def __init__(self, cf, logger):
super(net, self).__init__()
self.cf = cf
self.logger = logger
backbone = utils.import_module('bbone', cf.backbone_path)
self.logger.info("loaded backbone from {}".format(
self.cf.backbone_path))
conv_gen = backbone.ConvGenerator(cf.dim)
# set operate_stride1=True to generate a unet-like FPN.)
self.fpn = backbone.FPN(cf,
conv=conv_gen,
relu_enc=cf.relu,
operate_stride1=True)
self.conv_final = conv_gen(cf.end_filts,
cf.num_seg_classes,
ks=1,
pad=0,
norm=cf.norm,
relu=None)
#initialize parameters
if self.cf.weight_init == "custom":
logger.info(
"Tried to use custom weight init which is not defined. Using pytorch default."
)
elif self.cf.weight_init:
mutils.initialize_weights(self)
else:
logger.info("using default pytorch weight init")
def __init__(self, cf, logger):
super(net, self).__init__()
self.cf = cf
self.logger = logger
self.build()
loss_order = [
'rpn_class', 'rpn_bbox', 'mrcnn_bbox', 'mrcnn_mask', 'mrcnn_class',
'mrcnn_rg'
]
if hasattr(cf, "mrcnn_loss_weights"):
# bring into right order
self.loss_weights = np.array(
[cf.mrcnn_loss_weights[k] for k in loss_order])
else:
self.loss_weights = np.array([1.] * len(loss_order))
if self.cf.weight_init == "custom":
logger.info(
"Tried to use custom weight init which is not defined. Using pytorch default."
)
elif self.cf.weight_init:
mutils.initialize_weights(self)
else:
logger.info("using default pytorch weight init")
def __init__(self, cf, logger):
super(net, self).__init__()
self.cf = cf
self.logger = logger
self.build()
if self.cf.weight_init is not None:
logger.info("using pytorch weight init of type {}".format(self.cf.weight_init))
mutils.initialize_weights(self)
else:
logger.info("using default pytorch weight init")
def __init__(self, cf, logger):
super(net, self).__init__()
self.cf = cf
self.dim = cf.dim
self.norm = cf.norm
self.logger = logger
backbone = utils.import_module('bbone', cf.backbone_path)
self.c_gen = backbone.ConvGenerator(cf.dim)
self.Interpolator = backbone.Interpolate
#down = DownBlockGen(cf.dim)
#up = UpBlockGen(cf.dim, backbone.Interpolate)
down = self.down
up = self.up
pad = cf.pad
if pad=="same":
pad = (cf.kernel_size-1)//2
self.dims = "not yet recorded"
self.is_cuda = False
self.init = horiz_conv(len(cf.channels), cf.init_filts, cf.kernel_size, self.c_gen, self.norm, pad=pad,
relu=cf.relu)
self.down1 = down(cf.init_filts, cf.init_filts*2, cf.kernel_size, cf.kernel_size_m, pad=pad, relu=cf.relu)
self.down2 = down(cf.init_filts*2, cf.init_filts*4, cf.kernel_size, cf.kernel_size_m, pad=pad, relu=cf.relu)
self.down3 = down(cf.init_filts*4, cf.init_filts*6, cf.kernel_size, cf.kernel_size_m, pad=pad, relu=cf.relu)
self.down4 = down(cf.init_filts*6, cf.init_filts*8, cf.kernel_size, cf.kernel_size_m, pad=pad, relu=cf.relu,
maintain_z=True)
self.down5 = down(cf.init_filts*8, cf.init_filts*12, cf.kernel_size, cf.kernel_size_m, pad=pad, relu=cf.relu,
maintain_z=True)
#self.down6 = down(cf.init_filts*10, cf.init_filts*14, cf.kernel_size, cf.kernel_size_m, pad=pad, relu=cf.relu)
#self.up1 = up(cf.init_filts*14, cf.init_filts*10, cf.kernel_size, pad=pad, relu=cf.relu)
self.up2 = up(cf.init_filts*12, cf.init_filts*8, cf.kernel_size, pad=pad, relu=cf.relu, maintain_z=True)
self.up3 = up(cf.init_filts*8, cf.init_filts*6, cf.kernel_size, pad=pad, relu=cf.relu, maintain_z=True)
self.up4 = up(cf.init_filts*6, cf.init_filts*4, cf.kernel_size, pad=pad, relu=cf.relu)
self.up5 = up(cf.init_filts*4, cf.init_filts*2, cf.kernel_size, pad=pad, relu=cf.relu)
self.up6 = up(cf.init_filts*2, cf.init_filts, cf.kernel_size, pad=pad, relu=cf.relu)
self.seg = self.c_gen(cf.init_filts, cf.num_seg_classes, 1, norm=None, relu=None)
# initialize parameters
if self.cf.weight_init == "custom":
logger.info("Tried to use custom weight init which is not defined. Using pytorch default.")
elif self.cf.weight_init:
mutils.initialize_weights(self)
else:
logger.info("using default pytorch weight init")
def __init__(self, cf, logger):
super(net, self).__init__()
self.cf = cf
self.logger = logger
self.regress_flag = any(['regression' in task for task in self.cf.prediction_tasks])
self.build()
if self.cf.weight_init=="custom":
logger.info("Tried to use custom weight init which is not defined. Using pytorch default.")
elif self.cf.weight_init:
mutils.initialize_weights(self)
else:
logger.info("using default pytorch weight init")
def __init__(self, cf, logger):
"""
cf: A Sub-class of the cf class
model_dir: Directory to save training logs and trained weights
"""
super(net, self).__init__()
self.cf = cf
self.logger = logger
self.build()
if self.cf.weight_init is not None:
mutils.initialize_weights(self)
else:
logger.info("using default pytorch weight init")
self.debug_acm = []
def __init__(self, cf, logger):
super(net, self).__init__()
self.cf = cf
self.logger = logger
backbone = utils.import_module('bbone', cf.backbone_path)
conv = mutils.NDConvGenerator(cf.dim)
# set operate_stride1=True to generate a unet-like FPN.)
self.fpn = backbone.FPN(cf, conv, operate_stride1=True).cuda()
self.conv_final = conv(cf.end_filts, cf.num_seg_classes, ks=1, pad=0, norm=cf.norm, relu=None)
if self.cf.weight_init is not None:
logger.info("using pytorch weight init of type {}".format(self.cf.weight_init))
mutils.initialize_weights(self)
else:
logger.info("using default pytorch weight init")
