[RELAY] MobileNet (#1997)

python/tvm/relay/testing/__init__.py

@@ -5,3 +5,4 @@ from . import mlp
 from . import resnet
 from . import dqn
 from . import dcgan
+from . import mobilenet

python/tvm/relay/testing/mobilenet.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.
+"""
+Port of NNVM version of MobileNet to Relay.
+"""
+# pylint: disable=invalid-name
+
+from tvm import relay
+from . import layers
+from .init import create_workload
+
+def conv_block(data, name, channels, kernel_size=(3, 3), strides=(1, 1),
+               padding=(1, 1), epsilon=1e-5):
+    """Helper function to construct conv_bn-relu"""
+    # convolution + bn + relu
+    conv = layers.conv2d(
+        data=data,
+        channels=channels,
+        kernel_size=kernel_size,
+        strides=strides,
+        padding=padding,
+        data_layout='NCHW',
+        name=name+'_conv')
+    bn = layers.batch_norm_infer(data=conv, epsilon=epsilon, name=name + '_bn')
+    act = relay.nn.relu(data=bn)
+    return act
+
+
+def separable_conv_block(data, name, depthwise_channels, pointwise_channels,
+                         kernel_size=(3, 3), downsample=False, padding=(1, 1),
+                         epsilon=1e-5):
+    """Helper function to get a separable conv block"""
+    if downsample:
+        strides = (2, 2)
+    else:
+        strides = (1, 1)
+    # depthwise convolution + bn + relu
+    conv1 = layers.conv2d(
+        data=data,
+        channels=depthwise_channels,
+        groups=depthwise_channels,
+        kernel_size=kernel_size,
+        strides=strides,
+        padding=padding,
+        name=name+'_depthwise_conv1')
+    bn1 = layers.batch_norm_infer(data=conv1, epsilon=epsilon, name=name+'_bn1')
+    act1 = relay.nn.relu(data=bn1)
+    # pointwise convolution + bn + relu
+    conv2 = layers.conv2d(
+        data=act1,
+        channels=pointwise_channels,
+        kernel_size=(1, 1),
+        strides=(1, 1),
+        padding=(0, 0),
+        data_layout='NCHW',
+        name=name + '_conv2')
+    bn2 = layers.batch_norm_infer(data=conv2, epsilon=epsilon, name=name+'_bn2')
+    act2 = relay.nn.relu(data=bn2)
+    return act2
+
+
+def mobile_net(num_classes=1000, data_shape=(1, 3, 224, 224),
+               dtype='float32', alpha=1.0, is_shallow=False):
+    """Function to construct a MobileNet"""
+    data = relay.var("data", shape=data_shape, dtype=dtype)
+    body = conv_block(data, 'conv_block_1', int(32*alpha), strides=(2, 2))
+    body = separable_conv_block(body, 'separable_conv_block_1',
+                                int(32*alpha), int(64*alpha))
+    body = separable_conv_block(body, 'separable_conv_block_2',
+                                int(64*alpha), int(128*alpha), downsample=True)
+    body = separable_conv_block(body, 'separable_conv_block_3',
+                                int(128*alpha), int(128*alpha))
+    body = separable_conv_block(body, 'separable_conv_block_4',
+                                int(128*alpha), int(256*alpha), downsample=True)
+    body = separable_conv_block(body, 'separable_conv_block_5',
+                                int(256*alpha), int(256*alpha))
+    body = separable_conv_block(body, 'separable_conv_block_6',
+                                int(256*alpha), int(512*alpha), downsample=True)
+    if is_shallow:
+        body = separable_conv_block(body, 'separable_conv_block_7',
+                                    int(512*alpha), int(1024*alpha), downsample=True)
+        body = separable_conv_block(body, 'separable_conv_block_8',
+                                    int(1024*alpha), int(1024*alpha), downsample=True)
+    else:
+        for i in range(7, 12):
+            body = separable_conv_block(body, 'separable_conv_block_%d' % i,
+                                        int(512*alpha), int(512*alpha))
+        body = separable_conv_block(body, 'separable_conv_block_12',
+                                    int(512*alpha), int(1024*alpha), downsample=True)
+        body = separable_conv_block(body, 'separable_conv_block_13',
+                                    int(1024*alpha), int(1024*alpha))
+    pool = relay.nn.global_avg_pool2d(data=body)
+    flatten = relay.nn.batch_flatten(data=pool)
+    weight = relay.var('fc_weight')
+    fc = relay.nn.dense(data=flatten, weight=weight, units=num_classes)
+    softmax = relay.nn.softmax(data=fc)
+    return relay.Function(relay.ir_pass.free_vars(softmax), softmax)
+
+
+def get_workload(batch_size=1, num_classes=1000, image_shape=(3, 224, 224), dtype='float32'):
+    """Get benchmark workload for mobilenet
+
+    Parameters
+    ----------
+    batch_size : int, optional
+        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 : relay.Function
+        The computational graph
+
+    params : dict of str to NDArray
+        The parameters.
+    """
+    data_shape = tuple([batch_size] + list(image_shape))
+    net = mobile_net(num_classes=num_classes, data_shape=data_shape,
+                     dtype=dtype, alpha=1.0, is_shallow=False)
+    return create_workload(net)

tests/python/relay/test_ir_text_printer.py

@@ -104,6 +104,10 @@ def test_resnet():
     net, params = tvm.relay.testing.resnet.get_workload(batch_size=1)
     net.astext()
 
+def test_mobilenet():
+    net, params = tvm.relay.testing.mobilenet.get_workload(batch_size=1)
+    net.astext()
+
 def test_dqn():
     net, params = tvm.relay.testing.dqn.get_workload(batch_size=1)
     net.astext()
@@ -115,6 +119,7 @@ def test_dcgan():
 if __name__ == "__main__":
     do_print[0] = True
     test_resnet()
+    test_mobilenet()
     test_mlp()
     test_dqn()
     test_dcgan()