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| import unittest |
| import hypothesis.strategies as st |
| from hypothesis import given, settings |
| import numpy as np |
| from caffe2.python import core, workspace, model_helper |
| import caffe2.python.hypothesis_test_util as hu |
| import caffe2.python.ideep_test_util as mu |
|
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|
| @unittest.skipIf(not workspace.C.use_mkldnn, "No MKLDNN support.") |
| class LeakyReluTest(hu.HypothesisTestCase): |
| def _get_inputs(self, N, C, H, W, order): |
| input_data = np.random.rand(N, C, H, W).astype(np.float32) - 0.5 |
|
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| |
| input_data[np.logical_and( |
| input_data >= 0, input_data <= 0.051)] = 0.051 |
| input_data[np.logical_and( |
| input_data <= 0, input_data >= -0.051)] = -0.051 |
|
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| return input_data, |
|
|
| def _get_op(self, device_option, alpha, order, inplace=False): |
| outputs = ['output' if not inplace else "input"] |
| op = core.CreateOperator( |
| 'LeakyRelu', |
| ['input'], |
| outputs, |
| alpha=alpha, |
| device_option=device_option) |
| return op |
|
|
| def _feed_inputs(self, input_blobs, device_option): |
| names = ['input', 'scale', 'bias'] |
| for name, blob in zip(names, input_blobs): |
| self.ws.create_blob(name).feed(blob, device_option=device_option) |
|
|
| @given(N=st.integers(2, 3), |
| C=st.integers(2, 3), |
| H=st.integers(2, 3), |
| W=st.integers(2, 3), |
| alpha=st.floats(0, 1), |
| seed=st.integers(0, 1000), |
| **mu.gcs) |
| @settings(deadline=1000) |
| def test_leaky_relu_gradients(self, gc, dc, N, C, H, W, alpha, seed): |
| np.random.seed(seed) |
|
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| op = self._get_op( |
| device_option=gc, |
| alpha=alpha, |
| order='NCHW') |
| input_blobs = self._get_inputs(N, C, H, W, "NCHW") |
|
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| self.assertDeviceChecks(dc, op, input_blobs, [0]) |
| self.assertGradientChecks(gc, op, input_blobs, 0, [0]) |
|
|
| @given(N=st.integers(2, 10), |
| C=st.integers(3, 10), |
| H=st.integers(5, 10), |
| W=st.integers(7, 10), |
| alpha=st.floats(0, 1), |
| seed=st.integers(0, 1000)) |
| def test_leaky_relu_model_helper_helper(self, N, C, H, W, alpha, seed): |
| np.random.seed(seed) |
| order = 'NCHW' |
| arg_scope = {'order': order} |
| model = model_helper.ModelHelper(name="test_model", arg_scope=arg_scope) |
| model.LeakyRelu( |
| 'input', |
| 'output', |
| alpha=alpha) |
|
|
| input_blob = np.random.rand(N, C, H, W).astype(np.float32) |
|
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| self.ws.create_blob('input').feed(input_blob) |
|
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| self.ws.create_net(model.param_init_net).run() |
| self.ws.create_net(model.net).run() |
|
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| output_blob = self.ws.blobs['output'].fetch() |
|
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| assert output_blob.shape == (N, C, H, W) |
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|
|
| if __name__ == "__main__": |
| unittest.main() |
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|
