Wolfram Research

Multi-scale Context Aggregation Net Trained on CamVid Data

Segment an image of a driving scenario into semantic component classes

Released in 2016, this is the first model featuring a systematic use of dilated convolutions for pixel-wise classification. A context aggregation module featuring convolutions with exponentially increasing dilations is appended to a VGG-style front end.

Number of layers: 53 | Parameter count: 134,313,443 | Trained size: 537 MB

Training Set Information

Performance

Examples

Resource retrieval

Get the pre-trained net:

In[1]:=
NetModel["Multi-scale Context Aggregation Net Trained on CamVid Data"]
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Evaluation function

Write an evaluation function to handle padding and tiling of the input image:

In[2]:=
netevaluate[img_, device_: "CPU"] := Block[ {net, marginImg, inputSize, windowSize, zoom, imgPad, imgSize, takeSpecs, tiles, marginTile, prob}, (* Parameters *) net = NetModel[ "Multi-scale Context Aggregation Net Trained on CamVid Data"]; marginImg = 186; inputSize = {900, 1100}; zoom = 8; windowSize = inputSize - 2*marginImg; (* Pad and tile input *) imgPad = ImagePad[img, marginImg, "Reflected"]; imgSize = ImageDimensions[imgPad]; takeSpecs = Table[ {{i, i + inputSize[[1]] - 1}, {j, j + inputSize[[2]] - 1}}, {i, 1, imgSize[[2]] - 2*marginImg, windowSize[[1]]}, {j, 1, imgSize[[1]] - 2*marginImg, windowSize[[1]]} ]; tiles = Map[ImageTake[imgPad, Sequence @@ #] &, takeSpecs, {2}]; (* Make all tiles 900x1100 *) marginTile = Reverse[windowSize] - Mod[imgSize - 2*marginImg, Reverse@windowSize]; tiles = MapAt[ImagePad[#, {{0, marginTile[[1]]}, {0, 0}}, "Reflected"] &, tiles, {All, -1}]; tiles = MapAt[ImagePad[#, {{0, 0}, {marginTile[[2]], 0}}, "Reflected"] &, tiles, {-1, All}]; (* Run net on tiles *) prob = net[Flatten@tiles, None, TargetDevice -> device]; prob = ArrayFlatten@ ArrayReshape[prob, Join[Dimensions@tiles, {66, 91, 11}]]; (* Resample probs by zoom factor and trim additional tile margin *) prob = ArrayResample[prob, Dimensions[prob]*{zoom, zoom, 1}, Resampling -> "Linear"]; prob = Take[prob, Sequence @@ Reverse[ImageDimensions@img], All]; (* Predict classes *) NetExtract[net, "Output"]@prob ]

Label list

Define the label list for this model. Integers in the model’s output correspond to elements in the label list:

In[3]:=
labels = {"building", "tree", "sky", "car", "sign, symbol", "road", "pedestrian", "fence", "column, pole", "sidewalk", "bicycle"};

Basic usage

Obtain a segmentation mask for a given image:

In[4]:=
CloudGet["https://www.wolframcloud.com/objects/c33a3c23-4a3d-41ff-9c19-919cd798bc5e"] (* Evaluate this cell to copy the example input from a cloud object *)

Inspect which classes are detected:

In[5]:=
detected = DeleteDuplicates@Flatten@mask
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In[6]:=
labels[[detected]]
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Visualize the mask:

In[7]:=
Colorize[mask]
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Advanced visualization

Associate classes to colors:

In[8]:=
colors = Apply[ RGBColor, {{70, 70, 70}, {107, 142, 35}, {70, 130, 180}, {0, 0, 142}, {220, 220, 0}, {128, 64, 128}, {220, 20, 60}, {190, 153, 153}, {250, 170, 30}, {244, 35, 232}, {119, 11, 32}}/255., {1}]
Out[8]=
In[9]:=
indexToColor = Thread[Range[11] -> colors];

Write a function to overlap the image and the mask with a legend:

In[10]:=
result[img_, device_: "CPU"] := Block[ {mask, classes, maskPlot, composition}, mask = netevaluate[img, device]; classes = DeleteDuplicates[Flatten@mask]; maskPlot = Colorize[mask, ColorRules -> indexToColor]; composition = ImageCompose[img, {maskPlot, 0.5}]; Legended[ Row[Image[#, ImageSize -> Large] & /@ {maskPlot, composition}], SwatchLegend[indexToColor[[classes, 2]], labels[[classes]]]] ]

Inspect the results:

In[11]:=
CloudGet["https://www.wolframcloud.com/objects/bcc70401-c06e-406f-b48a-32b083bed924"] (* Evaluate this cell to copy the example input from a cloud object *)
Out[11]=
In[12]:=
CloudGet["https://www.wolframcloud.com/objects/1b3d1880-6c42-42cc-9678-a019797e4466"] (* Evaluate this cell to copy the example input from a cloud object *)
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Net information

Inspect the number of parameters of all arrays in the net:

In[13]:=
NetInformation[ NetModel["Multi-scale Context Aggregation Net Trained on CamVid \ Data"], "ArraysElementCounts"]
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Obtain the total number of parameters:

In[14]:=
NetInformation[ NetModel["Multi-scale Context Aggregation Net Trained on CamVid \ Data"], "ArraysTotalElementCount"]
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Obtain the layer type counts:

In[15]:=
NetInformation[ NetModel["Multi-scale Context Aggregation Net Trained on CamVid \ Data"], "LayerTypeCounts"]
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Display the summary graphic:

In[16]:=
NetInformation[ NetModel["Multi-scale Context Aggregation Net Trained on CamVid \ Data"], "SummaryGraphic"]
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Export to MXNet

Export the net into a format that can be opened in MXNet:

In[17]:=
jsonPath = Export[FileNameJoin[{$TemporaryDirectory, "net.json"}], NetModel["Multi-scale Context Aggregation Net Trained on CamVid \ Data"], "MXNet"]
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Export also creates a net.params file containing parameters:

In[18]:=
paramPath = FileNameJoin[{DirectoryName[jsonPath], "net.params"}]
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Get the size of the parameter file:

In[19]:=
FileByteCount[paramPath]
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The size is similar to the byte count of the resource object:

In[20]:=
ResourceObject[ "Multi-scale Context Aggregation Net Trained on PASCAL VOC2012 \ Data"]["ByteCount"]
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Represent the MXNet net as a graph:

In[21]:=
Import[jsonPath, {"MXNet", "NodeGraphPlot"}]
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Requirements

Wolfram Language 11.3 (March 2018) or above

Resource History

  • Date Created: 15 May 2018
  • Latest Update: 21 June 2018

Reference