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ruff manual fixes

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AUTOMATIC
2023-05-10 11:19:16 +03:00
parent 028d3f6425
commit 550256db1c
15 changed files with 69 additions and 41 deletions
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extensions-builtin/LDSR/sd_hijack_autoencoder.py
+5 -5
View File
@@ -24,7 +24,7 @@ class VQModel(pl.LightningModule):
n_embed,
embed_dim,
ckpt_path=None,
ignore_keys=[],
ignore_keys=None,
image_key="image",
colorize_nlabels=None,
monitor=None,
@@ -62,7 +62,7 @@ class VQModel(pl.LightningModule):
print(f"Keeping EMAs of {len(list(self.model_ema.buffers()))}.")
if ckpt_path is not None:
self.init_from_ckpt(ckpt_path, ignore_keys=ignore_keys)
self.init_from_ckpt(ckpt_path, ignore_keys=ignore_keys or [])
self.scheduler_config = scheduler_config
self.lr_g_factor = lr_g_factor
@@ -81,11 +81,11 @@ class VQModel(pl.LightningModule):
if context is not None:
print(f"{context}: Restored training weights")
def init_from_ckpt(self, path, ignore_keys=list()):
def init_from_ckpt(self, path, ignore_keys=None):
sd = torch.load(path, map_location="cpu")["state_dict"]
keys = list(sd.keys())
for k in keys:
for ik in ignore_keys:
for ik in ignore_keys or []:
if k.startswith(ik):
print("Deleting key {} from state_dict.".format(k))
del sd[k]
@@ -270,7 +270,7 @@ class VQModel(pl.LightningModule):
class VQModelInterface(VQModel):
def __init__(self, embed_dim, *args, **kwargs):
super().__init__(embed_dim=embed_dim, *args, **kwargs)
super().__init__(*args, embed_dim=embed_dim, **kwargs)
self.embed_dim = embed_dim
def encode(self, x):
extensions-builtin/LDSR/sd_hijack_ddpm_v1.py
+7 -7
View File
@@ -48,7 +48,7 @@ class DDPMV1(pl.LightningModule):
beta_schedule="linear",
loss_type="l2",
ckpt_path=None,
ignore_keys=[],
ignore_keys=None,
load_only_unet=False,
monitor="val/loss",
use_ema=True,
@@ -100,7 +100,7 @@ class DDPMV1(pl.LightningModule):
if monitor is not None:
self.monitor = monitor
if ckpt_path is not None:
self.init_from_ckpt(ckpt_path, ignore_keys=ignore_keys, only_model=load_only_unet)
self.init_from_ckpt(ckpt_path, ignore_keys=ignore_keys or [], only_model=load_only_unet)
self.register_schedule(given_betas=given_betas, beta_schedule=beta_schedule, timesteps=timesteps,
linear_start=linear_start, linear_end=linear_end, cosine_s=cosine_s)
@@ -182,13 +182,13 @@ class DDPMV1(pl.LightningModule):
if context is not None:
print(f"{context}: Restored training weights")
def init_from_ckpt(self, path, ignore_keys=list(), only_model=False):
def init_from_ckpt(self, path, ignore_keys=None, only_model=False):
sd = torch.load(path, map_location="cpu")
if "state_dict" in list(sd.keys()):
sd = sd["state_dict"]
keys = list(sd.keys())
for k in keys:
for ik in ignore_keys:
for ik in ignore_keys or []:
if k.startswith(ik):
print("Deleting key {} from state_dict.".format(k))
del sd[k]
@@ -444,7 +444,7 @@ class LatentDiffusionV1(DDPMV1):
conditioning_key = None
ckpt_path = kwargs.pop("ckpt_path", None)
ignore_keys = kwargs.pop("ignore_keys", [])
super().__init__(conditioning_key=conditioning_key, *args, **kwargs)
super().__init__(*args, conditioning_key=conditioning_key, **kwargs)
self.concat_mode = concat_mode
self.cond_stage_trainable = cond_stage_trainable
self.cond_stage_key = cond_stage_key
@@ -1418,10 +1418,10 @@ class Layout2ImgDiffusionV1(LatentDiffusionV1):
# TODO: move all layout-specific hacks to this class
def __init__(self, cond_stage_key, *args, **kwargs):
assert cond_stage_key == 'coordinates_bbox', 'Layout2ImgDiffusion only for cond_stage_key="coordinates_bbox"'
super().__init__(cond_stage_key=cond_stage_key, *args, **kwargs)
super().__init__(*args, cond_stage_key=cond_stage_key, **kwargs)
def log_images(self, batch, N=8, *args, **kwargs):
logs = super().log_images(batch=batch, N=N, *args, **kwargs)
logs = super().log_images(*args, batch=batch, N=N, **kwargs)
key = 'train' if self.training else 'validation'
dset = self.trainer.datamodule.datasets[key]
extensions-builtin/SwinIR/swinir_model_arch.py
+5 -1
View File
@@ -644,13 +644,17 @@ class SwinIR(nn.Module):
"""
def __init__(self, img_size=64, patch_size=1, in_chans=3,
embed_dim=96, depths=[6, 6, 6, 6], num_heads=[6, 6, 6, 6],
embed_dim=96, depths=None, num_heads=None,
window_size=7, mlp_ratio=4., qkv_bias=True, qk_scale=None,
drop_rate=0., attn_drop_rate=0., drop_path_rate=0.1,
norm_layer=nn.LayerNorm, ape=False, patch_norm=True,
use_checkpoint=False, upscale=2, img_range=1., upsampler='', resi_connection='1conv',
**kwargs):
super(SwinIR, self).__init__()
depths = depths or [6, 6, 6, 6]
num_heads = num_heads or [6, 6, 6, 6]
num_in_ch = in_chans
num_out_ch = in_chans
num_feat = 64
extensions-builtin/SwinIR/swinir_model_arch_v2.py
+9 -2
View File
@@ -74,9 +74,12 @@ class WindowAttention(nn.Module):
"""
def __init__(self, dim, window_size, num_heads, qkv_bias=True, attn_drop=0., proj_drop=0.,
pretrained_window_size=[0, 0]):
pretrained_window_size=None):
super().__init__()
pretrained_window_size = pretrained_window_size or [0, 0]
self.dim = dim
self.window_size = window_size # Wh, Ww
self.pretrained_window_size = pretrained_window_size
@@ -698,13 +701,17 @@ class Swin2SR(nn.Module):
"""
def __init__(self, img_size=64, patch_size=1, in_chans=3,
embed_dim=96, depths=[6, 6, 6, 6], num_heads=[6, 6, 6, 6],
embed_dim=96, depths=None, num_heads=None,
window_size=7, mlp_ratio=4., qkv_bias=True,
drop_rate=0., attn_drop_rate=0., drop_path_rate=0.1,
norm_layer=nn.LayerNorm, ape=False, patch_norm=True,
use_checkpoint=False, upscale=2, img_range=1., upsampler='', resi_connection='1conv',
**kwargs):
super(Swin2SR, self).__init__()
depths = depths or [6, 6, 6, 6]
num_heads = num_heads or [6, 6, 6, 6]
num_in_ch = in_chans
num_out_ch = in_chans
num_feat = 64