From 54d704d0bf2b3dd4297a58f0f775e2e8b92e24ac Mon Sep 17 00:00:00 2001
From: 199******99 <9200608+doglikegodness@user.noreply.gitee.com>
Date: Sat, 10 Jul 2021 09:48:35 +0000
Subject: [PATCH] =?UTF-8?q?add=20code/2021=5Fspring/=E5=9C=BA=E6=99=AF?=
 =?UTF-8?q?=E8=AF=86=E5=88=AB/=E9=BB=84=E7=8E=AE=E7=90=AA2019302110409/con?=
 =?UTF-8?q?vert=5Fmodel.py.?=
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---
 .../convert_model.py"                         | 26 +++++++++++++++++++
 1 file changed, 26 insertions(+)
 create mode 100644 "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"

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 0000000..f95ebec
--- /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')
-- 
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