aljaz/DALLE2-pytorch
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Changed types to be generic instead of functions (#215)

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This allows pylance to do proper type hinting and makes developing
extensions to the package much easier
This commit is contained in:
Aidan Dempster
2022-07-22 16:16:29 -04:00
committed by GitHub
parent 48a1302428
commit f3d7e226ba
1 changed files with 16 additions and 18 deletions
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34
dalle2_pytorch/train_configs.py
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@@ -1,7 +1,7 @@
import json
from torchvision import transforms as T
from pydantic import BaseModel, validator, root_validator
from typing import List, Iterable, Optional, Union, Tuple, Dict, Any
from typing import List, Optional, Union, Tuple, Dict, Any, TypeVar
from x_clip import CLIP as XCLIP
from coca_pytorch import CoCa
@@ -25,11 +25,9 @@ def exists(val):
def default(val, d):
return val if exists(val) else d
def ListOrTuple(inner_type):
return Union[List[inner_type], Tuple[inner_type]]
def SingularOrIterable(inner_type):
return Union[inner_type, ListOrTuple(inner_type)]
InnerType = TypeVar('InnerType')
ListOrTuple = Union[List[InnerType], Tuple[InnerType]]
SingularOrIterable = Union[InnerType, ListOrTuple[InnerType]]
# general pydantic classes
@@ -222,13 +220,13 @@ class TrainDiffusionPriorConfig(BaseModel):
class UnetConfig(BaseModel):
dim: int
dim_mults: ListOrTuple(int)
dim_mults: ListOrTuple[int]
image_embed_dim: int = None
text_embed_dim: int = None
cond_on_text_encodings: bool = None
cond_dim: int = None
channels: int = 3
self_attn: ListOrTuple(int)
self_attn: ListOrTuple[int]
attn_dim_head: int = 32
attn_heads: int = 16
init_cross_embed: bool = True
@@ -237,16 +235,16 @@ class UnetConfig(BaseModel):
extra = "allow"
class DecoderConfig(BaseModel):
unets: ListOrTuple(UnetConfig)
unets: ListOrTuple[UnetConfig]
image_size: int = None
image_sizes: ListOrTuple(int) = None
image_sizes: ListOrTuple[int] = None
clip: Optional[AdapterConfig] # The clip model to use if embeddings are not provided
channels: int = 3
timesteps: int = 1000
sample_timesteps: Optional[SingularOrIterable(int)] = None
sample_timesteps: Optional[SingularOrIterable[int]] = None
loss_type: str = 'l2'
beta_schedule: ListOrTuple(str) = 'cosine'
learned_variance: bool = True
beta_schedule: ListOrTuple[str] = None # None means all cosine
learned_variance: SingularOrIterable[bool] = True
image_cond_drop_prob: float = 0.1
text_cond_drop_prob: float = 0.5
@@ -305,11 +303,11 @@ class DecoderDataConfig(BaseModel):
class DecoderTrainConfig(BaseModel):
epochs: int = 20
lr: SingularOrIterable(float) = 1e-4
wd: SingularOrIterable(float) = 0.01
warmup_steps: Optional[SingularOrIterable(int)] = None
lr: SingularOrIterable[float] = 1e-4
wd: SingularOrIterable[float] = 0.01
warmup_steps: Optional[SingularOrIterable[int]] = None
find_unused_parameters: bool = True
max_grad_norm: SingularOrIterable(float) = 0.5
max_grad_norm: SingularOrIterable[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
cond_scale: Union[float, List[float]] = 1.0
@@ -320,7 +318,7 @@ class DecoderTrainConfig(BaseModel):
use_ema: bool = True
ema_beta: float = 0.999
amp: bool = False
unet_training_mask: ListOrTuple(bool) = None # If None, use all unets
unet_training_mask: ListOrTuple[bool] = None # If None, use all unets
class DecoderEvaluateConfig(BaseModel):
n_evaluation_samples: int = 1000