Sourcery refactored master branch - #3
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| from highresnet import HighRes2DNet | ||
| model = HighRes2DNet(*args, **kwargs) | ||
| return model | ||
| return HighRes2DNet(*args, **kwargs) |
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Function highres2dnet refactored with the following changes:
- Inline variable that is only used once
| masked_img = ma.masked_array(img, np.logical_not(mask)).compressed() | ||
| perc_results = np.percentile(masked_img, 100 * np.array(perc)) | ||
| return perc_results | ||
| return np.percentile(masked_img, 100 * np.array(perc)) |
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Function __compute_percentiles refactored with the following changes:
- Inline variable that is only used once
| print(f'Detected spacing: {spacing}. Resampling to 1 mm iso...') | ||
| needs_resampling = not is_ras or not is_1_iso | ||
| return needs_resampling | ||
| return not is_ras or not is_1_iso |
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Function check_header refactored with the following changes:
- Inline variable that is only used once
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| model = torch.hub.load(repo, model_name, pretrained=True) | ||
| return model | ||
| return torch.hub.load(repo, model_name, pretrained=True) |
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Function get_model refactored with the following changes:
- Inline variable that is only used once
| sitk.WriteImage(resampled, temp_path) | ||
| nifti_resampled = nib.load(temp_path) | ||
| return nifti_resampled | ||
| return nib.load(temp_path) |
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Function resample_spacing refactored with the following changes:
- Inline variable that is only used once
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| nii_resampled = resample_spacing( | ||
| nii_ras, | ||
| output_spacing=one_iso, | ||
| interpolation=interpolation, | ||
| ) | ||
| return nii_resampled | ||
| return resample_spacing( | ||
| nii_ras, | ||
| output_spacing=one_iso, | ||
| interpolation=interpolation, | ||
| ) |
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Function resample_ras_1mm_iso refactored with the following changes:
- Inline variable that is only used once
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| sample = dict( | ||
| image=window, | ||
| location=location, | ||
| ) | ||
| return sample | ||
| return dict( | ||
| image=window, | ||
| location=location, | ||
| ) |
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Function GridSampler.__getitem__ refactored with the following changes:
- Inline variable that is only used once
| input_output_diff = (3 - 1) + torch.sum(B * N * 2 ** (d + 1)) | ||
| receptive_field = input_output_diff + 1 | ||
| return receptive_field | ||
| return input_output_diff + 1 |
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Function HighResNet.receptive_field refactored with the following changes:
- Inline variable that is only used once
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| if self.change_dimension: | ||
| if residual_type == 'project': | ||
| conv_class = nn.Conv2d if dimensions == 2 else nn.Conv3d | ||
| self.change_dim_layer = conv_class( | ||
| in_channels, | ||
| out_channels, | ||
| kernel_size=1, | ||
| dilation=dilation, | ||
| bias=False, # as in NiftyNet and PyTorch's ResNet model | ||
| ) | ||
| if self.change_dimension and residual_type == 'project': | ||
| conv_class = nn.Conv2d if dimensions == 2 else nn.Conv3d | ||
| self.change_dim_layer = conv_class( | ||
| in_channels, | ||
| out_channels, | ||
| kernel_size=1, | ||
| dilation=dilation, | ||
| bias=False, # as in NiftyNet and PyTorch's ResNet model | ||
| ) |
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Function ResidualBlock.__init__ refactored with the following changes:
- Merge nested if conditions
| config.read_string(config_string) | ||
| data_url = config['data']['url'] | ||
| return data_url | ||
| return config['data']['url'] |
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Function get_data_url_from_model_zoo refactored with the following changes:
- Inline variable that is only used once
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