from __future__ import division from __future__ import absolute_import from __future__ import print_function from __future__ import unicode_literals from caffe2.python import core, workspace import caffe2.python.hypothesis_test_util as hu import caffe2.python.serialized_test.serialized_test_util as serial import hypothesis.strategies as st import numpy as np class TestScaleOps(serial.SerializedTestCase): @serial.given(dim=st.sampled_from([[1, 386, 1], [386, 1, 1], [1, 256, 1], [256, 1, 1], [1024, 256, 1], [1, 1024, 1], [1, 1, 1]]), scale=st.floats(0.0, 10.0), num_tensors=st.integers(1, 10), **hu.gcs) def test_scale_ops(self, dim, scale, num_tensors, gc, dc): in_tensors = [] in_tensor_ps = [] out_tensors = [] out_ref_tensors = [] # initialize tensors for i in range(num_tensors): tensor = "X_{}".format(i) X = np.random.rand(*dim).astype(np.float32) - 0.5 in_tensors.append(tensor) in_tensor_ps.append(X) out_tensor = "O_{}".format(i) out_tensors.append(out_tensor) workspace.FeedBlob(tensor, X, device_option=gc) # run ScaleBlobs operator scale_blobs_op = core.CreateOperator( "ScaleBlobs", in_tensors, out_tensors, scale=scale, ) scale_blobs_op.device_option.CopyFrom(gc) workspace.RunOperatorOnce(scale_blobs_op) # run Scale op for each tensor and compare with ScaleBlobs for i in range(num_tensors): tensor = "X_{}".format(i) out_ref_tensor = "O_ref_{}".format(i) scale_op = core.CreateOperator( "Scale", [tensor], [out_ref_tensor], scale=scale, ) scale_op.device_option.CopyFrom(gc) workspace.RunOperatorOnce(scale_op) o_ref = workspace.FetchBlob(out_ref_tensor) o = workspace.FetchBlob(out_tensors[i]) np.testing.assert_allclose(o, o_ref) if __name__ == '__main__': unittest.main()