def test_qparams_init():
# net = vgg16(pretrained=True)
net = mobilenet1_0(pretrained=True)
convert_model(net)
qparams_init(net)
input = nd.random.uniform(shape=(1, 3, 224, 224))
print(net(input))
def test_collect_qparams():
net = vgg16(pretrained=True)
convert_model(net)
qparams_init(net)
print("collect_qparams: ", collect_qparams(net))
print("")
print_all_qparams(net)
def test_quantized_mobilnet():
x = nd.uniform(shape=(1, 3, 224, 224))
from models.quantized_mobilenet import mobilenet1_0 as my_mobilenet
my_net = my_mobilenet(pretrained=True)
my_res = my_net(x)
from gluoncv.model_zoo import mobilenet1_0 as ref_mobilenet
ref_net = ref_mobilenet(pretrained=True)
ref_res = ref_net(x)
sim_net = ref_mobilenet(pretrained=True)
convert_model(sim_net, exclude=[sim_net.features[0]])
qparams_init(sim_net)
sim_res = sim_net(x)
# print(">> Difference between my_quantized_mobilenet and simulated_mobilenet -- ")
# print(">> ", ((my_res - sim_res).abs() / sim_res.abs()).reshape(-1).sort()[-100:])
return (my_res.argsort(is_ascend=False)[0, :20],
ref_res.argsort(is_ascend=False)[0, :20],
sim_res.argsort(is_ascend=False)[0, :20])
exclude_blocks = []
if opt.exclude_first_conv == 'true':
exclude_blocks.extend([net.features[0], net.features[1]])
if model_name.startswith('mobilenetv2_'):
exclude_blocks.append(net.output[0])
if model_name.startswith('cifar_resnet'):
exclude_blocks.extend(
[net.features[2][0].body[0], net.features[2][0].body[1]])
print('*' * 25 + ' Exclude blocks ' + '*' * 25)
for b in exclude_blocks:
print(b.name)
print('*' * (25 * 2 + len(' Exclude blocks ')))
print()
convert.convert_model(
net,
exclude=exclude_blocks,
convert_fn=convert_fn,
)
# initialize for quantization parameters and reset context
qparams_init(net)
ctx = gpu(opt.use_gpu) if opt.use_gpu != -1 else cpu()
net.collect_params().reset_ctx(ctx)
# construct transformer
if opt.dataset == 'imagenet':
eval_transformer = T.Compose([
T.Resize(256, keep_ratio=True),
T.CenterCrop(224),
T.ToTensor(),
T.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])
def test_merge_bn():
net = mobilenet1_0(pretrained=True)
convert_model(net)
qinit(net)
_merge_bn(net)