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export_model_with_decoder.py
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export_model_with_decoder.py
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"""
Author: Guanghan Ning
Date: August, 2019
"""
import sys, os, time
sys.path.insert(0, "/export/guanghan/CenterNet-Gluon/dataset")
sys.path.insert(0, "/Users/guanghan.ning/Desktop/dev/CenterNet-Gluon/dataset")
import mxnet as mx
from mxnet import nd, gluon, init, autograd
from gluoncv.data.batchify import Tuple, Stack, Pad
from opts import opts
from models.model import create_model, load_model, save_model
from models.losses import MultiPoseLoss
from cocohp_centernet import CenterMultiPoseDataset
from detectors.pose_detector import PoseDetector
from progress.bar import Bar
from utils.misc import AverageMeter
import warnings
def get_coco(opt, coco_path):
"""Get coco dataset."""
train_dataset = CenterMultiPoseDataset(opt, split = 'train') # custom dataset
val_dataset = CenterMultiPoseDataset(opt, split = 'val') # custom dataset
opt.val_interval = 10
return train_dataset, val_dataset
def get_dataloader(train_dataset, data_shape, batch_size, num_workers, ctx):
"""Get dataloader."""
width, height = data_shape, data_shape
batchify_fn = Tuple(Stack(), Stack(), Stack(), Stack(), Stack(), Stack(), Stack(), Stack(), Stack(), Stack(), Stack(), Stack())
train_loader = gluon.data.DataLoader(train_dataset, batch_size, True, batchify_fn=batchify_fn, last_batch='rollover', num_workers=num_workers)
return train_loader
def validate(model, dataset, opt, ctx):
"""Test on validation dataset."""
detector = PoseDetector(opt)
detector.model = model
results = {}
num_iters = len(dataset)
for ind in range(2):
img_id = dataset.images[ind]
img_info = dataset.coco.loadImgs(ids=[img_id])[0]
img_path = os.path.join(dataset.img_dir, img_info['file_name'])
ret = detector.run(img_path)
results[img_id] = ret['results']
#print(model)
print("Calling hybridize")
detector.model.hybridize()
#print(model)
for ind in range(2):
img_id = dataset.images[ind]
img_info = dataset.coco.loadImgs(ids=[img_id])[0]
img_path = os.path.join(dataset.img_dir, img_info['file_name'])
ret = detector.run(img_path)
results[img_id] = ret['results']
print("Save Mode: symbolic")
prefix = "2DPose_{}_with_decode".format(opt.arch)
epoch = 999
#detector.save_symbols(img_path)
model.export(prefix, epoch)
def test_hybridize(model, ctx):
x = nd.random.normal(shape=(1, 3, 512, 512)).as_in_context(ctx)
model(x)
model.hybridize()
model(x)
return
if __name__ == "__main__":
opt = opts()
opt.task = "multi_pose"
opt = opt.init()
ctx = [mx.gpu(int(i)) for i in opt.gpus_str.split(',') if i.strip()]
ctx = ctx if ctx else [mx.cpu()]
print("Using Devices: ", ctx)
""" 1. network """
print('Creating model...')
print("Using network architecture: ", opt.arch)
if opt.mode == "symbolic":
print("Mode: symbolic")
if opt.flag_finetune:
with warnings.catch_warnings():
warnings.simplefilter("ignore")
opt.cur_epoch = int(opt.pretrained_path.split('.')[0][-4:])
params_path = opt.pretrained_path
json_path = opt.pretrained_path[:-11] + "symbol.json"
model = gluon.nn.SymbolBlock.imports(json_path, ['data'], params_path, ctx=ctx)
else:
opt.cur_epoch = 0
model = create_model(opt.arch, opt.heads, opt.head_conv, ctx = ctx)
#model.hybridize()
#print(model)
else:
print("Mode: imperative")
opt.cur_epoch = 0
model = create_model(opt.arch, opt.heads, opt.head_conv, ctx = ctx)
if opt.flag_finetune:
model = load_model(model, opt.pretrained_path, ctx = ctx)
#model = model.load_parameters(opt.pretrained_path, ctx=ctx, ignore_extra=True, allow_missing = True)
opt.cur_epoch = int(opt.pretrained_path.split('.')[0][-4:])
elif opt.arch != "res_18":
model.collect_params().initialize(init=init.Xavier(), ctx = ctx)
""" 2. Dataset """
train_dataset, val_dataset = get_coco(opt, "./data/coco")
data_shape = opt.input_res
batch_size = opt.batch_size
num_workers = opt.num_workers
train_loader = get_dataloader(train_dataset, data_shape, batch_size, num_workers, ctx)
""" 3. Training """
#train(model, train_loader, val_dataset, ctx, opt)
#validate(model, val_dataset, opt, ctx[-1])
#test_hybridize(model, ctx[-1])
x = nd.random.normal(shape=(1, 3, 512, 512)).as_in_context(ctx[-1])
print(model(x))
model.hybridize()
print(model(x))