# Copyright (c) 2017-present, Facebook, Inc. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. ############################################################################## from __future__ import absolute_import from __future__ import division from __future__ import print_function from __future__ import unicode_literals import numpy as np import unittest from caffe2.proto import caffe2_pb2 from caffe2.python import core from caffe2.python import gradient_checker from caffe2.python import workspace import utils.c2 import utils.logging class SpatialNarrowAsOpTest(unittest.TestCase): def _run_test(self, A, B, check_grad=False): with core.DeviceScope(core.DeviceOption(caffe2_pb2.CUDA, 0)): op = core.CreateOperator('SpatialNarrowAs', ['A', 'B'], ['C']) workspace.FeedBlob('A', A) workspace.FeedBlob('B', B) workspace.RunOperatorOnce(op) C = workspace.FetchBlob('C') if check_grad: gc = gradient_checker.GradientChecker( stepsize=0.005, threshold=0.005, device_option=core.DeviceOption(caffe2_pb2.CUDA, 0) ) res, grad, grad_estimated = gc.CheckSimple(op, [A, B], 0, [0]) self.assertTrue(res, 'Grad check failed') dims = C.shape C_ref = A[:dims[0], :dims[1], :dims[2], :dims[3]] np.testing.assert_allclose(C, C_ref, rtol=1e-5, atol=1e-08) def test_small_forward_and_gradient(self): A = np.random.randn(2, 3, 5, 7).astype(np.float32) B = np.random.randn(2, 3, 2, 2).astype(np.float32) self._run_test(A, B, check_grad=True) A = np.random.randn(2, 3, 5, 7).astype(np.float32) B = np.random.randn(2, 3, 5).astype(np.float32) self._run_test(A, B, check_grad=True) def test_large_forward(self): A = np.random.randn(2, 256, 42, 100).astype(np.float32) B = np.random.randn(2, 256, 35, 87).astype(np.float32) self._run_test(A, B) A = np.random.randn(2, 256, 42, 87).astype(np.float32) B = np.random.randn(2, 256, 35, 87).astype(np.float32) self._run_test(A, B) def test_size_exceptions(self): A = np.random.randn(2, 256, 42, 86).astype(np.float32) B = np.random.randn(2, 256, 35, 87).astype(np.float32) with self.assertRaises(RuntimeError): self._run_test(A, B) A = np.random.randn(2, 255, 42, 88).astype(np.float32) B = np.random.randn(2, 256, 35, 87).astype(np.float32) with self.assertRaises(RuntimeError): self._run_test(A, B) if __name__ == '__main__': workspace.GlobalInit(['caffe2', '--caffe2_log_level=0']) utils.c2.import_detectron_ops() assert 'SpatialNarrowAs' in workspace.RegisteredOperators() utils.logging.setup_logging(__name__) unittest.main()