def make_layers(config, in_channel=3, accelerator=None):
assert config is not None
in_height = int(config["DataConfig"]["image_height"])
in_width = int(config["DataConfig"]["image_width"])
in_channel = in_channel
assert accelerator is not None
acc = accelerator
layers = []
# Conv(output channel, input channel, input height, input width, kerSize, stride)
layers += [
fpga_nn.Conv2D(64,
in_channel,
in_height,
in_width,
ker=3,
s=1,
accelerator=acc)
]
layers += [
fpga_nn.Conv2DPool(128,
64,
in_height,
in_width,
ker=3,
poolWin=2,
accelerator=acc)
]
layers += [
fpga_nn.Conv2D(128,
128,
int(in_height / 2),
int(in_width / 2),
ker=3,
s=1,
accelerator=acc)
]
layers += [
fpga_nn.Conv2DPool(256,
128,
int(in_height / 2),
int(in_width / 2),
ker=3,
poolWin=2,
accelerator=acc)
]
layers += [
fpga_nn.Conv2D(256,
256,
int(in_height / 4),
int(in_width / 4),
ker=3,
s=1,
accelerator=acc)
]
layers += [
fpga_nn.Conv2D(256,
256,
int(in_height / 4),
int(in_width / 4),
ker=3,
s=1,
accelerator=acc)
]
layers += [
fpga_nn.Conv2DPool(512,
256,
int(in_height / 4),
int(in_width / 4),
ker=3,
poolWin=2,
accelerator=acc)
]
layers += [
fpga_nn.Conv2D(512,
512,
int(in_height / 8),
int(in_width / 8),
ker=3,
s=1,
accelerator=acc)
]
layers += [
fpga_nn.Conv2D(512,
512,
int(in_height / 8),
int(in_width / 8),
ker=3,
s=1,
accelerator=acc)
]
layers += [
fpga_nn.Conv2DPool(512,
512,
int(in_height / 8),
int(in_width / 8),
ker=3,
poolWin=2,
accelerator=acc)
]
layers += [
fpga_nn.Conv2D(512,
512,
int(in_height / 16),
int(in_width / 16),
ker=3,
s=1,
accelerator=acc)
]
layers += [
fpga_nn.Conv2D(512,
512,
int(in_height / 16),
int(in_width / 16),
ker=3,
s=1,
accelerator=acc)
]
layers += [
fpga_nn.Conv2DPool(512,
512,
int(in_height / 16),
int(in_width / 16),
ker=3,
poolWin=2,
accelerator=acc)
]
# conv output size = (8,8,512)
layers += [fpga_nn.Flatten(int(in_height / 32), int(in_width / 32), 512)]
layers += [
fpga_nn.Linear(4096,
int(in_height / 32) * int(in_width / 32) * 512)
]
layers += [fpga_nn.Linear(101, 4096)]
return layers
def make_layers(config, in_channel=20, accelerator=None):
assert config is not None
in_height = int(config["DataConfig"]["image_height"])
in_width = int(config["DataConfig"]["image_width"])
in_channel = in_channel
assert accelerator is not None
acc = accelerator
layers = []
#Conv(output channel, input channel, input height, input width, kerSize, stride)
layers += [
fpga_nn.Conv2DPool(32,
in_channel,
in_height,
in_width,
ker=3,
poolWin=2,
accelerator=acc)
]
layers += [
fpga_nn.Conv2DPool(64,
32,
int(in_height / 2),
int(in_width / 2),
ker=3,
poolWin=2,
accelerator=acc)
]
layers += [
fpga_nn.Conv2DPool(64,
64,
int(in_height / 4),
int(in_width / 4),
ker=3,
poolWin=2,
accelerator=acc)
]
layers += [
fpga_nn.Conv2DPool(64,
64,
int(in_height / 8),
int(in_width / 8),
ker=3,
poolWin=2,
accelerator=acc)
]
layers += [
fpga_nn.Conv2DPool(64,
64,
int(in_height / 16),
int(in_width / 16),
ker=3,
poolWin=2,
accelerator=acc)
]
# layers += [fpga_nn.Conv2DPool(64, 64, int(in_height/32), int(in_width/32), ker = 3, poolWin = 2, accelerator=acc)]
# conv output size = (8,8,512)
layers += [fpga_nn.Flatten(int(in_height / 32), int(in_width / 32), 64)]
layers += [
fpga_nn.Linear(512,
int(in_height / 32) * int(in_width / 32) * 64)
]
layers += [fpga_nn.Linear(101, 512, quantize=False)]
return layers