How to use remove_module method in lisa

Best Python code snippet using lisa_python

KernelSetterService_pb2_grpc.py

Source:KernelSetterService_pb2_grpc.py

1# Generated by the gRPC Python protocol compiler plugin. DO NOT EDIT!2"""Client and server classes corresponding to protobuf-defined services."""3import grpc4import Setter.KernelSetterService_pb2 as KernelSetterService__pb25class SETTERStub(object):6    """Missing associated documentation comment in .proto file."""7    def __init__(self, channel):8        """Constructor.9        Args:10            channel: A grpc.Channel.11        """12        self.REMOVE_MODULE = channel.unary_unary(13                '/SETTER/REMOVE_MODULE',14                request_serializer=KernelSetterService__pb2.REQUEST.SerializeToString,15                response_deserializer=KernelSetterService__pb2.REPLY.FromString,16                )17        self.MODPROBE = channel.unary_unary(18                '/SETTER/MODPROBE',19                request_serializer=KernelSetterService__pb2.REQUEST.SerializeToString,20                response_deserializer=KernelSetterService__pb2.REPLY.FromString,21                )22        self.DEPLOY_MODULE = channel.unary_unary(23                '/SETTER/DEPLOY_MODULE',24                request_serializer=KernelSetterService__pb2.REQUEST.SerializeToString,25                response_deserializer=KernelSetterService__pb2.REPLY.FromString,26                )27        self.INSTALL_KERNEL_OBJECT = channel.unary_unary(28                '/SETTER/INSTALL_KERNEL_OBJECT',29                request_serializer=KernelSetterService__pb2.KERNEL_OBJECT.SerializeToString,30                response_deserializer=KernelSetterService__pb2.REPLY.FromString,31                )32class SETTERServicer(object):33    """Missing associated documentation comment in .proto file."""34    def REMOVE_MODULE(self, request, context):35        """Missing associated documentation comment in .proto file."""36        context.set_code(grpc.StatusCode.UNIMPLEMENTED)37        context.set_details('Method not implemented!')38        raise NotImplementedError('Method not implemented!')39    def MODPROBE(self, request, context):40        """Missing associated documentation comment in .proto file."""41        context.set_code(grpc.StatusCode.UNIMPLEMENTED)42        context.set_details('Method not implemented!')43        raise NotImplementedError('Method not implemented!')44    def DEPLOY_MODULE(self, request, context):45        """Missing associated documentation comment in .proto file."""46        context.set_code(grpc.StatusCode.UNIMPLEMENTED)47        context.set_details('Method not implemented!')48        raise NotImplementedError('Method not implemented!')49    def INSTALL_KERNEL_OBJECT(self, request, context):50        """Missing associated documentation comment in .proto file."""51        context.set_code(grpc.StatusCode.UNIMPLEMENTED)52        context.set_details('Method not implemented!')53        raise NotImplementedError('Method not implemented!')54def add_SETTERServicer_to_server(servicer, server):55    rpc_method_handlers = {56            'REMOVE_MODULE': grpc.unary_unary_rpc_method_handler(57                    servicer.REMOVE_MODULE,58                    request_deserializer=KernelSetterService__pb2.REQUEST.FromString,59                    response_serializer=KernelSetterService__pb2.REPLY.SerializeToString,60            ),61            'MODPROBE': grpc.unary_unary_rpc_method_handler(62                    servicer.MODPROBE,63                    request_deserializer=KernelSetterService__pb2.REQUEST.FromString,64                    response_serializer=KernelSetterService__pb2.REPLY.SerializeToString,65            ),66            'DEPLOY_MODULE': grpc.unary_unary_rpc_method_handler(67                    servicer.DEPLOY_MODULE,68                    request_deserializer=KernelSetterService__pb2.REQUEST.FromString,69                    response_serializer=KernelSetterService__pb2.REPLY.SerializeToString,70            ),71            'INSTALL_KERNEL_OBJECT': grpc.unary_unary_rpc_method_handler(72                    servicer.INSTALL_KERNEL_OBJECT,73                    request_deserializer=KernelSetterService__pb2.KERNEL_OBJECT.FromString,74                    response_serializer=KernelSetterService__pb2.REPLY.SerializeToString,75            ),76    }77    generic_handler = grpc.method_handlers_generic_handler(78            'SETTER', rpc_method_handlers)79    server.add_generic_rpc_handlers((generic_handler,))80 # This class is part of an EXPERIMENTAL API.81class SETTER(object):82    """Missing associated documentation comment in .proto file."""83    @staticmethod84    def REMOVE_MODULE(request,85            target,86            options=(),87            channel_credentials=None,88            call_credentials=None,89            insecure=False,90            compression=None,91            wait_for_ready=None,92            timeout=None,93            metadata=None):94        return grpc.experimental.unary_unary(request, target, '/SETTER/REMOVE_MODULE',95            KernelSetterService__pb2.REQUEST.SerializeToString,96            KernelSetterService__pb2.REPLY.FromString,97            options, channel_credentials,98            insecure, call_credentials, compression, wait_for_ready, timeout, metadata)99    @staticmethod100    def MODPROBE(request,101            target,102            options=(),103            channel_credentials=None,104            call_credentials=None,105            insecure=False,106            compression=None,107            wait_for_ready=None,108            timeout=None,109            metadata=None):110        return grpc.experimental.unary_unary(request, target, '/SETTER/MODPROBE',111            KernelSetterService__pb2.REQUEST.SerializeToString,112            KernelSetterService__pb2.REPLY.FromString,113            options, channel_credentials,114            insecure, call_credentials, compression, wait_for_ready, timeout, metadata)115    @staticmethod116    def DEPLOY_MODULE(request,117            target,118            options=(),119            channel_credentials=None,120            call_credentials=None,121            insecure=False,122            compression=None,123            wait_for_ready=None,124            timeout=None,125            metadata=None):126        return grpc.experimental.unary_unary(request, target, '/SETTER/DEPLOY_MODULE',127            KernelSetterService__pb2.REQUEST.SerializeToString,128            KernelSetterService__pb2.REPLY.FromString,129            options, channel_credentials,130            insecure, call_credentials, compression, wait_for_ready, timeout, metadata)131    @staticmethod132    def INSTALL_KERNEL_OBJECT(request,133            target,134            options=(),135            channel_credentials=None,136            call_credentials=None,137            insecure=False,138            compression=None,139            wait_for_ready=None,140            timeout=None,141            metadata=None):142        return grpc.experimental.unary_unary(request, target, '/SETTER/INSTALL_KERNEL_OBJECT',143            KernelSetterService__pb2.KERNEL_OBJECT.SerializeToString,144            KernelSetterService__pb2.REPLY.FromString,145            options, channel_credentials,...

checkpoint.py

Source:checkpoint.py

1# Copyright (c) 2018- Salas Lin (leVirve)2#3# This software is released under the MIT License.4# https://opensource.org/licenses/MIT5import os6from pathlib import Path7from collections import OrderedDict8import torch9from .base import Extension, unique_experiment_name10def export_checkpoint_weight(checkpoint_path, remove_module=True):11    def clean_module(state_dict):12        new_state_dict = OrderedDict()13        for k, v in state_dict.items():14            new_state_dict[k.replace('module.', '')] = v15        return new_state_dict16    ckpt = torch.load(checkpoint_path)17    weight = ckpt['weight']18    return clean_module(weight) if remove_module else weight19class Checkpoint(Extension):20    r""" Checkpoint manager for model saving and restoring.21    Args:22        rootdir (str): the root folder for checkpoint manager (default: ``exp/checkpoints``).23        name (str): subfolder name for current experiment (default: ``default``).24        save_interval (int): interval of epochs to save the checkpoint (default: 10)25    """26    def __init__(self, rootdir='exp/checkpoints/', name='default', save_interval=10):27        self.rootdir = rootdir28        self.name = name29        self.save_interval = save_interval30    @property31    def savedir(self):32        if not hasattr(self, '_savedir'):33            self._savedir = unique_experiment_name(self.rootdir, self.name)34            os.makedirs(self._savedir, exist_ok=True)35        return Path(self._savedir)36    def get_checkpoint_dir(self, unique=False):37        if unique:38            return self.savedir39        return Path(self.savedir) / self.name40    def load_trained_model(self, weight_path, remove_module=False):41        """ another loader method for `model`42        It can recover changed model module from dumped `latest.pt` and load43        pre-trained weights.44        Args:45            weight_path (str): full path or short weight name to the dumped weight46            remove_module (bool)47        """48        folder = self.get_checkpoint_dir()49        if str(folder) not in weight_path:50            folder = Path(weight_path).parent51        ckpt = torch.load(folder / 'latest.pt')52        full_model = ckpt['model']53        if 'latest.pt' not in weight_path:54            state_dict = export_checkpoint_weight(weight_path, remove_module)55            full_model.load_state_dict(state_dict)56        return full_model57    def load(self, path=None, model=None, remove_module=False, resume=False):58        """ load method for `model` and `optimizer`59        If `resume` is True, full `model` and `optimizer` modules will be returned;60        or the loaded model will be returned.61        Args:62            path (str): full path to the dumped weight or full module63            model (nn.Module)64            remove_module (bool)65            resume (bool)66        Return:67            - dict() of dumped data inside `latest.pt`68            - OrderedDict() of `state_dict`69            - nn.Module of input model with loaded state_dict70            - nn.Module of dumped full module with loaded state_dict71        """72        if resume:73            latest_ckpt = torch.load(path)74            return latest_ckpt75        try:76            state_dict = export_checkpoint_weight(path, remove_module)77            if model is None:78                return state_dict79            model.load_state_dict(state_dict)80            return model81        except KeyError:82            self.logger.warn('Use fallback solution: load `latest.pt` as module')83            return self.load_trained_model(path, remove_module)84    def save(self, model, optimizer=None, epoch=None):85        """ save method for `model` and `optimizer`86        Args:87            model (nn.Module)88            optimizer (nn.Module)89            epoch (int): epoch step of training90        """91        if (epoch + 1) % self.save_interval:92            return93        folder = self.get_checkpoint_dir(unique=True)94        torch.save({'weight': model.state_dict()}, folder / f'net-{epoch}.pt')95        torch.save({96            'model': model,97            'optimizer': optimizer,98            'epoch': epoch + 199        }, folder / 'latest.pt')100    def get_weights(self, weight_path, model=None, remove_module=False, path_only=False):101        """ model weights searcher102        Args:103            weight_path (str): the path to single weight file or the folder of weights104            model (nn.Module): if given, the model will be filled with state_dict105            remove_module (bool): remove the `module.` string from the keys of state_dict106            path_only (bool): if true, the return value will be only path to weights107        Returns:108            - payload, path: if model is given, payload will be loaded model else will be state_dict109            - path: the path to the weight110        """111        weight_path = Path(weight_path)112        if weight_path.is_file():113            path = str(weight_path)114            if path_only:115                yield path116            payload = self.load(path, model=model, remove_module=remove_module)117            return payload, path118        paths = list(weight_path.glob('*.pt'))119        if weight_path.is_dir():120            assert len(paths), 'Weights folder contains nothing.'121        for path in paths:122            path = str(path)123            if 'latest.pt' in path:124                continue125            if path_only:126                yield path127                continue128            payload = self.load(path, model=model, remove_module=remove_module)129            model = payload  # use corrected model_def130            yield payload, path131# deprecated132class GANCheckpoint(Checkpoint):133    def load(self, trainer, gnet_path=None, dnet_path=None, resume=False):134        epoch = self._load(dnet_path, trainer.dnet, trainer.d_optim)135        epoch = self._load(gnet_path, trainer.gnet, trainer.g_optim)136        if resume:137            trainer.start_epoch = epoch138    def save(self, trainer, epoch):139        if (epoch + 1) % self.save_interval:140            return141        self._save('dnet-{epoch}.pth', trainer.model_d, trainer.optim_d, epoch)...

command.py

Source:command.py

...14class Canvas:15    color = "white"16    def add_module(self):17        print("æ·»å äºåç½®æ¨¡å")18    def remove_module(self):19        print("å é¤äºåç½®æ¨¡å")20    def set_background(self, color):21        self.color = color22        print("è®¾ç½®äºèæ¯é¢è²ä¸ºï¼%s" % color)23    def get_background(self):24        return self.color25# å½ä»¤26class Command:27    # æ§è¡å½ä»¤28    def execute(self):29        pass30    # æ¤é31    def undo(self):32        pass33# å·ä½çå½ä»¤34class AddModuleCommand(Command):35    def __init__(self, canvas):36        self.canvas = canvas37    def execute(self):38        self.canvas.add_module()39    def undo(self):40        self.canvas.remove_module()41class RemoveModuleCommand(Command):42    def __init__(self, canvas):43        self.canvas = canvas44    def execute(self):45        self.canvas.remove_module()46    def undo(self):47        self.canvas.add_module()48class SetBackgroundCommand(Command):49    def __init__(self, canvas, color):50        self.canvas = canvas51        self.origin_color = canvas.get_background()52        self.color = color53    def execute(self):54        self.canvas.set_background(self.color)55    def undo(self):56        self.canvas.set_background(self.origin_color)57# è¯·æ±ç±»58class Invoke:59    def __init__(self):60        self.add_module_command = None61        self.remove_module_command = None62        self.set_background_command = None63        self.undo_command = None64    def set_command(self, add_module_command, remove_module_command, set_background_command):65        self.add_module_command = add_module_command66        self.remove_module_command = remove_module_command67        self.set_background_command = set_background_command68    def add_module(self):69        self.add_module_command.execute()70        self.undo_command = self.add_module_command71    def remove_module(self):72        self.remove_module_command.execute()73        self.undo_command = self.remove_module_command74    def set_background(self):75        self.set_background_command.execute()76        self.undo_command = self.set_background_command77    def undo(self):78        self.undo_command.undo()79# å®¢æ·ç«¯80if __name__ == "__main__":81    canvas = Canvas()82    add_module_command = AddModuleCommand(canvas)83    remove_module_command = RemoveModuleCommand(canvas)84    set_background_command = SetBackgroundCommand(canvas, "blue")85    invoke = Invoke()86    invoke.set_command(add_module_command, remove_module_command, set_background_command)87    invoke.add_module()88    invoke.remove_module()89    invoke.undo()90    invoke.set_background()...

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