| 130 | return x |
| 131 | |
| 132 | class DECLIP(CLIP): |
| 133 | def __init__(self,image_encode, text_encode, use_allgather, nn_size=2**16, nn_topk=1, \ |
| 134 | return_dense=False, return_simsiam_text=False, return_simsiam_nn_text=False, return_caption=False, return_nn_bank=False, text_mask_type=None, |
| 135 | EDA=True, feature_dim=1024, forward_type='split'): |
| 136 | super(DECLIP, self).__init__(image_encode, text_encode, use_allgather) |
| 137 | # TODO change for r50 checkpoint |
| 138 | self.projector = projection_MLP(feature_dim) |
| 139 | # self.projector = projection_MLP(1024) |
| 140 | self.predictor = prediction_MLP(1024) |
| 141 | self.return_dense = return_dense |
| 142 | self.return_simsiam_nn_text = return_simsiam_nn_text |
| 143 | self.return_nn_bank = return_nn_bank |
| 144 | self.return_caption = return_caption |
| 145 | self.return_simsiam_text = return_simsiam_text |
| 146 | self.return_simsiam_nn_text = return_simsiam_nn_text |
| 147 | self.text_mask_type = text_mask_type |
| 148 | self.EDA = EDA |
| 149 | self.forward_type = forward_type |
| 150 | #import gensim |
| 151 | #from textaugment import Word2vec |
| 152 | #model = gensim.models.KeyedVectors.load_word2vec_format('/mnt/cache/liyangguang/GoogleNews-vectors-negative300.bin.gz', binary=True) |
| 153 | #self.word2vec = Word2vec(model=model) |
| 154 | from textaugment import EDA |
| 155 | self.emd = EDA() |
| 156 | |
| 157 | if self.return_dense: |
| 158 | raise NotImplementedError('These are bugs in the model, Please Check The Codes!') |
| 159 | self.projector_d = projection_MLP(2048) # dense |
| 160 | self.predictor_d = prediction_MLP(1024) |
| 161 | if self.return_simsiam_text: |
| 162 | self.projector_text = projection_MLP(feature_dim) |
| 163 | self.predictor_text = prediction_MLP(1024) |
| 164 | if self.return_simsiam_nn_text: |
| 165 | self.projector_nn_text = projection_MLP(feature_dim) |
| 166 | self.predictor_nn_text = prediction_MLP(1024) |
| 167 | if self.return_caption: |
| 168 | raise NotImplementedError('Not Available') |
| 169 | if text_mask_type is not None: |
| 170 | enc_dim = self.encode_text.text_projection.weight.shape[-1] |
| 171 | self.text_label_predictor = nn.Linear(enc_dim, self.encode_text.vocab_size) |
| 172 | if self.return_nn_bank: |
| 173 | #nn.init.normal_(self.text_projection, std=self.transformer.width ** -0.5) |
| 174 | self.nn_replacer_img = NNMemoryBankModule(size=nn_size, topk=nn_topk) |
| 175 | self.nn_replacer_text = NNMemoryBankModule(size=nn_size, topk=nn_topk) |
| 176 | |
| 177 | def text_modules(self): |
| 178 | ret = super(self).text_modules() |
| 179 | if self.text_mask_type is not None: |
| 180 | ret.append(self.text_label_predictor) |
| 181 | return ret |
| 182 | |
| 183 | def visual_modules(self): |
| 184 | ret = super(self).visual_modules() |
| 185 | ret.extend([self.predictor, self.projector]) |
| 186 | return ret |
| 187 | |
| 188 | def encode_image(self, image, return_dense=False): |
| 189 | if return_dense: |
no outgoing calls
no test coverage detected