init commit

src/unifolm_wma/utils/utils.py

@@ -0,0 +1,81 @@
+import importlib
+import numpy as np
+import cv2
+import torch
+import torch.distributed as dist
+
+
+def count_params(model, verbose=False):
+    total_params = sum(p.numel() for p in model.parameters())
+    if verbose:
+        print(
+            f"{model.__class__.__name__} has {total_params*1.e-6:.2f} M params."
+        )
+    return total_params
+
+
+def check_istarget(name, para_list):
+    """ 
+    name: full name of source para
+    para_list: partial name of target para 
+    """
+    istarget = False
+    for para in para_list:
+        if para in name:
+            return True
+    return istarget
+
+
+def instantiate_from_config(config):
+    if not "target" in config:
+        if config == '__is_first_stage__':
+            return None
+        elif config == "__is_unconditional__":
+            return None
+        raise KeyError("Expected key `target` to instantiate.")
+    return get_obj_from_str(config["target"])(**config.get("params", dict()))
+
+
+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 load_npz_from_dir(data_dir):
+    data = [
+        np.load(os.path.join(data_dir, data_name))['arr_0']
+        for data_name in os.listdir(data_dir)
+    ]
+    data = np.concatenate(data, axis=0)
+    return data
+
+
+def load_npz_from_paths(data_paths):
+    data = [np.load(data_path)['arr_0'] for data_path in data_paths]
+    data = np.concatenate(data, axis=0)
+    return data
+
+
+def resize_numpy_image(image,
+                       max_resolution=512 * 512,
+                       resize_short_edge=None):
+    h, w = image.shape[:2]
+    if resize_short_edge is not None:
+        k = resize_short_edge / min(h, w)
+    else:
+        k = max_resolution / (h * w)
+        k = k**0.5
+    h = int(np.round(h * k / 64)) * 64
+    w = int(np.round(w * k / 64)) * 64
+    image = cv2.resize(image, (w, h), interpolation=cv2.INTER_LANCZOS4)
+    return image
+
+
+def setup_dist(args):
+    if dist.is_initialized():
+        return
+    torch.cuda.set_device(args.local_rank)
+    torch.distributed.init_process_group('nccl', init_method='env://')