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Hi,
Thanks for sharing your work. Btw, when I tried to reproduce using the ADE20K pretrained checkpoint, I came across the following error. I hope you can take a look:
`
LGGAN/semantic_image_synthesis$ sh test_ade.sh
----------------- Options ---------------
aspect_ratio: 1.0
batchSize: 1 [default: 2]
cache_filelist_read: False
cache_filelist_write: False
checkpoints_dir: ./checkpoints
contain_dontcare_label: True
crop_size: 256
dataroot: ./datasets/ade20k [default: ./datasets/cityscapes/]
dataset_mode: ade20k [default: coco]
display_winsize: 256
gpu_ids: 0 [default: 0,1]
how_many: inf
init_type: xavier
init_variance: 0.02
isTrain: False [default: None]
label_nc: 150
load_from_opt_file: False
load_size: 256
max_dataset_size: 9223372036854775807
model: pix2pix
nThreads: 0
name: LGGAN_ade [default: label2coco]
nef: 16
netG: lggan
ngf: 64
no_flip: True
no_instance: True
no_pairing_check: False
norm_D: spectralinstance
norm_E: spectralinstance
norm_G: spectralspadesyncbatch3x3
num_upsampling_layers: normal
output_nc: 3
phase: test
preprocess_mode: resize_and_crop
results_dir: ./results [default: ./results/]
serial_batches: True
use_vae: False
which_epoch: 200 [default: latest]
z_dim: 256
----------------- End -------------------
dataset [ADE20KDataset] of size 2000 was created
Network [LGGANGenerator] was created. Total number of parameters: 114.6 million. To see the architecture, do print(network).
Traceback (most recent call last):
File "test_ade.py", line 20, in
model = Pix2PixModel(opt)
File "/home/you/Work/LGGAN/semantic_image_synthesis/models/pix2pix_model.py", line 25, in init
self.netG, self.netD, self.netE = self.initialize_networks(opt)
File "/home/you/Work/LGGAN/semantic_image_synthesis/models/pix2pix_model.py", line 121, in initialize_networks
netG = util.load_network(netG, 'G', opt.which_epoch, opt)
File "/home/you/Work/LGGAN/semantic_image_synthesis/util/util.py", line 208, in load_network
net.load_state_dict(weights)
File "/home/you/anaconda3/envs/torch1.4-py36-cuda10.1-tf1.14/lib/python3.6/site-packages/torch/nn/modules/module.py", line 830, in load_state_dict
self.class.name, "\n\t".join(error_msgs)))
RuntimeError: Error(s) in loading state_dict for LGGANGenerator:
Unexpected key(s) in state_dict: "deconv5_35.weight", "deconv5_35.bias", "deconv5_36.weight", "deconv5_36.bias", "deconv5_37.weight", "deconv5_37.bias", "deconv5_38.weight", "deconv5_38.bias", "deconv5_39.weight", "deconv5_39.bias", "deconv5_40.weight", "deconv5_40.bias", "deconv5_41.weight", "deconv5_41.bias", "deconv5_42.weight", "deconv5_42.bias", "deconv5_43.weight", "deconv5_43.bias", "deconv5_44.weight", "deconv5_44.bias", "deconv5_45.weight", "deconv5_45.bias", "deconv5_46.weight", "deconv5_46.bias", "deconv5_47.weight", "deconv5_47.bias", "deconv5_48.weight", "deconv5_48.bias", "deconv5_49.weight", "deconv5_49.bias", "deconv5_50.weight", "deconv5_50.bias", "deconv5_51.weight", "deconv5_51.bias".
size mismatch for conv1.weight: copying a param with shape torch.Size([64, 151, 7, 7]) from checkpoint, the shape in current model is torch.Size([64, 36, 7, 7]).
size mismatch for deconv9.weight: copying a param with shape torch.Size([3, 156, 3, 3]) from checkpoint, the shape in current model is torch.Size([3, 105, 3, 3]).
size mismatch for fc2.weight: copying a param with shape torch.Size([51, 64]) from checkpoint, the shape in current model is torch.Size([35, 64]).
size mismatch for fc2.bias: copying a param with shape torch.Size([51]) from checkpoint, the shape in current model is torch.Size([35]).
`
The text was updated successfully, but these errors were encountered:
Hi,
Thanks for sharing your work. Btw, when I tried to reproduce using the ADE20K pretrained checkpoint, I came across the following error. I hope you can take a look:
`
LGGAN/semantic_image_synthesis$ sh test_ade.sh
----------------- Options ---------------
aspect_ratio: 1.0
batchSize: 1 [default: 2]
cache_filelist_read: False
cache_filelist_write: False
checkpoints_dir: ./checkpoints
contain_dontcare_label: True
crop_size: 256
dataroot: ./datasets/ade20k [default: ./datasets/cityscapes/]
dataset_mode: ade20k [default: coco]
display_winsize: 256
gpu_ids: 0 [default: 0,1]
how_many: inf
init_type: xavier
init_variance: 0.02
isTrain: False [default: None]
label_nc: 150
load_from_opt_file: False
load_size: 256
max_dataset_size: 9223372036854775807
model: pix2pix
nThreads: 0
name: LGGAN_ade [default: label2coco]
nef: 16
netG: lggan
ngf: 64
no_flip: True
no_instance: True
no_pairing_check: False
norm_D: spectralinstance
norm_E: spectralinstance
norm_G: spectralspadesyncbatch3x3
num_upsampling_layers: normal
output_nc: 3
phase: test
preprocess_mode: resize_and_crop
results_dir: ./results [default: ./results/]
serial_batches: True
use_vae: False
which_epoch: 200 [default: latest]
z_dim: 256
----------------- End -------------------
dataset [ADE20KDataset] of size 2000 was created
Network [LGGANGenerator] was created. Total number of parameters: 114.6 million. To see the architecture, do print(network).
Traceback (most recent call last):
File "test_ade.py", line 20, in
model = Pix2PixModel(opt)
File "/home/you/Work/LGGAN/semantic_image_synthesis/models/pix2pix_model.py", line 25, in init
self.netG, self.netD, self.netE = self.initialize_networks(opt)
File "/home/you/Work/LGGAN/semantic_image_synthesis/models/pix2pix_model.py", line 121, in initialize_networks
netG = util.load_network(netG, 'G', opt.which_epoch, opt)
File "/home/you/Work/LGGAN/semantic_image_synthesis/util/util.py", line 208, in load_network
net.load_state_dict(weights)
File "/home/you/anaconda3/envs/torch1.4-py36-cuda10.1-tf1.14/lib/python3.6/site-packages/torch/nn/modules/module.py", line 830, in load_state_dict
self.class.name, "\n\t".join(error_msgs)))
RuntimeError: Error(s) in loading state_dict for LGGANGenerator:
Unexpected key(s) in state_dict: "deconv5_35.weight", "deconv5_35.bias", "deconv5_36.weight", "deconv5_36.bias", "deconv5_37.weight", "deconv5_37.bias", "deconv5_38.weight", "deconv5_38.bias", "deconv5_39.weight", "deconv5_39.bias", "deconv5_40.weight", "deconv5_40.bias", "deconv5_41.weight", "deconv5_41.bias", "deconv5_42.weight", "deconv5_42.bias", "deconv5_43.weight", "deconv5_43.bias", "deconv5_44.weight", "deconv5_44.bias", "deconv5_45.weight", "deconv5_45.bias", "deconv5_46.weight", "deconv5_46.bias", "deconv5_47.weight", "deconv5_47.bias", "deconv5_48.weight", "deconv5_48.bias", "deconv5_49.weight", "deconv5_49.bias", "deconv5_50.weight", "deconv5_50.bias", "deconv5_51.weight", "deconv5_51.bias".
size mismatch for conv1.weight: copying a param with shape torch.Size([64, 151, 7, 7]) from checkpoint, the shape in current model is torch.Size([64, 36, 7, 7]).
size mismatch for deconv9.weight: copying a param with shape torch.Size([3, 156, 3, 3]) from checkpoint, the shape in current model is torch.Size([3, 105, 3, 3]).
size mismatch for fc2.weight: copying a param with shape torch.Size([51, 64]) from checkpoint, the shape in current model is torch.Size([35, 64]).
size mismatch for fc2.bias: copying a param with shape torch.Size([51]) from checkpoint, the shape in current model is torch.Size([35]).
`
The text was updated successfully, but these errors were encountered: