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https://github.com/saymrwulf/pytorch.git
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27c7158166
Summary: There is a module called `2to3` which you can target for future specifically to remove these, the directory of `caffe2` has the most redundant imports: ```2to3 -f future -w caffe2``` Pull Request resolved: https://github.com/pytorch/pytorch/pull/45033 Reviewed By: seemethere Differential Revision: D23808648 Pulled By: bugra fbshipit-source-id: 38971900f0fe43ab44a9168e57f2307580d36a38
75 lines
2.3 KiB
Python
75 lines
2.3 KiB
Python
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import hypothesis.strategies as st
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from caffe2.python import core, workspace
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from hypothesis import given
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import caffe2.python.hypothesis_test_util as hu
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import numpy as np
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class TestNGramOps(hu.HypothesisTestCase):
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@given(
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seed=st.integers(0, 2**32 - 1),
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N=st.integers(min_value=10, max_value=100),
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D=st.integers(min_value=2, max_value=10),
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out_of_vcb=st.floats(min_value=0, max_value=0.5),
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max_categorical_limit=st.integers(min_value=5, max_value=20),
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max_in_vcb_val=st.integers(min_value=1000, max_value=10000),
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**hu.gcs_cpu_only
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)
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def test_ngram_from_categorical_op(
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self,
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seed,
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N,
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D,
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out_of_vcb,
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max_categorical_limit,
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max_in_vcb_val,
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gc,
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dc,
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):
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np.random.seed(seed)
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col_num = max(int(D / 2), 1)
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col_ids = np.random.choice(D, col_num, False).astype(np.int32)
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categorical_limits = np.random.randint(
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2, high=max_categorical_limit, size=col_num
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).astype(np.int32)
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vcb = [
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np.random.choice(max_in_vcb_val, x, False)
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for x in categorical_limits
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]
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vals = np.array([x for l in vcb for x in l], dtype=np.int32)
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# Enforce round(floats) to be negative.
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floats = np.random.rand(N, D).astype(np.float32) - 2
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expected_output = []
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for i in range(N):
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val = 0
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for (k, j) in enumerate(col_ids):
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base = np.prod(categorical_limits[:k])
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r = np.random.randint(categorical_limits[k])
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p = np.random.rand()
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if p > out_of_vcb:
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val += base * r
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floats[i][j] = vcb[k][r]
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expected_output.append(val)
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expected_output = np.array(expected_output, dtype=np.int32)
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workspace.ResetWorkspace()
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workspace.FeedBlob('floats', floats)
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op = core.CreateOperator(
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"NGramFromCategorical",
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['floats'],
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['output'],
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col_ids=col_ids,
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categorical_limits=categorical_limits,
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vals=vals,
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)
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workspace.RunOperatorOnce(op)
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output = workspace.blobs['output']
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np.testing.assert_array_equal(output, expected_output)
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