def schedule(xgraph: XGraph, target: str, **kwargs) -> XGraph:
"""
Schedule a xgraph for execution on the given target
Returns
XGraph containing only executable operations
"""
fancy_logger.banner("SCHEDULE `{}` EXECUTION GRAPH".format(target))
xgraph = target_registry.get_target_build_func(target)(xgraph.copy(),
**kwargs)
return xgraph
def test_copy(self):
xgraph = XGraph()
xgraph.add(XLayer(
name='in1',
type=['Input'],
bottoms=[],
tops=[],
targets=[]
))
xgraph.add(XLayer(
name='in2',
type=['Input'],
bottoms=[],
tops=[],
targets=[]
))
xgraph.add(XLayer(
name='conv1',
type=['Convolution'],
bottoms=['in1'],
tops=[],
data=ConvData(
weights=np.array([[[[1, 2], [3, 4]]]], dtype=np.float32),
biases=np.array([0., 1.], dtype=np.float32)
),
targets=[]
))
xgraph.add(XLayer(
name='add1',
type=['Eltwise'],
bottoms=['conv1', 'in2'],
tops=[],
targets=[]
))
xgraph.insert(XLayer(
name='conv2',
type=['Convolution'],
bottoms=['in2'],
tops=['add1'],
data=ConvData(
weights=np.array([[[[1, 2], [3, 4]]]], dtype=np.float32),
biases=np.array([0., 1.], dtype=np.float32)
),
targets=[]
))
xgraph.add(XLayer(
name='pool1',
type=['Pooling'],
bottoms=['add1'],
tops=[],
targets=[]
))
assert len(xgraph) == 6
assert xgraph.get_layer_names() == \
['in1', 'conv1', 'in2', 'conv2', 'add1', 'pool1']
xg_copy = xgraph.copy()
assert len(xg_copy) == 6
assert xg_copy.get_layer_names() == \
['in1', 'conv1', 'in2', 'conv2', 'add1', 'pool1']
xgc_layers = xg_copy.get_layers()
assert xgc_layers[1].type == ['Convolution']
assert xg_copy.get('conv1').type == ['Convolution']
xgc_layers[1].type = ['Convolution2']
assert xg_copy.get('conv1').type == ['Convolution2']
xgc_layers[1].type = ['Convolution']
assert xgc_layers[1].type == ['Convolution']
assert xg_copy.get('conv1').type == ['Convolution']
np.testing.assert_array_equal(
xgc_layers[1].data.weights,
np.array([[[[1, 2], [3, 4]]]], dtype=np.float32)
)
np.testing.assert_array_equal(
xgc_layers[1].data.biases,
np.array([0., 1.], dtype=np.float32)
)
xgraph.get('conv1').data = ConvData(
weights=xgc_layers[1].data.weights * 2,
biases=xgc_layers[1].data.biases
)
np.testing.assert_array_equal(
xgraph.get('conv1').data.weights,
np.array([[[[2, 4], [6, 8]]]], dtype=np.float32)
)
np.testing.assert_array_equal(
xgc_layers[1].data.weights,
np.array([[[[1, 2], [3, 4]]]], dtype=np.float32)
)
np.testing.assert_array_equal(
xgc_layers[1].data.biases,
np.array([0., 1.], dtype=np.float32)
)
def _test_copy(
in1_name: str,
in2_name: str,
conv1_name: str,
add_name: str,
conv2_name: str,
pool_name: str,
):
expected_in1_name = px.stringify(in1_name)
expected_in2_name = px.stringify(in2_name)
expected_conv1_name = px.stringify(conv1_name)
expected_conv2_name = px.stringify(conv2_name)
expected_pool_name = px.stringify(pool_name)
expected_add_name = px.stringify(add_name)
in1 = px.ops.input(op_name=in1_name, shape=[1, 2, 4, 4])
in2 = px.ops.input(op_name=in2_name, shape=[1, 2, 4, 4])
W = px.ops.constant(
"W", np.array([[[[1, 2], [3, 4]]]], dtype=np.float32))
X_conv = px.ops.conv2d(op_name=conv1_name,
input_layer=in1,
weights_layer=W,
kernel_size=[2, 2])
X_add = px.ops.eltwise(op_name=add_name,
lhs_layer=X_conv,
rhs_layer=in2)
X_conv2 = px.ops.conv2d(op_name=conv2_name,
input_layer=in2,
weights_layer=W,
kernel_size=[2, 2])
X_pool = px.ops.pool2d(op_name=pool_name,
input_layer=X_add,
pool_type="Avg",
pool_size=[2, 2])
xgraph = XGraph()
xgraph.add(in1)
xgraph.add(in2)
xgraph.add(X_conv)
xgraph.add(X_add)
xgraph.insert(
XLayer(
name=conv2_name,
type=["Convolution"],
bottoms=[in2_name],
tops=[add_name],
data=ConvData(
weights=np.array([[[[1, 2], [3, 4]]]],
dtype=np.float32),
biases=np.array([0.0, 1.0], dtype=np.float32),
),
targets=[],
))
xgraph.add(X_pool)
assert len(xgraph) == 6
assert xgraph.get_layer_names() == [
expected_in1_name,
expected_conv1_name,
expected_in2_name,
expected_conv2_name,
expected_add_name,
expected_pool_name,
]
xg_copy = xgraph.copy()
assert len(xg_copy) == 6
assert xg_copy.get_layer_names() == [
expected_in1_name,
expected_conv1_name,
expected_in2_name,
expected_conv2_name,
expected_add_name,
expected_pool_name,
]
xgc_layers = xg_copy.get_layers()
assert xgc_layers[1].type == ["Convolution"]
assert xg_copy.get(conv1_name).type == ["Convolution"]
xgc_layers[1].type = ["Convolution2"]
assert xg_copy.get(conv1_name).type == ["Convolution2"]
xgc_layers[1].type = ["Convolution"]
assert xgc_layers[1].type == ["Convolution"]
assert xg_copy.get(conv1_name).type == ["Convolution"]
np.testing.assert_array_equal(
xgc_layers[1].data.weights,
np.array([[[[1, 2], [3, 4]]]], dtype=np.float32),
)
np.testing.assert_array_equal(xgc_layers[1].data.biases,
np.array([0.0], dtype=np.float32))
xgraph.get(conv1_name).data = ConvData(
weights=xgc_layers[1].data.weights * 2,
biases=xgc_layers[1].data.biases)
np.testing.assert_array_equal(
xgraph.get(conv1_name).data.weights,
np.array([[[[2, 4], [6, 8]]]], dtype=np.float32),
)
np.testing.assert_array_equal(
xgc_layers[1].data.weights,
np.array([[[[1, 2], [3, 4]]]], dtype=np.float32),
)
np.testing.assert_array_equal(xgc_layers[1].data.biases,
np.array([0.0], dtype=np.float32))
