In [1]:

    # | default_exp model.optimization.nn.tsc.vittsc.face_detection_training_mask_tune
    %load_ext autoreload
    %autoreload 2

In [2]:

    # declare a list tasks whose products you want to use as inputs
    upstream = ['tabular_to_timeseries_face_detection']

In [3]:

    # Parameters
    upstream = {"tabular_to_timeseries_face_detection": {"nb": "/home/ubuntu/vitmtsc_nbdev/output/301_feature_preprocessing.face_detection.tabular_to_timeseries.html", "FaceDetection_TRAIN_MODEL_INPUT": "/home/ubuntu/vitmtsc_nbdev/output/FaceDetection/target_encoding-nn/train", "FaceDetection_VALID_MODEL_INPUT": "/home/ubuntu/vitmtsc_nbdev/output/FaceDetection/target_encoding-nn/valid", "FaceDetection_TEST_MODEL_INPUT": "/home/ubuntu/vitmtsc_nbdev/output/FaceDetection/target_encoding-nn/test"}}
    product = {"nb": "/home/ubuntu/vitmtsc_nbdev/output/401_model.optimization.nn.tsc.vittsc.face_detection_training_mask_tune.html", "FaceDetection_MODEL_TUNE_OUTPUT": "/home/ubuntu/vitmtsc_nbdev/output/FaceDetection/ray_results", "FaceDetection_MODEL_TRAINING_OUTPUT": "/home/ubuntu/vitmtsc_nbdev/output/FaceDetection/experiments_result", "FaceDetection_MODEL_TRAINING_CHECKPOINT_OUTPUT": "/home/ubuntu/vitmtsc_nbdev/output/FaceDetection/experiments_result/checkpoint", "FaceDetection_BEST_MODEL": "/home/ubuntu/vitmtsc_nbdev/output/FaceDetection/experiments_result/best_model.ckpt", "FaceDetection_BEST_MODEL_CONFIG": "/home/ubuntu/vitmtsc_nbdev/output/FaceDetection/experiments_result/best_model_config.json"}

In [4]:

    #| export
    upstream = {
        "tabular_to_timeseries_face_detection": {
            "nb": "/home/ubuntu/vitmtsc_nbdev/output/301_feature_preprocessing.face_detection.tabular_to_timeseries.html",
            "FaceDetection_TRAIN_MODEL_INPUT": "/home/ubuntu/vitmtsc_nbdev/output/FaceDetection/target_encoding-nn/train",
            "FaceDetection_VALID_MODEL_INPUT": "/home/ubuntu/vitmtsc_nbdev/output/FaceDetection/target_encoding-nn/valid",
            "FaceDetection_TEST_MODEL_INPUT": "/home/ubuntu/vitmtsc_nbdev/output/FaceDetection/target_encoding-nn/test",
        }
    }
    product = {
        "nb": "/home/ubuntu/vitmtsc_nbdev/output/401_model.optimization.nn.tsc.vittsc.face_detection_training_mask_tune.html",
        "FaceDetection_MODEL_TUNE_OUTPUT": "/home/ubuntu/vitmtsc_nbdev/output/FaceDetection/ray_results",
        "FaceDetection_MODEL_TRAINING_OUTPUT": "/home/ubuntu/vitmtsc_nbdev/output/FaceDetection/experiments_result",
        "FaceDetection_MODEL_TRAINING_CHECKPOINT_OUTPUT": "/home/ubuntu/vitmtsc_nbdev/output/FaceDetection/experiments_result/checkpoint",
        "FaceDetection_BEST_MODEL": "/home/ubuntu/vitmtsc_nbdev/output/FaceDetection/experiments_result/best_model.ckpt",
        "FaceDetection_BEST_MODEL_CONFIG": "/home/ubuntu/vitmtsc_nbdev/output/FaceDetection/experiments_result/best_model_config.json",
    }

In [5]:

    #| hide
    from nbdev.showdoc import *

In [6]:

    #| export
    import sys
    import pathlib as p

    def is_running_from_ipython():
        from IPython import get_ipython
        return get_ipython() is not None

    if not is_running_from_ipython() and __package__ is None:
        DIR = p.Path(__file__).resolve().parent
        sys.path.insert(0, str(DIR.parent))
        __package__ = DIR.name

In [7]:

    #| export
    import torch
    import pytorch_lightning
    import pandas as pd
    import numpy as np
    import os
    import math

    from torch.nn import functional as F
    from torch import nn
    from torchmetrics import functional as FM
    from pytorch_lightning import loggers as pl_loggers
    from pytorch_lightning.callbacks import ModelCheckpoint
    from pytorch_lightning.callbacks import LearningRateMonitor
    from petastorm import make_batch_reader
    from petastorm.pytorch import DataLoader
    from einops import rearrange, repeat
    from pytorch_lightning.callbacks.early_stopping import EarlyStopping
    from pytorch_lightning.loggers import TensorBoardLogger
    import pytorch_lightning as pl

## Vision Transformer for Multivariate Time-Series Classification (VitMTSC) Model Training with Masking - Hyperparameter search¶

> Classification Task

> Data Loader Module

> Hyperparameter Search

In [8]:

    # | export
    DATASET_NAME = "FaceDetection"
    NUM_TARGET = 2
    SEQUENCE_LENGTH = 62
    NUMBER_OF_FEATURES = 144
    NUM_WORKERS = 1
    NUM_GPUS = 1
    MAX_EPOCHS = 50
    TUNE_EPOCHS = 5
    NUM_SAMPLES = 1000

In [9]:

    #| export
    import dask_cudf
    import numpy as np
    import sklearn.utils.class_weight

    def get_train_dataset_size():
        gdf = dask_cudf.read_parquet(upstream['tabular_to_timeseries_face_detection']['FaceDetection_TRAIN_MODEL_INPUT'], columns = ['case_id'])
        return gdf.case_id.nunique().compute()

    def get_valid_dataset_size():
        gdf = dask_cudf.read_parquet(upstream['tabular_to_timeseries_face_detection']['FaceDetection_VALID_MODEL_INPUT'], columns = ['case_id'])
        return gdf.case_id.nunique().compute()

    def get_test_dataset_size():
        gdf = dask_cudf.read_parquet(upstream['tabular_to_timeseries_face_detection']['FaceDetection_TEST_MODEL_INPUT'], columns = ['case_id'])
        return gdf.case_id.nunique().compute()

    def get_class_weight():
        train_gdf = dask_cudf.read_parquet(upstream['tabular_to_timeseries_face_detection']['FaceDetection_TRAIN_MODEL_INPUT'], columns = ['case_id', 'class_vals'])
        y_train = train_gdf['class_vals'].compute().to_numpy()
        class_weight = sklearn.utils.class_weight.compute_class_weight('balanced', classes = np.unique(y_train), y = y_train)
        class_weight = class_weight/2
        print(f'class_weight: {class_weight}')
        return class_weight

In [10]:

    get_train_dataset_size(), get_valid_dataset_size(), get_test_dataset_size(), get_class_weight()

    class_weight: [0.49978787 0.50021231]

Out[10]:

    (4712, 1178, 3524, array([0.49978787, 0.50021231]))

### 1. Model Definition¶

In [11]:

    # | export
    class Residual(nn.Module):
        def __init__(self, fn):
            super().__init__()

            self.fn = fn

        def forward(self, x, **kwargs):
            return self.fn(x, **kwargs) + x

In [12]:

    # | export
    class PreNorm(nn.Module):
        def __init__(self, dim, fn):
            super().__init__()

            self.norm = nn.LayerNorm(dim)
            self.fn = fn

        def forward(self, x, **kwargs):
            return self.fn(self.norm(x), **kwargs)

In [13]:

    # | export
    class FeedForward(nn.Module):
        def __init__(self, dim, hidden_dim, dropout=0.0):
            super().__init__()

            self.net = nn.Sequential(
                nn.Linear(dim, hidden_dim),
                nn.GELU(),
                nn.Dropout(dropout),
                nn.Linear(hidden_dim, dim),
                nn.Dropout(dropout),
            )

        def forward(self, x):
            return self.net(x)

In [14]:

    # | export
    class Attention(nn.Module):
        def __init__(self, dim, heads=10, dim_head=32, dropout=0.0):
            super().__init__()

            inner_dim = dim_head * heads
            self.heads = heads
            self.scale = dim_head**-0.5

            self.to_qkv = nn.Linear(dim, inner_dim * 3, bias=False)
            self.to_out = nn.Sequential(nn.Linear(inner_dim, dim), nn.Dropout(dropout))

            self.attn_gradients = None
            self.attention_map = None

        def save_attn_gradients(self, attn_gradients):
            self.attn_gradients = attn_gradients

        def get_attn_gradients(self):
            return self.attn_gradients

        def save_attention_map(self, attention_map):
            self.attention_map = attention_map

        def get_attention_map(self):
            return self.attention_map

        def forward(self, x, mask=None, register_hook=False):
            b, n, _, h = *x.shape, self.heads
            qkv = self.to_qkv(x).chunk(3, dim=-1)
            q, k, v = map(lambda t: rearrange(t, "b n (h d) -> b h n d", h=h), qkv)

            dots = torch.einsum("bhid,bhjd->bhij", q, k) * self.scale
            mask_value = -torch.finfo(dots.dtype).max

            # print('mask1.shape', mask.shape)
            if mask is not None:
                # mask = F.pad(mask, (1, 0), value = True)
                mask = F.pad(mask.flatten(1), (1, 0), value=True)
                mask = mask.unsqueeze(1).unsqueeze(2)

                # print('mask2.shape', mask.shape)
                # print('mask:', mask)
                assert mask.shape[-1] == dots.shape[-1], "mask has incorrect dimensions"
                dots.masked_fill_(mask == 0.0, mask_value)
                del mask

            attn = dots.softmax(dim=-1)
            # print('attn.shape: ', attn.shape)
            # print('attn: ', attn)

            out = torch.einsum("bhij,bhjd->bhid", attn, v)

            if register_hook:
                self.save_attention_map(attn)
                attn.register_hook(self.save_attn_gradients)

            out = rearrange(out, "b h n d -> b n (h d)")
            out = self.to_out(out)
            # print('out.shape: ', out.shape)
            # print('out: ', out)
            return out

In [15]:

    # | export
    class Transformer(nn.Module):
        def __init__(self, dim, depth, heads, dim_head, mlp_dim, dropout):
            super().__init__()

            self.layers = nn.ModuleList([])
            for _ in range(depth):
                self.layers.append(
                    nn.ModuleList(
                        [
                            Residual(
                                PreNorm(
                                    dim,
                                    Attention(
                                        dim, heads=heads, dim_head=dim_head, dropout=dropout
                                    ),
                                )
                            ),
                            Residual(
                                PreNorm(dim, FeedForward(dim, mlp_dim, dropout=dropout))
                            ),
                        ]
                    )
                )

        def forward(self, x, mask=None, register_hook=False):
            for attn, ff in self.layers:
                x = attn(x, mask=mask, register_hook=register_hook)
                x = ff(x)
            return x

In [16]:

    # | export
    def petastorm_collate_fn(rows):
        data_df = pd.DataFrame(rows)
        # print(f'data_df.shape: {data_df.shape}') # data_df.shape: (2, 4402) 22 * 200 + 2

        case_id_df = data_df.iloc[
            :,
            NUMBER_OF_FEATURES * SEQUENCE_LENGTH
            + 1 : NUMBER_OF_FEATURES * SEQUENCE_LENGTH
            + 2,
        ]  # NUMBER_OF_FEATURES*SEQUENCE_LENGTH+1:NUMBER_OF_FEATURES*SEQUENCE_LENGTH+2
        case_id_tensor = torch.tensor(case_id_df.values.astype(np.float64))

        target_df = data_df.iloc[
            :,
            NUMBER_OF_FEATURES * SEQUENCE_LENGTH
            + 0 : NUMBER_OF_FEATURES * SEQUENCE_LENGTH
            + 1,
        ]  # NUMBER_OF_FEATURES*SEQUENCE_LENGTH+0:NUMBER_OF_FEATURES*SEQUENCE_LENGTH+1
        target_tensor = torch.tensor(target_df.values.astype(np.float32))

        data_tensor_df = data_df.iloc[
            :, 0 * SEQUENCE_LENGTH : NUMBER_OF_FEATURES * SEQUENCE_LENGTH
        ]  # 0*SEQUENCE_LENGTH:NUMBER_OF_FEATURES*SEQUENCE_LENGTH
        data_tensor = torch.tensor(data_tensor_df.values.astype(np.float32))
        data_tensor = rearrange(data_tensor, "t (b h)-> t h b", h=SEQUENCE_LENGTH)

        mask_df = data_df.iloc[
            :, 0 * SEQUENCE_LENGTH : 1 * SEQUENCE_LENGTH
        ]  # 0*SEQUENCE_LENGTH:1*SEQUENCE_LENGTH
        mask_tensor = torch.tensor(mask_df.values.astype(np.float32))

        return (
            data_tensor,
            target_tensor.squeeze(),
            case_id_tensor.squeeze(),
            mask_tensor.squeeze(),
        )

    class VitMTSCPetastormDataModule(pl.LightningDataModule):
        def __init__(
            self,
            config,
            #data_dir=f"file:///home/ubuntu/vitmtsc_nbdev/Multivariate_parquet/{DATASET_NAME}/target_encoding-nn/",
            num_workers=NUM_WORKERS,
            transform_spec=None,
            shard_count=NUM_GPUS,
            num_epochs=MAX_EPOCHS,
        ):
            super().__init__()
            self.train_files = f"file://{upstream['tabular_to_timeseries_face_detection']['FaceDetection_TRAIN_MODEL_INPUT']}"
            self.valid_files = f"file://{upstream['tabular_to_timeseries_face_detection']['FaceDetection_VALID_MODEL_INPUT']}"
            self.test_files = f"file://{upstream['tabular_to_timeseries_face_detection']['FaceDetection_TEST_MODEL_INPUT']}"
            self.batch_size = config["batch_size"]
            self.num_workers = num_workers
            self.transform_spec = transform_spec
            self.shard_count = shard_count
            self.num_epochs = num_epochs

        def train_dataloader(self):

            self.train_ds = make_batch_reader(
                self.train_files,
                workers_count=self.num_workers,
                transform_spec=self.transform_spec,
                cur_shard=int(os.environ["LOCAL_RANK"]),
                shard_count=self.shard_count,
                num_epochs=self.num_epochs,
            )
            return DataLoader(
                self.train_ds, batch_size=self.batch_size, collate_fn=petastorm_collate_fn
            )

        def val_dataloader(self):
            print(
                "val_dataloader: local rank :",
                int(os.environ["LOCAL_RANK"]),
                "shard count: ",
                self.shard_count,
            )
            self.val_ds = make_batch_reader(
                self.valid_files,
                workers_count=self.num_workers,
                transform_spec=self.transform_spec,
                cur_shard=int(os.environ["LOCAL_RANK"]),
                shard_count=self.shard_count,
                num_epochs=self.num_epochs,
            )
            return DataLoader(
                self.val_ds, batch_size=self.batch_size, collate_fn=petastorm_collate_fn
            )

        def test_dataloader(self):
            print(
                "test_dataloader: local rank :",
                int(os.environ["LOCAL_RANK"]),
                "shard count: ",
                self.shard_count,
            )
            self.test_ds = make_batch_reader(
                self.test_files,
                workers_count=self.num_workers,
                transform_spec=self.transform_spec,
                cur_shard=int(os.environ["LOCAL_RANK"]),
                shard_count=self.shard_count,
                num_epochs=self.num_epochs,
            )
            return DataLoader(
                self.test_ds, batch_size=self.batch_size, collate_fn=petastorm_collate_fn
            )

In [17]:

    # | export
    class VitTimeSeriesTransformer(pl.LightningModule):
        def __init__(
            self,
            config,
            c_in=NUMBER_OF_FEATURES,
            c_out=NUM_TARGET,
            seq_len=SEQUENCE_LENGTH,
            class_weight=torch.FloatTensor(get_class_weight()),
        ):
            super(VitTimeSeriesTransformer, self).__init__()

            self.d_model = config["d_model"]
            self.depth = config["depth"]
            self.heads = config["heads"]
            self.mlp_dim = config["mlp_dim"]
            self.dim_head = config["dim_head"]
            self.dropout_p = config["dropout"]
            self.emb_dropout_p = config["emb_dropout"]
            self.lr = config["lr"]
            self.weight_decay = config["weight_decay"]
            self.patience = config["patience"]

            self.pos_embedding = nn.Parameter(torch.randn(1, seq_len + 1, self.d_model))
            self.patch_to_embedding = nn.Linear(c_in, self.d_model)
            self.cls_token = nn.Parameter(torch.randn(1, 1, self.d_model))
            self.dropout = nn.Dropout(self.emb_dropout_p)
            self.transformer = Transformer(
                self.d_model,
                self.depth,
                self.heads,
                self.dim_head,
                self.mlp_dim,
                self.dropout_p,
            )
            self.to_cls_token = nn.Identity()
            self.mlp_head = nn.Sequential(
                nn.LayerNorm(self.d_model),
                nn.Linear(self.d_model, self.mlp_dim),
                nn.GELU(),
                nn.Dropout(self.dropout_p),
                nn.Linear(self.mlp_dim, c_out),
            )

            self.c_out = c_out
            self.register_buffer("class_weight", class_weight)

        def forward(self, x, mask=None, register_hook=False):
            # x = rearrange(x, 'b v s-> b s v') # bs x nvars x seq_len ->  bs x seq_len x nvars
            x = self.patch_to_embedding(x)  # bs x seq_len x nvars -> bs x seq_len x d_model
            b, n, _ = x.shape  # bs, seq_len

            cls_tokens = repeat(self.cls_token, "() n d -> b n d", b=b)  # bs x 1 x d_model
            x = torch.cat((cls_tokens, x), dim=1)  # bs x (seq_len + 1) x d_model
            x += self.pos_embedding[
                :, : (n + 1)
            ]  # +=  1 x (seq_len + 1) x d_model -> # bs x (seq_len + 1) x d_model
            x = self.dropout(x)  # bs x (seq_len + 1) x d_model

            x = self.transformer(
                x, mask=mask, register_hook=register_hook
            )  # bs x (seq_len + 1) x d_model

            x = self.to_cls_token(x[:, 0])  # bs x d_model
            return self.mlp_head(x)  # bs x num_classes

        def configure_optimizers(self):
            # optimizer = torch.optim.Adam(self.parameters(), lr=self.lr)
            # scheduler = torch.optim.lr_scheduler.StepLR(optimizer, step_size=self.step_size, gamma=self.gamma)
            optimizer = torch.optim.AdamW(
                self.parameters(), lr=self.lr, weight_decay=self.weight_decay
            )
            scheduler = torch.optim.lr_scheduler.ReduceLROnPlateau(
                optimizer, patience=self.patience
            )
            return {
                "optimizer": optimizer,
                "lr_scheduler": scheduler,
                "monitor": "train_loss",
            }

        def training_step(self, batch, batch_idx):
            x, y, _, mask = batch
            y_hat = self(x, mask)
            y = y.long()
            train_loss = F.cross_entropy(y_hat, y, weight=self.class_weight)
            train_auc = FM.accuracy(F.softmax(y_hat, dim=1), y, num_classes=self.c_out)
            train_auroc = FM.auroc(F.softmax(y_hat, dim=1), y, num_classes=self.c_out)
            self.log(
                "train_loss",
                train_loss,
                on_step=False,
                on_epoch=True,
                prog_bar=True,
                logger=True,
            )
            self.log(
                "train_auc",
                train_auc,
                on_step=False,
                on_epoch=True,
                prog_bar=True,
                logger=True,
            )
            return train_loss

        def validation_step(self, batch, batch_idx):
            x, y, _, mask = batch
            y_hat = self(x, mask)
            y = y.long()
            val_loss = F.cross_entropy(y_hat, y, weight=self.class_weight)
            val_auc = FM.accuracy(F.softmax(y_hat, dim=1), y, num_classes=self.c_out)
            val_auroc = FM.auroc(F.softmax(y_hat, dim=1), y, num_classes=self.c_out)
            self.log(
                "val_loss",
                val_loss,
                on_step=False,
                on_epoch=True,
                prog_bar=True,
                logger=True,
                sync_dist=True,
            )
            self.log(
                "val_auc", val_auc, on_step=False, on_epoch=True, prog_bar=True, logger=True
            )
            return val_loss

        def test_step(self, batch, batch_idx):
            x, y, _, mask = batch
            y_hat = self(x, mask)
            y = y.long()
            test_loss = F.cross_entropy(y_hat, y, weight=self.class_weight)
            test_auc = FM.accuracy(F.softmax(y_hat, dim=1), y, num_classes=self.c_out)
            test_auroc = FM.auroc(F.softmax(y_hat, dim=1), y, num_classes=self.c_out)
            self.log(
                "test_loss",
                test_loss,
                on_step=False,
                on_epoch=True,
                prog_bar=True,
                logger=True,
                sync_dist=True,
            )
            self.log(
                "test_auc",
                test_auc,
                on_step=False,
                on_epoch=True,
                prog_bar=True,
                logger=True,
            )
            return test_loss

    class_weight: [0.49978787 0.50021231]

### 2. Routine for Single/Multi-GPU DDP Training¶

In [18]:

    # | export
    def get_model(config):
        model = VitTimeSeriesTransformer(config)
        return model

    def get_datamodule(config):
        return VitMTSCPetastormDataModule(config)

In [19]:

    %env LOCAL_RANK=0

    env: LOCAL_RANK=0

In [20]:

    #| export
    import json
    import ray
    from ray.tune import ExperimentAnalysis
    from ray import tune
    from ray.tune import CLIReporter
    from ray.tune.schedulers import ASHAScheduler
    from ray.tune.integration.pytorch_lightning import TuneReportCallback

    def tune_training(config, num_epochs = TUNE_EPOCHS, num_gpus = NUM_GPUS):
        pl.seed_everything(42, workers=True)
        model = get_model(config)
        dm = get_datamodule(config)
        metrics = {"loss": "val_loss", "auc": "val_auc"}
        callbacks = [TuneReportCallback(metrics, on="validation_end")]

        trainer = pl.Trainer(
            max_epochs=num_epochs,
            # If fractional GPUs passed in, convert to int.
            #gpus= math.ceil(num_gpus),
            accelerator='gpu', devices=math.ceil(num_gpus),
            strategy= "dp",
            callbacks=callbacks,
            limit_train_batches= math.ceil(get_train_dataset_size()/config['batch_size']),
            limit_val_batches= math.ceil(get_valid_dataset_size()/config['batch_size']),
            val_check_interval= math.ceil(get_train_dataset_size()/config['batch_size']),
            num_sanity_val_steps=0,
            reload_dataloaders_every_n_epochs=1,
            deterministic=True
        )

        trainer.fit(model, dm)

In [21]:

    # | export
    def tune_training_asha(
        num_samples=NUM_SAMPLES,
        num_epochs=TUNE_EPOCHS,
        num_gpus=NUM_GPUS,
        gpus_per_trial=0.2,
    ):
        config = {
            "d_model": tune.choice([16, 32, 48, 64, 128, 256, 512]),
            "depth": tune.choice([2, 4, 6, 8]),
            "heads": tune.choice([2, 4, 6, 8]),
            "mlp_dim": tune.choice([8, 10, 12, 14, 16, 20, 24, 32]),
            "dim_head": tune.choice([8, 10, 12, 14, 16]),
            "dropout": tune.loguniform(1e-6, 1e-3),
            "emb_dropout": tune.loguniform(1e-6, 1e-3),
            "weight_decay": tune.loguniform(1e-3, 1e-1),
            "lr": tune.loguniform(1e-6, 1e-1),
            "patience": tune.choice([1, 2]),
            "batch_size": tune.choice([64, 128, 256, 512, 1024]),
        }

        scheduler = ASHAScheduler(max_t=num_epochs, grace_period=1, reduction_factor=2)

        reporter = CLIReporter(
            parameter_columns=[
                "d_model",
                "depth",
                "heads",
                "mlp_dim",
                "dim_head",
                "dropout",
                "emb_dropout",
                "weight_decay",
                "lr",
                "patience",
                "batch_size",
            ],
            metric_columns=["loss", "auc", "training_iteration"],
        )

        trainable = tune.with_parameters(
            tune_training, num_epochs=num_epochs, num_gpus=num_gpus
        )
        analysis = tune.run(
            trainable,
            resources_per_trial={"cpu": 1, "gpu": gpus_per_trial},
            metric="loss",
            mode="min",
            config=config,
            num_samples=num_samples,
            scheduler=scheduler,
            progress_reporter=reporter,
            verbose=1,
            name="FaceDetection",
            raise_on_failed_trial = False
        )

        print("Best hyperparameters found were: ", analysis.best_config)

In [22]:

    %env LOCAL_RANK=0

    env: LOCAL_RANK=0

In [23]:

    !rm -rf ~/ray_results/FaceDetection/
    !rm -rf ./output/FaceDetection/ray_results/
    !rm -rf ./output/FaceDetection/experiments_result
    !mkdir -p  output/FaceDetection/experiments_result

In [25]:

    !cp -rf ~/ray_results/FaceDetection/ output/FaceDetection/ray_results/

In [26]:

    pd.set_option('display.max_columns', None)
    pd.set_option('display.expand_frame_repr', False)
    pd.set_option('max_colwidth', None)

    #analysis = ExperimentAnalysis(f'~/ray_results/{DATASET_NAME}')
    analysis = ExperimentAnalysis(product['FaceDetection_MODEL_TUNE_OUTPUT'])
    tune_result_df = analysis.results_df[['loss', 'auc', 'training_iteration', 'experiment_tag']]
    tune_result_df.nsmallest(5, 'loss')

    2022-09-24 06:19:44,215	INFO experiment_analysis.py:757 -- No `self.trials`. Drawing logdirs from checkpoint file. This may result in some information that is out of sync, as checkpointing is periodic.
    /home/ubuntu/anaconda3/envs/rapids-22.08_ploomber/lib/python3.8/site-packages/ray/tune/analysis/experiment_analysis.py:303: UserWarning: Dataframes will use '/' instead of '.' to delimit nested result keys in future versions of Ray. For forward compatibility, set the environment variable TUNE_RESULT_DELIM='/'
      warnings.warn(

Out[26]:

| loss | auc | training_iteration | experiment_tag
--- | --- | --- | --- | ---
trial_id |  |  |  |
3b088_00135 | 0.523333 | 0.739062 | 5 | 135_batch_size=256,d_model=128,depth=6,dim_head=14,dropout=0.0000,emb_dropout=0.0003,heads=8,lr=0.0007,mlp_dim=8,patience=1,weight_decay=0.0092
3b088_00973 | 0.538751 | 0.741406 | 5 | 973_batch_size=128,d_model=512,depth=4,dim_head=8,dropout=0.0003,emb_dropout=0.0003,heads=8,lr=0.0001,mlp_dim=32,patience=1,weight_decay=0.0607
3b088_00218 | 0.539851 | 0.737500 | 5 | 218_batch_size=128,d_model=256,depth=8,dim_head=12,dropout=0.0002,emb_dropout=0.0000,heads=6,lr=0.0001,mlp_dim=14,patience=1,weight_decay=0.0965
3b088_00321 | 0.540390 | 0.749178 | 5 | 321_batch_size=64,d_model=512,depth=6,dim_head=12,dropout=0.0000,emb_dropout=0.0000,heads=4,lr=0.0001,mlp_dim=8,patience=2,weight_decay=0.0034
3b088_00008 | 0.541308 | 0.728906 | 5 | 8_batch_size=256,d_model=256,depth=6,dim_head=12,dropout=0.0000,emb_dropout=0.0003,heads=4,lr=0.0004,mlp_dim=20,patience=1,weight_decay=0.0025

In [27]:

    best_config = analysis.get_best_config('loss', 'min')
    print(best_config)

    {'d_model': 128, 'depth': 6, 'heads': 8, 'mlp_dim': 8, 'dim_head': 14, 'dropout': 2.339598278520724e-05, 'emb_dropout': 0.0002715732862353105, 'weight_decay': 0.009198666704068874, 'lr': 0.0006525079632904397, 'patience': 1, 'batch_size': 256}

In [28]:

    #| export
    import json
    def write_best_model_config():
        analysis = ExperimentAnalysis(product['FaceDetection_MODEL_TUNE_OUTPUT'])
        best_config = analysis.get_best_config('loss', 'min')
        with open(product['FaceDetection_BEST_MODEL_CONFIG'], 'w') as outfile:
            # Serializing json
            json_object = json.dumps(best_config, indent=4)
            outfile.write(json_object)

In [29]:

    #| export
    write_best_model_config()

    2022-09-24 06:20:16,757	INFO experiment_analysis.py:757 -- No `self.trials`. Drawing logdirs from checkpoint file. This may result in some information that is out of sync, as checkpointing is periodic.

In [30]:

    #| export
    def get_best_model_config():
        with open(product['FaceDetection_BEST_MODEL_CONFIG'], 'r') as json_file:
            return json.load(json_file)

    best_config = get_best_model_config()

In [31]:

    #| export
    def training_loop(TB_LOG_DIR, max_epochs = MAX_EPOCHS, config = best_config):
        pl.seed_everything(42, workers=True)
        model = get_model(config)
        dm = get_datamodule(config)

        checkpoint_callback = ModelCheckpoint(dirpath=product['FaceDetection_MODEL_TRAINING_CHECKPOINT_OUTPUT'],
                                              save_top_k = 1, #-1,
                                              filename=f"{DATASET_NAME}" + '-vittsc-mask-{epoch:02d}')
        tb_logger = pl_loggers.TensorBoardLogger(TB_LOG_DIR)

        lr_monitor = LearningRateMonitor(logging_interval='step')

        early_stop_callback = EarlyStopping(monitor="val_loss", min_delta=0.001, patience=3, verbose=False, mode="min")

        trainer = pl.Trainer(
            #gpus=1,
            accelerator='gpu', devices=1,
            #track_grad_norm=2,
            #plugins='deepspeed',
            #stochastic_weight_avg=True,
            #precision=16,
            max_epochs=max_epochs,
            strategy= 'dp',   #'ddp',
            logger=tb_logger,
            callbacks=[lr_monitor, checkpoint_callback, early_stop_callback],
            limit_train_batches= math.ceil(get_train_dataset_size()/config['batch_size']),
            limit_val_batches= math.ceil(get_valid_dataset_size()/config['batch_size']),
            val_check_interval= math.ceil(get_train_dataset_size()/config['batch_size']),
            num_sanity_val_steps=0,
            reload_dataloaders_every_n_epochs=1,
            deterministic=True
        )

        trainer.fit(model, dm)

In [32]:

    %env LOCAL_RANK=0

    env: LOCAL_RANK=0

### 4. Model Training¶

In [33]:

    #| export
    if __name__ == "__main__":
        training_loop(TB_LOG_DIR = product['FaceDetection_MODEL_TRAINING_OUTPUT'],
                      max_epochs = MAX_EPOCHS,
                      config = get_best_model_config())

    Global seed set to 42
    GPU available: True, used: True
    TPU available: False, using: 0 TPU cores
    IPU available: False, using: 0 IPUs
    HPU available: False, using: 0 HPUs
    Missing logger folder: /home/ubuntu/vitmtsc_nbdev/output/FaceDetection/experiments_result/lightning_logs
    LOCAL_RANK: 0 - CUDA_VISIBLE_DEVICES: [0,1,2,3]

      | Name               | Type        | Params
    ---------------------------------------------------
    0 | patch_to_embedding | Linear      | 18.6 K
    1 | dropout            | Dropout     | 0
    2 | transformer        | Transformer | 361 K
    3 | to_cls_token       | Identity    | 0
    4 | mlp_head           | Sequential  | 1.3 K
    ---------------------------------------------------
    389 K     Trainable params
    0         Non-trainable params
    389 K     Total params
    1.556     Total estimated model params size (MB)
    /home/ubuntu/anaconda3/envs/rapids-22.08_ploomber/lib/python3.8/site-packages/petastorm/fs_utils.py:88: FutureWarning: pyarrow.localfs is deprecated as of 2.0.0, please use pyarrow.fs.LocalFileSystem instead.
      self._filesystem = pyarrow.localfs
    /home/ubuntu/anaconda3/envs/rapids-22.08_ploomber/lib/python3.8/site-packages/petastorm/etl/dataset_metadata.py:402: FutureWarning: Specifying the 'metadata_nthreads' argument is deprecated as of pyarrow 8.0.0, and the argument will be removed in a future version
      dataset = pq.ParquetDataset(path_or_paths, filesystem=fs, validate_schema=False, metadata_nthreads=10)
    /home/ubuntu/anaconda3/envs/rapids-22.08_ploomber/lib/python3.8/site-packages/petastorm/etl/dataset_metadata.py:362: FutureWarning: 'ParquetDataset.common_metadata' attribute is deprecated as of pyarrow 5.0.0 and will be removed in a future version.
      if not dataset.common_metadata:
    /home/ubuntu/anaconda3/envs/rapids-22.08_ploomber/lib/python3.8/site-packages/petastorm/reader.py:405: FutureWarning: Specifying the 'metadata_nthreads' argument is deprecated as of pyarrow 8.0.0, and the argument will be removed in a future version
      self.dataset = pq.ParquetDataset(dataset_path, filesystem=pyarrow_filesystem,
    /home/ubuntu/anaconda3/envs/rapids-22.08_ploomber/lib/python3.8/site-packages/petastorm/unischema.py:317: FutureWarning: 'ParquetDataset.pieces' attribute is deprecated as of pyarrow 5.0.0 and will be removed in a future version. Specify 'use_legacy_dataset=False' while constructing the ParquetDataset, and then use the '.fragments' attribute instead.
      meta = parquet_dataset.pieces[0].get_metadata()
    /home/ubuntu/anaconda3/envs/rapids-22.08_ploomber/lib/python3.8/site-packages/petastorm/unischema.py:321: FutureWarning: 'ParquetDataset.partitions' attribute is deprecated as of pyarrow 5.0.0 and will be removed in a future version. Specify 'use_legacy_dataset=False' while constructing the ParquetDataset, and then use the '.partitioning' attribute instead.
      for partition in (parquet_dataset.partitions or []):
    /home/ubuntu/anaconda3/envs/rapids-22.08_ploomber/lib/python3.8/site-packages/petastorm/etl/dataset_metadata.py:253: FutureWarning: 'ParquetDataset.metadata' attribute is deprecated as of pyarrow 5.0.0 and will be removed in a future version.
      metadata = dataset.metadata
    /home/ubuntu/anaconda3/envs/rapids-22.08_ploomber/lib/python3.8/site-packages/petastorm/etl/dataset_metadata.py:254: FutureWarning: 'ParquetDataset.common_metadata' attribute is deprecated as of pyarrow 5.0.0 and will be removed in a future version.
      common_metadata = dataset.common_metadata
    /home/ubuntu/anaconda3/envs/rapids-22.08_ploomber/lib/python3.8/site-packages/petastorm/etl/dataset_metadata.py:350: FutureWarning: 'ParquetDataset.pieces' attribute is deprecated as of pyarrow 5.0.0 and will be removed in a future version. Specify 'use_legacy_dataset=False' while constructing the ParquetDataset, and then use the '.fragments' attribute instead.
      futures_list = [thread_pool.submit(_split_piece, piece, dataset.fs.open) for piece in dataset.pieces]
    /home/ubuntu/anaconda3/envs/rapids-22.08_ploomber/lib/python3.8/site-packages/petastorm/etl/dataset_metadata.py:350: FutureWarning: 'ParquetDataset.fs' attribute is deprecated as of pyarrow 5.0.0 and will be removed in a future version. Specify 'use_legacy_dataset=False' while constructing the ParquetDataset, and then use the '.filesystem' attribute instead.
      futures_list = [thread_pool.submit(_split_piece, piece, dataset.fs.open) for piece in dataset.pieces]
    /home/ubuntu/anaconda3/envs/rapids-22.08_ploomber/lib/python3.8/site-packages/petastorm/etl/dataset_metadata.py:334: FutureWarning: ParquetDatasetPiece is deprecated as of pyarrow 5.0.0 and will be removed in a future version.
      return [pq.ParquetDatasetPiece(piece.path, open_file_func=fs_open,
    /home/ubuntu/anaconda3/envs/rapids-22.08_ploomber/lib/python3.8/site-packages/pytorch_lightning/trainer/trainer.py:1933: PossibleUserWarning: The number of training batches (19) is smaller than the logging interval Trainer(log_every_n_steps=50). Set a lower value for log_every_n_steps if you want to see logs for the training epoch.
      rank_zero_warn(
    /home/ubuntu/anaconda3/envs/rapids-22.08_ploomber/lib/python3.8/site-packages/petastorm/arrow_reader_worker.py:138: FutureWarning: 'ParquetDataset.fs' attribute is deprecated as of pyarrow 5.0.0 and will be removed in a future version. Specify 'use_legacy_dataset=False' while constructing the ParquetDataset, and then use the '.filesystem' attribute instead.
      parquet_file = ParquetFile(self._dataset.fs.open(piece.path))
    /home/ubuntu/anaconda3/envs/rapids-22.08_ploomber/lib/python3.8/site-packages/petastorm/arrow_reader_worker.py:286: FutureWarning: 'ParquetDataset.partitions' attribute is deprecated as of pyarrow 5.0.0 and will be removed in a future version. Specify 'use_legacy_dataset=False' while constructing the ParquetDataset, and then use the '.partitioning' attribute instead.
      partition_names = self._dataset.partitions.partition_names if self._dataset.partitions else set()
    /home/ubuntu/anaconda3/envs/rapids-22.08_ploomber/lib/python3.8/site-packages/petastorm/arrow_reader_worker.py:289: FutureWarning: 'ParquetDataset.partitions' attribute is deprecated as of pyarrow 5.0.0 and will be removed in a future version. Specify 'use_legacy_dataset=False' while constructing the ParquetDataset, and then use the '.partitioning' attribute instead.
      table = piece.read(columns=column_names - partition_names, partitions=self._dataset.partitions)

    val_dataloader: local rank : 0 shard count:  1

    Training: 0it [00:00, ?it/s]

    Validation: 0it [00:00, ?it/s]

    val_dataloader: local rank : 0 shard count:  1

    Validation: 0it [00:00, ?it/s]

    val_dataloader: local rank : 0 shard count:  1

    Validation: 0it [00:00, ?it/s]

    val_dataloader: local rank : 0 shard count:  1

    Validation: 0it [00:00, ?it/s]

    val_dataloader: local rank : 0 shard count:  1

    Validation: 0it [00:00, ?it/s]

    val_dataloader: local rank : 0 shard count:  1

    Validation: 0it [00:00, ?it/s]

    val_dataloader: local rank : 0 shard count:  1

    Validation: 0it [00:00, ?it/s]

    val_dataloader: local rank : 0 shard count:  1

    Validation: 0it [00:00, ?it/s]

### 5. Training Results¶

In [34]:

    import glob
    import shutil
    source_file = glob.glob(product['FaceDetection_MODEL_TRAINING_CHECKPOINT_OUTPUT'] + '/*.ckpt')[0]
    print(source_file)
    shutil.copyfile(source_file, product['FaceDetection_BEST_MODEL'])

    /home/ubuntu/vitmtsc_nbdev/output/FaceDetection/experiments_result/checkpoint/FaceDetection-vittsc-mask-epoch=07.ckpt

Out[34]:

    '/home/ubuntu/vitmtsc_nbdev/output/FaceDetection/experiments_result/best_model.ckpt'

In [35]:

    #%load_ext tensorboard
    #%tensorboard --logdir experiments_result/FaceDetection/vittsc_mask --port 8199

We shutdown the kernel!!!

In [36]:

    from nbdev import nbdev_export
    nbdev_export()

Multi-GPU Training

In [ ]: