diff --git a/python/tvm/relay/testing/__init__.py b/python/tvm/relay/testing/__init__.py
index 43160d64549c95d72cec4088850797cbc4771e29..47a04b5319221d6fa5bf768495a0c12909cd7d1e 100644
--- a/python/tvm/relay/testing/__init__.py
+++ b/python/tvm/relay/testing/__init__.py
@@ -7,4 +7,7 @@ from . import dqn
from . import dcgan
from . import mobilenet
from . import lstm
+from . import inception_v3
+from . import squeezenet
+from . import vgg
from .config import ctx_list
diff --git a/python/tvm/relay/testing/inception_v3.py b/python/tvm/relay/testing/inception_v3.py
new file mode 100644
index 0000000000000000000000000000000000000000..96684c5d6e1d2a2a992aa28d42fba0ca268db55b
--- /dev/null
+++ b/python/tvm/relay/testing/inception_v3.py
@@ -0,0 +1,284 @@
+"""
+Inception V3, suitable for images with around 299 x 299
+
+Reference:
+Szegedy, Christian, et al. "Rethinking the Inception Architecture for Computer Vision."
+arXiv preprint arXiv:1512.00567 (2015).
+
+Adopted from https://github.com/apache/incubator-mxnet/blob/
+ master/example/image-classification/symbols/inception-v3.py
+"""
+# pylint: disable=invalid-name,missing-docstring,unused-argument
+from tvm import relay
+from .init import create_workload
+from . import layers
+
+def Conv(data, num_filter, kernel=(1, 1), stride=(1, 1), pad=(0, 0), name=None, suffix=''):
+ conv = layers.conv2d(
+ data=data,
+ channels=int(num_filter),
+ kernel_size=kernel,
+ strides=stride,
+ padding=pad,
+ name='%s%s_conv1' % (name, suffix))
+
+ bn = layers.batch_norm_infer(data=conv, epsilon=2e-5, name='%s%s_bn' % (name, suffix))
+ act = relay.nn.relu(data=bn)
+ return act
+
+def Pooling(data, kernel, stride, pad, pool_type, name):
+ if pool_type == 'max':
+ return relay.nn.max_pool2d(data=data, pool_size=kernel, strides=stride, padding=pad)
+ elif pool_type == 'avg':
+ return relay.nn.avg_pool2d(data=data, pool_size=kernel, strides=stride, padding=pad,
+ count_include_pad=True)
+ else:
+ raise ValueError("Invalid pooling type: " + pool_type)
+
+def Inception7A(data,
+ num_1x1,
+ num_3x3_red, num_3x3_1, num_3x3_2,
+ num_5x5_red, num_5x5,
+ pool, proj,
+ name):
+ 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=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 = relay.concatenate((tower_1x1, tower_5x5, tower_3x3, cproj), axis=0)
+ return concat
+
+# First Downsample
+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 = relay.concatenate((tower_3x3, tower_d3x3, pooling), axis=0)
+ 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
+ concat = relay.concatenate((tower_1x1, tower_d7, tower_q7, cproj), axis=0)
+ 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
+ concat = relay.concatenate((tower_3x3, tower_d7_3x3, pooling), axis=0)
+ return concat
+
+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
+ concat = relay.concatenate(
+ (tower_1x1, tower_d3_a, tower_d3_b, tower_3x3_d3_a, tower_3x3_d3_b, cproj), axis=0)
+ return concat
+
+def get_net(batch_size,
+ num_classes,
+ image_shape,
+ dtype):
+ """Get network a Inception v3 network.
+
+ batch_size : int
+ The batch size used in the model
+
+ num_classes : int, optional
+ Number of claseses
+
+ image_shape : tuple, optional
+ The input image shape
+
+ dtype : str, optional
+ The data type
+
+ Returns
+ -------
+ net : relay.Function
+ The dataflow.
+ """
+ data_shape = (batch_size,) + image_shape
+ data = relay.var("data",
+ shape=data_shape,
+ dtype=dtype)
+
+ # stage 1
+ conv = Conv(data, 32, kernel=(3, 3), stride=(2, 2), name="conv")
+ conv_1 = Conv(conv, 32, kernel=(3, 3), name="conv_1")
+ conv_2 = Conv(conv_1, 64, kernel=(3, 3), pad=(1, 1), name="conv_2")
+ pool = Pooling(data=conv_2, kernel=(3, 3), stride=(2, 2), pool_type="max", pad=(0, 0),
+ name="pool")
+ # stage 2
+ conv_3 = Conv(pool, 80, kernel=(1, 1), name="conv_3")
+ conv_4 = Conv(conv_3, 192, kernel=(3, 3), name="conv_4")
+ pool1 = Pooling(data=conv_4, kernel=(3, 3), stride=(2, 2), pool_type="max", pad=(0, 0),
+ name="pool1")
+
+ # stage 3
+ in3a = Inception7A(pool1, 64,
+ 64, 96, 96,
+ 48, 64,
+ "avg", 32, "mixed")
+
+ in3b = Inception7A(in3a, 64,
+ 64, 96, 96,
+ 48, 64,
+ "avg", 64, "mixed_1")
+ in3c = Inception7A(in3b, 64,
+ 64, 96, 96,
+ 48, 64,
+ "avg", 64, "mixed_2")
+ in3d = Inception7B(in3c, 384,
+ 64, 96, 96,
+ "max", "mixed_3")
+ # stage 4
+ in4a = Inception7C(in3d, 192,
+ 128, 128, 192,
+ 128, 128, 128, 128, 192,
+ "avg", 192, "mixed_4")
+ in4b = Inception7C(in4a, 192,
+ 160, 160, 192,
+ 160, 160, 160, 160, 192,
+ "avg", 192, "mixed_5")
+ in4c = Inception7C(in4b, 192,
+ 160, 160, 192,
+ 160, 160, 160, 160, 192,
+ "avg", 192, "mixed_6")
+ in4d = Inception7C(in4c, 192,
+ 192, 192, 192,
+ 192, 192, 192, 192, 192,
+ "avg", 192, "mixed_7")
+ in4e = Inception7D(in4d, 192, 320,
+ 192, 192, 192, 192,
+ "max", "mixed_8")
+ # stage 5
+ in5a = Inception7E(in4e, 320,
+ 384, 384, 384,
+ 448, 384, 384, 384,
+ "avg", 192, "mixed_9")
+ in5b = Inception7E(in5a, 320,
+ 384, 384, 384,
+ 448, 384, 384, 384,
+ "max", 192, "mixed_10")
+
+ # pool
+ pool = Pooling(data=in5b, kernel=(8, 8), stride=(1, 1), pool_type="avg", pad=(0, 0),
+ name="global_pool")
+
+ flatten = relay.nn.batch_flatten(pool)
+ fc1 = relay.nn.dense(flatten, relay.var("fc1_weight"), units=num_classes)
+ fc1 = relay.nn.bias_add(fc1, relay.var("fc2_bias"))
+ inception_v3 = relay.nn.softmax(data=fc1)
+ args = relay.ir_pass.free_vars(inception_v3)
+ return relay.Function(args, inception_v3)
+
+def get_workload(batch_size=1, num_classes=1000,
+ image_shape=(3, 299, 299), dtype="float32"):
+ """Get benchmark workload for InceptionV3
+
+ Parameters
+ ----------
+ batch_size : int
+ The batch size used in the model
+
+ num_classes : int, optional
+ Number of classes
+
+ image_shape : tuple, optional
+ The input image shape
+
+ 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, num_classes, image_shape, dtype)
+ return create_workload(net)
diff --git a/python/tvm/relay/testing/squeezenet.py b/python/tvm/relay/testing/squeezenet.py
new file mode 100644
index 0000000000000000000000000000000000000000..fa55cafbf2b473cb88d1dbc46b8656e254ff6732
--- /dev/null
+++ b/python/tvm/relay/testing/squeezenet.py
@@ -0,0 +1,151 @@
+# Licensed to the Apache Software Foundation (ASF) under one
+# or more contributor license agreements. See the NOTICE file
+# distributed with this work for additional information
+# regarding copyright ownership. The ASF licenses this file
+# to you under the Apache License, Version 2.0 (the
+# "License"); you may not use this file except in compliance
+# with the License. You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing,
+# software distributed under the License is distributed on an
+# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+# KIND, either express or implied. See the License for the
+# specific language governing permissions and limitations
+# under the License.
+
+# coding: utf-8
+# pylint: disable=unused-argument
+
+"""
+Symbol of SqueezeNet
+
+Reference:
+Iandola, Forrest N., et al.
+"Squeezenet: Alexnet-level accuracy with 50x fewer parameters and< 0.5 mb model size." (2016).
+"""
+
+from tvm import relay
+from .init import create_workload
+from . import layers
+
+# Helpers
+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 = relay.concatenate((left, right), axis=1)
+
+ return net
+
+def _make_fire_conv(net, channels, kernel_size, padding=0):
+ net = layers.conv2d(net, channels=channels, kernel_size=(kernel_size, kernel_size),
+ padding=(padding, padding), name="conv2d")
+ net = relay.nn.relu(net)
+ return net
+
+# Net
+def get_net(batch_size, image_shape, num_classes, version, dtype):
+ """Get symbol of SqueezeNet
+
+ Parameters
+ ----------
+ batch_size : int
+ The batch size used in the model
+
+ image_shape : tuple, optional
+ The input image shape
+
+ num_classes: int
+ The number of classification results
+
+ version : str, optional
+ "1.0" or "1.1" of SqueezeNet
+ """
+ assert version in ['1.0', '1.1'], ("Unsupported SqueezeNet version {version}:"
+ "1.0 or 1.1 expected".format(version=version))
+ data_shape = (batch_size,) + image_shape
+ net = relay.var("data", shape=data_shape, dtype=dtype)
+ if version == '1.0':
+ net = layers.conv2d(net,
+ channels=96,
+ kernel_size=(7, 7),
+ strides=(2, 2),
+ padding=(3, 3),
+ name="conv2d")
+ net = relay.nn.bias_add(net, relay.var("dense1_bias"))
+ net = relay.nn.relu(net)
+ net = relay.nn.max_pool2d(net, pool_size=(3, 3), strides=(2, 2))
+ net = _make_fire(net, 16, 64, 64)
+ net = _make_fire(net, 16, 64, 64)
+ net = _make_fire(net, 32, 128, 128)
+ net = relay.nn.max_pool2d(net, pool_size=(3, 3), strides=(2, 2))
+ net = _make_fire(net, 32, 128, 128)
+ net = _make_fire(net, 48, 192, 192)
+ net = _make_fire(net, 48, 192, 192)
+ net = _make_fire(net, 64, 256, 256)
+ net = relay.nn.max_pool2d(net, pool_size=(3, 3), strides=(2, 2))
+ net = _make_fire(net, 64, 256, 256)
+ else:
+ net = layers.conv2d(net,
+ channels=64,
+ kernel_size=(3, 3),
+ strides=(2, 2),
+ padding=(1, 1),
+ name="conv2d")
+ net = relay.nn.relu(net)
+ net = relay.nn.max_pool2d(net, pool_size=(3, 3), strides=(2, 2))
+ net = _make_fire(net, 16, 64, 64)
+ net = _make_fire(net, 16, 64, 64)
+ net = relay.nn.max_pool2d(net, pool_size=(3, 3), strides=(2, 2))
+ net = _make_fire(net, 32, 128, 128)
+ net = _make_fire(net, 32, 128, 128)
+ net = relay.nn.max_pool2d(net, pool_size=(3, 3), strides=(2, 2))
+ net = _make_fire(net, 48, 192, 192)
+ net = _make_fire(net, 48, 192, 192)
+ net = _make_fire(net, 64, 256, 256)
+ net = _make_fire(net, 64, 256, 256)
+ net = relay.nn.dropout(net, rate=0.5)
+ net = layers.conv2d(net, channels=num_classes, kernel_size=(1, 1), name="conv2d")
+ net = relay.nn.relu(net)
+ net = relay.nn.global_avg_pool2d(net)
+ net = relay.nn.batch_flatten(net)
+ net = relay.nn.softmax(net)
+ args = relay.ir_pass.free_vars(net)
+ return relay.Function(args, net)
+
+def get_workload(batch_size=1, num_classes=1000, version='1.0',
+ image_shape=(3, 224, 224), dtype="float32"):
+ """Get benchmark workload for SqueezeNet
+
+ Parameters
+ ----------
+ batch_size : int
+ The batch size used in the model
+
+ num_classes : int, optional
+ Number of classes
+
+ version : str, optional
+ "1.0" or "1.1" of SqueezeNet
+
+ image_shape : tuple, optional
+ The input image shape
+
+ 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, image_shape, num_classes, version, dtype)
+ return create_workload(net)
diff --git a/python/tvm/relay/testing/vgg.py b/python/tvm/relay/testing/vgg.py
new file mode 100644
index 0000000000000000000000000000000000000000..7ec6669f6346e6db19a46683d7d5ba80c0ff863b
--- /dev/null
+++ b/python/tvm/relay/testing/vgg.py
@@ -0,0 +1,117 @@
+# Licensed to the Apache Software Foundation (ASF) under one
+# or more contributor license agreements. See the NOTICE file
+# distributed with this work for additional information
+# regarding copyright ownership. The ASF licenses this file
+# to you under the Apache License, Version 2.0 (the
+# "License"); you may not use this file except in compliance
+# with the License. You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing,
+# software distributed under the License is distributed on an
+# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+# KIND, either express or implied. See the License for the
+# specific language governing permissions and limitations
+# under the License.
+
+"""References:
+
+Simonyan, Karen, and Andrew Zisserman. "Very deep convolutional networks for
+large-scale image recognition." arXiv preprint arXiv:1409.1556 (2014).
+"""
+from tvm import relay
+from .init import create_workload
+from . import layers as wrapper
+
+def get_feature(internel_layer, layers, filters, batch_norm=False):
+ """Get VGG feature body as stacks of convoltions."""
+ for i, num in enumerate(layers):
+ for j in range(num):
+ internel_layer = wrapper.conv2d(
+ data=internel_layer, kernel_size=(3, 3), padding=(1, 1),
+ channels=filters[i], name="conv%s_%s"%(i + 1, j + 1))
+ if batch_norm:
+ internel_layer = wrapper.batch_norm_infer(
+ data=internel_layer, name="bn%s_%s" %(i + 1, j + 1))
+ internel_layer = relay.nn.relu(data=internel_layer)
+ internel_layer = relay.nn.max_pool2d(
+ data=internel_layer, pool_size=(2, 2), strides=(2, 2))
+ return internel_layer
+
+def get_classifier(input_data, num_classes):
+ """Get VGG classifier layers as fc layers."""
+ flatten = relay.nn.batch_flatten(data=input_data)
+ fc6 = wrapper.dense_add_bias(data=flatten, units=4096, name="fc6")
+ relu6 = relay.nn.relu(data=fc6)
+ drop6 = relay.nn.dropout(data=relu6, rate=0.5)
+ fc7 = wrapper.dense_add_bias(data=drop6, units=4096, name="fc7")
+ relu7 = relay.nn.relu(data=fc7)
+ drop7 = relay.nn.dropout(data=relu7, rate=0.5)
+ fc8 = wrapper.dense_add_bias(data=drop7, units=num_classes, name="fc8")
+ return fc8
+
+def get_net(batch_size, image_shape, num_classes, dtype, num_layers=11, batch_norm=False):
+ """
+ Parameters
+ ----------
+ batch_size : int
+ The batch size used in the model
+
+ image_shape : tuple, optional
+ The input image shape
+
+ num_classes : int, optional
+ Number of claseses
+
+ dtype : str, optional
+ The data type
+
+ num_layers : int
+ Number of layers for the variant of densenet. Options are 11, 13, 16, 19.
+
+ batch_norm : bool, default False
+ Use batch normalization.
+ """
+ vgg_spec = {11: ([1, 1, 2, 2, 2], [64, 128, 256, 512, 512]),
+ 13: ([2, 2, 2, 2, 2], [64, 128, 256, 512, 512]),
+ 16: ([2, 2, 3, 3, 3], [64, 128, 256, 512, 512]),
+ 19: ([2, 2, 4, 4, 4], [64, 128, 256, 512, 512])}
+ if num_layers not in vgg_spec:
+ raise ValueError("Invalide num_layers {}. Choices are 11,13,16,19.".format(num_layers))
+ layers, filters = vgg_spec[num_layers]
+ data_shape = (batch_size,) + image_shape
+ data = relay.var("data", shape=data_shape, dtype=dtype)
+ feature = get_feature(data, layers, filters, batch_norm)
+ classifier = get_classifier(feature, num_classes)
+ symbol = relay.nn.softmax(data=classifier)
+ args = relay.ir_pass.free_vars(symbol)
+ return relay.Function(args, symbol)
+
+def get_workload(batch_size, num_classes=1000, image_shape=(3, 224, 224), dtype="float32"):
+ """Get benchmark workload for VGG nets.
+
+ Parameters
+ ----------
+ batch_size : int
+ The batch size used in the model
+
+ num_classes : int, optional
+ Number of claseses
+
+ image_shape : tuple, optional
+ The input image shape
+
+ 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, image_shape, num_classes, dtype)
+ return create_workload(net)
diff --git a/tests/python/relay/test_ir_text_printer.py b/tests/python/relay/test_ir_text_printer.py
index 30130fd7bcac7c7451012428069c6e0624f9f206..f6a1236c89e6b987e1d16891939f6495f858078d 100644
--- a/tests/python/relay/test_ir_text_printer.py
+++ b/tests/python/relay/test_ir_text_printer.py
@@ -124,6 +124,18 @@ def test_lstm():
net, params = tvm.relay.testing.lstm.get_workload(4, 4)
net.astext()
+def test_inception_v3():
+ net, params = tvm.relay.testing.inception_v3.get_workload(batch_size=1)
+ net.astext()
+
+def test_squeezenet():
+ for version in ['1.0', '1.1']:
+ net, params = tvm.relay.testing.squeezenet.get_workload(batch_size=1, version=version)
+ net.astext()
+
+def test_vgg():
+ net, params = tvm.relay.testing.vgg.get_workload(batch_size=1)
+ net.astext()
if __name__ == "__main__":
do_print[0] = True
@@ -132,6 +144,9 @@ if __name__ == "__main__":
test_mlp()
test_dqn()
test_dcgan()
+ test_squeezenet()
+ test_inception_v3()
+ test_vgg()
test_func()
test_env()
test_meta_data()