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Feature/group normed res net #2

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Feature/group normed res net #2

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c3ba8a1
Add pooling for group norm ResNet
Jun 4, 2019
61683df
Merge branch 'master' into feature/group_normed_res_net
Jun 12, 2019
2fea452
Fix group normed resnet
Jun 18, 2019
838a777
Merge branch 'master' into feature/group_normed_res_net
Jun 18, 2019
ef24f0a
Merge branch 'master' into feature/group_normed_res_net
Jun 19, 2019
068fdb7
Merge branch 'master' into feature/group_normed_res_net
Jun 19, 2019
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16 changes: 12 additions & 4 deletions custom_model.py
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Original file line number Diff line number Diff line change
@@ -1,4 +1,5 @@
from chainer import functions as F
from chainer.functions.pooling.average_pooling_2d import average_pooling_2d
from chainer import links as L
from chainer import Chain
from resnet_group_norm import ResNet as ResNetGroupNorm
Expand All @@ -8,17 +9,24 @@ class CustomModel(Chain):
def __init__(self, n_actions):
super(CustomModel, self).__init__()
with self.init_scope():
self.resNet=L.ResNet50Layers()
self.l1=L.Linear(2138, 1024)
self.resNet=ResNetGroupNorm(n_layers=18)
self.l1=L.Linear(602, 1024)
self.l2=L.Linear(1024, 1024)
self.l3=L.Linear(1024, n_actions)

def forward(self, x):
image, history = x[0], x[1]
image = F.reshape(image, (-1,3,224,224))
history = F.reshape(history.astype('float32'),(-1,90))
h1 = F.relu(self.resNet(image, layers=['pool5'])['pool5'])
h1 = F.reshape(F.concat((h1, history), axis=1), (-1,2138))
h1 = self.resNet(image)

# pooling as done here: https://github.com/chainer/chainer/blob/v6.0.0/chainer/links/model/vision/resnet.py#L655
n, channel, rows, cols = h1.shape
h1 = average_pooling_2d(h1, (rows, cols), stride=1)
h1 = F.reshape(h1, (n, channel))

h1 = F.relu(h1)
h1 = F.reshape(F.concat((h1, history), axis=1), (-1,602))
h2 = F.relu(self.l1(h1))
h3 = F.relu(self.l2(h2))
return F.relu(self.l3(h3))
40 changes: 20 additions & 20 deletions resnet_group_norm.py
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Original file line number Diff line number Diff line change
Expand Up @@ -47,11 +47,11 @@ def __init__(self, n_layers, class_labels=None):
self.res4 = BasicBlock(block[2], 512)
elif n_layers in [18, 20, 21, 34]:
self.conv1 = L.Convolution2D(3, 64, 7, 2, 3, initialW=w, nobias=True)
self.bn1 = L.GroupNormalization(16)
self.res2 = BasicBlock(block[0], 64, 1, num_groups=16)
self.res3 = BasicBlock(block[1], 128)
self.res4 = BasicBlock(block[2], 256)
self.res5 = BasicBlock(block[3], 512)
self.bn1 = L.GroupNormalization(16, 64)
self.res2 = BasicBlock(block[0], 64, 64, 1, num_groups=16)
self.res3 = BasicBlock(block[1], 64, 128)
self.res4 = BasicBlock(block[2], 128, 256)
self.res5 = BasicBlock(block[3], 256, 512)
elif n_layers in [32, 44, 56, 110]:
self.conv1 = L.Convolution2D(3, 16, 7, 2, 3, initialW=w, nobias=True)
self.bn1 = L.GroupNormalization(8)
Expand Down Expand Up @@ -98,12 +98,12 @@ def __call__(self, x):

class BasicBlock(chainer.ChainList):

def __init__(self, layer, ch, stride=2, num_groups=32):
def __init__(self, layer, input_ch, output_ch, stride=2, num_groups=32):
super(BasicBlock, self).__init__()
with self.init_scope():
self.add_link(BasicA(ch, stride, num_groups))
self.add_link(BasicA(input_ch, output_ch, stride, num_groups))
for i in range(layer - 1):
self.add_link(BasicB(ch, num_groups))
self.add_link(BasicB(output_ch, num_groups))

def __call__(self, x):
for f in self.children():
Expand All @@ -127,18 +127,18 @@ def __call__(self, x):

class BasicA(chainer.Chain):

def __init__(self, ch, stride, num_groups):
def __init__(self, input_ch, output_ch, stride, num_groups):
super(BasicA, self).__init__()
w = chainer.initializers.HeNormal()

with self.init_scope():
self.conv1 = L.Convolution2D(None, ch, 3, stride, 1, initialW=w, nobias=True)
self.bn1 = L.GroupNormalization(num_groups)
self.conv2 = L.Convolution2D(None, ch, 3, 1, 1, initialW=w, nobias=True)
self.bn2 = L.GroupNormalization(num_groups)
self.conv1 = L.Convolution2D(input_ch, output_ch, 3, stride, 1, initialW=w, nobias=True)
self.bn1 = L.GroupNormalization(num_groups, output_ch)
self.conv2 = L.Convolution2D(output_ch, output_ch, 3, 1, 1, initialW=w, nobias=True)
self.bn2 = L.GroupNormalization(num_groups, output_ch)

self.conv3 = L.Convolution2D(None, ch, 3, stride, 1, initialW=w, nobias=True)
self.bn3 = L.GroupNormalization(num_groups)
self.conv3 = L.Convolution2D(input_ch, output_ch, 3, stride, 1, initialW=w, nobias=True)
self.bn3 = L.GroupNormalization(num_groups, output_ch)

def __call__(self, x):
h1 = F.relu(self.bn1(self.conv1(x)))
Expand All @@ -155,10 +155,10 @@ def __init__(self, ch, num_groups):
w = chainer.initializers.HeNormal()

with self.init_scope():
self.conv1 = L.Convolution2D(None, ch, 3, 1, 1, initialW=w, nobias=True)
self.bn1 = L.GroupNormalization(num_groups)
self.conv2 = L.Convolution2D(None, ch, 3, 1, 1, initialW=w, nobias=True)
self.bn2 = L.GroupNormalization(num_groups)
self.conv1 = L.Convolution2D(ch, ch, 3, 1, 1, initialW=w, nobias=True)
self.bn1 = L.GroupNormalization(num_groups, ch)
self.conv2 = L.Convolution2D(ch, ch, 3, 1, 1, initialW=w, nobias=True)
self.bn2 = L.GroupNormalization(num_groups, ch)

def __call__(self, x):
h = F.relu(self.bn1(self.conv1(x)))
Expand Down Expand Up @@ -220,4 +220,4 @@ def __call__(self, x):
h = F.relu(self.bn2(self.conv2(h)))
h = self.bn3(self.conv3(h))

return F.relu(h + x)
return F.relu(h + x)