293 lines
11 KiB
Python
293 lines
11 KiB
Python
# coding=utf-8
|
|
# 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.
|
|
""" Testing suite for the PyTorch SwiftFormer model. """
|
|
|
|
|
|
import copy
|
|
import unittest
|
|
|
|
from transformers import PretrainedConfig, SwiftFormerConfig
|
|
from transformers.testing_utils import (
|
|
require_torch,
|
|
require_vision,
|
|
slow,
|
|
torch_device,
|
|
)
|
|
from transformers.utils import cached_property, is_torch_available, is_vision_available
|
|
|
|
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 SwiftFormerForImageClassification, SwiftFormerModel
|
|
|
|
|
|
if is_vision_available():
|
|
from PIL import Image
|
|
|
|
from transformers import ViTImageProcessor
|
|
|
|
|
|
class SwiftFormerModelTester:
|
|
def __init__(
|
|
self,
|
|
parent,
|
|
batch_size=13,
|
|
num_channels=3,
|
|
is_training=True,
|
|
use_labels=True,
|
|
hidden_dropout_prob=0.1,
|
|
attention_probs_dropout_prob=0.1,
|
|
image_size=224,
|
|
num_labels=3,
|
|
layer_depths=[1, 1, 1, 1],
|
|
embed_dims=[16, 16, 32, 32],
|
|
):
|
|
self.parent = parent
|
|
self.batch_size = batch_size
|
|
self.num_channels = num_channels
|
|
self.is_training = is_training
|
|
self.use_labels = use_labels
|
|
self.hidden_dropout_prob = hidden_dropout_prob
|
|
self.attention_probs_dropout_prob = attention_probs_dropout_prob
|
|
self.num_labels = num_labels
|
|
self.image_size = image_size
|
|
self.layer_depths = layer_depths
|
|
self.embed_dims = embed_dims
|
|
|
|
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 SwiftFormerConfig(
|
|
depths=self.layer_depths,
|
|
embed_dims=self.embed_dims,
|
|
mlp_ratio=4,
|
|
downsamples=[True, True, True, True],
|
|
hidden_act="gelu",
|
|
num_labels=self.num_labels,
|
|
down_patch_size=3,
|
|
down_stride=2,
|
|
down_pad=1,
|
|
drop_rate=0.0,
|
|
drop_path_rate=0.0,
|
|
use_layer_scale=True,
|
|
layer_scale_init_value=1e-5,
|
|
)
|
|
|
|
def create_and_check_model(self, config, pixel_values, labels):
|
|
model = SwiftFormerModel(config=config)
|
|
model.to(torch_device)
|
|
model.eval()
|
|
result = model(pixel_values)
|
|
self.parent.assertEqual(result.last_hidden_state.shape, (self.batch_size, self.embed_dims[-1], 7, 7))
|
|
|
|
def create_and_check_for_image_classification(self, config, pixel_values, labels):
|
|
config.num_labels = self.num_labels
|
|
model = SwiftFormerForImageClassification(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))
|
|
|
|
model = SwiftFormerForImageClassification(config)
|
|
model.to(torch_device)
|
|
model.eval()
|
|
|
|
pixel_values = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size])
|
|
result = model(pixel_values)
|
|
self.parent.assertEqual(result.logits.shape, (self.batch_size, self.num_labels))
|
|
|
|
def prepare_config_and_inputs_for_common(self):
|
|
(config, pixel_values, labels) = self.prepare_config_and_inputs()
|
|
inputs_dict = {"pixel_values": pixel_values}
|
|
return config, inputs_dict
|
|
|
|
|
|
@require_torch
|
|
class SwiftFormerModelTest(ModelTesterMixin, PipelineTesterMixin, unittest.TestCase):
|
|
"""
|
|
Here we also overwrite some of the tests of test_modeling_common.py, as SwiftFormer does not use input_ids, inputs_embeds,
|
|
attention_mask and seq_length.
|
|
"""
|
|
|
|
all_model_classes = (SwiftFormerModel, SwiftFormerForImageClassification) if is_torch_available() else ()
|
|
pipeline_model_mapping = (
|
|
{"image-feature-extraction": SwiftFormerModel, "image-classification": SwiftFormerForImageClassification}
|
|
if is_torch_available()
|
|
else {}
|
|
)
|
|
|
|
fx_compatible = False
|
|
test_pruning = False
|
|
test_resize_embeddings = False
|
|
test_head_masking = False
|
|
has_attentions = False
|
|
|
|
def setUp(self):
|
|
self.model_tester = SwiftFormerModelTester(self)
|
|
self.config_tester = ConfigTester(
|
|
self,
|
|
config_class=SwiftFormerConfig,
|
|
has_text_modality=False,
|
|
hidden_size=37,
|
|
num_attention_heads=12,
|
|
num_hidden_layers=12,
|
|
)
|
|
|
|
def test_config(self):
|
|
self.config_tester.run_common_tests()
|
|
|
|
@unittest.skip(reason="SwiftFormer does not use inputs_embeds")
|
|
def test_inputs_embeds(self):
|
|
pass
|
|
|
|
def test_model_common_attributes(self):
|
|
config, _ = self.model_tester.prepare_config_and_inputs_for_common()
|
|
|
|
for model_class in self.all_model_classes:
|
|
model = model_class(config)
|
|
x = model.get_output_embeddings()
|
|
self.assertTrue(x is None or isinstance(x, nn.Linear))
|
|
|
|
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_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 = "MBZUAI/swiftformer-xs"
|
|
model = SwiftFormerModel.from_pretrained(model_name)
|
|
self.assertIsNotNone(model)
|
|
|
|
@unittest.skip(reason="SwiftFormer does not output attentions")
|
|
def test_attention_outputs(self):
|
|
pass
|
|
|
|
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.hidden_states
|
|
|
|
expected_num_stages = 8
|
|
self.assertEqual(len(hidden_states), expected_num_stages) # TODO
|
|
|
|
# SwiftFormer's feature maps are of shape (batch_size, embed_dims, height, width)
|
|
# with the width and height being successively divided by 2, after every 2 blocks
|
|
for i in range(len(hidden_states)):
|
|
self.assertEqual(
|
|
hidden_states[i].shape,
|
|
torch.Size(
|
|
[
|
|
self.model_tester.batch_size,
|
|
self.model_tester.embed_dims[i // 2],
|
|
(self.model_tester.image_size // 4) // 2 ** (i // 2),
|
|
(self.model_tester.image_size // 4) // 2 ** (i // 2),
|
|
]
|
|
),
|
|
)
|
|
|
|
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_initialization(self):
|
|
def _config_zero_init(config):
|
|
configs_no_init = copy.deepcopy(config)
|
|
for key in configs_no_init.__dict__.keys():
|
|
if "_range" in key or "_std" in key or "initializer_factor" in key or "layer_scale" in key:
|
|
setattr(configs_no_init, key, 1e-10)
|
|
if isinstance(getattr(configs_no_init, key, None), PretrainedConfig):
|
|
no_init_subconfig = _config_zero_init(getattr(configs_no_init, key))
|
|
setattr(configs_no_init, key, no_init_subconfig)
|
|
return configs_no_init
|
|
|
|
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:
|
|
self.assertIn(
|
|
((param.data.mean() * 1e9) / 1e9).round().item(),
|
|
[0.0, 1.0],
|
|
msg=f"Parameter {name} of model {model_class} seems not properly initialized",
|
|
)
|
|
|
|
|
|
# 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 SwiftFormerModelIntegrationTest(unittest.TestCase):
|
|
@cached_property
|
|
def default_image_processor(self):
|
|
return ViTImageProcessor.from_pretrained("MBZUAI/swiftformer-xs") if is_vision_available() else None
|
|
|
|
@slow
|
|
def test_inference_image_classification_head(self):
|
|
model = SwiftFormerForImageClassification.from_pretrained("MBZUAI/swiftformer-xs").to(torch_device)
|
|
|
|
image_processor = self.default_image_processor
|
|
image = prepare_img()
|
|
inputs = image_processor(images=image, return_tensors="pt").to(torch_device)
|
|
|
|
# forward pass
|
|
with torch.no_grad():
|
|
outputs = model(**inputs)
|
|
|
|
# verify the logits
|
|
expected_shape = torch.Size((1, 1000))
|
|
self.assertEqual(outputs.logits.shape, expected_shape)
|
|
|
|
expected_slice = torch.tensor([[-2.1703e00, 2.1107e00, -2.0811e00]]).to(torch_device)
|
|
self.assertTrue(torch.allclose(outputs.logits[0, :3], expected_slice, atol=1e-4))
|