From 6f7f6031d194b3b936a308a63a1eb4030d75c23e Mon Sep 17 00:00:00 2001 From: TraceLumen Date: Sat, 12 Sep 2026 03:29:38 +0300 Subject: [PATCH] =?UTF-8?q?init=20+=20inference.py=20=D0=BF=D0=B0=D1=82?= =?UTF-8?q?=D1=87?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- .gitignore | 9 + LICENSE | 21 + README.md | 69 ++ data/MBD/MBD.py | 110 +++ data/MBD/MBD_utils.py | 291 +++++++ data/MBD/infer.py | 151 ++++ data/MBD/model/__init__.py | 50 ++ data/MBD/model/cbam.py | 95 +++ data/MBD/model/deep_lab_model/__init__.py | 0 data/MBD/model/deep_lab_model/aspp.py | 95 +++ .../model/deep_lab_model/backbone/__init__.py | 13 + data/MBD/model/deep_lab_model/backbone/drn.py | 402 ++++++++++ .../deep_lab_model/backbone/mobilenet.py | 151 ++++ .../model/deep_lab_model/backbone/resnet.py | 170 ++++ .../model/deep_lab_model/backbone/xception.py | 288 +++++++ data/MBD/model/deep_lab_model/decoder.py | 59 ++ data/MBD/model/deep_lab_model/deeplab.py | 81 ++ 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create mode 100644 data/MBD/tps_grid_gen.py create mode 100644 data/MBD/utils.py create mode 100644 data/README.md create mode 100644 data/images/1.png create mode 100644 data/images/2.png create mode 100644 data/images/3.png create mode 100644 data/images/4.png create mode 100644 data/images/5.png create mode 100644 data/images/6.png create mode 100644 data/preprocess/crop_merge_image.py create mode 100644 data/preprocess/sauvola_binarize.py create mode 100644 data/preprocess/shadow_extraction.py create mode 100644 eval.py create mode 100644 images/motivation.jpg create mode 100644 inference.py create mode 100644 loaders/docres_loader.py create mode 100644 models/restormer_arch.py create mode 100644 requirements.txt create mode 100644 start_train.sh create mode 100644 train.py create mode 100644 utils.py diff --git a/.gitignore b/.gitignore new file mode 100644 index 0000000..5c0d7e6 --- /dev/null +++ b/.gitignore @@ -0,0 +1,9 @@ +__pycache__/ +**/__pycache__/ +*.pyc +*.pyo +*.egg-info/ +dist/ +build/ +checkpoints/ +*.pkl diff --git a/LICENSE b/LICENSE new file mode 100644 index 0000000..c479874 --- /dev/null +++ b/LICENSE @@ -0,0 +1,21 @@ +MIT License + +Copyright (c) 2024 Jiaxin Zhang + +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. diff --git a/README.md b/README.md new file mode 100644 index 0000000..4038b7b --- /dev/null +++ b/README.md @@ -0,0 +1,69 @@ + +
+ +# DocRes: A Generalist Model Toward Unifying Document Image Restoration Tasks + +[![Open in Spaces](https://huggingface.co/datasets/huggingface/badges/resolve/main/open-in-hf-spaces-sm.svg)](https://huggingface.co/spaces/qubvel-hf/documents-restoration) + +
+ +

+ +

+ +This is the official implementation of our paper [DocRes: A Generalist Model Toward Unifying Document Image Restoration Tasks](https://arxiv.org/abs/2405.04408). + +## News +🔥 [2025.7] Our paper ["Aesthetics is Cheap, Show me the Text: An Empirical Evaluation of State-of-the-Art Generative Models for OCR"](https://arxiv.org/abs/2507.15085), which conducts a comprehensive evaluation of SOTA generative models has been online at arXiv. 🔥 + +🔥 [2025.6] Beyond GPT-4o, we evaluate more SOTA generative models' image generation abilities in various document processing tasks. Check [here](https://github.com/NiceRingNode/Awesome-Image-Generators-for-OCR-Image-Generation-and-Editing)! 🔥 + +🎉 [2025.5] We evaluate the image generation ability of GPT-4o, including various document processing tasks. Check [here](https://github.com/NiceRingNode/Awesome-Image-Generators-for-OCR-Image-Generation-and-Editing)! 🔥 + +🎉 [2025.2] Our new work [LGGPT](https://github.com/NiceRingNode/LGGPT) has been accepted to IJCV 2025, an LLM that unifies versatile layout generation tasks! Welcome to follow! + +🔥 A comprehensive [Recommendation for Document Image Processing](https://github.com/ZZZHANG-jx/Recommendations-Document-Image-Processing) is available. + + +## Inference +1. Put MBD model weights [mbd.pkl](https://1drv.ms/f/s!Ak15mSdV3Wy4iahoKckhDPVP5e2Czw?e=iClwdK) to `./data/MBD/checkpoint/` +2. Put DocRes model weights [docres.pkl](https://1drv.ms/f/s!Ak15mSdV3Wy4iahoKckhDPVP5e2Czw?e=iClwdK) to `./checkpoints/` +3. Run the following script and the results will be saved in `./restorted/`. We have provided some distorted examples in `./input/`. +```bash +python inference.py --im_path ./input/for_dewarping.png --task dewarping --save_dtsprompt 1 +``` + +- `--im_path`: the path of input document image +- `--task`: task that need to be executed, it must be one of _dewarping_, _deshadowing_, _appearance_, _deblurring_, _binarization_, or _end2end_ +- `--save_dtsprompt`: whether to save the DTSPrompt + +## Evaluation + +1. Dataset preparation, see [dataset instruction](./data/README.md) +2. Put MBD model weights [mbd.pkl](https://1drv.ms/f/s!Ak15mSdV3Wy4iahoKckhDPVP5e2Czw?e=iClwdK) to `data/MBD/checkpoint/` +3. Put DocRes model weights [docres.pkl](https://1drv.ms/f/s!Ak15mSdV3Wy4iahoKckhDPVP5e2Czw?e=iClwdK) to `./checkpoints/` +2. Run the following script +```bash +python eval.py --dataset realdae +``` +- `--dataset`: dataset that need to be evaluated, it can be set as _dir300_, _kligler_, _jung_, _osr_, _docunet\_docaligner_, _realdae_, _tdd_, and _dibco18_. + +## Training +1. Dataset preparation, see [dataset instruction](./data/README.md) +2. Specify the datasets_setting within `train.py` based on your dataset path and experimental setting. +3. Run the following script +```bash +bash start_train.sh +``` + + +## Citation +``` +@inproceedings{zhangdocres2024, +Author = {Jiaxin Zhang, Dezhi Peng, Chongyu Liu , Peirong Zhang and Lianwen Jin}, +Booktitle = {In Proceedings of the IEEE/CV Conference on Computer Vision and Pattern Recognition}, +Title = {{DocRes: A Generalist Model Toward Unifying Document Image Restoration Tasks}}, +Year = {2024}} +``` +## ⭐ Star Rising +[![Star Rising](https://api.star-history.com/svg?repos=ZZZHANG-jx/DocRes&type=Timeline)](https://star-history.com/#ZZZHANG-jx/DocRes&Timeline) diff --git a/data/MBD/MBD.py b/data/MBD/MBD.py new file mode 100644 index 0000000..a41c8c2 --- /dev/null +++ b/data/MBD/MBD.py @@ -0,0 +1,110 @@ +import cv2 +import numpy as np +import MBD_utils +import torch +import torch.nn.functional as F + + +def mask_base_dewarper(image,mask): + ''' + input: + image -> ndarray HxWx3 uint8 + mask -> ndarray HxW uint8 + return + dewarped -> ndarray HxWx3 uint8 + grid (optional) -> ndarray HxWx2 -1~1 + ''' + + ## get contours + # _, contours, hierarchy = cv2.findContours(mask,cv2.RETR_EXTERNAL,cv2.CHAIN_APPROX_NONE) ## cv2.__version__ == 3.x + contours,hierarchy = cv2.findContours(mask,cv2.RETR_EXTERNAL,method=cv2.CHAIN_APPROX_SIMPLE) ## cv2.__version__ == 4.x + + ## get biggest contours and four corners based on Douglas-Peucker algorithm + four_corners, maxArea, contour= MBD_utils.DP_algorithm(contours) + four_corners = MBD_utils.reorder(four_corners) + + ## reserve biggest contours and remove other noisy contours + new_mask = np.zeros_like(mask) + new_mask = cv2.drawContours(new_mask,[contour],-1,255,cv2.FILLED) + + ## obtain middle points + # ratios = [0.25,0.5,0.75] # ratios = [0.125,0.25,0.375,0.5,0.625,0.75,0.875] + ratios = [0.25,0.5,0.75] + # ratios = [0.0625,0.125,0.1875,0.25,0.3125,0.375,0.4475,0.5,0.5625,0.625,0.06875,0.75,0.8125,0.875,0.9375] + middle = MBD_utils.findMiddle(corners=four_corners,mask=new_mask,points=ratios) + + ## all points + source_points = np.concatenate((four_corners,middle),axis=0) ## all_point = four_corners(topleft,topright,bottom)+top+bottom+left+right + + ## target points + h,w = image.shape[:2] + padding = 0 + target_points = [[padding, padding],[w-padding, padding], [padding, h-padding],[w-padding, h-padding]] + for ratio in ratios: + target_points.append([int((w-2*padding)*ratio)+padding,padding]) + for ratio in ratios: + target_points.append([int((w-2*padding)*ratio)+padding,h-padding]) + for ratio in ratios: + target_points.append([padding,int((h-2*padding)*ratio)+padding]) + for ratio in ratios: + target_points.append([w-padding,int((h-2*padding)*ratio)+padding]) + + ## dewarp base on cv2 + # pts1 = np.float32(source_points) + # pts2 = np.float32(target_points) + # tps = cv2.createThinPlateSplineShapeTransformer() + # matches = [] + # N = pts1.shape[0] + # for i in range(0,N): + # matches.append(cv2.DMatch(i,i,0)) + # pts1 = pts1.reshape(1,-1,2) + # pts2 = pts2.reshape(1,-1,2) + # tps.estimateTransformation(pts2,pts1,matches) + # dewarped = tps.warpImage(image) + + ## dewarp base on generated grid + source_points = source_points.reshape(-1,2)/np.array([image.shape[:2][::-1]]).reshape(1,2) + source_points = torch.from_numpy(source_points).float().cuda() + source_points = source_points.unsqueeze(0) + source_points = (source_points-0.5)*2 + target_points = np.asarray(target_points).reshape(-1,2)/np.array([image.shape[:2][::-1]]).reshape(1,2) + target_points = torch.from_numpy(target_points).float() + target_points = (target_points-0.5)*2 + + model = MBD_utils.TPSGridGen(target_height=256,target_width=256,target_control_points=target_points) + model = model.cuda() + grid = model(source_points).view(-1,256,256,2).permute(0,3,1,2) + grid = F.interpolate(grid,(h,w),mode='bilinear').permute(0,2,3,1) + dewarped = MBD_utils.torch2cvimg(F.grid_sample(MBD_utils.cvimg2torch(image).cuda(),grid))[0] + return dewarped,grid[0].cpu().numpy() + +def mask_base_cropper(image,mask): + ''' + input: + image -> ndarray HxWx3 uint8 + mask -> ndarray HxW uint8 + return + dewarped -> ndarray HxWx3 uint8 + grid (optional) -> ndarray HxWx2 -1~1 + ''' + + ## get contours + _, contours, hierarchy = cv2.findContours(mask,cv2.RETR_EXTERNAL,cv2.CHAIN_APPROX_NONE) ## cv2.__version__ == 3.x + # contours,hierarchy = cv2.findContours(mask,cv2.RETR_EXTERNAL,method=cv2.CHAIN_APPROX_SIMPLE) ## cv2.__version__ == 4.x + + ## get biggest contours and four corners based on Douglas-Peucker algorithm + four_corners, maxArea, contour= MBD_utils.DP_algorithm(contours) + four_corners = MBD_utils.reorder(four_corners) + + ## reserve biggest contours and remove other noisy contours + new_mask = np.zeros_like(mask) + new_mask = cv2.drawContours(new_mask,[contour],-1,255,cv2.FILLED) + + ## 最小外接矩形 + rect = cv2.minAreaRect(contour) # 得到最小外接矩形的（中心(x,y), (宽,高), 旋转角度） + box = cv2.boxPoints(rect) # cv2.boxPoints(rect) for OpenCV 3.x 获取最小外接矩形的4个顶点坐标 + box = np.int0(box) + box = box.reshape((4,1,2)) + + + diff --git a/data/MBD/MBD_utils.py b/data/MBD/MBD_utils.py new file mode 100644 index 0000000..9761f1b --- /dev/null +++ b/data/MBD/MBD_utils.py @@ -0,0 +1,291 @@ +import cv2 +import numpy as np +import copy +import torch +import torch +import itertools +import torch.nn as nn +from torch.autograd import Function, Variable + +def reorder(myPoints): + myPoints = myPoints.reshape((4, 2)) + myPointsNew = np.zeros((4, 1, 2), dtype=np.int32) + add = myPoints.sum(1) + myPointsNew[0] = myPoints[np.argmin(add)] + myPointsNew[3] =myPoints[np.argmax(add)] + diff = np.diff(myPoints, axis=1) + myPointsNew[1] =myPoints[np.argmin(diff)] + myPointsNew[2] = myPoints[np.argmax(diff)] + return myPointsNew + + +def findMiddle(corners,mask,points=[0.25,0.5,0.75]): + num_middle_points = len(points) + top = [np.array([])]*num_middle_points + bottom = [np.array([])]*num_middle_points + left = [np.array([])]*num_middle_points + right = [np.array([])]*num_middle_points + + center_top = [] + center_bottom = [] + center_left = [] + center_right = [] + + center = (int((corners[0][0][1]+corners[3][0][1])/2),int((corners[0][0][0]+corners[3][0][0])/2)) + for ratio in points: + + center_top.append( (center[0],int(corners[0][0][0]*(1-ratio)+corners[1][0][0]*ratio)) ) + + center_bottom.append( (center[0],int(corners[2][0][0]*(1-ratio)+corners[3][0][0]*ratio)) ) + + center_left.append( (int(corners[0][0][1]*(1-ratio)+corners[2][0][1]*ratio),center[1]) ) + + center_right.append( (int(corners[1][0][1]*(1-ratio)+corners[3][0][1]*ratio),center[1]) ) + + for i in range(0,center[0],1): + for j in range(num_middle_points): + if top[j].size==0: + if mask[i,center_top[j][1]]==255: + top[j] = np.asarray([center_top[j][1],i]) + top[j] = top[j].reshape(1,2) + + for i in range(mask.shape[0]-1,center[0],-1): + for j in range(num_middle_points): + if bottom[j].size==0: + if mask[i,center_bottom[j][1]]==255: + bottom[j] = np.asarray([center_bottom[j][1],i]) + bottom[j] = bottom[j].reshape(1,2) + + for i in range(mask.shape[1]-1,center[1],-1): + for j in range(num_middle_points): + if right[j].size==0: + if mask[center_right[j][0],i]==255: + right[j] = np.asarray([i,center_right[j][0]]) + right[j] = right[j].reshape(1,2) + + for i in range(0,center[1]): + for j in range(num_middle_points): + if left[j].size==0: + if mask[center_left[j][0],i]==255: + left[j] = np.asarray([i,center_left[j][0]]) + left[j] = left[j].reshape(1,2) + + return np.asarray(top+bottom+left+right) + +def DP_algorithmv1(contours): + biggest = np.array([]) + max_area = 0 + step = 0.001 + count = 0 + # while biggest.size==0: + while True: + for i in contours: + # print(i.shape) + area = cv2.contourArea(i) + # print(area,cv2.arcLength(i, True)) + if area > cv2.arcLength(i, True)*10: + peri = cv2.arcLength(i, True) + approx = cv2.approxPolyDP(i, (0.01+step*count) * peri, True) + if area > max_area and len(approx) == 4: + max_area = area + biggest_contours = i + biggest = approx + break + if abs(max_area - cv2.contourArea(biggest))/max_area > 0.3: + biggest = np.array([]) + count += 1 + if count > 200: + break + temp = biggest[0] + return biggest,max_area, biggest_contours + +def DP_algorithm(contours): + biggest = np.array([]) + max_area = 0 + step = 0.001 + count = 0 + + ### largest contours + for i in contours: + area = cv2.contourArea(i) + if area > max_area: + max_area = area + biggest_contours = i + peri = cv2.arcLength(biggest_contours, True) + + ### find four corners + while True: + approx = cv2.approxPolyDP(biggest_contours, (0.01+step*count) * peri, True) + if len(approx) == 4: + biggest = approx + break + # if abs(max_area - cv2.contourArea(biggest))/max_area > 0.2: + # if abs(max_area - cv2.contourArea(biggest))/max_area > 0.4: + # biggest = np.array([]) + count += 1 + if count > 200: + break + return biggest,max_area, biggest_contours + +def drawRectangle(img,biggest,color,thickness): + cv2.line(img, (biggest[0][0][0], biggest[0][0][1]), (biggest[1][0][0], biggest[1][0][1]), color, thickness) + cv2.line(img, (biggest[0][0][0], biggest[0][0][1]), (biggest[2][0][0], biggest[2][0][1]), color, thickness) + cv2.line(img, (biggest[3][0][0], biggest[3][0][1]), (biggest[2][0][0], biggest[2][0][1]), color, thickness) + cv2.line(img, (biggest[3][0][0], biggest[3][0][1]), (biggest[1][0][0], biggest[1][0][1]), color, thickness) + return img + +def minAreaRect(contours,img): + # biggest = np.array([]) + max_area = 0 + for i in contours: + area = cv2.contourArea(i) + if area > max_area: + peri = cv2.arcLength(i, True) + rect = cv2.minAreaRect(i) + points = cv2.boxPoints(rect) + max_area = area + return points + +def cropRectangle(img,biggest): + # print(biggest) + w = np.abs(biggest[0][0][0] - biggest[1][0][0]) + h = np.abs(biggest[0][0][1] - biggest[2][0][1]) + new_img = np.zeros((w,h,img.shape[-1]),dtype=np.uint8) + new_img = img[biggest[0][0][1]:biggest[0][0][1]+h,biggest[0][0][0]:biggest[0][0][0]+w] + return new_img + +def cvimg2torch(img,min=0,max=1): + ''' + input: + im -> ndarray uint8 HxWxC + return + tensor -> torch.tensor BxCxHxW + ''' + if len(img.shape)==2: + img = np.expand_dims(img,axis=-1) + img = img.astype(float) / 255.0 + img = img.transpose(2, 0, 1) # NHWC -> NCHW + img = np.expand_dims(img, 0) + img = torch.from_numpy(img).float() + return img + +def torch2cvimg(tensor,min=0,max=1): + ''' + input: + tensor -> torch.tensor BxCxHxW C can be 1,3 + return + im -> ndarray uint8 HxWxC + ''' + im_list = [] + for i in range(tensor.shape[0]): + im = tensor.detach().cpu().data.numpy()[i] + im = im.transpose(1,2,0) + im = np.clip(im,min,max) + im = ((im-min)/(max-min)*255).astype(np.uint8) + im_list.append(im) + return im_list + + + +class TPSGridGen(nn.Module): + def __init__(self, target_height, target_width, target_control_points): + ''' + target_control_points -> torch.tensor num_pointx2 -1~1 + source_control_points -> torch.tensor batch_size x num_point x 2 -1~1 + return: + grid -> batch_size x hw x 2 -1~1 + ''' + super(TPSGridGen, self).__init__() + assert target_control_points.ndimension() == 2 + assert target_control_points.size(1) == 2 + N = target_control_points.size(0) + self.num_points = N + target_control_points = target_control_points.float() + + # create padded kernel matrix + forward_kernel = torch.zeros(N + 3, N + 3) + target_control_partial_repr = self.compute_partial_repr(target_control_points, target_control_points) + forward_kernel[:N, :N].copy_(target_control_partial_repr) + forward_kernel[:N, -3].fill_(1) + forward_kernel[-3, :N].fill_(1) + forward_kernel[:N, -2:].copy_(target_control_points) + forward_kernel[-2:, :N].copy_(target_control_points.transpose(0, 1)) + # compute inverse matrix + inverse_kernel = torch.inverse(forward_kernel) + + # create target cordinate matrix + HW = target_height * target_width + target_coordinate = list(itertools.product(range(target_height), range(target_width))) + target_coordinate = torch.Tensor(target_coordinate) # HW x 2 + Y, X = target_coordinate.split(1, dim = 1) + Y = Y * 2 / (target_height - 1) - 1 + X = X * 2 / (target_width - 1) - 1 + target_coordinate = torch.cat([X, Y], dim = 1) # convert from (y, x) to (x, y) + target_coordinate_partial_repr = self.compute_partial_repr(target_coordinate.to(target_control_points.device), target_control_points) + target_coordinate_repr = torch.cat([ + target_coordinate_partial_repr, torch.ones(HW, 1), target_coordinate + ], dim = 1) + + # register precomputed matrices + self.register_buffer('inverse_kernel', inverse_kernel) + self.register_buffer('padding_matrix', torch.zeros(3, 2)) + self.register_buffer('target_coordinate_repr', target_coordinate_repr) + + def forward(self, source_control_points): + assert source_control_points.ndimension() == 3 + assert source_control_points.size(1) == self.num_points + assert source_control_points.size(2) == 2 + batch_size = source_control_points.size(0) + + Y = torch.cat([source_control_points, Variable(self.padding_matrix.expand(batch_size, 3, 2))], 1) + mapping_matrix = torch.matmul(Variable(self.inverse_kernel), Y) + source_coordinate = torch.matmul(Variable(self.target_coordinate_repr), mapping_matrix) + return source_coordinate + # phi(x1, x2) = r^2 * log(r), where r = ||x1 - x2||_2 + def compute_partial_repr(self, input_points, control_points): + N = input_points.size(0) + M = control_points.size(0) + pairwise_diff = input_points.view(N, 1, 2) - control_points.view(1, M, 2) + # original implementation, very slow + # pairwise_dist = torch.sum(pairwise_diff ** 2, dim = 2) # square of distance + pairwise_diff_square = pairwise_diff * pairwise_diff + pairwise_dist = pairwise_diff_square[:, :, 0] + pairwise_diff_square[:, :, 1] + repr_matrix = 0.5 * pairwise_dist * torch.log(pairwise_dist) + # fix numerical error for 0 * log(0), substitute all nan with 0 + mask = repr_matrix != repr_matrix + repr_matrix.masked_fill_(mask, 0) + return repr_matrix + + + + + + ### deside wheather further process + # point_area = cv2.contourArea(np.concatenate((biggest_angle[0].reshape(1,1,2),middle[0:3],biggest_angle[1].reshape(1,1,2),middle[9:12],biggest_angle[3].reshape(1,1,2),middle[3:6][::-1],biggest_angle[2].reshape(1,1,2),middle[6:9][::-1]),axis=0)) + #### 最小外接矩形 + # rect = cv2.minAreaRect(contour) # 得到最小外接矩形的（中心(x,y), (宽,高), 旋转角度） + # box = cv2.boxPoints(rect) # cv2.boxPoints(rect) for OpenCV 3.x 获取最小外接矩形的4个顶点坐标 + # box = np.int0(box) + # box = box.reshape((4,1,2)) + # minrect_area = cv2.contourArea(box) + # print(abs(minrect_area-point_area)/point_area) + #### 四个角点 IOU + # biggest_box = np.concatenate((biggest_angle[0,:,:].reshape(1,1,2),biggest_angle[2,:,:].reshape(1,1,2),biggest_angle[3,:,:].reshape(1,1,2),biggest_angle[1,:,:].reshape(1,1,2)),axis=0) + # biggest_mask = np.zeros_like(mask) + # # corner_area = cv2.contourArea(biggest_box) + # cv2.drawContours(biggest_mask,[biggest_box], -1, color=255, thickness=-1) + + # smooth = 1e-5 + # biggest_mask_ = biggest_mask > 50 + # mask_ = mask > 50 + # intersection = (biggest_mask_ & mask_).sum() + # union = (biggest_mask_ | mask_).sum() + # iou = (intersection + smooth) / (union + smooth) + # if iou > 0.975: + # skip = True + # else: + # skip = False + # print(iou) + # cv2.imshow('mask',cv2.resize(mask,(512,512))) + # cv2.imshow('biggest_mask',cv2.resize(biggest_mask,(512,512))) + # cv2.waitKey(0) diff --git a/data/MBD/infer.py b/data/MBD/infer.py new file mode 100644 index 0000000..4afe4bb --- /dev/null +++ b/data/MBD/infer.py @@ -0,0 +1,151 @@ +import torch +import argparse +import numpy as np +import torch.nn.functional as F +import glob +import cv2 +from tqdm import tqdm + +import time +import os +from model.deep_lab_model.deeplab import * +from MBD import mask_base_dewarper +import time + +from utils import cvimg2torch,torch2cvimg + + + +def net1_net2_infer(model,img_paths,args): + + ### validate on the real datasets + seg_model=model + seg_model.eval() + for img_path in tqdm(img_paths): + if os.path.exists(img_path.replace('_origin','_capture')): + continue + t1 = time.time() + ### segmentation mask predict + img_org = cv2.imread(img_path) + h_org,w_org = img_org.shape[:2] + img = cv2.resize(img_org,(448, 448)) + img = cv2.GaussianBlur(img,(15,15),0,0) + img = cv2.cvtColor(img,cv2.COLOR_BGR2RGB) + img = cvimg2torch(img) + + with torch.no_grad(): + pred = seg_model(img.cuda()) + mask_pred = pred[:,0,:,:].unsqueeze(1) + mask_pred = F.interpolate(mask_pred,(h_org,w_org)) + mask_pred = mask_pred.squeeze(0).squeeze(0).cpu().numpy() + mask_pred = (mask_pred*255).astype(np.uint8) + kernel = np.ones((3,3)) + mask_pred = cv2.dilate(mask_pred,kernel,iterations=3) + mask_pred = cv2.erode(mask_pred,kernel,iterations=3) + mask_pred[mask_pred>100] = 255 + mask_pred[mask_pred<100] = 0 + ### tps transform base on the mask + # dewarp, grid = mask_base_dewarper(img_org,mask_pred) + try: + dewarp, grid = mask_base_dewarper(img_org,mask_pred) + except: + print('fail') + grid = np.meshgrid(np.arange(w_org),np.arange(h_org))/np.array([w_org,h_org]).reshape(2,1,1) + grid = torch.from_numpy((grid-0.5)*2).float().unsqueeze(0).permute(0,2,3,1) + dewarp = torch2cvimg(F.grid_sample(cvimg2torch(img_org),grid))[0] + grid = grid[0].numpy() + # cv2.imshow('in',cv2.resize(img_org,(512,512))) + # cv2.imshow('out',cv2.resize(dewarp,(512,512))) + # cv2.waitKey(0) + cv2.imwrite(img_path.replace('_origin','_capture'),dewarp) + cv2.imwrite(img_path.replace('_origin','_mask_new'),mask_pred) + + grid0 = cv2.resize(grid[:,:,0],(128,128)) + grid1 = cv2.resize(grid[:,:,1],(128,128)) + grid = np.stack((grid0,grid1),axis=-1) + np.save(img_path.replace('_origin','_grid1'),grid) + + +def net1_net2_infer_single_im(img,model_path): + seg_model = DeepLab(num_classes=1, + backbone='resnet', + output_stride=16, + sync_bn=None, + freeze_bn=False) + seg_model = torch.nn.DataParallel(seg_model, device_ids=range(torch.cuda.device_count())) + seg_model.cuda() + checkpoint = torch.load(model_path) + seg_model.load_state_dict(checkpoint['model_state']) + ### validate on the real datasets + seg_model.eval() + ### segmentation mask predict + img_org = img + h_org,w_org = img_org.shape[:2] + img = cv2.resize(img_org,(448, 448)) + img = cv2.GaussianBlur(img,(15,15),0,0) + img = cv2.cvtColor(img,cv2.COLOR_BGR2RGB) + img = cvimg2torch(img) + + with torch.no_grad(): + # from torchtoolbox.tools import summary + # print(summary(seg_model,torch.rand((1, 3, 448, 448)).cuda())) 59.4M 135.6G + + pred = seg_model(img.cuda()) + mask_pred = pred[:,0,:,:].unsqueeze(1) + mask_pred = F.interpolate(mask_pred,(h_org,w_org)) + mask_pred = mask_pred.squeeze(0).squeeze(0).cpu().numpy() + mask_pred = (mask_pred*255).astype(np.uint8) + kernel = np.ones((3,3)) + mask_pred = cv2.dilate(mask_pred,kernel,iterations=3) + mask_pred = cv2.erode(mask_pred,kernel,iterations=3) + mask_pred[mask_pred>100] = 255 + mask_pred[mask_pred<100] = 0 + ### tps transform base on the mask + # dewarp, grid = mask_base_dewarper(img_org,mask_pred) + # try: + # dewarp, grid = mask_base_dewarper(img_org,mask_pred) + # except: + # print('fail') + # grid = np.meshgrid(np.arange(w_org),np.arange(h_org))/np.array([w_org,h_org]).reshape(2,1,1) + # grid = torch.from_numpy((grid-0.5)*2).float().unsqueeze(0).permute(0,2,3,1) + # dewarp = torch2cvimg(F.grid_sample(cvimg2torch(img_org),grid))[0] + # grid = grid[0].numpy() + # cv2.imshow('in',cv2.resize(img_org,(512,512))) + # cv2.imshow('out',cv2.resize(dewarp,(512,512))) + # cv2.waitKey(0) + # cv2.imwrite(img_path.replace('_origin','_capture'),dewarp) + # cv2.imwrite(img_path.replace('_origin','_mask_new'),mask_pred) + + # grid0 = cv2.resize(grid[:,:,0],(128,128)) + # grid1 = cv2.resize(grid[:,:,1],(128,128)) + # grid = np.stack((grid0,grid1),axis=-1) + # np.save(img_path.replace('_origin','_grid1'),grid) + return mask_pred + + + +if __name__ == '__main__': + parser = argparse.ArgumentParser(description='Hyperparams') + parser.add_argument('--img_folder', nargs='?', type=str, default='./all_data',help='Data path to load data') + parser.add_argument('--img_rows', nargs='?', type=int, default=448, + help='Height of the input image') + parser.add_argument('--img_cols', nargs='?', type=int, default=448, + help='Width of the input image') + parser.add_argument('--seg_model_path', nargs='?', type=str, default='checkpoints/mbd.pkl', + help='Path to previous saved model to restart from') + args = parser.parse_args() + + seg_model = DeepLab(num_classes=1, + backbone='resnet', + output_stride=16, + sync_bn=None, + freeze_bn=False) + seg_model = torch.nn.DataParallel(seg_model, device_ids=range(torch.cuda.device_count())) + seg_model.cuda() + checkpoint = torch.load(args.seg_model_path) + seg_model.load_state_dict(checkpoint['model_state']) + + im_paths = glob.glob(os.path.join(args.img_folder,'*_origin.*')) + + net1_net2_infer(seg_model,im_paths,args) + diff --git a/data/MBD/model/__init__.py b/data/MBD/model/__init__.py new file mode 100644 index 0000000..882d90a --- /dev/null +++ b/data/MBD/model/__init__.py @@ -0,0 +1,50 @@ +import torchvision.models as models +from model.densenetccnl import * +from model.unetnc import * +from model.gienet import * + + +def get_model(name, n_classes=1, filters=64,version=None,in_channels=3, is_batchnorm=True, norm='batch', model_path=None, use_sigmoid=True, layers=3,img_size=512): + model = _get_model_instance(name) + + + if name == 'dnetccnl': + model = model(img_size=128, in_channels=in_channels, out_channels=n_classes, filters=32) + elif name == 'dnetccnl512': + model = model(img_size=img_size, in_channels=in_channels, out_channels=n_classes, filters=32) + elif name == 'unetnc': + model = model(input_nc=in_channels, output_nc=n_classes, num_downs=7) + elif name == 'gie': + model = model(input_nc=in_channels, output_nc=n_classes, num_downs=7) + elif name == 'giecbam': + model = model(input_nc=in_channels, output_nc=n_classes, num_downs=7) + elif name == 'gie2head': + model = model(input_nc=in_channels, output_nc=n_classes, num_downs=7) + elif name == 'giemask': + model = model(input_nc=in_channels, output_nc=n_classes, num_downs=7) + elif name == 'giemask2': + model = model(input_nc=in_channels, output_nc=n_classes, num_downs=7) + elif name == 'giedilated': + model = model(input_nc=in_channels, output_nc=n_classes, num_downs=7) + elif name == 'bmp': + model = model(input_nc=in_channels, output_nc=n_classes, num_downs=7) + elif name == 'displacement': + model = model(n_classes=2, num_filter=32, BatchNorm='GN', in_channels=5) + return model + +def _get_model_instance(name): + try: + return { + 'dnetccnl': dnetccnl, + 'dnetccnl512': dnetccnl512, + 'unetnc': UnetGenerator, + 'gie':GieGenerator, + 'giecbam':GiecbamGenerator, + 'giedilated':DilatedSingleUnet, + 'gie2head':Gie2headGenerator, + 'giemask':GiemaskGenerator, + 'giemask2':Giemask2Generator, + 'bmp':BmpGenerator, + }[name] + except: + print('Model {} not available'.format(name)) diff --git a/data/MBD/model/cbam.py b/data/MBD/model/cbam.py new file mode 100644 index 0000000..d4f908a --- /dev/null +++ b/data/MBD/model/cbam.py @@ -0,0 +1,95 @@ +import torch +import math +import torch.nn as nn +import torch.nn.functional as F + +class BasicConv(nn.Module): + def __init__(self, in_planes, out_planes, kernel_size, stride=1, padding=0, dilation=1, groups=1, relu=True, bn=True, bias=False): + super(BasicConv, self).__init__() + self.out_channels = out_planes + self.conv = nn.Conv2d(in_planes, out_planes, kernel_size=kernel_size, stride=stride, padding=padding, dilation=dilation, groups=groups, bias=bias) + self.bn = nn.BatchNorm2d(out_planes,eps=1e-5, momentum=0.01, affine=True) if bn else None + self.relu = nn.ReLU() if relu else None + + def forward(self, x): + x = self.conv(x) + if self.bn is not None: + x = self.bn(x) + if self.relu is not None: + x = self.relu(x) + return x + +class Flatten(nn.Module): + def forward(self, x): + return x.view(x.size(0), -1) + +class ChannelGate(nn.Module): + def __init__(self, gate_channels, reduction_ratio=16, pool_types=['avg', 'max']): + super(ChannelGate, self).__init__() + self.gate_channels = gate_channels + self.mlp = nn.Sequential( + Flatten(), + nn.Linear(gate_channels, gate_channels // reduction_ratio), + nn.ReLU(), + nn.Linear(gate_channels // reduction_ratio, gate_channels) + ) + self.pool_types = pool_types + def forward(self, x): + channel_att_sum = None + for pool_type in self.pool_types: + if pool_type=='avg': + avg_pool = F.avg_pool2d( x, (x.size(2), x.size(3)), stride=(x.size(2), x.size(3))) + channel_att_raw = self.mlp( avg_pool ) + elif pool_type=='max': + max_pool = F.max_pool2d( x, (x.size(2), x.size(3)), stride=(x.size(2), x.size(3))) + channel_att_raw = self.mlp( max_pool ) + elif pool_type=='lp': + lp_pool = F.lp_pool2d( x, 2, (x.size(2), x.size(3)), stride=(x.size(2), x.size(3))) + channel_att_raw = self.mlp( lp_pool ) + elif pool_type=='lse': + # LSE pool only + lse_pool = logsumexp_2d(x) + channel_att_raw = self.mlp( lse_pool ) + + if channel_att_sum is None: + channel_att_sum = channel_att_raw + else: + channel_att_sum = channel_att_sum + channel_att_raw + + scale = F.sigmoid( channel_att_sum ).unsqueeze(2).unsqueeze(3).expand_as(x) + return x * scale + +def logsumexp_2d(tensor): + tensor_flatten = tensor.view(tensor.size(0), tensor.size(1), -1) + s, _ = torch.max(tensor_flatten, dim=2, keepdim=True) + outputs = s + (tensor_flatten - s).exp().sum(dim=2, keepdim=True).log() + return outputs + +class ChannelPool(nn.Module): + def forward(self, x): + return torch.cat( (torch.max(x,1)[0].unsqueeze(1), torch.mean(x,1).unsqueeze(1)), dim=1 ) + +class SpatialGate(nn.Module): + def __init__(self): + super(SpatialGate, self).__init__() + kernel_size = 7 + self.compress = ChannelPool() + self.spatial = BasicConv(2, 1, kernel_size, stride=1, padding=(kernel_size-1) // 2, relu=False) + def forward(self, x): + x_compress = self.compress(x) + x_out = self.spatial(x_compress) + scale = F.sigmoid(x_out) # broadcasting + return x * scale + +class CBAM(nn.Module): + def __init__(self, gate_channels, reduction_ratio=16, pool_types=['avg', 'max'], no_spatial=False): + super(CBAM, self).__init__() + self.ChannelGate = ChannelGate(gate_channels, reduction_ratio, pool_types) + self.no_spatial=no_spatial + if not no_spatial: + self.SpatialGate = SpatialGate() + def forward(self, x): + x_out = self.ChannelGate(x) + if not self.no_spatial: + x_out = self.SpatialGate(x_out) + return x_out \ No newline at end of file diff --git a/data/MBD/model/deep_lab_model/__init__.py b/data/MBD/model/deep_lab_model/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/data/MBD/model/deep_lab_model/aspp.py b/data/MBD/model/deep_lab_model/aspp.py new file mode 100644 index 0000000..6b80fc2 --- /dev/null +++ b/data/MBD/model/deep_lab_model/aspp.py @@ -0,0 +1,95 @@ +import math +import torch +import torch.nn as nn +import torch.nn.functional as F +from model.deep_lab_model.sync_batchnorm.batchnorm import SynchronizedBatchNorm2d + +class _ASPPModule(nn.Module): + def __init__(self, inplanes, planes, kernel_size, padding, dilation, BatchNorm): + super(_ASPPModule, self).__init__() + self.atrous_conv = nn.Conv2d(inplanes, planes, kernel_size=kernel_size, + stride=1, padding=padding, dilation=dilation, bias=False) + self.bn = BatchNorm(planes) + self.relu = nn.ReLU() + + self._init_weight() + + def forward(self, x): + x = self.atrous_conv(x) + x = self.bn(x) + + return self.relu(x) + + def _init_weight(self): + for m in self.modules(): + if isinstance(m, nn.Conv2d): + torch.nn.init.kaiming_normal_(m.weight) + elif isinstance(m, SynchronizedBatchNorm2d): + m.weight.data.fill_(1) + m.bias.data.zero_() + elif isinstance(m, nn.BatchNorm2d): + m.weight.data.fill_(1) + m.bias.data.zero_() + +class ASPP(nn.Module): + def __init__(self, backbone, output_stride, BatchNorm): + super(ASPP, self).__init__() + if backbone == 'drn': + inplanes = 512 + elif backbone == 'mobilenet': + inplanes = 320 + else: + inplanes = 2048 + if output_stride == 16: + dilations = [1, 6, 12, 18] + elif output_stride == 8: + dilations = [1, 12, 24, 36] + else: + raise NotImplementedError + + self.aspp1 = _ASPPModule(inplanes, 256, 1, padding=0, dilation=dilations[0], BatchNorm=BatchNorm) + self.aspp2 = _ASPPModule(inplanes, 256, 3, padding=dilations[1], dilation=dilations[1], BatchNorm=BatchNorm) + self.aspp3 = _ASPPModule(inplanes, 256, 3, padding=dilations[2], dilation=dilations[2], BatchNorm=BatchNorm) + self.aspp4 = _ASPPModule(inplanes, 256, 3, padding=dilations[3], dilation=dilations[3], BatchNorm=BatchNorm) + + self.global_avg_pool = nn.Sequential(nn.AdaptiveAvgPool2d((1, 1)), + nn.Conv2d(inplanes, 256, 1, stride=1, bias=False), + BatchNorm(256), + nn.ReLU()) + self.conv1 = nn.Conv2d(1280, 256, 1, bias=False) + self.bn1 = BatchNorm(256) + self.relu = nn.ReLU() + self.dropout = nn.Dropout(0.5) + self._init_weight() + + def forward(self, x): + x1 = self.aspp1(x) + x2 = self.aspp2(x) + x3 = self.aspp3(x) + x4 = self.aspp4(x) + x5 = self.global_avg_pool(x) + x5 = F.interpolate(x5, size=x4.size()[2:], mode='bilinear', align_corners=True) + x = torch.cat((x1, x2, x3, x4, x5), dim=1) + + x = self.conv1(x) + x = self.bn1(x) + x = self.relu(x) + + return self.dropout(x) + + def _init_weight(self): + for m in self.modules(): + if isinstance(m, nn.Conv2d): + # n = m.kernel_size[0] * m.kernel_size[1] * m.out_channels + # m.weight.data.normal_(0, math.sqrt(2. / n)) + torch.nn.init.kaiming_normal_(m.weight) + elif isinstance(m, SynchronizedBatchNorm2d): + m.weight.data.fill_(1) + m.bias.data.zero_() + elif isinstance(m, nn.BatchNorm2d): + m.weight.data.fill_(1) + m.bias.data.zero_() + + +def build_aspp(backbone, output_stride, BatchNorm): + return ASPP(backbone, output_stride, BatchNorm) \ No newline at end of file diff --git a/data/MBD/model/deep_lab_model/backbone/__init__.py b/data/MBD/model/deep_lab_model/backbone/__init__.py new file mode 100644 index 0000000..6b54db9 --- /dev/null +++ b/data/MBD/model/deep_lab_model/backbone/__init__.py @@ -0,0 +1,13 @@ +from model.deep_lab_model.backbone import resnet, xception, drn, mobilenet + +def build_backbone(backbone, output_stride, BatchNorm): + if backbone == 'resnet': + return resnet.ResNet101(output_stride, BatchNorm) + elif backbone == 'xception': + return xception.AlignedXception(output_stride, BatchNorm) + elif backbone == 'drn': + return drn.drn_d_54(BatchNorm) + elif backbone == 'mobilenet': + return mobilenet.MobileNetV2(output_stride, BatchNorm) + else: + raise NotImplementedError diff --git a/data/MBD/model/deep_lab_model/backbone/drn.py b/data/MBD/model/deep_lab_model/backbone/drn.py new file mode 100644 index 0000000..298c75e --- /dev/null +++ b/data/MBD/model/deep_lab_model/backbone/drn.py @@ -0,0 +1,402 @@ +import torch.nn as nn +import math +import torch.utils.model_zoo as model_zoo +from model.deep_lab_model.sync_batchnorm.batchnorm import SynchronizedBatchNorm2d + +webroot = 'http://dl.yf.io/drn/' + +model_urls = { + 'resnet50': 'https://download.pytorch.org/models/resnet50-19c8e357.pth', + 'drn-c-26': webroot + 'drn_c_26-ddedf421.pth', + 'drn-c-42': webroot + 'drn_c_42-9d336e8c.pth', + 'drn-c-58': webroot + 'drn_c_58-0a53a92c.pth', + 'drn-d-22': webroot + 'drn_d_22-4bd2f8ea.pth', + 'drn-d-38': webroot + 'drn_d_38-eebb45f0.pth', + 'drn-d-54': webroot + 'drn_d_54-0e0534ff.pth', + 'drn-d-105': webroot + 'drn_d_105-12b40979.pth' +} + + +def conv3x3(in_planes, out_planes, stride=1, padding=1, dilation=1): + return nn.Conv2d(in_planes, out_planes, kernel_size=3, stride=stride, + padding=padding, bias=False, dilation=dilation) + + +class BasicBlock(nn.Module): + expansion = 1 + + def __init__(self, inplanes, planes, stride=1, downsample=None, + dilation=(1, 1), residual=True, BatchNorm=None): + super(BasicBlock, self).__init__() + self.conv1 = conv3x3(inplanes, planes, stride, + padding=dilation[0], dilation=dilation[0]) + self.bn1 = BatchNorm(planes) + self.relu = nn.ReLU(inplace=True) + self.conv2 = conv3x3(planes, planes, + padding=dilation[1], dilation=dilation[1]) + self.bn2 = BatchNorm(planes) + self.downsample = downsample + self.stride = stride + self.residual = residual + + def forward(self, x): + residual = x + + out = self.conv1(x) + out = self.bn1(out) + out = self.relu(out) + + out = self.conv2(out) + out = self.bn2(out) + + if self.downsample is not None: + residual = self.downsample(x) + if self.residual: + out += residual + out = self.relu(out) + + return out + + +class Bottleneck(nn.Module): + expansion = 4 + + def __init__(self, inplanes, planes, stride=1, downsample=None, + dilation=(1, 1), residual=True, BatchNorm=None): + super(Bottleneck, self).__init__() + self.conv1 = nn.Conv2d(inplanes, planes, kernel_size=1, bias=False) + self.bn1 = BatchNorm(planes) + self.conv2 = nn.Conv2d(planes, planes, kernel_size=3, stride=stride, + padding=dilation[1], bias=False, + dilation=dilation[1]) + self.bn2 = BatchNorm(planes) + self.conv3 = nn.Conv2d(planes, planes * 4, kernel_size=1, bias=False) + self.bn3 = BatchNorm(planes * 4) + self.relu = nn.ReLU(inplace=True) + self.downsample = downsample + self.stride = stride + + def forward(self, x): + residual = x + + out = self.conv1(x) + out = self.bn1(out) + out = self.relu(out) + + out = self.conv2(out) + out = self.bn2(out) + out = self.relu(out) + + out = self.conv3(out) + out = self.bn3(out) + + if self.downsample is not None: + residual = self.downsample(x) + + out += residual + out = self.relu(out) + + return out + + +class DRN(nn.Module): + + def __init__(self, block, layers, arch='D', + channels=(16, 32, 64, 128, 256, 512, 512, 512), + BatchNorm=None): + super(DRN, self).__init__() + self.inplanes = channels[0] + self.out_dim = channels[-1] + self.arch = arch + + if arch == 'C': + self.conv1 = nn.Conv2d(3, channels[0], kernel_size=7, stride=1, + padding=3, bias=False) + self.bn1 = BatchNorm(channels[0]) + self.relu = nn.ReLU(inplace=True) + + self.layer1 = self._make_layer( + BasicBlock, channels[0], layers[0], stride=1, BatchNorm=BatchNorm) + self.layer2 = self._make_layer( + BasicBlock, channels[1], layers[1], stride=2, BatchNorm=BatchNorm) + + elif arch == 'D': + self.layer0 = nn.Sequential( + nn.Conv2d(3, channels[0], kernel_size=7, stride=1, padding=3, + bias=False), + BatchNorm(channels[0]), + nn.ReLU(inplace=True) + ) + + self.layer1 = self._make_conv_layers( + channels[0], layers[0], stride=1, BatchNorm=BatchNorm) + self.layer2 = self._make_conv_layers( + channels[1], layers[1], stride=2, BatchNorm=BatchNorm) + + self.layer3 = self._make_layer(block, channels[2], layers[2], stride=2, BatchNorm=BatchNorm) + self.layer4 = self._make_layer(block, channels[3], layers[3], stride=2, BatchNorm=BatchNorm) + self.layer5 = self._make_layer(block, channels[4], layers[4], + dilation=2, new_level=False, BatchNorm=BatchNorm) + self.layer6 = None if layers[5] == 0 else \ + self._make_layer(block, channels[5], layers[5], dilation=4, + new_level=False, BatchNorm=BatchNorm) + + if arch == 'C': + self.layer7 = None if layers[6] == 0 else \ + self._make_layer(BasicBlock, channels[6], layers[6], dilation=2, + new_level=False, residual=False, BatchNorm=BatchNorm) + self.layer8 = None if layers[7] == 0 else \ + self._make_layer(BasicBlock, channels[7], layers[7], dilation=1, + new_level=False, residual=False, BatchNorm=BatchNorm) + elif arch == 'D': + self.layer7 = None if layers[6] == 0 else \ + self._make_conv_layers(channels[6], layers[6], dilation=2, BatchNorm=BatchNorm) + self.layer8 = None if layers[7] == 0 else \ + self._make_conv_layers(channels[7], layers[7], dilation=1, BatchNorm=BatchNorm) + + self._init_weight() + + def _init_weight(self): + for m in self.modules(): + if isinstance(m, nn.Conv2d): + n = m.kernel_size[0] * m.kernel_size[1] * m.out_channels + m.weight.data.normal_(0, math.sqrt(2. / n)) + elif isinstance(m, SynchronizedBatchNorm2d): + m.weight.data.fill_(1) + m.bias.data.zero_() + elif isinstance(m, nn.BatchNorm2d): + m.weight.data.fill_(1) + m.bias.data.zero_() + + + def _make_layer(self, block, planes, blocks, stride=1, dilation=1, + new_level=True, residual=True, BatchNorm=None): + assert dilation == 1 or dilation % 2 == 0 + downsample = None + if stride != 1 or self.inplanes != planes * block.expansion: + downsample = nn.Sequential( + nn.Conv2d(self.inplanes, planes * block.expansion, + kernel_size=1, stride=stride, bias=False), + BatchNorm(planes * block.expansion), + ) + + layers = list() + layers.append(block( + self.inplanes, planes, stride, downsample, + dilation=(1, 1) if dilation == 1 else ( + dilation // 2 if new_level else dilation, dilation), + residual=residual, BatchNorm=BatchNorm)) + self.inplanes = planes * block.expansion + for i in range(1, blocks): + layers.append(block(self.inplanes, planes, residual=residual, + dilation=(dilation, dilation), BatchNorm=BatchNorm)) + + return nn.Sequential(*layers) + + def _make_conv_layers(self, channels, convs, stride=1, dilation=1, BatchNorm=None): + modules = [] + for i in range(convs): + modules.extend([ + nn.Conv2d(self.inplanes, channels, kernel_size=3, + stride=stride if i == 0 else 1, + padding=dilation, bias=False, dilation=dilation), + BatchNorm(channels), + nn.ReLU(inplace=True)]) + self.inplanes = channels + return nn.Sequential(*modules) + + def forward(self, x): + if self.arch == 'C': + x = self.conv1(x) + x = self.bn1(x) + x = self.relu(x) + elif self.arch == 'D': + x = self.layer0(x) + + x = self.layer1(x) + x = self.layer2(x) + + x = self.layer3(x) + low_level_feat = x + + x = self.layer4(x) + x = self.layer5(x) + + if self.layer6 is not None: + x = self.layer6(x) + + if self.layer7 is not None: + x = self.layer7(x) + + if self.layer8 is not None: + x = self.layer8(x) + + return x, low_level_feat + + +class DRN_A(nn.Module): + + def __init__(self, block, layers, BatchNorm=None): + self.inplanes = 64 + super(DRN_A, self).__init__() + self.out_dim = 512 * block.expansion + self.conv1 = nn.Conv2d(3, 64, kernel_size=7, stride=2, padding=3, + bias=False) + self.bn1 = BatchNorm(64) + self.relu = nn.ReLU(inplace=True) + self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1) + self.layer1 = self._make_layer(block, 64, layers[0], BatchNorm=BatchNorm) + self.layer2 = self._make_layer(block, 128, layers[1], stride=2, BatchNorm=BatchNorm) + self.layer3 = self._make_layer(block, 256, layers[2], stride=1, + dilation=2, BatchNorm=BatchNorm) + self.layer4 = self._make_layer(block, 512, layers[3], stride=1, + dilation=4, BatchNorm=BatchNorm) + + self._init_weight() + + def _init_weight(self): + for m in self.modules(): + if isinstance(m, nn.Conv2d): + n = m.kernel_size[0] * m.kernel_size[1] * m.out_channels + m.weight.data.normal_(0, math.sqrt(2. / n)) + elif isinstance(m, SynchronizedBatchNorm2d): + m.weight.data.fill_(1) + m.bias.data.zero_() + elif isinstance(m, nn.BatchNorm2d): + m.weight.data.fill_(1) + m.bias.data.zero_() + + def _make_layer(self, block, planes, blocks, stride=1, dilation=1, BatchNorm=None): + downsample = None + if stride != 1 or self.inplanes != planes * block.expansion: + downsample = nn.Sequential( + nn.Conv2d(self.inplanes, planes * block.expansion, + kernel_size=1, stride=stride, bias=False), + BatchNorm(planes * block.expansion), + ) + + layers = [] + layers.append(block(self.inplanes, planes, stride, downsample, BatchNorm=BatchNorm)) + self.inplanes = planes * block.expansion + for i in range(1, blocks): + layers.append(block(self.inplanes, planes, + dilation=(dilation, dilation, ), BatchNorm=BatchNorm)) + + return nn.Sequential(*layers) + + def forward(self, x): + x = self.conv1(x) + x = self.bn1(x) + x = self.relu(x) + x = self.maxpool(x) + + x = self.layer1(x) + x = self.layer2(x) + x = self.layer3(x) + x = self.layer4(x) + + return x + +def drn_a_50(BatchNorm, pretrained=True): + model = DRN_A(Bottleneck, [3, 4, 6, 3], BatchNorm=BatchNorm) + if pretrained: + model.load_state_dict(model_zoo.load_url(model_urls['resnet50'])) + return model + + +def drn_c_26(BatchNorm, pretrained=True): + model = DRN(BasicBlock, [1, 1, 2, 2, 2, 2, 1, 1], arch='C', BatchNorm=BatchNorm) + if pretrained: + pretrained = model_zoo.load_url(model_urls['drn-c-26']) + del pretrained['fc.weight'] + del pretrained['fc.bias'] + model.load_state_dict(pretrained) + return model + + +def drn_c_42(BatchNorm, pretrained=True): + model = DRN(BasicBlock, [1, 1, 3, 4, 6, 3, 1, 1], arch='C', BatchNorm=BatchNorm) + if pretrained: + pretrained = model_zoo.load_url(model_urls['drn-c-42']) + del pretrained['fc.weight'] + del pretrained['fc.bias'] + model.load_state_dict(pretrained) + return model + + +def drn_c_58(BatchNorm, pretrained=True): + model = DRN(Bottleneck, [1, 1, 3, 4, 6, 3, 1, 1], arch='C', BatchNorm=BatchNorm) + if pretrained: + pretrained = model_zoo.load_url(model_urls['drn-c-58']) + del pretrained['fc.weight'] + del pretrained['fc.bias'] + model.load_state_dict(pretrained) + return model + + +def drn_d_22(BatchNorm, pretrained=True): + model = DRN(BasicBlock, [1, 1, 2, 2, 2, 2, 1, 1], arch='D', BatchNorm=BatchNorm) + if pretrained: + pretrained = model_zoo.load_url(model_urls['drn-d-22']) + del pretrained['fc.weight'] + del pretrained['fc.bias'] + model.load_state_dict(pretrained) + return model + + +def drn_d_24(BatchNorm, pretrained=True): + model = DRN(BasicBlock, [1, 1, 2, 2, 2, 2, 2, 2], arch='D', BatchNorm=BatchNorm) + if pretrained: + pretrained = model_zoo.load_url(model_urls['drn-d-24']) + del pretrained['fc.weight'] + del pretrained['fc.bias'] + model.load_state_dict(pretrained) + return model + + +def drn_d_38(BatchNorm, pretrained=True): + model = DRN(BasicBlock, [1, 1, 3, 4, 6, 3, 1, 1], arch='D', BatchNorm=BatchNorm) + if pretrained: + pretrained = model_zoo.load_url(model_urls['drn-d-38']) + del pretrained['fc.weight'] + del pretrained['fc.bias'] + model.load_state_dict(pretrained) + return model + + +def drn_d_40(BatchNorm, pretrained=True): + model = DRN(BasicBlock, [1, 1, 3, 4, 6, 3, 2, 2], arch='D', BatchNorm=BatchNorm) + if pretrained: + pretrained = model_zoo.load_url(model_urls['drn-d-40']) + del pretrained['fc.weight'] + del pretrained['fc.bias'] + model.load_state_dict(pretrained) + return model + + +def drn_d_54(BatchNorm, pretrained=True): + model = DRN(Bottleneck, [1, 1, 3, 4, 6, 3, 1, 1], arch='D', BatchNorm=BatchNorm) + if pretrained: + pretrained = model_zoo.load_url(model_urls['drn-d-54']) + del pretrained['fc.weight'] + del pretrained['fc.bias'] + model.load_state_dict(pretrained) + return model + + +def drn_d_105(BatchNorm, pretrained=True): + model = DRN(Bottleneck, [1, 1, 3, 4, 23, 3, 1, 1], arch='D', BatchNorm=BatchNorm) + if pretrained: + pretrained = model_zoo.load_url(model_urls['drn-d-105']) + del pretrained['fc.weight'] + del pretrained['fc.bias'] + model.load_state_dict(pretrained) + return model + +if __name__ == "__main__": + import torch + model = drn_a_50(BatchNorm=nn.BatchNorm2d, pretrained=True) + input = torch.rand(1, 3, 512, 512) + output, low_level_feat = model(input) + print(output.size()) + print(low_level_feat.size()) diff --git a/data/MBD/model/deep_lab_model/backbone/mobilenet.py b/data/MBD/model/deep_lab_model/backbone/mobilenet.py new file mode 100644 index 0000000..c5f7f06 --- /dev/null +++ b/data/MBD/model/deep_lab_model/backbone/mobilenet.py @@ -0,0 +1,151 @@ +import torch +import torch.nn.functional as F +import torch.nn as nn +import math +from model.deep_lab_model.sync_batchnorm.batchnorm import SynchronizedBatchNorm2d +import torch.utils.model_zoo as model_zoo + +def conv_bn(inp, oup, stride, BatchNorm): + return nn.Sequential( + nn.Conv2d(inp, oup, 3, stride, 1, bias=False), + BatchNorm(oup), + nn.ReLU6(inplace=True) + ) + + +def fixed_padding(inputs, kernel_size, dilation): + kernel_size_effective = kernel_size + (kernel_size - 1) * (dilation - 1) + pad_total = kernel_size_effective - 1 + pad_beg = pad_total // 2 + pad_end = pad_total - pad_beg + padded_inputs = F.pad(inputs, (pad_beg, pad_end, pad_beg, pad_end)) + return padded_inputs + + +class InvertedResidual(nn.Module): + def __init__(self, inp, oup, stride, dilation, expand_ratio, BatchNorm): + super(InvertedResidual, self).__init__() + self.stride = stride + assert stride in [1, 2] + + hidden_dim = round(inp * expand_ratio) + self.use_res_connect = self.stride == 1 and inp == oup + self.kernel_size = 3 + self.dilation = dilation + + if expand_ratio == 1: + self.conv = nn.Sequential( + # dw + nn.Conv2d(hidden_dim, hidden_dim, 3, stride, 0, dilation, groups=hidden_dim, bias=False), + BatchNorm(hidden_dim), + nn.ReLU6(inplace=True), + # pw-linear + nn.Conv2d(hidden_dim, oup, 1, 1, 0, 1, 1, bias=False), + BatchNorm(oup), + ) + else: + self.conv = nn.Sequential( + # pw + nn.Conv2d(inp, hidden_dim, 1, 1, 0, 1, bias=False), + BatchNorm(hidden_dim), + nn.ReLU6(inplace=True), + # dw + nn.Conv2d(hidden_dim, hidden_dim, 3, stride, 0, dilation, groups=hidden_dim, bias=False), + BatchNorm(hidden_dim), + nn.ReLU6(inplace=True), + # pw-linear + nn.Conv2d(hidden_dim, oup, 1, 1, 0, 1, bias=False), + BatchNorm(oup), + ) + + def forward(self, x): + x_pad = fixed_padding(x, self.kernel_size, dilation=self.dilation) + if self.use_res_connect: + x = x + self.conv(x_pad) + else: + x = self.conv(x_pad) + return x + + +class MobileNetV2(nn.Module): + def __init__(self, output_stride=8, BatchNorm=None, width_mult=1., pretrained=True): + super(MobileNetV2, self).__init__() + block = InvertedResidual + input_channel = 32 + current_stride = 1 + rate = 1 + interverted_residual_setting = [ + # t, c, n, s + [1, 16, 1, 1], + [6, 24, 2, 2], + [6, 32, 3, 2], + [6, 64, 4, 2], + [6, 96, 3, 1], + [6, 160, 3, 2], + [6, 320, 1, 1], + ] + + # building first layer + input_channel = int(input_channel * width_mult) + self.features = [conv_bn(3, input_channel, 2, BatchNorm)] + current_stride *= 2 + # building inverted residual blocks + for t, c, n, s in interverted_residual_setting: + if current_stride == output_stride: + stride = 1 + dilation = rate + rate *= s + else: + stride = s + dilation = 1 + current_stride *= s + output_channel = int(c * width_mult) + for i in range(n): + if i == 0: + self.features.append(block(input_channel, output_channel, stride, dilation, t, BatchNorm)) + else: + self.features.append(block(input_channel, output_channel, 1, dilation, t, BatchNorm)) + input_channel = output_channel + self.features = nn.Sequential(*self.features) + self._initialize_weights() + + if pretrained: + self._load_pretrained_model() + + self.low_level_features = self.features[0:4] + self.high_level_features = self.features[4:] + + def forward(self, x): + low_level_feat = self.low_level_features(x) + x = self.high_level_features(low_level_feat) + return x, low_level_feat + + def _load_pretrained_model(self): + pretrain_dict = model_zoo.load_url('http://jeff95.me/models/mobilenet_v2-6a65762b.pth') + model_dict = {} + state_dict = self.state_dict() + for k, v in pretrain_dict.items(): + if k in state_dict: + model_dict[k] = v + state_dict.update(model_dict) + self.load_state_dict(state_dict) + + def _initialize_weights(self): + for m in self.modules(): + if isinstance(m, nn.Conv2d): + # n = m.kernel_size[0] * m.kernel_size[1] * m.out_channels + # m.weight.data.normal_(0, math.sqrt(2. / n)) + torch.nn.init.kaiming_normal_(m.weight) + elif isinstance(m, SynchronizedBatchNorm2d): + m.weight.data.fill_(1) + m.bias.data.zero_() + elif isinstance(m, nn.BatchNorm2d): + m.weight.data.fill_(1) + m.bias.data.zero_() + +if __name__ == "__main__": + input = torch.rand(1, 3, 512, 512) + model = MobileNetV2(output_stride=16, BatchNorm=nn.BatchNorm2d) + output, low_level_feat = model(input) + print(output.size()) + print(low_level_feat.size()) diff --git a/data/MBD/model/deep_lab_model/backbone/resnet.py b/data/MBD/model/deep_lab_model/backbone/resnet.py new file mode 100644 index 0000000..8e0ca87 --- /dev/null +++ b/data/MBD/model/deep_lab_model/backbone/resnet.py @@ -0,0 +1,170 @@ +import math +import torch.nn as nn +import torch.utils.model_zoo as model_zoo +from model.deep_lab_model.sync_batchnorm.batchnorm import SynchronizedBatchNorm2d + +class Bottleneck(nn.Module): + expansion = 4 + + def __init__(self, inplanes, planes, stride=1, dilation=1, downsample=None, BatchNorm=None): + super(Bottleneck, self).__init__() + self.conv1 = nn.Conv2d(inplanes, planes, kernel_size=1, bias=False) + self.bn1 = BatchNorm(planes) + self.conv2 = nn.Conv2d(planes, planes, kernel_size=3, stride=stride, + dilation=dilation, padding=dilation, bias=False) + self.bn2 = BatchNorm(planes) + self.conv3 = nn.Conv2d(planes, planes * 4, kernel_size=1, bias=False) + self.bn3 = BatchNorm(planes * 4) + self.relu = nn.ReLU(inplace=True) + self.downsample = downsample + self.stride = stride + self.dilation = dilation + + def forward(self, x): + residual = x + + out = self.conv1(x) + out = self.bn1(out) + out = self.relu(out) + + out = self.conv2(out) + out = self.bn2(out) + out = self.relu(out) + + out = self.conv3(out) + out = self.bn3(out) + + if self.downsample is not None: + residual = self.downsample(x) + + out += residual + out = self.relu(out) + + return out + +class ResNet(nn.Module): + + def __init__(self, block, layers, output_stride, BatchNorm, pretrained=True): + self.inplanes = 64 + super(ResNet, self).__init__() + blocks = [1, 2, 4] + if output_stride == 16: + strides = [1, 2, 2, 1] + dilations = [1, 1, 1, 2] + elif output_stride == 8: + strides = [1, 2, 1, 1] + dilations = [1, 1, 2, 4] + else: + raise NotImplementedError + + # Modules + self.conv1 = nn.Conv2d(3, 64, kernel_size=7, stride=2, padding=3, + bias=False) + self.bn1 = BatchNorm(64) + self.relu = nn.ReLU(inplace=True) + self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1) + + self.layer1 = self._make_layer(block, 64, layers[0], stride=strides[0], dilation=dilations[0], BatchNorm=BatchNorm) + self.layer2 = self._make_layer(block, 128, layers[1], stride=strides[1], dilation=dilations[1], BatchNorm=BatchNorm) + self.layer3 = self._make_layer(block, 256, layers[2], stride=strides[2], dilation=dilations[2], BatchNorm=BatchNorm) + self.layer4 = self._make_MG_unit(block, 512, blocks=blocks, stride=strides[3], dilation=dilations[3], BatchNorm=BatchNorm) + # self.layer4 = self._make_layer(block, 512, layers[3], stride=strides[3], dilation=dilations[3], BatchNorm=BatchNorm) + self._init_weight() + + # if pretrained: + # self._load_pretrained_model() + + def _make_layer(self, block, planes, blocks, stride=1, dilation=1, BatchNorm=None): + downsample = None + if stride != 1 or self.inplanes != planes * block.expansion: + downsample = nn.Sequential( + nn.Conv2d(self.inplanes, planes * block.expansion, + kernel_size=1, stride=stride, bias=False), + BatchNorm(planes * block.expansion), + ) + + layers = [] + layers.append(block(self.inplanes, planes, stride, dilation, downsample, BatchNorm)) + self.inplanes = planes * block.expansion + for i in range(1, blocks): + layers.append(block(self.inplanes, planes, dilation=dilation, BatchNorm=BatchNorm)) + + return nn.Sequential(*layers) + + def _make_MG_unit(self, block, planes, blocks, stride=1, dilation=1, BatchNorm=None): + downsample = None + if stride != 1 or self.inplanes != planes * block.expansion: + downsample = nn.Sequential( + nn.Conv2d(self.inplanes, planes * block.expansion, + kernel_size=1, stride=stride, bias=False), + BatchNorm(planes * block.expansion), + ) + + layers = [] + layers.append(block(self.inplanes, planes, stride, dilation=blocks[0]*dilation, + downsample=downsample, BatchNorm=BatchNorm)) + self.inplanes = planes * block.expansion + for i in range(1, len(blocks)): + layers.append(block(self.inplanes, planes, stride=1, + dilation=blocks[i]*dilation, BatchNorm=BatchNorm)) + + return nn.Sequential(*layers) + + def forward(self, input): + x = self.conv1(input) + x = self.bn1(x) + x = self.relu(x) + x = self.maxpool(x) + + x = self.layer1(x) + low_level_feat = x + x = self.layer2(x) + x = self.layer3(x) + x = self.layer4(x) + return x, low_level_feat + + def _init_weight(self): + for m in self.modules(): + if isinstance(m, nn.Conv2d): + n = m.kernel_size[0] * m.kernel_size[1] * m.out_channels + m.weight.data.normal_(0, math.sqrt(2. / n)) + elif isinstance(m, SynchronizedBatchNorm2d): + m.weight.data.fill_(1) + m.bias.data.zero_() + elif isinstance(m, nn.BatchNorm2d): + m.weight.data.fill_(1) + m.bias.data.zero_() + + def _load_pretrained_model(self): + + import urllib.request + import ssl + ssl._create_default_https_context = ssl._create_unverified_context + response = urllib.request.urlopen('https://download.pytorch.org/models/resnet101-5d3b4d8f.pth') + + pretrain_dict = model_zoo.load_url('https://download.pytorch.org/models/resnet101-5d3b4d8f.pth') + model_dict = {} + state_dict = self.state_dict() + for k, v in pretrain_dict.items(): + if k in state_dict: + # if 'conv1' in k: + # continue + model_dict[k] = v + state_dict.update(model_dict) + self.load_state_dict(state_dict) + +def ResNet101(output_stride, BatchNorm, pretrained=True): + """Constructs a ResNet-101 model. + Args: + pretrained (bool): If True, returns a model pre-trained on ImageNet + """ + model = ResNet(Bottleneck, [3, 4, 23, 3], output_stride, BatchNorm, pretrained=pretrained) + return model + +if __name__ == "__main__": + import torch + model = ResNet101(BatchNorm=nn.BatchNorm2d, pretrained=True, output_stride=8) + input = torch.rand(1, 3, 512, 512) + output, low_level_feat = model(input) + print(output.size()) + print(low_level_feat.size()) \ No newline at end of file diff --git a/data/MBD/model/deep_lab_model/backbone/xception.py b/data/MBD/model/deep_lab_model/backbone/xception.py new file mode 100644 index 0000000..fdfb415 --- /dev/null +++ b/data/MBD/model/deep_lab_model/backbone/xception.py @@ -0,0 +1,288 @@ +import math +import torch +import torch.nn as nn +import torch.nn.functional as F +import torch.utils.model_zoo as model_zoo +from model.deep_lab_model.sync_batchnorm.batchnorm import SynchronizedBatchNorm2d + +def fixed_padding(inputs, kernel_size, dilation): + kernel_size_effective = kernel_size + (kernel_size - 1) * (dilation - 1) + pad_total = kernel_size_effective - 1 + pad_beg = pad_total // 2 + pad_end = pad_total - pad_beg + padded_inputs = F.pad(inputs, (pad_beg, pad_end, pad_beg, pad_end)) + return padded_inputs + + +class SeparableConv2d(nn.Module): + def __init__(self, inplanes, planes, kernel_size=3, stride=1, dilation=1, bias=False, BatchNorm=None): + super(SeparableConv2d, self).__init__() + + self.conv1 = nn.Conv2d(inplanes, inplanes, kernel_size, stride, 0, dilation, + groups=inplanes, bias=bias) + self.bn = BatchNorm(inplanes) + self.pointwise = nn.Conv2d(inplanes, planes, 1, 1, 0, 1, 1, bias=bias) + + def forward(self, x): + x = fixed_padding(x, self.conv1.kernel_size[0], dilation=self.conv1.dilation[0]) + x = self.conv1(x) + x = self.bn(x) + x = self.pointwise(x) + return x + + +class Block(nn.Module): + def __init__(self, inplanes, planes, reps, stride=1, dilation=1, BatchNorm=None, + start_with_relu=True, grow_first=True, is_last=False): + super(Block, self).__init__() + + if planes != inplanes or stride != 1: + self.skip = nn.Conv2d(inplanes, planes, 1, stride=stride, bias=False) + self.skipbn = BatchNorm(planes) + else: + self.skip = None + + self.relu = nn.ReLU(inplace=True) + rep = [] + + filters = inplanes + if grow_first: + rep.append(self.relu) + rep.append(SeparableConv2d(inplanes, planes, 3, 1, dilation, BatchNorm=BatchNorm)) + rep.append(BatchNorm(planes)) + filters = planes + + for i in range(reps - 1): + rep.append(self.relu) + rep.append(SeparableConv2d(filters, filters, 3, 1, dilation, BatchNorm=BatchNorm)) + rep.append(BatchNorm(filters)) + + if not grow_first: + rep.append(self.relu) + rep.append(SeparableConv2d(inplanes, planes, 3, 1, dilation, BatchNorm=BatchNorm)) + rep.append(BatchNorm(planes)) + + if stride != 1: + rep.append(self.relu) + rep.append(SeparableConv2d(planes, planes, 3, 2, BatchNorm=BatchNorm)) + rep.append(BatchNorm(planes)) + + if stride == 1 and is_last: + rep.append(self.relu) + rep.append(SeparableConv2d(planes, planes, 3, 1, BatchNorm=BatchNorm)) + rep.append(BatchNorm(planes)) + + if not start_with_relu: + rep = rep[1:] + + self.rep = nn.Sequential(*rep) + + def forward(self, inp): + x = self.rep(inp) + + if self.skip is not None: + skip = self.skip(inp) + skip = self.skipbn(skip) + else: + skip = inp + + x = x + skip + + return x + + +class AlignedXception(nn.Module): + """ + Modified Alighed Xception + """ + def __init__(self, output_stride, BatchNorm, + pretrained=True): + super(AlignedXception, self).__init__() + + if output_stride == 16: + entry_block3_stride = 2 + middle_block_dilation = 1 + exit_block_dilations = (1, 2) + elif output_stride == 8: + entry_block3_stride = 1 + middle_block_dilation = 2 + exit_block_dilations = (2, 4) + else: + raise NotImplementedError + + + # Entry flow + self.conv1 = nn.Conv2d(3, 32, 3, stride=2, padding=1, bias=False) + self.bn1 = BatchNorm(32) + self.relu = nn.ReLU(inplace=True) + + self.conv2 = nn.Conv2d(32, 64, 3, stride=1, padding=1, bias=False) + self.bn2 = BatchNorm(64) + + self.block1 = Block(64, 128, reps=2, stride=2, BatchNorm=BatchNorm, start_with_relu=False) + self.block2 = Block(128, 256, reps=2, stride=2, BatchNorm=BatchNorm, start_with_relu=False, + grow_first=True) + self.block3 = Block(256, 728, reps=2, stride=entry_block3_stride, BatchNorm=BatchNorm, + start_with_relu=True, grow_first=True, is_last=True) + + # Middle flow + self.block4 = Block(728, 728, reps=3, stride=1, dilation=middle_block_dilation, + BatchNorm=BatchNorm, start_with_relu=True, grow_first=True) + self.block5 = Block(728, 728, reps=3, stride=1, dilation=middle_block_dilation, + BatchNorm=BatchNorm, start_with_relu=True, grow_first=True) + self.block6 = Block(728, 728, reps=3, stride=1, dilation=middle_block_dilation, + BatchNorm=BatchNorm, start_with_relu=True, grow_first=True) + self.block7 = Block(728, 728, reps=3, stride=1, dilation=middle_block_dilation, + BatchNorm=BatchNorm, start_with_relu=True, grow_first=True) + self.block8 = Block(728, 728, reps=3, stride=1, dilation=middle_block_dilation, + BatchNorm=BatchNorm, start_with_relu=True, grow_first=True) + self.block9 = Block(728, 728, reps=3, stride=1, dilation=middle_block_dilation, + BatchNorm=BatchNorm, start_with_relu=True, grow_first=True) + self.block10 = Block(728, 728, reps=3, stride=1, dilation=middle_block_dilation, + BatchNorm=BatchNorm, start_with_relu=True, grow_first=True) + self.block11 = Block(728, 728, reps=3, stride=1, dilation=middle_block_dilation, + BatchNorm=BatchNorm, start_with_relu=True, grow_first=True) + self.block12 = Block(728, 728, reps=3, stride=1, dilation=middle_block_dilation, + BatchNorm=BatchNorm, start_with_relu=True, grow_first=True) + self.block13 = Block(728, 728, reps=3, stride=1, dilation=middle_block_dilation, + BatchNorm=BatchNorm, start_with_relu=True, grow_first=True) + self.block14 = Block(728, 728, reps=3, stride=1, dilation=middle_block_dilation, + BatchNorm=BatchNorm, start_with_relu=True, grow_first=True) + self.block15 = Block(728, 728, reps=3, stride=1, dilation=middle_block_dilation, + BatchNorm=BatchNorm, start_with_relu=True, grow_first=True) + self.block16 = Block(728, 728, reps=3, stride=1, dilation=middle_block_dilation, + BatchNorm=BatchNorm, start_with_relu=True, grow_first=True) + self.block17 = Block(728, 728, reps=3, stride=1, dilation=middle_block_dilation, + BatchNorm=BatchNorm, start_with_relu=True, grow_first=True) + self.block18 = Block(728, 728, reps=3, stride=1, dilation=middle_block_dilation, + BatchNorm=BatchNorm, start_with_relu=True, grow_first=True) + self.block19 = Block(728, 728, reps=3, stride=1, dilation=middle_block_dilation, + BatchNorm=BatchNorm, start_with_relu=True, grow_first=True) + + # Exit flow + self.block20 = Block(728, 1024, reps=2, stride=1, dilation=exit_block_dilations[0], + BatchNorm=BatchNorm, start_with_relu=True, grow_first=False, is_last=True) + + self.conv3 = SeparableConv2d(1024, 1536, 3, stride=1, dilation=exit_block_dilations[1], BatchNorm=BatchNorm) + self.bn3 = BatchNorm(1536) + + self.conv4 = SeparableConv2d(1536, 1536, 3, stride=1, dilation=exit_block_dilations[1], BatchNorm=BatchNorm) + self.bn4 = BatchNorm(1536) + + self.conv5 = SeparableConv2d(1536, 2048, 3, stride=1, dilation=exit_block_dilations[1], BatchNorm=BatchNorm) + self.bn5 = BatchNorm(2048) + + # Init weights + self._init_weight() + + # Load pretrained model + if pretrained: + self._load_pretrained_model() + + def forward(self, x): + # Entry flow + x = self.conv1(x) + x = self.bn1(x) + x = self.relu(x) + + x = self.conv2(x) + x = self.bn2(x) + x = self.relu(x) + + x = self.block1(x) + # add relu here + x = self.relu(x) + low_level_feat = x + x = self.block2(x) + x = self.block3(x) + + # Middle flow + x = self.block4(x) + x = self.block5(x) + x = self.block6(x) + x = self.block7(x) + x = self.block8(x) + x = self.block9(x) + x = self.block10(x) + x = self.block11(x) + x = self.block12(x) + x = self.block13(x) + x = self.block14(x) + x = self.block15(x) + x = self.block16(x) + x = self.block17(x) + x = self.block18(x) + x = self.block19(x) + + # Exit flow + x = self.block20(x) + x = self.relu(x) + x = self.conv3(x) + x = self.bn3(x) + x = self.relu(x) + + x = self.conv4(x) + x = self.bn4(x) + x = self.relu(x) + + x = self.conv5(x) + x = self.bn5(x) + x = self.relu(x) + + return x, low_level_feat + + def _init_weight(self): + for m in self.modules(): + if isinstance(m, nn.Conv2d): + n = m.kernel_size[0] * m.kernel_size[1] * m.out_channels + m.weight.data.normal_(0, math.sqrt(2. / n)) + elif isinstance(m, SynchronizedBatchNorm2d): + m.weight.data.fill_(1) + m.bias.data.zero_() + elif isinstance(m, nn.BatchNorm2d): + m.weight.data.fill_(1) + m.bias.data.zero_() + + + def _load_pretrained_model(self): + pretrain_dict = model_zoo.load_url('http://data.lip6.fr/cadene/pretrainedmodels/xception-b5690688.pth') + model_dict = {} + state_dict = self.state_dict() + + for k, v in pretrain_dict.items(): + if k in state_dict: + if 'pointwise' in k: + v = v.unsqueeze(-1).unsqueeze(-1) + if k.startswith('block11'): + model_dict[k] = v + model_dict[k.replace('block11', 'block12')] = v + model_dict[k.replace('block11', 'block13')] = v + model_dict[k.replace('block11', 'block14')] = v + model_dict[k.replace('block11', 'block15')] = v + model_dict[k.replace('block11', 'block16')] = v + model_dict[k.replace('block11', 'block17')] = v + model_dict[k.replace('block11', 'block18')] = v + model_dict[k.replace('block11', 'block19')] = v + elif k.startswith('block12'): + model_dict[k.replace('block12', 'block20')] = v + elif k.startswith('bn3'): + model_dict[k] = v + model_dict[k.replace('bn3', 'bn4')] = v + elif k.startswith('conv4'): + model_dict[k.replace('conv4', 'conv5')] = v + elif k.startswith('bn4'): + model_dict[k.replace('bn4', 'bn5')] = v + else: + model_dict[k] = v + state_dict.update(model_dict) + self.load_state_dict(state_dict) + + + +if __name__ == "__main__": + import torch + model = AlignedXception(BatchNorm=nn.BatchNorm2d, pretrained=True, output_stride=16) + input = torch.rand(1, 3, 512, 512) + output, low_level_feat = model(input) + print(output.size()) + print(low_level_feat.size()) diff --git a/data/MBD/model/deep_lab_model/decoder.py b/data/MBD/model/deep_lab_model/decoder.py new file mode 100644 index 0000000..56efcb8 --- /dev/null +++ b/data/MBD/model/deep_lab_model/decoder.py @@ -0,0 +1,59 @@ +import math +import torch +import torch.nn as nn +import torch.nn.functional as F +from model.deep_lab_model.sync_batchnorm.batchnorm import SynchronizedBatchNorm2d + +class Decoder(nn.Module): + def __init__(self, num_classes, backbone, BatchNorm): + super(Decoder, self).__init__() + if backbone == 'resnet' or backbone == 'drn': + low_level_inplanes = 256 + elif backbone == 'xception': + low_level_inplanes = 128 + elif backbone == 'mobilenet': + low_level_inplanes = 24 + else: + raise NotImplementedError + + self.conv1 = nn.Conv2d(low_level_inplanes, 48, 1, bias=False) + self.bn1 = BatchNorm(48) + self.relu = nn.ReLU() + self.last_conv = nn.Sequential(nn.Conv2d(304, 256, kernel_size=3, stride=1, padding=1, bias=False), + BatchNorm(256), + nn.ReLU(), + nn.Dropout(0.5), + nn.Conv2d(256, 256, kernel_size=3, stride=1, padding=1, bias=False), + BatchNorm(256), + nn.ReLU(), + nn.Dropout(0.1), + nn.Conv2d(256, num_classes, kernel_size=1, stride=1), + nn.Sigmoid() + ) + self._init_weight() + + + def forward(self, x, low_level_feat): + low_level_feat = self.conv1(low_level_feat) + low_level_feat = self.bn1(low_level_feat) + low_level_feat = self.relu(low_level_feat) + + x = F.interpolate(x, size=low_level_feat.size()[2:], mode='bilinear', align_corners=True) + x = torch.cat((x, low_level_feat), dim=1) + x = self.last_conv(x) + + return x + + def _init_weight(self): + for m in self.modules(): + if isinstance(m, nn.Conv2d): + torch.nn.init.kaiming_normal_(m.weight) + elif isinstance(m, SynchronizedBatchNorm2d): + m.weight.data.fill_(1) + m.bias.data.zero_() + elif isinstance(m, nn.BatchNorm2d): + m.weight.data.fill_(1) + m.bias.data.zero_() + +def build_decoder(num_classes, backbone, BatchNorm): + return Decoder(num_classes, backbone, BatchNorm) \ No newline at end of file diff --git a/data/MBD/model/deep_lab_model/deeplab.py b/data/MBD/model/deep_lab_model/deeplab.py new file mode 100644 index 0000000..4533467 --- /dev/null +++ b/data/MBD/model/deep_lab_model/deeplab.py @@ -0,0 +1,81 @@ +import torch +import torch.nn as nn +import torch.nn.functional as F +from model.deep_lab_model.sync_batchnorm.batchnorm import SynchronizedBatchNorm2d +from model.deep_lab_model.aspp import build_aspp +from model.deep_lab_model.decoder import build_decoder +from model.deep_lab_model.backbone import build_backbone + +class DeepLab(nn.Module): + def __init__(self, backbone='resnet', output_stride=16, num_classes=21, + sync_bn=True, freeze_bn=False): + super(DeepLab, self).__init__() + if backbone == 'drn': + output_stride = 8 + + if sync_bn == True: + BatchNorm = SynchronizedBatchNorm2d + else: + BatchNorm = nn.BatchNorm2d + + self.backbone = build_backbone(backbone, output_stride, BatchNorm) + self.aspp = build_aspp(backbone, output_stride, BatchNorm) + self.decoder = build_decoder(num_classes, backbone, BatchNorm) + + self.freeze_bn = freeze_bn + + def forward(self, input): + x, low_level_feat = self.backbone(input) + x = self.aspp(x) + x = self.decoder(x, low_level_feat) + x = F.interpolate(x, size=input.size()[2:], mode='bilinear', align_corners=True) + + return x + + def freeze_bn(self): + for m in self.modules(): + if isinstance(m, SynchronizedBatchNorm2d): + m.eval() + elif isinstance(m, nn.BatchNorm2d): + m.eval() + + def get_1x_lr_params(self): + modules = [self.backbone] + for i in range(len(modules)): + for m in modules[i].named_modules(): + if self.freeze_bn: + if isinstance(m[1], nn.Conv2d): + for p in m[1].parameters(): + if p.requires_grad: + yield p + else: + if isinstance(m[1], nn.Conv2d) or isinstance(m[1], SynchronizedBatchNorm2d) \ + or isinstance(m[1], nn.BatchNorm2d): + for p in m[1].parameters(): + if p.requires_grad: + yield p + + def get_10x_lr_params(self): + modules = [self.aspp, self.decoder] + for i in range(len(modules)): + for m in modules[i].named_modules(): + if self.freeze_bn: + if isinstance(m[1], nn.Conv2d): + for p in m[1].parameters(): + if p.requires_grad: + yield p + else: + if isinstance(m[1], nn.Conv2d) or isinstance(m[1], SynchronizedBatchNorm2d) \ + or isinstance(m[1], nn.BatchNorm2d): + for p in m[1].parameters(): + if p.requires_grad: + yield p + +if __name__ == "__main__": + model = DeepLab(backbone='mobilenet', output_stride=16) + model.eval() + input = torch.rand(1, 3, 513, 513) + output = model(input) + print(output.size()) + + diff --git a/data/MBD/model/deep_lab_model/sync_batchnorm/__init__.py b/data/MBD/model/deep_lab_model/sync_batchnorm/__init__.py new file mode 100644 index 0000000..540118f --- /dev/null +++ b/data/MBD/model/deep_lab_model/sync_batchnorm/__init__.py @@ -0,0 +1,12 @@ +# -*- coding: utf-8 -*- +# File : __init__.py +# Author : Jiayuan Mao +# Email : maojiayuan@gmail.com +# Date : 27/01/2018 +# +# This file is part of Synchronized-BatchNorm-PyTorch. +# https://github.com/vacancy/Synchronized-BatchNorm-PyTorch +# Distributed under MIT License. + +from .batchnorm import SynchronizedBatchNorm1d, SynchronizedBatchNorm2d, SynchronizedBatchNorm3d +from .replicate import DataParallelWithCallback, patch_replication_callback \ No newline at end of file diff --git a/data/MBD/model/deep_lab_model/sync_batchnorm/batchnorm.py b/data/MBD/model/deep_lab_model/sync_batchnorm/batchnorm.py new file mode 100644 index 0000000..aa9dd37 --- /dev/null +++ b/data/MBD/model/deep_lab_model/sync_batchnorm/batchnorm.py @@ -0,0 +1,282 @@ +# -*- coding: utf-8 -*- +# File : batchnorm.py +# Author : Jiayuan Mao +# Email : maojiayuan@gmail.com +# Date : 27/01/2018 +# +# This file is part of Synchronized-BatchNorm-PyTorch. +# https://github.com/vacancy/Synchronized-BatchNorm-PyTorch +# Distributed under MIT License. + +import collections + +import torch +import torch.nn.functional as F + +from torch.nn.modules.batchnorm import _BatchNorm +from torch.nn.parallel._functions import ReduceAddCoalesced, Broadcast + +from .comm import SyncMaster + +__all__ = ['SynchronizedBatchNorm1d', 'SynchronizedBatchNorm2d', 'SynchronizedBatchNorm3d'] + + +def _sum_ft(tensor): + """sum over the first and last dimention""" + return tensor.sum(dim=0).sum(dim=-1) + + +def _unsqueeze_ft(tensor): + """add new dementions at the front and the tail""" + return tensor.unsqueeze(0).unsqueeze(-1) + + +_ChildMessage = collections.namedtuple('_ChildMessage', ['sum', 'ssum', 'sum_size']) +_MasterMessage = collections.namedtuple('_MasterMessage', ['sum', 'inv_std']) + + +class _SynchronizedBatchNorm(_BatchNorm): + def __init__(self, num_features, eps=1e-5, momentum=0.1, affine=True): + super(_SynchronizedBatchNorm, self).__init__(num_features, eps=eps, momentum=momentum, affine=affine) + + self._sync_master = SyncMaster(self._data_parallel_master) + + self._is_parallel = False + self._parallel_id = None + self._slave_pipe = None + + def forward(self, input): + # If it is not parallel computation or is in evaluation mode, use PyTorch's implementation. + if not (self._is_parallel and self.training): + return F.batch_norm( + input, self.running_mean, self.running_var, self.weight, self.bias, + self.training, self.momentum, self.eps) + + # Resize the input to (B, C, -1). + input_shape = input.size() + input = input.view(input.size(0), self.num_features, -1) + + # Compute the sum and square-sum. + sum_size = input.size(0) * input.size(2) + input_sum = _sum_ft(input) + input_ssum = _sum_ft(input ** 2) + + # Reduce-and-broadcast the statistics. + if self._parallel_id == 0: + mean, inv_std = self._sync_master.run_master(_ChildMessage(input_sum, input_ssum, sum_size)) + else: + mean, inv_std = self._slave_pipe.run_slave(_ChildMessage(input_sum, input_ssum, sum_size)) + + # Compute the output. + if self.affine: + # MJY:: Fuse the multiplication for speed. + output = (input - _unsqueeze_ft(mean)) * _unsqueeze_ft(inv_std * self.weight) + _unsqueeze_ft(self.bias) + else: + output = (input - _unsqueeze_ft(mean)) * _unsqueeze_ft(inv_std) + + # Reshape it. + return output.view(input_shape) + + def __data_parallel_replicate__(self, ctx, copy_id): + self._is_parallel = True + self._parallel_id = copy_id + + # parallel_id == 0 means master device. + if self._parallel_id == 0: + ctx.sync_master = self._sync_master + else: + self._slave_pipe = ctx.sync_master.register_slave(copy_id) + + def _data_parallel_master(self, intermediates): + """Reduce the sum and square-sum, compute the statistics, and broadcast it.""" + + # Always using same "device order" makes the ReduceAdd operation faster. + # Thanks to:: Tete Xiao (http://tetexiao.com/) + intermediates = sorted(intermediates, key=lambda i: i[1].sum.get_device()) + + to_reduce = [i[1][:2] for i in intermediates] + to_reduce = [j for i in to_reduce for j in i] # flatten + target_gpus = [i[1].sum.get_device() for i in intermediates] + + sum_size = sum([i[1].sum_size for i in intermediates]) + sum_, ssum = ReduceAddCoalesced.apply(target_gpus[0], 2, *to_reduce) + mean, inv_std = self._compute_mean_std(sum_, ssum, sum_size) + + broadcasted = Broadcast.apply(target_gpus, mean, inv_std) + + outputs = [] + for i, rec in enumerate(intermediates): + outputs.append((rec[0], _MasterMessage(*broadcasted[i * 2:i * 2 + 2]))) + + return outputs + + def _compute_mean_std(self, sum_, ssum, size): + """Compute the mean and standard-deviation with sum and square-sum. This method + also maintains the moving average on the master device.""" + assert size > 1, 'BatchNorm computes unbiased standard-deviation, which requires size > 1.' + mean = sum_ / size + sumvar = ssum - sum_ * mean + unbias_var = sumvar / (size - 1) + bias_var = sumvar / size + + self.running_mean = (1 - self.momentum) * self.running_mean + self.momentum * mean.data + self.running_var = (1 - self.momentum) * self.running_var + self.momentum * unbias_var.data + + return mean, bias_var.clamp(self.eps) ** -0.5 + + +class SynchronizedBatchNorm1d(_SynchronizedBatchNorm): + r"""Applies Synchronized Batch Normalization over a 2d or 3d input that is seen as a + mini-batch. + .. math:: + y = \frac{x - mean[x]}{ \sqrt{Var[x] + \epsilon}} * gamma + beta + This module differs from the built-in PyTorch BatchNorm1d as the mean and + standard-deviation are reduced across all devices during training. + For example, when one uses `nn.DataParallel` to wrap the network during + training, PyTorch's implementation normalize the tensor on each device using + the statistics only on that device, which accelerated the computation and + is also easy to implement, but the statistics might be inaccurate. + Instead, in this synchronized version, the statistics will be computed + over all training samples distributed on multiple devices. + + Note that, for one-GPU or CPU-only case, this module behaves exactly same + as the built-in PyTorch implementation. + The mean and standard-deviation are calculated per-dimension over + the mini-batches and gamma and beta are learnable parameter vectors + of size C (where C is the input size). + During training, this layer keeps a running estimate of its computed mean + and variance. The running sum is kept with a default momentum of 0.1. + During evaluation, this running mean/variance is used for normalization. + Because the BatchNorm is done over the `C` dimension, computing statistics + on `(N, L)` slices, it's common terminology to call this Temporal BatchNorm + Args: + num_features: num_features from an expected input of size + `batch_size x num_features [x width]` + eps: a value added to the denominator for numerical stability. + Default: 1e-5 + momentum: the value used for the running_mean and running_var + computation. Default: 0.1 + affine: a boolean value that when set to ``True``, gives the layer learnable + affine parameters. Default: ``True`` + Shape: + - Input: :math:`(N, C)` or :math:`(N, C, L)` + - Output: :math:`(N, C)` or :math:`(N, C, L)` (same shape as input) + Examples: + >>> # With Learnable Parameters + >>> m = SynchronizedBatchNorm1d(100) + >>> # Without Learnable Parameters + >>> m = SynchronizedBatchNorm1d(100, affine=False) + >>> input = torch.autograd.Variable(torch.randn(20, 100)) + >>> output = m(input) + """ + + def _check_input_dim(self, input): + if input.dim() != 2 and input.dim() != 3: + raise ValueError('expected 2D or 3D input (got {}D input)' + .format(input.dim())) + super(SynchronizedBatchNorm1d, self)._check_input_dim(input) + + +class SynchronizedBatchNorm2d(_SynchronizedBatchNorm): + r"""Applies Batch Normalization over a 4d input that is seen as a mini-batch + of 3d inputs + .. math:: + y = \frac{x - mean[x]}{ \sqrt{Var[x] + \epsilon}} * gamma + beta + This module differs from the built-in PyTorch BatchNorm2d as the mean and + standard-deviation are reduced across all devices during training. + For example, when one uses `nn.DataParallel` to wrap the network during + training, PyTorch's implementation normalize the tensor on each device using + the statistics only on that device, which accelerated the computation and + is also easy to implement, but the statistics might be inaccurate. + Instead, in this synchronized version, the statistics will be computed + over all training samples distributed on multiple devices. + + Note that, for one-GPU or CPU-only case, this module behaves exactly same + as the built-in PyTorch implementation. + The mean and standard-deviation are calculated per-dimension over + the mini-batches and gamma and beta are learnable parameter vectors + of size C (where C is the input size). + During training, this layer keeps a running estimate of its computed mean + and variance. The running sum is kept with a default momentum of 0.1. + During evaluation, this running mean/variance is used for normalization. + Because the BatchNorm is done over the `C` dimension, computing statistics + on `(N, H, W)` slices, it's common terminology to call this Spatial BatchNorm + Args: + num_features: num_features from an expected input of + size batch_size x num_features x height x width + eps: a value added to the denominator for numerical stability. + Default: 1e-5 + momentum: the value used for the running_mean and running_var + computation. Default: 0.1 + affine: a boolean value that when set to ``True``, gives the layer learnable + affine parameters. Default: ``True`` + Shape: + - Input: :math:`(N, C, H, W)` + - Output: :math:`(N, C, H, W)` (same shape as input) + Examples: + >>> # With Learnable Parameters + >>> m = SynchronizedBatchNorm2d(100) + >>> # Without Learnable Parameters + >>> m = SynchronizedBatchNorm2d(100, affine=False) + >>> input = torch.autograd.Variable(torch.randn(20, 100, 35, 45)) + >>> output = m(input) + """ + + def _check_input_dim(self, input): + if input.dim() != 4: + raise ValueError('expected 4D input (got {}D input)' + .format(input.dim())) + super(SynchronizedBatchNorm2d, self)._check_input_dim(input) + + +class SynchronizedBatchNorm3d(_SynchronizedBatchNorm): + r"""Applies Batch Normalization over a 5d input that is seen as a mini-batch + of 4d inputs + .. math:: + y = \frac{x - mean[x]}{ \sqrt{Var[x] + \epsilon}} * gamma + beta + This module differs from the built-in PyTorch BatchNorm3d as the mean and + standard-deviation are reduced across all devices during training. + For example, when one uses `nn.DataParallel` to wrap the network during + training, PyTorch's implementation normalize the tensor on each device using + the statistics only on that device, which accelerated the computation and + is also easy to implement, but the statistics might be inaccurate. + Instead, in this synchronized version, the statistics will be computed + over all training samples distributed on multiple devices. + + Note that, for one-GPU or CPU-only case, this module behaves exactly same + as the built-in PyTorch implementation. + The mean and standard-deviation are calculated per-dimension over + the mini-batches and gamma and beta are learnable parameter vectors + of size C (where C is the input size). + During training, this layer keeps a running estimate of its computed mean + and variance. The running sum is kept with a default momentum of 0.1. + During evaluation, this running mean/variance is used for normalization. + Because the BatchNorm is done over the `C` dimension, computing statistics + on `(N, D, H, W)` slices, it's common terminology to call this Volumetric BatchNorm + or Spatio-temporal BatchNorm + Args: + num_features: num_features from an expected input of + size batch_size x num_features x depth x height x width + eps: a value added to the denominator for numerical stability. + Default: 1e-5 + momentum: the value used for the running_mean and running_var + computation. Default: 0.1 + affine: a boolean value that when set to ``True``, gives the layer learnable + affine parameters. Default: ``True`` + Shape: + - Input: :math:`(N, C, D, H, W)` + - Output: :math:`(N, C, D, H, W)` (same shape as input) + Examples: + >>> # With Learnable Parameters + >>> m = SynchronizedBatchNorm3d(100) + >>> # Without Learnable Parameters + >>> m = SynchronizedBatchNorm3d(100, affine=False) + >>> input = torch.autograd.Variable(torch.randn(20, 100, 35, 45, 10)) + >>> output = m(input) + """ + + def _check_input_dim(self, input): + if input.dim() != 5: + raise ValueError('expected 5D input (got {}D input)' + .format(input.dim())) + super(SynchronizedBatchNorm3d, self)._check_input_dim(input) \ No newline at end of file diff --git a/data/MBD/model/deep_lab_model/sync_batchnorm/comm.py b/data/MBD/model/deep_lab_model/sync_batchnorm/comm.py new file mode 100644 index 0000000..8f2f701 --- /dev/null +++ b/data/MBD/model/deep_lab_model/sync_batchnorm/comm.py @@ -0,0 +1,129 @@ +# -*- coding: utf-8 -*- +# File : comm.py +# Author : Jiayuan Mao +# Email : maojiayuan@gmail.com +# Date : 27/01/2018 +# +# This file is part of Synchronized-BatchNorm-PyTorch. +# https://github.com/vacancy/Synchronized-BatchNorm-PyTorch +# Distributed under MIT License. + +import queue +import collections +import threading + +__all__ = ['FutureResult', 'SlavePipe', 'SyncMaster'] + + +class FutureResult(object): + """A thread-safe future implementation. Used only as one-to-one pipe.""" + + def __init__(self): + self._result = None + self._lock = threading.Lock() + self._cond = threading.Condition(self._lock) + + def put(self, result): + with self._lock: + assert self._result is None, 'Previous result has\'t been fetched.' + self._result = result + self._cond.notify() + + def get(self): + with self._lock: + if self._result is None: + self._cond.wait() + + res = self._result + self._result = None + return res + + +_MasterRegistry = collections.namedtuple('MasterRegistry', ['result']) +_SlavePipeBase = collections.namedtuple('_SlavePipeBase', ['identifier', 'queue', 'result']) + + +class SlavePipe(_SlavePipeBase): + """Pipe for master-slave communication.""" + + def run_slave(self, msg): + self.queue.put((self.identifier, msg)) + ret = self.result.get() + self.queue.put(True) + return ret + + +class SyncMaster(object): + """An abstract `SyncMaster` object. + - During the replication, as the data parallel will trigger an callback of each module, all slave devices should + call `register(id)` and obtain an `SlavePipe` to communicate with the master. + - During the forward pass, master device invokes `run_master`, all messages from slave devices will be collected, + and passed to a registered callback. + - After receiving the messages, the master device should gather the information and determine to message passed + back to each slave devices. + """ + + def __init__(self, master_callback): + """ + Args: + master_callback: a callback to be invoked after having collected messages from slave devices. + """ + self._master_callback = master_callback + self._queue = queue.Queue() + self._registry = collections.OrderedDict() + self._activated = False + + def __getstate__(self): + return {'master_callback': self._master_callback} + + def __setstate__(self, state): + self.__init__(state['master_callback']) + + def register_slave(self, identifier): + """ + Register an slave device. + Args: + identifier: an identifier, usually is the device id. + Returns: a `SlavePipe` object which can be used to communicate with the master device. + """ + if self._activated: + assert self._queue.empty(), 'Queue is not clean before next initialization.' + self._activated = False + self._registry.clear() + future = FutureResult() + self._registry[identifier] = _MasterRegistry(future) + return SlavePipe(identifier, self._queue, future) + + def run_master(self, master_msg): + """ + Main entry for the master device in each forward pass. + The messages were first collected from each devices (including the master device), and then + an callback will be invoked to compute the message to be sent back to each devices + (including the master device). + Args: + master_msg: the message that the master want to send to itself. This will be placed as the first + message when calling `master_callback`. For detailed usage, see `_SynchronizedBatchNorm` for an example. + Returns: the message to be sent back to the master device. + """ + self._activated = True + + intermediates = [(0, master_msg)] + for i in range(self.nr_slaves): + intermediates.append(self._queue.get()) + + results = self._master_callback(intermediates) + assert results[0][0] == 0, 'The first result should belongs to the master.' + + for i, res in results: + if i == 0: + continue + self._registry[i].result.put(res) + + for i in range(self.nr_slaves): + assert self._queue.get() is True + + return results[0][1] + + @property + def nr_slaves(self): + return len(self._registry) diff --git a/data/MBD/model/deep_lab_model/sync_batchnorm/replicate.py b/data/MBD/model/deep_lab_model/sync_batchnorm/replicate.py new file mode 100644 index 0000000..3734266 --- /dev/null +++ b/data/MBD/model/deep_lab_model/sync_batchnorm/replicate.py @@ -0,0 +1,88 @@ +# -*- coding: utf-8 -*- +# File : replicate.py +# Author : Jiayuan Mao +# Email : maojiayuan@gmail.com +# Date : 27/01/2018 +# +# This file is part of Synchronized-BatchNorm-PyTorch. +# https://github.com/vacancy/Synchronized-BatchNorm-PyTorch +# Distributed under MIT License. + +import functools + +from torch.nn.parallel.data_parallel import DataParallel + +__all__ = [ + 'CallbackContext', + 'execute_replication_callbacks', + 'DataParallelWithCallback', + 'patch_replication_callback' +] + + +class CallbackContext(object): + pass + + +def execute_replication_callbacks(modules): + """ + Execute an replication callback `__data_parallel_replicate__` on each module created by original replication. + The callback will be invoked with arguments `__data_parallel_replicate__(ctx, copy_id)` + Note that, as all modules are isomorphism, we assign each sub-module with a context + (shared among multiple copies of this module on different devices). + Through this context, different copies can share some information. + We guarantee that the callback on the master copy (the first copy) will be called ahead of calling the callback + of any slave copies. + """ + master_copy = modules[0] + nr_modules = len(list(master_copy.modules())) + ctxs = [CallbackContext() for _ in range(nr_modules)] + + for i, module in enumerate(modules): + for j, m in enumerate(module.modules()): + if hasattr(m, '__data_parallel_replicate__'): + m.__data_parallel_replicate__(ctxs[j], i) + + +class DataParallelWithCallback(DataParallel): + """ + Data Parallel with a replication callback. + An replication callback `__data_parallel_replicate__` of each module will be invoked after being created by + original `replicate` function. + The callback will be invoked with arguments `__data_parallel_replicate__(ctx, copy_id)` + Examples: + > sync_bn = SynchronizedBatchNorm1d(10, eps=1e-5, affine=False) + > sync_bn = DataParallelWithCallback(sync_bn, device_ids=[0, 1]) + # sync_bn.__data_parallel_replicate__ will be invoked. + """ + + def replicate(self, module, device_ids): + modules = super(DataParallelWithCallback, self).replicate(module, device_ids) + execute_replication_callbacks(modules) + return modules + + +def patch_replication_callback(data_parallel): + """ + Monkey-patch an existing `DataParallel` object. Add the replication callback. + Useful when you have customized `DataParallel` implementation. + Examples: + > sync_bn = SynchronizedBatchNorm1d(10, eps=1e-5, affine=False) + > sync_bn = DataParallel(sync_bn, device_ids=[0, 1]) + > patch_replication_callback(sync_bn) + # this is equivalent to + > sync_bn = SynchronizedBatchNorm1d(10, eps=1e-5, affine=False) + > sync_bn = DataParallelWithCallback(sync_bn, device_ids=[0, 1]) + """ + + assert isinstance(data_parallel, DataParallel) + + old_replicate = data_parallel.replicate + + @functools.wraps(old_replicate) + def new_replicate(module, device_ids): + modules = old_replicate(module, device_ids) + execute_replication_callbacks(modules) + return modules + + data_parallel.replicate = new_replicate \ No newline at end of file diff --git a/data/MBD/model/deep_lab_model/sync_batchnorm/unittest.py b/data/MBD/model/deep_lab_model/sync_batchnorm/unittest.py new file mode 100644 index 0000000..f826560 --- /dev/null +++ b/data/MBD/model/deep_lab_model/sync_batchnorm/unittest.py @@ -0,0 +1,29 @@ +# -*- coding: utf-8 -*- +# File : unittest.py +# Author : Jiayuan Mao +# Email : maojiayuan@gmail.com +# Date : 27/01/2018 +# +# This file is part of Synchronized-BatchNorm-PyTorch. +# https://github.com/vacancy/Synchronized-BatchNorm-PyTorch +# Distributed under MIT License. + +import unittest + +import numpy as np +from torch.autograd import Variable + + +def as_numpy(v): + if isinstance(v, Variable): + v = v.data + return v.cpu().numpy() + + +class TorchTestCase(unittest.TestCase): + def assertTensorClose(self, a, b, atol=1e-3, rtol=1e-3): + npa, npb = as_numpy(a), as_numpy(b) + self.assertTrue( + np.allclose(npa, npb, atol=atol), + 'Tensor close check failed\n{}\n{}\nadiff={}, rdiff={}'.format(a, b, np.abs(npa - npb).max(), np.abs((npa - npb) / np.fmax(npa, 1e-5)).max()) + ) diff --git a/data/MBD/model/densenetccnl.py b/data/MBD/model/densenetccnl.py new file mode 100644 index 0000000..8c9a2e7 --- /dev/null +++ b/data/MBD/model/densenetccnl.py @@ -0,0 +1,382 @@ +# Densenet decoder encoder with intermediate fully connected layers and dropout + +import torch +import torch.backends.cudnn as cudnn +import torch.nn as nn +import torch.nn.functional as F +import functools +from torch.autograd import gradcheck +from torch.autograd import Function +from torch.autograd import Variable +from torch.autograd import gradcheck +from torch.autograd import Function +import numpy as np + + +def add_coordConv_channels(t): + n,c,h,w=t.size() + xx_channel=np.ones((h, w)) + xx_range=np.array(range(h)) + xx_range=np.expand_dims(xx_range,-1) + xx_coord=xx_channel*xx_range + yy_coord=xx_coord.transpose() + + xx_coord=xx_coord/(h-1) + yy_coord=yy_coord/(h-1) + xx_coord=xx_coord*2 - 1 + yy_coord=yy_coord*2 - 1 + xx_coord=torch.from_numpy(xx_coord).float() + yy_coord=torch.from_numpy(yy_coord).float() + + if t.is_cuda: + xx_coord=xx_coord.cuda() + yy_coord=yy_coord.cuda() + + xx_coord=xx_coord.unsqueeze(0).unsqueeze(0).repeat(n,1,1,1) + yy_coord=yy_coord.unsqueeze(0).unsqueeze(0).repeat(n,1,1,1) + + t_cc=torch.cat((t,xx_coord,yy_coord),dim=1) + + return t_cc + + + +class DenseBlockEncoder(nn.Module): + def __init__(self, n_channels, n_convs, activation=nn.ReLU, args=[False]): + super(DenseBlockEncoder, self).__init__() + assert(n_convs > 0) + + self.n_channels = n_channels + self.n_convs = n_convs + self.layers = nn.ModuleList() + for i in range(n_convs): + self.layers.append(nn.Sequential( + nn.BatchNorm2d(n_channels), + activation(*args), + nn.Conv2d(n_channels, n_channels, 3, stride=1, padding=1, bias=False),)) + + def forward(self, inputs): + outputs = [] + + for i, layer in enumerate(self.layers): + if i > 0: + next_output = 0 + for no in outputs: + next_output = next_output + no + outputs.append(next_output) + else: + outputs.append(layer(inputs)) + return outputs[-1] + +# Dense block in encoder. +class DenseBlockDecoder(nn.Module): + def __init__(self, n_channels, n_convs, activation=nn.ReLU, args=[False]): + super(DenseBlockDecoder, self).__init__() + assert(n_convs > 0) + + self.n_channels = n_channels + self.n_convs = n_convs + self.layers = nn.ModuleList() + for i in range(n_convs): + self.layers.append(nn.Sequential( + nn.BatchNorm2d(n_channels), + activation(*args), + nn.ConvTranspose2d(n_channels, n_channels, 3, stride=1, padding=1, bias=False),)) + + def forward(self, inputs): + outputs = [] + + for i, layer in enumerate(self.layers): + if i > 0: + next_output = 0 + for no in outputs: + next_output = next_output + no + outputs.append(next_output) + else: + outputs.append(layer(inputs)) + return outputs[-1] + +class DenseTransitionBlockEncoder(nn.Module): + def __init__(self, n_channels_in, n_channels_out, mp, activation=nn.ReLU, args=[False]): + super(DenseTransitionBlockEncoder, self).__init__() + self.n_channels_in = n_channels_in + self.n_channels_out = n_channels_out + self.mp = mp + self.main = nn.Sequential( + nn.BatchNorm2d(n_channels_in), + activation(*args), + nn.Conv2d(n_channels_in, n_channels_out, 1, stride=1, padding=0, bias=False), + nn.MaxPool2d(mp), + ) + def forward(self, inputs): + # print(inputs.shape,'222222222222222',self.main(inputs).shape) + return self.main(inputs) + + +class DenseTransitionBlockDecoder(nn.Module): + def __init__(self, n_channels_in, n_channels_out, activation=nn.ReLU, args=[False]): + super(DenseTransitionBlockDecoder, self).__init__() + self.n_channels_in = n_channels_in + self.n_channels_out = n_channels_out + self.main = nn.Sequential( + nn.BatchNorm2d(n_channels_in), + activation(*args), + nn.ConvTranspose2d(n_channels_in, n_channels_out, 4, stride=2, padding=1, bias=False), + ) + def forward(self, inputs): + # print(inputs.shape,'333333333333',self.main(inputs).shape) + return self.main(inputs) + +## Dense encoders and decoders for image of size 128 128 +class waspDenseEncoder128(nn.Module): + def __init__(self, nc=1, ndf = 32, ndim = 128, activation=nn.LeakyReLU, args=[0.2, False], f_activation=nn.Tanh, f_args=[]): + super(waspDenseEncoder128, self).__init__() + self.ndim = ndim + + self.main = nn.Sequential( + # input is (nc) x 128 x 128 + nn.BatchNorm2d(nc), + nn.ReLU(True), + nn.Conv2d(nc, ndf, 4, stride=2, padding=1), + + # state size. (ndf) x 64 x 64 + DenseBlockEncoder(ndf, 6), + DenseTransitionBlockEncoder(ndf, ndf*2, 2, activation=activation, args=args), + + # state size. (ndf*2) x 32 x 32 + DenseBlockEncoder(ndf*2, 12), + DenseTransitionBlockEncoder(ndf*2, ndf*4, 2, activation=activation, args=args), + + # state size. (ndf*4) x 16 x 16 + DenseBlockEncoder(ndf*4, 16), + DenseTransitionBlockEncoder(ndf*4, ndf*8, 2, activation=activation, args=args), + + # state size. (ndf*4) x 8 x 8 + DenseBlockEncoder(ndf*8, 16), + DenseTransitionBlockEncoder(ndf*8, ndf*8, 2, activation=activation, args=args), + + # state size. (ndf*8) x 4 x 4 + DenseBlockEncoder(ndf*8, 16), + DenseTransitionBlockEncoder(ndf*8, ndim, 4, activation=activation, args=args), + f_activation(*f_args), + ) + + def forward(self, input): + input=add_coordConv_channels(input) + output = self.main(input).view(-1,self.ndim) + #print(output.size()) + return output + +class waspDenseDecoder128(nn.Module): + def __init__(self, nz=128, nc=1, ngf=32, lb=0, ub=1, activation=nn.ReLU, args=[False], f_activation=nn.Hardtanh, f_args=[]): + super(waspDenseDecoder128, self).__init__() + self.main = nn.Sequential( + # input is Z, going into convolution + nn.BatchNorm2d(nz), + activation(*args), + nn.ConvTranspose2d(nz, ngf * 8, 4, 1, 0, bias=False), + + # state size. (ngf*8) x 4 x 4 + DenseBlockDecoder(ngf*8, 16), + DenseTransitionBlockDecoder(ngf*8, ngf*8), + + # state size. (ngf*4) x 8 x 8 + DenseBlockDecoder(ngf*8, 16), + DenseTransitionBlockDecoder(ngf*8, ngf*4), + + # state size. (ngf*2) x 16 x 16 + DenseBlockDecoder(ngf*4, 12), + DenseTransitionBlockDecoder(ngf*4, ngf*2), + + # state size. (ngf) x 32 x 32 + DenseBlockDecoder(ngf*2, 6), + DenseTransitionBlockDecoder(ngf*2, ngf), + + # state size. (ngf) x 64 x 64 + DenseBlockDecoder(ngf, 6), + DenseTransitionBlockDecoder(ngf, ngf), + + # state size (ngf) x 128 x 128 + nn.BatchNorm2d(ngf), + activation(*args), + nn.ConvTranspose2d(ngf, nc, 3, stride=1, padding=1, bias=False), + f_activation(*f_args), + ) + # self.smooth=nn.Sequential( + # nn.Conv2d(nc, nc, 1, stride=1, padding=0, bias=False), + # f_activation(*f_args), + # ) + def forward(self, inputs): + # return self.smooth(self.main(inputs)) + return self.main(inputs) + + + +## Dense encoders and decoders for image of size 512 512 +class waspDenseEncoder512(nn.Module): + def __init__(self, nc=1, ndf = 32, ndim = 128, activation=nn.LeakyReLU, args=[0.2, False], f_activation=nn.Tanh, f_args=[]): + super(waspDenseEncoder512, self).__init__() + self.ndim = ndim + + self.main = nn.Sequential( + # input is (nc) x 128 x 128 > *4 + nn.BatchNorm2d(nc), + nn.ReLU(True), + nn.Conv2d(nc, ndf, 4, stride=2, padding=1), + + # state size. (ndf) x 64 x 64 > *4 + DenseBlockEncoder(ndf, 6), + DenseTransitionBlockEncoder(ndf, ndf*2, 2, activation=activation, args=args), + + # state size. (ndf*2) x 32 x 32 > *4 + DenseBlockEncoder(ndf*2, 12), + DenseTransitionBlockEncoder(ndf*2, ndf*4, 2, activation=activation, args=args), + + # state size. (ndf*4) x 16 x 16 > *4 + DenseBlockEncoder(ndf*4, 16), + DenseTransitionBlockEncoder(ndf*4, ndf*8, 2, activation=activation, args=args), + + # state size. (ndf*8) x 8 x 8 *4 + DenseBlockEncoder(ndf*8, 16), + DenseTransitionBlockEncoder(ndf*8, ndf*8, 2, activation=activation, args=args), + + # state size. (ndf*8) x 4 x 4 > *4 + DenseBlockEncoder(ndf*8, 16), + DenseTransitionBlockEncoder(ndf*8, ndf*8, 4, activation=activation, args=args), + f_activation(*f_args), + + # state size. (ndf*8) x 2 x 2 > *4 + DenseBlockEncoder(ndf*8, 16), + DenseTransitionBlockEncoder(ndf*8, ndim, 4, activation=activation, args=args), + f_activation(*f_args), + ) + + def forward(self, input): + input=add_coordConv_channels(input) + output = self.main(input).view(-1,self.ndim) + # output = self.main(input).view(8,-1) + # print(input.shape,'---------------------') + #print(output.size()) + return output + +class waspDenseDecoder512(nn.Module): + def __init__(self, nz=128, nc=1, ngf=32, lb=0, ub=1, activation=nn.ReLU, args=[False], f_activation=nn.Tanh, f_args=[]): + super(waspDenseDecoder512, self).__init__() + self.main = nn.Sequential( + # input is Z, going into convolution + nn.BatchNorm2d(nz), + activation(*args), + nn.ConvTranspose2d(nz, ngf * 8, 4, 1, 0, bias=False), + + # state size. (ngf*8) x 4 x 4 + DenseBlockDecoder(ngf*8, 16), + DenseTransitionBlockDecoder(ngf*8, ngf*8), + + # state size. (ngf*8) x 8 x 8 + DenseBlockDecoder(ngf*8, 16), + DenseTransitionBlockDecoder(ngf*8, ngf*8), + + # state size. (ngf*4) x 16 x 16 + DenseBlockDecoder(ngf*8, 16), + DenseTransitionBlockDecoder(ngf*8, ngf*4), + + # state size. (ngf*2) x 32 x 32 + DenseBlockDecoder(ngf*4, 12), + DenseTransitionBlockDecoder(ngf*4, ngf*2), + + # state size. (ngf) x 64 x 64 + DenseBlockDecoder(ngf*2, 6), + DenseTransitionBlockDecoder(ngf*2, ngf), + + # state size. (ngf) x 128 x 128 + DenseBlockDecoder(ngf, 6), + DenseTransitionBlockDecoder(ngf, ngf), + + # state size. (ngf) x 256 x 256 + DenseBlockDecoder(ngf, 6), + DenseTransitionBlockDecoder(ngf, ngf), + + # state size (ngf) x 512 x 512 + nn.BatchNorm2d(ngf), + activation(*args), + nn.ConvTranspose2d(ngf, nc, 3, stride=1, padding=1, bias=False), + f_activation(*f_args), + ) + # self.smooth=nn.Sequential( + # nn.Conv2d(nc, nc, 1, stride=1, padding=0, bias=False), + # f_activation(*f_args), + # ) + def forward(self, inputs): + # return self.smooth(self.main(inputs)) + return self.main(inputs) + + +class dnetccnl(nn.Module): + #in_channels -> nc | encoder first layer + #filters -> ndf | encoder first layer + #img_size(h,w) -> ndim + #out_channels -> optical flow (x,y) + + def __init__(self, img_size=448, in_channels=3, out_channels=2, filters=32,fc_units=100): + super(dnetccnl, self).__init__() + self.nc=in_channels + self.nf=filters + self.ndim=img_size + self.oc=out_channels + self.fcu=fc_units + + self.encoder=waspDenseEncoder128(nc=self.nc+2,ndf=self.nf,ndim=self.ndim) + self.decoder=waspDenseDecoder128(nz=self.ndim,nc=self.oc,ngf=self.nf) + # self.fc_layers= nn.Sequential(nn.Linear(self.ndim, self.fcu), + # nn.ReLU(True), + # nn.Dropout(0.25), + # nn.Linear(self.fcu,self.ndim), + # nn.ReLU(True), + # nn.Dropout(0.25), + # ) + + def forward(self, inputs): + + encoded=self.encoder(inputs) + encoded=encoded.unsqueeze(-1).unsqueeze(-1) + decoded=self.decoder(encoded) + # print torch.max(decoded) + # print torch.min(decoded) + # print(decoded.shape,'11111111111111111',encoded.shape) + + return decoded + +class dnetccnl512(nn.Module): + #in_channels -> nc | encoder first layer + #filters -> ndf | encoder first layer + #img_size(h,w) -> ndim + #out_channels -> optical flow (x,y) + + def __init__(self, img_size=448, in_channels=3, out_channels=2, filters=32,fc_units=100): + super(dnetccnl512, self).__init__() + self.nc=in_channels + self.nf=filters + self.ndim=img_size + self.oc=out_channels + self.fcu=fc_units + + self.encoder=waspDenseEncoder512(nc=self.nc+2,ndf=self.nf,ndim=self.ndim) + self.decoder=waspDenseDecoder512(nz=self.ndim,nc=self.oc,ngf=self.nf) + # self.fc_layers= nn.Sequential(nn.Linear(self.ndim, self.fcu), + # nn.ReLU(True), + # nn.Dropout(0.25), + # nn.Linear(self.fcu,self.ndim), + # nn.ReLU(True), + # nn.Dropout(0.25), + # ) + + def forward(self, inputs): + + encoded=self.encoder(inputs) + encoded=encoded.unsqueeze(-1).unsqueeze(-1) + decoded=self.decoder(encoded) + # print torch.max(decoded) + # print torch.min(decoded) + # print(decoded.shape,'11111111111111111',encoded.shape) + + return decoded \ No newline at end of file diff --git a/data/MBD/model/gienet.py b/data/MBD/model/gienet.py new file mode 100644 index 0000000..aa6ab11 --- /dev/null +++ b/data/MBD/model/gienet.py @@ -0,0 +1,742 @@ +from math import log +import torch +import torch.nn as nn +from torch.nn import init +import functools +from model.cbam import CBAM +# Defines the Unet generator. +# |num_downs|: number of downsamplings in UNet. For example, +# if |num_downs| == 7, image of size 128x128 will become of size 1x1 +# at the bottleneck +class SingleConv(nn.Module): + """(convolution => [BN] => ReLU) * 2""" + + def __init__(self, in_channels, out_channels): + super().__init__() + self.double_conv = nn.Sequential( + nn.ReflectionPad2d(1), + nn.Conv2d(in_channels, out_channels, kernel_size=3, padding=0,stride=1), + nn.BatchNorm2d(out_channels), + nn.ReLU(inplace=True), + # nn.ReflectionPad2d(1), + # nn.Conv2d(out_channels, out_channels, kernel_size=3, padding=0,stride=1), + # nn.BatchNorm2d(out_channels), + # nn.ReLU(inplace=True) + ) + + def forward(self, x): + return self.double_conv(x) +class Down_single(nn.Module): + """Downscaling with maxpool then double conv""" + + def __init__(self, in_channels, out_channels): + super().__init__() + self.maxpool_conv = nn.Sequential( + nn.MaxPool2d(2), + SingleConv(in_channels, out_channels) + ) + + def forward(self, x): + return self.maxpool_conv(x) +class Up_single(nn.Module): + """Upscaling then double conv""" + def __init__(self, in_channels, out_channels, bilinear=True): + super().__init__() + self.up = nn.Upsample(scale_factor=2, mode='nearest') + self.conv = SingleConv(in_channels, out_channels) + self.deconv = nn.ConvTranspose2d(in_channels, out_channels,kernel_size=4, stride=2,padding=1, bias=True) + def forward(self, x1, x2): + x1 = self.deconv(x1) + # input is BCHW + x = torch.cat([x2, x1], dim=1) + return self.conv(x) +class DoubleConv(nn.Module): + """(convolution => [BN] => ReLU) * 2""" + + def __init__(self, in_channels, out_channels): + super().__init__() + self.double_conv = nn.Sequential( + nn.ReflectionPad2d(1), + nn.Conv2d(in_channels, out_channels, kernel_size=3, padding=0,stride=1), + nn.BatchNorm2d(out_channels), + nn.ReLU(inplace=True), + nn.ReflectionPad2d(1), + nn.Conv2d(out_channels, out_channels, kernel_size=3, padding=0,stride=1), + nn.BatchNorm2d(out_channels), + nn.ReLU(inplace=True) + ) + + def forward(self, x): + return self.double_conv(x) +class Down(nn.Module): + """Downscaling with maxpool then double conv""" + + def __init__(self, in_channels, out_channels): + super().__init__() + self.maxpool_conv = nn.Sequential( + nn.MaxPool2d(2), + DoubleConv(in_channels, out_channels) + ) + + def forward(self, x): + return self.maxpool_conv(x) +class Up(nn.Module): + """Upscaling then double conv""" + def __init__(self, in_channels, out_channels, bilinear=True): + super().__init__() + self.up = nn.Upsample(scale_factor=2, mode='nearest') + self.conv = DoubleConv(in_channels, out_channels) + self.deconv = nn.ConvTranspose2d(in_channels, out_channels,kernel_size=4, stride=2,padding=1, bias=True) + def forward(self, x1, x2): + x1 = self.deconv(x1) + # input is BCHW + x = torch.cat([x2, x1], dim=1) + return self.conv(x) + +class OutConv(nn.Module): + def __init__(self, in_channels, out_channels): + super(OutConv, self).__init__() + self.conv = nn.Conv2d(in_channels, out_channels, kernel_size=1) + self.tanh = nn.Tanh() + self.hardtanh = nn.Hardtanh() + self.sigmoid = nn.Sigmoid() + + def forward(self, x1): + x = self.conv(x1) + # x = self.sigmoid(x) + # x = self.hardtanh(x) + # x = (x+1)/2 + return x +class GiemaskGenerator(nn.Module): + """Create a Unet-based generator""" + + def __init__(self, input_nc, output_nc, num_downs, ngf=64, biline=True, norm_layer=nn.BatchNorm2d, use_dropout=False): + """Construct a Unet generator + Parameters: + input_nc (int) -- the number of channels in input images + output_nc (int) -- the number of channels in output images + num_downs (int) -- the number of downsamplings in UNet. For example, # if |num_downs| == 7, + image of size 128x128 will become of size 1x1 # at the bottleneck + ngf (int) -- the number of filters in the last conv layer + norm_layer -- normalization layer + + We construct the U-Net from the innermost layer to the outermost layer. + It is a recursive process. + """ + super(GiemaskGenerator, self).__init__() + self.init_channel =32 + self.inc = DoubleConv(3,self.init_channel) + self.down1 = Down(self.init_channel, self.init_channel*2) + self.down2 = Down(self.init_channel*2, self.init_channel*4) + self.down3 = Down(self.init_channel*4, self.init_channel*8) + self.down4 = Down(self.init_channel*8, self.init_channel*16) + self.down5 = Down(self.init_channel*16, self.init_channel*32) + + self.up1 = Up(self.init_channel*32, self.init_channel*16) + self.up2 = Up(self.init_channel*16, self.init_channel*8) + self.up3 = Up(self.init_channel*8, self.init_channel*4) + self.up4 = Up(self.init_channel*4,self.init_channel*2) + self.up5 = Up(self.init_channel*2, self.init_channel) + self.outc = OutConv(self.init_channel, 1) + self.up1_1 = Up_single(self.init_channel*32, self.init_channel*16) + self.up2_1 = Up_single(self.init_channel*16, self.init_channel*8) + self.up3_1 = Up_single(self.init_channel*8, self.init_channel*4) + self.up4_1 = Up_single(self.init_channel*4,self.init_channel*2) + self.up5_1 = Up_single(self.init_channel*2, self.init_channel) + self.outc_1 = OutConv(self.init_channel, 1) + # self.dropout = nn.Dropout(p=0.5) + def forward(self, input): + x1 = self.inc(input) + x2 = self.down1(x1) + x3 = self.down2(x2) + x4 = self.down3(x3) + x5 = self.down4(x4) + x6 = self.down5(x5) + + + x_1 = self.up1_1(x6, x5) + x_1 = self.up2_1(x_1, x4) + x_1 = self.up3_1(x_1, x3) + x_1 = self.up4_1(x_1, x2) + x_1 = self.up5_1(x_1, x1) + mask = self.outc_1(x_1) + + x = self.up1(x6, x5) +# x = self.dropout(x) + x = self.up2(x, x4) +# x = self.dropout(x) + x = self.up3(x, x3) +# x = self.dropout(x) + x = self.up4(x, x2) +# x = self.dropout(x) + x = self.up5(x, x1) +# x = self.dropout(x) + depth = self.outc(x) + return depth,mask + """Create a Unet-based generator""" +class Giemask2Generator(nn.Module): + """Create a Unet-based generator""" + + def __init__(self, input_nc, output_nc, num_downs, ngf=64, biline=True, norm_layer=nn.BatchNorm2d, use_dropout=False): + """Construct a Unet generator + Parameters: + input_nc (int) -- the number of channels in input images + output_nc (int) -- the number of channels in output images + num_downs (int) -- the number of downsamplings in UNet. For example, # if |num_downs| == 7, + image of size 128x128 will become of size 1x1 # at the bottleneck + ngf (int) -- the number of filters in the last conv layer + norm_layer -- normalization layer + + We construct the U-Net from the innermost layer to the outermost layer. + It is a recursive process. + """ + super(Giemask2Generator, self).__init__() + self.init_channel =32 + self.inc = DoubleConv(3,self.init_channel) + self.down1 = Down(self.init_channel, self.init_channel*2) + self.down2 = Down(self.init_channel*2, self.init_channel*4) + self.down3 = Down(self.init_channel*4, self.init_channel*8) + self.down4 = Down(self.init_channel*8, self.init_channel*16) + self.down5 = Down(self.init_channel*16, self.init_channel*32) + + self.up1 = Up(self.init_channel*32, self.init_channel*16) + self.up2 = Up(self.init_channel*16, self.init_channel*8) + self.up3 = Up(self.init_channel*8, self.init_channel*4) + self.up4 = Up(self.init_channel*4,self.init_channel*2) + self.up5 = Up(self.init_channel*2, self.init_channel) + self.outc = OutConv(self.init_channel, 1) + self.up1_1 = Up_single(self.init_channel*32, self.init_channel*16) + self.up2_1 = Up_single(self.init_channel*16, self.init_channel*8) + self.up3_1 = Up_single(self.init_channel*8, self.init_channel*4) + self.up4_1 = Up_single(self.init_channel*4,self.init_channel*2) + self.up5_1 = Up_single(self.init_channel*2, self.init_channel) + self.outc_1 = OutConv(self.init_channel, 1) + self.outc_2 = OutConv(self.init_channel, 1) + # self.dropout = nn.Dropout(p=0.5) + def forward(self, input): + x1 = self.inc(input) + x2 = self.down1(x1) + x3 = self.down2(x2) + x4 = self.down3(x3) + x5 = self.down4(x4) + x6 = self.down5(x5) + + + x_1 = self.up1_1(x6, x5) + x_1 = self.up2_1(x_1, x4) + x_1 = self.up3_1(x_1, x3) + x_1 = self.up4_1(x_1, x2) + x_1 = self.up5_1(x_1, x1) + mask = self.outc_1(x_1) + edge = self.outc_2(x_1) + + x = self.up1(x6, x5) +# x = self.dropout(x) + x = self.up2(x, x4) +# x = self.dropout(x) + x = self.up3(x, x3) +# x = self.dropout(x) + x = self.up4(x, x2) +# x = self.dropout(x) + x = self.up5(x, x1) +# x = self.dropout(x) + depth = self.outc(x) + return depth,mask,edge + """Create a Unet-based generator""" +class GieGenerator(nn.Module): + def __init__(self, input_nc, output_nc, num_downs, ngf=64, biline=True, norm_layer=nn.BatchNorm2d, use_dropout=False): + """Construct a Unet generator + Parameters: + input_nc (int) -- the number of channels in input images + output_nc (int) -- the number of channels in output images + num_downs (int) -- the number of downsamplings in UNet. For example, # if |num_downs| == 7, + image of size 128x128 will become of size 1x1 # at the bottleneck + ngf (int) -- the number of filters in the last conv layer + norm_layer -- normalization layer + + We construct the U-Net from the innermost layer to the outermost layer. + It is a recursive process. + """ + super(GieGenerator, self).__init__() + self.init_channel =32 + self.inc = DoubleConv(input_nc,self.init_channel) + self.down1 = Down(self.init_channel, self.init_channel*2) + self.down2 = Down(self.init_channel*2, self.init_channel*4) + self.down3 = Down(self.init_channel*4, self.init_channel*8) + self.down4 = Down(self.init_channel*8, self.init_channel*16) + self.down5 = Down(self.init_channel*16, self.init_channel*32) + + self.up1 = Up(self.init_channel*32, self.init_channel*16) + self.up2 = Up(self.init_channel*16, self.init_channel*8) + self.up3 = Up(self.init_channel*8, self.init_channel*4) + self.up4 = Up(self.init_channel*4,self.init_channel*2) + self.up5 = Up(self.init_channel*2, self.init_channel) + self.outc = OutConv(self.init_channel, 2) + # self.dropout = nn.Dropout(p=0.5) + def forward(self, input): + x1 = self.inc(input) + x2 = self.down1(x1) + x3 = self.down2(x2) + x4 = self.down3(x3) + x5 = self.down4(x4) + x6 = self.down5(x5) + + x = self.up1(x6, x5) +# x = self.dropout(x) + x = self.up2(x, x4) +# x = self.dropout(x) + x = self.up3(x, x3) +# x = self.dropout(x) + x = self.up4(x, x2) +# x = self.dropout(x) + x = self.up5(x, x1) +# x = self.dropout(x) + logits1 = self.outc(x) + return logits1 + + +class GiecbamGenerator(nn.Module): + def __init__(self, input_nc, output_nc, num_downs, ngf=64, biline=True, norm_layer=nn.BatchNorm2d, use_dropout=False): + """Construct a Unet generator + Parameters: + input_nc (int) -- the number of channels in input images + output_nc (int) -- the number of channels in output images + num_downs (int) -- the number of downsamplings in UNet. For example, # if |num_downs| == 7, + image of size 128x128 will become of size 1x1 # at the bottleneck + ngf (int) -- the number of filters in the last conv layer + norm_layer -- normalization layer + + We construct the U-Net from the innermost layer to the outermost layer. + It is a recursive process. + """ + super(GiecbamGenerator, self).__init__() + self.init_channel =32 + self.inc = DoubleConv(input_nc,self.init_channel) + self.down1 = Down(self.init_channel, self.init_channel*2) + self.down2 = Down(self.init_channel*2, self.init_channel*4) + self.down3 = Down(self.init_channel*4, self.init_channel*8) + self.down4 = Down(self.init_channel*8, self.init_channel*16) + self.down5 = Down(self.init_channel*16, self.init_channel*32) + self.cbam = CBAM(gate_channels=self.init_channel*32) + self.up1 = Up(self.init_channel*32, self.init_channel*16) + self.up2 = Up(self.init_channel*16, self.init_channel*8) + self.up3 = Up(self.init_channel*8, self.init_channel*4) + self.up4 = Up(self.init_channel*4,self.init_channel*2) + self.up5 = Up(self.init_channel*2, self.init_channel) + self.outc = OutConv(self.init_channel, 2) + self.dropout = nn.Dropout(p=0.1) + def forward(self, input): + x1 = self.inc(input) + x2 = self.down1(x1) + x3 = self.down2(x2) + x4 = self.down3(x3) + x5 = self.down4(x4) + x6 = self.down5(x5) + x6 = self.cbam(x6) + x = self.up1(x6, x5) + x = self.up2(x, x4) + x = self.up3(x, x3) + x = self.up4(x, x2) + x = self.up5(x, x1) + x = self.dropout(x) + logits1 = self.outc(x) + return logits1 + + + + +class Gie2headGenerator(nn.Module): + def __init__(self, input_nc, output_nc, num_downs, ngf=64, biline=True, norm_layer=nn.BatchNorm2d, use_dropout=False): + """Construct a Unet generator + Parameters: + input_nc (int) -- the number of channels in input images + output_nc (int) -- the number of channels in output images + num_downs (int) -- the number of downsamplings in UNet. For example, # if |num_downs| == 7, + image of size 128x128 will become of size 1x1 # at the bottleneck + ngf (int) -- the number of filters in the last conv layer + norm_layer -- normalization layer + + We construct the U-Net from the innermost layer to the outermost layer. + It is a recursive process. + """ + super(Gie2headGenerator, self).__init__() + self.init_channel =32 + self.inc = DoubleConv(input_nc,self.init_channel) + self.down1 = Down(self.init_channel, self.init_channel*2) + self.down2 = Down(self.init_channel*2, self.init_channel*4) + self.down3 = Down(self.init_channel*4, self.init_channel*8) + self.down4 = Down(self.init_channel*8, self.init_channel*16) + self.down5 = Down(self.init_channel*16, self.init_channel*32) + + self.up1_1 = Up(self.init_channel*32, self.init_channel*16) + self.up2_1 = Up(self.init_channel*16, self.init_channel*8) + self.up3_1 = Up(self.init_channel*8, self.init_channel*4) + self.up4_1 = Up(self.init_channel*4,self.init_channel*2) + self.up5_1 = Up(self.init_channel*2, self.init_channel) + self.outc_1 = OutConv(self.init_channel, 1) + + self.up1_2 = Up(self.init_channel*32, self.init_channel*16) + self.up2_2 = Up(self.init_channel*16, self.init_channel*8) + self.up3_2 = Up(self.init_channel*8, self.init_channel*4) + self.up4_2 = Up(self.init_channel*4,self.init_channel*2) + self.up5_2 = Up(self.init_channel*2, self.init_channel) + self.outc_2 = OutConv(self.init_channel, 1) + + def forward(self, input): + x1 = self.inc(input) + x2 = self.down1(x1) + x3 = self.down2(x2) + x4 = self.down3(x3) + x5 = self.down4(x4) + x6 = self.down5(x5) + + x_1 = self.up1_1(x6, x5) + x_1 = self.up2_1(x_1, x4) + x_1 = self.up3_1(x_1, x3) + x_1 = self.up4_1(x_1, x2) + x_1 = self.up5_1(x_1, x1) + logits_1 = self.outc_1(x_1) + + x_2 = self.up1_2(x6, x5) + x_2 = self.up2_2(x_2, x4) + x_2 = self.up3_2(x_2, x3) + x_2 = self.up4_2(x_2, x2) + x_2 = self.up5_2(x_2, x1) + logits_2 = self.outc_2(x_2) + + logits = torch.cat((logits_1,logits_2),1) + + return logits + + + +class BmpGenerator(nn.Module): + def __init__(self, input_nc, output_nc, num_downs, ngf=64, biline=True, norm_layer=nn.BatchNorm2d, use_dropout=False): + """Construct a Unet generator + Parameters: + input_nc (int) -- the number of channels in input images + output_nc (int) -- the number of channels in output images + num_downs (int) -- the number of downsamplings in UNet. For example, # if |num_downs| == 7, + image of size 128x128 will become of size 1x1 # at the bottleneck + ngf (int) -- the number of filters in the last conv layer + norm_layer -- normalization layer + + We construct the U-Net from the innermost layer to the outermost layer. + It is a recursive process. + """ + super(BmpGenerator, self).__init__() + self.init_channel =32 + self.output_nc = output_nc + self.inc = DoubleConv(input_nc,self.init_channel) + self.down1 = Down(self.init_channel, self.init_channel*2) + self.down2 = Down(self.init_channel*2, self.init_channel*4) + self.down3 = Down(self.init_channel*4, self.init_channel*8) + self.down4 = Down(self.init_channel*8, self.init_channel*16) + self.down5 = Down(self.init_channel*16, self.init_channel*32) + + self.up1 = Up(self.init_channel*32, self.init_channel*16) + self.up2 = Up(self.init_channel*16, self.init_channel*8) + self.up3 = Up(self.init_channel*8, self.init_channel*4) + self.up4 = Up(self.init_channel*4,self.init_channel*2) + self.up5 = Up(self.init_channel*2, self.init_channel) + self.outc = OutConv(self.init_channel, self.output_nc) + # self.dropout = nn.Dropout(p=0.5) + def forward(self, input): + x1 = self.inc(input) + x2 = self.down1(x1) + x3 = self.down2(x2) + x4 = self.down3(x3) + x5 = self.down4(x4) + x6 = self.down5(x5) + + x = self.up1(x6, x5) +# x = self.dropout(x) + x = self.up2(x, x4) +# x = self.dropout(x) + x = self.up3(x, x3) +# x = self.dropout(x) + x = self.up4(x, x2) +# x = self.dropout(x) + x = self.up5(x, x1) +# x = self.dropout(x) + logits1 = self.outc(x) + return logits1 +class Bmp2Generator(nn.Module): + """Create a Unet-based generator""" + + def __init__(self, input_nc, output_nc, num_downs, ngf=64, biline=True, norm_layer=nn.BatchNorm2d, use_dropout=False): + """Construct a Unet generator + Parameters: + input_nc (int) -- the number of channels in input images + output_nc (int) -- the number of channels in output images + num_downs (int) -- the number of downsamplings in UNet. For example, # if |num_downs| == 7, + image of size 128x128 will become of size 1x1 # at the bottleneck + ngf (int) -- the number of filters in the last conv layer + norm_layer -- normalization layer + + We construct the U-Net from the innermost layer to the outermost layer. + It is a recursive process. + """ + super(Bmp2Generator, self).__init__() + #gienet + self.init_channel =32 + self.inc = DoubleConv(3,self.init_channel) + self.down1 = Down(self.init_channel, self.init_channel*2) + self.down2 = Down(self.init_channel*2, self.init_channel*4) + self.down3 = Down(self.init_channel*4, self.init_channel*8) + self.down4 = Down(self.init_channel*8, self.init_channel*16) + self.down5 = Down(self.init_channel*16, self.init_channel*32) + + self.up1 = Up(self.init_channel*32, self.init_channel*16) + self.up2 = Up(self.init_channel*16, self.init_channel*8) + self.up3 = Up(self.init_channel*8, self.init_channel*4) + self.up4 = Up(self.init_channel*4,self.init_channel*2) + self.up5 = Up(self.init_channel*2, self.init_channel) + self.outc = OutConv(self.init_channel, 1) + self.up1_1 = Up_single(self.init_channel*32, self.init_channel*16) + self.up2_1 = Up_single(self.init_channel*16, self.init_channel*8) + self.up3_1 = Up_single(self.init_channel*8, self.init_channel*4) + self.up4_1 = Up_single(self.init_channel*4,self.init_channel*2) + self.up5_1 = Up_single(self.init_channel*2, self.init_channel) + self.outc_1 = OutConv(self.init_channel, 1) + self.outc_2 = OutConv(self.init_channel, 1) + + #bpm net + self.inc_b = DoubleConv(4,self.init_channel) + self.down1_b = Down(self.init_channel, self.init_channel*2) + self.down2_b = Down(self.init_channel*2, self.init_channel*4) + self.down3_b = Down(self.init_channel*4, self.init_channel*8) + self.down4_b = Down(self.init_channel*8, self.init_channel*16) + self.down5_b = Down(self.init_channel*16, self.init_channel*32) + + self.up1_b = Up(self.init_channel*32, self.init_channel*16) + self.up2_b = Up(self.init_channel*16, self.init_channel*8) + self.up3_b = Up(self.init_channel*8, self.init_channel*4) + self.up4_b = Up(self.init_channel*4,self.init_channel*2) + self.up5_b = Up(self.init_channel*2, self.init_channel) + self.outc_b = OutConv(self.init_channel, 2) + # self.dropout = nn.Dropout(p=0.5) + def forward(self, input): + #gienet + x1 = self.inc(input) + x2 = self.down1(x1) + x3 = self.down2(x2) + x4 = self.down3(x3) + x5 = self.down4(x4) + x6 = self.down5(x5) + + x_1 = self.up1_1(x6, x5) + x_1 = self.up2_1(x_1, x4) + x_1 = self.up3_1(x_1, x3) + x_1 = self.up4_1(x_1, x2) + x_1 = self.up5_1(x_1, x1) + mask = self.outc_1(x_1) + edge = self.outc_2(x_1) + + x = self.up1(x6, x5) + x = self.up2(x, x4) + x = self.up3(x, x3) + x = self.up4(x, x2) + x = self.up5(x, x1) + depth = self.outc(x) + + #bmpnet + mask[mask>0.5]=1. + mask[mask<=0.5]=0. + image_cat_depth = torch.cat((input*mask,depth*mask),dim=1) + x1_b = self.inc_b(image_cat_depth) + x2_b = self.down1_b(x1_b) + x3_b = self.down2_b(x2_b) + x4_b = self.down3_b(x3_b) + x5_b = self.down4_b(x4_b) + x6_b = self.down5_b(x5_b) + x_b = self.up1_b(x6_b, x5_b) + x_b = self.up2_b(x_b, x4_b) + x_b = self.up3_b(x_b, x3_b) + x_b = self.up4_b(x_b, x2_b) + x_b = self.up5_b(x_b, x1_b) + bm = self.outc_b(x_b) + # return depth,mask,edge,bm + return bm +class UnetGenerator(nn.Module): + def __init__(self, input_nc, output_nc, num_downs, ngf=64, + norm_layer=nn.BatchNorm2d, use_dropout=False): + super(UnetGenerator, self).__init__() + + # construct unet structure + unet_block = UnetSkipConnectionBlock(ngf * 8, ngf * 8, input_nc=None, submodule=None, norm_layer=norm_layer, innermost=True) + for i in range(num_downs - 5): + unet_block = UnetSkipConnectionBlock(ngf * 8, ngf * 8, input_nc=None, submodule=unet_block, norm_layer=norm_layer, use_dropout=use_dropout) + unet_block = UnetSkipConnectionBlock(ngf * 4, ngf * 8, input_nc=None, submodule=unet_block, norm_layer=norm_layer) + unet_block = UnetSkipConnectionBlock(ngf * 2, ngf * 4, input_nc=None, submodule=unet_block, norm_layer=norm_layer) + unet_block = UnetSkipConnectionBlock(ngf, ngf * 2, input_nc=None, submodule=unet_block, norm_layer=norm_layer) + unet_block = UnetSkipConnectionBlock(output_nc, ngf, input_nc=input_nc, submodule=unet_block, outermost=True, norm_layer=norm_layer) + + self.model = unet_block + + def forward(self, input): + return self.model(input) + +#class GieGenerator(nn.Module): +# def __init__(self, input_nc, output_nc, num_downs, ngf=64, +# norm_layer=nn.BatchNorm2d, use_dropout=False): +# super(GieGenerator, self).__init__() +# +# # construct unet structure +# unet_block = UnetSkipConnectionBlock(ngf * 8, ngf * 8, input_nc=None, submodule=None, norm_layer=norm_layer, innermost=True) +# for i in range(num_downs - 5): +# unet_block = UnetSkipConnectionBlock(ngf * 8, ngf * 8, input_nc=None, submodule=unet_block, norm_layer=norm_layer, use_dropout=use_dropout) +# unet_block = UnetSkipConnectionBlock(ngf * 4, ngf * 8, input_nc=None, submodule=unet_block, norm_layer=norm_layer) +# unet_block = UnetSkipConnectionBlock(ngf * 2, ngf * 4, input_nc=None, submodule=unet_block, norm_layer=norm_layer) +# unet_block = UnetSkipConnectionBlock(ngf, ngf * 2, input_nc=None, submodule=unet_block, norm_layer=norm_layer) +# unet_block = UnetSkipConnectionBlock(output_nc, ngf, input_nc=input_nc, submodule=unet_block, outermost=True, norm_layer=norm_layer) +# +# self.model = unet_block +# +# def forward(self, input): +# return self.model(input) + +# Defines the submodule with skip connection. +# X -------------------identity---------------------- X +# |-- downsampling -- |submodule| -- upsampling --| +class UnetSkipConnectionBlock(nn.Module): + def __init__(self, outer_nc, inner_nc, input_nc=None, + submodule=None, outermost=False, innermost=False, norm_layer=nn.BatchNorm2d, use_dropout=False): + super(UnetSkipConnectionBlock, self).__init__() + self.outermost = outermost + if type(norm_layer) == functools.partial: + use_bias = norm_layer.func == nn.InstanceNorm2d + else: + use_bias = norm_layer == nn.InstanceNorm2d + if input_nc is None: + input_nc = outer_nc + downconv = nn.Conv2d(input_nc, inner_nc, kernel_size=4, + stride=2, padding=1, bias=use_bias) + downrelu = nn.LeakyReLU(0.2, True) + downnorm = norm_layer(inner_nc) + uprelu = nn.ReLU(True) + upnorm = norm_layer(outer_nc) + + if outermost: + upconv = nn.ConvTranspose2d(inner_nc * 2, outer_nc, + kernel_size=4, stride=2, + padding=1) + down = [downconv] + up = [uprelu, upconv, nn.Tanh()] + model = down + [submodule] + up + elif innermost: + # resize = nn.Upsample(scale_factor=2) + # conv = nn.Conv2d(inner_nc,outer_nc,kernel_size=4,stride=2,padding=1,bias=use_bias) + upconv = nn.ConvTranspose2d(inner_nc, outer_nc, + kernel_size=4, stride=2, + padding=1, bias=use_bias) + down = [downrelu, downconv] + up = [uprelu, upconv, upnorm] + #up = [uprelu, resize, conv, upnorm] + model = down + up + else: + upconv = nn.ConvTranspose2d(inner_nc * 2, outer_nc, + kernel_size=4, stride=2, + padding=1, bias=use_bias) + down = [downrelu, downconv, downnorm] + up = [uprelu, upconv, upnorm] + + if use_dropout: + model = down + [submodule] + up + [nn.Dropout(0.5)] + else: + model = down + [submodule] + up + + self.model = nn.Sequential(*model) + + def forward(self, x): + if self.outermost: + return self.model(x) + else: + return torch.cat([x, self.model(x)], 1) + + + +##=================================================================================================== +class DilatedDoubleConv(nn.Module): + """(convolution => [BN] => ReLU) * 2""" + + def __init__(self, in_channels, out_channels): + super().__init__() + self.double_conv = nn.Sequential( + nn.Conv2d(in_channels, out_channels, kernel_size=3, padding=4,stride=1,dilation=4), + nn.BatchNorm2d(out_channels), + nn.ReLU(inplace=True), + nn.Conv2d(out_channels, out_channels, kernel_size=3, padding=4,stride=1,dilation=4), + nn.BatchNorm2d(out_channels), + nn.ReLU(inplace=True) + ) + + def forward(self, x): + return self.double_conv(x) + +class DilatedDown(nn.Module): + """Downscaling with maxpool then double conv""" + + def __init__(self, in_channels, out_channels): + super().__init__() + self.maxpool_conv = nn.Sequential( + nn.MaxPool2d(2), + DilatedDoubleConv(in_channels, out_channels) + ) + + def forward(self, x): + return self.maxpool_conv(x) + +class DilatedUp(nn.Module): + """Upscaling then double conv""" + def __init__(self, in_channels, out_channels, bilinear=True): + super().__init__() + self.up = nn.Upsample(scale_factor=2, mode='nearest') + self.conv = DilatedDoubleConv(in_channels, out_channels) + + self.conv1 = nn.Sequential( + nn.Conv2d(in_channels, out_channels, kernel_size=3, padding=4,stride=1,dilation=4), + nn.BatchNorm2d(out_channels), + nn.ReLU(inplace=True), + ) + # self.deconv = nn.ConvTranspose2d(in_channels, out_channels,kernel_size=4, stride=2,padding=1, bias=True) + def forward(self, x1, x2): + x1 = self.up(x1) + x1 = self.conv1(x1) + # x1 = self.deconv(x1) + # input is BCHW + x = torch.cat([x2, x1], dim=1) + return self.conv(x) +class DilatedSingleUnet(nn.Module): + def __init__(self, input_nc, output_nc, num_downs, ngf=64, biline=True, norm_layer=nn.BatchNorm2d, use_dropout=False): + super(DilatedSingleUnet, self).__init__() + self.init_channel = 32 + self.inc = DilatedDoubleConv(input_nc,self.init_channel) + self.down1 = DilatedDown(self.init_channel, self.init_channel*2) + self.down2 = DilatedDown(self.init_channel*2, self.init_channel*4) + self.down3 = DilatedDown(self.init_channel*4, self.init_channel*8) + self.down4 = DilatedDown(self.init_channel*8, self.init_channel*16) + self.down5 = DilatedDown(self.init_channel*16, self.init_channel*32) + self.cbam = CBAM(gate_channels=self.init_channel*32) + + self.up1 = DilatedUp(self.init_channel*32, self.init_channel*16) + self.up2 = DilatedUp(self.init_channel*16, self.init_channel*8) + self.up3 = DilatedUp(self.init_channel*8, self.init_channel*4) + self.up4 = DilatedUp(self.init_channel*4,self.init_channel*2) + self.up5 = DilatedUp(self.init_channel*2, self.init_channel) + self.outc = OutConv(self.init_channel, output_nc) + def forward(self, input): + x1 = self.inc(input) + x2 = self.down1(x1) + x3 = self.down2(x2) + x4 = self.down3(x3) + x5 = self.down4(x4) + x6 = self.down5(x5) + x6 = self.cbam(x6) + x = self.up1(x6, x5) + x = self.up2(x, x4) + x = self.up3(x, x3) + x = self.up4(x, x2) + x = self.up5(x, x1) + logits1 = self.outc(x) + return logits1 \ No newline at end of file diff --git a/data/MBD/model/unetnc.py b/data/MBD/model/unetnc.py new file mode 100644 index 0000000..a1423f9 --- /dev/null +++ b/data/MBD/model/unetnc.py @@ -0,0 +1,86 @@ +import torch +import torch.nn as nn +from torch.nn import init +import functools + +# Defines the Unet generator. +# |num_downs|: number of downsamplings in UNet. For example, +# if |num_downs| == 7, image of size 128x128 will become of size 1x1 +# at the bottleneck +class UnetGenerator(nn.Module): + def __init__(self, input_nc, output_nc, num_downs, ngf=64, + norm_layer=nn.BatchNorm2d, use_dropout=False): + super(UnetGenerator, self).__init__() + + # construct unet structure + unet_block = UnetSkipConnectionBlock(ngf * 8, ngf * 8, input_nc=None, submodule=None, norm_layer=norm_layer, innermost=True) + for i in range(num_downs - 5): + unet_block = UnetSkipConnectionBlock(ngf * 8, ngf * 8, input_nc=None, submodule=unet_block, norm_layer=norm_layer, use_dropout=use_dropout) + unet_block = UnetSkipConnectionBlock(ngf * 4, ngf * 8, input_nc=None, submodule=unet_block, norm_layer=norm_layer) + unet_block = UnetSkipConnectionBlock(ngf * 2, ngf * 4, input_nc=None, submodule=unet_block, norm_layer=norm_layer) + unet_block = UnetSkipConnectionBlock(ngf, ngf * 2, input_nc=None, submodule=unet_block, norm_layer=norm_layer) + unet_block = UnetSkipConnectionBlock(output_nc, ngf, input_nc=input_nc, submodule=unet_block, outermost=True, norm_layer=norm_layer) + + self.model = unet_block + + def forward(self, input): + return self.model(input) + + + def forward(self, input): + return self.model(input) + +# Defines the submodule with skip connection. +# X -------------------identity---------------------- X +# |-- downsampling -- |submodule| -- upsampling --| +class UnetSkipConnectionBlock(nn.Module): + def __init__(self, outer_nc, inner_nc, input_nc=None, + submodule=None, outermost=False, innermost=False, norm_layer=nn.BatchNorm2d, use_dropout=False): + super(UnetSkipConnectionBlock, self).__init__() + self.outermost = outermost + if type(norm_layer) == functools.partial: + use_bias = norm_layer.func == nn.InstanceNorm2d + else: + use_bias = norm_layer == nn.InstanceNorm2d + if input_nc is None: + input_nc = outer_nc + downconv = nn.Conv2d(input_nc, inner_nc, kernel_size=4, + stride=2, padding=1, bias=use_bias) + downrelu = nn.LeakyReLU(0.2, True) + downnorm = norm_layer(inner_nc) + uprelu = nn.ReLU(True) + upnorm = norm_layer(outer_nc) + + if outermost: + upconv = nn.ConvTranspose2d(inner_nc * 2, outer_nc, + kernel_size=4, stride=2, + padding=1) + down = [downconv] + up = [uprelu, upconv, nn.Tanh()] + model = down + [submodule] + up + elif innermost: + upconv = nn.ConvTranspose2d(inner_nc, outer_nc, + kernel_size=4, stride=2, + padding=1, bias=use_bias) + down = [downrelu, downconv] + up = [uprelu, upconv, upnorm] + model = down + up + else: + upconv = nn.ConvTranspose2d(inner_nc * 2, outer_nc, + kernel_size=4, stride=2, + padding=1, bias=use_bias) + down = [downrelu, downconv, downnorm] + up = [uprelu, upconv, upnorm] + + if use_dropout: + model = down + [submodule] + up + [nn.Dropout(0.5)] + else: + model = down + [submodule] + up + + self.model = nn.Sequential(*model) + + def forward(self, x): + if self.outermost: + return self.model(x) + else: + return torch.cat([x, self.model(x)], 1) diff --git a/data/MBD/modify_stn_model/stn_head.py b/data/MBD/modify_stn_model/stn_head.py new file mode 100644 index 0000000..9521a67 --- /dev/null +++ b/data/MBD/modify_stn_model/stn_head.py @@ -0,0 +1,123 @@ +from __future__ import absolute_import + +import math +import numpy as np +import sys + +import torch +from torch import nn +from torch.nn import functional as F +from torch.nn import init + + +def conv3x3_block(in_planes, out_planes, stride=1): + """3x3 convolution with padding""" + conv_layer = nn.Conv2d(in_planes, out_planes, kernel_size=3, stride=1, padding=1) + + block = nn.Sequential( + conv_layer, + nn.BatchNorm2d(out_planes), + nn.ReLU(inplace=True), + ) + return block + + +class STNHead(nn.Module): + def __init__(self, in_planes, num_ctrlpoints, activation='none'): + super(STNHead, self).__init__() + + self.in_planes = in_planes + self.num_ctrlpoints = num_ctrlpoints + self.activation = activation + self.stn_convnet = nn.Sequential( + conv3x3_block(in_planes, 32), # 32*64 + nn.MaxPool2d(kernel_size=2, stride=2), + conv3x3_block(32, 64), # 16*32 + nn.MaxPool2d(kernel_size=2, stride=2), + conv3x3_block(64, 128), # 8*16 + nn.MaxPool2d(kernel_size=2, stride=2), + conv3x3_block(128, 256), # 4*8 + nn.MaxPool2d(kernel_size=2, stride=2), + conv3x3_block(256, 256), # 2*4, + nn.MaxPool2d(kernel_size=2, stride=2), + conv3x3_block(256, 256)) # 1*2 > 256*8*8 + + self.stn_fc1 = nn.Sequential( + # nn.Linear(2*256, 512), + nn.Linear(8*8*256, 512), + nn.BatchNorm1d(512), + nn.ReLU(inplace=True)) + self.stn_fc2 = nn.Linear(512, num_ctrlpoints*2) + + self.init_weights(self.stn_convnet) + self.init_weights(self.stn_fc1) + self.init_stn(self.stn_fc2) + + def init_weights(self, module): + for m in module.modules(): + if isinstance(m, nn.Conv2d): + n = m.kernel_size[0] * m.kernel_size[1] * m.out_channels + m.weight.data.normal_(0, math.sqrt(2. / n)) + if m.bias is not None: + m.bias.data.zero_() + elif isinstance(m, nn.BatchNorm2d): + m.weight.data.fill_(1) + m.bias.data.zero_() + elif isinstance(m, nn.Linear): + m.weight.data.normal_(0, 0.001) + m.bias.data.zero_() + + def init_stn(self, stn_fc2): + # margin = 0.01 + # sampling_num_per_side = int(self.num_ctrlpoints / 2) + # ctrl_pts_x = np.linspace(margin, 1.-margin, sampling_num_per_side) + # ctrl_pts_y_top = np.ones(sampling_num_per_side) * margin + # ctrl_pts_y_bottom = np.ones(sampling_num_per_side) * (1-margin) + # ctrl_pts_top = np.stack([ctrl_pts_x, ctrl_pts_y_top], axis=1) + # ctrl_pts_bottom = np.stack([ctrl_pts_x, ctrl_pts_y_bottom], axis=1) + # ctrl_points = np.concatenate([ctrl_pts_top, ctrl_pts_bottom], axis=0).astype(np.float32) + + margin_x, margin_y = 0.35,0.35 + # margin_x, margin_y = 0,0 + num_ctrl_pts_per_side = (self.num_ctrlpoints-4) // 4 +2 + ctrl_pts_x = np.linspace(margin_x, 1.0 - margin_x, num_ctrl_pts_per_side) + ctrl_pts_y_top = np.ones(num_ctrl_pts_per_side) * margin_y + ctrl_pts_y_bottom = np.ones(num_ctrl_pts_per_side) * (1.0 - margin_y) + ctrl_pts_top = np.stack([ctrl_pts_x, ctrl_pts_y_top], axis=1) + ctrl_pts_bottom = np.stack([ctrl_pts_x, ctrl_pts_y_bottom], axis=1) + + ctrl_pts_x_left = np.ones(num_ctrl_pts_per_side) * margin_x + ctrl_pts_x_right = np.ones(num_ctrl_pts_per_side) * (1.0-margin_x) + ctrl_pts_left = np.stack([ctrl_pts_x_left[1:-1], ctrl_pts_x[1:-1]], axis=1) + ctrl_pts_right = np.stack([ctrl_pts_x_right[1:-1], ctrl_pts_x[1:-1]], axis=1) + + ctrl_points = np.concatenate([ctrl_pts_top, ctrl_pts_bottom, ctrl_pts_left, ctrl_pts_right], axis=0).astype(np.float32) + + + if self.activation is 'none': + pass + elif self.activation == 'sigmoid': + ctrl_points = -np.log(1. / ctrl_points - 1.) + stn_fc2.weight.data.zero_() + stn_fc2.bias.data = torch.Tensor(ctrl_points).view(-1) + + def forward(self, x): + x = self.stn_convnet(x) + batch_size, _, h, w = x.size() + x = x.view(batch_size, -1) + img_feat = self.stn_fc1(x) + x = self.stn_fc2(0.1 * img_feat) + if self.activation == 'sigmoid': + x = F.sigmoid(x) + x = x.view(-1, self.num_ctrlpoints, 2) + return img_feat, x + + +if __name__ == "__main__": + in_planes = 3 + num_ctrlpoints = 20 + activation='none' # 'sigmoid' + stn_head = STNHead(in_planes, num_ctrlpoints, activation) + input = torch.randn(10, 3, 32, 64) + control_points = stn_head(input) + print(control_points.size()) \ No newline at end of file diff --git a/data/MBD/modify_stn_model/tps_spatial_transformer.py b/data/MBD/modify_stn_model/tps_spatial_transformer.py new file mode 100644 index 0000000..3aca926 --- /dev/null +++ b/data/MBD/modify_stn_model/tps_spatial_transformer.py @@ -0,0 +1,194 @@ +from __future__ import absolute_import + +import numpy as np +import itertools + +import torch +import torch.nn as nn +import torch.nn.functional as F + +def grid_sample(input, grid, canvas = None): + output = F.grid_sample(input, grid) + if canvas is None: + return output + else: + input_mask = input.data.new(input.size()).fill_(1) + output_mask = F.grid_sample(input_mask, grid) + padded_output = output * output_mask + canvas * (1 - output_mask) + return padded_output + + +# phi(x1, x2) = r^2 * log(r), where r = ||x1 - x2||_2 +def compute_partial_repr(input_points, control_points): + N = input_points.size(0) + M = control_points.size(0) + pairwise_diff = input_points.view(N, 1, 2) - control_points.view(1, M, 2) + # original implementation, very slow + # pairwise_dist = torch.sum(pairwise_diff ** 2, dim = 2) # square of distance + pairwise_diff_square = pairwise_diff * pairwise_diff + pairwise_dist = pairwise_diff_square[:, :, 0] + pairwise_diff_square[:, :, 1] + repr_matrix = 0.5 * pairwise_dist * torch.log(pairwise_dist) + # fix numerical error for 0 * log(0), substitute all nan with 0 + mask = repr_matrix != repr_matrix + repr_matrix.masked_fill_(mask, 0) + return repr_matrix + + +# # output_ctrl_pts are specified, according to our task. +# def build_output_control_points(num_control_points, margins): +# margin_x, margin_y = margins +# margin_x, margin_y = 0,0 +# num_ctrl_pts_per_side = num_control_points // 2 +# ctrl_pts_x = np.linspace(margin_x, 1.0 - margin_x, num_ctrl_pts_per_side) +# ctrl_pts_y_top = np.ones(num_ctrl_pts_per_side) * margin_y +# ctrl_pts_y_bottom = np.ones(num_ctrl_pts_per_side) * (1.0 - margin_y) +# ctrl_pts_top = np.stack([ctrl_pts_x, ctrl_pts_y_top], axis=1) +# ctrl_pts_bottom = np.stack([ctrl_pts_x, ctrl_pts_y_bottom], axis=1) +# # ctrl_pts_top = ctrl_pts_top[1:-1,:] +# # ctrl_pts_bottom = ctrl_pts_bottom[1:-1,:] +# output_ctrl_pts_arr = np.concatenate([ctrl_pts_top, ctrl_pts_bottom], axis=0) +# output_ctrl_pts = torch.Tensor(output_ctrl_pts_arr) +# return output_ctrl_pts + +# output_ctrl_pts are specified, according to our task. +# def build_output_control_points(num_control_points, margins): +# margin_x, margin_y = margins +# # margin_x, margin_y = 0,0 +# num_ctrl_pts_per_side = (num_control_points-4) // 4 +2 +# ctrl_pts_x = np.linspace(margin_x, 1.0 - margin_x, num_ctrl_pts_per_side) +# ctrl_pts_y_top = np.ones(num_ctrl_pts_per_side) * margin_y +# ctrl_pts_y_bottom = np.ones(num_ctrl_pts_per_side) * (1.0 - margin_y) +# ctrl_pts_top = np.stack([ctrl_pts_x, ctrl_pts_y_top], axis=1) +# ctrl_pts_bottom = np.stack([ctrl_pts_x, ctrl_pts_y_bottom], axis=1) + +# ctrl_pts_x_left = np.ones(num_ctrl_pts_per_side) * margin_x +# ctrl_pts_x_right = np.ones(num_ctrl_pts_per_side) * (1.0-margin_x) +# ctrl_pts_left = np.stack([ctrl_pts_x_left[1:-1], ctrl_pts_x[1:-1]], axis=1) +# ctrl_pts_right = np.stack([ctrl_pts_x_right[1:-1], ctrl_pts_x[1:-1]], axis=1) + +# output_ctrl_pts_arr = np.concatenate([ctrl_pts_top, ctrl_pts_bottom, ctrl_pts_left, ctrl_pts_right], axis=0) +# output_ctrl_pts = torch.Tensor(output_ctrl_pts_arr) +# return output_ctrl_pts + +def build_output_control_points(num_control_points, margins): + points = [0.25,0.5,0.75] + pts2 = [[0, 0],[1, 0], [0, 1],[1, 1]] + # pts22 = [] + for ratio in points: + pts2.append([1*ratio,0]) + for ratio in points: + pts2.append([1*ratio,1]) + for ratio in points: + pts2.append([0,1*ratio]) + for ratio in points: + pts2.append([1,1*ratio]) + pts2 = np.float32(pts2) + margin_x, margin_y = margins + # margin_x, margin_y = 0,0 + num_ctrl_pts_per_side = (num_control_points-4) // 4 +2 + ctrl_pts_x = np.linspace(margin_x, 1.0 - margin_x, num_ctrl_pts_per_side) + ctrl_pts_y_top = np.ones(num_ctrl_pts_per_side) * margin_y + ctrl_pts_y_bottom = np.ones(num_ctrl_pts_per_side) * (1.0 - margin_y) + ctrl_pts_top = np.stack([ctrl_pts_x, ctrl_pts_y_top], axis=1) + ctrl_pts_bottom = np.stack([ctrl_pts_x, ctrl_pts_y_bottom], axis=1) + + ctrl_pts_x_left = np.ones(num_ctrl_pts_per_side) * margin_x + ctrl_pts_x_right = np.ones(num_ctrl_pts_per_side) * (1.0-margin_x) + ctrl_pts_left = np.stack([ctrl_pts_x_left[1:-1], ctrl_pts_x[1:-1]], axis=1) + ctrl_pts_right = np.stack([ctrl_pts_x_right[1:-1], ctrl_pts_x[1:-1]], axis=1) + + output_ctrl_pts_arr = np.concatenate([ctrl_pts_top, ctrl_pts_bottom, ctrl_pts_left, ctrl_pts_right], axis=0) + # output_ctrl_pts_arr = np.asarray([[0,0],[1,0],[1,1],[0,1], + # [],[],[],[], + # [],[],[],[], + # [],[],[],[]]) + output_ctrl_pts_arr = pts2 + # print(output_ctrl_pts_arr.shape,'=================') + output_ctrl_pts = torch.FloatTensor(output_ctrl_pts_arr) + return output_ctrl_pts + + + +# demo: ~/test/models/test_tps_transformation.py +class TPSSpatialTransformer(nn.Module): + + def __init__(self, output_image_size=None, num_control_points=None, margins=None): + super(TPSSpatialTransformer, self).__init__() + self.output_image_size = output_image_size + self.num_control_points = num_control_points + self.margins = margins + + self.target_height, self.target_width = output_image_size + target_control_points = build_output_control_points(num_control_points, margins) + N = num_control_points + # N = N - 4 + + # create padded kernel matrix + forward_kernel = torch.zeros(N + 3, N + 3) + target_control_partial_repr = compute_partial_repr(target_control_points, target_control_points) + forward_kernel[:N, :N].copy_(target_control_partial_repr) + forward_kernel[:N, -3].fill_(1) + forward_kernel[-3, :N].fill_(1) + forward_kernel[:N, -2:].copy_(target_control_points) + forward_kernel[-2:, :N].copy_(target_control_points.transpose(0, 1)) + # compute inverse matrix + # print(forward_kernel.shape) + inverse_kernel = torch.inverse(forward_kernel) + + # create target cordinate matrix + HW = self.target_height * self.target_width + target_coordinate = list(itertools.product(range(self.target_height), range(self.target_width))) + target_coordinate = torch.Tensor(target_coordinate) # HW x 2 + Y, X = target_coordinate.split(1, dim = 1) + Y = Y / (self.target_height - 1) + X = X / (self.target_width - 1) + target_coordinate = torch.cat([X, Y], dim = 1) # convert from (y, x) to (x, y) + target_coordinate_partial_repr = compute_partial_repr(target_coordinate, target_control_points) + target_coordinate_repr = torch.cat([ + target_coordinate_partial_repr, torch.ones(HW, 1), target_coordinate + ], dim = 1) + + # register precomputed matrices + self.register_buffer('inverse_kernel', inverse_kernel) + self.register_buffer('padding_matrix', torch.zeros(3, 2)) + self.register_buffer('target_coordinate_repr', target_coordinate_repr) + self.register_buffer('target_control_points', target_control_points) + + def forward(self, input, source_control_points,direction='dewarp'): + if direction == 'dewarp': + assert source_control_points.ndimension() == 3 + assert source_control_points.size(1) == self.num_control_points + assert source_control_points.size(2) == 2 + batch_size = source_control_points.size(0) + + Y = torch.cat([source_control_points, self.padding_matrix.expand(batch_size, 3, 2)], 1) + mapping_matrix = torch.matmul(self.inverse_kernel, Y) + source_coordinate = torch.matmul(self.target_coordinate_repr, mapping_matrix) + + grid = source_coordinate.view(-1, self.target_height, self.target_width, 2) + grid = torch.clamp(grid, 0, 1) # the source_control_points may be out of [0, 1]. + # the input to grid_sample is normalized [-1, 1], but what we get is [0, 1] + grid = 2.0 * grid - 1.0 + output = grid_sample(input, grid, canvas=None) + return output, grid + + # elif direction == 'warp': + # target_control_points = source_control_points.clone() + # source_control_points = (build_output_control_points(self.num_control_points, self.margins)).clone() + # source_control_points = source_control_points.unsqueeze(0) + # source_control_points = source_control_points.expand(target_control_points.size(0),self.num_control_points,2) + # assert source_control_points.ndimension() == 3 + # assert source_control_points.size(1) == self.num_control_points + # assert source_control_points.size(2) == 2 + # batch_size = source_control_points.size(0) + + # Y = torch.cat([source_control_points.to('cuda'), self.padding_matrix.expand(batch_size, 3, 2)], 1) + # mapping_matrix = torch.matmul(self.inverse_kernel, Y) + # source_coordinate = torch.matmul(self.target_coordinate_repr, mapping_matrix) + + # grid = source_coordinate.view(-1, self.target_height, self.target_width, 2) + # grid = torch.clamp(grid, 0, 1) # the source_control_points may be out of [0, 1]. + # # the input to grid_sample is normalized [-1, 1], but what we get is [0, 1] + # grid = 2.0 * grid - 1.0 + # output_maps = grid_sample(input, grid, canvas=None) + # return output_maps, source_coordinate diff --git a/data/MBD/stn_model/stn_head.py b/data/MBD/stn_model/stn_head.py new file mode 100644 index 0000000..9521a67 --- /dev/null +++ b/data/MBD/stn_model/stn_head.py @@ -0,0 +1,123 @@ +from __future__ import absolute_import + +import math +import numpy as np +import sys + +import torch +from torch import nn +from torch.nn import functional as F +from torch.nn import init + + +def conv3x3_block(in_planes, out_planes, stride=1): + """3x3 convolution with padding""" + conv_layer = nn.Conv2d(in_planes, out_planes, kernel_size=3, stride=1, padding=1) + + block = nn.Sequential( + conv_layer, + nn.BatchNorm2d(out_planes), + nn.ReLU(inplace=True), + ) + return block + + +class STNHead(nn.Module): + def __init__(self, in_planes, num_ctrlpoints, activation='none'): + super(STNHead, self).__init__() + + self.in_planes = in_planes + self.num_ctrlpoints = num_ctrlpoints + self.activation = activation + self.stn_convnet = nn.Sequential( + conv3x3_block(in_planes, 32), # 32*64 + nn.MaxPool2d(kernel_size=2, stride=2), + conv3x3_block(32, 64), # 16*32 + nn.MaxPool2d(kernel_size=2, stride=2), + conv3x3_block(64, 128), # 8*16 + nn.MaxPool2d(kernel_size=2, stride=2), + conv3x3_block(128, 256), # 4*8 + nn.MaxPool2d(kernel_size=2, stride=2), + conv3x3_block(256, 256), # 2*4, + nn.MaxPool2d(kernel_size=2, stride=2), + conv3x3_block(256, 256)) # 1*2 > 256*8*8 + + self.stn_fc1 = nn.Sequential( + # nn.Linear(2*256, 512), + nn.Linear(8*8*256, 512), + nn.BatchNorm1d(512), + nn.ReLU(inplace=True)) + self.stn_fc2 = nn.Linear(512, num_ctrlpoints*2) + + self.init_weights(self.stn_convnet) + self.init_weights(self.stn_fc1) + self.init_stn(self.stn_fc2) + + def init_weights(self, module): + for m in module.modules(): + if isinstance(m, nn.Conv2d): + n = m.kernel_size[0] * m.kernel_size[1] * m.out_channels + m.weight.data.normal_(0, math.sqrt(2. / n)) + if m.bias is not None: + m.bias.data.zero_() + elif isinstance(m, nn.BatchNorm2d): + m.weight.data.fill_(1) + m.bias.data.zero_() + elif isinstance(m, nn.Linear): + m.weight.data.normal_(0, 0.001) + m.bias.data.zero_() + + def init_stn(self, stn_fc2): + # margin = 0.01 + # sampling_num_per_side = int(self.num_ctrlpoints / 2) + # ctrl_pts_x = np.linspace(margin, 1.-margin, sampling_num_per_side) + # ctrl_pts_y_top = np.ones(sampling_num_per_side) * margin + # ctrl_pts_y_bottom = np.ones(sampling_num_per_side) * (1-margin) + # ctrl_pts_top = np.stack([ctrl_pts_x, ctrl_pts_y_top], axis=1) + # ctrl_pts_bottom = np.stack([ctrl_pts_x, ctrl_pts_y_bottom], axis=1) + # ctrl_points = np.concatenate([ctrl_pts_top, ctrl_pts_bottom], axis=0).astype(np.float32) + + margin_x, margin_y = 0.35,0.35 + # margin_x, margin_y = 0,0 + num_ctrl_pts_per_side = (self.num_ctrlpoints-4) // 4 +2 + ctrl_pts_x = np.linspace(margin_x, 1.0 - margin_x, num_ctrl_pts_per_side) + ctrl_pts_y_top = np.ones(num_ctrl_pts_per_side) * margin_y + ctrl_pts_y_bottom = np.ones(num_ctrl_pts_per_side) * (1.0 - margin_y) + ctrl_pts_top = np.stack([ctrl_pts_x, ctrl_pts_y_top], axis=1) + ctrl_pts_bottom = np.stack([ctrl_pts_x, ctrl_pts_y_bottom], axis=1) + + ctrl_pts_x_left = np.ones(num_ctrl_pts_per_side) * margin_x + ctrl_pts_x_right = np.ones(num_ctrl_pts_per_side) * (1.0-margin_x) + ctrl_pts_left = np.stack([ctrl_pts_x_left[1:-1], ctrl_pts_x[1:-1]], axis=1) + ctrl_pts_right = np.stack([ctrl_pts_x_right[1:-1], ctrl_pts_x[1:-1]], axis=1) + + ctrl_points = np.concatenate([ctrl_pts_top, ctrl_pts_bottom, ctrl_pts_left, ctrl_pts_right], axis=0).astype(np.float32) + + + if self.activation is 'none': + pass + elif self.activation == 'sigmoid': + ctrl_points = -np.log(1. / ctrl_points - 1.) + stn_fc2.weight.data.zero_() + stn_fc2.bias.data = torch.Tensor(ctrl_points).view(-1) + + def forward(self, x): + x = self.stn_convnet(x) + batch_size, _, h, w = x.size() + x = x.view(batch_size, -1) + img_feat = self.stn_fc1(x) + x = self.stn_fc2(0.1 * img_feat) + if self.activation == 'sigmoid': + x = F.sigmoid(x) + x = x.view(-1, self.num_ctrlpoints, 2) + return img_feat, x + + +if __name__ == "__main__": + in_planes = 3 + num_ctrlpoints = 20 + activation='none' # 'sigmoid' + stn_head = STNHead(in_planes, num_ctrlpoints, activation) + input = torch.randn(10, 3, 32, 64) + control_points = stn_head(input) + print(control_points.size()) \ No newline at end of file diff --git a/data/MBD/stn_model/tps_spatial_transformer.py b/data/MBD/stn_model/tps_spatial_transformer.py new file mode 100644 index 0000000..7e6cbee --- /dev/null +++ b/data/MBD/stn_model/tps_spatial_transformer.py @@ -0,0 +1,155 @@ +from __future__ import absolute_import + +import numpy as np +import itertools + +import torch +import torch.nn as nn +import torch.nn.functional as F + +def grid_sample(input, grid, canvas = None): + output = F.grid_sample(input, grid) + if canvas is None: + return output + else: + input_mask = input.data.new(input.size()).fill_(1) + output_mask = F.grid_sample(input_mask, grid) + padded_output = output * output_mask + canvas * (1 - output_mask) + return padded_output + + +# phi(x1, x2) = r^2 * log(r), where r = ||x1 - x2||_2 +def compute_partial_repr(input_points, control_points): + N = input_points.size(0) + M = control_points.size(0) + pairwise_diff = input_points.view(N, 1, 2) - control_points.view(1, M, 2) + # original implementation, very slow + # pairwise_dist = torch.sum(pairwise_diff ** 2, dim = 2) # square of distance + pairwise_diff_square = pairwise_diff * pairwise_diff + pairwise_dist = pairwise_diff_square[:, :, 0] + pairwise_diff_square[:, :, 1] + repr_matrix = 0.5 * pairwise_dist * torch.log(pairwise_dist) + # fix numerical error for 0 * log(0), substitute all nan with 0 + mask = repr_matrix != repr_matrix + repr_matrix.masked_fill_(mask, 0) + return repr_matrix + + +# # output_ctrl_pts are specified, according to our task. +# def build_output_control_points(num_control_points, margins): +# margin_x, margin_y = margins +# margin_x, margin_y = 0,0 +# num_ctrl_pts_per_side = num_control_points // 2 +# ctrl_pts_x = np.linspace(margin_x, 1.0 - margin_x, num_ctrl_pts_per_side) +# ctrl_pts_y_top = np.ones(num_ctrl_pts_per_side) * margin_y +# ctrl_pts_y_bottom = np.ones(num_ctrl_pts_per_side) * (1.0 - margin_y) +# ctrl_pts_top = np.stack([ctrl_pts_x, ctrl_pts_y_top], axis=1) +# ctrl_pts_bottom = np.stack([ctrl_pts_x, ctrl_pts_y_bottom], axis=1) +# # ctrl_pts_top = ctrl_pts_top[1:-1,:] +# # ctrl_pts_bottom = ctrl_pts_bottom[1:-1,:] +# output_ctrl_pts_arr = np.concatenate([ctrl_pts_top, ctrl_pts_bottom], axis=0) +# output_ctrl_pts = torch.Tensor(output_ctrl_pts_arr) +# return output_ctrl_pts + +# output_ctrl_pts are specified, according to our task. +def build_output_control_points(num_control_points, margins): + margin_x, margin_y = margins + # margin_x, margin_y = 0,0 + num_ctrl_pts_per_side = (num_control_points-4) // 4 +2 + ctrl_pts_x = np.linspace(margin_x, 1.0 - margin_x, num_ctrl_pts_per_side) + ctrl_pts_y_top = np.ones(num_ctrl_pts_per_side) * margin_y + ctrl_pts_y_bottom = np.ones(num_ctrl_pts_per_side) * (1.0 - margin_y) + ctrl_pts_top = np.stack([ctrl_pts_x, ctrl_pts_y_top], axis=1) + ctrl_pts_bottom = np.stack([ctrl_pts_x, ctrl_pts_y_bottom], axis=1) + + ctrl_pts_x_left = np.ones(num_ctrl_pts_per_side) * margin_x + ctrl_pts_x_right = np.ones(num_ctrl_pts_per_side) * (1.0-margin_x) + ctrl_pts_left = np.stack([ctrl_pts_x_left[1:-1], ctrl_pts_x[1:-1]], axis=1) + ctrl_pts_right = np.stack([ctrl_pts_x_right[1:-1], ctrl_pts_x[1:-1]], axis=1) + + output_ctrl_pts_arr = np.concatenate([ctrl_pts_top, ctrl_pts_bottom, ctrl_pts_left, ctrl_pts_right], axis=0) + output_ctrl_pts = torch.Tensor(output_ctrl_pts_arr) + return output_ctrl_pts + +# demo: ~/test/models/test_tps_transformation.py +class TPSSpatialTransformer(nn.Module): + + def __init__(self, output_image_size=None, num_control_points=None, margins=None): + super(TPSSpatialTransformer, self).__init__() + self.output_image_size = output_image_size + self.num_control_points = num_control_points + self.margins = margins + + self.target_height, self.target_width = output_image_size + target_control_points = build_output_control_points(num_control_points, margins) + N = num_control_points + # N = N - 4 + + # create padded kernel matrix + forward_kernel = torch.zeros(N + 3, N + 3) + target_control_partial_repr = compute_partial_repr(target_control_points, target_control_points) + forward_kernel[:N, :N].copy_(target_control_partial_repr) + forward_kernel[:N, -3].fill_(1) + forward_kernel[-3, :N].fill_(1) + forward_kernel[:N, -2:].copy_(target_control_points) + forward_kernel[-2:, :N].copy_(target_control_points.transpose(0, 1)) + # compute inverse matrix + # print(forward_kernel.shape) + inverse_kernel = torch.inverse(forward_kernel) + + # create target cordinate matrix + HW = self.target_height * self.target_width + target_coordinate = list(itertools.product(range(self.target_height), range(self.target_width))) + target_coordinate = torch.Tensor(target_coordinate) # HW x 2 + Y, X = target_coordinate.split(1, dim = 1) + Y = Y / (self.target_height - 1) + X = X / (self.target_width - 1) + target_coordinate = torch.cat([X, Y], dim = 1) # convert from (y, x) to (x, y) + target_coordinate_partial_repr = compute_partial_repr(target_coordinate, target_control_points) + target_coordinate_repr = torch.cat([ + target_coordinate_partial_repr, torch.ones(HW, 1), target_coordinate + ], dim = 1) + + # register precomputed matrices + self.register_buffer('inverse_kernel', inverse_kernel) + self.register_buffer('padding_matrix', torch.zeros(3, 2)) + self.register_buffer('target_coordinate_repr', target_coordinate_repr) + self.register_buffer('target_control_points', target_control_points) + + def forward(self, input, source_control_points,direction='dewarp'): + if direction == 'dewarp': + assert source_control_points.ndimension() == 3 + assert source_control_points.size(1) == self.num_control_points + assert source_control_points.size(2) == 2 + batch_size = source_control_points.size(0) + + Y = torch.cat([source_control_points, self.padding_matrix.expand(batch_size, 3, 2)], 1) + mapping_matrix = torch.matmul(self.inverse_kernel, Y) + source_coordinate = torch.matmul(self.target_coordinate_repr, mapping_matrix) + + grid = source_coordinate.view(-1, self.target_height, self.target_width, 2) + grid = torch.clamp(grid, 0, 1) # the source_control_points may be out of [0, 1]. + # the input to grid_sample is normalized [-1, 1], but what we get is [0, 1] + grid = 2.0 * grid - 1.0 + output_maps = grid_sample(input, grid, canvas=None) + return output_maps, source_coordinate + + # elif direction == 'warp': + # target_control_points = source_control_points.clone() + # source_control_points = (build_output_control_points(self.num_control_points, self.margins)).clone() + # source_control_points = source_control_points.unsqueeze(0) + # source_control_points = source_control_points.expand(target_control_points.size(0),self.num_control_points,2) + # assert source_control_points.ndimension() == 3 + # assert source_control_points.size(1) == self.num_control_points + # assert source_control_points.size(2) == 2 + # batch_size = source_control_points.size(0) + + # Y = torch.cat([source_control_points.to('cuda'), self.padding_matrix.expand(batch_size, 3, 2)], 1) + # mapping_matrix = torch.matmul(self.inverse_kernel, Y) + # source_coordinate = torch.matmul(self.target_coordinate_repr, mapping_matrix) + + # grid = source_coordinate.view(-1, self.target_height, self.target_width, 2) + # grid = torch.clamp(grid, 0, 1) # the source_control_points may be out of [0, 1]. + # # the input to grid_sample is normalized [-1, 1], but what we get is [0, 1] + # grid = 2.0 * grid - 1.0 + # output_maps = grid_sample(input, grid, canvas=None) + # return output_maps, source_coordinate diff --git a/data/MBD/tps_grid_gen.py b/data/MBD/tps_grid_gen.py new file mode 100644 index 0000000..418bd1f --- /dev/null +++ b/data/MBD/tps_grid_gen.py @@ -0,0 +1,70 @@ +# encoding: utf-8 + +import torch +import itertools +import torch.nn as nn +from torch.autograd import Function, Variable + +class TPSGridGen(nn.Module): + + def __init__(self, target_height, target_width, target_control_points): + super(TPSGridGen, self).__init__() + assert target_control_points.ndimension() == 2 + assert target_control_points.size(1) == 2 + N = target_control_points.size(0) + self.num_points = N + target_control_points = target_control_points.float() + + # create padded kernel matrix + forward_kernel = torch.zeros(N + 3, N + 3) + target_control_partial_repr = self.compute_partial_repr(target_control_points, target_control_points) + forward_kernel[:N, :N].copy_(target_control_partial_repr) + forward_kernel[:N, -3].fill_(1) + forward_kernel[-3, :N].fill_(1) + forward_kernel[:N, -2:].copy_(target_control_points) + forward_kernel[-2:, :N].copy_(target_control_points.transpose(0, 1)) + # compute inverse matrix + inverse_kernel = torch.inverse(forward_kernel) + + # create target cordinate matrix + HW = target_height * target_width + target_coordinate = list(itertools.product(range(target_height), range(target_width))) + target_coordinate = torch.Tensor(target_coordinate) # HW x 2 + Y, X = target_coordinate.split(1, dim = 1) + Y = Y * 2 / (target_height - 1) - 1 + X = X * 2 / (target_width - 1) - 1 + target_coordinate = torch.cat([X, Y], dim = 1) # convert from (y, x) to (x, y) + target_coordinate_partial_repr = self.compute_partial_repr(target_coordinate, target_control_points) + target_coordinate_repr = torch.cat([ + target_coordinate_partial_repr, torch.ones(HW, 1), target_coordinate + ], dim = 1) + + # register precomputed matrices + self.register_buffer('inverse_kernel', inverse_kernel) + self.register_buffer('padding_matrix', torch.zeros(3, 2)) + self.register_buffer('target_coordinate_repr', target_coordinate_repr) + + def forward(self, source_control_points): + assert source_control_points.ndimension() == 3 + assert source_control_points.size(1) == self.num_points + assert source_control_points.size(2) == 2 + batch_size = source_control_points.size(0) + + Y = torch.cat([source_control_points, Variable(self.padding_matrix.expand(batch_size, 3, 2))], 1) + mapping_matrix = torch.matmul(Variable(self.inverse_kernel), Y) + source_coordinate = torch.matmul(Variable(self.target_coordinate_repr), mapping_matrix) + return source_coordinate + # phi(x1, x2) = r^2 * log(r), where r = ||x1 - x2||_2 + def compute_partial_repr(self, input_points, control_points): + N = input_points.size(0) + M = control_points.size(0) + pairwise_diff = input_points.view(N, 1, 2) - control_points.view(1, M, 2) + # original implementation, very slow + # pairwise_dist = torch.sum(pairwise_diff ** 2, dim = 2) # square of distance + pairwise_diff_square = pairwise_diff * pairwise_diff + pairwise_dist = pairwise_diff_square[:, :, 0] + pairwise_diff_square[:, :, 1] + repr_matrix = 0.5 * pairwise_dist * torch.log(pairwise_dist) + # fix numerical error for 0 * log(0), substitute all nan with 0 + mask = repr_matrix != repr_matrix + repr_matrix.masked_fill_(mask, 0) + return repr_matrix \ No newline at end of file diff --git a/data/MBD/utils.py b/data/MBD/utils.py new file mode 100644 index 0000000..4b6d377 --- /dev/null +++ b/data/MBD/utils.py @@ -0,0 +1,234 @@ +''' +Misc Utility functions +''' +from collections import OrderedDict +import os +import numpy as np +import torch +import random +import torchvision + +def recursive_glob(rootdir='.', suffix=''): + """Performs recursive glob with given suffix and rootdir + :param rootdir is the root directory + :param suffix is the suffix to be searched + """ + return [os.path.join(looproot, filename) + for looproot, _, filenames in os.walk(rootdir) + for filename in filenames if filename.endswith(suffix)] + +def poly_lr_scheduler(optimizer, init_lr, iter, lr_decay_iter=1, max_iter=30000, power=0.9,): + """Polynomial decay of learning rate + :param init_lr is base learning rate + :param iter is a current iteration + :param lr_decay_iter how frequently decay occurs, default is 1 + :param max_iter is number of maximum iterations + :param power is a polymomial power + + """ + if iter % lr_decay_iter or iter > max_iter: + return optimizer + + for param_group in optimizer.param_groups: + param_group['lr'] = init_lr*(1 - iter/max_iter)**power + + +def adjust_learning_rate(optimizer, init_lr, epoch): + """Sets the learning rate to the initial LR decayed by 10 every 30 epochs""" + lr = init_lr * (0.1 ** (epoch // 30)) + for param_group in optimizer.param_groups: + param_group['lr'] = lr + + +def alpha_blend(input_image, segmentation_mask, alpha=0.5): + """Alpha Blending utility to overlay RGB masks on RBG images + :param input_image is a np.ndarray with 3 channels + :param segmentation_mask is a np.ndarray with 3 channels + :param alpha is a float value + + """ + blended = np.zeros(input_image.size, dtype=np.float32) + blended = input_image * alpha + segmentation_mask * (1 - alpha) + return blended + +def convert_state_dict(state_dict): + """Converts a state dict saved from a dataParallel module to normal + module state_dict inplace + :param state_dict is the loaded DataParallel model_state + + """ + new_state_dict = OrderedDict() + for k, v in state_dict.items(): + name = k[7:] # remove `module.` + new_state_dict[name] = v + return new_state_dict + + +class ImagePool(): + def __init__(self, pool_size): + self.pool_size = pool_size + if self.pool_size > 0: + self.num_imgs = 0 + self.images = [] + + def query(self, images): + if self.pool_size == 0: + return images + return_images = [] + for image in images: + image = torch.unsqueeze(image.data, 0) + if self.num_imgs < self.pool_size: + self.num_imgs = self.num_imgs + 1 + self.images.append(image) + return_images.append(image) + else: + p = random.uniform(0, 1) + if p > 0.5: + random_id = random.randint(0, self.pool_size - 1) # randint is inclusive + tmp = self.images[random_id].clone() + self.images[random_id] = image + return_images.append(tmp) + else: + return_images.append(image) + return_images = torch.cat(return_images, 0) + return return_images + + +def set_requires_grad(nets, requires_grad=False): + if not isinstance(nets, list): + nets = [nets] + for net in nets: + if net is not None: + for param in net.parameters(): + param.requires_grad = requires_grad + + + +def get_lr(optimizer): + for param_group in optimizer.param_groups: + return float(param_group['lr']) + +def visualize(epoch,model,layer): + #get conv layers + conv_layers=[] + for m in model.modules(): + if isinstance(m,torch.nn.modules.conv.Conv2d): + conv_layers.append(m) + + # print conv_layers[layer].weight.data.cpu().numpy().shape + tensor=conv_layers[layer].weight.data.cpu() + vistensor(tensor, epoch, ch=0, allkernels=False, nrow=8, padding=1) + + +def vistensor(tensor, epoch, ch=0, allkernels=False, nrow=8, padding=1): + ''' + vistensor: visuzlization tensor + @ch: visualization channel + @allkernels: visualization all tensors + https://github.com/pedrodiamel/pytorchvision/blob/a14672fe4b07995e99f8af755de875daf8aababb/pytvision/visualization.py#L325 + ''' + + n,c,w,h = tensor.shape + if allkernels: tensor = tensor.view(n*c,-1,w,h ) + elif c != 3: tensor = tensor[:,ch,:,:].unsqueeze(dim=1) + + rows = np.min( (tensor.shape[0]//nrow + 1, 64 ) ) + # print rows + # print tensor.shape + grid = utils.make_grid(tensor, nrow=8, normalize=True, padding=padding) + # print grid.shape + plt.figure( figsize=(10,10), dpi=200 ) + plt.imshow(grid.numpy().transpose((1, 2, 0))) + plt.savefig('./generated/filters_layer1_dwuv_'+str(epoch)+'.png') + plt.close() + + +def show_uloss(uwpred,uworg,inp_img, samples=7): + + n,c,h,w=inp_img.shape + # print(labels.shape) + uwpred=uwpred.detach().cpu().numpy() + uworg=uworg.detach().cpu().numpy() + inp_img=inp_img.detach().cpu().numpy() + + #NCHW->NHWC + uwpred=uwpred.transpose((0, 2, 3, 1)) + uworg=uworg.transpose((0, 2, 3, 1)) + + choices=random.sample(range(n), min(n,samples)) + f, axarr = plt.subplots(samples, 3) + for j in range(samples): + # print(np.min(labels[j])) + # print imgs[j].shape + img=inp_img[j].transpose(1,2,0) + axarr[j][0].imshow(img[:,:,::-1]) + axarr[j][1].imshow(uworg[j]) + axarr[j][2].imshow(uwpred[j]) + + plt.savefig('./generated/unwarp.png') + plt.close() + + +def show_uloss_visdom(vis,uwpred,uworg,labels_win,out_win,labelopts,outopts,args): + samples=7 + n,c,h,w=uwpred.shape + uwpred=uwpred.detach().cpu().numpy() + uworg=uworg.detach().cpu().numpy() + out_arr=np.full((samples,3,args.img_rows,args.img_cols),0.0) + label_arr=np.full((samples,3,args.img_rows,args.img_cols),0.0) + choices=random.sample(range(n), min(n,samples)) + idx=0 + for c in choices: + out_arr[idx,:,:,:]=uwpred[c] + label_arr[idx,:,:,:]=uworg[c] + idx+=1 + + vis.images(out_arr, + win=out_win, + opts=outopts) + vis.images(label_arr, + win=labels_win, + opts=labelopts) + +def show_unwarp_tnsboard(global_step,writer,uwpred,uworg,grid_samples,gt_tag,pred_tag): + idxs=torch.LongTensor(random.sample(range(images.shape[0]), min(grid_samples,images.shape[0]))) + grid_uworg = torchvision.utils.make_grid(uworg[idxs],normalize=True, scale_each=True) + writer.add_image(gt_tag, grid_uworg, global_step) + grid_uwpr = torchvision.utils.make_grid(uwpred[idxs],normalize=True, scale_each=True) + writer.add_image(pred_tag, grid_uwpr, global_step) + +def show_wc_tnsboard(global_step,writer,images,labels, pred, grid_samples,inp_tag, gt_tag, pred_tag): + idxs=torch.LongTensor(random.sample(range(images.shape[0]), min(grid_samples,images.shape[0]))) + grid_inp = torchvision.utils.make_grid(images[idxs],normalize=True, scale_each=True) + writer.add_image(inp_tag, grid_inp, global_step) + grid_lbl = torchvision.utils.make_grid(labels[idxs],normalize=True, scale_each=True) + writer.add_image(gt_tag, grid_lbl, global_step) + grid_pred = torchvision.utils.make_grid(pred[idxs],normalize=True, scale_each=True) + writer.add_image(pred_tag, grid_pred, global_step) +def torch2cvimg(tensor,min=0,max=1): + ''' + input: + tensor -> torch.tensor BxCxHxW C can be 1,3 + return + im -> ndarray uint8 HxWxC + ''' + im_list = [] + for i in range(tensor.shape[0]): + im = tensor.detach().cpu().data.numpy()[i] + im = im.transpose(1,2,0) + im = np.clip(im,min,max) + im = ((im-min)/(max-min)*255).astype(np.uint8) + im_list.append(im) + return im_list +def cvimg2torch(img,min=0,max=1): + ''' + input: + im -> ndarray uint8 HxWxC + return + tensor -> torch.tensor BxCxHxW + ''' + img = img.astype(float) / 255.0 + img = img.transpose(2, 0, 1) # NHWC -> NCHW + img = np.expand_dims(img, 0) + img = torch.from_numpy(img).float() + return img \ No newline at end of file diff --git a/data/README.md b/data/README.md new file mode 100644 index 0000000..02a72fb --- /dev/null +++ b/data/README.md @@ -0,0 +1,147 @@ +# Dataset Preparation +The data files tree should be look like: +``` +data/ + eval/ + dir300/ + 1_in.png + 1_gt.png + ... + kligler/ + jung/ + osr/ + realdae/ + docunet_docaligner/ + dibco18/ + train/ + dewarping/ + doc3d/ + deshadowing/ + fsdsrd/ + tdd/ + appearance/ + clean_pdfs/ + realdae/ + deblurring/ + tdd/ + binarization/ + bickly/ + dibco/ + noise_office/ + phibd/ + msi/ +``` + +## Evaluation Dataset +You can find the links for downloading the dataset we used for evaluation (Tables 1 and 2) in [this](https://github.com/ZZZHANG-jx/Recommendations-Document-Image-Processing/tree/master) repository, including DIR300 (300 samples), Kligler (300 samples), Jung (87 samples), OSR (237 samples), RealDAE (150 samples), DocUNet_DocAligner (150 samples), TDD (16000 samples) and DIBCO18 (10 samples). After downloading, add the suffix of `_in` and `_gt` to the input image and gt image respectively, and place them in the folder of the corresponding dataset + + +## Training Dataset +You can find the links for downloading the dataset we used for training in [this](https://github.com/ZZZHANG-jx/Recommendations-Document-Image-Processing/tree/master) repository. +### Dewarping +- Doc3D + - Mask extraction: you should extract the mask for each image from the uv data in Doc3D + - Background preparation: you can download the background data from [here](https://www.robots.ox.ac.uk/~vgg/data/dtd/) and specify it for self.background_paths in `loaders/docres_loader.py` +- JSON preparation: +``` + +[ + ## you need to specify the paths of 'in_path', 'mask_path and 'gt_path': + { + "in_path": "dewarping/doc3d/img/1/102_1-pp_Page_048-xov0001.png", + "mask_path": "dewarping/doc3d/mask/1/102_1-pp_Page_048-xov0001.png", + "gt_path": "dewarping/doc3d/bm/1/102_1-pp_Page_048-xov0001.npy" + } +] + +``` +### Deshadowing +- RDD +- FSDSRD +- JSON preparation +``` +[ ## you need to specify the paths of 'in_path' and 'gt_path', for example: + { + "in_path": "deshadowing/fsdsrd/im/00004.png", + "gt_path": "deshadowing/fsdsrd/gt/00004.png" + }, + { + "in_path": "deshadowing/rdd/im/00004.png", + "gt_path": "deshadowing/rdd/gt/00004.png" + } +] +``` +### Appearance enhancement +- Doc3DShade + - Clean PDFs collection: You should collection PDFs files from the internet and convert them as images to serve as the source for synthesis. + - Shadow extraction: + - Run `python data/preprocess/shadow_extraction.py` to extract shadows. + - The principle of `data/preprocess/shadow_extraction.py`: + - step1: utilize the the mask (as shown in img1) to dewarp the original images (as shown in img2) and alb_images (as shown in img3), resulting in the dewarped images img4 and img5 + - step2: extract the shadow images (as shown in img6) based on img4 and img5. + - Point self.shadow_paths in `loaders/docres_loader.py` to the folder containing the shadow images. + +|img1|img2|img3| +|----|----|----| +|![img1](./images/1.png)|![img2](./images/2.png)|![img3](./images/3.png)| +|img4|img5|img6| +|![img4](./images/4.png)|![img5](./images/5.png)|![img6](./images/6.png)| + +- RealDAE +- JSON preparation: +``` +[ + ## for Doc3DShade dataset, you only need to specify the path of image from PDF, for example: + { + 'gt_path':'appearance/clean_pdfs/1.jpg' + }, + + ## for RealDAE dataset, you need to specify the paths of both input and gt, for example: + { + 'in_path': 'appearance/realdae/1_in.jpg', + 'gt_path': 'appearance/realdae/1_gt.jpg' + } +] + +``` + +### Debluring +- TDD +- JSON preparation +``` +[ ## you need to specify the paths of 'in_path' and 'gt_path', for example: + { + "in_path": "debluring/tdd/im/00004.png", + "gt_path": "debluring/tdd/gt/00004.png" + }, +] +``` +### Binarization +- Bickly + - DTPrompt preparation: Since the DTPrompt for binarization is time-expensive, we obtain it offline before training. Use `data/preprocess/sauvola_binarize.py` +- DIBCO + - DTPrompt preparation: the same as Bickly +- Noise Office + - DTPrompt preparation: the same as Bickly +- PHIDB + - DTPrompt preparation: the same as Bickly +- MSI + - DTPrompt preparation: the same as Bickly +- JSON preparation +``` +[ + ## you need to specify the paths of 'in_path', 'gt_path', 'bin_path', 'thr_path' and 'gradient_path', for example: + { + "in_path": "binarization/noise_office/imgs/1.png", + "gt_path": "binarization/noise_office/gt_imgs/1.png", + "bin_path": "binarization/noise_office/imgs/1_bin.png", + "thr_path": "binarization/noise_office/imgs/1_thr.png", + "gradient_path": "binarization/noise_office/imgs/1_gradient.png" + }, +] +``` + +After all the data are prepared, you should specify the dataset_setting in `train.py`. + + + diff --git a/data/images/1.png b/data/images/1.png new file mode 100644 index 0000000000000000000000000000000000000000..505cc8a1ccdc0739455f9a34927cf697fe61dd74 GIT binary patch literal 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b/data/preprocess/crop_merge_image.py new file mode 100644 index 0000000..5d9887f --- /dev/null +++ b/data/preprocess/crop_merge_image.py @@ -0,0 +1,142 @@ +import os + +import cv2 +import numpy as np +# SIZE =256 +# BATCH_SIZE = 32 +# STRIDES = 256 + +def split_img(img, size_x, size_y, strides): + max_y, max_x = img.shape[:2] + border_y = 0 + if max_y % size_y != 0: + border_y = size_y - (max_y % size_y) + img = cv2.copyMakeBorder(img,border_y,0,0,0,cv2.BORDER_REPLICATE) + # img = cv2.copyMakeBorder(img, border_y, 0, 0, 0, cv2.BORDER_CONSTANT, value=[255,255,255]) + border_x = 0 + if max_x % size_x != 0: + border_x = size_x - (max_x % size_x) + # img = cv2.copyMakeBorder(img, 0, 0, border_x, 0, cv2.BORDER_CONSTANT, value=[255,255,255]) + img = cv2.copyMakeBorder(img,0,0,border_x,0,cv2.BORDER_REPLICATE) + # h,w + max_y, max_x = img.shape[:2] + parts = [] + curr_y = 0 + x = 0 + y = 0 + # TODO: rewrite with generators. + while (curr_y + size_y) <= max_y: + curr_x = 0 + while (curr_x + size_x) <= max_x: + parts.append(img[curr_y:curr_y + size_y, curr_x:curr_x + size_x]) + curr_x += strides + y += 1 + curr_y += strides + # parts is a list + # (windows_number_x*windows_number_y,SIZE,SIZE,3) + # print(max_y,max_x) + # print(y,x) + # print(np.array(parts).shape) + return parts, border_x, border_y, max_x, max_y + + +def combine_imgs(border_x,border_y,imgs, max_y, max_x,size_x, size_y, strides): + + # weighted_img + + index = int(size_x / strides) + weight_img = np.ones(shape=(max_y,max_x)) + weight_img[0:strides] = index + weight_img[-strides:] = index + weight_img[:,0:strides]=index + weight_img[:,-strides:]=index + + # 边上 + i = 0 + for j in range(1,index+1): + # 左上 + weight_img[0:strides,i:i+strides] = np.ones(shape=(strides,strides))*j + weight_img[i:i+strides,0:strides] = np.ones(shape=(strides,strides))*j + # 右上 + weight_img[i:i+strides,-strides:] = np.ones(shape=(strides,strides))*j + if i == 0: + weight_img[0:strides,-strides:] = np.ones(shape=(strides,strides))*j + else: + weight_img[0:strides,-strides-i:-i] = np.ones(shape=(strides,strides))*j + # 左下 + weight_img[-strides:,i:i+strides] = np.ones(shape=(strides,strides))*j + if i == 0: + weight_img[-strides:,0:strides] = np.ones(shape=(strides,strides))*j + else: + weight_img[-strides-i:-i:,0:strides] = np.ones(shape=(strides,strides))*j + # 右下 + if i == 0: + weight_img[-strides:,-strides:] = np.ones(shape=(strides,strides))*j + else: + weight_img[-strides-i:-i,-strides:] = np.ones(shape=(strides,strides))*j + weight_img[-strides:,-strides-i:-i] = np.ones(shape=(strides,strides))*j + + + i += strides + + for i in range(strides,max_y-strides,strides): + for j in range(strides,max_x-strides,strides): + weight_img[i:i+strides,j:j+strides] = np.ones(shape=(strides,strides))*weight_img[i][0]*weight_img[0][j] + + + if len(imgs[0].shape)==2: + new_img = np.zeros(shape=(max_y,max_x)) + weight_img = (1 / weight_img) + else: + new_img = np.zeros(shape=(max_y,max_x,imgs[0].shape[-1])) + weight_img = (1 / weight_img).reshape((max_y,max_x,1)) + weight_img = np.tile(weight_img,(1,1,imgs[0].shape[-1])) + + curr_y = 0 + x = 0 + y = 0 + i = 0 + # TODO: rewrite with generators. + while (curr_y + size_y) <= max_y: + curr_x = 0 + while (curr_x + size_x) <= max_x: + new_img[curr_y:curr_y + size_y, curr_x:curr_x + size_x] += weight_img[curr_y:curr_y + size_y, curr_x:curr_x + size_x]*imgs[i] + i += 1 + curr_x += strides + y += 1 + curr_y += strides + + + new_img = new_img[border_y:, border_x:] + # print(border_y,border_x) + + return new_img + + +def stride_integral(img,stride=32): + h,w = img.shape[:2] + + if (h%stride)!=0: + padding_h = stride - (h%stride) + img = cv2.copyMakeBorder(img,padding_h,0,0,0,borderType=cv2.BORDER_REPLICATE) + else: + padding_h = 0 + + if (w%stride)!=0: + padding_w = stride - (w%stride) + img = cv2.copyMakeBorder(img,0,0,padding_w,0,borderType=cv2.BORDER_REPLICATE) + else: + padding_w = 0 + + return img,padding_h,padding_w + + +def mkdir_s(path: str): + """Create directory in specified path, if not exists.""" + if not os.path.exists(path): + os.makedirs(path) + + +if __name__ =='__main__': + parts, border_x, border_y, max_x, max_y = split_img(im,512,512,strides=512) + result = combine_imgs(border_x,border_y,parts, max_y, max_x,512, 512, 512) diff --git a/data/preprocess/sauvola_binarize.py b/data/preprocess/sauvola_binarize.py new file mode 100644 index 0000000..8edab8e --- /dev/null +++ b/data/preprocess/sauvola_binarize.py @@ -0,0 +1,91 @@ +import cv2 +# importing required libraries +import numpy as np +import cv2 +from skimage.filters import threshold_sauvola +import glob +from tqdm import tqdm +import os +from skimage import io + +def SauvolaModBinarization(image,n1=51,n2=51,k1=0.3,k2=0.3,default=True): + ''' + Binarization using Sauvola's algorithm + @name : SauvolaModBinarization + parameters + @param image (numpy array of shape (3/1) of type np.uint8): color or gray scale image + optional parameters + @param n1 (int) : window size for running sauvola during the first pass + @param n2 (int): window size for running sauvola during the second pass + @param k1 (float): k value corresponding to sauvola during the first pass + @param k2 (float): k value corresponding to sauvola during the second pass + @param default (bool) : bollean variable to set the above parameter as default. + @param default is set to True : thus default values of the above optional parameters (n1,n2,k1,k2) are set to + n1 = 5 % of min(image height, image width) + n2 = 10 % of min(image height, image width) + k1 = 0.5 + k2 = 0.5 + Returns + @return A binary image of same size as @param image + + @cite https://drive.google.com/file/d/1D3CyI5vtodPJeZaD2UV5wdcaIMtkBbdZ/view?usp=sharing + ''' + + if(default): + n1 = int(0.05*min(image.shape[0],image.shape[1])) + if (n1%2==0): + n1 = n1+1 + n2 = int(0.1*min(image.shape[0],image.shape[1])) + if (n2%2==0): + n2 = n2+1 + k1 = 0.5 + k2 = 0.5 + if(image.ndim==3): + gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY) + else: + gray = np.copy(image) + T1 = threshold_sauvola(gray, window_size=n1,k=k1) + max_val = np.amax(gray) + min_val = np.amin(gray) + C = np.copy(T1) + C = C.astype(np.float32) + C[gray > T1] = (gray[gray > T1] - T1[gray > T1])/(max_val - T1[gray > T1]) + C[gray <= T1] = 0 + C = C * 255.0 + new_in = np.copy(C.astype(np.uint8)) + T2 = threshold_sauvola(new_in, window_size=n2,k=k2) + binary = np.copy(gray) + binary[new_in <= T2] = 0 + binary[new_in > T2] = 255 + return binary,T2 + + +def dtprompt(img): + x = cv2.Sobel(img,cv2.CV_16S,1,0) + y = cv2.Sobel(img,cv2.CV_16S,0,1) + absX = cv2.convertScaleAbs(x) # 转回uint8 + absY = cv2.convertScaleAbs(y) + high_frequency = cv2.addWeighted(absX,0.5,absY,0.5,0) + high_frequency = cv2.cvtColor(high_frequency,cv2.COLOR_BGR2GRAY) + return high_frequency + + +im_paths = glob.glob('imgs/*') + + + +for im_path in tqdm(im_paths): + if '_bin.' in im_path: + continue + if '_thr.' in im_path: + continue + if '_gradient.' in im_path: + continue + + im = cv2.imread(im_path) + result,thresh = SauvolaModBinarization(im) + gradient = dtprompt(im) + thresh = thresh.astype(np.uint8) + cv2.imwrite(im_path.replace('.','_bin.'),result) + cv2.imwrite(im_path.replace('.','_thr.'),thresh) + cv2.imwrite(im_path.replace('.','_gradient.'),gradient) \ No newline at end of file diff --git a/data/preprocess/shadow_extraction.py b/data/preprocess/shadow_extraction.py new file mode 100644 index 0000000..d99d30b --- /dev/null +++ b/data/preprocess/shadow_extraction.py @@ -0,0 +1,60 @@ +import cv2 +import numpy as np +import glob +import os +from tqdm import tqdm +import random +import sys +sys.path.append('./data/MBD') +from MBD import mask_base_dewarper + +def shadowExtract(cap_im, alb_im): + im = cap_im + alb = alb_im + + ## Avoid some bad cases + skip = False + im_min = np.min(im,axis=-1) + kernel = np.ones((3,3)) + _, mask = cv2.threshold(cv2.cvtColor(alb,cv2.COLOR_BGR2GRAY), 1, 255, cv2.THRESH_BINARY) + mask_erode = cv2.dilate(mask,kernel=kernel) + mask_erode = cv2.erode(mask_erode,kernel=kernel) + mask_erode = cv2.erode(mask_erode,iterations=4,kernel=kernel) + metric = np.min(im_min[mask_erode==255]) + metric_num = 0 + if metric==0 or metric==1: + metric_num = np.sum(im_min[mask_erode==255]==metric) + if metric_num>=20: + skip = True + pass + # return None + # it is recommended to skip this sample as it will introduce some artifacts. + + alb_temp = alb.astype(np.float64) + alb_temp[alb_temp==0] = alb_temp[alb_temp==0]+1e-5 + shadow = np.clip(im.astype(np.float64)/alb_temp,0,1) + shadow = (shadow*255).astype(np.uint8) + return shadow,skip + + +cap_im = cv2.imread('./data/images/2.png') +alb_im = cv2.imread('./data/images/3.png') + +## get mask by binarizing alb_im +_, mask = cv2.threshold(cv2.cvtColor(alb,cv2.COLOR_BGR2GRAY), 1, 255, cv2.THRESH_BINARY) +kernel = np.ones((3,3)) +mask = cv2.dilate(mask,iterations=2,kernel=kernel) +mask = cv2.erode(mask,iterations=2,kernel=kernel) + +## dewarp cap and alb based on the mask by using MBD method +cap_im, _ = mask_base_dewarper(cap_im, mask_im) +alb_im, _ = mask_base_dewarper(alb_im, mask_im) + + +shadow_im,skip = shadowExtract(cap_im,alb_im) # It is recommended to skip this sample if skip is True. Based on our observations, images that meet this condition often introduce noise. + +cv2.imshow('shadow_im',shadow_im) +cv2.imshow('cap_im',cap_im) +cv2.imshow('alb_im',alb_im) +cv2.imshow('mask',mask) +cv2.waitKey(0) \ No newline at end of file diff --git a/eval.py b/eval.py new file mode 100644 index 0000000..5b2994a --- /dev/null +++ b/eval.py @@ -0,0 +1,369 @@ +import os +import cv2 +import glob +import utils +import argparse +import numpy as np +from tqdm import tqdm +from skimage.metrics import structural_similarity,peak_signal_noise_ratio + +import torch + +from utils import convert_state_dict +from models import restormer_arch +from data.preprocess.crop_merge_image import stride_integral + +os.sys.path.append('./data/MBD/') +from data.MBD.infer import net1_net2_infer_single_im + + +def dewarp_prompt(img): + mask = net1_net2_infer_single_im(img,'data/MBD/checkpoint/mbd.pkl') + base_coord = utils.getBasecoord(256,256)/256 + img[mask==0]=0 + mask = cv2.resize(mask,(256,256))/255 + return img,np.concatenate((base_coord,np.expand_dims(mask,-1)),-1) + +def deshadow_prompt(img): + h,w = img.shape[:2] + # img = cv2.resize(img,(128,128)) + img = cv2.resize(img,(1024,1024)) + rgb_planes = cv2.split(img) + result_planes = [] + result_norm_planes = [] + bg_imgs = [] + for plane in rgb_planes: + dilated_img = cv2.dilate(plane, np.ones((7,7), np.uint8)) + bg_img = cv2.medianBlur(dilated_img, 21) + bg_imgs.append(bg_img) + diff_img = 255 - cv2.absdiff(plane, bg_img) + norm_img = cv2.normalize(diff_img,None, alpha=0, beta=255, norm_type=cv2.NORM_MINMAX, dtype=cv2.CV_8UC1) + result_planes.append(diff_img) + result_norm_planes.append(norm_img) + bg_imgs = cv2.merge(bg_imgs) + bg_imgs = cv2.resize(bg_imgs,(w,h)) + # result = cv2.merge(result_planes) + result_norm = cv2.merge(result_norm_planes) + result_norm[result_norm==0]=1 + shadow_map = np.clip(img.astype(float)/result_norm.astype(float)*255,0,255).astype(np.uint8) + shadow_map = cv2.resize(shadow_map,(w,h)) + shadow_map = cv2.cvtColor(shadow_map,cv2.COLOR_BGR2GRAY) + shadow_map = cv2.cvtColor(shadow_map,cv2.COLOR_GRAY2BGR) + # return shadow_map + return bg_imgs + +def deblur_prompt(img): + x = cv2.Sobel(img,cv2.CV_16S,1,0) + y = cv2.Sobel(img,cv2.CV_16S,0,1) + absX = cv2.convertScaleAbs(x) # 转回uint8 + absY = cv2.convertScaleAbs(y) + high_frequency = cv2.addWeighted(absX,0.5,absY,0.5,0) + high_frequency = cv2.cvtColor(high_frequency,cv2.COLOR_BGR2GRAY) + high_frequency = cv2.cvtColor(high_frequency,cv2.COLOR_GRAY2BGR) + return high_frequency + +def appearance_prompt(img): + h,w = img.shape[:2] + # img = cv2.resize(img,(128,128)) + img = cv2.resize(img,(1024,1024)) + rgb_planes = cv2.split(img) + result_planes = [] + result_norm_planes = [] + for plane in rgb_planes: + dilated_img = cv2.dilate(plane, np.ones((7,7), np.uint8)) + bg_img = cv2.medianBlur(dilated_img, 21) + diff_img = 255 - cv2.absdiff(plane, bg_img) + norm_img = cv2.normalize(diff_img,None, alpha=0, beta=255, norm_type=cv2.NORM_MINMAX, dtype=cv2.CV_8UC1) + result_planes.append(diff_img) + result_norm_planes.append(norm_img) + result_norm = cv2.merge(result_norm_planes) + result_norm = cv2.resize(result_norm,(w,h)) + return result_norm + +def binarization_promptv2(img): + result,thresh = utils.SauvolaModBinarization(img) + thresh = thresh.astype(np.uint8) + result[result>155]=255 + result[result<=155]=0 + + x = cv2.Sobel(img,cv2.CV_16S,1,0) + y = cv2.Sobel(img,cv2.CV_16S,0,1) + absX = cv2.convertScaleAbs(x) # 转回uint8 + absY = cv2.convertScaleAbs(y) + high_frequency = cv2.addWeighted(absX,0.5,absY,0.5,0) + high_frequency = cv2.cvtColor(high_frequency,cv2.COLOR_BGR2GRAY) + return np.concatenate((np.expand_dims(thresh,-1),np.expand_dims(high_frequency,-1),np.expand_dims(result,-1)),-1) + +def dewarping(model,im_path): + INPUT_SIZE=256 + im_org = cv2.imread(im_path) + im_masked, prompt_org = dewarp_prompt(im_org.copy()) + + h,w = im_masked.shape[:2] + im_masked = im_masked.copy() + im_masked = cv2.resize(im_masked,(INPUT_SIZE,INPUT_SIZE)) + im_masked = im_masked / 255.0 + im_masked = torch.from_numpy(im_masked.transpose(2,0,1)).unsqueeze(0) + im_masked = im_masked.float().to(DEVICE) + + prompt = torch.from_numpy(prompt_org.transpose(2,0,1)).unsqueeze(0) + prompt = prompt.float().to(DEVICE) + + in_im = torch.cat((im_masked,prompt),dim=1) + + # inference + base_coord = utils.getBasecoord(INPUT_SIZE,INPUT_SIZE)/INPUT_SIZE + model = model.float() + with torch.no_grad(): + pred = model(in_im) + pred = pred[0][:2].permute(1,2,0).cpu().numpy() + pred = pred+base_coord + ## smooth + for i in range(15): + pred = cv2.blur(pred,(3,3),borderType=cv2.BORDER_REPLICATE) + pred = cv2.resize(pred,(w,h))*(w,h) + pred = pred.astype(np.float32) + out_im = cv2.remap(im_org,pred[:,:,0],pred[:,:,1],cv2.INTER_LINEAR) + + prompt_org = (prompt_org*255).astype(np.uint8) + prompt_org = cv2.resize(prompt_org,im_org.shape[:2][::-1]) + + return prompt_org[:,:,0],prompt_org[:,:,1],prompt_org[:,:,2],out_im + +def appearance(model,im_path): + MAX_SIZE=1600 + # obtain im and prompt + im_org = cv2.imread(im_path) + h,w = im_org.shape[:2] + prompt = appearance_prompt(im_org) + in_im = np.concatenate((im_org,prompt),-1) + + # constrain the max resolution + if max(w,h) < MAX_SIZE: + in_im,padding_h,padding_w = stride_integral(in_im,8) + else: + in_im = cv2.resize(in_im,(MAX_SIZE,MAX_SIZE)) + + # normalize + in_im = in_im / 255.0 + in_im = torch.from_numpy(in_im.transpose(2,0,1)).unsqueeze(0) + + # inference + in_im = in_im.half().to(DEVICE) + model = model.half() + with torch.no_grad(): + pred = model(in_im) + pred = torch.clamp(pred,0,1) + pred = pred[0].permute(1,2,0).cpu().numpy() + pred = (pred*255).astype(np.uint8) + + if max(w,h) < MAX_SIZE: + out_im = pred[padding_h:,padding_w:] + else: + pred[pred==0] = 1 + shadow_map = cv2.resize(im_org,(MAX_SIZE,MAX_SIZE)).astype(float)/pred.astype(float) + shadow_map = cv2.resize(shadow_map,(w,h)) + shadow_map[shadow_map==0]=0.00001 + out_im = np.clip(im_org.astype(float)/shadow_map,0,255).astype(np.uint8) + + return prompt[:,:,0],prompt[:,:,1],prompt[:,:,2],out_im + + +def deshadowing(model,im_path): + MAX_SIZE=1600 + # obtain im and prompt + im_org = cv2.imread(im_path) + h,w = im_org.shape[:2] + prompt = deshadow_prompt(im_org) + in_im = np.concatenate((im_org,prompt),-1) + + # constrain the max resolution + if max(w,h) < MAX_SIZE: + in_im,padding_h,padding_w = stride_integral(in_im,8) + else: + in_im = cv2.resize(in_im,(MAX_SIZE,MAX_SIZE)) + + # normalize + in_im = in_im / 255.0 + in_im = torch.from_numpy(in_im.transpose(2,0,1)).unsqueeze(0) + + # inference + in_im = in_im.half().to(DEVICE) + model = model.half() + with torch.no_grad(): + pred = model(in_im) + pred = torch.clamp(pred,0,1) + pred = pred[0].permute(1,2,0).cpu().numpy() + pred = (pred*255).astype(np.uint8) + + if max(w,h) < MAX_SIZE: + out_im = pred[padding_h:,padding_w:] + else: + pred[pred==0]=1 + shadow_map = cv2.resize(im_org,(MAX_SIZE,MAX_SIZE)).astype(float)/pred.astype(float) + shadow_map = cv2.resize(shadow_map,(w,h)) + shadow_map[shadow_map==0]=0.00001 + out_im = np.clip(im_org.astype(float)/shadow_map,0,255).astype(np.uint8) + + return prompt[:,:,0],prompt[:,:,1],prompt[:,:,2],out_im + + +def deblurring(model,im_path): + # setup image + im_org = cv2.imread(im_path) + in_im,padding_h,padding_w = stride_integral(im_org,8) + prompt = deblur_prompt(in_im) + in_im = np.concatenate((in_im,prompt),-1) + in_im = in_im / 255.0 + in_im = torch.from_numpy(in_im.transpose(2,0,1)).unsqueeze(0) + in_im = in_im.half().to(DEVICE) + # inference + model.to(DEVICE) + model.eval() + model = model.half() + with torch.no_grad(): + pred = model(in_im) + pred = torch.clamp(pred,0,1) + pred = pred[0].permute(1,2,0).cpu().numpy() + pred = (pred*255).astype(np.uint8) + out_im = pred[padding_h:,padding_w:] + + return prompt[:,:,0],prompt[:,:,1],prompt[:,:,2],out_im + + + +def binarization(model,im_path): + im_org = cv2.imread(im_path) + im,padding_h,padding_w = stride_integral(im_org,8) + prompt = binarization_promptv2(im) + h,w = im.shape[:2] + in_im = np.concatenate((im,prompt),-1) + + in_im = in_im / 255.0 + in_im = torch.from_numpy(in_im.transpose(2,0,1)).unsqueeze(0) + in_im = in_im.to(DEVICE) + model = model.half() + in_im = in_im.half() + with torch.no_grad(): + pred = model(in_im,'binarization') + pred = pred[:,:2,:,:] + pred = torch.max(torch.softmax(pred,1),1)[1] + pred = pred[0].cpu().numpy() + pred = (pred*255).astype(np.uint8) + pred = cv2.resize(pred,(w,h)) + out_im = pred[padding_h:,padding_w:] + + return prompt[:,:,0],prompt[:,:,1],prompt[:,:,2],out_im + + + + + +def get_args(): + parser = argparse.ArgumentParser(description='Params') + parser.add_argument('--model_path', nargs='?', type=str, default='./checkpoints/docres.pkl',help='Path of the saved checkpoint') + parser.add_argument('--dataset', nargs='?', type=str, default='./distorted/',help='Path of input document image') + args = parser.parse_args() + assert args.dataset in all_datasets.keys(), 'Unregisted dataset, dataset must be one of '+', '.join(all_datasets) + return args + +def model_init(args): + # prepare model + model = restormer_arch.Restormer( + inp_channels=6, + out_channels=3, + dim = 48, + num_blocks = [2,3,3,4], + num_refinement_blocks = 4, + heads = [1,2,4,8], + ffn_expansion_factor = 2.66, + bias = False, + LayerNorm_type = 'WithBias', + dual_pixel_task = True + ) + + if DEVICE.type == 'cpu': + state = convert_state_dict(torch.load(args.model_path, map_location='cpu')['model_state']) + else: + state = convert_state_dict(torch.load(args.model_path, map_location='cuda:0')['model_state']) + model.load_state_dict(state) + + model.eval() + model = model.to(DEVICE) + return model + +def inference_one_im(model,im_path,task): + if task=='dewarping': + prompt1,prompt2,prompt3,restorted = dewarping(model,im_path) + elif task=='deshadowing': + prompt1,prompt2,prompt3,restorted = deshadowing(model,im_path) + elif task=='appearance': + prompt1,prompt2,prompt3,restorted = appearance(model,im_path) + elif task=='deblurring': + prompt1,prompt2,prompt3,restorted = deblurring(model,im_path) + elif task=='binarization': + prompt1,prompt2,prompt3,restorted = binarization(model,im_path) + elif task=='end2end': + prompt1,prompt2,prompt3,restorted = dewarping(model,im_path) + cv2.imwrite('./temp.jpg',restorted) + prompt1,prompt2,prompt3,restorted = deshadowing(model,'./temp.jpg') + cv2.imwrite('./temp.jpg',restorted) + prompt1,prompt2,prompt3,restorted = appearance(model,'./temp.jpg') + os.remove('./temp.jpg') + + return prompt1,prompt2,prompt3,restorted + + + +if __name__ == '__main__': + all_datasets = {'dir300':'dewarping','kligler':'deshadowing','jung':'deshadowing','osr':'deshadowing','docunet_docaligner':'appearance','realdae':'appearance','tdd':'deblurring','dibco18':'binarization'} + + ## model init + DEVICE = torch.device('cuda' if torch.cuda.is_available() else 'cpu') + args = get_args() + model = model_init(args) + + ## inference + print('Predicting') + task = all_datasets[args.dataset] + im_paths = glob.glob(os.path.join('./data/eval/',args.dataset,'*_in.*')) + for im_path in tqdm(im_paths): + _,_,_,restorted = inference_one_im(model,im_path,task) + cv2.imwrite(im_path.replace('_in','_docres'),restorted) + + ## obtain metric + print('Metric calculating') + if task == 'dewarping': + exit() + elif task=='deshadowing' or task=='appearance' or task=='deblurring': + psnr = [] + ssim = [] + for im_path in tqdm(im_paths): + pred = cv2.imread(im_path.replace('_in','_docres')) + gt = cv2.imread(im_path.replace('_in','_gt')) + ssim.append(structural_similarity(pred,gt,multichannel=True)) + psnr.append(peak_signal_noise_ratio(pred, gt)) + print(args.dataset) + print('ssim:',np.mean(ssim)) + print('psnr:',np.mean(psnr)) + elif task=='binarization': + fmeasures, pfmeasures,psnrs = [],[],[] + for im_path in tqdm(im_paths): + pred = cv2.imread(im_path.replace('_in','_docres')) + gt = cv2.imread(im_path.replace('_in','_gt')) + pred = cv2.cvtColor(pred,cv2.COLOR_BGR2GRAY) + gt = cv2.cvtColor(gt,cv2.COLOR_BGR2GRAY) + pred[pred>155]=255 + pred[pred<=155]=0 + gt[gt>155]=255 + gt[gt<=155]=0 + fmeasure, pfmeasure,psnr,_,_,_ = utils.bin_metric(pred,gt) + fmeasures.append(fmeasure) + pfmeasures.append(pfmeasure) + psnrs.append(psnr) + print(args.dataset) + print('fmeasure:',np.mean(fmeasures)) + print('pfmeasure:',np.mean(pfmeasures)) + print('psnr:',np.mean(psnrs)) + + diff --git a/images/motivation.jpg b/images/motivation.jpg new file mode 100644 index 0000000000000000000000000000000000000000..90072a3e59b6bb84e25e7bc2c9219a5f26cc87fd GIT binary patch literal 1093794 zcmeFZ2T)YY)-bvO2@*we92F!Zk~4!MS#nac0+IwIBWb_@O3nxZBRL6(D;u8`Q;1duK5nsJRL`+IdKyZcf3Mm;m z1qB5m2^BRZIrUX?3i69iFtETjm#{BmV_zmGA|N9FU;dmo0;G6&Lj<{47&ib+QVc9o zjPn)%0st7;7uaH80DpWiFhLw~aPcnV6Mz6^R{%^5EG*1RSQl7>&_3XM;1VhJ)$6=> zaL668rqvItZeKY0)j%qBBEk4_hjYd 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