378 lines
15 KiB
Python
378 lines
15 KiB
Python
# Copyright (c) 2020 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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from __future__ import unicode_literals
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import os
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import numpy as np
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import paddle
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import paddle.nn as nn
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from .download import get_weights_path
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from .logger import setup_logger
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logger = setup_logger(__name__)
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def is_url(path):
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"""
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Whether path is URL.
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Args:
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path (string): URL string or not.
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"""
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return path.startswith('http://') \
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or path.startswith('https://') \
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or path.startswith('ppdet://')
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def _strip_postfix(path):
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path, ext = os.path.splitext(path)
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assert ext in ['', '.pdparams', '.pdopt', '.pdmodel'], \
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"Unknown postfix {} from weights".format(ext)
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return path
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def load_weight(model, weight, optimizer=None, ema=None, exchange=True):
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if is_url(weight):
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weight = get_weights_path(weight)
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path = _strip_postfix(weight)
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pdparam_path = path + '.pdparams'
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if not os.path.exists(pdparam_path):
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raise ValueError("Model pretrain path {} does not "
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"exists.".format(pdparam_path))
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if ema is not None and os.path.exists(path + '.pdema'):
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if exchange:
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# Exchange model and ema_model to load
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logger.info('Exchange model and ema_model to load:')
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ema_state_dict = paddle.load(pdparam_path)
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logger.info('Loading ema_model weights from {}'.format(path +
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'.pdparams'))
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param_state_dict = paddle.load(path + '.pdema')
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logger.info('Loading model weights from {}'.format(path + '.pdema'))
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else:
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ema_state_dict = paddle.load(path + '.pdema')
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logger.info('Loading ema_model weights from {}'.format(path +
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'.pdema'))
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param_state_dict = paddle.load(pdparam_path)
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logger.info('Loading model weights from {}'.format(path +
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'.pdparams'))
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else:
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ema_state_dict = None
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param_state_dict = paddle.load(pdparam_path)
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if hasattr(model, 'modelTeacher') and hasattr(model, 'modelStudent'):
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print('Loading pretrain weights for Teacher-Student framework.')
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print('Loading pretrain weights for Student model.')
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student_model_dict = model.modelStudent.state_dict()
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student_param_state_dict = match_state_dict(
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student_model_dict, param_state_dict, mode='student')
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model.modelStudent.set_dict(student_param_state_dict)
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print('Loading pretrain weights for Teacher model.')
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teacher_model_dict = model.modelTeacher.state_dict()
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teacher_param_state_dict = match_state_dict(
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teacher_model_dict, param_state_dict, mode='teacher')
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model.modelTeacher.set_dict(teacher_param_state_dict)
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else:
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model_dict = model.state_dict()
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model_weight = {}
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incorrect_keys = 0
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for key in model_dict.keys():
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if key in param_state_dict.keys():
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model_weight[key] = param_state_dict[key]
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else:
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logger.info('Unmatched key: {}'.format(key))
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incorrect_keys += 1
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assert incorrect_keys == 0, "Load weight {} incorrectly, \
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{} keys unmatched, please check again.".format(weight,
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incorrect_keys)
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logger.info('Finish resuming model weights: {}'.format(pdparam_path))
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model.set_dict(model_weight)
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last_epoch = 0
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if optimizer is not None and os.path.exists(path + '.pdopt'):
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optim_state_dict = paddle.load(path + '.pdopt')
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# to solve resume bug, will it be fixed in paddle 2.0
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for key in optimizer.state_dict().keys():
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if not key in optim_state_dict.keys():
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optim_state_dict[key] = optimizer.state_dict()[key]
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if 'last_epoch' in optim_state_dict:
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last_epoch = optim_state_dict.pop('last_epoch')
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optimizer.set_state_dict(optim_state_dict)
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if ema_state_dict is not None:
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ema.resume(ema_state_dict,
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optim_state_dict['LR_Scheduler']['last_epoch'])
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elif ema_state_dict is not None:
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ema.resume(ema_state_dict)
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return last_epoch
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def match_state_dict(model_state_dict, weight_state_dict, mode='default'):
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"""
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Match between the model state dict and pretrained weight state dict.
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Return the matched state dict.
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The method supposes that all the names in pretrained weight state dict are
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subclass of the names in models`, if the prefix 'backbone.' in pretrained weight
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keys is stripped. And we could get the candidates for each model key. Then we
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select the name with the longest matched size as the final match result. For
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example, the model state dict has the name of
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'backbone.res2.res2a.branch2a.conv.weight' and the pretrained weight as
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name of 'res2.res2a.branch2a.conv.weight' and 'branch2a.conv.weight'. We
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match the 'res2.res2a.branch2a.conv.weight' to the model key.
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"""
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model_keys = sorted(model_state_dict.keys())
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weight_keys = sorted(weight_state_dict.keys())
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def teacher_match(a, b):
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# skip student params
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if b.startswith('modelStudent'):
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return False
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return a == b or a.endswith("." + b) or b.endswith("." + a)
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def student_match(a, b):
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# skip teacher params
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if b.startswith('modelTeacher'):
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return False
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return a == b or a.endswith("." + b) or b.endswith("." + a)
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def match(a, b):
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if b.startswith('backbone.res5'):
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b = b[9:]
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return a == b or a.endswith("." + b)
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if mode == 'student':
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match_op = student_match
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elif mode == 'teacher':
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match_op = teacher_match
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else:
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match_op = match
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match_matrix = np.zeros([len(model_keys), len(weight_keys)])
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for i, m_k in enumerate(model_keys):
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for j, w_k in enumerate(weight_keys):
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if match_op(m_k, w_k):
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match_matrix[i, j] = len(w_k)
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max_id = match_matrix.argmax(1)
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max_len = match_matrix.max(1)
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max_id[max_len == 0] = -1
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load_id = set(max_id)
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load_id.discard(-1)
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not_load_weight_name = []
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if weight_keys[0].startswith('modelStudent') or weight_keys[0].startswith(
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'modelTeacher'):
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for match_idx in range(len(max_id)):
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if max_id[match_idx] == -1:
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not_load_weight_name.append(model_keys[match_idx])
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if len(not_load_weight_name) > 0:
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logger.info('{} in model is not matched with pretrained weights, '
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'and its will be trained from scratch'.format(
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not_load_weight_name))
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else:
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for idx in range(len(weight_keys)):
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if idx not in load_id:
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not_load_weight_name.append(weight_keys[idx])
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if len(not_load_weight_name) > 0:
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logger.info('{} in pretrained weight is not used in the model, '
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'and its will not be loaded'.format(
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not_load_weight_name))
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matched_keys = {}
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result_state_dict = {}
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for model_id, weight_id in enumerate(max_id):
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if weight_id == -1:
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continue
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model_key = model_keys[model_id]
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weight_key = weight_keys[weight_id]
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weight_value = weight_state_dict[weight_key]
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model_value_shape = list(model_state_dict[model_key].shape)
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if list(weight_value.shape) != model_value_shape:
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logger.info(
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'The shape {} in pretrained weight {} is unmatched with '
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'the shape {} in model {}. And the weight {} will not be '
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'loaded'.format(weight_value.shape, weight_key,
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model_value_shape, model_key, weight_key))
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continue
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assert model_key not in result_state_dict
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result_state_dict[model_key] = weight_value
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if weight_key in matched_keys:
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raise ValueError('Ambiguity weight {} loaded, it matches at least '
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'{} and {} in the model'.format(
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weight_key, model_key, matched_keys[
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weight_key]))
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matched_keys[weight_key] = model_key
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return result_state_dict
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def load_pretrain_weight(model, pretrain_weight, ARSL_eval=False):
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if is_url(pretrain_weight):
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pretrain_weight = get_weights_path(pretrain_weight)
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path = _strip_postfix(pretrain_weight)
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if not (os.path.isdir(path) or os.path.isfile(path) or
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os.path.exists(path + '.pdparams')):
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raise ValueError("Model pretrain path `{}` does not exists. "
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"If you don't want to load pretrain model, "
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"please delete `pretrain_weights` field in "
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"config file.".format(path))
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teacher_student_flag = False
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if not ARSL_eval:
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if hasattr(model, 'modelTeacher') and hasattr(model, 'modelStudent'):
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print('Loading pretrain weights for Teacher-Student framework.')
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print(
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'Assert Teacher model has the same structure with Student model.'
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)
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model_dict = model.modelStudent.state_dict()
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teacher_student_flag = True
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else:
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model_dict = model.state_dict()
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weights_path = path + '.pdparams'
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param_state_dict = paddle.load(weights_path)
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param_state_dict = match_state_dict(model_dict, param_state_dict)
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for k, v in param_state_dict.items():
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if isinstance(v, np.ndarray):
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v = paddle.to_tensor(v)
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if model_dict[k].dtype != v.dtype:
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param_state_dict[k] = v.astype(model_dict[k].dtype)
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if teacher_student_flag:
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model.modelStudent.set_dict(param_state_dict)
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model.modelTeacher.set_dict(param_state_dict)
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else:
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model.set_dict(param_state_dict)
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logger.info('Finish loading model weights: {}'.format(weights_path))
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else:
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weights_path = path + '.pdparams'
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param_state_dict = paddle.load(weights_path)
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student_model_dict = model.modelStudent.state_dict()
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student_param_state_dict = match_state_dict(
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student_model_dict, param_state_dict, mode='student')
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model.modelStudent.set_dict(student_param_state_dict)
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print('Loading pretrain weights for Teacher model.')
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teacher_model_dict = model.modelTeacher.state_dict()
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teacher_param_state_dict = match_state_dict(
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teacher_model_dict, param_state_dict, mode='teacher')
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model.modelTeacher.set_dict(teacher_param_state_dict)
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logger.info('Finish loading model weights: {}'.format(weights_path))
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def save_model(model,
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optimizer,
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save_dir,
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save_name,
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last_epoch,
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ema_model=None):
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"""
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save model into disk.
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Args:
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model (dict): the model state_dict to save parameters.
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optimizer (paddle.optimizer.Optimizer): the Optimizer instance to
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save optimizer states.
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save_dir (str): the directory to be saved.
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save_name (str): the path to be saved.
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last_epoch (int): the epoch index.
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ema_model (dict|None): the ema_model state_dict to save parameters.
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"""
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if paddle.distributed.get_rank() != 0:
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return
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save_dir = os.path.normpath(save_dir)
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if not os.path.exists(save_dir):
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os.makedirs(save_dir)
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if save_name == "best_model":
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best_model_path = os.path.join(save_dir, 'best_model')
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if not os.path.exists(best_model_path):
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os.makedirs(best_model_path)
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save_path = os.path.join(save_dir, save_name)
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# save model
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if isinstance(model, nn.Layer):
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paddle.save(model.state_dict(), save_path + ".pdparams")
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best_model = model.state_dict()
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else:
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assert isinstance(model,
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dict), 'model is not a instance of nn.layer or dict'
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if ema_model is None:
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paddle.save(model, save_path + ".pdparams")
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best_model = model
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else:
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assert isinstance(ema_model,
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dict), ("ema_model is not a instance of dict, "
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"please call model.state_dict() to get.")
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# Exchange model and ema_model to save
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paddle.save(ema_model, save_path + ".pdparams")
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paddle.save(model, save_path + ".pdema")
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best_model = ema_model
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if save_name == 'best_model':
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best_model_path = os.path.join(best_model_path, 'model')
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paddle.save(best_model, best_model_path + ".pdparams")
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# save optimizer
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state_dict = optimizer.state_dict()
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state_dict['last_epoch'] = last_epoch
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paddle.save(state_dict, save_path + ".pdopt")
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logger.info("Save checkpoint: {}".format(save_dir))
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def save_semi_model(teacher_model, student_model, optimizer, save_dir,
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save_name, last_epoch, last_iter):
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"""
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save teacher and student model into disk.
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Args:
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teacher_model (dict): the teacher_model state_dict to save parameters.
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student_model (dict): the student_model state_dict to save parameters.
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optimizer (paddle.optimizer.Optimizer): the Optimizer instance to
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save optimizer states.
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save_dir (str): the directory to be saved.
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save_name (str): the path to be saved.
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last_epoch (int): the epoch index.
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last_iter (int): the iter index.
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"""
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if paddle.distributed.get_rank() != 0:
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return
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assert isinstance(teacher_model, dict), (
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"teacher_model is not a instance of dict, "
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"please call teacher_model.state_dict() to get.")
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assert isinstance(student_model, dict), (
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"student_model is not a instance of dict, "
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"please call student_model.state_dict() to get.")
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if not os.path.exists(save_dir):
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os.makedirs(save_dir)
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save_path = os.path.join(save_dir, save_name)
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# save model
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paddle.save(teacher_model, save_path + str(last_epoch) + "epoch_t.pdparams")
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paddle.save(student_model, save_path + str(last_epoch) + "epoch_s.pdparams")
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# save optimizer
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state_dict = optimizer.state_dict()
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state_dict['last_epoch'] = last_epoch
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state_dict['last_iter'] = last_iter
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paddle.save(state_dict, save_path + str(last_epoch) + "epoch.pdopt")
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logger.info("Save checkpoint: {}".format(save_dir))
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