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Fix bugs in TAS: missing ReLU in the end of each searching block

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D-X-Y
2020-04-09 06:19:31 +00:00
parent 569b9b406a
commit 076f9c2d41
9 changed files with 108 additions and 13 deletions
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4
lib/models/cell_operations.py
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@@ -15,8 +15,8 @@ OPS = {
'nor_conv_1x1' : lambda C_in, C_out, stride, affine, track_running_stats: ReLUConvBN(C_in, C_out, (1,1), (stride,stride), (0,0), (1,1), affine, track_running_stats),
'dua_sepc_3x3' : lambda C_in, C_out, stride, affine, track_running_stats: DualSepConv(C_in, C_out, (3,3), (stride,stride), (1,1), (1,1), affine, track_running_stats),
'dua_sepc_5x5' : lambda C_in, C_out, stride, affine, track_running_stats: DualSepConv(C_in, C_out, (5,5), (stride,stride), (2,2), (1,1), affine, track_running_stats),
'dil_sepc_3x3' : lambda C_in, C_out, stride, affine, track_running_stats: SepConv(C_in, C_out, (3,3), (stride,stride), (2,2), (2,2), affine, track_running_stats),
'dil_sepc_5x5' : lambda C_in, C_out, stride, affine, track_running_stats: SepConv(C_in, C_out, (5,5), (stride,stride), (4,4), (2,2), affine, track_running_stats),
'dil_sepc_3x3' : lambda C_in, C_out, stride, affine, track_running_stats: SepConv(C_in, C_out, (3,3), (stride,stride), (2,2), (2,2), affine, track_running_stats),
'dil_sepc_5x5' : lambda C_in, C_out, stride, affine, track_running_stats: SepConv(C_in, C_out, (5,5), (stride,stride), (4,4), (2,2), affine, track_running_stats),
'skip_connect' : lambda C_in, C_out, stride, affine, track_running_stats: Identity() if stride == 1 and C_in == C_out else FactorizedReduce(C_in, C_out, stride, affine, track_running_stats),
}

5
lib/models/shape_searchs/SearchCifarResNet.py
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@@ -172,7 +172,7 @@ class ResNetBasicblock(nn.Module):
else:
residual, expected_flop_c = inputs, 0
out = additive_func(residual, out_b)
return out, expected_inC_b, sum([expected_flop_a, expected_flop_b, expected_flop_c])
return nn.functional.relu(out, inplace=True), expected_inC_b, sum([expected_flop_a, expected_flop_b, expected_flop_c])
def basic_forward(self, inputs):
basicblock = self.conv_a(inputs)
@@ -244,8 +244,7 @@ class ResNetBottleneck(nn.Module):
else:
residual, expected_flop_c = inputs, 0
out = additive_func(residual, out_1x4)
return out, expected_inC_1x4, sum([expected_flop_1x1, expected_flop_3x3, expected_flop_1x4, expected_flop_c])
return nn.functional.relu(out, inplace=True), expected_inC_1x4, sum([expected_flop_1x1, expected_flop_3x3, expected_flop_1x4, expected_flop_c])
class SearchShapeCifarResNet(nn.Module):

5
lib/models/shape_searchs/SearchCifarResNet_width.py
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@@ -156,7 +156,7 @@ class ResNetBasicblock(nn.Module):
else:
residual, expected_flop_c = inputs, 0
out = additive_func(residual, out_b)
return out, expected_inC_b, sum([expected_flop_a, expected_flop_b, expected_flop_c])
return nn.functional.relu(out, inplace=True), expected_inC_b, sum([expected_flop_a, expected_flop_b, expected_flop_c])
def basic_forward(self, inputs):
basicblock = self.conv_a(inputs)
@@ -228,8 +228,7 @@ class ResNetBottleneck(nn.Module):
else:
residual, expected_flop_c = inputs, 0
out = additive_func(residual, out_1x4)
return out, expected_inC_1x4, sum([expected_flop_1x1, expected_flop_3x3, expected_flop_1x4, expected_flop_c])
return nn.functional.relu(out, inplace=True), expected_inC_1x4, sum([expected_flop_1x1, expected_flop_3x3, expected_flop_1x4, expected_flop_c])
class SearchWidthCifarResNet(nn.Module):

5
lib/models/shape_searchs/SearchImagenetResNet.py
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@@ -171,7 +171,7 @@ class ResNetBasicblock(nn.Module):
else:
residual, expected_flop_c = inputs, 0
out = additive_func(residual, out_b)
return out, expected_inC_b, sum([expected_flop_a, expected_flop_b, expected_flop_c])
return nn.functional.relu(out, inplace=True), expected_inC_b, sum([expected_flop_a, expected_flop_b, expected_flop_c])
def basic_forward(self, inputs):
basicblock = self.conv_a(inputs)
@@ -243,8 +243,7 @@ class ResNetBottleneck(nn.Module):
else:
residual, expected_flop_c = inputs, 0
out = additive_func(residual, out_1x4)
return out, expected_inC_1x4, sum([expected_flop_1x1, expected_flop_3x3, expected_flop_1x4, expected_flop_c])
return nn.functional.relu(out, inplace=True), expected_inC_1x4, sum([expected_flop_1x1, expected_flop_3x3, expected_flop_1x4, expected_flop_c])
class SearchShapeImagenetResNet(nn.Module):

2
lib/models/shape_searchs/SearchSimResNet_width.py
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@@ -153,7 +153,7 @@ class SimBlock(nn.Module):
else:
residual, expected_flop_c = inputs, 0
out = additive_func(residual, out)
return out, expected_next_inC, sum([expected_flop, expected_flop_c])
return nn.functional.relu(out, inplace=True), expected_next_inC, sum([expected_flop, expected_flop_c])
def basic_forward(self, inputs):
basicblock = self.conv(inputs)