def test_compute_norm_for_blobs_modify_output_record(self): model = model_helper.ModelHelper(name="test") data = model.net.AddExternalInput("data") fc1 = brew.fc(model, data, "fc1", dim_in=4, dim_out=2) # no operator name set, will use default brew.fc(model, fc1, "fc2", dim_in=2, dim_out=1) net_modifier = ComputeNormForBlobs( blobs=['fc1_w', 'fc2_w'], logging_frequency=10, ) net_modifier(model.net, modify_output_record=True) workspace.FeedBlob('data', np.random.rand(10, 4).astype(np.float32)) workspace.RunNetOnce(model.param_init_net) workspace.RunNetOnce(model.net) fc1_w = workspace.FetchBlob('fc1_w') fc1_w_l2_norm = workspace.FetchBlob('fc1_w_l2_norm') self.assertEqual(fc1_w_l2_norm.size, 1) self.assertAlmostEqual(fc1_w_l2_norm[0], np.linalg.norm(fc1_w)**2, delta=1e-5) self.assertEqual(len(model.net.Proto().op), 10) assert 'fc1_w' + net_modifier.field_name_suffix() in\ model.net.output_record().field_blobs(),\ model.net.output_record().field_blobs() assert 'fc2_w' + net_modifier.field_name_suffix() in\ model.net.output_record().field_blobs(),\ model.net.output_record().field_blobs()
def test_compute_l1_averaged_norm_for_blobs(self): model = model_helper.ModelHelper(name="test") data = model.net.AddExternalInput("data") fc1 = brew.fc(model, data, "fc1", dim_in=4, dim_out=2) # no operator name set, will use default brew.fc(model, fc1, "fc2", dim_in=2, dim_out=1) net_modifier = ComputeNormForBlobs( blobs=['fc1_w', 'fc2_w'], logging_frequency=10, p=1, compute_averaged_norm=True, ) net_modifier(model.net) workspace.FeedBlob('data', np.random.rand(10, 4).astype(np.float32)) workspace.RunNetOnce(model.param_init_net) workspace.RunNetOnce(model.net) fc1_w = workspace.FetchBlob('fc1_w') fc1_w_averaged_l1_norm = workspace.FetchBlob('fc1_w_averaged_l1_norm') self.assertEqual(fc1_w_averaged_l1_norm.size, 1) self.assertAlmostEqual(fc1_w_averaged_l1_norm[0], np.sum(np.abs(fc1_w)) / fc1_w.size, delta=1e-5) self.assertEqual(len(model.net.Proto().op), 8)
def test_compute_norm_row_index_for_blobs(self): model = model_helper.ModelHelper(name="test") data = model.net.AddExternalInput("data") fc1 = brew.fc(model, data, "fc1", dim_in=4, dim_out=2) net_modifier = ComputeNormForBlobs(blobs=['fc1_w'], logging_frequency=10, compute_averaged_norm=True, row_index=1) net_modifier(model.net) workspace.FeedBlob('data', np.random.rand(10, 4).astype(np.float32)) workspace.RunNetOnce(model.param_init_net) workspace.RunNetOnce(model.net) fc1_w = workspace.FetchBlob('fc1_w') fc1_w_row_1_averaged_l2_norm = workspace.FetchBlob( 'fc1_w_row_1_averaged_l2_norm') self.assertEqual(fc1_w_row_1_averaged_l2_norm.size, 1) self.assertAlmostEqual(fc1_w_row_1_averaged_l2_norm[0], np.linalg.norm(fc1_w[1])**2 / fc1_w[1].size, delta=1e-5)
def test_compute_l1_averaged_norm_for_blobs(self): model = model_helper.ModelHelper(name="test") data = model.net.AddExternalInput("data") fc1 = brew.fc(model, data, "fc1", dim_in=4, dim_out=2) # no operator name set, will use default brew.fc(model, fc1, "fc2", dim_in=2, dim_out=1) net_modifier = ComputeNormForBlobs( blobs=['fc1_w', 'fc2_w'], logging_frequency=10, p=1, compute_averaged_norm=True, ) net_modifier(model.net) workspace.FeedBlob('data', np.random.rand(10, 4).astype(np.float32)) workspace.RunNetOnce(model.param_init_net) workspace.RunNetOnce(model.net) fc1_w = workspace.FetchBlob('fc1_w') fc1_w_averaged_l1_norm = workspace.FetchBlob('fc1_w_averaged_l1_norm') self.assertEqual(fc1_w_averaged_l1_norm.size, 1) self.assertAlmostEqual(fc1_w_averaged_l1_norm[0], np.sum(np.abs(fc1_w)) / fc1_w.size, delta=1e-5) self.assertEqual(len(model.net.Proto().op), 6) assert 'fc1_w' + net_modifier.field_name_suffix() in\ model.net.output_record().field_blobs(),\ model.net.output_record().field_blobs() assert 'fc2_w' + net_modifier.field_name_suffix() in\ model.net.output_record().field_blobs(),\ model.net.output_record().field_blobs()