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https://github.com/lucidrains/DALLE2-pytorch.git
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add sampels-seen and ema decay (#166)
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@@ -268,6 +268,7 @@ def train(
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validation_losses = []
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next_task = 'train'
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sample = 0
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samples_seen = 0
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val_sample = 0
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step = lambda: int(trainer.step.item())
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@@ -312,6 +313,7 @@ def train(
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all_samples = accelerator.gather(sample_length_tensor) # TODO: accelerator.reduce is broken when this was written. If it is fixed replace this.
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total_samples = all_samples.sum().item()
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sample += total_samples
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samples_seen += total_samples
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img, emb = send_to_device((img, emb))
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trainer.train()
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@@ -334,14 +336,20 @@ def train(
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mask = unet_all_losses != 0
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unet_average_loss = (unet_all_losses * mask).sum(dim=0) / mask.sum(dim=0)
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loss_map = { f"Unet {index} Training Loss": loss.item() for index, loss in enumerate(unet_average_loss) if loss != 0 }
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# gather decay rate on each UNet
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ema_decay_list = {f"Unet {index} EMA Decay": ema_unet.get_current_decay() for index, ema_unet in enumerate(trainer.ema_unets)}
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log_data = {
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"Epoch": epoch,
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"Sample": sample,
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"Step": i,
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"Samples per second": samples_per_sec,
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"Samples Seen": samples_seen,
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**ema_decay_list,
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**loss_map
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}
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# print(f"I am rank {accelerator.state.process_index}. Example weight: {trainer.decoder.state_dict()['module.unets.0.init_conv.convs.0.weight'][0,0,0,0]}")
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if is_master:
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tracker.log(log_data, step=step(), verbose=True)
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