diff --git "a/code/2021_spring/\345\234\272\346\231\257\350\257\206\345\210\253/\351\273\204\347\216\256\347\220\2522019302110409/convert_model.py" "b/code/2021_spring/\345\234\272\346\231\257\350\257\206\345\210\253/\351\273\204\347\216\256\347\220\2522019302110409/convert_model.py"
new file mode 100644
index 0000000000000000000000000000000000000000..f95ebec4eaabcd98962572ba947ba84c0cb98c3a
--- /dev/null
+++ "b/code/2021_spring/\345\234\272\346\231\257\350\257\206\345\210\253/\351\273\204\347\216\256\347\220\2522019302110409/convert_model.py"
@@ -0,0 +1,26 @@
+import torch
+from torch.autograd import Variable as V
+import torchvision.models as models
+from PIL import Image
+from torchvision import transforms as trn
+from torch.nn import functional as F
+import os
+
+# th architecture to use
+arch = 'resnet18'
+model_weight = '/data/vision/torralba/deepscene/moments/models/2stream-simple/model/kinetics_rgb_resnet18_2d_single_stack1_fromscratch_best.pth.tar'
+model_name = 'resnet18_kinetics_fromscratch'
+
+# create the network architecture
+model = models.__dict__[arch](num_classes=400)
+
+#model_weight = '%s_places365.pth.tar' % arch
+
+checkpoint = torch.load(model_weight, map_location=lambda storage, loc: storage) # model trained in GPU could be deployed in CPU machine like this!
+state_dict = {str.replace(k,'module.',''): v for k,v in checkpoint['state_dict'].items()} # the data parallel layer will add 'module' before each layer name
+model.load_state_dict(state_dict)
+model.eval()
+
+model.cpu()
+torch.save(model, 'whole_' + model_name + '.pth.tar')
+print('save to ' + 'whole_' + model_name + '.pth.tar')