# This file is the old version, used to compute activation
# but the order in act.npy is wrong
import numpy as np
from dnnbrain.dnn.models import AlexNet
from dnnbrain.dnn.core import Mask, Stimulus
from os.path import join as pjoin
# load pic to compute activation
main_path = '/nfs/s2/userhome/zhouming/workingdir/numerosity/stimulus'
out_path = '/nfs/s2/userhome/zhouming/workingdir/numerosity/out/activation'
stim_path = '/nfs/s2/userhome/zhouming/workingdir/numerosity/stim/csv/sci_BD.stim.csv'
dnn = AlexNet()
stim = Stimulus()
stim.load(stim_path)
layer_all = ['fc1_relu', 'fc2_relu']
chn = 'all'
# start computing
for layer in layer_all:
mask = Mask(layer,chn)
act = dnn.compute_activation(stim, mask).get(layer)
np.save(pjoin(out_path, f'act_{layer}_test_numerosity.npy'), act)
del act
# using activation to select the numerosity unit
#for stim in stim_set:
# stim_path = pjoin(main_path, stim)
# pic_all = np.zeros((len(os.listdir(stim_path)), 3, 224, 224), dtype=np.uint8)
# for idx,pic_name in enumerate(os.listdir(stim_path)):
# pic_path = pjoin(stim_path, pic_name)
# pic = plt.imread(pic_path).astype(np.uint8)
if net == 'AlexNet':
layer_name = [
'conv1' + pf, 'conv2' + pf, 'conv3' + pf, 'conv4' + pf, 'conv5' + pf,
'fc1' + pf, 'fc2' + pf
]
elif net == 'Vgg11':
layer_name = [
'conv1' + pf, 'conv2' + pf, 'conv3' + pf, 'conv4' + pf, 'conv5' + pf,
'conv6' + pf, 'conv7' + pf, 'conv8' + pf, 'fc1' + pf, 'fc2' + pf
]
#%% Load DNN, stimuli and define ablated models
from dnnbrain.dnn import models as db_models # used by eval
dnn = eval('db_models.{}()'.format(net)) # load DNN
stimuli = Stimulus()
stimuli.load(stim_path) # load stimuli
dmask = gen_dmask(layer_name) # generate DNN mask
# permut weights pretrained network for each layer, finest granularity purmutation
def permut_weight_model(dnn):
new_dict = {}
for key in dnn.model.state_dict().keys():
if key.split('.')[-1] == 'weight':
para = dnn.model.state_dict()[key]
ori_shape = para.shape
new_dict[key] = para.view(-1)[torch.randperm(
*para.view(-1).size())].reshape(ori_shape)
else:
new_dict[key] = dnn.model.state_dict()[key]
# print preliminary selection number
# print(class_name[:5], ' has ', len(filtered_class_path), ' images\n')
# construct data dict
data = dict()
data['stimID'] = class_path
# create .stim.csv
stim_file = StimulusFile(file_path)
# write .stim.csv
stim_file.write('image', pjoin(data_path, train_fold), data,
class_index={'index': index, 'name': class_name})
del class_path, label_number, class_name
# generate activation
# create stimulus
stimuli = Stimulus()
stimuli.load(file_path)
# create mask for absolute value
dmask = Mask()
layer = 'fc'
# activation value
dmask.set(layer, channels='all')
# extract activation
dnn = Resnet152()
activation = np.array(dnn.compute_activation(stimuli, dmask).get(layer)).astype(np.float32)
activation = activation.squeeze()
# # delete .stim.csv file
# os.remove(file_path)
#stim.load('sci_test_simple.stim.csv')
#act_fc1_relu = dnn.compute_activation(stim, mask).get(layer)
#np.save('act_fc1_relu_test_simple.npy', act_fc1_relu)
#
#layer = 'fc2_relu'
#chn = 'all'
#mask = Mask(layer, chn)
#stim = Stimulus()
#stim.load('sci_test_simple.stim.csv')
#act_fc2_relu = dnn.compute_activation(stim, mask).get(layer)
#np.save('act_fc2_relu_test_simple.npy', act_fc2_relu)
layer = 'fc3'
chn = 'all'
mask = Mask(layer, chn)
stim = Stimulus()
stim.load('sci_test_simple.stim.csv')
act_fc3 = dnn.compute_activation(stim, mask).get(layer)
np.save('act_fc3_test_simple.npy', act_fc3)
layer = 'fc1_relu'
chn = 'all'
mask = Mask(layer, chn)
stim = Stimulus()
stim.load('sci_test.stim.csv')
act_fc1_relu = dnn.compute_activation(stim, mask).get(layer)
np.save('act_fc1_relu_test.npy', act_fc1_relu)
layer = 'fc2_relu'
chn = 'all'
mask = Mask(layer, chn)
import numpy as np
from dnnbrain.dnn.core import Stimulus, Mask
from dnnbrain.dnn import models as db_models
model = eval('db_models.{}()'.format('alexnet'))
stim = Stimulus()
stim.load(r"C:\Users\xuwei\Desktop\test\test.dmask.csv")
dmask = Mask(r"C:\Users\xuwei\Desktop\test\test.stim.csv")
activation = model.compute_activation(stim, dmask)
activation.layers
activation.get('conv5')