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test.py
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import os
import time
import string
import argparse
import re
import PIL
import validators
import torch
import torch.backends.cudnn as cudnn
import torch.utils.data
import torch.nn.functional as F
import numpy as np
from nltk.metrics.distance import edit_distance
from matplotlib import pyplot as plt
from matplotlib import colors
import cv2
from torchvision import transforms
import torchvision.utils as vutils
from utils import Averager, TokenLabelConverter
from dataset import hierarchical_dataset, AlignCollate, ImgDataset
from models import Model
from utils import get_args
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
def benchmark_all_eval(model, criterion, converter, opt): #, calculate_infer_time=False):
""" evaluation with 10 benchmark evaluation datasets """
if opt.fast_acc:
# # To easily compute the total accuracy of our paper.
eval_data_list = ['IC13_857', 'SVT', 'IIIT5k_3000', 'IC15_1811', 'SVTP', 'CUTE80']
else:
# The evaluation datasets, dataset order is same with Table 1 in our paper.
eval_data_list = ['IIIT5k_3000', 'SVT', 'IC03_860', 'IC03_867', 'IC13_857',
'IC13_1015', 'IC15_1811', 'IC15_2077', 'SVTP', 'CUTE80']
if opt.calculate_infer_time:
evaluation_batch_size = 1 # batch_size should be 1 to calculate the GPU inference time per image.
else:
evaluation_batch_size = opt.batch_size
char_list_accuracy = []
bpe_list_accuracy = []
wp_list_accuracy = []
fused_list_accuracy = []
total_forward_time = 0
total_evaluation_data_number = 0
char_total_correct_number = 0
bpe_total_correct_number = 0
wp_total_correct_number = 0
fused_total_correct_number = 0
log = open(f'./result/{opt.exp_name}/log_all_evaluation.txt', 'a')
dashed_line = '-' * 80
print(dashed_line)
log.write(dashed_line + '\n')
for eval_data in eval_data_list:
if opt.eval_img:
eval_data_path = os.path.join(opt.eval_data, eval_data+'.txt')
eval_data = ImgDataset(root=eval_data_path, opt=opt)
else:
eval_data_path = os.path.join(opt.eval_data, eval_data)
print(eval_data_path)
eval_data, eval_data_log = hierarchical_dataset(root=eval_data_path, opt=opt)
AlignCollate_evaluation = AlignCollate(imgH=opt.imgH, imgW=opt.imgW, keep_ratio_with_pad=opt.PAD, opt=opt)
evaluation_loader = torch.utils.data.DataLoader(
eval_data, batch_size=evaluation_batch_size,
shuffle=False,
num_workers=int(opt.workers),
collate_fn=AlignCollate_evaluation, pin_memory=True)
_, accuracys, _, _, _, infer_time, length_of_data, accur_numbers = validation(
model, criterion, evaluation_loader, converter, opt)
char_list_accuracy.append(f'{accuracys[0]:0.3f}')
bpe_list_accuracy.append(f'{accuracys[1]:0.3f}')
wp_list_accuracy.append(f'{accuracys[2]:0.3f}')
fused_list_accuracy.append(f'{accuracys[3]:0.3f}')
total_forward_time += infer_time
total_evaluation_data_number += len(eval_data)
char_total_correct_number += accur_numbers[0]
bpe_total_correct_number += accur_numbers[1]
wp_total_correct_number += accur_numbers[2]
fused_total_correct_number += accur_numbers[3]
#log.write(eval_data_log)
print(f'char_Acc {accuracys[0]:0.3f}\t bpe_Acc {accuracys[1]:0.3f}\t wp_Acc {accuracys[2]:0.3f}\t fused_Acc {accuracys[3]:0.3f}')
log.write(f'char_Acc {accuracys[0]:0.3f}\t bpe_Acc {accuracys[1]:0.3f}\t wp_Acc {accuracys[2]:0.3f}\t fused_Acc {accuracys[3]:0.3f}')
print(dashed_line)
log.write(dashed_line + '\n')
averaged_forward_time = total_forward_time / total_evaluation_data_number * 1000
char_total_accuracy = round(char_total_correct_number/total_evaluation_data_number*100,3)
bpe_total_accuracy = round(bpe_total_correct_number/total_evaluation_data_number*100,3)
wp_total_accuracy = round(wp_total_correct_number/total_evaluation_data_number*100,3)
fused_total_accuracy = round(fused_total_correct_number/total_evaluation_data_number*100,3)
params_num = sum([np.prod(p.size()) for p in model.parameters()])
evaluation_log = 'accuracy: ' + '\n'
evaluation_log += 'char_total_Acc:'+str(char_total_accuracy)+'\n'+'bpe_total_Acc:'+str(bpe_total_accuracy)+'\n'+'wp_total_Acc:'+str(wp_total_accuracy)+'\n'+'fused_total_Acc:'+str(fused_total_accuracy)+'\n'
evaluation_log += f'averaged_infer_time: {averaged_forward_time:0.3f}\t# parameters: {params_num/1e6:0.3f}'
if opt.flops:
evaluation_log += get_flops(model, opt, converter)
print(evaluation_log)
log.write(evaluation_log + '\n')
log.close()
return [char_total_accuracy, bpe_total_accuracy, wp_total_accuracy, fused_total_accuracy]
def validation(model, criterion, evaluation_loader, converter, opt):
""" validation or evaluation """
char_n_correct = 0
bpe_n_correct = 0
wp_n_correct = 0
out_n_correct = 0
length_of_data = 0
infer_time = 0
valid_loss_avg = Averager()
for i, (image_tensors, labels, imgs_path) in enumerate(evaluation_loader):
batch_size = image_tensors.size(0)
length_of_data = length_of_data + batch_size
image = image_tensors.to(device)
# For max length prediction
if opt.Transformer:
target = converter.encode(labels)
else:
length_for_pred = torch.IntTensor([opt.batch_max_length] * batch_size).to(device)
text_for_pred = torch.LongTensor(batch_size, opt.batch_max_length + 1).fill_(0).to(device)
text_for_loss, length_for_loss = converter.encode(labels, batch_max_length=opt.batch_max_length)
start_time = time.time()
if opt.Transformer in ["mgp-str"]:
attens, char_preds, bpe_preds, wp_preds = model(image, is_eval=True) # final
forward_time = time.time() - start_time
cost = criterion(char_preds.contiguous().view(-1, char_preds.shape[-1]), target.contiguous().view(-1))
# char pred
_, char_pred_index = char_preds.topk(1, dim=-1, largest=True, sorted=True)
char_pred_index = char_pred_index.view(-1, converter.batch_max_length)
length_for_pred = torch.IntTensor([converter.batch_max_length - 1] * batch_size).to(device)
char_preds_str = converter.char_decode(char_pred_index[:, 1:], length_for_pred)
char_pred_prob = F.softmax(char_preds, dim=2)
char_pred_max_prob, _ = char_pred_prob.max(dim=2)
char_preds_max_prob = char_pred_max_prob[:, 1:]
# bpe pred
_, bpe_preds_index = bpe_preds.topk(1, dim=-1, largest=True, sorted=True)
bpe_preds_index = bpe_preds_index.view(-1, converter.batch_max_length)
bpe_preds_str = converter.bpe_decode(bpe_preds_index[:,1:], length_for_pred)
bpe_preds_prob = F.softmax(bpe_preds, dim=2)
bpe_preds_max_prob, _ = bpe_preds_prob.max(dim=2)
bpe_preds_max_prob = bpe_preds_max_prob[:, 1:]
bpe_preds_index = bpe_preds_index[:, 1:]
# wp pred
_, wp_preds_index = wp_preds.topk(1, dim=-1, largest=True, sorted=True)
wp_preds_index = wp_preds_index.view(-1, converter.batch_max_length)
wp_preds_str = converter.wp_decode(wp_preds_index[:,1:], length_for_pred)
wp_preds_prob = F.softmax(wp_preds, dim=2)
wp_preds_max_prob, _ = wp_preds_prob.max(dim=2)
wp_preds_max_prob = wp_preds_max_prob[:, 1:]
wp_preds_index = wp_preds_index[:, 1:]
infer_time += forward_time
valid_loss_avg.add(cost)
# calculate accuracy & confidence score
confidence_score_list = []
for index,gt in enumerate(labels):
max_confidence_score = 0.0
out_pred = None
# char
char_pred = char_preds_str[index]
char_pred_max_prob = char_preds_max_prob[index]
char_pred_EOS = char_pred.find('[s]')
char_pred = char_pred[:char_pred_EOS] # prune after "end of sentence" token ([s])
if char_pred == gt:
char_n_correct += 1
char_pred_max_prob = char_pred_max_prob[:char_pred_EOS+1]
try:
char_confidence_score = char_pred_max_prob.cumprod(dim=0)[-1]
except:
char_confidence_score = 0.0
if char_confidence_score > max_confidence_score:
max_confidence_score = char_confidence_score
out_pred = char_pred
# bpe
bpe_pred = bpe_preds_str[index]
bpe_pred_max_prob = bpe_preds_max_prob[index]
bpe_pred_EOS = bpe_pred.find('#')
bpe_pred = bpe_pred[:bpe_pred_EOS]
if bpe_pred == gt:
bpe_n_correct += 1
bpe_pred_index = bpe_preds_index[index].cpu().tolist()
try:
bpe_pred_EOS_index = bpe_pred_index.index(2)
except:
bpe_pred_EOS_index = -1
bpe_pred_max_prob = bpe_pred_max_prob[:bpe_pred_EOS_index+1]
try:
bpe_confidence_score = bpe_pred_max_prob.cumprod(dim=0)[-1]
except:
bpe_confidence_score = 0.0
if bpe_confidence_score > max_confidence_score:
max_confidence_score = bpe_confidence_score
out_pred = bpe_pred
# wp
wp_pred = wp_preds_str[index]
wp_pred_max_prob = wp_preds_max_prob[index]
wp_pred_EOS = wp_pred.find('[SEP]')
wp_pred = wp_pred[:wp_pred_EOS]
if wp_pred == gt:
wp_n_correct += 1
wp_pred_index = wp_preds_index[index].cpu().tolist()
try:
wp_pred_EOS_index = wp_pred_index.index(102)
except:
wp_pred_EOS_index = -1
wp_pred_max_prob = wp_pred_max_prob[:wp_pred_EOS_index+1]
try:
wp_confidence_score = wp_pred_max_prob.cumprod(dim=0)[-1]
except:
wp_confidence_score = 0.0
if wp_confidence_score > max_confidence_score:
max_confidence_score = wp_confidence_score
out_pred = wp_pred
if out_pred == gt:
out_n_correct += 1
confidence_score_list.append(char_confidence_score)
elif opt.Transformer in ["char-str"]:
attens, char_preds = model(image, is_eval=True) # final
forward_time = time.time() - start_time
cost = criterion(char_preds.contiguous().view(-1, char_preds.shape[-1]), target.contiguous().view(-1))
# char pred
_, char_pred_index = char_preds.topk(1, dim=-1, largest=True, sorted=True)
char_pred_index = char_pred_index.view(-1, converter.batch_max_length)
length_for_pred = torch.IntTensor([converter.batch_max_length - 1] * batch_size).to(device)
char_preds_str = converter.char_decode(char_pred_index[:, 1:], length_for_pred)
char_pred_prob = F.softmax(char_preds, dim=2)
char_pred_max_prob, _ = char_pred_prob.max(dim=2)
char_preds_max_prob = char_pred_max_prob[:, 1:]
infer_time += forward_time
valid_loss_avg.add(cost)
# calculate accuracy & confidence score
confidence_score_list = []
for index,gt in enumerate(labels):
max_confidence_score = 0.0
out_pred = None
# char
char_pred = char_preds_str[index]
char_pred_max_prob = char_preds_max_prob[index]
char_pred_EOS = char_pred.find('[s]')
char_pred = char_pred[:char_pred_EOS] # prune after "end of sentence" token ([s])
if char_pred == gt:
char_n_correct += 1
char_pred_max_prob = char_pred_max_prob[:char_pred_EOS+1]
try:
char_confidence_score = char_pred_max_prob.cumprod(dim=0)[-1]
except:
char_confidence_score = 0.0
if char_confidence_score > max_confidence_score:
max_confidence_score = char_confidence_score
out_pred = char_pred
if out_pred == gt:
out_n_correct += 1
confidence_score_list.append(char_confidence_score)
char_accuracy = char_n_correct/float(length_of_data) * 100
bpe_accuracy = bpe_n_correct / float(length_of_data) * 100
wp_accuracy = wp_n_correct / float(length_of_data) * 100
out_accuracy = out_n_correct / float(length_of_data) * 100
return valid_loss_avg.val(), [char_accuracy, bpe_accuracy, wp_accuracy, out_accuracy], char_preds_str, confidence_score_list, labels, infer_time, length_of_data, [char_n_correct, bpe_n_correct, wp_n_correct, out_n_correct]
def draw_atten(img_path, gt, pred, attn, pil, tensor, resize, count, flag=0):
image = PIL.Image.open(img_path).convert('RGB')
image = cv2.resize(np.array(image), (128, 32))
image = tensor(image)
image_np = np.array(pil(image))
attn_pil = [pil(a) for a in attn[:, None, :, :]]
attn = [tensor(resize(a)).repeat(3, 1, 1) for a in attn_pil]
attn_sum = np.array([np.array(a) for a in attn_pil[:len(pred)]]).sum(axis=0)
blended_sum = tensor(blend_mask(image_np, attn_sum))
blended = [tensor(blend_mask(image_np, np.array(a))) for a in attn_pil]
save_image = torch.stack([image] + attn + [blended_sum] + blended)
save_image = save_image.view(2, -1, *save_image.shape[1:])
save_image = save_image.permute(1, 0, 2, 3, 4).flatten(0, 1)
vutils.save_image(save_image, f'atten_imgs/TwoBiTokenViT/{gt}_{count}_{flag}_{pred}.jpg', nrow=2, normalize=True, scale_each=True)
def blend_mask(image, mask, alpha=0.5, cmap='jet', color='b', color_alpha=1.0):
# normalize mask
mask = (mask-mask.min()) / (mask.max() - mask.min() + np.finfo(float).eps)
if mask.shape != image.shape:
mask = cv2.resize(mask,(image.shape[1], image.shape[0]))
# get color map
color_map = plt.get_cmap(cmap)
mask = color_map(mask)[:,:,:3]
# convert float to uint8
mask = (mask * 255).astype(dtype=np.uint8)
# set the basic color
basic_color = np.array(colors.to_rgb(color)) * 255
basic_color = np.tile(basic_color, [image.shape[0], image.shape[1], 1])
basic_color = basic_color.astype(dtype=np.uint8)
# blend with basic color
blended_img = cv2.addWeighted(image, color_alpha, basic_color, 1-color_alpha, 0)
# blend with mask
blended_img = cv2.addWeighted(blended_img, alpha, mask, 1-alpha, 0)
return blended_img
def test(opt):
""" model configuration """
converter = TokenLabelConverter(opt)
opt.num_class = len(converter.character)
if opt.rgb:
opt.input_channel = 3
model = Model(opt)
print('model input parameters', opt.imgH, opt.imgW, opt.num_fiducial, opt.input_channel, opt.output_channel,
opt.hidden_size, opt.num_class, opt.batch_max_length, opt.Transformation, opt.FeatureExtraction,
opt.SequenceModeling, opt.Prediction)
model = torch.nn.DataParallel(model).to(device)
# load model
print('loading pretrained model from %s' % opt.saved_model)
if validators.url(opt.saved_model):
model.load_state_dict(torch.hub.load_state_dict_from_url(opt.saved_model, progress=True, map_location=device))
else:
model.load_state_dict(torch.load(opt.saved_model, map_location=device))
opt.exp_name = '_'.join(opt.saved_model.split('/')[1:])
# print(model)
""" keep evaluation model and result logs """
os.makedirs(f'./result/{opt.exp_name}', exist_ok=True)
os.system(f'cp {opt.saved_model} ./result/{opt.exp_name}/')
""" setup loss """
criterion = torch.nn.CrossEntropyLoss(ignore_index=0).to(device) # ignore [GO] token = ignore index 0
""" evaluation """
model.eval()
opt.eval = True
with torch.no_grad():
if opt.benchmark_all_eval: # evaluation with 10 benchmark evaluation datasets
return benchmark_all_eval(model, criterion, converter, opt)
else:
log = open(f'./result/{opt.exp_name}/log_evaluation.txt', 'a')
AlignCollate_evaluation = AlignCollate(imgH=opt.imgH, imgW=opt.imgW, keep_ratio_with_pad=opt.PAD, opt=opt)
eval_data, eval_data_log = hierarchical_dataset(root=opt.eval_data, opt=opt)
evaluation_loader = torch.utils.data.DataLoader(
eval_data, batch_size=opt.batch_size,
shuffle=False,
num_workers=int(opt.workers),
collate_fn=AlignCollate_evaluation, pin_memory=True)
_, accuracy_by_best_model, _, _, _, _, _, _ = validation(
model, criterion, evaluation_loader, converter, opt)
log.write(eval_data_log)
print(f'{accuracy_by_best_model[0]:0.3f}')
log.write(f'{accuracy_by_best_model[0]:0.3f}\n')
log.close()
# https://github.com/clovaai/deep-text-recognition-benchmark/issues/125
def get_flops(model, opt, converter):
from thop import profile
input = torch.randn(1, 1, opt.imgH, opt.imgW).to(device)
model = model.to(device)
if opt.Transformer:
seqlen = converter.batch_max_length
text_for_pred = torch.LongTensor(1, seqlen).fill_(0).to(device)
#preds = model(image, text=target, seqlen=converter.batch_max_length)
MACs, params = profile(model, inputs=(input, text_for_pred, True, seqlen))
else:
text_for_pred = torch.LongTensor(1, opt.batch_max_length + 1).fill_(0).to(device)
#model_ = Model(opt).to(device)
MACs, params = profile(model, inputs=(input, text_for_pred, ))
flops = 2 * MACs # approximate FLOPS
return f'Approximate FLOPS: {flops:0.3f}'
if __name__ == '__main__':
opt = get_args(is_train=False)
""" vocab / character number configuration """
if opt.sensitive:
opt.character = string.printable[:-6] # same with ASTER setting (use 94 char).
cudnn.benchmark = True
cudnn.deterministic = True
opt.num_gpu = torch.cuda.device_count()
from tabulate import tabulate
if opt.range is not None:
start_range, end_range = sorted([int(e) for e in opt.range.split('-')])
print("eval range: ",start_range,end_range)
if os.path.isdir(opt.model_dir):
result = []
model_list = os.listdir(opt.model_dir)
model_list = [model for model in model_list if model.startswith('iter_')]
model_list = sorted(model_list, key=lambda x: int(x.split('.')[0].split('_')[-1]), reverse=True)
err_list = []
for model in model_list:
if opt.range is not None:
num_epoch = int(str(model).split('_')[1].split('.')[0])
if not (num_epoch>=start_range and num_epoch <=end_range):
continue
opt.saved_model = os.path.join(opt.model_dir, model)
result.append(test(opt)+[opt.saved_model])
print('opt.model_path :', opt.saved_model)
tab_title = ['char_acc', 'bpe_acc', 'wp_acc', 'fused_acc','model']
result = sorted(result, key=lambda x: x[3], reverse=True)
print(tabulate(result, tab_title, numalign='right'))
else:
opt.saved_model = opt.model_dir
test(opt)