PaddleX/paddlex/repo_apis/PaddleSeg_api/seg/model.py

# copyright (c) 2024 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
#
#    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 ...base import BaseModel
from ...base.utils.arg import CLIArgument
from ...base.utils.subprocess import CompletedProcess
from ....utils.misc import abspath


class SegModel(BaseModel):
    """ Semantic Segmentation Model """

    def train(self,
              batch_size: int=None,
              learning_rate: float=None,
              epochs_iters: int=None,
              ips: str=None,
              device: str='gpu',
              resume_path: str=None,
              dy2st: bool=False,
              amp: str='OFF',
              num_workers: int=None,
              use_vdl: bool=True,
              save_dir: str=None,
              **kwargs) -> CompletedProcess:
        """train self

        Args:
            batch_size (int, optional): the train batch size value. Defaults to None.
            learning_rate (float, optional): the train learning rate value. Defaults to None.
            epochs_iters (int, optional): the train epochs value. Defaults to None.
            ips (str, optional): the ip addresses of nodes when using distribution. Defaults to None.
            device (str, optional): the running device. Defaults to 'gpu'.
            resume_path (str, optional): the checkpoint file path to resume training. Train from scratch if it is set
                to None. Defaults to None.
            dy2st (bool, optional): Enable dynamic to static. Defaults to False.
            amp (str, optional): the amp settings. Defaults to 'OFF'.
            num_workers (int, optional): the workers number. Defaults to None.
            use_vdl (bool, optional): enable VisualDL. Defaults to True.
            save_dir (str, optional): the directory path to save train output. Defaults to None.

        Returns:
           CompletedProcess: the result of training subprocess execution.
        """
        config = self.config.copy()
        cli_args = []

        if batch_size is not None:
            cli_args.append(CLIArgument('--batch_size', batch_size))

        if learning_rate is not None:
            cli_args.append(CLIArgument('--learning_rate', learning_rate))

        if epochs_iters is not None:
            cli_args.append(CLIArgument('--iters', epochs_iters))

        # No need to handle `ips`

        if device is not None:
            device_type, _ = self.runner.parse_device(device)
            cli_args.append(CLIArgument('--device', device_type))

        # For compatibility
        resume_dir = kwargs.pop('resume_dir', None)
        if resume_path is None and resume_dir is not None:
            resume_path = os.path.join(resume_dir, 'model.pdparams')

        if resume_path is not None:
            # NOTE: We must use an absolute path here,
            # so we can run the scripts either inside or outside the repo dir.
            resume_path = abspath(resume_path)
            if os.path.basename(resume_path) != 'model.pdparams':
                raise ValueError(f"{resume_path} has an incorrect file name.")
            if not os.path.exists(resume_path):
                raise FileNotFoundError(f"{resume_path} does not exist.")
            resume_dir = os.path.dirname(resume_path)
            opts_path = os.path.join(resume_dir, 'model.pdopt')
            if not os.path.exists(opts_path):
                raise FileNotFoundError(f"{opts_path} must exist.")
            cli_args.append(CLIArgument('--resume_model', resume_dir))

        if dy2st:
            config.update_dy2st(dy2st)

        if use_vdl:
            cli_args.append(CLIArgument('--use_vdl'))

        if save_dir is not None:
            save_dir = abspath(save_dir)
        else:
            # `save_dir` is None
            save_dir = abspath(os.path.join('output', 'train'))
        cli_args.append(CLIArgument('--save_dir', save_dir))

        save_interval = kwargs.pop('save_interval', None)
        if save_interval is not None:
            cli_args.append(CLIArgument('--save_interval', save_interval))

        do_eval = kwargs.pop('do_eval', True)
        repeats = kwargs.pop('repeats', None)
        seed = kwargs.pop('seed', None)

        profile = kwargs.pop('profile', None)
        if profile is not None:
            cli_args.append(CLIArgument('--profiler_options', profile))

        log_iters = kwargs.pop('log_iters', None)
        if log_iters is not None:
            cli_args.append(CLIArgument('--log_iters', log_iters))

        # Benchmarking mode settings
        benchmark = kwargs.pop('benchmark', None)
        if benchmark is not None:
            envs = benchmark.get('env', None)
            seed = benchmark.get('seed', None)
            repeats = benchmark.get('repeats', None)
            do_eval = benchmark.get('do_eval', False)
            num_workers = benchmark.get('num_workers', None)
            config.update_log_ranks(device)
            amp = benchmark.get('amp', None)
            config.update_print_mem_info(benchmark.get('print_mem_info', True))
            if repeats is not None:
                assert isinstance(repeats, int), 'repeats must be an integer.'
                cli_args.append(CLIArgument('--repeats', repeats))
            if num_workers is not None:
                assert isinstance(num_workers,
                                  int), 'num_workers must be an integer.'
                cli_args.append(CLIArgument('--num_workers', num_workers))
            if seed is not None:
                assert isinstance(seed, int), 'seed must be an integer.'
                cli_args.append(CLIArgument('--seed', seed))
            if amp in ['O1', 'O2']:
                cli_args.append(CLIArgument('--precision', 'fp16'))
                cli_args.append(CLIArgument('--amp_level', amp))
            if envs is not None:
                for env_name, env_value in envs.items():
                    os.environ[env_name] = str(env_value)
        else:
            if amp is not None:
                if amp != 'OFF':
                    cli_args.append(CLIArgument('--precision', 'fp16'))
                    cli_args.append(CLIArgument('--amp_level', amp))
            if num_workers is not None:
                cli_args.append(CLIArgument('--num_workers', num_workers))
            if repeats is not None:
                cli_args.append(CLIArgument('--repeats', repeats))
            if seed is not None:
                cli_args.append(CLIArgument('--seed', seed))

        self._assert_empty_kwargs(kwargs)

        with self._create_new_config_file() as config_path:
            config.dump(config_path)

            return self.runner.train(
                config_path, cli_args, device, ips, save_dir, do_eval=do_eval)

    def evaluate(self,
                 weight_path: str,
                 batch_size: int=None,
                 ips: str=None,
                 device: str='gpu',
                 amp: str='OFF',
                 num_workers: int=None,
                 **kwargs) -> CompletedProcess:
        """evaluate self using specified weight

        Args:
            weight_path (str): the path of model weight file to be evaluated.
            batch_size (int, optional): the batch size value in evaluating. Defaults to None.
            ips (str, optional): the ip addresses of nodes when using distribution. Defaults to None.
            device (str, optional): the running device. Defaults to 'gpu'.
            amp (str, optional): the AMP setting. Defaults to 'OFF'.
            num_workers (int, optional): the workers number in evaluating. Defaults to None.

        Returns:
            CompletedProcess: the result of evaluating subprocess execution.
        """
        config = self.config.copy()
        cli_args = []

        weight_path = abspath(weight_path)
        cli_args.append(CLIArgument('--model_path', weight_path))

        if batch_size is not None:
            if batch_size != 1:
                raise ValueError("Batch size other than 1 is not supported.")

        # No need to handle `ips`

        if device is not None:
            device_type, _ = self.runner.parse_device(device)
            cli_args.append(CLIArgument('--device', device_type))

        if amp is not None:
            if amp != 'OFF':
                cli_args.append(CLIArgument('--precision', 'fp16'))
                cli_args.append(CLIArgument('--amp_level', amp))

        if num_workers is not None:
            cli_args.append(CLIArgument('--num_workers', num_workers))

        self._assert_empty_kwargs(kwargs)

        with self._create_new_config_file() as config_path:
            config.dump(config_path)
            cp = self.runner.evaluate(config_path, cli_args, device, ips)
            return cp

    def predict(self,
                weight_path: str,
                input_path: str,
                device: str='gpu',
                save_dir: str=None,
                **kwargs) -> CompletedProcess:
        """predict using specified weight

        Args:
            weight_path (str): the path of model weight file used to predict.
            input_path (str): the path of image file to be predicted.
            device (str, optional): the running device. Defaults to 'gpu'.
            save_dir (str, optional): the directory path to save predict output. Defaults to None.

        Returns:
            CompletedProcess: the result of predicting subprocess execution.
        """
        config = self.config.copy()
        cli_args = []

        weight_path = abspath(weight_path)
        cli_args.append(CLIArgument('--model_path', weight_path))

        input_path = abspath(input_path)
        cli_args.append(CLIArgument('--image_path', input_path))

        if device is not None:
            device_type, _ = self.runner.parse_device(device)
            cli_args.append(CLIArgument('--device', device_type))

        if save_dir is not None:
            save_dir = abspath(save_dir)
        else:
            # `save_dir` is None
            save_dir = abspath(os.path.join('output', 'predict'))
        cli_args.append(CLIArgument('--save_dir', save_dir))

        self._assert_empty_kwargs(kwargs)

        with self._create_new_config_file() as config_path:
            config.dump(config_path)
            return self.runner.predict(config_path, cli_args, device)

    def analyse(self,
                weight_path,
                ips=None,
                device='gpu',
                save_dir=None,
                **kwargs):
        """ analyse """
        config = self.config.copy()
        cli_args = []

        weight_path = abspath(weight_path)
        cli_args.append(CLIArgument('--model_path', weight_path))

        if device is not None:
            device_type, _ = self.runner.parse_device(device)
            cli_args.append(CLIArgument('--device', device_type))

        if save_dir is not None:
            save_dir = abspath(save_dir)
        else:
            # `save_dir` is None
            save_dir = abspath(os.path.join('output', 'analysis'))
        cli_args.append(CLIArgument('--save_dir', save_dir))

        self._assert_empty_kwargs(kwargs)

        with self._create_new_config_file() as config_path:
            config.dump(config_path)
            cp = self.runner.analyse(config_path, cli_args, device, ips)
            return cp

    def export(self, weight_path: str, save_dir: str,
               **kwargs) -> CompletedProcess:
        """export the dynamic model to static model

        Args:
            weight_path (str): the model weight file path that used to export.
            save_dir (str): the directory path to save export output.

        Returns:
            CompletedProcess: the result of exporting subprocess execution.
        """
        config = self.config.copy()
        cli_args = []

        weight_path = abspath(weight_path)
        cli_args.append(CLIArgument('--model_path', weight_path))

        if save_dir is not None:
            save_dir = abspath(save_dir)
        else:
            # `save_dir` is None
            save_dir = abspath(os.path.join('output', 'export'))
        cli_args.append(CLIArgument('--save_dir', save_dir))

        input_shape = kwargs.pop('input_shape', None)
        if input_shape is not None:
            cli_args.append(CLIArgument('--input_shape', *input_shape))

        output_op = kwargs.pop('output_op', None)
        if output_op is not None:
            assert output_op in ['softmax', 'argmax'
                                 ], "`output_op` must be 'softmax' or 'argmax'."
            cli_args.append(CLIArgument('--output_op', output_op))

        self._assert_empty_kwargs(kwargs)

        with self._create_new_config_file() as config_path:
            config.dump(config_path)
            return self.runner.export(config_path, cli_args, None)

    def infer(self,
              model_dir: str,
              input_path: str,
              device: str='gpu',
              save_dir: str=None,
              **kwargs) -> CompletedProcess:
        """predict image using infernece model

        Args:
            model_dir (str): the directory path of inference model files that would use to predict.
            input_path (str): the path of image that would be predict.
            device (str, optional): the running device. Defaults to 'gpu'.
            save_dir (str, optional): the directory path to save output. Defaults to None.

        Returns:
            CompletedProcess: the result of infering subprocess execution.
        """
        config = self.config.copy()
        cli_args = []

        model_dir = abspath(model_dir)
        input_path = abspath(input_path)
        cli_args.append(CLIArgument('--image_path', input_path))

        if device is not None:
            device_type, _ = self.runner.parse_device(device)
            cli_args.append(CLIArgument('--device', device_type))

        if save_dir is not None:
            save_dir = abspath(save_dir)
        else:
            # `save_dir` is None
            save_dir = abspath(os.path.join('output', 'infer'))
        cli_args.append(CLIArgument('--save_dir', save_dir))

        self._assert_empty_kwargs(kwargs)

        with self._create_new_config_file() as config_path:
            config.dump(config_path)
            deploy_config_path = os.path.join(model_dir, 'inference.yml')
            return self.runner.infer(deploy_config_path, cli_args, device)

    def compression(self,
                    weight_path: str,
                    batch_size: int=None,
                    learning_rate: float=None,
                    epochs_iters: int=None,
                    device: str='gpu',
                    use_vdl: bool=True,
                    save_dir: str=None,
                    **kwargs) -> CompletedProcess:
        """compression model

        Args:
            weight_path (str): the path to weight file of model.
            batch_size (int, optional): the batch size value of compression training. Defaults to None.
            learning_rate (float, optional): the learning rate value of compression training. Defaults to None.
            epochs_iters (int, optional): the epochs or iters of compression training. Defaults to None.
            device (str, optional): the device to run compression training. Defaults to 'gpu'.
            use_vdl (bool, optional): whether or not to use VisualDL. Defaults to True.
            save_dir (str, optional): the directory to save output. Defaults to None.

        Returns:
            CompletedProcess: the result of compression subprocess execution.
        """
        # Update YAML config file
        # NOTE: In PaddleSeg, QAT does not use a different config file than regular training
        # Reusing `self.config` preserves the config items modified by the user when
        # `SegModel` is initialized with a `SegConfig` object.
        config = self.config.copy()
        train_cli_args = []
        export_cli_args = []

        weight_path = abspath(weight_path)
        train_cli_args.append(CLIArgument('--model_path', weight_path))

        if batch_size is not None:
            train_cli_args.append(CLIArgument('--batch_size', batch_size))

        if learning_rate is not None:
            train_cli_args.append(CLIArgument('--learning_rate', learning_rate))

        if epochs_iters is not None:
            train_cli_args.append(CLIArgument('--iters', epochs_iters))

        if device is not None:
            device_type, _ = self.runner.parse_device(device)
            train_cli_args.append(CLIArgument('--device', device_type))

        if use_vdl:
            train_cli_args.append(CLIArgument('--use_vdl'))

        if save_dir is not None:
            save_dir = abspath(save_dir)
        else:
            # `save_dir` is None
            save_dir = abspath(os.path.join('output', 'compress'))
        train_cli_args.append(CLIArgument('--save_dir', save_dir))
        # The exported model saved in a subdirectory named `export`
        export_cli_args.append(
            CLIArgument('--save_dir', os.path.join(save_dir, 'export')))

        input_shape = kwargs.pop('input_shape', None)
        if input_shape is not None:
            export_cli_args.append(CLIArgument('--input_shape', *input_shape))

        self._assert_empty_kwargs(kwargs)

        with self._create_new_config_file() as config_path:
            config.dump(config_path)
            return self.runner.compression(config_path, train_cli_args,
                                           export_cli_args, device, save_dir)