The mobilenetv2 cannot be evaluated with your pretrained models.

[majian-stu]

Hi! Thank you for your great work!
I have evaluated the ResNetDCT_Upscaled_Static with your pretrained parameters successfully.
But I cannot evaluate the "mobilenetv2dct_upscaled_subset" with your pretrained parameters (mobilenetv2dct_upscaled_static_24/32). Because the parameters do not match the model you define.
Actually, there is not anyone model matching with your pretrained parameters.
Did I miss something? I'm looking forward to your reply！

RuntimeError: Error(s) in loading state_dict for MobileNetV2DCT_Upscaled_Subset:
Missing key(s) in state_dict: "upconv_y.0.weight", "upconv_y.1.weight", "upconv_y.1.bias", "upconv_y.1.running_mean", "upconv_y.1.running_var", "upconv_cb.0.weight", "upconv_cb.2.weight", "upconv_cb.2.bias", "upconv_cb.2.running_mean", "upconv_cb.2.running_var", "upconv_cr.0.weight", "upconv_cr.2.weight", "upconv_cr.2.bias", "upconv_cr.2.running_mean", "upconv_cr.2.running_var".
size mismatch for features.0.conv.0.weight: copying a param with shape torch.Size([24, 1, 3, 3]) from checkpoint, the shape in current model is torch.Size([32, 1, 3, 3]).
size mismatch for features.0.conv.1.weight: copying a param with shape torch.Size([24]) from checkpoint, the shape in current model is torch.Size([32]).
size mismatch for features.0.conv.1.bias: copying a param with shape torch.Size([24]) from checkpoint, the shape in current model is torch.Size([32]).
size mismatch for features.0.conv.1.running_mean: copying a param with shape torch.Size([24]) from checkpoint, the shape in current model is torch.Size([32]).
size mismatch for features.0.conv.1.running_var: copying a param with shape torch.Size([24]) from checkpoint, the shape in current model is torch.Size([32]).
size mismatch for features.0.conv.3.weight: copying a param with shape torch.Size([16, 24, 1, 1]) from checkpoint, the shape in current model is torch.Size([16, 32, 1, 1]).

[liliiscaiji]

Excuse me, where can I download the available pre training weights？