def get_assembly():
image_names = []
for i in range(1, 21):
image_names.append(f'images/{i}.png')
assembly = NeuroidAssembly(
(np.arange(40 * 5) + np.random.standard_normal(40 * 5)).reshape(
(5, 40, 1)),
coords={
'image_id': ('presentation', image_names * 2),
'object_name': ('presentation', ['a'] * 40),
'repetition': ('presentation', ([1] * 20 + [2] * 20)),
'neuroid_id': ('neuroid', np.arange(5)),
'region': ('neuroid', ['IT'] * 5),
'time_bin_start': ('time_bin', [70]),
'time_bin_end': ('time_bin', [170])
},
dims=['neuroid', 'presentation', 'time_bin'])
labels = ['a'] * 10 + ['b'] * 10
stimulus_set = StimulusSet([{
'image_id': image_names[i],
'object_name': 'a',
'image_label': labels[i]
} for i in range(20)])
stimulus_set.image_paths = {
image_name: os.path.join(os.path.dirname(__file__), image_name)
for image_name in image_names
}
stimulus_set.identifier = 'test'
assembly.attrs['stimulus_set'] = stimulus_set
assembly.attrs['stimulus_set_name'] = stimulus_set.identifier
assembly = assembly.squeeze("time_bin")
return assembly.transpose('presentation', 'neuroid')
def test_creates_probabilities(self):
activations_model = pytorch_custom()
brain_model = ModelCommitment(identifier=activations_model.identifier,
activations_model=activations_model,
layers=None,
behavioral_readout_layer='relu2')
fitting_stimuli = StimulusSet({
'image_id': ['rgb1', 'rgb2'],
'image_label': ['label1', 'label2']
})
fitting_stimuli.image_paths = {
'rgb1': os.path.join(os.path.dirname(__file__), 'rgb1.jpg'),
'rgb2': os.path.join(os.path.dirname(__file__), 'rgb2.jpg')
}
fitting_stimuli.identifier = 'test_probabilities_mapping.creates_probabilities'
fitting_stimuli = place_on_screen(
fitting_stimuli,
target_visual_degrees=brain_model.visual_degrees(),
source_visual_degrees=8)
brain_model.start_task(BrainModel.Task.probabilities, fitting_stimuli)
probabilities = brain_model.look_at(fitting_stimuli)
np.testing.assert_array_equal(probabilities.dims,
['presentation', 'choice'])
np.testing.assert_array_equal(probabilities.shape, [2, 2])
np.testing.assert_array_almost_equal(
probabilities.sel(image_id='rgb1', choice='label1').values,
probabilities.sel(image_id='rgb2', choice='label2').values)
assert probabilities.sel(image_id='rgb1', choice='label1') + \
probabilities.sel(image_id='rgb1', choice='label2') == approx(1)
def _build_stimulus_set(image_names):
stimulus_set = StimulusSet([{
'image_id': image_name,
'some_meta': image_name[::-1]
} for image_name in image_names])
stimulus_set.image_paths = {
image_name: os.path.join(os.path.dirname(__file__), image_name)
for image_name in image_names
}
return stimulus_set
def test_commit(self, model_ctr, layers, region):
activations_model = model_ctr()
layer_model = LayerMappedModel(identifier=activations_model.identifier, activations_model=activations_model,
region_layer_map={region: layers})
layer_model.start_recording(region)
stimulus_set = StimulusSet([{'image_id': 'test'}])
stimulus_set.image_paths = {'test': os.path.join(os.path.dirname(__file__), 'rgb1.jpg')}
stimulus_set.identifier = self.__class__.__name__
predictions = layer_model.look_at(stimulus_set)
assert set(predictions['region'].values) == {region}
assert set(predictions['layer'].values) == {layers} if isinstance(layers, str) else set(layers)
def load(self):
stimulus_set = pd.read_csv(self.csv_path)[::self.sampling_factor]
stimulus_set = StimulusSet(stimulus_set)
stimulus_set.image_paths = {
row['image_id']: os.path.join(self.stimuli_directory,
row['filename'])
for _, row in stimulus_set.iterrows()
}
assert all(
os.path.isfile(image_path)
for image_path in stimulus_set.image_paths.values())
return stimulus_set
def visual_degree_samples(visual_degrees_samples=(8, 4, 12), base_degrees=8):
base_image = Path(__file__).parent / 'base.png'
stimulus_set = StimulusSet([{'image_id': 'base'}])
stimulus_set.image_paths = {'base': base_image}
stimulus_set.identifier = 'visual_degrees_base'
for visual_degrees in visual_degrees_samples:
converted_stimulus_set = place_on_screen(
stimulus_set,
source_visual_degrees=8,
target_visual_degrees=visual_degrees)
converted_path = converted_stimulus_set.get_image('base')
target_path = static_directory / 'visual_degrees' / f"{base_degrees}_to_{visual_degrees}.png"
converted_image = Image.open(converted_path)
converted_image.save(target_path)
def _place_on_screen(stimuli_identifier: str,
stimulus_set: StimulusSet,
target_visual_degrees: int,
source_visual_degrees: int = None):
converted_stimuli_id = f"{stimuli_identifier}--target{target_visual_degrees}--source{source_visual_degrees}"
source_visual_degrees = _determine_visual_degrees(source_visual_degrees,
stimulus_set)
target_dir = root_path / converted_stimuli_id
target_dir.mkdir(parents=True, exist_ok=False)
image_converter = ImageConverter(target_dir=target_dir)
converted_image_paths = {}
for image_id, image_degrees in tqdm(zip(stimulus_set['image_id'],
source_visual_degrees),
total=len(stimulus_set),
desc='convert image degrees'):
converted_image_path = image_converter.convert_image(
image_path=stimulus_set.get_image(image_id),
source_degrees=image_degrees,
target_degrees=target_visual_degrees)
converted_image_paths[image_id] = converted_image_path
converted_stimuli = StimulusSet(stimulus_set.copy(
deep=True)) # without copy, it will link to the previous stim set
converted_stimuli.image_paths = converted_image_paths
converted_stimuli.identifier = converted_stimuli_id
converted_stimuli['degrees'] = target_visual_degrees
converted_stimuli.original_paths = copy.deepcopy(stimulus_set.image_paths)
return converted_stimuli
def __init__(self):
stimulus_set = pd.read_csv(os.path.join(os.path.dirname(__file__), 'imagenet2012.csv'))
stimulus_set = StimulusSet(stimulus_set)
stimulus_set.image_paths = {row.image_id: row.filepath for row in stimulus_set.itertuples()}
self._stimulus_set = stimulus_set
self._similarity_metric = Accuracy()
ceiling = Score([1, np.nan], coords={'aggregation': ['center', 'error']}, dims=['aggregation'])
super(Imagenet2012, self).__init__(identifier='fei-fei.Deng2009-top1', version=1,
ceiling_func=lambda: ceiling,
parent='ImageNet',
bibtex="""@INPROCEEDINGS{5206848,
author={J. {Deng} and W. {Dong} and R. {Socher} and L. {Li} and {Kai Li} and {Li Fei-Fei}},
booktitle={2009 IEEE Conference on Computer Vision and Pattern Recognition},
title={ImageNet: A large-scale hierarchical image database},
year={2009},
volume={},
number={},
pages={248-255},
url = {https://ieeexplore.ieee.org/document/5206848}
}""")
def test_creates_synset(self, model_ctr):
np.random.seed(0)
activations_model = model_ctr()
brain_model = ModelCommitment(
identifier=activations_model.identifier,
activations_model=activations_model,
layers=None,
behavioral_readout_layer='dummy') # not needed
stimuli = StimulusSet({
'image_id': ['1', '2'],
'filename': ['rgb1', 'rgb2']
})
stimuli.image_paths = {
'1': os.path.join(os.path.dirname(__file__), 'rgb1.jpg'),
'2': os.path.join(os.path.dirname(__file__), 'rgb2.jpg')
}
stimuli.identifier = 'test_logits_behavior.creates_synset'
brain_model.start_task(BrainModel.Task.label, 'imagenet')
behavior = brain_model.look_at(stimuli)
assert isinstance(behavior, BehavioralAssembly)
assert set(behavior['image_id'].values) == {'1', '2'}
assert len(behavior['synset']) == 2
assert behavior['synset'].values[0].startswith('n')