def test_json_to_prototxt(self):
tests = open(
os.path.join(settings.BASE_DIR, 'tests', 'unit', 'ide',
'caffe_export_test.json'), 'r')
response = json.load(tests)
tests.close()
net = yaml.safe_load(json.dumps(response['net']))
net = {'l0': net['Input']}
prototxt, input_dim = jsonToPrototxt(net, response['net_name'])
self.assertEqual(net['l0']['info']['type'], 'Input')
def exportToCaffe(request):
if request.method == 'POST':
net = yaml.safe_load(request.POST.get('net'))
net_name = request.POST.get('net_name')
if net_name == '':
net_name = 'Net'
prototxt,input_dim = jsonToPrototxt(net,net_name)
randomId=datetime.now().strftime('%Y%m%d%H%M%S')+randomword(5)
with open(BASE_DIR+'/media/'+randomId+'.prototxt', 'w') as f:
f.write(prototxt)
return JsonResponse({'result': 'success','id': randomId, 'name': randomId+'.prototxt', 'url': '/media/'+randomId+'.prototxt'})
def test_json_to_prototxt(self):
tests = open(
os.path.join(settings.BASE_DIR, 'tests', 'unit', 'ide',
'caffe_export_test.json'), 'r')
response = json.load(tests)
tests.close()
net = yaml.safe_load(json.dumps(response['net']))
net = {'l0': net['Input'], 'l1': net['Accuracy']}
net['l0']['connection']['output'].append('l1')
prototxt, input_dim = jsonToPrototxt(net, response['net_name'])
self.assertGreater(len(prototxt), 9)
self.assertEqual(net['l1']['info']['type'], 'Accuracy')
def exportToCaffe(request):
if request.method == 'POST':
net = yaml.safe_load(request.POST.get('net'))
net_name = request.POST.get('net_name')
if net_name == '':
net_name = 'Net'
prototxt, input_dim = jsonToPrototxt(net, net_name)
randomId = datetime.now().strftime('%Y%m%d%H%M%S')+randomword(5)
with open(BASE_DIR+'/media/'+randomId+'.prototxt', 'w') as f:
f.write(prototxt)
return JsonResponse({'result': 'success', 'id': randomId,
'name': randomId+'.prototxt', 'url': '/media/'+randomId+'.prototxt'})
def exportToTensorflow(request):
if request.method == 'POST':
net = yaml.safe_load(request.POST.get('net'))
net_name = request.POST.get('net_name')
if net_name == '':
net_name = 'Net'
prototxt, input_dim = jsonToPrototxt(net, net_name)
randomId = datetime.now().strftime('%Y%m%d%H%M%S') + randomword(5)
with open(BASE_DIR + '/media/' + randomId + '.prototxt', 'w') as f:
f.write(prototxt)
os.system('python ' + BASE_DIR +
'/tensorflow_app/caffe-tensorflow/convert.py ' + BASE_DIR +
'/media/' + randomId + '.prototxt --code-output-path=' +
BASE_DIR + '/media/' + randomId + '.py')
# NCHW to NHWC data format
input_caffe = map(int, input_dim.split(','))
input_tensorflow = []
for i in [0, 2, 3, 1]:
input_tensorflow.append(input_caffe[i])
# converting generated caffe-tensorflow code to graphdef
try:
net = __import__(str(randomId))
images = tf.placeholder(tf.float32, input_tensorflow)
net = getattr(net, net_name)({'blob0': images})
graph_def = tf.get_default_graph().as_graph_def(add_shapes=True)
with open(BASE_DIR + '/media/' + randomId + '.pbtxt', 'w') as f:
f.write(str(graph_def))
except AssertionError:
return JsonResponse({
'result': 'error',
'error': 'Cannot convert to GraphDef'
})
except AttributeError:
return JsonResponse({
'result': 'error',
'error': 'GraphDef not supported'
})
return JsonResponse({
'result': 'success',
'id': randomId,
'name': randomId + '.pbtxt',
'url': '/media/' + randomId + '.pbtxt'
})
def exportToTensorflow(request):
if request.method == 'POST':
net = yaml.safe_load(request.POST.get('net'))
net_name = request.POST.get('net_name')
if net_name == '':
net_name = 'Net'
# rename input layers to 'data'
inputs = get_inputs(net)
for i in inputs:
net[i]['props']['name'] = 'data'
prototxt,input_dim = jsonToPrototxt(net,net_name)
randomId=datetime.now().strftime('%Y%m%d%H%M%S')+randomword(5)
with open(BASE_DIR+'/media/'+randomId+'.prototxt', 'w') as f:
f.write(prototxt)
convert(BASE_DIR+'/media/'+randomId+'.prototxt', None, None, BASE_DIR+'/media/'+randomId+'.py', 'test')
# NCHW to NHWC data format
input_caffe = input_dim
input_tensorflow = []
for i in [0,2,3,1]:
input_tensorflow.append(input_caffe[i])
# converting generated caffe-tensorflow code to graphdef
try:
net = __import__ (str(randomId))
images = tf.placeholder(tf.float32, input_tensorflow)
# the name of the first layer should be 'data' !
net = getattr(net, net_name)({'data': images})
graph_def = tf.get_default_graph().as_graph_def(add_shapes=True)
with open(BASE_DIR+'/media/'+randomId+'.pbtxt', 'w') as f: f.write(str(graph_def))
except AssertionError:
return JsonResponse({'result': 'error', 'error': 'Cannot convert to GraphDef'})
except AttributeError:
return JsonResponse({'result': 'error', 'error': 'GraphDef not supported'})
return JsonResponse({'result': 'success','id': randomId, 'name': randomId+'.pbtxt', 'url': '/media/'+randomId+'.pbtxt'})
def exportToTensorflow(request):
if request.method == 'POST':
net = yaml.safe_load(request.POST.get('net'))
net_name = request.POST.get('net_name')
if net_name == '':
net_name = 'Net'
prototxt, input_dim = jsonToPrototxt(net, net_name)
randomId = datetime.now().strftime('%Y%m%d%H%M%S') + randomword(5)
with open(BASE_DIR+'/media/'+randomId+'.prototxt', 'w') as f:
f.write(prototxt)
os.system('python ' + BASE_DIR + '/tensorflow_app/caffe-tensorflow/convert.py ' + BASE_DIR
+ '/media/' + randomId + '.prototxt --code-output-path=' + BASE_DIR +
'/media/' + randomId + '.py')
# NCHW to NHWC data format
input_caffe = map(int, input_dim.split(','))
input_tensorflow = []
for i in [0, 2, 3, 1]:
input_tensorflow.append(input_caffe[i])
# converting generated caffe-tensorflow code to graphdef
try:
net = __import__(str(randomId))
images = tf.placeholder(tf.float32, input_tensorflow)
net = getattr(net, net_name)({'blob0': images})
graph_def = tf.get_default_graph().as_graph_def(add_shapes=True)
with open(BASE_DIR+'/media/'+randomId+'.pbtxt', 'w') as f:
f.write(str(graph_def))
except AssertionError:
return JsonResponse({'result': 'error', 'error': 'Cannot convert to GraphDef'})
except AttributeError:
return JsonResponse({'result': 'error', 'error': 'GraphDef not supported'})
return JsonResponse({'result': 'success', 'id': randomId, 'name': randomId+'.pbtxt',
'url': '/media/'+randomId+'.pbtxt'})
