def feature2image(self, feature_tensor):
output_channels = 3*self.recon_dist_param_num
hgd = [
{"type": "conv2d", "depth": 64, "decoder_depth": output_channels, "decoder_activation_fn": None},
{"type": "conv2d", "depth": 64, "decoder_depth": 32},
{"type": "skip", "layer_num": 2},
{"type": "pool", "pool": "max", "kernel": 2, "stride": 2}, # 40 x 40
{"type": "conv2d", "depth": 128, "decoder_depth": 64},
{"type": "skip", "layer_num": 2},
{"type": "pool", "pool": "max", "kernel": 2, "stride": 2}, # 20x20
{"type": "conv2d", "depth": 256},
{"type": "skip", "layer_num": 2},
{"type": "pool", "pool": "max", "kernel": 2, "stride": 2}, # 10x10
{"type": "conv2d", "depth": 512},
{"type": "skip", "layer_num": 2},
{"type": "pool", "pool": "max", "kernel": 2, "stride": 2}, # 5x5
{"type": "conv2d", "depth": 512},
]
with pt.defaults_scope(**self.pt_defaults_scope_value()):
output_tensor = hourglass(
feature_tensor, hgd,
net_type=self.options["hourglass_type"] if "hourglass_type" in self.options else None,
extra_highlevel_feature=None
)
return output_tensor
def image2feature(self, image_tensor):
if self.patch_feature_dim == 0:
return None
mid_tensor = (
pt.wrap(image_tensor).
conv2d(3, 32).
max_pool(2, 2)
).tensor # 64x64
hgd = [
{"type": "conv2d", "depth": 64},
{"type": "skip", "layer_num": 2},
{"type": "pool", "pool": "max", "kernel": 2, "stride": 2}, # 32 x 32
{"type": "conv2d", "depth": 128},
{"type": "skip", "layer_num": 2},
{"type": "pool", "pool": "max", "kernel": 2, "stride": 2}, # 16 x 16
{"type": "conv2d", "depth": 256},
{"type": "skip", "layer_num": 2},
{"type": "pool", "pool": "max", "kernel": 2, "stride": 2}, # 8 x 8
{"type": "conv2d", "depth": 512},
{"type": "skip", "layer_num": 2},
{"type": "pool", "pool": "max", "kernel": 2, "stride": 2}, # 4 x 4
{"type": "conv2d", "depth": 512},
]
with pt.defaults_scope(**self.pt_defaults_scope_value()):
feature_map = hourglass(
mid_tensor, hgd,
net_type=self.options["hourglass_type"] if "hourglass_type" in self.options else None
)
return feature_map
def encoder_map(input_tensor, hourglass_type=None):
hgd = [
{
"type": "conv2d",
"depth": 32,
"decoder_depth": 64
},
{
"type": "conv2d",
"depth": 64,
"decoder_depth": 64
},
{
"type": "skip",
"layer_num": 2
},
{
"type": "pool",
"pool": "max",
"kernel": 2,
"stride": 2
}, # 40 x 40
{
"type": "conv2d",
"depth": 128
},
{
"type": "skip",
"layer_num": 2
},
{
"type": "pool",
"pool": "max",
"kernel": 2,
"stride": 2
}, # 20x20
{
"type": "conv2d",
"depth": 256
},
{
"type": "skip",
"layer_num": 2
},
{
"type": "pool",
"pool": "max",
"kernel": 2,
"stride": 2
}, # 10x10
{
"type": "conv2d",
"depth": 512
},
]
output_tensor = hourglass(input_tensor, hgd, net_type=hourglass_type)
return output_tensor
def image2heatmap(self, image_tensor):
hgd = [
{
"type": "conv2d",
"depth": 32,
"decoder_depth": self.options["keypoint_num"] + 1,
"decoder_activation_fn": None
},
# plus one for bg
{
"type": "conv2d",
"depth": 32
},
{
"type": "skip",
"layer_num": 3,
},
{
"type": "pool",
"pool": "max"
},
{
"type": "conv2d",
"depth": 64
},
{
"type": "conv2d",
"depth": 64
},
{
"type": "skip",
"layer_num": 3,
},
{
"type": "pool",
"pool": "max"
},
{
"type": "conv2d",
"depth": 64
},
{
"type": "conv2d",
"depth": 64
},
{
"type": "skip",
"layer_num": 3,
},
{
"type": "pool",
"pool": "max"
},
{
"type": "conv2d",
"depth": 64
},
{
"type": "conv2d",
"depth": 64
},
]
with pt.defaults_scope(**self.pt_defaults_scope_value()):
raw_heatmap = hourglass(image_tensor,
hgd,
net_type=self.options["hourglass_type"] if
"hourglass_type" in self.options else None)
# raw_heatmap = pt.wrap(raw_heatmap).pixel_bias(activation_fn=None).tensor
return raw_heatmap
def image2feature(self, image_tensor):
if self.patch_feature_dim == 0:
return None
hgd = [
{
"type": "conv2d",
"depth": 32,
"decoder_depth": 64
},
{
"type": "conv2d",
"depth": 64,
"decoder_depth": 64
},
{
"type": "skip",
"layer_num": 2
},
{
"type": "pool",
"pool": "max",
"kernel": 2,
"stride": 2
}, # 40 x 40
{
"type": "conv2d",
"depth": 128
},
{
"type": "skip",
"layer_num": 2
},
{
"type": "pool",
"pool": "max",
"kernel": 2,
"stride": 2
}, # 20x20
{
"type": "conv2d",
"depth": 256
},
{
"type": "skip",
"layer_num": 2
},
{
"type": "pool",
"pool": "max",
"kernel": 2,
"stride": 2
}, # 10x10
{
"type": "conv2d",
"depth": 512
},
]
with pt.defaults_scope(**self.pt_defaults_scope_value()):
feature_map = hourglass(image_tensor,
hgd,
net_type=self.options["hourglass_type"] if
"hourglass_type" in self.options else None)
return feature_map
def feature2image(self, feature_map):
output_channels = 3 * self.recon_dist_param_num
hgd = [
{
"type": "conv2d",
"depth": 64
},
{
"type": "skip",
"layer_num": 2
},
{
"type": "pool",
"pool": "max",
"kernel": 2,
"stride": 2
}, # 32x32
{
"type": "conv2d",
"depth": 128
},
{
"type": "skip",
"layer_num": 2
},
{
"type": "pool",
"pool": "max",
"kernel": 2,
"stride": 2
}, # 16x16
{
"type": "conv2d",
"depth": 256
},
{
"type": "skip",
"layer_num": 2
},
{
"type": "pool",
"pool": "max",
"kernel": 2,
"stride": 2
}, # 8x8
{
"type": "conv2d",
"depth": 512
},
{
"type": "skip",
"layer_num": 2
},
{
"type": "pool",
"pool": "max",
"kernel": 2,
"stride": 2
}, # 4x4
{
"type": "conv2d",
"depth": 512
},
]
with pt.defaults_scope(**self.pt_defaults_scope_value()):
output_tensor = hourglass(
feature_map,
hgd,
net_type=self.options["hourglass_type"]
if "hourglass_type" in self.options else None,
extra_highlevel_feature=None)
output_tensor = (pt.wrap(output_tensor).deconv2d(
3, 32, stride=2).conv2d(3, output_channels,
activation_fn=None)).tensor
return output_tensor