def build_and_switch_demo_model(cfg):
# Build the video model and print model statistics.
model = model_builder.build_model(cfg)
model.eval()
misc.log_model_info(model)
return model
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 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 load_model(self):
#logger.info("Model Config")
#logger.info(self.cfg)
self.model = build_model(self.cfg)
self.model.eval()
#if du.is_master_proc():
misc.log_model_info(self.model, self.cfg, is_train=False)
model_path = cfg.TRAIN.CHECKPOINT_FILE_PATH
assert os.path.exists(
model_path), "%s. Model Path Not Found" % model_path
cu.load_checkpoint(model_path, self.model, self.cfg.NUM_GPUS > 1) #
def __init__(self, cfg, backbone):
if SlowFast.__instance != None:
raise Exception('This class is a singleton!')
else:
SlowFast.__instance = self
self.cfg = cfg
self.backbone = backbone
print(self.cfg.MODEL.ARCH)
self.model = model_builder.build_model(self.cfg)
self.model.eval()
misc.log_model_info(self.model)
self.checkpoint = cfg.TRAIN.CHECKPOINT_FILE_PATH
cu.load_checkpoint(
self.checkpoint,
self.model,
self.cfg.NUM_GPUS > 1,
None,
inflation=False,
convert_from_caffe2="caffe2"
in [self.cfg.TEST.CHECKPOINT_TYPE, self.cfg.TRAIN.CHECKPOINT_TYPE],
)
if self.backbone == 'yolo':
self.object_predictor = YOLO.get_instance(
self.cfg.DEMO.YOLO_LIB, self.cfg.DEMO.YOLO_CFG,
self.cfg.DEMO.YOLO_META, self.cfg.DEMO.YOLO_CLASS,
self.cfg.DEMO.YOLO_WEIGHTS)
else:
dtron2_cfg_file = self.cfg.DEMO.DETECTRON2_OBJECT_DETECTION_MODEL_CFG
dtron2_cfg = get_cfg()
dtron2_cfg.merge_from_file(
model_zoo.get_config_file(dtron2_cfg_file))
dtron2_cfg.MODEL.ROI_HEADS.SCORE_THRESH_TEST = .5
dtron2_cfg.MODEL.WEIGHTS = self.cfg.DEMO.DETECTRON2_OBJECT_DETECTION_MODEL_WEIGHTS
self.object_predictor = DefaultPredictor(dtron2_cfg)
with open(self.cfg.DEMO.LABEL_FILE_PATH) as f:
self.labels = f.read().split('\n')[:-1]
self.palette = np.random.randint(64, 128,
(len(self.labels), 3)).tolist()
self.images_queue = queue.Queue()
self.write_queue = queue.Queue()
self.cap = cv2.VideoCapture(self.cfg.DEMO.DATA_SOURCE)
self.seq_len = self.cfg.DATA.NUM_FRAMES * self.cfg.DATA.SAMPLING_RATE
def __init__(self, cfg):
"""
Args:
cfg (CfgNode): configs. Details can be found in
slowfast/config/defaults.py
"""
# Build the video model and print model statistics.
self.model = build_model(cfg)
self.model.eval()
self.cfg = cfg
logger.info("Start loading model info")
misc.log_model_info(self.model, cfg, use_train_input=False)
logger.info("Start loading model weights")
cu.load_test_checkpoint(cfg, self.model)
logger.info("Finish loading model weights")
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 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 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 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 demo(cfg, backbone):
# 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 = model_builder.build_model(cfg)
model.eval()
misc.log_model_info(model)
# Load a checkpoint to test if applicable.
if cfg.TEST.CHECKPOINT_FILE_PATH != "":
ckpt = cfg.TEST.CHECKPOINT_FILE_PATH
elif cu.has_checkpoint(cfg.OUTPUT_DIR):
ckpt = cu.get_last_checkpoint(cfg.OUTPUT_DIR)
elif cfg.TRAIN.CHECKPOINT_FILE_PATH != "":
ckpt = cfg.TRAIN.CHECKPOINT_FILE_PATH
else:
raise NotImplementedError("Unknown way to load checkpoint.")
cu.load_checkpoint(
ckpt,
model,
cfg.NUM_GPUS > 1,
None,
inflation=False,
convert_from_caffe2= "caffe2" in [cfg.TEST.CHECKPOINT_TYPE, cfg.TRAIN.CHECKPOINT_TYPE],
)
darknetlib_path = '/home/ubuntu/hanhbd/SlowFast/detector/libdarknet.so'
config_path = '/home/ubuntu/hanhbd/SlowFast/detector/yolov4.cfg'
meta_path = '/home/ubuntu/hanhbd/SlowFast/detector/coco.data'
classes_path = '/home/ubuntu/hanhbd/SlowFast/detector/coco.names'
weight_path = '/home/ubuntu/hanhbd/SlowFast/detector/yolov4.weights'
if backbone == 'yolo':
object_predictor = YOLO.get_instance(darknetlib_path, config_path, meta_path, classes_path, weight_path)
else:
dtron2_cfg_file = cfg.DEMO.DETECTRON2_OBJECT_DETECTION_MODEL_CFG
dtron2_cfg = get_cfg()
dtron2_cfg.merge_from_file(model_zoo.get_config_file(dtron2_cfg_file))
dtron2_cfg.MODEL.ROI_HEADS.SCORE_THRESH_TEST = .5
dtron2_cfg.MODEL.WEIGHTS = cfg.DEMO.DETECTRON2_OBJECT_DETECTION_MODEL_WEIGHTS
object_predictor = DefaultPredictor(dtron2_cfg)
with open(cfg.DEMO.LABEL_FILE_PATH) as f:
labels = f.read().split('\n')[:-1]
palette = np.random.randint(64, 128, (len(labels), 3)).tolist()
count_xxx = 0
seq_len = cfg.DATA.NUM_FRAMES*cfg.DATA.SAMPLING_RATE
frames = []
org_frames = []
mid_frame = None
pred_labels = []
draw_imgs = []
cap = cv2.VideoCapture(cfg.DEMO.DATA_SOURCE)
was_read, frame = cap.read()
display_width = int(cap.get(cv2.CAP_PROP_FRAME_WIDTH))
display_height = int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT))
fps = int(cap.get(cv2.CAP_PROP_FPS))
fourcc = cv2.VideoWriter_fourcc(*'DIVX')
videowriter = cv2.VideoWriter('./result/testset_fighting_05.avi',fourcc, fps, (display_width,display_height))
while was_read :
was_read, frame = cap.read()
if not was_read:
videowriter.release()
break
if len(frames) != seq_len:
frame_processed = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
frame_processed = scale(cfg.DATA.TEST_CROP_SIZE, frame_processed)
frames.append(frame_processed)
org_frames.append(frame)
else:
#predict all person box in all frame
start = time()
mid_frame = org_frames[seq_len//2 - 2]
# just draw half of number frame because we will use slide = 1/2 length of sequence
if cfg.DETECTION.ENABLE and len(draw_imgs) == 0:
for idx in range(seq_len//2 - 1):
image = org_frames[idx]
boxes = detector(object_predictor , image, backbone, cfg , display_height, display_width )
# boxes = object_predictor.detect_image(img)
# boxes = torch.as_tensor(boxes).float().cuda()
boxes = torch.cat([torch.full((boxes.shape[0], 1), float(0)).cuda(), boxes], axis=1)
boxes = boxes.cpu().detach().numpy()
if backbone == 'yolo':
boxes = boxes[:, 1:]
else:
ratio = np.min(
[display_height, display_width]
) / cfg.DATA.TEST_CROP_SIZE
boxes = boxes[:, 1:] * ratio
for box in boxes:
xmin, ymin, xmax, ymax = box
cv2.rectangle(image, (xmin, ymin), (xmax , ymax), (0, 255, 0), thickness=2)
draw_imgs.append(image)
# detect box in mid frame
if cfg.DETECTION.ENABLE:
boxes = detector(object_predictor , mid_frame, backbone, cfg , display_height, display_width )
boxes = torch.cat([torch.full((boxes.shape[0], 1), float(0)).cuda(), boxes], axis=1)
inputs = torch.from_numpy(np.array(frames)).float()
inputs = inputs / 255.0
# Perform color normalization.
inputs = inputs - torch.tensor(cfg.DATA.MEAN)
inputs = inputs / torch.tensor(cfg.DATA.STD)
# T H W C -> C T H W.
inputs = inputs.permute(3, 0, 1, 2)
# 1 C T H W.
inputs = inputs.unsqueeze(0)
# Sample frames for the fast pathway.
index = torch.linspace(0, inputs.shape[2] - 1, cfg.DATA.NUM_FRAMES).long()
fast_pathway = torch.index_select(inputs, 2, index)
# Sample frames for the slow pathway.
index = torch.linspace(0, fast_pathway.shape[2] - 1,
fast_pathway.shape[2]//cfg.SLOWFAST.ALPHA).long()
slow_pathway = torch.index_select(fast_pathway, 2, index)
inputs = [slow_pathway, fast_pathway]
# 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)
# use a dummy variable to disable all computations below.
if not len(boxes):
preds = torch.tensor([])
else:
preds = model(inputs, boxes)
# Gather all the predictions across all the devices to perform ensemble.
if cfg.NUM_GPUS > 1:
preds = du.all_gather(preds)[0]
# post processing
preds = preds.cpu().detach().numpy()
pred_masks = preds > .1
label_ids = [np.nonzero(pred_mask)[0] for pred_mask in pred_masks]
pred_labels = [
[labels[label_id] for label_id in perbox_label_ids]
for perbox_label_ids in label_ids
]
print(pred_labels)
boxes = boxes.cpu().detach().numpy()
if backbone == 'yolo':
boxes = boxes[:, 1:]
else:
ratio = np.min(
[display_height, display_width]
) / cfg.DATA.TEST_CROP_SIZE
boxes = boxes[:, 1:] * ratio
# draw result on mid frame
if pred_labels and boxes.any():
for box, box_labels in zip(boxes.astype(int), pred_labels):
xmin, ymin, xmax, ymax = box
cv2.rectangle(mid_frame, (xmin, ymin), (xmax , ymax), (0, 255, 0), thickness=2)
label_origin = box[:2]
for label in box_labels:
label_origin[-1] -= 5
(label_width, label_height), _ = cv2.getTextSize(label, cv2.FONT_HERSHEY_SIMPLEX, .5, 2)
cv2.rectangle(
mid_frame,
(label_origin[0], label_origin[1] + 5),
(label_origin[0] + label_width, label_origin[1] - label_height - 5),
palette[labels.index(label)], -1
)
cv2.putText(
mid_frame, label, tuple(label_origin),
cv2.FONT_HERSHEY_SIMPLEX, .5, (255, 255, 255), 1
)
label_origin[-1] -= label_height + 5
# append mid frame to the draw array
draw_imgs.append(mid_frame)
# write image to videos
for img_ in draw_imgs:
videowriter.write(img_)
print("time process", (time() - start) /64 )
# clean the buffer of frames and org_frames with slide 1/2 seq_len
# frames = frames[seq_len//2 - 1:]
# org_frames = org_frames[seq_len//2 - 1:]
frames = frames[1:]
org_frames = org_frames[1:]
draw_imgs = draw_imgs[-1:]
count_xxx += 1
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()
# ADD Demo
if cfg.TEST.DEMO_PATH != "":
generate_subclip(cfg)
# 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():
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 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),
(cfg.TEST.DEMO_PATH != ""),
)
# # Perform multi-view test on the entire dataset.
perform_test(test_loader, model, test_meter, cfg)
if cfg.TEST.DEMO_PATH != "":
end_time_dir = os.path.join(cfg.DATA.PATH_TO_DATA_DIR, "end_time.npy")
end_time_array = np.load(end_time_dir)
cwd = os.getcwd()
probability_dir = os.path.join(cwd, "tmp/probability.npy")
p_array = np.load(probability_dir)
print(end_time_array)
print(p_array)
end_time_dir = os.path.join(cfg.DATA.PATH_TO_DATA_DIR)
if end_time_dir.split("/")[-1] == "demo":
try:
shutil.rmtree(end_time_dir)
except OSError as e:
print("Error: %s - %s." % (e.filename, e.strerror))
json_list = []
for i in range(len(end_time_array)):
json_list.append([end_time_array[i], p_array[i]])
print(json_list)
with open('timeLabel.json', 'w') as f:
json.dump({"jogging": str(json_list)}, f)
plt.plot(end_time_array, p_array, 'ro-')
plt.axis(
[0, end_time_array[len(end_time_array) - 1] + 0.5, -0.01, 1.01])
plt.xlabel('time(s)')
plt.ylabel('probability')
plt.suptitle("Jogging/Running")
plt.savefig("fig.png")
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 test(cfg):
"""
Perform multi-view testing/feature extraction 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.")
vid_root = cfg.DATA.PATH_TO_DATA_DIR
videos_list_file = os.path.join(vid_root, "vid_list.csv")
print("Loading Video List ...")
with open(videos_list_file) as f:
videos = sorted(
[x.strip() for x in f.readlines() if len(x.strip()) > 0])
print("Done")
print("----------------------------------------------------------")
print("{} videos to be processed...".format(len(videos)))
print("----------------------------------------------------------")
start_time = time.time()
for vid in videos:
# Create video testing loaders.
path_to_vid = os.path.join(vid_root, os.path.split(vid)[0])
vid_id = os.path.split(vid)[1]
out_path = os.path.join(cfg.OUTPUT_DIR, os.path.split(vid)[0])
out_file = vid_id.split(".")[0] + "_{}.npy".format(cfg.DATA.NUM_FRAMES)
if os.path.exists(os.path.join(out_path, out_file)):
print("{} already exists".format(out_file))
continue
print("Processing {}...".format(vid))
dataset = VideoSet(cfg, path_to_vid, vid_id)
test_loader = torch.utils.data.DataLoader(
dataset,
batch_size=cfg.TEST.BATCH_SIZE,
shuffle=False,
sampler=None,
num_workers=cfg.DATA_LOADER.NUM_WORKERS,
pin_memory=cfg.DATA_LOADER.PIN_MEMORY,
drop_last=False,
)
# Perform multi-view test on the entire dataset.
feat_arr = multi_view_test(test_loader, model, cfg)
os.makedirs(out_path, exist_ok=True)
np.save(os.path.join(out_path, out_file), feat_arr)
print("Done.")
print("----------------------------------------------------------")
end_time = time.time()
hours, minutes, seconds = calculate_time_taken(start_time, end_time)
print("Time taken: {} hour(s), {} minute(s) and {} second(s)".format(
hours, minutes, seconds))
print("----------------------------------------------------------")
def test(cfg):
"""
Perform multi-view testing on the pretrained audio 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 audio 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)
cu.load_test_checkpoint(cfg, model)
# Create audio 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
== 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_ENSEMBLE_VIEWS,
cfg.MODEL.NUM_CLASSES,
len(test_loader),
cfg.DATA.ENSEMBLE_METHOD,
)
else:
test_meter = TestMeter(
len(test_loader.dataset)
// cfg.TEST.NUM_ENSEMBLE_VIEWS,
cfg.TEST.NUM_ENSEMBLE_VIEWS,
cfg.MODEL.NUM_CLASSES[0],
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, preds, preds_clips, labels, metadata = perform_test(test_loader, model, test_meter, cfg, writer)
if du.is_master_proc():
if cfg.TEST.DATASET == 'epickitchens':
results = {'verb_output': preds[0],
'noun_output': preds[1],
'narration_id': metadata}
scores_path = os.path.join(cfg.OUTPUT_DIR, 'scores')
if not os.path.exists(scores_path):
os.makedirs(scores_path)
file_path = os.path.join(scores_path, cfg.EPICKITCHENS.TEST_SPLIT+'.pkl')
pickle.dump(results, open(file_path, 'wb'))
else:
if cfg.TEST.DATASET == 'vggsound':
get_stats(preds, labels)
results = {'scores': preds, 'labels': labels}
scores_path = os.path.join(cfg.OUTPUT_DIR, 'scores')
if not os.path.exists(scores_path):
os.makedirs(scores_path)
file_path = os.path.join(scores_path, 'test.pkl')
pickle.dump(results, open(file_path, 'wb'))
if writer is not None:
writer.close()
def train(cfg):
"""
Train an audio 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)
# Print config.
logger.info("Train with config:")
logger.info(pprint.pformat(cfg))
# Build the audio 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)
if cfg.BN.FREEZE:
model.module.freeze_fn(
'bn_parameters') if cfg.NUM_GPUS > 1 else model.freeze_fn(
'bn_parameters')
# 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 audio 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")
precise_bn_loader = (loader.construct_loader(cfg, "train")
if cfg.BN.USE_PRECISE_STATS else None)
else:
train_loader = loader.construct_loader(cfg, "train+val")
val_loader = loader.construct_loader(cfg, "val")
precise_bn_loader = (loader.construct_loader(cfg, "train+val")
if cfg.BN.USE_PRECISE_STATS else None)
# Create meters.
if cfg.TRAIN.DATASET == 'epickitchens':
train_meter = EPICTrainMeter(len(train_loader), cfg)
val_meter = EPICValMeter(len(val_loader), cfg)
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
if cfg.WANDB.ENABLE and du.is_master_proc(cfg.NUM_GPUS * cfg.NUM_SHARDS):
wandb_log = True
if cfg.TRAIN.AUTO_RESUME and cfg.WANDB.RUN_ID != "":
wandb.init(project='slowfast',
config=cfg,
sync_tensorboard=True,
resume=cfg.WANDB.RUN_ID)
else:
wandb.init(project='slowfast', config=cfg, sync_tensorboard=True)
wandb.watch(model)
else:
wandb_log = False
# 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, writer, wandb_log)
is_checkp_epoch = cu.is_checkpoint_epoch(
cfg,
cur_epoch,
)
is_eval_epoch = misc.is_eval_epoch(
cfg,
cur_epoch,
)
# 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:
is_best_epoch, _ = eval_epoch(val_loader, model, val_meter,
cur_epoch, cfg, writer, wandb_log)
if is_best_epoch:
cu.save_checkpoint(cfg.OUTPUT_DIR,
model,
optimizer,
cur_epoch,
cfg,
is_best_epoch=is_best_epoch)
if writer is not None:
writer.close()
def feature_extract(kwargs):
"""
Perform multi-view testing on the pretrained video model.
Args:
cfg (CfgNode): configs. Details can be found in
slowfast/config/defaults.py
"""
cfg = kwargs.pop('cfg')
path_to_video_list = kwargs.pop('path_to_video_list')
path_to_video_dir = kwargs.pop('path_to_video_dir')
path_to_feat_dir = kwargs.pop('path_to_feat_dir')
num_features = kwargs.pop('num_features')
if not os.path.isdir(path_to_feat_dir):
os.makedirs(path_to_feat_dir)
# Setup logging format.
logging.setup_logging()
# Print config.
logger.info("Extract feature with config:")
logger.info(cfg)
# Build the video model and print model statistics.
model = model_builder.build_model(cfg, feature_extraction=True)
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("Extracting features with random initialization. Only for debugging.")
feature_extract_fn = perform_bbox_feature_extract if cfg.DETECTION.ENABLE else perform_feature_extract
# Create video feature extraction loaders.
if osp.isfile(path_to_video_list):
path_to_videos = []
with open(path_to_video_list, 'r') as f:
video_list = json.load(f)
for video_name in video_list:
path_to_video = osp.join(path_to_video_dir, video_name)
if osp.isfile(path_to_video):
path_to_videos.append(path_to_video)
else:
path_to_videos = glob.glob(osp.join(path_to_video_dir, '*'))
for video_idx, path_to_video in enumerate(tqdm(path_to_videos)):
path_to_feature = osp.join(path_to_feat_dir, osp.splitext(osp.basename(path_to_video))[0] + '.json')
if osp.isfile(path_to_feature) and json.load(open(path_to_feature))['num_features'] == num_features:
continue
video_extraction_loader = loader.construct_loader(cfg, path_to_video)
video_data = {
'num_features': 0,
'video_features': {}
}
if len(video_extraction_loader) > 0:
video_features = feature_extract_fn(video_extraction_loader, model, cfg)
assert all([feature is not None for feature in video_features]), 'Missing some features !'
video_data['num_features'] = len(video_features)
video_data['video_features'] = video_features
if video_data['num_features'] != num_features:
print('Warning! Video %s has %d features.' % (path_to_video, video_data['num_features']))
with open(path_to_feature, 'w') as f:
json.dump(video_data, f)
def test(cfg):
"""
Perform multi-view testing/feature extraction 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(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)
vid_root = os.path.join(cfg.DATA.PATH_TO_DATA_DIR, cfg.DATA.PATH_PREFIX)
videos_list_file = os.path.join(cfg.DATA.PATH_TO_DATA_DIR, "vid_list.csv")
print("Loading Video List ...")
with open(videos_list_file) as f:
videos = sorted([x.strip() for x in f.readlines() if len(x.strip()) > 0])
print("Done")
print("----------------------------------------------------------")
if cfg.DATA.READ_VID_FILE:
rejected_vids = []
print("{} videos to be processed...".format(len(videos)))
print("----------------------------------------------------------")
start_time = time.time()
for vid_no, vid in enumerate(videos):
# Create video testing loaders.
path_to_vid = os.path.join(vid_root, os.path.split(vid)[0])
vid_id = os.path.split(vid)[1]
if cfg.DATA.READ_VID_FILE:
try:
_ = VideoFileClip(
os.path.join(path_to_vid, vid_id) + cfg.DATA.VID_FILE_EXT,
audio=False,
fps_source="fps",
)
except Exception as e:
print("{}. {} cannot be read with error {}".format(vid_no, vid, e))
print("----------------------------------------------------------")
rejected_vids.append(vid)
continue
out_path = os.path.join(cfg.OUTPUT_DIR, os.path.split(vid)[0])
out_file = vid_id.split(".")[0] + "_{}.npy".format(cfg.DATA.NUM_FRAMES)
if os.path.exists(os.path.join(out_path, out_file)):
print("{}. {} already exists".format(vid_no, out_file))
print("----------------------------------------------------------")
continue
print("{}. Processing {}...".format(vid_no, vid))
dataset = VideoSet(
cfg, path_to_vid, vid_id, read_vid_file=cfg.DATA.READ_VID_FILE
)
test_loader = torch.utils.data.DataLoader(
dataset,
batch_size=cfg.TEST.BATCH_SIZE,
shuffle=False,
sampler=None,
num_workers=cfg.DATA_LOADER.NUM_WORKERS,
pin_memory=cfg.DATA_LOADER.PIN_MEMORY,
drop_last=False,
)
# Perform multi-view test on the entire dataset.
feat_arr = perform_inference(test_loader, model, cfg)
os.makedirs(out_path, exist_ok=True)
np.save(os.path.join(out_path, out_file), feat_arr)
print("Done.")
print("----------------------------------------------------------")
if cfg.DATA.READ_VID_FILE:
print("Rejected Videos: {}".format(rejected_vids))
end_time = time.time()
hours, minutes, seconds = calculate_time_taken(start_time, end_time)
print(
"Time taken: {} hour(s), {} minute(s) and {} second(s)".format(
hours, minutes, seconds
)
)
print("----------------------------------------------------------")
def get_predictions(self):
"""
Predict and append prediction results to each box in each keyframe in
`self.pred_boxes` dictionary.
"""
# Set random seed from configs.
np.random.seed(self.cfg.RNG_SEED)
torch.manual_seed(self.cfg.RNG_SEED)
# Setup logging format.
logging.setup_logging(self.cfg.OUTPUT_DIR)
# Print config.
logger.info("Run demo with config:")
logger.info(self.cfg)
assert (self.cfg.NUM_GPUS <=
1), "Cannot run demo visualization on multiple GPUs."
# Build the video model and print model statistics.
model = build_model(self.cfg)
model.eval()
logger.info("Start loading model info")
misc.log_model_info(model, self.cfg, use_train_input=False)
logger.info("Start loading model weights")
cu.load_test_checkpoint(self.cfg, model)
logger.info("Finish loading model weights")
logger.info("Start making predictions for precomputed boxes.")
for keyframe_idx, boxes_and_labels in tqdm.tqdm(
self.pred_boxes.items()):
inputs = self.get_input_clip(keyframe_idx)
boxes = boxes_and_labels[0]
boxes = torch.from_numpy(np.array(boxes)).float()
box_transformed = scale_boxes(
self.cfg.DATA.TEST_CROP_SIZE,
boxes,
self.display_height,
self.display_width,
)
# Pad frame index for each box.
box_inputs = torch.cat(
[
torch.full((box_transformed.shape[0], 1), float(0)),
box_transformed,
],
axis=1,
)
if self.cfg.NUM_GPUS:
# 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)
box_inputs = box_inputs.cuda()
preds = model(inputs, box_inputs)
preds = preds.detach()
if self.cfg.NUM_GPUS:
preds = preds.cpu()
boxes_and_labels[1] = preds
def demo(cfg):
# 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("Run demo with config:")
logger.info(cfg)
# Build the video model and print model statistics.
model = model_builder.build_model(cfg)
model.eval()
misc.log_model_info(model)
# Load a checkpoint to test if applicable.
if cfg.TEST.CHECKPOINT_FILE_PATH != "":
ckpt = cfg.TEST.CHECKPOINT_FILE_PATH
elif cu.has_checkpoint(cfg.OUTPUT_DIR):
ckpt = cu.get_last_checkpoint(cfg.OUTPUT_DIR)
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.
ckpt = cfg.TRAIN.CHECKPOINT_FILE_PATH
else:
raise NotImplementedError("Unknown way to load checkpoint.")
cu.load_checkpoint(
ckpt,
model,
cfg.NUM_GPUS > 1,
None,
inflation=False,
convert_from_caffe2="caffe2"
in [cfg.TEST.CHECKPOINT_TYPE, cfg.TRAIN.CHECKPOINT_TYPE],
)
if cfg.DETECTION.ENABLE:
# Load object detector from detectron2
dtron2_cfg_file = cfg.DEMO.DETECTRON2_OBJECT_DETECTION_MODEL_CFG
dtron2_cfg = get_cfg()
dtron2_cfg.merge_from_file(model_zoo.get_config_file(dtron2_cfg_file))
dtron2_cfg.MODEL.ROI_HEADS.SCORE_THRESH_TEST = .5
dtron2_cfg.MODEL.WEIGHTS = cfg.DEMO.DETECTRON2_OBJECT_DETECTION_MODEL_WEIGHTS
object_predictor = DefaultPredictor(dtron2_cfg)
# Load the labels of AVA dataset
with open(cfg.DEMO.LABEL_FILE_PATH) as f:
labels = f.read().split('\n')[:-1]
palette = np.random.randint(64, 128, (len(labels), 3)).tolist()
boxes = []
else:
# Load the labels of Kinectics-400 dataset
labels_df = pd.read_csv(cfg.DEMO.LABEL_FILE_PATH)
labels = labels_df['name'].values
frame_provider = VideoReader(cfg)
seq_len = cfg.DATA.NUM_FRAMES * cfg.DATA.SAMPLING_RATE
frames = []
pred_labels = []
s = 0.
for able_to_read, frame in frame_provider:
if not able_to_read:
# when reaches the end frame, clear the buffer and continue to the next one.
frames = []
continue
if len(frames) != seq_len:
frame_processed = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
frame_processed = scale(cfg.DATA.TEST_CROP_SIZE, frame_processed)
frames.append(frame_processed)
if cfg.DETECTION.ENABLE and len(frames) == seq_len // 2 - 1:
mid_frame = frame
if len(frames) == seq_len:
start = time()
if cfg.DETECTION.ENABLE:
outputs = object_predictor(mid_frame)
fields = outputs["instances"]._fields
pred_classes = fields["pred_classes"]
selection_mask = pred_classes == 0
# acquire person boxes
pred_classes = pred_classes[selection_mask]
pred_boxes = fields["pred_boxes"].tensor[selection_mask]
scores = fields["scores"][selection_mask]
boxes = cv2_transform.scale_boxes(
cfg.DATA.TEST_CROP_SIZE, pred_boxes,
frame_provider.display_height,
frame_provider.display_width)
boxes = torch.cat(
[torch.full((boxes.shape[0], 1), float(0)).cuda(), boxes],
axis=1)
inputs = torch.as_tensor(frames).float()
inputs = inputs / 255.0
# Perform color normalization.
inputs = inputs - torch.tensor(cfg.DATA.MEAN)
inputs = inputs / torch.tensor(cfg.DATA.STD)
# T H W C -> C T H W.
inputs = inputs.permute(3, 0, 1, 2)
# 1 C T H W.
inputs = inputs.unsqueeze(0)
# Sample frames for the fast pathway.
index = torch.linspace(0, inputs.shape[2] - 1,
cfg.DATA.NUM_FRAMES).long()
fast_pathway = torch.index_select(inputs, 2, index)
# logger.info('fast_pathway.shape={}'.format(fast_pathway.shape))
# Sample frames for the slow pathway.
index = torch.linspace(0, fast_pathway.shape[2] - 1,
fast_pathway.shape[2] //
cfg.SLOWFAST.ALPHA).long()
slow_pathway = torch.index_select(fast_pathway, 2, index)
# logger.info('slow_pathway.shape={}'.format(slow_pathway.shape))
inputs = [slow_pathway, fast_pathway]
"""
# 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)
"""
# Perform the forward pass.
if cfg.DETECTION.ENABLE:
# When there is nothing in the scene,
# use a dummy variable to disable all computations below.
if not len(boxes):
preds = torch.tensor([])
else:
preds = model(inputs, boxes)
else:
preds = model(inputs)
# Gather all the predictions across all the devices to perform ensemble.
if cfg.NUM_GPUS > 1:
preds = du.all_gather(preds)[0]
if cfg.DETECTION.ENABLE:
# This post processing was intendedly assigned to the cpu since my laptop GPU
# RTX 2080 runs out of its memory, if your GPU is more powerful, I'd recommend
# to change this section to make CUDA does the processing.
preds = preds.cpu().detach().numpy()
pred_masks = preds > .1
label_ids = [
np.nonzero(pred_mask)[0] for pred_mask in pred_masks
]
pred_labels = [[
labels[label_id] for label_id in perbox_label_ids
] for perbox_label_ids in label_ids]
# I'm unsure how to detectron2 rescales boxes to image original size, so I use
# input boxes of slowfast and rescale back it instead, it's safer and even if boxes
# was not rescaled by cv2_transform.rescale_boxes, it still works.
boxes = boxes.cpu().detach().numpy()
ratio = np.min([
frame_provider.display_height, frame_provider.display_width
]) / cfg.DATA.TEST_CROP_SIZE
boxes = boxes[:, 1:] * ratio
else:
## Option 1: single label inference selected from the highest probability entry.
# label_id = preds.argmax(-1).cpu()
# pred_label = labels[label_id]
# Option 2: multi-label inferencing selected from probability entries > threshold
label_ids = torch.nonzero(
preds.squeeze() > .1).reshape(-1).cpu().detach().numpy()
pred_labels = labels[label_ids]
logger.info(pred_labels)
if not list(pred_labels):
pred_labels = ['Unknown']
# # option 1: remove the oldest frame in the buffer to make place for the new one.
# frames.pop(0)
# option 2: empty the buffer
frames = []
s = time() - start
if cfg.DETECTION.ENABLE and pred_labels and boxes.any():
for box, box_labels in zip(boxes.astype(int), pred_labels):
cv2.rectangle(frame,
tuple(box[:2]),
tuple(box[2:]), (0, 255, 0),
thickness=2)
label_origin = box[:2]
for label in box_labels:
label_origin[-1] -= 5
(label_width, label_height), _ = cv2.getTextSize(
label, cv2.FONT_HERSHEY_SIMPLEX, .5, 2)
cv2.rectangle(frame,
(label_origin[0], label_origin[1] + 5),
(label_origin[0] + label_width,
label_origin[1] - label_height - 5),
palette[labels.index(label)], -1)
cv2.putText(frame, label, tuple(label_origin),
cv2.FONT_HERSHEY_SIMPLEX, .5, (255, 255, 255),
1)
label_origin[-1] -= label_height + 5
if not cfg.DETECTION.ENABLE:
# Display predicted labels to frame.
y_offset = 50
cv2.putText(frame,
'Action:', (10, y_offset),
fontFace=cv2.FONT_HERSHEY_SIMPLEX,
fontScale=.65,
color=(0, 235, 0),
thickness=2)
for pred_label in pred_labels:
y_offset += 30
cv2.putText(frame,
'{}'.format(pred_label), (20, y_offset),
fontFace=cv2.FONT_HERSHEY_SIMPLEX,
fontScale=.65,
color=(0, 235, 0),
thickness=2)
# Display prediction speed
cv2.putText(frame,
'Speed: {:.2f}s'.format(s), (10, 25),
fontFace=cv2.FONT_HERSHEY_SIMPLEX,
fontScale=.65,
color=(0, 235, 0),
thickness=2)
# Display the frame
cv2.imshow('SlowFast', frame)
# hit Esc to quit the demo.
key = cv2.waitKey(1)
if key == 27:
break
frame_provider.clean()
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)
out_str_prefix = "lin" if cfg.MODEL.DETACH_FINAL_FC else ""
if du.is_master_proc() and cfg.LOG_MODEL_INFO:
misc.log_model_info(model, cfg, use_train_input=False)
if (cfg.TASK == "ssl" and cfg.MODEL.MODEL_NAME == "ContrastiveModel"
and cfg.CONTRASTIVE.KNN_ON):
train_loader = loader.construct_loader(cfg, "train")
out_str_prefix = "knn"
if hasattr(model, "module"):
model.module.init_knn_labels(train_loader)
else:
model.init_knn_labels(train_loader)
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 if not cfg.TASK == "ssl" else
cfg.CONTRASTIVE.NUM_CLASSES_DOWNSTREAM,
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()
result_string = (
"_a{}{}{} Top1 Acc: {} Top5 Acc: {} MEM: {:.2f} dataset: {}{}"
"".format(
out_str_prefix,
cfg.TEST.DATASET[0],
test_meter.stats["top1_acc"],
test_meter.stats["top1_acc"],
test_meter.stats["top5_acc"],
misc.gpu_mem_usage(),
cfg.TEST.DATASET[0],
cfg.MODEL.NUM_CLASSES,
))
logger.info("testing done: {}".format(result_string))
return result_string
def test_videos(cfg):
"""
Ouputs a reconstruction of 1 video from each class.
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)
assert cfg.PREDICTIVE.ENABLE, "Model doesn't generate frames"
# Build the video model and print model statistics.
model = build_model(cfg)
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 != "":
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")
test_set = build_dataset(cfg.TEST.DATASET, cfg, "test")
logger.info("Testing model for {} iterations".format(len(test_loader)))
# # Perform multi-view test on the entire dataset.
# perform_test(test_loader, model, test_meter, cfg)
# create directory with video for output
for i in range(cfg.MODEL.NUM_CLASSES):
# get video input
# get model prediction
# output frames or GIF of video+pred side by side + cpc_loss + predictive loss
## needs function
frames, label, index, _ = test_set.get_example_by_class(i)
input_frames = frames.cuda(non_blocking=True)
labels = labels.cuda()
preds = model(input_frames[None, :], return_frames=True)
class_name = test_set._classes[i]
loss_pred
loss_cpc
if cfg.PREDICTIVE.ENABLE:
errors = preds['pred_errors']
if cfg.PREDICTIVE.CPC:
cpc_loss = preds['cpc_loss']
preds = preds['frames']
frames = frames.permute(0, 2, 3, 4, 1)
preds = preds.permute(0, 2, 3, 4, 1)
images = [
Image.fromarray(image.astype(np.uint8), 'RGB') for image in images
]
images[0].save(os.path.join(
cfg.OUTPUT_DIR,
'%s_pred-%f_cpc-%d.gif' % (class_name, loss_pred, loss_cpc)),
save_all=True,
append_images=images[1:],
optimize=False,
duration=40,
loop=0)
def demo(cfg):
# 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("Run demo with config:")
logger.info(cfg)
# Build the video model and print model statistics.
model = model_builder.build_model(cfg)
model.eval()
misc.log_model_info(model)
# Load a checkpoint to test if applicable.
if cfg.TEST.CHECKPOINT_FILE_PATH != "":
ckpt = cfg.TEST.CHECKPOINT_FILE_PATH
elif cu.has_checkpoint(cfg.OUTPUT_DIR):
ckpt = cu.get_last_checkpoint(cfg.OUTPUT_DIR)
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.
ckpt = cfg.TRAIN.CHECKPOINT_FILE_PATH
else:
raise NotImplementedError("Unknown way to load checkpoint.")
cu.load_checkpoint(
ckpt,
model,
cfg.NUM_GPUS > 1,
None,
inflation=False,
convert_from_caffe2="caffe2"
in [cfg.TEST.CHECKPOINT_TYPE, cfg.TRAIN.CHECKPOINT_TYPE],
)
# Load the labels of Kinectics-400 dataset
labels_df = pd.read_csv(cfg.DEMO.LABEL_FILE_PATH)
labels = labels_df['name'].values
img_provider = VideoReader(cfg)
frames = []
# # Option 1
# pred_label = ''
# Option 2
pred_labels = []
s = 0.
for able_to_read, frame in img_provider:
if not able_to_read:
# when reaches the end frame, clear the buffer and continue to the next one.
frames = []
continue
if len(frames) != cfg.DATA.NUM_FRAMES * cfg.DATA.SAMPLING_RATE:
frame_processed = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
frame_processed = scale(256, frame_processed)
frames.append(frame_processed)
if len(frames) == cfg.DATA.NUM_FRAMES * cfg.DATA.SAMPLING_RATE:
start = time()
# Perform color normalization.
inputs = torch.tensor(frames).float()
inputs = inputs / 255.0
inputs = inputs - torch.tensor(cfg.DATA.MEAN)
inputs = inputs / torch.tensor(cfg.DATA.STD)
# T H W C -> C T H W.
inputs = inputs.permute(3, 0, 1, 2)
# 1 C T H W.
inputs = inputs[None, :, :, :, :]
# Sample frames for the fast pathway.
index = torch.linspace(0, inputs.shape[2] - 1,
cfg.DATA.NUM_FRAMES).long()
fast_pathway = torch.index_select(inputs, 2, index)
logger.info('fast_pathway.shape={}'.format(fast_pathway.shape))
# Sample frames for the slow pathway.
index = torch.linspace(0, fast_pathway.shape[2] - 1,
fast_pathway.shape[2] //
cfg.SLOWFAST.ALPHA).long()
slow_pathway = torch.index_select(fast_pathway, 2, index)
logger.info('slow_pathway.shape={}'.format(slow_pathway.shape))
inputs = [slow_pathway, fast_pathway]
# 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)
# Perform the forward pass.
preds = model(inputs)
# Gather all the predictions across all the devices to perform ensemble.
if cfg.NUM_GPUS > 1:
preds = du.all_gather(preds)[0]
## Option 1: single label inference selected from the highest probability entry.
# label_id = preds.argmax(-1).cpu()
# pred_label = labels[label_id]
# Option 2: multi-label inferencing selected from probability entries > threshold
label_ids = torch.nonzero(
preds.squeeze() > .1).reshape(-1).cpu().detach().numpy()
pred_labels = labels[label_ids]
logger.info(pred_labels)
if not list(pred_labels):
pred_labels = ['Unknown']
# remove the oldest frame in the buffer to make place for the new one.
# frames.pop(0)
frames = []
s = time() - start
# #************************************************************
# # Option 1
# #************************************************************
# # Display prediction speed to frame
# cv2.putText(frame, 'Speed: {:.2f}s'.format(s), (20, 30),
# fontFace=cv2.FONT_HERSHEY_SIMPLEX,
# fontScale=1, color=(0, 235, 0), thickness=3)
# # Display predicted label to frame.
# cv2.putText(frame, 'Action: {}'.format(pred_label), (20, 60),
# fontFace=cv2.FONT_HERSHEY_SIMPLEX,
# fontScale=1, color=(0, 235, 0), thickness=3)
#************************************************************
# Option 2
#************************************************************
# Display prediction speed to frame
cv2.putText(frame,
'Speed: {:.2f}s'.format(s), (20, 30),
fontFace=cv2.FONT_HERSHEY_SIMPLEX,
fontScale=1,
color=(0, 235, 0),
thickness=3)
# Display predicted labels to frame.
y_offset = 60
cv2.putText(frame,
'Action:', (20, y_offset),
fontFace=cv2.FONT_HERSHEY_SIMPLEX,
fontScale=1,
color=(0, 235, 0),
thickness=3)
for pred_label in pred_labels:
y_offset += 30
cv2.putText(frame,
'{}'.format(pred_label), (20, y_offset),
fontFace=cv2.FONT_HERSHEY_SIMPLEX,
fontScale=1,
color=(0, 235, 0),
thickness=3)
# Display the frame
cv2.imshow('SlowFast', frame)
# hit Esc to quit the demo.
key = cv2.waitKey(1)
if key == 27:
break
img_provider.clean()
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_epoch(self,
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()
train_meter.iter_tic()
data_size = len(train_loader)
start = time.time()
btch = cfg.TRAIN.BATCH_SIZE * self.cfg.NUM_SHARDS
rankE = os.environ.get("RANK", None)
worldE = os.environ.get("WORLD_SIZE", None)
dSize = data_size * btch
self.logger.info(
"Train Epoch {} dLen {} Batch {} dSize {} localRank {} rank {} {} world {} {}"
.format(cur_epoch, data_size, btch, dSize, du.get_local_rank(),
du.get_rank(), rankE, du.get_world_size(), worldE))
tot = 0
first = True
predsAll = []
labelsAll = []
for cur_iter, (inputs, labels, _, meta) in enumerate(train_loader):
# Transfer the data to the current GPU device.
tot += len(labels)
if isinstance(inputs, (list, )):
if first:
self.logger.info(
"rank {} LEN {} {} shape Slow {} Fast {} {} tot {}".
format(du.get_rank(), len(labels), len(inputs),
inputs[0].shape, inputs[1].shape,
labels[0].shape, tot))
first = False
for i in range(len(inputs)):
inputs[i] = inputs[i].cuda(non_blocking=True)
else:
if first:
self.logger.info(
"rank {} LEN {} shape {} {} tot {}".format(
du.get_rank(), len(labels), inputs.shape,
labels[0].shape, tot))
first = False
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)
# Update the learning rate.
lr = optim.get_epoch_lr(cur_epoch + float(cur_iter) / data_size,
cfg)
optim.set_lr(optimizer, lr)
if cfg.DETECTION.ENABLE:
# Compute the predictions.
preds = model(inputs, meta["boxes"])
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)
# check Nan Loss.
misc.check_nan_losses(loss)
# Perform the backward pass.
optimizer.zero_grad()
loss.backward()
# Update the parameters.
optimizer.step()
if cfg.DETECTION.ENABLE:
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,
)
ite = data_size * cur_epoch + cur_iter
if du.is_master_proc():
self.logger.log_row(name='TrainLoss',
iter=ite,
loss=loss,
description="train loss")
self.logger.log_row(name='TrainLr',
iter=ite,
lr=lr,
description="train learn rate")
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:
# Binary classifier - save preds / labels for metrics
if cfg.MODEL.NUM_CLASSES == 2:
predsAll.extend(preds.detach().cpu().numpy()[:, -1])
labelsAll.extend(labels.detach().cpu().numpy())
# Compute the errors.
num_topks_correct = metrics.topks_correct(
preds, labels, (1, min(5, cfg.MODEL.NUM_CLASSES)))
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.
# self.logger.info("UPDATING stat {} {} {}".format(inputs[0].size(0), cfg.NUM_GPUS, inputs[0].size(0) * cfg.NUM_GPUS))
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,
)
stats = train_meter.log_iter_stats(cur_epoch, cur_iter, predsAll,
labelsAll)
ite = dSize * cur_epoch + btch * (cur_iter + 1)
self.plotStats(stats, ite, 'TrainIter')
train_meter.iter_tic()
if du.is_master_proc() and cfg.LOG_MODEL_INFO:
misc.log_model_info(model, cfg, use_train_input=True)
# Log epoch stats.
gathered = du.all_gather([
torch.tensor(predsAll).to(torch.device("cuda")),
torch.tensor(labelsAll).to(torch.device("cuda"))
])
stats = train_meter.log_epoch_stats(cur_epoch,
gathered[0].detach().cpu().numpy(),
gathered[1].detach().cpu().numpy())
ite = (cur_epoch + 1) * dSize
self.plotStats(stats, ite, 'TrainEpoch')
train_meter.reset()
end = time.time()
el = end - start
totAll = du.all_reduce([torch.tensor(tot).cuda()], average=False)
tSum = totAll[0].item()
elT = torch.tensor(el).cuda()
elMax = du.all_reduce([elT], op=dist.ReduceOp.MAX,
average=False)[0].item()
jobRate = tSum / elMax
self.logger.info(
"totSampCnt {} workerSampCnt {} eTimeMax {} eTimeWorker {} SampPerSecJob {:.1f} SampPerSecWorker {:.1f}"
.format(tSum, tot, elMax, el, jobRate, tot / el))
return jobRate
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 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 = build_model(cfg)
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 != "":
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.
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.
preds, labels, metadata = perform_test(test_loader, model, test_meter, cfg)
if du.is_master_proc():
if cfg.TEST.DATASET == 'epickitchens':
results = {
'scores': {
'verb': preds[0],
'noun': preds[1]
},
'labels': {
'verb': labels[0],
'noun': 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)
file_path = os.path.join(scores_path,
cfg.EPICKITCHENS.TEST_SPLIT + '.pkl')
pickle.dump(results, open(file_path, 'wb'))
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(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)
# Create a GradScaler for mixed precision training
scaler = torch.cuda.amp.GradScaler(enabled=cfg.TRAIN.MIXED_PRECISION)
# 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, task=cfg.TASK)
if last_checkpoint is not None:
checkpoint_epoch = cu.load_checkpoint(
last_checkpoint,
model,
cfg.NUM_GPUS > 1,
optimizer,
scaler if cfg.TRAIN.MIXED_PRECISION else None,
)
start_epoch = checkpoint_epoch + 1
elif "ssl_eval" in cfg.TASK:
last_checkpoint = cu.get_last_checkpoint(cfg.OUTPUT_DIR,
task="ssl")
checkpoint_epoch = cu.load_checkpoint(
last_checkpoint,
model,
cfg.NUM_GPUS > 1,
optimizer,
scaler if cfg.TRAIN.MIXED_PRECISION else None,
epoch_reset=True,
clear_name_pattern=cfg.TRAIN.CHECKPOINT_CLEAR_NAME_PATTERN,
)
start_epoch = checkpoint_epoch + 1
else:
start_epoch = 0
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,
scaler if cfg.TRAIN.MIXED_PRECISION else None,
inflation=cfg.TRAIN.CHECKPOINT_INFLATE,
convert_from_caffe2=cfg.TRAIN.CHECKPOINT_TYPE == "caffe2",
epoch_reset=cfg.TRAIN.CHECKPOINT_EPOCH_RESET,
clear_name_pattern=cfg.TRAIN.CHECKPOINT_CLEAR_NAME_PATTERN,
)
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")
precise_bn_loader = (loader.construct_loader(
cfg, "train", is_precise_bn=True)
if cfg.BN.USE_PRECISE_STATS else None)
# if (
# cfg.TASK == "ssl"
# and cfg.MODEL.MODEL_NAME == "ContrastiveModel"
# and cfg.CONTRASTIVE.KNN_ON
# ):
# if hasattr(model, "module"):
# model.module.init_knn_labels(train_loader)
# else:
# model.init_knn_labels(train_loader)
# 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(1e6, cfg)
val_meter = ValMeter(1e6, 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))
epoch_timer = EpochTimer()
for cur_epoch in range(start_epoch, cfg.SOLVER.MAX_EPOCH):
if cur_epoch > 0 and cfg.DATA.LOADER_CHUNK_SIZE > 0:
num_chunks = math.ceil(cfg.DATA.LOADER_CHUNK_OVERALL_SIZE /
cfg.DATA.LOADER_CHUNK_SIZE)
skip_rows = (cur_epoch) % num_chunks * cfg.DATA.LOADER_CHUNK_SIZE
logger.info(
f"=================+++ num_chunks {num_chunks} skip_rows {skip_rows}"
)
cfg.DATA.SKIP_ROWS = skip_rows
logger.info(f"|===========| skip_rows {skip_rows}")
train_loader = loader.construct_loader(cfg, "train")
loader.shuffle_dataset(train_loader, cur_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,
task=cfg.TASK)
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)
if hasattr(train_loader.dataset, "_set_epoch_num"):
train_loader.dataset._set_epoch_num(cur_epoch)
# Train for one epoch.
epoch_timer.epoch_tic()
train_epoch(
train_loader,
model,
optimizer,
scaler,
train_meter,
cur_epoch,
cfg,
writer,
)
epoch_timer.epoch_toc()
logger.info(
f"Epoch {cur_epoch} takes {epoch_timer.last_epoch_time():.2f}s. Epochs "
f"from {start_epoch} to {cur_epoch} take "
f"{epoch_timer.avg_epoch_time():.2f}s in average and "
f"{epoch_timer.median_epoch_time():.2f}s in median.")
logger.info(
f"For epoch {cur_epoch}, each iteraction takes "
f"{epoch_timer.last_epoch_time()/len(train_loader):.2f}s in average. "
f"From epoch {start_epoch} to {cur_epoch}, each iteraction takes "
f"{epoch_timer.avg_epoch_time()/len(train_loader):.2f}s in average."
)
is_checkp_epoch = (cu.is_checkpoint_epoch(
cfg,
cur_epoch,
None if multigrid is None else multigrid.schedule,
) or cur_epoch == cfg.SOLVER.MAX_EPOCH - 1)
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,
scaler if cfg.TRAIN.MIXED_PRECISION else None,
)
# Evaluate the model on validation set.
if is_eval_epoch:
eval_epoch(
val_loader,
model,
val_meter,
cur_epoch,
cfg,
train_loader,
writer,
)
if writer is not None:
writer.close()
result_string = "Top1 Acc: {:.2f} Top5 Acc: {:.2f} MEM: {:.2f}" "".format(
100 - val_meter.min_top1_err,
100 - val_meter.min_top5_err,
misc.gpu_mem_usage(),
)
logger.info("training done: {}".format(result_string))
return result_string
