Comments (1)
I actually get an error when defining the model. There is a mismatch between the shape of their pre-trained weights of the first convolutional layer (64,1,7,7)
with that of the architecture definition (64,3,7,7)
.
Should one adjust the architecture definition of the TaskonomyEncoder
to have only one channel in the input conv layer, or repeat three times the weights of the first conv layer of checkpoints['state_dict']
along channel dimensions?
from midlevel-reps.
Related Issues (9)
- visualpriors source code HOT 1
- Variable not defined HOT 1
- Semseg networks shouldn't apply tanh before softmax HOT 1
- Error while importing environment
- size mismatch for decoder_output.0.weight and decoder_output.0.bias HOT 3
- Feature readout does not work with semantic networks HOT 3
- screen command failed "No screen session found." HOT 2
- Pretrained checkpoint HOT 1
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from midlevel-reps.