[NNVM] Add symbol for inception v3 (#1604)

nnvm/python/nnvm/testing/__init__.py

@@ -8,6 +8,7 @@ from . import mlp
 from . import resnet
 from . import vgg
 from . import squeezenet
+from . import inception_v3
 from . import dcgan
 from . import dqn
 from . import yolo2_detection

nnvm/python/nnvm/testing/squeezenet.py

@@ -98,7 +98,7 @@ def get_symbol(num_classes, version, **kwargs):
 
 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
+    """Get benchmark workload for SqueezeNet
 
     Parameters
     ----------

nnvm/src/compiler/graph_hash.cc

@@ -125,7 +125,7 @@ std::string GraphDeepCompare(const Graph& a,
   const IndexedGraph& idxb = b.indexed_graph();
   std::ostringstream err;
   if (idxa.num_nodes() != idxb.num_nodes()) {
-    err << "Number of nodes mismatch";
+    err << "Number of nodes mismatch (" <<  idxa.num_nodes() << " v.s " << idxb.num_nodes() << ")";
     return err.str();
   }
   if (idxa.num_node_entries() != idxb.num_node_entries()) {

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

 """MXNet and NNVM model zoo."""
 from __future__ import absolute_import
-from . import mlp, resnet, vgg, dqn, dcgan, squeezenet
+from . import mlp, resnet, vgg, dqn, dcgan, squeezenet, inception_v3
 import nnvm.testing
 
-__all__ = ['mx_mlp', 'nnvm_mlp', 'mx_resnet', 'nnvm_resnet', 'mx_vgg', 'nnvm_vgg',
-           'mx_squeezenet', 'nnvm_squeezenet']
-
 _num_class = 1000
 
 # mlp fc
@@ -35,6 +32,10 @@ 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]
 
+# inception
+mx_inception_v3 = inception_v3.get_symbol()
+nnvm_inception_v3 = nnvm.testing.inception_v3.get_workload(1)[0]
+
 # dqn
 mx_dqn = dqn.get_symbol()
 nnvm_dqn = nnvm.testing.dqn.get_workload(1)[0]

nnvm/tests/python/frontend/mxnet/model_zoo/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
+"""
+import mxnet as mx
+import numpy as np
+
+def Conv(data, num_filter, kernel=(1, 1), stride=(1, 1), pad=(0, 0), name=None, suffix=''):
+    conv = mx.sym.Convolution(data=data, num_filter=num_filter, kernel=kernel, stride=stride, pad=pad, no_bias=True, name='%s%s_conv2d' %(name, suffix))
+    bn = mx.sym.BatchNorm(data=conv, eps=2e-5, name='%s%s_batchnorm' % (name, suffix))
+    act = mx.sym.Activation(data=bn, act_type='relu', name='%s%s_relu' %(name, suffix))
+    return act
+
+
+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 = mx.sym.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 = mx.sym.Concat(*[tower_1x1, tower_5x5, tower_3x3, cproj], name='ch_concat_%s_chconcat' % name)
+    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 = mx.sym.Pooling(data=data, kernel=(3, 3), stride=(2, 2), pad=(0,0), pool_type="max", name=('max_pool_%s_pool' % name))
+    concat = mx.sym.Concat(*[tower_3x3, tower_d3x3, pooling], name='ch_concat_%s_chconcat' % name)
+    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 = mx.sym.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 = mx.sym.Concat(*[tower_1x1, tower_d7, tower_q7, cproj], name='ch_concat_%s_chconcat' % name)
+    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 = mx.sym.Pooling(data=data, kernel=(3, 3), stride=(2, 2), pool_type=pool, name=('%s_pool_%s_pool' % (pool, name)))
+    # concat
+    concat = mx.sym.Concat(*[tower_3x3, tower_d7_3x3, pooling], name='ch_concat_%s_chconcat' % name)
+    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 = mx.sym.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 = mx.sym.Concat(*[tower_1x1, tower_d3_a, tower_d3_b, tower_3x3_d3_a, tower_3x3_d3_b, cproj], name='ch_concat_%s_chconcat' % name)
+    return concat
+
+def get_symbol(num_classes=1000, **kwargs):
+    data = mx.sym.Variable(name="data")
+    # 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 = mx.sym.Pooling(data=conv_2, kernel=(3, 3), stride=(2, 2), pool_type="max", 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 = mx.sym.Pooling(data=conv_4, kernel=(3, 3), stride=(2, 2), pool_type="max", 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 = mx.sym.Pooling(data=in5b, kernel=(8, 8), stride=(1, 1), pool_type="avg", name="global_pool")
+    flatten = mx.sym.Flatten(data=pool, name="flatten")
+    fc1 = mx.sym.FullyConnected(data=flatten, num_hidden=num_classes, name='fc1', flatten=False)
+    softmax = mx.sym.SoftmaxOutput(data=fc1, name='softmax')
+    return softmax

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

@@ -39,17 +39,23 @@ def test_squeezenet():
         nnvm_sym = model_zoo.nnvm_squeezenet[version]
         compare_graph(from_mx_sym, nnvm_sym)
 
+def test_inception_v3():
+    mx_sym = model_zoo.mx_inception_v3
+    from_mx_sym, _ = nnvm.frontend.from_mxnet(mx_sym)
+    nnvm_sym = model_zoo.nnvm_inception_v3
+    compare_graph(from_mx_sym, nnvm_sym, ishape=(2, 3, 299, 299))
+
 def test_dqn():
     mx_sym = model_zoo.mx_dqn
     from_mx_sym, _ = nnvm.frontend.from_mxnet(mx_sym)
     nnvm_sym = model_zoo.nnvm_dqn
-    compare_graph(from_mx_sym, nnvm_sym)
+    compare_graph(from_mx_sym, nnvm_sym, ishape=(2, 4, 84, 84))
 
 def test_dcgan():
     mx_sym = model_zoo.mx_dcgan
     from_mx_sym, _ = nnvm.frontend.from_mxnet(mx_sym)
     nnvm_sym = model_zoo.nnvm_dcgan
-    compare_graph(from_mx_sym, nnvm_sym)
+    compare_graph(from_mx_sym, nnvm_sym, ishape=(2, 100))
 
 def test_multi_outputs():
     def compose(F, **kwargs):
@@ -70,3 +76,4 @@ if __name__ == '__main__':
     test_dqn()
     test_dcgan()
     test_squeezenet()
+    test_inception_v3()