def __init__(self, model_path, use_cuda=True,num_class = 751):
self.net = Net(reid=True,num_classes = num_class)
self.device = "cuda" if torch.cuda.is_available() and use_cuda else "cpu"
state_dict = torch.load(model_path, map_location=lambda storage, loc: storage)['net_dict']
self.net.load_state_dict(state_dict)
logger = logging.getLogger("root.tracker")
logger.info("Loading weights from {}... Done!".format(model_path))
self.net.to(self.device)
self.size = (64, 128)
self.norm = transforms.Compose([
transforms.ToTensor(),
transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225]),
])
class Extractor(object):
def __init__(self, model_path, use_cuda=True):
self.net = Net(reid=True)
self.device = "cuda" if torch.cuda.is_available(
) and use_cuda else "cpu"
state_dict = torch.load(
model_path, map_location=lambda storage, loc: storage)['net_dict']
self.net.load_state_dict(state_dict)
print("Loading weights from {}... Done!".format(model_path))
self.net.to(self.device)
self.size = (64, 128)
self.norm = transforms.Compose([
transforms.ToTensor(),
transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225]),
])
def _preprocess(self, im_crops):
"""
TODO:
1. to float with scale from 0 to 1
2. resize to (64, 128) as Market1501 dataset did
3. concatenate to a numpy array
3. to torch Tensor
4. normalize
"""
def _resize(im, size):
return cv2.resize(im.astype(np.float32) / 255., size)
im_batch = torch.cat([
self.norm(_resize(im, self.size)).unsqueeze(0) for im in im_crops
],
dim=0).float()
return im_batch
def __call__(self, im_crops):
im_batch = self._preprocess(im_crops)
with torch.no_grad():
im_batch = im_batch.to(self.device)
features = self.net(im_batch)
return features.cpu().numpy()
torchvision.transforms.Resize((128, 64)),
torchvision.transforms.ToTensor(),
torchvision.transforms.Normalize([0.485, 0.456, 0.406],
[0.229, 0.224, 0.225])
])
queryloader = torch.utils.data.DataLoader(torchvision.datasets.ImageFolder(
query_dir, transform=transform),
batch_size=64,
shuffle=False)
galleryloader = torch.utils.data.DataLoader(torchvision.datasets.ImageFolder(
gallery_dir, transform=transform),
batch_size=64,
shuffle=False)
# net definition
net = Net(reid=True)
assert os.path.isfile(
"./checkpoint/ckpt.t7"), "Error: no checkpoint file found!"
print('Loading from checkpoint/ckpt.t7')
checkpoint = torch.load("./checkpoint/ckpt.t7")
net_dict = checkpoint['net_dict']
net.load_state_dict(net_dict, strict=False)
net.eval()
net.to(device)
# compute features
query_features = torch.tensor([]).float()
query_labels = torch.tensor([]).long()
gallery_features = torch.tensor([]).float()
gallery_labels = torch.tensor([]).long()
def export_net_onnx():
model_path = '/home/liyongjing/Egolee_2021/programs/deep_sort_pytorch-master/deep_sort/deep/checkpoint/ckpt.t7'
model = Net(reid=True)
state_dict = torch.load(
model_path, map_location=lambda storage, loc: storage)['net_dict']
model.load_state_dict(state_dict)
img_size = (64, 128)
batch_size = 2
img = torch.randn(batch_size, 3, *img_size[::-1])
# img = torch.ones(batch_size, 3, *img_size[::-1])
model.eval()
y = model(img) # dry run
try:
print('\nStarting ONNX export with onnx %s...' % onnx.__version__)
f = osp.splitext(model_path)[0] + '.onnx'
torch.onnx.export(
model,
img,
f,
verbose=True,
opset_version=9,
input_names=['images'],
output_names=['classes', 'boxes'] if y is None else ['output'])
# Checks
onnx_model = onnx.load(f) # load onnx model
onnx.checker.check_model(onnx_model) # check onnx model
# print(onnx.helper.printable_graph(onnx_model.graph)) # print a human readable model
print('====ONNX export success, saved as %s' % f)
# simpily onnx
from onnxsim import simplify
model_simp, check = simplify(onnx_model)
assert check, "Simplified ONNX model could not be validated"
f2 = f.replace('.onnx', '_sim.onnx') # filename
onnx.save(model_simp, f2)
print('====ONNX SIM export success, saved as %s' % f2)
# check output different between pytorch and onnx: y, y_onnx
import onnxruntime as rt
input_all = [node.name for node in onnx_model.graph.input]
input_initializer = [
node.name for node in onnx_model.graph.initializer
]
net_feed_input = list(set(input_all) - set(input_initializer))
# net_feed_input = input_all
assert (len(net_feed_input) == 1)
sess = rt.InferenceSession(f2)
y_onnx = sess.run(None, {net_feed_input[0]: img.detach().numpy()})[0]
# for i, (_y, _y_onnx) in enumerate(zip(y, y_onnx)):
y_numpy = y.detach().numpy()
# all_close = np.allclose(_y_numpy, _y_onnx, rtol=1e-05, atol=1e-06)
# for x, y in zip(y_numpy[0, 0:20], y_onnx[0, 0:20]):
# print(x)
# print(y)
# print('*' * 10)
#
print(y_numpy.shape)
print(y_onnx.shape)
print(y_numpy[0, 0:20])
diff = y_numpy - y_onnx
print('max diff {}'.format(np.max(diff)))
# assert(np.max(diff) > 1e-5)
from onnx import shape_inference
f3 = f2.replace('.onnx', '_shape.onnx') # filename
onnx.save(onnx.shape_inference.infer_shapes(onnx.load(f2)), f3)
print('====ONNX shape inference export success, saved as %s' % f3)
except Exception as e:
print('ONNX export failure: %s' % e)
torchvision.transforms.Normalize([0.485, 0.456, 0.406],
[0.229, 0.224, 0.225])
])
trainloader = torch.utils.data.DataLoader(torchvision.datasets.ImageFolder(
train_dir, transform=transform_train),
batch_size=64,
shuffle=True)
testloader = torch.utils.data.DataLoader(torchvision.datasets.ImageFolder(
test_dir, transform=transform_test),
batch_size=64,
shuffle=True)
num_classes = len(trainloader.dataset.classes)
# net definition
start_epoch = 0
net = Net(num_classes=num_classes)
if args.resume:
assert os.path.isfile(
"./checkpoint/ckpt.t7"), "Error: no checkpoint file found!"
print('Loading from checkpoint/ckpt.t7')
checkpoint = torch.load("./checkpoint/ckpt.t7")
# import ipdb; ipdb.set_trace()
net_dict = checkpoint['net_dict']
net.load_state_dict(net_dict)
best_acc = checkpoint['acc']
start_epoch = checkpoint['epoch']
net.to(device)
# loss and optimizer
criterion = torch.nn.CrossEntropyLoss()
optimizer = torch.optim.SGD(net.parameters(),