def ResNext_bottleneck_B(split, bottom, dim_in, features, stride):
dim_out = features * 4
dim_branch_out = int(math.floor(features * (opt.baseWidth / 64)))
branch_container = list()
for i in range(opt.cardinalty):
# torch#75: s:add(Convolution(nInputPlane,d,1,1,1,1,0,0))
scale1, relu1 = conv_BN_scale_relu(split, bottom, dim_branch_out, 1, 1,
0)
# torch#76: s:add(Convolution(d,d,3,3,stride,stride,1,1))
# NOTE: I don't think stride is needed, as we need to keep dim the same
scale2, relu2 = conv_BN_scale_relu(split, relu1, dim_branch_out, 3,
stride, 1)
branch_container.append(relu2)
comb = L.Concat(*branch_container, in_place=True)
# torch#96: s:add(Convolution(D*C,n*4,1,1,1,1,0,0))
scale_comb, relu_comb = conv_BN_scale_relu(split, comb, dim_out, 1, 1, 0)
if dim_in == dim_out:
scale0 = bottom
relu0 = L.ReLU(scale0, in_place=True)
else:
scale0, relu0 = conv_BN_scale_relu(split, bottom, dim_out, 1, stride,
0)
return L.Eltwise(relu_comb, relu0, operation=P.Eltwise.SUM)
def decompose(split, bottom, dim_in, dim_branch_out, cardinality):
branch_container = list()
for i in range(cardinality):
# torch#75: s:add(Convolution(nInputPlane,d,1,1,1,1,0,0))
scale1, relu1 = conv_BN_scale_relu(split, bottom, dim_branch_out, 1, 1, 0);
# torch#76: s:add(Convolution(d,d,3,3,stride,stride,1,1))
# NOTE: I don't think stride is needed, as we need to keep dim the same
scale2, relu2 = conv_BN_scale_relu(split, relu1, dim_branch_out, 3, 1, 1)
branch_container.append(relu2)
comb = L.Concat(*branch_container, in_place=True)
return comb
def expand_dim_n_bottleneck_B(split, bottom, dim_in, features, stride):
dim_branch_out = int(math.floor(features * (opt.baseWidth / 64)))
dim_out = dim_in * 4
branch_container = list()
for i in range(opt.cardinalty):
scale1, relu1 = conv_BN_scale_relu(split, bottom, dim_branch_out, 1, 1,
0)
scale2, relu2 = conv_BN_scale_relu(split, relu1, dim_branch_out, 3,
stride, 1)
branch_container.append(relu2)
comb = L.Concat(*branch_container, in_place=True)
scale_comb, relu_comb = conv_BN_scale_relu(split, comb, dim_out, 1, 1, 0)
scale_i, relu_i = conv_BN_scale_relu(split, bottom, dim_out, 1, stride, 0)
return L.Eltwise(relu_comb, relu_i, operation=P.Eltwise.SUM)
def ResNext(split, n):
if split == "train":
data, labels = L.Data(source=TRAIN_FILE,
backend=P.Data.LMDB,
batch_size=opt.batch_size,
ntop=2,
transform_param=dict(mean_file=MEAN_FILE,
crop_size=28,
mirror=True))
elif split == "test":
data, labels = L.Data(source=TEST_FILE,
backend=P.Data.LMDB,
batch_size=opt.batch_size,
ntop=2,
transform_param=dict(mean_file=MEAN_FILE,
crop_size=28))
scale, result = conv_BN_scale_relu(
split, data, nout=64, ks=3, stride=1,
pad=1) #NOT CHANGE dim_out 64, 64x{28x28}
features = 64
t = expand_dim_n_bottleneck_B(split, result, features, features,
1) #64->256, 256x{28x28}
t = same_dim_n_bottlneck_B(split, t, features * 4, features,
1) #256->256, 256x{28x28}
t = same_dim_n_bottlneck_B(split, t, features * 4, features,
1) #256->256, 256x{28x28}
features = 128
t = expand_dim_n_bottleneck_B(split, t, features * 2, features,
2) #256->512, 512x{14x14}
t = same_dim_n_bottlneck_B(split, t, features * 4, features,
1) #512->512, 512x{14x14}
t = same_dim_n_bottlneck_B(split, t, features * 4, features,
1) #512->512, 512x{14x14}
features = 256
t = expand_dim_n_bottleneck_B(split, t, features * 2, features,
2) #512->1024, 1024x{7x7}
t = same_dim_n_bottlneck_B(split, t, features * 4, features,
1) #1024->1024, 1024x{7x7}
t = same_dim_n_bottlneck_B(split, t, features * 4, features,
1) #1024->1024, 1024x{7x7}
#pool = L.Pooling(result, pool = P.Pooling.AVE, global_pooling = True, kernel_size = 8, stride = 1)
pool = L.Pooling(t, pool=P.Pooling.AVE, global_pooling=True)
IP = L.InnerProduct(pool,
num_output=10,
weight_filler=dict(type='xavier'),
bias_filler=dict(type='constant'))
acc = L.Accuracy(IP, labels)
loss = L.SoftmaxWithLoss(IP, labels)
return to_proto(acc, loss)
def ResNext(split, n):
DATAPATH_PREFIX = "../CIFAR10/"
TRAIN_FILE = DATAPATH_PREFIX + "cifar10_train_lmdb"
TEST_FILE = DATAPATH_PREFIX + "cifar10_test_lmdb"
MEAN_FILE = DATAPATH_PREFIX + "mean.binaryproto"
if split == "train":
data, labels = L.Data(source=TRAIN_FILE,
backend=P.Data.LMDB,
batch_size=opt.batch_size,
ntop=2,
transform_param=dict(mean_file=MEAN_FILE,
crop_size=28,
mirror=True))
elif split == "test":
data, labels = L.Data(source=TEST_FILE,
backend=P.Data.LMDB,
batch_size=opt.batch_size,
ntop=2,
transform_param=dict(mean_file=MEAN_FILE,
crop_size=28))
scale, result = conv_BN_scale_relu(split,
data,
nout=64,
ks=3,
stride=1,
pad=1)
# Conv2
t = n_bottleneck_layer_naive(split, result, ResNext_bottleneck_B, 64, n,
1) # Attention all three stride is 1
# Conv3
t = n_bottleneck_layer_naive(
split, t, ResNext_bottleneck_B, 128, n,
2) # Attention 1st stride is 2, the 2nd and 3rd is 1
# Conv4
r = n_bottleneck_layer_naive(
split, t, ResNext_bottleneck_B, 256, n,
2) # Attention 1st stride is 2, the 2nd and 3rd is 1
#pool = L.Pooling(result, pool = P.Pooling.AVE, global_pooling = True, kernel_size = 8, stride = 1)
pool = L.Pooling(r, pool=P.Pooling.AVE, global_pooling=True)
IP = L.InnerProduct(pool,
num_output=int(opt.dataset[5:]),
weight_filler=dict(type='xavier'),
bias_filler=dict(type='constant'))
acc = L.Accuracy(IP, labels)
loss = L.SoftmaxWithLoss(IP, labels)
return to_proto(acc, loss)
def ResNext(split, n):
DATAPATH_PREFIX = "../CIFAR10/"
TRAIN_FILE = DATAPATH_PREFIX + "cifar10_train_lmdb"
TEST_FILE = DATAPATH_PREFIX + "cifar10_test_lmdb"
MEAN_FILE = DATAPATH_PREFIX + "mean.binaryproto"
if split == "train":
data, labels = L.Data(source=TRAIN_FILE,
backend=P.Data.LMDB,
batch_size=opt.batch_size,
ntop=2,
transform_param=dict(mean_file=MEAN_FILE,
crop_size=28,
mirror=True))
elif split == "test":
data, labels = L.Data(source=TEST_FILE,
backend=P.Data.LMDB,
batch_size=opt.batch_size,
ntop=2,
transform_param=dict(mean_file=MEAN_FILE,
crop_size=28))
scale, result = conv_BN_scale_relu(
split, data, nout=64, ks=3, stride=1,
pad=1) #NOT CHANGE dim_out 64, 64x{28x28}
features = 64
t = expand_dim_n_bottleneck_B(split, result, features, features, 1)
t = same_dim_n_bottlneck_B(split, t, features * 4, features, 1)
t = same_dim_n_bottlneck_B(split, t, features * 4, features, 1)
features = 128
t = expand_dim_n_bottleneck_B(split, t, features * 2, features, 2)
t = same_dim_n_bottlneck_B(split, t, features * 4, features, 1)
t = same_dim_n_bottlneck_B(split, t, features * 4, features, 1)
features = 256
t = expand_dim_n_bottleneck_B(split, t, features * 2, features, 2)
t = same_dim_n_bottlneck_B(split, t, features * 4, features, 1)
t = same_dim_n_bottlneck_B(split, t, features * 4, features, 1)
#pool = L.Pooling(result, pool = P.Pooling.AVE, global_pooling = True, kernel_size = 8, stride = 1)
pool = L.Pooling(t, pool=P.Pooling.AVE, global_pooling=True)
IP = L.InnerProduct(pool,
num_output=10,
weight_filler=dict(type='xavier'),
bias_filler=dict(type='constant'))
acc = L.Accuracy(IP, labels)
loss = L.SoftmaxWithLoss(IP, labels)
return to_proto(acc, loss)
def ResNext(split, n):
if split == "train":
data, labels = L.Data(source=TRAIN_FILE,
backend=P.Data.LMDB,
batch_size=opt.batch_size,
ntop=2,
transform_param=dict(mean_file=MEAN_FILE,
crop_size=28,
mirror=True))
elif split == "test":
data, labels = L.Data(source=TEST_FILE,
backend=P.Data.LMDB,
batch_size=opt.batch_size,
ntop=2,
transform_param=dict(mean_file=MEAN_FILE,
crop_size=28))
scale, result = conv_BN_scale_relu(
split, data, nout=64, ks=3, stride=1,
pad=1) #NOT CHANGE dim_out 64, 64x{28x28}
# Block 1
# The first bottleneck
bc = []
features = 64
dim_branch_out = int(math.floor(features * (opt.baseWidth / 64.0)))
print("features: ", features, "dim_branch_out: ", dim_branch_out)
for i in range(opt.cardinalty):
scale1, relu1 = conv_BN_scale_relu(split, result, dim_branch_out, 1, 1,
0)
scale2, relu2 = conv_BN_scale_relu(split, relu1, dim_branch_out, 3, 1,
1)
bc.append(relu2)
comb = L.Concat(*bc)
sf, rf = conv_BN_scale_relu(split, comb, 256, 1, 1, 0)
scale_i, relu_i = conv_BN_scale_relu(split, result, 256, 1, 1, 0)
rf1 = L.Eltwise(rf, relu_i, operation=P.Eltwise.SUM)
# The second bottleneck
bc = []
for i in range(opt.cardinalty):
scale1, relu1 = conv_BN_scale_relu(split, rf1, dim_branch_out, 1, 1, 0)
scale2, relu2 = conv_BN_scale_relu(split, relu1, dim_branch_out, 3, 1,
1)
bc.append(relu2)
comb = L.Concat(*bc)
sf, rf = conv_BN_scale_relu(split, comb, 256, 1, 1, 0)
scale_i, relu_i = conv_BN_scale_relu(split, rf1, 256, 1, 1, 0)
rf2 = L.Eltwise(rf, relu_i, operation=P.Eltwise.SUM)
# The third bottleneck
bc = []
for i in range(opt.cardinalty):
scale1, relu1 = conv_BN_scale_relu(split, rf2, dim_branch_out, 1, 1, 0)
scale2, relu2 = conv_BN_scale_relu(split, relu1, dim_branch_out, 3, 1,
1)
bc.append(relu2)
comb = L.Concat(*bc)
sf, rf = conv_BN_scale_relu(split, comb, 256, 1, 1, 0)
scale_i, relu_i = conv_BN_scale_relu(split, rf2, 256, 1, 1, 0)
rf3 = L.Eltwise(rf, relu_i, operation=P.Eltwise.SUM)
# The 4th bttleneck
bc = []
features = 128
dim_branch_out = int(math.floor(features * (opt.baseWidth / 64.0)))
print("features: ", features, "dim_branch_out: ", dim_branch_out)
for i in range(opt.cardinalty):
scale1, relu1 = conv_BN_scale_relu(split, rf3, dim_branch_out, 1, 2, 0)
scale2, relu2 = conv_BN_scale_relu(split, relu1, dim_branch_out, 3, 1,
1)
bc.append(relu2)
comb = L.Concat(*bc)
sf, rf = conv_BN_scale_relu(split, comb, 512, 1, 1, 0)
scale_i, relu_i = conv_BN_scale_relu(split, rf3, 512, 1, 2, 0)
rf4 = L.Eltwise(rf, relu_i, operation=P.Eltwise.SUM)
# The 5th bottleneck
bc = []
for i in range(opt.cardinalty):
scale1, relu1 = conv_BN_scale_relu(split, rf4, dim_branch_out, 1, 1, 0)
scale2, relu2 = conv_BN_scale_relu(split, relu1, dim_branch_out, 3, 1,
1)
bc.append(relu2)
comb = L.Concat(*bc)
sf, rf = conv_BN_scale_relu(split, comb, 512, 1, 1, 0)
scale_i, relu_i = conv_BN_scale_relu(split, rf4, 512, 1, 1, 0)
rf5 = L.Eltwise(rf, relu_i, operation=P.Eltwise.SUM)
# The 6th bottleneck
bc = []
for i in range(opt.cardinalty):
scale1, relu1 = conv_BN_scale_relu(split, rf5, dim_branch_out, 1, 1, 0)
scale2, relu2 = conv_BN_scale_relu(split, relu1, dim_branch_out, 3, 1,
1)
bc.append(relu2)
comb = L.Concat(*bc)
sf, rf = conv_BN_scale_relu(split, comb, 512, 1, 1, 0)
scale_i, relu_i = conv_BN_scale_relu(split, rf5, 512, 1, 1, 0)
rf6 = L.Eltwise(rf, relu_i, operation=P.Eltwise.SUM)
# The 7th bttleneck
bc = []
features = 256
dim_branch_out = int(math.floor(features * (opt.baseWidth / 64.0)))
print("features: ", features, "dim_branch_out: ", dim_branch_out)
for i in range(opt.cardinalty):
scale1, relu1 = conv_BN_scale_relu(split, rf6, dim_branch_out, 1, 2, 0)
scale2, relu2 = conv_BN_scale_relu(split, relu1, dim_branch_out, 3, 1,
1)
bc.append(relu2)
comb = L.Concat(*bc)
sf, rf = conv_BN_scale_relu(split, comb, 1024, 1, 1, 0)
scale_i, relu_i = conv_BN_scale_relu(split, rf6, 1024, 1, 2, 0)
rf7 = L.Eltwise(rf, relu_i, operation=P.Eltwise.SUM)
# The 8th bottleneck
bc = []
for i in range(opt.cardinalty):
scale1, relu1 = conv_BN_scale_relu(split, rf7, dim_branch_out, 1, 1, 0)
scale2, relu2 = conv_BN_scale_relu(split, relu1, dim_branch_out, 3, 1,
1)
bc.append(relu2)
comb = L.Concat(*bc)
sf, rf = conv_BN_scale_relu(split, comb, 1024, 1, 1, 0)
scale_i, relu_i = conv_BN_scale_relu(split, rf7, 1024, 1, 1, 0)
rf8 = L.Eltwise(rf, relu_i, operation=P.Eltwise.SUM)
# The 9th bottleneck
bc = []
for i in range(opt.cardinalty):
scale1, relu1 = conv_BN_scale_relu(split, rf8, dim_branch_out, 1, 1, 0)
scale2, relu2 = conv_BN_scale_relu(split, relu1, dim_branch_out, 3, 1,
1)
bc.append(relu2)
comb = L.Concat(*bc)
sf, rf = conv_BN_scale_relu(split, comb, 1024, 1, 1, 0)
scale_i, relu_i = conv_BN_scale_relu(split, rf8, 1024, 1, 1, 0)
rf9 = L.Eltwise(rf, relu_i, operation=P.Eltwise.SUM)
#pool = L.Pooling(result, pool = P.Pooling.AVE, global_pooling = True, kernel_size = 8, stride = 1)
pool = L.Pooling(rf9, pool=P.Pooling.AVE, global_pooling=True)
IP = L.InnerProduct(pool,
num_output=10,
weight_filler=dict(type='xavier'),
bias_filler=dict(type='constant'))
acc = L.Accuracy(IP, labels)
loss = L.SoftmaxWithLoss(IP, labels)
return to_proto(acc, loss)
