335 lines
13 KiB
Python
335 lines
13 KiB
Python
# coding=utf-8
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# Copyright 2022 The HuggingFace Inc. team. All rights reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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"""Testing suite for the PyTorch Swin2SR model."""
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import unittest
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from transformers import Swin2SRConfig
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from transformers.testing_utils import require_torch, require_vision, slow, torch_device
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from transformers.utils import is_torch_available, is_vision_available
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from ...test_configuration_common import ConfigTester
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from ...test_modeling_common import ModelTesterMixin, _config_zero_init, floats_tensor, ids_tensor
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from ...test_pipeline_mixin import PipelineTesterMixin
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if is_torch_available():
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import torch
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from torch import nn
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from transformers import Swin2SRForImageSuperResolution, Swin2SRModel
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if is_vision_available():
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from PIL import Image
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from transformers import Swin2SRImageProcessor
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class Swin2SRModelTester:
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def __init__(
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self,
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parent,
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batch_size=13,
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image_size=32,
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patch_size=1,
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num_channels=3,
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num_channels_out=1,
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embed_dim=16,
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depths=[1, 2, 1],
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num_heads=[2, 2, 4],
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window_size=2,
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mlp_ratio=2.0,
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qkv_bias=True,
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hidden_dropout_prob=0.0,
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attention_probs_dropout_prob=0.0,
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drop_path_rate=0.1,
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hidden_act="gelu",
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use_absolute_embeddings=False,
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patch_norm=True,
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initializer_range=0.02,
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layer_norm_eps=1e-5,
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is_training=True,
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scope=None,
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use_labels=False,
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upscale=2,
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):
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self.parent = parent
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self.batch_size = batch_size
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self.image_size = image_size
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self.patch_size = patch_size
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self.num_channels = num_channels
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self.num_channels_out = num_channels_out
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self.embed_dim = embed_dim
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self.depths = depths
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self.num_heads = num_heads
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self.window_size = window_size
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self.mlp_ratio = mlp_ratio
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self.qkv_bias = qkv_bias
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self.hidden_dropout_prob = hidden_dropout_prob
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self.attention_probs_dropout_prob = attention_probs_dropout_prob
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self.drop_path_rate = drop_path_rate
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self.hidden_act = hidden_act
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self.use_absolute_embeddings = use_absolute_embeddings
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self.patch_norm = patch_norm
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self.layer_norm_eps = layer_norm_eps
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self.initializer_range = initializer_range
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self.is_training = is_training
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self.scope = scope
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self.use_labels = use_labels
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self.upscale = upscale
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# here we set some attributes to make tests pass
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self.num_hidden_layers = len(depths)
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self.hidden_size = embed_dim
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self.seq_length = (image_size // patch_size) ** 2
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def prepare_config_and_inputs(self):
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pixel_values = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size])
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labels = None
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if self.use_labels:
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labels = ids_tensor([self.batch_size], self.type_sequence_label_size)
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config = self.get_config()
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return config, pixel_values, labels
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def get_config(self):
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return Swin2SRConfig(
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image_size=self.image_size,
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patch_size=self.patch_size,
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num_channels=self.num_channels,
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num_channels_out=self.num_channels_out,
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embed_dim=self.embed_dim,
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depths=self.depths,
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num_heads=self.num_heads,
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window_size=self.window_size,
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mlp_ratio=self.mlp_ratio,
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qkv_bias=self.qkv_bias,
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hidden_dropout_prob=self.hidden_dropout_prob,
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attention_probs_dropout_prob=self.attention_probs_dropout_prob,
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drop_path_rate=self.drop_path_rate,
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hidden_act=self.hidden_act,
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use_absolute_embeddings=self.use_absolute_embeddings,
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path_norm=self.patch_norm,
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layer_norm_eps=self.layer_norm_eps,
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initializer_range=self.initializer_range,
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upscale=self.upscale,
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)
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def create_and_check_model(self, config, pixel_values, labels):
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model = Swin2SRModel(config=config)
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model.to(torch_device)
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model.eval()
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result = model(pixel_values)
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self.parent.assertEqual(
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result.last_hidden_state.shape, (self.batch_size, self.embed_dim, self.image_size, self.image_size)
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)
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def create_and_check_for_image_super_resolution(self, config, pixel_values, labels):
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model = Swin2SRForImageSuperResolution(config)
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model.to(torch_device)
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model.eval()
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result = model(pixel_values)
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expected_image_size = self.image_size * self.upscale
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self.parent.assertEqual(
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result.reconstruction.shape,
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(self.batch_size, self.num_channels_out, expected_image_size, expected_image_size),
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)
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def prepare_config_and_inputs_for_common(self):
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config_and_inputs = self.prepare_config_and_inputs()
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config, pixel_values, labels = config_and_inputs
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inputs_dict = {"pixel_values": pixel_values}
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return config, inputs_dict
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@require_torch
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class Swin2SRModelTest(ModelTesterMixin, PipelineTesterMixin, unittest.TestCase):
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all_model_classes = (Swin2SRModel, Swin2SRForImageSuperResolution) if is_torch_available() else ()
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pipeline_model_mapping = (
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{"image-feature-extraction": Swin2SRModel, "image-to-image": Swin2SRForImageSuperResolution}
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if is_torch_available()
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else {}
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)
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fx_compatible = False
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test_pruning = False
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test_resize_embeddings = False
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test_head_masking = False
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test_torchscript = False
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def setUp(self):
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self.model_tester = Swin2SRModelTester(self)
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self.config_tester = ConfigTester(self, config_class=Swin2SRConfig, embed_dim=37)
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def test_config(self):
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self.config_tester.create_and_test_config_to_json_string()
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self.config_tester.create_and_test_config_to_json_file()
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self.config_tester.create_and_test_config_from_and_save_pretrained()
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self.config_tester.create_and_test_config_with_num_labels()
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self.config_tester.check_config_can_be_init_without_params()
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self.config_tester.check_config_arguments_init()
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def test_model(self):
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config_and_inputs = self.model_tester.prepare_config_and_inputs()
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self.model_tester.create_and_check_model(*config_and_inputs)
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def test_model_for_image_super_resolution(self):
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config_and_inputs = self.model_tester.prepare_config_and_inputs()
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self.model_tester.create_and_check_for_image_super_resolution(*config_and_inputs)
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# TODO: check if this works again for PyTorch 2.x.y
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@unittest.skip(reason="Got `CUDA error: misaligned address` with PyTorch 2.0.0.")
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def test_multi_gpu_data_parallel_forward(self):
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pass
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@unittest.skip(reason="Swin2SR does not use inputs_embeds")
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def test_inputs_embeds(self):
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pass
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@unittest.skip(reason="Swin2SR does not support training yet")
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def test_training(self):
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pass
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@unittest.skip(reason="Swin2SR does not support training yet")
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def test_training_gradient_checkpointing(self):
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pass
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@unittest.skip(
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reason="This architecure seem to not compute gradients properly when using GC, check: https://github.com/huggingface/transformers/pull/27124"
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)
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def test_training_gradient_checkpointing_use_reentrant(self):
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pass
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@unittest.skip(
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reason="This architecure seem to not compute gradients properly when using GC, check: https://github.com/huggingface/transformers/pull/27124"
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)
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def test_training_gradient_checkpointing_use_reentrant_false(self):
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pass
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def test_model_common_attributes(self):
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config, _ = self.model_tester.prepare_config_and_inputs_for_common()
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for model_class in self.all_model_classes:
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model = model_class(config)
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self.assertIsInstance(model.get_input_embeddings(), (nn.Module))
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x = model.get_output_embeddings()
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self.assertTrue(x is None or isinstance(x, nn.Linear))
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@slow
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def test_model_from_pretrained(self):
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model_name = "caidas/swin2SR-classical-sr-x2-64"
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model = Swin2SRModel.from_pretrained(model_name)
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self.assertIsNotNone(model)
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# overwriting because of `logit_scale` parameter
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def test_initialization(self):
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config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
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configs_no_init = _config_zero_init(config)
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for model_class in self.all_model_classes:
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model = model_class(config=configs_no_init)
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for name, param in model.named_parameters():
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if "logit_scale" in name:
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continue
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if param.requires_grad:
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self.assertIn(
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((param.data.mean() * 1e9).round() / 1e9).item(),
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[0.0, 1.0],
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msg=f"Parameter {name} of model {model_class} seems not properly initialized",
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)
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def test_attention_outputs(self):
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config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
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config.return_dict = True
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for model_class in self.all_model_classes:
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inputs_dict["output_attentions"] = True
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inputs_dict["output_hidden_states"] = False
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config.return_dict = True
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model = model_class(config)
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model.to(torch_device)
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model.eval()
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with torch.no_grad():
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outputs = model(**self._prepare_for_class(inputs_dict, model_class))
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attentions = outputs.attentions
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expected_num_attentions = len(self.model_tester.depths)
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self.assertEqual(len(attentions), expected_num_attentions)
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# check that output_attentions also work using config
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del inputs_dict["output_attentions"]
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config.output_attentions = True
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window_size_squared = config.window_size**2
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model = model_class(config)
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model.to(torch_device)
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model.eval()
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with torch.no_grad():
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outputs = model(**self._prepare_for_class(inputs_dict, model_class))
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attentions = outputs.attentions
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self.assertEqual(len(attentions), expected_num_attentions)
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self.assertListEqual(
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list(attentions[0].shape[-3:]),
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[self.model_tester.num_heads[0], window_size_squared, window_size_squared],
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)
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out_len = len(outputs)
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# Check attention is always last and order is fine
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inputs_dict["output_attentions"] = True
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inputs_dict["output_hidden_states"] = True
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model = model_class(config)
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model.to(torch_device)
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model.eval()
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with torch.no_grad():
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outputs = model(**self._prepare_for_class(inputs_dict, model_class))
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self.assertEqual(out_len + 1, len(outputs))
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self_attentions = outputs.attentions
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self.assertEqual(len(self_attentions), expected_num_attentions)
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self.assertListEqual(
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list(self_attentions[0].shape[-3:]),
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[self.model_tester.num_heads[0], window_size_squared, window_size_squared],
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)
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@require_vision
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@require_torch
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@slow
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class Swin2SRModelIntegrationTest(unittest.TestCase):
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def test_inference_image_super_resolution_head(self):
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processor = Swin2SRImageProcessor()
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model = Swin2SRForImageSuperResolution.from_pretrained("caidas/swin2SR-classical-sr-x2-64").to(torch_device)
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image = Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png")
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inputs = processor(images=image, return_tensors="pt").to(torch_device)
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# forward pass
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with torch.no_grad():
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outputs = model(**inputs)
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# verify the logits
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expected_shape = torch.Size([1, 3, 976, 1296])
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self.assertEqual(outputs.reconstruction.shape, expected_shape)
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expected_slice = torch.tensor(
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[[0.5458, 0.5546, 0.5638], [0.5526, 0.5565, 0.5651], [0.5396, 0.5426, 0.5621]]
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).to(torch_device)
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self.assertTrue(torch.allclose(outputs.reconstruction[0, 0, :3, :3], expected_slice, atol=1e-4))
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