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# coding=utf-8
# Copyright 2025 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 MLCD model."""
import unittest
import requests
from PIL import Image
from transformers import (
AutoProcessor,
MLCDVisionConfig,
MLCDVisionModel,
is_torch_available,
)
from transformers.testing_utils import (
require_torch,
slow,
torch_device,
)
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, _config_zero_init, floats_tensor
if is_torch_available():
import torch
class MLCDVisionModelTester:
def __init__(
self,
parent,
batch_size=12,
image_size=30,
patch_size=2,
num_channels=3,
is_training=True,
hidden_size=32,
num_hidden_layers=2,
num_attention_heads=4,
intermediate_size=37,
dropout=0.1,
attention_dropout=0.1,
initializer_range=0.02,
scope=None,
):
self.parent = parent
self.batch_size = batch_size
self.image_size = image_size
self.patch_size = patch_size
self.num_channels = num_channels
self.is_training = is_training
self.hidden_size = hidden_size
self.num_hidden_layers = num_hidden_layers
self.num_attention_heads = num_attention_heads
self.intermediate_size = intermediate_size
self.dropout = dropout
self.attention_dropout = attention_dropout
self.initializer_range = initializer_range
self.scope = scope
# in MLCD, the seq length equals the number of patches + 1 (we add 1 for the [CLS] token)
num_patches = (image_size // patch_size) ** 2
self.seq_length = num_patches + 1
def prepare_config_and_inputs(self):
pixel_values = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size])
config = self.get_config()
return config, pixel_values
def get_config(self):
return MLCDVisionConfig(
image_size=self.image_size,
patch_size=self.patch_size,
num_channels=self.num_channels,
hidden_size=self.hidden_size,
num_hidden_layers=self.num_hidden_layers,
num_attention_heads=self.num_attention_heads,
intermediate_size=self.intermediate_size,
dropout=self.dropout,
attention_dropout=self.attention_dropout,
initializer_range=self.initializer_range,
)
def create_and_check_model(self, config, pixel_values):
model = MLCDVisionModel(config=config)
model.to(torch_device)
model.eval()
with torch.no_grad():
result = model(pixel_values)
# expected sequence length = num_patches + 1 (we add 1 for the [CLS] token)
image_size = (self.image_size, self.image_size)
patch_size = (self.patch_size, self.patch_size)
num_patches = (image_size[1] // patch_size[1]) * (image_size[0] // patch_size[0])
self.parent.assertEqual(result.last_hidden_state.shape, (self.batch_size, num_patches + 1, self.hidden_size))
self.parent.assertEqual(result.pooler_output.shape, (self.batch_size, self.hidden_size))
def prepare_config_and_inputs_for_common(self):
config_and_inputs = self.prepare_config_and_inputs()
config, pixel_values = config_and_inputs
inputs_dict = {"pixel_values": pixel_values}
return config, inputs_dict
@require_torch
class MLCDVisionModelTest(ModelTesterMixin, unittest.TestCase):
"""
Model tester for `MLCDVisionModel`.
"""
all_model_classes = (MLCDVisionModel,) if is_torch_available() else ()
test_pruning = False
test_head_masking = False
test_torchscript = False
test_resize_embeddings = False
test_torch_exportable = True
def setUp(self):
self.model_tester = MLCDVisionModelTester(self)
self.config_tester = ConfigTester(self, config_class=MLCDVisionConfig, has_text_modality=False)
def test_model_get_set_embeddings(self):
config, _ = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
model = model_class(config)
self.assertIsInstance(model.get_input_embeddings(), (torch.nn.Module))
x = model.get_output_embeddings()
self.assertTrue(x is None or isinstance(x, torch.nn.Linear))
def test_initialization(self):
config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
configs_no_init = _config_zero_init(config)
for model_class in self.all_model_classes:
model = model_class(config=configs_no_init)
for name, param in model.named_parameters():
if param.requires_grad and "class_pos_emb" not in name:
self.assertIn(
((param.data.mean() * 1e9).round() / 1e9).item(),
[0.0, 1.0],
msg=f"Parameter {name} of model {model_class} seems not properly initialized",
)
@require_torch
class MLCDVisionModelIntegrationTest(unittest.TestCase):
@slow
def test_inference(self):
model_name = "DeepGlint-AI/mlcd-vit-bigG-patch14-448"
model = MLCDVisionModel.from_pretrained(model_name).to(torch_device)
processor = AutoProcessor.from_pretrained(model_name)
# process single image
url = "http://images.cocodataset.org/val2017/000000039769.jpg"
image = Image.open(requests.get(url, stream=True).raw)
inputs = processor(images=image, return_tensors="pt")
# move inputs to the same device as the model
inputs = {k: v.to(torch_device) for k, v in inputs.items()}
# forward pass
with torch.no_grad():
outputs = model(**inputs, output_attentions=True)
last_hidden_state = outputs.last_hidden_state
last_attention = outputs.attentions[-1]
# verify the shapes of last_hidden_state and last_attention
self.assertEqual(
last_hidden_state.shape,
torch.Size([1, 1025, 1664]),
)
self.assertEqual(
last_attention.shape,
torch.Size([1, 16, 1025, 1025]),
)
# verify the values of last_hidden_state and last_attention
# fmt: off
expected_partial_5x5_last_hidden_state = torch.tensor(
[
[-0.8978, -0.1181, 0.4769, 0.4761, -0.5779],
[ 0.2640, -2.6150, 0.4853, 0.5743, -1.1003],
[ 0.3314, -0.3328, -0.4305, -0.1874, -0.7701],
[-1.5174, -1.0238, -1.1854, 0.1749, -0.8786],
[ 0.2323, -0.8346, -0.9680, -0.2951, 0.0867],
]
).to(torch_device)
expected_partial_5x5_last_attention = torch.tensor(
[
[2.0930e-01, 6.3073e-05, 1.4717e-03, 2.6881e-05, 3.0513e-03],
[1.5828e-04, 2.1056e-03, 4.6784e-04, 1.8276e-03, 5.3233e-04],
[5.7824e-04, 1.1446e-03, 1.3854e-03, 1.1775e-03, 1.2750e-03],
[9.6343e-05, 1.6365e-03, 2.9066e-04, 3.1089e-03, 2.0607e-04],
[6.2688e-04, 1.1656e-03, 1.5030e-03, 8.2819e-04, 2.6992e-03],
]
).to(torch_device)
# fmt: on
torch.testing.assert_close(
last_hidden_state[0, :5, :5], expected_partial_5x5_last_hidden_state, rtol=1e-3, atol=1e-3
)
torch.testing.assert_close(
last_attention[0, 0, :5, :5], expected_partial_5x5_last_attention, rtol=1e-4, atol=1e-4
)