[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/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()