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# Owner(s): ["module: onnx"]
import os
import unittest
from collections import OrderedDict
from typing import List, Mapping, Tuple
import onnx_test_common
import parameterized
import PIL
import test_models
import torch
import torchvision
from pytorch_test_common import skipIfUnsupportedMinOpsetVersion, skipScriptTest
from torch import nn
from torch.testing._internal import common_utils
from torchvision import ops
from torchvision.models.detection import (
faster_rcnn,
image_list,
keypoint_rcnn,
mask_rcnn,
roi_heads,
rpn,
transform,
)
def exportTest(
self,
model,
inputs,
rtol=1e-2,
atol=1e-7,
opset_versions=None,
acceptable_error_percentage=None,
):
opset_versions = opset_versions if opset_versions else [7, 8, 9, 10, 11, 12, 13, 14]
for opset_version in opset_versions:
self.opset_version = opset_version
self.onnx_shape_inference = True
onnx_test_common.run_model_test(
self,
model,
input_args=inputs,
rtol=rtol,
atol=atol,
acceptable_error_percentage=acceptable_error_percentage,
)
if self.is_script_test_enabled and opset_version > 11:
script_model = torch.jit.script(model)
onnx_test_common.run_model_test(
self,
script_model,
input_args=inputs,
rtol=rtol,
atol=atol,
acceptable_error_percentage=acceptable_error_percentage,
)
TestModels = type(
"TestModels",
(common_utils.TestCase,),
dict(
test_models.TestModels.__dict__,
is_script_test_enabled=False,
is_script=False,
exportTest=exportTest,
),
)
# model tests for scripting with new JIT APIs and shape inference
TestModels_new_jit_API = type(
"TestModels_new_jit_API",
(common_utils.TestCase,),
dict(
TestModels.__dict__,
exportTest=exportTest,
is_script_test_enabled=True,
is_script=True,
onnx_shape_inference=True,
),
)
def _get_image(rel_path: str, size: Tuple[int, int]) -> torch.Tensor:
data_dir = os.path.join(os.path.dirname(__file__), "assets")
path = os.path.join(data_dir, *rel_path.split("/"))
image = PIL.Image.open(path).convert("RGB").resize(size, PIL.Image.BILINEAR)
return torchvision.transforms.ToTensor()(image)
def _get_test_images() -> Tuple[List[torch.Tensor], List[torch.Tensor]]:
return (
[_get_image("grace_hopper_517x606.jpg", (100, 320))],
[_get_image("rgb_pytorch.png", (250, 380))],
)
def _get_features(images):
s0, s1 = images.shape[-2:]
features = [
("0", torch.rand(2, 256, s0 // 4, s1 // 4)),
("1", torch.rand(2, 256, s0 // 8, s1 // 8)),
("2", torch.rand(2, 256, s0 // 16, s1 // 16)),
("3", torch.rand(2, 256, s0 // 32, s1 // 32)),
("4", torch.rand(2, 256, s0 // 64, s1 // 64)),
]
features = OrderedDict(features)
return features
def _init_test_generalized_rcnn_transform():
min_size = 100
max_size = 200
image_mean = [0.485, 0.456, 0.406]
image_std = [0.229, 0.224, 0.225]
return transform.GeneralizedRCNNTransform(min_size, max_size, image_mean, image_std)
def _init_test_rpn():
anchor_sizes = ((32,), (64,), (128,), (256,), (512,))
aspect_ratios = ((0.5, 1.0, 2.0),) * len(anchor_sizes)
rpn_anchor_generator = rpn.AnchorGenerator(anchor_sizes, aspect_ratios)
out_channels = 256
rpn_head = rpn.RPNHead(
out_channels, rpn_anchor_generator.num_anchors_per_location()[0]
)
rpn_fg_iou_thresh = 0.7
rpn_bg_iou_thresh = 0.3
rpn_batch_size_per_image = 256
rpn_positive_fraction = 0.5
rpn_pre_nms_top_n = dict(training=2000, testing=1000)
rpn_post_nms_top_n = dict(training=2000, testing=1000)
rpn_nms_thresh = 0.7
rpn_score_thresh = 0.0
return rpn.RegionProposalNetwork(
rpn_anchor_generator,
rpn_head,
rpn_fg_iou_thresh,
rpn_bg_iou_thresh,
rpn_batch_size_per_image,
rpn_positive_fraction,
rpn_pre_nms_top_n,
rpn_post_nms_top_n,
rpn_nms_thresh,
score_thresh=rpn_score_thresh,
)
def _init_test_roi_heads_faster_rcnn():
out_channels = 256
num_classes = 91
box_fg_iou_thresh = 0.5
box_bg_iou_thresh = 0.5
box_batch_size_per_image = 512
box_positive_fraction = 0.25
bbox_reg_weights = None
box_score_thresh = 0.05
box_nms_thresh = 0.5
box_detections_per_img = 100
box_roi_pool = ops.MultiScaleRoIAlign(
featmap_names=["0", "1", "2", "3"], output_size=7, sampling_ratio=2
)
resolution = box_roi_pool.output_size[0]
representation_size = 1024
box_head = faster_rcnn.TwoMLPHead(
out_channels * resolution**2, representation_size
)
representation_size = 1024
box_predictor = faster_rcnn.FastRCNNPredictor(representation_size, num_classes)
return roi_heads.RoIHeads(
box_roi_pool,
box_head,
box_predictor,
box_fg_iou_thresh,
box_bg_iou_thresh,
box_batch_size_per_image,
box_positive_fraction,
bbox_reg_weights,
box_score_thresh,
box_nms_thresh,
box_detections_per_img,
)
@parameterized.parameterized_class(
("is_script",),
[(True,), (False,)],
class_name_func=onnx_test_common.parameterize_class_name,
)
class TestModelsONNXRuntime(onnx_test_common._TestONNXRuntime):
@skipIfUnsupportedMinOpsetVersion(11)
@skipScriptTest() # Faster RCNN model is not scriptable
def test_faster_rcnn(self):
model = faster_rcnn.fasterrcnn_resnet50_fpn(
pretrained=False, pretrained_backbone=True, min_size=200, max_size=300
)
model.eval()
x1 = torch.randn(3, 200, 300, requires_grad=True)
x2 = torch.randn(3, 200, 300, requires_grad=True)
self.run_test(model, ([x1, x2],), rtol=1e-3, atol=1e-5)
self.run_test(
model,
([x1, x2],),
input_names=["images_tensors"],
output_names=["outputs"],
dynamic_axes={"images_tensors": [0, 1, 2], "outputs": [0, 1, 2]},
rtol=1e-3,
atol=1e-5,
)
dummy_image = [torch.ones(3, 100, 100) * 0.3]
images, test_images = _get_test_images()
self.run_test(
model,
(images,),
additional_test_inputs=[(images,), (test_images,), (dummy_image,)],
input_names=["images_tensors"],
output_names=["outputs"],
dynamic_axes={"images_tensors": [0, 1, 2], "outputs": [0, 1, 2]},
rtol=1e-3,
atol=1e-5,
)
self.run_test(
model,
(dummy_image,),
additional_test_inputs=[(dummy_image,), (images,)],
input_names=["images_tensors"],
output_names=["outputs"],
dynamic_axes={"images_tensors": [0, 1, 2], "outputs": [0, 1, 2]},
rtol=1e-3,
atol=1e-5,
)
@skipIfUnsupportedMinOpsetVersion(11)
@skipScriptTest()
def test_mask_rcnn(self):
model = mask_rcnn.maskrcnn_resnet50_fpn(
pretrained=False, pretrained_backbone=True, min_size=200, max_size=300
)
images, test_images = _get_test_images()
self.run_test(model, (images,), rtol=1e-3, atol=1e-5)
self.run_test(
model,
(images,),
input_names=["images_tensors"],
output_names=["boxes", "labels", "scores", "masks"],
dynamic_axes={
"images_tensors": [0, 1, 2],
"boxes": [0, 1],
"labels": [0],
"scores": [0],
"masks": [0, 1, 2],
},
rtol=1e-3,
atol=1e-5,
)
dummy_image = [torch.ones(3, 100, 100) * 0.3]
self.run_test(
model,
(images,),
additional_test_inputs=[(images,), (test_images,), (dummy_image,)],
input_names=["images_tensors"],
output_names=["boxes", "labels", "scores", "masks"],
dynamic_axes={
"images_tensors": [0, 1, 2],
"boxes": [0, 1],
"labels": [0],
"scores": [0],
"masks": [0, 1, 2],
},
rtol=1e-3,
atol=1e-5,
)
self.run_test(
model,
(dummy_image,),
additional_test_inputs=[(dummy_image,), (images,)],
input_names=["images_tensors"],
output_names=["boxes", "labels", "scores", "masks"],
dynamic_axes={
"images_tensors": [0, 1, 2],
"boxes": [0, 1],
"labels": [0],
"scores": [0],
"masks": [0, 1, 2],
},
rtol=1e-3,
atol=1e-5,
)
@unittest.skip("Failing, see https://github.com/pytorch/pytorch/issues/66528")
@skipIfUnsupportedMinOpsetVersion(11)
@skipScriptTest()
def test_keypoint_rcnn(self):
model = keypoint_rcnn.keypointrcnn_resnet50_fpn(
pretrained=False, pretrained_backbone=False, min_size=200, max_size=300
)
images, test_images = _get_test_images()
self.run_test(model, (images,), rtol=1e-3, atol=1e-5)
self.run_test(
model,
(images,),
input_names=["images_tensors"],
output_names=["outputs1", "outputs2", "outputs3", "outputs4"],
dynamic_axes={"images_tensors": [0, 1, 2]},
rtol=1e-3,
atol=1e-5,
)
dummy_images = [torch.ones(3, 100, 100) * 0.3]
self.run_test(
model,
(images,),
additional_test_inputs=[(images,), (test_images,), (dummy_images,)],
input_names=["images_tensors"],
output_names=["outputs1", "outputs2", "outputs3", "outputs4"],
dynamic_axes={"images_tensors": [0, 1, 2]},
rtol=5e-3,
atol=1e-5,
)
self.run_test(
model,
(dummy_images,),
additional_test_inputs=[(dummy_images,), (test_images,)],
input_names=["images_tensors"],
output_names=["outputs1", "outputs2", "outputs3", "outputs4"],
dynamic_axes={"images_tensors": [0, 1, 2]},
rtol=5e-3,
atol=1e-5,
)
@skipIfUnsupportedMinOpsetVersion(11)
@skipScriptTest()
def test_roi_heads(self):
class RoIHeadsModule(torch.nn.Module):
def __init__(self):
super().__init__()
self.transform = _init_test_generalized_rcnn_transform()
self.rpn = _init_test_rpn()
self.roi_heads = _init_test_roi_heads_faster_rcnn()
def forward(self, images, features: Mapping[str, torch.Tensor]):
original_image_sizes = [
(img.shape[-1], img.shape[-2]) for img in images
]
images_m = image_list.ImageList(
images, [(i.shape[-1], i.shape[-2]) for i in images]
)
proposals, _ = self.rpn(images_m, features)
detections, _ = self.roi_heads(
features, proposals, images_m.image_sizes
)
detections = self.transform.postprocess(
detections, images_m.image_sizes, original_image_sizes
)
return detections
images = torch.rand(2, 3, 100, 100)
features = _get_features(images)
images2 = torch.rand(2, 3, 150, 150)
test_features = _get_features(images2)
model = RoIHeadsModule()
model.eval()
model(images, features)
self.run_test(
model,
(images, features),
input_names=["input1", "input2", "input3", "input4", "input5", "input6"],
dynamic_axes={
"input1": [0, 1, 2, 3],
"input2": [0, 1, 2, 3],
"input3": [0, 1, 2, 3],
"input4": [0, 1, 2, 3],
"input5": [0, 1, 2, 3],
"input6": [0, 1, 2, 3],
},
additional_test_inputs=[(images, features), (images2, test_features)],
)
@skipScriptTest() # TODO: #75625
def test_transformer_encoder(self):
class MyModule(torch.nn.Module):
def __init__(self, ninp, nhead, nhid, dropout, nlayers):
super().__init__()
encoder_layers = nn.TransformerEncoderLayer(ninp, nhead, nhid, dropout)
self.transformer_encoder = nn.TransformerEncoder(
encoder_layers, nlayers
)
def forward(self, input):
return self.transformer_encoder(input)
x = torch.rand(10, 32, 512)
self.run_test(MyModule(512, 8, 2048, 0.0, 3), (x,), atol=1e-5)
@skipScriptTest()
def test_mobilenet_v3(self):
model = torchvision.models.quantization.mobilenet_v3_large(pretrained=False)
dummy_input = torch.randn(1, 3, 224, 224)
self.run_test(model, (dummy_input,))
@skipIfUnsupportedMinOpsetVersion(11)
@skipScriptTest()
def test_shufflenet_v2_dynamic_axes(self):
model = torchvision.models.shufflenet_v2_x0_5(pretrained=False)
dummy_input = torch.randn(1, 3, 224, 224, requires_grad=True)
test_inputs = torch.randn(3, 3, 224, 224, requires_grad=True)
self.run_test(
model,
(dummy_input,),
additional_test_inputs=[(dummy_input,), (test_inputs,)],
input_names=["input_images"],
output_names=["outputs"],
dynamic_axes={
"input_images": {0: "batch_size"},
"output": {0: "batch_size"},
},
rtol=1e-3,
atol=1e-5,
)
if __name__ == "__main__":
common_utils.run_tests()
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