- 1.Download Link Container File
- 2.DIALux Light Control File
The Evil DLC expands on what makes Little Big Workshop great! Your factory is turning profits week after week but you still feel unfulfilled? Then it’s time to crank the corporate greed dial all the way to sinister! This is more than just a new look. Products, companies, sabotage, skills, and malevolent tricks to outplay the competition,. The information does not usually directly identify you, but the use of cookies enables a faster and more personalized experience for you. For more information about the different types of cookies we use or to change your default settings, please click on the category headings below.
File Type 1Download Link Container File| Developer | N/A |
| Popularity | |
| Category | Encoded Files |
| Format | N/A |
Encrypted container format used for storing downloadable content; uses client-server encryption in which links are processed locally and keys are distributed and recrypted by a Web service; can be processed by JDownloader, a Java-based download management program.
- 1.Download Link Container File
- 2.DIALux Light Control File
The Evil DLC expands on what makes Little Big Workshop great! Your factory is turning profits week after week but you still feel unfulfilled? Then it’s time to crank the corporate greed dial all the way to sinister! This is more than just a new look. Products, companies, sabotage, skills, and malevolent tricks to outplay the competition,. The information does not usually directly identify you, but the use of cookies enables a faster and more personalized experience for you. For more information about the different types of cookies we use or to change your default settings, please click on the category headings below.
File Type 1Download Link Container File| Developer | N/A |
| Popularity | |
| Category | Encoded Files |
| Format | N/A |
Encrypted container format used for storing downloadable content; uses client-server encryption in which links are processed locally and keys are distributed and recrypted by a Web service; can be processed by JDownloader, a Java-based download management program.
Open over 300 file formats with File Viewer Plus. Programs that open DLC files| Developer | DIAL |
| Popularity | |
| Category | Data Files |
| Format | XML |
File created by DIALux, a professional application that enables you to model and plan lighting for indoor and outdoor area; contains information about light scenes and control groups used by the DIALux program to control the dimming and color value of light fixtures; exported in order to be read by a Digital Addressable Lighting Interface (DALI) system and program the settings for a light system.
To create the DLC file, select File → Export → Save DIALux light scene file..., choose the save location, name the file, and click .
NOTE: There currently are no DALI systems that use the DLC file.
Programs that open DLC filesThis chapter describes the various SDK tools and features.
snpe-net-run loads a DLC file, loads the data for the input tensor(s), and executes the network on the specified runtime.
This binary outputs raw output tensors into the output folder by default. Examples of using snpe-net-run can be found in Running AlexNet tutorial.
Additional details:
- Running batched inputs:
snpe-net-run is able to automatically batch the input data. The batch size is indicated in the model container (DLC file) but can also be set using the 'input_dimensions' argument passed to snpe-net-run. Users do not need to batch their input data. If the input data is not batch, the input size needs to be a multiple of the size of the input data files. snpe-net-run would group the provided inputs into batches and pad the incomplete batches (if present) with zeros.
In the example below, the model is set to accept batches of three inputs. So, the inputs are automatically grouped together to form batches by snpe-net-run and padding is done to the final batch. Note that there are five output files generated by snpe-net-run:
- input_list argument:
snpe-net-run can take multiple input files as input data per iteration, and specify multiple output names, in an input list file formated as below:
The first line starting with a '#' specifies the output layers' names. If there is more than one output, a whitespace should be used as a delimiter. Following the first line, you can use multiple lines to supply input files, one line per iteration, and each line only supply one layer.If there is more than one input per line, a whitespace should be used as a delimiter.
Here is an example, where the layer names are 'Input_1' and 'Input_2', and inputs are located in the path 'Placeholder_1/real_input_inputs_1/'. Its input list file should look like this:
Note: If the batch dimension of the model is greater than 1, the number of batch elements in the input file has to either match the batch dimension specified in the DLC or it has to be one. In the latter case, snpe-net-run will combine multiple lines into a single input tensor.
- Running AIP Runtime:
- AIP Runtime requires a DLC which was quantized, and HTA sections were generated offline. See Adding HTA sections
- AIP Runtime does not support debug_mode
- AIP Runtime requires a DLC with all the layers partitioned to HTA to support batched inputs
python script snpe_bench.py runs a DLC neural network and collects benchmark performance information.
snpe-caffe-to-dlc converts a Caffe model into an SNPE DLC file.
Examples of using this script can be found in Converting Models from Caffe to SNPE.
Additional details:
- input_encoding argument:
- Specifies the encoding type of input images.
- A preprocessing layer is added to the network to convert input images from the specified encoding to BGR, the encoding used by Caffe.
- The encoding preprocessing layer can be seen when using snpe-dlc-info.
- Allowed options are:
- argb32: The ARGB32 format consists of 4 bytes per pixel: one byte for Red, one for Green, one for Blue and one for the alpha channel. The alpha channel is ignored. For little endian CPUs, the byte order is BGRA. For big endian CPUs, the byte order is ARGB.
- rgba: The RGBA format consists of 4 bytes per pixel: one byte for Red, one for Green, one for Blue and one for the alpha channel. The alpha channel is ignored. The byte ordering is endian independent and is always RGBA byte order.
- nv21: NV21 is the Android version of YUV. The Chrominance is down sampled and has a sub sampling ratio of 4:2:0. Note that this image format has 3 channels, but the U and V channels are subsampled. For every four Y pixels there is one U and one V pixel.
- bgr: The BGR format consists of 3 bytes per pixel: one byte for Red, one for Green and one for Blue. The byte ordering is endian independent and is always BGR byte order.
- This argument is optional. If omitted then input image encoding is assumed to be BGR and no preprocessing layer is added.
- See input_preprocessing for more details.
- disable_batchnorm_folding argument:
- The disable batchnorm folding argument allows the user to turn off the optimization that folds batchnorm and batchnorm + scaling layers into previous convolution layers when possible.
- This argument is optional. If omitted then the converter will fold batchnorm and batchnorm + scaling layers into previous convolution layers wherever possible as an optimization. When this occurs the names of the folded batchnorm and scale layers are concatenated to the convolution layer it was folded into.
- For example: if batchnorm layer named 'bn' and scale layer named 'scale' are folded into a convolution layer named 'conv', the resulting dlc will show the convolution layer to be named 'conv.bn.scale'.
- input_type argument:
- Specifies the expected data type for a certain input layer name.
- This argument can be passed more than once if you want to specify the expected data type of two or more input layers.
- input_type argument takes INPUT_NAME followed by INPUT_TYPE.
- This argument is optional. If omitted for a certain input layer then the expected data type will be of type:default.
- Allowed options are:
- default: Specifies that the input contains floating-point values.
- image: Specifies that the input contains floating-point values that are all integers in the range 0..255.
- opaque: Specifies that the input contains floating-point values that should be passed to the selected runtime without modification.
For example an opaque tensor is passed directly to the DSP without quantization.
- For example: [–input_type 'data' image –input_type 'roi' opaque].
snpe-caffe2-to-dlc converts a Caffe2 model into an SNPE DLC file.
snpe-diagview loads a DiagLog file generated by snpe-net-run whenever it operates on input tensor data. The DiagLog file contains timing information information for each layer as well as the entire forward propagate time. If the run uses an input list of input tensors, the timing info reported by snpe-diagview is an average over the entire input set.
The snpe-net-run generates a file called 'SNPEDiag_0.log', 'SNPEDiag_1.log' ... , 'SNPEDiag_n.log', where n corresponds to the nth iteration of the snpe-net-run execution.
snpe-dlc-info outputs layer information from a DLC file, which provides information about the network model.
snpe-dlc-diff compares two DLCs and by default outputs some of the following differences in them in a tabular format:
- unique layers between the two DLCs
- parameter differences in common layers
- differences in dimensions of buffers associated with common layers
- weight differences in common layers
- output tensor names differences in common layers
- unique records between the two DLCs (currently checks for AIP records only)
snpe-dlc-viewer visualizes the network structure of a DLC in a web browser.
Additional details:
The DLC viewer tool renders the specified network DLC in HTML format that may be viewed on a web browser.
On installations that support a native web browser a browser instance is opened on which the network is automatically rendered.
Users can optionally save the HTML content anywhere on their systems and open on a chosen web browser independently at a later time.
- Features:
- Graph-based representation of network model with nodes depicting layers and edges depicting buffer connections.
- Colored legend to indicate layer types.
- Zoom and drag options available for ease of visualization.
- Tool-tips upon mouse hover to describe detailed layer parameters.
- Sections showing metadata from DLC records
- Supported browsers:
- Google Chrome
- Firefox
- Internet Explorer on Windows
- Microsoft Edge Browser on Windows
- Safari on Mac
snpe-dlc-quantize converts non-quantized DLC models into quantized DLC models.
Additional details:
- For specifying input_list, refer to input_list argument in snpe-net-run for supported input formats (in order to calculate output activation encoding information for all layers, do not include the line which specifies desired outputs).
- The tool requires the batch dimension of the DLC input file to be set to 1 during the original model conversion step.
- An example of quantization using snpe-dlc-quantize can be found in the C++ Tutorial section:Running the Inception v3 Model. For details on quantization see Quantized vs Non-Quantized Models.
- Using snpe-dlc-quantize is mandatory for running on HTA. See Adding HTA sections
snpe-tensorflow-to-dlc converts a TensorFlow model into an SNPE DLC file.
Dlc Info For Mac CatalinaExamples of using this script can be found in Converting Models from TensorFlow to SNPE.
Additional details:
- input_network argument:
- The converter supports either a single frozen graph .pb file or a pair of graph meta and checkpoint files.
- If you are using the TensorFlow Saver to save your graph during training, 3 files will be generated as described below:
- <model-name>.meta
- <model-name>
- checkpoint
- The converter --input_network option specifies the path to the graph meta file. The converter will also use the checkpoint file to read the graph nodes parameters during conversion. The checkpoint file must have the same name without the .meta suffix.
- This argument is required.
- input_dim argument:
- Specifies the input dimensions of the graph's input node(s)
- The converter requires a node name along with dimensions as input from which it will create an input layer by using the node output tensor dimensions. When defining a graph, there is typically a placeholder name used as input during training in the graph. The placeholder tensor name is the name you must use as the argument. It is also possible to use other types of nodes as input, however the node used as input will not be used as part of a layer other than the input layer.
- Multiple Inputs
- Networks with multiple inputs must provide --input_dim INPUT_NAME INPUT_DIM, one for each input node.
- This argument is required.
- out_node argument:
The name of the last node in your TensorFlow graph which will represent the output layer of your network.
- Multiple Outputs
- Networks with multiple outputs must provide several --out_node arguments, one for each output node.
- output_path argument:
- Specifies the output DLC file name.
- This argument is optional. If not provided the converter will create a DLC file file with the same name as the graph file name, with a .dlc file extension.
snpe-onnx-to-dlc converts a serialized ONNX model into a SNPE DLC file.
For more information, see ONNX Model Conversion
Additional details:
- File needed to be pushed to device:
snpe-throughput-net-run concurrently runs multiple instances of SNPE for a certain duration of time and measures inference throughput. Each instance of SNPE can have its own model, designated runtime and performance profile. Please note that the '--duration' parameter is common for all instances of SNPE created.
