merge control

modules/control/proc/normalbae/__init__.py

@@ -0,0 +1,107 @@
+import os
+import types
+import warnings
+
+import cv2
+import numpy as np
+import torch
+import torchvision.transforms as transforms
+from einops import rearrange
+from huggingface_hub import hf_hub_download
+from PIL import Image
+
+from modules.control.util import HWC3, resize_image
+from .nets.NNET import NNET
+
+
+# load model
+def load_checkpoint(fpath, model):
+    ckpt = torch.load(fpath, map_location='cpu')['model']
+
+    load_dict = {}
+    for k, v in ckpt.items():
+        if k.startswith('module.'):
+            k_ = k.replace('module.', '')
+            load_dict[k_] = v
+        else:
+            load_dict[k] = v
+
+    model.load_state_dict(load_dict)
+    return model
+
+class NormalBaeDetector:
+    def __init__(self, model):
+        self.model = model
+        self.norm = transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])
+
+    @classmethod
+    def from_pretrained(cls, pretrained_model_or_path, filename=None, cache_dir=None):
+        filename = filename or "scannet.pt"
+
+        if os.path.isdir(pretrained_model_or_path):
+            model_path = os.path.join(pretrained_model_or_path, filename)
+        else:
+            model_path = hf_hub_download(pretrained_model_or_path, filename, cache_dir=cache_dir)
+
+        args = types.SimpleNamespace()
+        args.mode = 'client'
+        args.architecture = 'BN'
+        args.pretrained = 'scannet'
+        args.sampling_ratio = 0.4
+        args.importance_ratio = 0.7
+        model = NNET(args)
+        model = load_checkpoint(model_path, model)
+        model.eval()
+
+        return cls(model)
+
+    def to(self, device):
+        self.model.to(device)
+        return self
+
+
+    def __call__(self, input_image, detect_resolution=512, image_resolution=512, output_type="pil", **kwargs):
+        if "return_pil" in kwargs:
+            warnings.warn("return_pil is deprecated. Use output_type instead.", DeprecationWarning)
+            output_type = "pil" if kwargs["return_pil"] else "np"
+        if type(output_type) is bool:
+            warnings.warn("Passing `True` or `False` to `output_type` is deprecated and will raise an error in future versions")
+            if output_type:
+                output_type = "pil"
+
+        device = next(iter(self.model.parameters())).device
+        if not isinstance(input_image, np.ndarray):
+            input_image = np.array(input_image, dtype=np.uint8)
+
+        input_image = HWC3(input_image)
+        input_image = resize_image(input_image, detect_resolution)
+
+        assert input_image.ndim == 3
+        image_normal = input_image
+        image_normal = torch.from_numpy(image_normal).float().to(device)
+        image_normal = image_normal / 255.0
+        image_normal = rearrange(image_normal, 'h w c -> 1 c h w')
+        image_normal = self.norm(image_normal)
+
+        normal = self.model(image_normal)
+        normal = normal[0][-1][:, :3]
+        # d = torch.sum(normal ** 2.0, dim=1, keepdim=True) ** 0.5
+        # d = torch.maximum(d, torch.ones_like(d) * 1e-5)
+        # normal /= d
+        normal = ((normal + 1) * 0.5).clip(0, 1)
+
+        normal = rearrange(normal[0], 'c h w -> h w c').cpu().numpy()
+        normal_image = (normal * 255.0).clip(0, 255).astype(np.uint8)
+
+        detected_map = normal_image
+        detected_map = HWC3(detected_map)
+
+        img = resize_image(input_image, image_resolution)
+        H, W, _C = img.shape
+
+        detected_map = cv2.resize(detected_map, (W, H), interpolation=cv2.INTER_LINEAR)
+
+        if output_type == "pil":
+            detected_map = Image.fromarray(detected_map)
+
+        return detected_map