rework the code to not use the walrus operator because colab's 3.7 does not support it
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@ -429,13 +429,16 @@ def train_hypernetwork(hypernetwork_name, learn_rate, batch_size, data_root, log
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weights = hypernetwork.weights()
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weights = hypernetwork.weights()
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for weight in weights:
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for weight in weights:
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weight.requires_grad = True
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weight.requires_grad = True
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# Here we use optimizer from saved HN, or we can specify as UI option.
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# Here we use optimizer from saved HN, or we can specify as UI option.
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if (optimizer_name := hypernetwork.optimizer_name) in optimizer_dict:
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if hypernetwork.optimizer_name in optimizer_dict:
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optimizer = optimizer_dict[hypernetwork.optimizer_name](params=weights, lr=scheduler.learn_rate)
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optimizer = optimizer_dict[hypernetwork.optimizer_name](params=weights, lr=scheduler.learn_rate)
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optimizer_name = hypernetwork.optimizer_name
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else:
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else:
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print(f"Optimizer type {optimizer_name} is not defined!")
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print(f"Optimizer type {hypernetwork.optimizer_name} is not defined!")
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optimizer = torch.optim.AdamW(params=weights, lr=scheduler.learn_rate)
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optimizer = torch.optim.AdamW(params=weights, lr=scheduler.learn_rate)
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optimizer_name = 'AdamW'
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optimizer_name = 'AdamW'
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if hypernetwork.optimizer_state_dict: # This line must be changed if Optimizer type can be different from saved optimizer.
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if hypernetwork.optimizer_state_dict: # This line must be changed if Optimizer type can be different from saved optimizer.
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try:
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try:
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optimizer.load_state_dict(hypernetwork.optimizer_state_dict)
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optimizer.load_state_dict(hypernetwork.optimizer_state_dict)
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