forked from PulseFocusPlatform/PulseFocusPlatform
420 lines
16 KiB
Python
420 lines
16 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 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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from collections import OrderedDict
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from paddleslim.dist.single_distiller import merge, l2_loss
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from paddle import fluid
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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.data.reader import create_reader
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from ppdet.utils.eval_utils import parse_fetches, eval_results, eval_run
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from ppdet.utils.stats import TrainingStats
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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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import ppdet.utils.checkpoint as checkpoint
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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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def l2_distill(pairs, weight):
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"""
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Add l2 distillation losses composed of multi pairs of feature maps,
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each pair of feature maps is the input of teacher and student's
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yolov3_loss respectively
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"""
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loss = []
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for pair in pairs:
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loss.append(l2_loss(pair[0], pair[1]))
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loss = fluid.layers.sum(loss)
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weighted_loss = loss * weight
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return weighted_loss
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def split_distill(split_output_names, weight):
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"""
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Add fine grained distillation losses.
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Each loss is composed by distill_reg_loss, distill_cls_loss and
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distill_obj_loss
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"""
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student_var = []
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for name in split_output_names:
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student_var.append(fluid.default_main_program().global_block().var(
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name))
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s_x0, s_y0, s_w0, s_h0, s_obj0, s_cls0 = student_var[0:6]
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s_x1, s_y1, s_w1, s_h1, s_obj1, s_cls1 = student_var[6:12]
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s_x2, s_y2, s_w2, s_h2, s_obj2, s_cls2 = student_var[12:18]
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teacher_var = []
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for name in split_output_names:
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teacher_var.append(fluid.default_main_program().global_block().var(
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'teacher_' + name))
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t_x0, t_y0, t_w0, t_h0, t_obj0, t_cls0 = teacher_var[0:6]
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t_x1, t_y1, t_w1, t_h1, t_obj1, t_cls1 = teacher_var[6:12]
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t_x2, t_y2, t_w2, t_h2, t_obj2, t_cls2 = teacher_var[12:18]
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def obj_weighted_reg(sx, sy, sw, sh, tx, ty, tw, th, tobj):
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loss_x = fluid.layers.sigmoid_cross_entropy_with_logits(
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sx, fluid.layers.sigmoid(tx))
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loss_y = fluid.layers.sigmoid_cross_entropy_with_logits(
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sy, fluid.layers.sigmoid(ty))
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loss_w = fluid.layers.abs(sw - tw)
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loss_h = fluid.layers.abs(sh - th)
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loss = fluid.layers.sum([loss_x, loss_y, loss_w, loss_h])
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weighted_loss = fluid.layers.reduce_mean(loss *
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fluid.layers.sigmoid(tobj))
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return weighted_loss
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def obj_weighted_cls(scls, tcls, tobj):
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loss = fluid.layers.sigmoid_cross_entropy_with_logits(
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scls, fluid.layers.sigmoid(tcls))
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weighted_loss = fluid.layers.reduce_mean(
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fluid.layers.elementwise_mul(
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loss, fluid.layers.sigmoid(tobj), axis=0))
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return weighted_loss
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def obj_loss(sobj, tobj):
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obj_mask = fluid.layers.cast(tobj > 0., dtype="float32")
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obj_mask.stop_gradient = True
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loss = fluid.layers.reduce_mean(
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fluid.layers.sigmoid_cross_entropy_with_logits(sobj, obj_mask))
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return loss
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distill_reg_loss0 = obj_weighted_reg(s_x0, s_y0, s_w0, s_h0, t_x0, t_y0,
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t_w0, t_h0, t_obj0)
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distill_reg_loss1 = obj_weighted_reg(s_x1, s_y1, s_w1, s_h1, t_x1, t_y1,
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t_w1, t_h1, t_obj1)
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distill_reg_loss2 = obj_weighted_reg(s_x2, s_y2, s_w2, s_h2, t_x2, t_y2,
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t_w2, t_h2, t_obj2)
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distill_reg_loss = fluid.layers.sum(
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[distill_reg_loss0, distill_reg_loss1, distill_reg_loss2])
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distill_cls_loss0 = obj_weighted_cls(s_cls0, t_cls0, t_obj0)
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distill_cls_loss1 = obj_weighted_cls(s_cls1, t_cls1, t_obj1)
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distill_cls_loss2 = obj_weighted_cls(s_cls2, t_cls2, t_obj2)
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distill_cls_loss = fluid.layers.sum(
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[distill_cls_loss0, distill_cls_loss1, distill_cls_loss2])
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distill_obj_loss0 = obj_loss(s_obj0, t_obj0)
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distill_obj_loss1 = obj_loss(s_obj1, t_obj1)
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distill_obj_loss2 = obj_loss(s_obj2, t_obj2)
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distill_obj_loss = fluid.layers.sum(
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[distill_obj_loss0, distill_obj_loss1, distill_obj_loss2])
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loss = (distill_reg_loss + distill_cls_loss + distill_obj_loss) * weight
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return loss
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def main():
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env = os.environ
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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_version()
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main_arch = cfg.architecture
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if cfg.use_gpu:
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devices_num = fluid.core.get_cuda_device_count()
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else:
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devices_num = int(os.environ.get('CPU_NUM', 1))
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if 'FLAGS_selected_gpus' in env:
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device_id = int(env['FLAGS_selected_gpus'])
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else:
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device_id = 0
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place = fluid.CUDAPlace(device_id) if cfg.use_gpu else fluid.CPUPlace()
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exe = fluid.Executor(place)
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# build program
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model = create(main_arch)
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inputs_def = cfg['TrainReader']['inputs_def']
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train_feed_vars, train_loader = model.build_inputs(**inputs_def)
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train_fetches = model.train(train_feed_vars)
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loss = train_fetches['loss']
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start_iter = 0
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train_reader = create_reader(cfg.TrainReader, (cfg.max_iters - start_iter) *
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devices_num, cfg)
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# When iterable mode, set set_sample_list_generator(train_reader, place)
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train_loader.set_sample_list_generator(train_reader)
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# get all student variables
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student_vars = []
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for v in fluid.default_main_program().list_vars():
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try:
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student_vars.append((v.name, v.shape))
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except:
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pass
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# uncomment the following lines to print all student variables
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# print("="*50 + "student_model_vars" + "="*50)
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# print(student_vars)
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eval_prog = fluid.Program()
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with fluid.program_guard(eval_prog, fluid.default_startup_program()):
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with fluid.unique_name.guard():
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model = create(main_arch)
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inputs_def = cfg['EvalReader']['inputs_def']
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test_feed_vars, eval_loader = model.build_inputs(**inputs_def)
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fetches = model.eval(test_feed_vars)
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eval_prog = eval_prog.clone(True)
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eval_reader = create_reader(cfg.EvalReader)
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# When iterable mode, set set_sample_list_generator(eval_reader, place)
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eval_loader.set_sample_list_generator(eval_reader)
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# parse eval fetches
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extra_keys = []
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if cfg.metric == 'COCO':
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extra_keys = ['im_info', 'im_id', 'im_shape']
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if cfg.metric == 'VOC':
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extra_keys = ['gt_bbox', 'gt_class', 'is_difficult']
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eval_keys, eval_values, eval_cls = parse_fetches(fetches, eval_prog,
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extra_keys)
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teacher_cfg = load_config(FLAGS.teacher_config)
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merge_config(FLAGS.opt)
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teacher_arch = teacher_cfg.architecture
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teacher_program = fluid.Program()
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teacher_startup_program = fluid.Program()
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with fluid.program_guard(teacher_program, teacher_startup_program):
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with fluid.unique_name.guard():
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teacher_feed_vars = OrderedDict()
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for name, var in train_feed_vars.items():
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teacher_feed_vars[name] = teacher_program.global_block(
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)._clone_variable(
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var, force_persistable=False)
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model = create(teacher_arch)
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train_fetches = model.train(teacher_feed_vars)
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teacher_loss = train_fetches['loss']
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# get all teacher variables
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teacher_vars = []
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for v in teacher_program.list_vars():
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try:
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teacher_vars.append((v.name, v.shape))
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except:
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pass
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# uncomment the following lines to print all teacher variables
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# print("="*50 + "teacher_model_vars" + "="*50)
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# print(teacher_vars)
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exe.run(teacher_startup_program)
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assert FLAGS.teacher_pretrained, "teacher_pretrained should be set"
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checkpoint.load_params(exe, teacher_program, FLAGS.teacher_pretrained)
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teacher_program = teacher_program.clone(for_test=True)
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cfg = load_config(FLAGS.config)
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merge_config(FLAGS.opt)
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data_name_map = {
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'target0': 'target0',
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'target1': 'target1',
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'target2': 'target2',
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'image': 'image',
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'gt_bbox': 'gt_bbox',
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'gt_class': 'gt_class',
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'gt_score': 'gt_score'
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}
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merge(teacher_program, fluid.default_main_program(), data_name_map, place)
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yolo_output_names = [
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'strided_slice_0.tmp_0', 'strided_slice_1.tmp_0',
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'strided_slice_2.tmp_0', 'strided_slice_3.tmp_0',
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'strided_slice_4.tmp_0', 'transpose_0.tmp_0', 'strided_slice_5.tmp_0',
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'strided_slice_6.tmp_0', 'strided_slice_7.tmp_0',
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'strided_slice_8.tmp_0', 'strided_slice_9.tmp_0', 'transpose_2.tmp_0',
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'strided_slice_10.tmp_0', 'strided_slice_11.tmp_0',
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'strided_slice_12.tmp_0', 'strided_slice_13.tmp_0',
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'strided_slice_14.tmp_0', 'transpose_4.tmp_0'
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]
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distill_pairs = [['teacher_conv2d_6.tmp_1', 'conv2d_20.tmp_1'],
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['teacher_conv2d_14.tmp_1', 'conv2d_28.tmp_1'],
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['teacher_conv2d_22.tmp_1', 'conv2d_36.tmp_1']]
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distill_loss = l2_distill(
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distill_pairs, 100) if not cfg.use_fine_grained_loss else split_distill(
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yolo_output_names, 1000)
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loss = distill_loss + loss
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lr_builder = create('LearningRate')
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optim_builder = create('OptimizerBuilder')
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lr = lr_builder()
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opt = optim_builder(lr)
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opt.minimize(loss)
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exe.run(fluid.default_startup_program())
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fuse_bn = getattr(model.backbone, 'norm_type', None) == 'affine_channel'
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ignore_params = cfg.finetune_exclude_pretrained_params \
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if 'finetune_exclude_pretrained_params' in cfg else []
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if FLAGS.resume_checkpoint:
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checkpoint.load_checkpoint(exe,
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fluid.default_main_program(),
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FLAGS.resume_checkpoint)
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start_iter = checkpoint.global_step()
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elif cfg.pretrain_weights and fuse_bn and not ignore_params:
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checkpoint.load_and_fusebn(exe,
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fluid.default_main_program(),
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cfg.pretrain_weights)
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elif cfg.pretrain_weights:
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checkpoint.load_params(
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exe,
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fluid.default_main_program(),
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cfg.pretrain_weights,
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ignore_params=ignore_params)
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build_strategy = fluid.BuildStrategy()
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build_strategy.fuse_all_reduce_ops = False
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build_strategy.fuse_all_optimizer_ops = False
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# only enable sync_bn in multi GPU devices
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sync_bn = getattr(model.backbone, 'norm_type', None) == 'sync_bn'
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build_strategy.sync_batch_norm = sync_bn and devices_num > 1 \
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and cfg.use_gpu
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exec_strategy = fluid.ExecutionStrategy()
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# iteration number when CompiledProgram tries to drop local execution scopes.
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# Set it to be 1 to save memory usages, so that unused variables in
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# local execution scopes can be deleted after each iteration.
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exec_strategy.num_iteration_per_drop_scope = 1
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parallel_main = fluid.CompiledProgram(fluid.default_main_program(
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)).with_data_parallel(
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loss_name=loss.name,
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build_strategy=build_strategy,
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exec_strategy=exec_strategy)
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compiled_eval_prog = fluid.CompiledProgram(eval_prog)
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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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map_type = cfg.map_type if 'map_type' in cfg else '11point'
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best_box_ap_list = [0.0, 0] #[map, iter]
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cfg_name = os.path.basename(FLAGS.config).split('.')[0]
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save_dir = os.path.join(cfg.save_dir, cfg_name)
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train_loader.start()
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for step_id in range(start_iter, cfg.max_iters):
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teacher_loss_np, distill_loss_np, loss_np, lr_np = exe.run(
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parallel_main,
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fetch_list=[
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'teacher_' + teacher_loss.name, distill_loss.name, loss.name,
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lr.name
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])
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if step_id % cfg.log_iter == 0:
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logger.info(
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"step {} lr {:.6f}, loss {:.6f}, distill_loss {:.6f}, teacher_loss {:.6f}".
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format(step_id, lr_np[0], loss_np[0], distill_loss_np[0],
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teacher_loss_np[0]))
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if step_id % cfg.snapshot_iter == 0 and step_id != 0 or step_id == cfg.max_iters - 1:
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save_name = str(
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step_id) if step_id != cfg.max_iters - 1 else "model_final"
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checkpoint.save(exe,
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fluid.default_main_program(),
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os.path.join(save_dir, save_name))
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if FLAGS.save_inference:
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feeded_var_names = ['image', 'im_size']
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targets = list(fetches.values())
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fluid.io.save_inference_model(save_dir + '/infer',
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feeded_var_names, targets, exe,
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eval_prog)
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# eval
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results = eval_run(exe, compiled_eval_prog, eval_loader, eval_keys,
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eval_values, eval_cls, cfg)
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resolution = None
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box_ap_stats = eval_results(results, cfg.metric, cfg.num_classes,
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resolution, is_bbox_normalized,
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FLAGS.output_eval, map_type,
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cfg['EvalReader']['dataset'])
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if box_ap_stats[0] > best_box_ap_list[0]:
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best_box_ap_list[0] = box_ap_stats[0]
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best_box_ap_list[1] = step_id
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checkpoint.save(exe,
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fluid.default_main_program(),
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os.path.join(save_dir, "best_model"))
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if FLAGS.save_inference:
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feeded_var_names = ['image', 'im_size']
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targets = list(fetches.values())
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fluid.io.save_inference_model(save_dir + '/infer',
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feeded_var_names, targets,
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exe, eval_prog)
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logger.info("Best test box ap: {}, in step: {}".format(
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best_box_ap_list[0], best_box_ap_list[1]))
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train_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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"-r",
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"--resume_checkpoint",
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default=None,
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type=str,
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help="Checkpoint path for resuming training.")
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parser.add_argument(
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"-t",
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"--teacher_config",
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default=None,
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type=str,
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help="Config file of teacher architecture.")
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parser.add_argument(
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"--teacher_pretrained",
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default=None,
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type=str,
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help="Whether to use pretrained model.")
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parser.add_argument(
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"--output_eval",
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default=None,
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type=str,
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help="Evaluation directory, default is current directory.")
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parser.add_argument(
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"--save_inference",
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default=False,
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type=bool,
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help="Whether to save inference model.")
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FLAGS = parser.parse_args()
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main()
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