mirror of
https://github.com/huggingface/transformers.git
synced 2025-07-04 21:30:07 +06:00
e355c0a11c
* fix GroundingDino * fix SuperGlue * fix GroundingDino * fix MambaModel * fix OmDetTurbo * fix SegGpt * fix Qwen2Audio * fix Mamba2 * fix DabDetr * fix Dac * fix FalconMamba * skip timm initialization * fix Encodec and MusicgenMelody * fix Musicgen * skip timm initialization test * fix OmDetTurbo * clean the code Co-authored-by: Cyril Vallez <cyril.vallez@gmail.com> * add reviewed changes * add back timm * style * better check for parametrizations --------- Co-authored-by: Cyril Vallez <cyril.vallez@gmail.com>
443 lines
17 KiB
Python
443 lines
17 KiB
Python
# Copyright 2024 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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import inspect
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import tempfile
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import unittest
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from transformers import pipeline
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from transformers.testing_utils import (
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require_bitsandbytes,
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require_timm,
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require_torch,
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require_vision,
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slow,
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torch_device,
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)
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from transformers.utils.import_utils import is_timm_available, 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, floats_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 transformers import TimmWrapperConfig, TimmWrapperForImageClassification, TimmWrapperModel
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if is_timm_available():
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import timm
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if is_vision_available():
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from PIL import Image
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from transformers import TimmWrapperImageProcessor
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class TimmWrapperModelTester:
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def __init__(
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self,
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parent,
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model_name="timm/resnet18.a1_in1k",
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batch_size=3,
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image_size=32,
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num_channels=3,
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is_training=True,
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):
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self.parent = parent
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self.model_name = model_name
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self.batch_size = batch_size
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self.image_size = image_size
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self.num_channels = num_channels
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self.is_training = is_training
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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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config = self.get_config()
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return config, pixel_values
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def get_config(self):
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return TimmWrapperConfig.from_pretrained(self.model_name)
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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 = 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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@require_timm
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class TimmWrapperModelTest(ModelTesterMixin, PipelineTesterMixin, unittest.TestCase):
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all_model_classes = (TimmWrapperModel, TimmWrapperForImageClassification) if is_torch_available() else ()
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pipeline_model_mapping = (
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{"image-feature-extraction": TimmWrapperModel, "image-classification": TimmWrapperForImageClassification}
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if is_torch_available()
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else {}
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)
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test_resize_embeddings = False
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test_head_masking = False
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test_pruning = False
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has_attentions = False
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test_model_parallel = False
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def setUp(self):
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self.config_class = TimmWrapperConfig
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self.model_tester = TimmWrapperModelTester(self)
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self.config_tester = ConfigTester(
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self,
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config_class=self.config_class,
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has_text_modality=False,
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common_properties=[],
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model_name="timm/resnet18.a1_in1k",
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)
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def test_config(self):
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self.config_tester.run_common_tests()
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def test_hidden_states_output(self):
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config, inputs_dict = 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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# check all hidden states
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with torch.no_grad():
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outputs = model(**inputs_dict, output_hidden_states=True)
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self.assertTrue(
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len(outputs.hidden_states) == 5, f"expected 5 hidden states, but got {len(outputs.hidden_states)}"
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)
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expected_shapes = [[16, 16], [8, 8], [4, 4], [2, 2], [1, 1]]
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resulted_shapes = [list(h.shape[2:]) for h in outputs.hidden_states]
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self.assertListEqual(expected_shapes, resulted_shapes)
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# check we can select hidden states by indices
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with torch.no_grad():
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outputs = model(**inputs_dict, output_hidden_states=[-2, -1])
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self.assertTrue(
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len(outputs.hidden_states) == 2, f"expected 2 hidden states, but got {len(outputs.hidden_states)}"
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)
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expected_shapes = [[2, 2], [1, 1]]
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resulted_shapes = [list(h.shape[2:]) for h in outputs.hidden_states]
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self.assertListEqual(expected_shapes, resulted_shapes)
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@unittest.skip(reason="TimmWrapper models doesn't have inputs_embeds")
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def test_inputs_embeds(self):
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pass
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@unittest.skip(reason="TimmWrapper models doesn't have inputs_embeds")
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def test_model_get_set_embeddings(self):
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pass
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@unittest.skip(reason="TimmWrapper doesn't support output_attentions=True.")
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def test_torchscript_output_attentions(self):
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pass
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@unittest.skip(reason="TimmWrapper doesn't support this.")
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def test_retain_grad_hidden_states_attentions(self):
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pass
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@unittest.skip(reason="TimmWrapper initialization is managed on the timm side")
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def test_can_init_all_missing_weights(self):
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pass
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@unittest.skip(reason="TimmWrapper initialization is managed on the timm side")
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def test_initialization(self):
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pass
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@unittest.skip(reason="TimmWrapper initialization is managed on the timm side")
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def test_mismatched_shapes_have_properly_initialized_weights(self):
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pass
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@unittest.skip(reason="Need to use a timm model and there is no tiny model available.")
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def test_model_is_small(self):
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pass
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def test_forward_signature(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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signature = inspect.signature(model.forward)
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# signature.parameters is an OrderedDict => so arg_names order is deterministic
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arg_names = [*signature.parameters.keys()]
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expected_arg_names = ["pixel_values"]
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self.assertListEqual(arg_names[:1], expected_arg_names)
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def test_do_pooling_option(self):
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config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
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config.do_pooling = False
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model = TimmWrapperModel._from_config(config)
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# check there is no pooling
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with torch.no_grad():
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output = model(**inputs_dict)
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self.assertIsNone(output.pooler_output)
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# check there is pooler output
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with torch.no_grad():
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output = model(**inputs_dict, do_pooling=True)
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self.assertIsNotNone(output.pooler_output)
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def test_timm_config_labels(self):
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# test timm config with no labels
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checkpoint = "timm/resnet18.a1_in1k"
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config = TimmWrapperConfig.from_pretrained(checkpoint)
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self.assertIsNone(config.label2id)
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self.assertIsInstance(config.id2label, dict)
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self.assertEqual(len(config.id2label), 1000)
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self.assertEqual(config.id2label[1], "goldfish, Carassius auratus")
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# test timm config with labels in config
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checkpoint = "timm/eva02_large_patch14_clip_336.merged2b_ft_inat21"
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config = TimmWrapperConfig.from_pretrained(checkpoint)
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self.assertIsInstance(config.id2label, dict)
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self.assertEqual(len(config.id2label), 10000)
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self.assertEqual(config.id2label[1], "Sabella spallanzanii")
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self.assertIsInstance(config.label2id, dict)
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self.assertEqual(len(config.label2id), 10000)
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self.assertEqual(config.label2id["Sabella spallanzanii"], 1)
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# test custom labels are provided
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checkpoint = "timm/resnet18.a1_in1k"
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config = TimmWrapperConfig.from_pretrained(checkpoint, num_labels=2)
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self.assertEqual(config.num_labels, 2)
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self.assertEqual(config.id2label, {0: "LABEL_0", 1: "LABEL_1"})
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self.assertEqual(config.label2id, {"LABEL_0": 0, "LABEL_1": 1})
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# test with provided id2label and label2id
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checkpoint = "timm/resnet18.a1_in1k"
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config = TimmWrapperConfig.from_pretrained(
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checkpoint, num_labels=2, id2label={0: "LABEL_0", 1: "LABEL_1"}, label2id={"LABEL_0": 0, "LABEL_1": 1}
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)
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self.assertEqual(config.num_labels, 2)
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self.assertEqual(config.id2label, {0: "LABEL_0", 1: "LABEL_1"})
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self.assertEqual(config.label2id, {"LABEL_0": 0, "LABEL_1": 1})
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# test save load
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checkpoint = "timm/resnet18.a1_in1k"
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config = TimmWrapperConfig.from_pretrained(checkpoint)
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with tempfile.TemporaryDirectory() as tmpdirname:
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config.save_pretrained(tmpdirname)
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restored_config = TimmWrapperConfig.from_pretrained(tmpdirname)
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self.assertEqual(config.num_labels, restored_config.num_labels)
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self.assertEqual(config.id2label, restored_config.id2label)
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self.assertEqual(config.label2id, restored_config.label2id)
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def test_model_init_args(self):
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# test init from config
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config = TimmWrapperConfig.from_pretrained(
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"timm/vit_base_patch32_clip_448.laion2b_ft_in12k_in1k",
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model_args={"depth": 3},
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)
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model = TimmWrapperModel(config)
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self.assertEqual(len(model.timm_model.blocks), 3)
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cls_model = TimmWrapperForImageClassification(config)
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self.assertEqual(len(cls_model.timm_model.blocks), 3)
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# test save load
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with tempfile.TemporaryDirectory() as tmpdirname:
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model.save_pretrained(tmpdirname)
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restored_model = TimmWrapperModel.from_pretrained(tmpdirname)
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self.assertEqual(len(restored_model.timm_model.blocks), 3)
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# We will verify our results on an image of cute cats
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def prepare_img():
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image = Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png")
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return image
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@require_torch
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@require_timm
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@require_vision
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class TimmWrapperModelIntegrationTest(unittest.TestCase):
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# some popular ones
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model_names_to_test = [
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"vit_small_patch16_384.augreg_in21k_ft_in1k",
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"resnet50.a1_in1k",
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"tf_mobilenetv3_large_minimal_100.in1k",
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"swin_tiny_patch4_window7_224.ms_in1k",
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"ese_vovnet19b_dw.ra_in1k",
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"hrnet_w18.ms_aug_in1k",
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]
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@slow
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def test_inference_image_classification_head(self):
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checkpoint = "timm/resnet18.a1_in1k"
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model = TimmWrapperForImageClassification.from_pretrained(checkpoint, device_map=torch_device).eval()
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image_processor = TimmWrapperImageProcessor.from_pretrained(checkpoint)
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image = prepare_img()
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inputs = image_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 shape and logits
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expected_shape = torch.Size((1, 1000))
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self.assertEqual(outputs.logits.shape, expected_shape)
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expected_label = 281 # tabby cat
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self.assertEqual(torch.argmax(outputs.logits).item(), expected_label)
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expected_slice = torch.tensor([-11.2618, -9.6192, -10.3205]).to(torch_device)
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resulted_slice = outputs.logits[0, :3]
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is_close = torch.allclose(resulted_slice, expected_slice, atol=1e-3)
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self.assertTrue(is_close, f"Expected {expected_slice}, but got {resulted_slice}")
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@slow
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def test_inference_with_pipeline(self):
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image = prepare_img()
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classifier = pipeline(model="timm/resnet18.a1_in1k", device=torch_device)
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result = classifier(image)
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# verify result
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expected_label = "tabby, tabby cat"
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expected_score = 0.4329
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self.assertEqual(result[0]["label"], expected_label)
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self.assertAlmostEqual(result[0]["score"], expected_score, places=3)
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@slow
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@require_bitsandbytes
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def test_inference_image_classification_quantized(self):
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from transformers import BitsAndBytesConfig
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checkpoint = "timm/vit_small_patch16_384.augreg_in21k_ft_in1k"
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quantization_config = BitsAndBytesConfig(load_in_8bit=True)
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model = TimmWrapperForImageClassification.from_pretrained(
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checkpoint, quantization_config=quantization_config, device_map=torch_device
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).eval()
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image_processor = TimmWrapperImageProcessor.from_pretrained(checkpoint)
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image = prepare_img()
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inputs = image_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 shape and logits
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expected_shape = torch.Size((1, 1000))
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self.assertEqual(outputs.logits.shape, expected_shape)
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expected_label = 281 # tabby cat
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self.assertEqual(torch.argmax(outputs.logits).item(), expected_label)
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expected_slice = torch.tensor([-2.4043, 1.4492, -0.5127]).to(outputs.logits.dtype)
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resulted_slice = outputs.logits[0, :3].cpu()
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is_close = torch.allclose(resulted_slice, expected_slice, atol=0.1)
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self.assertTrue(is_close, f"Expected {expected_slice}, but got {resulted_slice}")
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@slow
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def test_transformers_model_for_classification_is_equivalent_to_timm(self):
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# check that wrapper logits are the same as timm model logits
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image = prepare_img()
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for model_name in self.model_names_to_test:
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checkpoint = f"timm/{model_name}"
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with self.subTest(msg=model_name):
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# prepare inputs
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image_processor = TimmWrapperImageProcessor.from_pretrained(checkpoint)
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pixel_values = image_processor(images=image).pixel_values.to(torch_device)
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# load models
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model = TimmWrapperForImageClassification.from_pretrained(checkpoint, device_map=torch_device).eval()
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timm_model = timm.create_model(model_name, pretrained=True).to(torch_device).eval()
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with torch.inference_mode():
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outputs = model(pixel_values)
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timm_outputs = timm_model(pixel_values)
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# check shape is the same
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self.assertEqual(outputs.logits.shape, timm_outputs.shape)
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# check logits are the same
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diff = (outputs.logits - timm_outputs).max().item()
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self.assertLess(diff, 1e-4)
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@slow
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def test_transformers_model_is_equivalent_to_timm(self):
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# check that wrapper logits are the same as timm model logits
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image = prepare_img()
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models_to_test = ["vit_small_patch16_224.dino"] + self.model_names_to_test
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for model_name in models_to_test:
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checkpoint = f"timm/{model_name}"
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with self.subTest(msg=model_name):
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# prepare inputs
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image_processor = TimmWrapperImageProcessor.from_pretrained(checkpoint)
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pixel_values = image_processor(images=image).pixel_values.to(torch_device)
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# load models
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model = TimmWrapperModel.from_pretrained(checkpoint, device_map=torch_device).eval()
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timm_model = timm.create_model(model_name, pretrained=True, num_classes=0).to(torch_device).eval()
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with torch.inference_mode():
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outputs = model(pixel_values)
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timm_outputs = timm_model(pixel_values)
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# check shape is the same
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self.assertEqual(outputs.pooler_output.shape, timm_outputs.shape)
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# check logits are the same
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diff = (outputs.pooler_output - timm_outputs).max().item()
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self.assertLess(diff, 1e-4)
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@slow
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def test_save_load_to_timm(self):
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# test that timm model can be loaded to transformers, saved and then loaded back into timm
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model = TimmWrapperForImageClassification.from_pretrained(
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"timm/resnet18.a1_in1k", num_labels=10, ignore_mismatched_sizes=True
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)
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with tempfile.TemporaryDirectory() as tmpdirname:
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model.save_pretrained(tmpdirname)
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# there is no direct way to load timm model from folder, use the same config + path to weights
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timm_model = timm.create_model(
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"resnet18", num_classes=10, checkpoint_path=f"{tmpdirname}/model.safetensors"
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)
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# check that all weights are the same after reload
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different_weights = []
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for (name1, param1), (name2, param2) in zip(
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model.timm_model.named_parameters(), timm_model.named_parameters()
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):
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if param1.shape != param2.shape or not torch.equal(param1, param2):
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different_weights.append((name1, name2))
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if different_weights:
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self.fail(f"Found different weights after reloading: {different_weights}")
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