8e55eff8f3cd1fd6a932d02098bdfdd6b959e306,classification_models/resnet/builder.py,,build_resnet,#,21

Before Change

    conv_params = get_conv_params()
    init_filters = 64

    if block_type == "basic":
        conv_block = basic_conv_block
        identity_block = basic_identity_block
    else:
        conv_block = usual_conv_block
        identity_block = usual_identity_block
    
    // resnet bottom
    x = BatchNormalization(name="bn_data", **no_scale_bn_params)(img_input)
    x = ZeroPadding2D(padding=(3, 3))(x)
    x = Conv2D(init_filters, (7, 7), strides=(2, 2), name="conv0", **conv_params)(x)
    x = BatchNormalization(name="bn0", **bn_params)(x)

After Change

        attention_block = spatial_se_block()
    elif attention == "cse":
        attention_block = channel_se_block(ratio=16)
    elif attention == "csse":
        attention_block = csse_block(ratio=2)
    elif attention is None:
        attention_block = None
    else:
        raise ValueError("Supported attention blocks are: sse, cse, csse. Got "{}".".format(attention))

    // get parameters for model layers
    no_scale_bn_params = get_bn_params(scale=False)

Instances