# 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 detectron.utils.c2 as c2_utils import detectron.utils.logging as logging_utils class SmoothL1LossTest(unittest.TestCase): def test_forward_and_gradient(self): Y = np.random.randn(128, 4 * 21).astype(np.float32) Y_hat = np.random.randn(128, 4 * 21).astype(np.float32) inside_weights = np.random.randn(128, 4 * 21).astype(np.float32) inside_weights[inside_weights < 0] = 0 outside_weights = np.random.randn(128, 4 * 21).astype(np.float32) outside_weights[outside_weights < 0] = 0 scale = np.random.random() beta = np.random.random() op = core.CreateOperator( 'SmoothL1Loss', ['Y_hat', 'Y', 'inside_weights', 'outside_weights'], ['loss'], scale=scale, beta=beta ) 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, [Y_hat, Y, inside_weights, outside_weights], 0, [0] ) self.assertTrue( grad.shape == grad_estimated.shape, 'Fail check: grad.shape != grad_estimated.shape' ) # To inspect the gradient and estimated gradient: # np.set_printoptions(precision=3, suppress=True) # print('grad:') # print(grad) # print('grad_estimated:') # print(grad_estimated) self.assertTrue(res) if __name__ == '__main__': c2_utils.import_detectron_ops() assert 'SmoothL1Loss' in workspace.RegisteredOperators() logging_utils.setup_logging(__name__) unittest.main()