Crop Image / Training now works / Got memory error while training
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@@ -1732,7 +1826,7 @@ MonoBehaviour:
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agentParameters:
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agentRenderTextures:
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resetOnDone: 1
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onDemandDecision: 1
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@@ -6,7 +6,19 @@ using System.Threading.Tasks;
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public class ImageProcessor : MonoBehaviour
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{
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public MeshRenderer processedImageRenderer;
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[Header("RenderTexture")]
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[Tooltip("RenderTexture that will be passed to the LearningBrain.")]
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public RenderTexture renderTextureCropped;
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[Header("Debug Helper")]
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//[Tooltip("Reference to the MeshRenderer that will show the processed Image from Cozmo")]
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//public MeshRenderer processedImageRenderer;
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[Tooltip("Reference to the MeshRenderer that will show the processed and cropped Image from Cozmo")]
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public MeshRenderer processedImageRendererCropped;
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/// <summary>
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/// Center of Gravity in the cropped canny image
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/// </summary>
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public Point CenterOfGravity { get; private set; }
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// OpenCVSharp parameters
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@@ -17,34 +29,56 @@ public class ImageProcessor : MonoBehaviour
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private byte[] cannyImageData;
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private const int imWidth = 320; //TODO: Set width and height based on agent observation size
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private const int imHeight = 240;
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private Camera textureCamera;
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private int imWidth = 320; // Width of the camera image from the virtual cozmo
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private int imHeight = 240; // Height of the camera image from the virtual cozmo
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private int croppedImHeight = 120; // Height of the cropped camera image from the virtual cozmo
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private Camera textureCamera; // Virtual Cozmo camera
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private void Start()
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{
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// Get reference to the cozmo camera
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textureCamera = GetComponent<Camera>();
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// Set image widths and heights based on the given RenderTextures
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imWidth = textureCamera.targetTexture.width;
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imHeight = textureCamera.targetTexture.height;
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croppedImHeight = renderTextureCropped.height;
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//assign the processed targetTexture to the renderer to display the image
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processedImageRenderer.material.mainTexture = textureCamera.targetTexture;
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//processedImageRenderer.material.mainTexture = textureCamera.targetTexture;
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processedImageRendererCropped.material.mainTexture = renderTextureCropped;
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// initialize video / image with given size
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videoSourceImage = new Mat(imHeight, imWidth, MatType.CV_8UC3);
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videoSourceImageData = new Vec3b[imHeight * imWidth];
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cannyImage = new Mat(imHeight, imWidth, MatType.CV_8UC1);
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cannyImageData = new byte[imHeight * imWidth];
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cannyImageData = new byte[croppedImHeight * imWidth];
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}
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/// <summary>
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/// Gets called when a new image arrives from the camera this script lies on
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/// </summary>
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/// <param name="source"></param>
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/// <param name="destination"></param>
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private void OnRenderImage(RenderTexture source, RenderTexture destination)
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{
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Texture2D tex = RenderTextureToTexture2D(source);
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videoSourceImage = TextureToMat(tex);
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cannyImage = ProcessImage(videoSourceImage);
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cannyImage = CropImage(cannyImage);
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CenterOfGravityTest(cannyImage);
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processedTexture = MatToTexture(cannyImage);
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Graphics.Blit(processedTexture, destination);
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//Graphics.Blit(processedTexture, destination);
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Graphics.Blit(processedTexture, renderTextureCropped);
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}
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// Crop image to just see the middle of the original image
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private Mat CropImage(Mat image)
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{
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//cut a fourth out of the top and bottom of the image
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OpenCvSharp.Rect rectCroped = new OpenCvSharp.Rect(0, image.Height / 4, image.Width, image.Height / 2);
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Mat croppedImage = new Mat(image, rectCroped);
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return croppedImage;
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}
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private Texture2D RenderTextureToTexture2D(RenderTexture rTex)
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@@ -101,10 +135,10 @@ public class ImageProcessor : MonoBehaviour
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// cannyImageData is byte array, because canny image is grayscale
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mat.GetArray(0, 0, cannyImageData);
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// create Color32 array that can be assigned to Texture2D directly
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Color32[] c = new Color32[imHeight * imWidth];
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Color32[] c = new Color32[croppedImHeight * imWidth];
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// parallel for loop
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Parallel.For(0, imHeight, i =>
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Parallel.For(0, croppedImHeight, i =>
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{
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for (var j = 0; j < imWidth; j++)
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{
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@@ -120,7 +154,7 @@ public class ImageProcessor : MonoBehaviour
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}
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});
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Texture2D texture = new Texture2D(imWidth, imHeight, TextureFormat.RGBA32, true, true);
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Texture2D texture = new Texture2D(imWidth, croppedImHeight, TextureFormat.RGBA32, true, true);
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texture.SetPixels32(c);
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// to update the texture, OpenGL manner
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texture.Apply();
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@@ -94,8 +94,8 @@ public class CozmoAgent : Agent
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}
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else if (centerOfGravity.X > renderCamera.targetTexture.width / 2 - farAreaLimit && centerOfGravity.X < renderCamera.targetTexture.width / 2 + farAreaLimit)
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{
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SetReward(-0.7f);
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print("Reward: -0.7");
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SetReward(-0.3f);
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print("Reward: -0.3");
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}
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else
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{
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@@ -0,0 +1,34 @@
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%YAML 1.1
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m_ForcedFallbackFormat: 4
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m_DownscaleFallback: 0
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m_Width: 320
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m_Height: 120
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m_AntiAliasing: 1
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m_DepthFormat: 2
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m_ColorFormat: 0
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m_MipMap: 0
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m_GenerateMips: 1
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m_SRGB: 0
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m_UseDynamicScale: 0
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m_TextureSettings:
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m_WrapV: 1
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m_WrapW: 1
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@@ -0,0 +1,22 @@
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default:
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trainer: ppo
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batch_size: 1024
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beta: 5.0e-3
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buffer_size: 10240
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epsilon: 0.2
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gamma: 0.99
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hidden_units: 128
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lambd: 0.95
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learning_rate: 3.0e-4
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max_steps: 5.0e4
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memory_size: 256
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normalize: false
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num_epoch: 3
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num_layers: 2
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time_horizon: 64
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sequence_length: 64
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summary_freq: 1000
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use_recurrent: false
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use_curiosity: false
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curiosity_strength: 0.01
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curiosity_enc_size: 128
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+58020
File diff suppressed because it is too large.
Load diff
@@ -136,7 +136,7 @@
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},
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{
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"data": {
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"image/png": 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truncated
|
||||
"image/png": "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\n",
|
||||
"text/plain": [
|
||||
"<Figure size 432x288 with 1 Axes>"
|
||||
]
|
||||
@@ -195,27 +195,19 @@
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"Total reward this episode: 762.3000014424324\n",
|
||||
"Total reward this episode: -992.6999998688698\n",
|
||||
"Total reward this episode: -1000.0\n",
|
||||
"Total reward this episode: -992.1999996900558\n",
|
||||
"Total reward this episode: -999.0999999642372\n"
|
||||
"Total reward this episode: -699.7999988794327\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"ename": "MemoryError",
|
||||
"evalue": "",
|
||||
"ename": "KeyError",
|
||||
"evalue": "'CozmoLearningBrain'",
|
||||
"output_type": "error",
|
||||
"traceback": [
|
||||
"\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
|
||||
"\u001b[1;31mMemoryError\u001b[0m Traceback (most recent call last)",
|
||||
"\u001b[1;31mKeyError\u001b[0m Traceback (most recent call last)",
|
||||
"\u001b[1;32m<ipython-input-5-3140409a1130>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m 10\u001b[0m \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 11\u001b[0m \u001b[0maction\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcolumn_stack\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mrandom\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mrandint\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0maction_size\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mi\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0msize\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mlen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0menv_info\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0magents\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0mi\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mrange\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mlen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0maction_size\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 12\u001b[1;33m \u001b[0menv_info\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0menv\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mstep\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0maction\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mdefault_brain\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 13\u001b[0m \u001b[0mepisode_rewards\u001b[0m \u001b[1;33m+=\u001b[0m \u001b[0menv_info\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mrewards\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 14\u001b[0m \u001b[0mdone\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0menv_info\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mlocal_done\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
|
||||
"\u001b[1;32mc:\\users\\tobi\\downloads\\ml-agents-0.8.1\\ml-agents-envs\\mlagents\\envs\\environment.py\u001b[0m in \u001b[0;36mstep\u001b[1;34m(self, vector_action, memory, text_action, value, custom_action)\u001b[0m\n\u001b[0;32m 436\u001b[0m \u001b[1;32mraise\u001b[0m \u001b[0mKeyboardInterrupt\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 437\u001b[0m \u001b[0mrl_output\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0moutputs\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mrl_output\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 438\u001b[1;33m \u001b[0mstate\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_get_state\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mrl_output\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 439\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_global_done\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mstate\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 440\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0m_b\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_external_brain_names\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
|
||||
"\u001b[1;32mc:\\users\\tobi\\downloads\\ml-agents-0.8.1\\ml-agents-envs\\mlagents\\envs\\environment.py\u001b[0m in \u001b[0;36m_get_state\u001b[1;34m(self, output)\u001b[0m\n\u001b[0;32m 496\u001b[0m \u001b[0magent_info_list\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0moutput\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0magentInfos\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mbrain_name\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mvalue\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 497\u001b[0m _data[brain_name] = BrainInfo.from_agent_proto(agent_info_list,\n\u001b[1;32m--> 498\u001b[1;33m self.brains[brain_name])\n\u001b[0m\u001b[0;32m 499\u001b[0m \u001b[1;32mreturn\u001b[0m \u001b[0m_data\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mglobal_done\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 500\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
|
||||
"\u001b[1;32mc:\\users\\tobi\\downloads\\ml-agents-0.8.1\\ml-agents-envs\\mlagents\\envs\\brain.py\u001b[0m in \u001b[0;36mfrom_agent_proto\u001b[1;34m(agent_info_list, brain_params)\u001b[0m\n\u001b[0;32m 91\u001b[0m obs = [BrainInfo.process_pixels(x.visual_observations[i],\n\u001b[0;32m 92\u001b[0m brain_params.camera_resolutions[i]['blackAndWhite'])\n\u001b[1;32m---> 93\u001b[1;33m for x in agent_info_list]\n\u001b[0m\u001b[0;32m 94\u001b[0m \u001b[0mvis_obs\u001b[0m \u001b[1;33m+=\u001b[0m \u001b[1;33m[\u001b[0m\u001b[0mobs\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 95\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0mlen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0magent_info_list\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;33m==\u001b[0m \u001b[1;36m0\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
|
||||
"\u001b[1;32mc:\\users\\tobi\\downloads\\ml-agents-0.8.1\\ml-agents-envs\\mlagents\\envs\\brain.py\u001b[0m in \u001b[0;36m<listcomp>\u001b[1;34m(.0)\u001b[0m\n\u001b[0;32m 91\u001b[0m obs = [BrainInfo.process_pixels(x.visual_observations[i],\n\u001b[0;32m 92\u001b[0m brain_params.camera_resolutions[i]['blackAndWhite'])\n\u001b[1;32m---> 93\u001b[1;33m for x in agent_info_list]\n\u001b[0m\u001b[0;32m 94\u001b[0m \u001b[0mvis_obs\u001b[0m \u001b[1;33m+=\u001b[0m \u001b[1;33m[\u001b[0m\u001b[0mobs\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 95\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0mlen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0magent_info_list\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;33m==\u001b[0m \u001b[1;36m0\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
|
||||
"\u001b[1;32mc:\\users\\tobi\\downloads\\ml-agents-0.8.1\\ml-agents-envs\\mlagents\\envs\\brain.py\u001b[0m in \u001b[0;36mprocess_pixels\u001b[1;34m(image_bytes, gray_scale)\u001b[0m\n\u001b[0;32m 76\u001b[0m \u001b[0ms\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mbytearray\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mimage_bytes\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 77\u001b[0m \u001b[0mimage\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mImage\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mopen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mio\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mBytesIO\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0ms\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 78\u001b[1;33m \u001b[0ms\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0marray\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mimage\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;33m/\u001b[0m \u001b[1;36m255.0\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 79\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0mgray_scale\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 80\u001b[0m \u001b[0ms\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmean\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0ms\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m2\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
|
||||
"\u001b[1;31mMemoryError\u001b[0m: "
|
||||
"\u001b[1;32mc:\\users\\tobi\\downloads\\ml-agents-0.8.1\\ml-agents-envs\\mlagents\\envs\\environment.py\u001b[0m in \u001b[0;36mstep\u001b[1;34m(self, vector_action, memory, text_action, value, custom_action)\u001b[0m\n\u001b[0;32m 439\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_global_done\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mstate\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 440\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0m_b\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_external_brain_names\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 441\u001b[1;33m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_n_agents\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0m_b\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mlen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mstate\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0m_b\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0magents\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 442\u001b[0m \u001b[1;32mreturn\u001b[0m \u001b[0mstate\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 443\u001b[0m \u001b[1;32melif\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_loaded\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
|
||||
"\u001b[1;31mKeyError\u001b[0m: 'CozmoLearningBrain'"
|
||||
]
|
||||
}
|
||||
],
|
||||
|
||||
Reference in new issue
Block a user