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Class HECTOR

model.py:136–320  ·  view source on GitHub ↗

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134 return x
135
136class HECTOR(nn.Module):
137 def __init__(
138 self,
139 input_feature_size=1024,
140 precompression_layer=True,
141 feature_size_comp = 512,
142 feature_size_attn = 256,
143 postcompression_layer=True,
144 feature_size_comp_post = 128,
145 dropout=True,
146 p_dropout_fc=0.25,
147 p_dropout_atn=0.25,
148 n_classes=2,
149 input_stage_size=6,
150 embedding_dim_stage=16,
151 depth_dim_stage=1,
152 act_fct_stage='elu',
153 dropout_stage=True,
154 p_dropout_stage=0.25,
155 input_mol_size=4,
156 embedding_dim_mol=16,
157 depth_dim_mol=1,
158 act_fct_mol='elu',
159 dropout_mol=True,
160 p_dropout_mol=0.25,
161 fusion_type='kron',
162 use_bilinear=[True,True,True],
163 gate_hist=False,
164 gate_stage=False,
165 gate_mol=False,
166 scale=[1,1,1],
167 ):
168 super(HECTOR, self).__init__()
169
170 self.fusion_type =fusion_type
171 self.input_stage_size=input_stage_size
172 self.use_bilinear = use_bilinear
173 self.gate_hist = gate_hist
174 self.gate_stage = gate_stage
175 self.gate_mol = gate_mol
176
177 # Reduce dimension of H&E patch features.
178 if precompression_layer:
179 self.compression_layer = nn.Sequential(*[FC_block(input_feature_size, feature_size_comp*4, p_dropout_fc=p_dropout_fc),
180 FC_block(feature_size_comp*4, feature_size_comp*2, p_dropout_fc=p_dropout_fc),
181 FC_block(feature_size_comp*2, feature_size_comp, p_dropout_fc=p_dropout_fc),])
182
183 dim_post_compression = feature_size_comp
184 else:
185 self.compression_layer = nn.Identity()
186 dim_post_compression = input_feature_size
187
188 # Get embeddings of categorical features.
189 self.encoding_stage_net = Categorical_encoding(taxonomy_in=self.input_stage_size,
190 embedding_dim=embedding_dim_stage,
191 depth=depth_dim_stage,
192 act_fct=act_fct_stage,
193 dropout=dropout_stage,

Callers 1

run_train_eval_loopFunction · 0.90

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