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Original file line number | Diff line number | Diff line change |
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# An example of how to run all the configuration files in the current directory, in parallel. | ||
# Usage: python3 run_all.py | ||
|
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import logging | ||
import random | ||
import threading | ||
import time | ||
import queue | ||
import glob | ||
import os | ||
import json | ||
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logging.basicConfig(level=logging.DEBUG, | ||
format='(%(threadName)-10s) %(message)s', | ||
) | ||
class Trainer(object): | ||
def __init__(self, card=0): | ||
self.lock = threading.Lock() | ||
self.card = card | ||
self.steps = 100000 | ||
#number_samples = 60*25 # one minute video | ||
number_samples = 10*25 # ten second video | ||
self.sample_every = self.steps//number_samples | ||
self.format = 'jpg' | ||
self.dataset = "/ml/datasets/faces/128x128_sketched/all" | ||
self.batch_size = 8 | ||
def run(self, json_file): | ||
obj = json.loads(open(json_file+".json", 'r').read()) | ||
if "width" in obj["runtime"]: | ||
size = " --size "+str(obj["runtime"]["width"])+"x"+ str(obj["runtime"]["height"])+"x"+str(obj["runtime"]["channels"]) | ||
else: | ||
print("NULL SIZE", obj) | ||
size = "" | ||
command = obj["runtime"]["train"] | ||
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logging.debug('Run %s on card %d', json_file, self.card) | ||
if os.path.exists("samples/"+json_file): | ||
logging.info('Skipping '+json_file+', samples exist') | ||
else: | ||
command = ("CUDA_VISIBLE_DEVICES=%d " % self.card) + command | ||
command = command.replace("[dataset]", self.dataset) | ||
command = command.replace("[hypergan]", "/ml/dev/hypergan/") | ||
command += " --sample_every %d -b %d --format %s -c %s --save_every %d --steps %d --save_samples --resize" % (self.sample_every, self.batch_size, self.format, json_file, self.steps-1, self.steps) | ||
command += size | ||
logging.debug(command) | ||
os.system(command) | ||
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def worker(c): | ||
thread = threading.currentThread() | ||
if queue.empty(): | ||
return | ||
json_file = queue.get() | ||
config = json_file.replace(".json","") | ||
c.run(config) | ||
worker(c) | ||
queue.task_done() | ||
logging.debug('Done') | ||
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gpus = [] | ||
gpu_count = 2 | ||
gpu_offset = 0 | ||
per_gpu = 2 | ||
queue = queue.Queue() | ||
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files = glob.glob("*.json") | ||
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for f in files: | ||
queue.put(f) | ||
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for j in range(per_gpu): | ||
for i in range(gpu_count): | ||
gpu = Trainer(i+gpu_offset) | ||
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t=threading.Thread(target=worker, args=(gpu,)) | ||
gpus.append(t) | ||
t.start() | ||
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logging.debug('Waiting for worker threads') | ||
main_thread = threading.currentThread() | ||
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queue.join() | ||
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for t in threading.enumerate(): | ||
if t is not main_thread: | ||
t.join() | ||
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||
logging.debug("Goodbye") |
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