看看重塑发生在哪里。在此之前，您可以在其中插入一个全局平均池化层。这样您就可以处理任何大小。

但是，我建议裁剪和诈骗。如有必要，创建多个作物并对结果进行平均。这可能仍然比使用更大的图像更快。

如何使用 Xception

#!/usr/bin/env python
# -*- coding: utf-8 -*-
"""See https://martin-thoma.com/image-classification/ for details."""
from __future__ import print_function

import numpy as np
import json
import os
import time

from keras import backend as K
from keras.preprocessing import image
from keras.applications.xception import Xception
from keras.utils.data_utils import get_file

CLASS_INDEX = None
CLASS_INDEX_PATH = ('https://s3.amazonaws.com/deep-learning-models/'
                    'image-models/imagenet_class_index.json')


def preprocess_input(x, dim_ordering='default'):
    """
    Standard preprocessing of image data.

    1. Make sure the order of the channels is correct (RGB, BGR, depending on
       the backend)
    2. Mean subtraction by channel.

    Parameters
    ----------
    x : numpy array
        The image
    dim_ordering : string, optional (default: 'default')
        Either 'th' for Theano or 'tf' for Tensorflow

    Returns
    -------
    numpy array
        The preprocessed image
    """
    if dim_ordering == 'default':
        dim_ordering = K.image_dim_ordering()
    assert dim_ordering in {'tf', 'th'}

    if dim_ordering == 'th':
        x[:, 0, :, :] -= 103.939
        x[:, 1, :, :] -= 116.779
        x[:, 2, :, :] -= 123.68
        # 'RGB'->'BGR'
        x = x[:, ::-1, :, :]
    else:
        x[:, :, :, 0] -= 103.939
        x[:, :, :, 1] -= 116.779
        x[:, :, :, 2] -= 123.68
        # 'RGB'->'BGR'
        x = x[:, :, :, ::-1]
    return x


def decode_predictions(preds, top=5):
    """
    Decode the predictionso of the ImageNet trained network.

    Parameters
    ----------
    preds : numpy array
    top : int
        How many predictions to return

    Returns
    -------
    list of tuples
        e.g. (u'n02206856', u'bee', 0.71072823) for the WordNet identifier,
        the class name and the probability.
    """
    global CLASS_INDEX
    if len(preds.shape) != 2 or preds.shape[1] != 1000:
        raise ValueError('`decode_predictions` expects '
                         'a batch of predictions '
                         '(i.e. a 2D array of shape (samples, 1000)). '
                         'Found array with shape: ' + str(preds.shape))
    if CLASS_INDEX is None:
        fpath = get_file('imagenet_class_index.json',
                         CLASS_INDEX_PATH,
                         cache_subdir='models')
        CLASS_INDEX = json.load(open(fpath))
    results = []
    for pred in preds:
        top_indices = pred.argsort()[-top:][::-1]
        result = [tuple(CLASS_INDEX[str(i)]) + (pred[i],) for i in top_indices]
        results.append(result)
    return results


def is_valid_file(parser, arg):
    """
    Check if arg is a valid file that already exists on the file system.

    Parameters
    ----------
    parser : argparse object
    arg : str

    Returns
    -------
    arg
    """
    arg = os.path.abspath(arg)
    if not os.path.exists(arg):
        parser.error("The file %s does not exist!" % arg)
    else:
        return arg


def get_parser():
    """Get parser object."""
    from argparse import ArgumentParser, ArgumentDefaultsHelpFormatter
    parser = ArgumentParser(description=__doc__,
                            formatter_class=ArgumentDefaultsHelpFormatter)
    parser.add_argument("-f", "--file",
                        dest="filename",
                        type=lambda x: is_valid_file(parser, x),
                        help="Classify image",
                        metavar="IMAGE",
                        required=True)
    return parser


if __name__ == "__main__":
    args = get_parser().parse_args()

    # Load model
    model = Xception(include_top=True, weights='imagenet')

    img_path = args.filename
    img = image.load_img(img_path, target_size=(224, 224))
    x = image.img_to_array(img)
    x = np.expand_dims(x, axis=0)
    x = preprocess_input(x)
    print('Input image shape:', x.shape)
    t0 = time.time()
    preds = model.predict(x)
    t1 = time.time()
    print("Prediction time: {:0.3f}s".format(t1 - t0))
    for wordnet_id, class_name, prob in decode_predictions(preds)[0]:
        print("{wid}\t{prob:>6}%\t{name}".format(wid=wordnet_id,
                                                 name=class_name,
                                                 prob="%0.2f" % (prob * 100)))

为什么它适用于任何尺寸

看看model.summary()Xception 的，尤其是输出形状。注意到全局平均池化层了吗？在此之前，形状由输入决定。意思是在那之前，它可以是任何东西。

全局池化是另一种类型的过渡层。它在整个特征图大小上应用池化，以将输入缩小为恒定的 1×1 特征图，从而允许一个网络具有不同的输入大小。

资料来源：我：卷积神经网络架构的分析与优化