def train():
net = plate_recognition_net(train=True)
model = mx.model.FeedForward(symbol=net,
ctx=mx.gpu() if list_gpus() else mx.cpu(),
num_epoch=1,
learning_rate=0.001,
wd=0.00001,
initializer=mx.init.Xavier(factor_type="in",
magnitude=2.34),
momentum=0.9)
batch_size = 20
# data_train = PlateRecognitionIter(2000000, batch_size, 7, 30, 120)
data_train = PlateRecognitionIter(5000000, batch_size, 7, 30, 120)
data_test = PlateRecognitionIter(1000, batch_size, 7, 30, 120)
import logging
logging.basicConfig(level=logging.DEBUG,
format='%(asctime)-15s %(message)s')
model.fit(X=data_train,
eval_data=data_test,
eval_metric=accuracy,
batch_end_callback=mx.callback.Speedometer(batch_size, 50))
model.save("models/cnn-ocr")
def recognize_one(img_filename):
img = cv2.imread(img_filename)
img = cv2.resize(img, (120, 30))
img = np.swapaxes(img, 0, 2)
img = np.swapaxes(img, 1, 2)
batch_size = 1
_, arg_params, __ = mx.model.load_checkpoint("models/cnn-ocr", 1)
data_shape = [("data", (batch_size, 3, 30, 120))]
input_shapes = dict(data_shape)
sym = plate_recognition_net(train=False)
executor = sym.simple_bind(ctx=mx.gpu() if list_gpus() else mx.cpu(),
**input_shapes)
for key in executor.arg_dict.keys():
if key in arg_params:
arg_params[key].copyto(executor.arg_dict[key])
executor.forward(is_train=True, data=mx.nd.array([img]))
probs = executor.outputs[0].asnumpy()
line = ''
for i in range(probs.shape[0]):
if i == 0:
result = np.argmax(probs[i][0:31])
elif i == 1:
result = np.argmax(probs[i][41:65]) + 41
else:
result = np.argmax(probs[i][31:65]) + 31
line += chars[result]
print('recognized as: ' + line)
def _get_nGPUs(self):
# caution: if not using all the GPUs, please mannually set nGPUs
nGPUs = len(test_utils.list_gpus())
# for CPU
nGPUs = nGPUs if nGPUs > 0 else 1
return nGPUs
def _get_num_devices(self):
# caution: if not using all the GPUs, please mannually set num_devices
num_devices = len(test_utils.list_gpus())
# for CPU
num_devices = num_devices if num_devices > 0 else 1
return num_devices
def try_gpu():
if len(test_utils.list_gpus()) > 0:
return mx.gpu()
else:
return mx.cpu()
def _get_num_devices(self):
# caution: if not using all the GPUs, please mannually set num_devices
num_devices = len(test_utils.list_gpus())
# for CPU
num_devices = num_devices if num_devices > 0 else 1
return num_devices
from mydataset import _test_data
from mxnet.gluon.data.vision import transforms
from mxnet.gluon.data import DataLoader
from myNet import resnet18
from mxnet import cpu, gpu
from mxnet import ndarray as nd
from mxnet.test_utils import list_gpus
import pandas as pd
BATCH_SIZE = 1
MODEL_PATH = 'resnet18.params'
if list_gpus():
CTX = gpu()
else:
CTX = cpu()
transform_test = transforms.Compose([
transforms.ToTensor(),
transforms.Normalize([0.4914, 0.4822, 0.4465], [0.2023, 0.1994, 0.2010])
])
test_dataloader = DataLoader(_test_data.transform_first(transform_test),
batch_size=BATCH_SIZE,
shuffle=True,
last_batch='keep')
net = resnet18(10)
net.load_parameters(MODEL_PATH, ctx=CTX)
# net.initialize(ctx=CTX)