init

hy3dshape/hy3dshape/utils/misc.py

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+# -*- coding: utf-8 -*-
+
+import importlib
+from omegaconf import OmegaConf, DictConfig, ListConfig
+
+import torch
+import torch.distributed as dist
+from typing import Union
+
+
+def get_config_from_file(config_file: str) -> Union[DictConfig, ListConfig]:
+    config_file = OmegaConf.load(config_file)
+
+    if 'base_config' in config_file.keys():
+        if config_file['base_config'] == "default_base":
+            base_config = OmegaConf.create()
+            # base_config = get_default_config()
+        elif config_file['base_config'].endswith(".yaml"):
+            base_config = get_config_from_file(config_file['base_config'])
+        else:
+            raise ValueError(f"{config_file} must be `.yaml` file or it contains `base_config` key.")
+
+        config_file = {key: value for key, value in config_file if key != "base_config"}
+
+        return OmegaConf.merge(base_config, config_file)
+
+    return config_file
+
+
+def get_obj_from_str(string, reload=False):
+    module, cls = string.rsplit(".", 1)
+    if reload:
+        module_imp = importlib.import_module(module)
+        importlib.reload(module_imp)
+    return getattr(importlib.import_module(module, package=None), cls)
+
+
+def get_obj_from_config(config):
+    if "target" not in config:
+        raise KeyError("Expected key `target` to instantiate.")
+
+    return get_obj_from_str(config["target"])
+
+
+def instantiate_from_config(config, **kwargs):
+    if "target" not in config:
+        raise KeyError("Expected key `target` to instantiate.")
+
+    cls = get_obj_from_str(config["target"])
+
+    if config.get("from_pretrained", None):
+        return cls.from_pretrained(config["from_pretrained"])
+
+    params = config.get("params", dict())
+    # params.update(kwargs)
+    # instance = cls(**params)
+    kwargs.update(params)
+    instance = cls(**kwargs)
+
+    return instance
+
+
+def disabled_train(self, mode=True):
+    """Overwrite model.train with this function to make sure train/eval mode
+    does not change anymore."""
+    return self
+
+
+def instantiate_non_trainable_model(config):
+    model = instantiate_from_config(config)
+    model = model.eval()
+    model.train = disabled_train
+    for param in model.parameters():
+        param.requires_grad = False
+
+    return model
+
+
+def is_dist_avail_and_initialized():
+    if not dist.is_available():
+        return False
+    if not dist.is_initialized():
+        return False
+    return True
+
+
+def get_rank():
+    if not is_dist_avail_and_initialized():
+        return 0
+    return dist.get_rank()
+
+
+def get_world_size():
+    if not is_dist_avail_and_initialized():
+        return 1
+    return dist.get_world_size()
+
+
+def all_gather_batch(tensors):
+    """
+    Performs all_gather operation on the provided tensors.
+    """
+    # Queue the gathered tensors
+    world_size = get_world_size()
+    # There is no need for reduction in the single-proc case
+    if world_size == 1:
+        return tensors
+    tensor_list = []
+    output_tensor = []
+    for tensor in tensors:
+        tensor_all = [torch.ones_like(tensor) for _ in range(world_size)]
+        dist.all_gather(
+            tensor_all,
+            tensor,
+            async_op=False  # performance opt
+        )
+
+        tensor_list.append(tensor_all)
+
+    for tensor_all in tensor_list:
+        output_tensor.append(torch.cat(tensor_all, dim=0))
+    return output_tensor