[RELAY]Testing Inception, Squeezenet, VGG port (#2013)

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

python/tvm/relay/testing/inception_v3.py

+"""
+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)

python/tvm/relay/testing/squeezenet.py

+# 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)

python/tvm/relay/testing/vgg.py

+# 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)

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()