mirror of
https://github.com/saymrwulf/pytorch.git
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51 lines
1.6 KiB
Python
51 lines
1.6 KiB
Python
from __future__ import absolute_import
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from __future__ import division
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from __future__ import print_function
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from __future__ import unicode_literals
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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.NumCudaDevices() > 0:
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devices.append(core.DeviceOption(caffe2_pb2.CUDA, 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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