conversion script for resnet

scripts/resnetport.py

+import os
+from typing import OrderedDict
+import torch
+
+model_dir = 'pretrained/resnet/'
+new_state_dict = {}
+
+weights : OrderedDict = torch.load(model_dir + 'resnet_18.pth')
+net_conf = (False, (2, 2, 2, 2)) 
+counter = 0
+new_state_dict['layerin.0.weight'] = weights['conv1.weight']
+new_state_dict['layerin.2.weight'] = weights['bn1.weight']
+new_state_dict['layerin.2.bias'] = weights['bn1.bias']
+for i, j in enumerate(net_conf[1], 1):
+    for k in range(j):
+        curr_layer = f"layer{i}.{k}."
+        curr_state_dict_key = f"resblocks.{counter}."
+        new_state_dict[curr_state_dict_key + "conv1.weight"] = weights[curr_layer + "conv1.weight"]
+        new_state_dict[curr_state_dict_key + "conv2.weight"] = weights[curr_layer + "conv2.weight"]
+
+        new_state_dict[curr_state_dict_key + "bn1.weight"] = weights[curr_layer + "bn1.weight"]
+        new_state_dict[curr_state_dict_key + "bn1.bias"] = weights[curr_layer + "bn1.bias"]
+        new_state_dict[curr_state_dict_key + "bn2.weight"] = weights[curr_layer + "bn2.weight"]
+        new_state_dict[curr_state_dict_key + "bn2.bias"] = weights[curr_layer + "bn2.bias"]
+
+        if net_conf[0]:
+            new_state_dict[curr_state_dict_key + "conv3.weight"] = weights[curr_layer + "conv3.weight"]
+            new_state_dict[curr_state_dict_key + "bn3.weight"] = weights[curr_layer + "bn3.weight"]
+            new_state_dict[curr_state_dict_key + "bn3.bias"] = weights[curr_layer + "bn3.bias"]
+        counter += 1
+
+torch.save(new_state_dict, "pretrained/resnet/modified/resnet_18.pth")
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