Python TensorflowUtils.max_pool_2x2方法代码示例

# 需要导入模块: import TensorflowUtils [as 别名]
# 或者: from TensorflowUtils import max_pool_2x2 [as 别名]
def inference_simple(dataset):
    with tf.name_scope("conv1") as scope:
        W1 = utils.weight_variable([5, 5, 1, 32], name="W1")
        b1 = utils.bias_variable([32], name="b1")
        tf.histogram_summary("W1", W1)
        tf.histogram_summary("b1", b1)
        h_conv1 = tf.nn.relu(utils.conv2d_basic(dataset, W1, b1), name="h_conv1")
        h_pool1 = utils.max_pool_2x2(h_conv1)

    with tf.name_scope("conv2") as scope:
        W2 = utils.weight_variable([3, 3, 32, 64], name="W2")
        b2 = utils.bias_variable([64], name="b2")
        tf.histogram_summary("W2", W2)
        tf.histogram_summary("b2", b2)
        h_conv2 = tf.nn.relu(utils.conv2d_basic(h_pool1, W2, b2), name="h_conv2")
        h_pool2 = utils.max_pool_2x2(h_conv2)

    with tf.name_scope("fc") as scope:
        image_size = IMAGE_SIZE // 4
        h_flat = tf.reshape(h_pool2, [-1, image_size * image_size * 64])
        W_fc = utils.weight_variable([image_size * image_size * 64, NUM_LABELS], name="W_fc")
        b_fc = utils.bias_variable([NUM_LABELS], name="b_fc")
        tf.histogram_summary("W_fc", W_fc)
        tf.histogram_summary("b_fc", b_fc)
        pred = tf.matmul(h_flat, W_fc) + b_fc

    return pred

# 需要导入模块: import TensorflowUtils [as 别名]
# 或者: from TensorflowUtils import max_pool_2x2 [as 别名]
def inference(dataset):
    with tf.name_scope("conv1") as scope:
        W1 = utils.weight_variable([5, 5, 1, 32], name="W1")
        b1 = utils.bias_variable([32], name="b1")
        tf.histogram_summary("W1", W1)
        tf.histogram_summary("b1", b1)
        h_conv1 = utils.conv2d_basic(dataset, W1, b1)
        h_norm1 = utils.local_response_norm(h_conv1)
        h_1 = tf.nn.relu(h_norm1, name="conv1")
        h_pool1 = utils.max_pool_2x2(h_1)

    with tf.name_scope("conv2") as scope:
        W2 = utils.weight_variable([3, 3, 32, 64], name="W2")
        b2 = utils.bias_variable([64], name="b2")
        tf.histogram_summary("W2", W2)
        tf.histogram_summary("b2", b2)
        h_conv2 = utils.conv2d_basic(h_pool1, W2, b2)
        h_norm2 = utils.local_response_norm(h_conv2)
        h_2 = tf.nn.relu(h_norm2, name="conv2")
        h_pool2 = utils.max_pool_2x2(h_2)

    with tf.name_scope("conv3") as scope:
        W3 = utils.weight_variable([3, 3, 64, 128], name="W3")
        b3 = utils.bias_variable([128], name="b3")
        tf.histogram_summary("W3", W3)
        tf.histogram_summary("b3", b3)
        h_conv3 = utils.conv2d_basic(h_pool2, W3, b3)
        h_norm3 = utils.local_response_norm(h_conv3)
        h_3 = tf.nn.relu(h_norm3, name="conv3")
        h_pool3 = utils.max_pool_2x2(h_3)

    with tf.name_scope("conv4") as scope:
        W4 = utils.weight_variable([3, 3, 128, 256], name="W4")
        b4 = utils.bias_variable([256], name="b4")
        tf.histogram_summary("W4", W4)
        tf.histogram_summary("b4", b4)
        h_conv4 = utils.conv2d_basic(h_pool3, W4, b4)
        h_norm4 = utils.local_response_norm(h_conv4)
        h_4 = tf.nn.relu(h_norm4, name="conv4")

    with tf.name_scope("fc1") as scope:
        image_size = IMAGE_SIZE // 8
        h_flat = tf.reshape(h_4, [-1, image_size * image_size * 256])
        W_fc1 = utils.weight_variable([image_size * image_size * 256, 512], name="W_fc1")
        b_fc1 = utils.bias_variable([512], name="b_fc1")
        tf.histogram_summary("W_fc1", W_fc1)
        tf.histogram_summary("b_fc1", b_fc1)
        h_fc1 = tf.nn.relu(tf.matmul(h_flat, W_fc1) + b_fc1)

    with tf.name_scope("fc2") as scope:
        W_fc2 = utils.weight_variable([512, NUM_LABELS], name="W_fc2")
        b_fc2 = utils.bias_variable([NUM_LABELS], name="b_fc2")
        tf.histogram_summary("W_fc2", W_fc2)
        tf.histogram_summary("b_fc2", b_fc2)
        pred = tf.matmul(h_fc1, W_fc2) + b_fc2

    return pred

# 需要导入模块: import TensorflowUtils [as 别名]
# 或者: from TensorflowUtils import max_pool_2x2 [as 别名]
def inferece(dataset, prob):
    with tf.name_scope("conv1") as scope:
        W_conv1 = utils.weight_variable([5, 5, 1, 32])
        b_conv1 = utils.bias_variable([32])
        tf.histogram_summary("W_conv1", W_conv1)
        tf.histogram_summary("b_conv1", b_conv1)
        h_conv1 = utils.conv2d_basic(dataset, W_conv1, b_conv1)
        h_1 = tf.nn.relu(h_conv1)
        h_pool1 = utils.max_pool_2x2(h_1)
        add_to_regularization_loss(W_conv1, b_conv1)

    with tf.name_scope("conv2") as scope:
        W_conv2 = utils.weight_variable([3, 3, 32, 64])
        b_conv2 = utils.bias_variable([64])
        tf.histogram_summary("W_conv2", W_conv2)
        tf.histogram_summary("b_conv2", b_conv2)
        h_conv2 = utils.conv2d_basic(h_pool1, W_conv2, b_conv2)
        h_2 = tf.nn.relu(h_conv2)
        h_pool2 = utils.max_pool_2x2(h_2)
        add_to_regularization_loss(W_conv2, b_conv2)

    with tf.name_scope("fc_1") as scope:
        image_size = IMAGE_SIZE / 4
        h_flat = tf.reshape(h_pool2, [-1, image_size * image_size * 64])
        W_fc1 = utils.weight_variable([image_size * image_size * 64, 256])
        b_fc1 = utils.bias_variable([256])
        tf.histogram_summary("W_fc1", W_fc1)
        tf.histogram_summary("b_fc1", b_fc1)
        h_fc1 = tf.nn.relu(tf.matmul(h_flat, W_fc1) + b_fc1)
        h_fc1_dropout = tf.nn.dropout(h_fc1, prob)
    with tf.name_scope("fc_2") as scope:
        W_fc2 = utils.weight_variable([256, NUM_LABELS])
        b_fc2 = utils.bias_variable([NUM_LABELS])
        tf.histogram_summary("W_fc2", W_fc2)
        tf.histogram_summary("b_fc2", b_fc2)
        pred = tf.matmul(h_fc1, W_fc2) + b_fc2

    return pred

# 需要导入模块: import TensorflowUtils [as 别名]
# 或者: from TensorflowUtils import max_pool_2x2 [as 别名]
def vgg_net(weights, image):
    layers = (
        'conv1_1', 'relu1_1', 'conv1_2', 'relu1_2', 'pool1',

        'conv2_1', 'relu2_1', 'conv2_2', 'relu2_2', 'pool2',

        'conv3_1', 'relu3_1', 'conv3_2', 'relu3_2', 'conv3_3',
        'relu3_3', 'conv3_4', 'relu3_4', 'pool3',

        'conv4_1', 'relu4_1', 'conv4_2', 'relu4_2', 'conv4_3',
        'relu4_3', 'conv4_4', 'relu4_4', 'pool4',

        'conv5_1', 'relu5_1', 'conv5_2', 'relu5_2', 'conv5_3',
        'relu5_3', 'conv5_4', 'relu5_4'
    )

    net = {}
    current = image
    for i, name in enumerate(layers):
        kind = name[:4]
        if kind == 'conv':
            kernels, bias = weights[i][0][0][0][0]
            # matconvnet: weights are [width, height, in_channels, out_channels]
            # tensorflow: weights are [height, width, in_channels, out_channels]
            kernels = np.transpose(kernels, (1, 0, 2, 3))
            bias = bias.reshape(-1)
            current = utils.conv2d_basic(current, kernels, bias)
        elif kind == 'relu':
            current = tf.nn.relu(current)
        elif kind == 'pool':
            current = utils.max_pool_2x2(current)
        elif kind == 'norm':
            current = tf.nn.lrn(current, 4, bias=1.0, alpha=0.001 / 9.0, beta=0.75)

        net[name] = current

    assert len(net) == len(layers)
    return net

# 需要导入模块: import TensorflowUtils [as 别名]
# 或者: from TensorflowUtils import max_pool_2x2 [as 别名]
def inference(image, keep_prob):
    """
    Semantic segmentation network definition
    :param image: input image. Should have values in range 0-255
    :param keep_prob:
    :return:
    """
    print("setting up vgg initialized conv layers ...")
    model_data = utils.get_model_data(FLAGS.model_dir, MODEL_URL)

    mean = model_data['normalization'][0][0][0]
    mean_pixel = np.mean(mean, axis=(0, 1))

    weights = np.squeeze(model_data['layers'])

    #processed_image = utils.process_image(image, mean_pixel)

    with tf.variable_scope("inference"):
        image_net = vgg_net(weights, image)
        conv_final_layer = image_net["conv5_3"]

        pool5 = utils.max_pool_2x2(conv_final_layer)

        W6 = utils.weight_variable([7, 7, 512, 4096], name="W6")
        b6 = utils.bias_variable([4096], name="b6")
        conv6 = utils.conv2d_basic(pool5, W6, b6)
        relu6 = tf.nn.relu(conv6, name="relu6")
        if FLAGS.debug:
            utils.add_activation_summary(relu6)
        relu_dropout6 = tf.nn.dropout(relu6, keep_prob=keep_prob)

        W7 = utils.weight_variable([1, 1, 4096, 4096], name="W7")
        b7 = utils.bias_variable([4096], name="b7")
        conv7 = utils.conv2d_basic(relu_dropout6, W7, b7)
        relu7 = tf.nn.relu(conv7, name="relu7")
        if FLAGS.debug:
            utils.add_activation_summary(relu7)
        relu_dropout7 = tf.nn.dropout(relu7, keep_prob=keep_prob)

        W8 = utils.weight_variable([1, 1, 4096, NUM_OF_CLASSESS], name="W8")
        b8 = utils.bias_variable([NUM_OF_CLASSESS], name="b8")
        conv8 = utils.conv2d_basic(relu_dropout7, W8, b8)
        # annotation_pred1 = tf.argmax(conv8, dimension=3, name="prediction1")

        # now to upscale to actual image size
        deconv_shape1 = image_net["pool4"].get_shape()
        W_t1 = utils.weight_variable([4, 4, deconv_shape1[3].value, NUM_OF_CLASSESS], name="W_t1")
        b_t1 = utils.bias_variable([deconv_shape1[3].value], name="b_t1")
        conv_t1 = utils.conv2d_transpose_strided(conv8, W_t1, b_t1, output_shape=tf.shape(image_net["pool4"]))
        fuse_1 = tf.add(conv_t1, image_net["pool4"], name="fuse_1")

        deconv_shape2 = image_net["pool3"].get_shape()
        W_t2 = utils.weight_variable([4, 4, deconv_shape2[3].value, deconv_shape1[3].value], name="W_t2")
        b_t2 = utils.bias_variable([deconv_shape2[3].value], name="b_t2")
        conv_t2 = utils.conv2d_transpose_strided(fuse_1, W_t2, b_t2, output_shape=tf.shape(image_net["pool3"]))
        fuse_2 = tf.add(conv_t2, image_net["pool3"], name="fuse_2")

        shape = tf.shape(image)
        deconv_shape3 = tf.stack([shape[0], shape[1], shape[2], NUM_OF_CLASSESS])
        W_t3 = utils.weight_variable([16, 16, NUM_OF_CLASSESS, deconv_shape2[3].value], name="W_t3")
        b_t3 = utils.bias_variable([NUM_OF_CLASSESS], name="b_t3")
        conv_t3 = utils.conv2d_transpose_strided(fuse_2, W_t3, b_t3, output_shape=deconv_shape3, stride=8)

        annotation_pred = tf.argmax(conv_t3, dimension=3, name="prediction")

    return tf.expand_dims(annotation_pred, dim=3), conv_t3