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# Copyright (c) Facebook, Inc. and its affiliates.
import unittest
import torch
from torch import nn
from detectron2.utils.analysis import find_unused_parameters, flop_count_operators, parameter_count
from detectron2.utils.testing import get_model_no_weights
class RetinaNetTest(unittest.TestCase):
def setUp(self):
self.model = get_model_no_weights("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), "test_unused": "abcd"}]
res = flop_count_operators(self.model, inputs)
self.assertEqual(int(res["conv"]), 146) # 146B flops
def test_param_count(self):
res = parameter_count(self.model)
self.assertEqual(res[""], 37915572)
self.assertEqual(res["backbone"], 31452352)
class FasterRCNNTest(unittest.TestCase):
def setUp(self):
self.model = get_model_no_weights("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.assertEqual(int(res["conv"]), 117)
def test_flop_with_output_shape(self):
inputs = [{"image": torch.rand(3, 800, 800), "height": 700, "width": 700}]
res = flop_count_operators(self.model, inputs)
self.assertEqual(int(res["conv"]), 117)
def test_param_count(self):
res = parameter_count(self.model)
self.assertEqual(res[""], 41699936)
self.assertEqual(res["backbone"], 26799296)
class MaskRCNNTest(unittest.TestCase):
def setUp(self):
self.model = get_model_no_weights("COCO-InstanceSegmentation/mask_rcnn_R_50_FPN_1x.yaml")
def test_flop(self):
inputs1 = [{"image": torch.rand(3, 800, 800)}]
inputs2 = [{"image": torch.rand(3, 800, 800), "height": 700, "width": 700}]
for inputs in [inputs1, inputs2]:
res = flop_count_operators(self.model, inputs)
# The mask head could have extra conv flops, so total >= 117
self.assertGreaterEqual(int(res["conv"]), 117)
class UnusedParamTest(unittest.TestCase):
def test_unused(self):
class TestMod(nn.Module):
def __init__(self):
super().__init__()
self.fc1 = nn.Linear(10, 10)
self.t = nn.Linear(10, 10)
def forward(self, x):
return self.fc1(x).mean()
m = TestMod()
ret = find_unused_parameters(m, torch.randn(10, 10))
self.assertEqual(set(ret), {"t.weight", "t.bias"})
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