-
-
Current backend: {{ app_state.backend }}
- {% if app_state.latest_ckpt_name is defined %}
-
Current checkpoint: {{ app_state.latest_ckpt_name }}
- {% endif %}
- {% if app_state.latest_ckpt_time is defined %}
-
Last updated: {{ app_state.latest_ckpt_time }}
- {% endif %}
+
+
-
+
+
+
{% block body %}{% endblock %}
+
+
+
+
+
diff --git a/picasso/visualizations/__init__.py b/picasso/visualizations/__init__.py
index 0146dd3..6c1fd87 100644
--- a/picasso/visualizations/__init__.py
+++ b/picasso/visualizations/__init__.py
@@ -11,32 +11,58 @@
class BaseVisualization:
- """Template for visualizations
-
- Attributes:
- description (:obj:`str`): short description of the visualization
- model (instance of :class:`.ml_frameworks.model.Model` or derived class):
- backend to use
- settings (:obj:`dict`): a settings dictionary. Settings defined
- here will be rendered in html for the user to select. See
- derived classes for examples.
+ """Interface encapsulating a NN visualization.
+
+ This interface defines how a visualization is computed for a given NN
+ model.
+
"""
+ # (:obj:`str`): Short description of the visualization.
+ DESCRIPTION = None
+
+ # (:obj:`str`): Optional link to the paper specifying the visualization.
+ REFERENCE_LINK = None
+
+ # (:obj:`dict`): Optional visuzalization settings that the user can select.
+ # Should be a dict mapping setting names to lists of their allowed values.
+ AVAILABLE_SETTINGS = None
+
def __init__(self, model):
- self.model = model
+ """Create a new instance of this visualization.
+
+ `BaseVisualization` is an interface and should only be instantiated via
+ a subclass.
+
+ Args:
+ model (:obj:`.ml_frameworks.model.BaseModel`): NN model to be
+ visualized.
+
+ """
+ self._model = model
+
+ @property
+ def model(self):
+ """NN model to be visualized.
+
+ (:obj:`.ml_frameworks.model.BaseModel`)
+
+ """
+ return self._model
def make_visualization(self, inputs, output_dir, settings=None):
- """Generate the visualization
+ """Generate the visualization.
All visualizations must implement this method.
Args:
- inputs (iterable of :class:`PIL.Image`): images uploaded by the
- user. Will have already been converted to :obj:`Image`
- objects.
- output_dir (:obj:`str`): a directory to store outputs (e.g. plots)
+ inputs (iterable of :class:`PIL.Image`): Batch of input images to
+ make visualizations for, as PIL :obj:`Image` objects.
+ output_dir (:obj:`str`): A directory to write outputs to (e.g.,
+ plots).
Returns:
data needed to render the visualization. Since there is an
associated HTML template, the return type is arbitrary.
+
"""
raise NotImplementedError
diff --git a/picasso/visualizations/class_probabilities.py b/picasso/visualizations/class_probabilities.py
index f87d5f3..ca49cbe 100644
--- a/picasso/visualizations/class_probabilities.py
+++ b/picasso/visualizations/class_probabilities.py
@@ -9,10 +9,9 @@ class probabilities of the input image.
"""
- description = 'Predict class probabilities from new examples'
+ DESCRIPTION = 'Predict class probabilities from new examples'
- def make_visualization(self, inputs,
- output_dir, settings=None):
+ def make_visualization(self, inputs, output_dir, settings=None):
pre_processed_arrays = self.model.preprocess([example['data']
for example in inputs])
predictions = self.model.sess.run(self.model.tf_predict_var,
diff --git a/picasso/visualizations/partial_occlusion.py b/picasso/visualizations/partial_occlusion.py
index 239b4d6..711454d 100644
--- a/picasso/visualizations/partial_occlusion.py
+++ b/picasso/visualizations/partial_occlusion.py
@@ -22,16 +22,17 @@ class PartialOcclusion(BaseVisualization):
classifying on the image feature we expect.
"""
- settings = {
+ DESCRIPTION = ('Partially occlude image to determine regions '
+ 'important to classification')
+
+ REFERENCE_LINK = 'https://arxiv.org/abs/1311.2901'
+
+ AVAILABLE_SETTINGS = {
'Window': ['0.50', '0.40', '0.30', '0.20', '0.10', '0.05'],
'Strides': ['2', '5', '10', '20', '30'],
'Occlusion': ['grey', 'black', 'white']
}
- description = ('Partially occlude image to determine regions '
- 'important to classification')
- reference_link = 'https://arxiv.org/abs/1311.2901'
-
def __init__(self, model):
super(PartialOcclusion, self).__init__(model)
self.predict_tensor = self.get_predict_tensor()
@@ -53,11 +54,10 @@ def make_visualization(self, inputs, output_dir, settings=None):
# get class predictions as in ClassProbabilities
pre_processed_arrays = self.model.preprocess([example['data']
- for example in inputs])
- class_predictions = \
- self.model.sess.run(self.model.tf_predict_var,
- feed_dict={self.model.tf_input_var:
- pre_processed_arrays})
+ for example in inputs])
+ class_predictions = self.model.sess.run(
+ self.model.tf_predict_var,
+ feed_dict={self.model.tf_input_var: pre_processed_arrays})
decoded_predictions = self.model.decode_prob(class_predictions)
results = []
@@ -100,8 +100,8 @@ def make_visualization(self, inputs, output_dir, settings=None):
def get_predict_tensor(self):
# Assume that predict is the softmax
# tensor in the computation graph
- return self.model.sess.graph. \
- get_tensor_by_name(self.model.tf_predict_var.name)
+ return self.model.sess.graph.get_tensor_by_name(
+ self.model.tf_predict_var.name)
def update_settings(self, settings):
def error_string(setting, setting_val):
@@ -112,19 +112,19 @@ def error_string(setting, setting_val):
vis=self.__class__.__name__)
if 'Window' in settings:
- if settings['Window'] in self.settings['Window']:
+ if settings['Window'] in self.AVAILABLE_SETTINGS['Window']:
self.window = float(settings['Window'])
else:
raise ValueError(error_string(settings['Window'], 'Window'))
if 'Strides' in settings:
- if settings['Strides'] in self.settings['Strides']:
+ if settings['Strides'] in self.AVAILABLE_SETTINGS['Strides']:
self.num_windows = int(settings['Strides'])
else:
raise ValueError(error_string(settings['Strides'], 'Strides'))
if 'Occlusion' in settings:
- if settings['Occlusion'] in self.settings['Occlusion']:
+ if settings['Occlusion'] in self.AVAILABLE_SETTINGS['Occlusion']:
self.occlusion_method = settings['Occlusion']
else:
raise ValueError(error_string(settings['Occlusion'],
diff --git a/picasso/visualizations/saliency_maps.py b/picasso/visualizations/saliency_maps.py
index 060df75..f3d8160 100644
--- a/picasso/visualizations/saliency_maps.py
+++ b/picasso/visualizations/saliency_maps.py
@@ -21,24 +21,27 @@ class SaliencyMaps(BaseVisualization):
classification (as changing them would change the classification).
"""
- description = ('See maximal derivates against class with respect '
+ DESCRIPTION = ('See maximal derivates against class with respect '
'to input')
- reference_link = 'https://arxiv.org/pdf/1312.6034'
+
+ REFERENCE_LINK = 'https://arxiv.org/pdf/1312.6034'
def __init__(self, model, logit_tensor_name=None):
super(SaliencyMaps, self).__init__(model)
if logit_tensor_name:
- self.logit_tensor = self.model.sess.graph \
- .get_tensor_by_name(logit_tensor_name)
+ self.logit_tensor = self.model.sess.graph.get_tensor_by_name(
+ logit_tensor_name)
else:
self.logit_tensor = self.get_logit_tensor()
+ self.input_shape = self.model.tf_input_var.get_shape()[1:].as_list()
+
def get_gradient_wrt_class(self, class_index):
- gradient_name = 'bv_{class_index}_gradient' \
- .format(class_index=class_index)
+ gradient_name = 'bv_{class_index}_gradient'.format(
+ class_index=class_index)
try:
- return self.model.sess.graph. \
- get_tensor_by_name('{}:0'.format(gradient_name))
+ return self.model.sess.graph.get_tensor_by_name(
+ '{}:0'.format(gradient_name))
except KeyError:
class_logit = tf.slice(self.logit_tensor,
[0, class_index],
@@ -61,24 +64,29 @@ def make_visualization(self, inputs, output_dir, settings=None):
results = []
for i, inp in enumerate(inputs):
class_gradients = []
- output_images = []
relevant_class_indices = [pred['index']
for pred in decoded_predictions[i]]
- gradients_wrt_class = [self.get_gradient_wrt_class(index) for index
- in relevant_class_indices]
+ gradients_wrt_class = [self.get_gradient_wrt_class(index)
+ for index in relevant_class_indices]
for gradient_wrt_class in gradients_wrt_class:
class_gradients.append([self.model.sess.run(
gradient_wrt_class,
feed_dict={self.model.tf_input_var: [arr]})
for arr in pre_processed_arrays])
- output_fns = []
- output_arrays = np.array([gradient[i] for
- gradient in class_gradients])
+
+ output_arrays = np.array([gradient[i]
+ for gradient in class_gradients])
# if images are color, take the maximum channel
if output_arrays.shape[-1] == 3:
output_arrays = output_arrays.max(-1)
+ # we care about the size of the derivative, not the sign
+ output_arrays = np.abs(output_arrays)
+
+ # We want each array to be represented as a 1-channel image of
+ # the same size as the model's input image.
+ output_images = output_arrays.reshape([-1] + self.input_shape[0:2])
- output_images = self.model.postprocess(np.abs(output_arrays))
+ output_fns = []
for j, image in enumerate(output_images):
output_fn = '{fn}-{j}-{ts}.png'.format(ts=str(time.time()),
j=j,
@@ -106,8 +114,7 @@ def get_logit_tensor(self):
# Assume that the logits are the tensor input to the last softmax
# operation in the computation graph
sm = [node for node in self.model.sess.graph_def.node
- if node.name ==
- self.model.tf_predict_var.name.split(':')[0]][-1]
+ if node.name == self.model.tf_predict_var.name.split(':')[0]][-1]
logit_op_name = sm.input[0]
- return self.model.sess.graph. \
- get_tensor_by_name('{}:0'.format(logit_op_name))
+ return self.model.sess.graph.get_tensor_by_name(
+ '{}:0'.format(logit_op_name))
diff --git a/tests/conftest.py b/tests/conftest.py
index 777ac6f..238e5f7 100644
--- a/tests/conftest.py
+++ b/tests/conftest.py
@@ -27,5 +27,8 @@ def example_prob_array():
@pytest.fixture
def base_model():
- from picasso.ml_frameworks.model import Model
- return Model()
+ from picasso.ml_frameworks.model import BaseModel
+ class BaseModelForTest(BaseModel):
+ def _load(self, data_dir):
+ pass
+ return BaseModelForTest("")