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CME-CASIA-B.py
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import tensorflow as tf
from tensorflow.python.layers.core import Dense
import numpy as np
import time
import matplotlib as mpl
import copy
import os
from tensorflow.python.ops import rnn_cell_impl
# mpl.use('Agg')
# import matplotlib.pyplot as plt
import os
# Number of Epochs
epochs = 100
# Batch Size
batch_size = 128
# RNN Size k = 256
rnn_size = 256
# Number of Layers, 2-layer LSTM
num_layers = 2
# Time Steps of Input, f = 6 skeleton frames
time_steps = 6
# Length of Series, J = 20 body joints in a sequence
series_length = 20
# Learning Rate
learning_rate = 0.0005
lr_decay = 0.95
momentum = 0.5
lambda_l2_reg = 0.02
dataset = False
attention = False
manner = False
gpu = False
permutation_flag = False
permutation_test_flag = False
permutation_test_2_flag = False
permutation = 0
test_permutation = 0
test_2_permutation = 0
Reverse = True
use_attention = True
Bi_LSTM = False
AGEs = True
Frozen = False
tf.app.flags.DEFINE_string('attention', 'LA', "(LA) Locality-oriented Attention Alignment or BA (Basic Attention Alignment)")
tf.app.flags.DEFINE_string('manner', 'sc', "average prediction (ap) or sequence-level concatenation (sc)")
tf.app.flags.DEFINE_string('dataset', 'CASIA_B', "Dataset: BIWI or IAS, CASIA_B or KGBD")
tf.app.flags.DEFINE_string('length', '50', "For CVE setup on CASIA B, 50, 60 or 70")
tf.app.flags.DEFINE_string('gpu', '0', "GPU number")
tf.app.flags.DEFINE_string('frozen', '0', "Freeze CAGEs for contrastive learning")
tf.app.flags.DEFINE_string('c_reid', '0', "Peform re-id use projection vectors")
tf.app.flags.DEFINE_string('t', '0.15', "Temperature for contrastive learning")
tf.app.flags.DEFINE_string('train_flag', '1', "Choose to train (1) or test (0)")
tf.app.flags.DEFINE_string('view', 'None', "Choose different views for CASIA B")
tf.app.flags.DEFINE_string('transfer', 'None', "Choose a dataset's encoding model to transfer encoding")
tf.app.flags.DEFINE_string('model', 'rev_rec', "prediction, sorting, rev_rec (Rev. Rec.), rev_rec_plus(Rev. Rec. Plus)")
tf.app.flags.DEFINE_string('double_pretext', '', "rev_rec+pred, rev_rec+sort")
# tf.app.flags.DEFINE_string('p_num', '', "number of persons for training")
tf.app.flags.DEFINE_string('probe_type', 'nm.nm', "probe type (nm, cl, bg)")
tf.app.flags.DEFINE_string('norm', '1', "1 or 0")
FLAGS = tf.app.flags.FLAGS
config = tf.ConfigProto()
os.environ['CUDA_VISIBLE_DEVICES'] = '0'
temperature = 0.1
config.gpu_options.allow_growth = True
view = 'view_'
probe_type = ''
transfer = 'None'
Model = 'rev_rec'
pre_task = 'rev_rec'
# revise 1
double_pretext = ''
p_num = ''
CASIA_scale = ''
multi_view_results = ''
normalize = False
LSTM_layer = ''
view_num = ''
change = '_test_adj_2'
change = '_mid_match'
gallery_num = '0'
def main(_):
global permutation_test_flag, permutation_flag, probe_type, \
batch_size, learning_rate, normalize, LSTM_layer, gallery_num
global attention, dataset, series_length, epochs, time_steps, gpu, manner, frames_ps, \
temperature, Frozen, C_reid, temperature, train_flag, view, use_attention, transfer, Model, pre_task
attention, dataset, gpu, manner, length, Frozen, C_reid, temperature, train_flag, \
view_num, transfer, Model, double_pretext, probe_type_t, norm_t = FLAGS.attention, \
FLAGS.dataset, FLAGS.gpu, FLAGS.manner, \
FLAGS.length, FLAGS.frozen, FLAGS.c_reid, \
FLAGS.t, FLAGS.train_flag, FLAGS.view, \
FLAGS.transfer, FLAGS.model, FLAGS.double_pretext, \
FLAGS.probe_type, FLAGS.norm
if attention not in ['BA', 'LA']:
raise Exception('Attention must be BA or LA.')
if manner not in ['sc']:
raise Exception('Training manner must be sc.')
if dataset not in ['CASIA_B']:
raise Exception('Dataset must be CASIA_B.')
if not gpu.isdigit() or int(gpu) < 0:
raise Exception('GPU number must be a positive integer.')
# if length not in ['4', '6', '8', '10', '40', '20']:
# raise Exception('Length number must be 4, 6, 8, 10, 20 or 40.')
if Frozen not in ['0']:
raise Exception('Frozen state must be 0.')
if C_reid not in ['0']:
raise Exception('C_reid state must be 0.')
if train_flag not in ['0', '1', '2']:
raise Exception('Train_flag must be 0, 1, or 2 (Only Evaluation).')
if view_num not in ['None']:
raise Exception('View_num must be None for CME setup.')
if transfer not in ['None']:
raise Exception('Transfer dataset must be None.')
if Model not in ['rev_rec', 'rev_rec_plus', 'prediction', 'sorting']:
raise Exception('Model must be prediction, sorting, rev_rec or rev_rec_plus')
# Revise 1
if double_pretext not in ['', 'rev_rec+pred', 'rev_rec+sort', 'pred+sort']:
raise Exception('Model must be '' (default), rev_rec+pred, rev_rec+sort, pred+sort')
# old
# if p_num not in ['', '24', '62', '74']:
# raise Exception('Number must be 24, 62, 74')
if double_pretext != '' and Model != 'rev_rec_plus':
raise Exception('Model must be rev_rec_plus')
if probe_type_t not in ['nm.nm', 'cl.cl', 'bg.bg', 'cl.nm', 'bg.nm']:
raise Exception('[Probe].[Gallery] must be nm.nm (default), cl.cl, bg.bg, cl.nm, bg.nm')
os.environ['CUDA_VISIBLE_DEVICES'] = gpu
# os.environ['CUDA_VISIBLE_DEVICES'] = '0,1,2,3'
folder_name = dataset + '_' + attention
series_length = 20
setting = ''
LSTM_layer = ''
if dataset == 'CASIA_B':
series_length = 14
view += view_num
if gallery_num == '':
gallery_num = view_num
if norm_t == '1':
normalize = True
probe_type = probe_type_t
if view_num == 'None':
view = ''
if num_layers == 4:
LSTM_layer = '_L4'
if dataset == 'KS20':
series_length = 25
view += view_num
if view_num == 'None':
view = ''
if transfer != 'None':
train_flag = '0'
time_steps = int(length)
temperature = float(temperature)
pre_task = Model
frames_ps = dataset + '_match/' + str(time_steps) + setting + '/'
epochs = 400
if dataset != 'KS20' and dataset != 'CASIA_B':
view = ''
# if dataset == 'KGBD':
# epochs = 100
if dataset == 'CASIA_B':
epochs = 100
# Obtain CAGEs
# Train self-supervised gait encoding model on X, Y, Z
if dataset == 'KGBD':
temperature = 0.5
elif dataset != 'CASIA_B':
temperature = 0.1
epochs = 400
if Model == 'rev_rec_plus':
# rev_rec uses 'LA' and other two pretext tasks use 'BA'
attention = 'BA'
print(
' ## Dataset: %s\n ## Attention: %s\n ## Re-ID Manner: %s\n ## Sequence Length: %s\n'
' ## Tempearture: %s\n ## Pretext Task: %s\n ## Probe set: %s\n ## Gallery set: %s\n ## GPU: %s\n' %
(dataset, attention, manner, str(time_steps), str(temperature), Model, probe_type.split('.')[0],
probe_type.split('.')[1], str(gpu)))
if train_flag == '1':
print(' ## Training Gait Encoding Model: True')
else:
print(' ## Training Gait Encoding Model: False')
print(' ## Training Recognition Network: True\n')
if train_flag == '1' and Model != 'rev_rec_plus':
try:
os.mkdir('./Models/Gait_Encoding_models')
except:
pass
folder_name = './Models/Gait_Encoding_models/' + folder_name
for i in ['x', 'y', 'z']:
try:
os.mkdir(folder_name + '_' + i + '_' + str(time_steps) + '_' + str(temperature) + '_' + Frozen + view + 'pre_' + pre_task + LSTM_layer + change)
print("### LSTM layers", LSTM_layer)
except:
pass
train(folder_name + '_' + i + '_' + str(time_steps) + '_' + str(temperature)+ '_' + Frozen + view + 'pre_' + pre_task + LSTM_layer + change, i, train_dataset=dataset)
elif train_flag == '1' and Model == 'rev_rec_plus':
print(' ## Training Three Types of Gait Encoding Model: (1) Rev. Rec. (2) Prediction (3) Sorting, and Combine CAGEs to Train RN')
attention = 'LA'
folder_name = dataset + '_' + attention
try:
os.mkdir('./Models/Gait_Encoding_models')
except:
pass
folder_name = './Models/Gait_Encoding_models/' + folder_name
# Rev. Rec.
pre_task = 'rev_rec'
for i in ['x', 'y', 'z']:
try:
os.mkdir(folder_name + '_' + i + '_' + str(time_steps) + '_' + str(temperature) + '_' + Frozen + view + 'pre_' + pre_task)
except:
pass
train(folder_name + '_' + i + '_' + str(time_steps) + '_' + str(temperature)+ '_' + Frozen + view + 'pre_' + pre_task, i, train_dataset=dataset)
# Prediction
attention = 'BA'
folder_name = dataset + '_' + attention
folder_name = './Models/Gait_Encoding_models/' + folder_name
pre_task = 'prediction'
for i in ['x', 'y', 'z']:
try:
os.mkdir(folder_name + '_' + i + '_' + str(time_steps) + '_' + str(temperature) + '_' + Frozen + view + 'pre_' + pre_task)
except:
pass
train(folder_name + '_' + i + '_' + str(time_steps) + '_' + str(temperature)+ '_' + Frozen + view + 'pre_' + pre_task, i, train_dataset=dataset)
# Sorting
attention = 'BA'
folder_name = dataset + '_' + attention
folder_name = './Models/Gait_Encoding_models/' + folder_name
pre_task = 'sorting'
for i in ['x', 'y', 'z']:
try:
os.mkdir(folder_name + '_' + i + '_' + str(time_steps) + '_' + str(temperature) + '_' + Frozen + view + 'pre_' + pre_task)
except:
pass
train(folder_name + '_' + i + '_' + str(time_steps) + '_' + str(temperature)+ '_' + Frozen + view + 'pre_' + pre_task, i, train_dataset=dataset)
pre_task = 'rev_rec_plus'
print('Generate CAGEs')
if dataset == 'IAS':
X, X_y, t_X, t_X_y, t_2_X, t_2_X_y, t_X_att = encoder_classify(dataset + '_' + attention + 'x',
'x', 'att', dataset)
Y, Y_y, t_Y, t_Y_y, t_2_Y, t_2_Y_y, t_Y_att = encoder_classify(dataset + '_' + attention + 'y',
'y', 'att', dataset)
Z, Z_y, t_Z, t_Z_y, t_2_Z, t_2_Z_y, t_Z_att = encoder_classify(dataset + '_' + attention + 'z',
'z', 'att', dataset)
else:
X, X_y, t_X, t_X_y, t_X_att = encoder_classify(dataset + '_' + attention + 'x', 'x', 'att', dataset)
Y, Y_y, t_Y, t_Y_y, t_Y_att = encoder_classify(dataset + '_' + attention + 'y', 'y', 'att', dataset)
Z, Z_y, t_Z, t_Z_y, t_Z_att = encoder_classify(dataset + '_' + attention + 'z', 'z', 'att', dataset)
assert X_y.tolist() == Y_y.tolist() and Y_y.tolist() == Z_y.tolist()
assert t_X_y.tolist() == t_Y_y.tolist() and t_Y_y.tolist() == t_Z_y.tolist()
X = np.column_stack([X,Y,Z])
y = X_y
t_X = np.column_stack([t_X, t_Y, t_Z])
t_y = t_X_y
if dataset == 'IAS':
t_2_X = np.column_stack([t_2_X, t_2_Y, t_2_Z])
t_2_y = t_2_X_y
if train_flag == '0' or train_flag == '1':
# direct evaluation
print('Using CAGEs to match pedestrians')
encoder_match_union_directly(X, y, t_X, t_y, './Models/CAGEs_RN_models',
dataset + '_' + attention + '_RN_' + manner + '_' + str(
time_steps) + '_' + str(temperature) + '_' + str(
Frozen) + view + 'pre_' + Model + LSTM_layer + change, dataset)
def get_inputs():
inputs = tf.placeholder(tf.float32, [batch_size, time_steps, series_length], name='inputs')
targets = tf.placeholder(tf.float32, [batch_size, time_steps, series_length], name='targets')
learning_rate = tf.Variable(0.001, trainable=False, dtype=tf.float32, name='learning_rate')
learning_rate_decay_op = learning_rate.assign(learning_rate * 0.5)
target_sequence_length = tf.placeholder(tf.int32, (None, ), name='target_sequence_length')
max_target_sequence_length = tf.reduce_max(target_sequence_length, name='max_target_len')
source_sequence_length = tf.placeholder(tf.int32, (None, ), name='source_sequence_length')
keep_prob = tf.placeholder(tf.float32, name='keep_prob')
return inputs, targets, learning_rate, learning_rate_decay_op, target_sequence_length, max_target_sequence_length, source_sequence_length, keep_prob
def get_data_CASIA_B(dimension, fr):
global view, probe_type
if view != '':
view_num = view.split('_')[1]
if view != '':
view_dir = view + '/'
else:
view_dir = ''
input_data = np.load('Datasets/'+ frames_ps + view_dir + 'CASIA_B_train_npy_data/source_' + dimension + '_CASIA_B_' + str(fr) + '.npy')
input_data = input_data.reshape([-1,time_steps, series_length])
input_data = input_data.tolist()
targets = np.load('Datasets/'+ frames_ps + view_dir +'CASIA_B_train_npy_data/target_' + dimension + '_CASIA_B_' + str(fr) + '.npy')
if time_steps == 25:
targets = targets[:targets.shape[0]//(time_steps*series_length) * (time_steps*series_length)]
targets = targets.reshape([-1,time_steps, series_length])
targets = targets.tolist()
return input_data, targets
def pad_batch(batch_data, pad_int):
'''
padding the first skeleton of target sequence with zeros —— Z
transform the target sequence (1,2,3,...,f) to (Z,1,2,3,...,f-1) as input to decoder in training
parameters：
- batch_data
- pad_int: position (0)
'''
max_sentence = max([len(sentence) for sentence in batch_data])
return [sentence + [pad_int] * (max_sentence - len(sentence)) for sentence in batch_data]
def get_batches(targets, sources, batch_size, source_pad_int, target_pad_int):
for batch_i in range(0, len(sources) // batch_size):
start_i = batch_i * batch_size
sources_batch = sources[start_i:start_i + batch_size]
targets_batch = targets[start_i:start_i + batch_size]
# transform the target sequence (1,2,3,...,f) to (Z,1,2,3,...,f-1) as input to decoder in training
pad_sources_batch = np.array(pad_batch(sources_batch, source_pad_int))
pad_targets_batch = np.array(pad_batch(targets_batch, target_pad_int))
# record the lengths of sequence (not neccessary)
targets_lengths = []
for target in targets_batch:
targets_lengths.append(len(target))
source_lengths = []
for source in sources_batch:
source_lengths.append(len(source))
yield pad_targets_batch, pad_sources_batch, targets_lengths, source_lengths
def get_batches_plain(targets, sources, batch_size, source_pad_int, target_pad_int):
for batch_i in range(0, len(sources) // batch_size):
start_i = batch_i * batch_size
sources_batch = sources[start_i:start_i + batch_size]
targets_batch = sources[start_i:start_i + batch_size]
# transform the target sequence (1,2,3,...,f) to (Z,1,2,3,...,f-1) as input to decoder in training
pad_sources_batch = np.array(pad_batch(sources_batch, source_pad_int))
pad_targets_batch = np.array(pad_batch(targets_batch, target_pad_int))
# record the lengths of sequence (not neccessary)
targets_lengths = []
for target in targets_batch:
targets_lengths.append(len(target))
source_lengths = []
for source in sources_batch:
source_lengths.append(len(source))
yield pad_targets_batch, pad_sources_batch, targets_lengths, source_lengths
def GE(input_data, rnn_size, num_layers, source_sequence_length, encoding_embedding_size):
'''
Gait Encoder (GE)
Parameters:
- input_data: skeleton sequences (X,Y,Z series)
- rnn_size: 256
- num_layers: 2
- source_sequence_length:
- encoding_embedding_size: embedding size
'''
encoder_embed_input = input_data
def get_lstm_cell(rnn_size):
lstm_cell = tf.contrib.rnn.LSTMCell(rnn_size, initializer=tf.random_uniform_initializer(-0.1, 0.1, seed=2))
# if use_dropout:
# lstm_cell = tf.contrib.rnn.DropoutWrapper(lstm_cell, output_keep_prob=0.5)
return lstm_cell
if Bi_LSTM:
fw_cell = tf.contrib.rnn.MultiRNNCell([get_lstm_cell(rnn_size) for _ in range(num_layers)])
bw_cell = tf.contrib.rnn.MultiRNNCell([get_lstm_cell(rnn_size) for _ in range(num_layers)])
encoder_output, encoder_state = \
tf.nn.bidirectional_dynamic_rnn(fw_cell, bw_cell, encoder_embed_input, sequence_length=source_sequence_length, dtype=tf.float32)
weights = fw_cell.variables
# print(encoder_state)
# print('1')
# print(encoder_output)
# exit(1)
else:
cell = tf.contrib.rnn.MultiRNNCell([get_lstm_cell(rnn_size) for _ in range(num_layers)])
encoder_output, encoder_state = tf.nn.dynamic_rnn(cell, encoder_embed_input,
sequence_length=source_sequence_length, dtype=tf.float32)
weights = cell.variables
if Bi_LSTM:
# print(encoder_state)
# exit(1)
c_1 = encoder_state[1][1][0] + encoder_state[0][1][0]
h_1 = encoder_state[1][1][1] + encoder_state[0][1][1]
c_0 = encoder_state[1][0][0] + encoder_state[0][0][0]
h_0 = encoder_state[1][0][1] + encoder_state[0][0][1]
# bidirectional_rnn/fw/fw/transpose_1:0
# ReverseSequence: 0
return encoder_output[0], (rnn_cell_impl.LSTMStateTuple(c_0, h_0), rnn_cell_impl.LSTMStateTuple(c_1, h_1)), weights, source_sequence_length
else:
return encoder_output, encoder_state, weights, source_sequence_length
def GD(decoding_embedding_size, num_layers, rnn_size,
target_sequence_length, source_sequence_length, max_target_sequence_length, encoder_output, encoder_state, decoder_input):
'''
Gait Decoder (GD)
parameters：
- decoding_embedding_size: embedding size
- num_layers: 2
- rnn_size: 256
- target_sequence_length: 6
- max_target_sequence_length: 6
- encoder_state: gait encoded state
- decoder_input:
'''
decoder_embed_input = decoder_input
def get_decoder_cell(rnn_size):
decoder_cell = tf.contrib.rnn.LSTMCell(rnn_size,
initializer=tf.random_uniform_initializer(-0.1, 0.1, seed=2))
return decoder_cell
cell = tf.contrib.rnn.MultiRNNCell([get_decoder_cell(rnn_size) for _ in range(num_layers)])
if use_attention:
attention_mechanism = tf.contrib.seq2seq.LuongAttention(num_units=rnn_size, memory=encoder_output,
memory_sequence_length=source_sequence_length)
cell = tf.contrib.seq2seq.AttentionWrapper(cell=cell, attention_mechanism=attention_mechanism,
attention_layer_size=rnn_size, alignment_history=True, output_attention=True,
name='Attention_Wrapper')
# FC layer
output_layer = Dense(series_length,
use_bias=True,
kernel_initializer=tf.truncated_normal_initializer(mean=0.0, stddev=0.1))
with tf.variable_scope("decode"):
training_helper = tf.contrib.seq2seq.TrainingHelper(inputs=decoder_embed_input,
sequence_length=target_sequence_length,
time_major=False)
if not use_attention:
training_decoder = tf.contrib.seq2seq.BasicDecoder(cell,
training_helper,
encoder_state,
output_layer)
else:
decoder_initial_state = cell.zero_state(batch_size=batch_size, dtype=tf.float32).clone(
cell_state=encoder_state)
training_decoder = tf.contrib.seq2seq.BasicDecoder(cell,
training_helper,
initial_state=decoder_initial_state,
output_layer=output_layer,
)
training_decoder_output, training_decoder_state, _ = tf.contrib.seq2seq.dynamic_decode(training_decoder,
impute_finished=True,
maximum_iterations=max_target_sequence_length)
with tf.variable_scope("decode", reuse=True):
def initialize_fn():
finished = tf.tile([False], [batch_size])
start_inputs = decoder_embed_input[:, 0]
return (finished, start_inputs)
def sample_fn(time, outputs, state):
del time, state
return tf.constant([0] * batch_size)
def next_inputs_fn(time, outputs, state, sample_ids):
del sample_ids
finished = time >= tf.shape(decoder_embed_input)[1]
all_finished = tf.reduce_all(finished)
next_inputs = tf.cond(
all_finished,
lambda: tf.zeros_like(outputs),
lambda: outputs)
return (finished, next_inputs, state)
predicting_helper = tf.contrib.seq2seq.CustomHelper(initialize_fn=initialize_fn,
sample_fn=sample_fn,
next_inputs_fn=next_inputs_fn)
if not use_attention:
predicting_decoder = tf.contrib.seq2seq.BasicDecoder(cell,
predicting_helper,
encoder_state,
output_layer)
else:
decoder_initial_state = cell.zero_state(batch_size=batch_size, dtype=tf.float32).clone(
cell_state=encoder_state)
predicting_decoder = tf.contrib.seq2seq.BasicDecoder(cell,
predicting_helper,
initial_state=decoder_initial_state,
output_layer=output_layer)
predicting_decoder_output, predicting_decoder_state, _ = tf.contrib.seq2seq.dynamic_decode(predicting_decoder,
impute_finished=True,
maximum_iterations=max_target_sequence_length)
return training_decoder_output, predicting_decoder_output, training_decoder_state, predicting_decoder_state
def process_decoder_input(data, batch_size):
'''
transform the target sequence (1,2,3,...,f) to (Z,1,2,3,...,f-1) as input to decoder in training
'''
ending = tf.strided_slice(data, [0, 0, 0], [batch_size, -1, series_length], [1, 1, 1])
decoder_input = tf.concat([tf.fill([batch_size, time_steps, series_length], 0.), ending], 1)
return decoder_input
def encoder_decoder(input_data, targets, lr, target_sequence_length,
max_target_sequence_length, source_sequence_length,
encoder_embedding_size, decoder_embedding_size,
rnn_size, num_layers):
encoding_embedding_size = 128
decoding_embedding_size = 128
encoder_output, encoder_state, weights, source_sequence_length = GE(input_data,
rnn_size,
num_layers,
source_sequence_length,
encoding_embedding_size)
decoder_input = process_decoder_input(targets, batch_size)
lstm_weights_1 = tf.Variable(weights[0], dtype=tf.float32, name='lstm_weights_layer_1')
lstm_weights_2 = tf.Variable(weights[3], dtype=tf.float32, name='lstm_weights_layer_2')
training_decoder_output, predicting_decoder_output, training_state, predicting_state = GD(
decoding_embedding_size,
num_layers,
rnn_size,
target_sequence_length,
source_sequence_length,
max_target_sequence_length,
encoder_output,
encoder_state,
decoder_input)
if use_attention:
attention_matrices = training_state.alignment_history.stack()
return training_decoder_output, predicting_decoder_output, lstm_weights_1, lstm_weights_2, attention_matrices
else:
return training_decoder_output, predicting_decoder_output, lstm_weights_1, lstm_weights_2
def train(folder_name, dim, train_dataset=False):
global series_length, time_steps, dataset, attention, Frozen, epochs
if train_dataset == 'KGBD':
input_data_, targets_ = get_data_KGBD(dim, fr=time_steps)
epochs = 150
elif train_dataset == 'IAS':
input_data_, targets_ = get_data_IAS(dim, fr=time_steps)
elif train_dataset == 'BIWI':
input_data_, targets_, t_input_data_, t_targets_ = get_data_BIWI(dim, fr=time_steps)
elif train_dataset == 'CASIA_B':
input_data_, targets_ = get_data_CASIA_B(dim, fr=time_steps)
if normalize:
# normalized joint 0
input_data_ = np.array(input_data_)
targets_ = np.array(targets_)
input_data_ = (input_data_ - np.tile(np.expand_dims(input_data_[:, :, 0], axis=-1), [1, 1, series_length]))
targets_ = (targets_ - np.tile(np.expand_dims(targets_[:, :, 0], axis=-1), [1, 1, series_length]))
else:
input_data_= np.array(input_data_).reshape([-1, time_steps*series_length])
targets_ = np.array(targets_).reshape([-1, time_steps*series_length])
# print(input_data_.shape, np.min(input_data_, axis=0).shape)
# print(targets_.shape, np.min(targets_, axis=0).shape)
# exit()
input_data_ = (input_data_ - np.min(input_data_, axis=0)) / \
(np.max(input_data_, axis=0) - np.min(input_data_, axis=0) + 1)
targets_ = (targets_ - np.min(targets_, axis=0)) / \
(np.max(targets_, axis=0) - np.min(targets_, axis=0) + 1)
input_data_ = np.array(input_data_).reshape([-1, time_steps, series_length])
targets_ = np.array(targets_).reshape([-1, time_steps, series_length])
input_data_ = input_data_.tolist()
targets_ = targets_.tolist()
# # if dim == 'z':
# print(input_data_.shape, targets_.shape)
# print(input_data_[0])
# return
elif train_dataset == 'KS20':
input_data_, targets_ = get_data_KS20(dim, fr=time_steps)
else:
raise Error('No dataset is chosen!')
if not Reverse:
targets_ = copy.deepcopy(input_data_)
if train_dataset == 'BIWI':
t_targets_ = copy.deepcopy(t_input_data_)
train_graph = tf.Graph()
encoding_embedding_size = 128
decoding_embedding_size = 128
with train_graph.as_default():
input_data, targets, lr, lr_decay_op, target_sequence_length, max_target_sequence_length, source_sequence_length, keep_prob = get_inputs()
if use_attention:
training_decoder_output, predicting_decoder_output, lstm_weights_1, lstm_weights_2, attention_matrices = encoder_decoder(input_data,
targets,
lr,
target_sequence_length,
max_target_sequence_length,
source_sequence_length,
encoding_embedding_size,
decoding_embedding_size,
rnn_size,
num_layers)
else:
training_decoder_output, predicting_decoder_output, lstm_weights_1, lstm_weights_2 = encoder_decoder(input_data,
targets,
lr,
target_sequence_length,
max_target_sequence_length,
source_sequence_length,
encoding_embedding_size,
decoding_embedding_size,
rnn_size,
num_layers)
training_decoder_output = training_decoder_output.rnn_output
predicting_output = tf.identity(predicting_decoder_output.rnn_output, name='predictions')
training_output = tf.identity(predicting_decoder_output.rnn_output, name='train_output')
train_loss = tf.reduce_mean(tf.nn.l2_loss(training_decoder_output - targets))
real_loss = tf.identity(train_loss, name='real_loss')
encoder_output = train_graph.get_tensor_by_name('rnn/transpose_1:0')
if use_attention:
attention_matrices = tf.identity(attention_matrices, name='train_attention_matrix')
# Locality-oriented attention loss
if attention == 'LA' or attention == 'LA-R':
objective_attention = np.ones(shape=[time_steps, time_steps])
for index, _ in enumerate(objective_attention.tolist()):
if not Reverse:
pt = index
else:
pt = time_steps - 1 - index
D = time_steps
objective_attention[index][pt] = 1
for i in range(1, D+1):
if pt + i <= time_steps - 1:
objective_attention[index][min(pt + i, time_steps - 1)] = np.exp(-(i)**2/(2*(D/2)**2))
if pt-i >= 0:
objective_attention[index][max(pt-i, 0)] = np.exp(-(i)**2/(2*(D/2)**2))
objective_attention[index][pt] = 1
objective_attention = np.tile(objective_attention, [batch_size, 1, 1])
objective_attention = objective_attention.swapaxes(1,0)
att_loss = tf.reduce_mean(tf.nn.l2_loss(attention_matrices - attention_matrices * objective_attention))
train_loss += att_loss
if Frozen == '0':
attention_trans = tf.transpose(attention_matrices, [1, 0, 2])
AGEs = tf.matmul(attention_trans, encoder_output)
AGEs = tf.reshape(AGEs, [batch_size, -1])
first_size = rnn_size * time_steps
# C_input = tf.placeholder(tf.float32, [None, first_size], name='C_input')
C_lr = tf.Variable(0.0005, trainable=False, dtype=tf.float32, name='learning_rate')
# learning_rate_1:0
W1 = tf.Variable(tf.random_normal([first_size, rnn_size]), name='W1')
b1 = tf.Variable(tf.zeros(shape=[rnn_size, ]), name='b1')
Wx_plus_b1 = tf.matmul(AGEs, W1) + b1
l1 = tf.nn.relu(Wx_plus_b1)
W = tf.Variable(tf.random_normal([rnn_size, rnn_size]), name='W')
b = tf.Variable(tf.zeros(shape=[rnn_size, ], name='b'))
contrast_v = tf.matmul(l1, W) + b
# print (encoder_output)
# print(attention_trans)
# print(AGEs)
# print(contrast_v)
# exit(1)
# add_2:0
# with tf.name_scope("C_train"):
t = temperature
C_optimizer = tf.train.AdamOptimizer(learning_rate, name="Adam_C")
z1 = contrast_v[1:]
z2 = contrast_v[:-1]
z = tf.concat((z1, z2), axis=0)
unorm_sim = tf.matmul(z, tf.transpose(z))
z_norm = tf.sqrt(tf.reduce_sum(tf.pow(z, 2), axis=1))
z_norm = tf.expand_dims(z_norm, axis=1)
norm_matrix = tf.matmul(z_norm, tf.transpose(z_norm))
sim = unorm_sim / (t * norm_matrix)
C_loss = tf.zeros(1)
sample_num = batch_size - 1
for i in range(sample_num):
C_loss = C_loss - tf.log(
tf.exp(sim[i, i + sample_num]) / (tf.reduce_sum(tf.exp(sim[i, :])) - tf.exp(sim[i, i])))
C_loss = C_loss - tf.log(tf.exp(sim[i + sample_num, i]) / (
tf.reduce_sum(tf.exp(sim[i + sample_num, :])) - tf.exp(
sim[i + sample_num, i + sample_num])))
C_loss = C_loss / (2 * sample_num)
# C_train_op = C_optimizer.minimize(C_loss)
train_loss += C_loss
# print (C_lr, C_loss, contrast_v)
# <tf.Variable 'learning_rate_1:0' shape=() dtype=float32_ref>
# Tensor("C_train/truediv_255:0", shape=(1,), dtype=float32)
# Tensor("add_2:0", shape=(128, 256), dtype=float32)
# exit(1)
else:
h_s = tf.reshape(encoder_output, [batch_size, -1])
first_size = rnn_size * time_steps
C_lr = tf.Variable(0.0005, trainable=False, dtype=tf.float32, name='learning_rate')
W1 = tf.Variable(tf.random_normal([first_size, rnn_size]), name='W1')
b1 = tf.Variable(tf.zeros(shape=[rnn_size, ]), name='b1')
Wx_plus_b1 = tf.matmul(h_s, W1) + b1
l1 = tf.nn.relu(Wx_plus_b1)
W = tf.Variable(tf.random_normal([rnn_size, rnn_size]), name='W')
b = tf.Variable(tf.zeros(shape=[rnn_size, ], name='b'))
contrast_v = tf.matmul(l1, W) + b
t = temperature
C_optimizer = tf.train.AdamOptimizer(learning_rate, name="Adam_C")
z1 = contrast_v[1:]
z2 = contrast_v[:-1]
z = tf.concat((z1, z2), axis=0)
unorm_sim = tf.matmul(z, tf.transpose(z))
z_norm = tf.sqrt(tf.reduce_sum(tf.pow(z, 2), axis=1))
z_norm = tf.expand_dims(z_norm, axis=1)
norm_matrix = tf.matmul(z_norm, tf.transpose(z_norm))
sim = unorm_sim / (t * norm_matrix)
C_loss = tf.zeros(1)
sample_num = batch_size - 1
for i in range(sample_num):
C_loss = C_loss - tf.log(
tf.exp(sim[i, i + sample_num]) / (tf.reduce_sum(tf.exp(sim[i, :])) - tf.exp(sim[i, i])))
C_loss = C_loss - tf.log(tf.exp(sim[i + sample_num, i]) / (
tf.reduce_sum(tf.exp(sim[i + sample_num, :])) - tf.exp(
sim[i + sample_num, i + sample_num])))
C_loss = C_loss / (2 * sample_num)
train_loss += C_loss
l2 = lambda_l2_reg * sum(
tf.nn.l2_loss(tf_var)
for tf_var in tf.trainable_variables()
if not ("noreg" in tf_var.name or "Bias" in tf_var.name)
)
# train_loss += att_loss
cost = tf.add(l2, train_loss, name='cost')
with tf.name_scope("optimization"):
# Optimizer
optimizer = tf.train.AdamOptimizer(lr, name='Adam')
# Gradient Clipping
gradients = optimizer.compute_gradients(cost)
capped_gradients = [(tf.clip_by_value(grad, -5., 5.), var) for grad, var in gradients if grad is not None]
train_op = optimizer.apply_gradients(capped_gradients, name='train_op')
# Contrast learning after freezing AGEs
if Frozen == '1':
first_size = rnn_size * time_steps
C_input = tf.placeholder(tf.float32, [None, first_size], name='C_input')
lr = tf.Variable(0.0005, trainable=False, dtype=tf.float32, name='learning_rate')
# learning_rate_1:0
W1 = tf.Variable(tf.random_normal([first_size, rnn_size]), name='W1')
b1 = tf.Variable(tf.zeros(shape=[rnn_size, ]), name='b1')
Wx_plus_b1 = tf.matmul(C_input, W1) + b1
l1 = tf.nn.relu(Wx_plus_b1)
W = tf.Variable(tf.random_normal([rnn_size, rnn_size]), name='W')
b = tf.Variable(tf.zeros(shape=[rnn_size, ], name='b'))
contrast_v = tf.matmul(l1, W) + b
# add_16:0
with tf.name_scope("C_train"):
t = temperature
C_optimizer = tf.train.AdamOptimizer(learning_rate, name="Adam_C")
z1 = contrast_v[1:]
z2 = contrast_v[:-1]
# cost = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(logits=pred, labels=y_input))
# (batch_size-1, )
z = tf.concat((z1, z2), axis=0)
unorm_sim = tf.matmul(z, tf.transpose(z))
z_norm = tf.sqrt(tf.reduce_sum(tf.pow(z, 2), axis=1))
z_norm = tf.expand_dims(z_norm, axis=1)
# z_norm = z_norm.unsqueeze(1)
norm_matrix = tf.matmul(z_norm, tf.transpose(z_norm))
# norm_matrix = z_norm.mm(z_norm.t())
sim = unorm_sim / (t * norm_matrix)
# print (z, unorm_sim, z_norm, norm_matrix, sim)
# exit(1)
# sim = unorm_sim / (self.t * norm_matrix)
# # print(sim[batch_size+2, 5])
# # print(sim[5, batch_size+2])
# # exit(1)
# loss = torch.zeros(1, requires_grad=True)
C_loss = tf.zeros(1)
# loss = Variable(loss.type(Tensor))
# sample_num = z1.size(0)
sample_num = batch_size - 1
# for i in range(sample_num):
# loss = loss - torch.log(
# torch.exp(sim[i, i + sample_num]) / (torch.sum(torch.exp(sim[i, :])) - torch.exp(sim[i, i])))
# loss = loss - torch.log(torch.exp(sim[i + sample_num, i]) / (
# torch.sum(torch.exp(sim[i + sample_num, :])) - torch.exp(
# sim[i + sample_num, i + sample_num])))
for i in range(sample_num):
C_loss = C_loss - tf.log(
tf.exp(sim[i, i + sample_num]) / (tf.reduce_sum(tf.exp(sim[i, :])) - tf.exp(sim[i, i])))
C_loss = C_loss - tf.log(tf.exp(sim[i + sample_num, i]) / (
tf.reduce_sum(tf.exp(sim[i + sample_num, :])) - tf.exp(
sim[i + sample_num, i + sample_num])))
C_loss = C_loss / (2 * sample_num)
# gradients = optimizer.compute_gradients(cost)
# capped_gradients = [(tf.clip_by_value(grad, -5., 5.), var) for grad, var in gradients if grad is not None]
C_train_op = C_optimizer.minimize(C_loss)
# correct_pred = tf.equal(tf.argmax(pred, 1),tf.argmax(y_input, 1))
# accuracy = tf.reduce_mean(tf.cast(correct_pred, tf.float32))
input_data_ = np.array(input_data_)
targets_ = np.array(targets_)
# just not permuated first
# permutation = np.random.permutation(input_data_.shape[0])
# input_data_= input_data_[permutation]
# targets_ = targets_[permutation]
train_source = input_data_
train_target = targets_
train_source = train_source.tolist()
train_target =train_target.tolist()
# input_data_ = input_data_.tolist()
# targets_ = targets_.tolist()
valid_source = train_source[:batch_size]
valid_target = train_target[:batch_size]
# print(len(train_source), len(train_target), len(valid_source), len(valid_target))
(valid_targets_batch, valid_sources_batch, valid_targets_lengths, valid_sources_lengths) = next(
get_batches(valid_target, valid_source, batch_size, source_pad_int=0, target_pad_int=0))
display_step = 50
checkpoint = "./" + folder_name + "/trained_model.ckpt"
best_checkpoint = './' + folder_name + '/best_model.ckpt'
with tf.Session(graph=train_graph, config=config) as sess:
sess.run(tf.global_variables_initializer())
print('Begin Training on Dimension [' + dim.upper() + ']')
train_loss = []
test_loss = []
c_train_loss = []
losses = [0, 0, 0]
loss_cnt = 0
conv_cnt = 0
best_val = 100000
over_flag = False
if use_attention:
alignment_history = train_graph.get_tensor_by_name('train_attention_matrix:0')
encoder_output = train_graph.get_tensor_by_name('rnn/transpose_1:0')
for epoch_i in range(1, epochs + 1):
if over_flag:
break
for batch_i, (targets_batch, sources_batch, targets_lengths, sources_lengths) in enumerate(
get_batches(train_target, train_source, batch_size, source_pad_int=0, target_pad_int=0)):
# print (sources_batch[5, 3:, :])
# print (sources_batch[6, :3, :])
# print (sources_batch[9, 3:, :])
# print (sources_batch[10, :3, :])
# exit(1)
if use_attention:
if Frozen == '0':
_, loss, c_loss, att, en_outputs, att_history = sess.run(
[train_op, real_loss, C_loss, attention_matrices, encoder_output, alignment_history],
{input_data: sources_batch,
targets: targets_batch,
lr: learning_rate,
C_lr: learning_rate,
target_sequence_length: targets_lengths,
source_sequence_length: sources_lengths,
keep_prob: 0.5})
else:
_, loss, att, en_outputs, att_history = sess.run([train_op, real_loss, attention_matrices, encoder_output, alignment_history],
{input_data: sources_batch,
targets: targets_batch,
lr: learning_rate,
target_sequence_length: targets_lengths,
source_sequence_length: sources_lengths,
keep_prob: 0.5})
att_batch = []
for index in range(en_outputs.shape[0]):
t1 = np.reshape(en_outputs[index], [-1]).tolist()
if use_attention and AGEs:
weights = att_history[:, index, :]
f_o = en_outputs[index, :, :]
att_op = np.matmul(weights, f_o)
# if manner == 'sc':
att_op = np.reshape(att_op, [-1]).tolist()
att_batch.append(att_op)
att_batch = np.array(att_batch)
_, c_loss, c_vec = sess.run([C_train_op, C_loss, contrast_v],
{C_input: att_batch,
lr: learning_rate
})
# print(c_loss)
# else:
# X.extend(att_op.tolist())
else:
_, loss = sess.run([train_op, real_loss],
{input_data: sources_batch,
targets: targets_batch,
lr: learning_rate,
target_sequence_length: targets_lengths,
source_sequence_length: sources_lengths,
keep_prob: 0.5})
# if batch_i % display_step == 0:
if epoch_i % 1 == 0:
if Frozen == '0':
validation_loss, c_loss = sess.run(
[real_loss, C_loss],
{input_data: valid_sources_batch,
targets: valid_targets_batch,
lr: learning_rate,
C_lr: learning_rate,
target_sequence_length: valid_targets_lengths,
source_sequence_length: valid_sources_lengths,
keep_prob: 1.0})
else:
validation_loss = sess.run(
[real_loss],
{input_data: valid_sources_batch,
targets: valid_targets_batch,
lr: learning_rate,
target_sequence_length: valid_targets_lengths,
source_sequence_length: valid_sources_lengths,
keep_prob: 1.0})
# if epoch_i % 25 == 0 and validation_loss[0] < best_val:
# saver = tf.train.Saver()
# saver.save(sess, best_checkpoint)
# print('The Best Model Saved Again')
# best_val = validation_loss[0]
train_loss.append(loss)
if Frozen == '1':
c_train_loss.append(c_loss[0])
test_loss.append(validation_loss[0])
losses[loss_cnt % 3] = validation_loss[0]
print(
'Epoch {:>3}/{} Batch {:>4}/{} - Training Loss: {:>6.3f} - Validation loss: {:>6.3f} - Contrastive loss: {:>6.3f}'
.format(epoch_i,
epochs,
batch_i,
len(train_source) // batch_size,
loss,
validation_loss[0],
c_loss[0]))
else:
c_train_loss.append(c_loss[0])
test_loss.append(validation_loss)
losses[loss_cnt % 3] = validation_loss
print(
'Epoch {:>3}/{} Batch {:>4}/{} - Training Loss: {:>6.3f} - Validation loss: {:>6.3f} - Contrastive loss: {:>6.3f}'
.format(epoch_i,
epochs,
batch_i,
len(train_source) // batch_size,
loss,
validation_loss,
c_loss[0]))
loss_cnt += 1
# print(losses)
if conv_cnt > 0 and validation_loss[0] >= max(losses):
over_flag = True
break
if (round(losses[(loss_cnt - 1) % 3], 5) == round(losses[loss_cnt % 3], 5)) and (round(losses[(loss_cnt - 2) % 3], 5)\
== round(losses[loss_cnt % 3], 5)) :
sess.run(lr_decay_op)
conv_cnt += 1
saver = tf.train.Saver()
saver.save(sess, checkpoint)
print('Model Trained and Saved')
np.save(folder_name + '/train_loss.npy', np.array(train_loss))
np.save(folder_name + '/test_loss.npy', np.array(test_loss))
np.save(folder_name + '/c_train_loss.npy', np.array(c_train_loss))
def encoder_classify(model_name, dimension, type, dataset):
global manner, transfer, LSTM_layer, gallery_num, view
if view != '':
view_num = view.split('_')[1]
number = ord(dimension) - ord('x') + 1
print('Run the gait encoding model to obtain CAGEs (%d / 3)' % number)
global epochs, series_length, attention, Frozen, C_reid
epochs = 200
if view != '':
view_dir = view + '/'
else:
if probe_type != '':
gallery_dir = probe_type.split('.')[1] + '/'
probe_dir = probe_type.split('.')[0] + '/'
# w_dir = probe_type + '/'
else:
view_dir = ''
_input_data = np.load(
'Datasets/' + frames_ps + dataset + '_test_npy_data/' + gallery_dir + 't_source_' + dimension + '_' + dataset + '_' + str(
time_steps) + '.npy')
if time_steps == 25:
_input_data = _input_data[:_input_data.shape[0] // (time_steps * series_length) * (time_steps * series_length)]
_input_data = _input_data.reshape([-1, time_steps, series_length])
if Model == 'rev_rec' or Model == 'rev_rec_plus':
_targets = np.load(
'Datasets/' + frames_ps + dataset + '_test_npy_data/' + gallery_dir + 't_target_' + dimension + '_' + dataset + '_' + str(
time_steps) + '.npy')
_targets = _targets.reshape([-1, time_steps, series_length])
# prediction
elif Model == 'prediction':
_targets = np.concatenate((_input_data[1:, :, :], _input_data[-1, :, :].reshape([1, time_steps, series_length])),
axis=0)
# _input_data = _input_data[:-1]
# permutation
elif Model == 'sorting':
_targets = copy.deepcopy(_input_data)
for i in range(_input_data.shape[0]):
permutation_ = np.random.permutation(time_steps)
_input_data[i] = _input_data[i, permutation_]
if dataset == 'IAS':
t_input_data = np.load(
'Datasets/' + frames_ps + dataset + '_test_npy_data/t_source_' + dimension + '_' + dataset + '-A_' + str(
time_steps) + '.npy')
t_input_data = t_input_data.reshape([-1, time_steps, series_length])
if Model == 'rev_rec' or Model == 'rev_rec_plus':
t_targets = np.load(