visualizer-2

utilities/visualize-2.py

+#python visualize-2.py results-video.npz
+
+import numpy as np
+import matplotlib.pyplot as plt
+import sys
+import math
+import scipy.signal
+CELLS_PER_FRAME=0
+SQRT_CELLS=0
+
+if __name__ == '__main__':
+    RESULT_FILE_NAME=sys.argv[1]
+    CELLS_PER_FRAME=(int(sys.argv[2]))
+    SQRT_CELLS=math.sqrt(CELLS_PER_FRAME)
+    data=np.load(RESULT_FILE_NAME)
+
+
+
+
+def countMisses(yTrue,yPred,windowH,windowW):
+    global SQRT_CELLS
+    truePred=np.zeros(shape=(windowH*windowW,windowH*windowW),dtype=np.int32)
+
+
+    yTrue=np.reshape(yTrue,(yTrue.shape[0],SQRT_CELLS,SQRT_CELLS))
+    yPred=np.reshape(yPred,(yPred.shape[0],SQRT_CELLS,SQRT_CELLS))
+
+    convKer=np.zeros((windowH,windowW),dtype=np.int32)
+
+    convTrue=np.zeros((yTrue.shape[0],SQRT_CELLS-windowH+1,SQRT_CELLS-windowW+1))
+    convPred=np.zeros((yPred.shape[0],SQRT_CELLS-windowH+1,SQRT_CELLS-windowW+1))
+
+    for f in range(yTrue.shape[0]):
+        convTrue[f]=scipy.signal.convolve2d(yTrue[f],convKer,mode='valid')
+
+    for f in range(yPred.shape[0]):
+        convPred[f]=scipy.signal.convolve2d(yPred[f],convKer,mode='valid')
+
+
+
+
+
+
+
+
+
+
+
+
+def drawTrainingHistograms(data):
+    global SQRT_CELLS
+    VID='cropped-tvstatic-video'
+    FIG_NAME='{}-{}x{}-inception'.format(VID,SQRT_CELLS,SQRT_CELLS)
+    TITLE='{}-{}x{}->inception'.format(VID,SQRT_CELLS,SQRT_CELLS)
+    CAPTION=" inception v3 network"
+
+    FIG_NAME='./fig/'+FIG_NAME+'.png'
+    trc=data['trainCost']
+    tra=data['trainAcc']
+    tec=data['testCost']
+    tea=data['testAcc']
+
+    #print(np.array(tra)-np.array(tea))
+
+    EPOCHS=len(trc)
+    e=range(EPOCHS)
+
+
+    fig, ax = plt.subplots(figsize=(10, 10))
+    fig.subplots_adjust(bottom=0.5, left=0.2)
+    ax.plot(e,trc,'r',e,tra,'k',e,tec,'b',e,tea,'y')
+    ax.set_xlabel('Batch iter\n\n\n----------------------\n'+CAPTION,horizontalalignment='left', x=0)
+    ax.set_ylabel("Performance")
+
+    plt.legend(['Train cost','Train acc','Test cost','Test acc'])
+
+
+    major_ticksY = np.arange(0, 1.05, 0.2)
+    minor_ticksY = np.arange(0, 1.05, 0.05)
+
+    #ax.set_xticks(major_ticksY)
+    #ax.set_xticks(minor_ticksY, minor=True)
+    ax.set_yticks(major_ticksY)
+    ax.set_yticks(minor_ticksY, minor=True)
+
+    # And a corresponding grid
+    ax.grid(which='both')
+
+    # Or if you want different settings for the grids:
+    ax.grid(which='minor', alpha=0.2)
+    ax.grid(which='major', alpha=0.5)
+
+
+    plt.title(TITLE)
+
+    plt.savefig(FIG_NAME,dpi=1000)
+    plt.show()
+
+
+