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Make more test models smaller (#25005)

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* Make more test models tiny

* Make more test models tiny

* More models

* More models
This commit is contained in:
Sylvain Gugger 2023-07-24 10:08:47 -04:00 committed by GitHub
parent 8f1f0bf50f
commit 42571f6eb8
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GPG Key ID: 4AEE18F83AFDEB23
22 changed files with 149 additions and 137 deletions
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6
tests/models/ctrl/test_modeling_ctrl.py
View File

@ -133,7 +133,7 @@ class CTRLModelTester:
n_embd=self.hidden_size,
n_layer=self.num_hidden_layers,
n_head=self.num_attention_heads,
# intermediate_size=self.intermediate_size,
dff=self.intermediate_size,
# hidden_act=self.hidden_act,
# hidden_dropout_prob=self.hidden_dropout_prob,
# attention_probs_dropout_prob=self.attention_probs_dropout_prob,
@ -243,10 +243,6 @@ class CTRLModelTest(ModelTesterMixin, GenerationTesterMixin, PipelineTesterMixin
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_lm_head_model(*config_and_inputs)
@unittest.skip("Will be fixed soon by reducing the size of the model used for common tests.")
def test_model_is_small(self):
pass
@slow
def test_model_from_pretrained(self):
for model_name in CTRL_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:

2
tests/models/ctrl/test_modeling_tf_ctrl.py
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@ -95,7 +95,7 @@ class TFCTRLModelTester(object):
n_embd=self.hidden_size,
n_layer=self.num_hidden_layers,
n_head=self.num_attention_heads,
# intermediate_size=self.intermediate_size,
dff=self.intermediate_size,
# hidden_act=self.hidden_act,
# hidden_dropout_prob=self.hidden_dropout_prob,
# attention_probs_dropout_prob=self.attention_probs_dropout_prob,

8
tests/models/cvt/test_modeling_cvt.py
View File

@ -55,8 +55,8 @@ class CvtModelTester:
batch_size=13,
image_size=64,
num_channels=3,
embed_dim=[16, 48, 96],
num_heads=[1, 3, 6],
embed_dim=[16, 32, 48],
num_heads=[1, 2, 3],
depth=[1, 2, 10],
patch_sizes=[7, 3, 3],
patch_stride=[4, 2, 2],
@ -247,10 +247,6 @@ class CvtModelTest(ModelTesterMixin, PipelineTesterMixin, unittest.TestCase):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_image_classification(*config_and_inputs)
@unittest.skip("Will be fixed soon by reducing the size of the model used for common tests.")
def test_model_is_small(self):
pass
@slow
def test_model_from_pretrained(self):
for model_name in CVT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:

4
tests/models/cvt/test_modeling_tf_cvt.py
View File

@ -45,8 +45,8 @@ class TFCvtModelTester:
batch_size=13,
image_size=64,
num_channels=3,
embed_dim=[16, 48, 96],
num_heads=[1, 3, 6],
embed_dim=[16, 32, 48],
num_heads=[1, 2, 3],
depth=[1, 2, 10],
patch_sizes=[7, 3, 3],
patch_stride=[4, 2, 2],

19
tests/models/deta/test_modeling_deta.py
View File

@ -19,7 +19,7 @@ import inspect
import math
import unittest
from transformers import DetaConfig, is_torch_available, is_torchvision_available, is_vision_available
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
@ -49,7 +49,7 @@ class DetaModelTester:
batch_size=8,
is_training=True,
use_labels=True,
hidden_size=256,
hidden_size=32,
num_hidden_layers=2,
num_attention_heads=8,
intermediate_size=4,
@ -118,6 +118,16 @@ class DetaModelTester:
return config, pixel_values, pixel_mask, labels
def get_config(self):
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],
)
return DetaConfig(
d_model=self.hidden_size,
encoder_layers=self.num_hidden_layers,
@ -134,6 +144,7 @@ class DetaModelTester:
encoder_n_points=self.encoder_n_points,
decoder_n_points=self.decoder_n_points,
two_stage=self.two_stage,
backbone_config=resnet_config,
)
def prepare_config_and_inputs_for_common(self):
@ -423,10 +434,6 @@ class DetaModelTest(ModelTesterMixin, GenerationTesterMixin, PipelineTesterMixin
def test_tied_model_weights_key_ignore(self):
pass
@unittest.skip("Will be fixed soon by reducing the size of the model used for common tests.")
def test_model_is_small(self):
pass
def test_initialization(self):
config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()

8
tests/models/dpt/test_modeling_dpt.py
View File

@ -62,6 +62,7 @@ class DPTModelTester:
attention_probs_dropout_prob=0.1,
initializer_range=0.02,
num_labels=3,
neck_hidden_sizes=[16, 16, 32, 32],
is_hybrid=False,
scope=None,
):
@ -84,6 +85,7 @@ class DPTModelTester:
self.num_labels = num_labels
self.scope = scope
self.is_hybrid = is_hybrid
self.neck_hidden_sizes = neck_hidden_sizes
# sequence length of DPT = num_patches + 1 (we add 1 for the [CLS] token)
num_patches = (image_size // patch_size) ** 2
self.seq_length = num_patches + 1
@ -105,6 +107,7 @@ class DPTModelTester:
patch_size=self.patch_size,
num_channels=self.num_channels,
hidden_size=self.hidden_size,
fusion_hidden_size=self.hidden_size,
num_hidden_layers=self.num_hidden_layers,
backbone_out_indices=self.backbone_out_indices,
num_attention_heads=self.num_attention_heads,
@ -115,6 +118,7 @@ class DPTModelTester:
is_decoder=False,
initializer_range=self.initializer_range,
is_hybrid=self.is_hybrid,
neck_hidden_sizes=self.neck_hidden_sizes,
)
def create_and_check_model(self, config, pixel_values, labels):
@ -275,10 +279,6 @@ class DPTModelTest(ModelTesterMixin, PipelineTesterMixin, unittest.TestCase):
msg=f"Parameter {name} of model {model_class} seems not properly initialized",
)
@unittest.skip("Will be fixed soon by reducing the size of the model used for common tests.")
def test_model_is_small(self):
pass
@slow
def test_model_from_pretrained(self):
for model_name in DPT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:

12
tests/models/dpt/test_modeling_dpt_hybrid.py
View File

@ -62,7 +62,8 @@ class DPTModelTester:
attention_probs_dropout_prob=0.1,
initializer_range=0.02,
num_labels=3,
backbone_featmap_shape=[1, 384, 24, 24],
backbone_featmap_shape=[1, 32, 24, 24],
neck_hidden_sizes=[16, 16, 32, 32],
is_hybrid=True,
scope=None,
):
@ -86,6 +87,7 @@ class DPTModelTester:
self.backbone_featmap_shape = backbone_featmap_shape
self.scope = scope
self.is_hybrid = is_hybrid
self.neck_hidden_sizes = neck_hidden_sizes
# sequence length of DPT = num_patches + 1 (we add 1 for the [CLS] token)
num_patches = (image_size // patch_size) ** 2
self.seq_length = num_patches + 1
@ -108,7 +110,7 @@ class DPTModelTester:
"depths": [3, 4, 9],
"out_features": ["stage1", "stage2", "stage3"],
"embedding_dynamic_padding": True,
"hidden_sizes": [96, 192, 384, 768],
"hidden_sizes": [16, 16, 32, 32],
"num_groups": 2,
}
@ -117,6 +119,7 @@ class DPTModelTester:
patch_size=self.patch_size,
num_channels=self.num_channels,
hidden_size=self.hidden_size,
fusion_hidden_size=self.hidden_size,
num_hidden_layers=self.num_hidden_layers,
backbone_out_indices=self.backbone_out_indices,
num_attention_heads=self.num_attention_heads,
@ -129,6 +132,7 @@ class DPTModelTester:
is_hybrid=self.is_hybrid,
backbone_config=backbone_config,
backbone_featmap_shape=self.backbone_featmap_shape,
neck_hidden_sizes=self.neck_hidden_sizes,
)
def create_and_check_model(self, config, pixel_values, labels):
@ -289,10 +293,6 @@ class DPTModelTest(ModelTesterMixin, PipelineTesterMixin, unittest.TestCase):
msg=f"Parameter {name} of model {model_class} seems not properly initialized",
)
@unittest.skip("Will be fixed soon by reducing the size of the model used for common tests.")
def test_model_is_small(self):
pass
@slow
def test_model_from_pretrained(self):
for model_name in DPT_PRETRAINED_MODEL_ARCHIVE_LIST[1:]:

6
tests/models/efficientnet/test_modeling_efficientnet.py
View File

@ -49,7 +49,7 @@ class EfficientNetModelTester:
num_channels=3,
kernel_sizes=[3, 3, 5],
in_channels=[32, 16, 24],
out_channels=[16, 24, 40],
out_channels=[16, 24, 20],
strides=[1, 1, 2],
num_block_repeats=[1, 1, 2],
expand_ratios=[1, 6, 6],
@ -223,10 +223,6 @@ class EfficientNetModelTest(ModelTesterMixin, PipelineTesterMixin, unittest.Test
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_image_classification(*config_and_inputs)
@unittest.skip("Will be fixed soon by reducing the size of the model used for common tests.")
def test_model_is_small(self):
pass
@slow
def test_model_from_pretrained(self):
for model_name in EFFICIENTNET_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:

30
tests/models/encodec/test_modeling_encodec.py
View File

@ -77,16 +77,25 @@ class EncodecModelTester:
batch_size=12,
num_channels=2,
is_training=False,
num_hidden_layers=4,
intermediate_size=40,
hidden_size=32,
num_filters=8,
num_residual_layers=1,
upsampling_ratios=[8, 4],
num_lstm_layers=1,
codebook_size=64,
):
self.parent = parent
self.batch_size = batch_size
self.num_channels = num_channels
self.is_training = is_training
self.num_hidden_layers = num_hidden_layers
self.intermediate_size = intermediate_size
self.hidden_size = hidden_size
self.num_filters = num_filters
self.num_residual_layers = num_residual_layers
self.upsampling_ratios = upsampling_ratios
self.num_lstm_layers = num_lstm_layers
self.codebook_size = codebook_size
def prepare_config_and_inputs(self):
input_values = floats_tensor([self.batch_size, self.num_channels, self.intermediate_size], scale=1.0)
@ -99,7 +108,16 @@ class EncodecModelTester:
return config, inputs_dict
def get_config(self):
return EncodecConfig(audio_channels=self.num_channels, chunk_in_sec=None)
return EncodecConfig(
audio_channels=self.num_channels,
chunk_in_sec=None,
hidden_size=self.hidden_size,
num_filters=self.num_filters,
num_residual_layers=self.num_residual_layers,
upsampling_ratios=self.upsampling_ratios,
num_lstm_layers=self.num_lstm_layers,
codebook_size=self.codebook_size,
)
def create_and_check_model_forward(self, config, inputs_dict):
model = EncodecModel(config=config).to(torch_device).eval()
@ -397,10 +415,6 @@ class EncodecModelTest(ModelTesterMixin, PipelineTesterMixin, unittest.TestCase)
msg=f"Parameter {name} of model {model_class} seems not properly initialized",
)
@unittest.skip("Will be fixed soon by reducing the size of the model used for common tests.")
def test_model_is_small(self):
pass
def test_identity_shortcut(self):
config, inputs_dict = self.model_tester.prepare_config_and_inputs()
config.use_conv_shortcut = False

4
tests/models/esm/test_modeling_esm.py
View File

@ -279,10 +279,6 @@ class EsmModelTest(ModelTesterMixin, PipelineTesterMixin, unittest.TestCase):
def test_resize_tokens_embeddings(self):
pass
@unittest.skip("Will be fixed soon by reducing the size of the model used for common tests.")
def test_model_is_small(self):
pass
@require_torch
class EsmModelIntegrationTest(TestCasePlus):

32
tests/models/esm/test_modeling_esmfold.py
View File

@ -100,6 +100,28 @@ class EsmFoldModelTester:
return config, input_ids, input_mask, sequence_labels, token_labels, choice_labels
def get_config(self):
esmfold_config = {
"trunk": {
"num_blocks": 2,
"sequence_state_dim": 64,
"pairwise_state_dim": 16,
"sequence_head_width": 4,
"pairwise_head_width": 4,
"position_bins": 4,
"chunk_size": 16,
"structure_module": {
"ipa_dim": 16,
"num_angles": 7,
"num_blocks": 2,
"num_heads_ipa": 4,
"pairwise_dim": 16,
"resnet_dim": 16,
"sequence_dim": 48,
},
},
"fp16_esm": False,
"lddt_head_hid_dim": 16,
}
config = EsmConfig(
vocab_size=33,
hidden_size=self.hidden_size,
@ -114,7 +136,7 @@ class EsmFoldModelTester:
type_vocab_size=self.type_vocab_size,
initializer_range=self.initializer_range,
is_folding_model=True,
esmfold_config={"trunk": {"num_blocks": 2}, "fp16_esm": False},
esmfold_config=esmfold_config,
)
return config
@ -126,8 +148,8 @@ class EsmFoldModelTester:
result = model(input_ids)
result = model(input_ids)
self.parent.assertEqual(result.positions.shape, (8, self.batch_size, self.seq_length, 14, 3))
self.parent.assertEqual(result.angles.shape, (8, self.batch_size, self.seq_length, 7, 2))
self.parent.assertEqual(result.positions.shape, (2, self.batch_size, self.seq_length, 14, 3))
self.parent.assertEqual(result.angles.shape, (2, self.batch_size, self.seq_length, 7, 2))
def prepare_config_and_inputs_for_common(self):
config_and_inputs = self.prepare_config_and_inputs()
@ -243,10 +265,6 @@ class EsmFoldModelTest(ModelTesterMixin, PipelineTesterMixin, unittest.TestCase)
def test_multi_gpu_data_parallel_forward(self):
pass
@unittest.skip("Will be fixed soon by reducing the size of the model used for common tests.")
def test_model_is_small(self):
pass
@require_torch
class EsmModelIntegrationTest(TestCasePlus):

42
tests/models/flava/test_modeling_flava.py
View File

@ -92,7 +92,7 @@ class FlavaImageModelTester:
num_channels=3,
qkv_bias=True,
mask_token=True,
vocab_size=8192,
vocab_size=99,
):
self.parent = parent
self.batch_size = batch_size
@ -321,10 +321,6 @@ class FlavaImageModelTest(ModelTesterMixin, unittest.TestCase):
def test_save_load_fast_init_to_base(self):
pass
@unittest.skip("Will be fixed soon by reducing the size of the model used for common tests.")
def test_model_is_small(self):
pass
@slow
def test_model_from_pretrained(self):
for model_name in FLAVA_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
@ -341,7 +337,7 @@ class FlavaTextModelTester:
is_training=True,
use_input_mask=True,
use_token_type_ids=True,
vocab_size=30522,
vocab_size=102,
type_vocab_size=2,
max_position_embeddings=512,
position_embedding_type="absolute",
@ -476,10 +472,6 @@ class FlavaTextModelTest(ModelTesterMixin, unittest.TestCase):
def test_save_load_fast_init_to_base(self):
pass
@unittest.skip("Will be fixed soon by reducing the size of the model used for common tests.")
def test_model_is_small(self):
pass
@slow
def test_model_from_pretrained(self):
for model_name in FLAVA_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
@ -632,10 +624,6 @@ class FlavaMultimodalModelTest(ModelTesterMixin, unittest.TestCase):
def test_save_load_fast_init_to_base(self):
pass
@unittest.skip("Will be fixed soon by reducing the size of the model used for common tests.")
def test_model_is_small(self):
pass
@slow
def test_model_from_pretrained(self):
for model_name in FLAVA_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
@ -644,11 +632,23 @@ class FlavaMultimodalModelTest(ModelTesterMixin, unittest.TestCase):
class FlavaImageCodebookTester:
def __init__(self, parent, batch_size=12, image_size=112, num_channels=3):
def __init__(
self,
parent,
batch_size=12,
image_size=112,
num_channels=3,
hidden_size=32,
num_groups=2,
vocab_size=99,
):
self.parent = parent
self.batch_size = batch_size
self.image_size = image_size
self.num_channels = num_channels
self.hidden_size = hidden_size
self.num_groups = num_groups
self.vocab_size = vocab_size
def prepare_config_and_inputs(self):
pixel_values = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size])
@ -657,7 +657,9 @@ class FlavaImageCodebookTester:
return config, pixel_values
def get_config(self):
return FlavaImageCodebookConfig()
return FlavaImageCodebookConfig(
hidden_size=self.hidden_size, num_groups=self.num_groups, vocab_size=self.vocab_size
)
def create_and_check_model(self, config, pixel_values):
model = FlavaImageCodebook(config=config)
@ -743,10 +745,6 @@ class FlavaImageCodebookTest(ModelTesterMixin, unittest.TestCase):
def test_save_load_fast_init_to_base(self):
pass
@unittest.skip("Will be fixed soon by reducing the size of the model used for common tests.")
def test_model_is_small(self):
pass
@slow
def test_model_from_pretrained(self):
for model_name in FLAVA_CODEBOOK_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
@ -929,10 +927,6 @@ class FlavaModelTest(ModelTesterMixin, PipelineTesterMixin, unittest.TestCase):
msg=f"Parameter {name} of model {model_class} seems not properly initialized",
)
@unittest.skip("Will be fixed soon by reducing the size of the model used for common tests.")
def test_model_is_small(self):
pass
def _create_and_check_torchscript(self, config, inputs_dict):
if not self.test_torchscript:
return

10
tests/models/git/test_modeling_git.py
View File

@ -203,7 +203,7 @@ class GitModelTester:
use_labels=True,
vocab_size=99,
hidden_size=32,
num_hidden_layers=5,
num_hidden_layers=4,
num_attention_heads=4,
intermediate_size=37,
hidden_act="gelu",
@ -268,6 +268,10 @@ class GitModelTester:
"num_channels": self.num_channels,
"image_size": self.image_size,
"patch_size": self.patch_size,
"hidden_size": self.hidden_size,
"projection_dim": 32,
"num_hidden_layers": self.num_hidden_layers,
"num_attention_heads": self.num_attention_heads,
},
vocab_size=self.vocab_size,
hidden_size=self.hidden_size,
@ -454,10 +458,6 @@ class GitModelTest(ModelTesterMixin, GenerationTesterMixin, PipelineTesterMixin,
def test_greedy_generate_dict_outputs_use_cache(self):
pass
@unittest.skip("Will be fixed soon by reducing the size of the model used for common tests.")
def test_model_is_small(self):
pass
@require_torch
@require_vision

10
tests/models/gptsan_japanese/test_modeling_gptsan_japanese.py
View File

@ -38,7 +38,7 @@ class GPTSanJapaneseTester:
def __init__(
self,
parent,
vocab_size=36000,
vocab_size=99,
batch_size=13,
num_contexts=7,
# For common tests
@ -182,10 +182,6 @@ class GPTSanJapaneseTest(ModelTesterMixin, PipelineTesterMixin, unittest.TestCas
def test_model_parallelism(self):
super().test_model_parallelism()
@unittest.skip("Will be fixed soon by reducing the size of the model used for common tests.")
def test_model_is_small(self):
pass
@require_torch
class GPTSanJapaneseForConditionalGenerationTest(ModelTesterMixin, GenerationTesterMixin, unittest.TestCase):
@ -216,10 +212,6 @@ class GPTSanJapaneseForConditionalGenerationTest(ModelTesterMixin, GenerationTes
def test_model_parallelism(self):
super().test_model_parallelism()
@unittest.skip("Will be fixed soon by reducing the size of the model used for common tests.")
def test_model_is_small(self):
pass
@slow
def test_logits(self):
model = GPTSanJapaneseForConditionalGeneration.from_pretrained("Tanrei/GPTSAN-japanese")

28
tests/models/graphormer/test_modeling_graphormer.py
View File

@ -42,22 +42,22 @@ class GraphormerModelTester:
self,
parent,
num_classes=1,
num_atoms=512 * 9,
num_edges=512 * 3,
num_in_degree=512,
num_out_degree=512,
num_spatial=512,
num_edge_dis=128,
num_atoms=32 * 9,
num_edges=32 * 3,
num_in_degree=32,
num_out_degree=32,
num_spatial=32,
num_edge_dis=16,
multi_hop_max_dist=5, # sometimes is 20
spatial_pos_max=1024,
spatial_pos_max=32,
edge_type="multi_hop",
init_fn=None,
max_nodes=512,
max_nodes=32,
share_input_output_embed=False,
num_hidden_layers=12,
embedding_dim=768,
ffn_embedding_dim=768,
num_attention_heads=32,
num_hidden_layers=2,
embedding_dim=32,
ffn_embedding_dim=32,
num_attention_heads=4,
dropout=0.1,
attention_dropout=0.1,
activation_dropout=0.1,
@ -470,10 +470,6 @@ class GraphormerModelTest(ModelTesterMixin, PipelineTesterMixin, unittest.TestCa
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_graph_classification(*config_and_inputs)
@unittest.skip("Will be fixed soon by reducing the size of the model used for common tests.")
def test_model_is_small(self):
pass
@slow
def test_model_from_pretrained(self):
for model_name in GRAPHORMER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:

10
tests/models/levit/test_modeling_levit.py
View File

@ -67,10 +67,10 @@ class LevitModelTester:
stride=2,
padding=1,
patch_size=16,
hidden_sizes=[128, 256, 384],
num_attention_heads=[4, 6, 8],
hidden_sizes=[16, 32, 48],
num_attention_heads=[1, 2, 3],
depths=[2, 3, 4],
key_dim=[16, 16, 16],
key_dim=[8, 8, 8],
drop_path_rate=0,
mlp_ratio=[2, 2, 2],
attention_ratio=[2, 2, 2],
@ -282,10 +282,6 @@ class LevitModelTest(ModelTesterMixin, PipelineTesterMixin, unittest.TestCase):
check_hidden_states_output(inputs_dict, config, model_class)
@unittest.skip("Will be fixed soon by reducing the size of the model used for common tests.")
def test_model_is_small(self):
pass
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)

22
tests/models/mask2former/test_modeling_mask2former.py
View File

@ -54,6 +54,8 @@ class Mask2FormerModelTester:
max_size=32 * 8,
num_labels=4,
hidden_dim=64,
num_attention_heads=4,
num_hidden_layers=2,
):
self.parent = parent
self.batch_size = batch_size
@ -66,6 +68,8 @@ class Mask2FormerModelTester:
self.num_labels = num_labels
self.hidden_dim = hidden_dim
self.mask_feature_size = hidden_dim
self.num_attention_heads = num_attention_heads
self.num_hidden_layers = num_hidden_layers
def prepare_config_and_inputs(self):
pixel_values = floats_tensor([self.batch_size, self.num_channels, self.min_size, self.max_size]).to(
@ -85,15 +89,25 @@ class Mask2FormerModelTester:
def get_config(self):
config = Mask2FormerConfig(
hidden_size=self.hidden_dim,
num_attention_heads=self.num_attention_heads,
num_hidden_layers=self.num_hidden_layers,
encoder_feedforward_dim=16,
dim_feedforward=32,
num_queries=self.num_queries,
num_labels=self.num_labels,
decoder_layers=2,
encoder_layers=2,
feature_size=16,
)
config.num_queries = self.num_queries
config.num_labels = self.num_labels
config.backbone_config.embed_dim = 16
config.backbone_config.depths = [1, 1, 1, 1]
config.backbone_config.hidden_size = 16
config.backbone_config.num_channels = self.num_channels
config.backbone_config.num_heads = [1, 1, 2, 2]
config.encoder_feedforward_dim = 64
config.dim_feedforward = 128
config.hidden_dim = self.hidden_dim
config.mask_feature_size = self.hidden_dim
config.feature_size = self.hidden_dim
@ -220,10 +234,6 @@ class Mask2FormerModelTest(ModelTesterMixin, PipelineTesterMixin, unittest.TestC
def test_multi_gpu_data_parallel_forward(self):
pass
@unittest.skip("Will be fixed soon by reducing the size of the model used for common tests.")
def test_model_is_small(self):
pass
def test_forward_signature(self):
config, _ = self.model_tester.prepare_config_and_inputs_for_common()

12
tests/models/maskformer/test_modeling_maskformer.py
View File

@ -85,9 +85,15 @@ class MaskFormerModelTester:
return MaskFormerConfig.from_backbone_and_decoder_configs(
backbone_config=SwinConfig(
depths=[1, 1, 1, 1],
embed_dim=16,
hidden_size=32,
num_heads=[1, 1, 2, 2],
),
decoder_config=DetrConfig(
decoder_ffn_dim=128,
decoder_ffn_dim=64,
decoder_layers=2,
encoder_ffn_dim=64,
encoder_layers=2,
num_queries=self.num_queries,
decoder_attention_heads=2,
d_model=self.mask_feature_size,
@ -224,10 +230,6 @@ class MaskFormerModelTest(ModelTesterMixin, PipelineTesterMixin, unittest.TestCa
def test_multi_gpu_data_parallel_forward(self):
pass
@unittest.skip("Will be fixed soon by reducing the size of the model used for common tests.")
def test_model_is_small(self):
pass
def test_forward_signature(self):
config, _ = self.model_tester.prepare_config_and_inputs_for_common()

8
tests/models/mobilevit/test_modeling_mobilevit.py
View File

@ -56,7 +56,7 @@ class MobileViTModelTester:
image_size=32,
patch_size=2,
num_channels=3,
last_hidden_size=640,
last_hidden_size=32,
num_attention_heads=4,
hidden_act="silu",
conv_kernel_size=3,
@ -115,6 +115,8 @@ class MobileViTModelTester:
attention_probs_dropout_prob=self.attention_probs_dropout_prob,
classifier_dropout_prob=self.classifier_dropout_prob,
initializer_range=self.initializer_range,
hidden_sizes=[12, 16, 20],
neck_hidden_sizes=[8, 8, 16, 16, 32, 32, 32],
)
def create_and_check_model(self, config, pixel_values, labels, pixel_labels):
@ -231,10 +233,6 @@ class MobileViTModelTest(ModelTesterMixin, PipelineTesterMixin, unittest.TestCas
expected_arg_names = ["pixel_values"]
self.assertListEqual(arg_names[:1], expected_arg_names)
@unittest.skip("Will be fixed soon by reducing the size of the model used for common tests.")
def test_model_is_small(self):
pass
def test_model(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*config_and_inputs)

4
tests/models/mobilevit/test_modeling_tf_mobilevit.py
View File

@ -59,7 +59,7 @@ class TFMobileViTModelTester:
image_size=32,
patch_size=2,
num_channels=3,
last_hidden_size=640,
last_hidden_size=32,
num_attention_heads=4,
hidden_act="silu",
conv_kernel_size=3,
@ -118,6 +118,8 @@ class TFMobileViTModelTester:
attention_probs_dropout_prob=self.attention_probs_dropout_prob,
classifier_dropout_prob=self.classifier_dropout_prob,
initializer_range=self.initializer_range,
hidden_sizes=[12, 16, 20],
neck_hidden_sizes=[8, 8, 16, 16, 32, 32, 32],
)
def create_and_check_model(self, config, pixel_values, labels, pixel_labels):

7
tests/models/mobilevitv2/test_modeling_mobilevitv2.py
View File

@ -115,6 +115,9 @@ class MobileViTV2ModelTester:
width_multiplier=self.width_multiplier,
ffn_dropout=self.ffn_dropout_prob,
attn_dropout=self.attn_dropout_prob,
base_attn_unit_dims=[16, 24, 32],
n_attn_blocks=[1, 1, 2],
aspp_out_channels=32,
)
def create_and_check_model(self, config, pixel_values, labels, pixel_labels):
@ -225,10 +228,6 @@ class MobileViTV2ModelTest(ModelTesterMixin, PipelineTesterMixin, unittest.TestC
def test_multi_gpu_data_parallel_forward(self):
pass
@unittest.skip("Will be fixed soon by reducing the size of the model used for common tests.")
def test_model_is_small(self):
pass
def test_forward_signature(self):
config, _ = self.model_tester.prepare_config_and_inputs_for_common()

2
tests/test_modeling_common.py
View File

@ -2708,7 +2708,7 @@ class ModelTesterMixin:
def test_model_is_small(self):
# Just a consistency check to make sure we are not running tests on 80M parameter models.
config, _ = self.model_tester.prepare_config_and_inputs_for_common()
# print(config)
print(config)
for model_class in self.all_model_classes:
model = model_class(config)