Name	Size	Mode	Actions
__pycache__/	-	0755	rm
compute_histogram_for_blobs.py	3508	0644	editdlrm
compute_histogram_for_blobs_test.py	4947	0644	editdlrm
compute_norm_for_blobs.py	3642	0644	editdlrm
compute_norm_for_blobs_test.py	7942	0644	editdlrm
compute_statistics_for_blobs.py	1909	0644	editdlrm
compute_statistics_for_blobs_test.py	2870	0644	editdlrm
get_entry_from_blobs.py	3130	0644	editdlrm
get_entry_from_blobs_test.py	3538	0644	editdlrm
gradient_clipping.py	5766	0644	editdlrm
gradient_clipping_test.py	10016	0644	editdlrm
initializers.py	5378	0644	editdlrm
initializers_test.py	2106	0644	editdlrm
net_modifier.py	823	0644	editdlrm
parameter_info.py	1438	0644	editdlrm
parameter_sharing.py	3848	0644	editdlrm
parameter_sharing_test.py	7223	0644	editdlrm
__init__.py	0	0644	editdlrm

import unittest from caffe2.python import workspace, brew, model_helper from caffe2.python.modeling.compute_norm_for_blobs import ComputeNormForBlobs import numpy as np class ComputeNormForBlobsTest(unittest.TestCase): def test_compute_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, ) 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_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 model.net.output_record() is None 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_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, 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_l2_norm = workspace.FetchBlob('fc1_w_averaged_l2_norm') self.assertEqual(fc1_w_averaged_l2_norm.size, 1) self.assertAlmostEqual(fc1_w_averaged_l2_norm[0], np.linalg.norm(fc1_w)**2 / fc1_w.size, delta=1e-5) self.assertEqual(len(model.net.Proto().op), 10) def test_compute_norm_for_blobs_no_print(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=-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_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), 8) def test_compute_l1_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, ) 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_l1_norm = workspace.FetchBlob('fc1_w_l1_norm') self.assertEqual(fc1_w_l1_norm.size, 1) self.assertAlmostEqual(fc1_w_l1_norm[0], np.sum(np.abs(fc1_w)), delta=1e-5) self.assertEqual(len(model.net.Proto().op), 10) 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), 10) 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)