def __init__(self, in_channels, growth_rate, bn_size): super(_DenseLayer, self).__init__() self.layer = layers.SequentialLayer([ _bn(in_channels), layers.ReLU(), _conv1x1(in_channels, bn_size * growth_rate), _bn(bn_size * growth_rate), layers.ReLU(), _conv3x3(bn_size * growth_rate, growth_rate), ]) self.ops = Concat(axis=1)
def __init__(self, features, class_num=1000): super(VGG, self).__init__() self.features = features self.flatten = layers.Flatten() self.classifier = layers.SequentialLayer([ layers.Dense(512 * 7 * 7, 4096), layers.ReLU(), layers.Dropout(), layers.Dense(4096, 4096), layers.ReLU(), layers.Dropout(), layers.Dense(4096, class_num), ])
def make_layers(cfg, batch_norm=False): Layers = [] in_channels = 3 for v in cfg: if v == 'M': Layers += [layers.MaxPool2d(kernel_size=2, stride=2)] else: conv2d = _conv3x3(in_channels, v) if batch_norm: Layers += [conv2d, layers.BatchNorm2d(v), layers.ReLU()] else: Layers += [conv2d, layers.ReLU()] in_channels = v return layers.SequentialLayer(Layers)
def __init__(self, in_channels, out_channels): super(_Transition, self).__init__() self.layer = layers.SequentialLayer([ _bn(in_channels), layers.ReLU(), _conv1x1(in_channels, out_channels), AvgPool2d(kernel_size=2, stride=2, pad_mode='same', data_format='NCHW') ])
def test_sequential(): context.set_context(mode=context.GRAPH_MODE, device_target="CPU") net = layers.SequentialLayer([ layers.Conv2d(1, 6, 5, pad_mode='valid', weight_init="ones"), layers.ReLU(), layers.MaxPool2d(kernel_size=2, stride=2) ]) model = Model(net) model.compile() z = model.predict(ts.ones((1, 1, 32, 32))) print(z.asnumpy())
def __init__(self, in_planes, out_planes, kernel_size=4, stride=2, alpha=0.2, norm_mode='batch', pad_mode='CONSTANT', use_relu=True, padding=None): super(ConvTransposeNormReLU, self).__init__() conv = layers.Conv2dTranspose(in_planes, out_planes, kernel_size, stride=stride, pad_mode='same') norm = layers.BatchNorm2d(out_planes) if norm_mode == 'instance': # Use BatchNorm2d with batchsize=1, affine=False, training=True instead of InstanceNorm2d norm = layers.BatchNorm2d(out_planes, affine=False) has_bias = (norm_mode == 'instance') if padding is None: padding = (kernel_size - 1) // 2 if pad_mode == 'CONSTANT': conv = layers.Conv2dTranspose(in_planes, out_planes, kernel_size, stride, pad_mode='same', has_bias=has_bias) layer_list = [conv, norm] else: paddings = ((0, 0), (0, 0), (padding, padding), (padding, padding)) pad = layers.Pad(paddings=paddings, mode=pad_mode) conv = layers.Conv2dTranspose(in_planes, out_planes, kernel_size, stride, pad_mode='pad', has_bias=has_bias) layer_list = [pad, conv, norm] if use_relu: relu = layers.ReLU() if alpha > 0: relu = layers.LeakyReLU(alpha) layer_list.append(relu) self.features = layers.SequentialLayer(layer_list)
def __init__(self, outer_nc, inner_nc, in_planes=None, dropout=False, submodule=None, outermost=False, innermost=False, alpha=0.2, norm_mode='batch'): super(UnetSkipConnectionBlock, self).__init__() downnorm = layers.BatchNorm2d(inner_nc) upnorm = layers.BatchNorm2d(outer_nc) use_bias = False if norm_mode == 'instance': downnorm = layers.BatchNorm2d(inner_nc, affine=False) upnorm = layers.BatchNorm2d(outer_nc, affine=False) use_bias = True if in_planes is None: in_planes = outer_nc downconv = layers.Conv2d(in_planes, inner_nc, kernel_size=4, stride=2, padding=1, has_bias=use_bias, pad_mode='pad') downrelu = layers.LeakyReLU(alpha) uprelu = layers.ReLU() if outermost: upconv = layers.Conv2dTranspose(inner_nc * 2, outer_nc, kernel_size=4, stride=2, padding=1, pad_mode='pad') down = [downconv] up = [uprelu, upconv, layers.Tanh()] model = down + [submodule] + up elif innermost: upconv = layers.Conv2dTranspose(inner_nc, outer_nc, kernel_size=4, stride=2, padding=1, has_bias=use_bias, pad_mode='pad') down = [downrelu, downconv] up = [uprelu, upconv, upnorm] model = down + up else: upconv = layers.Conv2dTranspose(inner_nc * 2, outer_nc, kernel_size=4, stride=2, padding=1, has_bias=use_bias, pad_mode='pad') down = [downrelu, downconv, downnorm] up = [uprelu, upconv, upnorm] model = down + [submodule] + up if dropout: model.append(layers.Dropout(0.5)) self.model = layers.SequentialLayer(model) self.skip_connections = not outermost self.concat = Concat(axis=1)
def __init__(self, in_channel, out_channel, stride=1): super(ResidualBlock, self).__init__() channel = out_channel // self.expansion self.conv1 = _conv1x1(in_channel, channel, stride=1) self.bn1 = _bn(channel) self.conv2 = _conv3x3(channel, channel, stride=stride) self.bn2 = _bn(channel) self.conv3 = _conv1x1(channel, out_channel, stride=1) self.bn3 = _bn_last(out_channel) self.relu = layers.ReLU() self.down_sample = False self.down_sample_layer = None if stride != 1 or in_channel != out_channel: self.down_sample = True if self.down_sample: self.down_sample_layer = layers.SequentialLayer( [_conv1x1(in_channel, out_channel, stride), _bn(out_channel)])
def __init__(self, block, layer_nums, in_channels, out_channels, strides, num_classes): super(ResNet, self).__init__() if not len(layer_nums) == len(in_channels) == len(out_channels) == 4: raise ValueError("the length of layer_num, in_channels, out_channels list must be 4!") self.conv1 = _conv7x7(3, 64, stride=2) self.bn1 = _bn(64) self.relu = layers.ReLU() self.maxpool = layers.MaxPool2d(kernel_size=3, stride=2, pad_mode="same") self.layer1 = self._make_layer(block, layer_nums[0], in_channel=in_channels[0], out_channel=out_channels[0], stride=strides[0]) self.layer2 = self._make_layer(block, layer_nums[1], in_channel=in_channels[1], out_channel=out_channels[1], stride=strides[1]) self.layer3 = self._make_layer(block, layer_nums[2], in_channel=in_channels[2], out_channel=out_channels[2], stride=strides[2]) self.layer4 = self._make_layer(block, layer_nums[3], in_channel=in_channels[3], out_channel=out_channels[3], stride=strides[3]) self.mean = ReduceMean(keep_dims=True) self.flatten = layers.Flatten() self.end_point = _fc(out_channels[3], num_classes)