dump_path = sys.argv[1]
model_data = pickle.load(open(dump_path, 'r'))
# Setting some vars for easier ref.
chunk_size = model_data['chunk_size'] * 2
batch_size = model_data['batch_size']
l_out = model_data['l_out']
l_ins = model_data['l_ins']
# Print some basic stuff about the model.
num_params = nn.layers.count_params(l_out)
print("\n\t\tNumber of parameters: %d" % num_params)
model_arch = architecture_string(model_data['l_out'])
print(model_arch)
# Set up Theano stuff to compute output.
output = nn.layers.get_output(l_out, deterministic=True)
input_ndims = [len(nn.layers.get_output_shape(l_in))
for l_in in l_ins]
xs_shared = [nn.utils.shared_empty(dim=ndim)
for ndim in input_ndims]
idx = T.lscalar('idx')
givens = {}
for l_in, x_shared in zip(l_ins, xs_shared):
givens[l_in.input_var] = x_shared[
idx * batch_size:(idx + 1) * batch_size
# doesn't have the attribute
if lr_decay:
lr_init = model.lr_init
lr_final = model.lr_final
else:
lr_init = LEARNING_RATE_SCHEDULE[1]
np.set_printoptions(precision=3)
np.set_printoptions(suppress=True)
model_id = strftime("%Y_%m_%d_%H%M%S", gmtime())
dump_path = '/' + model_id + '_' + model.config_name + '.pkl'
# prints the architecture of the model
model_arch = architecture_string(l_out)
print model_arch
num_params = nn.layers.count_params(l_out, trainable=True)
print "\n\t\tNumber of trainable parameters: %d\n" % num_params
print "\t\tModel id: %s\n" % model_id
print "\t\tModel name: %s\n" % model.config_name
input_ndims = [len(nn.layers.get_output_shape(l_in)) for l_in in l_ins]
xs_shared = [nn.utils.shared_empty(dim=ndim) for ndim in input_ndims]
y_shared = nn.utils.shared_empty(dim=2)
idx = T.lscalar('idx')
obj = model.build_objective(l_out)
train_loss = obj.get_loss()
if lr_decay:
lr_init = model.lr_init
lr_final = model.lr_final
else:
lr_init = LEARNING_RATE_SCHEDULE[1]
np.set_printoptions(precision=3)
np.set_printoptions(suppress=True)
model_id = strftime("%Y_%m_%d_%H%M%S", gmtime())
dump_path = 'dumps/' + model_id + '_' + model.config_name + '.pkl'
model_arch = architecture_string(l_out)
print model_arch
num_params = nn.layers.count_params(l_out, trainable=True)
print "\n\t\tNumber of trainable parameters: %d\n" % num_params
print "\t\tModel id: %s\n" % model_id
print "\t\tModel name: %s\n" % model.config_name
input_ndims = [len(nn.layers.get_output_shape(l_in))
for l_in in l_ins]
xs_shared = [nn.utils.shared_empty(dim=ndim)
for ndim in input_ndims]
y_shared = nn.utils.shared_empty(dim=2)
idx = T.lscalar('idx')
dump_path = sys.argv[1]
model_data = pickle.load(open(dump_path, 'r'))
# Setting some vars for easier ref.
chunk_size = model_data['chunk_size'] * 2
batch_size = model_data['batch_size']
l_out = model_data['l_out']
l_ins = model_data['l_ins']
# Print some basic stuff about the model.
num_params = nn.layers.count_params(l_out)
print "\n\t\tNumber of parameters: %d" % num_params
model_arch = architecture_string(model_data['l_out'])
print model_arch
# Set up Theano stuff to compute output.
output = nn.layers.get_output(l_out, deterministic=True)
input_ndims = [len(nn.layers.get_output_shape(l_in))
for l_in in l_ins]
xs_shared = [nn.utils.shared_empty(dim=ndim)
for ndim in input_ndims]
idx = T.lscalar('idx')
givens = {}
for l_in, x_shared in zip(l_ins, xs_shared):
givens[l_in.input_var] = x_shared[
idx * batch_size:(idx + 1) * batch_size
def main(data,
cls_num,
dim,
pos_ratio,
w=None,
increase=True,
n=2,
num_epochs=500,
strategy='momentum',
model=None,
thres=None,
lb_num=None):
x_train = data['training']
x_val = data['val']
# Prepare Theano variables for inputs and targets
input_var = T.tensor4('inputs')
target_var = T.ivector('targets')
w_var = T.vector('w')
num_boxes_train = len(x_train['image_ids'])
pos_idx = (x_train['boxes'][:, -1] == 1).nonzero()[0]
print(num_boxes_train, 'pos:{}'.format(pos_idx.shape[0]))
# Create neural network model
print("Building model and compiling functions...")
cls_network, kl_network = build_cnn(cls_num, input_var, n, dim)
epsilon = 1e-35
margin = 0.5
batchsize = target_var.shape[0]
cls_prediction = lasagne.layers.get_output(cls_network)
# cls_prediction = T.clip(cls_prediction, 1e-7, 1-(1e-7))
kl_prediction = lasagne.layers.get_output(kl_network)
# add weight decay
all_layers = lasagne.layers.get_all_layers([cls_network, kl_network])
l2_penalty = lasagne.regularization.regularize_layer_params(
all_layers, lasagne.regularization.l2) * 0.0005
cls_loss = lasagne.objectives.categorical_crossentropy(
cls_prediction, target_var)
cls_loss = aggregate(cls_loss, w_var)
kl_prediction = kl_prediction + epsilon
kl_loss = get_klloss(batchsize, margin, kl_prediction, target_var)
loss = l2_penalty + 0 * kl_loss + cls_loss
lr = 0.1
sh_lr = theano.shared(lasagne.utils.floatX(lr))
params = lasagne.layers.get_all_params([cls_network, kl_network],
trainable=True)
if strategy == 'momentum':
updates = lasagne.updates.momentum(loss,
params,
learning_rate=sh_lr,
momentum=0.9) #nesterov_
else:
updates = lasagne.updates.adam(loss, params, learning_rate=1e-4)
train_fn = theano.function([input_var, target_var, w_var],
[loss, cls_prediction, kl_prediction],
updates=updates,
allow_input_downcast=True) #, w_var
# Create a loss expression for validation/testing
test_cls_prediction = lasagne.layers.get_output(cls_network,
deterministic=True)
test_cls_prediction = T.clip(test_cls_prediction, 1e-7, 1 - 1e-7)
test_cls_loss = lasagne.objectives.categorical_crossentropy(
test_cls_prediction, target_var)
test_cls_loss = test_cls_loss.mean()
test_kl_prediction = lasagne.layers.get_output(kl_network,
deterministic=True)
test_kl_prediction = test_kl_prediction + epsilon
test_kl_loss = get_klloss(batchsize, margin, test_kl_prediction,
target_var)
test_loss = l2_penalty + test_kl_loss + test_cls_loss
# Compile a second function computing the validation loss and accuracy:
val_fn = theano.function(
[input_var, target_var],
[test_loss, test_cls_prediction, test_kl_prediction],
allow_input_downcast=True)
print(
"number of parameters in model: %d" %
lasagne.layers.count_params([cls_network, kl_network], trainable=True))
print(architecture_string([cls_network, kl_network]))
if model is not None and os.path.exists(model):
# load network weights from model file
with np.load(model) as f:
param_values = [f['arr_%d' % i] for i in range(len(f.files))]
with np.load('weighted_indexes_{}'.format(model)) as f:
indexes = f['arr_0']
lasagne.layers.set_all_param_values([cls_network, kl_network],
param_values)
else:
# launch the training loop
print("Starting training...")
for epoch in np.arange(num_epochs):
print("Epoch {} of {}:".format(epoch + 1, num_epochs))
cls_preds, kl_preds, my_hat, indexes, cm = get_mtresults(x_train,\
'train','train', train_fn, cls_num, dim, pos_ratio, True, w=w, batchsize=128)
cls_preds, kl_preds, my_hat, indexes, cm = get_mtresults(x_val,\
'val', 'train',val_fn, cls_num, dim, pos_ratio, indexes = indexes)
if (epoch + 1) == 30 or (epoch + 1) == 50 or (epoch + 1) == 70:
new_lr = sh_lr.get_value() * 0.1
print("New LR:" + str(new_lr))
sh_lr.set_value(lasagne.utils.floatX(new_lr))
# dump the network weights to a file :
if model is not None:
np.savez(
model,
*lasagne.layers.get_all_param_values([cls_network,
kl_network]))
np.savez('weighted_indexes_{}'.format(model), indexes)
return input_var, cls_network, kl_network, val_fn, indexes
def main(data, cls_num, dim, pos_ratio, w=None, increase=True, n=2, num_epochs=500, strategy='momentum', last_model=None, model=None, thres=None,lb_num=None):
x_train = data['training']
x_val = data['val']
# Prepare Theano variables for inputs and targets
input_var = T.tensor4('inputs')
target_var = T.ivector('targets')
w_var = T.vector('w')
neg_idx = (x_train['boxes'][:,-1]==0).nonzero()[0]
pos_idx = (x_train['boxes'][:,-1]==1).nonzero()[0]
print('neg:{}'.format(neg_idx.shape[0]), 'pos:{}'.format(pos_idx.shape[0]))
# Create neural network model
print("Building model and compiling functions...")
network = build_alexnet(cls_num, input_var, dim)
fc8 = network['fc8']
print(architecture_string(fc8))
batchsize = target_var.shape[0]
prediction = lasagne.layers.get_output(fc8)
# add weight decay
all_layers = lasagne.layers.get_all_layers(fc8)
l2_penalty = lasagne.regularization.regularize_layer_params(
all_layers, lasagne.regularization.l2) * 0.0005
loss = lasagne.objectives.categorical_crossentropy(prediction, target_var)
#loss = loss.mean()
loss = aggregate(loss, w_var)
loss = loss + l2_penalty
acc = T.mean(T.eq(T.argmax(prediction, axis=1), target_var), dtype=theano.config.floatX)
lr = 0.001
sh_lr = theano.shared(lasagne.utils.floatX(lr))
params = lasagne.layers.get_all_params(fc8, trainable=True)
if strategy=='momentum':
updates = lasagne.updates.momentum(loss, params, learning_rate=sh_lr, momentum=0.9)#nesterov_
else:
updates = lasagne.updates.adam(loss, params, learning_rate=1e-4)
train_fn = theano.function([input_var, target_var, w_var], [loss, acc, prediction],
updates=updates, allow_input_downcast=True)
# Create a loss expression for validation/testing
test_prediction = lasagne.layers.get_output(fc8, deterministic=True)
test_loss = lasagne.objectives.categorical_crossentropy(test_prediction, target_var)
test_loss = test_loss.mean()
test_acc = T.mean(T.eq(T.argmax(test_prediction, axis=1), target_var),
dtype=theano.config.floatX)
# Compile a second function computing the validation loss and accuracy:
val_fn = theano.function([input_var, target_var], [test_loss, test_acc,
test_prediction], allow_input_downcast=True)
print("number of parameters in model: %d" % lasagne.layers.count_params(fc8, trainable=True))
# print(architecture_string(fc8))
if model is not None and os.path.exists(model):
# load network weights from model file
with np.load(model) as f:
param_values = [f['arr_%d' % i] for i in range(len(f.files))]
#with np.load('weighted_indexes_{}'.format(model)) as f:
# indexes = f['arr_0']
lasagne.layers.set_all_param_values(fc8, param_values)
else:
if last_model is None:
# first loop of training, copy model from caffe
caffe_root = '/home/dl/caffe/'
sys.path.insert(0, caffe_root + 'python')
import caffe
caffe.set_mode_gpu()
caffe_net = caffe.Net(caffe_root + 'models/bvlc_reference_caffenet/deploy.prototxt',
caffe_root + 'models/bvlc_reference_caffenet/bvlc_reference_caffenet.caffemodel',
caffe.TEST)
layers_caffe = dict(zip(list(caffe_net._layer_names), caffe_net.layers))
for name, layer in network.items():
try:
if name == 'conv2':
W = layers_caffe[name].blobs[0].data[:,:,::-1,::-1]
b = layers_caffe[name].blobs[1].data
network['conv2_part1'].W.set_value(W[0:128,:,:,:])
network['conv2_part1'].b.set_value(b[0:128])
network['conv2_part2'].W.set_value(W[128:,:,:,:])
network['conv2_part2'].b.set_value(b[128:])
elif name == 'conv4':
W = layers_caffe[name].blobs[0].data[:,:,::-1,::-1]
b = layers_caffe[name].blobs[1].data
network['conv4_part1'].W.set_value(W[0:192,:,:,:])
network['conv4_part1'].b.set_value(b[0:192])
network['conv4_part2'].W.set_value(W[192:,:,:,:])
network['conv4_part2'].b.set_value(b[192:])
elif name == 'conv5':
W = layers_caffe[name].blobs[0].data[:,:,::-1,::-1]
b = layers_caffe[name].blobs[1].data
network['conv5_part1'].W.set_value(W[0:128,:,:,:])
network['conv5_part1'].b.set_value(b[0:128])
network['conv5_part2'].W.set_value(W[128:,:,:,:])
network['conv5_part2'].b.set_value(b[128:])
elif name == 'fc6' or name == 'fc7':
layer.W.set_value(np.transpose(layers_caffe[name].blobs[0].data))
layer.b.set_value(layers_caffe[name].blobs[1].data)
elif name != 'fc8':
layer.W.set_value(layers_caffe[name].blobs[0].data[:,:,::-1,::-1])
layer.b.set_value(layers_caffe[name].blobs[1].data)
except AttributeError:
continue
except KeyError:
continue
print(architecture_string(fc8))
elif os.path.exists(last_model):
with np.load(last_model) as f:
param_values = [f['arr_%d' % i] for i in range(len(f.files))]
#with np.load('weighted_indexes_{}'.format(last_model)) as f:
# indexes = f['arr_0']
lasagne.layers.set_all_param_values(fc8, param_values)
print("Starting training...")
for epoch in np.arange(num_epochs):
print("Epoch {} of {}:".format(epoch + 1, num_epochs))
preds = get_mtresults(x_train,\
'train','train', train_fn, cls_num, dim, pos_ratio, True, w=w, batchsize=128)
preds = get_mtresults(x_val,\
'val', 'train', val_fn, cls_num, dim, pos_ratio)
if (epoch+1) == 10 or (epoch+1) == 20 or (epoch+1)==30:
new_lr = sh_lr.get_value() * 0.1
print("New LR:"+str(new_lr))
sh_lr.set_value(lasagne.utils.floatX(new_lr))
# dump the network weights to a file :
if model is not None:
np.savez(model, *lasagne.layers.get_all_param_values(fc8))
#np.savez('weighted_indexes_{}'.format(model), indexes)
return input_var, fc8, val_fn