68 lines
3.0 KiB
Python
68 lines
3.0 KiB
Python
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, ResidualModule
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from ml_lib.modules.utils import LightningBaseModule
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from util.module_mixins import (BaseOptimizerMixin, BaseTrainMixin, BaseValMixin, BinaryMaskDatasetMixin,
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BaseDataloadersMixin)
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class ResidualConvClassifier(BinaryMaskDatasetMixin,
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BaseDataloadersMixin,
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BaseTrainMixin,
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BaseValMixin,
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BaseOptimizerMixin,
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LightningBaseModule
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):
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def __init__(self, hparams):
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super(ResidualConvClassifier, self).__init__(hparams)
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# Dataset
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# =============================================================================
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self.dataset = self.build_dataset()
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# Model Paramters
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# =============================================================================
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# Additional parameters
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self.in_shape = self.dataset.train_dataset.sample_shape
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self.conv_filters = self.params.filters
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# Modules with Parameters
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self.conv_list = ModuleList()
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last_shape = self.in_shape
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k = 3 # Base Kernel Value
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conv_module_params = self.params.module_kwargs
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conv_module_params.update(conv_kernel=(k, k), conv_stride=(1, 1), conv_padding=1)
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self.conv_list.append(ConvModule(last_shape, self.conv_filters[0], (k, k), conv_stride=(2, 2), conv_padding=1,
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**self.params.module_kwargs))
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last_shape = self.conv_list[-1].shape
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for idx in range(len(self.conv_filters)):
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conv_module_params.update(conv_filters=self.conv_filters[idx])
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self.conv_list.append(ResidualModule(last_shape, ConvModule, 2, **conv_module_params))
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last_shape = self.conv_list[-1].shape
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try:
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self.conv_list.append(ConvModule(last_shape, self.conv_filters[idx+1], (k, k), conv_stride=(2, 2), conv_padding=2,
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**self.params.module_kwargs))
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last_shape = self.conv_list[-1].shape
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except IndexError:
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pass
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self.full_1 = LinearModule(self.conv_list[-1].shape, self.params.lat_dim, **self.params.module_kwargs)
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self.full_2 = LinearModule(self.full_1.shape, self.full_1.shape * 2, **self.params.module_kwargs)
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self.full_3 = LinearModule(self.full_2.shape, self.full_2.shape // 2, **self.params.module_kwargs)
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self.full_out = LinearModule(self.full_3.shape, 1, bias=self.params.bias, activation=nn.Sigmoid)
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def forward(self, batch, **kwargs):
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tensor = batch
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for conv in self.conv_list:
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tensor = conv(tensor)
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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_3(tensor)
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tensor = self.full_out(tensor)
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return Namespace(main_out=tensor)
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