def __call__(self, candidate: BrainModel):
fitting_stimuli = place_on_screen(
self._fitting_stimuli,
target_visual_degrees=candidate.visual_degrees(),
source_visual_degrees=self._visual_degrees)
candidate.start_task(BrainModel.Task.probabilities, fitting_stimuli)
stimulus_set = place_on_screen(
self._assembly.stimulus_set,
target_visual_degrees=candidate.visual_degrees(),
source_visual_degrees=self._visual_degrees)
probabilities = candidate.look_at(stimulus_set)
score = self._metric(probabilities, self._assembly)
score = self.ceil_score(score, self.ceiling)
return score
def __call__(self, candidate: BrainModel):
candidate.start_recording('IT', time_bins=self._time_bins)
stimulus_set = place_on_screen(
self._assembly.stimulus_set,
target_visual_degrees=candidate.visual_degrees(),
source_visual_degrees=self._visual_degrees)
# Temporal recordings from large candidates take up a lot of memory and compute time.
# In order to quickly reject recordings that are static over time,
# we will show one image and check whether the recordings vary over time at all or not.
# If they don't we can quickly score the candidate with a failure state
# since it will not be able to predict temporal differences with the OST metric
check_stimulus_set = stimulus_set[:1]
check_stimulus_set.identifier = None # unset identifier to avoid storing (interferes with actual stimulus_set)
check_recordings = candidate.look_at(
check_stimulus_set, number_of_trials=self._number_of_trials)
if not temporally_varying(check_recordings):
score = Score([np.nan, np.nan],
coords={'aggregation': ['center', 'error']},
dims=['aggregation'])
else:
recordings = candidate.look_at(
stimulus_set, number_of_trials=self._number_of_trials)
score = self._similarity_metric(recordings, self._assembly)
score = ceil_score(score, self.ceiling)
return score
def __call__(self, candidate: BrainModel):
candidate.start_recording(self.region, time_bins=self.timebins)
stimulus_set = place_on_screen(
self._assembly.stimulus_set,
target_visual_degrees=candidate.visual_degrees(),
source_visual_degrees=self._visual_degrees)
source_assembly = candidate.look_at(stimulus_set)
if 'time_bin' in source_assembly.dims:
source_assembly = source_assembly.squeeze(
'time_bin') # static case for these benchmarks
raw_score = self._similarity_metric(source_assembly, self._assembly)
return explained_variance(raw_score, self.ceiling)
def record_from_model(model: BrainModel, stimulus_identifier,
number_of_trials):
stimulus_set = get_stimulus_set(stimulus_identifier)
stimulus_set = place_on_screen(
stimulus_set, target_visual_degrees=model.visual_degrees())
activations = model.look_at(stimulus_set, number_of_trials)
if 'time_bin' in activations.dims:
activations = activations.squeeze(
'time_bin') # static case for these benchmarks
if not activations.values.flags['WRITEABLE']:
activations.values.setflags(write=1)
return activations
def __call__(self, candidate: BrainModel):
time_bins = [(time_bin_start, time_bin_start + 10)
for time_bin_start in range(70, 250, 10)]
candidate.start_recording('IT', time_bins=time_bins)
stimulus_set = place_on_screen(
self._assembly.stimulus_set,
target_visual_degrees=candidate.visual_degrees(),
source_visual_degrees=self._visual_degrees)
recordings = candidate.look_at(stimulus_set,
number_of_trials=self._number_of_trials)
score = self._similarity_metric(recordings, self._assembly)
score = ceil_score(score, self.ceiling)
return score