From 17371311a1dd893876f732326bc241a77a7fabc4 Mon Sep 17 00:00:00 2001 From: aayush Date: Thu, 19 Sep 2019 18:56:47 -0400 Subject: [PATCH] RIT-MVRL model --- .gitignore | 5 + README.md | 31 +- ...neration from train image 000000240768.pdf | Bin 0 -> 591416 bytes __pycache__/dataset.cpython-36.pyc | Bin 0 -> 7419 bytes __pycache__/densenet.cpython-36.pyc | Bin 0 -> 5093 bytes __pycache__/models.cpython-36.pyc | Bin 0 -> 330 bytes __pycache__/opt.cpython-36.pyc | Bin 0 -> 1505 bytes __pycache__/utils.cpython-36.pyc | Bin 0 -> 7425 bytes best_model.pkl | Bin 0 -> 1018397 bytes dataset.py | 213 ++++++++++++ densenet.py | 139 ++++++++ environment.yml | 311 ++++++++++++++++++ models.py | 13 + opt.py | 40 +++ requirements.txt | 9 + starburst_black.png | Bin 0 -> 24512 bytes test.py | 77 +++++ test/labels/000000337021.npy | Bin 0 -> 2048128 bytes test/labels/000000337043.npy | Bin 0 -> 2048128 bytes test/labels/000000337047.npy | Bin 0 -> 2048128 bytes test/labels/000000337720.npy | Bin 0 -> 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Bin 0 -> 118682 bytes train.py | 193 +++++++++++ utils.py | 222 +++++++++++++ 99 files changed, 1233 insertions(+), 20 deletions(-) create mode 100644 .gitignore create mode 100644 Starburst generation from train image 000000240768.pdf create mode 100644 __pycache__/dataset.cpython-36.pyc create mode 100644 __pycache__/densenet.cpython-36.pyc create mode 100644 __pycache__/models.cpython-36.pyc create mode 100644 __pycache__/opt.cpython-36.pyc create mode 100644 __pycache__/utils.cpython-36.pyc create mode 100644 best_model.pkl create mode 100644 dataset.py create mode 100644 densenet.py create mode 100644 environment.yml create mode 100644 models.py create mode 100644 opt.py create mode 100644 requirements.txt create mode 100644 starburst_black.png create mode 100644 test.py create mode 100644 test/labels/000000337021.npy create mode 100644 test/labels/000000337043.npy create mode 100644 test/labels/000000337047.npy create mode 100644 test/labels/000000337720.npy create mode 100644 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create mode 100644 test/mask/000000348897.jpg create mode 100644 test/mask/000000349618.jpg create mode 100644 test/mask/000000350263.jpg create mode 100644 test/mask/000000350293.jpg create mode 100644 test/mask/000000352383.jpg create mode 100644 test/mask/000000352385.jpg create mode 100644 test/mask/000000352398.jpg create mode 100644 test/mask/000000352410.jpg create mode 100644 test/mask/000000353117.jpg create mode 100644 test/mask/000000353820.jpg create mode 100644 test/mask/000000355220.jpg create mode 100644 test/mask/000000355243.jpg create mode 100644 train.py create mode 100644 utils.py diff --git a/.gitignore b/.gitignore new file mode 100644 index 0000000..744b649 --- /dev/null +++ b/.gitignore @@ -0,0 +1,5 @@ +/logs/ +/0try/ +/_pycache_/ +/Semantic_Segmentation_Dataset/ + diff --git a/README.md b/README.md index 39af52c..6abc0bd 100644 --- a/README.md +++ b/README.md @@ -1,29 +1,20 @@ -# README # +Instructions: -This README would normally document whatever steps are necessary to get your application up and running. +To train the model + +```python train.py --help``` +eg. -### What is this repository for? ### +```python train.py --model densenet --expname FINAL --bs 4 --useGPU True --dataset Semantic_Segmentation_Dataset/``` -* Quick summary -* Version -* [Learn Markdown](https://bitbucket.org/tutorials/markdowndemo) -### How do I get set up? ### +To test the result: + +```python test.py --model densenet --load best_model.pkl --bs 4 --dataset Semantic_Segmentation_Dataset/``` -* Summary of set up -* Configuration -* Dependencies -* Database configuration -* How to run tests -* Deployment instructions -### Contribution guidelines ### -* Writing tests -* Code review -* Other guidelines +The requirements.txt file contains all the packages necessary for the code to run. We have also included an environment.yml file of the system which runs the code successfully. Please refer to that file if there is an error with specific packages. + -### Who do I talk to? ### -* Repo owner or admin -* Other community or team contact \ No newline at end of file diff --git a/Starburst generation from train image 000000240768.pdf b/Starburst generation from train image 000000240768.pdf new file mode 100644 index 0000000000000000000000000000000000000000..4333918b005b7c058cb978043a8a4cdd99fcccda GIT binary patch literal 591416 zcmeFY1yq#Z-Y-6Yq|zas(hS4UF@$t?gX9b)IW*D;(%miH(xnp8EfRu&AT81&g4BQT z*XR7sd(V6CyZ(3Gwa%W!JkRXsyT85bvmY4MrDcI&PF`%r)wPK!Y%DM>7p;?p9X134 zQh~XdL(SdHX}J+~njm#^YnUr7&(C{pZ)X@t3h~U|$y!Vd8|DbTZF5WaYg=WQqqUn2 zEgv7h5J=12!tIt`Uqu=8v*B-y#l&3Q;4pIsY%GYlm$SL$W0)JQ1^t3-o+#ZY4KfHZFtFNL?E9C@-(eiQf11-V4T(km0oM3(~UOq4fEjJff z0K~-y;^GH#frYsQg}K4BzZQBiEW{lI4z&{2l$QNf9O9QKy^WijvoHwc>FLSo$;0Uc zw+4ZQgoHp`+#qglAc6wu>h0)e?geyoW%z~3Z+xU-u9k3HXE$3XN7`Gy<`z!wZld(` zw}O5zzuM*C{JS7WSI(a{a#}h$fV|9|L10cU(BCzJTK?X~*&S~GQ#Gh12xbp+fH}Ik 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zUX><{)MR?S&!zwUcg&9vh*=!_bL)M8n1$io`|gutp583XrVaD_002ovPDHLkV1g2qvJ3zK literal 0 HcmV?d00001 diff --git a/test.py b/test.py new file mode 100644 index 0000000..0a8ca96 --- /dev/null +++ b/test.py @@ -0,0 +1,77 @@ +#!/usr/bin/env python3 +# -*- coding: utf-8 -*- +""" +Created on Mon Sep 2 11:37:59 2019 + +@author: aaa +""" +import torch +from dataset import IrisDataset +from torch.utils.data import DataLoader +import numpy as np +import matplotlib.pyplot as plt +from dataset import transform +import os +from opt import parse_args +from models import model_dict +from tqdm import tqdm +from utils import get_predictions +#%% + +if __name__ == '__main__': + + args = parse_args() + + if args.model not in model_dict: + print ("Model not found !!!") + print ("valid models are:",list(model_dict.keys())) + exit(1) + + if args.useGPU: + device=torch.device("cuda") + else: + device=torch.device("cpu") + + model = model_dict[args.model] + model = model.to(device) + filename = args.load + if not os.path.exists(filename): + print("model path not found !!!") + exit(1) + + model.load_state_dict(torch.load(filename)) + model = model.to(device) + model.eval() + + test_set = IrisDataset(filepath = 'Semantic_Segmentation_Dataset/',\ + split = 'test',transform = transform) + + testloader = DataLoader(test_set, batch_size = args.bs, + shuffle=False, num_workers=2) + counter=0 + + os.makedirs('test/labels/',exist_ok=True) + os.makedirs('test/output/',exist_ok=True) + os.makedirs('test/mask/',exist_ok=True) + + with torch.no_grad(): + for i, batchdata in tqdm(enumerate(testloader),total=len(testloader)): + img,labels,index,x,y= batchdata + data = img.to(device) + output = model(data) + predict = get_predictions(output) + for j in range (len(index)): + np.save('test/labels/{}.npy'.format(index[j]),predict[j].cpu().numpy()) + try: + plt.imsave('test/output/{}.jpg'.format(index[j]),255*labels[j].cpu().numpy()) + except: + pass + + pred_img = predict[j].cpu().numpy()/3.0 + inp = 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import DataLoader +from dataset import IrisDataset +import torch +from utils import mIoU, CrossEntropyLoss2d,total_metric,get_nparams,Logger,GeneralizedDiceLoss,SurfaceLoss +import numpy as np +from dataset import transform +from opt import parse_args +import os +from utils import get_predictions +from tqdm import tqdm +import matplotlib.pyplot as plt +#%% + +def lossandaccuracy(loader,model,factor): + epoch_loss = [] + ious = [] + model.eval() + with torch.no_grad(): + for i, batchdata in enumerate(loader): +# print (len(batchdata)) + img,labels,index,spatialWeights,maxDist=batchdata + data = img.to(device) + + target = labels.to(device).long() + output = model(data) + + ## loss from cross entropy is weighted sum of pixel wise loss and Canny edge loss *20 + CE_loss = criterion(output,target) + loss = CE_loss*(torch.from_numpy(np.ones(spatialWeights.shape)).to(torch.float32).to(device)()+(spatialWeights).to(torch.float32).to(device)()) + + loss=torch.mean(loss).to(torch.float32).to(device) + loss_dice = criterion_DICE(output,target) + loss_sl = torch.mean(criterion_SL(output.to(device),(maxDist).to(device))) + + ##total loss is the weighted sum of suface loss and dice loss plus the boundary weighted cross entropy loss + loss = (1-factor)*loss_sl+factor*(loss_dice)+loss + + epoch_loss.append(loss.item()) + predict = get_predictions(output) + iou = mIoU(predict,labels) + ious.append(iou) + return np.average(epoch_loss),np.average(ious) + +#%% +if __name__ == '__main__': + + args = parse_args() + kwargs = vars(args) + +# device = torch.device("cuda" if torch.cuda.is_available() else "cpu") + + if args.useGPU: + device=torch.device("cuda") + torch.cuda.manual_seed(12) + else: + device=torch.device("cpu") + torch.manual_seed(12) + + torch.backends.cudnn.deterministic=False + + if args.model not in model_dict: + print ("Model not found !!!") + print ("valid models are:",list(model_dict.keys())) + exit(1) + + LOGDIR = 'logs/{}'.format(args.expname) + os.makedirs(LOGDIR,exist_ok=True) + os.makedirs(LOGDIR+'/models',exist_ok=True) + logger = Logger(os.path.join(LOGDIR,'logs.log')) + + model = model_dict[args.model] + model = model.to(device) + torch.save(model.state_dict(), '{}/models/dense_net{}.pkl'.format(LOGDIR,'_0')) + model.train() + nparams = get_nparams(model) + + try: + from torchsummary import summary + summary(model,input_size=(1,640,400)) + print("Max params:", 1024*1024/4.0) + logger.write_summary(str(model.parameters)) + except: + print ("Torch summary not found !!!") + + optimizer = torch.optim.Adam(model.parameters(), lr = args.lr) + scheduler = torch.optim.lr_scheduler.ReduceLROnPlateau(optimizer, 'min',patience=5) + + criterion = CrossEntropyLoss2d() + criterion_DICE = GeneralizedDiceLoss(softmax=True, reduction=True) + criterion_SL = SurfaceLoss() + + Path2file = args.dataset + train = IrisDataset(filepath = Path2file,split='train', + transform = transform, **kwargs) + + valid = IrisDataset(filepath = Path2file , split='validation', + transform = transform, **kwargs) + + trainloader = DataLoader(train, batch_size = args.bs, + shuffle=True, num_workers = args.workers) + + validloader = DataLoader(valid, batch_size = args.bs, + shuffle= False, num_workers = args.workers) + + test = IrisDataset(filepath = Path2file , split='test', + transform = transform, **kwargs) + + testloader = DataLoader(test, batch_size = args.bs, + shuffle=False, num_workers = args.workers) + + +# alpha = 1 - np.arange(1,args.epochs)/args.epoch + ##The weighing function for the dice loss and surface loss + alpha=np.zeros(((args.epochs))) + alpha[0:np.min([125,args.epochs])]=1 - np.arange(1,np.min([125,args.epochs])+1)/np.min([125,args.epochs]) + if args.epochs>125: + alpha[125:]=1 + ious = [] + for epoch in range(args.epochs): + for i, batchdata in enumerate(trainloader): +# print (len(batchdata)) + img,labels,index,spatialWeights,maxDist= batchdata + data = img.to(device) + target = labels.to(device).long() + optimizer.zero_grad() + output = model(data) + ## loss from cross entropy is weighted sum of pixel wise loss and Canny edge loss *20 + CE_loss = criterion(output,target) + loss = CE_loss*(torch.from_numpy(np.ones(spatialWeights.shape)).to(torch.float32).to(device)+(spatialWeights).to(torch.float32).to(device)) + + loss=torch.mean(loss).to(torch.float32).to(device) + loss_dice = criterion_DICE(output,target) + loss_sl = torch.mean(criterion_SL(output.to(device),(maxDist).to(device))) + + ##total loss is the weighted sum of suface loss and dice loss plus the boundary weighted cross entropy loss + loss = (1-alpha[epoch])*loss_sl+alpha[epoch]*(loss_dice)+loss +# + predict = get_predictions(output) + iou = mIoU(predict,labels) + ious.append(iou) + + if i%10 == 0: + logger.write('Epoch:{} [{}/{}], Loss: {:.3f}'.format(epoch,i,len(trainloader),loss.item())) + + loss.backward() + optimizer.step() + + logger.write('Epoch:{}, Train mIoU: {}'.format(epoch,np.average(ious))) + lossvalid , miou = lossandaccuracy(validloader,model,alpha[epoch]) + totalperf = total_metric(nparams,miou) + f = 'Epoch:{}, Valid Loss: {:.3f} mIoU: {} Complexity: {} total: {}' + logger.write(f.format(epoch,lossvalid, miou,nparams,totalperf)) + + scheduler.step(lossvalid) + + ##save the model every epoch + if epoch %1 == 0: + torch.save(model.state_dict(), '{}/models/dense_net{}.pkl'.format(LOGDIR,epoch)) + + ##visualize the ouput every 5 epoch + if epoch %5 ==0: + os.makedirs('test/epoch/labels/',exist_ok=True) + os.makedirs('test/epoch/output/',exist_ok=True) + os.makedirs('test/epoch/mask/',exist_ok=True) + + with torch.no_grad(): + for i, batchdata in tqdm(enumerate(testloader),total=len(testloader)): + img,labels,index,x,maxDist= batchdata + data = img.to(device) + output = model(data) + predict = get_predictions(output) + for j in range (len(index)): + np.save('test/epoch/labels/{}.npy'.format(index[j]),predict[j].cpu().numpy()) + try: + plt.imsave('test/epoch/output/{}.jpg'.format(index[j]),255*labels[j].cpu().numpy()) + except: + pass + pred_img = predict[j].cpu().numpy()/3.0 + inp = img[j].squeeze() * 0.5 + 0.5 + img_orig = np.clip(inp,0,1) + img_orig = np.array(img_orig) + combine = np.hstack([img_orig,pred_img]) + plt.imsave('test/epoch/mask/{}.jpg'.format(index[j]),combine) + diff --git a/utils.py b/utils.py new file mode 100644 index 0000000..3c4d6b7 --- /dev/null +++ b/utils.py @@ -0,0 +1,222 @@ +#!/usr/bin/env python3 +# -*- coding: utf-8 -*- +""" +Created on Tue Aug 27 16:04:18 2019 + +@author: Aayush Chaudhary +""" +##https://github.com/Bjarten/early-stopping-pytorch/blob/master/pytorchtools.py +import numpy as np +import torch +import torch.nn as nn +import torch.nn.functional as F +import torchvision +import cv2 +import os + +from sklearn.metrics import precision_score , recall_score,f1_score +from scipy.ndimage import distance_transform_edt as distance +#%% +class FocalLoss2d(nn.Module): + def __init__(self, weight=None,gamma=2): + super(FocalLoss2d,self).__init__() + self.gamma = gamma + self.loss = nn.NLLLoss(weight) + def forward(self, outputs, targets): + return self.loss((1 - nn.Softmax2d()(outputs)).pow(self.gamma) * torch.log(nn.Softmax2d()(outputs)), targets) + +###https://github.com/ycszen/pytorch-segmentation/blob/master/loss.py +# https://discuss.pytorch.org/t/using-cross-entropy-loss-with-semantic-segmentation-model/31988 +class CrossEntropyLoss2d(nn.Module): + + def __init__(self, weight=None): + super(CrossEntropyLoss2d,self).__init__() + self.loss = nn.NLLLoss(weight) + + def forward(self, outputs, targets): + return self.loss(F.log_softmax(outputs,dim=1), targets) + +class SurfaceLoss(nn.Module): + # Author: Rakshit Kothari + def __init__(self, epsilon=1e-5, softmax=True): + super(SurfaceLoss, self).__init__() + self.weight_map = [] + def forward(self, x, distmap): + x = torch.softmax(x, dim=1) + self.weight_map = distmap + score = x.flatten(start_dim=2)*distmap.flatten(start_dim=2) + score = torch.mean(score, dim=2) # Mean between pixels per channel + score = torch.mean(score, dim=1) # Mean between channels + return score + + +class GeneralizedDiceLoss(nn.Module): + # Author: Rakshit Kothari + # Input: (B, C, ...) + # Target: (B, C, ...) + def __init__(self, epsilon=1e-5, weight=None, softmax=True, reduction=True): + super(GeneralizedDiceLoss, self).__init__() + self.epsilon = epsilon + self.weight = [] + self.reduction = reduction + if softmax: + self.norm = nn.Softmax(dim=1) + else: + self.norm = nn.Sigmoid() + + def forward(self, ip, target): + + # Rapid way to convert to one-hot. For future version, use functional + Label = (np.arange(4) == target.cpu().numpy()[..., None]).astype(np.uint8) + target = torch.from_numpy(np.rollaxis(Label, 3,start=1)).cuda() + + assert ip.shape == target.shape + ip = self.norm(ip) + + # Flatten for multidimensional data + ip = torch.flatten(ip, start_dim=2, end_dim=-1).cuda().to(torch.float32) + target = torch.flatten(target, start_dim=2, end_dim=-1).cuda().to(torch.float32) + + numerator = ip*target + denominator = ip + target + + class_weights = 1./(torch.sum(target, dim=2)**2).clamp(min=self.epsilon) + + A = class_weights*torch.sum(numerator, dim=2) + B = class_weights*torch.sum(denominator, dim=2) + + dice_metric = 2.*torch.sum(A, dim=1)/torch.sum(B, dim=1) + if self.reduction: + return torch.mean(1. - dice_metric.clamp(min=self.epsilon)) + else: + return 1. - dice_metric.clamp(min=self.epsilon) + +def one_hot2dist(posmask): + # Input: Mask. Will be converted to Bool. + # Author: Rakshit Kothari + assert len(posmask.shape) == 2 + h, w = posmask.shape + res = np.zeros_like(posmask) + posmask = posmask.astype(np.bool) + mxDist = np.sqrt((h-1)**2 + (w-1)**2) + if posmask.any(): + negmask = ~posmask + res = distance(negmask) * negmask - (distance(posmask) - 1) * posmask + return res/mxDist + +def mIoU(predictions, targets,info=False): ###Mean per class accuracy + unique_labels = np.unique(targets) + num_unique_labels = len(unique_labels) + ious = [] + for index in range(num_unique_labels): + pred_i = predictions == index + label_i = targets == index + intersection = np.logical_and(label_i, pred_i) + union = np.logical_or(label_i, pred_i) + iou_score = np.sum(intersection.numpy())/np.sum(union.numpy()) + ious.append(iou_score) + if info: + print ("per-class mIOU: ", ious) + return np.mean(ious) + +#GA: Global Pixel Accuracy +#CA: Mean Class Accuracy for different classes +# +#Back: Background (non-eye part of peri-ocular region) +#Sclera: Sclera +#Iris: Iris +#Pupil: Pupil +#Precision: Computed using sklearn.metrics.precision_score(pred, gt, ‘weighted’) +#Recall: Computed using sklearn.metrics.recall_score(pred, gt, ‘weighted’) +#F1: Computed using sklearn.metrics.f1_score(pred, gt, ‘weighted’) +#IoU: Computed using the function below +def compute_mean_iou(flat_pred, flat_label,info=False): + ''' + compute mean intersection over union (IOU) over all classes + :param flat_pred: flattened prediction matrix + :param flat_label: flattened label matrix + :return: mean IOU + ''' + unique_labels = np.unique(flat_label) + num_unique_labels = len(unique_labels) + + Intersect = np.zeros(num_unique_labels) + Union = np.zeros(num_unique_labels) + precision = np.zeros(num_unique_labels) + recall = np.zeros(num_unique_labels) + f1 = np.zeros(num_unique_labels) + + for index, val in enumerate(unique_labels): + pred_i = flat_pred == val + label_i = flat_label == val + + if info: + precision[index] = precision_score(pred_i, label_i, 'weighted') + recall[index] = recall_score(pred_i, label_i, 'weighted') + f1[index] = f1_score(pred_i, label_i, 'weighted') + + Intersect[index] = float(np.sum(np.logical_and(label_i, pred_i))) + Union[index] = float(np.sum(np.logical_or(label_i, pred_i))) + + if info: + print ("per-class mIOU: ", Intersect / Union) + print ("per-class precision: ", precision) + print ("per-class recall: ", recall) + print ("per-class f1: ", f1) + mean_iou = np.mean(Intersect / Union) + return mean_iou + +def total_metric(nparams,miou): + S = nparams * 4.0 / (1024 * 1024) + total = min(1,1.0/S) + miou + return total * 0.5 + + +def get_nparams(model): + return sum(p.numel() for p in model.parameters() if p.requires_grad) + + +def get_predictions(output): + bs,c,h,w = output.size() + values, indices = output.cpu().max(1) + indices = indices.view(bs,h,w) # bs x h x w + return indices + + +class Logger(): + def __init__(self, output_name): + dirname = os.path.dirname(output_name) + if not os.path.exists(dirname): + os.mkdir(dirname) + self.dirname = dirname + self.log_file = open(output_name, 'a+') + self.infos = {} + + def append(self, key, val): + vals = self.infos.setdefault(key, []) + vals.append(val) + + def log(self, extra_msg=''): + msgs = [extra_msg] + for key, vals in self.infos.iteritems(): + msgs.append('%s %.6f' % (key, np.mean(vals))) + msg = '\n'.join(msgs) + self.log_file.write(msg + '\n') + self.log_file.flush() + self.infos = {} + return msg + + def write_silent(self, msg): + self.log_file.write(msg + '\n') + self.log_file.flush() + + def write(self, msg): + self.log_file.write(msg + '\n') + self.log_file.flush() + print (msg) + def write_summary(self,msg): + self.log_file.write(msg) + self.log_file.write('\n') + self.log_file.flush() + print (msg) +

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