cannot import name 'get_model' from 'torchvision.models'

这个错误通常是因为您的 PyTorch 和 Torchvision 版本不兼容造成的。请尝试更新 Torchvision 或降级 PyTorch。

您可以使用以下命令升级 Torchvision：`pip install --upgrade torchvision`

如果您想降级 PyTorch，请使用以下命令：`pip install torch==<version>`，其中 `<version>` 是您想要降级的版本号。

相关问题

from keras_frcnn import metrics as metrics_fn ImportError: cannot import name 'metrics' from 'keras_frcnn' (D:\程序\Keras-Faster-RCNN-master-master\keras_frcnn\__init__.py)

这个错误可能是由于`keras_frcnn`包中没有`metrics`模块导致的。为此，你可以尝试使用`keras_frcnn.metrics`代替`from keras_frcnn import metrics as metrics_fn`，并修改`class_acc`和`mean_overlapping_bboxes`为`keras_frcnn.metrics.class_acc`和`keras_frcnn.metrics.mean_overlapping_bboxes`。修改后的代码如下：

from keras.models import Model
from keras_frcnn import config, data_generators
from keras_frcnn import losses as losses_fn
from keras_frcnn import metrics
from keras.models import load_model

# 加载模型配置
config_output_filename = "config.pickle"
with open(config_output_filename, "rb") as f_in:
    C = pickle.load(f_in)

# 创建数据生成器
test_imgs, _, _ = data_generators.get_data(
    C,
    C.test_path,
    mode='test'
)
test_gen = data_generators.get_anchor_gt(
    test_imgs,
    C,
    mode='test'
)

# 加载模型
model_path = "model_frcnn.hdf5"
model = load_model(model_path, custom_objects={
    "rpn_loss_cls": losses_fn.rpn_loss_cls,
    "rpn_loss_regr": losses_fn.rpn_loss_regr,
    "class_loss_cls": losses_fn.class_loss_cls,
    "class_loss_regr": losses_fn.class_loss_regr,
    "class_acc": metrics.class_acc,
    "mean_overlapping_bboxes": metrics.mean_overlapping_bboxes
})

# 评估模型性能
X, Y, image_data, debug_img, debug_num_pos = next(test_gen)
result = model.evaluate(X, Y)

# 打印性能指标
print("Losses: ", result)
print("Classification accuracy: ", result[1])
print("Classification loss: ", result[2])
print("Regression loss: ", result[3])

如果还有其他问题，请告诉我。

依然报错--------------------------------------------------------------------------- ValueError Traceback (most recent call last) <ipython-input-41-f53d3c8bb89c> in <cell line: 2>() 3 input_tensor = graph.get_tensor_by_name('ImageTensor:0') 4 output_tensor = graph.get_tensor_by_name('SemanticPredictions:0') ----> 5 output = sess.run(output_tensor, feed_dict={input_tensor: np.expand_dims(image_resized, axis=0)}) 1 frames /usr/local/lib/python3.10/dist-packages/tensorflow/python/client/session.py in _run(self, handle, fetches, feed_dict, options, run_metadata) 1163 if (not is_tensor_handle_feed and 1164 not subfeed_t.get_shape().is_compatible_with(np_val.shape)): -> 1165 raise ValueError( 1166 f'Cannot feed value of shape {str(np_val.shape)} for Tensor ' 1167 f'{subfeed_t.name}, which has shape ' ValueError: Cannot feed value of shape (1, 804, 804) for Tensor ImageTensor:0, which has shape (1, None, None, 3)

非常抱歉，我之前的回答存在错误。感谢你的耐心！在调整图像形状时，应该保持与DeepLabv3+模型期望的形状一致。下面是修改后的代码：

import cv2
import numpy as np
import urllib.request
import tensorflow as tf
from cellpose import models

# 下载DeepLabv3+模型权重文件
model_url = "http://download.tensorflow.org/models/deeplabv3_mnv2_pascal_train_aug_2018_01_29.tar.gz"
tar_filename = "deeplabv3_mnv2_pascal_train_aug.tar.gz"
urllib.request.urlretrieve(model_url, tar_filename)

# 解压缩
with tarfile.open(tar_filename, "r:gz") as tar:
    tar.extractall()
model_filename = "deeplabv3_mnv2_pascal_train_aug/frozen_inference_graph.pb"

# 加载DeepLabv3+模型
graph = tf.Graph()
with graph.as_default():
    od_graph_def = tf.GraphDef()
    with tf.io.gfile.GFile(model_filename, 'rb') as fid:
        serialized_graph = fid.read()
        od_graph_def.ParseFromString(serialized_graph)
        tf.import_graph_def(od_graph_def, name='')

# 加载Cellpose模型
model1 = models.CellposeModel(gpu=True, model_type='livecell')
model2 = models.CellposeModel(gpu=True, model_type='nuclei')
model3 = models.CellposeModel(gpu=True, model_type='cyto2')

# 读取图像
image_path = "your_image.jpg"
image = cv2.imread(image_path)

# 调整图像形状
image_resized = cv2.resize(image, (None, None), fx=0.5, fy=0.5)  # 调整图像的大小，可以根据需要修改缩放因子

# 进行图像分割（DeepLabv3+模型）
with tf.compat.v1.Session(graph=graph) as sess:
    input_tensor = graph.get_tensor_by_name('ImageTensor:0')
    output_tensor = graph.get_tensor_by_name('SemanticPredictions:0')
    output = sess.run(output_tensor, feed_dict={input_tensor: np.expand_dims(image_resized, axis=0)})

# 解码并可视化分割结果（DeepLabv3+模型）
segmentation_mask = np.squeeze(output)
segmentation_mask = np.uint8(segmentation_mask)
segmentation_mask = cv2.resize(segmentation_mask, (image.shape[1], image.shape[0]), interpolation=cv2.INTER_NEAREST)

# 进行细胞分割（Cellpose模型）
masks1, _, _, _ = model1.eval(image_resized)
masks2, _, _, _ = model2.eval(image_resized)
masks3, _, _, _ = model3.eval(image_resized)

# 可视化结果
cv2.imshow("Original Image", image)
cv2.imshow("Segmentation Mask (DeepLabv3+)", segmentation_mask)
cv2.imshow("Cell Masks (Model 1)", masks1)
cv2.imshow("Cell Masks (Model 2)", masks2)
cv2.imshow("Cell Masks (Model 3)", masks3)
cv2.waitKey(0)
cv2.destroyAllWindows()

在这个代码中，我将图像进行了缩放，并将缩放后的图像作为输入传递给DeepLabv3+模型和Cellpose模型。请根据需要调整缩放因子。

非常抱歉之前的错误带来的困扰。希望这个修改过的代码能够解决你的问题。如果还有其他疑问，请随时告诉我。