def conv_stage(data, names, filters, kernel=3, stride=1, dilate=1, pad=-1, groups=1, no_bias=True, dropout_rate=0.): i = 0 bn1 = bn(data, names[i]) i += 1 relu1 = relu(bn1, names[i]) i += 1 if dropout_rate > 0.: dropout1 = dropout(relu1, names[i], p=dropout_rate) i += 1 top = conv(dropout1 if dropout_rate > 0. else relu1, names[i], filters, kernel=kernel, stride=stride, dilate=dilate, pad=pad, groups=groups, no_bias=no_bias) return relu1, top
def conv_stage(data, name, filters, kernel=3, stride=1, dilate=1, pad=-1, groups=1, no_bias=False, has_bn=False, dropout_rate=0., has_relu=True): top = conv(data, name, filters, kernel=kernel, stride=stride, dilate=dilate, pad=pad, groups=groups, no_bias=no_bias) if has_bn: top = bn(top, name='{}_bn'.format(name)) if dropout_rate > 0.0: top = dropout(top, '{}_do'.format(name), p=dropout_rate) if has_relu: top = relu(top, '{}_relu'.format(name)) return top
def res_conv_stage(data, name, filters, kernel=3, stride=1, dilate=1, pad=-1, groups=1, no_bias=True, has_relu=True, dropout_rate=0.): top = conv(data, 'res{}'.format(name), filters, kernel=kernel, stride=stride, dilate=dilate, pad=pad, groups=groups, no_bias=no_bias) top = bn(data=top, name='bn{}'.format(name)) if dropout_rate > 0.0: top = dropout(top, 'res{}_do'.format(name), p=dropout_rate) if has_relu: top = relu(top, 'res{}_relu'.format(name)) return top
def res_conv_stage(data, name, filters, kernel=3, stride=1, dilate=1, pad=-1, groups=1, no_bias=True, has_relu=True, dropout_rate=0.): top = conv(data, 'res{}'.format(name), filters, kernel=kernel, stride=stride, dilate=dilate, pad=pad, groups=groups, no_bias=no_bias) top = bn(data=top, name='bn{}'.format(name)) if dropout_rate > 0.0: top = dropout(top, 'res{}_do'.format(name), p=dropout_rate) if has_relu: top = relu(top, 'res{}_relu'.format(name)) return top
def conv_stage(data, name, filters, kernel=3, stride=1, dilate=1, pad=-1, groups=1, no_bias=False, has_bn=False, dropout_rate=0., has_relu=True): top = conv(data, name, filters, kernel=kernel, stride=stride, dilate=dilate, pad=pad, groups=groups, no_bias=no_bias) if has_bn: top = bn(top, name='{}_bn'.format(name)) if dropout_rate > 0.0: top = dropout(top, '{}_do'.format(name), p=dropout_rate) if has_relu: top = relu(top, '{}_relu'.format(name)) return top
def conv_stage(data, names, filters, kernel=3, stride=1, dilate=1, pad=-1, groups=1, no_bias=True, dropout_rate=0.): i = 0 bn1 = bn(data, names[i]) i += 1 relu1 = relu(bn1, names[i]) i += 1 if dropout_rate > 0.: dropout1 = dropout(relu1, names[i], p=dropout_rate) i += 1 top = conv(dropout1 if dropout_rate > 0. else relu1, names[i], filters, kernel=kernel, stride=stride, dilate=dilate, pad=pad, groups=groups, no_bias=no_bias) return relu1, top