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add test case and fix bug #6

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merged 4 commits into from
Aug 13, 2021
Merged

add test case and fix bug #6

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4520515
add test case and fix bug
heliqi Aug 11, 2021
1d599c1
delete import pandas
heliqi Aug 11, 2021
75ef19b
add paddlepaddle tests
heliqi Aug 13, 2021
67ca20d
modify the variable name of convert_reshape
heliqi Aug 13, 2021
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93 changes: 67 additions & 26 deletions python/tvm/relay/frontend/paddlepaddle.py
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Original file line number Diff line number Diff line change
Expand Up @@ -68,10 +68,13 @@ def convert_arg_max(g, op, block):
axis = op.attr('axis')
keepdims = op.attr('keepdims')
flatten = op.attr('flatten')
assert not flatten, "Only flatten==True is supported for PaddlePaddle's arg_max"

x = g.get_node(x.input('X')[0])
out = _op.argmax(x, axis=axis, keepdims=keepdims)
x = g.get_node(op.input('X')[0])
if axis is None or flatten:
x = _op.reshape(x, [-1])
out = _op.argmax(x, axis=None, keepdims=True)
else:
out = _op.argmax(x, axis=axis, keepdims=keepdims)
g.add_node(op.output('Out')[0], out)


Expand Down Expand Up @@ -166,9 +169,9 @@ def convert_cumsum(g, op, block):
flatten = op.attr('flatten')
reverse = op.attr('reverse')

assert not flatten, "Only flatten==False is supported for PaddlePaddle's cumsum"

x = g.get_node(op.input('X')[0])
if axis is None or flatten:
x = _op.reshape(x, [-1])
if reverse:
x = _op.reverse(x, axis=axis)
out = _op.cumsum(x, axis=axis, exclusive=exclusive)
Expand Down Expand Up @@ -281,6 +284,12 @@ def convert_fill_constant(g, op, block):
shape = block.var(op.output('Out')[0]).shape
dtype = block.var(op.output('Out')[0]).dtype
dtype = str(dtype).strip().split('.')[1]
if op.input('ValueTensor'):
shape = g.get_node(op.input('ValueTensor')[0])
shape = infer_value(shape, g.get_params()).numpy()
if op.input('ShapeTensor'):
shape = g.get_node(op.input('ShapeTensor')[0])
shape = infer_value(shape, g.get_params()).numpy()
value = np.full(shape, value, dtype)
out = _expr.const(value.astype(dtype)).astype(dtype)
g.add_node(op.output('Out')[0], out)
Expand Down Expand Up @@ -333,8 +342,22 @@ def convert_layer_norm(g, op, block):
begin_norm_axis = op.attr('begin_norm_axis')
epsilon = op.attr('epsilon')
x = g.get_node(op.input('X')[0])
bias = g.get_node(op.input('Bias')[0])
scale = g.get_node(op.input('Scale')[0])
bias_input = op.input('Bias')
scale_input = op.input('Scale')

x_shape = infer_shape(x)
assert begin_norm_axis == -1 or begin_norm_axis == len(x_shape) - 1, "Support only normalization over last one dimension."

if bias_input:
bias = g.get_node(bias_input[0])
else:
bias = _expr.const(np.zeros(x_shape[begin_norm_axis]))

if scale_input:
scale = g.get_node(scale_input[0])
else:
scale = _expr.const(np.ones(x_shape[begin_norm_axis]))

out = _op.nn.layer_norm(x,
gamma=scale,
beta=bias,
Expand All @@ -351,7 +374,7 @@ def convert_leaky_relu(g, op, block):
alpha = op.attr('alpha')
x = g.get_node(op.input('X')[0])
out = _op.nn.leaky_relu(x, alpha=alpha)
g.add_node(op.output('Out')[0])
g.add_node(op.output('Out')[0], out)


def convert_lookup_table(g, op, block):
Expand Down Expand Up @@ -540,7 +563,7 @@ def convert_pool2d(g, op, block):
if global_pooling:
adaptive = True
ksize = [1, 1]

input = g.get_node(op.input('X')[0])
in_h, in_w = infer_shape(input)[2:]

Expand Down Expand Up @@ -587,8 +610,27 @@ def convert_pool2d(g, op, block):
def convert_reshape(g, op, block):
"""Operator converter for reshape."""

shape = op.attr('shape')
out = _op.reshape(g.get_node(op.input('X')[0]), shape)
input_shape = op.input('Shape')
input_shape_tensor = op.input('ShapeTensor')
data = g.get_node(op.input('X')[0])
if input_shape:
new_shape = g.get_node(input_shape[0])
elif input_shape_tensor:
tmp_shape = []
for shape_name in input_shape_tensor:
shape = g.get_node(shape_name)
if len(infer_shape(shape)) == 0:
shape = _op.reshape(shape, [-1])
if isinstance(shape, _expr.Constant):
tmp_shape.append(shape)
elif isinstance(shape, _expr.Expr):
tmp_shape.append(shape)
else:
tmp_shape.append(_expr.const(np.array(shape).astype('int64')))
new_shape = _op.concatenate(tmp_shape, axis=0)
else:
new_shape = op.attr('shape')
out = _op.reshape(data, new_shape)
g.add_node(op.output('Out')[0], out)


Expand Down Expand Up @@ -627,7 +669,7 @@ def convert_shape(g, op, block):

def convert_slice(g, op, block):
"""Operator converter for slice."""
def parameter_process(starts, ends, axes):
def parameter_process(starts, ends, axes, dshape):
new_axes = []
new_starts = []
new_ends = []
Expand All @@ -640,22 +682,29 @@ def parameter_process(starts, ends, axes):
pop_index += 1
else:
new_starts.append(0)
new_ends.append(np.iinfo(np.int32).max)
new_ends.append(dshape[i])
return new_starts, new_ends, new_axes

data = g.get_node(op.input('Input')[0])
dshape = infer_shape(data)
starts = op.attr('starts')
ends = op.attr('ends')
axes = op.attr('axes')
decrease_axis = op.attr('decrease_axis')
if isinstance(starts, int):
starts = [starts]
if isinstance(ends, int):
ends = [ends]
if isinstance(axes, int):
axes = [axes]
starts, ends, axes = parameter_process(starts, ends, axes)
out = _op.strided_slice(g.get_node(op.input('Input')[0]),
if isinstance(decrease_axis, int):
decrease_axis = [decrease_axis]
starts, ends, axes = parameter_process(starts, ends, axes, dshape)
out = _op.strided_slice(data,
begin=starts,
end=ends)
if decrease_axis:
out = _op.squeeze(out, axis=decrease_axis)
g.add_node(op.output('Out')[0], out)


Expand All @@ -672,15 +721,6 @@ def convert_softmax(g, op, block):
out = e / _op.sum(e, axis, keepdims=True)
g.add_node(op.output('Out')[0], out)


def convert_transpose(g, op, block):
"""Operator converter for transpose."""

perm = op.attr('axis')
out = _op.transpose(g.get_node(op.input('X')[0]), axes=perm)
g.add_node(op.output('Out')[0], out)


def convert_unsqueeze(g, op, block):
"""Operator converter for unsqueeze."""

Expand Down Expand Up @@ -727,7 +767,6 @@ def convert_unsqueeze(g, op, block):
'slice': convert_slice,
'softmax': convert_softmax,
'tanh': convert_activation,
'transpose2': convert_transpose,
'unsqueeze2': convert_unsqueeze,
}

Expand All @@ -747,7 +786,9 @@ def get_node(self, name):
def add_node(self, name, node):
self.nodes[name] = fold_constant(node)

def get_params(self, name):
def get_params(self, name=None):
if name is None:
return self.params
assert name in self.params
return self.params[name]

Expand Down
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