def testSingleImg():
NetHelper.gpu()
#submission()
nh = NetHelper(deploy=cfgs.deploy_pt, model=cfgs.best_model_dir)
img = Data.imFromFile(os.path.join(cfgs.train_mask_path, "1_1_mask.tif"))
res = nh.bin_pred_map(img)
print(np.histogram(res))
def testSingleImg():
NetHelper.gpu()
#submission()
nh=NetHelper(deploy=cfgs.deploy_pt,model=cfgs.best_model_dir)
img=Data.imFromFile(os.path.join(cfgs.train_mask_path,"1_1_mask.tif"))
res=nh.bin_pred_map(img)
print(np.histogram(res))
def submission():
NetHelper.gpu(2)
#submission()
nh = NetHelper(deploy=cfgs.deploy_pt, model=cfgs.best_model_dir)
if debug:
l = Data.folder_opt(cfgs.train_data_path, func, nh)
else:
l = Data.folder_opt(cfgs.test_data_path, func, nh)
l = np.array(l, dtype=[('x', int), ('y', object)])
l.sort(order='x')
first_row = 'img,pixels'
file_name = 'submission.csv'
with open(file_name, 'w+') as f:
f.write(first_row)
for i in l:
s = str(i[0]) + ',' + i[1]
f.write(('\n' + s))
def submission():
NetHelper.gpu(2)
#submission()
nh=NetHelper(deploy=cfgs.deploy_pt,model=cfgs.best_model_dir)
if debug:
l=Data.folder_opt(cfgs.train_data_path,func,nh)
else:
l=Data.folder_opt(cfgs.test_data_path,func,nh)
l=np.array(l,dtype=[('x',int),('y',object)])
l.sort(order='x')
first_row = 'img,pixels'
file_name = 'submission.csv'
with open(file_name, 'w+') as f:
f.write(first_row)
for i in l:
s = str(i[0]) + ',' + i[1]
f.write(('\n'+s))
def train_model(self):
for iter in range(500 * 2000):
if debug:
if iter % 100 == 0 and iter != 0:
nethelper = NetHelper(self.solver.net)
# nethelper.hist('label')
# nethelper.hist('prob', filters=2,attr="blob")
# nethelper.hist('data', filters=2,attr="blob")
if False:
for i in range(
nethelper.net.blobs['data'].data.shape[0]):
plt.subplot(221)
plt.imshow(nethelper.net.blobs['data'].data[i, 0])
plt.subplot(222)
plt.imshow(nethelper.net.blobs['prob'].data[i, 0])
plt.subplot(223)
plt.imshow(nethelper.net.blobs['label'].data[i, 0])
plt.show()
self.solver.step(1)
weights = cfgs.init
# init
caffe.set_device(2)
caffe.set_mode_gpu()
# caffe.set_mode_cpu()
solver = caffe.SGDSolver(cfgs.solver_pt)
if weights is not None:
solver.net.copy_from(weights)
for iter in range(500*2000):
if debug:
if iter % 100 == 0 and iter !=0:
nethelper=NetHelper(solver.net)
# nethelper.hist('data')
# nethelper.hist('label')
nethelper.hist('prob', filters=2,attr="blob")
if False:
for i in range(nethelper.net.blobs['data1'].data.shape[0]):
plt.subplot(221)
plt.imshow(nethelper.net.blobs['data1'].data[i,0])
plt.subplot(222)
plt.imshow(nethelper.net.blobs['prob'].data[i,0])
plt.subplot(223)
plt.imshow(nethelper.net.blobs['label'].data[i,0])
plt.show()
import cfgs_res as cfgs
#import score
#import surgery
import os
# gen solver prototxt
# solver=CaffeSolver(debug=cfgs.debug)
# solver.sp=cfgs.sp.copy()
# solver.write(cfgs.solver_pt)
debug = True
weights = cfgs.init
# init
caffe.set_device(1)
caffe.set_mode_gpu()
# caffe.set_mode_cpu()
solver = caffe.SGDSolver(cfgs.solver_pt)
if weights is not None:
solver.net.copy_from(weights)
for iter in range(500 * 2000):
if debug:
if iter % 100 == 0:
nethelper = NetHelper(solver.net)
# nethelper.hist('res5c_branch2c',attr="param")
solver.step(1)