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https://github.com/qurator-spk/eynollah.git
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Predictor: handle multi-input and/or multi-output cases
This commit is contained in:
Robert Sachunsky
parent
c79b73dcc8
commit
a391ee24e6
1 changed files with 51 additions and 10 deletions
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@ -1,5 +1,5 @@
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from contextlib import ExitStack
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from typing import List, Dict
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from typing import List, Dict, Tuple, Union
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import logging
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import logging.handlers
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import multiprocessing as mp
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@ -8,6 +8,7 @@ import numpy as np
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from .utils.shm import share_ndarray, ndarray_shared
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QSIZE = 200
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ArrayT = Union[np.ndarray, Tuple[np.ndarray]]
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class Predictor(mp.context.SpawnProcess):
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@ -40,10 +41,10 @@ class Predictor(mp.context.SpawnProcess):
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def input_shape(self):
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return self({})
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def predict(self, data: dict, verbose=0):
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def predict(self, data: ArrayT, verbose=0) -> ArrayT:
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return self(data)
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def __call__(self, data: dict):
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def __call__(self, data: Union[ArrayT, Dict]) -> Union[ArrayT, Tuple]:
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# unusable as per python/cpython#79967
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#with self.jobid.get_lock():
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# would work, but not public:
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@ -55,7 +56,15 @@ class Predictor(mp.context.SpawnProcess):
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self.taskq.put((jobid, data))
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#self.logger.debug("sent shape query task '%d' for model '%s'", jobid, self.name)
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return self.result(jobid)
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with share_ndarray(data) as shared_data:
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with ExitStack() as stack:
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if isinstance(data, tuple):
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# multi-input
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shared_data = []
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for data0 in data:
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shared_data.append(stack.enter_context(share_ndarray(data0)))
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shared_data = tuple(shared_data)
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else:
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shared_data = stack.enter_context(share_ndarray(data))
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self.taskq.put((jobid, shared_data))
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#self.logger.debug("sent prediction task '%d' for model '%s': %s", jobid, self.name, shared_data)
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return self.result(jobid)
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@ -67,6 +76,14 @@ class Predictor(mp.context.SpawnProcess):
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result = self.results.pop(jobid)
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if isinstance(result, Exception):
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raise Exception(f"predictor {self.name} failed for {jobid}") from result
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elif isinstance(result, tuple) and isinstance(result[0], dict):
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# multi-output
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result1 = []
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for result0 in result:
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with ndarray_shared(result0) as shared_result0:
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result1.append(np.copy(shared_result0))
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result = result1
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self.closable.append(jobid)
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elif isinstance(result, dict):
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with ndarray_shared(result) as shared_result:
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result = np.copy(shared_result)
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@ -111,6 +128,7 @@ class Predictor(mp.context.SpawnProcess):
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"binarization": 4,
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"enhancement": 4,
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"reading_order": 4,
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"ocr": 8,
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# medium size (672x672x3)...
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"textline": 2,
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# large models...
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@ -126,8 +144,13 @@ class Predictor(mp.context.SpawnProcess):
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self.resultq.put((jobid, result))
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#self.logger.debug("sent result for '%d': %s", jobid, result)
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else:
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if isinstance(shared_data, tuple):
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multi_input = True
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batch_size = shared_data[0]['shape'][0]
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else:
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multi_input = False
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batch_size = shared_data['shape'][0]
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tasks = [(jobid, shared_data)]
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batch_size = shared_data['shape'][0]
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while (not self.taskq.empty() and
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# climb to target batch size
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batch_size * len(tasks) < REBATCH_SIZE):
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@ -136,7 +159,7 @@ class Predictor(mp.context.SpawnProcess):
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# add to our batch
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tasks.append((jobid0, shared_data0))
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else:
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# immediately anser
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# immediately answer
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self.resultq.put((jobid0, self.model.input_shape))
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if len(tasks) > 1:
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self.logger.debug("rebatching %d '%s' tasks of batch size %d",
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@ -147,12 +170,26 @@ class Predictor(mp.context.SpawnProcess):
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for jobid, shared_data in tasks:
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#self.logger.debug("predicting '%d' with model '%s': %s", jobid, self.name, shared_data)
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jobs.append(jobid)
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data.append(stack.enter_context(ndarray_shared(shared_data)))
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data = np.concatenate(data)
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if multi_input:
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data.append(tuple(stack.enter_context(ndarray_shared(shared_data0))
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for shared_data0 in shared_data))
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else:
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data.append(stack.enter_context(ndarray_shared(shared_data)))
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if multi_input:
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data = tuple(np.concatenate(data0)
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for data0 in zip(*data))
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else:
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data = np.concatenate(data)
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#result = self.model.predict(data, verbose=0)
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# faster, less VRAM
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result = self.model.predict_on_batch(data)
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results = np.split(result, len(jobs))
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if isinstance(result, tuple):
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multi_output = True
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results = zip(*(np.split(result0, len(jobs))
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for result0 in result))
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else:
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multi_output = False
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results = np.split(result, len(jobs))
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#self.logger.debug("sharing result array for '%d'", jobid)
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with ExitStack() as stack:
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for jobid, result in zip(jobs, results):
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@ -160,7 +197,11 @@ class Predictor(mp.context.SpawnProcess):
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# but don't want to wait either, so track closing
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# context per job, and wait for closable signal
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# from client
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result = stack.enter_context(share_ndarray(result))
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if multi_output:
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result = tuple(stack.enter_context(share_ndarray(result0))
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for result0 in result)
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else:
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result = stack.enter_context(share_ndarray(result))
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closing[jobid] = stack.pop_all()
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self.resultq.put((jobid, result))
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#self.logger.debug("sent result for '%d': %s", jobid, result)
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