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2025-10-09 16:47:16 +08:00
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transformers/tests/models/rt_detr/__init__.py Normal file
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transformers/tests/models/rt_detr/test_image_processing_rt_detr.py Normal file
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# Copyright 2024 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import json
import unittest
from transformers.image_utils import load_image
from transformers.testing_utils import (
require_torch,
require_torch_accelerator,
require_torchvision,
require_vision,
slow,
torch_device,
)
from transformers.utils import is_torch_available, is_torchvision_available, is_vision_available
from ...test_image_processing_common import ImageProcessingTestMixin, prepare_image_inputs
from ...test_processing_common import url_to_local_path
if is_vision_available():
from PIL import Image
from transformers import RTDetrImageProcessor, RTDetrImageProcessorFast
if is_torch_available():
import torch
class RTDetrImageProcessingTester:
def __init__(
self,
parent,
batch_size=4,
num_channels=3,
do_resize=True,
size=None,
do_rescale=True,
rescale_factor=1 / 255,
do_normalize=False,
do_pad=False,
return_tensors="pt",
):
self.parent = parent
self.batch_size = batch_size
self.num_channels = num_channels
self.do_resize = do_resize
self.size = size if size is not None else {"height": 640, "width": 640}
self.do_rescale = do_rescale
self.rescale_factor = rescale_factor
self.do_normalize = do_normalize
self.do_pad = do_pad
self.return_tensors = return_tensors
def prepare_image_processor_dict(self):
return {
"do_resize": self.do_resize,
"size": self.size,
"do_rescale": self.do_rescale,
"rescale_factor": self.rescale_factor,
"do_normalize": self.do_normalize,
"do_pad": self.do_pad,
"return_tensors": self.return_tensors,
}
def get_expected_values(self):
return self.size["height"], self.size["width"]
def expected_output_image_shape(self, images):
height, width = self.get_expected_values()
return self.num_channels, height, width
def prepare_image_inputs(self, equal_resolution=False, numpify=False, torchify=False):
return prepare_image_inputs(
batch_size=self.batch_size,
num_channels=self.num_channels,
min_resolution=30,
max_resolution=400,
equal_resolution=equal_resolution,
numpify=numpify,
torchify=torchify,
)
@require_torch
@require_vision
class RtDetrImageProcessingTest(ImageProcessingTestMixin, unittest.TestCase):
image_processing_class = RTDetrImageProcessor if is_vision_available() else None
fast_image_processing_class = RTDetrImageProcessorFast if is_torchvision_available() else None
def setUp(self):
super().setUp()
self.image_processor_tester = RTDetrImageProcessingTester(self)
@property
def image_processor_dict(self):
return self.image_processor_tester.prepare_image_processor_dict()
def test_image_processor_properties(self):
for image_processing_class in self.image_processor_list:
image_processing = image_processing_class(**self.image_processor_dict)
self.assertTrue(hasattr(image_processing, "do_resize"))
self.assertTrue(hasattr(image_processing, "size"))
self.assertTrue(hasattr(image_processing, "resample"))
self.assertTrue(hasattr(image_processing, "do_rescale"))
self.assertTrue(hasattr(image_processing, "rescale_factor"))
self.assertTrue(hasattr(image_processing, "return_tensors"))
def test_image_processor_from_dict_with_kwargs(self):
for image_processing_class in self.image_processor_list:
image_processor = image_processing_class.from_dict(self.image_processor_dict)
self.assertEqual(image_processor.size, {"height": 640, "width": 640})
def test_valid_coco_detection_annotations(self):
# prepare image and target
image = Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png")
with open("./tests/fixtures/tests_samples/COCO/coco_annotations.txt") as f:
target = json.loads(f.read())
params = {"image_id": 39769, "annotations": target}
for image_processing_class in self.image_processor_list:
# encode them
image_processing = image_processing_class.from_pretrained("PekingU/rtdetr_r50vd")
# legal encodings (single image)
_ = image_processing(images=image, annotations=params, return_tensors="pt")
_ = image_processing(images=image, annotations=[params], return_tensors="pt")
# legal encodings (batch of one image)
_ = image_processing(images=[image], annotations=params, return_tensors="pt")
_ = image_processing(images=[image], annotations=[params], return_tensors="pt")
# legal encoding (batch of more than one image)
n = 5
_ = image_processing(images=[image] * n, annotations=[params] * n, return_tensors="pt")
# example of an illegal encoding (missing the 'image_id' key)
with self.assertRaises(ValueError) as e:
image_processing(images=image, annotations={"annotations": target}, return_tensors="pt")
self.assertTrue(str(e.exception).startswith("Invalid COCO detection annotations"))
# example of an illegal encoding (unequal lengths of images and annotations)
with self.assertRaises(ValueError) as e:
image_processing(images=[image] * n, annotations=[params] * (n - 1), return_tensors="pt")
self.assertTrue(str(e.exception) == "The number of images (5) and annotations (4) do not match.")
@slow
def test_call_pytorch_with_coco_detection_annotations(self):
# prepare image and target
image = Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png")
with open("./tests/fixtures/tests_samples/COCO/coco_annotations.txt") as f:
target = json.loads(f.read())
target = {"image_id": 39769, "annotations": target}
for image_processing_class in self.image_processor_list:
# encode them
image_processing = image_processing_class.from_pretrained("PekingU/rtdetr_r50vd")
encoding = image_processing(images=image, annotations=target, return_tensors="pt")
# verify pixel values
expected_shape = torch.Size([1, 3, 640, 640])
self.assertEqual(encoding["pixel_values"].shape, expected_shape)
expected_slice = torch.tensor([0.5490, 0.5647, 0.5725])
torch.testing.assert_close(encoding["pixel_values"][0, 0, 0, :3], expected_slice, rtol=1e-4, atol=1e-4)
# verify area
expected_area = torch.tensor([2827.9883, 5403.4761, 235036.7344, 402070.2188, 71068.8281, 79601.2812])
torch.testing.assert_close(encoding["labels"][0]["area"], expected_area)
# verify boxes
expected_boxes_shape = torch.Size([6, 4])
self.assertEqual(encoding["labels"][0]["boxes"].shape, expected_boxes_shape)
expected_boxes_slice = torch.tensor([0.5503, 0.2765, 0.0604, 0.2215])
torch.testing.assert_close(encoding["labels"][0]["boxes"][0], expected_boxes_slice, rtol=1e-3, atol=1e-3)
# verify image_id
expected_image_id = torch.tensor([39769])
torch.testing.assert_close(encoding["labels"][0]["image_id"], expected_image_id)
# verify is_crowd
expected_is_crowd = torch.tensor([0, 0, 0, 0, 0, 0])
torch.testing.assert_close(encoding["labels"][0]["iscrowd"], expected_is_crowd)
# verify class_labels
expected_class_labels = torch.tensor([75, 75, 63, 65, 17, 17])
torch.testing.assert_close(encoding["labels"][0]["class_labels"], expected_class_labels)
# verify orig_size
expected_orig_size = torch.tensor([480, 640])
torch.testing.assert_close(encoding["labels"][0]["orig_size"], expected_orig_size)
# verify size
expected_size = torch.tensor([640, 640])
torch.testing.assert_close(encoding["labels"][0]["size"], expected_size)
@slow
def test_image_processor_outputs(self):
image = Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png")
for image_processing_class in self.image_processor_list:
image_processing = image_processing_class(**self.image_processor_dict)
encoding = image_processing(images=image, return_tensors="pt")
# verify pixel values: shape
expected_shape = torch.Size([1, 3, 640, 640])
self.assertEqual(encoding["pixel_values"].shape, expected_shape)
# verify pixel values: output values
expected_slice = torch.tensor([0.5490196347236633, 0.5647059082984924, 0.572549045085907])
torch.testing.assert_close(encoding["pixel_values"][0, 0, 0, :3], expected_slice, rtol=1e-5, atol=1e-5)
def test_multiple_images_processor_outputs(self):
images_urls = [
"http://images.cocodataset.org/val2017/000000000139.jpg",
"http://images.cocodataset.org/val2017/000000000285.jpg",
"http://images.cocodataset.org/val2017/000000000632.jpg",
"http://images.cocodataset.org/val2017/000000000724.jpg",
"http://images.cocodataset.org/val2017/000000000776.jpg",
"http://images.cocodataset.org/val2017/000000000785.jpg",
"http://images.cocodataset.org/val2017/000000000802.jpg",
"http://images.cocodataset.org/val2017/000000000872.jpg",
]
images = []
for url in images_urls:
image = load_image(url_to_local_path(url))
images.append(image)
for image_processing_class in self.image_processor_list:
# apply image processing
image_processing = image_processing_class(**self.image_processor_dict)
encoding = image_processing(images=images, return_tensors="pt")
# verify if pixel_values is part of the encoding
self.assertIn("pixel_values", encoding)
# verify pixel values: shape
expected_shape = torch.Size([8, 3, 640, 640])
self.assertEqual(encoding["pixel_values"].shape, expected_shape)
# verify pixel values: output values
expected_slices = torch.tensor(
[
[0.5333333611488342, 0.5568627715110779, 0.5647059082984924],
[0.5372549295425415, 0.4705882668495178, 0.4274510145187378],
[0.3960784673690796, 0.35686275362968445, 0.3686274588108063],
[0.20784315466880798, 0.1882353127002716, 0.15294118225574493],
[0.364705890417099, 0.364705890417099, 0.3686274588108063],
[0.8078432083129883, 0.8078432083129883, 0.8078432083129883],
[0.4431372880935669, 0.4431372880935669, 0.4431372880935669],
[0.19607844948768616, 0.21176472306251526, 0.3607843220233917],
]
)
torch.testing.assert_close(encoding["pixel_values"][:, 1, 0, :3], expected_slices, rtol=1e-5, atol=1e-5)
@slow
def test_batched_coco_detection_annotations(self):
image_0 = Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png")
image_1 = Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png").resize((800, 800))
with open("./tests/fixtures/tests_samples/COCO/coco_annotations.txt") as f:
target = json.loads(f.read())
annotations_0 = {"image_id": 39769, "annotations": target}
annotations_1 = {"image_id": 39769, "annotations": target}
# Adjust the bounding boxes for the resized image
w_0, h_0 = image_0.size
w_1, h_1 = image_1.size
for i in range(len(annotations_1["annotations"])):
coords = annotations_1["annotations"][i]["bbox"]
new_bbox = [
coords[0] * w_1 / w_0,
coords[1] * h_1 / h_0,
coords[2] * w_1 / w_0,
coords[3] * h_1 / h_0,
]
annotations_1["annotations"][i]["bbox"] = new_bbox
images = [image_0, image_1]
annotations = [annotations_0, annotations_1]
for image_processing_class in self.image_processor_list:
image_processing = image_processing_class()
encoding = image_processing(
images=images,
annotations=annotations,
return_segmentation_masks=True,
return_tensors="pt", # do_convert_annotations=True
)
# Check the pixel values have been padded
postprocessed_height, postprocessed_width = 640, 640
expected_shape = torch.Size([2, 3, postprocessed_height, postprocessed_width])
self.assertEqual(encoding["pixel_values"].shape, expected_shape)
# Check the bounding boxes have been adjusted for padded images
self.assertEqual(encoding["labels"][0]["boxes"].shape, torch.Size([6, 4]))
self.assertEqual(encoding["labels"][1]["boxes"].shape, torch.Size([6, 4]))
expected_boxes_0 = torch.tensor(
[
[0.6879, 0.4609, 0.0755, 0.3691],
[0.2118, 0.3359, 0.2601, 0.1566],
[0.5011, 0.5000, 0.9979, 1.0000],
[0.5010, 0.5020, 0.9979, 0.9959],
[0.3284, 0.5944, 0.5884, 0.8112],
[0.8394, 0.5445, 0.3213, 0.9110],
]
)
expected_boxes_1 = torch.tensor(
[
[0.5503, 0.2765, 0.0604, 0.2215],
[0.1695, 0.2016, 0.2080, 0.0940],
[0.5006, 0.4933, 0.9977, 0.9865],
[0.5008, 0.5002, 0.9983, 0.9955],
[0.2627, 0.5456, 0.4707, 0.8646],
[0.7715, 0.4115, 0.4570, 0.7161],
]
)
torch.testing.assert_close(encoding["labels"][0]["boxes"], expected_boxes_0, atol=1e-3, rtol=1e-3)
torch.testing.assert_close(encoding["labels"][1]["boxes"], expected_boxes_1, atol=1e-3, rtol=1e-3)
# Check if do_convert_annotations=False, then the annotations are not converted to centre_x, centre_y, width, height
# format and not in the range [0, 1]
encoding = image_processing(
images=images,
annotations=annotations,
return_segmentation_masks=True,
do_convert_annotations=False,
return_tensors="pt",
)
self.assertEqual(encoding["labels"][0]["boxes"].shape, torch.Size([6, 4]))
self.assertEqual(encoding["labels"][1]["boxes"].shape, torch.Size([6, 4]))
# Convert to absolute coordinates
unnormalized_boxes_0 = torch.vstack(
[
expected_boxes_0[:, 0] * postprocessed_width,
expected_boxes_0[:, 1] * postprocessed_height,
expected_boxes_0[:, 2] * postprocessed_width,
expected_boxes_0[:, 3] * postprocessed_height,
]
).T
unnormalized_boxes_1 = torch.vstack(
[
expected_boxes_1[:, 0] * postprocessed_width,
expected_boxes_1[:, 1] * postprocessed_height,
expected_boxes_1[:, 2] * postprocessed_width,
expected_boxes_1[:, 3] * postprocessed_height,
]
).T
# Convert from centre_x, centre_y, width, height to x_min, y_min, x_max, y_max
expected_boxes_0 = torch.vstack(
[
unnormalized_boxes_0[:, 0] - unnormalized_boxes_0[:, 2] / 2,
unnormalized_boxes_0[:, 1] - unnormalized_boxes_0[:, 3] / 2,
unnormalized_boxes_0[:, 0] + unnormalized_boxes_0[:, 2] / 2,
unnormalized_boxes_0[:, 1] + unnormalized_boxes_0[:, 3] / 2,
]
).T
expected_boxes_1 = torch.vstack(
[
unnormalized_boxes_1[:, 0] - unnormalized_boxes_1[:, 2] / 2,
unnormalized_boxes_1[:, 1] - unnormalized_boxes_1[:, 3] / 2,
unnormalized_boxes_1[:, 0] + unnormalized_boxes_1[:, 2] / 2,
unnormalized_boxes_1[:, 1] + unnormalized_boxes_1[:, 3] / 2,
]
).T
torch.testing.assert_close(encoding["labels"][0]["boxes"], expected_boxes_0, atol=1, rtol=1)
torch.testing.assert_close(encoding["labels"][1]["boxes"], expected_boxes_1, atol=1, rtol=1)
@slow
@require_torch_accelerator
@require_torchvision
# Copied from tests.models.detr.test_image_processing_detr.DetrImageProcessingTest.test_fast_processor_equivalence_cpu_accelerator_coco_detection_annotations
def test_fast_processor_equivalence_cpu_accelerator_coco_detection_annotations(self):
# prepare image and target
image = Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png")
with open("./tests/fixtures/tests_samples/COCO/coco_annotations.txt") as f:
target = json.loads(f.read())
target = {"image_id": 39769, "annotations": target}
processor = self.image_processor_list[1]()
# 1. run processor on CPU
encoding_cpu = processor(images=image, annotations=target, return_tensors="pt", device="cpu")
# 2. run processor on accelerator
encoding_gpu = processor(images=image, annotations=target, return_tensors="pt", device=torch_device)
# verify pixel values
self.assertEqual(encoding_cpu["pixel_values"].shape, encoding_gpu["pixel_values"].shape)
self.assertTrue(
torch.allclose(
encoding_cpu["pixel_values"][0, 0, 0, :3],
encoding_gpu["pixel_values"][0, 0, 0, :3].to("cpu"),
atol=1e-4,
)
)
# verify area
torch.testing.assert_close(encoding_cpu["labels"][0]["area"], encoding_gpu["labels"][0]["area"].to("cpu"))
# verify boxes
self.assertEqual(encoding_cpu["labels"][0]["boxes"].shape, encoding_gpu["labels"][0]["boxes"].shape)
self.assertTrue(
torch.allclose(
encoding_cpu["labels"][0]["boxes"][0], encoding_gpu["labels"][0]["boxes"][0].to("cpu"), atol=1e-3
)
)
# verify image_id
torch.testing.assert_close(
encoding_cpu["labels"][0]["image_id"], encoding_gpu["labels"][0]["image_id"].to("cpu")
)
# verify is_crowd
torch.testing.assert_close(
encoding_cpu["labels"][0]["iscrowd"], encoding_gpu["labels"][0]["iscrowd"].to("cpu")
)
# verify class_labels
self.assertTrue(
torch.allclose(
encoding_cpu["labels"][0]["class_labels"], encoding_gpu["labels"][0]["class_labels"].to("cpu")
)
)
# verify orig_size
torch.testing.assert_close(
encoding_cpu["labels"][0]["orig_size"], encoding_gpu["labels"][0]["orig_size"].to("cpu")
)
# verify size
torch.testing.assert_close(encoding_cpu["labels"][0]["size"], encoding_gpu["labels"][0]["size"].to("cpu"))

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transformers/tests/models/rt_detr/test_modeling_rt_detr.py Normal file
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# coding = utf-8
# Copyright 2024 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""Testing suite for the PyTorch RT_DETR model."""
import inspect
import math
import tempfile
import unittest
from functools import cached_property
from parameterized import parameterized
from transformers import (
RTDetrConfig,
RTDetrImageProcessor,
RTDetrResNetConfig,
is_torch_available,
is_vision_available,
)
from transformers.testing_utils import (
Expectations,
require_torch,
require_torch_accelerator,
require_vision,
slow,
torch_device,
)
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, _config_zero_init, floats_tensor
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from transformers import RTDetrForObjectDetection, RTDetrModel
if is_vision_available():
from PIL import Image
CHECKPOINT = "PekingU/rtdetr_r50vd" # TODO: replace
class RTDetrModelTester:
def __init__(
self,
parent,
batch_size=3,
is_training=True,
use_labels=True,
n_targets=3,
num_labels=10,
initializer_range=0.02,
layer_norm_eps=1e-5,
batch_norm_eps=1e-5,
# backbone
backbone_config=None,
# encoder HybridEncoder
encoder_hidden_dim=32,
encoder_in_channels=[128, 256, 512],
feat_strides=[8, 16, 32],
encoder_layers=1,
encoder_ffn_dim=64,
encoder_attention_heads=2,
dropout=0.0,
activation_dropout=0.0,
encode_proj_layers=[2],
positional_encoding_temperature=10000,
encoder_activation_function="gelu",
activation_function="silu",
eval_size=None,
normalize_before=False,
# decoder RTDetrTransformer
d_model=32,
num_queries=30,
decoder_in_channels=[32, 32, 32],
decoder_ffn_dim=64,
num_feature_levels=3,
decoder_n_points=4,
decoder_layers=2,
decoder_attention_heads=2,
decoder_activation_function="relu",
attention_dropout=0.0,
num_denoising=0,
label_noise_ratio=0.5,
box_noise_scale=1.0,
learn_initial_query=False,
anchor_image_size=None,
image_size=64,
disable_custom_kernels=True,
with_box_refine=True,
):
self.parent = parent
self.batch_size = batch_size
self.num_channels = 3
self.is_training = is_training
self.use_labels = use_labels
self.n_targets = n_targets
self.num_labels = num_labels
self.initializer_range = initializer_range
self.layer_norm_eps = layer_norm_eps
self.batch_norm_eps = batch_norm_eps
self.backbone_config = backbone_config
self.encoder_hidden_dim = encoder_hidden_dim
self.encoder_in_channels = encoder_in_channels
self.feat_strides = feat_strides
self.encoder_layers = encoder_layers
self.encoder_ffn_dim = encoder_ffn_dim
self.encoder_attention_heads = encoder_attention_heads
self.dropout = dropout
self.activation_dropout = activation_dropout
self.encode_proj_layers = encode_proj_layers
self.positional_encoding_temperature = positional_encoding_temperature
self.encoder_activation_function = encoder_activation_function
self.activation_function = activation_function
self.eval_size = eval_size
self.normalize_before = normalize_before
self.d_model = d_model
self.num_queries = num_queries
self.decoder_in_channels = decoder_in_channels
self.decoder_ffn_dim = decoder_ffn_dim
self.num_feature_levels = num_feature_levels
self.decoder_n_points = decoder_n_points
self.decoder_layers = decoder_layers
self.decoder_attention_heads = decoder_attention_heads
self.decoder_activation_function = decoder_activation_function
self.attention_dropout = attention_dropout
self.num_denoising = num_denoising
self.label_noise_ratio = label_noise_ratio
self.box_noise_scale = box_noise_scale
self.learn_initial_query = learn_initial_query
self.anchor_image_size = anchor_image_size
self.image_size = image_size
self.disable_custom_kernels = disable_custom_kernels
self.with_box_refine = with_box_refine
self.encoder_seq_length = math.ceil(self.image_size / 32) * math.ceil(self.image_size / 32)
def prepare_config_and_inputs(self):
pixel_values = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size])
pixel_mask = torch.ones([self.batch_size, self.image_size, self.image_size], device=torch_device)
labels = None
if self.use_labels:
# labels is a list of Dict (each Dict being the labels for a given example in the batch)
labels = []
for i in range(self.batch_size):
target = {}
target["class_labels"] = torch.randint(
high=self.num_labels, size=(self.n_targets,), device=torch_device
)
target["boxes"] = torch.rand(self.n_targets, 4, device=torch_device)
labels.append(target)
config = self.get_config()
config.num_labels = self.num_labels
return config, pixel_values, pixel_mask, labels
def get_config(self):
hidden_sizes = [10, 20, 30, 40]
backbone_config = RTDetrResNetConfig(
embeddings_size=10,
hidden_sizes=hidden_sizes,
depths=[1, 1, 2, 1],
out_features=["stage2", "stage3", "stage4"],
out_indices=[2, 3, 4],
)
return RTDetrConfig.from_backbone_configs(
backbone_config=backbone_config,
encoder_hidden_dim=self.encoder_hidden_dim,
encoder_in_channels=hidden_sizes[1:],
feat_strides=self.feat_strides,
encoder_layers=self.encoder_layers,
encoder_ffn_dim=self.encoder_ffn_dim,
encoder_attention_heads=self.encoder_attention_heads,
dropout=self.dropout,
activation_dropout=self.activation_dropout,
encode_proj_layers=self.encode_proj_layers,
positional_encoding_temperature=self.positional_encoding_temperature,
encoder_activation_function=self.encoder_activation_function,
activation_function=self.activation_function,
eval_size=self.eval_size,
normalize_before=self.normalize_before,
d_model=self.d_model,
num_queries=self.num_queries,
decoder_in_channels=self.decoder_in_channels,
decoder_ffn_dim=self.decoder_ffn_dim,
num_feature_levels=self.num_feature_levels,
decoder_n_points=self.decoder_n_points,
decoder_layers=self.decoder_layers,
decoder_attention_heads=self.decoder_attention_heads,
decoder_activation_function=self.decoder_activation_function,
attention_dropout=self.attention_dropout,
num_denoising=self.num_denoising,
label_noise_ratio=self.label_noise_ratio,
box_noise_scale=self.box_noise_scale,
learn_initial_query=self.learn_initial_query,
anchor_image_size=self.anchor_image_size,
image_size=self.image_size,
disable_custom_kernels=self.disable_custom_kernels,
with_box_refine=self.with_box_refine,
)
def prepare_config_and_inputs_for_common(self):
config, pixel_values, pixel_mask, labels = self.prepare_config_and_inputs()
inputs_dict = {"pixel_values": pixel_values}
return config, inputs_dict
def create_and_check_rt_detr_model(self, config, pixel_values, pixel_mask, labels):
model = RTDetrModel(config=config)
model.to(torch_device)
model.eval()
result = model(pixel_values=pixel_values, pixel_mask=pixel_mask)
result = model(pixel_values)
self.parent.assertEqual(result.last_hidden_state.shape, (self.batch_size, self.num_queries, self.d_model))
def create_and_check_rt_detr_object_detection_head_model(self, config, pixel_values, pixel_mask, labels):
model = RTDetrForObjectDetection(config=config)
model.to(torch_device)
model.eval()
result = model(pixel_values=pixel_values, pixel_mask=pixel_mask)
result = model(pixel_values)
self.parent.assertEqual(result.logits.shape, (self.batch_size, self.num_queries, self.num_labels))
self.parent.assertEqual(result.pred_boxes.shape, (self.batch_size, self.num_queries, 4))
result = model(pixel_values=pixel_values, pixel_mask=pixel_mask, labels=labels)
self.parent.assertEqual(result.loss.shape, ())
self.parent.assertEqual(result.logits.shape, (self.batch_size, self.num_queries, self.num_labels))
self.parent.assertEqual(result.pred_boxes.shape, (self.batch_size, self.num_queries, 4))
@require_torch
class RTDetrModelTest(ModelTesterMixin, PipelineTesterMixin, unittest.TestCase):
all_model_classes = (RTDetrModel, RTDetrForObjectDetection) if is_torch_available() else ()
pipeline_model_mapping = (
{"image-feature-extraction": RTDetrModel, "object-detection": RTDetrForObjectDetection}
if is_torch_available()
else {}
)
is_encoder_decoder = True
test_torchscript = False
test_pruning = False
test_head_masking = False
test_missing_keys = False
test_torch_exportable = True
# special case for head models
def _prepare_for_class(self, inputs_dict, model_class, return_labels=False):
inputs_dict = super()._prepare_for_class(inputs_dict, model_class, return_labels=return_labels)
if return_labels:
if model_class.__name__ == "RTDetrForObjectDetection":
labels = []
for i in range(self.model_tester.batch_size):
target = {}
target["class_labels"] = torch.ones(
size=(self.model_tester.n_targets,), device=torch_device, dtype=torch.long
)
target["boxes"] = torch.ones(
self.model_tester.n_targets, 4, device=torch_device, dtype=torch.float
)
labels.append(target)
inputs_dict["labels"] = labels
return inputs_dict
def setUp(self):
self.model_tester = RTDetrModelTester(self)
self.config_tester = ConfigTester(
self,
config_class=RTDetrConfig,
has_text_modality=False,
common_properties=["hidden_size", "num_attention_heads"],
)
def test_config(self):
self.config_tester.run_common_tests()
def test_rt_detr_model(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_rt_detr_model(*config_and_inputs)
def test_rt_detr_object_detection_head_model(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_rt_detr_object_detection_head_model(*config_and_inputs)
@unittest.skip(reason="RTDetr does not use inputs_embeds")
def test_inputs_embeds(self):
pass
@unittest.skip(reason="RTDetr does not use test_inputs_embeds_matches_input_ids")
def test_inputs_embeds_matches_input_ids(self):
pass
@unittest.skip(reason="RTDetr does not support input and output embeddings")
def test_model_get_set_embeddings(self):
pass
@unittest.skip(reason="RTDetr does not support input and output embeddings")
def test_model_common_attributes(self):
pass
@unittest.skip(reason="RTDetr does not use token embeddings")
def test_resize_tokens_embeddings(self):
pass
@unittest.skip(reason="Feed forward chunking is not implemented")
def test_feed_forward_chunking(self):
pass
def test_attention_outputs(self):
config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
config.return_dict = True
for model_class in self.all_model_classes:
inputs_dict["output_attentions"] = True
inputs_dict["output_hidden_states"] = False
config.return_dict = True
model = model_class._from_config(config, attn_implementation="eager")
config = model.config
model.to(torch_device)
model.eval()
with torch.no_grad():
outputs = model(**self._prepare_for_class(inputs_dict, model_class))
attentions = outputs.encoder_attentions
self.assertEqual(len(attentions), self.model_tester.encoder_layers)
# check that output_attentions also work using config
del inputs_dict["output_attentions"]
config.output_attentions = True
model = model_class(config)
model.to(torch_device)
model.eval()
with torch.no_grad():
outputs = model(**self._prepare_for_class(inputs_dict, model_class))
attentions = outputs.encoder_attentions
self.assertEqual(len(attentions), self.model_tester.encoder_layers)
self.assertListEqual(
list(attentions[0].shape[-3:]),
[
self.model_tester.encoder_attention_heads,
self.model_tester.encoder_seq_length,
self.model_tester.encoder_seq_length,
],
)
out_len = len(outputs)
correct_outlen = 13
# loss is at first position
if "labels" in inputs_dict:
correct_outlen += 1 # loss is added to beginning
# Object Detection model returns pred_logits and pred_boxes
if model_class.__name__ == "RTDetrForObjectDetection":
correct_outlen += 2
self.assertEqual(out_len, correct_outlen)
# decoder attentions
decoder_attentions = outputs.decoder_attentions
self.assertIsInstance(decoder_attentions, (list, tuple))
self.assertEqual(len(decoder_attentions), self.model_tester.decoder_layers)
self.assertListEqual(
list(decoder_attentions[0].shape[-3:]),
[
self.model_tester.decoder_attention_heads,
self.model_tester.num_queries,
self.model_tester.num_queries,
],
)
# cross attentions
cross_attentions = outputs.cross_attentions
self.assertIsInstance(cross_attentions, (list, tuple))
self.assertEqual(len(cross_attentions), self.model_tester.decoder_layers)
self.assertListEqual(
list(cross_attentions[0].shape[-3:]),
[
self.model_tester.decoder_attention_heads,
self.model_tester.num_feature_levels,
self.model_tester.decoder_n_points,
],
)
# Check attention is always last and order is fine
inputs_dict["output_attentions"] = True
inputs_dict["output_hidden_states"] = True
model = model_class(config)
model.to(torch_device)
model.eval()
with torch.no_grad():
outputs = model(**self._prepare_for_class(inputs_dict, model_class))
if hasattr(self.model_tester, "num_hidden_states_types"):
added_hidden_states = self.model_tester.num_hidden_states_types
else:
# RTDetr should maintin encoder_hidden_states output
added_hidden_states = 2
self.assertEqual(out_len + added_hidden_states, len(outputs))
self_attentions = outputs.encoder_attentions
self.assertEqual(len(self_attentions), self.model_tester.encoder_layers)
self.assertListEqual(
list(self_attentions[0].shape[-3:]),
[
self.model_tester.encoder_attention_heads,
self.model_tester.encoder_seq_length,
self.model_tester.encoder_seq_length,
],
)
def test_hidden_states_output(self):
def check_hidden_states_output(inputs_dict, config, model_class):
model = model_class(config)
model.to(torch_device)
model.eval()
with torch.no_grad():
outputs = model(**self._prepare_for_class(inputs_dict, model_class))
hidden_states = outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states
expected_num_layers = getattr(
self.model_tester, "expected_num_hidden_layers", len(self.model_tester.encoder_in_channels) - 1
)
self.assertEqual(len(hidden_states), expected_num_layers)
self.assertListEqual(
list(hidden_states[1].shape[-2:]),
[
self.model_tester.image_size // self.model_tester.feat_strides[-1],
self.model_tester.image_size // self.model_tester.feat_strides[-1],
],
)
if config.is_encoder_decoder:
hidden_states = outputs.decoder_hidden_states
expected_num_layers = getattr(
self.model_tester, "expected_num_hidden_layers", self.model_tester.decoder_layers + 1
)
self.assertIsInstance(hidden_states, (list, tuple))
self.assertEqual(len(hidden_states), expected_num_layers)
self.assertListEqual(
list(hidden_states[0].shape[-2:]),
[self.model_tester.num_queries, self.model_tester.d_model],
)
config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
inputs_dict["output_hidden_states"] = True
check_hidden_states_output(inputs_dict, config, model_class)
# check that output_hidden_states also work using config
del inputs_dict["output_hidden_states"]
config.output_hidden_states = True
check_hidden_states_output(inputs_dict, config, model_class)
def test_retain_grad_hidden_states_attentions(self):
config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
config.output_hidden_states = True
config.output_attentions = True
model_class = self.all_model_classes[0]
model = model_class(config)
model.to(torch_device)
inputs = self._prepare_for_class(inputs_dict, model_class)
outputs = model(**inputs)
# we take the first output since last_hidden_state is the first item
output = outputs[0]
encoder_hidden_states = outputs.encoder_hidden_states[0]
encoder_attentions = outputs.encoder_attentions[0]
encoder_hidden_states.retain_grad()
encoder_attentions.retain_grad()
decoder_attentions = outputs.decoder_attentions[0]
decoder_attentions.retain_grad()
cross_attentions = outputs.cross_attentions[0]
cross_attentions.retain_grad()
output.flatten()[0].backward(retain_graph=True)
self.assertIsNotNone(encoder_hidden_states.grad)
self.assertIsNotNone(encoder_attentions.grad)
self.assertIsNotNone(decoder_attentions.grad)
self.assertIsNotNone(cross_attentions.grad)
def test_forward_signature(self):
config, _ = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
model = model_class(config)
signature = inspect.signature(model.forward)
arg_names = [*signature.parameters.keys()]
expected_arg_names = ["pixel_values"]
self.assertListEqual(arg_names[:1], expected_arg_names)
def test_different_timm_backbone(self):
config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
# let's pick a random timm backbone
config.backbone = "tf_mobilenetv3_small_075"
config.backbone_config = None
config.use_timm_backbone = True
config.backbone_kwargs = {"out_indices": [2, 3, 4]}
for model_class in self.all_model_classes:
model = model_class(config)
model.to(torch_device)
model.eval()
with torch.no_grad():
outputs = model(**self._prepare_for_class(inputs_dict, model_class))
if model_class.__name__ == "RTDetrForObjectDetection":
expected_shape = (
self.model_tester.batch_size,
self.model_tester.num_queries,
self.model_tester.num_labels,
)
self.assertEqual(outputs.logits.shape, expected_shape)
# Confirm out_indices was propagated to backbone
self.assertEqual(len(model.model.backbone.intermediate_channel_sizes), 3)
else:
# Confirm out_indices was propagated to backbone
self.assertEqual(len(model.backbone.intermediate_channel_sizes), 3)
self.assertTrue(outputs)
def test_hf_backbone(self):
config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
# Load a pretrained HF checkpoint as backbone
config.backbone = "microsoft/resnet-18"
config.backbone_config = None
config.use_timm_backbone = False
config.use_pretrained_backbone = True
config.backbone_kwargs = {"out_indices": [2, 3, 4]}
for model_class in self.all_model_classes:
model = model_class(config)
model.to(torch_device)
model.eval()
with torch.no_grad():
outputs = model(**self._prepare_for_class(inputs_dict, model_class))
if model_class.__name__ == "RTDetrForObjectDetection":
expected_shape = (
self.model_tester.batch_size,
self.model_tester.num_queries,
self.model_tester.num_labels,
)
self.assertEqual(outputs.logits.shape, expected_shape)
# Confirm out_indices was propagated to backbone
self.assertEqual(len(model.model.backbone.intermediate_channel_sizes), 3)
else:
# Confirm out_indices was propagated to backbone
self.assertEqual(len(model.backbone.intermediate_channel_sizes), 3)
self.assertTrue(outputs)
def test_initialization(self):
config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
configs_no_init = _config_zero_init(config)
configs_no_init.initializer_bias_prior_prob = 0.2
bias_value = -1.3863 # log_e ((1 - 0.2) / 0.2)
failed_cases = []
for model_class in self.all_model_classes:
model = model_class(config=configs_no_init)
# Skip the check for the backbone
for name, module in model.named_modules():
if module.__class__.__name__ == "RTDetrConvEncoder":
backbone_params = [f"{name}.{key}" for key in module.state_dict()]
break
for name, param in model.named_parameters():
if param.requires_grad:
if ("class_embed" in name and "bias" in name) or "enc_score_head.bias" in name:
bias_tensor = torch.full_like(param.data, bias_value)
if not torch.allclose(param.data, bias_tensor, atol=1e-4):
failed_cases.append(
f"Parameter {name} of model {model_class} seems not properly initialized. "
f"Biases should be initialized to {bias_value}, got {param.data}"
)
elif (
"level_embed" in name
or "sampling_offsets.bias" in name
or "value_proj" in name
or "output_proj" in name
or "reference_points" in name
or "enc_score_head.weight" in name
or ("class_embed" in name and "weight" in name)
or name in backbone_params
):
continue
else:
mean = param.data.mean()
round_mean = (mean * 1e9).round() / 1e9
round_mean = round_mean.item()
if round_mean not in [0.0, 1.0]:
failed_cases.append(
f"Parameter {name} of model {model_class} seems not properly initialized. "
f"Mean is {round_mean}, but should be in [0, 1]"
)
message = "\n" + "\n".join(failed_cases)
self.assertTrue(not failed_cases, message)
@parameterized.expand(["float32", "float16", "bfloat16"])
@require_torch_accelerator
@slow
def test_inference_with_different_dtypes(self, dtype_str):
dtype = {
"float32": torch.float32,
"float16": torch.float16,
"bfloat16": torch.bfloat16,
}[dtype_str]
config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
model = model_class(config)
model.to(torch_device).to(dtype)
model.eval()
for key, tensor in inputs_dict.items():
if tensor.dtype == torch.float32:
inputs_dict[key] = tensor.to(dtype)
with torch.no_grad():
_ = model(**self._prepare_for_class(inputs_dict, model_class))
@parameterized.expand(["float32", "float16", "bfloat16"])
@require_torch_accelerator
@slow
def test_inference_equivalence_for_static_and_dynamic_anchors(self, dtype_str):
dtype = {
"float32": torch.float32,
"float16": torch.float16,
"bfloat16": torch.bfloat16,
}[dtype_str]
config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
h, w = inputs_dict["pixel_values"].shape[-2:]
# convert inputs to the desired dtype
for key, tensor in inputs_dict.items():
if tensor.dtype == torch.float32:
inputs_dict[key] = tensor.to(dtype)
for model_class in self.all_model_classes:
with tempfile.TemporaryDirectory() as tmpdirname:
model_class(config).save_pretrained(tmpdirname)
model_static = model_class.from_pretrained(
tmpdirname, anchor_image_size=[h, w], device_map=torch_device, dtype=dtype
).eval()
model_dynamic = model_class.from_pretrained(
tmpdirname, anchor_image_size=None, device_map=torch_device, dtype=dtype
).eval()
self.assertIsNotNone(model_static.config.anchor_image_size)
self.assertIsNone(model_dynamic.config.anchor_image_size)
with torch.no_grad():
outputs_static = model_static(**self._prepare_for_class(inputs_dict, model_class))
outputs_dynamic = model_dynamic(**self._prepare_for_class(inputs_dict, model_class))
self.assertTrue(
torch.allclose(
outputs_static.last_hidden_state, outputs_dynamic.last_hidden_state, rtol=1e-4, atol=1e-4
),
f"Max diff: {(outputs_static.last_hidden_state - outputs_dynamic.last_hidden_state).abs().max()}",
)
TOLERANCE = 1e-4
# We will verify our results on an image of cute cats
def prepare_img():
image = Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png")
return image
@require_torch
@require_vision
class RTDetrModelIntegrationTest(unittest.TestCase):
@cached_property
def default_image_processor(self):
return RTDetrImageProcessor.from_pretrained(CHECKPOINT) if is_vision_available() else None
def test_inference_object_detection_head(self):
model = RTDetrForObjectDetection.from_pretrained(CHECKPOINT).to(torch_device)
image_processor = self.default_image_processor
image = prepare_img()
inputs = image_processor(images=image, return_tensors="pt").to(torch_device)
with torch.no_grad():
outputs = model(**inputs)
expected_shape_logits = torch.Size((1, 300, model.config.num_labels))
self.assertEqual(outputs.logits.shape, expected_shape_logits)
expectations = Expectations(
{
(None, None): [
[-4.64763879776001, -5.001153945922852, -4.978509902954102],
[-4.159348487854004, -4.703853607177734, -5.946484565734863],
[-4.437461853027344, -4.65836238861084, -6.235235691070557],
],
("cuda", 8): [[-4.6471, -5.0008, -4.9786], [-4.1599, -4.7041, -5.9458], [-4.4374, -4.6582, -6.2340]],
}
)
expected_logits = torch.tensor(expectations.get_expectation()).to(torch_device)
expectations = Expectations(
{
(None, None): [
[0.1688060760498047, 0.19992263615131378, 0.21225441992282867],
[0.768376350402832, 0.41226309537887573, 0.4636859893798828],
[0.25953856110572815, 0.5483334064483643, 0.4777486026287079],
],
("cuda", 8): [[0.1688, 0.1999, 0.2123], [0.7684, 0.4123, 0.4637], [0.2596, 0.5483, 0.4777]],
}
)
expected_boxes = torch.tensor(expectations.get_expectation()).to(torch_device)
torch.testing.assert_close(outputs.logits[0, :3, :3], expected_logits, rtol=2e-4, atol=2e-4)
expected_shape_boxes = torch.Size((1, 300, 4))
self.assertEqual(outputs.pred_boxes.shape, expected_shape_boxes)
torch.testing.assert_close(outputs.pred_boxes[0, :3, :3], expected_boxes, rtol=2e-4, atol=2e-4)
# verify postprocessing
results = image_processor.post_process_object_detection(
outputs, threshold=0.0, target_sizes=[image.size[::-1]]
)[0]
expectations = Expectations(
{
(None, None): [0.9703017473220825, 0.9599503874778748, 0.9575679302215576, 0.9506784677505493],
("cuda", 8): [0.9704, 0.9599, 0.9576, 0.9507],
}
)
expected_scores = torch.tensor(expectations.get_expectation()).to(torch_device)
expected_labels = [57, 15, 15, 65]
expectations = Expectations(
{
(None, None): [
[0.13774872, 0.37821293, 640.13074, 476.21088],
[343.38132, 24.276838, 640.1404, 371.49573],
[13.225126, 54.179348, 318.98422, 472.2207],
[40.114475, 73.44104, 175.9573, 118.48469],
],
("cuda", 8): [
[1.3775e-01, 3.7821e-01, 6.4013e02, 4.7621e02],
[3.4338e02, 2.4277e01, 6.4014e02, 3.7150e02],
[1.3225e01, 5.4179e01, 3.1898e02, 4.7222e02],
[4.0114e01, 7.3441e01, 1.7596e02, 1.1848e02],
],
}
)
expected_slice_boxes = torch.tensor(expectations.get_expectation()).to(torch_device)
torch.testing.assert_close(results["scores"][:4], expected_scores, rtol=2e-4, atol=2e-4)
self.assertSequenceEqual(results["labels"][:4].tolist(), expected_labels)
torch.testing.assert_close(results["boxes"][:4], expected_slice_boxes, rtol=2e-4, atol=2e-4)

100
transformers/tests/models/rt_detr/test_modeling_rt_detr_resnet.py Normal file
View File

@@ -0,0 +1,100 @@
# Copyright 2023 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import unittest
from transformers import RTDetrResNetConfig
from transformers.testing_utils import require_torch
from transformers.utils.import_utils import is_torch_available
from ...test_backbone_common import BackboneTesterMixin
from ...test_modeling_common import floats_tensor, ids_tensor
if is_torch_available():
from transformers import RTDetrResNetBackbone
class RTDetrResNetModelTester:
def __init__(
self,
parent,
batch_size=3,
image_size=32,
num_channels=3,
embeddings_size=10,
hidden_sizes=[10, 20, 30, 40],
depths=[1, 1, 2, 1],
is_training=True,
use_labels=True,
hidden_act="relu",
num_labels=3,
scope=None,
out_features=["stage2", "stage3", "stage4"],
out_indices=[2, 3, 4],
):
self.parent = parent
self.batch_size = batch_size
self.image_size = image_size
self.num_channels = num_channels
self.embeddings_size = embeddings_size
self.hidden_sizes = hidden_sizes
self.depths = depths
self.is_training = is_training
self.use_labels = use_labels
self.hidden_act = hidden_act
self.num_labels = num_labels
self.scope = scope
self.num_stages = len(hidden_sizes)
self.out_features = out_features
self.out_indices = out_indices
def prepare_config_and_inputs(self):
pixel_values = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size])
labels = None
if self.use_labels:
labels = ids_tensor([self.batch_size], self.num_labels)
config = self.get_config()
return config, pixel_values, labels
def get_config(self):
return RTDetrResNetConfig(
num_channels=self.num_channels,
embeddings_size=self.embeddings_size,
hidden_sizes=self.hidden_sizes,
depths=self.depths,
hidden_act=self.hidden_act,
num_labels=self.num_labels,
out_features=self.out_features,
out_indices=self.out_indices,
)
def prepare_config_and_inputs_for_common(self):
config_and_inputs = self.prepare_config_and_inputs()
config, pixel_values, labels = config_and_inputs
inputs_dict = {"pixel_values": pixel_values}
return config, inputs_dict
@require_torch
class RTDetrResNetBackboneTest(BackboneTesterMixin, unittest.TestCase):
all_model_classes = (RTDetrResNetBackbone,) if is_torch_available() else ()
has_attentions = False
config_class = RTDetrResNetConfig
def setUp(self):
self.model_tester = RTDetrResNetModelTester(self)