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inference.py
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inference.py
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import argparse
import time
import os
import sys
import json
import shutil
import numpy as np
import torch
from torch.autograd import Variable
from sklearn.metrics import confusion_matrix
from torch.nn import functional as F
from opts import parse_opts
from model import generate_model
from dataset import get_training_set, get_validation_set, get_test_set
from mean import get_mean, get_std
from spatial_transforms import *
from temporal_transforms import *
from target_transforms import ClassLabel, VideoID
from target_transforms import Compose as TargetCompose
from dataset import get_training_set, get_validation_set, get_test_set
from utils import Logger
from train import train_epoch
from validation import val_epoch
import test
from utils import AverageMeter
"""
def calculate_accuracy(outputs, targets, topk=(1,)):
maxk = max(topk)
batch_size = targets.size(0)
_, pred = outputs.topk(maxk, 1, True, True)
pred = pred.t()
correct = pred.eq(targets.view(1, -1).expand_as(pred))
ret = []
for k in topk:
correct_k = correct[:k].float().sum().data[0]
ret.append(correct_k / batch_size)
return ret
"""
def calculate_accuracy(outputs, targets, topk=(1,)):
maxk = max(topk)
batch_size = targets.size(0)
_, pred = outputs.topk(maxk, 1, True, True)
pred = pred.t()
correct = pred.eq(targets.view(1, -1).expand_as(pred))
ret = []
for k in topk:
correct_k = correct[:k].float().sum().data[0]
ret.append(correct_k / batch_size)
return ret
opt = parse_opts()
if opt.root_path != '':
opt.video_path = os.path.join(opt.root_path, opt.video_path)
opt.annotation_path = os.path.join(opt.root_path, opt.annotation_path)
opt.result_path = os.path.join(opt.root_path, opt.result_path)
if opt.resume_path:
opt.resume_path = os.path.join(opt.root_path, opt.resume_path)
if opt.pretrain_path:
opt.pretrain_path = os.path.join(opt.root_path, opt.pretrain_path)
opt.scales = [opt.initial_scale]
for i in range(1, opt.n_scales):
opt.scales.append(opt.scales[-1] * opt.scale_step)
opt.arch = '{}-{}'.format(opt.model, opt.model_depth)
opt.mean = get_mean(opt.norm_value, dataset=opt.mean_dataset)
opt.std = get_std(opt.norm_value)
print(opt)
with open(os.path.join(opt.result_path, 'opts.json'), 'w') as opt_file:
json.dump(vars(opt), opt_file)
torch.manual_seed(opt.manual_seed)
model, parameters = generate_model(opt)
print(model)
pytorch_total_params = sum(p.numel() for p in model.parameters() if
p.requires_grad)
print("Total number of trainable parameters: ", pytorch_total_params)
if opt.no_mean_norm and not opt.std_norm:
norm_method = Normalize([0, 0, 0], [1, 1, 1])
elif not opt.std_norm:
norm_method = Normalize(opt.mean, [1, 1, 1])
else:
norm_method = Normalize(opt.mean, opt.std)
spatial_transform = Compose([
#Scale(opt.sample_size),
Scale(112),
CenterCrop(112),
ToTensor(opt.norm_value), norm_method
])
temporal_transform = TemporalCenterCrop(opt.sample_duration)
#temporal_transform = TemporalBeginCrop(opt.sample_duration)
#temporal_transform = TemporalEndCrop(opt.sample_duration)
target_transform = ClassLabel()
validation_data = get_validation_set(
opt, spatial_transform, temporal_transform, target_transform)
data_loader = torch.utils.data.DataLoader(
validation_data,
batch_size=1,
shuffle=False,
num_workers=opt.n_threads,
pin_memory=True)
val_logger = Logger(os.path.join(opt.result_path, 'val.log'), ['epoch', 'loss', 'acc'])
if opt.resume_path:
print('loading checkpoint {}'.format(opt.resume_path))
checkpoint = torch.load(opt.resume_path)
assert opt.arch == checkpoint['arch']
opt.begin_epoch = checkpoint['epoch']
model.load_state_dict(checkpoint['state_dict'])
recorder = []
print('run')
model.eval()
batch_time = AverageMeter()
top1 = AverageMeter()
top5 = AverageMeter()
end_time = time.time()
for i, (inputs, targets) in enumerate(data_loader):
if not opt.no_cuda:
targets = targets.cuda(async=True)
#inputs = Variable(torch.squeeze(inputs), volatile=True)
inputs = Variable(inputs, volatile=True)
targets = Variable(targets, volatile=True)
outputs = model(inputs)
recorder.append(outputs.data.cpu().numpy().copy())
#outputs = torch.unsqueeze(torch.mean(outputs, 0), 0)
prec1, prec5 = calculate_accuracy(outputs, targets, topk=(1, 5))
top1.update(prec1, inputs.size(0))
top5.update(prec5, inputs.size(0))
batch_time.update(time.time() - end_time)
end_time = time.time()
print('[{0}/{1}]\t'
'Time {batch_time.val:.5f} ({batch_time.avg:.5f})\t'
'prec@1 {top1.avg:.5f} prec@5 {top5.avg:.5f}'.format(
i + 1,
len(data_loader),
batch_time=batch_time,
top1 =top1,
top5=top5))
video_pred = [np.argmax(np.mean(x, axis=0)) for x in recorder]
print(video_pred)
with open('annotation_Something/categories.txt') as f:
lines = f.readlines()
categories = [item.rstrip() for item in lines]
name_list = [x.strip().split()[0] for x in open('annotation_Something/testlist01.txt')]
order_dict = {e:i for i, e in enumerate(sorted(name_list))}
reorder_output = [None] * len(recorder)
reorder_pred = [None] * len(recorder)
output_csv = []
for i in range(len(recorder)):
idx = order_dict[name_list[i]]
reorder_output[idx] = recorder[i]
reorder_pred[idx] = video_pred[i]
output_csv.append('%s;%s'%(name_list[i],
categories[video_pred[i]]))
with open('something_predictions.csv','w') as f:
f.write('\n'.join(output_csv))
print('-----Evaluation is finished------')
print('Overall Prec@1 {:.05f}% Prec@5 {:.05f}%'.format(top1.avg, top5.avg))