From 025e3dc38732c9b41eed475e1fe3adb6aa202b63 Mon Sep 17 00:00:00 2001 From: Rune Frohn Date: Thu, 6 May 2021 16:17:17 +0200 Subject: [PATCH 01/32] first working models --- cutpaste.py | 69 ++++++++++++++++ dataset.py | 52 ++++++++++++ eval.py | 209 ++++++++++++++++++++++++++++++++++++++++++++++++ model.py | 36 +++++++++ run_training.py | 143 +++++++++++++++++++++++++++++++++ 5 files changed, 509 insertions(+) create mode 100644 cutpaste.py create mode 100644 dataset.py create mode 100644 eval.py create mode 100644 model.py create mode 100644 run_training.py diff --git a/cutpaste.py b/cutpaste.py new file mode 100644 index 0000000..27c88d0 --- /dev/null +++ b/cutpaste.py @@ -0,0 +1,69 @@ +import random +import math +from torchvision import transforms +import torch + +def cut_paste_collate_fn(batch): + # cutPaste return 2 tuples of tuples we convert them into a list of tuples + org_imgs, imgs = list(zip(*batch)) +# print(list(zip(*batch))) + return torch.stack(org_imgs), torch.stack(imgs) + +class CutPaste(object): + """Randomly mask out one or more patches from an image. + Args: + n_holes (int): Number of patches to cut out of each image. + length (int): The length (in pixels) of each square patch. + """ + def __init__(self, area_ratio=[0.02,0.15], aspect_ratio=0.3, colorJitter=0.1, transform=None): + self.area_ratio = area_ratio + self.aspect_ratio = aspect_ratio + self.transform = transform + + # setup colorJitter + if colorJitter is None: + self.colorJitter = None + else: + self.colorJitter = transforms.ColorJitter(brightness = colorJitter, + contrast = colorJitter, + saturation = colorJitter, + hue = colorJitter) + + def __call__(self, img): + #TODO: we might want to use the pytorch implementation to calculate the patches from https://pytorch.org/vision/stable/_modules/torchvision/transforms/transforms.html#RandomErasing + h = img.size[0] + w = img.size[1] + + # ratio between area_ratio[0] and area_ratio[1] + ratio_area = random.uniform(self.area_ratio[0], self.area_ratio[1]) * w * h + + # TODO: check if this is realy uniform in (aspect_ratio, 1) ∪ (1, 1/aspect_ratio). + # so first we sample from witch bucket and than in the range + aspect = random.uniform(self.aspect_ratio, 1/self.aspect_ratio) + + cut_w = int(round(math.sqrt(ratio_area * aspect))) + cut_h = int(round(math.sqrt(ratio_area / aspect))) + + # BIG TODO: also sample from other images. currently we only sample from the image itself + from_location_h = int(random.uniform(0, h - cut_h)) + from_location_w = int(random.uniform(0, w - cut_w)) + + box = [from_location_w, from_location_h, from_location_w + cut_w, from_location_h + cut_h] + patch = img.crop(box) + + if self.colorJitter: + patch = self.colorJitter(patch) + + to_location_h = int(random.uniform(0, h - cut_h)) + to_location_w = int(random.uniform(0, w - cut_w)) + + insert_box = [to_location_w, to_location_h, to_location_w + cut_w, to_location_h + cut_h] + org_img = img.copy() + img.paste(patch, insert_box) + + # apply transforms to both images + if self.transform: + img = self.transform(img) + org_img = self.transform(org_img) + + return org_img, img \ No newline at end of file diff --git a/dataset.py b/dataset.py new file mode 100644 index 0000000..52b4ed5 --- /dev/null +++ b/dataset.py @@ -0,0 +1,52 @@ +from torch.utils.data import Dataset +from pathlib import Path +from PIL import Image + + +class MVTecAT(Dataset): + """Face Landmarks dataset.""" + + def __init__(self, root_dir, defect_name, size, transform=None, mode="train"): + """ + Args: + root_dir (string): Directory with the MVTec AD dataset. + defect_name (string): defect to load. + transform: Transform to apply to data + mode: "train" loads training sammples "test" test samples default "train" + """ + self.root_dir = Path(root_dir) + self.defect_name = defect_name + self.transform = transform + self.mode = mode + self.size = size + + # find test images + if self.mode == "train": + self.image_names = list((self.root_dir / defect_name / "train" / "good").glob("*.png")) + print("loading images") + # during training we cache the smaller images for performance reasons (not a good coding style) + # TODO: maybe use multiple transforms where the first one is cached :D OR resize every image of the dataset beforehand + self.imgs = [Image.open(file).resize((size,size)).convert("RGB") for file in self.image_names] + else: + #test mode + self.image_names = list((self.root_dir / defect_name / "test").glob(str(Path("*") / "*.png"))) + + def __len__(self): + return len(self.image_names) + + def __getitem__(self, idx): + if self.mode == "train": + # img = Image.open(self.image_names[idx]) + # img = img.convert("RGB") + img = self.imgs[idx].copy() + if self.transform is not None: + img = self.transform(img) + return img + else: + filename = self.image_names[idx] + label = filename.parts[-2] + img = Image.open(filename) + img = img.resize((self.size,self.size)).convert("RGB") + if self.transform is not None: + img = self.transform(img) + return img, label != "good" \ No newline at end of file diff --git a/eval.py b/eval.py new file mode 100644 index 0000000..0e503d6 --- /dev/null +++ b/eval.py @@ -0,0 +1,209 @@ +from sklearn.metrics import roc_curve, auc +from sklearn.manifold import TSNE +from sklearn.neighbors import KernelDensity +from torchvision import transforms +from torch.utils.data import DataLoader +import torch +from dataset import MVTecAT +from cutpaste import CutPaste +from model import ProjectionNet +import matplotlib.pyplot as plt +import argparse +from pathlib import Path +from cutpaste import CutPaste, cut_paste_collate_fn +from sklearn.utils import shuffle + +def eval_model(modelname, defect_type, device="cpu", save_plots=False, size=256, show_training_data=True): + # create test dataset + test_transform = transforms.Compose([]) + test_transform.transforms.append(transforms.Resize((size,size))) + test_transform.transforms.append(transforms.ToTensor()) + test_data = MVTecAT("Data", defect_type, size, transform = test_transform, mode="test") + + dataloader_test = DataLoader(test_data, batch_size=64, + shuffle=False, num_workers=0) + + # create model + print(f"loading model {modelname}") + model = ProjectionNet(pretrained=False) + model.load_state_dict(torch.load(modelname)) + model.to(device) + model.eval() + + #get embeddings for test data + labels = [] + embeds = [] + with torch.no_grad(): + for x, label in dataloader_test: + embed, logit = model(x.to(device)) + + # save + embeds.append(embed.cpu()) + labels.append(label.cpu()) + labels = torch.cat(labels) + embeds = torch.cat(embeds) + + # train data / train kde + # TODO: put the GDE stuff into the Model class and do this at the end of the training + test_data = MVTecAT("Data", defect_type, size, transform=test_transform, mode="train") + + dataloader_train = DataLoader(test_data, batch_size=64, + shuffle=False, num_workers=0) + train_embed = [] + with torch.no_grad(): + for x in dataloader_train: + embed, logit = model(x.to(device)) + + train_embed.append(embed.cpu()) + train_embed = torch.cat(train_embed) + + #create eval plot dir + if save_plots: + eval_dir = Path("eval") / modelname + eval_dir.mkdir(parents=True, exist_ok=True) + + # plot tsne + # also show some of the training data + show_training_data = False + if show_training_data: + #augmentation settig + # TODO: but all of this in the same place and put it into the args + min_scale = 0.5 + + # create Training Dataset and Dataloader + after_cutpaste_transform = transforms.Compose([]) + after_cutpaste_transform.transforms.append(transforms.ToTensor()) + #TODO: we might want to normalize the images. + + train_transform = transforms.Compose([]) + #train_transform.transforms.append(transforms.RandomResizedCrop(size, scale=(min_scale,1))) + #train_transform.transforms.append(transforms.GaussianBlur(int(size/10), sigma=(0.1,2.0))) + train_transform.transforms.append(CutPaste(transform=after_cutpaste_transform)) + # train_transform.transforms.append(transforms.ToTensor()) + + train_data = MVTecAT("Data", defect_type, transform=train_transform, size=size) + dataloader_train = DataLoader(train_data, batch_size=32, + shuffle=True, num_workers=8, collate_fn=cut_paste_collate_fn, + persistent_workers=True) + # inference training data + train_labels = [] + train_embeds = [] + with torch.no_grad(): + for x1, x2 in dataloader_train: + x = torch.cat([x1,x2], axis=0) + embed, logit = model(x.to(device)) + + # generate labels: + y = torch.tensor([0, 1]) + y = y.repeat_interleave(x1.size(0)) + + # save + train_embeds.append(embed.cpu()) + train_labels.append(y) + # only less data + break + train_labels = torch.cat(train_labels) + train_embeds = torch.cat(train_embeds) + + # for tsne we encode training data as 2, and augmentet data as 3 + tsne_labels = torch.cat([labels, train_labels + 2]) + tsne_embeds = torch.cat([embeds, train_embeds]) + else: + tsne_labels = labels + tsne_embeds = embeds + plot_tsne(tsne_labels, tsne_embeds, eval_dir / "tsne.png") + # estemate KDE parameters + kde = KernelDensity(kernel='gaussian', bandwidth=1).fit(train_embed) + scores = kde.score_samples(embeds) + # we get the probability to be in the correct distribution + # but our labels are inverted (1 for out of distribution) + # so we have to relabel + + roc_auc = plot_roc(labels == False, scores, eval_dir / "roc_plot.png", modelname=modelname, save_plots=save_plots) + + + return roc_auc + + +def plot_roc(labels, scores, filename, modelname="", save_plots=False): + + fpr, tpr, _ = roc_curve(labels, scores) + roc_auc = auc(fpr, tpr) + + #plot roc + if save_plots: + plt.figure() + lw = 2 + plt.plot(fpr, tpr, color='darkorange', + lw=lw, label='ROC curve (area = %0.2f)' % roc_auc) + plt.plot([0, 1], [0, 1], color='navy', lw=lw, linestyle='--') + plt.xlim([0.0, 1.0]) + plt.ylim([0.0, 1.05]) + plt.xlabel('False Positive Rate') + plt.ylabel('True Positive Rate') + plt.title(f'Receiver operating characteristic {modelname}') + plt.legend(loc="lower right") + # plt.show() + plt.savefig(filename) + plt.close() + + return roc_auc + +def plot_tsne(labels, embeds, filename): + tsne = TSNE(n_components=2, verbose=1, perplexity=15, n_iter=300) + embeds, labels = shuffle(embeds, labels) + tsne_results = tsne.fit_transform(embeds) + fig, ax = plt.subplots(1) + colormap = ["b", "r", "c", "y"] + + ax.scatter(tsne_results[:,0], tsne_results[:,1], color=[colormap[l] for l in labels]) + fig.savefig(filename) + plt.close() + +if __name__ == '__main__': + parser = argparse.ArgumentParser(description='eval models') + parser.add_argument('--type', default="all", + help='MVTec defection dataset type to train seperated by , (default: "all": train all defect types)') + + parser.add_argument('--model_dir', default="models", + help=' directory contating models to evaluate (default: models)') + + parser.add_argument('--cuda', default=False, + help='use cuda for model predictions (default: False)') + + + + args = parser.parse_args() + + args = parser.parse_args() + print(args) + all_types = ['bottle', + 'cable', + 'capsule', + 'carpet', + 'grid', + 'hazelnut', + 'leather', + 'metal_nut', + 'pill', + 'screw', + 'tile', + 'toothbrush', + 'transistor', + 'wood', + 'zipper'] + + if args.type == "all": + types = all_types + else: + types = args.type.split(",") + + device = "cuda" if args.cuda else "cpu" + + # find models + model_names = [list(Path(args.model_dir).glob(f"model-{data_type}*"))[0] for data_type in types] + for model_name, data_type in zip(model_names, types): + print(f"evaluating {data_type}") + + roc_auc = eval_model(model_name, data_type, save_plots=True, device=device) + print(f"{data_type} AUC: {roc_auc}") \ No newline at end of file diff --git a/model.py b/model.py new file mode 100644 index 0000000..acda7a7 --- /dev/null +++ b/model.py @@ -0,0 +1,36 @@ +import torch +import torch.nn as nn +import torch.nn.functional as F +from torchvision.models import resnet18 + + +class ProjectionNet(nn.Module): + def __init__(self, pretrained=True, head_layers=[512,512,512,512,512,512,512,512,128]): + super(ProjectionNet, self).__init__() + #self.resnet18 = torch.hub.load('pytorch/vision:v0.9.0', 'resnet18', pretrained=pretrained) + self.resnet18 = resnet18(pretrained=pretrained) + + # create MPL head as seen in the code in: https://github.com/uoguelph-mlrg/Cutout/blob/master/util/cutout.py + # TODO: check if this is really the right architecture + last_layer = 512 + sequential_layers = [] + for num_neurons in head_layers[:-1]: + sequential_layers.append(nn.Linear(last_layer, num_neurons)) + #TODO: use Batchnormalization? + sequential_layers.append(nn.ReLU(inplace=True)) + last_layer = num_neurons + + #the last layer without activation + sequential_layers.append(nn.Linear(last_layer, head_layers[-1])) + last_layer = head_layers[-1] + + head = nn.Sequential( + *sequential_layers + ) + self.resnet18.fc = head + self.out = nn.Linear(last_layer, 2) + + def forward(self, x): + embeds = self.resnet18(x) + logits = self.out(embeds) + return embeds, logits \ No newline at end of file diff --git a/run_training.py b/run_training.py new file mode 100644 index 0000000..936a2c8 --- /dev/null +++ b/run_training.py @@ -0,0 +1,143 @@ +# head dims:512,512,512,512,512,512,512,512,128 +# code is basicly:https://github.com/google-research/deep_representation_one_class +from pathlib import Path +from tqdm import tqdm +import datetime +import argparse + +import torch +from torch import optim +from torch.utils.data import DataLoader +from torch.utils.tensorboard import SummaryWriter +from torchvision import transforms + +from dataset import MVTecAT +from cutpaste import CutPaste, cut_paste_collate_fn +from model import ProjectionNet +from torch.optim.lr_scheduler import CosineAnnealingWarmRestarts + +def run_training(data_type="screw"): + torch.multiprocessing.freeze_support() + # TODO: use script params for hyperparameter + # Temperature Hyperparameter currently not used + temperature = 0.2 + epochs = 64*4 + device = "cuda" + pretrained = True + + weight_decay = 0.00003 + learninig_rate = 0.03 + momentum = 0.9 + #TODO: use f strings also for the date LOL + model_name = f"model-{data_type}" + '-{date:%Y-%m-%d_%H_%M_%S}'.format(date=datetime.datetime.now() ) + + #augmentation: + size = 256 + min_scale = 0.5 + + # create Training Dataset and Dataloader + after_cutpaste_transform = transforms.Compose([]) + after_cutpaste_transform.transforms.append(transforms.ToTensor()) + #TODO: we might want to normalize the images. + + train_transform = transforms.Compose([]) + # train_transform.transforms.append(transforms.Resize((256,256))) + train_transform.transforms.append(transforms.RandomResizedCrop(size, scale=(min_scale,1))) + train_transform.transforms.append(transforms.GaussianBlur(int(size/10), sigma=(0.1,2.0))) + train_transform.transforms.append(CutPaste(transform = after_cutpaste_transform)) + # train_transform.transforms.append(transforms.ToTensor()) + + train_data = MVTecAT("Data", data_type, transform = train_transform, size=size) + dataloader = DataLoader(train_data, batch_size=32, + shuffle=True, num_workers=8, collate_fn=cut_paste_collate_fn, + persistent_workers=True) + + # Writer will output to ./runs/ directory by default + writer = SummaryWriter(Path("logdirs") / model_name) + + # create Model: + model = ProjectionNet(pretrained=pretrained) + model.to(device) + + loss_fn = torch.nn.CrossEntropyLoss() + optimizer = optim.SGD(model.parameters(), lr=learninig_rate, momentum=momentum, weight_decay=weight_decay) + scheduler = CosineAnnealingWarmRestarts(optimizer, epochs) + + step = 0 + import torch.autograd.profiler as profiler + num_batches = len(dataloader) + for epoch in tqdm(range(epochs)): + for batch_idx, data in enumerate(dataloader): + x1, x2 = data + x1 = x1.to(device) + x2 = x2.to(device) + + # zero the parameter gradients + optimizer.zero_grad() + + xc = torch.cat((x1, x2), axis=0) + embeds, logits = model(xc) + + # embeds = F.normalize(embeds, p=2, dim=1) + # embeds1, embeds2 = torch.split(embeds,x1.size(0),dim=0) + # ip = torch.matmul(embeds1, embeds2.T) + # ip = ip / temperature + + # y = torch.arange(0,x1.size(0), device=device) + # loss = loss_fn(ip, torch.arange(0,x1.size(0), device=device)) + + y = torch.tensor([0, 1], device=device) + y = y.repeat_interleave(x1.size(0)) + loss = loss_fn(logits, y) + + + # regulize weights: + loss.backward() + optimizer.step() + scheduler.step(epoch + batch_idx / num_batches) + + writer.add_scalar('loss', loss.item(), step) + + # predicted = torch.argmax(ip,axis=0) + predicted = torch.argmax(logits,axis=1) + # print(logits) + # print(predicted) + # print(y) + accuracy = torch.true_divide(torch.sum(predicted==y), predicted.size(0)) + writer.add_scalar('acc', accuracy, step) + + step += 1 + writer.add_scalar('epoch', epoch, step) + torch.save(model.state_dict(), Path("models") / f"{model_name}.tch") + +if __name__ == '__main__': + parser = argparse.ArgumentParser(description='Training defect detection as described in the CutPaste Paper.') + parser.add_argument('--type', default="all", + help='MVTec defection dataset type to train seperated by , (default: "all": train all defect types)') + + args = parser.parse_args() + print(args) + all_types = ['bottle', + 'cable', + 'capsule', + 'carpet', + 'grid', + 'hazelnut', + 'leather', + 'metal_nut', + 'pill', + 'screw', + 'tile', + 'toothbrush', + 'transistor', + 'wood', + 'zipper'] + + if args.type == "all": + types = all_types + else: + types = args.type.split(",") + + for data_type in types: + print(f"training {data_type}") + run_training(data_type) \ No newline at end of file From 795146b17256cfdbb3e9623590f5270ed98aa245 Mon Sep 17 00:00:00 2001 From: Rune Frohn Date: Sat, 8 May 2021 07:31:42 +0200 Subject: [PATCH 02/32] add freezing, some learning paramerters fix labels in eval --- eval.py | 35 +++++++++++++++++------ model.py | 16 ++++++++++- run_training.py | 75 +++++++++++++++++++++++++++++++++++++++++-------- 3 files changed, 105 insertions(+), 21 deletions(-) diff --git a/eval.py b/eval.py index 0e503d6..fb9af1d 100644 --- a/eval.py +++ b/eval.py @@ -12,23 +12,28 @@ from pathlib import Path from cutpaste import CutPaste, cut_paste_collate_fn from sklearn.utils import shuffle +from sklearn.model_selection import GridSearchCV +import numpy as np -def eval_model(modelname, defect_type, device="cpu", save_plots=False, size=256, show_training_data=True): +def eval_model(modelname, defect_type, device="cpu", save_plots=False, size=256, show_training_data=True, model=None): # create test dataset test_transform = transforms.Compose([]) test_transform.transforms.append(transforms.Resize((size,size))) test_transform.transforms.append(transforms.ToTensor()) + test_transform.transforms.append(transforms.Normalize(mean=[0.485, 0.456, 0.406], + std=[0.229, 0.224, 0.225])) test_data = MVTecAT("Data", defect_type, size, transform = test_transform, mode="test") dataloader_test = DataLoader(test_data, batch_size=64, shuffle=False, num_workers=0) # create model - print(f"loading model {modelname}") - model = ProjectionNet(pretrained=False) - model.load_state_dict(torch.load(modelname)) - model.to(device) - model.eval() + if model is None: + print(f"loading model {modelname}") + model = ProjectionNet(pretrained=False) + model.load_state_dict(torch.load(modelname)) + model.to(device) + model.eval() #get embeddings for test data labels = [] @@ -73,6 +78,8 @@ def eval_model(modelname, defect_type, device="cpu", save_plots=False, size=256, # create Training Dataset and Dataloader after_cutpaste_transform = transforms.Compose([]) after_cutpaste_transform.transforms.append(transforms.ToTensor()) + after_cutpaste_transform.transforms.append(transforms.Normalize(mean=[0.485, 0.456, 0.406], + std=[0.229, 0.224, 0.225])) #TODO: we might want to normalize the images. train_transform = transforms.Compose([]) @@ -112,14 +119,26 @@ def eval_model(modelname, defect_type, device="cpu", save_plots=False, size=256, tsne_labels = labels tsne_embeds = embeds plot_tsne(tsne_labels, tsne_embeds, eval_dir / "tsne.png") - # estemate KDE parameters + else: + eval_dir = Path("unused") + # # estemate KDE parameters + # # use grid search cross-validation to optimize the bandwidth + # params = {'bandwidth': np.logspace(-10, 10, 50)} + # grid = GridSearchCV(KernelDensity(), params) + # grid.fit(embeds) + + # print("best bandwidth: {0}".format(grid.best_estimator_.bandwidth)) + + # # use the best estimator to compute the kernel density estimate + # kde = grid.best_estimator_ kde = KernelDensity(kernel='gaussian', bandwidth=1).fit(train_embed) scores = kde.score_samples(embeds) + # print(scores) # we get the probability to be in the correct distribution # but our labels are inverted (1 for out of distribution) # so we have to relabel - roc_auc = plot_roc(labels == False, scores, eval_dir / "roc_plot.png", modelname=modelname, save_plots=save_plots) + roc_auc = plot_roc(labels, scores, eval_dir / "roc_plot.png", modelname=modelname, save_plots=save_plots) return roc_auc diff --git a/model.py b/model.py index acda7a7..873bd1d 100644 --- a/model.py +++ b/model.py @@ -33,4 +33,18 @@ def __init__(self, pretrained=True, head_layers=[512,512,512,512,512,512,512,512 def forward(self, x): embeds = self.resnet18(x) logits = self.out(embeds) - return embeds, logits \ No newline at end of file + return embeds, logits + + def freeze_resnet(self): + # freez full resnet18 + for param in self.resnet18.parameters(): + param.requires_grad = False + + #unfreeze head: + for param in self.resnet18.fc.parameters(): + param.requires_grad = True + + def unfreeze(self): + #unfreeze all: + for param in self.parameters(): + param.requires_grad = True \ No newline at end of file diff --git a/run_training.py b/run_training.py index 936a2c8..cf0db65 100644 --- a/run_training.py +++ b/run_training.py @@ -8,25 +8,30 @@ import torch from torch import optim from torch.utils.data import DataLoader +from torch.optim.lr_scheduler import CosineAnnealingWarmRestarts from torch.utils.tensorboard import SummaryWriter from torchvision import transforms + from dataset import MVTecAT from cutpaste import CutPaste, cut_paste_collate_fn from model import ProjectionNet -from torch.optim.lr_scheduler import CosineAnnealingWarmRestarts - -def run_training(data_type="screw"): +from eval import eval_model + +def run_training(data_type="screw", + model_dir="models", + epochs=256, + pretrained=True, + test_epochs=10, + freeze_resnet=20, + learninig_rate=0.03): torch.multiprocessing.freeze_support() # TODO: use script params for hyperparameter # Temperature Hyperparameter currently not used temperature = 0.2 - epochs = 64*4 device = "cuda" - pretrained = True weight_decay = 0.00003 - learninig_rate = 0.03 momentum = 0.9 #TODO: use f strings also for the date LOL model_name = f"model-{data_type}" + '-{date:%Y-%m-%d_%H_%M_%S}'.format(date=datetime.datetime.now() ) @@ -38,19 +43,21 @@ def run_training(data_type="screw"): # create Training Dataset and Dataloader after_cutpaste_transform = transforms.Compose([]) after_cutpaste_transform.transforms.append(transforms.ToTensor()) + after_cutpaste_transform.transforms.append(transforms.Normalize(mean=[0.485, 0.456, 0.406], + std=[0.229, 0.224, 0.225])) #TODO: we might want to normalize the images. train_transform = transforms.Compose([]) - # train_transform.transforms.append(transforms.Resize((256,256))) train_transform.transforms.append(transforms.RandomResizedCrop(size, scale=(min_scale,1))) train_transform.transforms.append(transforms.GaussianBlur(int(size/10), sigma=(0.1,2.0))) + train_transform.transforms.append(transforms.Resize((256,256))) train_transform.transforms.append(CutPaste(transform = after_cutpaste_transform)) # train_transform.transforms.append(transforms.ToTensor()) - train_data = MVTecAT("Data", data_type, transform = train_transform, size=size) - dataloader = DataLoader(train_data, batch_size=32, + train_data = MVTecAT("Data", data_type, transform = train_transform, size=int(size * (1/min_scale))) + dataloader = DataLoader(train_data, batch_size=64, shuffle=True, num_workers=8, collate_fn=cut_paste_collate_fn, - persistent_workers=True) + persistent_workers=True, pin_memory=True, prefetch_factor=5) # Writer will output to ./runs/ directory by default writer = SummaryWriter(Path("logdirs") / model_name) @@ -59,6 +66,9 @@ def run_training(data_type="screw"): model = ProjectionNet(pretrained=pretrained) model.to(device) + if freeze_resnet > 0: + model.freeze_resnet() + loss_fn = torch.nn.CrossEntropyLoss() optimizer = optim.SGD(model.parameters(), lr=learninig_rate, momentum=momentum, weight_decay=weight_decay) scheduler = CosineAnnealingWarmRestarts(optimizer, epochs) @@ -67,6 +77,8 @@ def run_training(data_type="screw"): import torch.autograd.profiler as profiler num_batches = len(dataloader) for epoch in tqdm(range(epochs)): + if epoch == freeze_resnet: + model.unfreeze() for batch_idx, data in enumerate(dataloader): x1, x2 = data x1 = x1.to(device) @@ -105,16 +117,46 @@ def run_training(data_type="screw"): # print(y) accuracy = torch.true_divide(torch.sum(predicted==y), predicted.size(0)) writer.add_scalar('acc', accuracy, step) + writer.add_scalar('lr', scheduler.get_last_lr()[0], step) step += 1 writer.add_scalar('epoch', epoch, step) - torch.save(model.state_dict(), Path("models") / f"{model_name}.tch") + + # run tests + if test_epochs > 0: + if epoch % test_epochs == 0: + # run auc calculation + #TODO: create dataset only once. + #TODO: train predictor here + roc_auc= eval_model(model_name, data_type, device=device, save_plots=False, size=size, show_training_data=False, model=model) + writer.add_scalar('eval_auc', roc_auc, step) + + + torch.save(model.state_dict(), model_dir / f"{model_name}.tch") if __name__ == '__main__': parser = argparse.ArgumentParser(description='Training defect detection as described in the CutPaste Paper.') parser.add_argument('--type', default="all", help='MVTec defection dataset type to train seperated by , (default: "all": train all defect types)') + parser.add_argument('--epochs', default=256, type=int, + help='number of epochs to train the model , (default: 256)') + + parser.add_argument('--model_dir', default="models", + help='output folder of the models , (default: models)') + + parser.add_argument('--no-pretrained', dest='pretrained', default=True, action='store_false', + help='use pretrained values to initalize ResNet18 , (default: True)') + + parser.add_argument('--test_epochs', default=10, + help='interval to calculate the auc during trainig, if -1 do not calculate test scores, (default: 10)') + + parser.add_argument('--freeze_resnet', default=20, + help='number of epochs to freeze resnet (default: 20)') + + parser.add_argument('--lr', default=0.03, type=float, + help='learning rate (default: 0.03)') + args = parser.parse_args() print(args) all_types = ['bottle', @@ -138,6 +180,15 @@ def run_training(data_type="screw"): else: types = args.type.split(",") + # create modle dir + Path(args.model_dir).mkdir(exist_ok=True, parents=True) + for data_type in types: print(f"training {data_type}") - run_training(data_type) \ No newline at end of file + run_training(data_type, + model_dir=Path(args.model_dir), + epochs=args.epochs, + pretrained=args.pretrained, + test_epochs=args.test_epochs, + freeze_resnet=args.freeze_resnet, + learninig_rate=args.lr) \ No newline at end of file From cf518cadf2892ffd9aac8ed414147d33e4ae7dce Mon Sep 17 00:00:00 2001 From: Rune Frohn Date: Mon, 10 May 2021 13:00:58 +0200 Subject: [PATCH 03/32] using custom mahalanobis distance impelmentation, recalculate embedings for evaluation during training --- dataset.py | 1 - eval.py | 108 +++++++++++++++++++++++++++++++++++++----------- model.py | 15 ++++--- run_training.py | 85 +++++++++++++++++++++++++------------ 4 files changed, 152 insertions(+), 57 deletions(-) diff --git a/dataset.py b/dataset.py index 52b4ed5..3dfb5f0 100644 --- a/dataset.py +++ b/dataset.py @@ -25,7 +25,6 @@ def __init__(self, root_dir, defect_name, size, transform=None, mode="train"): self.image_names = list((self.root_dir / defect_name / "train" / "good").glob("*.png")) print("loading images") # during training we cache the smaller images for performance reasons (not a good coding style) - # TODO: maybe use multiple transforms where the first one is cached :D OR resize every image of the dataset beforehand self.imgs = [Image.open(file).resize((size,size)).convert("RGB") for file in self.image_names] else: #test mode diff --git a/eval.py b/eval.py index fb9af1d..9fa0427 100644 --- a/eval.py +++ b/eval.py @@ -14,8 +14,26 @@ from sklearn.utils import shuffle from sklearn.model_selection import GridSearchCV import numpy as np +from sklearn.covariance import LedoitWolf +from collections import defaultdict +import pandas as pd -def eval_model(modelname, defect_type, device="cpu", save_plots=False, size=256, show_training_data=True, model=None): +def get_train_embeds(model, size, defect_type, transform, device): + # train data / train kde + test_data = MVTecAT("Data", defect_type, size, transform=transform, mode="train") + + dataloader_train = DataLoader(test_data, batch_size=64, + shuffle=False, num_workers=0) + train_embed = [] + with torch.no_grad(): + for x in dataloader_train: + embed, logit = model(x.to(device)) + + train_embed.append(embed.cpu()) + train_embed = torch.cat(train_embed) + return train_embed + +def eval_model(modelname, defect_type, device="cpu", save_plots=False, size=256, show_training_data=True, model=None, train_embed=None, head_layer=8): # create test dataset test_transform = transforms.Compose([]) test_transform.transforms.append(transforms.Resize((size,size))) @@ -25,12 +43,14 @@ def eval_model(modelname, defect_type, device="cpu", save_plots=False, size=256, test_data = MVTecAT("Data", defect_type, size, transform = test_transform, mode="test") dataloader_test = DataLoader(test_data, batch_size=64, - shuffle=False, num_workers=0) + shuffle=False, num_workers=0) # create model if model is None: print(f"loading model {modelname}") - model = ProjectionNet(pretrained=False) + head_layers = [512]*head_layer+[128] + print(head_layers) + model = ProjectionNet(pretrained=False, head_layers=head_layers) model.load_state_dict(torch.load(modelname)) model.to(device) model.eval() @@ -48,19 +68,12 @@ def eval_model(modelname, defect_type, device="cpu", save_plots=False, size=256, labels = torch.cat(labels) embeds = torch.cat(embeds) - # train data / train kde - # TODO: put the GDE stuff into the Model class and do this at the end of the training - test_data = MVTecAT("Data", defect_type, size, transform=test_transform, mode="train") - - dataloader_train = DataLoader(test_data, batch_size=64, - shuffle=False, num_workers=0) - train_embed = [] - with torch.no_grad(): - for x in dataloader_train: - embed, logit = model(x.to(device)) + if train_embed is None: + train_embed = get_train_embeds(model, size, defect_type, test_transform, device) - train_embed.append(embed.cpu()) - train_embed = torch.cat(train_embed) + # norm embeds + embeds = torch.nn.functional.normalize(embeds, p=2, dim=1) + train_embed = torch.nn.functional.normalize(train_embed, p=2, dim=1) #create eval plot dir if save_plots: @@ -72,7 +85,8 @@ def eval_model(modelname, defect_type, device="cpu", save_plots=False, size=256, show_training_data = False if show_training_data: #augmentation settig - # TODO: but all of this in the same place and put it into the args + # TODO: do all of this in a seperate function that we can call in training and evaluation. + # very ugly to just copy the code lol min_scale = 0.5 # create Training Dataset and Dataloader @@ -80,7 +94,6 @@ def eval_model(modelname, defect_type, device="cpu", save_plots=False, size=256, after_cutpaste_transform.transforms.append(transforms.ToTensor()) after_cutpaste_transform.transforms.append(transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])) - #TODO: we might want to normalize the images. train_transform = transforms.Compose([]) #train_transform.transforms.append(transforms.RandomResizedCrop(size, scale=(min_scale,1))) @@ -121,6 +134,7 @@ def eval_model(modelname, defect_type, device="cpu", save_plots=False, size=256, plot_tsne(tsne_labels, tsne_embeds, eval_dir / "tsne.png") else: eval_dir = Path("unused") + # TODO: put the GDE stuff into the Model class and do this at the end of the training # # estemate KDE parameters # # use grid search cross-validation to optimize the bandwidth # params = {'bandwidth': np.logspace(-10, 10, 50)} @@ -131,14 +145,45 @@ def eval_model(modelname, defect_type, device="cpu", save_plots=False, size=256, # # use the best estimator to compute the kernel density estimate # kde = grid.best_estimator_ - kde = KernelDensity(kernel='gaussian', bandwidth=1).fit(train_embed) - scores = kde.score_samples(embeds) + # kde = KernelDensity(kernel='gaussian', bandwidth=1).fit(train_embed) + # scores = kde.score_samples(embeds) # print(scores) # we get the probability to be in the correct distribution # but our labels are inverted (1 for out of distribution) # so we have to relabel - roc_auc = plot_roc(labels, scores, eval_dir / "roc_plot.png", modelname=modelname, save_plots=save_plots) + # use own formulation with malanobis distance + # from https://github.com/ORippler/gaussian-ad-mvtec/blob/4e85fb5224eee13e8643b684c8ef15ab7d5d016e/src/gaussian/model.py#L308 + def mahalanobis_distance( + values: torch.Tensor, mean: torch.Tensor, inv_covariance: torch.Tensor + ) -> torch.Tensor: + """Compute the batched mahalanobis distance. + values is a batch of feature vectors. + mean is either the mean of the distribution to compare, or a second + batch of feature vectors. + inv_covariance is the inverse covariance of the target distribution. + """ + assert values.dim() == 2 + assert 1 <= mean.dim() <= 2 + assert len(inv_covariance.shape) == 2 + assert values.shape[1] == mean.shape[-1] + assert mean.shape[-1] == inv_covariance.shape[0] + assert inv_covariance.shape[0] == inv_covariance.shape[1] + + if mean.dim() == 1: # Distribution mean. + mean = mean.unsqueeze(0) + x_mu = values - mean # batch x features + # Same as dist = x_mu.t() * inv_covariance * x_mu batch wise + dist = torch.einsum("im,mn,in->i", x_mu, inv_covariance, x_mu) + return dist.sqrt() + # claculate mean + mean = torch.mean(train_embed, axis=0) + inv_cov = torch.Tensor(LedoitWolf().fit(train_embed.cpu()).precision_,device="cpu") + + distances = mahalanobis_distance(embeds, mean, inv_cov) + #TODO: set threshold on mahalanobis distances and use "real" probabilities + + roc_auc = plot_roc(labels, distances, eval_dir / "roc_plot.png", modelname=modelname, save_plots=save_plots) return roc_auc @@ -169,7 +214,7 @@ def plot_roc(labels, scores, filename, modelname="", save_plots=False): return roc_auc def plot_tsne(labels, embeds, filename): - tsne = TSNE(n_components=2, verbose=1, perplexity=15, n_iter=300) + tsne = TSNE(n_components=2, verbose=1, perplexity=30, n_iter=500) embeds, labels = shuffle(embeds, labels) tsne_results = tsne.fit_transform(embeds) fig, ax = plt.subplots(1) @@ -190,6 +235,9 @@ def plot_tsne(labels, embeds, filename): parser.add_argument('--cuda', default=False, help='use cuda for model predictions (default: False)') + parser.add_argument('--head_layer', default=8, type=int, + help='number of layers in the projection head (default: 8)') + args = parser.parse_args() @@ -220,9 +268,21 @@ def plot_tsne(labels, embeds, filename): device = "cuda" if args.cuda else "cpu" # find models - model_names = [list(Path(args.model_dir).glob(f"model-{data_type}*"))[0] for data_type in types] + model_names = [list(Path(args.model_dir).glob(f"model-{data_type}*"))[0] for data_type in types if len(list(Path(args.model_dir).glob(f"model-{data_type}*"))) > 0] + if len(model_names) < len(all_types): + print("warning: not all types present in folder") + + obj = defaultdict(list) for model_name, data_type in zip(model_names, types): print(f"evaluating {data_type}") - roc_auc = eval_model(model_name, data_type, save_plots=True, device=device) - print(f"{data_type} AUC: {roc_auc}") \ No newline at end of file + roc_auc = eval_model(model_name, data_type, save_plots=True, device=device, head_layer=args.head_layer) + print(f"{data_type} AUC: {roc_auc}") + obj["defect_type"].append(data_type) + obj["roc_auc"].append(roc_auc) + + # save pandas dataframe + eval_dir = Path("eval") / model_name + eval_dir.mkdir(parents=True, exist_ok=True) + df = pd.DataFrame(obj) + df.to_csv(str(eval_dir) + "_perf.csv") \ No newline at end of file diff --git a/model.py b/model.py index 873bd1d..420ac70 100644 --- a/model.py +++ b/model.py @@ -14,25 +14,28 @@ def __init__(self, pretrained=True, head_layers=[512,512,512,512,512,512,512,512 # TODO: check if this is really the right architecture last_layer = 512 sequential_layers = [] - for num_neurons in head_layers[:-1]: + for num_neurons in head_layers: sequential_layers.append(nn.Linear(last_layer, num_neurons)) - #TODO: use Batchnormalization? + sequential_layers.append(nn.BatchNorm1d(num_neurons)) sequential_layers.append(nn.ReLU(inplace=True)) last_layer = num_neurons #the last layer without activation - sequential_layers.append(nn.Linear(last_layer, head_layers[-1])) - last_layer = head_layers[-1] + #TODO: is this correct? check one classe representation framework paper/code + # sequential_layers.append(nn.Linear(last_layer, head_layers[-1])) + # last_layer = head_layers[-1] head = nn.Sequential( *sequential_layers ) - self.resnet18.fc = head + self.resnet18.fc = nn.Identity() + self.head = head self.out = nn.Linear(last_layer, 2) def forward(self, x): embeds = self.resnet18(x) - logits = self.out(embeds) + tmp = self.head(embeds) + logits = self.out(tmp) return embeds, logits def freeze_resnet(self): diff --git a/run_training.py b/run_training.py index cf0db65..b818b8c 100644 --- a/run_training.py +++ b/run_training.py @@ -24,7 +24,9 @@ def run_training(data_type="screw", pretrained=True, test_epochs=10, freeze_resnet=20, - learninig_rate=0.03): + learninig_rate=0.03, + optim_name="SGD", + batch_size=64): torch.multiprocessing.freeze_support() # TODO: use script params for hyperparameter # Temperature Hyperparameter currently not used @@ -45,17 +47,16 @@ def run_training(data_type="screw", after_cutpaste_transform.transforms.append(transforms.ToTensor()) after_cutpaste_transform.transforms.append(transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])) - #TODO: we might want to normalize the images. train_transform = transforms.Compose([]) - train_transform.transforms.append(transforms.RandomResizedCrop(size, scale=(min_scale,1))) - train_transform.transforms.append(transforms.GaussianBlur(int(size/10), sigma=(0.1,2.0))) + # train_transform.transforms.append(transforms.RandomResizedCrop(size, scale=(min_scale,1))) + # train_transform.transforms.append(transforms.GaussianBlur(int(size/10), sigma=(0.1,2.0))) train_transform.transforms.append(transforms.Resize((256,256))) train_transform.transforms.append(CutPaste(transform = after_cutpaste_transform)) # train_transform.transforms.append(transforms.ToTensor()) train_data = MVTecAT("Data", data_type, transform = train_transform, size=int(size * (1/min_scale))) - dataloader = DataLoader(train_data, batch_size=64, + dataloader = DataLoader(train_data, batch_size=batch_size, shuffle=True, num_workers=8, collate_fn=cut_paste_collate_fn, persistent_workers=True, pin_memory=True, prefetch_factor=5) @@ -63,15 +64,22 @@ def run_training(data_type="screw", writer = SummaryWriter(Path("logdirs") / model_name) # create Model: - model = ProjectionNet(pretrained=pretrained) + model = ProjectionNet(pretrained=pretrained, head_layers=[512,512,512,512,512,512,512,512,128]) model.to(device) if freeze_resnet > 0: model.freeze_resnet() loss_fn = torch.nn.CrossEntropyLoss() - optimizer = optim.SGD(model.parameters(), lr=learninig_rate, momentum=momentum, weight_decay=weight_decay) - scheduler = CosineAnnealingWarmRestarts(optimizer, epochs) + if optim_name == "sgd": + optimizer = optim.SGD(model.parameters(), lr=learninig_rate, momentum=momentum, weight_decay=weight_decay) + scheduler = CosineAnnealingWarmRestarts(optimizer, epochs) + #scheduler = None + elif optim_name == "adam": + optimizer = optim.Adam(model.parameters(), lr=learninig_rate, weight_decay=weight_decay) + scheduler = None + else: + print(f"ERROR unkown optimizer: {optim_name}") step = 0 import torch.autograd.profiler as profiler @@ -79,6 +87,8 @@ def run_training(data_type="screw", for epoch in tqdm(range(epochs)): if epoch == freeze_resnet: model.unfreeze() + + batch_embeds = [] for batch_idx, data in enumerate(dataloader): x1, x2 = data x1 = x1.to(device) @@ -90,13 +100,13 @@ def run_training(data_type="screw", xc = torch.cat((x1, x2), axis=0) embeds, logits = model(xc) - # embeds = F.normalize(embeds, p=2, dim=1) - # embeds1, embeds2 = torch.split(embeds,x1.size(0),dim=0) - # ip = torch.matmul(embeds1, embeds2.T) - # ip = ip / temperature +# embeds = F.normalize(embeds, p=2, dim=1) +# embeds1, embeds2 = torch.split(embeds,x1.size(0),dim=0) +# ip = torch.matmul(embeds1, embeds2.T) +# ip = ip / temperature - # y = torch.arange(0,x1.size(0), device=device) - # loss = loss_fn(ip, torch.arange(0,x1.size(0), device=device)) +# y = torch.arange(0,x1.size(0), device=device) +# loss = loss_fn(ip, torch.arange(0,x1.size(0), device=device)) y = torch.tensor([0, 1], device=device) y = y.repeat_interleave(x1.size(0)) @@ -106,7 +116,8 @@ def run_training(data_type="screw", # regulize weights: loss.backward() optimizer.step() - scheduler.step(epoch + batch_idx / num_batches) + if scheduler is not None: + scheduler.step(epoch + batch_idx / num_batches) writer.add_scalar('loss', loss.item(), step) @@ -117,19 +128,33 @@ def run_training(data_type="screw", # print(y) accuracy = torch.true_divide(torch.sum(predicted==y), predicted.size(0)) writer.add_scalar('acc', accuracy, step) - writer.add_scalar('lr', scheduler.get_last_lr()[0], step) + if scheduler is not None: + writer.add_scalar('lr', scheduler.get_last_lr()[0], step) + # save embed for validation: + if test_epochs > 0 and epoch % test_epochs == 0: + batch_embeds.append(embeds.cpu().detach()) + step += 1 writer.add_scalar('epoch', epoch, step) # run tests - if test_epochs > 0: - if epoch % test_epochs == 0: - # run auc calculation - #TODO: create dataset only once. - #TODO: train predictor here - roc_auc= eval_model(model_name, data_type, device=device, save_plots=False, size=size, show_training_data=False, model=model) - writer.add_scalar('eval_auc', roc_auc, step) + if test_epochs > 0 and epoch % test_epochs == 0: + # run auc calculation + #TODO: create dataset only once. + #TODO: train predictor here or in the model class itself. Should not be in the eval part + #TODO: we might not want to use the training datat because of droupout etc. but it should give a indecation of the model performance??? + # batch_embeds = torch.cat(batch_embeds) + # print(batch_embeds.shape) + model.eval() + roc_auc= eval_model(model_name, data_type, device=device, + save_plots=False, + size=size, + show_training_data=False, + model=model) + #train_embed=batch_embeds) + model.train() + writer.add_scalar('eval_auc', roc_auc, step) torch.save(model.state_dict(), model_dir / f"{model_name}.tch") @@ -151,11 +176,17 @@ def run_training(data_type="screw", parser.add_argument('--test_epochs', default=10, help='interval to calculate the auc during trainig, if -1 do not calculate test scores, (default: 10)') - parser.add_argument('--freeze_resnet', default=20, + parser.add_argument('--freeze_resnet', default=20, type=int, help='number of epochs to freeze resnet (default: 20)') parser.add_argument('--lr', default=0.03, type=float, - help='learning rate (default: 0.03)') + help='learning rate (default: 0.03)') + + parser.add_argument('--optim', default="sgd", + help='optimizing algorithm (dafault: "sgd")') + + parser.add_argument('--batch_size', default=64, type=int, + help='batch size, real batchsize is depending on cut paste config normal cutaout has effective batchsize of 2x batchsize (dafault: "64")') args = parser.parse_args() print(args) @@ -191,4 +222,6 @@ def run_training(data_type="screw", pretrained=args.pretrained, test_epochs=args.test_epochs, freeze_resnet=args.freeze_resnet, - learninig_rate=args.lr) \ No newline at end of file + learninig_rate=args.lr, + optim_name=args.optim, + batch_size=args.batch_size) \ No newline at end of file From 859ee4596408deb7995be7c877a6ccdfafacc0fc Mon Sep 17 00:00:00 2001 From: Rune Frohn Date: Mon, 10 May 2021 13:04:30 +0200 Subject: [PATCH 04/32] add head_layer argument --- run_training.py | 16 ++++++++++++---- 1 file changed, 12 insertions(+), 4 deletions(-) diff --git a/run_training.py b/run_training.py index b818b8c..30e278b 100644 --- a/run_training.py +++ b/run_training.py @@ -26,7 +26,8 @@ def run_training(data_type="screw", freeze_resnet=20, learninig_rate=0.03, optim_name="SGD", - batch_size=64): + batch_size=64, + head_layer=8): torch.multiprocessing.freeze_support() # TODO: use script params for hyperparameter # Temperature Hyperparameter currently not used @@ -64,7 +65,9 @@ def run_training(data_type="screw", writer = SummaryWriter(Path("logdirs") / model_name) # create Model: - model = ProjectionNet(pretrained=pretrained, head_layers=[512,512,512,512,512,512,512,512,128]) + head_layers = [512]*head_layer+[128] + print(head_layers) + model = ProjectionNet(pretrained=pretrained, head_layers=head_layers) model.to(device) if freeze_resnet > 0: @@ -183,11 +186,15 @@ def run_training(data_type="screw", help='learning rate (default: 0.03)') parser.add_argument('--optim', default="sgd", - help='optimizing algorithm (dafault: "sgd")') + help='optimizing algorithm values:[sgd, adam] (dafault: "sgd")') parser.add_argument('--batch_size', default=64, type=int, help='batch size, real batchsize is depending on cut paste config normal cutaout has effective batchsize of 2x batchsize (dafault: "64")') + parser.add_argument('--head_layer', default=8, type=int, + help='number of layers in the projection head (default: 8)') + + args = parser.parse_args() print(args) all_types = ['bottle', @@ -224,4 +231,5 @@ def run_training(data_type="screw", freeze_resnet=args.freeze_resnet, learninig_rate=args.lr, optim_name=args.optim, - batch_size=args.batch_size) \ No newline at end of file + batch_size=args.batch_size, + head_layer=args.head_layer) \ No newline at end of file From 5d3f6d81729d26b18b849e22ed7f14c717ea0ca5 Mon Sep 17 00:00:00 2001 From: Rune Frohn Date: Mon, 10 May 2021 13:05:35 +0200 Subject: [PATCH 05/32] first version of the readme --- README.md | 32 ++++++++++++++++++++++++++++++++ 1 file changed, 32 insertions(+) create mode 100644 README.md diff --git a/README.md b/README.md new file mode 100644 index 0000000..8a446c5 --- /dev/null +++ b/README.md @@ -0,0 +1,32 @@ +# Implementation of CutPaste + +This is a work in progress PyTorch implementation of [CutPaste: Self-Supervised Learning for Anomaly Detection and Localization](https://arxiv.org/abs/2104.04015). + +## Setup +Download the MVTec Anomaly detection Dataset from [here](https://www.mvtec.com/company/research/datasets/mvtec-ad) and extract it into a new folder named `Data`. + +Install the following requirements: +1. Pytorch and torchvision +2. sklearn +3. pandas +4. seaborn +5. tqdm +6. tensorboard + +For example with [Anaconda](https://docs.conda.io/projects/conda/en/latest/user-guide/install/download.html): +``` +conda crate -n cutpaste pytorch torchvision torchaudio cudatoolkit=10.2 seaborn pandas tqdm tensorboard -c pytorch +conda activate cutpaste +``` + +## Run Training +``` +python run_training.py --model_dir models --head_layer 2 +``` +The Script will train a model for each defect type and save it in the `model_dir` Folder. + +## Run Evaluation +``` +python eval.py --model_dir models --head_layer 2 +``` +This will create a new directory `Eval` with plots for each defect type/model. From b16495981c515a6a9463cf8159afc7bce4220c93 Mon Sep 17 00:00:00 2001 From: Runinho Date: Sat, 29 May 2021 10:20:57 +0200 Subject: [PATCH 06/32] mark implementation as unofficial in the readme --- README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/README.md b/README.md index 8a446c5..c1988e1 100644 --- a/README.md +++ b/README.md @@ -1,6 +1,6 @@ # Implementation of CutPaste -This is a work in progress PyTorch implementation of [CutPaste: Self-Supervised Learning for Anomaly Detection and Localization](https://arxiv.org/abs/2104.04015). +This is a **unofficial** work in progress PyTorch implementation of [CutPaste: Self-Supervised Learning for Anomaly Detection and Localization](https://arxiv.org/abs/2104.04015). ## Setup Download the MVTec Anomaly detection Dataset from [here](https://www.mvtec.com/company/research/datasets/mvtec-ad) and extract it into a new folder named `Data`. From c1c60e0ca2f0154ed4929980c35804a694320536 Mon Sep 17 00:00:00 2001 From: Runinho Date: Sat, 29 May 2021 10:38:56 +0200 Subject: [PATCH 07/32] Update README.md --- README.md | 9 +++++++-- 1 file changed, 7 insertions(+), 2 deletions(-) diff --git a/README.md b/README.md index c1988e1..41ebf1d 100644 --- a/README.md +++ b/README.md @@ -1,6 +1,6 @@ # Implementation of CutPaste -This is a **unofficial** work in progress PyTorch implementation of [CutPaste: Self-Supervised Learning for Anomaly Detection and Localization](https://arxiv.org/abs/2104.04015). +This is a **unofficial** work in progress PyTorch reimplementation of [CutPaste: Self-Supervised Learning for Anomaly Detection and Localization](https://arxiv.org/abs/2104.04015) and in no way affiliated with the original authors. Use at own risk. Pull requestes and feedback is appreciated. ## Setup Download the MVTec Anomaly detection Dataset from [here](https://www.mvtec.com/company/research/datasets/mvtec-ad) and extract it into a new folder named `Data`. @@ -15,7 +15,7 @@ Install the following requirements: For example with [Anaconda](https://docs.conda.io/projects/conda/en/latest/user-guide/install/download.html): ``` -conda crate -n cutpaste pytorch torchvision torchaudio cudatoolkit=10.2 seaborn pandas tqdm tensorboard -c pytorch +conda create -n cutpaste pytorch torchvision torchaudio cudatoolkit=10.2 seaborn pandas tqdm tensorboard scikit-learn -c pytorch conda activate cutpaste ``` @@ -25,6 +25,11 @@ python run_training.py --model_dir models --head_layer 2 ``` The Script will train a model for each defect type and save it in the `model_dir` Folder. +One can track the training progress of the models with tensorboard: +``` +tensorboard --logdir logdirs +``` + ## Run Evaluation ``` python eval.py --model_dir models --head_layer 2 From 01085c01f6195b845cbeb76998350e982595e85a Mon Sep 17 00:00:00 2001 From: Runinho Date: Sat, 29 May 2021 10:59:02 +0200 Subject: [PATCH 08/32] add implemenation details and TODOs to readme --- README.md | 13 +++++++++++++ 1 file changed, 13 insertions(+) diff --git a/README.md b/README.md index 41ebf1d..243dd95 100644 --- a/README.md +++ b/README.md @@ -35,3 +35,16 @@ tensorboard --logdir logdirs python eval.py --model_dir models --head_layer 2 ``` This will create a new directory `Eval` with plots for each defect type/model. + +## Some implementation details +Only the normal CutPaste augmentation and 2-Class classification variant is implemented. + +The pasted image patch always origins from the same image it is pasted to. I'm not sure if this is a Problem and if this is also the case in the original paper/code. + +# TODOs +- [ ] implement Cut-Paste Scar +- [ ] implement gradCam +- [ ] implement localization variant +- [ ] add option to finetune on EfficientNet(B4) +- [ ] clean up parameters and move them into the arguments of the scripts +- [ ] compare results of this reimplementation with the results of the paper From f06210329cb54a9d757a1a3f696af7f8ee77d35d Mon Sep 17 00:00:00 2001 From: Rune Frohn Date: Thu, 24 Jun 2021 20:54:37 +0200 Subject: [PATCH 09/32] using epoch definition from paper --- run_training.py | 83 +++++++++++++++++++++++++++---------------------- 1 file changed, 45 insertions(+), 38 deletions(-) diff --git a/run_training.py b/run_training.py index 30e278b..0a670c2 100644 --- a/run_training.py +++ b/run_training.py @@ -87,22 +87,30 @@ def run_training(data_type="screw", step = 0 import torch.autograd.profiler as profiler num_batches = len(dataloader) - for epoch in tqdm(range(epochs)): + def get_data_inf(): + while True: + for out in enumerate(dataloader): + yield out + dataloader_inf = get_data_inf() + # From paper: "Note that, unlike conventional definition for an epoch, + # we define 256 parameter update steps as one epoch. + for step in tqdm(range(epochs*256)): + epoch = int(step / 256) if epoch == freeze_resnet: model.unfreeze() batch_embeds = [] - for batch_idx, data in enumerate(dataloader): - x1, x2 = data - x1 = x1.to(device) - x2 = x2.to(device) + batch_idx, data = next(dataloader_inf) + x1, x2 = data + x1 = x1.to(device) + x2 = x2.to(device) - # zero the parameter gradients - optimizer.zero_grad() + # zero the parameter gradients + optimizer.zero_grad() - xc = torch.cat((x1, x2), axis=0) - embeds, logits = model(xc) - + xc = torch.cat((x1, x2), axis=0) + embeds, logits = model(xc) + # embeds = F.normalize(embeds, p=2, dim=1) # embeds1, embeds2 = torch.split(embeds,x1.size(0),dim=0) # ip = torch.matmul(embeds1, embeds2.T) @@ -111,34 +119,33 @@ def run_training(data_type="screw", # y = torch.arange(0,x1.size(0), device=device) # loss = loss_fn(ip, torch.arange(0,x1.size(0), device=device)) - y = torch.tensor([0, 1], device=device) - y = y.repeat_interleave(x1.size(0)) - loss = loss_fn(logits, y) - - - # regulize weights: - loss.backward() - optimizer.step() - if scheduler is not None: - scheduler.step(epoch + batch_idx / num_batches) - - writer.add_scalar('loss', loss.item(), step) - - # predicted = torch.argmax(ip,axis=0) - predicted = torch.argmax(logits,axis=1) - # print(logits) - # print(predicted) - # print(y) - accuracy = torch.true_divide(torch.sum(predicted==y), predicted.size(0)) - writer.add_scalar('acc', accuracy, step) - if scheduler is not None: - writer.add_scalar('lr', scheduler.get_last_lr()[0], step) - - # save embed for validation: - if test_epochs > 0 and epoch % test_epochs == 0: - batch_embeds.append(embeds.cpu().detach()) - - step += 1 + y = torch.tensor([0, 1], device=device) + y = y.repeat_interleave(x1.size(0)) + loss = loss_fn(logits, y) + + + # regulize weights: + loss.backward() + optimizer.step() + if scheduler is not None: + scheduler.step(epoch + batch_idx / num_batches) + + writer.add_scalar('loss', loss.item(), step) + +# predicted = torch.argmax(ip,axis=0) + predicted = torch.argmax(logits,axis=1) +# print(logits) +# print(predicted) +# print(y) + accuracy = torch.true_divide(torch.sum(predicted==y), predicted.size(0)) + writer.add_scalar('acc', accuracy, step) + if scheduler is not None: + writer.add_scalar('lr', scheduler.get_last_lr()[0], step) + + # save embed for validation: + if test_epochs > 0 and epoch % test_epochs == 0: + batch_embeds.append(embeds.cpu().detach()) + writer.add_scalar('epoch', epoch, step) # run tests From be3d04af40518c77e8cfebc3fa19dae9bcf3080a Mon Sep 17 00:00:00 2001 From: Rune Frohn Date: Mon, 5 Jul 2021 09:55:14 +0200 Subject: [PATCH 10/32] fix epoch definition --- run_training.py | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/run_training.py b/run_training.py index 0a670c2..3f957f1 100644 --- a/run_training.py +++ b/run_training.py @@ -94,8 +94,8 @@ def get_data_inf(): dataloader_inf = get_data_inf() # From paper: "Note that, unlike conventional definition for an epoch, # we define 256 parameter update steps as one epoch. - for step in tqdm(range(epochs*256)): - epoch = int(step / 256) + for step in tqdm(range(epochs)): + epoch = int(step / 1) if epoch == freeze_resnet: model.unfreeze() @@ -128,7 +128,7 @@ def get_data_inf(): loss.backward() optimizer.step() if scheduler is not None: - scheduler.step(epoch + batch_idx / num_batches) + scheduler.step(epoch) writer.add_scalar('loss', loss.item(), step) From 70d3988789c05e5369ea19dcfc192c235a67bbee Mon Sep 17 00:00:00 2001 From: Rune Frohn Date: Mon, 5 Jul 2021 09:55:43 +0200 Subject: [PATCH 11/32] add other cutpaste variants --- cutpaste.py | 93 ++++++++++++++++++++++++++++++++++++++++++++--------- 1 file changed, 78 insertions(+), 15 deletions(-) diff --git a/cutpaste.py b/cutpaste.py index 27c88d0..5e1289f 100644 --- a/cutpaste.py +++ b/cutpaste.py @@ -8,19 +8,12 @@ def cut_paste_collate_fn(batch): org_imgs, imgs = list(zip(*batch)) # print(list(zip(*batch))) return torch.stack(org_imgs), torch.stack(imgs) + class CutPaste(object): - """Randomly mask out one or more patches from an image. - Args: - n_holes (int): Number of patches to cut out of each image. - length (int): The length (in pixels) of each square patch. - """ - def __init__(self, area_ratio=[0.02,0.15], aspect_ratio=0.3, colorJitter=0.1, transform=None): - self.area_ratio = area_ratio - self.aspect_ratio = aspect_ratio + def __init__(self, colorJitter=0.1, transform=None): self.transform = transform - # setup colorJitter if colorJitter is None: self.colorJitter = None else: @@ -28,6 +21,24 @@ def __init__(self, area_ratio=[0.02,0.15], aspect_ratio=0.3, colorJitter=0.1, tr contrast = colorJitter, saturation = colorJitter, hue = colorJitter) + def __call__(self, org_img, img): + # apply transforms to both images + if self.transform: + img = self.transform(img) + org_img = self.transform(org_img) + return org_img, img + +class CutPasteNormal(object): + """Randomly mask out one or more patches from an image. + Args: + n_holes (int): Number of patches to cut out of each image. + length (int): The length (in pixels) of each square patch. + """ + def __init__(self, area_ratio=[0.02,0.15], aspect_ratio=0.3, **kwags): + super(CutPasteNormal, self).__init__(**kwags) + self.area_ratio = area_ratio + self.aspect_ratio = aspect_ratio + self.transform = transform def __call__(self, img): #TODO: we might want to use the pytorch implementation to calculate the patches from https://pytorch.org/vision/stable/_modules/torchvision/transforms/transforms.html#RandomErasing @@ -39,7 +50,10 @@ def __call__(self, img): # TODO: check if this is realy uniform in (aspect_ratio, 1) ∪ (1, 1/aspect_ratio). # so first we sample from witch bucket and than in the range - aspect = random.uniform(self.aspect_ratio, 1/self.aspect_ratio) + log_ratio = torch.log(torch.tensor((self.aspect_ratio, 1/self.aspect_ratio))) + aspect = torch.exp( + torch.empty(1).uniform_(log_ratio[0], log_ratio[1]) + ).item() cut_w = int(round(math.sqrt(ratio_area * aspect))) cut_h = int(round(math.sqrt(ratio_area / aspect))) @@ -61,9 +75,58 @@ def __call__(self, img): org_img = img.copy() img.paste(patch, insert_box) - # apply transforms to both images - if self.transform: - img = self.transform(img) - org_img = self.transform(org_img) + return org_img, img + +class CutPasteScar(CutPaste): + def __init__(self, width=[2,16], height=[10,25], rotation=[-45,45], **kwags): + super(CutPasteScar, self).__init__(**kwags) + self.width = width + self.height = height + self.rotation = rotation + + def __call__(self, img): + h = img.size[0] + w = img.size[1] + + # cut region + cut_w = random.uniform(*self.width) + cut_h = random.uniform(*self.height) + + from_location_h = int(random.uniform(0, h - cut_h)) + from_location_w = int(random.uniform(0, w - cut_w)) + + box = [from_location_w, from_location_h, from_location_w + cut_w, from_location_h + cut_h] + patch = img.crop(box) + + # rotate + rot_deg = random.uniform(*self.rotation) + patch = patch.convert("RGBA").rotate(rot_deg,expand=True) + + #paste + to_location_h = int(random.uniform(0, h - patch.size[0])) + to_location_w = int(random.uniform(0, w - patch.size[1])) + + insert_box = [to_location_w, to_location_h, to_location_w + patch.size[1], to_location_h + patch.size[0]] + mask = patch.split()[-1] + patch = patch.convert("RGB") - return org_img, img \ No newline at end of file + if self.colorJitter: + patch = self.colorJitter(patch) + + org_img = img.copy() + img.paste(patch, (to_location_w, to_location_h), mask=mask) + + return super().__call__(org_img, img) + +class CutPasteUnion(object): + + def __init__(self, **kwags): + self.normal = CutPasteNormal(**kwags) + self.scar = CutPasteScar(**kwags) + + def __call__(self, img): + r = random.uniform(0, 1) + if r < 0.5: + return self.normal(img) + else: + return self.scar(img) \ No newline at end of file From 5ebeeafa29596813638d81964783f78919607360 Mon Sep 17 00:00:00 2001 From: Rune Frohn Date: Wed, 7 Jul 2021 15:35:30 +0200 Subject: [PATCH 12/32] add other variants to training script --- cutpaste.py | 42 +++++++++++++++++++++++++++++------------- run_training.py | 44 ++++++++++++++++++++++++++++++-------------- 2 files changed, 59 insertions(+), 27 deletions(-) diff --git a/cutpaste.py b/cutpaste.py index 5e1289f..987dce0 100644 --- a/cutpaste.py +++ b/cutpaste.py @@ -5,12 +5,13 @@ def cut_paste_collate_fn(batch): # cutPaste return 2 tuples of tuples we convert them into a list of tuples - org_imgs, imgs = list(zip(*batch)) + img_types = list(zip(*batch)) # print(list(zip(*batch))) - return torch.stack(org_imgs), torch.stack(imgs) + return [torch.stack(imgs) for imgs in img_types] class CutPaste(object): + """Base class for both cutpaste variants with common operations""" def __init__(self, colorJitter=0.1, transform=None): self.transform = transform @@ -28,17 +29,16 @@ def __call__(self, org_img, img): org_img = self.transform(org_img) return org_img, img -class CutPasteNormal(object): - """Randomly mask out one or more patches from an image. +class CutPasteNormal(CutPaste): + """Randomly copy one patche from the image and paste it somewere else. Args: - n_holes (int): Number of patches to cut out of each image. - length (int): The length (in pixels) of each square patch. + area_ratio (list): list with 2 floats for maximum and minimum area to cut out + aspect_ratio (float): minimum area ration. Ration is sampled between aspect_ratio and 1/aspect_ratio. """ def __init__(self, area_ratio=[0.02,0.15], aspect_ratio=0.3, **kwags): super(CutPasteNormal, self).__init__(**kwags) self.area_ratio = area_ratio self.aspect_ratio = aspect_ratio - self.transform = transform def __call__(self, img): #TODO: we might want to use the pytorch implementation to calculate the patches from https://pytorch.org/vision/stable/_modules/torchvision/transforms/transforms.html#RandomErasing @@ -48,8 +48,7 @@ def __call__(self, img): # ratio between area_ratio[0] and area_ratio[1] ratio_area = random.uniform(self.area_ratio[0], self.area_ratio[1]) * w * h - # TODO: check if this is realy uniform in (aspect_ratio, 1) ∪ (1, 1/aspect_ratio). - # so first we sample from witch bucket and than in the range + # sample in log space log_ratio = torch.log(torch.tensor((self.aspect_ratio, 1/self.aspect_ratio))) aspect = torch.exp( torch.empty(1).uniform_(log_ratio[0], log_ratio[1]) @@ -58,7 +57,7 @@ def __call__(self, img): cut_w = int(round(math.sqrt(ratio_area * aspect))) cut_h = int(round(math.sqrt(ratio_area / aspect))) - # BIG TODO: also sample from other images. currently we only sample from the image itself + # one might also want to sample from other images. currently we only sample from the image itself from_location_h = int(random.uniform(0, h - cut_h)) from_location_w = int(random.uniform(0, w - cut_w)) @@ -75,9 +74,15 @@ def __call__(self, img): org_img = img.copy() img.paste(patch, insert_box) - return org_img, img + return super().__call__(org_img, img) class CutPasteScar(CutPaste): + """Randomly copy one patche from the image and paste it somewere else. + Args: + width (list): width to sample from. List of [min, max] + height (list): height to sample from. List of [min, max] + rotation (list): rotation to sample from. List of [min, max] + """ def __init__(self, width=[2,16], height=[10,25], rotation=[-45,45], **kwags): super(CutPasteScar, self).__init__(**kwags) self.width = width @@ -119,7 +124,6 @@ def __call__(self, img): return super().__call__(org_img, img) class CutPasteUnion(object): - def __init__(self, **kwags): self.normal = CutPasteNormal(**kwags) self.scar = CutPasteScar(**kwags) @@ -129,4 +133,16 @@ def __call__(self, img): if r < 0.5: return self.normal(img) else: - return self.scar(img) \ No newline at end of file + return self.scar(img) + +class CutPaste3Way(object): + def __init__(self, **kwags): + self.normal = CutPasteNormal(**kwags) + self.scar = CutPasteScar(**kwags) + + def __call__(self, img): + org, cutpaste_normal = self.normal(img) + _, cutpaste_scar = self.scar(img) + + return org, cutpaste_normal, cutpaste_scar + diff --git a/run_training.py b/run_training.py index 3f957f1..f5f5fd8 100644 --- a/run_training.py +++ b/run_training.py @@ -14,7 +14,7 @@ from dataset import MVTecAT -from cutpaste import CutPaste, cut_paste_collate_fn +from cutpaste import CutPasteNormal,CutPasteScar, CutPaste3Way, CutPasteUnion, cut_paste_collate_fn from model import ProjectionNet from eval import eval_model @@ -27,12 +27,14 @@ def run_training(data_type="screw", learninig_rate=0.03, optim_name="SGD", batch_size=64, - head_layer=8): + head_layer=8, + cutpate_type=CutPasteNormal, + device = "cuda", + workers=8): torch.multiprocessing.freeze_support() # TODO: use script params for hyperparameter # Temperature Hyperparameter currently not used temperature = 0.2 - device = "cuda" weight_decay = 0.00003 momentum = 0.9 @@ -53,12 +55,12 @@ def run_training(data_type="screw", # train_transform.transforms.append(transforms.RandomResizedCrop(size, scale=(min_scale,1))) # train_transform.transforms.append(transforms.GaussianBlur(int(size/10), sigma=(0.1,2.0))) train_transform.transforms.append(transforms.Resize((256,256))) - train_transform.transforms.append(CutPaste(transform = after_cutpaste_transform)) + train_transform.transforms.append(cutpate_type(transform = after_cutpaste_transform)) # train_transform.transforms.append(transforms.ToTensor()) train_data = MVTecAT("Data", data_type, transform = train_transform, size=int(size * (1/min_scale))) dataloader = DataLoader(train_data, batch_size=batch_size, - shuffle=True, num_workers=8, collate_fn=cut_paste_collate_fn, + shuffle=True, num_workers=workers, collate_fn=cut_paste_collate_fn, persistent_workers=True, pin_memory=True, prefetch_factor=5) # Writer will output to ./runs/ directory by default @@ -101,14 +103,12 @@ def get_data_inf(): batch_embeds = [] batch_idx, data = next(dataloader_inf) - x1, x2 = data - x1 = x1.to(device) - x2 = x2.to(device) + xs = [x.to(device) for x in data] # zero the parameter gradients optimizer.zero_grad() - xc = torch.cat((x1, x2), axis=0) + xc = torch.cat(xs, axis=0) embeds, logits = model(xc) # embeds = F.normalize(embeds, p=2, dim=1) @@ -119,8 +119,9 @@ def get_data_inf(): # y = torch.arange(0,x1.size(0), device=device) # loss = loss_fn(ip, torch.arange(0,x1.size(0), device=device)) - y = torch.tensor([0, 1], device=device) - y = y.repeat_interleave(x1.size(0)) + # calculate label + y = torch.arange(len(xs), device=device) + y = y.repeat_interleave(xs[0].size(0)) loss = loss_fn(logits, y) @@ -198,8 +199,15 @@ def get_data_inf(): parser.add_argument('--batch_size', default=64, type=int, help='batch size, real batchsize is depending on cut paste config normal cutaout has effective batchsize of 2x batchsize (dafault: "64")') - parser.add_argument('--head_layer', default=8, type=int, - help='number of layers in the projection head (default: 8)') + parser.add_argument('--head_layer', default=1, type=int, + help='number of layers in the projection head (default: 1)') + + parser.add_argument('--variant', default="3way", choices=['normal', 'scar', '3way', 'union'], help='cutpaste variant to use (dafault: "3way")') + + parser.add_argument('--cuda', default=False, + help='use cuda for training (default: False)') + + parser.add_argument('--workers', default=8, type=int, help="number of workers to use for data loading (default:8)") args = parser.parse_args() @@ -225,6 +233,11 @@ def get_data_inf(): else: types = args.type.split(",") + variant_map = {'normal':CutPasteNormal, 'scar':CutPasteScar, '3way':CutPaste3Way, 'union':CutPasteUnion} + variant = variant_map[args.variant] + + device = "cuda" if args.cuda else "cpu" + # create modle dir Path(args.model_dir).mkdir(exist_ok=True, parents=True) @@ -239,4 +252,7 @@ def get_data_inf(): learninig_rate=args.lr, optim_name=args.optim, batch_size=args.batch_size, - head_layer=args.head_layer) \ No newline at end of file + head_layer=args.head_layer, + device=device, + cutpate_type=variant, + workers=args.workers) \ No newline at end of file From a48aba3a56c28273d084466c411e60beb31387fb Mon Sep 17 00:00:00 2001 From: Rune Frohn Date: Wed, 7 Jul 2021 15:54:25 +0200 Subject: [PATCH 13/32] add suport for 3 classes in model --- model.py | 4 ++-- run_training.py | 3 ++- 2 files changed, 4 insertions(+), 3 deletions(-) diff --git a/model.py b/model.py index 420ac70..dd8f350 100644 --- a/model.py +++ b/model.py @@ -5,7 +5,7 @@ class ProjectionNet(nn.Module): - def __init__(self, pretrained=True, head_layers=[512,512,512,512,512,512,512,512,128]): + def __init__(self, pretrained=True, head_layers=[512,512,512,512,512,512,512,512,128], num_classes=2): super(ProjectionNet, self).__init__() #self.resnet18 = torch.hub.load('pytorch/vision:v0.9.0', 'resnet18', pretrained=pretrained) self.resnet18 = resnet18(pretrained=pretrained) @@ -30,7 +30,7 @@ def __init__(self, pretrained=True, head_layers=[512,512,512,512,512,512,512,512 ) self.resnet18.fc = nn.Identity() self.head = head - self.out = nn.Linear(last_layer, 2) + self.out = nn.Linear(last_layer, num_classes) def forward(self, x): embeds = self.resnet18(x) diff --git a/run_training.py b/run_training.py index f5f5fd8..161b4be 100644 --- a/run_training.py +++ b/run_training.py @@ -69,7 +69,8 @@ def run_training(data_type="screw", # create Model: head_layers = [512]*head_layer+[128] print(head_layers) - model = ProjectionNet(pretrained=pretrained, head_layers=head_layers) + num_classes = 2 if cutpate_type is not CutPaste3Way else 3 + model = ProjectionNet(pretrained=pretrained, head_layers=head_layers, num_classes=num_classes) model.to(device) if freeze_resnet > 0: From 84508943eaebb33e8514c0c3e79cc40d456f46e4 Mon Sep 17 00:00:00 2001 From: Rune Frohn Date: Sat, 10 Jul 2021 15:06:07 +0200 Subject: [PATCH 14/32] multithread image loading --- dataset.py | 7 ++++--- 1 file changed, 4 insertions(+), 3 deletions(-) diff --git a/dataset.py b/dataset.py index 3dfb5f0..a47f3c5 100644 --- a/dataset.py +++ b/dataset.py @@ -1,7 +1,7 @@ from torch.utils.data import Dataset from pathlib import Path from PIL import Image - +from joblib import Parallel, delayed class MVTecAT(Dataset): """Face Landmarks dataset.""" @@ -25,7 +25,8 @@ def __init__(self, root_dir, defect_name, size, transform=None, mode="train"): self.image_names = list((self.root_dir / defect_name / "train" / "good").glob("*.png")) print("loading images") # during training we cache the smaller images for performance reasons (not a good coding style) - self.imgs = [Image.open(file).resize((size,size)).convert("RGB") for file in self.image_names] + #self.imgs = [Image.open(file).resize((size,size)).convert("RGB") for file in self.image_names] + self.imgs = Parallel(n_jobs=10)(delayed(lambda file: Image.open(file).resize((size,size)).convert("RGB"))(file) for file in self.image_names) else: #test mode self.image_names = list((self.root_dir / defect_name / "test").glob(str(Path("*") / "*.png"))) @@ -48,4 +49,4 @@ def __getitem__(self, idx): img = img.resize((self.size,self.size)).convert("RGB") if self.transform is not None: img = self.transform(img) - return img, label != "good" \ No newline at end of file + return img, label != "good" From a719b30a94fd311e29f596a6dc0018079d1b5440 Mon Sep 17 00:00:00 2001 From: Rune Frohn Date: Sat, 10 Jul 2021 15:17:21 +0200 Subject: [PATCH 15/32] fix argument parsing and add script argument debug info into output --- run_training.py | 10 ++++++---- 1 file changed, 6 insertions(+), 4 deletions(-) diff --git a/run_training.py b/run_training.py index 161b4be..9baecc1 100644 --- a/run_training.py +++ b/run_training.py @@ -68,7 +68,6 @@ def run_training(data_type="screw", # create Model: head_layers = [512]*head_layer+[128] - print(head_layers) num_classes = 2 if cutpate_type is not CutPaste3Way else 3 model = ProjectionNet(pretrained=pretrained, head_layers=head_layers, num_classes=num_classes) model.to(device) @@ -88,7 +87,6 @@ def run_training(data_type="screw", print(f"ERROR unkown optimizer: {optim_name}") step = 0 - import torch.autograd.profiler as profiler num_batches = len(dataloader) def get_data_inf(): while True: @@ -185,7 +183,7 @@ def get_data_inf(): parser.add_argument('--no-pretrained', dest='pretrained', default=True, action='store_false', help='use pretrained values to initalize ResNet18 , (default: True)') - parser.add_argument('--test_epochs', default=10, + parser.add_argument('--test_epochs', default=10, type=int, help='interval to calculate the auc during trainig, if -1 do not calculate test scores, (default: 10)') parser.add_argument('--freeze_resnet', default=20, type=int, @@ -238,9 +236,13 @@ def get_data_inf(): variant = variant_map[args.variant] device = "cuda" if args.cuda else "cpu" + print(f"using device: {device}") # create modle dir Path(args.model_dir).mkdir(exist_ok=True, parents=True) + # save config. + with open(Path(args.model_dir) / "run_config.txt", "w") as f: + f.write(str(args)) for data_type in types: print(f"training {data_type}") @@ -256,4 +258,4 @@ def get_data_inf(): head_layer=args.head_layer, device=device, cutpate_type=variant, - workers=args.workers) \ No newline at end of file + workers=args.workers) From c16ce5afec02c92d8603ddcad7724edb94fcd142 Mon Sep 17 00:00:00 2001 From: Runinho Date: Thu, 22 Jul 2021 10:18:47 +0200 Subject: [PATCH 16/32] optimizing repeat --- dataset.py | 12 ++++++++++++ run_training.py | 4 ++-- 2 files changed, 14 insertions(+), 2 deletions(-) diff --git a/dataset.py b/dataset.py index a47f3c5..d2cf80e 100644 --- a/dataset.py +++ b/dataset.py @@ -3,6 +3,18 @@ from PIL import Image from joblib import Parallel, delayed +class Repeat(Dataset): + def __init__(self, org_dataset, new_length): + self.org_dataset = org_dataset + self.org_length = len(self.org_dataset) + self.new_length = new_length + + def __len__(self): + return self.new_length + + def __getitem__(self, idx): + return self.org_dataset[idx % self.org_length] + class MVTecAT(Dataset): """Face Landmarks dataset.""" diff --git a/run_training.py b/run_training.py index 9baecc1..0f344b5 100644 --- a/run_training.py +++ b/run_training.py @@ -13,7 +13,7 @@ from torchvision import transforms -from dataset import MVTecAT +from dataset import MVTecAT, Repeat from cutpaste import CutPasteNormal,CutPasteScar, CutPaste3Way, CutPasteUnion, cut_paste_collate_fn from model import ProjectionNet from eval import eval_model @@ -59,7 +59,7 @@ def run_training(data_type="screw", # train_transform.transforms.append(transforms.ToTensor()) train_data = MVTecAT("Data", data_type, transform = train_transform, size=int(size * (1/min_scale))) - dataloader = DataLoader(train_data, batch_size=batch_size, + dataloader = DataLoader(Repeat(train_data, 3000), batch_size=batch_size, shuffle=True, num_workers=workers, collate_fn=cut_paste_collate_fn, persistent_workers=True, pin_memory=True, prefetch_factor=5) From 5712b825908ba635d35506536e922ae4866e6c93 Mon Sep 17 00:00:00 2001 From: Runinho Date: Thu, 22 Jul 2021 11:58:47 +0200 Subject: [PATCH 17/32] update progress in the todo --- README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/README.md b/README.md index 243dd95..1bac24a 100644 --- a/README.md +++ b/README.md @@ -42,7 +42,7 @@ Only the normal CutPaste augmentation and 2-Class classification variant is impl The pasted image patch always origins from the same image it is pasted to. I'm not sure if this is a Problem and if this is also the case in the original paper/code. # TODOs -- [ ] implement Cut-Paste Scar +- [x] implement Cut-Paste Scar - [ ] implement gradCam - [ ] implement localization variant - [ ] add option to finetune on EfficientNet(B4) From 92634374940af8065fbef05820c733aaf6181f37 Mon Sep 17 00:00:00 2001 From: Runinho Date: Thu, 22 Jul 2021 14:39:04 +0200 Subject: [PATCH 18/32] remove unnecessary print --- cutpaste.py | 1 - 1 file changed, 1 deletion(-) diff --git a/cutpaste.py b/cutpaste.py index a828299..ca276f6 100644 --- a/cutpaste.py +++ b/cutpaste.py @@ -102,7 +102,6 @@ def __call__(self, img): box = [from_location_w, from_location_h, from_location_w + cut_w, from_location_h + cut_h] patch = img.crop(box) - print(patch.size) if self.colorJitter: patch = self.colorJitter(patch) From 60eed82b0d1741d727e3ec2daa723de4d472d3ae Mon Sep 17 00:00:00 2001 From: Runinho Date: Thu, 22 Jul 2021 14:40:14 +0200 Subject: [PATCH 19/32] add augmentation before cutpaste --- run_training.py | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/run_training.py b/run_training.py index cb6c5a8..92e3b27 100644 --- a/run_training.py +++ b/run_training.py @@ -52,7 +52,8 @@ def run_training(data_type="screw", std=[0.229, 0.224, 0.225])) train_transform = transforms.Compose([]) - # train_transform.transforms.append(transforms.RandomResizedCrop(size, scale=(min_scale,1))) + train_transform.transforms.append(transforms.RandomResizedCrop(size, scale=(min_scale,1))) + train_transform.transforms.append(transforms.ColorJitter(brightness=0.1, contrast=0.1, saturation=0.1, hue=0.1)) # train_transform.transforms.append(transforms.GaussianBlur(int(size/10), sigma=(0.1,2.0))) train_transform.transforms.append(transforms.Resize((size,size))) train_transform.transforms.append(cutpate_type(transform = after_cutpaste_transform)) From d34aba1bcd60be21c7e6970d2664ce0a5ac4d34a Mon Sep 17 00:00:00 2001 From: Runinho Date: Sun, 25 Jul 2021 10:47:45 +0200 Subject: [PATCH 20/32] disable randomResizedCrop --- run_training.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/run_training.py b/run_training.py index 25c9e45..064e7c9 100644 --- a/run_training.py +++ b/run_training.py @@ -43,7 +43,7 @@ def run_training(data_type="screw", model_name = f"model-{data_type}" + '-{date:%Y-%m-%d_%H_%M_%S}'.format(date=datetime.datetime.now() ) #augmentation: - min_scale = 0.5 + min_scale = 1 # create Training Dataset and Dataloader after_cutpaste_transform = transforms.Compose([]) @@ -52,7 +52,7 @@ def run_training(data_type="screw", std=[0.229, 0.224, 0.225])) train_transform = transforms.Compose([]) - train_transform.transforms.append(transforms.RandomResizedCrop(size, scale=(min_scale,1))) + #train_transform.transforms.append(transforms.RandomResizedCrop(size, scale=(min_scale,1))) train_transform.transforms.append(transforms.ColorJitter(brightness=0.1, contrast=0.1, saturation=0.1, hue=0.1)) # train_transform.transforms.append(transforms.GaussianBlur(int(size/10), sigma=(0.1,2.0))) train_transform.transforms.append(transforms.Resize((size,size))) From b23481f0b950ce1b95deca853c2e41e5a96b4366 Mon Sep 17 00:00:00 2001 From: Runinho Date: Mon, 26 Jul 2021 09:34:26 +0200 Subject: [PATCH 21/32] fix naming of csv eval result --- eval.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/eval.py b/eval.py index 523585b..4ef2dbd 100644 --- a/eval.py +++ b/eval.py @@ -293,7 +293,7 @@ def plot_tsne(labels, embeds, filename): obj["roc_auc"].append(roc_auc) # save pandas dataframe - eval_dir = Path("eval") / model_name + eval_dir = Path("eval") / args.model_dir eval_dir.mkdir(parents=True, exist_ok=True) df = pd.DataFrame(obj) df.to_csv(str(eval_dir) + "_perf.csv") From d02c206d4552dfa2926faa5ef5c891f942aa23ba Mon Sep 17 00:00:00 2001 From: Runinho Date: Mon, 26 Jul 2021 09:35:47 +0200 Subject: [PATCH 22/32] add results to README --- README.md | 80 +++++++++++++++++- doc/imgs/3way_acc.png | Bin 0 -> 84866 bytes doc/imgs/3way_eval_auc.png | Bin 0 -> 26099 bytes doc/imgs/3way_loss.png | Bin 0 -> 103662 bytes doc/imgs/author_vs_thisimpl_CutPaste.png | Bin 0 -> 20453 bytes doc/imgs/author_vs_thisimpl_CutPaste_3way.png | Bin 0 -> 23238 bytes doc/imgs/author_vs_thisimpl_CutPaste_scar.png | Bin 0 -> 22412 bytes doc/imgs/compare_all.png | Bin 0 -> 31048 bytes doc/imgs/normal_acc.png | Bin 0 -> 63738 bytes doc/imgs/normal_eval_auc.png | Bin 0 -> 72715 bytes doc/imgs/normal_loss.png | Bin 0 -> 84114 bytes doc/imgs/scar_acc.png | Bin 0 -> 86657 bytes doc/imgs/scar_eval_auc.png | Bin 0 -> 101110 bytes doc/imgs/scar_loss.png | Bin 0 -> 101969 bytes 14 files changed, 77 insertions(+), 3 deletions(-) create mode 100644 doc/imgs/3way_acc.png create mode 100644 doc/imgs/3way_eval_auc.png create mode 100644 doc/imgs/3way_loss.png create mode 100644 doc/imgs/author_vs_thisimpl_CutPaste.png create mode 100644 doc/imgs/author_vs_thisimpl_CutPaste_3way.png create mode 100644 doc/imgs/author_vs_thisimpl_CutPaste_scar.png create mode 100644 doc/imgs/compare_all.png create mode 100644 doc/imgs/normal_acc.png create mode 100644 doc/imgs/normal_eval_auc.png create mode 100644 doc/imgs/normal_loss.png create mode 100644 doc/imgs/scar_acc.png create mode 100644 doc/imgs/scar_eval_auc.png create mode 100644 doc/imgs/scar_loss.png diff --git a/README.md b/README.md index 1bac24a..51137ff 100644 --- a/README.md +++ b/README.md @@ -1,6 +1,6 @@ # Implementation of CutPaste -This is a **unofficial** work in progress PyTorch reimplementation of [CutPaste: Self-Supervised Learning for Anomaly Detection and Localization](https://arxiv.org/abs/2104.04015) and in no way affiliated with the original authors. Use at own risk. Pull requestes and feedback is appreciated. +This is an **unofficial** work in progress PyTorch reimplementation of [CutPaste: Self-Supervised Learning for Anomaly Detection and Localization](https://arxiv.org/abs/2104.04015) and in no way affiliated with the original authors. Use at own risk. Pull requests and feedback is appreciated. ## Setup Download the MVTec Anomaly detection Dataset from [here](https://www.mvtec.com/company/research/datasets/mvtec-ad) and extract it into a new folder named `Data`. @@ -36,11 +36,85 @@ python eval.py --model_dir models --head_layer 2 ``` This will create a new directory `Eval` with plots for each defect type/model. -## Some implementation details -Only the normal CutPaste augmentation and 2-Class classification variant is implemented. +# Implementation details +### CutPaste Location The pasted image patch always origins from the same image it is pasted to. I'm not sure if this is a Problem and if this is also the case in the original paper/code. +### Epochs +Li et al. define "256 parameter update steps" as one epoch. The `--epoch` parameter takes the number of update steps and not their definition of epochs. + +### Batch Size +Li et al. use a "batch size of 64 (or 96 for 3-way)". Because the number of images feed into the model changes from the normal to the 3-way variant I suspect that they always start with 32 images that get augmented. The `--batch_size` parameter specifies the number of images read from disk. So for the all variants `--batch_size=32` should correspond with the batch size used by Li et al. + +### Projection head +I did not find a model description of the projection head Li et al. use. +The `--head_layer` parameter is used to vary the number of layers used in this implementation. +Actually `head_layer + 2` fully connected layers are used. +Starting with `head_layer` Layer with 512 neurons, followed by a layer with 128 neurons and the output layer with 2 or 3 neurons. The number of neurons depends on the variant. 2 for `normal` and `scar` and 3 for `3way`. + +### Augmentations used before CutPaste +Li et al. "apply random translation and +color jitters for data augmentation". +This implementation only applies color jitter before the CutPaste augmentation. I tried to use [torchvision.transforms.RandomResizedCrop](https://pytorch.org/vision/stable/transforms.html#torchvision.transforms.RandomResizedCrop) as translation, but in a brief test I did not find that it improves performance. + +### Tensorflow vs PyTorch +Li et al. use tensorflow for their implementation. This Implementation is using PyTorch. + +# Results +This implementation only tries to recreate the main results from section 4.1 and shown in table 1. +## CutPaste +``` +python run_training.py --epochs 10000 --test_epochs 32 --no-pretrained --cuda 1 --head_layer 1 --batch_size 32 --variant normal +``` +![training loss](doc/imgs/normal_loss.png) +The blue line is the real value and the orange line is an average over 100 epochs. +![training accuracy](doc/imgs/normal_acc.png) +![validation accuracy](doc/imgs/normal_eval_auc.png) +We only run the ROC AUC every 32nd update step, here the orange line is an average over 320 update steps (10 ROC AUC values). +Note: The validation accuracy (named test set ROC AUC) is using the Mahalanobis distance as anomaly score. It can not be directly compared with the accuracy during training. + +![comparison with Li et al.](doc/imgs/author_vs_thisimpl_CutPaste.png) +Note that for readability, the y-axis starts at 40% AUC ROC. +## CutPaste (scar) +``` +python run_training.py --epochs 10000 --test_epochs 32 --no-pretrained --cuda 1 --head_layer 1 --batch_size 32 --variant scar +``` +![training loss](doc/imgs/scar_loss.png) +![training accuracy](doc/imgs/scar_acc.png) +![validation accuracy](doc/imgs/scar_eval_auc.png) + +![comparision with Li et al.](doc/imgs/author_vs_thisimpl_CutPaste_scar.png) +## CutPaste (3-way) +Due to limited computing resources, the evaluation during training is disabled. +``` +python run_training.py --epochs 10000 --test_epochs -1 --no-pretrained --cuda 1 --head_layer 1 --batch_size 32 --variant 3way +``` +![training loss](doc/imgs/3way_loss.png) +![training accuracy](doc/imgs/3way_acc.png) +![comparison with Li et al.](doc/imgs/author_vs_thisimpl_CutPaste.png) + +# Comparison to Li et al. +| defect_type | CutPaste | Li et al. CutPaste | CutPaste (scar) | Li et al. CutPaste (scar) | CutPaste (3-way) | Li et al. CutPaste (3-way) | +|:--------------|-----------:|---------------------:|------------------:|----------------------------:|-------------------:|-----------------------------:| +| bottle | 99.7 | 99.2 | 97.9 | 98.0 | 99.6 | 98.3 | +| cable | 92.3 | 87.1 | 75.0 | 78.8 | 77.2 | 80.6 | +| capsule | 86.2 | 87.9 | 84.5 | 95.3 | 92.4 | 96.2 | +| carpet | 59.8 | 67.9 | 88.6 | 94.6 | 60.1 | 93.1 | +| grid | 100.0 | 99.9 | 99.9 | 95.5 | 100.0 | 99.9 | +| hazelnut | 83.7 | 91.3 | 87.5 | 96.7 | 86.8 | 97.3 | +| leather | 99.5 | 99.7 | 99.5 | 100.0 | 100.0 | 100.0 | +| metal_nut | 91.5 | 96.8 | 80.6 | 97.9 | 87.8 | 99.3 | +| pill | 89.4 | 93.4 | 78.4 | 85.8 | 91.7 | 92.4 | +| screw | 44.1 | 54.4 | 80.7 | 83.7 | 86.8 | 86.3 | +| tile | 88.7 | 95.9 | 95.3 | 89.4 | 97.2 | 93.4 | +| toothbrush | 96.7 | 99.2 | 88.3 | 96.7 | 94.7 | 98.3 | +| transistor | 95.1 | 96.4 | 86.8 | 91.1 | 93.0 | 95.5 | +| wood | 98.6 | 94.9 | 98.0 | 98.7 | 99.4 | 98.6 | +| zipper | 99.6 | 99.4 | 95.9 | 99.5 | 98.8 | 99.4 | +| average | 88.3 | 90.9 | 89.1 | 93.4 | 91.0 | 95.2 | + +![comparison with Li et al.](doc/imgs/compare_all.png) # TODOs - [x] implement Cut-Paste Scar - [ ] implement gradCam diff --git a/doc/imgs/3way_acc.png b/doc/imgs/3way_acc.png new file mode 100644 index 0000000000000000000000000000000000000000..f40da1aaf4c7a5efd2d86b15bcdbd4d44b081619 GIT binary patch literal 84866 zcmb??WmHsQ*zEuch=fQtf`D{43@Y79cMT!k-67rGh?I19cMd4s9nvKXFvNGbU)(?U zu62LhA2Tp#oqXOW_ueP$i-IHu8ZjCO1j3M(5>otM#p z8jk%MILRwJDNRQZ=rzvs2LY6xNeDs!fuzMgskmhxt+;0DsNN2qPjEVWuF2O77s7(L zl%!B!y(v+_rpJ$^`gtb$LhKEm%8R4$Uw#;GiUXALVVLwm@v*B*vu@CQLAid_~u)D?>jsRo7)7i;b;Vv-Cqnzu;Dhq262~ zNlB)-L;f`>CN-4_)kRb2(c3sQ>}F9hiO?a&>xUJp3s&smh$Yj&P%lL>F!F(TAB444 zPb@h?svz`URBUpyfnQTBIb13!w2hi7;b`aS1u~N2!U|dC@$uZp;#FFzDY0!)>|(q) zN%3t%>}LMO`rqSonCleftt8g_%8Q#!PS+NS7xcckreQa#{P|y3X z*bDEdW~eTLL*=Sc^bx&?nr6WRLU@f!^i&ts?a*Gh<5&%ar6c;$t@_8}ZS&LG^w8Ak zRXCMHZm5^3FS(bVSnxlah$I;dDu++^_L5?=p+nU6Fy4qETT$A{KP<{XlOj 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z!j<3*?yT*{J&PVPsc{C3nI00&l?$y&`W+9kzL>qPw!1sRI{Go6o@4Nc4Kh!YL2F=R z$Kk=n%fPStH<8YZwFniFwPN172H%%E76ZN)vbs8&eO*Sh8Sq?b_|58z@n{6fzu6v> z6!JM>GVD)3tsg^9`ile4qf60gRCy}|c~XA#O|PK}ZdUQky`A0Qc>Hmg!Xc9hhm{Eh z#t#{qO>*DL@rxNR{p2(&A4ZwM2pRQy?*%I!R}{Z#kXYg0&08?FnQT8J(|_2vaH4k4}%|@e!WAZ0)-J>TqDK*80&d5J52OBXap z9YnNhmxf6I>YNjZ4?EXmJryr?t#73X8b|(STZL;}f@=)qkc3n{y&bh31ebp2OBJvK zllvDzmU}-h5vY0;+!vLoBn{lXIV;xfo{DoZaa?vxNxto0TPG-w<#Q&N&X+DIvUG=QmF+f9bcO{v9n@QPUl*M(*(IKx{$6bh>;6 z>BYYjRjD}hVpL>QEY{pJcswnC2yc>rtxxdky@x%n_UUAf%sUa}FKYJ;K@7A(44>q` z+%4}%1mn`TT(7(gYWUfz`y>exy-8dxIF7AF9|TD<6CSgZ7M5B6&5OG367xW#% zhAiS7gJ5{}=cE`qiFV5vG>5>2><8C}M)d(=0Lnf^ZmX*>7>7A5TJ$@t@l*;a@M{m{6pt z`7z%*joop;`<~jgBZ;9-5c1K&gph7gF`eo!;Rl;;5|Nb}kmiWCH@Kq}QFGj1`agEw zEZU&ROUKQvyL`P&(K88>h8>^8tS<{V#bhCjn^uOP$}im|sM|gk^K07O#(AgHZ>o92y~JzC>gaB?gD=C)PHx*A=iG$7TR8J)t8aYsA$GlQLSU?#9N5xN4Zq=bA(O$={XV5p zXAsZjf&n!HIB#;A|M+7r|N8}~GAO9}0 Date: Wed, 28 Jul 2021 07:35:29 +0200 Subject: [PATCH 23/32] fix typo in filename --- README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/README.md b/README.md index 51137ff..9fc2557 100644 --- a/README.md +++ b/README.md @@ -92,7 +92,7 @@ python run_training.py --epochs 10000 --test_epochs -1 --no-pretrained --cuda 1 ``` ![training loss](doc/imgs/3way_loss.png) ![training accuracy](doc/imgs/3way_acc.png) -![comparison with Li et al.](doc/imgs/author_vs_thisimpl_CutPaste.png) +![comparison with Li et al.](doc/imgs/author_vs_thisimpl_CutPaste_3way.png) # Comparison to Li et al. | defect_type | CutPaste | Li et al. CutPaste | CutPaste (scar) | Li et al. CutPaste (scar) | CutPaste (3-way) | Li et al. CutPaste (3-way) | From ac29885eaeb2c7998d6e8c9307c5b60b94213e3f Mon Sep 17 00:00:00 2001 From: Runinho Date: Wed, 28 Jul 2021 07:37:17 +0200 Subject: [PATCH 24/32] fix some formating --- README.md | 1 + 1 file changed, 1 insertion(+) diff --git a/README.md b/README.md index 9fc2557..218bc89 100644 --- a/README.md +++ b/README.md @@ -75,6 +75,7 @@ We only run the ROC AUC every 32nd update step, here the orange line is an avera Note: The validation accuracy (named test set ROC AUC) is using the Mahalanobis distance as anomaly score. It can not be directly compared with the accuracy during training. ![comparison with Li et al.](doc/imgs/author_vs_thisimpl_CutPaste.png) + Note that for readability, the y-axis starts at 40% AUC ROC. ## CutPaste (scar) ``` From c4bb088375578065790414f586685e105dfc307e Mon Sep 17 00:00:00 2001 From: Runinho Date: Fri, 30 Jul 2021 08:49:35 +0200 Subject: [PATCH 25/32] Spelling --- README.md | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/README.md b/README.md index 218bc89..dace782 100644 --- a/README.md +++ b/README.md @@ -51,7 +51,7 @@ Li et al. use a "batch size of 64 (or 96 for 3-way)". Because the number of imag I did not find a model description of the projection head Li et al. use. The `--head_layer` parameter is used to vary the number of layers used in this implementation. Actually `head_layer + 2` fully connected layers are used. -Starting with `head_layer` Layer with 512 neurons, followed by a layer with 128 neurons and the output layer with 2 or 3 neurons. The number of neurons depends on the variant. 2 for `normal` and `scar` and 3 for `3way`. +Starting with `head_layer` layers with 512 neurons, followed by a layer with 128 neurons and the output layer with 2 or 3 neurons. The number of neurons depends on the variant. 2 for `normal` and `scar` and 3 for `3way`. ### Augmentations used before CutPaste Li et al. "apply random translation and @@ -59,7 +59,7 @@ color jitters for data augmentation". This implementation only applies color jitter before the CutPaste augmentation. I tried to use [torchvision.transforms.RandomResizedCrop](https://pytorch.org/vision/stable/transforms.html#torchvision.transforms.RandomResizedCrop) as translation, but in a brief test I did not find that it improves performance. ### Tensorflow vs PyTorch -Li et al. use tensorflow for their implementation. This Implementation is using PyTorch. +Li et al. use tensorflow for their implementation. This implementation is using PyTorch. # Results This implementation only tries to recreate the main results from section 4.1 and shown in table 1. From 453c197720eaa400bdd63dd6db9822e52aef315b Mon Sep 17 00:00:00 2001 From: Runinho Date: Fri, 15 Oct 2021 16:01:28 +0200 Subject: [PATCH 26/32] fix boolean conversion and some spelling --- README.md | 2 +- dataset.py | 2 +- eval.py | 7 ++++--- model.py | 3 --- requirements.txt | 7 +++++++ run_training.py | 3 ++- utils.py | 15 +++++++++++++++ 7 files changed, 30 insertions(+), 9 deletions(-) create mode 100644 requirements.txt create mode 100644 utils.py diff --git a/README.md b/README.md index dace782..bdebb4f 100644 --- a/README.md +++ b/README.md @@ -1,6 +1,6 @@ # Implementation of CutPaste -This is an **unofficial** work in progress PyTorch reimplementation of [CutPaste: Self-Supervised Learning for Anomaly Detection and Localization](https://arxiv.org/abs/2104.04015) and in no way affiliated with the original authors. Use at own risk. Pull requests and feedback is appreciated. +This is an **unofficial** work in progress PyTorch reimplementation of [CutPaste: Self-Supervised Learning for Anomaly Detection and Localization](https://arxiv.org/abs/2104.04015) and in no way affiliated with the original authors. Use at own risk. Pull requests and feedback is appreciated. ## Setup Download the MVTec Anomaly detection Dataset from [here](https://www.mvtec.com/company/research/datasets/mvtec-ad) and extract it into a new folder named `Data`. diff --git a/dataset.py b/dataset.py index bc0c4d3..257cdd9 100644 --- a/dataset.py +++ b/dataset.py @@ -24,7 +24,7 @@ def __init__(self, root_dir, defect_name, size, transform=None, mode="train"): root_dir (string): Directory with the MVTec AD dataset. defect_name (string): defect to load. transform: Transform to apply to data - mode: "train" loads training sammples "test" test samples default "train" + mode: "train" loads training samples "test" test samples default "train" """ self.root_dir = Path(root_dir) self.defect_name = defect_name diff --git a/eval.py b/eval.py index d43e5e0..2876eea 100644 --- a/eval.py +++ b/eval.py @@ -17,6 +17,7 @@ from sklearn.covariance import LedoitWolf from collections import defaultdict import pandas as pd +from utils import str2bool test_data_eval = None test_transform = None @@ -95,8 +96,8 @@ def eval_model(modelname, defect_type, device="cpu", save_plots=False, size=256, # also show some of the training data show_training_data = False if show_training_data: - #augmentation settig - # TODO: do all of this in a seperate function that we can call in training and evaluation. + #augmentation setting + # TODO: do all of this in a separate function that we can call in training and evaluation. # very ugly to just copy the code lol min_scale = 0.5 @@ -243,7 +244,7 @@ def plot_tsne(labels, embeds, filename): parser.add_argument('--model_dir', default="models", help=' directory contating models to evaluate (default: models)') - parser.add_argument('--cuda', default=False, + parser.add_argument('--cuda', default=False, type=str2bool, help='use cuda for model predictions (default: False)') parser.add_argument('--head_layer', default=8, type=int, diff --git a/model.py b/model.py index 3a94879..998fd9d 100644 --- a/model.py +++ b/model.py @@ -21,9 +21,6 @@ def __init__(self, pretrained=True, head_layers=[512,512,512,512,512,512,512,512 last_layer = num_neurons #the last layer without activation - #TODO: is this correct? check one classe representation framework paper/code - # sequential_layers.append(nn.Linear(last_layer, head_layers[-1])) - # last_layer = head_layers[-1] head = nn.Sequential( *sequential_layers diff --git a/requirements.txt b/requirements.txt new file mode 100644 index 0000000..74c1afb --- /dev/null +++ b/requirements.txt @@ -0,0 +1,7 @@ +torch +torchvision +sklearn +pandas +seaborn +tqdm +tensorboard diff --git a/run_training.py b/run_training.py index 064e7c9..a9be4d4 100644 --- a/run_training.py +++ b/run_training.py @@ -17,6 +17,7 @@ from cutpaste import CutPasteNormal,CutPasteScar, CutPaste3Way, CutPasteUnion, cut_paste_collate_fn from model import ProjectionNet from eval import eval_model +from util import str2bool def run_training(data_type="screw", model_dir="models", @@ -204,7 +205,7 @@ def get_data_inf(): parser.add_argument('--variant', default="3way", choices=['normal', 'scar', '3way', 'union'], help='cutpaste variant to use (dafault: "3way")') - parser.add_argument('--cuda', default=False, + parser.add_argument('--cuda', default=False, type=str2bool, help='use cuda for training (default: False)') parser.add_argument('--workers', default=8, type=int, help="number of workers to use for data loading (default:8)") diff --git a/utils.py b/utils.py new file mode 100644 index 0000000..5c0489f --- /dev/null +++ b/utils.py @@ -0,0 +1,15 @@ +import distutils + +def str2bool(v): + """argparse handels type=bool in a weird way. + See this stack overflow: https://stackoverflow.com/questions/15008758/parsing-boolean-values-with-argparse + we can use this function as type converter for boolean values + """ + if isinstance(v, bool): + return v + if v.lower() in ('yes', 'true', 't', 'y', '1'): + return True + elif v.lower() in ('no', 'false', 'f', 'n', '0'): + return False + else: + raise argparse.ArgumentTypeError('Boolean value expected.') \ No newline at end of file From 60b34cc352e7c83ef3d348e7b90e12f8c74d4d9f Mon Sep 17 00:00:00 2001 From: Runinho Date: Fri, 15 Oct 2021 16:02:39 +0200 Subject: [PATCH 27/32] refactor density estimation --- .gitignore | 1 + README.md | 42 ++++++++++++++++++++++++++++++++ density.py | 68 +++++++++++++++++++++++++++++++++++++++++++++++++++ eval.py | 71 +++++++++++++++--------------------------------------- 4 files changed, 130 insertions(+), 52 deletions(-) create mode 100644 .gitignore create mode 100644 density.py diff --git a/.gitignore b/.gitignore new file mode 100644 index 0000000..9e3d04c --- /dev/null +++ b/.gitignore @@ -0,0 +1 @@ +venv* diff --git a/README.md b/README.md index bdebb4f..6f236bd 100644 --- a/README.md +++ b/README.md @@ -61,6 +61,48 @@ This implementation only applies color jitter before the CutPaste augmentation. ### Tensorflow vs PyTorch Li et al. use tensorflow for their implementation. This implementation is using PyTorch. +### Kernel Density Estimation +I implemented two Kernel Density Estimation and mahalanobis distance pipelines. +Li et al. use sklearn for the density estimation but [Ripple et al.](https://github.com/ORippler/gaussian-ad-mvtec) have their own. +The `eval.py` has a `--density` flag that can be toggled between `torch` for the Ripple et al. implementation and `sklearn` for my sklearn implementation. +In my limited testing both implementations have small differences between the resulting ROC AUCs: +``` +> python eval.py --density torch --cuda 1 --head_layer 2 --save_plots 0| grep AUC +bottle AUC: 0.9944444444444445 +cable AUC: 0.8549475262368815 +capsule AUC: 0.8232947746310331 +carpet AUC: 0.9329855537720706 +grid AUC: 0.982456140350877 +hazelnut AUC: 0.9160714285714285 +leather AUC: 1.0 +metal_nut AUC: 0.9403714565004888 +pill AUC: 0.8046917621385706 +screw AUC: 0.701988112318098 +tile AUC: 0.9430014430014431 +toothbrush AUC: 0.8972222222222221 +transistor AUC: 0.9008333333333334 +wood AUC: 0.9815789473684211 +zipper AUC: 0.9997373949579832 + +> python eval.py --density sklearn --cuda 1 --head_layer 2 --save_plots 0| grep AUC +bottle AUC: 0.9944444444444445 +cable AUC: 0.8549475262368815 +capsule AUC: 0.8232947746310331 +carpet AUC: 0.9329855537720706 +grid AUC: 0.982456140350877 +hazelnut AUC: 0.9160714285714285 +leather AUC: 1.0 +metal_nut AUC: 0.9403714565004888 +pill AUC: 0.8046917621385706 +screw AUC: 0.701988112318098 +tile AUC: 0.9430014430014431 +toothbrush AUC: 0.8972222222222221 +transistor AUC: 0.9008333333333334 +wood AUC: 0.9815789473684211 +zipper AUC: 0.9997373949579832 +``` + + # Results This implementation only tries to recreate the main results from section 4.1 and shown in table 1. ## CutPaste diff --git a/density.py b/density.py new file mode 100644 index 0000000..848223d --- /dev/null +++ b/density.py @@ -0,0 +1,68 @@ + +from sklearn.covariance import LedoitWolf +from sklearn.neighbors import KernelDensity +import torch + + +class Density(object): + def fit(self, embeddings): + raise NotImplementedError + + def predict(self, embeddings): + raise NotImplementedError + + +class GaussianDensityTorch(object): + """Gaussian Density estimation similar to the implementation used by Ripple et al. + The code of Ripple et al. can be found here: https://github.com/ORippler/gaussian-ad-mvtec. + """ + def fit(self, embeddings): + self.mean = torch.mean(embeddings, axis=0) + self.inv_cov = torch.Tensor(LedoitWolf().fit(embeddings.cpu()).precision_,device="cpu") + + def predict(self, embeddings): + distances = self.mahalanobis_distance(embeddings, self.mean, self.inv_cov) + return distances + + @staticmethod + def mahalanobis_distance( + values: torch.Tensor, mean: torch.Tensor, inv_covariance: torch.Tensor + ) -> torch.Tensor: + """Compute the batched mahalanobis distance. + values is a batch of feature vectors. + mean is either the mean of the distribution to compare, or a second + batch of feature vectors. + inv_covariance is the inverse covariance of the target distribution. + + from https://github.com/ORippler/gaussian-ad-mvtec/blob/4e85fb5224eee13e8643b684c8ef15ab7d5d016e/src/gaussian/model.py#L308 + """ + assert values.dim() == 2 + assert 1 <= mean.dim() <= 2 + assert len(inv_covariance.shape) == 2 + assert values.shape[1] == mean.shape[-1] + assert mean.shape[-1] == inv_covariance.shape[0] + assert inv_covariance.shape[0] == inv_covariance.shape[1] + + if mean.dim() == 1: # Distribution mean. + mean = mean.unsqueeze(0) + x_mu = values - mean # batch x features + # Same as dist = x_mu.t() * inv_covariance * x_mu batch wise + dist = torch.einsum("im,mn,in->i", x_mu, inv_covariance, x_mu) + return dist.sqrt() + +class GaussianDensitySklearn(): + """Li et al. use sklearn for density estimation. + This implementation uses sklearn KernelDensity module for fitting and predicting. + """ + def fit(self, embeddings): + # estimate KDE parameters + # use grid search cross-validation to optimize the bandwidth + self.kde = KernelDensity(kernel='gaussian', bandwidth=1).fit(embeddings) + + def predict(self, embeddings): + scores = self.kde.score_samples(embeddings) + + # invert scores, so they fit to the class labels for the auc calculation + scores = -scores + + return scores diff --git a/eval.py b/eval.py index 2876eea..22009ae 100644 --- a/eval.py +++ b/eval.py @@ -1,6 +1,5 @@ from sklearn.metrics import roc_curve, auc from sklearn.manifold import TSNE -from sklearn.neighbors import KernelDensity from torchvision import transforms from torch.utils.data import DataLoader import torch @@ -14,15 +13,15 @@ from sklearn.utils import shuffle from sklearn.model_selection import GridSearchCV import numpy as np -from sklearn.covariance import LedoitWolf from collections import defaultdict +from density import GaussianDensitySklearn, GaussianDensityTorch import pandas as pd from utils import str2bool test_data_eval = None test_transform = None cached_type = None - + def get_train_embeds(model, size, defect_type, transform, device): # train data / train kde test_data = MVTecAT("Data", defect_type, size, transform=transform, mode="train") @@ -38,7 +37,7 @@ def get_train_embeds(model, size, defect_type, transform, device): train_embed = torch.cat(train_embed) return train_embed -def eval_model(modelname, defect_type, device="cpu", save_plots=False, size=256, show_training_data=True, model=None, train_embed=None, head_layer=8): +def eval_model(modelname, defect_type, device="cpu", save_plots=False, size=256, show_training_data=True, model=None, train_embed=None, head_layer=8, density=GaussianDensityTorch()): # create test dataset global test_data_eval,test_transform, cached_type @@ -146,53 +145,10 @@ def eval_model(modelname, defect_type, device="cpu", save_plots=False, size=256, plot_tsne(tsne_labels, tsne_embeds, eval_dir / "tsne.png") else: eval_dir = Path("unused") - # TODO: put the GDE stuff into the Model class and do this at the end of the training - # # estemate KDE parameters - # # use grid search cross-validation to optimize the bandwidth - # params = {'bandwidth': np.logspace(-10, 10, 50)} - # grid = GridSearchCV(KernelDensity(), params) - # grid.fit(embeds) - - # print("best bandwidth: {0}".format(grid.best_estimator_.bandwidth)) - - # # use the best estimator to compute the kernel density estimate - # kde = grid.best_estimator_ - # kde = KernelDensity(kernel='gaussian', bandwidth=1).fit(train_embed) - # scores = kde.score_samples(embeds) - # print(scores) - # we get the probability to be in the correct distribution - # but our labels are inverted (1 for out of distribution) - # so we have to relabel - - # use own formulation with malanobis distance - # from https://github.com/ORippler/gaussian-ad-mvtec/blob/4e85fb5224eee13e8643b684c8ef15ab7d5d016e/src/gaussian/model.py#L308 - def mahalanobis_distance( - values: torch.Tensor, mean: torch.Tensor, inv_covariance: torch.Tensor - ) -> torch.Tensor: - """Compute the batched mahalanobis distance. - values is a batch of feature vectors. - mean is either the mean of the distribution to compare, or a second - batch of feature vectors. - inv_covariance is the inverse covariance of the target distribution. - """ - assert values.dim() == 2 - assert 1 <= mean.dim() <= 2 - assert len(inv_covariance.shape) == 2 - assert values.shape[1] == mean.shape[-1] - assert mean.shape[-1] == inv_covariance.shape[0] - assert inv_covariance.shape[0] == inv_covariance.shape[1] - - if mean.dim() == 1: # Distribution mean. - mean = mean.unsqueeze(0) - x_mu = values - mean # batch x features - # Same as dist = x_mu.t() * inv_covariance * x_mu batch wise - dist = torch.einsum("im,mn,in->i", x_mu, inv_covariance, x_mu) - return dist.sqrt() - # claculate mean - mean = torch.mean(train_embed, axis=0) - inv_cov = torch.Tensor(LedoitWolf().fit(train_embed.cpu()).precision_,device="cpu") - - distances = mahalanobis_distance(embeds, mean, inv_cov) + + print(f"using density estimation {density.__class__.__name__}") + density.fit(train_embed) + distances = density.predict(embeds) #TODO: set threshold on mahalanobis distances and use "real" probabilities roc_auc = plot_roc(labels, distances, eval_dir / "roc_plot.png", modelname=modelname, save_plots=save_plots) @@ -250,6 +206,11 @@ def plot_tsne(labels, embeds, filename): parser.add_argument('--head_layer', default=8, type=int, help='number of layers in the projection head (default: 8)') + parser.add_argument('--density', default="torch", choices=["torch", "sklearn"], + help='density implementation to use. See `density.py` for both implementations. (default: torch)') + + parser.add_argument('--save_plots', default=True, type=str2bool, + help='save TSNE and roc plots') args = parser.parse_args() @@ -279,6 +240,12 @@ def plot_tsne(labels, embeds, filename): device = "cuda" if args.cuda else "cpu" + density_mapping = { + "torch": GaussianDensityTorch, + "sklearn": GaussianDensitySklearn + } + density = density_mapping[args.density] + # find models model_names = [list(Path(args.model_dir).glob(f"model-{data_type}*"))[0] for data_type in types if len(list(Path(args.model_dir).glob(f"model-{data_type}*"))) > 0] if len(model_names) < len(all_types): @@ -288,7 +255,7 @@ def plot_tsne(labels, embeds, filename): for model_name, data_type in zip(model_names, types): print(f"evaluating {data_type}") - roc_auc = eval_model(model_name, data_type, save_plots=True, device=device, head_layer=args.head_layer) + roc_auc = eval_model(model_name, data_type, save_plots=args.save_plots, device=device, head_layer=args.head_layer, density=density()) print(f"{data_type} AUC: {roc_auc}") obj["defect_type"].append(data_type) obj["roc_auc"].append(roc_auc) From fb6a214812ff246b3959c0b4c4066c3e5ad5bc02 Mon Sep 17 00:00:00 2001 From: Runinho Date: Fri, 15 Oct 2021 16:04:36 +0200 Subject: [PATCH 28/32] fix import --- run_training.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/run_training.py b/run_training.py index a9be4d4..c5c514e 100644 --- a/run_training.py +++ b/run_training.py @@ -17,7 +17,7 @@ from cutpaste import CutPasteNormal,CutPasteScar, CutPaste3Way, CutPasteUnion, cut_paste_collate_fn from model import ProjectionNet from eval import eval_model -from util import str2bool +from utils import str2bool def run_training(data_type="screw", model_dir="models", From c636d59376d6b03a2b4a2579dc0643e161f3aec6 Mon Sep 17 00:00:00 2001 From: Runinho Date: Fri, 15 Oct 2021 17:19:52 +0200 Subject: [PATCH 29/32] remove unused import --- utils.py | 2 -- 1 file changed, 2 deletions(-) diff --git a/utils.py b/utils.py index 5c0489f..d925fa0 100644 --- a/utils.py +++ b/utils.py @@ -1,5 +1,3 @@ -import distutils - def str2bool(v): """argparse handels type=bool in a weird way. See this stack overflow: https://stackoverflow.com/questions/15008758/parsing-boolean-values-with-argparse From 3f2f55393d8d6e3e4395d557ab3a88267b6d99ad Mon Sep 17 00:00:00 2001 From: Runinho Date: Mon, 18 Oct 2021 12:46:33 +0200 Subject: [PATCH 30/32] add note for gpu training --- README.md | 5 +++++ 1 file changed, 5 insertions(+) diff --git a/README.md b/README.md index 6f236bd..52a742e 100644 --- a/README.md +++ b/README.md @@ -25,6 +25,11 @@ python run_training.py --model_dir models --head_layer 2 ``` The Script will train a model for each defect type and save it in the `model_dir` Folder. +To enable training on an Nvidia GPU use the `--cuda 1` flag. +``` +python run_training.py --model_dir models --head_layer 2 --cuda 1 +``` + One can track the training progress of the models with tensorboard: ``` tensorboard --logdir logdirs From f26ac4ad179610fb36c8c1212f35a8634a9dcd9e Mon Sep 17 00:00:00 2001 From: Runinho <3237686+Runinho@users.noreply.github.com> Date: Tue, 7 May 2024 15:51:27 +0200 Subject: [PATCH 31/32] fix typo --- model.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/model.py b/model.py index 998fd9d..018816a 100644 --- a/model.py +++ b/model.py @@ -10,7 +10,7 @@ def __init__(self, pretrained=True, head_layers=[512,512,512,512,512,512,512,512 #self.resnet18 = torch.hub.load('pytorch/vision:v0.9.0', 'resnet18', pretrained=pretrained) self.resnet18 = resnet18(pretrained=pretrained) - # create MPL head as seen in the code in: https://github.com/uoguelph-mlrg/Cutout/blob/master/util/cutout.py + # create MLP head as seen in the code in: https://github.com/uoguelph-mlrg/Cutout/blob/master/util/cutout.py # TODO: check if this is really the right architecture last_layer = 512 sequential_layers = [] From 10d8bf71df76d3a97f0106efee1d76f81d983149 Mon Sep 17 00:00:00 2001 From: Runinho <3237686+Runinho@users.noreply.github.com> Date: Tue, 7 May 2024 15:53:37 +0200 Subject: [PATCH 32/32] improve documentation --- dataset.py | 4 +++- 1 file changed, 3 insertions(+), 1 deletion(-) diff --git a/dataset.py b/dataset.py index 257cdd9..4447c80 100644 --- a/dataset.py +++ b/dataset.py @@ -16,7 +16,9 @@ def __getitem__(self, idx): return self.org_dataset[idx % self.org_length] class MVTecAT(Dataset): - """Face Landmarks dataset.""" + """MVTec anomaly detection dataset. + Link: https://www.mvtec.com/company/research/datasets/mvtec-ad + """ def __init__(self, root_dir, defect_name, size, transform=None, mode="train"): """