def main_eval(model_json, model_weights, num_stack, num_class, matfile):
xnet = HourglassNet(num_class, num_stack, (256, 256), (64, 64))
xnet.load_model(model_json, model_weights)
valdata = MPIIDataGen("../../data/mpii/mpii_annotations.json", "../../data/mpii/images",
inres=(256, 256), outres=(64, 64), is_train=False)
print 'val data size', valdata.get_dataset_size()
valkps = np.zeros(shape=(valdata.get_dataset_size(), 16, 2), dtype=np.float)
count = 0
batch_size = 8
for _img, _gthmap, _meta in valdata.generator(batch_size, num_stack, sigma=1, is_shuffle=False , with_meta=True):
count += batch_size
if count > valdata.get_dataset_size():
break
out = xnet.model.predict(_img)
get_final_pred_kps(valkps, out[-1], _meta)
scipy.io.savemat(matfile, mdict={'preds' : valkps})
run_pckh(model_json, matfile)
def main_test():
xnet = HourglassNet(16, 8, (256, 256), (64, 64))
xnet.load_model("../../trained_models/hg_s8_b1_v1_adam/net_arch.json",
"../../trained_models/hg_s8_b1_v1_adam/weights_epoch22.h5")
valdata = MPIIDataGen("../../data/mpii/mpii_annotations.json", "../../data/mpii/images",
inres=(256, 256), outres=(64, 64), is_train=False)
print 'val data size', valdata.get_dataset_size()
valkps = np.zeros(shape=(valdata.get_dataset_size(), 16, 2), dtype=np.float)
count = 0
batch_size = 8
for _img, _gthmap, _meta in valdata.generator(batch_size, 8, sigma=2, is_shuffle=False , with_meta=True):
count += batch_size
if count > valdata.get_dataset_size():
break
out = xnet.model.predict(_img)
get_final_pred_kps(valkps, out[-1], _meta)
matfile = os.path.join( "../../trained_models/hg_s8_b1_v1_adam/", 'preds_e22.mat')
scipy.io.savemat(matfile, mdict={'preds' : valkps})
run_pckh('hg_s8_b1_epoch22', matfile)
def main_eval(model_json, model_weights, num_stack, num_class, matfile, tiny):
inres = (192, 192) if tiny else (256, 256)
outres = (48, 48) if tiny else (64, 64)
num_channles = 128 if tiny else 256
xnet = HourglassNet(num_classes=num_class,
num_stacks=num_stack,
num_channels=num_channles,
inres=inres,
outres=outres)
xnet.load_model(model_json, model_weights)
# dataset_path = '/home/tomas_bordac/nyu_croped'
dataset_path = os.path.join('D:\\', 'nyu_croped')
valdata = NYUHandDataGen('joint_data.mat',
dataset_path,
inres=inres,
outres=outres,
is_train=False)
print('val data size', valdata.get_dataset_size())
valkps = np.zeros(shape=(valdata.get_dataset_size(), 11, 2),
dtype=np.float)
count = 0
batch_size = 8
for _img, _gthmap, _meta in valdata.generator(batch_size,
num_stack,
sigma=3,
is_shuffle=False,
with_meta=True):
count += batch_size
if count > valdata.get_dataset_size():
break
out = xnet.model.predict(_img)
get_final_pred_kps(valkps, out[-1], _meta, outres)
scipy.io.savemat(matfile, mdict={'preds': valkps})
run_pckh(model_json, matfile)
def main_eval(model_path, num_stack, num_class, matfile, tiny):
inres = (192, 192) if tiny else (256, 256)
outres = (48, 48) if tiny else (64, 64)
num_channles = 128 if tiny else 256
xnet = HourglassNet(num_classes=num_class,
num_stacks=num_stack,
num_channels=num_channles,
inres=inres,
outres=outres)
xnet.load_model(model_path)
valdata = MPIIDataGen("../../data/mpii/mpii_annotations.json",
"../../data/mpii/images",
inres=inres,
outres=outres,
is_train=False)
print('val data size', valdata.get_dataset_size())
valkps = np.zeros(shape=(valdata.get_dataset_size(), 16, 2),
dtype=np.float)
count = 0
batch_size = 8
for _img, _gthmap, _meta in valdata.generator(batch_size,
num_stack,
sigma=1,
is_shuffle=False,
with_meta=True):
count += batch_size
if count > valdata.get_dataset_size():
break
out = xnet.model.predict(_img)
get_final_pred_kps(valkps, out[-1], _meta, outres)
scipy.io.savemat(matfile, mdict={'preds': valkps})
run_pckh(model_path, matfile)
print 'val data size', valdata.get_dataset_size()
valkps = np.zeros(shape=(valdata.get_dataset_size(), 16, 2), dtype=np.float)
count = 0
batch_size = 8
for _img, _gthmap, _meta in valdata.generator(batch_size, 8, sigma=2, is_shuffle=False , with_meta=True):
count += batch_size
if count > valdata.get_dataset_size():
break
out = xnet.model.predict(_img)
get_final_pred_kps(valkps, out[-1], _meta)
matfile = os.path.join( "../../trained_models/hg_s8_b1_v1_adam/", 'preds_e22.mat')
scipy.io.savemat(matfile, mdict={'preds' : valkps})
run_pckh('hg_s8_b1_epoch22', matfile)
if __name__ == '__main__':
os.environ["CUDA_DEVICE_ORDER"] = "PCI_BUS_ID"
os.environ["CUDA_VISIBLE_DEVICES"] = '2'
#main_test()
matfile = os.path.join( "../../trained_models/hg_s8_b1_v1_adam/", 'preds_e22.mat')
run_pckh('hg_s8_b1_epoch22', matfile)