I am trying to load a model I have trained, I get the following error and can’t understand how to solve it.
## error:
---------------------------------------------------------------------------
RuntimeError Traceback (most recent call last)
<ipython-input-53-fb3124c9db63> in <module>
38 return model
39
---> 40 model = load_checkpoint('test_MODEL.pt')
<ipython-input-53-fb3124c9db63> in load_checkpoint(checkpoint_path)
34
35
---> 36 model.load_state_dict(checkpoint['state_dict'])
37
38 return model
/opt/anaconda3/lib/python3.7/site-packages/torch/nn/modules/module.py in load_state_dict(self, state_dict, strict)
767 if len(error_msgs) > 0:
768 raise RuntimeError('Error(s) in loading state_dict for {}:\n\t{}'.format(
--> 769 self.__class__.__name__, "\n\t".join(error_msgs)))
770
771 def _named_members(self, get_members_fn, prefix='', recurse=True):
RuntimeError: Error(s) in loading state_dict for ResNet:
Missing key(s) in state_dict: "fc.weight", "fc.bias", "classifier.fc1.weight", "classifier.fc1.bias", "classifier.fc2.weight", "classifier.fc2.bias".
Unexpected key(s) in state_dict: "fc.fc1.weight", "fc.fc1.bias", "fc.fc2.weight", "fc.fc2.bias".
Code to load is the following:
import torch
import json
from torch import nn
import torch.nn.functional as F
from torchvision import models
class RES50Classifier(nn.Module):
def __init__(self, in_features, hidden_features,
out_features, drop_prob=0.1):
super().__init__()
self.fc1 = nn.Linear(in_features, hidden_features)
self.fc2 = nn.Linear(hidden_features, out_features)
self.drop = nn.Dropout(p=drop_prob)
def forward(self, x):
x = self.drop(F.relu(self.fc1(x)))
x = self.fc2(x)
x = F.log_softmax(x, dim=1)
return x
def load_checkpoint(checkpoint_path):
checkpoint = torch.load(checkpoint_path)
model = models.resnet50(pretrained=False)
for param in model.parameters():
param.requires_grad = False
# Put the classifier on the pretrained network
model.classifier = RES50Classifier(2048, 500, 102)
model.load_state_dict(checkpoint['state_dict'])
return model
model = load_checkpoint('test_MODEL.pt')
The code I use to define the model is the following:
class RES50Classifier(nn.Module):
def __init__(self, in_features, hidden_features,
out_features, drop_prob=0.1):
super().__init__()
self.fc1 = nn.Linear(in_features, hidden_features)
self.fc2 = nn.Linear(hidden_features, out_features)
self.drop = nn.Dropout(p=drop_prob)
def forward(self, x):
x = self.drop(F.relu(self.fc1(x)))
x = self.fc2(x)
x = F.log_softmax(x, dim=1)
return x
model = models.resnet50(pretrained=True)
for param in model.parameters():
param.requires_grad = False
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
model.fc = RES50Classifier(2048, 500, 102)
criterion = nn.CrossEntropyLoss()
# Only train the classifier parameters, feature parameters are frozen
optimizer = optim.Adam(model.fc.parameters(), lr=0.003)
model.to(device);
