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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)
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