mirror of
https://github.com/huggingface/transformers.git
synced 2025-07-04 21:30:07 +06:00
b912f5ee43
* use torch.testing.assertclose instead to get more details about error in cis * fix * style * test_all * revert for I bert * fixes and updates * more image processing fixes * more image processors * fix mamba and co * style * less strick * ok I won't be strict * skip and be done * up
775 lines
31 KiB
Python
775 lines
31 KiB
Python
# 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 parameterized import parameterized
|
|
|
|
from transformers import (
|
|
RTDetrConfig,
|
|
RTDetrImageProcessor,
|
|
RTDetrResNetConfig,
|
|
is_torch_available,
|
|
is_vision_available,
|
|
)
|
|
from transformers.testing_utils import (
|
|
require_torch,
|
|
require_torch_accelerator,
|
|
require_vision,
|
|
slow,
|
|
torch_device,
|
|
)
|
|
from transformers.utils import cached_property
|
|
|
|
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
|
|
|
|
# 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(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 propogated to backbone
|
|
self.assertEqual(len(model.model.backbone.intermediate_channel_sizes), 3)
|
|
else:
|
|
# Confirm out_indices was propogated 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 propogated to backbone
|
|
self.assertEqual(len(model.model.backbone.intermediate_channel_sizes), 3)
|
|
else:
|
|
# Confirm out_indices was propogated 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().keys()]
|
|
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, torch_dtype_str):
|
|
torch_dtype = {
|
|
"float32": torch.float32,
|
|
"float16": torch.float16,
|
|
"bfloat16": torch.bfloat16,
|
|
}[torch_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(torch_dtype)
|
|
model.eval()
|
|
for key, tensor in inputs_dict.items():
|
|
if tensor.dtype == torch.float32:
|
|
inputs_dict[key] = tensor.to(torch_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, torch_dtype_str):
|
|
torch_dtype = {
|
|
"float32": torch.float32,
|
|
"float16": torch.float16,
|
|
"bfloat16": torch.bfloat16,
|
|
}[torch_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(torch_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, torch_dtype=torch_dtype
|
|
).eval()
|
|
model_dynamic = model_class.from_pretrained(
|
|
tmpdirname, anchor_image_size=None, device_map=torch_device, torch_dtype=torch_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)
|
|
|
|
expected_logits = torch.tensor(
|
|
[
|
|
[-4.64763879776001, -5.001153945922852, -4.978509902954102],
|
|
[-4.159348487854004, -4.703853607177734, -5.946484565734863],
|
|
[-4.437461853027344, -4.65836238861084, -6.235235691070557],
|
|
]
|
|
).to(torch_device)
|
|
expected_boxes = torch.tensor(
|
|
[
|
|
[0.1688060760498047, 0.19992263615131378, 0.21225441992282867],
|
|
[0.768376350402832, 0.41226309537887573, 0.4636859893798828],
|
|
[0.25953856110572815, 0.5483334064483643, 0.4777486026287079],
|
|
]
|
|
).to(torch_device)
|
|
|
|
torch.testing.assert_close(outputs.logits[0, :3, :3], expected_logits, rtol=1e-4, atol=1e-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=1e-4, atol=1e-4)
|
|
|
|
# verify postprocessing
|
|
results = image_processor.post_process_object_detection(
|
|
outputs, threshold=0.0, target_sizes=[image.size[::-1]]
|
|
)[0]
|
|
expected_scores = torch.tensor(
|
|
[0.9703017473220825, 0.9599503874778748, 0.9575679302215576, 0.9506784677505493], device=torch_device
|
|
)
|
|
expected_labels = [57, 15, 15, 65]
|
|
expected_slice_boxes = torch.tensor(
|
|
[
|
|
[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],
|
|
],
|
|
device=torch_device,
|
|
)
|
|
|
|
torch.testing.assert_close(results["scores"][:4], expected_scores, rtol=1e-4, atol=1e-4)
|
|
self.assertSequenceEqual(results["labels"][:4].tolist(), expected_labels)
|
|
torch.testing.assert_close(results["boxes"][:4], expected_slice_boxes, rtol=1e-4, atol=1e-4)
|