10 examples of 'torch save model' in Python

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492def save_model(self):
493 torch.save(self.G.state_dict(), self.save_dir + "/G.pt")
494 torch.save(self.E.state_dict(), self.save_dir + "/E.pt")
495 torch.save(self.D.state_dict(), self.save_dir + "/D.pt")
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103def saveModel(state, epoch, loss_epoch, valid_epoch, is_best, episode_idx):
104 torch.save({
105 "epoch": epoch,
106 "episodes": episode_idx + 1,
107 "state_dict": state,
108 "epoch_avg_loss": round(loss_epoch, 10),
109 "epoch_avg_valid": round(valid_epoch, 10)
110 }, MODEL_PATH)
111 if is_best:
112 shutil.copyfile(MODEL_PATH, MODEL_PATH_BEST)
101def saveModel(state, epoch, loss_epoch, diff_epoch, is_best, episode_idx):
102 torch.save({
103 "epoch": epoch,
104 "episodes": episode_idx + 1,
105 "state_dict": state,
106 "epoch_avg_loss": np.mean(loss_epoch),
107 "epoch_avg_diff": np.mean(diff_epoch)
108 }, MODEL_PATH)
109 if is_best:
110 shutil.copyfile(MODEL_PATH, MODEL_PATH_BEST)
26def save_model(model, name, epoch, folder_name):
27 print("Saving Model")
28 torch.save(model.state_dict(),
29 (folder_name + "trained_{}.pth").format(epoch))
30 print("Done saving Model")
79def save_model(model, optim, epoch, path):
80 torch.save({
81 'epoch': epoch + 1,
82 'state_dict': model.state_dict(),
83 'optimizer': optim.state_dict()}, path)
53def save_model(net, model_path):
54 model_dir = os.path.dirname(model_path)
55 if not os.path.exists(model_dir):
56 os.makedirs(model_dir)
57 torch.save(net.state_dict(), model_path)
29def save_checkpoint(now_epoch, net, optimizer, lr_scheduler, file_name):
30 checkpoint = {'epoch': now_epoch,
31 'state_dict': net.state_dict(),
32 'optimizer_state_dict': optimizer.state_dict(),
33 'lr_scheduler_state_dict':lr_scheduler.state_dict()}
34 if os.path.exists(file_name):
35 print('Overwriting {}'.format(file_name))
36 torch.save(checkpoint, file_name)
251def save_model(self):
252
253 path = self.output_dir/'model_out'
254 path.mkdir(exist_ok=True)
255
256 torch.cuda.empty_cache()
257 # Save a trained model
258 model_to_save = self.model.module if hasattr(self.model, 'module') else self.model # Only save the model it-self
259 model_to_save.save_pretrained(path)
260
261 # save the tokenizer
262 self.data.tokenizer.save_pretrained(path)
78def save_model(self):
79 torch.save(self.net, self.file_path + "unet_model.pkl")
20def save(self, path, epoch):
21 # must be rewritten if self is not nn.Module
22 f = open(os.path.join(path, "%s.txt" % self.__name__), "w")
23 f.write(str(self))
24 f.close()
25 torch.save(
26 self.state_dict(), os.path.join(path, "%s_%s.pth" % (self.__name__, epoch))
27 )

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