TensorFlow Lite，ML Kit 和 Flutter 移动深度学习：6~11（4）

void refreshBoard(String from, String to) {
   setState(() {
     board[to] = board[from];
     board[from] = " ";
   });
 }

Widget getImage(String squareName) {
   return Expanded(
     child: DragTarget<List>(builder: (context, accepted, rejected) {
             return Draggable<List>(
                 child: mapImages(squareName),
                 feedback: mapImages(squareName),
                 onDragCompleted: () {},
                 data: [
                   squareName,
                 ],
               );
       }, onWillAccept: (willAccept) {
         return true;
       }, onAccept: (List moveInfo) {
         String from = moveInfo[0];
         String to = squareName;
         refreshBoard(from, to);
       })
     );
 }

String getPositionString(String move) {
    String s = "";
    for(int i = 8; i >= 1; i--) {
        int count = 0;
        for(int j = 97; j <= 104; j++) {
            String ch = String.fromCharCode(j)+'$i';
            if(board[ch] == " ") {
                count += 1;
                if(j == 104) 
                    s = s + "$count";
            } else {
                if(count > 0) 
                    s = s + "$count";
                s = s + board[ch];count = 0;
            }
        }
    s = s + "/";
    }
    String position = s.substring(0, s.length-1) + " w KQkq - 0 1";
    var json = jsonEncode({"position": position, "moves": move});
}

void refreshBoard(String from, String to) {
    String move= from + to;
    getPositionString(move);
    .....
}

void makePOSTRequest(var json) async{
    var url = 'http://35.200.253.0:8080/play';
    var response = await http.post(url, headers: {"Content-Type": "application/json"} ,body: json);
    String rsp = response.body;
    String from = rsp.substring(0,3);
    String to = rsp.substring(3);
}

String getPositionString(String move) {
    .....
    makePOSTRequest(json);
}

void refreshBoardFromServer(String from, String to) {
    setState(() {    
        board[to] = board[from];
        board[from] = " ";
    });
}

void makePOSTRequest(var json) async{
    ......
    refreshBoardFromServer(from, to);
    buildChessBoard();
}

class MyApp extends StatelessWidget {
    @override
    Widget build(BuildContext context) {
        return MaterialApp(
            title: 'Chess',
            theme: ThemeData(primarySwatch: Colors.blue,),
            home: MyHomePage(title: 'Chess'),
        );
    }
}

@override
Widget build(BuildContext context) {
    return Scaffold(
        appBar: AppBar(title: Text('Chess'),),
        body: Center(
            child: Column(
                mainAxisAlignment: MainAxisAlignment.center,
                children: <Widget>[ChessGame()
                ],
            ),
        ),
    );
}

void main() => runApp(MyApp());

scipy.io.loadmat(file_name, mdict=None, appendmat=True, **kwargs)

# content loss 
        content_loss = content_weight * (2 * tf.nn.l2_loss( 
                net[CONTENT_LAYER] - content_features[CONTENT_LAYER]) / 
                content_features[CONTENT_LAYER].size)

best_loss = float('inf') 
        best = None 
        with tf.Session() as sess: 
            sess.run(tf.initialize_all_variables()) 
            for i in range(iterations): 
                last_step = (i == iterations - 1) 
                print_progress(i, last=last_step) 
                train_step.run() 
                if (checkpoint_iterations and i % checkpoint_iterations == 0) or last_step: 
                    this_loss = loss.eval() 
                    if this_loss < best_loss: 
                        best_loss = this_loss 
                        best = image.eval() 
                    yield ( 
                        (None if last_step else i), 
                        vgg.unprocess(best.reshape(shape[1:]), mean_pixel) 
                    )

python neural_style.py --content examples/2-content.jpg --styles examples/2-style1.jpg  --checkpoint-iterations=100 --iterations=1000 --checkpoint-output=out%s.jpg --output=outfinal

Iteration 1/1000
Iteration 2/1000
Iteration 3/1000
Iteration 4/1000
...
Iteration 999/1000
Iteration 1000/1000
  content loss: 908786
    style loss: 261789
       tv loss: 25639.9
    total loss: 1.19621e+06

import os 
import numpy as np 
import scipy.misc 
from stylize import stylize 
import math 
from argparse import ArgumentParser 
# default arguments 
CONTENT_WEIGHT = 5e0 
STYLE_WEIGHT = 1e2 
TV_WEIGHT = 1e2 
LEARNING_RATE = 1e1 
STYLE_SCALE = 1.0 
ITERATIONS = 100 
VGG_PATH = 'imagenet-vgg-verydeep-19.mat' 
def build_parser(): 
    parser = ArgumentParser() 
    parser.add_argument('--content', 
            dest='content', help='content image', 
            metavar='CONTENT', required=True) 
    parser.add_argument('--styles', 
            dest='styles', 
            nargs='+', help='one or more style images', 
            metavar='STYLE', required=True) 
    parser.add_argument('--output', 
            dest='output', help='output path', 
            metavar='OUTPUT', required=True) 
    parser.add_argument('--checkpoint-output', 
            dest='checkpoint_output', help='checkpoint output format', 
            metavar='OUTPUT') 
    parser.add_argument('--iterations', type=int, 
            dest='iterations', help='iterations (default %(default)s)', 
            metavar='ITERATIONS', default=ITERATIONS) 
    parser.add_argument('--width', type=int, 
            dest='width', help='output width', 
            metavar='WIDTH') 
    parser.add_argument('--style-scales', type=float, 
            dest='style_scales', 
            nargs='+', help='one or more style scales', 
            metavar='STYLE_SCALE') 
    parser.add_argument('--network', 
            dest='network', help='path to network parameters (default %(default)s)', 
            metavar='VGG_PATH', default=VGG_PATH) 
    parser.add_argument('--content-weight', type=float, 
            dest='content_weight', help='content weight (default %(default)s)', 
            metavar='CONTENT_WEIGHT', default=CONTENT_WEIGHT) 
    parser.add_argument('--style-weight', type=float, 
            dest='style_weight', help='style weight (default %(default)s)', 
            metavar='STYLE_WEIGHT', default=STYLE_WEIGHT) 
    parser.add_argument('--style-blend-weights', type=float, 
            dest='style_blend_weights', help='style blending weights', 
            nargs='+', metavar='STYLE_BLEND_WEIGHT') 
    parser.add_argument('--tv-weight', type=float, 
            dest='tv_weight', help='total variation regularization weight (default %(default)s)', 
            metavar='TV_WEIGHT', default=TV_WEIGHT) 
    parser.add_argument('--learning-rate', type=float, 
            dest='learning_rate', help='learning rate (default %(default)s)', 
            metavar='LEARNING_RATE', default=LEARNING_RATE) 
    parser.add_argument('--initial', 
            dest='initial', help='initial image', 
            metavar='INITIAL') 
    parser.add_argument('--print-iterations', type=int, 
            dest='print_iterations', help='statistics printing frequency', 
            metavar='PRINT_ITERATIONS') 
    parser.add_argument('--checkpoint-iterations', type=int, 
            dest='checkpoint_iterations', help='checkpoint frequency', 
            metavar='CHECKPOINT_ITERATIONS') 
    return parser 
def main(): 
    parser = build_parser() 
    options = parser.parse_args() 
    if not os.path.isfile(options.network): 
        parser.error("Network %s does not exist. (Did you forget to download it?)" % options.network) 
    content_image = imread(options.content) 
    style_images = [imread(style) for style in options.styles] 
    width = options.width 
    if width is not None: 
        new_shape = (int(math.floor(float(content_image.shape[0]) / 
                content_image.shape[1] * width)), width) 
        content_image = scipy.misc.imresize(content_image, new_shape) 
    target_shape = content_image.shape 
    for i in range(len(style_images)): 
        style_scale = STYLE_SCALE 
        if options.style_scales is not None: 
            style_scale = options.style_scales[i] 
        style_images[i] = scipy.misc.imresize(style_images[i], style_scale * 
                target_shape[1] / style_images[i].shape[1]) 
    style_blend_weights = options.style_blend_weights 
    if style_blend_weights is None: 
        # default is equal weights 
        style_blend_weights = [1.0/len(style_images) for _ in style_images] 
    else: 
        total_blend_weight = sum(style_blend_weights) 
        style_blend_weights = [weight/total_blend_weight 
                               for weight in style_blend_weights] 
    initial = options.initial 
    if initial is not None: 
        initial = scipy.misc.imresize(imread(initial), content_image.shape[:2]) 
    if options.checkpoint_output and "%s" not in options.checkpoint_output: 
        parser.error("To save intermediate images, the checkpoint output " 
                     "parameter must contain `%s` (e.g. `foo%s.jpg`)") 
    for iteration, image in stylize( 
        network=options.network, 
        initial=initial, 
        content=content_image, 
        styles=style_images, 
        iterations=options.iterations, 
        content_weight=options.content_weight, 
        style_weight=options.style_weight, 
        style_blend_weights=style_blend_weights, 
        tv_weight=options.tv_weight, 
        learning_rate=options.learning_rate, 
        print_iterations=options.print_iterations, 
        checkpoint_iterations=options.checkpoint_iterations 
    ): 
        output_file = None 
        if iteration is not None: 
            if options.checkpoint_output: 
                output_file = options.checkpoint_output % iteration 
        else: 
            output_file = options.output 
        if output_file: 
            imsave(output_file, image) 
def imread(path): 
    return scipy.misc.imread(path).astype(np.float) 
def imsave(path, img): 
    img = np.clip(img, 0, 255).astype(np.uint8) 
    scipy.misc.imsave(path, img) 
if __name__ == '__main__': 
    main()

import vgg 
import tensorflow as tf 
import numpy as np 
from sys import stderr 
CONTENT_LAYER = 'relu4_2' 
STYLE_LAYERS = ('relu1_1', 'relu2_1', 'relu3_1', 'relu4_1', 'relu5_1') 
try: 
    reduce 
except NameError: 
    from functools import reduce 
def stylize(network, initial, content, styles, iterations, 
        content_weight, style_weight, style_blend_weights, tv_weight, 
        learning_rate, print_iterations=None, checkpoint_iterations=None): 
    """ 
    Stylize images. 
    This function yields tuples (iteration, image); `iteration` is None 
    if this is the final image (the last iteration).  Other tuples are yielded 
    every `checkpoint_iterations` iterations. 
    :rtype: iterator[tuple[int|None,image]] 
    """ 
    shape = (1,) + content.shape 
    style_shapes = [(1,) + style.shape for style in styles] 
    content_features = {} 
    style_features = [{} for _ in styles] 
    # compute content features in feedforward mode 
    g = tf.Graph() 
    with g.as_default(), g.device('/cpu:0'), tf.Session() as sess: 
        image = tf.placeholder('float', shape=shape) 
        net, mean_pixel = vgg.net(network, image) 
        content_pre = np.array([vgg.preprocess(content, mean_pixel)]) 
        content_features[CONTENT_LAYER] = net[CONTENT_LAYER].eval( 
                feed_dict={image: content_pre}) 
    # compute style features in feedforward mode 
    for i in range(len(styles)): 
        g = tf.Graph() 
        with g.as_default(), g.device('/cpu:0'), tf.Session() as sess: 
            image = tf.placeholder('float', shape=style_shapes[i]) 
            net, _ = vgg.net(network, image) 
            style_pre = np.array([vgg.preprocess(styles[i], mean_pixel)]) 
            for layer in STYLE_LAYERS: 
                features = net[layer].eval(feed_dict={image: style_pre}) 
                features = np.reshape(features, (-1, features.shape[3])) 
                gram = np.matmul(features.T, features) / features.size 
                style_features[i][layer] = gram 
    # make stylized image using backpropogation 
    with tf.Graph().as_default(): 
        if initial is None: 
            noise = np.random.normal(size=shape, scale=np.std(content) * 0.1) 
            initial = tf.random_normal(shape) * 0.256 
        else: 
            initial = np.array([vgg.preprocess(initial, mean_pixel)]) 
            initial = initial.astype('float32') 
        image = tf.Variable(initial) 
        net, _ = vgg.net(network, image) 
        # content loss 
        content_loss = content_weight * (2 * tf.nn.l2_loss( 
                net[CONTENT_LAYER] - content_features[CONTENT_LAYER]) / 
                content_features[CONTENT_LAYER].size) 
        # style loss 
        style_loss = 0 
        for i in range(len(styles)): 
            style_losses = [] 
            for style_layer in STYLE_LAYERS: 
                layer = net[style_layer] 
                _, height, width, number = map(lambda i: i.value, layer.get_shape()) 
                size = height * width * number 
                feats = tf.reshape(layer, (-1, number)) 
                gram = tf.matmul(tf.transpose(feats), feats) / size 
                style_gram = style_features[i][style_layer] 
                style_losses.append(2 * tf.nn.l2_loss(gram - style_gram) / style_gram.size) 
            style_loss += style_weight * style_blend_weights[i] * reduce(tf.add, style_losses) 
        # total variation denoising 
        tv_y_size = _tensor_size(image[:,1:,:,:]) 
        tv_x_size = _tensor_size(image[:,:,1:,:]) 
        tv_loss = tv_weight * 2 * ( 
                (tf.nn.l2_loss(image[:,1:,:,:] - image[:,:shape[1]-1,:,:]) / 
                    tv_y_size) + 
                (tf.nn.l2_loss(image[:,:,1:,:] - image[:,:,:shape[2]-1,:]) / 
                    tv_x_size)) 
        # overall loss 
        loss = content_loss + style_loss + tv_loss 
        # optimizer setup 
        train_step = tf.train.AdamOptimizer(learning_rate).minimize(loss) 
        def print_progress(i, last=False): 
            stderr.write('Iteration %d/%d\n' % (i + 1, iterations)) 
            if last or (print_iterations and i % print_iterations == 0): 
                stderr.write('  content loss: %g\n' % content_loss.eval()) 
                stderr.write('    style loss: %g\n' % style_loss.eval()) 
                stderr.write('       tv loss: %g\n' % tv_loss.eval()) 
                stderr.write('    total loss: %g\n' % loss.eval()) 
        # optimization 
        best_loss = float('inf') 
        best = None 
        with tf.Session() as sess: 
            sess.run(tf.initialize_all_variables()) 
            for i in range(iterations): 
                last_step = (i == iterations - 1) 
                print_progress(i, last=last_step) 
                train_step.run() 
                if (checkpoint_iterations and i % checkpoint_iterations == 0) or last_step: 
                    this_loss = loss.eval() 
                    if this_loss < best_loss: 
                        best_loss = this_loss 
                        best = image.eval() 
                    yield ( 
                        (None if last_step else i), 
                        vgg.unprocess(best.reshape(shape[1:]), mean_pixel) 
                    ) 
def _tensor_size(tensor): 
    from operator import mul 
    return reduce(mul, (d.value for d in tensor.get_shape()), 1) 
 vgg.py 
import tensorflow as tf 
import numpy as np 
import scipy.io 
def net(data_path, input_image): 
    layers = ( 
        'conv1_1', 'relu1_1', 'conv1_2', 'relu1_2', 'pool1', 
        'conv2_1', 'relu2_1', 'conv2_2', 'relu2_2', 'pool2', 
        'conv3_1', 'relu3_1', 'conv3_2', 'relu3_2', 'conv3_3', 
        'relu3_3', 'conv3_4', 'relu3_4', 'pool3', 
        'conv4_1', 'relu4_1', 'conv4_2', 'relu4_2', 'conv4_3', 
        'relu4_3', 'conv4_4', 'relu4_4', 'pool4', 
        'conv5_1', 'relu5_1', 'conv5_2', 'relu5_2', 'conv5_3', 
        'relu5_3', 'conv5_4', 'relu5_4' 
    ) 
    data = scipy.io.loadmat(data_path) 
    mean = data['normalization'][0][0][0] 
    mean_pixel = np.mean(mean, axis=(0, 1)) 
    weights = data['layers'][0] 
    net = {} 
    current = input_image 
    for i, name in enumerate(layers): 
        kind = name[:4] 
        if kind == 'conv': 
            kernels, bias = weights[i][0][0][0][0] 
            # matconvnet: weights are [width, height, in_channels, out_channels] 
            # tensorflow: weights are [height, width, in_channels, out_channels] 
            kernels = np.transpose(kernels, (1, 0, 2, 3)) 
            bias = bias.reshape(-1) 
            current = _conv_layer(current, kernels, bias) 
        elif kind == 'relu': 
            current = tf.nn.relu(current) 
        elif kind == 'pool': 
            current = _pool_layer(current) 
        net[name] = current 
    assert len(net) == len(layers) 
    return net, mean_pixel 
def _conv_layer(input, weights, bias): 
    conv = tf.nn.conv2d(input, tf.constant(weights), strides=(1, 1, 1, 1), 
            padding='SAME') 
    return tf.nn.bias_add(conv, bias) 
def _pool_layer(input): 
    return tf.nn.max_pool(input, ksize=(1, 2, 2, 1), strides=(1, 2, 2, 1), 
            padding='SAME') 
def preprocess(image, mean_pixel): 
    return image - mean_pixel 
def unprocess(image, mean_pixel): 
    return image + mean_pixel

import os
from data import DIV2K
from model.srgan import generator, discriminator
from train import SrganTrainer, SrganGeneratorTrainer

weights_dir = 'weights'
weights_file = lambda filename: os.path.join(weights_dir, filename)
os.makedirs(weights_dir, exist_ok=True)

div2k_train = DIV2K(scale=4, subset='train', downgrade='bicubic')
div2k_valid = DIV2K(scale=4, subset='valid', downgrade='bicubic')

train_ds = div2k_train.dataset(batch_size=16, random_transform=True)
valid_ds = div2k_valid.dataset(batch_size=16, random_transform=True, repeat_count=1)

pre_trainer = SrganGeneratorTrainer(model=generator(), checkpoint_dir=f'.ckpt/pre_generator')
pre_trainer.train(train_ds,
                  valid_ds.take(10),
                  steps=1000000, 
                  evaluate_every=1000, 
                  save_best_only=False)
pre_trainer.model.save_weights(weights_file('pre_generator.h5'))

gan_generator = generator()
gan_generator.load_weights(weights_file('pre_generator.h5'))
gan_trainer = SrganTrainer(generator=gan_generator, discriminator=discriminator())
gan_trainer.train(train_ds, steps=200000)

gan_trainer.generator.save_weights(weights_file('gan_generator.h5'))
gan_trainer.discriminator.save_weights(weights_file('gan_discriminator.h5'))

Widget buildImage1() {
     return Expanded(
         child: Container(
             width: 200, 
             height: 200, 
             child: img1
         )
     );
 }

var img1 = Image.asset('assets/place_holder_image.png');

flutter:
    assets:
        - assets/place_holder_image.png

Widget buildImageOutput() {
     return Expanded(
         child: Container(
             width: 200, 
             height: 200, 
             child: imageOutput
         )
     );
 }

Widget imageOutput = Image.asset('assets/place_holder_image.png');

Widget buildPickImageButton() {
     return Container(
         margin: EdgeInsets.all(8),
         child: FloatingActionButton(
             elevation: 8,
             child: Icon(Icons.camera_alt),
             onPressed: () => {},
        )
     );
 }

@override
 Widget build(BuildContext context) {
     return Scaffold(
         appBar: AppBar(title: Text('Image Super Resolution')),
         body: Container(
             child: Column(
                 crossAxisAlignment: CrossAxisAlignment.center,
                 children: <Widget>[
                     buildImageInput(),
                     buildImageOutput(),
                     buildPickImageButton()
                 ]
             )
         )
     );
 }