forked from mrq/DL-Art-School
Fix val behavior for ExtensibleTrainer
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@ -137,8 +137,8 @@ class ExtensibleTrainer(BaseModel):
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# Some models need to make parametric adjustments per-step. Do that here.
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for net in self.networks.values():
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if hasattr(net, "update_for_step"):
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net.update_for_step(step, os.path.join(self.opt['path']['models'], ".."))
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if hasattr(net.module, "update_for_step"):
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net.module.update_for_step(step, os.path.join(self.opt['path']['models'], ".."))
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# Iterate through the steps, performing them one at a time.
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state = self.dstate
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@ -101,7 +101,10 @@ def gather_2d(input, index):
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ind_nd = ind_nd.repeat((1, c))
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ind_nd = ind_nd.unsqueeze(2)
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result = torch.gather(nodim, dim=2, index=ind_nd)
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return result.squeeze()
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result = result.squeeze()
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if b == 1:
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result = result.unsqueeze(0)
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return result
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# Computes a linear latent by performing processing on the reference image and returning the filters of a single point,
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@ -215,7 +215,7 @@ def main():
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logger.info(message)
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#### validation
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if opt['datasets'].get('val', None) and current_step % opt['train']['val_freq'] == 0:
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if opt['model'] in ['sr', 'srgan', 'corruptgan', 'spsrgan'] and rank <= 0: # image restoration validation
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if opt['model'] in ['sr', 'srgan', 'corruptgan', 'spsrgan', 'extensibletrainer'] and rank <= 0: # image restoration validation
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model.force_restore_swapout()
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val_batch_sz = 1 if 'batch_size' not in opt['datasets']['val'].keys() else opt['datasets']['val']['batch_size']
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# does not support multi-GPU validation
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@ -32,7 +32,7 @@ def init_dist(backend='nccl', **kwargs):
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def main():
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#### options
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parser = argparse.ArgumentParser()
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parser.add_argument('-opt', type=str, help='Path to option YAML file.', default='../options/train_mi1_nt_spsr_switched.yml')
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parser.add_argument('-opt', type=str, help='Path to option YAML file.', default='../options/train_imgset_spsr_switched2_fullimgref.yml')
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parser.add_argument('--launcher', choices=['none', 'pytorch'], default='none',
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help='job launcher')
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parser.add_argument('--local_rank', type=int, default=0)
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