class TestBeliefState(object):
def setup(self):
d = Dataset('test_pascal_trainval',force=True)
d2 = Dataset('test_pascal_test',force=True)
config = {'detectors': ['csc_default']}
self.dp = DatasetPolicy(d,d2,**config)
self.bs = BeliefState(d,self.dp.actions)
def test_featurization(self):
ff = self.bs.compute_full_feature()
np.set_printoptions(precision=2)
print self.bs.block_out_action(ff,-1)
print self.bs.block_out_action(ff,0)
print self.bs.block_out_action(ff,3)
class TestBeliefState(object):
def setup(self):
d = Dataset('test_pascal_trainval', force=True)
d2 = Dataset('test_pascal_test', force=True)
config = {'detectors': ['csc_default']}
self.dp = DatasetPolicy(d, d2, **config)
self.bs = BeliefState(d, self.dp.actions)
def test_featurization(self):
ff = self.bs.compute_full_feature()
np.set_printoptions(precision=2)
print self.bs.block_out_action(ff, -1)
print self.bs.block_out_action(ff, 0)
print self.bs.block_out_action(ff, 3)
def setup(self):
d = Dataset('test_pascal_trainval',force=True)
d2 = Dataset('test_pascal_test',force=True)
config = {'detectors': ['csc_default']}
self.dp = DatasetPolicy(d,d2,**config)
self.bs = BeliefState(d,self.dp.actions)
def run_on_image(self, image, dataset, verbose=False, epsilon=0.01):
"""
Return
- list of detections in the image, with each row as self.det_columns
- list of multi-label classification outputs, with each row as self.get_cls_cols()
- list of <s,a,r,s',dt> samples.
"""
gt = image.get_det_gt(with_diff=True)
self.tt.tic('run_on_image')
all_detections = []
all_clses = []
samples = []
prev_ap = 0
img_ind = dataset.get_img_ind(image)
# If we have previously run_on_image(), then we already have a reference to an inf_model
# Otherwise, we make a new one and store a reference to it, to keep it alive
if hasattr(self, 'inf_model'):
b = BeliefState(self.train_dataset, self.actions,
self.inference_mode, self.bounds, self.inf_model,
self.fastinf_model_name)
else:
b = BeliefState(self.train_dataset,
self.actions,
self.inference_mode,
self.bounds,
fastinf_model_name=self.fastinf_model_name)
self.b = b
self.inf_model = b.model
self.update_actions(b)
action_ind = self.select_action(b, epsilon)
step_ind = 0
initial_clses = np.array(b.get_p_c().tolist() + [img_ind, 0])
entropy_prev = np.mean(b.get_entropies())
while True:
# Populate the sample with stuff we know
sample = Sample()
sample.step_ind = step_ind
sample.img_ind = img_ind
sample.state = b.compute_full_feature()
sample.action_ind = action_ind
# TODO: this is incorrect, and results in samples at t=0 to already have detections
sample.t = b.t
# prepare for AUC reward stuff
# TODO: should set time_to_deadline to -Inf if no bounds
time_to_deadline = 0
if self.bounds:
# this should never be less than zero, except for when running oracle
time_to_deadline = max(0, self.bounds[1] - b.t)
sample.auc_ap_raw = 0
sample.auc_ap = 0
# Take the action and get the observations as a dict
action = self.actions[action_ind]
obs = action.obj.get_observations(image)
dt = obs['dt']
# If observations include detections, compute the relevant
# stuff for the sample collection
sample.det_naive_ap = 0
sample.det_actual_ap = 0
if not 'dets' in obs:
all_detections.append(np.array([]))
else:
det = action.obj
dets = obs['dets']
cls_ind = dataset.classes.index(det.cls)
if dets.shape[0] > 0:
c_vector = np.tile(cls_ind, (np.shape(dets)[0], 1))
i_vector = np.tile(img_ind, (np.shape(dets)[0], 1))
detections = np.hstack((dets, c_vector, i_vector))
else:
detections = np.array([])
dets_table = Table(detections,
det.columns + ['cls_ind', 'img_ind'])
# compute the 'naive' det AP increase,
# as if we were adding dets to an empty set
#ap,rec,prec = self.ev.compute_det_pr(dets_table,gt)
ap = self.ev.compute_det_map(dets_table, gt)
sample.det_naive_ap = ap
# TODO: am I needlessly recomputing this table?
all_detections.append(detections)
nonempty_dets = [
dets for dets in all_detections if dets.shape[0] > 0
]
all_dets_table = Table(np.array([]), dets_table.cols)
if len(nonempty_dets) > 0:
all_dets_table = Table(np.concatenate(nonempty_dets, 0),
dets_table.cols)
# compute the actual AP increase: adding dets to dets so far
#ap,rec,prec = self.ev.compute_det_pr(all_dets_table,gt)
ap = self.ev.compute_det_map(all_dets_table, gt)
ap_diff = ap - prev_ap
sample.det_actual_ap = ap_diff
# Compure detector AUC reward
# If the action took longer than we have time, benefit is 0 (which is already set above)
if dt <= time_to_deadline:
midway_point = b.t + dt / 2.
if midway_point > self.bounds[0]:
length = max(0, self.bounds[1] - midway_point)
else:
length = self.bounds[1] - self.bounds[0]
auc_ap = 1. * ap_diff * length
sample.auc_ap_raw = auc_ap
# Now divide by the potential gain to compute the "normalized" reward
# Note that there are two cases: the curve goes up, or it turns down.
# In the first case, the normalizing area should be the area to ap=1.
# In the second case, the normalizing area should be the area to ap=0.
if ap_diff < 0:
divisor = time_to_deadline * (prev_ap)
else:
divisor = time_to_deadline * (1. - prev_ap)
if divisor < 0:
divisor = 0
auc_ap = 1 if divisor == 0 else auc_ap / divisor
assert (auc_ap >= -1 and auc_ap <= 1)
sample.auc_ap = auc_ap
prev_ap = ap
# Update the belief state with the observations
if action.name == 'gist':
b.update_with_gist(action_ind, obs['scores'])
else:
b.update_with_score(action_ind, obs['score'])
# mean entropy
entropy = np.mean(b.get_entropies())
dh = entropy_prev - entropy # this is actually -dh :)
sample.entropy = dh
auc_entropy = time_to_deadline * dh - dh * dt / 2
divisor = (time_to_deadline * entropy_prev)
if divisor == 0:
auc_entropy = 1
else:
auc_entropy /= divisor
if dt > time_to_deadline:
auc_entropy = 0
if not (auc_entropy >= -1 and auc_entropy <= 1):
auc_entropy = 0
sample.auc_entropy = auc_entropy
entropy_prev = entropy
# TODO: the below line of code should occur before the state is stored in the sample
b.t += dt
sample.dt = dt
samples.append(sample)
step_ind += 1
# The updated belief state posterior over C is our classification result
clses = b.get_p_c().tolist() + [img_ind, b.t]
all_clses.append(clses)
# Update action values and pick the next action
self.update_actions(b)
action_ind = self.select_action(b, epsilon)
# check for stopping conditions
if action_ind < 0:
break
if self.bounds and not self.policy_mode == 'oracle':
if b.t > self.bounds[1]:
break
# in case of 'oracle' mode, re-sort the detections and times in order of AP
# contributions, and actually re-gather p_c's for clses.
action_inds = [s.action_ind for s in samples]
if self.policy_mode == 'oracle':
naive_aps = np.array([s.det_naive_ap for s in samples])
sorted_inds = np.argsort(-naive_aps,
kind='merge') # order-preserving
all_detections = np.take(all_detections, sorted_inds)
sorted_action_inds = np.take(action_inds, sorted_inds)
# actually go through the whole thing again, getting new p_c's
b.reset()
all_clses = []
for action_ind in sorted_action_inds:
action = self.actions[action_ind]
obs = action.obj.get_observations(image)
b.t += obs['dt']
if action.name == 'gist':
b.update_with_gist(action_ind, obs['scores'])
else:
b.update_with_score(action_ind, obs['score'])
clses = b.get_p_c().tolist() + [img_ind, b.t]
all_clses.append(clses)
# now construct the final dets array, with correct times
times = [s.dt for s in samples]
#assert(len(all_detections)==len(all_clses)==len(times))
cum_times = np.cumsum(times)
all_times = []
all_nonempty_detections = []
for i, dets in enumerate(all_detections):
num_dets = dets.shape[0]
if num_dets > 0:
all_nonempty_detections.append(dets)
t_vector = np.tile(cum_times[i], (num_dets, 1))
all_times.append(t_vector)
if len(all_nonempty_detections) > 0:
all_detections = np.concatenate(all_nonempty_detections, 0)
all_times = np.concatenate(all_times, 0)
# appending 'time' column at end, as promised
all_detections = np.hstack((all_detections, all_times))
# we probably went over deadline with the oracle mode, so trim it down
if self.bounds:
if np.max(all_times) > self.bounds[1]:
first_overdeadline_ind = np.flatnonzero(
all_times > self.bounds[1])[0]
all_detections = all_detections[:first_overdeadline_ind, :]
else:
all_detections = np.array([])
all_clses = np.array(all_clses)
if verbose:
print("DatasetPolicy on image with ind %d took %.3f s" %
(img_ind, self.tt.qtoc('run_on_image')))
# TODO: temp debug thing
if False:
print("Action sequence was: %s" % [s.action_ind for s in samples])
print("here's an image:")
X = np.vstack((all_clses[:, :-2], image.get_cls_ground_truth()))
np.set_printoptions(precision=2, suppress=True)
print X
plt.pcolor(np.flipud(X))
plt.show()
return (all_detections, all_clses, samples)
def run_on_image(self, image, dataset, verbose=False, epsilon=0.01):
"""
Return
- list of detections in the image, with each row as self.det_columns
- list of multi-label classification outputs, with each row as self.get_cls_cols()
- list of <s,a,r,s',dt> samples.
"""
gt = image.get_det_gt(with_diff=True)
self.tt.tic('run_on_image')
all_detections = []
all_clses = []
samples = []
prev_ap = 0
img_ind = dataset.get_img_ind(image)
# If we have previously run_on_image(), then we already have a reference to an inf_model
# Otherwise, we make a new one and store a reference to it, to keep it alive
if hasattr(self,'inf_model'):
b = BeliefState(self.train_dataset, self.actions, self.inference_mode,
self.bounds, self.inf_model, self.fastinf_model_name)
else:
b = BeliefState(self.train_dataset, self.actions, self.inference_mode,
self.bounds, fastinf_model_name=self.fastinf_model_name)
self.b = b
self.inf_model = b.model
self.update_actions(b)
action_ind = self.select_action(b,epsilon)
step_ind = 0
initial_clses = np.array(b.get_p_c().tolist() + [img_ind,0])
entropy_prev = np.mean(b.get_entropies())
while True:
# Populate the sample with stuff we know
sample = Sample()
sample.step_ind = step_ind
sample.img_ind = img_ind
sample.state = b.compute_full_feature()
sample.action_ind = action_ind
# TODO: this is incorrect, and results in samples at t=0 to already have detections
sample.t = b.t
# prepare for AUC reward stuff
# TODO: should set time_to_deadline to -Inf if no bounds
time_to_deadline = 0
if self.bounds:
# this should never be less than zero, except for when running oracle
time_to_deadline = max(0,self.bounds[1]-b.t)
sample.auc_ap_raw = 0
sample.auc_ap = 0
# Take the action and get the observations as a dict
action = self.actions[action_ind]
obs = action.obj.get_observations(image)
dt = obs['dt']
# If observations include detections, compute the relevant
# stuff for the sample collection
sample.det_naive_ap = 0
sample.det_actual_ap = 0
if not 'dets' in obs:
all_detections.append(np.array([]))
else:
det = action.obj
dets = obs['dets']
cls_ind = dataset.classes.index(det.cls)
if dets.shape[0]>0:
c_vector = np.tile(cls_ind,(np.shape(dets)[0],1))
i_vector = np.tile(img_ind,(np.shape(dets)[0],1))
detections = np.hstack((dets, c_vector, i_vector))
else:
detections = np.array([])
dets_table = Table(detections,det.columns+['cls_ind','img_ind'])
# compute the 'naive' det AP increase,
# as if we were adding dets to an empty set
#ap,rec,prec = self.ev.compute_det_pr(dets_table,gt)
ap = self.ev.compute_det_map(dets_table,gt)
sample.det_naive_ap = ap
# TODO: am I needlessly recomputing this table?
all_detections.append(detections)
nonempty_dets = [dets for dets in all_detections if dets.shape[0]>0]
all_dets_table = Table(np.array([]),dets_table.cols)
if len(nonempty_dets)>0:
all_dets_table = Table(np.concatenate(nonempty_dets,0),dets_table.cols)
# compute the actual AP increase: adding dets to dets so far
#ap,rec,prec = self.ev.compute_det_pr(all_dets_table,gt)
ap = self.ev.compute_det_map(all_dets_table,gt)
ap_diff = ap-prev_ap
sample.det_actual_ap = ap_diff
# Compure detector AUC reward
# If the action took longer than we have time, benefit is 0 (which is already set above)
if dt <= time_to_deadline:
midway_point = b.t+dt/2.
if midway_point > self.bounds[0]:
length = max(0, self.bounds[1]-midway_point)
else:
length = self.bounds[1]-self.bounds[0]
auc_ap = 1.*ap_diff * length
sample.auc_ap_raw = auc_ap
# Now divide by the potential gain to compute the "normalized" reward
# Note that there are two cases: the curve goes up, or it turns down.
# In the first case, the normalizing area should be the area to ap=1.
# In the second case, the normalizing area should be the area to ap=0.
if ap_diff<0:
divisor = time_to_deadline*(prev_ap)
else:
divisor = time_to_deadline*(1.-prev_ap)
if divisor < 0:
divisor = 0
auc_ap = 1 if divisor == 0 else auc_ap/divisor
assert(auc_ap>=-1 and auc_ap<=1)
sample.auc_ap = auc_ap
prev_ap = ap
# Update the belief state with the observations
if action.name=='gist':
b.update_with_gist(action_ind, obs['scores'])
else:
b.update_with_score(action_ind, obs['score'])
# mean entropy
entropy = np.mean(b.get_entropies())
dh = entropy_prev-entropy # this is actually -dh :)
sample.entropy = dh
auc_entropy = time_to_deadline * dh - dh * dt / 2
divisor = (time_to_deadline * entropy_prev)
if divisor == 0:
auc_entropy = 1
else:
auc_entropy /= divisor
if dt > time_to_deadline:
auc_entropy = 0
if not (auc_entropy>=-1 and auc_entropy<=1):
auc_entropy = 0
sample.auc_entropy = auc_entropy
entropy_prev = entropy
# TODO: the below line of code should occur before the state is stored in the sample
b.t += dt
sample.dt = dt
samples.append(sample)
step_ind += 1
# The updated belief state posterior over C is our classification result
clses = b.get_p_c().tolist() + [img_ind,b.t]
all_clses.append(clses)
# Update action values and pick the next action
self.update_actions(b)
action_ind = self.select_action(b,epsilon)
# check for stopping conditions
if action_ind < 0:
break
if self.bounds and not self.policy_mode=='oracle':
if b.t > self.bounds[1]:
break
# in case of 'oracle' mode, re-sort the detections and times in order of AP
# contributions, and actually re-gather p_c's for clses.
action_inds = [s.action_ind for s in samples]
if self.policy_mode=='oracle':
naive_aps = np.array([s.det_naive_ap for s in samples])
sorted_inds = np.argsort(-naive_aps,kind='merge') # order-preserving
all_detections = np.take(all_detections, sorted_inds)
sorted_action_inds = np.take(action_inds, sorted_inds)
# actually go through the whole thing again, getting new p_c's
b.reset()
all_clses = []
for action_ind in sorted_action_inds:
action = self.actions[action_ind]
obs = action.obj.get_observations(image)
b.t += obs['dt']
if action.name=='gist':
b.update_with_gist(action_ind, obs['scores'])
else:
b.update_with_score(action_ind, obs['score'])
clses = b.get_p_c().tolist() + [img_ind,b.t]
all_clses.append(clses)
# now construct the final dets array, with correct times
times = [s.dt for s in samples]
#assert(len(all_detections)==len(all_clses)==len(times))
cum_times = np.cumsum(times)
all_times = []
all_nonempty_detections = []
for i,dets in enumerate(all_detections):
num_dets = dets.shape[0]
if num_dets > 0:
all_nonempty_detections.append(dets)
t_vector = np.tile(cum_times[i],(num_dets,1))
all_times.append(t_vector)
if len(all_nonempty_detections)>0:
all_detections = np.concatenate(all_nonempty_detections,0)
all_times = np.concatenate(all_times,0)
# appending 'time' column at end, as promised
all_detections = np.hstack((all_detections,all_times))
# we probably went over deadline with the oracle mode, so trim it down
if self.bounds:
if np.max(all_times)>self.bounds[1]:
first_overdeadline_ind = np.flatnonzero(all_times>self.bounds[1])[0]
all_detections = all_detections[:first_overdeadline_ind,:]
else:
all_detections = np.array([])
all_clses = np.array(all_clses)
if verbose:
print("DatasetPolicy on image with ind %d took %.3f s"%(
img_ind,self.tt.qtoc('run_on_image')))
# TODO: temp debug thing
if False:
print("Action sequence was: %s"%[s.action_ind for s in samples])
print("here's an image:")
X = np.vstack((all_clses[:,:-2],image.get_cls_ground_truth()))
np.set_printoptions(precision=2, suppress=True)
print X
plt.pcolor(np.flipud(X))
plt.show()
return (all_detections,all_clses,samples)
def setup(self):
d = Dataset('test_pascal_trainval', force=True)
d2 = Dataset('test_pascal_test', force=True)
config = {'detectors': ['csc_default']}
self.dp = DatasetPolicy(d, d2, **config)
self.bs = BeliefState(d, self.dp.actions)
