def FasterRcnn_eval(dataset_path, ckpt_path, ann_file):
"""FasterRcnn evaluation."""
ds = create_fasterrcnn_dataset(dataset_path, batch_size=config.test_batch_size, is_training=False)
net = Faster_Rcnn_Resnet50(config)
param_dict = load_checkpoint(ckpt_path)
load_param_into_net(net, param_dict)
net.set_train(False)
eval_iter = 0
total = ds.get_dataset_size()
outputs = []
dataset_coco = COCO(ann_file)
print("\n========================================\n")
print("total images num: ", total)
print("Processing, please wait a moment.")
max_num = 128
for data in ds.create_dict_iterator(num_epochs=1):
eval_iter = eval_iter + 1
img_data = data['image']
img_metas = data['image_shape']
gt_bboxes = data['box']
gt_labels = data['label']
gt_num = data['valid_num']
start = time.time()
# run net
output = net(img_data, img_metas, gt_bboxes, gt_labels, gt_num)
end = time.time()
print("Iter {} cost time {}".format(eval_iter, end - start))
# output
all_bbox = output[0]
all_label = output[1]
all_mask = output[2]
for j in range(config.test_batch_size):
all_bbox_squee = np.squeeze(all_bbox.asnumpy()[j, :, :])
all_label_squee = np.squeeze(all_label.asnumpy()[j, :, :])
all_mask_squee = np.squeeze(all_mask.asnumpy()[j, :, :])
all_bboxes_tmp_mask = all_bbox_squee[all_mask_squee, :]
all_labels_tmp_mask = all_label_squee[all_mask_squee]
if all_bboxes_tmp_mask.shape[0] > max_num:
inds = np.argsort(-all_bboxes_tmp_mask[:, -1])
inds = inds[:max_num]
all_bboxes_tmp_mask = all_bboxes_tmp_mask[inds]
all_labels_tmp_mask = all_labels_tmp_mask[inds]
outputs_tmp = bbox2result_1image(all_bboxes_tmp_mask, all_labels_tmp_mask, config.num_classes)
outputs.append(outputs_tmp)
eval_types = ["bbox"]
result_files = results2json(dataset_coco, outputs, "./results.pkl")
coco_eval(result_files, eval_types, dataset_coco, single_result=True)
type=str,
default='',
help='fasterrcnn ckpt file.')
parser.add_argument('--output_file',
type=str,
default='',
help='fasterrcnn output air name.')
parser.add_argument('--file_format',
type=str,
choices=["AIR", "ONNX", "MINDIR"],
default='AIR',
help='file format')
args = parser.parse_args()
if __name__ == '__main__':
net = Faster_Rcnn_Resnet50(config=config)
param_dict = load_checkpoint(args.ckpt_file)
load_param_into_net(net, param_dict)
img = Tensor(
np.zeros(
[config.test_batch_size, 3, config.img_height, config.img_width]),
ms.float16)
img_metas = Tensor(
np.random.uniform(0.0, 1.0, size=[config.test_batch_size, 4]),
ms.float16)
gt_bboxes = Tensor(
np.random.uniform(0.0,
1.0,
size=[config.test_batch_size, config.num_gts]),
def create_network(name, *args, **kwargs):
if name == "faster_rcnn":
return Faster_Rcnn_Resnet50(config=config)
raise NotImplementedError(f"{name} is not implemented in the repo")
