segmentation and depth ds

configs/meta_arch/depth-segmentator.yaml

@@ -0,0 +1,6 @@
+# @package _group_
+#
+# target: sharpf.modeling.DepthSegmentator
+# monitor: val_balanced_accuracy #TODO: add balanced accuracy metric class
+# loss:
+#   target: torch.nn.BCEWithLogitsLoss

configs/model/unet-segmentator.yaml

@@ -0,0 +1,19 @@
+# @package _group_
+model_name: PixelSegmentator
+params:
+  feature_extractor:
+    target: sharpf.modeling.Unet
+    params:
+      encoder_name: resnet50
+      decoder_use_batchnorm: true
+      decoder_channels: [256, 128, 64, 32, 16]
+      decoder_attention_type: null
+      in_channels: 1
+  segmentation_head:
+    - target: torch.nn.Conv2d
+      params:
+        in_channels: 16
+        out_channels: 1
+        kernel_size: 3
+        padding: 1
+    - target: torch.nn.Sigmoid

configs/transforms/depth-norm.yaml

@@ -0,0 +1,15 @@
+# @package _group_
+
+train:
+  - target: sharpf.utils.abc_utils.torch.TypeCast
+
+val:
+  - target: sharpf.utils.abc_utils.torch.TypeCast
+
+test:
+  - target: sharpf.utils.abc_utils.torch.TypeCast
+
+normalisation:
+  - data: ['standartize', 'quantile']
+
+

sharpf/modeling/meta_arch/depth_regressor.py

@@ -30,6 +30,7 @@ def __init__(self, cfg):
         super().__init__()
         self.hparams = flatten_omegaconf(cfg)  # there should be better official way later
         self.cfg = cfg
+        self.task = 'regression'
         self.model = build_model(cfg.model)
         self.example_input_array = torch.rand(1, 1, 64, 64)
         self.data_dir = hydra.utils.to_absolute_path(self.cfg.data.data_dir)
@@ -102,6 +103,7 @@ def _get_dataset(self, partition):
             io=DepthMapIO,
             data_label=self.cfg.data.data_label,
             target_label=self.cfg.data.target_label,
+            task=self.task,
             partition=partition,
             transform=transform,
             max_loaded_files=self.cfg.data.max_loaded_files

sharpf/modeling/meta_arch/depth_segmentator.py

@@ -0,0 +1,139 @@
+import logging
+
+import hydra
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+from pytorch_lightning import TrainResult
+from pytorch_lightning.core.lightning import LightningModule
+from pytorch_lightning.metrics import tensor_metric
+from torch.utils.data import DataLoader
+
+from sharpf.utils.comm import get_batch_size
+from sharpf.utils.losses import balanced_accuracy
+from ..model.build import build_model
+from ...data import DepthMapIO
+from ...utils.abc_utils import LotsOfHdf5Files, DepthDataset
+from ...utils.abc_utils.torch import CompositeTransform
+from ...utils.config import flatten_omegaconf
+
+log = logging.getLogger(__name__)
+
+
+@tensor_metric()
+def gather_sum(x: torch.Tensor) -> torch.Tensor:
+    return x
+
+
+class DepthSegmentator(LightningModule):
+
+    def __init__(self, cfg):
+        super().__init__()
+        self.hparams = flatten_omegaconf(cfg)  # there should be better official way later
+        self.cfg = cfg
+        self.task = 'segmentation'
+        self.model = build_model(cfg.model)
+        self.example_input_array = torch.rand(1, 1, 64, 64)
+        self.data_dir = hydra.utils.to_absolute_path(self.cfg.data.data_dir)
+
+        dist_backend = self.cfg.trainer.distributed_backend
+        if (dist_backend is not None and 'ddp' in dist_backend) or (
+                dist_backend is None and self.cfg.trainer.gpus is not None and (
+                self.cfg.trainer.gpus > 1 or self.cfg.trainer.num_nodes > 1)):
+            log.info('Converting BatchNorm to SyncBatchNorm. Do not forget other batch-dimension dependent operations.')
+            self.model = nn.SyncBatchNorm.convert_sync_batchnorm(self.model)
+
+    def forward(self, x):
+        return self.model(x)
+
+    def training_step(self, batch, batch_idx):
+        points, distances = batch['image'], batch['distances']
+        points = points.unsqueeze(1) if points.dim() == 3 else points
+        preds = self.forward(points)
+        loss = hydra.utils.instantiate(self.cfg.meta_arch.loss, preds, distances)
+        result = TrainResult(minimize=loss)
+        result.log('train_loss', loss, prog_bar=True)
+        return result
+
+    def _shared_eval_step(self, batch, batch_idx, prefix):
+        metric_name = 'balanced_accuracy'
+        metric = balanced_accuracy
+        points, distances = batch['image'], batch['distances']
+        points = points.unsqueeze(1) if points.dim() == 3 else points
+        preds = self.forward(points)
+
+        metric_value = metric(preds, distances)  # (batch)
+        # loss = hydra.utils.instantiate(self.cfg.meta_arch.loss, preds, distances)
+        # self.logger[0].experiment.add_scalars('losses', {f'{prefix}_loss': loss})
+        # TODO Consider pl.EvalResult, once there are good examples how to use it
+        return {f'{metric_name}_sum': metric_value.sum(),
+                'batch_size': torch.tensor(points.size(0), device=self.device)}
+
+    def _shared_eval_epoch_end(self, outputs, prefix):
+        metric_name = 'balanced_accuracy'
+        metric_sum = 0
+        size = 0
+        for output in outputs:
+            metric_sum += output[f'{metric_name}_sum']
+            size += output['batch_size']
+        mean_metric = gather_sum(metric_sum) / gather_sum(size)
+        logs = {f'{prefix}_mean_{metric_name}': mean_metric}
+        return {f'{prefix}_mean_{metric_name}': mean_metric, 'log': logs}
+
+    def validation_step(self, batch, batch_idx):
+        return self._shared_eval_step(batch, batch_idx, prefix='val')
+
+    def test_step(self, batch, batch_idx):
+        return self._shared_eval_step(batch, batch_idx, prefix='test')
+
+    def validation_epoch_end(self, outputs):
+        return self._shared_eval_epoch_end(outputs, prefix='val')
+
+    def test_epoch_end(self, outputs):
+        return self._shared_eval_epoch_end(outputs, prefix='test')
+
+    def configure_optimizers(self):
+        optimizer = hydra.utils.instantiate(self.cfg.opt, params=self.parameters())
+        scheduler = hydra.utils.instantiate(self.cfg.scheduler, optimizer=optimizer)
+        return [optimizer], [scheduler]
+
+    def _get_dataset(self, partition):
+        if hasattr(self, f'{partition}_set') and getattr(self, f'{partition}_set') is not None:
+            return getattr(self, f'{partition}_set')
+        transform = CompositeTransform([hydra.utils.instantiate(tf) for tf in self.cfg.transforms[partition]])
+        if 'normalisation' in self.cfg.transforms.keys:
+            normalisation = self.cfg.transforms['normalisation']
+        else:
+            normalisation = None
+
+        return DepthDataset(
+            data_dir=self.data_dir,
+            io=DepthMapIO,
+            data_label=self.cfg.data.data_label,
+            target_label=self.cfg.data.target_label,
+            task=self.task,
+            partition=partition,
+            transform=transform,
+            max_loaded_files=self.cfg.data.max_loaded_files,
+            normalisation=normalisation
+        )
+
+    def _get_dataloader(self, partition):
+        dataset = self._get_dataset(partition)
+        num_workers = self.cfg.data_loader[partition].num_workers
+        batch_size = get_batch_size(self.cfg.data_loader[partition].total_batch_size)
+        return DataLoader(dataset, batch_size=batch_size, num_workers=num_workers, pin_memory=True)
+
+    def setup(self, stage: str):
+        self.train_set = self._get_dataset('train') if stage == 'fit' else None
+        self.val_set = self._get_dataset('val') if stage == 'fit' else None
+        self.test_set = self._get_dataset('test') if stage == 'test' else None
+
+    def train_dataloader(self):
+        return self._get_dataloader('train')
+
+    def val_dataloader(self):
+        return self._get_dataloader('val')
+
+    def test_dataloader(self):
+        return self._get_dataloader('val')  # FIXME

sharpf/utils/abc_utils/hdf5/dataset.py

@@ -133,3 +133,79 @@ def __getitem__(self, index):
             file_index_to_unload = np.random.choice(loaded_file_indexes)
             self.files[file_index_to_unload].unload()
         return item
+
+class DepthDataset(LotsOfHdf5Files):
+
+    def __init__(self, io, data_dir, data_label, target_label, task, partition=None,
+                 transform=None, normalisation=['quantile', 'standartize'], max_loaded_files=0):
+        super().__init__(data_dir=data_dir, io=io,
+                         data_label=data_label, target_label=target_label,
+                         labels=None,
+                         partition=partition,
+                         transform=transform,
+                         max_loaded_files=max_loaded_files)
+        self.task = task
+        self.quality = self._get_quantity()
+        self.normalisation = normalisation
+
+    def _get_quantity(self):
+        data_dir_split = self.data_dir.split('_')
+        if 'high' in data_dir_split:
+            return 'high'
+        elif 'low' in data_dir_split:
+            return 'low'
+        elif 'med' in data_dir_split:
+            return 'med'
+
+    def quantile_normalize(self, data):
+        # mask -> min shift -> quantile
+
+        norm_data = np.copy(data)
+        mask_obj = np.where(norm_data != 0)
+        mask_back = np.where(norm_data == 0)
+        norm_data[mask_back] = norm_data.max() + 1.0  # new line
+        norm_data -= norm_data[mask_obj].min()
+
+        norm_data /= high_res_quantile
+
+        return norm_data
+
+    def standartize(self, data):
+        # zero mean, unit variance
+
+        standart_data = np.copy(data)
+        standart_data -= np.mean(standart_data)
+        std = np.linalg.norm(standart_data, axis=1).max()
+        if std > 0:
+            standart_data /= std
+
+        return standart_data
+
+    def __getitem__(self, index):
+
+        data, target = super.__getitem__(index)
+        mask_1 = (np.copy(data) != 0.0).astype(float)  # mask for object
+        mask_2 = np.where(data == 0)  # mask for background
+
+        if 'quantile' in self.normalisation:
+            data = self.quantile_normalize(data)
+        if 'standartize' in self.normalisation:
+            data = self.standartize(data)
+
+        dist_new = np.copy(target)
+        dist_mask = dist_new * mask_1  # select object points
+        dist_mask[mask_2] = 1.0  # background points has max distance to sharp features
+        close_to_sharp = np.array((dist_mask != np.nan) & (dist_mask < 1.)).astype(float)
+
+        if self.task == 'two-heads':
+            # regression + segmentation (or two-head network) has to targets:
+            # distance field and segmented close-to-sharp region of the object
+            target = torch.cat([torch.FloatTensor(dist_mask).unsqueeze(0), torch.FloatTensor(close_to_sharp).unsqueeze(0)], dim=0)
+        if self.task == 'segmentation':
+            target = torch.FloatTensor(close_to_sharp)
+        elif self.task == 'regression':
+            target = torch.FloatTensor(dist_mask)
+
+        data = torch.FloatTensor(data).unsqueeze(0)
+
+        return {'data': data, 'target': target}