[NNVM] Add symbol squeezenet

nnvm/python/nnvm/testing/__init__.py

@@ -7,6 +7,7 @@
 from . import mlp
 from . import resnet
 from . import vgg
+from . import squeezenet
 from . import dcgan
 from . import dqn
 from . import yolo2_detection

nnvm/python/nnvm/testing/mobilenet.py

@@ -86,7 +86,7 @@ def get_workload(batch_size, num_classes=1000, image_shape=(3, 224, 224), dtype=
         The batch size used in the model
 
     num_classes : int, optional
-        Number of claseses
+        Number of classes
 
     image_shape : tuple, optional
         The input image shape

nnvm/python/nnvm/testing/resnet.py

@@ -199,7 +199,7 @@ def get_workload(batch_size=1, num_classes=1000, num_layers=18,
         The batch size used in the model
 
     num_classes : int, optional
-        Number of claseses
+        Number of classes
 
     num_layers : int, optional
         Number of layers

nnvm/python/nnvm/testing/squeezenet.py

@@ -0,0 +1,132 @@
+# 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 .. import symbol as sym
+from . utils import create_workload
+
+# 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 = sym.concatenate(left, right, axis=1)
+
+    return net
+
+def _make_fire_conv(net, channels, kernel_size, padding=0):
+    net = sym.conv2d(net, channels=channels, kernel_size=(kernel_size, kernel_size),
+                     padding=(padding, padding))
+    net = sym.relu(net)
+    return net
+
+# Net
+def get_symbol(num_classes, version, **kwargs):
+    """Get symbol of SqueezeNet
+
+    Parameters
+    ----------
+    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))
+    net = sym.Variable("data")
+    if version == '1.0':
+        net = sym.conv2d(net, channels=96, kernel_size=(7, 7), strides=(2, 2), padding=(3, 3))
+        net = sym.relu(net)
+        net = sym.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 = sym.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 = sym.max_pool2d(net, pool_size=(3, 3), strides=(2, 2))
+        net = _make_fire(net, 64, 256, 256)
+    else:
+        net = sym.conv2d(net, channels=64, kernel_size=(3, 3), strides=(2, 2), padding=(1, 1))
+        net = sym.relu(net)
+        net = sym.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 = sym.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 = sym.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 = sym.dropout(net, rate=0.5)
+    net = sym.conv2d(net, channels=num_classes, kernel_size=(1, 1))
+    net = sym.relu(net)
+    net = sym.global_avg_pool2d(net)
+    net = sym.flatten(net)
+    return sym.softmax(net)
+
+def get_workload(batch_size=1, num_classes=1000, version='1.0',
+                 image_shape=(3, 224, 224), dtype="float32", **kwargs):
+    """Get benchmark workload for resnet
+
+    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
+
+    kwargs : dict
+        Extra arguments
+
+    Returns
+    -------
+    net : nnvm.Symbol
+        The computational graph
+
+    params : dict of str to NDArray
+        The parameters.
+    """
+    net = get_symbol(num_classes=num_classes, version=version, **kwargs)
+    return create_workload(net, batch_size, image_shape, dtype)

nnvm/tests/python/frontend/mxnet/model_zoo/__init__.py

@@ -1,9 +1,10 @@
 """MXNet and NNVM model zoo."""
 from __future__ import absolute_import
-from . import mlp, resnet, vgg, dqn, dcgan
+from . import mlp, resnet, vgg, dqn, dcgan, squeezenet
 import nnvm.testing
 
-__all__ = ['mx_mlp', 'nnvm_mlp', 'mx_resnet', 'nnvm_resnet', 'mx_vgg', 'nnvm_vgg']
+__all__ = ['mx_mlp', 'nnvm_mlp', 'mx_resnet', 'nnvm_resnet', 'mx_vgg', 'nnvm_vgg',
+           'mx_squeezenet', 'nnvm_squeezenet']
 
 _num_class = 1000
 
@@ -27,6 +28,13 @@
     nnvm_vgg[num_layer] = nnvm.testing.vgg.get_workload(
         1, _num_class, num_layers=num_layer)[0]
 
+# squeezenet
+mx_squeezenet = {}
+nnvm_squeezenet = {}
+for version in ['1.0', '1.1']:
+    mx_squeezenet[version] = squeezenet.get_symbol(version=version)
+    nnvm_squeezenet[version] = nnvm.testing.squeezenet.get_workload(1, version=version)[0]
+
 # dqn
 mx_dqn = dqn.get_symbol()
 nnvm_dqn = nnvm.testing.dqn.get_workload(1)[0]

nnvm/tests/python/frontend/mxnet/model_zoo/squeezenet.py

@@ -0,0 +1,76 @@
+"""
+Symbol of SqueezeNet
+
+Reference:
+Iandola, Forrest N., et al.
+"Squeezenet: Alexnet-level accuracy with 50x fewer parameters and< 0.5 mb model size." (2016).
+"""
+
+import mxnet as mx
+
+# 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 = mx.sym.concat(left, right, dim=1)
+
+    return net
+
+def _make_fire_conv(net, channels, kernel_size, padding=0):
+    net = mx.sym.Convolution(net, num_filter=channels, kernel=(kernel_size, kernel_size),
+                             pad=(padding, padding))
+    net = mx.sym.Activation(net, act_type='relu')
+    return net
+
+# Net
+def get_symbol(num_classes=1000, version='1.0', **kwargs):
+    """Get symbol of SqueezeNet
+
+    Parameters
+    ----------
+    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))
+    net = mx.sym.Variable("data")
+    if version == '1.0':
+        net = mx.sym.Convolution(net, num_filter=96, kernel=(7, 7), stride=(2, 2), pad=(3, 3))
+        net = mx.sym.Activation(net, act_type='relu')
+        net = mx.sym.Pooling(data=net, kernel=(3, 3), pool_type='max', stride=(2, 2))
+        net = _make_fire(net, 16, 64, 64)
+        net = _make_fire(net, 16, 64, 64)
+        net = _make_fire(net, 32, 128, 128)
+        net = mx.sym.Pooling(data=net, kernel=(3, 3), pool_type='max', stride=(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 = mx.sym.Pooling(data=net, kernel=(3, 3), pool_type='max', stride=(2, 2))
+        net = _make_fire(net, 64, 256, 256)
+    else:
+        net = mx.sym.Convolution(net, num_filter=64, kernel=(3, 3), stride=(2, 2), pad=(1, 1))
+        net = mx.sym.Activation(net, act_type='relu')
+        net = mx.sym.Pooling(data=net, kernel=(3, 3), pool_type='max', stride=(2, 2))
+        net = _make_fire(net, 16, 64, 64)
+        net = _make_fire(net, 16, 64, 64)
+        net = mx.sym.Pooling(data=net, kernel=(3, 3), pool_type='max',  stride=(2, 2))
+        net = _make_fire(net, 32, 128, 128)
+        net = _make_fire(net, 32, 128, 128)
+        net = mx.sym.Pooling(data=net, kernel=(3, 3), pool_type='max',  stride=(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 = mx.sym.Dropout(net, p=0.5)
+    net = mx.sym.Convolution(net, num_filter=num_classes, kernel=(1, 1))
+    net = mx.sym.Activation(net, act_type='relu')
+    net = mx.sym.Pooling(data=net, global_pool=True, kernel=(13, 13), pool_type='avg')
+    net = mx.sym.flatten(net)
+    return mx.sym.softmax(net)

nnvm/tests/python/frontend/mxnet/test_graph.py

@@ -32,6 +32,13 @@ def test_resnet():
         nnvm_sym = model_zoo.nnvm_resnet[n]
         compare_graph(from_mx_sym, nnvm_sym)
 
+def test_squeezenet():
+    for version in ['1.0', '1.1']:
+        mx_sym = model_zoo.mx_squeezenet[version]
+        from_mx_sym, _ = nnvm.frontend.from_mxnet(mx_sym)
+        nnvm_sym = model_zoo.nnvm_squeezenet[version]
+        compare_graph(from_mx_sym, nnvm_sym)
+
 def test_dqn():
     mx_sym = model_zoo.mx_dqn
     from_mx_sym, _ = nnvm.frontend.from_mxnet(mx_sym)
@@ -62,3 +69,4 @@ def compose(F, **kwargs):
     test_multi_outputs()
     test_dqn()
     test_dcgan()
+    test_squeezenet()

tutorials/autotvm/tune_cuda_conv2d.py

@@ -21,7 +21,7 @@
 # ---------------------------------
 # There are plenty of useful schedule primitives in tvm. You can also find 
 # some tutorials that describe them in more details, such as 
-# (1). :doc:``Optimizing Conv2d on NVIDIA GPU <../optimize/opt_conv_cuda>`
+# (1). :ref:`opt-conv-gpu`
 # (2). `Optimizing DepthwiseConv on NVIDIA GPU <https://tvm.ai/2017/08/22/Optimize-Deep-Learning-GPU-Operators-with-TVM-A-Depthwise-Convolution-Example.html>`_
 # 
 # However, their implementations are manually tuned for some special input

tutorials/nnvm/imagenet_inference_gpu.py

@@ -20,7 +20,7 @@
 # To get the maximum performance, we need to enable nvcc's compiler hook.
 # This usually gives better performance than nvrtc mode.
 
-@tvm.register_func
+@tvm.register_func("tvm_callback_cuda_compile", override=True)
 def tvm_callback_cuda_compile(code):
     ptx = nvcc.compile_cuda(code, target="ptx")
     return ptx

tutorials/optimize/opt_conv_cuda.py

@@ -1,4 +1,7 @@
-"""How to optimize convolution on GPU
+"""
+.. _opt-conv-gpu:
+
+How to optimize convolution on GPU
 ==================================
 **Author**: `Haichen Shen <https://homes.cs.washington.edu/~haichen/>`_