mirror of
https://github.com/brycedrennan/imaginAIry
synced 2024-11-17 09:25:47 +00:00
103 lines
2.9 KiB
Python
103 lines
2.9 KiB
Python
from functools import lru_cache
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import numpy as np
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import torch
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from huggingface_hub import hf_hub_download
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from PIL import Image
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from torch import nn
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from torchvision import transforms
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from .decoder import Decoder
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from .encoder import Encoder
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from .utils import get_device
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class NNET(nn.Module):
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def __init__(self, architecture="BN", sampling_ratio=0.4, importance_ratio=0.7):
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super().__init__()
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self.encoder = Encoder()
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self.decoder = Decoder(
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architecture=architecture,
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sampling_ratio=sampling_ratio,
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importance_ratio=importance_ratio,
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)
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def forward(self, img, **kwargs):
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return self.decoder(self.encoder(img), **kwargs)
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def create_normal_map_pil_img(img, device=get_device()):
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img_t = pillow_img_to_torch_normal_map_input(img).to(device)
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pred_norm = create_normal_map_torch_img(img_t, device=device)
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return torch_normal_map_to_pillow_img(pred_norm)
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def create_normal_map_torch_img(img_t, device=get_device()):
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with torch.no_grad():
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model = load_model(device=device)
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img_t = img_t.to(device)
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norm_out_list, _, _ = model(img_t) # noqa
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norm_out = norm_out_list[-1]
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pred_norm_t = norm_out[:, :3, :, :]
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return pred_norm_t
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normalize_img = transforms.Normalize(
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mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]
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)
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def pillow_img_to_torch_normal_map_input(img):
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img = np.array(img).astype(np.float32) / 255.0
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img = torch.from_numpy(img).permute(2, 0, 1)
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img = normalize_img(img)
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img = img.unsqueeze(0)
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# Resize image to nearest multiple of 8 using interpolate()
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h, w = img.size(2), img.size(3)
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h_new = int(round(h / 8) * 8)
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w_new = int(round(w / 8) * 8)
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img = torch.nn.functional.interpolate(
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img, size=(h_new, w_new), mode="bilinear", align_corners=False
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)
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return img
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def torch_normal_map_to_pillow_img(norm_map_t):
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norm_map_np = norm_map_t.detach().cpu().permute(0, 2, 3, 1).numpy() # (B, H, W, 3)
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pred_norm_rgb = ((norm_map_np + 1) * 0.5) * 255
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pred_norm_rgb = np.clip(pred_norm_rgb, a_min=0, a_max=255)
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pred_norm_rgb = pred_norm_rgb.astype(np.uint8) # (B, H, W, 3)
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return Image.fromarray(pred_norm_rgb[0])
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def load_checkpoint(fpath, model):
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ckpt = torch.load(fpath, map_location="cpu")["model"]
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load_dict = {}
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for k, v in ckpt.items():
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load_dict[k] = v
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model.load_state_dict(load_dict)
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return model
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@lru_cache(maxsize=1)
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def load_model(device=None, sampling_ratio=0.4, importance_ratio=0.7) -> NNET:
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device = device or get_device()
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weights_path = hf_hub_download(
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repo_id="imaginairy/imaginairy-normal-uncertainty-map", filename="scannet.pt"
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)
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architecture = "BN"
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model = NNET(
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architecture=architecture,
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sampling_ratio=sampling_ratio,
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importance_ratio=importance_ratio,
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).to(device)
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model = load_checkpoint(weights_path, model)
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model.eval()
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return model
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