pytorch

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import numpy as np
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from caffe2.python import core, workspace
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from caffe2.python.test_util import TestCase
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from caffe2.proto import caffe2_pb2
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class TestPrependDim(TestCase):
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    def _test_fwd_bwd(self):
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        old_shape = (128, 2, 4)
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        new_shape = (8, 16, 2, 4)
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        X = np.random.rand(*old_shape).astype(np.float32)
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        Y = np.random.rand(*new_shape).astype(np.float32)
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        net = core.Net('net')
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        net.GivenTensorFill([], 'X', shape=old_shape, values=X.flatten())
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        net.GivenTensorFill([], 'Y', shape=new_shape, values=Y.flatten())
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        net.PrependDim(['X'], ['X_out'], dim_size=8)
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        net.DotProduct(['X_out', 'Y'], 'Z')
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        net.AddGradientOperators(['Z'])
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        workspace.RunNetOnce(net)
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        X_out = workspace.FetchBlob('X_out')
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        X_grad = workspace.FetchBlob('X_grad')
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        Y_grad = workspace.FetchBlob('Y_grad')
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        # Check the shape of the gradient
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        np.testing.assert_array_equal(X_out.shape, Y.shape)
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        np.testing.assert_array_equal(X_grad.shape, X.shape)
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        np.testing.assert_array_equal(Y_grad.shape, Y.shape)
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    def test_prepend_dim(self):
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        devices = [core.DeviceOption(caffe2_pb2.CPU, 0)]
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        if workspace.NumGpuDevices() > 0:
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            devices.append(core.DeviceOption(workspace.GpuDeviceType, 0))
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        for device_opt in devices:
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            with core.DeviceScope(device_opt):
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                self._test_fwd_bwd()
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if __name__ == "__main__":
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    import unittest
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    unittest.main()
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