def test_concatenate():
x1 = sym.Variable("x", shape=(10, 20))
x2 = sym.Variable("y", shape=(10, 30))
z = sym.concatenate(x1, x2, name="concat")
g, ldict = correct_layout(z, {"x": "HW", "y": "HW"})
assert (ldict["x"][0] == "HW")
assert (ldict["y"][0] == "HW")
assert (ldict["concat"][0] == "HW")
# second pass will insert layout transform
_, ldict = correct_layout(g, {"x": "HW16w", "y": "HW16w"})
assert (ldict["x"][0] == "HW16w")
assert (ldict["y"][0] == "HW16w")
assert (ldict["concat"][0] == "HW16w")
x1 = sym.Variable("x", shape=(10, 20, 60))
x2 = sym.Variable("y", shape=(10, 20, 40))
z = sym.concatenate(x1, x2, axis=2, name="concat")
g, ldict = correct_layout(z, {"x": "H20wW", "y": "H20wW"})
assert (ldict["x"][0] == "H20wW")
assert (ldict["y"][0] == "H20wW")
assert (ldict["concat"][0] == "H20wW")
# second pass will insert layout transform
_, ldict = correct_layout(g, {"x": "HW", "y": "HW"})
assert (ldict["x_H20wW"][0] == "H20wW")
assert (ldict["x_H20wW"][0] == "H20wW")
assert (ldict["concat"][0] == "H20wW")
def test_concatenate():
x1 = sym.Variable("x", shape=(10, 20))
x2 = sym.Variable("y", shape=(10, 30))
z = sym.concatenate(x1, x2, name="concat")
g, ldict = correct_layout(z, {"x": "HW", "y": "HW"})
assert(ldict["x"][0] == "HW")
assert(ldict["y"][0] == "HW")
assert(ldict["concat"][0] == "HW")
# second pass will insert layout transform
_, ldict = correct_layout(g, {"x": "HW16w", "y": "HW16w"})
assert(ldict["x"][0] == "HW16w")
assert(ldict["y"][0] == "HW16w")
assert(ldict["concat"][0] == "HW16w")
x1 = sym.Variable("x", shape=(10, 20, 60))
x2 = sym.Variable("y", shape=(10, 20, 40))
z = sym.concatenate(x1, x2, axis=2, name="concat")
g, ldict = correct_layout(z, {"x": "H20wW", "y": "H20wW"})
assert(ldict["x"][0] == "H20wW")
assert(ldict["y"][0] == "H20wW")
assert(ldict["concat"][0] == "H20wW")
# second pass will insert layout transform
_, ldict = correct_layout(g, {"x": "HW", "y": "HW"})
assert(ldict["x_H20wW"][0] == "H20wW")
assert(ldict["x_H20wW"][0] == "H20wW")
assert(ldict["concat"][0] == "H20wW")
def test_concatenate():
x1 = sym.Variable("x", shape=(10, 20))
x2 = sym.Variable("y", shape=(10, 30))
z = sym.concatenate(x1, x2, name="concat")
sdict = infer_shape(z)
assert(sdict["concat"][0] == [10, 50])
z = sym.concatenate(x1, x1, axis=0, name="concat")
sdict = infer_shape(z)
assert(sdict["concat"][0] == [20, 20])
def test_concatenate():
x1 = sym.Variable("x", shape=(10, 20))
x2 = sym.Variable("y", shape=(10, 30))
z = sym.concatenate(x1, x2, name="concat")
sdict = infer_shape(z)
assert(sdict["concat"][0] == [10, 50])
z = sym.concatenate(x1, x1, axis=0, name="concat")
sdict = infer_shape(z)
assert(sdict["concat"][0] == [20, 20])
def test_concatenate_conv2d():
ch = 3
size = 8
data = sym.Variable(name="data")
concat = sym.concatenate(data, data, axis=1)
conv = sym.conv2d(data=concat,
kernel_size=(1, 1),
channels=ch * 2,
use_bias=False,
name="conv")
net = sym.elemwise_add(concat, conv)
dtype = "float32"
dshape = (1, ch, size, size)
kshape = (ch * 2, ch * 2, 1, 1)
oshape = (1, ch * 2, size, size)
shape_dict = {"data": dshape}
for target, ctx in ctx_list():
graph, lib, _ = nnvm.compiler.build(net, target, shape_dict)
# data, conv weight, conv op, concat
assert graph.index.num_nodes == 4
data = tvm.nd.array(np.random.uniform(size=dshape).astype(dtype))
kernel = tvm.nd.array(np.random.uniform(size=kshape).astype(dtype))
m = graph_runtime.create(graph, lib, ctx)
m.run(data=data, conv_weight=kernel)
# get output
out = m.get_output(0, tvm.nd.empty(oshape, dtype))
concat = np.concatenate((data.asnumpy(), data.asnumpy()), axis=1)
conv = topi.testing.conv2d_nchw_python(concat, kernel.asnumpy(),
(1, 1), 'SAME')
ref = concat + conv
np.testing.assert_allclose(out.asnumpy(), ref, rtol=1e-5)
def test_concatenate_conv2d():
ch = 3
size = 8
data = sym.Variable(name="data")
concat = sym.concatenate(data, data, axis=1)
conv = sym.conv2d(data=concat, kernel_size=(1,1), channels=ch*2, use_bias=False, name="conv")
net = sym.elemwise_add(concat, conv)
dtype="float32"
dshape = (1, ch, size, size)
kshape = (ch*2, ch*2, 1, 1)
oshape = (1, ch*2, size, size)
shape_dict = {"data": dshape}
for target, ctx in ctx_list():
graph, lib, _ = nnvm.compiler.build(net, target, shape_dict)
# data, conv weight, conv op, concat
assert graph.index.num_nodes == 4
data = tvm.nd.array(np.random.uniform(size=dshape).astype(dtype))
kernel = tvm.nd.array(np.random.uniform(size=kshape).astype(dtype))
m = graph_runtime.create(graph, lib, ctx)
m.run(data=data, conv_weight=kernel)
# get output
out = m.get_output(0, tvm.nd.empty(oshape, dtype))
concat = np.concatenate((data.asnumpy(), data.asnumpy()), axis=1)
conv = topi.testing.conv2d_nchw_python(
concat, kernel.asnumpy(), (1,1), 'SAME')
ref = concat + conv
tvm.testing.assert_allclose(out.asnumpy(), ref, rtol=1e-5)
def verify_concatenate(ishape, axis):
x = [sym.Variable("x%d" % i, shape=ishape[i]) for i in range(len(ishape))]
y = sym.concatenate(*x, axis=axis) + 1
def forward(**kwargs):
return np.concatenate(list(kwargs.values()), axis=axis) + 1
check_function(y, forward)
def verify_concatenate(ishape, axis):
x = [sym.Variable("x%d" % i, shape=ishape[i]) for i in range(len(ishape))]
y = sym.concatenate(*x, axis=axis) + 1
def forward(**kwargs):
return np.concatenate(list(kwargs.values()), axis=axis) + 1
check_function(y, forward)
def _make_fire(net, squeeze_channels, expand1x1_channels, expand3x3_channels):
net = _make_fire_conv(net, squeeze_channels, 1, 0)
left = _make_fire_conv(net, expand1x1_channels, 1, 0)
right = _make_fire_conv(net, expand3x3_channels, 3, 1)
# NOTE : Assume NCHW layout here
net = sym.concatenate(left, right, axis=1)
return net
def test_concatenate_split():
x = sym.Variable('x')
y = sym.Variable('y')
y = sym.concatenate(x, y)
assert y.list_input_names() == ['x', 'y']
z = sym.split(y, indices_or_sections=10)
assert len(z.list_output_names()) == 10
z = sym.split(y, indices_or_sections=[10, 20])
assert len(z.list_output_names()) == 3
def _make_fire(net, squeeze_channels, expand1x1_channels, expand3x3_channels):
net = _make_fire_conv(net, squeeze_channels, 1, 0)
left = _make_fire_conv(net, expand1x1_channels, 1, 0)
right = _make_fire_conv(net, expand3x3_channels, 3, 1)
# NOTE : Assume NCHW layout here
net = sym.concatenate(left, right, axis=1)
return net
def test_concatenate_split():
x = sym.Variable('x')
y = sym.Variable('y')
y = sym.concatenate(x, y)
assert y.list_input_names() == ['x', 'y']
z = sym.split(y, indices_or_sections=10)
assert len(z.list_output_names()) == 10
z = sym.split(y, indices_or_sections=[10, 20])
assert len(z.list_output_names()) == 3
def verify_concatenate(ishape, axis):
x = [sym.Variable("x%d" % i) for i in range(len(ishape))]
y = sym.concatenate(*x, axis=axis) + 1
def forward(**kwargs):
return np.concatenate(tuple(kwargs.values()), axis=axis) + 1
dtype = "float32"
inputs = dict(("x%d" % i, (ishape[i], )) for (i, input) in enumerate(x))
helper(y, inputs, dtype, forward)
def Inception7A(data, num_1x1, num_3x3_red, num_3x3_1, num_3x3_2,
num_5x5_red, num_5x5, pool, proj, name):
# num_1x1 = 64
# num_3x3_red = 64
# num_3x3_1 = 96
# num_3x3_2 = 96
# num_5x5_red = 48
# num_5x5 = 64
tower_1x1 = Conv(data, num_1x1, name=('%s_conv' % name))
tower_5x5 = Conv(data,
num_5x5_red,
name=('%s_tower' % name),
suffix='_conv')
tower_5x5 = Conv(tower_5x5,
num_5x5,
kernel=(5, 5),
pad=(2, 2),
name=('%s_tower' % name),
suffix='_conv_1')
tower_3x3 = Conv(data,
num_3x3_red,
name=('%s_tower_1' % name),
suffix="_conv")
tower_3x3 = Conv(tower_3x3,
num_3x3_1,
kernel=(3, 3),
pad=(1, 1),
name=('%s_tower_1' % name),
suffix='_conv_1')
tower_3x3 = Conv(tower_3x3,
num_3x3_2,
kernel=(3, 3),
pad=(1, 1),
name=('%s_tower_1' % name),
suffix='_conv_2')
pooling = Pooling(data,
kernel=(3, 3),
stride=(1, 1),
pad=(1, 1),
pool_type=pool,
name=('%s_pool_%s_pool' % (pool, name)))
cproj = Conv(pooling, proj, name=('%s_tower_2' % name), suffix='_conv')
concat = sym.concatenate(*[tower_1x1, tower_5x5, tower_3x3, cproj],
axis=3,
name='ch_concat_%s_chconcat' % name)
return concat
def Inception7D(data, num_3x3_red, num_3x3, num_d7_3x3_red, num_d7_1,
num_d7_2, num_d7_3x3, pool, name):
tower_3x3 = Conv(data=data,
num_filter=num_3x3_red,
name=('%s_tower' % name),
suffix='_conv')
tower_3x3 = Conv(data=tower_3x3,
num_filter=num_3x3,
kernel=(3, 3),
pad=(0, 0),
stride=(2, 2),
name=('%s_tower' % name),
suffix='_conv_1')
tower_d7_3x3 = Conv(data=data,
num_filter=num_d7_3x3_red,
name=('%s_tower_1' % name),
suffix='_conv')
tower_d7_3x3 = Conv(data=tower_d7_3x3,
num_filter=num_d7_1,
kernel=(1, 7),
pad=(0, 3),
name=('%s_tower_1' % name),
suffix='_conv_1')
tower_d7_3x3 = Conv(data=tower_d7_3x3,
num_filter=num_d7_2,
kernel=(7, 1),
pad=(3, 0),
name=('%s_tower_1' % name),
suffix='_conv_2')
tower_d7_3x3 = Conv(data=tower_d7_3x3,
num_filter=num_d7_3x3,
kernel=(3, 3),
stride=(2, 2),
name=('%s_tower_1' % name),
suffix='_conv_3')
pooling = Pooling(data=data,
kernel=(3, 3),
stride=(2, 2),
pool_type=pool,
pad=(0, 0),
name=('%s_pool_%s_pool' % (pool, name)))
concat = sym.concatenate(*[tower_3x3, tower_d7_3x3, pooling],
axis=3,
name='ch_concat_%s_chconcat' % name)
return concat
def verify_concatenate(ishape, axis):
x = [sym.Variable("x%d" % i) for i in range(len(ishape))]
y = sym.concatenate(*x, axis=axis) + 1
dtype = "float32"
for target, ctx in ctx_list():
# set input
data = []
for i, shape in enumerate(ishape):
data.append(np.random.uniform(size=shape).astype(dtype))
pdict = {"x%d" % i: v for i, v in enumerate(data)}
shape = {"x%d" % i: v.shape for i, v in enumerate(data)}
graph, lib, _ = nnvm.compiler.build(y, target, shape)
m = graph_runtime.create(graph, lib, ctx)
m.run(**pdict)
out_np = np.concatenate(data, axis=axis) + 1
out = m.get_output(0, tvm.nd.empty(out_np.shape))
np.testing.assert_allclose(out.asnumpy(), out_np, atol=1e-5, rtol=1e-5)
def verify_concatenate(ishape, axis):
x = [sym.Variable("x%d" % i) for i in range(len(ishape))]
y = sym.concatenate(*x, axis=axis) + 1
dtype = "float32"
for target, ctx in ctx_list():
# set input
data = []
for i, shape in enumerate(ishape):
data.append(np.random.uniform(size=shape).astype(dtype))
pdict = {"x%d" % i : v for i, v in enumerate(data)}
shape = {"x%d" % i : v.shape for i, v in enumerate(data)}
graph, lib, _ = nnvm.compiler.build(y, target, shape)
m = graph_runtime.create(graph, lib, ctx)
m.run(**pdict)
out_np = np.concatenate(data, axis=axis) + 1
out = m.get_output(0, tvm.nd.empty(out_np.shape))
np.testing.assert_allclose(out.asnumpy(), out_np, atol=1e-5, rtol=1e-5)
def test1():
in_shape = [3, 3, 3]
out_shape = [3, 3, 3, 2]
data = {
"x": np.arange(np.prod(in_shape), dtype=np.float32).reshape(in_shape),
"y": np.arange(np.prod(in_shape), dtype=np.float32).reshape(in_shape)
}
axis = -4
x = sym.Variable("x")
y = sym.Variable("y")
x = sym.expand_dims(x, axis=axis, num_newaxis=1) # sym.elemwise_add(x,y)
y = sym.expand_dims(y, axis=axis, num_newaxis=1) # sym.elemwise_add(x,y)
z = sym.concatenate(x, y, axis=-4)
nnvm_graph = nnvm.graph.create(z)
print('Got NNVM graph')
print(nnvm_graph.json())
in_shapes_dict = {n: list(np.shape(v)) for n, v in data.items()}
tvm_graph, lib, params = nnvm.compiler.build(nnvm_graph, 'llvm',
in_shapes_dict)
print('Got TVM graph')
ctx = tvm.cpu(0)
graph_module = graph_runtime.create(tvm_graph, lib, ctx)
print('Got graph module')
print(tvm_graph.__dir__())
print(tvm_graph.json())
for name, value in data.items():
graph_module.set_input(name, value)
graph_module.run()
out_value = graph_module.get_output(0, tvm.nd.empty((out_shape),
'float32'))
# print('Inputs:\nX:', data["x"], "\nY:", data["y"])
print('Output value:', type(out_value), '\nShape:', out_value.shape,
'\nO:', out_value)
def Inception7B(data, num_3x3, num_d3x3_red, num_d3x3_1, num_d3x3_2, pool,
name):
tower_3x3 = Conv(data,
num_3x3,
kernel=(3, 3),
pad=(0, 0),
stride=(2, 2),
name=('%s_conv' % name))
tower_d3x3 = Conv(data,
num_d3x3_red,
name=('%s_tower' % name),
suffix='_conv')
tower_d3x3 = Conv(tower_d3x3,
num_d3x3_1,
kernel=(3, 3),
pad=(1, 1),
stride=(1, 1),
name=('%s_tower' % name),
suffix='_conv_1')
tower_d3x3 = Conv(tower_d3x3,
num_d3x3_2,
kernel=(3, 3),
pad=(0, 0),
stride=(2, 2),
name=('%s_tower' % name),
suffix='_conv_2')
pooling = Pooling(data=data,
kernel=(3, 3),
stride=(2, 2),
pad=(0, 0),
pool_type="max",
name=('max_pool_%s_pool' % name))
concat = sym.concatenate(*[tower_3x3, tower_d3x3, pooling],
axis=3,
name='ch_concat_%s_chconcat' % name)
return concat
def test_alter_conv2d_layout():
data = sym.Variable("data", shape=(1, 32, 512, 512))
conv = sym.conv2d(data,
name="conv",
channels=16,
kernel_size=(3, 3),
padding=(1, 1),
use_bias=False,
layout="NCHW")
# split here
convs = sym.split(conv, indices_or_sections=2)
relus = [sym.relu(x, name="relu") for x in convs]
relu = sym.concatenate(*relus)
flatten = sym.flatten(relu, name="flatten")
softmax = sym.softmax(flatten, name="softmax")
g = graph.create(softmax)
g = g.apply("CorrectLayout")
g = graph_attr.set_dtype_inputs(g, "float32")
g = g.apply(["InferShape", "InferType"])
layouts_origin = get_layouts(g)
@reg.register_alter_op_layout("conv2d", level=100)
def alter_conv2d_layout(attrs, inputs, tinfos):
new_attrs = {k: attrs[k] for k in attrs.keys()}
new_attrs["layout"] = "NCHW16c"
new_attrs["kernel_layout"] = "NCHW16c"
new_attrs["name"] = "conv_alter"
return sym.conv2d(inputs[0], inputs[1], **new_attrs)
g = g.apply("AlterOpLayout")
layouts = get_layouts(g)
# check copy layouts
for node in ["data", "relu", "flatten", "softmax", "conv_weight"]:
assert layouts[node] == layouts_origin[node]
assert layouts["conv_alter"] == layouts_origin["conv"]
def Inception7E(data, num_1x1, num_d3_red, num_d3_1, num_d3_2,
num_3x3_d3_red, num_3x3, num_3x3_d3_1, num_3x3_d3_2, pool,
proj, name):
tower_1x1 = Conv(data=data,
num_filter=num_1x1,
kernel=(1, 1),
name=('%s_conv' % name))
tower_d3 = Conv(data=data,
num_filter=num_d3_red,
name=('%s_tower' % name),
suffix='_conv')
tower_d3_a = Conv(data=tower_d3,
num_filter=num_d3_1,
kernel=(1, 3),
pad=(0, 1),
name=('%s_tower' % name),
suffix='_mixed_conv')
tower_d3_b = Conv(data=tower_d3,
num_filter=num_d3_2,
kernel=(3, 1),
pad=(1, 0),
name=('%s_tower' % name),
suffix='_mixed_conv_1')
tower_3x3_d3 = Conv(data=data,
num_filter=num_3x3_d3_red,
name=('%s_tower_1' % name),
suffix='_conv')
tower_3x3_d3 = Conv(data=tower_3x3_d3,
num_filter=num_3x3,
kernel=(3, 3),
pad=(1, 1),
name=('%s_tower_1' % name),
suffix='_conv_1')
tower_3x3_d3_a = Conv(data=tower_3x3_d3,
num_filter=num_3x3_d3_1,
kernel=(1, 3),
pad=(0, 1),
name=('%s_tower_1' % name),
suffix='_mixed_conv')
tower_3x3_d3_b = Conv(data=tower_3x3_d3,
num_filter=num_3x3_d3_2,
kernel=(3, 1),
pad=(1, 0),
name=('%s_tower_1' % name),
suffix='_mixed_conv_1')
pooling = Pooling(data=data,
kernel=(3, 3),
stride=(1, 1),
pad=(1, 1),
pool_type=pool,
name=('%s_pool_%s_pool' % (pool, name)))
cproj = Conv(data=pooling,
num_filter=proj,
kernel=(1, 1),
name=('%s_tower_2' % name),
suffix='_conv')
concat = sym.concatenate(*[
tower_1x1, tower_d3_a, tower_d3_b, tower_3x3_d3_a, tower_3x3_d3_b,
cproj
],
axis=3,
name='ch_concat_%s_chconcat' % name)
return concat
def Inception7C(data, num_1x1, num_d7_red, num_d7_1, num_d7_2, num_q7_red,
num_q7_1, num_q7_2, num_q7_3, num_q7_4, pool, proj, name):
tower_1x1 = Conv(data=data,
num_filter=num_1x1,
kernel=(1, 1),
name=('%s_conv' % name))
tower_d7 = Conv(data=data,
num_filter=num_d7_red,
name=('%s_tower' % name),
suffix='_conv')
tower_d7 = Conv(data=tower_d7,
num_filter=num_d7_1,
kernel=(1, 7),
pad=(0, 3),
name=('%s_tower' % name),
suffix='_conv_1')
tower_d7 = Conv(data=tower_d7,
num_filter=num_d7_2,
kernel=(7, 1),
pad=(3, 0),
name=('%s_tower' % name),
suffix='_conv_2')
tower_q7 = Conv(data=data,
num_filter=num_q7_red,
name=('%s_tower_1' % name),
suffix='_conv')
tower_q7 = Conv(data=tower_q7,
num_filter=num_q7_1,
kernel=(7, 1),
pad=(3, 0),
name=('%s_tower_1' % name),
suffix='_conv_1')
tower_q7 = Conv(data=tower_q7,
num_filter=num_q7_2,
kernel=(1, 7),
pad=(0, 3),
name=('%s_tower_1' % name),
suffix='_conv_2')
tower_q7 = Conv(data=tower_q7,
num_filter=num_q7_3,
kernel=(7, 1),
pad=(3, 0),
name=('%s_tower_1' % name),
suffix='_conv_3')
tower_q7 = Conv(data=tower_q7,
num_filter=num_q7_4,
kernel=(1, 7),
pad=(0, 3),
name=('%s_tower_1' % name),
suffix='_conv_4')
pooling = Pooling(data=data,
kernel=(3, 3),
stride=(1, 1),
pad=(1, 1),
pool_type=pool,
name=('%s_pool_%s_pool' % (pool, name)))
cproj = Conv(data=pooling,
num_filter=proj,
kernel=(1, 1),
name=('%s_tower_2' % name),
suffix='_conv')
concat = sym.concatenate(*[tower_1x1, tower_d7, tower_q7, cproj],
axis=3,
name='ch_concat_%s_chconcat' % name)
return concat
