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| ''' | |
| Netdissect package. | |
| To run dissection: | |
| 1. Load up the convolutional model you wish to dissect, and wrap it | |
| in an InstrumentedModel. Call imodel.retain_layers([layernames,..]) | |
| to analyze a specified set of layers. | |
| 2. Load the segmentation dataset using the BrodenDataset class; | |
| use the transform_image argument to normalize images to be | |
| suitable for the model, or the size argument to truncate the dataset. | |
| 3. Write a function to recover the original image (with RGB scaled to | |
| [0...1]) given a normalized dataset image; ReverseNormalize in this | |
| package inverts transforms.Normalize for this purpose. | |
| 4. Choose a directory in which to write the output, and call | |
| dissect(outdir, model, dataset). | |
| Example: | |
| from netdissect import InstrumentedModel, dissect | |
| from netdissect import BrodenDataset, ReverseNormalize | |
| model = InstrumentedModel(load_my_model()) | |
| model.eval() | |
| model.cuda() | |
| model.retain_layers(['conv1', 'conv2', 'conv3', 'conv4', 'conv5']) | |
| bds = BrodenDataset('dataset/broden1_227', | |
| transform_image=transforms.Compose([ | |
| transforms.ToTensor(), | |
| transforms.Normalize(IMAGE_MEAN, IMAGE_STDEV)]), | |
| size=1000) | |
| dissect('result/dissect', model, bds, | |
| recover_image=ReverseNormalize(IMAGE_MEAN, IMAGE_STDEV), | |
| examples_per_unit=10) | |
| ''' | |
| from .dissection import dissect, ReverseNormalize | |
| from .dissection import ClassifierSegRunner, GeneratorSegRunner | |
| from .dissection import ImageOnlySegRunner | |
| from .broden import BrodenDataset, ScaleSegmentation, scatter_batch | |
| from .segdata import MultiSegmentDataset | |
| from .nethook import InstrumentedModel | |
| from .zdataset import z_dataset_for_model, z_sample_for_model, standard_z_sample | |
| from . import actviz | |
| from . import progress | |
| from . import runningstats | |
| from . import sampler | |
| __all__ = [ | |
| 'dissect', 'ReverseNormalize', | |
| 'ClassifierSegRunner', 'GeneratorSegRunner', 'ImageOnlySegRunner', | |
| 'BrodenDataset', 'ScaleSegmentation', 'scatter_batch', | |
| 'MultiSegmentDataset', | |
| 'InstrumentedModel', | |
| 'z_dataset_for_model', 'z_sample_for_model', 'standard_z_sample' | |
| 'actviz', | |
| 'progress', | |
| 'runningstats', | |
| 'sampler' | |
| ] | |