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from collections import OrderedDict |
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import torch |
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from torch import nn |
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from maskrcnn_benchmark.modeling import registry |
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from . import bert_model |
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from . import rnn_model |
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from . import clip_model |
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from . import word_utils |
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@registry.LANGUAGE_BACKBONES.register("bert-base-uncased") |
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def build_bert_backbone(cfg): |
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body = bert_model.BertEncoder(cfg) |
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model = nn.Sequential(OrderedDict([("body", body)])) |
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return model |
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@registry.LANGUAGE_BACKBONES.register("roberta-base") |
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def build_bert_backbone(cfg): |
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body = bert_model.BertEncoder(cfg) |
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model = nn.Sequential(OrderedDict([("body", body)])) |
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return model |
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@registry.LANGUAGE_BACKBONES.register("rnn") |
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def build_rnn_backbone(cfg): |
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body = rnn_model.RNNEnoder(cfg) |
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model = nn.Sequential(OrderedDict([("body", body)])) |
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return model |
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@registry.LANGUAGE_BACKBONES.register("clip") |
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def build_clip_backbone(cfg): |
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body = clip_model.CLIPTransformer(cfg) |
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model = nn.Sequential(OrderedDict([("body", body)])) |
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return model |
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def build_backbone(cfg): |
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assert cfg.MODEL.LANGUAGE_BACKBONE.MODEL_TYPE in registry.LANGUAGE_BACKBONES, \ |
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"cfg.MODEL.LANGUAGE_BACKBONE.TYPE: {} is not registered in registry".format( |
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cfg.MODEL.LANGUAGE_BACKBONE.MODEL_TYPE |
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) |
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return registry.LANGUAGE_BACKBONES[cfg.MODEL.LANGUAGE_BACKBONE.MODEL_TYPE](cfg) |
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