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| from caffe2.python import brew, core, scope, workspace |
| from caffe2.python.modeling.parameter_info import ParameterTags |
| from caffe2.python.model_helper import ModelHelper |
| from caffe2.python.cnn import CNNModelHelper |
| |
| import unittest |
| import numpy as np |
| |
| |
| class BrewTest(unittest.TestCase): |
| def setUp(self): |
| |
| def myhelper(model, val=-1): |
| return val |
| |
| if not brew.has_helper(myhelper): |
| brew.Register(myhelper) |
| self.myhelper = myhelper |
| |
| def myhelper2(model, val=-1): |
| return val |
| |
| if not brew.has_helper(myhelper2): |
| brew.Register(myhelper2) |
| self.myhelper2 = myhelper2 |
| self.model = ModelHelper(name="test_model") |
| |
| def test_dropout(self): |
| p = 0.2 |
| X = np.ones((100, 100)).astype(np.float32) - p |
| workspace.FeedBlob("x", X) |
| model = ModelHelper(name="test_model") |
| brew.dropout(model, "x", "out", is_test=False) |
| workspace.RunNetOnce(model.param_init_net) |
| workspace.RunNetOnce(model.net) |
| out = workspace.FetchBlob("out") |
| self.assertLess(abs(out.mean() - (1 - p)), 0.05) |
| |
| def test_fc(self): |
| m, n, k = (15, 15, 15) |
| X = np.random.rand(m, k).astype(np.float32) - 0.5 |
| |
| workspace.FeedBlob("x", X) |
| model = ModelHelper(name="test_model") |
| brew.fc(model, "x", "out_1", k, n) |
| model.Validate() |
| workspace.RunNetOnce(model.param_init_net) |
| workspace.RunNetOnce(model.net) |
| |
| def test_relu(self): |
| Xpos = np.ones((5, 5)).astype(np.float32) - 0.5 |
| Xneg = np.ones((5, 5)).astype(np.float32) - 1.5 |
| |
| workspace.FeedBlob("xpos", Xpos) |
| workspace.FeedBlob("xneg", Xneg) |
| model = ModelHelper(name="test_model") |
| brew.relu(model, "xpos", "out_xpos") |
| brew.relu(model, "xneg", "out_xneg") |
| model.Validate() |
| workspace.RunNetOnce(model.param_init_net) |
| workspace.RunNetOnce(model.net) |
| |
| pos = workspace.FetchBlob("out_xpos") |
| self.assertAlmostEqual(pos.mean(), 0.5) |
| neg = workspace.FetchBlob("out_xneg") |
| self.assertAlmostEqual(neg.mean(), 0) |
| |
| def test_tanh(self): |
| X = np.ones((5, 5)).astype(np.float32) - 0.5 |
| |
| workspace.FeedBlob("x", X) |
| model = ModelHelper(name="test_model") |
| brew.tanh(model, "x", "out_tanh") |
| model.Validate() |
| workspace.RunNetOnce(model.param_init_net) |
| workspace.RunNetOnce(model.net) |
| |
| out = workspace.FetchBlob("out_tanh") |
| self.assertAlmostEqual(out.mean(), np.tanh(0.5), places=5) |
| |
| def test_validate(self): |
| model = ModelHelper(name="test_model") |
| model.params.append("aaa") |
| model.params.append("bbb") |
| self.assertEqual(model._Validate(), []) |
| |
| model.params.append("xxx") |
| model.params.append("bbb") |
| self.assertEqual(model._Validate(), ["bbb"]) |
| |
| def test_arg_scope(self): |
| myhelper = self.myhelper |
| myhelper2 = self.myhelper2 |
| n = 15 |
| with brew.arg_scope([myhelper], val=n): |
| res = brew.myhelper(self.model) |
| self.assertEqual(n, res) |
| |
| with brew.arg_scope([myhelper, myhelper2], val=n): |
| res1 = brew.myhelper(self.model) |
| res2 = brew.myhelper2(self.model) |
| self.assertEqual([n, n], [res1, res2]) |
| |
| def test_arg_scope_single(self): |
| X = np.random.rand(64, 3, 32, 32).astype(np.float32) - 0.5 |
| |
| workspace.FeedBlob("x", X) |
| model = ModelHelper(name="test_model") |
| with brew.arg_scope( |
| brew.conv, |
| stride=2, |
| pad=2, |
| weight_init=('XavierFill', {}), |
| bias_init=('ConstantFill', {}) |
| ): |
| brew.conv( |
| model=model, |
| blob_in="x", |
| blob_out="out", |
| dim_in=3, |
| dim_out=64, |
| kernel=3, |
| ) |
| model.Validate() |
| workspace.RunNetOnce(model.param_init_net) |
| workspace.RunNetOnce(model.net) |
| out = workspace.FetchBlob("out") |
| self.assertEqual(out.shape, (64, 64, 17, 17)) |
| |
| def test_arg_scope_nested(self): |
| myhelper = self.myhelper |
| n = 16 |
| with brew.arg_scope([myhelper], val=-3), \ |
| brew.arg_scope([myhelper], val=-2): |
| with brew.arg_scope([myhelper], val=n): |
| res = brew.myhelper(self.model) |
| self.assertEqual(n, res) |
| res = brew.myhelper(self.model) |
| self.assertEqual(res, -2) |
| |
| res = brew.myhelper(self.model, val=15) |
| self.model.Validate() |
| self.assertEqual(res, 15) |
| |
| def test_double_register(self): |
| myhelper = self.myhelper |
| with self.assertRaises(AttributeError): |
| brew.Register(myhelper) |
| |
| def test_has_helper(self): |
| self.assertTrue(brew.has_helper(brew.conv)) |
| self.assertTrue(brew.has_helper("conv")) |
| |
| def myhelper3(): |
| pass |
| |
| self.assertFalse(brew.has_helper(myhelper3)) |
| |
| def test_model_helper(self): |
| X = np.random.rand(64, 32, 32, 3).astype(np.float32) - 0.5 |
| |
| workspace.FeedBlob("x", X) |
| my_arg_scope = {'order': 'NHWC'} |
| model = ModelHelper(name="test_model", arg_scope=my_arg_scope) |
| with brew.arg_scope( |
| brew.conv, |
| stride=2, |
| pad=2, |
| weight_init=('XavierFill', {}), |
| bias_init=('ConstantFill', {}) |
| ): |
| brew.conv( |
| model=model, |
| blob_in="x", |
| blob_out="out", |
| dim_in=3, |
| dim_out=64, |
| kernel=[8, 3] |
| ) |
| model.Validate() |
| workspace.RunNetOnce(model.param_init_net) |
| workspace.RunNetOnce(model.net) |
| out = workspace.FetchBlob("out") |
| self.assertEqual(out.shape, (64, 15, 17, 64)) |
| |
| def test_cnn_model_helper_deprecated(self): |
| X = np.random.rand(64, 32, 32, 3).astype(np.float32) - 0.5 |
| |
| workspace.FeedBlob("x", X) |
| # CNNModelHelper is going to be deprecated soon. This test is only |
| # covering some CNNModelHelper logic |
| model = CNNModelHelper(name="test_model", order='NHWC') |
| self.assertEqual(model.arg_scope['order'], 'NHWC') |
| |
| def test_get_params(self): |
| def param(x): |
| return core.ScopedBlobReference(x) |
| |
| def to_str_list(x): |
| return sorted([str(p) for p in x]) |
| |
| model = ModelHelper(name="test_model") |
| model.AddParameter(param("a")) |
| model.AddParameter(param("b"), tags=ParameterTags.COMPUTED_PARAM) |
| with scope.NameScope("c"): |
| model.AddParameter(param("a")) |
| model.AddParameter(param("d"), tags=ParameterTags.COMPUTED_PARAM) |
| self.assertEqual(to_str_list(model.GetParams()), ['c/a']) |
| self.assertEqual(to_str_list(model.GetComputedParams()), ['c/d']) |
| self.assertEqual(to_str_list(model.GetAllParams()), ['c/a', 'c/d']) |
| # Get AllParams from the global Scope |
| self.assertEqual(to_str_list(model.GetAllParams('')), [ |
| 'a', 'b', 'c/a', 'c/d']) |
| self.assertEqual(to_str_list(model.GetParams()), ['a', 'c/a']) |
| self.assertEqual(to_str_list(model.GetComputedParams()), ['b', 'c/d']) |
| self.assertEqual(to_str_list(model.GetAllParams()), |
| ['a', 'b', 'c/a', 'c/d']) |
| self.assertEqual(to_str_list(model.GetAllParams('')), |
| ['a', 'b', 'c/a', 'c/d']) |
| # Get AllParams from the scope 'c' |
| self.assertEqual(to_str_list(model.GetAllParams('c')), ['c/a', 'c/d']) |
| self.assertEqual(to_str_list(model.GetAllParams('c/')), ['c/a', 'c/d']) |
| |
| def test_param_consistence(self): |
| model = ModelHelper(name='test_mode') |
| cnv = brew.conv(model, 'data', 'cnv', 32, 32, 4) |
| step_model = ModelHelper(name='step_model', param_model=model) |
| a = brew.fc(step_model, cnv, 'a', 100, 200) |
| brew.fc(model, a, 'b', 200, 5) |
| # test the _parameters_info is shared between model and step_model |
| self.assertEqual(model._parameters_info, step_model._parameters_info) |
| |
| def test_cond(self): |
| workspace.FeedBlob("cond", np.array(True)) |
| workspace.FeedBlob("then_value", np.array(1)) |
| workspace.FeedBlob("else_value", np.array(2)) |
| |
| then_model = ModelHelper(name="then_test_model") |
| then_model.net.Copy("then_value", "output_blob") |
| |
| else_model = ModelHelper(name="else_test_model") |
| else_model.net.Copy("else_value", "output_blob") |
| |
| model = ModelHelper(name="test_model") |
| brew.cond( |
| model=model, |
| cond_blob="cond", |
| external_blobs=["then_value", "else_value", "output_blob"], |
| then_model=then_model, |
| else_model=else_model) |
| |
| workspace.RunNetOnce(model.param_init_net) |
| workspace.RunNetOnce(model.net) |
| output_value = workspace.FetchBlob("output_blob") |
| self.assertEqual(output_value, 1) |
| workspace.FeedBlob("cond", np.array(False)) |
| workspace.RunNetOnce(model.param_init_net) |
| workspace.RunNetOnce(model.net) |
| output_value = workspace.FetchBlob("output_blob") |
| self.assertEqual(output_value, 2) |
| |
| def test_loop(self): |
| workspace.FeedBlob("cond", np.array(True)) |
| workspace.FeedBlob("ONE", np.array(1)) |
| workspace.FeedBlob("TWO", np.array(2)) |
| workspace.FeedBlob("TEN", np.array(10)) |
| workspace.FeedBlob("counter", np.array(0)) |
| workspace.FeedBlob("output_blob", np.array(0)) |
| |
| loop_model = ModelHelper(name="loop_test_model") |
| loop_model.net.Add(["output_blob", "TWO"], "output_blob") |
| |
| cond_model = ModelHelper(name="cond_test_model") |
| cond_model.net.Add(["counter", "ONE"], "counter") |
| comp_res = cond_model.net.LT(["counter", "TEN"]) |
| cond_model.net.Copy(comp_res, "cond") |
| |
| model = ModelHelper(name="test_model") |
| brew.loop( |
| model=model, |
| cond_blob="cond", |
| external_blobs=["cond", "ONE", "TWO", "TEN", "counter", "output_blob"], |
| loop_model=loop_model, |
| cond_model=cond_model) |
| |
| workspace.RunNetOnce(model.param_init_net) |
| workspace.RunNetOnce(model.net) |
| output_value = workspace.FetchBlob("output_blob") |
| self.assertEqual(output_value, 18) |
| |
| |
| @unittest.skipIf(not workspace.has_gpu_support, "No gpu support.") |
| class BrewGPUTest(unittest.TestCase): |
| def test_relu(self): |
| Xpos = np.ones((5, 5)).astype(np.float32) - 0.5 |
| Xneg = np.ones((5, 5)).astype(np.float32) - 1.5 |
| |
| workspace.FeedBlob("xpos", Xpos) |
| workspace.FeedBlob("xneg", Xneg) |
| model = ModelHelper(name="test_model") |
| brew.relu(model, "xpos", "out_xpos", use_cudnn=True) |
| brew.relu(model, "xneg", "out_xneg", use_cudnn=True) |
| model.Validate() |
| workspace.RunNetOnce(model.param_init_net) |
| workspace.RunNetOnce(model.net) |
| |
| pos = workspace.FetchBlob("out_xpos") |
| self.assertAlmostEqual(pos.mean(), 0.5) |
| neg = workspace.FetchBlob("out_xneg") |
| self.assertAlmostEqual(neg.mean(), 0) |
| |
| def test_tanh(self): |
| X = np.ones((5, 5)).astype(np.float32) - 0.5 |
| |
| workspace.FeedBlob("x", X) |
| model = ModelHelper(name="test_model") |
| brew.tanh(model, "x", "out_tanh", use_cudnn=True) |
| model.Validate() |
| workspace.RunNetOnce(model.param_init_net) |
| workspace.RunNetOnce(model.net) |
| |
| out = workspace.FetchBlob("out_tanh") |
| self.assertAlmostEqual(out.mean(), np.tanh(0.5), places=5) |
