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zero-cost-nas/foresight/models/nasbench2_ops.py

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+# Copyright 2021 Samsung Electronics Co., Ltd.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+
+#     http://www.apache.org/licenses/LICENSE-2.0
+
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# =============================================================================
+
+import os
+import argparse
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+
+class ReLUConvBN(nn.Module):
+
+    def __init__(self, in_channels, out_channels, kernel_size, stride, padding, dilation, affine, track_running_stats=True, use_bn=True, name='ReLUConvBN'):
+        super(ReLUConvBN, self).__init__()
+        self.name = name
+        if use_bn:
+            self.op = nn.Sequential(
+                nn.ReLU(inplace=False),
+                nn.Conv2d(in_channels, out_channels, kernel_size, stride=stride, padding=padding, dilation=dilation, bias=not affine),
+                nn.BatchNorm2d(out_channels, affine=affine, track_running_stats=track_running_stats)
+                )
+        else:
+            self.op = nn.Sequential(
+                nn.ReLU(inplace=False),
+                nn.Conv2d(in_channels, out_channels, kernel_size, stride=stride, padding=padding, dilation=dilation, bias=not affine)
+                )
+
+    def forward(self, x):
+        return self.op(x)
+
+class Identity(nn.Module):
+    def __init__(self, name='Identity'):
+        self.name = name
+        super(Identity, self).__init__()
+
+    def forward(self, x):
+        return x
+
+class Zero(nn.Module):
+
+  def __init__(self, stride, name='Zero'):
+    self.name = name
+    super(Zero, self).__init__()
+    self.stride = stride
+
+  def forward(self, x):
+    if self.stride == 1:
+      return x.mul(0.)
+    return x[:,:,::self.stride,::self.stride].mul(0.)
+
+class POOLING(nn.Module):
+    def __init__(self, kernel_size, stride, padding, name='POOLING'):
+        super(POOLING, self).__init__()
+        self.name = name
+        self.avgpool = nn.AvgPool2d(kernel_size=kernel_size, stride=1, padding=1, count_include_pad=False)
+
+    def forward(self, x):
+        return self.avgpool(x)
+
+
+class reduction(nn.Module):
+    def __init__(self, in_channels, out_channels):
+        super(reduction, self).__init__()
+        self.residual = nn.Sequential(
+                            nn.AvgPool2d(kernel_size=2, stride=2, padding=0),
+                            nn.Conv2d(in_channels=in_channels, out_channels=out_channels, kernel_size=1, stride=1, padding=0, bias=False))
+
+        self.conv_a = ReLUConvBN(in_channels=in_channels, out_channels=out_channels, kernel_size=3, stride=2, padding=1, dilation=1, affine=True, track_running_stats=True)
+        self.conv_b = ReLUConvBN(in_channels=out_channels, out_channels=out_channels, kernel_size=3, stride=1, padding=1, dilation=1, affine=True, track_running_stats=True)
+
+    def forward(self, x):
+        basicblock = self.conv_a(x)
+        basicblock = self.conv_b(basicblock)
+        residual = self.residual(x)
+        return residual + basicblock
+
+class stem(nn.Module):
+    def __init__(self, out_channels, use_bn=True):
+        super(stem, self).__init__()
+        if use_bn:
+            self.net = nn.Sequential(
+                    nn.Conv2d(in_channels=3, out_channels=out_channels, kernel_size=3, padding=1, bias=False),
+                    nn.BatchNorm2d(out_channels))
+        else:
+            self.net = nn.Sequential(
+                    nn.Conv2d(in_channels=3, out_channels=out_channels, kernel_size=3, padding=1, bias=False)
+            )
+
+    def forward(self, x):
+        return self.net(x)
+
+class top(nn.Module):
+    def __init__(self, in_dims, num_classes, use_bn=True):
+        super(top, self).__init__()
+        if use_bn:
+            self.lastact = nn.Sequential(nn.BatchNorm2d(in_dims), nn.ReLU(inplace=True))
+        else:
+            self.lastact = nn.ReLU(inplace=True)
+        self.global_pooling = nn.AdaptiveAvgPool2d(1)
+        self.classifier  = nn.Linear(in_dims, num_classes)
+
+    def forward(self, x):
+        x = self.lastact(x)
+        x = self.global_pooling(x)
+        x = x.view(x.size(0), -1)
+        logits = self.classifier(x)
+        return logits
+
+
+class SearchCell(nn.Module):
+
+    def __init__(self, in_channels, out_channels, stride, affine, track_running_stats, use_bn=True, num_nodes=4, keep_mask=None):
+        super(SearchCell, self).__init__()
+        self.num_nodes = num_nodes
+        self.options = nn.ModuleList()
+        for curr_node in range(self.num_nodes-1):
+            for prev_node in range(curr_node+1): 
+                for _op_name in OPS.keys():
+                    op = OPS[_op_name](in_channels, out_channels, stride, affine, track_running_stats, use_bn)
+                    self.options.append(op)
+
+        if keep_mask is not None:
+            self.keep_mask = keep_mask
+        else:
+            self.keep_mask = [True]*len(self.options)
+
+    def forward(self, x):
+        outs = [x]
+
+        idx = 0
+        for curr_node in range(self.num_nodes-1):
+            edges_in = []
+            for prev_node in range(curr_node+1): # n-1 prev nodes
+                for op_idx in range(len(OPS.keys())):
+                    if self.keep_mask[idx]:
+                        edges_in.append(self.options[idx](outs[prev_node]))
+                    idx += 1
+            node_output = sum(edges_in)
+            outs.append(node_output)
+        
+        return outs[-1]
+
+
+
+OPS = {
+    'none' : lambda in_channels, out_channels, stride, affine, track_running_stats, use_bn: Zero(stride, name='none'),
+    'avg_pool_3x3' : lambda in_channels, out_channels, stride, affine, track_running_stats, use_bn: POOLING(3, 1, 1, name='avg_pool_3x3'),
+    'nor_conv_3x3' : lambda in_channels, out_channels, stride, affine, track_running_stats, use_bn: ReLUConvBN(in_channels, out_channels, 3, 1, 1, 1, affine, track_running_stats, use_bn, name='nor_conv_3x3'),
+    'nor_conv_1x1' : lambda in_channels, out_channels, stride, affine, track_running_stats, use_bn: ReLUConvBN(in_channels, out_channels, 1, 1, 0, 1, affine, track_running_stats, use_bn, name='nor_conv_1x1'),
+    'skip_connect' : lambda in_channels, out_channels, stride, affine, track_running_stats, use_bn: Identity(name='skip_connect'),
+}
+
+