Configure ruff and black for linting #52
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@benjaminaubin rebased |
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@benjaminaubin Rebased again 😁 I also enabled import sorting via Ruff (ref. #59). |
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Rebased once again. |
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Rebased 🍂 |
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Rebased on 059d8e9. |
akx
commented
Nov 22, 2023
| @@ -402,7 +402,6 @@ def log_img(self, pl_module, batch, batch_idx, split="train"): | |||
| # batch_idx > 5 and | |||
| self.max_images > 0 | |||
| ): | |||
| logger = type(pl_module.logger) | |||
akx
commented
Nov 22, 2023
| @@ -648,7 +647,7 @@ def init_wandb(save_dir, opt, config, group_name, name_str): | |||
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| ckpt_resume_path = opt.resume_from_checkpoint | |||
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| if not "devices" in trainer_config and trainer_config["accelerator"] != "gpu": | |||
| if "devices" not in trainer_config and trainer_config["accelerator"] != "gpu": | |||
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x not in y is idiomatic.
akx
commented
Nov 22, 2023
| @@ -691,7 +690,7 @@ def init_wandb(save_dir, opt, config, group_name, name_str): | |||
| # TODO change once leaving "swiffer" config directory | |||
| try: | |||
| group_name = nowname.split(now)[-1].split("-")[1] | |||
| except: | |||
| except Exception: | |||
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except: is dangerous as it catches SystemExits and KeyboardInterrupts.
akx
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Nov 22, 2023
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| print(f"sigmas after discretization, before pruning img2img: ", sigmas) | ||
| print("sigmas after discretization, before pruning img2img: ", sigmas) | ||
| sigmas = torch.flip(sigmas, (0,)) | ||
| sigmas = sigmas[: max(int(self.strength * len(sigmas)), 1)] | ||
| print("prune index:", max(int(self.strength * len(sigmas)), 1)) | ||
| sigmas = torch.flip(sigmas, (0,)) | ||
| print(f"sigmas after pruning: ", sigmas) | ||
| print("sigmas after pruning: ", sigmas) |
akx
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Nov 22, 2023
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| def get_interactive_image() -> Image.Image: | ||
| image = st.file_uploader("Input", type=["jpg", "JPEG", "png"]) | ||
| if image is not None: | ||
| image = Image.open(image) | ||
| if not image.mode == "RGB": | ||
| image = image.convert("RGB") | ||
| return image | ||
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akx
commented
Nov 22, 2023
| @@ -87,7 +87,7 @@ def benchmark_torch_function_in_microseconds(f, *args, **kwargs): | |||
| ) as prof: | |||
| with record_function("Default detailed stats"): | |||
| for _ in range(25): | |||
| o = F.scaled_dot_product_attention(query, key, value) | |||
| _o = F.scaled_dot_product_attention(query, key, value) | |||
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Unused local, mark it as such.
akx
commented
Nov 22, 2023
| with autocast(device) as precision_scope: | ||
| with autocast(device): |
akx
commented
Nov 22, 2023
| print(key, [len(l) for l in batch[key]]) | ||
| print(key, [len(lst) for lst in batch[key]]) |
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l is easily confusable for I and 1.
akx
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Nov 22, 2023
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| denoiser = lambda input, sigma, c: self.denoiser( | ||
| self.model, input, sigma, c, **kwargs | ||
| ) | ||
| def denoiser(input, sigma, c): | ||
| return self.denoiser(self.model, input, sigma, c, **kwargs) |
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Don't use a lambda where a def will do. (That said, this is really just partial()...)
akx
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Nov 22, 2023
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| # from .diffusionmodules.util import mixed_checkpoint as checkpoint | ||
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akx
commented
Nov 22, 2023
| # compute in_ch_mult, block_in and curr_res at lowest res | ||
| in_ch_mult = (1,) + tuple(ch_mult) | ||
| # compute block_in and curr_res at lowest res |
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This PR configures linting with the Ruff linter (and the Black formatter that's already in use in the repository) to be easily runnable with pre-commit. It also adds a GitHub Actions workflow to run those checks for every pushed commit.
It then fixes the issues that the default set of configuration for Ruff (with small changes, see pyproject.toml) notes.
This PR includes #146 and #147 because they would otherwise cause lint errors.