def get_model(self): image = paddle.static.data( name='image', shape=[None, 1, 28, 28], dtype='float32') label = paddle.static.data(name='label', shape=[None, 1], dtype='int64') model = MobileNet() out = model.net(input=image, class_dim=10) cost = paddle.nn.functional.loss.cross_entropy(input=out, label=label) avg_cost = paddle.mean(x=cost) startup_prog = paddle.static.default_startup_program() train_prog = paddle.static.default_main_program() return startup_prog, train_prog
def get_model(self): image = fluid.layers.data( name='image', shape=[1, 28, 28], dtype='float32') label = fluid.layers.data(name='label', shape=[1], dtype='int64') model = MobileNet() out = model.net(input=image, class_dim=10) cost = fluid.layers.cross_entropy(input=out, label=label) avg_cost = fluid.layers.mean(x=cost) startup_prog = fluid.default_startup_program() train_prog = fluid.default_main_program() return startup_prog, train_prog
def test_analysis_helper(self): image = paddle.static.data( name='image', shape=[None, 1, 28, 28], dtype='float32') label = paddle.static.data(name='label', shape=[None, 1], dtype='int64') model = MobileNet() out = model.net(input=image, class_dim=10) cost = paddle.nn.functional.loss.cross_entropy(input=out, label=label) avg_cost = paddle.mean(x=cost) acc_top1 = paddle.metric.accuracy(input=out, label=label, k=1) acc_top5 = paddle.metric.accuracy(input=out, label=label, k=5) optimizer = paddle.optimizer.Momentum( momentum=0.9, learning_rate=0.01, weight_decay=paddle.regularizer.L2Decay(4e-5)) optimizer.minimize(avg_cost) main_prog = paddle.static.default_main_program() places = paddle.static.cuda_places() if paddle.is_compiled_with_cuda( ) else paddle.static.cpu_places() exe = paddle.static.Executor(places[0]) def transform(x): return np.reshape(x, [1, 28, 28]) train_dataset = paddle.vision.datasets.MNIST( mode='train', backend='cv2', transform=transform) train_loader = paddle.io.DataLoader( train_dataset, places=places, feed_list=[image, label], drop_last=True, return_list=False, batch_size=64) exe.run(paddle.static.default_startup_program()) vars = ['conv2d_0.tmp_0', 'fc_0.tmp_0', 'fc_0.tmp_1', 'fc_0.tmp_2'] var_collector1 = VarCollector(main_prog, vars, use_ema=True) values = var_collector1.abs_max_run( train_loader, exe, step=None, loss_name=avg_cost.name) vars = [v.name for v in main_prog.list_vars() if v.persistable] var_collector2 = VarCollector(main_prog, vars, use_ema=False) values = var_collector2.run(train_loader, exe, step=None, loss_name=avg_cost.name) var_collector2.pdf(values)
def test_analysis_helper(self): image = fluid.layers.data(name='image', shape=[1, 28, 28], dtype='float32') label = fluid.layers.data(name='label', shape=[1], dtype='int64') model = MobileNet() out = model.net(input=image, class_dim=10) 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) acc_top5 = fluid.layers.accuracy(input=out, label=label, k=5) optimizer = fluid.optimizer.Momentum( momentum=0.9, learning_rate=0.01, regularization=fluid.regularizer.L2Decay(4e-5)) optimizer.minimize(avg_cost) main_prog = fluid.default_main_program() places = fluid.cuda_places() if fluid.is_compiled_with_cuda( ) else fluid.cpu_places() exe = fluid.Executor(places[0]) train_reader = paddle.fluid.io.batch(paddle.dataset.mnist.train(), batch_size=64) train_loader = fluid.io.DataLoader.from_generator( feed_list=[image, label], capacity=512, use_double_buffer=True, iterable=True) train_loader.set_sample_list_generator(train_reader, places) exe.run(fluid.default_startup_program()) vars = ['conv2d_0.tmp_0', 'fc_0.tmp_0', 'fc_0.tmp_1', 'fc_0.tmp_2'] var_collector1 = VarCollector(main_prog, vars, use_ema=True) values = var_collector1.abs_max_run(train_loader, exe, step=None, loss_name=avg_cost.name) vars = [v.name for v in main_prog.list_vars() if v.persistable] var_collector2 = VarCollector(main_prog, vars, use_ema=False) values = var_collector2.run(train_loader, exe, step=None, loss_name=avg_cost.name) var_collector2.pdf(values)
def test_accuracy(self): float_infer_model_path_prefix = "./mv1_float_inference" image = paddle.static.data(name='image', shape=[None, 1, 28, 28], dtype='float32') label = paddle.static.data(name='label', shape=[None, 1], dtype='int64') model = MobileNet() out = model.net(input=image, class_dim=10) cost = paddle.nn.functional.loss.cross_entropy(input=out, label=label) avg_cost = paddle.mean(x=cost) acc_top1 = paddle.metric.accuracy(input=out, label=label, k=1) acc_top5 = paddle.metric.accuracy(input=out, label=label, k=5) optimizer = paddle.optimizer.Momentum( momentum=0.9, learning_rate=0.01, weight_decay=paddle.regularizer.L2Decay(4e-5)) optimizer.minimize(avg_cost) main_prog = paddle.static.default_main_program() val_prog = main_prog.clone(for_test=True) #place = paddle.CPUPlace() place = paddle.CUDAPlace( 0) if paddle.is_compiled_with_cuda() else paddle.CPUPlace() exe = paddle.static.Executor(place) exe.run(paddle.static.default_startup_program()) def transform(x): return np.reshape(x, [1, 28, 28]) train_dataset = paddle.vision.datasets.MNIST(mode='train', backend='cv2', transform=transform) test_dataset = paddle.vision.datasets.MNIST(mode='test', backend='cv2', transform=transform) train_loader = paddle.io.DataLoader(train_dataset, places=place, feed_list=[image, label], drop_last=True, batch_size=64, return_list=False) valid_loader = paddle.io.DataLoader(test_dataset, places=place, feed_list=[image, label], batch_size=64, return_list=False) def sample_generator_creator(): def __reader__(): for data in test_dataset: image, label = data yield image, label return __reader__ def train(program): iter = 0 for data in train_loader(): cost, top1, top5 = exe.run( program, feed=data, fetch_list=[avg_cost, acc_top1, acc_top5]) iter += 1 if iter % 100 == 0: print( 'train iter={}, avg loss {}, acc_top1 {}, acc_top5 {}'. format(iter, cost, top1, top5)) def test(program, outputs=[avg_cost, acc_top1, acc_top5]): iter = 0 result = [[], [], []] for data in valid_loader(): cost, top1, top5 = exe.run(program, feed=data, fetch_list=outputs) iter += 1 if iter % 100 == 0: print( 'eval iter={}, avg loss {}, acc_top1 {}, acc_top5 {}'. format(iter, cost, top1, top5)) result[0].append(cost) result[1].append(top1) result[2].append(top5) print(' avg loss {}, acc_top1 {}, acc_top5 {}'.format( np.mean(result[0]), np.mean(result[1]), np.mean(result[2]))) return np.mean(result[1]), np.mean(result[2]) train(main_prog) top1_1, top5_1 = test(val_prog) paddle.static.save_inference_model( path_prefix=float_infer_model_path_prefix, feed_vars=[image, label], fetch_vars=[avg_cost, acc_top1, acc_top5], executor=exe, program=val_prog) quant_config = { 'weight_quantize_type': 'channel_wise_abs_max', 'activation_quantize_type': 'moving_average_abs_max', 'not_quant_pattern': ['skip_quant'], 'quantize_op_types': ['conv2d', 'depthwise_conv2d', 'mul'] } train_config = { "num_epoch": 1, # training epoch num "max_iter": 20, "save_iter_step": 10, "learning_rate": 0.0001, "weight_decay": 0.0001, "use_pact": False, "quant_model_ckpt_path": "./quantaware_with_infermodel_checkpoints/", "teacher_model_path_prefix": float_infer_model_path_prefix, "model_path_prefix": float_infer_model_path_prefix, "distill_node_pair": [ "teacher_fc_0.tmp_0", "fc_0.tmp_0", "teacher_batch_norm_24.tmp_4", "batch_norm_24.tmp_4", "teacher_batch_norm_22.tmp_4", "batch_norm_22.tmp_4", "teacher_batch_norm_18.tmp_4", "batch_norm_18.tmp_4", "teacher_batch_norm_13.tmp_4", "batch_norm_13.tmp_4", "teacher_batch_norm_5.tmp_4", "batch_norm_5.tmp_4" ] } def test_callback(compiled_test_program, feed_names, fetch_list, checkpoint_name): outputs = fetch_list iter = 0 result = [[], [], []] for data in valid_loader(): cost, top1, top5 = exe.run(compiled_test_program, feed=data, fetch_list=fetch_list) iter += 1 if iter % 100 == 0: print( 'eval iter={}, avg loss {}, acc_top1 {}, acc_top5 {}'. format(iter, cost, top1, top5)) result[0].append(cost) result[1].append(top1) result[2].append(top5) print("quant model checkpoint: " + checkpoint_name + ' avg loss {}, acc_top1 {}, acc_top5 {}'.format( np.mean(result[0]), np.mean(result[1]), np.mean( result[2]))) return np.mean(result[1]), np.mean(result[2]) def test_quant_aware_with_infermodel(exe, place): quant_aware_with_infermodel(exe, place, scope=None, train_reader=train_loader, quant_config=quant_config, train_config=train_config, test_callback=test_callback) def test_export_quant_infermodel(exe, place, checkpoint_path, quant_infermodel_save_path): export_quant_infermodel( exe, place, scope=None, quant_config=quant_config, train_config=train_config, checkpoint_path=checkpoint_path, export_inference_model_path_prefix=quant_infermodel_save_path) #place = paddle.CPUPlace() place = paddle.CUDAPlace( 0) if paddle.is_compiled_with_cuda() else paddle.CPUPlace() exe = paddle.static.Executor(place) test_quant_aware_with_infermodel(exe, place) checkpoint_path = "./quantaware_with_infermodel_checkpoints/epoch_0_iter_10" quant_infermodel_save_path = "./quantaware_with_infermodel_export" test_export_quant_infermodel(exe, place, checkpoint_path, quant_infermodel_save_path) train_config["use_pact"] = True test_quant_aware_with_infermodel(exe, place) train_config["use_pact"] = False checkpoint_path = "./quantaware_with_infermodel_checkpoints/epoch_0_iter_10" quant_infermodel_save_path = "./quantaware_with_infermodel_export" test_export_quant_infermodel(exe, place, checkpoint_path, quant_infermodel_save_path)
def test_accuracy(self): image = paddle.static.data(name='image', shape=[None, 1, 28, 28], dtype='float32') label = paddle.static.data(name='label', shape=[None, 1], dtype='int64') model = MobileNet() out = model.net(input=image, class_dim=10) cost = paddle.nn.functional.loss.cross_entropy(input=out, label=label) avg_cost = paddle.mean(x=cost) acc_top1 = paddle.metric.accuracy(input=out, label=label, k=1) acc_top5 = paddle.metric.accuracy(input=out, label=label, k=5) optimizer = paddle.optimizer.Momentum( momentum=0.9, learning_rate=0.01, weight_decay=paddle.regularizer.L2Decay(4e-5)) optimizer.minimize(avg_cost) main_prog = paddle.static.default_main_program() val_prog = main_prog.clone(for_test=True) place = paddle.CUDAPlace( 0) if paddle.is_compiled_with_cuda() else paddle.CPUPlace() exe = paddle.static.Executor(place) exe.run(paddle.static.default_startup_program()) def transform(x): return np.reshape(x, [1, 28, 28]) train_dataset = paddle.vision.datasets.MNIST(mode='train', backend='cv2', transform=transform) test_dataset = paddle.vision.datasets.MNIST(mode='test', backend='cv2', transform=transform) train_loader = paddle.io.DataLoader(train_dataset, places=place, feed_list=[image, label], drop_last=True, batch_size=64, return_list=False) valid_loader = paddle.io.DataLoader(test_dataset, places=place, feed_list=[image, label], batch_size=64, return_list=False) def sample_generator_creator(): def __reader__(): for data in test_dataset: image, label = data yield image, label return __reader__ def train(program): iter = 0 for data in train_loader(): cost, top1, top5 = exe.run( program, feed=data, fetch_list=[avg_cost, acc_top1, acc_top5]) iter += 1 if iter % 100 == 0: print( 'train iter={}, avg loss {}, acc_top1 {}, acc_top5 {}'. format(iter, cost, top1, top5)) def test(program, outputs=[avg_cost, acc_top1, acc_top5]): iter = 0 result = [[], [], []] for data in valid_loader(): cost, top1, top5 = exe.run(program, feed=data, fetch_list=outputs) iter += 1 if iter % 100 == 0: print( 'eval iter={}, avg loss {}, acc_top1 {}, acc_top5 {}'. format(iter, cost, top1, top5)) result[0].append(cost) result[1].append(top1) result[2].append(top5) print(' avg loss {}, acc_top1 {}, acc_top5 {}'.format( np.mean(result[0]), np.mean(result[1]), np.mean(result[2]))) return np.mean(result[1]), np.mean(result[2]) train(main_prog) top1_1, top5_1 = test(val_prog) paddle.fluid.io.save_inference_model( dirname='./test_quant_post', feeded_var_names=[image.name, label.name], target_vars=[avg_cost, acc_top1, acc_top5], main_program=val_prog, executor=exe, model_filename='model', params_filename='params') quant_post_static(exe, './test_quant_post', './test_quant_post_inference', sample_generator=sample_generator_creator(), model_filename='model', params_filename='params', batch_nums=10) quant_post_prog, feed_target_names, fetch_targets = paddle.fluid.io.load_inference_model( dirname='./test_quant_post_inference', executor=exe, model_filename='__model__', params_filename='__params__') top1_2, top5_2 = test(quant_post_prog, fetch_targets) print("before quantization: top1: {}, top5: {}".format(top1_1, top5_1)) print("after quantization: top1: {}, top5: {}".format(top1_2, top5_2))
def test_accuracy(self): image = paddle.static.data( name='image', shape=[None, 1, 28, 28], dtype='float32') image.stop_gradient = False label = paddle.static.data(name='label', shape=[None, 1], dtype='int64') model = MobileNet() out = model.net(input=image, class_dim=10) cost = paddle.nn.functional.loss.cross_entropy(input=out, label=label) avg_cost = paddle.mean(x=cost) acc_top1 = paddle.metric.accuracy(input=out, label=label, k=1) acc_top5 = paddle.metric.accuracy(input=out, label=label, k=5) optimizer = paddle.optimizer.Momentum( momentum=0.9, learning_rate=0.01, weight_decay=paddle.regularizer.L2Decay(4e-5)) optimizer.minimize(avg_cost) main_prog = paddle.static.default_main_program() val_prog = main_prog.clone(for_test=True) place = paddle.CUDAPlace(0) if paddle.is_compiled_with_cuda( ) else paddle.CPUPlace() exe = paddle.static.Executor(place) exe.run(paddle.static.default_startup_program()) def transform(x): return np.reshape(x, [1, 28, 28]) train_dataset = paddle.vision.datasets.MNIST( mode='train', backend='cv2', transform=transform) test_dataset = paddle.vision.datasets.MNIST( mode='test', backend='cv2', transform=transform) train_loader = paddle.io.DataLoader( train_dataset, places=place, feed_list=[image, label], drop_last=True, return_list=False, batch_size=64) valid_loader = paddle.io.DataLoader( test_dataset, places=place, feed_list=[image, label], batch_size=64, return_list=False) def train(program): iter = 0 for data in train_loader(): cost, top1, top5 = exe.run( program, feed=data, fetch_list=[avg_cost, acc_top1, acc_top5]) iter += 1 if iter % 100 == 0: print( 'train iter={}, avg loss {}, acc_top1 {}, acc_top5 {}'. format(iter, cost, top1, top5)) def test(program): iter = 0 result = [[], [], []] for data in valid_loader(): cost, top1, top5 = exe.run( program, feed=data, fetch_list=[avg_cost, acc_top1, acc_top5]) iter += 1 if iter % 100 == 0: print('eval iter={}, avg loss {}, acc_top1 {}, acc_top5 {}'. format(iter, cost, top1, top5)) result[0].append(cost) result[1].append(top1) result[2].append(top5) print(' avg loss {}, acc_top1 {}, acc_top5 {}'.format( np.mean(result[0]), np.mean(result[1]), np.mean(result[2]))) return np.mean(result[1]), np.mean(result[2]) train(main_prog) top1_1, top5_1 = test(main_prog) config = { 'weight_quantize_type': 'channel_wise_abs_max', 'activation_quantize_type': 'moving_average_abs_max', 'quantize_op_types': ['depthwise_conv2d', 'mul', 'conv2d'], } quant_train_prog_pact = quant_aware( main_prog, place, config, for_test=False, act_preprocess_func=pact, optimizer_func=get_optimizer, executor=exe) quant_eval_prog = quant_aware(val_prog, place, config, for_test=True) train(quant_train_prog_pact) quant_eval_prog, int8_prog = convert( quant_eval_prog, place, config, save_int8=True) top1_2, top5_2 = test(quant_eval_prog) # values before quantization and after quantization should be close print("before quantization: top1: {}, top5: {}".format(top1_1, top5_1)) print("after quantization: top1: {}, top5: {}".format(top1_2, top5_2))
def test_accuracy(self): image = paddle.static.data( name='image', shape=[None, 1, 28, 28], dtype='float32') label = paddle.static.data(name='label', shape=[None, 1], dtype='int64') model = MobileNet() out = model.net(input=image, class_dim=10) cost = paddle.nn.functional.loss.cross_entropy(input=out, label=label) avg_cost = paddle.mean(x=cost) acc_top1 = paddle.metric.accuracy(input=out, label=label, k=1) acc_top5 = paddle.metric.accuracy(input=out, label=label, k=5) optimizer = paddle.optimizer.Momentum( momentum=0.9, learning_rate=0.01, weight_decay=paddle.regularizer.L2Decay(4e-5)) optimizer.minimize(avg_cost) main_prog = paddle.static.default_main_program() val_prog = main_prog.clone(for_test=True) place = paddle.CUDAPlace(0) if paddle.fluid.is_compiled_with_cuda( ) else paddle.CPUPlace() exe = paddle.static.Executor(place) exe.run(paddle.static.default_startup_program()) train_loader = paddle.io.DataLoader.from_generator( feed_list=[image, label], capacity=512, use_double_buffer=True, iterable=True) valid_loader = paddle.io.DataLoader.from_generator( feed_list=[image, label], capacity=512, use_double_buffer=True, iterable=True) train_reader = paddle.batch(paddle.dataset.mnist.train(), batch_size=64) eval_reader = paddle.batch(paddle.dataset.mnist.test(), batch_size=64) train_loader.set_sample_list_generator(train_reader, place) valid_loader.set_sample_list_generator(eval_reader, place) def train(program): iter = 0 for data in train_loader(): cost, top1, top5 = exe.run( program, feed=data, fetch_list=[avg_cost, acc_top1, acc_top5]) iter += 1 if iter % 100 == 0: print( 'train iter={}, avg loss {}, acc_top1 {}, acc_top5 {}'. format(iter, cost, top1, top5)) def test(program, outputs=[avg_cost, acc_top1, acc_top5]): iter = 0 result = [[], [], []] for data in valid_loader(): cost, top1, top5 = exe.run(program, feed=data, fetch_list=outputs) iter += 1 if iter % 100 == 0: print('eval iter={}, avg loss {}, acc_top1 {}, acc_top5 {}'. format(iter, cost, top1, top5)) result[0].append(cost) result[1].append(top1) result[2].append(top5) print(' avg loss {}, acc_top1 {}, acc_top5 {}'.format( np.mean(result[0]), np.mean(result[1]), np.mean(result[2]))) return np.mean(result[1]), np.mean(result[2]) train(main_prog) top1_1, top5_1 = test(val_prog) paddle.static.save_inference_model( dirname='./test_quant_post_dynamic', feeded_var_names=[image.name, label.name], target_vars=[avg_cost, acc_top1, acc_top5], main_program=val_prog, executor=exe, model_filename='model', params_filename='params') quant_post_dynamic( model_dir='./test_quant_post_dynamic', save_model_dir='./test_quant_post_inference', model_filename='model', params_filename='params', generate_test_model=True) quant_post_prog, feed_target_names, fetch_targets = paddle.static.load_inference_model( dirname='./test_quant_post_inference/test_model', executor=exe) top1_2, top5_2 = test(quant_post_prog, fetch_targets) print("before quantization: top1: {}, top5: {}".format(top1_1, top5_1)) print("after quantization: top1: {}, top5: {}".format(top1_2, top5_2))
def test_accuracy(self): image = fluid.layers.data(name='image', shape=[1, 28, 28], dtype='float32') label = fluid.layers.data(name='label', shape=[1], dtype='int64') model = MobileNet() out = model.net(input=image, class_dim=10) 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) acc_top5 = fluid.layers.accuracy(input=out, label=label, k=5) optimizer = fluid.optimizer.Momentum( momentum=0.9, learning_rate=0.01, regularization=fluid.regularizer.L2Decay(4e-5)) optimizer.minimize(avg_cost) main_prog = fluid.default_main_program() val_prog = main_prog.clone(for_test=True) place = fluid.CUDAPlace( 0) if fluid.is_compiled_with_cuda() else fluid.CPUPlace() exe = fluid.Executor(place) exe.run(fluid.default_startup_program()) feeder = fluid.DataFeeder([image, label], place, program=main_prog) train_reader = paddle.fluid.io.batch(paddle.dataset.mnist.train(), batch_size=64) eval_reader = paddle.fluid.io.batch(paddle.dataset.mnist.test(), batch_size=64) def train(program): iter = 0 for data in train_reader(): cost, top1, top5 = exe.run( program, feed=feeder.feed(data), fetch_list=[avg_cost, acc_top1, acc_top5]) iter += 1 if iter % 100 == 0: print( 'train iter={}, avg loss {}, acc_top1 {}, acc_top5 {}'. format(iter, cost, top1, top5)) def test(program, outputs=[avg_cost, acc_top1, acc_top5]): iter = 0 result = [[], [], []] for data in train_reader(): cost, top1, top5 = exe.run(program, feed=feeder.feed(data), fetch_list=outputs) iter += 1 if iter % 100 == 0: print( 'eval iter={}, avg loss {}, acc_top1 {}, acc_top5 {}'. format(iter, cost, top1, top5)) result[0].append(cost) result[1].append(top1) result[2].append(top5) print(' avg loss {}, acc_top1 {}, acc_top5 {}'.format( np.mean(result[0]), np.mean(result[1]), np.mean(result[2]))) return np.mean(result[1]), np.mean(result[2]) train(main_prog) top1_1, top5_1 = test(val_prog) fluid.io.save_inference_model( dirname='./test_quant_post', feeded_var_names=[image.name, label.name], target_vars=[avg_cost, acc_top1, acc_top5], main_program=val_prog, executor=exe, model_filename='model', params_filename='params') quant_post_static(exe, './test_quant_post', './test_quant_post_inference', sample_generator=paddle.dataset.mnist.test(), model_filename='model', params_filename='params', batch_nums=10) quant_post_prog, feed_target_names, fetch_targets = fluid.io.load_inference_model( dirname='./test_quant_post_inference', executor=exe, model_filename='__model__', params_filename='__params__') top1_2, top5_2 = test(quant_post_prog, fetch_targets) print("before quantization: top1: {}, top5: {}".format(top1_1, top5_1)) print("after quantization: top1: {}, top5: {}".format(top1_2, top5_2))
def test_accuracy(self): image = fluid.layers.data(name='image', shape=[1, 28, 28], dtype='float32') image.stop_gradient = False label = fluid.layers.data(name='label', shape=[1], dtype='int64') model = MobileNet() out = model.net(input=image, class_dim=10) 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) acc_top5 = fluid.layers.accuracy(input=out, label=label, k=5) optimizer = fluid.optimizer.Momentum( momentum=0.9, learning_rate=0.01, regularization=fluid.regularizer.L2Decay(4e-5)) optimizer.minimize(avg_cost) main_prog = fluid.default_main_program() val_prog = main_prog.clone(for_test=True) place = fluid.CUDAPlace( 0) if fluid.is_compiled_with_cuda() else fluid.CPUPlace() exe = fluid.Executor(place) exe.run(fluid.default_startup_program()) feeder = fluid.DataFeeder([image, label], place, program=main_prog) train_reader = paddle.fluid.io.batch(paddle.dataset.mnist.train(), batch_size=64) eval_reader = paddle.fluid.io.batch(paddle.dataset.mnist.test(), batch_size=64) def train(program): iter = 0 for data in train_reader(): cost, top1, top5 = exe.run( program, feed=feeder.feed(data), fetch_list=[avg_cost, acc_top1, acc_top5]) iter += 1 if iter % 100 == 0: print( 'train iter={}, avg loss {}, acc_top1 {}, acc_top5 {}'. format(iter, cost, top1, top5)) def test(program): iter = 0 result = [[], [], []] for data in eval_reader(): cost, top1, top5 = exe.run( program, feed=feeder.feed(data), fetch_list=[avg_cost, acc_top1, acc_top5]) iter += 1 if iter % 100 == 0: print( 'eval iter={}, avg loss {}, acc_top1 {}, acc_top5 {}'. format(iter, cost, top1, top5)) result[0].append(cost) result[1].append(top1) result[2].append(top5) print(' avg loss {}, acc_top1 {}, acc_top5 {}'.format( np.mean(result[0]), np.mean(result[1]), np.mean(result[2]))) return np.mean(result[1]), np.mean(result[2]) train(main_prog) top1_1, top5_1 = test(main_prog) config = { 'weight_quantize_type': 'channel_wise_abs_max', 'activation_quantize_type': 'moving_average_abs_max', 'quantize_op_types': ['depthwise_conv2d', 'mul', 'conv2d'], } quant_train_prog_pact = quant_aware(main_prog, place, config, for_test=False, act_preprocess_func=pact, optimizer_func=get_optimizer, executor=exe) quant_eval_prog = quant_aware(val_prog, place, config, for_test=True) train(quant_train_prog_pact) quant_eval_prog, int8_prog = convert(quant_eval_prog, place, config, save_int8=True) top1_2, top5_2 = test(quant_eval_prog) # values before quantization and after quantization should be close print("before quantization: top1: {}, top5: {}".format(top1_1, top5_1)) print("after quantization: top1: {}, top5: {}".format(top1_2, top5_2))
def test_accuracy(self): image = paddle.static.data(name='image', shape=[None, 1, 28, 28], dtype='float32') label = paddle.static.data(name='label', shape=[None, 1], dtype='int64') model = MobileNet() out = model.net(input=image, class_dim=10) cost = paddle.nn.functional.loss.cross_entropy(input=out, label=label) avg_cost = paddle.mean(x=cost) acc_top1 = paddle.metric.accuracy(input=out, label=label, k=1) acc_top5 = paddle.metric.accuracy(input=out, label=label, k=5) optimizer = paddle.optimizer.Momentum( momentum=0.9, learning_rate=0.01, weight_decay=paddle.regularizer.L2Decay(4e-5)) optimizer.minimize(avg_cost) main_prog = paddle.static.default_main_program() val_prog = main_prog.clone(for_test=True) place = paddle.CUDAPlace(0) if paddle.fluid.is_compiled_with_cuda( ) else paddle.static.CPUPlace() exe = paddle.static.Executor(place) exe.run(paddle.static.default_startup_program()) train_loader = paddle.io.DataLoader.from_generator( feed_list=[image, label], capacity=512, use_double_buffer=True, iterable=True) valid_loader = paddle.io.DataLoader.from_generator( feed_list=[image, label], capacity=512, use_double_buffer=True, iterable=True) train_reader = paddle.batch(paddle.dataset.mnist.train(), batch_size=64) eval_reader = paddle.batch(paddle.dataset.mnist.test(), batch_size=64) train_loader.set_sample_list_generator(train_reader, place) valid_loader.set_sample_list_generator(eval_reader, place) def train(program): iter = 0 for data in train_loader(): cost, top1, top5 = exe.run( program, feed=data, fetch_list=[avg_cost, acc_top1, acc_top5]) iter += 1 if iter % 100 == 0: print( 'train iter={}, avg loss {}, acc_top1 {}, acc_top5 {}'. format(iter, cost, top1, top5)) def test(program): iter = 0 result = [[], [], []] for data in valid_loader(): cost, top1, top5 = exe.run( program, feed=data, fetch_list=[avg_cost, acc_top1, acc_top5]) iter += 1 if iter % 100 == 0: print( 'eval iter={}, avg loss {}, acc_top1 {}, acc_top5 {}'. format(iter, cost, top1, top5)) result[0].append(cost) result[1].append(top1) result[2].append(top5) print(' avg loss {}, acc_top1 {}, acc_top5 {}'.format( np.mean(result[0]), np.mean(result[1]), np.mean(result[2]))) return np.mean(result[1]), np.mean(result[2]) train(main_prog) top1_1, top5_1 = test(main_prog) config = { 'weight_quantize_type': 'channel_wise_abs_max', 'activation_quantize_type': 'moving_average_abs_max', 'quantize_op_types': ['depthwise_conv2d', 'mul', 'conv2d'], } quant_train_prog = quant_aware(main_prog, place, config, for_test=False) quant_eval_prog = quant_aware(val_prog, place, config, for_test=True) train(quant_train_prog) quant_eval_prog, int8_prog = convert(quant_eval_prog, place, config, save_int8=True) top1_2, top5_2 = test(quant_eval_prog) # values before quantization and after quantization should be close print("before quantization: top1: {}, top5: {}".format(top1_1, top5_1)) print("after quantization: top1: {}, top5: {}".format(top1_2, top5_2))