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How to use dump_variables method in lisa

Best Python code snippet using lisa_python

vgg16_top_tf_keras.py

Source:vgg16_top_tf_keras.py

...235      summaries_op = tf.summary.merge_all()236      #summary_writer.add_graph(sess.graph)237      sess.run(init)238      239      #dump_variables()240      241      # note that it is necessary to start with a fully-trained242      # classifier, including the top classifier,243      # in order to successfully do fine-tuning244      245      # prepare data augmentation configuration246      train_datagen = ImageDataGenerator(247          rescale=1. / 255,248          shear_range=0.2,249          zoom_range=0.2,250          horizontal_flip=True)251      252      test_datagen = ImageDataGenerator(rescale=1. / 255)253      ...

trainer.py

Source:trainer.py

1import chainer2from chainer import cuda, serializers3from chainer import computational_graph as cg4import numpy as np5import tqdm6import sys7import copy8from deepnet.core import config9from deepnet import utils10import os.path11import subprocess12import gc13from time import sleep14import corenet15class Trainer:16    def __init__(self,17                 network, train_iter, valid_iter,18                 visualizers, train_config, optimizer,19                 logger, archive_dir, archive_nodes,20                 postprocessor, redirect, architecture_loss,21                 ):22        config.set_global_config('main_network', network)23        self.network = network24        self.train_config = train_config25        self.n_max_train_iter = train_config['n_max_train_iter']26        self.n_max_valid_iter = train_config['n_max_valid_iter'] if train_config['n_max_valid_iter'] is not None else len(27            valid_iter.dataset)28        self.n_valid_step = train_config['n_valid_step']29        self.progress_vars = train_config['progress_vars']30        self.train_iter = train_iter31        self.valid_iter = valid_iter32        self.archive_dir = archive_dir33        self.archive_nodes = archive_nodes34        self.visualizers = visualizers35        self.postprocessor = postprocessor36        self.optimizer = optimizer37        for key, optimizer in self.optimizer.items():38            corenet.ChainerNode.add_updater(key, optimizer)39        self.logger = logger40        self.dump_variables = []41        self.redirect = redirect42        self.architecture_loss = architecture_loss43        for l in self.logger:44            for var_name in l.dump_variables:45                pos = var_name.find('.')46                if pos == -1:47                    self.dump_variables.append(var_name)48                else:49                    self.dump_variables.append(var_name[pos+1:])50        self.dump_variables = list(set(self.dump_variables))51    def train(self):52        with tqdm.tqdm(total=self.n_max_train_iter) as pbar:53            for i, batch in enumerate(self.train_iter):54                self.train_iteration = i55                variables = {}56                variables['__iteration__'] = i57                variables['__train_iteration__'] = self.train_iteration58                input_vars = self.batch_to_vars(batch)59                # Inference current batch.60                for stage_input in input_vars:61                    self.inference(stage_input, is_train=True)62                sleep(1e-3)63                # Back propagation and update network64                self.network.update()65                # Update variables.66                variables.update(self.network.variables)67                self.network.variables.clear()68                # Save network architecture69                if self.train_iteration == 0:70                    self.write_network_architecture(71                        self.architecture_loss[0],72                        variables[self.architecture_loss[1]]73                    )74                # Update variables and unwrapping chainer variable75                for var_name, value in variables.items():76                    variables[var_name] = utils.unwrapped(value)77                variables.update({'train.' + name: utils.unwrapped(value)78                                  for name, value in variables.items()})79                # validation if current iteraiton is multiplier as n_valid_step80                valid_keys = []81                if i % self.n_valid_step == 0:82                    valid_variables = self.validate(variables=variables)83                    variables.update(84                        {'valid.' + name: value for name, value in valid_variables.items()})85                    self.network.variables.clear()86                    del valid_variables87                # Write log88                for logger in self.logger:89                    logger(variables, is_valid=False)90                # Update progress bar91                self.print_description(pbar, variables)92                pbar.update()93                if self.n_max_train_iter <= i:94                    break95                # Refresh variables96                variables.clear()97                gc.collect()98    def validate(self, variables):99        valid_variables = dict()100        with tqdm.tqdm(total=self.n_max_valid_iter) as pbar, \101                chainer.no_backprop_mode():102            self.valid_iter.reset()103            for i, batch in enumerate(self.valid_iter):104                sleep(1e-3)105                self.valid_iteration = i106                variables['__iteration__'] = i107                variables['__valid_iteration__'] = self.valid_iteration108                input_vars = self.batch_to_vars(batch)109                # Inference110                for j, stage_input in enumerate(input_vars):111                    self.inference(stage_input, is_train=False)112                    variables['__stage__'] = j113                    variables.update(self.network.variables)114                for visualizer in self.visualizers:115                    visualizer(variables)116                # Update variables117                for var_name in self.dump_variables:118                    var = variables[var_name]119                    if var_name not in valid_variables:  # Initialize variable120                        if isinstance(var, chainer.Variable):121                            valid_variables[var_name] = chainer.functions.copy(122                                var, -1)123                        else:124                            valid_variables[var_name] = var125                    else:126                        if isinstance(var, chainer.Variable):127                            valid_variables[var_name] += chainer.functions.copy(128                                var, -1)129                # Post processing130                self.postprocessor(variables, 'valid', True)131                pbar.update(self.valid_iter.batch_size)132                if self.n_max_valid_iter <= (i + 1) * self.valid_iter.batch_size:133                    break134            pbar.close()135        for node_name in self.archive_nodes:136            try:137                serializers.save_npz(138                    os.path.join(self.archive_dir, node_name +139                                 '_{:08d}.npz'.format(variables['__train_iteration__'])),140                    self.network.get_node(node_name).model141                )142            except KeyError:143                raise KeyError('Failed to save npz file: ' + node_name)144        # Post processing145        self.postprocessor(variables, 'valid', False)146        # Compute mean variables147        for var_name in self.dump_variables:148            var = valid_variables[var_name]149            denom = float(self.n_max_valid_iter) / self.valid_iter.batch_size150            if isinstance(var, chainer.Variable):151                if self.train_config['gpu'][0] >= 0:152                    valid_variables[var_name] = float(153                        chainer.cuda.to_cpu((var / denom).data)154                    )155                else:156                    valid_variables[var_name] = float((var / denom).data)157        # Save visualized results158        for visualizer in self.visualizers:159            visualizer(variables)160            visualizer.save()161            visualizer.clear()162        return valid_variables163    def print_description(self, pbar, variables):164        disp_vars = {}165        display_var_formats = []166        for var_format in self.progress_vars:167            #168            var_name = ''169            pos = var_format.find(':')170            if pos == -1:171                var_name = var_format172            else:173                var_name = var_format[:pos]174            # cast variable175            var = variables[var_name]176            display_var_formats.append(var_name + '=' + '{' + var_format + '}')177            if isinstance(var, chainer.Variable):178                value = None179                if self.train_config['gpu'][0] >= 0:180                    value = chainer.cuda.to_cpu(var.data)181                else:182                    value = var.data183                if isinstance(value, np.ndarray):184                    if value.ndim == 0 or value.size == 1:185                        disp_vars[var_name] = float(value)186                    else:187                        disp_vars[var_name] = value.to_list()188            elif isinstance(var, np.ndarray):189                if var.ndim == 0 or var.size == 1:190                    disp_vars[var_name] = float(var)191                else:192                    disp_vars[var_name] = var.to_list()193            else:194                disp_vars[var_name] = var195        display_format = 'train[' + ','.join(display_var_formats) + ']'196        pbar.set_description(display_format.format(197            **disp_vars, __iteration__=variables['__iteration__']))198    def batch_to_vars(self, batch):199        # batch to vars200        input_vars = [dict() for elem in batch[0]]201        for elem in batch:              # loop about batch202            for i, stage_input in enumerate(elem):    # loop about stage input203                for name, input_ in stage_input.items():204                    input_vars[i].setdefault(name, []).append(input_)205        return input_vars206    def inference(self, stage_input, is_train=False):207        for key, value in list(stage_input.items()):208            if key not in self.redirect:209                continue210            stage_input[self.redirect[key]] = value211        self.network(mode='train' if is_train else 'valid', **stage_input)212    def write_network_architecture(self, graph_filename, loss):213        with open(graph_filename, 'w+') as o:214            o.write(cg.build_computational_graph((loss, )).dump())215        try:216            subprocess.call('dot -T png {} -o {}'.format(graph_filename,217                                                         graph_filename.replace('.dot', '.png')),218                            shell=True)219        except:220            warnings.warn('please install graphviz and set your environment.')221        try:222            subprocess.call('dot -T svg {} -o {}'.format(graph_filename,223                                                         graph_filename.replace('.dot', '.svg')),224                            shell=True)225        except:...

__init__.py

Source:__init__.py

1from floxcore.command import Stage2from floxcore.context import Flox3from floxcore.plugin import Plugin4from flox_sentry.configure import SentryConfiguration5from flox_sentry.project import create_team, create_project, assing_teams, dump_variables6class SentryPlugin(Plugin):7    def configuration(self):8        return SentryConfiguration()9    def handle_variables(self, flox: Flox):10        return (11            Stage(dump_variables, 1900),12        )13    def handle_project(self, flox: Flox):14        return [15            Stage(create_team, require=["sentry.create_team"]),16            Stage(create_project, 1900),17            Stage(dump_variables, 1900),18            Stage(assing_teams),19        ]20    def configured(self, flox) -> bool:21        return all([flox.settings.sentry.default_team, flox.settings.sentry.organization])22def plugin():...

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