Update info about MENet models for TensorFlow

README.md

@@ -507,6 +507,13 @@ bottleneck block. Respectively a network without b-suffix has the stride in the
 | ShuffleNetV2 x1.0 | 33.80 | 13.48 | 2,278,604 | 147.92M | Converted from GL model ([log](https://github.com/osmr/imgclsmob/releases/download/v0.0.85/shufflenetv2_w1-1348-7fd0afe5.tf.npz.log)) |
 | ShuffleNetV2 x1.5 | 32.51 | 12.50 | 4,406,098 | 318.61M | Converted from GL model ([log](https://github.com/osmr/imgclsmob/releases/download/v0.0.85/shufflenetv2_w3d2-1250-5dd7b5b1.tf.npz.log)) |
 | ShuffleNetV2 x2.0 | 31.99 | 12.26 | 7,601,686 | 593.66M | Converted from GL model ([log](https://github.com/osmr/imgclsmob/releases/download/v0.0.85/shufflenetv2_w2-1226-f66f6987.tf.npz.log)) |
+| 108-MENet-8x1 (g=3) | 46.11 | 22.32 | 654,516 | 40.64M | From [clavichord93/MENet] ([log](https://github.com/osmr/imgclsmob/releases/download/v0.0.87/menet108_8x1_g3-2232-372c412f.tf.npz.log)) |
+| 128-MENet-8x1 (g=4) | 45.86 | 21.94 | 750,796 | 43.58M | From [clavichord93/MENet] ([log](https://github.com/osmr/imgclsmob/releases/download/v0.0.87/menet128_8x1_g4-2194-d4f6a78e.tf.npz.log)) |
+| 228-MENet-12x1 (g=3) | 35.01 | 14.05 | 1,806,568 | 148.93M | From [clavichord93/MENet] ([log](https://github.com/osmr/imgclsmob/releases/download/v0.0.87/menet228_12x1_g3-1405-724b4422.tf.npz.log)) |
+| 256-MENet-12x1 (g=4) | 34.48 | 13.95 | 1,888,240 | 146.11M | From [clavichord93/MENet] ([log](https://github.com/osmr/imgclsmob/releases/download/v0.0.87/menet256_12x1_g4-1395-d0ce72b1.tf.npz.log)) |
+| 348-MENet-12x1 (g=3) | 31.19 | 11.41 | 3,368,128 | 306.31M | From [clavichord93/MENet] ([log](https://github.com/osmr/imgclsmob/releases/download/v0.0.87/menet348_12x1_g3-1141-f90f3c12.tf.npz.log)) |
+| 352-MENet-12x1 (g=8) | 34.65 | 13.71 | 2,272,872 | 151.03M | From [clavichord93/MENet] ([log](https://github.com/osmr/imgclsmob/releases/download/v0.0.87/menet352_12x1_g8-1371-3621d3c0.tf.npz.log)) |
+| 456-MENet-24x1 (g=3) | 29.56 | 10.46 | 5,304,784 | 560.72M | From [clavichord93/MENet] ([log](https://github.com/osmr/imgclsmob/releases/download/v0.0.87/menet456_24x1_g3-1046-6d70fb21.tf.npz.log)) |
 | MobileNet x0.25 | 45.78 | 22.21 | 470,072 | 42.30M | Converted from GL model ([log](https://github.com/osmr/imgclsmob/releases/download/v0.0.80/mobilenet_wd4-2221-15ee9820.tf.npz.log)) |
 | MobileNet x0.5 | 36.18 | 14.84 | 1,331,592 | 152.04M | Converted from GL model ([log](https://github.com/osmr/imgclsmob/releases/download/v0.0.80/mobilenet_wd2-1484-84d1de07.tf.npz.log)) |
 | MobileNet x0.75 | 32.70 | 12.27 | 2,585,560 | 329.22M | Converted from Gluon Model Zoo ([log](https://github.com/osmr/imgclsmob/releases/download/v0.0.80/mobilenet_w3d4-1227-7f3f25dc.tf.npz.log)) |

tensorflow_/tensorflowcv/models/common.py

@@ -2,7 +2,7 @@
     Common routines for models in TensorFlow.
 """
 
-__all__ = ['conv2d', 'conv1x1', 'batchnorm', 'maxpool2d', 'se_block', 'channel_shuffle']
+__all__ = ['conv2d', 'conv1x1', 'batchnorm', 'maxpool2d', 'avgpool2d', 'se_block', 'channel_shuffle']
 
 import math
 import tensorflow as tf

tensorflow_/tensorflowcv/models/menet.py

@@ -163,7 +163,6 @@ def me_unit(x,
     if downsample:
         out_channels -= in_channels
 
-    # identity = tf.identity(x)
     identity = x
 
     # pointwise group convolution 1
@@ -232,7 +231,6 @@ def me_unit(x,
     y = tf.nn.sigmoid(y, name=name + "/s_evolve_activ")
 
     x = x * y
-    # x = tf.multiply(x, y, name=name + "/mul")
 
     # pointwise group convolution 2
     x = group_conv1x1(
@@ -253,13 +251,6 @@ def me_unit(x,
             strides=2,
             padding=1,
             name=name + "/avgpool")
-        # identity = tf.layers.average_pooling2d(
-        #     inputs=identity,
-        #     pool_size=3,
-        #     strides=2,
-        #     padding='same',
-        #     data_format='channels_first',
-        #     name=name + "/avgpool")
         x = tf.concat([x, identity], axis=1, name=name + "/concat")
     else:
         x = x + identity

tensorflow_/tensorflowcv/models/model_store.py

@@ -55,6 +55,13 @@
     ('shufflenetv2_w1', '1348', '7fd0afe56d93af7552934d5e1d77fa3597f1b3b7', 'v0.0.85'),
     ('shufflenetv2_w3d2', '1250', '5dd7b5b1d7de186adf32da4f81204c5a3a6e64c3', 'v0.0.85'),
     ('shufflenetv2_w2', '1226', 'f66f6987ad0dd8f67f6c8a53f0f729a415aa67bf', 'v0.0.85'),
+    ('menet108_8x1_g3', '2232', '372c412f91fb628a2e2d8330958e2d26a1f5a59e', 'v0.0.87'),
+    ('menet128_8x1_g4', '2194', 'd4f6a78eb1ed563dabfb16bca6008147c9163eac', 'v0.0.87'),
+    ('menet228_12x1_g3', '1405', '724b4422a8e979d944521bf3cf57a409a2ba43f1', 'v0.0.87'),
+    ('menet256_12x1_g4', '1395', 'd0ce72b17f895ae67e61de78dccd2a42ffd9368e', 'v0.0.87'),
+    ('menet348_12x1_g3', '1141', 'f90f3c12eb38e9fe0d4e43e48d68456d24c20a45', 'v0.0.87'),
+    ('menet352_12x1_g8', '1371', '3621d3c0cddd0a0f4fce4b2a91d6562ba584e7b2', 'v0.0.87'),
+    ('menet456_24x1_g3', '1046', '6d70fb2177a326b3c94e66592238867c3bc8bcea', 'v0.0.87'),
     ('mobilenet_wd4', '2221', '15ee9820a315d20c732c085a4cd1edd0e3c0658a', 'v0.0.80'),
     ('mobilenet_wd2', '1484', '84d1de07c3d939c08bf1bdd4583c6b739aca0b04', 'v0.0.80'),
     ('mobilenet_w3d4', '1227', '7f3f25dc1e01ca59df627fbab0fadef7a9b9b14c', 'v0.0.80'),