update readme.

README.md

@@ -39,7 +39,7 @@ The current version supports attribution methods and video classification models
 * **pretrain_dataset**: Dataset name that test model pretrained on. Choices include 'kinetics', 'epic-kitchens-verb', 'epic-kitchens-noun'.
 * **vis_method**: Name of visualization methods. Choices include 'grad', 'grad*input', 'integrated_grad', 'grad_cam', 'perturb'. Here the 'perturb' means is spatiotemporal perturbation method.
 * **save_label**: Extra label for saving results. If given, visualization results will be saved in ./visual_res/$vis_method$/$model$/$save_label$.
-* no_gpu: If set, the demo will be run on CPU, else run on only one GPU.
+* **no_gpu**: If set, the demo will be run on CPU, else run on only one GPU.
 
 Arguments for perturb:
 * **num_iter**: Number of iterations to get the perturbation results. Default is set to 2000 for better convergence.
@@ -51,13 +51,13 @@ Arguments for gradient methods:
 ### Examples
 
 #### Saptiotemporal Perturbation + I3D (pretrained on Kinetics-400)
-`$ python main.py --videos_dir /home/acb11711tx/lzq/VideoVisual/test_data/kinetics/sampled_frames --model i3d --pretrain_dataset kinetics --vis_method perturb --num_iter 2000 --perturb_area 0.1`
+`$ python main.py --videos_dir VideoVisual/test_data/kinetics/sampled_frames --model i3d --pretrain_dataset kinetics --vis_method perturb --num_iter 2000 --perturb_area 0.1`
 
 #### Spatiotemporal Perturbation + TSM (pretrained on EPIC-Kitchens-noun)
-`$ python main.py --videos_dir /home/acb11711tx/lzq/VideoVisual/test_data/epic-kitchens-noun/sampled_frames --model tsm --pretrain_dataset epic-kitchens-noun --vis_method perturb --num_iter 2000 --perturb_area 0.05`
+`$ python main.py --videos_dir VideoVisual/test_data/epic-kitchens-noun/sampled_frames --model tsm --pretrain_dataset epic-kitchens-noun --vis_method perturb --num_iter 2000 --perturb_area 0.05`
 
 #### Integrated Gradients + R(2+1)D (pretrained on Kinetics-400)
-`$ python main.py --videos_dir /home/acb11711tx/lzq/VideoVisual/test_data/kinetics/sampled_frames --model r2plus1d --pretrain_dataset kinetics --vis_method integrated_grad`
+`$ python main.py --videos_dir VideoVisual/test_data/kinetics/sampled_frames --model r2plus1d --pretrain_dataset kinetics --vis_method integrated_grad`
 
 
 ## Results

model_def/tsm.py

@@ -1,6 +1,11 @@
 import torch
 from torch import nn
 
+import os
+import sys
+sys.path.append(".")
+sys.path.append("..")
+
 class tsm (nn.Module):
     def __init__ (self, num_classes, segment_count, pretrained):
         super(tsm, self).__init__()
@@ -14,7 +19,10 @@ def __init__ (self, num_classes, segment_count, pretrained):
             kinetics_classes_num = 400
             self.model = torch.hub.load(self.repo, 'TSM', kinetics_classes_num, segment_count, 'RGB',
                                             base_model='resnet50')
-            checkpoint_path = '/home/acb11711tx/lzq/ModelVisualization/model_param/TSM_kinetics_RGB_resnet50_shift8_blockres_avg_segment8_e100_dense.pth'
+            checkpoint_path = 'model_param/TSM_kinetics_RGB_resnet50_shift8_blockres_avg_segment8_e100_dense.pth'
+            assert os.path.isfile(checkpoint_path), \
+                    f'Something wrong with pretrained parameters of TSM-Kinetics, Given {checkpoint_path}.'
+            print(f'Load checkpoint of TSM from {checkpoint_path}.')
             state_dict = torch.load(checkpoint_path)['state_dict']
             state_dict = {k[7:]: v for k, v in state_dict.items()}
             self.model.load_state_dict(state_dict)

path_dict.py

@@ -5,10 +5,6 @@ class PathDict (object):
     def __init__ (self):
         hostname = socket.gethostname()
         if 'abci' in hostname:
-            self.surgery_ds_root = '/groups1/gcb50205/lzq/dataset/JIGSAWS'
             self.proj_root = os.path.dirname(os.path.abspath(__file__))
-            print(self.proj_root)
         else:
-            # self.surgery_ds_root = '/home/shinkyo/dataset/JIGSAWS'
-            # self.proj_root = '/home/shinkyo/lzq/ModelVisualization'
             self.proj_root = os.path.dirname(os.path.abspath(__file__))