def test_conv2d_transpose():
x = sym.Variable("x")
y = sym.conv2d_transpose(x,
channels=10,
kernel_size=(3, 3),
strides=(2, 2),
name="y",
padding=(1, 1),
output_padding=(2, 2))
dtype = "float32"
dshape = (1, 3, 18, 18)
kshape = (3, 10, 3, 3)
oshape = (1, 10, 37, 37)
shape_dict = {"x": dshape}
for target, ctx in ctx_list():
graph, lib, _ = nnvm.compiler.build(y, target, shape_dict)
m = graph_runtime.create(graph, lib, ctx)
data = tvm.nd.array(np.random.uniform(size=dshape).astype(dtype))
kernel = tvm.nd.array(np.random.uniform(size=kshape).astype(dtype))
bias = tvm.nd.array(np.random.uniform(size=kshape[1]).astype(dtype))
m.run(x=data, y_weight=kernel, y_bias=bias)
out = m.get_output(0, tvm.nd.empty(oshape, dtype))
c_np = topi.testing.conv2d_transpose_nchw_python(
data.asnumpy(), kernel.asnumpy(), 2, 1)
c_np = c_np + bias.asnumpy().reshape(kshape[1], 1, 1)
d_np = np.zeros(shape=oshape)
d_np[:, :, 0:c_np.shape[2], 0:c_np.shape[3]] = c_np
tvm.testing.assert_allclose(out.asnumpy(), d_np, rtol=1e-5)
def test_conv2d_transpose():
x = sym.Variable("data", shape=(1, 32, 512, 512))
y = sym.conv2d_transpose(x, name="conv", channels=12,
kernel_size=(3,3), padding=(1,1), layout="NCHW")
_, ldict = correct_layout(y)
assert(ldict["data"][0] == "NCHW")
assert(ldict["conv_weight"][0] == "OIHW")
assert(ldict["conv_bias"][0] == "C")
assert(ldict["conv"][0] == "NCHW")
def test_conv2d_transpose():
x = sym.Variable("data", shape=(1, 32, 512, 512))
y = sym.conv2d_transpose(x,
name="conv",
channels=12,
kernel_size=(3, 3),
padding=(1, 1),
layout="NCHW")
_, ldict = correct_layout(y)
assert (ldict["data"][0] == "NCHW")
assert (ldict["conv_weight"][0] == "OIHW")
assert (ldict["conv_bias"][0] == "C")
assert (ldict["conv"][0] == "NCHW")
def test_conv2d_transpose():
x = sym.Variable("x")
y = sym.conv2d_transpose(x, channels=10, kernel_size=(3,3), strides=(2,2),
name="y", padding=(1,1), output_padding=(2,2))
dtype = "float32"
dshape = (1, 3, 18, 18)
kshape = (3, 10, 3, 3)
oshape = (1, 10, 37, 37)
shape_dict = {"x": dshape}
for target, ctx in ctx_list():
graph, lib, _ = nnvm.compiler.build(y, target, shape_dict)
m = graph_runtime.create(graph, lib, ctx)
data = tvm.nd.array(np.random.uniform(size=dshape).astype(dtype))
kernel = tvm.nd.array(np.random.uniform(size=kshape).astype(dtype))
bias = tvm.nd.array(np.random.uniform(size=kshape[1]).astype(dtype))
m.run(x=data, y_weight=kernel, y_bias=bias)
out = m.get_output(0, tvm.nd.empty(oshape, dtype))
c_np = topi.testing.conv2d_transpose_nchw_python(
data.asnumpy(), kernel.asnumpy(), 2, 1)
c_np = c_np + bias.asnumpy().reshape(kshape[1], 1, 1)
d_np = np.zeros(shape=oshape)
d_np[:,:,0:c_np.shape[2],0:c_np.shape[3]] = c_np
tvm.testing.assert_allclose(out.asnumpy(), d_np, rtol=1e-5)
def check(in_shape, out_shape, **kwargs):
x = sym.Variable("x", shape=in_shape)
y = sym.conv2d_transpose(x, name="y", **kwargs)
sdict = infer_shape(y)
assert(tuple(sdict["y"][0]) == tuple(out_shape))
def check(in_shape, out_shape, **kwargs):
x = sym.Variable("x", shape=in_shape)
y = sym.conv2d_transpose(x, name="y", **kwargs)
sdict = infer_shape(y)
assert(tuple(sdict["y"][0]) == tuple(out_shape))