eval DF

experiments/scripts/eval_linemod.sh

@@ -7,5 +7,4 @@ export PYTHONUNBUFFERED="True"
 export CUDA_VISIBLE_DEVICES=0
 
 python3 ./tools/eval_linemod.py --dataset_root ./datasets/linemod/Linemod_preprocessed\
-  --model trained_checkpoints/linemod/pose_model_9_0.01310166542980859.pth\
-  --refine_model trained_checkpoints/linemod/pose_refine_model_493_0.006761023565178073.pth
+  --model trained_models/linemod8/pose_model_4_0.012983739659874712.pth --refine_model trained_models/linemod8/pose_refine_model_9_0.01186443073513208.pth
\ No newline at end of file

tools/eval_linemod.py

@@ -132,8 +132,10 @@ parser.add_argument('--model', type=str, default='', help='resume PoseNet model'
 parser.add_argument('--refine_model', type=str, default='', help='resume PoseRefineNet model')
 opt = parser.parse_args()
 
+# num_objects = 5
 num_objects = 8
-objlist = [1, 2, 3, 4, 5, 6, 7, 8]
+# objlist = [1, 3, 6, 7, 8]
+objlist = [1, 2, 3, 4 ,5 ,6, 7, 8]
 num_points = 500
 iteration = 4
 bs = 1
@@ -143,7 +145,7 @@ cam_fx = 543.2527222420504  # TODO
 cam_fy = 724.3369629894005  # TODO
 
 # ["banana1", "kiwi1", "pear2", "strawberry1", "apricot", "orange2", "peach1", "lemon2", "apple2" ]
-map_id_obj = {
+"""map_id_obj = {
     1: 'banana1',
     2: 'kiwi1',
     3: 'pear2',
@@ -152,7 +154,14 @@ map_id_obj = {
     6: 'peach1',
     7: 'lemon2',
     8: 'apple2',
-}
+}"""
+#map_id_obj = {1: 'apple2', 3: 'banana1'}
+map_id_obj = {1: 'apple2', 2: 'apricot', 3: 'banana1', 4: 'kiwi1', 5:'lemon2',  6: 'orange2', 7: 'peach1', 8: 'pear2'}
+        # apple2, apricot, banana1, kiwi1, lemon2, orange2, peach1, pear2
+        #self.objlist = [1, 2, 3, 4, 5, 6, 7, 8]
+        #self.objlist = [1, 3, 6, 7, 8]
+
+
 K = np.array([[cam_fx, 0, cam_cx], [0, cam_fy, cam_cy], [0, 0, 1]])
 dataset_config_dir = 'datasets/linemod/dataset_config'
 output_result_dir = 'experiments/eval_result/linemod'
@@ -176,8 +185,9 @@ criterion = Loss(num_points_mesh, sym_list)
 criterion_refine = Loss_refine(num_points_mesh, sym_list)
 
 diameter = []
+print('{0}/models_info.yml'.format(dataset_config_dir))
 meta_file = open('{0}/models_info.yml'.format(dataset_config_dir), 'r')
-meta = yaml.load(meta_file)
+meta = yaml.load(meta_file, yaml.Loader)
 for obj in objlist:
     diameter.append(meta[obj]['diameter'] / 1000.0 * 0.1)
 print(diameter)