def main():
args = get_argument()
assert args.relu or args.relu == args.equalize, 'must replace relu6 to relu while equalization'
assert args.equalize or args.absorption == args.equalize, 'must use absorption with equalize'
data = torch.ones((4, 3, 224, 224)) #.cuda()
model = mobilenet_v2(
'modeling/classification/mobilenetv2_1.0-f2a8633.pth.tar')
model.eval()
transformer = TorchTransformer()
module_dict = {}
if args.quantize:
if args.trainable:
module_dict[1] = [(nn.Conv2d, QuantConv2d),
(nn.Linear, QuantLinear)]
else:
module_dict[1] = [(nn.Conv2d, QuantNConv2d),
(nn.Linear, QuantNLinear)]
if args.relu:
module_dict[0] = [(torch.nn.ReLU6, torch.nn.ReLU)]
# transformer.summary(model, data)
# transformer.visualize(model, data, 'graph_cls', graph_size=120)
model, transformer = switch_layers(model,
transformer,
data,
module_dict,
ignore_layer=[QuantMeasure],
quant_op=args.quantize)
graph = transformer.log.getGraph()
bottoms = transformer.log.getBottoms()
output_shape = transformer.log.getOutShapes()
if args.quantize:
if args.trainable:
targ_layer = [QuantConv2d, QuantLinear]
else:
targ_layer = [QuantNConv2d, QuantNLinear]
else:
targ_layer = [nn.Conv2d, nn.Linear]
model = merge_batchnorm(model, graph, bottoms, targ_layer)
#create relations
if args.equalize:
res = create_relation(graph, bottoms, targ_layer)
cross_layer_equalization(graph,
res,
targ_layer,
visualize_state=False,
converge_thres=2e-7)
if args.absorption:
bias_absorption(graph, res, bottoms, 3)
if args.clip_weight:
clip_weight(graph, range_clip=[-15, 15], targ_type=targ_layer)
if args.correction:
bias_correction(graph, bottoms, targ_layer)
if args.quantize:
if not args.trainable:
graph = quantize_targ_layer(graph, targ_layer)
set_quant_minmax(graph, bottoms, output_shape)
model = model.cuda()
model.eval()
if args.quantize:
replace_op()
acc = inference_all(model)
print("Acc: {}".format(acc))
if args.quantize:
restore_op()
cross_layer_equalization(graph,
res,
targ_layer,
visualize_state=False,
converge_thres=2e-7,
s_range=(1 / args.equal_range,
args.equal_range))
# if args.distill:
# set_scale(res, graph, bottoms, targ_layer)
if args.absorption:
bias_absorption(graph, res, bottoms, 3)
if args.clip_weight:
clip_weight(graph, range_clip=[-15, 15], targ_type=targ_layer)
if args.correction:
bias_correction(graph, bottoms, targ_layer)
if args.quantize:
if not args.trainable and not args.distill_range:
graph = quantize_targ_layer(graph, args.bits_weight,
args.bits_bias, targ_layer)
if args.distill_range:
set_update_stat(net, [QuantMeasure], True)
net = update_quant_range(net.cuda(),
data_distill,
graph,
bottoms,
def main():
args = get_argument()
assert args.relu or args.relu == args.equalize, 'must replace relu6 to relu while equalization'
assert args.equalize or args.absorption == args.equalize, 'must use absorption with equalize'
data = torch.ones((4, 3, 224, 224)) #.cuda()
if args.resnet:
import torchvision.models as models
model = models.resnet18(pretrained=True)
else:
model = mobilenet_v2(
'modeling/classification/mobilenetv2_1.0-f2a8633.pth.tar')
model.eval()
if args.distill_range:
import copy
# define FP32 model
model_original = copy.deepcopy(model)
model_original.eval()
transformer = TorchTransformer()
transformer._build_graph(model_original, data, [QuantMeasure])
graph = transformer.log.getGraph()
bottoms = transformer.log.getBottoms()
if not args.true_data:
data_distill = getDistilData(model_original, 'imagenet', args.dis_batch_size, bn_merged=False,\
num_batch=args.dis_num_batch, gpu=True, value_range=[-2.11790393, 2.64], size=[224, 224], early_break_factor=1.2 if args.resnet else 0.5)
else:
imagenet_dataset = datasets.ImageFolder(
'/home/jakc4103/windows/Toshiba/workspace/dataset/ILSVRC/Data/CLS-LOC/train',
transforms.Compose([
transforms.Resize(256),
transforms.CenterCrop(224),
transforms.ToTensor(),
transforms.Normalize(mean=[0.485, 0.456, 0.406],
std=[0.229, 0.224, 0.225]),
]))
data_distill = []
dataloader = DataLoader(imagenet_dataset,
batch_size=args.dis_batch_size,
shuffle=True,
num_workers=4,
pin_memory=True)
for idx, sample in enumerate(dataloader):
if idx >= args.dis_num_batch:
break
image = sample[0]
data_distill.append(image)
del dataloader, imagenet_dataset
transformer = TorchTransformer()
module_dict = {}
if args.quantize:
if args.distill_range:
module_dict[1] = [(nn.Conv2d, QConv2d), (nn.Linear, QLinear)]
elif args.trainable:
module_dict[1] = [(nn.Conv2d, QuantConv2d),
(nn.Linear, QuantLinear)]
else:
module_dict[1] = [(nn.Conv2d, QuantNConv2d),
(nn.Linear, QuantNLinear)]
if args.relu:
module_dict[0] = [(torch.nn.ReLU6, torch.nn.ReLU)]
# transformer.summary(model, data)
# transformer.visualize(model, data, 'graph_cls', graph_size=120)
model, transformer = switch_layers(model,
transformer,
data,
module_dict,
ignore_layer=[QuantMeasure],
quant_op=args.quantize)
graph = transformer.log.getGraph()
bottoms = transformer.log.getBottoms()
if args.quantize:
if args.distill_range:
targ_layer = [QConv2d, QLinear]
elif args.trainable:
targ_layer = [QuantConv2d, QuantLinear]
else:
targ_layer = [QuantNConv2d, QuantNLinear]
else:
targ_layer = [nn.Conv2d, nn.Linear]
if args.quantize:
set_layer_bits(graph, args.bits_weight, args.bits_activation,
args.bits_bias, targ_layer)
model = merge_batchnorm(model, graph, bottoms, targ_layer)
#create relations
if args.equalize or args.distill_range:
res = create_relation(graph, bottoms, targ_layer, delete_single=False)
if args.equalize:
cross_layer_equalization(graph,
res,
targ_layer,
visualize_state=False,
converge_thres=2e-7)
# if args.distill:
# set_scale(res, graph, bottoms, targ_layer)
if args.absorption:
bias_absorption(graph, res, bottoms, 3)
if args.clip_weight:
clip_weight(graph, range_clip=[-15, 15], targ_type=targ_layer)
if args.correction:
# if args.distill:
# model_original = copy.deepcopy(model.cpu())
# model_original.eval()
# transformer = TorchTransformer()
# transformer.register(targ_layer[0], nn.Conv2d)
# transformer.register(targ_layer[1], nn.Linear)
# model_original = transformer.trans_layers(model_original, update=True)
# bias_correction_distill(model, model_original, data_distill, targ_layer, [nn.Conv2d, nn.Linear])
# else:
bias_correction(graph,
bottoms,
targ_layer,
bits_weight=args.bits_weight)
if args.quantize:
if not args.trainable and not args.distill_range:
graph = quantize_targ_layer(graph, args.bits_weight,
args.bits_bias, targ_layer)
if args.distill_range:
set_update_stat(model, [QuantMeasure], True)
model = update_quant_range(model.cuda(), data_distill, graph,
bottoms)
set_update_stat(model, [QuantMeasure], False)
else:
set_quant_minmax(graph, bottoms)
torch.cuda.empty_cache()
# if args.distill:
# model = update_scale(model, model_original, data_distill, graph, bottoms, res, targ_layer, num_epoch=1000)
# set_quant_minmax(graph, bottoms)
model = model.cuda()
model.eval()
if args.quantize:
replace_op()
acc = inference_all(model)
print("Acc: {}".format(acc))
if args.quantize:
restore_op()
if args.log:
with open("cls_result.txt", 'a+') as ww:
ww.write(
"resnet: {}, quant: {}, relu: {}, equalize: {}, absorption: {}, correction: {}, clip: {}, distill_range: {}\n"
.format(args.resnet, args.quantize, args.relu, args.equalize,
args.absorption, args.correction, args.clip_weight,
args.distill_range))
ww.write("Acc: {}\n\n".format(acc))
def main():
args = get_argument()
assert args.relu or args.relu == args.equalize, 'must replace relu6 to relu while equalization'
assert args.equalize or args.absorption == args.equalize, 'must use absorption with equalize'
data = torch.ones((4, 3, 513, 513))#.cuda()
model = DeepLab(sync_bn=False)
state_dict = torch.load('modeling/segmentation/deeplab-mobilenet.pth.tar')['state_dict']
model.load_state_dict(state_dict)
model.eval()
if args.distill_range:
import copy
# define FP32 model
model_original = copy.deepcopy(model)
model_original.eval()
transformer = TorchTransformer()
transformer._build_graph(model_original, data, [QuantMeasure])
graph = transformer.log.getGraph()
bottoms = transformer.log.getBottoms()
data_distill = getDistilData(model_original, 'imagenet', 32, bn_merged=False,\
num_batch=8, gpu=True, value_range=[-2.11790393, 2.64], size=[513, 513], early_break_factor=0.2)
transformer = TorchTransformer()
module_dict = {}
if args.quantize:
if args.distill_range:
module_dict[1] = [(nn.Conv2d, QConv2d)]
elif args.trainable:
module_dict[1] = [(nn.Conv2d, QuantConv2d)]
else:
module_dict[1] = [(nn.Conv2d, QuantNConv2d)]
if args.relu:
module_dict[0] = [(torch.nn.ReLU6, torch.nn.ReLU)]
# transformer.summary(model, data)
# transformer.visualize(model, data, 'graph_deeplab', graph_size=120)
model, transformer = switch_layers(model, transformer, data, module_dict, ignore_layer=[QuantMeasure], quant_op=args.quantize)
graph = transformer.log.getGraph()
bottoms = transformer.log.getBottoms()
if args.quantize:
if args.distill_range:
targ_layer = [QConv2d]
elif args.trainable:
targ_layer = [QuantConv2d]
else:
targ_layer = [QuantNConv2d]
else:
targ_layer = [nn.Conv2d]
if args.quantize:
set_layer_bits(graph, args.bits_weight, args.bits_activation, args.bits_bias, targ_layer)
model = merge_batchnorm(model, graph, bottoms, targ_layer)
#create relations
if args.equalize or args.distill_range:
res = create_relation(graph, bottoms, targ_layer)
if args.equalize:
cross_layer_equalization(graph, res, targ_layer, visualize_state=False)
# if args.distill:
# set_scale(res, graph, bottoms, targ_layer)
if args.absorption:
bias_absorption(graph, res, bottoms, 3)
if args.clip_weight:
clip_weight(graph, range_clip=[-15, 15], targ_type=targ_layer)
if args.correction:
bias_correction(graph, bottoms, targ_layer)
if args.quantize:
if not args.trainable and not args.distill_range:
graph = quantize_targ_layer(graph, args.bits_weight, args.bits_bias, targ_layer)
if args.distill_range:
set_update_stat(model, [QuantMeasure], True)
model = update_quant_range(model.cuda(), data_distill, graph, bottoms)
set_update_stat(model, [QuantMeasure], False)
else:
set_quant_minmax(graph, bottoms)
torch.cuda.empty_cache()
model = model.cuda()
model.eval()
if args.quantize:
replace_op()
inference_all(model, args.dataset, args if args.log else None)
if args.quantize:
restore_op()
def main():
args = get_argument()
# An instance of your model
if args.resnet:
import torchvision.models as models
model = models.resnet18(pretrained=True)
model = ProbModel(model)
else:
model = mobilenet_v2(
'modeling/classification/mobilenetv2_1.0-f2a8633.pth.tar')
model = ProbModel(model)
model.eval()
if args.quantize:
data = torch.ones((4, 3, 224, 224)) #.cuda()
if args.distill_range:
import copy
# define FP32 model
model_original = copy.deepcopy(model)
model_original.eval()
transformer = TorchTransformer()
transformer._build_graph(model_original, data, [QuantMeasure])
graph = transformer.log.getGraph()
bottoms = transformer.log.getBottoms()
data_distill = getDistilData(model_original, 'imagenet', args.dis_batch_size, bn_merged=False,\
num_batch=args.dis_num_batch, gpu=True, value_range=[-2.11790393, 2.64], size=[224, 224], early_break_factor=1.2 if args.resnet else 0.5)
transformer = TorchTransformer()
module_dict = {}
if args.distill_range:
module_dict[1] = [(torch.nn.Conv2d, QConv2d),
(torch.nn.Linear, QLinear)]
else:
module_dict[1] = [(torch.nn.Conv2d, QuantNConv2d),
(torch.nn.Linear, QuantNLinear)]
if args.relu or args.equalize:
module_dict[0] = [(torch.nn.ReLU6, torch.nn.ReLU)]
# transformer.summary(model, data)
# transformer.visualize(model, data, 'graph_cls', graph_size=120)
model, transformer = switch_layers(model,
transformer,
data,
module_dict,
ignore_layer=[QuantMeasure],
quant_op=True)
graph = transformer.log.getGraph()
bottoms = transformer.log.getBottoms()
if args.distill_range:
targ_layer = [QConv2d, QLinear]
else:
targ_layer = [QuantNConv2d, QuantNLinear]
set_layer_bits(graph, args.bits_weight, args.bits_activation,
args.bits_bias, targ_layer)
model = merge_batchnorm(model, graph, bottoms, targ_layer)
#create relations
if args.equalize or args.distill_range:
res = create_relation(graph,
bottoms,
targ_layer,
delete_single=False)
if args.equalize:
cross_layer_equalization(graph,
res,
targ_layer,
visualize_state=False,
converge_thres=2e-7,
signed=True)
if args.clip_weight:
clip_weight(graph, range_clip=[-15, 15], targ_type=targ_layer)
if args.correction:
bias_correction(graph,
bottoms,
targ_layer,
bits_weight=args.bits_weight,
signed=True)
if args.distill_range:
set_update_stat(model, [QuantMeasure], True)
model = update_quant_range(model.cuda(), data_distill, graph,
bottoms)
set_update_stat(model, [QuantMeasure], False)
else:
set_quant_minmax(graph, bottoms)
torch.cuda.empty_cache()
# restore custom conv layer to torch.nn.conv2d
module_dict = {}
if args.distill_range:
module_dict[1] = [(QConv2d, torch.nn.Conv2d),
(QLinear, torch.nn.Linear)]
else:
module_dict[1] = [(QuantNConv2d, torch.nn.Conv2d),
(QuantNLinear, torch.nn.Linear)]
model, transformer = switch_layers(model,
transformer,
data,
module_dict,
ignore_layer=[QuantMeasure],
quant_op=False)
graph = transformer.log.getGraph()
bottoms = transformer.log.getBottoms()
# An example input you would normally provide to your model's forward() method
x = torch.rand(1, 3, 224, 224)
# Export the onnx model
torch_out = torch.onnx._export(model, x, "model.onnx", export_params=True)
# Simplify model using onnx-simplifier
os.system("python3 -m onnxsim model.onnx model-sim.onnx")
os.system("rm model.onnx")
cur_path = os.path.abspath(os.getcwd())
os.system("mv model-sim.onnx {}".format(
os.path.join(args.ncnn_build, 'tools/onnx', 'model-sim.onnx')))
os.chdir(os.path.join(args.ncnn_build, 'tools/onnx'))
# Convert onnx to ncnn
os.system("./onnx2ncnn model-sim.onnx model.param model.bin")
# Add input image size to .param
lines = [line.strip() for line in open("model.param", "r")]
with open("model.param", 'w') as ww:
for idx, line in enumerate(lines):
if idx == 2 and 'input' in line.lower():
line += ' 0=224 1=224 2=3'
ww.write(line + '\n')
if not os.path.exists(os.path.join(cur_path, 'modeling/ncnn')):
os.makedirs(os.path.join(cur_path, 'modeling/ncnn'))
os.system("rm model-sim.onnx")
if args.quantize:
os.system("mv model.param {}".format(
os.path.join(args.ncnn_build, 'tools/quantize', 'model.param')))
os.system("mv model.bin {}".format(
os.path.join(args.ncnn_build, 'tools/quantize', 'model.bin')))
os.chdir(os.path.join(args.ncnn_build, 'tools/quantize'))
# Estimate activation range using https://github.com/Tencent/ncnn/tree/master/tools/quantize
os.system("./ncnn2table --param=model.param --bin=model.bin\
--images={} --output=model_int8_channel.table\
--mean={},{},{} --norm={},{},{} --size=224,224 --thread=2".
format(args.image_path, 0.485 * 255, 0.456 * 255,
0.406 * 255, 1 / (0.229 * 255), 1 / (0.224 * 255),
1 / (0.225 * 255)))
# modify activation min/max range and weight min/max range to values calculated in DFQ
table_old = [
line.strip() for line in open("model_int8_channel.table", 'r')
]
table_new = []
count = 0
for ii in range(2):
for idx in graph:
if type(graph[idx]) in [torch.nn.Conv2d, torch.nn.Linear]:
if ii == 0: #min/max for layer weight
mi = float(torch.min(graph[idx].weight))
ma = float(torch.max(graph[idx].weight))
else:
mi = float(torch.min(graph[idx].quant.running_min))
ma = float(torch.max(graph[idx].quant.running_max))
scale = 128. / (max(abs(ma), abs(mi)))
if ii == 0: #min/max for activation
table_new.append(
' '.join(table_old[count].split(' ')[0:1] +
[str(scale)] *
graph[idx].weight.shape[0]))
else:
table_new.append(
' '.join(table_old[count].split(' ')[0:1] +
[str(scale)]))
count += 1
with open("model_int8_tensor.table", 'w') as ww:
for line in table_new:
ww.write(line + '\n')
# Convert to Int8 model
os.system(
"./ncnn2int8 model.param model.bin model_int8.param model_int8.bin model_int8_tensor.table"
)
lines = [line.strip() for line in open("model_int8.param", "r")]
os.system("cp model_int8.param {}".format(
os.path.join(cur_path, args.param)))
os.system("cp model_int8.bin {}".format(
os.path.join(cur_path, args.bin)))
os.system("cp model_int8_tensor.table {}".format(
os.path.join(cur_path, args.table)))
else:
os.system("mv model.param {}".format(os.path.join(
cur_path, args.param)))
os.system("mv model.bin {}".format(os.path.join(cur_path, args.bin)))
os.chdir(cur_path)
line = ' '.join([l.strip()
for l in open(args.param, 'r')][-1].split()).split(' ')[1]
print("=" * 100)
print("Target layer name '{}'".format(line))
print("=" * 100)
def main():
args = get_argument()
assert args.relu or args.relu == args.equalize, 'must replace relu6 to relu while equalization'
assert args.equalize or args.absorption == args.equalize, 'must use absorption with equalize'
data = torch.ones((4, 3, 513, 513)) #.cuda()
model = DeepLab(sync_bn=False)
state_dict = torch.load(
'modeling/segmentation/deeplab-mobilenet.pth.tar')['state_dict']
model.load_state_dict(state_dict)
model.eval()
transformer = TorchTransformer()
module_dict = {}
if args.quantize:
if args.trainable:
module_dict[1] = [(nn.Conv2d, QuantConv2d)]
else:
module_dict[1] = [(nn.Conv2d, QuantNConv2d)]
if args.relu:
module_dict[0] = [(torch.nn.ReLU6, torch.nn.ReLU)]
# transformer.summary(model, data)
# transformer.visualize(model, data, 'graph_deeplab', graph_size=120)
model, transformer = switch_layers(model,
transformer,
data,
module_dict,
ignore_layer=[QuantMeasure],
quant_op=args.quantize)
graph = transformer.log.getGraph()
bottoms = transformer.log.getBottoms()
output_shape = transformer.log.getOutShapes()
if args.quantize:
if args.trainable:
targ_layer = [QuantConv2d]
else:
targ_layer = [QuantNConv2d]
else:
targ_layer = [nn.Conv2d]
model = merge_batchnorm(model, graph, bottoms, targ_layer)
#create relations
if args.equalize:
res = create_relation(graph, bottoms, targ_layer)
cross_layer_equalization(graph, res, targ_layer, visualize_state=False)
if args.absorption:
bias_absorption(graph, res, bottoms, 3)
if args.clip_weight:
clip_weight(graph, range_clip=[-15, 15], targ_type=targ_layer)
if args.correction:
bias_correction(graph, bottoms, targ_layer)
if args.quantize:
if not args.trainable:
graph = quantize_targ_layer(graph, targ_layer)
set_quant_minmax(graph, bottoms, output_shape)
model = model.cuda()
model.eval()
if args.quantize:
replace_op()
inference_all(model, args.dataset)
if args.quantize:
restore_op()