Generalize for each net quantization

anson0910 · Nov 14, 2015 · 3ed8307 · 3ed8307
1 parent 3990078
commit 3ed8307
Showing 1 changed file with 13 additions and 7 deletions.
diff --git a/face_net_surgery/face_12_cal_quantize_3_to_9.py b/face_net_surgery/face_12_cal_quantize_3_to_9.py
@@ -1,6 +1,9 @@
 import numpy as np
 import sys
 
+modelName = 'face_12_cal'
+modelFileName = 'face_12_cal_train_iter_400000.caffemodel'
+
 # ================== caffe ======================================
 caffe_root = '/home/anson/caffe-master/' # this file is expected to be in {caffe_root}/examples
 sys.path.insert(0, caffe_root + 'python')
@@ -38,26 +41,28 @@ def round_number(num, fixedPointList):
  return result
 
 # ================== load face12c_full_conv ======================================
-MODEL_FILE = '/home/anson/caffe-master/models/face_12_cal/deploy.prototxt'
-PRETRAINED = '/home/anson/caffe-master/models/face_12_cal/face_12_cal_train_iter_400000.caffemodel'
+MODEL_FILE = '/home/anson/caffe-master/models/' + modelName + '/deploy.prototxt'
+PRETRAINED = '/home/anson/caffe-master/models/' + modelName + '/' + modelFileName
 caffe.set_mode_gpu()
 net = caffe.Net(MODEL_FILE, PRETRAINED, caffe.TEST)
+# ============ should be modified for different files ================
 params = ['conv1', 'fc2', 'fc3']
+# =====================================================================
 # fc_params = {name: (weights, biases)}
 original_params = {pr: (net.params[pr][0].data, net.params[pr][1].data) for pr in params}
 
 for quantize_bit_num in range(3, 10):
  # ================== load file to save quantized parameters =======================
- MODEL_FILE = '/home/anson/caffe-master/models/face_12_cal/deploy.prototxt'
- PRETRAINED = '/home/anson/caffe-master/models/face_12_cal/face_12_cal_quantize_' \
+ MODEL_FILE = '/home/anson/caffe-master/models/' + modelName +'/deploy.prototxt'
+ PRETRAINED = '/home/anson/caffe-master/models/' + modelName + '/' + modelName + '_quantize_' \
  + str(quantize_bit_num) +'.caffemodel'
  quantized_model = open(PRETRAINED, 'w')
  net_quantized = caffe.Net(MODEL_FILE, PRETRAINED, caffe.TEST)
  params_quantized = params
  # conv_params = {name: (weights, biases)}
  quantized_params = {pr: (net_quantized.params[pr][0].data, net_quantized.params[pr][1].data) for pr in params_quantized}
 
- print "\n============face_12_cal================="
+ print "\n============" + modelName + "================="
 
  # transplant
  for pr, pr_quantized in zip(params, params_quantized):
@@ -69,6 +74,7 @@ def round_number(num, fixedPointList):
  filters_weights = net_quantized.params[k][0].data
  filters_bias = net_quantized.params[k][1].data
 
+ # ============ should be modified for different files ================
  if k == 'conv1':
  a_weight = 0
  a_bias = -4
@@ -78,6 +84,7 @@ def round_number(num, fixedPointList):
  elif k == 'fc3':
  a_weight = -5
  a_bias = 0
+ # =====================================================================
 
  b_weight = quantize_bit_num - 1 - a_weight
  b_bias = quantize_bit_num - 1 - a_bias
@@ -97,7 +104,6 @@ def round_number(num, fixedPointList):
  for currentNum in np.nditer(filters_bias, op_flags=['readwrite']):
  currentNum[...] = round_number(currentNum[...], biasFixedPointList)
 
- net_quantized.save('/home/anson/caffe-master/models/face_12_cal/face_12_cal_quantize_'
- + str(quantize_bit_num) +'.caffemodel')
+ net_quantized.save(PRETRAINED)
 
  quantized_model.close()