Thank you so much ptrblck for your reply.

After using this its giving error

checkpoint = torch.load(PATH)

model = CQCCModel()

optimizer = torch.optim.Adam(model.parameters(), lr=0.0001)

model.load_state_dict(checkpoint[‘model’])

error: model.load_state_dict(checkpoint[‘model’])

KeyError: ‘model’

If I am using “model.load_state_dict(checkpoint[model])” than its showing error

**error**:

KeyError: CQCCModel(

(layer1): Sequential(

(0): Conv2d(1, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))

(1): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)

(2): LeakyReLU(negative_slope=0.03)

)

(layer2): Sequential(

(0): ResNetBlock(

(conv1): Conv2d(32, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))

(bn1): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)

(lrelu): LeakyReLU(negative_slope=0.01)

(dropout): Dropout(p=0.5)

(conv2): Conv2d(32, 32, kernel_size=(3, 3), stride=(3, 3), padding=(1, 1))

(conv11): Conv2d(32, 32, kernel_size=(3, 3), stride=(3, 3), padding=(1, 1))

)

)

(layer3): Sequential(

(0): ResNetBlock(

(conv1): Conv2d(32, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))

(bn1): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)

(lrelu): LeakyReLU(negative_slope=0.01)

(dropout): Dropout(p=0.5)

(conv2): Conv2d(32, 32, kernel_size=(3, 3), stride=(3, 3), padding=(1, 1))

(conv11): Conv2d(32, 32, kernel_size=(3, 3), stride=(3, 3), padding=(1, 1))

(pre_bn): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)

)

(1): MaxPool2d(kernel_size=3, stride=3, padding=1, dilation=1, ceil_mode=False)

)

(layer4): Sequential(

(0): ResNetBlock(

(conv1): Conv2d(32, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))

(bn1): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)

(lrelu): LeakyReLU(negative_slope=0.01)

(dropout): Dropout(p=0.5)

(conv2): Conv2d(32, 32, kernel_size=(3, 3), stride=(3, 3), padding=(1, 1))

(conv11): Conv2d(32, 32, kernel_size=(3, 3), stride=(3, 3), padding=(1, 1))

(pre_bn): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)

)

(1): MaxPool2d(kernel_size=3, stride=3, padding=1, dilation=1, ceil_mode=False)

)

why this error is coming after defining the model (model = CQCCModel())?

Any suggestions is useful.

Thanks