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提交 b116b11e 编写于 作者: W wjj19950828
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Merge remote-tracking branch 'upstream/develop' into Add_Fourier_transform

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docs/inference_model_convertor/demo/tensorflow2paddle.ipynb
浏览文件 @ b116b11e
......@@ -81,7 +81,7 @@
"source": [
"## 模型迁移\n",
"### 1. 获取MobileNetV1的FrozenModel\n",
"由于X2Paddle只支持TensorFlow中FrozenModel的转换，如果为纯checkpoint模型，需要参考参考X2Paddle官方[文档](https://github.com/PaddlePaddle/X2Paddle/blob/develop/docs/user_guides/export_tf_model.md)，将其转换为FrozenModel，本示例中提供的模型为FrozenModel，所以无需转换。"
"由于X2Paddle只支持TensorFlow中FrozenModel的转换，如果为纯checkpoint模型，需要参考参考X2Paddle官方[文档](https://github.com/PaddlePaddle/X2Paddle/blob/release-1.1/docs/user_guides/export_tf_model.md)，将其转换为FrozenModel，本示例中提供的模型为FrozenModel，所以无需转换。"
]
},
{
......@@ -210,4 +210,4 @@
},
"nbformat": 4,
"nbformat_minor": 4
}
}
\ No newline at end of file
docs/pytorch_project_convertor/API_docs/ops/README.md
浏览文件 @ b116b11e
......@@ -152,7 +152,7 @@
| 147 | [torch.matmul](https://pytorch.org/docs/stable/generated/torch.matmul.html?highlight=matmul#torch.matmul) | [paddle.matmul](https://www.paddlepaddle.org.cn/documentation/docs/zh/api/paddle/matmul_cn.html) | [差异对比](https://github.com/PaddlePaddle/X2Paddle/tree/develop/docs/pytorch_project_convertor/API_docs/ops/torch.matmul.md) |
| 148 | [torch.mm](https://pytorch.org/docs/stable/generated/torch.mm.html?highlight=mm#torch.mm) | [paddle.matmul](https://www.paddlepaddle.org.cn/documentation/docs/zh/api/paddle/matmul_cn.html) | [差异对比](https://github.com/PaddlePaddle/X2Paddle/tree/develop/docs/pytorch_project_convertor/API_docs/ops/torch.mm.md) |
| 149 | [torch.mv](https://pytorch.org/docs/stable/generated/torch.mv.html?highlight=mv#torch.mv) | 无对应实现 | [组合实现](https://github.com/PaddlePaddle/X2Paddle/tree/develop/docs/pytorch_project_convertor/API_docs/ops/torch.mv.md) |
| 150 | [torch.scatter](https://pytorch.org/docs/stable/generated/torch.scatter.html?highlight=scatter#torch.scatter) | [paddle.scatter_nd_add](https://www.paddlepaddle.org.cn/documentation/docs/zh/api/paddle/scatter_nd_add_cn.html) | [组合实现](https://github.com/PaddlePaddle/X2Paddle/tree/develop/docs/pytorch_project_convertor/API_docs/ops/torch.scatter.md) |
......
docs/pytorch_project_convertor/API_docs/ops/torch.scatter.md 0 → 100644
浏览文件 @ b116b11e
## torch.scatter
### [torch.scatter](https://pytorch.org/docs/stable/generated/torch.scatter.html?highlight=scatter#torch.scatter)
```python
torch.scatter(tensor,
dim,
index,
src)
```
### [paddle.scatter_nd_add](https://www.paddlepaddle.org.cn/documentation/docs/zh/api/paddle/scatter_nd_add_cn.html)
```python
paddle.scatter_nd_add(x,
index,
updates,
name=None)
```
### 参数差异
| PyTorch | PaddlePaddle | 备注 |
| ------------- | ------------ | ------------------------------------------------------ |
| tensor | x | 表示输入Tensor。 |
| dim | - | 表示在哪一个维度scatter,Paddle无此参数 |
| index | index | 输入的索引张量 |
| src | updates | 输入的更新张量 |
### 功能差异
#### 使用方式
因 torch.scatter 与 paddle.scatter_nd_add 差异较大，必须使用 paddle.flatten + paddle.meshgrid + paddle.scatter_nd_add 组合实现，看如下例子
### 代码示例
``` python
# PyTorch 示例：
src = torch.arange(1, 11).reshape((2, 5))
# 输出
# tensor([[ 1, 2, 3, 4, 5],
# [ 6, 7, 8, 9, 10]])
index = torch.tensor([[0, 1, 2], [0, 1, 4]])
torch.zeros(3, 5, dtype=src.dtype).scatter_(1, index, src)
# 输出
# tensor([[1, 2, 3, 0, 0],
# [6, 7, 0, 0, 8],
# [0, 0, 0, 0, 0]])
```
``` python
# PaddlePaddle 组合实现：
x = paddle.zeros([3, 5], dtype="int64")
updates = paddle.arange(1, 11).reshape([2,5])
# 输出
# Tensor(shape=[2, 5], dtype=int64, place=CUDAPlace(0), stop_gradient=True,
# [[1 , 2 , 3 , 4 , 5 ],
# [6 , 7 , 8 , 9 , 10]])
index = paddle.to_tensor([[0, 1, 2], [0, 1, 4]])
i, j = index.shape
grid_x , grid_y = paddle.meshgrid(paddle.arange(i), paddle.arange(j))
# 若 PyTorch 的 dim 取 0
# index = paddle.stack([index.flatten(), grid_y.flatten()], axis=1)
# 若 PyTorch 的 dim 取 1
index = paddle.stack([grid_x.flatten(), index.flatten()], axis=1)
# PaddlePaddle updates 的 shape 大小必须与 index 对应
updates_index = paddle.stack([grid_x.flatten(), grid_y.flatten()], axis=1)
updates = paddle.gather_nd(updates, index=updates_index)
paddle.scatter_nd_add(x, index, updates)
# 输出
# Tensor(shape=[3, 5], dtype=int64, place=CUDAPlace(0), stop_gradient=True,
# [[1, 2, 3, 0, 0],
# [6, 7, 0, 0, 8],
# [0, 0, 0, 0, 0]])
```
x2paddle/core/program.py 100644 → 100755
浏览文件 @ b116b11e
......@@ -27,22 +27,23 @@ from x2paddle.core.util import *
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.
......@@ -164,11 +165,31 @@ class PaddleGraph(object):
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 (
......@@ -186,6 +207,7 @@ class PaddleGraph(object):
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
......@@ -496,16 +518,20 @@ class PaddleGraph(object):
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(", ")
......@@ -532,9 +558,9 @@ class PaddleGraph(object):
paddle.save(self.parameters, save_path)
def dygraph2static(self, save_dir, input_shapes=[], input_types=[]):
sepc_list = list()
spec_list = list()
for i, name in enumerate(self.inputs):
sepc_list.append(
spec_list.append(
paddle.static.InputSpec(
shape=input_shapes[i], name=name, dtype=input_types[i]))
path = osp.abspath(save_dir)
......@@ -548,7 +574,7 @@ class PaddleGraph(object):
else:
model.set_dict(restore)
model.eval()
static_model = paddle.jit.to_static(model, input_spec=sepc_list)
static_model = paddle.jit.to_static(model, input_spec=spec_list)
try:
paddle.jit.save(static_model,
osp.join(save_dir, "inference_model/model"))
......
x2paddle/decoder/onnx_decoder.py
浏览文件 @ b116b11e
......@@ -583,6 +583,9 @@ class ONNXDecoder(object):
item.name = self.make_variable_name(item.name)
for node in graph.node:
node.name = node.output[0]
if ":" in node.name and len(
node.output) > 1 and node.op_type != "LSTM":
node.name = node.name.split(':')[0]
node.name = self.make_variable_name(node.name)
for i in range(len(node.input)):
if node.input[i] == '':
......
x2paddle/op_mapper/caffe2paddle/caffe_op_mapper.py
浏览文件 @ b116b11e
......@@ -966,11 +966,12 @@ class CaffeOpMapper():
inputs={"x": input.name},
outputs=[node.layer_name],
**layer_attrs)
self.paddle_graph.add_layer(
"paddle.pow",
inputs={"x": node.layer_name},
outputs=[node.layer_name],
exponent=params.power)
if params.power != 1:
self.paddle_graph.add_layer(
"paddle.pow",
inputs={"x": node.layer_name,
"y": params.power},
outputs=[node.layer_name])
def Reduction(self, node):
assert len(
......
x2paddle/op_mapper/onnx2paddle/opset9/opset.py
浏览文件 @ b116b11e
......@@ -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:
......@@ -169,6 +170,8 @@ class OpSet9():
'Floor': ['paddle.floor'],
'Abs': ['paddle.abs'],
'Erf': ['paddle.erf'],
'Sin': ['paddle.sin'],
'Cos': ['paddle.cos'],
}
def __init__(self, decoder, paddle_graph):
......@@ -248,6 +251,7 @@ class OpSet9():
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",
......@@ -302,6 +306,7 @@ class OpSet9():
elif len(node.layer.input) == 4:
# opset 11
val_sizes = self.graph.get_input_node(node, idx=3, copy=True)
size_values = _const_weight_or_none(val_sizes)
val_x_shape = val_x.out_shapes[0]
if len(val_x_shape) == 3:
var_n, var_hw = val_sizes.name + '_n', val_sizes.name + '_hw'
......@@ -347,23 +352,26 @@ class OpSet9():
outputs=[node.name],
axis=0)
else:
var_nc, var_hw = val_sizes.name + '_nc', val_sizes.name + '_hw'
self.paddle_graph.add_layer(
'paddle.split',
inputs={"x": val_sizes.name},
outputs=[var_nc, var_hw],
num_or_sections=[2, 2],
axis=0)
self.paddle_graph.add_layer(
"paddle.cast",
inputs={"x": var_hw},
outputs=[var_hw],
dtype=string('int32'))
inputs['size'] = var_hw
attrs = {
if size_values is not None:
attrs["size"] = [size_values[2], size_values[3]]
else:
var_nc, var_hw = val_sizes.name + '_nc', val_sizes.name + '_hw'
self.paddle_graph.add_layer(
'paddle.split',
inputs={"x": val_sizes.name},
outputs=[var_nc, var_hw],
num_or_sections=[2, 2],
axis=0)
self.paddle_graph.add_layer(
"paddle.cast",
inputs={"x": var_hw},
outputs=[var_hw],
dtype=string('int32'))
inputs['size'] = var_hw
attrs.update({
"align_corners": False,
"mode": string(node.get_attr('mode', 'nearest'))
}
})
mode = node.get_attr('mode', 'nearest')
if mode == "linear":
attrs["mode"] = string("bilinear")
......@@ -381,15 +389,18 @@ class OpSet9():
**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({
......@@ -397,6 +408,8 @@ class OpSet9():
"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")
......@@ -676,8 +689,7 @@ class OpSet9():
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',
......@@ -798,11 +810,19 @@ class OpSet9():
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}
......@@ -834,6 +854,11 @@ class OpSet9():
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',
......@@ -1136,6 +1161,10 @@ class OpSet9():
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
......@@ -1176,7 +1205,6 @@ class OpSet9():
@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(
......@@ -1342,7 +1370,6 @@ class OpSet9():
@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(
......@@ -1366,8 +1393,6 @@ class OpSet9():
val_x = self.graph.get_input_node(node, idx=0, copy=True)
paddle_op = 'split'
split = node.get_attr('split')
if split is None:
split = len(node.outputs)
axis = node.get_attr('axis', 0)
if split is None:
split_num = len(node.layer.output)
......@@ -1972,6 +1997,143 @@ class OpSet9():
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)
......@@ -2126,14 +2288,35 @@ class OpSet9():
paddings, var_x = self._pad_if_asymmetric(node, pads, val_x)
output_size = [0, 0]
if len(output_size) != 0:
paddings = [0] * 4
total_paddings = list()
total_paddings.append((val_x.out_shapes[0][2] - 1) * strides[
0] + dilations[0] * (kernel_shape[0] - 1) + 1 + out_padding[0] -
output_size[0])
total_paddings.append((val_x.out_shapes[0][3] - 1) * strides[
1] + dilations[1] * (kernel_shape[1] - 1) + 1 + out_padding[1] -
output_size[1])
if auto_pad == "SAME_UPPER":
for i in range(len(total_paddings)):
paddings[2 * i] = total_paddings[0] - total_paddings[0] // 2
paddings[2 * i + 1] = total_paddings[0] // 2
else:
for i in range(len(total_paddings)):
paddings[2 * i] = total_paddings[0] // 2
paddings[2 * i + 1] = total_paddings[0] - total_paddings[
0] // 2
else:
output_size = [0, 0]
output_size[0] = (val_x.out_shapes[0][2] - 1
) * strides[0] - 2 * paddings[0] + dilations[0] * (
kernel_shape[0] - 1) + 1 + out_padding[0]
output_size[1] = (val_x.out_shapes[0][3] - 1
) * strides[1] - 2 * paddings[1] + dilations[1] * (
kernel_shape[1] - 1) + 1 + out_padding[1]
output_size[0] = (
val_x.out_shapes[0][2] - 1
) * strides[0] - 2 * paddings[0] + dilations[0] * (
kernel_shape[0] - 1) + 1 + out_padding[0]
output_size[1] = (
val_x.out_shapes[0][3] - 1
) * strides[1] - 2 * paddings[1] + dilations[1] * (
kernel_shape[1] - 1) + 1 + out_padding[1]
# Conv2DTranspose缺少output_size，只能在forward里头传进output_size
inputs_dict = {'x': val_x if isinstance(val_x, str) else val_x.name}
......@@ -2176,6 +2359,8 @@ class OpSet9():
if val_b is not None:
_rename_or_remove_weight(self.weights, val_b.name,
op_name + '.bias')
else:
layer_attrs["bias_attr"] = False
self.paddle_graph.add_layer(
kernel=paddle_op,
inputs=inputs_dict,
......
x2paddle/op_mapper/pytorch2paddle/aten.py
浏览文件 @ b116b11e
......@@ -1315,8 +1315,10 @@ def aten__convolution(mapper, graph, node):
weights = mapper.pytorch_params[inputs_name[1]]
if len(weights.shape) == 3:
op_name = name_generator("conv1d", mapper.nn_name2id)
else:
elif len(weights.shape) == 4:
op_name = name_generator("conv2d", mapper.nn_name2id)
else:
op_name = name_generator("conv3d", mapper.nn_name2id)
output_name = mapper._get_outputs_name(node)[0]
layer_outputs = [op_name, output_name]
layer_inputs = {}
......@@ -1364,7 +1366,22 @@ def aten__convolution(mapper, graph, node):
else:
layer_attrs['in_channels'] = weights.shape[1] * mapper.attrs[
inputs_name[8]]
if len(weights.shape) == 4:
if len(weights.shape) == 3:
if mapper.attrs[inputs_name[6]]:
graph.add_layer(
"paddle.nn.Conv1DTranspose",
inputs=layer_inputs,
outputs=layer_outputs,
scope_name=scope_name,
**layer_attrs)
else:
graph.add_layer(
"paddle.nn.Conv1D",
inputs=layer_inputs,
outputs=layer_outputs,
scope_name=scope_name,
**layer_attrs)
elif len(weights.shape) == 4:
if mapper.attrs[inputs_name[6]]:
graph.add_layer(
"paddle.nn.Conv2DTranspose",
......@@ -1382,14 +1399,14 @@ def aten__convolution(mapper, graph, node):
else:
if mapper.attrs[inputs_name[6]]:
graph.add_layer(
"paddle.nn.Conv1DTranspose",
"paddle.nn.Conv3DTranspose",
inputs=layer_inputs,
outputs=layer_outputs,
scope_name=scope_name,
**layer_attrs)
else:
graph.add_layer(
"paddle.nn.Conv1D",
"paddle.nn.Conv3D",
inputs=layer_inputs,
outputs=layer_outputs,
scope_name=scope_name,
......
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