70 lines
3.0 KiB
Python
70 lines
3.0 KiB
Python
import inspect
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from argparse import Namespace
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from torch import nn
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from torch.nn import ModuleList
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from ml_lib.modules.blocks import ConvModule, LinearModule
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from ml_lib.modules.util import (LightningBaseModule, Splitter, Merger)
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from util.module_mixins import CombinedModelMixins
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class BandwiseConvClassifier(CombinedModelMixins,
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LightningBaseModule
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):
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def __init__(self, in_shape, n_classes, weight_init, activation,
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use_bias, use_norm, dropout, lat_dim, filters,
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lr, weight_decay, sto_weight_avg, lr_warm_restart_epochs, opt_reset_interval,
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loss, scheduler, lr_scheduler_parameter
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):
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# TODO: Move this to parent class, or make it much easieer to access....
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a = dict(locals())
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params = {arg: a[arg] for arg in inspect.signature(self.__init__).parameters.keys() if arg != 'self'}
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super(BandwiseConvClassifier, self).__init__(params)
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# Model Paramters
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# =============================================================================
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# Additional parameters
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self.n_band_sections = 8
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# Modules
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# =============================================================================
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self.split = Splitter(in_shape, self.n_band_sections)
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k = 3
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self.band_list = ModuleList()
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for band in range(self.n_band_sections):
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last_shape = self.split.shape[band]
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conv_list = ModuleList()
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for conv_filters in self.params.filters:
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conv_list.append(ConvModule(last_shape, conv_filters, (k, k), conv_stride=(2, 2), conv_padding=2,
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**self.params.module_kwargs))
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last_shape = conv_list[-1].shape
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# self.conv_list.append(ConvModule(last_shape, 1, 1, conv_stride=1, **self.params.module_kwargs))
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# last_shape = self.conv_list[-1].shape
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self.band_list.append(conv_list)
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self.merge = Merger(self.band_list[-1][-1].shape, self.n_band_sections)
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self.full_1 = LinearModule(self.merge.shape, self.params.lat_dim, **self.params.module_kwargs)
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self.full_2 = LinearModule(self.full_1.shape, self.params.lat_dim, **self.params.module_kwargs)
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# Make Decision between binary and Multiclass Classification
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logits = n_classes if n_classes > 2 else 1
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module_kwargs = self.params.module_kwargs
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module_kwargs.update(activation=(nn.Softmax if logits > 1 else nn.Sigmoid))
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self.full_out = LinearModule(self.full_2.shape, logits, **module_kwargs)
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def forward(self, batch, **kwargs):
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tensors = self.split(batch)
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for idx, (tensor, convs) in enumerate(zip(tensors, self.band_list)):
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for conv in convs:
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tensor = conv(tensor)
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tensors[idx] = tensor
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tensor = self.merge(tensors)
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tensor = self.full_1(tensor)
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tensor = self.full_2(tensor)
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tensor = self.full_out(tensor)
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return Namespace(main_out=tensor)
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