mirror of
https://github.com/huggingface/transformers.git
synced 2025-07-07 23:00:08 +06:00
88f50a1e89
* Add tf code for efficientformer * Fix return dict bug - return last hidden state after last stage * Fix corresponding return dict bug * Override test tol * Change default values of training to False * Set training to default False X3 * Rm axis from ln * Set init in dense projection * Rm debug stuff * Make style; all tests pass. * Modify year to 2023 * Fix attention biases codes * Update the shape list logic * Add a batch norm eps config * Remove extract comments in test files * Add conditional attn and hidden states return for serving output * Change channel dim checking logic * Add exception for withteacher model in training mode * Revert layer count for now * Add layer count for conditional layer naming * Transpose for conv happens only in main layer * Make tests smaller * Make style * Update doc * Rm from_pt * Change to actual expect image class label * Remove stray print in tests * Update image processor test * Remove the old serving output logic * Make style * Make style * Complete test
495 lines
20 KiB
Python
495 lines
20 KiB
Python
# coding=utf-8
|
|
# Copyright 2022 The HuggingFace Inc. team. All rights reserved.
|
|
#
|
|
# Licensed under the Apache License, Version 2.0 (the "License");
|
|
# you may not use this file except in compliance with the License.
|
|
# You may obtain a copy of the License at
|
|
#
|
|
# http://www.apache.org/licenses/LICENSE-2.0
|
|
#
|
|
# Unless required by applicable law or agreed to in writing, software
|
|
# distributed under the License is distributed on an "AS IS" BASIS,
|
|
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
|
# See the License for the specific language governing permissions and
|
|
# limitations under the License.
|
|
""" Testing suite for the PyTorch EfficientFormer model. """
|
|
|
|
|
|
import inspect
|
|
import unittest
|
|
import warnings
|
|
from typing import List
|
|
|
|
from transformers import EfficientFormerConfig
|
|
from transformers.models.auto import get_values
|
|
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
|
|
from transformers.utils import cached_property, is_torch_available, is_vision_available
|
|
|
|
from ...test_configuration_common import ConfigTester
|
|
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
|
|
from ...test_pipeline_mixin import PipelineTesterMixin
|
|
|
|
|
|
if is_torch_available():
|
|
import torch
|
|
|
|
from transformers import (
|
|
MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING,
|
|
MODEL_MAPPING,
|
|
EfficientFormerForImageClassification,
|
|
EfficientFormerForImageClassificationWithTeacher,
|
|
EfficientFormerModel,
|
|
)
|
|
from transformers.models.efficientformer.modeling_efficientformer import (
|
|
EFFICIENTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
|
|
)
|
|
|
|
|
|
if is_vision_available():
|
|
from PIL import Image
|
|
|
|
from transformers import EfficientFormerImageProcessor
|
|
|
|
|
|
class EfficientFormerModelTester:
|
|
def __init__(
|
|
self,
|
|
parent,
|
|
batch_size: int = 13,
|
|
image_size: int = 64,
|
|
patch_size: int = 2,
|
|
embed_dim: int = 3,
|
|
num_channels: int = 3,
|
|
is_training: bool = True,
|
|
use_labels: bool = True,
|
|
hidden_size: int = 128,
|
|
hidden_sizes=[16, 32, 64, 128],
|
|
num_hidden_layers: int = 7,
|
|
num_attention_heads: int = 4,
|
|
intermediate_size: int = 37,
|
|
hidden_act: str = "gelu",
|
|
hidden_dropout_prob: float = 0.1,
|
|
attention_probs_dropout_prob: float = 0.1,
|
|
type_sequence_label_size: int = 10,
|
|
initializer_range: float = 0.02,
|
|
encoder_stride: int = 2,
|
|
num_attention_outputs: int = 1,
|
|
dim: int = 128,
|
|
depths: List[int] = [2, 2, 2, 2],
|
|
resolution: int = 2,
|
|
mlp_expansion_ratio: int = 2,
|
|
):
|
|
self.parent = parent
|
|
self.batch_size = batch_size
|
|
self.image_size = image_size
|
|
self.patch_size = patch_size
|
|
self.num_channels = num_channels
|
|
self.is_training = is_training
|
|
self.use_labels = use_labels
|
|
self.hidden_size = hidden_size
|
|
self.num_hidden_layers = num_hidden_layers
|
|
self.num_attention_heads = num_attention_heads
|
|
self.intermediate_size = intermediate_size
|
|
self.hidden_act = hidden_act
|
|
self.hidden_dropout_prob = hidden_dropout_prob
|
|
self.attention_probs_dropout_prob = attention_probs_dropout_prob
|
|
self.type_sequence_label_size = type_sequence_label_size
|
|
self.initializer_range = initializer_range
|
|
self.encoder_stride = encoder_stride
|
|
self.num_attention_outputs = num_attention_outputs
|
|
self.embed_dim = embed_dim
|
|
self.seq_length = embed_dim + 1
|
|
self.resolution = resolution
|
|
self.depths = depths
|
|
self.hidden_sizes = hidden_sizes
|
|
self.dim = dim
|
|
self.mlp_expansion_ratio = mlp_expansion_ratio
|
|
|
|
def prepare_config_and_inputs(self):
|
|
pixel_values = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size])
|
|
|
|
labels = None
|
|
if self.use_labels:
|
|
labels = ids_tensor([self.batch_size], self.type_sequence_label_size)
|
|
|
|
config = self.get_config()
|
|
return config, pixel_values, labels
|
|
|
|
def get_config(self):
|
|
return EfficientFormerConfig(
|
|
image_size=self.image_size,
|
|
patch_size=self.patch_size,
|
|
num_channels=self.num_channels,
|
|
hidden_size=self.hidden_size,
|
|
num_hidden_layers=self.num_hidden_layers,
|
|
num_attention_heads=self.num_attention_heads,
|
|
intermediate_size=self.intermediate_size,
|
|
hidden_act=self.hidden_act,
|
|
hidden_dropout_prob=self.hidden_dropout_prob,
|
|
attention_probs_dropout_prob=self.attention_probs_dropout_prob,
|
|
is_decoder=False,
|
|
initializer_range=self.initializer_range,
|
|
encoder_stride=self.encoder_stride,
|
|
resolution=self.resolution,
|
|
depths=self.depths,
|
|
hidden_sizes=self.hidden_sizes,
|
|
dim=self.dim,
|
|
mlp_expansion_ratio=self.mlp_expansion_ratio,
|
|
)
|
|
|
|
def create_and_check_model(self, config, pixel_values, labels):
|
|
model = EfficientFormerModel(config=config)
|
|
model.to(torch_device)
|
|
model.eval()
|
|
result = model(pixel_values)
|
|
self.parent.assertEqual(result.last_hidden_state.shape, (self.batch_size, self.seq_length, self.hidden_size))
|
|
|
|
def create_and_check_for_image_classification(self, config, pixel_values, labels):
|
|
config.num_labels = self.type_sequence_label_size
|
|
model = EfficientFormerForImageClassification(config)
|
|
model.to(torch_device)
|
|
model.eval()
|
|
result = model(pixel_values, labels=labels)
|
|
self.parent.assertEqual(result.logits.shape, (self.batch_size, self.type_sequence_label_size))
|
|
|
|
# test greyscale images
|
|
config.num_channels = 1
|
|
model = EfficientFormerForImageClassification(config)
|
|
model.to(torch_device)
|
|
model.eval()
|
|
|
|
pixel_values = floats_tensor([self.batch_size, 1, self.image_size, self.image_size])
|
|
result = model(pixel_values)
|
|
self.parent.assertEqual(result.logits.shape, (self.batch_size, self.type_sequence_label_size))
|
|
|
|
def prepare_config_and_inputs_for_common(self):
|
|
config_and_inputs = self.prepare_config_and_inputs()
|
|
(
|
|
config,
|
|
pixel_values,
|
|
labels,
|
|
) = config_and_inputs
|
|
inputs_dict = {"pixel_values": pixel_values}
|
|
return config, inputs_dict
|
|
|
|
|
|
@require_torch
|
|
class EfficientFormerModelTest(ModelTesterMixin, PipelineTesterMixin, unittest.TestCase):
|
|
"""
|
|
Here we also overwrite some of the tests of test_modeling_common.py, as EfficientFormer does not use input_ids, inputs_embeds,
|
|
attention_mask and seq_length.
|
|
"""
|
|
|
|
all_model_classes = (
|
|
(
|
|
EfficientFormerModel,
|
|
EfficientFormerForImageClassificationWithTeacher,
|
|
EfficientFormerForImageClassification,
|
|
)
|
|
if is_torch_available()
|
|
else ()
|
|
)
|
|
pipeline_model_mapping = (
|
|
{
|
|
"feature-extraction": EfficientFormerModel,
|
|
"image-classification": (
|
|
EfficientFormerForImageClassification,
|
|
EfficientFormerForImageClassificationWithTeacher,
|
|
),
|
|
}
|
|
if is_torch_available()
|
|
else {}
|
|
)
|
|
fx_compatible = False
|
|
|
|
test_pruning = False
|
|
test_resize_embeddings = False
|
|
test_head_masking = False
|
|
|
|
def setUp(self):
|
|
self.model_tester = EfficientFormerModelTester(self)
|
|
self.config_tester = ConfigTester(
|
|
self, config_class=EfficientFormerConfig, has_text_modality=False, hidden_size=37
|
|
)
|
|
|
|
def test_config(self):
|
|
self.config_tester.run_common_tests()
|
|
|
|
@unittest.skip(reason="EfficientFormer does not use inputs_embeds")
|
|
def test_inputs_embeds(self):
|
|
pass
|
|
|
|
@unittest.skip(reason="EfficientFormer does not support input and output embeddings")
|
|
def test_model_common_attributes(self):
|
|
pass
|
|
|
|
def test_forward_signature(self):
|
|
config, _ = self.model_tester.prepare_config_and_inputs_for_common()
|
|
|
|
for model_class in self.all_model_classes:
|
|
model = model_class(config)
|
|
signature = inspect.signature(model.forward)
|
|
# signature.parameters is an OrderedDict => so arg_names order is deterministic
|
|
arg_names = [*signature.parameters.keys()]
|
|
|
|
expected_arg_names = ["pixel_values"]
|
|
self.assertListEqual(arg_names[:1], expected_arg_names)
|
|
|
|
def test_hidden_states_output(self):
|
|
def check_hidden_states_output(inputs_dict, config, model_class):
|
|
model = model_class(config)
|
|
model.to(torch_device)
|
|
model.eval()
|
|
|
|
with torch.no_grad():
|
|
outputs = model(**self._prepare_for_class(inputs_dict, model_class))
|
|
|
|
hidden_states = outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states
|
|
|
|
expected_num_layers = getattr(
|
|
self.model_tester, "expected_num_hidden_layers", self.model_tester.num_hidden_layers + 1
|
|
)
|
|
self.assertEqual(len(hidden_states), expected_num_layers)
|
|
|
|
if hasattr(self.model_tester, "encoder_seq_length"):
|
|
seq_length = self.model_tester.encoder_seq_length
|
|
if hasattr(self.model_tester, "chunk_length") and self.model_tester.chunk_length > 1:
|
|
seq_length = seq_length * self.model_tester.chunk_length
|
|
else:
|
|
seq_length = self.model_tester.seq_length
|
|
|
|
self.assertListEqual(
|
|
list(hidden_states[-1].shape[-2:]),
|
|
[seq_length, self.model_tester.hidden_size],
|
|
)
|
|
|
|
if config.is_encoder_decoder:
|
|
hidden_states = outputs.decoder_hidden_states
|
|
|
|
self.assertIsInstance(hidden_states, (list, tuple))
|
|
self.assertEqual(len(hidden_states), expected_num_layers)
|
|
seq_len = getattr(self.model_tester, "seq_length", None)
|
|
decoder_seq_length = getattr(self.model_tester, "decoder_seq_length", seq_len)
|
|
|
|
self.assertListEqual(
|
|
list(hidden_states[-1].shape[-2:]),
|
|
[decoder_seq_length, self.model_tester.hidden_size],
|
|
)
|
|
|
|
config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
|
|
|
|
for model_class in self.all_model_classes:
|
|
inputs_dict["output_hidden_states"] = True
|
|
check_hidden_states_output(inputs_dict, config, model_class)
|
|
|
|
# check that output_hidden_states also work using config
|
|
del inputs_dict["output_hidden_states"]
|
|
config.output_hidden_states = True
|
|
|
|
check_hidden_states_output(inputs_dict, config, model_class)
|
|
|
|
def _prepare_for_class(self, inputs_dict, model_class, return_labels=False):
|
|
inputs_dict = super()._prepare_for_class(inputs_dict, model_class, return_labels=return_labels)
|
|
|
|
if return_labels:
|
|
if model_class.__name__ == "EfficientFormerForImageClassificationWithTeacher":
|
|
del inputs_dict["labels"]
|
|
|
|
return inputs_dict
|
|
|
|
def test_model(self):
|
|
config_and_inputs = self.model_tester.prepare_config_and_inputs()
|
|
self.model_tester.create_and_check_model(*config_and_inputs)
|
|
|
|
@unittest.skip(reason="EfficientFormer does not implement masked image modeling yet")
|
|
def test_for_masked_image_modeling(self):
|
|
config_and_inputs = self.model_tester.prepare_config_and_inputs()
|
|
self.model_tester.create_and_check_for_masked_image_modeling(*config_and_inputs)
|
|
|
|
def test_for_image_classification(self):
|
|
config_and_inputs = self.model_tester.prepare_config_and_inputs()
|
|
self.model_tester.create_and_check_for_image_classification(*config_and_inputs)
|
|
|
|
# special case for EfficientFormerForImageClassificationWithTeacher model
|
|
def test_training(self):
|
|
if not self.model_tester.is_training:
|
|
return
|
|
|
|
config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
|
|
config.return_dict = True
|
|
|
|
for model_class in self.all_model_classes:
|
|
# EfficientFormerForImageClassificationWithTeacher supports inference-only
|
|
if (
|
|
model_class in get_values(MODEL_MAPPING)
|
|
or model_class.__name__ == "EfficientFormerForImageClassificationWithTeacher"
|
|
):
|
|
continue
|
|
model = model_class(config)
|
|
model.to(torch_device)
|
|
model.train()
|
|
inputs = self._prepare_for_class(inputs_dict, model_class, return_labels=True)
|
|
loss = model(**inputs).loss
|
|
loss.backward()
|
|
|
|
def test_problem_types(self):
|
|
config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
|
|
|
|
problem_types = [
|
|
{"title": "multi_label_classification", "num_labels": 2, "dtype": torch.float},
|
|
{"title": "single_label_classification", "num_labels": 1, "dtype": torch.long},
|
|
{"title": "regression", "num_labels": 1, "dtype": torch.float},
|
|
]
|
|
|
|
for model_class in self.all_model_classes:
|
|
if (
|
|
model_class
|
|
not in [
|
|
*get_values(MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING),
|
|
]
|
|
or model_class.__name__ == "EfficientFormerForImageClassificationWithTeacher"
|
|
):
|
|
continue
|
|
|
|
for problem_type in problem_types:
|
|
with self.subTest(msg=f"Testing {model_class} with {problem_type['title']}"):
|
|
config.problem_type = problem_type["title"]
|
|
config.num_labels = problem_type["num_labels"]
|
|
|
|
model = model_class(config)
|
|
model.to(torch_device)
|
|
model.train()
|
|
|
|
inputs = self._prepare_for_class(inputs_dict, model_class, return_labels=True)
|
|
|
|
if problem_type["num_labels"] > 1:
|
|
inputs["labels"] = inputs["labels"].unsqueeze(1).repeat(1, problem_type["num_labels"])
|
|
|
|
inputs["labels"] = inputs["labels"].to(problem_type["dtype"])
|
|
|
|
# This tests that we do not trigger the warning form PyTorch "Using a target size that is different
|
|
# to the input size. This will likely lead to incorrect results due to broadcasting. Please ensure
|
|
# they have the same size." which is a symptom something in wrong for the regression problem.
|
|
# See https://github.com/huggingface/transformers/issues/11780
|
|
with warnings.catch_warnings(record=True) as warning_list:
|
|
loss = model(**inputs).loss
|
|
for w in warning_list:
|
|
if "Using a target size that is different to the input size" in str(w.message):
|
|
raise ValueError(
|
|
f"Something is going wrong in the regression problem: intercepted {w.message}"
|
|
)
|
|
|
|
loss.backward()
|
|
|
|
@slow
|
|
def test_model_from_pretrained(self):
|
|
for model_name in EFFICIENTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
|
|
model = EfficientFormerModel.from_pretrained(model_name)
|
|
self.assertIsNotNone(model)
|
|
|
|
def test_attention_outputs(self):
|
|
config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
|
|
config.return_dict = True
|
|
|
|
seq_len = getattr(self.model_tester, "seq_length", None)
|
|
encoder_seq_length = getattr(self.model_tester, "encoder_seq_length", seq_len)
|
|
encoder_key_length = getattr(self.model_tester, "key_length", encoder_seq_length)
|
|
chunk_length = getattr(self.model_tester, "chunk_length", None)
|
|
|
|
if chunk_length is not None and hasattr(self.model_tester, "num_hashes"):
|
|
encoder_seq_length = encoder_seq_length * self.model_tester.num_hashes
|
|
|
|
for model_class in self.all_model_classes:
|
|
inputs_dict["output_attentions"] = True
|
|
inputs_dict["output_hidden_states"] = False
|
|
config.return_dict = True
|
|
model = model_class(config)
|
|
model.to(torch_device)
|
|
model.eval()
|
|
with torch.no_grad():
|
|
outputs = model(**self._prepare_for_class(inputs_dict, model_class))
|
|
attentions = outputs.encoder_attentions if config.is_encoder_decoder else outputs.attentions
|
|
self.assertEqual(len(attentions), self.model_tester.num_attention_outputs)
|
|
|
|
# check that output_attentions also work using config
|
|
del inputs_dict["output_attentions"]
|
|
config.output_attentions = True
|
|
model = model_class(config)
|
|
model.to(torch_device)
|
|
model.eval()
|
|
with torch.no_grad():
|
|
outputs = model(**self._prepare_for_class(inputs_dict, model_class))
|
|
attentions = outputs.encoder_attentions if config.is_encoder_decoder else outputs.attentions
|
|
self.assertEqual(len(attentions), self.model_tester.num_attention_outputs)
|
|
|
|
if chunk_length is not None:
|
|
self.assertListEqual(
|
|
list(attentions[0].shape[-4:]),
|
|
[self.model_tester.num_attention_heads, encoder_seq_length, chunk_length, encoder_key_length],
|
|
)
|
|
else:
|
|
self.assertListEqual(
|
|
list(attentions[0].shape[-3:]),
|
|
[self.model_tester.num_attention_heads, encoder_seq_length, encoder_key_length],
|
|
)
|
|
|
|
|
|
# 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 EfficientFormerModelIntegrationTest(unittest.TestCase):
|
|
@cached_property
|
|
def default_feature_extractor(self):
|
|
return (
|
|
EfficientFormerImageProcessor.from_pretrained("snap-research/efficientformer-l1-300")
|
|
if is_vision_available()
|
|
else None
|
|
)
|
|
|
|
@slow
|
|
def test_inference_image_classification_head(self):
|
|
model = EfficientFormerForImageClassification.from_pretrained("snap-research/efficientformer-l1-300").to(
|
|
torch_device
|
|
)
|
|
|
|
feature_extractor = self.default_feature_extractor
|
|
image = prepare_img()
|
|
inputs = feature_extractor(images=image, return_tensors="pt").to(torch_device)
|
|
|
|
# forward pass
|
|
with torch.no_grad():
|
|
outputs = model(**inputs)
|
|
|
|
# verify the logits
|
|
expected_shape = (1, 1000)
|
|
self.assertEqual(outputs.logits.shape, expected_shape)
|
|
|
|
expected_slice = torch.tensor([-0.0555, 0.4825, -0.0852]).to(torch_device)
|
|
self.assertTrue(torch.allclose(outputs.logits[0][:3], expected_slice, atol=1e-4))
|
|
|
|
@slow
|
|
def test_inference_image_classification_head_with_teacher(self):
|
|
model = EfficientFormerForImageClassificationWithTeacher.from_pretrained(
|
|
"snap-research/efficientformer-l1-300"
|
|
).to(torch_device)
|
|
|
|
feature_extractor = self.default_feature_extractor
|
|
image = prepare_img()
|
|
inputs = feature_extractor(images=image, return_tensors="pt").to(torch_device)
|
|
|
|
# forward pass
|
|
with torch.no_grad():
|
|
outputs = model(**inputs)
|
|
|
|
# verify the logits
|
|
expected_shape = (1, 1000)
|
|
self.assertEqual(outputs.logits.shape, expected_shape)
|
|
|
|
expected_slice = torch.tensor([-0.1312, 0.4353, -1.0499]).to(torch_device)
|
|
self.assertTrue(torch.allclose(outputs.logits[0][:3], expected_slice, atol=1e-4))
|