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authornunzip <np.scarh@gmail.com>2018-12-11 13:07:36 +0000
committernunzip <np.scarh@gmail.com>2018-12-11 13:07:36 +0000
commitb1c5d8a82395a8545794b0ce22cccb16ca380647 (patch)
tree8980806822be3836e635ca6a27b5df6ae123e560
parent46bdc8b2ea4618efc606d509d4de37dc8f50a929 (diff)
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Optimizer (run no flags)
-rw-r--r--opt.py358
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+#!/usr/bin/env python
+# Author: Vasil Zlatanov, Nunzio Pucci
+# EE4 Pattern Recognition coursework
+#
+# usage: part2.py [-h] [-t] [-cm] [-km] [-ma] [-e] [-r] [-ka RERANKA]
+# [-kb RERANKB] [-v]
+
+import matplotlib.pyplot as plt
+from mpl_toolkits.mplot3d import Axes3D
+import sys
+import random
+import os
+import json
+import scipy.io
+from random import randint
+from sklearn.neighbors import KNeighborsClassifier
+from sklearn.neighbors import NearestNeighbors
+from sklearn.neighbors import DistanceMetric
+from sklearn.cluster import KMeans
+from sklearn.decomposition import PCA
+from sklearn.discriminant_analysis import LinearDiscriminantAnalysis
+from sklearn.model_selection import train_test_split
+from sklearn.preprocessing import StandardScaler
+from sklearn.metrics import confusion_matrix
+from sklearn.metrics import accuracy_score
+import argparse
+import numpy as np
+from numpy import genfromtxt
+from numpy import linalg as LA
+from timeit import default_timer as timer
+from scipy.spatial.distance import cdist
+sys.path.append('lib')
+from rerank import re_ranking
+from kmean import create_kmean_clusters
+import logging
+from logging import debug
+
+parser = argparse.ArgumentParser()
+parser.add_argument("-t", "--train", help="Use train data instead of query and gallery", action='store_true')
+parser.add_argument("-c", "--conf_mat", help="Show visual confusion matrix", action='store_true')
+parser.add_argument("-k", "--kmean_alt", help="Perform clustering with generalized labels(not actual kmean)", action='store_true', default=0)
+parser.add_argument("-m", "--mahalanobis", help="Perform Mahalanobis Distance metric", action='store_true', default=0)
+parser.add_argument("-e", "--euclidean", help="Use standard euclidean distance", action='store_true', default=0)
+parser.add_argument("-r", "--rerank", help="Use k-reciprocal rernaking", action='store_true')
+parser.add_argument("-p", "--reranka", help="Parameter k1 for Rerank -p '$k1val' -ARGUMENT REQUIRED, default=9-", type=int, default = 9)
+parser.add_argument("-q", "--rerankb", help="Parameter k2 for rerank -q '$k2val' -ARGUMENT REQUIRED, default=3-", type=int, default = 3)
+parser.add_argument("-l", "--rerankl", help="Coefficient to combine distances(lambda) -l '$lambdaval' -ARGUMENT REQUIRED, default=0.3-", type=float, default = 0.3)
+parser.add_argument("-n", "--neighbors", help="Use customized ranklist size -n 'size' -ARGUMENT REQUIRED, default=1-", type=int, default = 1)
+parser.add_argument("-v", "--verbose", help="Use verbose output", action='store_true')
+parser.add_argument("-s", "--showrank", help="Save ranklist pics id in a txt file. Number of ranklists saved specified as -s '$number' -ARGUMENT REQUIRED, default=0-", type=int, default = 0)
+parser.add_argument("-1", "--normalise", help="Normalise features", action='store_true', default=0)
+parser.add_argument("-M", "--multrank", help="Run for different ranklist sizes equal to M -ARGUMENT REQUIRED, default=1-", type=int, default=1)
+parser.add_argument("-C", "--comparison", help="Set to 2 to obtain a comparison of baseline and improved metric -ARGUMENT REQUIRED, default=1-", type=int, default=1)
+parser.add_argument("--data", help="You can either put the data in a folder called 'data', or specify the location with --data 'path' -ARGUMENT REQUIRED, default='data'-", default='data')
+parser.add_argument("-K", "--kmean", help="Perform Kmean clustering of size specified through -K '$size' -ARGUMENT REQUIRED, default=0-", type=int, default=0)
+parser.add_argument("-P", "--mAP", help="Display Mean Average Precision for ranklist of size -n '$size'", action='store_true')
+parser.add_argument("-2", "--PCA", help="Use PCA with -2 '$n_components' -ARGUMENT REQUIRED, default=0-", type=int, default=0)
+
+args = parser.parse_args()
+
+if args.verbose:
+ logging.basicConfig(level=logging.DEBUG)
+
+def draw_results(test_label, pred_label):
+ acc_sc = accuracy_score(test_label, pred_label)
+ cm = confusion_matrix(test_label, pred_label)
+ print('Accuracy: ', acc_sc)
+ if (args.conf_mat):
+ plt.matshow(cm, cmap='Blues')
+ plt.colorbar()
+ plt.ylabel('Actual')
+ plt.xlabel('Predicted')
+ plt.show()
+ return acc_sc
+
+def test_model(gallery_data, probe_data, gallery_label, probe_label, gallery_cam, probe_cam, showfiles_train, showfiles_test, args):
+
+ debug("probe shape: %s", probe_data.shape)
+ debug("gallery shape: %s", gallery_data.shape)
+
+ if args.rerank:
+ distances = re_ranking(probe_data, gallery_data,
+ args.reranka, args.rerankb, args.rerankl,
+ MemorySave = False, Minibatch = 2000)
+ else:
+ if args.mahalanobis:
+ # metric = 'jaccard' is also valid
+ cov_inv = np.linalg.inv(np.cov(gallery_data.T))
+ distances = np.zeros((probe_data.shape[0], gallery_data.shape[0]))
+ for i in range(int(probe_data.shape[0]/10)):
+ print("Comupting from", i*10, "to", (i+1)*10-1)
+ distances[i*10:(i+1)*10-1] = cdist(probe_data[i*10:(i+1)*10-1], gallery_data, 'mahalanobis', VI=cov_inv)
+ else:
+ distances = cdist(probe_data, gallery_data, 'euclidean')
+
+ ranklist = np.argsort(distances, axis=1)
+
+ test_table = np.arange(1, args.multrank+1)
+ target_pred = np.zeros((args.multrank, ranklist.shape[0]))
+ nsize = args.neighbors
+ if (args.multrank != 1):
+ nsize = test_table[args.multrank-1]
+ nneighbors = np.zeros((ranklist.shape[0],nsize))
+ nnshowrank = (np.zeros((ranklist.shape[0],nsize))).astype(object)
+
+ for i in range(args.multrank):
+ if args.multrank!= 1:
+ args.neighbors = test_table[i]
+ for probe_idx in range(probe_data.shape[0]):
+ row = ranklist[probe_idx]
+ n = 0
+ q = 0
+ while (q < args.neighbors):
+ while (probe_cam[probe_idx] == gallery_cam[row[n]] and
+ probe_label[probe_idx] == gallery_label[row[n]]):
+ n += 1
+ nneighbors[probe_idx][q] = gallery_label[row[n]]
+ nnshowrank[probe_idx][q] = showfiles_train[row[n]] #
+ q += 1
+ n += 1
+
+ if (args.neighbors) and (probe_label[probe_idx] in nneighbors[probe_idx]):
+ target_pred[i][probe_idx] = probe_label[probe_idx]
+ else:
+ target_pred[i][probe_idx] = nneighbors[probe_idx][0]
+
+
+ if (args.showrank):
+ with open("ranklist.txt", "w") as text_file:
+ text_file.write(np.array2string(nnshowrank[:args.showrank]))
+ with open("query.txt", "w") as text_file:
+ text_file.write(np.array2string(showfiles_test[:args.showrank]))
+
+ if args.mAP:
+ precision = np.zeros((probe_label.shape[0], args.neighbors))
+ recall = np.zeros((probe_label.shape[0], args.neighbors))
+ mAP = np.zeros(probe_label.shape[0])
+ max_level_precision = np.zeros((probe_label.shape[0],11))
+
+ for i in range(probe_label.shape[0]):
+ truth_count=0
+ false_count=0
+ for j in range(args.neighbors):
+ if probe_label[i] == nneighbors[i][j]:
+ truth_count+=1
+ precision[i][j] = truth_count/(j+1)
+ else:
+ false_count+=1
+ precision[i][j]= 1 - false_count/(j+1)
+ if truth_count!=0:
+ recall_step = 1/truth_count
+ for j in range(args.neighbors):
+ if probe_label[i] == nneighbors[i][j]:
+ recall[i][j:] += recall_step
+ else:
+ recall[i][:] = 1
+ for i in range(probe_label.shape[0]):
+ for j in range(11):
+ max_level_precision[i][j] = np.max(precision[i][np.where(recall[i]>=(j/10))])
+ #print(mAP[i])
+ for i in range(probe_label.shape[0]):
+ #mAP[i] = sum(max_level_precision[i])/11
+ mAP[i] = sum(precision[i])/args.neighbors
+ print('mAP:',np.mean(mAP))
+
+ return target_pred
+
+def eval(camId, filelist, labels, gallery_idx, train_idx, feature_vectors, args):
+
+ if args.train:
+ cam = camId[train_idx]
+ cam = cam.reshape((cam.shape[0],1))
+ labs = labels[train_idx].reshape((labels[train_idx].shape[0],1))
+ tt = np.hstack((train_idx, cam))
+ train, test, train_label, test_label = train_test_split(tt, labs, test_size=0.3, random_state=0)
+ #to make it smaller we do a double split
+ del labs
+ del cam
+ train_data = feature_vectors[train[:,0]]
+ test_data = feature_vectors[test[:,0]]
+ train_cam = train[:,1]
+ test_cam = test[:,1]
+ showfiles_train = filelist[train[:,0]]
+ showfiles_test = filelist[train[:,0]]
+ del train
+ del test
+ del tt
+ else:
+ query_idx = query_idx.reshape(query_idx.shape[0])
+ gallery_idx = gallery_idx.reshape(gallery_idx.shape[0])
+ camId = camId.reshape(camId.shape[0])
+
+ showfiles_train = filelist[gallery_idx]
+ showfiles_test = filelist[query_idx]
+ train_data = feature_vectors[gallery_idx]
+ test_data = feature_vectors[query_idx]
+ train_label = labels[gallery_idx]
+ test_label = labels[query_idx]
+ train_cam = camId[gallery_idx]
+ test_cam = camId[query_idx]
+
+ train_idx = train_idx.reshape(train_idx.shape[0])
+ train_model = feature_vectors[train_idx]
+
+ if(args.PCA):
+ pca=PCA(n_components=args.PCA) #Data variance @100 is 94%
+ train_model=pca.fit_transform(train_model)
+ train_data=pca.transform(train_data)
+ test_data=pca.transform(test_data)
+
+ accuracy = np.zeros((2, args.multrank))
+ test_table = np.arange(1, args.multrank+1)
+
+ if (args.normalise):
+ debug("Normalising data")
+ train_data = np.divide(train_data,LA.norm(train_data,axis=0))
+ test_data = np.divide(test_data, LA.norm(test_data,axis=0))
+ if(args.kmean_alt):
+ debug("Using Kmeans")
+ train_data, train_label, train_cam = create_kmean_clusters(feature_vectors, labels,gallery_idx,camId)
+
+ if args.kmean:
+ kmeans = KMeans(n_clusters=args.kmean, random_state=0).fit(train_data)
+ neigh = NearestNeighbors(n_neighbors=1)
+ neigh.fit(kmeans.cluster_centers_)
+ neighbors = neigh.kneighbors(test_data, return_distance=False)
+ target_pred = np.zeros(test_data.shape[0])
+
+ for i in range(test_data.shape[0]):
+ td = test_data[i].reshape(1,test_data.shape[1])
+ tc = np.array([test_cam[i]])
+ tl = np.array([test_label[i]])
+ target_pred[i] = (test_model(train_data[np.where(kmeans.labels_==neighbors[i])], td, train_label[np.where(kmeans.labels_==neighbors[i])], tl, train_cam[np.where(kmeans.labels_==neighbors[i])], tc, showfiles_train[np.where(kmeans.labels_==neighbors[i])], showfiles_test[i], args))
+
+ accuracy[0] = draw_results(test_label, target_pred)
+ else:
+ for q in range(args.comparison):
+ target_pred = test_model(train_data, test_data, train_label, test_label, train_cam, test_cam, showfiles_train, showfiles_test, args)
+ for i in range(args.multrank):
+ return draw_results(test_label, target_pred[i])
+ args.rerank = True
+ args.neighbors = 1
+
+ if(args.multrank != 1):
+ plt.plot(test_table[:(args.multrank)], 100*accuracy[0])
+ if(args.comparison!=1):
+ plt.plot(test_table[:(args.multrank)], 100*accuracy[1])
+ plt.legend(['Baseline NN', 'NN+Reranking'], loc='upper left')
+ plt.xlabel('Top k')
+ plt.ylabel('Identification Accuracy (%)')
+ plt.grid(True)
+ plt.show()
+
+def main():
+ mat = scipy.io.loadmat(os.path.join(args.data,'cuhk03_new_protocol_config_labeled.mat'))
+ camId = mat['camId']
+ filelist = mat['filelist']
+ labels = mat['labels']
+ gallery_idx = mat['gallery_idx'] - 1
+ query_idx = mat['query_idx'] - 1
+ train_idx = mat['train_idx'] - 1
+ with open(os.path.join(args.data,'feature_data.json'), 'r') as read_file:
+ feature_vectors = np.array(json.load(read_file))
+
+ axis = 0
+ search = 0
+ steps = 0
+ vertical = True
+ neg = False
+ outofaxis = False
+ start = np.array([1,1])
+ args.PCA = 10
+ args.train = True
+ args.rerank = True
+ args.reranka = 1
+ args.rerankb = 1
+ opt = np.array([1,1])
+ max_acc = eval(camId, filelist, labels, gallery_idx, train_idx, feature_vectors, args)
+ print('origin')
+ print('vertical')
+ while steps<3:
+ steps+=1
+ while axis<4:
+ axis+=1
+ p = start[0]
+ q = start[1]
+ while search <5:
+ search+=1
+ if vertical:
+ if neg:
+ p = start[0] - 2*search
+ if p < 1:
+ p = 1
+ search = 5
+ outofaxis = True
+ else:
+ p = search*2 + start[0]
+ args.reranka = p
+ if not outofaxis:
+ print('p:',p,' q:',q)
+ acc = eval(camId, filelist, labels, gallery_idx, train_idx, feature_vectors, args)
+ if acc > max_acc:
+ print('new p:',p, ' for accuracy:', acc)
+ max_acc=acc
+ opt[0] = p
+ start[0] = p
+ axis=0
+ steps=0
+ search=6
+ else:
+ if neg:
+ q = start[1] - 2*search
+ if q < 1:
+ q = 1
+ search = 5
+ outofaxis = True
+ else:
+ q = search*2 + start[1]
+ args.rerankb = q
+ if not outofaxis:
+ print('p:',p,' q:',q)
+ acc = eval(camId, filelist, labels, gallery_idx, train_idx, feature_vectors, args)
+ if acc > max_acc:
+ print('new q:',q, ' for accuracy:', acc)
+ max_acc=acc
+ opt[1] = q
+ start[1] = q
+ axis=0
+ steps=0
+ search=6
+ if search==5:
+ outofaxis = False
+ vertical = not vertical
+ print('vertical:',vertical)
+ search=0
+ if axis==2 or axis == 4:
+ neg = not neg
+ axis=0
+ start[0]+=2
+ start[1]+=2
+ p=start[0]
+ q=start[1]
+ args.reranka = start[0]
+ args.rerankb = start[1]
+ print('p:',p,' q:',q)
+ acc = eval(camId, filelist, labels, gallery_idx, train_idx, feature_vectors, args)
+ if acc > max_acc:
+ print('new p:',p,'new q:',q, ' for accuracy:', acc)
+ max_acc=acc
+ opt[0] = start[0]
+ opt[1] = start[1]
+ steps=0
+ vertical=True
+ print('Maximum Accuracy:',max_acc,' found at p:',opt[0],'|q:',opt[1])
+
+if __name__ == "__main__":
+ main()
+