def get_default_args():
parser = classifier.init_classifier_compression_arg_parser()
parser.add_argument(
'--qe-no-quant-layers',
'--qenql',
type=str,
nargs='+',
metavar='LAYER_NAME',
default=[],
help='List of layer names for which to skip quantization.')
parser.add_argument(
'--qe-calib-portion',
type=float,
default=1.0,
help=
'The portion of the dataset to use for calibration stats collection.')
parser.add_argument(
'--qe-calib-batchsize',
type=int,
default=256,
help=
'The portion of the dataset to use for calibration stats collection.')
parser.add_argument('--base-score', type=float, default=None)
parser.add_argument('--quantize-inputs',
type=str,
nargs='+',
metavar='LAYER_NAME#INPUT_IDX',
default=[],
help='The inputs of layers to quantize')
parser.add_argument('--resume-search-from',
type=str,
help='Search checkpoint file to resume.',
default=None)
args = parser.parse_args()
return args
def get_default_args():
parser = classifier.init_classifier_compression_arg_parser()
parser.add_argument(
'--grid_resolution',
'-gr',
type=int,
help='Number of intervals in the grid, one coordinate.',
default=11)
parser.add_argument('--min_ratio',
'-minr',
type=float,
help='min ration of the scale',
default=0.7)
parser.add_argument('--max_ratio',
'-maxr',
type=float,
help='max ration of the scale',
default=1.3)
parser.add_argument('--bcorr_w',
'-bcw',
action='store_true',
help='Bias correction for weights',
default=False)
parser.add_argument('--experiment',
'-exp',
help='Name of the experiment',
default='default')
parser.add_argument('--w_index', '-w_idx', nargs='+', type=int)
args = parser.parse_args()
return args
def main():
import amc_args
# Parse arguments
args = classifier.init_classifier_compression_arg_parser()
args = amc_args.add_automl_args(args).parse_args()
app = AutoCompressionSampleApp(args, script_dir=os.path.dirname(__file__))
return app.train_auto_compressor()
def get_default_args():
parser = classifier.init_classifier_compression_arg_parser()
parser.add_argument('--bcorr_w', '-bcw', action='store_true', help='Bias correction for weights', default=False)
parser.add_argument('--experiment', '-exp', help='Name of the experiment', default='default')
parser.add_argument('--w_index', '-w_idx', nargs='+', type=int)
args = parser.parse_args()
return args
def get_default_args():
parser = classifier.init_classifier_compression_arg_parser()
parser.add_argument('--opt-maxiter',
dest='maxiter',
default=None,
type=int,
help='Max iteration for minimization method.')
parser.add_argument('--opt-maxfev',
dest='maxfev',
default=None,
type=int,
help='Max iteration for minimization method.')
parser.add_argument(
'--opt-method',
dest='method',
default='Powell',
help='Minimization method used by scip.optimize.minimize.')
parser.add_argument(
'--opt-bh',
dest='basinhopping',
action='store_true',
default=False,
help='Use scipy.optimize.basinhopping stochastic global minimum search.'
)
parser.add_argument(
'--opt-bh-niter',
dest='niter',
default=100,
help='Number of iterations for the basinhopping algorithm.')
parser.add_argument('--opt-init-mode',
dest='init_mode',
default='NONE',
choices=list(_INIT_MODES),
help='The mode of quant initalization. Choices: ' +
'|'.join(list(_INIT_MODES)))
parser.add_argument(
'--opt-init-method',
dest='init_mode_method',
help=
'If --opt-init-mode was specified as L1/L2/L3, this specifies the method of '
'minimization.')
parser.add_argument('--opt-val-size',
type=float,
default=1,
help='Use portion of the test size.')
parser.add_argument(
'--opt-eval-memoize-dataloader',
dest='memoize_dataloader',
action='store_true',
default=False,
help='Stores the input batch in memory to optimize performance.')
parser.add_argument('--base-score', type=float, default=None)
parser.add_argument(
'--opt-search-clipping',
dest='search_clipping',
action='store_true',
help='Search on clipping values instead of scale/zero_point.')
args = parser.parse_args()
return args
def main():
# Parse arguments
args = parser.add_cmdline_args(classifier.init_classifier_compression_arg_parser(True)).parse_args()
app = ClassifierCompressorSampleApp(args, script_dir=os.path.dirname(__file__))
if app.handle_subapps():
return
init_knowledge_distillation(app.args, app.model, app.compression_scheduler)
app.run_training_loop()
# Finally run results on the test set
return app.test()
def main(individual):
# Parse arguments
#print(individual)
args = parser.add_cmdline_args(
classifier.init_classifier_compression_arg_parser(True)).parse_args()
args.compress = 'FB_gene1.yaml'
idx = 0
num = 0 #initial 5%
yaml_list = []
for i in range(int(len(individual) / 7)): #各ビット数/7
num = 0
for j in range(7): #長さ
if (individual[idx] == 1):
num += pow(2, j)
idx += 1
if num >= 100:
num = 90
elif i == 59 and num <= 30:
num = 31
elif num <= 5:
num = 6
yaml_list.append(num / 100)
print("yaml_list")
print(yaml_list)
write_yaml(yaml_list)
app = ClassifierCompressorSampleApp(args,
script_dir=os.path.dirname(__file__))
if app.handle_subapps():
return
init_knowledge_distillation(app.args, app.model, app.compression_scheduler)
app.run_training_loop()
# Finally run results on the test set
# return top1, top5, losssesが来る
loaded_array = np.load(
'/home/oza/pre-experiment/speeding/distiller/distiller/apputils/simple_gene.npz'
)
accuracy = loaded_array['array_1']
sparce = loaded_array['array_2']
print("accuracy: " + str(accuracy))
print("sparce: " + str(sparce))
accuracy /= 100
sparce /= 100
#return app.test()
score = accuracy * sparce
print("score: " + str(score))
global max
if (score > max):
max = score
print("max score: " + str(score))
print("max individual: " + str(yaml_list))
return accuracy * sparce
def main():
# Parse arguments
args = parser.add_cmdline_args(
classifier.init_classifier_compression_arg_parser(True)).parse_args()
performance_tracker = apputils.SparsityAccuracyTracker(
args.num_best_scores)
#args.compress = 'OZA'
#print(args.compress)
#quit()
app = ClassifierCompressorSampleApp(args,
script_dir=os.path.dirname(__file__))
if app.handle_subapps():
return
init_knowledge_distillation(app.args, app.model, app.compression_scheduler)
app.run_training_loop()
# Finally run results on the test set
# return top1, top5, losssesが来る
return app.test()
df_amc_results = pd.read_csv(os.path.join(ft_dir, "amc.csv"))
top1_sorted_df_amc_results = df_amc_results.sort_values(
by=['top1'], ascending=False)
top1_sorted_df_amc_results = top1_sorted_df_amc_results[0:best_nets]
checkpoints = [
os.path.join(ft_dir, ckpt + "_checkpoint.pth.tar")
for ckpt in top1_sorted_df_amc_results.ckpt_name
]
return checkpoints
try:
set_start_method('forkserver')
except RuntimeError:
pass
# Parse arguments
argparser = classifier.init_classifier_compression_arg_parser()
add_parallel_args(argparser)
app_args = argparser.parse_args()
print("Starting fine-tuning")
stats_file = FTStatsLogger(
os.path.join(app_args.scan_dir, app_args.output_csv))
ft_dirs = get_immediate_subdirs(app_args.scan_dir)
for ft_dir in ft_dirs:
checkpoints = None
if app_args.top_performing_chkpts:
checkpoints = get_best_checkpoints(ft_dir)
finetune_directory(ft_dir,
stats_file,
app_args,
checkpoints=checkpoints)
args.device = device
if args.resumed_checkpoint_path:
args.load_model_path = args.resumed_checkpoint_path
if args.load_model_path:
msglogger.info("Loading checkpoint from %s" % args.load_model_path)
model = apputils.load_lean_checkpoint(model, args.load_model_path,
model_device=args.device)
quantizer = distiller.quantization.PostTrainLinearQuantizer.from_args(model, args)
dummy_input = torch.rand(*model.input_shape, device=args.device)
model, qp_dict = lapq.ptq_coordinate_search(quantizer, dummy_input, eval_fn, test_fn=test_fn,
**lapq.cmdline_args_to_dict(args))
results = test_fn(quantizer.model)
msglogger.info("Arch: %s \tTest: \t top1 = %.3f \t top5 = %.3f \t loss = %.3f" %
(args.arch, results['top-1'], results['top-5'], results['loss']))
distiller.yaml_ordered_save('%s.quant_params_dict.yaml' % args.arch, qp_dict)
distiller.apputils.save_checkpoint(0, args.arch, model,
extras={'top1': results['top-1'], 'qp_dict': qp_dict}, name=args.name,
dir=msglogger.logdir)
if __name__ == "__main__":
parser = classifier.init_classifier_compression_arg_parser(include_ptq_lapq_args=True)
args = parser.parse_args()
args.epochs = float('inf') # hack for args parsing so there's no error in epochs
cc = classifier.ClassifierCompressor(args, script_dir=os.path.dirname(__file__))
image_classifier_ptq_lapq(cc.model, cc.criterion, [cc.pylogger, cc.tflogger], cc.args)
