How to use do_checkpoint method in localstack

Best Python code snippet using localstack_python

train.py

Source:train.py

1if __name__ == "__main__":2    import argparse3    from src.IdentificationNetwork import IdentificationNetwork4    from src.TrackingNetwork import TrackingNetwork5    from src.Util import get_fixed_path6    import tensorflow.keras.backend as K7    import src.nets89    parser = argparse.ArgumentParser()1011    parser.add_argument("-i", "--datasets_folder", help="Location of the \"Datasets\" folder", type=str, required=True)12    parser.add_argument("-o", "--save_folder", help="Where to save the models", type=str, required=True)1314    parser.add_argument("-l", "--load_folder", help="If and from where to load the models' weights", type=str,15                        required=False, default=None)1617    parser.add_argument("-pte", "--pre_tracking_epochs", help="Epochs for the pre tracking network", type=int,18                        required=False, default=20)19    parser.add_argument("-te", "--tracking_epochs", help="Epochs for the tracking network", type=int,20                        required=False, default=50)21    parser.add_argument("-ie", "--identification_epochs", help="Epochs for the identification network", type=int,22                        required=False, default=25)2324    parser.add_argument("-spe", "--steps_per_epoch", help="Steps per epoch", type=int, required=False, default=128)25    parser.add_argument("-bs", "--batch_size", help="Batch size", type=int, required=False, default=2)2627    parser.add_argument("-dc", "--do_checkpoint", help="Should save checkpoints", type=bool, required=False, default=True)2829    args = parser.parse_args()3031    datasets_folder = get_fixed_path(args.datasets_folder, replace_backslash=True, add_backslash=True)32    save_folder = get_fixed_path(args.save_folder, replace_backslash=True, add_backslash=True)33    load_folder = get_fixed_path(args.load_folder, replace_backslash=True, add_backslash=True) if args.load_folder is not None else None3435    ####################################################################################################################36    # Pre-tracking network37    ####################################################################################################################38    if args.pre_tracking_epochs > 0:39        K.clear_session()4041        if load_folder is None:42            net_pre_tracking = IdentificationNetwork(src.nets.generate_pre_tracking_model(input_shape=(416, 416, 1), number_of_classes=7))4344            net_pre_tracking.train_model(epochs=args.pre_tracking_epochs,45                                         steps_per_epoch=args.steps_per_epoch,46                                         weights_save_path=save_folder + "pre_tracking.h5",47                                         weights_load_path=None,48                                         do_checkpoint=args.do_checkpoint,49                                         generator=IdentificationNetwork.generator(50                                             folder_images=datasets_folder + "PreTracking/",51                                             batch_size=args.batch_size,52                                             image_ext=(".jpg", ".png")53                                         ))5455    ####################################################################################################################56    # Tracking network57    ####################################################################################################################58    if args.tracking_epochs > 0:59        K.clear_session()6061        net_tracking = TrackingNetwork(src.nets.generate_tracking_model(input_shape=(416, 416, 1),62                                                                        output_shape=(13, 13, 11),63                                                                        number_of_classes=6))6465        load_path_tracking = load_folder + "tracking.h5" if load_folder is not None else save_folder + "pre_tracking.h5"6667        net_tracking.get_model().summary()6869        net_tracking.train_model(epochs=args.tracking_epochs,70                                 steps_per_epoch=args.steps_per_epoch,71                                 weights_save_path=save_folder + "tracking.h5",72                                 weights_load_path=load_path_tracking,73                                 do_checkpoint=args.do_checkpoint,74                                 generator=TrackingNetwork.generator(75                                     folder_images=datasets_folder + "Tracking/",76                                     folder_grids=datasets_folder + "Tracking/",77                                     batch_size=args.batch_size78                                 ))7980    ####################################################################################################################81    # Identification network82    ####################################################################################################################83    if args.identification_epochs > 0:84        K.clear_session()8586        net_identification = IdentificationNetwork(models=(87            src.nets.generate_identification_model(input_shape=(50, 50, 1), number_of_classes=9),88            src.nets.generate_identification_model(input_shape=(50, 50, 1), number_of_classes=4),89            src.nets.generate_identification_model(input_shape=(50, 50, 1), number_of_classes=2),90            src.nets.generate_identification_model(input_shape=(50, 50, 1), number_of_classes=2),91            src.nets.generate_identification_model(input_shape=(50, 50, 1), number_of_classes=4),92            src.nets.generate_identification_model(input_shape=(50, 50, 1), number_of_classes=2)93        ))9495        load_path_identification = load_folder + "identification.h5" if load_folder is not None else None9697        net_identification.train_models(number_of_models=6,98                                        epochs=args.identification_epochs,99                                        steps_per_epoch=args.steps_per_epoch,100                                        weights_save_path=save_folder + "identification.h5",101                                        weights_load_path=None,102                                        do_checkpoint=args.do_checkpoint,103                                        generators=IdentificationNetwork.generators(104                                            folder_images=datasets_folder + "Identification/",105                                            batch_size=args.batch_size,106                                            image_ext=(".jpg", ".png")
...

classifier.py

Source:classifier.py

1import torch2import torch.nn as nn3from torch.utils.checkpoint import checkpoint4from tortoise.models.arch_util import Upsample, Downsample, normalization, zero_module, AttentionBlock5class ResBlock(nn.Module):6    def __init__(7        self,8        channels,9        dropout,10        out_channels=None,11        use_conv=False,12        use_scale_shift_norm=False,13        dims=2,14        up=False,15        down=False,16        kernel_size=3,17        do_checkpoint=True,18    ):19        super().__init__()20        self.channels = channels21        self.dropout = dropout22        self.out_channels = out_channels or channels23        self.use_conv = use_conv24        self.use_scale_shift_norm = use_scale_shift_norm25        self.do_checkpoint = do_checkpoint26        padding = 1 if kernel_size == 3 else 227        self.in_layers = nn.Sequential(28            normalization(channels),29            nn.SiLU(),30            nn.Conv1d(channels, self.out_channels, kernel_size, padding=padding),31        )32        self.updown = up or down33        if up:34            self.h_upd = Upsample(channels, False, dims)35            self.x_upd = Upsample(channels, False, dims)36        elif down:37            self.h_upd = Downsample(channels, False, dims)38            self.x_upd = Downsample(channels, False, dims)39        else:40            self.h_upd = self.x_upd = nn.Identity()41        self.out_layers = nn.Sequential(42            normalization(self.out_channels),43            nn.SiLU(),44            nn.Dropout(p=dropout),45            zero_module(46                nn.Conv1d(self.out_channels, self.out_channels, kernel_size, padding=padding)47            ),48        )49        if self.out_channels == channels:50            self.skip_connection = nn.Identity()51        elif use_conv:52            self.skip_connection = nn.Conv1d(53                dims, channels, self.out_channels, kernel_size, padding=padding54            )55        else:56            self.skip_connection = nn.Conv1d(dims, channels, self.out_channels, 1)57    def forward(self, x):58        if self.do_checkpoint:59            return checkpoint(60                self._forward, x61            )62        else:63            return self._forward(x)64    def _forward(self, x):65        if self.updown:66            in_rest, in_conv = self.in_layers[:-1], self.in_layers[-1]67            h = in_rest(x)68            h = self.h_upd(h)69            x = self.x_upd(x)70            h = in_conv(h)71        else:72            h = self.in_layers(x)73        h = self.out_layers(h)74        return self.skip_connection(x) + h75class AudioMiniEncoder(nn.Module):76    def __init__(self,77                 spec_dim,78                 embedding_dim,79                 base_channels=128,80                 depth=2,81                 resnet_blocks=2,82                 attn_blocks=4,83                 num_attn_heads=4,84                 dropout=0,85                 downsample_factor=2,86                 kernel_size=3):87        super().__init__()88        self.init = nn.Sequential(89            nn.Conv1d(spec_dim, base_channels, 3, padding=1)90        )91        ch = base_channels92        res = []93        self.layers = depth94        for l in range(depth):95            for r in range(resnet_blocks):96                res.append(ResBlock(ch, dropout, do_checkpoint=False, kernel_size=kernel_size))97            res.append(Downsample(ch, use_conv=True, out_channels=ch*2, factor=downsample_factor))98            ch *= 299        self.res = nn.Sequential(*res)100        self.final = nn.Sequential(101            normalization(ch),102            nn.SiLU(),103            nn.Conv1d(ch, embedding_dim, 1)104        )105        attn = []106        for a in range(attn_blocks):107            attn.append(AttentionBlock(embedding_dim, num_attn_heads, do_checkpoint=False))108        self.attn = nn.Sequential(*attn)109        self.dim = embedding_dim110    def forward(self, x):111        h = self.init(x)112        h = self.res(h)113        h = self.final(h)114        for blk in self.attn:115            h = checkpoint(blk, h)116        return h[:, :, 0]117class AudioMiniEncoderWithClassifierHead(nn.Module):118    def __init__(self, classes, distribute_zero_label=True, **kwargs):119        super().__init__()120        self.enc = AudioMiniEncoder(**kwargs)121        self.head = nn.Linear(self.enc.dim, classes)122        self.num_classes = classes123        self.distribute_zero_label = distribute_zero_label124    def forward(self, x, labels=None):125        h = self.enc(x)126        logits = self.head(h)127        if labels is None:128            return logits129        else:130            if self.distribute_zero_label:131                oh_labels = nn.functional.one_hot(labels, num_classes=self.num_classes)132                zeros_indices = (labels == 0).unsqueeze(-1)133                # Distribute 20% of the probability mass on all classes when zero is specified, to compensate for dataset noise.134                zero_extra_mass = torch.full_like(oh_labels, dtype=torch.float, fill_value=.2/(self.num_classes-1))135                zero_extra_mass[:, 0] = -.2136                zero_extra_mass = zero_extra_mass * zeros_indices137                oh_labels = oh_labels + zero_extra_mass138            else:139                oh_labels = labels140            loss = nn.functional.cross_entropy(logits, oh_labels)...

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