imnet_evaluate/pnasnet.py [7:414]: - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - ''' Code from https://github.com/Cadene/pretrained-models.pytorch/blob/master/pretrainedmodels/models/pnasnet.py with some adaptations ''' from __future__ import print_function, division, absolute_import from collections import OrderedDict import torch import torch.nn as nn import torch.utils.model_zoo as model_zoo pretrained_settings = { 'pnasnet5large': { 'imagenet': { 'url': 'http://data.lip6.fr/cadene/pretrainedmodels/pnasnet5large-bf079911.pth', 'input_space': 'RGB', 'input_size': [3, 331, 331], 'input_range': [0, 1], 'mean': [0.5, 0.5, 0.5], 'std': [0.5, 0.5, 0.5], 'num_classes': 1000 }, 'imagenet+background': { 'url': 'http://data.lip6.fr/cadene/pretrainedmodels/pnasnet5large-bf079911.pth', 'input_space': 'RGB', 'input_size': [3, 331, 331], 'input_range': [0, 1], 'mean': [0.5, 0.5, 0.5], 'std': [0.5, 0.5, 0.5], 'num_classes': 1001 } } } class MaxPool(nn.Module): def __init__(self, kernel_size, stride=1, padding=1, zero_pad=False): super(MaxPool, self).__init__() self.zero_pad = nn.ZeroPad2d((1, 0, 1, 0)) if zero_pad else None self.pool = nn.MaxPool2d(kernel_size, stride=stride, padding=padding) def forward(self, x): if self.zero_pad: x = self.zero_pad(x) x = self.pool(x) if self.zero_pad: x = x[:, :, 1:, 1:] return x class SeparableConv2d(nn.Module): def __init__(self, in_channels, out_channels, dw_kernel_size, dw_stride, dw_padding): super(SeparableConv2d, self).__init__() self.depthwise_conv2d = nn.Conv2d(in_channels, in_channels, kernel_size=dw_kernel_size, stride=dw_stride, padding=dw_padding, groups=in_channels, bias=False) self.pointwise_conv2d = nn.Conv2d(in_channels, out_channels, kernel_size=1, bias=False) def forward(self, x): x = self.depthwise_conv2d(x) x = self.pointwise_conv2d(x) return x class BranchSeparables(nn.Module): def __init__(self, in_channels, out_channels, kernel_size, stride=1, stem_cell=False, zero_pad=False): super(BranchSeparables, self).__init__() padding = kernel_size // 2 middle_channels = out_channels if stem_cell else in_channels self.zero_pad = nn.ZeroPad2d((1, 0, 1, 0)) if zero_pad else None self.relu_1 = nn.ReLU() self.separable_1 = SeparableConv2d(in_channels, middle_channels, kernel_size, dw_stride=stride, dw_padding=padding) self.bn_sep_1 = nn.BatchNorm2d(middle_channels, eps=0.001) self.relu_2 = nn.ReLU() self.separable_2 = SeparableConv2d(middle_channels, out_channels, kernel_size, dw_stride=1, dw_padding=padding) self.bn_sep_2 = nn.BatchNorm2d(out_channels, eps=0.001) def forward(self, x): x = self.relu_1(x) if self.zero_pad: x = self.zero_pad(x) x = self.separable_1(x) if self.zero_pad: x = x[:, :, 1:, 1:].contiguous() x = self.bn_sep_1(x) x = self.relu_2(x) x = self.separable_2(x) x = self.bn_sep_2(x) return x class ReluConvBn(nn.Module): def __init__(self, in_channels, out_channels, kernel_size, stride=1): super(ReluConvBn, self).__init__() self.relu = nn.ReLU() self.conv = nn.Conv2d(in_channels, out_channels, kernel_size=kernel_size, stride=stride, bias=False) self.bn = nn.BatchNorm2d(out_channels, eps=0.001) def forward(self, x): x = self.relu(x) x = self.conv(x) x = self.bn(x) return x class FactorizedReduction(nn.Module): def __init__(self, in_channels, out_channels): super(FactorizedReduction, self).__init__() self.relu = nn.ReLU() self.path_1 = nn.Sequential(OrderedDict([ ('avgpool', nn.AvgPool2d(1, stride=2, count_include_pad=False)), ('conv', nn.Conv2d(in_channels, out_channels // 2, kernel_size=1, bias=False)), ])) self.path_2 = nn.Sequential(OrderedDict([ ('pad', nn.ZeroPad2d((0, 1, 0, 1))), ('avgpool', nn.AvgPool2d(1, stride=2, count_include_pad=False)), ('conv', nn.Conv2d(in_channels, out_channels // 2, kernel_size=1, bias=False)), ])) self.final_path_bn = nn.BatchNorm2d(out_channels, eps=0.001) def forward(self, x): x = self.relu(x) x_path1 = self.path_1(x) x_path2 = self.path_2.pad(x) x_path2 = x_path2[:, :, 1:, 1:] x_path2 = self.path_2.avgpool(x_path2) x_path2 = self.path_2.conv(x_path2) out = self.final_path_bn(torch.cat([x_path1, x_path2], 1)) return out class CellBase(nn.Module): def cell_forward(self, x_left, x_right): x_comb_iter_0_left = self.comb_iter_0_left(x_left) x_comb_iter_0_right = self.comb_iter_0_right(x_left) x_comb_iter_0 = x_comb_iter_0_left + x_comb_iter_0_right x_comb_iter_1_left = self.comb_iter_1_left(x_right) x_comb_iter_1_right = self.comb_iter_1_right(x_right) x_comb_iter_1 = x_comb_iter_1_left + x_comb_iter_1_right x_comb_iter_2_left = self.comb_iter_2_left(x_right) x_comb_iter_2_right = self.comb_iter_2_right(x_right) x_comb_iter_2 = x_comb_iter_2_left + x_comb_iter_2_right x_comb_iter_3_left = self.comb_iter_3_left(x_comb_iter_2) x_comb_iter_3_right = self.comb_iter_3_right(x_right) x_comb_iter_3 = x_comb_iter_3_left + x_comb_iter_3_right x_comb_iter_4_left = self.comb_iter_4_left(x_left) if self.comb_iter_4_right: x_comb_iter_4_right = self.comb_iter_4_right(x_right) else: x_comb_iter_4_right = x_right x_comb_iter_4 = x_comb_iter_4_left + x_comb_iter_4_right 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) return x_out class CellStem0(CellBase): def __init__(self, in_channels_left, out_channels_left, in_channels_right, out_channels_right): super(CellStem0, self).__init__() self.conv_1x1 = ReluConvBn(in_channels_right, out_channels_right, kernel_size=1) self.comb_iter_0_left = BranchSeparables(in_channels_left, out_channels_left, kernel_size=5, stride=2, stem_cell=True) self.comb_iter_0_right = nn.Sequential(OrderedDict([ ('max_pool', MaxPool(3, stride=2)), ('conv', nn.Conv2d(in_channels_left, out_channels_left, kernel_size=1, bias=False)), ('bn', nn.BatchNorm2d(out_channels_left, eps=0.001)), ])) self.comb_iter_1_left = BranchSeparables(out_channels_right, out_channels_right, kernel_size=7, stride=2) self.comb_iter_1_right = MaxPool(3, stride=2) self.comb_iter_2_left = BranchSeparables(out_channels_right, out_channels_right, kernel_size=5, stride=2) self.comb_iter_2_right = BranchSeparables(out_channels_right, out_channels_right, kernel_size=3, stride=2) self.comb_iter_3_left = BranchSeparables(out_channels_right, out_channels_right, kernel_size=3) self.comb_iter_3_right = MaxPool(3, stride=2) self.comb_iter_4_left = BranchSeparables(in_channels_right, out_channels_right, kernel_size=3, stride=2, stem_cell=True) self.comb_iter_4_right = ReluConvBn(out_channels_right, out_channels_right, kernel_size=1, stride=2) def forward(self, x_left): x_right = self.conv_1x1(x_left) x_out = self.cell_forward(x_left, x_right) return x_out class Cell(CellBase): def __init__(self, in_channels_left, out_channels_left, in_channels_right, out_channels_right, is_reduction=False, zero_pad=False, match_prev_layer_dimensions=False): super(Cell, self).__init__() # If `is_reduction` is set to `True` stride 2 is used for # convolutional and pooling layers to reduce the spatial size of # the output of a cell approximately by a factor of 2. stride = 2 if is_reduction else 1 # If `match_prev_layer_dimensions` is set to `True` # `FactorizedReduction` is used to reduce the spatial size # of the left input of a cell approximately by a factor of 2. self.match_prev_layer_dimensions = match_prev_layer_dimensions if match_prev_layer_dimensions: self.conv_prev_1x1 = FactorizedReduction(in_channels_left, out_channels_left) else: self.conv_prev_1x1 = ReluConvBn(in_channels_left, out_channels_left, kernel_size=1) self.conv_1x1 = ReluConvBn(in_channels_right, out_channels_right, kernel_size=1) self.comb_iter_0_left = BranchSeparables(out_channels_left, out_channels_left, kernel_size=5, stride=stride, zero_pad=zero_pad) self.comb_iter_0_right = MaxPool(3, stride=stride, zero_pad=zero_pad) self.comb_iter_1_left = BranchSeparables(out_channels_right, out_channels_right, kernel_size=7, stride=stride, zero_pad=zero_pad) self.comb_iter_1_right = MaxPool(3, stride=stride, zero_pad=zero_pad) self.comb_iter_2_left = BranchSeparables(out_channels_right, out_channels_right, kernel_size=5, stride=stride, zero_pad=zero_pad) self.comb_iter_2_right = BranchSeparables(out_channels_right, out_channels_right, kernel_size=3, stride=stride, zero_pad=zero_pad) self.comb_iter_3_left = BranchSeparables(out_channels_right, out_channels_right, kernel_size=3) self.comb_iter_3_right = MaxPool(3, stride=stride, zero_pad=zero_pad) self.comb_iter_4_left = BranchSeparables(out_channels_left, out_channels_left, kernel_size=3, stride=stride, zero_pad=zero_pad) if is_reduction: self.comb_iter_4_right = ReluConvBn(out_channels_right, out_channels_right, kernel_size=1, stride=stride) else: self.comb_iter_4_right = None def forward(self, x_left, x_right): x_left = self.conv_prev_1x1(x_left) x_right = self.conv_1x1(x_right) x_out = self.cell_forward(x_left, x_right) return x_out class PNASNet5Large(nn.Module): def __init__(self, num_classes=1001): super(PNASNet5Large, self).__init__() self.num_classes = num_classes self.conv_0 = nn.Sequential(OrderedDict([ ('conv', nn.Conv2d(3, 96, kernel_size=3, stride=2, bias=False)), ('bn', nn.BatchNorm2d(96, eps=0.001)) ])) self.cell_stem_0 = CellStem0(in_channels_left=96, out_channels_left=54, in_channels_right=96, out_channels_right=54) self.cell_stem_1 = Cell(in_channels_left=96, out_channels_left=108, in_channels_right=270, out_channels_right=108, match_prev_layer_dimensions=True, is_reduction=True) self.cell_0 = Cell(in_channels_left=270, out_channels_left=216, in_channels_right=540, out_channels_right=216, match_prev_layer_dimensions=True) self.cell_1 = Cell(in_channels_left=540, out_channels_left=216, in_channels_right=1080, out_channels_right=216) self.cell_2 = Cell(in_channels_left=1080, out_channels_left=216, in_channels_right=1080, out_channels_right=216) self.cell_3 = Cell(in_channels_left=1080, out_channels_left=216, in_channels_right=1080, out_channels_right=216) self.cell_4 = Cell(in_channels_left=1080, out_channels_left=432, in_channels_right=1080, out_channels_right=432, is_reduction=True, zero_pad=True) self.cell_5 = Cell(in_channels_left=1080, out_channels_left=432, in_channels_right=2160, out_channels_right=432, match_prev_layer_dimensions=True) self.cell_6 = Cell(in_channels_left=2160, out_channels_left=432, in_channels_right=2160, out_channels_right=432) self.cell_7 = Cell(in_channels_left=2160, out_channels_left=432, in_channels_right=2160, out_channels_right=432) self.cell_8 = Cell(in_channels_left=2160, out_channels_left=864, in_channels_right=2160, out_channels_right=864, is_reduction=True) self.cell_9 = Cell(in_channels_left=2160, out_channels_left=864, in_channels_right=4320, out_channels_right=864, match_prev_layer_dimensions=True) self.cell_10 = Cell(in_channels_left=4320, out_channels_left=864, in_channels_right=4320, out_channels_right=864) self.cell_11 = Cell(in_channels_left=4320, out_channels_left=864, in_channels_right=4320, out_channels_right=864) self.relu = nn.ReLU() self.avg_pool = nn.AdaptiveAvgPool2d((1, 1)) self.dropout = nn.Dropout(0.5) self.last_linear = nn.Linear(4320, num_classes) def features(self, x): x_conv_0 = self.conv_0(x) x_stem_0 = self.cell_stem_0(x_conv_0) x_stem_1 = self.cell_stem_1(x_conv_0, x_stem_0) x_cell_0 = self.cell_0(x_stem_0, x_stem_1) x_cell_1 = self.cell_1(x_stem_1, x_cell_0) x_cell_2 = self.cell_2(x_cell_0, x_cell_1) x_cell_3 = self.cell_3(x_cell_1, x_cell_2) x_cell_4 = self.cell_4(x_cell_2, x_cell_3) x_cell_5 = self.cell_5(x_cell_3, x_cell_4) x_cell_6 = self.cell_6(x_cell_4, x_cell_5) x_cell_7 = self.cell_7(x_cell_5, x_cell_6) x_cell_8 = self.cell_8(x_cell_6, x_cell_7) x_cell_9 = self.cell_9(x_cell_7, x_cell_8) x_cell_10 = self.cell_10(x_cell_8, x_cell_9) x_cell_11 = self.cell_11(x_cell_9, x_cell_10) return x_cell_11 def logits(self, features): x = self.relu(features) x = self.avg_pool(x) x = x.view(x.size(0), -1) x = self.dropout(x) x = self.last_linear(x) return x def forward(self, input): x = self.features(input) x = self.logits(x) return x def pnasnet5large(num_classes=1000, pretrained='imagenet'): r"""PNASNet-5 model architecture from the `"Progressive Neural Architecture Search" `_ paper. """ if pretrained: settings = pretrained_settings['pnasnet5large'][pretrained] assert num_classes == settings[ 'num_classes'], 'num_classes should be {}, but is {}'.format( settings['num_classes'], num_classes) # both 'imagenet'&'imagenet+background' are loaded from same parameters model = PNASNet5Large(num_classes=1001) model.load_state_dict(model_zoo.load_url(settings['url'])) if pretrained == 'imagenet': new_last_linear = nn.Linear(model.last_linear.in_features, 1000) new_last_linear.weight.data = model.last_linear.weight.data[1:] new_last_linear.bias.data = model.last_linear.bias.data[1:] model.last_linear = new_last_linear model.input_space = settings['input_space'] model.input_size = settings['input_size'] model.input_range = settings['input_range'] model.mean = settings['mean'] model.std = settings['std'] else: model = PNASNet5Large(num_classes=num_classes) return model - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - imnet_finetune/pnasnet.py [7:414]: - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - ''' Code from https://github.com/Cadene/pretrained-models.pytorch/blob/master/pretrainedmodels/models/pnasnet.py with some adaptations ''' from __future__ import print_function, division, absolute_import from collections import OrderedDict import torch import torch.nn as nn import torch.utils.model_zoo as model_zoo pretrained_settings = { 'pnasnet5large': { 'imagenet': { 'url': 'http://data.lip6.fr/cadene/pretrainedmodels/pnasnet5large-bf079911.pth', 'input_space': 'RGB', 'input_size': [3, 331, 331], 'input_range': [0, 1], 'mean': [0.5, 0.5, 0.5], 'std': [0.5, 0.5, 0.5], 'num_classes': 1000 }, 'imagenet+background': { 'url': 'http://data.lip6.fr/cadene/pretrainedmodels/pnasnet5large-bf079911.pth', 'input_space': 'RGB', 'input_size': [3, 331, 331], 'input_range': [0, 1], 'mean': [0.5, 0.5, 0.5], 'std': [0.5, 0.5, 0.5], 'num_classes': 1001 } } } class MaxPool(nn.Module): def __init__(self, kernel_size, stride=1, padding=1, zero_pad=False): super(MaxPool, self).__init__() self.zero_pad = nn.ZeroPad2d((1, 0, 1, 0)) if zero_pad else None self.pool = nn.MaxPool2d(kernel_size, stride=stride, padding=padding) def forward(self, x): if self.zero_pad: x = self.zero_pad(x) x = self.pool(x) if self.zero_pad: x = x[:, :, 1:, 1:] return x class SeparableConv2d(nn.Module): def __init__(self, in_channels, out_channels, dw_kernel_size, dw_stride, dw_padding): super(SeparableConv2d, self).__init__() self.depthwise_conv2d = nn.Conv2d(in_channels, in_channels, kernel_size=dw_kernel_size, stride=dw_stride, padding=dw_padding, groups=in_channels, bias=False) self.pointwise_conv2d = nn.Conv2d(in_channels, out_channels, kernel_size=1, bias=False) def forward(self, x): x = self.depthwise_conv2d(x) x = self.pointwise_conv2d(x) return x class BranchSeparables(nn.Module): def __init__(self, in_channels, out_channels, kernel_size, stride=1, stem_cell=False, zero_pad=False): super(BranchSeparables, self).__init__() padding = kernel_size // 2 middle_channels = out_channels if stem_cell else in_channels self.zero_pad = nn.ZeroPad2d((1, 0, 1, 0)) if zero_pad else None self.relu_1 = nn.ReLU() self.separable_1 = SeparableConv2d(in_channels, middle_channels, kernel_size, dw_stride=stride, dw_padding=padding) self.bn_sep_1 = nn.BatchNorm2d(middle_channels, eps=0.001) self.relu_2 = nn.ReLU() self.separable_2 = SeparableConv2d(middle_channels, out_channels, kernel_size, dw_stride=1, dw_padding=padding) self.bn_sep_2 = nn.BatchNorm2d(out_channels, eps=0.001) def forward(self, x): x = self.relu_1(x) if self.zero_pad: x = self.zero_pad(x) x = self.separable_1(x) if self.zero_pad: x = x[:, :, 1:, 1:].contiguous() x = self.bn_sep_1(x) x = self.relu_2(x) x = self.separable_2(x) x = self.bn_sep_2(x) return x class ReluConvBn(nn.Module): def __init__(self, in_channels, out_channels, kernel_size, stride=1): super(ReluConvBn, self).__init__() self.relu = nn.ReLU() self.conv = nn.Conv2d(in_channels, out_channels, kernel_size=kernel_size, stride=stride, bias=False) self.bn = nn.BatchNorm2d(out_channels, eps=0.001) def forward(self, x): x = self.relu(x) x = self.conv(x) x = self.bn(x) return x class FactorizedReduction(nn.Module): def __init__(self, in_channels, out_channels): super(FactorizedReduction, self).__init__() self.relu = nn.ReLU() self.path_1 = nn.Sequential(OrderedDict([ ('avgpool', nn.AvgPool2d(1, stride=2, count_include_pad=False)), ('conv', nn.Conv2d(in_channels, out_channels // 2, kernel_size=1, bias=False)), ])) self.path_2 = nn.Sequential(OrderedDict([ ('pad', nn.ZeroPad2d((0, 1, 0, 1))), ('avgpool', nn.AvgPool2d(1, stride=2, count_include_pad=False)), ('conv', nn.Conv2d(in_channels, out_channels // 2, kernel_size=1, bias=False)), ])) self.final_path_bn = nn.BatchNorm2d(out_channels, eps=0.001) def forward(self, x): x = self.relu(x) x_path1 = self.path_1(x) x_path2 = self.path_2.pad(x) x_path2 = x_path2[:, :, 1:, 1:] x_path2 = self.path_2.avgpool(x_path2) x_path2 = self.path_2.conv(x_path2) out = self.final_path_bn(torch.cat([x_path1, x_path2], 1)) return out class CellBase(nn.Module): def cell_forward(self, x_left, x_right): x_comb_iter_0_left = self.comb_iter_0_left(x_left) x_comb_iter_0_right = self.comb_iter_0_right(x_left) x_comb_iter_0 = x_comb_iter_0_left + x_comb_iter_0_right x_comb_iter_1_left = self.comb_iter_1_left(x_right) x_comb_iter_1_right = self.comb_iter_1_right(x_right) x_comb_iter_1 = x_comb_iter_1_left + x_comb_iter_1_right x_comb_iter_2_left = self.comb_iter_2_left(x_right) x_comb_iter_2_right = self.comb_iter_2_right(x_right) x_comb_iter_2 = x_comb_iter_2_left + x_comb_iter_2_right x_comb_iter_3_left = self.comb_iter_3_left(x_comb_iter_2) x_comb_iter_3_right = self.comb_iter_3_right(x_right) x_comb_iter_3 = x_comb_iter_3_left + x_comb_iter_3_right x_comb_iter_4_left = self.comb_iter_4_left(x_left) if self.comb_iter_4_right: x_comb_iter_4_right = self.comb_iter_4_right(x_right) else: x_comb_iter_4_right = x_right x_comb_iter_4 = x_comb_iter_4_left + x_comb_iter_4_right 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) return x_out class CellStem0(CellBase): def __init__(self, in_channels_left, out_channels_left, in_channels_right, out_channels_right): super(CellStem0, self).__init__() self.conv_1x1 = ReluConvBn(in_channels_right, out_channels_right, kernel_size=1) self.comb_iter_0_left = BranchSeparables(in_channels_left, out_channels_left, kernel_size=5, stride=2, stem_cell=True) self.comb_iter_0_right = nn.Sequential(OrderedDict([ ('max_pool', MaxPool(3, stride=2)), ('conv', nn.Conv2d(in_channels_left, out_channels_left, kernel_size=1, bias=False)), ('bn', nn.BatchNorm2d(out_channels_left, eps=0.001)), ])) self.comb_iter_1_left = BranchSeparables(out_channels_right, out_channels_right, kernel_size=7, stride=2) self.comb_iter_1_right = MaxPool(3, stride=2) self.comb_iter_2_left = BranchSeparables(out_channels_right, out_channels_right, kernel_size=5, stride=2) self.comb_iter_2_right = BranchSeparables(out_channels_right, out_channels_right, kernel_size=3, stride=2) self.comb_iter_3_left = BranchSeparables(out_channels_right, out_channels_right, kernel_size=3) self.comb_iter_3_right = MaxPool(3, stride=2) self.comb_iter_4_left = BranchSeparables(in_channels_right, out_channels_right, kernel_size=3, stride=2, stem_cell=True) self.comb_iter_4_right = ReluConvBn(out_channels_right, out_channels_right, kernel_size=1, stride=2) def forward(self, x_left): x_right = self.conv_1x1(x_left) x_out = self.cell_forward(x_left, x_right) return x_out class Cell(CellBase): def __init__(self, in_channels_left, out_channels_left, in_channels_right, out_channels_right, is_reduction=False, zero_pad=False, match_prev_layer_dimensions=False): super(Cell, self).__init__() # If `is_reduction` is set to `True` stride 2 is used for # convolutional and pooling layers to reduce the spatial size of # the output of a cell approximately by a factor of 2. stride = 2 if is_reduction else 1 # If `match_prev_layer_dimensions` is set to `True` # `FactorizedReduction` is used to reduce the spatial size # of the left input of a cell approximately by a factor of 2. self.match_prev_layer_dimensions = match_prev_layer_dimensions if match_prev_layer_dimensions: self.conv_prev_1x1 = FactorizedReduction(in_channels_left, out_channels_left) else: self.conv_prev_1x1 = ReluConvBn(in_channels_left, out_channels_left, kernel_size=1) self.conv_1x1 = ReluConvBn(in_channels_right, out_channels_right, kernel_size=1) self.comb_iter_0_left = BranchSeparables(out_channels_left, out_channels_left, kernel_size=5, stride=stride, zero_pad=zero_pad) self.comb_iter_0_right = MaxPool(3, stride=stride, zero_pad=zero_pad) self.comb_iter_1_left = BranchSeparables(out_channels_right, out_channels_right, kernel_size=7, stride=stride, zero_pad=zero_pad) self.comb_iter_1_right = MaxPool(3, stride=stride, zero_pad=zero_pad) self.comb_iter_2_left = BranchSeparables(out_channels_right, out_channels_right, kernel_size=5, stride=stride, zero_pad=zero_pad) self.comb_iter_2_right = BranchSeparables(out_channels_right, out_channels_right, kernel_size=3, stride=stride, zero_pad=zero_pad) self.comb_iter_3_left = BranchSeparables(out_channels_right, out_channels_right, kernel_size=3) self.comb_iter_3_right = MaxPool(3, stride=stride, zero_pad=zero_pad) self.comb_iter_4_left = BranchSeparables(out_channels_left, out_channels_left, kernel_size=3, stride=stride, zero_pad=zero_pad) if is_reduction: self.comb_iter_4_right = ReluConvBn(out_channels_right, out_channels_right, kernel_size=1, stride=stride) else: self.comb_iter_4_right = None def forward(self, x_left, x_right): x_left = self.conv_prev_1x1(x_left) x_right = self.conv_1x1(x_right) x_out = self.cell_forward(x_left, x_right) return x_out class PNASNet5Large(nn.Module): def __init__(self, num_classes=1001): super(PNASNet5Large, self).__init__() self.num_classes = num_classes self.conv_0 = nn.Sequential(OrderedDict([ ('conv', nn.Conv2d(3, 96, kernel_size=3, stride=2, bias=False)), ('bn', nn.BatchNorm2d(96, eps=0.001)) ])) self.cell_stem_0 = CellStem0(in_channels_left=96, out_channels_left=54, in_channels_right=96, out_channels_right=54) self.cell_stem_1 = Cell(in_channels_left=96, out_channels_left=108, in_channels_right=270, out_channels_right=108, match_prev_layer_dimensions=True, is_reduction=True) self.cell_0 = Cell(in_channels_left=270, out_channels_left=216, in_channels_right=540, out_channels_right=216, match_prev_layer_dimensions=True) self.cell_1 = Cell(in_channels_left=540, out_channels_left=216, in_channels_right=1080, out_channels_right=216) self.cell_2 = Cell(in_channels_left=1080, out_channels_left=216, in_channels_right=1080, out_channels_right=216) self.cell_3 = Cell(in_channels_left=1080, out_channels_left=216, in_channels_right=1080, out_channels_right=216) self.cell_4 = Cell(in_channels_left=1080, out_channels_left=432, in_channels_right=1080, out_channels_right=432, is_reduction=True, zero_pad=True) self.cell_5 = Cell(in_channels_left=1080, out_channels_left=432, in_channels_right=2160, out_channels_right=432, match_prev_layer_dimensions=True) self.cell_6 = Cell(in_channels_left=2160, out_channels_left=432, in_channels_right=2160, out_channels_right=432) self.cell_7 = Cell(in_channels_left=2160, out_channels_left=432, in_channels_right=2160, out_channels_right=432) self.cell_8 = Cell(in_channels_left=2160, out_channels_left=864, in_channels_right=2160, out_channels_right=864, is_reduction=True) self.cell_9 = Cell(in_channels_left=2160, out_channels_left=864, in_channels_right=4320, out_channels_right=864, match_prev_layer_dimensions=True) self.cell_10 = Cell(in_channels_left=4320, out_channels_left=864, in_channels_right=4320, out_channels_right=864) self.cell_11 = Cell(in_channels_left=4320, out_channels_left=864, in_channels_right=4320, out_channels_right=864) self.relu = nn.ReLU() self.avg_pool = nn.AdaptiveAvgPool2d((1, 1)) self.dropout = nn.Dropout(0.5) self.last_linear = nn.Linear(4320, num_classes) def features(self, x): x_conv_0 = self.conv_0(x) x_stem_0 = self.cell_stem_0(x_conv_0) x_stem_1 = self.cell_stem_1(x_conv_0, x_stem_0) x_cell_0 = self.cell_0(x_stem_0, x_stem_1) x_cell_1 = self.cell_1(x_stem_1, x_cell_0) x_cell_2 = self.cell_2(x_cell_0, x_cell_1) x_cell_3 = self.cell_3(x_cell_1, x_cell_2) x_cell_4 = self.cell_4(x_cell_2, x_cell_3) x_cell_5 = self.cell_5(x_cell_3, x_cell_4) x_cell_6 = self.cell_6(x_cell_4, x_cell_5) x_cell_7 = self.cell_7(x_cell_5, x_cell_6) x_cell_8 = self.cell_8(x_cell_6, x_cell_7) x_cell_9 = self.cell_9(x_cell_7, x_cell_8) x_cell_10 = self.cell_10(x_cell_8, x_cell_9) x_cell_11 = self.cell_11(x_cell_9, x_cell_10) return x_cell_11 def logits(self, features): x = self.relu(features) x = self.avg_pool(x) x = x.view(x.size(0), -1) x = self.dropout(x) x = self.last_linear(x) return x def forward(self, input): x = self.features(input) x = self.logits(x) return x def pnasnet5large(num_classes=1000, pretrained='imagenet'): r"""PNASNet-5 model architecture from the `"Progressive Neural Architecture Search" `_ paper. """ if pretrained: settings = pretrained_settings['pnasnet5large'][pretrained] assert num_classes == settings[ 'num_classes'], 'num_classes should be {}, but is {}'.format( settings['num_classes'], num_classes) # both 'imagenet'&'imagenet+background' are loaded from same parameters model = PNASNet5Large(num_classes=1001) model.load_state_dict(model_zoo.load_url(settings['url'])) if pretrained == 'imagenet': new_last_linear = nn.Linear(model.last_linear.in_features, 1000) new_last_linear.weight.data = model.last_linear.weight.data[1:] new_last_linear.bias.data = model.last_linear.bias.data[1:] model.last_linear = new_last_linear model.input_space = settings['input_space'] model.input_size = settings['input_size'] model.input_range = settings['input_range'] model.mean = settings['mean'] model.std = settings['std'] else: model = PNASNet5Large(num_classes=num_classes) return model - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -