if os.path.isdir(config.IMAGE_DIR) and os.path.exists(
config.ANNO_PATH):
print("Create Mindrecord.")
data_to_mindrecord_byte_image("other", True, prefix)
print("Create Mindrecord Done, at {}".format(mindrecord_dir))
else:
raise Exception("IMAGE_DIR or ANNO_PATH not exits.")
while not os.path.exists(mindrecord_file + ".db"):
time.sleep(5)
if not args_opt.only_create_dataset:
loss_scale = float(config.loss_scale)
# When create MindDataset, using the fitst mindrecord file, such as MaskRcnn.mindrecord0.
dataset = create_maskrcnn_dataset(mindrecord_file,
batch_size=config.batch_size,
device_num=device_num,
rank_id=rank)
dataset_size = dataset.get_dataset_size()
print("total images num: ", dataset_size)
print("Create dataset done!")
net = Mask_Rcnn_Resnet50(config=config)
net = net.set_train()
load_path = args_opt.pre_trained
if load_path != "":
param_dict = load_checkpoint(load_path)
if config.pretrain_epoch_size == 0:
for item in list(param_dict.keys()):
if not (item.startswith('backbone')
def MaskRcnn_eval(dataset_path, ckpt_path, ann_file):
"""MaskRcnn evaluation."""
ds = create_maskrcnn_dataset(dataset_path,
batch_size=config.test_batch_size,
is_training=False)
net = Mask_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(output_numpy=True, 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']
gt_mask = data["mask"]
start = time.time()
# run net
output = net(Tensor(img_data), Tensor(img_metas), Tensor(gt_bboxes),
Tensor(gt_labels), Tensor(gt_num), Tensor(gt_mask))
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]
all_mask_fb = output[3]
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_mask_fb_squee = np.squeeze(all_mask_fb.asnumpy()[j, :, :, :])
all_bboxes_tmp_mask = all_bbox_squee[all_mask_squee, :]
all_labels_tmp_mask = all_label_squee[all_mask_squee]
all_mask_fb_tmp_mask = all_mask_fb_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]
all_mask_fb_tmp_mask = all_mask_fb_tmp_mask[inds]
bbox_results = bbox2result_1image(all_bboxes_tmp_mask,
all_labels_tmp_mask,
config.num_classes)
segm_results = get_seg_masks(all_mask_fb_tmp_mask,
all_bboxes_tmp_mask,
all_labels_tmp_mask, img_metas[j],
True, config.num_classes)
outputs.append((bbox_results, segm_results))
eval_types = ["bbox", "segm"]
result_files = results2json(dataset_coco, outputs, "./results.pkl")
coco_eval(result_files, eval_types, dataset_coco, single_result=False)