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[misc] update pre-commit and run all files (#4752)

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* [misc] update pre-commit

* [misc] run pre-commit

* [misc] remove useless configuration files

* [misc] ignore cuda for clang-format
This commit is contained in:
Hongxin Liu
2023-09-19 14:20:26 +08:00
committed by GitHub
parent 3c6b831c26
commit 079bf3cb26
1268 changed files with 50037 additions and 38444 deletions
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33
examples/images/diffusion/ldm/modules/midas/api.py
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@@ -3,13 +3,11 @@
import cv2
import torch
import torch.nn as nn
from torchvision.transforms import Compose
from ldm.modules.midas.midas.dpt_depth import DPTDepthModel
from ldm.modules.midas.midas.midas_net import MidasNet
from ldm.modules.midas.midas.midas_net_custom import MidasNet_small
from ldm.modules.midas.midas.transforms import Resize, NormalizeImage, PrepareForNet
from ldm.modules.midas.midas.transforms import NormalizeImage, PrepareForNet, Resize
from torchvision.transforms import Compose
ISL_PATHS = {
"dpt_large": "midas_models/dpt_large-midas-2f21e586.pt",
@@ -98,18 +96,20 @@ def load_model(model_type):
model = MidasNet(model_path, non_negative=True)
net_w, net_h = 384, 384
resize_mode = "upper_bound"
normalization = NormalizeImage(
mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]
)
normalization = NormalizeImage(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])
elif model_type == "midas_v21_small":
model = MidasNet_small(model_path, features=64, backbone="efficientnet_lite3", exportable=True,
non_negative=True, blocks={'expand': True})
model = MidasNet_small(
model_path,
features=64,
backbone="efficientnet_lite3",
exportable=True,
non_negative=True,
blocks={"expand": True},
)
net_w, net_h = 256, 256
resize_mode = "upper_bound"
normalization = NormalizeImage(
mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]
)
normalization = NormalizeImage(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])
else:
print(f"model_type '{model_type}' not implemented, use: --model_type large")
@@ -135,11 +135,7 @@ def load_model(model_type):
class MiDaSInference(nn.Module):
MODEL_TYPES_TORCH_HUB = [
"DPT_Large",
"DPT_Hybrid",
"MiDaS_small"
]
MODEL_TYPES_TORCH_HUB = ["DPT_Large", "DPT_Hybrid", "MiDaS_small"]
MODEL_TYPES_ISL = [
"dpt_large",
"dpt_hybrid",
@@ -149,7 +145,7 @@ class MiDaSInference(nn.Module):
def __init__(self, model_type):
super().__init__()
assert (model_type in self.MODEL_TYPES_ISL)
assert model_type in self.MODEL_TYPES_ISL
model, _ = load_model(model_type)
self.model = model
self.model.train = disabled_train
@@ -167,4 +163,3 @@ class MiDaSInference(nn.Module):
)
assert prediction.shape == (x.shape[0], 1, x.shape[2], x.shape[3])
return prediction