[DEEP-180] Add mmsegmentation integration

README.md

@@ -58,7 +58,7 @@ Deep Lake comes with built-in dataloaders for Pytorch and TensorFlow. Train your
 </details>
 <details>
   <summary><b>Integrations with Powerful Tools</b></summary>
-Deep Lake has integrations with <a href="https://github.com/hwchase17/langchain">Langchain</a> and <a href="https://github.com/jerryjliu/llama_index">LLamaIndex</a> as a vector store for LLM apps, <a href="https://wandb.ai/">Weights & Biases</a> for data lineage during model training, and <a href="https://github.com/open-mmlab/mmdetection">MMDetection</a> for training object detection models.
+Deep Lake has integrations with <a href="https://github.com/hwchase17/langchain">Langchain</a> and <a href="https://github.com/jerryjliu/llama_index">LLamaIndex</a> as a vector store for LLM apps, <a href="https://wandb.ai/">Weights & Biases</a> for data lineage during model training, <a href="https://github.com/open-mmlab/mmdetection">MMDetection</a> for training object detection models, and <a href="https://github.com/open-mmlab/mmsegmentation">MMSegmentation</a> for training semantic segmentation models.
 </details>
 <details>
   <summary><b>100+ most-popular image, video, and audio datasets available in seconds</b></summary>

deeplake/client/client.py

@@ -101,9 +101,6 @@ def request(
             headers (dict, optional): Dictionary of HTTP Headers to send with the request.
             timeout (float,optional): How many seconds to wait for the server to send data before giving up.
 
-        Raises:
-            InvalidPasswordException: `password` cannot be `None` inside `json`.
-
         Returns:
             requests.Response: The response received from the server.
         """

deeplake/integrations/mm/mm_common.py

@@ -0,0 +1,154 @@
+import os
+import torch
+import mmcv  # type: ignore
+import deeplake as dp
+from deeplake.util.warnings import always_warn
+from deeplake.util.exceptions import EmptyTokenException
+from deeplake.client.config import DEEPLAKE_AUTH_TOKEN
+
+
+def ddp_setup(rank: int, world_size: int, port: int):
+    """
+    Args:
+        rank: Unique identifier of each process
+        world_size: Total number of processes
+        port: Port number
+    """
+    os.environ["MASTER_ADDR"] = "localhost"
+    os.environ["MASTER_PORT"] = str(port)
+    torch.distributed.init_process_group(
+        backend="nccl", rank=rank, world_size=world_size
+    )
+
+
+def force_cudnn_initialization(device_id):
+    dev = torch.device(f"cuda:{device_id}")
+    torch.nn.functional.conv2d(
+        torch.zeros(32, 32, 32, 32, device=dev), torch.zeros(32, 32, 32, 32, device=dev)
+    )
+
+
+def load_ds_from_cfg(cfg: mmcv.utils.config.ConfigDict):
+    creds = cfg.get("deeplake_credentials", {})
+    token = creds.get("token", None)
+    token = token or os.environ.get(DEEPLAKE_AUTH_TOKEN)
+    if token is None:
+        raise EmptyTokenException()
+
+    ds_path = cfg.deeplake_path
+    ds = dp.load(ds_path, token=token, read_only=True)
+    deeplake_commit = cfg.get("deeplake_commit")
+    deeplake_view_id = cfg.get("deeplake_view_id")
+    deeplake_query = cfg.get("deeplake_query")
+
+    if deeplake_view_id and deeplake_query:
+        raise Exception(
+            "A query and view_id were specified simultaneously for a dataset in the config. Please specify either the deeplake_query or the deeplake_view_id."
+        )
+
+    if deeplake_commit:
+        ds.checkout(deeplake_commit)
+
+    if deeplake_view_id:
+        ds = ds.load_view(id=deeplake_view_id)
+
+    if deeplake_query:
+        ds = ds.query(deeplake_query)
+
+    return ds
+
+
+def get_collect_keys(cfg):
+    pipeline = cfg.train_pipeline
+    for transform in pipeline:
+        if transform["type"] == "Collect":
+            return transform["keys"]
+    raise ValueError("collection keys were not specified")
+
+
+def check_persistent_workers(train_persistent_workers, val_persistent_workers):
+    if train_persistent_workers != val_persistent_workers:
+        if train_persistent_workers:
+            always_warn(
+                "persistent workers for training and evaluation should be identical, "
+                "otherwise, this could lead to performance issues. "
+                "Either both of then should be `True` or both of them should `False`. "
+                "If you want to use persistent workers set True for validation"
+            )
+        else:
+            always_warn(
+                "persistent workers for training and evaluation should be identical, "
+                "otherwise, this could lead to performance issues. "
+                "Either both of then should be `True` or both of them should `False`. "
+                "If you want to use persistent workers set True for training"
+            )
+
+
+def find_tensor_with_htype(ds: dp.Dataset, htype: str, mm_class=None):
+    tensors = [k for k, v in ds.tensors.items() if v.meta.htype == htype]
+    if mm_class is not None:
+        always_warn(
+            f"No deeplake tensor name specified for '{mm_class} in config. Fetching it using htype '{htype}'."
+        )
+    if not tensors:
+        always_warn(f"No tensor found with htype='{htype}'")
+        return None
+    t = tensors[0]
+    if len(tensors) > 1:
+        always_warn(f"Multiple tensors with htype='{htype}' found. choosing '{t}'.")
+    return t
+
+
+def check_unsupported_functionalities(cfg):
+    check_unused_dataset_fields(cfg)
+    check_unsupported_train_pipeline_fields(cfg, mode="train")
+    check_unsupported_train_pipeline_fields(cfg, mode="val")
+    check_dataset_augmentation_formats(cfg)
+
+
+def check_unused_dataset_fields(cfg):
+    if cfg.get("dataset_type"):
+        always_warn(
+            "The deeplake mmdet integration does not use dataset_type to work with the data and compute metrics. All deeplake datasets are in the same deeplake format. To specify a metrics format, you should deeplake_metrics_format "
+        )
+
+    if cfg.get("data_root"):
+        always_warn(
+            "The deeplake mmdet integration does not use data_root, this input will be ignored"
+        )
+
+
+def check_unsupported_train_pipeline_fields(cfg, mode="train"):
+    transforms = cfg.data[mode].pipeline
+
+    for transform in transforms:
+        transform_type = transform.get("type")
+
+        if transform_type == "LoadImageFromFile":
+            always_warn(
+                "LoadImageFromFile is going to be skipped because deeplake mmdet integration does not use it"
+            )
+
+        if transform_type == "LoadAnnotations":
+            always_warn(
+                "LoadAnnotations is going to be skipped because deeplake mmdet integration does not use it"
+            )
+
+        if transform_type == "Corrupt":
+            raise Exception("Corrupt augmentation is not supported yet.")
+
+        elif transform_type == "CopyPaste":  # TO DO: @adolkhan resolve this
+            raise Exception("CopyPaste augmentation is not supported yet")
+
+        elif transform_type == "CutOut":  # TO DO: @adolkhan resolve this
+            raise Exception("CutOut augmentation is not supported yet")
+
+        elif transform_type == "Mosaic":  # TO DO: @adolkhan resolve this
+            raise Exception("Mosaic augmentation is not supported yet")
+
+
+def check_dataset_augmentation_formats(cfg):
+    if cfg.get("train_dataset"):
+        always_warn(
+            "train_dataset is going to be unused. Datset types like: ConcatDataset, RepeatDataset, ClassBalancedDataset, MultiImageMixDataset are not supported."
+        )