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test.py
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from __future__ import print_function
import argparse
import os
import shutil
import time
from tqdm import tqdm
import torch
import torch.nn as nn
import torch.nn.parallel
import torch.backends.cudnn as cudnn
import torch.optim
#import torch.utils.data
import torch.nn.functional as F
import torchvision.transforms as transforms
import torchvision.datasets as datasets
#from torchsummary import summary
import torchvision.models as models
# from models import *
from collections import OrderedDict
from torch.autograd import Variable
from scipy.ndimage import uniform_filter1d
# import scipy as sp
from scipy import signal
import pickle
from utils.utils import Normalize
from utils.utils import calc_scores
import logging
# import models.resnet as ResNet
import utils
import matplotlib.pyplot as plt
import numpy as np
# import cv2
import sys
from EvaluationMetrics.cccmetric import ccc
import math
from losses.CCC import CCC
#import wandb
def Test(val_loader, model, cam):
# switch to evaluate mode
global Val_acc
global best_Val_acc
global best_Val_acc_epoch
#model.eval()
model.eval()
cam.eval()
PrivateTest_loss = 0
correct = 0
total = 0
running_val_loss = 0
running_val_accuracy = 0
vout = []
vtar = []
aout = []
atar = []
#torch.cuda.synchronize()
#t7 = time.time()
pred_a = dict()
pred_v = dict()
label_a = dict()
label_v = dict()
#files_dict = {}
count = 0
for batch_idx, (visualdata, audiodata, frame_ids, videos, vid_lengths) in tqdm(enumerate(val_loader),
total=len(val_loader), position=0, leave=True):
#if(batch_idx > 2):#int(65844/64)):
# break
#torch.cuda.synchronize()
#t8 = time.time()
#print('data loading time', t8-t7)
audiodata = audiodata.cuda()#.unsqueeze(2)
visualdata = visualdata.cuda()
#torch.cuda.synchronize()
#t9 = time.time()
with torch.no_grad():
b, seq_t, c, subseq_t, h, w = visualdata.size()
#sub_seq_len = 16
#visualdata = visual_data.view(b, c, -1, sub_seq_len, h, w)
#visual_feats = []
#aud_feats = []
visual_feats = torch.empty((b, seq_t, 25088), dtype=visualdata.dtype, device = visualdata.device)
aud_feats = torch.empty((b, seq_t, 512), dtype=visualdata.dtype, device = visualdata.device)
for i in range(visualdata.shape[0]):
#vis_dat = visualdata[i, :, :, :,:,:].transpose(0,1)
audio_feat, visualfeat, _ = model(audiodata[i,:,:,:], visualdata[i, :, :, :,:,:])
visual_feats[i,:,:] = visualfeat
aud_feats[i,:,:] = audio_feat
#visualfeat = visual_model(visualdata[i, :, :, :,:,:].transpose(0,1))#[:,-1,:]
#visualfeat, _ = torch.max(visualfeat,1)
#visual_feats.append(visualfeat)
#aud_data = audiodata[i,:,:,:]#.unsqueeze(1)
#audio_feat = audio_model(aud_data)
#aud_feats.append(audio_feat) #.squeeze(3))
#visual_feat = torch.stack(visual_feats)#.squeeze(3).squeeze(3).squeeze(3)#.transpose(1,2)
#audio_feat = torch.stack(aud_feats)#.squeeze(3)#.transpose(1,2)
#torch.cuda.synchronize()
#t8 = time.time()
#audio_feat, audio_out = audio_model(audiodata)
#audio_feat = audio_feat.squeeze(3)
#audio_feat, audio_out = audio_model(audiodata)
#visualfeat, visual_out = visual_model(visualdata)#.unsqueeze(0))
#visual_feat = visualfeat.squeeze(2).squeeze(2).squeeze(2)
#visual_feat = torch.max(visualfeat, dim = 2)[0].squeeze(2).squeeze(2)
#vis_data = visualdata.view(b*visualdata.shape[2], c, subseq_t ,h , w)
#visualfeatures, _ = visual_model(vis_data)
#visual_feat = visualfeatures.view(b, -1, visualfeatures.shape[1])
#aud_data = audiodata.view(audiodata.shape[0]*audiodata.shape[1], audiodata.shape[2], audiodata.shape[3]).unsqueeze(1)
#aud_feat, audio_out = audio_model(aud_data)
#audio_feat = aud_feat.view(b, -1, aud_feat.shape[1])
#print(audio_feat.shape)
#print(visual_feat.shape)
#audio_feat_norm = F.normalize(avalidateudio_feat, p=2, dim=2, eps=1e-12)
#visual_feat_norm = F.normalize(visual_feat, p=2, dim=2, eps=1e-12)
#audio_attfeat, visual_attfeat = cam(audio_feat, visual_feat)
#audiovisual_outs = model(audio_feat_norm, visual_feat_norm)
audiovisual_vouts,audiovisual_aouts = cam(aud_feats, visual_feats)
#outputs = audiovisual_outs.view(-1, audiovisual_outs.shape[0]*audiovisual_outs.shape[1])
#targets = labels.view(-1, labels.shape[0]*labels.shape[1]).cuda()
audiovisual_vouts = audiovisual_vouts.detach().cpu().numpy()
audiovisual_aouts = audiovisual_aouts.detach().cpu().numpy()
#sys.exit()
#flags = valence == -5.0
#v = np.delete(v, flags)
#a = np.delete(a, flags)
#print(len(frame_ids))
#print(len(list(zip(*vid_lengths))))
#print(len(list(zip(*videos))))
#sys.exit()
for voutputs, aoutputs, frameids, video, vid_length in zip(audiovisual_vouts, audiovisual_aouts, frame_ids, videos, vid_lengths):
for voutput, aoutput, frameid, vid, length in zip(voutputs, aoutputs, frameids, video, vid_length):
if vid not in pred_a:
if frameid>1:
print(vid)
print(length)
print("something is wrong")
sys.exit()
count = count + 1
#files_dict[vid] = [0]*length
pred_a[vid] = [0]*length
pred_v[vid] = [0]*length
#files_dict[vid][frameid-1] = [voutput, aoutput, labV, labA]
pred_a[vid][frameid-1] = aoutput
pred_v[vid][frameid-1] = voutput
else:
if frameid <= length:
#print(frameid)
#files_dict[vid][frameid-1] = [torch.tanh(output), lab]
#pred_a[vid][frameid-1] = [voutput, aoutput, labV, labA]
#files_dict[vid][frameid-1] = [voutput, aoutput, labV, labA]
pred_a[vid][frameid-1] = aoutput
pred_v[vid][frameid-1] = voutput
if not os.path.isdir("results"):
os.makedirs("results")
for key in pred_a.keys():
label_file_path = os.path.join('results', key + ".txt")
text_file = open(label_file_path, "w")
clipped_preds_v = np.clip(pred_v[key], -1.0, 1.0)
clipped_preds_a = np.clip(pred_a[key], -1.0, 1.0)
smoothened_preds_v = uniform_filter1d(clipped_preds_v, size=20, mode='constant')
smoothened_preds_a = uniform_filter1d(clipped_preds_a, size=50, mode='constant')
n = text_file.write("valence,arousal")
n = text_file.write('\n')
for i in range(len(smoothened_preds_a)):
#vout.append(np.clip(smoothened_preds_v[i], -1.0, 1.0))
#aout.append(np.clip(smoothened_preds_a[i], -1.0, 1.0))
str_data = ','.join([str("{0:.5f}".format(smoothened_preds_v[i])), str("{0:.5f}".format(smoothened_preds_a[i]))])
n = text_file.write(str_data)
n = text_file.write('\n')
text_file.close()
sys.exit()