def setUp(self):
self.num_output = 13
net_f = simple_net_file(self.num_output)
f = tempfile.NamedTemporaryFile(mode="w+", delete=False)
f.write(
"""net: '"""
+ net_f
+ """'
test_iter: 10 test_interval: 10 base_lr: 0.01 momentum: 0.9
weight_decay: 0.0005 lr_policy: 'inv' gamma: 0.0001 power: 0.75
display: 100 max_iter: 100 snapshot_after_train: false
snapshot_prefix: "models" """
)
f.close()
self.solver = caffe.SGDSolver(f.name)
# also make sure get_solver runs
caffe.get_solver(f.name)
caffe.set_mode_cpu()
# fill in valid labels
self.solver.net.blobs["label"].data[...] = np.random.randint(
self.num_output, size=self.solver.net.blobs["label"].data.shape
)
self.solver.test_nets[0].blobs["label"].data[...] = np.random.randint(
self.num_output, size=self.solver.test_nets[0].blobs["label"].data.shape
)
os.remove(f.name)
os.remove(net_f)
def setUp(self):
self.num_output = 13
net_f = simple_net_file(self.num_output)
f = tempfile.NamedTemporaryFile(mode='w+', delete=False)
net_f_mod = net_f
if os.name == 'nt':
net_f_mod = net_f_mod.replace("\\", "/")
f.write("""net: '""" + net_f_mod + """'
test_iter: 10 test_interval: 10 base_lr: 0.01 momentum: 0.9
weight_decay: 0.0005 lr_policy: 'inv' gamma: 0.0001 power: 0.75
display: 100 max_iter: 100 snapshot_after_train: false
snapshot_prefix: "model" """)
f.close()
self.solver = caffe.SGDSolver(f.name)
# also make sure get_solver runs
caffe.get_solver(f.name)
caffe.set_mode_cpu()
# fill in valid labels
self.solver.net.blobs['label'].data[...] = \
np.random.randint(self.num_output,
size=self.solver.net.blobs['label'].data.shape)
self.solver.test_nets[0].blobs['label'].data[...] = \
np.random.randint(self.num_output,
size=self.solver.test_nets[0].blobs['label'].data.shape)
os.remove(f.name)
os.remove(net_f)
def init_solver(self):
""" Helper method to initialize the solver. """
solver_param = SolverParameter()
solver_param.snapshot_prefix = self._hyperparams['weights_file_prefix']
solver_param.display = 0 # Don't display anything.
solver_param.base_lr = self._hyperparams['lr']
solver_param.lr_policy = self._hyperparams['lr_policy']
solver_param.momentum = self._hyperparams['momentum']
solver_param.weight_decay = self._hyperparams['weight_decay']
solver_param.type = self._hyperparams['solver_type']
solver_param.random_seed = self._hyperparams['random_seed']
# Pass in net parameter either by filename or protostring.
if isinstance(self._hyperparams['network_model'], basestring):
self.solver = caffe.get_solver(self._hyperparams['network_model'])
else:
network_arch_params = self._hyperparams['network_arch_params']
network_arch_params['dim_input'] = self._dO
network_arch_params['dim_output'] = self._dU
network_arch_params['batch_size'] = self.batch_size
network_arch_params['phase'] = TRAIN
solver_param.train_net_param.CopyFrom(
self._hyperparams['network_model'](**network_arch_params)
)
# For running forward in python.
network_arch_params['batch_size'] = 1
network_arch_params['phase'] = TEST
solver_param.test_net_param.add().CopyFrom(
self._hyperparams['network_model'](**network_arch_params)
)
# For running forward on the robot.
network_arch_params['batch_size'] = 1
network_arch_params['phase'] = 'deploy'
solver_param.test_net_param.add().CopyFrom(
self._hyperparams['network_model'](**network_arch_params)
)
# These are required by Caffe to be set, but not used.
solver_param.test_iter.append(1)
solver_param.test_iter.append(1)
solver_param.test_interval = 1000000
f = tempfile.NamedTemporaryFile(mode='w+', delete=False)
f.write(MessageToString(solver_param))
f.close()
self.solver = caffe.get_solver(f.name)
def __get_solver(self, solver_proto_path):
'''
Returns a caffe.SGDSolver for the given protofile path,
ignoring Caffe command line chatter if debug mode is not set
to True.
'''
if not self.debug_mode:
# disable Caffe init chatter when not in debug
with Suppressor():
# return caffe.SGDSolver(solver_proto_path)
return caffe.get_solver(solver_proto_path)
else:
# return caffe.SGDSolver(solver_proto_path)
return caffe.get_solver(solver_proto_path)
def init_solver(self):
""" Helper method to initialize the solver. """
solver_param = SolverParameter()
solver_param.snapshot_prefix = self._hyperparams['weights_file_prefix']
solver_param.display = 0 # Don't display anything.
solver_param.base_lr = self._hyperparams['lr']
solver_param.lr_policy = self._hyperparams['lr_policy']
solver_param.momentum = self._hyperparams['momentum']
solver_param.weight_decay = self._hyperparams['weight_decay']
solver_param.type = self._hyperparams['solver_type']
solver_param.random_seed = self._hyperparams['random_seed']
# Pass in net parameter either by filename or protostring.
if isinstance(self._hyperparams['network_model'], basestring):
self.solver = caffe.get_solver(self._hyperparams['network_model'])
else:
network_arch_params = self._hyperparams['network_arch_params']
network_arch_params['dim_input'] = self._dO
network_arch_params['dim_output'] = self._dU
network_arch_params['batch_size'] = self.batch_size
network_arch_params['phase'] = TRAIN
solver_param.train_net_param.CopyFrom(
self._hyperparams['network_model'](**network_arch_params)
)
# For running forward in python.
network_arch_params['batch_size'] = 1
network_arch_params['phase'] = TEST
solver_param.test_net_param.add().CopyFrom(
self._hyperparams['network_model'](**network_arch_params)
)
# For running forward on the robot.
network_arch_params['batch_size'] = 1
network_arch_params['phase'] = 'deploy'
solver_param.test_net_param.add().CopyFrom(
self._hyperparams['network_model'](**network_arch_params)
)
# These are required by Caffe to be set, but not used.
solver_param.test_iter.append(1)
solver_param.test_iter.append(1)
solver_param.test_interval = 1000000
f = tempfile.NamedTemporaryFile(mode='w+', delete=False)
f.write(MessageToString(solver_param))
f.close()
self.solver = caffe.get_solver(f.name)
def train_and_test(niter=200, learn_all_pre=False, learn_all_scra=False):
global typeNet
typeNet = 'pretrained_net'
# Initialize chartnet with pretrained ImageNet weights.
chart_solver = caffe.get_solver(
solver(chart_net(train=True, learn_all=learn_all_pre)))
chart_solver.net.copy_from(weights_pretrained)
typeNet = 'scratch_net'
# Create chartnet that is initialized randomly.
scratch_chart_solver = caffe.get_solver(
solver(chart_net(train=True, learn_all=learn_all_scra)))
print 'Running solvers for %d iterations...' % niter
solvers = [('pretrained', chart_solver), ('scratch', scratch_chart_solver)]
loss, acc, weights = run_solvers(niter, solvers, disp_interval=50)
print 'Done.'
train_loss, scratch_train_loss = loss['pretrained'], loss['scratch']
train_acc, scratch_train_acc = acc['pretrained'], acc['scratch']
chart_weights, scratch_chart_weights = weights['pretrained'], weights[
'scratch']
# Delete solvers to save memory.
del chart_solver, scratch_chart_solver, solvers
plt.figure()
plt.plot(np.vstack([train_loss, scratch_train_loss]).T)
plt.xlabel('Iteration #')
plt.ylabel('Loss')
plt.figure()
plt.plot(np.vstack([train_acc, scratch_train_acc]).T)
plt.xlabel('Iteration #')
plt.ylabel('Accuracy')
typeNet = 'pretrained_net'
test_net, accuracy = eval_chart_net(chart_weights)
print 'Accuracy, trained from ImageNet initialization: %3.1f%%' % (
100 * accuracy, )
typeNet = 'scratch_net'
scratch_test_net, scratch_accuracy = eval_chart_net(scratch_chart_weights)
print 'Accuracy, trained from random initialization: %3.1f%%' % (
100 * scratch_accuracy, )
classify_image(test_net, '00024.png')
classify_image(scratch_test_net, '00024.png')
return chart_weights, scratch_chart_weights
def train_network(solver_file, output_weights, batch_size, num_iterations, stop_loss_thres=0.01, use_gpu=True) :
if use_gpu :
caffe.set_mode_gpu()
else :
caffe.set_mode_cpu()
solver = caffe.get_solver(solver_file)
solver.net.blobs['data'].reshape(batch_size, 1, 64, 64)
solver.net.blobs['target'].reshape(batch_size, 1, 64, 64)
solver.net.reshape()
for i in range(num_iterations):
data, target = get_data(batch_size, numclasses=3)
solver.net.blobs['data'].data[...] = data
solver.net.blobs['target'].data[...] = target
solver.step(1)
output = solver.net.blobs['seg'].data[...]
loss = solver.net.blobs['loss'].data
if loss < stop_loss_thres:
solver.net.save(output_weights)
fig, sub = plt.subplots(ncols=3, figsize=(15, 5))
sub[0].set_title('Input')
sub[0].imshow(data[0, 0, :, :])
sub[1].set_title('Ground Truth')
sub[1].imshow(target[0, 0, :, :])
sub[2].set_title('Segmentation')
sub[2].imshow(np.argmax(output[0, :, :, :], axis=0))
plt.show()
break
def simple_train_example():
if 'posix' == os.name:
caffe_home_dir_path = '/home/sangwook/lib_repo/cpp/caffe_github'
else:
caffe_home_dir_path = 'D:/lib_repo/cpp/rnd/caffe_github'
#caffe_home_dir_path = 'D:/lib_repo/cpp/caffe_github'
solver_filepath = caffe_home_dir_path + '/models/bvlc_reference_caffenet/solver.prototxt'
solver = caffe.get_solver(solver_filepath)
#solver = caffe.SGDSolver(solver_filepath)
#solver = caffe.AdaDelta(solver_filepath)
#solver = caffe.AdaGrad(solver_filepath)
#solver = caffe.Adam(solver_filepath)
#solver = caffe.Nesterov(solver_filepath)
#solver = caffe.RMSprop(solver_filepath)
# Now, it's time to begin to see if everything works well and to fill the layers in a forward propagation in the net (computation of net.blobs[k].data from input layer until the loss layer).
# Trains net.
solver.net.forward()
# Tests net (there can be more than one).
solver.test_nets[0].forward()
# For the computation of the gradients (computation of the net.blobs[k].diff and net.params[k][j].diff from the loss layer until input layer).
solver.net.backward()
# To launch one step of the gradient descent, that is a forward propagation, a backward propagation and the update of the net params given the gradients (update of the net.params[k][j].data).
solver.step(1)
# To run the full gradient descent, that is the max_iter steps.
solver.solve()
# Computes accuracy of the model on the test data.
"""
def train_net(args, with_val_net=False):
train_net_file = miniplaces_net(get_split('train'),
args.crop,
args.batch,
args.image_root,
train=True,
cudnn=args.cudnn)
# Set with_val_net=True to test during training.
# Environment variable GLOG_minloglevel should be set to 0 to display
# Caffe output in this case; otherwise, the test result will not be
# displayed.
if with_val_net:
val_net_file = miniplaces_net(get_split('val'),
args.crop,
args.batch,
args.image_root,
train=False,
cudnn=args.cudnn)
else:
val_net_file = None
solver_file = miniplaces_solver(args, train_net_file, val_net_file)
solver = caffe.get_solver(solver_file)
solver.net.params['conv1'][0].data[...] = weights.make_weights()
if args.weights_file:
solver.net.copy_from(args.weights_file)
outputs = sorted(solver.net.outputs)
def str_output(output):
value = solver.net.blobs[output].data
if output.startswith('accuracy'):
valstr = '%5.2f%%' % (100 * value, )
else:
valstr = '%6f' % value
return '%s = %s' % (output, valstr)
def disp_outputs(iteration, iter_pad_len=len(str(args.iters))):
metrics = '; '.join(str_output(o) for o in outputs)
return 'Iteration %*d: %s' % (iter_pad_len, iteration, metrics)
# We could just call `solver.solve()` rather than `step()`ing in a loop.
# (If we hadn't set GLOG_minloglevel = 3 at the top of this file, Caffe
# would display loss/accuracy information during training.)
previous_time = None
for iteration in xrange(args.iters):
solver.step(1)
if (args.disp > 0) and (iteration % args.disp == 0):
current_time = time.clock()
if previous_time is None:
benchmark = ''
else:
time_per_iter = (current_time - previous_time) / args.disp
benchmark = ' (%5f s/it)' % time_per_iter
previous_time = current_time
print disp_outputs(iteration), benchmark
# Print accuracy for last iteration.
solver.net.forward()
disp_outputs(args.iters)
solver.net.save(
snapshot_at_iteration(args.iters, args.snapshot_dir,
args.snapshot_prefix))
def run_solver(niter = 18000, disp_interval = 10):
solver = caffe.get_solver(vgg_new_model + 'VGG_dfi_solver.prototxt')
print 'Solver loaded'
if os.path.isfile(vgg_face_model + 'VGG_FACE.caffemodel'):
weights = vgg_face_model + 'VGG_FACE.caffemodel'
print 'Caffe VGG weights found'
solver.net.copy_from(weights)
print 'weights copied'
loss = np.zeros(niter)
acc = np.zeros(niter)
for it in range(niter):
solver.step(1) #Run a single SGD step # Simulate a batch size of
loss[it] = solver.net.blobs['loss'].data.copy()
acc[it] = solver.net.blobs['acc'].data.copy()
if it % disp_interval == 0 or it + 1 == niter:
loss_disp = 'Loss:', loss[it], ' Acc:', np.round(100*acc[it])
print '%3d) %s' % (it, loss_disp)
# Save the learned weights.
weights = os.path.join(vgg_new_model, 'weights.VGG_dfi.caffemodel')
solver.net.save(weights)
return
def train_equivariance_model(model_type, loss, input_train_features, input_test_features, target_train_features,
target_test_features, train_labels, test_labels, target_train_output_probs, target_test_output_probs,
classifier_weights, classifier_bias, args):
num_train_instances = input_train_features.shape[0]
num_test_instances = input_test_features.shape[0]
num_activations = input_train_features.shape[1]
num_classes = target_train_output_probs.shape[1]
mlp = model_type == 'mlp'
write_hdf5s(args, input_train_features, target_train_features, target_train_output_probs, train_labels,
input_test_features, target_test_features, target_test_output_probs, test_labels)
# Write the prototxt for the train and test nets, as well as the solver
net_param = equivariance_proto(args, num_activations, num_classes, loss, mlp)
with open(args.train_file, 'w') as f:
f.write(re.sub("VAL_", "", str(net_param)))
solver_param = create_solver(args, num_train_instances, num_test_instances)
with open(args.solver_file, 'w') as f:
f.write(str(solver_param))
# load the solver and the network files it references
solver = caffe.get_solver(args.solver_file)
# fix the classificaiton weights/biases to be the passed in weights/biases
classify_layer_params = solver.net.params['classify']
classify_layer_params[0].data[:] = classifier_weights[:] # data copy, not object reassignment
classify_layer_params[1].data[:] = classifier_bias[:]
solver.solve()
return solver.net, solver.test_nets[0]
def solve_sphconv(layer,
network='faster-rcnn',
log=False,
random_init=False,
state=None):
log_dir = os.path.join(LOG_ROOT, "SphConv")
if not os.path.isdir(log_dir):
os.makedirs(log_dir)
log_path = os.path.join(log_dir, "{0}{1}.log".format(network, layer))
if os.path.isfile(log_path):
sys.stderr.write("Log already exists, please delete log file first.\n")
return
if log:
stderr = os.dup(2)
log = os.open(log_path, os.O_WRONLY | os.O_CREAT)
os.dup2(log, 2)
solver_proto = os.path.join(
SphConv_ROOT, "prototxt", "SphConv",
"{0}{1}.solver.prototxt".format(network, layer))
solver = caffe.get_solver(solver_proto)
if state is not None:
solver.restore(state)
elif not random_init:
init_sphconv(solver.net, layer, pretrained=False)
solver.solve()
def __init__(self, config):
# "Use Caffe as self."
# caffe constructor: network_file, phase, level, stages, weight, engine
topology_path = os.path.expanduser(str(config.model.topology))
if (hasattr(config.backend,
'engine')) and (config.backend.engine != "default"):
engine = str(config.backend.engine)
else:
engine = 'CAFFE'
if hasattr(config.model, 'weight'):
logger.debug("loading weights from: {}".format(
config.model.weight))
weight_path = os.path.expanduser(str(config.model.weight))
else:
weight_path = None
if config.model.type == "test":
phase = caffe.TEST
else:
phase = caffe.TRAIN
caffe.set_mode_cpu()
caffe.set_random_seed(0)
if hasattr(config,
'batch_size') and config.model.prototxt_type == 'train_val':
topology_path = self.reshape_in_train_val( topology_path, config.batch_size, \
config.out_dir,)
if config.model.prototxt_type == 'solver':
logger.debug("using engine: {}".format(engine))
modified_solver_path = os.path.join(str(config.out_dir),
'modified_solver.prototxt')
if not os.path.exists(os.path.dirname(modified_solver_path)):
os.makedirs(os.path.dirname(modified_solver_path))
solver_params = caffe_pb2.SolverParameter()
with open(config.model.topology) as f:
s = f.read()
txtf.Merge(s, solver_params)
solver_params.engine = engine
if hasattr(config, 'batch_size'):
solver_params.net = self.reshape_in_train_val( str(solver_params.net), \
config.batch_size, config.out_dir)
with open(modified_solver_path, 'w') as fp:
fp.write(str(solver_params))
self.solver = caffe.get_solver(modified_solver_path)
self.net = self.solver.net
if weight_path != None:
self.net.copy_from(weight_path)
else:
try:
logger.debug("using engine: {}".format(engine))
self.net = caffe.Net(topology_path,
phase,
weights=weight_path,
engine=engine)
except:
self.net = caffe.Net(topology_path, phase, weights=weight_path)
def train(solver_proto, datasets, initialization, gpu_id):
"""Train a network"""
caffe.set_device(gpu_id)
caffe.set_mode_gpu()
solver = caffe.get_solver(solver_proto)
if initialization is not None:
assert osp.exists(initialization), 'Path to weights/solverstate does not exist: {}'.format(initialization)
if initialization.endswith('.solverstate'):
print 'Restoring solverstate from {}'.format(initialization)
solver.restore(initialization)
elif initialization.endswith('.caffemodel'):
print 'Initializing weights from {}'.format(initialization)
solver.net.copy_from(initialization)
else:
raise ValueError('ERROR: {} is not supported for initailization'.format(initialization))
else:
warnings.warn("Warning: No initialization provided. Training from scratch.")
for dataset in datasets:
solver.net.layers[0].add_dataset(dataset)
solver.net.layers[0].print_params()
solver.net.layers[0].generate_datum_ids()
# train according to solver params
solver.solve()
def train(solver_prototxt_filename):
'''
Train the ANN
'''
caffe.set_mode_cpu()
solver = caffe.get_solver(solver_prototxt_filename)
solver.solve()
def train(config):
with open('./track_model/proto_train.prototxt', 'w') as f:
f.write(str(track_model.generate_model('train', config)))
caffe.set_device(config.gpu_id)
caffe.set_mode_gpu()
solver = caffe.get_solver('./track_model/solver_sgd.prototxt')
solver.net.copy_from(config.weights)
#solver.net.save('./snapshots/track_model/_iter_0.caffemodel')
#solver.restore('./snapshots/track_model/_iter_50000.solverstate')
loss_avg = 0.0
avg_accuracy_all, avg_accuracy_pos, avg_accuracy_neg = 0, 0, 0
decay = 0.99
for it in range(config.max_iter):
solver.step(1)
loss_val = solver.net.blobs['loss'].data
#scores_val = solver.net.blobs['fcn_scaled_scores'].data.copy()
#label_val = solver.net.blobs['label'].data.copy()
#import ipdb; ipdb.set_trace()
loss_avg = decay * loss_avg + (1 - decay) * loss_val
if it % config.iter_display == 0:
print('\titer = %d, cls_loss (cur) = %f, cls_loss (avg) = %f' %
(it, loss_val, loss_avg))
def __init__(self,
CNN_NETWORK_PATH,
CNN_SOLVER_PATH,
CAFFEMODEL_PATH,
SOLVERSTATE_PATH,
USE_GPU=True,
resume_training=False):
self.CNN_NETWORK_PATH = CNN_NETWORK_PATH
self.CNN_SOLVER_PATH = CNN_SOLVER_PATH
self.CAFFEMODEL_PATH = CAFFEMODEL_PATH
self.SOLVERSTATE_PATH = SOLVERSTATE_PATH
self.resume_training = resume_training
if USE_GPU:
caffe.set_device(0)
caffe.set_mode_gpu()
else:
caffe.set_mode_cpu()
if self.resume_training:
self.net = caffe.Net(self.CNN_NETWORK_PATH, self.CAFFEMODEL_PATH,
caffe.TRAIN)
else:
self.net = caffe.Net(self.CNN_NETWORK_PATH, caffe.TRAIN)
self.solver = caffe.get_solver(self.CNN_SOLVER_PATH)
def train(solver_proto):
'''
Train the ANN
'''
caffe.set_mode_cpu()
solver = caffe.get_solver(solver_proto)
solver.solve()
def train_model(model_type, loss, classifier_weights, classifier_bias, num_train_instances,
num_test_instances, num_features, num_classes, args):
net_param = equivalence_proto(args, num_features, num_classes, loss, model_type == 'mlp')
with open(args.train_file, 'w') as f:
f.write(re.sub("VAL_", "", str(net_param)))
solver_param = create_solver(args, num_train_instances, num_test_instances)
with open(args.solver_file, 'w') as f:
f.write(str(solver_param))
# load the solver and the network files it references
solver = caffe.get_solver(args.solver_file)
# fix the classificaiton weights/biases to be the passed in weights/biases
classify_layer_params = solver.net.params['classify']
classify_layer_params[0].data[:] = classifier_weights[:] # data copy, not object reassignment
classify_layer_params[1].data[:] = classifier_bias[:]
# apparently necessary, though I thought the weights were shared between the two networks
classify_layer_params = solver.test_nets[0].params['classify']
classify_layer_params[0].data[:] = classifier_weights[:] # data copy, not object reassignment
classify_layer_params[1].data[:] = classifier_bias[:]
solver.solve()
return solver.net, solver.test_nets[0]
def train():
# Instantiate a solver object load the prototxt
solver = caffe.get_solver(solverPATH)
solver.max_iter = MAX_ITER
solver.test_iter = 92
solver.test_bs = 64
solver.test_interval = TEST_INTERVAL
# the main solver loop
for it in range(1, solver.max_iter + 1):
#train the next batch
solver.step(1)
#Test if the right interval
if it % solver.test_interval == 0:
print 'Iteration', it, 'testing'
acc = 0
loss = 0
label = []
out1 = []
out2 = []
out3 = []
for test_it in range(solver.test_iter):
#Run the test data through the network
solver.test_nets[0].forward()
label = np.concatenate(
(label, solver.test_nets[0].blobs['label'].data), axis=0)
out1 = np.concatenate(
(out1, solver.test_nets[0].
blobs['loss1/classifier_transfer'].data.argmax(1)),
axis=0)
out2 = np.concatenate(
(out2, solver.test_nets[0].
blobs['loss2/classifier_transfer'].data.argmax(1)),
axis=0)
out3 = np.concatenate(
(out3, solver.test_nets[0].
blobs['loss3/classifier_transfer'].data.argmax(1)),
axis=0)
# loss += solver.test_nets[0].blobs['loss'].data
# acc += solver.test_nets[0].blobs['accuracy'].data
# Record the test data
# print float(loss) / solver.test_iter
# print float(acc) / solver.test_iter
labell.append(label)
out1l.append(out1)
out2l.append(out2)
out3l.append(out3)
return
def tune_VGG_for_falls():
# simple python wrapper for running already written prototxt files
solver_file = '/home/nestsense/code/ns-main/models/vgg_solver.prototxt'
weights_file = '/home/nestsense/code/ns-main/snapshot/VGG_ILSVRC_16_layers.caffemodel'
solver = caffe.get_solver(solver_file)
solver.net.copy_from(weights_file)
solver.solve()
def train(solver_proto_path,
snapshot_solver_path=None,
init_weights=None,
GPU_ID=0):
"""
Train the defined net. While we did not use this function for our final net,
we used the caffe executable for multi-gpu use, this was used for prototyping
"""
t0 = time.time()
if GPU_ID >= 0:
caffe.set_mode_gpu()
caffe.set_device(GPU_ID)
else:
caffe.set_mode_cpu()
solver = caffe.get_solver(solver_proto_path)
if snapshot_solver_path is not None:
solver.solve(snapshot_solver_path) # train from previous solverstate
else:
if init_weights is not None:
solver.net.copy_from(
init_weights
) # for copying weights from a model without solverstate
solver.solve() # train form scratch
t1 = time.time()
print 'Total training time: ', t1 - t0, ' sec'
model_dir = "calc_" + time.strftime("%d-%m-%Y_%I%M%S")
moveModel(model_dir=model_dir) # move all the model files to a directory
print "Moved model to model/" + model_dir
def train():
with open('./seg_low_res_model/proto_train.prototxt', 'w') as f:
f.write(str(segmodel.generate_model('train', config.N)))
caffe.set_device(config.gpu_id)
caffe.set_mode_gpu()
solver = caffe.get_solver('./seg_low_res_model/solver.prototxt')
if config.weights is not None:
solver.net.copy_from(config.weights)
cls_loss_avg = 0.0
avg_accuracy_all, avg_accuracy_pos, avg_accuracy_neg = 0.0, 0.0, 0.0
decay = 0.99
for it in range(config.max_iter):
solver.step(1)
cls_loss_val = solver.net.blobs['loss'].data
scores_val = solver.net.blobs['fcn_scores'].data.copy()
label_val = solver.net.blobs['label'].data.copy()
cls_loss_avg = decay*cls_loss_avg + (1-decay)*cls_loss_val
print('\titer = %d, cls_loss (cur) = %f, cls_loss (avg) = %f'
% (it, cls_loss_val, cls_loss_avg))
# Accuracy
accuracy_all, accuracy_pos, accuracy_neg = compute_accuracy(scores_val, label_val)
avg_accuracy_all = decay*avg_accuracy_all + (1-decay)*accuracy_all
avg_accuracy_pos = decay*avg_accuracy_pos + (1-decay)*accuracy_pos
avg_accuracy_neg = decay*avg_accuracy_neg + (1-decay)*accuracy_neg
print('\titer = %d, accuracy (cur) = %f (all), %f (pos), %f (neg)'
% (it, accuracy_all, accuracy_pos, accuracy_neg))
print('\titer = %d, accuracy (avg) = %f (all), %f (pos), %f (neg)'
% (it, avg_accuracy_all, avg_accuracy_pos, avg_accuracy_neg))
def solve2(solver, args, uid, rank):
if args.cpu:
caffe.set_mode_cpu()
else:
caffe.set_mode_gpu()
caffe.set_device(args.gpus[rank])
caffe.set_solver_count(len(args.gpus))
caffe.set_solver_rank(rank)
caffe.set_multiprocess(True)
solver = caffe.get_solver(solver)
if args.init_model:
if args.init_model.endswith('.caffemodel'):
solver.net.copy_from(args.init_model)
else:
solver.net.copy_from(os.path.join(exp_dir, '{}_iter_{}.caffemodel'.format(category, args.init_model)))
if args.init_state:
if args.init_state.endswith('.solverstate'):
solver.restore(args.init_state)
else:
solver.restore(os.path.join(exp_dir, '{}_iter_{}.solverstate'.format(category, args.init_state)))
nccl = caffe.NCCL(solver, uid)
nccl.bcast()
if solver.param.layer_wise_reduce:
solver.net.after_backward(nccl)
print(rank)
#pdb.set_trace()
solver.step(solver.param.max_iter)
def train(config):
with open('./seg_model/proto_train.prototxt', 'w') as f:
f.write(str(segmodel.generate_model('train', config)))
caffe.set_device(config.gpu_id)
caffe.set_mode_gpu()
solver = caffe.get_solver('./seg_model/solver.prototxt')
if config.weights is not None:
solver.net.copy_from(config.weights)
cls_loss_avg = 0.0
avg_accuracy_all, avg_accuracy_pos, avg_accuracy_neg = 0.0, 0.0, 0.0
decay = 0.99
for it in range(config.max_iter):
solver.step(1)
cls_loss_val = solver.net.blobs['loss'].data
scores_val = solver.net.blobs['upscores'].data.copy()
label_val = solver.net.blobs['label'].data.copy()
cls_loss_avg = decay*cls_loss_avg + (1-decay)*cls_loss_val
print('\titer = %d, cls_loss (cur) = %f, cls_loss (avg) = %f'
% (it, cls_loss_val, cls_loss_avg))
# Accuracy
accuracy_all, accuracy_pos, accuracy_neg = compute_accuracy(scores_val, label_val)
avg_accuracy_all = decay*avg_accuracy_all + (1-decay)*accuracy_all
avg_accuracy_pos = decay*avg_accuracy_pos + (1-decay)*accuracy_pos
avg_accuracy_neg = decay*avg_accuracy_neg + (1-decay)*accuracy_neg
print('\titer = %d, accuracy (cur) = %f (all), %f (pos), %f (neg)'
% (it, accuracy_all, accuracy_pos, accuracy_neg))
print('\titer = %d, accuracy (avg) = %f (all), %f (pos), %f (neg)'
% (it, avg_accuracy_all, avg_accuracy_pos, avg_accuracy_neg))
def main():
if not os.path.exists('./result'):
os.makedirs('./result')
question_vocab, answer_vocab = {}, {}
if os.path.exists('./result/vdict.json') and os.path.exists('./result/adict.json'):
write_log('restoring vocab', 'log.txt')
with open('./result/vdict.json','r') as f:
question_vocab = json.load(f)
with open('./result/adict.json','r') as f:
answer_vocab = json.load(f)
else:
question_vocab, answer_vocab = make_vocab_files()
with open('./result/vdict.json','w') as f:
json.dump(question_vocab, f)
with open('./result/adict.json','w') as f:
json.dump(answer_vocab, f)
write_log('question vocab size: '+ str(len(question_vocab)), 'log.txt')
write_log('answer vocab size: '+ str(len(answer_vocab)), 'log.txt')
with open('./result/proto_train.prototxt', 'w') as f:
f.write(str(qlstm(config.TRAIN_DATA_SPLITS, config.BATCH_SIZE, \
config.MAX_WORDS_IN_QUESTION, len(question_vocab))))
with open('./result/proto_test.prototxt', 'w') as f:
f.write(str(qlstm('val', config.VAL_BATCH_SIZE, \
config.MAX_WORDS_IN_QUESTION, len(question_vocab))))
caffe.set_device(config.GPU_ID)
caffe.set_mode_gpu()
solver = caffe.get_solver('./qlstm_solver.prototxt')
train_loss = np.zeros(config.MAX_ITERATIONS)
# results = []
for it in range(config.MAX_ITERATIONS):
solver.step(1)
# store the train loss
train_loss[it] = solver.net.blobs['loss'].data
if it != 0 and it % config.PRINT_INTERVAL == 0:
write_log('------------------------------------', 'log.txt')
write_log('Iteration: ' + str(it), 'log.txt')
c_mean_loss = train_loss[it-config.PRINT_INTERVAL:it].mean()
write_log('Train loss: ' + str(c_mean_loss), 'log.txt')
if it != 0 and it % config.VALIDATE_INTERVAL == 0: # acutually test
solver.test_nets[0].save('./result/tmp.caffemodel')
write_log('Validating...', 'log.txt')
test_loss, acc_overall, acc_per_ques, acc_per_ans = exec_validation(config.GPU_ID, 'val', it=it)
write_log('Iteration: ' + str(it), 'log.txt')
write_log('Test loss: ' + str(test_loss), 'log.txt')
write_log('Overall Accuracy: ' + str(acc_overall), 'log.txt')
write_log('Per Question Type Accuracy is the following:', 'log.txt')
for quesType in acc_per_ques:
write_log("%s : %.02f" % (quesType, acc_per_ques[quesType]), 'log.txt')
write_log('Per Answer Type Accuracy is the following:', 'log.txt')
for ansType in acc_per_ans:
write_log("%s : %.02f" % (ansType, acc_per_ans[ansType]), 'log.txt')
def train(self):
train_data, __ = utils.load_train_data()
# raw = train_data['input']
# r = raw[0]
# g = raw[1]
# b = raw[2]
# print len(r.flatten())
# r_std = np.std(r.flatten())
# r_mean = np.mean(r.flatten())
# adjusted_stddev = max(r_std, 1.0 / math.sqrt(3072))
# r = (np.array(r, dtype=np.float32) - r_mean) / adjusted_stddev
#
# g_std = np.std(g.flatten())
# g_mean = np.mean(g.flatten())
# adjusted_stddev = max(g_std, 1.0 / math.sqrt(3072))
# g = (np.array(g, dtype=np.float32) - g_mean) / adjusted_stddev
#
#
# b_std = np.std(b.flatten())
# b_mean = np.mean(b.flatten())
# adjusted_stddev = max(b_std, 1.0 / math.sqrt(3072))
# b = (np.array(b, dtype=np.float32) - b_mean) / adjusted_stddev
#
# raw[0] = r
# raw[1] = g
# raw[2] = b
# print raw
# train_data['input'] = raw
# # train_data['input'] = utils.additive_gaussian_noise(raw)
# print train_data['input']
# utils.save_data_as_lmdb('cifar6_train_data_lmdb',train_data)
caffe.set_mode_gpu()
solver = caffe.get_solver('alexnet_solver_6.prototxt')
solver.solve()
pass
def _train_net (solver_prototxt, data_provider, output_dir, maxiter,\
resume_file=None, weight_file=None, log_to_file=False):
'''
This is private helper function to train a deep net
INPUT:
solver_prototxt: the file name and pull path of the solver prototxt
data_provider: should be in the form of [train_data_provider, test_data_provider]
NOTE:
This fucntion assumes the first layer is a data layer
'''
# get the solver from the prototxt
solver = caffe.get_solver(solver_prototxt)
# assuming the data layer is located at the very very bottom
# set the data for training
solver.net.layers[0].set_data(data_provider[0])
# set the data for testing if necessary
if len(solver.test_nets) > 0:
solver.test_nets[0].layers[0].set_data(data_provider[1])
if resume_file is not None:
# if a resume file exists, we want to resume the process
solver.solve(resume_file)
elif weight_file is not None:
# reload the weight
solver.net.copy_from(weight_file)
solver.solve()
else:
# train from the begining
solver.solve()
def train(solver_prototxt_filename):
'''
Train the ANN
'''
caffe.set_mode_cpu()
solver = caffe.get_solver(solver_prototxt_filename)
solver.solve()
def solve(self,max_iter):
caffe.set_mode_gpu()
if self.gpu_id is not None:
caffe.set_device(self.gpu_id)
solver = caffe.get_solver(self.solverfile)
for i in xrange( max_iter+1):
solver.step(1)
def run(dataset, gpu, learning_rates, momentums, weight_decays, steps,
num_hiddens, runs):
DEVICE = 'GPU'
EXP_PATH = '/home/szymon.zareba/dev/phd_pycaffe/experiments/rbm_pm1_geo/'
if DEVICE == 'CPU':
caffe.set_mode_cpu()
elif DEVICE == 'GPU':
caffe.set_device(gpu)
caffe.set_mode_gpu()
else:
raise ValueError(DEVICE)
EXP_PATH = os.path.join(EXP_PATH, dataset)
all_params = [learning_rates, momentums, weight_decays, steps, num_hiddens]
for setup in tqdm(list(product(*all_params))):
for run in range(1, runs + 1):
run = str(run)
print 'setup: {}'.format(setup)
learning_rate, momentum, weight_decay, step, num_hidden = setup
name = '_'.join([str(s) for s in setup])
print 'name: pm_geo_{}'.format(name)
if os.path.exists(os.path.join(EXP_PATH, name, run)):
print 'experiment exists'
else:
model_proto, solver_proto = create_protos(
EXP_PATH, name, DEVICE, learning_rate, momentum,
weight_decay, step, num_hidden, dataset, run)
solver = caffe.get_solver(solver_proto)
solver.solve()
def model_prepare(gpu_id=GPU_ID):
logging.info('Prepare Model ...')
caffe.set_mode_gpu()
caffe.set_device(gpu_id)
solver = caffe.get_solver(solver_file)
solver.net.copy_from(pretrained_model)
logging.info('Done.')
return solver
def init_solver(solver_config, options):
caffe.set_mode_gpu()
caffe.select_device(options.train_device, False)
solver_inst = caffe.get_solver(solver_config)
if options.test_net is None:
return solver_inst, None
else:
return solver_inst, init_testnet(options.test_net, test_device=options.test_device, level=options.test_level, stages=options.test_stages)
def Training(solver_file):
"""
training
"""
set_device()
solver = caffe.get_solver(solver_file)
#solver.solve() # solve completely
number_iteration = 10000
# collect the information
display = 100
# test information
test_iteration = 100
test_interval = 100
# loss and accuracy information
train_loss = np.zeros( int(np.ceil(number_iteration * 1.0 / display)) )
test_loss = np.zeros( int(np.ceil(number_iteration * 1.0 / test_interval)) )
test_accuracy = np.zeros( int(np.ceil(number_iteration * 1.0 / test_interval)) )
# tmp variables
_train_loss = 0; _test_loss = 0; _test_accuracy = 0;
# main loop
for iter in range(number_iteration):
solver.step(1)
# save model during training
#~ if iter == number_iteration - 1: #in [10, 30, 60, 100, 300, 600, 1000, 3000, 6000, number_iteration - 1]:
#~ string = 'lenet_iter_%(iter)d.caffemodel'%{'iter': iter}
#~ solver.net.save(string)
if 0 == iter % display:
train_loss[iter // display] = solver.net.blobs['loss'].data
'''
# accumulate the train loss
_train_loss += solver.net.blobs['SoftmaxWithLoss1'].data
if 0 == iter % display:
train_loss[iter // display] = _train_loss / display
_train_loss = 0
'''
if 0 == iter % test_interval:
for test_iter in range(test_iteration):
solver.test_nets[0].forward()
_test_loss += solver.test_nets[0].blobs['loss'].data
_test_accuracy += solver.test_nets[0].blobs['accuracy'].data
test_loss[iter / test_interval] = _test_loss / test_iteration
test_accuracy[iter / test_interval] = _test_accuracy / test_iteration
_test_loss = 0
_test_accuracy = 0
def main():
if not os.path.exists('./result'):
os.makedirs('./result')
question_vocab, answer_vocab = {}, {}
if os.path.exists('./result/vdict.json') and os.path.exists('./result/adict.json'):
print 'restoring vocab'
with open('./result/vdict.json','r') as f:
question_vocab = json.load(f)
with open('./result/adict.json','r') as f:
answer_vocab = json.load(f)
else:
question_vocab, answer_vocab = make_vocab_files()
with open('./result/vdict.json','w') as f:
json.dump(question_vocab, f)
with open('./result/adict.json','w') as f:
json.dump(answer_vocab, f)
print 'question vocab size:', len(question_vocab)
print 'answer vocab size:', len(answer_vocab)
with open('./result/proto_train.prototxt', 'w') as f:
f.write(str(qlstm(config.TRAIN_DATA_SPLITS, config.BATCH_SIZE, \
config.MAX_WORDS_IN_QUESTION, len(question_vocab))))
with open('./result/proto_test.prototxt', 'w') as f:
f.write(str(qlstm('val', config.VAL_BATCH_SIZE, \
config.MAX_WORDS_IN_QUESTION, len(question_vocab))))
raise
caffe.set_device(config.GPU_ID)
caffe.set_mode_gpu()
solver = caffe.get_solver('./qlstm_solver.prototxt')
train_loss = np.zeros(config.MAX_ITERATIONS)
results = []
for it in range(config.MAX_ITERATIONS):
solver.step(1)
# store the train loss
train_loss[it] = solver.net.blobs['loss'].data
if it % config.PRINT_INTERVAL == 0:
print 'Iteration:', it
c_mean_loss = train_loss[it-config.PRINT_INTERVAL:it].mean()
print 'Train loss:', c_mean_loss
if it != 0 and it % config.VALIDATE_INTERVAL == 0:
solver.test_nets[0].save('./result/tmp.caffemodel')
print 'Validating...'
test_loss, acc_overall, acc_per_ques, acc_per_ans = exec_validation(config.GPU_ID, 'val', it=it)
print 'Test loss:', test_loss
print 'Accuracy:', acc_overall
results.append([it, c_mean_loss, test_loss, acc_overall, acc_per_ques, acc_per_ans])
best_result_idx = np.array([x[3] for x in results]).argmax()
print 'Best accuracy of', results[best_result_idx][3], 'was at iteration', results[best_result_idx][0]
drawgraph(results)
def create_solver(solver_param, file_name=""):
if file_name:
f = open(file_name, 'w')
else:
f = tempfile.NamedTemporaryFile(mode='w+', delete=False)
f.write(str(solver_param))
f.close()
solver = caffe.get_solver(f.name)
return solver
def Train(solver = "Data/solver.prototxt"):
# Tell caffe to use cpu. switch to caffe.set_mode_gpu() to try using the gpu
caffe.set_mode_cpu()
# Load solver from file
solver = caffe.get_solver(solver)
# Train a new model
solver.solve()
def train_cnn(solver_config_path, s, output_plot_path):
### load the solver and create train and test nets
solver = None # ignore this workaround for lmdb data (can't instantiate two solvers on the same data)
solver = caffe.get_solver(solver_config_path)
test_interval = s.test_interval
global niter
def train(solver_prototxt_filename):
"""
Train the ANN
"""
caffe.set_mode_gpu()
solver = caffe.get_solver(solver_prototxt_filename)
solver.net.copy_from(caffemodel_path)
solver.restore(caffe_solverstate_path)
solver.solve()
def create_solver(solver_param, file_name=""):
if file_name:
f = open(file_name, 'w')
else:
f = tempfile.NamedTemporaryFile(mode='w+', delete=False)
f.write(str(solver_param))
f.close()
solver = caffe.get_solver(f.name)
return solver
def objective(parameters):
#print("Parameters:")
#pprint(parameters)
#print()
base_lr = parameters['base_lr']
momentum = parameters['momentum']
batch_size = int(parameters['batch_size'])
weight_decay = parameters['weight_decay']
proba = parameters['dropout']
solver_options = {}
solver_options["base_lr"] = base_lr
solver_options["momentum"] = momentum
solver_options["weight_decay"] = weight_decay
mlab.update_solver(
"/home/sanand2/hypercaffe/caffe/examples/cifar10/cifar10_full_solver.prototxt",
solver_options)
solver_options = {}
solver_options["batch_size"] = batch_size
mlab.update_solver(
"/home/sanand2/hypercaffe/caffe/examples/cifar10/cifar10_full_solver.prototxt",
solver_options)
net = caffe.Net(
'/home/sanand2/hypercaffe/caffe/examples/cifar10/cifar10_full_train_test.prototxt',
caffe.TRAIN)
solver = caffe.get_solver(
'/home/sanand2/hypercaffe/caffe/examples/cifar10/cifar10_full_solver.prototxt'
)
solver.solve()
min = solver.net.blobs['loss'].data
print min
#loss_val = np.zeros(10)
#min = 0
#for it in range(10):
# solver.solve()
# loss_val[it] = solver.net.blobs['loss'].data
# if min<loss_val[it] :
# min = loss_val[it]
#print "loss value is " + str(loss_val[it])
# print min
#print net.blobs['mnist'].batch_size
#min = 0
#for i in range(10):
#print str(loss_val[i]) + " "
#sum += int(loss_val[i])
#sum = sum /10
#print "loss value is " + loss_val
#os.system("cd /home/syazdani/caffe")
#os.system("./examples/mnist/train_lenet.sh")
#os.system("cd hyperopt")
#sys.stdout.close()
#sys.stdout = tmp
return {'loss': min, 'status': STATUS_OK}
def train(self):
train_data, __ = utils.load_train_data()
print train_data['input'].shape
print train_data['output'].shape
utils.save_data_as_lmdb(const.LMDB_TRAIN_DATA_PATH, train_data)
caffe.set_mode_gpu()
solver = caffe.get_solver(const.ALEXNET_SOLVER)
solver.solve()
pass
def train(self):
train_data, __ = utils.load_train_data()
print train_data['input'].shape
print train_data['output'].shape
utils.save_data_as_lmdb(const.LMDB_TRAIN_WHITEN_DATA_PATH,train_data, False, True)
caffe.set_mode_gpu()
solver = caffe.get_solver('alexnet_solver_3.prototxt')
solver.solve()
pass
def __init__(self):
train_net_file = "yelp_train.prototxt"
test_net_file = "yelp_test.prototxt"
weights = "weights/weights.pretrained.caffemodel"
yelp_solver_filename = "yelp_multilabel_solver.prototxt"
yelp_solver = caffe.get_solver(yelp_solver_filename)
yelp_solver.net.copy_from(weights)
self.net = yelp_solver.test_nets[0]
def init_solver(solver_config, options):
caffe.set_mode_gpu()
caffe.select_device(options.train_device, False)
solver_inst = caffe.get_solver(solver_config)
if (options.test_net == None):
return (solver_inst, None)
else:
return (solver_inst, init_testnet(options.test_net, test_device=options.test_device))
def trainNewSolver(trainSolverPath, saveToSolverPath):
"""
Here we want to train a new solver with a certain features:
1. All biased nodes are zero
2. Construct a certain net and train
:return: void. Save the solverstate to a directory
"""
global solver
solver = caffe.get_solver(trainSolverPath)
solver.step(10000)
def main():
#read arguments
parser = argparser()
args = parser.parse_args()
solver = args.solver
train_loss_path = args.train_loss
train_acc_path = args.train_accuracy
val_loss_path = args.val_loss
val_acc_path = args.val_accuracy
snapshot = args.snapshot
threshold = args.threshold
max_iter = args.max_iter
record_iter = args.record_iter
test_iter = args.test_iter
early_stop = args.early_stop
caffe.set_mode_gpu()
caffe.set_device(0)
solver = caffe.get_solver(solver)
if snapshot != "":
solver.net.copy_from(snapshot)
train_loss = np.zeros(max_iter / record_iter)
train_acc = np.zeros(max_iter / record_iter)
val_loss = np.zeros(max_iter / record_iter)
val_acc = np.zeros(max_iter / record_iter)
pre_loss = 0
pre_acc = 0
for i in range(max_iter):
solver.step(1)
cur_train_loss = solver.net.blobs["loss"].data
cur_train_acc = solver.net.blobs["accuracy"].data
cur_val_loss = solver.test_nets[0].blobs["loss"].data
cur_val_acc = solver.test_nets[0].blobs["accuracy"].data
if i % record_iter == 0:
train_loss[i / record_iter] = cur_train_loss
train_acc[i / record_iter] = cur_train_acc
val_loss[i / record_iter] = cur_val_loss
val_acc[i / record_iter] = cur_val_acc
if early_stop and (i % test_iter == 0):
if i == 0:
pre_train_loss = cur_train_loss
pre_train_acc = cur_train_acc
pre_val_loss = cur_val_loss
pre_val_acc = cur_val_acc
elif (pre_loss - cur_loss) / pre_loss < threshold:
print "Converged, stopping...."
solver.snapshot()
train_loss = train_loss[0 : i / record_iter + 1]
train_acc = train_acc[0 : i / record_iter + 1]
np.savetxt(train_loss_path, train_loss)
np.savetxt(train_acc_path, train_acc)
np.savetxt(val_loss_path, val_loss)
np.savetxt(val_acc_path, val_acc)
def __init__(self, action_space, model=pms.newModel):
self.action_space = action_space
actionSolver = None
actionSolver = caffe.get_solver(pms.actionSolverPath)
actionSolver.net.copy_from(model)
# test net share weights with train net
actionSolver.test_nets[0].share_with(actionSolver.net)
self.solver = actionSolver
self.targetNet = caffe.Net(pms.actionTestNetPath, model, caffe.TEST)
def learn_and_test(solver_file, size_test):
caffe.set_mode_cpu()
solver = caffe.get_solver(solver_file)
solver.solve()
accuracy = 0
test_iters = int(size_test / solver.test_nets[0].blobs['data'].num)
for i in range(test_iters):
solver.test_nets[0].forward()
accuracy += solver.test_nets[0].blobs['accuracy'].data
accuracy /= test_iters
return accuracy
def train_test_net_command(solver_config_path):
"""
Train/test process launching cpp solver from shell.
"""
# Load solver
solver = None
solver = caffe.get_solver(solver_config_path)
# Launch training command
command = "{caffe} train -solver {solver}".format(caffe=caffe_root + caffe_path,
solver=solver_config_path)
subprocess.call(command, shell=True)
def learn(solver_file, label_num):
#net = caffe.Net(solver_file)
#net.forward() # this will load the next mini-batch as defined in the net
#label1 = net.blobs['label1'].data # or whatever you want
print '========'
print 'currently solving for label number: ' + str(label_num)
caffe.set_mode_gpu()
solver = caffe.get_solver(solver_file)
solver.step(3000)
def train_net(with_val_net=False):
train_net_file = miniplaces_net(get_split('train'), train=True)
# Set with_val_net=True to test during training.
# Environment variable GLOG_minloglevel should be set to 0 to display
# Caffe output in this case; otherwise, the test result will not be
# displayed.
if with_val_net:
val_net_file = miniplaces_net(get_split('val'), train=False)
else:
val_net_file = None
solver_file = miniplaces_solver(train_net_file, val_net_file)
solver = caffe.get_solver(solver_file)
outputs = sorted(solver.net.outputs)
def str_output(output):
value = solver.net.blobs[output].data
if output.startswith('accuracy'):
valstr = '%5.2f%%' % (100 * value, )
else:
valstr = '%6f' % value
return '%s = %s' % (output, valstr)
def disp_outputs(iteration, iter_pad_len=len(str(args.iters))):
metrics = '; '.join(str_output(o) for o in outputs)
return 'Iteration %*d: %s' % (iter_pad_len, iteration, metrics)
# We could just call `solver.solve()` rather than `step()`ing in a loop.
# (If we hadn't set GLOG_minloglevel = 3 at the top of this file, Caffe
# would display loss/accuracy information during training.)
previous_time = None
loss_line = []
for iteration in xrange(args.iters):
solver.step(1)
if (args.disp > 0) and (iteration % args.disp == 0):
loss_line.append( [iteration, float(solver.net.blobs['loss'].data)] )
current_time = time.clock()
if previous_time is None:
benchmark = ''
else:
time_per_iter = (current_time - previous_time) / args.disp
benchmark = ' (%5f s/it)' % time_per_iter
previous_time = current_time
print disp_outputs(iteration), benchmark
# Print accuracy for last iteration.
solver.net.forward()
disp_outputs(args.iters)
solver.net.save(snapshot_at_iteration(args.iters))
with open('./results/loss.json' + time_str, 'w') as f:
json.dump(loss_line,f)
def train_net(fn, with_val_net=False):
train_net_file = miniplaces_net(get_split('train'), fn, train=True)
# Set with_val_net=True to test during training.
# Environment variable GLOG_minloglevel should be set to 0 to display
# Caffe output in this case; otherwise, the test result will not be
# displayed.
if with_val_net:
val_net_file = miniplaces_net(get_split('val'), fn, train=False)
else:
val_net_file = None
solver_file = miniplaces_solver(train_net_file, val_net_file)
solver = caffe.get_solver(solver_file)
solver.restore('snapshot/place_net_iter_24000.solverstate')
filters = solver.net.params['Convolution1'][0].data
vis_square(filters.transpose(0, 2, 3, 1))
crash
# solver.net.copy_from('snapshot/place_net_iter_45000.caffemodel')
outputs = sorted(solver.net.outputs)
def str_output(output):
value = solver.net.blobs[output].data
if output.startswith('accuracy'):
valstr = '%5.2f%%' % (100 * value, )
else:
valstr = '%6f' % value
return '%s = %s' % (output, valstr)
def disp_outputs(iteration, iter_pad_len=len(str(args.iters))):
metrics = '; '.join(str_output(o) for o in outputs)
return 'Iteration %*d: %s' % (iter_pad_len, iteration, metrics)
# We could just call `solver.solve()` rather than `step()`ing in a loop.
# (If we hadn't set GLOG_minloglevel = 3 at the top of this file, Caffe
# would display loss/accuracy information during training.)
previous_time = None
for iteration in xrange(args.iters):
solver.step(1)
if (args.disp > 0) and (iteration % args.disp == 0):
current_time = time.clock()
if previous_time is None:
benchmark = ''
else:
time_per_iter = (current_time - previous_time) / args.disp
benchmark = ' (%5f s/it)' % time_per_iter
previous_time = current_time
print disp_outputs(iteration), benchmark
# Print accuracy for last iteration.
solver.net.forward()
disp_outputs(args.iters)
solver.net.save(snapshot_at_iteration(args.iters))
def training(solver_file):
"""
Training function
:param solver_file: prototxt solver
:return: void
"""
solver = caffe.get_solver(solver_file)
# solver.solve() # solve completely
number_iteration = 10000
# collect the information
display = 100
# test information
test_iteration = 100
test_interval = 100
# loss and accuracy information
train_loss = np.zeros(int(np.ceil(number_iteration * 1.0 / display)))
test_loss = np.zeros(int(np.ceil(number_iteration * 1.0 / test_interval)))
test_accuracy = np.zeros(int(np.ceil(number_iteration * 1.0 / test_interval)))
# tmp variables
_test_loss = 0
_test_accuracy = 0
# main loop
for iteration in range(number_iteration):
solver.step(1)
if 0 == iteration % display:
train_loss[iteration // display] = solver.net.blobs['loss'].data
if 0 == iteration % test_interval:
for test_iter in range(test_iteration):
solver.test_nets[0].forward()
_test_loss += solver.test_nets[0].blobs['loss'].data
_test_accuracy += solver.test_nets[0].blobs['accuracy'].data
test_loss[iteration / test_interval] = _test_loss / test_iteration
test_accuracy[iteration / test_interval] = _test_accuracy / test_iteration
_test_loss = 0
_test_accuracy = 0
def _init_solver(self): """ Helper method to initialize the solver. """ solver_param = SolverParameter() solver_param.display = 0 # Don't display anything. solver_param.base_lr = self._hyperparams['lr'] solver_param.lr_policy = self._hyperparams['lr_policy'] solver_param.momentum = self._hyperparams['momentum'] solver_param.weight_decay = self._hyperparams['weight_decay'] solver_param.type = self._hyperparams['solver_type'] solver_param.random_seed = self._hyperparams['random_seed'] # Pass in net parameter by protostring (could add option to input prototxt file). network_arch_params = self._hyperparams['network_arch_params'] network_arch_params['dim_input'] = self._dO network_arch_params['demo_batch_size'] = self._hyperparams['demo_batch_size'] network_arch_params['sample_batch_size'] = self._hyperparams['sample_batch_size'] network_arch_params['T'] = self._T network_arch_params['phase'] = TRAIN solver_param.train_net_param.CopyFrom( self._hyperparams['network_model'](**network_arch_params) ) # For running forward in python. network_arch_params['phase'] = TEST solver_param.test_net_param.add().CopyFrom( self._hyperparams['network_model'](**network_arch_params) ) network_arch_params['phase'] = 'forward_feat' solver_param.test_net_param.add().CopyFrom( self._hyperparams['network_model'](**network_arch_params) ) # These are required by Caffe to be set, but not used. solver_param.test_iter.append(1) solver_param.test_iter.append(1) solver_param.test_interval = 1000000 f = tempfile.NamedTemporaryFile(mode='w+', delete=False) f.write(MessageToString(solver_param)) f.close() self.solver = caffe.get_solver(f.name)
def main():
options, args = getOptions()
# In[6]:
niter = options.niter # 200 epochs # number of iterations to train
quick_solver = caffe.get_solver(options.solver_path)
quick_solver.net.copy_from(options.weights_path)
print "Options: %s" % options
print 'Running solvers for %d iterations...' % niter
solvers = [('pretrained', quick_solver)]
train_loss, train_acc, val_loss, val_acc, weights = run_solvers(niter, solvers, options.model_name)
pickle_filename = "%d_iter.%s.pickle" % (niter, options.model_name)
with open(os.path.join(pickle_dir, pickle_filename), 'w+') as f:
pickle.dump([train_loss, train_acc, val_loss, val_acc], f)
# Make sure that caffe is on the python path:
import matplotlib
matplotlib.use('Agg')
import sys,caffe,os
from os.path import join
solver_file = sys.argv[1]
outdir = sys.argv[2]
gpunum = int(sys.argv[3])
sys.stdout = open(join(outdir,'train.out'), 'w')
sys.stderr = open(join(outdir,'train.err'), 'w')
caffe.set_device(gpunum)
caffe.set_mode_gpu()
solver = caffe.get_solver(solver_file)
solver.solve()
__author__ = 'luispeinado'
import sys
#caffe_root = '/Users/luispeinado/caffe/' # this file should be run from {caffe_root}/examples (otherwise change this line)
#sys.path.insert(0, caffe_root + 'python')
#from sklearn.datasets import load_iris
#import sklearn.metrics
#import numpy as np
#from sklearn.cross_validation import StratifiedShuffleSplit
#import matplotlib.pyplot as plt
import h5py
import caffe
#import caffe.draw
solver_prototxt_filename="solver.prototxt"
caffe.set_mode_cpu()
#caffe.set_device(0)
#caffe.set_mode_gpu()
solver_prototxt_filename = sys.argv[1]
solver = caffe.get_solver(solver_prototxt_filename)
solver.solve()
weight_dir = tempfile.mkdtemp()
weights = {}
for name, s in solvers:
filename = 'weights.%s.caffemodel' % name
weights[name] = os.path.join(weight_dir, filename)
s.net.save(weights[name])
return loss, acc, weights
# In[ ]:
niter = 500 # number of iterations to train
# Reset style_solver as before.
drink_solver_filename = solver(drink_net(train=True))
drink_solver = caffe.get_solver(drink_solver_filename)
drink_solver.net.copy_from(weights)
# For reference, we also create a solver that isn't initialized from
# the pretrained ImageNet weights.
scratch_drink_solver_filename = solver(drink_net(train=True))
scratch_drink_solver = caffe.get_solver(scratch_drink_solver_filename)
print 'Running solvers for %d iterations...' % niter
solvers = [('pretrained', drink_solver),
('scratch', scratch_drink_solver)]
loss, acc, weights = run_solvers(niter, solvers)
print 'Done.'
train_loss, scratch_train_loss = loss['pretrained'], loss['scratch']
train_acc, scratch_train_acc = acc['pretrained'], acc['scratch']
weight_dir = '/home/ldy/workspace/caffe/models/finetune_UCMerced_LandUse/'
weights = {}
for name, s in solvers:
filename = 'weights_finally.%s.caffemodel' % name
weights[name] = os.path.join(weight_dir, filename)
print weights[name]
s.net.save(weights[name])
return loss, acc, weights
print 'training...'
niter = 95 # number of iterations to train
# Reset style_solver as before.
style_solver_filename = solver(style_net(train=True))
style_solver = caffe.get_solver(style_solver_filename)
style_solver.net.copy_from(weights)
print 'Running solvers for %d iterations...' % niter
solvers = [('pretrained', style_solver)]
loss, acc, finetuned_weights = run_solvers(niter, solvers)
print 'Done.'
style_weights_ft = finetuned_weights['pretrained']
# Delete solvers to save memory.
del style_solver, solvers
def eval_style_net(weights, test_iters=10):
