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import caffe2.python.hypothesis_test_util as hu
import hypothesis.strategies as st
import numpy as np
from caffe2.python import core, dyndep, workspace
from hypothesis import given, settings
dyndep.InitOpsLibrary("//caffe2/caffe2/quantization/server:dnnlowp_ops")
workspace.GlobalInit(["caffe2", "--caffe2_omp_num_threads=11"])
class DNNLowPBatchPermutationOpTest(hu.HypothesisTestCase):
@given(N=st.integers(min_value=1, max_value=100), **hu.gcs_cpu_only)
@settings(max_examples=10, deadline=None)
def test_batch_permutation(self, N, gc, dc):
X = np.round(np.random.rand(N, 10, 20, 3) * 255).astype(np.float32)
indices = np.arange(N).astype(np.int32)
np.random.shuffle(indices)
quantize = core.CreateOperator("Quantize", ["X"], ["X_q"], engine="DNNLOWP")
batch_perm = core.CreateOperator(
"BatchPermutation", ["X_q", "indices"], ["Y_q"], engine="DNNLOWP"
)
net = core.Net("test_net")
net.Proto().op.extend([quantize, batch_perm])
workspace.FeedBlob("X", X)
workspace.FeedBlob("indices", indices)
workspace.RunNetOnce(net)
X_q = workspace.FetchInt8Blob("X_q").data
Y_q = workspace.FetchInt8Blob("Y_q").data
def batch_permutation_ref(X, indices):
return np.array([X[i] for i in indices])
Y_q_ref = batch_permutation_ref(X_q, indices)
np.testing.assert_allclose(Y_q, Y_q_ref)
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