FM / modeling

/usr/local/lib64/python3.6/site-packages/caffe2/python/modeling/

/usr/local/lib64/python3.6/site-packages/caffe2/python/modeling

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

Edit: /usr/local/lib64/python3.6/site-packages/caffe2/python/modeling/compute_histogram_for_blobs_test.py (4947B)

import unittest
from caffe2.python import workspace, brew, model_helper
from caffe2.python.modeling.compute_histogram_for_blobs import (
    ComputeHistogramForBlobs
)

import numpy as np

class ComputeHistogramForBlobsTest(unittest.TestCase):

def histogram(self, X, lower_bound=0.0, upper_bound=1.0, num_buckets=20):
        assert X.ndim == 2, ('this test assume 2d array,  but X.ndim is {0}'.
            format(X.ndim))
        N, M = X.shape
        hist = np.zeros((num_buckets + 2, ), dtype=np.int32)
        segment = (upper_bound - lower_bound) / num_buckets
        Y = np.zeros((N, M), dtype=np.int32)
        Y[X < lower_bound] = 0
        Y[X >= upper_bound] = num_buckets + 1
        Y[(X >= lower_bound) & (X < upper_bound)] = \
            ((X[(X >= lower_bound) & (X < upper_bound)] - lower_bound) /
                    segment + 1).astype(np.int32)

for i in range(Y.shape[0]):
            for j in range(Y.shape[1]):
                hist[Y[i][j]] += 1

cur_hist = hist.astype(np.float32) / (N * M)
        acc_hist = cur_hist
        return [cur_hist, acc_hist]

def test_compute_histogram_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)

num_buckets = 20
        lower_bound = 0.2
        upper_bound = 0.8
        accumulate = False
        net_modifier = ComputeHistogramForBlobs(blobs=['fc1_w', 'fc2_w'],
                                                logging_frequency=10,
                                                num_buckets=num_buckets,
                                                lower_bound=lower_bound,
                                                upper_bound=upper_bound,
                                                accumulate=accumulate)
        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_curr_normalized_hist = workspace.FetchBlob('fc1_w_curr_normalized_hist')
        cur_hist, acc_hist = self.histogram(fc1_w,
                                            lower_bound=lower_bound,
                                            upper_bound=upper_bound,
                                            num_buckets=num_buckets)

self.assertEqual(fc1_w_curr_normalized_hist.size, num_buckets + 2)
        self.assertAlmostEqual(np.linalg.norm(
            fc1_w_curr_normalized_hist - cur_hist), 0.0, delta=1e-5)
        self.assertEqual(len(model.net.Proto().op), 12)

assert model.net.output_record() is None

def test_compute_histogram_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)

workspace.FeedBlob('data', np.random.rand(10, 4).astype(np.float32))

workspace.RunNetOnce(model.param_init_net)
        workspace.RunNetOnce(model.net)

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()

import unittest from caffe2.python import workspace, brew, model_helper from caffe2.python.modeling.compute_histogram_for_blobs import ( ComputeHistogramForBlobs ) import numpy as np class ComputeHistogramForBlobsTest(unittest.TestCase): def histogram(self, X, lower_bound=0.0, upper_bound=1.0, num_buckets=20): assert X.ndim == 2, ('this test assume 2d array, but X.ndim is {0}'. format(X.ndim)) N, M = X.shape hist = np.zeros((num_buckets + 2, ), dtype=np.int32) segment = (upper_bound - lower_bound) / num_buckets Y = np.zeros((N, M), dtype=np.int32) Y[X < lower_bound] = 0 Y[X >= upper_bound] = num_buckets + 1 Y[(X >= lower_bound) & (X < upper_bound)] = \ ((X[(X >= lower_bound) & (X < upper_bound)] - lower_bound) / segment + 1).astype(np.int32) for i in range(Y.shape[0]): for j in range(Y.shape[1]): hist[Y[i][j]] += 1 cur_hist = hist.astype(np.float32) / (N * M) acc_hist = cur_hist return [cur_hist, acc_hist] def test_compute_histogram_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) num_buckets = 20 lower_bound = 0.2 upper_bound = 0.8 accumulate = False net_modifier = ComputeHistogramForBlobs(blobs=['fc1_w', 'fc2_w'], logging_frequency=10, num_buckets=num_buckets, lower_bound=lower_bound, upper_bound=upper_bound, accumulate=accumulate) 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_curr_normalized_hist = workspace.FetchBlob('fc1_w_curr_normalized_hist') cur_hist, acc_hist = self.histogram(fc1_w, lower_bound=lower_bound, upper_bound=upper_bound, num_buckets=num_buckets) self.assertEqual(fc1_w_curr_normalized_hist.size, num_buckets + 2) self.assertAlmostEqual(np.linalg.norm( fc1_w_curr_normalized_hist - cur_hist), 0.0, delta=1e-5) self.assertEqual(len(model.net.Proto().op), 12) assert model.net.output_record() is None def test_compute_histogram_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) num_buckets = 20 lower_bound = 0.2 upper_bound = 0.8 accumulate = False net_modifier = ComputeHistogramForBlobs(blobs=['fc1_w', 'fc2_w'], logging_frequency=10, num_buckets=num_buckets, lower_bound=lower_bound, upper_bound=upper_bound, accumulate=accumulate) 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_curr_normalized_hist = workspace.FetchBlob('fc1_w_curr_normalized_hist') cur_hist, acc_hist = self.histogram(fc1_w, lower_bound=lower_bound, upper_bound=upper_bound, num_buckets=num_buckets) self.assertEqual(fc1_w_curr_normalized_hist.size, num_buckets + 2) self.assertAlmostEqual(np.linalg.norm( fc1_w_curr_normalized_hist - cur_hist), 0.0, delta=1e-5) self.assertEqual(len(model.net.Proto().op), 12) 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()