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new 8acc413 [Relay][Frontend][ONNX] Support auto_pad in Conv and
ConvTranspose (#4563)
8acc413 is described below
commit 8acc413cf820d25b4039d8ae7bd455607ee1bae8
Author: Neo Chien <[email protected]>
AuthorDate: Mon Dec 23 01:03:39 2019 +0800
[Relay][Frontend][ONNX] Support auto_pad in Conv and ConvTranspose (#4563)
---
python/tvm/relay/frontend/onnx.py | 73 ++++++++++++++++++++++---
tests/python/frontend/onnx/test_forward.py | 88 +++++++++++++++++++++++++++++-
2 files changed, 150 insertions(+), 11 deletions(-)
diff --git a/python/tvm/relay/frontend/onnx.py
b/python/tvm/relay/frontend/onnx.py
index 2dec7e2..52211f8 100644
--- a/python/tvm/relay/frontend/onnx.py
+++ b/python/tvm/relay/frontend/onnx.py
@@ -66,6 +66,17 @@ def revert_caffe2_pad(pads):
return pads
+def get_pad_pair(input1d, kernel1d, stride1d):
+ """infer pad size"""
+ if input1d % stride1d == 0:
+ pad = max(kernel1d - stride1d, 0)
+ else:
+ pad = max(kernel1d - (input1d % stride1d), 0)
+ pad_before = pad // 2
+ pad_after = pad - pad_before
+ return [pad_before, pad_after]
+
+
def onnx_storage_order2layout(storage_order):
"""converter of onnx storage order parameter to tvm storage order format"""
if storage_order not in (0, 1):
@@ -202,14 +213,37 @@ class Conv(OnnxOpConverter):
@classmethod
def _impl_v1(cls, inputs, attr, params):
- out = AttrCvt(op_name=dimension_picker('conv'),
- transforms={
- 'kernel_shape': 'kernel_size',
- 'dilations': ('dilation', (0, 0)),
- 'pads': ('padding', (0, 0), revert_caffe2_pad),
- 'group': ('groups', 1)},
- ignores=['auto_pad'],
- custom_check=dimension_constraint())(inputs[:2], attr,
params)
+ # infer pads for auto_pad
+ if 'auto_pad' in attr:
+ attr['auto_pad'] = attr['auto_pad'].decode('utf-8')
+ if attr['auto_pad'] in ('SAME_UPPER', 'SAME_LOWER'):
+ input_shape = infer_shape(inputs[0])
+ in_h, in_w = input_shape[2], input_shape[3]
+ stride_h, stride_w = attr['strides']
+ kernel_h, kernel_w = attr['kernel_shape']
+ dilation_h, dilation_w = attr['dilations']
+ dilated_kernel_h = (kernel_h - 1) * dilation_h + 1
+ dilated_kernel_w = (kernel_w - 1) * dilation_w + 1
+ pad_v = get_pad_pair(in_h, dilated_kernel_h, stride_h)
+ pad_h = get_pad_pair(in_w, dilated_kernel_w, stride_w)
+ attr['pads'] = (pad_v[0], pad_h[0], pad_v[1], pad_h[1])
+ elif attr['auto_pad'] == 'VALID':
+ attr['pads'] = (0, 0)
+ elif attr['auto_pad'] == 'NOTSET':
+ pass
+ else:
+ msg = 'Value {} in attribute "auto_pad" of operator Conv is
invalid.'
+ raise
tvm.error.OpAttributeInvalid(msg.format(attr['auto_pad']))
+ attr.pop('auto_pad')
+
+ out = AttrCvt(
+ op_name=dimension_picker('conv'),
+ transforms={
+ 'kernel_shape': 'kernel_size',
+ 'dilations': ('dilation', (0, 0)),
+ 'pads': ('padding', (0, 0), revert_caffe2_pad),
+ 'group': ('groups', 1)},
+ custom_check=dimension_constraint())(inputs[:2], attr, params)
use_bias = len(inputs) == 3
if use_bias:
out = _op.nn.bias_add(out, inputs[2])
@@ -226,6 +260,29 @@ class ConvTranspose(OnnxOpConverter):
attr['channels'] = channels
groups = attr.pop('group')
attr['groups'] = groups
+ # infer pads for auto_pad
+ if 'auto_pad' in attr:
+ attr['auto_pad'] = attr['auto_pad'].decode('utf-8')
+ if attr['auto_pad'] in ('SAME_UPPER', 'SAME_LOWER'):
+ input_shape = infer_shape(inputs[0])
+ in_h, in_w = input_shape[2], input_shape[3]
+ stride_h, stride_w = attr['strides']
+ kernel_h, kernel_w = attr['kernel_shape']
+ dilation_h, dilation_w = attr['dilations']
+ dilated_kernel_h = (kernel_h - 1) * dilation_h + 1
+ dilated_kernel_w = (kernel_w - 1) * dilation_w + 1
+ pad_v = get_pad_pair(in_h, dilated_kernel_h, stride_h)
+ pad_h = get_pad_pair(in_w, dilated_kernel_w, stride_w)
+ attr['pads'] = (pad_v[0], pad_h[0], pad_v[1], pad_h[1])
+ elif attr['auto_pad'] == 'VALID':
+ attr['pads'] = (0, 0)
+ elif attr['auto_pad'] == 'NOTSET':
+ pass
+ else:
+ msg = 'Value {} in attribute "auto_pad" of operator Conv is
invalid.'
+ raise
tvm.error.OpAttributeInvalid(msg.format(attr['auto_pad']))
+ attr.pop('auto_pad')
+
out = AttrCvt(
op_name=dimension_picker('conv', '_transpose'),
transforms={
diff --git a/tests/python/frontend/onnx/test_forward.py
b/tests/python/frontend/onnx/test_forward.py
index cdd2df4..12dee0f 100644
--- a/tests/python/frontend/onnx/test_forward.py
+++ b/tests/python/frontend/onnx/test_forward.py
@@ -77,11 +77,14 @@ def get_tvm_output(graph_def, input_data, target, ctx,
output_shape=None, output
return tvm_output.asnumpy()
-def get_onnxruntime_output(model, x, dtype='float32'):
+def get_onnxruntime_output(model, inputs, dtype='float32'):
import onnxruntime.backend
rep = onnxruntime.backend.prepare(model, 'CPU')
- x = x.astype(dtype)
- ort_out = rep.run(x)[0]
+ if isinstance(inputs, list) and len(inputs) > 1:
+ ort_out = rep.run(inputs)
+ else:
+ x = inputs.astype(dtype)
+ ort_out = rep.run(x)[0]
return ort_out
@@ -1746,6 +1749,83 @@ def test_or():
verify_or(indata=[x, y], dtype=bool)
+def verify_conv(x_shape, w_shape, y_shape, p):
+ node = helper.make_node('Conv',
+ inputs=['x', 'W'],
+ outputs=['y'],
+ kernel_shape=[3, 3],
+ # Default values for other attributes:
+ # strides=[1, 1],
+ # dilations=[1, 1],
+ # groups=1
+ pads=p,)
+
+ graph = helper.make_graph([node],
+ 'conv_test',
+ inputs=[helper.make_tensor_value_info("x",
TensorProto.FLOAT, list(x_shape)),
+ helper.make_tensor_value_info("W",
TensorProto.FLOAT, list(w_shape))],
+ outputs=[helper.make_tensor_value_info("y",
TensorProto.FLOAT, list(y_shape))])
+
+ model = helper.make_model(graph, producer_name='conv_test')
+
+ for target, ctx in ctx_list():
+ x = np.random.uniform(size=x_shape).astype('float32')
+ W = np.random.uniform(size=w_shape).astype('float32')
+ tvm_out = get_tvm_output(model, [x, W], target, ctx, y_shape)
+ onnx_out = get_onnxruntime_output(model, [x, W], 'float32')[0]
+ tvm.testing.assert_allclose(onnx_out, tvm_out, rtol=1e-5, atol=1e-5)
+
+
+def test_conv():
+ # Convolution with padding
+ # (1, 1, 5, 5) input tensor
+ # (1, 1, 3, 3) tensor for convolution weights
+ # (1, 1, 5, 5) output tensor
+ # [1, 1, 1, 1] list for pads
+ verify_conv((1, 1, 5, 5), (1, 1, 3, 3), (1, 1, 5, 5), [1, 1, 1, 1])
+
+ # Convolution without padding
+ # (1, 1, 5, 5) input tensor
+ # (1, 1, 3, 3) tensor for convolution weights
+ # (1, 1, 3, 3) output tensor
+ # [0, 0, 0, 0] list for pads
+ verify_conv((1, 1, 5, 5), (1, 1, 3, 3), (1, 1, 3, 3), [0, 0, 0, 0])
+
+
+def verify_convtranspose(x_shape, w_shape, y_shape, p):
+ node = onnx.helper.make_node("ConvTranspose",
+ inputs=["x", "W"],
+ outputs=['y'],
+ strides=[3, 2],
+ group=1,
+ kernel_shape=[3, 3],
+ pads=p)
+
+ graph = helper.make_graph([node],
+ 'verify_convtranspose_test',
+ inputs=[helper.make_tensor_value_info("x",
TensorProto.FLOAT, list(x_shape)),
+ helper.make_tensor_value_info("W",
TensorProto.FLOAT, list(w_shape))],
+ outputs=[helper.make_tensor_value_info("y",
TensorProto.FLOAT, list(y_shape))])
+
+ model = helper.make_model(graph, producer_name='convtranspose_trest')
+
+ for target, ctx in ctx_list():
+ x = np.random.uniform(size=x_shape).astype('float32')
+ W = np.random.uniform(size=w_shape).astype('float32')
+ tvm_out = get_tvm_output(model, [x, W], target, ctx, y_shape)
+ onnx_out = get_onnxruntime_output(model, [x, W], 'float32')[0]
+ tvm.testing.assert_allclose(onnx_out, tvm_out, rtol=1e-5, atol=1e-5)
+
+
+def test_convtranspose():
+ # Convolution Transpose with padding
+ # (1, 1, 3, 3) input tensor
+ # (1, 2, 3, 3) tensor for convolution weights
+ # (1, 2, 7, 3) output tensor
+ # [1, 2, 1, 2] list for pads
+ verify_convtranspose((1, 1, 3, 3), (1, 2, 3, 3), (1, 2, 7, 3), [1, 2, 1,
2])
+
+
if __name__ == '__main__':
test_flatten()
test_reshape()
@@ -1800,3 +1880,5 @@ if __name__ == '__main__':
test_or()
test_depth_to_space()
test_space_to_depth()
+ test_conv()
+ test_convtranspose()
