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How to use _setup_parameters method in tempest
Best Python code snippet using tempest_python
qlearning_test.py
Source:qlearning_test.py 
...24 def test_init_dqn(self,25 mock_parameters,26 mock_model,27 mock_replay_memory):28 self._setup_parameters(mock_parameters.return_value)29 mock_model.return_value = self._setup_test_model()30 action_space = spaces.Discrete(2)31 observation_space = spaces.Box(0, 1, (1,))32 sut = QLearning('', observation_space, action_space)33 self.assertEqual(sut._num_actions, 2)34 self.assertIsNone(sut._num_states)35 self.assertEqual(sut._shape_of_inputs, (1,))36 self.assertFalse(sut._discrete_observation_space)37 self.assertIsNone(sut._space_discretizer)38 self.assertIsNone(sut._preprocessor)39 self.assertFalse(hasattr(sut, 'weight_variables'))40 self.assertIsNotNone(sut._trainer)41 mock_model.assert_called_with((1,), 2, '[2]', None)42 mock_replay_memory.assert_called_with(100, False)43 @patch('cntk.contrib.deeprl.agent.qlearning.ReplayMemory')44 @patch('cntk.contrib.deeprl.agent.qlearning.QLearningParameters')45 def test_init_dqn_prioritized_replay(self,46 mock_parameters,47 mock_replay_memory):48 self._setup_parameters(mock_parameters.return_value)49 mock_parameters.return_value.use_prioritized_replay = True50 action_space = spaces.Discrete(2)51 observation_space = spaces.Box(0, 1, (1,))52 sut = QLearning('', observation_space, action_space)53 self.assertIsNotNone(sut._weight_variables)54 mock_replay_memory.assert_called_with(100, True)55 @patch('cntk.contrib.deeprl.agent.qlearning.ReplayMemory')56 @patch('cntk.contrib.deeprl.agent.qlearning.QLearningParameters')57 def test_init_dqn_preprocessing(self,58 mock_parameters,59 mock_replay_memory):60 self._setup_parameters(mock_parameters.return_value)61 mock_parameters.return_value.preprocessing = \62 'cntk.contrib.deeprl.agent.shared.preprocessing.AtariPreprocessing'63 mock_parameters.return_value.preprocessing_args = '()'64 action_space = spaces.Discrete(2)65 observation_space = spaces.Box(0, 1, (1,))66 sut = QLearning('', observation_space, action_space)67 # Preprocessor with default arguments.68 self.assertIsNotNone(sut._preprocessor)69 self.assertEqual(sut._preprocessor.output_shape(), (4, 84, 84))70 # Preprocessor with arguments passed as a tuple.71 mock_parameters.return_value.preprocessing_args = '(3,)'72 sut = QLearning('', observation_space, action_space)73 self.assertEqual(sut._preprocessor.output_shape(), (3, 84, 84))74 # Preprocessor with inappropriate arguments.75 mock_parameters.return_value.preprocessing_args = '(3, 4)'76 self.assertRaises(77 TypeError, QLearning, '', observation_space, action_space)78 # Undefined preprocessor.79 mock_parameters.return_value.preprocessing = 'undefined'80 self.assertRaises(81 ValueError, QLearning, '', observation_space, action_space)82 @patch('cntk.contrib.deeprl.agent.qlearning.Models.dueling_network')83 @patch('cntk.contrib.deeprl.agent.qlearning.QLearningParameters')84 def test_init_dueling_dqn(self, mock_parameters, mock_model):85 self._setup_parameters(mock_parameters.return_value)86 mock_parameters.return_value.q_representation = 'dueling-dqn'87 mock_parameters.return_value.hidden_layers = '[2, [2], [2]]'88 mock_model.return_value = self._setup_test_model()89 action_space = spaces.Discrete(2)90 observation_space = spaces.Box(0, 1, (1,))91 sut = QLearning('', observation_space, action_space)92 self.assertEqual(sut._num_actions, 2)93 self.assertIsNone(sut._num_states)94 self.assertEqual(sut._shape_of_inputs, (1,))95 self.assertFalse(sut._discrete_observation_space)96 self.assertIsNone(sut._space_discretizer)97 self.assertIsNone(sut._preprocessor)98 mock_model.assert_called_with((1,), 2, '[2, [2], [2]]', None)99 @patch('cntk.contrib.deeprl.agent.shared.customized_models.conv_dqn')100 @patch('cntk.contrib.deeprl.agent.qlearning.QLearningParameters')101 def test_init_customized_q(self, mock_parameters, mock_model):102 self._setup_parameters(mock_parameters.return_value)103 mock_parameters.return_value.q_representation = \104 'cntk.contrib.deeprl.agent.shared.customized_models.conv_dqn'105 mock_model.return_value = self._setup_test_model()106 action_space = spaces.Discrete(2)107 observation_space = spaces.Box(0, 1, (1,))108 sut = QLearning('', observation_space, action_space)109 self.assertEqual(sut._num_actions, 2)110 self.assertIsNone(sut._num_states)111 self.assertEqual(sut._shape_of_inputs, (1,))112 self.assertFalse(sut._discrete_observation_space)113 self.assertIsNone(sut._space_discretizer)114 self.assertIsNone(sut._preprocessor)115 mock_model.assert_called_with((1,), 2, None)116 @patch('cntk.contrib.deeprl.agent.qlearning.QLearningParameters')117 def test_init_unsupported_q(self, mock_parameters):118 instance = mock_parameters.return_value119 instance.q_representation = 'undefined'120 instance.preprocessing = ''121 action_space = spaces.Discrete(2)122 observation_space = spaces.Box(0, 1, (1,))123 self.assertRaises(124 ValueError, QLearning, '', observation_space, action_space)125 @patch('cntk.contrib.deeprl.agent.qlearning.Models.feedforward_network')126 @patch('cntk.contrib.deeprl.agent.qlearning.QLearningParameters')127 def test_init_dqn_huber_loss(self, mock_parameters, mock_model):128 self._setup_parameters(mock_parameters.return_value)129 mock_parameters.return_value.use_error_clipping = True130 mock_model.return_value = self._setup_test_model()131 action_space = spaces.Discrete(2)132 observation_space = spaces.Box(0, 1, (1,))133 sut = QLearning('', observation_space, action_space)134 mock_model.assert_called_with((1,), 2, '[2]', huber_loss)135 @patch('cntk.contrib.deeprl.agent.qlearning.ReplayMemory')136 @patch('cntk.contrib.deeprl.agent.qlearning.QLearningParameters')137 def test_update_q(self,138 mock_parameters,139 mock_replay_memory):140 """Test if _update_q_periodically() can finish successfully."""141 self._setup_parameters(mock_parameters.return_value)142 self._setup_replay_memory(mock_replay_memory.return_value)143 action_space = spaces.Discrete(2)144 observation_space = spaces.Box(0, 1, (1,))145 sut = QLearning('', observation_space, action_space)146 sut._trainer.train_minibatch = MagicMock()147 sut._choose_action = MagicMock(side_effect=[148 (1, 'GREEDY'),149 (0, 'GREEDY'),150 (1, 'RANDOM'),151 ])152 action, debug_info = sut.start(np.array([0.1], np.float32))153 self.assertEqual(action, 1)154 self.assertEqual(debug_info['action_behavior'], 'GREEDY')155 self.assertEqual(sut.episode_count, 1)156 self.assertEqual(sut.step_count, 0)157 self.assertEqual(sut._epsilon, 0.1)158 self.assertEqual(sut._trainer.parameter_learners[0].learning_rate(), 0.1)159 self.assertEqual(sut._last_state, np.array([0.1], np.float32))160 self.assertEqual(sut._last_action, 1)161 action, debug_info = sut.step(1, np.array([0.2], np.float32))162 self.assertEqual(action, 0)163 self.assertEqual(debug_info['action_behavior'], 'GREEDY')164 self.assertEqual(sut.episode_count, 1)165 self.assertEqual(sut.step_count, 1)166 self.assertEqual(sut._epsilon, 0.09)167 # learning rate remains 0.1 as Q is not updated during this time step.168 self.assertEqual(sut._trainer.parameter_learners[0].learning_rate(), 0.1)169 self.assertEqual(sut._last_state, np.array([0.2], np.float32))170 self.assertEqual(sut._last_action, 0)171 action, debug_info = sut.step(2, np.array([0.3], np.float32))172 self.assertEqual(action, 1)173 self.assertEqual(debug_info['action_behavior'], 'RANDOM')174 self.assertEqual(sut.episode_count, 1)175 self.assertEqual(sut.step_count, 2)176 self.assertEqual(sut._epsilon, 0.08)177 self.assertEqual(sut._trainer.parameter_learners[0].learning_rate(), 0.08)178 self.assertEqual(sut._last_state, np.array([0.3], np.float32))179 self.assertEqual(sut._last_action, 1)180 sut.end(3, np.array([0.4], np.float32))181 self.assertEqual(sut.episode_count, 1)182 self.assertEqual(sut.step_count, 3)183 self.assertEqual(sut._epsilon, 0.08)184 # learning rate remains 0.08 as Q is not updated during this time step.185 self.assertEqual(sut._trainer.parameter_learners[0].learning_rate(), 0.08)186 @patch('cntk.contrib.deeprl.agent.qlearning.ReplayMemory')187 @patch('cntk.contrib.deeprl.agent.qlearning.QLearningParameters')188 def test_update_q_dqn(self,189 mock_parameters,190 mock_replay_memory):191 self._setup_parameters(mock_parameters.return_value)192 self._setup_replay_memory(mock_replay_memory.return_value)193 action_space = spaces.Discrete(2)194 observation_space = spaces.Box(0, 1, (1,))195 sut = QLearning('', observation_space, action_space)196 sut._q.eval = \197 MagicMock(return_value=np.array([[[0.2, 0.1]]], np.float32))198 sut._target_q.eval = \199 MagicMock(return_value=np.array([[[0.3, 0.4]]], np.float32))200 sut._trainer = MagicMock()201 sut._update_q_periodically()202 np.testing.assert_array_equal(203 sut._trainer.train_minibatch.call_args[0][0][sut._input_variables],204 [np.array([0.1], np.float32)])205 # 10 (reward) + 0.9 (gamma) x 0.4 (max q_target) -> update action 0206 np.testing.assert_array_equal(207 sut._trainer.train_minibatch.call_args[0][0][sut._output_variables],208 [np.array([10.36, 0.1], np.float32)])209 @patch('cntk.contrib.deeprl.agent.qlearning.ReplayMemory')210 @patch('cntk.contrib.deeprl.agent.qlearning.QLearningParameters')211 def test_update_q_dqn_prioritized_replay(self,212 mock_parameters,213 mock_replay_memory):214 self._setup_parameters(mock_parameters.return_value)215 mock_parameters.return_value.use_prioritized_replay = True216 self._setup_prioritized_replay_memory(mock_replay_memory.return_value)217 action_space = spaces.Discrete(2)218 observation_space = spaces.Box(0, 1, (1,))219 sut = QLearning('', observation_space, action_space)220 def new_q_value(self):221 return np.array([[[0.2, 0.1]]], np.float32)222 sut._q.eval = MagicMock(side_effect=new_q_value)223 sut._target_q.eval = MagicMock(224 return_value=np.array([[[0.3, 0.4]]], np.float32))225 sut._trainer = MagicMock()226 sut._update_q_periodically()227 self.assertEqual(sut._trainer.train_minibatch.call_count, 1)228 np.testing.assert_array_equal(229 sut._trainer.train_minibatch.call_args[0][0][sut._input_variables],230 [231 np.array([0.1], np.float32),232 np.array([0.3], np.float32),233 np.array([0.1], np.float32)234 ])235 np.testing.assert_array_equal(236 sut._trainer.train_minibatch.call_args[0][0][sut._output_variables],237 [238 # 10 (reward) + 0.9 (gamma) x 0.4 (max q_target)239 np.array([10.36, 0.1], np.float32),240 # 11 (reward) + 0.9 (gamma) x 0.4 (max q_target)241 np.array([0.2, 11.36], np.float32),242 np.array([10.36, 0.1], np.float32)243 ])244 np.testing.assert_almost_equal(245 sut._trainer.train_minibatch.call_args[0][0][sut._weight_variables],246 [247 [0.16666667],248 [0.66666667],249 [0.16666667]250 ])251 self.assertAlmostEqual(252 sut._replay_memory.update_priority.call_args[0][0][3],253 105.2676) # (10.16 + 0.1)^2254 self.assertAlmostEqual(255 sut._replay_memory.update_priority.call_args[0][0][4],256 129.0496,257 places=6) # (11.26 + 0.1) ^ 2258 @patch('cntk.contrib.deeprl.agent.qlearning.ReplayMemory')259 @patch('cntk.contrib.deeprl.agent.qlearning.QLearningParameters')260 def test_update_q_double_dqn(self,261 mock_parameters,262 mock_replay_memory):263 self._setup_parameters(mock_parameters.return_value)264 mock_parameters.return_value.double_q_learning = True265 self._setup_replay_memory(mock_replay_memory.return_value)266 action_space = spaces.Discrete(2)267 observation_space = spaces.Box(0, 1, (1,))268 sut = QLearning('', observation_space, action_space)269 sut._q.eval = \270 MagicMock(return_value=np.array([[[0.2, 0.1]]], np.float32))271 sut._target_q.eval = \272 MagicMock(return_value=np.array([[[0.3, 0.4]]], np.float32))273 sut._trainer = MagicMock()274 sut._update_q_periodically()275 # 10 (reward) + 0.9 (gamma) x 0.3 -> update action 0276 np.testing.assert_array_equal(277 sut._trainer.train_minibatch.call_args[0][0][sut._output_variables],278 [np.array([10.27, 0.1], np.float32)])279 @patch('cntk.contrib.deeprl.agent.qlearning.QLearningParameters')280 def test_populate_replay_memory(self, mock_parameters):281 self._setup_parameters(mock_parameters.return_value)282 mock_parameters.return_value.preprocessing = \283 'cntk.contrib.deeprl.agent.shared.preprocessing.SlidingWindow'284 mock_parameters.return_value.preprocessing_args = '(2, )'285 action_space = spaces.Discrete(2)286 observation_space = spaces.Box(0, 1, (1,))287 sut = QLearning('', observation_space, action_space)288 sut._compute_priority = Mock(side_effect=[1, 2, 3])289 sut._choose_action = Mock(290 side_effect=[(0, ''), (0, ''), (1, ''), (1, '')])291 sut._replay_memory = MagicMock()292 sut._update_q_periodically = MagicMock()293 sut.start(np.array([0.1], np.float32))294 sut.step(0.1, np.array([0.2], np.float32))295 sut.step(0.2, np.array([0.3], np.float32))296 sut.end(0.3, np.array([0.4], np.float32))297 self.assertEqual(sut._replay_memory.store.call_count, 3)298 call_args = sut._replay_memory.store.call_args_list[0]299 np.testing.assert_array_equal(300 call_args[0][0],301 np.array([[0], [0.1]], np.float32))302 self.assertEqual(call_args[0][1], 0)303 self.assertEqual(call_args[0][2], 0.1)304 np.testing.assert_array_equal(305 call_args[0][3],306 np.array([[0.1], [0.2]], np.float32))307 self.assertEqual(call_args[0][4], 1)308 call_args = sut._replay_memory.store.call_args_list[2]309 np.testing.assert_array_equal(310 call_args[0][0],311 np.array([[0.2], [0.3]], np.float32))312 self.assertEqual(call_args[0][1], 1)313 self.assertEqual(call_args[0][2], 0.3)314 self.assertIsNone(call_args[0][3])315 self.assertEqual(call_args[0][4], 3)316 @patch('cntk.contrib.deeprl.agent.qlearning.QLearningParameters')317 def test_replay_start_size(self, mock_parameters):318 self._setup_parameters(mock_parameters.return_value)319 # Set exploration rate to 0320 mock_parameters.return_value.initial_epsilon = 0321 mock_parameters.return_value.epsilon_decay_step_count = 100322 mock_parameters.return_value.epsilon_minimum = 0323 mock_parameters.return_value.replay_start_size = 3324 action_space = spaces.Discrete(2)325 observation_space = spaces.Box(0, 1, (1,))326 sut = QLearning('', observation_space, action_space)327 sut._trainer = MagicMock()328 sut._replay_memory = MagicMock()329 _, debug = sut.start(np.array([0.1], np.float32))330 self.assertEqual(sut.step_count, 0)331 self.assertEqual(sut._trainer.train_minibatch.call_count, 0)332 self.assertEqual(debug['action_behavior'], 'RANDOM')333 _, debug = sut.step(0.1, np.array([0.2], np.float32))334 self.assertEqual(sut.step_count, 1)335 self.assertEqual(sut._trainer.train_minibatch.call_count, 0)336 self.assertEqual(debug['action_behavior'], 'RANDOM')337 sut.end(0.2, np.array([0.3], np.float32))338 self.assertEqual(sut.step_count, 2)339 self.assertEqual(sut._trainer.train_minibatch.call_count, 0)340 _, debug = sut.start(np.array([0.4], np.float32))341 self.assertEqual(sut.step_count, 2)342 self.assertEqual(sut._trainer.train_minibatch.call_count, 0)343 self.assertEqual(debug['action_behavior'], 'RANDOM')344 a, debug = sut.step(0.3, np.array([0.5], np.float32))345 self.assertEqual(sut.step_count, 3)346 self.assertEqual(sut._trainer.train_minibatch.call_count, 0)347 self.assertEqual(debug['action_behavior'], 'GREEDY')348 a, debug = sut.start(np.array([0.6], np.float32))349 self.assertEqual(sut.step_count, 3)350 self.assertEqual(sut._trainer.train_minibatch.call_count, 0)351 self.assertEqual(debug['action_behavior'], 'GREEDY')352 a, debug = sut.step(0.4, np.array([0.7], np.float32))353 self.assertEqual(sut.step_count, 4)354 self.assertEqual(sut._trainer.train_minibatch.call_count, 1)355 self.assertEqual(debug['action_behavior'], 'GREEDY')356 def _setup_parameters(self, parameters):357 parameters.q_representation = 'dqn'358 parameters.hidden_layers = '[2]'359 parameters.initial_epsilon = 0.1360 parameters.epsilon_decay_step_count = 9361 parameters.epsilon_minimum = 0.01362 parameters.initial_eta = 0.1363 parameters.eta_decay_step_count = 9364 parameters.eta_minimum = 0.01365 parameters.momentum = 0.95366 parameters.gradient_clipping_threshold = 10367 parameters.q_update_frequency = 2368 parameters.gamma = 0.9369 parameters.double_q_learning = False370 parameters.replay_start_size = 0...
models.py
Source:models.py
...7from .scene import SceneModel8# If we are using autograd, then we need to use a special version of numpy.9from .config import numpy as np10class PointSource(SubsampledModelComponent):11 def _setup_parameters(self):12 self._add_parameter('amplitude', 1., (None, None), 'AMP',13 'Point source amplitude', coefficient=True)14 self._add_parameter('center_x', 0., (None, None), 'XC',15 'Point source center position X')16 self._add_parameter('center_y', 0., (None, None), 'YC',17 'Point source center position Y')18 def _evaluate_fourier(self, kx, ky, subsampling, grid_info, amplitude,19 center_x, center_y, **kwargs):20 # A delta function is a complex exponential in Fourier space.21 point_source_fourier = amplitude * np.exp(22 - 1j * (center_x * kx + center_y * ky)23 )24 return point_source_fourier25class GaussianPointSource(SubsampledModelComponent):26 """A point source convolved with a Gaussian.27 This can be evaluated entirely in real space, so it is useful to use this28 for simple Gaussian fits over a model where a point source is explicitly29 convolved with a Gaussian PSF.30 """31 def _setup_parameters(self):32 self._add_parameter('amplitude', None, (None, None), 'AMP',33 'Point source amplitude', coefficient=True)34 self._add_parameter('center_x', None, (None, None), 'XC',35 'Point source center position X')36 self._add_parameter('center_y', None, (None, None), 'YC',37 'Point source center position Y')38 self._add_parameter('sigma_x', 1., (0.1, 20.), 'SIGX',39 'Gaussian width in X direction')40 self._add_parameter('sigma_y', 1., (0.1, 20.), 'SIGY',41 'Gaussian width in Y direction')42 self._add_parameter('rho', 0., (-1., 1.), 'RHO',43 'Gaussian correlation')44 def _evaluate(self, x, y, subsampling, grid_info, amplitude, center_x,45 center_y, sigma_x, sigma_y, rho, **kwargs):46 gaussian = np.exp(-0.5 / (1 - rho**2) * (47 (x - center_x)**2 / sigma_x**2 +48 (y - center_y)**2 / sigma_y**2 +49 -2. * x * y * rho / sigma_x / sigma_y50 ))51 # Normalize52 gaussian /= 2 * np.pi * sigma_x * sigma_y * np.sqrt(1 - rho**2)53 gaussian /= subsampling**254 gaussian *= amplitude55 return gaussian56class SimpleGaussianPointSource(GaussianPointSource):57 """A Gaussian point source with no ellipticity.58 See GaussianPointSource for details. We fix rho to 0, and force sigma_x to59 be equal to sigma_y, removing 2 parameters from the fit.60 """61 def _setup_parameters(self):62 super(SimpleGaussianPointSource, self)._setup_parameters()63 # sigma_y and sigma_x are now just one sigma parameter.64 self._add_parameter('sigma', 1., (0.1, 20.), 'SIG', 'Gaussian width')65 self._modify_parameter('sigma_x', derived=True)66 self._modify_parameter('sigma_y', derived=True)67 self.fix(rho=0)68 def _calculate_derived_parameters(self, parameters):69 p = parameters70 p['sigma_x'] = p['sigma']71 p['sigma_y'] = p['sigma']72 # Update parameters from the superclass73 parent_parameters = super(SimpleGaussianPointSource, self).\74 _calculate_derived_parameters(p)75 return parent_parameters76class Background(PixelModelComponent):77 def _setup_parameters(self):78 self._add_parameter('background', None, (None, None), 'BKG',79 'Background', coefficient=True)80 def _evaluate(self, x, y, grid_info, background, **kwargs):81 return np.ones(x.shape) * background82class PolynomialBackground(PixelModelComponent):83 def __init__(self, background_degree=0, normalization_scale=10., *args,84 **kwargs):85 self.background_degree = background_degree86 self.normalization_scale = normalization_scale87 super(PolynomialBackground, self).__init__(*args, **kwargs)88 def _setup_parameters(self):89 """Setup the polynomial background parameters.90 This is a 2-dimensional polynomial background. We label the flat91 background level parameters as background, and the higher order terms92 as background_x_y where x is the degree in the x-direction and y is the93 degree in the y direction. eg: background_1_2 is degree 1 in x and94 degree 2 in y.95 """96 self._add_parameter('background', None, (None, None), 'BKG',97 'Background', coefficient=True)98 for x_degree in range(self.background_degree + 1):99 for y_degree in range(self.background_degree + 1 - x_degree):100 if x_degree == 0 and y_degree == 0:101 # Already added the constant background.102 continue103 self._add_parameter(104 'background_%d_%d' % (x_degree, y_degree),105 0.,106 (None, None),107 'BKG%d%d' % (x_degree, y_degree),108 'Polynomial background, x-degree=%d, y-degree=%d' %109 (x_degree, y_degree),110 coefficient=True111 )112 def _evaluate(self, x, y, grid_info, background, **parameters):113 components = []114 # Zeroth order background115 components.append(background * np.ones(x.shape))116 # Normalize so that things vary on a reasonable scale117 norm_x = x / self.normalization_scale118 norm_y = y / self.normalization_scale119 # Polynomial background components120 for x_degree in range(self.background_degree + 1):121 for y_degree in range(self.background_degree + 1 - x_degree):122 if x_degree == 0 and y_degree == 0:123 # Already added the constant background.124 continue125 name = 'background_%d_%d' % (x_degree, y_degree)126 coefficient = parameters[name]127 component = (128 coefficient * (norm_x**x_degree) * (norm_y**y_degree)129 )130 components.append(component)131 return components132class GaussianPsfElement(PsfElement):133 def _setup_parameters(self):134 self._add_parameter('sigma_x', 1., (0.01, 20.), 'SIGX',135 'Gaussian width in X direction')136 self._add_parameter('sigma_y', 1., (0.01, 20.), 'SIGY',137 'Gaussian width in Y direction')138 self._add_parameter('rho', 0., (-1., 1.), 'RHO',139 'Gaussian correlation')140 def _evaluate(self, x, y, subsampling, grid_info, sigma_x, sigma_y, rho,141 **kwargs):142 gaussian = np.exp(-0.5 / (1 - rho**2) * (143 x**2 / sigma_x**2 +144 y**2 / sigma_y**2 +145 -2. * x * y * rho / sigma_x / sigma_y146 ))147 # Normalize148 gaussian /= 2 * np.pi * sigma_x * sigma_y * np.sqrt(1 - rho**2)149 gaussian /= subsampling**2150 return gaussian151 def _evaluate_fourier(self, kx, ky, subsampling, grid_info, sigma_x,152 sigma_y, rho, **kwargs):153 gaussian = np.exp(-0.5 * (154 kx**2 * sigma_x**2 +155 ky**2 * sigma_y**2 +156 2. * kx * ky * rho * sigma_x * sigma_y157 ))158 return gaussian159class GaussianMoffatPsfElement(PsfElement):160 def _setup_parameters(self):161 self._add_parameter('alpha', 2.5, (0.1, 15.), 'ALPHA', 'Moffat width')162 self._add_parameter('sigma', 1., (0.5, 5.), 'SIGMA', 'Gaussian width')163 self._add_parameter('beta', 2., (1.5, 50.), 'BETA', 'Moffat power')164 self._add_parameter('eta', 1., (0., None), 'ETA', 'Gaussian ratio')165 self._add_parameter('ell', 1., (0.2, 5.), 'E0', 'Ellipticity')166 self._add_parameter('xy', 0., (-0.6, 0.6), 'XY', 'XY coefficient')167 def _evaluate(self, x, y, subsampling, grid_info, alpha, sigma, beta, eta,168 ell, xy, **kwargs):169 # Issue: with the pipeline parametrization, the radius can sometimes be170 # negative which is obviously not physical. Restrict the xy parameter171 # so that when we evaluate it we get a radius very close to but not172 # exactly 0 when that happens.173 max_ratio = 0.99999174 max_xy = max_ratio * np.sqrt(ell)175 xy = np.clip(xy, -max_xy, max_xy)176 r2 = x**2 + ell * y**2 + 2 * xy * x * y177 gaussian = np.exp(-0.5 * r2 / sigma**2)178 moffat = (1 + r2 / alpha**2)**(-beta)179 model = moffat + eta * gaussian180 model /= subsampling**2181 model /= (np.pi / np.sqrt(ell - xy**2) * (2 * eta * sigma**2 + alpha**2182 / (beta - 1)))183 return model184class ExponentialPowerPsfElement(PsfElement):185 """A Psf model element that has a profile in Fourier space of186 exp(-(w * width)**power).187 When power is 5/3, this is a Kolmogorov PSF.188 """189 def _setup_parameters(self):190 self._add_parameter('power', 1.6, (0., 2.), 'POW', 'power')191 self._add_parameter('width', 0.5, (0.001, 30.), 'WID', 'width')192 def _evaluate_fourier(self, kx, ky, subsampling, grid_info, power, width,193 **kwargs):194 k = np.sqrt(kx*kx + ky*ky)195 fourier_profile = np.exp(-width**power * k**power)196 return fourier_profile197class DeltaExponentialPsfElement(PsfElement):198 """A Psf model element that is the sum of a delta function and a199 Fourier exponential profile .200 """201 def _setup_parameters(self):202 self._add_parameter('delta_fraction', 0.5, (0., 1.), 'DELT',203 'delta function fraction')204 self._add_parameter('power', 1.6, (0., 2.), 'POW', 'power')205 self._add_parameter('width', 0.5, (0.001, 30.), 'WID', 'width')206 def _evaluate_fourier(self, kx, ky, subsampling, grid_info, delta_fraction,207 power, width, **kwargs):208 k = np.sqrt(kx*kx + ky*ky)209 fourier_profile = (210 delta_fraction +211 (1 - delta_fraction) * np.exp(-width**power * k**power)212 )213 return fourier_profile214class FourierMoffatPsfElement(PsfElement):215 """A Psf model element that has a Fourier profile of a Moffat distribution.216 This is not theoretically motivated in any way.217 """218 def _setup_parameters(self):219 self._add_parameter('alpha', 1., (0.0001, 100.), 'ALPHA', 'alpha')220 self._add_parameter('beta', 2., (0., 5.), 'BETA', 'beta')221 def _evaluate_fourier(self, kx, ky, subsampling, grid_info, alpha, beta,222 **kwargs):223 k2 = kx*kx + ky*ky224 fourier_profile = (1 + k2 / alpha**2)**(-beta)225 return fourier_profile226class KolmogorovPsfElement(ExponentialPowerPsfElement):227 """A Kolmogorov PSF.228 This is just an ExponentialPowerPsfElement with the power set to 5/3229 """230 def _setup_parameters(self):231 super(KolmogorovPsfElement, self)._setup_parameters()232 self.fix(power=5./3.)233class VonKarmanPsfElement(PsfElement):234 """VonKarman PSF.235 In Fourier space, this has the form:236 r0^(-5/3) * (f^2 + L0^-2)^(-11/6)237 where r0 is the Fried parameter and L0 is the outer scale.238 """239 def _setup_parameters(self):240 self._add_parameter('r0', 1., (0., None), 'R0',241 'von Karman Fried parameter')242 self._add_parameter('L0', 20., (1., None), 'L0',243 'von Karman outer scale')244 def _evaluate_fourier(self, kx, ky, subsampling, grid_info, r0, L0,245 **kwargs):246 k = np.sqrt(kx*kx + ky*ky)247 # fourier_profile = np.exp(-r0**(5/3.) * (k**2 + L0**-2)**(11/6.))248 # fourier_profile = np.exp(249 # -(L0 / r0)**(5/3.) * (250 # + 1.87439 * (k / L0)**(5/3.)251 # - 1.50845 * (k / L0)**2.252 # )253 # )254 from scipy.special import kv, gamma255 fourier_profile = np.exp(256 -(L0 / r0)**(5/3.) * (257 gamma(5/6.) / 2**(1/6.) -258 (k / L0)**(5/6.) * kv(5/6., k / L0)259 )260 )261 # Set the (0, 0) bin to 1. The profile has that as the limit, but the262 # above calculation will give nan.263 fourier_profile[0, 0] = 1.264 return fourier_profile265class ChromaticExponentialPowerPsfElement(ExponentialPowerPsfElement):266 """A chromatic ExponentialPowerPsfElement to represent seeing.267 The width of the PSF takes the form:268 width = ref_width * (wave / ref_wave) ** (ref_power)269 So the full PSF profile in Fourier space is:270 width = exp(-(w * ref_width * (wave / ref_wave) ** (ref_power))**power)271 """272 def _setup_parameters(self):273 super(ChromaticExponentialPowerPsfElement, self)._setup_parameters()274 # Width is now a derived parameter275 self._modify_parameter('width', derived=True)276 self._add_parameter('wavelength', None, (None, None), 'WAVE',277 'wavelength [A]', fixed=True, apply_prefix=False)278 self._add_parameter('ref_width', 1., (0.01, 30.), 'RWID',279 'width at reference wavelength')280 self._add_parameter('ref_power', -0.3, (-2., 2.), 'RPOW',281 'powerlaw power')282 def _calculate_derived_parameters(self, parameters):283 """Calculate the seeing width parameter using a power-law in wavelength284 """285 p = parameters286 # Ensure that all required variables have been set properly.287 if p['wavelength'] is None:...
modules.py
Source:modules.py
...19 mean: Tuple[int, int, int] = (0.485, 0.456, 0.406),20 std: Tuple[int, int, int] = (0.229, 0.224, 0.225),21 ) -> None:22 super().__init__()23 self.mean = self._setup_parameters(mean)24 self.std = self._setup_parameters(std)25 @staticmethod26 def _setup_parameters(param: float) -> Parameter:27 """expand parameter dimensions for future broadcasting, return28 as `Parameter` for easy movement to/from GPU.29 """30 return Parameter(31 torch.as_tensor(param)[None, :, None, None], requires_grad=False32 )33 def forward(self, x: Tensor) -> Tensor:34 """input[channel] = (input[channel] - mean[channel]) / std[channel]"""...
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