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test_tde.py
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test_tde.py
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######################################
# Kaihua Tang
######################################
import torch
import torch.nn as nn
import torch.nn.functional as F
import utils.general_utils as utils
from data.dataloader import get_loader
INVALID_ATTRIBUTE_DATASET = ('MSCOCO-LT', 'MSCOCO-BL', 'Places-GLT')
class acc_by_splits():
def __init__(self, logger, stype):
self.logger = logger
self.stype = stype
def print_score(self, predictions, labels, num_class, split, csv_results=None, premask=False):
split_set = sorted(list(set(split.tolist())))
self.logger.info('------------- Detailed Splits by {} -----------'.format(self.stype))
output_log = 'Recall/AC ==> '
for s in split_set:
s_mask = (split==s)
if premask:
s_acc = utils.calculate_recall(predictions[s_mask].view(-1), labels[s_mask].view(-1))
else:
s_acc = utils.calculate_recall(predictions.view(-1), labels.view(-1), split_mask=s_mask)
output_log = output_log + '{}_{} Acc : {:7.4f} / {:5d}, '.format(self.stype, str(s), s_acc, s_mask.sum().item())
if csv_results is not None:
csv_results['recall'].append('{:.4f}'.format(s_acc))
self.logger.info(output_log)
output_log = 'Precision ==> '
for s in split_set:
s_mask = (split==s)
if premask:
s_prc = utils.calculate_precision(predictions[s_mask].view(-1), labels[s_mask].view(-1), num_class)
else:
s_prc = utils.calculate_precision(predictions.view(-1), labels.view(-1), num_class, split_mask=s_mask)
output_log = output_log + '{}_{} Prc : {:7.4f} / {:5d}, '.format(self.stype, str(s), s_prc, s_mask.sum().item())
if csv_results is not None:
csv_results['precision'].append('{:.4f}'.format(s_prc))
self.logger.info(output_log)
class test_tde():
def __init__(self, config, logger, model, classifier, val=False, specify_testset=None, add_ckpt=None):
self.config = config
self.logger = logger
self.model = model
self.classifier = classifier
self.add_ckpt = add_ckpt
# get dataloader
if val:
self.phase = 'val'
self.loader = get_loader(config, 'val', config['dataset']['testset'], logger)
elif specify_testset is not None:
self.phase = 'test'
self.loader = get_loader(config, 'test', specify_testset, logger)
else:
self.phase = 'test'
self.loader = get_loader(config, 'test', config['dataset']['testset'], logger)
def run_test(self):
currect_split = self.config['dataset']['testset']
self.logger.info('------------- Start Testing at Split: {} -----------'.format(currect_split))
if currect_split in ('test_bl', 'test_bbl'):
frq_accs = acc_by_splits(self.logger, 'frequency')
if currect_split in ('test_bbl'):
if self.config['dataset']['name'] not in INVALID_ATTRIBUTE_DATASET:
att_accs = acc_by_splits(self.logger, 'attribute')
# set model to evaluation
self.model.eval()
self.classifier.eval()
# run batch
with torch.no_grad():
all_preds = []
all_labs = []
all_frqs = []
all_atts = []
# load embed
self.embed = self.add_ckpt['embed'].cuda()
for _, (inputs, labels, freq_labels, attributes, indexes) in enumerate(self.loader):
# additional inputs
inputs, labels, freq_labels, attributes = inputs.cuda(), labels.cuda(), freq_labels.cuda(), attributes.cuda()
add_inputs = {}
add_inputs['embed'] = self.embed.view(1, -1).repeat(inputs.shape[0], 1)
features = self.model(inputs)
predictions = self.classifier(features, add_inputs)
all_preds.append(predictions)
all_labs.append(labels)
all_frqs.append(freq_labels)
all_atts.append(attributes)
all_preds = torch.cat(all_preds, dim=0)
num_class = all_preds.shape[-1]
all_preds = all_preds.max(-1)[1].view(-1)
all_labs = torch.cat(all_labs, dim=0).view(-1)
all_frqs = torch.cat(all_frqs, dim=0).view(-1)
all_atts = torch.cat(all_atts, dim=0).view(-1)
csv_results = {'recall': [currect_split, 'recall'],
'precision': [currect_split, 'precision'],
'f1': [currect_split, 'f1']}
# frequency splits by intra-class attributes
if currect_split in ('test_bl', 'test_bbl'):
frq_accs.print_score(all_preds, all_labs, num_class, split=all_frqs, csv_results=csv_results)
if currect_split in ('test_bbl'):
if self.config['dataset']['name'] not in INVALID_ATTRIBUTE_DATASET:
att_accs.print_score(all_preds, all_labs, num_class, split=all_atts, csv_results=csv_results, premask=True)
# overall performance
total_num = all_preds.shape[0]
# calculate recall
recall_score = utils.calculate_recall(all_preds, all_labs)
self.logger.info('Test Complete ==> Overall Recall/AC : {:9.4f}, Number Samples : {:9d}'.format(recall_score, total_num))
csv_results['recall'].append('{:.4f}'.format(recall_score))
# calculate precision
precision_score = utils.calculate_precision(all_preds, all_labs, num_class)
self.logger.info('Test Complete ==> Overall Precision : {:9.4f}, Number Samples : {:9d}'.format(precision_score, total_num))
csv_results['precision'].append('{:.4f}'.format(precision_score))
# F1 score
F1_score = utils.calculate_f1(recall_score, precision_score)
self.logger.info('Test Complete ==> Overall F1 Score : {:9.4f}, Number Samples : {:9d}'.format(F1_score, total_num))
csv_results['f1'].append('{:.4f}'.format(F1_score))
# save csv results
self.logger.write_results(csv_results['recall'])
self.logger.write_results(csv_results['precision'])
self.logger.write_results(csv_results['f1'])
# set back to training mode again
self.model.train()
self.classifier.train()
return recall_score
def run_val(self, epoch, embed=None):
self.logger.info('------------- Start Validation at Epoch {} -----------'.format(epoch))
frq_accs = acc_by_splits(self.logger, 'frequency')
if self.config['dataset']['name'] not in INVALID_ATTRIBUTE_DATASET:
att_accs = acc_by_splits(self.logger, 'attribute')
# set model to evaluation
self.model.eval()
self.classifier.eval()
# run batch
with torch.no_grad():
all_preds = []
all_labs = []
all_frqs = []
all_atts = []
if embed is not None:
self.embed = embed.cuda()
else:
self.embed = self.add_ckpt['embed'].cuda()
for _, (inputs, labels, freq_labels, attributes, indexes) in enumerate(self.loader):
# additional inputs
inputs, labels, freq_labels, attributes = inputs.cuda(), labels.cuda(), freq_labels.cuda(), attributes.cuda()
add_inputs = {}
add_inputs['embed'] = self.embed.view(1, -1).repeat(inputs.shape[0], 1)
features = self.model(inputs)
predictions = self.classifier(features, add_inputs)
all_preds.append(predictions)
all_labs.append(labels)
all_frqs.append(freq_labels)
all_atts.append(attributes)
all_preds = torch.cat(all_preds, dim=0)
num_class = all_preds.shape[-1]
all_preds = all_preds.max(-1)[1].view(-1)
all_labs = torch.cat(all_labs, dim=0).view(-1)
all_frqs = torch.cat(all_frqs, dim=0).view(-1)
all_atts = torch.cat(all_atts, dim=0).view(-1)
# overall performance
total_num = all_preds.shape[0]
# calculate recall
recall_score = utils.calculate_recall(all_preds, all_labs)
self.logger.info('Test Complete ==> Overall Recall/AC : {:9.4f}, Number Samples : {:9d}'.format(recall_score, total_num))
# calculate precision
precision_score = utils.calculate_precision(all_preds, all_labs, num_class)
self.logger.info('Test Complete ==> Overall Precision : {:9.4f}, Number Samples : {:9d}'.format(precision_score, total_num))
# F1 score
F1_score = utils.calculate_f1(recall_score, precision_score)
self.logger.info('Test Complete ==> Overall F1 Score : {:9.4f}, Number Samples : {:9d}'.format(F1_score, total_num))
# frequency splits by intra-class attributes
frq_accs.print_score(all_preds, all_labs, num_class, split=all_frqs)
if self.config['dataset']['name'] not in INVALID_ATTRIBUTE_DATASET:
att_accs.print_score(all_preds, all_labs, num_class, split=all_atts, premask=True)
# set back to training mode again
self.model.train()
self.classifier.train()
return recall_score