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提交 ba689267 编写于 作者: S SunAhong1993
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add tf ops

上级 18cc4a40
隐藏空白更改
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Showing with 520 addition and 108 deletion
x2paddle/op_mapper/dygraph/tf2paddle/tf_op_mapper.py
浏览文件 @ ba689267
......@@ -69,13 +69,19 @@ class TFOpMapper(OpMapper):
'Add': 'paddle.add',
'AddV2': 'paddle.add',
'RealDiv': 'paddle.divide',
'DivNoNan': 'paddle.divide',
'Sub': 'fluid.layers.elementwise_sub',
'Maximum': 'paddle.maximum',
'Minimum': 'paddle.minimum',
'LessEqual': 'paddle.less_equal',
'GreaterEqual': 'paddle.greater_equal',
'Greater': 'paddle.greater_than',
'NotEqual': 'paddle.not_equal',
'Equal': 'paddle.equal',
'Mul': 'paddle.multiply',
'FloorDiv': 'paddle.floor_divide'
'FloorDiv': 'paddle.floor_divide',
'FloorMod': 'paddle.floor_mod',
'LogicalAnd': 'logical_and',
}
def __init__(self, decoder):
......@@ -185,16 +191,6 @@ class TFOpMapper(OpMapper):
outputs=[node.name])
self.paddle_graph.layers[layer_id].input_shapes = {"x": x_shape, "y": y_shape}
def NotEqual(self, node):
x = self.graph.get_input_node(node, 0)
y = self.graph.get_input_node(node, 1)
self.paddle_graph.add_layer(
kernel="paddle.not_equal",
inputs={"x": x.name,
"y": y.name},
outputs=[node.name])
def Placeholder(self, node):
shape = node.out_shapes[0]
assert len(shape) != 0, "Unknown shape of input nodes[{}].".format(
......@@ -249,6 +245,24 @@ class TFOpMapper(OpMapper):
outputs=[node.name],
perm=perm)
def Where(self, node):
if len(node.layer.input) == 1:
cond = self.graph.get_input_node(node, 0)
self.paddle_graph.add_layer(
"paddle.nonzero",
inputs={"x": cond.name},
outputs=[node.name])
else:
cond = self.graph.get_input_node(node, 0)
x = self.graph.get_input_node(node, 1)
y = self.graph.get_input_node(node, 2)
self.paddle_graph.add_layer(
"paddle.where",
inputs={"condition": cond.name,
"x": x.name,
"y": y.name},
outputs=[node.name])
def Neg(self, node):
input = self.graph.get_input_node(node, 0)
......@@ -437,6 +451,71 @@ class TFOpMapper(OpMapper):
inputs={"x": node.name},
outputs=[node.name],
perm=[0, 2, 3, 1])
def Conv3D(self, node):
op_name = name_generator("conv", self.nn_name2id)
output_name = node.name
layer_outputs = [op_name, output_name]
input = self.graph.get_input_node(node, 0)
kernel = self.graph.get_input_node(node, 1)
k_size = kernel.out_shapes[0]
strides = node.get_attr("strides")
dilations = node.get_attr("dilations")
data_format = node.get_attr("data_format").decode()
pad_mode = node.get_attr("padding").decode()
if data_format == "NDHWC":
n, d, h, w, c = input.out_shapes[0]
else:
n, c, d, h, w = input.out_shapes[0]
if kernel.layer_type == 'Const':
kernel_value = kernel.value
else:
kernel_value = self.decoder.infer_tensor(kernel, use_diff_inputs=False)
kernel_weight_name = op_name + ".weight"
self.params[kernel_weight_name] = numpy.transpose(kernel_value,
(4, 3, 0, 1, 2))
input_name = input.name
if data_format == "NDHWC":
strides = [strides[i] for i in [0, 4, 1, 2, 3]]
dilations = [dilations[i] for i in [0, 4, 1, 2, 3]]
transpose_name = gen_name("conv3d", "transpose")
self.paddle_graph.add_layer(
kernel="paddle.transpose",
inputs={"x": input.name},
outputs=[transpose_name],
perm=[0, 4, 1, 2, 3])
input_name = transpose_name
if c == -1:
attr = {"shape": [0, k_size[2], 0, 0, 0]}
self.paddle_graph.add_layer(
kernel="paddle.reshape",
inputs={"x": input_name},
outputs=[input_name],
shape=[0, k_size[2], 0, 0, 0])
self.paddle_graph.add_layer(
kernel="paddle.nn.Conv3D",
inputs={"input": input_name},
outputs=layer_outputs,
weight_attr=string(kernel_weight_name),
bias_attr=False,
in_channels=k_size[3],
out_channels=k_size[4],
kernel_size=k_size[0:3],
stride=strides[2:5],
dilation=dilations[2:5],
padding=string(pad_mode))
if data_format == "NDHWC":
self.paddle_graph.add_layer(
kernel="paddle.transpose",
inputs={"x": node.name},
outputs=[node.name],
perm=[0, 2, 3, 4, 1])
def BiasAdd(self, node):
input = self.graph.get_input_node(node, 0)
......@@ -575,6 +654,33 @@ class TFOpMapper(OpMapper):
inputs={"x": input.name},
outputs=[node.name],
pad=paddings)
def MirrorPad(self, node):
op_name = name_generator("pad", self.nn_name2id)
output_name = node.name
layer_outputs = [op_name, output_name]
input = self.graph.get_input_node(node, 0)
paddings = self.graph.get_input_node(node, 1)
assert paddings.layer_type == "Const", "Padding should be Const"
paddings = np.flip(paddings.value, 0).flatten().tolist()
dim = int(len(paddings) / 2)
transpose_name = gen_name("pad", "transpose")
self.paddle_graph.add_layer(
kernel="paddle.transpose",
inputs={"x": input.name},
outputs=[transpose_name],
perm=[0, 3, 1, 2])
self.paddle_graph.add_layer(
kernel="paddle.nn.Pad{}D".format(dim),
inputs={"x": transpose_name},
outputs=layer_outputs,
pad=new_padding)
self.paddle_graph.add_layer(
kernel="paddle.transpose",
inputs={"x": node.name},
outputs=[node.name],
perm=[0, 2, 3, 1])
def Squeeze(self, node):
input = self.graph.get_input_node(node, 0)
......@@ -592,6 +698,25 @@ class TFOpMapper(OpMapper):
kernel="paddle.shape",
inputs={"input": input_name},
outputs=[node.name])
def Size(self, node):
input = self.graph.get_input_node(node, 0)
input_name = input.name
self.paddle_graph.add_layer(
kernel="paddle.shape",
inputs={"input": input_name},
outputs=[node.name])
self.paddle_graph.add_layer(
kernel="paddle.prod",
inputs={"x": node.name},
outputs=[node.name])
def Ceil(self, node):
input = self.graph.get_input_node(node, 0)
self.paddle_graph.add_layer(
kernel="paddle.ceil",
inputs={"x": input.name},
outputs=[node.name])
def ArgMax(self, node):
input = self.graph.get_input_node(node, 0)
......@@ -603,6 +728,19 @@ class TFOpMapper(OpMapper):
inputs={"x": input.name},
outputs=[node.name],
axis=axis)
def TopKV2(self, node):
input = self.graph.get_input_node(node, 0)
k = self.graph.get_input_node(node, 1)
assert k.layer_type == "Const", "ArgMax only support Const parameter"
k = k.value
sort = node.get_attr('sorted')
self.paddle_graph.add_layer(
kernel="paddle.topk",
inputs={"x": input.name},
outputs=[node.name],
k=k,
sorted=sort)
def MatMul(self, node):
x = self.graph.get_input_node(node, 0)
......@@ -765,10 +903,13 @@ class TFOpMapper(OpMapper):
axis = 1
else:
raise Exception("Unexpected situation happend in Unpack OP")
layer_outputs = ["{}_p{}".format(node.layer_name, i) for i in range(num)]
if len(layer_outputs) == 1:
layer_outputs[0] = "[{}]".format(node.layer_name)
self.paddle_graph.add_layer(
kernel="paddle.unstack",
inputs={"x": input_name},
outputs=["{}_p{}".format(node.layer_name, i) for i in range(num)],
outputs=layer_outputs,
axis=axis,
num=num)
......@@ -776,7 +917,6 @@ class TFOpMapper(OpMapper):
inputs_list = list()
for i in range(len(node.inputs) - 1):
inputs_list.append(self.graph.get_input_node(node, i))
# inputs_list = [self.graph.get_node(name) for name in node.layer.input[:-1]]
axis = self.graph.get_input_node(node, -1)
assert axis.layer_type == "Const", "axis for ConcatV2 must be type Const"
axis = axis.value
......@@ -789,6 +929,17 @@ class TFOpMapper(OpMapper):
inputs={"x": input_names},
outputs=[node.name],
axis=axis)
def AddN(self, node):
inputs_list = list()
for i in range(len(node.inputs) - 1):
inputs_list.append(self.graph.get_input_node(node, i))
input_names = [i.name for i in inputs_list]
self.paddle_graph.add_layer(
kernel="paddle.add_n",
inputs={"inputs": input_names},
outputs=[node.name])
def StridedSlice(self, node):
input = self.graph.get_input_node(node, 0)
......@@ -894,6 +1045,20 @@ class TFOpMapper(OpMapper):
inputs={"x": node.name},
outputs=[node.name],
axis=shrink_axes)
def Prod(self, node):
input = self.graph.get_input_node(node, 0)
reduction_indices = self.graph.get_input_node(node, 1)
assert reduction_indices.layer_type == "Const"
keep_dims = node.get_attr('keep_dims')
axis = reduction_indices.value
self.paddle_graph.add_layer(
kernel="paddle.prod",
inputs={"x": input.name},
outputs=[node.layer_name],
keepdim=keep_dims,
axis=axis)
def Split(self, node):
dim = self.graph.get_input_node(node, 0)
......@@ -1177,15 +1342,15 @@ class TFOpMapper(OpMapper):
def Tile(self, node):
input = self.graph.get_input_node(node, 0)
expand_times = self.graph.get_input_node(node, 1)
repeat_times = self.graph.get_input_node(node, 1)
inputs = {"x": input.name}
attr = dict()
in_shape = input.out_shapes[0]
if expand_times.layer_type == "Const":
expand_times = expand_times.value.tolist()
attr["repeat_times"] = expand_times
if repeat_times.layer_type == "Const":
repeat_times = repeat_times.value.tolist()
attr["repeat_times"] = repeat_times
else:
inputs["repeat_times"] = expand_times.name
inputs["repeat_times"] = repeat_times.name
self.paddle_graph.add_layer(
kernel="paddle.tile",
......@@ -1206,6 +1371,7 @@ class TFOpMapper(OpMapper):
if start.layer_type == "Const":
attr["start"] = start.value
else:
inputs["start"] = start.name
if limit.dtype.startswith('float'):
dtype = limit.dtype
......@@ -1309,8 +1475,7 @@ class TFOpMapper(OpMapper):
index = self.graph.get_input_node(node, 1)
axis = self.graph.get_input_node(node, 2)
assert axis.layer_type == 'Const', "Only support Const parameter[axis]"
axis = axis.value.tolist()
assert axis == 0, "Only support axis=0 in GatherV2 OP"
axis = axis.value
index_name = index.name
if len(index.out_shapes[0]) != 1:
reshape_name = gen_name("gather", "reshape")
......@@ -1324,7 +1489,8 @@ class TFOpMapper(OpMapper):
self.paddle_graph.add_layer(
"paddle.gather",
inputs=inputs,
outputs=[node.name])
outputs=[node.name],
axis=axis)
if len(index.out_shapes[0]) != 1:
out_shape = node.out_shapes[0]
self.paddle_graph.add_layer(
......@@ -1332,6 +1498,15 @@ class TFOpMapper(OpMapper):
inputs={"x": node.name},
outputs=[node.name],
shape=out_shape)
def GatherNd(self, node):
x = self.graph.get_input_node(node, 0)
index = self.graph.get_input_node(node, 1)
inputs = {'x': x.name, 'index': index.name}
self.paddle_graph.add_layer(
"paddle.gather_nd",
inputs=inputs,
outputs=[node.name])
def ExpandDims(self, node):
x = self.graph.get_input_node(node, 0, copy=True)
......
x2paddle/op_mapper/static/tf2paddle/tf_op_mapper.py
浏览文件 @ ba689267
......@@ -12,7 +12,7 @@
# See the License for the specific language governing permissions and
# limitations under the License.
from x2paddle.decoder.tf_decoder import TFGraph
from x2paddle.decoder.tf_decoder import TFGraph, TFGraphNode
from x2paddle.core.program import PaddleGraph
from x2paddle.core.op_mapper import OpMapper
from x2paddle.core.util import *
......@@ -67,22 +67,30 @@ class TFOpMapper(OpMapper):
'Square': ['square']
}
elementwise_ops = {
'Add': 'elementwise_add',
'AddV2': 'elementwise_add',
'RealDiv': 'elementwise_div',
'Sub': 'elementwise_sub',
'Maximum': 'elementwise_max',
'Minimum': 'elementwise_min',
'LessEqual': 'less_equal',
'GreaterEqual': 'greater_equal',
'Mul': 'elementwise_mul',
'FloorDiv': 'elementwise_floordiv'
'Add': 'paddle.add',
'AddV2': 'paddle.add',
'RealDiv': 'paddle.divide',
'DivNoNan': 'paddle.divide',
'Sub': 'fluid.layers.elementwise_sub',
'Maximum': 'paddle.maximum',
'Minimum': 'paddle.minimum',
'LessEqual': 'paddle.less_equal',
'GreaterEqual': 'paddle.greater_equal',
'Greater': 'paddle.greater_than',
'NotEqual': 'paddle.not_equal',
'Equal': 'paddle.equal',
'Mul': 'paddle.multiply',
'FloorDiv': 'paddle.floor_divide',
'FloorMod': 'paddle.floor_mod',
'LogicalAnd': 'logical_and',
}
def __init__(self, decoder):
super(TFOpMapper, self).__init__()
self.decoder = decoder
self.graph = decoder.tf_graph
if not self.op_checker():
raise Exception("Model is not supported yet.")
self.params = dict()
self.paddle_graph = PaddleGraph(parent_layer=None, graph_type="static", source_type="tf")
......@@ -101,40 +109,45 @@ class TFOpMapper(OpMapper):
self.paddle_graph.inputs = self.graph.input_nodes
self.paddle_graph.outputs = self.graph.output_nodes
unsupported_ops = set()
sys.stderr.write("Total nodes: {}\n".format(len(self.graph.topo_sort)))
print("Total nodes: {}".format(
sum([
isinstance(node, TFGraphNode)
for name, node in self.graph.node_map.items()
])))
print("Nodes converting ...")
for i, node_name in enumerate(self.graph.topo_sort):
sys.stderr.write("\rConverting node {} ... ".format(i + 1))
node = self.graph.get_node(node_name)
op = node.layer_type
if op in self.directly_map_ops:
if len(unsupported_ops) > 0:
continue
self.directly_map(node)
elif op in self.elementwise_ops:
if len(unsupported_ops) > 0:
continue
self.elementwise_map(node)
elif hasattr(self, op):
if len(unsupported_ops) > 0:
continue
func = getattr(self, op)
try:
func(node)
except Exception as e:
unsupported_ops.add(op)
print("\n{}\n".format(traceback.format_exc()))
else:
func(node)
print("\nNodes converted.")
self.paddle_graph.set_name(self.graph.graph_name)
self.paddle_graph.set_parameters(self.params)
def op_checker(self):
unsupported_ops = set()
for node_name in self.graph.topo_sort:
node = self.graph.get_node(node_name)
op = node.layer_type
if not hasattr(self, op) and \
op not in self.directly_map_ops and \
op not in self.elementwise_ops:
unsupported_ops.add(op)
if len(unsupported_ops) > 0:
print("\n========= {} OPs are not supported yet ===========".format(
len(unsupported_ops)))
if len(unsupported_ops) == 0:
return True
else:
if len(unsupported_ops) > 0:
print("\n========= {} OPs are not supported yet ===========".format(
len(unsupported_ops)))
for op in unsupported_ops:
print("========== {} ============".format(op))
sys.exit(-1)
sys.stderr.write("\nDone!\n")
self.paddle_graph.set_name(self.graph.graph_name)
self.paddle_graph.set_parameters(self.params)
return False
def directly_map(self, node):
assert node.layer_type in self.directly_map_ops
......@@ -161,22 +174,12 @@ class TFOpMapper(OpMapper):
x_shape = x.out_shapes[0]
y_shape = y.out_shapes[0]
layer_id = self.paddle_graph.add_layer(
kernel="fluid.layers.{}".format(op_type),
kernel=op_type,
inputs={"x": x.name,
"y": y.name},
outputs=[node.name])
self.paddle_graph.layers[layer_id].input_shapes = {"x": x_shape, "y": y_shape}
def NotEqual(self, node):
x = self.graph.get_node(node.layer.input[0])
y = self.graph.get_node(node.layer.input[1])
self.paddle_graph.add_layer(
kernel="fluid.layers.not_equal",
inputs={"x": x.name,
"y": y.name},
outputs=[node.name])
def Placeholder(self, node):
shape = node.out_shapes[0]
assert len(shape) != 0, "Unknown shape of input nodes[{}].".format(
......@@ -249,6 +252,12 @@ class TFOpMapper(OpMapper):
inputs=inputs,
outputs=[node.name],
**attr)
if dims.layer_type != "Const":
self.paddle_graph.add_layer(
"paddle.reshape",
inputs={"x": node.name},
outputs=[node.name],
shape=node.out_shapes[0])
def DepthToSpace(self, node):
input = self.graph.get_node(node.layer.input[0])
......@@ -305,6 +314,24 @@ class TFOpMapper(OpMapper):
outputs=[node.name],
perm=[0, 2, 3, 1])
def Where(self, node):
if len(node.layer.input) == 1:
cond = self.graph.get_input_node(node, 0)
self.paddle_graph.add_layer(
"paddle.nonzero",
inputs={"x": cond.name},
outputs=[node.name])
else:
cond = self.graph.get_input_node(node, 0)
x = self.graph.get_input_node(node, 1)
y = self.graph.get_input_node(node, 2)
self.paddle_graph.add_layer(
"paddle.where",
inputs={"condition": cond.name,
"x": x.name,
"y": y.name},
outputs=[node.name])
def Neg(self, node):
input = self.graph.get_input_node(node, 0)
......@@ -417,6 +444,83 @@ class TFOpMapper(OpMapper):
inputs={"x": node.name},
outputs=[node.name],
perm=[0, 2, 3, 1])
def Conv3D(self, node):
input = self.graph.get_input_node(node, 0)
kernel = self.graph.get_input_node(node, 1)
k_size = kernel.out_shapes[0]
strides = node.get_attr("strides")
dilations = node.get_attr("dilations")
data_format = node.get_attr("data_format").decode()
pad_mode = node.get_attr("padding").decode()
if data_format == "NDHWC":
n, d, h, w, c = input.out_shapes[0]
else:
n, c, d, h, w = input.out_shapes[0]
if kernel.layer_type == 'Const':
kernel_value = kernel.value
kernel_weight_name = kernel.name.replace('/', '_')
self.paddle_graph.add_layer(
kernel="paddle.static.nn.create_parameter",
inputs={},
outputs=[kernel_weight_name],
shape=self.params[kernel_weight_name].shape,
dtype=string(str(self.params[kernel_weight_name].dtype)),
name=string(kernel_weight_name))
self.params[kernel_weight_name] = numpy.transpose(kernel_value,
(4, 3, 0, 1, 2))
else:
kernel_value = self.decoder.infer_tensor(kernel, use_diff_inputs=False)
if kernel.layer_type == 'Split':
kernel_weight_name = "{}_{}_kernel".format(node.name,
kernel.name)
else:
kernel_weight_name = kernel.name.replace('/', '_')
self.paddle_graph.add_layer(
kernel="paddle.transpose",
inputs={"x": kernel_weight_name},
outputs=[kernel_weight_name],
perm=[4, 3, 0, 1, 2])
input_name = input.name
if data_format == "NDHWC":
strides = [strides[i] for i in [0, 4, 1, 2, 3]]
dilations = [dilations[i] for i in [0, 4, 1, 2, 3]]
transpose_name = gen_name("conv3d", "transpose")
self.paddle_graph.add_layer(
kernel="paddle.transpose",
inputs={"x": input.name},
outputs=[transpose_name],
perm=[0, 4, 1, 2, 3])
input_name = transpose_name
if c == -1:
attr = {"shape": [0, k_size[2], 0, 0, 0]}
self.paddle_graph.add_layer(
kernel="paddle.reshape",
inputs={"x": input_name},
outputs=[input_name],
shape=[0, k_size[2], 0, 0, 0])
self.paddle_graph.add_layer(
kernel="paddle.nn.functional.conv3d",
inputs={"x": input_name},
outputs=[node.name],
weight=kernel_weight_name,
bias=None,
stride=strides[2:5],
dilation=dilations[2:5],
padding=string(pad_mode))
if data_format == "NDHWC":
self.paddle_graph.add_layer(
kernel="paddle.transpose",
inputs={"x": node.name},
outputs=[node.name],
perm=[0, 2, 3, 4, 1])
def BiasAdd(self, node):
input = self.graph.get_node(node.layer.input[0])
......@@ -476,36 +580,28 @@ class TFOpMapper(OpMapper):
keep_dims = node.get_attr("keep_dims")
self.paddle_graph.add_layer(
kernel="fluid.layers.reduce_mean",
inputs={"input": input.name},
kernel="paddle.mean",
inputs={"x": input.name},
outputs=[node.name],
dim=dims,
keep_dim=keep_dims)
axis=dims,
keepdim=keep_dims)
def Reshape(self, node):
input = self.graph.get_node(node.layer.input[0])
param = self.graph.get_node(node.layer.input[1])
input = self.graph.get_input_node(node, 0)
param = self.graph.get_input_node(node, 1)
input_name = input.name
if input.dtype == 'bool':
cast_name = gen_name('reshape', 'cast')
self.paddle_graph.add_layer(
kernel="fluid.layers.cast",
inputs={"x": input_name},
outputs=[cast_name],
dtype="'int32'")
input_name = cast_name
if param.layer_type == "Const":
shape = param.value.tolist()
self.paddle_graph.add_layer(
kernel="fluid.layers.reshape",
kernel="paddle.reshape",
inputs={"x": input_name},
outputs=[node.name],
shape=shape)
else:
self.paddle_graph.add_layer(
kernel="fluid.layers.reshape",
kernel="paddle.reshape",
inputs={"x": input_name,
"shape": param.name},
outputs=[node.name])
......@@ -514,17 +610,52 @@ class TFOpMapper(OpMapper):
if (out_shape > 0).any():
out_shape[out_shape < 0] = 0
self.paddle_graph.add_layer(
kernel="fluid.layers.reshape",
kernel="paddle.reshape",
inputs={"x": node.name},
outputs=[node.name],
shape=out_shape.tolist())
if input.dtype == 'bool':
self.paddle_graph.add_layer(
kernel="fluid.layers.cast",
inputs={"x": node.name},
outputs=[node.name],
dtype="'bool'")
# input = self.graph.get_node(node.layer.input[0])
# param = self.graph.get_node(node.layer.input[1])
# input_name = input.name
# if input.dtype == 'bool':
# cast_name = gen_name('reshape', 'cast')
# self.paddle_graph.add_layer(
# kernel="fluid.layers.cast",
# inputs={"x": input_name},
# outputs=[cast_name],
# dtype="'int32'")
# input_name = cast_name
# if param.layer_type == "Const":
# shape = param.value.tolist()
# self.paddle_graph.add_layer(
# kernel="fluid.layers.reshape",
# inputs={"x": input_name},
# outputs=[node.name],
# shape=shape)
# else:
# self.paddle_graph.add_layer(
# kernel="fluid.layers.reshape",
# inputs={"x": input_name,
# "shape": param.name},
# outputs=[node.name])
# if param.layer_type != "Const":
# out_shape = numpy.array(node.out_shapes[0])
# if (out_shape > 0).any():
# out_shape[out_shape < 0] = 0
# self.paddle_graph.add_layer(
# kernel="fluid.layers.reshape",
# inputs={"x": node.name},
# outputs=[node.name],
# shape=out_shape.tolist())
# if input.dtype == 'bool':
# self.paddle_graph.add_layer(
# kernel="fluid.layers.cast",
# inputs={"x": node.name},
# outputs=[node.name],
# dtype="'bool'")
def Pad(self, node):
input = self.graph.get_node(node.layer.input[0])
......@@ -558,6 +689,32 @@ class TFOpMapper(OpMapper):
inputs={"x": input.name},
outputs=[node.name],
paddings=paddings)
def MirrorPad(self, node):
op_name = name_generator("pad", self.nn_name2id)
output_name = node.name
layer_outputs = [op_name, output_name]
input = self.graph.get_input_node(node, 0)
paddings = self.graph.get_input_node(node, 1)
assert paddings.layer_type == "Const", "Padding should be Const"
paddings = np.flip(paddings.value, 0).flatten().tolist()
dim = int(len(paddings) / 2)
transpose_name = gen_name("pad", "transpose")
self.paddle_graph.add_layer(
kernel="paddle.transpose",
inputs={"x": input.name},
outputs=[transpose_name],
perm=[0, 3, 1, 2])
self.paddle_graph.add_layer(
kernel="paddle.nn.Pad{}D".format(dim),
inputs={"x": transpose_name},
outputs=layer_outputs,
pad=new_padding)
self.paddle_graph.add_layer(
kernel="paddle.transpose",
inputs={"x": node.name},
outputs=[node.name],
perm=[0, 2, 3, 1])
def Squeeze(self, node):
input = self.graph.get_node(node.layer.input[0])
......@@ -578,21 +735,36 @@ class TFOpMapper(OpMapper):
axis=axis)
def Shape(self, node):
input = self.graph.get_node(node.layer.input[0])
input = self.graph.get_input_node(node, 0)
input_name = input.name
if input.dtype == 'bool':
cast_name = gen_name('shape', 'cast')
self.paddle_graph.add_layer(
kernel="fluid.layers.cast",
inputs={"x": input.name},
outputs=[cast_name],
dtype="'int32'")
input_name = cast_name
self.paddle_graph.add_layer(
kernel="fluid.layers.shape",
kernel="paddle.shape",
inputs={"input": input_name},
outputs=[node.name])
def Size(self, node):
input = self.graph.get_input_node(node, 0)
input_name = input.name
self.paddle_graph.add_layer(
kernel="fluid.layers.size",
inputs={"input": input_name},
outputs=[node.name])
# self.paddle_graph.add_layer(
# kernel="paddle.shape",
# inputs={"input": input_name},
# outputs=[node.name])
# self.paddle_graph.add_layer(
# kernel="paddle.prod",
# inputs={"x": node.name},
# outputs=[node.name])
def Ceil(self, node):
input = self.graph.get_input_node(node, 0)
self.paddle_graph.add_layer(
kernel="paddle.ceil",
inputs={"x": input.name},
outputs=[node.name])
def ArgMax(self, node):
input = self.graph.get_node(node.layer.input[0])
axis = self.graph.get_node(node.layer.input[1])
......@@ -603,6 +775,19 @@ class TFOpMapper(OpMapper):
inputs={"x": input.name},
outputs=[node.name],
axis=axis)
def TopKV2(self, node):
input = self.graph.get_input_node(node, 0)
k = self.graph.get_input_node(node, 1)
assert k.layer_type == "Const", "ArgMax only support Const parameter"
k = k.value
sort = node.get_attr('sorted')
self.paddle_graph.add_layer(
kernel="paddle.topk",
inputs={"x": input.name},
outputs=[node.name],
k=k,
sorted=sort)
def MatMul(self, node):
x = self.graph.get_node(node.layer.input[0])
......@@ -744,10 +929,13 @@ class TFOpMapper(OpMapper):
axis = 1
else:
raise Exception("Unexpected situation happend in Unpack OP")
layer_outputs = ["{}_p{}".format(node.layer_name, i) for i in range(num)]
if len(layer_outputs) == 1:
layer_outputs[0] = "[{}]".format(node.layer_name)
self.paddle_graph.add_layer(
kernel="fluid.layers.unstack",
inputs={"x": input_name},
outputs=["{}_p{}".format(node.layer_name, i) for i in range(num)],
outputs=layer_outputs,
axis=axis,
num=num)
......@@ -780,6 +968,17 @@ class TFOpMapper(OpMapper):
inputs={"x": node.name},
outputs=[node.name],
dtype="'bool'")
def AddN(self, node):
inputs_list = list()
for i in range(len(node.inputs) - 1):
inputs_list.append(self.graph.get_input_node(node, i))
input_names = [i.name for i in inputs_list]
self.paddle_graph.add_layer(
kernel="paddle.add_n",
inputs={"inputs": input_names},
outputs=[node.name])
def StridedSlice(self, node):
input = self.graph.get_node(node.layer.input[0])
......@@ -870,6 +1069,20 @@ class TFOpMapper(OpMapper):
inputs={"input": node.name},
outputs=[node.name],
axes=shrink_axes)
def Prod(self, node):
input = self.graph.get_input_node(node, 0)
reduction_indices = self.graph.get_input_node(node, 1)
assert reduction_indices.layer_type == "Const"
keep_dims = node.get_attr('keep_dims')
axis = reduction_indices.value
self.paddle_graph.add_layer(
kernel="paddle.prod",
inputs={"x": input.name},
outputs=[node.layer_name],
keepdim=keep_dims,
axis=axis)
def Split(self, node):
dim = self.graph.get_node(node.layer.input[0])
......@@ -1128,20 +1341,27 @@ class TFOpMapper(OpMapper):
def Tile(self, node):
input = self.graph.get_node(node.layer.input[0])
expand_times = self.graph.get_node(node.layer.input[1])
repeat_times = self.graph.get_node(node.layer.input[1])
inputs = {"x": input.name}
attr = dict()
if expand_times.layer_type == "Const":
expand_times = expand_times.value.tolist()
attr["expand_times"] = expand_times
if repeat_times.layer_type == "Const":
repeat_times = repeat_times.value.tolist()
attr["repeat_times"] = repeat_times
else:
inputs["expand_times"] = expand_times.name
inputs["repeat_times"] = repeat_times.name
self.paddle_graph.add_layer(
kernel="fluid.layers.expand",
kernel="paddle.tile",
inputs=inputs,
outputs=[node.name],
**attr)
if not isinstance(repeat_times, list) and repeat_times.layer_type != "Const":
self.paddle_graph.add_layer(
kernel="paddle.reshape",
inputs={"x": node.name},
outputs=[node.name],
shape=node.out_shapes[0])
def Range(self, node):
start = self.graph.get_node(node.layer.input[0])
......@@ -1173,10 +1393,18 @@ class TFOpMapper(OpMapper):
attr["dtype"] = string(node.dtype)
self.paddle_graph.add_layer(
kernel="fluid.layers.range",
kernel="paddle.arange",
inputs=inputs,
outputs=[node.name],
**attr)
if start.layer_type != "Const" or \
limit.layer_type != "Const" or \
delta.layer_type != "Const":
self.paddle_graph.add_layer(
kernel="paddle.reshape",
inputs={"x": node.name},
outputs=[node.name],
shape=node.out_shapes[0])
def SquaredDifference(self, node):
x = self.graph.get_node(node.layer.input[0])
......@@ -1259,7 +1487,7 @@ class TFOpMapper(OpMapper):
index = self.graph.get_node(node.layer.input[1])
axis = self.graph.get_node(node.layer.input[2])
assert axis.layer_type == 'Const', "Only support Const parameter[axis]"
axis = axis.value.tolist()
axis = axis.value
assert axis == 0, "Only support axis=0 in GatherV2 OP"
index_name = index.name
if len(index.out_shapes[0]) != 1:
......@@ -1283,6 +1511,15 @@ class TFOpMapper(OpMapper):
inputs={"x": node.name},
outputs=[node.name],
shape=out_shape)
def GatherNd(self, node):
x = self.graph.get_input_node(node, 0)
index = self.graph.get_input_node(node, 1)
inputs = {'x': x.name, 'index': index.name}
self.paddle_graph.add_layer(
"paddle.gather_nd",
inputs=inputs,
outputs=[node.name])
def ExpandDims(self, node):
x = self.graph.get_node(node.layer.input[0], copy=True)
......
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