def update_votes_cubam(show_progress=False):
""" This function is automatically called by
mturk.tasks.mturk_update_votes_cubam_task """
from mturk.cubam import update_votes_cubam
changed_objects = []
changed_objects += update_votes_cubam(
MaterialShape, ShapePlanarityLabel.objects.filter(
invalid=False, shape__invalid=False),
'shape_id', 'planar', 'planar', quality_method_attr='planar_method',
score_threshold=0, min_votes=5,
show_progress=show_progress,
return_changed_objects=True,
experiment_filter={'slug': 'label_planarity'}
)
changed_objects += update_votes_cubam(
MaterialShape, MaterialShapeQuality.objects.filter(
invalid=False, shape__invalid=False),
'shape_id', 'correct', 'correct',
score_threshold=0, min_votes=5,
show_progress=show_progress,
return_changed_objects=True,
experiment_filter={'slug': 'quality_material'}
)
return changed_objects
def update_votes_cubam(show_progress=False):
""" This function is automatically called by
mturk.tasks.mturk_update_votes_cubam_task """
from mturk.cubam import update_votes_cubam
changed_objects = []
changed_objects += update_votes_cubam(
MaterialShape,
ShapePlanarityLabel.objects.filter(invalid=False,
shape__invalid=False),
'shape_id',
'planar',
'planar',
quality_method_attr='planar_method',
score_threshold=0,
min_votes=5,
show_progress=show_progress,
return_changed_objects=True,
experiment_filter={'slug': 'label_planarity'})
changed_objects += update_votes_cubam(
MaterialShape,
MaterialShapeQuality.objects.filter(invalid=False,
shape__invalid=False),
'shape_id',
'correct',
'correct',
score_threshold=0,
min_votes=5,
show_progress=show_progress,
return_changed_objects=True,
experiment_filter={'slug': 'quality_material'})
return changed_objects
def update_votes_cubam(show_progress=False):
""" This function is automatically called by
mturk.tasks.mturk_update_votes_cubam_task """
from mturk.cubam import update_votes_cubam
changed_objects = []
changed_objects += update_votes_cubam(
Photo, PhotoSceneQualityLabel.objects.filter(invalid=False),
'photo_id', 'correct', 'scene_category_correct',
quality_method_attr='scene_category_correct_method',
score_threshold=0, min_votes=5,
show_progress=show_progress,
return_changed_objects=True,
experiment_filter={'slug': 'quality_scene'}
)
changed_objects += update_votes_cubam(
Photo, PhotoWhitebalanceLabel.objects.filter(invalid=False),
'photo_id', 'whitebalanced', 'whitebalanced',
score_threshold=0, min_votes=5,
show_progress=show_progress, return_changed_objects=True,
experiment_filter={'slug': 'label_whitebalance'}
)
return changed_objects
def update_votes_cubam(show_progress=False):
""" This function is automatically called by
mturk.tasks.mturk_update_votes_cubam_task """
from mturk.cubam import update_votes_cubam
changed_objects = []
changed_objects += update_votes_cubam(
Photo,
PhotoSceneQualityLabel.objects.filter(invalid=False),
'photo_id',
'correct',
'scene_category_correct',
quality_method_attr='scene_category_correct_method',
score_threshold=0,
min_votes=5,
show_progress=show_progress,
return_changed_objects=True,
experiment_filter={'slug': 'quality_scene'})
changed_objects += update_votes_cubam(
Photo,
PhotoWhitebalanceLabel.objects.filter(invalid=False),
'photo_id',
'whitebalanced',
'whitebalanced',
score_threshold=0,
min_votes=5,
show_progress=show_progress,
return_changed_objects=True,
experiment_filter={'slug': 'label_whitebalance'})
return changed_objects
def update_votes_cubam(show_progress=False):
""" This function is automatically called by
mturk.tasks.mturk_update_votes_cubam_task """
from mturk.cubam import update_votes_cubam
changed_objects = []
for bsdf_version in ('wd',):
bsdf_ct = ContentType.objects.get(
app_label="bsdfs", model="shapebsdflabel_%s" % bsdf_version)
bsdf_model = bsdf_ct.model_class()
# gloss
changed_objects += update_votes_cubam(
bsdf_model, ShapeBsdfQuality.objects.filter(
invalid=False, content_type=bsdf_ct,
gloss_correct__isnull=False),
'object_id', 'gloss_correct', 'gloss_correct',
score_threshold=0, min_votes=5,
show_progress=show_progress,
return_changed_objects=True,
experiment_filter={
'slug': 'quality_bsdf_gloss',
'variant': json.dumps({'bsdf_version': bsdf_version}),
}
)
# color
changed_objects += update_votes_cubam(
bsdf_model, ShapeBsdfQuality.objects.filter(
invalid=False, content_type=bsdf_ct,
color_correct__isnull=False),
'object_id', 'color_correct', 'color_correct',
score_threshold=0, min_votes=5,
show_progress=show_progress,
return_changed_objects=True,
experiment_filter={
'slug': 'quality_bsdf_color',
'variant': json.dumps({'bsdf_version': bsdf_version}),
}
)
return changed_objects
def update_votes_cubam(show_progress=False):
""" This function is automatically called by
mturk.tasks.mturk_update_votes_cubam_task """
from mturk.cubam import update_votes_cubam
changed_objects = []
for bsdf_version in ("wd",):
bsdf_ct = ContentType.objects.get(app_label="bsdfs", model="shapebsdflabel_%s" % bsdf_version)
bsdf_model = bsdf_ct.model_class()
# gloss
changed_objects += update_votes_cubam(
bsdf_model,
ShapeBsdfQuality.objects.filter(invalid=False, content_type=bsdf_ct, gloss_correct__isnull=False),
"object_id",
"gloss_correct",
"gloss_correct",
score_threshold=0,
min_votes=5,
show_progress=show_progress,
return_changed_objects=True,
experiment_filter={"slug": "quality_bsdf_gloss", "variant": json.dumps({"bsdf_version": bsdf_version})},
)
# color
changed_objects += update_votes_cubam(
bsdf_model,
ShapeBsdfQuality.objects.filter(invalid=False, content_type=bsdf_ct, color_correct__isnull=False),
"object_id",
"color_correct",
"color_correct",
score_threshold=0,
min_votes=5,
show_progress=show_progress,
return_changed_objects=True,
experiment_filter={"slug": "quality_bsdf_color", "variant": json.dumps({"bsdf_version": bsdf_version})},
)
return changed_objects
def update_votes_cubam(show_progress=False):
""" This function is automatically called by
mturk.tasks.mturk_update_votes_cubam_task """
from mturk.cubam import update_votes_cubam
changed_objects = []
changed_objects += update_votes_cubam(
ShapeRectifiedNormalLabel,
ShapeRectifiedNormalQuality.objects.filter(
invalid=False, rectified_normal__invalid=False),
'rectified_normal_id', 'correct', 'correct',
score_threshold=0, min_votes=5,
show_progress=show_progress,
return_changed_objects=True,
experiment_filter={'slug': 'quality_rectify'}
)
return changed_objects
def update_votes_cubam(show_progress=False):
""" This function is automatically called by
mturk.tasks.mturk_update_votes_cubam_task """
from mturk.models import Experiment
from mturk.cubam import update_votes_cubam
# responses that we will consider
opacity_responses = IntrinsicPointOpacityResponse.objects \
.filter(invalid=False) \
.exclude(point__opaque_method='A') \
.order_by()
update_votes_cubam(object_model=IntrinsicPoint,
labels=opacity_responses,
object_attr='point_id',
label_attr='opaque',
object_label_attr='opaque',
quality_method_attr='opaque_method',
score_threshold=0,
min_votes=4,
show_progress=show_progress,
return_changed_objects=False,
experiment_filter={'slug': 'intrinsic_opacity'})
# Since we have a 3-way answer, we break it down into two questions:
# 1) are the two points the same reflectance?
# 2) if not, does the darker pixel have darker reflectance?
#
# With this breakdown, we hope to capture use biases. A relatively common
# failure case of the experiment is that users always click the
# darker pixel. Another common case is that they always indicate "E"
# (same). These biases should be well captured by this breakdown.
# we have to manually mark it as dirty inbetween our two stages since
# our dirty tracking system assumes only one run of CUBAM
cubam_dirty = any(
Experiment.objects.filter(slug='intrinsic_compare').values_list(
'cubam_dirty', flat=True))
# responses that we will consider
darker_responses = IntrinsicPointComparisonResponse.objects \
.filter(invalid=False, user__exclude_from_aggregation=False) \
.order_by()
# CUBAM for question 1
update_votes_cubam(object_model=IntrinsicPointComparison,
labels=darker_responses,
object_attr='comparison_id',
label_attr='reflectance_eq',
object_label_attr='reflectance_eq',
quality_method_attr='darker_method',
score_threshold=0,
min_votes=4,
show_progress=show_progress,
return_changed_objects=False,
experiment_filter={'slug': 'intrinsic_compare'})
# items that are updated
comparisons = IntrinsicPointComparison.objects \
.exclude(darker_method='A') \
.order_by()
# convert back to our 3-way representation (1, 2, E)
comparisons.filter(reflectance_eq=True).update(
darker="E",
reflectance_dd=None,
reflectance_dd_score=None,
darker_score=F('reflectance_eq_score'))
# the dirty-tracking doesn't handle two-stage updates like this one,
# so we manually mark it as dirty
if cubam_dirty:
Experiment.objects \
.filter(slug='intrinsic_compare') \
.update(cubam_dirty=True)
# CUBAM for question 2, only considering the entries that branched as False
# from question 1
dd_responses = darker_responses.filter(reflectance_eq=False,
reflectance_dd__isnull=False)
update_votes_cubam(object_model=IntrinsicPointComparison,
labels=dd_responses,
object_attr='comparison_id',
label_attr='reflectance_dd',
object_label_attr='reflectance_dd',
quality_method_attr='darker_method',
score_threshold=0,
min_votes=1,
show_progress=show_progress,
return_changed_objects=False,
experiment_filter={'slug': 'intrinsic_compare'})
if cubam_dirty:
if show_progress:
print 'Updating changed IntrinsicPointComparison instances...'
# convert back to our 3-way representation (1, 2, E)
comparisons.filter(reflectance_eq=False) \
.filter(Q(point1_image_darker=True, reflectance_dd=True) |
Q(point1_image_darker=False, reflectance_dd=False)) \
.update(darker="1")
comparisons.filter(reflectance_eq=False) \
.filter(Q(point1_image_darker=True, reflectance_dd=False) |
Q(point1_image_darker=False, reflectance_dd=True)) \
.update(darker="2")
comparisons.filter(reflectance_eq=False, reflectance_dd=True) \
.update(darker_score=F('reflectance_dd_score'))
comparisons.filter(reflectance_eq=False, reflectance_dd=False) \
.update(darker_score=0 - F('reflectance_dd_score'))
if show_progress:
print 'Updating all Photo instances...'
# update photos (faster to just list all the ids than to try and figure out
# what changed since there are so many objects)
from photos.tasks import update_photos_num_intrinsic
photo_ids = Photo.objects.all().order_by().values_list('id', flat=True)
update_photos_num_intrinsic(list(photo_ids),
show_progress=show_progress)
return None
def update_votes_cubam(show_progress=False):
""" This function is automatically called by
mturk.tasks.mturk_update_votes_cubam_task """
from mturk.models import Experiment
from mturk.cubam import update_votes_cubam
# responses that we will consider
opacity_responses = IntrinsicPointOpacityResponse.objects \
.filter(invalid=False) \
.exclude(point__opaque_method='A') \
.order_by()
update_votes_cubam(
object_model=IntrinsicPoint,
labels=opacity_responses,
object_attr='point_id',
label_attr='opaque',
object_label_attr='opaque',
quality_method_attr='opaque_method',
score_threshold=0,
min_votes=4,
show_progress=show_progress,
return_changed_objects=False,
experiment_filter={'slug': 'intrinsic_opacity'}
)
# Since we have a 3-way answer, we break it down into two questions:
# 1) are the two points the same reflectance?
# 2) if not, does the darker pixel have darker reflectance?
#
# With this breakdown, we hope to capture use biases. A relatively common
# failure case of the experiment is that users always click the
# darker pixel. Another common case is that they always indicate "E"
# (same). These biases should be well captured by this breakdown.
# we have to manually mark it as dirty inbetween our two stages since
# our dirty tracking system assumes only one run of CUBAM
cubam_dirty = any(Experiment.objects
.filter(slug='intrinsic_compare')
.values_list('cubam_dirty', flat=True))
# responses that we will consider
darker_responses = IntrinsicPointComparisonResponse.objects \
.filter(invalid=False, user__exclude_from_aggregation=False) \
.order_by()
# CUBAM for question 1
update_votes_cubam(
object_model=IntrinsicPointComparison,
labels=darker_responses,
object_attr='comparison_id',
label_attr='reflectance_eq',
object_label_attr='reflectance_eq',
quality_method_attr='darker_method',
score_threshold=0,
min_votes=4,
show_progress=show_progress,
return_changed_objects=False,
experiment_filter={'slug': 'intrinsic_compare'}
)
# items that are updated
comparisons = IntrinsicPointComparison.objects \
.exclude(darker_method='A') \
.order_by()
# convert back to our 3-way representation (1, 2, E)
comparisons.filter(reflectance_eq=True).update(
darker="E", reflectance_dd=None, reflectance_dd_score=None,
darker_score=F('reflectance_eq_score')
)
# the dirty-tracking doesn't handle two-stage updates like this one,
# so we manually mark it as dirty
if cubam_dirty:
Experiment.objects \
.filter(slug='intrinsic_compare') \
.update(cubam_dirty=True)
# CUBAM for question 2, only considering the entries that branched as False
# from question 1
dd_responses = darker_responses.filter(
reflectance_eq=False, reflectance_dd__isnull=False)
update_votes_cubam(
object_model=IntrinsicPointComparison,
labels=dd_responses,
object_attr='comparison_id',
label_attr='reflectance_dd',
object_label_attr='reflectance_dd',
quality_method_attr='darker_method',
score_threshold=0,
min_votes=1,
show_progress=show_progress,
return_changed_objects=False,
experiment_filter={'slug': 'intrinsic_compare'}
)
if cubam_dirty:
if show_progress:
print 'Updating changed IntrinsicPointComparison instances...'
# convert back to our 3-way representation (1, 2, E)
comparisons.filter(reflectance_eq=False) \
.filter(Q(point1_image_darker=True, reflectance_dd=True) |
Q(point1_image_darker=False, reflectance_dd=False)) \
.update(darker="1")
comparisons.filter(reflectance_eq=False) \
.filter(Q(point1_image_darker=True, reflectance_dd=False) |
Q(point1_image_darker=False, reflectance_dd=True)) \
.update(darker="2")
comparisons.filter(reflectance_eq=False, reflectance_dd=True) \
.update(darker_score=F('reflectance_dd_score'))
comparisons.filter(reflectance_eq=False, reflectance_dd=False) \
.update(darker_score=0 - F('reflectance_dd_score'))
if show_progress:
print 'Updating all Photo instances...'
# update photos (faster to just list all the ids than to try and figure out
# what changed since there are so many objects)
from photos.tasks import update_photos_num_intrinsic
photo_ids = Photo.objects.all().order_by().values_list('id', flat=True)
update_photos_num_intrinsic(list(photo_ids), show_progress=show_progress)
return None