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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 DNNLowPResizeNearestOpTest(hu.HypothesisTestCase):
@given(
N=st.integers(0, 3),
H=st.integers(10, 300),
W=st.integers(10, 300),
C=st.integers(1, 32),
scale_w=st.floats(0.25, 4.0) | st.just(2.0),
scale_h=st.floats(0.25, 4.0) | st.just(2.0),
**hu.gcs_cpu_only
)
@settings(deadline=None, max_examples=50)
def test_resize_nearest(self, N, H, W, C, scale_w, scale_h, gc, dc):
X = np.round(np.random.rand(N, H, W, C) * 255).astype(np.float32)
quantize = core.CreateOperator("Quantize", ["X"], ["X_q"], engine="DNNLOWP")
resize_nearest = core.CreateOperator(
"Int8ResizeNearest",
["X_q"],
["Y_q"],
width_scale=scale_w,
height_scale=scale_h,
engine="DNNLOWP",
)
net = core.Net("test_net")
net.Proto().op.extend([quantize, resize_nearest])
workspace.FeedBlob("X", X)
workspace.RunNetOnce(net)
X_q = workspace.FetchInt8Blob("X_q").data
Y_q = workspace.FetchInt8Blob("Y_q").data
def resize_nearest_ref(X):
outH = np.int32(H * scale_h)
outW = np.int32(W * scale_w)
outH_idxs, outW_idxs = np.meshgrid(
np.arange(outH), np.arange(outW), indexing="ij"
)
inH_idxs = np.minimum(outH_idxs / scale_h, H - 1).astype(np.int32)
inW_idxs = np.minimum(outW_idxs / scale_w, W - 1).astype(np.int32)
Y = X[:, inH_idxs, inW_idxs, :]
return Y
Y_q_ref = resize_nearest_ref(X_q)
np.testing.assert_allclose(Y_q, Y_q_ref)
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