| import torch
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| import wandb
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| import time
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| from sklearn.manifold import TSNE
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| import matplotlib.pyplot as plt
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| import pandas as pd
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| import pickle
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| import os
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| from torchvision.transforms import ToTensor, ToPILImage
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| from tqdm import tqdm
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| import random
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| from math import sqrt
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|
|
| def dummy(lists):
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| new = []
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| for i in lists:
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| new.append(i.item())
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| return new
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|
|
| def display(subject, ordered_t,count, num_images = 5):
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|
|
| def l1_dist(x, y):
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| return torch.sum(x - y).item()
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|
|
| def l2_dist(x, y):
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|
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| return sqrt(torch.sum((x - y) ** 2).item())
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|
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|
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| ordered_t = sorted(ordered_t, key=lambda elem: l2_dist(subject[0], elem[0]))
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|
|
| subject_repeated = [subject for _ in range(num_images)]
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| nearest_10_images = ordered_t[:num_images]
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| farthest_10_images = ordered_t[-num_images:]
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|
|
| def make_panel(list_of_images):
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| images = [image[1] for image in list_of_images]
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| labels = [image[2].cpu().numpy() for image in list_of_images]
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| filenames = [image[3] for image in list_of_images]
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| panel = torch.cat(images, dim=2)
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| panel_pil = ToPILImage().__call__(panel)
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| return panel_pil,labels, filenames
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|
|
| panel_of_subject,labels_sub, filenames = make_panel(subject_repeated)
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|
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| labels_sub = labels_sub[0]
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| filename_sub = filenames[0]
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|
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| panel_of_nearest_10,labels_near,_ = make_panel(nearest_10_images)
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| labels_near = dummy(labels_near)
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|
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| panel_of_farthest_10,labels_far,_ = make_panel(farthest_10_images)
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| return labels_sub, labels_near, filename_sub
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|
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|
|
| class Nearest_Neigbours:
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| def __init__(self, device, model, dataloaders, target_names, output_folders, name):
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| self.device = device
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| self.model = model
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| self.dataloaders = dataloaders
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| self.target_names = target_names
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| self.output_folders = output_folders
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| self.name = name
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|
|
| def extract(self):
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| self.model.eval()
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| minibatches_t = []
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| with torch.no_grad():
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| for i, (inputs, labels, img_name) in enumerate(self.dataloaders):
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| inputs, labels = inputs.to(self.device), labels.long().to(self.device)
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|
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| features, feat = self.model(inputs)
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|
|
| print(features.shape)
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| i_t = inputs.detach().cpu().unbind(0)
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| e_t = features.detach().cpu().unbind(0)
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| sublist_t = [elem_t for elem_t in zip(e_t, i_t, labels, img_name)]
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| minibatches_t.append(sublist_t)
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| ordered_t = []
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| for minibatch_t in minibatches_t:
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| while minibatch_t:
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| ordered_t.append(minibatch_t.pop())
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|
|
|
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| with open(self.output_folders + 'ordered_features_'+self.name , "wb") as fp:
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| pickle.dump(ordered_t, fp)
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|
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|
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| return ordered_t
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|
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|
|
| def main_loop(self):
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| '''
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| if os.path.exists(self.output_folders + 'ordered_features_train'):
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| with open(self.output_folders + 'ordered_features_train', "rb") as fp: # Unpickling
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| ordered_t = pickle.load(fp)
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| #with open(self.output_folders + 'labels_t'+self.name, "rb") as fp: # Unpickling
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| # labels_t = pickle.load(fp)
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| print('')
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| else:
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| ordered_t = self.extract()
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| '''
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| if os.path.exists(self.output_folders + 'ordered_features_train_roi'):
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| with open(self.output_folders + 'ordered_features_train_roi', "rb") as fp:
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| ordered_t_roi = pickle.load(fp)
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|
|
|
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| print('')
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| else:
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| ordered_t_roi = self.extract()
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|
|
|
|
| num_features = len(ordered_t_roi)
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| inputs=[]
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| nns1 = []
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| nns2 = []
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| nns3 = []
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| nns4 = []
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| nns5 = []
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| filess =[]
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| encoded_representations = []
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| count = 0
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| for i in range(num_features):
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| count+=1
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|
|
| print(count)
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| subject = ordered_t_roi[i]
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|
|
|
|
| labels_sub, labels_near, filename_sub = display(subject, ordered_t_roi, count, num_images = 5)
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| extract_features = ordered_t_roi[i][0]
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|
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| pth_filename = os.path.splitext(os.path.basename(filename_sub))[0]
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| pth_filename = self.output_folders + 'extracted_features/' + pth_filename +'.pth'
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| torch.save(extract_features,pth_filename)
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| filess.append(filename_sub)
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| inputs.append(labels_sub)
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| nns1.append(labels_near[0])
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| nns2.append(labels_near[1])
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| nns3.append(labels_near[2])
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| nns4.append(labels_near[3])
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| nns5.append(labels_near[4])
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| encoded_representations.append(pth_filename)
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| dataframe = pd.DataFrame({'Filename':filess,'Input':inputs,'NN1':nns1,'NN2':nns2,'NN3':nns3,'NN4':nns4,'NN5':nns5, 'Features':encoded_representations})
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| dataframe.to_csv(self.output_folders+'New' +self.name+'_nn.csv', index = False)
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| return |
