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, 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 _bn_last(channel): return layers.BatchNorm2d(channel, eps=1e-4, momentum=0.9, gamma_init=0, beta_init=0, moving_mean_init=0, moving_var_init=1)
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 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, 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)