def main():
startup_prog, eval_program, place, config, train_alg_type = program.preprocess(
)
eval_build_outputs = program.build(config,
eval_program,
startup_prog,
mode='test')
eval_fetch_name_list = eval_build_outputs[1]
eval_fetch_varname_list = eval_build_outputs[2]
eval_program = eval_program.clone(for_test=True)
exe = fluid.Executor(place)
exe.run(startup_prog)
init_model(config, eval_program, exe)
if train_alg_type == 'det':
eval_reader = reader_main(config=config, mode="eval")
eval_info_dict = {'program':eval_program,\
'reader':eval_reader,\
'fetch_name_list':eval_fetch_name_list,\
'fetch_varname_list':eval_fetch_varname_list}
metrics = eval_det_run(exe, config, eval_info_dict, "eval")
logger.info("Eval result: {}".format(metrics))
elif train_alg_type == 'cls':
eval_reader = reader_main(config=config, mode="eval")
eval_info_dict = {'program': eval_program, \
'reader': eval_reader, \
'fetch_name_list': eval_fetch_name_list, \
'fetch_varname_list': eval_fetch_varname_list}
metrics = eval_cls_run(exe, eval_info_dict)
logger.info("Eval result: {}".format(metrics))
else:
reader_type = config['Global']['reader_yml']
if "benchmark" not in reader_type:
eval_reader = reader_main(config=config, mode="eval")
eval_info_dict = {'program': eval_program, \
'reader': eval_reader, \
'fetch_name_list': eval_fetch_name_list, \
'fetch_varname_list': eval_fetch_varname_list}
metrics = eval_rec_run(exe, config, eval_info_dict, "eval")
logger.info("Eval result: {}".format(metrics))
else:
eval_info_dict = {'program':eval_program,\
'fetch_name_list':eval_fetch_name_list,\
'fetch_varname_list':eval_fetch_varname_list}
test_rec_benchmark(exe, config, eval_info_dict)
def train_eval_cls_run(config,
exe,
train_info_dict,
eval_info_dict,
is_slim=None):
train_batch_id = 0
log_smooth_window = config['Global']['log_smooth_window']
epoch_num = config['Global']['epoch_num']
print_batch_step = config['Global']['print_batch_step']
eval_batch_step = config['Global']['eval_batch_step']
start_eval_step = 0
if type(eval_batch_step) == list and len(eval_batch_step) >= 2:
start_eval_step = eval_batch_step[0]
eval_batch_step = eval_batch_step[1]
logger.info(
"During the training process, after the {}th iteration, an evaluation is run every {} iterations"
.format(start_eval_step, eval_batch_step))
save_epoch_step = config['Global']['save_epoch_step']
save_model_dir = config['Global']['save_model_dir']
if not os.path.exists(save_model_dir):
os.makedirs(save_model_dir)
train_stats = TrainingStats(log_smooth_window, ['loss', 'acc'])
best_eval_acc = -1
best_batch_id = 0
best_epoch = 0
train_loader = train_info_dict['reader']
for epoch in range(epoch_num):
train_loader.start()
try:
while True:
t1 = time.time()
train_outs = exe.run(
program=train_info_dict['compile_program'],
fetch_list=train_info_dict['fetch_varname_list'],
return_numpy=False)
fetch_map = dict(
zip(train_info_dict['fetch_name_list'],
range(len(train_outs))))
loss = np.mean(np.array(train_outs[fetch_map['total_loss']]))
lr = np.mean(np.array(train_outs[fetch_map['lr']]))
acc = np.mean(np.array(train_outs[fetch_map['acc']]))
t2 = time.time()
train_batch_elapse = t2 - t1
stats = {'loss': loss, 'acc': acc}
train_stats.update(stats)
if train_batch_id > start_eval_step and (train_batch_id - start_eval_step) \
% print_batch_step == 0:
logs = train_stats.log()
strs = 'epoch: {}, iter: {}, lr: {:.6f}, {}, time: {:.3f}'.format(
epoch, train_batch_id, lr, logs, train_batch_elapse)
logger.info(strs)
if train_batch_id > 0 and\
train_batch_id % eval_batch_step == 0:
model_average = train_info_dict['model_average']
if model_average != None:
model_average.apply(exe)
metrics = eval_cls_run(exe, eval_info_dict)
eval_acc = metrics['avg_acc']
eval_sample_num = metrics['total_sample_num']
if eval_acc > best_eval_acc:
best_eval_acc = eval_acc
best_batch_id = train_batch_id
best_epoch = epoch
save_path = save_model_dir + "/best_accuracy"
if is_slim is None:
save_model(train_info_dict['train_program'],
save_path)
else:
import paddleslim as slim
if is_slim == "prune":
slim.prune.save_model(
exe, train_info_dict['train_program'],
save_path)
elif is_slim == "quant":
save_model(eval_info_dict['program'],
save_path)
else:
raise ValueError(
"Only quant and prune are supported currently. But received {}"
.format(is_slim))
strs = 'Test iter: {}, acc:{:.6f}, best_acc:{:.6f}, best_epoch:{}, best_batch_id:{}, eval_sample_num:{}'.format(
train_batch_id, eval_acc, best_eval_acc, best_epoch,
best_batch_id, eval_sample_num)
logger.info(strs)
train_batch_id += 1
except fluid.core.EOFException:
train_loader.reset()
if epoch == 0 and save_epoch_step == 1:
save_path = save_model_dir + "/iter_epoch_0"
if is_slim is None:
save_model(train_info_dict['train_program'], save_path)
else:
import paddleslim as slim
if is_slim == "prune":
slim.prune.save_model(exe,
train_info_dict['train_program'],
save_path)
elif is_slim == "quant":
save_model(eval_info_dict['program'], save_path)
else:
raise ValueError(
"Only quant and prune are supported currently. But received {}"
.format(is_slim))
if epoch > 0 and epoch % save_epoch_step == 0:
save_path = save_model_dir + "/iter_epoch_%d" % (epoch)
if is_slim is None:
save_model(train_info_dict['train_program'], save_path)
else:
import paddleslim as slim
if is_slim == "prune":
slim.prune.save_model(exe,
train_info_dict['train_program'],
save_path)
elif is_slim == "quant":
save_model(eval_info_dict['program'], save_path)
else:
raise ValueError(
"Only quant and prune are supported currently. But received {}"
.format(is_slim))
return
def main():
# 1. quantization configs
quant_config = {
# weight quantize type, default is 'channel_wise_abs_max'
'weight_quantize_type': 'channel_wise_abs_max',
# activation quantize type, default is 'moving_average_abs_max'
'activation_quantize_type': 'moving_average_abs_max',
# weight quantize bit num, default is 8
'weight_bits': 8,
# activation quantize bit num, default is 8
'activation_bits': 8,
# ops of name_scope in not_quant_pattern list, will not be quantized
'not_quant_pattern': ['skip_quant'],
# ops of type in quantize_op_types, will be quantized
'quantize_op_types': ['conv2d', 'depthwise_conv2d', 'mul'],
# data type after quantization, such as 'uint8', 'int8', etc. default is 'int8'
'dtype': 'int8',
# window size for 'range_abs_max' quantization. defaulf is 10000
'window_size': 10000,
# The decay coefficient of moving average, default is 0.9
'moving_rate': 0.9,
}
# Run code with static graph mode.
try:
paddle.enable_static()
except:
pass
startup_prog, eval_program, place, config, alg_type = program.preprocess()
feeded_var_names, target_vars, fetches_var_name = program.build_export(
config, eval_program, startup_prog)
eval_program = eval_program.clone(for_test=True)
exe = fluid.Executor(place)
exe.run(startup_prog)
eval_program = quant_aware(
eval_program, place, quant_config, scope=None, for_test=True)
init_model(config, eval_program, exe)
# 2. Convert the program before save inference program
# The dtype of eval_program's weights is float32, but in int8 range.
eval_program = convert(eval_program, place, quant_config, scope=None)
eval_fetch_name_list = fetches_var_name
eval_fetch_varname_list = [v.name for v in target_vars]
eval_reader = reader_main(config=config, mode="eval")
quant_info_dict = {'program':eval_program,\
'reader':eval_reader,\
'fetch_name_list':eval_fetch_name_list,\
'fetch_varname_list':eval_fetch_varname_list}
if alg_type == 'det':
final_metrics = eval_det_run(exe, config, quant_info_dict, "eval")
elif alg_type == 'cls':
final_metrics = eval_cls_run(exe, quant_info_dict)
else:
final_metrics = eval_rec_run(exe, config, quant_info_dict, "eval")
print(final_metrics)
# 3. Save inference model
model_path = "./quant_model"
if not os.path.isdir(model_path):
os.makedirs(model_path)
fluid.io.save_inference_model(
dirname=model_path,
feeded_var_names=feeded_var_names,
target_vars=target_vars,
executor=exe,
main_program=eval_program,
model_filename=model_path + '/model',
params_filename=model_path + '/params')
print("model saved as {}".format(model_path))