import numpy as np import argparse import time import paddle import paddle.fluid as fluid import paddle.fluid.profiler as profiler from functools import reduce SEED = 90 DTYPE = "float32" # random seed must set before configuring the network. fluid.default_startup_program().random_seed = SEED def parse_args(): parser = argparse.ArgumentParser("mnist model benchmark.") parser.add_argument( '--batch_size', type=int, default=128, help='The minibatch size.') parser.add_argument( '--iterations', type=int, default=35, help='The number of minibatches.') parser.add_argument( '--pass_num', type=int, default=5, help='The number of passes.') parser.add_argument( '--device', type=str, default='GPU', choices=['CPU', 'GPU'], help='The device type.') parser.add_argument( '--infer_only', action='store_true', help='If set, run forward only.') parser.add_argument( '--use_cprof', action='store_true', help='If set, use cProfile.') parser.add_argument( '--use_nvprof', action='store_true', help='If set, use nvprof for CUDA.') args = parser.parse_args() return args def print_arguments(args): vars(args)['use_nvprof'] = (vars(args)['use_nvprof'] and vars(args)['device'] == 'GPU') print('----------- Configuration Arguments -----------') for arg, value in sorted(vars(args).items()): print('%s: %s' % (arg, value)) print('------------------------------------------------') def cnn_model(data): conv_pool_1 = fluid.nets.simple_img_conv_pool( input=data, filter_size=5, num_filters=20, pool_size=2, pool_stride=2, act="relu") conv_pool_2 = fluid.nets.simple_img_conv_pool( input=conv_pool_1, filter_size=5, num_filters=50, pool_size=2, pool_stride=2, act="relu") # TODO(dzhwinter) : refine the initializer and random seed settting SIZE = 10 input_shape = conv_pool_2.shape param_shape = [reduce(lambda a, b: a * b, input_shape[1:], 1)] + [SIZE] scale = (2.0 / (param_shape[0]**2 * SIZE))**0.5 predict = fluid.layers.fc( input=conv_pool_2, size=SIZE, act="softmax", param_attr=fluid.param_attr.ParamAttr( initializer=fluid.initializer.NormalInitializer( loc=0.0, scale=scale))) return predict def eval_test(exe, batch_acc, batch_size_tensor, inference_program): test_reader = paddle.batch( paddle.dataset.mnist.test(), batch_size=args.batch_size) test_pass_acc = fluid.average.WeightedAverage() for batch_id, data in enumerate(test_reader()): img_data = np.array([x[0].reshape([1, 28, 28]) for x in data]).astype(DTYPE) y_data = np.array([x[1] for x in data]).astype("int64") y_data = y_data.reshape([len(y_data), 1]) acc, weight = exe.run(inference_program, feed={"pixel": img_data, "label": y_data}, fetch_list=[batch_acc, batch_size_tensor]) test_pass_acc.add(value=acc, weight=weight) pass_acc = test_pass_acc.eval() return pass_acc def run_benchmark(model, args): if args.use_cprof: pr = cProfile.Profile() pr.enable() start_time = time.time() # Input data images = fluid.layers.data(name='pixel', shape=[1, 28, 28], dtype=DTYPE) label = fluid.layers.data(name='label', shape=[1], dtype='int64') # Train program predict = model(images) cost = fluid.layers.cross_entropy(input=predict, label=label) avg_cost = fluid.layers.mean(x=cost) # Evaluator batch_size_tensor = fluid.layers.create_tensor(dtype='int64') batch_acc = fluid.layers.accuracy( input=predict, label=label, total=batch_size_tensor) # inference program inference_program = fluid.default_main_program().clone() with fluid.program_guard(inference_program): inference_program = fluid.io.get_inference_program( target_vars=[batch_acc, batch_size_tensor]) # Optimization opt = fluid.optimizer.AdamOptimizer( learning_rate=0.001, beta1=0.9, beta2=0.999) opt.minimize(avg_cost) fluid.memory_optimize(fluid.default_main_program()) # Initialize executor place = fluid.CPUPlace() if args.device == 'CPU' else fluid.CUDAPlace(0) exe = fluid.Executor(place) # Parameter initialization exe.run(fluid.default_startup_program()) # Reader train_reader = paddle.batch( paddle.dataset.mnist.train(), batch_size=args.batch_size) accuracy = fluid.average.WeightedAverage() for pass_id in range(args.pass_num): accuracy.reset() pass_start = time.time() every_pass_loss = [] for batch_id, data in enumerate(train_reader()): img_data = np.array( [x[0].reshape([1, 28, 28]) for x in data]).astype(DTYPE) y_data = np.array([x[1] for x in data]).astype("int64") y_data = y_data.reshape([len(y_data), 1]) start = time.time() loss, acc, weight = exe.run( fluid.default_main_program(), feed={"pixel": img_data, "label": y_data}, fetch_list=[avg_cost, batch_acc, batch_size_tensor] ) # The accuracy is the accumulation of batches, but not the current batch. end = time.time() accuracy.add(value=acc, weight=weight) every_pass_loss.append(loss) print("Pass = %d, Iter = %d, Loss = %f, Accuracy = %f" % (pass_id, batch_id, loss, acc)) pass_end = time.time() train_avg_acc = accuracy.eval() train_avg_loss = np.mean(every_pass_loss) test_avg_acc = eval_test(exe, batch_acc, batch_size_tensor, inference_program) print( "pass=%d, train_avg_acc=%f,train_avg_loss=%f, test_avg_acc=%f, elapse=%f" % (pass_id, train_avg_acc, train_avg_loss, test_avg_acc, (pass_end - pass_start))) #Note: The following logs are special for CE monitoring. #Other situations do not need to care about these logs. print("kpis train_acc %f" % train_avg_acc) print("kpis train_cost %f" % train_avg_loss) print("kpis test_acc %f" % test_avg_acc) print("kpis train_duration %f" % (pass_end - pass_start)) if __name__ == '__main__': args = parse_args() print_arguments(args) if args.use_nvprof and args.device == 'GPU': with profiler.cuda_profiler("cuda_profiler.txt", 'csv') as nvprof: run_benchmark(cnn_model, args) else: run_benchmark(cnn_model, args)