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, do_behavior=False):
# Do brain region task
candidate.start_recording(self.region, time_bins=self.timebins)
source_assembly = candidate.look_at(self.assembly.stimulus_set)
# Do behavior task
if do_behavior:
candidate.start_task(BrainModel.Task.probabilities,
self.assembly.stimulus_set)
candidate.look_at(self.assembly.stimulus_set)
raw_score = self._similarity_metric(source_assembly, self.assembly)
return ceil_score(raw_score, self.ceiling)
def __call__(self, candidate: BrainModel, do_behavior=False):
# Check neural recordings
candidate.start_recording(self.region, time_bins=self.timebins)
source_assembly = candidate.look_at(self.assembly.stimulus_set)
# Check behavioral tasks
if do_behavior:
candidate.start_task(BrainModel.Task.probabilities,
self.assembly.stimulus_set)
candidate.look_at(self.assembly.stimulus_set)
candidate.start_task(BrainModel.Task.label, 'imagenet')
candidate.look_at(self.assembly.stimulus_set)
raw_score = self._similarity_metric(source_assembly, self.assembly)
return ceil_score(raw_score, self.ceiling)
def __call__(self, candidate: BrainModel):
candidate.start_task(BrainModel.Task.probabilities,
self._fitting_stimuli)
probabilities = candidate.look_at(self._assembly.stimulus_set)
score = self._metric(probabilities, self._assembly)
score = self.ceil_score(score, self.ceiling)
return score
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)
recordings = candidate.look_at(self._assembly.stimulus_set)
score = self._similarity_metric(recordings, self._assembly)
score = ceil_score(score, self.ceiling)
return score
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)
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, number_of_trials=self._number_of_trials)
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 __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, number_of_trials=self._number_of_trials)
score = self._metric(probabilities, self._assembly)
score = self.ceil_score(score, self.ceiling)
return score
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):
self._metric = ScanMatchPy.initialize()
self._logger.info("## Starting visual search task...")
candidate.start_task(BrainModel.Task.visual_search,
max_fix=self.max_fix,
data_len=self.data_len,
ior_size=self.ior_size)
self.cumm_perf, self.saccades = candidate.look_at(self._stimuli)
# in saccades the last index denotes the index at which the target was found
fix_model = self.saccades[:, :self.max_fix + 1, :] # first n saccades
I_fix_model = self.saccades[:, self.max_fix + 1, :
1] # index at which the target was found
fix1 = matlab.int32(fix_model.tolist())
I_fix1 = matlab.int32(I_fix_model.tolist())
self._logger.info("## Search task done...\n")
self._logger.info("## Calculating score...")
scores = []
for sub_id in tqdm(range(self.num_sub),
desc="comparing with human data: "):
data_human = self._assemblies.values[sub_id *
self.data_len:(sub_id + 1) *
self.data_len]
fix_human = data_human[:, :self.max_fix + 1, :]
I_fix_human = data_human[:, self.max_fix + 1, :1]
fix2 = matlab.int32(fix_human.tolist())
I_fix2 = matlab.int32(I_fix_human.tolist())
score = self._metric.findScore(fix1, fix2, I_fix1, I_fix2)
scores.append(score)
scores = np.asarray(scores)
self.raw_score = np.mean(scores)
self.std = np.std(scores) / np.sqrt(scores.shape[0])
self.model_score = Score([self.raw_score, self.std],
coords={'aggregation': ['center', 'error']},
dims=['aggregation'])
self._metric.terminate()
ceiled_score = ceil_score(self.model_score, self.ceiling)
self._logger.info("## Score calculated...\n")
return ceiled_score