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CL_PE.py
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CL_PE.py
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## streaming data + updating prototypes
import time
import numpy as np
import tensorflow as tf
import sys
import pickle
import os
import copy
from sklearn.metrics import confusion_matrix, f1_score, precision_score, recall_score,accuracy_score
from sklearn import utils
import matplotlib.pyplot as plt
import itertools
import csv
from sklearn.decomposition import PCA
from inc_pca import IncPCA
from sklearn import metrics
from enum import Enum
import librosa.display
import sys
from scipy import stats
import datetime
from scipy.fftpack import dct
import _pickle as cp
import copy
import os
from collections import Counter
import random
from imblearn.over_sampling import SMOTE
from subprocess import call
from models import *
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
import torch.utils.data
from torch.utils.data import TensorDataset
#os.environ["CUDA_VISIBLE_DEVICES"]="0"
from data_handler import *
import shutil
from prototype_memory import *
from replay_memory import *
from proto_net import *
from losses import *
from utils import *
import json
import argparse
parser = argparse.ArgumentParser(description="Offline ProtoNet")
parser.add_argument('--data', default='Opportunity')
parser.add_argument('--baseClasses', type=int, default = 5)
parser.add_argument('--newClasses', type=int, default = 0)
parser.add_argument('--percentage', type=float, default = 1.)
parser.add_argument('--batch_size', type=int, default = 200)
parser.add_argument('--window_length_PAMAP2', type=float, default = 1.)
parser.add_argument('--window_step_PAMAP2', type=float, default = 0.5)
parser.add_argument('--epochs', type=int, default = 100)
parser.add_argument('--support', type=int, default = 10)
parser.add_argument('--online_epochs', type=int, default=1)
parser.add_argument('--alpha', type=float, default=.5)
parser.add_argument('--replay_size', type=int, default=6)
parser.add_argument('--not_all_buffer_classes', action='store_true', default=False)
parser.add_argument('--random_stream', action='store_true', default=False)
parser.add_argument('--cuda_device', type=str, default='0')
parser.add_argument('--online_batch', type=int, default=20)
parser.add_argument('--contrastive_loss', action='store_true', default=False)
parser.add_argument('--margin', type=float, default=1)
parser.add_argument('--prototypical_contrastive_loss', action='store_true', default=False)
parser.add_argument('--T', type=float, default=1.)
parser.add_argument('--window_length_Skoda', type=int, default=98)
parser.add_argument('--window_step_Skoda', type=int, default=49)
parser.add_argument('--window_length_HAPT', type=float, default=2.56)
parser.add_argument('--contrastive_loss_with_prototypes', action='store_true', default=False)
parser.add_argument('--seed', type=int, default=1)
parser.add_argument('--contrastive_loss_offline', action='store_true', default=False)
params = parser.parse_args()
seed = params.seed
torch.backends.cudnn.deterministic = True
random.seed(seed)
if params.data =='Skoda':
torch.manual_seed(seed)
torch.cuda.manual_seed(seed)
else:
torch.manual_seed(1)
torch.cuda.manual_seed(1)
np.random.seed(seed)
os.environ["CUDA_VISIBLE_DEVICES"]=params.cuda_device
def plotCNNStatistics(statistics_path):
statistics_dict = cPickle.load(open(statistics_path, 'rb'))
# Plot
fig, ax = plt.subplots(1, 1, figsize=(15, 8))
lines = []
bal_alpha = 0.3
test_alpha = 1.0
bal_map = np.array([statistics['Trainloss'].cpu().data.numpy() for statistics in statistics_dict['Trainloss']]) # (N, classes_num)
test_map = np.array([statistics['Testloss'] for statistics in statistics_dict['Testloss']]) # (N, classes_num)
test_f1 = np.array([statistics['test_f1'] for statistics in statistics_dict['test_f1']]) # (N, classes_num)
basetrain_map = np.array([statistics['BaseTrainloss'].cpu().data.numpy() for statistics in statistics_dict['BaseTrainloss']])
basetrain_f1 = np.array([statistics['BaseTrain_f1'] for statistics in statistics_dict['BaseTrain_f1']])
line, = ax.plot(bal_map, color='r', alpha=bal_alpha)
line, = ax.plot(test_map, color='r', alpha=test_alpha)
lines.append(line)
ax.grid(color='b', linestyle='solid', linewidth=0.3)
plt.legend(labels=['Training Loss','Testing Loss'], loc=2)
fig, ax = plt.subplots(1, 1, figsize=(15, 8))
line, = ax.plot(test_f1, color='r', alpha=test_alpha)
ax.yaxis.set_ticks(np.arange(0, 1.01, 0.05))
ax.yaxis.set_ticklabels(np.around(np.arange(0, 1.01, 0.05), decimals=2))
plt.ylabel('Test Average Fscore')
fig, ax = plt.subplots(1, 1, figsize=(15, 8))
line, = ax.plot(basetrain_map, color='r', alpha=test_alpha)
plt.ylabel('Base Train Loss')
fig, ax = plt.subplots(1, 1, figsize=(15, 8))
line, = ax.plot(basetrain_f1, color='r', alpha=test_alpha)
ax.yaxis.set_ticks(np.arange(0, 1.01, 0.05))
ax.yaxis.set_ticklabels(np.around(np.arange(0, 1.01, 0.05), decimals=2))
plt.ylabel('Base train Average Fscore')
def plotForgettingScore(statistics_path):
statistics_dict = cPickle.load(open(statistics_path, 'rb'))
# Plot
fig, ax = plt.subplots(1, 1, figsize=(15, 8))
lines = []
bal_alpha = 0.3
test_alpha = 1.0
bal_map = np.array([statistics['Forgetting Score'] for statistics in statistics_dict['ForgettingScore']]) # (N, classes_num)
line, = ax.plot(bal_map, color='r', alpha=bal_alpha)
lines.append(line)
ax.grid(color='b', linestyle='solid', linewidth=0.3)
plt.legend(labels=['Forgetting Score'], loc=2)
if params.data == 'Opportunity':
##################### Opportunity Dataset ##########################
print("Downloading opportunity dataset...")
if not os.path.exists("OpportunityUCIDataset.zip"):
call(
'wget "https://archive.ics.uci.edu/ml/machine-learning-databases/00226/OpportunityUCIDataset.zip"',
shell=True
)
print("Downloading done.\n")
else:
print("Dataset already downloaded. Did not download twice.\n")
print("Extracting...")
if not os.path.exists("oppChallenge_gestures.data"):
from preprocess_Oppdata import generate_data
generate_data("OpportunityUCIDataset.zip", "oppChallenge_gestures.data", "gestures")
print("Extracting successfully done to oppChallenge_gestures.data.")
else:
print("Dataset already extracted. Did not extract twice.\n")
#--------------------------------------------
# Dataset-specific constants and functions
#--------------------------------------------
# Hardcoded number of sensor channels employed in the OPPORTUNITY challenge
NB_SENSOR_CHANNELS = 113
NB_SENSOR_CHANNELS_WITH_FILTERING = 149
# Hardcoded number of classes in the gesture recognition problem
NUM_CLASSES = 18
# Hardcoded length of the sliding window mechanism employed to segment the data
SLIDING_WINDOW_LENGTH =24
# Hardcoded step of the sliding window mechanism employed to segment the data
SLIDING_WINDOW_STEP = int(SLIDING_WINDOW_LENGTH/2)
# Batch Size
BATCH_SIZE = params.batch_size
BATCH_SIZE_VAL = params.batch_size
print("Loading data...")
X_train, y_train, X_test, y_test = load_dataset('oppChallenge_gestures.data')
print(np.shape(y_train))
assert (NB_SENSOR_CHANNELS_WITH_FILTERING == X_train.shape[1] or NB_SENSOR_CHANNELS == X_train.shape[1])
X_train, y_train_segments = rearrange(X_train, y_train.reshape((-1,1)), SLIDING_WINDOW_LENGTH, SLIDING_WINDOW_STEP)
X_test, y_test_segments = rearrange(X_test, y_test.reshape((-1,1)), SLIDING_WINDOW_LENGTH, SLIDING_WINDOW_STEP)
#sys.exit()
# Data is reshaped
X_train = X_train.reshape((-1, SLIDING_WINDOW_LENGTH, NB_SENSOR_CHANNELS)) # for input to Conv1D
X_test = X_test.reshape((-1, SLIDING_WINDOW_LENGTH, NB_SENSOR_CHANNELS)) # for input to Conv1D
print(" ..after sliding and reshaping, train data: inputs {0}, targets {1}".format(X_train.shape, y_train_segments.shape))
print(" ..after sliding and reshaping, test data : inputs {0}, targets {1}".format(X_test.shape, y_test_segments.shape))
print(np.shape(X_train))
print(Counter(y_train_segments))
# remove null class
X_train = X_train[y_train_segments != 0]
X_test = X_test[y_test_segments != 0]
y_train_segments = y_train_segments[y_train_segments != 0]
y_train_segments = y_train_segments -1
y_test_segments = y_test_segments[y_test_segments != 0]
y_test_segments = y_test_segments - 1
print(Counter(y_train_segments))
classes = np.unique(y_test_segments)
elif params.data == 'PAMAP2':
################ PAMAP2 Dataset #############################
NB_SENSOR_CHANNELS = 52
NUM_CLASSES = 12
SAMPLING_FREQ = 100 # 100Hz
#SLIDING_WINDOW_LENGTH = int(5.12 * SAMPLING_FREQ)
SLIDING_WINDOW_LENGTH = int(params.window_length_PAMAP2*SAMPLING_FREQ)
#SLIDING_WINDOW_STEP = int(1*SAMPLING_FREQ)
SLIDING_WINDOW_STEP = int(params.window_step_PAMAP2*SAMPLING_FREQ)
print("Extracting...")
if not os.path.exists("./PAMAP2_Dataset/PAMAP2_Train_Test_{}_{}.data".format(SLIDING_WINDOW_LENGTH,SLIDING_WINDOW_STEP)):
print('PAMAP2_Train_Test.data not found. Please run python3 PAMAP2_preprocessing.py to extract data')
raise FileNotFoundError
else:
print("Loading data...")
X_train, y_train_segments, X_test, y_test_segments = load_dataset("./PAMAP2_Dataset/PAMAP2_Train_Test_{}_{}_normalized.data".format(SLIDING_WINDOW_LENGTH,SLIDING_WINDOW_STEP))
print(" ..train data: inputs {0}, targets {1}".format(X_train.shape, y_train_segments.shape))
print(" ..test data : inputs {0}, targets {1}".format(X_test.shape, y_test_segments.shape))
print(Counter(y_train_segments))
classes = np.unique(y_train_segments)
NUM_CLASSES = len(classes)
# Batch Size
BATCH_SIZE = params.batch_size
BATCH_SIZE_VAL = params.batch_size
elif params.data == 'DSADS':
################ DSADS Dataset #############################
NB_SENSOR_CHANNELS =45
NUM_CLASSES = 19
SAMPLING_FREQ = 25 # 100Hz
SLIDING_WINDOW_LENGTH = int(5*SAMPLING_FREQ)
print("Extracting...")
if not os.path.exists("./DSADS_Train_Test_normalized.data"):
print('DSADS_Train_Test_normalized.data not found. Please run python3 DSADS_preprocessing.py to extract data')
raise FileNotFoundError
else:
print("Loading data...")
X_train, y_train_segments, X_test, y_test_segments = load_dataset("./DSADS_Train_Test_normalized.data")
print(" ..train data: inputs {0}, targets {1}".format(X_train.shape, y_train_segments.shape))
print(" ..test data : inputs {0}, targets {1}".format(X_test.shape, y_test_segments.shape))
print(Counter(y_train_segments))
classes = np.unique(y_train_segments)
NUM_CLASSES = len(classes)
# Batch Size
BATCH_SIZE = params.batch_size
BATCH_SIZE_VAL = params.batch_size
elif params.data == 'Skoda':
################ DSADS Dataset #############################
NB_SENSOR_CHANNELS =30
NUM_CLASSES = 11
#SAMPLING_FREQ = 98 # 100Hz
SLIDING_WINDOW_LENGTH = int(params.window_length_Skoda)
#SLIDING_WINDOW_STEP = int(1*SAMPLING_FREQ)
SLIDING_WINDOW_STEP = int(params.window_step_Skoda)
print("Extracting...")
if not os.path.exists("./Skoda_data/Skoda_Train_Test_{}_{}.data".format(SLIDING_WINDOW_LENGTH,SLIDING_WINDOW_STEP)):
print('Skdoa_Train_Test not found. Please run python3 Skdoa_processing.py to extract data')
raise FileNotFoundError
else:
print("Loading data...")
X_train, y_train_segments, X_test, y_test_segments = load_dataset("./Skoda_data/Skoda_Train_Test_{}_{}.data".format(SLIDING_WINDOW_LENGTH,SLIDING_WINDOW_STEP))
print(" ..train data: inputs {0}, targets {1}".format(X_train.shape, y_train_segments.shape))
print(" ..test data : inputs {0}, targets {1}".format(X_test.shape, y_test_segments.shape))
print(Counter(y_train_segments))
classes = np.unique(y_train_segments)
NUM_CLASSES = len(classes)
# Batch Size
BATCH_SIZE = params.batch_size
BATCH_SIZE_VAL = params.batch_size
elif params.data == 'HAPT':
################ DSADS Dataset #############################
NB_SENSOR_CHANNELS =6
NUM_CLASSES = 12
SAMPLING_FREQ = 50 # 100Hz
SLIDING_WINDOW_LENGTH = int(params.window_length_HAPT*SAMPLING_FREQ)
#SLIDING_WINDOW_STEP = int(1*SAMPLING_FREQ)
SLIDING_WINDOW_STEP = int(SLIDING_WINDOW_LENGTH/2)
print("Extracting...")
if not os.path.exists("./HAPT_data/HAPT_Train_Test_{}_{}.data".format(SLIDING_WINDOW_LENGTH,SLIDING_WINDOW_STEP)):
print('HAPT_Train_Test not found. Please run python3 HAPT_processing.py to extract data')
raise FileNotFoundError
else:
print("Loading data...")
X_train, y_train_segments, X_test, y_test_segments = load_dataset("./HAPT_data/HAPT_Train_Test_{}_{}.data".format(SLIDING_WINDOW_LENGTH,SLIDING_WINDOW_STEP))
print(" ..train data: inputs {0}, targets {1}".format(X_train.shape, y_train_segments.shape))
print(" ..test data : inputs {0}, targets {1}".format(X_test.shape, y_test_segments.shape))
print(Counter(y_train_segments))
classes = np.unique(y_train_segments)
NUM_CLASSES = len(classes)
# Batch Size
BATCH_SIZE = params.batch_size
BATCH_SIZE_VAL = params.batch_size
"""
Get Base Data and Streaming Data
"""
## streaming data
baseClassesNb = params.baseClasses
percentage = params.percentage #.05 # 20%
dataHandler = DataHandler(nb_baseClasses=baseClassesNb, seed=seed, train={'data':X_train,'label':y_train_segments}, ClassPercentage=percentage)
dataHandler.streaming_data(nb_NewClasses=params.newClasses)
baseData = copy.deepcopy(dataHandler.getBaseData())
baseClasses = np.unique(baseData['label'])
NewClasses = dataHandler.NewClasses
newClassesNb = len(NewClasses)
mapping = {}
for i in np.arange(baseClassesNb + newClassesNb):
if i >= baseClassesNb:
mapping[NewClasses[i-baseClassesNb]] = i
else:
mapping[baseClasses[i]] = i
for x in range(len(baseData['label'])):
baseData['label'][x] = mapping[baseData['label'][x]]
print(mapping)
## select base classes in test data
X_test_select = []
y_test_select = []
for c in baseClasses:
d,l = X_test[y_test_segments == c,:], y_test_segments[y_test_segments == c]
X_test_select.extend(d)
y_test_select.extend(l)
for x in range(len(y_test_select)):
y_test_select[x] = mapping[y_test_select[x]]
## select new classes in test data
X_test_newClasses = []
y_test_newClasses = []
for c in NewClasses:
d,l = X_test[y_test_segments == c,:], y_test_segments[y_test_segments == c]
X_test_newClasses.extend(d)
y_test_newClasses.extend(l)
for x in range(len(y_test_newClasses)):
y_test_newClasses[x] = mapping[y_test_newClasses[x]]
y_train = tf.keras.utils.to_categorical(baseData['label'], num_classes=baseClassesNb, dtype='int32')
y_test = tf.keras.utils.to_categorical(y_test_select, num_classes=baseClassesNb, dtype='int32')
y_test_newClasses_cat = tf.keras.utils.to_categorical(y_test_newClasses, num_classes=baseClassesNb + newClassesNb, dtype='int32')
#model = InceptionNN(NUM_CLASSES)
extractor = DeepConvLSTM(n_classes=len(np.unique(baseData['label'])), NB_SENSOR_CHANNELS = NB_SENSOR_CHANNELS, SLIDING_WINDOW_LENGTH = SLIDING_WINDOW_LENGTH)
model = ProtoNet(extractor,128,baseClassesNb+newClassesNb)
if torch.cuda.is_available():
model.cuda()
torch.backends.cudnn.deterministic = True
random.seed(seed)
if params.data =='Skoda':
torch.manual_seed(seed)
torch.cuda.manual_seed(seed)
else:
torch.manual_seed(1)
torch.cuda.manual_seed(1)
np.random.seed(seed)
# Statistics
statistics_path = './statistics/OnlineProtoNet_DeepConvLSTM_{}_baseClasses_{}_percentage_{}_online_epochs_{}_WeightedUpdate_{}_not_all_buffer_classes_{}_random_stream_{}.pkl'.format(params.data,
params.baseClasses, params.percentage, params.online_epochs, params.alpha, params.not_all_buffer_classes, params.random_stream)
forgetting_path = './forgetting_score/OnlineProtoNet_DeepConvLSTM_{}_baseClasses_{}_percentage_{}_online_epochs_{}_WeightedUpdate_{}_contrastive_loss_{}.pkl'.format(params.data, params.baseClasses, params.percentage, params.online_epochs, params.alpha, params.contrastive_loss)
if not os.path.exists(os.path.dirname(statistics_path)):
os.makedirs(os.path.dirname(statistics_path))
statistics_container = StatisticsContainer(statistics_path)
if not os.path.exists(os.path.dirname(forgetting_path)):
os.makedirs(os.path.dirname(forgetting_path))
forgetting_container = ForgettingContainer(forgetting_path)
## pretrain base model
x_train_tensor = torch.from_numpy(np.array(baseData['data'])).float()
y_train_tensor = torch.from_numpy(np.array(y_train)).float()
x_test_tensor = torch.from_numpy(np.array(X_test_select)).float()
x_test_newclasses_tensor = torch.from_numpy(np.array(X_test_newClasses)).float()
y_test_tensor = torch.from_numpy(np.array(y_test)).float()
y_test_newClasses_tensor = torch.from_numpy(np.array(y_test_newClasses_cat)).float()
train_data = TensorDataset(x_train_tensor, y_train_tensor)
test_data = TensorDataset(x_test_tensor, y_test_tensor)
test_newClasses_data = TensorDataset(x_test_newclasses_tensor, y_test_newClasses_tensor)
train_loader = torch.utils.data.DataLoader(dataset=train_data,
batch_size=BATCH_SIZE,
num_workers=1, pin_memory=True, shuffle = True,drop_last=False)
test_loader = torch.utils.data.DataLoader(dataset=test_data,
batch_size=BATCH_SIZE_VAL,
num_workers=1, pin_memory=True, shuffle = True,drop_last=False)
if newClassesNb > 1:
test_newClasses_loader = torch.utils.data.DataLoader(dataset=test_newClasses_data,
batch_size=BATCH_SIZE_VAL,
num_workers=1, pin_memory=True, shuffle = True,drop_last=False)
optimizer = optim.Adam(model.parameters(), lr=1e-3,betas=(0.9, 0.999), eps=1e-08, weight_decay=0., amsgrad=True)
if params.contrastive_loss or params.contrastive_loss_offline:
ContrastiveLoss = OnlineContrastiveLoss(model, PairSelector(balance=False), margin=params.margin)
if params.prototypical_contrastive_loss:
PrototypicalContrastiveLoss = OnlinePrototypicalContrastiveLoss(model, params.T, baseClassesNb+newClassesNb)
if params.contrastive_loss_with_prototypes:
ContrastiveLossWithPrototypes = OnlineContrastiveLossWithPrototypes(model, PairSelector(balance=False), margin=params.margin)
n_epochs = params.epochs
n_support = params.support ## HARD CODED
iteration = 0
for epoch in range(n_epochs):
model.train()
running_loss = 0.0
n_steps = 0
for d in train_loader:
# get the inputs; data is a list of [inputs, labels]
inputs, labels = d
x_support, y_support, x_query, y_query = extract_sample(len(np.unique(np.argmax(labels,axis=1))), n_support, n_support, inputs, np.argmax(labels, axis = 1), seed = iteration,shuffle=True)
h = model.extractor.init_hidden(len(x_support))
query_h = model.extractor.init_hidden(len(x_query))
#print(y_support)
y_support = tf.keras.utils.to_categorical(y_support, num_classes=baseClassesNb, dtype='int32')
#print(y_support)
#sys.exit()
y_support = torch.from_numpy(y_support).float().cuda()
x_support = x_support.cuda()
x_query = x_query.cuda()
#y_query = tf.keras.utils.to_categorical(y_query, num_classes=baseClassesNb, dtype='int32')
y_query = torch.from_numpy(y_query).long().cuda()
h = tuple([each.data for each in h])
query_h = tuple([each.data for each in query_h])
# zero the parameter gradients
optimizer.zero_grad()
log_p,h = model.forward_offline(x_support,y_support,x_query,h,query_h)
key2idx = torch.empty(baseClassesNb,dtype=torch.long).cuda()
proto_keys = list(model.memory.prototypes.keys())
#import pdb; pdb.set_trace()
#import pdb; pdb.set_trace()
key2idx[proto_keys] = torch.arange(len(proto_keys)).cuda()
y_query = key2idx[y_query].view(-1,1)
y_query = tf.keras.utils.to_categorical(y_query.cpu().numpy(), num_classes=len(proto_keys), dtype='int32')
y_query = torch.from_numpy(y_query).float().cuda()
#print(np.argmax(log_p.data.cpu().numpy(),axis=1),np.argmax(y_query.data.cpu().numpy(),axis=1))
loss = F.binary_cross_entropy(log_p, y_query)
if params.contrastive_loss or params.contrastive_loss_offline:
_,_,z_query = model.extractor(x_query,query_h, x_query.size(0))
loss += ContrastiveLoss(z_query, y_query)
if params.contrastive_loss_with_prototypes:
_,_,z_query = model.extractor(x_query,query_h, x_query.size(0))
loss += ContrastiveLossWithPrototypes(z_query, y_query,model)
# if params.prototypical_contrastive_loss:
# _,_,z_query = model.extractor(x_query,query_h, x_query.size(0))
# loss += PrototypicalContrastiveLoss(z_query, y_query)
running_loss += loss
loss.backward()
optimizer.step()
n_steps += 1
print('[Epoch %d]' % (epoch + 1))
epoch_train_loss = running_loss / n_steps
print('Train loss: {}'.format(epoch_train_loss))
eval_output = []
true_output = []
test_output = []
true_test_output = []
model.eval()
with torch.no_grad():
print('TESTING !!')
running_test_loss = 0.0
n_steps = 0
for d in test_loader:
inputs, labels = d
val_h = model.extractor.init_hidden(len(inputs))
support_h = model.extractor.init_hidden(len(x_train_tensor))
####### Testing without selecting random support ########################################################################
#inputs , labels = order_classes(inputs,np.argmax(labels, axis = 1),iteration)
#labels = tf.keras.utils.to_categorical(labels,num_classes=baseClassesNb,dtype='int32')
#labels = torch.from_numpy(labels).float()
inputs = inputs.cuda()
#labels = torch.from_numpy(tf.keras.utils.to_categorical(np.argmax(labels, axis = 1), num_classes=baseClassesNb, dtype='int32')).float()
labels = labels.cuda()
val_h = tuple([each.data for each in val_h])
support_h = tuple([each.data for each in support_h])
#print(np.shape(x_support), np.shape(x_query))
# zero the parameter gradients
#print(np.shape(labels))
#log_p,val_h = model.forward_offline(inputs,labels,inputs,support_h,val_h)
log_p,val_h = model.forward_inference(x_train_tensor.cuda(),y_train_tensor.cuda(),inputs,support_h,val_h)
#print(log_p, y_query)
#clipwise_output = model(inputs,inputs.shape[0])
#print("....",np.shape(clipwise_output))
#clipwise_output = outputs['clipwise_output']
test_loss = F.binary_cross_entropy(log_p, labels)
test_output.append(log_p.data.cpu().numpy())
true_test_output.append(labels.data.cpu().numpy())
running_test_loss += test_loss
n_steps += 1
##########################################################################################################################
test_oo = np.argmax(np.vstack(test_output), axis = 1)
true_test_oo = np.argmax(np.vstack(true_test_output), axis = 1)
accuracy = metrics.accuracy_score(true_test_oo, test_oo)
precision, recall, fscore,_ = metrics.precision_recall_fscore_support(true_test_oo, test_oo, labels=np.unique(true_test_oo), average='macro')
try:
auc_test = metrics.roc_auc_score(np.vstack(true_test_output), np.vstack(test_output), labels=np.unique(true_test_oo), average='macro')
except ValueError:
auc_test = None
epoch_test_loss = running_test_loss / n_steps
print('Test loss: {}'.format(epoch_test_loss))
print('TEST average_precision: {}'.format(precision))
print('TEST average f1: {}'.format(fscore))
print('TEST average recall: {}'.format(recall))
print('TEST auc: {}'.format(accuracy))
trainLoss = {'Trainloss': epoch_train_loss}
#trainLoss = {'Trainloss': loss}
statistics_container.append(iteration, trainLoss, data_type='Trainloss')
testLoss = {'Testloss': epoch_test_loss}
#testLoss = {'Testloss': test_loss}
statistics_container.append(iteration, testLoss, data_type='Testloss')
test_f1 = {'test_f1':fscore}
statistics_container.append(iteration, test_f1, data_type='test_f1')
statistics_container.dump()
iteration += 1
C = confusion_matrix(true_test_oo, test_oo)
labels = copy.deepcopy(true_test_oo)
for i in range(len(true_test_oo)):
labels[i] = list(mapping.keys())[true_test_oo[i]]
plt.figure(figsize=(10,10))
plot_confusion_matrix(C, class_list=np.unique(labels), normalize=True, title='Predicted Results')
#plotCNNStatistics(statistics_path)
model.eval()
#model.extractor.eval()
embeddings_list = dict()
embeddings_list['embeddings'] = []
embeddings_list['labels'] = []
with torch.no_grad():
iteration = 0
for tr_input, y_tr in train_loader:
tr_h = model.extractor.init_hidden(len(tr_input))
# tr_input , y_tr = order_classes(tr_input,np.argmax(y_tr, axis = 1),iteration)
# y_tr = tf.keras.utils.to_categorical(y_tr,num_classes=baseClassesNb,dtype='int32')
# y_tr = torch.from_numpy(y_tr).float()
tr_input = tr_input.cuda()
#labels = torch.from_numpy(tf.keras.utils.to_categorical(np.argmax(labels, axis = 1), num_classes=baseClassesNb, dtype='int32')).float()
y_tr = y_tr.cuda()
tr_h = tuple(each.data for each in tr_h)
_,tr_h, embeddings = model.extractor(tr_input, tr_h, len(y_tr))
embeddings_list['embeddings'].extend(embeddings.data.cpu().numpy())
embeddings_list['labels'].extend(y_tr.data.cpu().numpy())
labels = torch.from_numpy(np.array(embeddings_list['labels'])).float()
z_proto = torch.from_numpy(np.array(embeddings_list['embeddings'])).float().cuda()
labels = labels.cuda()
model.update_protoMemory(z_proto,labels)
### setup replay memory
replay_buffer = ReplayMemory(params.replay_size)
yy = [np.argmax(l) for l in y_train]
replay_buffer.update((np.array(baseData['data']), np.array(yy)))
## save prototypes
json_dict = copy.deepcopy(model.memory.prototypes)
for key in model.memory.prototypes.keys():
print(key, type(key))
if type(key) is not str:
json_dict[str(key)] = str(json_dict[key])
del json_dict[key]
with open("./prototypes_json/Debugging_{}_Data_OfflineShuffle_ModelAdaptation.json".format(percentage), "w") as write_file:
str_ = json.dumps(json_dict)
write_file.write(str_)
##ge get train performance on base training data
model.eval()
eval_output = []
true_output = []
train_output = []
true_train_output = []
with torch.no_grad():
print('Getting Performance on Base Data !!')
running_train_loss = 0.0
n_steps = 0
for d in train_loader:
inputs, labels = d
val_h = model.extractor.init_hidden(len(inputs))
####### Testing without selecting random support ########################################################################
#inputs , labels = order_classes(inputs,np.argmax(labels, axis = 1),iteration)
#labels = tf.keras.utils.to_categorical(labels,num_classes=baseClassesNb,dtype='int32')
#labels = torch.from_numpy(labels).float()
inputs = inputs.cuda()
#labels = torch.from_numpy(tf.keras.utils.to_categorical(np.argmax(labels, axis = 1), num_classes=baseClassesNb, dtype='int32')).float()
labels = labels.cuda()
val_h = tuple([each.data for each in val_h])
#print(np.shape(x_support), np.shape(x_query))
# zero the parameter gradients
#print(np.shape(labels))
#log_p,val_h = model.forward_offline(inputs,labels,inputs,support_h,val_h)
log_p,val_h = model.forward_offline(inputs, labels,inputs,val_h,val_h)
#print(log_p, y_query)
#clipwise_output = model(inputs,inputs.shape[0])
#print("....",np.shape(clipwise_output))
#clipwise_output = outputs['clipwise_output']
train_loss = F.binary_cross_entropy(log_p, labels)
train_output.append(log_p.data.cpu().numpy())
true_train_output.append(labels.data.cpu().numpy())
running_train_loss += train_loss
n_steps += 1
##########################################################################################################################
train_oo = np.argmax(np.vstack(train_output), axis = 1)
true_train_oo = np.argmax(np.vstack(true_train_output), axis = 1)
accuracy = metrics.accuracy_score(true_train_oo, train_oo)
precision, recall, fscore,_ = metrics.precision_recall_fscore_support(true_train_oo, train_oo, labels=np.unique(true_train_oo), average='macro')
try:
auc_test = metrics.roc_auc_score(np.vstack(true_train_output), np.vstack(train_output), labels=np.unique(true_train_oo), average='macro')
except ValueError:
auc_test = None
epoch_train_loss = running_train_loss / n_steps
print("----------------------------------------------------------------------")
print('Base Train loss: {}'.format(epoch_train_loss))
print('Base Train average_precision: {}'.format(precision))
print('Base Train average f1: {}'.format(fscore))
print('Base Train average recall: {}'.format(recall))
print('Base Train auc: {}'.format(accuracy))
print("----------------------------------------------------------------------")
trainLoss = {'BaseTrainloss': epoch_train_loss}
baseTrainF1 = {'BaseTrain_f1': fscore}
#trainLoss = {'Trainloss': loss}
statistics_container.append(iteration, trainLoss, data_type='BaseTrainloss')
statistics_container.append(iteration, baseTrainF1, data_type='BaseTrain_f1')
statistics_container.dump()
cm = plt.get_cmap('gist_rainbow')
NUM_COLORS = len(classes)
colors = [cm((1.*i)/NUM_COLORS) for i in np.arange(NUM_COLORS)]
markers=['.', 'x', 'h','1']
## OPPORTUNITY
if params.data == 'Opportunity':
LABELS = ['OpenDoor1', 'OpenDoor2','CloseDoor1','CloseDoor2','OpenFridge','CloseFridge','OpenDishwasher','CloseDishwasher','OpenDrawer1','CloseDrawer1','OpenDrawer2','CloseDrawer2','OpenDrawer3','CloseDrawer3','CleanTable','DrinkFromCup','ToogleSwitch']
elif params.data == 'PAMAP2':
## PAMAP2
LABELS = {1:'lying',2:'sitting',3:'standing',4: 'walking',5: 'running',6: 'cycling',7: 'Nordic walking',9: 'watching TV',10: 'computer work',11: 'car driving', 12: 'ascending stairs',
13:'descending stairs',16: 'vacuum cleaning',17: 'ironing',18: 'folding laundry',19: 'house cleaning',20:'playing soccer',24: 'rope jumping'}
elif params.data == 'DSADS':
LABELS = {1:'sitting',2:'standing',3:'lying on back',4: 'lying on right side',5: 'ascending stairs',6: 'descending stairs',7: 'standing in elevator still',8: 'moving around in elevator',9: 'walking in parking lot',10: 'walking on treadmill w/ speed 4km/h in flat', 11:'walking on treadmill w/ speed 4km/h in 15 deg',12: 'running on treadmill',
13:'exercising on stepper',14: 'exercising on cross trainer',15: 'cycling on exercise bike in horizontal',16: 'cycling on exercise bike in vertical',17: 'rowing',18: 'jumping',19:'playing basketbal'}
elif params.data == 'Skoda':
LABELS = {0: 'null class', 1: 'write on notepad', 2: 'open hood', 3: 'close hood',
4: 'check gaps on the front door', 5: 'open left front door',
6: 'close left front door', 7: 'close both left door', 8: 'check trunk gaps',
9: 'open and close trunk', 10: 'check steering wheel'}
elif params.data == 'HAPT':
LABELS = {1:'walking',2:'walking upstairs',3:'walking downstairs',4:'sitting',5:'standing',6:'laying',7:'stand to sit',8:'sit to stand',
9:'sit to lie',10:'lie to sit',11:'stand to lie',12:'lie to stand'}
### starting streaming
N = params.online_batch
prototypes_check =copy.deepcopy(list(model.memory.prototypes.values()))
### plot prototypes before updating
pca = IncPCA(n_components=2)
pca.partial_fit(list(model.memory.prototypes.values()))
#prototypes_pca = pca.transform(list(prot_mem.prototypes.values()))
prototypes_pca = pca.transform(list(model.memory.prototypes.values()))
fig, ax = plt.subplots(figsize=(10,10))
# ax.set_xlim(-6,6)
# ax.set_ylim(-6,6)
xdata, ydata = [], []
ln, = plt.plot([],[],'ro')
xdata.extend(prototypes_pca[:,0])
ydata.extend(prototypes_pca[:,1])
annotations= set()
def plt_dynamic(x,y,labels,ax,fig,colors,markers=['.', 'x', 'h','1']):
#print(x,y,labels,x[labels==6],y[labels==6])
for k, col in zip(np.unique(labels),colors):
#print(k,x,y,labels)
xx,yy = x[labels == k], y[labels == k]
ax.plot(xx,yy, 'o',
markerfacecolor=col, markeredgecolor=col,
marker=markers[k%len(markers)],markersize=20)
#add label
if annotate and LABELS[list(mapping.keys())[k]] not in annotations:
annotations.add(LABELS[list(mapping.keys())[k]])
ax.annotate(LABELS[list(mapping.keys())[k]], (xx, yy),
horizontalalignment='center',
verticalalignment='center',
size=10, weight='bold',rotation=45,
color='k')
fig.canvas.draw()
#ytrue = np.array(list(prot_mem.prototypes.keys()), dtype=np.int32)
ytrue = np.array(list(model.memory.prototypes.keys()), dtype=np.int32)
annotate = True
#print(xdata, ydata, ytrue)
plt_dynamic(np.array(xdata), np.array(ydata), ytrue, ax,fig, colors)
plt.title("Prototypes after updating using all training data")
annotate = True
plt.show(block=False)
val_h = model.extractor.init_hidden(N)
xdata, ydata, ytrue = [], [], []
ll=[]
#optimizer = optim.Adam(model.parameters(), lr=1e-6,betas=(0.9, 0.999), eps=1e-08, weight_decay=0., amsgrad=True)
print("Started Streaming Data ...")
support_set = []
labels_set = []
map_labels = []
# query_set = [d for d in baseData['data']]
# query_labels_set = [l for l in y_train]
# query_map_labels = [np.argmax(l) for l in y_train]
counter = 1
#running_loss = 0.0
n_steps = 0
online_epochs = params.online_epochs
embeddings_list = dict()
embeddings_list['embeddings'] = []
embeddings_list['labels'] = []
excluded_classes = []
embx_data, emby_data, embyTrue = [], [], []
max_f1_score = {class_k: 0. for class_k in range(len(baseClasses))}
f1_score_t = {}
while not dataHandler.endOfStream():
#print(counter)
#print(counter)
if params.random_stream:
d, l = dataHandler.getNextData()
support_set.append(copy.deepcopy(d))
labels_set.append(copy.deepcopy(l))
map_labels.append(copy.deepcopy(mapping[l]))
else:
d, l = dataHandler.getNextBatch_controlled(N)
support_set = copy.deepcopy(d)
labels_set = copy.deepcopy(l)
map_labels = copy.deepcopy([mapping[ll] for ll in l])
print(np.shape(support_set), np.shape(labels_set), np.shape(map_labels))
model.train()
if not params.random_stream or counter % N == 0:
val_h = model.extractor.init_hidden(len(support_set))
query_h = model.extractor.init_hidden(len(support_set))
# saved_map_labels = copy.deepcopy(query_map_labels)
saved_support_set = copy.deepcopy(support_set)
saved_map_labels = copy.deepcopy(map_labels)
support_set = torch.from_numpy(np.array(support_set)).float()
# query_set = torch.from_numpy(np.array(query_set)).float()
map_labels = tf.keras.utils.to_categorical(map_labels, num_classes=baseClassesNb + newClassesNb, dtype='int32')
map_labels = torch.from_numpy(np.array(map_labels)).float()
# query_map_labels = tf.keras.utils.to_categorical(query_map_labels, num_classes=baseClassesNb + newClassesNb, dtype='int32')
# query_map_labels = torch.from_numpy(np.array(query_map_labels)).float()
# query_set = query_set.cuda()
# query_map_labels = query_map_labels.cuda()
#sys.exit()
support_set = support_set.cuda()
map_labels = map_labels.cuda()
val_h = tuple([each.data for each in val_h])
query_h = tuple([each.data for each in query_h])
optimizer.zero_grad()
log_p, val_h,_ = model.forward_online(support_set,map_labels, support_set, val_h,query_h)
#embeddings_list['embeddings'].extend(embds.data.cpu().numpy())
#embeddings_list['labels'].extend(map_labels.data.cpu().numpy())
loss = F.binary_cross_entropy(log_p, map_labels)
print(loss)
if params.contrastive_loss:
_,_,z_query = model.extractor(support_set,query_h, support_set.size(0))
loss += ContrastiveLoss(z_query, map_labels)
if params.prototypical_contrastive_loss:
_,_,z_query = model.extractor(support_set,query_h, support_set.size(0))
loss += PrototypicalContrastiveLoss(z_query, map_labels)
if params.contrastive_loss_with_prototypes:
_,_,z_query = model.extractor(support_set,query_h, support_set.size(0))
loss += ContrastiveLossWithPrototypes(z_query, map_labels, model)
#running_loss += loss
print(loss)
loss.backward()
optimizer.step()
#model.online_update_prototypes(support_set,map_labels, val_h)
for j in range(1, online_epochs):
query_h = model.extractor.init_hidden(len(support_set))
query_set1, query_map_labels1 = utils.shuffle(support_set,map_labels, random_state=j)
#sys.exit()
query_set1 = query_set1.cuda()
query_map_labels1 = query_map_labels1.cuda()
query_h = tuple([each.data for each in query_h])
optimizer.zero_grad()
log_p, query_h = model.forward_online_QUERY(query_set1, query_h)
#embeddings_list['embeddings'].extend(embds.data.cpu().numpy())
#embeddings_list['labels'].extend(map_labels.data.cpu().numpy())
loss = F.binary_cross_entropy(log_p, query_map_labels1)
if params.contrastive_loss:
_,_,z_query = model.extractor(query_set1,query_h, query_set1.size(0))
loss += ContrastiveLoss(z_query, query_map_labels1)
if params.prototypical_contrastive_loss:
_,_,z_query = model.extractor(query_set1,query_h, query_set1.size(0))
loss += PrototypicalContrastiveLoss(z_query, query_map_labels1)
#running_loss += loss
loss.backward()
optimizer.step()
#model.online_update_prototypes(support_set,map_labels, val_h)
# update prototypes of classes not found in support set using weighted sum of old prototypes and prototypes from replay data (high momentum update set alpha = 0.99)
model.eval()
if params.not_all_buffer_classes:
excluded_classes = np.unique(saved_map_labels)
print("Excluded Classes: ",excluded_classes)
query_set, query_map_labels = replay_buffer.exemplar_train(excluded_classes)
print("Replay Buffer: ", np.shape(query_set), np.shape(query_map_labels))
query_set = torch.from_numpy(np.array(query_set)).float()
query_map_labels = tf.keras.utils.to_categorical(query_map_labels, num_classes=baseClassesNb + newClassesNb, dtype='int32')
query_map_labels = torch.from_numpy(np.array(query_map_labels)).float()
query_set = query_set.cuda()
query_map_labels = query_map_labels.cuda()
query_h = model.extractor.init_hidden(len(query_set))
query_h = tuple([each.data for each in query_h])
if len(query_set) > 0:
model.prototype_update_momentum(query_set, query_map_labels, params.alpha, query_h)
# query_set = list(query_set.data.cpu().numpy())
# query_map_labels = copy.deepcopy(saved_map_labels)
support_set = []
labels_set = []
map_labels = []
eval_output = []
true_output = []
test_output = []
true_test_output = []
#h = model.extractor.init_hidden(n_support*baseClassesNb)
#print(np.shape(val_h[0]))
model.eval()
with torch.no_grad():
iteration = 0
running_test_loss = 0.0