334 lines
13 KiB
Python
334 lines
13 KiB
Python
# coding=utf-8
|
|
# Copyright 2022 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 Swin2SR model. """
|
|
import unittest
|
|
|
|
from transformers import Swin2SRConfig
|
|
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
|
|
from transformers.utils import is_torch_available, is_vision_available
|
|
|
|
from ...test_configuration_common import ConfigTester
|
|
from ...test_modeling_common import ModelTesterMixin, _config_zero_init, floats_tensor, ids_tensor
|
|
from ...test_pipeline_mixin import PipelineTesterMixin
|
|
|
|
|
|
if is_torch_available():
|
|
import torch
|
|
from torch import nn
|
|
|
|
from transformers import Swin2SRForImageSuperResolution, Swin2SRModel
|
|
|
|
if is_vision_available():
|
|
from PIL import Image
|
|
|
|
from transformers import Swin2SRImageProcessor
|
|
|
|
|
|
class Swin2SRModelTester:
|
|
def __init__(
|
|
self,
|
|
parent,
|
|
batch_size=13,
|
|
image_size=32,
|
|
patch_size=1,
|
|
num_channels=3,
|
|
num_channels_out=1,
|
|
embed_dim=16,
|
|
depths=[1, 2, 1],
|
|
num_heads=[2, 2, 4],
|
|
window_size=2,
|
|
mlp_ratio=2.0,
|
|
qkv_bias=True,
|
|
hidden_dropout_prob=0.0,
|
|
attention_probs_dropout_prob=0.0,
|
|
drop_path_rate=0.1,
|
|
hidden_act="gelu",
|
|
use_absolute_embeddings=False,
|
|
patch_norm=True,
|
|
initializer_range=0.02,
|
|
layer_norm_eps=1e-5,
|
|
is_training=True,
|
|
scope=None,
|
|
use_labels=False,
|
|
upscale=2,
|
|
):
|
|
self.parent = parent
|
|
self.batch_size = batch_size
|
|
self.image_size = image_size
|
|
self.patch_size = patch_size
|
|
self.num_channels = num_channels
|
|
self.num_channels_out = num_channels_out
|
|
self.embed_dim = embed_dim
|
|
self.depths = depths
|
|
self.num_heads = num_heads
|
|
self.window_size = window_size
|
|
self.mlp_ratio = mlp_ratio
|
|
self.qkv_bias = qkv_bias
|
|
self.hidden_dropout_prob = hidden_dropout_prob
|
|
self.attention_probs_dropout_prob = attention_probs_dropout_prob
|
|
self.drop_path_rate = drop_path_rate
|
|
self.hidden_act = hidden_act
|
|
self.use_absolute_embeddings = use_absolute_embeddings
|
|
self.patch_norm = patch_norm
|
|
self.layer_norm_eps = layer_norm_eps
|
|
self.initializer_range = initializer_range
|
|
self.is_training = is_training
|
|
self.scope = scope
|
|
self.use_labels = use_labels
|
|
self.upscale = upscale
|
|
|
|
# here we set some attributes to make tests pass
|
|
self.num_hidden_layers = len(depths)
|
|
self.hidden_size = embed_dim
|
|
self.seq_length = (image_size // patch_size) ** 2
|
|
|
|
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.type_sequence_label_size)
|
|
|
|
config = self.get_config()
|
|
|
|
return config, pixel_values, labels
|
|
|
|
def get_config(self):
|
|
return Swin2SRConfig(
|
|
image_size=self.image_size,
|
|
patch_size=self.patch_size,
|
|
num_channels=self.num_channels,
|
|
num_channels_out=self.num_channels_out,
|
|
embed_dim=self.embed_dim,
|
|
depths=self.depths,
|
|
num_heads=self.num_heads,
|
|
window_size=self.window_size,
|
|
mlp_ratio=self.mlp_ratio,
|
|
qkv_bias=self.qkv_bias,
|
|
hidden_dropout_prob=self.hidden_dropout_prob,
|
|
attention_probs_dropout_prob=self.attention_probs_dropout_prob,
|
|
drop_path_rate=self.drop_path_rate,
|
|
hidden_act=self.hidden_act,
|
|
use_absolute_embeddings=self.use_absolute_embeddings,
|
|
path_norm=self.patch_norm,
|
|
layer_norm_eps=self.layer_norm_eps,
|
|
initializer_range=self.initializer_range,
|
|
upscale=self.upscale,
|
|
)
|
|
|
|
def create_and_check_model(self, config, pixel_values, labels):
|
|
model = Swin2SRModel(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_dim, self.image_size, self.image_size)
|
|
)
|
|
|
|
def create_and_check_for_image_super_resolution(self, config, pixel_values, labels):
|
|
model = Swin2SRForImageSuperResolution(config)
|
|
model.to(torch_device)
|
|
model.eval()
|
|
result = model(pixel_values)
|
|
|
|
expected_image_size = self.image_size * self.upscale
|
|
self.parent.assertEqual(
|
|
result.reconstruction.shape,
|
|
(self.batch_size, self.num_channels_out, expected_image_size, expected_image_size),
|
|
)
|
|
|
|
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 Swin2SRModelTest(ModelTesterMixin, PipelineTesterMixin, unittest.TestCase):
|
|
all_model_classes = (Swin2SRModel, Swin2SRForImageSuperResolution) if is_torch_available() else ()
|
|
pipeline_model_mapping = (
|
|
{"image-feature-extraction": Swin2SRModel, "image-to-image": Swin2SRForImageSuperResolution}
|
|
if is_torch_available()
|
|
else {}
|
|
)
|
|
|
|
fx_compatible = False
|
|
test_pruning = False
|
|
test_resize_embeddings = False
|
|
test_head_masking = False
|
|
test_torchscript = False
|
|
|
|
def setUp(self):
|
|
self.model_tester = Swin2SRModelTester(self)
|
|
self.config_tester = ConfigTester(self, config_class=Swin2SRConfig, embed_dim=37)
|
|
|
|
def test_config(self):
|
|
self.config_tester.create_and_test_config_to_json_string()
|
|
self.config_tester.create_and_test_config_to_json_file()
|
|
self.config_tester.create_and_test_config_from_and_save_pretrained()
|
|
self.config_tester.create_and_test_config_with_num_labels()
|
|
self.config_tester.check_config_can_be_init_without_params()
|
|
self.config_tester.check_config_arguments_init()
|
|
|
|
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_model_for_image_super_resolution(self):
|
|
config_and_inputs = self.model_tester.prepare_config_and_inputs()
|
|
self.model_tester.create_and_check_for_image_super_resolution(*config_and_inputs)
|
|
|
|
# TODO: check if this works again for PyTorch 2.x.y
|
|
@unittest.skip(reason="Got `CUDA error: misaligned address` with PyTorch 2.0.0.")
|
|
def test_multi_gpu_data_parallel_forward(self):
|
|
pass
|
|
|
|
@unittest.skip(reason="Swin2SR does not use inputs_embeds")
|
|
def test_inputs_embeds(self):
|
|
pass
|
|
|
|
@unittest.skip(reason="Swin2SR does not support training yet")
|
|
def test_training(self):
|
|
pass
|
|
|
|
@unittest.skip(reason="Swin2SR does not support training yet")
|
|
def test_training_gradient_checkpointing(self):
|
|
pass
|
|
|
|
@unittest.skip(
|
|
reason="This architecure seem to not compute gradients properly when using GC, check: https://github.com/huggingface/transformers/pull/27124"
|
|
)
|
|
def test_training_gradient_checkpointing_use_reentrant(self):
|
|
pass
|
|
|
|
@unittest.skip(
|
|
reason="This architecure seem to not compute gradients properly when using GC, check: https://github.com/huggingface/transformers/pull/27124"
|
|
)
|
|
def test_training_gradient_checkpointing_use_reentrant_false(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)
|
|
self.assertIsInstance(model.get_input_embeddings(), (nn.Module))
|
|
x = model.get_output_embeddings()
|
|
self.assertTrue(x is None or isinstance(x, nn.Linear))
|
|
|
|
@slow
|
|
def test_model_from_pretrained(self):
|
|
model_name = "caidas/swin2SR-classical-sr-x2-64"
|
|
model = Swin2SRModel.from_pretrained(model_name)
|
|
self.assertIsNotNone(model)
|
|
|
|
# overwriting because of `logit_scale` parameter
|
|
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 "logit_scale" in name:
|
|
continue
|
|
if param.requires_grad:
|
|
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",
|
|
)
|
|
|
|
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.attentions
|
|
expected_num_attentions = len(self.model_tester.depths)
|
|
self.assertEqual(len(attentions), expected_num_attentions)
|
|
|
|
# check that output_attentions also work using config
|
|
del inputs_dict["output_attentions"]
|
|
config.output_attentions = True
|
|
window_size_squared = config.window_size**2
|
|
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.attentions
|
|
self.assertEqual(len(attentions), expected_num_attentions)
|
|
|
|
self.assertListEqual(
|
|
list(attentions[0].shape[-3:]),
|
|
[self.model_tester.num_heads[0], window_size_squared, window_size_squared],
|
|
)
|
|
out_len = len(outputs)
|
|
|
|
# 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))
|
|
|
|
self.assertEqual(out_len + 1, len(outputs))
|
|
|
|
self_attentions = outputs.attentions
|
|
|
|
self.assertEqual(len(self_attentions), expected_num_attentions)
|
|
|
|
self.assertListEqual(
|
|
list(self_attentions[0].shape[-3:]),
|
|
[self.model_tester.num_heads[0], window_size_squared, window_size_squared],
|
|
)
|
|
|
|
|
|
@require_vision
|
|
@require_torch
|
|
@slow
|
|
class Swin2SRModelIntegrationTest(unittest.TestCase):
|
|
def test_inference_image_super_resolution_head(self):
|
|
processor = Swin2SRImageProcessor()
|
|
model = Swin2SRForImageSuperResolution.from_pretrained("caidas/swin2SR-classical-sr-x2-64").to(torch_device)
|
|
|
|
image = Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png")
|
|
inputs = 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, 3, 976, 1296])
|
|
self.assertEqual(outputs.reconstruction.shape, expected_shape)
|
|
expected_slice = torch.tensor(
|
|
[[0.5458, 0.5546, 0.5638], [0.5526, 0.5565, 0.5651], [0.5396, 0.5426, 0.5621]]
|
|
).to(torch_device)
|
|
self.assertTrue(torch.allclose(outputs.reconstruction[0, 0, :3, :3], expected_slice, atol=1e-4))
|