def get_next_batch(self):
if self.data_dic is None or len(self.batch_range) > 1:
self.data_dic = self.get_batch(self.curr_batchnum)
epoch, batchnum = self.curr_epoch, self.curr_batchnum
self.advance_batch()
cur_ndata = len(self.data_dic['cur_batch_indexes'])
cur_batch_indexes = self.data_dic['cur_batch_indexes']
self.data_dic['cur_candidate_indexes']=self.generate_candidate_index(cur_batch_indexes,self.random_prob)
cur_candidate_indexes = self.data_dic['cur_candidate_indexes']
gt_jt_rel = self.batch_meta['feature_list'][0][..., cur_batch_indexes]
candidate_jt_rel = self.batch_meta['feature_list'][0][..., cur_candidate_indexes]
img_feature = self.batch_meta['feature_list'][1][..., cur_batch_indexes]
jt_candidate_feature = self.batch_meta['feature_list'][2][..., cur_candidate_indexes]
z = (gt_jt_rel - candidate_jt_rel).reshape((3, self.num_joints,cur_ndata), order='F')
score = dutils.calc_RBF_score(z, self.rbf_sigma, 3)
mpjpe = dutils.calc_mpjpe_from_residual(z, self.num_joints)
alldata = [np.require(gt_jt_rel/self.max_depth, dtype=np.single, requirements='C'), \
np.require(img_feature.reshape((-1,cur_ndata),order='F'), \
dtype=np.single, requirements='C'), \
np.require(jt_candidate_feature.reshape((-1,cur_ndata),order='F'), \
dtype=np.single, requirements='C'), \
np.require(score.reshape((-1,cur_ndata)), \
dtype=np.single, requirements='C'),\
np.require(mpjpe.reshape((-1,cur_ndata))/self.max_depth, \
dtype=np.single, requirements='C')]
# import iutils as iu
# iu.print_common_statistics(alldata[0], 'gt')
# iu.print_common_statistics(alldata[1], 'imgfeature')
# iu.print_common_statistics(alldata[2], 'jtfeature')
# iu.print_common_statistics(alldata[3], 'mpjpe')
return epoch, batchnum, alldata
def show_bm_cmp_from_saved():
save_path = "/public/sijinli2/ibuffer/2015-01-22/saved_batch_data_test_net4_K_rbf_test"
save_path = "/public/sijinli2/ibuffer/2015-01-22/saved_batch_data_test_net4_K_rbf_correct_test"
d = mio.unpickle(save_path)
print d.keys()
bm_target, gt_score, mv_score, mv_target, gt_target, bm_score = (
d["bm_target"],
d["gt_score"],
d["mv_score"],
d["mv_target"],
d["gt_target"],
d["bm_score"],
)
ndata = bm_target.shape[-1]
bm_margin = MMLSSolver.calc_margin(gt_target - bm_target).flatten() * 1200
mv_margin = MMLSSolver.calc_margin(gt_target - mv_target).flatten() * 1200
# bm_rbf_margin = 1 - dutils.calc_RBF_score(gt_target - bm_target, 50/1200.0,3).flatten()
# mv_rbf_margin = 1 - dutils.calc_RBF_score(gt_target - mv_target, 50/1200.0,3).flatten()
# pl.ylabel('bm rbf margin')
# pl.xlabel('highest score rbf margin')
# pl.scatter(mv_rbf_margin, bm_rbf_margin, s=15, c='b')
# pl.show()
score_diff = mv_score - bm_score
pl.ylabel("score_diff")
pl.xlabel("mpjpe (bestmatch_pose, max_score_pose)")
pl.scatter(bm_margin, score_diff, s=15, c="b")
pl.show()
# show the scatter plot for mpjpe vs RBF score
residuals = (gt_target - bm_target) * 1200
bm_margin = bm_margin / 1200.0
t = 0
ncol = 4
sigma_list = [1, 5, 10, 20, 50, 100, 200, 500, 1000, 1500, 2000, 5000]
nrow = (len(sigma_list) - 1) // ncol + 1
# mm = np.max(bm_margin)
for r in range(nrow):
for c in range(ncol):
sigma = float(sigma_list[t])
t = t + 1
pl.subplot(nrow, ncol, t)
bm_rbf_score = 1 - dutils.calc_RBF_score(residuals, sigma)
pl.xlabel("mpjpe (bestmatch_pose, max_score_pose) ")
pl.ylabel("RBF sigma = {:.1f}".format(sigma))
pl.scatter(bm_margin, bm_rbf_score)
if t == len(sigma_list):
break
pl.show()
def show_bm_cmp_from_saved():
save_path = '/public/sijinli2/ibuffer/2015-01-22/saved_batch_data_test_net4_K_rbf_test'
save_path = '/public/sijinli2/ibuffer/2015-01-22/saved_batch_data_test_net4_K_rbf_correct_test'
d = mio.unpickle(save_path)
print d.keys()
bm_target, gt_score, mv_score, mv_target, gt_target, bm_score = d['bm_target'], \
d['gt_score'], \
d['mv_score'], \
d['mv_target'],\
d['gt_target'],\
d['bm_score']
ndata = bm_target.shape[-1]
bm_margin = MMLSSolver.calc_margin(gt_target - bm_target).flatten() * 1200
mv_margin = MMLSSolver.calc_margin(gt_target - mv_target).flatten() * 1200
# bm_rbf_margin = 1 - dutils.calc_RBF_score(gt_target - bm_target, 50/1200.0,3).flatten()
# mv_rbf_margin = 1 - dutils.calc_RBF_score(gt_target - mv_target, 50/1200.0,3).flatten()
# pl.ylabel('bm rbf margin')
# pl.xlabel('highest score rbf margin')
# pl.scatter(mv_rbf_margin, bm_rbf_margin, s=15, c='b')
# pl.show()
score_diff = mv_score - bm_score
pl.ylabel('score_diff')
pl.xlabel('mpjpe (bestmatch_pose, max_score_pose)')
pl.scatter(bm_margin, score_diff, s=15, c='b')
pl.show()
# show the scatter plot for mpjpe vs RBF score
residuals = (gt_target - bm_target) * 1200
bm_margin = bm_margin / 1200.0
t = 0
ncol = 4
sigma_list = [1, 5, 10, 20, 50, 100, 200, 500, 1000, 1500, 2000, 5000]
nrow = (len(sigma_list) - 1) // ncol + 1
# mm = np.max(bm_margin)
for r in range(nrow):
for c in range(ncol):
sigma = float(sigma_list[t])
t = t + 1
pl.subplot(nrow, ncol, t)
bm_rbf_score = 1 - dutils.calc_RBF_score(residuals, sigma)
pl.xlabel('mpjpe (bestmatch_pose, max_score_pose) ')
pl.ylabel('RBF sigma = {:.1f}'.format(sigma))
pl.scatter(bm_margin, bm_rbf_score)
if t == len(sigma_list):
break
pl.show()
def get_next_batch(self):
if self.data_dic is None or len(self.batch_range) > 1:
self.data_dic = self.get_batch(self.curr_batchnum)
epoch, batchnum = self.curr_epoch, self.curr_batchnum
self.advance_batch()
cur_ndata = len(self.data_dic['cur_batch_indexes'])
cur_batch_indexes = self.data_dic['cur_batch_indexes']
self.data_dic['cur_candidate_indexes'] = self.generate_candidate_index(
cur_batch_indexes, self.random_prob)
cur_candidate_indexes = self.data_dic['cur_candidate_indexes']
gt_jt_rel = self.batch_meta['feature_list'][0][..., cur_batch_indexes]
candidate_jt_rel = self.batch_meta['feature_list'][0][
..., cur_candidate_indexes]
img_feature = self.batch_meta['feature_list'][1][...,
cur_batch_indexes]
jt_candidate_feature = self.batch_meta['feature_list'][2][
..., cur_candidate_indexes]
z = (gt_jt_rel - candidate_jt_rel).reshape(
(3, self.num_joints, cur_ndata), order='F')
score = dutils.calc_RBF_score(z, self.rbf_sigma, 3)
mpjpe = dutils.calc_mpjpe_from_residual(z, self.num_joints)
alldata = [np.require(gt_jt_rel/self.max_depth, dtype=np.single, requirements='C'), \
np.require(img_feature.reshape((-1,cur_ndata),order='F'), \
dtype=np.single, requirements='C'), \
np.require(jt_candidate_feature.reshape((-1,cur_ndata),order='F'), \
dtype=np.single, requirements='C'), \
np.require(score.reshape((-1,cur_ndata)), \
dtype=np.single, requirements='C'),\
np.require(mpjpe.reshape((-1,cur_ndata))/self.max_depth, \
dtype=np.single, requirements='C')]
# import iutils as iu
# iu.print_common_statistics(alldata[0], 'gt')
# iu.print_common_statistics(alldata[1], 'imgfeature')
# iu.print_common_statistics(alldata[2], 'jtfeature')
# iu.print_common_statistics(alldata[3], 'mpjpe')
return epoch, batchnum, alldata
