--no_instance doesn't work about spade HOT 3 CLOSED

It is because the checkpoint model requires instance map. The number of input channel using instance map will be one larger than not using instance map. Please refer to this issue

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When I'm trying to run test.py with --no_instance flag, I get the following error (I tried this flag on COCO and Cityscapes checkpoints, and the error happens on both):

Traceback (most recent call last): File "test.py", line 19, in <module> model = Pix2PixModel(opt) File "D:\...path...\SPADE\models\pix2pix_model.py", line 26, in __init__ self.netG, self.netD, self.netE = self.initialize_networks(opt) File "D:\...path...\SPADE\models\pix2pix_model.py", line 98, in initialize_networks netG = util.load_network(netG, 'G', opt.which_epoch, opt) File "D:\...path...\SPADE\util\util.py", line 194, in load_network net.load_state_dict(weights) File "D:\...path...\Python\Python36\lib\site-packages\torch\nn\modules\module.py", line 769, in load_state_dict self.__class__.__name__, "\n\t".join(error_msgs))) RuntimeError: Error(s) in loading state_dict for SPADEGenerator: size mismatch for fc.weight: copying a param with shape torch.Size([1024, 184, 3, 3]) from checkpoint, the shape in current model is torch.Size([1024, 183, 3, 3]). size mismatch for head_0.norm_0.mlp_shared.0.weight: copying a param with shape torch.Size([128, 184, 3, 3]) from checkpoint, the shape in current model is torch.Size([128, 183, 3, 3]). size mismatch for head_0.norm_1.mlp_shared.0.weight: copying a param with shape torch.Size([128, 184, 3, 3]) from checkpoint, the shape in current model is torch.Size([128, 183, 3, 3]). size mismatch for G_middle_0.norm_0.mlp_shared.0.weight: copying a param with shape torch.Size([128, 184, 3, 3]) from checkpoint, the shape in current model is torch.Size([128, 183, 3, 3]). size mismatch for G_middle_0.norm_1.mlp_shared.0.weight: copying a param with shape torch.Size([128, 184, 3, 3]) from checkpoint, the shape in current model is torch.Size([128, 183, 3, 3]). size mismatch for G_middle_1.norm_0.mlp_shared.0.weight: copying a param with shape torch.Size([128, 184, 3, 3]) from checkpoint, the shape in current model is torch.Size([128, 183, 3, 3]). size mismatch for G_middle_1.norm_1.mlp_shared.0.weight: copying a param with shape torch.Size([128, 184, 3, 3]) from checkpoint, the shape in current model is torch.Size([128, 183, 3, 3]). size mismatch for up_0.norm_0.mlp_shared.0.weight: copying a param with shape torch.Size([128, 184, 3, 3]) from checkpoint, the shape in current model is torch.Size([128, 183, 3, 3]). size mismatch for up_0.norm_1.mlp_shared.0.weight: copying a param with shape torch.Size([128, 184, 3, 3]) from checkpoint, the shape in current model is torch.Size([128, 183, 3, 3]). size mismatch for up_0.norm_s.mlp_shared.0.weight: copying a param with shape torch.Size([128, 184, 3, 3]) from checkpoint, the shape in current model is torch.Size([128, 183, 3, 3]). size mismatch for up_1.norm_0.mlp_shared.0.weight: copying a param with shape torch.Size([128, 184, 3, 3]) from checkpoint, the shape in current model is torch.Size([128, 183, 3, 3]). size mismatch for up_1.norm_1.mlp_shared.0.weight: copying a param with shape torch.Size([128, 184, 3, 3]) from checkpoint, the shape in current model is torch.Size([128, 183, 3, 3]). size mismatch for up_1.norm_s.mlp_shared.0.weight: copying a param with shape torch.Size([128, 184, 3, 3]) from checkpoint, the shape in current model is torch.Size([128, 183, 3, 3]). size mismatch for up_2.norm_0.mlp_shared.0.weight: copying a param with shape torch.Size([128, 184, 3, 3]) from checkpoint, the shape in current model is torch.Size([128, 183, 3, 3]). size mismatch for up_2.norm_1.mlp_shared.0.weight: copying a param with shape torch.Size([128, 184, 3, 3]) from checkpoint, the shape in current model is torch.Size([128, 183, 3, 3]). size mismatch for up_2.norm_s.mlp_shared.0.weight: copying a param with shape torch.Size([128, 184, 3, 3]) from checkpoint, the shape in current model is torch.Size([128, 183, 3, 3]). size mismatch for up_3.norm_0.mlp_shared.0.weight: copying a param with shape torch.Size([128, 184, 3, 3]) from checkpoint, the shape in current model is torch.Size([128, 183, 3, 3]). size mismatch for up_3.norm_1.mlp_shared.0.weight: copying a param with shape torch.Size([128, 184, 3, 3]) from checkpoint, the shape in current model is torch.Size([128, 183, 3, 3]). size mismatch for up_3.norm_s.mlp_shared.0.weight: copying a param with shape torch.Size([128, 184, 3, 3]) from checkpoint, the shape in current model is torch.Size([128, 183, 3, 3]).

Thank you for the code and the great work
I also got the same , I am using Input label maps (my own label maps but generated using model trained on COCO), Since instances maps is needed to use the pretrained model on coco stuff

I tried to use coco generated instance file to generated instances maps for them but i got the following error
/mnt/HDD/VE_Saida/lib/python3.6/site-packages/skimage/io/_io.py:48: UserWarning: as_grey has been deprecated in favor of as_gray
warn('as_grey has been deprecated in favor of as_gray')
Traceback (most recent call last):
File "/mnt/HDD/VE_Saida/Updated_SPADE/SPADE-master/datasets/coco_generate_instance_map.py", line 54, in
img[rr, cc] = count
IndexError: index 256 is out of bounds for axis 0 with size 256

Process finished with exit code 1

Can you please let me know how can i use pre-trained mode for coco stuff on my lables maps dataset, I mean how i can generated instances for them .

Thank you very much

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Hello @code-de , did you solve this issue ?

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