136 lines
6.5 KiB
Python
136 lines
6.5 KiB
Python
from distutils import extension
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import numpy as np
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import cv2
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import torch
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from einops import rearrange
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import os
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from modules import devices
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from modules.paths import models_path
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from annotator.util import safe_step, nms
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class Network(torch.nn.Module):
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def __init__(self, model_path):
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super().__init__()
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self.netVggOne = torch.nn.Sequential(
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torch.nn.Conv2d(in_channels=3, out_channels=64, kernel_size=3, stride=1, padding=1),
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torch.nn.ReLU(inplace=False),
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torch.nn.Conv2d(in_channels=64, out_channels=64, kernel_size=3, stride=1, padding=1),
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torch.nn.ReLU(inplace=False)
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)
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self.netVggTwo = torch.nn.Sequential(
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torch.nn.MaxPool2d(kernel_size=2, stride=2),
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torch.nn.Conv2d(in_channels=64, out_channels=128, kernel_size=3, stride=1, padding=1),
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torch.nn.ReLU(inplace=False),
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torch.nn.Conv2d(in_channels=128, out_channels=128, kernel_size=3, stride=1, padding=1),
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torch.nn.ReLU(inplace=False)
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)
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self.netVggThr = torch.nn.Sequential(
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torch.nn.MaxPool2d(kernel_size=2, stride=2),
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torch.nn.Conv2d(in_channels=128, out_channels=256, kernel_size=3, stride=1, padding=1),
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torch.nn.ReLU(inplace=False),
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torch.nn.Conv2d(in_channels=256, out_channels=256, kernel_size=3, stride=1, padding=1),
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torch.nn.ReLU(inplace=False),
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torch.nn.Conv2d(in_channels=256, out_channels=256, kernel_size=3, stride=1, padding=1),
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torch.nn.ReLU(inplace=False)
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)
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self.netVggFou = torch.nn.Sequential(
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torch.nn.MaxPool2d(kernel_size=2, stride=2),
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torch.nn.Conv2d(in_channels=256, out_channels=512, kernel_size=3, stride=1, padding=1),
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torch.nn.ReLU(inplace=False),
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torch.nn.Conv2d(in_channels=512, out_channels=512, kernel_size=3, stride=1, padding=1),
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torch.nn.ReLU(inplace=False),
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torch.nn.Conv2d(in_channels=512, out_channels=512, kernel_size=3, stride=1, padding=1),
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torch.nn.ReLU(inplace=False)
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)
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self.netVggFiv = torch.nn.Sequential(
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torch.nn.MaxPool2d(kernel_size=2, stride=2),
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torch.nn.Conv2d(in_channels=512, out_channels=512, kernel_size=3, stride=1, padding=1),
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torch.nn.ReLU(inplace=False),
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torch.nn.Conv2d(in_channels=512, out_channels=512, kernel_size=3, stride=1, padding=1),
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torch.nn.ReLU(inplace=False),
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torch.nn.Conv2d(in_channels=512, out_channels=512, kernel_size=3, stride=1, padding=1),
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torch.nn.ReLU(inplace=False)
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)
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self.netScoreOne = torch.nn.Conv2d(in_channels=64, out_channels=1, kernel_size=1, stride=1, padding=0)
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self.netScoreTwo = torch.nn.Conv2d(in_channels=128, out_channels=1, kernel_size=1, stride=1, padding=0)
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self.netScoreThr = torch.nn.Conv2d(in_channels=256, out_channels=1, kernel_size=1, stride=1, padding=0)
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self.netScoreFou = torch.nn.Conv2d(in_channels=512, out_channels=1, kernel_size=1, stride=1, padding=0)
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self.netScoreFiv = torch.nn.Conv2d(in_channels=512, out_channels=1, kernel_size=1, stride=1, padding=0)
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self.netCombine = torch.nn.Sequential(
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torch.nn.Conv2d(in_channels=5, out_channels=1, kernel_size=1, stride=1, padding=0),
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torch.nn.Sigmoid()
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)
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self.load_state_dict({strKey.replace('module', 'net'): tenWeight for strKey, tenWeight in torch.load(model_path).items()})
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# end
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def forward(self, tenInput):
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tenInput = tenInput * 255.0
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tenInput = tenInput - torch.tensor(data=[104.00698793, 116.66876762, 122.67891434], dtype=tenInput.dtype, device=tenInput.device).view(1, 3, 1, 1)
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tenVggOne = self.netVggOne(tenInput)
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tenVggTwo = self.netVggTwo(tenVggOne)
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tenVggThr = self.netVggThr(tenVggTwo)
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tenVggFou = self.netVggFou(tenVggThr)
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tenVggFiv = self.netVggFiv(tenVggFou)
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tenScoreOne = self.netScoreOne(tenVggOne)
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tenScoreTwo = self.netScoreTwo(tenVggTwo)
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tenScoreThr = self.netScoreThr(tenVggThr)
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tenScoreFou = self.netScoreFou(tenVggFou)
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tenScoreFiv = self.netScoreFiv(tenVggFiv)
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tenScoreOne = torch.nn.functional.interpolate(input=tenScoreOne, size=(tenInput.shape[2], tenInput.shape[3]), mode='bilinear', align_corners=False)
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tenScoreTwo = torch.nn.functional.interpolate(input=tenScoreTwo, size=(tenInput.shape[2], tenInput.shape[3]), mode='bilinear', align_corners=False)
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tenScoreThr = torch.nn.functional.interpolate(input=tenScoreThr, size=(tenInput.shape[2], tenInput.shape[3]), mode='bilinear', align_corners=False)
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tenScoreFou = torch.nn.functional.interpolate(input=tenScoreFou, size=(tenInput.shape[2], tenInput.shape[3]), mode='bilinear', align_corners=False)
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tenScoreFiv = torch.nn.functional.interpolate(input=tenScoreFiv, size=(tenInput.shape[2], tenInput.shape[3]), mode='bilinear', align_corners=False)
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return self.netCombine(torch.cat([ tenScoreOne, tenScoreTwo, tenScoreThr, tenScoreFou, tenScoreFiv ], 1))
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# end
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# end
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netNetwork = None
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remote_model_path = "https://huggingface.co/lllyasviel/ControlNet/resolve/main/annotator/ckpts/network-bsds500.pth"
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modeldir = os.path.join(models_path, "hed")
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old_modeldir = os.path.dirname(os.path.realpath(__file__))
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def apply_hed(input_image, is_safe=False):
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global netNetwork
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if netNetwork is None:
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modelpath = os.path.join(modeldir, "network-bsds500.pth")
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old_modelpath = os.path.join(old_modeldir, "network-bsds500.pth")
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if os.path.exists(old_modelpath):
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modelpath = old_modelpath
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elif not os.path.exists(modelpath):
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from basicsr.utils.download_util import load_file_from_url
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load_file_from_url(remote_model_path, model_dir=modeldir)
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netNetwork = Network(modelpath)
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netNetwork.to(devices.get_device_for("controlnet")).eval()
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assert input_image.ndim == 3
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input_image = input_image[:, :, ::-1].copy()
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with torch.no_grad():
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image_hed = torch.from_numpy(input_image).float().to(devices.get_device_for("controlnet"))
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image_hed = image_hed / 255.0
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image_hed = rearrange(image_hed, 'h w c -> 1 c h w')
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edge = netNetwork(image_hed)[0]
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edge = edge.cpu().numpy()
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if is_safe:
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edge = safe_step(edge)
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edge = (edge * 255.0).clip(0, 255).astype(np.uint8)
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return edge[0]
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def unload_hed_model():
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global netNetwork
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if netNetwork is not None:
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netNetwork.cpu()
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