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- # Copyright 2021 Huawei Technologies Co., Ltd
- #
- # Licensed under the Apache License, Version 2.0 (the "License");
- # you may not use this file except in compliance with the License.
- # You may obtain a copy of the License at
- #
- # http://www.apache.org/licenses/LICENSE-2.0
- #
- # Unless required by applicable law or agreed to in writing, software
- # distributed under the License is distributed on an "AS IS" BASIS,
- # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- # See the License for the specific language governing permissions and
- # limitations under the License.
- # ============================================================================
-
- """Evaluation for retinanet"""
-
- import os
- import time
- import numpy as np
- import mindspore
- from mindspore import nn
- from mindspore import ops
- from mindspore import context, Tensor
- from mindspore.train.serialization import load_checkpoint, load_param_into_net
- from src.retinahead import retinahead, retinanetInferWithDecoder
- from src.backbone import resnet101
- from src.dataset import create_retinanet_dataset, data_to_mindrecord_byte_image, voc_data_to_mindrecord
- from src.model_utils.config import config
- from src.model_utils.moxing_adapter import moxing_wrapper
- from src.model_utils.device_adapter import get_device_id
- from src.coco_eval import metrics
- from src.box_utils import default_boxes,default_square_boxes_ltrb
-
- def retinanet_eval(dataset_path, ckpt_path):
- """retinanet evaluation."""
- softmax = nn.Softmax(axis=-1)
- sigmoid = nn.Sigmoid()
- batch_size = 1
- ds = create_retinanet_dataset(dataset_path, batch_size=batch_size, repeat_num=1, is_training=False)
- backbone = resnet101(config.num_classes)
- net = retinahead(backbone, config)
- # net = retinanetInferWithDecoder(net, Tensor(default_boxes), config)
- net = retinanetInferWithDecoder(net, Tensor(default_square_boxes_ltrb), config)
- print("Load Checkpoint!")
- print("ckpt_path: ", ckpt_path)
- param_dict = load_checkpoint(ckpt_path)
- net.init_parameters_data()
- load_param_into_net(net, param_dict)
-
- net.set_train(False)
- i = batch_size
- total = ds.get_dataset_size() * batch_size
- start = time.time()
- pred_data = []
- print("\n========================================\n")
- print("total images num: ", total)
- print("Processing, please wait a moment.")
- for data in ds.create_dict_iterator(output_numpy=True):
- img_id = data['img_id']
- img_np = data['image']
- image_shape = data['image_shape']
- scale = data['scale']
-
- # import pdb
- # pdb.set_trace()
- output = net(Tensor(img_np)) # bboxes, labels, scores, confids labels是0-79 不是独热编码
- for batch_idx in range(img_np.shape[0]):
- bboxes, labels, scores, confids = output[0].asnumpy(), output[1].asnumpy(), output[2].asnumpy(), output[3].asnumpy() # 做了squeezee而且val的时候batch_size都是1 所以不用[batch_idx]了
- bboxes = bboxes / scale
- pred_data.append({"boxes": bboxes.astype(np.float16), # 转为float16之后,显存保持3000不变,否则会一直涨,最后能涨到30000
- "box_scores": (scores * confids).astype(np.float16), # 在retinahead的decoder中转为了(ymin xmin ymax xmax)
- "labels":labels.astype(np.int16),
- "img_id": int(np.squeeze(img_id[batch_idx])),
- "img_shape": image_shape[batch_idx]})
- percent = round(i / total * 100., 2)
- print(" {}% [{}/{}]".format(str(percent), i, total))
- i += batch_size
- cost_time = int((time.time() - start) * 1000)
- print(" 100% [{arg1}/{arg1}] cost {arg2} ms".format(arg1=total, arg2=cost_time))
- mAP = metrics(pred_data) # pred_data为长度4541的列表,列表元素是字典
- # pred_data[0]['boxes'].shape (7555, 4)
- # pred_data[0]['box_scores'].shape (7555, 81)
- print("\n========================================\n")
- print("mAP: {}".format(mAP))
-
-
- def modelarts_process():
- if config.need_modelarts_dataset_unzip:
- config.coco_root = os.path.join(config.coco_root, config.modelarts_dataset_unzip_name)
- print(os.listdir(os.path.join(config.data_path, config.modelarts_dataset_unzip_name)))
-
-
- @moxing_wrapper(pre_process=modelarts_process)
- def eval_retinanet_resnet101():
- """ eval_retinanet_resnet101 """
- # context.set_context(mode=context.GRAPH_MODE, device_target=config.run_platform, device_id=get_device_id())
- if config.train_mode == 'Graph':
- context.set_context(mode=context.GRAPH_MODE, device_target=config.run_platform, device_id=get_device_id()) #静态图
- else :
- context.set_context(mode=context.PYNATIVE_MODE, device_target=config.run_platform, device_id=get_device_id()) #动态图
-
- prefix = "retinanet_eval.mindrecord"
- mindrecord_dir = config.mindrecord_dir
- mindrecord_file = os.path.join(mindrecord_dir, prefix + "0")
- if config.dataset == "voc":
- config.coco_root = config.voc_root
- if not os.path.exists(mindrecord_file):
- if not os.path.isdir(mindrecord_dir):
- os.makedirs(mindrecord_dir)
- if config.dataset == "coco":
- if os.path.isdir(config.coco_root):
- print("Create Mindrecord.")
- data_to_mindrecord_byte_image("coco", False, prefix)
- print("Create Mindrecord Done, at {}".format(mindrecord_dir))
- else:
- print("coco_root not exits.")
- elif config.dataset == "voc":
- if os.path.isdir(config.voc_dir) and os.path.isdir(config.voc_root):
- print("Create Mindrecord.")
- voc_data_to_mindrecord(mindrecord_dir, False, prefix)
- print("Create Mindrecord Done, at {}".format(mindrecord_dir))
- else:
- print("voc_root or voc_dir not exits.")
- else:
- if os.path.isdir(config.image_dir) and os.path.exists(config.anno_path):
- print("Create Mindrecord.")
- data_to_mindrecord_byte_image("other", False, prefix)
- print("Create Mindrecord Done, at {}".format(mindrecord_dir))
- else:
- print("IMAGE_DIR or ANNO_PATH not exits.")
- else:
- print("Mindrecord file exists.")
-
- print("Start Eval!")
- retinanet_eval(mindrecord_file, config.checkpoint_path)
-
-
- if __name__ == '__main__':
- eval_retinanet_resnet101()
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