OK, I see. The float/double cast was only necessary in older versions, but I assumed it could be the mistake as I’ve found it in the other branch.
Regarding the other thread, it looks like you are performing some random transformations on your dataset.
Are you performing the same random transforms on the eval set currently?
Also, could you check, if this code snippet returns True?
model = VGG_5()
model.eval()
x = torch.randn(10, 5, 64, 64)
output_all, pred_all = model(x)
output_1, pred_1 = model(x[:5])
output_2, pred_2 = model(x[5:])
output_stacked = torch.cat((output_1, output_2), dim=0)

print(torch.allclose(output_all, output_stacked))

If so, the issue is probably located in the data not the model.