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# Copyright (c) Facebook, Inc. and its affiliates.
import json
import os
import random
import tempfile
import unittest
import torch
from torch import Tensor, nn
from detectron2 import model_zoo
from detectron2.config import get_cfg
from detectron2.config.instantiate import dump_dataclass, instantiate
from detectron2.export import dump_torchscript_IR, scripting_with_instances
from detectron2.export.flatten import TracingAdapter, flatten_to_tuple
from detectron2.export.torchscript_patch import patch_builtin_len
from detectron2.layers import ShapeSpec
from detectron2.modeling import build_backbone
from detectron2.modeling.postprocessing import detector_postprocess
from detectron2.modeling.roi_heads import KRCNNConvDeconvUpsampleHead
from detectron2.structures import Boxes, Instances
from detectron2.utils.env import TORCH_VERSION
from detectron2.utils.testing import (
assert_instances_allclose,
convert_scripted_instances,
get_sample_coco_image,
random_boxes,
)
"""
https://detectron2.readthedocs.io/tutorials/deployment.html
contains some explanations of this file.
"""
SLOW_PUBLIC_CPU_TEST = unittest.skipIf(
os.environ.get("CI") and not torch.cuda.is_available(),
"The test is too slow on CPUs and will be executed on CircleCI's GPU jobs.",
)
class TestScripting(unittest.TestCase):
def testMaskRCNNFPN(self):
self._test_rcnn_model("COCO-InstanceSegmentation/mask_rcnn_R_50_FPN_3x.yaml")
@SLOW_PUBLIC_CPU_TEST
def testMaskRCNNC4(self):
self._test_rcnn_model("COCO-InstanceSegmentation/mask_rcnn_R_50_C4_3x.yaml")
def testRetinaNet(self):
self._test_retinanet_model("COCO-Detection/retinanet_R_50_FPN_3x.yaml")
def _test_rcnn_model(self, config_path):
model = model_zoo.get(config_path, trained=True)
model.eval()
fields = {
"proposal_boxes": Boxes,
"objectness_logits": Tensor,
"pred_boxes": Boxes,
"scores": Tensor,
"pred_classes": Tensor,
"pred_masks": Tensor,
}
script_model = scripting_with_instances(model, fields)
# Test that batch inference with different shapes are supported
image = get_sample_coco_image()
small_image = nn.functional.interpolate(image, scale_factor=0.5)
inputs = [{"image": image}, {"image": small_image}]
with torch.no_grad():
instance = model.inference(inputs, do_postprocess=False)[0]
scripted_instance = script_model.inference(inputs, do_postprocess=False)[0]
assert_instances_allclose(instance, scripted_instance)
def _test_retinanet_model(self, config_path):
model = model_zoo.get(config_path, trained=True)
model.eval()
fields = {
"pred_boxes": Boxes,
"scores": Tensor,
"pred_classes": Tensor,
}
script_model = scripting_with_instances(model, fields)
img = get_sample_coco_image()
inputs = [{"image": img}] * 2
with torch.no_grad():
instance = model(inputs)[0]["instances"]
scripted_instance = convert_scripted_instances(script_model(inputs)[0])
scripted_instance = detector_postprocess(scripted_instance, img.shape[1], img.shape[2])
assert_instances_allclose(instance, scripted_instance)
# Note that the model currently cannot be saved and loaded into a new process:
# https://github.com/pytorch/pytorch/issues/46944
# TODO: this test requires manifold access, see: T88318502
class TestTracing(unittest.TestCase):
def testMaskRCNNFPN(self):
def inference_func(model, image):
inputs = [{"image": image}]
return model.inference(inputs, do_postprocess=False)[0]
self._test_model("COCO-InstanceSegmentation/mask_rcnn_R_50_FPN_3x.yaml", inference_func)
def testMaskRCNNFPN_with_postproc(self):
def inference_func(model, image):
inputs = [{"image": image, "height": image.shape[1], "width": image.shape[2]}]
return model.inference(inputs, do_postprocess=True)[0]["instances"]
self._test_model("COCO-InstanceSegmentation/mask_rcnn_R_50_FPN_3x.yaml", inference_func)
@SLOW_PUBLIC_CPU_TEST
def testMaskRCNNC4(self):
def inference_func(model, image):
inputs = [{"image": image}]
return model.inference(inputs, do_postprocess=False)[0]
self._test_model("COCO-InstanceSegmentation/mask_rcnn_R_50_C4_3x.yaml", inference_func)
@SLOW_PUBLIC_CPU_TEST
def testCascadeRCNN(self):
def inference_func(model, image):
inputs = [{"image": image}]
return model.inference(inputs, do_postprocess=False)[0]
self._test_model("Misc/cascade_mask_rcnn_R_50_FPN_3x.yaml", inference_func)
# bug fixed by https://github.com/pytorch/pytorch/pull/67734
@unittest.skipIf(TORCH_VERSION == (1, 10) and os.environ.get("CI"), "1.10 has bugs.")
def testRetinaNet(self):
def inference_func(model, image):
return model.forward([{"image": image}])[0]["instances"]
self._test_model("COCO-Detection/retinanet_R_50_FPN_3x.yaml", inference_func)
def _test_model(self, config_path, inference_func, batch=1):
model = model_zoo.get(config_path, trained=True)
image = get_sample_coco_image()
inputs = tuple(image.clone() for _ in range(batch))
wrapper = TracingAdapter(model, inputs, inference_func)
wrapper.eval()
with torch.no_grad():
# trace with smaller images, and the trace must still work
trace_inputs = tuple(
nn.functional.interpolate(image, scale_factor=random.uniform(0.5, 0.7))
for _ in range(batch)
)
traced_model = torch.jit.trace(wrapper, trace_inputs)
outputs = inference_func(model, *inputs)
traced_outputs = wrapper.outputs_schema(traced_model(*inputs))
if batch > 1:
for output, traced_output in zip(outputs, traced_outputs):
assert_instances_allclose(output, traced_output, size_as_tensor=True)
else:
assert_instances_allclose(outputs, traced_outputs, size_as_tensor=True)
@SLOW_PUBLIC_CPU_TEST
def testMaskRCNNFPN_batched(self):
def inference_func(model, image1, image2):
inputs = [{"image": image1}, {"image": image2}]
return model.inference(inputs, do_postprocess=False)
self._test_model(
"COCO-InstanceSegmentation/mask_rcnn_R_50_FPN_3x.yaml", inference_func, batch=2
)
def testKeypointHead(self):
class M(nn.Module):
def __init__(self):
super().__init__()
self.model = KRCNNConvDeconvUpsampleHead(
ShapeSpec(channels=4, height=14, width=14), num_keypoints=17, conv_dims=(4,)
)
def forward(self, x, predbox1, predbox2):
inst = [
Instances((100, 100), pred_boxes=Boxes(predbox1)),
Instances((100, 100), pred_boxes=Boxes(predbox2)),
]
ret = self.model(x, inst)
return tuple(x.pred_keypoints for x in ret)
model = M()
model.eval()
def gen_input(num1, num2):
feat = torch.randn((num1 + num2, 4, 14, 14))
box1 = random_boxes(num1)
box2 = random_boxes(num2)
return feat, box1, box2
with torch.no_grad(), patch_builtin_len():
trace = torch.jit.trace(model, gen_input(15, 15), check_trace=False)
inputs = gen_input(12, 10)
trace_outputs = trace(*inputs)
true_outputs = model(*inputs)
for trace_output, true_output in zip(trace_outputs, true_outputs):
self.assertTrue(torch.allclose(trace_output, true_output))
class TestTorchscriptUtils(unittest.TestCase):
# TODO: add test to dump scripting
def test_dump_IR_tracing(self):
cfg = get_cfg()
cfg.MODEL.RESNETS.DEPTH = 18
cfg.MODEL.RESNETS.RES2_OUT_CHANNELS = 64
class Mod(nn.Module):
def forward(self, x):
return tuple(self.m(x).values())
model = Mod()
model.m = build_backbone(cfg)
model.eval()
with torch.no_grad():
ts_model = torch.jit.trace(model, (torch.rand(2, 3, 224, 224),))
with tempfile.TemporaryDirectory(prefix="detectron2_test") as d:
dump_torchscript_IR(ts_model, d)
# check that the files are created
for name in ["model_ts_code", "model_ts_IR", "model_ts_IR_inlined", "model"]:
fname = os.path.join(d, name + ".txt")
self.assertTrue(os.stat(fname).st_size > 0, fname)
def test_dump_IR_function(self):
@torch.jit.script
def gunc(x, y):
return x + y
def func(x, y):
return x + y + gunc(x, y)
ts_model = torch.jit.trace(func, (torch.rand(3), torch.rand(3)))
with tempfile.TemporaryDirectory(prefix="detectron2_test") as d:
dump_torchscript_IR(ts_model, d)
for name in ["model_ts_code", "model_ts_IR", "model_ts_IR_inlined"]:
fname = os.path.join(d, name + ".txt")
self.assertTrue(os.stat(fname).st_size > 0, fname)
def test_flatten_basic(self):
obj = [3, ([5, 6], {"name": [7, 9], "name2": 3})]
res, schema = flatten_to_tuple(obj)
self.assertEqual(res, (3, 5, 6, 7, 9, 3))
new_obj = schema(res)
self.assertEqual(new_obj, obj)
_, new_schema = flatten_to_tuple(new_obj)
self.assertEqual(schema, new_schema) # test __eq__
self._check_schema(schema)
def _check_schema(self, schema):
dumped_schema = dump_dataclass(schema)
# Check that the schema is json-serializable
# Although in reality you might want to use yaml because it often has many levels
json.dumps(dumped_schema)
# Check that the schema can be deserialized
new_schema = instantiate(dumped_schema)
self.assertEqual(schema, new_schema)
def test_flatten_instances_boxes(self):
inst = Instances(
torch.tensor([5, 8]), pred_masks=torch.tensor([3]), pred_boxes=Boxes(torch.ones((1, 4)))
)
obj = [3, ([5, 6], inst)]
res, schema = flatten_to_tuple(obj)
self.assertEqual(res[:3], (3, 5, 6))
for r, expected in zip(res[3:], (inst.pred_boxes.tensor, inst.pred_masks, inst.image_size)):
self.assertIs(r, expected)
new_obj = schema(res)
assert_instances_allclose(new_obj[1][1], inst, rtol=0.0, size_as_tensor=True)
self._check_schema(schema)
def test_allow_non_tensor(self):
data = (torch.tensor([5, 8]), 3) # contains non-tensor
class M(nn.Module):
def forward(self, input, number):
return input
model = M()
with self.assertRaisesRegex(ValueError, "must only contain tensors"):
adap = TracingAdapter(model, data, allow_non_tensor=False)
adap = TracingAdapter(model, data, allow_non_tensor=True)
_ = adap(*adap.flattened_inputs)
newdata = (data[0].clone(),)
with self.assertRaisesRegex(ValueError, "cannot generalize"):
_ = adap(*newdata)
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