move config parsing logic to own file, consider whether to find an off-the-shelf solution at future date

README.md

@@ -1076,7 +1076,6 @@ This library would not have gotten to this working state without the help of
 - [x] bring in cross-scale embedding from iclr paper https://github.com/lucidrains/vit-pytorch/blob/main/vit_pytorch/crossformer.py#L14
 - [x] cross embed layers for downsampling, as an option
 - [x] use an experimental tracker agnostic setup, as done <a href="https://github.com/lucidrains/tf-bind-transformer#simple-trainer-class-for-fine-tuning">here</a>
-- [x] use pydantic for config drive training
 - [ ] become an expert with unets, cleanup unet code, make it fully configurable, port all learnings over to https://github.com/lucidrains/x-unet (test out unet² in ddpm repo) - consider https://github.com/lucidrains/uformer-pytorch attention-based unet
 - [ ] transcribe code to Jax, which lowers the activation energy for distributed training, given access to TPUs
 - [ ] train on a toy task, offer in colab

configs/README.md

@@ -4,7 +4,7 @@ For more complex configuration, we provide the option of using a configuration f
 
 ### Decoder Trainer
 
-The decoder trainer has 7 main configuration options. A full example of their use can be found in the [example decoder configuration](train_decoder_config.example.json).
+The decoder trainer has 7 main configuration options. A full example of their use can be found in the [example decoder configuration](train_decoder_config.json.example).
 
 **<ins>Unets</ins>:**
 

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
+    }
+}

configs/train_decoder_config.json.example

@@ -12,6 +12,7 @@
     ],
     "decoder": {
         "image_sizes": [64],
+        "image_size": [64],
         "channels": 3,
         "timesteps": 1000,
         "loss_type": "l2",
@@ -62,7 +63,7 @@
         "unet_training_mask": [true]
     },
     "evaluate": {
-        "n_evaluation_samples": 1000,
+        "n_evalation_samples": 1000,
         "FID": {
             "feature": 64
         },

dalle2_pytorch/train_configs.py

@@ -1,125 +1,47 @@
-import json
 from torchvision import transforms as T
-from pydantic import BaseModel, validator, root_validator
+from configs.decoder_defaults import default_config, ConfigField
-from typing import List, Iterable, Optional, Union, Tuple, Dict, Any
 
-def exists(val):
+class TrainDecoderConfig:
-    return val is not None
+    def __init__(self, config):
+        self.config = self.map_config(config, default_config)
 
-def default(val, d):
+    def map_config(self, config, defaults):
-    return val if exists(val) else d
+        """
+        Returns a dictionary containing all config options in the union of config and defaults.
+        If the config value is an array, apply the default value to each element.
+        If the default values dict has a value of ConfigField.REQUIRED for a key, it is required and a runtime error should be thrown if a value is not supplied from config
+        """
+        def _check_option(option, option_config, option_defaults):
+            for key, value in option_defaults.items():
+                if key not in option_config:
+                    if value == ConfigField.REQUIRED:
+                        raise RuntimeError("Required config value '{}' of option '{}' not supplied".format(key, option))
+                    option_config[key] = value
+        
+        for key, value in defaults.items():
+            if key not in config:
+                # Then they did not pass in one of the main configs. If the default is an array or object, then we can fill it in. If is a required object, we must error
+                if value == ConfigField.REQUIRED:
+                    raise RuntimeError("Required config value '{}' not supplied".format(key))
+                elif isinstance(value, dict):
+                    config[key] = {}
+                elif isinstance(value, list):
+                    config[key] = [{}]
+            # Config[key] is now either a dict, list of dicts, or an object that cannot be checked. 
+            # If it is a list, then we need to check each element
+            if isinstance(value, list):
+                assert isinstance(config[key], list)
+                for element in config[key]:
+                    _check_option(key, element, value[0])
+            elif isinstance(value, dict):
+                _check_option(key, config[key], value)
+            # This object does not support checking
+        return config
 
-class UnetConfig(BaseModel):
+    def get_preprocessing(self):
-    dim: int
+        """
-    dim_mults: List[int]
+        Takes the preprocessing dictionary and converts it to a composition of torchvision transforms
-    image_embed_dim: int = None
+        """
-    cond_dim: int = None
-    channels: int = 3
-    attn_dim_head: int = 32
-    attn_heads: int = 16
-
-    class Config:
-        extra = "allow"
-
-class DecoderConfig(BaseModel):
-    image_size: int = None
-    image_sizes: Union[List[int], Tuple[int]] = None
-    channels: int = 3
-    timesteps: int = 1000
-    loss_type: str = 'l2'
-    beta_schedule: str = 'cosine'
-    learned_variance: bool = True
-
-    @validator('image_sizes')
-    def check_image_sizes(cls, image_sizes, values):
-        if exists(values.get('image_size')) ^ exists(image_sizes):
-            return image_sizes
-        raise ValueError('either image_size or image_sizes is required, but not both')
-
-    class Config:
-        extra = "allow"
-
-class TrainSplitConfig(BaseModel):
-    train: float = 0.75
-    val: float = 0.15
-    test: float = 0.1
-
-    @root_validator
-    def validate_all(cls, fields):
-        if sum([*fields.values()]) != 1.:
-            raise ValueError(f'{fields.keys()} must sum to 1.0')
-        return fields
-
-class DecoderDataConfig(BaseModel):
-    webdataset_base_url: str     # path to a webdataset with jpg images
-    embeddings_url: str          # path to .npy files with embeddings
-    num_workers: int = 4
-    batch_size: int = 64
-    start_shard: int = 0
-    end_shard: int = 9999999
-    shard_width: int = 6
-    index_width: int = 4
-    splits: TrainSplitConfig
-    shuffle_train: bool = True
-    resample_train: bool = False
-    preprocessing: Dict[str, Any] = {'ToTensor': True}
-
-class DecoderTrainConfig(BaseModel):
-    epochs: int = 20
-    lr: float = 1e-4
-    wd: float = 0.01
-    max_grad_norm: float = 0.5
-    save_every_n_samples: int = 100000
-    n_sample_images: int = 6                # The number of example images to produce when sampling the train and test dataset
-    device: str = 'cuda:0'
-    epoch_samples: int = 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: int = None          # Same as above but for validation.
-    use_ema: bool = True
-    ema_beta: float = 0.99
-    amp: bool = False
-    save_all: bool = False                  # Whether to preserve all checkpoints
-    save_latest: bool = True                # Whether to always save the latest checkpoint
-    save_best: bool = True                  # Whether to save the best checkpoint
-    unet_training_mask: List[bool] = None   # If None, use all unets
-
-class DecoderEvaluateConfig(BaseModel):
-    n_evaluation_samples: int = 1000
-    FID: Dict[str, Any] = None
-    IS: Dict[str, Any] = None
-    KID: Dict[str, Any] = None
-    LPIPS: Dict[str, Any] = None
-
-class TrackerConfig(BaseModel):
-    tracker_type: str = 'console'           # Decoder currently supports console and wandb
-    data_path: str = './models'             # The path where files will be saved locally
-    init_config: Dict[str, Any] = None
-    wandb_entity: str = ''                  # Only needs to be set if tracker_type is wandb
-    wandb_project: str = ''
-    verbose: bool = False                   # Whether to print console logging for non-console trackers
-
-class DecoderLoadConfig(BaseModel):
-    source: str = None                      # Supports file and wandb
-    run_path: str = ''                      # Used only if source is wandb
-    file_path: str = ''                     # The local filepath if source is file. If source is wandb, the relative path to the model file in wandb.
-    resume: bool = False                    # If using wandb, whether to resume the run
-
-class TrainDecoderConfig(BaseModel):
-    unets: List[UnetConfig]
-    decoder: DecoderConfig
-    data: DecoderDataConfig
-    train: DecoderTrainConfig
-    evaluate: DecoderEvaluateConfig
-    tracker: TrackerConfig
-    load: DecoderLoadConfig
-
-    @classmethod
-    def from_json_path(cls, json_path):
-        with open(json_path) as f:
-            config = json.load(f)
-        return cls(**config)
-
-    @property
-    def img_preproc(self):
         def _get_transformation(transformation_name, **kwargs):
             if transformation_name == "RandomResizedCrop":
                 return T.RandomResizedCrop(**kwargs)
@@ -128,8 +50,13 @@ class TrainDecoderConfig(BaseModel):
             elif transformation_name == "ToTensor":
                 return T.ToTensor()
         
-        transforms = []
+        transformations = []
-        for transform_name, transform_kwargs_or_bool in self.data.preprocessing.items():
+        for transformation_name, transformation_kwargs in self.config["data"]["preprocessing"].items():
-            transform_kwargs = {} if not isinstance(transform_kwargs_or_bool, dict) else transform_kwargs_or_bool
+            if isinstance(transformation_kwargs, dict):
-            transforms.append(_get_transformation(transform_name, **transform_kwargs))
+                transformations.append(_get_transformation(transformation_name, **transformation_kwargs))
-        return T.Compose(transforms)
+            else:
+                transformations.append(_get_transformation(transformation_name))
+        return T.Compose(transformations)
+    
+    def __getitem__(self, key):
+        return self.config[key]

setup.py

@@ -10,7 +10,7 @@ setup(
       'dream = dalle2_pytorch.cli:dream'
     ],
   },
-  version = '0.4.2',
+  version = '0.3.9',
   license='MIT',
   description = 'DALL-E 2',
   author = 'Phil Wang',
@@ -32,7 +32,6 @@ setup(
     'kornia>=0.5.4',
     'numpy',
     'pillow',
-    'pydantic',
     'resize-right>=0.0.2',
     'rotary-embedding-torch',
     'torch>=1.10',

train_decoder.py

@@ -5,6 +5,7 @@ from dalle2_pytorch.trackers import WandbTracker, ConsoleTracker
 from dalle2_pytorch.train_configs import TrainDecoderConfig
 from dalle2_pytorch.utils import Timer
 
+import json
 import torchvision
 import torch
 from torchmetrics.image.fid import FrechetInceptionDistance
@@ -89,11 +90,11 @@ def create_dataloaders(
 def create_decoder(device, decoder_config, unets_config):
     """Creates a sample decoder"""
 
-    unets = [Unet(**config.dict()) for config in unets_config]
+    unets = [Unet(**config) for config in unets_config]
 
     decoder = Decoder(
         unet=unets,
-        **decoder_config.dict()
+        **decoder_config
     )
 
     decoder.to(device=device)
@@ -153,13 +154,13 @@ def generate_grid_samples(trainer, examples, text_prepend=""):
     grid_images = [torchvision.utils.make_grid([original_image, generated_image]) for original_image, generated_image in zip(real_images, generated_images)]
     return grid_images, captions
                     
-def evaluate_trainer(trainer, dataloader, device, n_evaluation_samples=1000, FID=None, IS=None, KID=None, LPIPS=None):
+def evaluate_trainer(trainer, dataloader, device, n_evalation_samples=1000, FID=None, IS=None, KID=None, LPIPS=None):
     """
     Computes evaluation metrics for the decoder
     """
     metrics = {}
     # Prepare the data
-    examples = get_example_data(dataloader, device, n_evaluation_samples)
+    examples = get_example_data(dataloader, device, n_evalation_samples)
     real_images, generated_images, captions = generate_samples(trainer, examples)
     real_images = torch.stack(real_images).to(device=device, dtype=torch.float)
     generated_images = torch.stack(generated_images).to(device=device, dtype=torch.float)
@@ -251,8 +252,8 @@ def train(
     start_epoch = 0
     validation_losses = []
 
-    if exists(load_config) and exists(load_config.source):
+    if exists(load_config) and exists(load_config["source"]):
-        start_epoch, start_step, validation_losses = recall_trainer(tracker, trainer, recall_source=load_config.source, **load_config)
+        start_epoch, start_step, validation_losses = recall_trainer(tracker, trainer, recall_source=load_config["source"], **load_config)
     trainer.to(device=inference_device)
 
     if not exists(unet_training_mask):
@@ -270,6 +271,7 @@ def train(
 
     for epoch in range(start_epoch, epochs):
         print(print_ribbon(f"Starting epoch {epoch}", repeat=40))
+        trainer.train()
 
         timer = Timer()
 
@@ -278,13 +280,11 @@ def train(
         last_snapshot = 0
 
         losses = []
-
         for i, (img, emb) in enumerate(dataloaders["train"]):
             step += 1
             sample += img.shape[0]
             img, emb = send_to_device((img, emb))
             
-            trainer.train()
             for unet in range(1, trainer.num_unets+1):
                 # Check if this is a unet we are training
                 if not unet_training_mask[unet-1]: # Unet index is the unet number - 1
@@ -299,7 +299,7 @@ def train(
             timer.reset()
             last_sample = sample
 
-            if i % TRAIN_CALC_LOSS_EVERY_ITERS == 0:
+            if i % CALC_LOSS_EVERY_ITERS == 0:
                 average_loss = sum(losses) / len(losses)
                 log_data = {
                     "Training loss": average_loss,
@@ -319,12 +319,11 @@ def train(
                     save_paths.append("latest.pth")
                 if save_all:
                     save_paths.append(f"checkpoints/epoch_{epoch}_step_{step}.pth")
-
                 save_trainer(tracker, trainer, epoch, step, validation_losses, save_paths)
-
                 if exists(n_sample_images) and n_sample_images > 0:
                     trainer.eval()
                     train_images, train_captions = generate_grid_samples(trainer, train_example_data, "Train: ")
+                    trainer.train()
                     tracker.log_images(train_images, captions=train_captions, image_section="Train Samples", step=step)
 
             if exists(epoch_samples) and sample >= epoch_samples:
@@ -359,6 +358,7 @@ def train(
             tracker.log(log_data, step=step, verbose=True)
 
         # Compute evaluation metrics
+        trainer.eval()
         if exists(evaluate_config):
             print(print_ribbon(f"Starting Evaluation {epoch}", repeat=40))
             evaluation = evaluate_trainer(trainer, dataloaders["val"], inference_device, **evaluate_config)
@@ -385,25 +385,21 @@ def create_tracker(config, tracker_type=None, data_path=None, **kwargs):
     """
     Creates a tracker of the specified type and initializes special features based on the full config
     """
-    tracker_config = config.tracker
+    tracker_config = config["tracker"]
     init_config = {}
-
+    init_config["config"] = config.config
-    if exists(tracker_config.init_config):
-        init_config["config"] = tracker_config.init_config
-
     if tracker_type == "console":
         tracker = ConsoleTracker(**init_config)
     elif tracker_type == "wandb":
         # We need to initialize the resume state here
-        load_config = config.load
+        load_config = config["load"]
-        if load_config.source == "wandb" and load_config.resume:
+        if load_config["source"] == "wandb" and load_config["resume"]:
             # Then we are resuming the run load_config["run_path"]
-            run_id = load_config.run_path.split("/")[-1]
+            run_id = config["resume"]["wandb_run_path"].split("/")[-1]
             init_config["id"] = run_id
             init_config["resume"] = "must"
-
+        init_config["entity"] = tracker_config["wandb_entity"]
-        init_config["entity"] = tracker_config.wandb_entity
+        init_config["project"] = tracker_config["wandb_project"]
-        init_config["project"] = tracker_config.wandb_project
         tracker = WandbTracker(data_path)
         tracker.init(**init_config)
     else:
@@ -412,35 +408,35 @@ def create_tracker(config, tracker_type=None, data_path=None, **kwargs):
     
 def initialize_training(config):
     # Create the save path
-    if "cuda" in config.train.device:
+    if "cuda" in config["train"]["device"]:
         assert torch.cuda.is_available(), "CUDA is not available"
-    device = torch.device(config.train.device)
+    device = torch.device(config["train"]["device"])
     torch.cuda.set_device(device)
-    all_shards = list(range(config.data.start_shard, config.data.end_shard + 1))
+    all_shards = list(range(config["data"]["start_shard"], config["data"]["end_shard"] + 1))
 
     dataloaders = create_dataloaders (
         available_shards=all_shards,
-        img_preproc = config.img_preproc,
+        img_preproc = config.get_preprocessing(),
-        train_prop = config.data.splits.train,
+        train_prop = config["data"]["splits"]["train"],
-        val_prop = config.data.splits.val,
+        val_prop = config["data"]["splits"]["val"],
-        test_prop = config.data.splits.test,
+        test_prop = config["data"]["splits"]["test"],
-        n_sample_images=config.train.n_sample_images,
+        n_sample_images=config["train"]["n_sample_images"],
-        **config.data.dict()
+        **config["data"]
     )
 
-    decoder = create_decoder(device, config.decoder, config.unets)
+    decoder = create_decoder(device, config["decoder"], config["unets"])
     num_parameters = sum(p.numel() for p in decoder.parameters())
     print(print_ribbon("Loaded Config", repeat=40))
     print(f"Number of parameters: {num_parameters}")
 
-    tracker = create_tracker(config, **config.tracker.dict())
+    tracker = create_tracker(config, **config["tracker"])
 
     train(dataloaders, decoder, 
         tracker=tracker,
         inference_device=device,
-        load_config=config.load,
+        load_config=config["load"],
-        evaluate_config=config.evaluate,
+        evaluate_config=config["evaluate"],
-        **config.train.dict(),
+        **config["train"],
     )
 
 # Create a simple click command line interface to load the config and start the training
@@ -448,7 +444,9 @@ def initialize_training(config):
 @click.option("--config_file", default="./train_decoder_config.json", help="Path to config file")
 def main(config_file):
     print("Recalling config from {}".format(config_file))
-    config = TrainDecoderConfig.from_json_path(config_file)
+    with open(config_file) as f:
+        config = json.load(f)
+    config = TrainDecoderConfig(config)
     initialize_training(config)