与this question非常相似，除了我想知道如何获取输入大小为(128, 128, 3)张图像的预训练模型，并保持其权重，将其用于predict，用于输入大小不同的图像。
当我尝试输入任意大小的图像时，我得到了它：
Traceback (most recent call last):
File "arg_test.py", line 127, in <module>
predict(args)
File "arg_test.py", line 71, in predict
predictions.append(model.predict(input_img)[0]) # returns a list of lists, one for each image in the batch
File "C:\Users\payne\Anaconda3\envs\ml-gpu\lib\site-packages\keras\engine\training.py", line 1147, in predict
x, _, _ = self._standardize_user_data(x)
File "C:\Users\payne\Anaconda3\envs\ml-gpu\lib\site-packages\keras\engine\training.py", line 749, in _standardize_user_data
exception_prefix='input')
File "C:\Users\payne\Anaconda3\envs\ml-gpu\lib\site-packages\keras\engine\training_utils.py", line 137, in standardize_input_data
str(data_shape))
ValueError: Error when checking input: expected input_1 to have shape (128, 128, 3) but got array with shape (2736, 3648, 3)
这里是my model：
def setUpModel(x_train, y_train):
filters = 256
kernel_size = 3
strides = 1
# Head module
input = Input(shape=(img_height//scale_fact, img_width//scale_fact, img_depth))
conv0 = Conv2D(filters, kernel_size, strides=strides, padding='same')(input)
# Body module
res = Conv2D(filters, kernel_size, strides=strides, padding='same')(conv0)
act = ReLU()(res)
res = Conv2D(filters, kernel_size, strides=strides, padding='same')(act)
res_rec = Add()([conv0, res])
for i in range(res_blocks):
res1 = Conv2D(filters, kernel_size, strides=strides, padding='same')(res_rec)
act = ReLU()(res1)
res2 = Conv2D(filters, kernel_size, strides=strides, padding='same')(act)
res_rec = Add()([res_rec, res2])
conv = Conv2D(filters, kernel_size, strides=strides, padding='same')(res_rec)
add = Add()([conv0, conv])
# Tail module
conv = Conv2D(filters, kernel_size, strides=strides, padding='same')(add)
act = ReLU()(conv)
up = UpSampling2D(size=scale_fact if scale_fact != 4 else 2)(act) # TODO: try "Conv2DTranspose"
# mul = Multiply([np.zeros((img_width,img_height,img_depth)).fill(0.1), up])(up)
# When it's a 4X factor, we want the upscale split in two procedures
if(scale_fact == 4):
conv = Conv2D(filters, kernel_size, strides=strides, padding='same')(up)
act = ReLU()(conv)
up = UpSampling2D(size=2)(act) # TODO: try "Conv2DTranspose"
output = Conv2D(filters=3,
kernel_size=1,
strides=1,
padding='same')(up)
model = Model(inputs=input, outputs=output)
这只是训练期间使用的模型的架构，但是训练落后了：我有model.h5文件，是通过model.save()获得的。
这是我得到预测的方式：
import argparse
import numpy as np
import matplotlib.pyplot as plt
import skimage.io
from keras.models import load_model
from keras.optimizers import Adam
from keras.optimizers import Adadelta
from constants import save_dir
from constants import model_name
from constants import crops_p_img
from constants import tests_path
from constants import img_height
from constants import img_width
from constants import scale_fact
from utils import float_im
from utils import crop_center
parser = argparse.ArgumentParser(formatter_class=argparse.ArgumentDefaultsHelpFormatter)
parser.add_argument('-a', '--amount', type=int, default=crops_p_img,
help='how many (cropped to 128x128) samples to predict from within the image')
parser.add_argument('image', type=str,
help='image name (example: "bird.png") that must be inside the "./input/" folder')
parser.add_argument('-m', '--model', type=str, default=model_name,
help='model name (in the "./save/" folder), followed by ".h5"')
parser.add_argument('-r', '--random', action="store_true", # if var is in args, set to TRUE, else, set to FALSE
help='flag that will select a random 128x128 area in the input image instead of the center')
parser.add_argument('-f', '--full', action="store_true", # if var is in args, set to TRUE, else, set to FALSE
help='(WIP) flag that will get the whole image to be processed by the network')
args = parser.parse_args()
def predict(args):
model = load_model(save_dir + '/' + args.model)
# Setting up the proper optimizer TODO: needed?
if args.model == "my_full_model.h5":
optimizer = Adadelta(lr=1.0,
rho=0.95,
epsilon=None,
decay=0.0)
else:
optimizer = Adam(lr=0.001,
beta_1=0.9,
beta_2=0.999,
epsilon=None,
decay=0.0,
amsgrad=False)
model.compile(optimizer=optimizer,
loss='mean_squared_error')
image = skimage.io.imread(tests_path + args.image)
if image.shape[0] == 128:
args.amount = 1
predictions = []
images = []
# TODO: integrate FULL IMAGE
# if args.full:
# images.append(image)
# # Hack because GPU can only handle one image at a time
# input_img = (np.expand_dims(images[0], 0)) # Add the image to a batch where it's the only member
# predictions.append(model.predict(input_img)[0]) # returns a list of lists, one for each image in the batch
# else:
if True:
for i in range(args.amount):
# Cropping to fit input size
if (args.random or args.amount > 1) and image.shape[0] > 128:
images.append(random_crop(image))
else:
images.append(crop_center(image, img_width//scale_fact, img_height//scale_fact))
input_img = (np.expand_dims(images[i], 0))
predictions.append(model.predict(input_img)[0])
for i in range(len(predictions)):
show_pred_output(images[i], predictions[i])
# adapted from: https://stackoverflow.com/a/52463034/9768291
def random_crop(img):
crop_h, crop_w = img_width//scale_fact, img_height//scale_fact
print("Shape of input image to crop:", img.shape[0], img.shape[1])
if (img.shape[0] >= crop_h) and (img.shape[1] >= crop_w):
# Cropping a random part of the image
rand_h = np.random.randint(0, img.shape[0]-crop_h)
rand_w = np.random.randint(0, img.shape[1]-crop_w)
print("Random position for the crop:", rand_h, rand_w)
tmp_img = img[rand_h:rand_h+crop_h, rand_w:rand_w+crop_w]
new_img = float_im(tmp_img) # From [0,255] to [0.,1.]
else:
return img
return new_img
def show_pred_output(input, pred):
plt.figure(figsize=(20, 20))
plt.suptitle("Results")
plt.subplot(1, 2, 1)
plt.title("Input: 128x128")
plt.imshow(input, cmap=plt.cm.binary).axes.get_xaxis().set_visible(False)
plt.subplot(1, 2, 2)
plt.title("Output: 512x512")
plt.imshow(pred, cmap=plt.cm.binary).axes.get_xaxis().set_visible(False)
plt.show()
if __name__ == '__main__':
print(" - ", args)
predict(args)
答案 0 :(得分：0)
您应该替换此行：
input = Input(shape=(None, None, img_depth))
None的形状表示可变大小。由于该模型只是卷积，因此可以处理任何大小的图像。