def create_CRP(data, num_filter, name, workspace=512, lr_type="alex10"):
relu1 = syms.relu(data)
c1 = syms.conv(relu1, kernel=3, pad=1, num_filter=num_filter, name=name+"_conv1", no_bias=True, workspace=workspace, lr_type=lr_type)
p1 = syms.maxpool(c1, kernel=5, pad=2)
c2 = syms.conv(p1, kernel=3, pad=1, num_filter=num_filter, name=name+"_conv2", no_bias=True, workspace=workspace, lr_type=lr_type)
p2 = syms.maxpool(c2, kernel=5, pad=2)
c3 = syms.conv(p2, kernel=3, pad=1, num_filter=num_filter, name=name+"_conv3", no_bias=True, workspace=workspace, lr_type=lr_type)
p3 = syms.maxpool(c3, kernel=5, pad=2)
c4 = syms.conv(p3, kernel=3, pad=1, num_filter=num_filter, name=name+"_conv4", no_bias=True, workspace=workspace, lr_type=lr_type)
p4 = syms.maxpool(c4, kernel=5, pad=2)
return relu1+p1+p2+p3+p4
def create_block(data,
name,
num_filter,
kernel,
pad=0,
stride=1,
dilate=1,
workspace=512,
use_global_stats=True,
for_g=False,
lr_type="alex"):
res_name = "res" + name
bn_name = "bn" + name
if for_g:
res_name += "_g"
bn_name += "_g"
res = syms.conv(data=data,
name=res_name,
num_filter=num_filter,
pad=pad,
kernel=kernel,
stride=stride,
dilate=dilate,
no_bias=True,
workspace=workspace,
lr_type=lr_type)
bn = syms.bn(res,
name=bn_name,
use_global_stats=use_global_stats,
lr_type=lr_type)
return bn
def create_classifier(data, class_num, lr_type="alex10", workspace=512):
fc8_SEC = syms.conv(data,
"fc8_SEC",
num_filter=class_num,
lr_type=lr_type,
workspace=workspace)
return fc8_SEC
def create_refine_block(input1, name, num_filter1, num_filter2=None, input2=None, workspace=512, lr_type="alex10"):
rcu1 = create_big_RCU(input1, num_filter=num_filter1, name=name+"_rcu1", workspace=workspace, lr_type=lr_type)
if input2 is not None:
assert num_filter2 is not None
rcu2 = create_big_RCU(input2, num_filter=num_filter2, name=name+"_rcu2", workspace=workspace, lr_type=lr_type)
adapt1 = syms.conv(data=rcu1, kernel=3, pad=1, num_filter=num_filter2, name=name+"_adapt1", workspace=workspace, no_bias=True, lr_type=lr_type)
adapt2 = syms.conv(data=rcu2, kernel=3, pad=1, num_filter=num_filter2, name=name+"_adapt2", workspace=workspace, no_bias=True, lr_type=lr_type)
up1 = syms.upsample(adapt1, name=name+"_up1", scale=2, num_filter=num_filter2)
res = up1+adapt2
crp = create_CRP(res, name=name+"_crp", num_filter=num_filter2, workspace=workspace, lr_type=lr_type)
output_rcu = create_RCU(crp, num_filter=num_filter2, name=name+"_outputrcu", workspace=workspace, lr_type=lr_type)
return output_rcu
else:
crp = create_CRP(rcu1, name=name+"_crp", num_filter=num_filter1, workspace=workspace, lr_type=lr_type)
rcu2 = create_RCU(crp, num_filter=num_filter1, name=name+"_outputrcu", workspace=workspace, lr_type=lr_type)
return rcu2
def create_classifier(data, class_num, lr_type="alex10", workspace=512):
CAM_pool = syms.avgpool(data, global_pool=True)
CAM_fc = syms.conv(CAM_pool,
"scores",
num_filter=class_num,
lr_type=lr_type,
workspace=workspace)
flattened = mx.sym.flatten(CAM_fc)
return flattened
def create_part1(data, workspace=512, use_global_stats=True, lr_type="alex"):
data = mx.symbol.Variable(name="data")
conv1 = syms.conv(data,
name="conv1",
num_filter=64,
pad=3,
kernel=7,
stride=2,
workspace=workspace,
lr_type=lr_type)
bn = syms.bn(conv1,
name="bn_conv1",
use_global_stats=use_global_stats,
lr_type=lr_type)
relu = syms.relu(bn)
pool1 = syms.maxpool(relu, kernel=3, stride=2, pad=1)
res2a = create_big_block(pool1,
"2a",
64,
256,
identity_map=False,
workspace=workspace,
use_global_stats=use_global_stats,
lr_type=lr_type)
res2b = create_big_block(res2a,
"2b",
64,
256,
workspace=workspace,
use_global_stats=use_global_stats,
lr_type=lr_type)
res2c = create_big_block(res2b,
"2c",
64,
256,
workspace=workspace,
use_global_stats=use_global_stats)
res3a = create_big_block(res2c,
"3a",
128,
512,
stride=2,
identity_map=False,
workspace=workspace,
use_global_stats=use_global_stats,
lr_type=lr_type)
res3b = create_big_block(res3a,
"3b",
128,
512,
workspace=workspace,
use_global_stats=use_global_stats,
lr_type=lr_type)
return res3b
def create_aspp_block(data, dilate, name, workspace=512):
conv1 = syms.conv(data,
name="fc6_" + name,
num_filter=1024,
pad=dilate,
kernel=3,
stride=1,
dilate=dilate,
workspace=workspace,
lr_type="alex")
relu1 = syms.relu(conv1)
fc = syms.conv(relu1,
name="fc7_" + name,
num_filter=1024,
kernel=1,
workspace=workspace,
lr_type="alex")
fc_relu = syms.relu(fc)
return fc_relu
def create_main(class_num, workspace=512, use_global_stats=True, lr_type="alex"):
data = mx.symbol.Variable(name="data")
conv1 = syms.conv(data, name="conv1", num_filter=64, pad=3, kernel=7, stride=2, workspace=workspace, lr_type=lr_type)
bn = syms.bn(conv1, name="bn_conv1", use_global_stats=use_global_stats, lr_type=lr_type)
relu = syms.relu(bn)
pool1 = syms.maxpool(relu, kernel=3, stride=2, pad=1)
res2a = create_big_block(pool1, "2a", 64, 256, identity_map=False, workspace=workspace
, use_global_stats=use_global_stats, lr_type=lr_type)
res2b = create_big_block(res2a, "2b", 64, 256, workspace=workspace, use_global_stats=use_global_stats, lr_type=lr_type)
res2c = create_big_block(res2b, "2c", 64, 256, workspace=workspace, use_global_stats=use_global_stats, lr_type=lr_type)
res3a = create_big_block(res2c, "3a", 128, 512, stride=2,identity_map=False, workspace=workspace,
use_global_stats=use_global_stats, lr_type=lr_type)
res3b = create_big_block(res3a, "3b", 128, 512, workspace=workspace, use_global_stats=use_global_stats, lr_type=lr_type)
res3c = create_big_block(res3b, "3c", 128, 512, workspace=workspace, use_global_stats=use_global_stats, lr_type=lr_type)
res3d = create_big_block(res3c, "3d", 128, 512, workspace=workspace, use_global_stats=use_global_stats, lr_type=lr_type)
res4a = create_big_block(res3d, "4a", 256, 1024, stride=1, identity_map=False, pad=2, dilate=2,
workspace=workspace, use_global_stats=use_global_stats, lr_type=lr_type)
res4b = create_big_block(res4a, "4b", 256, 1024, workspace=workspace, pad=2, dilate=2,
use_global_stats=use_global_stats, lr_type=lr_type)
res4c = create_big_block(res4b, "4c", 256, 1024, workspace=workspace, pad=2, dilate=2,
use_global_stats=use_global_stats, lr_type=lr_type)
res4d = create_big_block(res4c, "4d", 256, 1024, workspace=workspace, pad=2, dilate=2,
use_global_stats=use_global_stats, lr_type=lr_type)
res4e = create_big_block(res4d, "4e", 256, 1024, workspace=workspace, pad=2, dilate=2,
use_global_stats=use_global_stats, lr_type=lr_type)
res4f = create_big_block(res4e, "4f", 256, 1024, workspace=workspace, pad=2, dilate=2,
use_global_stats=use_global_stats, lr_type=lr_type)
res5a = create_big_block(res4f, "5a", 512, 2048, stride=1, identity_map=False, pad=4, dilate=4,
workspace=workspace, use_global_stats=use_global_stats, lr_type=lr_type)
res5b = create_big_block(res5a, "5b", 512, 2048, workspace=workspace, pad=4, dilate=4,
use_global_stats=use_global_stats, lr_type=lr_type)
res5c = create_big_block(res5b, "5c", 512, 2048, workspace=workspace, pad=4, dilate=4,
use_global_stats=use_global_stats, lr_type=lr_type)
new_conv1 = syms.conv(res5c, name="new_conv1", num_filter=512, pad=12, kernel=3, dilate=12,
workspace=workspace, lr_type="alex10")
new_relu1 = syms.relu(new_conv1)
dp1 = syms.dropout(new_relu1)
fc = syms.conv(dp1, name="fc", num_filter=512, workspace=workspace, lr_type="alex10")
fc_relu = syms.relu(fc)
net = syms.conv(fc_relu, name="score", num_filter=class_num, workspace=workspace, lr_type="alex10")
return net
def create_body(data, lr_type="alex", workspace=512):
# group1
g1_1 = create_convrelu_unit(data, "conv1_1", 64, lr_type=lr_type, workspace=workspace)
g1_2 = create_convrelu_unit(g1_1, "conv1_2", 64, lr_type=lr_type, workspace=workspace)
pool1 = syms.maxpool(g1_2, 3, 1, 2)
# group2
g2_1 = create_convrelu_unit(pool1, "conv2_1", 128, lr_type=lr_type, workspace=workspace)
g2_2 = create_convrelu_unit(g2_1, "conv2_2", 128, lr_type=lr_type, workspace=workspace)
pool2 = syms.maxpool(g2_2, 3, 1, 2)
# group3
g3_1 = create_convrelu_unit(pool2, "conv3_1", 256, lr_type=lr_type, workspace=workspace)
g3_2 = create_convrelu_unit(g3_1, "conv3_2", 256, lr_type=lr_type, workspace=workspace)
g3_3 = create_convrelu_unit(g3_2, "conv3_3", 256, lr_type=lr_type, workspace=workspace)
pool3 = syms.maxpool(g3_3, 3, 1, 2)
# group4
g4_1 = create_convrelu_unit(pool3, "conv4_1", 512, lr_type=lr_type, workspace=workspace)
g4_2 = create_convrelu_unit(g4_1, "conv4_2", 512, lr_type=lr_type, workspace=workspace)
g4_3 = create_convrelu_unit(g4_2, "conv4_3", 512, lr_type=lr_type, workspace=workspace)
pool4 = syms.maxpool(g4_3, 3, 1, 1)
# group5
g5_1 = create_convrelu_unit(pool4, "conv5_1", 512, lr_type=lr_type, dilate=2, pad=2, workspace=workspace)
g5_2 = create_convrelu_unit(g5_1, "conv5_2", 512, lr_type=lr_type, dilate=2, pad=2, workspace=workspace)
g5_3 = create_convrelu_unit(g5_2, "conv5_3", 512, lr_type=lr_type, dilate=2, pad=2, workspace=workspace)
pool5 = syms.maxpool(g5_3, 3, 1, 1)
pool5a = syms.avgpool(pool5, 3, 1, 1)
# group6
conv6 = syms.conv(pool5a, "fc6", 1024, kernel=3, lr_type=lr_type, dilate=12, pad=12, workspace=workspace)
relu6 = syms.relu(conv6)
# group7
conv7 = syms.conv(relu6, "fc7", 1024, lr_type=lr_type, workspace=workspace)
relu7 = syms.relu(conv7)
return relu7
def create_main(data, class_num, workspace=512, lr_type="alex10"):
outputs = create_res101_main(data, workspace=workspace)
outputs = outputs[::-1]
refine1_adapt1 = syms.conv(data=outputs[0], kernel=3, pad=1, num_filter=512, name="refine1_input_adapt1", no_bias=True, workspace=workspace, lr_type=lr_type)
refine_block1 = create_refine_block(refine1_adapt1, name="refine1", num_filter1=512, workspace=workspace, lr_type=lr_type)
refine2_adapt2 = syms.conv(data=outputs[1], kernel=3, pad=1, num_filter=256, name="refine2_input_adapt2", no_bias=True, workspace=workspace, lr_type=lr_type)
refine_block2 = create_refine_block(input1=refine_block1, input2=refine2_adapt2, num_filter1=512, num_filter2=256, name="refine2", workspace=workspace, lr_type=lr_type)
refine3_adapt2 = syms.conv(data=outputs[2], kernel=3, pad=1, num_filter=256, name="refine3_input_adapt2", no_bias=True, workspace=workspace, lr_type=lr_type)
refine_block3 = create_refine_block(input1=refine_block2, input2=refine3_adapt2, num_filter1=256, num_filter2=256, name="refine3", workspace=workspace, lr_type=lr_type)
refine4_adapt2 = syms.conv(data=outputs[3], kernel=3, pad=1, num_filter=256, name="refine4_input_adapt2", no_bias=True, workspace=workspace, lr_type=lr_type)
refine_block4 = create_refine_block(input1=refine_block3, input2=refine4_adapt2, num_filter1=256, num_filter2=256, name="refine4", workspace=workspace, lr_type=lr_type)
final_rcu = create_big_RCU(refine_block4, num_filter=256, name="final_rcu1" , workspace=workspace, lr_type=lr_type)
dp = syms.dropout(data=final_rcu, p=0.5)
classifier = syms.conv(data=dp, kernel=3, num_filter=class_num, name="score", workspace=workspace, pad=1, lr_type=lr_type)
return classifier
def create_body(lr_type="alex", workspace=512):
data = mx.symbol.Variable(name="data")
# group1
g1_1 = create_convrelu_unit(data, "conv1_1", 64, lr_type=lr_type, workspace=workspace)
g1_2 = create_convrelu_unit(g1_1, "conv1_2", 64, lr_type=lr_type, workspace=workspace)
pool1 = syms.maxpool(g1_2, 3, 1, 2, pooling_convention="valid")
# group2
g2_1 = create_convrelu_unit(pool1, "conv2_1", 128, lr_type=lr_type, workspace=workspace)
g2_2 = create_convrelu_unit(g2_1, "conv2_2", 128, lr_type=lr_type, workspace=workspace)
pool2 = syms.maxpool(g2_2, 3, 1, 2, pooling_convention="valid")
# group3
g3_1 = create_convrelu_unit(pool2, "conv3_1", 256, lr_type=lr_type, workspace=workspace)
g3_2 = create_convrelu_unit(g3_1, "conv3_2", 256, lr_type=lr_type, workspace=workspace)
g3_3 = create_convrelu_unit(g3_2, "conv3_3", 256, lr_type=lr_type, workspace=workspace)
pool3 = syms.maxpool(g3_3, 3, 1, 2, pooling_convention="valid")
# group4
g4_1 = create_convrelu_unit(pool3, "conv4_1", 512, lr_type=lr_type, workspace=workspace)
g4_2 = create_convrelu_unit(g4_1, "conv4_2", 512, lr_type=lr_type, workspace=workspace)
g4_3 = create_convrelu_unit(g4_2, "conv4_3", 512, lr_type=lr_type, workspace=workspace)
# group5
g5_1 = create_convrelu_unit(g4_3, "conv5_1", 512, lr_type=lr_type, workspace=workspace)
g5_2 = create_convrelu_unit(g5_1, "conv5_2", 512, lr_type=lr_type, workspace=workspace)
g5_3 = create_convrelu_unit(g5_2, "conv5_3", 512, lr_type=lr_type, workspace=workspace)
# group6
conv6 = syms.conv(g5_3, "conv6_CAM", 1024, pad=1, kernel=3, lr_type="alex10", workspace=workspace)
relu6 = syms.relu(conv6)
relu6_dp = syms.dropout(relu6, 0.5)
# group7
conv7 = syms.conv(relu6_dp, "conv7_CAM", 1024, pad=1, kernel=3, lr_type="alex10", workspace=workspace)
relu7 = syms.relu(conv7)
relu7_dp = syms.dropout(relu7, 0.5)
return relu7_dp
def create_convrelu_unit(data,
name,
num_filter,
lr_type="alex",
pad=1,
kernel=3,
stride=1,
workspace=512):
conv = syms.conv(data,
name=name,
num_filter=num_filter,
pad=pad,
kernel=kernel,
stride=stride,
workspace=workspace,
lr_type=lr_type)
relu = syms.relu(conv)
return relu
def create_res101_main(data, workspace=512, use_global_stats=True):
conv1 = syms.conv(data, name="conv1", num_filter=64, pad=3, kernel=7, stride=2, workspace=workspace, no_bias=True)
bn = syms.bn(conv1, name="bn_conv1", use_global_stats=use_global_stats)
relu = syms.relu(bn)
pool1 = syms.maxpool(relu, kernel=3, stride=2, pad=1)
res2a = create_big_block(pool1, "2a", 64, 256, identity_map=False, workspace=workspace, use_global_stats=use_global_stats)
res2b = create_big_block(res2a, "2b", 64, 256, workspace=workspace, use_global_stats=use_global_stats)
res2c = create_big_block(res2b, "2c", 64, 256, workspace=workspace, use_global_stats=use_global_stats)
res3a = create_big_block(res2c, "3a", 128, 512, stride=2, identity_map=False, workspace=workspace, use_global_stats=use_global_stats)
res3b1 = create_big_block(res3a, "3b1", 128, 512, workspace=workspace, use_global_stats=use_global_stats)
res3b2 = create_big_block(res3b1, "3b2", 128, 512, workspace=workspace, use_global_stats=use_global_stats)
res3b3 = create_big_block(res3b2, "3b3", 128, 512, workspace=workspace, use_global_stats=use_global_stats)
res4a = create_big_block(res3b3, "4a", 256, 1024, stride=2, identity_map=False, workspace=workspace, use_global_stats=use_global_stats)
res4b1 = create_big_block(res4a, "4b1", 256, 1024, workspace=workspace, use_global_stats=use_global_stats)
res4b2 = create_big_block(res4b1, "4b2", 256, 1024, workspace=workspace, use_global_stats=use_global_stats)
res4b3 = create_big_block(res4b2, "4b3", 256, 1024, workspace=workspace, use_global_stats=use_global_stats)
res4b4 = create_big_block(res4b3, "4b4", 256, 1024, workspace=workspace, use_global_stats=use_global_stats)
res4b5 = create_big_block(res4b4, "4b5", 256, 1024, workspace=workspace, use_global_stats=use_global_stats)
res4b6 = create_big_block(res4b5, "4b6", 256, 1024, workspace=workspace, use_global_stats=use_global_stats)
res4b7 = create_big_block(res4b6, "4b7", 256, 1024, workspace=workspace, use_global_stats=use_global_stats)
res4b8 = create_big_block(res4b7, "4b8", 256, 1024, workspace=workspace, use_global_stats=use_global_stats)
res4b9 = create_big_block(res4b8, "4b9", 256, 1024, workspace=workspace, use_global_stats=use_global_stats)
res4b10 = create_big_block(res4b9, "4b10", 256, 1024, workspace=workspace, use_global_stats=use_global_stats)
res4b11 = create_big_block(res4b10, "4b11", 256, 1024, workspace=workspace, use_global_stats=use_global_stats)
res4b12 = create_big_block(res4b11, "4b12", 256, 1024, workspace=workspace, use_global_stats=use_global_stats)
res4b13 = create_big_block(res4b12, "4b13", 256, 1024, workspace=workspace, use_global_stats=use_global_stats)
res4b14 = create_big_block(res4b13, "4b14", 256, 1024, workspace=workspace, use_global_stats=use_global_stats)
res4b15 = create_big_block(res4b14, "4b15", 256, 1024, workspace=workspace, use_global_stats=use_global_stats)
res4b16 = create_big_block(res4b15, "4b16", 256, 1024, workspace=workspace, use_global_stats=use_global_stats)
res4b17 = create_big_block(res4b16, "4b17", 256, 1024, workspace=workspace, use_global_stats=use_global_stats)
res4b18 = create_big_block(res4b17, "4b18", 256, 1024, workspace=workspace, use_global_stats=use_global_stats)
res4b19 = create_big_block(res4b18, "4b19", 256, 1024, workspace=workspace, use_global_stats=use_global_stats)
res4b20 = create_big_block(res4b19, "4b20", 256, 1024, workspace=workspace, use_global_stats=use_global_stats)
res4b21 = create_big_block(res4b20, "4b21", 256, 1024, workspace=workspace, use_global_stats=use_global_stats)
res4b22 = create_big_block(res4b21, "4b22", 256, 1024, workspace=workspace, use_global_stats=use_global_stats)
res5a = create_big_block(res4b22, "5a", 512, 2048, stride=2, identity_map=False, workspace=workspace, use_global_stats=use_global_stats)
res5b = create_big_block(res5a, "5b", 512, 2048, workspace=workspace, use_global_stats=use_global_stats)
res5c = create_big_block(res5b, "5c", 512, 2048, workspace=workspace, use_global_stats=use_global_stats)
return [res2c, res3b3, res4b22, res5c]
def create_seg_part(data,
class_num,
workspace=512,
lr_type="alex",
use_global_stats=True):
res4a = create_big_block(data,
"4a",
256,
1024,
stride=1,
identity_map=False,
pad=2,
dilate=2,
workspace=workspace,
use_global_stats=use_global_stats,
lr_type=lr_type)
res4b = create_big_block(res4a,
"4b",
256,
1024,
workspace=workspace,
pad=2,
dilate=2,
use_global_stats=use_global_stats,
lr_type=lr_type)
res4c = create_big_block(res4b,
"4c",
256,
1024,
workspace=workspace,
pad=2,
dilate=2,
use_global_stats=use_global_stats,
lr_type=lr_type)
res4d = create_big_block(res4c,
"4d",
256,
1024,
workspace=workspace,
pad=2,
dilate=2,
use_global_stats=use_global_stats,
lr_type=lr_type)
res4e = create_big_block(res4d,
"4e",
256,
1024,
workspace=workspace,
pad=2,
dilate=2,
use_global_stats=use_global_stats,
lr_type=lr_type)
res4f = create_big_block(res4e,
"4f",
256,
1024,
workspace=workspace,
pad=2,
dilate=2,
use_global_stats=use_global_stats,
lr_type=lr_type)
res5a = create_big_block(res4f,
"5a",
512,
2048,
stride=1,
identity_map=False,
pad=4,
dilate=4,
workspace=workspace,
use_global_stats=use_global_stats,
lr_type=lr_type)
res5b = create_big_block(res5a,
"5b",
512,
2048,
workspace=workspace,
pad=4,
dilate=4,
use_global_stats=use_global_stats,
lr_type=lr_type)
res5c = create_big_block(res5b,
"5c",
512,
2048,
workspace=workspace,
pad=4,
dilate=4,
use_global_stats=use_global_stats,
lr_type=lr_type)
new_conv1 = syms.conv(res5c,
name="new_conv1",
num_filter=512,
pad=12,
kernel=3,
dilate=12,
workspace=workspace)
new_relu1 = syms.relu(new_conv1)
dp1 = syms.dropout(new_relu1)
fc = syms.conv(dp1, name="fc", num_filter=512, workspace=workspace)
fc_relu = syms.relu(fc)
net = syms.conv(fc_relu,
name="score",
num_filter=class_num,
workspace=workspace)
return net
def create_g_part(data,
class_num,
workspace=512,
lr_type="alex",
use_global_stats=True):
res4a = create_big_block(data,
"4a",
256,
1024,
stride=2,
identity_map=False,
workspace=workspace,
use_global_stats=use_global_stats,
for_g=True,
lr_type=lr_type)
res4b = create_big_block(res4a,
"4b",
256,
1024,
workspace=workspace,
use_global_stats=use_global_stats,
for_g=True,
lr_type=lr_type)
res4c = create_big_block(res4b,
"4c",
256,
1024,
workspace=workspace,
use_global_stats=use_global_stats,
for_g=True,
lr_type=lr_type)
res4d = create_big_block(res4c,
"4d",
256,
1024,
workspace=workspace,
use_global_stats=use_global_stats,
for_g=True,
lr_type=lr_type)
res4e = create_big_block(res4d,
"4e",
256,
1024,
workspace=workspace,
use_global_stats=use_global_stats,
for_g=True,
lr_type=lr_type)
res4f = create_big_block(res4e,
"4f",
256,
1024,
workspace=workspace,
use_global_stats=use_global_stats,
for_g=True,
lr_type=lr_type)
res5a = create_big_block(res4f,
"5a",
512,
2048,
stride=2,
identity_map=False,
workspace=workspace,
use_global_stats=use_global_stats,
for_g=True,
lr_type=lr_type)
res5b = create_big_block(res5a,
"5b",
512,
2048,
workspace=workspace,
use_global_stats=use_global_stats,
for_g=True,
lr_type=lr_type)
res5c = create_big_block(res5b,
"5c",
512,
2048,
workspace=workspace,
use_global_stats=use_global_stats,
for_g=True,
lr_type=lr_type)
pool = syms.maxpool(res5c, kernel=1, pad=0, global_pool=True)
fc1 = syms.conv(data=pool,
kernel=1,
num_filter=1024,
workspace=workspace,
name="g_multi_fc1")
fc1_relu = syms.relu(fc1)
dp1 = syms.dropout(data=fc1_relu)
fc2 = syms.conv(data=dp1,
kernel=1,
num_filter=1024,
workspace=workspace,
name="g_multi_fc2")
fc2_relu = syms.relu(fc2)
dp2 = syms.dropout(data=fc2_relu)
score = syms.conv(data=dp2,
kernel=1,
num_filter=class_num,
workspace=workspace,
name="g_score")
return score
def create_RCU(data, num_filter, name, workspace=512, lr_type="alex10"):
relu1 = syms.relu(data)
res1 = syms.conv(relu1, num_filter=num_filter, name=name+"_b1", kernel=3, pad=1, workspace=workspace, lr_type=lr_type)
relu2 = syms.relu(res1)
res2 = syms.conv(relu2, num_filter=num_filter, name=name+"_b2", kernel=3, pad=1, workspace=workspace, no_bias=True, lr_type=lr_type)
return data+res2
def create_part2(data,
num_class,
workspace=512,
use_global_stats=True,
lr_type="alex"):
res3c = create_big_block(data,
"3c",
128,
512,
workspace=workspace,
use_global_stats=use_global_stats,
lr_type=lr_type)
res3d = create_big_block(res3c,
"3d",
128,
512,
workspace=workspace,
use_global_stats=use_global_stats,
lr_type=lr_type)
res4a = create_big_block(res3d,
"4a",
256,
1024,
stride=2,
identity_map=False,
workspace=workspace,
use_global_stats=use_global_stats,
lr_type=lr_type)
res4b = create_big_block(res4a,
"4b",
256,
1024,
workspace=workspace,
use_global_stats=use_global_stats,
lr_type=lr_type)
res4c = create_big_block(res4b,
"4c",
256,
1024,
workspace=workspace,
use_global_stats=use_global_stats,
lr_type=lr_type)
res4d = create_big_block(res4c,
"4d",
256,
1024,
workspace=workspace,
use_global_stats=use_global_stats,
lr_type=lr_type)
res4e = create_big_block(res4d,
"4e",
256,
1024,
workspace=workspace,
use_global_stats=use_global_stats,
lr_type=lr_type)
res4f = create_big_block(res4e,
"4f",
256,
1024,
workspace=workspace,
use_global_stats=use_global_stats,
lr_type=lr_type)
res5a = create_big_block(res4f,
"5a",
512,
2048,
stride=2,
identity_map=False,
workspace=workspace,
use_global_stats=use_global_stats,
lr_type=lr_type)
res5b = create_big_block(res5a,
"5b",
512,
2048,
workspace=workspace,
use_global_stats=use_global_stats,
lr_type=lr_type)
res5c = create_big_block(res5b,
"5c",
512,
2048,
workspace=workspace,
use_global_stats=use_global_stats,
lr_type=lr_type)
adapt = syms.conv(res5c,
"adapt",
512,
kernel=1,
lr_type="alex10",
workspace=workspace)
adapt_relu = syms.relu(adapt)
fl = mx.sym.flatten(adapt_relu)
fc6 = syms.fullyconnected(fl,
num_hidden=1024,
name="CAMfc6",
lr_type="alex10")
relu6 = syms.relu(fc6)
# group7
fc7 = syms.fullyconnected(relu6,
num_hidden=1024,
name="CAMfc7",
lr_type="alex10")
relu7 = syms.relu(fc7)
fc8 = syms.fullyconnected(relu7,
num_hidden=num_class,
name="CAMfc8",
lr_type="alex10")
return fc8
def create_block(data, name, num_filter, kernel, pad=0, stride=1, dilate=1, workspace=512, use_global_stats=True):
res = syms.conv(data=data, name="res" + name, num_filter=num_filter, pad=pad, kernel=kernel, stride=stride, dilate=dilate, no_bias=True, workspace=workspace)
bn = syms.bn(res, name="bn" + name, use_global_stats=use_global_stats)
return bn
def create_g_part(data, class_num, lr_type="alex", workspace=512):
pool3 = syms.maxpool(data, 2, 0, 2, pooling_convention='valid')
# group4
g4_1 = create_convrelu_unit(pool3,
"conv4_1_g",
512,
lr_type=lr_type,
workspace=workspace)
g4_2 = create_convrelu_unit(g4_1,
"conv4_2_g",
512,
lr_type=lr_type,
workspace=workspace)
g4_3 = create_convrelu_unit(g4_2,
"conv4_3_g",
512,
lr_type=lr_type,
workspace=workspace)
pool4 = syms.maxpool(g4_3, 2, 0, 2, pooling_convention='valid')
# group5
g5_1 = create_convrelu_unit(pool4,
"conv5_1_g",
512,
lr_type=lr_type,
workspace=workspace)
g5_2 = create_convrelu_unit(g5_1,
"conv5_2_g",
512,
lr_type=lr_type,
workspace=workspace)
g5_3 = create_convrelu_unit(g5_2,
"conv5_3_g",
512,
lr_type=lr_type,
workspace=workspace)
pool5 = syms.maxpool(g5_3, 2, 0, 2, pooling_convention='valid')
# group6
conv6 = syms.conv(pool5,
"fc6_g",
1024,
kernel=3,
lr_type=lr_type,
pad=1,
workspace=workspace)
relu6 = syms.relu(conv6)
# group7
conv7 = syms.conv(relu6,
"fc7_g",
1024,
lr_type=lr_type,
workspace=workspace)
relu7 = syms.relu(conv7)
pool = syms.maxpool(relu7, kernel=1, pad=0, global_pool=True)
fc1 = syms.conv(pool,
name="g_multi_fc1",
num_filter=1024,
workspace=workspace,
lr_type="alex10")
fc1_relu = syms.relu(fc1)
dp1 = syms.dropout(data=fc1_relu)
fc2 = syms.conv(dp1,
name="g_multi_fc2",
num_filter=1024,
workspace=workspace,
lr_type="alex10")
fc2_relu = syms.relu(fc2)
dp2 = syms.dropout(data=fc2_relu)
g_score = syms.conv(dp2,
name="g_score",
num_filter=class_num,
workspace=workspace,
lr_type="alex10")
return g_score
def create_body(lr_type="alex", workspace=512, use_global_stats=True):
data = mx.symbol.Variable(name="data")
conv1 = syms.conv(data,
name="conv1",
num_filter=64,
pad=3,
kernel=7,
stride=2,
workspace=workspace,
lr_type=lr_type)
bn = syms.bn(conv1,
name="bn_conv1",
use_global_stats=use_global_stats,
lr_type=lr_type)
relu = syms.relu(bn)
pool1 = syms.maxpool(relu, kernel=3, stride=2, pad=1)
res2a = create_big_block(pool1,
"2a",
64,
256,
identity_map=False,
workspace=workspace,
use_global_stats=use_global_stats,
lr_type=lr_type)
res2b = create_big_block(res2a,
"2b",
64,
256,
workspace=workspace,
use_global_stats=use_global_stats,
lr_type=lr_type)
res2c = create_big_block(res2b,
"2c",
64,
256,
workspace=workspace,
use_global_stats=use_global_stats)
res3a = create_big_block(res2c,
"3a",
128,
512,
stride=2,
identity_map=False,
workspace=workspace,
use_global_stats=use_global_stats,
lr_type=lr_type)
res3b = create_big_block(res3a,
"3b",
128,
512,
workspace=workspace,
use_global_stats=use_global_stats,
lr_type=lr_type)
res3c = create_big_block(res3b,
"3c",
128,
512,
workspace=workspace,
use_global_stats=use_global_stats,
lr_type=lr_type)
res3d = create_big_block(res3c,
"3d",
128,
512,
workspace=workspace,
use_global_stats=use_global_stats,
lr_type=lr_type)
res4a = create_big_block(res3d,
"4a",
256,
1024,
stride=1,
identity_map=False,
workspace=workspace,
use_global_stats=use_global_stats,
lr_type=lr_type)
res4b = create_big_block(res4a,
"4b",
256,
1024,
workspace=workspace,
use_global_stats=use_global_stats,
lr_type=lr_type)
res4c = create_big_block(res4b,
"4c",
256,
1024,
workspace=workspace,
use_global_stats=use_global_stats,
lr_type=lr_type)
res4d = create_big_block(res4c,
"4d",
256,
1024,
workspace=workspace,
use_global_stats=use_global_stats,
lr_type=lr_type)
res4e = create_big_block(res4d,
"4e",
256,
1024,
workspace=workspace,
use_global_stats=use_global_stats,
lr_type=lr_type)
res4f = create_big_block(res4e,
"4f",
256,
1024,
workspace=workspace,
use_global_stats=use_global_stats,
lr_type=lr_type)
res5a = create_big_block(res4f,
"5a",
512,
2048,
stride=1,
identity_map=False,
workspace=workspace,
use_global_stats=use_global_stats,
lr_type=lr_type)
res5b = create_big_block(res5a,
"5b",
512,
2048,
workspace=workspace,
use_global_stats=use_global_stats,
lr_type=lr_type)
res5c = create_big_block(res5b,
"5c",
512,
2048,
workspace=workspace,
use_global_stats=use_global_stats,
lr_type=lr_type)
# group6
conv6 = syms.conv(res5c,
"conv6_CAM",
1024,
pad=1,
kernel=3,
lr_type="alex10",
workspace=workspace)
relu6 = syms.relu(conv6)
relu6_dp = syms.dropout(relu6, 0.5)
# group7
conv7 = syms.conv(relu6_dp,
"conv7_CAM",
1024,
pad=1,
kernel=3,
lr_type="alex10",
workspace=workspace)
relu7 = syms.relu(conv7)
relu7_dp = syms.dropout(relu7, 0.5)
return relu7_dp
def create_seg_part(data, class_num, workspace=512, lr_type="alex"):
pool3 = syms.maxpool(data, 3, 1, 2, pooling_convention='valid')
# group4
g4_1 = create_convrelu_unit(pool3,
"conv4_1",
512,
lr_type=lr_type,
workspace=workspace)
g4_2 = create_convrelu_unit(g4_1,
"conv4_2",
512,
lr_type=lr_type,
workspace=workspace)
g4_3 = create_convrelu_unit(g4_2,
"conv4_3",
512,
lr_type=lr_type,
workspace=workspace)
pool4 = syms.maxpool(g4_3, 3, 1, 1, pooling_convention='valid')
# group5
g5_1 = create_convrelu_unit(pool4,
"conv5_1",
512,
lr_type=lr_type,
dilate=2,
pad=2,
workspace=workspace)
g5_2 = create_convrelu_unit(g5_1,
"conv5_2",
512,
lr_type=lr_type,
dilate=2,
pad=2,
workspace=workspace)
g5_3 = create_convrelu_unit(g5_2,
"conv5_3",
512,
lr_type=lr_type,
dilate=2,
pad=2,
workspace=workspace)
pool5 = syms.maxpool(g5_3, 3, 1, 1, pooling_convention='valid')
c1 = create_aspp_block(pool5, 6, "1")
c2 = create_aspp_block(pool5, 12, "2")
c3 = create_aspp_block(pool5, 18, "3")
c4 = create_aspp_block(pool5, 24, "4")
merged = c1 + c2 + c3 + c4
dp1 = syms.dropout(merged)
fc2 = syms.conv(dp1,
name="fc2",
num_filter=512,
workspace=workspace,
lr_type="alex10")
fc2_relu = syms.relu(fc2)
dp2 = syms.dropout(fc2_relu)
net = syms.conv(dp2,
name="score",
num_filter=class_num,
workspace=workspace,
lr_type="alex10")
return net
def create_main(class_num,
lr_type_old="alex",
lr_type_new="alex",
workspace=512):
data = mx.symbol.Variable(name="data")
# group1
g1_1 = create_convrelu_unit(data,
"conv1_1",
64,
lr_type=lr_type_old,
workspace=workspace)
g1_2 = create_convrelu_unit(g1_1,
"conv1_2",
64,
lr_type=lr_type_old,
workspace=workspace)
pool1 = syms.maxpool(g1_2, 3, 1, 2)
# group2
g2_1 = create_convrelu_unit(pool1,
"conv2_1",
128,
lr_type=lr_type_old,
workspace=workspace)
g2_2 = create_convrelu_unit(g2_1,
"conv2_2",
128,
lr_type=lr_type_old,
workspace=workspace)
pool2 = syms.maxpool(g2_2, 3, 1, 2)
# group3
g3_1 = create_convrelu_unit(pool2,
"conv3_1",
256,
lr_type=lr_type_old,
workspace=workspace)
g3_2 = create_convrelu_unit(g3_1,
"conv3_2",
256,
lr_type=lr_type_old,
workspace=workspace)
g3_3 = create_convrelu_unit(g3_2,
"conv3_3",
256,
lr_type=lr_type_old,
workspace=workspace)
pool3 = syms.maxpool(g3_3, 3, 1, 2)
# group4
g4_1 = create_convrelu_unit(pool3,
"conv4_1",
512,
lr_type=lr_type_old,
workspace=workspace)
g4_2 = create_convrelu_unit(g4_1,
"conv4_2",
512,
lr_type=lr_type_old,
workspace=workspace)
g4_3 = create_convrelu_unit(g4_2,
"conv4_3",
512,
lr_type=lr_type_old,
workspace=workspace)
pool4 = syms.maxpool(g4_3, 3, 1, 1)
# group5
g5_1 = create_convrelu_unit(pool4,
"conv5_1",
512,
lr_type=lr_type_old,
dilate=2,
pad=2,
workspace=workspace)
g5_2 = create_convrelu_unit(g5_1,
"conv5_2",
512,
lr_type=lr_type_old,
dilate=2,
pad=2,
workspace=workspace)
g5_3 = create_convrelu_unit(g5_2,
"conv5_3",
512,
lr_type=lr_type_old,
dilate=2,
pad=2,
workspace=workspace)
pool5 = syms.maxpool(g5_3, 3, 1, 1)
# group6
conv6 = syms.conv(pool5,
"fc6",
1024,
kernel=3,
lr_type=lr_type_old,
dilate=4,
pad=4,
workspace=workspace)
relu6 = syms.relu(conv6)
# group7
conv7 = syms.conv(relu6,
"fc7",
1024,
lr_type=lr_type_old,
workspace=workspace)
relu7 = syms.relu(conv7)
new1 = syms.conv(relu7,
name="new1",
num_filter=512,
pad=12,
kernel=3,
dilate=12,
workspace=workspace,
lr_type="alex10")
new1_relu = syms.relu(new1)
dp1 = syms.dropout(new1_relu)
new2 = syms.conv(dp1,
name="new2",
num_filter=512,
workspace=workspace,
lr_type="alex10")
new2_relu = syms.relu(new2)
net = syms.conv(new2_relu,
name="score",
num_filter=class_num,
workspace=workspace,
lr_type="alex10")
return net
