[NNVM] Add symbol for inception v3 (#1604)
This commit is contained in:
Lianmin Zheng
Tianqi Chen
Родитель
7751a6babc
Коммит
a80356bbe0
|
|
@ -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
|
||||
|
|
|
|||
|
|
@ -0,0 +1,255 @@
|
|||
"""
|
||||
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 .. import symbol as sym
|
||||
from .utils import create_workload
|
||||
|
||||
def Conv(data, num_filter, kernel=(1, 1), stride=(1, 1), pad=(0, 0), name=None, suffix=''):
|
||||
conv = sym.conv2d(data=data, channels=num_filter, kernel_size=kernel,
|
||||
strides=stride, padding=pad, use_bias=False,
|
||||
name='%s%s_conv2d' % (name, suffix))
|
||||
bn = sym.batch_norm(data=conv, name='%s%s_batchnorm' % (name, suffix), epsilon=2e-5)
|
||||
act = sym.relu(data=bn, name='%s%s_relu' % (name, suffix))
|
||||
return act
|
||||
|
||||
def Pooling(data, kernel, stride, pad, pool_type, name):
|
||||
if pool_type == 'max':
|
||||
return sym.max_pool2d(data=data, pool_size=kernel, strides=stride, padding=pad, name=name)
|
||||
elif pool_type == 'avg':
|
||||
return sym.avg_pool2d(data=data, pool_size=kernel, strides=stride, padding=pad, name=name,
|
||||
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 = sym.concatenate(*[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 = Pooling(data=data, kernel=(3, 3), stride=(2, 2), pad=(0, 0), pool_type="max",
|
||||
name=('max_pool_%s_pool' % name))
|
||||
concat = sym.concatenate(*[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 = 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 = sym.concatenate(*[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 = Pooling(data=data, kernel=(3, 3), stride=(2, 2), pool_type=pool, pad=(0, 0),
|
||||
name=('%s_pool_%s_pool' % (pool, name)))
|
||||
# concat
|
||||
concat = sym.concatenate(*[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 = 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 = sym.concatenate(
|
||||
*[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 = 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 = 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 = sym.flatten(data=pool, name="flatten")
|
||||
fc1 = sym.dense(data=flatten, units=num_classes, name='fc1')
|
||||
softmax = sym.softmax(data=fc1, name='softmax')
|
||||
return softmax
|
||||
|
||||
def get_workload(batch_size=1, num_classes=1000,
|
||||
image_shape=(3, 299, 299), dtype="float32", **kwargs):
|
||||
"""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
|
||||
|
||||
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, **kwargs)
|
||||
return create_workload(net, batch_size, image_shape, dtype)
|
||||
|
|
@ -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
|
||||
----------
|
||||
|
|
|
|||
|
|
@ -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()) {
|
||||
|
|
|
|||
|
|
@ -1,11 +1,8 @@
|
|||
"""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]
|
||||
|
|
|
|||
|
|
@ -0,0 +1,170 @@
|
|||
"""
|
||||
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
|
||||
|
|
@ -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()
|
||||
|
|
|
|||
Загрузка…
Ссылка в новой задаче