def test_d4_speed():
df = defaultdict(list)
n = 100
model = resnet34_unet32().cuda().eval()
x = torch.rand((4, 3, 224, 224)).float().cuda()
y1 = tta.d4_image2mask(model, x)
y2 = tta.d4_image_deaugment(model(tta.d4_image_augment(x)))
np.testing.assert_allclose(to_numpy(y1), to_numpy(y2), atol=1e-6, rtol=1e-6)
for deterministic in [False, True]:
for benchmark in [False, True]:
for dtype in [torch.float16, torch.float32]:
torch.cuda.empty_cache()
torch.backends.cuda.deterministic = deterministic
torch.backends.cuda.benchmark = benchmark
model = resnet34_unet32().to(dtype).cuda().eval()
speed_v1 = 0
speed_v2 = 0
for i in range(n):
x = torch.rand((4, 3, 224, 224)).to(dtype).cuda(non_blocking=False)
start = cv2.getTickCount()
y = tta.d4_image2mask(model, x)
v = y.sum().item()
finish = cv2.getTickCount()
speed_v1 += finish - start
np.testing.assert_allclose(v, v, atol=1e-6, rtol=1e-6)
for i in range(n):
x = torch.rand((4, 3, 224, 224)).to(dtype).cuda(non_blocking=False)
start = cv2.getTickCount()
x_a = tta.d4_image_augment(x)
x_a = model(x_a)
y = tta.d4_image_deaugment(x_a)
v = y.sum().item()
finish = cv2.getTickCount()
speed_v2 += finish - start
np.testing.assert_allclose(v, v, atol=1e-6, rtol=1e-6)
df["mode"].append("fp16" if dtype == torch.float16 else "fp32")
df["deterministic"].append(deterministic)
df["benchmark"].append(benchmark)
df["d4_image2mask (ms)"].append(1000.0 * speed_v1 / (cv2.getTickFrequency() * n))
df["d4_augment (ms)"].append(1000.0 * speed_v2 / (cv2.getTickFrequency() * n))
import pandas as pd
df = pd.DataFrame.from_dict(df)
pd.set_option("display.max_columns", None)
pd.set_option("display.max_rows", None)
print(df)
df.to_csv("tta_eval.csv", index=False)
def test_d4_image2mask():
x = torch.rand((4, 3, 224, 224))
model = NoOp()
output = tta.d4_image2mask(model, x)
np.testing.assert_allclose(to_numpy(output),
to_numpy(x),
atol=1e-6,
rtol=1e-6)
def forward(self, x):
return d4_image2mask(self.model, x)