-
Notifications
You must be signed in to change notification settings - Fork 16
Commit
This commit does not belong to any branch on this repository, and may belong to a fork outside of the repository.
- Loading branch information
Showing
4 changed files
with
533 additions
and
411 deletions.
There are no files selected for viewing
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -1,113 +1,139 @@ | ||
| from __future__ import division | ||
| import keras.backend as K | ||
| import theano.tensor as T | ||
| from keras.layers import Layer, InputSpec | ||
| from keras import initializations, regularizers, constraints | ||
| import theano | ||
| import numpy as np | ||
| floatX = theano.config.floatX | ||
|
|
||
|
|
||
| class LearningPrior(Layer): | ||
| def __init__(self, nb_gaussian, init='normal', weights=None, | ||
| W_regularizer=None, activity_regularizer=None, | ||
| W_constraint=None, **kwargs): | ||
| self.nb_gaussian = nb_gaussian | ||
| self.init = initializations.get(init, dim_ordering='th') | ||
|
|
||
| self.W_regularizer = regularizers.get(W_regularizer) | ||
| self.activity_regularizer = regularizers.get(activity_regularizer) | ||
|
|
||
| self.W_constraint = constraints.get(W_constraint) | ||
|
|
||
| self.input_spec = [InputSpec(ndim=4)] | ||
| self.initial_weights = weights | ||
| super(LearningPrior, self).__init__(**kwargs) | ||
|
|
||
| def build(self, input_shape): | ||
| self.W_shape = (self.nb_gaussian*4, ) | ||
| self.W = self.init(self.W_shape, name='{}_W'.format(self.name)) | ||
|
|
||
| self.trainable_weights = [self.W] | ||
|
|
||
| self.regularizers = [] | ||
| if self.W_regularizer: | ||
| self.W_regularizer.set_param(self.W) | ||
| self.regularizers.append(self.W_regularizer) | ||
|
|
||
| if self.activity_regularizer: | ||
| self.activity_regularizer.set_layer(self) | ||
| self.regularizers.append(self.activity_regularizer) | ||
|
|
||
| if self.initial_weights is not None: | ||
| self.set_weights(self.initial_weights) | ||
| del self.initial_weights | ||
|
|
||
| self.constraints = {} | ||
| if self.W_constraint: | ||
| self.constraints[self.W] = self.W_constraint | ||
|
|
||
| def get_output_shape_for(self, input_shape): | ||
| self.b_s = input_shape[0] | ||
| self.height = input_shape[2] | ||
| self.width = input_shape[3] | ||
|
|
||
| return self.b_s, self.nb_gaussian, self.height, self.width | ||
|
|
||
| def call(self, x, mask=None): | ||
| mu_x = self.W[:self.nb_gaussian] | ||
| mu_y = self.W[self.nb_gaussian:self.nb_gaussian*2] | ||
| sigma_x = self.W[self.nb_gaussian*2:self.nb_gaussian*3] | ||
| sigma_y = self.W[self.nb_gaussian*3:] | ||
|
|
||
| self.b_s = x.shape[0] | ||
| self.height = x.shape[2] | ||
| self.width = x.shape[3] | ||
|
|
||
| e = self.height / self.width | ||
| e1 = (1 - e) / 2 | ||
| e2 = e1 + e | ||
|
|
||
| mu_x = K.clip(mu_x, 0.25, 0.75) | ||
| mu_y = K.clip(mu_y, 0.35, 0.65) | ||
|
|
||
| sigma_x = K.clip(sigma_x, 0.1, 0.9) | ||
| sigma_y = K.clip(sigma_y, 0.2, 0.8) | ||
|
|
||
| x_t = T.dot(T.ones((self.height, 1)), self._linspace(0, 1.0, self.width).dimshuffle('x', 0)) | ||
| y_t = T.dot(self._linspace(e1, e2, self.height).dimshuffle(0, 'x'), T.ones((1, self.width))) | ||
|
|
||
| x_t = K.repeat_elements(K.expand_dims(x_t, dim=-1), self.nb_gaussian, axis=-1) | ||
| y_t = K.repeat_elements(K.expand_dims(y_t, dim=-1), self.nb_gaussian, axis=-1) | ||
|
|
||
| gaussian = 1 / (2 * np.pi * sigma_x * sigma_y + K.epsilon()) * \ | ||
| T.exp(-((x_t - mu_x) ** 2 / (2 * sigma_x ** 2 + K.epsilon()) + | ||
| (y_t - mu_y) ** 2 / (2 * sigma_y ** 2 + K.epsilon()))) | ||
|
|
||
| gaussian = K.permute_dimensions(gaussian, (2, 0, 1)) | ||
| max_gauss = K.repeat_elements(K.expand_dims(K.repeat_elements(K.expand_dims(K.max(K.max(gaussian, axis=1), axis=1)), self.height, axis=-1)), self.width, axis=-1) | ||
| gaussian = gaussian / max_gauss | ||
|
|
||
| output = K.repeat_elements(K.expand_dims(gaussian, dim=0), self.b_s, axis=0) | ||
|
|
||
| return output | ||
|
|
||
| @staticmethod | ||
| def _linspace(start, stop, num): | ||
| # produces results identical to: | ||
| # np.linspace(start, stop, num) | ||
| start = T.cast(start, floatX) | ||
| stop = T.cast(stop, floatX) | ||
| num = T.cast(num, floatX) | ||
| step = (stop - start) / (num - 1) | ||
| return T.arange(num, dtype=floatX) * step + start | ||
|
|
||
| def get_config(self): | ||
| config = {'nb_gaussian': self.nb_gaussian, | ||
| 'init': self.init.__name__, | ||
| 'W_regularizer': self.W_regularizer.get_config() if self.W_regularizer else None, | ||
| 'activity_regularizer': self.activity_regularizer.get_config() if self.activity_regularizer else None, | ||
| 'W_constraint': self.W_constraint.get_config() if self.W_constraint else None, | ||
| } | ||
| base_config = super(LearningPrior, self).get_config() | ||
| return dict(list(base_config.items()) + list(config.items())) | ||
| from __future__ import division | ||
| import keras.backend as K | ||
| import theano.tensor as T | ||
| from keras.layers import Layer, InputSpec | ||
| from keras import initializations, regularizers, constraints | ||
| import theano | ||
| import numpy as np | ||
| floatX = theano.config.floatX | ||
|
|
||
|
|
||
| class LearningPrior(Layer): | ||
| def __init__(self, | ||
| nb_gaussian, | ||
| init='normal', | ||
| weights=None, | ||
| W_regularizer=None, | ||
| activity_regularizer=None, | ||
| W_constraint=None, | ||
| **kwargs): | ||
| self.nb_gaussian = nb_gaussian | ||
| self.init = initializations.get(init, dim_ordering='th') | ||
|
|
||
| self.W_regularizer = regularizers.get(W_regularizer) | ||
| self.activity_regularizer = regularizers.get(activity_regularizer) | ||
|
|
||
| self.W_constraint = constraints.get(W_constraint) | ||
|
|
||
| self.input_spec = [InputSpec(ndim=4)] | ||
| self.initial_weights = weights | ||
| super(LearningPrior, self).__init__(**kwargs) | ||
|
|
||
| def build(self, input_shape): | ||
| self.W_shape = (self.nb_gaussian * 4, ) | ||
| self.W = self.init(self.W_shape, name='{}_W'.format(self.name)) | ||
|
|
||
| self.trainable_weights = [self.W] | ||
|
|
||
| self.regularizers = [] | ||
| if self.W_regularizer: | ||
| self.W_regularizer.set_param(self.W) | ||
| self.regularizers.append(self.W_regularizer) | ||
|
|
||
| if self.activity_regularizer: | ||
| self.activity_regularizer.set_layer(self) | ||
| self.regularizers.append(self.activity_regularizer) | ||
|
|
||
| if self.initial_weights is not None: | ||
| self.set_weights(self.initial_weights) | ||
| del self.initial_weights | ||
|
|
||
| self.constraints = {} | ||
| if self.W_constraint: | ||
| self.constraints[self.W] = self.W_constraint | ||
|
|
||
| def get_output_shape_for(self, input_shape): | ||
| self.b_s = input_shape[0] | ||
| self.height = input_shape[2] | ||
| self.width = input_shape[3] | ||
|
|
||
| return self.b_s, self.nb_gaussian, self.height, self.width | ||
|
|
||
| def call(self, x, mask=None): | ||
| mu_x = self.W[:self.nb_gaussian] | ||
| mu_y = self.W[self.nb_gaussian:self.nb_gaussian * 2] | ||
| sigma_x = self.W[self.nb_gaussian * 2:self.nb_gaussian * 3] | ||
| sigma_y = self.W[self.nb_gaussian * 3:] | ||
|
|
||
| self.b_s = x.shape[0] | ||
| self.height = x.shape[2] | ||
| self.width = x.shape[3] | ||
|
|
||
| e = self.height / self.width | ||
| e1 = (1 - e) / 2 | ||
| e2 = e1 + e | ||
|
|
||
| mu_x = K.clip(mu_x, 0.25, 0.75) | ||
| mu_y = K.clip(mu_y, 0.35, 0.65) | ||
|
|
||
| sigma_x = K.clip(sigma_x, 0.1, 0.9) | ||
| sigma_y = K.clip(sigma_y, 0.2, 0.8) | ||
|
|
||
| x_t = T.dot( | ||
| T.ones((self.height, 1)), | ||
| self._linspace(0, 1.0, self.width).dimshuffle('x', 0)) | ||
| y_t = T.dot( | ||
| self._linspace(e1, e2, self.height).dimshuffle(0, 'x'), | ||
| T.ones((1, self.width))) | ||
|
|
||
| x_t = K.repeat_elements( | ||
| K.expand_dims(x_t, dim=-1), self.nb_gaussian, axis=-1) | ||
| y_t = K.repeat_elements( | ||
| K.expand_dims(y_t, dim=-1), self.nb_gaussian, axis=-1) | ||
|
|
||
| gaussian = 1 / (2 * np.pi * sigma_x * sigma_y + K.epsilon()) * \ | ||
| T.exp(-((x_t - mu_x) ** 2 / (2 * sigma_x ** 2 + K.epsilon()) + | ||
| (y_t - mu_y) ** 2 / (2 * sigma_y ** 2 + K.epsilon()))) | ||
|
|
||
| gaussian = K.permute_dimensions(gaussian, (2, 0, 1)) | ||
| max_gauss = K.repeat_elements( | ||
| K.expand_dims( | ||
| K.repeat_elements( | ||
| K.expand_dims(K.max(K.max(gaussian, axis=1), axis=1)), | ||
| self.height, | ||
| axis=-1)), | ||
| self.width, | ||
| axis=-1) | ||
| gaussian = gaussian / max_gauss | ||
|
|
||
| output = K.repeat_elements( | ||
| K.expand_dims(gaussian, dim=0), self.b_s, axis=0) | ||
|
|
||
| return output | ||
|
|
||
| @staticmethod | ||
| def _linspace(start, stop, num): | ||
| # produces results identical to: | ||
| # np.linspace(start, stop, num) | ||
| start = T.cast(start, floatX) | ||
| stop = T.cast(stop, floatX) | ||
| num = T.cast(num, floatX) | ||
| step = (stop - start) / (num - 1) | ||
| return T.arange(num, dtype=floatX) * step + start | ||
|
|
||
| def get_config(self): | ||
| config = { | ||
| 'nb_gaussian': | ||
| self.nb_gaussian, | ||
| 'init': | ||
| self.init.__name__, | ||
| 'W_regularizer': | ||
| self.W_regularizer.get_config() if self.W_regularizer else None, | ||
| 'activity_regularizer': | ||
| self.activity_regularizer.get_config() | ||
| if self.activity_regularizer else None, | ||
| 'W_constraint': | ||
| self.W_constraint.get_config() if self.W_constraint else None, | ||
| } | ||
| base_config = super(LearningPrior, self).get_config() | ||
| return dict(list(base_config.items()) + list(config.items())) |
Oops, something went wrong.