# Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. from __future__ import print_function import numpy as np from PIL import Image import os import paddle import paddle.fluid as fluid from paddle.fluid.optimizer import AdamOptimizer from paddle.fluid.dygraph.nn import Conv2D, Pool2D, FC from paddle.fluid.dygraph.base import to_variable class SimpleImgConvPool(fluid.dygraph.Layer): def __init__(self, name_scope, num_channels, num_filters, filter_size, pool_size, pool_stride, pool_padding=0, pool_type='max', global_pooling=False, conv_stride=1, conv_padding=0, conv_dilation=1, conv_groups=1, act=None, use_cudnn=False, param_attr=None, bias_attr=None): super(SimpleImgConvPool, self).__init__(name_scope) self._conv2d = Conv2D( self.full_name(), num_channels=num_channels, num_filters=num_filters, filter_size=filter_size, stride=conv_stride, padding=conv_padding, dilation=conv_dilation, groups=conv_groups, param_attr=None, bias_attr=None, use_cudnn=use_cudnn) self._pool2d = Pool2D( self.full_name(), pool_size=pool_size, pool_type=pool_type, pool_stride=pool_stride, pool_padding=pool_padding, global_pooling=global_pooling, use_cudnn=use_cudnn) def forward(self, inputs): x = self._conv2d(inputs) x = self._pool2d(x) return x class MNIST(fluid.dygraph.Layer): def __init__(self, name_scope): super(MNIST, self).__init__(name_scope) self._simple_img_conv_pool_1 = SimpleImgConvPool( self.full_name(), 1, 20, 5, 2, 2, act="relu") self._simple_img_conv_pool_2 = SimpleImgConvPool( self.full_name(), 20, 50, 5, 2, 2, act="relu") pool_2_shape = 50 * 4 * 4 SIZE = 10 scale = (2.0 / (pool_2_shape**2 * SIZE))**0.5 self._fc = FC(self.full_name(), 10, param_attr=fluid.param_attr.ParamAttr( initializer=fluid.initializer.NormalInitializer( loc=0.0, scale=scale)), act="softmax") def forward(self, inputs, label=None): x = self._simple_img_conv_pool_1(inputs) x = self._simple_img_conv_pool_2(x) x = self._fc(x) if label is not None: acc = fluid.layers.accuracy(input=x, label=label) return x, acc else: return x def test_train(reader, model, batch_size): acc_set = [] avg_loss_set = [] for batch_id, data in enumerate(reader()): dy_x_data = np.array( [x[0].reshape(1, 28, 28) for x in data]).astype('float32') y_data = np.array( [x[1] for x in data]).astype('int64').reshape(batch_size, 1) img = to_variable(dy_x_data) label = to_variable(y_data) label.stop_gradient = True prediction, acc = model(img, label) loss = fluid.layers.cross_entropy(input=prediction, label=label) avg_loss = fluid.layers.mean(loss) acc_set.append(float(acc.numpy())) avg_loss_set.append(float(avg_loss.numpy())) # get test acc and loss acc_val_mean = np.array(acc_set).mean() avg_loss_val_mean = np.array(avg_loss_set).mean() return avg_loss_val_mean, acc_val_mean def train_mnist(): epoch_num = 5 BATCH_SIZE = 64 with fluid.dygraph.guard(): mnist = MNIST("mnist") adam = AdamOptimizer(learning_rate=0.001) train_reader = paddle.batch( paddle.dataset.mnist.train(), batch_size=BATCH_SIZE, drop_last=True) test_reader = paddle.batch( paddle.dataset.mnist.test(), batch_size=BATCH_SIZE, drop_last=True) for epoch in range(epoch_num): for batch_id, data in enumerate(train_reader()): dy_x_data = np.array( [x[0].reshape(1, 28, 28) for x in data]).astype('float32') y_data = np.array( [x[1] for x in data]).astype('int64').reshape(BATCH_SIZE, 1) img = to_variable(dy_x_data) label = to_variable(y_data) label.stop_gradient = True cost, acc = mnist(img, label) loss = fluid.layers.cross_entropy(cost, label) avg_loss = fluid.layers.mean(loss) avg_loss.backward() adam.minimize(avg_loss) # save checkpoint mnist.clear_gradients() if batch_id % 100 == 0: print("Loss at epoch {} step {}: {:}".format(epoch, batch_id, avg_loss.numpy())) mnist.eval() test_cost, test_acc = test_train(test_reader, mnist, BATCH_SIZE) mnist.train() print("Loss at epoch {} , Test avg_loss is: {}, acc is: {}".format(epoch, test_cost, test_acc)) fluid.dygraph.save_persistables(mnist.state_dict(), "save_dir") print("checkpoint saved") with fluid.dygraph.guard(): mnist_infer = MNIST("mnist") # load checkpoint mnist_infer.load_dict( fluid.dygraph.load_persistables("save_dir")) print("checkpoint loaded") # start evaluate mode mnist_infer.eval() def load_image(file): im = Image.open(file).convert('L') im = im.resize((28, 28), Image.ANTIALIAS) im = np.array(im).reshape(1, 1, 28, 28).astype(np.float32) im = im / 255.0 * 2.0 - 1.0 return im cur_dir = os.path.dirname(os.path.realpath(__file__)) tensor_img = load_image(cur_dir + '/image/infer_3.png') results = mnist_infer(to_variable(tensor_img)) lab = np.argsort(results.numpy()) print("Inference result of image/infer_3.png is: %d" % lab[0][-1]) if __name__ == '__main__': train_mnist()