FM / python

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

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

Name	Size	Mode	Actions
__pycache__/	-	0755	rm
convnet_benchmarks.py	20369	0644	edit dl rm
device_reduce_sum_bench.py	3946	0644	edit dl rm
funhash_op_test.py	2740	0644	edit dl rm
net_construct_bench.py	4124	0644	edit dl rm
SparseTransformer.py	6888	0644	edit dl rm
sparse_funhash_op_test.py	2502	0644	edit dl rm
sparse_reshape_op_test.py	3412	0644	edit dl rm
tt_contraction_op_test.py	2099	0644	edit dl rm
tt_pad_op_test.py	1944	0644	edit dl rm
__init__.py	0	0644	edit dl rm

Edit: /usr/local/lib64/python3.6/site-packages/caffe2/experiments/python/funhash_op_test.py (2740B)

import numpy as np
from scipy.sparse import coo_matrix

from hypothesis import given
import hypothesis.strategies as st

from caffe2.python import core
import caffe2.python.hypothesis_test_util as hu

class TestFunHash(hu.HypothesisTestCase):
    @given(n_out=st.integers(min_value=5, max_value=20),
           n_in=st.integers(min_value=10, max_value=20),
           n_data=st.integers(min_value=2, max_value=8),
           n_weight=st.integers(min_value=8, max_value=15),
           n_alpha=st.integers(min_value=3, max_value=8),
           sparsity=st.floats(min_value=0.1, max_value=1.0),
           **hu.gcs)
    def test_funhash(self, n_out, n_in, n_data, n_weight, n_alpha, sparsity,
                     gc, dc):
        A = np.random.rand(n_data, n_in)
        A[A > sparsity] = 0
        A_coo = coo_matrix(A)
        val, key, seg = A_coo.data, A_coo.col, A_coo.row

weight = np.random.rand(n_weight).astype(np.float32)
        alpha = np.random.rand(n_alpha).astype(np.float32)
        val = val.astype(np.float32)
        key = key.astype(np.int64)
        seg = seg.astype(np.int32)

op = core.CreateOperator(
            'FunHash',
            ['val', 'key', 'seg', 'weight', 'alpha'],
            ['out'],
            num_outputs=n_out)

# Check over multiple devices
        self.assertDeviceChecks(
            dc, op, [val, key, seg, weight, alpha], [0])
        # Gradient check wrt weight
        self.assertGradientChecks(
            gc, op, [val, key, seg, weight, alpha], 3, [0])
        # Gradient check wrt alpha
        self.assertGradientChecks(
            gc, op, [val, key, seg, weight, alpha], 4, [0])

op2 = core.CreateOperator(
            'FunHash',
            ['val', 'key', 'seg', 'weight'],
            ['out'],
            num_outputs=n_out)

# Check over multiple devices
        self.assertDeviceChecks(
            dc, op2, [val, key, seg, weight], [0])
        # Gradient check wrt weight
        self.assertGradientChecks(
            gc, op2, [val, key, seg, weight], 3, [0])

# Copyright (c) 2016-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. ############################################################################## import numpy as np from scipy.sparse import coo_matrix from hypothesis import given import hypothesis.strategies as st from caffe2.python import core import caffe2.python.hypothesis_test_util as hu class TestFunHash(hu.HypothesisTestCase): @given(n_out=st.integers(min_value=5, max_value=20), n_in=st.integers(min_value=10, max_value=20), n_data=st.integers(min_value=2, max_value=8), n_weight=st.integers(min_value=8, max_value=15), n_alpha=st.integers(min_value=3, max_value=8), sparsity=st.floats(min_value=0.1, max_value=1.0), **hu.gcs) def test_funhash(self, n_out, n_in, n_data, n_weight, n_alpha, sparsity, gc, dc): A = np.random.rand(n_data, n_in) A[A > sparsity] = 0 A_coo = coo_matrix(A) val, key, seg = A_coo.data, A_coo.col, A_coo.row weight = np.random.rand(n_weight).astype(np.float32) alpha = np.random.rand(n_alpha).astype(np.float32) val = val.astype(np.float32) key = key.astype(np.int64) seg = seg.astype(np.int32) op = core.CreateOperator( 'FunHash', ['val', 'key', 'seg', 'weight', 'alpha'], ['out'], num_outputs=n_out) # Check over multiple devices self.assertDeviceChecks( dc, op, [val, key, seg, weight, alpha], [0]) # Gradient check wrt weight self.assertGradientChecks( gc, op, [val, key, seg, weight, alpha], 3, [0]) # Gradient check wrt alpha self.assertGradientChecks( gc, op, [val, key, seg, weight, alpha], 4, [0]) op2 = core.CreateOperator( 'FunHash', ['val', 'key', 'seg', 'weight'], ['out'], num_outputs=n_out) # Check over multiple devices self.assertDeviceChecks( dc, op2, [val, key, seg, weight], [0]) # Gradient check wrt weight self.assertGradientChecks( gc, op2, [val, key, seg, weight], 3, [0])