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350 lines
15 KiB
350 lines
15 KiB
"""
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pnasnet5large implementation grabbed from Cadene's pretrained models
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Additional credit to https://github.com/creafz
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https://github.com/Cadene/pretrained-models.pytorch/blob/master/pretrainedmodels/models/pnasnet.py
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"""
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from collections import OrderedDict
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from functools import partial
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import torch
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import torch.nn as nn
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import torch.nn.functional as F
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from .helpers import build_model_with_cfg
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from .layers import ConvNormAct, create_conv2d, create_pool2d, create_classifier
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from .registry import register_model
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__all__ = ['PNASNet5Large']
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default_cfgs = {
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'pnasnet5large': {
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'url': 'https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-cadene/pnasnet5large-bf079911.pth',
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'input_size': (3, 331, 331),
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'pool_size': (11, 11),
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'crop_pct': 0.911,
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'interpolation': 'bicubic',
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'mean': (0.5, 0.5, 0.5),
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'std': (0.5, 0.5, 0.5),
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'num_classes': 1000,
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'first_conv': 'conv_0.conv',
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'classifier': 'last_linear',
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'label_offset': 1, # 1001 classes in pretrained weights
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},
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}
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class SeparableConv2d(nn.Module):
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def __init__(self, in_channels, out_channels, kernel_size, stride, padding=''):
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super(SeparableConv2d, self).__init__()
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self.depthwise_conv2d = create_conv2d(
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in_channels, in_channels, kernel_size=kernel_size,
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stride=stride, padding=padding, groups=in_channels)
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self.pointwise_conv2d = create_conv2d(
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in_channels, out_channels, kernel_size=1, padding=padding)
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def forward(self, x):
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x = self.depthwise_conv2d(x)
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x = self.pointwise_conv2d(x)
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return x
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class BranchSeparables(nn.Module):
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def __init__(self, in_channels, out_channels, kernel_size, stride=1, stem_cell=False, padding=''):
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super(BranchSeparables, self).__init__()
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middle_channels = out_channels if stem_cell else in_channels
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self.act_1 = nn.ReLU()
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self.separable_1 = SeparableConv2d(
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in_channels, middle_channels, kernel_size, stride=stride, padding=padding)
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self.bn_sep_1 = nn.BatchNorm2d(middle_channels, eps=0.001)
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self.act_2 = nn.ReLU()
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self.separable_2 = SeparableConv2d(
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middle_channels, out_channels, kernel_size, stride=1, padding=padding)
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self.bn_sep_2 = nn.BatchNorm2d(out_channels, eps=0.001)
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def forward(self, x):
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x = self.act_1(x)
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x = self.separable_1(x)
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x = self.bn_sep_1(x)
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x = self.act_2(x)
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x = self.separable_2(x)
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x = self.bn_sep_2(x)
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return x
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class ActConvBn(nn.Module):
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def __init__(self, in_channels, out_channels, kernel_size, stride=1, padding=''):
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super(ActConvBn, self).__init__()
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self.act = nn.ReLU()
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self.conv = create_conv2d(
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in_channels, out_channels, kernel_size=kernel_size, stride=stride, padding=padding)
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self.bn = nn.BatchNorm2d(out_channels, eps=0.001)
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def forward(self, x):
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x = self.act(x)
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x = self.conv(x)
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x = self.bn(x)
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return x
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class FactorizedReduction(nn.Module):
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def __init__(self, in_channels, out_channels, padding=''):
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super(FactorizedReduction, self).__init__()
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self.act = nn.ReLU()
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self.path_1 = nn.Sequential(OrderedDict([
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('avgpool', nn.AvgPool2d(1, stride=2, count_include_pad=False)),
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('conv', create_conv2d(in_channels, out_channels // 2, kernel_size=1, padding=padding)),
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]))
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self.path_2 = nn.Sequential(OrderedDict([
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('pad', nn.ZeroPad2d((-1, 1, -1, 1))), # shift
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('avgpool', nn.AvgPool2d(1, stride=2, count_include_pad=False)),
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('conv', create_conv2d(in_channels, out_channels // 2, kernel_size=1, padding=padding)),
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]))
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self.final_path_bn = nn.BatchNorm2d(out_channels, eps=0.001)
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def forward(self, x):
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x = self.act(x)
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x_path1 = self.path_1(x)
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x_path2 = self.path_2(x)
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out = self.final_path_bn(torch.cat([x_path1, x_path2], 1))
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return out
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class CellBase(nn.Module):
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def cell_forward(self, x_left, x_right):
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x_comb_iter_0_left = self.comb_iter_0_left(x_left)
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x_comb_iter_0_right = self.comb_iter_0_right(x_left)
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x_comb_iter_0 = x_comb_iter_0_left + x_comb_iter_0_right
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x_comb_iter_1_left = self.comb_iter_1_left(x_right)
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x_comb_iter_1_right = self.comb_iter_1_right(x_right)
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x_comb_iter_1 = x_comb_iter_1_left + x_comb_iter_1_right
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x_comb_iter_2_left = self.comb_iter_2_left(x_right)
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x_comb_iter_2_right = self.comb_iter_2_right(x_right)
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x_comb_iter_2 = x_comb_iter_2_left + x_comb_iter_2_right
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x_comb_iter_3_left = self.comb_iter_3_left(x_comb_iter_2)
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x_comb_iter_3_right = self.comb_iter_3_right(x_right)
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x_comb_iter_3 = x_comb_iter_3_left + x_comb_iter_3_right
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x_comb_iter_4_left = self.comb_iter_4_left(x_left)
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if self.comb_iter_4_right is not None:
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x_comb_iter_4_right = self.comb_iter_4_right(x_right)
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else:
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x_comb_iter_4_right = x_right
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x_comb_iter_4 = x_comb_iter_4_left + x_comb_iter_4_right
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x_out = torch.cat([x_comb_iter_0, x_comb_iter_1, x_comb_iter_2, x_comb_iter_3, x_comb_iter_4], 1)
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return x_out
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class CellStem0(CellBase):
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def __init__(self, in_chs_left, out_chs_left, in_chs_right, out_chs_right, pad_type=''):
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super(CellStem0, self).__init__()
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self.conv_1x1 = ActConvBn(in_chs_right, out_chs_right, kernel_size=1, padding=pad_type)
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self.comb_iter_0_left = BranchSeparables(
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in_chs_left, out_chs_left, kernel_size=5, stride=2, stem_cell=True, padding=pad_type)
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self.comb_iter_0_right = nn.Sequential(OrderedDict([
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('max_pool', create_pool2d('max', 3, stride=2, padding=pad_type)),
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('conv', create_conv2d(in_chs_left, out_chs_left, kernel_size=1, padding=pad_type)),
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('bn', nn.BatchNorm2d(out_chs_left, eps=0.001)),
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]))
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self.comb_iter_1_left = BranchSeparables(
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out_chs_right, out_chs_right, kernel_size=7, stride=2, padding=pad_type)
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self.comb_iter_1_right = create_pool2d('max', 3, stride=2, padding=pad_type)
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self.comb_iter_2_left = BranchSeparables(
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out_chs_right, out_chs_right, kernel_size=5, stride=2, padding=pad_type)
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self.comb_iter_2_right = BranchSeparables(
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out_chs_right, out_chs_right, kernel_size=3, stride=2, padding=pad_type)
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self.comb_iter_3_left = BranchSeparables(
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out_chs_right, out_chs_right, kernel_size=3, padding=pad_type)
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self.comb_iter_3_right = create_pool2d('max', 3, stride=2, padding=pad_type)
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self.comb_iter_4_left = BranchSeparables(
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in_chs_right, out_chs_right, kernel_size=3, stride=2, stem_cell=True, padding=pad_type)
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self.comb_iter_4_right = ActConvBn(
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out_chs_right, out_chs_right, kernel_size=1, stride=2, padding=pad_type)
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def forward(self, x_left):
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x_right = self.conv_1x1(x_left)
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x_out = self.cell_forward(x_left, x_right)
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return x_out
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class Cell(CellBase):
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def __init__(self, in_chs_left, out_chs_left, in_chs_right, out_chs_right, pad_type='',
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is_reduction=False, match_prev_layer_dims=False):
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super(Cell, self).__init__()
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# If `is_reduction` is set to `True` stride 2 is used for
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# convolution and pooling layers to reduce the spatial size of
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# the output of a cell approximately by a factor of 2.
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stride = 2 if is_reduction else 1
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# If `match_prev_layer_dimensions` is set to `True`
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# `FactorizedReduction` is used to reduce the spatial size
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# of the left input of a cell approximately by a factor of 2.
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self.match_prev_layer_dimensions = match_prev_layer_dims
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if match_prev_layer_dims:
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self.conv_prev_1x1 = FactorizedReduction(in_chs_left, out_chs_left, padding=pad_type)
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else:
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self.conv_prev_1x1 = ActConvBn(in_chs_left, out_chs_left, kernel_size=1, padding=pad_type)
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self.conv_1x1 = ActConvBn(in_chs_right, out_chs_right, kernel_size=1, padding=pad_type)
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self.comb_iter_0_left = BranchSeparables(
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out_chs_left, out_chs_left, kernel_size=5, stride=stride, padding=pad_type)
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self.comb_iter_0_right = create_pool2d('max', 3, stride=stride, padding=pad_type)
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self.comb_iter_1_left = BranchSeparables(
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out_chs_right, out_chs_right, kernel_size=7, stride=stride, padding=pad_type)
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self.comb_iter_1_right = create_pool2d('max', 3, stride=stride, padding=pad_type)
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self.comb_iter_2_left = BranchSeparables(
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out_chs_right, out_chs_right, kernel_size=5, stride=stride, padding=pad_type)
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self.comb_iter_2_right = BranchSeparables(
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out_chs_right, out_chs_right, kernel_size=3, stride=stride, padding=pad_type)
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self.comb_iter_3_left = BranchSeparables(out_chs_right, out_chs_right, kernel_size=3)
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self.comb_iter_3_right = create_pool2d('max', 3, stride=stride, padding=pad_type)
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self.comb_iter_4_left = BranchSeparables(
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out_chs_left, out_chs_left, kernel_size=3, stride=stride, padding=pad_type)
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if is_reduction:
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self.comb_iter_4_right = ActConvBn(
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out_chs_right, out_chs_right, kernel_size=1, stride=stride, padding=pad_type)
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else:
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self.comb_iter_4_right = None
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def forward(self, x_left, x_right):
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x_left = self.conv_prev_1x1(x_left)
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x_right = self.conv_1x1(x_right)
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x_out = self.cell_forward(x_left, x_right)
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return x_out
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class PNASNet5Large(nn.Module):
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def __init__(self, num_classes=1000, in_chans=3, output_stride=32, drop_rate=0., global_pool='avg', pad_type=''):
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super(PNASNet5Large, self).__init__()
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self.num_classes = num_classes
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self.drop_rate = drop_rate
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self.num_features = 4320
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assert output_stride == 32
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self.conv_0 = ConvNormAct(
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in_chans, 96, kernel_size=3, stride=2, padding=0,
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norm_layer=partial(nn.BatchNorm2d, eps=0.001, momentum=0.1), apply_act=False)
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self.cell_stem_0 = CellStem0(
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in_chs_left=96, out_chs_left=54, in_chs_right=96, out_chs_right=54, pad_type=pad_type)
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self.cell_stem_1 = Cell(
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in_chs_left=96, out_chs_left=108, in_chs_right=270, out_chs_right=108, pad_type=pad_type,
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match_prev_layer_dims=True, is_reduction=True)
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self.cell_0 = Cell(
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in_chs_left=270, out_chs_left=216, in_chs_right=540, out_chs_right=216, pad_type=pad_type,
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match_prev_layer_dims=True)
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self.cell_1 = Cell(
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in_chs_left=540, out_chs_left=216, in_chs_right=1080, out_chs_right=216, pad_type=pad_type)
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self.cell_2 = Cell(
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in_chs_left=1080, out_chs_left=216, in_chs_right=1080, out_chs_right=216, pad_type=pad_type)
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self.cell_3 = Cell(
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in_chs_left=1080, out_chs_left=216, in_chs_right=1080, out_chs_right=216, pad_type=pad_type)
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self.cell_4 = Cell(
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in_chs_left=1080, out_chs_left=432, in_chs_right=1080, out_chs_right=432, pad_type=pad_type,
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is_reduction=True)
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self.cell_5 = Cell(
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in_chs_left=1080, out_chs_left=432, in_chs_right=2160, out_chs_right=432, pad_type=pad_type,
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match_prev_layer_dims=True)
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self.cell_6 = Cell(
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in_chs_left=2160, out_chs_left=432, in_chs_right=2160, out_chs_right=432, pad_type=pad_type)
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self.cell_7 = Cell(
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in_chs_left=2160, out_chs_left=432, in_chs_right=2160, out_chs_right=432, pad_type=pad_type)
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self.cell_8 = Cell(
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in_chs_left=2160, out_chs_left=864, in_chs_right=2160, out_chs_right=864, pad_type=pad_type,
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is_reduction=True)
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self.cell_9 = Cell(
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in_chs_left=2160, out_chs_left=864, in_chs_right=4320, out_chs_right=864, pad_type=pad_type,
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match_prev_layer_dims=True)
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self.cell_10 = Cell(
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in_chs_left=4320, out_chs_left=864, in_chs_right=4320, out_chs_right=864, pad_type=pad_type)
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self.cell_11 = Cell(
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in_chs_left=4320, out_chs_left=864, in_chs_right=4320, out_chs_right=864, pad_type=pad_type)
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self.act = nn.ReLU()
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self.feature_info = [
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dict(num_chs=96, reduction=2, module='conv_0'),
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dict(num_chs=270, reduction=4, module='cell_stem_1.conv_1x1.act'),
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dict(num_chs=1080, reduction=8, module='cell_4.conv_1x1.act'),
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dict(num_chs=2160, reduction=16, module='cell_8.conv_1x1.act'),
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dict(num_chs=4320, reduction=32, module='act'),
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]
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self.global_pool, self.last_linear = create_classifier(
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self.num_features, self.num_classes, pool_type=global_pool)
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def get_classifier(self):
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return self.last_linear
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def reset_classifier(self, num_classes, global_pool='avg'):
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self.num_classes = num_classes
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self.global_pool, self.last_linear = create_classifier(
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self.num_features, self.num_classes, pool_type=global_pool)
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def forward_features(self, x):
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x_conv_0 = self.conv_0(x)
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x_stem_0 = self.cell_stem_0(x_conv_0)
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x_stem_1 = self.cell_stem_1(x_conv_0, x_stem_0)
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x_cell_0 = self.cell_0(x_stem_0, x_stem_1)
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x_cell_1 = self.cell_1(x_stem_1, x_cell_0)
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x_cell_2 = self.cell_2(x_cell_0, x_cell_1)
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x_cell_3 = self.cell_3(x_cell_1, x_cell_2)
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x_cell_4 = self.cell_4(x_cell_2, x_cell_3)
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x_cell_5 = self.cell_5(x_cell_3, x_cell_4)
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x_cell_6 = self.cell_6(x_cell_4, x_cell_5)
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x_cell_7 = self.cell_7(x_cell_5, x_cell_6)
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x_cell_8 = self.cell_8(x_cell_6, x_cell_7)
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x_cell_9 = self.cell_9(x_cell_7, x_cell_8)
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x_cell_10 = self.cell_10(x_cell_8, x_cell_9)
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x_cell_11 = self.cell_11(x_cell_9, x_cell_10)
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x = self.act(x_cell_11)
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return x
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def forward(self, x):
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x = self.forward_features(x)
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x = self.global_pool(x)
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if self.drop_rate > 0:
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x = F.dropout(x, self.drop_rate, training=self.training)
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x = self.last_linear(x)
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return x
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def _create_pnasnet(variant, pretrained=False, **kwargs):
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return build_model_with_cfg(
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PNASNet5Large, variant, pretrained,
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feature_cfg=dict(feature_cls='hook', no_rewrite=True), # not possible to re-write this model
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**kwargs)
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@register_model
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def pnasnet5large(pretrained=False, **kwargs):
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r"""PNASNet-5 model architecture from the
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`"Progressive Neural Architecture Search"
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<https://arxiv.org/abs/1712.00559>`_ paper.
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"""
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model_kwargs = dict(pad_type='same', **kwargs)
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return _create_pnasnet('pnasnet5large', pretrained, **model_kwargs)
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