修改以下代码使正确import random def proc(data): cls_no = random.choice(list(data.keys())) stu_no = random.randint(1,10) return "{}{:02}".format(cls_no, stu_no) if __name__ == "__main__": data = {"A001":32, "A002":47, "B001":39, "B002":42} result = set() while len(result) < 5: result.add(list[set()] ) print(result)
时间: 2024-03-09 08:45:38 浏览: 28
以下是修改后的代码:
```python
import random
def proc(data):
cls_no = random.choice(list(data.keys()))
stu_no = random.randint(1, data[cls_no])
return "{}{:02}".format(cls_no, stu_no)
if __name__ == "__main__":
data = {"A001":32, "A002":47, "B001":39, "B002":42}
result = set()
while len(result) < 5:
result.add(proc(data))
print(result)
```
主要修改如下:
1. 在第五行中,修改了 randint() 函数的第二个参数,使其取值范围为对应班级的学生人数,避免了随机生成不在学生人数范围内的学号。
2. 在第11行中,修改了调用的函数名称,将 list[set()] 改为 proc(data),这是修正后的函数名称。
相关问题
给下列代码加注释: def merge_accumulate_client_update(self, list_num_proc, list_state_dict, lr): total_num_proc = sum(list_num_proc) # merged_state_dict = dict() dict_keys = list_state_dict[0].keys() for state_dict in list_state_dict[1:]: assert state_dict.keys() == dict_keys # accumulate extra sgrad and remove from state_dict if self.use_adaptive and self.is_adj_round(): prefix = "extra." for state_dict in list_state_dict: del_list = [] for key, param in state_dict.items(): if key[:len(prefix)] == prefix: sgrad_key = key[len(prefix):] mask_0 = self.model.get_mask_by_name(sgrad_key) == 0. dense_sgrad = torch.zeros_like(mask_0, dtype=torch.float) dense_sgrad.masked_scatter_(mask_0, param) # no need to divide by lr self.control.accumulate(sgrad_key, dense_sgrad) del_list.append(key) for del_key in del_list: del state_dict[del_key]
```python
def merge_accumulate_client_update(self, list_num_proc, list_state_dict, lr):
total_num_proc = sum(list_num_proc)
# merged_state_dict = dict()
dict_keys = list_state_dict[0].keys()
# Check if all state dicts have the same keys
for state_dict in list_state_dict[1:]:
assert state_dict.keys() == dict_keys
# accumulate extra sgrad and remove from state_dict
if self.use_adaptive and self.is_adj_round():
prefix = "extra."
for state_dict in list_state_dict:
del_list = []
for key, param in state_dict.items():
# Check if the key starts with 'extra.'
if key[:len(prefix)] == prefix:
# Get the corresponding sgrad key
sgrad_key = key[len(prefix):]
# Create a mask of zeroes
mask_0 = self.model.get_mask_by_name(sgrad_key) == 0.
# Create a dense tensor and fill it with values from param based on the mask
dense_sgrad = torch.zeros_like(mask_0, dtype=torch.float)
dense_sgrad.masked_scatter_(mask_0, param)
# Accumulate the dense sgrad without dividing by lr
self.control.accumulate(sgrad_key, dense_sgrad)
# Add the key to the delete list
del_list.append(key)
# Remove the keys from the state_dict
for del_key in del_list:
del state_dict[del_key]
```
这段代码实现了一个`merge_accumulate_client_update`方法,主要功能是合并和累加`list_state_dict`中的状态字典。以下是对代码的注释:
- `total_num_proc`:所有进程数的总和。
- `dict_keys`:状态字典的键列表。
- 检查所有状态字典是否具有相同的键。
- 如果使用自适应且处于调整轮次,则累加额外的`sgrad`并从状态字典中删除。
- `prefix`:额外`sgrad`的前缀。
- 对于每个状态字典,遍历键和参数。
- 如果键以`prefix`开头,则获取相应的`sgrad`键。
- 创建一个零填充的掩码。
- 创建一个稠密张量,并根据掩码从参数中填充值。
- 累加不除以`lr`的稠密`sgrad`。
- 将键添加到删除列表。
- 从状态字典中删除键。
程序无法执行,修改class Processor(): def __init__(self): self._inspect_step = int(cfg.get('PROCESS', 'INSPECT_STEP')) def capture_img(self): global aco aco = aco + 1 self._cam.stream_on() raw_image = self._cam.data_stream[0].get_image() if raw_image is None: print("Getting image failed.\n") return None print("Frame ID: {} Height: {} Width: {} Count: {}\n" .format(raw_image.get_frame_id(), raw_image.get_height(), raw_image.get_width(), aco - 2)) numpy_image = raw_image.get_numpy_array() if numpy_image is None: return None img = Image.fromarray(numpy_image, 'L') if self._issave: picfile = '{}/{}.bmp'.format(self._picpath, self._piccount) self._piccount = self._piccount + 1 img.save(picfile) if self._isshow: w, h = img.size scale = min(1.0 * IMG_RESIZE_W / w, 1.0 * IMG_RESIZE_H / h) self._img = ImageTk.PhotoImage(image=img.resize((int(w * scale), int(h * scale)), Image.ANTIALIAS)) self._show_cb(self._img) self._cam.stream_off() return img def process_img(self, img): return PROC_OK def inspect(self): print("process2") time.sleep(1) def rotate(self): count = 0 aco = 0 self.threadsignal = 0 while self._running: v = self._dgl.qpin(ROT) if len(v) > 0: count = count + sum(v) if count > self._inspect_step: aco += 1 img = self.capture_img() count = count - self._inspect_step if __name__ == '__main__': task2 = multiprocessing.Process(target=self.inspect) task2.start() task1 = multiprocessing.Process(target=self.rotate) task1.start()
在这段代码中,有几个问题可以被改进。首先,类 `Processor` 中的全局变量 `aco` 可能会导致并发问题,在多进程环境下应该避免使用全局变量。其次,代码中出现了 `time.sleep()`,这会阻塞当前进程并造成性能瓶颈,应该尽量避免使用。
以下是可能的改进方案:
1. 将 `aco` 变量改为实例变量,避免多进程之间的并发问题。
2. 使用 `Queue` 或其他进程间通信机制替代全局变量,避免多进程之间的竞争。
3. 将 `time.sleep()` 改为使用定时器或者事件,避免阻塞进程并提高程序的性能。
4. 在多进程环境中,使用 `if __name__ == '__main__':` 来避免创建子进程时出现递归调用的问题。
以下是修改后的代码示例:
```python
import multiprocessing
from queue import Queue
import time
class Processor():
def __init__(self):
self._inspect_step = int(cfg.get('PROCESS', 'INSPECT_STEP'))
self._aco = 0
self._queue = Queue()
def capture_img(self):
self._aco += 1
self._cam.stream_on()
raw_image = self._cam.data_stream[0].get_image()
if raw_image is None:
print("Getting image failed.\n")
return None
print("Frame ID: {} Height: {} Width: {} Count: {}\n".format(raw_image.get_frame_id(), raw_image.get_height(), raw_image.get_width(), self._aco - 2))
numpy_image = raw_image.get_numpy_array()
if numpy_image is None:
return None
img = Image.fromarray(numpy_image, 'L')
if self._issave:
picfile = '{}/{}.bmp'.format(self._picpath, self._piccount)
self._piccount = self._piccount + 1
img.save(picfile)
if self._isshow:
w, h = img.size
scale = min(1.0 * IMG_RESIZE_W / w, 1.0 * IMG_RESIZE_H / h)
self._img = ImageTk.PhotoImage(image=img.resize((int(w * scale), int(h * scale)), Image.ANTIALIAS))
self._show_cb(self._img)
self._cam.stream_off()
return img
def process_img(self, img):
return PROC_OK
def inspect(self):
print("process2")
while self._running:
time.sleep(1)
self._queue.put("task")
def rotate(self):
count = 0
while self._running:
v = self._dgl.qpin(ROT)
if len(v) > 0:
count = count + sum(v)
if count > self._inspect_step:
self._aco += 1
img = self.capture_img()
count = count - self._inspect_step
try:
task = self._queue.get(block=False)
if task == "task":
print("processing task")
except:
pass
if __name__ == '__main__':
p = Processor()
p._running = True
task2 = multiprocessing.Process(target=p.inspect)
task2.start()
task1 = multiprocessing.Process(target=p.rotate)
task1.start()
```
请注意,这里只是对原始代码的一些修改建议,并不能保证这样的修改能够完全解决问题。在实际环境中,还需要根据具体情况进行调整和优化。
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