def get_trainer_by_name(name, param_dict): net = FastNet(param_dict['image_shape']) if name == 'layerwise': param_dict['net'] = net return ImageNetLayerwisedTrainer(**param_dict) net = FastNet(param_dict['image_shape']) load_model(net, param_dict['init_model']) param_dict['net'] = net if name == 'normal': return Trainer(**param_dict) elif name == 'minibatch': return MiniBatchTrainer(**param_dict) else: raise Exception, 'No trainer found for name: %s' % name
def train(self): MiniBatchTrainer.train(self) for i, cate in enumerate(self.num_caterange_list): self.set_category_range(cate) self.curr_batch = self.curr_epoch = 0 self.num_batch = self.num_batch_list[i] model = self.checkpoint_dumper.get_checkpoint() layers = model['layers'] fc = layers[-2] fc['weight'] = None fc['bias'] = None fc['weightIncr'] = None fc['biasIncr'] = None # for l in layers: # if l['type'] == 'fc': # l['weight'] = None # l['bias'] = None # l['weightIncr'] = None # l['biasIncr'] = None # fc = layers[-2] fc['outputSize'] = cate self.learning_rate = self.learning_rate_list[i] self.net = FastNet(self.learning_rate, self.image_shape, init_model=model) self.net.clear_weight_incr() MiniBatchTrainer.train(self)
def train(self): MiniBatchTrainer.train(self) for i, group in enumerate(self.num_group_list): self.set_num_group(group) self.curr_batch = self.curr_epoch = 0 self.num_batch = self.num_batch_list[i] model = self.checkpoint_dumper.get_checkpoint() layers = model['layers'] fc = layers[-2] fc['outputSize'] = group fc['weight'] = None fc['bias'] = None fc['weightIncr'] = None fc['biasIncr'] = None self.learning_rate = self.learning_rate_list[i] self.net = FastNet(self.learning_rate, self.image_shape, init_model=model) self.net.clear_weight_incr() MiniBatchTrainer.train(self)
def train_replaynet(self, stack): self.container.append(self.save_freq) self.container.append(self.test_freq) self.container.append(self.train_dp) self.container.append(self.test_dp) self.container.append(self.layer_output_dumper) self.container.append(self.net) self.save_freq = self.curr_batch + 100 self.test_freq = self.curr_batch + 100 self.curr_batch = self.curr_epoch = 0 self.init_replaynet_data_provider() model = [] model.extend(stack) model.extend(self.fc_tmp) self.layer_output_dumper = None size = self.net['fc8'].get_input_size() image_shape = (size, 1, 1, self.batch_size) self.net = FastNet(self.learning_rate, image_shape, model) self.replaynet = self.net self.num_epoch = self.replaynet_epoch Trainer.train(self) self.net = self.container.pop() self.layer_output_dumper = self.container.pop() self.layer_output_dumper.reset() self.test_dp = self.container.pop() self.train_dp = self.container.pop() self.test_freq = self.container.pop() self.save_freq = self.container.pop()
def _finish_init(self): self.num_batch_list = self.num_batch[1:] self.num_batch = self.num_batch[0] self.learning_rate_list = self.learning_rate[1:] self.learning_rate = self.learning_rate[0] layers = self.init_model fc = layers[-2] fc['outputSize'] = self.num_group_list[0] self.num_group_list = self.num_group_list[1:] self.set_num_group(fc['outputSize']) self.net = FastNet(self.learning_rate, self.image_shape, init_model=self.init_model) MiniBatchTrainer._finish_init(self)
def get_trainer_by_name(name, param_dict): net = FastNet(param_dict['learning_rate'], param_dict['image_shape'], init_model=None) param_dict['net'] = net if name == 'layerwise': return ImageNetLayerwisedTrainer(**param_dict) if name == 'catewise': return ImageNetCatewisedTrainer(**param_dict) if name == 'categroup': return ImageNetCateGroupTrainer(**param_dict) net = FastNet(param_dict['learning_rate'], param_dict['image_shape'], param_dict['init_model']) param_dict['net'] = net if name == 'normal': return Trainer(**param_dict) if name == 'minibatch': return MiniBatchTrainer(**param_dict) raise Exception, 'No trainer found for name: %s' % name
def _finish_init(self): assert len(self.num_caterange_list) == len(self.num_batch) and self.num_caterange_list[-1] == 1000 self.num_batch_list = self.num_batch[1:] self.num_batch = self.num_batch[0] init_output = self.num_caterange_list[0] self.num_caterange_list = self.num_caterange_list[1:] fc = self.init_model[-2] fc['outputSize'] = init_output self.learning_rate_list = self.learning_rate[1:] self.learning_rate = self.learning_rate[0] self.set_category_range(init_output) self.net = FastNet(self.learning_rate, self.image_shape, init_model=self.init_model) MiniBatchTrainer._finish_init(self)
def _finish_init(self): self.final_num_epoch = self.num_epoch self.curr_model = [] self.divide_layers_to_stack() self.conv_stack = FastNet.split_conv_to_stack(self.conv_params) self.fc_stack = FastNet.split_fc_to_stack(self.fc_params) self.fc_tmp = [self.fc_stack['fc8'][0], self.softmax_param] del self.fc_stack['fc8'] self.stack = self.fc_stack self.initialize_model() pprint.pprint(self.stack) self.num_epoch = self.frag_epoch self.net = FastNet(self.learning_rate, self.image_shape, self.curr_model) self.container = deque()