Update utility.py

ppocr/utils/utility.py

-# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
+# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserve.
 #
-# 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
+#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
+#    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 logging
-import os
+#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.
 
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
 
-def initial_logger():
-    FORMAT = '%(asctime)s-%(levelname)s: %(message)s'
-    logging.basicConfig(level=logging.INFO, format=FORMAT)
-    logger = logging.getLogger(__name__)
-    return logger
-
-
-import importlib
-
-
-def create_module(module_str):
-    tmpss = module_str.split(",")
-    assert len(tmpss) == 2, "Error formate\
-        of the module path: {}".format(module_str)
-    module_name, function_name = tmpss[0], tmpss[1]
-    somemodule = importlib.import_module(module_name, __package__)
-    function = getattr(somemodule, function_name)
-    return function
-
-
-def get_check_global_params(mode):
-    check_params = ['use_gpu', 'max_text_length', 'image_shape',\
-        'image_shape', 'character_type', 'loss_type']
-    if mode == "train_eval":
-        check_params = check_params + [\
-            'train_batch_size_per_card', 'test_batch_size_per_card']
-    elif mode == "test":
-        check_params = check_params + ['test_batch_size_per_card']
-    return check_params
-
-
-def get_check_reader_params(mode):
-    check_params = []
-    if mode == "train_eval":
-        check_params = ['TrainReader', 'EvalReader']
-    elif mode == "test":
-        check_params = ['TestReader']
-    return check_params
-
-
-def get_image_file_list(img_file):
-    imgs_lists = []
-    if img_file is None or not os.path.exists(img_file):
-        raise Exception("not found any img file in {}".format(img_file))
-
-    img_end = ['jpg', 'png', 'jpeg', 'JPEG', 'JPG', 'bmp']
-    if os.path.isfile(img_file) and img_file.split('.')[-1] in img_end:
-        imgs_lists.append(img_file)
-    elif os.path.isdir(img_file):
-        for single_file in os.listdir(img_file):
-            if single_file.split('.')[-1] in img_end:
-                imgs_lists.append(os.path.join(img_file, single_file))
-    if len(imgs_lists) == 0:
-        raise Exception("not found any img file in {}".format(img_file))
-    return imgs_lists
-
-
-from paddle import fluid
-
-
-def create_multi_devices_program(program, loss_var_name):
-    build_strategy = fluid.BuildStrategy()
-    build_strategy.memory_optimize = False
-    build_strategy.enable_inplace = True
-    exec_strategy = fluid.ExecutionStrategy()
-    exec_strategy.num_iteration_per_drop_scope = 1
-    compile_program = fluid.CompiledProgram(program).with_data_parallel(
-        loss_name=loss_var_name,
-        build_strategy=build_strategy,
-        exec_strategy=exec_strategy)
-    return compile_program
+import errno
+import os
+import shutil
+import tempfile
+
+import paddle.fluid as fluid
+
+from .utility import initial_logger
+import re
+logger = initial_logger()
+
+
+def _mkdir_if_not_exist(path):
+    """
+    mkdir if not exists, ignore the exception when multiprocess mkdir together
+    """
+    if not os.path.exists(path):
+        try:
+            os.makedirs(path)
+        except OSError as e:
+            if e.errno == errno.EEXIST and os.path.isdir(path):
+                logger.warning(
+                    'be happy if some process has already created {}'.format(
+                        path))
+            else:
+                raise OSError('Failed to mkdir {}'.format(path))
+
+
+def _load_state(path):
+    if os.path.exists(path + '.pdopt'):
+        # XXX another hack to ignore the optimizer state
+        tmp = tempfile.mkdtemp()
+        dst = os.path.join(tmp, os.path.basename(os.path.normpath(path)))
+        shutil.copy(path + '.pdparams', dst + '.pdparams')
+        state = fluid.io.load_program_state(dst)
+        shutil.rmtree(tmp)
+    else:
+        state = fluid.io.load_program_state(path)
+    return state
+
+
+def load_params(exe, prog, path, ignore_params=[]):
+    """
+    Load model from the given path.
+    Args:
+        exe (fluid.Executor): The fluid.Executor object.
+        prog (fluid.Program): load weight to which Program object.
+        path (string): URL string or loca model path.
+        ignore_params (list): ignore variable to load when finetuning.
+            It can be specified by finetune_exclude_pretrained_params
+            and the usage can refer to docs/advanced_tutorials/TRANSFER_LEARNING.md
+    """
+    if not (os.path.isdir(path) or os.path.exists(path + '.pdparams')):
+        raise ValueError("Model pretrain path {} does not "
+                         "exists.".format(path))
+
+    logger.info('Loading parameters from {}...'.format(path))
+
+    ignore_set = set()
+    state = _load_state(path)
+
+    # ignore the parameter which mismatch the shape
+    # between the model and pretrain weight.
+    all_var_shape = {}
+    for block in prog.blocks:
+        for param in block.all_parameters():
+            all_var_shape[param.name] = param.shape
+    ignore_set.update([
+        name for name, shape in all_var_shape.items()
+        if name in state and shape != state[name].shape
+    ])
+
+    if ignore_params:
+        all_var_names = [var.name for var in prog.list_vars()]
+        ignore_list = filter(
+            lambda var: any([re.match(name, var) for name in ignore_params]),
+            all_var_names)
+        ignore_set.update(list(ignore_list))
+
+    if len(ignore_set) > 0:
+        for k in ignore_set:
+            if k in state:
+                logger.warning('variable {} not used'.format(k))
+                del state[k]
+    fluid.io.set_program_state(prog, state)
+
+
+def init_model(config, program, exe):
+    """
+    load model from checkpoint or pretrained_model
+    """
+    checkpoints = config['Global'].get('checkpoints')
+    if checkpoints:
+        path = checkpoints
+        fluid.load(program, path, exe)
+        logger.info("Finish initing model from {}".format(path))
+
+    pretrain_weights = config['Global'].get('pretrain_weights')
+    if pretrain_weights:
+        path = pretrain_weights
+        load_params(exe, program, path)
+        logger.info("Finish initing model from {}".format(path))
+
+
+def save_model(program, model_path):
+    """
+    save model to the target path
+    """
+    fluid.save(program, model_path)
+    logger.info("Already save model in {}".format(model_path))