mkdir(logdir)
sys.stdout = open(str(logdir) + '/log.txt', 'w')
cfg = ModelConfig(mpii_dataconf.input_shape, pa17j3d, num_pyramids=8,
action_pyramids=[], num_levels=4)
num_predictions = spnet.get_num_predictions(cfg.num_pyramids, cfg.num_levels)
start_lr = 0.001
weights_path = os.path.join(logdir, 'weights_posebaseline_{epoch:03d}.hdf5')
batch_size_mpii = 14
batch_size_ar = 2
"""Load datasets"""
mpii = MpiiSinglePerson(datasetpath('MPII'), dataconf=mpii_dataconf,
poselayout=pa17j3d)
# h36m = Human36M(datasetpath('Human3.6M'), dataconf=human36m_dataconf,
# poselayout=pa17j3d, topology='frames')f
penn_sf = PennAction(datasetpath('Penn_Action'), pennaction_dataconf,
poselayout=pa17j3d, topology='frames', use_gt_bbox=True)
ntu_sf = Ntu(datasetpath('NTU'), ntu_pe_dataconf, poselayout=pa17j3d,
topology='frames', use_gt_bbox=True)
"""Create an object to load data from all datasets."""
data_tr = BatchLoader([mpii, penn_sf, ntu_sf], ['frame'], ['pose'],
TRAIN_MODE, batch_size=[batch_size_mpii, batch_size_ar,
batch_size_ar], num_predictions=num_predictions, shuffle=True)
weights_path = get_file(weights_file,
TF_WEIGHTS_PATH,
file_hash=md5_hash,
cache_subdir='models')
"""Load pre-trained model."""
model.load_weights(weights_path)
"""Merge pose and visibility as a single output."""
outputs = []
for b in range(int(len(model.outputs) / 2)):
outputs.append(
concatenate([model.outputs[2 * b], model.outputs[2 * b + 1]],
name='blk%d' % (b + 1)))
model = Model(model.input, outputs, name=model.name)
logger.debug("DATASETS")
"""Load the MPII dataset."""
mpii = MpiiSinglePerson('datasets/MPII', dataconf=mpii_sp_dataconf)
logger.debug(mpii.dataset_path)
"""Pre-load validation samples and generate the eval. callback."""
mpii_val = BatchLoader(mpii,
x_dictkeys=['frame'],
y_dictkeys=['pose', 'afmat', 'headsize'],
mode=VALID_MODE,
batch_size=mpii.get_length(VALID_MODE),
num_predictions=1,
shuffle=False)
logger.debug(mpii_val.datasets)
printcn(OKBLUE, 'Pre-loading MPII validation data...')
[x_val], [p_val, afmat_val, head_val] = mpii_val[0]
pose_pred = np.zeros_like(p_val)
num_joints = 16
model = reception.build(input_shape, num_joints, dim=2,
num_blocks=num_blocks, num_context_per_joint=2, ksize=(5, 5),
concat_pose_confidence=False)
"""Load pre-trained model."""
# weights_path = get_file(weights_file, TF_WEIGHTS_PATH, md5_hash=md5_hash, cache_subdir='models')
model.load_weights(weights_path)
"""Merge pose and visibility as a single output."""
outputs = []
for b in range(int(len(model.outputs) / 2)):
outputs.append(concatenate([model.outputs[2*b], model.outputs[2*b + 1]],
name='blk%d' % (b + 1)))
model = Model(model.input, outputs, name=model.name)
"""Load the MPII dataset."""
mpii = MpiiSinglePerson(f"{dataset_path}datasets/MPII", dataconf=mpii_sp_dataconf)
"""Pre-load validation samples and generate the eval. callback."""
mpii_val = BatchLoader(mpii, x_dictkeys=['frame'],
y_dictkeys=['pose', 'afmat', 'headsize'], mode=VALID_MODE,
batch_size=mpii.get_length(VALID_MODE), num_predictions=1,
shuffle=False)
printcn(OKBLUE, 'Pre-loading MPII validation data...')
[x_val], [p_val, afmat_val, head_val] = mpii_val[0]
eval_singleperson_pckh(model, x_val, p_val[:,:,0:2], afmat_val, head_val)
pa17j3d,
num_pyramids=8,
action_pyramids=[],
num_levels=4)
num_predictions = spnet.get_num_predictions(cfg.num_pyramids, cfg.num_levels)
start_lr = 0.001
weights_path = os.path.join(
"E:\\Bachelorarbeit-SS20\\weights\\deephar\\output\\ntu_baseline\\0603",
'weights_posebaseline_{epoch:03d}.hdf5')
batch_size_mpii = 10
batch_size_ar = 2
"""Load datasets"""
mpii = MpiiSinglePerson("D:\\MPII", dataconf=mpii_dataconf, poselayout=pa17j3d)
h36m = Human36M("B:\\Human3.6M",
dataconf=human36m_dataconf,
poselayout=pa17j3d,
topology='frames')
penn_sf = PennAction("D:\\PennAction",
pennaction_dataconf,
poselayout=pa17j3d,
topology='frames',
use_gt_bbox=True)
ntu_sf = Ntu("E:\\Bachelorarbeit-SS20\\datasets\\NTU",
ntu_pe_dataconf,
poselayout=pa17j3d,
num_actions=[15],
num_pyramids=6,
action_pyramids=[5, 6],
num_levels=4,
pose_replica=True,
num_pose_features=160,
num_visual_features=160)
num_predictions = spnet.get_num_predictions(cfg.num_pyramids, cfg.num_levels)
num_action_predictions = \
spnet.get_num_predictions(len(cfg.action_pyramids), cfg.num_levels)
logger.info("Loading Datasets")
"""Load datasets"""
mpii = MpiiSinglePerson(os.getcwd() + '/datasets/MPII',
dataconf=mpii_dataconf,
poselayout=pa16j2d)
logger.info("MPII Loaded")
# Check file with bounding boxes
penn_data_path = os.getcwd() + '/datasets/PennAction'
penn_bbox_file = 'penn_pred_bboxes_multitask.json'
if os.path.isfile(os.path.join(penn_data_path, penn_bbox_file)) == False:
logger.debug(
f'Error: file {penn_bbox_file} not found in {penn_data_path}!')
logger.debug(
f'\nPlease download it from https://drive.google.com/file/d/1qXpEKF0d9KxmQdd2_QSIA1c3WGj1D3Y3/view?usp=sharing'
)
sys.stdout.flush()
sys.exit()
"""Architecture configuration."""
num_blocks = 8
batch_size = 24
input_shape = mpii_sp_dataconf.input_shape
num_joints = 16
print("***********************")
print(input_shape)
model = reception.build(input_shape,
num_joints,
dim=2,
num_blocks=num_blocks,
num_context_per_joint=2,
ksize=(5, 5))
"""Load the MPII dataset."""
mpii = MpiiSinglePerson('datasets/MPII', dataconf=mpii_sp_dataconf)
data_tr = BatchLoader(mpii, ['frame'], ['pose'],
TRAIN_MODE,
batch_size=batch_size,
num_predictions=num_blocks,
shuffle=True)
"""Pre-load validation samples and generate the eval. callback."""
mpii_val = BatchLoader(mpii,
x_dictkeys=['frame'],
y_dictkeys=['pose', 'afmat', 'headsize'],
mode=VALID_MODE,
batch_size=mpii.get_length(VALID_MODE),
num_predictions=1,
shuffle=False)
printcn(OKBLUE, 'Pre-loading MPII validation data...')
from deephar.config import mpii_sp_dataconf
from deephar.data import MpiiSinglePerson
from deephar.data import BatchLoader
from deephar.utils import *
sys.path.append(os.path.join(os.getcwd(), 'exp/common'))
from mpii_tools import eval_singleperson_pckh
sys.path.append(os.path.join(os.getcwd(), 'datasets'))
import annothelper
annothelper.check_mpii_dataset()
"""Load the MPII dataset."""
mpii = MpiiSinglePerson('datasets/MPII', dataconf=mpii_sp_dataconf)
"""Pre-load validation samples and generate the eval. callback."""
mpii_val = BatchLoader(
mpii,
x_dictkeys=['frame'],
y_dictkeys=['pose'],
mode=TRAIN_MODE,
# batch_size=mpii.get_length(VALID_MODE), num_predictions=1,
batch_size=1,
num_predictions=1,
shuffle=False)
printcn(OKBLUE, 'Pre-loading MPII validation data...')
# img 256x256
[x_val], [p_val] = mpii_val[10]
print(p_val)
num_pose_features=192,
num_visual_features=192)
num_predictions = spnet.get_num_predictions(cfg.num_pyramids, cfg.num_levels)
num_action_predictions = \
spnet.get_num_predictions(len(cfg.action_pyramids), cfg.num_levels)
start_lr = 0.01
action_weight = 0.1
batch_size_mpii = 3
batch_size_h36m = 4
batch_size_ntu = 8 #1
batch_clips = 4 # 8/4
"""Load datasets"""
mpii = MpiiSinglePerson(datasetpath('MPII'),
dataconf=mpii_dataconf,
poselayout=pa17j3d)
# h36m = Human36M(datasetpath('Human3.6M'), dataconf=human36m_dataconf,
# poselayout=pa17j3d, topology='frames')
ntu_sf = Ntu(datasetpath('NTU'),
ntu_pe_dataconf,
poselayout=pa17j3d,
topology='frames',
use_gt_bbox=True)
ntu = Ntu(datasetpath('NTU'),
ntu_dataconf,
poselayout=pa17j3d,
topology='sequences',
num_pose_features=192,
num_visual_features=192)
num_predictions = spnet.get_num_predictions(cfg.num_pyramids, cfg.num_levels)
num_action_predictions = \
spnet.get_num_predictions(len(cfg.action_pyramids), cfg.num_levels)
start_lr = 0.01
action_weight = 0.1
batch_size_mpii = 3
#batch_size_h36m = 4
batch_size_ntu = 6 #1
batch_clips = 3 # 8/4
"""Load datasets"""
mpii = MpiiSinglePerson("E:\\Bachelorarbeit-SS20\\datasets\\MPII",
dataconf=mpii_dataconf,
poselayout=pa17j3d)
# h36m = Human36M(datasetpath('Human3.6M'), dataconf=human36m_dataconf,
# poselayout=pa17j3d, topology='frames')
ntu_sf = Ntu("E:\\Bachelorarbeit-SS20\\datasets\\NTU",
ntu_pe_dataconf,
poselayout=pa17j3d,
topology='frames',
use_gt_bbox=True)
ntu = Ntu("E:\\Bachelorarbeit-SS20\\datasets\\NTU",
ntu_dataconf,
poselayout=pa17j3d,
topology='sequences',
sys.stdout = open(str(logdir) + '/log.txt', 'w')
weights_file = os.path.join(logdir, 'weights_mpii_{epoch:03d}.h5')
"""Architecture configuration."""
num_blocks = 8
batch_size = 24
input_shape = mpii_sp_dataconf.input_shape
num_joints = 16
model = reception.build(input_shape, num_joints, dim=2,
num_blocks=num_blocks, num_context_per_joint=2, ksize=(5, 5))
"""Load the MPII dataset."""
mpii = MpiiSinglePerson('../../datasets/MPII', dataconf=mpii_sp_dataconf)
data_tr = BatchLoader(mpii, ['frame'], ['pose'], TRAIN_MODE,
batch_size=batch_size, num_predictions=num_blocks, shuffle=True)
"""Pre-load validation samples and generate the eval. callback."""
mpii_val = BatchLoader(mpii, x_dictkeys=['frame'],
y_dictkeys=['pose', 'afmat', 'headsize'], mode=VALID_MODE,
batch_size=mpii.get_length(VALID_MODE), num_predictions=1,
shuffle=False)
printcn(OKBLUE, 'Pre-loading MPII validation data...')
[x_val], [p_val, afmat_val, head_val] = mpii_val[0]
eval_callback = MpiiEvalCallback(x_val, p_val, afmat_val, head_val,
eval_model=model, batch_size=2, pred_per_block=1, logdir=logdir)
loss = pose_regression_loss('l1l2bincross', 0.01)
num_levels=4, pose_replica=False,
num_pose_features=192, num_visual_features=192)
num_predictions = spnet.get_num_predictions(cfg.num_pyramids, cfg.num_levels)
num_action_predictions = \
spnet.get_num_predictions(len(cfg.action_pyramids), cfg.num_levels)
start_lr = 0.01
action_weight = 0.1
batch_size_mpii = 3
#batch_size_h36m = 4
batch_size_ntu = 6 #1
batch_clips = 3 # 8/4
"""Load datasets"""
mpii = MpiiSinglePerson("/home/ispl-ex39/Downloads/deephar-master/datasets/MPII", dataconf=mpii_dataconf,
poselayout=pa17j3d)
# h36m = Human36M(datasetpath('Human3.6M'), dataconf=human36m_dataconf,
# poselayout=pa17j3d, topology='frames')
ntu_sf = Ntu("/home/ispl-ex39/hdd_ext/hdd2000/NTU", ntu_pe_dataconf, poselayout=pa17j3d,
topology='frames', use_gt_bbox=True)
ntu = Ntu("/home/ispl-ex39/hdd_ext/hdd2000/NTU", ntu_dataconf, poselayout=pa17j3d,
topology='sequences', use_gt_bbox=True, clip_size=num_frames)
ntu_s1 = Ntu("/home/ispl-ex39/hdd_ext/hdd2000/NTU", ntu_dataconf, poselayout=pa17j3d,
topology='sequences', use_gt_bbox=True, clip_size=num_frames)
# topology='sequences', use_gt_bbox=True, clip_size=num_frames, num_S=1)
pe_data_tr = BatchLoader([ntu_sf], ['frame'], ['pose'], TRAIN_MODE,