def slice_dyn(test_shape=[2,8,10,10]):
paddle.disable_static()
data = paddle.rand(shape=test_shape, dtype='float32')
'''
slice w/ decrease_axis
'''
@paddle.jit.to_static
def test_slice_decrease_axis(x):
return x[0, 1:3, :, 5]
exportModel('slice_decrease_axis', test_slice_decrease_axis, [data], target_dir=sys.argv[1]) # output shape (2, 10)
'''
slice w/o decrease_axis
'''
@paddle.jit.to_static
def test_slice(x):
return paddle.slice(x, axes=[0,1,3], starts=[0,1,5], ends=[1,3,6])
# exportModel('slice_dyn', test_slice, [data], target_dir=sys.argv[1]) # output shape (1, 2, 10, 1) # disable it by default as this kind of test model already there. It's for comparsion only.
'''
slice w/ decrease_axis of all dims
'''
@paddle.jit.to_static
def test_slice_decrease_axis_all(x):
return x[0, 0, 0, 0]
exportModel('slice_decrease_axis_all', test_slice_decrease_axis_all, [data], target_dir=sys.argv[1]) # output shape (1,)
'''
slice w/o decrease_axis of all dims
'''
@paddle.jit.to_static
def test_slice_alldim(x):
return paddle.slice(x, axes=[0,1,2,3], starts=[0,0,0,0], ends=[1,1,1,1])
def slice_reshape(B=1, C=256, H=16, W=32):
paddle.disable_static()
data = paddle.rand(shape=[B, C, H*W], dtype='float32')
@paddle.jit.to_static
def test_model(x):
x2 = paddle.assign([-1, -1, 16, 32]).astype('int32')
node_reshape = paddle.reshape(x, [0, 256, x2[2], x2[3]])
return node_reshape
exportModel('slice_reshape', test_model, [data], target_dir=sys.argv[1])
def meshgrid():
paddle.disable_static()
@paddle.jit.to_static
def test_model(x, y, z):
return paddle.meshgrid(x, y, z)
x = paddle.randint(low=0, high=100, shape=[5])
y = paddle.randint(low=0, high=100, shape=[3])
z = paddle.randint(low=0, high=100, shape=[2])
return exportModel('meshgrid',
test_model, [x, y, z],
target_dir=sys.argv[1])
from save_model import exportModel
'''
assign w/ ouput
'''
@paddle.jit.to_static
def test_assign_output(array):
result1 = paddle.zeros(shape=[3, 2], dtype='float32')
paddle.assign(array, result1) # result1 = [[1, 1], [3 4], [1, 3]]
return result1
array = np.array([[1, 1], [3, 4], [1, 3]]).astype(np.int64)
exportModel('assign_output',
test_assign_output, [array],
target_dir=sys.argv[1])
'''
assign w/o ouput
'''
@paddle.jit.to_static
def test_assign_none(data):
result2 = paddle.assign(
data) # result2 = [[2.5, 2.5], [2.5, 2.5], [2.5, 2.5]]
return result2
data = paddle.full(shape=[3, 2], fill_value=2.5,
dtype='float32') # [[2.5, 2.5], [2.5, 2.5], [2.5, 2.5]]