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How to use cond method in hypothesis
Best Python code snippet using hypothesis
cond_v2_test.py
Source:cond_v2_test.py 
...45 if not feed_dict:46 feed_dict = {}47 with self.session(graph=ops.get_default_graph()) as sess:48 pred = array_ops.placeholder(dtypes.bool, name="pred")49 expected = control_flow_ops.cond(pred, true_fn, false_fn, name="expected")50 actual = cond_v2.cond_v2(pred, true_fn, false_fn, name="actual")51 expected_grad = gradients_impl.gradients(expected, train_vals)52 actual_grad = gradients_impl.gradients(actual, train_vals)53 sess_run_args = {pred: True}54 sess_run_args.update(feed_dict)55 expected_val, actual_val, expected_grad_val, actual_grad_val = sess.run(56 (expected, actual, expected_grad, actual_grad), sess_run_args)57 self.assertEqual(expected_val, actual_val)58 self.assertEqual(expected_grad_val, actual_grad_val)59 sess_run_args = {pred: False}60 sess_run_args.update(feed_dict)61 expected_val, actual_val, expected_grad_val, actual_grad_val = sess.run(62 (expected, actual, expected_grad, actual_grad), sess_run_args)63 self.assertEqual(expected_val, actual_val)64 self.assertEqual(expected_grad_val, actual_grad_val)65 @test_util.run_deprecated_v166 def testBasic(self):67 x = constant_op.constant(1.0, name="x")68 y = constant_op.constant(2.0, name="y")69 def true_fn():70 return x * 2.071 def false_fn():72 return y * 3.073 self._testCond(true_fn, false_fn, [x])74 self._testCond(true_fn, false_fn, [x, y])75 self._testCond(true_fn, false_fn, [y])76 def testExternalControlDependencies(self):77 with ops.Graph().as_default(), self.test_session():78 v = variables.Variable(1.0)79 v.initializer.run()80 op = v.assign_add(1.0)81 def true_branch():82 with ops.control_dependencies([op]):83 return 1.084 cond_v2.cond_v2(array_ops.placeholder_with_default(False, None),85 true_branch,86 lambda: 2.0).eval()87 self.assertAllEqual(self.evaluate(v), 2.0)88 @test_util.run_deprecated_v189 def testMultipleOutputs(self):90 x = constant_op.constant(1.0, name="x")91 y = constant_op.constant(3.0, name="y")92 def true_fn():93 return x * y, y94 def false_fn():95 return x, y * 3.096 self._testCond(true_fn, false_fn, [x])97 self._testCond(true_fn, false_fn, [x, y])98 self._testCond(true_fn, false_fn, [y])99 @test_util.run_deprecated_v1100 def testBasic2(self):101 x = constant_op.constant(1.0, name="x")102 y = constant_op.constant(2.0, name="y")103 def true_fn():104 return x * y * 2.0105 def false_fn():106 return 2.0107 self._testCond(true_fn, false_fn, [x])108 self._testCond(true_fn, false_fn, [x, y])109 self._testCond(true_fn, false_fn, [y])110 @test_util.run_deprecated_v1111 def testNoInputs(self):112 with self.cached_session() as sess:113 pred = array_ops.placeholder(dtypes.bool, name="pred")114 def true_fn():115 return constant_op.constant(1.0)116 def false_fn():117 return constant_op.constant(2.0)118 out = cond_v2.cond_v2(pred, true_fn, false_fn)119 self.assertEqual(sess.run(out, {pred: True}), (1.0,))120 self.assertEqual(sess.run(out, {pred: False}), (2.0,))121 def _createCond(self, name):122 """Creates a cond_v2 call and returns the output tensor and the cond op."""123 pred = constant_op.constant(True, name="pred")124 x = constant_op.constant(1.0, name="x")125 def true_fn():126 return x127 def false_fn():128 return x + 1129 output = cond_v2.cond_v2(pred, true_fn, false_fn, name=name)130 cond_op = output.op.inputs[0].op131 self.assertEqual(cond_op.type, "If")132 return output, cond_op133 def _createNestedCond(self, name):134 """Like _createCond but creates a nested cond_v2 call as well."""135 pred = constant_op.constant(True, name="pred")136 x = constant_op.constant(1.0, name="x")137 def true_fn():138 return cond_v2.cond_v2(pred, lambda: x, lambda: x + 1)139 def false_fn():140 return x + 2141 output = cond_v2.cond_v2(pred, true_fn, false_fn, name=name)142 cond_op = output.op.inputs[0].op143 self.assertEqual(cond_op.type, "If")144 return output, cond_op145 def testDefaultName(self):146 with ops.Graph().as_default():147 _, cond_op = self._createCond(None)148 self.assertEqual(cond_op.name, "cond")149 self.assertRegexpMatches(150 cond_op.get_attr("then_branch").name, r"cond_true_\d*")151 self.assertRegexpMatches(152 cond_op.get_attr("else_branch").name, r"cond_false_\d*")153 with ops.Graph().as_default():154 with ops.name_scope("foo"):155 _, cond1_op = self._createCond("")156 self.assertEqual(cond1_op.name, "foo/cond")157 self.assertRegexpMatches(158 cond1_op.get_attr("then_branch").name, r"foo_cond_true_\d*")159 self.assertRegexpMatches(160 cond1_op.get_attr("else_branch").name, r"foo_cond_false_\d*")161 _, cond2_op = self._createCond(None)162 self.assertEqual(cond2_op.name, "foo/cond_1")163 self.assertRegexpMatches(164 cond2_op.get_attr("then_branch").name, r"foo_cond_1_true_\d*")165 self.assertRegexpMatches(166 cond2_op.get_attr("else_branch").name, r"foo_cond_1_false_\d*")167 @test_util.run_v1_only("b/120545219")168 def testDefunInCond(self):169 x = constant_op.constant(1.0, name="x")170 y = constant_op.constant(2.0, name="y")171 def true_fn():172 @function.defun173 def fn():174 return x * y * 2.0175 return fn()176 def false_fn():177 return 2.0178 self._testCond(true_fn, false_fn, [x])179 self._testCond(true_fn, false_fn, [x, y])180 self._testCond(true_fn, false_fn, [y])181 @test_util.run_deprecated_v1182 def testNestedDefunInCond(self):183 x = constant_op.constant(1.0, name="x")184 y = constant_op.constant(2.0, name="y")185 def true_fn():186 return 2.0187 def false_fn():188 @function.defun189 def fn():190 @function.defun191 def nested_fn():192 return x * y * 2.0193 return nested_fn()194 return fn()195 self._testCond(true_fn, false_fn, [x])196 self._testCond(true_fn, false_fn, [x, y])197 self._testCond(true_fn, false_fn, [y])198 @test_util.run_deprecated_v1199 def testDoubleNestedDefunInCond(self):200 x = constant_op.constant(1.0, name="x")201 y = constant_op.constant(2.0, name="y")202 def true_fn():203 @function.defun204 def fn():205 @function.defun206 def nested_fn():207 @function.defun208 def nested_nested_fn():209 return x * y * 2.0210 return nested_nested_fn()211 return nested_fn()212 return fn()213 def false_fn():214 return 2.0215 self._testCond(true_fn, false_fn, [x])216 self._testCond(true_fn, false_fn, [x, y])217 self._testCond(true_fn, false_fn, [y])218 def testNestedCond(self):219 def run_test(pred_value):220 def build_graph():221 pred = array_ops.placeholder(dtypes.bool, name="pred")222 x = constant_op.constant(1.0, name="x")223 y = constant_op.constant(2.0, name="y")224 def true_fn():225 return 2.0226 def false_fn():227 def false_true_fn():228 return x * y * 2.0229 def false_false_fn():230 return x * 5.0231 return _cond(pred, false_true_fn, false_false_fn, "inside_false_fn")232 return x, y, pred, true_fn, false_fn233 with ops.Graph().as_default():234 x, y, pred, true_fn, false_fn = build_graph()235 self._testCond(true_fn, false_fn, [x, y], {pred: pred_value})236 self._testCond(true_fn, false_fn, [x], {pred: pred_value})237 self._testCond(true_fn, false_fn, [y], {pred: pred_value})238 run_test(True)239 run_test(False)240 def testNestedCondBothBranches(self):241 def run_test(pred_value):242 def build_graph():243 pred = array_ops.placeholder(dtypes.bool, name="pred")244 x = constant_op.constant(1.0, name="x")245 y = constant_op.constant(2.0, name="y")246 def true_fn():247 return _cond(pred, lambda: x + y, lambda: x * x, name=None)248 def false_fn():249 return _cond(pred, lambda: x - y, lambda: y * y, name=None)250 return x, y, pred, true_fn, false_fn251 with ops.Graph().as_default():252 x, y, pred, true_fn, false_fn = build_graph()253 self._testCond(true_fn, false_fn, [x, y], {pred: pred_value})254 self._testCond(true_fn, false_fn, [x], {pred: pred_value})255 self._testCond(true_fn, false_fn, [y], {pred: pred_value})256 run_test(True)257 run_test(False)258 def testDoubleNestedCond(self):259 def run_test(pred1_value, pred2_value):260 def build_graph():261 pred1 = array_ops.placeholder(dtypes.bool, name="pred1")262 pred2 = array_ops.placeholder(dtypes.bool, name="pred2")263 x = constant_op.constant(1.0, name="x")264 y = constant_op.constant(2.0, name="y")265 def true_fn():266 return 2.0267 def false_fn():268 def false_true_fn():269 def false_true_true_fn():270 return x * y * 2.0271 def false_true_false_fn():272 return x * 10.0273 return _cond(274 pred1,275 false_true_true_fn,276 false_true_false_fn,277 name="inside_false_true_fn")278 def false_false_fn():279 return x * 5.0280 return _cond(281 pred2, false_true_fn, false_false_fn, name="inside_false_fn")282 return x, y, pred1, pred2, true_fn, false_fn283 with ops.Graph().as_default():284 x, y, pred1, pred2, true_fn, false_fn = build_graph()285 self._testCond(true_fn, false_fn, [x, y], {286 pred1: pred1_value,287 pred2: pred2_value288 })289 x, y, pred1, pred2, true_fn, false_fn = build_graph()290 self._testCond(true_fn, false_fn, [x], {291 pred1: pred1_value,292 pred2: pred2_value293 })294 x, y, pred1, pred2, true_fn, false_fn = build_graph()295 self._testCond(true_fn, false_fn, [y], {296 pred1: pred1_value,297 pred2: pred2_value298 })299 run_test(True, True)300 run_test(True, False)301 run_test(False, False)302 run_test(False, True)303 def testGradientFromInsideDefun(self):304 def build_graph():305 pred_outer = array_ops.placeholder(dtypes.bool, name="pred_outer")306 pred_inner = array_ops.placeholder(dtypes.bool, name="pred_inner")307 x = constant_op.constant(1.0, name="x")308 y = constant_op.constant(2.0, name="y")309 def true_fn():310 return 2.0311 def false_fn():312 def inner_true_fn():313 return x * y * 2.0314 def inner_false_fn():315 return x * 5.0316 return cond_v2.cond_v2(317 pred_inner, inner_true_fn, inner_false_fn, name="inner_cond")318 cond_outer = cond_v2.cond_v2(319 pred_outer, true_fn, false_fn, name="outer_cond")320 # Compute grads inside a Defun.321 @function.defun322 def nesting_fn():323 return gradients_impl.gradients(cond_outer, [x, y])324 grads = nesting_fn()325 return grads, pred_outer, pred_inner326 with ops.Graph().as_default():327 grads, pred_outer, pred_inner = build_graph()328 with self.session(graph=ops.get_default_graph()) as sess:329 self.assertSequenceEqual(330 sess.run(grads, {331 pred_outer: True,332 pred_inner: True333 }), [0., 0.])334 self.assertSequenceEqual(335 sess.run(grads, {336 pred_outer: True,337 pred_inner: False338 }), [0., 0.])339 self.assertSequenceEqual(340 sess.run(grads, {341 pred_outer: False,342 pred_inner: True343 }), [4., 2.])344 self.assertSequenceEqual(345 sess.run(grads, {346 pred_outer: False,347 pred_inner: False348 }), [5., 0.])349 def testGradientFromInsideNestedDefun(self):350 def build_graph():351 pred_outer = array_ops.placeholder(dtypes.bool, name="pred_outer")352 pred_inner = array_ops.placeholder(dtypes.bool, name="pred_inner")353 x = constant_op.constant(1.0, name="x")354 y = constant_op.constant(2.0, name="y")355 def true_fn():356 return 2.0357 def false_fn():358 def inner_true_fn():359 return x * y * 2.0360 def inner_false_fn():361 return x * 5.0362 return cond_v2.cond_v2(363 pred_inner, inner_true_fn, inner_false_fn, name="inner_cond")364 cond_outer = cond_v2.cond_v2(365 pred_outer, true_fn, false_fn, name="outer_cond")366 # Compute grads inside a Defun.367 @function.defun368 def nesting_fn():369 @function.defun370 def inner_nesting_fn():371 return gradients_impl.gradients(cond_outer, [x, y])372 return inner_nesting_fn()373 grads = nesting_fn()374 return grads, pred_outer, pred_inner375 with ops.Graph().as_default():376 grads, pred_outer, pred_inner = build_graph()377 with self.session(graph=ops.get_default_graph()) as sess:378 self.assertSequenceEqual(379 sess.run(grads, {380 pred_outer: True,381 pred_inner: True382 }), [0., 0.])383 self.assertSequenceEqual(384 sess.run(grads, {385 pred_outer: True,386 pred_inner: False387 }), [0., 0.])388 self.assertSequenceEqual(389 sess.run(grads, {390 pred_outer: False,391 pred_inner: True392 }), [4., 2.])393 self.assertSequenceEqual(394 sess.run(grads, {395 pred_outer: False,396 pred_inner: False397 }), [5., 0.])398 def testBuildCondAndGradientInsideDefun(self):399 def build_graph():400 pred_outer = array_ops.placeholder(dtypes.bool, name="pred_outer")401 pred_inner = array_ops.placeholder(dtypes.bool, name="pred_inner")402 x = constant_op.constant(1.0, name="x")403 y = constant_op.constant(2.0, name="y")404 # Build cond and its gradient inside a Defun.405 @function.defun406 def fn():407 def true_fn():408 return 2.0409 def false_fn():410 def inner_true_fn():411 return x * y * 2.0412 def inner_false_fn():413 return x * 5.0414 return cond_v2.cond_v2(415 pred_inner, inner_true_fn, inner_false_fn, name="inner_cond")416 cond_outer = cond_v2.cond_v2(417 pred_outer, true_fn, false_fn, name="outer_cond")418 return gradients_impl.gradients(cond_outer, [x, y])419 grads = fn()420 return grads, pred_outer, pred_inner421 with ops.Graph().as_default(), self.session(422 graph=ops.get_default_graph()) as sess:423 grads, pred_outer, pred_inner = build_graph()424 self.assertSequenceEqual(425 sess.run(grads, {426 pred_outer: True,427 pred_inner: True428 }), [0., 0.])429 self.assertSequenceEqual(430 sess.run(grads, {431 pred_outer: True,432 pred_inner: False433 }), [0., 0.])434 self.assertSequenceEqual(435 sess.run(grads, {436 pred_outer: False,437 pred_inner: True438 }), [4., 2.])439 self.assertSequenceEqual(440 sess.run(grads, {441 pred_outer: False,442 pred_inner: False443 }), [5., 0.])444 @test_util.run_deprecated_v1445 def testSecondDerivative(self):446 with self.cached_session() as sess:447 pred = array_ops.placeholder(dtypes.bool, name="pred")448 x = constant_op.constant(3.0, name="x")449 def true_fn():450 return math_ops.pow(x, 3)451 def false_fn():452 return x453 cond = cond_v2.cond_v2(pred, true_fn, false_fn, name="cond")454 cond_grad = gradients_impl.gradients(cond, [x])455 cond_grad_grad = gradients_impl.gradients(cond_grad, [x])456 # d[x^3]/dx = 3x^2457 true_val = sess.run(cond_grad, {pred: True})458 self.assertEqual(true_val, [27.0])459 # d[x]/dx = 1460 false_val = sess.run(cond_grad, {pred: False})461 self.assertEqual(false_val, [1.0])462 true_val = sess.run(cond_grad_grad, {pred: True})463 # d2[x^3]/dx2 = 6x464 self.assertEqual(true_val, [18.0])465 false_val = sess.run(cond_grad_grad, {pred: False})466 # d2[x]/dx2 = 0467 self.assertEqual(false_val, [0.0])468 def testGradientOfDeserializedCond(self):469 with ops.Graph().as_default():470 pred = array_ops.placeholder(dtypes.bool, name="pred")471 x = constant_op.constant(3.0, name="x")472 ops.add_to_collection("x", x)473 def true_fn():474 return math_ops.pow(x, 3)475 def false_fn():476 return x477 ops.add_to_collection("pred", pred)478 cond = cond_v2.cond_v2(pred, true_fn, false_fn, name="cond")479 ops.add_to_collection("cond", cond)480 meta_graph = saver.export_meta_graph()481 with ops.Graph().as_default() as g:482 with self.session(graph=g) as sess:483 saver.import_meta_graph(meta_graph)484 x = ops.get_collection("x")[0]485 pred = ops.get_collection("pred")[0]486 cond = ops.get_collection("cond")487 cond_grad = gradients_impl.gradients(cond, [x], name="cond_grad")488 cond_grad_grad = gradients_impl.gradients(489 cond_grad, [x], name="cond_grad_grad")490 # d[x^3]/dx = 3x^2491 true_val = sess.run(cond_grad, {pred: True})492 self.assertEqual(true_val, [27.0])493 # d[x]/dx = 1494 false_val = sess.run(cond_grad, {pred: False})495 self.assertEqual(false_val, [1.0])496 true_val = sess.run(cond_grad_grad, {pred: True})497 # d2[x^3]/dx2 = 6x498 self.assertEqual(true_val, [18.0])499 false_val = sess.run(cond_grad_grad, {pred: False})500 # d2[x]/dx2 = 0501 self.assertEqual(false_val, [0.0])502 def testGradientTapeOfCondWithResourceVariableInFunction(self):503 with context.eager_mode():504 v = variables.Variable(2.)505 @def_function.function506 def fnWithCond(): # pylint: disable=invalid-name507 with backprop.GradientTape() as tape:508 pred = constant_op.constant(True, dtype=dtypes.bool)509 def true_fn():510 return math_ops.pow(v, 3)511 def false_fn():512 return v513 cond = cond_v2.cond_v2(pred, true_fn, false_fn, name="cond")514 return tape.gradient(cond, v)515 self.assertAllEqual(fnWithCond(), 12.0)516 def testLowering(self):517 with ops.Graph().as_default() as g:518 with self.session(graph=g) as sess:519 cond_output, _ = self._createCond("cond")520 run_options = config_pb2.RunOptions(output_partition_graphs=True)521 run_metadata = config_pb2.RunMetadata()522 sess.run(cond_output, options=run_options, run_metadata=run_metadata)523 # If lowering was enabled, there should be a `Switch` node524 switch_found = any(525 any(node.op == "Switch" for node in graph.node)526 for graph in run_metadata.partition_graphs527 )528 self.assertTrue(switch_found,529 "A `Switch` op should exist if the graph was lowered.")530 # If lowering was enabled, there should be no `If` node531 if_found = any(532 any(node.op == "If" for node in graph.node)533 for graph in run_metadata.partition_graphs534 )535 self.assertFalse(if_found,536 "An `If` op was found, but it should be lowered.")537 @test_util.run_deprecated_v1538 def testLoweringDisabledInXLA(self):539 with self.session(graph=ops.Graph()) as sess:540 # Build the cond_v2 in an XLA context541 xla_context = control_flow_ops.XLAControlFlowContext()542 xla_context.Enter()543 cond_output, cond_op = self._createCond("cond")544 xla_context.Exit()545 # Check lowering attr is not set.546 with self.assertRaises(ValueError):547 cond_op.get_attr("_lower_using_switch_merge")548 # Check the actual graph that is run.549 run_options = config_pb2.RunOptions(output_partition_graphs=True)550 run_metadata = config_pb2.RunMetadata()551 sess.run(cond_output, options=run_options, run_metadata=run_metadata)552 # Lowering disabled in XLA, there should be no `Switch` node553 switch_found = any(554 any(node.op == "Switch" for node in graph.node)555 for graph in run_metadata.partition_graphs556 )557 self.assertFalse(558 switch_found,559 "A `Switch` op exists, but the graph should not be lowered.")560 # Lowering disabled in XLA, there should still be an `If` node561 if_found = any(562 any(node.op == "If" for node in graph.node)563 for graph in run_metadata.partition_graphs564 )565 self.assertTrue(566 if_found,567 "An `If` op was not found, but the graph should not be lowered.")568 @test_util.run_deprecated_v1569 def testNestedLoweringDisabledInXLA(self):570 # Build the cond_v2 in an XLA context571 xla_context = control_flow_ops.XLAControlFlowContext()572 xla_context.Enter()573 _, cond_op = self._createNestedCond("cond")574 xla_context.Exit()575 # Check lowering attr is not set for either If node.576 with self.assertRaises(ValueError):577 cond_op.get_attr("_lower_using_switch_merge")578 nested_if_ops = []579 for func in ops.get_default_graph()._functions.values():580 nested_if_ops.extend(op for op in func.graph.get_operations()581 if op.type == "If")582 self.assertEqual(len(nested_if_ops), 1)583 with self.assertRaises(ValueError):584 nested_if_ops[0].get_attr("_lower_using_switch_merge")585 # TODO(skyewm): check the actual graphs that are run once we have a way to586 # programmatically access those graphs.587 # b/131355614588 @test_util.run_deprecated_v1589 def testNoOptionalsInXla(self):590 @def_function.function591 def func_with_cond():592 pred = constant_op.constant(True, name="pred")593 x = constant_op.constant(1.0, name="x")594 def true_fn():595 intermediate = x + 1596 return intermediate * x597 def false_fn():598 return x + 1599 output = cond_v2.cond_v2(pred, true_fn, false_fn)600 grad = gradients_impl.gradients(output, x)[0]601 forward_if_op = output.op.inputs[0].op602 gradient_if_op = grad.op.inputs[0].op603 def verify_no_optional_ops(op, branch_name):604 branch_function = ops.get_default_graph()._get_function(605 op.get_attr(branch_name).name)606 function_def = branch_function.definition607 for node_def in function_def.node_def:608 self.assertNotIn(node_def.op, _OPTIONAL_OPS)609 verify_no_optional_ops(forward_if_op, "then_branch")610 verify_no_optional_ops(forward_if_op, "else_branch")611 verify_no_optional_ops(gradient_if_op, "then_branch")612 verify_no_optional_ops(gradient_if_op, "else_branch")613 return grad614 xla_context = control_flow_ops.XLAControlFlowContext()615 xla_context.Enter()616 func_with_cond()617 xla_context.Exit()618 @test_util.run_deprecated_v1619 def testLoweringDisabledWithSingleThreadedExecutorContext(self):620 with self.session(graph=ops.Graph()) as sess:621 @function.defun622 def _add_cond(x):623 return cond_v2.cond_v2(624 constant_op.constant(True, name="pred"),625 lambda: x,626 lambda: x + 1)627 x = array_ops.placeholder(shape=None, dtype=dtypes.float32)628 with context.function_executor_type("SINGLE_THREADED_EXECUTOR"):629 out_cond = _add_cond(x)630 # The fact that sess.run() succeeds means lowering is disabled, because631 # the single threaded executor does not support cond v1 ops.632 sess.run(out_cond, feed_dict={x: 1.0})633 @test_util.enable_control_flow_v2634 def testStructuredOutputs(self):635 x = constant_op.constant(1.0, name="x")636 y = constant_op.constant(3.0, name="y")637 def true_fn():638 return ((x * y,), y)639 def false_fn():640 return ((x,), y * 3.0)641 output = control_flow_ops.cond(642 constant_op.constant(False), true_fn, false_fn)643 self.assertEqual(self.evaluate(output[0][0]), 1.)644 self.assertEqual(self.evaluate(output[1]), 9.)645 @test_util.enable_control_flow_v2646 @test_util.run_deprecated_v1647 def testRaisesOutputStructuresMismatch(self):648 x = constant_op.constant(1.0, name="x")649 y = constant_op.constant(3.0, name="y")650 def true_fn():651 return x * y, y652 def false_fn():653 return ((x,), y * 3.0)654 with self.assertRaisesRegexp(655 TypeError, "true_fn and false_fn arguments to tf.cond must have the "656 "same number, type, and overall structure of return values."):657 control_flow_ops.cond(constant_op.constant(False), true_fn, false_fn)658 @test_util.enable_control_flow_v2659 def testCondAndTensorArray(self):660 x = math_ops.range(-5, 5)661 output = tensor_array_ops.TensorArray(dtype=dtypes.int32, size=x.shape[0])662 def loop_body(i, output):663 def if_true():664 return output.write(i, x[i]**2)665 def if_false():666 return output.write(i, x[i])667 output = control_flow_ops.cond(x[i] > 0, if_true, if_false)668 return i + 1, output669 _, output = control_flow_ops.while_loop(670 lambda i, arr: i < x.shape[0],671 loop_body,672 loop_vars=(constant_op.constant(0), output))673 output_t = output.stack()674 self.assertAllEqual(675 self.evaluate(output_t), [-5, -4, -3, -2, -1, 0, 1, 4, 9, 16])676 @test_util.enable_control_flow_v2677 def testCondAndTensorArrayInDefun(self):678 @function.defun679 def f():680 x = math_ops.range(-5, 5)681 output = tensor_array_ops.TensorArray(dtype=dtypes.int32, size=x.shape[0])682 def loop_body(i, output):683 def if_true():684 return output.write(i, x[i]**2)685 def if_false():686 return output.write(i, x[i])687 output = control_flow_ops.cond(x[i] > 0, if_true, if_false)688 return i + 1, output689 _, output = control_flow_ops.while_loop(690 lambda i, arr: i < x.shape[0],691 loop_body,692 loop_vars=(constant_op.constant(0), output))693 return output.stack()694 output_t = f()695 self.assertAllEqual(696 self.evaluate(output_t), [-5, -4, -3, -2, -1, 0, 1, 4, 9, 16])697 @test_util.run_deprecated_v1698 def testForwardPassRewrite(self):699 x = constant_op.constant(1.0, name="x")700 output = cond_v2.cond_v2(constant_op.constant(True),701 lambda: x * 2.0,702 lambda: x)703 if_op = output.op.inputs[0].op704 self.assertEqual(if_op.type, "If")705 # pylint: disable=g-deprecated-assert706 self.assertEqual(len(if_op.outputs), 1)707 gradients_impl.gradients(output, x)708 # if_op should have been rewritten to output 2.0 intermediate.709 self.assertEqual(len(if_op.outputs), 2)710 gradients_impl.gradients(output, x)711 # Computing the gradient again shouldn't rewrite if_op again.712 self.assertEqual(len(if_op.outputs), 2)713 # pylint: enable=g-deprecated-assert714class CondV2CollectionTest(test.TestCase):715 def testCollectionIntValueAccessInCond(self):716 """Read values from graph collections inside of cond_v2."""717 with ops.Graph().as_default() as g:718 with self.session(graph=g):719 x = 2720 y = 5721 ops.add_to_collection("x", x)722 ops.add_to_collection("y", y)723 def fn():724 x_const = constant_op.constant(ops.get_collection("x")[0])725 y_const = constant_op.constant(ops.get_collection("y")[0])726 return math_ops.add(x_const, y_const)727 cnd = cond_v2.cond_v2(constant_op.constant(True), fn, fn)728 self.assertEquals(cnd.eval(), 7)729 def testCollectionTensorValueAccessInCond(self):730 """Read tensors from collections inside of cond_v2 & use them."""731 with ops.Graph().as_default() as g:732 with self.session(graph=g):733 x = constant_op.constant(2)734 y = constant_op.constant(5)735 ops.add_to_collection("x", x)736 ops.add_to_collection("y", y)737 def fn():738 x_read = ops.get_collection("x")[0]739 y_read = ops.get_collection("y")[0]740 return math_ops.add(x_read, y_read)741 cnd = cond_v2.cond_v2(math_ops.less(x, y), fn, fn)742 self.assertEquals(cnd.eval(), 7)743 def testCollectionIntValueWriteInCond(self):744 """Make sure Int writes to collections work inside of cond_v2."""745 with ops.Graph().as_default() as g:746 with self.session(graph=g):747 x = constant_op.constant(2)748 y = constant_op.constant(5)749 def true_fn():750 z = math_ops.add(x, y)751 ops.add_to_collection("z", 7)752 return math_ops.mul(x, z)753 def false_fn():754 z = math_ops.add(x, y)755 return math_ops.mul(x, z)756 cnd = cond_v2.cond_v2(constant_op.constant(True), true_fn, false_fn)757 self.assertEquals(cnd.eval(), 14)758 read_z_collection = ops.get_collection("z")759 self.assertEquals(read_z_collection, [7])760class CondV2ContainerTest(test.TestCase):761 def testContainer(self):762 """Set containers outside & inside of cond_v2.763 Make sure the containers are set correctly for both variable creation764 (tested by variables.Variable) and for stateful ops (tested by FIFOQueue)765 """766 self.skipTest("b/113048653")767 with ops.Graph().as_default() as g:768 with self.session(graph=g):769 v0 = variables.Variable([0])770 q0 = data_flow_ops.FIFOQueue(1, dtypes.float32)771 def container(node):772 return node.op.get_attr("container")773 self.assertEqual(compat.as_bytes(""), container(v0))774 self.assertEqual(compat.as_bytes(""), container(q0.queue_ref))775 def true_fn():776 # When this branch is created in cond below,777 # the container should begin with 'l1'778 v1 = variables.Variable([1])779 q1 = data_flow_ops.FIFOQueue(1, dtypes.float32)780 with ops.container("l2t"):781 v2 = variables.Variable([2])782 q2 = data_flow_ops.FIFOQueue(1, dtypes.float32)783 v3 = variables.Variable([1])784 q3 = data_flow_ops.FIFOQueue(1, dtypes.float32)785 self.assertEqual(compat.as_bytes("l1"), container(v1))786 self.assertEqual(compat.as_bytes("l1"), container(q1.queue_ref))787 self.assertEqual(compat.as_bytes("l2t"), container(v2))788 self.assertEqual(compat.as_bytes("l2t"), container(q2.queue_ref))789 self.assertEqual(compat.as_bytes("l1"), container(v3))790 self.assertEqual(compat.as_bytes("l1"), container(q3.queue_ref))791 return constant_op.constant(2.0)792 def false_fn():793 # When this branch is created in cond below,794 # the container should begin with 'l1'795 v1 = variables.Variable([1])796 q1 = data_flow_ops.FIFOQueue(1, dtypes.float32)797 with ops.container("l2f"):798 v2 = variables.Variable([2])799 q2 = data_flow_ops.FIFOQueue(1, dtypes.float32)800 v3 = variables.Variable([1])801 q3 = data_flow_ops.FIFOQueue(1, dtypes.float32)802 self.assertEqual(compat.as_bytes("l1"), container(v1))803 self.assertEqual(compat.as_bytes("l1"), container(q1.queue_ref))804 self.assertEqual(compat.as_bytes("l2f"), container(v2))805 self.assertEqual(compat.as_bytes("l2f"), container(q2.queue_ref))806 self.assertEqual(compat.as_bytes("l1"), container(v3))807 self.assertEqual(compat.as_bytes("l1"), container(q3.queue_ref))808 return constant_op.constant(6.0)809 with ops.container("l1"):810 cnd_true = cond_v2.cond_v2(811 constant_op.constant(True), true_fn, false_fn)812 self.assertEquals(cnd_true.eval(), 2)813 cnd_false = cond_v2.cond_v2(814 constant_op.constant(False), true_fn, false_fn)815 self.assertEquals(cnd_false.eval(), 6)816 v4 = variables.Variable([3])817 q4 = data_flow_ops.FIFOQueue(1, dtypes.float32)818 v5 = variables.Variable([4])819 q5 = data_flow_ops.FIFOQueue(1, dtypes.float32)820 self.assertEqual(compat.as_bytes("l1"), container(v4))821 self.assertEqual(compat.as_bytes("l1"), container(q4.queue_ref))822 self.assertEqual(compat.as_bytes(""), container(v5))823 self.assertEqual(compat.as_bytes(""), container(q5.queue_ref))824class CondV2ColocationGroupAndDeviceTest(test.TestCase):825 def testColocateWithBeforeCond(self):826 with ops.Graph().as_default() as g:827 with self.session(graph=g):828 a = constant_op.constant([2.0], name="a")829 b = constant_op.constant([2.0], name="b")830 def fn():831 c = constant_op.constant(3.0)832 self.assertEqual([b"loc:@a"], c.op.colocation_groups())833 return c834 with ops.colocate_with(a.op):835 self.assertEquals(836 cond_v2.cond_v2(constant_op.constant(True), fn, fn).eval(), 3)837 def fn2():838 c = constant_op.constant(3.0)839 self.assertEqual([b"loc:@a", b"loc:@b"], c.op.colocation_groups())840 return c841 with ops.colocate_with(a.op):842 with ops.colocate_with(b.op):843 self.assertEquals(844 cond_v2.cond_v2(constant_op.constant(True), fn2, fn2).eval(), 3)845 def testColocateWithInAndOutOfCond(self):846 with ops.Graph().as_default() as g:847 with self.session(graph=g):848 a = constant_op.constant([2.0], name="a")849 b = constant_op.constant([2.0], name="b")850 def fn2():851 with ops.colocate_with(b.op):852 c = constant_op.constant(3.0)853 self.assertEqual([b"loc:@a", b"loc:@b"], c.op.colocation_groups())854 return c855 with ops.colocate_with(a.op):856 self.assertEquals(857 cond_v2.cond_v2(constant_op.constant(True), fn2, fn2).eval(), 3)858 d = constant_op.constant([2.0], name="d")859 self.assertEqual([b"loc:@a"], d.op.colocation_groups())860 def testColocateWithInCondGraphPartitioning(self):861 with ops.Graph().as_default() as g:862 with self.session(863 graph=g,864 config=config_pb2.ConfigProto(device_count={"CPU": 2})865 ) as sess:866 with ops.device("/device:CPU:0"):867 a = constant_op.constant([2.0], name="a")868 with ops.device("/device:CPU:1"):869 b = constant_op.constant([2.0], name="b")870 def fn():871 with ops.colocate_with(b.op):872 c = math_ops.add(a, a, name="c")873 return c874 out_cond_2 = cond_v2.cond_v2(constant_op.constant(True), fn, fn)875 run_options = config_pb2.RunOptions(output_partition_graphs=True)876 run_metadata = config_pb2.RunMetadata()877 sess.run(out_cond_2, options=run_options, run_metadata=run_metadata)878 # We expect there to be two partitions because of the879 # colocate_with. We are only running the cond, which has a data880 # dependency on `a` but not on `b`. So, without the colocate_with881 # we would expect execution on just one device.882 self.assertTrue(len(run_metadata.partition_graphs) >= 2)883 def testDeviceBeforeCond(self):884 with ops.Graph().as_default() as g:885 with self.session(graph=g):886 def fn():887 self.assertEqual("", constant_op.constant(3.0).op.device)888 return test_ops.device_placement_op()889 with ops.device("/device:CPU:0"):890 self.assertIn(891 compat.as_bytes("CPU:0"),892 self.evaluate(cond_v2.cond_v2(constant_op.constant(True),893 fn, fn)))894 def fn2():895 self.assertEqual("", constant_op.constant(3.0).op.device)896 return test_ops.device_placement_op()897 if test_util.is_gpu_available():898 with ops.device("/device:GPU:0"):899 self.assertIn(900 compat.as_bytes("GPU:0"),901 self.evaluate(cond_v2.cond_v2(constant_op.constant(True),902 fn2, fn2)))903 else:904 self.skipTest("Test requires a GPU to check GPU device placement.")905 def testDeviceInAndOutOfCond(self):906 with ops.Graph().as_default() as g:907 with self.session(908 graph=g, config=config_pb2.ConfigProto(device_count={"CPU": 2})):909 def fn2():910 with ops.device("/device:CPU:1"):911 c = constant_op.constant(3.0)912 self.assertEqual("/device:CPU:1", c.op.device)913 return c914 with ops.device("/device:CPU:0"):915 self.assertEquals(916 cond_v2.cond_v2(constant_op.constant(True), fn2, fn2).eval(), 3)917 d = constant_op.constant(4.0)918 self.assertEqual("/device:CPU:0", d.op.device)919 def testDeviceInCondGraphPartitioning(self):920 with ops.Graph().as_default() as g:921 with self.session(922 graph=g,923 config=config_pb2.ConfigProto(device_count={"CPU": 2})924 ) as sess:925 def fn():926 with ops.device("/device:CPU:1"):927 c = math_ops.add(a, a, name="c")928 return c929 with ops.device("/device:CPU:0"):930 a = constant_op.constant([2.0], name="a")931 out_cond_2 = cond_v2.cond_v2(constant_op.constant(True), fn, fn)932 run_options = config_pb2.RunOptions(output_partition_graphs=True)933 run_metadata = config_pb2.RunMetadata()934 sess.run(out_cond_2, options=run_options, run_metadata=run_metadata)935 self.assertTrue(len(run_metadata.partition_graphs) >= 2)936def _cond(pred, true_fn, false_fn, name):937 if _is_old_cond():938 return control_flow_ops.cond(pred, true_fn, false_fn, name=name)939 else:940 return cond_v2.cond_v2(pred, true_fn, false_fn, name=name)941def _is_old_cond():942 return isinstance(ops.get_default_graph()._get_control_flow_context(),943 control_flow_ops.CondContext)944if __name__ == "__main__":...
test_locks.py
Source:test_locks.py
...528 self.assertTrue(RGX_REPR.match(repr(cond)))529 def test_context_manager(self):530 cond = asyncio.Condition(loop=self.loop)531 @asyncio.coroutine532 def acquire_cond():533 return (yield from cond)534 with self.loop.run_until_complete(acquire_cond()):535 self.assertTrue(cond.locked())536 self.assertFalse(cond.locked())537 def test_context_manager_no_yield(self):538 cond = asyncio.Condition(loop=self.loop)539 try:540 with cond:541 self.fail('RuntimeError is not raised in with expression')542 except RuntimeError as err:543 self.assertEqual(544 str(err),545 '"yield from" should be used as context manager expression')546 self.assertFalse(cond.locked())547 def test_explicit_lock(self):548 lock = asyncio.Lock(loop=self.loop)...
mk_star_flag.py
Source:mk_star_flag.py
1import os2import numpy as np3import astropy.io.fits as fitsio4import matplotlib5import matplotlib.pyplot as plt6import matplotlib.gridspec as gridspec7from useful import calc_sn8def get_mags(catalog):9 flx_b,err_b,cov_b = catalog["FLUX_APER_supcam_b"][:,2], catalog["FLUXERR_APER_supcam_b"][:,2], catalog["COVERAGE_FLAG_supcam_b"]10 flx_z,err_z,cov_z = catalog["FLUX_APER_supcam_z"][:,2], catalog["FLUXERR_APER_supcam_z"][:,2], catalog["COVERAGE_FLAG_supcam_z"]11 flx_k,err_k,cov_k = catalog["FLUX_APER_video_ks"][:,2], catalog["FLUXERR_APER_video_ks"][:,2], catalog["COVERAGE_FLAG_video_ks"]12 sn_b = flx_b/err_b13 sn_z = flx_z/err_z14 sn_k = flx_k/err_k15 cond_cov = (cov_b == 1) & (cov_z == 1) & (cov_k == 1)16 cond_det = (sn_b>1) & (sn_z>3) & (sn_k>3) & cond_cov17 cond_non = (sn_b<1) & (sn_z>3) & (sn_k>3) & cond_cov18 mag_b,mag_z,mag_k = np.zeros((3,len(catalog))) - 99.19 mag_b[cond_det] = -2.5*np.log10(flx_b[cond_det]) + 23.9320 mag_z[cond_det] = -2.5*np.log10(flx_z[cond_det]) + 23.9321 mag_k[cond_det] = -2.5*np.log10(flx_k[cond_det]) + 23.9322 mag_b[cond_non] = -2.5*np.log10(err_b[cond_non]) + 23.9323 mag_z[cond_non] = -2.5*np.log10(flx_z[cond_non]) + 23.9324 mag_k[cond_non] = -2.5*np.log10(flx_k[cond_non]) + 23.9325 return mag_b, mag_z, mag_k, catalog["LPH_Z_BEST"], cond_det, cond_non, cond_cov26def get_star_flag(catalog):27 mag_b, mag_z, mag_k, z, cond_det, cond_non, cond_cov = get_mags(catalog)28 cond_chi = (catalog["LPH_CHI_BEST"] - catalog["LPH_CHI_STAR"]) > 029 cond_bzk = (mag_z - mag_k) < (mag_b - mag_z) * 0.3 - 0.2530 cond_star = cond_cov & (cond_chi & cond_bzk)31 cond_gal = cond_cov & ~(cond_chi & cond_bzk)32 print ("Star/Galaxy classification: %i stars and %i gals (no classification for %i)" % (np.sum(cond_star),np.sum(cond_gal),np.sum(~cond_cov)))33 return cond_star, cond_gal, cond_cov34def add_sg_classification(catalog):35 cond_star, cond_gal, cond_cov = get_star_flag(catalog)36 catalog["STAR_FLAG"][cond_star] = 137 catalog["STAR_FLAG"][cond_gal ] = 038 catalog["STAR_FLAG"][~cond_cov] = -9939 catalog["ZPHOT"][ cond_star] = 0.0040 return catalog41def mk_plot():42 catalog = fitsio.getdata("final_cats/final_catalog_errfix_zphot.fits")43 mag_b, mag_z, mag_k, z, cond_det, cond_non, cond_cov = get_mags(catalog)44 cond_star,cond_gal,cond_cov = get_star_flag(catalog)45 fig = plt.figure(figsize=(10,9),dpi=75)46 fig.subplots_adjust(left=0.09,right=0.91,top=0.98,bottom=0.08,wspace=0.02,hspace=0.14)47 ogs = gridspec.GridSpec(1,2,width_ratios=[25,1])48 ax = fig.add_subplot(ogs[0,0])49 cax = fig.add_subplot(ogs[0,1])50 # ax2 = fig.add_subplot(ogs[1,:])51 vmax = 4.552 cmap = matplotlib.cm.get_cmap('RdYlBu_r')53 norm = matplotlib.colors.Normalize(vmin=0, vmax=vmax)54 zz = np.arange(-1,7,0.1)55 ss = np.linspace(0.5,1.5,len(zz)-1)**256 for z0,z1,s in zip (zz[:-1],zz[1:],ss):57 cond_z = (z0<=z) & (z<z1)58 im = ax.scatter(-99,-99,s=0.5,c=0,cmap=plt.cm.RdYlBu_r,vmin=0,vmax=vmax)59 ax.scatter((mag_b-mag_z)[cond_det&cond_z],(mag_z-mag_k)[cond_det&cond_z],c=cmap(norm(z[cond_det&cond_z])),s=s,alpha=0.4)60 ax.errorbar((mag_b-mag_z)[cond_non&cond_z],(mag_z-mag_k)[cond_non&cond_z],xerr=0.15,xlolims=True,ecolor=cmap(norm(0.5*(z0+z1))),linestyle='',marker='',alpha=0.4)61 ax.scatter((mag_b-mag_z)[cond_det&cond_star],(mag_z-mag_k)[cond_det&cond_star],s=2,c='k',alpha=0.4)62 ax.errorbar((mag_b-mag_z)[cond_non&cond_star],(mag_z-mag_k)[cond_non&cond_star],xerr=0.15,xlolims=True,ecolor='k',linestyle='',marker='',alpha=0.4)63 plt.colorbar(im, cax=cax)64 # ax2.hist(catalog["LPH_Z_BEST"],bins=np.arange(-1,7,0.05),color='k',alpha=0.4)65 # ax2.hist(catalog["LPH_Z_BEST"][cond_star],bins=np.arange(-1,7,0.05),color='k',alpha=0.4)66 ax.set_xlabel("$B-z$",fontsize=24)67 ax.set_ylabel("$z-K$",fontsize=24)68 ax.set_xlim(-0.8,7.8)69 ax.set_ylim(-1.5,4.0)70 cax.set_ylabel("photo-z",fontsize=24)71 # ax2.set_xlabel('z',fontsize=18)72 # ax2.set_xlim(0-0.1,6.1)73 _ = [label.set_fontsize(16) for label in ax.get_yticklabels()+ax.get_xticklabels()+cax.get_yticklabels()]74 # _ = [label.set_visible(False) for label in ax2.get_yticklabels()]75 fig.savefig("final_cats/plots/star_flag_bzk.png")76if __name__ == '__main__':77 mk_plot()...
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