Skip to content

GitLab

"benchmark/vscode:/vscode.git/clone" did not exist on "fab0f6e77dd70f3f4d0d974896739e6b4ca8b2dd"
Unverified Commit a564d10a authored by amyeroberts's avatar amyeroberts Committed by GitHub
Browse files

Deprecate low use models (#30781) 

* Deprecate models
- graphormer
- time_series_transformer
- xlm_prophetnet
- qdqbert
- nat
- ernie_m
- tvlt
- nezha
- mega
- jukebox
- vit_hybrid
- x_clip
- deta
- speech_to_text_2
- efficientformer
- realm
- gptsan_japanese

* Fix up

* Fix speech2text2 imports

* Make sure message isn't indented

* Fix docstrings

* Correctly map for deprecated models from model_type

* Uncomment out

* Add back time series transformer and x-clip

* Import fix and fix-up

* Fix up with updated ruff
parent 7f08817b
Hide whitespace changes
Inline Side-by-side
Showing with 639 additions and 1959 deletions
src/transformers/models/vit_hybrid/__init__.py → src/transformers/models/deprecated/vit_hybrid/__init__.py
View file @ a564d10a
......@@ -13,7 +13,7 @@
# limitations under the License.
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
from ....utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
_import_structure = {"configuration_vit_hybrid": ["ViTHybridConfig"]}
......
src/transformers/models/vit_hybrid/configuration_vit_hybrid.py → src/transformers/models/deprecated/vit_hybrid/configuration_vit_hybrid.py
View file @ a564d10a
......@@ -14,10 +14,10 @@
# limitations under the License.
"""ViT Hybrid model configuration"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto.configuration_auto import CONFIG_MAPPING
from ..bit import BitConfig
from ....configuration_utils import PretrainedConfig
from ....utils import logging
from ...auto.configuration_auto import CONFIG_MAPPING
from ...bit import BitConfig
logger = logging.get_logger(__name__)
......
src/transformers/models/vit_hybrid/image_processing_vit_hybrid.py → src/transformers/models/deprecated/vit_hybrid/image_processing_vit_hybrid.py
View file @ a564d10a
......@@ -18,14 +18,14 @@ from typing import Dict, List, Optional, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
from ....image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ....image_transforms import (
convert_to_rgb,
get_resize_output_image_size,
resize,
to_channel_dimension_format,
)
from ...image_utils import (
from ....image_utils import (
OPENAI_CLIP_MEAN,
OPENAI_CLIP_STD,
ChannelDimension,
......@@ -39,7 +39,7 @@ from ...image_utils import (
validate_kwargs,
validate_preprocess_arguments,
)
from ...utils import TensorType, is_vision_available, logging
from ....utils import TensorType, is_vision_available, logging
logger = logging.get_logger(__name__)
......
src/transformers/models/vit_hybrid/modeling_vit_hybrid.py → src/transformers/models/deprecated/vit_hybrid/modeling_vit_hybrid.py
View file @ a564d10a
......@@ -23,12 +23,12 @@ import torch.utils.checkpoint
from torch import nn
from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
from ...activations import ACT2FN
from ...modeling_outputs import BaseModelOutput, BaseModelOutputWithPooling, ImageClassifierOutput
from ...modeling_utils import PreTrainedModel
from ...pytorch_utils import find_pruneable_heads_and_indices, prune_linear_layer
from ...utils import add_code_sample_docstrings, add_start_docstrings, add_start_docstrings_to_model_forward, logging
from ...utils.backbone_utils import load_backbone
from ....activations import ACT2FN
from ....modeling_outputs import BaseModelOutput, BaseModelOutputWithPooling, ImageClassifierOutput
from ....modeling_utils import PreTrainedModel
from ....pytorch_utils import find_pruneable_heads_and_indices, prune_linear_layer
from ....utils import add_code_sample_docstrings, add_start_docstrings, add_start_docstrings_to_model_forward, logging
from ....utils.backbone_utils import load_backbone
from .configuration_vit_hybrid import ViTHybridConfig
......
src/transformers/models/xlm_prophetnet/__init__.py → src/transformers/models/deprecated/xlm_prophetnet/__init__.py
View file @ a564d10a
......@@ -13,7 +13,7 @@
# limitations under the License.
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_sentencepiece_available, is_torch_available
from ....utils import OptionalDependencyNotAvailable, _LazyModule, is_sentencepiece_available, is_torch_available
_import_structure = {
......
src/transformers/models/xlm_prophetnet/configuration_xlm_prophetnet.py → src/transformers/models/deprecated/xlm_prophetnet/configuration_xlm_prophetnet.py
View file @ a564d10a
......@@ -16,8 +16,8 @@
from typing import Callable, Optional, Union
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ....configuration_utils import PretrainedConfig
from ....utils import logging
logger = logging.get_logger(__name__)
......
src/transformers/models/xlm_prophetnet/modeling_xlm_prophetnet.py → src/transformers/models/deprecated/xlm_prophetnet/modeling_xlm_prophetnet.py
View file @ a564d10a
......@@ -25,10 +25,10 @@ import torch.utils.checkpoint
from torch import Tensor, nn
from torch.nn import LayerNorm
from ...activations import ACT2FN
from ...modeling_outputs import BaseModelOutput
from ...modeling_utils import PreTrainedModel
from ...utils import (
from ....activations import ACT2FN
from ....modeling_outputs import BaseModelOutput
from ....modeling_utils import PreTrainedModel
from ....utils import (
ModelOutput,
add_start_docstrings,
add_start_docstrings_to_model_forward,
......
src/transformers/models/xlm_prophetnet/tokenization_xlm_prophetnet.py → src/transformers/models/deprecated/xlm_prophetnet/tokenization_xlm_prophetnet.py
View file @ a564d10a
......@@ -18,8 +18,8 @@ import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
from ....tokenization_utils import PreTrainedTokenizer
from ....utils import logging
logger = logging.get_logger(__name__)
......
src/transformers/models/dinat/modeling_dinat.py
View file @ a564d10a
......@@ -71,7 +71,6 @@ _IMAGE_CLASS_EXPECTED_OUTPUT = "tabby, tabby cat"
@dataclass
# Copied from transformers.models.nat.modeling_nat.NatEncoderOutput with Nat->Dinat
class DinatEncoderOutput(ModelOutput):
"""
Dinat encoder's outputs, with potential hidden states and attentions.
......@@ -105,7 +104,6 @@ class DinatEncoderOutput(ModelOutput):
@dataclass
# Copied from transformers.models.nat.modeling_nat.NatModelOutput with Nat->Dinat
class DinatModelOutput(ModelOutput):
"""
Dinat model's outputs that also contains a pooling of the last hidden states.
......@@ -142,7 +140,6 @@ class DinatModelOutput(ModelOutput):
@dataclass
# Copied from transformers.models.nat.modeling_nat.NatImageClassifierOutput with Nat->Dinat
class DinatImageClassifierOutput(ModelOutput):
"""
Dinat outputs for image classification.
......@@ -178,7 +175,6 @@ class DinatImageClassifierOutput(ModelOutput):
reshaped_hidden_states: Optional[Tuple[torch.FloatTensor, ...]] = None
# Copied from transformers.models.nat.modeling_nat.NatEmbeddings with Nat->Dinat
class DinatEmbeddings(nn.Module):
"""
Construct the patch and position embeddings.
......@@ -201,7 +197,6 @@ class DinatEmbeddings(nn.Module):
return embeddings
# Copied from transformers.models.nat.modeling_nat.NatPatchEmbeddings with Nat->Dinat
class DinatPatchEmbeddings(nn.Module):
"""
This class turns `pixel_values` of shape `(batch_size, num_channels, height, width)` into the initial
......@@ -238,7 +233,6 @@ class DinatPatchEmbeddings(nn.Module):
return embeddings
# Copied from transformers.models.nat.modeling_nat.NatDownsampler with Nat->Dinat
class DinatDownsampler(nn.Module):
"""
Convolutional Downsampling Layer.
......@@ -321,7 +315,6 @@ class NeighborhoodAttention(nn.Module):
self.dropout = nn.Dropout(config.attention_probs_dropout_prob)
# Copied from transformers.models.nat.modeling_nat.NeighborhoodAttention.transpose_for_scores with Nat->Dinat
def transpose_for_scores(self, x):
new_x_shape = x.size()[:-1] + (self.num_attention_heads, self.attention_head_size)
x = x.view(new_x_shape)
......@@ -361,7 +354,6 @@ class NeighborhoodAttention(nn.Module):
return outputs
# Copied from transformers.models.nat.modeling_nat.NeighborhoodAttentionOutput
class NeighborhoodAttentionOutput(nn.Module):
def __init__(self, config, dim):
super().__init__()
......@@ -382,7 +374,6 @@ class NeighborhoodAttentionModule(nn.Module):
self.output = NeighborhoodAttentionOutput(config, dim)
self.pruned_heads = set()
# Copied from transformers.models.nat.modeling_nat.NeighborhoodAttentionModule.prune_heads
def prune_heads(self, heads):
if len(heads) == 0:
return
......@@ -401,7 +392,6 @@ class NeighborhoodAttentionModule(nn.Module):
self.self.all_head_size = self.self.attention_head_size * self.self.num_attention_heads
self.pruned_heads = self.pruned_heads.union(heads)
# Copied from transformers.models.nat.modeling_nat.NeighborhoodAttentionModule.forward
def forward(
self,
hidden_states: torch.Tensor,
......@@ -413,7 +403,6 @@ class NeighborhoodAttentionModule(nn.Module):
return outputs
# Copied from transformers.models.nat.modeling_nat.NatIntermediate with Nat->Dinat
class DinatIntermediate(nn.Module):
def __init__(self, config, dim):
super().__init__()
......@@ -429,7 +418,6 @@ class DinatIntermediate(nn.Module):
return hidden_states
# Copied from transformers.models.nat.modeling_nat.NatOutput with Nat->Dinat
class DinatOutput(nn.Module):
def __init__(self, config, dim):
super().__init__()
......@@ -539,7 +527,6 @@ class DinatStage(nn.Module):
self.pointing = False
# Copied from transformers.models.nat.modeling_nat.NatStage.forward
def forward(
self,
hidden_states: torch.Tensor,
......@@ -582,7 +569,6 @@ class DinatEncoder(nn.Module):
]
)
# Copied from transformers.models.nat.modeling_nat.NatEncoder.forward with Nat->Dinat
def forward(
self,
hidden_states: torch.Tensor,
......@@ -687,7 +673,6 @@ DINAT_INPUTS_DOCSTRING = r"""
"The bare Dinat Model transformer outputting raw hidden-states without any specific head on top.",
DINAT_START_DOCSTRING,
)
# Copied from transformers.models.nat.modeling_nat.NatModel with Nat->Dinat, NAT->DINAT
class DinatModel(DinatPreTrainedModel):
def __init__(self, config, add_pooling_layer=True):
super().__init__(config)
......
src/transformers/utils/dummy_pt_objects.py
View file @ a564d10a
......@@ -2609,6 +2609,174 @@ class DeiTPreTrainedModel(metaclass=DummyObject):
requires_backends(self, ["torch"])
class DetaForObjectDetection(metaclass=DummyObject):
_backends = ["torch"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["torch"])
class DetaModel(metaclass=DummyObject):
_backends = ["torch"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["torch"])
class DetaPreTrainedModel(metaclass=DummyObject):
_backends = ["torch"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["torch"])
class EfficientFormerForImageClassification(metaclass=DummyObject):
_backends = ["torch"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["torch"])
class EfficientFormerForImageClassificationWithTeacher(metaclass=DummyObject):
_backends = ["torch"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["torch"])
class EfficientFormerModel(metaclass=DummyObject):
_backends = ["torch"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["torch"])
class EfficientFormerPreTrainedModel(metaclass=DummyObject):
_backends = ["torch"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["torch"])
class ErnieMForInformationExtraction(metaclass=DummyObject):
_backends = ["torch"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["torch"])
class ErnieMForMultipleChoice(metaclass=DummyObject):
_backends = ["torch"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["torch"])
class ErnieMForQuestionAnswering(metaclass=DummyObject):
_backends = ["torch"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["torch"])
class ErnieMForSequenceClassification(metaclass=DummyObject):
_backends = ["torch"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["torch"])
class ErnieMForTokenClassification(metaclass=DummyObject):
_backends = ["torch"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["torch"])
class ErnieMModel(metaclass=DummyObject):
_backends = ["torch"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["torch"])
class ErnieMPreTrainedModel(metaclass=DummyObject):
_backends = ["torch"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["torch"])
class GPTSanJapaneseForConditionalGeneration(metaclass=DummyObject):
_backends = ["torch"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["torch"])
class GPTSanJapaneseModel(metaclass=DummyObject):
_backends = ["torch"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["torch"])
class GPTSanJapanesePreTrainedModel(metaclass=DummyObject):
_backends = ["torch"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["torch"])
class GraphormerForGraphClassification(metaclass=DummyObject):
_backends = ["torch"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["torch"])
class GraphormerModel(metaclass=DummyObject):
_backends = ["torch"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["torch"])
class GraphormerPreTrainedModel(metaclass=DummyObject):
_backends = ["torch"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["torch"])
class JukeboxModel(metaclass=DummyObject):
_backends = ["torch"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["torch"])
class JukeboxPreTrainedModel(metaclass=DummyObject):
_backends = ["torch"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["torch"])
class JukeboxPrior(metaclass=DummyObject):
_backends = ["torch"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["torch"])
class JukeboxVQVAE(metaclass=DummyObject):
_backends = ["torch"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["torch"])
class MCTCTForCTC(metaclass=DummyObject):
_backends = ["torch"]
......@@ -2616,69 +2784,343 @@ class MCTCTForCTC(metaclass=DummyObject):
requires_backends(self, ["torch"])
class MCTCTModel(metaclass=DummyObject):
class MCTCTModel(metaclass=DummyObject):
_backends = ["torch"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["torch"])
class MCTCTPreTrainedModel(metaclass=DummyObject):
_backends = ["torch"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["torch"])
class MegaForCausalLM(metaclass=DummyObject):
_backends = ["torch"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["torch"])
class MegaForMaskedLM(metaclass=DummyObject):
_backends = ["torch"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["torch"])
class MegaForMultipleChoice(metaclass=DummyObject):
_backends = ["torch"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["torch"])
class MegaForQuestionAnswering(metaclass=DummyObject):
_backends = ["torch"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["torch"])
class MegaForSequenceClassification(metaclass=DummyObject):
_backends = ["torch"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["torch"])
class MegaForTokenClassification(metaclass=DummyObject):
_backends = ["torch"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["torch"])
class MegaModel(metaclass=DummyObject):
_backends = ["torch"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["torch"])
class MegaPreTrainedModel(metaclass=DummyObject):
_backends = ["torch"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["torch"])
class MMBTForClassification(metaclass=DummyObject):
_backends = ["torch"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["torch"])
class MMBTModel(metaclass=DummyObject):
_backends = ["torch"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["torch"])
class ModalEmbeddings(metaclass=DummyObject):
_backends = ["torch"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["torch"])
class NatBackbone(metaclass=DummyObject):
_backends = ["torch"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["torch"])
class NatForImageClassification(metaclass=DummyObject):
_backends = ["torch"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["torch"])
class NatModel(metaclass=DummyObject):
_backends = ["torch"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["torch"])
class NatPreTrainedModel(metaclass=DummyObject):
_backends = ["torch"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["torch"])
class NezhaForMaskedLM(metaclass=DummyObject):
_backends = ["torch"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["torch"])
class NezhaForMultipleChoice(metaclass=DummyObject):
_backends = ["torch"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["torch"])
class NezhaForNextSentencePrediction(metaclass=DummyObject):
_backends = ["torch"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["torch"])
class NezhaForPreTraining(metaclass=DummyObject):
_backends = ["torch"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["torch"])
class NezhaForQuestionAnswering(metaclass=DummyObject):
_backends = ["torch"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["torch"])
class NezhaForSequenceClassification(metaclass=DummyObject):
_backends = ["torch"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["torch"])
class NezhaForTokenClassification(metaclass=DummyObject):
_backends = ["torch"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["torch"])
class NezhaModel(metaclass=DummyObject):
_backends = ["torch"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["torch"])
class NezhaPreTrainedModel(metaclass=DummyObject):
_backends = ["torch"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["torch"])
class OpenLlamaForCausalLM(metaclass=DummyObject):
_backends = ["torch"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["torch"])
class MCTCTPreTrainedModel(metaclass=DummyObject):
class OpenLlamaForSequenceClassification(metaclass=DummyObject):
_backends = ["torch"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["torch"])
class MMBTForClassification(metaclass=DummyObject):
class OpenLlamaModel(metaclass=DummyObject):
_backends = ["torch"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["torch"])
class MMBTModel(metaclass=DummyObject):
class OpenLlamaPreTrainedModel(metaclass=DummyObject):
_backends = ["torch"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["torch"])
class ModalEmbeddings(metaclass=DummyObject):
class QDQBertForMaskedLM(metaclass=DummyObject):
_backends = ["torch"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["torch"])
class OpenLlamaForCausalLM(metaclass=DummyObject):
class QDQBertForMultipleChoice(metaclass=DummyObject):
_backends = ["torch"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["torch"])
class OpenLlamaForSequenceClassification(metaclass=DummyObject):
class QDQBertForNextSentencePrediction(metaclass=DummyObject):
_backends = ["torch"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["torch"])
class OpenLlamaModel(metaclass=DummyObject):
class QDQBertForQuestionAnswering(metaclass=DummyObject):
_backends = ["torch"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["torch"])
class OpenLlamaPreTrainedModel(metaclass=DummyObject):
class QDQBertForSequenceClassification(metaclass=DummyObject):
_backends = ["torch"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["torch"])
class QDQBertForTokenClassification(metaclass=DummyObject):
_backends = ["torch"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["torch"])
class QDQBertLayer(metaclass=DummyObject):
_backends = ["torch"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["torch"])
class QDQBertLMHeadModel(metaclass=DummyObject):
_backends = ["torch"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["torch"])
class QDQBertModel(metaclass=DummyObject):
_backends = ["torch"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["torch"])
class QDQBertPreTrainedModel(metaclass=DummyObject):
_backends = ["torch"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["torch"])
def load_tf_weights_in_qdqbert(*args, **kwargs):
requires_backends(load_tf_weights_in_qdqbert, ["torch"])
class RealmEmbedder(metaclass=DummyObject):
_backends = ["torch"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["torch"])
class RealmForOpenQA(metaclass=DummyObject):
_backends = ["torch"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["torch"])
class RealmKnowledgeAugEncoder(metaclass=DummyObject):
_backends = ["torch"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["torch"])
class RealmPreTrainedModel(metaclass=DummyObject):
_backends = ["torch"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["torch"])
class RealmReader(metaclass=DummyObject):
_backends = ["torch"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["torch"])
class RealmRetriever(metaclass=DummyObject):
_backends = ["torch"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["torch"])
class RealmScorer(metaclass=DummyObject):
_backends = ["torch"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["torch"])
def load_tf_weights_in_realm(*args, **kwargs):
requires_backends(load_tf_weights_in_realm, ["torch"])
class RetriBertModel(metaclass=DummyObject):
_backends = ["torch"]
......@@ -2693,6 +3135,20 @@ class RetriBertPreTrainedModel(metaclass=DummyObject):
requires_backends(self, ["torch"])
class Speech2Text2ForCausalLM(metaclass=DummyObject):
_backends = ["torch"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["torch"])
class Speech2Text2PreTrainedModel(metaclass=DummyObject):
_backends = ["torch"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["torch"])
class TrajectoryTransformerModel(metaclass=DummyObject):
_backends = ["torch"]
......@@ -2746,536 +3202,529 @@ def load_tf_weights_in_transfo_xl(*args, **kwargs):
requires_backends(load_tf_weights_in_transfo_xl, ["torch"])
class VanForImageClassification(metaclass=DummyObject):
_backends = ["torch"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["torch"])
class VanModel(metaclass=DummyObject):
class TvltForAudioVisualClassification(metaclass=DummyObject):
_backends = ["torch"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["torch"])
class VanPreTrainedModel(metaclass=DummyObject):
class TvltForPreTraining(metaclass=DummyObject):
_backends = ["torch"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["torch"])
class DepthAnythingForDepthEstimation(metaclass=DummyObject):
class TvltModel(metaclass=DummyObject):
_backends = ["torch"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["torch"])
class DepthAnythingPreTrainedModel(metaclass=DummyObject):
class TvltPreTrainedModel(metaclass=DummyObject):
_backends = ["torch"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["torch"])
class DetaForObjectDetection(metaclass=DummyObject):
class VanForImageClassification(metaclass=DummyObject):
_backends = ["torch"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["torch"])
class DetaModel(metaclass=DummyObject):
class VanModel(metaclass=DummyObject):
_backends = ["torch"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["torch"])
class DetaPreTrainedModel(metaclass=DummyObject):
class VanPreTrainedModel(metaclass=DummyObject):
_backends = ["torch"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["torch"])
class DetrForObjectDetection(metaclass=DummyObject):
class ViTHybridForImageClassification(metaclass=DummyObject):
_backends = ["torch"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["torch"])
class DetrForSegmentation(metaclass=DummyObject):
class ViTHybridModel(metaclass=DummyObject):
_backends = ["torch"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["torch"])
class DetrModel(metaclass=DummyObject):
class ViTHybridPreTrainedModel(metaclass=DummyObject):
_backends = ["torch"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["torch"])
class DetrPreTrainedModel(metaclass=DummyObject):
class XLMProphetNetDecoder(metaclass=DummyObject):
_backends = ["torch"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["torch"])
class DinatBackbone(metaclass=DummyObject):
class XLMProphetNetEncoder(metaclass=DummyObject):
_backends = ["torch"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["torch"])
class DinatForImageClassification(metaclass=DummyObject):
class XLMProphetNetForCausalLM(metaclass=DummyObject):
_backends = ["torch"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["torch"])
class DinatModel(metaclass=DummyObject):
class XLMProphetNetForConditionalGeneration(metaclass=DummyObject):
_backends = ["torch"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["torch"])
class DinatPreTrainedModel(metaclass=DummyObject):
class XLMProphetNetModel(metaclass=DummyObject):
_backends = ["torch"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["torch"])
class Dinov2Backbone(metaclass=DummyObject):
class XLMProphetNetPreTrainedModel(metaclass=DummyObject):
_backends = ["torch"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["torch"])
class Dinov2ForImageClassification(metaclass=DummyObject):
class DepthAnythingForDepthEstimation(metaclass=DummyObject):
_backends = ["torch"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["torch"])
class Dinov2Model(metaclass=DummyObject):
class DepthAnythingPreTrainedModel(metaclass=DummyObject):
_backends = ["torch"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["torch"])
class Dinov2PreTrainedModel(metaclass=DummyObject):
class DetrForObjectDetection(metaclass=DummyObject):
_backends = ["torch"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["torch"])
class DistilBertForMaskedLM(metaclass=DummyObject):
class DetrForSegmentation(metaclass=DummyObject):
_backends = ["torch"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["torch"])
class DistilBertForMultipleChoice(metaclass=DummyObject):
class DetrModel(metaclass=DummyObject):
_backends = ["torch"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["torch"])
class DistilBertForQuestionAnswering(metaclass=DummyObject):
class DetrPreTrainedModel(metaclass=DummyObject):
_backends = ["torch"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["torch"])
class DistilBertForSequenceClassification(metaclass=DummyObject):
class DinatBackbone(metaclass=DummyObject):
_backends = ["torch"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["torch"])
class DistilBertForTokenClassification(metaclass=DummyObject):
class DinatForImageClassification(metaclass=DummyObject):
_backends = ["torch"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["torch"])
class DistilBertModel(metaclass=DummyObject):
class DinatModel(metaclass=DummyObject):
_backends = ["torch"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["torch"])
class DistilBertPreTrainedModel(metaclass=DummyObject):
class DinatPreTrainedModel(metaclass=DummyObject):
_backends = ["torch"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["torch"])
class DonutSwinModel(metaclass=DummyObject):
class Dinov2Backbone(metaclass=DummyObject):
_backends = ["torch"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["torch"])
class DonutSwinPreTrainedModel(metaclass=DummyObject):
class Dinov2ForImageClassification(metaclass=DummyObject):
_backends = ["torch"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["torch"])
class DPRContextEncoder(metaclass=DummyObject):
class Dinov2Model(metaclass=DummyObject):
_backends = ["torch"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["torch"])
class DPRPretrainedContextEncoder(metaclass=DummyObject):
class Dinov2PreTrainedModel(metaclass=DummyObject):
_backends = ["torch"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["torch"])
class DPRPreTrainedModel(metaclass=DummyObject):
class DistilBertForMaskedLM(metaclass=DummyObject):
_backends = ["torch"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["torch"])
class DPRPretrainedQuestionEncoder(metaclass=DummyObject):
class DistilBertForMultipleChoice(metaclass=DummyObject):
_backends = ["torch"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["torch"])
class DPRPretrainedReader(metaclass=DummyObject):
class DistilBertForQuestionAnswering(metaclass=DummyObject):
_backends = ["torch"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["torch"])
class DPRQuestionEncoder(metaclass=DummyObject):
class DistilBertForSequenceClassification(metaclass=DummyObject):
_backends = ["torch"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["torch"])
class DPRReader(metaclass=DummyObject):
class DistilBertForTokenClassification(metaclass=DummyObject):
_backends = ["torch"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["torch"])
class DPTForDepthEstimation(metaclass=DummyObject):
class DistilBertModel(metaclass=DummyObject):
_backends = ["torch"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["torch"])
class DPTForSemanticSegmentation(metaclass=DummyObject):
class DistilBertPreTrainedModel(metaclass=DummyObject):
_backends = ["torch"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["torch"])
class DPTModel(metaclass=DummyObject):
class DonutSwinModel(metaclass=DummyObject):
_backends = ["torch"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["torch"])
class DPTPreTrainedModel(metaclass=DummyObject):
class DonutSwinPreTrainedModel(metaclass=DummyObject):
_backends = ["torch"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["torch"])
class EfficientFormerForImageClassification(metaclass=DummyObject):
class DPRContextEncoder(metaclass=DummyObject):
_backends = ["torch"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["torch"])
class EfficientFormerForImageClassificationWithTeacher(metaclass=DummyObject):
class DPRPretrainedContextEncoder(metaclass=DummyObject):
_backends = ["torch"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["torch"])
class EfficientFormerModel(metaclass=DummyObject):
class DPRPreTrainedModel(metaclass=DummyObject):
_backends = ["torch"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["torch"])
class EfficientFormerPreTrainedModel(metaclass=DummyObject):
class DPRPretrainedQuestionEncoder(metaclass=DummyObject):
_backends = ["torch"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["torch"])
class EfficientNetForImageClassification(metaclass=DummyObject):
class DPRPretrainedReader(metaclass=DummyObject):
_backends = ["torch"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["torch"])
class EfficientNetModel(metaclass=DummyObject):
class DPRQuestionEncoder(metaclass=DummyObject):
_backends = ["torch"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["torch"])
class EfficientNetPreTrainedModel(metaclass=DummyObject):
class DPRReader(metaclass=DummyObject):
_backends = ["torch"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["torch"])
class ElectraForCausalLM(metaclass=DummyObject):
class DPTForDepthEstimation(metaclass=DummyObject):
_backends = ["torch"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["torch"])
class ElectraForMaskedLM(metaclass=DummyObject):
class DPTForSemanticSegmentation(metaclass=DummyObject):
_backends = ["torch"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["torch"])
class ElectraForMultipleChoice(metaclass=DummyObject):
class DPTModel(metaclass=DummyObject):
_backends = ["torch"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["torch"])
class ElectraForPreTraining(metaclass=DummyObject):
class DPTPreTrainedModel(metaclass=DummyObject):
_backends = ["torch"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["torch"])
class ElectraForQuestionAnswering(metaclass=DummyObject):
class EfficientNetForImageClassification(metaclass=DummyObject):
_backends = ["torch"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["torch"])
class ElectraForSequenceClassification(metaclass=DummyObject):
class EfficientNetModel(metaclass=DummyObject):
_backends = ["torch"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["torch"])
class ElectraForTokenClassification(metaclass=DummyObject):
class EfficientNetPreTrainedModel(metaclass=DummyObject):
_backends = ["torch"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["torch"])
class ElectraModel(metaclass=DummyObject):
class ElectraForCausalLM(metaclass=DummyObject):
_backends = ["torch"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["torch"])
class ElectraPreTrainedModel(metaclass=DummyObject):
class ElectraForMaskedLM(metaclass=DummyObject):
_backends = ["torch"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["torch"])
def load_tf_weights_in_electra(*args, **kwargs):
requires_backends(load_tf_weights_in_electra, ["torch"])
class EncodecModel(metaclass=DummyObject):
class ElectraForMultipleChoice(metaclass=DummyObject):
_backends = ["torch"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["torch"])
class EncodecPreTrainedModel(metaclass=DummyObject):
class ElectraForPreTraining(metaclass=DummyObject):
_backends = ["torch"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["torch"])
class EncoderDecoderModel(metaclass=DummyObject):
class ElectraForQuestionAnswering(metaclass=DummyObject):
_backends = ["torch"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["torch"])
class ErnieForCausalLM(metaclass=DummyObject):
class ElectraForSequenceClassification(metaclass=DummyObject):
_backends = ["torch"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["torch"])
class ErnieForMaskedLM(metaclass=DummyObject):
class ElectraForTokenClassification(metaclass=DummyObject):
_backends = ["torch"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["torch"])
class ErnieForMultipleChoice(metaclass=DummyObject):
class ElectraModel(metaclass=DummyObject):
_backends = ["torch"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["torch"])
class ErnieForNextSentencePrediction(metaclass=DummyObject):
class ElectraPreTrainedModel(metaclass=DummyObject):
_backends = ["torch"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["torch"])
class ErnieForPreTraining(metaclass=DummyObject):
def load_tf_weights_in_electra(*args, **kwargs):
requires_backends(load_tf_weights_in_electra, ["torch"])
class EncodecModel(metaclass=DummyObject):
_backends = ["torch"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["torch"])
class ErnieForQuestionAnswering(metaclass=DummyObject):
class EncodecPreTrainedModel(metaclass=DummyObject):
_backends = ["torch"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["torch"])
class ErnieForSequenceClassification(metaclass=DummyObject):
class EncoderDecoderModel(metaclass=DummyObject):
_backends = ["torch"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["torch"])
class ErnieForTokenClassification(metaclass=DummyObject):
class ErnieForCausalLM(metaclass=DummyObject):
_backends = ["torch"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["torch"])
class ErnieModel(metaclass=DummyObject):
class ErnieForMaskedLM(metaclass=DummyObject):
_backends = ["torch"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["torch"])
class ErniePreTrainedModel(metaclass=DummyObject):
class ErnieForMultipleChoice(metaclass=DummyObject):
_backends = ["torch"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["torch"])
class ErnieMForInformationExtraction(metaclass=DummyObject):
class ErnieForNextSentencePrediction(metaclass=DummyObject):
_backends = ["torch"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["torch"])
class ErnieMForMultipleChoice(metaclass=DummyObject):
class ErnieForPreTraining(metaclass=DummyObject):
_backends = ["torch"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["torch"])
class ErnieMForQuestionAnswering(metaclass=DummyObject):
class ErnieForQuestionAnswering(metaclass=DummyObject):
_backends = ["torch"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["torch"])
class ErnieMForSequenceClassification(metaclass=DummyObject):
class ErnieForSequenceClassification(metaclass=DummyObject):
_backends = ["torch"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["torch"])
class ErnieMForTokenClassification(metaclass=DummyObject):
class ErnieForTokenClassification(metaclass=DummyObject):
_backends = ["torch"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["torch"])
class ErnieMModel(metaclass=DummyObject):
class ErnieModel(metaclass=DummyObject):
_backends = ["torch"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["torch"])
class ErnieMPreTrainedModel(metaclass=DummyObject):
class ErniePreTrainedModel(metaclass=DummyObject):
_backends = ["torch"]
def __init__(self, *args, **kwargs):
......@@ -4043,48 +4492,6 @@ class GPTJPreTrainedModel(metaclass=DummyObject):
requires_backends(self, ["torch"])
class GPTSanJapaneseForConditionalGeneration(metaclass=DummyObject):
_backends = ["torch"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["torch"])
class GPTSanJapaneseModel(metaclass=DummyObject):
_backends = ["torch"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["torch"])
class GPTSanJapanesePreTrainedModel(metaclass=DummyObject):
_backends = ["torch"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["torch"])
class GraphormerForGraphClassification(metaclass=DummyObject):
_backends = ["torch"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["torch"])
class GraphormerModel(metaclass=DummyObject):
_backends = ["torch"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["torch"])
class GraphormerPreTrainedModel(metaclass=DummyObject):
_backends = ["torch"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["torch"])
class GroundingDinoForObjectDetection(metaclass=DummyObject):
_backends = ["torch"]
......@@ -4404,34 +4811,6 @@ class JetMoePreTrainedModel(metaclass=DummyObject):
requires_backends(self, ["torch"])
class JukeboxModel(metaclass=DummyObject):
_backends = ["torch"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["torch"])
class JukeboxPreTrainedModel(metaclass=DummyObject):
_backends = ["torch"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["torch"])
class JukeboxPrior(metaclass=DummyObject):
_backends = ["torch"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["torch"])
class JukeboxVQVAE(metaclass=DummyObject):
_backends = ["torch"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["torch"])
class Kosmos2ForConditionalGeneration(metaclass=DummyObject):
_backends = ["torch"]
......@@ -5125,62 +5504,6 @@ class MBartPreTrainedModel(metaclass=DummyObject):
requires_backends(self, ["torch"])
class MegaForCausalLM(metaclass=DummyObject):
_backends = ["torch"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["torch"])
class MegaForMaskedLM(metaclass=DummyObject):
_backends = ["torch"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["torch"])
class MegaForMultipleChoice(metaclass=DummyObject):
_backends = ["torch"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["torch"])
class MegaForQuestionAnswering(metaclass=DummyObject):
_backends = ["torch"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["torch"])
class MegaForSequenceClassification(metaclass=DummyObject):
_backends = ["torch"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["torch"])
class MegaForTokenClassification(metaclass=DummyObject):
_backends = ["torch"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["torch"])
class MegaModel(metaclass=DummyObject):
_backends = ["torch"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["torch"])
class MegaPreTrainedModel(metaclass=DummyObject):
_backends = ["torch"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["torch"])
class MegatronBertForCausalLM(metaclass=DummyObject):
_backends = ["torch"]
......@@ -5746,175 +6069,84 @@ class MusicgenModel(metaclass=DummyObject):
requires_backends(self, ["torch"])
class MusicgenPreTrainedModel(metaclass=DummyObject):
_backends = ["torch"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["torch"])
class MusicgenProcessor(metaclass=DummyObject):
_backends = ["torch"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["torch"])
class MusicgenMelodyForCausalLM(metaclass=DummyObject):
_backends = ["torch"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["torch"])
class MusicgenMelodyForConditionalGeneration(metaclass=DummyObject):
_backends = ["torch"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["torch"])
class MusicgenMelodyModel(metaclass=DummyObject):
_backends = ["torch"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["torch"])
class MusicgenMelodyPreTrainedModel(metaclass=DummyObject):
_backends = ["torch"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["torch"])
class MvpForCausalLM(metaclass=DummyObject):
_backends = ["torch"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["torch"])
class MvpForConditionalGeneration(metaclass=DummyObject):
_backends = ["torch"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["torch"])
class MvpForQuestionAnswering(metaclass=DummyObject):
_backends = ["torch"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["torch"])
class MvpForSequenceClassification(metaclass=DummyObject):
_backends = ["torch"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["torch"])
class MvpModel(metaclass=DummyObject):
_backends = ["torch"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["torch"])
class MvpPreTrainedModel(metaclass=DummyObject):
_backends = ["torch"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["torch"])
class NatBackbone(metaclass=DummyObject):
_backends = ["torch"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["torch"])
class NatForImageClassification(metaclass=DummyObject):
class MusicgenPreTrainedModel(metaclass=DummyObject):
_backends = ["torch"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["torch"])
class NatModel(metaclass=DummyObject):
class MusicgenProcessor(metaclass=DummyObject):
_backends = ["torch"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["torch"])
class NatPreTrainedModel(metaclass=DummyObject):
class MusicgenMelodyForCausalLM(metaclass=DummyObject):
_backends = ["torch"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["torch"])
class NezhaForMaskedLM(metaclass=DummyObject):
class MusicgenMelodyForConditionalGeneration(metaclass=DummyObject):
_backends = ["torch"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["torch"])
class NezhaForMultipleChoice(metaclass=DummyObject):
class MusicgenMelodyModel(metaclass=DummyObject):
_backends = ["torch"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["torch"])
class NezhaForNextSentencePrediction(metaclass=DummyObject):
class MusicgenMelodyPreTrainedModel(metaclass=DummyObject):
_backends = ["torch"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["torch"])
class NezhaForPreTraining(metaclass=DummyObject):
class MvpForCausalLM(metaclass=DummyObject):
_backends = ["torch"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["torch"])
class NezhaForQuestionAnswering(metaclass=DummyObject):
class MvpForConditionalGeneration(metaclass=DummyObject):
_backends = ["torch"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["torch"])
class NezhaForSequenceClassification(metaclass=DummyObject):
class MvpForQuestionAnswering(metaclass=DummyObject):
_backends = ["torch"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["torch"])
class NezhaForTokenClassification(metaclass=DummyObject):
class MvpForSequenceClassification(metaclass=DummyObject):
_backends = ["torch"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["torch"])
class NezhaModel(metaclass=DummyObject):
class MvpModel(metaclass=DummyObject):
_backends = ["torch"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["torch"])
class NezhaPreTrainedModel(metaclass=DummyObject):
class MvpPreTrainedModel(metaclass=DummyObject):
_backends = ["torch"]
def __init__(self, *args, **kwargs):
......@@ -6716,80 +6948,6 @@ class PvtV2PreTrainedModel(metaclass=DummyObject):
requires_backends(self, ["torch"])
class QDQBertForMaskedLM(metaclass=DummyObject):
_backends = ["torch"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["torch"])
class QDQBertForMultipleChoice(metaclass=DummyObject):
_backends = ["torch"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["torch"])
class QDQBertForNextSentencePrediction(metaclass=DummyObject):
_backends = ["torch"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["torch"])
class QDQBertForQuestionAnswering(metaclass=DummyObject):
_backends = ["torch"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["torch"])
class QDQBertForSequenceClassification(metaclass=DummyObject):
_backends = ["torch"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["torch"])
class QDQBertForTokenClassification(metaclass=DummyObject):
_backends = ["torch"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["torch"])
class QDQBertLayer(metaclass=DummyObject):
_backends = ["torch"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["torch"])
class QDQBertLMHeadModel(metaclass=DummyObject):
_backends = ["torch"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["torch"])
class QDQBertModel(metaclass=DummyObject):
_backends = ["torch"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["torch"])
class QDQBertPreTrainedModel(metaclass=DummyObject):
_backends = ["torch"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["torch"])
def load_tf_weights_in_qdqbert(*args, **kwargs):
requires_backends(load_tf_weights_in_qdqbert, ["torch"])
class Qwen2ForCausalLM(metaclass=DummyObject):
_backends = ["torch"]
......@@ -6888,59 +7046,6 @@ class RagTokenForGeneration(metaclass=DummyObject):
requires_backends(self, ["torch"])
class RealmEmbedder(metaclass=DummyObject):
_backends = ["torch"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["torch"])
class RealmForOpenQA(metaclass=DummyObject):
_backends = ["torch"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["torch"])
class RealmKnowledgeAugEncoder(metaclass=DummyObject):
_backends = ["torch"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["torch"])
class RealmPreTrainedModel(metaclass=DummyObject):
_backends = ["torch"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["torch"])
class RealmReader(metaclass=DummyObject):
_backends = ["torch"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["torch"])
class RealmRetriever(metaclass=DummyObject):
_backends = ["torch"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["torch"])
class RealmScorer(metaclass=DummyObject):
_backends = ["torch"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["torch"])
def load_tf_weights_in_realm(*args, **kwargs):
requires_backends(load_tf_weights_in_realm, ["torch"])
class RecurrentGemmaForCausalLM(metaclass=DummyObject):
_backends = ["torch"]
......@@ -7716,20 +7821,6 @@ class Speech2TextPreTrainedModel(metaclass=DummyObject):
requires_backends(self, ["torch"])
class Speech2Text2ForCausalLM(metaclass=DummyObject):
_backends = ["torch"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["torch"])
class Speech2Text2PreTrainedModel(metaclass=DummyObject):
_backends = ["torch"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["torch"])
class SpeechT5ForSpeechToSpeech(metaclass=DummyObject):
_backends = ["torch"]
......@@ -8277,34 +8368,6 @@ class TrOCRPreTrainedModel(metaclass=DummyObject):
requires_backends(self, ["torch"])
class TvltForAudioVisualClassification(metaclass=DummyObject):
_backends = ["torch"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["torch"])
class TvltForPreTraining(metaclass=DummyObject):
_backends = ["torch"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["torch"])
class TvltModel(metaclass=DummyObject):
_backends = ["torch"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["torch"])
class TvltPreTrainedModel(metaclass=DummyObject):
_backends = ["torch"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["torch"])
class TvpForVideoGrounding(metaclass=DummyObject):
_backends = ["torch"]
......@@ -8725,27 +8788,6 @@ class ViTPreTrainedModel(metaclass=DummyObject):
requires_backends(self, ["torch"])
class ViTHybridForImageClassification(metaclass=DummyObject):
_backends = ["torch"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["torch"])
class ViTHybridModel(metaclass=DummyObject):
_backends = ["torch"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["torch"])
class ViTHybridPreTrainedModel(metaclass=DummyObject):
_backends = ["torch"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["torch"])
class ViTMAEForPreTraining(metaclass=DummyObject):
_backends = ["torch"]
......@@ -9194,48 +9236,6 @@ class XLMWithLMHeadModel(metaclass=DummyObject):
requires_backends(self, ["torch"])
class XLMProphetNetDecoder(metaclass=DummyObject):
_backends = ["torch"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["torch"])
class XLMProphetNetEncoder(metaclass=DummyObject):
_backends = ["torch"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["torch"])
class XLMProphetNetForCausalLM(metaclass=DummyObject):
_backends = ["torch"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["torch"])
class XLMProphetNetForConditionalGeneration(metaclass=DummyObject):
_backends = ["torch"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["torch"])
class XLMProphetNetModel(metaclass=DummyObject):
_backends = ["torch"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["torch"])
class XLMProphetNetPreTrainedModel(metaclass=DummyObject):
_backends = ["torch"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["torch"])
class XLMRobertaForCausalLM(metaclass=DummyObject):
_backends = ["torch"]
......
src/transformers/utils/dummy_sentencepiece_objects.py
View file @ a564d10a
......@@ -72,6 +72,13 @@ class ErnieMTokenizer(metaclass=DummyObject):
requires_backends(self, ["sentencepiece"])
class XLMProphetNetTokenizer(metaclass=DummyObject):
_backends = ["sentencepiece"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["sentencepiece"])
class FNetTokenizer(metaclass=DummyObject):
_backends = ["sentencepiece"]
......@@ -233,13 +240,6 @@ class XGLMTokenizer(metaclass=DummyObject):
requires_backends(self, ["sentencepiece"])
class XLMProphetNetTokenizer(metaclass=DummyObject):
_backends = ["sentencepiece"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["sentencepiece"])
class XLMRobertaTokenizer(metaclass=DummyObject):
_backends = ["sentencepiece"]
......
src/transformers/utils/dummy_tf_objects.py
View file @ a564d10a
......@@ -1038,168 +1038,168 @@ class TFDeiTPreTrainedModel(metaclass=DummyObject):
requires_backends(self, ["tf"])
class TFAdaptiveEmbedding(metaclass=DummyObject):
class TFEfficientFormerForImageClassification(metaclass=DummyObject):
_backends = ["tf"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["tf"])
class TFTransfoXLForSequenceClassification(metaclass=DummyObject):
class TFEfficientFormerForImageClassificationWithTeacher(metaclass=DummyObject):
_backends = ["tf"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["tf"])
class TFTransfoXLLMHeadModel(metaclass=DummyObject):
class TFEfficientFormerModel(metaclass=DummyObject):
_backends = ["tf"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["tf"])
class TFTransfoXLMainLayer(metaclass=DummyObject):
class TFEfficientFormerPreTrainedModel(metaclass=DummyObject):
_backends = ["tf"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["tf"])
class TFTransfoXLModel(metaclass=DummyObject):
class TFAdaptiveEmbedding(metaclass=DummyObject):
_backends = ["tf"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["tf"])
class TFTransfoXLPreTrainedModel(metaclass=DummyObject):
class TFTransfoXLForSequenceClassification(metaclass=DummyObject):
_backends = ["tf"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["tf"])
class TFDistilBertForMaskedLM(metaclass=DummyObject):
class TFTransfoXLLMHeadModel(metaclass=DummyObject):
_backends = ["tf"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["tf"])
class TFDistilBertForMultipleChoice(metaclass=DummyObject):
class TFTransfoXLMainLayer(metaclass=DummyObject):
_backends = ["tf"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["tf"])
class TFDistilBertForQuestionAnswering(metaclass=DummyObject):
class TFTransfoXLModel(metaclass=DummyObject):
_backends = ["tf"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["tf"])
class TFDistilBertForSequenceClassification(metaclass=DummyObject):
class TFTransfoXLPreTrainedModel(metaclass=DummyObject):
_backends = ["tf"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["tf"])
class TFDistilBertForTokenClassification(metaclass=DummyObject):
class TFDistilBertForMaskedLM(metaclass=DummyObject):
_backends = ["tf"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["tf"])
class TFDistilBertMainLayer(metaclass=DummyObject):
class TFDistilBertForMultipleChoice(metaclass=DummyObject):
_backends = ["tf"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["tf"])
class TFDistilBertModel(metaclass=DummyObject):
class TFDistilBertForQuestionAnswering(metaclass=DummyObject):
_backends = ["tf"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["tf"])
class TFDistilBertPreTrainedModel(metaclass=DummyObject):
class TFDistilBertForSequenceClassification(metaclass=DummyObject):
_backends = ["tf"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["tf"])
class TFDPRContextEncoder(metaclass=DummyObject):
class TFDistilBertForTokenClassification(metaclass=DummyObject):
_backends = ["tf"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["tf"])
class TFDPRPretrainedContextEncoder(metaclass=DummyObject):
class TFDistilBertMainLayer(metaclass=DummyObject):
_backends = ["tf"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["tf"])
class TFDPRPretrainedQuestionEncoder(metaclass=DummyObject):
class TFDistilBertModel(metaclass=DummyObject):
_backends = ["tf"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["tf"])
class TFDPRPretrainedReader(metaclass=DummyObject):
class TFDistilBertPreTrainedModel(metaclass=DummyObject):
_backends = ["tf"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["tf"])
class TFDPRQuestionEncoder(metaclass=DummyObject):
class TFDPRContextEncoder(metaclass=DummyObject):
_backends = ["tf"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["tf"])
class TFDPRReader(metaclass=DummyObject):
class TFDPRPretrainedContextEncoder(metaclass=DummyObject):
_backends = ["tf"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["tf"])
class TFEfficientFormerForImageClassification(metaclass=DummyObject):
class TFDPRPretrainedQuestionEncoder(metaclass=DummyObject):
_backends = ["tf"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["tf"])
class TFEfficientFormerForImageClassificationWithTeacher(metaclass=DummyObject):
class TFDPRPretrainedReader(metaclass=DummyObject):
_backends = ["tf"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["tf"])
class TFEfficientFormerModel(metaclass=DummyObject):
class TFDPRQuestionEncoder(metaclass=DummyObject):
_backends = ["tf"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["tf"])
class TFEfficientFormerPreTrainedModel(metaclass=DummyObject):
class TFDPRReader(metaclass=DummyObject):
_backends = ["tf"]
def __init__(self, *args, **kwargs):
......
src/transformers/utils/dummy_tokenizers_objects.py
View file @ a564d10a
......@@ -121,6 +121,13 @@ class DebertaV2TokenizerFast(metaclass=DummyObject):
requires_backends(self, ["tokenizers"])
class RealmTokenizerFast(metaclass=DummyObject):
_backends = ["tokenizers"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["tokenizers"])
class RetriBertTokenizerFast(metaclass=DummyObject):
_backends = ["tokenizers"]
......@@ -352,13 +359,6 @@ class Qwen2TokenizerFast(metaclass=DummyObject):
requires_backends(self, ["tokenizers"])
class RealmTokenizerFast(metaclass=DummyObject):
_backends = ["tokenizers"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["tokenizers"])
class ReformerTokenizerFast(metaclass=DummyObject):
_backends = ["tokenizers"]
......
src/transformers/utils/dummy_vision_objects.py
View file @ a564d10a
......@@ -142,49 +142,63 @@ class DetaImageProcessor(metaclass=DummyObject):
requires_backends(self, ["vision"])
class DetrFeatureExtractor(metaclass=DummyObject):
class EfficientFormerImageProcessor(metaclass=DummyObject):
_backends = ["vision"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["vision"])
class DetrImageProcessor(metaclass=DummyObject):
class TvltImageProcessor(metaclass=DummyObject):
_backends = ["vision"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["vision"])
class DonutFeatureExtractor(metaclass=DummyObject):
class ViTHybridImageProcessor(metaclass=DummyObject):
_backends = ["vision"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["vision"])
class DonutImageProcessor(metaclass=DummyObject):
class DetrFeatureExtractor(metaclass=DummyObject):
_backends = ["vision"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["vision"])
class DPTFeatureExtractor(metaclass=DummyObject):
class DetrImageProcessor(metaclass=DummyObject):
_backends = ["vision"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["vision"])
class DPTImageProcessor(metaclass=DummyObject):
class DonutFeatureExtractor(metaclass=DummyObject):
_backends = ["vision"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["vision"])
class EfficientFormerImageProcessor(metaclass=DummyObject):
class DonutImageProcessor(metaclass=DummyObject):
_backends = ["vision"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["vision"])
class DPTFeatureExtractor(metaclass=DummyObject):
_backends = ["vision"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["vision"])
class DPTImageProcessor(metaclass=DummyObject):
_backends = ["vision"]
def __init__(self, *args, **kwargs):
......@@ -520,13 +534,6 @@ class Swin2SRImageProcessor(metaclass=DummyObject):
requires_backends(self, ["vision"])
class TvltImageProcessor(metaclass=DummyObject):
_backends = ["vision"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["vision"])
class TvpImageProcessor(metaclass=DummyObject):
_backends = ["vision"]
......@@ -590,13 +597,6 @@ class ViTImageProcessor(metaclass=DummyObject):
requires_backends(self, ["vision"])
class ViTHybridImageProcessor(metaclass=DummyObject):
_backends = ["vision"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["vision"])
class VitMatteImageProcessor(metaclass=DummyObject):
_backends = ["vision"]
......
tests/models/deta/test_image_processing_deta.py deleted 100644 → 0
View file @ 7f08817b
# coding=utf-8
# Copyright 2022 HuggingFace Inc.
#
# 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.
import json
import pathlib
import unittest
from transformers.testing_utils import require_torch, require_vision, slow
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import AnnotationFormatTestMixin, ImageProcessingTestMixin, prepare_image_inputs
if is_torch_available():
import torch
if is_vision_available():
from PIL import Image
from transformers import DetaImageProcessor
class DetaImageProcessingTester(unittest.TestCase):
def __init__(
self,
parent,
batch_size=7,
num_channels=3,
min_resolution=30,
max_resolution=400,
do_resize=True,
size=None,
do_normalize=True,
image_mean=[0.5, 0.5, 0.5],
image_std=[0.5, 0.5, 0.5],
do_rescale=True,
rescale_factor=1 / 255,
do_pad=True,
):
# by setting size["longest_edge"] > max_resolution we're effectively not testing this :p
size = size if size is not None else {"shortest_edge": 18, "longest_edge": 1333}
self.parent = parent
self.batch_size = batch_size
self.num_channels = num_channels
self.min_resolution = min_resolution
self.max_resolution = max_resolution
self.do_resize = do_resize
self.size = size
self.do_normalize = do_normalize
self.image_mean = image_mean
self.image_std = image_std
self.do_rescale = do_rescale
self.rescale_factor = rescale_factor
self.do_pad = do_pad
def prepare_image_processor_dict(self):
return {
"do_resize": self.do_resize,
"size": self.size,
"do_normalize": self.do_normalize,
"image_mean": self.image_mean,
"image_std": self.image_std,
"do_rescale": self.do_rescale,
"rescale_factor": self.rescale_factor,
"do_pad": self.do_pad,
}
def get_expected_values(self, image_inputs, batched=False):
"""
This function computes the expected height and width when providing images to DetaImageProcessor,
assuming do_resize is set to True with a scalar size.
"""
if not batched:
image = image_inputs[0]
if isinstance(image, Image.Image):
w, h = image.size
else:
h, w = image.shape[1], image.shape[2]
if w < h:
expected_height = int(self.size["shortest_edge"] * h / w)
expected_width = self.size["shortest_edge"]
elif w > h:
expected_height = self.size["shortest_edge"]
expected_width = int(self.size["shortest_edge"] * w / h)
else:
expected_height = self.size["shortest_edge"]
expected_width = self.size["shortest_edge"]
else:
expected_values = []
for image in image_inputs:
expected_height, expected_width = self.get_expected_values([image])
expected_values.append((expected_height, expected_width))
expected_height = max(expected_values, key=lambda item: item[0])[0]
expected_width = max(expected_values, key=lambda item: item[1])[1]
return expected_height, expected_width
def expected_output_image_shape(self, images):
height, width = self.get_expected_values(images, batched=True)
return self.num_channels, height, width
def prepare_image_inputs(self, equal_resolution=False, numpify=False, torchify=False):
return prepare_image_inputs(
batch_size=self.batch_size,
num_channels=self.num_channels,
min_resolution=self.min_resolution,
max_resolution=self.max_resolution,
equal_resolution=equal_resolution,
numpify=numpify,
torchify=torchify,
)
@require_torch
@require_vision
class DetaImageProcessingTest(AnnotationFormatTestMixin, ImageProcessingTestMixin, unittest.TestCase):
image_processing_class = DetaImageProcessor if is_vision_available() else None
def setUp(self):
self.image_processor_tester = DetaImageProcessingTester(self)
@property
def image_processor_dict(self):
return self.image_processor_tester.prepare_image_processor_dict()
def test_image_processor_properties(self):
image_processing = self.image_processing_class(**self.image_processor_dict)
self.assertTrue(hasattr(image_processing, "image_mean"))
self.assertTrue(hasattr(image_processing, "image_std"))
self.assertTrue(hasattr(image_processing, "do_normalize"))
self.assertTrue(hasattr(image_processing, "do_resize"))
self.assertTrue(hasattr(image_processing, "do_rescale"))
self.assertTrue(hasattr(image_processing, "do_pad"))
self.assertTrue(hasattr(image_processing, "size"))
def test_image_processor_from_dict_with_kwargs(self):
image_processor = self.image_processing_class.from_dict(self.image_processor_dict)
self.assertEqual(image_processor.size, {"shortest_edge": 18, "longest_edge": 1333})
self.assertEqual(image_processor.do_pad, True)
@slow
def test_call_pytorch_with_coco_detection_annotations(self):
# prepare image and target
image = Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png")
with open("./tests/fixtures/tests_samples/COCO/coco_annotations.txt", "r") as f:
target = json.loads(f.read())
target = {"image_id": 39769, "annotations": target}
# encode them
image_processing = DetaImageProcessor()
encoding = image_processing(images=image, annotations=target, return_tensors="pt")
# verify pixel values
expected_shape = torch.Size([1, 3, 800, 1066])
self.assertEqual(encoding["pixel_values"].shape, expected_shape)
expected_slice = torch.tensor([0.2796, 0.3138, 0.3481])
self.assertTrue(torch.allclose(encoding["pixel_values"][0, 0, 0, :3], expected_slice, atol=1e-4))
# verify area
expected_area = torch.tensor([5887.9600, 11250.2061, 489353.8438, 837122.7500, 147967.5156, 165732.3438])
self.assertTrue(torch.allclose(encoding["labels"][0]["area"], expected_area))
# verify boxes
expected_boxes_shape = torch.Size([6, 4])
self.assertEqual(encoding["labels"][0]["boxes"].shape, expected_boxes_shape)
expected_boxes_slice = torch.tensor([0.5503, 0.2765, 0.0604, 0.2215])
self.assertTrue(torch.allclose(encoding["labels"][0]["boxes"][0], expected_boxes_slice, atol=1e-3))
# verify image_id
expected_image_id = torch.tensor([39769])
self.assertTrue(torch.allclose(encoding["labels"][0]["image_id"], expected_image_id))
# verify is_crowd
expected_is_crowd = torch.tensor([0, 0, 0, 0, 0, 0])
self.assertTrue(torch.allclose(encoding["labels"][0]["iscrowd"], expected_is_crowd))
# verify class_labels
expected_class_labels = torch.tensor([75, 75, 63, 65, 17, 17])
self.assertTrue(torch.allclose(encoding["labels"][0]["class_labels"], expected_class_labels))
# verify orig_size
expected_orig_size = torch.tensor([480, 640])
self.assertTrue(torch.allclose(encoding["labels"][0]["orig_size"], expected_orig_size))
# verify size
expected_size = torch.tensor([800, 1066])
self.assertTrue(torch.allclose(encoding["labels"][0]["size"], expected_size))
@slow
def test_call_pytorch_with_coco_panoptic_annotations(self):
# prepare image, target and masks_path
image = Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png")
with open("./tests/fixtures/tests_samples/COCO/coco_panoptic_annotations.txt", "r") as f:
target = json.loads(f.read())
target = {"file_name": "000000039769.png", "image_id": 39769, "segments_info": target}
masks_path = pathlib.Path("./tests/fixtures/tests_samples/COCO/coco_panoptic")
# encode them
image_processing = DetaImageProcessor(format="coco_panoptic")
encoding = image_processing(images=image, annotations=target, masks_path=masks_path, return_tensors="pt")
# verify pixel values
expected_shape = torch.Size([1, 3, 800, 1066])
self.assertEqual(encoding["pixel_values"].shape, expected_shape)
expected_slice = torch.tensor([0.2796, 0.3138, 0.3481])
self.assertTrue(torch.allclose(encoding["pixel_values"][0, 0, 0, :3], expected_slice, atol=1e-4))
# verify area
expected_area = torch.tensor([147979.6875, 165527.0469, 484638.5938, 11292.9375, 5879.6562, 7634.1147])
self.assertTrue(torch.allclose(encoding["labels"][0]["area"], expected_area))
# verify boxes
expected_boxes_shape = torch.Size([6, 4])
self.assertEqual(encoding["labels"][0]["boxes"].shape, expected_boxes_shape)
expected_boxes_slice = torch.tensor([0.2625, 0.5437, 0.4688, 0.8625])
self.assertTrue(torch.allclose(encoding["labels"][0]["boxes"][0], expected_boxes_slice, atol=1e-3))
# verify image_id
expected_image_id = torch.tensor([39769])
self.assertTrue(torch.allclose(encoding["labels"][0]["image_id"], expected_image_id))
# verify is_crowd
expected_is_crowd = torch.tensor([0, 0, 0, 0, 0, 0])
self.assertTrue(torch.allclose(encoding["labels"][0]["iscrowd"], expected_is_crowd))
# verify class_labels
expected_class_labels = torch.tensor([17, 17, 63, 75, 75, 93])
self.assertTrue(torch.allclose(encoding["labels"][0]["class_labels"], expected_class_labels))
# verify masks
expected_masks_sum = 822873
self.assertEqual(encoding["labels"][0]["masks"].sum().item(), expected_masks_sum)
# verify orig_size
expected_orig_size = torch.tensor([480, 640])
self.assertTrue(torch.allclose(encoding["labels"][0]["orig_size"], expected_orig_size))
# verify size
expected_size = torch.tensor([800, 1066])
self.assertTrue(torch.allclose(encoding["labels"][0]["size"], expected_size))
@slow
# Copied from tests.models.detr.test_image_processing_detr.DetrImageProcessingTest.test_batched_coco_detection_annotations with Detr->Deta
def test_batched_coco_detection_annotations(self):
image_0 = Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png")
image_1 = Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png").resize((800, 800))
with open("./tests/fixtures/tests_samples/COCO/coco_annotations.txt", "r") as f:
target = json.loads(f.read())
annotations_0 = {"image_id": 39769, "annotations": target}
annotations_1 = {"image_id": 39769, "annotations": target}
# Adjust the bounding boxes for the resized image
w_0, h_0 = image_0.size
w_1, h_1 = image_1.size
for i in range(len(annotations_1["annotations"])):
coords = annotations_1["annotations"][i]["bbox"]
new_bbox = [
coords[0] * w_1 / w_0,
coords[1] * h_1 / h_0,
coords[2] * w_1 / w_0,
coords[3] * h_1 / h_0,
]
annotations_1["annotations"][i]["bbox"] = new_bbox
images = [image_0, image_1]
annotations = [annotations_0, annotations_1]
image_processing = DetaImageProcessor()
encoding = image_processing(
images=images,
annotations=annotations,
return_segmentation_masks=True,
return_tensors="pt", # do_convert_annotations=True
)
# Check the pixel values have been padded
postprocessed_height, postprocessed_width = 800, 1066
expected_shape = torch.Size([2, 3, postprocessed_height, postprocessed_width])
self.assertEqual(encoding["pixel_values"].shape, expected_shape)
# Check the bounding boxes have been adjusted for padded images
self.assertEqual(encoding["labels"][0]["boxes"].shape, torch.Size([6, 4]))
self.assertEqual(encoding["labels"][1]["boxes"].shape, torch.Size([6, 4]))
expected_boxes_0 = torch.tensor(
[
[0.6879, 0.4609, 0.0755, 0.3691],
[0.2118, 0.3359, 0.2601, 0.1566],
[0.5011, 0.5000, 0.9979, 1.0000],
[0.5010, 0.5020, 0.9979, 0.9959],
[0.3284, 0.5944, 0.5884, 0.8112],
[0.8394, 0.5445, 0.3213, 0.9110],
]
)
expected_boxes_1 = torch.tensor(
[
[0.4130, 0.2765, 0.0453, 0.2215],
[0.1272, 0.2016, 0.1561, 0.0940],
[0.3757, 0.4933, 0.7488, 0.9865],
[0.3759, 0.5002, 0.7492, 0.9955],
[0.1971, 0.5456, 0.3532, 0.8646],
[0.5790, 0.4115, 0.3430, 0.7161],
]
)
self.assertTrue(torch.allclose(encoding["labels"][0]["boxes"], expected_boxes_0, rtol=1e-3))
self.assertTrue(torch.allclose(encoding["labels"][1]["boxes"], expected_boxes_1, rtol=1e-3))
# Check the masks have also been padded
self.assertEqual(encoding["labels"][0]["masks"].shape, torch.Size([6, 800, 1066]))
self.assertEqual(encoding["labels"][1]["masks"].shape, torch.Size([6, 800, 1066]))
# Check if do_convert_annotations=False, then the annotations are not converted to centre_x, centre_y, width, height
# format and not in the range [0, 1]
encoding = image_processing(
images=images,
annotations=annotations,
return_segmentation_masks=True,
do_convert_annotations=False,
return_tensors="pt",
)
self.assertEqual(encoding["labels"][0]["boxes"].shape, torch.Size([6, 4]))
self.assertEqual(encoding["labels"][1]["boxes"].shape, torch.Size([6, 4]))
# Convert to absolute coordinates
unnormalized_boxes_0 = torch.vstack(
[
expected_boxes_0[:, 0] * postprocessed_width,
expected_boxes_0[:, 1] * postprocessed_height,
expected_boxes_0[:, 2] * postprocessed_width,
expected_boxes_0[:, 3] * postprocessed_height,
]
).T
unnormalized_boxes_1 = torch.vstack(
[
expected_boxes_1[:, 0] * postprocessed_width,
expected_boxes_1[:, 1] * postprocessed_height,
expected_boxes_1[:, 2] * postprocessed_width,
expected_boxes_1[:, 3] * postprocessed_height,
]
).T
# Convert from centre_x, centre_y, width, height to x_min, y_min, x_max, y_max
expected_boxes_0 = torch.vstack(
[
unnormalized_boxes_0[:, 0] - unnormalized_boxes_0[:, 2] / 2,
unnormalized_boxes_0[:, 1] - unnormalized_boxes_0[:, 3] / 2,
unnormalized_boxes_0[:, 0] + unnormalized_boxes_0[:, 2] / 2,
unnormalized_boxes_0[:, 1] + unnormalized_boxes_0[:, 3] / 2,
]
).T
expected_boxes_1 = torch.vstack(
[
unnormalized_boxes_1[:, 0] - unnormalized_boxes_1[:, 2] / 2,
unnormalized_boxes_1[:, 1] - unnormalized_boxes_1[:, 3] / 2,
unnormalized_boxes_1[:, 0] + unnormalized_boxes_1[:, 2] / 2,
unnormalized_boxes_1[:, 1] + unnormalized_boxes_1[:, 3] / 2,
]
).T
self.assertTrue(torch.allclose(encoding["labels"][0]["boxes"], expected_boxes_0, rtol=1))
self.assertTrue(torch.allclose(encoding["labels"][1]["boxes"], expected_boxes_1, rtol=1))
# Copied from tests.models.detr.test_image_processing_detr.DetrImageProcessingTest.test_batched_coco_panoptic_annotations with Detr->Deta
def test_batched_coco_panoptic_annotations(self):
# prepare image, target and masks_path
image_0 = Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png")
image_1 = Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png").resize((800, 800))
with open("./tests/fixtures/tests_samples/COCO/coco_panoptic_annotations.txt", "r") as f:
target = json.loads(f.read())
annotation_0 = {"file_name": "000000039769.png", "image_id": 39769, "segments_info": target}
annotation_1 = {"file_name": "000000039769.png", "image_id": 39769, "segments_info": target}
w_0, h_0 = image_0.size
w_1, h_1 = image_1.size
for i in range(len(annotation_1["segments_info"])):
coords = annotation_1["segments_info"][i]["bbox"]
new_bbox = [
coords[0] * w_1 / w_0,
coords[1] * h_1 / h_0,
coords[2] * w_1 / w_0,
coords[3] * h_1 / h_0,
]
annotation_1["segments_info"][i]["bbox"] = new_bbox
masks_path = pathlib.Path("./tests/fixtures/tests_samples/COCO/coco_panoptic")
images = [image_0, image_1]
annotations = [annotation_0, annotation_1]
# encode them
image_processing = DetaImageProcessor(format="coco_panoptic")
encoding = image_processing(
images=images,
annotations=annotations,
masks_path=masks_path,
return_tensors="pt",
return_segmentation_masks=True,
)
# Check the pixel values have been padded
postprocessed_height, postprocessed_width = 800, 1066
expected_shape = torch.Size([2, 3, postprocessed_height, postprocessed_width])
self.assertEqual(encoding["pixel_values"].shape, expected_shape)
# Check the bounding boxes have been adjusted for padded images
self.assertEqual(encoding["labels"][0]["boxes"].shape, torch.Size([6, 4]))
self.assertEqual(encoding["labels"][1]["boxes"].shape, torch.Size([6, 4]))
expected_boxes_0 = torch.tensor(
[
[0.2625, 0.5437, 0.4688, 0.8625],
[0.7719, 0.4104, 0.4531, 0.7125],
[0.5000, 0.4927, 0.9969, 0.9854],
[0.1688, 0.2000, 0.2063, 0.0917],
[0.5492, 0.2760, 0.0578, 0.2187],
[0.4992, 0.4990, 0.9984, 0.9979],
]
)
expected_boxes_1 = torch.tensor(
[
[0.1576, 0.3262, 0.2814, 0.5175],
[0.4634, 0.2463, 0.2720, 0.4275],
[0.3002, 0.2956, 0.5985, 0.5913],
[0.1013, 0.1200, 0.1238, 0.0550],
[0.3297, 0.1656, 0.0347, 0.1312],
[0.2997, 0.2994, 0.5994, 0.5987],
]
)
self.assertTrue(torch.allclose(encoding["labels"][0]["boxes"], expected_boxes_0, rtol=1e-3))
self.assertTrue(torch.allclose(encoding["labels"][1]["boxes"], expected_boxes_1, rtol=1e-3))
# Check the masks have also been padded
self.assertEqual(encoding["labels"][0]["masks"].shape, torch.Size([6, 800, 1066]))
self.assertEqual(encoding["labels"][1]["masks"].shape, torch.Size([6, 800, 1066]))
# Check if do_convert_annotations=False, then the annotations are not converted to centre_x, centre_y, width, height
# format and not in the range [0, 1]
encoding = image_processing(
images=images,
annotations=annotations,
masks_path=masks_path,
return_segmentation_masks=True,
do_convert_annotations=False,
return_tensors="pt",
)
self.assertEqual(encoding["labels"][0]["boxes"].shape, torch.Size([6, 4]))
self.assertEqual(encoding["labels"][1]["boxes"].shape, torch.Size([6, 4]))
# Convert to absolute coordinates
unnormalized_boxes_0 = torch.vstack(
[
expected_boxes_0[:, 0] * postprocessed_width,
expected_boxes_0[:, 1] * postprocessed_height,
expected_boxes_0[:, 2] * postprocessed_width,
expected_boxes_0[:, 3] * postprocessed_height,
]
).T
unnormalized_boxes_1 = torch.vstack(
[
expected_boxes_1[:, 0] * postprocessed_width,
expected_boxes_1[:, 1] * postprocessed_height,
expected_boxes_1[:, 2] * postprocessed_width,
expected_boxes_1[:, 3] * postprocessed_height,
]
).T
# Convert from centre_x, centre_y, width, height to x_min, y_min, x_max, y_max
expected_boxes_0 = torch.vstack(
[
unnormalized_boxes_0[:, 0] - unnormalized_boxes_0[:, 2] / 2,
unnormalized_boxes_0[:, 1] - unnormalized_boxes_0[:, 3] / 2,
unnormalized_boxes_0[:, 0] + unnormalized_boxes_0[:, 2] / 2,
unnormalized_boxes_0[:, 1] + unnormalized_boxes_0[:, 3] / 2,
]
).T
expected_boxes_1 = torch.vstack(
[
unnormalized_boxes_1[:, 0] - unnormalized_boxes_1[:, 2] / 2,
unnormalized_boxes_1[:, 1] - unnormalized_boxes_1[:, 3] / 2,
unnormalized_boxes_1[:, 0] + unnormalized_boxes_1[:, 2] / 2,
unnormalized_boxes_1[:, 1] + unnormalized_boxes_1[:, 3] / 2,
]
).T
self.assertTrue(torch.allclose(encoding["labels"][0]["boxes"], expected_boxes_0, rtol=1))
self.assertTrue(torch.allclose(encoding["labels"][1]["boxes"], expected_boxes_1, rtol=1))
# Copied from tests.models.detr.test_image_processing_detr.DetrImageProcessingTest.test_max_width_max_height_resizing_and_pad_strategy with Detr->Deta
def test_max_width_max_height_resizing_and_pad_strategy(self):
image_1 = torch.ones([200, 100, 3], dtype=torch.uint8)
# do_pad=False, max_height=100, max_width=100, image=200x100 -> 100x50
image_processor = DetaImageProcessor(
size={"max_height": 100, "max_width": 100},
do_pad=False,
)
inputs = image_processor(images=[image_1], return_tensors="pt")
self.assertEqual(inputs["pixel_values"].shape, torch.Size([1, 3, 100, 50]))
# do_pad=False, max_height=300, max_width=100, image=200x100 -> 200x100
image_processor = DetaImageProcessor(
size={"max_height": 300, "max_width": 100},
do_pad=False,
)
inputs = image_processor(images=[image_1], return_tensors="pt")
# do_pad=True, max_height=100, max_width=100, image=200x100 -> 100x100
image_processor = DetaImageProcessor(
size={"max_height": 100, "max_width": 100}, do_pad=True, pad_size={"height": 100, "width": 100}
)
inputs = image_processor(images=[image_1], return_tensors="pt")
self.assertEqual(inputs["pixel_values"].shape, torch.Size([1, 3, 100, 100]))
# do_pad=True, max_height=300, max_width=100, image=200x100 -> 300x100
image_processor = DetaImageProcessor(
size={"max_height": 300, "max_width": 100},
do_pad=True,
pad_size={"height": 301, "width": 101},
)
inputs = image_processor(images=[image_1], return_tensors="pt")
self.assertEqual(inputs["pixel_values"].shape, torch.Size([1, 3, 301, 101]))
### Check for batch
image_2 = torch.ones([100, 150, 3], dtype=torch.uint8)
# do_pad=True, max_height=150, max_width=100, images=[200x100, 100x150] -> 150x100
image_processor = DetaImageProcessor(
size={"max_height": 150, "max_width": 100},
do_pad=True,
pad_size={"height": 150, "width": 100},
)
inputs = image_processor(images=[image_1, image_2], return_tensors="pt")
self.assertEqual(inputs["pixel_values"].shape, torch.Size([2, 3, 150, 100]))
tests/models/deta/test_modeling_deta.py deleted 100644 → 0
View file @ 7f08817b
# 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 DETA model."""
import collections
import inspect
import math
import re
import unittest
from transformers import DetaConfig, ResNetConfig, is_torch_available, is_torchvision_available, is_vision_available
from transformers.file_utils import cached_property
from transformers.testing_utils import require_torchvision, require_vision, slow, torch_device
from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, _config_zero_init, floats_tensor
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from transformers.pytorch_utils import id_tensor_storage
if is_torchvision_available():
from transformers import DetaForObjectDetection, DetaModel
if is_vision_available():
from PIL import Image
from transformers import AutoImageProcessor
class DetaModelTester:
def __init__(
self,
parent,
batch_size=8,
is_training=True,
use_labels=True,
hidden_size=32,
num_hidden_layers=2,
num_attention_heads=8,
intermediate_size=4,
hidden_act="gelu",
hidden_dropout_prob=0.1,
attention_probs_dropout_prob=0.1,
num_queries=12,
two_stage_num_proposals=12,
num_channels=3,
image_size=224,
n_targets=8,
num_labels=91,
num_feature_levels=4,
encoder_n_points=2,
decoder_n_points=6,
two_stage=True,
assign_first_stage=True,
assign_second_stage=True,
):
self.parent = parent
self.batch_size = batch_size
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.num_queries = num_queries
self.two_stage_num_proposals = two_stage_num_proposals
self.num_channels = num_channels
self.image_size = image_size
self.n_targets = n_targets
self.num_labels = num_labels
self.num_feature_levels = num_feature_levels
self.encoder_n_points = encoder_n_points
self.decoder_n_points = decoder_n_points
self.two_stage = two_stage
self.assign_first_stage = assign_first_stage
self.assign_second_stage = assign_second_stage
# we also set the expected seq length for both encoder and decoder
self.encoder_seq_length = (
math.ceil(self.image_size / 8) ** 2
+ math.ceil(self.image_size / 16) ** 2
+ math.ceil(self.image_size / 32) ** 2
+ math.ceil(self.image_size / 64) ** 2
)
self.decoder_seq_length = self.num_queries
def prepare_config_and_inputs(self, model_class_name):
pixel_values = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size])
pixel_mask = torch.ones([self.batch_size, self.image_size, self.image_size], device=torch_device)
labels = None
if self.use_labels:
# labels is a list of Dict (each Dict being the labels for a given example in the batch)
labels = []
for i in range(self.batch_size):
target = {}
target["class_labels"] = torch.randint(
high=self.num_labels, size=(self.n_targets,), device=torch_device
)
target["boxes"] = torch.rand(self.n_targets, 4, device=torch_device)
target["masks"] = torch.rand(self.n_targets, self.image_size, self.image_size, device=torch_device)
labels.append(target)
config = self.get_config(model_class_name)
return config, pixel_values, pixel_mask, labels
def get_config(self, model_class_name):
resnet_config = ResNetConfig(
num_channels=3,
embeddings_size=10,
hidden_sizes=[10, 20, 30, 40],
depths=[1, 1, 2, 1],
hidden_act="relu",
num_labels=3,
out_features=["stage2", "stage3", "stage4"],
out_indices=[2, 3, 4],
)
two_stage = model_class_name == "DetaForObjectDetection"
assign_first_stage = model_class_name == "DetaForObjectDetection"
assign_second_stage = model_class_name == "DetaForObjectDetection"
return DetaConfig(
d_model=self.hidden_size,
encoder_layers=self.num_hidden_layers,
decoder_layers=self.num_hidden_layers,
encoder_attention_heads=self.num_attention_heads,
decoder_attention_heads=self.num_attention_heads,
encoder_ffn_dim=self.intermediate_size,
decoder_ffn_dim=self.intermediate_size,
dropout=self.hidden_dropout_prob,
attention_dropout=self.attention_probs_dropout_prob,
num_queries=self.num_queries,
two_stage_num_proposals=self.two_stage_num_proposals,
num_labels=self.num_labels,
num_feature_levels=self.num_feature_levels,
encoder_n_points=self.encoder_n_points,
decoder_n_points=self.decoder_n_points,
two_stage=two_stage,
assign_first_stage=assign_first_stage,
assign_second_stage=assign_second_stage,
backbone_config=resnet_config,
backbone=None,
)
def prepare_config_and_inputs_for_common(self, model_class_name="DetaModel"):
config, pixel_values, pixel_mask, labels = self.prepare_config_and_inputs(model_class_name)
inputs_dict = {"pixel_values": pixel_values, "pixel_mask": pixel_mask}
return config, inputs_dict
def create_and_check_deta_model(self, config, pixel_values, pixel_mask, labels):
model = DetaModel(config=config)
model.to(torch_device)
model.eval()
result = model(pixel_values=pixel_values, pixel_mask=pixel_mask)
result = model(pixel_values)
self.parent.assertEqual(result.last_hidden_state.shape, (self.batch_size, self.num_queries, self.hidden_size))
def create_and_check_deta_freeze_backbone(self, config, pixel_values, pixel_mask, labels):
model = DetaModel(config=config)
model.to(torch_device)
model.eval()
model.freeze_backbone()
for _, param in model.backbone.model.named_parameters():
self.parent.assertEqual(False, param.requires_grad)
def create_and_check_deta_unfreeze_backbone(self, config, pixel_values, pixel_mask, labels):
model = DetaModel(config=config)
model.to(torch_device)
model.eval()
model.unfreeze_backbone()
for _, param in model.backbone.model.named_parameters():
self.parent.assertEqual(True, param.requires_grad)
def create_and_check_deta_object_detection_head_model(self, config, pixel_values, pixel_mask, labels):
model = DetaForObjectDetection(config=config)
model.to(torch_device)
model.eval()
result = model(pixel_values=pixel_values, pixel_mask=pixel_mask)
result = model(pixel_values)
self.parent.assertEqual(result.logits.shape, (self.batch_size, self.two_stage_num_proposals, self.num_labels))
self.parent.assertEqual(result.pred_boxes.shape, (self.batch_size, self.two_stage_num_proposals, 4))
result = model(pixel_values=pixel_values, pixel_mask=pixel_mask, labels=labels)
self.parent.assertEqual(result.loss.shape, ())
self.parent.assertEqual(result.logits.shape, (self.batch_size, self.two_stage_num_proposals, self.num_labels))
self.parent.assertEqual(result.pred_boxes.shape, (self.batch_size, self.two_stage_num_proposals, 4))
@require_torchvision
class DetaModelTest(ModelTesterMixin, GenerationTesterMixin, PipelineTesterMixin, unittest.TestCase):
all_model_classes = (DetaModel, DetaForObjectDetection) if is_torchvision_available() else ()
pipeline_model_mapping = (
{"image-feature-extraction": DetaModel, "object-detection": DetaForObjectDetection}
if is_torchvision_available()
else {}
)
is_encoder_decoder = True
test_torchscript = False
test_pruning = False
test_head_masking = False
test_missing_keys = False
# TODO: Fix the failed tests when this model gets more usage
def is_pipeline_test_to_skip(
self, pipeline_test_casse_name, config_class, model_architecture, tokenizer_name, processor_name
):
if pipeline_test_casse_name == "ObjectDetectionPipelineTests":
return True
return False
@unittest.skip("Skip for now. PR #22437 causes some loading issue. See (not merged) #22656 for some discussions.")
def test_can_use_safetensors(self):
super().test_can_use_safetensors()
# special case for head models
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__ == "DetaForObjectDetection":
labels = []
for i in range(self.model_tester.batch_size):
target = {}
target["class_labels"] = torch.ones(
size=(self.model_tester.n_targets,), device=torch_device, dtype=torch.long
)
target["boxes"] = torch.ones(
self.model_tester.n_targets, 4, device=torch_device, dtype=torch.float
)
target["masks"] = torch.ones(
self.model_tester.n_targets,
self.model_tester.image_size,
self.model_tester.image_size,
device=torch_device,
dtype=torch.float,
)
labels.append(target)
inputs_dict["labels"] = labels
return inputs_dict
def setUp(self):
self.model_tester = DetaModelTester(self)
self.config_tester = ConfigTester(self, config_class=DetaConfig, has_text_modality=False)
def test_config(self):
# we don't test common_properties and arguments_init as these don't apply for DETA
self.config_tester.create_and_test_config_to_json_string()
self.config_tester.create_and_test_config_to_json_file()
self.config_tester.create_and_test_config_from_and_save_pretrained()
self.config_tester.create_and_test_config_with_num_labels()
self.config_tester.check_config_can_be_init_without_params()
def test_deta_model(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs(model_class_name="DetaModel")
self.model_tester.create_and_check_deta_model(*config_and_inputs)
def test_deta_freeze_backbone(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs(model_class_name="DetaModel")
self.model_tester.create_and_check_deta_freeze_backbone(*config_and_inputs)
def test_deta_unfreeze_backbone(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs(model_class_name="DetaModel")
self.model_tester.create_and_check_deta_unfreeze_backbone(*config_and_inputs)
def test_deta_object_detection_head_model(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs(model_class_name="DetaForObjectDetection")
self.model_tester.create_and_check_deta_object_detection_head_model(*config_and_inputs)
@unittest.skip(reason="DETA does not use inputs_embeds")
def test_inputs_embeds(self):
pass
@unittest.skip(reason="DETA does not use inputs_embeds")
def test_inputs_embeds_matches_input_ids(self):
pass
@unittest.skip(reason="DETA does not have a get_input_embeddings method")
def test_model_common_attributes(self):
pass
@unittest.skip(reason="DETA is not a generative model")
def test_generate_without_input_ids(self):
pass
@unittest.skip(reason="DETA does not use token embeddings")
def test_resize_tokens_embeddings(self):
pass
@unittest.skip(reason="Feed forward chunking is not implemented")
def test_feed_forward_chunking(self):
pass
def test_attention_outputs(self):
config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
config.return_dict = True
for model_class in self.all_model_classes:
inputs_dict["output_attentions"] = True
inputs_dict["output_hidden_states"] = False
config.return_dict = True
model = model_class(config)
model.to(torch_device)
model.eval()
with torch.no_grad():
outputs = model(**self._prepare_for_class(inputs_dict, model_class))
attentions = outputs.encoder_attentions
self.assertEqual(len(attentions), self.model_tester.num_hidden_layers)
# 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
self.assertEqual(len(attentions), self.model_tester.num_hidden_layers)
self.assertListEqual(
list(attentions[0].shape[-3:]),
[
self.model_tester.num_attention_heads,
self.model_tester.num_feature_levels,
self.model_tester.encoder_n_points,
],
)
out_len = len(outputs)
correct_outlen = 8
# loss is at first position
if "labels" in inputs_dict:
correct_outlen += 1 # loss is added to beginning
# Object Detection model returns pred_logits and pred_boxes
if model_class.__name__ == "DetaForObjectDetection":
correct_outlen += 2
self.assertEqual(out_len, correct_outlen)
# decoder attentions
decoder_attentions = outputs.decoder_attentions
self.assertIsInstance(decoder_attentions, (list, tuple))
self.assertEqual(len(decoder_attentions), self.model_tester.num_hidden_layers)
self.assertListEqual(
list(decoder_attentions[0].shape[-3:]),
[self.model_tester.num_attention_heads, self.model_tester.num_queries, self.model_tester.num_queries],
)
# cross attentions
cross_attentions = outputs.cross_attentions
self.assertIsInstance(cross_attentions, (list, tuple))
self.assertEqual(len(cross_attentions), self.model_tester.num_hidden_layers)
self.assertListEqual(
list(cross_attentions[0].shape[-3:]),
[
self.model_tester.num_attention_heads,
self.model_tester.num_feature_levels,
self.model_tester.decoder_n_points,
],
)
# Check attention is always last and order is fine
inputs_dict["output_attentions"] = True
inputs_dict["output_hidden_states"] = True
model = model_class(config)
model.to(torch_device)
model.eval()
with torch.no_grad():
outputs = model(**self._prepare_for_class(inputs_dict, model_class))
if hasattr(self.model_tester, "num_hidden_states_types"):
added_hidden_states = self.model_tester.num_hidden_states_types
elif self.is_encoder_decoder:
added_hidden_states = 2
else:
added_hidden_states = 1
self.assertEqual(out_len + added_hidden_states, len(outputs))
self_attentions = outputs.encoder_attentions
self.assertEqual(len(self_attentions), self.model_tester.num_hidden_layers)
self.assertListEqual(
list(self_attentions[0].shape[-3:]),
[
self.model_tester.num_attention_heads,
self.model_tester.num_feature_levels,
self.model_tester.encoder_n_points,
],
)
# removed retain_grad and grad on decoder_hidden_states, as queries don't require grad
def test_retain_grad_hidden_states_attentions(self):
config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
config.output_hidden_states = True
config.output_attentions = True
# no need to test all models as different heads yield the same functionality
model_class = self.all_model_classes[0]
model = model_class(config)
model.to(torch_device)
inputs = self._prepare_for_class(inputs_dict, model_class)
outputs = model(**inputs)
# we take the second output since last_hidden_state is the second item
output = outputs[1]
encoder_hidden_states = outputs.encoder_hidden_states[0]
encoder_attentions = outputs.encoder_attentions[0]
encoder_hidden_states.retain_grad()
encoder_attentions.retain_grad()
decoder_attentions = outputs.decoder_attentions[0]
decoder_attentions.retain_grad()
cross_attentions = outputs.cross_attentions[0]
cross_attentions.retain_grad()
output.flatten()[0].backward(retain_graph=True)
self.assertIsNotNone(encoder_hidden_states.grad)
self.assertIsNotNone(encoder_attentions.grad)
self.assertIsNotNone(decoder_attentions.grad)
self.assertIsNotNone(cross_attentions.grad)
def test_forward_auxiliary_loss(self):
config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
config.auxiliary_loss = True
# only test for object detection and segmentation model
for model_class in self.all_model_classes[1:]:
model = model_class(config)
model.to(torch_device)
inputs = self._prepare_for_class(inputs_dict, model_class, return_labels=True)
outputs = model(**inputs)
self.assertIsNotNone(outputs.auxiliary_outputs)
self.assertEqual(len(outputs.auxiliary_outputs), self.model_tester.num_hidden_layers - 1)
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()]
if model.config.is_encoder_decoder:
expected_arg_names = ["pixel_values", "pixel_mask"]
expected_arg_names.extend(
["head_mask", "decoder_head_mask", "encoder_outputs"]
if "head_mask" and "decoder_head_mask" in arg_names
else []
)
self.assertListEqual(arg_names[: len(expected_arg_names)], expected_arg_names)
else:
expected_arg_names = ["pixel_values", "pixel_mask"]
self.assertListEqual(arg_names[:1], expected_arg_names)
@unittest.skip(reason="Model doesn't use tied weights")
def test_tied_model_weights_key_ignore(self):
pass
def test_initialization(self):
config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
configs_no_init = _config_zero_init(config)
for model_class in self.all_model_classes:
model = model_class(config=configs_no_init)
# Skip the check for the backbone
for name, module in model.named_modules():
if module.__class__.__name__ == "DetaBackboneWithPositionalEncodings":
backbone_params = [f"{name}.{key}" for key in module.state_dict().keys()]
break
for name, param in model.named_parameters():
if param.requires_grad:
if (
"level_embed" in name
or "sampling_offsets.bias" in name
or "value_proj" in name
or "output_proj" in name
or "reference_points" in name
or name in backbone_params
):
continue
self.assertIn(
((param.data.mean() * 1e9).round() / 1e9).item(),
[0.0, 1.0],
msg=f"Parameter {name} of model {model_class} seems not properly initialized",
)
@unittest.skip("No support for low_cpu_mem_usage=True.")
def test_save_load_low_cpu_mem_usage(self):
pass
@unittest.skip("No support for low_cpu_mem_usage=True.")
def test_save_load_low_cpu_mem_usage_checkpoints(self):
pass
@unittest.skip("No support for low_cpu_mem_usage=True.")
def test_save_load_low_cpu_mem_usage_no_safetensors(self):
pass
# Inspired by tests.test_modeling_common.ModelTesterMixin.test_tied_weights_keys
def test_tied_weights_keys(self):
for model_class in self.all_model_classes:
# We need to pass model class name to correctly initialize the config.
# If we don't pass it, the config for `DetaForObjectDetection`` will be initialized
# with `two_stage=False` and the test will fail because for that case `class_embed`
# weights are not tied.
config, _ = self.model_tester.prepare_config_and_inputs_for_common(model_class_name=model_class.__name__)
config.tie_word_embeddings = True
model_tied = model_class(config)
ptrs = collections.defaultdict(list)
for name, tensor in model_tied.state_dict().items():
ptrs[id_tensor_storage(tensor)].append(name)
# These are all the pointers of shared tensors.
tied_params = [names for _, names in ptrs.items() if len(names) > 1]
tied_weight_keys = model_tied._tied_weights_keys if model_tied._tied_weights_keys is not None else []
# Detect we get a hit for each key
for key in tied_weight_keys:
is_tied_key = any(re.search(key, p) for group in tied_params for p in group)
self.assertTrue(is_tied_key, f"{key} is not a tied weight key for {model_class}.")
# Removed tied weights found from tied params -> there should only be one left after
for key in tied_weight_keys:
for i in range(len(tied_params)):
tied_params[i] = [p for p in tied_params[i] if re.search(key, p) is None]
tied_params = [group for group in tied_params if len(group) > 1]
self.assertListEqual(
tied_params,
[],
f"Missing `_tied_weights_keys` for {model_class}: add all of {tied_params} except one.",
)
TOLERANCE = 1e-4
# 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_torchvision
@require_vision
@slow
class DetaModelIntegrationTests(unittest.TestCase):
@cached_property
def default_image_processor(self):
return AutoImageProcessor.from_pretrained("jozhang97/deta-resnet-50") if is_vision_available() else None
def test_inference_object_detection_head(self):
model = DetaForObjectDetection.from_pretrained("jozhang97/deta-resnet-50").to(torch_device)
image_processor = self.default_image_processor
image = prepare_img()
inputs = image_processor(images=image, return_tensors="pt").to(torch_device)
with torch.no_grad():
outputs = model(**inputs)
expected_shape_logits = torch.Size((1, 300, model.config.num_labels))
self.assertEqual(outputs.logits.shape, expected_shape_logits)
expected_logits = torch.tensor(
[[-7.3978, -2.5406, -4.1668], [-8.2684, -3.9933, -3.8096], [-7.0515, -3.7973, -5.8516]]
).to(torch_device)
expected_boxes = torch.tensor(
[[0.5043, 0.4973, 0.9998], [0.2542, 0.5489, 0.4748], [0.5490, 0.2765, 0.0570]]
).to(torch_device)
self.assertTrue(torch.allclose(outputs.logits[0, :3, :3], expected_logits, atol=1e-4))
expected_shape_boxes = torch.Size((1, 300, 4))
self.assertEqual(outputs.pred_boxes.shape, expected_shape_boxes)
self.assertTrue(torch.allclose(outputs.pred_boxes[0, :3, :3], expected_boxes, atol=1e-4))
# verify postprocessing
results = image_processor.post_process_object_detection(
outputs, threshold=0.3, target_sizes=[image.size[::-1]]
)[0]
expected_scores = torch.tensor([0.6392, 0.6276, 0.5546, 0.5260, 0.4706], device=torch_device)
expected_labels = [75, 17, 17, 75, 63]
expected_slice_boxes = torch.tensor([40.5866, 73.2107, 176.1421, 117.1751], device=torch_device)
self.assertTrue(torch.allclose(results["scores"], expected_scores, atol=1e-4))
self.assertSequenceEqual(results["labels"].tolist(), expected_labels)
self.assertTrue(torch.allclose(results["boxes"][0, :], expected_slice_boxes))
def test_inference_object_detection_head_swin_backbone(self):
model = DetaForObjectDetection.from_pretrained("jozhang97/deta-swin-large").to(torch_device)
image_processor = self.default_image_processor
image = prepare_img()
inputs = image_processor(images=image, return_tensors="pt").to(torch_device)
with torch.no_grad():
outputs = model(**inputs)
expected_shape_logits = torch.Size((1, 300, model.config.num_labels))
self.assertEqual(outputs.logits.shape, expected_shape_logits)
expected_logits = torch.tensor(
[[-7.6308, -2.8485, -5.3737], [-7.2037, -4.5505, -4.8027], [-7.2943, -4.2611, -4.6617]]
).to(torch_device)
expected_boxes = torch.tensor(
[[0.4987, 0.4969, 0.9999], [0.2549, 0.5498, 0.4805], [0.5498, 0.2757, 0.0569]]
).to(torch_device)
self.assertTrue(torch.allclose(outputs.logits[0, :3, :3], expected_logits, atol=1e-4))
expected_shape_boxes = torch.Size((1, 300, 4))
self.assertEqual(outputs.pred_boxes.shape, expected_shape_boxes)
self.assertTrue(torch.allclose(outputs.pred_boxes[0, :3, :3], expected_boxes, atol=1e-4))
# verify postprocessing
results = image_processor.post_process_object_detection(
outputs, threshold=0.3, target_sizes=[image.size[::-1]]
)[0]
expected_scores = torch.tensor([0.6831, 0.6826, 0.5684, 0.5464, 0.4392], device=torch_device)
expected_labels = [17, 17, 75, 75, 63]
expected_slice_boxes = torch.tensor([345.8478, 23.6754, 639.8562, 372.8265], device=torch_device)
self.assertTrue(torch.allclose(results["scores"], expected_scores, atol=1e-4))
self.assertSequenceEqual(results["labels"].tolist(), expected_labels)
self.assertTrue(torch.allclose(results["boxes"][0, :], expected_slice_boxes))
tests/models/efficientformer/test_image_processing_efficientformer.py deleted 100644 → 0
View file @ 7f08817b
# coding=utf-8
# Copyright 2021 HuggingFace Inc.
#
# 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.
import unittest
from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_vision_available
from ...test_image_processing_common import ImageProcessingTestMixin, prepare_image_inputs
if is_vision_available():
from transformers import ViTImageProcessor
class EfficientFormerImageProcessorTester(unittest.TestCase):
def __init__(
self,
parent,
batch_size=13,
num_channels=3,
image_size=224,
min_resolution=30,
max_resolution=400,
do_resize=True,
size=None,
do_normalize=True,
image_mean=[0.5, 0.5, 0.5],
image_std=[0.5, 0.5, 0.5],
):
size = size if size is not None else {"height": 18, "width": 18}
self.parent = parent
self.batch_size = batch_size
self.num_channels = num_channels
self.image_size = image_size
self.min_resolution = min_resolution
self.max_resolution = max_resolution
self.do_resize = do_resize
self.size = size
self.do_normalize = do_normalize
self.image_mean = image_mean
self.image_std = image_std
def prepare_image_processor_dict(self):
return {
"image_mean": self.image_mean,
"image_std": self.image_std,
"do_normalize": self.do_normalize,
"do_resize": self.do_resize,
"size": self.size,
}
def expected_output_image_shape(self, images):
return self.num_channels, self.size["height"], self.size["width"]
def prepare_image_inputs(self, equal_resolution=False, numpify=False, torchify=False):
return prepare_image_inputs(
batch_size=self.batch_size,
num_channels=self.num_channels,
min_resolution=self.min_resolution,
max_resolution=self.max_resolution,
equal_resolution=equal_resolution,
numpify=numpify,
torchify=torchify,
)
@require_torch
@require_vision
class EfficientFormerImageProcessorTest(ImageProcessingTestMixin, unittest.TestCase):
image_processing_class = ViTImageProcessor if is_vision_available() else None
def setUp(self):
self.image_processor_tester = EfficientFormerImageProcessorTester(self)
@property
def image_processor_dict(self):
return self.image_processor_tester.prepare_image_processor_dict()
def test_image_proc_properties(self):
image_processor = self.image_processing_class(**self.image_processor_dict)
self.assertTrue(hasattr(image_processor, "image_mean"))
self.assertTrue(hasattr(image_processor, "image_std"))
self.assertTrue(hasattr(image_processor, "do_normalize"))
self.assertTrue(hasattr(image_processor, "do_resize"))
self.assertTrue(hasattr(image_processor, "size"))
Markdown is supported
0% or .
You are about to add 0 people to the discussion. Proceed with caution.
Finish editing this message first!
Please register or to comment