From ef39eac34de4e8a3070b3d9083626ea9fddada8c Mon Sep 17 00:00:00 2001
From: Siju <sijusamuel@gmail.com>
Date: Thu, 13 Sep 2018 23:14:32 +0530
Subject: [PATCH] [NNVM]Tensorflow and Onnx basic ops (#1666)
---
nnvm/python/nnvm/frontend/onnx.py | 24 +++-
nnvm/python/nnvm/frontend/tensorflow.py | 20 ++++
.../python/frontend/onnx/test_forward.py | 106 ++++++++++++++++++
.../frontend/tensorflow/test_forward.py | 64 +++++++++++
4 files changed, 212 insertions(+), 2 deletions(-)
diff --git a/nnvm/python/nnvm/frontend/onnx.py b/nnvm/python/nnvm/frontend/onnx.py
index ed885dfcd..22602d748 100644
--- a/nnvm/python/nnvm/frontend/onnx.py
+++ b/nnvm/python/nnvm/frontend/onnx.py
@@ -577,6 +577,26 @@ class HardSigmoid(OnnxOpConverter):
attr = {'a_min':0, 'a_max':1}
return AttrCvt(op_name='clip')([transformX], attr)
+class ArgMax(OnnxOpConverter):
+ """ Operator converter for ArgMax.
+ """
+ @classmethod
+ def _impl_v1(cls, inputs, attr, params):
+ axis = attr.get('axis', 0)
+ keepdims = attr.get('keepdims', True)
+ attr = {'axis':axis, 'keepdims':keepdims}
+ return AttrCvt(op_name='argmax')(inputs, attr)
+
+class ArgMin(OnnxOpConverter):
+ """ Operator converter for ArgMin.
+ """
+ @classmethod
+ def _impl_v1(cls, inputs, attr, params):
+ axis = attr.get('axis', 0)
+ keepdims = attr.get('keepdims', True)
+ attr = {'axis':axis, 'keepdims':keepdims}
+ return AttrCvt(op_name='argmin')(inputs, attr)
+
# compatible operators that do NOT require any conversion.
_identity_list = []
@@ -673,8 +693,8 @@ def _get_convert_map(opset):
# 'ReduceMean'
# 'ReduceProd'
# 'ReduceLogSumExp'
- # 'ArgMax'
- # 'ArgMin'
+ 'ArgMax': ArgMax.get_converter(opset),
+ 'ArgMin': ArgMin.get_converter(opset),
# defs/tensor
'Cast': Cast.get_converter(opset),
diff --git a/nnvm/python/nnvm/frontend/tensorflow.py b/nnvm/python/nnvm/frontend/tensorflow.py
index d9406601d..ab5664678 100644
--- a/nnvm/python/nnvm/frontend/tensorflow.py
+++ b/nnvm/python/nnvm/frontend/tensorflow.py
@@ -650,6 +650,7 @@ def _pad(name):
ignores=['Tpaddings'],)(new_inputs, attr)
return _impl
+
def _transpose():
def _impl(inputs, attr, params):
# If perm is not specified, axes is left empty,
@@ -680,6 +681,19 @@ def _range():
return _sym.Variable(name=name, shape=params[name].shape)
return _impl
+def _elu():
+ def _impl(inputs, attr, params):
+ alpha = 1.0
+ return -alpha * _sym.relu(1 - _sym.exp(inputs[0])) + _sym.relu(inputs[0])
+ return _impl
+
+def _selu():
+ def _impl(inputs, attr, params):
+ alpha = 1.6732632423543772848170429916717
+ gamma = 1.0507009873554804934193349852946
+ return gamma * (-alpha * _sym.relu(1 - _sym.exp(inputs[0])) + _sym.relu(inputs[0]))
+ return _impl
+
# compatible operators that do NOT require any conversion.
_identity_list = []
@@ -695,12 +709,15 @@ _convert_map = {
'BatchNormWithGlobalNormalization' : _batch_norm(),
'BiasAdd' : _bias_add(),
'Cast' : _cast(),
+ 'Ceil' : AttrCvt('ceil'),
'CheckNumerics' : _check_numerics(),
'Concat' : _concat(),
'ConcatV2' : _concatV2(),
'Conv2D' : _conv('conv'),
'DecodeJpeg' : _decode_image(),
+ 'Elu' : _elu(),
'ExpandDims' : _expand_dims(),
+ 'Floor' : AttrCvt('floor'),
'Identity' : _identity(),
'MatMul' : _matmul(),
'MaxPool' : _pooling('max_pool'),
@@ -712,9 +729,11 @@ _convert_map = {
'Sum' : _sum(),
'Square' : _square(),
'Pack' : _pack(),
+ 'LeakyRelu' : AttrCvt('leaky_relu'),
'Relu' : AttrCvt('relu'),
'Reshape' : _reshape(),
'ResizeBilinear' : _resize_bilinear(),
+ 'Selu' : _selu(),
'Softmax' : AttrCvt('softmax', {'axis': ('axis', 1)}),
'Rsqrt' : _rsqrt(),
'Squeeze' : _squeeze(),
@@ -732,6 +751,7 @@ _convert_map = {
'Range' : _range(),
'Rank' : _rank(),
'Transpose' : _transpose(),
+ 'Tanh' : AttrCvt('tanh'),
}
# _convert_map_rnn defines maps of rnn operator name to
diff --git a/nnvm/tests/python/frontend/onnx/test_forward.py b/nnvm/tests/python/frontend/onnx/test_forward.py
index 5e199b452..7939796ae 100644
--- a/nnvm/tests/python/frontend/onnx/test_forward.py
+++ b/nnvm/tests/python/frontend/onnx/test_forward.py
@@ -548,6 +548,111 @@ def test_forward_hardsigmoid():
verify_hardsigmoid((1, 3, 20, 20), 0.5, 0.6)
verify_hardsigmoid((20, 20), 0.3, 0.4)
+def verify_argmin(input_dim, axis=None, keepdims=None):
+ def _argmin_numpy(data, axis=0, keepdims=True):
+ result = np.argmin(data, axis=axis)
+ if (keepdims == 1):
+ result = np.expand_dims(result, axis)
+ return result.astype(data.dtype)
+
+ a_np1 = np.random.uniform(-10, 10, input_dim).astype(np.int32)
+ if keepdims is None and axis is None:
+ b_np = _argmin_numpy(a_np1)
+ node = onnx.helper.make_node('ArgMin',
+ inputs=['a_np1'],
+ outputs=['out'])
+ elif axis is None:
+ b_np = _argmin_numpy(a_np1, keepdims=keepdims)
+ node = onnx.helper.make_node('ArgMin',
+ inputs=['a_np1'],
+ outputs=['out'],
+ keepdims=keepdims)
+ elif keepdims is None:
+ b_np = _argmin_numpy(a_np1, axis=axis)
+ node = onnx.helper.make_node('ArgMin',
+ inputs=['a_np1'],
+ outputs=['out'],
+ axis=axis)
+ else:
+ b_np = _argmin_numpy(a_np1, axis=axis, keepdims=keepdims)
+ node = onnx.helper.make_node('ArgMin',
+ inputs=['a_np1'],
+ outputs=['out'],
+ axis=axis,
+ keepdims=keepdims)
+ graph = helper.make_graph([node],
+ "argmin_test",
+ inputs = [helper.make_tensor_value_info("a_np1",
+ TensorProto.INT32, list(a_np1.shape))],
+ outputs = [helper.make_tensor_value_info("out",
+ TensorProto.INT32, list(b_np.shape))])
+
+ model = helper.make_model(graph, producer_name='argmin_test')
+
+ for target, ctx in ctx_list():
+ tvm_out = get_tvm_output(model, [a_np1], target, ctx, b_np.shape, b_np.dtype)
+ np.testing.assert_allclose(b_np, tvm_out, rtol=1e-5, atol=1e-5)
+
+def verify_argmax(input_dim, axis=None, keepdims=None):
+ def _argmax_numpy(data, axis=0, keepdims=True):
+ result = np.argmax(data, axis=axis)
+ if (keepdims == 1):
+ result = np.expand_dims(result, axis)
+ return result.astype(data.dtype)
+
+ a_np1 = np.random.uniform(-10, 10, input_dim).astype(np.int32)
+
+ if keepdims is None and axis is None:
+ b_np = _argmax_numpy(a_np1)
+ node = onnx.helper.make_node('ArgMax',
+ inputs=['a_np1'],
+ outputs=['out'])
+ elif axis is None:
+ b_np = _argmax_numpy(a_np1, keepdims=keepdims)
+ node = onnx.helper.make_node('ArgMax',
+ inputs=['a_np1'],
+ outputs=['out'],
+ keepdims=keepdims)
+ elif keepdims is None:
+ b_np = _argmax_numpy(a_np1, axis=axis)
+ node = onnx.helper.make_node('ArgMax',
+ inputs=['a_np1'],
+ outputs=['out'],
+ axis=axis)
+ else:
+ b_np = _argmax_numpy(a_np1, axis=axis, keepdims=keepdims)
+ node = onnx.helper.make_node('ArgMax',
+ inputs=['a_np1'],
+ outputs=['out'],
+ axis=axis,
+ keepdims=keepdims)
+
+ graph = helper.make_graph([node],
+ "argmax_test",
+ inputs = [helper.make_tensor_value_info("a_np1",
+ TensorProto.INT32, list(a_np1.shape))],
+ outputs = [helper.make_tensor_value_info("out",
+ TensorProto.INT32, list(b_np.shape))])
+
+ model = helper.make_model(graph, producer_name='argmax_test')
+
+ for target, ctx in ctx_list():
+ tvm_out = get_tvm_output(model, [a_np1], target, ctx, b_np.shape, b_np.dtype)
+ np.testing.assert_allclose(b_np, tvm_out, rtol=1e-5, atol=1e-5)
+
+def test_forward_arg_min_max():
+ '''Verify argmin and argmax'''
+ verify_argmin([3,4,4])
+ verify_argmax([3,4,4])
+ verify_argmin([3,4,4], axis=1)
+ verify_argmax([3,4,4], axis=0)
+ verify_argmin([3,4,4], keepdims=0)
+ verify_argmax([3,4,4], keepdims=1)
+ for axis in [0,1,2]:
+ for keepdims in [True,False]:
+ verify_argmin([3,4,4], axis, keepdims)
+ verify_argmax([3,4,4], axis, keepdims)
+
if __name__ == '__main__':
# verify_super_resolution_example()
# verify_squeezenet1_1()
@@ -570,3 +675,4 @@ if __name__ == '__main__':
test_forward_max()
test_forward_mean()
test_forward_hardsigmoid()
+ test_forward_arg_min_max()
diff --git a/nnvm/tests/python/frontend/tensorflow/test_forward.py b/nnvm/tests/python/frontend/tensorflow/test_forward.py
index af69a0549..616259504 100644
--- a/nnvm/tests/python/frontend/tensorflow/test_forward.py
+++ b/nnvm/tests/python/frontend/tensorflow/test_forward.py
@@ -877,6 +877,63 @@ def test_forward_transpose():
_test_forward_transpose((2, 3, 4), (0, 1, 2))
_test_forward_transpose((2, 3, 4, 5), (3, 0, 1, 2))
+
+def test_forward_ceil():
+ ishape = (1, 3, 10, 10)
+ inp_array = np.random.uniform(size=ishape).astype(np.float32)
+ with tf.Graph().as_default():
+ in1 = tf.placeholder(shape=inp_array.shape, dtype=inp_array.dtype)
+ tf.ceil(in1)
+ compare_tf_with_tvm(inp_array, 'Placeholder:0', 'Ceil:0')
+
+def test_forward_floor():
+ ishape = (1, 3, 10, 10)
+ inp_array = np.random.uniform(size=ishape).astype(np.float32)
+ with tf.Graph().as_default():
+ in1 = tf.placeholder(shape=inp_array.shape, dtype=inp_array.dtype)
+ tf.floor(in1)
+ compare_tf_with_tvm(inp_array, 'Placeholder:0', 'Floor:0')
+
+def test_forward_relu():
+ ishape = (1, 3, 10, 10)
+ inp_array = np.random.uniform(-5, 5, size=ishape).astype(np.float32)
+ with tf.Graph().as_default():
+ in1 = tf.placeholder(shape=inp_array.shape, dtype=inp_array.dtype)
+ tf.nn.relu(in1)
+ compare_tf_with_tvm(inp_array, 'Placeholder:0', 'Relu:0')
+
+def test_forward_leaky_relu():
+ ishape = (1, 3, 10, 10)
+ inp_array = np.random.uniform(-5, 5, size=ishape).astype(np.float32)
+ with tf.Graph().as_default():
+ in1 = tf.placeholder(shape=inp_array.shape, dtype=inp_array.dtype)
+ tf.nn.leaky_relu(in1, alpha=0.4)
+ compare_tf_with_tvm(inp_array, 'Placeholder:0', 'LeakyRelu:0')
+
+def test_forward_elu():
+ ishape = (1, 3, 10, 10)
+ inp_array = np.random.uniform(-5, 5, size=ishape).astype(np.float32)
+ with tf.Graph().as_default():
+ in1 = tf.placeholder(shape=inp_array.shape, dtype=inp_array.dtype)
+ tf.nn.elu(in1)
+ compare_tf_with_tvm(inp_array, 'Placeholder:0', 'Elu:0')
+
+def test_forward_selu():
+ ishape = (1, 3, 10, 10)
+ inp_array = np.random.uniform(-5, 5, size=ishape).astype(np.float32)
+ with tf.Graph().as_default():
+ in1 = tf.placeholder(shape=inp_array.shape, dtype=inp_array.dtype)
+ tf.nn.selu(in1)
+ compare_tf_with_tvm(inp_array, 'Placeholder:0', 'Selu:0')
+
+def test_forward_tanh():
+ ishape = (1, 3, 10, 10)
+ inp_array = np.random.uniform(-5, 5, size=ishape).astype(np.float32)
+ with tf.Graph().as_default():
+ in1 = tf.placeholder(shape=inp_array.shape, dtype=inp_array.dtype)
+ tf.nn.tanh(in1)
+ compare_tf_with_tvm(inp_array, 'Placeholder:0', 'Tanh:0')
+
#######################################################################
# Main
# ----
@@ -905,3 +962,10 @@ if __name__ == '__main__':
test_forward_ptb()
test_forward_lrn()
test_forward_l2_normalize()
+ test_forward_ceil()
+ test_forward_floor()
+ test_forward_relu()
+ test_forward_leaky_relu()
+ test_forward_elu()
+ test_forward_selu()
+ test_forward_tanh()
--
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