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rank_visualize.py
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import numpy as np
import matplotlib.pyplot as plt
number_blocks = [6,12,24,16]
pre = 'rank_adaptive_local2/densenet121_limit3'
rank_visual = 'rank_visual_adaptive_local2'
rank_Type = 'rank'
if rank_Type == 'rank':
y_max = 56
rank = pre + '/rank_conv%d'%(1) + '.npy'
data = np.load(rank)
plt.plot(data , 'ro')
plt.title( 'conv%d'%(0) + '_rank', fontsize=15 )
plt.ylim([-1, y_max+1])
plt.xlabel('featrue maps', fontsize=15) # x축 label : 'Students_num'
# plt.xscale("log")
plt.ylabel('rank', fontsize=15) # y축 label : 'Score'
plt.savefig(rank_visual + '/conv%d'%(1) + '_rank')
plt.close()
cnt=1
for i in range(4):
for j in range(number_blocks[i]):
cnt += 1
rank = pre + '/rank_conv%d'%(cnt) + '.npy'
data = np.load(rank)
plt.plot(data , 'ro')
plt.title( 'block%d'%(i+1) + '-layer%d'%(j+1) +'_1x1' + '_rank' ,fontsize=15)
plt.ylim([-1, y_max+1])
plt.xlabel('featrue maps', fontsize=15) # x축 label : 'Students_num'
# plt.xscale("log")
plt.ylabel('rank', fontsize=15) # y축 label : 'Score'
plt.savefig(rank_visual + '/rank_conv%d'%(cnt))
plt.close()
cnt += 1
rank = pre + '/rank_conv%d'%(cnt) + '.npy'
data = np.load(rank)
plt.plot(data , 'ro')
plt.title( 'block%d'%(i+1) + '-layer%d'%(j+1) +'_3x3' + '_rank' ,fontsize=15 )
plt.ylim([-1, y_max+1])
plt.xlabel('featrue maps', fontsize=15) # x축 label : 'Students_num'
# plt.xscale("log")
plt.ylabel('rank', fontsize=15) # y축 label : 'Score'
plt.savefig(rank_visual + '/rank_conv%d'%(cnt))
plt.close()
y_max = y_max // 2
if i != 3:
cnt += 1
rank = pre + '/rank_conv%d'%(cnt) + '.npy'
data = np.load(rank)
plt.plot(data , 'ro')
plt.title( 'trainsition%d'%(i+1) + '_rank' ,fontsize=15)
plt.ylim([-1, y_max+1])
plt.xlabel('featrue maps', fontsize=15) # x축 label : 'Students_num'
# plt.xscale("log")
plt.ylabel('rank', fontsize=15) # y축 label : 'Score'
plt.savefig(rank_visual + '/rank_conv%d'%(cnt))
plt.close()
else :
rank = pre + '/rank_conv%d'%(1) + '.npy'
data = np.load(rank)
plt.plot(data , 'ro')
plt.title( 'conv%d'%(0) + '_rank',fontsize=15 )
plt.xlabel('featrue maps', fontsize=15) # x축 label : 'Students_num'
# plt.xscale("log")
plt.ylabel('rank', fontsize=15) # y축 label : 'Score'
plt.savefig(rank_visual + '/conv%d'%(1) + '_rank')
plt.close()
cnt=1
for i in range(4):
for j in range(number_blocks[i]):
cnt += 1
rank = pre + '/rank_conv%d'%(cnt) + '.npy'
data = np.load(rank)
plt.plot(data , 'ro')
plt.title( 'block%d'%(i+1) + '-layer%d'%(j+1) +'_1x1' + '_rank',fontsize=15 )
plt.xlabel('featrue maps', fontsize=15) # x축 label : 'Students_num'
# plt.xscale("log")
plt.ylabel('rank', fontsize=15) # y축 label : 'Score'
plt.savefig(rank_visual + '/rank_conv%d'%(cnt))
plt.close()
cnt += 1
rank = pre + '/rank_conv%d'%(cnt) + '.npy'
data = np.load(rank)
plt.plot(data , 'ro')
plt.title( 'block%d'%(i+1) + '-layer%d'%(j+1) +'_3x3' + '_rank' ,fontsize=15 )
plt.xlabel('featrue maps', fontsize=15) # x축 label : 'Students_num'
# plt.xscale("log")
plt.ylabel('rank', fontsize=15) # y축 label : 'Score'
plt.savefig(rank_visual + '/rank_conv%d'%(cnt))
plt.close()
if i != 3:
cnt += 1
rank = pre + '/rank_conv%d'%(cnt) + '.npy'
data = np.load(rank)
plt.plot(data , 'ro')
plt.title( 'trainsition%d'%(i+1) + '_rank',fontsize=15 )
plt.xlabel('featrue maps', fontsize=15) # x축 label : 'Students_num'
# plt.xscale("log")
plt.ylabel('rank', fontsize=15) # y축 label : 'Score'
plt.savefig(rank_visual + '/rank_conv%d'%(cnt))
plt.close()