mirror of
https://github.com/huggingface/transformers.git
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747f361da1
* Add sdpa for Beit * Updates * [run-slow] beit * Update inference benchmarks * Update * Fix - add missed to super().forward() * Updates * Fix missing import
790 lines
34 KiB
Python
790 lines
34 KiB
Python
# coding=utf-8
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# Copyright 2021 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 BEiT model."""
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import inspect
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import tempfile
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import unittest
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import numpy as np
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from datasets import load_dataset
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from packaging import version
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from parameterized import parameterized
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from transformers import BeitConfig
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from transformers.testing_utils import (
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require_torch,
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require_torch_multi_gpu,
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require_torch_sdpa,
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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 (
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cached_property,
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is_torch_available,
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is_torch_bf16_available_on_device,
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is_torch_fp16_available_on_device,
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is_vision_available,
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)
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from ...test_backbone_common import BackboneTesterMixin
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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, sdpa_kernel
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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 (
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BeitBackbone,
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BeitForImageClassification,
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BeitForMaskedImageModeling,
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BeitForSemanticSegmentation,
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BeitModel,
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)
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from transformers.models.auto.modeling_auto import MODEL_FOR_BACKBONE_MAPPING_NAMES, MODEL_MAPPING_NAMES
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if is_vision_available():
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import PIL
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from PIL import Image
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from transformers import BeitImageProcessor
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class BeitModelTester:
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def __init__(
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self,
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parent,
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vocab_size=100,
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batch_size=13,
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image_size=30,
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patch_size=2,
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num_channels=3,
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is_training=True,
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use_labels=True,
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hidden_size=32,
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num_hidden_layers=4,
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num_attention_heads=4,
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intermediate_size=37,
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hidden_act="gelu",
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hidden_dropout_prob=0.1,
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attention_probs_dropout_prob=0.1,
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type_sequence_label_size=10,
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initializer_range=0.02,
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num_labels=3,
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scope=None,
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out_indices=[1, 2, 3, 4],
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out_features=["stage1", "stage2", "stage3", "stage4"],
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attn_implementation="eager",
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mask_ratio=0.5,
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):
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self.parent = parent
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self.vocab_size = vocab_size
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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.is_training = is_training
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self.use_labels = use_labels
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self.hidden_size = hidden_size
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self.num_hidden_layers = num_hidden_layers
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self.num_attention_heads = num_attention_heads
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self.intermediate_size = intermediate_size
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self.hidden_act = hidden_act
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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.type_sequence_label_size = type_sequence_label_size
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self.initializer_range = initializer_range
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self.scope = scope
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self.out_indices = out_indices
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self.out_features = out_features
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self.num_labels = num_labels
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# in BeiT, the seq length equals the number of patches + 1 (we add 1 for the [CLS] token)
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num_patches = (image_size // patch_size) ** 2
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self.seq_length = num_patches + 1
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self.num_masks = int(mask_ratio * self.seq_length)
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self.attn_implementation = attn_implementation
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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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pixel_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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pixel_labels = ids_tensor([self.batch_size, self.image_size, self.image_size], self.num_labels)
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config = self.get_config()
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return config, pixel_values, labels, pixel_labels
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def get_config(self):
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return BeitConfig(
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vocab_size=self.vocab_size,
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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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hidden_size=self.hidden_size,
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num_hidden_layers=self.num_hidden_layers,
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num_attention_heads=self.num_attention_heads,
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intermediate_size=self.intermediate_size,
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hidden_act=self.hidden_act,
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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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is_decoder=False,
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initializer_range=self.initializer_range,
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out_indices=self.out_indices,
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out_features=self.out_features,
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attn_implementation=self.attn_implementation,
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)
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def create_and_check_model(self, config, pixel_values, labels, pixel_labels):
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model = BeitModel(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(result.last_hidden_state.shape, (self.batch_size, self.seq_length, self.hidden_size))
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def create_and_check_backbone(self, config, pixel_values, labels, pixel_labels):
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model = BeitBackbone(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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# verify hidden states
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self.parent.assertEqual(len(result.feature_maps), len(config.out_features))
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expected_height = expected_width = self.image_size // config.patch_size
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self.parent.assertListEqual(
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list(result.feature_maps[0].shape), [self.batch_size, self.hidden_size, expected_height, expected_width]
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)
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# verify channels
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self.parent.assertEqual(len(model.channels), len(config.out_features))
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# verify backbone works with out_features=None
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config.out_features = None
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model = BeitBackbone(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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# verify feature maps
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self.parent.assertEqual(len(result.feature_maps), 1)
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self.parent.assertListEqual(
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list(result.feature_maps[0].shape), [self.batch_size, self.hidden_size, expected_height, expected_width]
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)
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# verify channels
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self.parent.assertEqual(len(model.channels), 1)
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def create_and_check_for_masked_lm(self, config, pixel_values, labels, pixel_labels):
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model = BeitForMaskedImageModeling(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(result.logits.shape, (self.batch_size, self.seq_length - 1, self.vocab_size))
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def create_and_check_for_image_classification(self, config, pixel_values, labels, pixel_labels):
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config.num_labels = self.type_sequence_label_size
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model = BeitForImageClassification(config)
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model.to(torch_device)
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model.eval()
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result = model(pixel_values, labels=labels)
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self.parent.assertEqual(result.logits.shape, (self.batch_size, self.type_sequence_label_size))
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# test greyscale images
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config.num_channels = 1
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model = BeitForImageClassification(config)
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model.to(torch_device)
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model.eval()
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pixel_values = floats_tensor([self.batch_size, 1, self.image_size, self.image_size])
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result = model(pixel_values, labels=labels)
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self.parent.assertEqual(result.logits.shape, (self.batch_size, self.type_sequence_label_size))
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def create_and_check_for_semantic_segmentation(self, config, pixel_values, labels, pixel_labels):
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config.num_labels = self.num_labels
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model = BeitForSemanticSegmentation(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.logits.shape, (self.batch_size, self.num_labels, self.image_size * 2, self.image_size * 2)
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)
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result = model(pixel_values, labels=pixel_labels)
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self.parent.assertEqual(
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result.logits.shape, (self.batch_size, self.num_labels, self.image_size * 2, self.image_size * 2)
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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, pixel_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 BeitModelTest(ModelTesterMixin, PipelineTesterMixin, unittest.TestCase):
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"""
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Here we also overwrite some of the tests of test_modeling_common.py, as BEiT does not use input_ids, inputs_embeds,
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attention_mask and seq_length.
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"""
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all_model_classes = (
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(
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BeitModel,
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BeitForImageClassification,
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BeitForMaskedImageModeling,
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BeitForSemanticSegmentation,
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BeitBackbone,
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)
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if is_torch_available()
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else ()
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)
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pipeline_model_mapping = (
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{
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"image-feature-extraction": BeitModel,
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"image-classification": BeitForImageClassification,
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"image-segmentation": BeitForSemanticSegmentation,
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}
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if is_torch_available()
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else {}
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)
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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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def setUp(self):
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self.model_tester = BeitModelTester(self)
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self.config_tester = ConfigTester(self, config_class=BeitConfig, has_text_modality=False, hidden_size=37)
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def test_config(self):
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self.config_tester.run_common_tests()
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@unittest.skip(reason="BEiT does not use inputs_embeds")
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def test_inputs_embeds(self):
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pass
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@require_torch_multi_gpu
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@unittest.skip(reason="BEiT has some layers using `add_module` which doesn't work well with `nn.DataParallel`")
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def test_multi_gpu_data_parallel_forward(self):
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pass
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@unittest.skip(reason="BEiT does not support feedforward chunking yet")
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def test_feed_forward_chunking(self):
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pass
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def test_model_get_set_embeddings(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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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_backbone(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_backbone(*config_and_inputs)
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def test_for_masked_lm(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_masked_lm(*config_and_inputs)
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def test_for_image_classification(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_classification(*config_and_inputs)
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def test_for_semantic_segmentation(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_semantic_segmentation(*config_and_inputs)
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def test_training(self):
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if not self.model_tester.is_training:
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self.skipTest(reason="model_tester.is_training is set to False")
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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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# we don't test BeitForMaskedImageModeling
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if model_class.__name__ in [
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*MODEL_MAPPING_NAMES.values(),
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*MODEL_FOR_BACKBONE_MAPPING_NAMES.values(),
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"BeitForMaskedImageModeling",
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]:
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continue
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model = model_class(config)
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model.to(torch_device)
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model.train()
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inputs = self._prepare_for_class(inputs_dict, model_class, return_labels=True)
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loss = model(**inputs).loss
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loss.backward()
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def test_training_gradient_checkpointing(self):
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config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
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if not self.model_tester.is_training:
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self.skipTest(reason="model_tester.is_training is set to False")
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config.use_cache = False
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config.return_dict = True
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for model_class in self.all_model_classes:
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# we don't test BeitForMaskedImageModeling
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if (
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model_class.__name__
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in [
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*MODEL_MAPPING_NAMES.values(),
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*MODEL_FOR_BACKBONE_MAPPING_NAMES.values(),
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"BeitForMaskedImageModeling",
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]
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or not model_class.supports_gradient_checkpointing
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):
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continue
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model = model_class(config)
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model.gradient_checkpointing_enable()
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model.to(torch_device)
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model.train()
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inputs = self._prepare_for_class(inputs_dict, model_class, return_labels=True)
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loss = model(**inputs).loss
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loss.backward()
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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_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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# we skip lambda parameters as these require special initial values
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# determined by config.layer_scale_init_value
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if "lambda" 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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@slow
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def test_model_from_pretrained(self):
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model_name = "microsoft/beit-base-patch16-224"
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model = BeitModel.from_pretrained(model_name)
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self.assertIsNotNone(model)
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@parameterized.expand([("float16",), ("bfloat16",), ("float32",)])
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@require_torch_sdpa
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def test_eager_matches_sdpa_inference(self, torch_dtype: str):
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# The common test modifies the num_hidden_layers to be 1. However, for Beit we want to
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# avoid that because the num_hidden_layers is generally assumed to be 4. Also, the code
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# related to attention masks in the original common tests is not required as the Beit
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# model does not handle attention masks. Furthermore, some extra code like modifying
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# the norm layers eps values for specialized configs and checking for the 'noise'
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# has been omitted to simply the test.
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if not self.has_attentions:
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self.skipTest(reason="Model architecture does not support attentions")
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if not self.all_model_classes[0]._supports_sdpa:
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self.skipTest(f"{self.all_model_classes[0].__name__} does not support SDPA")
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if torch_dtype == "float16" and not is_torch_fp16_available_on_device(torch_device):
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self.skipTest(f"float16 not supported on {torch_device} (on the specific device currently used)")
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if torch_dtype == "bfloat16" and not is_torch_bf16_available_on_device(torch_device):
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self.skipTest(
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f"bfloat16 not supported on {torch_device} (on the specific device currently used, e.g. Nvidia T4 GPU)"
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)
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# Not sure whether it's fine to put torch.XXX in a decorator if torch is not available so hacking it here instead.
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if torch_dtype == "float16":
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torch_dtype = torch.float16
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elif torch_dtype == "bfloat16":
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torch_dtype = torch.bfloat16
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elif torch_dtype == "float32":
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torch_dtype = torch.float32
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atols = {
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("cpu", False, torch.float32): 1e-6,
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("cpu", False, torch.float16): 5e-3,
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("cpu", False, torch.bfloat16): 1e-2,
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("cpu", True, torch.float32): 1e-6,
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("cpu", True, torch.float16): 5e-3,
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("cpu", True, torch.bfloat16): 1e-2,
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("cuda", False, torch.float32): 1e-6,
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("cuda", False, torch.bfloat16): 1e-2,
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("cuda", False, torch.float16): 5e-3,
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("cuda", True, torch.float32): 1e-6,
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("cuda", True, torch.bfloat16): 1e-2,
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("cuda", True, torch.float16): 5e-3,
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}
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rtols = {
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("cpu", False, torch.float32): 1e-4,
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("cpu", False, torch.float16): 5e-3,
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("cpu", False, torch.bfloat16): 1e-2,
|
|
("cpu", True, torch.float32): 1e-4,
|
|
("cpu", True, torch.float16): 5e-3,
|
|
("cpu", True, torch.bfloat16): 1e-2,
|
|
("cuda", False, torch.float32): 1e-4,
|
|
("cuda", False, torch.bfloat16): 1e-2,
|
|
("cuda", False, torch.float16): 5e-3,
|
|
("cuda", True, torch.float32): 1e-4,
|
|
("cuda", True, torch.bfloat16): 3e-2,
|
|
("cuda", True, torch.float16): 5e-3,
|
|
}
|
|
|
|
def get_mean_reldiff(failcase, x, ref, atol, rtol):
|
|
return f"{failcase}: mean relative difference: {((x - ref).abs() / (ref.abs() + 1e-12)).mean():.3e}, torch atol = {atol}, torch rtol = {rtol}"
|
|
|
|
for model_class in self.all_model_classes:
|
|
config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
|
|
|
|
config.rms_norm_eps = 1.0
|
|
config.layer_norm_eps = 1.0
|
|
config.norm_eps = 1.0
|
|
config.norm_epsilon = 1.0
|
|
config.layer_norm_epsilon = 1.0
|
|
|
|
model = model_class(config)
|
|
with tempfile.TemporaryDirectory() as tmpdirname:
|
|
model.save_pretrained(tmpdirname)
|
|
model_sdpa = model_class.from_pretrained(tmpdirname, torch_dtype=torch_dtype, use_mask_token=True)
|
|
model_sdpa = model_sdpa.eval().to(torch_device, dtype=torch_dtype)
|
|
|
|
model_eager = model_class.from_pretrained(
|
|
tmpdirname,
|
|
torch_dtype=torch_dtype,
|
|
attn_implementation="eager",
|
|
use_mask_token=True,
|
|
)
|
|
model_eager = model_eager.eval().to(torch_device, dtype=torch_dtype)
|
|
|
|
# Another way to make sure norm layers have desired epsilon. (Some models don't set it from its config.)
|
|
for x in model_eager.modules():
|
|
if isinstance(x, (nn.LayerNorm, nn.GroupNorm)):
|
|
x.eps = 1.0
|
|
for x in model_sdpa.modules():
|
|
if isinstance(x, (nn.LayerNorm, nn.GroupNorm)):
|
|
x.eps = 1.0
|
|
|
|
# We use these for loops instead of parameterized.expand just for the interest of avoiding loading/saving 16 times the model,
|
|
# but it would be nicer to have an efficient way to use parameterized.expand
|
|
fail_cases = []
|
|
for padding_side in ["left", "right"]:
|
|
for use_mask in [False, True]:
|
|
for output_attentions in [True, False]:
|
|
can_output_attn = "output_attentions" in inspect.signature(model_sdpa.forward).parameters
|
|
if not (self.has_attentions and can_output_attn) and output_attentions:
|
|
continue
|
|
# TODO: if we can also check with `batch_size=1` without being flaky?
|
|
for batch_size in [7]:
|
|
dummy_input = inputs_dict[model.main_input_name]
|
|
|
|
if dummy_input.dtype in [torch.float32, torch.bfloat16, torch.float16]:
|
|
dummy_input = dummy_input.to(torch_dtype)
|
|
|
|
dummy_input = dummy_input[:batch_size]
|
|
for enable_kernels in [False, True]:
|
|
failcase = f"padding_side={padding_side}, use_mask={use_mask}, enable_kernels={enable_kernels}"
|
|
processed_inputs = {
|
|
model.main_input_name: dummy_input,
|
|
"output_hidden_states": True,
|
|
}
|
|
|
|
if (
|
|
self.has_attentions
|
|
and "output_attentions" in inspect.signature(model_sdpa.forward).parameters
|
|
):
|
|
processed_inputs["output_attentions"] = output_attentions
|
|
|
|
if "bool_masked_pos" in inspect.signature(model_eager.forward).parameters:
|
|
dummy_mask = torch.ones((self.model_tester.num_masks,))
|
|
mask_length = self.model_tester.seq_length - 1 - dummy_mask.size(0)
|
|
dummy_mask = torch.cat([dummy_mask, torch.zeros(mask_length)])
|
|
dummy_bool_masked_pos = dummy_mask.expand(batch_size, -1).bool()
|
|
processed_inputs["bool_masked_pos"] = dummy_bool_masked_pos.to(torch_device)
|
|
|
|
with torch.no_grad():
|
|
with sdpa_kernel(
|
|
enable_flash=enable_kernels,
|
|
enable_math=True,
|
|
enable_mem_efficient=enable_kernels,
|
|
):
|
|
prepared_inputs = self._prepare_for_class(processed_inputs, model_class)
|
|
outputs_eager = model_eager(**prepared_inputs)
|
|
outputs_sdpa = model_sdpa(**prepared_inputs)
|
|
|
|
logits_eager = outputs_eager.hidden_states[-1]
|
|
logits_sdpa = outputs_sdpa.hidden_states[-1]
|
|
if torch_device in ["cpu", "cuda"]:
|
|
atol = atols[torch_device, enable_kernels, torch_dtype]
|
|
rtol = rtols[torch_device, enable_kernels, torch_dtype]
|
|
elif torch_device == "xpu":
|
|
# As of PyTorch 2.5 XPU backend supports only torch.nn.attention.SDPBackend.MATH
|
|
# which is implemented on PyTorch level using aten operators and is
|
|
# device agnostic with respect to implementation of each aten operator.
|
|
atol = atols["cuda", False, torch_dtype]
|
|
rtol = rtols["cuda", False, torch_dtype]
|
|
else:
|
|
atol = 1e-7
|
|
rtol = 1e-4
|
|
|
|
# Masked tokens output slightly deviates - we don't mind that.
|
|
if use_mask:
|
|
_logits_sdpa = torch.zeros_like(input=logits_sdpa)
|
|
_logits_eager = torch.zeros_like(input=logits_eager)
|
|
|
|
_logits_sdpa[:-1] = logits_sdpa[:-1]
|
|
_logits_eager[:-1] = logits_eager[:-1]
|
|
|
|
if padding_side == "left":
|
|
_logits_sdpa[-1:, 2:] = logits_sdpa[-1:, 2:]
|
|
_logits_eager[-1:, 2:] = logits_eager[-1:, 2:]
|
|
|
|
elif padding_side == "right":
|
|
_logits_sdpa[-1:, 2:] = logits_sdpa[-1:, :-2]
|
|
_logits_eager[-1:, 2:] = logits_eager[-1:, :-2]
|
|
|
|
logits_sdpa = _logits_sdpa
|
|
logits_eager = _logits_eager
|
|
|
|
results = [
|
|
torch.allclose(_logits_sdpa, _logits_eager, atol=atol, rtol=rtol)
|
|
for (_logits_sdpa, _logits_eager) in zip(logits_sdpa, logits_eager)
|
|
]
|
|
# If 80% batch elements have matched results, it's fine
|
|
if np.mean(results) < 0.8:
|
|
fail_cases.append(
|
|
get_mean_reldiff(failcase, logits_sdpa, logits_eager, atol, rtol)
|
|
)
|
|
|
|
self.assertTrue(len(fail_cases) == 0, "\n".join(fail_cases))
|
|
|
|
|
|
# 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 BeitModelIntegrationTest(unittest.TestCase):
|
|
@cached_property
|
|
def default_image_processor(self):
|
|
return BeitImageProcessor.from_pretrained("microsoft/beit-base-patch16-224") if is_vision_available() else None
|
|
|
|
@slow
|
|
def test_inference_masked_image_modeling_head(self):
|
|
model = BeitForMaskedImageModeling.from_pretrained("microsoft/beit-base-patch16-224-pt22k").to(torch_device)
|
|
|
|
image_processor = self.default_image_processor
|
|
image = prepare_img()
|
|
pixel_values = image_processor(images=image, return_tensors="pt").pixel_values.to(torch_device)
|
|
|
|
# prepare bool_masked_pos
|
|
bool_masked_pos = torch.ones((1, 196), dtype=torch.bool).to(torch_device)
|
|
|
|
# forward pass
|
|
with torch.no_grad():
|
|
outputs = model(pixel_values=pixel_values, bool_masked_pos=bool_masked_pos)
|
|
logits = outputs.logits
|
|
|
|
# verify the logits
|
|
expected_shape = torch.Size((1, 196, 8192))
|
|
self.assertEqual(logits.shape, expected_shape)
|
|
|
|
expected_slice = torch.tensor(
|
|
[[-3.2437, 0.5072, -13.9174], [-3.2456, 0.4948, -13.9401], [-3.2033, 0.5121, -13.8550]]
|
|
).to(torch_device)
|
|
|
|
self.assertTrue(torch.allclose(logits[bool_masked_pos][:3, :3], expected_slice, atol=1e-2))
|
|
|
|
@slow
|
|
def test_inference_image_classification_head_imagenet_1k(self):
|
|
model = BeitForImageClassification.from_pretrained("microsoft/beit-base-patch16-224").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)
|
|
logits = outputs.logits
|
|
|
|
# verify the logits
|
|
expected_shape = torch.Size((1, 1000))
|
|
self.assertEqual(logits.shape, expected_shape)
|
|
|
|
expected_slice = torch.tensor([-1.2385, -1.0987, -1.0108]).to(torch_device)
|
|
|
|
self.assertTrue(torch.allclose(logits[0, :3], expected_slice, atol=1e-4))
|
|
|
|
expected_class_idx = 281
|
|
self.assertEqual(logits.argmax(-1).item(), expected_class_idx)
|
|
|
|
@slow
|
|
def test_inference_image_classification_head_imagenet_22k(self):
|
|
model = BeitForImageClassification.from_pretrained("microsoft/beit-large-patch16-224-pt22k-ft22k").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)
|
|
logits = outputs.logits
|
|
|
|
# verify the logits
|
|
expected_shape = torch.Size((1, 21841))
|
|
self.assertEqual(logits.shape, expected_shape)
|
|
|
|
expected_slice = torch.tensor([1.6881, -0.2787, 0.5901]).to(torch_device)
|
|
|
|
self.assertTrue(torch.allclose(logits[0, :3], expected_slice, atol=1e-4))
|
|
|
|
expected_class_idx = 2396
|
|
self.assertEqual(logits.argmax(-1).item(), expected_class_idx)
|
|
|
|
@slow
|
|
def test_inference_semantic_segmentation(self):
|
|
model = BeitForSemanticSegmentation.from_pretrained("microsoft/beit-base-finetuned-ade-640-640")
|
|
model = model.to(torch_device)
|
|
|
|
image_processor = BeitImageProcessor(do_resize=True, size=640, do_center_crop=False)
|
|
|
|
ds = load_dataset("hf-internal-testing/fixtures_ade20k", split="test", trust_remote_code=True)
|
|
image = Image.open(ds[0]["file"])
|
|
inputs = image_processor(images=image, return_tensors="pt").to(torch_device)
|
|
|
|
# forward pass
|
|
with torch.no_grad():
|
|
outputs = model(**inputs)
|
|
logits = outputs.logits
|
|
|
|
# verify the logits
|
|
expected_shape = torch.Size((1, 150, 160, 160))
|
|
self.assertEqual(logits.shape, expected_shape)
|
|
|
|
is_pillow_less_than_9 = version.parse(PIL.__version__) < version.parse("9.0.0")
|
|
|
|
if is_pillow_less_than_9:
|
|
expected_slice = torch.tensor(
|
|
[
|
|
[[-4.9225, -2.3954, -3.0522], [-2.8822, -1.0046, -1.7561], [-2.9549, -1.3228, -2.1347]],
|
|
[[-5.8168, -3.4129, -4.0778], [-3.8651, -2.2214, -3.0277], [-3.8356, -2.4643, -3.3535]],
|
|
[[-0.0078, 3.9952, 4.0754], [2.9856, 4.6944, 5.0035], [3.2413, 4.7813, 4.9969]],
|
|
],
|
|
device=torch_device,
|
|
)
|
|
else:
|
|
expected_slice = torch.tensor(
|
|
[
|
|
[[-4.8960, -2.3688, -3.0355], [-2.8478, -0.9836, -1.7418], [-2.9449, -1.3332, -2.1456]],
|
|
[[-5.8081, -3.4124, -4.1006], [-3.8561, -2.2081, -3.0323], [-3.8365, -2.4601, -3.3669]],
|
|
[[-0.0309, 3.9868, 4.0540], [2.9640, 4.6877, 4.9976], [3.2081, 4.7690, 4.9942]],
|
|
],
|
|
device=torch_device,
|
|
)
|
|
|
|
self.assertTrue(torch.allclose(logits[0, :3, :3, :3], expected_slice, atol=1e-4))
|
|
|
|
@slow
|
|
def test_post_processing_semantic_segmentation(self):
|
|
model = BeitForSemanticSegmentation.from_pretrained("microsoft/beit-base-finetuned-ade-640-640")
|
|
model = model.to(torch_device)
|
|
|
|
image_processor = BeitImageProcessor(do_resize=True, size=640, do_center_crop=False)
|
|
|
|
ds = load_dataset("hf-internal-testing/fixtures_ade20k", split="test", trust_remote_code=True)
|
|
image = Image.open(ds[0]["file"])
|
|
inputs = image_processor(images=image, return_tensors="pt").to(torch_device)
|
|
|
|
# forward pass
|
|
with torch.no_grad():
|
|
outputs = model(**inputs)
|
|
|
|
outputs.logits = outputs.logits.detach().cpu()
|
|
|
|
segmentation = image_processor.post_process_semantic_segmentation(outputs=outputs, target_sizes=[(500, 300)])
|
|
expected_shape = torch.Size((500, 300))
|
|
self.assertEqual(segmentation[0].shape, expected_shape)
|
|
|
|
segmentation = image_processor.post_process_semantic_segmentation(outputs=outputs)
|
|
expected_shape = torch.Size((160, 160))
|
|
self.assertEqual(segmentation[0].shape, expected_shape)
|
|
|
|
@slow
|
|
def test_inference_interpolate_pos_encoding(self):
|
|
model_name = "microsoft/beit-base-patch16-224-pt22k"
|
|
model = BeitModel.from_pretrained(model_name, **{"use_absolute_position_embeddings": True}).to(torch_device)
|
|
|
|
image = Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png")
|
|
processor = BeitImageProcessor.from_pretrained(model_name)
|
|
inputs = processor(images=image, return_tensors="pt", size={"height": 480, "width": 480})
|
|
pixel_values = inputs.pixel_values.to(torch_device)
|
|
|
|
# with interpolate_pos_encoding being False an exception should be raised with higher resolution
|
|
# images than what the model supports.
|
|
self.assertFalse(processor.do_center_crop)
|
|
with torch.no_grad():
|
|
with self.assertRaises(ValueError, msg="doesn't match model"):
|
|
model(pixel_values, interpolate_pos_encoding=False)
|
|
|
|
# with interpolate_pos_encoding being True the model should process the higher resolution image
|
|
# successfully and produce the expected output.
|
|
with torch.no_grad():
|
|
outputs = model(pixel_values, interpolate_pos_encoding=True)
|
|
|
|
expected_shape = torch.Size((1, 1801, 768))
|
|
self.assertEqual(outputs.last_hidden_state.shape, expected_shape)
|
|
|
|
|
|
@require_torch
|
|
class BeitBackboneTest(unittest.TestCase, BackboneTesterMixin):
|
|
all_model_classes = (BeitBackbone,) if is_torch_available() else ()
|
|
config_class = BeitConfig
|
|
|
|
def setUp(self):
|
|
self.model_tester = BeitModelTester(self)
|