How to use the dragon.vm.caffe.layers.Convolution function in dragon

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github seetaresearch / Dragon / Dragon / python / dragon / vm / caffe / model_libs.py View on Github external
net.relu2_1 = L.ReLU(net.conv2_1, in_place=True)
    net.conv2_2 = L.Convolution(net.relu2_1, num_output=128, pad=1, kernel_size=3, **kwargs)
    net.relu2_2 = L.ReLU(net.conv2_2, in_place=True)

    if nopool:
        name = 'conv2_3'
        net[name] = L.Convolution(net.relu2_2, num_output=128, pad=1, kernel_size=3, stride=2, **kwargs)
    else:
        name = 'pool2'
        net[name] = L.Pooling(net.relu2_2, pool=P.Pooling.MAX, kernel_size=2, stride=2)

    net.conv3_1 = L.Convolution(net[name], num_output=256, pad=1, kernel_size=3, **kwargs)
    net.relu3_1 = L.ReLU(net.conv3_1, in_place=True)
    net.conv3_2 = L.Convolution(net.relu3_1, num_output=256, pad=1, kernel_size=3, **kwargs)
    net.relu3_2 = L.ReLU(net.conv3_2, in_place=True)
    net.conv3_3 = L.Convolution(net.relu3_2, num_output=256, pad=1, kernel_size=3, **kwargs)
    net.relu3_3 = L.ReLU(net.conv3_3, in_place=True)

    if nopool:
        name = 'conv3_4'
        net[name] = L.Convolution(net.relu3_3, num_output=256, pad=1, kernel_size=3, stride=2, **kwargs)
    else:
        name = 'pool3'
        net[name] = L.Pooling(net.relu3_3, pool=P.Pooling.MAX, kernel_size=2, stride=2)

    net.conv4_1 = L.Convolution(net[name], num_output=512, pad=1, kernel_size=3, **kwargs)
    net.relu4_1 = L.ReLU(net.conv4_1, in_place=True)
    net.conv4_2 = L.Convolution(net.relu4_1, num_output=512, pad=1, kernel_size=3, **kwargs)
    net.relu4_2 = L.ReLU(net.conv4_2, in_place=True)
    net.conv4_3 = L.Convolution(net.relu4_2, num_output=512, pad=1, kernel_size=3, **kwargs)
    net.relu4_3 = L.ReLU(net.conv4_3, in_place=True)
github seetaresearch / Dragon / Dragon / python / dragon / vm / caffe / model_libs.py View on Github external
'param': [
            dict(lr_mult=lr_mult, decay_mult=1),
            dict(lr_mult=2 * lr_mult, decay_mult=0)],
        'weight_filler': dict(type='xavier'),
        'bias_filler': dict(type='constant', value=0)
        }

  conv_name = '{}{}{}'.format(conv_prefix, out_layer, conv_postfix)
  [kernel_h, kernel_w] = UnpackVariable(kernel_size, 2)
  [pad_h, pad_w] = UnpackVariable(pad, 2)
  [stride_h, stride_w] = UnpackVariable(stride, 2)
  if kernel_h == kernel_w:
    net[conv_name] = L.Convolution(net[from_layer], num_output=num_output,
        kernel_size=kernel_h, pad=pad_h, stride=stride_h, **kwargs)
  else:
    net[conv_name] = L.Convolution(net[from_layer], num_output=num_output,
        kernel_h=kernel_h, kernel_w=kernel_w, pad_h=pad_h, pad_w=pad_w,
        stride_h=stride_h, stride_w=stride_w, **kwargs)
  if dilation > 1:
    net.update(conv_name, {'dilation': dilation})
  if use_bn:
    bn_name = '{}{}{}'.format(bn_prefix, out_layer, bn_postfix)
    net[bn_name] = L.BatchNorm(net[conv_name], in_place=True, **bn_kwargs)
    if use_scale:
      sb_name = '{}{}{}'.format(scale_prefix, out_layer, scale_postfix)
      net[sb_name] = L.Scale(net[bn_name], in_place=True, **sb_kwargs)
    else:
      bias_name = '{}{}{}'.format(bias_prefix, out_layer, bias_postfix)
      net[bias_name] = L.Bias(net[bn_name], in_place=True, **bias_kwargs)
  if use_relu:
    relu_name = '{}_relu'.format(conv_name)
    net[relu_name] = L.ReLU(net[conv_name], in_place=True)
github seetaresearch / Dragon / Dragon / python / dragon / vm / caffe / model_libs.py View on Github external
net.relu3_1 = L.ReLU(net.conv3_1, in_place=True)
    net.conv3_2 = L.Convolution(net.relu3_1, num_output=256, pad=1, kernel_size=3, **kwargs)
    net.relu3_2 = L.ReLU(net.conv3_2, in_place=True)
    net.conv3_3 = L.Convolution(net.relu3_2, num_output=256, pad=1, kernel_size=3, **kwargs)
    net.relu3_3 = L.ReLU(net.conv3_3, in_place=True)

    if nopool:
        name = 'conv3_4'
        net[name] = L.Convolution(net.relu3_3, num_output=256, pad=1, kernel_size=3, stride=2, **kwargs)
    else:
        name = 'pool3'
        net[name] = L.Pooling(net.relu3_3, pool=P.Pooling.MAX, kernel_size=2, stride=2)

    net.conv4_1 = L.Convolution(net[name], num_output=512, pad=1, kernel_size=3, **kwargs)
    net.relu4_1 = L.ReLU(net.conv4_1, in_place=True)
    net.conv4_2 = L.Convolution(net.relu4_1, num_output=512, pad=1, kernel_size=3, **kwargs)
    net.relu4_2 = L.ReLU(net.conv4_2, in_place=True)
    net.conv4_3 = L.Convolution(net.relu4_2, num_output=512, pad=1, kernel_size=3, **kwargs)
    net.relu4_3 = L.ReLU(net.conv4_3, in_place=True)

    if nopool:
        name = 'conv4_4'
        net[name] = L.Convolution(net.relu4_3, num_output=512, pad=1, kernel_size=3, stride=2, **kwargs)
    else:
        name = 'pool4'
        if dilate_pool4:
            net[name] = L.Pooling(net.relu4_3, pool=P.Pooling.MAX, kernel_size=3, stride=1, pad=1)
            dilation = 2
        else:
            net[name] = L.Pooling(net.relu4_3, pool=P.Pooling.MAX, kernel_size=2, stride=2)
            dilation = 1
github seetaresearch / Dragon / Dragon / python / dragon / vm / caffe / model_libs.py View on Github external
net.relu5_2 = L.ReLU(net.conv5_2, in_place=True)
    net.conv5_3 = L.Convolution(net.relu5_2, num_output=512, pad=pad, kernel_size=kernel_size, dilation=dilation, **kwargs)
    net.relu5_3 = L.ReLU(net.conv5_3, in_place=True)

    if need_fc:
        if dilated:
            if nopool:
                name = 'conv5_4'
                net[name] = L.Convolution(net.relu5_3, num_output=512, pad=1, kernel_size=3, stride=1, **kwargs)
            else:
                name = 'pool5'
                net[name] = L.Pooling(net.relu5_3, pool=P.Pooling.MAX, pad=1, kernel_size=3, stride=1)
        else:
            if nopool:
                name = 'conv5_4'
                net[name] = L.Convolution(net.relu5_3, num_output=512, pad=1, kernel_size=3, stride=2, **kwargs)
            else:
                name = 'pool5'
                net[name] = L.Pooling(net.relu5_3, pool=P.Pooling.MAX, kernel_size=2, stride=2)

        if fully_conv:
            if dilated:
                if reduced:
                    dilation = dilation * 6
                    kernel_size = 3
                    num_output = 1024
                else:
                    dilation = dilation * 2
                    kernel_size = 7
                    num_output = 4096
            else:
                if reduced:
github seetaresearch / Dragon / Dragon / python / dragon / vm / caffe / model_libs.py View on Github external
name = 'conv2_3'
        net[name] = L.Convolution(net.relu2_2, num_output=128, pad=1, kernel_size=3, stride=2, **kwargs)
    else:
        name = 'pool2'
        net[name] = L.Pooling(net.relu2_2, pool=P.Pooling.MAX, kernel_size=2, stride=2)

    net.conv3_1 = L.Convolution(net[name], num_output=256, pad=1, kernel_size=3, **kwargs)
    net.relu3_1 = L.ReLU(net.conv3_1, in_place=True)
    net.conv3_2 = L.Convolution(net.relu3_1, num_output=256, pad=1, kernel_size=3, **kwargs)
    net.relu3_2 = L.ReLU(net.conv3_2, in_place=True)
    net.conv3_3 = L.Convolution(net.relu3_2, num_output=256, pad=1, kernel_size=3, **kwargs)
    net.relu3_3 = L.ReLU(net.conv3_3, in_place=True)

    if nopool:
        name = 'conv3_4'
        net[name] = L.Convolution(net.relu3_3, num_output=256, pad=1, kernel_size=3, stride=2, **kwargs)
    else:
        name = 'pool3'
        net[name] = L.Pooling(net.relu3_3, pool=P.Pooling.MAX, kernel_size=2, stride=2)

    net.conv4_1 = L.Convolution(net[name], num_output=512, pad=1, kernel_size=3, **kwargs)
    net.relu4_1 = L.ReLU(net.conv4_1, in_place=True)
    net.conv4_2 = L.Convolution(net.relu4_1, num_output=512, pad=1, kernel_size=3, **kwargs)
    net.relu4_2 = L.ReLU(net.conv4_2, in_place=True)
    net.conv4_3 = L.Convolution(net.relu4_2, num_output=512, pad=1, kernel_size=3, **kwargs)
    net.relu4_3 = L.ReLU(net.conv4_3, in_place=True)

    if nopool:
        name = 'conv4_4'
        net[name] = L.Convolution(net.relu4_3, num_output=512, pad=1, kernel_size=3, stride=2, **kwargs)
    else:
        name = 'pool4'
github seetaresearch / Dragon / Dragon / python / dragon / vm / caffe / model_libs.py View on Github external
'bias_term': True}
  else:
    kwargs = {
        'param': [
            dict(lr_mult=lr_mult, decay_mult=1),
            dict(lr_mult=2 * lr_mult, decay_mult=0)],
        'weight_filler': dict(type='xavier'),
        'bias_filler': dict(type='constant', value=0)
        }

  conv_name = '{}{}{}'.format(conv_prefix, out_layer, conv_postfix)
  [kernel_h, kernel_w] = UnpackVariable(kernel_size, 2)
  [pad_h, pad_w] = UnpackVariable(pad, 2)
  [stride_h, stride_w] = UnpackVariable(stride, 2)
  if kernel_h == kernel_w:
    net[conv_name] = L.Convolution(net[from_layer], num_output=num_output,
        kernel_size=kernel_h, pad=pad_h, stride=stride_h, **kwargs)
  else:
    net[conv_name] = L.Convolution(net[from_layer], num_output=num_output,
        kernel_h=kernel_h, kernel_w=kernel_w, pad_h=pad_h, pad_w=pad_w,
        stride_h=stride_h, stride_w=stride_w, **kwargs)
  if dilation > 1:
    net.update(conv_name, {'dilation': dilation})
  if use_bn:
    bn_name = '{}{}{}'.format(bn_prefix, out_layer, bn_postfix)
    net[bn_name] = L.BatchNorm(net[conv_name], in_place=True, **bn_kwargs)
    if use_scale:
      sb_name = '{}{}{}'.format(scale_prefix, out_layer, scale_postfix)
      net[sb_name] = L.Scale(net[bn_name], in_place=True, **sb_kwargs)
    else:
      bias_name = '{}{}{}'.format(bias_prefix, out_layer, bias_postfix)
      net[bias_name] = L.Bias(net[bn_name], in_place=True, **bias_kwargs)
github seetaresearch / Dragon / Dragon / python / dragon / vm / caffe / model_libs.py View on Github external
kernel_size = 3
                    num_output = 1024
                else:
                    kernel_size = 7
                    num_output = 4096
            pad = int(int((kernel_size + (dilation - 1) * (kernel_size - 1)) - 1) / 2)
            net.fc6 = L.Convolution(net[name], num_output=num_output, pad=pad, kernel_size=kernel_size, dilation=dilation, **kwargs)

            net.relu6 = L.ReLU(net.fc6, in_place=True)
            if dropout:
                net.drop6 = L.Dropout(net.relu6, dropout_ratio=0.5, in_place=True)

            if reduced:
                net.fc7 = L.Convolution(net.relu6, num_output=1024, kernel_size=1, **kwargs)
            else:
                net.fc7 = L.Convolution(net.relu6, num_output=4096, kernel_size=1, **kwargs)
            net.relu7 = L.ReLU(net.fc7, in_place=True)
            if dropout:
                net.drop7 = L.Dropout(net.relu7, dropout_ratio=0.5, in_place=True)
        else:
            net.fc6 = L.InnerProduct(net.pool5, num_output=4096)
            net.relu6 = L.ReLU(net.fc6, in_place=True)
            if dropout:
                net.drop6 = L.Dropout(net.relu6, dropout_ratio=0.5, in_place=True)
            net.fc7 = L.InnerProduct(net.relu6, num_output=4096)
            net.relu7 = L.ReLU(net.fc7, in_place=True)
            if dropout:
                net.drop7 = L.Dropout(net.relu7, dropout_ratio=0.5, in_place=True)

    # Update freeze layers.
    kwargs['param'] = [dict(lr_mult=0, decay_mult=0), dict(lr_mult=0, decay_mult=0)]
    layers = net.keys()
github seetaresearch / Dragon / Dragon / python / dragon / vm / caffe / model_libs.py View on Github external
def VGGNetBody(net, from_layer, need_fc=True, fully_conv=False, reduced=False,
        dilated=False, nopool=False, dropout=True, freeze_layers=[], dilate_pool4=False):
    kwargs = {
            'param': [dict(lr_mult=1, decay_mult=1), dict(lr_mult=2, decay_mult=0)],
            'weight_filler': dict(type='xavier'),
            'bias_filler': dict(type='constant', value=0)}

    assert from_layer in net.keys()
    net.conv1_1 = L.Convolution(net[from_layer], num_output=64, pad=1, kernel_size=3, **kwargs)

    net.relu1_1 = L.ReLU(net.conv1_1, in_place=True)
    net.conv1_2 = L.Convolution(net.relu1_1, num_output=64, pad=1, kernel_size=3, **kwargs)
    net.relu1_2 = L.ReLU(net.conv1_2, in_place=True)

    if nopool:
        name = 'conv1_3'
        net[name] = L.Convolution(net.relu1_2, num_output=64, pad=1, kernel_size=3, stride=2, **kwargs)
    else:
        name = 'pool1'
        net.pool1 = L.Pooling(net.relu1_2, pool=P.Pooling.MAX, kernel_size=2, stride=2)

    net.conv2_1 = L.Convolution(net[name], num_output=128, pad=1, kernel_size=3, **kwargs)
    net.relu2_1 = L.ReLU(net.conv2_1, in_place=True)
    net.conv2_2 = L.Convolution(net.relu2_1, num_output=128, pad=1, kernel_size=3, **kwargs)
    net.relu2_2 = L.ReLU(net.conv2_2, in_place=True)
github seetaresearch / Dragon / Dragon / python / dragon / vm / caffe / model_libs.py View on Github external
if nopool:
        name = 'conv4_4'
        net[name] = L.Convolution(net.relu4_3, num_output=512, pad=1, kernel_size=3, stride=2, **kwargs)
    else:
        name = 'pool4'
        if dilate_pool4:
            net[name] = L.Pooling(net.relu4_3, pool=P.Pooling.MAX, kernel_size=3, stride=1, pad=1)
            dilation = 2
        else:
            net[name] = L.Pooling(net.relu4_3, pool=P.Pooling.MAX, kernel_size=2, stride=2)
            dilation = 1

    kernel_size = 3
    pad = int(int((kernel_size + (dilation - 1) * (kernel_size - 1)) - 1) / 2)
    net.conv5_1 = L.Convolution(net[name], num_output=512, pad=pad, kernel_size=kernel_size, dilation=dilation, **kwargs)
    net.relu5_1 = L.ReLU(net.conv5_1, in_place=True)
    net.conv5_2 = L.Convolution(net.relu5_1, num_output=512, pad=pad, kernel_size=kernel_size, dilation=dilation, **kwargs)
    net.relu5_2 = L.ReLU(net.conv5_2, in_place=True)
    net.conv5_3 = L.Convolution(net.relu5_2, num_output=512, pad=pad, kernel_size=kernel_size, dilation=dilation, **kwargs)
    net.relu5_3 = L.ReLU(net.conv5_3, in_place=True)

    if need_fc:
        if dilated:
            if nopool:
                name = 'conv5_4'
                net[name] = L.Convolution(net.relu5_3, num_output=512, pad=1, kernel_size=3, stride=1, **kwargs)
            else:
                name = 'pool5'
                net[name] = L.Pooling(net.relu5_3, pool=P.Pooling.MAX, pad=1, kernel_size=3, stride=1)
        else:
            if nopool:
github seetaresearch / Dragon / Dragon / python / dragon / vm / caffe / model_libs.py View on Github external
net.conv1_1 = L.Convolution(net[from_layer], num_output=64, pad=1, kernel_size=3, **kwargs)

    net.relu1_1 = L.ReLU(net.conv1_1, in_place=True)
    net.conv1_2 = L.Convolution(net.relu1_1, num_output=64, pad=1, kernel_size=3, **kwargs)
    net.relu1_2 = L.ReLU(net.conv1_2, in_place=True)

    if nopool:
        name = 'conv1_3'
        net[name] = L.Convolution(net.relu1_2, num_output=64, pad=1, kernel_size=3, stride=2, **kwargs)
    else:
        name = 'pool1'
        net.pool1 = L.Pooling(net.relu1_2, pool=P.Pooling.MAX, kernel_size=2, stride=2)

    net.conv2_1 = L.Convolution(net[name], num_output=128, pad=1, kernel_size=3, **kwargs)
    net.relu2_1 = L.ReLU(net.conv2_1, in_place=True)
    net.conv2_2 = L.Convolution(net.relu2_1, num_output=128, pad=1, kernel_size=3, **kwargs)
    net.relu2_2 = L.ReLU(net.conv2_2, in_place=True)

    if nopool:
        name = 'conv2_3'
        net[name] = L.Convolution(net.relu2_2, num_output=128, pad=1, kernel_size=3, stride=2, **kwargs)
    else:
        name = 'pool2'
        net[name] = L.Pooling(net.relu2_2, pool=P.Pooling.MAX, kernel_size=2, stride=2)

    net.conv3_1 = L.Convolution(net[name], num_output=256, pad=1, kernel_size=3, **kwargs)
    net.relu3_1 = L.ReLU(net.conv3_1, in_place=True)
    net.conv3_2 = L.Convolution(net.relu3_1, num_output=256, pad=1, kernel_size=3, **kwargs)
    net.relu3_2 = L.ReLU(net.conv3_2, in_place=True)
    net.conv3_3 = L.Convolution(net.relu3_2, num_output=256, pad=1, kernel_size=3, **kwargs)
    net.relu3_3 = L.ReLU(net.conv3_3, in_place=True)