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new a4902e0 Remove developer facing api from frontend exports. (#5375)
a4902e0 is described below
commit a4902e0590f608e3f22281e49d5433a8f2c574a8
Author: shoubhik <[email protected]>
AuthorDate: Sat Apr 18 21:09:32 2020 -0700
Remove developer facing api from frontend exports. (#5375)
---
python/tvm/relay/frontend/__init__.py | 4 ---
tests/python/frontend/mxnet/test_qnn_ops_utils.py | 37 ++++++++++++-----------
2 files changed, 19 insertions(+), 22 deletions(-)
diff --git a/python/tvm/relay/frontend/__init__.py
b/python/tvm/relay/frontend/__init__.py
index fa258f4..aba9eea 100644
--- a/python/tvm/relay/frontend/__init__.py
+++ b/python/tvm/relay/frontend/__init__.py
@@ -24,10 +24,6 @@ for Relay.
from __future__ import absolute_import
from .mxnet import from_mxnet
-from .mxnet_qnn_op_utils import dequantize_mxnet_min_max
-from .mxnet_qnn_op_utils import quantize_mxnet_min_max
-from .mxnet_qnn_op_utils import get_mkldnn_int8_scale
-from .mxnet_qnn_op_utils import get_mkldnn_uint8_scale
from .mxnet_qnn_op_utils import quantize_conv_bias_mkldnn_from_var
from .keras import from_keras
from .onnx import from_onnx
diff --git a/tests/python/frontend/mxnet/test_qnn_ops_utils.py
b/tests/python/frontend/mxnet/test_qnn_ops_utils.py
index 3204256..d130eef 100644
--- a/tests/python/frontend/mxnet/test_qnn_ops_utils.py
+++ b/tests/python/frontend/mxnet/test_qnn_ops_utils.py
@@ -16,10 +16,14 @@
# under the License.
import tvm
-from tvm import te
import numpy as np
from tvm import relay
from tvm.contrib import graph_runtime
+from tvm.relay.frontend.mxnet_qnn_op_utils import dequantize_mxnet_min_max, \
+ quantize_mxnet_min_max, \
+ get_mkldnn_int8_scale, \
+ get_mkldnn_uint8_scale, \
+
quantize_conv_bias_mkldnn_from_var
def test_mkldnn_dequantize():
@@ -29,11 +33,10 @@ def test_mkldnn_dequantize():
input_data = relay.var("input_data", shape=shape, dtype=in_dtype)
min_range = quant_args['min_range']
max_range = quant_args['max_range']
- dequantized_output = \
- relay.frontend.dequantize_mxnet_min_max(input_data,
- min_range=min_range,
- max_range=max_range,
- in_dtype=in_dtype)
+ dequantized_output = dequantize_mxnet_min_max(input_data,
+ min_range=min_range,
+ max_range=max_range,
+ in_dtype=in_dtype)
mod = relay.Function(relay.analysis.free_vars(dequantized_output),
dequantized_output)
mod = tvm.IRModule.from_expr(mod)
with relay.build_config(opt_level=3):
@@ -79,17 +82,15 @@ def test_mkldnn_dequantize():
def test_mkldnn_quantize():
-
def quantize_test_driver(out_dtype, quant_args, in_data,
verify_output_data):
shape = in_data.shape
input_data = relay.var("input_data", shape=shape, dtype='float32')
min_range = quant_args['min_range']
max_range = quant_args['max_range']
- quantized_output, _, _ = \
- relay.frontend.quantize_mxnet_min_max(input_data,
- min_range=min_range,
- max_range=max_range,
- out_dtype=out_dtype)
+ quantized_output, _, _ = quantize_mxnet_min_max(input_data,
+ min_range=min_range,
+ max_range=max_range,
+ out_dtype=out_dtype)
mod = relay.Function(relay.analysis.free_vars(quantized_output),
quantized_output)
mod = tvm.IRModule.from_expr(mod)
with relay.build_config(opt_level=3):
@@ -140,8 +141,8 @@ def test_get_mkldnn_int8_scale():
range_min = -3.904039
range_max = 3.904039
expected = 0.03061991354976495
- output = relay.frontend.get_mkldnn_int8_scale(range_max=range_max,
- range_min=range_min)
+ output = get_mkldnn_int8_scale(range_max=range_max,
+ range_min=range_min)
assert np.allclose(output, expected)
@@ -149,15 +150,15 @@ def test_get_mkldnn_uint8_scale():
range_min = 0.0
range_max = 55.77269
expected = 0.21828841189047482
- output = relay.frontend.get_mkldnn_uint8_scale(range_max=range_max,
- range_min=range_min)
+ output = get_mkldnn_uint8_scale(range_max=range_max,
+ range_min=range_min)
assert np.allclose(output, expected)
def test_quantize_conv_bias_mkldnn_from_var():
bias_var = relay.var('bias', shape=(3,), dtype='float32')
bias_scale = tvm.nd.array(np.array([0.5, 0.6, 0.7]))
- output = relay.frontend.quantize_conv_bias_mkldnn_from_var(bias_var,
bias_scale)
+ output = quantize_conv_bias_mkldnn_from_var(bias_var, bias_scale)
assert isinstance(output, tvm.relay.expr.Call)
attrs = output.attrs
assert attrs.axis == 0
@@ -171,4 +172,4 @@ if __name__ == "__main__":
test_mkldnn_quantize()
test_get_mkldnn_int8_scale()
test_get_mkldnn_uint8_scale()
- test_quantize_conv_bias_mkldnn_from_var()
\ No newline at end of file
+ test_quantize_conv_bias_mkldnn_from_var()
