def _test_add_remove(in_name: str, conv_name: str):
in1 = px.ops.input(op_name=in_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=conv_name,
input_layer=in1,
weights_layer=W,
kernel_size=[2, 2])
xgraph = XGraph()
xgraph.add(in1)
assert len(xgraph) == 1
assert len(xgraph.get_layer_names()) == 1
assert len(xgraph.get_output_names()) == 1
assert len(xgraph.get_input_names()) == 1
xgraph.add(X_conv)
assert len(xgraph) == 2
assert len(xgraph.get_layer_names()) == 2
assert len(xgraph.get_output_names()) == 1
assert len(xgraph.get_input_names()) == 1
xgraph.remove(X_conv.name)
assert len(xgraph) == 1
assert len(xgraph.get_layer_names()) == 1
assert len(xgraph.get_output_names()) == 1
assert len(xgraph.get_input_names()) == 1
def test_xgraph_add_remove(self):
xgraph = XGraph()
xgraph.add(
XLayer(name='in1', type=['Input'], bottoms=[], tops=[],
targets=[]))
assert (len(xgraph) == 1)
assert (len(xgraph.get_layer_names()) == 1)
assert (len(xgraph.get_output_names()) == 1)
assert (len(xgraph.get_input_names()) == 1)
X_conv = 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(X_conv)
assert (len(xgraph) == 2)
assert (len(xgraph.get_layer_names()) == 2)
assert (len(xgraph.get_output_names()) == 1)
assert (len(xgraph.get_input_names()) == 1)
xgraph.remove(X_conv.name)
assert (len(xgraph) == 1)
assert (len(xgraph.get_layer_names()) == 1)
assert (len(xgraph.get_output_names()) == 1)
assert (len(xgraph.get_input_names()) == 1)
def test_xgraph_add_get(self):
xgraph = XGraph()
xgraph.add(XLayer(
name='in1',
type=['Input'],
bottoms=[],
tops=[],
targets=[]
))
assert len(xgraph) == 1
assert len(xgraph.get_layer_names()) == 1
assert len(xgraph.get_output_names()) == 1
assert len(xgraph.get_input_names()) == 1
assert isinstance(xgraph.get('in1'), XLayer)
assert xgraph.get('in1').bottoms == []
assert xgraph.get('in1').tops == []
X_conv = 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(X_conv)
assert len(xgraph) == 2
assert xgraph.get_layer_names() == ['in1', 'conv1']
assert xgraph.get_output_names() == ['conv1']
assert xgraph.get_input_names() == ['in1']
assert xgraph.get('in1').tops == ['conv1']
assert isinstance(xgraph.get('conv1'), XLayer)
assert xgraph.get('conv1').bottoms == ['in1']
assert xgraph.get('conv1').tops == []
assert xgraph.get('conv1').type == ['Convolution']
np.testing.assert_array_equal(
xgraph.get('conv1').data.weights,
np.array([[[[1, 2], [3, 4]]]], dtype=np.float32)
)
np.testing.assert_array_equal(
xgraph.get('conv1').data.biases,
np.array([0., 1.], dtype=np.float32)
)
xgraph.get('conv1').data = ConvData(
weights=xgraph.get('conv1').data.weights * 2,
biases=xgraph.get('conv1').data.biases
)
np.testing.assert_array_equal(
xgraph.get('conv1').data.weights,
np.array([[[[2, 4], [6, 8]]]], dtype=np.float32)
)
xgraph.remove(X_conv.name)
assert len(xgraph) == 1
assert 'in1' in xgraph
assert len(xgraph.get_layer_names()) == 1
assert len(xgraph.get_output_names()) == 1
assert len(xgraph.get_input_names()) == 1
def test_visualize(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='conv3',
type=['Convolution'],
bottoms=['add1'],
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='pool1',
type=['Pooling'],
bottoms=['add1'],
tops=[],
targets=[]
))
xgraph.add(XLayer(
name='add2',
type=['Eltwise'],
bottoms=['conv3', 'pool1'],
tops=[],
targets=[]
))
assert len(xgraph) == 8
assert xgraph.get_layer_names() == \
['in1', 'conv1', 'in2', 'conv2', 'add1', 'conv3', 'pool1', 'add2']
out_file = os.path.join(FILE_DIR, 'viz.png')
xgraph.visualize(out_file)
os.remove(out_file)
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_xgraph_device_tagging(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=[]
))
xgraph = partition(xgraph, ['cpu'])
assert len(xgraph) == 6
xlayers = xgraph.get_layers()
assert xgraph.get_layer_names() == \
['in1', 'conv1', 'in2', 'conv2', 'add1', 'pool1']
assert set(xlayers[0].targets) == set(['cpu', 'qsim'])
assert set(xlayers[1].targets) == set(['cpu', 'qsim', 'test'])
assert set(xlayers[2].targets) == set(['cpu', 'qsim'])
assert set(xlayers[3].targets) == set(['cpu', 'qsim', 'test'])
assert set(xlayers[4].targets) == set(['cpu', 'qsim'])
assert set(xlayers[5].targets) == set(['cpu', 'qsim', 'test'])
xgraph.remove('conv1')
assert len(xgraph) == 5
xlayers = xgraph.get_layers()
assert xgraph.get_layer_names() == \
['in1', 'in2', 'conv2', 'add1', 'pool1']
assert xlayers[3].type[0] == 'Eltwise'
assert xlayers[3].bottoms == ['in1', 'conv2']
assert set(xlayers[0].targets) == set(['cpu', 'qsim'])
assert set(xlayers[1].targets) == set(['cpu', 'qsim'])
assert set(xlayers[2].targets) == set(['cpu', 'qsim', 'test'])
assert set(xlayers[3].targets) == set(['cpu', 'qsim'])
assert set(xlayers[4].targets) == set(['cpu', 'qsim', 'test'])
def test_xgraph_insert(self):
xgraph = XGraph()
xgraph.add(XLayer(
name='in1',
type=['Input'],
bottoms=[],
tops=[],
targets=[]
))
assert(len(xgraph) == 1)
assert(len(xgraph.get_layer_names()) == 1)
assert(len(xgraph.get_output_names()) == 1)
assert(len(xgraph.get_input_names()) == 1)
X_conv = 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(X_conv)
assert len(xgraph) == 2
assert len(xgraph.get_layer_names()) == 2
assert len(xgraph.get_output_names()) == 1
assert len(xgraph.get_input_names()) == 1
X_pool = XLayer(
name='pool1',
type=['Pooling'],
bottoms=['in1'],
tops=['conv1'],
targets=[]
)
xgraph.insert(X_pool)
assert len(xgraph) == 3
assert len(xgraph.get_layer_names()) == 3
assert len(xgraph.get_output_names()) == 1
assert len(xgraph.get_input_names()) == 1
xlayers = xgraph.get_layers()
assert xlayers[0].name == 'in1'
assert xlayers[0].bottoms == []
assert xlayers[0].tops == ['pool1']
assert xlayers[1].name == 'pool1'
assert xlayers[1].bottoms == ['in1']
assert xlayers[1].tops == ['conv1']
assert xlayers[2].name == 'conv1'
assert xlayers[2].bottoms == ['pool1']
assert xlayers[2].tops == []
X_in2 = XLayer(
name='in2',
type=['Input'],
bottoms=[],
tops=[],
targets=[]
)
xgraph.add(X_in2)
X_add = XLayer(
name='add1',
type=['Eltwise'],
bottoms=['conv1', 'in2'],
tops=[],
targets=[]
)
xgraph.add(X_add)
X_conv2 = 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.insert(X_conv2)
assert(len(xgraph) == 6)
assert(len(xgraph.get_layer_names()) == 6)
assert(len(xgraph.get_output_names()) == 1)
assert(len(xgraph.get_input_names()) == 2)
xlayers = xgraph.get_layers()
assert xlayers[0].name == 'in1'
assert xlayers[0].bottoms == []
assert xlayers[0].tops == ['pool1']
assert xlayers[1].name == 'pool1'
assert xlayers[1].bottoms == ['in1']
assert xlayers[1].tops == ['conv1']
assert xlayers[2].name == 'conv1'
assert xlayers[2].bottoms == ['pool1']
assert xlayers[2].tops == ['add1']
assert xlayers[3].name == 'in2'
assert xlayers[3].bottoms == []
assert xlayers[3].tops == ['conv2']
assert xlayers[4].name == 'conv2'
assert xlayers[4].bottoms == ['in2']
assert xlayers[4].tops == ['add1']
assert xlayers[5].name == 'add1'
assert xlayers[5].bottoms == ['conv1', 'conv2']
assert xlayers[5].tops == []
def _test_add_get(in_name: str, conv_name: str):
expected_in_name = px.stringify(in_name)
expected_conv_name = px.stringify(conv_name)
in1 = px.ops.input(op_name=expected_in_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=conv_name,
input_layer=in1,
weights_layer=W,
kernel_size=[2, 2])
xgraph = XGraph()
xgraph.add(in1)
assert len(xgraph) == 1
assert len(xgraph.get_layer_names()) == 1
assert len(xgraph.get_output_names()) == 1
assert len(xgraph.get_input_names()) == 1
assert isinstance(xgraph.get(in_name), XLayer)
assert xgraph.get(in_name).bottoms == []
assert xgraph.get(in_name).tops == []
xgraph.add(X_conv)
assert len(xgraph) == 2
assert xgraph.get_layer_names() == [
expected_in_name, expected_conv_name
]
assert xgraph.get_output_names() == [expected_conv_name]
assert xgraph.get_input_names() == [expected_in_name]
assert xgraph.get(in_name).tops == [expected_conv_name]
assert isinstance(xgraph.get(conv_name), XLayer)
assert xgraph.get(conv_name).bottoms == [expected_in_name]
assert xgraph.get(conv_name).tops == []
assert xgraph.get(conv_name).type == ["Convolution"]
np.testing.assert_array_equal(
xgraph.get(conv_name).data.weights,
np.array([[[[1, 2], [3, 4]]]], dtype=np.float32),
)
np.testing.assert_array_equal(
xgraph.get(conv_name).data.biases,
np.array([0.0], dtype=np.float32),
)
xgraph.get(conv_name).data = ConvData(
weights=xgraph.get(conv_name).data.weights * 2,
biases=xgraph.get(conv_name).data.biases,
)
np.testing.assert_array_equal(
xgraph.get(conv_name).data.weights,
np.array([[[[2, 4], [6, 8]]]], dtype=np.float32),
)
xgraph.remove(X_conv.name)
assert len(xgraph) == 1
assert in_name in xgraph
assert len(xgraph.get_layer_names()) == 1
assert len(xgraph.get_output_names()) == 1
assert len(xgraph.get_input_names()) == 1
def test_visualize(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([0.0, 1.0], 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([0.0, 1.0], dtype=np.float32),
),
targets=[],
))
xgraph.add(
XLayer(
name="conv3",
type=["Convolution"],
bottoms=["add1"],
tops=[],
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(
XLayer(name="pool1",
type=["Pooling"],
bottoms=["add1"],
tops=[],
targets=[]))
xgraph.add(
XLayer(
name="add2",
type=["Eltwise"],
bottoms=["conv3", "pool1"],
tops=[],
targets=[],
))
assert len(xgraph) == 8
assert xgraph.get_layer_names() == [
"in1",
"conv1",
"in2",
"conv2",
"add1",
"conv3",
"pool1",
"add2",
]
out_file = os.path.join(FILE_DIR, "viz.png")
xgraph.visualize(out_file)
os.remove(out_file)
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))
def test_xgraph_device_tagging(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([0.0, 1.0], 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([0.0, 1.0], dtype=np.float32),
),
targets=[],
))
xgraph.add(
XLayer(name="pool1",
type=["Pooling"],
bottoms=["add1"],
tops=[],
targets=[]))
xgraph = partition(xgraph, ["cpu"])
assert len(xgraph) == 6
xlayers = xgraph.get_layers()
assert xgraph.get_layer_names() == [
"in1",
"conv1",
"in2",
"conv2",
"add1",
"pool1",
]
assert set(xlayers[0].targets) == set(["cpu", "qsim"])
assert set(xlayers[1].targets) == set(["cpu", "qsim", "test"])
assert set(xlayers[2].targets) == set(["cpu", "qsim"])
assert set(xlayers[3].targets) == set(["cpu", "qsim", "test"])
assert set(xlayers[4].targets) == set(["cpu", "qsim"])
assert set(xlayers[5].targets) == set(["cpu", "qsim", "test"])
xgraph.remove("conv1")
assert len(xgraph) == 5
xlayers = xgraph.get_layers()
assert xgraph.get_layer_names() == [
"in1", "in2", "conv2", "add1", "pool1"
]
assert xlayers[3].type[0] == "Eltwise"
assert xlayers[3].bottoms == ["in1", "conv2"]
assert set(xlayers[0].targets) == set(["cpu", "qsim"])
assert set(xlayers[1].targets) == set(["cpu", "qsim"])
assert set(xlayers[2].targets) == set(["cpu", "qsim", "test"])
assert set(xlayers[3].targets) == set(["cpu", "qsim"])
assert set(xlayers[4].targets) == set(["cpu", "qsim", "test"])
def _test_xgraph_insert(
in_name: str,
in2_name: str,
conv_name: str,
pool_name: str,
add_name: str,
conv2_name: str,
):
expected_in_name = px.stringify(in_name)
expected_in2_name = px.stringify(in2_name)
expected_conv_name = px.stringify(conv_name)
expected_pool_name = px.stringify(pool_name)
expected_add_name = px.stringify(add_name)
expected_conv2_name = px.stringify(conv2_name)
in1 = px.ops.input(op_name=in_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=conv_name,
input_layer=in1,
weights_layer=W,
kernel_size=[2, 2])
# X_pool = px.ops.pool2d(op_name=pool_name, input_layer=X_conv, )
xgraph = XGraph()
xgraph.add(in1)
assert len(xgraph) == 1
assert len(xgraph.get_layer_names()) == 1
assert len(xgraph.get_output_names()) == 1
assert len(xgraph.get_input_names()) == 1
xgraph.add(X_conv)
assert len(xgraph) == 2
assert len(xgraph.get_layer_names()) == 2
assert len(xgraph.get_output_names()) == 1
assert len(xgraph.get_input_names()) == 1
X_pool = XLayer(
name=pool_name,
type=["Pooling"],
bottoms=[in_name],
tops=[conv_name],
targets=[],
)
xgraph.insert(X_pool)
assert len(xgraph) == 3
assert len(xgraph.get_layer_names()) == 3
assert len(xgraph.get_output_names()) == 1
assert len(xgraph.get_input_names()) == 1
xlayers = xgraph.get_layers()
assert xlayers[0].name == expected_in_name
assert xlayers[0].bottoms == []
assert xlayers[0].tops == [expected_pool_name]
assert xlayers[1].name == expected_pool_name
assert xlayers[1].bottoms == [expected_in_name]
assert xlayers[1].tops == [expected_conv_name]
assert xlayers[2].name == expected_conv_name
assert xlayers[2].bottoms == [expected_pool_name]
assert xlayers[2].tops == []
X_in2 = px.ops.input(op_name=in2_name, shape=[1, 2, 4, 4])
xgraph.add(X_in2)
X_add = XLayer(
name=add_name,
type=["Eltwise"],
bottoms=[conv_name, in2_name],
tops=[],
targets=[],
)
xgraph.add(X_add)
X_conv2 = 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.insert(X_conv2)
assert len(xgraph) == 6
assert len(xgraph.get_layer_names()) == 6
assert len(xgraph.get_output_names()) == 1
assert len(xgraph.get_input_names()) == 2
xlayers = xgraph.get_layers()
assert xlayers[0].name == expected_in_name
assert xlayers[0].bottoms == []
assert xlayers[0].tops == [expected_pool_name]
assert xlayers[1].name == expected_pool_name
assert xlayers[1].bottoms == [expected_in_name]
assert xlayers[1].tops == [expected_conv_name]
assert xlayers[2].name == expected_conv_name
assert xlayers[2].bottoms == [expected_pool_name]
assert xlayers[2].tops == [expected_add_name]
assert xlayers[3].name == expected_in2_name
assert xlayers[3].bottoms == []
assert xlayers[3].tops == [expected_conv2_name]
assert xlayers[4].name == expected_conv2_name
assert xlayers[4].bottoms == [expected_in2_name]
assert xlayers[4].tops == [expected_add_name]
assert xlayers[5].name == expected_add_name
assert xlayers[5].bottoms == [
expected_conv_name, expected_conv2_name
]
assert xlayers[5].tops == []
