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move neural network creations off the configuration file into the pydantic classes

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This commit is contained in:
Phil Wang
2022-05-22 19:18:18 -07:00
parent 0f4edff214
commit 5c397c9d66
6 changed files with 44 additions and 41 deletions
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6
configs/README.md
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@@ -6,9 +6,10 @@ For more complex configuration, we provide the option of using a configuration f
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).
**<ins>Unets</ins>:**
**<ins>Unet</ins>:**
This is a single unet config, which belongs as an array nested under the decoder config as a list of `unets`
Each member of this array defines a single unet that will be added to the decoder.
| Option | Required | Default | Description |
| ------ | -------- | ------- | ----------- |
| `dim` | Yes | N/A | The starting channels of the unet. |
@@ -22,6 +23,7 @@ Any parameter from the `Unet` constructor can also be given here.
Defines the configuration options for the decoder model. The unets defined above will automatically be inserted.
| Option | Required | Default | Description |
| ------ | -------- | ------- | ----------- |
| `unets` | Yes | N/A | A list of unets, using the configuration above |
| `image_sizes` | Yes | N/A | The resolution of the image after each upsampling step. The length of this array should be the number of unets defined. |
| `image_size` | Yes | N/A | Not used. Can be any number. |
| `timesteps` | No | `1000` | The number of diffusion timesteps used for generation. |

22
configs/train_decoder_config.example.json
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@@ -1,16 +1,16 @@
{
"unets": [
{
"dim": 128,
"image_embed_dim": 768,
"cond_dim": 64,
"channels": 3,
"dim_mults": [1, 2, 4, 8],
"attn_dim_head": 32,
"attn_heads": 16
}
],
"decoder": {
"unets": [
{
"dim": 128,
"image_embed_dim": 768,
"cond_dim": 64,
"channels": 3,
"dim_mults": [1, 2, 4, 8],
"attn_dim_head": 32,
"attn_heads": 16
}
],
"image_sizes": [64],
"channels": 3,
"timesteps": 1000,

4
dalle2_pytorch/dalle2_pytorch.py
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@@ -1712,7 +1712,7 @@ class Decoder(BaseGaussianDiffusion):
self.unconditional = unconditional
assert not (condition_on_text_encodings and unconditional), 'unconditional decoder image generation cannot be set to True if conditioning on text is present'
assert self.unconditional or (exists(clip) ^ exists(image_size)), 'either CLIP is supplied, or you must give the image_size and channels (usually 3 for RGB)'
assert self.unconditional or (exists(clip) ^ (exists(image_size) or exists(image_sizes))), 'either CLIP is supplied, or you must give the image_size and channels (usually 3 for RGB)'
self.clip = None
if exists(clip):
@@ -1728,7 +1728,7 @@ class Decoder(BaseGaussianDiffusion):
self.clip_image_size = clip.image_size
self.channels = clip.image_channels
else:
self.clip_image_size = image_size
self.clip_image_size = default(image_size, lambda: image_sizes[-1])
self.channels = channels
self.condition_on_text_encodings = condition_on_text_encodings

35
dalle2_pytorch/train_configs.py
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@@ -3,15 +3,24 @@ from torchvision import transforms as T
from pydantic import BaseModel, validator, root_validator
from typing import List, Iterable, Optional, Union, Tuple, Dict, Any
from dalle2_pytorch.dalle2_pytorch import Unet, Decoder
# helper functions
def exists(val):
return val is not None
def default(val, d):
return val if exists(val) else d
def ListOrTuple(inner_type):
return Union[List[inner_type], Tuple[inner_type]]
# pydantic classes
class UnetConfig(BaseModel):
dim: int
dim_mults: List[int]
dim_mults: ListOrTuple(int)
image_embed_dim: int = None
cond_dim: int = None
channels: int = 3
@@ -22,14 +31,21 @@ class UnetConfig(BaseModel):
extra = "allow"
class DecoderConfig(BaseModel):
unets: Union[List[UnetConfig], Tuple[UnetConfig]]
image_size: int = None
image_sizes: Union[List[int], Tuple[int]] = None
image_sizes: ListOrTuple(int) = None
channels: int = 3
timesteps: int = 1000
loss_type: str = 'l2'
beta_schedule: str = 'cosine'
learned_variance: bool = True
def create(self):
decoder_kwargs = self.dict()
unet_configs = decoder_kwargs.pop('unets')
unets = [Unet(**config) for config in unet_configs]
return Decoder(unets, **decoder_kwargs)
@validator('image_sizes')
def check_image_sizes(cls, image_sizes, values):
if exists(values.get('image_size')) ^ exists(image_sizes):
@@ -86,17 +102,17 @@ class DecoderTrainConfig(BaseModel):
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
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.
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
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: ListOrTuple(bool) = None # If None, use all unets
class DecoderEvaluateConfig(BaseModel):
n_evaluation_samples: int = 1000
@@ -120,7 +136,6 @@ class DecoderLoadConfig(BaseModel):
resume: bool = False # If using wandb, whether to resume the run
class TrainDecoderConfig(BaseModel):
unets: List[UnetConfig]
decoder: DecoderConfig
data: DecoderDataConfig
train: DecoderTrainConfig

2
setup.py
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@@ -10,7 +10,7 @@ setup(
'dream = dalle2_pytorch.cli:dream'
],
},
version = '0.4.7',
version = '0.4.8',
license='MIT',
description = 'DALL-E 2',
author = 'Phil Wang',

16
train_decoder.py
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@@ -85,20 +85,6 @@ def create_dataloaders(
"test_sampling": test_sampling_dataloader
}
def create_decoder(device, decoder_config, unets_config):
"""Creates a sample decoder"""
unets = [Unet(**config.dict()) for config in unets_config]
decoder = Decoder(
unet=unets,
**decoder_config.dict()
)
decoder.to(device=device)
return decoder
def get_dataset_keys(dataloader):
"""
It is sometimes neccesary to get the keys the dataloader is returning. Since the dataset is burried in the dataloader, we need to do a process to recover it.
@@ -428,7 +414,7 @@ def initialize_training(config):
**config.data.dict()
)
decoder = create_decoder(device, config.decoder, config.unets)
decoder = config.decoder.create().to(device = device)
num_parameters = sum(p.numel() for p in decoder.parameters())
print(print_ribbon("Loaded Config", repeat=40))
print(f"Number of parameters: {num_parameters}")