def test_int8_gen_quant_params_min_max_op(self, n, m, preserve_sparsity,
rnd_seed, gc, dc):
X_min = 0 if preserve_sparsity else -77
X_max = X_min + 255
np.random.seed(rnd_seed)
X = np.round(np.random.rand(n, m) * (X_max - X_min) + X_min).astype(
np.float32)
# Calculate X_qparam
hist, bin_edges = np.histogram(X.flatten(), bins=2048)
X_qparam = dnnlowp_pybind11.ChooseStaticQuantizationParams(
np.min(X), np.max(X), hist, preserve_sparsity, 8,
"MIN_MAX_QUANTIZATION")
# Build a net to generate X's qparam using the Int8GenQuantParamsMinMax op
workspace.FeedBlob("X", X, device_option=gc)
workspace.FeedBlob("X_min", np.array([np.min(X)]), device_option=gc)
workspace.FeedBlob("X_max", np.array([np.max(X)]), device_option=gc)
dnnlowp_pybind11.CreateInt8QuantSchemeBlob("quant_scheme",
"MIN_MAX_QUANTIZATION",
preserve_sparsity)
assert workspace.HasBlob(
"quant_scheme"
), "Failed to create the quant_scheme blob in current workspace"
gen_quant_params_net = core.Net("gen_quant_params_min_max")
gen_quant_params_op = core.CreateOperator(
"Int8GenQuantParamsMinMax",
["X_min", "X_max", "quant_scheme"],
["quant_param"],
device_option=gc,
)
gen_quant_params_net.Proto().op.extend([gen_quant_params_op])
assert workspace.RunNetOnce(
gen_quant_params_net), "Failed to run the gen_quant_params net"
scale, zero_point = dnnlowp_pybind11.ObserveInt8QuantParamsBlob(
"quant_param")
shapes, types = workspace.InferShapesAndTypes([gen_quant_params_net],
blob_dimensions={
"X": [n, m],
"X_min": [1],
"X_max": [1],
"quant_scheme": [1]
},
blob_types={
"X":
core.DataType.FLOAT,
"X_min":
core.DataType.FLOAT,
"X_max":
core.DataType.FLOAT,
"quant_scheme":
core.DataType.STRING
})
self.assertEqual(shapes["quant_param"], [1])
self.assertEqual(types["quant_param"], core.DataType.FLOAT)
np.testing.assert_equal(scale, X_qparam.scale)
np.testing.assert_equal(zero_point, X_qparam.zero_point)
def test_int8_gen_quant_params_op(self, n, m, k, quantization_kind,
preserve_sparsity, rnd_seed, gc, dc):
assert n > 0, "Zero samples in the input data"
X_min = 0 if preserve_sparsity else -77
X_max = X_min + 255
np.random.seed(rnd_seed)
X = np.round(np.random.rand(n, m, k) * (X_max - X_min) + X_min).astype(
np.float32)
# Calculate X_qparam
hist, bin_edges = np.histogram(X.flatten(), bins=2048)
X_qparam = dnnlowp_pybind11.ChooseStaticQuantizationParams(
np.min(X), np.max(X), hist, preserve_sparsity, 8,
quantization_kind)
# Build a net to generate X's qparam using the Int8GenQuantParams op
workspace.FeedBlob("X", X, device_option=gc)
dnnlowp_pybind11.CreateInt8QuantSchemeBlob("quant_scheme",
quantization_kind,
preserve_sparsity)
assert workspace.HasBlob(
"quant_scheme"
), "Failed to create the quant_scheme blob in current workspace"
gen_quant_params_net = core.Net("gen_quant_params")
gen_quant_params_op = core.CreateOperator(
"Int8GenQuantParams",
["X", "quant_scheme"],
["quant_param"],
device_option=gc,
)
gen_quant_params_net.Proto().op.extend([gen_quant_params_op])
assert workspace.RunNetOnce(
gen_quant_params_net), "Failed to run the gen_quant_params net"
scale, zero_point = dnnlowp_pybind11.ObserveInt8QuantParamsBlob(
"quant_param")
np.testing.assert_equal(scale, X_qparam.scale)
np.testing.assert_equal(zero_point, X_qparam.zero_point)