def eval_config(id, config, f):
opts = Options()
opts.train_data_path = '../../data/train_data.in'
opts.eval_data_path = '../../data/eval_data.in'
opts.lr = get_lr(config)
opts.embedding_dim = get_embedding_dim(config)
opts.train_ws = get_train_ws(config)
opts.min_count = 30
opts.t = 0.025
opts.verbose = 1
opts.min_count_label = 5
opts.label = "__label__"
opts.batch_size = get_batch_size(config)
opts.num_sampled = get_num_sampled(config)
opts.max_train_steps = None
opts.epoch = 2
opts.hidden_units = get_hidden_units(config)
opts.model_dir = 'model_dir'
opts.export_model_dir = 'export_model_dir'
opts.prefetch_size = 100000
opts.save_summary_steps = 100
opts.save_checkpoints_secs = 600
opts.keep_checkpoint_max = 2
opts.log_step_count_steps = 1000
opts.recall_k = 10
opts.dict_dir = 'dict_dir'
opts.use_saved_dict = False
opts.use_profile_hook = False
opts.profile_steps = 100
opts.root_ops_path = 'lib/'
opts.remove_model_dir = 1
opts.optimize_level = 1
opts.receive_ws = 100
opts.use_subset = True
opts.dropout = 0.0
opts.ntargets = 1
opts.chief_lock = 'model_dir/chief.lock'
opts.max_distribute_train_steps = -1
opts.train_nce_biases = get_train_nce_biases(config)
opts.shuffle_batch = get_shuffle_batch(config)
opts.predict_ws = 20
opts.sample_dropout = 0.0
opts.optimizer_type = get_optimizer_type(config)
opts.tfrecord_file = ''
opts.num_tfrecord_file = 1
opts.train_data_format = 'fasttext'
opts.tfrecord_map_num_parallel_calls = 1
opts.train_parallel_mode = 'train_op_parallel'
opts.num_train_op_parallel = 4
opts.use_batch_normalization = get_use_batch_normalization(config)
opts.tf_config = None
opts.task_type = model_keys.TaskType.LOCAL # default mode
result = {}
start = int(time.time())
try:
result = train.train(opts, export=False)
except Exception:
pass
end = int(time.time())
elapsed = end - start
f.write(str(opts.lr))
f.write('\t')
f.write(str(opts.embedding_dim))
f.write('\t')
f.write(str(opts.train_ws))
f.write('\t')
f.write(str(opts.batch_size))
f.write('\t')
f.write(str(opts.num_sampled))
f.write('\t')
f.write(str(opts.hidden_units))
f.write('\t')
f.write(str(opts.shuffle_batch))
f.write('\t')
f.write(str(opts.optimizer_type))
f.write('\t')
f.write(str(opts.use_batch_normalization))
f.write('\t')
f.write(str(opts.train_nce_biases))
f.write('\t')
f.write(str(result.get('loss', -1)))
f.write('\t')
f.write(str(result.get('precision_at_top_10', -1)))
f.write('\t')
f.write(str(result.get('recall_at_top_10', -1)))
f.write('\t')
f.write(str(result.get('average_precision_at_10', -1)))
f.write('\t')
f.write(str(result.get('accuracy', -1)))
f.write('\t')
f.write(str(elapsed))
f.write('\n')
f.flush()
def eval_config(config):
lr = config['lr']
embedding_dim = config['embedding_dim']
train_ws = config['train_ws']
batch_size = config['batch_size']
num_sampled = config['num_sampled']
hidden_units = config['hidden_units']
shuffle_batch = config['shuffle_batch']
optimizer_type = config['optimizer_type']
use_batch_normalization = config['use_batch_normalization']
train_nce_biases = config['train_nce_biases']
hidden_units = get_hidden_units(hidden_units, embedding_dim)
opts = Options()
opts.train_data_path = '../../data/train_data.in'
opts.eval_data_path = '../../data/eval_data.in'
opts.lr = lr
opts.embedding_dim = embedding_dim
opts.train_ws = train_ws
opts.min_count = 30
opts.t = 0.025
opts.verbose = 1
opts.min_count_label = 5
opts.label = "__label__"
opts.batch_size = batch_size
opts.num_sampled = num_sampled
opts.max_train_steps = None
opts.epoch = 2
opts.hidden_units = hidden_units
opts.model_dir = 'model_dir'
opts.export_model_dir = 'export_model_dir'
opts.prefetch_size = 100000
opts.save_summary_steps = 100
opts.save_checkpoints_secs = 600
opts.keep_checkpoint_max = 2
opts.log_step_count_steps = 1000
opts.recall_k = 10
opts.dict_dir = 'dict_dir'
opts.use_saved_dict = False
opts.use_profile_hook = False
opts.profile_steps = 100
opts.root_ops_path = 'lib/'
opts.remove_model_dir = 1
opts.optimize_level = 1
opts.receive_ws = 100
opts.use_subset = True
opts.dropout = 0.0
opts.ntargets = 1
opts.chief_lock = 'model_dir/chief.lock'
opts.max_distribute_train_steps = -1
opts.train_nce_biases = train_nce_biases
opts.shuffle_batch = shuffle_batch
opts.predict_ws = 20
opts.sample_dropout = 0.0
opts.optimizer_type = optimizer_type
opts.tfrecord_file = ''
opts.num_tfrecord_file = 1
opts.train_data_format = 'fasttext'
opts.tfrecord_map_num_parallel_calls = 1
opts.train_parallel_mode = 'train_op_parallel'
opts.num_train_op_parallel = 4
opts.use_batch_normalization = use_batch_normalization
opts.tf_config = None
opts.task_type = model_keys.TaskType.LOCAL # default mode
result = {}
try:
result = train.train(opts, export=False)
except Exception as e:
print(e)
return -result.get('loss', -1)
root = opt.meta_dataroot
dictTrainAs, dictTrainBs, dataset_num = meta_preprocess(root)
for test_dataset_indx in range(dataset_num):
dataset = MetaDataloader(root, k_shot=opt.k_spt,
k_query=opt.k_qry,
batchsz=2000, resize=opt.load_size, crop_size = opt.crop_size,
test_dataset_indx= test_dataset_indx, dictTrainAs=dictTrainAs, dictTrainBs=dictTrainBs, dataset_num=dataset_num)
dataset_test = MetaTestDataloader(root, k_shot=opt.k_spt,
k_query=opt.k_qry,
batchsz=2, resize=opt.load_size, crop_size = opt.crop_size,
test_dataset_indx= test_dataset_indx, dictTrainAs=dictTrainAs, dictTrainBs=dictTrainBs, dataset_num=dataset_num)
dataset_size = len(dataset)
print('The number of training images = %d' % dataset_size)
dataset_loader = DataLoader(dataset, opt.task_num, shuffle=True, num_workers=opt.num_threads, pin_memory=True)
dataset_loader_test = DataLoader(dataset_test, opt.task_num_val, shuffle=True, num_workers=opt.num_threads, pin_memory=True)
opt.epoch = test_dataset_indx
model = create_model(opt)
model.setup(opt)
for j, (A_spt, B_spt, A_qry, B_qry) in enumerate(dataset_loader_test):
model.set_input(A_spt, B_spt, A_qry, B_qry)
model.finetunning(test_dataset_indx,opt.load_iter, j)
model.finetunning_withoutmeta(test_dataset_indx,opt.load_iter,j)
model.plot_training_loss(test_dataset_indx)
