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test.py
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test.py
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import torch
from torch.autograd import Variable
import torch.nn.functional as F
import time
import os
import sys
import json
import pdb
from utils import AverageMeter
def calculate_video_results(output_buffer, video_id, test_results, class_names):
video_outputs = torch.stack(output_buffer)
average_scores = torch.mean(video_outputs, dim=0)
sorted_scores, locs = torch.topk(average_scores, k=2)
video_results = []
for i in range(sorted_scores.size(0)):
video_results.append({
'label': class_names[locs[i].item()],
'score': sorted_scores[i].item()
})
test_results['results'][video_id] = video_results
def test(data_loader, model, opt, class_names):
print('test')
model.eval()
batch_time = AverageMeter()
data_time = AverageMeter()
end_time = time.time()
output_buffer = []
previous_video_id = ''
test_results = {'results': {}}
for i, (inputs, targets) in enumerate(data_loader):
data_time.update(time.time() - end_time)
with torch.no_grad():
inputs = Variable(inputs)
targets = Variable(targets)
outputs = model(inputs)
if not opt.no_softmax_in_test:
outputs = F.softmax(outputs)
for j in range(outputs.size(0)):
if not (i == 0 and j == 0) and targets[j].item() != previous_video_id:
calculate_video_results(output_buffer, previous_video_id,
test_results, class_names)
output_buffer = []
output_buffer.append(outputs[j].data.cpu())
previous_video_id = targets[j].item()
if (i % 100) == 0:
with open(
os.path.join(opt.result_path, '{}.json'.format(
opt.test_subset)), 'w') as f:
json.dump(test_results, f)
batch_time.update(time.time() - end_time)
end_time = time.time()
print('[{}/{}]\t'
'Time {batch_time.val:.3f} ({batch_time.avg:.3f})\t'
'Data {data_time.val:.3f} ({data_time.avg:.3f})\t'.format(
i + 1,
len(data_loader),
batch_time=batch_time,
data_time=data_time))
with open(
os.path.join(opt.result_path, '{}.json'.format(opt.test_subset)),
'w') as f:
json.dump(test_results, f)