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# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
import unittest
import torch
import detectron2.model_zoo as model_zoo
from detectron2.config import get_cfg
from detectron2.modeling import build_model
from detectron2.utils.analysis import flop_count_operators, parameter_count
def get_model_zoo(config_path):
"""
Like model_zoo.get, but do not load any weights (even pretrained)
"""
cfg_file = model_zoo.get_config_file(config_path)
cfg = get_cfg()
cfg.merge_from_file(cfg_file)
if not torch.cuda.is_available():
cfg.MODEL.DEVICE = "cpu"
return build_model(cfg)
class RetinaNetTest(unittest.TestCase):
def setUp(self):
self.model = get_model_zoo("COCO-Detection/retinanet_R_50_FPN_1x.yaml")
def test_flop(self):
# RetinaNet supports flop-counting with random inputs
inputs = [{"image": torch.rand(3, 800, 800)}]
res = flop_count_operators(self.model, inputs)
self.assertTrue(int(res["conv"]), 146) # 146B flops
def test_param_count(self):
res = parameter_count(self.model)
self.assertTrue(res[""], 37915572)
self.assertTrue(res["backbone"], 31452352)
class FasterRCNNTest(unittest.TestCase):
def setUp(self):
self.model = get_model_zoo("COCO-Detection/faster_rcnn_R_50_FPN_1x.yaml")
def test_flop(self):
# Faster R-CNN supports flop-counting with random inputs
inputs = [{"image": torch.rand(3, 800, 800)}]
res = flop_count_operators(self.model, inputs)
# This only checks flops for backbone & proposal generator
# Flops for box head is not conv, and depends on #proposals, which is
# almost 0 for random inputs.
self.assertTrue(int(res["conv"]), 117)
def test_param_count(self):
res = parameter_count(self.model)
self.assertTrue(res[""], 41699936)
self.assertTrue(res["backbone"], 26799296)
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