models.py 文件源码

def create_generator(hr_image_bilinear, num_channels, cfg):
    layers = []
    print(hr_image_bilinear.get_shape())
    conv = slim.conv2d(hr_image_bilinear, cfg.ngf, [3,3], stride = 2, scope = 'encoder0')
    layers.append(conv)

    layers_specs = [
        cfg.ngf*2, 
        cfg.ngf*4,
        cfg.ngf*8,
        cfg.ngf*8,
        cfg.ngf*8,
        cfg.ngf*8,
    ]
    for idx, out_channels in enumerate(layers_specs):
        with slim.arg_scope([slim.conv2d], activation_fn = lrelu, stride = 2, padding = 'VALID'):
            conv = conv2d(layers[-1], out_channels, scope = 'encoder%d'%(idx+1))
            print(conv.get_shape())
            layers.append(conv)
    ### decoder part

    layers_specs = [
        (cfg.ngf*8, 0.5),
        (cfg.ngf*8, 0.5),
        (cfg.ngf*8, 0.0),
        (cfg.ngf*4, 0.0),
        (cfg.ngf*2, 0.0),
        (cfg.ngf, 0.0)
    ]
    num_encoder_layers = len(layers)

    for decoder_layer_idx, (out_channels, dropout) in enumerate(layers_specs):
        skip_layer = num_encoder_layers - decoder_layer_idx - 1
        with slim.arg_scope([slim.conv2d], activation_fn = lrelu):
            if decoder_layer_idx == 0:
                input = layers[-1]
            else:
                input = tf.concat([layers[-1], layers[skip_layer]], axis = 3)
            output = upsample_layer(input, out_channels, mode = 'deconv')
            print(output.get_shape())
            if dropout > 0.0:
                output = tf.nn.dropout(output, keep_prob = 1 - dropout)
            layers.append(output)
    input = tf.concat([layers[-1],layers[0]], axis = 3)
    output = slim.conv2d_transpose(input, num_channels, [4,4], stride = 2, activation_fn = tf.tanh)
    return output