diff --git a/CCEdit-main/src/controlnet11/annotator/zoe/zoedepth/data/ddad.py b/CCEdit-main/src/controlnet11/annotator/zoe/zoedepth/data/ddad.py new file mode 100644 index 0000000000000000000000000000000000000000..4bd0492bdec767685d3a21992b4a26e62d002d97 --- /dev/null +++ b/CCEdit-main/src/controlnet11/annotator/zoe/zoedepth/data/ddad.py @@ -0,0 +1,117 @@ +# MIT License + +# Copyright (c) 2022 Intelligent Systems Lab Org + +# Permission is hereby granted, free of charge, to any person obtaining a copy +# of this software and associated documentation files (the "Software"), to deal +# in the Software without restriction, including without limitation the rights +# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell +# copies of the Software, and to permit persons to whom the Software is +# furnished to do so, subject to the following conditions: + +# The above copyright notice and this permission notice shall be included in all +# copies or substantial portions of the Software. + +# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE +# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER +# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, +# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE +# SOFTWARE. + +# File author: Shariq Farooq Bhat + +import os + +import numpy as np +import torch +from PIL import Image +from torch.utils.data import DataLoader, Dataset +from torchvision import transforms + + +class ToTensor(object): + def __init__(self, resize_shape): + # self.normalize = transforms.Normalize( + # mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]) + self.normalize = lambda x : x + self.resize = transforms.Resize(resize_shape) + + def __call__(self, sample): + image, depth = sample['image'], sample['depth'] + image = self.to_tensor(image) + image = self.normalize(image) + depth = self.to_tensor(depth) + + image = self.resize(image) + + return {'image': image, 'depth': depth, 'dataset': "ddad"} + + def to_tensor(self, pic): + + if isinstance(pic, np.ndarray): + img = torch.from_numpy(pic.transpose((2, 0, 1))) + return img + + # # handle PIL Image + if pic.mode == 'I': + img = torch.from_numpy(np.array(pic, np.int32, copy=False)) + elif pic.mode == 'I;16': + img = torch.from_numpy(np.array(pic, np.int16, copy=False)) + else: + img = torch.ByteTensor( + torch.ByteStorage.from_buffer(pic.tobytes())) + # PIL image mode: 1, L, P, I, F, RGB, YCbCr, RGBA, CMYK + if pic.mode == 'YCbCr': + nchannel = 3 + elif pic.mode == 'I;16': + nchannel = 1 + else: + nchannel = len(pic.mode) + img = img.view(pic.size[1], pic.size[0], nchannel) + + img = img.transpose(0, 1).transpose(0, 2).contiguous() + + if isinstance(img, torch.ByteTensor): + return img.float() + else: + return img + + +class DDAD(Dataset): + def __init__(self, data_dir_root, resize_shape): + import glob + + # image paths are of the form /{outleft, depthmap}/*.png + self.image_files = glob.glob(os.path.join(data_dir_root, '*.png')) + self.depth_files = [r.replace("_rgb.png", "_depth.npy") + for r in self.image_files] + self.transform = ToTensor(resize_shape) + + def __getitem__(self, idx): + + image_path = self.image_files[idx] + depth_path = self.depth_files[idx] + + image = np.asarray(Image.open(image_path), dtype=np.float32) / 255.0 + depth = np.load(depth_path) # meters + + # depth[depth > 8] = -1 + depth = depth[..., None] + + sample = dict(image=image, depth=depth) + sample = self.transform(sample) + + if idx == 0: + print(sample["image"].shape) + + return sample + + def __len__(self): + return len(self.image_files) + + +def get_ddad_loader(data_dir_root, resize_shape, batch_size=1, **kwargs): + dataset = DDAD(data_dir_root, resize_shape) + return DataLoader(dataset, batch_size, **kwargs) diff --git a/CCEdit-main/src/controlnet11/annotator/zoe/zoedepth/data/diml_indoor_test.py b/CCEdit-main/src/controlnet11/annotator/zoe/zoedepth/data/diml_indoor_test.py new file mode 100644 index 0000000000000000000000000000000000000000..f720ad9aefaee78ef4ec363dfef0f82ace850a6d --- /dev/null +++ b/CCEdit-main/src/controlnet11/annotator/zoe/zoedepth/data/diml_indoor_test.py @@ -0,0 +1,125 @@ +# MIT License + +# Copyright (c) 2022 Intelligent Systems Lab Org + +# Permission is hereby granted, free of charge, to any person obtaining a copy +# of this software and associated documentation files (the "Software"), to deal +# in the Software without restriction, including without limitation the rights +# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell +# copies of the Software, and to permit persons to whom the Software is +# furnished to do so, subject to the following conditions: + +# The above copyright notice and this permission notice shall be included in all +# copies or substantial portions of the Software. + +# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE +# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER +# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, +# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE +# SOFTWARE. + +# File author: Shariq Farooq Bhat + +import os + +import numpy as np +import torch +from PIL import Image +from torch.utils.data import DataLoader, Dataset +from torchvision import transforms + + +class ToTensor(object): + def __init__(self): + # self.normalize = transforms.Normalize( + # mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]) + self.normalize = lambda x : x + self.resize = transforms.Resize((480, 640)) + + def __call__(self, sample): + image, depth = sample['image'], sample['depth'] + image = self.to_tensor(image) + image = self.normalize(image) + depth = self.to_tensor(depth) + + image = self.resize(image) + + return {'image': image, 'depth': depth, 'dataset': "diml_indoor"} + + def to_tensor(self, pic): + + if isinstance(pic, np.ndarray): + img = torch.from_numpy(pic.transpose((2, 0, 1))) + return img + + # # handle PIL Image + if pic.mode == 'I': + img = torch.from_numpy(np.array(pic, np.int32, copy=False)) + elif pic.mode == 'I;16': + img = torch.from_numpy(np.array(pic, np.int16, copy=False)) + else: + img = torch.ByteTensor( + torch.ByteStorage.from_buffer(pic.tobytes())) + # PIL image mode: 1, L, P, I, F, RGB, YCbCr, RGBA, CMYK + if pic.mode == 'YCbCr': + nchannel = 3 + elif pic.mode == 'I;16': + nchannel = 1 + else: + nchannel = len(pic.mode) + img = img.view(pic.size[1], pic.size[0], nchannel) + + img = img.transpose(0, 1).transpose(0, 2).contiguous() + if isinstance(img, torch.ByteTensor): + return img.float() + else: + return img + + +class DIML_Indoor(Dataset): + def __init__(self, data_dir_root): + import glob + + # image paths are of the form /{HR, LR}//{color, depth_filled}/*.png + self.image_files = glob.glob(os.path.join( + data_dir_root, "LR", '*', 'color', '*.png')) + self.depth_files = [r.replace("color", "depth_filled").replace( + "_c.png", "_depth_filled.png") for r in self.image_files] + self.transform = ToTensor() + + def __getitem__(self, idx): + image_path = self.image_files[idx] + depth_path = self.depth_files[idx] + + image = np.asarray(Image.open(image_path), dtype=np.float32) / 255.0 + depth = np.asarray(Image.open(depth_path), + dtype='uint16') / 1000.0 # mm to meters + + # print(np.shape(image)) + # print(np.shape(depth)) + + # depth[depth > 8] = -1 + depth = depth[..., None] + + sample = dict(image=image, depth=depth) + + # return sample + sample = self.transform(sample) + + if idx == 0: + print(sample["image"].shape) + + return sample + + def __len__(self): + return len(self.image_files) + + +def get_diml_indoor_loader(data_dir_root, batch_size=1, **kwargs): + dataset = DIML_Indoor(data_dir_root) + return DataLoader(dataset, batch_size, **kwargs) + +# get_diml_indoor_loader(data_dir_root="datasets/diml/indoor/test/HR") +# get_diml_indoor_loader(data_dir_root="datasets/diml/indoor/test/LR") diff --git a/CCEdit-main/src/controlnet11/annotator/zoe/zoedepth/data/diml_outdoor_test.py b/CCEdit-main/src/controlnet11/annotator/zoe/zoedepth/data/diml_outdoor_test.py new file mode 100644 index 0000000000000000000000000000000000000000..8670b48f5febafb819dac22848ad79ccb5dd5ae4 --- /dev/null +++ b/CCEdit-main/src/controlnet11/annotator/zoe/zoedepth/data/diml_outdoor_test.py @@ -0,0 +1,114 @@ +# MIT License + +# Copyright (c) 2022 Intelligent Systems Lab Org + +# Permission is hereby granted, free of charge, to any person obtaining a copy +# of this software and associated documentation files (the "Software"), to deal +# in the Software without restriction, including without limitation the rights +# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell +# copies of the Software, and to permit persons to whom the Software is +# furnished to do so, subject to the following conditions: + +# The above copyright notice and this permission notice shall be included in all +# copies or substantial portions of the Software. + +# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE +# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER +# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, +# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE +# SOFTWARE. + +# File author: Shariq Farooq Bhat + +import os + +import numpy as np +import torch +from PIL import Image +from torch.utils.data import DataLoader, Dataset +from torchvision import transforms + + +class ToTensor(object): + def __init__(self): + # self.normalize = transforms.Normalize( + # mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]) + self.normalize = lambda x : x + + def __call__(self, sample): + image, depth = sample['image'], sample['depth'] + image = self.to_tensor(image) + image = self.normalize(image) + depth = self.to_tensor(depth) + + return {'image': image, 'depth': depth, 'dataset': "diml_outdoor"} + + def to_tensor(self, pic): + + if isinstance(pic, np.ndarray): + img = torch.from_numpy(pic.transpose((2, 0, 1))) + return img + + # # handle PIL Image + if pic.mode == 'I': + img = torch.from_numpy(np.array(pic, np.int32, copy=False)) + elif pic.mode == 'I;16': + img = torch.from_numpy(np.array(pic, np.int16, copy=False)) + else: + img = torch.ByteTensor( + torch.ByteStorage.from_buffer(pic.tobytes())) + # PIL image mode: 1, L, P, I, F, RGB, YCbCr, RGBA, CMYK + if pic.mode == 'YCbCr': + nchannel = 3 + elif pic.mode == 'I;16': + nchannel = 1 + else: + nchannel = len(pic.mode) + img = img.view(pic.size[1], pic.size[0], nchannel) + + img = img.transpose(0, 1).transpose(0, 2).contiguous() + if isinstance(img, torch.ByteTensor): + return img.float() + else: + return img + + +class DIML_Outdoor(Dataset): + def __init__(self, data_dir_root): + import glob + + # image paths are of the form /{outleft, depthmap}/*.png + self.image_files = glob.glob(os.path.join( + data_dir_root, "*", 'outleft', '*.png')) + self.depth_files = [r.replace("outleft", "depthmap") + for r in self.image_files] + self.transform = ToTensor() + + def __getitem__(self, idx): + image_path = self.image_files[idx] + depth_path = self.depth_files[idx] + + image = np.asarray(Image.open(image_path), dtype=np.float32) / 255.0 + depth = np.asarray(Image.open(depth_path), + dtype='uint16') / 1000.0 # mm to meters + + # depth[depth > 8] = -1 + depth = depth[..., None] + + sample = dict(image=image, depth=depth, dataset="diml_outdoor") + + # return sample + return self.transform(sample) + + def __len__(self): + return len(self.image_files) + + +def get_diml_outdoor_loader(data_dir_root, batch_size=1, **kwargs): + dataset = DIML_Outdoor(data_dir_root) + return DataLoader(dataset, batch_size, **kwargs) + +# get_diml_outdoor_loader(data_dir_root="datasets/diml/outdoor/test/HR") +# get_diml_outdoor_loader(data_dir_root="datasets/diml/outdoor/test/LR") diff --git a/CCEdit-main/src/controlnet11/annotator/zoe/zoedepth/data/diode.py b/CCEdit-main/src/controlnet11/annotator/zoe/zoedepth/data/diode.py new file mode 100644 index 0000000000000000000000000000000000000000..1510c87116b8f70ce2e1428873a8e4da042bee23 --- /dev/null +++ b/CCEdit-main/src/controlnet11/annotator/zoe/zoedepth/data/diode.py @@ -0,0 +1,125 @@ +# MIT License + +# Copyright (c) 2022 Intelligent Systems Lab Org + +# Permission is hereby granted, free of charge, to any person obtaining a copy +# of this software and associated documentation files (the "Software"), to deal +# in the Software without restriction, including without limitation the rights +# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell +# copies of the Software, and to permit persons to whom the Software is +# furnished to do so, subject to the following conditions: + +# The above copyright notice and this permission notice shall be included in all +# copies or substantial portions of the Software. + +# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE +# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER +# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, +# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE +# SOFTWARE. + +# File author: Shariq Farooq Bhat + +import os + +import numpy as np +import torch +from PIL import Image +from torch.utils.data import DataLoader, Dataset +from torchvision import transforms + + +class ToTensor(object): + def __init__(self): + # self.normalize = transforms.Normalize( + # mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]) + self.normalize = lambda x : x + self.resize = transforms.Resize(480) + + def __call__(self, sample): + image, depth = sample['image'], sample['depth'] + image = self.to_tensor(image) + image = self.normalize(image) + depth = self.to_tensor(depth) + + image = self.resize(image) + + return {'image': image, 'depth': depth, 'dataset': "diode"} + + def to_tensor(self, pic): + + if isinstance(pic, np.ndarray): + img = torch.from_numpy(pic.transpose((2, 0, 1))) + return img + + # # handle PIL Image + if pic.mode == 'I': + img = torch.from_numpy(np.array(pic, np.int32, copy=False)) + elif pic.mode == 'I;16': + img = torch.from_numpy(np.array(pic, np.int16, copy=False)) + else: + img = torch.ByteTensor( + torch.ByteStorage.from_buffer(pic.tobytes())) + # PIL image mode: 1, L, P, I, F, RGB, YCbCr, RGBA, CMYK + if pic.mode == 'YCbCr': + nchannel = 3 + elif pic.mode == 'I;16': + nchannel = 1 + else: + nchannel = len(pic.mode) + img = img.view(pic.size[1], pic.size[0], nchannel) + + img = img.transpose(0, 1).transpose(0, 2).contiguous() + + if isinstance(img, torch.ByteTensor): + return img.float() + else: + return img + + +class DIODE(Dataset): + def __init__(self, data_dir_root): + import glob + + # image paths are of the form /scene_#/scan_#/*.png + self.image_files = glob.glob( + os.path.join(data_dir_root, '*', '*', '*.png')) + self.depth_files = [r.replace(".png", "_depth.npy") + for r in self.image_files] + self.depth_mask_files = [ + r.replace(".png", "_depth_mask.npy") for r in self.image_files] + self.transform = ToTensor() + + def __getitem__(self, idx): + image_path = self.image_files[idx] + depth_path = self.depth_files[idx] + depth_mask_path = self.depth_mask_files[idx] + + image = np.asarray(Image.open(image_path), dtype=np.float32) / 255.0 + depth = np.load(depth_path) # in meters + valid = np.load(depth_mask_path) # binary + + # depth[depth > 8] = -1 + # depth = depth[..., None] + + sample = dict(image=image, depth=depth, valid=valid) + + # return sample + sample = self.transform(sample) + + if idx == 0: + print(sample["image"].shape) + + return sample + + def __len__(self): + return len(self.image_files) + + +def get_diode_loader(data_dir_root, batch_size=1, **kwargs): + dataset = DIODE(data_dir_root) + return DataLoader(dataset, batch_size, **kwargs) + +# get_diode_loader(data_dir_root="datasets/diode/val/outdoor") diff --git a/CCEdit-main/src/controlnet11/annotator/zoe/zoedepth/data/hypersim.py b/CCEdit-main/src/controlnet11/annotator/zoe/zoedepth/data/hypersim.py new file mode 100644 index 0000000000000000000000000000000000000000..4334198971830200f72ea2910d03f4c7d6a43334 --- /dev/null +++ b/CCEdit-main/src/controlnet11/annotator/zoe/zoedepth/data/hypersim.py @@ -0,0 +1,138 @@ +# MIT License + +# Copyright (c) 2022 Intelligent Systems Lab Org + +# Permission is hereby granted, free of charge, to any person obtaining a copy +# of this software and associated documentation files (the "Software"), to deal +# in the Software without restriction, including without limitation the rights +# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell +# copies of the Software, and to permit persons to whom the Software is +# furnished to do so, subject to the following conditions: + +# The above copyright notice and this permission notice shall be included in all +# copies or substantial portions of the Software. + +# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE +# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER +# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, +# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE +# SOFTWARE. + +# File author: Shariq Farooq Bhat + +import glob +import os + +import h5py +import numpy as np +import torch +from PIL import Image +from torch.utils.data import DataLoader, Dataset +from torchvision import transforms + + +def hypersim_distance_to_depth(npyDistance): + intWidth, intHeight, fltFocal = 1024, 768, 886.81 + + npyImageplaneX = np.linspace((-0.5 * intWidth) + 0.5, (0.5 * intWidth) - 0.5, intWidth).reshape( + 1, intWidth).repeat(intHeight, 0).astype(np.float32)[:, :, None] + npyImageplaneY = np.linspace((-0.5 * intHeight) + 0.5, (0.5 * intHeight) - 0.5, + intHeight).reshape(intHeight, 1).repeat(intWidth, 1).astype(np.float32)[:, :, None] + npyImageplaneZ = np.full([intHeight, intWidth, 1], fltFocal, np.float32) + npyImageplane = np.concatenate( + [npyImageplaneX, npyImageplaneY, npyImageplaneZ], 2) + + npyDepth = npyDistance / np.linalg.norm(npyImageplane, 2, 2) * fltFocal + return npyDepth + + +class ToTensor(object): + def __init__(self): + # self.normalize = transforms.Normalize( + # mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]) + self.normalize = lambda x: x + self.resize = transforms.Resize((480, 640)) + + def __call__(self, sample): + image, depth = sample['image'], sample['depth'] + image = self.to_tensor(image) + image = self.normalize(image) + depth = self.to_tensor(depth) + + image = self.resize(image) + + return {'image': image, 'depth': depth, 'dataset': "hypersim"} + + def to_tensor(self, pic): + + if isinstance(pic, np.ndarray): + img = torch.from_numpy(pic.transpose((2, 0, 1))) + return img + + # # handle PIL Image + if pic.mode == 'I': + img = torch.from_numpy(np.array(pic, np.int32, copy=False)) + elif pic.mode == 'I;16': + img = torch.from_numpy(np.array(pic, np.int16, copy=False)) + else: + img = torch.ByteTensor( + torch.ByteStorage.from_buffer(pic.tobytes())) + # PIL image mode: 1, L, P, I, F, RGB, YCbCr, RGBA, CMYK + if pic.mode == 'YCbCr': + nchannel = 3 + elif pic.mode == 'I;16': + nchannel = 1 + else: + nchannel = len(pic.mode) + img = img.view(pic.size[1], pic.size[0], nchannel) + + img = img.transpose(0, 1).transpose(0, 2).contiguous() + if isinstance(img, torch.ByteTensor): + return img.float() + else: + return img + + +class HyperSim(Dataset): + def __init__(self, data_dir_root): + # image paths are of the form //images/scene_cam_#_final_preview/*.tonemap.jpg + # depth paths are of the form //images/scene_cam_#_final_preview/*.depth_meters.hdf5 + self.image_files = glob.glob(os.path.join( + data_dir_root, '*', 'images', 'scene_cam_*_final_preview', '*.tonemap.jpg')) + self.depth_files = [r.replace("_final_preview", "_geometry_hdf5").replace( + ".tonemap.jpg", ".depth_meters.hdf5") for r in self.image_files] + self.transform = ToTensor() + + def __getitem__(self, idx): + image_path = self.image_files[idx] + depth_path = self.depth_files[idx] + + image = np.asarray(Image.open(image_path), dtype=np.float32) / 255.0 + + # depth from hdf5 + depth_fd = h5py.File(depth_path, "r") + # in meters (Euclidean distance) + distance_meters = np.array(depth_fd['dataset']) + depth = hypersim_distance_to_depth( + distance_meters) # in meters (planar depth) + + # depth[depth > 8] = -1 + depth = depth[..., None] + + sample = dict(image=image, depth=depth) + sample = self.transform(sample) + + if idx == 0: + print(sample["image"].shape) + + return sample + + def __len__(self): + return len(self.image_files) + + +def get_hypersim_loader(data_dir_root, batch_size=1, **kwargs): + dataset = HyperSim(data_dir_root) + return DataLoader(dataset, batch_size, **kwargs) diff --git a/CCEdit-main/src/controlnet11/annotator/zoe/zoedepth/data/ibims.py b/CCEdit-main/src/controlnet11/annotator/zoe/zoedepth/data/ibims.py new file mode 100644 index 0000000000000000000000000000000000000000..b66abfabcf4cfc617d4a60ec818780c3548d9920 --- /dev/null +++ b/CCEdit-main/src/controlnet11/annotator/zoe/zoedepth/data/ibims.py @@ -0,0 +1,81 @@ +# MIT License + +# Copyright (c) 2022 Intelligent Systems Lab Org + +# Permission is hereby granted, free of charge, to any person obtaining a copy +# of this software and associated documentation files (the "Software"), to deal +# in the Software without restriction, including without limitation the rights +# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell +# copies of the Software, and to permit persons to whom the Software is +# furnished to do so, subject to the following conditions: + +# The above copyright notice and this permission notice shall be included in all +# copies or substantial portions of the Software. + +# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE +# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER +# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, +# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE +# SOFTWARE. + +# File author: Shariq Farooq Bhat + +import os + +import numpy as np +import torch +from PIL import Image +from torch.utils.data import DataLoader, Dataset +from torchvision import transforms as T + + +class iBims(Dataset): + def __init__(self, config): + root_folder = config.ibims_root + with open(os.path.join(root_folder, "imagelist.txt"), 'r') as f: + imglist = f.read().split() + + samples = [] + for basename in imglist: + img_path = os.path.join(root_folder, 'rgb', basename + ".png") + depth_path = os.path.join(root_folder, 'depth', basename + ".png") + valid_mask_path = os.path.join( + root_folder, 'mask_invalid', basename+".png") + transp_mask_path = os.path.join( + root_folder, 'mask_transp', basename+".png") + + samples.append( + (img_path, depth_path, valid_mask_path, transp_mask_path)) + + self.samples = samples + # self.normalize = T.Normalize( + # mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]) + self.normalize = lambda x : x + + def __getitem__(self, idx): + img_path, depth_path, valid_mask_path, transp_mask_path = self.samples[idx] + + img = np.asarray(Image.open(img_path), dtype=np.float32) / 255.0 + depth = np.asarray(Image.open(depth_path), + dtype=np.uint16).astype('float')*50.0/65535 + + mask_valid = np.asarray(Image.open(valid_mask_path)) + mask_transp = np.asarray(Image.open(transp_mask_path)) + + # depth = depth * mask_valid * mask_transp + depth = np.where(mask_valid * mask_transp, depth, -1) + + img = torch.from_numpy(img).permute(2, 0, 1) + img = self.normalize(img) + depth = torch.from_numpy(depth).unsqueeze(0) + return dict(image=img, depth=depth, image_path=img_path, depth_path=depth_path, dataset='ibims') + + def __len__(self): + return len(self.samples) + + +def get_ibims_loader(config, batch_size=1, **kwargs): + dataloader = DataLoader(iBims(config), batch_size=batch_size, **kwargs) + return dataloader diff --git a/CCEdit-main/src/controlnet11/annotator/zoe/zoedepth/data/preprocess.py b/CCEdit-main/src/controlnet11/annotator/zoe/zoedepth/data/preprocess.py new file mode 100644 index 0000000000000000000000000000000000000000..e08cc309dc823ae6efd7cda8db9eb37130dc5499 --- /dev/null +++ b/CCEdit-main/src/controlnet11/annotator/zoe/zoedepth/data/preprocess.py @@ -0,0 +1,154 @@ +# MIT License + +# Copyright (c) 2022 Intelligent Systems Lab Org + +# Permission is hereby granted, free of charge, to any person obtaining a copy +# of this software and associated documentation files (the "Software"), to deal +# in the Software without restriction, including without limitation the rights +# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell +# copies of the Software, and to permit persons to whom the Software is +# furnished to do so, subject to the following conditions: + +# The above copyright notice and this permission notice shall be included in all +# copies or substantial portions of the Software. + +# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE +# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER +# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, +# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE +# SOFTWARE. + +# File author: Shariq Farooq Bhat + +import numpy as np +from dataclasses import dataclass +from typing import Tuple, List + +# dataclass to store the crop parameters +@dataclass +class CropParams: + top: int + bottom: int + left: int + right: int + + + +def get_border_params(rgb_image, tolerance=0.1, cut_off=20, value=0, level_diff_threshold=5, channel_axis=-1, min_border=5) -> CropParams: + gray_image = np.mean(rgb_image, axis=channel_axis) + h, w = gray_image.shape + + + def num_value_pixels(arr): + return np.sum(np.abs(arr - value) < level_diff_threshold) + + def is_above_tolerance(arr, total_pixels): + return (num_value_pixels(arr) / total_pixels) > tolerance + + # Crop top border until number of value pixels become below tolerance + top = min_border + while is_above_tolerance(gray_image[top, :], w) and top < h-1: + top += 1 + if top > cut_off: + break + + # Crop bottom border until number of value pixels become below tolerance + bottom = h - min_border + while is_above_tolerance(gray_image[bottom, :], w) and bottom > 0: + bottom -= 1 + if h - bottom > cut_off: + break + + # Crop left border until number of value pixels become below tolerance + left = min_border + while is_above_tolerance(gray_image[:, left], h) and left < w-1: + left += 1 + if left > cut_off: + break + + # Crop right border until number of value pixels become below tolerance + right = w - min_border + while is_above_tolerance(gray_image[:, right], h) and right > 0: + right -= 1 + if w - right > cut_off: + break + + + return CropParams(top, bottom, left, right) + + +def get_white_border(rgb_image, value=255, **kwargs) -> CropParams: + """Crops the white border of the RGB. + + Args: + rgb: RGB image, shape (H, W, 3). + Returns: + Crop parameters. + """ + if value == 255: + # assert range of values in rgb image is [0, 255] + assert np.max(rgb_image) <= 255 and np.min(rgb_image) >= 0, "RGB image values are not in range [0, 255]." + assert rgb_image.max() > 1, "RGB image values are not in range [0, 255]." + elif value == 1: + # assert range of values in rgb image is [0, 1] + assert np.max(rgb_image) <= 1 and np.min(rgb_image) >= 0, "RGB image values are not in range [0, 1]." + + return get_border_params(rgb_image, value=value, **kwargs) + +def get_black_border(rgb_image, **kwargs) -> CropParams: + """Crops the black border of the RGB. + + Args: + rgb: RGB image, shape (H, W, 3). + + Returns: + Crop parameters. + """ + + return get_border_params(rgb_image, value=0, **kwargs) + +def crop_image(image: np.ndarray, crop_params: CropParams) -> np.ndarray: + """Crops the image according to the crop parameters. + + Args: + image: RGB or depth image, shape (H, W, 3) or (H, W). + crop_params: Crop parameters. + + Returns: + Cropped image. + """ + return image[crop_params.top:crop_params.bottom, crop_params.left:crop_params.right] + +def crop_images(*images: np.ndarray, crop_params: CropParams) -> Tuple[np.ndarray]: + """Crops the images according to the crop parameters. + + Args: + images: RGB or depth images, shape (H, W, 3) or (H, W). + crop_params: Crop parameters. + + Returns: + Cropped images. + """ + return tuple(crop_image(image, crop_params) for image in images) + +def crop_black_or_white_border(rgb_image, *other_images: np.ndarray, tolerance=0.1, cut_off=20, level_diff_threshold=5) -> Tuple[np.ndarray]: + """Crops the white and black border of the RGB and depth images. + + Args: + rgb: RGB image, shape (H, W, 3). This image is used to determine the border. + other_images: The other images to crop according to the border of the RGB image. + Returns: + Cropped RGB and other images. + """ + # crop black border + crop_params = get_black_border(rgb_image, tolerance=tolerance, cut_off=cut_off, level_diff_threshold=level_diff_threshold) + cropped_images = crop_images(rgb_image, *other_images, crop_params=crop_params) + + # crop white border + crop_params = get_white_border(cropped_images[0], tolerance=tolerance, cut_off=cut_off, level_diff_threshold=level_diff_threshold) + cropped_images = crop_images(*cropped_images, crop_params=crop_params) + + return cropped_images + \ No newline at end of file diff --git a/CCEdit-main/src/controlnet11/annotator/zoe/zoedepth/data/sun_rgbd_loader.py b/CCEdit-main/src/controlnet11/annotator/zoe/zoedepth/data/sun_rgbd_loader.py new file mode 100644 index 0000000000000000000000000000000000000000..9e2bdb9aefe68ca4439f41eff3bba722c49fb976 --- /dev/null +++ b/CCEdit-main/src/controlnet11/annotator/zoe/zoedepth/data/sun_rgbd_loader.py @@ -0,0 +1,106 @@ +# MIT License + +# Copyright (c) 2022 Intelligent Systems Lab Org + +# Permission is hereby granted, free of charge, to any person obtaining a copy +# of this software and associated documentation files (the "Software"), to deal +# in the Software without restriction, including without limitation the rights +# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell +# copies of the Software, and to permit persons to whom the Software is +# furnished to do so, subject to the following conditions: + +# The above copyright notice and this permission notice shall be included in all +# copies or substantial portions of the Software. + +# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE +# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER +# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, +# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE +# SOFTWARE. + +# File author: Shariq Farooq Bhat + +import os + +import numpy as np +import torch +from PIL import Image +from torch.utils.data import DataLoader, Dataset +from torchvision import transforms + + +class ToTensor(object): + def __init__(self): + # self.normalize = transforms.Normalize( + # mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]) + self.normalize = lambda x : x + + def __call__(self, sample): + image, depth = sample['image'], sample['depth'] + image = self.to_tensor(image) + image = self.normalize(image) + depth = self.to_tensor(depth) + + return {'image': image, 'depth': depth, 'dataset': "sunrgbd"} + + def to_tensor(self, pic): + + if isinstance(pic, np.ndarray): + img = torch.from_numpy(pic.transpose((2, 0, 1))) + return img + + # # handle PIL Image + if pic.mode == 'I': + img = torch.from_numpy(np.array(pic, np.int32, copy=False)) + elif pic.mode == 'I;16': + img = torch.from_numpy(np.array(pic, np.int16, copy=False)) + else: + img = torch.ByteTensor( + torch.ByteStorage.from_buffer(pic.tobytes())) + # PIL image mode: 1, L, P, I, F, RGB, YCbCr, RGBA, CMYK + if pic.mode == 'YCbCr': + nchannel = 3 + elif pic.mode == 'I;16': + nchannel = 1 + else: + nchannel = len(pic.mode) + img = img.view(pic.size[1], pic.size[0], nchannel) + + img = img.transpose(0, 1).transpose(0, 2).contiguous() + if isinstance(img, torch.ByteTensor): + return img.float() + else: + return img + + +class SunRGBD(Dataset): + def __init__(self, data_dir_root): + # test_file_dirs = loadmat(train_test_file)['alltest'].squeeze() + # all_test = [t[0].replace("/n/fs/sun3d/data/", "") for t in test_file_dirs] + # self.all_test = [os.path.join(data_dir_root, t) for t in all_test] + import glob + self.image_files = glob.glob( + os.path.join(data_dir_root, 'rgb', 'rgb', '*')) + self.depth_files = [ + r.replace("rgb/rgb", "gt/gt").replace("jpg", "png") for r in self.image_files] + self.transform = ToTensor() + + def __getitem__(self, idx): + image_path = self.image_files[idx] + depth_path = self.depth_files[idx] + + image = np.asarray(Image.open(image_path), dtype=np.float32) / 255.0 + depth = np.asarray(Image.open(depth_path), dtype='uint16') / 1000.0 + depth[depth > 8] = -1 + depth = depth[..., None] + return self.transform(dict(image=image, depth=depth)) + + def __len__(self): + return len(self.image_files) + + +def get_sunrgbd_loader(data_dir_root, batch_size=1, **kwargs): + dataset = SunRGBD(data_dir_root) + return DataLoader(dataset, batch_size, **kwargs) diff --git a/CCEdit-main/src/controlnet11/annotator/zoe/zoedepth/data/transforms.py b/CCEdit-main/src/controlnet11/annotator/zoe/zoedepth/data/transforms.py new file mode 100644 index 0000000000000000000000000000000000000000..374416dff24fb4fd55598f3946d6d6b091ddefc9 --- /dev/null +++ b/CCEdit-main/src/controlnet11/annotator/zoe/zoedepth/data/transforms.py @@ -0,0 +1,481 @@ +# MIT License + +# Copyright (c) 2022 Intelligent Systems Lab Org + +# Permission is hereby granted, free of charge, to any person obtaining a copy +# of this software and associated documentation files (the "Software"), to deal +# in the Software without restriction, including without limitation the rights +# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell +# copies of the Software, and to permit persons to whom the Software is +# furnished to do so, subject to the following conditions: + +# The above copyright notice and this permission notice shall be included in all +# copies or substantial portions of the Software. + +# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE +# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER +# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, +# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE +# SOFTWARE. + +# File author: Shariq Farooq Bhat + +import math +import random + +import cv2 +import numpy as np + + +class RandomFliplr(object): + """Horizontal flip of the sample with given probability. + """ + + def __init__(self, probability=0.5): + """Init. + + Args: + probability (float, optional): Flip probability. Defaults to 0.5. + """ + self.__probability = probability + + def __call__(self, sample): + prob = random.random() + + if prob < self.__probability: + for k, v in sample.items(): + if len(v.shape) >= 2: + sample[k] = np.fliplr(v).copy() + + return sample + + +def apply_min_size(sample, size, image_interpolation_method=cv2.INTER_AREA): + """Rezise the sample to ensure the given size. Keeps aspect ratio. + + Args: + sample (dict): sample + size (tuple): image size + + Returns: + tuple: new size + """ + shape = list(sample["disparity"].shape) + + if shape[0] >= size[0] and shape[1] >= size[1]: + return sample + + scale = [0, 0] + scale[0] = size[0] / shape[0] + scale[1] = size[1] / shape[1] + + scale = max(scale) + + shape[0] = math.ceil(scale * shape[0]) + shape[1] = math.ceil(scale * shape[1]) + + # resize + sample["image"] = cv2.resize( + sample["image"], tuple(shape[::-1]), interpolation=image_interpolation_method + ) + + sample["disparity"] = cv2.resize( + sample["disparity"], tuple(shape[::-1]), interpolation=cv2.INTER_NEAREST + ) + sample["mask"] = cv2.resize( + sample["mask"].astype(np.float32), + tuple(shape[::-1]), + interpolation=cv2.INTER_NEAREST, + ) + sample["mask"] = sample["mask"].astype(bool) + + return tuple(shape) + + +class RandomCrop(object): + """Get a random crop of the sample with the given size (width, height). + """ + + def __init__( + self, + width, + height, + resize_if_needed=False, + image_interpolation_method=cv2.INTER_AREA, + ): + """Init. + + Args: + width (int): output width + height (int): output height + resize_if_needed (bool, optional): If True, sample might be upsampled to ensure + that a crop of size (width, height) is possbile. Defaults to False. + """ + self.__size = (height, width) + self.__resize_if_needed = resize_if_needed + self.__image_interpolation_method = image_interpolation_method + + def __call__(self, sample): + + shape = sample["disparity"].shape + + if self.__size[0] > shape[0] or self.__size[1] > shape[1]: + if self.__resize_if_needed: + shape = apply_min_size( + sample, self.__size, self.__image_interpolation_method + ) + else: + raise Exception( + "Output size {} bigger than input size {}.".format( + self.__size, shape + ) + ) + + offset = ( + np.random.randint(shape[0] - self.__size[0] + 1), + np.random.randint(shape[1] - self.__size[1] + 1), + ) + + for k, v in sample.items(): + if k == "code" or k == "basis": + continue + + if len(sample[k].shape) >= 2: + sample[k] = v[ + offset[0]: offset[0] + self.__size[0], + offset[1]: offset[1] + self.__size[1], + ] + + return sample + + +class Resize(object): + """Resize sample to given size (width, height). + """ + + def __init__( + self, + width, + height, + resize_target=True, + keep_aspect_ratio=False, + ensure_multiple_of=1, + resize_method="lower_bound", + image_interpolation_method=cv2.INTER_AREA, + letter_box=False, + ): + """Init. + + Args: + width (int): desired output width + height (int): desired output height + resize_target (bool, optional): + True: Resize the full sample (image, mask, target). + False: Resize image only. + Defaults to True. + keep_aspect_ratio (bool, optional): + True: Keep the aspect ratio of the input sample. + Output sample might not have the given width and height, and + resize behaviour depends on the parameter 'resize_method'. + Defaults to False. + ensure_multiple_of (int, optional): + Output width and height is constrained to be multiple of this parameter. + Defaults to 1. + resize_method (str, optional): + "lower_bound": Output will be at least as large as the given size. + "upper_bound": Output will be at max as large as the given size. (Output size might be smaller than given size.) + "minimal": Scale as least as possible. (Output size might be smaller than given size.) + Defaults to "lower_bound". + """ + self.__width = width + self.__height = height + + self.__resize_target = resize_target + self.__keep_aspect_ratio = keep_aspect_ratio + self.__multiple_of = ensure_multiple_of + self.__resize_method = resize_method + self.__image_interpolation_method = image_interpolation_method + self.__letter_box = letter_box + + def constrain_to_multiple_of(self, x, min_val=0, max_val=None): + y = (np.round(x / self.__multiple_of) * self.__multiple_of).astype(int) + + if max_val is not None and y > max_val: + y = (np.floor(x / self.__multiple_of) + * self.__multiple_of).astype(int) + + if y < min_val: + y = (np.ceil(x / self.__multiple_of) + * self.__multiple_of).astype(int) + + return y + + def get_size(self, width, height): + # determine new height and width + scale_height = self.__height / height + scale_width = self.__width / width + + if self.__keep_aspect_ratio: + if self.__resize_method == "lower_bound": + # scale such that output size is lower bound + if scale_width > scale_height: + # fit width + scale_height = scale_width + else: + # fit height + scale_width = scale_height + elif self.__resize_method == "upper_bound": + # scale such that output size is upper bound + if scale_width < scale_height: + # fit width + scale_height = scale_width + else: + # fit height + scale_width = scale_height + elif self.__resize_method == "minimal": + # scale as least as possbile + if abs(1 - scale_width) < abs(1 - scale_height): + # fit width + scale_height = scale_width + else: + # fit height + scale_width = scale_height + else: + raise ValueError( + f"resize_method {self.__resize_method} not implemented" + ) + + if self.__resize_method == "lower_bound": + new_height = self.constrain_to_multiple_of( + scale_height * height, min_val=self.__height + ) + new_width = self.constrain_to_multiple_of( + scale_width * width, min_val=self.__width + ) + elif self.__resize_method == "upper_bound": + new_height = self.constrain_to_multiple_of( + scale_height * height, max_val=self.__height + ) + new_width = self.constrain_to_multiple_of( + scale_width * width, max_val=self.__width + ) + elif self.__resize_method == "minimal": + new_height = self.constrain_to_multiple_of(scale_height * height) + new_width = self.constrain_to_multiple_of(scale_width * width) + else: + raise ValueError( + f"resize_method {self.__resize_method} not implemented") + + return (new_width, new_height) + + def make_letter_box(self, sample): + top = bottom = (self.__height - sample.shape[0]) // 2 + left = right = (self.__width - sample.shape[1]) // 2 + sample = cv2.copyMakeBorder( + sample, top, bottom, left, right, cv2.BORDER_CONSTANT, None, 0) + return sample + + def __call__(self, sample): + width, height = self.get_size( + sample["image"].shape[1], sample["image"].shape[0] + ) + + # resize sample + sample["image"] = cv2.resize( + sample["image"], + (width, height), + interpolation=self.__image_interpolation_method, + ) + + if self.__letter_box: + sample["image"] = self.make_letter_box(sample["image"]) + + if self.__resize_target: + if "disparity" in sample: + sample["disparity"] = cv2.resize( + sample["disparity"], + (width, height), + interpolation=cv2.INTER_NEAREST, + ) + + if self.__letter_box: + sample["disparity"] = self.make_letter_box( + sample["disparity"]) + + if "depth" in sample: + sample["depth"] = cv2.resize( + sample["depth"], (width, + height), interpolation=cv2.INTER_NEAREST + ) + + if self.__letter_box: + sample["depth"] = self.make_letter_box(sample["depth"]) + + sample["mask"] = cv2.resize( + sample["mask"].astype(np.float32), + (width, height), + interpolation=cv2.INTER_NEAREST, + ) + + if self.__letter_box: + sample["mask"] = self.make_letter_box(sample["mask"]) + + sample["mask"] = sample["mask"].astype(bool) + + return sample + + +class ResizeFixed(object): + def __init__(self, size): + self.__size = size + + def __call__(self, sample): + sample["image"] = cv2.resize( + sample["image"], self.__size[::-1], interpolation=cv2.INTER_LINEAR + ) + + sample["disparity"] = cv2.resize( + sample["disparity"], self.__size[::- + 1], interpolation=cv2.INTER_NEAREST + ) + + sample["mask"] = cv2.resize( + sample["mask"].astype(np.float32), + self.__size[::-1], + interpolation=cv2.INTER_NEAREST, + ) + sample["mask"] = sample["mask"].astype(bool) + + return sample + + +class Rescale(object): + """Rescale target values to the interval [0, max_val]. + If input is constant, values are set to max_val / 2. + """ + + def __init__(self, max_val=1.0, use_mask=True): + """Init. + + Args: + max_val (float, optional): Max output value. Defaults to 1.0. + use_mask (bool, optional): Only operate on valid pixels (mask == True). Defaults to True. + """ + self.__max_val = max_val + self.__use_mask = use_mask + + def __call__(self, sample): + disp = sample["disparity"] + + if self.__use_mask: + mask = sample["mask"] + else: + mask = np.ones_like(disp, dtype=np.bool) + + if np.sum(mask) == 0: + return sample + + min_val = np.min(disp[mask]) + max_val = np.max(disp[mask]) + + if max_val > min_val: + sample["disparity"][mask] = ( + (disp[mask] - min_val) / (max_val - min_val) * self.__max_val + ) + else: + sample["disparity"][mask] = np.ones_like( + disp[mask]) * self.__max_val / 2.0 + + return sample + + +# mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225] +class NormalizeImage(object): + """Normlize image by given mean and std. + """ + + def __init__(self, mean, std): + self.__mean = mean + self.__std = std + + def __call__(self, sample): + sample["image"] = (sample["image"] - self.__mean) / self.__std + + return sample + + +class DepthToDisparity(object): + """Convert depth to disparity. Removes depth from sample. + """ + + def __init__(self, eps=1e-4): + self.__eps = eps + + def __call__(self, sample): + assert "depth" in sample + + sample["mask"][sample["depth"] < self.__eps] = False + + sample["disparity"] = np.zeros_like(sample["depth"]) + sample["disparity"][sample["depth"] >= self.__eps] = ( + 1.0 / sample["depth"][sample["depth"] >= self.__eps] + ) + + del sample["depth"] + + return sample + + +class DisparityToDepth(object): + """Convert disparity to depth. Removes disparity from sample. + """ + + def __init__(self, eps=1e-4): + self.__eps = eps + + def __call__(self, sample): + assert "disparity" in sample + + disp = np.abs(sample["disparity"]) + sample["mask"][disp < self.__eps] = False + + # print(sample["disparity"]) + # print(sample["mask"].sum()) + # exit() + + sample["depth"] = np.zeros_like(disp) + sample["depth"][disp >= self.__eps] = ( + 1.0 / disp[disp >= self.__eps] + ) + + del sample["disparity"] + + return sample + + +class PrepareForNet(object): + """Prepare sample for usage as network input. + """ + + def __init__(self): + pass + + def __call__(self, sample): + image = np.transpose(sample["image"], (2, 0, 1)) + sample["image"] = np.ascontiguousarray(image).astype(np.float32) + + if "mask" in sample: + sample["mask"] = sample["mask"].astype(np.float32) + sample["mask"] = np.ascontiguousarray(sample["mask"]) + + if "disparity" in sample: + disparity = sample["disparity"].astype(np.float32) + sample["disparity"] = np.ascontiguousarray(disparity) + + if "depth" in sample: + depth = sample["depth"].astype(np.float32) + sample["depth"] = np.ascontiguousarray(depth) + + return sample diff --git a/CCEdit-main/src/controlnet11/annotator/zoe/zoedepth/data/vkitti.py b/CCEdit-main/src/controlnet11/annotator/zoe/zoedepth/data/vkitti.py new file mode 100644 index 0000000000000000000000000000000000000000..72a2e5a8346f6e630ede0e28d6959725af8d7e72 --- /dev/null +++ b/CCEdit-main/src/controlnet11/annotator/zoe/zoedepth/data/vkitti.py @@ -0,0 +1,151 @@ +# MIT License + +# Copyright (c) 2022 Intelligent Systems Lab Org + +# Permission is hereby granted, free of charge, to any person obtaining a copy +# of this software and associated documentation files (the "Software"), to deal +# in the Software without restriction, including without limitation the rights +# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell +# copies of the Software, and to permit persons to whom the Software is +# furnished to do so, subject to the following conditions: + +# The above copyright notice and this permission notice shall be included in all +# copies or substantial portions of the Software. + +# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE +# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER +# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, +# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE +# SOFTWARE. + +# File author: Shariq Farooq Bhat + +import torch +from torch.utils.data import Dataset, DataLoader +from torchvision import transforms +import os + +from PIL import Image +import numpy as np +import cv2 + + +class ToTensor(object): + def __init__(self): + self.normalize = transforms.Normalize( + mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]) + # self.resize = transforms.Resize((375, 1242)) + + def __call__(self, sample): + image, depth = sample['image'], sample['depth'] + + image = self.to_tensor(image) + image = self.normalize(image) + depth = self.to_tensor(depth) + + # image = self.resize(image) + + return {'image': image, 'depth': depth, 'dataset': "vkitti"} + + def to_tensor(self, pic): + + if isinstance(pic, np.ndarray): + img = torch.from_numpy(pic.transpose((2, 0, 1))) + return img + + # # handle PIL Image + if pic.mode == 'I': + img = torch.from_numpy(np.array(pic, np.int32, copy=False)) + elif pic.mode == 'I;16': + img = torch.from_numpy(np.array(pic, np.int16, copy=False)) + else: + img = torch.ByteTensor( + torch.ByteStorage.from_buffer(pic.tobytes())) + # PIL image mode: 1, L, P, I, F, RGB, YCbCr, RGBA, CMYK + if pic.mode == 'YCbCr': + nchannel = 3 + elif pic.mode == 'I;16': + nchannel = 1 + else: + nchannel = len(pic.mode) + img = img.view(pic.size[1], pic.size[0], nchannel) + + img = img.transpose(0, 1).transpose(0, 2).contiguous() + if isinstance(img, torch.ByteTensor): + return img.float() + else: + return img + + +class VKITTI(Dataset): + def __init__(self, data_dir_root, do_kb_crop=True): + import glob + # image paths are of the form /{HR, LR}//{color, depth_filled}/*.png + self.image_files = glob.glob(os.path.join( + data_dir_root, "test_color", '*.png')) + self.depth_files = [r.replace("test_color", "test_depth") + for r in self.image_files] + self.do_kb_crop = True + self.transform = ToTensor() + + def __getitem__(self, idx): + image_path = self.image_files[idx] + depth_path = self.depth_files[idx] + + image = Image.open(image_path) + depth = Image.open(depth_path) + depth = cv2.imread(depth_path, cv2.IMREAD_ANYCOLOR | + cv2.IMREAD_ANYDEPTH) + print("dpeth min max", depth.min(), depth.max()) + + # print(np.shape(image)) + # print(np.shape(depth)) + + # depth[depth > 8] = -1 + + if self.do_kb_crop and False: + height = image.height + width = image.width + top_margin = int(height - 352) + left_margin = int((width - 1216) / 2) + depth = depth.crop( + (left_margin, top_margin, left_margin + 1216, top_margin + 352)) + image = image.crop( + (left_margin, top_margin, left_margin + 1216, top_margin + 352)) + # uv = uv[:, top_margin:top_margin + 352, left_margin:left_margin + 1216] + + image = np.asarray(image, dtype=np.float32) / 255.0 + # depth = np.asarray(depth, dtype=np.uint16) /1. + depth = depth[..., None] + sample = dict(image=image, depth=depth) + + # return sample + sample = self.transform(sample) + + if idx == 0: + print(sample["image"].shape) + + return sample + + def __len__(self): + return len(self.image_files) + + +def get_vkitti_loader(data_dir_root, batch_size=1, **kwargs): + dataset = VKITTI(data_dir_root) + return DataLoader(dataset, batch_size, **kwargs) + + +if __name__ == "__main__": + loader = get_vkitti_loader( + data_dir_root="/home/bhatsf/shortcuts/datasets/vkitti_test") + print("Total files", len(loader.dataset)) + for i, sample in enumerate(loader): + print(sample["image"].shape) + print(sample["depth"].shape) + print(sample["dataset"]) + print(sample['depth'].min(), sample['depth'].max()) + if i > 5: + break diff --git a/CCEdit-main/src/controlnet11/annotator/zoe/zoedepth/data/vkitti2.py b/CCEdit-main/src/controlnet11/annotator/zoe/zoedepth/data/vkitti2.py new file mode 100644 index 0000000000000000000000000000000000000000..9bcfb0414b7f3f21859f30ae34bd71689516a3e7 --- /dev/null +++ b/CCEdit-main/src/controlnet11/annotator/zoe/zoedepth/data/vkitti2.py @@ -0,0 +1,187 @@ +# MIT License + +# Copyright (c) 2022 Intelligent Systems Lab Org + +# Permission is hereby granted, free of charge, to any person obtaining a copy +# of this software and associated documentation files (the "Software"), to deal +# in the Software without restriction, including without limitation the rights +# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell +# copies of the Software, and to permit persons to whom the Software is +# furnished to do so, subject to the following conditions: + +# The above copyright notice and this permission notice shall be included in all +# copies or substantial portions of the Software. + +# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE +# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER +# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, +# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE +# SOFTWARE. + +# File author: Shariq Farooq Bhat + +import os + +import cv2 +import numpy as np +import torch +from PIL import Image +from torch.utils.data import DataLoader, Dataset +from torchvision import transforms + + +class ToTensor(object): + def __init__(self): + # self.normalize = transforms.Normalize( + # mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]) + self.normalize = lambda x: x + # self.resize = transforms.Resize((375, 1242)) + + def __call__(self, sample): + image, depth = sample['image'], sample['depth'] + + image = self.to_tensor(image) + image = self.normalize(image) + depth = self.to_tensor(depth) + + # image = self.resize(image) + + return {'image': image, 'depth': depth, 'dataset': "vkitti"} + + def to_tensor(self, pic): + + if isinstance(pic, np.ndarray): + img = torch.from_numpy(pic.transpose((2, 0, 1))) + return img + + # # handle PIL Image + if pic.mode == 'I': + img = torch.from_numpy(np.array(pic, np.int32, copy=False)) + elif pic.mode == 'I;16': + img = torch.from_numpy(np.array(pic, np.int16, copy=False)) + else: + img = torch.ByteTensor( + torch.ByteStorage.from_buffer(pic.tobytes())) + # PIL image mode: 1, L, P, I, F, RGB, YCbCr, RGBA, CMYK + if pic.mode == 'YCbCr': + nchannel = 3 + elif pic.mode == 'I;16': + nchannel = 1 + else: + nchannel = len(pic.mode) + img = img.view(pic.size[1], pic.size[0], nchannel) + + img = img.transpose(0, 1).transpose(0, 2).contiguous() + if isinstance(img, torch.ByteTensor): + return img.float() + else: + return img + + +class VKITTI2(Dataset): + def __init__(self, data_dir_root, do_kb_crop=True, split="test"): + import glob + + # image paths are of the form /rgb///frames//Camera<0,1>/rgb_{}.jpg + self.image_files = glob.glob(os.path.join( + data_dir_root, "rgb", "**", "frames", "rgb", "Camera_0", '*.jpg'), recursive=True) + self.depth_files = [r.replace("/rgb/", "/depth/").replace( + "rgb_", "depth_").replace(".jpg", ".png") for r in self.image_files] + self.do_kb_crop = True + self.transform = ToTensor() + + # If train test split is not created, then create one. + # Split is such that 8% of the frames from each scene are used for testing. + if not os.path.exists(os.path.join(data_dir_root, "train.txt")): + import random + scenes = set([os.path.basename(os.path.dirname( + os.path.dirname(os.path.dirname(f)))) for f in self.image_files]) + train_files = [] + test_files = [] + for scene in scenes: + scene_files = [f for f in self.image_files if os.path.basename( + os.path.dirname(os.path.dirname(os.path.dirname(f)))) == scene] + random.shuffle(scene_files) + train_files.extend(scene_files[:int(len(scene_files) * 0.92)]) + test_files.extend(scene_files[int(len(scene_files) * 0.92):]) + with open(os.path.join(data_dir_root, "train.txt"), "w") as f: + f.write("\n".join(train_files)) + with open(os.path.join(data_dir_root, "test.txt"), "w") as f: + f.write("\n".join(test_files)) + + if split == "train": + with open(os.path.join(data_dir_root, "train.txt"), "r") as f: + self.image_files = f.read().splitlines() + self.depth_files = [r.replace("/rgb/", "/depth/").replace( + "rgb_", "depth_").replace(".jpg", ".png") for r in self.image_files] + elif split == "test": + with open(os.path.join(data_dir_root, "test.txt"), "r") as f: + self.image_files = f.read().splitlines() + self.depth_files = [r.replace("/rgb/", "/depth/").replace( + "rgb_", "depth_").replace(".jpg", ".png") for r in self.image_files] + + def __getitem__(self, idx): + image_path = self.image_files[idx] + depth_path = self.depth_files[idx] + + image = Image.open(image_path) + # depth = Image.open(depth_path) + depth = cv2.imread(depth_path, cv2.IMREAD_ANYCOLOR | + cv2.IMREAD_ANYDEPTH) / 100.0 # cm to m + depth = Image.fromarray(depth) + # print("dpeth min max", depth.min(), depth.max()) + + # print(np.shape(image)) + # print(np.shape(depth)) + + if self.do_kb_crop: + if idx == 0: + print("Using KB input crop") + height = image.height + width = image.width + top_margin = int(height - 352) + left_margin = int((width - 1216) / 2) + depth = depth.crop( + (left_margin, top_margin, left_margin + 1216, top_margin + 352)) + image = image.crop( + (left_margin, top_margin, left_margin + 1216, top_margin + 352)) + # uv = uv[:, top_margin:top_margin + 352, left_margin:left_margin + 1216] + + image = np.asarray(image, dtype=np.float32) / 255.0 + # depth = np.asarray(depth, dtype=np.uint16) /1. + depth = np.asarray(depth, dtype=np.float32) / 1. + depth[depth > 80] = -1 + + depth = depth[..., None] + sample = dict(image=image, depth=depth) + + # return sample + sample = self.transform(sample) + + if idx == 0: + print(sample["image"].shape) + + return sample + + def __len__(self): + return len(self.image_files) + + +def get_vkitti2_loader(data_dir_root, batch_size=1, **kwargs): + dataset = VKITTI2(data_dir_root) + return DataLoader(dataset, batch_size, **kwargs) + + +if __name__ == "__main__": + loader = get_vkitti2_loader( + data_dir_root="/home/bhatsf/shortcuts/datasets/vkitti2") + print("Total files", len(loader.dataset)) + for i, sample in enumerate(loader): + print(sample["image"].shape) + print(sample["depth"].shape) + print(sample["dataset"]) + print(sample['depth'].min(), sample['depth'].max()) + if i > 5: + break diff --git a/CCEdit-main/src/controlnet11/annotator/zoe/zoedepth/models/__pycache__/__init__.cpython-39.pyc b/CCEdit-main/src/controlnet11/annotator/zoe/zoedepth/models/__pycache__/__init__.cpython-39.pyc new file mode 100644 index 0000000000000000000000000000000000000000..03f50fb6cbb2b9d30955791b997e3d205914f9f8 Binary files /dev/null and b/CCEdit-main/src/controlnet11/annotator/zoe/zoedepth/models/__pycache__/__init__.cpython-39.pyc differ diff --git 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a/CCEdit-main/src/controlnet11/annotator/zoe/zoedepth/models/base_models/midas_repo/midas/backbones/levit.py b/CCEdit-main/src/controlnet11/annotator/zoe/zoedepth/models/base_models/midas_repo/midas/backbones/levit.py new file mode 100644 index 0000000000000000000000000000000000000000..6d023a98702a0451806d26f33f8bccf931814f10 --- /dev/null +++ b/CCEdit-main/src/controlnet11/annotator/zoe/zoedepth/models/base_models/midas_repo/midas/backbones/levit.py @@ -0,0 +1,106 @@ +import timm +import torch +import torch.nn as nn +import numpy as np + +from .utils import activations, get_activation, Transpose + + +def forward_levit(pretrained, x): + pretrained.model.forward_features(x) + + layer_1 = pretrained.activations["1"] + layer_2 = pretrained.activations["2"] + layer_3 = pretrained.activations["3"] + + layer_1 = pretrained.act_postprocess1(layer_1) + layer_2 = pretrained.act_postprocess2(layer_2) + layer_3 = pretrained.act_postprocess3(layer_3) + + return layer_1, layer_2, layer_3 + + +def _make_levit_backbone( + model, + hooks=[3, 11, 21], + patch_grid=[14, 14] +): + pretrained = nn.Module() + + pretrained.model = model + pretrained.model.blocks[hooks[0]].register_forward_hook(get_activation("1")) + pretrained.model.blocks[hooks[1]].register_forward_hook(get_activation("2")) + pretrained.model.blocks[hooks[2]].register_forward_hook(get_activation("3")) + + pretrained.activations = activations + + patch_grid_size = np.array(patch_grid, dtype=int) + + pretrained.act_postprocess1 = nn.Sequential( + Transpose(1, 2), + nn.Unflatten(2, torch.Size(patch_grid_size.tolist())) + ) + pretrained.act_postprocess2 = nn.Sequential( + Transpose(1, 2), + nn.Unflatten(2, torch.Size((np.ceil(patch_grid_size / 2).astype(int)).tolist())) + ) + pretrained.act_postprocess3 = nn.Sequential( + Transpose(1, 2), + nn.Unflatten(2, torch.Size((np.ceil(patch_grid_size / 4).astype(int)).tolist())) + ) + + return pretrained + + +class ConvTransposeNorm(nn.Sequential): + """ + Modification of + https://github.com/rwightman/pytorch-image-models/blob/master/timm/models/levit.py: ConvNorm + such that ConvTranspose2d is used instead of Conv2d. + """ + + def __init__( + self, in_chs, out_chs, kernel_size=1, stride=1, pad=0, dilation=1, + groups=1, bn_weight_init=1): + super().__init__() + self.add_module('c', + nn.ConvTranspose2d(in_chs, out_chs, kernel_size, stride, pad, dilation, groups, bias=False)) + self.add_module('bn', nn.BatchNorm2d(out_chs)) + + nn.init.constant_(self.bn.weight, bn_weight_init) + + @torch.no_grad() + def fuse(self): + c, bn = self._modules.values() + w = bn.weight / (bn.running_var + bn.eps) ** 0.5 + w = c.weight * w[:, None, None, None] + b = bn.bias - bn.running_mean * bn.weight / (bn.running_var + bn.eps) ** 0.5 + m = nn.ConvTranspose2d( + w.size(1), w.size(0), w.shape[2:], stride=self.c.stride, + padding=self.c.padding, dilation=self.c.dilation, groups=self.c.groups) + m.weight.data.copy_(w) + m.bias.data.copy_(b) + return m + + +def stem_b4_transpose(in_chs, out_chs, activation): + """ + Modification of + https://github.com/rwightman/pytorch-image-models/blob/master/timm/models/levit.py: stem_b16 + such that ConvTranspose2d is used instead of Conv2d and stem is also reduced to the half. + """ + return nn.Sequential( + ConvTransposeNorm(in_chs, out_chs, 3, 2, 1), + activation(), + ConvTransposeNorm(out_chs, out_chs // 2, 3, 2, 1), + activation()) + + +def _make_pretrained_levit_384(pretrained, hooks=None): + model = timm.create_model("levit_384", pretrained=pretrained) + + hooks = [3, 11, 21] if hooks == None else hooks + return _make_levit_backbone( + model, + hooks=hooks + ) diff --git a/CCEdit-main/src/controlnet11/annotator/zoe/zoedepth/models/base_models/midas_repo/midas/backbones/next_vit.py b/CCEdit-main/src/controlnet11/annotator/zoe/zoedepth/models/base_models/midas_repo/midas/backbones/next_vit.py new file mode 100644 index 0000000000000000000000000000000000000000..8afdd8b743b5ab023a359dc3b721e601b1a40d11 --- /dev/null +++ b/CCEdit-main/src/controlnet11/annotator/zoe/zoedepth/models/base_models/midas_repo/midas/backbones/next_vit.py @@ -0,0 +1,39 @@ +import timm + +import torch.nn as nn + +from pathlib import Path +from .utils import activations, forward_default, get_activation + +from ..external.next_vit.classification.nextvit import * + + +def forward_next_vit(pretrained, x): + return forward_default(pretrained, x, "forward") + + +def _make_next_vit_backbone( + model, + hooks=[2, 6, 36, 39], +): + pretrained = nn.Module() + + pretrained.model = model + pretrained.model.features[hooks[0]].register_forward_hook(get_activation("1")) + pretrained.model.features[hooks[1]].register_forward_hook(get_activation("2")) + pretrained.model.features[hooks[2]].register_forward_hook(get_activation("3")) + pretrained.model.features[hooks[3]].register_forward_hook(get_activation("4")) + + pretrained.activations = activations + + return pretrained + + +def _make_pretrained_next_vit_large_6m(hooks=None): + model = timm.create_model("nextvit_large") + + hooks = [2, 6, 36, 39] if hooks == None else hooks + return _make_next_vit_backbone( + model, + hooks=hooks, + ) diff --git a/CCEdit-main/src/controlnet11/annotator/zoe/zoedepth/models/base_models/midas_repo/midas/backbones/swin.py b/CCEdit-main/src/controlnet11/annotator/zoe/zoedepth/models/base_models/midas_repo/midas/backbones/swin.py new file mode 100644 index 0000000000000000000000000000000000000000..f8c71367e3e78b087f80b2ab3e2f495a9c372f1a --- /dev/null +++ b/CCEdit-main/src/controlnet11/annotator/zoe/zoedepth/models/base_models/midas_repo/midas/backbones/swin.py @@ -0,0 +1,13 @@ +import timm + +from .swin_common import _make_swin_backbone + + +def _make_pretrained_swinl12_384(pretrained, hooks=None): + model = timm.create_model("swin_large_patch4_window12_384", pretrained=pretrained) + + hooks = [1, 1, 17, 1] if hooks == None else hooks + return _make_swin_backbone( + model, + hooks=hooks + ) diff --git a/CCEdit-main/src/controlnet11/annotator/zoe/zoedepth/models/base_models/midas_repo/midas/backbones/swin_common.py b/CCEdit-main/src/controlnet11/annotator/zoe/zoedepth/models/base_models/midas_repo/midas/backbones/swin_common.py new file mode 100644 index 0000000000000000000000000000000000000000..94d63d408f18511179d90b3ac6f697385d1e556d --- /dev/null +++ b/CCEdit-main/src/controlnet11/annotator/zoe/zoedepth/models/base_models/midas_repo/midas/backbones/swin_common.py @@ -0,0 +1,52 @@ +import torch + +import torch.nn as nn +import numpy as np + +from .utils import activations, forward_default, get_activation, Transpose + + +def forward_swin(pretrained, x): + return forward_default(pretrained, x) + + +def _make_swin_backbone( + model, + hooks=[1, 1, 17, 1], + patch_grid=[96, 96] +): + pretrained = nn.Module() + + pretrained.model = model + pretrained.model.layers[0].blocks[hooks[0]].register_forward_hook(get_activation("1")) + pretrained.model.layers[1].blocks[hooks[1]].register_forward_hook(get_activation("2")) + pretrained.model.layers[2].blocks[hooks[2]].register_forward_hook(get_activation("3")) + pretrained.model.layers[3].blocks[hooks[3]].register_forward_hook(get_activation("4")) + + pretrained.activations = activations + + if hasattr(model, "patch_grid"): + used_patch_grid = model.patch_grid + else: + used_patch_grid = patch_grid + + patch_grid_size = np.array(used_patch_grid, dtype=int) + + pretrained.act_postprocess1 = nn.Sequential( + Transpose(1, 2), + nn.Unflatten(2, torch.Size(patch_grid_size.tolist())) + ) + pretrained.act_postprocess2 = nn.Sequential( + Transpose(1, 2), + nn.Unflatten(2, torch.Size((patch_grid_size // 2).tolist())) + ) + pretrained.act_postprocess3 = nn.Sequential( + Transpose(1, 2), + nn.Unflatten(2, torch.Size((patch_grid_size // 4).tolist())) + ) + pretrained.act_postprocess4 = nn.Sequential( + Transpose(1, 2), + nn.Unflatten(2, torch.Size((patch_grid_size // 8).tolist())) + ) + + return pretrained diff --git a/CCEdit-main/src/controlnet11/annotator/zoe/zoedepth/models/base_models/midas_repo/midas/backbones/vit.py b/CCEdit-main/src/controlnet11/annotator/zoe/zoedepth/models/base_models/midas_repo/midas/backbones/vit.py new file mode 100644 index 0000000000000000000000000000000000000000..413f9693bd4548342280e329c9128c1a52cea920 --- /dev/null +++ b/CCEdit-main/src/controlnet11/annotator/zoe/zoedepth/models/base_models/midas_repo/midas/backbones/vit.py @@ -0,0 +1,221 @@ +import torch +import torch.nn as nn +import timm +import types +import math +import torch.nn.functional as F + +from .utils import (activations, forward_adapted_unflatten, get_activation, get_readout_oper, + make_backbone_default, Transpose) + + +def forward_vit(pretrained, x): + return forward_adapted_unflatten(pretrained, x, "forward_flex") + + +def _resize_pos_embed(self, posemb, gs_h, gs_w): + posemb_tok, posemb_grid = ( + posemb[:, : self.start_index], + posemb[0, self.start_index:], + ) + + gs_old = int(math.sqrt(len(posemb_grid))) + + posemb_grid = posemb_grid.reshape(1, gs_old, gs_old, -1).permute(0, 3, 1, 2) + posemb_grid = F.interpolate(posemb_grid, size=(gs_h, gs_w), mode="bilinear") + posemb_grid = posemb_grid.permute(0, 2, 3, 1).reshape(1, gs_h * gs_w, -1) + + posemb = torch.cat([posemb_tok, posemb_grid], dim=1) + + return posemb + + +def forward_flex(self, x): + b, c, h, w = x.shape + + pos_embed = self._resize_pos_embed( + self.pos_embed, h // self.patch_size[1], w // self.patch_size[0] + ) + + B = x.shape[0] + + if hasattr(self.patch_embed, "backbone"): + x = self.patch_embed.backbone(x) + if isinstance(x, (list, tuple)): + x = x[-1] # last feature if backbone outputs list/tuple of features + + x = self.patch_embed.proj(x).flatten(2).transpose(1, 2) + + if getattr(self, "dist_token", None) is not None: + cls_tokens = self.cls_token.expand( + B, -1, -1 + ) # stole cls_tokens impl from Phil Wang, thanks + dist_token = self.dist_token.expand(B, -1, -1) + x = torch.cat((cls_tokens, dist_token, x), dim=1) + else: + if self.no_embed_class: + x = x + pos_embed + cls_tokens = self.cls_token.expand( + B, -1, -1 + ) # stole cls_tokens impl from Phil Wang, thanks + x = torch.cat((cls_tokens, x), dim=1) + + if not self.no_embed_class: + x = x + pos_embed + x = self.pos_drop(x) + + for blk in self.blocks: + x = blk(x) + + x = self.norm(x) + + return x + + +def _make_vit_b16_backbone( + model, + features=[96, 192, 384, 768], + size=[384, 384], + hooks=[2, 5, 8, 11], + vit_features=768, + use_readout="ignore", + start_index=1, + start_index_readout=1, +): + pretrained = make_backbone_default(model, features, size, hooks, vit_features, use_readout, start_index, + start_index_readout) + + # We inject this function into the VisionTransformer instances so that + # we can use it with interpolated position embeddings without modifying the library source. + pretrained.model.forward_flex = types.MethodType(forward_flex, pretrained.model) + pretrained.model._resize_pos_embed = types.MethodType( + _resize_pos_embed, pretrained.model + ) + + return pretrained + + +def _make_pretrained_vitl16_384(pretrained, use_readout="ignore", hooks=None): + model = timm.create_model("vit_large_patch16_384", pretrained=pretrained) + + hooks = [5, 11, 17, 23] if hooks == None else hooks + return _make_vit_b16_backbone( + model, + features=[256, 512, 1024, 1024], + hooks=hooks, + vit_features=1024, + use_readout=use_readout, + ) + + +def _make_pretrained_vitb16_384(pretrained, use_readout="ignore", hooks=None): + model = timm.create_model("vit_base_patch16_384", pretrained=pretrained) + + hooks = [2, 5, 8, 11] if hooks == None else hooks + return _make_vit_b16_backbone( + model, features=[96, 192, 384, 768], hooks=hooks, use_readout=use_readout + ) + + +def _make_vit_b_rn50_backbone( + model, + features=[256, 512, 768, 768], + size=[384, 384], + hooks=[0, 1, 8, 11], + vit_features=768, + patch_size=[16, 16], + number_stages=2, + use_vit_only=False, + use_readout="ignore", + start_index=1, +): + pretrained = nn.Module() + + pretrained.model = model + + used_number_stages = 0 if use_vit_only else number_stages + for s in range(used_number_stages): + pretrained.model.patch_embed.backbone.stages[s].register_forward_hook( + get_activation(str(s + 1)) + ) + for s in range(used_number_stages, 4): + pretrained.model.blocks[hooks[s]].register_forward_hook(get_activation(str(s + 1))) + + pretrained.activations = activations + + readout_oper = get_readout_oper(vit_features, features, use_readout, start_index) + + for s in range(used_number_stages): + value = nn.Sequential(nn.Identity(), nn.Identity(), nn.Identity()) + exec(f"pretrained.act_postprocess{s + 1}=value") + for s in range(used_number_stages, 4): + if s < number_stages: + final_layer = nn.ConvTranspose2d( + in_channels=features[s], + out_channels=features[s], + kernel_size=4 // (2 ** s), + stride=4 // (2 ** s), + padding=0, + bias=True, + dilation=1, + groups=1, + ) + elif s > number_stages: + final_layer = nn.Conv2d( + in_channels=features[3], + out_channels=features[3], + kernel_size=3, + stride=2, + padding=1, + ) + else: + final_layer = None + + layers = [ + readout_oper[s], + Transpose(1, 2), + nn.Unflatten(2, torch.Size([size[0] // 16, size[1] // 16])), + nn.Conv2d( + in_channels=vit_features, + out_channels=features[s], + kernel_size=1, + stride=1, + padding=0, + ), + ] + if final_layer is not None: + layers.append(final_layer) + + value = nn.Sequential(*layers) + exec(f"pretrained.act_postprocess{s + 1}=value") + + pretrained.model.start_index = start_index + pretrained.model.patch_size = patch_size + + # We inject this function into the VisionTransformer instances so that + # we can use it with interpolated position embeddings without modifying the library source. + pretrained.model.forward_flex = types.MethodType(forward_flex, pretrained.model) + + # We inject this function into the VisionTransformer instances so that + # we can use it with interpolated position embeddings without modifying the library source. + pretrained.model._resize_pos_embed = types.MethodType( + _resize_pos_embed, pretrained.model + ) + + return pretrained + + +def _make_pretrained_vitb_rn50_384( + pretrained, use_readout="ignore", hooks=None, use_vit_only=False +): + model = timm.create_model("vit_base_resnet50_384", pretrained=pretrained) + + hooks = [0, 1, 8, 11] if hooks == None else hooks + return _make_vit_b_rn50_backbone( + model, + features=[256, 512, 768, 768], + size=[384, 384], + hooks=hooks, + use_vit_only=use_vit_only, + use_readout=use_readout, + ) diff --git a/CCEdit-main/src/controlnet11/annotator/zoe/zoedepth/models/base_models/midas_repo/mobile/android/app/src/androidTest/assets/fox-mobilenet_v1_1.0_224_support.txt b/CCEdit-main/src/controlnet11/annotator/zoe/zoedepth/models/base_models/midas_repo/mobile/android/app/src/androidTest/assets/fox-mobilenet_v1_1.0_224_support.txt new file mode 100644 index 0000000000000000000000000000000000000000..bdfad31f9b3e694817025d8b8f2ca0b40aa436bb --- /dev/null +++ b/CCEdit-main/src/controlnet11/annotator/zoe/zoedepth/models/base_models/midas_repo/mobile/android/app/src/androidTest/assets/fox-mobilenet_v1_1.0_224_support.txt @@ -0,0 +1,3 @@ +red_fox 0.79403335 +kit_fox 0.16753247 +grey_fox 0.03619214 diff --git a/CCEdit-main/src/controlnet11/annotator/zoe/zoedepth/models/base_models/midas_repo/mobile/android/app/src/androidTest/assets/fox-mobilenet_v1_1.0_224_task_api.txt b/CCEdit-main/src/controlnet11/annotator/zoe/zoedepth/models/base_models/midas_repo/mobile/android/app/src/androidTest/assets/fox-mobilenet_v1_1.0_224_task_api.txt new file mode 100644 index 0000000000000000000000000000000000000000..3668ce54df0d1e57e31c58281d6085b83928f991 --- /dev/null +++ b/CCEdit-main/src/controlnet11/annotator/zoe/zoedepth/models/base_models/midas_repo/mobile/android/app/src/androidTest/assets/fox-mobilenet_v1_1.0_224_task_api.txt @@ -0,0 +1,3 @@ +red_fox 0.85 +kit_fox 0.13 +grey_fox 0.02 diff --git a/CCEdit-main/src/controlnet11/annotator/zoe/zoedepth/models/base_models/midas_repo/mobile/android/app/src/androidTest/java/AndroidManifest.xml b/CCEdit-main/src/controlnet11/annotator/zoe/zoedepth/models/base_models/midas_repo/mobile/android/app/src/androidTest/java/AndroidManifest.xml new file mode 100644 index 0000000000000000000000000000000000000000..3653d8799092492ebbb16c7c956eb50e3d404aa4 --- /dev/null +++ b/CCEdit-main/src/controlnet11/annotator/zoe/zoedepth/models/base_models/midas_repo/mobile/android/app/src/androidTest/java/AndroidManifest.xml @@ -0,0 +1,5 @@ + + + + \ No newline at end of file diff --git a/CCEdit-main/src/controlnet11/annotator/zoe/zoedepth/models/base_models/midas_repo/mobile/android/app/src/androidTest/java/org/tensorflow/lite/examples/classification/ClassifierTest.java b/CCEdit-main/src/controlnet11/annotator/zoe/zoedepth/models/base_models/midas_repo/mobile/android/app/src/androidTest/java/org/tensorflow/lite/examples/classification/ClassifierTest.java new file mode 100644 index 0000000000000000000000000000000000000000..0194132890aae659c2a70d33106306ed665b22e8 --- /dev/null +++ b/CCEdit-main/src/controlnet11/annotator/zoe/zoedepth/models/base_models/midas_repo/mobile/android/app/src/androidTest/java/org/tensorflow/lite/examples/classification/ClassifierTest.java @@ -0,0 +1,121 @@ +/* + * Copyright 2019 The TensorFlow Authors. All Rights Reserved. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package org.tensorflow.lite.examples.classification; + +import static com.google.common.truth.Truth.assertThat; + +import android.content.res.AssetManager; +import android.graphics.Bitmap; +import android.graphics.BitmapFactory; +import android.util.Log; +import androidx.test.ext.junit.runners.AndroidJUnit4; +import androidx.test.platform.app.InstrumentationRegistry; +import androidx.test.rule.ActivityTestRule; +import java.io.IOException; +import java.io.InputStream; +import java.util.ArrayList; +import java.util.Iterator; +import java.util.List; +import java.util.Scanner; +import org.junit.Assert; +import org.junit.Rule; +import org.junit.Test; +import org.junit.runner.RunWith; +import org.tensorflow.lite.examples.classification.tflite.Classifier; +import org.tensorflow.lite.examples.classification.tflite.Classifier.Device; +import org.tensorflow.lite.examples.classification.tflite.Classifier.Model; +import org.tensorflow.lite.examples.classification.tflite.Classifier.Recognition; + +/** Golden test for Image Classification Reference app. */ +@RunWith(AndroidJUnit4.class) +public class ClassifierTest { + + @Rule + public ActivityTestRule rule = + new ActivityTestRule<>(ClassifierActivity.class); + + private static final String[] INPUTS = {"fox.jpg"}; + private static final String[] GOLDEN_OUTPUTS_SUPPORT = {"fox-mobilenet_v1_1.0_224_support.txt"}; + private static final String[] GOLDEN_OUTPUTS_TASK = {"fox-mobilenet_v1_1.0_224_task_api.txt"}; + + @Test + public void classificationResultsShouldNotChange() throws IOException { + ClassifierActivity activity = rule.getActivity(); + Classifier classifier = Classifier.create(activity, Model.FLOAT_MOBILENET, Device.CPU, 1); + for (int i = 0; i < INPUTS.length; i++) { + String imageFileName = INPUTS[i]; + String goldenOutputFileName; + // TODO(b/169379396): investigate the impact of the resize algorithm on accuracy. + // This is a temporary workaround to set different golden rest results as the preprocessing + // of lib_support and lib_task_api are different. Will merge them once the above TODO is + // resolved. + if (Classifier.TAG.equals("ClassifierWithSupport")) { + goldenOutputFileName = GOLDEN_OUTPUTS_SUPPORT[i]; + } else { + goldenOutputFileName = GOLDEN_OUTPUTS_TASK[i]; + } + Bitmap input = loadImage(imageFileName); + List goldenOutput = loadRecognitions(goldenOutputFileName); + + List result = classifier.recognizeImage(input, 0); + Iterator goldenOutputIterator = goldenOutput.iterator(); + + for (Recognition actual : result) { + Assert.assertTrue(goldenOutputIterator.hasNext()); + Recognition expected = goldenOutputIterator.next(); + assertThat(actual.getTitle()).isEqualTo(expected.getTitle()); + assertThat(actual.getConfidence()).isWithin(0.01f).of(expected.getConfidence()); + } + } + } + + private static Bitmap loadImage(String fileName) { + AssetManager assetManager = + InstrumentationRegistry.getInstrumentation().getContext().getAssets(); + InputStream inputStream = null; + try { + inputStream = assetManager.open(fileName); + } catch (IOException e) { + Log.e("Test", "Cannot load image from assets"); + } + return BitmapFactory.decodeStream(inputStream); + } + + private static List loadRecognitions(String fileName) { + AssetManager assetManager = + InstrumentationRegistry.getInstrumentation().getContext().getAssets(); + InputStream inputStream = null; + try { + inputStream = assetManager.open(fileName); + } catch (IOException e) { + Log.e("Test", "Cannot load probability results from assets"); + } + Scanner scanner = new Scanner(inputStream); + List result = new ArrayList<>(); + while (scanner.hasNext()) { + String category = scanner.next(); + category = category.replace('_', ' '); + if (!scanner.hasNextFloat()) { + break; + } + float probability = scanner.nextFloat(); + Recognition recognition = new Recognition(null, category, probability, null); + result.add(recognition); + } + return result; + } +} diff --git a/CCEdit-main/src/controlnet11/annotator/zoe/zoedepth/models/base_models/midas_repo/mobile/android/app/src/main/res/drawable-v24/ic_launcher_foreground.xml b/CCEdit-main/src/controlnet11/annotator/zoe/zoedepth/models/base_models/midas_repo/mobile/android/app/src/main/res/drawable-v24/ic_launcher_foreground.xml new file mode 100644 index 0000000000000000000000000000000000000000..b1517edf496ef5800b97d046b92012a9f94a34d0 --- /dev/null +++ b/CCEdit-main/src/controlnet11/annotator/zoe/zoedepth/models/base_models/midas_repo/mobile/android/app/src/main/res/drawable-v24/ic_launcher_foreground.xml @@ -0,0 +1,34 @@ + + + + + + + + + + + diff --git a/CCEdit-main/src/controlnet11/annotator/zoe/zoedepth/models/base_models/midas_repo/mobile/android/lib_task_api/src/main/java/org/tensorflow/lite/examples/classification/tflite/Classifier.java b/CCEdit-main/src/controlnet11/annotator/zoe/zoedepth/models/base_models/midas_repo/mobile/android/lib_task_api/src/main/java/org/tensorflow/lite/examples/classification/tflite/Classifier.java new file mode 100644 index 0000000000000000000000000000000000000000..45da52a0d0dfa203255e0f2d44901ee0618e739f --- /dev/null +++ b/CCEdit-main/src/controlnet11/annotator/zoe/zoedepth/models/base_models/midas_repo/mobile/android/lib_task_api/src/main/java/org/tensorflow/lite/examples/classification/tflite/Classifier.java @@ -0,0 +1,278 @@ +/* Copyright 2019 The TensorFlow Authors. All Rights Reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +==============================================================================*/ + +package org.tensorflow.lite.examples.classification.tflite; + +import static java.lang.Math.min; + +import android.app.Activity; +import android.graphics.Bitmap; +import android.graphics.Rect; +import android.graphics.RectF; +import android.os.SystemClock; +import android.os.Trace; +import android.util.Log; +import java.io.IOException; +import java.nio.MappedByteBuffer; +import java.util.ArrayList; +import java.util.List; +import org.tensorflow.lite.examples.classification.tflite.Classifier.Device; +import org.tensorflow.lite.support.common.FileUtil; +import org.tensorflow.lite.support.image.TensorImage; +import org.tensorflow.lite.support.label.Category; +import org.tensorflow.lite.support.metadata.MetadataExtractor; +import org.tensorflow.lite.task.core.vision.ImageProcessingOptions; +import org.tensorflow.lite.task.core.vision.ImageProcessingOptions.Orientation; +import org.tensorflow.lite.task.vision.classifier.Classifications; +import org.tensorflow.lite.task.vision.classifier.ImageClassifier; +import org.tensorflow.lite.task.vision.classifier.ImageClassifier.ImageClassifierOptions; + +/** A classifier specialized to label images using TensorFlow Lite. */ +public abstract class Classifier { + public static final String TAG = "ClassifierWithTaskApi"; + + /** The model type used for classification. */ + public enum Model { + FLOAT_MOBILENET, + QUANTIZED_MOBILENET, + FLOAT_EFFICIENTNET, + QUANTIZED_EFFICIENTNET + } + + /** The runtime device type used for executing classification. */ + public enum Device { + CPU, + NNAPI, + GPU + } + + /** Number of results to show in the UI. */ + private static final int MAX_RESULTS = 3; + + /** Image size along the x axis. */ + private final int imageSizeX; + + /** Image size along the y axis. */ + private final int imageSizeY; + /** An instance of the driver class to run model inference with Tensorflow Lite. */ + protected final ImageClassifier imageClassifier; + + /** + * Creates a classifier with the provided configuration. + * + * @param activity The current Activity. + * @param model The model to use for classification. + * @param device The device to use for classification. + * @param numThreads The number of threads to use for classification. + * @return A classifier with the desired configuration. + */ + public static Classifier create(Activity activity, Model model, Device device, int numThreads) + throws IOException { + if (model == Model.QUANTIZED_MOBILENET) { + return new ClassifierQuantizedMobileNet(activity, device, numThreads); + } else if (model == Model.FLOAT_MOBILENET) { + return new ClassifierFloatMobileNet(activity, device, numThreads); + } else if (model == Model.FLOAT_EFFICIENTNET) { + return new ClassifierFloatEfficientNet(activity, device, numThreads); + } else if (model == Model.QUANTIZED_EFFICIENTNET) { + return new ClassifierQuantizedEfficientNet(activity, device, numThreads); + } else { + throw new UnsupportedOperationException(); + } + } + + /** An immutable result returned by a Classifier describing what was recognized. */ + public static class Recognition { + /** + * A unique identifier for what has been recognized. Specific to the class, not the instance of + * the object. + */ + private final String id; + + /** Display name for the recognition. */ + private final String title; + + /** + * A sortable score for how good the recognition is relative to others. Higher should be better. + */ + private final Float confidence; + + /** Optional location within the source image for the location of the recognized object. */ + private RectF location; + + public Recognition( + final String id, final String title, final Float confidence, final RectF location) { + this.id = id; + this.title = title; + this.confidence = confidence; + this.location = location; + } + + public String getId() { + return id; + } + + public String getTitle() { + return title; + } + + public Float getConfidence() { + return confidence; + } + + public RectF getLocation() { + return new RectF(location); + } + + public void setLocation(RectF location) { + this.location = location; + } + + @Override + public String toString() { + String resultString = ""; + if (id != null) { + resultString += "[" + id + "] "; + } + + if (title != null) { + resultString += title + " "; + } + + if (confidence != null) { + resultString += String.format("(%.1f%%) ", confidence * 100.0f); + } + + if (location != null) { + resultString += location + " "; + } + + return resultString.trim(); + } + } + + /** Initializes a {@code Classifier}. */ + protected Classifier(Activity activity, Device device, int numThreads) throws IOException { + if (device != Device.CPU || numThreads != 1) { + throw new IllegalArgumentException( + "Manipulating the hardware accelerators and numbers of threads is not allowed in the Task" + + " library currently. Only CPU + single thread is allowed."); + } + + // Create the ImageClassifier instance. + ImageClassifierOptions options = + ImageClassifierOptions.builder().setMaxResults(MAX_RESULTS).build(); + imageClassifier = ImageClassifier.createFromFileAndOptions(activity, getModelPath(), options); + Log.d(TAG, "Created a Tensorflow Lite Image Classifier."); + + // Get the input image size information of the underlying tflite model. + MappedByteBuffer tfliteModel = FileUtil.loadMappedFile(activity, getModelPath()); + MetadataExtractor metadataExtractor = new MetadataExtractor(tfliteModel); + // Image shape is in the format of {1, height, width, 3}. + int[] imageShape = metadataExtractor.getInputTensorShape(/*inputIndex=*/ 0); + imageSizeY = imageShape[1]; + imageSizeX = imageShape[2]; + } + + /** Runs inference and returns the classification results. */ + public List recognizeImage(final Bitmap bitmap, int sensorOrientation) { + // Logs this method so that it can be analyzed with systrace. + Trace.beginSection("recognizeImage"); + + TensorImage inputImage = TensorImage.fromBitmap(bitmap); + int width = bitmap.getWidth(); + int height = bitmap.getHeight(); + int cropSize = min(width, height); + // TODO(b/169379396): investigate the impact of the resize algorithm on accuracy. + // Task Library resize the images using bilinear interpolation, which is slightly different from + // the nearest neighbor sampling algorithm used in lib_support. See + // https://github.com/tensorflow/examples/blob/0ef3d93e2af95d325c70ef3bcbbd6844d0631e07/lite/examples/image_classification/android/lib_support/src/main/java/org/tensorflow/lite/examples/classification/tflite/Classifier.java#L310. + ImageProcessingOptions imageOptions = + ImageProcessingOptions.builder() + .setOrientation(getOrientation(sensorOrientation)) + // Set the ROI to the center of the image. + .setRoi( + new Rect( + /*left=*/ (width - cropSize) / 2, + /*top=*/ (height - cropSize) / 2, + /*right=*/ (width + cropSize) / 2, + /*bottom=*/ (height + cropSize) / 2)) + .build(); + + // Runs the inference call. + Trace.beginSection("runInference"); + long startTimeForReference = SystemClock.uptimeMillis(); + List results = imageClassifier.classify(inputImage, imageOptions); + long endTimeForReference = SystemClock.uptimeMillis(); + Trace.endSection(); + Log.v(TAG, "Timecost to run model inference: " + (endTimeForReference - startTimeForReference)); + + Trace.endSection(); + + return getRecognitions(results); + } + + /** Closes the interpreter and model to release resources. */ + public void close() { + if (imageClassifier != null) { + imageClassifier.close(); + } + } + + /** Get the image size along the x axis. */ + public int getImageSizeX() { + return imageSizeX; + } + + /** Get the image size along the y axis. */ + public int getImageSizeY() { + return imageSizeY; + } + + /** + * Converts a list of {@link Classifications} objects into a list of {@link Recognition} objects + * to match the interface of other inference method, such as using the TFLite + * Support Library.. + */ + private static List getRecognitions(List classifications) { + + final ArrayList recognitions = new ArrayList<>(); + // All the demo models are single head models. Get the first Classifications in the results. + for (Category category : classifications.get(0).getCategories()) { + recognitions.add( + new Recognition( + "" + category.getLabel(), category.getLabel(), category.getScore(), null)); + } + return recognitions; + } + + /* Convert the camera orientation in degree into {@link ImageProcessingOptions#Orientation}.*/ + private static Orientation getOrientation(int cameraOrientation) { + switch (cameraOrientation / 90) { + case 3: + return Orientation.BOTTOM_LEFT; + case 2: + return Orientation.BOTTOM_RIGHT; + case 1: + return Orientation.TOP_RIGHT; + default: + return Orientation.TOP_LEFT; + } + } + + /** Gets the name of the model file stored in Assets. */ + protected abstract String getModelPath(); +} diff --git a/CCEdit-main/src/controlnet11/annotator/zoe/zoedepth/models/base_models/midas_repo/mobile/android/lib_task_api/src/main/java/org/tensorflow/lite/examples/classification/tflite/ClassifierFloatEfficientNet.java b/CCEdit-main/src/controlnet11/annotator/zoe/zoedepth/models/base_models/midas_repo/mobile/android/lib_task_api/src/main/java/org/tensorflow/lite/examples/classification/tflite/ClassifierFloatEfficientNet.java new file mode 100644 index 0000000000000000000000000000000000000000..250794cc12d0e603aa47502322dc646d50689848 --- /dev/null +++ b/CCEdit-main/src/controlnet11/annotator/zoe/zoedepth/models/base_models/midas_repo/mobile/android/lib_task_api/src/main/java/org/tensorflow/lite/examples/classification/tflite/ClassifierFloatEfficientNet.java @@ -0,0 +1,45 @@ +/* Copyright 2019 The TensorFlow Authors. All Rights Reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +==============================================================================*/ + +package org.tensorflow.lite.examples.classification.tflite; + +import android.app.Activity; +import java.io.IOException; +import org.tensorflow.lite.examples.classification.tflite.Classifier.Device; + +/** This TensorFlowLite classifier works with the float EfficientNet model. */ +public class ClassifierFloatEfficientNet extends Classifier { + + /** + * Initializes a {@code ClassifierFloatMobileNet}. + * + * @param device a {@link Device} object to configure the hardware accelerator + * @param numThreads the number of threads during the inference + * @throws IOException if the model is not loaded correctly + */ + public ClassifierFloatEfficientNet(Activity activity, Device device, int numThreads) + throws IOException { + super(activity, device, numThreads); + } + + @Override + protected String getModelPath() { + // you can download this file from + // see build.gradle for where to obtain this file. It should be auto + // downloaded into assets. + //return "efficientnet-lite0-fp32.tflite"; + return "model.tflite"; + } +} diff --git a/CCEdit-main/src/controlnet11/annotator/zoe/zoedepth/models/base_models/midas_repo/mobile/android/lib_task_api/src/main/java/org/tensorflow/lite/examples/classification/tflite/ClassifierQuantizedEfficientNet.java b/CCEdit-main/src/controlnet11/annotator/zoe/zoedepth/models/base_models/midas_repo/mobile/android/lib_task_api/src/main/java/org/tensorflow/lite/examples/classification/tflite/ClassifierQuantizedEfficientNet.java new file mode 100644 index 0000000000000000000000000000000000000000..05ca4fa6c409d0274a396c9b26c3c39ca8a8194e --- /dev/null +++ b/CCEdit-main/src/controlnet11/annotator/zoe/zoedepth/models/base_models/midas_repo/mobile/android/lib_task_api/src/main/java/org/tensorflow/lite/examples/classification/tflite/ClassifierQuantizedEfficientNet.java @@ -0,0 +1,43 @@ +/* Copyright 2017 The TensorFlow Authors. All Rights Reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +==============================================================================*/ + +package org.tensorflow.lite.examples.classification.tflite; + +import android.app.Activity; +import java.io.IOException; + +/** This TensorFlow Lite classifier works with the quantized EfficientNet model. */ +public class ClassifierQuantizedEfficientNet extends Classifier { + + /** + * Initializes a {@code ClassifierQuantizedMobileNet}. + * + * @param device a {@link Device} object to configure the hardware accelerator + * @param numThreads the number of threads during the inference + * @throws IOException if the model is not loaded correctly + */ + public ClassifierQuantizedEfficientNet(Activity activity, Device device, int numThreads) + throws IOException { + super(activity, device, numThreads); + } + + @Override + protected String getModelPath() { + // you can download this file from + // see build.gradle for where to obtain this file. It should be auto + // downloaded into assets. + return "efficientnet-lite0-int8.tflite"; + } +} diff --git a/CCEdit-main/src/controlnet11/annotator/zoe/zoedepth/models/base_models/midas_repo/mobile/android/lib_task_api/src/main/java/org/tensorflow/lite/examples/classification/tflite/ClassifierQuantizedMobileNet.java b/CCEdit-main/src/controlnet11/annotator/zoe/zoedepth/models/base_models/midas_repo/mobile/android/lib_task_api/src/main/java/org/tensorflow/lite/examples/classification/tflite/ClassifierQuantizedMobileNet.java new file mode 100644 index 0000000000000000000000000000000000000000..978b08eeaf52a23eede437d61045db08d1dff163 --- /dev/null +++ b/CCEdit-main/src/controlnet11/annotator/zoe/zoedepth/models/base_models/midas_repo/mobile/android/lib_task_api/src/main/java/org/tensorflow/lite/examples/classification/tflite/ClassifierQuantizedMobileNet.java @@ -0,0 +1,44 @@ +/* Copyright 2017 The TensorFlow Authors. All Rights Reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +==============================================================================*/ + +package org.tensorflow.lite.examples.classification.tflite; + +import android.app.Activity; +import java.io.IOException; +import org.tensorflow.lite.examples.classification.tflite.Classifier.Device; + +/** This TensorFlow Lite classifier works with the quantized MobileNet model. */ +public class ClassifierQuantizedMobileNet extends Classifier { + + /** + * Initializes a {@code ClassifierQuantizedMobileNet}. + * + * @param device a {@link Device} object to configure the hardware accelerator + * @param numThreads the number of threads during the inference + * @throws IOException if the model is not loaded correctly + */ + public ClassifierQuantizedMobileNet(Activity activity, Device device, int numThreads) + throws IOException { + super(activity, device, numThreads); + } + + @Override + protected String getModelPath() { + // you can download this file from + // see build.gradle for where to obtain this file. It should be auto + // downloaded into assets. + return "mobilenet_v1_1.0_224_quant.tflite"; + } +} diff --git a/CCEdit-main/src/controlnet11/annotator/zoe/zoedepth/models/base_models/midas_repo/mobile/android/models/build.gradle b/CCEdit-main/src/controlnet11/annotator/zoe/zoedepth/models/base_models/midas_repo/mobile/android/models/build.gradle new file mode 100644 index 0000000000000000000000000000000000000000..8d825707af20cbbead6c4599f075599148e3511c --- /dev/null +++ b/CCEdit-main/src/controlnet11/annotator/zoe/zoedepth/models/base_models/midas_repo/mobile/android/models/build.gradle @@ -0,0 +1,40 @@ +apply plugin: 'com.android.library' +apply plugin: 'de.undercouch.download' + +android { + compileSdkVersion 28 + buildToolsVersion "28.0.0" + + defaultConfig { + minSdkVersion 21 + targetSdkVersion 28 + versionCode 1 + versionName "1.0" + + testInstrumentationRunner "androidx.test.runner.AndroidJUnitRunner" + + } + + buildTypes { + release { + minifyEnabled false + proguardFiles getDefaultProguardFile('proguard-android-optimize.txt'), 'proguard-rules.pro' + } + } + + aaptOptions { + noCompress "tflite" + } + + lintOptions { + checkReleaseBuilds false + // Or, if you prefer, you can continue to check for errors in release builds, + // but continue the build even when errors are found: + abortOnError false + } +} + +// Download default models; if you wish to use your own models then +// place them in the "assets" directory and comment out this line. +project.ext.ASSET_DIR = projectDir.toString() + '/src/main/assets' +apply from:'download.gradle' diff --git a/CCEdit-main/src/controlnet11/annotator/zoe/zoedepth/models/base_models/midas_repo/mobile/android/models/download.gradle b/CCEdit-main/src/controlnet11/annotator/zoe/zoedepth/models/base_models/midas_repo/mobile/android/models/download.gradle new file mode 100644 index 0000000000000000000000000000000000000000..ce76974a2c3bc6f8214461028e0dfa6ebc25d588 --- /dev/null +++ b/CCEdit-main/src/controlnet11/annotator/zoe/zoedepth/models/base_models/midas_repo/mobile/android/models/download.gradle @@ -0,0 +1,10 @@ +def modelFloatDownloadUrl = "https://github.com/isl-org/MiDaS/releases/download/v2_1/model_opt.tflite" +def modelFloatFile = "model_opt.tflite" + +task downloadModelFloat(type: Download) { + src "${modelFloatDownloadUrl}" + dest project.ext.ASSET_DIR + "/${modelFloatFile}" + overwrite false +} + +preBuild.dependsOn downloadModelFloat diff --git a/CCEdit-main/src/controlnet11/annotator/zoe/zoedepth/models/base_models/midas_repo/mobile/android/models/proguard-rules.pro b/CCEdit-main/src/controlnet11/annotator/zoe/zoedepth/models/base_models/midas_repo/mobile/android/models/proguard-rules.pro new file mode 100644 index 0000000000000000000000000000000000000000..f1b424510da51fd82143bc74a0a801ae5a1e2fcd --- /dev/null +++ b/CCEdit-main/src/controlnet11/annotator/zoe/zoedepth/models/base_models/midas_repo/mobile/android/models/proguard-rules.pro @@ -0,0 +1,21 @@ +# Add project specific ProGuard rules here. +# You can control the set of applied configuration files using the +# proguardFiles setting in build.gradle. +# +# For more details, see +# http://developer.android.com/guide/developing/tools/proguard.html + +# If your project uses WebView with JS, uncomment the following +# and specify the fully qualified class name to the JavaScript interface +# class: +#-keepclassmembers class fqcn.of.javascript.interface.for.webview { +# public *; +#} + +# Uncomment this to preserve the line number information for +# debugging stack traces. +#-keepattributes SourceFile,LineNumberTable + +# If you keep the line number information, uncomment this to +# hide the original source file name. +#-renamesourcefileattribute SourceFile diff --git a/CCEdit-main/src/controlnet11/annotator/zoe/zoedepth/models/base_models/midas_repo/mobile/android/models/src/main/AndroidManifest.xml b/CCEdit-main/src/controlnet11/annotator/zoe/zoedepth/models/base_models/midas_repo/mobile/android/models/src/main/AndroidManifest.xml new file mode 100644 index 0000000000000000000000000000000000000000..42951a56497c5f947efe4aea6a07462019fb152c --- /dev/null +++ b/CCEdit-main/src/controlnet11/annotator/zoe/zoedepth/models/base_models/midas_repo/mobile/android/models/src/main/AndroidManifest.xml @@ -0,0 +1,3 @@ + + diff --git a/CCEdit-main/src/controlnet11/annotator/zoe/zoedepth/models/base_models/midas_repo/mobile/android/models/src/main/assets/run_tflite.py b/CCEdit-main/src/controlnet11/annotator/zoe/zoedepth/models/base_models/midas_repo/mobile/android/models/src/main/assets/run_tflite.py new file mode 100644 index 0000000000000000000000000000000000000000..4b8ebe235758d3d0f3d357c51ed54d78ac7eea8e --- /dev/null +++ b/CCEdit-main/src/controlnet11/annotator/zoe/zoedepth/models/base_models/midas_repo/mobile/android/models/src/main/assets/run_tflite.py @@ -0,0 +1,75 @@ +# Flex ops are included in the nightly build of the TensorFlow Python package. You can use TFLite models containing Flex ops by the same Python API as normal TFLite models. The nightly TensorFlow build can be installed with this command: +# Flex ops will be added to the TensorFlow Python package's and the tflite_runtime package from version 2.3 for Linux and 2.4 for other environments. +# https://www.tensorflow.org/lite/guide/ops_select#running_the_model + +# You must use: tf-nightly +# pip install tf-nightly + +import os +import glob +import cv2 +import numpy as np + +import tensorflow as tf + +width=256 +height=256 +model_name="model.tflite" +#model_name="model_quant.tflite" +image_name="dog.jpg" + +# input +img = cv2.imread(image_name) +img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB) / 255.0 + +mean=[0.485, 0.456, 0.406] +std=[0.229, 0.224, 0.225] +img = (img - mean) / std + +img_resized = tf.image.resize(img, [width,height], method='bicubic', preserve_aspect_ratio=False) +#img_resized = tf.transpose(img_resized, [2, 0, 1]) +img_input = img_resized.numpy() +reshape_img = img_input.reshape(1,width,height,3) +tensor = tf.convert_to_tensor(reshape_img, dtype=tf.float32) + +# load model +print("Load model...") +interpreter = tf.lite.Interpreter(model_path=model_name) +print("Allocate tensor...") +interpreter.allocate_tensors() +print("Get input/output details...") +input_details = interpreter.get_input_details() +output_details = interpreter.get_output_details() +print("Get input shape...") +input_shape = input_details[0]['shape'] +print(input_shape) +print(input_details) +print(output_details) +#input_data = np.array(np.random.random_sample(input_shape), dtype=np.float32) +print("Set input tensor...") +interpreter.set_tensor(input_details[0]['index'], tensor) + +print("invoke()...") +interpreter.invoke() + +# The function `get_tensor()` returns a copy of the tensor data. +# Use `tensor()` in order to get a pointer to the tensor. +print("get output tensor...") +output = interpreter.get_tensor(output_details[0]['index']) +#output = np.squeeze(output) +output = output.reshape(width, height) +#print(output) +prediction = np.array(output) +print("reshape prediction...") +prediction = prediction.reshape(width, height) + +# output file +#prediction = cv2.resize(prediction, (img.shape[1], img.shape[0]), interpolation=cv2.INTER_CUBIC) +print(" Write image to: output.png") +depth_min = prediction.min() +depth_max = prediction.max() +img_out = (255 * (prediction - depth_min) / (depth_max - depth_min)).astype("uint8") +print("save output image...") +cv2.imwrite("output.png", img_out) + +print("finished") \ No newline at end of file diff --git a/CCEdit-main/src/controlnet11/annotator/zoe/zoedepth/models/base_models/midas_repo/ros/additions/do_catkin_make.sh b/CCEdit-main/src/controlnet11/annotator/zoe/zoedepth/models/base_models/midas_repo/ros/additions/do_catkin_make.sh new file mode 100644 index 0000000000000000000000000000000000000000..0d416fc00282aab146326bbba12a9274e1ba29b8 --- /dev/null +++ b/CCEdit-main/src/controlnet11/annotator/zoe/zoedepth/models/base_models/midas_repo/ros/additions/do_catkin_make.sh @@ -0,0 +1,5 @@ +mkdir src +catkin_make +source devel/setup.bash +echo $ROS_PACKAGE_PATH +chmod +x ./devel/setup.bash diff --git a/CCEdit-main/src/controlnet11/annotator/zoe/zoedepth/models/base_models/midas_repo/ros/additions/install_ros_melodic_ubuntu_17_18.sh b/CCEdit-main/src/controlnet11/annotator/zoe/zoedepth/models/base_models/midas_repo/ros/additions/install_ros_melodic_ubuntu_17_18.sh new file mode 100644 index 0000000000000000000000000000000000000000..b868112631e9d9bc7bccb601407dfc857b8a99d5 --- /dev/null +++ b/CCEdit-main/src/controlnet11/annotator/zoe/zoedepth/models/base_models/midas_repo/ros/additions/install_ros_melodic_ubuntu_17_18.sh @@ -0,0 +1,34 @@ +#@title { display-mode: "code" } + +#from http://wiki.ros.org/indigo/Installation/Ubuntu + +#1.2 Setup sources.list +sudo sh -c 'echo "deb http://packages.ros.org/ros/ubuntu $(lsb_release -sc) main" > /etc/apt/sources.list.d/ros-latest.list' + +# 1.3 Setup keys +sudo apt-key adv --keyserver 'hkp://keyserver.ubuntu.com:80' --recv-key C1CF6E31E6BADE8868B172B4F42ED6FBAB17C654 +sudo apt-key adv --keyserver 'hkp://ha.pool.sks-keyservers.net:80' --recv-key 421C365BD9FF1F717815A3895523BAEEB01FA116 + +curl -sSL 'http://keyserver.ubuntu.com/pks/lookup?op=get&search=0xC1CF6E31E6BADE8868B172B4F42ED6FBAB17C654' | sudo apt-key add - + +# 1.4 Installation +sudo apt-get update +sudo apt-get upgrade + +# Desktop-Full Install: +sudo apt-get install ros-melodic-desktop-full + +printf "\nsource /opt/ros/melodic/setup.bash\n" >> ~/.bashrc + +# 1.5 Initialize rosdep +sudo rosdep init +rosdep update + + +# 1.7 Getting rosinstall (python) +sudo apt-get install python-rosinstall +sudo apt-get install python-catkin-tools +sudo apt-get install python-rospy +sudo apt-get install python-rosdep +sudo apt-get install python-roscd +sudo apt-get install python-pip \ No newline at end of file diff --git a/CCEdit-main/src/controlnet11/annotator/zoe/zoedepth/models/base_models/midas_repo/ros/midas_cpp/CMakeLists.txt b/CCEdit-main/src/controlnet11/annotator/zoe/zoedepth/models/base_models/midas_repo/ros/midas_cpp/CMakeLists.txt new file mode 100644 index 0000000000000000000000000000000000000000..885341691d217f9c4c8fcb1e4ff568d87788c7b8 --- /dev/null +++ b/CCEdit-main/src/controlnet11/annotator/zoe/zoedepth/models/base_models/midas_repo/ros/midas_cpp/CMakeLists.txt @@ -0,0 +1,189 @@ +cmake_minimum_required(VERSION 3.0.2) +project(midas_cpp) + +## Compile as C++11, supported in ROS Kinetic and newer +# add_compile_options(-std=c++11) + +## Find catkin macros and libraries +## if COMPONENTS list like find_package(catkin REQUIRED COMPONENTS xyz) +## is used, also find other catkin packages +find_package(catkin REQUIRED COMPONENTS + cv_bridge + image_transport + roscpp + rospy + sensor_msgs + std_msgs +) + +## System dependencies are found with CMake's conventions +# find_package(Boost REQUIRED COMPONENTS system) + +list(APPEND CMAKE_PREFIX_PATH "~/libtorch") +list(APPEND CMAKE_PREFIX_PATH "/usr/local/lib/python3.6/dist-packages/torch/lib") +list(APPEND CMAKE_PREFIX_PATH "/usr/local/lib/python2.7/dist-packages/torch/lib") + +if(NOT EXISTS "~/libtorch") + if (EXISTS "/usr/local/lib/python3.6/dist-packages/torch") + include_directories(/usr/local/include) + include_directories(/usr/local/lib/python3.6/dist-packages/torch/include/torch/csrc/api/include) + include_directories(/usr/local/lib/python3.6/dist-packages/torch/include) + + link_directories(/usr/local/lib) + link_directories(/usr/local/lib/python3.6/dist-packages/torch/lib) + + set(CMAKE_PREFIX_PATH /usr/local/lib/python3.6/dist-packages/torch) + set(Boost_USE_MULTITHREADED ON) + set(Torch_DIR /usr/local/lib/python3.6/dist-packages/torch) + + elseif (EXISTS "/usr/local/lib/python2.7/dist-packages/torch") + + include_directories(/usr/local/include) + include_directories(/usr/local/lib/python2.7/dist-packages/torch/include/torch/csrc/api/include) + include_directories(/usr/local/lib/python2.7/dist-packages/torch/include) + + link_directories(/usr/local/lib) + link_directories(/usr/local/lib/python2.7/dist-packages/torch/lib) + + set(CMAKE_PREFIX_PATH /usr/local/lib/python2.7/dist-packages/torch) + set(Boost_USE_MULTITHREADED ON) + set(Torch_DIR /usr/local/lib/python2.7/dist-packages/torch) + endif() +endif() + + + +find_package(Torch REQUIRED) +find_package(OpenCV REQUIRED) +include_directories( ${OpenCV_INCLUDE_DIRS} ) + +add_executable(midas_cpp src/main.cpp) +target_link_libraries(midas_cpp "${TORCH_LIBRARIES}" "${OpenCV_LIBS} ${catkin_LIBRARIES}") +set_property(TARGET midas_cpp PROPERTY CXX_STANDARD 14) + + + +################################### +## catkin specific configuration ## +################################### +## The catkin_package macro generates cmake config files for your package +## Declare things to be passed to dependent projects +## INCLUDE_DIRS: uncomment this if your package contains header files +## LIBRARIES: libraries you create in this project that dependent projects also need +## CATKIN_DEPENDS: catkin_packages dependent projects also need +## DEPENDS: system dependencies of this project that dependent projects also need +catkin_package( +# INCLUDE_DIRS include +# LIBRARIES midas_cpp +# CATKIN_DEPENDS cv_bridge image_transport roscpp sensor_msgs std_msgs +# DEPENDS system_lib +) + +########### +## Build ## +########### + +## Specify additional locations of header files +## Your package locations should be listed before other locations +include_directories( +# include + ${catkin_INCLUDE_DIRS} +) + +## Declare a C++ library +# add_library(${PROJECT_NAME} +# src/${PROJECT_NAME}/midas_cpp.cpp +# ) + +## Add cmake target dependencies of the library +## as an example, code may need to be generated before libraries +## either from message generation or dynamic reconfigure +# add_dependencies(${PROJECT_NAME} ${${PROJECT_NAME}_EXPORTED_TARGETS} ${catkin_EXPORTED_TARGETS}) + +## Declare a C++ executable +## With catkin_make all packages are built within a single CMake context +## The recommended prefix ensures that target names across packages don't collide +# add_executable(${PROJECT_NAME}_node src/midas_cpp_node.cpp) + +## Rename C++ executable without prefix +## The above recommended prefix causes long target names, the following renames the +## target back to the shorter version for ease of user use +## e.g. "rosrun someones_pkg node" instead of "rosrun someones_pkg someones_pkg_node" +# set_target_properties(${PROJECT_NAME}_node PROPERTIES OUTPUT_NAME node PREFIX "") + +## Add cmake target dependencies of the executable +## same as for the library above +# add_dependencies(${PROJECT_NAME}_node ${${PROJECT_NAME}_EXPORTED_TARGETS} ${catkin_EXPORTED_TARGETS}) + +## Specify libraries to link a library or executable target against +# target_link_libraries(${PROJECT_NAME}_node +# ${catkin_LIBRARIES} +# ) + +############# +## Install ## +############# + +# all install targets should use catkin DESTINATION variables +# See http://ros.org/doc/api/catkin/html/adv_user_guide/variables.html + +## Mark executable scripts (Python etc.) for installation +## in contrast to setup.py, you can choose the destination +# catkin_install_python(PROGRAMS +# scripts/my_python_script +# DESTINATION ${CATKIN_PACKAGE_BIN_DESTINATION} +# ) + +## Mark executables for installation +## See http://docs.ros.org/melodic/api/catkin/html/howto/format1/building_executables.html +# install(TARGETS ${PROJECT_NAME}_node +# RUNTIME DESTINATION ${CATKIN_PACKAGE_BIN_DESTINATION} +# ) + +## Mark libraries for installation +## See http://docs.ros.org/melodic/api/catkin/html/howto/format1/building_libraries.html +# install(TARGETS ${PROJECT_NAME} +# ARCHIVE DESTINATION ${CATKIN_PACKAGE_LIB_DESTINATION} +# LIBRARY DESTINATION ${CATKIN_PACKAGE_LIB_DESTINATION} +# RUNTIME DESTINATION ${CATKIN_GLOBAL_BIN_DESTINATION} +# ) + +## Mark cpp header files for installation +# install(DIRECTORY include/${PROJECT_NAME}/ +# DESTINATION ${CATKIN_PACKAGE_INCLUDE_DESTINATION} +# FILES_MATCHING PATTERN "*.h" +# PATTERN ".svn" EXCLUDE +# ) + +## Mark other files for installation (e.g. launch and bag files, etc.) +# install(FILES +# # myfile1 +# # myfile2 +# DESTINATION ${CATKIN_PACKAGE_SHARE_DESTINATION} +# ) + +############# +## Testing ## +############# + +## Add gtest based cpp test target and link libraries +# catkin_add_gtest(${PROJECT_NAME}-test test/test_midas_cpp.cpp) +# if(TARGET ${PROJECT_NAME}-test) +# target_link_libraries(${PROJECT_NAME}-test ${PROJECT_NAME}) +# endif() + +## Add folders to be run by python nosetests +# catkin_add_nosetests(test) + +install(TARGETS ${PROJECT_NAME} + ARCHIVE DESTINATION ${CATKIN_PACKAGE_LIB_DESTINATION} + LIBRARY DESTINATION ${CATKIN_PACKAGE_LIB_DESTINATION} + RUNTIME DESTINATION ${CATKIN_PACKAGE_BIN_DESTINATION} +) + +add_custom_command( + TARGET midas_cpp POST_BUILD + COMMAND ${CMAKE_COMMAND} -E copy + ${CMAKE_CURRENT_BINARY_DIR}/midas_cpp + ${CMAKE_SOURCE_DIR}/midas_cpp +) \ No newline at end of file diff --git a/CCEdit-main/src/controlnet11/annotator/zoe/zoedepth/models/base_models/midas_repo/ros/midas_cpp/launch/midas_cpp.launch b/CCEdit-main/src/controlnet11/annotator/zoe/zoedepth/models/base_models/midas_repo/ros/midas_cpp/launch/midas_cpp.launch new file mode 100644 index 0000000000000000000000000000000000000000..88e86f42f668e76ad4976ec6794a8cb0f20cac65 --- /dev/null +++ b/CCEdit-main/src/controlnet11/annotator/zoe/zoedepth/models/base_models/midas_repo/ros/midas_cpp/launch/midas_cpp.launch @@ -0,0 +1,19 @@ + + + + + + + + + + + + + + + + + + + \ No newline at end of file diff --git a/CCEdit-main/src/controlnet11/annotator/zoe/zoedepth/models/base_models/midas_repo/ros/midas_cpp/launch/midas_talker_listener.launch b/CCEdit-main/src/controlnet11/annotator/zoe/zoedepth/models/base_models/midas_repo/ros/midas_cpp/launch/midas_talker_listener.launch new file mode 100644 index 0000000000000000000000000000000000000000..8817a4f4933c56986fe0edc0886b2fded3d3406d --- /dev/null +++ b/CCEdit-main/src/controlnet11/annotator/zoe/zoedepth/models/base_models/midas_repo/ros/midas_cpp/launch/midas_talker_listener.launch @@ -0,0 +1,23 @@ + + + + + + + + + + + + + + + + + + + + + + + \ No newline at end of file diff --git a/CCEdit-main/src/controlnet11/annotator/zoe/zoedepth/models/base_models/midas_repo/ros/midas_cpp/package.xml b/CCEdit-main/src/controlnet11/annotator/zoe/zoedepth/models/base_models/midas_repo/ros/midas_cpp/package.xml new file mode 100644 index 0000000000000000000000000000000000000000..9cac90eba75409bd170f73531c54c83c52ff047a --- /dev/null +++ b/CCEdit-main/src/controlnet11/annotator/zoe/zoedepth/models/base_models/midas_repo/ros/midas_cpp/package.xml @@ -0,0 +1,77 @@ + + + midas_cpp + 0.1.0 + The midas_cpp package + + Alexey Bochkovskiy + MIT + https://github.com/isl-org/MiDaS/tree/master/ros + + + + + + + TODO + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + catkin + cv_bridge + image_transport + roscpp + rospy + sensor_msgs + std_msgs + cv_bridge + image_transport + roscpp + rospy + sensor_msgs + std_msgs + cv_bridge + image_transport + roscpp + rospy + sensor_msgs + std_msgs + + + + + + + + diff --git a/CCEdit-main/src/controlnet11/annotator/zoe/zoedepth/models/base_models/midas_repo/ros/midas_cpp/scripts/listener.py b/CCEdit-main/src/controlnet11/annotator/zoe/zoedepth/models/base_models/midas_repo/ros/midas_cpp/scripts/listener.py new file mode 100644 index 0000000000000000000000000000000000000000..6927ea7a83ac9309e5f883ee974a5dcfa8a2aa3b --- /dev/null +++ b/CCEdit-main/src/controlnet11/annotator/zoe/zoedepth/models/base_models/midas_repo/ros/midas_cpp/scripts/listener.py @@ -0,0 +1,61 @@ +#!/usr/bin/env python3 +from __future__ import print_function + +import roslib +#roslib.load_manifest('my_package') +import sys +import rospy +import cv2 +import numpy as np +from std_msgs.msg import String +from sensor_msgs.msg import Image +from cv_bridge import CvBridge, CvBridgeError + +class video_show: + + def __init__(self): + self.show_output = rospy.get_param('~show_output', True) + self.save_output = rospy.get_param('~save_output', False) + self.output_video_file = rospy.get_param('~output_video_file','result.mp4') + # rospy.loginfo(f"Listener - params: show_output={self.show_output}, save_output={self.save_output}, output_video_file={self.output_video_file}") + + self.bridge = CvBridge() + self.image_sub = rospy.Subscriber("midas_topic", Image, self.callback) + + def callback(self, data): + try: + cv_image = self.bridge.imgmsg_to_cv2(data) + except CvBridgeError as e: + print(e) + return + + if cv_image.size == 0: + return + + rospy.loginfo("Listener: Received new frame") + cv_image = cv_image.astype("uint8") + + if self.show_output==True: + cv2.imshow("video_show", cv_image) + cv2.waitKey(10) + + if self.save_output==True: + if self.video_writer_init==False: + fourcc = cv2.VideoWriter_fourcc(*'XVID') + self.out = cv2.VideoWriter(self.output_video_file, fourcc, 25, (cv_image.shape[1], cv_image.shape[0])) + + self.out.write(cv_image) + + + +def main(args): + rospy.init_node('listener', anonymous=True) + ic = video_show() + try: + rospy.spin() + except KeyboardInterrupt: + print("Shutting down") + cv2.destroyAllWindows() + +if __name__ == '__main__': + main(sys.argv) \ No newline at end of file diff --git a/CCEdit-main/src/controlnet11/annotator/zoe/zoedepth/models/base_models/midas_repo/ros/midas_cpp/scripts/listener_original.py b/CCEdit-main/src/controlnet11/annotator/zoe/zoedepth/models/base_models/midas_repo/ros/midas_cpp/scripts/listener_original.py new file mode 100644 index 0000000000000000000000000000000000000000..20e235f6958d644b89383752ab18e9e2275f55e5 --- /dev/null +++ b/CCEdit-main/src/controlnet11/annotator/zoe/zoedepth/models/base_models/midas_repo/ros/midas_cpp/scripts/listener_original.py @@ -0,0 +1,61 @@ +#!/usr/bin/env python3 +from __future__ import print_function + +import roslib +#roslib.load_manifest('my_package') +import sys +import rospy +import cv2 +import numpy as np +from std_msgs.msg import String +from sensor_msgs.msg import Image +from cv_bridge import CvBridge, CvBridgeError + +class video_show: + + def __init__(self): + self.show_output = rospy.get_param('~show_output', True) + self.save_output = rospy.get_param('~save_output', False) + self.output_video_file = rospy.get_param('~output_video_file','result.mp4') + # rospy.loginfo(f"Listener original - params: show_output={self.show_output}, save_output={self.save_output}, output_video_file={self.output_video_file}") + + self.bridge = CvBridge() + self.image_sub = rospy.Subscriber("image_topic", Image, self.callback) + + def callback(self, data): + try: + cv_image = self.bridge.imgmsg_to_cv2(data) + except CvBridgeError as e: + print(e) + return + + if cv_image.size == 0: + return + + rospy.loginfo("Listener_original: Received new frame") + cv_image = cv_image.astype("uint8") + + if self.show_output==True: + cv2.imshow("video_show_orig", cv_image) + cv2.waitKey(10) + + if self.save_output==True: + if self.video_writer_init==False: + fourcc = cv2.VideoWriter_fourcc(*'XVID') + self.out = cv2.VideoWriter(self.output_video_file, fourcc, 25, (cv_image.shape[1], cv_image.shape[0])) + + self.out.write(cv_image) + + + +def main(args): + rospy.init_node('listener_original', anonymous=True) + ic = video_show() + try: + rospy.spin() + except KeyboardInterrupt: + print("Shutting down") + cv2.destroyAllWindows() + +if __name__ == '__main__': + main(sys.argv) \ No newline at end of file diff --git a/CCEdit-main/src/controlnet11/annotator/zoe/zoedepth/models/base_models/midas_repo/ros/midas_cpp/scripts/talker.py b/CCEdit-main/src/controlnet11/annotator/zoe/zoedepth/models/base_models/midas_repo/ros/midas_cpp/scripts/talker.py new file mode 100644 index 0000000000000000000000000000000000000000..8219cc8632484a2efd02984347c615efad6b78b2 --- /dev/null +++ b/CCEdit-main/src/controlnet11/annotator/zoe/zoedepth/models/base_models/midas_repo/ros/midas_cpp/scripts/talker.py @@ -0,0 +1,53 @@ +#!/usr/bin/env python3 + + +import roslib +#roslib.load_manifest('my_package') +import sys +import rospy +import cv2 +from std_msgs.msg import String +from sensor_msgs.msg import Image +from cv_bridge import CvBridge, CvBridgeError + + +def talker(): + rospy.init_node('talker', anonymous=True) + + use_camera = rospy.get_param('~use_camera', False) + input_video_file = rospy.get_param('~input_video_file','test.mp4') + # rospy.loginfo(f"Talker - params: use_camera={use_camera}, input_video_file={input_video_file}") + + # rospy.loginfo("Talker: Trying to open a video stream") + if use_camera == True: + cap = cv2.VideoCapture(0) + else: + cap = cv2.VideoCapture(input_video_file) + + pub = rospy.Publisher('image_topic', Image, queue_size=1) + rate = rospy.Rate(30) # 30hz + bridge = CvBridge() + + while not rospy.is_shutdown(): + ret, cv_image = cap.read() + if ret==False: + print("Talker: Video is over") + rospy.loginfo("Video is over") + return + + try: + image = bridge.cv2_to_imgmsg(cv_image, "bgr8") + except CvBridgeError as e: + rospy.logerr("Talker: cv2image conversion failed: ", e) + print(e) + continue + + rospy.loginfo("Talker: Publishing frame") + pub.publish(image) + rate.sleep() + +if __name__ == '__main__': + try: + talker() + except rospy.ROSInterruptException: + pass diff --git a/CCEdit-main/src/controlnet11/annotator/zoe/zoedepth/models/base_models/midas_repo/ros/midas_cpp/src/main.cpp b/CCEdit-main/src/controlnet11/annotator/zoe/zoedepth/models/base_models/midas_repo/ros/midas_cpp/src/main.cpp new file mode 100644 index 0000000000000000000000000000000000000000..e4fc72c6955f66af71c9cb1fc7a7b1f643129685 --- /dev/null +++ b/CCEdit-main/src/controlnet11/annotator/zoe/zoedepth/models/base_models/midas_repo/ros/midas_cpp/src/main.cpp @@ -0,0 +1,285 @@ +#include +#include +#include +#include + +#include + +#include // One-stop header. + +#include +#include +#include +#include + +#include +#include + +// includes for OpenCV >= 3.x +#ifndef CV_VERSION_EPOCH +#include +#include +#include +#endif + +// OpenCV includes for OpenCV 2.x +#ifdef CV_VERSION_EPOCH +#include +#include +#include +#include +#endif + +static const std::string OPENCV_WINDOW = "Image window"; + +class Midas +{ + ros::NodeHandle nh_; + image_transport::ImageTransport it_; + image_transport::Subscriber image_sub_; + image_transport::Publisher image_pub_; + + torch::jit::script::Module module; + torch::Device device; + + auto ToTensor(cv::Mat img, bool show_output = false, bool unsqueeze = false, int unsqueeze_dim = 0) + { + //std::cout << "image shape: " << img.size() << std::endl; + at::Tensor tensor_image = torch::from_blob(img.data, { img.rows, img.cols, 3 }, at::kByte); + + if (unsqueeze) + { + tensor_image.unsqueeze_(unsqueeze_dim); + //std::cout << "tensors new shape: " << tensor_image.sizes() << std::endl; + } + + if (show_output) + { + std::cout << tensor_image.slice(2, 0, 1) << std::endl; + } + //std::cout << "tenor shape: " << tensor_image.sizes() << std::endl; + return tensor_image; + } + + auto ToInput(at::Tensor tensor_image) + { + // Create a vector of inputs. + return std::vector{tensor_image}; + } + + auto ToCvImage(at::Tensor tensor, int cv_type = CV_8UC3) + { + int width = tensor.sizes()[0]; + int height = tensor.sizes()[1]; + try + { + cv::Mat output_mat; + if (cv_type == CV_8UC4 || cv_type == CV_8UC3 || cv_type == CV_8UC2 || cv_type == CV_8UC1) { + cv::Mat cv_image(cv::Size{ height, width }, cv_type, tensor.data_ptr()); + output_mat = cv_image; + } + else if (cv_type == CV_32FC4 || cv_type == CV_32FC3 || cv_type == CV_32FC2 || cv_type == CV_32FC1) { + cv::Mat cv_image(cv::Size{ height, width }, cv_type, tensor.data_ptr()); + output_mat = cv_image; + } + else if (cv_type == CV_64FC4 || cv_type == CV_64FC3 || cv_type == CV_64FC2 || cv_type == CV_64FC1) { + cv::Mat cv_image(cv::Size{ height, width }, cv_type, tensor.data_ptr()); + output_mat = cv_image; + } + + //show_image(output_mat, "converted image from tensor"); + return output_mat.clone(); + } + catch (const c10::Error& e) + { + std::cout << "an error has occured : " << e.msg() << std::endl; + } + return cv::Mat(height, width, CV_8UC3); + } + + std::string input_topic, output_topic, model_name; + bool out_orig_size; + int net_width, net_height; + torch::NoGradGuard guard; + at::Tensor mean, std; + at::Tensor output, tensor; + +public: + Midas() + : nh_(), it_(nh_), device(torch::Device(torch::kCPU)) + { + ros::param::param("~input_topic", input_topic, "image_topic"); + ros::param::param("~output_topic", output_topic, "midas_topic"); + ros::param::param("~model_name", model_name, "model-small-traced.pt"); + ros::param::param("~out_orig_size", out_orig_size, true); + ros::param::param("~net_width", net_width, 256); + ros::param::param("~net_height", net_height, 256); + + std::cout << ", input_topic = " << input_topic << + ", output_topic = " << output_topic << + ", model_name = " << model_name << + ", out_orig_size = " << out_orig_size << + ", net_width = " << net_width << + ", net_height = " << net_height << + std::endl; + + // Subscrive to input video feed and publish output video feed + image_sub_ = it_.subscribe(input_topic, 1, &Midas::imageCb, this); + image_pub_ = it_.advertise(output_topic, 1); + + std::cout << "Try to load torchscript model \n"; + + try { + // Deserialize the ScriptModule from a file using torch::jit::load(). + module = torch::jit::load(model_name); + } + catch (const c10::Error& e) { + std::cerr << "error loading the model\n"; + exit(0); + } + + std::cout << "ok\n"; + + try { + module.eval(); + torch::jit::getProfilingMode() = false; + torch::jit::setGraphExecutorOptimize(true); + + mean = torch::tensor({ 0.485, 0.456, 0.406 }); + std = torch::tensor({ 0.229, 0.224, 0.225 }); + + if (torch::hasCUDA()) { + std::cout << "cuda is available" << std::endl; + at::globalContext().setBenchmarkCuDNN(true); + device = torch::Device(torch::kCUDA); + module.to(device); + mean = mean.to(device); + std = std.to(device); + } + } + catch (const c10::Error& e) + { + std::cerr << " module initialization: " << e.msg() << std::endl; + } + } + + ~Midas() + { + } + + void imageCb(const sensor_msgs::ImageConstPtr& msg) + { + cv_bridge::CvImagePtr cv_ptr; + try + { + // sensor_msgs::Image to cv::Mat + cv_ptr = cv_bridge::toCvCopy(msg, sensor_msgs::image_encodings::RGB8); + } + catch (cv_bridge::Exception& e) + { + ROS_ERROR("cv_bridge exception: %s", e.what()); + return; + } + + // pre-processing + auto tensor_cpu = ToTensor(cv_ptr->image); // OpenCV-image -> Libtorch-tensor + + try { + tensor = tensor_cpu.to(device); // move to device (CPU or GPU) + + tensor = tensor.toType(c10::kFloat); + tensor = tensor.permute({ 2, 0, 1 }); // HWC -> CHW + tensor = tensor.unsqueeze(0); + tensor = at::upsample_bilinear2d(tensor, { net_height, net_width }, true); // resize + tensor = tensor.squeeze(0); + tensor = tensor.permute({ 1, 2, 0 }); // CHW -> HWC + + tensor = tensor.div(255).sub(mean).div(std); // normalization + tensor = tensor.permute({ 2, 0, 1 }); // HWC -> CHW + tensor.unsqueeze_(0); // CHW -> NCHW + } + catch (const c10::Error& e) + { + std::cerr << " pre-processing exception: " << e.msg() << std::endl; + return; + } + + auto input_to_net = ToInput(tensor); // input to the network + + // inference + output; + try { + output = module.forward(input_to_net).toTensor(); // run inference + } + catch (const c10::Error& e) + { + std::cerr << " module.forward() exception: " << e.msg() << std::endl; + return; + } + + output = output.detach().to(torch::kF32); + + // move to CPU temporary + at::Tensor output_tmp = output; + output_tmp = output_tmp.to(torch::kCPU); + + // normalization + float min_val = std::numeric_limits::max(); + float max_val = std::numeric_limits::min(); + + for (int i = 0; i < net_width * net_height; ++i) { + float val = output_tmp.data_ptr()[i]; + if (min_val > val) min_val = val; + if (max_val < val) max_val = val; + } + float range_val = max_val - min_val; + + output = output.sub(min_val).div(range_val).mul(255.0F).clamp(0, 255).to(torch::kF32); // .to(torch::kU8); + + // resize to the original size if required + if (out_orig_size) { + try { + output = at::upsample_bilinear2d(output.unsqueeze(0), { cv_ptr->image.size().height, cv_ptr->image.size().width }, true); + output = output.squeeze(0); + } + catch (const c10::Error& e) + { + std::cout << " upsample_bilinear2d() exception: " << e.msg() << std::endl; + return; + } + } + output = output.permute({ 1, 2, 0 }).to(torch::kCPU); + + int cv_type = CV_32FC1; // CV_8UC1; + auto cv_img = ToCvImage(output, cv_type); + + sensor_msgs::Image img_msg; + + try { + // cv::Mat -> sensor_msgs::Image + std_msgs::Header header; // empty header + header.seq = 0; // user defined counter + header.stamp = ros::Time::now();// time + //cv_bridge::CvImage img_bridge = cv_bridge::CvImage(header, sensor_msgs::image_encodings::MONO8, cv_img); + cv_bridge::CvImage img_bridge = cv_bridge::CvImage(header, sensor_msgs::image_encodings::TYPE_32FC1, cv_img); + + img_bridge.toImageMsg(img_msg); // cv_bridge -> sensor_msgs::Image + } + catch (cv_bridge::Exception& e) + { + ROS_ERROR("cv_bridge exception: %s", e.what()); + return; + } + + // Output modified video stream + image_pub_.publish(img_msg); + } +}; + +int main(int argc, char** argv) +{ + ros::init(argc, argv, "midas", ros::init_options::AnonymousName); + Midas ic; + ros::spin(); + return 0; +} \ No newline at end of file diff --git a/CCEdit-main/src/controlnet11/annotator/zoe/zoedepth/models/base_models/midas_repo/tf/README.md b/CCEdit-main/src/controlnet11/annotator/zoe/zoedepth/models/base_models/midas_repo/tf/README.md new file mode 100644 index 0000000000000000000000000000000000000000..5b5fe0e63668eab45a55b140826cb3762862b17c --- /dev/null +++ b/CCEdit-main/src/controlnet11/annotator/zoe/zoedepth/models/base_models/midas_repo/tf/README.md @@ -0,0 +1,147 @@ +## Towards Robust Monocular Depth Estimation: Mixing Datasets for Zero-shot Cross-dataset Transfer + +### TensorFlow inference using `.pb` and `.onnx` models + +1. [Run inference on TensorFlow-model by using TensorFlow](#run-inference-on-tensorflow-model-by-using-tensorFlow) + +2. [Run inference on ONNX-model by using TensorFlow](#run-inference-on-onnx-model-by-using-tensorflow) + +3. [Make ONNX model from downloaded Pytorch model file](#make-onnx-model-from-downloaded-pytorch-model-file) + + +### Run inference on TensorFlow-model by using TensorFlow + +1) Download the model weights [model-f6b98070.pb](https://github.com/isl-org/MiDaS/releases/download/v2_1/model-f6b98070.pb) +and [model-small.pb](https://github.com/isl-org/MiDaS/releases/download/v2_1/model-small.pb) and place the +file in the `/tf/` folder. + +2) Set up dependencies: + +```shell +# install OpenCV +pip install --upgrade pip +pip install opencv-python + +# install TensorFlow +pip install -I grpcio tensorflow==2.3.0 tensorflow-addons==0.11.2 numpy==1.18.0 +``` + +#### Usage + +1) Place one or more input images in the folder `tf/input`. + +2) Run the model: + + ```shell + python tf/run_pb.py + ``` + + Or run the small model: + + ```shell + python tf/run_pb.py --model_weights model-small.pb --model_type small + ``` + +3) The resulting inverse depth maps are written to the `tf/output` folder. + + +### Run inference on ONNX-model by using ONNX-Runtime + +1) Download the model weights [model-f6b98070.onnx](https://github.com/isl-org/MiDaS/releases/download/v2_1/model-f6b98070.onnx) +and [model-small.onnx](https://github.com/isl-org/MiDaS/releases/download/v2_1/model-small.onnx) and place the +file in the `/tf/` folder. + +2) Set up dependencies: + +```shell +# install OpenCV +pip install --upgrade pip +pip install opencv-python + +# install ONNX +pip install onnx==1.7.0 + +# install ONNX Runtime +pip install onnxruntime==1.5.2 +``` + +#### Usage + +1) Place one or more input images in the folder `tf/input`. + +2) Run the model: + + ```shell + python tf/run_onnx.py + ``` + + Or run the small model: + + ```shell + python tf/run_onnx.py --model_weights model-small.onnx --model_type small + ``` + +3) The resulting inverse depth maps are written to the `tf/output` folder. + + + +### Make ONNX model from downloaded Pytorch model file + +1) Download the model weights [model-f6b98070.pt](https://github.com/isl-org/MiDaS/releases/download/v2_1/model-f6b98070.pt) and place the +file in the root folder. + +2) Set up dependencies: + +```shell +# install OpenCV +pip install --upgrade pip +pip install opencv-python + +# install PyTorch TorchVision +pip install -I torch==1.7.0 torchvision==0.8.0 + +# install TensorFlow +pip install -I grpcio tensorflow==2.3.0 tensorflow-addons==0.11.2 numpy==1.18.0 + +# install ONNX +pip install onnx==1.7.0 + +# install ONNX-TensorFlow +git clone https://github.com/onnx/onnx-tensorflow.git +cd onnx-tensorflow +git checkout 095b51b88e35c4001d70f15f80f31014b592b81e +pip install -e . +``` + +#### Usage + +1) Run the converter: + + ```shell + python tf/make_onnx_model.py + ``` + +2) The resulting `model-f6b98070.onnx` file is written to the `/tf/` folder. + + +### Requirements + + The code was tested with Python 3.6.9, PyTorch 1.5.1, TensorFlow 2.2.0, TensorFlow-addons 0.8.3, ONNX 1.7.0, ONNX-TensorFlow (GitHub-master-17.07.2020) and OpenCV 4.3.0. + +### Citation + +Please cite our paper if you use this code or any of the models: +``` +@article{Ranftl2019, + author = {Ren\'{e} Ranftl and Katrin Lasinger and David Hafner and Konrad Schindler and Vladlen Koltun}, + title = {Towards Robust Monocular Depth Estimation: Mixing Datasets for Zero-shot Cross-dataset Transfer}, + journal = {IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI)}, + year = {2020}, +} +``` + +### License + +MIT License + + diff --git a/CCEdit-main/src/controlnet11/annotator/zoe/zoedepth/models/base_models/midas_repo/tf/make_onnx_model.py b/CCEdit-main/src/controlnet11/annotator/zoe/zoedepth/models/base_models/midas_repo/tf/make_onnx_model.py new file mode 100644 index 0000000000000000000000000000000000000000..d14b0e4e1d2ea70fa315fd7ca7dfd72440a19376 --- /dev/null +++ b/CCEdit-main/src/controlnet11/annotator/zoe/zoedepth/models/base_models/midas_repo/tf/make_onnx_model.py @@ -0,0 +1,112 @@ +"""Compute depth maps for images in the input folder. +""" +import os +import ntpath +import glob +import torch +import utils +import cv2 +import numpy as np +from torchvision.transforms import Compose, Normalize +from torchvision import transforms + +from shutil import copyfile +import fileinput +import sys +sys.path.append(os.getcwd() + '/..') + +def modify_file(): + modify_filename = '../midas/blocks.py' + copyfile(modify_filename, modify_filename+'.bak') + + with open(modify_filename, 'r') as file : + filedata = file.read() + + filedata = filedata.replace('align_corners=True', 'align_corners=False') + filedata = filedata.replace('import torch.nn as nn', 'import torch.nn as nn\nimport torchvision.models as models') + filedata = filedata.replace('torch.hub.load("facebookresearch/WSL-Images", "resnext101_32x8d_wsl")', 'models.resnext101_32x8d()') + + with open(modify_filename, 'w') as file: + file.write(filedata) + +def restore_file(): + modify_filename = '../midas/blocks.py' + copyfile(modify_filename+'.bak', modify_filename) + +modify_file() + +from midas.midas_net import MidasNet +from midas.transforms import Resize, NormalizeImage, PrepareForNet + +restore_file() + + +class MidasNet_preprocessing(MidasNet): + """Network for monocular depth estimation. + """ + def forward(self, x): + """Forward pass. + + Args: + x (tensor): input data (image) + + Returns: + tensor: depth + """ + + mean = torch.tensor([0.485, 0.456, 0.406]) + std = torch.tensor([0.229, 0.224, 0.225]) + x.sub_(mean[None, :, None, None]).div_(std[None, :, None, None]) + + return MidasNet.forward(self, x) + + +def run(model_path): + """Run MonoDepthNN to compute depth maps. + + Args: + model_path (str): path to saved model + """ + print("initialize") + + # select device + + # load network + #model = MidasNet(model_path, non_negative=True) + model = MidasNet_preprocessing(model_path, non_negative=True) + + model.eval() + + print("start processing") + + # input + img_input = np.zeros((3, 384, 384), np.float32) + + # compute + with torch.no_grad(): + sample = torch.from_numpy(img_input).unsqueeze(0) + prediction = model.forward(sample) + prediction = ( + torch.nn.functional.interpolate( + prediction.unsqueeze(1), + size=img_input.shape[:2], + mode="bicubic", + align_corners=False, + ) + .squeeze() + .cpu() + .numpy() + ) + + torch.onnx.export(model, sample, ntpath.basename(model_path).rsplit('.', 1)[0]+'.onnx', opset_version=9) + + print("finished") + + +if __name__ == "__main__": + # set paths + # MODEL_PATH = "model.pt" + MODEL_PATH = "../model-f6b98070.pt" + + # compute depth maps + run(MODEL_PATH) diff --git a/CCEdit-main/src/controlnet11/annotator/zoe/zoedepth/models/base_models/midas_repo/tf/run_pb.py b/CCEdit-main/src/controlnet11/annotator/zoe/zoedepth/models/base_models/midas_repo/tf/run_pb.py new file mode 100644 index 0000000000000000000000000000000000000000..e46254f7b37f72e7d87672d70fd4b2f393ad7658 --- /dev/null +++ b/CCEdit-main/src/controlnet11/annotator/zoe/zoedepth/models/base_models/midas_repo/tf/run_pb.py @@ -0,0 +1,135 @@ +"""Compute depth maps for images in the input folder. +""" +import os +import glob +import utils +import cv2 +import argparse + +import tensorflow as tf + +from transforms import Resize, NormalizeImage, PrepareForNet + +def run(input_path, output_path, model_path, model_type="large"): + """Run MonoDepthNN to compute depth maps. + + Args: + input_path (str): path to input folder + output_path (str): path to output folder + model_path (str): path to saved model + """ + print("initialize") + + # the runtime initialization will not allocate all memory on the device to avoid out of GPU memory + gpus = tf.config.experimental.list_physical_devices('GPU') + if gpus: + try: + for gpu in gpus: + #tf.config.experimental.set_memory_growth(gpu, True) + tf.config.experimental.set_virtual_device_configuration(gpu, + [tf.config.experimental.VirtualDeviceConfiguration(memory_limit=4000)]) + except RuntimeError as e: + print(e) + + # network resolution + if model_type == "large": + net_w, net_h = 384, 384 + elif model_type == "small": + net_w, net_h = 256, 256 + else: + print(f"model_type '{model_type}' not implemented, use: --model_type large") + assert False + + # load network + graph_def = tf.compat.v1.GraphDef() + with tf.io.gfile.GFile(model_path, 'rb') as f: + graph_def.ParseFromString(f.read()) + tf.import_graph_def(graph_def, name='') + + + model_operations = tf.compat.v1.get_default_graph().get_operations() + input_node = '0:0' + output_layer = model_operations[len(model_operations) - 1].name + ':0' + print("Last layer name: ", output_layer) + + resize_image = Resize( + net_w, + net_h, + resize_target=None, + keep_aspect_ratio=False, + ensure_multiple_of=32, + resize_method="upper_bound", + image_interpolation_method=cv2.INTER_CUBIC, + ) + + def compose2(f1, f2): + return lambda x: f2(f1(x)) + + transform = compose2(resize_image, PrepareForNet()) + + # get input + img_names = glob.glob(os.path.join(input_path, "*")) + num_images = len(img_names) + + # create output folder + os.makedirs(output_path, exist_ok=True) + + print("start processing") + + with tf.compat.v1.Session() as sess: + try: + # load images + for ind, img_name in enumerate(img_names): + + print(" processing {} ({}/{})".format(img_name, ind + 1, num_images)) + + # input + img = utils.read_image(img_name) + img_input = transform({"image": img})["image"] + + # compute + prob_tensor = sess.graph.get_tensor_by_name(output_layer) + prediction, = sess.run(prob_tensor, {input_node: [img_input] }) + prediction = prediction.reshape(net_h, net_w) + prediction = cv2.resize(prediction, (img.shape[1], img.shape[0]), interpolation=cv2.INTER_CUBIC) + + # output + filename = os.path.join( + output_path, os.path.splitext(os.path.basename(img_name))[0] + ) + utils.write_depth(filename, prediction, bits=2) + + except KeyError: + print ("Couldn't find input node: ' + input_node + ' or output layer: " + output_layer + ".") + exit(-1) + + print("finished") + + +if __name__ == "__main__": + parser = argparse.ArgumentParser() + + parser.add_argument('-i', '--input_path', + default='input', + help='folder with input images' + ) + + parser.add_argument('-o', '--output_path', + default='output', + help='folder for output images' + ) + + parser.add_argument('-m', '--model_weights', + default='model-f6b98070.pb', + help='path to the trained weights of model' + ) + + parser.add_argument('-t', '--model_type', + default='large', + help='model type: large or small' + ) + + args = parser.parse_args() + + # compute depth maps + run(args.input_path, args.output_path, args.model_weights, args.model_type) diff --git a/CCEdit-main/src/controlnet11/annotator/zoe/zoedepth/models/base_models/midas_repo/tf/transforms.py b/CCEdit-main/src/controlnet11/annotator/zoe/zoedepth/models/base_models/midas_repo/tf/transforms.py new file mode 100644 index 0000000000000000000000000000000000000000..350cbc11662633ad7f8968eb10be2e7de6e384e9 --- /dev/null +++ b/CCEdit-main/src/controlnet11/annotator/zoe/zoedepth/models/base_models/midas_repo/tf/transforms.py @@ -0,0 +1,234 @@ +import numpy as np +import cv2 +import math + + +def apply_min_size(sample, size, image_interpolation_method=cv2.INTER_AREA): + """Rezise the sample to ensure the given size. Keeps aspect ratio. + + Args: + sample (dict): sample + size (tuple): image size + + Returns: + tuple: new size + """ + shape = list(sample["disparity"].shape) + + if shape[0] >= size[0] and shape[1] >= size[1]: + return sample + + scale = [0, 0] + scale[0] = size[0] / shape[0] + scale[1] = size[1] / shape[1] + + scale = max(scale) + + shape[0] = math.ceil(scale * shape[0]) + shape[1] = math.ceil(scale * shape[1]) + + # resize + sample["image"] = cv2.resize( + sample["image"], tuple(shape[::-1]), interpolation=image_interpolation_method + ) + + sample["disparity"] = cv2.resize( + sample["disparity"], tuple(shape[::-1]), interpolation=cv2.INTER_NEAREST + ) + sample["mask"] = cv2.resize( + sample["mask"].astype(np.float32), + tuple(shape[::-1]), + interpolation=cv2.INTER_NEAREST, + ) + sample["mask"] = sample["mask"].astype(bool) + + return tuple(shape) + + +class Resize(object): + """Resize sample to given size (width, height). + """ + + def __init__( + self, + width, + height, + resize_target=True, + keep_aspect_ratio=False, + ensure_multiple_of=1, + resize_method="lower_bound", + image_interpolation_method=cv2.INTER_AREA, + ): + """Init. + + Args: + width (int): desired output width + height (int): desired output height + resize_target (bool, optional): + True: Resize the full sample (image, mask, target). + False: Resize image only. + Defaults to True. + keep_aspect_ratio (bool, optional): + True: Keep the aspect ratio of the input sample. + Output sample might not have the given width and height, and + resize behaviour depends on the parameter 'resize_method'. + Defaults to False. + ensure_multiple_of (int, optional): + Output width and height is constrained to be multiple of this parameter. + Defaults to 1. + resize_method (str, optional): + "lower_bound": Output will be at least as large as the given size. + "upper_bound": Output will be at max as large as the given size. (Output size might be smaller than given size.) + "minimal": Scale as least as possible. (Output size might be smaller than given size.) + Defaults to "lower_bound". + """ + self.__width = width + self.__height = height + + self.__resize_target = resize_target + self.__keep_aspect_ratio = keep_aspect_ratio + self.__multiple_of = ensure_multiple_of + self.__resize_method = resize_method + self.__image_interpolation_method = image_interpolation_method + + def constrain_to_multiple_of(self, x, min_val=0, max_val=None): + y = (np.round(x / self.__multiple_of) * self.__multiple_of).astype(int) + + if max_val is not None and y > max_val: + y = (np.floor(x / self.__multiple_of) * self.__multiple_of).astype(int) + + if y < min_val: + y = (np.ceil(x / self.__multiple_of) * self.__multiple_of).astype(int) + + return y + + def get_size(self, width, height): + # determine new height and width + scale_height = self.__height / height + scale_width = self.__width / width + + if self.__keep_aspect_ratio: + if self.__resize_method == "lower_bound": + # scale such that output size is lower bound + if scale_width > scale_height: + # fit width + scale_height = scale_width + else: + # fit height + scale_width = scale_height + elif self.__resize_method == "upper_bound": + # scale such that output size is upper bound + if scale_width < scale_height: + # fit width + scale_height = scale_width + else: + # fit height + scale_width = scale_height + elif self.__resize_method == "minimal": + # scale as least as possbile + if abs(1 - scale_width) < abs(1 - scale_height): + # fit width + scale_height = scale_width + else: + # fit height + scale_width = scale_height + else: + raise ValueError( + f"resize_method {self.__resize_method} not implemented" + ) + + if self.__resize_method == "lower_bound": + new_height = self.constrain_to_multiple_of( + scale_height * height, min_val=self.__height + ) + new_width = self.constrain_to_multiple_of( + scale_width * width, min_val=self.__width + ) + elif self.__resize_method == "upper_bound": + new_height = self.constrain_to_multiple_of( + scale_height * height, max_val=self.__height + ) + new_width = self.constrain_to_multiple_of( + scale_width * width, max_val=self.__width + ) + elif self.__resize_method == "minimal": + new_height = self.constrain_to_multiple_of(scale_height * height) + new_width = self.constrain_to_multiple_of(scale_width * width) + else: + raise ValueError(f"resize_method {self.__resize_method} not implemented") + + return (new_width, new_height) + + def __call__(self, sample): + width, height = self.get_size( + sample["image"].shape[1], sample["image"].shape[0] + ) + + # resize sample + sample["image"] = cv2.resize( + sample["image"], + (width, height), + interpolation=self.__image_interpolation_method, + ) + + if self.__resize_target: + if "disparity" in sample: + sample["disparity"] = cv2.resize( + sample["disparity"], + (width, height), + interpolation=cv2.INTER_NEAREST, + ) + + if "depth" in sample: + sample["depth"] = cv2.resize( + sample["depth"], (width, height), interpolation=cv2.INTER_NEAREST + ) + + sample["mask"] = cv2.resize( + sample["mask"].astype(np.float32), + (width, height), + interpolation=cv2.INTER_NEAREST, + ) + sample["mask"] = sample["mask"].astype(bool) + + return sample + + +class NormalizeImage(object): + """Normlize image by given mean and std. + """ + + def __init__(self, mean, std): + self.__mean = mean + self.__std = std + + def __call__(self, sample): + sample["image"] = (sample["image"] - self.__mean) / self.__std + + return sample + + +class PrepareForNet(object): + """Prepare sample for usage as network input. + """ + + def __init__(self): + pass + + def __call__(self, sample): + image = np.transpose(sample["image"], (2, 0, 1)) + sample["image"] = np.ascontiguousarray(image).astype(np.float32) + + if "mask" in sample: + sample["mask"] = sample["mask"].astype(np.float32) + sample["mask"] = np.ascontiguousarray(sample["mask"]) + + if "disparity" in sample: + disparity = sample["disparity"].astype(np.float32) + sample["disparity"] = np.ascontiguousarray(disparity) + + if "depth" in sample: + depth = sample["depth"].astype(np.float32) + sample["depth"] = np.ascontiguousarray(depth) + + return sample diff --git a/CCEdit-main/src/controlnet11/annotator/zoe/zoedepth/models/base_models/midas_repo/tf/utils.py b/CCEdit-main/src/controlnet11/annotator/zoe/zoedepth/models/base_models/midas_repo/tf/utils.py new file mode 100644 index 0000000000000000000000000000000000000000..ff9a54bd55f5e31a90fad21242efbfda5a6cc1a7 --- /dev/null +++ b/CCEdit-main/src/controlnet11/annotator/zoe/zoedepth/models/base_models/midas_repo/tf/utils.py @@ -0,0 +1,82 @@ +import numpy as np +import sys +import cv2 + + +def write_pfm(path, image, scale=1): + """Write pfm file. + Args: + path (str): pathto file + image (array): data + scale (int, optional): Scale. Defaults to 1. + """ + + with open(path, "wb") as file: + color = None + + if image.dtype.name != "float32": + raise Exception("Image dtype must be float32.") + + image = np.flipud(image) + + if len(image.shape) == 3 and image.shape[2] == 3: # color image + color = True + elif ( + len(image.shape) == 2 or len(image.shape) == 3 and image.shape[2] == 1 + ): # greyscale + color = False + else: + raise Exception("Image must have H x W x 3, H x W x 1 or H x W dimensions.") + + file.write("PF\n" if color else "Pf\n".encode()) + file.write("%d %d\n".encode() % (image.shape[1], image.shape[0])) + + endian = image.dtype.byteorder + + if endian == "<" or endian == "=" and sys.byteorder == "little": + scale = -scale + + file.write("%f\n".encode() % scale) + + image.tofile(file) + +def read_image(path): + """Read image and output RGB image (0-1). + Args: + path (str): path to file + Returns: + array: RGB image (0-1) + """ + img = cv2.imread(path) + + if img.ndim == 2: + img = cv2.cvtColor(img, cv2.COLOR_GRAY2BGR) + + img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB) / 255.0 + + return img + +def write_depth(path, depth, bits=1): + """Write depth map to pfm and png file. + Args: + path (str): filepath without extension + depth (array): depth + """ + write_pfm(path + ".pfm", depth.astype(np.float32)) + + depth_min = depth.min() + depth_max = depth.max() + + max_val = (2**(8*bits))-1 + + if depth_max - depth_min > np.finfo("float").eps: + out = max_val * (depth - depth_min) / (depth_max - depth_min) + else: + out = 0 + + if bits == 1: + cv2.imwrite(path + ".png", out.astype("uint8")) + elif bits == 2: + cv2.imwrite(path + ".png", out.astype("uint16")) + + return \ No newline at end of file diff --git a/CCEdit-main/src/controlnet11/annotator/zoe/zoedepth/models/layers/__pycache__/attractor.cpython-39.pyc b/CCEdit-main/src/controlnet11/annotator/zoe/zoedepth/models/layers/__pycache__/attractor.cpython-39.pyc new file mode 100644 index 0000000000000000000000000000000000000000..f533664149f674e900ef7c1432c8553d7fa6f675 Binary files /dev/null and b/CCEdit-main/src/controlnet11/annotator/zoe/zoedepth/models/layers/__pycache__/attractor.cpython-39.pyc differ diff --git a/CCEdit-main/src/controlnet11/annotator/zoe/zoedepth/models/layers/__pycache__/dist_layers.cpython-39.pyc b/CCEdit-main/src/controlnet11/annotator/zoe/zoedepth/models/layers/__pycache__/dist_layers.cpython-39.pyc new file mode 100644 index 0000000000000000000000000000000000000000..02c97b146cdc1e2fe34d8ac4ee809b93261a3343 Binary files /dev/null and b/CCEdit-main/src/controlnet11/annotator/zoe/zoedepth/models/layers/__pycache__/dist_layers.cpython-39.pyc differ diff --git a/CCEdit-main/src/controlnet11/annotator/zoe/zoedepth/models/layers/__pycache__/localbins_layers.cpython-39.pyc b/CCEdit-main/src/controlnet11/annotator/zoe/zoedepth/models/layers/__pycache__/localbins_layers.cpython-39.pyc new file mode 100644 index 0000000000000000000000000000000000000000..48c2c46fb079ee7f9f078ab8dbc6d00748764ae0 Binary files /dev/null and b/CCEdit-main/src/controlnet11/annotator/zoe/zoedepth/models/layers/__pycache__/localbins_layers.cpython-39.pyc differ diff --git a/CCEdit-main/src/controlnet11/annotator/zoe/zoedepth/models/layers/attractor.py b/CCEdit-main/src/controlnet11/annotator/zoe/zoedepth/models/layers/attractor.py new file mode 100644 index 0000000000000000000000000000000000000000..2a8efe645adea1d88a12e2ac5cc6bb2a251eef9d --- /dev/null +++ b/CCEdit-main/src/controlnet11/annotator/zoe/zoedepth/models/layers/attractor.py @@ -0,0 +1,208 @@ +# MIT License + +# Copyright (c) 2022 Intelligent Systems Lab Org + +# Permission is hereby granted, free of charge, to any person obtaining a copy +# of this software and associated documentation files (the "Software"), to deal +# in the Software without restriction, including without limitation the rights +# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell +# copies of the Software, and to permit persons to whom the Software is +# furnished to do so, subject to the following conditions: + +# The above copyright notice and this permission notice shall be included in all +# copies or substantial portions of the Software. + +# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE +# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER +# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, +# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE +# SOFTWARE. + +# File author: Shariq Farooq Bhat + +import torch +import torch.nn as nn + + +@torch.jit.script +def exp_attractor(dx, alpha: float = 300, gamma: int = 2): + """Exponential attractor: dc = exp(-alpha*|dx|^gamma) * dx , where dx = a - c, a = attractor point, c = bin center, dc = shift in bin centermmary for exp_attractor + + Args: + dx (torch.Tensor): The difference tensor dx = Ai - Cj, where Ai is the attractor point and Cj is the bin center. + alpha (float, optional): Proportional Attractor strength. Determines the absolute strength. Lower alpha = greater attraction. Defaults to 300. + gamma (int, optional): Exponential Attractor strength. Determines the "region of influence" and indirectly number of bin centers affected. Lower gamma = farther reach. Defaults to 2. + + Returns: + torch.Tensor : Delta shifts - dc; New bin centers = Old bin centers + dc + """ + return torch.exp(-alpha*(torch.abs(dx)**gamma)) * (dx) + + +@torch.jit.script +def inv_attractor(dx, alpha: float = 300, gamma: int = 2): + """Inverse attractor: dc = dx / (1 + alpha*dx^gamma), where dx = a - c, a = attractor point, c = bin center, dc = shift in bin center + This is the default one according to the accompanying paper. + + Args: + dx (torch.Tensor): The difference tensor dx = Ai - Cj, where Ai is the attractor point and Cj is the bin center. + alpha (float, optional): Proportional Attractor strength. Determines the absolute strength. Lower alpha = greater attraction. Defaults to 300. + gamma (int, optional): Exponential Attractor strength. Determines the "region of influence" and indirectly number of bin centers affected. Lower gamma = farther reach. Defaults to 2. + + Returns: + torch.Tensor: Delta shifts - dc; New bin centers = Old bin centers + dc + """ + return dx.div(1+alpha*dx.pow(gamma)) + + +class AttractorLayer(nn.Module): + def __init__(self, in_features, n_bins, n_attractors=16, mlp_dim=128, min_depth=1e-3, max_depth=10, + alpha=300, gamma=2, kind='sum', attractor_type='exp', memory_efficient=False): + """ + Attractor layer for bin centers. Bin centers are bounded on the interval (min_depth, max_depth) + """ + super().__init__() + + self.n_attractors = n_attractors + self.n_bins = n_bins + self.min_depth = min_depth + self.max_depth = max_depth + self.alpha = alpha + self.gamma = gamma + self.kind = kind + self.attractor_type = attractor_type + self.memory_efficient = memory_efficient + + self._net = nn.Sequential( + nn.Conv2d(in_features, mlp_dim, 1, 1, 0), + nn.ReLU(inplace=True), + nn.Conv2d(mlp_dim, n_attractors*2, 1, 1, 0), # x2 for linear norm + nn.ReLU(inplace=True) + ) + + def forward(self, x, b_prev, prev_b_embedding=None, interpolate=True, is_for_query=False): + """ + Args: + x (torch.Tensor) : feature block; shape - n, c, h, w + b_prev (torch.Tensor) : previous bin centers normed; shape - n, prev_nbins, h, w + + Returns: + tuple(torch.Tensor,torch.Tensor) : new bin centers normed and scaled; shape - n, nbins, h, w + """ + if prev_b_embedding is not None: + if interpolate: + prev_b_embedding = nn.functional.interpolate( + prev_b_embedding, x.shape[-2:], mode='bilinear', align_corners=True) + x = x + prev_b_embedding + + A = self._net(x) + eps = 1e-3 + A = A + eps + n, c, h, w = A.shape + A = A.view(n, self.n_attractors, 2, h, w) + A_normed = A / A.sum(dim=2, keepdim=True) # n, a, 2, h, w + A_normed = A[:, :, 0, ...] # n, na, h, w + + b_prev = nn.functional.interpolate( + b_prev, (h, w), mode='bilinear', align_corners=True) + b_centers = b_prev + + if self.attractor_type == 'exp': + dist = exp_attractor + else: + dist = inv_attractor + + if not self.memory_efficient: + func = {'mean': torch.mean, 'sum': torch.sum}[self.kind] + # .shape N, nbins, h, w + delta_c = func(dist(A_normed.unsqueeze( + 2) - b_centers.unsqueeze(1)), dim=1) + else: + delta_c = torch.zeros_like(b_centers, device=b_centers.device) + for i in range(self.n_attractors): + # .shape N, nbins, h, w + delta_c += dist(A_normed[:, i, ...].unsqueeze(1) - b_centers) + + if self.kind == 'mean': + delta_c = delta_c / self.n_attractors + + b_new_centers = b_centers + delta_c + B_centers = (self.max_depth - self.min_depth) * \ + b_new_centers + self.min_depth + B_centers, _ = torch.sort(B_centers, dim=1) + B_centers = torch.clip(B_centers, self.min_depth, self.max_depth) + return b_new_centers, B_centers + + +class AttractorLayerUnnormed(nn.Module): + def __init__(self, in_features, n_bins, n_attractors=16, mlp_dim=128, min_depth=1e-3, max_depth=10, + alpha=300, gamma=2, kind='sum', attractor_type='exp', memory_efficient=False): + """ + Attractor layer for bin centers. Bin centers are unbounded + """ + super().__init__() + + self.n_attractors = n_attractors + self.n_bins = n_bins + self.min_depth = min_depth + self.max_depth = max_depth + self.alpha = alpha + self.gamma = gamma + self.kind = kind + self.attractor_type = attractor_type + self.memory_efficient = memory_efficient + + self._net = nn.Sequential( + nn.Conv2d(in_features, mlp_dim, 1, 1, 0), + nn.ReLU(inplace=True), + nn.Conv2d(mlp_dim, n_attractors, 1, 1, 0), + nn.Softplus() + ) + + def forward(self, x, b_prev, prev_b_embedding=None, interpolate=True, is_for_query=False): + """ + Args: + x (torch.Tensor) : feature block; shape - n, c, h, w + b_prev (torch.Tensor) : previous bin centers normed; shape - n, prev_nbins, h, w + + Returns: + tuple(torch.Tensor,torch.Tensor) : new bin centers unbounded; shape - n, nbins, h, w. Two outputs just to keep the API consistent with the normed version + """ + if prev_b_embedding is not None: + if interpolate: + prev_b_embedding = nn.functional.interpolate( + prev_b_embedding, x.shape[-2:], mode='bilinear', align_corners=True) + x = x + prev_b_embedding + + A = self._net(x) + n, c, h, w = A.shape + + b_prev = nn.functional.interpolate( + b_prev, (h, w), mode='bilinear', align_corners=True) + b_centers = b_prev + + if self.attractor_type == 'exp': + dist = exp_attractor + else: + dist = inv_attractor + + if not self.memory_efficient: + func = {'mean': torch.mean, 'sum': torch.sum}[self.kind] + # .shape N, nbins, h, w + delta_c = func( + dist(A.unsqueeze(2) - b_centers.unsqueeze(1)), dim=1) + else: + delta_c = torch.zeros_like(b_centers, device=b_centers.device) + for i in range(self.n_attractors): + delta_c += dist(A[:, i, ...].unsqueeze(1) - + b_centers) # .shape N, nbins, h, w + + if self.kind == 'mean': + delta_c = delta_c / self.n_attractors + + b_new_centers = b_centers + delta_c + B_centers = b_new_centers + + return b_new_centers, B_centers diff --git a/CCEdit-main/src/controlnet11/annotator/zoe/zoedepth/models/layers/dist_layers.py b/CCEdit-main/src/controlnet11/annotator/zoe/zoedepth/models/layers/dist_layers.py new file mode 100644 index 0000000000000000000000000000000000000000..3208405dfb78fdfc28d5765e5a6d5dbe31967a23 --- /dev/null +++ b/CCEdit-main/src/controlnet11/annotator/zoe/zoedepth/models/layers/dist_layers.py @@ -0,0 +1,121 @@ +# MIT License + +# Copyright (c) 2022 Intelligent Systems Lab Org + +# Permission is hereby granted, free of charge, to any person obtaining a copy +# of this software and associated documentation files (the "Software"), to deal +# in the Software without restriction, including without limitation the rights +# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell +# copies of the Software, and to permit persons to whom the Software is +# furnished to do so, subject to the following conditions: + +# The above copyright notice and this permission notice shall be included in all +# copies or substantial portions of the Software. + +# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE +# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER +# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, +# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE +# SOFTWARE. + +# File author: Shariq Farooq Bhat + +import torch +import torch.nn as nn + + +def log_binom(n, k, eps=1e-7): + """ log(nCk) using stirling approximation """ + n = n + eps + k = k + eps + return n * torch.log(n) - k * torch.log(k) - (n-k) * torch.log(n-k+eps) + + +class LogBinomial(nn.Module): + def __init__(self, n_classes=256, act=torch.softmax): + """Compute log binomial distribution for n_classes + + Args: + n_classes (int, optional): number of output classes. Defaults to 256. + """ + super().__init__() + self.K = n_classes + self.act = act + self.register_buffer('k_idx', torch.arange( + 0, n_classes).view(1, -1, 1, 1)) + self.register_buffer('K_minus_1', torch.Tensor( + [self.K-1]).view(1, -1, 1, 1)) + + def forward(self, x, t=1., eps=1e-4): + """Compute log binomial distribution for x + + Args: + x (torch.Tensor - NCHW): probabilities + t (float, torch.Tensor - NCHW, optional): Temperature of distribution. Defaults to 1.. + eps (float, optional): Small number for numerical stability. Defaults to 1e-4. + + Returns: + torch.Tensor -NCHW: log binomial distribution logbinomial(p;t) + """ + if x.ndim == 3: + x = x.unsqueeze(1) # make it nchw + + one_minus_x = torch.clamp(1 - x, eps, 1) + x = torch.clamp(x, eps, 1) + y = log_binom(self.K_minus_1, self.k_idx) + self.k_idx * \ + torch.log(x) + (self.K - 1 - self.k_idx) * torch.log(one_minus_x) + return self.act(y/t, dim=1) + + +class ConditionalLogBinomial(nn.Module): + def __init__(self, in_features, condition_dim, n_classes=256, bottleneck_factor=2, p_eps=1e-4, max_temp=50, min_temp=1e-7, act=torch.softmax): + """Conditional Log Binomial distribution + + Args: + in_features (int): number of input channels in main feature + condition_dim (int): number of input channels in condition feature + n_classes (int, optional): Number of classes. Defaults to 256. + bottleneck_factor (int, optional): Hidden dim factor. Defaults to 2. + p_eps (float, optional): small eps value. Defaults to 1e-4. + max_temp (float, optional): Maximum temperature of output distribution. Defaults to 50. + min_temp (float, optional): Minimum temperature of output distribution. Defaults to 1e-7. + """ + super().__init__() + self.p_eps = p_eps + self.max_temp = max_temp + self.min_temp = min_temp + self.log_binomial_transform = LogBinomial(n_classes, act=act) + bottleneck = (in_features + condition_dim) // bottleneck_factor + self.mlp = nn.Sequential( + nn.Conv2d(in_features + condition_dim, bottleneck, + kernel_size=1, stride=1, padding=0), + nn.GELU(), + # 2 for p linear norm, 2 for t linear norm + nn.Conv2d(bottleneck, 2+2, kernel_size=1, stride=1, padding=0), + nn.Softplus() + ) + + def forward(self, x, cond): + """Forward pass + + Args: + x (torch.Tensor - NCHW): Main feature + cond (torch.Tensor - NCHW): condition feature + + Returns: + torch.Tensor: Output log binomial distribution + """ + pt = self.mlp(torch.concat((x, cond), dim=1)) + p, t = pt[:, :2, ...], pt[:, 2:, ...] + + p = p + self.p_eps + p = p[:, 0, ...] / (p[:, 0, ...] + p[:, 1, ...]) + + t = t + self.p_eps + t = t[:, 0, ...] / (t[:, 0, ...] + t[:, 1, ...]) + t = t.unsqueeze(1) + t = (self.max_temp - self.min_temp) * t + self.min_temp + + return self.log_binomial_transform(p, t) diff --git a/CCEdit-main/src/controlnet11/annotator/zoe/zoedepth/models/layers/localbins_layers.py b/CCEdit-main/src/controlnet11/annotator/zoe/zoedepth/models/layers/localbins_layers.py new file mode 100644 index 0000000000000000000000000000000000000000..f94481605c3e6958ce50e73b2eb31d9f0c07dc67 --- /dev/null +++ b/CCEdit-main/src/controlnet11/annotator/zoe/zoedepth/models/layers/localbins_layers.py @@ -0,0 +1,169 @@ +# MIT License + +# Copyright (c) 2022 Intelligent Systems Lab Org + +# Permission is hereby granted, free of charge, to any person obtaining a copy +# of this software and associated documentation files (the "Software"), to deal +# in the Software without restriction, including without limitation the rights +# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell +# copies of the Software, and to permit persons to whom the Software is +# furnished to do so, subject to the following conditions: + +# The above copyright notice and this permission notice shall be included in all +# copies or substantial portions of the Software. + +# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE +# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER +# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, +# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE +# SOFTWARE. + +# File author: Shariq Farooq Bhat + +import torch +import torch.nn as nn + + +class SeedBinRegressor(nn.Module): + def __init__(self, in_features, n_bins=16, mlp_dim=256, min_depth=1e-3, max_depth=10): + """Bin center regressor network. Bin centers are bounded on (min_depth, max_depth) interval. + + Args: + in_features (int): input channels + n_bins (int, optional): Number of bin centers. Defaults to 16. + mlp_dim (int, optional): Hidden dimension. Defaults to 256. + min_depth (float, optional): Min depth value. Defaults to 1e-3. + max_depth (float, optional): Max depth value. Defaults to 10. + """ + super().__init__() + self.version = "1_1" + self.min_depth = min_depth + self.max_depth = max_depth + + self._net = nn.Sequential( + nn.Conv2d(in_features, mlp_dim, 1, 1, 0), + nn.ReLU(inplace=True), + nn.Conv2d(mlp_dim, n_bins, 1, 1, 0), + nn.ReLU(inplace=True) + ) + + def forward(self, x): + """ + Returns tensor of bin_width vectors (centers). One vector b for every pixel + """ + B = self._net(x) + eps = 1e-3 + B = B + eps + B_widths_normed = B / B.sum(dim=1, keepdim=True) + B_widths = (self.max_depth - self.min_depth) * \ + B_widths_normed # .shape NCHW + # pad has the form (left, right, top, bottom, front, back) + B_widths = nn.functional.pad( + B_widths, (0, 0, 0, 0, 1, 0), mode='constant', value=self.min_depth) + B_edges = torch.cumsum(B_widths, dim=1) # .shape NCHW + + B_centers = 0.5 * (B_edges[:, :-1, ...] + B_edges[:, 1:, ...]) + return B_widths_normed, B_centers + + +class SeedBinRegressorUnnormed(nn.Module): + def __init__(self, in_features, n_bins=16, mlp_dim=256, min_depth=1e-3, max_depth=10): + """Bin center regressor network. Bin centers are unbounded + + Args: + in_features (int): input channels + n_bins (int, optional): Number of bin centers. Defaults to 16. + mlp_dim (int, optional): Hidden dimension. Defaults to 256. + min_depth (float, optional): Not used. (for compatibility with SeedBinRegressor) + max_depth (float, optional): Not used. (for compatibility with SeedBinRegressor) + """ + super().__init__() + self.version = "1_1" + self._net = nn.Sequential( + nn.Conv2d(in_features, mlp_dim, 1, 1, 0), + nn.ReLU(inplace=True), + nn.Conv2d(mlp_dim, n_bins, 1, 1, 0), + nn.Softplus() + ) + + def forward(self, x): + """ + Returns tensor of bin_width vectors (centers). One vector b for every pixel + """ + B_centers = self._net(x) + return B_centers, B_centers + + +class Projector(nn.Module): + def __init__(self, in_features, out_features, mlp_dim=128): + """Projector MLP + + Args: + in_features (int): input channels + out_features (int): output channels + mlp_dim (int, optional): hidden dimension. Defaults to 128. + """ + super().__init__() + + self._net = nn.Sequential( + nn.Conv2d(in_features, mlp_dim, 1, 1, 0), + nn.ReLU(inplace=True), + nn.Conv2d(mlp_dim, out_features, 1, 1, 0), + ) + + def forward(self, x): + return self._net(x) + + + +class LinearSplitter(nn.Module): + def __init__(self, in_features, prev_nbins, split_factor=2, mlp_dim=128, min_depth=1e-3, max_depth=10): + super().__init__() + + self.prev_nbins = prev_nbins + self.split_factor = split_factor + self.min_depth = min_depth + self.max_depth = max_depth + + self._net = nn.Sequential( + nn.Conv2d(in_features, mlp_dim, 1, 1, 0), + nn.GELU(), + nn.Conv2d(mlp_dim, prev_nbins * split_factor, 1, 1, 0), + nn.ReLU() + ) + + def forward(self, x, b_prev, prev_b_embedding=None, interpolate=True, is_for_query=False): + """ + x : feature block; shape - n, c, h, w + b_prev : previous bin widths normed; shape - n, prev_nbins, h, w + """ + if prev_b_embedding is not None: + if interpolate: + prev_b_embedding = nn.functional.interpolate(prev_b_embedding, x.shape[-2:], mode='bilinear', align_corners=True) + x = x + prev_b_embedding + S = self._net(x) + eps = 1e-3 + S = S + eps + n, c, h, w = S.shape + S = S.view(n, self.prev_nbins, self.split_factor, h, w) + S_normed = S / S.sum(dim=2, keepdim=True) # fractional splits + + b_prev = nn.functional.interpolate(b_prev, (h,w), mode='bilinear', align_corners=True) + + + b_prev = b_prev / b_prev.sum(dim=1, keepdim=True) # renormalize for gurantees + # print(b_prev.shape, S_normed.shape) + # if is_for_query:(1).expand(-1, b_prev.size(0)//n, -1, -1, -1, -1).flatten(0,1) # TODO ? can replace all this with a single torch.repeat? + b = b_prev.unsqueeze(2) * S_normed + b = b.flatten(1,2) # .shape n, prev_nbins * split_factor, h, w + + # calculate bin centers for loss calculation + B_widths = (self.max_depth - self.min_depth) * b # .shape N, nprev * splitfactor, H, W + # pad has the form (left, right, top, bottom, front, back) + B_widths = nn.functional.pad(B_widths, (0,0,0,0,1,0), mode='constant', value=self.min_depth) + B_edges = torch.cumsum(B_widths, dim=1) # .shape NCHW + + B_centers = 0.5 * (B_edges[:, :-1, ...] + B_edges[:,1:,...]) + return b, B_centers \ No newline at end of file diff --git a/CCEdit-main/src/controlnet11/annotator/zoe/zoedepth/models/layers/patch_transformer.py b/CCEdit-main/src/controlnet11/annotator/zoe/zoedepth/models/layers/patch_transformer.py new file mode 100644 index 0000000000000000000000000000000000000000..99d9e51a06b981bae45ce7dd64eaef19a4121991 --- /dev/null +++ b/CCEdit-main/src/controlnet11/annotator/zoe/zoedepth/models/layers/patch_transformer.py @@ -0,0 +1,91 @@ +# MIT License + +# Copyright (c) 2022 Intelligent Systems Lab Org + +# Permission is hereby granted, free of charge, to any person obtaining a copy +# of this software and associated documentation files (the "Software"), to deal +# in the Software without restriction, including without limitation the rights +# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell +# copies of the Software, and to permit persons to whom the Software is +# furnished to do so, subject to the following conditions: + +# The above copyright notice and this permission notice shall be included in all +# copies or substantial portions of the Software. + +# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE +# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER +# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, +# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE +# SOFTWARE. + +# File author: Shariq Farooq Bhat + +import torch +import torch.nn as nn + + +class PatchTransformerEncoder(nn.Module): + def __init__(self, in_channels, patch_size=10, embedding_dim=128, num_heads=4, use_class_token=False): + """ViT-like transformer block + + Args: + in_channels (int): Input channels + patch_size (int, optional): patch size. Defaults to 10. + embedding_dim (int, optional): Embedding dimension in transformer model. Defaults to 128. + num_heads (int, optional): number of attention heads. Defaults to 4. + use_class_token (bool, optional): Whether to use extra token at the start for global accumulation (called as "class token"). Defaults to False. + """ + super(PatchTransformerEncoder, self).__init__() + self.use_class_token = use_class_token + encoder_layers = nn.TransformerEncoderLayer( + embedding_dim, num_heads, dim_feedforward=1024) + self.transformer_encoder = nn.TransformerEncoder( + encoder_layers, num_layers=4) # takes shape S,N,E + + self.embedding_convPxP = nn.Conv2d(in_channels, embedding_dim, + kernel_size=patch_size, stride=patch_size, padding=0) + + def positional_encoding_1d(self, sequence_length, batch_size, embedding_dim, device='cpu'): + """Generate positional encodings + + Args: + sequence_length (int): Sequence length + embedding_dim (int): Embedding dimension + + Returns: + torch.Tensor SBE: Positional encodings + """ + position = torch.arange( + 0, sequence_length, dtype=torch.float32, device=device).unsqueeze(1) + index = torch.arange( + 0, embedding_dim, 2, dtype=torch.float32, device=device).unsqueeze(0) + div_term = torch.exp(index * (-torch.log(torch.tensor(10000.0, device=device)) / embedding_dim)) + pos_encoding = position * div_term + pos_encoding = torch.cat([torch.sin(pos_encoding), torch.cos(pos_encoding)], dim=1) + pos_encoding = pos_encoding.unsqueeze(1).repeat(1, batch_size, 1) + return pos_encoding + + + def forward(self, x): + """Forward pass + + Args: + x (torch.Tensor - NCHW): Input feature tensor + + Returns: + torch.Tensor - SNE: Transformer output embeddings. S - sequence length (=HW/patch_size^2), N - batch size, E - embedding dim + """ + embeddings = self.embedding_convPxP(x).flatten( + 2) # .shape = n,c,s = n, embedding_dim, s + if self.use_class_token: + # extra special token at start ? + embeddings = nn.functional.pad(embeddings, (1, 0)) + + # change to S,N,E format required by transformer + embeddings = embeddings.permute(2, 0, 1) + S, N, E = embeddings.shape + embeddings = embeddings + self.positional_encoding_1d(S, N, E, device=embeddings.device) + x = self.transformer_encoder(embeddings) # .shape = S, N, E + return x diff --git a/CCEdit-main/src/controlnet11/annotator/zoe/zoedepth/models/zoedepth_nk/__init__.py b/CCEdit-main/src/controlnet11/annotator/zoe/zoedepth/models/zoedepth_nk/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..513a278b939c10c010e3c0250ec73544d5663886 --- /dev/null +++ b/CCEdit-main/src/controlnet11/annotator/zoe/zoedepth/models/zoedepth_nk/__init__.py @@ -0,0 +1,31 @@ +# MIT License + +# Copyright (c) 2022 Intelligent Systems Lab Org + +# Permission is hereby granted, free of charge, to any person obtaining a copy +# of this software and associated documentation files (the "Software"), to deal +# in the Software without restriction, including without limitation the rights +# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell +# copies of the Software, and to permit persons to whom the Software is +# furnished to do so, subject to the following conditions: + +# The above copyright notice and this permission notice shall be included in all +# copies or substantial portions of the Software. + +# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE +# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER +# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, +# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE +# SOFTWARE. + +# File author: Shariq Farooq Bhat + +from .zoedepth_nk_v1 import ZoeDepthNK + +all_versions = { + "v1": ZoeDepthNK, +} + +get_version = lambda v : all_versions[v] \ No newline at end of file diff --git a/CCEdit-main/src/controlnet11/annotator/zoe/zoedepth/models/zoedepth_nk/config_zoedepth_nk.json b/CCEdit-main/src/controlnet11/annotator/zoe/zoedepth/models/zoedepth_nk/config_zoedepth_nk.json new file mode 100644 index 0000000000000000000000000000000000000000..42bab2a3ad159a09599a5aba270c491021a3cf1a --- /dev/null +++ b/CCEdit-main/src/controlnet11/annotator/zoe/zoedepth/models/zoedepth_nk/config_zoedepth_nk.json @@ -0,0 +1,67 @@ +{ + "model": { + "name": "ZoeDepthNK", + "version_name": "v1", + "bin_conf" : [ + { + "name": "nyu", + "n_bins": 64, + "min_depth": 1e-3, + "max_depth": 10.0 + }, + { + "name": "kitti", + "n_bins": 64, + "min_depth": 1e-3, + "max_depth": 80.0 + } + ], + "bin_embedding_dim": 128, + "bin_centers_type": "softplus", + "n_attractors":[16, 8, 4, 1], + "attractor_alpha": 1000, + "attractor_gamma": 2, + "attractor_kind" : "mean", + "attractor_type" : "inv", + "min_temp": 0.0212, + "max_temp": 50.0, + "memory_efficient": true, + "midas_model_type" : "DPT_BEiT_L_384", + "img_size": [384, 512] + }, + + "train": { + "train_midas": true, + "use_pretrained_midas": true, + "trainer": "zoedepth_nk", + "epochs": 5, + "bs": 16, + "optim_kwargs": {"lr": 0.0002512, "wd": 0.01}, + "sched_kwargs": {"div_factor": 1, "final_div_factor": 10000, "pct_start": 0.7, "three_phase":false, "cycle_momentum": true}, + "same_lr": false, + "w_si": 1, + "w_domain": 100, + "avoid_boundary": false, + "random_crop": false, + "input_width": 640, + "input_height": 480, + "w_grad": 0, + "w_reg": 0, + "midas_lr_factor": 10, + "encoder_lr_factor":10, + "pos_enc_lr_factor":10 + }, + + "infer": { + "train_midas": false, + "pretrained_resource": "url::https://github.com/isl-org/ZoeDepth/releases/download/v1.0/ZoeD_M12_NK.pt", + "use_pretrained_midas": false, + "force_keep_ar": true + }, + + "eval": { + "train_midas": false, + "pretrained_resource": "url::https://github.com/isl-org/ZoeDepth/releases/download/v1.0/ZoeD_M12_NK.pt", + "use_pretrained_midas": false + } +} \ No newline at end of file diff --git a/CCEdit-main/src/controlnet11/annotator/zoe/zoedepth/models/zoedepth_nk/zoedepth_nk_v1.py b/CCEdit-main/src/controlnet11/annotator/zoe/zoedepth/models/zoedepth_nk/zoedepth_nk_v1.py new file mode 100644 index 0000000000000000000000000000000000000000..7368ae8031188a9f946d9d3f29633c96e791e68e --- /dev/null +++ b/CCEdit-main/src/controlnet11/annotator/zoe/zoedepth/models/zoedepth_nk/zoedepth_nk_v1.py @@ -0,0 +1,333 @@ +# MIT License + +# Copyright (c) 2022 Intelligent Systems Lab Org + +# Permission is hereby granted, free of charge, to any person obtaining a copy +# of this software and associated documentation files (the "Software"), to deal +# in the Software without restriction, including without limitation the rights +# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell +# copies of the Software, and to permit persons to whom the Software is +# furnished to do so, subject to the following conditions: + +# The above copyright notice and this permission notice shall be included in all +# copies or substantial portions of the Software. + +# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE +# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER +# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, +# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE +# SOFTWARE. + +# File author: Shariq Farooq Bhat + +import itertools + +import torch +import torch.nn as nn + +from zoedepth.models.depth_model import DepthModel +from zoedepth.models.base_models.midas import MidasCore +from zoedepth.models.layers.attractor import AttractorLayer, AttractorLayerUnnormed +from zoedepth.models.layers.dist_layers import ConditionalLogBinomial +from zoedepth.models.layers.localbins_layers import (Projector, SeedBinRegressor, + SeedBinRegressorUnnormed) +from zoedepth.models.layers.patch_transformer import PatchTransformerEncoder +from zoedepth.models.model_io import load_state_from_resource + + +class ZoeDepthNK(DepthModel): + def __init__(self, core, bin_conf, bin_centers_type="softplus", bin_embedding_dim=128, + n_attractors=[16, 8, 4, 1], attractor_alpha=300, attractor_gamma=2, attractor_kind='sum', attractor_type='exp', + min_temp=5, max_temp=50, + memory_efficient=False, train_midas=True, + is_midas_pretrained=True, midas_lr_factor=1, encoder_lr_factor=10, pos_enc_lr_factor=10, inverse_midas=False, **kwargs): + """ZoeDepthNK model. This is the version of ZoeDepth that has two metric heads and uses a learned router to route to experts. + + Args: + core (models.base_models.midas.MidasCore): The base midas model that is used for extraction of "relative" features + + bin_conf (List[dict]): A list of dictionaries that contain the bin configuration for each metric head. Each dictionary should contain the following keys: + "name" (str, typically same as the dataset name), "n_bins" (int), "min_depth" (float), "max_depth" (float) + + The length of this list determines the number of metric heads. + bin_centers_type (str, optional): "normed" or "softplus". Activation type used for bin centers. For "normed" bin centers, linear normalization trick is applied. This results in bounded bin centers. + For "softplus", softplus activation is used and thus are unbounded. Defaults to "normed". + bin_embedding_dim (int, optional): bin embedding dimension. Defaults to 128. + + n_attractors (List[int], optional): Number of bin attractors at decoder layers. Defaults to [16, 8, 4, 1]. + attractor_alpha (int, optional): Proportional attractor strength. Refer to models.layers.attractor for more details. Defaults to 300. + attractor_gamma (int, optional): Exponential attractor strength. Refer to models.layers.attractor for more details. Defaults to 2. + attractor_kind (str, optional): Attraction aggregation "sum" or "mean". Defaults to 'sum'. + attractor_type (str, optional): Type of attractor to use; "inv" (Inverse attractor) or "exp" (Exponential attractor). Defaults to 'exp'. + + min_temp (int, optional): Lower bound for temperature of output probability distribution. Defaults to 5. + max_temp (int, optional): Upper bound for temperature of output probability distribution. Defaults to 50. + + memory_efficient (bool, optional): Whether to use memory efficient version of attractor layers. Memory efficient version is slower but is recommended incase of multiple metric heads in order save GPU memory. Defaults to False. + + train_midas (bool, optional): Whether to train "core", the base midas model. Defaults to True. + is_midas_pretrained (bool, optional): Is "core" pretrained? Defaults to True. + midas_lr_factor (int, optional): Learning rate reduction factor for base midas model except its encoder and positional encodings. Defaults to 10. + encoder_lr_factor (int, optional): Learning rate reduction factor for the encoder in midas model. Defaults to 10. + pos_enc_lr_factor (int, optional): Learning rate reduction factor for positional encodings in the base midas model. Defaults to 10. + + """ + + super().__init__() + + self.core = core + self.bin_conf = bin_conf + self.min_temp = min_temp + self.max_temp = max_temp + self.memory_efficient = memory_efficient + self.train_midas = train_midas + self.is_midas_pretrained = is_midas_pretrained + self.midas_lr_factor = midas_lr_factor + self.encoder_lr_factor = encoder_lr_factor + self.pos_enc_lr_factor = pos_enc_lr_factor + self.inverse_midas = inverse_midas + + N_MIDAS_OUT = 32 + btlnck_features = self.core.output_channels[0] + num_out_features = self.core.output_channels[1:] + # self.scales = [16, 8, 4, 2] # spatial scale factors + + self.conv2 = nn.Conv2d( + btlnck_features, btlnck_features, kernel_size=1, stride=1, padding=0) + + # Transformer classifier on the bottleneck + self.patch_transformer = PatchTransformerEncoder( + btlnck_features, 1, 128, use_class_token=True) + self.mlp_classifier = nn.Sequential( + nn.Linear(128, 128), + nn.ReLU(), + nn.Linear(128, 2) + ) + + if bin_centers_type == "normed": + SeedBinRegressorLayer = SeedBinRegressor + Attractor = AttractorLayer + elif bin_centers_type == "softplus": + SeedBinRegressorLayer = SeedBinRegressorUnnormed + Attractor = AttractorLayerUnnormed + elif bin_centers_type == "hybrid1": + SeedBinRegressorLayer = SeedBinRegressor + Attractor = AttractorLayerUnnormed + elif bin_centers_type == "hybrid2": + SeedBinRegressorLayer = SeedBinRegressorUnnormed + Attractor = AttractorLayer + else: + raise ValueError( + "bin_centers_type should be one of 'normed', 'softplus', 'hybrid1', 'hybrid2'") + self.bin_centers_type = bin_centers_type + # We have bins for each bin conf. + # Create a map (ModuleDict) of 'name' -> seed_bin_regressor + self.seed_bin_regressors = nn.ModuleDict( + {conf['name']: SeedBinRegressorLayer(btlnck_features, conf["n_bins"], mlp_dim=bin_embedding_dim//2, min_depth=conf["min_depth"], max_depth=conf["max_depth"]) + for conf in bin_conf} + ) + + self.seed_projector = Projector( + btlnck_features, bin_embedding_dim, mlp_dim=bin_embedding_dim//2) + self.projectors = nn.ModuleList([ + Projector(num_out, bin_embedding_dim, mlp_dim=bin_embedding_dim//2) + for num_out in num_out_features + ]) + + # Create a map (ModuleDict) of 'name' -> attractors (ModuleList) + self.attractors = nn.ModuleDict( + {conf['name']: nn.ModuleList([ + Attractor(bin_embedding_dim, n_attractors[i], + mlp_dim=bin_embedding_dim, alpha=attractor_alpha, + gamma=attractor_gamma, kind=attractor_kind, + attractor_type=attractor_type, memory_efficient=memory_efficient, + min_depth=conf["min_depth"], max_depth=conf["max_depth"]) + for i in range(len(n_attractors)) + ]) + for conf in bin_conf} + ) + + last_in = N_MIDAS_OUT + # conditional log binomial for each bin conf + self.conditional_log_binomial = nn.ModuleDict( + {conf['name']: ConditionalLogBinomial(last_in, bin_embedding_dim, conf['n_bins'], bottleneck_factor=4, min_temp=self.min_temp, max_temp=self.max_temp) + for conf in bin_conf} + ) + + def forward(self, x, return_final_centers=False, denorm=False, return_probs=False, **kwargs): + """ + Args: + x (torch.Tensor): Input image tensor of shape (B, C, H, W). Assumes all images are from the same domain. + return_final_centers (bool, optional): Whether to return the final centers of the attractors. Defaults to False. + denorm (bool, optional): Whether to denormalize the input image. Defaults to False. + return_probs (bool, optional): Whether to return the probabilities of the bins. Defaults to False. + + Returns: + dict: Dictionary of outputs with keys: + - "rel_depth": Relative depth map of shape (B, 1, H, W) + - "metric_depth": Metric depth map of shape (B, 1, H, W) + - "domain_logits": Domain logits of shape (B, 2) + - "bin_centers": Bin centers of shape (B, N, H, W). Present only if return_final_centers is True + - "probs": Bin probabilities of shape (B, N, H, W). Present only if return_probs is True + """ + b, c, h, w = x.shape + self.orig_input_width = w + self.orig_input_height = h + rel_depth, out = self.core(x, denorm=denorm, return_rel_depth=True) + + outconv_activation = out[0] + btlnck = out[1] + x_blocks = out[2:] + + x_d0 = self.conv2(btlnck) + x = x_d0 + + # Predict which path to take + embedding = self.patch_transformer(x)[0] # N, E + domain_logits = self.mlp_classifier(embedding) # N, 2 + domain_vote = torch.softmax(domain_logits.sum( + dim=0, keepdim=True), dim=-1) # 1, 2 + + # Get the path + bin_conf_name = ["nyu", "kitti"][torch.argmax( + domain_vote, dim=-1).squeeze().item()] + + try: + conf = [c for c in self.bin_conf if c.name == bin_conf_name][0] + except IndexError: + raise ValueError( + f"bin_conf_name {bin_conf_name} not found in bin_confs") + + min_depth = conf['min_depth'] + max_depth = conf['max_depth'] + + seed_bin_regressor = self.seed_bin_regressors[bin_conf_name] + _, seed_b_centers = seed_bin_regressor(x) + if self.bin_centers_type == 'normed' or self.bin_centers_type == 'hybrid2': + b_prev = (seed_b_centers - min_depth)/(max_depth - min_depth) + else: + b_prev = seed_b_centers + prev_b_embedding = self.seed_projector(x) + + attractors = self.attractors[bin_conf_name] + for projector, attractor, x in zip(self.projectors, attractors, x_blocks): + b_embedding = projector(x) + b, b_centers = attractor( + b_embedding, b_prev, prev_b_embedding, interpolate=True) + b_prev = b + prev_b_embedding = b_embedding + + last = outconv_activation + + b_centers = nn.functional.interpolate( + b_centers, last.shape[-2:], mode='bilinear', align_corners=True) + b_embedding = nn.functional.interpolate( + b_embedding, last.shape[-2:], mode='bilinear', align_corners=True) + + clb = self.conditional_log_binomial[bin_conf_name] + x = clb(last, b_embedding) + + # Now depth value is Sum px * cx , where cx are bin_centers from the last bin tensor + # print(x.shape, b_centers.shape) + # b_centers = nn.functional.interpolate(b_centers, x.shape[-2:], mode='bilinear', align_corners=True) + out = torch.sum(x * b_centers, dim=1, keepdim=True) + + output = dict(domain_logits=domain_logits, metric_depth=out) + if return_final_centers or return_probs: + output['bin_centers'] = b_centers + + if return_probs: + output['probs'] = x + return output + + def get_lr_params(self, lr): + """ + Learning rate configuration for different layers of the model + + Args: + lr (float) : Base learning rate + Returns: + list : list of parameters to optimize and their learning rates, in the format required by torch optimizers. + """ + param_conf = [] + if self.train_midas: + def get_rel_pos_params(): + for name, p in self.core.core.pretrained.named_parameters(): + if "relative_position" in name: + yield p + + def get_enc_params_except_rel_pos(): + for name, p in self.core.core.pretrained.named_parameters(): + if "relative_position" not in name: + yield p + + encoder_params = get_enc_params_except_rel_pos() + rel_pos_params = get_rel_pos_params() + midas_params = self.core.core.scratch.parameters() + midas_lr_factor = self.midas_lr_factor if self.is_midas_pretrained else 1.0 + param_conf.extend([ + {'params': encoder_params, 'lr': lr / self.encoder_lr_factor}, + {'params': rel_pos_params, 'lr': lr / self.pos_enc_lr_factor}, + {'params': midas_params, 'lr': lr / midas_lr_factor} + ]) + + remaining_modules = [] + for name, child in self.named_children(): + if name != 'core': + remaining_modules.append(child) + remaining_params = itertools.chain( + *[child.parameters() for child in remaining_modules]) + param_conf.append({'params': remaining_params, 'lr': lr}) + return param_conf + + def get_conf_parameters(self, conf_name): + """ + Returns parameters of all the ModuleDicts children that are exclusively used for the given bin configuration + """ + params = [] + for name, child in self.named_children(): + if isinstance(child, nn.ModuleDict): + for bin_conf_name, module in child.items(): + if bin_conf_name == conf_name: + params += list(module.parameters()) + return params + + def freeze_conf(self, conf_name): + """ + Freezes all the parameters of all the ModuleDicts children that are exclusively used for the given bin configuration + """ + for p in self.get_conf_parameters(conf_name): + p.requires_grad = False + + def unfreeze_conf(self, conf_name): + """ + Unfreezes all the parameters of all the ModuleDicts children that are exclusively used for the given bin configuration + """ + for p in self.get_conf_parameters(conf_name): + p.requires_grad = True + + def freeze_all_confs(self): + """ + Freezes all the parameters of all the ModuleDicts children + """ + for name, child in self.named_children(): + if isinstance(child, nn.ModuleDict): + for bin_conf_name, module in child.items(): + for p in module.parameters(): + p.requires_grad = False + + @staticmethod + def build(midas_model_type="DPT_BEiT_L_384", pretrained_resource=None, use_pretrained_midas=False, train_midas=False, freeze_midas_bn=True, **kwargs): + core = MidasCore.build(midas_model_type=midas_model_type, use_pretrained_midas=use_pretrained_midas, + train_midas=train_midas, fetch_features=True, freeze_bn=freeze_midas_bn, **kwargs) + model = ZoeDepthNK(core, **kwargs) + if pretrained_resource: + assert isinstance(pretrained_resource, str), "pretrained_resource must be a string" + model = load_state_from_resource(model, pretrained_resource) + return model + + @staticmethod + def build_from_config(config): + return ZoeDepthNK.build(**config) diff --git a/CCEdit-main/src/controlnet11/annotator/zoe/zoedepth/trainers/base_trainer.py b/CCEdit-main/src/controlnet11/annotator/zoe/zoedepth/trainers/base_trainer.py new file mode 100644 index 0000000000000000000000000000000000000000..33fbbea3a7d49efe11b005adb5127f441eabfaf6 --- /dev/null +++ b/CCEdit-main/src/controlnet11/annotator/zoe/zoedepth/trainers/base_trainer.py @@ -0,0 +1,326 @@ +# MIT License + +# Copyright (c) 2022 Intelligent Systems Lab Org + +# Permission is hereby granted, free of charge, to any person obtaining a copy +# of this software and associated documentation files (the "Software"), to deal +# in the Software without restriction, including without limitation the rights +# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell +# copies of the Software, and to permit persons to whom the Software is +# furnished to do so, subject to the following conditions: + +# The above copyright notice and this permission notice shall be included in all +# copies or substantial portions of the Software. + +# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE +# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER +# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, +# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE +# SOFTWARE. + +# File author: Shariq Farooq Bhat + +import os +import uuid +import warnings +from datetime import datetime as dt +from typing import Dict + +import matplotlib.pyplot as plt +import numpy as np +import torch +import torch.distributed as dist +import torch.nn as nn +import torch.optim as optim +import wandb +from tqdm import tqdm + +from zoedepth.utils.config import flatten +from zoedepth.utils.misc import RunningAverageDict, colorize, colors + + +def is_rank_zero(args): + return args.rank == 0 + + +class BaseTrainer: + def __init__(self, config, model, train_loader, test_loader=None, device=None): + """ Base Trainer class for training a model.""" + + self.config = config + self.metric_criterion = "abs_rel" + if device is None: + device = torch.device( + 'cuda') if torch.cuda.is_available() else torch.device('cpu') + self.device = device + self.model = model + self.train_loader = train_loader + self.test_loader = test_loader + self.optimizer = self.init_optimizer() + self.scheduler = self.init_scheduler() + + def resize_to_target(self, prediction, target): + if prediction.shape[2:] != target.shape[-2:]: + prediction = nn.functional.interpolate( + prediction, size=target.shape[-2:], mode="bilinear", align_corners=True + ) + return prediction + + def load_ckpt(self, checkpoint_dir="./checkpoints", ckpt_type="best"): + import glob + import os + + from zoedepth.models.model_io import load_wts + + if hasattr(self.config, "checkpoint"): + checkpoint = self.config.checkpoint + elif hasattr(self.config, "ckpt_pattern"): + pattern = self.config.ckpt_pattern + matches = glob.glob(os.path.join( + checkpoint_dir, f"*{pattern}*{ckpt_type}*")) + if not (len(matches) > 0): + raise ValueError(f"No matches found for the pattern {pattern}") + checkpoint = matches[0] + else: + return + model = load_wts(self.model, checkpoint) + # TODO : Resuming training is not properly supported in this repo. Implement loading / saving of optimizer and scheduler to support it. + print("Loaded weights from {0}".format(checkpoint)) + warnings.warn( + "Resuming training is not properly supported in this repo. Implement loading / saving of optimizer and scheduler to support it.") + self.model = model + + def init_optimizer(self): + m = self.model.module if self.config.multigpu else self.model + + if self.config.same_lr: + print("Using same LR") + if hasattr(m, 'core'): + m.core.unfreeze() + params = self.model.parameters() + else: + print("Using diff LR") + if not hasattr(m, 'get_lr_params'): + raise NotImplementedError( + f"Model {m.__class__.__name__} does not implement get_lr_params. Please implement it or use the same LR for all parameters.") + + params = m.get_lr_params(self.config.lr) + + return optim.AdamW(params, lr=self.config.lr, weight_decay=self.config.wd) + + def init_scheduler(self): + lrs = [l['lr'] for l in self.optimizer.param_groups] + return optim.lr_scheduler.OneCycleLR(self.optimizer, lrs, epochs=self.config.epochs, steps_per_epoch=len(self.train_loader), + cycle_momentum=self.config.cycle_momentum, + base_momentum=0.85, max_momentum=0.95, div_factor=self.config.div_factor, final_div_factor=self.config.final_div_factor, pct_start=self.config.pct_start, three_phase=self.config.three_phase) + + def train_on_batch(self, batch, train_step): + raise NotImplementedError + + def validate_on_batch(self, batch, val_step): + raise NotImplementedError + + def raise_if_nan(self, losses): + for key, value in losses.items(): + if torch.isnan(value): + raise ValueError(f"{key} is NaN, Stopping training") + + @property + def iters_per_epoch(self): + return len(self.train_loader) + + @property + def total_iters(self): + return self.config.epochs * self.iters_per_epoch + + def should_early_stop(self): + if self.config.get('early_stop', False) and self.step > self.config.early_stop: + return True + + def train(self): + print(f"Training {self.config.name}") + if self.config.uid is None: + self.config.uid = str(uuid.uuid4()).split('-')[-1] + run_id = f"{dt.now().strftime('%d-%h_%H-%M')}-{self.config.uid}" + self.config.run_id = run_id + self.config.experiment_id = f"{self.config.name}{self.config.version_name}_{run_id}" + self.should_write = ((not self.config.distributed) + or self.config.rank == 0) + self.should_log = self.should_write # and logging + if self.should_log: + tags = self.config.tags.split( + ',') if self.config.tags != '' else None + wandb.init(project=self.config.project, name=self.config.experiment_id, config=flatten(self.config), dir=self.config.root, + tags=tags, notes=self.config.notes, settings=wandb.Settings(start_method="fork")) + + self.model.train() + self.step = 0 + best_loss = np.inf + validate_every = int(self.config.validate_every * self.iters_per_epoch) + + + if self.config.prefetch: + + for i, batch in tqdm(enumerate(self.train_loader), desc=f"Prefetching...", + total=self.iters_per_epoch) if is_rank_zero(self.config) else enumerate(self.train_loader): + pass + + losses = {} + def stringify_losses(L): return "; ".join(map( + lambda kv: f"{colors.fg.purple}{kv[0]}{colors.reset}: {round(kv[1].item(),3):.4e}", L.items())) + for epoch in range(self.config.epochs): + if self.should_early_stop(): + break + + self.epoch = epoch + ################################# Train loop ########################################################## + if self.should_log: + wandb.log({"Epoch": epoch}, step=self.step) + pbar = tqdm(enumerate(self.train_loader), desc=f"Epoch: {epoch + 1}/{self.config.epochs}. Loop: Train", + total=self.iters_per_epoch) if is_rank_zero(self.config) else enumerate(self.train_loader) + for i, batch in pbar: + if self.should_early_stop(): + print("Early stopping") + break + # print(f"Batch {self.step+1} on rank {self.config.rank}") + losses = self.train_on_batch(batch, i) + # print(f"trained batch {self.step+1} on rank {self.config.rank}") + + self.raise_if_nan(losses) + if is_rank_zero(self.config) and self.config.print_losses: + pbar.set_description( + f"Epoch: {epoch + 1}/{self.config.epochs}. Loop: Train. Losses: {stringify_losses(losses)}") + self.scheduler.step() + + if self.should_log and self.step % 50 == 0: + wandb.log({f"Train/{name}": loss.item() + for name, loss in losses.items()}, step=self.step) + + self.step += 1 + + ######################################################################################################## + + if self.test_loader: + if (self.step % validate_every) == 0: + self.model.eval() + if self.should_write: + self.save_checkpoint( + f"{self.config.experiment_id}_latest.pt") + + ################################# Validation loop ################################################## + # validate on the entire validation set in every process but save only from rank 0, I know, inefficient, but avoids divergence of processes + metrics, test_losses = self.validate() + # print("Validated: {}".format(metrics)) + if self.should_log: + wandb.log( + {f"Test/{name}": tloss for name, tloss in test_losses.items()}, step=self.step) + + wandb.log({f"Metrics/{k}": v for k, + v in metrics.items()}, step=self.step) + + if (metrics[self.metric_criterion] < best_loss) and self.should_write: + self.save_checkpoint( + f"{self.config.experiment_id}_best.pt") + best_loss = metrics[self.metric_criterion] + + self.model.train() + + if self.config.distributed: + dist.barrier() + # print(f"Validated: {metrics} on device {self.config.rank}") + + # print(f"Finished step {self.step} on device {self.config.rank}") + ################################################################################################# + + # Save / validate at the end + self.step += 1 # log as final point + self.model.eval() + self.save_checkpoint(f"{self.config.experiment_id}_latest.pt") + if self.test_loader: + + ################################# Validation loop ################################################## + metrics, test_losses = self.validate() + # print("Validated: {}".format(metrics)) + if self.should_log: + wandb.log({f"Test/{name}": tloss for name, + tloss in test_losses.items()}, step=self.step) + wandb.log({f"Metrics/{k}": v for k, + v in metrics.items()}, step=self.step) + + if (metrics[self.metric_criterion] < best_loss) and self.should_write: + self.save_checkpoint( + f"{self.config.experiment_id}_best.pt") + best_loss = metrics[self.metric_criterion] + + self.model.train() + + def validate(self): + with torch.no_grad(): + losses_avg = RunningAverageDict() + metrics_avg = RunningAverageDict() + for i, batch in tqdm(enumerate(self.test_loader), desc=f"Epoch: {self.epoch + 1}/{self.config.epochs}. Loop: Validation", total=len(self.test_loader), disable=not is_rank_zero(self.config)): + metrics, losses = self.validate_on_batch(batch, val_step=i) + + if losses: + losses_avg.update(losses) + if metrics: + metrics_avg.update(metrics) + + return metrics_avg.get_value(), losses_avg.get_value() + + def save_checkpoint(self, filename): + if not self.should_write: + return + root = self.config.save_dir + if not os.path.isdir(root): + os.makedirs(root) + + fpath = os.path.join(root, filename) + m = self.model.module if self.config.multigpu else self.model + torch.save( + { + "model": m.state_dict(), + "optimizer": None, # TODO : Change to self.optimizer.state_dict() if resume support is needed, currently None to reduce file size + "epoch": self.epoch + }, fpath) + + def log_images(self, rgb: Dict[str, list] = {}, depth: Dict[str, list] = {}, scalar_field: Dict[str, list] = {}, prefix="", scalar_cmap="jet", min_depth=None, max_depth=None): + if not self.should_log: + return + + if min_depth is None: + try: + min_depth = self.config.min_depth + max_depth = self.config.max_depth + except AttributeError: + min_depth = None + max_depth = None + + depth = {k: colorize(v, vmin=min_depth, vmax=max_depth) + for k, v in depth.items()} + scalar_field = {k: colorize( + v, vmin=None, vmax=None, cmap=scalar_cmap) for k, v in scalar_field.items()} + images = {**rgb, **depth, **scalar_field} + wimages = { + prefix+"Predictions": [wandb.Image(v, caption=k) for k, v in images.items()]} + wandb.log(wimages, step=self.step) + + def log_line_plot(self, data): + if not self.should_log: + return + + plt.plot(data) + plt.ylabel("Scale factors") + wandb.log({"Scale factors": wandb.Image(plt)}, step=self.step) + plt.close() + + def log_bar_plot(self, title, labels, values): + if not self.should_log: + return + + data = [[label, val] for (label, val) in zip(labels, values)] + table = wandb.Table(data=data, columns=["label", "value"]) + wandb.log({title: wandb.plot.bar(table, "label", + "value", title=title)}, step=self.step) diff --git a/CCEdit-main/src/controlnet11/annotator/zoe/zoedepth/trainers/builder.py b/CCEdit-main/src/controlnet11/annotator/zoe/zoedepth/trainers/builder.py new file mode 100644 index 0000000000000000000000000000000000000000..a663541b08912ebedce21a68c7599ce4c06e85d0 --- /dev/null +++ b/CCEdit-main/src/controlnet11/annotator/zoe/zoedepth/trainers/builder.py @@ -0,0 +1,48 @@ +# MIT License + +# Copyright (c) 2022 Intelligent Systems Lab Org + +# Permission is hereby granted, free of charge, to any person obtaining a copy +# of this software and associated documentation files (the "Software"), to deal +# in the Software without restriction, including without limitation the rights +# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell +# copies of the Software, and to permit persons to whom the Software is +# furnished to do so, subject to the following conditions: + +# The above copyright notice and this permission notice shall be included in all +# copies or substantial portions of the Software. + +# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE +# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER +# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, +# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE +# SOFTWARE. + +# File author: Shariq Farooq Bhat + +from importlib import import_module + + +def get_trainer(config): + """Builds and returns a trainer based on the config. + + Args: + config (dict): the config dict (typically constructed using utils.config.get_config) + config.trainer (str): the name of the trainer to use. The module named "{config.trainer}_trainer" must exist in trainers root module + + Raises: + ValueError: If the specified trainer does not exist under trainers/ folder + + Returns: + Trainer (inherited from zoedepth.trainers.BaseTrainer): The Trainer object + """ + assert "trainer" in config and config.trainer is not None and config.trainer != '', "Trainer not specified. Config: {0}".format( + config) + try: + Trainer = getattr(import_module( + f"zoedepth.trainers.{config.trainer}_trainer"), 'Trainer') + except ModuleNotFoundError as e: + raise ValueError(f"Trainer {config.trainer}_trainer not found.") from e + return Trainer diff --git a/CCEdit-main/src/controlnet11/annotator/zoe/zoedepth/trainers/loss.py b/CCEdit-main/src/controlnet11/annotator/zoe/zoedepth/trainers/loss.py new file mode 100644 index 0000000000000000000000000000000000000000..0c5a1c15cdf5628c1474c566fdc6e58159d7f5ab --- /dev/null +++ b/CCEdit-main/src/controlnet11/annotator/zoe/zoedepth/trainers/loss.py @@ -0,0 +1,316 @@ +# MIT License + +# Copyright (c) 2022 Intelligent Systems Lab Org + +# Permission is hereby granted, free of charge, to any person obtaining a copy +# of this software and associated documentation files (the "Software"), to deal +# in the Software without restriction, including without limitation the rights +# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell +# copies of the Software, and to permit persons to whom the Software is +# furnished to do so, subject to the following conditions: + +# The above copyright notice and this permission notice shall be included in all +# copies or substantial portions of the Software. + +# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE +# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER +# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, +# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE +# SOFTWARE. + +# File author: Shariq Farooq Bhat + +import torch +import torch.nn as nn +import torch.nn.functional as F +import torch.cuda.amp as amp +import numpy as np + + +KEY_OUTPUT = 'metric_depth' + + +def extract_key(prediction, key): + if isinstance(prediction, dict): + return prediction[key] + return prediction + + +# Main loss function used for ZoeDepth. Copy/paste from AdaBins repo (https://github.com/shariqfarooq123/AdaBins/blob/0952d91e9e762be310bb4cd055cbfe2448c0ce20/loss.py#L7) +class SILogLoss(nn.Module): + """SILog loss (pixel-wise)""" + def __init__(self, beta=0.15): + super(SILogLoss, self).__init__() + self.name = 'SILog' + self.beta = beta + + def forward(self, input, target, mask=None, interpolate=True, return_interpolated=False): + input = extract_key(input, KEY_OUTPUT) + if input.shape[-1] != target.shape[-1] and interpolate: + input = nn.functional.interpolate( + input, target.shape[-2:], mode='bilinear', align_corners=True) + intr_input = input + else: + intr_input = input + + if target.ndim == 3: + target = target.unsqueeze(1) + + if mask is not None: + if mask.ndim == 3: + mask = mask.unsqueeze(1) + + input = input[mask] + target = target[mask] + + with amp.autocast(enabled=False): # amp causes NaNs in this loss function + alpha = 1e-7 + g = torch.log(input + alpha) - torch.log(target + alpha) + + # n, c, h, w = g.shape + # norm = 1/(h*w) + # Dg = norm * torch.sum(g**2) - (0.85/(norm**2)) * (torch.sum(g))**2 + + Dg = torch.var(g) + self.beta * torch.pow(torch.mean(g), 2) + + loss = 10 * torch.sqrt(Dg) + + if torch.isnan(loss): + print("Nan SILog loss") + print("input:", input.shape) + print("target:", target.shape) + print("G", torch.sum(torch.isnan(g))) + print("Input min max", torch.min(input), torch.max(input)) + print("Target min max", torch.min(target), torch.max(target)) + print("Dg", torch.isnan(Dg)) + print("loss", torch.isnan(loss)) + + if not return_interpolated: + return loss + + return loss, intr_input + + +def grad(x): + # x.shape : n, c, h, w + diff_x = x[..., 1:, 1:] - x[..., 1:, :-1] + diff_y = x[..., 1:, 1:] - x[..., :-1, 1:] + mag = diff_x**2 + diff_y**2 + # angle_ratio + angle = torch.atan(diff_y / (diff_x + 1e-10)) + return mag, angle + + +def grad_mask(mask): + return mask[..., 1:, 1:] & mask[..., 1:, :-1] & mask[..., :-1, 1:] + + +class GradL1Loss(nn.Module): + """Gradient loss""" + def __init__(self): + super(GradL1Loss, self).__init__() + self.name = 'GradL1' + + def forward(self, input, target, mask=None, interpolate=True, return_interpolated=False): + input = extract_key(input, KEY_OUTPUT) + if input.shape[-1] != target.shape[-1] and interpolate: + input = nn.functional.interpolate( + input, target.shape[-2:], mode='bilinear', align_corners=True) + intr_input = input + else: + intr_input = input + + grad_gt = grad(target) + grad_pred = grad(input) + mask_g = grad_mask(mask) + + loss = nn.functional.l1_loss(grad_pred[0][mask_g], grad_gt[0][mask_g]) + loss = loss + \ + nn.functional.l1_loss(grad_pred[1][mask_g], grad_gt[1][mask_g]) + if not return_interpolated: + return loss + return loss, intr_input + + +class OrdinalRegressionLoss(object): + + def __init__(self, ord_num, beta, discretization="SID"): + self.ord_num = ord_num + self.beta = beta + self.discretization = discretization + + def _create_ord_label(self, gt): + N,one, H, W = gt.shape + # print("gt shape:", gt.shape) + + ord_c0 = torch.ones(N, self.ord_num, H, W).to(gt.device) + if self.discretization == "SID": + label = self.ord_num * torch.log(gt) / np.log(self.beta) + else: + label = self.ord_num * (gt - 1.0) / (self.beta - 1.0) + label = label.long() + mask = torch.linspace(0, self.ord_num - 1, self.ord_num, requires_grad=False) \ + .view(1, self.ord_num, 1, 1).to(gt.device) + mask = mask.repeat(N, 1, H, W).contiguous().long() + mask = (mask > label) + ord_c0[mask] = 0 + ord_c1 = 1 - ord_c0 + # implementation according to the paper. + # ord_label = torch.ones(N, self.ord_num * 2, H, W).to(gt.device) + # ord_label[:, 0::2, :, :] = ord_c0 + # ord_label[:, 1::2, :, :] = ord_c1 + # reimplementation for fast speed. + ord_label = torch.cat((ord_c0, ord_c1), dim=1) + return ord_label, mask + + def __call__(self, prob, gt): + """ + :param prob: ordinal regression probability, N x 2*Ord Num x H x W, torch.Tensor + :param gt: depth ground truth, NXHxW, torch.Tensor + :return: loss: loss value, torch.float + """ + # N, C, H, W = prob.shape + valid_mask = gt > 0. + ord_label, mask = self._create_ord_label(gt) + # print("prob shape: {}, ord label shape: {}".format(prob.shape, ord_label.shape)) + entropy = -prob * ord_label + loss = torch.sum(entropy, dim=1)[valid_mask.squeeze(1)] + return loss.mean() + + +class DiscreteNLLLoss(nn.Module): + """Cross entropy loss""" + def __init__(self, min_depth=1e-3, max_depth=10, depth_bins=64): + super(DiscreteNLLLoss, self).__init__() + self.name = 'CrossEntropy' + self.ignore_index = -(depth_bins + 1) + # self._loss_func = nn.NLLLoss(ignore_index=self.ignore_index) + self._loss_func = nn.CrossEntropyLoss(ignore_index=self.ignore_index) + self.min_depth = min_depth + self.max_depth = max_depth + self.depth_bins = depth_bins + self.alpha = 1 + self.zeta = 1 - min_depth + self.beta = max_depth + self.zeta + + def quantize_depth(self, depth): + # depth : N1HW + # output : NCHW + + # Quantize depth log-uniformly on [1, self.beta] into self.depth_bins bins + depth = torch.log(depth / self.alpha) / np.log(self.beta / self.alpha) + depth = depth * (self.depth_bins - 1) + depth = torch.round(depth) + depth = depth.long() + return depth + + + + def _dequantize_depth(self, depth): + """ + Inverse of quantization + depth : NCHW -> N1HW + """ + # Get the center of the bin + + + + + def forward(self, input, target, mask=None, interpolate=True, return_interpolated=False): + input = extract_key(input, KEY_OUTPUT) + # assert torch.all(input <= 0), "Input should be negative" + + if input.shape[-1] != target.shape[-1] and interpolate: + input = nn.functional.interpolate( + input, target.shape[-2:], mode='bilinear', align_corners=True) + intr_input = input + else: + intr_input = input + + # assert torch.all(input)<=1) + if target.ndim == 3: + target = target.unsqueeze(1) + + target = self.quantize_depth(target) + if mask is not None: + if mask.ndim == 3: + mask = mask.unsqueeze(1) + + # Set the mask to ignore_index + mask = mask.long() + input = input * mask + (1 - mask) * self.ignore_index + target = target * mask + (1 - mask) * self.ignore_index + + + + input = input.flatten(2) # N, nbins, H*W + target = target.flatten(1) # N, H*W + loss = self._loss_func(input, target) + + if not return_interpolated: + return loss + return loss, intr_input + + + + +def compute_scale_and_shift(prediction, target, mask): + # system matrix: A = [[a_00, a_01], [a_10, a_11]] + a_00 = torch.sum(mask * prediction * prediction, (1, 2)) + a_01 = torch.sum(mask * prediction, (1, 2)) + a_11 = torch.sum(mask, (1, 2)) + + # right hand side: b = [b_0, b_1] + b_0 = torch.sum(mask * prediction * target, (1, 2)) + b_1 = torch.sum(mask * target, (1, 2)) + + # solution: x = A^-1 . b = [[a_11, -a_01], [-a_10, a_00]] / (a_00 * a_11 - a_01 * a_10) . b + x_0 = torch.zeros_like(b_0) + x_1 = torch.zeros_like(b_1) + + det = a_00 * a_11 - a_01 * a_01 + # A needs to be a positive definite matrix. + valid = det > 0 + + x_0[valid] = (a_11[valid] * b_0[valid] - a_01[valid] * b_1[valid]) / det[valid] + x_1[valid] = (-a_01[valid] * b_0[valid] + a_00[valid] * b_1[valid]) / det[valid] + + return x_0, x_1 +class ScaleAndShiftInvariantLoss(nn.Module): + def __init__(self): + super().__init__() + self.name = "SSILoss" + + def forward(self, prediction, target, mask, interpolate=True, return_interpolated=False): + + if prediction.shape[-1] != target.shape[-1] and interpolate: + prediction = nn.functional.interpolate(prediction, target.shape[-2:], mode='bilinear', align_corners=True) + intr_input = prediction + else: + intr_input = prediction + + + prediction, target, mask = prediction.squeeze(), target.squeeze(), mask.squeeze() + assert prediction.shape == target.shape, f"Shape mismatch: Expected same shape but got {prediction.shape} and {target.shape}." + + scale, shift = compute_scale_and_shift(prediction, target, mask) + + scaled_prediction = scale.view(-1, 1, 1) * prediction + shift.view(-1, 1, 1) + + loss = nn.functional.l1_loss(scaled_prediction[mask], target[mask]) + if not return_interpolated: + return loss + return loss, intr_input + + + + +if __name__ == '__main__': + # Tests for DiscreteNLLLoss + celoss = DiscreteNLLLoss() + print(celoss(torch.rand(4, 64, 26, 32)*10, torch.rand(4, 1, 26, 32)*10, )) + + d = torch.Tensor([6.59, 3.8, 10.0]) + print(celoss.dequantize_depth(celoss.quantize_depth(d))) diff --git a/CCEdit-main/src/controlnet11/annotator/zoe/zoedepth/trainers/zoedepth_nk_trainer.py b/CCEdit-main/src/controlnet11/annotator/zoe/zoedepth/trainers/zoedepth_nk_trainer.py new file mode 100644 index 0000000000000000000000000000000000000000..d528ae126f1c51b2f25fd31f94a39591ceb2f43a --- /dev/null +++ b/CCEdit-main/src/controlnet11/annotator/zoe/zoedepth/trainers/zoedepth_nk_trainer.py @@ -0,0 +1,143 @@ +# MIT License + +# Copyright (c) 2022 Intelligent Systems Lab Org + +# Permission is hereby granted, free of charge, to any person obtaining a copy +# of this software and associated documentation files (the "Software"), to deal +# in the Software without restriction, including without limitation the rights +# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell +# copies of the Software, and to permit persons to whom the Software is +# furnished to do so, subject to the following conditions: + +# The above copyright notice and this permission notice shall be included in all +# copies or substantial portions of the Software. + +# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE +# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER +# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, +# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE +# SOFTWARE. + +# File author: Shariq Farooq Bhat + +import torch +import torch.cuda.amp as amp +import torch.nn as nn + +from zoedepth.trainers.loss import GradL1Loss, SILogLoss +from zoedepth.utils.config import DATASETS_CONFIG +from zoedepth.utils.misc import compute_metrics + +from .base_trainer import BaseTrainer + + +class Trainer(BaseTrainer): + def __init__(self, config, model, train_loader, test_loader=None, device=None): + super().__init__(config, model, train_loader, + test_loader=test_loader, device=device) + self.device = device + self.silog_loss = SILogLoss() + self.grad_loss = GradL1Loss() + self.domain_classifier_loss = nn.CrossEntropyLoss() + + self.scaler = amp.GradScaler(enabled=self.config.use_amp) + + def train_on_batch(self, batch, train_step): + """ + Expects a batch of images and depth as input + batch["image"].shape : batch_size, c, h, w + batch["depth"].shape : batch_size, 1, h, w + + Assumes all images in a batch are from the same dataset + """ + + images, depths_gt = batch['image'].to( + self.device), batch['depth'].to(self.device) + # batch['dataset'] is a tensor strings all valued either 'nyu' or 'kitti'. labels nyu -> 0, kitti -> 1 + dataset = batch['dataset'][0] + # Convert to 0s or 1s + domain_labels = torch.Tensor([dataset == 'kitti' for _ in range( + images.size(0))]).to(torch.long).to(self.device) + + # m = self.model.module if self.config.multigpu else self.model + + b, c, h, w = images.size() + mask = batch["mask"].to(self.device).to(torch.bool) + + losses = {} + + with amp.autocast(enabled=self.config.use_amp): + output = self.model(images) + pred_depths = output['metric_depth'] + domain_logits = output['domain_logits'] + + l_si, pred = self.silog_loss( + pred_depths, depths_gt, mask=mask, interpolate=True, return_interpolated=True) + loss = self.config.w_si * l_si + losses[self.silog_loss.name] = l_si + + if self.config.w_grad > 0: + l_grad = self.grad_loss(pred, depths_gt, mask=mask) + loss = loss + self.config.w_grad * l_grad + losses[self.grad_loss.name] = l_grad + else: + l_grad = torch.Tensor([0]) + + if self.config.w_domain > 0: + l_domain = self.domain_classifier_loss( + domain_logits, domain_labels) + loss = loss + self.config.w_domain * l_domain + losses["DomainLoss"] = l_domain + else: + l_domain = torch.Tensor([0.]) + + self.scaler.scale(loss).backward() + + if self.config.clip_grad > 0: + self.scaler.unscale_(self.optimizer) + nn.utils.clip_grad_norm_( + self.model.parameters(), self.config.clip_grad) + + self.scaler.step(self.optimizer) + + if self.should_log and self.step > 1 and (self.step % int(self.config.log_images_every * self.iters_per_epoch)) == 0: + depths_gt[torch.logical_not(mask)] = -99 + self.log_images(rgb={"Input": images[0, ...]}, depth={"GT": depths_gt[0], "PredictedMono": pred[0]}, prefix="Train", + min_depth=DATASETS_CONFIG[dataset]['min_depth'], max_depth=DATASETS_CONFIG[dataset]['max_depth']) + + self.scaler.update() + self.optimizer.zero_grad(set_to_none=True) + + return losses + + def validate_on_batch(self, batch, val_step): + images = batch['image'].to(self.device) + depths_gt = batch['depth'].to(self.device) + dataset = batch['dataset'][0] + if 'has_valid_depth' in batch: + if not batch['has_valid_depth']: + return None, None + + depths_gt = depths_gt.squeeze().unsqueeze(0).unsqueeze(0) + with amp.autocast(enabled=self.config.use_amp): + m = self.model.module if self.config.multigpu else self.model + pred_depths = m(images)["metric_depth"] + pred_depths = pred_depths.squeeze().unsqueeze(0).unsqueeze(0) + + mask = torch.logical_and( + depths_gt > self.config.min_depth, depths_gt < self.config.max_depth) + with amp.autocast(enabled=self.config.use_amp): + l_depth = self.silog_loss( + pred_depths, depths_gt, mask=mask.to(torch.bool), interpolate=True) + + metrics = compute_metrics(depths_gt, pred_depths, **self.config) + losses = {f"{self.silog_loss.name}": l_depth.item()} + + if val_step == 1 and self.should_log: + depths_gt[torch.logical_not(mask)] = -99 + self.log_images(rgb={"Input": images[0]}, depth={"GT": depths_gt[0], "PredictedMono": pred_depths[0]}, prefix="Test", + min_depth=DATASETS_CONFIG[dataset]['min_depth'], max_depth=DATASETS_CONFIG[dataset]['max_depth']) + + return metrics, losses diff --git a/CCEdit-main/src/controlnet11/annotator/zoe/zoedepth/trainers/zoedepth_trainer.py b/CCEdit-main/src/controlnet11/annotator/zoe/zoedepth/trainers/zoedepth_trainer.py new file mode 100644 index 0000000000000000000000000000000000000000..3ac1c24c0512c1c1b191670a7c24abb4fca47ba1 --- /dev/null +++ b/CCEdit-main/src/controlnet11/annotator/zoe/zoedepth/trainers/zoedepth_trainer.py @@ -0,0 +1,177 @@ +# MIT License + +# Copyright (c) 2022 Intelligent Systems Lab Org + +# Permission is hereby granted, free of charge, to any person obtaining a copy +# of this software and associated documentation files (the "Software"), to deal +# in the Software without restriction, including without limitation the rights +# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell +# copies of the Software, and to permit persons to whom the Software is +# furnished to do so, subject to the following conditions: + +# The above copyright notice and this permission notice shall be included in all +# copies or substantial portions of the Software. + +# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE +# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER +# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, +# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE +# SOFTWARE. + +# File author: Shariq Farooq Bhat + +import torch +import torch.cuda.amp as amp +import torch.nn as nn + +from zoedepth.trainers.loss import GradL1Loss, SILogLoss +from zoedepth.utils.config import DATASETS_CONFIG +from zoedepth.utils.misc import compute_metrics +from zoedepth.data.preprocess import get_black_border + +from .base_trainer import BaseTrainer +from torchvision import transforms +from PIL import Image +import numpy as np + +class Trainer(BaseTrainer): + def __init__(self, config, model, train_loader, test_loader=None, device=None): + super().__init__(config, model, train_loader, + test_loader=test_loader, device=device) + self.device = device + self.silog_loss = SILogLoss() + self.grad_loss = GradL1Loss() + self.scaler = amp.GradScaler(enabled=self.config.use_amp) + + def train_on_batch(self, batch, train_step): + """ + Expects a batch of images and depth as input + batch["image"].shape : batch_size, c, h, w + batch["depth"].shape : batch_size, 1, h, w + """ + + images, depths_gt = batch['image'].to( + self.device), batch['depth'].to(self.device) + dataset = batch['dataset'][0] + + b, c, h, w = images.size() + mask = batch["mask"].to(self.device).to(torch.bool) + + losses = {} + + with amp.autocast(enabled=self.config.use_amp): + + output = self.model(images) + pred_depths = output['metric_depth'] + + l_si, pred = self.silog_loss( + pred_depths, depths_gt, mask=mask, interpolate=True, return_interpolated=True) + loss = self.config.w_si * l_si + losses[self.silog_loss.name] = l_si + + if self.config.w_grad > 0: + l_grad = self.grad_loss(pred, depths_gt, mask=mask) + loss = loss + self.config.w_grad * l_grad + losses[self.grad_loss.name] = l_grad + else: + l_grad = torch.Tensor([0]) + + self.scaler.scale(loss).backward() + + if self.config.clip_grad > 0: + self.scaler.unscale_(self.optimizer) + nn.utils.clip_grad_norm_( + self.model.parameters(), self.config.clip_grad) + + self.scaler.step(self.optimizer) + + if self.should_log and (self.step % int(self.config.log_images_every * self.iters_per_epoch)) == 0: + # -99 is treated as invalid depth in the log_images function and is colored grey. + depths_gt[torch.logical_not(mask)] = -99 + + self.log_images(rgb={"Input": images[0, ...]}, depth={"GT": depths_gt[0], "PredictedMono": pred[0]}, prefix="Train", + min_depth=DATASETS_CONFIG[dataset]['min_depth'], max_depth=DATASETS_CONFIG[dataset]['max_depth']) + + if self.config.get("log_rel", False): + self.log_images( + scalar_field={"RelPred": output["relative_depth"][0]}, prefix="TrainRel") + + self.scaler.update() + self.optimizer.zero_grad() + + return losses + + @torch.no_grad() + def eval_infer(self, x): + with amp.autocast(enabled=self.config.use_amp): + m = self.model.module if self.config.multigpu else self.model + pred_depths = m(x)['metric_depth'] + return pred_depths + + @torch.no_grad() + def crop_aware_infer(self, x): + # if we are not avoiding the black border, we can just use the normal inference + if not self.config.get("avoid_boundary", False): + return self.eval_infer(x) + + # otherwise, we need to crop the image to avoid the black border + # For now, this may be a bit slow due to converting to numpy and back + # We assume no normalization is done on the input image + + # get the black border + assert x.shape[0] == 1, "Only batch size 1 is supported for now" + x_pil = transforms.ToPILImage()(x[0].cpu()) + x_np = np.array(x_pil, dtype=np.uint8) + black_border_params = get_black_border(x_np) + top, bottom, left, right = black_border_params.top, black_border_params.bottom, black_border_params.left, black_border_params.right + x_np_cropped = x_np[top:bottom, left:right, :] + x_cropped = transforms.ToTensor()(Image.fromarray(x_np_cropped)) + + # run inference on the cropped image + pred_depths_cropped = self.eval_infer(x_cropped.unsqueeze(0).to(self.device)) + + # resize the prediction to x_np_cropped's size + pred_depths_cropped = nn.functional.interpolate( + pred_depths_cropped, size=(x_np_cropped.shape[0], x_np_cropped.shape[1]), mode="bilinear", align_corners=False) + + + # pad the prediction back to the original size + pred_depths = torch.zeros((1, 1, x_np.shape[0], x_np.shape[1]), device=pred_depths_cropped.device, dtype=pred_depths_cropped.dtype) + pred_depths[:, :, top:bottom, left:right] = pred_depths_cropped + + return pred_depths + + + + def validate_on_batch(self, batch, val_step): + images = batch['image'].to(self.device) + depths_gt = batch['depth'].to(self.device) + dataset = batch['dataset'][0] + mask = batch["mask"].to(self.device) + if 'has_valid_depth' in batch: + if not batch['has_valid_depth']: + return None, None + + depths_gt = depths_gt.squeeze().unsqueeze(0).unsqueeze(0) + mask = mask.squeeze().unsqueeze(0).unsqueeze(0) + if dataset == 'nyu': + pred_depths = self.crop_aware_infer(images) + else: + pred_depths = self.eval_infer(images) + pred_depths = pred_depths.squeeze().unsqueeze(0).unsqueeze(0) + + with amp.autocast(enabled=self.config.use_amp): + l_depth = self.silog_loss( + pred_depths, depths_gt, mask=mask.to(torch.bool), interpolate=True) + + metrics = compute_metrics(depths_gt, pred_depths, **self.config) + losses = {f"{self.silog_loss.name}": l_depth.item()} + + if val_step == 1 and self.should_log: + depths_gt[torch.logical_not(mask)] = -99 + self.log_images(rgb={"Input": images[0]}, depth={"GT": depths_gt[0], "PredictedMono": pred_depths[0]}, prefix="Test", + min_depth=DATASETS_CONFIG[dataset]['min_depth'], max_depth=DATASETS_CONFIG[dataset]['max_depth']) + + return metrics, losses diff --git a/CCEdit-main/src/controlnet11/annotator/zoe/zoedepth/utils/__init__.py b/CCEdit-main/src/controlnet11/annotator/zoe/zoedepth/utils/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..5f2668792389157609abb2a0846fb620e7d67eb9 --- /dev/null +++ b/CCEdit-main/src/controlnet11/annotator/zoe/zoedepth/utils/__init__.py @@ -0,0 +1,24 @@ +# MIT License + +# Copyright (c) 2022 Intelligent Systems Lab Org + +# Permission is hereby granted, free of charge, to any person obtaining a copy +# of this software and associated documentation files (the "Software"), to deal +# in the Software without restriction, including without limitation the rights +# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell +# copies of the Software, and to permit persons to whom the Software is +# furnished to do so, subject to the following conditions: + +# The above copyright notice and this permission notice shall be included in all +# copies or substantial portions of the Software. + +# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE +# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER +# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, +# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE +# SOFTWARE. + +# File author: Shariq Farooq Bhat + diff --git a/CCEdit-main/src/controlnet11/annotator/zoe/zoedepth/utils/__pycache__/__init__.cpython-39.pyc b/CCEdit-main/src/controlnet11/annotator/zoe/zoedepth/utils/__pycache__/__init__.cpython-39.pyc new file mode 100644 index 0000000000000000000000000000000000000000..e0e88e9712fcef414fcf70b4bbab3f848f1c56d0 Binary files /dev/null and b/CCEdit-main/src/controlnet11/annotator/zoe/zoedepth/utils/__pycache__/__init__.cpython-39.pyc differ diff --git a/CCEdit-main/src/controlnet11/annotator/zoe/zoedepth/utils/__pycache__/arg_utils.cpython-39.pyc b/CCEdit-main/src/controlnet11/annotator/zoe/zoedepth/utils/__pycache__/arg_utils.cpython-39.pyc new file mode 100644 index 0000000000000000000000000000000000000000..2f0b0554d22f15e974c02924937b4705fb03f22b Binary files /dev/null and b/CCEdit-main/src/controlnet11/annotator/zoe/zoedepth/utils/__pycache__/arg_utils.cpython-39.pyc differ diff --git a/CCEdit-main/src/controlnet11/annotator/zoe/zoedepth/utils/__pycache__/config.cpython-39.pyc b/CCEdit-main/src/controlnet11/annotator/zoe/zoedepth/utils/__pycache__/config.cpython-39.pyc new file mode 100644 index 0000000000000000000000000000000000000000..6bc6200fcbf2c5ae4847b6857d84587f0971535d Binary files /dev/null and b/CCEdit-main/src/controlnet11/annotator/zoe/zoedepth/utils/__pycache__/config.cpython-39.pyc differ diff --git a/CCEdit-main/src/controlnet11/annotator/zoe/zoedepth/utils/arg_utils.py b/CCEdit-main/src/controlnet11/annotator/zoe/zoedepth/utils/arg_utils.py new file mode 100644 index 0000000000000000000000000000000000000000..8a3004ec3679c0a40fd8961253733fb4343ad545 --- /dev/null +++ b/CCEdit-main/src/controlnet11/annotator/zoe/zoedepth/utils/arg_utils.py @@ -0,0 +1,33 @@ + + +def infer_type(x): # hacky way to infer type from string args + if not isinstance(x, str): + return x + + try: + x = int(x) + return x + except ValueError: + pass + + try: + x = float(x) + return x + except ValueError: + pass + + return x + + +def parse_unknown(unknown_args): + clean = [] + for a in unknown_args: + if "=" in a: + k, v = a.split("=") + clean.extend([k, v]) + else: + clean.append(a) + + keys = clean[::2] + values = clean[1::2] + return {k.replace("--", ""): infer_type(v) for k, v in zip(keys, values)} diff --git a/CCEdit-main/src/controlnet11/annotator/zoe/zoedepth/utils/config.py b/CCEdit-main/src/controlnet11/annotator/zoe/zoedepth/utils/config.py new file mode 100644 index 0000000000000000000000000000000000000000..84996564663dadf0e720de2a68ef8c53106ed666 --- /dev/null +++ b/CCEdit-main/src/controlnet11/annotator/zoe/zoedepth/utils/config.py @@ -0,0 +1,437 @@ +# MIT License + +# Copyright (c) 2022 Intelligent Systems Lab Org + +# Permission is hereby granted, free of charge, to any person obtaining a copy +# of this software and associated documentation files (the "Software"), to deal +# in the Software without restriction, including without limitation the rights +# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell +# copies of the Software, and to permit persons to whom the Software is +# furnished to do so, subject to the following conditions: + +# The above copyright notice and this permission notice shall be included in all +# copies or substantial portions of the Software. + +# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE +# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER +# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, +# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE +# SOFTWARE. + +# File author: Shariq Farooq Bhat + +import json +import os + +from .easydict import EasyDict as edict +from .arg_utils import infer_type + +import pathlib +import platform + +ROOT = pathlib.Path(__file__).parent.parent.resolve() + +HOME_DIR = os.path.expanduser("~") + +COMMON_CONFIG = { + "save_dir": os.path.expanduser("~/shortcuts/monodepth3_checkpoints"), + "project": "ZoeDepth", + "tags": '', + "notes": "", + "gpu": None, + "root": ".", + "uid": None, + "print_losses": False +} + +DATASETS_CONFIG = { + "kitti": { + "dataset": "kitti", + "min_depth": 0.001, + "max_depth": 80, + "data_path": os.path.join(HOME_DIR, "shortcuts/datasets/kitti/raw"), + "gt_path": os.path.join(HOME_DIR, "shortcuts/datasets/kitti/gts"), + "filenames_file": "./train_test_inputs/kitti_eigen_train_files_with_gt.txt", + "input_height": 352, + "input_width": 1216, # 704 + "data_path_eval": os.path.join(HOME_DIR, "shortcuts/datasets/kitti/raw"), + "gt_path_eval": os.path.join(HOME_DIR, "shortcuts/datasets/kitti/gts"), + "filenames_file_eval": "./train_test_inputs/kitti_eigen_test_files_with_gt.txt", + + "min_depth_eval": 1e-3, + "max_depth_eval": 80, + + "do_random_rotate": True, + "degree": 1.0, + "do_kb_crop": True, + "garg_crop": True, + "eigen_crop": False, + "use_right": False + }, + "kitti_test": { + "dataset": "kitti", + "min_depth": 0.001, + "max_depth": 80, + "data_path": os.path.join(HOME_DIR, "shortcuts/datasets/kitti/raw"), + "gt_path": os.path.join(HOME_DIR, "shortcuts/datasets/kitti/gts"), + "filenames_file": "./train_test_inputs/kitti_eigen_train_files_with_gt.txt", + "input_height": 352, + "input_width": 1216, + "data_path_eval": os.path.join(HOME_DIR, "shortcuts/datasets/kitti/raw"), + "gt_path_eval": os.path.join(HOME_DIR, "shortcuts/datasets/kitti/gts"), + "filenames_file_eval": "./train_test_inputs/kitti_eigen_test_files_with_gt.txt", + + "min_depth_eval": 1e-3, + "max_depth_eval": 80, + + "do_random_rotate": False, + "degree": 1.0, + "do_kb_crop": True, + "garg_crop": True, + "eigen_crop": False, + "use_right": False + }, + "nyu": { + "dataset": "nyu", + "avoid_boundary": False, + "min_depth": 1e-3, # originally 0.1 + "max_depth": 10, + "data_path": os.path.join(HOME_DIR, "shortcuts/datasets/nyu_depth_v2/sync/"), + "gt_path": os.path.join(HOME_DIR, "shortcuts/datasets/nyu_depth_v2/sync/"), + "filenames_file": "./train_test_inputs/nyudepthv2_train_files_with_gt.txt", + "input_height": 480, + "input_width": 640, + "data_path_eval": os.path.join(HOME_DIR, "shortcuts/datasets/nyu_depth_v2/official_splits/test/"), + "gt_path_eval": os.path.join(HOME_DIR, "shortcuts/datasets/nyu_depth_v2/official_splits/test/"), + "filenames_file_eval": "./train_test_inputs/nyudepthv2_test_files_with_gt.txt", + "min_depth_eval": 1e-3, + "max_depth_eval": 10, + "min_depth_diff": -10, + "max_depth_diff": 10, + + "do_random_rotate": True, + "degree": 1.0, + "do_kb_crop": False, + "garg_crop": False, + "eigen_crop": True + }, + "ibims": { + "dataset": "ibims", + "ibims_root": os.path.join(HOME_DIR, "shortcuts/datasets/ibims/ibims1_core_raw/"), + "eigen_crop": True, + "garg_crop": False, + "do_kb_crop": False, + "min_depth_eval": 0, + "max_depth_eval": 10, + "min_depth": 1e-3, + "max_depth": 10 + }, + "sunrgbd": { + "dataset": "sunrgbd", + "sunrgbd_root": os.path.join(HOME_DIR, "shortcuts/datasets/SUNRGBD/test/"), + "eigen_crop": True, + "garg_crop": False, + "do_kb_crop": False, + "min_depth_eval": 0, + "max_depth_eval": 8, + "min_depth": 1e-3, + "max_depth": 10 + }, + "diml_indoor": { + "dataset": "diml_indoor", + "diml_indoor_root": os.path.join(HOME_DIR, "shortcuts/datasets/diml_indoor_test/"), + "eigen_crop": True, + "garg_crop": False, + "do_kb_crop": False, + "min_depth_eval": 0, + "max_depth_eval": 10, + "min_depth": 1e-3, + "max_depth": 10 + }, + "diml_outdoor": { + "dataset": "diml_outdoor", + "diml_outdoor_root": os.path.join(HOME_DIR, "shortcuts/datasets/diml_outdoor_test/"), + "eigen_crop": False, + "garg_crop": True, + "do_kb_crop": False, + "min_depth_eval": 2, + "max_depth_eval": 80, + "min_depth": 1e-3, + "max_depth": 80 + }, + "diode_indoor": { + "dataset": "diode_indoor", + "diode_indoor_root": os.path.join(HOME_DIR, "shortcuts/datasets/diode_indoor/"), + "eigen_crop": True, + "garg_crop": False, + "do_kb_crop": False, + "min_depth_eval": 1e-3, + "max_depth_eval": 10, + "min_depth": 1e-3, + "max_depth": 10 + }, + "diode_outdoor": { + "dataset": "diode_outdoor", + "diode_outdoor_root": os.path.join(HOME_DIR, "shortcuts/datasets/diode_outdoor/"), + "eigen_crop": False, + "garg_crop": True, + "do_kb_crop": False, + "min_depth_eval": 1e-3, + "max_depth_eval": 80, + "min_depth": 1e-3, + "max_depth": 80 + }, + "hypersim_test": { + "dataset": "hypersim_test", + "hypersim_test_root": os.path.join(HOME_DIR, "shortcuts/datasets/hypersim_test/"), + "eigen_crop": True, + "garg_crop": False, + "do_kb_crop": False, + "min_depth_eval": 1e-3, + "max_depth_eval": 80, + "min_depth": 1e-3, + "max_depth": 10 + }, + "vkitti": { + "dataset": "vkitti", + "vkitti_root": os.path.join(HOME_DIR, "shortcuts/datasets/vkitti_test/"), + "eigen_crop": False, + "garg_crop": True, + "do_kb_crop": True, + "min_depth_eval": 1e-3, + "max_depth_eval": 80, + "min_depth": 1e-3, + "max_depth": 80 + }, + "vkitti2": { + "dataset": "vkitti2", + "vkitti2_root": os.path.join(HOME_DIR, "shortcuts/datasets/vkitti2/"), + "eigen_crop": False, + "garg_crop": True, + "do_kb_crop": True, + "min_depth_eval": 1e-3, + "max_depth_eval": 80, + "min_depth": 1e-3, + "max_depth": 80, + }, + "ddad": { + "dataset": "ddad", + "ddad_root": os.path.join(HOME_DIR, "shortcuts/datasets/ddad/ddad_val/"), + "eigen_crop": False, + "garg_crop": True, + "do_kb_crop": True, + "min_depth_eval": 1e-3, + "max_depth_eval": 80, + "min_depth": 1e-3, + "max_depth": 80, + }, +} + +ALL_INDOOR = ["nyu", "ibims", "sunrgbd", "diode_indoor", "hypersim_test"] +ALL_OUTDOOR = ["kitti", "diml_outdoor", "diode_outdoor", "vkitti2", "ddad"] +ALL_EVAL_DATASETS = ALL_INDOOR + ALL_OUTDOOR + +COMMON_TRAINING_CONFIG = { + "dataset": "nyu", + "distributed": True, + "workers": 16, + "clip_grad": 0.1, + "use_shared_dict": False, + "shared_dict": None, + "use_amp": False, + + "aug": True, + "random_crop": False, + "random_translate": False, + "translate_prob": 0.2, + "max_translation": 100, + + "validate_every": 0.25, + "log_images_every": 0.1, + "prefetch": False, +} + + +def flatten(config, except_keys=('bin_conf')): + def recurse(inp): + if isinstance(inp, dict): + for key, value in inp.items(): + if key in except_keys: + yield (key, value) + if isinstance(value, dict): + yield from recurse(value) + else: + yield (key, value) + + return dict(list(recurse(config))) + + +def split_combined_args(kwargs): + """Splits the arguments that are combined with '__' into multiple arguments. + Combined arguments should have equal number of keys and values. + Keys are separated by '__' and Values are separated with ';'. + For example, '__n_bins__lr=256;0.001' + + Args: + kwargs (dict): key-value pairs of arguments where key-value is optionally combined according to the above format. + + Returns: + dict: Parsed dict with the combined arguments split into individual key-value pairs. + """ + new_kwargs = dict(kwargs) + for key, value in kwargs.items(): + if key.startswith("__"): + keys = key.split("__")[1:] + values = value.split(";") + assert len(keys) == len( + values), f"Combined arguments should have equal number of keys and values. Keys are separated by '__' and Values are separated with ';'. For example, '__n_bins__lr=256;0.001. Given (keys,values) is ({keys}, {values})" + for k, v in zip(keys, values): + new_kwargs[k] = v + return new_kwargs + + +def parse_list(config, key, dtype=int): + """Parse a list of values for the key if the value is a string. The values are separated by a comma. + Modifies the config in place. + """ + if key in config: + if isinstance(config[key], str): + config[key] = list(map(dtype, config[key].split(','))) + assert isinstance(config[key], list) and all([isinstance(e, dtype) for e in config[key]] + ), f"{key} should be a list of values dtype {dtype}. Given {config[key]} of type {type(config[key])} with values of type {[type(e) for e in config[key]]}." + + +def get_model_config(model_name, model_version=None): + """Find and parse the .json config file for the model. + + Args: + model_name (str): name of the model. The config file should be named config_{model_name}[_{model_version}].json under the models/{model_name} directory. + model_version (str, optional): Specific config version. If specified config_{model_name}_{model_version}.json is searched for and used. Otherwise config_{model_name}.json is used. Defaults to None. + + Returns: + easydict: the config dictionary for the model. + """ + config_fname = f"config_{model_name}_{model_version}.json" if model_version is not None else f"config_{model_name}.json" + config_file = os.path.join(ROOT, "models", model_name, config_fname) + if not os.path.exists(config_file): + return None + + with open(config_file, "r") as f: + config = edict(json.load(f)) + + # handle dictionary inheritance + # only training config is supported for inheritance + if "inherit" in config.train and config.train.inherit is not None: + inherit_config = get_model_config(config.train["inherit"]).train + for key, value in inherit_config.items(): + if key not in config.train: + config.train[key] = value + return edict(config) + + +def update_model_config(config, mode, model_name, model_version=None, strict=False): + model_config = get_model_config(model_name, model_version) + if model_config is not None: + config = {**config, ** + flatten({**model_config.model, **model_config[mode]})} + elif strict: + raise ValueError(f"Config file for model {model_name} not found.") + return config + + +def check_choices(name, value, choices): + # return # No checks in dev branch + if value not in choices: + raise ValueError(f"{name} {value} not in supported choices {choices}") + + +KEYS_TYPE_BOOL = ["use_amp", "distributed", "use_shared_dict", "same_lr", "aug", "three_phase", + "prefetch", "cycle_momentum"] # Casting is not necessary as their int casted values in config are 0 or 1 + + +def get_config(model_name, mode='train', dataset=None, **overwrite_kwargs): + """Main entry point to get the config for the model. + + Args: + model_name (str): name of the desired model. + mode (str, optional): "train" or "infer". Defaults to 'train'. + dataset (str, optional): If specified, the corresponding dataset configuration is loaded as well. Defaults to None. + + Keyword Args: key-value pairs of arguments to overwrite the default config. + + The order of precedence for overwriting the config is (Higher precedence first): + # 1. overwrite_kwargs + # 2. "config_version": Config file version if specified in overwrite_kwargs. The corresponding config loaded is config_{model_name}_{config_version}.json + # 3. "version_name": Default Model version specific config specified in overwrite_kwargs. The corresponding config loaded is config_{model_name}_{version_name}.json + # 4. common_config: Default config for all models specified in COMMON_CONFIG + + Returns: + easydict: The config dictionary for the model. + """ + + + check_choices("Model", model_name, ["zoedepth", "zoedepth_nk"]) + check_choices("Mode", mode, ["train", "infer", "eval"]) + if mode == "train": + check_choices("Dataset", dataset, ["nyu", "kitti", "mix", None]) + + config = flatten({**COMMON_CONFIG, **COMMON_TRAINING_CONFIG}) + config = update_model_config(config, mode, model_name) + + # update with model version specific config + version_name = overwrite_kwargs.get("version_name", config["version_name"]) + config = update_model_config(config, mode, model_name, version_name) + + # update with config version if specified + config_version = overwrite_kwargs.get("config_version", None) + if config_version is not None: + print("Overwriting config with config_version", config_version) + config = update_model_config(config, mode, model_name, config_version) + + # update with overwrite_kwargs + # Combined args are useful for hyperparameter search + overwrite_kwargs = split_combined_args(overwrite_kwargs) + config = {**config, **overwrite_kwargs} + + # Casting to bool # TODO: Not necessary. Remove and test + for key in KEYS_TYPE_BOOL: + if key in config: + config[key] = bool(config[key]) + + # Model specific post processing of config + parse_list(config, "n_attractors") + + # adjust n_bins for each bin configuration if bin_conf is given and n_bins is passed in overwrite_kwargs + if 'bin_conf' in config and 'n_bins' in overwrite_kwargs: + bin_conf = config['bin_conf'] # list of dicts + n_bins = overwrite_kwargs['n_bins'] + new_bin_conf = [] + for conf in bin_conf: + conf['n_bins'] = n_bins + new_bin_conf.append(conf) + config['bin_conf'] = new_bin_conf + + if mode == "train": + orig_dataset = dataset + if dataset == "mix": + dataset = 'nyu' # Use nyu as default for mix. Dataset config is changed accordingly while loading the dataloader + if dataset is not None: + config['project'] = f"MonoDepth3-{orig_dataset}" # Set project for wandb + + if dataset is not None: + config['dataset'] = dataset + config = {**DATASETS_CONFIG[dataset], **config} + + + config['model'] = model_name + typed_config = {k: infer_type(v) for k, v in config.items()} + # add hostname to config + config['hostname'] = platform.node() + return edict(typed_config) + + +def change_dataset(config, new_dataset): + config.update(DATASETS_CONFIG[new_dataset]) + return config diff --git a/CCEdit-main/src/controlnet11/annotator/zoe/zoedepth/utils/easydict/__init__.py b/CCEdit-main/src/controlnet11/annotator/zoe/zoedepth/utils/easydict/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..15928179b0182c6045d98bc0a7be1c6ca45f675e --- /dev/null +++ b/CCEdit-main/src/controlnet11/annotator/zoe/zoedepth/utils/easydict/__init__.py @@ -0,0 +1,158 @@ +""" +EasyDict +Copy/pasted from https://github.com/makinacorpus/easydict +Original author: Mathieu Leplatre +""" + +class EasyDict(dict): + """ + Get attributes + + >>> d = EasyDict({'foo':3}) + >>> d['foo'] + 3 + >>> d.foo + 3 + >>> d.bar + Traceback (most recent call last): + ... + AttributeError: 'EasyDict' object has no attribute 'bar' + + Works recursively + + >>> d = EasyDict({'foo':3, 'bar':{'x':1, 'y':2}}) + >>> isinstance(d.bar, dict) + True + >>> d.bar.x + 1 + + Bullet-proof + + >>> EasyDict({}) + {} + >>> EasyDict(d={}) + {} + >>> EasyDict(None) + {} + >>> d = {'a': 1} + >>> EasyDict(**d) + {'a': 1} + >>> EasyDict((('a', 1), ('b', 2))) + {'a': 1, 'b': 2} + + Set attributes + + >>> d = EasyDict() + >>> d.foo = 3 + >>> d.foo + 3 + >>> d.bar = {'prop': 'value'} + >>> d.bar.prop + 'value' + >>> d + {'foo': 3, 'bar': {'prop': 'value'}} + >>> d.bar.prop = 'newer' + >>> d.bar.prop + 'newer' + + + Values extraction + + >>> d = EasyDict({'foo':0, 'bar':[{'x':1, 'y':2}, {'x':3, 'y':4}]}) + >>> isinstance(d.bar, list) + True + >>> from operator import attrgetter + >>> list(map(attrgetter('x'), d.bar)) + [1, 3] + >>> list(map(attrgetter('y'), d.bar)) + [2, 4] + >>> d = EasyDict() + >>> list(d.keys()) + [] + >>> d = EasyDict(foo=3, bar=dict(x=1, y=2)) + >>> d.foo + 3 + >>> d.bar.x + 1 + + Still like a dict though + + >>> o = EasyDict({'clean':True}) + >>> list(o.items()) + [('clean', True)] + + And like a class + + >>> class Flower(EasyDict): + ... power = 1 + ... + >>> f = Flower() + >>> f.power + 1 + >>> f = Flower({'height': 12}) + >>> f.height + 12 + >>> f['power'] + 1 + >>> sorted(f.keys()) + ['height', 'power'] + + update and pop items + >>> d = EasyDict(a=1, b='2') + >>> e = EasyDict(c=3.0, a=9.0) + >>> d.update(e) + >>> d.c + 3.0 + >>> d['c'] + 3.0 + >>> d.get('c') + 3.0 + >>> d.update(a=4, b=4) + >>> d.b + 4 + >>> d.pop('a') + 4 + >>> d.a + Traceback (most recent call last): + ... + AttributeError: 'EasyDict' object has no attribute 'a' + """ + def __init__(self, d=None, **kwargs): + if d is None: + d = {} + else: + d = dict(d) + if kwargs: + d.update(**kwargs) + for k, v in d.items(): + setattr(self, k, v) + # Class attributes + for k in self.__class__.__dict__.keys(): + if not (k.startswith('__') and k.endswith('__')) and not k in ('update', 'pop'): + setattr(self, k, getattr(self, k)) + + def __setattr__(self, name, value): + if isinstance(value, (list, tuple)): + value = [self.__class__(x) + if isinstance(x, dict) else x for x in value] + elif isinstance(value, dict) and not isinstance(value, self.__class__): + value = self.__class__(value) + super(EasyDict, self).__setattr__(name, value) + super(EasyDict, self).__setitem__(name, value) + + __setitem__ = __setattr__ + + def update(self, e=None, **f): + d = e or dict() + d.update(f) + for k in d: + setattr(self, k, d[k]) + + def pop(self, k, d=None): + delattr(self, k) + return super(EasyDict, self).pop(k, d) + + +if __name__ == "__main__": + import doctest + doctest.testmod() \ No newline at end of file diff --git a/CCEdit-main/src/controlnet11/annotator/zoe/zoedepth/utils/easydict/__pycache__/__init__.cpython-39.pyc b/CCEdit-main/src/controlnet11/annotator/zoe/zoedepth/utils/easydict/__pycache__/__init__.cpython-39.pyc new file mode 100644 index 0000000000000000000000000000000000000000..0f643047e0cac6a684e0fb8d02ab2eb0e0d2c602 Binary files /dev/null and b/CCEdit-main/src/controlnet11/annotator/zoe/zoedepth/utils/easydict/__pycache__/__init__.cpython-39.pyc differ diff --git a/CCEdit-main/src/controlnet11/annotator/zoe/zoedepth/utils/geometry.py b/CCEdit-main/src/controlnet11/annotator/zoe/zoedepth/utils/geometry.py new file mode 100644 index 0000000000000000000000000000000000000000..e3da8c75b5a8e39b4b58a4dcd827b84d79b9115c --- /dev/null +++ b/CCEdit-main/src/controlnet11/annotator/zoe/zoedepth/utils/geometry.py @@ -0,0 +1,98 @@ +# MIT License + +# Copyright (c) 2022 Intelligent Systems Lab Org + +# Permission is hereby granted, free of charge, to any person obtaining a copy +# of this software and associated documentation files (the "Software"), to deal +# in the Software without restriction, including without limitation the rights +# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell +# copies of the Software, and to permit persons to whom the Software is +# furnished to do so, subject to the following conditions: + +# The above copyright notice and this permission notice shall be included in all +# copies or substantial portions of the Software. + +# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE +# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER +# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, +# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE +# SOFTWARE. + +# File author: Shariq Farooq Bhat + +import numpy as np + +def get_intrinsics(H,W): + """ + Intrinsics for a pinhole camera model. + Assume fov of 55 degrees and central principal point. + """ + f = 0.5 * W / np.tan(0.5 * 55 * np.pi / 180.0) + cx = 0.5 * W + cy = 0.5 * H + return np.array([[f, 0, cx], + [0, f, cy], + [0, 0, 1]]) + +def depth_to_points(depth, R=None, t=None): + + K = get_intrinsics(depth.shape[1], depth.shape[2]) + Kinv = np.linalg.inv(K) + if R is None: + R = np.eye(3) + if t is None: + t = np.zeros(3) + + # M converts from your coordinate to PyTorch3D's coordinate system + M = np.eye(3) + M[0, 0] = -1.0 + M[1, 1] = -1.0 + + height, width = depth.shape[1:3] + + x = np.arange(width) + y = np.arange(height) + coord = np.stack(np.meshgrid(x, y), -1) + coord = np.concatenate((coord, np.ones_like(coord)[:, :, [0]]), -1) # z=1 + coord = coord.astype(np.float32) + # coord = torch.as_tensor(coord, dtype=torch.float32, device=device) + coord = coord[None] # bs, h, w, 3 + + D = depth[:, :, :, None, None] + # print(D.shape, Kinv[None, None, None, ...].shape, coord[:, :, :, :, None].shape ) + pts3D_1 = D * Kinv[None, None, None, ...] @ coord[:, :, :, :, None] + # pts3D_1 live in your coordinate system. Convert them to Py3D's + pts3D_1 = M[None, None, None, ...] @ pts3D_1 + # from reference to targe tviewpoint + pts3D_2 = R[None, None, None, ...] @ pts3D_1 + t[None, None, None, :, None] + # pts3D_2 = pts3D_1 + # depth_2 = pts3D_2[:, :, :, 2, :] # b,1,h,w + return pts3D_2[:, :, :, :3, 0][0] + + +def create_triangles(h, w, mask=None): + """ + Reference: https://github.com/google-research/google-research/blob/e96197de06613f1b027d20328e06d69829fa5a89/infinite_nature/render_utils.py#L68 + Creates mesh triangle indices from a given pixel grid size. + This function is not and need not be differentiable as triangle indices are + fixed. + Args: + h: (int) denoting the height of the image. + w: (int) denoting the width of the image. + Returns: + triangles: 2D numpy array of indices (int) with shape (2(W-1)(H-1) x 3) + """ + x, y = np.meshgrid(range(w - 1), range(h - 1)) + tl = y * w + x + tr = y * w + x + 1 + bl = (y + 1) * w + x + br = (y + 1) * w + x + 1 + triangles = np.array([tl, bl, tr, br, tr, bl]) + triangles = np.transpose(triangles, (1, 2, 0)).reshape( + ((w - 1) * (h - 1) * 2, 3)) + if mask is not None: + mask = mask.reshape(-1) + triangles = triangles[mask[triangles].all(1)] + return triangles diff --git a/CCEdit-main/src/controlnet11/annotator/zoe/zoedepth/utils/misc.py b/CCEdit-main/src/controlnet11/annotator/zoe/zoedepth/utils/misc.py new file mode 100644 index 0000000000000000000000000000000000000000..4bbe403d3669829eecdf658458c76aa5e87e2b33 --- /dev/null +++ b/CCEdit-main/src/controlnet11/annotator/zoe/zoedepth/utils/misc.py @@ -0,0 +1,368 @@ +# MIT License + +# Copyright (c) 2022 Intelligent Systems Lab Org + +# Permission is hereby granted, free of charge, to any person obtaining a copy +# of this software and associated documentation files (the "Software"), to deal +# in the Software without restriction, including without limitation the rights +# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell +# copies of the Software, and to permit persons to whom the Software is +# furnished to do so, subject to the following conditions: + +# The above copyright notice and this permission notice shall be included in all +# copies or substantial portions of the Software. + +# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE +# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER +# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, +# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE +# SOFTWARE. + +# File author: Shariq Farooq Bhat + +"""Miscellaneous utility functions.""" + +from scipy import ndimage + +import base64 +import math +import re +from io import BytesIO + +import matplotlib +import matplotlib.cm +import numpy as np +import requests +import torch +import torch.distributed as dist +import torch.nn +import torch.nn as nn +import torch.utils.data.distributed +from PIL import Image +from torchvision.transforms import ToTensor + + +class RunningAverage: + def __init__(self): + self.avg = 0 + self.count = 0 + + def append(self, value): + self.avg = (value + self.count * self.avg) / (self.count + 1) + self.count += 1 + + def get_value(self): + return self.avg + + +def denormalize(x): + """Reverses the imagenet normalization applied to the input. + + Args: + x (torch.Tensor - shape(N,3,H,W)): input tensor + + Returns: + torch.Tensor - shape(N,3,H,W): Denormalized input + """ + mean = torch.Tensor([0.485, 0.456, 0.406]).view(1, 3, 1, 1).to(x.device) + std = torch.Tensor([0.229, 0.224, 0.225]).view(1, 3, 1, 1).to(x.device) + return x * std + mean + + +class RunningAverageDict: + """A dictionary of running averages.""" + def __init__(self): + self._dict = None + + def update(self, new_dict): + if new_dict is None: + return + + if self._dict is None: + self._dict = dict() + for key, value in new_dict.items(): + self._dict[key] = RunningAverage() + + for key, value in new_dict.items(): + self._dict[key].append(value) + + def get_value(self): + if self._dict is None: + return None + return {key: value.get_value() for key, value in self._dict.items()} + + +def colorize(value, vmin=None, vmax=None, cmap='gray_r', invalid_val=-99, invalid_mask=None, background_color=(128, 128, 128, 255), gamma_corrected=False, value_transform=None): + """Converts a depth map to a color image. + + Args: + value (torch.Tensor, numpy.ndarry): Input depth map. Shape: (H, W) or (1, H, W) or (1, 1, H, W). All singular dimensions are squeezed + vmin (float, optional): vmin-valued entries are mapped to start color of cmap. If None, value.min() is used. Defaults to None. + vmax (float, optional): vmax-valued entries are mapped to end color of cmap. If None, value.max() is used. Defaults to None. + cmap (str, optional): matplotlib colormap to use. Defaults to 'magma_r'. + invalid_val (int, optional): Specifies value of invalid pixels that should be colored as 'background_color'. Defaults to -99. + invalid_mask (numpy.ndarray, optional): Boolean mask for invalid regions. Defaults to None. + background_color (tuple[int], optional): 4-tuple RGB color to give to invalid pixels. Defaults to (128, 128, 128, 255). + gamma_corrected (bool, optional): Apply gamma correction to colored image. Defaults to False. + value_transform (Callable, optional): Apply transform function to valid pixels before coloring. Defaults to None. + + Returns: + numpy.ndarray, dtype - uint8: Colored depth map. Shape: (H, W, 4) + """ + if isinstance(value, torch.Tensor): + value = value.detach().cpu().numpy() + + value = value.squeeze() + if invalid_mask is None: + invalid_mask = value == invalid_val + mask = np.logical_not(invalid_mask) + + # normalize + vmin = np.percentile(value[mask],2) if vmin is None else vmin + vmax = np.percentile(value[mask],85) if vmax is None else vmax + if vmin != vmax: + value = (value - vmin) / (vmax - vmin) # vmin..vmax + else: + # Avoid 0-division + value = value * 0. + + # squeeze last dim if it exists + # grey out the invalid values + + value[invalid_mask] = np.nan + cmapper = matplotlib.cm.get_cmap(cmap) + if value_transform: + value = value_transform(value) + # value = value / value.max() + value = cmapper(value, bytes=True) # (nxmx4) + + # img = value[:, :, :] + img = value[...] + img[invalid_mask] = background_color + + # return img.transpose((2, 0, 1)) + if gamma_corrected: + # gamma correction + img = img / 255 + img = np.power(img, 2.2) + img = img * 255 + img = img.astype(np.uint8) + return img + + +def count_parameters(model, include_all=False): + return sum(p.numel() for p in model.parameters() if p.requires_grad or include_all) + + +def compute_errors(gt, pred): + """Compute metrics for 'pred' compared to 'gt' + + Args: + gt (numpy.ndarray): Ground truth values + pred (numpy.ndarray): Predicted values + + gt.shape should be equal to pred.shape + + Returns: + dict: Dictionary containing the following metrics: + 'a1': Delta1 accuracy: Fraction of pixels that are within a scale factor of 1.25 + 'a2': Delta2 accuracy: Fraction of pixels that are within a scale factor of 1.25^2 + 'a3': Delta3 accuracy: Fraction of pixels that are within a scale factor of 1.25^3 + 'abs_rel': Absolute relative error + 'rmse': Root mean squared error + 'log_10': Absolute log10 error + 'sq_rel': Squared relative error + 'rmse_log': Root mean squared error on the log scale + 'silog': Scale invariant log error + """ + thresh = np.maximum((gt / pred), (pred / gt)) + a1 = (thresh < 1.25).mean() + a2 = (thresh < 1.25 ** 2).mean() + a3 = (thresh < 1.25 ** 3).mean() + + abs_rel = np.mean(np.abs(gt - pred) / gt) + sq_rel = np.mean(((gt - pred) ** 2) / gt) + + rmse = (gt - pred) ** 2 + rmse = np.sqrt(rmse.mean()) + + rmse_log = (np.log(gt) - np.log(pred)) ** 2 + rmse_log = np.sqrt(rmse_log.mean()) + + err = np.log(pred) - np.log(gt) + silog = np.sqrt(np.mean(err ** 2) - np.mean(err) ** 2) * 100 + + log_10 = (np.abs(np.log10(gt) - np.log10(pred))).mean() + return dict(a1=a1, a2=a2, a3=a3, abs_rel=abs_rel, rmse=rmse, log_10=log_10, rmse_log=rmse_log, + silog=silog, sq_rel=sq_rel) + + +def compute_metrics(gt, pred, interpolate=True, garg_crop=False, eigen_crop=True, dataset='nyu', min_depth_eval=0.1, max_depth_eval=10, **kwargs): + """Compute metrics of predicted depth maps. Applies cropping and masking as necessary or specified via arguments. Refer to compute_errors for more details on metrics. + """ + if 'config' in kwargs: + config = kwargs['config'] + garg_crop = config.garg_crop + eigen_crop = config.eigen_crop + min_depth_eval = config.min_depth_eval + max_depth_eval = config.max_depth_eval + + if gt.shape[-2:] != pred.shape[-2:] and interpolate: + pred = nn.functional.interpolate( + pred, gt.shape[-2:], mode='bilinear', align_corners=True) + + pred = pred.squeeze().cpu().numpy() + pred[pred < min_depth_eval] = min_depth_eval + pred[pred > max_depth_eval] = max_depth_eval + pred[np.isinf(pred)] = max_depth_eval + pred[np.isnan(pred)] = min_depth_eval + + gt_depth = gt.squeeze().cpu().numpy() + valid_mask = np.logical_and( + gt_depth > min_depth_eval, gt_depth < max_depth_eval) + + if garg_crop or eigen_crop: + gt_height, gt_width = gt_depth.shape + eval_mask = np.zeros(valid_mask.shape) + + if garg_crop: + eval_mask[int(0.40810811 * gt_height):int(0.99189189 * gt_height), + int(0.03594771 * gt_width):int(0.96405229 * gt_width)] = 1 + + elif eigen_crop: + # print("-"*10, " EIGEN CROP ", "-"*10) + if dataset == 'kitti': + eval_mask[int(0.3324324 * gt_height):int(0.91351351 * gt_height), + int(0.0359477 * gt_width):int(0.96405229 * gt_width)] = 1 + else: + # assert gt_depth.shape == (480, 640), "Error: Eigen crop is currently only valid for (480, 640) images" + eval_mask[45:471, 41:601] = 1 + else: + eval_mask = np.ones(valid_mask.shape) + valid_mask = np.logical_and(valid_mask, eval_mask) + return compute_errors(gt_depth[valid_mask], pred[valid_mask]) + + +#################################### Model uilts ################################################ + + +def parallelize(config, model, find_unused_parameters=True): + + if config.gpu is not None: + torch.cuda.set_device(config.gpu) + model = model.cuda(config.gpu) + + config.multigpu = False + if config.distributed: + # Use DDP + config.multigpu = True + config.rank = config.rank * config.ngpus_per_node + config.gpu + dist.init_process_group(backend=config.dist_backend, init_method=config.dist_url, + world_size=config.world_size, rank=config.rank) + config.batch_size = int(config.batch_size / config.ngpus_per_node) + # config.batch_size = 8 + config.workers = int( + (config.num_workers + config.ngpus_per_node - 1) / config.ngpus_per_node) + print("Device", config.gpu, "Rank", config.rank, "batch size", + config.batch_size, "Workers", config.workers) + torch.cuda.set_device(config.gpu) + model = nn.SyncBatchNorm.convert_sync_batchnorm(model) + model = model.cuda(config.gpu) + model = torch.nn.parallel.DistributedDataParallel(model, device_ids=[config.gpu], output_device=config.gpu, + find_unused_parameters=find_unused_parameters) + + elif config.gpu is None: + # Use DP + config.multigpu = True + model = model.cuda() + model = torch.nn.DataParallel(model) + + return model + + +################################################################################################# + + +##################################################################################################### + + +class colors: + '''Colors class: + Reset all colors with colors.reset + Two subclasses fg for foreground and bg for background. + Use as colors.subclass.colorname. + i.e. colors.fg.red or colors.bg.green + Also, the generic bold, disable, underline, reverse, strikethrough, + and invisible work with the main class + i.e. colors.bold + ''' + reset = '\033[0m' + bold = '\033[01m' + disable = '\033[02m' + underline = '\033[04m' + reverse = '\033[07m' + strikethrough = '\033[09m' + invisible = '\033[08m' + + class fg: + black = '\033[30m' + red = '\033[31m' + green = '\033[32m' + orange = '\033[33m' + blue = '\033[34m' + purple = '\033[35m' + cyan = '\033[36m' + lightgrey = '\033[37m' + darkgrey = '\033[90m' + lightred = '\033[91m' + lightgreen = '\033[92m' + yellow = '\033[93m' + lightblue = '\033[94m' + pink = '\033[95m' + lightcyan = '\033[96m' + + class bg: + black = '\033[40m' + red = '\033[41m' + green = '\033[42m' + orange = '\033[43m' + blue = '\033[44m' + purple = '\033[45m' + cyan = '\033[46m' + lightgrey = '\033[47m' + + +def printc(text, color): + print(f"{color}{text}{colors.reset}") + +############################################ + +def get_image_from_url(url): + response = requests.get(url) + img = Image.open(BytesIO(response.content)).convert("RGB") + return img + +def url_to_torch(url, size=(384, 384)): + img = get_image_from_url(url) + img = img.resize(size, Image.ANTIALIAS) + img = torch.from_numpy(np.asarray(img)).float() + img = img.permute(2, 0, 1) + img.div_(255) + return img + +def pil_to_batched_tensor(img): + return ToTensor()(img).unsqueeze(0) + +def save_raw_16bit(depth, fpath="raw.png"): + if isinstance(depth, torch.Tensor): + depth = depth.squeeze().cpu().numpy() + + assert isinstance(depth, np.ndarray), "Depth must be a torch tensor or numpy array" + assert depth.ndim == 2, "Depth must be 2D" + depth = depth * 256 # scale for 16-bit png + depth = depth.astype(np.uint16) + depth = Image.fromarray(depth) + depth.save(fpath) + print("Saved raw depth to", fpath) \ No newline at end of file diff --git a/FateZero-main/data/attribute/cat_tiger_leopard_grass/00007.png b/FateZero-main/data/attribute/cat_tiger_leopard_grass/00007.png new file mode 100644 index 0000000000000000000000000000000000000000..7f1acc6abba41320958cd4577139062c642ab441 --- /dev/null +++ b/FateZero-main/data/attribute/cat_tiger_leopard_grass/00007.png @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:8119cf1f58670ec2cfc465389cd5e674a395768d35a1fa5e61386183842d8032 +size 208835 diff --git a/FateZero-main/data/attribute/fox_wolf_snow/00005.png b/FateZero-main/data/attribute/fox_wolf_snow/00005.png new file mode 100644 index 0000000000000000000000000000000000000000..b7adf53bd496ce76a3e8ea905fd6530645b18f66 --- /dev/null +++ b/FateZero-main/data/attribute/fox_wolf_snow/00005.png @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:de2c81bbe8bc1a97464890d92a5c368a9c50c6404582da968a0ce50271cb16a4 +size 205507