generator

问题 I am reading in a csv file with pandas, and give the column names stored in colname colnames=['file', 'label'] # Read data from file data = pd.read_csv('./Hand_Annotations_2.csv',names=colnames, header=None) # Preview the first 5 lines of the loaded data data.head() Then, I use ImageDataGenerator() and flow_fromdataframe() to get batches of data train_generator=datagen.flow_from_dataframe(dataframe=data, directory=None, x_col=colnames[0], y_col=colnames[1], class_indices=IDmap, class_mode=

问题 First look at the following code: pool = multiprocessing.Pool(processes=N) batch = [] for item in generator(): batch.append(item) if len(batch) == 10: pool.apply_async(my_fun, args=(batch,)) batch = [] # leftovers pool.apply_async(my_fun, args=(batch,)) Essentially I'm retrieving data from a generator, collecting in into a list and then spawning a process that consumes the batch of data. This may look fine but when the consumers (aka the pool processes) are slower than the producer (aka the

问题 I want to write a Python generator function that never actually yields anything. Basically it's a "do-nothing" drop-in that can be used by other code which expects to call a generator (but doesn't always need results from it). So far I have this: def empty_generator(): # ... do some stuff, but don't yield anything if False: yield Now, this works OK, but I'm wondering if there's a more expressive way to say the same thing, that is, declare a function to be a generator even if it never yields

问题 I want to write a Python generator function that never actually yields anything. Basically it's a "do-nothing" drop-in that can be used by other code which expects to call a generator (but doesn't always need results from it). So far I have this: def empty_generator(): # ... do some stuff, but don't yield anything if False: yield Now, this works OK, but I'm wondering if there's a more expressive way to say the same thing, that is, declare a function to be a generator even if it never yields

问题 I have a trained keras model, and I am trying to run predictions with CPU only. I want this to be as quick as possible, so I thought I would use predict_generator with multiple workers. All of the data for my prediction tensor are loaded into memory beforehand. Just for reference, array is a list of tensors, with the first tensor having shape [nsamples, x, y, nchannels]. I made a thread-safe generator following the instructions here (I followed this when using fit_generator as well). class

问题 I have a trained keras model, and I am trying to run predictions with CPU only. I want this to be as quick as possible, so I thought I would use predict_generator with multiple workers. All of the data for my prediction tensor are loaded into memory beforehand. Just for reference, array is a list of tensors, with the first tensor having shape [nsamples, x, y, nchannels]. I made a thread-safe generator following the instructions here (I followed this when using fit_generator as well). class

问题 I have the following code, in which I try to convert a string representation of ranges to a list of numbers. For example, if the input is '0-0,3-5,7-10' the expected output is [0,3,4,5,7,8,9,10] . However, I got an error at: for l,h in r.split('-') which it says not enough values to unpack. My reasoning is that, r should have the form 'x-y' , so if I split it by '-' , I should get two values back. What's wrong with that? def parse_ranges(ranges): """Return a list of numbers corresponding to

问题 I have the following code, in which I try to convert a string representation of ranges to a list of numbers. For example, if the input is '0-0,3-5,7-10' the expected output is [0,3,4,5,7,8,9,10] . However, I got an error at: for l,h in r.split('-') which it says not enough values to unpack. My reasoning is that, r should have the form 'x-y' , so if I split it by '-' , I should get two values back. What's wrong with that? def parse_ranges(ranges): """Return a list of numbers corresponding to

问题 I read here the following example: >>> def double_inputs(): ... while True: # Line 1 ... x = yield # Line 2 ... yield x * 2 # Line 3 ... >>> gen = double_inputs() >>> next(gen) # Run up to the first yield >>> gen.send(10) # goes into 'x' variable If I understand the above correctly, it seems to imply that Python actually waits until next(gen) to "run up to" to Line 2 in the body of the function. Put another way, the interpreter would not start executing the body of the function until we call

问题 I have been trying to understand asynchronous programming, particularly in Python. I understand that asyncio is built off of an event loop which schedules the execution of coroutines, but I have read about several different ways to define coroutines, and I am confused how they all relate to each other. I read this article for more background information on the topic. Although it covers each of the four types of coroutines I have mentioned, it does not entirely describe how they differ.