def __init__(self, in_channels, out_channels, kernel_size=3, stride=1, groups=1): super(ConvBNReLU, self).__init__() padding = (kernel_size - 1) // 2 if groups == 1: conv = layers.Conv2d(in_channels, out_channels, kernel_size, stride, pad_mode='pad', padding=padding) else: conv = layers.Conv2d(in_channels, in_channels, kernel_size, stride, pad_mode='pad', padding=padding, group=in_channels) self.features = layers.SequentialLayer( [conv, layers.BatchNorm2d(out_channels), layers.ReLU6()])
def __init__(self, in_planes=3, ndf=64, n_layers=3, alpha=0.2, norm_mode='batch'): super(Discriminator, self).__init__() kernel_size = 4 layer_list = [ layers.Conv2d(in_planes, ndf, kernel_size, 2, pad_mode='pad', padding=1), layers.LeakyReLU(alpha) ] nf_mult = ndf for i in range(1, n_layers): nf_mult_prev = nf_mult nf_mult = min(2**i, 8) * ndf layer_list.append( ConvNormReLU(nf_mult_prev, nf_mult, kernel_size, 2, alpha, norm_mode, padding=1)) nf_mult_prev = nf_mult nf_mult = min(2**n_layers, 8) * ndf layer_list.append( ConvNormReLU(nf_mult_prev, nf_mult, kernel_size, 1, alpha, norm_mode, padding=1)) layer_list.append( layers.Conv2d(nf_mult, 1, kernel_size, 1, pad_mode='pad', padding=1)) self.features = layers.SequentialLayer(layer_list)
def __init__(self, in_planes=3, ngf=64, n_layers=9, alpha=0.2, norm_mode='batch', dropout=True, pad_mode="CONSTANT"): super(ResNetGenerator, self).__init__() self.conv_in = ConvNormReLU(in_planes, ngf, 7, 1, alpha=alpha, norm_mode=norm_mode, pad_mode=pad_mode) self.down_1 = ConvNormReLU(ngf, ngf * 2, 3, 2, alpha, norm_mode) self.down_2 = ConvNormReLU(ngf * 2, ngf * 4, 3, 2, alpha, norm_mode) layer_list = [ ResidualBlock( ngf * 4, norm_mode, dropout=dropout, pad_mode=pad_mode) ] * n_layers self.residuals = layers.SequentialLayer(layer_list) self.up_2 = ConvTransposeNormReLU(ngf * 4, ngf * 2, 3, 2, alpha, norm_mode) self.up_1 = ConvTransposeNormReLU(ngf * 2, ngf, 3, 2, alpha, norm_mode) if pad_mode == "CONSTANT": self.conv_out = layers.Conv2d(ngf, 3, kernel_size=7, stride=1, pad_mode='pad', padding=3) else: pad = layers.Pad(paddings=((0, 0), (0, 0), (3, 3), (3, 3)), mode=pad_mode) conv = layers.Conv2d(ngf, 3, kernel_size=7, stride=1, pad_mode='pad') self.conv_out = layers.SequentialLayer([pad, conv]) self.activate = Tanh()
def _conv5x5(in_channel, out_channel, stride=1): weight_shape = (out_channel, in_channel, 5, 5) weight = _weight_variable(weight_shape) return layers.Conv2d(in_channel, out_channel, kernel_size=5, stride=stride, padding=2, pad_mode='pad', weight_init=weight)
def _conv7x7(in_channel, out_channel, stride=1): weight_shape = (out_channel, in_channel, 7, 7) weight = _weight_variable(weight_shape) return layers.Conv2d(in_channel, out_channel, kernel_size=7, stride=stride, padding=0, pad_mode='same', weight_init=weight)
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(ConvNormReLU, self).__init__() self.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.Conv2d(in_planes, out_planes, kernel_size, stride, pad_mode='pad', has_bias=has_bias, padding=padding) layer_list = [conv, norm] else: paddings = ((0, 0), (0, 0), (padding, padding), (padding, padding)) pad = layers.Pad(paddings=paddings, mode=pad_mode) conv = layers.Conv2d(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 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, 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, inp, oup, stride, expand_ratio): super(InvertedResidual, self).__init__() assert stride in [1, 2] hidden_dim = int(round(inp * expand_ratio)) self.use_res_connect = stride == 1 and inp == oup residual_layers = [] if expand_ratio != 1: residual_layers.append(ConvBNReLU(inp, hidden_dim, kernel_size=1)) residual_layers.extend([ ConvBNReLU(hidden_dim, hidden_dim, stride=stride, groups=hidden_dim), layers.Conv2d(hidden_dim, oup, kernel_size=1, stride=1, has_bias=False), layers.BatchNorm2d(oup), ]) self.conv = layers.SequentialLayer(residual_layers)