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transformers/tests/models/textnet/__init__.py Normal file
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transformers/tests/models/textnet/test_image_processing_textnet.py Normal file
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# Copyright 2024 HuggingFace Inc.
#
# 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.testing_utils import require_torch, require_vision
from transformers.utils import is_torchvision_available, is_vision_available
from ...test_image_processing_common import ImageProcessingTestMixin, prepare_image_inputs
if is_vision_available():
from transformers import TextNetImageProcessor
if is_torchvision_available():
from transformers import TextNetImageProcessorFast
class TextNetImageProcessingTester:
def __init__(
self,
parent,
batch_size=7,
num_channels=3,
image_size=18,
min_resolution=30,
max_resolution=400,
do_resize=True,
size=None,
size_divisor=32,
do_center_crop=True,
crop_size=None,
do_normalize=True,
image_mean=[0.48145466, 0.4578275, 0.40821073],
image_std=[0.26862954, 0.26130258, 0.27577711],
do_convert_rgb=True,
):
size = size if size is not None else {"shortest_edge": 20}
crop_size = crop_size if crop_size is not None else {"height": 18, "width": 18}
self.parent = parent
self.batch_size = batch_size
self.num_channels = num_channels
self.image_size = image_size
self.min_resolution = min_resolution
self.max_resolution = max_resolution
self.do_resize = do_resize
self.size = size
self.size_divisor = size_divisor
self.do_center_crop = do_center_crop
self.crop_size = crop_size
self.do_normalize = do_normalize
self.image_mean = image_mean
self.image_std = image_std
self.do_convert_rgb = do_convert_rgb
def prepare_image_processor_dict(self):
return {
"do_resize": self.do_resize,
"size": self.size,
"size_divisor": self.size_divisor,
"do_center_crop": self.do_center_crop,
"crop_size": self.crop_size,
"do_normalize": self.do_normalize,
"image_mean": self.image_mean,
"image_std": self.image_std,
"do_convert_rgb": self.do_convert_rgb,
}
def expected_output_image_shape(self, images):
return self.num_channels, self.crop_size["height"], self.crop_size["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=self.min_resolution,
max_resolution=self.max_resolution,
equal_resolution=equal_resolution,
numpify=numpify,
torchify=torchify,
)
@require_torch
@require_vision
class TextNetImageProcessingTest(ImageProcessingTestMixin, unittest.TestCase):
image_processing_class = TextNetImageProcessor if is_vision_available() else None
fast_image_processing_class = TextNetImageProcessorFast if is_torchvision_available() else None
def setUp(self):
super().setUp()
self.image_processor_tester = TextNetImageProcessingTester(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, "size_divisor"))
self.assertTrue(hasattr(image_processing, "do_center_crop"))
self.assertTrue(hasattr(image_processing, "center_crop"))
self.assertTrue(hasattr(image_processing, "do_normalize"))
self.assertTrue(hasattr(image_processing, "image_mean"))
self.assertTrue(hasattr(image_processing, "image_std"))
self.assertTrue(hasattr(image_processing, "do_convert_rgb"))
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, {"shortest_edge": 20})
self.assertEqual(image_processor.crop_size, {"height": 18, "width": 18})
image_processor = image_processing_class.from_dict(self.image_processor_dict, size=42, crop_size=84)
self.assertEqual(image_processor.size, {"shortest_edge": 42})
self.assertEqual(image_processor.crop_size, {"height": 84, "width": 84})

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transformers/tests/models/textnet/test_modeling_textnet.py Normal file
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# Copyright 2024 the Fast authors and 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 TextNet model."""
import unittest
import requests
from PIL import Image
from transformers import TextNetConfig
from transformers.models.textnet.image_processing_textnet import TextNetImageProcessor
from transformers.testing_utils import (
require_torch,
require_vision,
slow,
torch_device,
)
from transformers.utils import is_torch_available
from ...test_backbone_common import BackboneTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from torch import nn
from transformers import TextNetBackbone, TextNetForImageClassification, TextNetModel
class TextNetConfigTester(ConfigTester):
def create_and_test_config_common_properties(self):
config = self.config_class(**self.inputs_dict)
self.parent.assertTrue(hasattr(config, "hidden_sizes"))
self.parent.assertTrue(hasattr(config, "num_attention_heads"))
self.parent.assertTrue(hasattr(config, "num_encoder_blocks"))
class TextNetModelTester:
def __init__(
self,
parent,
stem_kernel_size=3,
stem_stride=2,
stem_in_channels=3,
stem_out_channels=32,
stem_act_func="relu",
dropout_rate=0,
ops_order="weight_bn_act",
conv_layer_kernel_sizes=[
[[3, 3]],
[[3, 3]],
[[3, 3]],
[[3, 3]],
],
conv_layer_strides=[
[2],
[2],
[2],
[2],
],
out_features=["stage1", "stage2", "stage3", "stage4"],
out_indices=[1, 2, 3, 4],
batch_size=3,
num_channels=3,
image_size=[32, 32],
is_training=True,
use_labels=True,
num_labels=3,
hidden_sizes=[32, 32, 32, 32, 32],
):
self.parent = parent
self.stem_kernel_size = stem_kernel_size
self.stem_stride = stem_stride
self.stem_in_channels = stem_in_channels
self.stem_out_channels = stem_out_channels
self.act_func = stem_act_func
self.dropout_rate = dropout_rate
self.ops_order = ops_order
self.conv_layer_kernel_sizes = conv_layer_kernel_sizes
self.conv_layer_strides = conv_layer_strides
self.out_features = out_features
self.out_indices = out_indices
self.batch_size = batch_size
self.num_channels = num_channels
self.image_size = image_size
self.is_training = is_training
self.use_labels = use_labels
self.num_labels = num_labels
self.hidden_sizes = hidden_sizes
self.num_stages = 5
def get_config(self):
return TextNetConfig(
stem_kernel_size=self.stem_kernel_size,
stem_stride=self.stem_stride,
stem_num_channels=self.stem_in_channels,
stem_out_channels=self.stem_out_channels,
act_func=self.act_func,
dropout_rate=self.dropout_rate,
ops_order=self.ops_order,
conv_layer_kernel_sizes=self.conv_layer_kernel_sizes,
conv_layer_strides=self.conv_layer_strides,
out_features=self.out_features,
out_indices=self.out_indices,
hidden_sizes=self.hidden_sizes,
image_size=self.image_size,
)
def create_and_check_model(self, config, pixel_values, labels):
model = TextNetModel(config=config)
model.to(torch_device)
model.eval()
result = model(pixel_values)
scale_h = self.image_size[0] // 32
scale_w = self.image_size[1] // 32
self.parent.assertEqual(
result.last_hidden_state.shape,
(self.batch_size, self.hidden_sizes[-1], scale_h, scale_w),
)
def create_and_check_for_image_classification(self, config, pixel_values, labels):
config.num_labels = self.num_labels
model = TextNetForImageClassification(config)
model.to(torch_device)
model.eval()
result = model(pixel_values, labels=labels)
self.parent.assertEqual(result.logits.shape, (self.batch_size, self.num_labels))
def prepare_config_and_inputs(self):
pixel_values = floats_tensor([self.batch_size, self.num_channels, self.image_size[0], self.image_size[1]])
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 create_and_check_backbone(self, config, pixel_values, labels):
model = TextNetBackbone(config=config)
model.to(torch_device)
model.eval()
result = model(pixel_values)
# verify feature maps
self.parent.assertEqual(len(result.feature_maps), len(config.out_features))
scale_h = self.image_size[0] // 32
scale_w = self.image_size[1] // 32
self.parent.assertListEqual(
list(result.feature_maps[0].shape), [self.batch_size, self.hidden_sizes[1], 8 * scale_h, 8 * scale_w]
)
# verify channels
self.parent.assertEqual(len(model.channels), len(config.out_features))
self.parent.assertListEqual(model.channels, config.hidden_sizes[1:])
# verify backbone works with out_features=None
config.out_features = None
model = TextNetBackbone(config=config)
model.to(torch_device)
model.eval()
result = model(pixel_values)
# verify feature maps
self.parent.assertEqual(len(result.feature_maps), 1)
scale_h = self.image_size[0] // 32
scale_w = self.image_size[1] // 32
self.parent.assertListEqual(
list(result.feature_maps[0].shape), [self.batch_size, self.hidden_sizes[0], scale_h, scale_w]
)
# verify channels
self.parent.assertEqual(len(model.channels), 1)
self.parent.assertListEqual(model.channels, [config.hidden_sizes[-1]])
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 TextNetModelTest(ModelTesterMixin, PipelineTesterMixin, unittest.TestCase):
"""
Here we also overwrite some tests of test_modeling_common.py, as TextNet does not use input_ids, inputs_embeds,
attention_mask and seq_length.
"""
all_model_classes = (TextNetModel, TextNetForImageClassification, TextNetBackbone) if is_torch_available() else ()
pipeline_model_mapping = (
{"feature-extraction": TextNetModel, "image-classification": TextNetForImageClassification}
if is_torch_available()
else {}
)
fx_compatible = False
test_pruning = False
test_resize_embeddings = False
test_head_masking = False
test_torch_exportable = True
has_attentions = False
def setUp(self):
self.model_tester = TextNetModelTester(self)
self.config_tester = TextNetConfigTester(self, config_class=TextNetConfig, has_text_modality=False)
@unittest.skip(reason="TextNet does not output attentions")
def test_attention_outputs(self):
pass
@unittest.skip(reason="TextNet does not have input/output embeddings")
def test_model_get_set_embeddings(self):
pass
@unittest.skip(reason="TextNet does not use inputs_embeds")
def test_inputs_embeds(self):
pass
@unittest.skip(reason="TextNet does not support input and output embeddings")
def test_model_common_attributes(self):
pass
def test_model(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*config_and_inputs)
def test_backbone(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_backbone(*config_and_inputs)
def test_initialization(self):
config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
model = model_class(config=config)
for name, module in model.named_modules():
if isinstance(module, (nn.BatchNorm2d, nn.GroupNorm)):
self.assertTrue(
torch.all(module.weight == 1),
msg=f"Parameter {name} of model {model_class} seems not properly initialized",
)
self.assertTrue(
torch.all(module.bias == 0),
msg=f"Parameter {name} of model {model_class} seems not properly initialized",
)
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
self.assertEqual(len(hidden_states), self.model_tester.num_stages)
self.assertListEqual(
list(hidden_states[0].shape[-2:]),
[self.model_tester.image_size[0] // 2, self.model_tester.image_size[1] // 2],
)
config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
layers_type = ["preactivation", "bottleneck"]
for model_class in self.all_model_classes:
for layer_type in layers_type:
config.layer_type = layer_type
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)
@unittest.skip(reason="TextNet does not use feedforward chunking")
def test_feed_forward_chunking(self):
pass
def test_for_image_classification(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_image_classification(*config_and_inputs)
@slow
def test_model_from_pretrained(self):
model_name = "czczup/textnet-base"
model = TextNetModel.from_pretrained(model_name)
self.assertIsNotNone(model)
@require_torch
@require_vision
class TextNetModelIntegrationTest(unittest.TestCase):
@slow
def test_inference_no_head(self):
processor = TextNetImageProcessor.from_pretrained("czczup/textnet-base")
model = TextNetModel.from_pretrained("czczup/textnet-base").to(torch_device)
# prepare 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").to(torch_device)
# forward pass
with torch.no_grad():
output = model(**inputs)
# verify output
self.assertEqual(output.last_hidden_state.shape, torch.Size([1, 512, 20, 27]))
expected_slice_backbone = torch.tensor(
[
[0.0000, 1.7415, 1.2660],
[0.0000, 1.0084, 1.9692],
[0.0000, 1.7464, 1.7892],
],
device=torch_device,
)
torch.testing.assert_close(
output.last_hidden_state[0, 12, :3, :3], expected_slice_backbone, rtol=1e-2, atol=1e-2
)
@require_torch
# Copied from tests.models.bit.test_modeling_bit.BitBackboneTest with Bit->TextNet
class TextNetBackboneTest(BackboneTesterMixin, unittest.TestCase):
all_model_classes = (TextNetBackbone,) if is_torch_available() else ()
config_class = TextNetConfig
has_attentions = False
def setUp(self):
self.model_tester = TextNetModelTester(self)