def testModel(modelidx):
dp = DataProvider(
config.batchsize, config.video_frames,
config.video_feature_num, config.video_feature_dim,
config.word_len_before, config.word_len_after,
video_dir=config.video_data_dir,
dataset_dir=config.text_data_dir,
word2vec_dir=config.word2vec_model_dir,
wordemb_dim=config.word_emb
)
modelFile = os.path.join(config.modelsave_dir, str(modelidx)+'_pool5.npy')
model = np.load(modelFile)
net, train_fun, test_fun, val_fun, sh_lr = compile_func(model)
# test
test_sample = 0
total_acc = 0
test_data = dp.load_dataset(datatype='test')
for batch in dp.iterator(test_data, shuffle=False):
num_samples,\
videos, video_masks,\
befores, before_masks, afters, afters_masks,\
lables = \
dp.loadOneBatch(batch)
prediction, acc = test_fun(
videos, video_masks,
befores, before_masks,
afters, afters_masks,
lables
)
total_acc += acc*num_samples
test_sample += num_samples
# fp_test.write('epoch: '+str(epoch)+'err: '+str(total_err)+'\n')
total_acc /= test_sample
print 'acc: '+str(total_acc)
def main():
dp = DataProvider(
config.batchsize, config.video_frames,
config.video_feature_num, config.video_feature_dim,
config.word_len_before, config.word_len_after,
video_dir=config.video_data_dir,
dataset_dir=config.text_data_dir,
word2vec_dir=config.word2vec_model_dir,
wordemb_dim=config.word_emb
)
train_data = dp.load_dataset(datatype='train')
val_data = dp.load_dataset(datatype='val')
model = None
if config.startidx > 0:
modelFile = os.path.join(config.modelsave_dir, str(config.startidx-1)+'_pool5.npy')
model = np.load(modelFile)
print '----------compile-------------'
net, train_fun, test_fun, val_fun, sh_lr = compile_func(model)
print '-------compile end------------'
train_file = os.path.join(config.performance_dir, 'train_pool5.txt')
val_file = os.path.join(config.performance_dir, 'val_pool5.txt')
fp_train = open(train_file, 'a+')
fp_train.write('--------------\n')
fp_train.write(time.strftime('%Y-%m-%d\t%H:%M:%S', time.localtime(time.time()))+'\n')
fp_train.write(str(config.batchsize)+'\n')
fp_train.write(str(config.num_epoches)+'\n')
fp_train.write(str(config.weight_decay)+'\n')
fp_train.write(str(config.lr)+'\n')
fp_train.write('-------result-------\n')
fp_train.close()
fp_val = open(val_file, 'a+')
fp_val.write('--------------\n')
fp_val.write(time.strftime('%Y-%m-%d\t%H:%M:%S', time.localtime(time.time()))+'\n')
fp_val.write(str(config.batchsize)+'\n')
fp_val.write(str(config.num_epoches)+'\n')
fp_val.write(str(config.weight_decay)+'\n')
fp_val.write(str(config.lr)+'\n')
fp_val.write('-------result-------\n')
fp_val.close()
for epoch in range(config.num_epoches):
if epoch < config.startidx:
continue
print '-----------epoch '+str(epoch)+'-------------'
# train
train_sample = 0
total_err = 0
i = 0
for batch in dp.iterator(train_data, shuffle=True):
num_samples,\
videos, video_masks,\
befores, before_masks, afters, afters_masks,\
lables = \
dp.loadOneBatch(batch)
err = train_fun(
videos, video_masks,
befores, before_masks,
afters, afters_masks,
lables
)
print 'train '+str(epoch)+':'+str(i)+':'+str(err)
i += 1
total_err += err*num_samples
train_sample += num_samples
model_train = get_all_param_values(net['fill'])
model_train_file = os.path.join(
config.modelsave_dir, str(epoch)+'_pool5.npy'
)
np.save(model_train_file, model_train)
total_err /= train_sample
fp_train = open(train_file, 'a+')
fp_train.write('epoch: '+str(epoch)+'\t err: '+str(total_err)+'\n')
fp_train.close()
print 'train:\t epoch: '+str(epoch)+'\t err: '+str(total_err)
# val
val_sample = 0
total_err = 0
total_acc = 0
i = 0
for batch in dp.iterator(val_data, shuffle=False):
num_samples,\
videos, video_masks,\
befores, before_masks, afters, afters_masks,\
lables = \
dp.loadOneBatch(batch)
prediction, err, acc = val_fun(
videos, video_masks,
befores, before_masks,
afters, afters_masks,
lables
)
print 'val '+str(epoch)+':'+str(i)
i += 1
total_err += err*num_samples
total_acc += acc*num_samples
val_sample += num_samples
total_err /= val_sample
total_acc /= val_sample
fp_val = open(val_file, 'a+')
fp_val.write('epoch: '+str(epoch)+'\t err: '+str(total_err)+'\t acc: '+str(total_acc)+'\n')
fp_val.close()
print 'val:\t epoch: '+str(epoch)+'\t err: '+str(total_err)+'\t acc: '+str(total_acc)
if (epoch+1) % config.lr_change == 0:
sh_lr.set_value(sh_lr.get_value()/10)
