diff --git a/fluid/image_classification/mobilenet.py b/fluid/image_classification/mobilenet.py index adfd6868f45db8d4e6751973b5f2ad91fe7d4048..edee1bf07532bb680c512dffe852ae3968257e9b 100644 --- a/fluid/image_classification/mobilenet.py +++ b/fluid/image_classification/mobilenet.py @@ -153,72 +153,3 @@ def mobile_net(img, class_dim, scale=1.0): act='softmax', param_attr=parameter_attr) return tmp - - -def train(learning_rate, batch_size, num_passes, model_save_dir='model'): - class_dim = 102 - image_shape = [3, 224, 224] - - image = fluid.layers.data(name='image', shape=image_shape, dtype='float32') - label = fluid.layers.data(name='label', shape=[1], dtype='int64') - - out = mobile_net(image, class_dim=class_dim) - - cost = fluid.layers.cross_entropy(input=out, label=label) - avg_cost = fluid.layers.mean(x=cost) - - optimizer = fluid.optimizer.Momentum( - learning_rate=learning_rate, - momentum=0.9, - regularization=fluid.regularizer.L2Decay(5 * 1e-5)) - opts = optimizer.minimize(avg_cost) - - b_size_var = fluid.layers.create_tensor(dtype='int64') - b_acc_var = fluid.layers.accuracy(input=out, label=label, total=b_size_var) - - inference_program = fluid.default_main_program().clone() - with fluid.program_guard(inference_program): - inference_program = fluid.io.get_inference_program( - target_vars=[b_acc_var, b_size_var]) - - place = fluid.CPUPlace() - exe = fluid.Executor(place) - exe.run(fluid.default_startup_program()) - - train_reader = paddle.batch( - paddle.dataset.flowers.train(), batch_size=batch_size) - test_reader = paddle.batch( - paddle.dataset.flowers.test(), batch_size=batch_size) - feeder = fluid.DataFeeder(place=place, feed_list=[image, label]) - - train_pass_acc_evaluator = fluid.average.WeightedAverage() - test_pass_acc_evaluator = fluid.average.WeightedAverage() - for pass_id in range(num_passes): - train_pass_acc_evaluator.reset() - for batch_id, data in enumerate(train_reader()): - loss, acc, size = exe.run( - fluid.default_main_program(), - feed=feeder.feed(data), - fetch_list=[avg_cost, b_acc_var, b_size_var]) - train_pass_acc_evaluator.add(value=acc, weight=size) - print("Pass {0}, batch {1}, loss {2}, acc {3}".format( - pass_id, batch_id, loss[0], acc[0])) - - test_pass_acc_evaluator.reset() - for data in test_reader(): - loss, acc, size = exe.run( - inference_program, - feed=feeder.feed(data), - fetch_list=[avg_cost, b_acc_var, b_size_var]) - test_pass_acc_evaluator.add(value=acc, weight=size) - print("End pass {0}, train_acc {1}, test_acc {2}".format( - pass_id, - train_pass_acc_evaluator.eval(), test_pass_acc_evaluator.eval())) - if pass_id % 10 == 0: - model_path = os.path.join(model_save_dir, str(pass_id)) - print 'save models to %s' % (model_path) - fluid.io.save_inference_model(model_path, ['image'], [out], exe) - - -if __name__ == '__main__': - train(learning_rate=0.005, batch_size=40, num_passes=300) diff --git a/fluid/image_classification/train.py b/fluid/image_classification/train.py index 4f1264281852094b2e0346ff9564ebb5986edf73..f0cc8b1d71e5e15bd274c55b324149c59c08288a 100644 --- a/fluid/image_classification/train.py +++ b/fluid/image_classification/train.py @@ -5,6 +5,7 @@ import sys import paddle.v2 as paddle import paddle.fluid as fluid from se_resnext import SE_ResNeXt +from mobilenet import mobile_net import reader import argparse @@ -18,6 +19,11 @@ add_arg('batch_size', int, 256, "Minibatch size.") add_arg('num_layers', int, 50, "How many layers for SE-ResNeXt model.") add_arg('with_mem_opt', bool, True, "Whether to use memory optimization or not.") add_arg('parallel_exe', bool, True, "Whether to use ParallelExecutor to train or not.") +add_arg('model', + type=str, + choices=['mobilenet', 'se_resnext'], + default='se_resnext', + help="Which model to use.") # yapf: enable @@ -44,7 +50,12 @@ def train_parallel_do(args, with pd.do(): image_ = pd.read_input(image) label_ = pd.read_input(label) - out = SE_ResNeXt(input=image_, class_dim=class_dim, layers=layers) + if args.model is 'se_resnext': + out = SE_ResNeXt( + input=image_, class_dim=class_dim, layers=layers) + else: + out = mobile_net(img=image_, class_dim=class_dim) + cost = fluid.layers.cross_entropy(input=out, label=label_) avg_cost = fluid.layers.mean(x=cost) acc_top1 = fluid.layers.accuracy(input=out, label=label_, k=1) @@ -58,7 +69,11 @@ def train_parallel_do(args, acc_top1 = fluid.layers.mean(x=acc_top1) acc_top5 = fluid.layers.mean(x=acc_top5) else: - out = SE_ResNeXt(input=image, class_dim=class_dim, layers=layers) + if args.model is 'se_resnext': + out = SE_ResNeXt(input=image, class_dim=class_dim, layers=layers) + else: + out = mobile_net(img=image, class_dim=class_dim) + cost = fluid.layers.cross_entropy(input=out, label=label) avg_cost = fluid.layers.mean(x=cost) acc_top1 = fluid.layers.accuracy(input=out, label=label, k=1) @@ -150,7 +165,8 @@ def train_parallel_do(args, test_acc5)) sys.stdout.flush() - model_path = os.path.join(model_save_dir, str(pass_id)) + model_path = os.path.join(model_save_dir + '/' + args.model, + str(pass_id)) if not os.path.isdir(model_path): os.makedirs(model_path) fluid.io.save_persistables(exe, model_path) @@ -171,7 +187,11 @@ def train_parallel_exe(args, image = fluid.layers.data(name='image', shape=image_shape, dtype='float32') label = fluid.layers.data(name='label', shape=[1], dtype='int64') - out = SE_ResNeXt(input=image, class_dim=class_dim, layers=layers) + if args.model is 'se_resnext': + out = SE_ResNeXt(input=image, class_dim=class_dim, layers=layers) + else: + out = mobile_net(img=image, class_dim=class_dim) + cost = fluid.layers.cross_entropy(input=out, label=label) acc_top1 = fluid.layers.accuracy(input=out, label=label, k=1) acc_top5 = fluid.layers.accuracy(input=out, label=label, k=5) @@ -270,7 +290,8 @@ def train_parallel_exe(args, test_acc5)) sys.stdout.flush() - model_path = os.path.join(model_save_dir, str(pass_id)) + model_path = os.path.join(model_save_dir + '/' + args.model, + str(pass_id)) if not os.path.isdir(model_path): os.makedirs(model_path) fluid.io.save_persistables(exe, model_path)