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init_net_surgery.py
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init_net_surgery.py
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import numpy as np
import os
os.environ["GLOG_minloglevel"] = "2"
import sys
sys.path.insert(0,'/data1/ravikiran/SketchObjPartSegmentation/src/caffe-switch/caffe/python')
import re
import caffe
import numpy as np
import skimage.io
import torch
import cv2
from torch.autograd import Variable
import torchvision.models as models
import torch.nn.functional as F
import deeplab_resnet
from collections import OrderedDict
class CaffeParamProvider():
def __init__(self, caffe_net):
self.caffe_net = caffe_net
def conv_kernel(self, name):
k = self.caffe_net.params[name][0].data
return k
def conv_biases(self, name):
k = self.caffe_net.params[name][1].data
return k
def bn_gamma(self, name):
return self.caffe_net.params[name][0].data
def bn_beta(self, name):
return self.caffe_net.params[name][1].data
def bn_mean(self, name):
return (self.caffe_net.params[name][0].data/self.caffe_net.params[name][2].data)
def bn_variance(self, name):
return (self.caffe_net.params[name][1].data/self.caffe_net.params[name][2].data)
def fc_weights(self, name):
w = self.caffe_net.params[name][0].data
#w = w.transpose((1, 0))
return w
def fc_biases(self, name):
b = self.caffe_net.params[name][1].data
return b
def preprocess(out):
"""Changes RGB [0,1] valued image to BGR [0,255] with mean subtracted."""
#out = np.copy(img) * 255.0
out = out[:, :, [2, 1, 0]] # swap channel from RGB to BGR
out[0] -= 104.008
out[1] -= 116.669
out[2] -= 122.675
return out
def assert_almost_equal(caffe_tensor, th_tensor):
t = th_tensor[0]
c = caffe_tensor[0]
if t.shape != c.shape:
print "t.shape", t.shape
print "c.shape", c.shape
d = np.linalg.norm(t - c)
print "d", d
assert d < 500
def dist_(caffe_tensor, th_tensor):
t = th_tensor[0]
c = caffe_tensor[0]
if t.shape != c.shape:
print "t.shape", t.shape
print "c.shape", c.shape
d = np.linalg.norm(t - c)
print "d", d
# returns image of shape [321, 321, 3]
# [height, width, depth]
def load_image(path, size=321):
img = cv2.imread(path)
resized_img = cv2.resize(img,(size,size)).astype(float)
return resized_img
def load_caffe(img_p, ):
caffe.set_mode_cpu()
#caffe.set_device(0)
prototxt = "data/test_Scale1.prototxt"
caffemodel = "data/init.caffemodel"
net = caffe.Net(prototxt,caffemodel, caffe.TEST)
net.blobs['data'].data[0] = img_p.transpose((2, 0, 1))
assert net.blobs['data'].data[0].shape == (3, 321, 321)
net.forward()
return net
def parse_pth_varnames(p, pth_varname, num_layers):
# this function has been modified to fix #4
post = ''
EXP = 'voc12'
# Scale3.layer5.conv2d_list.2.bias
if ('weight' in pth_varname and 'conv2d_list' in pth_varname):
# #print ('res%d%s_branch%d%s'+post) % x
if len(post)!=0:
post = post[1:]+'_'
y = (EXP,int(pth_varname[25]))
return p.conv_kernel(('fc1_%s_'+post+ 'c%d') % y)
if ('bias' in pth_varname and 'conv2d_list' in pth_varname):
# #print ('res%d%s_branch%d%s'+post) % x
if len(post)!=0:
post = post[1:]+'_'
y = (EXP,int(pth_varname[25]))
return p.conv_biases(('fc1_%s_'+post+'c%d') % y)
if pth_varname == 'Scale.conv1.weight':
return p.conv_kernel('conv1'+post)
elif pth_varname == 'Scale.bn1.weight':
return p.bn_gamma('scale_conv1'+post)
elif pth_varname == 'Scale.bn1.bias':
return p.bn_beta('scale_conv1'+post)
elif pth_varname == 'Scale.bn1.running_mean':
return p.bn_mean('bn_conv1'+post)
elif pth_varname == 'Scale.bn1.running_var':
return p.bn_variance('bn_conv1'+post)
elif pth_varname == 'fc.weight':
return p.fc_weights('fc1000')
elif pth_varname == 'fc.bias':
return p.fc_biases('fc1000')
re1 = 'Scale.layer(\d+).(\d+).(downsample|conv1|bn1|conv2|bn2|conv3|bn3)' #changed to handle issue #4
m = re.search(re1, pth_varname)
def letter(i):
return chr(ord('a') + i - 1)
scale_num = int(m.group(1)) + 1
block_num = int(m.group(2)) + 1
if scale_num == 2:
# scale 2 uses block letters
block_str = letter(block_num)
elif scale_num == 3 or scale_num == 4:
# scale 3 uses numbered blocks
# scale 4 uses numbered blocks
if num_layers == 50:
block_str = letter(block_num)
else:
if block_num == 1:
block_str = 'a'
else:
block_str = 'b%d' % (block_num - 1)
elif scale_num == 5:
# scale 5 uses block letters
block_str = letter(block_num)
else:
raise ValueError("unexpected scale_num %d" % scale_num)
branch = m.group(3)
if branch == "downsample":
branch_num = 1
conv_letter = ''
else:
branch_num = 2
conv_letter = letter(int(branch[-1]))
x = (scale_num, block_str, branch_num, conv_letter)
if ('weight' in pth_varname and 'conv' in pth_varname) or 'downsample.0.weight' in pth_varname:
return p.conv_kernel(('res%d%s_branch%d%s'+post) % x)
if ('weight' in pth_varname and 'bn' in pth_varname) or 'downsample.1.weight' in pth_varname:
return p.bn_gamma(('scale%d%s_branch%d%s'+post) % x)
if ('bias' in pth_varname and 'bn' in pth_varname) or 'downsample.1.bias' in pth_varname:
return p.bn_beta(('scale%d%s_branch%d%s'+post) % x)
if ('running_mean' in pth_varname and 'bn' in pth_varname) or 'downsample.1.running_mean' in pth_varname:
return p.bn_mean(('bn%d%s_branch%d%s'+post) % x)
if ('running_var' in pth_varname and 'bn' in pth_varname) or 'downsample.1.running_var' in pth_varname:
return p.bn_variance(('bn%d%s_branch%d%s'+post) % x)
raise ValueError('unhandled var ' + pth_varname)
def convert(img_p, layers):
caffe_model = load_caffe(img_p)
param_provider = CaffeParamProvider(caffe_model)
model = deeplab_resnet.Res_Deeplab(21)
old_dict = model.state_dict()
new_state_dict = OrderedDict()
keys = model.state_dict().keys()
for var_name in keys[:]:
data = parse_pth_varnames(param_provider, var_name, layers)
new_state_dict[var_name] = torch.from_numpy(data).float()
model.load_state_dict(new_state_dict)
o = []
def hook(module, input, output):
#print module
o.append(input[0].data.numpy())
model.Scale.conv1.register_forward_hook(hook) #0, data
model.Scale.bn1.register_forward_hook(hook) #1 conv1 out
model.Scale.relu.register_forward_hook(hook) #2 batch norm out
model.Scale.maxpool.register_forward_hook(hook) #3 bn1, relu out
model.Scale.layer1._modules['0'].conv1.register_forward_hook(hook) #4, pool1 out
model.Scale.layer1._modules['1'].conv1.register_forward_hook(hook) #5, res2a out
model.Scale.layer5.conv2d_list._modules['0'].register_forward_hook(hook) #6, res5c out
model.eval()
output = model(Variable(torch.from_numpy(img_p[np.newaxis, :].transpose(0,3,1,2)).float(),volatile=True))
dist_(caffe_model.blobs['data'].data,o[0])
dist_(caffe_model.blobs['conv1'].data,o[3])
dist_(caffe_model.blobs['pool1'].data,o[4])
dist_(caffe_model.blobs['res2a'].data,o[5])
dist_(caffe_model.blobs['res5c'].data,o[6])
dist_(caffe_model.blobs['fc1_voc12'].data,output[3].data.numpy())
print 'input image shape',img_p[np.newaxis, :].transpose(0,3,1,2).shape
print 'output shapes -'
for a in output:
print a.data.numpy().shape
torch.save(model.state_dict(),'data/MS_DeepLab_resnet_pretrained_COCO_init.pth')
def main():
img = load_image("data/cat.jpg")
img_p = preprocess(img)
print "CONVERTING Multi-scale DeepLab_resnet COCO init"
convert(img_p, layers = 101)
if __name__ == '__main__':
main()