network slimming v0.1

foolwood · Jan 15, 2018 · f3edf64 · f3edf64
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diff --git a/README.md b/README.md
@@ -2,33 +2,49 @@
 
 This is a **[PyTorch](http://pytorch.org/)** reimplementation of algorithm presented in "[Learning Efficient Convolutional Networks Through Network Slimming](http://openaccess.thecvf.com/content_iccv_2017/html/Liu_Learning_Efficient_Convolutional_ICCV_2017_paper.html)(ICCV2017)." . The source code is based on Torch. For more info, visit the author's [webpage](https://github.com/liuzhuang13/slimming)!.
 
-|  CIFAR10-VGG16BN  | Baseline | Trained with Sparsity | Pruned(70% Pruned) | Fine-tuned |
-| :---------------: | :------: | :-------------------: | :----------------: | :--------: |
-| Top1 Accuracy (%) |  93.72   |         93.56         |         80         |            |
-|    Parameters     |          |                       |                    |            |
+|  CIFAR10-VGG16BN  | Baseline | Trained with Sparsity (1e-4) | Pruned (0.7 Pruned) | Fine-tuned (40epochs) |
+| :---------------: | :------: | :--------------------------: | :-----------------: | :-------------------: |
+| Top1 Accuracy (%) |  93.62   |            93.77             |        10.00        |         93.56         |
+|    Parameters     |  20.04M  |            20.04M            |        2.42M        |         2.42M         |
+
+|             Pruned Ratio             |       0       |     0.1      |      0.2      |     0.3      |     0.4      |     0.5      |     0.6      |     0.7      |
+| :----------------------------------: | :-----------: | :----------: | :-----------: | :----------: | :----------: | :----------: | :----------: | :----------: |
+| Top1 Accuracy (%) without Fine-tuned |     93.77     |    93.72     |     93.76     |    93.75     |    93.75     |    93.40     |    37.83     |    10.00     |
+|       Parameters(M) / macc(M)        | 20.04/ 398.44 | 15.9/ 349.22 | 12.28/ 307.78 | 9.12/ 272.94 | 6.74/ 247.86 | 4.62/ 231.86 | 3.14/ 222.17 | 2.42/ 210.84 |
+
+| Pruned Ratio |               architecture               |
+| :----------: | :--------------------------------------: |
+|      0       | [64, 64, 'M', 128, 128, 'M', 256, 256, 256, 256, 'M', 512, 512, 512, 512, 'M', 512, 512, 512, 512] |
+|     0.1      | [60, 64, 'M', 128, 128, 'M', 256, 255, 253, 245, 'M', 436, 417, 425, 462, 'M', 463, 465, 472, 424] |
+|     0.2      | [58, 64, 'M', 128, 128, 'M', 256, 255, 250, 233, 'M', 360, 336, 329, 398, 'M', 420, 412, 435, 341] |
+|     0.3      | [56, 64, 'M', 128, 128, 'M', 256, 254, 249, 227, 'M', 284, 239, 244, 351, 'M', 369, 364, 384, 255] |
+|     0.4      | [52, 64, 'M', 128, 128, 'M', 256, 254, 247, 218, 'M', 218, 162, 166, 294, 'M', 317, 315, 318, 165] |
+|     0.5      | [52, 64, 'M', 128, 128, 'M', 256, 254, 245, 214, 'M', 179, 117, 116, 229, 'M', 228, 220, 210, 111] |
+|     0.6      | [51, 64, 'M', 128, 128, 'M', 256, 254, 245, 213, 'M', 165, 85, 92, 153, 'M', 83, 86, 87, 111] |
+|     0.7      | [49, 64, 'M', 128, 128, 'M', 256, 254, 234, 198, 'M', 114, 41, 24, 11, 'M', 14, 13, 19, 104] |
 
 ## Baseline 
 
-```python
+```shell
 python main.py
 ```
 
 ## Trained with Sparsity
 
-```python
+```shell
 python main.py -sr --s 0.0001
 ```
 
 ## Pruned
 
-```python
-python prune.py --model model_best.pth.tar --save pruned.pth.tar --percent 0.75
+```shell
+python prune.py --model model_best.pth.tar --save pruned.pth.tar --percent 0.7
 ```
 
 ## Fine-tuned
 
-```python
-python main.py -refine pruned.pth.tar
+```shell
+python main.py -refine pruned.pth.tar --epochs 40
 ```
 
 ## Reference

diff --git a/main.py b/main.py
@@ -17,8 +17,8 @@
                     help='training dataset (default: cifar10)')
 parser.add_argument('--sparsity-regularization', '-sr', dest='sr', action='store_true',
                     help='train with channel sparsity regularization')
-parser.add_argument('--s', type=float, default=0.00001,
-                    help='scale sparse rate (default: 0.00001)')
+parser.add_argument('--s', type=float, default=0.0001,
+                    help='scale sparse rate (default: 0.0001)')
 parser.add_argument('--refine', default='', type=str, metavar='PATH',
                     help='refine from prune model')
 parser.add_argument('--batch-size', type=int, default=100, metavar='N',

diff --git a/prune.py b/prune.py
@@ -16,8 +16,8 @@
                     help='input batch size for testing (default: 1000)')
 parser.add_argument('--no-cuda', action='store_true', default=False,
                     help='disables CUDA training')
-parser.add_argument('--percent', type=float, default=0.2,
-                    help='scale sparse rate (default: 0.2)')
+parser.add_argument('--percent', type=float, default=0.5,
+                    help='scale sparse rate (default: 0.5)')
 parser.add_argument('--model', default='', type=str, metavar='PATH',
                     help='path to raw trained model (default: none)')
 parser.add_argument('--save', default='', type=str, metavar='PATH',