[incubator-mxnet] branch master updated: Adding multiple pretrained models (#9215)

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zhasheng pushed a commit to branch master
in repository https://gitbox.apache.org/repos/asf/incubator-mxnet.git


The following commit(s) were added to refs/heads/master by this push:
     new 25dd12d  Adding multiple pretrained models (#9215)
25dd12d is described below

commit 25dd12dc32db516ad6862ac70de398bc538dc430
Author: Joshua Z. Zhang <ch...@gmail.com>
AuthorDate: Sat Dec 30 23:30:27 2017 -0800

    Adding multiple pretrained models (#9215)
    
    * update resnet50_v2 pretrained model
    
    * add models to store, fix resnetv1 and mobilenet
    
    * add resnet152_v1
---
 python/mxnet/gluon/model_zoo/model_store.py      | 14 ++++++++++----
 python/mxnet/gluon/model_zoo/vision/mobilenet.py |  2 +-
 python/mxnet/gluon/model_zoo/vision/resnet.py    |  4 ++--
 3 files changed, 13 insertions(+), 7 deletions(-)

diff --git a/python/mxnet/gluon/model_zoo/model_store.py b/python/mxnet/gluon/model_zoo/model_store.py
index bb54974..1a7dc54 100644
--- a/python/mxnet/gluon/model_zoo/model_store.py
+++ b/python/mxnet/gluon/model_zoo/model_store.py
@@ -31,14 +31,20 @@ _model_sha1 = {name: checksum for checksum, name in [
     ('2603f878403c6aa5a71a124c4a3307143d6820e9', 'densenet169'),
     ('1cdbc116bc3a1b65832b18cf53e1cb8e7da017eb', 'densenet201'),
     ('ed47ec45a937b656fcc94dabde85495bbef5ba1f', 'inceptionv3'),
-    ('d2b128fa89477c2e20061607a53a8d9f66ce239d', 'resnet101_v1'),
-    ('6562166cd597a6328a32a0ce47bb651df80b3bbb', 'resnet152_v1'),
+    ('9f83e440996887baf91a6aff1cccc1c903a64274', 'mobilenet0.25'),
+    ('8e9d539cc66aa5efa71c4b6af983b936ab8701c3', 'mobilenet0.5'),
+    ('529b2c7f4934e6cb851155b22c96c9ab0a7c4dc2', 'mobilenet0.75'),
+    ('6b8c5106c730e8750bcd82ceb75220a3351157cd', 'mobilenet1.0'),
     ('38d6d423c22828718ec3397924b8e116a03e6ac0', 'resnet18_v1'),
     ('4dc2c2390a7c7990e0ca1e53aeebb1d1a08592d1', 'resnet34_v1'),
-    ('2a903ab21260c85673a78fe65037819a843a1f43', 'resnet50_v1'),
+    ('c940b1a062b32e3a5762f397c9d1e178b5abd007', 'resnet50_v1'),
+    ('d992389084bc5475c370e9b52c3561706e755799', 'resnet101_v1'),
+    ('48ce7775d375987d019ec9aa96bc43b98165dfcb', 'resnet152_v1'),
     ('8aacf80ff4014c1efa2362a963ac5ec82cf92d5b', 'resnet18_v2'),
     ('0ed3cd06da41932c03dea1de7bc2506ef3fb97b3', 'resnet34_v2'),
-    ('eb7a368774aa34a12ed155126b641ae7556dad9d', 'resnet50_v2'),
+    ('81a4e66af7859a5aa904e2b4051aa0d3bc472b2f', 'resnet50_v2'),
+    ('7eb2b3cde097883c11941b927048a705ed334294', 'resnet101_v2'),
+    ('64c75ac8c292f6ac54f873f9ef62e0531105878b', 'resnet152_v2'),
     ('264ba4970a0cc87a4f15c96e25246a1307caf523', 'squeezenet1.0'),
     ('33ba0f93753c83d86e1eb397f38a667eaf2e9376', 'squeezenet1.1'),
     ('dd221b160977f36a53f464cb54648d227c707a05', 'vgg11'),
diff --git a/python/mxnet/gluon/model_zoo/vision/mobilenet.py b/python/mxnet/gluon/model_zoo/vision/mobilenet.py
index 0ba0933..34d8490 100644
--- a/python/mxnet/gluon/model_zoo/vision/mobilenet.py
+++ b/python/mxnet/gluon/model_zoo/vision/mobilenet.py
@@ -28,7 +28,7 @@ from ... import nn
 # Helpers
 def _add_conv(out, channels=1, kernel=1, stride=1, pad=0, num_group=1):
     out.add(nn.Conv2D(channels, kernel, stride, pad, groups=num_group, use_bias=False))
-    out.add(nn.BatchNorm(scale=False))
+    out.add(nn.BatchNorm(scale=True))
     out.add(nn.Activation('relu'))
 
 def _add_conv_dw(out, dw_channels, channels, stride):
diff --git a/python/mxnet/gluon/model_zoo/vision/resnet.py b/python/mxnet/gluon/model_zoo/vision/resnet.py
index 4474aee..fa86c64 100644
--- a/python/mxnet/gluon/model_zoo/vision/resnet.py
+++ b/python/mxnet/gluon/model_zoo/vision/resnet.py
@@ -102,10 +102,10 @@ class BottleneckV1(HybridBlock):
     def __init__(self, channels, stride, downsample=False, in_channels=0, **kwargs):
         super(BottleneckV1, self).__init__(**kwargs)
         self.body = nn.HybridSequential(prefix='')
-        self.body.add(nn.Conv2D(channels//4, kernel_size=1, strides=1))
+        self.body.add(nn.Conv2D(channels//4, kernel_size=1, strides=stride))
         self.body.add(nn.BatchNorm())
         self.body.add(nn.Activation('relu'))
-        self.body.add(_conv3x3(channels//4, stride, channels//4))
+        self.body.add(_conv3x3(channels//4, 1, channels//4))
         self.body.add(nn.BatchNorm())
         self.body.add(nn.Activation('relu'))
         self.body.add(nn.Conv2D(channels, kernel_size=1, strides=1))

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