Ejemplo n.º 1
0
def run():
    model_name = 'alexnet'
    directory_caffe = './caffemodel'
    directory_theano = './theanomodel'
    url_prototxt = 'https://raw.githubusercontent.com/BVLC/caffe/master/models/bvlc_alexnet/deploy.prototxt'
    url_caffemodel = 'http://dl.caffe.berkeleyvision.org/bvlc_alexnet.caffemodel'
    filename_prototxt = '%s/%s.prototxt' % (directory_caffe, model_name)
    filename_caffemodel = '%s/%s.caffemodel' % (directory_caffe, model_name)
    filename_theanomodel = '%s/%s.model' % (directory_theano, model_name)

    # download caffemodel
    print 'downloading caffemodel'
    if not os.path.exists(directory_caffe):
        os.mkdir(directory_caffe)
    if not os.path.exists(filename_prototxt):
        p = subprocess.Popen(('wget', url_prototxt, '-O', filename_prototxt))
        p.wait()
    if not os.path.exists(filename_caffemodel):
        p = subprocess.Popen((
            'wget',
            url_caffemodel,
            '-O',
            filename_caffemodel,
        ))
        p.wait()

    # load caffe model
    print 'loading caffe model'
    model_caffe = caffe.Net(filename_prototxt, filename_caffemodel, True)
    conv1_W = theano.shared(model_caffe.params['conv1'][0].data[:, :, ::-1, ::-1])
    conv2_W = theano.shared(model_caffe.params['conv2'][0].data[:, :, ::-1, ::-1])
    conv3_W = theano.shared(model_caffe.params['conv3'][0].data[:, :, ::-1, ::-1])
    conv4_W = theano.shared(model_caffe.params['conv4'][0].data[:, :, ::-1, ::-1])
    conv5_W = theano.shared(model_caffe.params['conv5'][0].data[:, :, ::-1, ::-1])
    conv1_b = theano.shared(model_caffe.params['conv1'][1].data.squeeze())
    conv2_b = theano.shared(model_caffe.params['conv2'][1].data.squeeze())
    conv3_b = theano.shared(model_caffe.params['conv3'][1].data.squeeze())
    conv4_b = theano.shared(model_caffe.params['conv4'][1].data.squeeze())
    conv5_b = theano.shared(model_caffe.params['conv5'][1].data.squeeze())
    fc6_W = theano.shared(model_caffe.params['fc6'][0].data.squeeze())
    fc7_W = theano.shared(model_caffe.params['fc7'][0].data.squeeze())
    fc8_W = theano.shared(model_caffe.params['fc8'][0].data.squeeze())
    fc6_b = theano.shared(model_caffe.params['fc6'][1].data.squeeze())
    fc7_b = theano.shared(model_caffe.params['fc7'][1].data.squeeze())
    fc8_b = theano.shared(model_caffe.params['fc8'][1].data.squeeze())

    # make theano model
    print 'building theano model'
    model_theano = collections.OrderedDict()
    model_theano['data'] = T.tensor4()
    model_theano['conv1'] = layers.convolution_layer(model_theano['data'], conv1_W, conv1_b, subsample=(4, 4))
    model_theano['relu1'] = layers.relu_layer(model_theano['conv1'])
    model_theano['norm1'] = layers.lrn_layer(model_theano['relu1'])
    model_theano['pool1'] = layers.pooling_layer(model_theano['norm1'])
    model_theano['conv2'] = layers.convolution_layer(model_theano['pool1'], conv2_W, conv2_b, border='same', group=2)
    model_theano['relu2'] = layers.relu_layer(model_theano['conv2'])
    model_theano['norm2'] = layers.lrn_layer(model_theano['relu2'])
    model_theano['pool2'] = layers.pooling_layer(model_theano['norm2'])
    model_theano['conv3'] = layers.convolution_layer(model_theano['pool2'], conv3_W, conv3_b, border='same')
    model_theano['relu3'] = layers.relu_layer(model_theano['conv3'])
    model_theano['conv4'] = layers.convolution_layer(model_theano['relu3'], conv4_W, conv4_b, border='same', group=2)
    model_theano['relu4'] = layers.relu_layer(model_theano['conv4'])
    model_theano['conv5'] = layers.convolution_layer(model_theano['relu4'], conv5_W, conv5_b, border='same', group=2)
    model_theano['relu5'] = layers.relu_layer(model_theano['conv5'])
    model_theano['pool5'] = layers.pooling_layer(model_theano['relu5'])
    model_theano['fc6'] = layers.inner_product_layer(model_theano['pool5'], fc6_W, fc6_b)
    model_theano['relu6'] = layers.relu_layer(model_theano['fc6'])
    model_theano['fc7'] = layers.inner_product_layer(model_theano['relu6'], fc7_W, fc7_b)
    model_theano['relu7'] = layers.relu_layer(model_theano['fc7'])
    model_theano['fc8'] = layers.inner_product_layer(model_theano['relu7'], fc8_W, fc8_b)
    model_theano['prob'] = layers.softmax_layer(model_theano['fc8'])

    # check
    print 'checking model'
    data = np.random.randn(*model_caffe.blobs['data'].data.shape)
    data = data.astype(np.float32) * 10
    model_caffe.blobs['data'].data[:] = data
    model_caffe.forward()
    theano_output = theano.function(
        [model_theano['data']],
        model_theano['prob'],
    )(data)
    error = (
        theano_output.squeeze() -
        model_caffe.blobs['prob'].data.squeeze()
    ).max()
    assert error < 1e-6

    # save
    print 'saving'
    if not os.path.exists(directory_theano):
        os.mkdir(directory_theano)
    sys.setrecursionlimit(100000)
    pickle.dump(
        model_theano, 
        open(filename_theanomodel, 'wb'),        
        protocol=pickle.HIGHEST_PROTOCOL,
    )
    print 'done'
Ejemplo n.º 2
0
def run():
    model_name = "vggnet"
    directory_caffe = "./caffemodel"
    directory_theano = "./theanomodel"
    url_prototxt = "https://gist.githubusercontent.com/ksimonyan/3785162f95cd2d5fee77/raw/f02f8769e64494bcd3d7e97d5d747ac275825721/VGG_ILSVRC_19_layers_deploy.prototxt"
    url_caffemodel = "http://www.robots.ox.ac.uk/~vgg/software/very_deep/caffe/VGG_ILSVRC_19_layers.caffemodel"
    filename_prototxt = "%s/%s.prototxt" % (directory_caffe, model_name)
    filename_caffemodel = "%s/%s.caffemodel" % (directory_caffe, model_name)
    filename_theanomodel = "%s/%s.model" % (directory_theano, model_name)

    # download caffemodel
    print "downloading caffemodel"
    if not os.path.exists(directory_caffe):
        os.mkdir(directory_caffe)
    if not os.path.exists(filename_prototxt):
        p = subprocess.Popen(("wget", url_prototxt, "-O", filename_prototxt))
        p.wait()
    if not os.path.exists(filename_caffemodel):
        p = subprocess.Popen(("wget", url_caffemodel, "-O", filename_caffemodel))
        p.wait()

    # load caffe model
    model_caffe = caffe.Net(filename_prototxt, filename_caffemodel, True)
    conv1_1_W = theano.shared(model_caffe.params["conv1_1"][0].data[:, :, ::-1, ::-1])
    conv1_2_W = theano.shared(model_caffe.params["conv1_2"][0].data[:, :, ::-1, ::-1])
    conv2_1_W = theano.shared(model_caffe.params["conv2_1"][0].data[:, :, ::-1, ::-1])
    conv2_2_W = theano.shared(model_caffe.params["conv2_2"][0].data[:, :, ::-1, ::-1])
    conv3_1_W = theano.shared(model_caffe.params["conv3_1"][0].data[:, :, ::-1, ::-1])
    conv3_2_W = theano.shared(model_caffe.params["conv3_2"][0].data[:, :, ::-1, ::-1])
    conv3_3_W = theano.shared(model_caffe.params["conv3_3"][0].data[:, :, ::-1, ::-1])
    conv3_4_W = theano.shared(model_caffe.params["conv3_4"][0].data[:, :, ::-1, ::-1])
    conv4_1_W = theano.shared(model_caffe.params["conv4_1"][0].data[:, :, ::-1, ::-1])
    conv4_2_W = theano.shared(model_caffe.params["conv4_2"][0].data[:, :, ::-1, ::-1])
    conv4_3_W = theano.shared(model_caffe.params["conv4_3"][0].data[:, :, ::-1, ::-1])
    conv4_4_W = theano.shared(model_caffe.params["conv4_4"][0].data[:, :, ::-1, ::-1])
    conv5_1_W = theano.shared(model_caffe.params["conv5_1"][0].data[:, :, ::-1, ::-1])
    conv5_2_W = theano.shared(model_caffe.params["conv5_2"][0].data[:, :, ::-1, ::-1])
    conv5_3_W = theano.shared(model_caffe.params["conv5_3"][0].data[:, :, ::-1, ::-1])
    conv5_4_W = theano.shared(model_caffe.params["conv5_4"][0].data[:, :, ::-1, ::-1])
    conv1_1_b = theano.shared(model_caffe.params["conv1_1"][1].data.squeeze())
    conv1_2_b = theano.shared(model_caffe.params["conv1_2"][1].data.squeeze())
    conv2_1_b = theano.shared(model_caffe.params["conv2_1"][1].data.squeeze())
    conv2_2_b = theano.shared(model_caffe.params["conv2_2"][1].data.squeeze())
    conv3_1_b = theano.shared(model_caffe.params["conv3_1"][1].data.squeeze())
    conv3_2_b = theano.shared(model_caffe.params["conv3_2"][1].data.squeeze())
    conv3_3_b = theano.shared(model_caffe.params["conv3_3"][1].data.squeeze())
    conv3_4_b = theano.shared(model_caffe.params["conv3_4"][1].data.squeeze())
    conv4_1_b = theano.shared(model_caffe.params["conv4_1"][1].data.squeeze())
    conv4_2_b = theano.shared(model_caffe.params["conv4_2"][1].data.squeeze())
    conv4_3_b = theano.shared(model_caffe.params["conv4_3"][1].data.squeeze())
    conv4_4_b = theano.shared(model_caffe.params["conv4_4"][1].data.squeeze())
    conv5_1_b = theano.shared(model_caffe.params["conv5_1"][1].data.squeeze())
    conv5_2_b = theano.shared(model_caffe.params["conv5_2"][1].data.squeeze())
    conv5_3_b = theano.shared(model_caffe.params["conv5_3"][1].data.squeeze())
    conv5_4_b = theano.shared(model_caffe.params["conv5_4"][1].data.squeeze())
    fc6_W = theano.shared(model_caffe.params["fc6"][0].data.squeeze())
    fc7_W = theano.shared(model_caffe.params["fc7"][0].data.squeeze())
    fc8_W = theano.shared(model_caffe.params["fc8"][0].data.squeeze())
    fc6_b = theano.shared(model_caffe.params["fc6"][1].data.squeeze())
    fc7_b = theano.shared(model_caffe.params["fc7"][1].data.squeeze())
    fc8_b = theano.shared(model_caffe.params["fc8"][1].data.squeeze())

    # make theano model
    model_theano = collections.OrderedDict()
    model_theano["data"] = T.tensor4()

    model_theano["conv1_1"] = layers.convolution_layer(model_theano["data"], conv1_1_W, conv1_1_b, border="same")
    model_theano["relu1_1"] = layers.relu_layer(model_theano["conv1_1"])
    model_theano["conv1_2"] = layers.convolution_layer(model_theano["relu1_1"], conv1_2_W, conv1_2_b, border="same")
    model_theano["relu1_2"] = layers.relu_layer(model_theano["conv1_2"])
    model_theano["pool1"] = layers.pooling_layer(model_theano["relu1_2"], size=(2, 2), stride=(2, 2))

    model_theano["conv2_1"] = layers.convolution_layer(model_theano["pool1"], conv2_1_W, conv2_1_b, border="same")
    model_theano["relu2_1"] = layers.relu_layer(model_theano["conv2_1"])
    model_theano["conv2_2"] = layers.convolution_layer(model_theano["relu2_1"], conv2_2_W, conv2_2_b, border="same")
    model_theano["relu2_2"] = layers.relu_layer(model_theano["conv2_2"])
    model_theano["pool2"] = layers.pooling_layer(model_theano["relu2_2"], size=(2, 2), stride=(2, 2))

    model_theano["conv3_1"] = layers.convolution_layer(model_theano["pool2"], conv3_1_W, conv3_1_b, border="same")
    model_theano["relu3_1"] = layers.relu_layer(model_theano["conv3_1"])
    model_theano["conv3_2"] = layers.convolution_layer(model_theano["relu3_1"], conv3_2_W, conv3_2_b, border="same")
    model_theano["relu3_2"] = layers.relu_layer(model_theano["conv3_2"])
    model_theano["conv3_3"] = layers.convolution_layer(model_theano["relu3_2"], conv3_3_W, conv3_3_b, border="same")
    model_theano["relu3_3"] = layers.relu_layer(model_theano["conv3_3"])
    model_theano["conv3_4"] = layers.convolution_layer(model_theano["relu3_3"], conv3_4_W, conv3_4_b, border="same")
    model_theano["relu3_4"] = layers.relu_layer(model_theano["conv3_4"])
    model_theano["pool3"] = layers.pooling_layer(model_theano["relu3_4"], size=(2, 2), stride=(2, 2))

    model_theano["conv4_1"] = layers.convolution_layer(model_theano["pool3"], conv4_1_W, conv4_1_b, border="same")
    model_theano["relu4_1"] = layers.relu_layer(model_theano["conv4_1"])
    model_theano["conv4_2"] = layers.convolution_layer(model_theano["relu4_1"], conv4_2_W, conv4_2_b, border="same")
    model_theano["relu4_2"] = layers.relu_layer(model_theano["conv4_2"])
    model_theano["conv4_3"] = layers.convolution_layer(model_theano["relu4_2"], conv4_3_W, conv4_3_b, border="same")
    model_theano["relu4_3"] = layers.relu_layer(model_theano["conv4_3"])
    model_theano["conv4_4"] = layers.convolution_layer(model_theano["relu4_3"], conv4_4_W, conv4_4_b, border="same")
    model_theano["relu4_4"] = layers.relu_layer(model_theano["conv4_4"])
    model_theano["pool4"] = layers.pooling_layer(model_theano["relu4_4"], size=(2, 2), stride=(2, 2))

    model_theano["conv5_1"] = layers.convolution_layer(model_theano["pool4"], conv5_1_W, conv5_1_b, border="same")
    model_theano["relu5_1"] = layers.relu_layer(model_theano["conv5_1"])
    model_theano["conv5_2"] = layers.convolution_layer(model_theano["relu5_1"], conv5_2_W, conv5_2_b, border="same")
    model_theano["relu5_2"] = layers.relu_layer(model_theano["conv5_2"])
    model_theano["conv5_3"] = layers.convolution_layer(model_theano["relu5_2"], conv5_3_W, conv5_3_b, border="same")
    model_theano["relu5_3"] = layers.relu_layer(model_theano["conv5_3"])
    model_theano["conv5_4"] = layers.convolution_layer(model_theano["relu5_3"], conv5_4_W, conv5_4_b, border="same")
    model_theano["relu5_4"] = layers.relu_layer(model_theano["conv5_4"])
    model_theano["pool5"] = layers.pooling_layer(model_theano["relu5_4"], size=(2, 2), stride=(2, 2))

    model_theano["fc6"] = layers.inner_product_layer(model_theano["pool5"], fc6_W, fc6_b)
    model_theano["relu6"] = layers.relu_layer(model_theano["fc6"])
    model_theano["fc7"] = layers.inner_product_layer(model_theano["relu6"], fc7_W, fc7_b)
    model_theano["relu7"] = layers.relu_layer(model_theano["fc7"])
    model_theano["fc8"] = layers.inner_product_layer(model_theano["relu7"], fc8_W, fc8_b)
    model_theano["prob"] = layers.softmax_layer(model_theano["fc8"])

    # check
    data = np.random.randn(*model_caffe.blobs["data"].data.shape).astype(np.float32) * 10
    model_caffe.blobs["data"].data[:] = data
    model_caffe.forward()
    theano_output = theano.function([model_theano["data"]], model_theano["prob"])(data)
    error = (theano_output.squeeze() - model_caffe.blobs["prob"].data.squeeze()).max()
    assert error < 1e-6

    # save
    print "saving"
    if not os.path.exists(directory_theano):
        os.mkdir(directory_theano)
    sys.setrecursionlimit(100000)
    pickle.dump(model_theano, open(filename_theanomodel, "wb"), protocol=pickle.HIGHEST_PROTOCOL)
    print "done"
Ejemplo n.º 3
0
def run():
    model_name = 'alexnet'
    directory_caffe = './caffemodel'
    directory_theano = './theanomodel'
    url_prototxt = 'https://raw.githubusercontent.com/BVLC/caffe/master/models/bvlc_alexnet/deploy.prototxt'
    url_caffemodel = 'http://dl.caffe.berkeleyvision.org/bvlc_alexnet.caffemodel'
    filename_prototxt = '%s/%s.prototxt' % (directory_caffe, model_name)
    filename_caffemodel = '%s/%s.caffemodel' % (directory_caffe, model_name)
    filename_theanomodel = '%s/%s.model' % (directory_theano, model_name)

    # download caffemodel
    print 'downloading caffemodel'
    if not os.path.exists(directory_caffe):
        os.mkdir(directory_caffe)
    if not os.path.exists(filename_prototxt):
        p = subprocess.Popen(('wget', url_prototxt, '-O', filename_prototxt))
        p.wait()
    if not os.path.exists(filename_caffemodel):
        p = subprocess.Popen((
            'wget',
            url_caffemodel,
            '-O',
            filename_caffemodel,
        ))
        p.wait()

    # load caffe model
    print 'loading caffe model'
    model_caffe = caffe.Net(filename_prototxt, filename_caffemodel, True)
    conv1_W = theano.shared(
        model_caffe.params['conv1'][0].data[:, :, ::-1, ::-1])
    conv2_W = theano.shared(
        model_caffe.params['conv2'][0].data[:, :, ::-1, ::-1])
    conv3_W = theano.shared(
        model_caffe.params['conv3'][0].data[:, :, ::-1, ::-1])
    conv4_W = theano.shared(
        model_caffe.params['conv4'][0].data[:, :, ::-1, ::-1])
    conv5_W = theano.shared(
        model_caffe.params['conv5'][0].data[:, :, ::-1, ::-1])
    conv1_b = theano.shared(model_caffe.params['conv1'][1].data.squeeze())
    conv2_b = theano.shared(model_caffe.params['conv2'][1].data.squeeze())
    conv3_b = theano.shared(model_caffe.params['conv3'][1].data.squeeze())
    conv4_b = theano.shared(model_caffe.params['conv4'][1].data.squeeze())
    conv5_b = theano.shared(model_caffe.params['conv5'][1].data.squeeze())
    fc6_W = theano.shared(model_caffe.params['fc6'][0].data.squeeze())
    fc7_W = theano.shared(model_caffe.params['fc7'][0].data.squeeze())
    fc8_W = theano.shared(model_caffe.params['fc8'][0].data.squeeze())
    fc6_b = theano.shared(model_caffe.params['fc6'][1].data.squeeze())
    fc7_b = theano.shared(model_caffe.params['fc7'][1].data.squeeze())
    fc8_b = theano.shared(model_caffe.params['fc8'][1].data.squeeze())

    # make theano model
    print 'building theano model'
    model_theano = collections.OrderedDict()
    model_theano['data'] = T.tensor4()
    model_theano['conv1'] = layers.convolution_layer(model_theano['data'],
                                                     conv1_W,
                                                     conv1_b,
                                                     subsample=(4, 4))
    model_theano['relu1'] = layers.relu_layer(model_theano['conv1'])
    model_theano['norm1'] = layers.lrn_layer(model_theano['relu1'])
    model_theano['pool1'] = layers.pooling_layer(model_theano['norm1'])
    model_theano['conv2'] = layers.convolution_layer(model_theano['pool1'],
                                                     conv2_W,
                                                     conv2_b,
                                                     border='same',
                                                     group=2)
    model_theano['relu2'] = layers.relu_layer(model_theano['conv2'])
    model_theano['norm2'] = layers.lrn_layer(model_theano['relu2'])
    model_theano['pool2'] = layers.pooling_layer(model_theano['norm2'])
    model_theano['conv3'] = layers.convolution_layer(model_theano['pool2'],
                                                     conv3_W,
                                                     conv3_b,
                                                     border='same')
    model_theano['relu3'] = layers.relu_layer(model_theano['conv3'])
    model_theano['conv4'] = layers.convolution_layer(model_theano['relu3'],
                                                     conv4_W,
                                                     conv4_b,
                                                     border='same',
                                                     group=2)
    model_theano['relu4'] = layers.relu_layer(model_theano['conv4'])
    model_theano['conv5'] = layers.convolution_layer(model_theano['relu4'],
                                                     conv5_W,
                                                     conv5_b,
                                                     border='same',
                                                     group=2)
    model_theano['relu5'] = layers.relu_layer(model_theano['conv5'])
    model_theano['pool5'] = layers.pooling_layer(model_theano['relu5'])
    model_theano['fc6'] = layers.inner_product_layer(model_theano['pool5'],
                                                     fc6_W, fc6_b)
    model_theano['relu6'] = layers.relu_layer(model_theano['fc6'])
    model_theano['fc7'] = layers.inner_product_layer(model_theano['relu6'],
                                                     fc7_W, fc7_b)
    model_theano['relu7'] = layers.relu_layer(model_theano['fc7'])
    model_theano['fc8'] = layers.inner_product_layer(model_theano['relu7'],
                                                     fc8_W, fc8_b)
    model_theano['prob'] = layers.softmax_layer(model_theano['fc8'])

    # check
    print 'checking model'
    data = np.random.randn(*model_caffe.blobs['data'].data.shape)
    data = data.astype(np.float32) * 10
    model_caffe.blobs['data'].data[:] = data
    model_caffe.forward()
    theano_output = theano.function(
        [model_theano['data']],
        model_theano['prob'],
    )(data)
    error = (theano_output.squeeze() -
             model_caffe.blobs['prob'].data.squeeze()).max()
    assert error < 1e-6

    # save
    print 'saving'
    if not os.path.exists(directory_theano):
        os.mkdir(directory_theano)
    sys.setrecursionlimit(100000)
    pickle.dump(
        model_theano,
        open(filename_theanomodel, 'wb'),
        protocol=pickle.HIGHEST_PROTOCOL,
    )
    print 'done'
Ejemplo n.º 4
0
def build_encoder(x):
    # The encoder uses the deep residual network.
    outputs = []
    pooling = [1, 2, 2, 1]

    shape = x.get_shape().as_list()
    bs = shape[0]
    seq = shape[1]
    temp_shape = [bs * seq] + shape[2:]
    x = tf.reshape(x, temp_shape)
    # print x.get_shape().as_list()

    # layer 0
    with tf.variable_scope("encoder_layer0", reuse=tf.AUTO_REUSE):
        conv0_0 = layers.conv_layer(name="conv0_0",
                                    x=x,
                                    filter_shape=layers.create_variable(
                                        "filter0_0", shape=[7, 7, 3, 96]))
        conv0_0 = layers.batch_normalization(conv0_0, "conv0_0_bn")
        conv0_0 = layers.relu_layer(conv0_0)
        conv0_1 = layers.conv_layer(name="conv0_1",
                                    x=conv0_0,
                                    filter_shape=layers.create_variable(
                                        "filter0_1", shape=[3, 3, 96, 96]))
        conv0_1 = layers.batch_normalization(conv0_1, "conv0_1_bn")
        conv0_1 = layers.relu_layer(conv0_1)
        shortcut0 = layers.conv_layer(name="shortcut",
                                      x=x,
                                      filter_shape=layers.create_variable(
                                          "filter0_2", shape=[1, 1, 3, 96]))
        shortcut0 = layers.batch_normalization(shortcut0, "shortcut0_bn")
        shortcut0 = layers.relu_layer(shortcut0)
        layer0 = layers.pooling_layer("pooling", conv0_1 + shortcut0, pooling)
        outputs.append(layer0)  # [bs * size, 64, 64, 96]

    # layer 1
    with tf.variable_scope("encoder_layer1", reuse=tf.AUTO_REUSE):
        conv1_0 = layers.conv_layer(name="conv1_0",
                                    x=layer0,
                                    filter_shape=layers.create_variable(
                                        "filter1_0", shape=[3, 3, 96, 128]))
        conv1_0 = layers.batch_normalization(conv1_0, "conv1_0_bn")
        conv1_0 = layers.relu_layer(conv1_0)
        conv1_1 = layers.conv_layer(name="conv1_1",
                                    x=conv1_0,
                                    filter_shape=layers.create_variable(
                                        "filter1_1", shape=[3, 3, 128, 128]))
        conv1_1 = layers.batch_normalization(conv1_1, "conv1_1_bn")
        conv1_1 = layers.relu_layer(conv1_1)
        shortcut1 = layers.conv_layer(name="shortcut",
                                      x=layer0,
                                      filter_shape=layers.create_variable(
                                          "filter1_2", shape=[1, 1, 96, 128]))
        shortcut1 = layers.batch_normalization(shortcut1, "shortcut1_bn")
        shortcut1 = layers.relu_layer(shortcut1)
        layer1 = layers.pooling_layer("pooling", conv1_1 + shortcut1, pooling)
        outputs.append(layer1)  # [bs * size, 32, 32, 128]

    # layer 2
    with tf.variable_scope("encoder_layer2", reuse=tf.AUTO_REUSE):
        conv2_0 = layers.conv_layer(name="conv2_0",
                                    x=layer1,
                                    filter_shape=layers.create_variable(
                                        "filter2_0", shape=[3, 3, 128, 256]))
        conv2_0 = layers.batch_normalization(conv2_0, "conv2_0_bn")
        conv2_0 = layers.relu_layer(conv2_0)
        conv2_1 = layers.conv_layer(name="conv2_1",
                                    x=conv2_0,
                                    filter_shape=layers.create_variable(
                                        "filter2_1", shape=[3, 3, 256, 256]))
        conv2_1 = layers.batch_normalization(conv2_1, "conv2_1_bn")
        conv2_1 = layers.relu_layer(conv2_1)
        shortcut2 = layers.conv_layer(name="shortcut",
                                      x=layer1,
                                      filter_shape=layers.create_variable(
                                          "filter2_2", shape=[1, 1, 128, 256]))
        shortcut2 = layers.batch_normalization(shortcut2, "shortcut2_bn")
        shortcut2 = layers.relu_layer(shortcut2)
        layer2 = layers.pooling_layer("pooling", conv2_1 + shortcut2, pooling)
        outputs.append(layer2)  # [bs * size, 16, 16, 256]

    # layer 3
    with tf.variable_scope("encoder_layer3", reuse=tf.AUTO_REUSE):
        conv3_0 = layers.conv_layer(name="conv3_0",
                                    x=layer2,
                                    filter_shape=layers.create_variable(
                                        "filter3_0", shape=[3, 3, 256, 256]))
        conv3_0 = layers.batch_normalization(conv3_0, "conv3_0_bn")
        conv3_0 = layers.relu_layer(conv3_0)
        conv3_1 = layers.conv_layer(name="conv3_1",
                                    x=conv3_0,
                                    filter_shape=layers.create_variable(
                                        "filter3_1", shape=[3, 3, 256, 256]))
        conv3_1 = layers.batch_normalization(conv3_1, "conv3_1_bn")
        conv3_1 = layers.relu_layer(conv3_1)
        layer3 = layers.pooling_layer("pooling", conv3_1, pooling)
        outputs.append(layer3)  # [bs * size, 8, 8, 256]

    # layer 4
    with tf.variable_scope("encoder_layer4", reuse=tf.AUTO_REUSE):
        conv4_0 = layers.conv_layer(name="conv4_0",
                                    x=layer3,
                                    filter_shape=layers.create_variable(
                                        "filter4_0", shape=[3, 3, 256, 256]))
        conv4_0 = layers.batch_normalization(conv4_0, "conv4_0_bn")
        conv4_0 = layers.relu_layer(conv4_0)
        conv4_1 = layers.conv_layer(name="conv4_1",
                                    x=conv4_0,
                                    filter_shape=layers.create_variable(
                                        "filter4_1", shape=[3, 3, 256, 256]))
        conv4_1 = layers.batch_normalization(conv4_1, "conv4_1_bn")
        conv4_1 = layers.relu_layer(conv4_1)
        shortcut4 = layers.conv_layer(name="shortcut",
                                      x=layer3,
                                      filter_shape=layers.create_variable(
                                          "filter4_2", shape=[1, 1, 256, 256]))
        shortcut4 = layers.batch_normalization(shortcut4, "shortcut4_bn")
        shortcut4 = layers.relu_layer(shortcut4)
        layer4 = layers.pooling_layer("pooling", conv4_1 + shortcut4, pooling)
        outputs.append(layer4)  # [bs * size, 4, 4, 256]

    # layer 5
    with tf.variable_scope("encoder_layer5", reuse=tf.AUTO_REUSE):
        conv5_0 = layers.conv_layer(name="conv5_0",
                                    x=layer4,
                                    filter_shape=layers.create_variable(
                                        "filter5_0", shape=[3, 3, 256, 256]))
        conv5_0 = layers.batch_normalization(conv5_0, "conv5_0_bn")
        conv5_0 = layers.relu_layer(conv5_0)
        conv5_1 = layers.conv_layer(name="conv5_1",
                                    x=conv5_0,
                                    filter_shape=layers.create_variable(
                                        "filter5_1", shape=[3, 3, 256, 256]))
        conv5_1 = layers.batch_normalization(conv5_1, "conv5_1_bn")
        conv5_1 = layers.relu_layer(conv5_1)
        shortcut5 = layers.conv_layer(name="shortcut",
                                      x=layer4,
                                      filter_shape=layers.create_variable(
                                          "filter5_2", shape=[1, 1, 256, 256]))
        shortcut5 = layers.batch_normalization(shortcut5, "shortcut5_bn")
        shortcut5 = layers.relu_layer(shortcut5)
        layer5 = layers.pooling_layer("pooling", conv5_1 + shortcut5, pooling)
        outputs.append(layer5)  # [bs * size, 2, 2, 256]

    final_shape = [bs, seq, fc_layer_size[0]]
    # Flatten layer and fully connected layer
    flatten = layers.flatten_layer(layer5)
    outputs.append(flatten)

    with tf.variable_scope("fc_layer", reuse=tf.AUTO_REUSE):
        layer_fc = layers.fully_connected_layer(flatten, fc_layer_size[0],
                                                "fclayer_w", "fclayer_b")
        # layer_fc = layers.batch_normalization(layer_fc, "fc_bn")
        layer_fc = layers.relu_layer(layer_fc)
        outputs.append(layer_fc)  # [bs * size, 1024]

    # [bs, size, 1024]
    return tf.reshape(outputs[-1], final_shape)