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test_zeroshot.py
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test_zeroshot.py
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# Zero shot evaluation based on low and high-level features
from scipy.stats import spearmanr, pearsonr
from Evaluation.zeroshot_ll_model import *
from Evaluation.zeroshot_hl_model import *
from networks import *
import pandas as pd
import numpy as np
import traceback
import datetime
import argparse
import torch
import time
def parse_option():
parser = argparse.ArgumentParser('arguments for evaluation')
parser.add_argument('--device', type=str,
default='cuda:0', help='Device (cpu/cuda)')
parser.add_argument('--ll_model_weights_path', type=str,
default='./Evaluation/pretrained_weights/low_level_model_weights.tar',
help='Saved weights for the low level model')
parser.add_argument('--hl_model_weights_path', type=str,
default='./Evaluation/pretrained_weights/high_level_model_weights.pth',
help='Saved weights for the high level model')
parser.add_argument('--eval_type', type=str,
default='zeroshot', help='Evaluation modes (zeroshot/zeroshot_single_img)')
# Arguments for zeroshot/zeroshot_single_img evaluation
parser.add_argument('--dataset', default='CLIVE', type=str,
help='Dataset to check in zeroshot evaluation.')
parser.add_argument('--img_dir', type=str,
default='../Databases/CLIVE/ChallengeDB_release/Images', help='Image directory for above chosen dataset')
parser.add_argument('--test_img_path', type=str,
default='../Databases/CLIVE/ChallengeDB_release/Images/3.bmp', help='Test image path for zeroshot_single_img evaluation')
# Arguments for statistical distance computation in zeroshot evaluation of LL model
parser.add_argument('--pristine_img_dir', type=str,
default='../Databases/pristine', help='Image directory for pristine images.')
parser.add_argument('--patch_size', default=96, type=int,
help='Patch size for pristine patches')
parser.add_argument('--sharpness_param', default=0.75, type=float,
help='Sharpness parameter for selecting pristine patches')
parser.add_argument('--colorfulness_param', default=0.8, type=float,
help='Colorfulness parameter for selecting pristine patches')
optn = parser.parse_args()
return optn
class ZeroshotEvaluation():
def __init__(self, args, ll_model, hl_model):
self.ll_model = ll_model
self.hl_model = hl_model
self.args = args
def zeroshot_eval(self):
test_dataset = self.args.dataset
if test_dataset == 'CLIVE':
data_loc = './Evaluation/datasets/LIVEC.csv'
elif test_dataset == 'KONIQ':
data_loc = './Evaluation/datasets/KONIQ.csv'
img_dir = self.args.img_dir
names_ll, scores_ll, mos_ll = compute_niqe_distance(self.ll_model, test_dataset, img_dir, data_loc, self.args)
df_ll = pd.DataFrame()
df_ll['file_name'] = names_ll
df_ll['mos'] = mos_ll
df_ll['score_ll'] = scores_ll
names_hl, scores_hl, mos_hl = compute_hlm_scores(self.hl_model, test_dataset, img_dir, data_loc)
df_hl = pd.DataFrame()
df_hl['file_name'] = names_hl
df_hl['mos'] = mos_hl
df_hl['score_hl'] = scores_hl
df_scores = pd.merge(df_ll, df_hl, on=['file_name', 'mos'])
df_scores['combined'] = np.array(df_scores['score_hl']) + np.array(df_scores['score_ll'])
test_correlation_srocc = spearmanr(np.array(df_scores['combined']), np.array(df_scores['mos']))[0]
polyfit_combined = np.poly1d(np.polyfit(df_scores['combined'], df_scores['mos'], deg=3))
norm_combined = polyfit_combined(df_scores['combined'])
test_correlation_plcc = pearsonr(norm_combined, df_scores['mos'])[0]
print(f"SROCC on {test_dataset} is {test_correlation_srocc}")
print(f"PLCC on {test_dataset} is {test_correlation_plcc}")
return
def zeroshot_eval_single_img(self):
test_image_path = self.args.test_img_path
score_ll = compute_niqe_distance_single_image(self.ll_model, test_image_path, self.args)
score_hl = compute_hlm_score_single_image(self.hl_model, test_image_path)
score = score_hl + score_ll
print(f"Quality scores (high, low): {score}")
return
# Evaluation mode for testing
def eval_mode(model):
for param in model.parameters():
param.requires_grad_(False)
model.eval()
return model
# Loads the pretrained model
def load_model(model_weights_path, network_type):
model_weights = model_weights_path
model = None
if network_type == 'll':
model = LLModel(encoder='resnet18', head='mlp').to("cuda")
load_dict = torch.load(model_weights)
model.load_state_dict(load_dict['model']['state_dict'], strict=True)
elif network_type == 'hl':
model = HLModel().to("cuda")
load_dict = torch.load(model_weights)
model.clip_model.visual.load_state_dict(load_dict, strict=False)
return model
def main():
args = parse_option()
ll_model_weights_path = args.ll_model_weights_path
hl_model_weights_path = args.hl_model_weights_path
# Indicates the Low Level model, ResNet18 backbone trained with quality aware contrastive loss
ll_model = load_model(model_weights_path= ll_model_weights_path, network_type= 'll')
ll_model = eval_mode(model= ll_model)
# Indicates the high level model, pretrained CLIP model finetuned with group contrastive loss
hl_model = load_model(model_weights_path= hl_model_weights_path, network_type= 'hl')
hl_model = eval_mode(model= hl_model)
zeroshot_eval = ZeroshotEvaluation(args, ll_model, hl_model)
if args.eval_type == 'zeroshot':
zeroshot_eval.zeroshot_eval()
elif args.eval_type == 'zeroshot_single_img':
zeroshot_eval.zeroshot_eval_single_img()
return
if __name__ == '__main__':
print('Program started at ' + datetime.datetime.now().strftime('%d/%m/%Y %I:%M:%S %p'))
start_time = time.time()
try:
main()
run_result = 'Program completed successfully!'
except Exception as e:
print(e)
traceback.print_exc()
run_result = str(e)
end_time = time.time()
print('Program ended at ' + datetime.datetime.now().strftime('%d/%m/%Y %I:%M:%S %p'))
print('Execution time: ' + str(datetime.timedelta(seconds=end_time - start_time)))