I tried to apply your code to a 512x512x3 set of images, in a real world dataset.
I made several modifications to the basic code for data-loading and used the cal_fid_stat.py script from AutoGAN to generate stats for my test-set.
CUDA_VISIBLE_DEVICES=3 python -u search.py \
-gen_bs 16 \
-dis_bs 8 \
--dataset stl10 \
--bottom_width 4 \
--img_size 512 \
--gen_model shared_gan \
--dis_model shared_gan \
--controller controller \
--latent_dim 512 \
--gf_dim 512 \
--df_dim 256 \
--g_spectral_norm False \
--d_spectral_norm True \
--g_lr 0.0002 \
--d_lr 0.0002 \
--beta1 0.0 \
--beta2 0.9 \
--init_type xavier_uniform \
--n_critic 5 \
--val_freq 20 \
--ctrl_sample_batch 1 \
--shared_epoch 15 \
--grow_step1 15 \
--grow_step2 35 \
--max_search_iter 65 \
--ctrl_step 30 \
--random_seed 12345 \
--exp_name e2gan_search --data_path /home/user/data-E2GAN | tee search.log
search progress: 0%| | 0/100 [00:35<?, ?it/s]
Traceback (most recent call last):
File "search.py", line 227, in <module>
main()
File "search.py", line 155, in main
action = Agent.select_action([layer, last_R,0.01*last_fid] + last_state,Best)
File "/home/user/E2GAN/search/sac.py", line 60, in select_action
action1,action2,action3,action4, action5,action6,_,_,_,_,_, _ ,_,_, _, _ ,_,_,= self.policy.sample(state)
File "/home/user/E2GAN/search/sac_model.py", line 117, in sample
mean_1, log_std_1,mean_2, log_std_2,mean_3, log_std_3,mean_4, log_std_4,mean_5, log_std_5,mean_6, log_std_6= self.forward(state)
File "/home/user/E2GAN/search/sac_model.py", line 84, in forward
x = F.relu(self.linear1(state.cuda()))
File "/home/user/miniconda3/envs/ganspace/lib/python3.7/site-packages/torch/nn/modules/module.py", line 541, in __call__
result = self.forward(*input, **kwargs)
File "/home/user/miniconda3/envs/ganspace/lib/python3.7/site-packages/torch/nn/modules/linear.py", line 87, in forward
return F.linear(input, self.weight, self.bias)
File "/home/user/miniconda3/envs/ganspace/lib/python3.7/site-packages/torch/nn/functional.py", line 1370, in linear
ret = torch.addmm(bias, input, weight.t())
RuntimeError: size mismatch, m1: [1 x 515], m2: [131 x 128] at /opt/conda/conda-bld/pytorch_1573049306803/work/aten/src/THC/generic/THCTensorMathBlas.cu:290
I guess the error has to do with downscaling/upscaling convolutions, but I am not sure.
I was curious if you had tried a 512px model in the past or if there is a straightforward problem you can observe in the configuration of my script file.