Added single GPU training script for decoder (#108)

Added config files for training

Changed example image generation to be more efficient

Added configuration description to README

Removed unused import

configs/decoder_defaults.py

@@ -0,0 +1,82 @@
+"""
+Defines the default values for the decoder config
+"""
+
+from enum import Enum
+class ConfigField(Enum):
+    REQUIRED = 0  # This had more options. It's a bit unnecessary now, but I can't think of a better way to do it.
+
+default_config = {
+    "unets": ConfigField.REQUIRED,
+    "decoder": {
+        "image_sizes": ConfigField.REQUIRED,  # The side lengths of the upsampled image at the end of each unet
+        "image_size": ConfigField.REQUIRED,  # Usually the same as image_sizes[-1] I think
+        "channels": 3,
+        "timesteps": 1000,
+        "loss_type": "l2",
+        "beta_schedule": "cosine",
+        "learned_variance": True
+    },
+    "data": {
+        "webdataset_base_url": ConfigField.REQUIRED,  # Path to a webdataset with jpg images
+        "embeddings_url": ConfigField.REQUIRED,  # Path to .npy files with embeddings
+        "num_workers": 4,
+        "batch_size": 64,
+        "start_shard": 0,
+        "end_shard": 9999999,
+        "shard_width": 6,
+        "index_width": 4,
+        "splits": {
+            "train": 0.75,
+            "val": 0.15,
+            "test": 0.1
+        },
+        "shuffle_train": True,
+        "resample_train": False,
+        "preprocessing": {
+            "ToTensor": True
+        }
+    },
+    "train": {
+        "epochs": 20,
+        "lr": 1e-4,
+        "wd": 0.01,
+        "max_grad_norm": 0.5,
+        "save_every_n_samples": 100000,
+        "n_sample_images": 6,  # The number of example images to produce when sampling the train and test dataset
+        "device": "cuda:0",
+        "epoch_samples": None,  # Limits the number of samples per epoch. None means no limit. Required if resample_train is true as otherwise the number of samples per epoch is infinite.
+        "validation_samples": None,  # Same as above but for validation.
+        "use_ema": True,
+        "ema_beta": 0.99,
+        "amp": False,
+        "save_all": False,  # Whether to preserve all checkpoints
+        "save_latest": True,  # Whether to always save the latest checkpoint
+        "save_best": True,  # Whether to save the best checkpoint
+        "unet_training_mask": None  # If None, use all unets
+    },
+    "evaluate": {
+        "n_evalation_samples": 1000,
+        "FID": None,
+        "IS": None,
+        "KID": None,
+        "LPIPS": None
+    },
+    "tracker": {
+        "tracker_type": "console",  # Decoder currently supports console and wandb
+        "data_path": "./models",  # The path where files will be saved locally
+
+        "wandb_entity": "",  # Only needs to be set if tracker_type is wandb
+        "wandb_project": "",
+
+        "verbose": False  # Whether to print console logging for non-console trackers
+    },
+    "load": {
+        "source": None,  # Supports file and wandb
+
+        "run_path": "",  # Used only if source is wandb
+        "file_path": "",  # The local filepath if source is file. If source is wandb, the relative path to the model file in wandb.
+
+        "resume": False  # If using wandb, whether to resume the run
+    }
+}