WIP Code for ISMRM23 (Sourcery refactored)#3
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WIP Code for ISMRM23 (Sourcery refactored)#3sourcery-ai[bot] wants to merge 1 commit intoWIP_ISMRM23from
sourcery-ai[bot] wants to merge 1 commit intoWIP_ISMRM23from
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| """ | |||
| """ | |||
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Lines 184-210 refactored with the following changes:
- Use f-string instead of string concatenation [×18] (
use-fstring-for-concatenation)
| self.model_type = cmd_args.model | ||
| self.lr_1 = cmd_args.learning_rate | ||
| self.logger.info("learning rate " + str(self.lr_1)) | ||
| self.logger.info(f"learning rate {str(self.lr_1)}") |
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Function Pipeline.__init__ refactored with the following changes:
- Use f-string instead of string concatenation (
use-fstring-for-concatenation)
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| trainDS = SRDataset(logger=self.logger, patch_size=self.patch_size, | ||
| dir_path=vol_path, | ||
| label_dir_path=label_path, | ||
| # TODO: implement non-iso patch-size, now only using the first element | ||
| stride_depth=self.stride_depth, stride_length=self.stride_length, | ||
| stride_width=self.stride_width, Size=None, fly_under_percent=None, | ||
| # TODO: implement fly_under_percent, if needed | ||
| patch_size_us=self.patch_size, pre_interpolate=None, norm_data=False, | ||
| pre_load=True, | ||
| return_coords=True, | ||
| files_us=crossvalidation_set) # TODO implement patch_size_us if required - patch_size//scaling_factor | ||
| if get_subjects_only: | ||
| return trainDS | ||
| # sampler = tio.data.UniformSampler(self.patch_size) | ||
| # patches_queue = tio.Queue( | ||
| # trainDS, | ||
| # max_length=(self.samples_per_epoch // len(trainDS.pre_loaded_data['pre_loaded_img'])) * 2, | ||
| # samples_per_volume=1, | ||
| # sampler=sampler, | ||
| # num_workers=0, | ||
| # start_background=True | ||
| # ) | ||
| # return patches_queue | ||
| return trainDS | ||
| return SRDataset( | ||
| logger=self.logger, | ||
| patch_size=self.patch_size, | ||
| dir_path=vol_path, | ||
| label_dir_path=label_path, | ||
| # TODO: implement non-iso patch-size, now only using the first element | ||
| stride_depth=self.stride_depth, | ||
| stride_length=self.stride_length, | ||
| stride_width=self.stride_width, | ||
| Size=None, | ||
| fly_under_percent=None, | ||
| # TODO: implement fly_under_percent, if needed | ||
| patch_size_us=self.patch_size, | ||
| pre_interpolate=None, | ||
| norm_data=False, | ||
| pre_load=True, | ||
| return_coords=True, | ||
| files_us=crossvalidation_set, | ||
| ) |
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Function Pipeline.create_TIOSubDS refactored with the following changes:
- Inline variable that is immediately returned (
inline-immediately-returned-variable) - Lift code into else after jump in control flow (
reintroduce-else) - Hoist repeated code outside conditional statement (
hoist-statement-from-if) - Replace index in for loop with direct reference (
for-index-replacement)
This removes the following comments ( why? ):
# num_workers=0,
# max_length=(self.samples_per_epoch // len(trainDS.pre_loaded_data['pre_loaded_img'])) * 2,
# return patches_queue
# TODO implement patch_size_us if required - patch_size//scaling_factor
# )
# start_background=True
# sampler = tio.data.UniformSampler(self.patch_size)
# samples_per_volume=1,
# patches_queue = tio.Queue(
# sampler=sampler,
# trainDS,
| union = torch.sum(y_true_f + y_pred_f) - intersection | ||
| score = (intersection + self.smooth) / (union + self.smooth) | ||
| return score | ||
| return (intersection + self.smooth) / (union + self.smooth) |
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Function IOU.forward refactored with the following changes:
- Inline variable that is immediately returned (
inline-immediately-returned-variable)
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| torch.save(param, os.path.join(self.output_dir, 'nan_floss_{}_param.pt'.format(name))) | ||
| raise RuntimeError(" classname " + self.__class__.__name__ + "i " + str( | ||
| i) + f" module: {module} classname {self.__class__.__name__} Found NAN in output {i} at indices: ", | ||
| nan_mask.nonzero(), "where:", out[nan_mask.nonzero()[:, 0].unique(sorted=True)]) | ||
| torch.save(param, os.path.join(self.output_dir, f'nan_floss_{name}_param.pt')) | ||
| raise RuntimeError( | ||
| ( | ||
| f" classname {self.__class__.__name__}i {str(i)}" | ||
| + f" module: {module} classname {self.__class__.__name__} Found NAN in output {i} at indices: " | ||
| ), | ||
| nan_mask.nonzero(), | ||
| "where:", | ||
| out[nan_mask.nonzero()[:, 0].unique(sorted=True)], | ||
| ) |
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Function FocalTverskyLoss.nan_hook refactored with the following changes:
- Replace call to format with f-string (
use-fstring-for-formatting) - Use f-string instead of string concatenation [×3] (
use-fstring-for-concatenation)
| underKSPVol = performUndersamplingKSP(fullKSPVol, mask, maskmatpath, zeropad) | ||
| underImgVol = ifft2c(underKSPVol) | ||
| return underImgVol | ||
| return ifft2c(underKSPVol) |
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Function performUndersampling refactored with the following changes:
- Inline variable that is immediately returned (
inline-immediately-returned-variable)
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| underKSPVol = np.multiply(fullKSPVol.transpose((2, 0, 1)), mask).transpose((1, 2, 0)) | ||
| else: | ||
| temp = [] | ||
| for i in range(mask.shape[0]): | ||
| maskline = mask[i, :] | ||
| if maskline.any(): | ||
| temp.append(fullKSPVol[i, ...]) | ||
| temp = np.array(temp) | ||
| underKSPVol = [] | ||
| for i in range(mask.shape[1]): | ||
| maskline = mask[:, i] | ||
| if maskline.any(): | ||
| underKSPVol.append(temp[:, i, ...]) | ||
| underKSPVol = np.array(underKSPVol).swapaxes(0, 1) | ||
| return underKSPVol | ||
| return np.multiply(fullKSPVol.transpose((2, 0, 1)), mask).transpose((1, 2, 0)) | ||
| temp = [] | ||
| for i in range(mask.shape[0]): | ||
| maskline = mask[i, :] | ||
| if maskline.any(): | ||
| temp.append(fullKSPVol[i, ...]) | ||
| temp = np.array(temp) | ||
| underKSPVol = [] | ||
| for i in range(mask.shape[1]): | ||
| maskline = mask[:, i] | ||
| if maskline.any(): | ||
| underKSPVol.append(temp[:, i, ...]) | ||
| return np.array(underKSPVol).swapaxes(0, 1) |
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Function performUndersamplingKSP refactored with the following changes:
- Lift return into if (
lift-return-into-if) - Remove unnecessary else after guard condition (
remove-unnecessary-else)
| if closure is not None: | ||
| loss = closure() | ||
|
|
||
| loss = closure() if closure is not None else None |
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Function Madam.step refactored with the following changes:
- Move setting of default value for variable into
elsebranch (introduce-default-else) - Replace if statement with if expression (
assign-if-exp)
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| print('convert_and_save_tif:size of image:'+ str(len(image_list))) | ||
| print(f'convert_and_save_tif:size of image:{len(image_list)}') | ||
| with TiffImagePlugin.AppendingTiffWriter(output_path + filename, True) as tifWriter: | ||
| for im in image_list: | ||
| # with open(DATASET_FOLDER+tiff_in) as tiff_in: | ||
| im.save(tifWriter) | ||
| tifWriter.newFrame() | ||
| print("Conversion to tiff completed, image saved as {}".format(filename)) | ||
| print(f"Conversion to tiff completed, image saved as {filename}") |
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Function convert_and_save_tif refactored with the following changes:
- Use f-string instead of string concatenation (
use-fstring-for-concatenation) - Replace call to format with f-string (
use-fstring-for-formatting) - Remove unnecessary calls to
str()from formatted values in f-strings (remove-str-from-fstring)
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| print('convert_and_save_tif:size of image:' + str(len(image_list))) | ||
| print(f'convert_and_save_tif:size of image:{len(image_list)}') | ||
| with TiffImagePlugin.AppendingTiffWriter(output_path + filename, True) as tifWriter: | ||
| for im in image_list: | ||
| # with open(DATASET_FOLDER+tiff_in) as tiff_in: | ||
| im.save(tifWriter) | ||
| tifWriter.newFrame() | ||
| print("Conversion to tiff completed, image saved as {}".format(filename)) | ||
| print(f"Conversion to tiff completed, image saved as {filename}") |
Author
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Function convert_and_save_tif_greyscale refactored with the following changes:
- Use f-string instead of string concatenation (
use-fstring-for-concatenation) - Replace call to format with f-string (
use-fstring-for-formatting) - Remove unnecessary calls to
str()from formatted values in f-strings (remove-str-from-fstring)
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Pull Request #2 refactored by Sourcery.
Since the original Pull Request was opened as a fork in a contributor's
repository, we are unable to create a Pull Request branching from it.
To incorporate these changes, you can either:
Merge this Pull Request instead of the original, or
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the original Pull Request
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NOTE: As code is pushed to the original Pull Request, Sourcery will
re-run and update (force-push) this Pull Request with new refactorings as
necessary. If Sourcery finds no refactorings at any point, this Pull Request
will be closed automatically.
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