Skip to content
New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

Sign up for GitHub

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

support inputs as list and add some OPs support #723

Merged
merged 12 commits into from
Jan 11, 2022
Merged

support inputs as list and add some OPs support #723

Show file tree
Hide file tree
Changes from all commits
Commits
Show all changes
12 commits
Select commit Hold shift + click to select a range
2f01932
fix onnx convert
yeliang2258 Dec 22, 2021
3967c64
add GatherElements, suport list input
yeliang2258 Dec 23, 2021
6ae67e3
fix
yeliang2258 Dec 29, 2021
57586e7
update
yeliang2258 Dec 29, 2021
73451bb
remove useless code
yeliang2258 Dec 29, 2021
47de9f5
Merge remote-tracking branch 'upstream/develop' into idg_dev
yeliang2258 Jan 4, 2022
a87f7d3
fix neg
yeliang2258 Jan 5, 2022
1ca897a
for test
yeliang2258 Jan 10, 2022
5768d03
add to and remove useless code
yeliang2258 Jan 10, 2022
1de6fa0
updtae tode
yeliang2258 Jan 10, 2022
55ac8ad
fix neg
yeliang2258 Jan 10, 2022
d7934bf
fix
yeliang2258 Jan 10, 2022
File filter

Filter by extension

Filter by extension
Conversations
Failed to load comments.
Loading
Jump to
Jump to file
Failed to load files.
Loading
Diff view
Diff view
78 changes: 52 additions & 26 deletions x2paddle/core/program.py
100644 → 100755
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -27,22 +27,23 @@

class PaddleLayer(object):
def __init__(self, id, kernel, inputs, outputs, scope_name="", **kwargs):
assert isinstance(
inputs,
dict), "parameter 'inputs' for PaddleLayer should be type of dict"
assert isinstance(inputs, (
dict, list
)), "parameter 'inputs' for PaddleLayer should be type of dict or list"
assert isinstance(
outputs,
list), "parameter 'outputs' for PaddleLayer should be type of list"
for k, v in inputs.items():
if isinstance(v, (list, tuple)):
for i in v:
assert isinstance(
i, six.string_types
if isinstance(inputs, dict):
for k, v in inputs.items():
if isinstance(v, (list, tuple)):
for i in v:
assert isinstance(
i, six.string_types
), "value in inputs should be type of string or list of string"
else:
assert isinstance(v, six.string_types) or isinstance(
v, list
), "value in inputs should be type of string or list of string"
else:
assert isinstance(v, six.string_types) or isinstance(
v, list
), "value in inputs should be type of string or list of string"
for v in outputs:
assert isinstance(
v, six.
Expand Down Expand Up @@ -164,11 +165,31 @@ def build(self, inputs=None, outputs=None):
self.clear_edges()
outputs_from_nodes = dict()
for layer_id, layer in self.layers.items():
for input_key, input_var in layer.inputs.items():
vs = input_var
if not isinstance(vs, (list, tuple)):
vs = [vs]
for v in vs:
if isinstance(layer.inputs, dict):
for input_key, input_var in layer.inputs.items():
vs = input_var
if not isinstance(vs, (list, tuple)):
vs = [vs]
for v in vs:
assert v in outputs_from_nodes or (
inputs is not None and v in list(inputs.values())
) or (
outputs is not None and v in outputs
), "Couldn't find {} in previous layers, the layers should be make by topological sort".format(
v)
if v in outputs_from_nodes:
in_layer_id = outputs_from_nodes[v]
else:
in_layer_id = -1
if in_layer_id not in self.edges_out:
self.edges_out[in_layer_id] = list()
self.edges_out[in_layer_id].append(layer_id)

if layer_id not in self.edges_in:
self.edges_in[layer_id] = list()
self.edges_in[layer_id].append(in_layer_id)
else:
for v in layer.inputs:
assert v in outputs_from_nodes or (
inputs is not None and v in list(inputs.values())
) or (
Expand All @@ -186,6 +207,7 @@ def build(self, inputs=None, outputs=None):
if layer_id not in self.edges_in:
self.edges_in[layer_id] = list()
self.edges_in[layer_id].append(in_layer_id)

for output in layer.outputs:
outputs_from_nodes[output] = layer_id

Expand Down Expand Up @@ -496,16 +518,20 @@ def write_code(code_dir):
else:
line = ','.join(layer.outputs)
line += " = {}(".format(layer.kernel)
for k, v in layer.inputs.items():
if isinstance(v, list):
line += "{}=[{}], ".format(k, ", ".join(v))
elif isinstance(v, tuple):
line += "{}=({}), ".format(k, ", ".join(v))
else:
if k == "args":
line += v
if isinstance(layer.inputs, dict):
for k, v in layer.inputs.items():
if isinstance(v, list):
line += "{}=[{}], ".format(k, ", ".join(v))
elif isinstance(v, tuple):
line += "{}=({}), ".format(k, ", ".join(v))
else:
line += "{}={}, ".format(k, v)
if k == "args":
line += v
else:
line += "{}={}, ".format(k, v)
else:
line += "{}".format(", ".join(layer.inputs))

for k, v in layer.attrs.items():
line += "{}={}, ".format(k, v)
line = line.strip(", ")
Expand Down
194 changes: 176 additions & 18 deletions x2paddle/op_mapper/onnx2paddle/opset9/opset.py
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -62,6 +62,7 @@ def _rename_or_remove_weight(weights,
if origin_name not in weights:
raise KeyError('{} not a key in {}'.format(origin_name, weights.keys()))
if is_remove:
# TODO There may be problems when the same data is used as an argument to multiple OPs.
# remove weight
data = weights.pop(origin_name)
else:
Expand Down Expand Up @@ -250,6 +251,7 @@ def create_parameter(self, node, parameter=None):
node = parameter
dtype = node.dtype
shape = node.out_shapes[0]

if hasattr(node.weight, "shape") and len(node.weight.shape) == 0:
self.paddle_graph.add_layer(
"paddle.full",
Expand Down Expand Up @@ -387,22 +389,27 @@ def _interpolate(self, node):
**attrs)
return
elif node.layer_type == 'Upsample':
val_scales = self.graph.get_input_node(node, idx=1, copy=True)
self.paddle_graph.add_layer(
"paddle.slice",
inputs={"input": val_scales.name},
outputs=[val_scales.name],
axes=[0],
starts=[2],
ends=[4])
inputs['scale_factor'] = val_scales.name
if len(node.layer.input) == 2:
val_scales = self.graph.get_input_node(node, idx=1, copy=True)
self.paddle_graph.add_layer(
"paddle.slice",
inputs={"input": val_scales.name},
outputs=[val_scales.name],
axes=[0],
starts=[2],
ends=[4])
inputs['scale_factor'] = val_scales.name
else:
val_scales = node.get_attr('scales')[2:]

mode = node.get_attr('mode', 'nearest')
attrs.update({
"align_corners": False,
"mode": string(mode),
"align_mode": 1
})
if len(node.layer.input) == 1:
attrs["scale_factor"] = val_scales
val_x_shape = val_x.out_shapes[0]
if mode == "linear" and len(val_x_shape) == 4:
attrs["mode"] = string("bilinear")
Expand Down Expand Up @@ -682,8 +689,7 @@ def Unsqueeze(self, node):
axes = node.get_attr('axes')
if axes is None:
axes = self.graph.get_input_node(node, idx=1, copy=True)

if len(val_x.out_shapes[0]) == 0:
if len(val_x.out_shapes[0]) == 0 and len(axes) == 1 and axes[0] == 0:
if node.name:
self.paddle_graph.add_layer(
'paddle.reshape',
Expand Down Expand Up @@ -804,11 +810,19 @@ def Expand(self, node):
val_shape = self.graph.get_input_node(node, idx=1, copy=True)
val_x_dtype = val_x.dtype
name_ones = node.name + '_ones'
attr_ones = {
'shape': val_shape.name,
'dtype': string(val_x_dtype),
'fill_value': 1
}
shape_values = _const_weight_or_none(val_shape)
if shape_values is None:
attr_ones = {
'shape': val_shape.name,
'dtype': string(val_x_dtype),
'fill_value': 1
}
else:
attr_ones = {
'shape': shape_values.tolist(),
'dtype': string(val_x_dtype),
'fill_value': 1
}
self.paddle_graph.add_layer(
'paddle.full', inputs={}, outputs=[name_ones], **attr_ones)
inputs_dict = {'x': name_ones, 'y': val_x.name}
Expand Down Expand Up @@ -840,6 +854,11 @@ def Gather(self, node):
outputs=[node.name])
elif len(val_x.out_shapes[0]) > 1:
if len(indices_shape) == 0:
self.paddle_graph.add_layer(
'paddle.reshape',
inputs={"x": indices.name},
outputs=[indices.name],
shape=[-1, ])
gather_ = node.name + '_1'
self.paddle_graph.add_layer(
'paddle.gather',
Expand Down Expand Up @@ -1142,6 +1161,10 @@ def Slice(self, node):
starts = node.get_attr('starts')
ends = node.get_attr('ends')
axes = node.get_attr('axes')
output_shape = val_x.out_shapes[0]

if axes is None:
axes = [i for i in range(len(starts))]
for idx in range(len(ends)):
if ends[idx] > 2**31 - 1:
ends[idx] = 2**31 - 1
Expand Down Expand Up @@ -1182,7 +1205,6 @@ def ConstantOfShape(self, node):

@print_mapping_info
def GatherND(self, node):
print(len(node.inputs), node.inputs)
val_x = self.graph.get_input_node(node, idx=0, copy=True)
val_y = self.graph.get_input_node(node, idx=1, copy=True)
self.paddle_graph.add_layer(
Expand Down Expand Up @@ -1348,7 +1370,6 @@ def GreaterOrEqual(self, node):

@print_mapping_info
def GatherND(self, node):
print(len(node.inputs), node.inputs)
val_x = self.graph.get_input_node(node, idx=0, copy=True)
val_y = self.graph.get_input_node(node, idx=1, copy=True)
self.paddle_graph.add_layer(
Expand Down Expand Up @@ -1976,6 +1997,143 @@ def GlobalMaxPool(self, node):
outputs=layer_outputs,
output_size=output_shape[2:])

@print_mapping_info
def Neg(self, node):
import paddle
val_x = self.graph.get_input_node(node, idx=0, copy=True)
v0, v1, v2 = paddle.__version__.split('.')
if int(v0) >= 2 and int(v1) >= 2:
self.paddle_graph.add_layer(
"paddle.neg", inputs={'x': val_x.name}, outputs=[node.name])
else:
val_y = node.name + "_y"
dtype = np.dtype(val_x.dtype)
self.paddle_graph.add_layer(
"paddle.full",
inputs={},
outputs=[val_y],
dtype=string(dtype),
shape=[1],
fill_value=-1)
self.paddle_graph.add_layer(
"paddle.multiply",
inputs={'x': val_x.name,
'y': val_y},
outputs=[node.name])

@print_mapping_info
def SpaceToDepth(self, node):
val_x = self.graph.get_input_node(node, idx=0, copy=True)
blocksize = node.get_attr('blocksize')
val_x_shape = val_x.out_shapes[0]
b, c, h, w = val_x_shape
self.paddle_graph.add_layer(
'paddle.reshape',
inputs={"x": val_x.name},
outputs=[node.name],
shape=[b, c, h // blocksize, blocksize, w // blocksize, blocksize])
self.paddle_graph.add_layer(
'paddle.transpose',
inputs={"x": node.name},
outputs=[node.name],
perm=[0, 3, 5, 1, 2, 4])
self.paddle_graph.add_layer(
'paddle.reshape',
inputs={"x": node.name},
outputs=[node.name],
shape=[b, c * (blocksize**2), h // blocksize, w // blocksize])

@print_mapping_info
def GatherElements(self, node):
val_x = self.graph.get_input_node(node, idx=0, copy=True)
indices = self.graph.get_input_node(node, idx=1, copy=True)
axis = node.get_attr('axis')
val_x_shape = val_x.out_shapes[0]
indices_shape = indices.out_shapes[0]
axis = axis if axis >= 0 else axis + len(val_x_shape)
if axis == 0:
axis_perm = [i for i in range(len(val_x_shape))]
data_swaped = val_x.name
index_swaped = indices.name
else:
axis_perm = [i for i in range(len(val_x_shape))]
axis_perm[axis] = 0
axis_perm[0] = axis
data_swaped = val_x.name + "_transpose"
self.paddle_graph.add_layer(
"paddle.transpose",
inputs={'x': val_x.name},
perm=axis_perm,
outputs=[data_swaped])
index_swaped = indices.name + "_transpose"
self.paddle_graph.add_layer(
"paddle.transpose",
inputs={'x': indices.name},
perm=axis_perm,
outputs=[index_swaped])
temp = indices_shape[0]
indices_shape[0] = indices_shape[axis]
indices_shape[axis] = temp

idx_tensors_per_axis_pre = [
indices_shape[i] for i in range(len(indices_shape))
]
name_list = list()
for i in range(len(idx_tensors_per_axis_pre)):
tensor_name = val_x.name + "_meshgrid_" + str(i)
self.paddle_graph.add_layer(
kernel="paddle.linspace",
inputs={},
outputs=[tensor_name],
start=0,
stop=idx_tensors_per_axis_pre[i] - 1,
num=idx_tensors_per_axis_pre[i])
name_list.append(tensor_name)

self.paddle_graph.add_layer(
"paddle.meshgrid", inputs=name_list, outputs=name_list)

self.paddle_graph.add_layer(
"paddle.cast",
inputs={"x": index_swaped},
outputs=[index_swaped],
dtype=string("float32"))
import copy
copy_name_list = copy.copy(name_list)
copy_name_list[0] = index_swaped
new_name_list = list()
for i in range(len(copy_name_list)):
unsqueeze_name = copy_name_list[i] + "_unsqueeze"
self.paddle_graph.add_layer(
"paddle.unsqueeze",
inputs={"x": copy_name_list[i]},
axis=-1,
outputs=[unsqueeze_name])
new_name_list.append(unsqueeze_name)
concat_name = val_x.name + "_concated_layer"
self.paddle_graph.add_layer(
"paddle.concat",
inputs={'x': new_name_list},
axis=-1,
outputs=[concat_name])
self.paddle_graph.add_layer(
"paddle.cast",
inputs={"x": concat_name},
outputs=[concat_name],
dtype=string("int32"))
gather_nd_name = "gather_nd_layer"
self.paddle_graph.add_layer(
"paddle.gather_nd",
inputs={'x': data_swaped,
"index": concat_name},
outputs=[gather_nd_name])

self.paddle_graph.add_layer(
"paddle.transpose",
inputs={'x': gather_nd_name},
perm=axis_perm,
outputs=[node.name])

@print_mapping_info
def GlobalAveragePool(self, node):
op_name = name_generator("pool", self.nn_name2id)
Expand Down