def get_wb_from_convnet2_checkpoint(name, sp, params, item_type):
"""
params = [check_point_path, layer_name, ]
"""
sys.path.append('/home/grads/sijinli2/Projects/cuda-convnet2/')
checkpath = params[0]
layer_name = name if len(params) == 1 else params[1]
filepath = os.path.join(
checkpath,
sorted(os.listdir(checkpath), key=alphanum_key)[-1])
saved = mio.unpickle(filepath)
model_state = saved['model_state']
layer = model_state['layers'][layer_name]
if item_type == 'weights':
# weights
n_w = len(sp)
print ' init from convnet {}--------'.format(checkpath)
idx_list = range(n_w) if len(params) < 3 else iu.get_int_list_from_str(
params[2])
print ','.join(
['{}'.format(layer['weights'][k].shape) for k in idx_list])
print '--------------\n---------\n'
return [layer['weights'][k] for k in idx_list]
else:
return [layer['biases']]
def gwfp(name, sp_list, params):
"""
get weights from saved model
params = [model_path, layer_name, [idx,...]]
"""
n_w = len(sp_list)
model_path = params[0]
layer_name = name if len(params) == 1 else params[1]
model = Solver.get_saved_model(model_path)
layers = get_layers(model)
lay = layers[layer_name][2]
if len(params) < 3:
idx_list = range(n_w)
else:
idx_list = iu.get_int_list_from_str(params[2])
return [lay['weights'][k] for k in idx_list]
def gwfp(name, sp_list, params):
"""
get weights from saved model
params = [model_path, layer_name, [idx,...]]
"""
n_w = len(sp_list)
model_path = params[0]
layer_name = name if len(params) == 1 else params[1]
model = Solver.get_saved_model(model_path)
layers = get_layers(model)
lay = layers[layer_name][2]
if len(params) < 3:
idx_list = range(n_w)
else:
idx_list = iu.get_int_list_from_str(params[2])
return [lay['weights'][k] for k in idx_list]
def get_wb_from_convnet2_checkpoint(name, sp, params, item_type):
"""
params = [check_point_path, layer_name, ]
"""
sys.path.append('/home/grads/sijinli2/Projects/cuda-convnet2/')
checkpath= params[0]
layer_name= name if len(params) == 1 else params[1]
filepath = os.path.join(checkpath, sorted(os.listdir(checkpath), key=alphanum_key)[-1])
saved = mio.unpickle(filepath)
model_state = saved['model_state']
layer = model_state['layers'][layer_name]
if item_type == 'weights':
# weights
n_w = len(sp)
print ' init from convnet {}--------'.format(checkpath)
idx_list = range(n_w) if len(params) < 3 else iu.get_int_list_from_str(params[2])
print ','.join(['{}'.format(layer['weights'][k].shape) for k in idx_list])
print '--------------\n---------\n'
return [layer['weights'][k] for k in idx_list]
else:
return [layer['biases']]
