def do_benchmark_test(args, model_file, weights_file, iterations=1000):
""" Calc the accuracy on the lmdb dataset"""
net = caffe.Net(model_file, weights_file, caffe.TEST)
top1_total = 0
top5_total = 0
lmdb_data = datasets.LMDBData(args.dataset)
lmdb_data.set_scale(SCALE)
lmdb_data.set_crop_size(CROP_SIZE)
if MEAN_FILE is not None:
lmdb_data.set_mean_file(MEAN_FILE)
else:
lmdb_data.set_mean_value(MEAN_VALUE)
for index in range(iterations):
data, labels = lmdb_data.get_blobs(BATCH_SIZE)
forward_kwargs = {MODEL_INPUT_BLOB_NAME: data}
blobs_out = net.forward(**forward_kwargs)
top1, top5 = img_postprocess(blobs_out[MODEL_OUTPUT_BLOB_NAME], labels)
top1_total += top1
top5_total += top5
print('*****************iteration:{}******************'.format(index))
print('top1_acc:{}'.format(top1))
print('top5_acc:{}'.format(top5))
print('******final top1:{}'.format(top1_total / iterations))
print('******final top5:{}'.format(top5_total / iterations))
return top1_total / iterations, top5_total / iterations
def do_benchmark_test(model_file, weights_file, iterations=150):
""" Calc the accuracy on the lmdb dataset"""
model_file = os.path.realpath(model_file)
weights_file = os.path.realpath(weights_file)
net = caffe.Net(model_file, weights_file, caffe.TEST)
top1_total = 0
lmdb_data = datasets.LMDBData(LMDB_DATASET_DIR)
lmdb_data.set_scale(SCALE)
for index in range(iterations):
data, labels = lmdb_data.get_blobs(BATCH_SIZE)
forward_kwargs = {MODEL_INPUT_BLOB_NAME: data}
blobs_out = net.forward(**forward_kwargs)
top1, _ = img_postprocess(blobs_out[MODEL_OUTPUT_BLOB_NAME], labels)
top1_total += top1
print('*****************iteration:{}******************'.format(index))
print('top1_acc:{}'.format(top1))
print('******final top1:{}'.format(top1_total / iterations))
return top1_total / iterations