def __init__(self):
super().__init__()
self.resnet50 = ResNet50(config.backbone_freeze_at, False)
self.FPN = FPN(self.resnet50, 2, 6)
self.RPN = RPN(config.rpn_channel)
self.RCNN = RCNN()
self.Cascade_0 = Cascade('cascade_0')
def __init__(self):
super().__init__()
# ----------------------- build the backbone ------------------------ #
self.resnet50 = ResNet50()
# ------------ freeze the weights of resnet stage1 and stage 2 ------ #
if config.backbone_freeze_at >= 1:
for p in self.resnet50.conv1.parameters():
p.requires_grad = False
if config.backbone_freeze_at >= 2:
for p in self.resnet50.layer1.parameters():
p.requires_grad = False
# -------------------------- build the FPN -------------------------- #
self.backbone = FPN(self.resnet50)
# -------------------------- build the RPN -------------------------- #
self.RPN = RPN(config.rpn_channel)
# ----------------------- build the RCNN head ----------------------- #
self.Cascade_0 = Cascade('cascade_0')
#self.Cascade_1 = Cascade('cascade_1')
self.RCNN = RCNN()
# -------------------------- input Tensor --------------------------- #
self.inputs = {
"image": mge.tensor(
np.random.random([2, 3, 224, 224]).astype(np.float32), dtype="float32",
),
"im_info": mge.tensor(
np.random.random([2, 5]).astype(np.float32), dtype="float32",
),
"gt_boxes": mge.tensor(
np.random.random([2, 100, 5]).astype(np.float32), dtype="float32",
),
}
def __init__(self):
super().__init__()
self.resnet50 = ResNet50(config.backbone_freeze_at, False)
self.FPN = FPN(self.resnet50, 3, 7)
self.R_Head = RetinaNet_Head()
self.R_Anchor = RetinaNet_Anchor()
self.R_Criteria = RetinaNet_Criteria()
def __init__(self):
super().__init__()
self.resnet50 = ResNet50(config.backbone_freeze_at, False)
self.FPN = FPN(self.resnet50, 2, 6)
self.RPN = RPN(config.rpn_channel)
self.RCNN = RCNN()
assert config.num_classes == 2, 'Only support two class(1fg/1bg).'
def create_triq_model(n_quality_levels,
input_shape=(None, None, 3),
backbone='resnet50',
transformer_params=(2, 32, 8, 64),
maximum_position_encoding=193,
vis=False):
"""
Creates the hybrid TRIQ model
:param n_quality_levels: number of quality levels, use 5 to predict quality distribution
:param input_shape: input shape
:param backbone: bakbone nets, supports ResNet50 and VGG16 now
:param transformer_params: Transformer parameters
:param maximum_position_encoding: the maximal number of positional embeddings
:param vis: flag to visualize attention weight maps
:return: TRIQ model
"""
inputs = Input(shape=input_shape)
if backbone == 'resnet50':
backbone_model = ResNet50(inputs,
return_feature_maps=False,
return_last_map=True)
elif backbone == 'vgg16':
backbone_model = VGG16(inputs, return_last_map=True)
else:
raise NotImplementedError
C5 = backbone_model.output
dropout_rate = 0.1
transformer = TriQImageQualityTransformer(
num_layers=transformer_params[0],
d_model=transformer_params[1],
num_heads=transformer_params[2],
mlp_dim=transformer_params[3],
dropout=dropout_rate,
n_quality_levels=n_quality_levels,
maximum_position_encoding=maximum_position_encoding,
vis=vis)
outputs = transformer(C5)
model = Model(inputs=inputs, outputs=outputs)
model.summary()
return model
def __init__(self):
super().__init__()
# ----------------------- build the backbone ------------------------ #
self.resnet50 = ResNet50()
# ------------ freeze the weights of resnet stage1 and stage 2 ------ #
if config.backbone_freeze_at >= 1:
for p in self.resnet50.conv1.parameters():
# p.requires_grad = False
p = p.detach()
if config.backbone_freeze_at >= 2:
for p in self.resnet50.layer1.parameters():
# p.requires_grad = False
p = p.detach()
# -------------------------- build the FPN -------------------------- #
self.backbone = FPN(self.resnet50)
# -------------------------- build the RPN -------------------------- #
# self.RPN = RPN(config.rpn_channel)
self.head = RetinaNetHead()
# -------------------------- buid the anchor generator -------------- #
self.anchor_generator = RetinaNetAnchorV2()
# -------------------------- buid the criteria ---------------------- #
self.criteria = RetinaNetCriteriaV2()
# -------------------------- input Tensor --------------------------- #
self.inputs = {
"image":
mge.tensor(
np.random.random([2, 3, 756, 1400]).astype(np.float32),
dtype="float32",
),
"im_info":
mge.tensor(
np.random.random([2, 6]).astype(np.float32),
dtype="float32",
),
"gt_boxes":
mge.tensor(
np.random.random([2, 500, 5]).astype(np.float32),
dtype="float32",
),
}