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train_svr.py
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import os
import numpy as np
import pandas as pd
import argparse
from tqdm import tqdm
from joblib import dump,load,Parallel,delayed
from sklearn.model_selection import train_test_split, GridSearchCV
from sklearn.preprocessing import StandardScaler
from sklearn import svm
from sklearn.svm import SVR
from scipy.io import savemat
from scipy.stats import spearmanr,pearsonr
from scipy.optimize import curve_fit
parser = argparse.ArgumentParser(description='Run a content-separated SVR model')
parser.add_argument('--score_file',help='File with video names and scores')
parser.add_argument('--feature_folder',help='Folder containing features')
parser.add_argument('--only_train',action='store_true',help='only train')
parser.add_argument('--only_test',action='store_true',help='only test')
parser.add_argument('--train_and_test',action='store_true',help='train and test')
args = parser.parse_args()
def results(all_preds,all_dmos):
all_preds = np.asarray(all_preds)
all_preds[np.isnan(all_preds)]=0
all_dmos = np.asarray(all_dmos)
try:
[[b0, b1, b2, b3, b4], _] = curve_fit(lambda t, b0, b1, b2, b3, b4: b0 * (0.5 - 1.0/(1 + np.exp(b1*(t - b2))) + b3 * t + b4),
all_preds, all_dmos, p0=0.5*np.ones((5,)), maxfev=20000)
preds_fitted = b0 * (0.5 - 1.0/(1 + np.exp(b1*(all_preds - b2))) + b3 * all_preds+ b4)
except:
preds_fitted = all_preds
preds_srocc = spearmanr(preds_fitted,all_dmos)
preds_lcc = pearsonr(preds_fitted,all_dmos)
preds_rmse = np.sqrt(np.mean((preds_fitted-all_dmos)**2))
return preds_srocc[0],preds_lcc[0],preds_rmse
scores_df = pd.read_csv(args.score_file)
video_names = scores_df['video']
scores = scores_df['dark_mos']
scores_df['content'] = [v.split('_')[2] for v in scores_df['video']]
srocc_list = []
def trainval_split(trainval_content,r):
train,val= train_test_split(trainval_content,test_size=0.2,random_state=r)
train_features = []
train_indices = []
val_features = []
train_scores = []
val_scores = []
feature_folder= args.feature_folder
train_names = []
val_names = []
for i,vid in enumerate(video_names):
featfile_name = vid+'_upscaled.npy'
feat_file = np.load(os.path.join(feature_folder,featfile_name), allow_pickle=True) #[:,0:4096] # shape = #framesX4096
#mean across the temporal dimension ()
feature = np.mean(feat_file, axis=0, dtype=np.float32)
feature = np.nan_to_num(feature)
#print(feature.shape)
score = scores[i]
if(scores_df.loc[i]['content'] in train):
train_features.append(feature)
train_scores.append(score)
train_indices.append(i)
train_names.append(scores_df.loc[i]['video'])
elif(scores_df.loc[i]['content'] in val):
val_features.append(feature)
val_scores.append(score)
val_names.append(scores_df.loc[i]['video'])
return np.asarray(train_features),train_scores,np.asarray(val_features),val_scores,train,val_names
def single_split(trainval_content,cv_index,C):
train_features,train_scores,val_features,val_scores,_,_ = trainval_split(trainval_content,cv_index)
clf = svm.SVR(kernel='linear',C=C)
scaler = StandardScaler()
X_train = scaler.fit_transform(train_features)
X_test = scaler.transform(val_features)
clf.fit(X_train,train_scores)
return clf.score(X_test,val_scores)
def grid_search(C_list,trainval_content):
best_score = -100
best_C = C_list[0]
for C in C_list:
cv_score = Parallel(n_jobs=-1)(delayed(single_split)(trainval_content,cv_index,C) for cv_index in range(5))
avg_cv_score = np.average(cv_score)
if(avg_cv_score>best_score):
best_score = avg_cv_score
best_C = C
return best_C
def train_test(r):
train_features,train_scores,test_features,test_scores,trainval_content,test_names = trainval_split(scores_df['content'].unique(),r)
best_C= grid_search(C_list=np.logspace(-7,2,10,base=2),trainval_content=trainval_content)
scaler = StandardScaler()
scaler.fit(train_features)
X_train = scaler.transform(train_features)
X_test = scaler.transform(test_features)
best_svr =SVR(kernel='linear',C=best_C)
best_svr.fit(X_train,train_scores)
preds = best_svr.predict(X_test)
srocc,lcc,rmse = results(preds,test_scores)
return srocc,lcc,rmse
def only_train(r):
train_features,train_scores,test_features,test_scores,trainval_content,test_names = trainval_split(scores_df['content'].unique(),r)
all_features = np.concatenate((np.asarray(train_features),np.asarray(test_features)),axis=0)
all_scores = np.concatenate((train_scores,test_scores),axis=0)
scaler = StandardScaler()
X_train = scaler.fit_transform(all_features)
grid_svr = GridSearchCV(svm.SVR(kernel='linear'),param_grid = {"C":np.logspace(-7,2,10,base=2)},cv=5)
grid_svr.fit(X_train, all_scores)
preds = grid_svr.predict(X_train)
srocc_test = spearmanr(preds,all_scores)
print(srocc_test)
dump(scaler,"./contrique_livehdr_fitted_scaler.z")
dump(grid_svr,"./contrique_livehdr_trained_svr.z")
return
def only_test(r):
train_features,train_scores,test_features,test_scores,trainval_content,test_names = trainval_split(scores_df['content'].unique(),r)
all_features = np.concatenate((np.asarray(train_features),np.asarray(test_features)),axis=0)
all_scores = np.concatenate((train_scores,test_scores),axis=0)
scaler = StandardScaler()
scaler = load('contrique_livehdr_fitted_scaler.z')
X_train = scaler.fit_transform(all_features)
grid_svr = load('contrique_livehdr_trained_svr.z')
preds = grid_svr.predict(X_train)
srocc_test = spearmanr(preds,all_scores)
print(srocc_test)
return
if(args.only_train):
only_train(0)
elif(args.only_test):
only_test(0)
elif(args.train_and_test):
"""
srocc_list = []
for i in tqdm(range(100)):
res = train_test(i)
srocc_list.append(res)
"""
srocc_list = Parallel(n_jobs=15,verbose=0)(delayed(train_test)(i) for i in tqdm(range(100)))
print("median srocc is")
print(np.median([s[0] for s in srocc_list]))
print("median lcc is")
print(np.median([s[1] for s in srocc_list]))
print("median rmse is")
print(np.median([s[2] for s in srocc_list]))
print("std of srocc is")
print(np.std([s[0] for s in srocc_list]))
print("std of lcc is")
print(np.std([s[1] for s in srocc_list]))
print("std of rmse is")
print(np.std([s[2] for s in srocc_list]))