def run_eval(self,epoch):
val_data = MPIIDataGen(jsonfile='../../data/mpii/mpii_annotations.json',imgpath='../../data/mpii/images',inres=self.inres,outres=self.outres,is_train=False)
total_success , total_fail = 0 ,0
threshold = 0.5
count = 0
batch_size = 8
for _img , _gthmap , _meta in val_data.generator(batch_size,8,sigma=2,is_shuffle=False,with_meta=True):
count += batch_size
if count > val_data.get_dataset_size():
break
out = self.model.predict(_img)
suc,bad = cal_heatmap_acc(out[-1],_meta,threshold)
total_success +=suc
total_fail += bad
acc = total_success * 1.0 / (total_success+total_fail)
print('Eval Accuracy ',acc,' @ Epoch ',epoch)
with open(os.path.join(self.get_folder_path(),'val.txt'),'a+') as xfile:
xfile.write('Epoch '+ str(epoch) + ':' + str(acc) +'\n')
def main_test():
xnet = HourglassNet(16, 8, (256, 256), (64, 64))
xnet.load_model("../../trained_models/hg_s8_b1_sigma1/net_arch.json", "../../trained_models/hg_s8_b1_sigma1/weights_epoch22.h5")
valdata = MPIIDataGen("../../data/mpii/mpii_annotations.json", "../../data/mpii/images",
inres=(256, 256), outres=(64, 64), is_train=False)
total_good, total_fail = 0, 0
threshold = 0.5
print('val data size', valdata.get_dataset_size())
count = 0
batch_size = 8
for _img, _gthmap, _meta in valdata.tt_generator(batch_size, 8, sigma=2, is_shuffle=False , with_meta=True):
count += batch_size
if count % (batch_size*100) == 0:
print(count, 'processed', total_good, total_fail)
if count > valdata.get_dataset_size():
break
out = xnet.model.predict(_img)
good, bad = cal_heatmap_acc(out[-1], _meta, threshold)
total_good += good
total_fail += bad
print(total_good, total_fail, threshold, total_good*1.0/(total_good + total_fail))
def run_eval(self, epoch):
# dataset_path = os.path.join('D:\\', 'nyu_croped')
# dataset_path = '/home/tomas_bordac/nyu_croped'
dataset_path = config_reader.load_path()
valdata = NYUHandDataGen('joint_data.mat',
dataset_path,
inres=self.inres,
outres=self.outres,
is_train=False,
is_testtrain=False)
total_suc, total_fail = 0, 0
threshold = 0.5
count = 0
batch_size = 8
for _img, _gthmap, _meta in valdata.generator(batch_size,
2,
sigma=3,
is_shuffle=False,
with_meta=True):
count += batch_size
if count > valdata.get_dataset_size():
break
out = self.model.predict(_img)
suc, bad = cal_heatmap_acc(out[-1], _meta, threshold)
total_suc += suc
total_fail += bad
acc = total_suc * 1.0 / (total_fail + total_suc)
print('Eval Accuray ', acc, '@ Epoch ', epoch)
with open(os.path.join(self.get_folder_path(), 'val.txt'),
'a+') as xfile:
xfile.write('Epoch ' + str(epoch) + ':' + str(acc) + '\n')
def run_eval(self, epoch):
valdata = MPIIDataGen(
"/home/mike/Documents/stacked_hourglass_tf2/data/mpii/mpii_annotations.json",
"/home/mike/datasets/mpii_human_pose_v1/images",
inres=self.inres,
outres=self.outres,
is_train=False)
total_suc, total_fail = 0, 0
threshold = 0.5
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 = self.model.predict(_img)
suc, bad = cal_heatmap_acc(out[-1], _meta, threshold)
total_suc += suc
total_fail += bad
acc = total_suc * 1.0 / (total_fail + total_suc)
print('Eval Accuray ', acc, '@ Epoch ', epoch)
with open(os.path.join(self.get_folder_path(), 'val.txt'),
'a+') as xfile:
xfile.write('Epoch ' + str(epoch) + ':' + str(acc) + '\n')
