下面提供了caffe python的六个测试demo，大家可以根据自己的需求进行修改。
Example 1
- From project FaceDetection_CNN-master, under directory , in source file test.py.
def convert_full_conv():
# Load the original network and extract the fully connected layers' parameters.
net = caffe.Net('deploy.prototxt',
'alexNet__iter_60000.caffemodel',
caffe.TEST)
params = ['fc6', 'fc7', 'fc8_flickr']
fc_params = {pr: (net.params[pr][0].data, net.params[pr][1].data) for pr in params}
# Load the fully convolutional network to transplant the parameters.
net_full_conv = caffe.Net('face_full_conv.prototxt',
'alexNet__iter_60000.caffemodel',
caffe.TEST)
params_full_conv = ['fc6-conv', 'fc7-conv', 'fc8-conv']
conv_params = {pr: (net_full_conv.params[pr][0].data, net_full_conv.params[pr][1].data) for pr in params_full_conv}
for pr, pr_conv in zip(params, params_full_conv):
conv_params[pr_conv][0].flat = fc_params[pr][0].flat # flat unrolls the arrays
conv_params[pr_conv][1][...] = fc_params[pr][1]
net_full_conv.save('face_full_conv.caffemodel')
Example 2
- From project visual-concepts-master, under directory , in source file test_model.py.
def load_model(prototxt_file, model_file, base_image_size, mean, vocab):
"""
Load the model from file. Includes pointers to the prototxt file,
caffemodel file name, and other settings - image mean, base_image_size, vocab
"""
model = {};
model['net']= caffe.Net(prototxt_file, model_file, caffe.TEST);
model['base_image_size'] = base_image_size;
model['means'] = mean; model['vocab'] = vocab;
return model
Example 3
- From project SketchingAI-master, under directory src, in source file gendraw.py.
- Caffe中只给出了分类模型classify.py，如果想写预测模型predict.py可以参考这个
def test_old():
with open(labelspath,"r") as opened_file:
labels = opened_file.readlines()
caffe.set_mode_gpu()
net = caffe.Net(model_file, pretrained, caffe.TEST)
transformer = caffe.io.Transformer({"data": net.blobs["data"].data.shape})
transformer.set_transpose("data",(2,0,1))
transformer.set_mean("data",numpy.load(caffe_root+"/python/caffe/imagenet/ilsvrc_2012_mean.npy").mean(1).mean(1))
transformer.set_raw_scale("data",255)
transformer.set_channel_swap("data",(2,1,0))
net.blobs["data"].reshape(1,3,227,227)
test_image = dataroot+"/homecat.jpg"
test_image1 = dataroot+"/241.png"
net.blobs["data"].data[...] = transformer.preprocess("data", caffe.io.load_image(test_image1))
out = net.forward()
print net.blobs["fc6"].data.shape
prediction = out["prob"]
indices = numpy.argpartition(prediction[0],-10)[-10:]
print prediction[0].argmax(), labels[prediction[0].argmax()]
net.blobs["data"].data[...] = transformer.preprocess("data", caffe.io.load_image(test_image))
out = net.forward()
print net.blobs["fc6"].data.shape
prediction = out["prob"]
indices = numpy.argpartition(prediction[0],-10)[-10:]
print prediction[0].argmax(), labels[prediction[0].argmax()]
for index in indices:
print labels[index]
Example 4
- From project fast-rcnn-master, under directory tools, in source file compress_net.py.
def main():
args = parse_args()
net = caffe.Net(args.prototxt, args.caffemodel, caffe.TEST)
net_svd = caffe.Net(args.prototxt_svd, args.caffemodel, caffe.TEST)
print('Uncompressed network {} : {}'.format(args.prototxt, args.caffemodel))
print('Compressed network prototxt {}'.format(args.prototxt_svd))
out = os.path.splitext(os.path.basename(args.caffemodel))[0] + '_svd'
out_dir = os.path.dirname(args.caffemodel)
# Compress fc6
if net_svd.params.has_key('fc6_L'):
l_fc6 = net_svd.params['fc6_L'][0].data.shape[0]
print(' fc6_L bottleneck size: {}'.format(l_fc6))
# uncompressed weights and biases
W_fc6 = net.params['fc6'][0].data
B_fc6 = net.params['fc6'][1].data
print(' compressing fc6...')
Ul_fc6, L_fc6 = compress_weights(W_fc6, l_fc6)
assert(len(net_svd.params['fc6_L']) == 1)
# install compressed matrix factors (and original biases)
net_svd.params['fc6_L'][0].data[...] = L_fc6
net_svd.params['fc6_U'][0].data[...] = Ul_fc6
net_svd.params['fc6_U'][1].data[...] = B_fc6
out += '_fc6_{}'.format(l_fc6)
# Compress fc7
if net_svd.params.has_key('fc7_L'):
l_fc7 = net_svd.params['fc7_L'][0].data.shape[0]
print ' fc7_L bottleneck size: {}'.format(l_fc7)
W_fc7 = net.params['fc7'][0].data
B_fc7 = net.params['fc7'][1].data
print(' compressing fc7...')
Ul_fc7, L_fc7 = compress_weights(W_fc7, l_fc7)
assert(len(net_svd.params['fc7_L']) == 1)
net_svd.params['fc7_L'][0].data[...] = L_fc7
net_svd.params['fc7_U'][0].data[...] = Ul_fc7
net_svd.params['fc7_U'][1].data[...] = B_fc7
out += '_fc7_{}'.format(l_fc7)
filename = '{}/{}.caffemodel'.format(out_dir, out)
net_svd.save(filename)
print 'Wrote svd model to: {:s}'.format(filename)
Example 5
- From project DIGITS-master, under directory digits/model/tasks, in source file caffe_train.py.
def get_net(self, epoch=None):
"""
Returns an instance of caffe.Net
Keyword Arguments:
epoch -- which snapshot to load (default is -1 to load the most recently generated snapshot)
"""
if not self.has_model():
return False
file_to_load = None
if not epoch:
epoch = self.snapshots[-1][1]
file_to_load = self.snapshots[-1][0]
else:
for snapshot_file, snapshot_epoch in self.snapshots:
if snapshot_epoch == epoch:
file_to_load = snapshot_file
break
if file_to_load is None:
raise Exception('snapshot not found for epoch "%s"' % epoch)
# check if already loaded
if self.loaded_snapshot_file and self.loaded_snapshot_file == file_to_load
and hasattr(self, '_caffe_net') and self._caffe_net is not None:
return self._caffe_net
if config_value('caffe_root')['cuda_enabled'] and
config_value('gpu_list'):
caffe.set_mode_gpu()
# load a new model
self._caffe_net = caffe.Net(
self.path(self.deploy_file),
file_to_load,
caffe.TEST)
self.loaded_snapshot_epoch = epoch
self.loaded_snapshot_file = file_to_load
return self._caffe_net
Example 6
- From project DIGITS-master, under directory examples/classification, in source file example.py.
def get_net(caffemodel, deploy_file, use_gpu=True):
"""
Returns an instance of caffe.Net
Arguments:
caffemodel -- path to a .caffemodel file
deploy_file -- path to a .prototxt file
Keyword arguments:
use_gpu -- if True, use the GPU for inference
"""
if use_gpu:
caffe.set_mode_gpu()
# load a new model
return caffe.Net(deploy_file, caffemodel, caffe.TEST)