def build_packed_bnn(input_image, w_conv1, bn_t1, w_conv2, bn_t2, w_fc1, b_fc1,
w_fc2, b_fc2): # 16*16*1
conv1 = bnn.packed_conv2d_nhwc(input_image,
w_conv1,
padding=[1, 1],
name="conv1",
out_dtype=qtype_int)
bn1 = bnn.packed_batch_norm_threshold_nhwc(conv1, bn_t1, name="bn1")
maxpool1 = bnn.packed_max_pool2d_nhwc_LB(bn1, [2, 2], [2, 2],
name="maxpool1")
conv2 = bnn.packed_conv2d_nhwc(maxpool1,
w_conv2,
padding=[1, 1],
name="conv2",
out_dtype=qtype_int)
bn2 = bnn.packed_batch_norm_threshold_nhwc(conv2, bn_t2, name="bn2")
maxpool2 = bnn.packed_max_pool2d_nhwc_LB(bn2, [2, 2], [2, 2],
name="maxpool2") # 32*4*4=512
pack = bnn.packed_flatten_nhwc(maxpool2, name="packed_flatten")
fc1 = bnn.packed_dense(pack, w_fc1, b_fc1, True,
name="fc1") # 512/32->256/32
fc2 = bnn.packed_dense(fc1, w_fc2, b_fc2, False, name="fc2") # 256/32->10
return fc2
def build_packed_bnn(input_image, w_conv1, bn_t1, w_conv2, bn_t2, w_fc1, b_fc1,
w_fc2, b_fc2): # 1*16*16
if PACK_CONV:
conv1 = bnn.packed_conv2d_nchw(input_image,
w_conv1,
padding=[1, 1],
name="conv1",
out_dtype=qtype_int) # 16*16*16
bn1 = bnn.packed_batch_norm_threshold(conv1, bn_t1, name="bn1")
# bn1 = bnn.packed_conv2d_nchw(input_image, w_conv1, threshold=bn_t1, padding=[1,1], name="conv1", out_dtype=qtype_int) # 16*16*16
else:
conv1 = bnn.conv2d_nchw(input_image,
w_conv1,
padding=[1, 1],
name="conv1",
out_dtype=qtype_int) # 16*16*16
bn1 = bnn.batch_norm_threshold(conv1, bn_t1, name="bn1")
maxpool1 = bnn.packed_max_pool2d_nchw(bn1, [2, 2], [2, 2],
name="maxpool1",
unpack=not PACK_CONV) # 16*8*8
# maxpool1 = bnn.packed_max_pool2d_LB(bn1, [2,2], [2,2], name="maxpool1") # 16*8*8
if PACK_CONV:
conv2 = bnn.packed_conv2d_nchw(maxpool1,
w_conv2,
padding=[1, 1],
name="conv2",
out_dtype=qtype_int) # 32*8*8
bn2 = bnn.packed_batch_norm_threshold(conv2, bn_t2, name="bn2")
# bn2 = bnn.packed_conv2d_nchw(maxpool1, w_conv2, threshold=bn_t2, padding=[1,1], name="conv2", out_dtype=qtype_int) # 32*8*8
else:
conv2 = bnn.conv2d_nchw(maxpool1,
w_conv2,
padding=[1, 1],
name="conv2",
out_dtype=qtype_int) # 32*8*8
bn2 = bnn.batch_norm_threshold(conv2, bn_t2, name="bn2")
maxpool2 = bnn.packed_max_pool2d_nchw(bn2, [2, 2], [2, 2],
name="maxpool2",
unpack=not PACK_CONV) # 32*4*4=512
# maxpool2 = bnn.packed_max_pool2d_LB(bn2, [2,2], [2,2], name="maxpool2") # 32*4*4=512
if PACK_CONV:
pack = bnn.packed_flatten(maxpool2, name="packed_flatten")
else:
flat = bnn.flatten(maxpool2, name="flatten")
pack = hcl.pack(flat,
axis=1,
factor=32,
dtype=qtype_packed,
name="pack") # 512/32=16
fc1 = bnn.packed_dense(pack, w_fc1, b_fc1, True,
name="fc1") # 512/32->256/32
fc2 = bnn.packed_dense(fc1, w_fc2, b_fc2, False, name="fc2") # 256/32->10
return fc2
def build_packed_bnn(*arrays): # 1*16*16
hcl_comp = []
for i, array in enumerate(arrays):
if i in [0, 1]:
dtype = qtype_bit
elif i == 3:
dtype = hcl.UInt(16)
elif i in [5, 7]:
dtype = qtype_packed
else:
dtype = qtype_float
hcl_comp.append(
hcl.compute(array.shape,
lambda *dim: array[dim],
name="copy_{}".format(i),
dtype=dtype))
input_image = hcl_comp[0]
w_conv1 = hcl_comp[1]
bn_t1 = hcl_comp[2]
w_conv2 = hcl_comp[3]
bn_t2 = hcl_comp[4]
w_fc1 = hcl_comp[5]
b_fc1 = hcl_comp[6]
w_fc2 = hcl_comp[7]
b_fc2 = hcl_comp[8]
conv1 = bnn.packed_conv2d_nchw(input_image,
w_conv1,
padding=[1, 1],
name="conv1",
out_dtype=qtype_int) # 16*16*16
bn1 = bnn.packed_batch_norm_threshold(conv1, bn_t1, name="bn1")
# maxpool1 = bnn.packed_max_pool2d_nchw(bn1, [2,2], [2,2], name="maxpool1",unpack=False) # 16*8*8
maxpool1 = bnn.packed_max_pool2d_LB(bn1, [2, 2], [2, 2], name="maxpool1")
conv2 = bnn.packed_conv2d_nchw(maxpool1,
w_conv2,
padding=[1, 1],
name="conv2",
out_dtype=qtype_int) # 32*8*8
bn2 = bnn.packed_batch_norm_threshold(conv2, bn_t2, name="bn2")
# maxpool2 = bnn.packed_max_pool2d_nchw(bn2, [2,2], [2,2], name="maxpool2",unpack=False) # 16*8*8
maxpool2 = bnn.packed_max_pool2d_LB(bn2, [2, 2], [2, 2],
name="maxpool2") # 32*4*4=512
pack = bnn.packed_flatten(maxpool2, name="packed_flatten")
fc1 = bnn.packed_dense(pack, w_fc1, b_fc1, True,
name="fc1") # 512/32->256/32
fc2 = bnn.packed_dense(fc1, w_fc2, b_fc2, False, name="fc2") # 256/32->10
return fc2
def build_packed_bnn(input_image):
w_conv1 = hcl.const_tensor(packed_params["w_conv1"], "w_conv1", qtype_bit)
bn_t1 = hcl.const_tensor(packed_params["bn_t1"], "bn_t1", qtype_float)
w_conv2 = hcl.const_tensor(packed_params["w_conv2"], "w_conv2",
hcl.UInt(16))
bn_t2 = hcl.const_tensor(packed_params["bn_t2"], "bn_t2", qtype_float)
w_fc1 = hcl.const_tensor(packed_params["w_fc1"], "w_fc1", qtype_packed)
b_fc1 = hcl.const_tensor(packed_params["b_fc1"], "b_fc1", qtype_float)
w_fc2 = hcl.const_tensor(packed_params["w_fc2"], "w_fc2", qtype_packed)
b_fc2 = hcl.const_tensor(packed_params["b_fc2"], "b_fc2", qtype_float)
conv1 = bnn.packed_conv2d_nhwc(input_image,
w_conv1,
padding=[1, 1],
name="conv1",
out_dtype=qtype_int)
bn1 = bnn.packed_batch_norm_threshold_nhwc(conv1, bn_t1, name="bn1")
if not args.stream:
maxpool1 = bnn.packed_max_pool2d_nhwc(bn1, [2, 2], [2, 2],
name="maxpool1")
else:
maxpool1 = bnn.packed_max_pool2d_nhwc_LB(bn1, [2, 2], [2, 2],
name="maxpool1")
conv2 = bnn.packed_conv2d_nhwc(maxpool1,
w_conv2,
padding=[1, 1],
name="conv2",
out_dtype=qtype_int)
bn2 = bnn.packed_batch_norm_threshold_nhwc(conv2, bn_t2, name="bn2")
if not args.stream:
maxpool2 = bnn.packed_max_pool2d_nhwc(bn2, [2, 2], [2, 2],
name="maxpool2") # 32*4*4=512
else:
maxpool2 = bnn.packed_max_pool2d_nhwc_LB(bn2, [2, 2], [2, 2],
name="maxpool2") # 32*4*4=512
pack = bnn.packed_flatten_nhwc(maxpool2, name="packed_flatten")
fc1 = bnn.packed_dense(pack, w_fc1, b_fc1, True,
name="fc1") # 512/32->256/32
fc2 = bnn.packed_dense(fc1,
w_fc2,
b_fc2,
False,
name="fc2",
dtype=dtype_out) # 256/32->10
return fc2
def build_packed_bnn(input_image): # 1*16*16
w_conv1 = hcl.const_tensor(packed_params["w_conv1"], "w_conv1", qtype_bit)
bn_t1 = hcl.const_tensor(packed_params["bn_t1"], "bn_t1", qtype_float)
w_conv2 = hcl.const_tensor(packed_params["w_conv2"], "w_conv2",
hcl.UInt(16))
bn_t2 = hcl.const_tensor(packed_params["bn_t2"], "bn_t2", qtype_float)
w_fc1 = hcl.const_tensor(packed_params["w_fc1"], "w_fc1", qtype_packed)
b_fc1 = hcl.const_tensor(packed_params["b_fc1"], "b_fc1", qtype_float)
w_fc2 = hcl.const_tensor(packed_params["w_fc2"], "w_fc2", qtype_packed)
b_fc2 = hcl.const_tensor(packed_params["b_fc2"], "b_fc2", qtype_float)
conv1 = bnn.packed_conv2d_nchw(input_image,
w_conv1,
padding=[1, 1],
name="conv1",
out_dtype=qtype_int,
bitwidth=1) # 16*16*16
bn1 = bnn.packed_batch_norm_threshold(conv1, bn_t1, name="bn1")
maxpool1 = bnn.packed_max_pool2d_LB(bn1, [2, 2], [2, 2],
name="maxpool1") # 16*8*8
conv2 = bnn.packed_conv2d_nchw(maxpool1,
w_conv2,
padding=[1, 1],
name="conv2",
out_dtype=qtype_int,
bitwidth=16) # 32*8*8
bn2 = bnn.packed_batch_norm_threshold(conv2, bn_t2, name="bn2")
maxpool2 = bnn.packed_max_pool2d_LB(bn2, [2, 2], [2, 2],
name="maxpool2") # 32*4*4=512
pack = bnn.packed_flatten(maxpool2, name="packed_flatten")
fc1 = bnn.packed_dense(pack, w_fc1, b_fc1, True,
name="fc1") # 512/32->256/32
fc2 = bnn.packed_dense(fc1,
w_fc2,
b_fc2,
False,
name="fc2",
dtype=dtype_out) # 256/32->10
return fc2