delete infer.py

dygraph/core/infer.py

-# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
-#
-# 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.
-
-import os
-
-from paddle.fluid.dygraph.base import to_variable
-import numpy as np
-import paddle.fluid as fluid
-import cv2
-import tqdm
-
-from dygraph import utils
-import dygraph.utils.logger as logger
-
-
-def mkdir(path):
-    sub_dir = os.path.dirname(path)
-    if not os.path.exists(sub_dir):
-        os.makedirs(sub_dir)
-
-
-def infer(model, test_dataset=None, model_dir=None, save_dir='output'):
-    ckpt_path = os.path.join(model_dir, 'model')
-    para_state_dict, opti_state_dict = fluid.load_dygraph(ckpt_path)
-    model.set_dict(para_state_dict)
-    model.eval()
-
-    added_saved_dir = os.path.join(save_dir, 'added')
-    pred_saved_dir = os.path.join(save_dir, 'prediction')
-
-    logger.info("Start to predict...")
-    for im, im_info, im_path in tqdm.tqdm(test_dataset):
-        im = to_variable(im)
-        pred, _ = model(im)
-        pred = pred.numpy()
-        pred = np.squeeze(pred).astype('uint8')
-        for info in im_info[::-1]:
-            if info[0] == 'resize':
-                h, w = info[1][0], info[1][1]
-                pred = cv2.resize(pred, (w, h), cv2.INTER_NEAREST)
-            elif info[0] == 'padding':
-                h, w = info[1][0], info[1][1]
-                pred = pred[0:h, 0:w]
-            else:
-                raise Exception("Unexpected info '{}' in im_info".format(
-                    info[0]))
-
-        im_file = im_path.replace(test_dataset.dataset_root, '')
-        if im_file[0] == '/':
-            im_file = im_file[1:]
-        # save added image
-        added_image = utils.visualize(im_path, pred, weight=0.6)
-        added_image_path = os.path.join(added_saved_dir, im_file)
-        mkdir(added_image_path)
-        cv2.imwrite(added_image_path, added_image)
-
-        # save prediction
-        pred_im = utils.visualize(im_path, pred, weight=0.0)
-        pred_saved_path = os.path.join(pred_saved_dir, im_file)
-        mkdir(pred_saved_path)
-        cv2.imwrite(pred_saved_path, pred_im)