfg.TEST.CHECKPOINT_FILE_PATH != "":
cu.load_checkpoint(
cfg.TEST.CHECKPOINT_FILE_PATH,
model,
cfg.NUM_GPUS > 1,
None,
inflation=False,
convert_from_caffe2=cfg.TEST.CHECKPOINT_TYPE == "caffe2",
)
elif cu.has_checkpoint(cfg.OUTPUT_DIR):
last_checkpoint = cu.get_last_checkpoint(cfg.OUTPUT_DIR)
cu.load_checkpoint(last_checkpoint, model, cfg.NUM_GPUS > 1)
elif cfg.TRAIN.CHECKPOINT_FILE_PATH != "":
# If no checkpoint found in TEST.CHECKPOINT_FILE_PATH or in the current
# checkpoint folder, try to load checkpint from
# TRAIN.CHECKPOINT_FILE_PATH and test it.
cu.load_checkpoint(
cfg.TRAIN.CHECKPOINT_FILE_PATH,
model,
cfg.NUM_GPUS > 1,
None,
inflation=False,
convert_from_caffe2=cfg.TRAIN.CHECKPOINT_TYPE == "caffe2",
)
else:
# raise NotImplementedError("Unknown way to load checkpoint.")
logger.info("Testing with random initialization. Only for debugging.")
# Create video testing loaders.
test_loader = loader.construct_loader(cfg, "test")
logger.info("Testing model for {} iterations".format(len(test_loader)))
if cfg.DETECTION.ENABLE:
assert cfg.NUM_GPUS == cfg.TEST.BATCH_SIZE
test_meter = AVAMeter(len(test_loader), cfg, mode="test")
else:
assert (
len(test_loader.dataset)
% (cfg.TEST.NUM_ENSEMBLE_VIEWS * cfg.TEST.NUM_SPATIAL_CROPS)
== 0
)
# Create meters for multi-view testing.
test_meter = TestMeter(
len(test_loader.dataset)
// (cfg.TEST.NUM_ENSEMBLE_VIEWS * cfg.TEST.NUM_SPATIAL_CROPS),
cfg.TEST.NUM_ENSEMBLE_VIEWS * cfg.TEST.NUM_SPATIAL_CROPS,
cfg.MODEL.NUM_CLASSES,
len(test_loader),
)
# # Perform multi-view test on the entire dataset.
perform_test(test_loader, model, test_meter, cfg)
def test(cfg):
"""
Perform multi-view testing on the pretrained video model.
Args:
cfg (CfgNode): configs. Details can be found in
slowfast/config/defaults.py
"""
# Set up environment.
du.init_distributed_training(cfg)
# Set random seed from configs.
np.random.seed(cfg.RNG_SEED)
torch.manual_seed(cfg.RNG_SEED)
# Setup logging format.
logging.setup_logging(cfg.OUTPUT_DIR)
# Print config.
logger.info("Test with config:")
logger.info(cfg)
# Build the video model and print model statistics.
model = build_model(cfg)
if du.is_master_proc() and cfg.LOG_MODEL_INFO:
misc.log_model_info(model, cfg, is_train=False)
# Load a checkpoint to test if applicable.
if cfg.TEST.CHECKPOINT_FILE_PATH != "":
cu.load_checkpoint(
cfg.TEST.CHECKPOINT_FILE_PATH,
model,
cfg.NUM_GPUS > 1,
None,
inflation=False,
convert_from_caffe2=cfg.TEST.CHECKPOINT_TYPE == "caffe2",
)
elif cu.has_checkpoint(cfg.OUTPUT_DIR):
last_checkpoint = cu.get_last_checkpoint(cfg.OUTPUT_DIR)
cu.load_checkpoint(last_checkpoint, model, cfg.NUM_GPUS > 1)
elif cfg.TRAIN.CHECKPOINT_FILE_PATH != "":
# If no checkpoint found in TEST.CHECKPOINT_FILE_PATH or in the current
# checkpoint folder, try to load checkpoint from
# TRAIN.CHECKPOINT_FILE_PATH and test it.
cu.load_checkpoint(
cfg.TRAIN.CHECKPOINT_FILE_PATH,
model,
cfg.NUM_GPUS > 1,
None,
inflation=False,
convert_from_caffe2=cfg.TRAIN.CHECKPOINT_TYPE == "caffe2",
)
else:
# raise NotImplementedError("Unknown way to load checkpoint.")
logger.info("Testing with random initialization. Only for debugging.")
# Create video testing loaders.
test_loader = loader.construct_loader(cfg, "test")
logger.info("Testing model for {} iterations".format(len(test_loader)))
assert (len(test_loader.dataset) %
(cfg.TEST.NUM_ENSEMBLE_VIEWS * cfg.TEST.NUM_SPATIAL_CROPS) == 0)
# Create meters for multi-view testing.
test_meter = TestMeter(
len(test_loader.dataset) //
(cfg.TEST.NUM_ENSEMBLE_VIEWS * cfg.TEST.NUM_SPATIAL_CROPS),
cfg.TEST.NUM_ENSEMBLE_VIEWS * cfg.TEST.NUM_SPATIAL_CROPS,
cfg.MODEL.NUM_CLASSES,
len(test_loader),
cfg.DATA.MULTI_LABEL,
cfg.DATA.ENSEMBLE_METHOD,
)
# Set up writer for logging to Tensorboard format.
if cfg.TENSORBOARD.ENABLE and du.is_master_proc(
cfg.NUM_GPUS * cfg.NUM_SHARDS):
writer = tb.TensorboardWriter(cfg)
else:
writer = None
# # Perform multi-view test on the entire dataset.
perform_test(test_loader, model, test_meter, cfg, writer)
if writer is not None:
writer.close()
def train(cfg):
"""
Train a video model for many epochs on train set and evaluate it on val set.
Args:
cfg (CfgNode): configs. Details can be found in
slowfast/config/defaults.py
"""
# Set up environment.
du.init_distributed_training(cfg)
# Set random seed from configs.
np.random.seed(cfg.RNG_SEED)
torch.manual_seed(cfg.RNG_SEED)
# Setup logging format.
logging.setup_logging(cfg.OUTPUT_DIR)
# Init multigrid.
multigrid = None
if cfg.MULTIGRID.LONG_CYCLE or cfg.MULTIGRID.SHORT_CYCLE:
multigrid = MultigridSchedule()
cfg = multigrid.init_multigrid(cfg)
if cfg.MULTIGRID.LONG_CYCLE:
cfg, _ = multigrid.update_long_cycle(cfg, cur_epoch=0)
# Print config.
logger.info("Train with config:")
logger.info(pprint.pformat(cfg))
# Build the video model and print model statistics.
model = build_model(cfg)
if du.is_master_proc() and cfg.LOG_MODEL_INFO:
misc.log_model_info(model, cfg, use_train_input=True)
# Construct the optimizer.
optimizer = optim.construct_optimizer(model, cfg)
# Load a checkpoint to resume training if applicable.
start_epoch = cu.load_train_checkpoint(cfg, model, optimizer)
# Create the video train and val loaders.
train_loader = loader.construct_loader(cfg, "train")
val_loader = loader.construct_loader(cfg, "val")
precise_bn_loader = (loader.construct_loader(
cfg, "train", is_precise_bn=True)
if cfg.BN.USE_PRECISE_STATS else None)
# Create meters.
if cfg.DETECTION.ENABLE:
train_meter = AVAMeter(len(train_loader), cfg, mode="train")
val_meter = AVAMeter(len(val_loader), cfg, mode="val")
else:
train_meter = TrainMeter(len(train_loader), cfg)
val_meter = ValMeter(len(val_loader), cfg)
# set up writer for logging to Tensorboard format.
if cfg.TENSORBOARD.ENABLE and du.is_master_proc(
cfg.NUM_GPUS * cfg.NUM_SHARDS):
writer = tb.TensorboardWriter(cfg)
else:
writer = None
# Perform the training loop.
logger.info("Start epoch: {}".format(start_epoch + 1))
for cur_epoch in range(start_epoch, cfg.SOLVER.MAX_EPOCH):
if cfg.MULTIGRID.LONG_CYCLE:
cfg, changed = multigrid.update_long_cycle(cfg, cur_epoch)
if changed:
(
model,
optimizer,
train_loader,
val_loader,
precise_bn_loader,
train_meter,
val_meter,
) = build_trainer(cfg)
# Load checkpoint.
if cu.has_checkpoint(cfg.OUTPUT_DIR):
last_checkpoint = cu.get_last_checkpoint(cfg.OUTPUT_DIR)
assert "{:05d}.pyth".format(cur_epoch) in last_checkpoint
else:
last_checkpoint = cfg.TRAIN.CHECKPOINT_FILE_PATH
logger.info("Load from {}".format(last_checkpoint))
cu.load_checkpoint(last_checkpoint, model, cfg.NUM_GPUS > 1,
optimizer)
# Shuffle the dataset.
loader.shuffle_dataset(train_loader, cur_epoch)
# Train for one epoch.
train_epoch(train_loader, model, optimizer, train_meter, cur_epoch,
cfg, writer)
is_checkp_epoch = (cu.is_checkpoint_epoch(
cfg,
cur_epoch,
None if multigrid is None else multigrid.schedule,
))
is_eval_epoch = misc.is_eval_epoch(
cfg, cur_epoch, None if multigrid is None else multigrid.schedule)
# Compute precise BN stats.
if ((is_checkp_epoch or is_eval_epoch) and cfg.BN.USE_PRECISE_STATS
and len(get_bn_modules(model)) > 0):
calculate_and_update_precise_bn(
precise_bn_loader,
model,
min(cfg.BN.NUM_BATCHES_PRECISE, len(precise_bn_loader)),
cfg.NUM_GPUS > 0,
)
_ = misc.aggregate_sub_bn_stats(model)
# Save a checkpoint.
if is_checkp_epoch:
cu.save_checkpoint(cfg.OUTPUT_DIR, model, optimizer, cur_epoch,
cfg)
# Evaluate the model on validation set.
if is_eval_epoch:
eval_epoch(val_loader, model, val_meter, cur_epoch, cfg, writer)
if writer is not None:
writer.close()
def train_epoch(
train_loader, model, optimizer, train_meter, cur_epoch, cfg, writer=None
):
"""
Perform the video training for one epoch.
Args:
train_loader (loader): video training loader.
model (model): the video model to train.
optimizer (optim): the optimizer to perform optimization on the model's
parameters.
train_meter (TrainMeter): training meters to log the training performance.
cur_epoch (int): current epoch of training.
cfg (CfgNode): configs. Details can be found in
slowfast/config/defaults.py
writer (TensorboardWriter, optional): TensorboardWriter object
to writer Tensorboard log.
"""
# Enable train mode.
model.train()
# Check if the correct params are set to requires_grad = True
assert_requires_grad_correctness(model, du.is_master_proc(), cfg)
train_meter.iter_tic()
data_size = len(train_loader)
np.set_printoptions(suppress=True)
for cur_iter, (inputs, labels, _, meta) in enumerate(train_loader):
# Transfer the data to the current GPU device.
if isinstance(inputs, (list,)):
for i in range(len(inputs)):
inputs[i] = inputs[i].cuda(non_blocking=True)
else:
inputs = inputs.cuda(non_blocking=True)
labels = labels.cuda()
for key, val in meta.items():
if isinstance(val, (list,)):
for i in range(len(val)):
val[i] = val[i].cuda(non_blocking=True)
else:
meta[key] = val.cuda(non_blocking=True)
if cfg.MODEL.HEAD_ACT == "softmax" and cfg.TRAIN.DATASET == "custom":
# We have to change our labels to long tensor
labels = labels.type(torch.LongTensor)
labels = labels.cuda()
# Update the learning rate.
lr = optim.get_epoch_lr(cur_epoch + float(cur_iter) / data_size, cfg)
optim.set_lr(optimizer, lr, cfg)
if cfg.DETECTION.ENABLE:
# Compute the predictions.
preds = model(inputs, meta["boxes"], is_train=True)
else:
# Perform the forward pass.
preds = model(inputs)
# Explicitly declare reduction to mean.
loss_fun = losses.get_loss_func(cfg.MODEL.LOSS_FUNC)(reduction="mean")
# Compute the loss.
loss = loss_fun(preds, labels)
"""
if cur_iter % 70 == 0:
softmax = torch.nn.Softmax(dim=1)
probabilities = softmax(preds)
loss_prob = loss_fun(probabilities, labels)
preds_numpy = probabilities.cpu().detach().numpy()
preds_numpy = np.round(preds_numpy, 4)
labels_numpy = labels.cpu().detach().numpy()
print("--------------------------")
for label, pred in zip (labels_numpy, preds_numpy):
print(str(label) + "---->", end= "")
print(pred[label])
"""
# check Nan Loss.
misc.check_nan_losses(loss)
# Perform the backward pass.
optimizer.zero_grad()
loss.backward()
# Update the parameters.
optimizer.step()
# Todo: adjust accordingly
if cfg.DETECTION.ENABLE: #and not (cfg.MODEL.HEAD_ACT == "softmax"):
if cfg.NUM_GPUS > 1:
loss = du.all_reduce([loss])[0]
loss = loss.item()
train_meter.iter_toc()
# Update and log stats.
train_meter.update_stats(None, None, None, loss, lr)
# write to tensorboard format if available.
if writer is not None:
writer.add_scalars(
{"Train/loss": loss, "Train/lr": lr},
global_step=data_size * cur_epoch + cur_iter,
)
else:
top1_err, top5_err = None, None
if cfg.DATA.MULTI_LABEL:
# Gather all the predictions across all the devices.
if cfg.NUM_GPUS > 1:
[loss] = du.all_reduce([loss])
loss = loss.item()
else:
# Compute the errors.
num_topks_correct = metrics.topks_correct(preds, labels, (1, 5))
top1_err, top5_err = [
(1.0 - x / preds.size(0)) * 100.0 for x in num_topks_correct
]
# Gather all the predictions across all the devices.
if cfg.NUM_GPUS > 1:
loss, top1_err, top5_err = du.all_reduce(
[loss, top1_err, top5_err]
)
# Copy the stats from GPU to CPU (sync point).
loss, top1_err, top5_err = (
loss.item(),
top1_err.item(),
top5_err.item(),
)
train_meter.iter_toc()
# Update and log stats.
train_meter.update_stats(
top1_err, top5_err, loss, lr, inputs[0].size(0) * cfg.NUM_GPUS
)
# write to tensorboard format if available.
if writer is not None:
writer.add_scalars(
{
"Train/loss": loss,
"Train/lr": lr,
"Train/Top1_err": top1_err,
"Train/Top5_err": top5_err,
},
global_step=data_size * cur_epoch + cur_iter,
)
train_meter.log_iter_stats(cur_epoch, cur_iter)
train_meter.iter_tic()
# Log epoch stats.
train_meter.log_epoch_stats(cur_epoch)
train_meter.reset()
