Anyone own working multi_test_models.py file?

[Raazzta]

@Phoenix1327 , @all After getting 10 clips, generated by multi_test_tea_something_rgb_8f.sh, and run multi_test_models.py, I face error something like "cannot broadcast bla bla " line ~45 or 46 and dont know the solution. Anyone has fixed this error? Really appreciate if someone willing to share if have it. Thanks!

[jwli6]

multi_test_tea_something_rgb_8f.sh

NetWeight="checkpoint/TEA_something_RGB_tea50_shift8_blockres_avg_segment8_e50/ckpt.best.pth.tar" &&\
Segments=8 &&\
Crops=3 &&\
Clips=10 &&\
Batch_size=64 &&\
GPUS="0 1 2 3" &&\
Works=6 &&\
CUDA_VISIBLE_DEVICES=0,1,2,3,4,5,6,7 python test_models.py something \
    --arch='tea50' \
    --weight=$NetWeight \
    --worker=$Works --gpus $GPUS \
    --test_segments=$Segments --test_crops=$Crops \
    --batch_size=$Batch_size --shift --multi_clip_test \
    --clip_index=0 --full_res &&\

CUDA_VISIBLE_DEVICES=0,1,2,3,4,5,6,7 python test_models.py something \
    --arch='tea50' \
    --weight=$NetWeight \
    --worker=$Works --gpus $GPUS \
    --test_segments=$Segments --test_crops=$Crops \
    --batch_size=$Batch_size --shift --multi_clip_test \
    --clip_index=1 --full_res &&\

CUDA_VISIBLE_DEVICES=0,1,2,3,4,5,6,7 python test_models.py something \
    --arch='tea50' \
    --weight=$NetWeight \
    --worker=$Works --gpus $GPUS \
    --test_segments=$Segments --test_crops=$Crops \
    --batch_size=$Batch_size --shift --multi_clip_test \
    --clip_index=2 --full_res &&\

CUDA_VISIBLE_DEVICES=0,1,2,3,4,5,6,7 python test_models.py something \
    --arch='tea50' \
    --weight=$NetWeight \
    --worker=$Works --gpus $GPUS \
    --test_segments=$Segments --test_crops=$Crops \
    --batch_size=$Batch_size --shift --multi_clip_test \
    --clip_index=3 --full_res &&\

CUDA_VISIBLE_DEVICES=0,1,2,3,4,5,6,7 python test_models.py something \
    --arch='tea50' \
    --weight=$NetWeight \
    --worker=$Works --gpus $GPUS \
    --test_segments=$Segments --test_crops=$Crops \
    --batch_size=$Batch_size --shift --multi_clip_test \
    --clip_index=4 --full_res &&\

CUDA_VISIBLE_DEVICES=0,1,2,3,4,5,6,7 python test_models.py something \
    --arch='tea50' \
    --weight=$NetWeight \
    --worker=$Works --gpus $GPUS \
    --test_segments=$Segments --test_crops=$Crops \
    --batch_size=$Batch_size --shift --multi_clip_test \
    --clip_index=5 --full_res &&\

CUDA_VISIBLE_DEVICES=0,1,2,3,4,5,6,7 python test_models.py something \
    --arch='tea50' \
    --weight=$NetWeight \
    --worker=$Works --gpus $GPUS \
    --test_segments=$Segments --test_crops=$Crops \
    --batch_size=$Batch_size --shift --multi_clip_test \
    --clip_index=6 --full_res &&\

CUDA_VISIBLE_DEVICES=0,1,2,3,4,5,6,7 python test_models.py something \
    --arch='tea50' \
    --weight=$NetWeight \
    --worker=$Works --gpus $GPUS \
    --test_segments=$Segments --test_crops=$Crops \
    --batch_size=$Batch_size --shift --multi_clip_test \
    --clip_index=7 --full_res &&\

CUDA_VISIBLE_DEVICES=0,1,2,3,4,5,6,7 python test_models.py something \
    --arch='tea50' \
    --weight=$NetWeight \
    --worker=$Works --gpus $GPUS \
    --test_segments=$Segments --test_crops=$Crops \
    --batch_size=$Batch_size --shift --multi_clip_test \
    --clip_index=8 --full_res &&\

CUDA_VISIBLE_DEVICES=0,1,2,3,4,5,6,7 python test_models.py something \
    --arch='tea50' \
    --weight=$NetWeight \
    --worker=$Works --gpus $GPUS \
    --test_segments=$Segments --test_crops=$Crops \
    --batch_size=$Batch_size --shift --multi_clip_test \
    --clip_index=9 --full_res &&\

python ./multi_test_models.py something --test_crops=$Crops --num_clips=$Clips

[jwli6]

multi_test_models.py

# -*- coding: utf-8 -*-
# phoenixyli 李岩 @2020-04-02 21:15:33

import os
import argparse
import time

import torch.nn.parallel
import torch.optim
from sklearn.metrics import confusion_matrix
from ops.dataset import TSNDataSet
from ops.models import TSN
from ops.transforms import *
from ops import dataset_config
from torch.nn import functional as F
import pickle

def accuracy(output, target, topk=(1,)):
    """Computes the precision@k for the specified values of k"""
    maxk = max(topk)
    # import pdb; pdb.set_trace()
    batch_size = target.size(0)
    _, pred = output.topk(maxk, 1, True, True)
    pred = pred.t()
    correct = pred.eq(target.view(1, -1).expand_as(pred))
    res = []
    correct=correct.contiguous()
    for k in topk:
         correct_k = correct[:k].view(-1).float().sum(0)
         res.append(correct_k.mul_(100.0 / batch_size))
    return res


parser = argparse.ArgumentParser(description="TEA testing on the full validation set")
parser.add_argument('dataset', type=str)
parser.add_argument('--test_crops', type=int, default=1)
parser.add_argument('--num_clips', type=int, default=10)

args = parser.parse_args()

output_dir = './result_file'
output_filepath = os.path.join(output_dir, '0'+'_'+'crop'+str(args.test_crops)+'.pkl')
with open(output_filepath, 'rb') as f:
    # output_file[0].shape: (11520, 174) output_file[1].shape:(11520,)
    output_file = pickle.load(f)
    num_videos = output_file[0].shape[0]
    num_classes = output_file[0].shape[1]

num_clips = args.num_clips
ens_pred_numpy = np.zeros((num_videos, num_classes))
ens_label_numpy = np.zeros((num_videos,))

for clip_index in range(num_clips):
    output_filepath = os.path.join(output_dir, str(clip_index)+'_'+'crop'+str(args.test_crops)+'.pkl')
    with open(output_filepath, 'rb') as f:
        output_file = pickle.load(f)
        pred_numpy = output_file[0]
        ens_pred_numpy  = ens_pred_numpy + pred_numpy
        label_numpy = output_file[1]
        ens_label_numpy = ens_label_numpy + label_numpy

ens_pred_numpy = ens_pred_numpy / num_clips
ens_label_numpy = ens_label_numpy / int(num_clips)

prec1, prec5 = accuracy(torch.from_numpy(ens_pred_numpy), torch.from_numpy(ens_label_numpy).type(torch.LongTensor), topk=(1, 5))

# import pdb; pdb.set_trace()

video_pred = [np.argmax(x) for x in ens_pred_numpy]
video_labels = [x for x in ens_label_numpy]

cf = confusion_matrix(video_labels, video_pred).astype(float)

cls_cnt = cf.sum(axis=1)
cls_hit = np.diag(cf)

cls_acc = cls_hit / cls_cnt
# print(cls_acc)
upper = np.mean(np.max(cf, axis=1) / cls_cnt)
# print('upper bound: {}'.format(upper))

print('-----Evaluation is finished------')
print('Class Accuracy {:.02f}%'.format(np.mean(cls_acc) * 100))
print('Overall Prec@1 {:.02f}% Prec@5 {:.02f}%'.format(prec1, prec5))

[jwli6]

然后把这里改成pickle.dump([all_results,all_labels], f, pickle.HIGHEST_PROTOCOL)

[fbing998]

multi_test_tea_something_rgb_8f.sh

NetWeight="checkpoint/TEA_something_RGB_tea50_shift8_blockres_avg_segment8_e50/ckpt.best.pth.tar" &&\
Segments=8 &&\
Crops=3 &&\
Clips=10 &&\
Batch_size=64 &&\
GPUS="0 1 2 3" &&\
Works=6 &&\
CUDA_VISIBLE_DEVICES=0,1,2,3,4,5,6,7 python test_models.py something \
    --arch='tea50' \
    --weight=$NetWeight \
    --worker=$Works --gpus $GPUS \
    --test_segments=$Segments --test_crops=$Crops \
    --batch_size=$Batch_size --shift --multi_clip_test \
    --clip_index=0 --full_res &&\

CUDA_VISIBLE_DEVICES=0,1,2,3,4,5,6,7 python test_models.py something \
    --arch='tea50' \
    --weight=$NetWeight \
    --worker=$Works --gpus $GPUS \
    --test_segments=$Segments --test_crops=$Crops \
    --batch_size=$Batch_size --shift --multi_clip_test \
    --clip_index=1 --full_res &&\

CUDA_VISIBLE_DEVICES=0,1,2,3,4,5,6,7 python test_models.py something \
    --arch='tea50' \
    --weight=$NetWeight \
    --worker=$Works --gpus $GPUS \
    --test_segments=$Segments --test_crops=$Crops \
    --batch_size=$Batch_size --shift --multi_clip_test \
    --clip_index=2 --full_res &&\

CUDA_VISIBLE_DEVICES=0,1,2,3,4,5,6,7 python test_models.py something \
    --arch='tea50' \
    --weight=$NetWeight \
    --worker=$Works --gpus $GPUS \
    --test_segments=$Segments --test_crops=$Crops \
    --batch_size=$Batch_size --shift --multi_clip_test \
    --clip_index=3 --full_res &&\

CUDA_VISIBLE_DEVICES=0,1,2,3,4,5,6,7 python test_models.py something \
    --arch='tea50' \
    --weight=$NetWeight \
    --worker=$Works --gpus $GPUS \
    --test_segments=$Segments --test_crops=$Crops \
    --batch_size=$Batch_size --shift --multi_clip_test \
    --clip_index=4 --full_res &&\

CUDA_VISIBLE_DEVICES=0,1,2,3,4,5,6,7 python test_models.py something \
    --arch='tea50' \
    --weight=$NetWeight \
    --worker=$Works --gpus $GPUS \
    --test_segments=$Segments --test_crops=$Crops \
    --batch_size=$Batch_size --shift --multi_clip_test \
    --clip_index=5 --full_res &&\

CUDA_VISIBLE_DEVICES=0,1,2,3,4,5,6,7 python test_models.py something \
    --arch='tea50' \
    --weight=$NetWeight \
    --worker=$Works --gpus $GPUS \
    --test_segments=$Segments --test_crops=$Crops \
    --batch_size=$Batch_size --shift --multi_clip_test \
    --clip_index=6 --full_res &&\

CUDA_VISIBLE_DEVICES=0,1,2,3,4,5,6,7 python test_models.py something \
    --arch='tea50' \
    --weight=$NetWeight \
    --worker=$Works --gpus $GPUS \
    --test_segments=$Segments --test_crops=$Crops \
    --batch_size=$Batch_size --shift --multi_clip_test \
    --clip_index=7 --full_res &&\

CUDA_VISIBLE_DEVICES=0,1,2,3,4,5,6,7 python test_models.py something \
    --arch='tea50' \
    --weight=$NetWeight \
    --worker=$Works --gpus $GPUS \
    --test_segments=$Segments --test_crops=$Crops \
    --batch_size=$Batch_size --shift --multi_clip_test \
    --clip_index=8 --full_res &&\

CUDA_VISIBLE_DEVICES=0,1,2,3,4,5,6,7 python test_models.py something \
    --arch='tea50' \
    --weight=$NetWeight \
    --worker=$Works --gpus $GPUS \
    --test_segments=$Segments --test_crops=$Crops \
    --batch_size=$Batch_size --shift --multi_clip_test \
    --clip_index=9 --full_res &&\

python ./multi_test_models.py something --test_crops=$Crops --num_clips=$Clips

Do you have the pretraining files?