def create_model(input_var, input_shape, options):
conv_num_filters1 = 100
conv_num_filters2 = 150
conv_num_filters3 = 200
filter_size1 = 5
filter_size2 = 5
filter_size3 = 3
pool_size = 2
encode_size = options['BOTTLENECK']
dense_mid_size = options['DENSE']
pad_in = 'valid'
pad_out = 'full'
scaled_tanh = create_scaled_tanh()
input = InputLayer(shape=input_shape, input_var=input_var, name='input')
conv2d1 = Conv2DLayer(input, num_filters=conv_num_filters1, filter_size=filter_size1, pad=pad_in, name='conv2d1', nonlinearity=scaled_tanh)
maxpool2d2 = MaxPool2DLayer(conv2d1, pool_size=pool_size, name='maxpool2d2')
conv2d3 = Conv2DLayer(maxpool2d2, num_filters=conv_num_filters2, filter_size=filter_size2, pad=pad_in, name='conv2d3', nonlinearity=scaled_tanh)
maxpool2d4 = MaxPool2DLayer(conv2d3, pool_size=pool_size, name='maxpool2d4', pad=(1,0))
conv2d5 = Conv2DLayer(maxpool2d4, num_filters=conv_num_filters3, filter_size=filter_size3, pad=pad_in, name='conv2d5', nonlinearity=scaled_tanh)
reshape6 = ReshapeLayer(conv2d5, shape=([0], -1), name='reshape6') # 3000
reshape6_output = reshape6.output_shape[1]
dense7 = DenseLayer(reshape6, num_units=dense_mid_size, name='dense7', nonlinearity=scaled_tanh)
bottleneck = DenseLayer(dense7, num_units=encode_size, name='bottleneck', nonlinearity=linear)
# print_network(bottleneck)
dense8 = DenseLayer(bottleneck, num_units=dense_mid_size, W=bottleneck.W.T, name='dense8', nonlinearity=linear)
dense9 = DenseLayer(dense8, num_units=reshape6_output, W=dense7.W.T, nonlinearity=scaled_tanh, name='dense9')
reshape10 = ReshapeLayer(dense9, shape=([0], conv_num_filters3, 3, 5), name='reshape10') # 32 x 4 x 7
deconv2d11 = Deconv2DLayer(reshape10, conv2d5.input_shape[1], conv2d5.filter_size, stride=conv2d5.stride,
W=conv2d5.W, flip_filters=not conv2d5.flip_filters, name='deconv2d11', nonlinearity=scaled_tanh)
upscale2d12 = Upscale2DLayer(deconv2d11, scale_factor=pool_size, name='upscale2d12')
deconv2d13 = Deconv2DLayer(upscale2d12, conv2d3.input_shape[1], conv2d3.filter_size, stride=conv2d3.stride,
W=conv2d3.W, flip_filters=not conv2d3.flip_filters, name='deconv2d13', nonlinearity=scaled_tanh)
upscale2d14 = Upscale2DLayer(deconv2d13, scale_factor=pool_size, name='upscale2d14')
deconv2d15 = Deconv2DLayer(upscale2d14, conv2d1.input_shape[1], conv2d1.filter_size, stride=conv2d1.stride,
crop=(1, 0), W=conv2d1.W, flip_filters=not conv2d1.flip_filters, name='deconv2d14', nonlinearity=scaled_tanh)
reshape16 = ReshapeLayer(deconv2d15, ([0], -1), name='reshape16')
print_network(reshape16)
return reshape16
def main():
configure_theano()
options = parse_options()
config_file = options['config']
config = ConfigParser.ConfigParser()
config.read(config_file)
print('CLI options: {}'.format(options.items()))
print('Reading Config File: {}...'.format(config_file))
print(config.items('stream1'))
print(config.items('stream2'))
print(config.items('lstm_classifier'))
print(config.items('training'))
print('preprocessing dataset...')
# stream 1
s1_data = load_mat_file(config.get('stream1', 'data'))
s1_imagesize = tuple([int(d) for d in config.get('stream1', 'imagesize').split(',')])
s1 = config.get('stream1', 'model')
s1_inputdim = config.getint('stream1', 'input_dimensions')
s1_shape = config.get('stream1', 'shape')
s1_nonlinearities = config.get('stream1', 'nonlinearities')
# stream 2
s2_data = load_mat_file(config.get('stream2', 'data'))
s2_imagesize = tuple([int(d) for d in config.get('stream2', 'imagesize').split(',')])
s2 = config.get('stream2', 'model')
s2_inputdim = config.getint('stream2', 'input_dimensions')
s2_shape = config.get('stream2', 'shape')
s2_nonlinearities = config.get('stream2', 'nonlinearities')
# lstm classifier
fusiontype = config.get('lstm_classifier', 'fusiontype')
weight_init = options['weight_init'] if 'weight_init' in options else config.get('lstm_classifier', 'weight_init')
use_peepholes = options['use_peepholes'] if 'use_peepholes' in options else config.getboolean('lstm_classifier',
'use_peepholes')
output_classes = config.getint('lstm_classifier', 'output_classes')
output_classnames = config.get('lstm_classifier', 'output_classnames').split(',')
lstm_size = config.getint('lstm_classifier', 'lstm_size')
matlab_target_offset = config.getboolean('lstm_classifier', 'matlab_target_offset')
# capture training parameters
validation_window = int(options['validation_window']) \
if 'validation_window' in options else config.getint('training', 'validation_window')
num_epoch = int(options['num_epoch']) if 'num_epoch' in options else config.getint('training', 'num_epoch')
learning_rate = options['learning_rate'] if 'learning_rate' in options \
else config.getfloat('training', 'learning_rate')
epochsize = config.getint('training', 'epochsize')
batchsize = config.getint('training', 'batchsize')
weight_init_fn = las.init.GlorotUniform()
if weight_init == 'glorot':
weight_init_fn = las.init.GlorotUniform()
if weight_init == 'norm':
weight_init_fn = las.init.Normal(0.1)
if weight_init == 'uniform':
weight_init_fn = las.init.Uniform()
if weight_init == 'ortho':
weight_init_fn = las.init.Orthogonal()
train_subject_ids = read_data_split_file(config.get('training', 'train_subjects_file'))
val_subject_ids = read_data_split_file(config.get('training', 'val_subjects_file'))
test_subject_ids = read_data_split_file(config.get('training', 'test_subjects_file'))
s1_data_matrix = s1_data['dataMatrix'].astype('float32')
s2_data_matrix = s2_data['dataMatrix'].astype('float32')
targets_vec = s1_data['targetsVec'].reshape((-1,))
subjects_vec = s1_data['subjectsVec'].reshape((-1,))
vidlen_vec = s1_data['videoLengthVec'].reshape((-1,))
if matlab_target_offset:
targets_vec -= 1
s1_data_matrix = presplit_dataprocessing(s1_data_matrix, vidlen_vec, config, 'stream1', imagesize=s1_imagesize)
s2_data_matrix = presplit_dataprocessing(s2_data_matrix, vidlen_vec, config, 'stream2', imagesize=s2_imagesize)
s1_train_X, s1_train_y, s1_train_vidlens, s1_train_subjects, \
s1_val_X, s1_val_y, s1_val_vidlens, s1_val_subjects, \
s1_test_X, s1_test_y, s1_test_vidlens, s1_test_subjects = split_seq_data(s1_data_matrix, targets_vec, subjects_vec,
vidlen_vec, train_subject_ids,
val_subject_ids, test_subject_ids)
s2_train_X, s2_train_y, s2_train_vidlens, s2_train_subjects, \
s2_val_X, s2_val_y, s2_val_vidlens, s2_val_subjects, \
s2_test_X, s2_test_y, s2_test_vidlens, s2_test_subjects = split_seq_data(s2_data_matrix, targets_vec, subjects_vec,
vidlen_vec, train_subject_ids,
val_subject_ids, test_subject_ids)
s1_train_X, s1_val_X, s1_test_X = postsplit_datapreprocessing(s1_train_X, s1_val_X, s1_test_X, config, 'stream1')
s2_train_X, s2_val_X, s2_test_X = postsplit_datapreprocessing(s2_train_X, s2_val_X, s2_test_X, config, 'stream2')
ae1 = load_decoder(s1, s1_shape, s1_nonlinearities)
ae2 = load_decoder(s2, s2_shape, s2_nonlinearities)
# IMPT: the encoder was trained with fortan ordered images, so to visualize
# convert all the images to C order using reshape_images_order()
# output = dbn.predict(test_X)
# test_X = reshape_images_order(test_X, (26, 44))
# output = reshape_images_order(output, (26, 44))
# visualize_reconstruction(test_X[:36, :], output[:36, :], shape=(26, 44))
inputs1 = T.tensor3('inputs1', dtype='float32')
inputs2 = T.tensor3('inputs2', dtype='float32')
mask = T.matrix('mask', dtype='uint8')
targets = T.imatrix('targets')
print('constructing end to end model...')
network, l_fuse = adenet_v2_nodelta.create_model(ae1, ae2, (None, None, s1_inputdim), inputs1,
(None, None), mask,
(None, None, s2_inputdim), inputs2,
lstm_size, output_classes, fusiontype,
w_init_fn=weight_init_fn,
use_peepholes=use_peepholes)
print_network(network)
# draw_to_file(las.layers.get_all_layers(network), 'network.png')
print('compiling model...')
predictions = las.layers.get_output(network, deterministic=False)
all_params = las.layers.get_all_params(network, trainable=True)
cost = temporal_softmax_loss(predictions, targets, mask)
updates = adam(cost, all_params, learning_rate=learning_rate)
train = theano.function(
[inputs1, targets, mask, inputs2],
cost, updates=updates, allow_input_downcast=True)
compute_train_cost = theano.function([inputs1, targets, mask, inputs2],
cost, allow_input_downcast=True)
test_predictions = las.layers.get_output(network, deterministic=True)
test_cost = temporal_softmax_loss(test_predictions, targets, mask)
compute_test_cost = theano.function(
[inputs1, targets, mask, inputs2], test_cost, allow_input_downcast=True)
val_fn = theano.function([inputs1, mask, inputs2], test_predictions, allow_input_downcast=True)
# We'll train the network with 10 epochs of 30 minibatches each
print('begin training...')
cost_train = []
cost_val = []
class_rate = []
STRIP_SIZE = 3
val_window = circular_list(validation_window)
train_strip = np.zeros((STRIP_SIZE,))
best_val = float('inf')
best_cr = 0.0
datagen = gen_lstm_batch_random(s1_train_X, s1_train_y, s1_train_vidlens, batchsize=batchsize)
integral_lens = compute_integral_len(s1_train_vidlens)
val_datagen = gen_lstm_batch_random(s1_val_X, s1_val_y, s1_val_vidlens, batchsize=len(s1_val_vidlens))
test_datagen = gen_lstm_batch_random(s1_test_X, s1_test_y, s1_test_vidlens, batchsize=len(s1_test_vidlens))
# We'll use this "validation set" to periodically check progress
X_val, y_val, mask_val, idxs_val = next(val_datagen)
integral_lens_val = compute_integral_len(s1_val_vidlens)
X_diff_val = gen_seq_batch_from_idx(s2_val_X, idxs_val, s1_val_vidlens, integral_lens_val, np.max(s1_val_vidlens))
# we use the test set to check final classification rate
X_test, y_test, mask_test, idxs_test = next(test_datagen)
integral_lens_test = compute_integral_len(s1_test_vidlens)
X_diff_test = gen_seq_batch_from_idx(s2_test_X, idxs_test, s1_test_vidlens, integral_lens_test, np.max(s1_test_vidlens))
# reshape the targets for validation
y_val_evaluate = y_val
y_val = y_val.reshape((-1, 1)).repeat(mask_val.shape[-1], axis=-1)
for epoch in range(num_epoch):
time_start = time.time()
for i in range(epochsize):
X, y, m, batch_idxs = next(datagen)
# repeat targets based on max sequence len
y = y.reshape((-1, 1))
y = y.repeat(m.shape[-1], axis=-1)
X_diff = gen_seq_batch_from_idx(s2_train_X, batch_idxs,
s1_train_vidlens, integral_lens, np.max(s1_train_vidlens))
print_str = 'Epoch {} batch {}/{}: {} examples using adam'.format(
epoch + 1, i + 1, epochsize, len(X))
print(print_str, end='')
sys.stdout.flush()
train(X, y, m, X_diff)
print('\r', end='')
cost = compute_train_cost(X, y, m, X_diff)
val_cost = compute_test_cost(X_val, y_val, mask_val, X_diff_val)
cost_train.append(cost)
cost_val.append(val_cost)
train_strip[epoch % STRIP_SIZE] = cost
val_window.push(val_cost)
gl = 100 * (cost_val[-1] / np.min(cost_val) - 1)
pk = 1000 * (np.sum(train_strip) / (STRIP_SIZE * np.min(train_strip)) - 1)
pq = gl / pk
cr, val_conf = evaluate_model2(X_val, y_val_evaluate, mask_val, X_diff_val, val_fn)
class_rate.append(cr)
if val_cost < best_val:
best_val = val_cost
best_cr = cr
test_cr, test_conf = evaluate_model2(X_test, y_test, mask_test,
X_diff_test, val_fn)
print("Epoch {} train cost = {}, val cost = {}, "
"GL loss = {:.3f}, GQ = {:.3f}, CR = {:.3f}, Test CR= {:.3f} ({:.1f}sec)"
.format(epoch + 1, cost_train[-1], cost_val[-1], gl, pq, cr, test_cr, time.time() - time_start))
best_params = las.layers.get_all_param_values(network)
else:
print("Epoch {} train cost = {}, val cost = {}, "
"GL loss = {:.3f}, GQ = {:.3f}, CR = {:.3f} ({:.1f}sec)"
.format(epoch + 1, cost_train[-1], cost_val[-1], gl, pq, cr, time.time() - time_start))
if epoch >= validation_window and early_stop2(val_window, best_val, validation_window):
break
print('Final Model')
print('CR: {}, val loss: {}, Test CR: {}'.format(best_cr, best_val, test_cr))
# plot confusion matrix
table_str = plot_confusion_matrix(test_conf, output_classnames, fmt='pipe')
print('confusion matrix: ')
print(table_str)
if 'save_plot' in options:
prefix = options['save_plot']
plot_validation_cost(cost_train, cost_val, savefilename='{}.validloss.png'.format(prefix))
with open('{}.confmat.txt'.format(prefix), mode='a') as f:
f.write(table_str)
f.write('\n\n')
if 'write_results' in options:
print('writing results to {}'.format(options['write_results']))
results_file = options['write_results']
with open(results_file, mode='a') as f:
f.write('{},{},{}\n'.format(test_cr, best_cr, best_val))
if 'save_best' in options:
print('saving best model...')
las.layers.set_all_param_values(network, best_params)
save_model_params(network, options['save_best'])
print('best model saved to {}'.format(options['save_best']))
def main():
configure_theano()
options = parse_options()
config_file = options['config']
config = ConfigParser.ConfigParser()
config.read(config_file)
print('CLI options: {}'.format(options.items()))
print('Reading Config File: {}...'.format(config_file))
print(config.items('data'))
print(config.items('models'))
print(config.items('training'))
print('preprocessing dataset...')
data = load_mat_file(config.get('data', 'images'))
dct_data = load_mat_file(config.get('data', 'dct'))
ae_pretrained = config.get('models', 'pretrained')
ae_pretrained_diff = config.get('models', 'pretrained_diff')
fusiontype = config.get('models', 'fusiontype')
lstm_size = config.getint('models', 'lstm_size')
output_classes = config.getint('models', 'output_classes')
use_peepholes = options[
'use_peepholes'] if 'use_peepholes' in options else config.getboolean(
'models', 'use_peepholes')
use_blstm = config.getboolean('models', 'use_blstm')
delta_window = config.getint('models', 'delta_window')
input_dimensions = config.getint('models', 'input_dimensions')
# capture training parameters
validation_window = int(options['validation_window']) \
if 'validation_window' in options else config.getint('training', 'validation_window')
num_epoch = int(
options['num_epoch']) if 'num_epoch' in options else config.getint(
'training', 'num_epoch')
weight_init = options[
'weight_init'] if 'weight_init' in options else config.get(
'training', 'weight_init')
use_finetuning = config.getboolean('training', 'use_finetuning')
learning_rate = config.getfloat('training', 'learning_rate')
batchsize = config.getint('training', 'batchsize')
epochsize = config.getint('training', 'epochsize')
weight_init_fn = las.init.GlorotUniform()
if weight_init == 'glorot':
weight_init_fn = las.init.GlorotUniform()
if weight_init == 'norm':
weight_init_fn = las.init.Normal(0.1)
if weight_init == 'uniform':
weight_init_fn = las.init.Uniform()
if weight_init == 'ortho':
weight_init_fn = las.init.Orthogonal()
# 53 subjects, 70 utterances, 5 view angles
# s[x]_v[y]_u[z].mp4
# resized, height, width = (26, 44)
# ['dataMatrix', 'targetH', 'targetsPerVideoVec', 'videoLengthVec', '__header__', 'targetsVec',
# '__globals__', 'iterVec', 'filenamesVec', 'dataMatrixCells', 'subjectsVec', 'targetW', '__version__']
print(data.keys())
X = data['dataMatrix'].astype('float32')
y = data['targetsVec'].astype('int32')
y = y.reshape((len(y), ))
dct_feats = dct_data['dctFeatures'].astype('float32')
uniques = np.unique(y)
print('number of classifications: {}'.format(len(uniques)))
subjects = data['subjectsVec'].astype('int')
subjects = subjects.reshape((len(subjects), ))
video_lens = data['videoLengthVec'].astype('int')
video_lens = video_lens.reshape((len(video_lens, )))
# X = reorder_data(X, (26, 44), 'f', 'c')
# print('performing sequencewise mean image removal...')
# X = sequencewise_mean_image_subtraction(X, video_lens)
# visualize_images(X[550:650], (26, 44))
X_diff = compute_diff_images(X, video_lens)
# mean remove dct features
dct_feats = sequencewise_mean_image_subtraction(dct_feats, video_lens)
train_subject_ids = read_data_split_file('data/train.txt')
val_subject_ids = read_data_split_file('data/val.txt')
test_subject_ids = read_data_split_file('data/test.txt')
print('Train: {}'.format(train_subject_ids))
print('Validation: {}'.format(val_subject_ids))
print('Test: {}'.format(test_subject_ids))
train_X, train_y, train_dct, train_X_diff, train_vidlens, train_subjects, \
val_X, val_y, val_dct, val_X_diff, val_vidlens, val_subjects, \
test_X, test_y, test_dct, test_X_diff, test_vidlens, test_subjects = \
split_data(X, y, dct_feats, X_diff, subjects, video_lens, train_subject_ids, val_subject_ids, test_subject_ids)
assert train_X.shape[0] + val_X.shape[0] + test_X.shape[0] == len(X)
assert train_y.shape[0] + val_y.shape[0] + test_y.shape[0] == len(y)
assert train_vidlens.shape[0] + val_vidlens.shape[0] + test_vidlens.shape[
0] == len(video_lens)
assert train_subjects.shape[0] + val_vidlens.shape[
0] + test_subjects.shape[0] == len(subjects)
train_X = normalize_input(train_X, centralize=True)
val_X = normalize_input(val_X, centralize=True)
test_X = normalize_input(test_X, centralize=True)
print('loading pretrained encoder: {}...'.format(ae_pretrained))
ae = load_dbn(ae_pretrained)
print('loading pretrained encoder: {}...'.format(ae_pretrained_diff))
ae_diff = load_dbn(ae_pretrained_diff)
# IMPT: the encoder was trained with fortan ordered images, so to visualize
# convert all the images to C order using reshape_images_order()
# output = dbn.predict(test_X)
# test_X = reshape_images_order(test_X, (26, 44))
# output = reshape_images_order(output, (26, 44))
# visualize_reconstruction(test_X[:36, :], output[:36, :], shape=(26, 44))
window = T.iscalar('theta')
inputs = T.tensor3('inputs', dtype='float32')
inputs_diff = T.tensor3('inputs_diff', dtype='float32')
mask = T.matrix('mask', dtype='uint8')
targets = T.imatrix('targets')
print('constructing end to end model...')
if use_blstm:
network, l_fuse = adenet_v2_2.create_model(
ae, ae_diff, (None, None, input_dimensions), inputs, (None, None),
mask, (None, None, input_dimensions), inputs_diff, lstm_size,
window, output_classes, fusiontype, weight_init_fn, use_peepholes)
else:
network, l_fuse = adenet_v2_4.create_model(
ae, ae_diff, (None, None, input_dimensions), inputs, (None, None),
mask, (None, None, input_dimensions), inputs_diff, lstm_size,
window, output_classes, fusiontype, weight_init_fn, use_peepholes)
print_network(network)
# draw_to_file(las.layers.get_all_layers(network), 'network.png')
print('compiling model...')
predictions = las.layers.get_output(network, deterministic=False)
all_params = las.layers.get_all_params(network, trainable=True)
cost = temporal_softmax_loss(predictions, targets, mask)
updates = adam(cost, all_params, learning_rate=learning_rate)
train = theano.function([inputs, targets, mask, inputs_diff, window],
cost,
updates=updates,
allow_input_downcast=True)
compute_train_cost = theano.function(
[inputs, targets, mask, inputs_diff, window],
cost,
allow_input_downcast=True)
test_predictions = las.layers.get_output(network, deterministic=True)
test_cost = temporal_softmax_loss(test_predictions, targets, mask)
compute_test_cost = theano.function(
[inputs, targets, mask, inputs_diff, window],
test_cost,
allow_input_downcast=True)
val_fn = theano.function([inputs, mask, inputs_diff, window],
test_predictions,
allow_input_downcast=True)
# We'll train the network with 10 epochs of 30 minibatches each
print('begin training...')
cost_train = []
cost_val = []
class_rate = []
STRIP_SIZE = 3
val_window = circular_list(validation_window)
train_strip = np.zeros((STRIP_SIZE, ))
best_val = float('inf')
best_cr = 0.0
datagen = gen_lstm_batch_random(train_X,
train_y,
train_vidlens,
batchsize=batchsize)
integral_lens = compute_integral_len(train_vidlens)
val_datagen = gen_lstm_batch_random(val_X,
val_y,
val_vidlens,
batchsize=len(val_vidlens))
test_datagen = gen_lstm_batch_random(test_X,
test_y,
test_vidlens,
batchsize=len(test_vidlens))
# We'll use this "validation set" to periodically check progress
X_val, y_val, mask_val, idxs_val = next(val_datagen)
integral_lens_val = compute_integral_len(val_vidlens)
X_diff_val = gen_seq_batch_from_idx(val_X_diff, idxs_val, val_vidlens,
integral_lens_val, np.max(val_vidlens))
# we use the test set to check final classification rate
X_test, y_test, mask_test, idxs_test = next(test_datagen)
integral_lens_test = compute_integral_len(test_vidlens)
X_diff_test = gen_seq_batch_from_idx(test_X_diff, idxs_test, test_vidlens,
integral_lens_test,
np.max(test_vidlens))
# reshape the targets for validation
y_val_evaluate = y_val
y_val = y_val.reshape((-1, 1)).repeat(mask_val.shape[-1], axis=-1)
for epoch in range(num_epoch):
time_start = time.time()
for i in range(epochsize):
X, y, m, batch_idxs = next(datagen)
# repeat targets based on max sequence len
y = y.reshape((-1, 1))
y = y.repeat(m.shape[-1], axis=-1)
X_diff = gen_seq_batch_from_idx(train_X_diff, batch_idxs,
train_vidlens, integral_lens,
np.max(train_vidlens))
print_str = 'Epoch {} batch {}/{}: {} examples using adam'.format(
epoch + 1, i + 1, epochsize, len(X))
print(print_str, end='')
sys.stdout.flush()
train(X, y, m, X_diff, delta_window)
print('\r', end='')
cost = compute_train_cost(X, y, m, X_diff, delta_window)
val_cost = compute_test_cost(X_val, y_val, mask_val, X_diff_val,
delta_window)
cost_train.append(cost)
cost_val.append(val_cost)
train_strip[epoch % STRIP_SIZE] = cost
val_window.push(val_cost)
gl = 100 * (cost_val[-1] / np.min(cost_val) - 1)
pk = 1000 * (np.sum(train_strip) /
(STRIP_SIZE * np.min(train_strip)) - 1)
pq = gl / pk
cr, val_conf = evaluate_model2(X_val, y_val_evaluate, mask_val,
X_diff_val, delta_window, val_fn)
class_rate.append(cr)
if val_cost < best_val:
best_val = val_cost
best_cr = cr
if fusiontype == 'adasum':
adascale_param = las.layers.get_all_param_values(
l_fuse, scaling_param=True)
test_cr, test_conf = evaluate_model2(X_test, y_test, mask_test,
X_diff_test, delta_window,
val_fn)
print(
"Epoch {} train cost = {}, val cost = {}, "
"GL loss = {:.3f}, GQ = {:.3f}, CR = {:.3f}, Test CR= {:.3f} ({:.1f}sec)"
.format(epoch + 1, cost_train[-1], cost_val[-1], gl, pq, cr,
test_cr,
time.time() - time_start))
else:
print("Epoch {} train cost = {}, val cost = {}, "
"GL loss = {:.3f}, GQ = {:.3f}, CR = {:.3f} ({:.1f}sec)".
format(epoch + 1, cost_train[-1], cost_val[-1], gl, pq, cr,
time.time() - time_start))
if epoch >= validation_window and early_stop2(val_window, best_val,
validation_window):
break
phrases = ['p1', 'p2', 'p3', 'p4', 'p5', 'p6', 'p7', 'p8', 'p9', 'p10']
print('Final Model')
print('CR: {}, val loss: {}, Test CR: {}'.format(best_cr, best_val,
test_cr))
if fusiontype == 'adasum':
print("final scaling params: {}".format(adascale_param))
print('confusion matrix: ')
plot_confusion_matrix(test_conf, phrases, fmt='latex')
plot_validation_cost(cost_train, cost_val, savefilename='valid_cost')
if 'write_results' in options:
results_file = options['write_results']
with open(results_file, mode='a') as f:
f.write('{},{},{},{},{}\n'.format(validation_window, weight_init,
use_peepholes, use_blstm,
use_finetuning))
s = ','.join([str(v) for v in cost_train])
f.write('{}\n'.format(s))
s = ','.join([str(v) for v in cost_val])
f.write('{}\n'.format(s))
s = ','.join([str(v) for v in class_rate])
f.write('{}\n'.format(s))
f.write('{},{},{}\n'.format(fusiontype, best_cr, best_val))
def main():
configure_theano()
options = parse_options()
config_file = 'config/leave_one_out.ini'
print('loading config file: {}'.format(config_file))
config = ConfigParser.ConfigParser()
config.read(config_file)
print('preprocessing dataset...')
data = load_mat_file(config.get('data', 'images'))
dct_data = load_mat_file(config.get('data', 'dct'))
ae_pretrained = config.get('models', 'pretrained')
ae_finetuned = config.get('models', 'finetuned')
ae_finetuned_diff = config.get('models', 'finetuned_diff')
learning_rate = float(config.get('training', 'learning_rate'))
decay_rate = float(config.get('training', 'decay_rate'))
decay_start = int(config.get('training', 'decay_start'))
do_finetune = config.getboolean('training', 'do_finetune')
save_finetune = config.getboolean('training', 'save_finetune')
load_finetune = config.getboolean('training', 'load_finetune')
load_finetune_diff = config.getboolean('training', 'load_finetune_diff')
# 53 subjects, 70 utterances, 5 view angles
# s[x]_v[y]_u[z].mp4
# resized, height, width = (26, 44)
# ['dataMatrix', 'targetH', 'targetsPerVideoVec', 'videoLengthVec', '__header__', 'targetsVec',
# '__globals__', 'iterVec', 'filenamesVec', 'dataMatrixCells', 'subjectsVec', 'targetW', '__version__']
print(data.keys())
X = data['dataMatrix'].astype('float32')
y = data['targetsVec'].astype('int32')
y = y.reshape((len(y), ))
dct_feats = dct_data['dctFeatures'].astype('float32')
uniques = np.unique(y)
print('number of classifications: {}'.format(len(uniques)))
subjects = data['subjectsVec'].astype('int')
subjects = subjects.reshape((len(subjects), ))
video_lens = data['videoLengthVec'].astype('int')
video_lens = video_lens.reshape((len(video_lens, )))
# X = reorder_data(X, (26, 44), 'f', 'c')
# print('performing sequencewise mean image removal...')
# X = sequencewise_mean_image_subtraction(X, video_lens)
# visualize_images(X[550:650], (26, 44))
X_diff = compute_diff_images(X, video_lens)
# mean remove dct features
dct_feats = sequencewise_mean_image_subtraction(dct_feats, video_lens)
test_subject_ids = [options['test_subj']]
train_subject_ids = range(1, 54)
for subj in test_subject_ids:
train_subject_ids.remove(subj)
if 'results' in options:
results_file = options['results']
f = open(results_file, mode='a')
print(train_subject_ids)
print(test_subject_ids)
train_X, train_y, train_dct, train_X_diff, train_vidlens, train_subjects, \
test_X, test_y, test_dct, test_X_diff, test_vidlens, test_subjects = \
split_data(X, y, dct_feats, X_diff, subjects, video_lens, train_subject_ids, test_subject_ids)
assert train_X.shape[0] + test_X.shape[0] == len(X)
assert train_y.shape[0] + test_y.shape[0] == len(y)
assert train_vidlens.shape[0] + test_vidlens.shape[0] == len(video_lens)
assert train_subjects.shape[0] + test_subjects.shape[0] == len(subjects)
train_X = normalize_input(train_X, centralize=True)
test_X = normalize_input(test_X, centralize=True)
# featurewise normalize dct features
train_dct, dct_mean, dct_std = featurewise_normalize_sequence(train_dct)
test_dct = (test_dct - dct_mean) / dct_std
if do_finetune:
print('performing finetuning on pretrained encoder: {}'.format(
ae_pretrained))
ae = load_dbn(ae_pretrained)
ae.initialize()
ae.fit(train_X, train_X)
if save_finetune:
print('saving finetuned encoder: {}...'.format(ae_finetuned))
pickle.dump(ae, open(ae_finetuned, 'wb'))
if load_finetune:
print('loading finetuned encoder: {}...'.format(ae_finetuned))
ae = pickle.load(open(ae_finetuned, 'rb'))
ae.initialize()
if load_finetune_diff:
print('loading finetuned encoder: {}...'.format(ae_finetuned_diff))
ae_diff = pickle.load(open(ae_finetuned_diff, 'rb'))
ae_diff.initialize()
# IMPT: the encoder was trained with fortan ordered images, so to visualize
# convert all the images to C order using reshape_images_order()
# output = dbn.predict(test_X)
# test_X = reshape_images_order(test_X, (26, 44))
# output = reshape_images_order(output, (26, 44))
# visualize_reconstruction(test_X[:36, :], output[:36, :], shape=(26, 44))
window = T.iscalar('theta')
dct = T.tensor3('dct', dtype='float32')
inputs = T.tensor3('inputs', dtype='float32')
inputs_diff = T.tensor3('inputs_diff', dtype='float32')
mask = T.matrix('mask', dtype='uint8')
targets = T.ivector('targets')
lr = theano.shared(np.array(learning_rate, dtype=theano.config.floatX),
name='learning_rate')
lr_decay = np.array(decay_rate, dtype=theano.config.floatX)
print('constructing end to end model...')
'''
network = create_end_to_end_model(dbn, (None, None, 1144), inputs,
(None, None), mask, 250, window)
'''
network = adenet_v5.create_model(ae, ae_diff, (None, None, 1144), inputs,
(None, None), mask, (None, None, 90), dct,
(None, None, 1144), inputs_diff, 250,
window, 10)
print_network(network)
print('compiling model...')
predictions = las.layers.get_output(network, deterministic=False)
all_params = las.layers.get_all_params(network, trainable=True)
cost = T.mean(las.objectives.categorical_crossentropy(
predictions, targets))
updates = adadelta(cost, all_params, learning_rate=lr)
# updates = adagrad(cost, all_params, learning_rate=lr)
use_max_constraint = False
if use_max_constraint:
MAX_NORM = 4
for param in las.layers.get_all_params(network, regularizable=True):
if param.ndim > 1: # only apply to dimensions larger than 1, exclude biases
updates[param] = norm_constraint(
param,
MAX_NORM *
las.utils.compute_norms(param.get_value()).mean())
train = theano.function([inputs, targets, mask, dct, inputs_diff, window],
cost,
updates=updates,
allow_input_downcast=True)
compute_train_cost = theano.function(
[inputs, targets, mask, dct, inputs_diff, window],
cost,
allow_input_downcast=True)
test_predictions = las.layers.get_output(network, deterministic=True)
test_cost = T.mean(
las.objectives.categorical_crossentropy(test_predictions, targets))
compute_test_cost = theano.function(
[inputs, targets, mask, dct, inputs_diff, window],
test_cost,
allow_input_downcast=True)
val_fn = theano.function([inputs, mask, dct, inputs_diff, window],
test_predictions,
allow_input_downcast=True)
# We'll train the network with 10 epochs of 30 minibatches each
print('begin training...')
cost_train = []
cost_val = []
class_rate = []
NUM_EPOCHS = 10
EPOCH_SIZE = 120
BATCH_SIZE = 10
WINDOW_SIZE = 9
STRIP_SIZE = 3
MAX_LOSS = 0.2
VALIDATION_WINDOW = 4
val_window = circular_list(VALIDATION_WINDOW)
train_strip = np.zeros((STRIP_SIZE, ))
best_val = float('inf')
best_conf = None
best_cr = 0.0
datagen = gen_lstm_batch_random(train_X,
train_y,
train_vidlens,
batchsize=BATCH_SIZE)
val_datagen = gen_lstm_batch_random(test_X,
test_y,
test_vidlens,
batchsize=len(test_vidlens))
integral_lens = compute_integral_len(train_vidlens)
# We'll use this "validation set" to periodically check progress
X_val, y_val, mask_val, idxs_val = next(val_datagen)
integral_lens_val = compute_integral_len(test_vidlens)
dct_val = gen_seq_batch_from_idx(test_dct, idxs_val, test_vidlens,
integral_lens_val, np.max(test_vidlens))
X_diff_val = gen_seq_batch_from_idx(test_X_diff, idxs_val,
test_vidlens, integral_lens_val,
np.max(test_vidlens))
def early_stop(cost_window):
if len(cost_window) < 2:
return False
else:
curr = cost_window[0]
for idx, cost in enumerate(cost_window):
if curr < cost or idx == 0:
curr = cost
else:
return False
return True
for epoch in range(NUM_EPOCHS):
time_start = time.time()
for i in range(EPOCH_SIZE):
X, y, m, batch_idxs = next(datagen)
d = gen_seq_batch_from_idx(train_dct, batch_idxs, train_vidlens,
integral_lens, np.max(train_vidlens))
X_diff = gen_seq_batch_from_idx(train_X_diff, batch_idxs,
train_vidlens, integral_lens,
np.max(train_vidlens))
print_str = 'Epoch {} batch {}/{}: {} examples at learning rate = {:.4f}'.format(
epoch + 1, i + 1, EPOCH_SIZE, len(X), float(lr.get_value()))
print(print_str, end='')
sys.stdout.flush()
train(X, y, m, d, X_diff, WINDOW_SIZE)
print('\r', end='')
cost = compute_train_cost(X, y, m, d, X_diff, WINDOW_SIZE)
val_cost = compute_test_cost(X_val, y_val, mask_val, dct_val,
X_diff_val, WINDOW_SIZE)
cost_train.append(cost)
cost_val.append(val_cost)
train_strip[epoch % STRIP_SIZE] = cost
val_window.push(val_cost)
gl = 100 * (cost_val[-1] / np.min(cost_val) - 1)
pk = 1000 * (np.sum(train_strip) /
(STRIP_SIZE * np.min(train_strip)) - 1)
pq = gl / pk
cr, val_conf = evaluate_model(X_val, y_val, mask_val, dct_val,
X_diff_val, WINDOW_SIZE, val_fn)
class_rate.append(cr)
print(
"Epoch {} train cost = {}, validation cost = {}, "
"generalization loss = {:.3f}, GQ = {:.3f}, classification rate = {:.3f} ({:.1f}sec)"
.format(epoch + 1, cost_train[-1], cost_val[-1], gl, pq, cr,
time.time() - time_start))
if val_cost < best_val:
best_val = val_cost
best_conf = val_conf
best_cr = cr
if epoch >= VALIDATION_WINDOW and early_stop(val_window):
break
# learning rate decay
if epoch >= decay_start - 1:
lr.set_value(lr.get_value() * lr_decay)
phrases = ['p1', 'p2', 'p3', 'p4', 'p5', 'p6', 'p7', 'p8', 'p9', 'p10']
print('Final Model')
print('classification rate: {}, validation loss: {}'.format(
best_cr, best_val))
print('confusion matrix: ')
plot_confusion_matrix(best_conf, phrases, fmt='grid')
plot_validation_cost(cost_train,
cost_val,
class_rate,
savefilename='valid_cost')
if 'results' in options:
print('writing to results file: {}...'.format(options['results']))
f.write('{}, {}, {}\n'.format(test_subject_ids[0], best_cr, best_val))
f.close()
def main():
configure_theano()
options = parse_options()
config_file = options['config']
config = ConfigParser.ConfigParser()
config.read(config_file)
print('CLI options: {}'.format(options.items()))
print('Reading Config File: {}...'.format(config_file))
print(config.items('data'))
print(config.items('models'))
print(config.items('training'))
print('preprocessing dataset...')
data = load_mat_file(config.get('data', 'images'))
dct_data = load_mat_file(config.get('data', 'dct'))
ae_pretrained = config.get('models', 'pretrained')
ae_finetuned = config.get('models', 'finetuned')
ae_finetuned_diff = config.get('models', 'finetuned_diff')
fusiontype = config.get('models', 'fusiontype')
learning_rate = float(config.get('training', 'learning_rate'))
decay_rate = float(config.get('training', 'decay_rate'))
decay_start = int(config.get('training', 'decay_start'))
do_finetune = config.getboolean('training', 'do_finetune')
save_finetune = config.getboolean('training', 'save_finetune')
load_finetune = config.getboolean('training', 'load_finetune')
load_finetune_diff = config.getboolean('training', 'load_finetune_diff')
savemodel = config.getboolean('training', 'savemodel')
t = data['filenamesVec']
target_filenames = list()
for r in t:
for j in r:
target_filenames.append(str(j[0]))
# 53 subjects, 70 utterances, 5 view angles
# s[x]_v[y]_u[z].mp4
# resized, height, width = (26, 44)
# ['dataMatrix', 'targetH', 'targetsPerVideoVec', 'videoLengthVec', '__header__', 'targetsVec',
# '__globals__', 'iterVec', 'filenamesVec', 'dataMatrixCells', 'subjectsVec', 'targetW', '__version__']
print(data.keys())
X = data['dataMatrix'].astype('float32')
y = data['targetsVec'].astype('int32')
y = y.reshape((len(y), ))
dct_feats = dct_data['dctFeatures'].astype('float32')
uniques = np.unique(y)
print('number of classifications: {}'.format(len(uniques)))
subjects = data['subjectsVec'].astype('int')
subjects = subjects.reshape((len(subjects), ))
video_lens = data['videoLengthVec'].astype('int')
video_lens = video_lens.reshape((len(video_lens, )))
# X = reorder_data(X, (26, 44), 'f', 'c')
# print('performing sequencewise mean image removal...')
# X = sequencewise_mean_image_subtraction(X, video_lens)
# visualize_images(X[550:650], (26, 44))
X_diff = compute_diff_images(X, video_lens)
# mean remove dct features
dct_feats = sequencewise_mean_image_subtraction(dct_feats, video_lens)
train_subject_ids = read_data_split_file('data/train.txt')
val_subject_ids = read_data_split_file('data/val.txt')
test_subject_ids = read_data_split_file('data/test.txt')
print('Train: {}'.format(train_subject_ids))
print('Validation: {}'.format(val_subject_ids))
print('Test: {}'.format(test_subject_ids))
train_X, train_y, train_dct, train_X_diff, train_vidlens, train_subjects, train_filenames, \
val_X, val_y, val_dct, val_X_diff, val_vidlens, val_subjects, val_filenames, \
test_X, test_y, test_dct, test_X_diff, test_vidlens, test_subjects, test_filenames = \
split_data(X, y, dct_feats, X_diff, subjects, video_lens, train_subject_ids,
val_subject_ids, test_subject_ids, target_filenames)
assert train_X.shape[0] + val_X.shape[0] + test_X.shape[0] == len(X)
assert train_y.shape[0] + val_y.shape[0] + test_y.shape[0] == len(y)
assert train_vidlens.shape[0] + val_vidlens.shape[0] + test_vidlens.shape[
0] == len(video_lens)
assert train_subjects.shape[0] + val_vidlens.shape[
0] + test_subjects.shape[0] == len(subjects)
train_X = normalize_input(train_X, centralize=True)
val_X = normalize_input(val_X, centralize=True)
test_X = normalize_input(test_X, centralize=True)
# featurewise normalize dct features
train_dct, dct_mean, dct_std = featurewise_normalize_sequence(train_dct)
val_dct = (val_dct - dct_mean) / dct_std
test_dct = (test_dct - dct_mean) / dct_std
# IMPT: the encoder was trained with fortan ordered images, so to visualize
# convert all the images to C order using reshape_images_order()
# output = dbn.predict(test_X)
# test_X = reshape_images_order(test_X, (26, 44))
# output = reshape_images_order(output, (26, 44))
# visualize_reconstruction(test_X[:36, :], output[:36, :], shape=(26, 44))
if load_finetune:
print('loading finetuned encoder: {}...'.format(ae_finetuned))
ae = pickle.load(open(ae_finetuned, 'rb'))
ae.initialize()
if load_finetune_diff:
print('loading finetuned encoder: {}...'.format(ae_finetuned_diff))
ae_diff = pickle.load(open(ae_finetuned_diff, 'rb'))
ae_diff.initialize()
window = T.iscalar('theta')
dct = T.tensor3('dct', dtype='float32')
inputs = T.tensor3('inputs', dtype='float32')
inputs_diff = T.tensor3('inputs_diff', dtype='float32')
mask = T.matrix('mask', dtype='uint8')
targets = T.ivector('targets')
print('loading end to end model...')
network, l_fuse = adenet_v3.create_model(ae, ae_diff, (None, None, 1144),
inputs, (None, None), mask,
(None, None, 90), dct,
(None, None, 1144), inputs_diff,
250, window, 10, fusiontype)
all_param_values = load_model('models/3stream.dat')
all_params = las.layers.get_all_params(network)
for p, v in zip(all_params, all_param_values):
p.set_value(v)
print_network(network)
print('compiling model...')
'''
train = theano.function(
[inputs, targets, mask, dct, inputs_diff, window],
cost, updates=updates, allow_input_downcast=True)
compute_train_cost = theano.function([inputs, targets, mask, dct, inputs_diff, window],
cost, allow_input_downcast=True)
'''
test_predictions = las.layers.get_output(network, deterministic=True)
test_cost = T.mean(
las.objectives.categorical_crossentropy(test_predictions, targets))
val_fn = theano.function([inputs, mask, dct, inputs_diff, window],
test_predictions,
allow_input_downcast=True)
datagen = gen_lstm_batch_random(train_X,
train_y,
train_vidlens,
batchsize=10)
integral_lens = compute_integral_len(train_vidlens)
val_datagen = gen_lstm_batch_random(val_X,
val_y,
val_vidlens,
batchsize=len(val_vidlens))
test_datagen = gen_lstm_batch_random(test_X,
test_y,
test_vidlens,
batchsize=len(test_vidlens))
# We'll use this "validation set" to periodically check progress
X_val, y_val, mask_val, idxs_val = next(val_datagen)
integral_lens_val = compute_integral_len(val_vidlens)
dct_val = gen_seq_batch_from_idx(val_dct, idxs_val, val_vidlens,
integral_lens_val, np.max(val_vidlens))
X_diff_val = gen_seq_batch_from_idx(val_X_diff, idxs_val, val_vidlens,
integral_lens_val, np.max(val_vidlens))
# we use the test set to check final classification rate
X_test, y_test, mask_test, idxs_test = next(test_datagen)
integral_lens_test = compute_integral_len(test_vidlens)
dct_test = gen_seq_batch_from_idx(test_dct, idxs_test, test_vidlens,
integral_lens_test, np.max(test_vidlens))
X_diff_test = gen_seq_batch_from_idx(test_X_diff, idxs_test, test_vidlens,
integral_lens_test,
np.max(test_vidlens))
raw_input("Press Enter to start demo...")
for idx in range(len(X_test)):
videofile = '../examples/data/{}'.format(
test_filenames[idxs_test[idx]])
print('video file: {}'.format(videofile))
cap = cv2.VideoCapture(videofile)
while cap.isOpened():
ret, frame = cap.read()
if ret:
frame = cv2.resize(frame,
None,
fx=0.5,
fy=0.5,
interpolation=cv2.INTER_LINEAR)
gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
cv2.imshow('frame', gray)
else:
break
if cv2.waitKey(1) & 0xFF == ord('q'):
break
pred = val_fn(X_test[idx:idx + 1], mask_test[idx:idx + 1],
dct_test[idx:idx + 1], X_diff_test[idx:idx + 1], 9)
pred_idx = np.argmax(pred, axis=1)
print('Prediction: {}, Target: {}'.format(get_phrase(pred_idx[0]),
get_phrase(y_test[idx])))
raw_input("Press Enter to continue...")
cap.release()
cv2.destroyAllWindows()
def main():
configure_theano()
options = parse_options()
config_file = options['config']
config = ConfigParser.ConfigParser()
config.read(config_file)
print('CLI options: {}'.format(options.items()))
print('Reading Config File: {}...'.format(config_file))
print(config.items('data'))
print(config.items('models'))
print(config.items('training'))
print('preprocessing dataset...')
data = load_mat_file(config.get('data', 'images'))
dct_data = load_mat_file(config.get('data', 'dct'))
ae_finetuned = config.get('models', 'finetuned')
ae_finetuned_diff = config.get('models', 'finetuned_diff')
fusiontype = config.get('models', 'fusiontype')
learning_rate = float(config.get('training', 'learning_rate'))
decay_rate = float(config.get('training', 'decay_rate'))
decay_start = int(config.get('training', 'decay_start'))
load_finetune = config.getboolean('training', 'load_finetune')
load_finetune_diff = config.getboolean('training', 'load_finetune_diff')
train_vidlens = data['trVideoLengthVec'].astype('int').reshape((-1, ))
val_vidlens = data['valVideoLengthVec'].astype('int').reshape((-1, ))
test_vidlens = data['testVideoLengthVec'].astype('int').reshape((-1, ))
train_X = data['trData'].astype('float32')
val_X = data['valData'].astype('float32')
test_X = data['testData'].astype('float32')
train_dct = dct_data['trDctFeatures'].astype('float32')
val_dct = dct_data['valDctFeatures'].astype('float32')
test_dct = dct_data['testDctFeatures'].astype('float32')
train_X_diff = compute_diff_images(train_X, train_vidlens)
val_X_diff = compute_diff_images(val_X, val_vidlens)
test_X_diff = compute_diff_images(test_X, test_vidlens)
train_y = data['trTargetsVec'].astype('int').reshape(
(-1, )) + 1 # +1 to handle the -1 introduced in lstm_gendata
val_y = data['valTargetsVec'].astype('int').reshape((-1, )) + 1
test_y = data['testTargetsVec'].astype('int').reshape((-1, )) + 1
# featurewise normalize dct features
train_dct, dct_mean, dct_std = featurewise_normalize_sequence(train_dct)
val_dct = (val_dct - dct_mean) / dct_std
test_dct = (test_dct - dct_mean) / dct_std
if load_finetune:
print('loading finetuned encoder: {}...'.format(ae_finetuned))
ae = pickle.load(open(ae_finetuned, 'rb'))
ae.initialize()
if load_finetune_diff:
print('loading finetuned encoder: {}...'.format(ae_finetuned_diff))
ae_diff = pickle.load(open(ae_finetuned_diff, 'rb'))
ae_diff.initialize()
# IMPT: the encoder was trained with fortan ordered images, so to visualize
# convert all the images to C order using reshape_images_order()
# output = dbn.predict(test_X)
# test_X = reshape_images_order(test_X, (26, 44))
# output = reshape_images_order(output, (26, 44))
# visualize_reconstruction(test_X[:36, :], output[:36, :], shape=(26, 44))
window = T.iscalar('theta')
dct = T.tensor3('dct', dtype='float32')
inputs = T.tensor3('inputs', dtype='float32')
inputs_diff = T.tensor3('inputs_diff', dtype='float32')
mask = T.matrix('mask', dtype='uint8')
targets = T.ivector('targets')
lr = theano.shared(np.array(learning_rate, dtype=theano.config.floatX),
name='learning_rate')
lr_decay = np.array(decay_rate, dtype=theano.config.floatX)
print('constructing end to end model...')
network, l_fuse = adenet_v3.create_model(ae, ae_diff, (None, None, 1500),
inputs, (None, None), mask,
(None, None, 90), dct,
(None, None, 1500), inputs_diff,
250, window, 10, fusiontype)
print_network(network)
# draw_to_file(las.layers.get_all_layers(network), 'network.png')
print('compiling model...')
predictions = las.layers.get_output(network, deterministic=False)
all_params = las.layers.get_all_params(network, trainable=True)
cost = T.mean(las.objectives.categorical_crossentropy(
predictions, targets))
updates = adadelta(cost, all_params, learning_rate=lr)
# updates = adagrad(cost, all_params, learning_rate=lr)
train = theano.function([inputs, targets, mask, dct, inputs_diff, window],
cost,
updates=updates,
allow_input_downcast=True)
compute_train_cost = theano.function(
[inputs, targets, mask, dct, inputs_diff, window],
cost,
allow_input_downcast=True)
test_predictions = las.layers.get_output(network, deterministic=True)
test_cost = T.mean(
las.objectives.categorical_crossentropy(test_predictions, targets))
compute_test_cost = theano.function(
[inputs, targets, mask, dct, inputs_diff, window],
test_cost,
allow_input_downcast=True)
val_fn = theano.function([inputs, mask, dct, inputs_diff, window],
test_predictions,
allow_input_downcast=True)
# We'll train the network with 10 epochs of 30 minibatches each
print('begin training...')
cost_train = []
cost_val = []
class_rate = []
NUM_EPOCHS = 30
EPOCH_SIZE = 45
BATCH_SIZE = 20
WINDOW_SIZE = 9
STRIP_SIZE = 3
MAX_LOSS = 0.2
VALIDATION_WINDOW = 4
val_window = circular_list(VALIDATION_WINDOW)
train_strip = np.zeros((STRIP_SIZE, ))
best_val = float('inf')
best_conf = None
best_cr = 0.0
datagen = gen_lstm_batch_random(train_X,
train_y,
train_vidlens,
batchsize=BATCH_SIZE)
integral_lens = compute_integral_len(train_vidlens)
val_datagen = gen_lstm_batch_random(val_X,
val_y,
val_vidlens,
batchsize=len(val_vidlens))
test_datagen = gen_lstm_batch_random(test_X,
test_y,
test_vidlens,
batchsize=len(test_vidlens))
# We'll use this "validation set" to periodically check progress
X_val, y_val, mask_val, idxs_val = next(val_datagen)
integral_lens_val = compute_integral_len(val_vidlens)
dct_val = gen_seq_batch_from_idx(val_dct, idxs_val, val_vidlens,
integral_lens_val, np.max(val_vidlens))
X_diff_val = gen_seq_batch_from_idx(val_X_diff, idxs_val, val_vidlens,
integral_lens_val, np.max(val_vidlens))
# we use the test set to check final classification rate
X_test, y_test, mask_test, idxs_test = next(test_datagen)
integral_lens_test = compute_integral_len(test_vidlens)
dct_test = gen_seq_batch_from_idx(test_dct, idxs_test, test_vidlens,
integral_lens_test, np.max(test_vidlens))
X_diff_test = gen_seq_batch_from_idx(test_X_diff, idxs_test, test_vidlens,
integral_lens_test,
np.max(test_vidlens))
def early_stop(cost_window):
if len(cost_window) < 2:
return False
else:
curr = cost_window[0]
for idx, cost in enumerate(cost_window):
if curr < cost or idx == 0:
curr = cost
else:
return False
return True
for epoch in range(NUM_EPOCHS):
time_start = time.time()
for i in range(EPOCH_SIZE):
X, y, m, batch_idxs = next(datagen)
d = gen_seq_batch_from_idx(train_dct, batch_idxs, train_vidlens,
integral_lens, np.max(train_vidlens))
X_diff = gen_seq_batch_from_idx(train_X_diff, batch_idxs,
train_vidlens, integral_lens,
np.max(train_vidlens))
print_str = 'Epoch {} batch {}/{}: {} examples at learning rate = {:.4f}'.format(
epoch + 1, i + 1, EPOCH_SIZE, len(X), float(lr.get_value()))
print(print_str, end='')
sys.stdout.flush()
train(X, y, m, d, X_diff, WINDOW_SIZE)
print('\r', end='')
cost = compute_train_cost(X, y, m, d, X_diff, WINDOW_SIZE)
val_cost = compute_test_cost(X_val, y_val, mask_val, dct_val,
X_diff_val, WINDOW_SIZE)
cost_train.append(cost)
cost_val.append(val_cost)
train_strip[epoch % STRIP_SIZE] = cost
val_window.push(val_cost)
gl = 100 * (cost_val[-1] / np.min(cost_val) - 1)
pk = 1000 * (np.sum(train_strip) /
(STRIP_SIZE * np.min(train_strip)) - 1)
pq = gl / pk
cr, val_conf = evaluate_model(X_val, y_val, mask_val, dct_val,
X_diff_val, WINDOW_SIZE, val_fn)
class_rate.append(cr)
if val_cost < best_val:
best_val = val_cost
best_cr = cr
if fusiontype == 'adasum':
adascale_param = las.layers.get_all_param_values(
l_fuse, scaling_param=True)
test_cr, test_conf = evaluate_model(X_test, y_test, mask_test,
dct_test, X_diff_test,
WINDOW_SIZE, val_fn)
print(
"Epoch {} train cost = {}, val cost = {}, "
"GL loss = {:.3f}, GQ = {:.3f}, CR = {:.3f}, Test CR= {:.3f} ({:.1f}sec)"
.format(epoch + 1, cost_train[-1], cost_val[-1], gl, pq, cr,
test_cr,
time.time() - time_start))
else:
print("Epoch {} train cost = {}, val cost = {}, "
"GL loss = {:.3f}, GQ = {:.3f}, CR = {:.3f} ({:.1f}sec)".
format(epoch + 1, cost_train[-1], cost_val[-1], gl, pq, cr,
time.time() - time_start))
if epoch >= VALIDATION_WINDOW and early_stop(val_window):
break
# learning rate decay
if epoch + 1 >= decay_start:
lr.set_value(lr.get_value() * lr_decay)
numbers = ['0', '1', '2', '3', '4', '5', '6', '7', '8', '9']
print('Final Model')
print('CR: {}, val loss: {}, Test CR: {}'.format(best_cr, best_val,
test_cr))
if fusiontype == 'adasum':
print("final scaling params: {}".format(adascale_param))
print('confusion matrix: ')
plot_confusion_matrix(test_conf, numbers, fmt='latex')
plot_validation_cost(cost_train, cost_val, savefilename='valid_cost')
if options['write_results']:
results_file = options['write_results']
with open(results_file, mode='a') as f:
f.write('{},{},{}\n'.format(fusiontype, test_cr, best_val))
def main():
configure_theano()
options = parse_options()
config_file = options['config']
config = ConfigParser.ConfigParser()
config.read(config_file)
print('CLI options: {}'.format(options.items()))
print('Reading Config File: {}...'.format(config_file))
print(config.items('stream1'))
print(config.items('stream2'))
print(config.items('stream3'))
print(config.items('lstm_classifier'))
print(config.items('training'))
print('preprocessing dataset...')
# stream 1
s1_data = load_mat_file(config.get('stream1', 'data'))
s1_imagesize = tuple(
[int(d) for d in config.get('stream1', 'imagesize').split(',')])
s1 = config.get('stream1', 'model')
s1_inputdim = config.getint('stream1', 'input_dimensions')
s1_shape = config.get('stream1', 'shape')
s1_nonlinearities = config.get('stream1', 'nonlinearities')
# stream 2
s2_data = load_mat_file(config.get('stream2', 'data'))
s2_imagesize = tuple(
[int(d) for d in config.get('stream2', 'imagesize').split(',')])
s2 = config.get('stream2', 'model')
s2_inputdim = config.getint('stream2', 'input_dimensions')
s2_shape = config.get('stream2', 'shape')
s2_nonlinearities = config.get('stream2', 'nonlinearities')
# stream 3
s3_data = load_mat_file(config.get('stream3', 'data'))
s3_imagesize = tuple(
[int(d) for d in config.get('stream3', 'imagesize').split(',')])
s3 = config.get('stream3', 'model')
s3_inputdim = config.getint('stream3', 'input_dimensions')
s3_shape = config.get('stream3', 'shape')
s3_nonlinearities = config.get('stream3', 'nonlinearities')
# lstm classifier
fusiontype = config.get('lstm_classifier', 'fusiontype')
weight_init = options[
'weight_init'] if 'weight_init' in options else config.get(
'lstm_classifier', 'weight_init')
use_peepholes = options[
'use_peepholes'] if 'use_peepholes' in options else config.getboolean(
'lstm_classifier', 'use_peepholes')
windowsize = config.getint('lstm_classifier', 'windowsize')
output_classes = config.getint('lstm_classifier', 'output_classes')
output_classnames = config.get('lstm_classifier',
'output_classnames').split(',')
lstm_size = config.getint('lstm_classifier', 'lstm_size')
matlab_target_offset = config.getboolean('lstm_classifier',
'matlab_target_offset')
use_dropout = config.getboolean('lstm_classifier', 'use_dropout')
# capture training parameters
validation_window = int(options['validation_window']) \
if 'validation_window' in options else config.getint('training', 'validation_window')
num_epoch = int(
options['num_epoch']) if 'num_epoch' in options else config.getint(
'training', 'num_epoch')
learning_rate = options['learning_rate'] if 'learning_rate' in options \
else config.getfloat('training', 'learning_rate')
epochsize = config.getint('training', 'epochsize')
batchsize = config.getint('training', 'batchsize')
weight_init_fn = las.init.GlorotUniform()
if weight_init == 'glorot':
weight_init_fn = las.init.GlorotUniform()
if weight_init == 'norm':
weight_init_fn = las.init.Normal(0.1)
if weight_init == 'uniform':
weight_init_fn = las.init.Uniform()
if weight_init == 'ortho':
weight_init_fn = las.init.Orthogonal()
train_subject_ids = read_data_split_file(
config.get('training', 'train_subjects_file'))
val_subject_ids = read_data_split_file(
config.get('training', 'val_subjects_file'))
test_subject_ids = read_data_split_file(
config.get('training', 'test_subjects_file'))
s1_data_matrix = s1_data['dataMatrix'].astype('float32')
s2_data_matrix = s2_data['dataMatrix'].astype('float32')
s3_data_matrix = s3_data['dataMatrix'].astype('float32')
targets_vec = s1_data['targetsVec'].reshape((-1, ))
subjects_vec = s1_data['subjectsVec'].reshape((-1, ))
vidlen_vec = s1_data['videoLengthVec'].reshape((-1, ))
force_align_data = config.getboolean('stream1', 'force_align_data')
if matlab_target_offset:
targets_vec -= 1
s1_data_matrix = presplit_dataprocessing(s1_data_matrix,
vidlen_vec,
config,
'stream1',
imagesize=s1_imagesize)
s2_data_matrix = presplit_dataprocessing(s2_data_matrix,
vidlen_vec,
config,
'stream2',
imagesize=s2_imagesize)
s3_data_matrix = presplit_dataprocessing(s3_data_matrix,
vidlen_vec,
config,
'stream3',
imagesize=s3_imagesize)
if force_align_data:
s2_targets_vec = s2_data['targetsVec'].reshape((-1, ))
s2_vidlen_vec = s2_data['videoLengthVec'].reshape((-1, ))
s3_targets_vec = s3_data['targetsVec'].reshape((-1, ))
s3_vidlen_vec = s3_data['videoLengthVec'].reshape((-1, ))
orig_streams = [
(s1_data_matrix, targets_vec, vidlen_vec),
(s2_data_matrix, s2_targets_vec, s2_vidlen_vec),
(s3_data_matrix, s3_targets_vec, s3_vidlen_vec),
]
new_streams = multistream_force_align(orig_streams)
s1_data_matrix, targets_vec, vidlen_vec = new_streams[0]
s2_data_matrix, _, _ = new_streams[1]
s3_data_matrix, _, _ = new_streams[2]
s1_train_X, s1_train_y, s1_train_vidlens, s1_train_subjects, \
s1_val_X, s1_val_y, s1_val_vidlens, s1_val_subjects, \
s1_test_X, s1_test_y, s1_test_vidlens, s1_test_subjects = split_seq_data(s1_data_matrix, targets_vec, subjects_vec,
vidlen_vec, train_subject_ids,
val_subject_ids, test_subject_ids)
s2_train_X, s2_train_y, s2_train_vidlens, s2_train_subjects, \
s2_val_X, s2_val_y, s2_val_vidlens, s2_val_subjects, \
s2_test_X, s2_test_y, s2_test_vidlens, s2_test_subjects = split_seq_data(s2_data_matrix, targets_vec, subjects_vec,
vidlen_vec, train_subject_ids,
val_subject_ids, test_subject_ids)
s3_train_X, s3_train_y, s3_train_vidlens, s3_train_subjects, \
s3_val_X, s3_val_y, s3_val_vidlens, s3_val_subjects, \
s3_test_X, s3_test_y, s3_test_vidlens, s3_test_subjects = split_seq_data(s3_data_matrix, targets_vec, subjects_vec,
vidlen_vec, train_subject_ids,
val_subject_ids, test_subject_ids)
s1_train_X, s1_val_X, s1_test_X = postsplit_datapreprocessing(
s1_train_X, s1_val_X, s1_test_X, config, 'stream1')
s2_train_X, s2_val_X, s2_test_X = postsplit_datapreprocessing(
s2_train_X, s2_val_X, s2_test_X, config, 'stream2')
s3_train_X, s3_val_X, s3_test_X = postsplit_datapreprocessing(
s3_train_X, s3_val_X, s3_test_X, config, 'stream3')
ae1 = load_decoder(s1, s1_shape, s1_nonlinearities)
ae2 = load_decoder(s2, s2_shape, s2_nonlinearities)
ae3 = load_decoder(s3, s3_shape, s3_nonlinearities)
# IMPT: the encoder was trained with fortan ordered images, so to visualize
# convert all the images to C order using reshape_images_order()
# output = dbn.predict(test_X)
# test_X = reshape_images_order(test_X, (26, 44))
# output = reshape_images_order(output, (26, 44))
# visualize_reconstruction(test_X[:36, :], output[:36, :], shape=(26, 44))
window = T.iscalar('theta')
inputs1 = T.tensor3('inputs1', dtype='float32')
inputs2 = T.tensor3('inputs2', dtype='float32')
inputs3 = T.tensor3('inputs3', dtype='float32')
mask = T.matrix('mask', dtype='uint8')
targets = T.imatrix('targets')
print('constructing end to end model...')
if use_dropout:
network, l_fuse = adenet_3stream_dropout.create_model(
ae1,
ae2,
ae3, (None, None, s1_inputdim),
inputs1, (None, None, s2_inputdim),
inputs2, (None, None, s3_inputdim),
inputs3, (None, None),
mask,
lstm_size,
window,
output_classes,
fusiontype,
w_init_fn=weight_init_fn,
use_peepholes=use_peepholes)
else:
network, l_fuse = adenet_3stream.create_model(
ae1,
ae2,
ae3, (None, None, s1_inputdim),
inputs1, (None, None, s2_inputdim),
inputs2, (None, None, s3_inputdim),
inputs3, (None, None),
mask,
lstm_size,
window,
output_classes,
fusiontype,
w_init_fn=weight_init_fn,
use_peepholes=use_peepholes)
print_network(network)
# draw_to_file(las.layers.get_all_layers(network), 'network.png')
print('compiling model...')
predictions = las.layers.get_output(network, deterministic=False)
all_params = las.layers.get_all_params(network, trainable=True)
cost = temporal_softmax_loss(predictions, targets, mask)
updates = adam(cost, all_params, learning_rate=learning_rate)
train = theano.function([inputs1, inputs2, inputs3, targets, mask, window],
cost,
updates=updates,
allow_input_downcast=True)
compute_train_cost = theano.function(
[inputs1, inputs2, inputs3, targets, mask, window],
cost,
allow_input_downcast=True)
test_predictions = las.layers.get_output(network, deterministic=True)
test_cost = temporal_softmax_loss(test_predictions, targets, mask)
compute_test_cost = theano.function(
[inputs1, inputs2, inputs3, targets, mask, window],
test_cost,
allow_input_downcast=True)
val_fn = theano.function([inputs1, inputs2, inputs3, mask, window],
test_predictions,
allow_input_downcast=True)
# We'll train the network with 10 epochs of 30 minibatches each
print('begin training...')
cost_train = []
cost_val = []
class_rate = []
STRIP_SIZE = 3
val_window = circular_list(validation_window)
train_strip = np.zeros((STRIP_SIZE, ))
best_val = float('inf')
best_cr = 0.0
datagen = gen_lstm_batch_random(s1_train_X,
s1_train_y,
s1_train_vidlens,
batchsize=batchsize)
integral_lens = compute_integral_len(s1_train_vidlens)
val_datagen = gen_lstm_batch_random(s1_val_X,
s1_val_y,
s1_val_vidlens,
batchsize=len(s1_val_vidlens))
test_datagen = gen_lstm_batch_random(s1_test_X,
s1_test_y,
s1_test_vidlens,
batchsize=len(s1_test_vidlens))
# We'll use this "validation set" to periodically check progress
X_s1_val, y_val, mask_val, idxs_val = next(val_datagen)
integral_lens_val = compute_integral_len(s1_val_vidlens)
X_s2_val = gen_seq_batch_from_idx(s2_val_X, idxs_val,
s1_val_vidlens, integral_lens_val,
np.max(s1_val_vidlens))
X_s3_val = gen_seq_batch_from_idx(s3_val_X, idxs_val,
s1_val_vidlens, integral_lens_val,
np.max(s1_val_vidlens))
# we use the test set to check final classification rate
X_s1_test, y_test, mask_test, idxs_test = next(test_datagen)
integral_lens_test = compute_integral_len(s1_test_vidlens)
X_s2_test = gen_seq_batch_from_idx(s2_test_X, idxs_test, s1_test_vidlens,
integral_lens_test,
np.max(s1_test_vidlens))
X_s3_test = gen_seq_batch_from_idx(s3_test_X, idxs_test, s1_test_vidlens,
integral_lens_test,
np.max(s1_test_vidlens))
# reshape the targets for validation
y_val_evaluate = y_val
y_val = y_val.reshape((-1, 1)).repeat(mask_val.shape[-1], axis=-1)
for epoch in range(num_epoch):
time_start = time.time()
for i in range(epochsize):
X_s1, y, m, batch_idxs = next(datagen)
# repeat targets based on max sequence len
y = y.reshape((-1, 1))
y = y.repeat(m.shape[-1], axis=-1)
X_s2 = gen_seq_batch_from_idx(s2_train_X, batch_idxs,
s1_train_vidlens, integral_lens,
np.max(s1_train_vidlens))
X_s3 = gen_seq_batch_from_idx(s3_train_X, batch_idxs,
s1_train_vidlens, integral_lens,
np.max(s1_train_vidlens))
print_str = 'Epoch {} batch {}/{}: {} examples using adam with learning rate = {}'.format(
epoch + 1, i + 1, epochsize, len(X_s1), learning_rate)
print(print_str, end='')
sys.stdout.flush()
train(X_s1, X_s2, X_s3, y, m, windowsize)
print('\r', end='')
cost = compute_train_cost(X_s1, X_s2, X_s3, y, m, windowsize)
val_cost = compute_test_cost(X_s1_val, X_s2_val, X_s3_val, y_val,
mask_val, windowsize)
cost_train.append(cost)
cost_val.append(val_cost)
train_strip[epoch % STRIP_SIZE] = cost
val_window.push(val_cost)
gl = 100 * (cost_val[-1] / np.min(cost_val) - 1)
pk = 1000 * (np.sum(train_strip) /
(STRIP_SIZE * np.min(train_strip)) - 1)
pq = gl / pk
cr, val_conf = evaluate_model2(X_s1_val, X_s2_val, X_s3_val,
y_val_evaluate, mask_val, windowsize,
val_fn)
class_rate.append(cr)
if val_cost < best_val:
best_val = val_cost
best_cr = cr
test_cr, test_conf = evaluate_model2(X_s1_test, X_s2_test,
X_s3_test, y_test, mask_test,
windowsize, val_fn)
print(
"Epoch {} train cost = {}, val cost = {}, "
"GL loss = {:.3f}, GQ = {:.3f}, CR = {:.3f}, Test CR= {:.3f} ({:.1f}sec)"
.format(epoch + 1, cost_train[-1], cost_val[-1], gl, pq, cr,
test_cr,
time.time() - time_start))
best_params = las.layers.get_all_param_values(network)
else:
print("Epoch {} train cost = {}, val cost = {}, "
"GL loss = {:.3f}, GQ = {:.3f}, CR = {:.3f} ({:.1f}sec)".
format(epoch + 1, cost_train[-1], cost_val[-1], gl, pq, cr,
time.time() - time_start))
if epoch >= validation_window and early_stop2(val_window, best_val,
validation_window):
break
print('Final Model')
print('CR: {}, val loss: {}, Test CR: {}'.format(best_cr, best_val,
test_cr))
# plot confusion matrix
table_str = plot_confusion_matrix(test_conf, output_classnames, fmt='pipe')
print('confusion matrix: ')
print(table_str)
if 'save_plot' in options:
prefix = options['save_plot']
plot_validation_cost(cost_train,
cost_val,
savefilename='{}.validloss.png'.format(prefix))
with open('{}.confmat.txt'.format(prefix), mode='a') as f:
f.write(table_str)
f.write('\n\n')
if 'write_results' in options:
print('writing results to {}'.format(options['write_results']))
results_file = options['write_results']
with open(results_file, mode='a') as f:
f.write('{},{},{}\n'.format(test_cr, best_cr, best_val))
if 'save_best' in options:
print('saving best model...')
las.layers.set_all_param_values(network, best_params)
save_model_params(network, options['save_best'])
print('best model saved to {}'.format(options['save_best']))
def main():
configure_theano()
options = parse_options()
config_file = options['config']
config = ConfigParser.ConfigParser()
config.read(config_file)
print('Reading Config File: {}...'.format(config_file))
print(config.items('stream1'))
print(config.items('lstm_classifier'))
print(config.items('training'))
print('CLI options: {}'.format(options.items()))
print('preprocessing dataset...')
data = load_mat_file(config.get('stream1', 'data'))
stream1_dim = config.getint('stream1', 'input_dimensions')
output_classes = config.getint('lstm_classifier', 'output_classes')
output_classnames = config.get('lstm_classifier',
'output_classnames').split(',')
matlab_target_offset = config.getboolean('lstm_classifier',
'matlab_target_offset')
lstm_size = config.getint('lstm_classifier', 'lstm_size')
weight_init = options[
'weight_init'] if 'weight_init' in options else config.get(
'lstm_classifier', 'weight_init')
use_peepholes = options[
'use_peepholes'] if 'use_peepholes' in options else config.getboolean(
'lstm_classifier', 'use_peepholes')
windowsize = config.getint('lstm_classifier', 'windowsize')
# data preprocessing options
meanremove = config.getboolean('stream1', 'meanremove')
samplewisenormalize = config.getboolean('stream1', 'samplewisenormalize')
featurewisenormalize = config.getboolean('stream1', 'featurewisenormalize')
# capture training parameters
validation_window = int(options['validation_window']) \
if 'validation_window' in options else config.getint('training', 'validation_window')
num_epoch = int(
options['num_epoch']) if 'num_epoch' in options else config.getint(
'training', 'num_epoch')
learning_rate = options['learning_rate'] if 'learning_rate' in options \
else config.getfloat('training', 'learning_rate')
epochsize = config.getint('training', 'epochsize')
batchsize = config.getint('training', 'batchsize')
weight_init_fn = las.init.GlorotUniform()
if weight_init == 'glorot':
weight_init_fn = las.init.GlorotUniform()
if weight_init == 'norm':
weight_init_fn = las.init.Normal(0.1)
if weight_init == 'uniform':
weight_init_fn = las.init.Uniform()
if weight_init == 'ortho':
weight_init_fn = las.init.Orthogonal()
train_subject_ids = read_data_split_file(
config.get('training', 'train_subjects_file'))
val_subject_ids = read_data_split_file(
config.get('training', 'val_subjects_file'))
test_subject_ids = read_data_split_file(
config.get('training', 'test_subjects_file'))
data_matrix = data['dataMatrix'].astype('float32')
targets_vec = data['targetsVec'].reshape((-1, ))
subjects_vec = data['subjectsVec'].reshape((-1, ))
vidlen_vec = data['videoLengthVec'].reshape((-1, ))
if samplewisenormalize:
data_matrix = normalize_input(data_matrix)
if meanremove:
data_matrix = sequencewise_mean_image_subtraction(
data_matrix, vidlen_vec)
data_matrix = concat_first_second_deltas(data_matrix, vidlen_vec,
windowsize)
train_dct, train_y, train_vidlens, train_subjects, \
val_dct, val_y, val_vidlens, val_subjects, \
test_dct, test_y, test_vidlens, test_subjects = split_seq_data(data_matrix, targets_vec, subjects_vec, vidlen_vec,
train_subject_ids, val_subject_ids, test_subject_ids)
if matlab_target_offset:
train_y -= 1
val_y -= 1
test_y -= 1
# featurewise normalize dct features
if featurewisenormalize:
train_dct, dct_mean, dct_std = featurewise_normalize_sequence(
train_dct)
val_dct = (val_dct - dct_mean) / dct_std
test_dct = (test_dct - dct_mean) / dct_std
# IMPT: the encoder was trained with fortan ordered images, so to visualize
# convert all the images to C order using reshape_images_order()
# output = dbn.predict(test_X)
# test_X = reshape_images_order(test_X, (26, 44))
# output = reshape_images_order(output, (26, 44))
# visualize_reconstruction(test_X[:36, :], output[:36, :], shape=(26, 44))
inputs = T.tensor3('inputs', dtype='float32')
mask = T.matrix('mask', dtype='uint8')
targets = T.imatrix('targets')
print('constructing end to end model...')
network = lstm_classifier_majority_vote.create_model(
(None, None, stream1_dim * 3), inputs, (None, None), mask, lstm_size,
output_classes, weight_init_fn, use_peepholes)
print_network(network)
print('compiling model...')
predictions = las.layers.get_output(network, deterministic=False)
all_params = las.layers.get_all_params(network, trainable=True)
cost = temporal_softmax_loss(predictions, targets, mask)
updates = adam(cost, all_params, learning_rate=learning_rate)
train = theano.function([inputs, targets, mask],
cost,
updates=updates,
allow_input_downcast=True)
compute_train_cost = theano.function([inputs, targets, mask],
cost,
allow_input_downcast=True)
test_predictions = las.layers.get_output(network, deterministic=True)
test_cost = temporal_softmax_loss(test_predictions, targets, mask)
compute_test_cost = theano.function([inputs, targets, mask],
test_cost,
allow_input_downcast=True)
val_fn = theano.function([inputs, mask],
test_predictions,
allow_input_downcast=True)
# We'll train the network with 10 epochs of 30 minibatches each
print('begin training...')
cost_train = []
cost_val = []
class_rate = []
STRIP_SIZE = 3
val_window = circular_list(validation_window)
train_strip = np.zeros((STRIP_SIZE, ))
best_val = float('inf')
best_tr = float('inf')
best_cr = 0.0
datagen = gen_lstm_batch_random(train_dct,
train_y,
train_vidlens,
batchsize=batchsize)
val_datagen = gen_lstm_batch_random(val_dct,
val_y,
val_vidlens,
batchsize=len(val_vidlens))
test_datagen = gen_lstm_batch_random(test_dct,
test_y,
test_vidlens,
batchsize=len(test_vidlens))
integral_lens = compute_integral_len(train_vidlens)
# We'll use this "validation set" to periodically check progress
X_val, y_val, mask_val, idxs_val = next(val_datagen)
integral_lens_val = compute_integral_len(val_vidlens)
dct_val = gen_seq_batch_from_idx(val_dct, idxs_val, val_vidlens,
integral_lens_val, np.max(val_vidlens))
X_test, y_test, mask_test, idxs_test = next(test_datagen)
integral_lens_test = compute_integral_len(test_vidlens)
dct_test = gen_seq_batch_from_idx(test_dct, idxs_test, test_vidlens,
integral_lens_test, np.max(test_vidlens))
# reshape the targets for validation
y_val_evaluate = y_val
y_val = y_val.reshape((-1, 1)).repeat(mask_val.shape[-1], axis=-1)
for epoch in range(num_epoch):
time_start = time.time()
for i in range(epochsize):
_, y, m, batch_idxs = next(datagen)
# repeat targets based on max sequence len
y = y.reshape((-1, 1))
y = y.repeat(m.shape[-1], axis=-1)
d = gen_seq_batch_from_idx(train_dct, batch_idxs, train_vidlens,
integral_lens, np.max(train_vidlens))
print_str = 'Epoch {} batch {}/{}: {} examples using adam with learning rate = {}'.format(
epoch + 1, i + 1, epochsize, len(y), learning_rate)
print(print_str, end='')
sys.stdout.flush()
train(d, y, m)
print('\r', end='')
cost = compute_train_cost(d, y, m)
val_cost = compute_test_cost(dct_val, y_val, mask_val)
cost_train.append(cost)
cost_val.append(val_cost)
train_strip[epoch % STRIP_SIZE] = cost
val_window.push(val_cost)
gl = 100 * (cost_val[-1] / np.min(cost_val) - 1)
pk = 1000 * (np.sum(train_strip) /
(STRIP_SIZE * np.min(train_strip)) - 1)
pq = gl / pk
cr, val_conf = evaluate_model2(dct_val, y_val_evaluate, mask_val,
val_fn)
class_rate.append(cr)
if val_cost < best_val:
best_val = val_cost
best_conf = val_conf
best_cr = cr
test_cr, test_conf = evaluate_model2(dct_test, y_test, mask_test,
val_fn)
print(
"Epoch {} train cost = {}, val cost = {}, "
"GL loss = {:.3f}, GQ = {:.3f}, CR = {:.3f}, Test CR= {:.3f} ({:.1f}sec)"
.format(epoch + 1, cost_train[-1], cost_val[-1], gl, pq, cr,
test_cr,
time.time() - time_start))
best_params = las.layers.get_all_param_values(network)
else:
print("Epoch {} train cost = {}, val cost = {}, "
"GL loss = {:.3f}, GQ = {:.3f}, CR = {:.3f} ({:.1f}sec)".
format(epoch + 1, cost_train[-1], cost_val[-1], gl, pq, cr,
time.time() - time_start))
if epoch >= validation_window and early_stop2(val_window, best_val,
validation_window):
break
print('Final Model')
print('CR: {}, val loss: {}, Test CR: {}'.format(best_cr, best_val,
test_cr))
# plot confusion matrix
table_str = plot_confusion_matrix(test_conf, output_classnames, fmt='pipe')
print('confusion matrix: ')
print(table_str)
if 'save_plot' in options:
prefix = options['save_plot']
plot_validation_cost(cost_train,
cost_val,
savefilename='{}.validloss.png'.format(prefix))
with open('{}.confmat.txt'.format(prefix), mode='a') as f:
f.write(table_str)
f.write('\n\n')
if 'write_results' in options:
print('writing results to {}'.format(options['write_results']))
results_file = options['write_results']
with open(results_file, mode='a') as f:
f.write('{},{},{}\n'.format(test_cr, best_cr, best_val))
if 'save_best' in options:
print('saving best model...')
las.layers.set_all_param_values(network, best_params)
save_model_params(network, options['save_best'])
print('best model saved to {}'.format(options['save_best']))
def main():
configure_theano()
options = parse_options()
config_file = options['config']
config = ConfigParser.ConfigParser()
config.read(config_file)
print('CLI options: {}'.format(options.items()))
print('Reading Config File: {}...'.format(config_file))
print(config.items('data'))
print(config.items('models'))
print(config.items('training'))
print('preprocessing dataset...')
data = load_mat_file(config.get('data', 'images'))
dct_data = load_mat_file(config.get('data', 'dct'))
ae_finetuned = config.get('models', 'finetuned')
ae_finetuned_diff = config.get('models', 'finetuned_diff')
fusiontype = config.get('models', 'fusiontype')
learning_rate = float(config.get('training', 'learning_rate'))
decay_rate = float(config.get('training', 'decay_rate'))
decay_start = int(config.get('training', 'decay_start'))
load_finetune = config.getboolean('training', 'load_finetune')
load_finetune_diff = config.getboolean('training', 'load_finetune_diff')
train_vidlens = data['trVideoLengthVec'].astype('int').reshape((-1,))
val_vidlens = data['valVideoLengthVec'].astype('int').reshape((-1,))
test_vidlens = data['testVideoLengthVec'].astype('int').reshape((-1,))
train_X = data['trData'].astype('float32')
val_X = data['valData'].astype('float32')
test_X = data['testData'].astype('float32')
train_dct = dct_data['trDctFeatures'].astype('float32')
val_dct = dct_data['valDctFeatures'].astype('float32')
test_dct = dct_data['testDctFeatures'].astype('float32')
train_X_diff = compute_diff_images(train_X, train_vidlens)
val_X_diff = compute_diff_images(val_X, val_vidlens)
test_X_diff = compute_diff_images(test_X, test_vidlens)
train_y = data['trTargetsVec'].astype('int').reshape((-1,)) + 1 # +1 to handle the -1 introduced in lstm_gendata
val_y = data['valTargetsVec'].astype('int').reshape((-1,)) + 1
test_y = data['testTargetsVec'].astype('int').reshape((-1,)) + 1
# featurewise normalize dct features
train_dct, dct_mean, dct_std = featurewise_normalize_sequence(train_dct)
val_dct = (val_dct - dct_mean) / dct_std
test_dct = (test_dct - dct_mean) / dct_std
if load_finetune:
print('loading finetuned encoder: {}...'.format(ae_finetuned))
ae = pickle.load(open(ae_finetuned, 'rb'))
ae.initialize()
if load_finetune_diff:
print('loading finetuned encoder: {}...'.format(ae_finetuned_diff))
ae_diff = pickle.load(open(ae_finetuned_diff, 'rb'))
ae_diff.initialize()
# IMPT: the encoder was trained with fortan ordered images, so to visualize
# convert all the images to C order using reshape_images_order()
# output = dbn.predict(test_X)
# test_X = reshape_images_order(test_X, (26, 44))
# output = reshape_images_order(output, (26, 44))
# visualize_reconstruction(test_X[:36, :], output[:36, :], shape=(26, 44))
window = T.iscalar('theta')
dct = T.tensor3('dct', dtype='float32')
inputs = T.tensor3('inputs', dtype='float32')
inputs_diff = T.tensor3('inputs_diff', dtype='float32')
mask = T.matrix('mask', dtype='uint8')
targets = T.ivector('targets')
lr = theano.shared(np.array(learning_rate, dtype=theano.config.floatX), name='learning_rate')
lr_decay = np.array(decay_rate, dtype=theano.config.floatX)
print('constructing end to end model...')
network, l_fuse = adenet_v3.create_model(ae, ae_diff, (None, None, 1500), inputs,
(None, None), mask,
(None, None, 90), dct,
(None, None, 1500), inputs_diff,
250, window, 10, fusiontype)
print_network(network)
# draw_to_file(las.layers.get_all_layers(network), 'network.png')
print('compiling model...')
predictions = las.layers.get_output(network, deterministic=False)
all_params = las.layers.get_all_params(network, trainable=True)
cost = T.mean(las.objectives.categorical_crossentropy(predictions, targets))
updates = adadelta(cost, all_params, learning_rate=lr)
# updates = adagrad(cost, all_params, learning_rate=lr)
train = theano.function(
[inputs, targets, mask, dct, inputs_diff, window],
cost, updates=updates, allow_input_downcast=True)
compute_train_cost = theano.function([inputs, targets, mask, dct, inputs_diff, window],
cost, allow_input_downcast=True)
test_predictions = las.layers.get_output(network, deterministic=True)
test_cost = T.mean(las.objectives.categorical_crossentropy(test_predictions, targets))
compute_test_cost = theano.function(
[inputs, targets, mask, dct, inputs_diff, window], test_cost, allow_input_downcast=True)
val_fn = theano.function([inputs, mask, dct, inputs_diff, window], test_predictions, allow_input_downcast=True)
# We'll train the network with 10 epochs of 30 minibatches each
print('begin training...')
cost_train = []
cost_val = []
class_rate = []
NUM_EPOCHS = 30
EPOCH_SIZE = 45
BATCH_SIZE = 20
WINDOW_SIZE = 9
STRIP_SIZE = 3
MAX_LOSS = 0.2
VALIDATION_WINDOW = 4
val_window = circular_list(VALIDATION_WINDOW)
train_strip = np.zeros((STRIP_SIZE,))
best_val = float('inf')
best_conf = None
best_cr = 0.0
datagen = gen_lstm_batch_random(train_X, train_y, train_vidlens, batchsize=BATCH_SIZE)
integral_lens = compute_integral_len(train_vidlens)
val_datagen = gen_lstm_batch_random(val_X, val_y, val_vidlens, batchsize=len(val_vidlens))
test_datagen = gen_lstm_batch_random(test_X, test_y, test_vidlens, batchsize=len(test_vidlens))
# We'll use this "validation set" to periodically check progress
X_val, y_val, mask_val, idxs_val = next(val_datagen)
integral_lens_val = compute_integral_len(val_vidlens)
dct_val = gen_seq_batch_from_idx(val_dct, idxs_val, val_vidlens, integral_lens_val, np.max(val_vidlens))
X_diff_val = gen_seq_batch_from_idx(val_X_diff, idxs_val, val_vidlens, integral_lens_val, np.max(val_vidlens))
# we use the test set to check final classification rate
X_test, y_test, mask_test, idxs_test = next(test_datagen)
integral_lens_test = compute_integral_len(test_vidlens)
dct_test = gen_seq_batch_from_idx(test_dct, idxs_test, test_vidlens, integral_lens_test, np.max(test_vidlens))
X_diff_test = gen_seq_batch_from_idx(test_X_diff, idxs_test, test_vidlens, integral_lens_test, np.max(test_vidlens))
def early_stop(cost_window):
if len(cost_window) < 2:
return False
else:
curr = cost_window[0]
for idx, cost in enumerate(cost_window):
if curr < cost or idx == 0:
curr = cost
else:
return False
return True
for epoch in range(NUM_EPOCHS):
time_start = time.time()
for i in range(EPOCH_SIZE):
X, y, m, batch_idxs = next(datagen)
d = gen_seq_batch_from_idx(train_dct, batch_idxs,
train_vidlens, integral_lens, np.max(train_vidlens))
X_diff = gen_seq_batch_from_idx(train_X_diff, batch_idxs,
train_vidlens, integral_lens, np.max(train_vidlens))
print_str = 'Epoch {} batch {}/{}: {} examples at learning rate = {:.4f}'.format(
epoch + 1, i + 1, EPOCH_SIZE, len(X), float(lr.get_value()))
print(print_str, end='')
sys.stdout.flush()
train(X, y, m, d, X_diff, WINDOW_SIZE)
print('\r', end='')
cost = compute_train_cost(X, y, m, d, X_diff, WINDOW_SIZE)
val_cost = compute_test_cost(X_val, y_val, mask_val, dct_val, X_diff_val, WINDOW_SIZE)
cost_train.append(cost)
cost_val.append(val_cost)
train_strip[epoch % STRIP_SIZE] = cost
val_window.push(val_cost)
gl = 100 * (cost_val[-1] / np.min(cost_val) - 1)
pk = 1000 * (np.sum(train_strip) / (STRIP_SIZE * np.min(train_strip)) - 1)
pq = gl / pk
cr, val_conf = evaluate_model(X_val, y_val, mask_val, dct_val, X_diff_val, WINDOW_SIZE, val_fn)
class_rate.append(cr)
if val_cost < best_val:
best_val = val_cost
best_cr = cr
if fusiontype == 'adasum':
adascale_param = las.layers.get_all_param_values(l_fuse, scaling_param=True)
test_cr, test_conf = evaluate_model(X_test, y_test, mask_test,
dct_test, X_diff_test, WINDOW_SIZE, val_fn)
print("Epoch {} train cost = {}, val cost = {}, "
"GL loss = {:.3f}, GQ = {:.3f}, CR = {:.3f}, Test CR= {:.3f} ({:.1f}sec)"
.format(epoch + 1, cost_train[-1], cost_val[-1], gl, pq, cr, test_cr, time.time() - time_start))
else:
print("Epoch {} train cost = {}, val cost = {}, "
"GL loss = {:.3f}, GQ = {:.3f}, CR = {:.3f} ({:.1f}sec)"
.format(epoch + 1, cost_train[-1], cost_val[-1], gl, pq, cr, time.time() - time_start))
if epoch >= VALIDATION_WINDOW and early_stop(val_window):
break
# learning rate decay
if epoch + 1 >= decay_start:
lr.set_value(lr.get_value() * lr_decay)
numbers = ['0', '1', '2', '3', '4', '5', '6', '7', '8', '9']
print('Final Model')
print('CR: {}, val loss: {}, Test CR: {}'.format(best_cr, best_val, test_cr))
if fusiontype == 'adasum':
print("final scaling params: {}".format(adascale_param))
print('confusion matrix: ')
plot_confusion_matrix(test_conf, numbers, fmt='latex')
plot_validation_cost(cost_train, cost_val, savefilename='valid_cost')
if options['write_results']:
results_file = options['write_results']
with open(results_file, mode='a') as f:
f.write('{},{},{}\n'.format(fusiontype, test_cr, best_val))
def main():
configure_theano()
options = parse_options()
config_file = options['config']
config = ConfigParser.ConfigParser()
config.read(config_file)
print('CLI options: {}'.format(options.items()))
print(config.items('data'))
print(config.items('models'))
print(config.items('training'))
print('preprocessing dataset...')
data = load_mat_file(config.get('data', 'images'))
dct_data = load_mat_file(options['dct_data'] if 'dct_data' in options else config.get('data', 'dct'))
no_coeff = options['no_coeff'] if 'no_coeff' in options else config.getint('models', 'no_coeff')
no_epochs = options['no_epochs'] if 'no_epochs' in options else config.getint('training', 'no_epochs')
validation_window = options['validation_window'] if 'validation_window' in options \
else config.getint('training', 'validation_window')
epochsize = options['epochsize'] if 'epochsize' in options else config.getint('training', 'epochsize')
batchsize = options['batchsize'] if 'batchsize' in options else config.getint('training', 'batchsize')
# 53 subjects, 70 utterances, 5 view angles
# s[x]_v[y]_u[z].mp4
# resized, height, width = (26, 44)
# ['dataMatrix', 'targetH', 'targetsPerVideoVec', 'videoLengthVec', '__header__', 'targetsVec',
# '__globals__', 'iterVec', 'filenamesVec', 'dataMatrixCells', 'subjectsVec', 'targetW', '__version__']
print(data.keys())
X = data['dataMatrix'].astype('float32')
y = data['targetsVec'].astype('int32')
y = y.reshape((len(y),))
dct_feats = dct_data['dctFeatures'].astype('float32')
uniques = np.unique(y)
print('number of classifications: {}'.format(len(uniques)))
subjects = data['subjectsVec'].astype('int')
subjects = subjects.reshape((len(subjects),))
video_lens = data['videoLengthVec'].astype('int')
video_lens = video_lens.reshape((len(video_lens,)))
# X = reorder_data(X, (26, 44), 'f', 'c')
# print('performing sequencewise mean image removal...')
# X = sequencewise_mean_image_subtraction(X, video_lens)
# visualize_images(X[550:650], (26, 44))
# mean remove dct features
# dct_feats = sequencewise_mean_image_subtraction(dct_feats, video_lens)
train_subject_ids = read_data_split_file('data/train.txt')
val_subject_ids = read_data_split_file('data/val.txt')
test_subject_ids = read_data_split_file('data/test.txt')
print('Train: {}'.format(train_subject_ids))
print('Validation: {}'.format(val_subject_ids))
print('Test: {}'.format(test_subject_ids))
train_X, train_y, train_dct, train_vidlens, train_subjects, \
val_X, val_y, val_dct, val_vidlens, val_subjects, \
test_X, test_y, test_dct, test_vidlens, test_subjects = \
split_data(X, y, dct_feats, subjects, video_lens, train_subject_ids, val_subject_ids, test_subject_ids)
assert train_X.shape[0] + val_X.shape[0] + test_X.shape[0] == len(X)
assert train_y.shape[0] + val_y.shape[0] + test_y.shape[0] == len(y)
assert train_vidlens.shape[0] + val_vidlens.shape[0] + test_vidlens.shape[0] == len(video_lens)
assert train_subjects.shape[0] + val_subjects.shape[0] + test_subjects.shape[0] == len(subjects)
train_X = normalize_input(train_X, centralize=True)
val_X = normalize_input(val_X, centralize=True)
test_X = normalize_input(test_X, centralize=True)
# featurewise normalize dct features
train_dct, dct_mean, dct_std = featurewise_normalize_sequence(train_dct)
val_dct = (val_dct - dct_mean) / dct_std
test_dct = (test_dct - dct_mean) / dct_std
# IMPT: the encoder was trained with fortan ordered images, so to visualize
# convert all the images to C order using reshape_images_order()
# output = dbn.predict(test_X)
# test_X = reshape_images_order(test_X, (26, 44))
# output = reshape_images_order(output, (26, 44))
# visualize_reconstruction(test_X[:36, :], output[:36, :], shape=(26, 44))
inputs = T.tensor3('inputs', dtype='float32')
mask = T.matrix('mask', dtype='uint8')
targets = T.ivector('targets')
print('constructing end to end model...')
network = lstm_classifier_baseline.create_model((None, None, no_coeff*3), inputs,
(None, None), mask,
250, 10)
print_network(network)
print('compiling model...')
predictions = las.layers.get_output(network, deterministic=False)
all_params = las.layers.get_all_params(network, trainable=True)
cost = T.mean(las.objectives.categorical_crossentropy(predictions, targets))
updates = adam(cost, all_params)
train = theano.function(
[inputs, targets, mask],
cost, updates=updates, allow_input_downcast=True)
compute_train_cost = theano.function([inputs, targets, mask], cost, allow_input_downcast=True)
test_predictions = las.layers.get_output(network, deterministic=True)
test_cost = T.mean(las.objectives.categorical_crossentropy(test_predictions, targets))
compute_test_cost = theano.function(
[inputs, targets, mask], test_cost, allow_input_downcast=True)
val_fn = theano.function([inputs, mask], test_predictions, allow_input_downcast=True)
# We'll train the network with 10 epochs of 30 minibatches each
print('begin training...')
cost_train = []
cost_val = []
class_rate = []
STRIP_SIZE = 3
val_window = circular_list(validation_window)
train_strip = np.zeros((STRIP_SIZE,))
best_val = float('inf')
best_cr = 0.0
datagen = gen_lstm_batch_random(train_X, train_y, train_vidlens, batchsize=batchsize)
val_datagen = gen_lstm_batch_random(val_X, val_y, val_vidlens, batchsize=len(val_vidlens))
test_datagen = gen_lstm_batch_random(test_X, test_y, test_vidlens, batchsize=len(test_vidlens))
integral_lens = compute_integral_len(train_vidlens)
# We'll use this "validation set" to periodically check progress
X_val, y_val, mask_val, idxs_val = next(val_datagen)
integral_lens_val = compute_integral_len(val_vidlens)
dct_val = gen_seq_batch_from_idx(val_dct, idxs_val, val_vidlens, integral_lens_val, np.max(val_vidlens))
X_test, y_test, mask_test, idxs_test = next(test_datagen)
integral_lens_test = compute_integral_len(test_vidlens)
dct_test = gen_seq_batch_from_idx(test_dct, idxs_test, test_vidlens, integral_lens_test, np.max(test_vidlens))
for epoch in range(no_epochs):
time_start = time.time()
for i in range(epochsize):
X, y, m, batch_idxs = next(datagen)
d = gen_seq_batch_from_idx(train_dct, batch_idxs,
train_vidlens, integral_lens, np.max(train_vidlens))
print_str = 'Epoch {} batch {}/{}: {} examples using adam'.format(
epoch + 1, i + 1, epochsize, len(X))
print(print_str, end='')
sys.stdout.flush()
train(d, y, m)
print('\r', end='')
cost = compute_train_cost(d, y, m)
val_cost = compute_test_cost(dct_val, y_val, mask_val)
cost_train.append(cost)
cost_val.append(val_cost)
train_strip[epoch % STRIP_SIZE] = cost
val_window.push(val_cost)
gl = 100 * (cost_val[-1] / np.min(cost_val) - 1)
pk = 1000 * (np.sum(train_strip) / (STRIP_SIZE * np.min(train_strip)) - 1)
pq = gl / pk
cr, val_conf = evaluate_model(dct_val, y_val, mask_val, val_fn)
class_rate.append(cr)
if val_cost < best_val:
best_val = val_cost
best_cr = cr
test_cr, test_conf = evaluate_model(dct_test, y_test, mask_test, val_fn)
print("Epoch {} train cost = {}, val cost = {}, "
"GL loss = {:.3f}, GQ = {:.3f}, CR = {:.3f}, Test CR= {:.3f} ({:.1f}sec)"
.format(epoch + 1, cost_train[-1], cost_val[-1], gl, pq, cr, test_cr, time.time() - time_start))
else:
print("Epoch {} train cost = {}, val cost = {}, "
"GL loss = {:.3f}, GQ = {:.3f}, CR = {:.3f} ({:.1f}sec)"
.format(epoch + 1, cost_train[-1], cost_val[-1], gl, pq, cr, time.time() - time_start))
if epoch >= validation_window and early_stop2(val_window, best_val, validation_window):
break
phrases = ['p1', 'p2', 'p3', 'p4', 'p5', 'p6', 'p7', 'p8', 'p9', 'p10']
print('Final Model')
print('CR: {}, val loss: {}, Test CR: {}'.format(best_cr, best_val, test_cr))
print('confusion matrix: ')
plot_confusion_matrix(test_conf, phrases, fmt='latex')
plot_validation_cost(cost_train, cost_val, savefilename='valid_cost')
if 'write_results' in options:
results_file = options['write_results']
with open(results_file, mode='a') as f:
f.write('{},{}\n'.format(test_cr, best_val))
def main():
def signal_handler(signal, frame):
global terminate
terminate = True
print('terminating...'.format(terminate))
signal.signal(signal.SIGINT, signal_handler)
configure_theano()
options = parse_options()
X, X_val = generate_data()
# X = np.reshape(X, (-1, 1, 30, 40))[:-5]
print('X type and shape:', X.dtype, X.shape)
print('X.min():', X.min())
print('X.max():', X.max())
# X_val = np.reshape(X_val, (-1, 1, 30, 40))[:-1]
print('X_val type and shape:', X_val.dtype, X_val.shape)
print('X_val.min():', X_val.min())
print('X_val.max():', X_val.max())
# we need our target to be 1 dimensional
X_out = X.reshape((X.shape[0], -1))
X_val_out = X_val.reshape((X_val.shape[0], -1))
print('X_out:', X_out.dtype, X_out.shape)
print('X_val_out', X_val_out.dtype, X_val_out.shape)
# X_noisy = apply_gaussian_noise(X_out)
# visualize_reconstruction(X_noisy[0:25], X_out[0:25], shape=(28, 28))
# X = np.reshape(X_noisy, (-1, 1, 28, 28))
print('constructing and compiling model...')
# input_var = T.tensor4('input', dtype='float32')
input_var = T.tensor3('input', dtype='float32')
target_var = T.matrix('output', dtype='float32')
lr = theano.shared(np.array(0.8, dtype=theano.config.floatX), name='learning_rate')
lr_decay = np.array(0.9, dtype=theano.config.floatX)
# try building a reshaping layer
# network = create_model(input_var, (None, 1, 30, 40), options)
l_input = InputLayer((None, None, 1200), input_var, name='input')
l_input = ReshapeLayer(l_input, (-1, 1, 30, 40), name='reshape_input')
# l_input = InputLayer((None, 1, 30, 40), input_var, name='input')
if options['MODEL'] == 'normal':
network, encoder = avletters_convae.create_model(l_input, options)
if options['MODEL'] == 'batchnorm':
network, encoder = avletters_convae_bn.create_model(l_input, options)
if options['MODEL'] == 'dropout':
network, encoder = avletters_convae_drop.create_model(l_input, options)
if options['MODEL'] == 'bn+dropout':
network, encoder = avletters_convae_bndrop.create_model(l_input, options)
print('AE Network architecture: {}'.format(options['MODEL']))
print_network(network)
recon = las.layers.get_output(network, deterministic=False)
all_params = las.layers.get_all_params(network, trainable=True)
cost = T.mean(squared_error(recon, target_var))
updates = adadelta(cost, all_params, lr)
# updates = las.updates.apply_nesterov_momentum(updates, all_params, momentum=0.90)
use_max_constraint = False
print('apply max norm constraint: {}'.format(use_max_constraint))
if use_max_constraint:
MAX_NORM = 4
for param in las.layers.get_all_params(network, regularizable=True):
if param.ndim > 1: # only apply to dimensions larger than 1, exclude biases
# updates[param] = norm_constraint(param, MAX_NORM * las.utils.compute_norms(param.get_value()).mean())
updates[param] = norm_constraint(param, MAX_NORM)
train = theano.function([input_var, target_var], recon, updates=updates, allow_input_downcast=True)
train_cost_fn = theano.function([input_var, target_var], cost, allow_input_downcast=True)
eval_recon = las.layers.get_output(network, deterministic=True)
eval_cost = T.mean(las.objectives.squared_error(eval_recon, target_var))
eval_cost_fn = theano.function([input_var, target_var], eval_cost, allow_input_downcast=True)
recon_fn = theano.function([input_var], eval_recon, allow_input_downcast=True)
if terminate:
exit()
NUM_EPOCHS = options['NUM_EPOCHS']
EPOCH_SIZE = options['EPOCH_SIZE']
NO_STRIDES = options['NO_STRIDES']
VAL_NO_STRIDES = options['VAL_NO_STRIDES']
print('begin training for {} epochs...'.format(NUM_EPOCHS))
datagen = batch_iterator(X, X_out, 128)
costs = []
val_costs = []
for epoch in range(NUM_EPOCHS):
time_start = time.time()
for i in range(EPOCH_SIZE):
batch_X, batch_y = next(datagen)
print_str = 'Epoch {} batch {}/{}: {} examples at learning rate = {:.4f}'.format(
epoch + 1, i + 1, EPOCH_SIZE, len(batch_X), lr.get_value())
print(print_str, end='')
sys.stdout.flush()
batch_X = batch_X.reshape((-1, 1, 1200))
train(batch_X, batch_y)
print('\r', end='')
if terminate:
break
if terminate:
break
cost = batch_compute_cost(X, X_out, NO_STRIDES, train_cost_fn)
val_cost = batch_compute_cost(X_val, X_val_out, VAL_NO_STRIDES, eval_cost_fn)
costs.append(cost)
val_costs.append(val_cost)
print("Epoch {} train cost = {}, validation cost = {} ({:.1f}sec) "
.format(epoch + 1, cost, val_cost, time.time() - time_start))
if epoch > 10:
lr.set_value(lr.get_value() * lr_decay)
X_val_recon = recon_fn(X_val)
visualize_reconstruction(X_val_out[450:550], X_val_recon[450:550], shape=(30, 40), savefilename='avletters')
plot_validation_cost(costs, val_costs, None, savefilename='valid_cost')
conv2d1 = las.layers.get_all_layers(network)[2]
visualize.plot_conv_weights(conv2d1, (15, 14)).savefig('conv2d1.png')
print('saving encoder...')
save_model(encoder, 'models/conv_encoder.dat')
save_model(network, 'models/conv_ae.dat')
def main():
configure_theano()
options = parse_options()
config_file = options['config']
config = ConfigParser.ConfigParser()
config.read(config_file)
print('CLI options: {}'.format(options.items()))
print('Reading Config File: {}...'.format(config_file))
print(config.items('data'))
print(config.items('models'))
print(config.items('training'))
print('preprocessing dataset...')
data = load_mat_file(config.get('data', 'images'))
data_audio = load_mat_file(config.get('data', 'audio'))
ae_pretrained = config.get('models', 'pretrained')
ae_diff_pretrained = config.get('models', 'pretrained_diff')
fusiontype = config.get('models', 'fusiontype')
lstm_size = config.getint('models', 'lstm_size')
output_classes = config.getint('models', 'output_classes')
nonlinearity = options[
'nonlinearity'] if 'nonlinearity' in options else config.get(
'models', 'nonlinearity')
if nonlinearity == 'sigmoid':
nonlinearity = sigmoid
if nonlinearity == 'rectify':
nonlinearity = rectify
# capture training parameters
validation_window = int(options['validation_window']) \
if 'validation_window' in options else config.getint('training', 'validation_window')
num_epoch = int(
options['num_epoch']) if 'num_epoch' in options else config.getint(
'training', 'num_epoch')
weight_init = options[
'weight_init'] if 'weight_init' in options else config.get(
'training', 'weight_init')
learning_rate = options['learning_rate'] if 'learning_rate' in options \
else config.getfloat('training', 'learning_rate')
use_peepholes = options[
'use_peepholes'] if 'use_peepholes' in options else config.getboolean(
'training', 'use_peepholes')
input_dimension = config.getint('models', 'input_dimension')
input_dimension2 = config.getint('models', 'input_dimension2')
use_blstm = config.getboolean('training', 'use_blstm')
use_finetuning = config.getboolean('training', 'use_finetuning')
weight_init_fn = las.init.GlorotUniform()
if weight_init == 'glorot':
weight_init_fn = las.init.GlorotUniform()
if weight_init == 'norm':
weight_init_fn = las.init.Normal(0.1)
if weight_init == 'uniform':
weight_init_fn = las.init.Uniform()
if weight_init == 'ortho':
weight_init_fn = las.init.Orthogonal()
train_vidlens = data['trVideoLengthVec'].astype('int').reshape((-1, ))
val_vidlens = data['valVideoLengthVec'].astype('int').reshape((-1, ))
test_vidlens = data['testVideoLengthVec'].astype('int').reshape((-1, ))
train_X = data['trData'].astype('float32')
val_X = data['valData'].astype('float32')
test_X = data['testData'].astype('float32')
train_X_audio = data_audio['trData'].astype('float32')
val_X_audio = data_audio['valData'].astype('float32')
test_X_audio = data_audio['testData'].astype('float32')
# +1 to handle the -1 introduced in lstm_gendata
train_y = data['trTargetsVec'].astype('int').reshape((-1, )) + 1
val_y = data['valTargetsVec'].astype('int').reshape((-1, )) + 1
test_y = data['testTargetsVec'].astype('int').reshape((-1, )) + 1
train_X = reorder_data(train_X, (30, 50))
val_X = reorder_data(val_X, (30, 50))
test_X = reorder_data(test_X, (30, 50))
visual_weights, visual_biases = load_dbn(ae_pretrained)
audio_weights, audio_biases = load_dbn(ae_diff_pretrained)
# IMPT: the encoder was trained with fortan ordered images, so to visualize
# convert all the images to C order using reshape_images_order()
# output = dbn.predict(test_X)
# test_X = reshape_images_order(test_X, (26, 44))
# output = reshape_images_order(output, (26, 44))
# visualize_reconstruction(test_X[:36, :], output[:36, :], shape=(26, 44))
window = T.iscalar('theta')
visual_input = T.tensor3('visual_input', dtype='float32')
audio_input = T.tensor3('audio_input', dtype='float32')
mask = T.matrix('mask', dtype='uint8')
targets = T.imatrix('targets')
print('constructing end to end model...')
visual_net = avnet.create_pretrained_substream(
visual_weights, visual_biases, (None, None, input_dimension),
visual_input, (None, None), mask, 'visual', lstm_size, window,
nonlinearity, weight_init_fn, use_peepholes)
audio_net = avnet.create_pretrained_substream(
audio_weights, audio_biases, (None, None, input_dimension2),
audio_input, (None, None), mask, 'audio', lstm_size, window,
nonlinearity, weight_init_fn, use_peepholes)
network, l_fuse = avnet.create_model([visual_net, audio_net], (None, None),
mask, lstm_size, output_classes,
fusiontype, weight_init_fn,
use_peepholes)
print_network(network)
# draw_to_file(las.layers.get_all_layers(network), 'network.png')
print('compiling model...')
predictions = las.layers.get_output(network, deterministic=False)
all_params = las.layers.get_all_params(network, trainable=True)
cost = temporal_softmax_loss(predictions, targets, mask)
updates = adam(cost, all_params, learning_rate=learning_rate)
train = theano.function([visual_input, targets, mask, audio_input, window],
cost,
updates=updates,
allow_input_downcast=True)
compute_train_cost = theano.function(
[visual_input, targets, mask, audio_input, window],
cost,
allow_input_downcast=True)
test_predictions = las.layers.get_output(network, deterministic=True)
test_cost = temporal_softmax_loss(test_predictions, targets, mask)
compute_test_cost = theano.function(
[visual_input, targets, mask, audio_input, window],
test_cost,
allow_input_downcast=True)
val_fn = theano.function([visual_input, mask, audio_input, window],
test_predictions,
allow_input_downcast=True)
# We'll train the network with 10 epochs of 30 minibatches each
print('begin training...')
cost_train = []
cost_val = []
class_rate = []
EPOCH_SIZE = 90
BATCH_SIZE = 10
WINDOW_SIZE = 9
STRIP_SIZE = 3
val_window = circular_list(validation_window)
train_strip = np.zeros((STRIP_SIZE, ))
best_val = float('inf')
best_tr = float('inf')
best_cr = 0.0
datagen = gen_lstm_batch_random(train_X,
train_y,
train_vidlens,
batchsize=BATCH_SIZE)
integral_lens = compute_integral_len(train_vidlens)
val_datagen = gen_lstm_batch_random(val_X,
val_y,
val_vidlens,
batchsize=len(val_vidlens))
test_datagen = gen_lstm_batch_random(test_X,
test_y,
test_vidlens,
batchsize=len(test_vidlens))
# We'll use this "validation set" to periodically check progress
X_val, y_val, mask_val, idxs_val = next(val_datagen)
integral_lens_val = compute_integral_len(val_vidlens)
X_diff_val = gen_seq_batch_from_idx(val_X_audio, idxs_val, val_vidlens,
integral_lens_val, np.max(val_vidlens))
# we use the test set to check final classification rate
X_test, y_test, mask_test, idxs_test = next(test_datagen)
integral_lens_test = compute_integral_len(test_vidlens)
X_diff_test = gen_seq_batch_from_idx(test_X_audio, idxs_test, test_vidlens,
integral_lens_test,
np.max(test_vidlens))
# reshape the targets for validation
y_val_evaluate = y_val
y_val = y_val.reshape((-1, 1)).repeat(mask_val.shape[-1], axis=-1)
for epoch in range(num_epoch):
time_start = time.time()
for i in range(EPOCH_SIZE):
X, y, m, batch_idxs = next(datagen)
# repeat targets based on max sequence len
y = y.reshape((-1, 1))
y = y.repeat(m.shape[-1], axis=-1)
X_diff = gen_seq_batch_from_idx(train_X_audio, batch_idxs,
train_vidlens, integral_lens,
np.max(train_vidlens))
print_str = 'Epoch {} batch {}/{}: {} examples using adam with learning rate {:.4f}'.format(
epoch + 1, i + 1, EPOCH_SIZE, len(X), learning_rate)
print(print_str, end='')
sys.stdout.flush()
train(X, y, m, X_diff, WINDOW_SIZE)
print('\r', end='')
cost = compute_train_cost(X, y, m, X_diff, WINDOW_SIZE)
val_cost = compute_test_cost(X_val, y_val, mask_val, X_diff_val,
WINDOW_SIZE)
cost_train.append(cost)
cost_val.append(val_cost)
train_strip[epoch % STRIP_SIZE] = cost
val_window.push(val_cost)
gl = 100 * (cost_val[-1] / np.min(cost_val) - 1)
pk = 1000 * (np.sum(train_strip) /
(STRIP_SIZE * np.min(train_strip)) - 1)
pq = gl / pk
cr, val_conf = evaluate_model2(X_val, y_val_evaluate, mask_val,
X_diff_val, WINDOW_SIZE, val_fn)
class_rate.append(cr)
if val_cost < best_val:
best_val = val_cost
best_tr = cost
best_cr = cr
if fusiontype == 'adasum':
adascale_param = las.layers.get_all_param_values(
l_fuse, scaling_param=True)
test_cr, test_conf = evaluate_model2(X_test, y_test, mask_test,
X_diff_test, WINDOW_SIZE,
val_fn)
print(
"Epoch {} train cost = {}, val cost = {}, "
"GL loss = {:.3f}, GQ = {:.3f}, CR = {:.3f}, Test CR= {:.3f} ({:.1f}sec)"
.format(epoch + 1, cost_train[-1], cost_val[-1], gl, pq, cr,
test_cr,
time.time() - time_start))
else:
print("Epoch {} train cost = {}, val cost = {}, "
"GL loss = {:.3f}, GQ = {:.3f}, CR = {:.3f} ({:.1f}sec)".
format(epoch + 1, cost_train[-1], cost_val[-1], gl, pq, cr,
time.time() - time_start))
if epoch >= validation_window and early_stop2(val_window, best_val,
validation_window):
break
numbers = ['0', '1', '2', '3', '4', '5', '6', '7', '8', '9']
print('Final Model')
print('CR: {}, val loss: {}, Test CR: {}'.format(best_cr, best_val,
test_cr))
if fusiontype == 'adasum':
print("final scaling params: {}".format(adascale_param))
print('confusion matrix: ')
plot_confusion_matrix(test_conf, numbers, fmt='latex')
plot_validation_cost(cost_train, cost_val, savefilename='valid_cost')
if 'write_results' in options:
results_file = options['write_results']
with open(results_file, mode='a') as f:
f.write('{},{},{},{},{},{},{},{},{},{},{},{}\n'.format(
use_finetuning, 'yes', use_peepholes, 'adam', weight_init,
'RELU', use_blstm, learning_rate, best_tr, best_val,
best_cr * 100, test_cr * 100))
s = ','.join([str(v) for v in cost_train])
f.write('{}\n'.format(s))
s = ','.join([str(v) for v in cost_val])
f.write('{}\n'.format(s))
s = ','.join([str(v) for v in class_rate])
f.write('{}\n'.format(s))
def main():
configure_theano()
options = parse_options()
config_file = options['config']
config = ConfigParser.ConfigParser()
config.read(config_file)
print('Reading Config File: {}...'.format(config_file))
print(config.items('data'))
print(config.items('models'))
print(config.items('training'))
print('CLI options: {}'.format(options.items()))
print('preprocessing dataset...')
data = load_mat_file(config.get('data', 'images'))
dct_data = load_mat_file(config.get('data', 'dct'))
no_coeff = config.getint('models', 'no_coeff')
output_classes = config.getint('models', 'output_classes')
lstm_size = config.getint('models', 'lstm_size')
# capture training parameters
validation_window = int(options['validation_window']) \
if 'validation_window' in options else config.getint('training', 'validation_window')
no_epochs = int(options['no_epochs']) if 'no_epochs' in options else config.getint('training', 'no_epochs')
weight_init = options['weight_init'] if 'weight_init' in options else config.get('training', 'weight_init')
learning_rate = options['learning_rate'] if 'learning_rate' in options \
else config.getfloat('training', 'learning_rate')
epochsize = options['epochsize'] if 'epochsize' in options else config.getint('training', 'epochsize')
batchsize = options['batchsize'] if 'batchsize' in options else config.getint('training', 'batchsize')
use_peepholes = options['use_peepholes'] if 'use_peepholes' in options else config.getboolean('training',
'use_peepholes')
use_blstm = config.getboolean('training', 'use_blstm')
use_finetuning = config.getboolean('training', 'use_finetuning')
weight_init_fn = las.init.GlorotUniform()
if weight_init == 'glorot':
weight_init_fn = las.init.GlorotUniform()
if weight_init == 'norm':
weight_init_fn = las.init.Normal(0.1)
if weight_init == 'uniform':
weight_init_fn = las.init.Uniform()
if weight_init == 'ortho':
weight_init_fn = las.init.Orthogonal()
train_vidlens = data['trVideoLengthVec'].astype('int').reshape((-1,))
val_vidlens = data['valVideoLengthVec'].astype('int').reshape((-1,))
test_vidlens = data['testVideoLengthVec'].astype('int').reshape((-1,))
train_X = data['trData'].astype('float32')
val_X = data['valData'].astype('float32')
test_X = data['testData'].astype('float32')
train_dct = dct_data['trDctFeatures'].astype('float32')
val_dct = dct_data['valDctFeatures'].astype('float32')
test_dct = dct_data['testDctFeatures'].astype('float32')
# +1 to handle the -1 introduced in lstm_gendata
train_y = data['trTargetsVec'].astype('int').reshape((-1,)) + 1
val_y = data['valTargetsVec'].astype('int').reshape((-1,)) + 1
test_y = data['testTargetsVec'].astype('int').reshape((-1,)) + 1
# featurewise normalize dct features
train_dct, dct_mean, dct_std = featurewise_normalize_sequence(train_dct)
val_dct = (val_dct - dct_mean) / dct_std
test_dct = (test_dct - dct_mean) / dct_std
# IMPT: the encoder was trained with fortan ordered images, so to visualize
# convert all the images to C order using reshape_images_order()
# output = dbn.predict(test_X)
# test_X = reshape_images_order(test_X, (26, 44))
# output = reshape_images_order(output, (26, 44))
# visualize_reconstruction(test_X[:36, :], output[:36, :], shape=(26, 44))
inputs = T.tensor3('inputs', dtype='float32')
mask = T.matrix('mask', dtype='uint8')
targets = T.imatrix('targets')
print('constructing end to end model...')
network = lstm_classifier_majority_vote.create_model((None, None, no_coeff*3), inputs,
(None, None), mask,
lstm_size, output_classes, w_init=weight_init_fn)
print_network(network)
print('compiling model...')
predictions = las.layers.get_output(network, deterministic=False)
all_params = las.layers.get_all_params(network, trainable=True)
cost = temporal_softmax_loss(predictions, targets, mask)
updates = adam(cost, all_params)
train = theano.function(
[inputs, targets, mask],
cost, updates=updates, allow_input_downcast=True)
compute_train_cost = theano.function([inputs, targets, mask], cost, allow_input_downcast=True)
test_predictions = las.layers.get_output(network, deterministic=True)
test_cost = temporal_softmax_loss(test_predictions, targets, mask)
compute_test_cost = theano.function(
[inputs, targets, mask], test_cost, allow_input_downcast=True)
val_fn = theano.function([inputs, mask], test_predictions, allow_input_downcast=True)
# We'll train the network with 10 epochs of 30 minibatches each
print('begin training...')
cost_train = []
cost_val = []
class_rate = []
STRIP_SIZE = 3
val_window = circular_list(validation_window)
train_strip = np.zeros((STRIP_SIZE,))
best_val = float('inf')
best_tr = float('inf')
best_cr = 0.0
datagen = gen_lstm_batch_random(train_X, train_y, train_vidlens, batchsize=batchsize)
val_datagen = gen_lstm_batch_random(val_X, val_y, val_vidlens, batchsize=len(val_vidlens))
test_datagen = gen_lstm_batch_random(test_X, test_y, test_vidlens, batchsize=len(test_vidlens))
integral_lens = compute_integral_len(train_vidlens)
# We'll use this "validation set" to periodically check progress
X_val, y_val, mask_val, idxs_val = next(val_datagen)
integral_lens_val = compute_integral_len(val_vidlens)
dct_val = gen_seq_batch_from_idx(val_dct, idxs_val, val_vidlens, integral_lens_val, np.max(val_vidlens))
X_test, y_test, mask_test, idxs_test = next(test_datagen)
integral_lens_test = compute_integral_len(test_vidlens)
dct_test = gen_seq_batch_from_idx(test_dct, idxs_test, test_vidlens, integral_lens_test, np.max(test_vidlens))
# reshape the targets for validation
y_val_evaluate = y_val
y_val = y_val.reshape((-1, 1)).repeat(mask_val.shape[-1], axis=-1)
for epoch in range(no_epochs):
time_start = time.time()
for i in range(epochsize):
_, y, m, batch_idxs = next(datagen)
# repeat targets based on max sequence len
y = y.reshape((-1, 1))
y = y.repeat(m.shape[-1], axis=-1)
d = gen_seq_batch_from_idx(train_dct, batch_idxs, train_vidlens, integral_lens, np.max(train_vidlens))
print_str = 'Epoch {} batch {}/{}: {} examples using adam'.format(
epoch + 1, i + 1, epochsize, len(y))
print(print_str, end='')
sys.stdout.flush()
train(d, y, m)
print('\r', end='')
cost = compute_train_cost(d, y, m)
val_cost = compute_test_cost(dct_val, y_val, mask_val)
cost_train.append(cost)
cost_val.append(val_cost)
train_strip[epoch % STRIP_SIZE] = cost
val_window.push(val_cost)
gl = 100 * (cost_val[-1] / np.min(cost_val) - 1)
pk = 1000 * (np.sum(train_strip) / (STRIP_SIZE * np.min(train_strip)) - 1)
pq = gl / pk
cr, val_conf = evaluate_model2(dct_val, y_val_evaluate, mask_val, val_fn)
class_rate.append(cr)
if val_cost < best_val:
best_val = val_cost
best_conf = val_conf
best_cr = cr
test_cr, test_conf = evaluate_model2(dct_test, y_test, mask_test, val_fn)
print("Epoch {} train cost = {}, val cost = {}, "
"GL loss = {:.3f}, GQ = {:.3f}, CR = {:.3f}, Test CR= {:.3f} ({:.1f}sec)"
.format(epoch + 1, cost_train[-1], cost_val[-1], gl, pq, cr, test_cr, time.time() - time_start))
else:
print("Epoch {} train cost = {}, val cost = {}, "
"GL loss = {:.3f}, GQ = {:.3f}, CR = {:.3f} ({:.1f}sec)"
.format(epoch + 1, cost_train[-1], cost_val[-1], gl, pq, cr, time.time() - time_start))
if epoch >= validation_window and early_stop2(val_window, best_val, validation_window):
break
numbers = ['0', '1', '2', '3', '4', '5', '6', '7', '8', '9']
print('Final Model')
print('CR: {}, val loss: {}, Test CR: {}'.format(best_cr, best_val, test_cr))
print('confusion matrix: ')
plot_confusion_matrix(test_conf, numbers, fmt='latex')
plot_validation_cost(cost_train, cost_val, savefilename='valid_cost')
if 'write_results' in options:
results_file = options['write_results']
with open(results_file, mode='a') as f:
f.write('{},{},{},{},{},{},{},{},{},{},{},{}\n'.format(use_finetuning, 'yes', use_peepholes,
'adam', weight_init, 'N/A',
use_blstm, learning_rate, best_tr,
best_val, best_cr*100, test_cr*100))
s = ','.join([str(v) for v in cost_train])
f.write('{}\n'.format(s))
s = ','.join([str(v) for v in cost_val])
f.write('{}\n'.format(s))
s = ','.join([str(v) for v in class_rate])
f.write('{}\n'.format(s))
def main():
configure_theano()
options = parse_options()
config_file = options['config']
config = ConfigParser.ConfigParser()
config.read(config_file)
print('CLI options: {}'.format(options.items()))
print('Reading Config File: {}...'.format(config_file))
print(config.items('data'))
print(config.items('models'))
print(config.items('training'))
print('preprocessing dataset...')
data = load_mat_file(config.get('data', 'images'))
ae_pretrained = config.get('models', 'pretrained')
ae_pretrained_diff = config.get('models', 'pretrained_diff')
fusiontype = config.get('models', 'fusiontype')
# capture training parameters
validation_window = int(options['validation_window']) \
if 'validation_window' in options else config.getint('training', 'validation_window')
num_epoch = int(options['num_epoch']) if 'num_epoch' in options else config.getint('training', 'num_epoch')
weight_init = options['weight_init'] if 'weight_init' in options else config.get('training', 'weight_init')
learning_rate = options['learning_rate'] if 'learning_rate' in options \
else config.getfloat('training', 'learning_rate')
use_peepholes = options['use_peepholes'] if 'use_peepholes' in options else config.getboolean('training',
'use_peepholes')
epochsize = config.getint('training', 'epochsize')
batchsize = config.getint('training', 'batchsize')
windowsize = config.getint('training', 'windowsize')
weight_init_fn = las.init.GlorotUniform()
if weight_init == 'glorot':
weight_init_fn = las.init.GlorotUniform()
if weight_init == 'norm':
weight_init_fn = las.init.Normal(0.1)
if weight_init == 'uniform':
weight_init_fn = las.init.Uniform()
if weight_init == 'ortho':
weight_init_fn = las.init.Orthogonal()
train_subject_ids = read_data_split_file('data/train.txt')
val_subject_ids = read_data_split_file('data/val.txt')
test_subject_ids = read_data_split_file('data/test.txt')
data_matrix = data['dataMatrix']
targets_vec = data['targetsVec'].reshape((-1,))
subjects_vec = data['subjectsVec'].reshape((-1,))
vidlen_vec = data['videoLengthVec'].reshape((-1,))
data_matrix = reorder_data(data_matrix, (30, 50))
train_X, train_y, train_vidlens, train_subjects, \
val_X, val_y, val_vidlens, val_subjects, \
test_X, test_y, test_vidlens, test_subjects = split_seq_data(data_matrix, targets_vec, subjects_vec, vidlen_vec,
train_subject_ids, val_subject_ids, test_subject_ids)
train_X_diff = compute_diff_images(train_X, train_vidlens)
val_X_diff = compute_diff_images(val_X, val_vidlens)
test_X_diff = compute_diff_images(test_X, test_vidlens)
train_X = sequencewise_mean_image_subtraction(train_X, train_vidlens)
val_X = sequencewise_mean_image_subtraction(val_X, val_vidlens)
test_X = sequencewise_mean_image_subtraction(test_X, test_vidlens)
ae = load_dbn(ae_pretrained)
ae_diff = load_dbn(ae_pretrained_diff)
# IMPT: the encoder was trained with fortan ordered images, so to visualize
# convert all the images to C order using reshape_images_order()
# output = dbn.predict(test_X)
# test_X = reshape_images_order(test_X, (26, 44))
# output = reshape_images_order(output, (26, 44))
# visualize_reconstruction(test_X[:36, :], output[:36, :], shape=(26, 44))
window = T.iscalar('theta')
inputs = T.tensor3('inputs', dtype='float32')
inputs_diff = T.tensor3('inputs_diff', dtype='float32')
mask = T.matrix('mask', dtype='uint8')
targets = T.imatrix('targets')
print('constructing end to end model...')
network, l_fuse = adenet_v2_2.create_model(ae, ae_diff, (None, None, 1500), inputs,
(None, None), mask,
(None, None, 1500), inputs_diff,
250, window, 10, fusiontype,
w_init_fn=weight_init_fn,
use_peepholes=use_peepholes)
print_network(network)
# draw_to_file(las.layers.get_all_layers(network), 'network.png')
print('compiling model...')
predictions = las.layers.get_output(network, deterministic=False)
all_params = las.layers.get_all_params(network, trainable=True)
cost = temporal_softmax_loss(predictions, targets, mask)
updates = adam(cost, all_params, learning_rate=learning_rate)
train = theano.function(
[inputs, targets, mask, inputs_diff, window],
cost, updates=updates, allow_input_downcast=True)
compute_train_cost = theano.function([inputs, targets, mask, inputs_diff, window],
cost, allow_input_downcast=True)
test_predictions = las.layers.get_output(network, deterministic=True)
test_cost = temporal_softmax_loss(test_predictions, targets, mask)
compute_test_cost = theano.function(
[inputs, targets, mask, inputs_diff, window], test_cost, allow_input_downcast=True)
val_fn = theano.function([inputs, mask, inputs_diff, window], test_predictions, allow_input_downcast=True)
# We'll train the network with 10 epochs of 30 minibatches each
print('begin training...')
cost_train = []
cost_val = []
class_rate = []
STRIP_SIZE = 3
val_window = circular_list(validation_window)
train_strip = np.zeros((STRIP_SIZE,))
best_val = float('inf')
best_tr = float('inf')
best_cr = 0.0
datagen = gen_lstm_batch_random(train_X, train_y, train_vidlens, batchsize=batchsize)
integral_lens = compute_integral_len(train_vidlens)
val_datagen = gen_lstm_batch_random(val_X, val_y, val_vidlens, batchsize=len(val_vidlens))
test_datagen = gen_lstm_batch_random(test_X, test_y, test_vidlens, batchsize=len(test_vidlens))
# We'll use this "validation set" to periodically check progress
X_val, y_val, mask_val, idxs_val = next(val_datagen)
integral_lens_val = compute_integral_len(val_vidlens)
X_diff_val = gen_seq_batch_from_idx(val_X_diff, idxs_val, val_vidlens, integral_lens_val, np.max(val_vidlens))
# we use the test set to check final classification rate
X_test, y_test, mask_test, idxs_test = next(test_datagen)
integral_lens_test = compute_integral_len(test_vidlens)
X_diff_test = gen_seq_batch_from_idx(test_X_diff, idxs_test, test_vidlens, integral_lens_test, np.max(test_vidlens))
# reshape the targets for validation
y_val_evaluate = y_val
y_val = y_val.reshape((-1, 1)).repeat(mask_val.shape[-1], axis=-1)
for epoch in range(num_epoch):
time_start = time.time()
for i in range(epochsize):
X, y, m, batch_idxs = next(datagen)
# repeat targets based on max sequence len
y = y.reshape((-1, 1))
y = y.repeat(m.shape[-1], axis=-1)
X_diff = gen_seq_batch_from_idx(train_X_diff, batch_idxs,
train_vidlens, integral_lens, np.max(train_vidlens))
print_str = 'Epoch {} batch {}/{}: {} examples using adam'.format(
epoch + 1, i + 1, epochsize, len(X))
print(print_str, end='')
sys.stdout.flush()
train(X, y, m, X_diff, windowsize)
print('\r', end='')
cost = compute_train_cost(X, y, m, X_diff, windowsize)
val_cost = compute_test_cost(X_val, y_val, mask_val, X_diff_val, windowsize)
cost_train.append(cost)
cost_val.append(val_cost)
train_strip[epoch % STRIP_SIZE] = cost
val_window.push(val_cost)
gl = 100 * (cost_val[-1] / np.min(cost_val) - 1)
pk = 1000 * (np.sum(train_strip) / (STRIP_SIZE * np.min(train_strip)) - 1)
pq = gl / pk
cr, val_conf = evaluate_model2(X_val, y_val_evaluate, mask_val, X_diff_val, windowsize, val_fn)
class_rate.append(cr)
if val_cost < best_val:
best_val = val_cost
best_tr = cost
best_cr = cr
if fusiontype == 'adasum':
adascale_param = las.layers.get_all_param_values(l_fuse, scaling_param=True)
test_cr, test_conf = evaluate_model2(X_test, y_test, mask_test,
X_diff_test, windowsize, val_fn)
print("Epoch {} train cost = {}, val cost = {}, "
"GL loss = {:.3f}, GQ = {:.3f}, CR = {:.3f}, Test CR= {:.3f} ({:.1f}sec)"
.format(epoch + 1, cost_train[-1], cost_val[-1], gl, pq, cr, test_cr, time.time() - time_start))
else:
print("Epoch {} train cost = {}, val cost = {}, "
"GL loss = {:.3f}, GQ = {:.3f}, CR = {:.3f} ({:.1f}sec)"
.format(epoch + 1, cost_train[-1], cost_val[-1], gl, pq, cr, time.time() - time_start))
if epoch >= validation_window and early_stop2(val_window, best_val, validation_window):
break
numbers = ['0', '1', '2', '3', '4', '5', '6', '7', '8', '9']
print('Final Model')
print('CR: {}, val loss: {}, Test CR: {}'.format(best_cr, best_val, test_cr))
if fusiontype == 'adasum':
print("final scaling params: {}".format(adascale_param))
print('confusion matrix: ')
plot_confusion_matrix(test_conf, numbers, fmt='latex')
plot_validation_cost(cost_train, cost_val, savefilename='valid_cost')
if 'write_results' in options:
results_file = options['write_results']
with open(results_file, mode='a') as f:
f.write('{},{},{}\n'.format(test_cr, best_cr, best_val))
def main():
def signal_handler(signal, frame):
global terminate
terminate = True
print('terminating...'.format(terminate))
signal.signal(signal.SIGINT, signal_handler)
configure_theano()
options = parse_options()
X, X_val = generate_data()
# X = np.reshape(X, (-1, 1, 30, 40))[:-5]
print('X type and shape:', X.dtype, X.shape)
print('X.min():', X.min())
print('X.max():', X.max())
# X_val = np.reshape(X_val, (-1, 1, 30, 40))[:-1]
print('X_val type and shape:', X_val.dtype, X_val.shape)
print('X_val.min():', X_val.min())
print('X_val.max():', X_val.max())
# we need our target to be 1 dimensional
X_out = X.reshape((X.shape[0], -1))
X_val_out = X_val.reshape((X_val.shape[0], -1))
print('X_out:', X_out.dtype, X_out.shape)
print('X_val_out', X_val_out.dtype, X_val_out.shape)
# X_noisy = apply_gaussian_noise(X_out)
# visualize_reconstruction(X_noisy[0:25], X_out[0:25], shape=(28, 28))
# X = np.reshape(X_noisy, (-1, 1, 28, 28))
print('constructing and compiling model...')
# input_var = T.tensor4('input', dtype='float32')
input_var = T.tensor3('input', dtype='float32')
target_var = T.matrix('output', dtype='float32')
lr = theano.shared(np.array(0.8, dtype=theano.config.floatX),
name='learning_rate')
lr_decay = np.array(0.9, dtype=theano.config.floatX)
# try building a reshaping layer
# network = create_model(input_var, (None, 1, 30, 40), options)
l_input = InputLayer((None, None, 1200), input_var, name='input')
l_input = ReshapeLayer(l_input, (-1, 1, 30, 40), name='reshape_input')
# l_input = InputLayer((None, 1, 30, 40), input_var, name='input')
if options['MODEL'] == 'normal':
network, encoder = avletters_convae.create_model(l_input, options)
if options['MODEL'] == 'batchnorm':
network, encoder = avletters_convae_bn.create_model(l_input, options)
if options['MODEL'] == 'dropout':
network, encoder = avletters_convae_drop.create_model(l_input, options)
if options['MODEL'] == 'bn+dropout':
network, encoder = avletters_convae_bndrop.create_model(
l_input, options)
print('AE Network architecture: {}'.format(options['MODEL']))
print_network(network)
recon = las.layers.get_output(network, deterministic=False)
all_params = las.layers.get_all_params(network, trainable=True)
cost = T.mean(squared_error(recon, target_var))
updates = adadelta(cost, all_params, lr)
# updates = las.updates.apply_nesterov_momentum(updates, all_params, momentum=0.90)
use_max_constraint = False
print('apply max norm constraint: {}'.format(use_max_constraint))
if use_max_constraint:
MAX_NORM = 4
for param in las.layers.get_all_params(network, regularizable=True):
if param.ndim > 1: # only apply to dimensions larger than 1, exclude biases
# updates[param] = norm_constraint(param, MAX_NORM * las.utils.compute_norms(param.get_value()).mean())
updates[param] = norm_constraint(param, MAX_NORM)
train = theano.function([input_var, target_var],
recon,
updates=updates,
allow_input_downcast=True)
train_cost_fn = theano.function([input_var, target_var],
cost,
allow_input_downcast=True)
eval_recon = las.layers.get_output(network, deterministic=True)
eval_cost = T.mean(las.objectives.squared_error(eval_recon, target_var))
eval_cost_fn = theano.function([input_var, target_var],
eval_cost,
allow_input_downcast=True)
recon_fn = theano.function([input_var],
eval_recon,
allow_input_downcast=True)
if terminate:
exit()
NUM_EPOCHS = options['NUM_EPOCHS']
EPOCH_SIZE = options['EPOCH_SIZE']
NO_STRIDES = options['NO_STRIDES']
VAL_NO_STRIDES = options['VAL_NO_STRIDES']
print('begin training for {} epochs...'.format(NUM_EPOCHS))
datagen = batch_iterator(X, X_out, 128)
costs = []
val_costs = []
for epoch in range(NUM_EPOCHS):
time_start = time.time()
for i in range(EPOCH_SIZE):
batch_X, batch_y = next(datagen)
print_str = 'Epoch {} batch {}/{}: {} examples at learning rate = {:.4f}'.format(
epoch + 1, i + 1, EPOCH_SIZE, len(batch_X), lr.get_value())
print(print_str, end='')
sys.stdout.flush()
batch_X = batch_X.reshape((-1, 1, 1200))
train(batch_X, batch_y)
print('\r', end='')
if terminate:
break
if terminate:
break
cost = batch_compute_cost(X, X_out, NO_STRIDES, train_cost_fn)
val_cost = batch_compute_cost(X_val, X_val_out, VAL_NO_STRIDES,
eval_cost_fn)
costs.append(cost)
val_costs.append(val_cost)
print("Epoch {} train cost = {}, validation cost = {} ({:.1f}sec) ".
format(epoch + 1, cost, val_cost,
time.time() - time_start))
if epoch > 10:
lr.set_value(lr.get_value() * lr_decay)
X_val_recon = recon_fn(X_val)
visualize_reconstruction(X_val_out[450:550],
X_val_recon[450:550],
shape=(30, 40),
savefilename='avletters')
plot_validation_cost(costs, val_costs, None, savefilename='valid_cost')
conv2d1 = las.layers.get_all_layers(network)[2]
visualize.plot_conv_weights(conv2d1, (15, 14)).savefig('conv2d1.png')
print('saving encoder...')
save_model(encoder, 'models/conv_encoder.dat')
save_model(network, 'models/conv_ae.dat')
def main():
configure_theano()
options = parse_options()
config_file = options['config']
config = ConfigParser.ConfigParser()
config.read(config_file)
print('CLI options: {}'.format(options.items()))
print('Reading Config File: {}...'.format(config_file))
print(config.items('data'))
print(config.items('models'))
print(config.items('training'))
print('preprocessing dataset...')
data = load_mat_file(config.get('data', 'images'))
dct_data = load_mat_file(config.get('data', 'dct'))
ae_pretrained = config.get('models', 'pretrained')
ae_pretrained_diff = config.get('models', 'pretrained_diff')
fusiontype = config.get('models', 'fusiontype')
lstm_size = config.getint('models', 'lstm_size')
output_classes = config.getint('models', 'output_classes')
use_peepholes = options['use_peepholes'] if 'use_peepholes' in options else config.getboolean('models',
'use_peepholes')
use_blstm = config.getboolean('models', 'use_blstm')
delta_window = config.getint('models', 'delta_window')
input_dimensions = config.getint('models', 'input_dimensions')
# capture training parameters
validation_window = int(options['validation_window']) \
if 'validation_window' in options else config.getint('training', 'validation_window')
num_epoch = int(options['num_epoch']) if 'num_epoch' in options else config.getint('training', 'num_epoch')
weight_init = options['weight_init'] if 'weight_init' in options else config.get('training', 'weight_init')
use_finetuning = config.getboolean('training', 'use_finetuning')
learning_rate = config.getfloat('training', 'learning_rate')
batchsize = config.getint('training', 'batchsize')
epochsize = config.getint('training', 'epochsize')
weight_init_fn = las.init.GlorotUniform()
if weight_init == 'glorot':
weight_init_fn = las.init.GlorotUniform()
if weight_init == 'norm':
weight_init_fn = las.init.Normal(0.1)
if weight_init == 'uniform':
weight_init_fn = las.init.Uniform()
if weight_init == 'ortho':
weight_init_fn = las.init.Orthogonal()
# 53 subjects, 70 utterances, 5 view angles
# s[x]_v[y]_u[z].mp4
# resized, height, width = (26, 44)
# ['dataMatrix', 'targetH', 'targetsPerVideoVec', 'videoLengthVec', '__header__', 'targetsVec',
# '__globals__', 'iterVec', 'filenamesVec', 'dataMatrixCells', 'subjectsVec', 'targetW', '__version__']
print(data.keys())
X = data['dataMatrix'].astype('float32')
y = data['targetsVec'].astype('int32')
y = y.reshape((len(y),))
dct_feats = dct_data['dctFeatures'].astype('float32')
uniques = np.unique(y)
print('number of classifications: {}'.format(len(uniques)))
subjects = data['subjectsVec'].astype('int')
subjects = subjects.reshape((len(subjects),))
video_lens = data['videoLengthVec'].astype('int')
video_lens = video_lens.reshape((len(video_lens,)))
# X = reorder_data(X, (26, 44), 'f', 'c')
# print('performing sequencewise mean image removal...')
# X = sequencewise_mean_image_subtraction(X, video_lens)
# visualize_images(X[550:650], (26, 44))
X_diff = compute_diff_images(X, video_lens)
# mean remove dct features
dct_feats = sequencewise_mean_image_subtraction(dct_feats, video_lens)
train_subject_ids = read_data_split_file('data/train_30_10_12.txt')
val_subject_ids = read_data_split_file('data/val_30_10_12.txt')
test_subject_ids = read_data_split_file('data/test_30_10_12.txt')
print('Train: {}'.format(train_subject_ids))
print('Validation: {}'.format(val_subject_ids))
print('Test: {}'.format(test_subject_ids))
train_X, train_y, train_dct, train_X_diff, train_vidlens, train_subjects, \
val_X, val_y, val_dct, val_X_diff, val_vidlens, val_subjects, \
test_X, test_y, test_dct, test_X_diff, test_vidlens, test_subjects = \
split_data(X, y, dct_feats, X_diff, subjects, video_lens, train_subject_ids, val_subject_ids, test_subject_ids)
assert train_X.shape[0] + val_X.shape[0] + test_X.shape[0] == len(X)
assert train_y.shape[0] + val_y.shape[0] + test_y.shape[0] == len(y)
assert train_vidlens.shape[0] + val_vidlens.shape[0] + test_vidlens.shape[0] == len(video_lens)
assert train_subjects.shape[0] + val_vidlens.shape[0] + test_subjects.shape[0] == len(subjects)
train_X = normalize_input(train_X, centralize=True)
val_X = normalize_input(val_X, centralize=True)
test_X = normalize_input(test_X, centralize=True)
train_y -= 1
val_y -= 1
test_y -= 1
print('loading pretrained encoder: {}...'.format(ae_pretrained))
ae = load_dbn(ae_pretrained)
print('loading pretrained encoder: {}...'.format(ae_pretrained_diff))
ae_diff = load_dbn(ae_pretrained_diff)
# IMPT: the encoder was trained with fortan ordered images, so to visualize
# convert all the images to C order using reshape_images_order()
# output = dbn.predict(test_X)
# test_X = reshape_images_order(test_X, (26, 44))
# output = reshape_images_order(output, (26, 44))
# visualize_reconstruction(test_X[:36, :], output[:36, :], shape=(26, 44))
window = T.iscalar('theta')
inputs = T.tensor3('inputs', dtype='float32')
inputs_diff = T.tensor3('inputs_diff', dtype='float32')
mask = T.matrix('mask', dtype='uint8')
targets = T.imatrix('targets')
print('constructing end to end model...')
if use_blstm:
network, l_fuse = adenet_v2_2.create_model(ae, ae_diff, (None, None, input_dimensions), inputs,
(None, None), mask,
(None, None, input_dimensions), inputs_diff,
lstm_size, window, output_classes, fusiontype,
weight_init_fn, use_peepholes)
else:
network, l_fuse = adenet_v2_4.create_model(ae, ae_diff, (None, None, input_dimensions), inputs,
(None, None), mask,
(None, None, input_dimensions), inputs_diff,
lstm_size, window, output_classes, fusiontype,
weight_init_fn, use_peepholes)
print_network(network)
# draw_to_file(las.layers.get_all_layers(network), 'network.png')
print('compiling model...')
predictions = las.layers.get_output(network, deterministic=False)
all_params = las.layers.get_all_params(network, trainable=True)
cost = temporal_softmax_loss(predictions, targets, mask)
updates = adam(cost, all_params, learning_rate=learning_rate)
train = theano.function(
[inputs, targets, mask, inputs_diff, window],
cost, updates=updates, allow_input_downcast=True)
compute_train_cost = theano.function([inputs, targets, mask, inputs_diff, window],
cost, allow_input_downcast=True)
test_predictions = las.layers.get_output(network, deterministic=True)
test_cost = temporal_softmax_loss(test_predictions, targets, mask)
compute_test_cost = theano.function(
[inputs, targets, mask, inputs_diff, window], test_cost, allow_input_downcast=True)
val_fn = theano.function([inputs, mask, inputs_diff, window], test_predictions, allow_input_downcast=True)
# We'll train the network with 10 epochs of 30 minibatches each
print('begin training...')
cost_train = []
cost_val = []
class_rate = []
STRIP_SIZE = 3
val_window = circular_list(validation_window)
train_strip = np.zeros((STRIP_SIZE,))
best_val = float('inf')
best_cr = 0.0
datagen = gen_lstm_batch_random(train_X, train_y, train_vidlens, batchsize=batchsize)
integral_lens = compute_integral_len(train_vidlens)
val_datagen = gen_lstm_batch_random(val_X, val_y, val_vidlens, batchsize=len(val_vidlens))
test_datagen = gen_lstm_batch_random(test_X, test_y, test_vidlens, batchsize=len(test_vidlens))
# We'll use this "validation set" to periodically check progress
X_val, y_val, mask_val, idxs_val = next(val_datagen)
integral_lens_val = compute_integral_len(val_vidlens)
X_diff_val = gen_seq_batch_from_idx(val_X_diff, idxs_val, val_vidlens, integral_lens_val, np.max(val_vidlens))
# we use the test set to check final classification rate
X_test, y_test, mask_test, idxs_test = next(test_datagen)
integral_lens_test = compute_integral_len(test_vidlens)
X_diff_test = gen_seq_batch_from_idx(test_X_diff, idxs_test, test_vidlens, integral_lens_test, np.max(test_vidlens))
# reshape the targets for validation
y_val_evaluate = y_val
y_val = y_val.reshape((-1, 1)).repeat(mask_val.shape[-1], axis=-1)
for epoch in range(num_epoch):
time_start = time.time()
for i in range(epochsize):
X, y, m, batch_idxs = next(datagen)
# repeat targets based on max sequence len
y = y.reshape((-1, 1))
y = y.repeat(m.shape[-1], axis=-1)
X_diff = gen_seq_batch_from_idx(train_X_diff, batch_idxs,
train_vidlens, integral_lens, np.max(train_vidlens))
print_str = 'Epoch {} batch {}/{}: {} examples using adam'.format(epoch + 1, i + 1, epochsize, len(X))
print(print_str, end='')
sys.stdout.flush()
train(X, y, m, X_diff, delta_window)
print('\r', end='')
cost = compute_train_cost(X, y, m, X_diff, delta_window)
val_cost = compute_test_cost(X_val, y_val, mask_val, X_diff_val, delta_window)
cost_train.append(cost)
cost_val.append(val_cost)
train_strip[epoch % STRIP_SIZE] = cost
val_window.push(val_cost)
gl = 100 * (cost_val[-1] / np.min(cost_val) - 1)
pk = 1000 * (np.sum(train_strip) / (STRIP_SIZE * np.min(train_strip)) - 1)
pq = gl / pk
cr, val_conf = evaluate_model2(X_val, y_val_evaluate, mask_val, X_diff_val, delta_window, val_fn)
class_rate.append(cr)
if val_cost < best_val:
best_val = val_cost
best_cr = cr
if fusiontype == 'adasum':
adascale_param = las.layers.get_all_param_values(l_fuse, scaling_param=True)
test_cr, test_conf = evaluate_model2(X_test, y_test, mask_test,
X_diff_test, delta_window, val_fn)
print("Epoch {} train cost = {}, val cost = {}, "
"GL loss = {:.3f}, GQ = {:.3f}, CR = {:.3f}, Test CR= {:.3f} ({:.1f}sec)"
.format(epoch + 1, cost_train[-1], cost_val[-1], gl, pq, cr, test_cr, time.time() - time_start))
else:
print("Epoch {} train cost = {}, val cost = {}, "
"GL loss = {:.3f}, GQ = {:.3f}, CR = {:.3f} ({:.1f}sec)"
.format(epoch + 1, cost_train[-1], cost_val[-1], gl, pq, cr, time.time() - time_start))
if epoch >= validation_window and early_stop2(val_window, best_val, validation_window):
break
phrases = ['p1', 'p2', 'p3', 'p4', 'p5', 'p6', 'p7', 'p8', 'p9', 'p10']
print('Final Model')
print('CR: {}, val loss: {}, Test CR: {}'.format(best_cr, best_val, test_cr))
if fusiontype == 'adasum':
print("final scaling params: {}".format(adascale_param))
print('confusion matrix: ')
plot_confusion_matrix(test_conf, phrases, fmt='latex')
plot_validation_cost(cost_train, cost_val, savefilename='valid_cost')
if 'write_results' in options:
results_file = options['write_results']
with open(results_file, mode='a') as f:
f.write('{},{},{},{},{}\n'.format(validation_window, weight_init, use_peepholes, use_blstm, use_finetuning))
s = ','.join([str(v) for v in cost_train])
f.write('{}\n'.format(s))
s = ','.join([str(v) for v in cost_val])
f.write('{}\n'.format(s))
s = ','.join([str(v) for v in class_rate])
f.write('{}\n'.format(s))
f.write('{},{},{}\n'.format(fusiontype, best_cr, best_val))
def create_model(input_var, input_shape, options):
conv_num_filters1 = 100
conv_num_filters2 = 150
conv_num_filters3 = 200
filter_size1 = 5
filter_size2 = 5
filter_size3 = 3
pool_size = 2
encode_size = options['BOTTLENECK']
dense_mid_size = options['DENSE']
pad_in = 'valid'
pad_out = 'full'
scaled_tanh = create_scaled_tanh()
input = InputLayer(shape=input_shape, input_var=input_var, name='input')
conv2d1 = Conv2DLayer(input,
num_filters=conv_num_filters1,
filter_size=filter_size1,
pad=pad_in,
name='conv2d1',
nonlinearity=scaled_tanh)
maxpool2d2 = MaxPool2DLayer(conv2d1,
pool_size=pool_size,
name='maxpool2d2')
conv2d3 = Conv2DLayer(maxpool2d2,
num_filters=conv_num_filters2,
filter_size=filter_size2,
pad=pad_in,
name='conv2d3',
nonlinearity=scaled_tanh)
maxpool2d4 = MaxPool2DLayer(conv2d3,
pool_size=pool_size,
name='maxpool2d4',
pad=(1, 0))
conv2d5 = Conv2DLayer(maxpool2d4,
num_filters=conv_num_filters3,
filter_size=filter_size3,
pad=pad_in,
name='conv2d5',
nonlinearity=scaled_tanh)
reshape6 = ReshapeLayer(conv2d5, shape=([0], -1), name='reshape6') # 3000
reshape6_output = reshape6.output_shape[1]
dense7 = DenseLayer(reshape6,
num_units=dense_mid_size,
name='dense7',
nonlinearity=scaled_tanh)
bottleneck = DenseLayer(dense7,
num_units=encode_size,
name='bottleneck',
nonlinearity=linear)
# print_network(bottleneck)
dense8 = DenseLayer(bottleneck,
num_units=dense_mid_size,
W=bottleneck.W.T,
name='dense8',
nonlinearity=linear)
dense9 = DenseLayer(dense8,
num_units=reshape6_output,
W=dense7.W.T,
nonlinearity=scaled_tanh,
name='dense9')
reshape10 = ReshapeLayer(dense9,
shape=([0], conv_num_filters3, 3, 5),
name='reshape10') # 32 x 4 x 7
deconv2d11 = Deconv2DLayer(reshape10,
conv2d5.input_shape[1],
conv2d5.filter_size,
stride=conv2d5.stride,
W=conv2d5.W,
flip_filters=not conv2d5.flip_filters,
name='deconv2d11',
nonlinearity=scaled_tanh)
upscale2d12 = Upscale2DLayer(deconv2d11,
scale_factor=pool_size,
name='upscale2d12')
deconv2d13 = Deconv2DLayer(upscale2d12,
conv2d3.input_shape[1],
conv2d3.filter_size,
stride=conv2d3.stride,
W=conv2d3.W,
flip_filters=not conv2d3.flip_filters,
name='deconv2d13',
nonlinearity=scaled_tanh)
upscale2d14 = Upscale2DLayer(deconv2d13,
scale_factor=pool_size,
name='upscale2d14')
deconv2d15 = Deconv2DLayer(upscale2d14,
conv2d1.input_shape[1],
conv2d1.filter_size,
stride=conv2d1.stride,
crop=(1, 0),
W=conv2d1.W,
flip_filters=not conv2d1.flip_filters,
name='deconv2d14',
nonlinearity=scaled_tanh)
reshape16 = ReshapeLayer(deconv2d15, ([0], -1), name='reshape16')
print_network(reshape16)
return reshape16
def main():
configure_theano()
options = parse_options()
config_file = options['config']
config = ConfigParser.ConfigParser()
config.read(config_file)
print('CLI options: {}'.format(options.items()))
print('Reading Config File: {}...'.format(config_file))
print(config.items('data'))
print(config.items('models'))
print(config.items('training'))
print('preprocessing dataset...')
data = load_mat_file(config.get('data', 'images'))
data_audio = load_mat_file(config.get('data', 'audio'))
ae_pretrained = config.get('models', 'pretrained')
ae_diff_pretrained = config.get('models', 'pretrained_diff')
fusiontype = config.get('models', 'fusiontype')
lstm_size = config.getint('models', 'lstm_size')
output_classes = config.getint('models', 'output_classes')
nonlinearity = options['nonlinearity'] if 'nonlinearity' in options else config.get('models', 'nonlinearity')
if nonlinearity == 'sigmoid':
nonlinearity = sigmoid
if nonlinearity == 'rectify':
nonlinearity = rectify
# capture training parameters
validation_window = int(options['validation_window']) \
if 'validation_window' in options else config.getint('training', 'validation_window')
num_epoch = int(options['num_epoch']) if 'num_epoch' in options else config.getint('training', 'num_epoch')
weight_init = options['weight_init'] if 'weight_init' in options else config.get('training', 'weight_init')
learning_rate = options['learning_rate'] if 'learning_rate' in options \
else config.getfloat('training', 'learning_rate')
use_peepholes = options['use_peepholes'] if 'use_peepholes' in options else config.getboolean('training',
'use_peepholes')
input_dimension = config.getint('models', 'input_dimension')
input_dimension2 = config.getint('models', 'input_dimension2')
use_blstm = config.getboolean('training', 'use_blstm')
use_finetuning = config.getboolean('training', 'use_finetuning')
weight_init_fn = las.init.GlorotUniform()
if weight_init == 'glorot':
weight_init_fn = las.init.GlorotUniform()
if weight_init == 'norm':
weight_init_fn = las.init.Normal(0.1)
if weight_init == 'uniform':
weight_init_fn = las.init.Uniform()
if weight_init == 'ortho':
weight_init_fn = las.init.Orthogonal()
train_vidlens = data['trVideoLengthVec'].astype('int').reshape((-1,))
val_vidlens = data['valVideoLengthVec'].astype('int').reshape((-1,))
test_vidlens = data['testVideoLengthVec'].astype('int').reshape((-1,))
train_X = data['trData'].astype('float32')
val_X = data['valData'].astype('float32')
test_X = data['testData'].astype('float32')
train_X_audio = data_audio['trData'].astype('float32')
val_X_audio = data_audio['valData'].astype('float32')
test_X_audio = data_audio['testData'].astype('float32')
# +1 to handle the -1 introduced in lstm_gendata
train_y = data['trTargetsVec'].astype('int').reshape((-1,)) + 1
val_y = data['valTargetsVec'].astype('int').reshape((-1,)) + 1
test_y = data['testTargetsVec'].astype('int').reshape((-1,)) + 1
train_X = reorder_data(train_X, (30, 50))
val_X = reorder_data(val_X, (30, 50))
test_X = reorder_data(test_X, (30, 50))
visual_weights, visual_biases = load_dbn(ae_pretrained)
audio_weights, audio_biases = load_dbn(ae_diff_pretrained)
# IMPT: the encoder was trained with fortan ordered images, so to visualize
# convert all the images to C order using reshape_images_order()
# output = dbn.predict(test_X)
# test_X = reshape_images_order(test_X, (26, 44))
# output = reshape_images_order(output, (26, 44))
# visualize_reconstruction(test_X[:36, :], output[:36, :], shape=(26, 44))
window = T.iscalar('theta')
visual_input = T.tensor3('visual_input', dtype='float32')
audio_input = T.tensor3('audio_input', dtype='float32')
mask = T.matrix('mask', dtype='uint8')
targets = T.imatrix('targets')
print('constructing end to end model...')
visual_net = avnet.create_pretrained_substream(visual_weights, visual_biases,
(None, None, input_dimension), visual_input,
(None, None), mask, 'visual',
lstm_size, window, nonlinearity, weight_init_fn, use_peepholes)
audio_net = avnet.create_pretrained_substream(audio_weights, audio_biases,
(None, None, input_dimension2), audio_input,
(None, None), mask, 'audio',
lstm_size, window, nonlinearity, weight_init_fn, use_peepholes)
network, l_fuse = avnet.create_model([visual_net, audio_net], (None, None), mask, lstm_size, output_classes,
fusiontype, weight_init_fn, use_peepholes)
print_network(network)
# draw_to_file(las.layers.get_all_layers(network), 'network.png')
print('compiling model...')
predictions = las.layers.get_output(network, deterministic=False)
all_params = las.layers.get_all_params(network, trainable=True)
cost = temporal_softmax_loss(predictions, targets, mask)
updates = adam(cost, all_params, learning_rate=learning_rate)
train = theano.function(
[visual_input, targets, mask, audio_input, window],
cost, updates=updates, allow_input_downcast=True)
compute_train_cost = theano.function([visual_input, targets, mask, audio_input, window],
cost, allow_input_downcast=True)
test_predictions = las.layers.get_output(network, deterministic=True)
test_cost = temporal_softmax_loss(test_predictions, targets, mask)
compute_test_cost = theano.function(
[visual_input, targets, mask, audio_input, window], test_cost, allow_input_downcast=True)
val_fn = theano.function([visual_input, mask, audio_input, window], test_predictions, allow_input_downcast=True)
# We'll train the network with 10 epochs of 30 minibatches each
print('begin training...')
cost_train = []
cost_val = []
class_rate = []
EPOCH_SIZE = 90
BATCH_SIZE = 10
WINDOW_SIZE = 9
STRIP_SIZE = 3
val_window = circular_list(validation_window)
train_strip = np.zeros((STRIP_SIZE,))
best_val = float('inf')
best_tr = float('inf')
best_cr = 0.0
datagen = gen_lstm_batch_random(train_X, train_y, train_vidlens, batchsize=BATCH_SIZE)
integral_lens = compute_integral_len(train_vidlens)
val_datagen = gen_lstm_batch_random(val_X, val_y, val_vidlens, batchsize=len(val_vidlens))
test_datagen = gen_lstm_batch_random(test_X, test_y, test_vidlens, batchsize=len(test_vidlens))
# We'll use this "validation set" to periodically check progress
X_val, y_val, mask_val, idxs_val = next(val_datagen)
integral_lens_val = compute_integral_len(val_vidlens)
X_diff_val = gen_seq_batch_from_idx(val_X_audio, idxs_val, val_vidlens, integral_lens_val, np.max(val_vidlens))
# we use the test set to check final classification rate
X_test, y_test, mask_test, idxs_test = next(test_datagen)
integral_lens_test = compute_integral_len(test_vidlens)
X_diff_test = gen_seq_batch_from_idx(test_X_audio, idxs_test, test_vidlens, integral_lens_test, np.max(test_vidlens))
# reshape the targets for validation
y_val_evaluate = y_val
y_val = y_val.reshape((-1, 1)).repeat(mask_val.shape[-1], axis=-1)
for epoch in range(num_epoch):
time_start = time.time()
for i in range(EPOCH_SIZE):
X, y, m, batch_idxs = next(datagen)
# repeat targets based on max sequence len
y = y.reshape((-1, 1))
y = y.repeat(m.shape[-1], axis=-1)
X_diff = gen_seq_batch_from_idx(train_X_audio, batch_idxs,
train_vidlens, integral_lens, np.max(train_vidlens))
print_str = 'Epoch {} batch {}/{}: {} examples using adam with learning rate {:.4f}'.format(
epoch + 1, i + 1, EPOCH_SIZE, len(X), learning_rate)
print(print_str, end='')
sys.stdout.flush()
train(X, y, m, X_diff, WINDOW_SIZE)
print('\r', end='')
cost = compute_train_cost(X, y, m, X_diff, WINDOW_SIZE)
val_cost = compute_test_cost(X_val, y_val, mask_val, X_diff_val, WINDOW_SIZE)
cost_train.append(cost)
cost_val.append(val_cost)
train_strip[epoch % STRIP_SIZE] = cost
val_window.push(val_cost)
gl = 100 * (cost_val[-1] / np.min(cost_val) - 1)
pk = 1000 * (np.sum(train_strip) / (STRIP_SIZE * np.min(train_strip)) - 1)
pq = gl / pk
cr, val_conf = evaluate_model2(X_val, y_val_evaluate, mask_val, X_diff_val, WINDOW_SIZE, val_fn)
class_rate.append(cr)
if val_cost < best_val:
best_val = val_cost
best_tr = cost
best_cr = cr
if fusiontype == 'adasum':
adascale_param = las.layers.get_all_param_values(l_fuse, scaling_param=True)
test_cr, test_conf = evaluate_model2(X_test, y_test, mask_test,
X_diff_test, WINDOW_SIZE, val_fn)
print("Epoch {} train cost = {}, val cost = {}, "
"GL loss = {:.3f}, GQ = {:.3f}, CR = {:.3f}, Test CR= {:.3f} ({:.1f}sec)"
.format(epoch + 1, cost_train[-1], cost_val[-1], gl, pq, cr, test_cr, time.time() - time_start))
else:
print("Epoch {} train cost = {}, val cost = {}, "
"GL loss = {:.3f}, GQ = {:.3f}, CR = {:.3f} ({:.1f}sec)"
.format(epoch + 1, cost_train[-1], cost_val[-1], gl, pq, cr, time.time() - time_start))
if epoch >= validation_window and early_stop2(val_window, best_val, validation_window):
break
numbers = ['0', '1', '2', '3', '4', '5', '6', '7', '8', '9']
print('Final Model')
print('CR: {}, val loss: {}, Test CR: {}'.format(best_cr, best_val, test_cr))
if fusiontype == 'adasum':
print("final scaling params: {}".format(adascale_param))
print('confusion matrix: ')
plot_confusion_matrix(test_conf, numbers, fmt='latex')
plot_validation_cost(cost_train, cost_val, savefilename='valid_cost')
if 'write_results' in options:
results_file = options['write_results']
with open(results_file, mode='a') as f:
f.write('{},{},{},{},{},{},{},{},{},{},{},{}\n'.format(use_finetuning, 'yes', use_peepholes,
'adam', weight_init, 'RELU',
use_blstm, learning_rate, best_tr,
best_val, best_cr*100, test_cr*100))
s = ','.join([str(v) for v in cost_train])
f.write('{}\n'.format(s))
s = ','.join([str(v) for v in cost_val])
f.write('{}\n'.format(s))
s = ','.join([str(v) for v in class_rate])
f.write('{}\n'.format(s))
def main():
configure_theano()
config_file = 'config/trimodal.ini'
print('loading config file: {}'.format(config_file))
config = ConfigParser.ConfigParser()
config.read(config_file)
print('Reading Config File: {}...'.format(config_file))
print(config.items('data'))
print(config.items('models'))
print(config.items('training'))
print('preprocessing dataset...')
data = load_mat_file(config.get('data', 'images'))
dct_data = load_mat_file(config.get('data', 'dct'))
ae_pretrained = config.get('models', 'pretrained')
ae_finetuned = config.get('models', 'finetuned')
ae_finetuned_diff = config.get('models', 'finetuned_diff')
use_adascale = config.getboolean('models', 'use_adascale')
learning_rate = float(config.get('training', 'learning_rate'))
decay_rate = float(config.get('training', 'decay_rate'))
decay_start = int(config.get('training', 'decay_start'))
do_finetune = config.getboolean('training', 'do_finetune')
save_finetune = config.getboolean('training', 'save_finetune')
load_finetune = config.getboolean('training', 'load_finetune')
load_finetune_diff = config.getboolean('training', 'load_finetune_diff')
# 53 subjects, 70 utterances, 5 view angles
# s[x]_v[y]_u[z].mp4
# resized, height, width = (26, 44)
# ['dataMatrix', 'targetH', 'targetsPerVideoVec', 'videoLengthVec', '__header__', 'targetsVec',
# '__globals__', 'iterVec', 'filenamesVec', 'dataMatrixCells', 'subjectsVec', 'targetW', '__version__']
print(data.keys())
X = data['dataMatrix'].astype('float32')
y = data['targetsVec'].astype('int32')
y = y.reshape((len(y),))
dct_feats = dct_data['dctFeatures'].astype('float32')
uniques = np.unique(y)
print('number of classifications: {}'.format(len(uniques)))
subjects = data['subjectsVec'].astype('int')
subjects = subjects.reshape((len(subjects),))
video_lens = data['videoLengthVec'].astype('int')
video_lens = video_lens.reshape((len(video_lens,)))
# X = reorder_data(X, (26, 44), 'f', 'c')
# print('performing sequencewise mean image removal...')
# X = sequencewise_mean_image_subtraction(X, video_lens)
# visualize_images(X[550:650], (26, 44))
X_diff = compute_diff_images(X, video_lens)
# mean remove dct features
dct_feats = sequencewise_mean_image_subtraction(dct_feats, video_lens)
train_subject_ids = read_data_split_file('data/train_val.txt')
test_subject_ids = read_data_split_file('data/test.txt')
print(train_subject_ids)
print(test_subject_ids)
train_X, train_y, train_dct, train_X_diff, train_vidlens, train_subjects, \
test_X, test_y, test_dct, test_X_diff, test_vidlens, test_subjects = \
split_data(X, y, dct_feats, X_diff, subjects, video_lens, train_subject_ids, test_subject_ids)
assert train_X.shape[0] + test_X.shape[0] == len(X)
assert train_y.shape[0] + test_y.shape[0] == len(y)
assert train_vidlens.shape[0] + test_vidlens.shape[0] == len(video_lens)
assert train_subjects.shape[0] + test_subjects.shape[0] == len(subjects)
train_X = normalize_input(train_X, centralize=True)
test_X = normalize_input(test_X, centralize=True)
# featurewise normalize dct features
train_dct, dct_mean, dct_std = featurewise_normalize_sequence(train_dct)
test_dct = (test_dct - dct_mean) / dct_std
if do_finetune:
print('performing finetuning on pretrained encoder: {}'.format(ae_pretrained))
ae = load_dbn(ae_pretrained)
ae.initialize()
ae.fit(train_X, train_X)
if save_finetune:
print('saving finetuned encoder: {}...'.format(ae_finetuned))
pickle.dump(ae, open(ae_finetuned, 'wb'))
if load_finetune:
print('loading finetuned encoder: {}...'.format(ae_finetuned))
ae = pickle.load(open(ae_finetuned, 'rb'))
ae.initialize()
if load_finetune_diff:
print('loading finetuned encoder: {}...'.format(ae_finetuned_diff))
ae_diff = pickle.load(open(ae_finetuned_diff, 'rb'))
ae_diff.initialize()
# IMPT: the encoder was trained with fortan ordered images, so to visualize
# convert all the images to C order using reshape_images_order()
# output = dbn.predict(test_X)
# test_X = reshape_images_order(test_X, (26, 44))
# output = reshape_images_order(output, (26, 44))
# visualize_reconstruction(test_X[:36, :], output[:36, :], shape=(26, 44))
window = T.iscalar('theta')
dct = T.tensor3('dct', dtype='float32')
inputs = T.tensor3('inputs', dtype='float32')
inputs_diff = T.tensor3('inputs_diff', dtype='float32')
mask = T.matrix('mask', dtype='uint8')
targets = T.ivector('targets')
lr = theano.shared(np.array(learning_rate, dtype=theano.config.floatX), name='learning_rate')
lr_decay = np.array(decay_rate, dtype=theano.config.floatX)
print('constructing end to end model...')
'''
network = create_end_to_end_model(dbn, (None, None, 1144), inputs,
(None, None), mask, 250, window)
'''
network, adascale = adenet_v5.create_model(ae, ae_diff, (None, None, 1144), inputs,
(None, None), mask,
(None, None, 90), dct,
(None, None, 1144), inputs_diff,
250, window, 10, use_adascale)
print_network(network)
print('compiling model...')
predictions = las.layers.get_output(network, deterministic=False)
all_params = las.layers.get_all_params(network, trainable=True)
cost = T.mean(las.objectives.categorical_crossentropy(predictions, targets))
updates = adadelta(cost, all_params, learning_rate=lr)
# updates = adagrad(cost, all_params, learning_rate=lr)
use_max_constraint = False
if use_max_constraint:
MAX_NORM = 4
for param in las.layers.get_all_params(network, regularizable=True):
if param.ndim > 1: # only apply to dimensions larger than 1, exclude biases
updates[param] = norm_constraint(param, MAX_NORM * las.utils.compute_norms(param.get_value()).mean())
train = theano.function(
[inputs, targets, mask, dct, inputs_diff, window],
cost, updates=updates, allow_input_downcast=True)
compute_train_cost = theano.function([inputs, targets, mask, dct, inputs_diff, window],
cost, allow_input_downcast=True)
test_predictions = las.layers.get_output(network, deterministic=True)
test_cost = T.mean(las.objectives.categorical_crossentropy(test_predictions, targets))
compute_test_cost = theano.function(
[inputs, targets, mask, dct, inputs_diff, window], test_cost, allow_input_downcast=True)
val_fn = theano.function([inputs, mask, dct, inputs_diff, window], test_predictions, allow_input_downcast=True)
# We'll train the network with 10 epochs of 30 minibatches each
print('begin training...')
cost_train = []
cost_val = []
class_rate = []
NUM_EPOCHS = 30
EPOCH_SIZE = 120
BATCH_SIZE = 10
WINDOW_SIZE = 9
STRIP_SIZE = 3
MAX_LOSS = 0.2
VALIDATION_WINDOW = 4
val_window = circular_list(VALIDATION_WINDOW)
train_strip = np.zeros((STRIP_SIZE,))
best_val = float('inf')
best_conf = None
best_cr = 0.0
datagen = gen_lstm_batch_random(train_X, train_y, train_vidlens, batchsize=BATCH_SIZE)
val_datagen = gen_lstm_batch_random(test_X, test_y, test_vidlens,
batchsize=len(test_vidlens))
integral_lens = compute_integral_len(train_vidlens)
# We'll use this "validation set" to periodically check progress
X_val, y_val, mask_val, idxs_val = next(val_datagen)
integral_lens_val = compute_integral_len(test_vidlens)
dct_val = gen_seq_batch_from_idx(test_dct, idxs_val, test_vidlens, integral_lens_val, np.max(test_vidlens))
X_diff_val = gen_seq_batch_from_idx(test_X_diff, idxs_val, test_vidlens, integral_lens_val, np.max(test_vidlens))
def early_stop(cost_window):
if len(cost_window) < 2:
return False
else:
curr = cost_window[0]
for idx, cost in enumerate(cost_window):
if curr < cost or idx == 0:
curr = cost
else:
return False
return True
for epoch in range(NUM_EPOCHS):
time_start = time.time()
for i in range(EPOCH_SIZE):
X, y, m, batch_idxs = next(datagen)
d = gen_seq_batch_from_idx(train_dct, batch_idxs,
train_vidlens, integral_lens, np.max(train_vidlens))
X_diff = gen_seq_batch_from_idx(train_X_diff, batch_idxs,
train_vidlens, integral_lens, np.max(train_vidlens))
print_str = 'Epoch {} batch {}/{}: {} examples at learning rate = {:.4f}'.format(
epoch + 1, i + 1, EPOCH_SIZE, len(X), float(lr.get_value()))
print(print_str, end='')
sys.stdout.flush()
train(X, y, m, d, X_diff, WINDOW_SIZE)
print('\r', end='')
cost = compute_train_cost(X, y, m, d, X_diff, WINDOW_SIZE)
val_cost = compute_test_cost(X_val, y_val, mask_val, dct_val, X_diff_val, WINDOW_SIZE)
cost_train.append(cost)
cost_val.append(val_cost)
train_strip[epoch % STRIP_SIZE] = cost
val_window.push(val_cost)
gl = 100 * (cost_val[-1] / np.min(cost_val) - 1)
pk = 1000 * (np.sum(train_strip) / (STRIP_SIZE * np.min(train_strip)) - 1)
pq = gl / pk
cr, val_conf = evaluate_model(X_val, y_val, mask_val, dct_val, X_diff_val, WINDOW_SIZE, val_fn)
class_rate.append(cr)
print("Epoch {} train cost = {}, validation cost = {}, "
"generalization loss = {:.3f}, GQ = {:.3f}, classification rate = {:.3f} ({:.1f}sec)"
.format(epoch + 1, cost_train[-1], cost_val[-1], gl, pq, cr, time.time() - time_start))
if val_cost < best_val:
best_val = val_cost
best_conf = val_conf
best_cr = cr
if use_adascale:
adascale_param = las.layers.get_all_param_values(adascale, scaling_param=True)
if epoch >= VALIDATION_WINDOW and early_stop(val_window):
break
# learning rate decay
if epoch >= decay_start - 1:
lr.set_value(lr.get_value() * lr_decay)
phrases = ['p1', 'p2', 'p3', 'p4', 'p5', 'p6', 'p7', 'p8', 'p9', 'p10']
print('Final Model')
print('classification rate: {}, validation loss: {}'.format(best_cr, best_val))
if use_adascale:
print("final scaling params: {}".format(adascale_param))
print('confusion matrix: ')
plot_confusion_matrix(best_conf, phrases, fmt='grid')
plot_validation_cost(cost_train, cost_val, class_rate, savefilename='valid_cost')
def main():
configure_theano()
options = parse_options()
config_file = options['config']
config = ConfigParser.ConfigParser()
config.read(config_file)
print('CLI options: {}'.format(options.items()))
print('Reading Config File: {}...'.format(config_file))
print(config.items('data'))
print(config.items('models'))
print(config.items('training'))
print('preprocessing dataset...')
data = load_mat_file(config.get('data', 'images'))
ae_pretrained = config.get('models', 'pretrained')
ae_pretrained_diff = config.get('models', 'pretrained_diff')
fusiontype = config.get('models', 'fusiontype')
# capture training parameters
validation_window = int(options['validation_window']) \
if 'validation_window' in options else config.getint('training', 'validation_window')
num_epoch = int(
options['num_epoch']) if 'num_epoch' in options else config.getint(
'training', 'num_epoch')
weight_init = options[
'weight_init'] if 'weight_init' in options else config.get(
'training', 'weight_init')
learning_rate = options['learning_rate'] if 'learning_rate' in options \
else config.getfloat('training', 'learning_rate')
use_peepholes = options[
'use_peepholes'] if 'use_peepholes' in options else config.getboolean(
'training', 'use_peepholes')
epochsize = config.getint('training', 'epochsize')
batchsize = config.getint('training', 'batchsize')
windowsize = config.getint('training', 'windowsize')
weight_init_fn = las.init.GlorotUniform()
if weight_init == 'glorot':
weight_init_fn = las.init.GlorotUniform()
if weight_init == 'norm':
weight_init_fn = las.init.Normal(0.1)
if weight_init == 'uniform':
weight_init_fn = las.init.Uniform()
if weight_init == 'ortho':
weight_init_fn = las.init.Orthogonal()
train_subject_ids = read_data_split_file('data/train.txt')
val_subject_ids = read_data_split_file('data/val.txt')
test_subject_ids = read_data_split_file('data/test.txt')
data_matrix = data['dataMatrix']
targets_vec = data['targetsVec'].reshape((-1, ))
subjects_vec = data['subjectsVec'].reshape((-1, ))
vidlen_vec = data['videoLengthVec'].reshape((-1, ))
data_matrix = reorder_data(data_matrix, (30, 50))
train_X, train_y, train_vidlens, train_subjects, \
val_X, val_y, val_vidlens, val_subjects, \
test_X, test_y, test_vidlens, test_subjects = split_seq_data(data_matrix, targets_vec, subjects_vec, vidlen_vec,
train_subject_ids, val_subject_ids, test_subject_ids)
train_X_diff = compute_diff_images(train_X, train_vidlens)
val_X_diff = compute_diff_images(val_X, val_vidlens)
test_X_diff = compute_diff_images(test_X, test_vidlens)
train_X = sequencewise_mean_image_subtraction(train_X, train_vidlens)
val_X = sequencewise_mean_image_subtraction(val_X, val_vidlens)
test_X = sequencewise_mean_image_subtraction(test_X, test_vidlens)
ae = load_dbn(ae_pretrained)
ae_diff = load_dbn(ae_pretrained_diff)
# IMPT: the encoder was trained with fortan ordered images, so to visualize
# convert all the images to C order using reshape_images_order()
# output = dbn.predict(test_X)
# test_X = reshape_images_order(test_X, (26, 44))
# output = reshape_images_order(output, (26, 44))
# visualize_reconstruction(test_X[:36, :], output[:36, :], shape=(26, 44))
window = T.iscalar('theta')
inputs = T.tensor3('inputs', dtype='float32')
inputs_diff = T.tensor3('inputs_diff', dtype='float32')
mask = T.matrix('mask', dtype='uint8')
targets = T.imatrix('targets')
print('constructing end to end model...')
network, l_fuse = adenet_v2_2.create_model(ae,
ae_diff, (None, None, 1500),
inputs, (None, None),
mask, (None, None, 1500),
inputs_diff,
250,
window,
10,
fusiontype,
w_init_fn=weight_init_fn,
use_peepholes=use_peepholes)
print_network(network)
# draw_to_file(las.layers.get_all_layers(network), 'network.png')
print('compiling model...')
predictions = las.layers.get_output(network, deterministic=False)
all_params = las.layers.get_all_params(network, trainable=True)
cost = temporal_softmax_loss(predictions, targets, mask)
updates = adam(cost, all_params, learning_rate=learning_rate)
train = theano.function([inputs, targets, mask, inputs_diff, window],
cost,
updates=updates,
allow_input_downcast=True)
compute_train_cost = theano.function(
[inputs, targets, mask, inputs_diff, window],
cost,
allow_input_downcast=True)
test_predictions = las.layers.get_output(network, deterministic=True)
test_cost = temporal_softmax_loss(test_predictions, targets, mask)
compute_test_cost = theano.function(
[inputs, targets, mask, inputs_diff, window],
test_cost,
allow_input_downcast=True)
val_fn = theano.function([inputs, mask, inputs_diff, window],
test_predictions,
allow_input_downcast=True)
# We'll train the network with 10 epochs of 30 minibatches each
print('begin training...')
cost_train = []
cost_val = []
class_rate = []
STRIP_SIZE = 3
val_window = circular_list(validation_window)
train_strip = np.zeros((STRIP_SIZE, ))
best_val = float('inf')
best_tr = float('inf')
best_cr = 0.0
datagen = gen_lstm_batch_random(train_X,
train_y,
train_vidlens,
batchsize=batchsize)
integral_lens = compute_integral_len(train_vidlens)
val_datagen = gen_lstm_batch_random(val_X,
val_y,
val_vidlens,
batchsize=len(val_vidlens))
test_datagen = gen_lstm_batch_random(test_X,
test_y,
test_vidlens,
batchsize=len(test_vidlens))
# We'll use this "validation set" to periodically check progress
X_val, y_val, mask_val, idxs_val = next(val_datagen)
integral_lens_val = compute_integral_len(val_vidlens)
X_diff_val = gen_seq_batch_from_idx(val_X_diff, idxs_val, val_vidlens,
integral_lens_val, np.max(val_vidlens))
# we use the test set to check final classification rate
X_test, y_test, mask_test, idxs_test = next(test_datagen)
integral_lens_test = compute_integral_len(test_vidlens)
X_diff_test = gen_seq_batch_from_idx(test_X_diff, idxs_test, test_vidlens,
integral_lens_test,
np.max(test_vidlens))
# reshape the targets for validation
y_val_evaluate = y_val
y_val = y_val.reshape((-1, 1)).repeat(mask_val.shape[-1], axis=-1)
for epoch in range(num_epoch):
time_start = time.time()
for i in range(epochsize):
X, y, m, batch_idxs = next(datagen)
# repeat targets based on max sequence len
y = y.reshape((-1, 1))
y = y.repeat(m.shape[-1], axis=-1)
X_diff = gen_seq_batch_from_idx(train_X_diff, batch_idxs,
train_vidlens, integral_lens,
np.max(train_vidlens))
print_str = 'Epoch {} batch {}/{}: {} examples using adam'.format(
epoch + 1, i + 1, epochsize, len(X))
print(print_str, end='')
sys.stdout.flush()
train(X, y, m, X_diff, windowsize)
print('\r', end='')
cost = compute_train_cost(X, y, m, X_diff, windowsize)
val_cost = compute_test_cost(X_val, y_val, mask_val, X_diff_val,
windowsize)
cost_train.append(cost)
cost_val.append(val_cost)
train_strip[epoch % STRIP_SIZE] = cost
val_window.push(val_cost)
gl = 100 * (cost_val[-1] / np.min(cost_val) - 1)
pk = 1000 * (np.sum(train_strip) /
(STRIP_SIZE * np.min(train_strip)) - 1)
pq = gl / pk
cr, val_conf = evaluate_model2(X_val, y_val_evaluate, mask_val,
X_diff_val, windowsize, val_fn)
class_rate.append(cr)
if val_cost < best_val:
best_val = val_cost
best_tr = cost
best_cr = cr
if fusiontype == 'adasum':
adascale_param = las.layers.get_all_param_values(
l_fuse, scaling_param=True)
test_cr, test_conf = evaluate_model2(X_test, y_test, mask_test,
X_diff_test, windowsize,
val_fn)
print(
"Epoch {} train cost = {}, val cost = {}, "
"GL loss = {:.3f}, GQ = {:.3f}, CR = {:.3f}, Test CR= {:.3f} ({:.1f}sec)"
.format(epoch + 1, cost_train[-1], cost_val[-1], gl, pq, cr,
test_cr,
time.time() - time_start))
else:
print("Epoch {} train cost = {}, val cost = {}, "
"GL loss = {:.3f}, GQ = {:.3f}, CR = {:.3f} ({:.1f}sec)".
format(epoch + 1, cost_train[-1], cost_val[-1], gl, pq, cr,
time.time() - time_start))
if epoch >= validation_window and early_stop2(val_window, best_val,
validation_window):
break
numbers = ['0', '1', '2', '3', '4', '5', '6', '7', '8', '9']
print('Final Model')
print('CR: {}, val loss: {}, Test CR: {}'.format(best_cr, best_val,
test_cr))
if fusiontype == 'adasum':
print("final scaling params: {}".format(adascale_param))
print('confusion matrix: ')
plot_confusion_matrix(test_conf, numbers, fmt='latex')
plot_validation_cost(cost_train, cost_val, savefilename='valid_cost')
if 'write_results' in options:
results_file = options['write_results']
with open(results_file, mode='a') as f:
f.write('{},{},{}\n'.format(test_cr, best_cr, best_val))
def main():
configure_theano()
options = parse_options()
config_file = options['config']
config = ConfigParser.ConfigParser()
config.read(config_file)
print('Reading Config File: {}...'.format(config_file))
print(config.items('data'))
print(config.items('models'))
print(config.items('training'))
print('CLI options: {}'.format(options.items()))
print('preprocessing dataset...')
data = load_mat_file(config.get('data', 'images'))
dct_data = load_mat_file(config.get('data', 'dct'))
no_coeff = config.getint('models', 'no_coeff')
output_classes = config.getint('models', 'output_classes')
lstm_size = config.getint('models', 'lstm_size')
# capture training parameters
validation_window = int(options['validation_window']) \
if 'validation_window' in options else config.getint('training', 'validation_window')
no_epochs = int(
options['no_epochs']) if 'no_epochs' in options else config.getint(
'training', 'no_epochs')
weight_init = options[
'weight_init'] if 'weight_init' in options else config.get(
'training', 'weight_init')
learning_rate = options['learning_rate'] if 'learning_rate' in options \
else config.getfloat('training', 'learning_rate')
epochsize = options[
'epochsize'] if 'epochsize' in options else config.getint(
'training', 'epochsize')
batchsize = options[
'batchsize'] if 'batchsize' in options else config.getint(
'training', 'batchsize')
use_peepholes = options[
'use_peepholes'] if 'use_peepholes' in options else config.getboolean(
'training', 'use_peepholes')
use_blstm = config.getboolean('training', 'use_blstm')
use_finetuning = config.getboolean('training', 'use_finetuning')
weight_init_fn = las.init.GlorotUniform()
if weight_init == 'glorot':
weight_init_fn = las.init.GlorotUniform()
if weight_init == 'norm':
weight_init_fn = las.init.Normal(0.1)
if weight_init == 'uniform':
weight_init_fn = las.init.Uniform()
if weight_init == 'ortho':
weight_init_fn = las.init.Orthogonal()
train_vidlens = data['trVideoLengthVec'].astype('int').reshape((-1, ))
val_vidlens = data['valVideoLengthVec'].astype('int').reshape((-1, ))
test_vidlens = data['testVideoLengthVec'].astype('int').reshape((-1, ))
train_X = data['trData'].astype('float32')
val_X = data['valData'].astype('float32')
test_X = data['testData'].astype('float32')
train_dct = dct_data['trDctFeatures'].astype('float32')
val_dct = dct_data['valDctFeatures'].astype('float32')
test_dct = dct_data['testDctFeatures'].astype('float32')
# +1 to handle the -1 introduced in lstm_gendata
train_y = data['trTargetsVec'].astype('int').reshape((-1, )) + 1
val_y = data['valTargetsVec'].astype('int').reshape((-1, )) + 1
test_y = data['testTargetsVec'].astype('int').reshape((-1, )) + 1
# featurewise normalize dct features
train_dct, dct_mean, dct_std = featurewise_normalize_sequence(train_dct)
val_dct = (val_dct - dct_mean) / dct_std
test_dct = (test_dct - dct_mean) / dct_std
# IMPT: the encoder was trained with fortan ordered images, so to visualize
# convert all the images to C order using reshape_images_order()
# output = dbn.predict(test_X)
# test_X = reshape_images_order(test_X, (26, 44))
# output = reshape_images_order(output, (26, 44))
# visualize_reconstruction(test_X[:36, :], output[:36, :], shape=(26, 44))
inputs = T.tensor3('inputs', dtype='float32')
mask = T.matrix('mask', dtype='uint8')
targets = T.imatrix('targets')
print('constructing end to end model...')
network = lstm_classifier_majority_vote.create_model(
(None, None, no_coeff * 3),
inputs, (None, None),
mask,
lstm_size,
output_classes,
w_init=weight_init_fn)
print_network(network)
print('compiling model...')
predictions = las.layers.get_output(network, deterministic=False)
all_params = las.layers.get_all_params(network, trainable=True)
cost = temporal_softmax_loss(predictions, targets, mask)
updates = adam(cost, all_params)
train = theano.function([inputs, targets, mask],
cost,
updates=updates,
allow_input_downcast=True)
compute_train_cost = theano.function([inputs, targets, mask],
cost,
allow_input_downcast=True)
test_predictions = las.layers.get_output(network, deterministic=True)
test_cost = temporal_softmax_loss(test_predictions, targets, mask)
compute_test_cost = theano.function([inputs, targets, mask],
test_cost,
allow_input_downcast=True)
val_fn = theano.function([inputs, mask],
test_predictions,
allow_input_downcast=True)
# We'll train the network with 10 epochs of 30 minibatches each
print('begin training...')
cost_train = []
cost_val = []
class_rate = []
STRIP_SIZE = 3
val_window = circular_list(validation_window)
train_strip = np.zeros((STRIP_SIZE, ))
best_val = float('inf')
best_tr = float('inf')
best_cr = 0.0
datagen = gen_lstm_batch_random(train_X,
train_y,
train_vidlens,
batchsize=batchsize)
val_datagen = gen_lstm_batch_random(val_X,
val_y,
val_vidlens,
batchsize=len(val_vidlens))
test_datagen = gen_lstm_batch_random(test_X,
test_y,
test_vidlens,
batchsize=len(test_vidlens))
integral_lens = compute_integral_len(train_vidlens)
# We'll use this "validation set" to periodically check progress
X_val, y_val, mask_val, idxs_val = next(val_datagen)
integral_lens_val = compute_integral_len(val_vidlens)
dct_val = gen_seq_batch_from_idx(val_dct, idxs_val, val_vidlens,
integral_lens_val, np.max(val_vidlens))
X_test, y_test, mask_test, idxs_test = next(test_datagen)
integral_lens_test = compute_integral_len(test_vidlens)
dct_test = gen_seq_batch_from_idx(test_dct, idxs_test, test_vidlens,
integral_lens_test, np.max(test_vidlens))
# reshape the targets for validation
y_val_evaluate = y_val
y_val = y_val.reshape((-1, 1)).repeat(mask_val.shape[-1], axis=-1)
for epoch in range(no_epochs):
time_start = time.time()
for i in range(epochsize):
_, y, m, batch_idxs = next(datagen)
# repeat targets based on max sequence len
y = y.reshape((-1, 1))
y = y.repeat(m.shape[-1], axis=-1)
d = gen_seq_batch_from_idx(train_dct, batch_idxs, train_vidlens,
integral_lens, np.max(train_vidlens))
print_str = 'Epoch {} batch {}/{}: {} examples using adam'.format(
epoch + 1, i + 1, epochsize, len(y))
print(print_str, end='')
sys.stdout.flush()
train(d, y, m)
print('\r', end='')
cost = compute_train_cost(d, y, m)
val_cost = compute_test_cost(dct_val, y_val, mask_val)
cost_train.append(cost)
cost_val.append(val_cost)
train_strip[epoch % STRIP_SIZE] = cost
val_window.push(val_cost)
gl = 100 * (cost_val[-1] / np.min(cost_val) - 1)
pk = 1000 * (np.sum(train_strip) /
(STRIP_SIZE * np.min(train_strip)) - 1)
pq = gl / pk
cr, val_conf = evaluate_model2(dct_val, y_val_evaluate, mask_val,
val_fn)
class_rate.append(cr)
if val_cost < best_val:
best_val = val_cost
best_conf = val_conf
best_cr = cr
test_cr, test_conf = evaluate_model2(dct_test, y_test, mask_test,
val_fn)
print(
"Epoch {} train cost = {}, val cost = {}, "
"GL loss = {:.3f}, GQ = {:.3f}, CR = {:.3f}, Test CR= {:.3f} ({:.1f}sec)"
.format(epoch + 1, cost_train[-1], cost_val[-1], gl, pq, cr,
test_cr,
time.time() - time_start))
else:
print("Epoch {} train cost = {}, val cost = {}, "
"GL loss = {:.3f}, GQ = {:.3f}, CR = {:.3f} ({:.1f}sec)".
format(epoch + 1, cost_train[-1], cost_val[-1], gl, pq, cr,
time.time() - time_start))
if epoch >= validation_window and early_stop2(val_window, best_val,
validation_window):
break
numbers = ['0', '1', '2', '3', '4', '5', '6', '7', '8', '9']
print('Final Model')
print('CR: {}, val loss: {}, Test CR: {}'.format(best_cr, best_val,
test_cr))
print('confusion matrix: ')
plot_confusion_matrix(test_conf, numbers, fmt='latex')
plot_validation_cost(cost_train, cost_val, savefilename='valid_cost')
if 'write_results' in options:
results_file = options['write_results']
with open(results_file, mode='a') as f:
f.write('{},{},{},{},{},{},{},{},{},{},{},{}\n'.format(
use_finetuning, 'yes', use_peepholes, 'adam', weight_init,
'N/A', use_blstm, learning_rate, best_tr, best_val,
best_cr * 100, test_cr * 100))
s = ','.join([str(v) for v in cost_train])
f.write('{}\n'.format(s))
s = ','.join([str(v) for v in cost_val])
f.write('{}\n'.format(s))
s = ','.join([str(v) for v in class_rate])
f.write('{}\n'.format(s))
def main():
configure_theano()
options = parse_options()
config_file = options['config']
config = ConfigParser.ConfigParser()
config.read(config_file)
print('CLI options: {}'.format(options.items()))
print('Reading Config File: {}...'.format(config_file))
print(config.items('stream1'))
print(config.items('lstm_classifier'))
print(config.items('training'))
print('preprocessing dataset...')
data = load_mat_file(config.get('stream1', 'data'))
stream1 = config.get('stream1', 'model')
imagesize = tuple([int(d) for d in config.get('stream1', 'imagesize').split(',')])
stream1_dim = config.getint('stream1', 'input_dimensions')
stream1_shape = config.get('stream1', 'shape')
stream1_nonlinearities = config.get('stream1', 'nonlinearities')
# lstm classifier
output_classes = config.getint('lstm_classifier', 'output_classes')
output_classnames = config.get('lstm_classifier', 'output_classnames').split(',')
lstm_size = config.getint('lstm_classifier', 'lstm_size')
matlab_target_offset = config.getboolean('lstm_classifier', 'matlab_target_offset')
# data preprocessing options
reorderdata = config.getboolean('stream1', 'reorderdata')
diffimage = config.getboolean('stream1', 'diffimage')
meanremove = config.getboolean('stream1', 'meanremove')
samplewisenormalize = config.getboolean('stream1', 'samplewisenormalize')
featurewisenormalize = config.getboolean('stream1', 'featurewisenormalize')
# lstm classifier configurations
weight_init = options['weight_init'] if 'weight_init' in options else config.get('lstm_classifier', 'weight_init')
use_peepholes = options['use_peepholes'] if 'use_peepholes' in options else config.getboolean('lstm_classifier',
'use_peepholes')
windowsize = config.getint('lstm_classifier', 'windowsize')
# capture training parameters
validation_window = int(options['validation_window']) \
if 'validation_window' in options else config.getint('training', 'validation_window')
num_epoch = int(options['num_epoch']) if 'num_epoch' in options else config.getint('training', 'num_epoch')
learning_rate = options['learning_rate'] if 'learning_rate' in options \
else config.getfloat('training', 'learning_rate')
epochsize = config.getint('training', 'epochsize')
batchsize = config.getint('training', 'batchsize')
weight_init_fn = las.init.GlorotUniform()
if weight_init == 'glorot':
weight_init_fn = las.init.GlorotUniform()
if weight_init == 'norm':
weight_init_fn = las.init.Normal(0.1)
if weight_init == 'uniform':
weight_init_fn = las.init.Uniform()
if weight_init == 'ortho':
weight_init_fn = las.init.Orthogonal()
train_subject_ids = read_data_split_file(config.get('training', 'train_subjects_file'))
val_subject_ids = read_data_split_file(config.get('training', 'val_subjects_file'))
test_subject_ids = read_data_split_file(config.get('training', 'test_subjects_file'))
data_matrix = data['dataMatrix'].astype('float32')
targets_vec = data['targetsVec'].reshape((-1,))
subjects_vec = data['subjectsVec'].reshape((-1,))
vidlen_vec = data['videoLengthVec'].reshape((-1,))
if reorderdata:
data_matrix = reorder_data(data_matrix, (imagesize[0], imagesize[1]))
train_X, train_y, train_vidlens, train_subjects, \
val_X, val_y, val_vidlens, val_subjects, \
test_X, test_y, test_vidlens, test_subjects = split_seq_data(data_matrix, targets_vec, subjects_vec, vidlen_vec,
train_subject_ids, val_subject_ids, test_subject_ids)
if matlab_target_offset:
train_y -= 1
val_y -= 1
test_y -= 1
if meanremove:
train_X = sequencewise_mean_image_subtraction(train_X, train_vidlens)
val_X = sequencewise_mean_image_subtraction(val_X, val_vidlens)
test_X = sequencewise_mean_image_subtraction(test_X, test_vidlens)
if diffimage:
train_X = compute_diff_images(train_X, train_vidlens)
val_X = compute_diff_images(val_X, val_vidlens)
test_X = compute_diff_images(test_X, test_vidlens)
if samplewisenormalize:
train_X = normalize_input(train_X)
val_X = normalize_input(val_X)
test_X = normalize_input(test_X)
if featurewisenormalize:
train_X, mean, std = featurewise_normalize_sequence(train_X)
val_X = (val_X - mean) / std
test_X = (test_X - mean) / std
ae1 = load_decoder(stream1, stream1_shape, stream1_nonlinearities)
# IMPT: the encoder was trained with fortan ordered images, so to visualize
# convert all the images to C order using reshape_images_order()
# output = dbn.predict(test_X)
# test_X = reshape_images_order(test_X, (26, 44))
# output = reshape_images_order(output, (26, 44))
# visualize_reconstruction(test_X[:36, :], output[:36, :], shape=(26, 44))
window = T.iscalar('theta')
inputs1 = T.tensor3('inputs1', dtype='float32')
mask = T.matrix('mask', dtype='uint8')
targets = T.imatrix('targets')
print('constructing end to end model...')
network = deltanet_majority_vote.create_model(ae1, (None, None, stream1_dim), inputs1,
(None, None), mask,
lstm_size, window, output_classes,
weight_init_fn, use_peepholes)
print_network(network)
print('compiling model...')
predictions = las.layers.get_output(network, deterministic=False)
all_params = las.layers.get_all_params(network, trainable=True)
cost = temporal_softmax_loss(predictions, targets, mask)
default_learning_rate = theano.shared(las.utils.floatX(learning_rate), 'default_lr')
lr_config = {
'fc1': theano.shared(las.utils.floatX(0.001)),
'fc2': theano.shared(las.utils.floatX(0.001)),
'fc3': theano.shared(las.utils.floatX(0.001))
}
lr_map = custom.updates.generate_lr_map(all_params, lr_config, default_learning_rate)
# updates = adam(cost, all_params, default_learning_rate)
updates = custom.updates.adam_vlr(cost, all_params, lr_map)
train = theano.function(
[inputs1, targets, mask, window],
cost, updates=updates, allow_input_downcast=True)
compute_train_cost = theano.function([inputs1, targets, mask, window],
cost, allow_input_downcast=True)
test_predictions = las.layers.get_output(network, deterministic=True)
test_cost = temporal_softmax_loss(test_predictions, targets, mask)
compute_test_cost = theano.function(
[inputs1, targets, mask, window], test_cost, allow_input_downcast=True)
val_fn = theano.function([inputs1, mask, window], test_predictions, allow_input_downcast=True)
# We'll train the network with 10 epochs of 30 minibatches each
print('begin training...')
cost_train = []
cost_val = []
class_rate = []
STRIP_SIZE = 3
val_window = circular_list(validation_window)
train_strip = np.zeros((STRIP_SIZE,))
best_val = float('inf')
best_cr = 0.0
datagen = gen_lstm_batch_random(train_X, train_y, train_vidlens, batchsize=batchsize)
val_datagen = gen_lstm_batch_random(val_X, val_y, val_vidlens, batchsize=len(val_vidlens))
test_datagen = gen_lstm_batch_random(test_X, test_y, test_vidlens, batchsize=len(test_vidlens))
# We'll use this "validation set" to periodically check progress
X_val, y_val, mask_val, idxs_val = next(val_datagen)
# we use the test set to check final classification rate
X_test, y_test, mask_test, idxs_test = next(test_datagen)
# reshape the targets for validation
y_val_evaluate = y_val
y_val = y_val.reshape((-1, 1)).repeat(mask_val.shape[-1], axis=-1)
for epoch in range(num_epoch):
time_start = time.time()
for i in range(epochsize):
X, y, m, batch_idxs = next(datagen)
# repeat targets based on max sequence len
y = y.reshape((-1, 1))
y = y.repeat(m.shape[-1], axis=-1)
print_str = 'Epoch {} batch {}/{}: {} examples using adam with learning rate = {}'.format(
epoch + 1, i + 1, epochsize, len(X), learning_rate)
print(print_str, end='')
sys.stdout.flush()
train(X, y, m, windowsize)
print('\r', end='')
cost = compute_train_cost(X, y, m, windowsize)
val_cost = compute_test_cost(X_val, y_val, mask_val, windowsize)
cost_train.append(cost)
cost_val.append(val_cost)
train_strip[epoch % STRIP_SIZE] = cost
val_window.push(val_cost)
gl = 100 * (cost_val[-1] / np.min(cost_val) - 1)
pk = 1000 * (np.sum(train_strip) / (STRIP_SIZE * np.min(train_strip)) - 1)
pq = gl / pk
cr, val_conf = evaluate_model2(X_val, y_val_evaluate, mask_val, windowsize, val_fn)
class_rate.append(cr)
if val_cost < best_val:
best_val = val_cost
best_cr = cr
test_cr, test_conf = evaluate_model2(X_test, y_test, mask_test, windowsize, val_fn)
print("Epoch {} train cost = {}, val cost = {}, "
"GL loss = {:.3f}, GQ = {:.3f}, CR = {:.3f}, Test CR= {:.3f} ({:.1f}sec)"
.format(epoch + 1, cost_train[-1], cost_val[-1], gl, pq, cr, test_cr, time.time() - time_start))
best_params = las.layers.get_all_param_values(network)
else:
print("Epoch {} train cost = {}, val cost = {}, "
"GL loss = {:.3f}, GQ = {:.3f}, CR = {:.3f} ({:.1f}sec)"
.format(epoch + 1, cost_train[-1], cost_val[-1], gl, pq, cr, time.time() - time_start))
if epoch >= validation_window and early_stop2(val_window, best_val, validation_window):
break
# Show that learning rates are changed by exploding learning rates for encoder layers
# The training loss should increase dramatically and learning should diverge
if epoch + 1 == 4:
print('explode fc1,fc2,fc3 learning rates to 100.0')
lr_config['fc1'].set_value(100.0)
lr_config['fc2'].set_value(100.0)
lr_config['fc3'].set_value(100.0)
print('Final Model')
print('CR: {}, val loss: {}, Test CR: {}'.format(best_cr, best_val, test_cr))
# plot confusion matrix
table_str = plot_confusion_matrix(test_conf, output_classnames, fmt='pipe')
print('confusion matrix: ')
print(table_str)
if 'save_plot' in options:
prefix = options['save_plot']
plot_validation_cost(cost_train, cost_val, savefilename='{}.validloss.png'.format(prefix))
with open('{}.confmat.txt'.format(prefix), mode='a') as f:
f.write(table_str)
f.write('\n\n')
if 'write_results' in options:
print('writing results to {}'.format(options['write_results']))
results_file = options['write_results']
with open(results_file, mode='a') as f:
f.write('{},{},{}\n'.format(test_cr, best_cr, best_val))
if 'save_best' in options:
print('saving best model...')
las.layers.set_all_param_values(network, best_params)
save_model_params(network, options['save_best'])
print('best model saved to {}'.format(options['save_best']))
