fix sinkhorn & update CIE results

chenzhongwang9811 · Aug 3, 2021 · 450bc48 · 450bc48
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diff --git a/README.md b/README.md
@@ -1,5 +1,7 @@
 # Think Match
 
+[![Documentation Status](https://readthedocs.org/projects/thinkmatch/badge/?version=latest)](https://thinkmatch.readthedocs.io/en/latest/?badge=latest)
+
 _ThinkMatch_ is developed and maintained by [ThinkLab](http://thinklab.sjtu.edu.cn) at Shanghai Jiao Tong University.
 This repository is developed for the following purposes:
 * **Providing modules** for developing deep graph matching algorithms to facilitate future research.
@@ -29,7 +31,6 @@ In this repository, we mainly focus on image keypoint matching because it is a p
 Readers are referred to the following survey for more technical details about graph matching:
 * Junchi Yan, Xu-Cheng Yin, Weiyao Lin, Cheng Deng, Hongyuan Zha, Xiaokang Yang. "A Short Survey of Recent Advances in Graph Matching."
 _ICMR 2016_.
-
 ## Deep Graph Matching Algorithms
 _ThinkMatch_ currently contains pytorch source code of the following deep graph matching methods:
 
@@ -67,10 +68,10 @@ _ThinkMatch_ currently contains pytorch source code of the following deep graph
 | [NGM](/models/NGM)     | 2019 | 0.5010 | 0.6350 | 0.5790 | 0.5340 | 0.7980 | 0.7710 | 0.7360 | 0.6820 | 0.4110 | 0.6640 | 0.4080 | 0.6030 | 0.6190 | 0.6350 | 0.4560 | 0.7710 | 0.6930 | 0.6550 | 0.7920 | 0.8820 | 0.6413 |
 | [NHGM](/models/NGM)    | 2019 | 0.5240 | 0.6220 | 0.5830 | 0.5570 | 0.7870 | 0.7770 | 0.7440 | 0.7070 | 0.4200 | 0.6460 | 0.5380 | 0.6100 | 0.6190 | 0.6080 | 0.4680 | 0.7910 | 0.6680 | 0.5510 | 0.8090 | 0.8870 | 0.6458 |
 | [IPCA-GM](/models/PCA) | 2020 | 0.5378 | 0.6622 | 0.6714 | 0.6120 | 0.8039 | 0.7527 | 0.7255 | 0.7252 | 0.4455 | 0.6524 | 0.5430 | 0.6724 | 0.6790 | 0.6421 | 0.4793 | 0.8435 | 0.7079 | 0.6398 | 0.8380 | 0.9083 | 0.6770 |
-| [CIE-H](/models/CIE)   | 2020 | 0.4994 | 0.6313 | 0.7065 | 0.5298 | 0.8243 | 0.7536 | 0.6766 | 0.7230 | 0.4235 | 0.6688 | 0.6990 | 0.6952 | 0.7074 | 0.6196 | 0.4667 | 0.8504 | 0.7000 | 0.6175 | 0.8023 | 0.9178 | 0.6756 |
+| [CIE-H](/models/CIE)   | 2020 | 0.5250 | 0.6858 | 0.7015 | 0.5706 | 0.8207 | 0.7700 | 0.7073 | 0.7313 | 0.4383 | 0.6994 | 0.6237 | 0.7018 | 0.7031 | 0.6641 | 0.4763 | 0.8525 | 0.7172 | 0.6400 | 0.8385 | 0.9168 | 0.6892 |
 | [BBGM](/models/BBGM)   | 2020 | 0.6187 | 0.7106 | 0.7969 | 0.7896 | 0.8740 | 0.9401 | 0.8947 | 0.8022 | 0.5676 | 0.7914 | 0.6458 | 0.7892 | 0.7615 | 0.7512 | 0.6519 | 0.9818 | 0.7729 | 0.7701 | 0.9494 | 0.9393 | 0.7899 |
-| [NGM-v2](/models/NGM)   | 2021 | 0.6184 | 0.7118 | 0.7762 | 0.7875 | 0.8733 | 0.9363 | 0.8770 | 0.7977 | 0.5535 | 0.7781 | 0.8952 | 0.7880 | 0.8011 | 0.7923 | 0.6258 | 0.9771 | 0.7769 | 0.7574 | 0.9665 | 0.9323 | 0.8011 |
-| [NHGM-v2](/models/NGM)  | 2021 | 0.5995 | 0.7154 | 0.7724 | 0.7902 | 0.8773 | 0.9457 | 0.8903 | 0.8181 | 0.5995 | 0.8129 | 0.8695 | 0.7811 | 0.7645 | 0.7750 | 0.6440 | 0.9872 | 0.7778 | 0.7538 | 0.9787 | 0.9280 | 0.8040 |
+| [NGM-v2](/models/NGM)  | 2021 | 0.6184 | 0.7118 | 0.7762 | 0.7875 | 0.8733 | 0.9363 | 0.8770 | 0.7977 | 0.5535 | 0.7781 | 0.8952 | 0.7880 | 0.8011 | 0.7923 | 0.6258 | 0.9771 | 0.7769 | 0.7574 | 0.9665 | 0.9323 | 0.8011 |
+| [NHGM-v2](/models/NGM) | 2021 | 0.5995 | 0.7154 | 0.7724 | 0.7902 | 0.8773 | 0.9457 | 0.8903 | 0.8181 | 0.5995 | 0.8129 | 0.8695 | 0.7811 | 0.7645 | 0.7750 | 0.6440 | 0.9872 | 0.7778 | 0.7538 | 0.9787 | 0.9280 | 0.8040 |
 
 ### Willow Object Class - 2GM & MGM
 
@@ -122,7 +123,7 @@ This repository is developed and tested with Ubuntu 16.04, Python 3.7, Pytorch 1
 1. Install building tools for LPMP:
     ```bash
     apt-get install -y findutils libhdf5-serial-dev git wget libssl-dev
-
+    
     wget https://github.com/Kitware/CMake/releases/download/v3.19.1/cmake-3.19.1.tar.gz && tar zxvf cmake-3.19.1.tar.gz
     cd cmake-3.19.1 && ./bootstrap && make && make install
     ```
@@ -135,7 +136,7 @@ This repository is developed and tested with Ubuntu 16.04, Python 3.7, Pytorch 1
    apt-get update
    apt-get install -y software-properties-common
    add-apt-repository ppa:ubuntu-toolchain-r/test
-
+   
    apt-get install -y gcc-9 g++-9
    update-alternatives --install /usr/bin/gcc gcc /usr/bin/gcc-9 60 --slave /usr/bin/g++ g++ /usr/bin/g++-9
    ```

diff --git a/models/CIE/README.md b/models/CIE/README.md
@@ -18,7 +18,7 @@ pretrained model: [google drive](https://drive.google.com/file/d/1oRwcnw06t1rCbr
 
 | model                 | year | aero   | bike   | bird   | boat   | bottle | bus    | car    | cat    | chair  | cow    | table  | dog    | horse  | mbkie  | person | plant  | sheep  | sofa   | train  | tv     | mean   |
 | --------------------- | ---- | ------ | ------ | ------ | ------ | ------ | ------ | ------ | ------ | ------ | ------ | ------ | ------ | ------ | ------ | ------ | ------ | ------ | ------ | ------ | ------ | ------ |
-| [CIE-H](/models/CIE)   | 2020 | 0.4994 | 0.6313 | 0.7065 | 0.5298 | 0.8243 | 0.7536 | 0.6766 | 0.7230 | 0.4235 | 0.6688 | 0.6990 | 0.6952 | 0.7074 | 0.6196 | 0.4667 | 0.8504 | 0.7000 | 0.6175 | 0.8023 | 0.9178 | 0.6756 |
+| [CIE-H](/models/CIE)   | 2020 | 0.5250 | 0.6858 | 0.7015 | 0.5706 | 0.8207 | 0.7700 | 0.7073 | 0.7313 | 0.4383 | 0.6994 | 0.6237 | 0.7018 | 0.7031 | 0.6641 | 0.4763 | 0.8525 | 0.7172 | 0.6400 | 0.8385 | 0.9168 | 0.6892 |
 
 ## File Organization
 ```

diff --git a/src/lap_solvers/sinkhorn.py b/src/lap_solvers/sinkhorn.py
@@ -288,7 +288,7 @@ def __init__(self, max_iter=10, tau=1., epsilon=1e-4, batched_operation=False):
         self.sinkhorn = Sinkhorn(max_iter, tau, epsilon, batched_operation=batched_operation)
 
     def forward(self, s: Tensor, nrows: Tensor=None, ncols: Tensor=None,
-                sample_num=5, dummy_row=False, dtype=torch.float32) -> Tensor:
+                sample_num=5, dummy_row=False) -> Tensor:
         r"""
         :param s: :math:`(b\times n_1 \times n_2)` input 3d tensor. :math:`b`: batch size
         :param nrows: :math:`(b)` number of objects in dim1
@@ -329,7 +329,7 @@ def sample_gumbel(t_like, eps=1e-20):
         s_rep = s_rep + sample_gumbel(s_rep)
         nrows_rep = torch.repeat_interleave(nrows, sample_num, dim=0)
         ncols_rep = torch.repeat_interleave(ncols, sample_num, dim=0)
-        s_rep = self.sinkhorn(s_rep, nrows_rep, ncols_rep, dummy_row, dtype)
+        s_rep = self.sinkhorn(s_rep, nrows_rep, ncols_rep, dummy_row)
         #s_rep = torch.reshape(s_rep, (-1, sample_num, s_rep.shape[1], s_rep.shape[2]))
         return s_rep