diff --git a/python/tvm/relay/testing/__init__.py b/python/tvm/relay/testing/__init__.py
index 2a9b81b842309f19ef28374a756c4a57751d06f4..547fff4255956a426ec37ee22e7ed754247da473 100644
--- a/python/tvm/relay/testing/__init__.py
+++ b/python/tvm/relay/testing/__init__.py
@@ -4,3 +4,4 @@ from __future__ import absolute_import as _abs
from . import mlp
from . import resnet
from . import dqn
+from . import dcgan
diff --git a/python/tvm/relay/testing/dcgan.py b/python/tvm/relay/testing/dcgan.py
new file mode 100644
index 0000000000000000000000000000000000000000..96cd871e4122b55780f96f44023124560603d34c
--- /dev/null
+++ b/python/tvm/relay/testing/dcgan.py
@@ -0,0 +1,96 @@
+# pylint: disable=unused-argument
+"""
+Net of the generator of DCGAN
+
+Adopted from:
+https://github.com/tqchen/mxnet-gan/blob/master/mxgan/generator.py
+
+Reference:
+Radford, Alec, Luke Metz, and Soumith Chintala.
+"Unsupervised representation learning with deep convolutional generative adversarial networks."
+arXiv preprint arXiv:1511.06434 (2015).
+"""
+from tvm import relay
+from . import layers
+from .init import create_workload
+
+def deconv2d(data, ishape, oshape, kshape, name, stride=(2, 2)):
+ """a deconv layer that enlarges the feature map"""
+ target_shape = (oshape[-2], oshape[-1])
+
+ pad_y = (kshape[0] - 1) // 2
+ pad_x = (kshape[1] - 1) // 2
+ adj_y = (target_shape[0] + 2 * pad_y - kshape[0]) % stride[0]
+ adj_x = (target_shape[1] + 2 * pad_x - kshape[1]) % stride[1]
+
+ net = layers.conv2d_transpose(data,
+ kernel_size=kshape,
+ strides=stride,
+ channels=oshape[0],
+ padding=(pad_y, pad_x),
+ output_padding=(adj_y, adj_x),
+ name=name)
+ return net
+
+def deconv2d_bn_relu(data, prefix, **kwargs):
+ """a block of deconv + batch norm + relu"""
+ eps = 1e-5 + 1e-12
+ net = deconv2d(data, name="%s_deconv" % prefix, **kwargs)
+ net = layers.batch_norm_infer(net, epsilon=eps, name="batch_norm")
+ net = relay.nn.relu(net)
+ return net
+
+def get_net(batch_size, random_len=100, oshape=(3, 64, 64), ngf=128, code=None, dtype="float32"):
+ """get net of dcgan generator"""
+ assert oshape[-1] == 64, "Only support 64x64 image"
+ assert oshape[-2] == 64, "Only support 64x64 image"
+
+ code = relay.var("data", dtype=dtype, shape=(batch_size, random_len)) if code is None else code
+ dense_weight = relay.var("dense_weight")
+ dense = relay.nn.dense(code, weight=dense_weight, units=4*4*ngf*8)
+ relu = relay.nn.relu(dense)
+ # 4 x 4
+ reshape = relay.reshape(relu, newshape=(-1, ngf * 8, 4, 4))
+ # 8 x 8
+ dc8 = deconv2d_bn_relu(
+ reshape, ishape=(ngf * 8, 4, 4), oshape=(ngf * 4, 8, 8), kshape=(4, 4), prefix="g2")
+ # 16x16
+ dc16 = deconv2d_bn_relu(
+ dc8, ishape=(ngf * 4, 8, 8), oshape=(ngf * 2, 16, 16), kshape=(4, 4), prefix="g3")
+ # 32x32
+ dc32 = deconv2d_bn_relu(
+ dc16, ishape=(ngf * 2, 16, 16), oshape=(ngf, 32, 32), kshape=(4, 4), prefix="g4")
+ # 64x64
+ dc64 = deconv2d(
+ dc32, ishape=(ngf, 32, 32), oshape=oshape[-3:], kshape=(4, 4), name="g5_deconv")
+ tanh = relay.tanh(dc64)
+
+ args = relay.ir_pass.free_vars(tanh)
+ return relay.Function(args, tanh)
+
+
+def get_workload(batch_size, oshape=(3, 64, 64), ngf=128, random_len=100, dtype="float32"):
+ """Get benchmark workload for a DCGAN generator
+
+ Parameters
+ ----------
+ batch_size : int
+ The batch size used in the model
+ oshape : tuple, optional
+ The shape of output image, layout="CHW"
+ ngf: int, optional
+ The number of final feature maps in the generator
+ random_len : int, optional
+ The length of random input
+ dtype : str, optional
+ The data type
+
+ Returns
+ -------
+ net : nnvm.symbol
+ The computational graph
+ params : dict of str to NDArray
+ The parameters.
+ """
+ net = get_net(batch_size, random_len, oshape=oshape, ngf=ngf, dtype=dtype)
+ return create_workload(net)
diff --git a/python/tvm/relay/testing/dqn.py b/python/tvm/relay/testing/dqn.py
index 736894612e199ae98287030db185c6cfe7b5772e..034ac0a6c2e5f7a68754346f633d4ef209f0c48f 100644
--- a/python/tvm/relay/testing/dqn.py
+++ b/python/tvm/relay/testing/dqn.py
@@ -30,15 +30,25 @@ def get_net(batch_size, num_actions=18, image_shape=(4, 84, 84), dtype="float32"
"""get symbol of nature dqn"""
data_shape = (batch_size,) + image_shape
data = relay.var("data", shape=data_shape, dtype=dtype)
+
+ conv1_bias = relay.var("conv1_bias")
conv1 = layers.conv2d(data, kernel_size=(8, 8), strides=(4, 4), padding=(0, 0),
channels=32, name="conv1")
+ conv1 = relay.nn.bias_add(conv1, conv1_bias)
relu1 = relay.nn.relu(conv1)
+
+ conv2_bias = relay.var("conv2_bias")
conv2 = layers.conv2d(relu1, kernel_size=(4, 4), strides=(2, 2), padding=(0, 0),
channels=64, name="conv2")
+ conv2 = relay.nn.bias_add(conv2, conv2_bias)
relu2 = relay.nn.relu(conv2)
+
+ conv3_bias = relay.var("conv3_bias")
conv3 = layers.conv2d(relu2, kernel_size=(3, 3), strides=(1, 1), padding=(0, 0),
channels=64, name="conv3")
+ conv3 = relay.nn.bias_add(conv3, conv3_bias)
relu3 = relay.nn.relu(conv3)
+
bf1 = relay.nn.batch_flatten(relu3)
dense1 = layers.dense_add_bias(bf1, units=512, name="dense1")
relu4 = relay.nn.relu(dense1)
diff --git a/python/tvm/relay/testing/layers.py b/python/tvm/relay/testing/layers.py
index fc06ca229f771b8b04839d6cb52c04b7de44887a..1b279d9e72af7ecc5be48b073f3865f1bd1b2e55 100644
--- a/python/tvm/relay/testing/layers.py
+++ b/python/tvm/relay/testing/layers.py
@@ -80,6 +80,30 @@ def conv2d(data, weight=None, **kwargs):
weight = relay.var(name + "_weight")
return relay.nn.conv2d(data, weight, **kwargs)
+def conv2d_transpose(data, weight=None, **kwargs):
+ """Wrapper of conv2d_transpose which automatically creates weights if not given.
+
+ Parameters
+ ----------
+ data : relay.Expr
+ The input expression.
+
+ weight : relay.Expr
+ The weight to conv2d_transpose.
+
+ kwargs : dict
+ Additional arguments.
+
+ Returns
+ -------
+ result : relay.Expr
+ The result.
+ """
+ name = kwargs.get("name")
+ kwargs.pop("name")
+ if not weight:
+ weight = relay.var(name + "_weight")
+ return relay.nn.conv2d_transpose(data, weight, **kwargs)
def dense_add_bias(data, weight=None, bias=None, **kwargs):
"""Wrapper of dense which automatically creates weights if not given.
diff --git a/tests/python/relay/test_ir_text_printer.py b/tests/python/relay/test_ir_text_printer.py
index 7b2c343b084444ed884e5555eb4cad98abd5881e..fd446f9b7f03c72fa5f7f9583016ac12a6c95629 100644
--- a/tests/python/relay/test_ir_text_printer.py
+++ b/tests/python/relay/test_ir_text_printer.py
@@ -106,13 +106,18 @@ def test_resnet():
def test_dqn():
net, params = tvm.relay.testing.dqn.get_workload(batch_size=1)
- show(net.astext())
+ net.astext()
+
+def test_dcgan():
+ net, params = tvm.relay.testing.dcgan.get_workload(batch_size=1)
+ net.astext()
if __name__ == "__main__":
do_print[0] = True
test_resnet()
test_mlp()
test_dqn()
+ test_dcgan()
test_func()
test_env()
test_meta_data()