def _model(self):
gpu_count = mx.context.num_gpus()
ctx = mx.gpu() if gpu_count > 0 else mx.cpu()
if self.ml_model_name and self.ml_model_name.split(
'_')[0] == 'resnet18':
model = vision.resnet18_v1(classes=len(self.LABELS), ctx=ctx)
else:
model = vision.mobilenet_v2_1_0(classes=len(self.LABELS), ctx=ctx)
model_path = Path(self.ml_model.path) / Path(
os.listdir(self.ml_model.path)[0])
model.load_parameters(str(model_path), ctx=ctx)
def get_value(img: Array3D) -> Tuple[Union[str, Tuple], float]:
data = img.reshape((1, ) + img.shape)
data = mx.nd.array(data)
scores = model(mx.nd.array(self.img_transforms(data),
ctx=ctx)).softmax().asnumpy()
val = self.LABELS[int(np.argmax(scores, axis=1)[0])]
prob = scores[0][int(np.argmax(scores, axis=1)[0])]
return val, prob
return get_value
def mobilenetmxnetload():
net = vision.mobilenet_v2_1_0(pretrained=True)
net.hybridize()
return net
out = out.as_in_context(mx.cpu())
out = out.asnumpy()
res = ''
for item in out:
res += str(item) +' '
#print(res)
fw1.write(res+'\n')
label = root.strip().split('/')[-1]
if(train == True):
fw2.write(label+'\n')
print(label)
else:
fw2.write(file+'\n')
print(file)
fw1.close()
fw2.close()
print('net done!!!')
pretrained_net = models.mobilenet_v2_1_0(pretrained=True)
net = nn.HybridSequential()
for layer in pretrained_net.features:
net.add(layer)
print(net)
ctx = mx.gpu()
net.collect_params().reset_ctx(ctx)
net.hybridize()
dir = 'data/test_b'
suffix=['jpg']
batch_net(dir,suffix,net,train=False)
def load_mobilenet_v2_1_0(ctx):
return vision.mobilenet_v2_1_0(pretrained=True, ctx=ctx, prefix="")
import sys, os
import numpy as np
import matplotlib.pyplot as plt
import matplotlib.axes as axes
from mxnet.gluon import loss as gloss, nn
from mxnet.gluon.model_zoo import vision
from mxnet.gluon import data as gdata
from common_mx import *
if __name__ == '__main__':
prefix = 'model_zoo'
syms = prefix + '-symbol.json'
params = prefix + '-0000.params'
onnx_file = prefix + '.onnx'
input_shape = (1, 3, 224, 224)
mobile_net = vision.mobilenet_v2_1_0(pretrained=True, ctx=ctx)
mobile_net.hybridize()
mobile_net(mx.nd.ones(input_shape, ctx=ctx))
mobile_net.export(prefix)
# convert to onnx
onnx_model_path = onnx_mxnet.export_model(syms, params, [input_shape],
np.float32, onnx_file)
# Load onnx model
model_proto = onnx.load_model(onnx_model_path)
# Check if converted ONNX protobuf is valid
checker.check_graph(model_proto.graph)
##################
# Hyperparameter #
#----------------#
ctx = mx.cpu()
lr = 0.05
epochs = 10
momentum = 0.9
batch_size = 64
#----------------#
# Hyperparameter #
##################
################## model
from mxnet.gluon.model_zoo import vision
net = vision.mobilenet_v2_1_0(classes=10, pretrained=False, ctx=ctx)
# net = vision.mobilenet_v2_0_75(classes=10, pretrained=False, ctx=ctx)
# net = vision.mobilenet_v2_0_5(classes=10, pretrained=False, ctx=ctx)
# net = vision.mobilenet_v2_0_25(classes=10, pretrained=False, ctx=ctx)
################## 그래프
import gluoncv
inputShape = (1, 3, 224, 224)
gluoncv.utils.viz.plot_network(net, shape=inputShape)
##### 전처리 ##############################################
def transformer(data, label):
data = mx.image.imresize(data, 224, 224)
data = mx.nd.transpose(data, (2, 0, 1))
data = data.astype(np.float32)