class BasicBlock(nn.Module):
    def __init__(self, in_planes, out_planes, stride, dropRate=0.0):
        super(BasicBlock, self).__init__()
        self.bn1 = nn.BatchNorm2d(in_planes)
        self.relu1 = nn.ReLU(inplace=True)
        self.conv1 = nn.Conv2d(in_planes, out_planes, kernel_size=3, stride=stride,
                               padding=1, bias=False) # 1
        self.bn2 = nn.BatchNorm2d(out_planes)
        self.relu2 = nn.ReLU(inplace=True)
        self.conv2 = nn.Conv2d(out_planes, out_planes, kernel_size=3, stride=1,
                               padding=1, bias=False)
        self.droprate = dropRate
        self.equalInOut = (in_planes == out_planes)
        self.convShortcut = (not self.equalInOut) and nn.Conv2d(in_planes, out_planes, kernel_size=1, stride=stride,
                                                                padding=0, bias=False) or None

    def forward(self, x):
        if not self.equalInOut:
            x = self.relu1(self.bn1(x))
        else:
            out = self.relu1(self.bn1(x))
        out = self.relu2(self.bn2(self.conv1(out if self.equalInOut else x)))
        if self.droprate > 0:
            out = F.dropout(out, p=self.droprate, training=self.training)
        out = self.conv2(out)
        if self.convShortcut is not None:
        return torch.add(x if self.equalInOut else self.convShortcut(x), out)


class NetworkBlock(nn.Module):
    def __init__(self, nb_layers, in_planes, out_planes, block, stride, dropRate=0.0):
        super(NetworkBlock, self).__init__()
        self.layer = self._make_layer(block, in_planes, out_planes, nb_layers, stride, dropRate)

    def _make_layer(self, block, in_planes, out_planes, nb_layers, stride, dropRate):
        layers = []
        for i in range(int(nb_layers)):
            layers.append(block(i == 0 and in_planes or out_planes, out_planes, i == 0 and stride or 1, dropRate))
        return nn.Sequential(*layers)

    def forward(self, x):
        return self.layer(x)

class WideResNet(nn.Module):
    def __init__(self, depth=34, num_classes=10, widen_factor=10, dropRate=0.0):
        super(WideResNet, self).__init__()
        nChannels = [16, 16 * widen_factor, 32 * widen_factor, 64 * widen_factor]
        assert ((depth - 4) % 6 == 0)
        n = (depth - 4) / 6
        block = BasicBlock
        # 1st conv before any network block
        self.conv1 = nn.Conv2d(3, nChannels[0], kernel_size=3, stride=1,
                               padding=1, bias=False)
        # 1st block
        self.block1 = NetworkBlock(n, nChannels[0], nChannels[1], block, 1, dropRate)
        # 1st sub-block
        self.sub_block1 = NetworkBlock(n, nChannels[0], nChannels[1], block, 1, dropRate)
        # 2nd block
        self.block2 = NetworkBlock(n, nChannels[1], nChannels[2], block, 2, dropRate) # 2
        # 3rd block
        self.block3 = NetworkBlock(n, nChannels[2], nChannels[3], block, 2, dropRate) # 2
        # global average pooling and classifier
        self.bn1 = nn.BatchNorm2d(nChannels[3])
        self.relu = nn.ReLU(inplace=True)
        self.fc = nn.Linear(nChannels[3], num_classes)
        self.nChannels = nChannels[3]

        for m in self.modules():
            if isinstance(m, nn.Conv2d):
                n = m.kernel_size[0] * m.kernel_size[1] * m.out_channels
                m.weight.data.normal_(0, math.sqrt(2. / n))
            elif isinstance(m, nn.BatchNorm2d):
                m.weight.data.fill_(1)
                m.bias.data.zero_()
            elif isinstance(m, nn.Linear):
                m.bias.data.zero_()

    def forward(self, x):
        out = self.conv1(x)
        out = self.block1(out)
        out = self.block2(out)
        out = self.block3(out)
        out = self.relu(self.bn1(out))
        out = F.avg_pool2d(out, 8)
        out = out.view(-1, self.nChannels)
        return self.fc(out)

  def _conv(self, name, x, filter_size, in_filters, out_filters, strides, padding='SAME'):
    """Convolution."""
    with tf.variable_scope(name):
      n = filter_size * filter_size * out_filters
      kernel = tf.get_variable(
          'DW', [filter_size, filter_size, in_filters, out_filters],
          tf.float32, initializer=tf.random_normal_initializer(
              stddev=np.sqrt(2.0/n)))
      return tf.nn.conv2d(x, kernel, strides, padding=padding)

  def _residual(self, x, in_filter, out_filter, stride,
                activate_before_residual=False, is_log=False):
    """Residual unit with 2 sub layers."""
    if activate_before_residual:
        x = self._batch_norm('bn1', x)
        x = self._relu(x)
        orig_x = x
    else:
        orig_x = x
        x = self._batch_norm('bn1', x)
        x = self._relu(x)

    x = self._conv('conv1', x, 3, in_filter, out_filter, stride)
    x = self._batch_norm('bn2', x)
    x = self._relu(x)
    x = self._conv('conv2', x, 3, out_filter, out_filter, [1, 1, 1, 1])
    if in_filter != out_filter:
        orig_x = self._conv('shortcut_conv', orig_x, filter_size=1, in_filters=in_filter, out_filters=out_filter,
                            strides=stride, padding="VALID")
    x += orig_x
    return x

  def _build_model(self):
    assert self.mode == 'train' or self.mode == 'eval'
    with tf.variable_scope('input'):

      self.x_input = tf.placeholder(tf.float32, shape=[None, 32, 32, 3])
      self.y_input = tf.placeholder(tf.float32, shape=[None, 10])
      self.is_training = tf.placeholder(tf.bool, shape=None)
    x = self._conv('conv1.weight', self.x_input, 3, 3, 16, self._stride_arr(1))

    strides = [1, 2, 2]
    activate_before_residual = [True, True, True]
    res_func = self._residual
    # wide residual network (https://arxiv.org/abs/1605.07146v1)
    filters = [16, 160, 320, 640]

    with tf.variable_scope('block1.layer.0'):
      x = res_func(x, filters[0], filters[1], self._stride_arr(strides[0]),
                   activate_before_residual[0])
    for i in range(1, 5):
      with tf.variable_scope('block1.layer.%d' % i):
        x = res_func(x, filters[1], filters[1], self._stride_arr(1), False)

    with tf.variable_scope('block2.layer.0'):
      x = res_func(x, filters[1], filters[2], self._stride_arr(strides[1]),
                   activate_before_residual[1], is_log=True)
    for i in range(1, 5):
      with tf.variable_scope('block2.layer.%d' % i):
        x = res_func(x, filters[2], filters[2], self._stride_arr(1), False)

    with tf.variable_scope('block3.layer.0'):
      x = res_func(x, filters[2], filters[3], self._stride_arr(strides[2]),
                   activate_before_residual[2])
    for i in range(1, 5):
      with tf.variable_scope('block3.layer.%d' % i):
        x = res_func(x, filters[3], filters[3], self._stride_arr(1), False)

    x = self._batch_norm('bn1', x)
    x = self._relu(x)
    x = self._global_avg_pool(x)

    with tf.variable_scope('fc'):
      self.pre_softmax = self._fully_connected(x, 10)

I’m doing experiment on “adversarial defense”, and I checked that the performances of pytorch and tensorflow is different with same weights (I exported it as numpy and loaded to pytorch and tensorflow) I printed out each result of WideResNet34 and calculate the difference of each output, then, the above output of below image comes out

image1140×660 11.9 KB

The results start to be different from block2. Then, I only change the stride of each block to all 1 (stride of block 2 and 3),  the below output of above image comes out
The differences are negligible at all layers, so I think the difference appear only when stride=2. I don’t know why there is no difference when stride=1 but different when stride=2… Who knows about this thing?