"""
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
from modules.transformation import TPS_SpatialTransformerNetwork
from modules.feature_extraction import VGG_FeatureExtractor, RCNN_FeatureExtractor, ResNet_FeatureExtractor
from modules.sequence_modeling import BidirectionalLSTM
from modules.prediction import Attention
from modules.vitstr import create_vitstr
import math
import sys
import settings
# singleChar - if -1 then STRScore outputs all char, however if
# 0 - N, then it will output the single character confidence of the index 0 to N
class STRScore(nn.Module):
def __init__(self, opt, converter, device, gtStr="", enableSingleCharAttrAve=False, model=None):
super(STRScore, self).__init__()
if opt.modelName:
settings.MODEL = opt.modelName
self.enableSingleCharAttrAve = enableSingleCharAttrAve
self.singleChar = -1
self.recentlyPredStr = None
self.opt = opt
self.converter = converter
self.device = device
self.gtStr = gtStr
self.model = model # Pass here if you want to use it
self.blank = torch.tensor([-1], dtype=torch.float).to(self.device)
self.separator = torch.tensor([-2], dtype=torch.float).to(self.device)
# 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
def forward(self, preds):
bs = preds.shape[0]
# text_for_loss, length_for_loss = self.converter.encode(labels, batch_max_length=self.opt.batch_max_length)
text_for_loss_length = self.opt.batch_max_length + 1
length_for_pred = torch.IntTensor([self.opt.batch_max_length] * bs).to(self.device)
if 'CTC' in self.opt.Prediction:
# Calculate evaluation loss for CTC decoder.
preds_size = torch.FloatTensor([preds.size(1)] * bs)
if self.opt.baiduCTC:
_, preds_index = preds.max(2)
preds_index = preds_index.view(-1)
else:
_, preds_index = preds.max(2)
# print("preds_index shape: ", preds_index.shape)
preds_str = self.converter.decode(preds_index.data, preds_size.data)
# preds_str = self.converter.decode(preds_index, length_for_pred)
preds = preds.log_softmax(2).permute(1, 0, 2)
elif self.opt.Transformer:
# preds_index = preds_index.view(-1, self.converter.batch_max_length)
# print("preds shape: ", preds.shape)
# print("preds_index: ", preds_index)
# preds_str = self.converter.decode(preds_index, length_for_pred)
if settings.MODEL == 'vitstr':
_, preds_index = preds.topk(1, dim=-1, largest=True, sorted=True)
preds_str = self.converter.decode(preds_index[:, 1:], length_for_pred)
elif settings.MODEL == 'parseq':
preds_str, confidence = self.model.tokenizer.decode(preds)
self.recentlyPredStr = preds_str[-1]
# print("preds_str: ", preds_str)
# print("preds_str: ", preds_str)
else:
preds = preds[:, :text_for_loss_length, :]
# select max probabilty (greedy decoding) then decode index to character
_, preds_index = preds.max(2)
# print("preds shape: ", preds.shape)
# print("preds_index: ", preds_index)
preds_str = self.converter.decode(preds_index, length_for_pred)
# print("preds_str: ", preds_str)
# Confidence score
# ARGMAX calculation
sum = torch.FloatTensor([0]*bs).to(self.device)
if self.enableSingleCharAttrAve:
sum = torch.zeros((bs, preds.shape[2])).to(self.device)
if self.opt.confidence_mode == 0:
preds_prob = F.softmax(preds, dim=2)
# preds_prob shape: torch.Size([1, 25, 96])
preds_max_prob, preds_max_idx = preds_prob.max(dim=2)
# preds_max_prob shape: torch.Size([1, 25])
confidence_score_list = []
count = 0
for one_hot_preds, pred, pred_max_prob in zip(preds_prob, preds_str, preds_max_prob):
if self.opt.Transformer:
if settings.MODEL == 'vitstr':
if self.enableSingleCharAttrAve:
one_hot = one_hot_preds[self.singleChar, :]
pred = pred[self.singleChar]
pred_max_prob = pred_max_prob[self.singleChar]
else:
pred_EOS = pred.find('[s]')
pred = pred[:pred_EOS]
pred_max_prob = pred_max_prob[:pred_EOS]
# if pred_max_prob.shape[0] == 0: continue
if self.enableSingleCharAttrAve:
sum[count] = one_hot
# sum = one_hot
# sum shape: torch.Size([96])
# sum = sum.unsqueeze(0)
else:
if self.opt.scorer == "cumprod":
confidence_score = pred_max_prob.cumprod(dim=0)[-1] ### Maximum is 1
sum[count] += confidence_score
elif self.opt.scorer == "mean":
confidence_score = torch.mean(pred_max_prob) ### Maximum is 1
sum[count] += confidence_score
sum = sum.unsqueeze(1)
elif settings.MODEL == 'parseq':
if self.enableSingleCharAttrAve:
one_hot = one_hot_preds[self.singleChar, :]
# pred = pred[self.singleChar]
pred_max_prob = pred_max_prob[self.singleChar]
else:
pred_EOS = len(pred) # Predition string already has no EOS, fully intact
pred_max_prob = pred_max_prob[:pred_EOS]
# if pred_max_prob.shape[0] == 0: continue
if self.enableSingleCharAttrAve:
sum[count] = one_hot
# sum shape: torch.Size([96])
# sum = sum.unsqueeze(0)
else:
if self.opt.scorer == "cumprod":
confidence_score = pred_max_prob.cumprod(dim=0)[-1] ### Maximum is 1
sum[count] += confidence_score
elif self.opt.scorer == "mean":
confidence_score = torch.mean(pred_max_prob) ### Maximum is 1
sum[count] += confidence_score
sum = sum.unsqueeze(1)
elif 'Attn' in self.opt.Prediction:
# if pred_max_prob.shape[0] == 0: continue
if self.enableSingleCharAttrAve:
one_hot = one_hot_preds[self.singleChar, :]
sum[count] = one_hot
else:
pred_EOS = pred.find('[s]')
pred = pred[:pred_EOS]
pred_max_prob = pred_max_prob[:pred_EOS] ### Use score of all letters
# pred_max_prob = pred_max_prob[0:1] ### Use score of first letter only
if pred_max_prob.shape[0] == 0: continue
confidence_score = pred_max_prob.cumprod(dim=0)[-1] ### Maximum is 1
sum[count] += confidence_score
sum = sum.unsqueeze(1)
elif 'CTC' in self.opt.Prediction:
confidence_score = pred_max_prob.cumprod(dim=0)[-1]
sum[count] += confidence_score
sum = sum.unsqueeze(1)
count += 1
# return sum.detach().cpu().numpy()
# print("sumshape: ", sum.shape)
elif self.opt.confidence_mode == 1:
preds_prob = F.softmax(preds, dim=2)
### Predicted indices
preds_max_prob = torch.argmax(preds_prob, 2)
# print("preds_max_prob shape: ", preds_max_prob.shape)
### Ground truth indices
gtIndices, _ = self.converter.encode([self.gtStr for i in range(0,preds_prob.shape[0])], batch_max_length=self.opt.batch_max_length-1)
# print("gtIndices shape: ", gtIndices.shape)
### Acquire levenstein distance
m = torch.tensor([preds_prob.shape[1] for i in range(0, gtIndices.shape[0])], dtype=torch.float).to(self.device)
n = torch.tensor([preds_prob.shape[1] for i in range(0, gtIndices.shape[0])], dtype=torch.float).to(self.device)
# print("m: ", m)
# print("preds_max_prob dtype: ", preds_max_prob.dtype)
# print("gtIndices dtype: ", gtIndices.dtype)
preds_max_prob = preds_max_prob.type(torch.float)
gtIndices = gtIndices.type(torch.float)
r = levenshtein_distance(preds_max_prob.to(self.device), gtIndices.to(self.device), n, m, torch.cat([self.blank, self.separator]), torch.empty([], dtype=torch.float).to(self.device))
# print("r shape: ", r.shape)
# confidence_score_list = []
# count = 0
# for pred, pred_max_prob in zip(preds_str, preds_max_prob):
# if 'Attn' in self.opt.Prediction:
# pred_EOS = pred.find('[s]')
# pred = pred[:pred_EOS]
# pred_max_prob = pred_max_prob[:pred_EOS] ### Use score of all letters
# # pred_max_prob = pred_max_prob[0:1] ### Use score of first letter only
# if pred_max_prob.shape[0] == 0: continue
# confidence_score = pred_max_prob.cumprod(dim=0)[-1]
# sum[count] += confidence_score
# count += 1
# return sum.detach().cpu().numpy()
# print("sumshape: ", sum.shape)
# sum = sum.unsqueeze(1)
rSoft = F.softmax(r[:,2].type(torch.float))
# rSoft = rSoft.contiguous()
rNorm = rSoft.max()-rSoft
sum = rNorm.unsqueeze(1)
return sum
class Model(nn.Module):
def __init__(self, opt, device=None, converter=None, gt_text=""):
super(Model, self).__init__()
self.opt = opt
self.device = device
self.converter = converter
self.gt_text = gt_text
self.stages = {'Trans': opt.Transformation, 'Feat': opt.FeatureExtraction,
'Seq': opt.SequenceModeling, 'Pred': opt.Prediction,
'ViTSTR': opt.Transformer}
""" Transformation """
if opt.Transformation == 'TPS':
self.Transformation = TPS_SpatialTransformerNetwork(
F=opt.num_fiducial, I_size=(opt.imgH, opt.imgW), I_r_size=(opt.imgH, opt.imgW), I_channel_num=opt.input_channel)
else:
print('No Transformation module specified')
if opt.Transformer:
self.vitstr = create_vitstr(num_tokens=opt.num_class, model=opt.TransformerModel)
return
""" FeatureExtraction """
if opt.FeatureExtraction == 'VGG':
self.FeatureExtraction = VGG_FeatureExtractor(opt.input_channel, opt.output_channel)
elif opt.FeatureExtraction == 'RCNN':
self.FeatureExtraction = RCNN_FeatureExtractor(opt.input_channel, opt.output_channel)
elif opt.FeatureExtraction == 'ResNet':
self.FeatureExtraction = ResNet_FeatureExtractor(opt.input_channel, opt.output_channel)
else:
raise Exception('No FeatureExtraction module specified')
self.FeatureExtraction_output = opt.output_channel # int(imgH/16-1) * 512
self.AdaptiveAvgPool = nn.AdaptiveAvgPool2d((None, 1)) # Transform final (imgH/16-1) -> 1
""" Sequence modeling"""
if opt.SequenceModeling == 'BiLSTM':
self.SequenceModeling = nn.Sequential(
BidirectionalLSTM(self.FeatureExtraction_output, opt.hidden_size, opt.hidden_size),
BidirectionalLSTM(opt.hidden_size, opt.hidden_size, opt.hidden_size))
self.SequenceModeling_output = opt.hidden_size
else:
print('No SequenceModeling module specified')
self.SequenceModeling_output = self.FeatureExtraction_output
""" Prediction """
if opt.Prediction == 'CTC':
self.Prediction = nn.Linear(self.SequenceModeling_output, opt.num_class)
elif opt.Prediction == 'Attn':
self.Prediction = Attention(self.SequenceModeling_output, opt.hidden_size, opt.num_class)
else:
raise Exception('Prediction is neither CTC or Attn')
def set_labels(self, labels):
self.labels = labels
def patch_embed_func(self):
if self.opt.Transformer:
return self.vitstr.patch_embed_func()
return None
def setGTText(self, text):
self.gt_text = text
def forward(self, input, text="", seqlen=25, is_train=False):
# text = torch.FloatTensor(input.shape[0], self.opt.batch_max_length + 1).fill_(0).to(self.device)
# text = self.converter.encode(self.labels)
if settings.MODEL == 'trba':
text = self.gt_text
if not self.stages['ViTSTR']:
assert(len(text)>0)
""" Transformation stage """
if not self.stages['Trans'] == "None":
input = self.Transformation(input)
if self.stages['ViTSTR']:
prediction = self.vitstr(input, seqlen=seqlen)
return prediction
""" Feature extraction stage """
visual_feature = self.FeatureExtraction(input)
visual_feature = self.AdaptiveAvgPool(visual_feature.permute(0, 3, 1, 2)) # [b, c, h, w] -> [b, w, c, h]
visual_feature = visual_feature.squeeze(3)
""" Sequence modeling stage """
if self.stages['Seq'] == 'BiLSTM':
contextual_feature = self.SequenceModeling(visual_feature)
else:
contextual_feature = visual_feature # for convenience. this is NOT contextually modeled by BiLSTM
""" Prediction stage """
if self.stages['Pred'] == 'CTC':
prediction = self.Prediction(contextual_feature.contiguous())
else:
prediction = self.Prediction(contextual_feature.contiguous(), text, is_train, batch_max_length=self.opt.batch_max_length)
return prediction