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apex-master/examples/dcgan/README.md

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+# Mixed Precision DCGAN Training in PyTorch
+
+`main_amp.py` is based on [https://github.com/pytorch/examples/tree/master/dcgan](https://github.com/pytorch/examples/tree/master/dcgan).
+It implements Automatic Mixed Precision (Amp) training of the DCGAN example for different datasets. Command-line flags forwarded to `amp.initialize` are used to easily manipulate and switch between various pure and mixed precision "optimization levels" or `opt_level`s.  For a detailed explanation of `opt_level`s, see the [updated API guide](https://nvidia.github.io/apex/amp.html).
+
+We introduce these changes to the PyTorch DCGAN example as described in the [Multiple models/optimizers/losses](https://nvidia.github.io/apex/advanced.html#multiple-models-optimizers-losses) section of the documentation::
+```
+# Added after models and optimizers construction
+[netD, netG], [optimizerD, optimizerG] = amp.initialize(
+    [netD, netG], [optimizerD, optimizerG], opt_level=opt.opt_level, num_losses=3)
+...
+# loss.backward() changed to:
+with amp.scale_loss(errD_real, optimizerD, loss_id=0) as errD_real_scaled:
+    errD_real_scaled.backward()
+...
+with amp.scale_loss(errD_fake, optimizerD, loss_id=1) as errD_fake_scaled:
+    errD_fake_scaled.backward()
+...
+with amp.scale_loss(errG, optimizerG, loss_id=2) as errG_scaled:
+    errG_scaled.backward()
+```
+
+Note that we use different `loss_scalers` for each computed loss.
+Using a separate loss scaler per loss is [optional, not required](https://nvidia.github.io/apex/advanced.html#optionally-have-amp-use-a-different-loss-scaler-per-loss).
+
+To improve the numerical stability, we swapped `nn.Sigmoid() + nn.BCELoss()` to `nn.BCEWithLogitsLoss()`.
+
+With the new Amp API **you never need to explicitly convert your model, or the input data, to half().**
+
+"Pure FP32" training:
+```
+$ python main_amp.py --opt_level O0
+```
+Recommended mixed precision training:
+```
+$ python main_amp.py --opt_level O1
+```
+
+Have a look at the original [DCGAN example](https://github.com/pytorch/examples/tree/master/dcgan) for more information about the used arguments.
+
+To enable mixed precision training, we introduce the `--opt_level` argument.