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base_mnasnet.py
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base_mnasnet.py
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from nni.retiarii import basic_unit
import nni.retiarii.nn.pytorch as nn
import warnings
import torch
import torch.nn as torch_nn
import torch.nn.functional as F
from nni.retiarii import model_wrapper
import sys
from pathlib import Path
sys.path.append(str(Path(__file__).resolve().parents[2]))
# Paper suggests 0.9997 momentum, for TensorFlow. Equivalent PyTorch momentum is
# 1.0 - tensorflow.
_BN_MOMENTUM = 1 - 0.9997
_FIRST_DEPTH = 32
_MOBILENET_V2_FILTERS = [16, 24, 32, 64, 96, 160, 320]
_MOBILENET_V2_NUM_LAYERS = [1, 2, 3, 4, 3, 3, 1]
class _ResidualBlock(nn.Module):
def __init__(self, net):
super().__init__()
self.net = net
def forward(self, x):
return self.net(x) + x
class _InvertedResidual(nn.Module):
def __init__(self, in_ch, out_ch, kernel_size, stride, expansion_factor, skip, bn_momentum=0.1):
super(_InvertedResidual, self).__init__()
assert stride in [1, 2]
assert kernel_size in [3, 5]
mid_ch = in_ch * expansion_factor
self.apply_residual = skip and in_ch == out_ch and stride == 1
self.layers = nn.Sequential(
# Pointwise
nn.Conv2d(in_ch, mid_ch, 1, bias=False),
nn.BatchNorm2d(mid_ch, momentum=bn_momentum),
nn.ReLU(inplace=True),
# Depthwise
nn.Conv2d(mid_ch, mid_ch, kernel_size, padding=kernel_size // 2,
stride=stride, groups=mid_ch, bias=False),
nn.BatchNorm2d(mid_ch, momentum=bn_momentum),
nn.ReLU(inplace=True),
# Linear pointwise. Note that there's no activation.
nn.Conv2d(mid_ch, out_ch, 1, bias=False),
nn.BatchNorm2d(out_ch, momentum=bn_momentum))
def forward(self, input):
if self.apply_residual:
ret = self.layers(input) + input
else:
ret = self.layers(input)
return ret
def _stack_inverted_residual(in_ch, out_ch, kernel_size, skip, stride, exp_factor, repeats, bn_momentum):
""" Creates a stack of inverted residuals. """
assert repeats >= 1
# First one has no skip, because feature map size changes.
first = _InvertedResidual(in_ch, out_ch, kernel_size, stride, exp_factor, skip, bn_momentum=bn_momentum)
remaining = []
for _ in range(1, repeats):
remaining.append(_InvertedResidual(out_ch, out_ch, kernel_size, 1, exp_factor, skip, bn_momentum=bn_momentum))
return nn.Sequential(first, *remaining)
def _stack_normal_conv(in_ch, out_ch, kernel_size, skip, dconv, stride, repeats, bn_momentum):
assert repeats >= 1
stack = []
for i in range(repeats):
s = stride if i == 0 else 1
if dconv:
modules = [
nn.Conv2d(in_ch, in_ch, kernel_size, padding=kernel_size // 2, stride=s, groups=in_ch, bias=False),
nn.BatchNorm2d(in_ch, momentum=bn_momentum),
nn.ReLU(inplace=True),
nn.Conv2d(in_ch, out_ch, 1, padding=0, stride=1, bias=False),
nn.BatchNorm2d(out_ch, momentum=bn_momentum)
]
else:
modules = [
nn.Conv2d(in_ch, out_ch, kernel_size, padding=kernel_size // 2, stride=s, bias=False),
nn.ReLU(inplace=True),
nn.BatchNorm2d(out_ch, momentum=bn_momentum)
]
if skip and in_ch == out_ch and s == 1:
# use different implementation for skip and noskip to align with pytorch
stack.append(_ResidualBlock(nn.Sequential(*modules)))
else:
stack += modules
in_ch = out_ch
return stack
def _round_to_multiple_of(val, divisor, round_up_bias=0.9):
""" Asymmetric rounding to make `val` divisible by `divisor`. With default
bias, will round up, unless the number is no more than 10% greater than the
smaller divisible value, i.e. (83, 8) -> 80, but (84, 8) -> 88. """
assert 0.0 < round_up_bias < 1.0
new_val = max(divisor, int(val + divisor / 2) // divisor * divisor)
return new_val if new_val >= round_up_bias * val else new_val + divisor
def _get_depths(depths, alpha):
""" Scales tensor depths as in reference MobileNet code, prefers rouding up
rather than down. """
return [_round_to_multiple_of(depth * alpha, 8) for depth in depths]
@model_wrapper
class MNASNet(nn.Module):
""" MNASNet, as described in https://arxiv.org/pdf/1807.11626.pdf. This
implements the B1 variant of the model.
>>> model = MNASNet(1000, 1.0)
>>> x = torch.rand(1, 3, 224, 224)
>>> y = model(x)
>>> y.dim()
1
>>> y.nelement()
1000
"""
# Version 2 adds depth scaling in the initial stages of the network.
_version = 2
def __init__(self, alpha, depths, convops, kernel_sizes, num_layers,
skips, num_classes=1000, dropout=0.2):
super().__init__()
assert alpha > 0.0
assert len(depths) == len(convops) == len(kernel_sizes) == len(num_layers) == len(skips) == 7
self.alpha = alpha
self.num_classes = num_classes
depths = _get_depths([_FIRST_DEPTH] + depths, alpha)
base_filter_sizes = [16, 24, 40, 80, 96, 192, 320]
exp_ratios = [3, 3, 3, 6, 6, 6, 6]
strides = [1, 2, 2, 2, 1, 2, 1]
layers = [
# First layer: regular conv.
nn.Conv2d(3, depths[0], 3, padding=1, stride=2, bias=False),
nn.BatchNorm2d(depths[0], momentum=_BN_MOMENTUM),
nn.ReLU(inplace=True),
]
count = 0
# for conv, prev_depth, depth, ks, skip, stride, repeat, exp_ratio in \
# zip(convops, depths[:-1], depths[1:], kernel_sizes, skips, strides, num_layers, exp_ratios):
for filter_size, exp_ratio, stride in zip(base_filter_sizes, exp_ratios, strides):
# TODO: restrict that "choose" can only be used within mutator
ph = nn.Placeholder(label=f'mutable_{count}', **{
'kernel_size_options': [1, 3, 5],
'n_layer_options': [1, 2, 3, 4],
'op_type_options': ['__mutated__.base_mnasnet.RegularConv',
'__mutated__.base_mnasnet.DepthwiseConv',
'__mutated__.base_mnasnet.MobileConv'],
# 'se_ratio_options': [0, 0.25],
'skip_options': ['identity', 'no'],
'n_filter_options': [int(filter_size*x) for x in [0.75, 1.0, 1.25]],
'exp_ratio': exp_ratio,
'stride': stride,
'in_ch': depths[0] if count == 0 else None
})
layers.append(ph)
'''if conv == "mconv":
# MNASNet blocks: stacks of inverted residuals.
layers.append(_stack_inverted_residual(prev_depth, depth, ks, skip,
stride, exp_ratio, repeat, _BN_MOMENTUM))
else:
# Normal conv and depth-separated conv
layers += _stack_normal_conv(prev_depth, depth, ks, skip, conv == "dconv",
stride, repeat, _BN_MOMENTUM)'''
count += 1
if count >= 2:
break
layers += [
# Final mapping to classifier input.
nn.Conv2d(depths[7], 1280, 1, padding=0, stride=1, bias=False),
nn.BatchNorm2d(1280, momentum=_BN_MOMENTUM),
nn.ReLU(inplace=True),
]
self.layers = nn.Sequential(*layers)
self.classifier = nn.Sequential(nn.Dropout(p=dropout),
nn.Linear(1280, num_classes))
self._initialize_weights()
#self.for_test = 10
def forward(self, x):
# if self.for_test == 10:
x = self.layers(x)
# Equivalent to global avgpool and removing H and W dimensions.
x = x.mean([2, 3])
x = F.relu(x)
return self.classifier(x)
def _initialize_weights(self):
for m in self.modules():
if isinstance(m, nn.Conv2d):
torch_nn.init.kaiming_normal_(m.weight, mode="fan_out",
nonlinearity="relu")
if m.bias is not None:
torch_nn.init.zeros_(m.bias)
elif isinstance(m, nn.BatchNorm2d):
torch_nn.init.ones_(m.weight)
torch_nn.init.zeros_(m.bias)
elif isinstance(m, nn.Linear):
torch_nn.init.kaiming_uniform_(m.weight, mode="fan_out",
nonlinearity="sigmoid")
torch_nn.init.zeros_(m.bias)
def test_model(model):
model(torch.randn(2, 3, 224, 224))
# ====================definition of candidate op classes
BN_MOMENTUM = 1 - 0.9997
class RegularConv(nn.Module):
def __init__(self, kernel_size, in_ch, out_ch, skip, exp_ratio, stride):
super().__init__()
self.kernel_size = kernel_size
self.in_ch = in_ch
self.out_ch = out_ch
self.skip = skip
self.exp_ratio = exp_ratio
self.stride = stride
self.conv = nn.Conv2d(in_ch, out_ch, kernel_size, padding=kernel_size // 2, stride=stride, bias=False)
self.relu = nn.ReLU(inplace=True)
self.bn = nn.BatchNorm2d(out_ch, momentum=BN_MOMENTUM)
def forward(self, x):
out = self.bn(self.relu(self.conv(x)))
if self.skip == 'identity':
out = out + x
return out
class DepthwiseConv(nn.Module):
def __init__(self, kernel_size, in_ch, out_ch, skip, exp_ratio, stride):
super().__init__()
self.kernel_size = kernel_size
self.in_ch = in_ch
self.out_ch = out_ch
self.skip = skip
self.exp_ratio = exp_ratio
self.stride = stride
self.conv1 = nn.Conv2d(in_ch, in_ch, kernel_size, padding=kernel_size // 2, stride=stride, groups=in_ch, bias=False)
self.bn1 = nn.BatchNorm2d(in_ch, momentum=BN_MOMENTUM)
self.relu = nn.ReLU(inplace=True)
self.conv2 = nn.Conv2d(in_ch, out_ch, 1, padding=0, stride=1, bias=False)
self.bn2 = nn.BatchNorm2d(out_ch, momentum=BN_MOMENTUM)
def forward(self, x):
out = self.relu(self.bn1(self.conv1(x)))
out = self.bn2(self.conv2(out))
if self.skip == 'identity':
out = out + x
return out
class MobileConv(nn.Module):
def __init__(self, kernel_size, in_ch, out_ch, skip, exp_ratio, stride):
super().__init__()
self.kernel_size = kernel_size
self.in_ch = in_ch
self.out_ch = out_ch
self.skip = skip
self.exp_ratio = exp_ratio
self.stride = stride
mid_ch = in_ch * exp_ratio
self.layers = nn.Sequential(
# Pointwise
nn.Conv2d(in_ch, mid_ch, 1, bias=False),
nn.BatchNorm2d(mid_ch, momentum=BN_MOMENTUM),
nn.ReLU(inplace=True),
# Depthwise
nn.Conv2d(mid_ch, mid_ch, kernel_size, padding=(kernel_size - 1) // 2,
stride=stride, groups=mid_ch, bias=False),
nn.BatchNorm2d(mid_ch, momentum=BN_MOMENTUM),
nn.ReLU(inplace=True),
# Linear pointwise. Note that there's no activation.
nn.Conv2d(mid_ch, out_ch, 1, bias=False),
nn.BatchNorm2d(out_ch, momentum=BN_MOMENTUM))
def forward(self, x):
out = self.layers(x)
if self.skip == 'identity':
out = out + x
return out
# mnasnet0_5
ir_module = _InvertedResidual(16, 16, 3, 1, 1, True)