Python chainer.functions.MaxPooling2D() Examples

The following are 5 code examples of chainer.functions.MaxPooling2D(). You can vote up the ones you like or vote down the ones you don't like, and go to the original project or source file by following the links above each example. You may also want to check out all available functions/classes of the module chainer.functions , or try the search function .
Example #1
Source File: spp_discriminator.py    From Semantic-Segmentation-using-Adversarial-Networks with MIT License 6 votes vote down vote up 
def __call__(self, x):
        h = F.relu(self.conv1_1(x))
        h = F.relu(self.conv1_2(h))
        h = F.max_pooling_2d(h, 2, stride=2)
        h = F.relu(self.conv2_1(h))
        h = F.relu(self.conv2_2(h))
        h = F.max_pooling_2d(h, 2, stride=2)
        h = F.relu(self.conv3_1(h))
        h = F.relu(self.conv3_2(h))
        h = F.max_pooling_2d(h, 2, stride=2)
        h = F.relu(self.conv4_1(h))
        h = F.relu(self.conv4_2(h))
        h = F.spatial_pyramid_pooling_2d(h, 3, F.MaxPooling2D)
        h = F.tanh(self.fc4(h))
        h = F.dropout(h, ratio=.5, train=self.train)
        h = F.tanh(self.fc5(h))
        h = F.dropout(h, ratio=.5, train=self.train)
        h = self.fc6(h)
        return h
Example #2
Source File: vgg.py    From chainer-visualization with MIT License 5 votes vote down vote up 
def __init__(self):
        super(VGG, self).__init__()
        with self.init_scope():
            self.conv1_1 = L.Convolution2D(3, 64, 3, stride=1, pad=1)
            self.conv1_2 = L.Convolution2D(64, 64, 3, stride=1, pad=1)
            self.conv2_1 = L.Convolution2D(64, 128, 3, stride=1, pad=1)
            self.conv2_2 = L.Convolution2D(128, 128, 3, stride=1, pad=1)
            self.conv3_1 = L.Convolution2D(128, 256, 3, stride=1, pad=1)
            self.conv3_2 = L.Convolution2D(256, 256, 3, stride=1, pad=1)
            self.conv3_3 = L.Convolution2D(256, 256, 3, stride=1, pad=1)
            self.conv4_1 = L.Convolution2D(256, 512, 3, stride=1, pad=1)
            self.conv4_2 = L.Convolution2D(512, 512, 3, stride=1, pad=1)
            self.conv4_3 = L.Convolution2D(512, 512, 3, stride=1, pad=1)
            self.conv5_1 = L.Convolution2D(512, 512, 3, stride=1, pad=1)
            self.conv5_2 = L.Convolution2D(512, 512, 3, stride=1, pad=1)
            self.conv5_3 = L.Convolution2D(512, 512, 3, stride=1, pad=1)
            self.fc6 = L.Linear(25088, 4096)
            self.fc7 = L.Linear(4096, 4096)
            self.fc8 = L.Linear(4096, 1000)

        # Keep track of the pooling indices inside each function instance
        self.conv_blocks = [
            [self.conv1_1, self.conv1_2],
            [self.conv2_1, self.conv2_2],
            [self.conv3_1, self.conv3_2, self.conv3_3],
            [self.conv4_1, self.conv4_2, self.conv4_3],
            [self.conv5_1, self.conv5_2, self.conv5_3]
        ]
        self.deconv_blocks = []
        self.mps = [F.MaxPooling2D(2, 2) for _ in self.conv_blocks]
Example #3
Source File: segnet.py    From chainer-segnet with MIT License 5 votes vote down vote up 
def __init__(self, in_channel, n_mid=64):
        w = math.sqrt(2)
        super(EncDec, self).__init__(
            enc=L.Convolution2D(in_channel, n_mid, 7, 1, 3, w),
            bn_m=L.BatchNormalization(n_mid),
            dec=L.Convolution2D(n_mid, n_mid, 7, 1, 3, w),
            bn_o=L.BatchNormalization(n_mid),
        )
        self.p = F.MaxPooling2D(2, 2, use_cudnn=False)
        self.inside = None
Example #4
Source File: test_upsampling_2d.py    From chainer-segnet with MIT License 5 votes vote down vote up 
def setUp(self):
        self.x = numpy.random.uniform(-1, 1, self.in_shape).astype('f')
        self.p = F.MaxPooling2D(2, 2, use_cudnn=False)
        self.pooled_y = self.p(self.x)
        self.gy = numpy.random.uniform(
            -1, 1, self.in_shape).astype(numpy.float32)
Example #5
Source File: VGG16.py    From deel with MIT License 5 votes vote down vote up 
def __init__(self, train=False):
        super(VGG16, self).__init__()
        self.trunk = [
            ('conv1_1', L.Convolution2D(3, 64, 3, 1, 1)),
            ('relu1_1', F.ReLU()),
            ('conv1_2', L.Convolution2D(64, 64, 3, 1, 1)),
            ('relu1_2', F.ReLU()),
            ('pool1', F.MaxPooling2D(2, 2)),
            ('conv2_1', L.Convolution2D(64, 128, 3, 1, 1)),
            ('relu2_1', F.ReLU()),
            ('conv2_2', L.Convolution2D(128, 128, 3, 1, 1)),
            ('relu2_2', F.ReLU()),
            ('pool2', F.MaxPooling2D(2, 2)),
            ('conv3_1', L.Convolution2D(128, 256, 3, 1, 1)),
            ('relu3_1', F.ReLU()),
            ('conv3_2', L.Convolution2D(256, 256, 3, 1, 1)),
            ('relu3_2', F.ReLU()),
            ('conv3_3', L.Convolution2D(256, 256, 3, 1, 1)),
            ('relu3_3', F.ReLU()),
            ('pool3', F.MaxPooling2D(2, 2)),
            ('conv4_1', L.Convolution2D(256, 512, 3, 1, 1)),
            ('relu4_1', F.ReLU()),
            ('conv4_2', L.Convolution2D(512, 512, 3, 1, 1)),
            ('relu4_2', F.ReLU()),
            ('conv4_3', L.Convolution2D(512, 512, 3, 1, 1)),
            ('relu4_3', F.ReLU()),
            ('pool4', F.MaxPooling2D(2, 2)),
            ('conv5_1', L.Convolution2D(512, 512, 3, 1, 1)),
            ('relu5_1', F.ReLU()),
            ('conv5_2', L.Convolution2D(512, 512, 3, 1, 1)),
            ('relu5_2', F.ReLU()),
            ('conv5_3', L.Convolution2D(512, 512, 3, 1, 1)),
            ('relu5_3', F.ReLU()),
            ('rpn_conv_3x3', L.Convolution2D(512, 512, 3, 1, 1)),
            ('rpn_relu_3x3', F.ReLU()),
        ]
        for name, link in self.trunk:
            if 'conv' in name:
                self.add_link(name, link)