def eval_mm_mpjpe(self):
"""
This function will use the score prediction as the baseline
And add Gaussian noise to
"""
train=False
feat_E = self.candidate_feat_E / self.train_dp.max_depth
# Note that noise has already been multiplied into feat_E
net = self.feature_net
output_layer_name = self.prediction_layer_name
pred_outputs = net.get_layer_by_names([output_layer_name])[0].outputs
pred_func = theano.function(inputs=net.inputs, outputs=pred_outputs,on_unused_input='ignore')
itm = iu.itimer()
itm.restart()
cur_data = self.get_next_batch(train)
sample_num = 500
topK = 10
pred_mpjpe_l = []
topK_mpjpe_l = []
itm.tic()
while True:
self.print_iteration()
ndata = cur_data[2][0].shape[-1]
input_data = self.prepare_data(cur_data[2])
gt_target = input_data[1].T
print 'gt_target.shape = {}'.format(gt_target.shape)
imgfeatures = self.calc_image_features([input_data[0]]).T
print 'imgfeatures.shape = {}'.format(imgfeatures.shape)
preds = pred_func(*[input_data[0]])[0].T
print 'Prediction.shape = {}'.format(preds.shape)
candidate_targets = self.add_pca_noise(preds, feat_E, sample_num,r=1)
candidate_features = self.calc_target_feature_func(self.gpu_require(candidate_targets.reshape((-1, sample_num * ndata)).T))[0].T
print 'candidate_features.shape = {}'.format(candidate_features.shape)
scores = (candidate_features.reshape((-1, sample_num, ndata),order='F') * imgfeatures.reshape((-1, 1, ndata),order='F')).sum(axis=0)
print 'score.shape = {}'.format(scores.shape)
sidx = np.argpartition(-scores, topK, axis=0)[:topK,...]
sidx_in_arr = sidx + np.array(range(ndata)) * sample_num
topK_target_list = [ candidate_targets[:, sidx[...,k].flatten() ,k].mean(axis=1,keepdims=True) for k in range(ndata)]
topK_target = np.concatenate(topK_target_list, axis=1)
topK_mpjpe = dutils.calc_mpjpe_from_residual(topK_target - gt_target, 17)* 1200
pred_mpjpe = dutils.calc_mpjpe_from_residual(preds - gt_target, 17) * 1200
print 'topK_mpjpe = {} pred_mpjpe = {}'.format(topK_mpjpe.mean(), pred_mpjpe.mean())
pred_mpjpe_l.append(pred_mpjpe.flatten())
topK_mpjpe_l.append(topK_mpjpe.flatten())
self.epoch, self.batchnum = self.test_dp.epoch, self.test_dp.batchnum
if self.epoch == 1:
break
cur_data = self.get_next_batch(train)
itm.toc()
pred_mpjpe_arr = np.concatenate(pred_mpjpe_l)
topK_mpjpe_arr = np.concatenate(topK_mpjpe_l)
print 'Compute {} batch {} data in total'.format(len(pred_mpjpe_l), pred_mpjpe_arr.shape)
print 'in total pred_mpjpe = {} \t topK mpjpe = {}'.format(pred_mpjpe_arr.mean(),
topK_mpjpe_arr.mean()
)
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 eval_mpjpe(self, op):
"""
This function will simple evaluate mpjpe
"""
train = False
net = self.feature_net
output_layer_name = self.prediction_layer_name
pred_outputs = net.get_layer_by_names([output_layer_name])[0].outputs
pred_func = theano.function(inputs=net.inputs,
outputs=pred_outputs,
on_unused_input='ignore')
itm = iu.itimer()
itm.restart()
cur_data = self.get_next_batch(train)
pred_mpjpe_l = []
pred_target_l = []
save_folder = op.get_value('save_res_path')
assert (save_folder is not None)
iu.ensure_dir(save_folder)
save_path = iu.fullfile(save_folder, 'pose_prediction')
net.set_train_mode(
False) # added in Oct 27, 2015 After ICCV submission
while True:
self.print_iteration()
ndata = cur_data[2][0].shape[-1]
input_data = self.prepare_data(cur_data[2])
gt_target = input_data[1].T
print 'gt_target.shape = {}'.format(gt_target.shape)
preds = pred_func(*[input_data[0]])[0].T
print 'Prediction.shape = {}'.format(preds.shape)
pred_mpjpe = dutils.calc_mpjpe_from_residual(
preds - gt_target, 17) * 1200
print 'pred_mpjpe = {}'.format(pred_mpjpe.mean())
self.epoch, self.batchnum = self.test_dp.epoch, self.test_dp.batchnum
pred_mpjpe_l.append(pred_mpjpe)
pred_target_l.append(preds)
if self.epoch == 1:
break
cur_data = self.get_next_batch(train)
mio.pickle(save_path, {'preds': pred_target_l, 'mpjpe': pred_mpjpe_l})
preds = np.concatenate(pred_target_l, axis=1)
mpjpe = np.concatenate(pred_mpjpe_l, axis=1)
mio.pickle(save_path, {'preds': pred_target_l, 'mpjpe': pred_mpjpe_l})
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 eval_mpjpe(self, op):
"""
This function will simple evaluate mpjpe
"""
train=False
net = self.feature_net
output_layer_name = self.prediction_layer_name
pred_outputs = net.get_layer_by_names([output_layer_name])[0].outputs
pred_func = theano.function(inputs=net.inputs, outputs=pred_outputs,on_unused_input='ignore')
itm = iu.itimer()
itm.restart()
cur_data = self.get_next_batch(train)
pred_mpjpe_l = []
pred_target_l = []
save_folder = op.get_value('save_res_path')
assert(save_folder is not None)
iu.ensure_dir(save_folder)
save_path = iu.fullfile(save_folder, 'pose_prediction')
net.set_train_mode(False) # added in Oct 27, 2015 After ICCV submission
while True:
self.print_iteration()
ndata = cur_data[2][0].shape[-1]
input_data = self.prepare_data(cur_data[2])
gt_target = input_data[1].T
print 'gt_target.shape = {}'.format(gt_target.shape)
preds = pred_func(*[input_data[0]])[0].T
print 'Prediction.shape = {}'.format(preds.shape)
pred_mpjpe = dutils.calc_mpjpe_from_residual(preds - gt_target, 17) * 1200
print 'pred_mpjpe = {}'.format(pred_mpjpe.mean())
self.epoch, self.batchnum = self.test_dp.epoch, self.test_dp.batchnum
pred_mpjpe_l.append(pred_mpjpe)
pred_target_l.append(preds)
if self.epoch == 1:
break
cur_data = self.get_next_batch(train)
mio.pickle(save_path, {'preds':pred_target_l, 'mpjpe':pred_mpjpe_l})
preds = np.concatenate(pred_target_l, axis=1)
mpjpe = np.concatenate(pred_mpjpe_l, axis=1)
mio.pickle(save_path, {'preds':pred_target_l, 'mpjpe':pred_mpjpe_l})
def read_inputs():
d = mio.unpickle(
'/opt/visal/tmp/for_sijin/Data/H36M/H36MExp/folder_FCJ0_act_12/batches.meta'
)
info = d['info']
print info.keys()
indexes = info['indexes']
Y = d['feature_list'][0]
X = d['feature_list'][1]
train_range = range(0, 76048)
test_range = range(76048, 105368)
print min(indexes[train_range]), max(indexes[train_range])
print min(indexes[test_range]), max(indexes[test_range])
print 'X '
iu.print_common_statistics(X)
X_train = X[..., train_range]
Y_train = Y[..., train_range]
feature_dim = X_train.shape[0]
X_test = X[..., test_range]
Y_test = Y[..., test_range]
params = {'Sigma': np.ones(feature_dim + 1) * 0.0001}
r = LinearRegression(params)
r.fit(simpleDP(X_train, Y_train))
Y_pred = r.apply(X_test)
print Y_pred.shape
print Y_test[:5, :5]
print Y_pred[:5, :5]
diff = Y_test - Y_pred
print 'abs diff = {}'.format(np.sum(diff.flatten()**2))
mpjpe = dutils.calc_mpjpe_from_residual(diff, 17)
print 'average mpjpe {}'.format(np.mean(mpjpe.flatten()))
def read_inputs():
d = mio.unpickle('/opt/visal/tmp/for_sijin/Data/H36M/H36MExp/folder_FCJ0_act_12/batches.meta')
info = d['info']
print info.keys()
indexes = info['indexes']
Y = d['feature_list'][0]
X = d['feature_list'][1]
train_range = range(0,76048)
test_range = range(76048,105368)
print min(indexes[train_range]), max(indexes[train_range])
print min(indexes[test_range]), max(indexes[test_range])
print 'X '
iu.print_common_statistics(X)
X_train = X[..., train_range]
Y_train = Y[..., train_range]
feature_dim = X_train.shape[0]
X_test = X[..., test_range]
Y_test = Y[..., test_range]
params = {'Sigma':np.ones(feature_dim + 1) * 0.0001}
r = LinearRegression(params)
r.fit(simpleDP(X_train,Y_train))
Y_pred = r.apply(X_test)
print Y_pred.shape
print Y_test[:5,:5]
print Y_pred[:5,:5]
diff = Y_test - Y_pred
print 'abs diff = {}'.format(np.sum(diff.flatten()**2))
mpjpe = dutils.calc_mpjpe_from_residual(diff,17)
print 'average mpjpe {}'.format(np.mean(mpjpe.flatten()))
def eval_mm_mpjpe(self):
"""
This function will use the score prediction as the baseline
And add Gaussian noise to
"""
train = False
feat_E = self.candidate_feat_E / self.train_dp.max_depth
# Note that noise has already been multiplied into feat_E
net = self.feature_net
output_layer_name = self.prediction_layer_name
pred_outputs = net.get_layer_by_names([output_layer_name])[0].outputs
pred_func = theano.function(inputs=net.inputs,
outputs=pred_outputs,
on_unused_input='ignore')
itm = iu.itimer()
itm.restart()
cur_data = self.get_next_batch(train)
sample_num = 500
topK = 10
pred_mpjpe_l = []
topK_mpjpe_l = []
itm.tic()
while True:
self.print_iteration()
ndata = cur_data[2][0].shape[-1]
input_data = self.prepare_data(cur_data[2])
gt_target = input_data[1].T
print 'gt_target.shape = {}'.format(gt_target.shape)
imgfeatures = self.calc_image_features([input_data[0]]).T
print 'imgfeatures.shape = {}'.format(imgfeatures.shape)
preds = pred_func(*[input_data[0]])[0].T
print 'Prediction.shape = {}'.format(preds.shape)
candidate_targets = self.add_pca_noise(preds,
feat_E,
sample_num,
r=1)
candidate_features = self.calc_target_feature_func(
self.gpu_require(
candidate_targets.reshape(
(-1, sample_num * ndata)).T))[0].T
print 'candidate_features.shape = {}'.format(
candidate_features.shape)
scores = (candidate_features.reshape(
(-1, sample_num, ndata), order='F') * imgfeatures.reshape(
(-1, 1, ndata), order='F')).sum(axis=0)
print 'score.shape = {}'.format(scores.shape)
sidx = np.argpartition(-scores, topK, axis=0)[:topK, ...]
sidx_in_arr = sidx + np.array(range(ndata)) * sample_num
topK_target_list = [
candidate_targets[:, sidx[..., k].flatten(),
k].mean(axis=1, keepdims=True)
for k in range(ndata)
]
topK_target = np.concatenate(topK_target_list, axis=1)
topK_mpjpe = dutils.calc_mpjpe_from_residual(
topK_target - gt_target, 17) * 1200
pred_mpjpe = dutils.calc_mpjpe_from_residual(
preds - gt_target, 17) * 1200
print 'topK_mpjpe = {} pred_mpjpe = {}'.format(
topK_mpjpe.mean(), pred_mpjpe.mean())
pred_mpjpe_l.append(pred_mpjpe.flatten())
topK_mpjpe_l.append(topK_mpjpe.flatten())
self.epoch, self.batchnum = self.test_dp.epoch, self.test_dp.batchnum
if self.epoch == 1:
break
cur_data = self.get_next_batch(train)
itm.toc()
pred_mpjpe_arr = np.concatenate(pred_mpjpe_l)
topK_mpjpe_arr = np.concatenate(topK_mpjpe_l)
print 'Compute {} batch {} data in total'.format(
len(pred_mpjpe_l), pred_mpjpe_arr.shape)
print 'in total pred_mpjpe = {} \t topK mpjpe = {}'.format(
pred_mpjpe_arr.mean(), topK_mpjpe_arr.mean())
def show_highest_score(train):
"""
This function will load data from train or test set
"""
solver = resume_solver()
# stat = solver.stat
# print stat.keys()
# mvc = stat['most_violated_counts']
# scc = stat['sample_candidate_counts']
# print 'mvc sum = {}, scc = {}'.format(mvc.sum(), scc.sum())
# test_shared_weights_online(solver.train_net.layers)
# print '<<<<<<<<<<<<<<<<{}'.format(solver.train_net.layers is solver.eval_net.layers)
# print 'train net inputs {}'.format(solver.train_net.inputs)
# print 'eval net inputs {}'.format(solver.eval_net.inputs)
# print 'eval net outputs {}'.format(solver.eval_net.outputs)
# GraphParser.print_graph_connections(solver.train_net.layers)
# return
dp = solver.train_dp if train else solver.test_dp
data = dp.get_next_batch(train)
ep, bn, alldata, ext_data = solver.find_most_violated_ext(
data, use_zero_margin=True, train=train)
print ep, bn, len(alldata)
gt_target = alldata[0]
gt_margin = alldata[4]
img_features = alldata[1]
mv_target = ext_data[0]
batch_candidate_indexes = ext_data[1]
print 'batch candidate indexes shape is {}'.format(
batch_candidate_indexes.shape)
mv_features = alldata[3]
gt_features = alldata[2]
# mv_margin = solver.calc_margin(gt_target - mv_target)
mv_margin = alldata[5]
print "mv shape {}, gt shape {}".format(mv_target.shape, gt_target.shape)
fl = solver.train_dp.data_dic['feature_list']
batch_candidate_targets = fl[0][..., batch_candidate_indexes]
ndata = gt_target.shape[-1]
data_to_eval = [
solver.gpu_require(img_features.T),
solver.gpu_require(mv_features.T),
solver.gpu_require(mv_margin.T)
]
print 'Eval inpus are {}'.format([l.name for l in solver.eval_net.inputs])
mv_score = solver.eval_net.outputs[0].eval(
dict(zip(solver.eval_net.inputs, data_to_eval)))
data_to_eval = [
solver.gpu_require(img_features.T),
solver.gpu_require(gt_features.T),
solver.gpu_require(gt_margin.T)
]
gt_score = solver.eval_net.outputs[0].eval(
dict(zip(solver.eval_net.inputs, data_to_eval)))
res_mpjpe, bmi = get_batch_best_match(batch_candidate_targets, gt_target,
solver)
print 'Current best match mpjpe is {}'.format(np.mean(res_mpjpe) * 1200)
bmi_raw = batch_candidate_indexes[bmi]
bm_features = fl[2][..., bmi_raw]
bm_targets = fl[0][..., bmi_raw]
residuals = bm_targets - gt_target
mpjpe = dutils.calc_mpjpe_from_residual(residuals,
17) # mpjpe for best match
print 'Calc Again mpjpe is {}'.format(np.mean(mpjpe.flatten()) * 1200)
data_to_eval = [
solver.gpu_require(img_features.T),
solver.gpu_require(bm_features.T),
solver.gpu_require(gt_margin.T)
]
bm_score = solver.eval_net.outputs[0].eval(
dict(zip(solver.eval_net.inputs, data_to_eval)))
# for evaluation
# inputs = [solver.train_net.inputs[0],solver.train_net.inputs[2],solver.train_net.inputs[4]]
# print 'inputs = {}'.format(inputs)
# ff = theano.function(inputs=inputs,
# outputs=solver.eval_net.layers['net2_score'][2].outputs
# )
# print solver.eval_net.layers['net1_score'][2].outputs
# res = solver.call_func(ff, data_to_eval)
# r = res[0]
# diff = r - gt_score
# print '=======The abs difference is {}==========='.format(np.abs(diff).sum())
all_input_data = [solver.gpu_require(e.T) for e in alldata[1:]]
solver.analyze_num_sv(all_input_data)
# all_input_data = [all_input_data[0], all_input_data[2], all_input_data[1],
# all_input_data[4], all_input_data[3]]
# solver.print_layer_outputs(all_input_data)
# Ignore the use_zero margin flag
whole_candidate_set = solver.train_dp.data_dic['feature_list'][0][
..., solver.train_dp.data_range]
# print 'Whole candidate_set shape is {}'.format(whole_candidate_set.shape)
# what_is_the_best_match( whole_candidate_set , mv_target, solver)
# show_what_is_best_all(solver.train_dp, solver.test_dp, solver)
mv_margin = solver.calc_margin(gt_target - mv_target) # MPJPE
print 'gt_margin<======================'
iu.print_common_statistics(gt_margin)
print 'mv_margin<======================'
iu.print_common_statistics(alldata[5])
show_bm_cmp(ndata, gt_target, mv_target, bm_targets, mv_score, gt_score,
bm_score, solver)
show_masked_plot(ndata, mv_margin, mv_score, gt_score, bm_score)
show_raw_plot(ndata, mv_margin, mv_score, gt_score)
# print 'Strange Here: {:.6f}% is correct'.format()
pl.show()
def show_highest_score(train):
"""
This function will load data from train or test set
"""
solver = resume_solver()
# stat = solver.stat
# print stat.keys()
# mvc = stat['most_violated_counts']
# scc = stat['sample_candidate_counts']
# print 'mvc sum = {}, scc = {}'.format(mvc.sum(), scc.sum())
# test_shared_weights_online(solver.train_net.layers)
# print '<<<<<<<<<<<<<<<<{}'.format(solver.train_net.layers is solver.eval_net.layers)
# print 'train net inputs {}'.format(solver.train_net.inputs)
# print 'eval net inputs {}'.format(solver.eval_net.inputs)
# print 'eval net outputs {}'.format(solver.eval_net.outputs)
# GraphParser.print_graph_connections(solver.train_net.layers)
# return
dp = solver.train_dp if train else solver.test_dp
data = dp.get_next_batch(train)
ep, bn, alldata, ext_data = solver.find_most_violated_ext(data, use_zero_margin=True, train=train)
print ep, bn, len(alldata)
gt_target = alldata[0]
gt_margin = alldata[4]
img_features = alldata[1]
mv_target = ext_data[0]
batch_candidate_indexes = ext_data[1]
print "batch candidate indexes shape is {}".format(batch_candidate_indexes.shape)
mv_features = alldata[3]
gt_features = alldata[2]
# mv_margin = solver.calc_margin(gt_target - mv_target)
mv_margin = alldata[5]
print "mv shape {}, gt shape {}".format(mv_target.shape, gt_target.shape)
fl = solver.train_dp.data_dic["feature_list"]
batch_candidate_targets = fl[0][..., batch_candidate_indexes]
ndata = gt_target.shape[-1]
data_to_eval = [
solver.gpu_require(img_features.T),
solver.gpu_require(mv_features.T),
solver.gpu_require(mv_margin.T),
]
print "Eval inpus are {}".format([l.name for l in solver.eval_net.inputs])
mv_score = solver.eval_net.outputs[0].eval(dict(zip(solver.eval_net.inputs, data_to_eval)))
data_to_eval = [
solver.gpu_require(img_features.T),
solver.gpu_require(gt_features.T),
solver.gpu_require(gt_margin.T),
]
gt_score = solver.eval_net.outputs[0].eval(dict(zip(solver.eval_net.inputs, data_to_eval)))
res_mpjpe, bmi = get_batch_best_match(batch_candidate_targets, gt_target, solver)
print "Current best match mpjpe is {}".format(np.mean(res_mpjpe) * 1200)
bmi_raw = batch_candidate_indexes[bmi]
bm_features = fl[2][..., bmi_raw]
bm_targets = fl[0][..., bmi_raw]
residuals = bm_targets - gt_target
mpjpe = dutils.calc_mpjpe_from_residual(residuals, 17) # mpjpe for best match
print "Calc Again mpjpe is {}".format(np.mean(mpjpe.flatten()) * 1200)
data_to_eval = [
solver.gpu_require(img_features.T),
solver.gpu_require(bm_features.T),
solver.gpu_require(gt_margin.T),
]
bm_score = solver.eval_net.outputs[0].eval(dict(zip(solver.eval_net.inputs, data_to_eval)))
# for evaluation
# inputs = [solver.train_net.inputs[0],solver.train_net.inputs[2],solver.train_net.inputs[4]]
# print 'inputs = {}'.format(inputs)
# ff = theano.function(inputs=inputs,
# outputs=solver.eval_net.layers['net2_score'][2].outputs
# )
# print solver.eval_net.layers['net1_score'][2].outputs
# res = solver.call_func(ff, data_to_eval)
# r = res[0]
# diff = r - gt_score
# print '=======The abs difference is {}==========='.format(np.abs(diff).sum())
all_input_data = [solver.gpu_require(e.T) for e in alldata[1:]]
solver.analyze_num_sv(all_input_data)
# all_input_data = [all_input_data[0], all_input_data[2], all_input_data[1],
# all_input_data[4], all_input_data[3]]
# solver.print_layer_outputs(all_input_data)
# Ignore the use_zero margin flag
whole_candidate_set = solver.train_dp.data_dic["feature_list"][0][..., solver.train_dp.data_range]
# print 'Whole candidate_set shape is {}'.format(whole_candidate_set.shape)
# what_is_the_best_match( whole_candidate_set , mv_target, solver)
# show_what_is_best_all(solver.train_dp, solver.test_dp, solver)
mv_margin = solver.calc_margin(gt_target - mv_target) # MPJPE
print "gt_margin<======================"
iu.print_common_statistics(gt_margin)
print "mv_margin<======================"
iu.print_common_statistics(alldata[5])
show_bm_cmp(ndata, gt_target, mv_target, bm_targets, mv_score, gt_score, bm_score, solver)
show_masked_plot(ndata, mv_margin, mv_score, gt_score, bm_score)
show_raw_plot(ndata, mv_margin, mv_score, gt_score)
# print 'Strange Here: {:.6f}% is correct'.format()
pl.show()
def show_highest_score(train, solver):
"""
This function will load data from train or test set
"""
dp = solver.train_dp if train else solver.test_dp
data = solver.get_next_batch(train)
alldata, ext_data = solver.find_most_violated_ext(data[2],use_zero_margin=True,
train=train
)
print len(alldata)
gt_target = alldata[0]
gt_margin= alldata[4]
img_features = alldata[1]
mv_target = ext_data[0]
batch_candidate_indexes =ext_data[1]
print 'batch candidate indexes shape is {}'.format(batch_candidate_indexes.shape)
mv_features = alldata[3]
gt_features = alldata[2]
# mv_margin = solver.calc_margin(gt_target - mv_target)
mv_margin = alldata[5]
print "mv shape {}, gt shape {}".format(mv_target.shape, gt_target.shape)
fl = solver.get_all_candidates(solver.train_dp)
batch_candidate_targets = fl[0][...,batch_candidate_indexes]
ndata = gt_target.shape[-1]
data_to_eval = [solver.gpu_require(img_features.T),
solver.gpu_require(mv_features.T),
solver.gpu_require(mv_margin.T)
]
print 'Eval inpus are {}'.format([l.name for l in solver.eval_net.inputs])
mv_score = solver.eval_net.outputs[0].eval(dict(zip(solver.eval_net.inputs, data_to_eval)))
data_to_eval = [solver.gpu_require(img_features.T),
solver.gpu_require(gt_features.T),
solver.gpu_require(gt_margin.T)
]
gt_score = solver.eval_net.outputs[0].eval(dict(zip(solver.eval_net.inputs, data_to_eval)))
res_mpjpe, bmi = get_batch_best_match(batch_candidate_targets, gt_target, solver)
print 'Current best match mpjpe is {}'.format(np.mean(res_mpjpe)* 1200)
bmi_raw = batch_candidate_indexes[bmi]
bm_features = fl[2][..., bmi_raw]
bm_targets = fl[0][..., bmi_raw]
residuals = bm_targets - gt_target
mpjpe = dutils.calc_mpjpe_from_residual(residuals, 17) # mpjpe for best match
print 'mpjpe(bm_target, gt_target) is {}'.format(np.mean(mpjpe.flatten())*1200)
data_to_eval = [solver.gpu_require(img_features.T),
solver.gpu_require(bm_features.T),
solver.gpu_require(gt_margin.T)
]
bm_score = solver.eval_net.outputs[0].eval(dict(zip(solver.eval_net.inputs, data_to_eval)))
all_input_data = [solver.gpu_require(e.T) for e in alldata[1:]]
solver.analyze_num_sv(all_input_data)
# all_input_data = [all_input_data[0], all_input_data[2], all_input_data[1],
# all_input_data[4], all_input_data[3]]
# solver.print_layer_outputs(all_input_data)
# Ignore the use_zero margin flag
whole_candidate_set = solver.train_dp.data_dic['feature_list'][0][..., solver.train_dp.data_range]
# print 'Whole candidate_set shape is {}'.format(whole_candidate_set.shape)
# what_is_the_best_match( whole_candidate_set , mv_target, solver)
# show_what_is_best_all(solver.train_dp, solver.test_dp, solver)
mv_margin = solver.calc_margin(gt_target - mv_target) # MPJPE
print 'gt_margin<======================'
iu.print_common_statistics(gt_margin)
print 'mv_margin<======================'
iu.print_common_statistics(alldata[5])
show_bm_cmp(ndata, gt_target, mv_target, bm_targets, mv_score, gt_score, bm_score, solver)
show_masked_plot(ndata, mv_margin, mv_score, gt_score, bm_score)
show_raw_plot(ndata, mv_margin, mv_score, gt_score)
# print 'Strange Here: {:.6f}% is correct'.format()
pl.show()
