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Fix Transpose + Pad handler, for Keras app MobilenetV2 model #907

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merged 6 commits into from
May 5, 2020
Merged

Fix Transpose + Pad handler, for Keras app MobilenetV2 model #907

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01c403b
Fix Transpose + Pad handler, for Keras app MobilenetV2 model
jignparm May 4, 2020
a312d15
Minor fix
jignparm May 4, 2020
59cee63
typo
jignparm May 4, 2020
f720b64
Debug win-tf-2.1.0
jignparm May 4, 2020
149c44c
Add support for Keras resnet50 pretrained model testing.
jignparm May 5, 2020
b9ba4e1
kick off pipepine
jignparm May 5, 2020
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12 changes: 12 additions & 0 deletions tests/run_pretrained_models.yaml
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Original file line number Diff line number Diff line change
Expand Up @@ -399,6 +399,18 @@ keras_resnet50:
model: ResNet50
model_type: keras
input_get: get_ramp
inputs:
"input_1:0": [1, 224, 224, 3]
outputs:
- Identity:0

keras_mobilenet_v2:
tf_min_version: 2.1
disabled: false
url: module://tensorflow.keras.applications.mobilenet_v2/MobileNetV2
model: MobileNetV2
model_type: keras
input_get: get_ramp
inputs:
"input_1:0": [1, 224, 224, 3]
outputs:
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7 changes: 7 additions & 0 deletions tf2onnx/graph.py
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Expand Up @@ -504,6 +504,13 @@ def make_const(self, name, np_val, skip_conversion=False, raw=True):
self.set_dtype(name, utils.map_numpy_to_onnx_dtype(np_val.dtype))
return node

def copy_const(self, node, name=None):
"""Copy a const node, using name if specified"""
# TODO: support attr copy starting opset 12
if name is None:
name = utils.make_name(node.name)
return self.make_const(name, node.get_tensor_value(as_list=False))

def make_node(self, op_type, inputs, attr=None, output_count=1, outputs=None, skip_conversion=True,
op_name_scope=None, name=None, shapes=None, dtypes=None, domain=constants.ONNX_DOMAIN,
infer_shape_dtype=True):
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15 changes: 10 additions & 5 deletions tf2onnx/optimizer/transpose_optimizer.py
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Expand Up @@ -602,14 +602,19 @@ def _pad_handler(self, trans, node):
new_pads = [pads[0], pads[3], pads[1], pads[2], pads[4], pads[7], pads[5], pads[6]]
node.set_attr("pads", new_pads)
return self._switch_transpose_and_node(node, trans)
if node.inputs[1].is_const():
if node.inputs[1].data_format in ["NHWC", "unkown"]:
pads = node.inputs[1].get_tensor_value()

input1 = node.inputs[1]
if input1.is_const():
if input1.data_format in ["NHWC", "unkown"]:
if not self._nodes_has_single_consumer_node([input1]):
input1 = self._g.copy_const(input1)
node.input[1] = input1.output[0]
pads = input1.get_tensor_value()
# NHWC->NCHW
new_pads = np.array([pads[0], pads[3], pads[1], pads[2], pads[4], pads[7], pads[5], pads[6]],
dtype=np.int64)
node.inputs[1].set_tensor_value(new_pads)
node.inputs[1].data_format = "NCHW"
input1.set_tensor_value(new_pads)
input1.data_format = "NCHW"
return self._switch_transpose_and_node(node, trans)
return False

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