我刚刚收到一条我不明白的错误消息，我在第一次运行后收到此错误消息。因此第一次运行有效，但随后从 Tensorflow 导入失败。在第二次运行时它停止工作。我没有改变任何东西。这是我的 Jupyter Notebook 中的完整代码。我正在尝试制作一个 AI 来玩 GTA。先驾驶，再射击。
我的代码：
# %%
### IMPORTS
## system
import psutil
import os
import sys
## input
import pyautogui
import time
import keyboard
import screeninfo
## AI
import numpy as np
import pandas as pd
import tensorflow as tf ## ERROR Line here
print(tf.__version__)
import tensorflow_hub as hub
print(hub.__version__)
import neat
print("The following GPU devices are available: %s" % tf.test.gpu_device_name())
## Window
import pygetwindow as gw
from src.grabscreen import grab_screen
## C2V
from PIL import Image, ImageGrab # ImageColor, ImageDraw, ImageFont, ImageOps
import cv2
cv2.ocl.setUseOpenCL(True)
import math
# %%
## Var list
DEBUG = True
crosshair = (224,224,224)
gta_window = [i for i in screeninfo.get_monitors() if i.is_primary == True][0]
moule_handle = "https://tfhub.dev/google/openimages_v4/ssd/mobilenet_v2/1" #@param ["https://tfhub.dev/google/openimages_v4/ssd/mobilenet_v2/1", "https://tfhub.dev/google/faster_rcnn/openimages_v4/inception_resnet_v2/1"]
detector = hub.load(moule_handle).signatures['default']
# %%
## function lib
def is_gta_running():
return any(process.name() == "GTA5.exe" for process in psutil.process_iter())
input_list = {}
def add_input(func):
input_list[func.__name__] = func
return func
def gta_loaded():
return pyautogui.locateOnScreen("img/story_mode.PNG", confidence=0.7)
def story_mode():
return pyautogui.locateOnScreen("img/story_mode.PNG", confidence=0.7)
def start_gta():
pyautogui.press("win")
pyautogui.write("Grand Theft Auto V")
pyautogui.press("enter")
def click_button(button):
pyautogui.click(button)
time.sleep(1)
pyautogui.mouseDown(button='left')
time.sleep(1)
pyautogui.mouseUp(button='left')
def click(button: str="left"):
pyautogui.mouseDown(button=button)
time.sleep(1)
pyautogui.mouseUp(button=button)
def press(key: str):
pyautogui.keyDown(key)
time.sleep(1)
pyautogui.keyUp(key)
def tab_in_gta():
x = gw.getWindowsWithTitle('Grand Theft Auto V')[0]
gta_window.x = x.left
gta_window.y = x.top
gta_window.width = x.width
gta_window.height = x.height
pyautogui.moveTo(gta_window.x + gta_window.width / 2, gta_window.y + gta_window.height / 2)
pyautogui.click()
time.sleep(1)
pyautogui.mouseDown(button='right')
time.sleep(1)
pyautogui.mouseUp(button='right')
time.sleep(1)
# %%
## start GTA
if not is_gta_running():
print("Starting GTA")
start_gta()
loaded: pyautogui = gta_loaded()
while not loaded and not DEBUG:
loaded = gta_loaded()
time.sleep(1)
else:
if not DEBUG:
print(loaded)
click_button(loaded)
loading = story_mode()
while loading:
loading = story_mode()
time.sleep(1)
tab_in_gta()
else:
tab_in_gta()
# %%
## combat
@add_input
def aim():
pyautogui.mouseDown(button='right')
@add_input
def shoot():
pyautogui.mouseDown(button='left')
@add_input
def stop_shoot():
pyautogui.mouseUp(button='left')
@add_input
def stop_aim():
pyautogui.mouseUp(button='right')
@add_input
def x_axis(value):
pyautogui.moveRel(value, 0, duration=0.1)
@add_input
def y_axis(value):
pyautogui.moveRel(0, value, duration=0.1)
@add_input
def cover():
keyboard.press('q')
time.sleep(0.1)
keyboard.release('q')
@add_input
def reload():
keyboard.press('r')
time.sleep(0.1)
keyboard.release('r')
@add_input
def jump():
keyboard.press('space')
time.sleep(0.1)
keyboard.release('space')
@add_input
def sprint():
keyboard.press('shift')
@add_input
def stop_sprint():
keyboard.release('shift')
@add_input
def enter_car():
keyboard.press('f')
time.sleep(0.1)
keyboard.release('f')
## movement
@add_input
def forward():
keyboard.press('w')
@add_input
def stop_forward():
keyboard.release('w')
@add_input
def backward():
keyboard.press('s')
@add_input
def stop_backward():
keyboard.release('s')
@add_input
def left():
keyboard.press('a')
@add_input
def stop_left():
keyboard.release('a')
@add_input
def right():
keyboard.press('d')
@add_input
def stop_right():
keyboard.release('d')
print(len(input_list))
# %%
## CV2 lib
def make_screenshot():
screenshot = grab_screen((gta_window.x, gta_window.y, gta_window.x + gta_window.width, gta_window.y + gta_window.height))
return screenshot
def process_img(image):
processed_img = cv2.resize(image, (720,405))
processed_img = cv2.cvtColor(processed_img, cv2.COLOR_BGR2RGB)
data = detect_img(processed_img)
processed_img = np.expand_dims(processed_img, axis=0)
return processed_img, data
def detect_img(img):
img_tensor = tf.convert_to_tensor(img, dtype=tf.float32)
start_time = time.time()
result = detector(img_tensor)
end_time = time.time()
result = {key:value.numpy() for key,value in result.items()}
print("Found %d objects." % len(result["detection_scores"]))
print("Inference time: ", end_time-start_time)
return result
def draw_boxes(image, boxes, class_names, scores, max_boxes=10, min_score=0.1):
colors = list(ImageColor.colormap.values())
try:
font = ImageFont.truetype("/usr/share/fonts/truetype/liberation/LiberationSansNarrow-Regular.ttf",
25)
except IOError:
print("Font not found, using default font.")
font = ImageFont.load_default()
for i in range(min(boxes.shape[0], max_boxes)):
if scores[i] >= min_score:
ymin, xmin, ymax, xmax = tuple(boxes[i])
display_str = "{}: {}%".format(class_names[i].decode("ascii"),
int(100 * scores[i]))
color = colors[hash(class_names[i]) % len(colors)]
image_pil = Image.fromarray(np.uint8(image)).convert("RGB")
draw_bounding_box_on_image(
image_pil,
ymin,
xmin,
ymax,
xmax,
color,
font,
display_str_list=[display_str])
np.copyto(image, np.array(image_pil))
return image
def draw_bounding_box_on_image(image,
ymin,
xmin,
ymax,
xmax,
color,
font,
thickness=4,
display_str_list=()):
"""Adds a bounding box to an image."""
draw = ImageDraw.Draw(image)
im_width, im_height = image.size
(left, right, top, bottom) = (xmin * im_width, xmax * im_width,
ymin * im_height, ymax * im_height)
draw.line([(left, top), (left, bottom), (right, bottom), (right, top),
(left, top)],
width=thickness,
fill=color)
# %%
tab_in_gta()
while (True):
screenshot = make_screenshot()
processed_img, data = process_img(screenshot)
image_with_boxes = draw_boxes(
processed_img, data["detection_boxes"],
data["detection_class_entities"], data["detection_scores"])
cv2.imshow('AI View', image_with_boxes)
if cv2.waitKey(25) & 0xFF == ord('q') or keyboard.is_pressed('q'):
cv2.destroyAllWindows()
break
{
"name": "AttributeError",
"message": "module 'tensorflow.security.fuzzing.py.annotation_types' has no attribute 'Float8e4m3fn'",
"stack": "---------------------------------------------------------------------------
AttributeError Traceback (most recent call last)
c:\\Users\\juliu\\Desktop\\AI\\app.ipynb Zelle 1 line 1
<a href='vscode-notebook-cell:/c%3A/Users/juliu/Desktop/AI/app.ipynb#W0sZmlsZQ%3D%3D?line=14'>15</a> import numpy as np
<a href='vscode-notebook-cell:/c%3A/Users/juliu/Desktop/AI/app.ipynb#W0sZmlsZQ%3D%3D?line=15'>16</a> import pandas as pd
---> <a href='vscode-notebook-cell:/c%3A/Users/juliu/Desktop/AI/app.ipynb#W0sZmlsZQ%3D%3D?line=17'>18</a> import tensorflow as tf
<a href='vscode-notebook-cell:/c%3A/Users/juliu/Desktop/AI/app.ipynb#W0sZmlsZQ%3D%3D?line=18'>19</a> print(tf.__version__)
<a href='vscode-notebook-cell:/c%3A/Users/juliu/Desktop/AI/app.ipynb#W0sZmlsZQ%3D%3D?line=19'>20</a> import tensorflow_hub as hub
File c:\\Users\\juliu\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages\\tensorflow\\__init__.py:39
36 import sys as _sys
37 import typing as _typing
---> 39 from tensorflow.python.tools import module_util as _module_util
40 from tensorflow.python.util.lazy_loader import LazyLoader as _LazyLoader
41 from tensorflow.python.util.lazy_loader import KerasLazyLoader as _KerasLazyLoader
File c:\\Users\\juliu\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages\\tensorflow\\python\\__init__.py:42
36 from tensorflow.python import pywrap_tensorflow as _pywrap_tensorflow
38 # pylint: enable=wildcard-import
39
40 # from tensorflow.python import keras
41 # from tensorflow.python.layers import layers
---> 42 from tensorflow.python.saved_model import saved_model
43 from tensorflow.python.tpu import api
45 # Sub-package for performing i/o directly instead of via ops in a graph.
File c:\\Users\\juliu\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages\\tensorflow\\python\\saved_model\\saved_model.py:31
29 from tensorflow.python.saved_model.fingerprinting import Fingerprint
30 from tensorflow.python.saved_model.fingerprinting import read_fingerprint
---> 31 from tensorflow.python.saved_model.load import load
32 from tensorflow.python.saved_model.save import save
33 # pylint: enable=unused-import
34 # pylint: disable=wildcard-import
File c:\\Users\\juliu\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages\\tensorflow\\python\\saved_model\\load.py:31
29 from tensorflow.python.checkpoint import restore
30 from tensorflow.python.distribute import distribute_lib
---> 31 from tensorflow.python.distribute import distribute_utils
32 from tensorflow.python.distribute import values_util
33 from tensorflow.python.eager import context
File c:\\Users\\juliu\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages\\tensorflow\\python\\distribute\\distribute_utils.py:22
19 import threading
21 from tensorflow.python.distribute import distribute_lib
---> 22 from tensorflow.python.distribute import tpu_values as tpu_values_lib
23 from tensorflow.python.distribute import values as values_lib
24 from tensorflow.python.distribute.reduce_util import ReduceOp
File c:\\Users\\juliu\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages\\tensorflow\\python\\distribute\\tpu_values.py:22
15 \"\"\"Various classes representing TPU distributed values.
16
17 Note that the tests are in values_test.py .
18
19 \"\"\"
21 from tensorflow.python.distribute import packed_distributed_variable as packed
---> 22 from tensorflow.python.distribute import tpu_replicated_variable
23 from tensorflow.python.distribute import tpu_util
24 from tensorflow.python.distribute import values
File c:\\Users\\juliu\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages\\tensorflow\\python\\distribute\\tpu_replicated_variable.py:23
20 from collections import abc
21 import contextlib
---> 23 from tensorflow.python.compiler.xla.experimental import xla_sharding
24 from tensorflow.python.distribute import tpu_util
25 from tensorflow.python.eager import context
File c:\\Users\\juliu\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages\\tensorflow\\python\\compiler\\xla\\__init__.py:19
17 # pylint: disable=unused-import
18 from tensorflow.python.compiler.xla import jit
---> 19 from tensorflow.python.compiler.xla import xla
20 # pylint: enable=unused-import
File c:\\Users\\juliu\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages\\tensorflow\\python\\compiler\\xla\\xla.py:20
15 \"\"\"xla is an experimental library that provides XLA support APIs.\"\"\"
17 import contextlib
---> 20 from tensorflow.compiler.jit.ops import xla_ops
21 from tensorflow.compiler.jit.ops import xla_ops_grad # pylint: disable=unused-import
22 from tensorflow.core.framework import attr_value_pb2
File c:\\Users\\juliu\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages\\tensorflow\\compiler\\jit\\ops\\xla_ops.py:25
22 from typing import TypeVar, List, Any
23 from typing_extensions import Annotated
---> 25 TV_XlaClusterOutput_T = TypeVar(\"TV_XlaClusterOutput_T\", _atypes.BFloat16, _atypes.Bool, _atypes.Complex128, _atypes.Complex64, _atypes.Float16, _atypes.Float32, _atypes.Float64, _atypes.Float8e4m3fn, _atypes.Float8e5m2, _atypes.Half, _atypes.Int16, _atypes.Int32, _atypes.Int4, _atypes.Int64, _atypes.Int8, _atypes.QInt16, _atypes.QInt32, _atypes.QInt8, _atypes.QUInt16, _atypes.QUInt8, _atypes.Resource, _atypes.String, _atypes.UInt16, _atypes.UInt32, _atypes.UInt4, _atypes.UInt64, _atypes.UInt8, _atypes.Variant)
27 @_dispatch.add_fallback_dispatch_list
28 @_dispatch.add_type_based_api_dispatcher
29 @tf_export('xla_cluster_output')
30 def xla_cluster_output(input: Annotated[Any, TV_XlaClusterOutput_T], name=None) -> Annotated[Any, TV_XlaClusterOutput_T]:
31 r\"\"\"Operator that connects the output of an XLA computation to other consumer graph nodes.
32
33 Args:
(...)
38 A `Tensor`. Has the same type as `input`.
39 \"\"\"
AttributeError: module 'tensorflow.security.fuzzing.py.annotation_types' has no attribute 'Float8e4m3fn'"
}
我重新安装了 Tensorflow，希望错误能够消失。
运行“pip install --upgrade tensorflow”为我解决了这个问题。