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new f1d0cb2 [COREML]Reduceops support added to frontend (#6252)
f1d0cb2 is described below
commit f1d0cb201febbb67d600fa00e1c65aaa74733f3e
Author: Siju Samuel <[email protected]>
AuthorDate: Wed Aug 12 21:20:42 2020 +0530
[COREML]Reduceops support added to frontend (#6252)
---
python/tvm/relay/frontend/coreml.py | 35 ++++++++++++++++
tests/python/frontend/coreml/test_forward.py | 62 ++++++++++++++++++++++++++++
2 files changed, 97 insertions(+)
diff --git a/python/tvm/relay/frontend/coreml.py
b/python/tvm/relay/frontend/coreml.py
index 6e1fce9..b8ef1f2 100644
--- a/python/tvm/relay/frontend/coreml.py
+++ b/python/tvm/relay/frontend/coreml.py
@@ -377,6 +377,40 @@ def _UnaryFunctionLayerParams(op, inexpr, etab):
raise tvm.error.OpAttributeUnImplemented(msg.format(op_type))
+def _ReduceLayerParams(op, inexpr, etab):
+ axis = op.axis
+ if axis == op.CHW:
+ axis = [-3, -2, -1]
+ elif axis == op.HW:
+ axis = [-2, -1]
+ elif axis == op.C:
+ axis = -3
+ elif axis == op.H:
+ axis = -2
+ elif axis == op.W:
+ axis = -1
+ else:
+ msg = 'Reduce axis value {} is not supported in frontend CoreML.'
+ raise tvm.error.OpAttributeUnImplemented(msg.format(axis))
+
+ mode = op.mode
+ if mode == op.SUM:
+ return _op.sum(inexpr, axis=axis, keepdims=True)
+ elif mode == op.AVG:
+ return _op.mean(inexpr, axis=axis, keepdims=True)
+ elif mode == op.PROD:
+ return _op.prod(inexpr, axis=axis, keepdims=True)
+ elif mode == op.MIN:
+ return _op.min(inexpr, axis=axis, keepdims=True)
+ elif mode == op.MAX:
+ return _op.max(inexpr, axis=axis, keepdims=True)
+ elif mode == op.ARGMAX:
+ return _op.argmax(inexpr, axis=axis, keepdims=True)
+ else:
+ msg = 'Reduce mode value {} is not supported in frontend CoreML.'
+ raise tvm.error.OpAttributeUnImplemented(msg.format(mode))
+
+
_convert_map = {
'NeuralNetworkMeanImage': _NeuralNetworkMeanImage,
'NeuralNetworkImageScaler': _NeuralNetworkImageScaler,
@@ -400,6 +434,7 @@ _convert_map = {
'MaxLayerParams': _MaxLayerParams,
'MinLayerParams': _MinLayerParams,
'UnaryFunctionLayerParams': _UnaryFunctionLayerParams,
+ 'ReduceLayerParams': _ReduceLayerParams,
}
# SAME padding: https://www.tensorflow.org/api_guides/python/nn
diff --git a/tests/python/frontend/coreml/test_forward.py
b/tests/python/frontend/coreml/test_forward.py
index 49f0987..cbae5f3 100644
--- a/tests/python/frontend/coreml/test_forward.py
+++ b/tests/python/frontend/coreml/test_forward.py
@@ -525,6 +525,67 @@ def test_forward_unary():
verify_unary_threshold((1, 3, 20, 20), alpha=5.0)
+def test_forward_reduce():
+ from enum import Enum
+ class ReduceAxis(Enum):
+ CHW = 0
+ HW = 1
+ C = 2
+ H = 3
+ W = 4
+
+ def _verify_reduce(input_dim, mode, axis, ref_func, dtype='float32'):
+ print(input_dim, mode, axis)
+ a_np = np.random.uniform(size=input_dim).astype(dtype)
+
+ # translate to axis from coreml format
+ if axis == ReduceAxis.CHW:
+ np_axis = (-3, -2, -1)
+ elif axis == ReduceAxis.HW:
+ np_axis = (-2, -1)
+ elif axis == ReduceAxis.C:
+ np_axis = -3
+ elif axis == ReduceAxis.H:
+ np_axis = -2
+ elif axis == ReduceAxis.W:
+ np_axis = -1
+
+ if ref_func == np.argmax:
+ ref_val = np.expand_dims(ref_func(a_np, np_axis),
np_axis).astype(dtype)
+ else:
+ ref_val = ref_func(a_np, np_axis, keepdims=True)
+
+ inputs = [('input', datatypes.Array(*input_dim))]
+ output = [('output', datatypes.Array(*ref_val.shape))]
+ builder = NeuralNetworkBuilder(inputs, output)
+ builder.add_reduce(name=mode,
+ input_name='input',
+ output_name='output',
+ axis=axis.name,
+ mode=mode)
+
+ model = cm.models.MLModel(builder.spec)
+ for target, ctx in ctx_list():
+ out = run_tvm_graph(model, target, ctx, [a_np],
+ ['input'], ref_val.shape, dtype)
+ tvm.testing.assert_allclose(out, ref_val, rtol=1e-5, atol=1e-5)
+
+ dshapes = [[10, 10], [1, 10, 10], [1, 3, 10, 10]]
+ for dshape in dshapes:
+ for axis in ReduceAxis:
+ if len(dshape) < 3 and axis in [ReduceAxis.CHW, ReduceAxis.C]:
+ # input must have rank at least 3
+ continue
+ _verify_reduce(dshape, "sum", axis, np.sum)
+ _verify_reduce(dshape, "avg", axis, np.mean)
+ _verify_reduce(dshape, "prod", axis, np.prod)
+ _verify_reduce(dshape, "min", axis, np.min)
+ _verify_reduce(dshape, "max", axis, np.max)
+ if axis in [ReduceAxis.C, ReduceAxis.H, ReduceAxis.W]:
+ # For mode ArgMax, axis must be [-1] or [-2] or [-3]
+ _verify_reduce(dshape, "argmax", axis, np.argmax,
dtype='int32')
+
+
def verify_image_scaler(input_dim, blue_bias=0.0, green_bias=0.0,
red_bias=0.0, image_scale=1.0):
dtype = 'float32'
a_np = np.random.uniform(size=input_dim).astype(dtype)
@@ -602,6 +663,7 @@ if __name__ == '__main__':
test_forward_max()
test_forward_min()
test_forward_unary()
+ test_forward_reduce()
test_mobilenet_checkonly()
test_resnet50_checkonly()
test_forward_image_scaler()
