def loadConfigs(pt, model):
if DEBUG_Mario:
print("\n--- load configs ---")
print("in pt:", pt)
print("in model:", model)
dcdic = dcfgs.dcdic
print("\nCurrent pruning configuration (loaded from lib/cfgs.c.dcdic)")
for key, value in dcdic.items():
print(key, value)
changeConfigs = checkOperation(
question='\nModify the prunig configuration?', default='no')
if changeConfigs:
net = Net(pt, model=model, phase=caffe.TEST)
convs = net.convs
for conv in convs:
print(
'input number remaining filters of %s after pruning [original number of filters %d ]'
% (conv, net.conv_param_num_output(conv)))
dc = input("[leave blank to skip]: ")
if dc == '':
dcdic[conv] = dcdic[conv]
else:
dcdic[conv] = int(dc)
return dcdic
def checkSensitivity(pt, model, graph_title='<Model>-<Dataset>'):
"""
This funtion prunes the model layer by layer, from 100% to
100/p_Nx % of its filter. It then plots the sensibility curves and
saves the plot data to a pickle file for later analysis.
NOTE: The pruning performed here does not alter the model shape, simply
makes filters zero, enough for accuracy evaluation.
"""
p_dic = {} #ploting dictionary
p_Nx = 20 #number of points to plot (step for decreasing % of prune weights; 5% in this case)
n_batches = 100 #number of test batches
net = Net(pt, model=model, noTF=1) # ground truth net
convs = net.convs
net, BNs_dic = registerBNs(net)
noBNs = net.noBNs
#initial accuracy test
print('@ Grand-truth model @')
acc1_init, acc5_init = accuracy(net, n_batches)
for conv, convnext in zip(convs[0:], convs[1:]):
net = Net(pt, model=model, noTF=1) #reset the model
bn, affine = checkIfBN(noBNs, conv, BNs_dic)
dc_init = net.conv_param_num_output(conv)
dc = dc_init
p_x = [dc_init] #plot points of x_axis
p_acc1 = [acc1_init]
p_acc5 = [acc5_init]
for i in range(
p_Nx - 1
): #the first point was already added (initial conditions), reduce range by 1
dc = round(dc - dc_init / p_Nx)
net = pseudoPruneLayer(net, conv, convnext, bn, affine, dc)
acc1, acc5 = accuracy(net, n_batches)
p_x.append(dc)
p_acc1.append(acc1)
p_acc5.append(acc5)
if DEBUG_Mario: print("Layer %s done.", conv)
p_dic[conv] = [p_x, p_acc1,
p_acc5] # load the dictionary with the ploting data
if convnext == convs[
-1]: # If conv is the last of the list prune convnext before leaving the loop
#(convnext will be the last pruned layer)
net = Net(pt, model=model, noTF=1) #reset the model
bn, affine = checkIfBN(noBNs, convnext, BNs_dic)
dc_init = net.conv_param_num_output(convnext)
dc = dc_init
p_x = [dc_init] #plot points of x_axis
p_acc1 = [acc1_init]
p_acc5 = [acc5_init]
for i in range(
p_Nx - 1
): #the first point was already added (initial conditions), reduce range by 1
dc = round(dc - dc_init / p_Nx)
net = pseudoPruneLastLayer(net, convnext, bn, affine, dc)
acc1, acc5 = accuracy(net, n_batches)
p_x.append(dc)
p_acc1.append(acc1)
p_acc5.append(acc5)
if DEBUG_Mario: print("Last layer %s done.", convnext)
p_dic[convnext] = [p_x, p_acc1, p_acc5
] # load the dictionary with the ploting data
### End of the for-loop####
saver(graph_title, p_dic)
return p_dic