def predict(image):
image = image_loader(image=image)
BACKBONE = IR_50(INPUT_SIZE)
HEAD = ArcFace(in_features=EMBEDDING_SIZE,
out_features=1000,
device_id=GPU_ID)
BACKBONE = BACKBONE.to(DEVICE)
HEAD = HEAD.to(DEVICE)
BACKBONE.load_state_dict(
torch.load('./trained_model/Backbone_IR_50_ArcFace_30.pth'))
HEAD.load_state_dict(
torch.load('./trained_model/Head_IR_50_ArcFace_30.pth'))
BACKBONE.eval()
HEAD.eval()
image = image.to(DEVICE)
bs, ncrops, c, h, w = image.size()
inputs = image.view(-1, c, h, w)
features = BACKBONE(inputs)
outputs = HEAD(features, None)
outputs = outputs.view(bs, ncrops, -1).mean(1)
top_probs, top_labs = outputs.data.topk(1)
top_labs = top_labs.cpu().numpy()
top_probs = top_probs.cpu().numpy()
return int(top_labs), float(top_probs)
def __init__(self, device, head_name = cfg['HEAD_NAME'], NUM_CLASS = cfg['NUM_CLASS'], GPU_ID = cfg['GPU_ID'], EMBEDDING_SIZE = cfg['EMBEDDING_SIZE'], HEAD_RESUME_ROOT = cfg['HEAD_RESUME_ROOT']):
super().__init__()
HEAD_DICT = {'ArcFace': ArcFace(in_features = EMBEDDING_SIZE, out_features = NUM_CLASS, device_id = GPU_ID),
'CosFace': CosFace(in_features = EMBEDDING_SIZE, out_features = NUM_CLASS, device_id = GPU_ID),
'SphereFace': SphereFace(in_features = EMBEDDING_SIZE, out_features = NUM_CLASS, device_id = GPU_ID),
'Am_softmax': Am_softmax(in_features = EMBEDDING_SIZE, out_features = NUM_CLASS, device_id = GPU_ID)}
self.device = device
self.head = HEAD_DICT[head_name]
self.head.load_state_dict(torch.load(HEAD_RESUME_ROOT))
self.head = self.head.to(device)
BACKBONE = shufflenetv2(cfg=cfg)
elif BACKBONE_NAME == 'Mobilenet':
BACKBONE = mobilenet()
elif BACKBONE_NAME == 'Mobilenetv2':
BACKBONE = mobilenetv2()
elif BACKBONE_NAME == 'mobileface':
BACKBONE = mobileface()
else:
raise NotImplementedError
print("=" * 60)
print(BACKBONE)
print("{} Backbone Generated".format(BACKBONE_NAME))
print("=" * 60)
if HEAD_NAME == 'ArcFace':
HEAD = ArcFace(in_features = EMBEDDING_SIZE, out_features = NUM_CLASS, device_id = GPU_ID)
elif HEAD_NAME == 'CosFace':
HEAD = CosFace(in_features = EMBEDDING_SIZE, out_features = NUM_CLASS, device_id = GPU_ID)
elif HEAD_NAME == 'SphereFace':
HEAD = SphereFace(in_features = EMBEDDING_SIZE, out_features = NUM_CLASS, device_id = GPU_ID)
elif HEAD_NAME == 'Am_softmax':
HEAD = Am_softmax(in_features = EMBEDDING_SIZE, out_features = NUM_CLASS, device_id = GPU_ID)
elif HEAD_NAME == 'Softmax':
HEAD = Softmax(in_features = EMBEDDING_SIZE, out_features = NUM_CLASS, device_id = GPU_ID)
else:
raise NotImplementedError
print("=" * 60)
print(HEAD)
print("{} Head Generated".format(HEAD_NAME))
print("=" * 60)
'IR_50': IR_50(INPUT_SIZE),
'IR_101': IR_101(INPUT_SIZE),
'IR_152': IR_152(INPUT_SIZE),
'IR_SE_50': IR_SE_50(INPUT_SIZE),
'IR_SE_101': IR_SE_101(INPUT_SIZE),
'IR_SE_152': IR_SE_152(INPUT_SIZE)
}
BACKBONE = BACKBONE_DICT[BACKBONE_NAME]
print("=" * 60)
print(BACKBONE)
print("{} Backbone Generated".format(BACKBONE_NAME))
print("=" * 60)
HEAD_DICT = {
'ArcFace':
ArcFace(in_features=EMBEDDING_SIZE, out_features=NUM_CLASS),
'CosFace':
CosFace(in_features=EMBEDDING_SIZE, out_features=NUM_CLASS),
'SphereFace':
SphereFace(in_features=EMBEDDING_SIZE, out_features=NUM_CLASS),
'Am_softmax':
Am_softmax(in_features=EMBEDDING_SIZE, out_features=NUM_CLASS)
}
HEAD = HEAD_DICT[HEAD_NAME]
print("=" * 60)
print(HEAD)
print("{} Head Generated".format(HEAD_NAME))
print("=" * 60)
LOSS_DICT = {'Focal': FocalLoss(), 'Softmax': nn.CrossEntropyLoss()}
LOSS = LOSS_DICT[LOSS_NAME]
'IR_SE_101': IR_SE_101(INPUT_SIZE),
'IR_SE_152': IR_SE_152(INPUT_SIZE)
}
BACKBONE = BACKBONE_DICT[BACKBONE_NAME]
if BACKBONE_NAME == 'ppResNet_50' and USE_PRETRAINED:
pretrained_weight = download()
load_weight(model=BACKBONE, weight_path=pretrained_weight)
logger.info("=" * 60)
# logger.info(BACKBONE)
logger.info("{} Backbone Generated".format(BACKBONE_NAME))
logger.info("=" * 60)
HEAD_DICT = {
'ArcFace':
ArcFace(embedding_size=EMBEDDING_SIZE, class_dim=NUM_CLASS),
'CosFace':
CosFace(in_features=EMBEDDING_SIZE, out_features=NUM_CLASS),
'SphereFace':
SphereFace(in_features=EMBEDDING_SIZE, out_features=NUM_CLASS),
'Am_softmax':
Am_softmax(in_features=EMBEDDING_SIZE, out_features=NUM_CLASS),
'Softmax':
Softmax(in_features=EMBEDDING_SIZE, out_features=NUM_CLASS)
}
HEAD = HEAD_DICT[HEAD_NAME]
logger.info("=" * 60)
# logger.info(HEAD)
logger.info("{} Head Generated".format(HEAD_NAME))
logger.info("=" * 60)
BACKBONE = shufflenetv2(cfg=cfg)
elif BACKBONE_NAME == 'Mobilenet':
BACKBONE = mobilenet()
elif BACKBONE_NAME == 'Mobilenetv2':
BACKBONE = mobilenetv2()
elif BACKBONE_NAME == 'mobileface':
BACKBONE = mobileface()
else:
raise NotImplementedError
print("=" * 60)
print(BACKBONE)
print("{} Backbone Generated".format(BACKBONE_NAME))
print("=" * 60)
if HEAD_NAME == 'ArcFace':
HEAD = ArcFace(in_features = EMBEDDING_SIZE, out_features = NUM_CLASS, device_id = GPU_ID)
elif HEAD_NAME == 'CosFace':
HEAD = CosFace(in_features = EMBEDDING_SIZE, out_features = NUM_CLASS, device_id = GPU_ID)
elif HEAD_NAME == 'SphereFace':
HEAD = SphereFace(in_features = EMBEDDING_SIZE, out_features = NUM_CLASS, device_id = GPU_ID)
elif HEAD_NAME == 'Am_softmax':
HEAD = Am_softmax(in_features = EMBEDDING_SIZE, out_features = NUM_CLASS, device_id = GPU_ID)
elif HEAD_NAME == 'Softmax':
HEAD = Softmax(in_features = EMBEDDING_SIZE, out_features = NUM_CLASS, device_id = GPU_ID)
else:
raise NotImplementedError
print("=" * 60)
print(HEAD)
print("{} Head Generated".format(HEAD_NAME))
print("=" * 60)
'IR_101': IR_101(INPUT_SIZE),
'IR_152': IR_152(INPUT_SIZE),
'IR_SE_50': IR_SE_50(INPUT_SIZE),
'IR_SE_101': IR_SE_101(INPUT_SIZE),
'IR_SE_152': IR_SE_152(INPUT_SIZE)
}
BACKBONE = BACKBONE_DICT[BACKBONE_NAME]
print("=" * 60)
print(BACKBONE)
print("{} Backbone Generated".format(BACKBONE_NAME))
print("=" * 60)
HEAD_DICT = {
'ArcFace':
ArcFace(in_features=EMBEDDING_SIZE,
out_features=NUM_CLASS,
device_id=GPU_ID),
'CosFace':
CosFace(in_features=EMBEDDING_SIZE,
out_features=NUM_CLASS,
device_id=GPU_ID),
'SphereFace':
SphereFace(in_features=EMBEDDING_SIZE,
out_features=NUM_CLASS,
device_id=GPU_ID),
'Am_softmax':
Am_softmax(in_features=EMBEDDING_SIZE,
out_features=NUM_CLASS,
device_id=GPU_ID)
}
HEAD = HEAD_DICT[HEAD_NAME]
BACKBONE_DICT = {'ResNet_50': ResNet_50(INPUT_SIZE),
'ResNet_101': ResNet_101(INPUT_SIZE),
'ResNet_152': ResNet_152(INPUT_SIZE),
'IR_50': IR_50(INPUT_SIZE),
'IR_101': IR_101(INPUT_SIZE),
'IR_152': IR_152(INPUT_SIZE),
'IR_SE_50': IR_SE_50(INPUT_SIZE),
'IR_SE_101': IR_SE_101(INPUT_SIZE),
'IR_SE_152': IR_SE_152(INPUT_SIZE)}
BACKBONE = BACKBONE_DICT[BACKBONE_NAME]
print("=" * 60)
print(BACKBONE)
print("{} Backbone Generated".format(BACKBONE_NAME))
print("=" * 60)
HEAD_DICT = {'ArcFace': ArcFace(in_features = EMBEDDING_SIZE, out_features = NUM_CLASS, device_id = GPU_ID),
'CosFace': CosFace(in_features = EMBEDDING_SIZE, out_features = NUM_CLASS, device_id = GPU_ID),
'SphereFace': SphereFace(in_features = EMBEDDING_SIZE, out_features = NUM_CLASS, device_id = GPU_ID),
'Am_softmax': Am_softmax(in_features = EMBEDDING_SIZE, out_features = NUM_CLASS, device_id = GPU_ID)}
HEAD = HEAD_DICT[HEAD_NAME]
print("=" * 60)
print(HEAD)
print("{} Head Generated".format(HEAD_NAME))
print("=" * 60)
LOSS_DICT = {'Focal': FocalLoss(),
'Softmax': nn.CrossEntropyLoss()
'AdaCos' : AdaCos(),
'AdaM_Softmax': AdaM_Softmax() ,
'ArcFace' : ArcFace() ,
'ArcNegFace': ArcNegFace(),
BACKBONE_DICT = {'ResNet_50': ResNet_50(INPUT_SIZE),
'ResNet_101': ResNet_101(INPUT_SIZE),
'ResNet_152': ResNet_152(INPUT_SIZE),
'IR_50': IR_50(INPUT_SIZE),
'IR_101': IR_101(INPUT_SIZE),
'IR_152': IR_152(INPUT_SIZE),
'IR_SE_50': IR_SE_50(INPUT_SIZE),
'IR_SE_101': IR_SE_101(INPUT_SIZE),
'IR_SE_152': IR_SE_152(INPUT_SIZE)}
BACKBONE = BACKBONE_DICT[BACKBONE_NAME]
print("=" * 60)
print(BACKBONE)
print("{} Backbone Generated".format(BACKBONE_NAME))
print("=" * 60)
HEAD_DICT = {'ArcFace': ArcFace(in_features=EMBEDDING_SIZE, out_features=NUM_CLASS),
'CosFace': CosFace(in_features=EMBEDDING_SIZE, out_features=NUM_CLASS),
'SphereFace': SphereFace(in_features=EMBEDDING_SIZE, out_features=NUM_CLASS),
'Am_softmax': Am_softmax(in_features=EMBEDDING_SIZE, out_features=NUM_CLASS)}
HEAD = HEAD_DICT[HEAD_NAME]
print("=" * 60)
print(HEAD)
print("{} Head Generated".format(HEAD_NAME))
print("=" * 60)
LOSS_DICT = {'Focal': FocalLoss(),
'Softmax': nn.CrossEntropyLoss()}
LOSS = LOSS_DICT[LOSS_NAME]
print("=" * 60)
print(LOSS)
print("{} Loss Generated".format(LOSS_NAME))