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| """ | |
| Copyright (c) 2019-present NAVER Corp. | |
| Licensed under the Apache License, Version 2.0 (the "License"); | |
| you may not use this file except in compliance with the License. | |
| You may obtain a copy of the License at | |
| http://www.apache.org/licenses/LICENSE-2.0 | |
| Unless required by applicable law or agreed to in writing, software | |
| distributed under the License is distributed on an "AS IS" BASIS, | |
| WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. | |
| See the License for the specific language governing permissions and | |
| limitations under the License. | |
| """ | |
| import torch | |
| import torch.nn as nn | |
| import torch.nn.functional as F | |
| import sys | |
| import math | |
| class STRScore(nn.Module): | |
| def __init__(self, config, charsetMapper, postprocessFunc, device, enableSingleCharAttrAve=False): | |
| super(STRScore, self).__init__() | |
| self.config = config | |
| self.charsetMapper = charsetMapper | |
| self.postprocess = postprocessFunc | |
| self.device = device | |
| self.enableSingleCharAttrAve = enableSingleCharAttrAve | |
| # singleChar - if >=0, then the output of STRScore will only be a single character | |
| # instead of a whole. The char index will be equal to the parameter "singleChar". | |
| def setSingleCharOutput(self, singleChar): | |
| self.singleChar = singleChar | |
| ### Output of ABINET model | |
| ### Shape with 1 batchsize: torch.Size([1, 26, 37]) | |
| def forward(self, preds): | |
| # Acquire predicted text | |
| pt_text, _, __ = self.postprocess(preds[0], self.charsetMapper, self.config.model_eval) | |
| preds = preds[0]["logits"] | |
| # preds shape: torch.Size([50, 26, 37]) | |
| # Confidence score | |
| bs = preds.shape[0] | |
| # ARGMAX calculation | |
| sum = torch.FloatTensor([0]*len(preds)).to(self.device) | |
| preds_prob = F.softmax(preds, dim=2) | |
| preds_max_prob, preds_max_index = preds_prob.max(dim=2) | |
| if self.enableSingleCharAttrAve: | |
| preds_max_prob = preds_max_prob[:,self.singleChar] | |
| preds_max_prob = preds_max_prob.unsqueeze(0) | |
| if self.enableSingleCharAttrAve: | |
| sum = torch.zeros((bs, len(self.config.character)-1)).to(self.device) | |
| # print("preds_max_prob shape: ", preds_max_prob.shape) (1,26) | |
| confidence_score_list = [] | |
| count = 0 | |
| for one_hot_preds, pred, pred_max_prob in zip(preds_prob, pt_text, preds_max_prob): | |
| if self.enableSingleCharAttrAve: | |
| one_hot = one_hot_preds[self.singleChar, :] | |
| sum[count] = one_hot | |
| # sum = sum.unsqueeze(0) | |
| else: | |
| pred_EOS = len(pred) | |
| # pred = pred[:pred_EOS] | |
| pred_max_prob = pred_max_prob[:pred_EOS] ### Use score of all letters excluding null char | |
| # pred_max_prob = pred_max_prob[0:1] ### Use score of first letter only | |
| if pred_max_prob.shape[0] == 0: continue | |
| if self.config.scorer == "cumprod": | |
| confidence_score = pred_max_prob.cumprod(dim=0)[-1] ### Maximum is 1 | |
| elif self.config.scorer == "mean": | |
| confidence_score = torch.mean(pred_max_prob) ### Maximum is 1 | |
| sum[count] += confidence_score | |
| count += 1 | |
| if self.enableSingleCharAttrAve: | |
| pass | |
| else: | |
| sum = sum.unsqueeze(1) | |
| return sum | |