forked from PulseFocusPlatform/PulseFocusPlatform
217 lines
7.6 KiB
Python
217 lines
7.6 KiB
Python
# Copyright (c) 2019 PaddlePaddle Authors. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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from __future__ import absolute_import
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from __future__ import division
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from __future__ import print_function
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import os
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import sys
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# add python path of PadleDetection to sys.path
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parent_path = os.path.abspath(os.path.join(__file__, *(['..'] * 3)))
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if parent_path not in sys.path:
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sys.path.append(parent_path)
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import numpy as np
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from PIL import Image
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from paddle import fluid
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import logging
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FORMAT = '%(asctime)s-%(levelname)s: %(message)s'
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logging.basicConfig(level=logging.INFO, format=FORMAT)
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logger = logging.getLogger(__name__)
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try:
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from ppdet.core.workspace import load_config, merge_config, create
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from ppdet.utils.eval_utils import parse_fetches
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from ppdet.utils.cli import ArgsParser
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from ppdet.utils.check import check_gpu, check_version, check_config, enable_static_mode
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from ppdet.utils.visualizer import visualize_results
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import ppdet.utils.checkpoint as checkpoint
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from ppdet.data.reader import create_reader
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from tools.infer import get_test_images, get_save_image_name
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except ImportError as e:
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if sys.argv[0].find('static') >= 0:
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logger.error("Importing ppdet failed when running static model "
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"with error: {}\n"
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"please try:\n"
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"\t1. run static model under PaddleDetection/static "
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"directory\n"
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"\t2. run 'pip uninstall ppdet' to uninstall ppdet "
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"dynamic version firstly.".format(e))
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sys.exit(-1)
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else:
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raise e
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from paddleslim.quant import quant_aware, convert
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def main():
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cfg = load_config(FLAGS.config)
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merge_config(FLAGS.opt)
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check_config(cfg)
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# check if set use_gpu=True in paddlepaddle cpu version
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check_gpu(cfg.use_gpu)
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# check if paddlepaddle version is satisfied
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check_version()
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main_arch = cfg.architecture
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dataset = cfg.TestReader['dataset']
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test_images = get_test_images(FLAGS.infer_dir, FLAGS.infer_img)
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dataset.set_images(test_images)
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place = fluid.CUDAPlace(0) if cfg.use_gpu else fluid.CPUPlace()
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exe = fluid.Executor(place)
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model = create(main_arch)
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startup_prog = fluid.Program()
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infer_prog = fluid.Program()
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with fluid.program_guard(infer_prog, startup_prog):
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with fluid.unique_name.guard():
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inputs_def = cfg['TestReader']['inputs_def']
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feed_vars, loader = model.build_inputs(**inputs_def)
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test_fetches = model.test(feed_vars)
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infer_prog = infer_prog.clone(True)
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reader = create_reader(cfg.TestReader)
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# When iterable mode, set set_sample_list_generator(reader, place)
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loader.set_sample_list_generator(reader)
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not_quant_pattern = []
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if FLAGS.not_quant_pattern:
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not_quant_pattern = FLAGS.not_quant_pattern
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config = {
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'weight_quantize_type': 'channel_wise_abs_max',
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'activation_quantize_type': 'moving_average_abs_max',
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'quantize_op_types': ['depthwise_conv2d', 'mul', 'conv2d'],
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'not_quant_pattern': not_quant_pattern
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}
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infer_prog = quant_aware(infer_prog, place, config, for_test=True)
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exe.run(startup_prog)
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if cfg.weights:
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checkpoint.load_params(exe, infer_prog, cfg.weights)
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infer_prog = convert(infer_prog, place, config, save_int8=False)
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# parse infer fetches
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assert cfg.metric in ['COCO', 'VOC', 'OID', 'WIDERFACE'], \
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"unknown metric type {}".format(cfg.metric)
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extra_keys = []
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if cfg['metric'] in ['COCO', 'OID']:
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extra_keys = ['im_info', 'im_id', 'im_shape']
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if cfg['metric'] == 'VOC' or cfg['metric'] == 'WIDERFACE':
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extra_keys = ['im_id', 'im_shape']
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keys, values, _ = parse_fetches(test_fetches, infer_prog, extra_keys)
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# parse dataset category
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if cfg.metric == 'COCO':
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from ppdet.utils.coco_eval import bbox2out, mask2out, get_category_info
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if cfg.metric == 'OID':
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from ppdet.utils.oid_eval import bbox2out, get_category_info
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if cfg.metric == "VOC":
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from ppdet.utils.voc_eval import bbox2out, get_category_info
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if cfg.metric == "WIDERFACE":
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from ppdet.utils.widerface_eval_utils import bbox2out, get_category_info
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anno_file = dataset.get_anno()
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with_background = dataset.with_background
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use_default_label = dataset.use_default_label
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clsid2catid, catid2name = get_category_info(anno_file, with_background,
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use_default_label)
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# whether output bbox is normalized in model output layer
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is_bbox_normalized = False
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if hasattr(model, 'is_bbox_normalized') and \
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callable(model.is_bbox_normalized):
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is_bbox_normalized = model.is_bbox_normalized()
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imid2path = dataset.get_imid2path()
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iter_id = 0
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try:
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loader.start()
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while True:
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outs = exe.run(infer_prog, fetch_list=values, return_numpy=False)
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res = {
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k: (np.array(v), v.recursive_sequence_lengths())
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for k, v in zip(keys, outs)
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}
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logger.info('Infer iter {}'.format(iter_id))
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iter_id += 1
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bbox_results = None
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mask_results = None
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if 'bbox' in res:
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bbox_results = bbox2out([res], clsid2catid, is_bbox_normalized)
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if 'mask' in res:
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mask_results = mask2out([res], clsid2catid,
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model.mask_head.resolution)
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# visualize result
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im_ids = res['im_id'][0]
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for im_id in im_ids:
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image_path = imid2path[int(im_id)]
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image = Image.open(image_path).convert('RGB')
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image = visualize_results(image,
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int(im_id), catid2name,
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FLAGS.draw_threshold, bbox_results,
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mask_results)
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save_name = get_save_image_name(FLAGS.output_dir, image_path)
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logger.info("Detection bbox results save in {}".format(
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save_name))
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image.save(save_name, quality=95)
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except (StopIteration, fluid.core.EOFException):
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loader.reset()
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if __name__ == '__main__':
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enable_static_mode()
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parser = ArgsParser()
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parser.add_argument(
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"--infer_dir",
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type=str,
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default=None,
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help="Directory for images to perform inference on.")
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parser.add_argument(
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"--infer_img",
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type=str,
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default=None,
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help="Image path, has higher priority over --infer_dir")
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parser.add_argument(
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"--output_dir",
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type=str,
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default="output",
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help="Directory for storing the output visualization files.")
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parser.add_argument(
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"--draw_threshold",
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type=float,
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default=0.5,
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help="Threshold to reserve the result for visualization.")
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parser.add_argument(
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"--not_quant_pattern",
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nargs='+',
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type=str,
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help="Layers which name_scope contains string in not_quant_pattern will not be quantized"
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)
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FLAGS = parser.parse_args()
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main()
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