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