Add results to readme

README.md

@@ -6,19 +6,21 @@
 - Download [CUHK03 Dataset](http://www.ee.cuhk.edu.hk/~xgwang/CUHK_identification.html)
 - Download [VIPeR Dataset](https://vision.soe.ucsc.edu/node/178)
 
-###Test
+###Testing
+
 ```bash
-python test_cuhk03.py --which_epoch 59 --name cuhk03_dense --use_dense
+python test_cuhk03.py --model_path ./cuhk03/model.pth --use_dense
 python eval_cuhk03.py
 ```
+
 ###Currents results after
 
 | Dataset | Rank 1 | Rank 5 | Rank 10 | Rank 20 | mAP |
 | --- | --- | --- | --- | --- | --- |
-| CUHK03-Dense Baseline | 0.679181 | 0.909378 | 0.953513 | 0.977772 | 0.781033 |
-| CUHK03-Dense SLS_ReID | 0.843203 | 0.971337 | 0.989171 | 0.996290 | 0.899174 |
-| CUHK03-ResNet Baseline |0.750155 | 0.950839 | 0.979171 | 0.991119 | 0.838676 |
-| CUHK03-ResNet SLS_ReID | 0.909938 | 0.982435 | 0.992477 | 0.997420 | 0.941790 |
+| `CUHK03-Dense Baseline` | 0.679181 | 0.909378 | 0.953513 | 0.977772 | 0.781033 |
+| `CUHK03-Dense SLS_ReID` | 0.843203 | 0.971337 | 0.989171 | 0.996290 | 0.899174 |
+| `CUHK03-ResNet Baseline` |0.750155 | 0.950839 | 0.979171 | 0.991119 | 0.838676 |
+| `CUHK03-ResNet SLS_ReID` | 0.909938 | 0.982435 | 0.992477 | 0.997420 | 0.941790 |
 
 
 

eval_cuhk03.py

@@ -1,62 +1,8 @@
-import numpy as np
 from collections import defaultdict
-import scipy.io
-import torch
-import torch.nn as nn
 
-def eval_market1501(distmat, q_pids, g_pids, q_camids, g_camids, max_rank):
-    """Evaluation with market1501 metric
-    Key: for each query identity, its gallery images from the same camera view are discarded.
-    """
-    num_q, num_g = distmat.shape
-    if num_g < max_rank:
-        max_rank = num_g
-        print("Note: number of gallery samples is quite small, got {}".format(num_g))
-    indices = np.argsort(distmat, axis=1)
-    matches = (g_pids[indices] == q_pids[:, np.newaxis]).astype(np.int32)
-
-    # compute cmc curve for each query
-    all_cmc = []
-    all_AP = []
-    num_valid_q = 0. # number of valid query
-    for q_idx in range(num_q):
-        # get query pid and camid
-        q_pid = q_pids[q_idx]
-        q_camid = q_camids[q_idx]
-
-        # remove gallery samples that have the same pid and camid with query
-        order = indices[q_idx]
-        remove = (g_pids[order] == q_pid) & (g_camids[order] == q_camid)
-        keep = np.invert(remove)
-
-        # compute cmc curve
-        orig_cmc = matches[q_idx][keep] # binary vector, positions with value 1 are correct matches
-        if not np.any(orig_cmc):
-            # this condition is true when query identity does not appear in gallery
-            continue
-
-        cmc = orig_cmc.cumsum()
-        cmc[cmc > 1] = 1
-
-        all_cmc.append(cmc[:max_rank])
-        num_valid_q += 1.
-
-        # compute average precision
-        # reference: https://en.wikipedia.org/wiki/Evaluation_measures_(information_retrieval)#Average_precision
-        num_rel = orig_cmc.sum()
-        tmp_cmc = orig_cmc.cumsum()
-        tmp_cmc = [x / (i+1.) for i, x in enumerate(tmp_cmc)]
-        tmp_cmc = np.asarray(tmp_cmc) * orig_cmc
-        AP = tmp_cmc.sum() / num_rel
-        all_AP.append(AP)
-
-    assert num_valid_q > 0, "Error: all query identities do not appear in gallery"
-
-    all_cmc = np.asarray(all_cmc).astype(np.float32)
-    all_cmc = all_cmc.sum(0) / num_valid_q
-    mAP = np.mean(all_AP)
+import numpy as np
+import scipy.io
 
-    return all_cmc, mAP
 
 def eval_cuhk03(distmat, q_pids, g_pids, q_camids, g_camids, max_rank, N=100):
     """Evaluation with cuhk03 metric
@@ -129,7 +75,7 @@ def eval_cuhk03(distmat, q_pids, g_pids, q_camids, g_camids, max_rank, N=100):
 
 
 if __name__ == '__main__':
-    result = scipy.io.loadmat('./plsro_result.mat')
+    result = scipy.io.loadmat('./result.mat')
     distmat = result['distmat']
     q_pids = np.squeeze(result['q_pids'])
     g_pids = np.squeeze(result['g_pids'])

test_cuhk03.py

@@ -15,13 +15,11 @@
 
 import argparse
 parser = argparse.ArgumentParser(description='Training')
-parser.add_argument('--gpu_ids',default='0', type=str,help='gpu_ids: e.g. 0  0,1,2  0,2')
-parser.add_argument('--which_epoch',default='last', type=str, help='0,1,2,3...or last')
-parser.add_argument('--data_dir',default='cuhk03',type=str, help='./test_data')
-parser.add_argument('--name', default='resnet', type=str, help='save model path')
+parser.add_argument('--model_path', default='resnet', type=str, help='save model path')
 parser.add_argument('--batchsize', default=32, type=int, help='batchsize')
-parser.add_argument('--use_dense', action='store_true', help='use densenet121' )
-parser.add_argument('--n_classe', default=1367, help='n classes' )
+parser.add_argument('--use_dense', action='store_true', help='use densenet')
+parser.add_argument('--n_classe', default=1367, help='n classes')
+parser.add_argument('--dataset', default='/home/paul/datasets', type=str, help='Path to the dataset')
 
 opt = parser.parse_args()
 n_classe = opt.n_classe
@@ -82,12 +80,12 @@ def test(model, queryloader, galleryloader, use_gpu, ranks=[1, 5, 10, 20]):
               'query_feature': qf.numpy(), 'gallery_feature': gf.numpy()}
     print(qf.numpy())
     print(gf.numpy())
-    scipy.io.savemat('./plsro_result.mat', result)
+    scipy.io.savemat('./result.mat', result)
 
 
 
 def load_network(network):
-    save_path = os.path.join('./'+opt.data_dir+'/'+opt.name+'/net_%s.pth' %opt.which_epoch)
+    save_path = os.path.join(opt.model_path)
     network.load_state_dict(torch.load(save_path))
     return network
 
@@ -97,11 +95,6 @@ def load_network(network):
 if __name__ == '__main__':
 
     use_gpu = torch.cuda.is_available()
-    height = 224
-    width = 224
-    if opt.use_dense:
-        height = 144
-        width = 288
     data_transforms = transforms.Compose([
         transforms.Resize((288, 144), interpolation=3),
         transforms.ToTensor(),
@@ -120,7 +113,7 @@ def load_network(network):
         model = model.cuda()
 
     dataset = data_manager.init_img_dataset(
-        root='/home/paul/datasets', name=opt.data_dir, split_id=0, cuhk03_classic_split=True)
+        root=opt.dataset, name='cuhk03', split_id=0, cuhk03_classic_split=True)
 
     queryloader = DataLoader(
         ImageDataset(dataset.query, transform=data_transforms),