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https://github.com/zebrajr/pytorch.git
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27c7158166
Summary: There is a module called `2to3` which you can target for future specifically to remove these, the directory of `caffe2` has the most redundant imports: ```2to3 -f future -w caffe2``` Pull Request resolved: https://github.com/pytorch/pytorch/pull/45033 Reviewed By: seemethere Differential Revision: D23808648 Pulled By: bugra fbshipit-source-id: 38971900f0fe43ab44a9168e57f2307580d36a38
93 lines
2.8 KiB
Python
93 lines
2.8 KiB
Python
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import unittest
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import hypothesis.strategies as st
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from hypothesis import given, settings
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import numpy as np
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from caffe2.python import core, workspace, model_helper
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import caffe2.python.hypothesis_test_util as hu
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import caffe2.python.ideep_test_util as mu
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@unittest.skipIf(not workspace.C.use_mkldnn, "No MKLDNN support.")
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class LeakyReluTest(hu.HypothesisTestCase):
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def _get_inputs(self, N, C, H, W, order):
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input_data = np.random.rand(N, C, H, W).astype(np.float32) - 0.5
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# default step size is 0.05
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input_data[np.logical_and(
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input_data >= 0, input_data <= 0.051)] = 0.051
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input_data[np.logical_and(
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input_data <= 0, input_data >= -0.051)] = -0.051
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return input_data,
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def _get_op(self, device_option, alpha, order, inplace=False):
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outputs = ['output' if not inplace else "input"]
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op = core.CreateOperator(
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'LeakyRelu',
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['input'],
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outputs,
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alpha=alpha,
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device_option=device_option)
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return op
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def _feed_inputs(self, input_blobs, device_option):
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names = ['input', 'scale', 'bias']
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for name, blob in zip(names, input_blobs):
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self.ws.create_blob(name).feed(blob, device_option=device_option)
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@given(N=st.integers(2, 3),
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C=st.integers(2, 3),
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H=st.integers(2, 3),
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W=st.integers(2, 3),
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alpha=st.floats(0, 1),
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seed=st.integers(0, 1000),
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**mu.gcs)
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@settings(deadline=1000)
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def test_leaky_relu_gradients(self, gc, dc, N, C, H, W, alpha, seed):
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np.random.seed(seed)
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op = self._get_op(
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device_option=gc,
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alpha=alpha,
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order='NCHW')
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input_blobs = self._get_inputs(N, C, H, W, "NCHW")
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self.assertDeviceChecks(dc, op, input_blobs, [0])
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self.assertGradientChecks(gc, op, input_blobs, 0, [0])
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@given(N=st.integers(2, 10),
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C=st.integers(3, 10),
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H=st.integers(5, 10),
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W=st.integers(7, 10),
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alpha=st.floats(0, 1),
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seed=st.integers(0, 1000))
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def test_leaky_relu_model_helper_helper(self, N, C, H, W, alpha, seed):
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np.random.seed(seed)
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order = 'NCHW'
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arg_scope = {'order': order}
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model = model_helper.ModelHelper(name="test_model", arg_scope=arg_scope)
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model.LeakyRelu(
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'input',
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'output',
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alpha=alpha)
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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()
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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__":
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unittest.main()
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