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add mnasnet; add save embedding only option
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,155 @@ | ||
| import sys | ||
| import os | ||
| import mxnet as mx | ||
| import mxnet.ndarray as nd | ||
| import mxnet.gluon as gluon | ||
| import mxnet.gluon.nn as nn | ||
| import mxnet.autograd as ag | ||
| import symbol_utils | ||
| sys.path.append(os.path.join(os.path.dirname(__file__), '..')) | ||
| from config import config | ||
|
|
||
| def ConvBlock(channels, kernel_size, strides, **kwargs): | ||
| out = nn.HybridSequential(**kwargs) | ||
| with out.name_scope(): | ||
| out.add( | ||
| nn.Conv2D(channels, kernel_size, strides=strides, padding=1, use_bias=False), | ||
| nn.BatchNorm(scale=True), | ||
| nn.Activation('relu') | ||
| ) | ||
| return out | ||
|
|
||
| def Conv1x1(channels, is_linear=False, **kwargs): | ||
| out = nn.HybridSequential(**kwargs) | ||
| with out.name_scope(): | ||
| out.add( | ||
| nn.Conv2D(channels, 1, padding=0, use_bias=False), | ||
| nn.BatchNorm(scale=True) | ||
| ) | ||
| if not is_linear: | ||
| out.add(nn.Activation('relu')) | ||
| return out | ||
|
|
||
| def DWise(channels, strides, kernel_size=3, **kwargs): | ||
| out = nn.HybridSequential(**kwargs) | ||
| with out.name_scope(): | ||
| out.add( | ||
| nn.Conv2D(channels, kernel_size, strides=strides, padding=kernel_size // 2, groups=channels, use_bias=False), | ||
| nn.BatchNorm(scale=True), | ||
| nn.Activation('relu') | ||
| ) | ||
| return out | ||
|
|
||
| class SepCONV(nn.HybridBlock): | ||
| def __init__(self, inp, output, kernel_size, depth_multiplier=1, with_bn=True, **kwargs): | ||
| super(SepCONV, self).__init__(**kwargs) | ||
| with self.name_scope(): | ||
| self.net = nn.HybridSequential() | ||
| cn = int(inp*depth_multiplier) | ||
|
|
||
| if output is None: | ||
| self.net.add( | ||
| nn.Conv2D(in_channels=inp, channels=cn, groups=inp, kernel_size=kernel_size, strides=(1,1), padding=kernel_size // 2 | ||
| , use_bias=not with_bn) | ||
| ) | ||
| else: | ||
| self.net.add( | ||
| nn.Conv2D(in_channels=inp, channels=cn, groups=inp, kernel_size=kernel_size, strides=(1,1), padding=kernel_size // 2 | ||
| , use_bias=False), | ||
| nn.BatchNorm(), | ||
| nn.Activation('relu'), | ||
| nn.Conv2D(in_channels=cn, channels=output, kernel_size=(1,1), strides=(1,1) | ||
| , use_bias=not with_bn) | ||
| ) | ||
|
|
||
| self.with_bn = with_bn | ||
| self.act = nn.Activation('relu') | ||
| if with_bn: | ||
| self.bn = nn.BatchNorm() | ||
| def hybrid_forward(self, F ,x): | ||
| x = self.net(x) | ||
| if self.with_bn: | ||
| x = self.bn(x) | ||
| if self.act is not None: | ||
| x = self.act(x) | ||
| return x | ||
|
|
||
| class ExpandedConv(nn.HybridBlock): | ||
| def __init__(self, inp, oup, t, strides, kernel=3, same_shape=True, **kwargs): | ||
| super(ExpandedConv, self).__init__(**kwargs) | ||
|
|
||
| self.same_shape = same_shape | ||
| self.strides = strides | ||
| with self.name_scope(): | ||
| self.bottleneck = nn.HybridSequential() | ||
| self.bottleneck.add( | ||
| Conv1x1(inp*t, prefix="expand_"), | ||
| DWise(inp*t, self.strides, kernel, prefix="dwise_"), | ||
| Conv1x1(oup, is_linear=True, prefix="linear_") | ||
| ) | ||
| def hybrid_forward(self, F, x): | ||
| out = self.bottleneck(x) | ||
| if self.strides == 1 and self.same_shape: | ||
| out = F.elemwise_add(out, x) | ||
| return out | ||
|
|
||
| def ExpandedConvSequence(t, k, inp, oup, repeats, first_strides, **kwargs): | ||
| seq = nn.HybridSequential(**kwargs) | ||
| with seq.name_scope(): | ||
| seq.add(ExpandedConv(inp, oup, t, first_strides, k, same_shape=False)) | ||
| curr_inp = oup | ||
| for i in range(1, repeats): | ||
| seq.add(ExpandedConv(curr_inp, oup, t, 1)) | ||
| curr_inp = oup | ||
| return seq | ||
|
|
||
| class MNasNet(nn.HybridBlock): | ||
| def __init__(self, m=1.0, **kwargs): | ||
| super(MNasNet, self).__init__(**kwargs) | ||
|
|
||
| m = config.net_multiplier | ||
| self.first_oup = int(32*m) | ||
| #self.second_oup = int(16*m) | ||
| self.second_oup = int(32*m) | ||
| self.interverted_residual_setting = [ | ||
| # t, c, n, s, k | ||
| [3, int(24*m), 3, 2, 3, "stage2_"], # -> 56x56 | ||
| [3, int(40*m), 3, 2, 5, "stage3_"], # -> 28x28 | ||
| [6, int(80*m), 3, 2, 5, "stage4_1_"], # -> 14x14 | ||
| [6, int(96*m), 2, 1, 3, "stage4_2_"], # -> 14x14 | ||
| [6, int(192*m), 4, 2, 5, "stage5_1_"], # -> 7x7 | ||
| [6, int(320*m), 1, 1, 3, "stage5_2_"], # -> 7x7 | ||
| ] | ||
| self.last_channels = int(1024*m) | ||
|
|
||
| with self.name_scope(): | ||
| self.features = nn.HybridSequential() | ||
| self.features.add(ConvBlock(self.first_oup, 3, 1, prefix="stage1_conv0_")) | ||
| self.features.add(SepCONV(self.first_oup, self.second_oup, 3, prefix="stage1_sepconv0_")) | ||
| inp = self.second_oup | ||
| for i, (t, c, n, s, k, prefix) in enumerate(self.interverted_residual_setting): | ||
| oup = c | ||
| self.features.add(ExpandedConvSequence(t, k, inp, oup, n, s, prefix=prefix)) | ||
| inp = oup | ||
|
|
||
| self.features.add(Conv1x1(self.last_channels, prefix="stage5_3_")) | ||
| #self.features.add(nn.GlobalAvgPool2D()) | ||
| #self.features.add(nn.Flatten()) | ||
| #self.output = nn.Dense(num_classes) | ||
| def hybrid_forward(self, F, x): | ||
| x = self.features(x) | ||
| #x = self.output(x) | ||
| return x | ||
|
|
||
| def num_output_channel(self): | ||
| return self.last_channels | ||
|
|
||
| def get_symbol(): | ||
| net = MNasNet(config.net_multiplier) | ||
| data = mx.sym.Variable(name='data') | ||
| data = data-127.5 | ||
| data = data*0.0078125 | ||
| body = net(data) | ||
| fc1 = symbol_utils.get_fc1(body, config.emb_size, config.net_output, input_channel=net.num_output_channel()) | ||
| return fc1 | ||
|
|
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