Move test model folders (#17034)
* move test model folders (TODO: fix imports and others) * fix (potentially partially) imports (in model test modules) * fix (potentially partially) imports (in tokenization test modules) * fix (potentially partially) imports (in feature extraction test modules) * fix import utils.test_modeling_tf_core * fix path ../fixtures/ * fix imports about generation.test_generation_flax_utils * fix more imports * fix fixture path * fix get_test_dir * update module_to_test_file * fix get_tests_dir from wrong transformers.utils * update config.yml (CircleCI) * fix style * remove missing imports * update new model script * update check_repo * update SPECIAL_MODULE_TO_TEST_MAP * fix style * add __init__ * update self-scheduled * fix add_new_model scripts * check one way to get location back * python setup.py build install * fix import in test auto * update self-scheduled.yml * update slack notification script * Add comments about artifact names * fix for yolos Co-authored-by: ydshieh <ydshieh@users.noreply.github.com>
This commit is contained in:
Yih-Dar
0
tests/models/vision_encoder_decoder/__init__.py
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0
tests/models/vision_encoder_decoder/__init__.py
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# coding=utf-8
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# Copyright 2021 HuggingFace Inc. team.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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import tempfile
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import unittest
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import numpy as np
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from transformers import is_flax_available, is_torch_available, is_vision_available
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from transformers.testing_utils import is_pt_flax_cross_test, require_flax, require_vision, slow, torch_device
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from ...test_modeling_flax_common import floats_tensor, ids_tensor
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from ..gpt2.test_modeling_flax_gpt2 import FlaxGPT2ModelTester
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from ..vit.test_modeling_flax_vit import FlaxViTModelTester
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if is_flax_available():
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from transformers import (
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AutoTokenizer,
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FlaxGPT2LMHeadModel,
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FlaxVisionEncoderDecoderModel,
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FlaxViTModel,
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VisionEncoderDecoderConfig,
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)
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from transformers.modeling_flax_pytorch_utils import (
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convert_pytorch_state_dict_to_flax,
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load_flax_weights_in_pytorch_model,
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)
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if is_torch_available():
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import torch
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from transformers import VisionEncoderDecoderModel
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if is_vision_available():
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from PIL import Image
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from transformers import ViTFeatureExtractor
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@require_flax
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class FlaxEncoderDecoderMixin:
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def get_encoder_decoder_model(self, config, decoder_config):
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raise NotImplementedError
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def prepare_config_and_inputs(self):
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raise NotImplementedError
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def get_pretrained_model(self):
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raise NotImplementedError
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def check_encoder_decoder_model_from_pretrained_configs(
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self,
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config,
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pixel_values,
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encoder_hidden_states,
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decoder_config,
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decoder_input_ids,
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decoder_attention_mask,
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**kwargs
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):
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encoder_decoder_config = VisionEncoderDecoderConfig.from_encoder_decoder_configs(config, decoder_config)
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self.assertTrue(encoder_decoder_config.decoder.is_decoder)
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enc_dec_model = FlaxVisionEncoderDecoderModel(encoder_decoder_config)
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self.assertTrue(enc_dec_model.config.is_encoder_decoder)
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outputs_encoder_decoder = enc_dec_model(
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pixel_values=pixel_values,
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decoder_input_ids=decoder_input_ids,
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decoder_attention_mask=decoder_attention_mask,
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)
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self.assertEqual(
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outputs_encoder_decoder["logits"].shape, (decoder_input_ids.shape + (decoder_config.vocab_size,))
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)
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self.assertEqual(outputs_encoder_decoder["encoder_last_hidden_state"].shape[0], pixel_values.shape[0])
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self.assertEqual(outputs_encoder_decoder["encoder_last_hidden_state"].shape[-1], config.hidden_size)
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def check_encoder_decoder_model_from_pretrained(
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self,
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config,
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pixel_values,
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encoder_hidden_states,
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decoder_config,
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decoder_input_ids,
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decoder_attention_mask,
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return_dict,
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**kwargs
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):
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encoder_model, decoder_model = self.get_encoder_decoder_model(config, decoder_config)
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kwargs = {"encoder_model": encoder_model, "decoder_model": decoder_model, "return_dict": return_dict}
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enc_dec_model = FlaxVisionEncoderDecoderModel.from_encoder_decoder_pretrained(**kwargs)
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outputs_encoder_decoder = enc_dec_model(
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pixel_values=pixel_values,
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decoder_input_ids=decoder_input_ids,
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decoder_attention_mask=decoder_attention_mask,
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return_dict=True,
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)
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self.assertEqual(
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outputs_encoder_decoder["logits"].shape, (decoder_input_ids.shape + (decoder_config.vocab_size,))
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)
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self.assertEqual(outputs_encoder_decoder["encoder_last_hidden_state"].shape[0], pixel_values.shape[0])
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self.assertEqual(outputs_encoder_decoder["encoder_last_hidden_state"].shape[-1], config.hidden_size)
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def check_save_and_load(
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self,
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config,
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pixel_values,
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encoder_hidden_states,
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decoder_config,
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decoder_input_ids,
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decoder_attention_mask,
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**kwargs
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):
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encoder_model, decoder_model = self.get_encoder_decoder_model(config, decoder_config)
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kwargs = {"encoder_model": encoder_model, "decoder_model": decoder_model}
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enc_dec_model = FlaxVisionEncoderDecoderModel.from_encoder_decoder_pretrained(**kwargs)
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outputs = enc_dec_model(
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pixel_values=pixel_values,
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decoder_input_ids=decoder_input_ids,
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decoder_attention_mask=decoder_attention_mask,
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)
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out_2 = np.array(outputs[0])
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out_2[np.isnan(out_2)] = 0
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with tempfile.TemporaryDirectory() as tmpdirname:
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enc_dec_model.save_pretrained(tmpdirname)
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FlaxVisionEncoderDecoderModel.from_pretrained(tmpdirname)
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after_outputs = enc_dec_model(
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pixel_values=pixel_values,
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decoder_input_ids=decoder_input_ids,
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decoder_attention_mask=decoder_attention_mask,
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)
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out_1 = np.array(after_outputs[0])
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out_1[np.isnan(out_1)] = 0
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max_diff = np.amax(np.abs(out_1 - out_2))
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self.assertLessEqual(max_diff, 1e-5)
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def check_encoder_decoder_model_output_attentions(
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self,
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config,
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pixel_values,
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encoder_hidden_states,
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decoder_config,
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decoder_input_ids,
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decoder_attention_mask,
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**kwargs
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):
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# make the decoder inputs a different shape from the encoder inputs to harden the test
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decoder_input_ids = decoder_input_ids[:, :-1]
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decoder_attention_mask = decoder_attention_mask[:, :-1]
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encoder_model, decoder_model = self.get_encoder_decoder_model(config, decoder_config)
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kwargs = {"encoder_model": encoder_model, "decoder_model": decoder_model}
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enc_dec_model = FlaxVisionEncoderDecoderModel.from_encoder_decoder_pretrained(**kwargs)
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outputs_encoder_decoder = enc_dec_model(
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pixel_values=pixel_values,
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decoder_input_ids=decoder_input_ids,
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decoder_attention_mask=decoder_attention_mask,
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output_attentions=True,
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)
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encoder_attentions = outputs_encoder_decoder["encoder_attentions"]
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self.assertEqual(len(encoder_attentions), config.num_hidden_layers)
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self.assertEqual(encoder_attentions[0].shape[-3:-2], (config.num_attention_heads,))
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decoder_attentions = outputs_encoder_decoder["decoder_attentions"]
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num_decoder_layers = (
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decoder_config.num_decoder_layers
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if hasattr(decoder_config, "num_decoder_layers")
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else decoder_config.num_hidden_layers
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)
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self.assertEqual(len(decoder_attentions), num_decoder_layers)
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self.assertEqual(
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decoder_attentions[0].shape[-3:],
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(decoder_config.num_attention_heads, decoder_input_ids.shape[-1], decoder_input_ids.shape[-1]),
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)
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cross_attentions = outputs_encoder_decoder["cross_attentions"]
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self.assertEqual(len(cross_attentions), num_decoder_layers)
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cross_attention_input_seq_len = decoder_input_ids.shape[-1] * (
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1 + (decoder_config.ngram if hasattr(decoder_config, "ngram") else 0)
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)
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self.assertEqual(
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cross_attentions[0].shape[-3:-1],
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(decoder_config.num_attention_heads, cross_attention_input_seq_len),
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)
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def check_encoder_decoder_model_generate(self, pixel_values, config, decoder_config, **kwargs):
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encoder_model, decoder_model = self.get_encoder_decoder_model(config, decoder_config)
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kwargs = {"encoder_model": encoder_model, "decoder_model": decoder_model}
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enc_dec_model = FlaxVisionEncoderDecoderModel.from_encoder_decoder_pretrained(**kwargs)
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pad_token_id = enc_dec_model.config.decoder.pad_token_id
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eos_token_id = enc_dec_model.config.decoder.eos_token_id
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decoder_start_token_id = enc_dec_model.config.decoder.decoder_start_token_id
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# Copied from generation_utils (GPT2 doesn't have `pad_token_id`)
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if pad_token_id is None and eos_token_id is not None:
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pad_token_id = eos_token_id
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if decoder_start_token_id is None:
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decoder_start_token_id = enc_dec_model.config.decoder.bos_token_id
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# Bert does not have a bos token id, so use pad_token_id instead
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# Copied from `test_modeling_encoder_decoder.py`
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if decoder_start_token_id is None:
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decoder_start_token_id = pad_token_id
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generated_output = enc_dec_model.generate(
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pixel_values,
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pad_token_id=pad_token_id,
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eos_token_id=eos_token_id,
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decoder_start_token_id=decoder_start_token_id,
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)
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generated_sequences = generated_output.sequences
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self.assertEqual(generated_sequences.shape, (pixel_values.shape[0],) + (decoder_config.max_length,))
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def check_pt_flax_equivalence(self, pt_model, fx_model, inputs_dict):
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pt_model.to(torch_device)
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pt_model.eval()
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# prepare inputs
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flax_inputs = inputs_dict
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pt_inputs = {k: torch.tensor(v.tolist()) for k, v in flax_inputs.items()}
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with torch.no_grad():
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pt_outputs = pt_model(**pt_inputs).to_tuple()
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fx_outputs = fx_model(**inputs_dict).to_tuple()
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self.assertEqual(len(fx_outputs), len(pt_outputs), "Output lengths differ between Flax and PyTorch")
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for fx_output, pt_output in zip(fx_outputs, pt_outputs):
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self.assert_almost_equals(fx_output, pt_output.numpy(), 1e-5)
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# PT -> Flax
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with tempfile.TemporaryDirectory() as tmpdirname:
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pt_model.save_pretrained(tmpdirname)
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fx_model_loaded = FlaxVisionEncoderDecoderModel.from_pretrained(tmpdirname, from_pt=True)
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fx_outputs_loaded = fx_model_loaded(**inputs_dict).to_tuple()
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self.assertEqual(len(fx_outputs_loaded), len(pt_outputs), "Output lengths differ between Flax and PyTorch")
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for fx_output_loaded, pt_output in zip(fx_outputs_loaded, pt_outputs):
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self.assert_almost_equals(fx_output_loaded, pt_output.numpy(), 1e-5)
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# Flax -> PT
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with tempfile.TemporaryDirectory() as tmpdirname:
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fx_model.save_pretrained(tmpdirname)
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pt_model_loaded = VisionEncoderDecoderModel.from_pretrained(tmpdirname, from_flax=True)
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pt_model_loaded.to(torch_device)
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pt_model_loaded.eval()
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with torch.no_grad():
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pt_outputs_loaded = pt_model_loaded(**pt_inputs).to_tuple()
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self.assertEqual(len(fx_outputs), len(pt_outputs_loaded), "Output lengths differ between Flax and PyTorch")
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for fx_output, pt_output_loaded in zip(fx_outputs, pt_outputs_loaded):
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self.assert_almost_equals(fx_output, pt_output_loaded.numpy(), 1e-5)
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def check_equivalence_pt_to_flax(self, config, decoder_config, inputs_dict):
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encoder_decoder_config = VisionEncoderDecoderConfig.from_encoder_decoder_configs(config, decoder_config)
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pt_model = VisionEncoderDecoderModel(encoder_decoder_config)
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fx_model = FlaxVisionEncoderDecoderModel(encoder_decoder_config)
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fx_state = convert_pytorch_state_dict_to_flax(pt_model.state_dict(), fx_model)
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fx_model.params = fx_state
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self.check_pt_flax_equivalence(pt_model, fx_model, inputs_dict)
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def check_equivalence_flax_to_pt(self, config, decoder_config, inputs_dict):
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encoder_decoder_config = VisionEncoderDecoderConfig.from_encoder_decoder_configs(config, decoder_config)
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pt_model = VisionEncoderDecoderModel(encoder_decoder_config)
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fx_model = FlaxVisionEncoderDecoderModel(encoder_decoder_config)
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pt_model = load_flax_weights_in_pytorch_model(pt_model, fx_model.params)
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self.check_pt_flax_equivalence(pt_model, fx_model, inputs_dict)
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def test_encoder_decoder_model_from_pretrained_configs(self):
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config_inputs_dict = self.prepare_config_and_inputs()
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self.check_encoder_decoder_model_from_pretrained_configs(**config_inputs_dict)
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def test_encoder_decoder_model_from_pretrained(self):
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config_inputs_dict = self.prepare_config_and_inputs()
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self.check_encoder_decoder_model_from_pretrained(**config_inputs_dict, return_dict=False)
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def test_encoder_decoder_model_from_pretrained_return_dict(self):
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config_inputs_dict = self.prepare_config_and_inputs()
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self.check_encoder_decoder_model_from_pretrained(**config_inputs_dict, return_dict=True)
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def test_save_and_load_from_pretrained(self):
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config_inputs_dict = self.prepare_config_and_inputs()
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self.check_save_and_load(**config_inputs_dict)
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def test_encoder_decoder_model_output_attentions(self):
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config_inputs_dict = self.prepare_config_and_inputs()
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self.check_encoder_decoder_model_output_attentions(**config_inputs_dict)
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def test_encoder_decoder_model_generate(self):
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config_inputs_dict = self.prepare_config_and_inputs()
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self.check_encoder_decoder_model_generate(**config_inputs_dict)
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def assert_almost_equals(self, a: np.ndarray, b: np.ndarray, tol: float):
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diff = np.abs((a - b)).max()
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self.assertLessEqual(diff, tol, f"Difference between torch and flax is {diff} (>= {tol}).")
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@is_pt_flax_cross_test
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def test_pt_flax_equivalence(self):
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config_inputs_dict = self.prepare_config_and_inputs()
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config = config_inputs_dict.pop("config")
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decoder_config = config_inputs_dict.pop("decoder_config")
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inputs_dict = config_inputs_dict
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# `encoder_hidden_states` is not used in model call/forward
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del inputs_dict["encoder_hidden_states"]
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# Avoid the case where a sequence has no place to attend (after combined with the causal attention mask)
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batch_size = inputs_dict["decoder_attention_mask"].shape[0]
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inputs_dict["decoder_attention_mask"] = np.concatenate(
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[np.ones(shape=(batch_size, 1)), inputs_dict["decoder_attention_mask"][:, 1:]], axis=1
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)
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# Flax models don't use the `use_cache` option and cache is not returned as a default.
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# So we disable `use_cache` here for PyTorch model.
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decoder_config.use_cache = False
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self.assertTrue(decoder_config.cross_attention_hidden_size is None)
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# check without `enc_to_dec_proj` projection
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self.assertTrue(config.hidden_size == decoder_config.hidden_size)
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self.check_equivalence_pt_to_flax(config, decoder_config, inputs_dict)
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self.check_equivalence_flax_to_pt(config, decoder_config, inputs_dict)
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# check `enc_to_dec_proj` work as expected
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decoder_config.hidden_size = decoder_config.hidden_size * 2
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self.assertTrue(config.hidden_size != decoder_config.hidden_size)
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self.check_equivalence_pt_to_flax(config, decoder_config, inputs_dict)
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self.check_equivalence_flax_to_pt(config, decoder_config, inputs_dict)
|
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|
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@slow
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def test_real_model_save_load_from_pretrained(self):
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model_2 = self.get_pretrained_model()
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pixel_values = floats_tensor(
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[
|
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13,
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model_2.config.encoder.num_channels,
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model_2.config.encoder.image_size,
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||||
model_2.config.encoder.image_size,
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||||
]
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||||
)
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decoder_input_ids = ids_tensor([13, 1], model_2.config.decoder.vocab_size)
|
||||
|
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outputs = model_2(
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pixel_values=pixel_values,
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decoder_input_ids=decoder_input_ids,
|
||||
)
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out_2 = np.array(outputs[0])
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out_2[np.isnan(out_2)] = 0
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|
||||
with tempfile.TemporaryDirectory() as tmp_dirname:
|
||||
model_2.save_pretrained(tmp_dirname)
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||||
model_1 = FlaxVisionEncoderDecoderModel.from_pretrained(tmp_dirname)
|
||||
|
||||
after_outputs = model_1(
|
||||
pixel_values=pixel_values,
|
||||
decoder_input_ids=decoder_input_ids,
|
||||
)
|
||||
out_1 = np.array(after_outputs[0])
|
||||
out_1[np.isnan(out_1)] = 0
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||||
max_diff = np.amax(np.abs(out_1 - out_2))
|
||||
self.assertLessEqual(max_diff, 1e-5)
|
||||
|
||||
|
||||
@require_flax
|
||||
class FlaxViT2GPT2EncoderDecoderModelTest(FlaxEncoderDecoderMixin, unittest.TestCase):
|
||||
def get_encoder_decoder_model(self, config, decoder_config):
|
||||
encoder_model = FlaxViTModel(config)
|
||||
decoder_model = FlaxGPT2LMHeadModel(decoder_config)
|
||||
return encoder_model, decoder_model
|
||||
|
||||
def prepare_config_and_inputs(self):
|
||||
model_tester_encoder = FlaxViTModelTester(self, batch_size=13)
|
||||
model_tester_decoder = FlaxGPT2ModelTester(self, batch_size=13)
|
||||
encoder_config_and_inputs = model_tester_encoder.prepare_config_and_inputs()
|
||||
decoder_config_and_inputs = model_tester_decoder.prepare_config_and_inputs_for_decoder()
|
||||
(config, pixel_values) = encoder_config_and_inputs
|
||||
(
|
||||
decoder_config,
|
||||
decoder_input_ids,
|
||||
decoder_attention_mask,
|
||||
encoder_hidden_states,
|
||||
encoder_attention_mask,
|
||||
) = decoder_config_and_inputs
|
||||
|
||||
# make sure that cross attention layers are added
|
||||
decoder_config.add_cross_attention = True
|
||||
return {
|
||||
"config": config,
|
||||
"pixel_values": pixel_values,
|
||||
"decoder_config": decoder_config,
|
||||
"decoder_input_ids": decoder_input_ids,
|
||||
"decoder_attention_mask": decoder_attention_mask,
|
||||
"encoder_hidden_states": encoder_hidden_states, # This is not used in the tests.
|
||||
}
|
||||
|
||||
def get_pretrained_model(self):
|
||||
return FlaxVisionEncoderDecoderModel.from_encoder_decoder_pretrained(
|
||||
"google/vit-base-patch16-224-in21k", "gpt2"
|
||||
)
|
||||
|
||||
|
||||
@require_flax
|
||||
class FlaxVisionEncoderDecoderModelTest(unittest.TestCase):
|
||||
def get_from_encoderdecoder_pretrained_model(self):
|
||||
return FlaxVisionEncoderDecoderModel.from_encoder_decoder_pretrained(
|
||||
"google/vit-base-patch16-224-in21k", "gpt2"
|
||||
)
|
||||
|
||||
def _check_configuration_tie(self, model):
|
||||
|
||||
module = model.module.bind(model.params)
|
||||
|
||||
assert id(module.decoder.config) == id(model.config.decoder)
|
||||
assert id(module.encoder.config) == id(model.config.encoder)
|
||||
|
||||
@slow
|
||||
def test_configuration_tie(self):
|
||||
model = self.get_from_encoderdecoder_pretrained_model()
|
||||
self._check_configuration_tie(model)
|
||||
|
||||
|
||||
# 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_vision
|
||||
@require_flax
|
||||
class FlaxViT2GPT2ModelIntegrationTest(unittest.TestCase):
|
||||
@slow
|
||||
def test_inference_coco_en(self):
|
||||
|
||||
loc = "ydshieh/vit-gpt2-coco-en"
|
||||
|
||||
feature_extractor = ViTFeatureExtractor.from_pretrained(loc)
|
||||
tokenizer = AutoTokenizer.from_pretrained(loc)
|
||||
model = FlaxVisionEncoderDecoderModel.from_pretrained(loc)
|
||||
|
||||
img = prepare_img()
|
||||
pixel_values = feature_extractor(images=img, return_tensors="np").pixel_values
|
||||
|
||||
decoder_input_ids = np.array([[model.config.decoder_start_token_id]])
|
||||
logits = model(pixel_values, decoder_input_ids)[0]
|
||||
logits = np.array(logits)
|
||||
|
||||
# verify the logits
|
||||
expected_shape = (1, 1, model.config.decoder.vocab_size)
|
||||
self.assertEqual(logits.shape, expected_shape)
|
||||
|
||||
EXPECTED_LOGIT_SLICE = np.array(
|
||||
[
|
||||
-38.705837,
|
||||
-30.639936,
|
||||
-31.41905,
|
||||
-39.01204,
|
||||
-38.38698,
|
||||
-34.887215,
|
||||
-33.29087,
|
||||
-35.684475,
|
||||
-38.50852,
|
||||
-36.124676,
|
||||
]
|
||||
)
|
||||
max_diff = np.amax(np.abs(logits[0, 0, :10] - EXPECTED_LOGIT_SLICE))
|
||||
self.assertLessEqual(max_diff, 1e-4)
|
||||
|
||||
def generate_step(pixel_values):
|
||||
|
||||
outputs = model.generate(pixel_values, max_length=16, num_beams=4)
|
||||
output_ids = outputs.sequences
|
||||
preds = tokenizer.batch_decode(output_ids, skip_special_tokens=True)
|
||||
preds = [pred.strip() for pred in preds]
|
||||
|
||||
return preds, outputs.scores
|
||||
|
||||
preds, scores = generate_step(pixel_values)
|
||||
|
||||
EXPECTED_SCORES = np.array([-0.59563464])
|
||||
scores = np.array(scores)
|
||||
max_diff = np.amax(np.abs(scores - EXPECTED_SCORES))
|
||||
self.assertLessEqual(max_diff, 1e-4)
|
||||
|
||||
# should produce
|
||||
# ["a cat laying on top of a couch next to another cat"]
|
||||
self.assertEqual(preds, ["a cat laying on top of a couch next to another cat"])
|
||||
@@ -0,0 +1,866 @@
|
||||
# coding=utf-8
|
||||
# Copyright 2022 HuggingFace Inc. team.
|
||||
#
|
||||
# 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 TensorFlow VisionEncoderDecoder model. """
|
||||
|
||||
|
||||
import copy
|
||||
import os
|
||||
import tempfile
|
||||
import unittest
|
||||
|
||||
import numpy as np
|
||||
|
||||
from transformers import is_tf_available, is_torch_available, is_vision_available
|
||||
from transformers.testing_utils import (
|
||||
is_pt_tf_cross_test,
|
||||
require_tf,
|
||||
require_torch,
|
||||
require_vision,
|
||||
slow,
|
||||
torch_device,
|
||||
)
|
||||
|
||||
from ...test_modeling_tf_common import floats_tensor, ids_tensor
|
||||
from ..gpt2.test_modeling_tf_gpt2 import TFGPT2ModelTester
|
||||
from ..vit.test_modeling_tf_vit import TFViTModelTester
|
||||
|
||||
|
||||
if is_tf_available():
|
||||
import tensorflow as tf
|
||||
|
||||
from transformers import (
|
||||
AutoConfig,
|
||||
AutoFeatureExtractor,
|
||||
AutoTokenizer,
|
||||
TFAutoModel,
|
||||
TFAutoModelForCausalLM,
|
||||
TFGPT2LMHeadModel,
|
||||
TFVisionEncoderDecoderModel,
|
||||
TFViTModel,
|
||||
VisionEncoderDecoderConfig,
|
||||
)
|
||||
from transformers.modeling_tf_outputs import TFBaseModelOutput
|
||||
|
||||
if is_torch_available():
|
||||
import torch
|
||||
|
||||
from transformers import GPT2LMHeadModel, VisionEncoderDecoderModel, ViTModel
|
||||
|
||||
if is_vision_available():
|
||||
from PIL import Image
|
||||
|
||||
from transformers import ViTFeatureExtractor
|
||||
|
||||
|
||||
@require_tf
|
||||
class TFVisionEncoderDecoderMixin:
|
||||
def get_encoder_decoder_model(self, config, decoder_config):
|
||||
raise NotImplementedError
|
||||
|
||||
def prepare_config_and_inputs(self):
|
||||
raise NotImplementedError
|
||||
|
||||
def get_pretrained_model(self):
|
||||
raise NotImplementedError
|
||||
|
||||
def check_encoder_decoder_model_from_pretrained_configs(
|
||||
self,
|
||||
config,
|
||||
pixel_values,
|
||||
encoder_hidden_states,
|
||||
decoder_config,
|
||||
decoder_input_ids,
|
||||
decoder_attention_mask,
|
||||
**kwargs
|
||||
):
|
||||
encoder_decoder_config = VisionEncoderDecoderConfig.from_encoder_decoder_configs(config, decoder_config)
|
||||
self.assertTrue(encoder_decoder_config.decoder.is_decoder)
|
||||
|
||||
enc_dec_model = TFVisionEncoderDecoderModel(encoder_decoder_config)
|
||||
|
||||
self.assertTrue(enc_dec_model.config.is_encoder_decoder)
|
||||
|
||||
outputs_encoder_decoder = enc_dec_model(
|
||||
pixel_values=pixel_values,
|
||||
decoder_input_ids=decoder_input_ids,
|
||||
decoder_attention_mask=decoder_attention_mask,
|
||||
kwargs=kwargs,
|
||||
)
|
||||
|
||||
self.assertEqual(
|
||||
outputs_encoder_decoder["logits"].shape, (decoder_input_ids.shape + (decoder_config.vocab_size,))
|
||||
)
|
||||
self.assertEqual(outputs_encoder_decoder["encoder_last_hidden_state"].shape[0], pixel_values.shape[0])
|
||||
self.assertEqual(outputs_encoder_decoder["encoder_last_hidden_state"].shape[-1], config.hidden_size)
|
||||
|
||||
def check_encoder_decoder_model(
|
||||
self,
|
||||
config,
|
||||
pixel_values,
|
||||
encoder_hidden_states,
|
||||
decoder_config,
|
||||
decoder_input_ids,
|
||||
decoder_attention_mask,
|
||||
**kwargs
|
||||
):
|
||||
encoder_model, decoder_model = self.get_encoder_decoder_model(config, decoder_config)
|
||||
enc_dec_model = TFVisionEncoderDecoderModel(encoder=encoder_model, decoder=decoder_model)
|
||||
self.assertTrue(enc_dec_model.config.decoder.is_decoder)
|
||||
self.assertTrue(enc_dec_model.config.decoder.add_cross_attention)
|
||||
self.assertTrue(enc_dec_model.config.is_encoder_decoder)
|
||||
|
||||
outputs_encoder_decoder = enc_dec_model(
|
||||
pixel_values=pixel_values,
|
||||
decoder_input_ids=decoder_input_ids,
|
||||
decoder_attention_mask=decoder_attention_mask,
|
||||
kwargs=kwargs,
|
||||
)
|
||||
self.assertEqual(
|
||||
outputs_encoder_decoder["logits"].shape, (decoder_input_ids.shape + (decoder_config.vocab_size,))
|
||||
)
|
||||
self.assertEqual(outputs_encoder_decoder["encoder_last_hidden_state"].shape[0], pixel_values.shape[0])
|
||||
self.assertEqual(outputs_encoder_decoder["encoder_last_hidden_state"].shape[-1], config.hidden_size)
|
||||
|
||||
encoder_outputs = TFBaseModelOutput(last_hidden_state=encoder_hidden_states)
|
||||
outputs_encoder_decoder = enc_dec_model(
|
||||
pixel_values=None,
|
||||
encoder_outputs=encoder_outputs,
|
||||
decoder_input_ids=decoder_input_ids,
|
||||
decoder_attention_mask=decoder_attention_mask,
|
||||
kwargs=kwargs,
|
||||
)
|
||||
|
||||
self.assertEqual(
|
||||
outputs_encoder_decoder["logits"].shape, (decoder_input_ids.shape + (decoder_config.vocab_size,))
|
||||
)
|
||||
self.assertEqual(outputs_encoder_decoder["encoder_last_hidden_state"].shape[0], pixel_values.shape[0])
|
||||
self.assertEqual(outputs_encoder_decoder["encoder_last_hidden_state"].shape[-1], config.hidden_size)
|
||||
|
||||
def check_encoder_decoder_model_from_pretrained(
|
||||
self,
|
||||
config,
|
||||
pixel_values,
|
||||
encoder_hidden_states,
|
||||
decoder_config,
|
||||
decoder_input_ids,
|
||||
decoder_attention_mask,
|
||||
return_dict,
|
||||
**kwargs
|
||||
):
|
||||
encoder_model, decoder_model = self.get_encoder_decoder_model(config, decoder_config)
|
||||
kwargs = {"encoder_model": encoder_model, "decoder_model": decoder_model, "return_dict": return_dict}
|
||||
enc_dec_model = TFVisionEncoderDecoderModel.from_encoder_decoder_pretrained(**kwargs)
|
||||
outputs_encoder_decoder = enc_dec_model(
|
||||
pixel_values=pixel_values,
|
||||
decoder_input_ids=decoder_input_ids,
|
||||
decoder_attention_mask=decoder_attention_mask,
|
||||
return_dict=True,
|
||||
kwargs=kwargs,
|
||||
)
|
||||
|
||||
self.assertEqual(
|
||||
outputs_encoder_decoder["logits"].shape, (decoder_input_ids.shape + (decoder_config.vocab_size,))
|
||||
)
|
||||
self.assertEqual(outputs_encoder_decoder["encoder_last_hidden_state"].shape[0], pixel_values.shape[0])
|
||||
self.assertEqual(outputs_encoder_decoder["encoder_last_hidden_state"].shape[-1], config.hidden_size)
|
||||
|
||||
def check_save_and_load(
|
||||
self,
|
||||
config,
|
||||
pixel_values,
|
||||
encoder_hidden_states,
|
||||
decoder_config,
|
||||
decoder_input_ids,
|
||||
decoder_attention_mask,
|
||||
**kwargs
|
||||
):
|
||||
encoder_model, decoder_model = self.get_encoder_decoder_model(config, decoder_config)
|
||||
enc_dec_model = TFVisionEncoderDecoderModel(encoder=encoder_model, decoder=decoder_model)
|
||||
|
||||
outputs = enc_dec_model(
|
||||
pixel_values=pixel_values,
|
||||
decoder_input_ids=decoder_input_ids,
|
||||
decoder_attention_mask=decoder_attention_mask,
|
||||
kwargs=kwargs,
|
||||
)
|
||||
out_2 = np.array(outputs[0])
|
||||
out_2[np.isnan(out_2)] = 0
|
||||
|
||||
with tempfile.TemporaryDirectory() as tmpdirname:
|
||||
enc_dec_model.save_pretrained(tmpdirname)
|
||||
enc_dec_model = TFVisionEncoderDecoderModel.from_pretrained(tmpdirname)
|
||||
|
||||
after_outputs = enc_dec_model(
|
||||
pixel_values=pixel_values,
|
||||
decoder_input_ids=decoder_input_ids,
|
||||
decoder_attention_mask=decoder_attention_mask,
|
||||
kwargs=kwargs,
|
||||
)
|
||||
out_1 = np.array(after_outputs[0])
|
||||
out_1[np.isnan(out_1)] = 0
|
||||
max_diff = np.amax(np.abs(out_1 - out_2))
|
||||
self.assertLessEqual(max_diff, 1e-5)
|
||||
|
||||
def check_encoder_decoder_model_labels(
|
||||
self,
|
||||
config,
|
||||
pixel_values,
|
||||
encoder_hidden_states,
|
||||
decoder_config,
|
||||
decoder_input_ids,
|
||||
decoder_attention_mask,
|
||||
labels,
|
||||
**kwargs
|
||||
):
|
||||
encoder_model, decoder_model = self.get_encoder_decoder_model(config, decoder_config)
|
||||
enc_dec_model = TFVisionEncoderDecoderModel(encoder=encoder_model, decoder=decoder_model)
|
||||
|
||||
outputs_encoder_decoder = enc_dec_model(
|
||||
pixel_values=pixel_values,
|
||||
decoder_input_ids=decoder_input_ids,
|
||||
decoder_attention_mask=decoder_attention_mask,
|
||||
labels=labels,
|
||||
kwargs=kwargs,
|
||||
)
|
||||
|
||||
# Make sure `loss` exist
|
||||
self.assertIn("loss", outputs_encoder_decoder)
|
||||
|
||||
batch_size, seq_len = decoder_input_ids.shape
|
||||
expected_shape = (batch_size, seq_len, decoder_config.vocab_size)
|
||||
self.assertEqual(outputs_encoder_decoder["logits"].shape, expected_shape)
|
||||
self.assertEqual(outputs_encoder_decoder["encoder_last_hidden_state"].shape[0], pixel_values.shape[0])
|
||||
self.assertEqual(outputs_encoder_decoder["encoder_last_hidden_state"].shape[-1], config.hidden_size)
|
||||
|
||||
def check_encoder_decoder_model_output_attentions(
|
||||
self,
|
||||
config,
|
||||
pixel_values,
|
||||
encoder_hidden_states,
|
||||
decoder_config,
|
||||
decoder_input_ids,
|
||||
decoder_attention_mask,
|
||||
**kwargs
|
||||
):
|
||||
# make the decoder inputs a different shape from the encoder inputs to harden the test
|
||||
decoder_input_ids = decoder_input_ids[:, :-1]
|
||||
decoder_attention_mask = decoder_attention_mask[:, :-1]
|
||||
encoder_model, decoder_model = self.get_encoder_decoder_model(config, decoder_config)
|
||||
enc_dec_model = TFVisionEncoderDecoderModel(encoder=encoder_model, decoder=decoder_model)
|
||||
outputs_encoder_decoder = enc_dec_model(
|
||||
pixel_values=pixel_values,
|
||||
decoder_input_ids=decoder_input_ids,
|
||||
decoder_attention_mask=decoder_attention_mask,
|
||||
output_attentions=True,
|
||||
kwargs=kwargs,
|
||||
)
|
||||
|
||||
encoder_attentions = outputs_encoder_decoder["encoder_attentions"]
|
||||
self.assertEqual(len(encoder_attentions), config.num_hidden_layers)
|
||||
|
||||
self.assertEqual(encoder_attentions[0].shape[-3:-2], (config.num_attention_heads,))
|
||||
|
||||
decoder_attentions = outputs_encoder_decoder["decoder_attentions"]
|
||||
num_decoder_layers = (
|
||||
decoder_config.num_decoder_layers
|
||||
if hasattr(decoder_config, "num_decoder_layers")
|
||||
else decoder_config.num_hidden_layers
|
||||
)
|
||||
self.assertEqual(len(decoder_attentions), num_decoder_layers)
|
||||
|
||||
self.assertEqual(
|
||||
decoder_attentions[0].shape[-3:],
|
||||
(decoder_config.num_attention_heads, decoder_input_ids.shape[-1], decoder_input_ids.shape[-1]),
|
||||
)
|
||||
|
||||
cross_attentions = outputs_encoder_decoder["cross_attentions"]
|
||||
self.assertEqual(len(cross_attentions), num_decoder_layers)
|
||||
|
||||
cross_attention_input_seq_len = decoder_input_ids.shape[-1] * (
|
||||
1 + (decoder_config.ngram if hasattr(decoder_config, "ngram") else 0)
|
||||
)
|
||||
self.assertEqual(
|
||||
cross_attentions[0].shape[-3:-1],
|
||||
(decoder_config.num_attention_heads, cross_attention_input_seq_len),
|
||||
)
|
||||
|
||||
def check_encoder_decoder_model_generate(self, pixel_values, config, decoder_config, **kwargs):
|
||||
encoder_model, decoder_model = self.get_encoder_decoder_model(config, decoder_config)
|
||||
enc_dec_model = TFVisionEncoderDecoderModel(encoder=encoder_model, decoder=decoder_model)
|
||||
|
||||
# Generate until max length
|
||||
if hasattr(enc_dec_model.config, "eos_token_id"):
|
||||
enc_dec_model.config.eos_token_id = None
|
||||
if hasattr(enc_dec_model.config, "decoder") and hasattr(enc_dec_model.config.decoder, "eos_token_id"):
|
||||
enc_dec_model.config.decoder.eos_token_id = None
|
||||
|
||||
# Bert does not have a bos token id, so use pad_token_id instead
|
||||
generated_output = enc_dec_model.generate(
|
||||
pixel_values, decoder_start_token_id=enc_dec_model.config.decoder.pad_token_id
|
||||
)
|
||||
self.assertEqual(
|
||||
tuple(generated_output.shape.as_list()), (pixel_values.shape[0],) + (decoder_config.max_length,)
|
||||
)
|
||||
|
||||
def check_pt_tf_equivalence(self, pt_model, tf_model, inputs_dict):
|
||||
|
||||
pt_model.to(torch_device)
|
||||
pt_model.eval()
|
||||
|
||||
# prepare inputs
|
||||
tf_inputs = inputs_dict
|
||||
pt_inputs = {k: torch.tensor(v.numpy()) for k, v in tf_inputs.items()}
|
||||
if "labels" in pt_inputs:
|
||||
pt_inputs["labels"] = pt_inputs["labels"].type(torch.LongTensor)
|
||||
|
||||
# send pytorch inputs to the correct device
|
||||
pt_inputs = {k: v.to(device=torch_device) if isinstance(v, torch.Tensor) else v for k, v in pt_inputs.items()}
|
||||
|
||||
with torch.no_grad():
|
||||
pt_outputs = pt_model(**pt_inputs).to_tuple()
|
||||
|
||||
tf_outputs = tf_model(**inputs_dict)
|
||||
if "loss" in tf_outputs:
|
||||
tf_outputs.loss = tf.math.reduce_mean(tf_outputs.loss)
|
||||
tf_outputs = tf_outputs.to_tuple()
|
||||
self.assertEqual(len(tf_outputs), len(pt_outputs), "Output lengths differ between TF and PyTorch")
|
||||
|
||||
for tf_output, pt_output in zip(tf_outputs, pt_outputs):
|
||||
self.assert_almost_equals(tf_output.numpy(), pt_output.detach().to("cpu").numpy(), 1e-3)
|
||||
|
||||
# PT -> TF
|
||||
with tempfile.TemporaryDirectory() as encoder_tmp_dirname, tempfile.TemporaryDirectory() as decoder_tmp_dirname:
|
||||
|
||||
pt_model.encoder.save_pretrained(encoder_tmp_dirname)
|
||||
pt_model.decoder.save_pretrained(decoder_tmp_dirname)
|
||||
tf_model_loaded = TFVisionEncoderDecoderModel.from_encoder_decoder_pretrained(
|
||||
encoder_tmp_dirname, decoder_tmp_dirname, encoder_from_pt=True, decoder_from_pt=True
|
||||
)
|
||||
# This is only for copying some specific attributes of this particular model.
|
||||
tf_model_loaded.config = pt_model.config
|
||||
|
||||
tf_outputs_loaded = tf_model_loaded(**inputs_dict)
|
||||
if "loss" in tf_outputs_loaded:
|
||||
tf_outputs_loaded.loss = tf.math.reduce_mean(tf_outputs_loaded.loss)
|
||||
tf_outputs_loaded = tf_outputs_loaded.to_tuple()
|
||||
self.assertEqual(len(tf_outputs_loaded), len(pt_outputs), "Output lengths differ between TF and PyTorch")
|
||||
|
||||
for tf_output_loaded, pt_output in zip(tf_outputs_loaded, pt_outputs):
|
||||
self.assert_almost_equals(tf_output_loaded.numpy(), pt_output.detach().to("cpu").numpy(), 1e-3)
|
||||
|
||||
def check_equivalence_pt_to_tf(self, config, decoder_config, inputs_dict):
|
||||
|
||||
encoder_decoder_config = VisionEncoderDecoderConfig.from_encoder_decoder_configs(config, decoder_config)
|
||||
|
||||
pt_model = VisionEncoderDecoderModel(encoder_decoder_config)
|
||||
|
||||
with tempfile.TemporaryDirectory() as encoder_tmp_dirname, tempfile.TemporaryDirectory() as decoder_tmp_dirname:
|
||||
|
||||
pt_model.encoder.save_pretrained(encoder_tmp_dirname)
|
||||
pt_model.decoder.save_pretrained(decoder_tmp_dirname)
|
||||
tf_model = TFVisionEncoderDecoderModel.from_encoder_decoder_pretrained(
|
||||
encoder_tmp_dirname, decoder_tmp_dirname, encoder_from_pt=True, decoder_from_pt=True
|
||||
)
|
||||
# This is only for copying some specific attributes of this particular model.
|
||||
tf_model.config = pt_model.config
|
||||
|
||||
self.check_pt_tf_equivalence(pt_model, tf_model, inputs_dict)
|
||||
|
||||
def check_equivalence_tf_to_pt(self, config, decoder_config, inputs_dict):
|
||||
|
||||
encoder_decoder_config = VisionEncoderDecoderConfig.from_encoder_decoder_configs(config, decoder_config)
|
||||
|
||||
# Using `_tf_model`, the test will fail, because the weights of `_tf_model` get extended before saving
|
||||
# the encoder/decoder models.
|
||||
# There was a (very) ugly potential fix, which wasn't integrated to `transformers`: see
|
||||
# https://github.com/huggingface/transformers/pull/13222/commits/dbb3c9de76eee235791d2064094654637c99f36d#r697304245
|
||||
# (the change in `src/transformers/modeling_tf_utils.py`)
|
||||
_tf_model = TFVisionEncoderDecoderModel(encoder_decoder_config)
|
||||
# Make sure model is built
|
||||
_tf_model(**inputs_dict)
|
||||
|
||||
# Using `tf_model` to pass the test.
|
||||
encoder = _tf_model.encoder.__class__(encoder_decoder_config.encoder)
|
||||
decoder = _tf_model.decoder.__class__(encoder_decoder_config.decoder)
|
||||
# Make sure models are built
|
||||
encoder(encoder.dummy_inputs)
|
||||
decoder(decoder.dummy_inputs)
|
||||
tf_model = TFVisionEncoderDecoderModel(encoder=encoder, decoder=decoder)
|
||||
|
||||
with tempfile.TemporaryDirectory() as encoder_tmp_dirname, tempfile.TemporaryDirectory() as decoder_tmp_dirname:
|
||||
|
||||
tf_model.encoder.save_pretrained(encoder_tmp_dirname)
|
||||
tf_model.decoder.save_pretrained(decoder_tmp_dirname)
|
||||
pt_model = VisionEncoderDecoderModel.from_encoder_decoder_pretrained(
|
||||
encoder_tmp_dirname, decoder_tmp_dirname, encoder_from_tf=True, decoder_from_tf=True
|
||||
)
|
||||
# This is only for copying some specific attributes of this particular model.
|
||||
pt_model.config = tf_model.config
|
||||
|
||||
self.check_pt_tf_equivalence(pt_model, tf_model, inputs_dict)
|
||||
|
||||
def test_encoder_decoder_model(self):
|
||||
config_inputs_dict = self.prepare_config_and_inputs()
|
||||
self.check_encoder_decoder_model(**config_inputs_dict)
|
||||
|
||||
def test_encoder_decoder_model_from_pretrained_configs(self):
|
||||
config_inputs_dict = self.prepare_config_and_inputs()
|
||||
self.check_encoder_decoder_model_from_pretrained_configs(**config_inputs_dict)
|
||||
|
||||
def test_encoder_decoder_model_from_pretrained(self):
|
||||
config_inputs_dict = self.prepare_config_and_inputs()
|
||||
self.check_encoder_decoder_model_from_pretrained(**config_inputs_dict, return_dict=False)
|
||||
|
||||
def test_encoder_decoder_model_from_pretrained_return_dict(self):
|
||||
config_inputs_dict = self.prepare_config_and_inputs()
|
||||
self.check_encoder_decoder_model_from_pretrained(**config_inputs_dict, return_dict=True)
|
||||
|
||||
def test_save_and_load_from_pretrained(self):
|
||||
config_inputs_dict = self.prepare_config_and_inputs()
|
||||
self.check_save_and_load(**config_inputs_dict)
|
||||
|
||||
def test_encoder_decoder_model_labels(self):
|
||||
config_inputs_dict = self.prepare_config_and_inputs()
|
||||
self.check_encoder_decoder_model_labels(**config_inputs_dict)
|
||||
|
||||
def test_encoder_decoder_model_output_attentions(self):
|
||||
config_inputs_dict = self.prepare_config_and_inputs()
|
||||
self.check_encoder_decoder_model_output_attentions(**config_inputs_dict)
|
||||
|
||||
def test_encoder_decoder_model_generate(self):
|
||||
config_inputs_dict = self.prepare_config_and_inputs()
|
||||
self.check_encoder_decoder_model_generate(**config_inputs_dict)
|
||||
|
||||
def assert_almost_equals(self, a: np.ndarray, b: np.ndarray, tol: float):
|
||||
diff = np.abs((a - b)).max()
|
||||
self.assertLessEqual(diff, tol, f"Difference between torch and tf is {diff} (>= {tol}).")
|
||||
|
||||
@is_pt_tf_cross_test
|
||||
def test_pt_tf_equivalence(self):
|
||||
|
||||
config_inputs_dict = self.prepare_config_and_inputs()
|
||||
labels = config_inputs_dict.pop("decoder_token_labels")
|
||||
|
||||
# Keep only common arguments
|
||||
arg_names = [
|
||||
"config",
|
||||
"pixel_values",
|
||||
"decoder_config",
|
||||
"decoder_input_ids",
|
||||
"decoder_attention_mask",
|
||||
"encoder_hidden_states",
|
||||
]
|
||||
config_inputs_dict = {k: v for k, v in config_inputs_dict.items() if k in arg_names}
|
||||
|
||||
config = config_inputs_dict.pop("config")
|
||||
decoder_config = config_inputs_dict.pop("decoder_config")
|
||||
|
||||
inputs_dict = config_inputs_dict
|
||||
# `encoder_hidden_states` is not used in model call/forward
|
||||
del inputs_dict["encoder_hidden_states"]
|
||||
|
||||
inputs_dict_with_labels = copy.copy(inputs_dict)
|
||||
inputs_dict_with_labels["labels"] = labels
|
||||
|
||||
# Avoid the case where a sequence has no place to attend (after combined with the causal attention mask)
|
||||
batch_size = inputs_dict["decoder_attention_mask"].shape[0]
|
||||
inputs_dict["decoder_attention_mask"] = tf.constant(
|
||||
np.concatenate([np.ones(shape=(batch_size, 1)), inputs_dict["decoder_attention_mask"][:, 1:]], axis=1)
|
||||
)
|
||||
|
||||
# TF models don't use the `use_cache` option and cache is not returned as a default.
|
||||
# So we disable `use_cache` here for PyTorch model.
|
||||
decoder_config.use_cache = False
|
||||
|
||||
self.assertTrue(decoder_config.cross_attention_hidden_size is None)
|
||||
|
||||
# check without `enc_to_dec_proj` projection
|
||||
self.assertTrue(config.hidden_size == decoder_config.hidden_size)
|
||||
self.check_equivalence_pt_to_tf(config, decoder_config, inputs_dict)
|
||||
self.check_equivalence_tf_to_pt(config, decoder_config, inputs_dict)
|
||||
|
||||
# check equivalence with labels
|
||||
self.check_equivalence_pt_to_tf(config, decoder_config, inputs_dict_with_labels)
|
||||
self.check_equivalence_tf_to_pt(config, decoder_config, inputs_dict_with_labels)
|
||||
|
||||
# This is not working, because pt/tf equivalence test for encoder-decoder use `from_encoder_decoder_pretrained`,
|
||||
# which randomly initialize `enc_to_dec_proj`.
|
||||
# # check `enc_to_dec_proj` work as expected
|
||||
# decoder_config.hidden_size = decoder_config.hidden_size * 2
|
||||
# self.assertTrue(config.hidden_size != decoder_config.hidden_size)
|
||||
# self.check_equivalence_pt_to_tf(config, decoder_config, inputs_dict)
|
||||
# self.check_equivalence_tf_to_pt(config, decoder_config, inputs_dict)
|
||||
|
||||
# Let's just check `enc_to_dec_proj` can run for now
|
||||
decoder_config.hidden_size = decoder_config.hidden_size * 2
|
||||
self.assertTrue(config.hidden_size != decoder_config.hidden_size)
|
||||
encoder_decoder_config = VisionEncoderDecoderConfig.from_encoder_decoder_configs(config, decoder_config)
|
||||
model = TFVisionEncoderDecoderModel(encoder_decoder_config)
|
||||
model(**inputs_dict)
|
||||
|
||||
@slow
|
||||
def test_real_model_save_load_from_pretrained(self):
|
||||
model_2 = self.get_pretrained_model()
|
||||
pixel_values = floats_tensor(
|
||||
[
|
||||
13,
|
||||
model_2.config.encoder.num_channels,
|
||||
model_2.config.encoder.image_size,
|
||||
model_2.config.encoder.image_size,
|
||||
]
|
||||
)
|
||||
decoder_input_ids = ids_tensor([13, 1], model_2.config.decoder.vocab_size)
|
||||
|
||||
outputs = model_2(
|
||||
pixel_values=pixel_values,
|
||||
decoder_input_ids=decoder_input_ids,
|
||||
)
|
||||
out_2 = np.array(outputs[0])
|
||||
out_2[np.isnan(out_2)] = 0
|
||||
|
||||
with tempfile.TemporaryDirectory() as tmp_dirname:
|
||||
model_2.save_pretrained(tmp_dirname)
|
||||
model_1 = TFVisionEncoderDecoderModel.from_pretrained(tmp_dirname)
|
||||
|
||||
after_outputs = model_1(pixel_values=pixel_values, decoder_input_ids=decoder_input_ids)
|
||||
out_1 = np.array(after_outputs[0])
|
||||
out_1[np.isnan(out_1)] = 0
|
||||
max_diff = np.amax(np.abs(out_1 - out_2))
|
||||
self.assertLessEqual(max_diff, 1e-5)
|
||||
|
||||
|
||||
@require_tf
|
||||
class TFViT2GPT2EncoderDecoderModelTest(TFVisionEncoderDecoderMixin, unittest.TestCase):
|
||||
def get_pretrained_model(self):
|
||||
return TFVisionEncoderDecoderModel.from_encoder_decoder_pretrained(
|
||||
"google/vit-base-patch16-224-in21k", "../gpt2"
|
||||
)
|
||||
|
||||
def get_encoder_decoder_model(self, config, decoder_config):
|
||||
encoder_model = TFViTModel(config, name="encoder")
|
||||
decoder_model = TFGPT2LMHeadModel(decoder_config, name="decoder")
|
||||
return encoder_model, decoder_model
|
||||
|
||||
def prepare_config_and_inputs(self):
|
||||
model_tester_encoder = TFViTModelTester(self, batch_size=13)
|
||||
model_tester_decoder = TFGPT2ModelTester(self)
|
||||
encoder_config_and_inputs = model_tester_encoder.prepare_config_and_inputs()
|
||||
decoder_config_and_inputs = model_tester_decoder.prepare_config_and_inputs_for_decoder()
|
||||
(config, pixel_values, labels) = encoder_config_and_inputs
|
||||
(
|
||||
decoder_config,
|
||||
decoder_input_ids,
|
||||
decoder_attention_mask,
|
||||
decoder_head_mask,
|
||||
decoder_token_type_ids,
|
||||
decoder_sequence_labels,
|
||||
decoder_token_labels,
|
||||
decoder_choice_labels,
|
||||
encoder_hidden_states,
|
||||
encoder_attention_mask,
|
||||
) = decoder_config_and_inputs
|
||||
|
||||
# make sure that cross attention layers are added
|
||||
decoder_config.add_cross_attention = True
|
||||
# disable cache for now
|
||||
decoder_config.use_cache = False
|
||||
return {
|
||||
"config": config,
|
||||
"pixel_values": pixel_values,
|
||||
"decoder_config": decoder_config,
|
||||
"decoder_input_ids": decoder_input_ids,
|
||||
"decoder_attention_mask": decoder_attention_mask,
|
||||
"decoder_token_labels": decoder_token_labels,
|
||||
"encoder_hidden_states": encoder_hidden_states, # This is not used in the tests.
|
||||
"labels": decoder_token_labels,
|
||||
}
|
||||
|
||||
|
||||
@require_tf
|
||||
class TFVisionEncoderDecoderModelTest(unittest.TestCase):
|
||||
def get_from_encoderdecoder_pretrained_model(self):
|
||||
return TFVisionEncoderDecoderModel.from_encoder_decoder_pretrained(
|
||||
"google/vit-base-patch16-224-in21k", "../gpt2"
|
||||
)
|
||||
|
||||
def get_decoder_config(self):
|
||||
config = AutoConfig.from_pretrained("../gpt2")
|
||||
config.is_decoder = True
|
||||
config.add_cross_attention = True
|
||||
return config
|
||||
|
||||
def get_encoderdecoder_model(self):
|
||||
return TFVisionEncoderDecoderModel.from_pretrained("ydshieh/vit-gpt2-coco-en")
|
||||
|
||||
def get_encoder_decoder_models(self):
|
||||
encoder_model = TFViTModel.from_pretrained("google/vit-base-patch16-224-in21k", name="encoder")
|
||||
decoder_model = TFGPT2LMHeadModel.from_pretrained("../gpt2", config=self.get_decoder_config(), name="decoder")
|
||||
return {"encoder": encoder_model, "decoder": decoder_model}
|
||||
|
||||
def _check_configuration_tie(self, model):
|
||||
assert id(model.decoder.config) == id(model.config.decoder)
|
||||
assert id(model.encoder.config) == id(model.config.encoder)
|
||||
|
||||
@slow
|
||||
def test_configuration_tie(self):
|
||||
model = self.get_from_encoderdecoder_pretrained_model()
|
||||
self._check_configuration_tie(model)
|
||||
|
||||
model = TFVisionEncoderDecoderModel(**self.get_encoder_decoder_models())
|
||||
self._check_configuration_tie(model)
|
||||
|
||||
model = self.get_encoderdecoder_model()
|
||||
self._check_configuration_tie(model)
|
||||
|
||||
|
||||
# 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_tf
|
||||
class TFVisionEncoderDecoderModelSaveLoadTests(unittest.TestCase):
|
||||
def get_encoder_decoder_config(self):
|
||||
encoder_config = AutoConfig.from_pretrained("google/vit-base-patch16-224-in21k")
|
||||
decoder_config = AutoConfig.from_pretrained("../gpt2", is_decoder=True, add_cross_attention=True)
|
||||
return VisionEncoderDecoderConfig.from_encoder_decoder_configs(encoder_config, decoder_config)
|
||||
|
||||
def get_encoder_decoder_config_small(self):
|
||||
encoder_config = AutoConfig.from_pretrained("hf-internal-testing/tiny-random-vit")
|
||||
decoder_config = AutoConfig.from_pretrained(
|
||||
"hf-internal-testing/tiny-random-gpt2", is_decoder=True, add_cross_attention=True
|
||||
)
|
||||
return VisionEncoderDecoderConfig.from_encoder_decoder_configs(encoder_config, decoder_config)
|
||||
|
||||
def test_encoder_decoder_save_load_from_encoder_decoder(self):
|
||||
config = self.get_encoder_decoder_config_small()
|
||||
|
||||
# create two random ViT/GPT2 models for vit-gpt2 & initialize weights (+cross_attention weights)
|
||||
encoder = TFViTModel(config.encoder)
|
||||
encoder(encoder.dummy_inputs)
|
||||
decoder = TFGPT2LMHeadModel(config.decoder)
|
||||
decoder(decoder.dummy_inputs)
|
||||
|
||||
encoder_decoder_orig = TFVisionEncoderDecoderModel(encoder=encoder, decoder=decoder)
|
||||
|
||||
pixel_values = floats_tensor(
|
||||
[
|
||||
13,
|
||||
encoder.config.num_channels,
|
||||
encoder.config.image_size,
|
||||
encoder.config.image_size,
|
||||
]
|
||||
)
|
||||
decoder_input_ids = ids_tensor([13, 1], decoder.config.vocab_size)
|
||||
|
||||
logits_orig = encoder_decoder_orig(pixel_values=pixel_values, decoder_input_ids=decoder_input_ids).logits
|
||||
|
||||
with tempfile.TemporaryDirectory() as tmp_dirname:
|
||||
encoder_path = os.path.join(tmp_dirname, "encoder")
|
||||
decoder_path = os.path.join(tmp_dirname, "decoder")
|
||||
|
||||
encoder.save_pretrained(encoder_path)
|
||||
decoder.save_pretrained(decoder_path)
|
||||
|
||||
encoder_decoder = TFVisionEncoderDecoderModel.from_encoder_decoder_pretrained(encoder_path, decoder_path)
|
||||
|
||||
logits_1 = encoder_decoder(pixel_values=pixel_values, decoder_input_ids=decoder_input_ids).logits
|
||||
|
||||
self.assertTrue(logits_orig.numpy().sum() - logits_1.numpy().sum() < 1e-3)
|
||||
|
||||
max_diff = np.max(np.abs(logits_1.numpy() - logits_orig.numpy()))
|
||||
self.assertAlmostEqual(max_diff, 0.0, places=4)
|
||||
|
||||
with tempfile.TemporaryDirectory() as tmp_dirname:
|
||||
encoder_decoder.save_pretrained(tmp_dirname)
|
||||
encoder_decoder = TFVisionEncoderDecoderModel.from_pretrained(tmp_dirname)
|
||||
|
||||
logits_2 = encoder_decoder(pixel_values=pixel_values, decoder_input_ids=decoder_input_ids).logits
|
||||
|
||||
max_diff = np.max(np.abs(logits_2.numpy() - logits_orig.numpy()))
|
||||
self.assertAlmostEqual(max_diff, 0.0, places=4)
|
||||
|
||||
@require_torch
|
||||
@is_pt_tf_cross_test
|
||||
def test_encoder_decoder_save_load_from_encoder_decoder_from_pt(self):
|
||||
config = self.get_encoder_decoder_config_small()
|
||||
|
||||
# create two random ViT/GPT2 models for vit-gpt2 & initialize weights (+cross_attention weights)
|
||||
encoder_pt = ViTModel(config.encoder).to(torch_device).eval()
|
||||
decoder_pt = GPT2LMHeadModel(config.decoder).to(torch_device).eval()
|
||||
|
||||
encoder_decoder_pt = VisionEncoderDecoderModel(encoder=encoder_pt, decoder=decoder_pt).to(torch_device).eval()
|
||||
|
||||
pixel_values = floats_tensor(
|
||||
[
|
||||
13,
|
||||
encoder_pt.config.num_channels,
|
||||
encoder_pt.config.image_size,
|
||||
encoder_pt.config.image_size,
|
||||
]
|
||||
)
|
||||
decoder_input_ids = ids_tensor([13, 1], decoder_pt.config.vocab_size)
|
||||
|
||||
pt_pixel_values = torch.tensor(pixel_values.numpy(), device=torch_device, dtype=torch.float)
|
||||
pt_decoder_input_ids = torch.tensor(decoder_input_ids.numpy(), device=torch_device, dtype=torch.long)
|
||||
|
||||
logits_pt = encoder_decoder_pt(pixel_values=pt_pixel_values, decoder_input_ids=pt_decoder_input_ids).logits
|
||||
|
||||
# PyTorch => TensorFlow
|
||||
with tempfile.TemporaryDirectory() as tmp_dirname_1, tempfile.TemporaryDirectory() as tmp_dirname_2:
|
||||
encoder_decoder_pt.encoder.save_pretrained(tmp_dirname_1)
|
||||
encoder_decoder_pt.decoder.save_pretrained(tmp_dirname_2)
|
||||
encoder_decoder_tf = TFVisionEncoderDecoderModel.from_encoder_decoder_pretrained(
|
||||
tmp_dirname_1, tmp_dirname_2, encoder_from_pt=True, decoder_from_pt=True
|
||||
)
|
||||
|
||||
logits_tf = encoder_decoder_tf(pixel_values=pixel_values, decoder_input_ids=decoder_input_ids).logits
|
||||
|
||||
max_diff = np.max(np.abs(logits_pt.detach().cpu().numpy() - logits_tf.numpy()))
|
||||
self.assertAlmostEqual(max_diff, 0.0, places=3)
|
||||
|
||||
# Make sure `from_pretrained` following `save_pretrained` work and give the same result
|
||||
# (See https://github.com/huggingface/transformers/pull/14016)
|
||||
with tempfile.TemporaryDirectory() as tmp_dirname:
|
||||
encoder_decoder_tf.save_pretrained(tmp_dirname)
|
||||
encoder_decoder_tf = TFVisionEncoderDecoderModel.from_pretrained(tmp_dirname)
|
||||
|
||||
logits_tf_2 = encoder_decoder_tf(pixel_values=pixel_values, decoder_input_ids=decoder_input_ids).logits
|
||||
|
||||
max_diff = np.max(np.abs(logits_tf_2.numpy() - logits_tf.numpy()))
|
||||
self.assertAlmostEqual(max_diff, 0.0, places=3)
|
||||
|
||||
@require_vision
|
||||
@slow
|
||||
def test_encoder_decoder_from_pretrained(self):
|
||||
load_weight_prefix = TFVisionEncoderDecoderModel.load_weight_prefix
|
||||
|
||||
config = self.get_encoder_decoder_config()
|
||||
feature_extractor = AutoFeatureExtractor.from_pretrained("google/vit-base-patch16-224-in21k")
|
||||
decoder_tokenizer = AutoTokenizer.from_pretrained("../gpt2")
|
||||
|
||||
img = prepare_img()
|
||||
pixel_values = feature_extractor(images=img, return_tensors="tf").pixel_values
|
||||
decoder_input_ids = decoder_tokenizer("Linda Davis", return_tensors="tf").input_ids
|
||||
|
||||
with tempfile.TemporaryDirectory() as tmp_dirname:
|
||||
|
||||
# Since most of HF's models don't have pretrained cross-attention layers, they are randomly
|
||||
# initialized even if we create models using `from_pretrained` method.
|
||||
# For the tests, the decoder need to be a model with pretrained cross-attention layers.
|
||||
# So we create pretrained models (without `load_weight_prefix`), save them, and later,
|
||||
# we load them using `from_pretrained`.
|
||||
# (we don't need to do this for encoder, but let's make the code more similar between encoder/decoder)
|
||||
encoder = TFAutoModel.from_pretrained("google/vit-base-patch16-224-in21k", name="encoder")
|
||||
# It's necessary to specify `add_cross_attention=True` here.
|
||||
decoder = TFAutoModelForCausalLM.from_pretrained(
|
||||
"../gpt2", is_decoder=True, add_cross_attention=True, name="decoder"
|
||||
)
|
||||
pretrained_encoder_dir = os.path.join(tmp_dirname, "pretrained_encoder")
|
||||
pretrained_decoder_dir = os.path.join(tmp_dirname, "pretrained_decoder")
|
||||
encoder.save_pretrained(pretrained_encoder_dir)
|
||||
decoder.save_pretrained(pretrained_decoder_dir)
|
||||
del encoder
|
||||
del decoder
|
||||
|
||||
enc_dec_model = TFVisionEncoderDecoderModel.from_encoder_decoder_pretrained(
|
||||
pretrained_encoder_dir,
|
||||
pretrained_decoder_dir,
|
||||
)
|
||||
# check that the from pretrained methods work
|
||||
enc_dec_model.save_pretrained(tmp_dirname)
|
||||
enc_dec_model = TFVisionEncoderDecoderModel.from_pretrained(tmp_dirname)
|
||||
|
||||
output = enc_dec_model(pixel_values, decoder_input_ids=decoder_input_ids, labels=decoder_input_ids)
|
||||
|
||||
loss_pretrained = output.loss
|
||||
del enc_dec_model
|
||||
|
||||
# Create the model using `__init__` with loaded ``pretrained`` encoder / decoder
|
||||
encoder = TFAutoModel.from_pretrained(
|
||||
pretrained_encoder_dir, load_weight_prefix=load_weight_prefix, name="encoder"
|
||||
)
|
||||
decoder = TFAutoModelForCausalLM.from_pretrained(
|
||||
pretrained_decoder_dir, load_weight_prefix=load_weight_prefix, name="decoder"
|
||||
)
|
||||
enc_dec_model = TFVisionEncoderDecoderModel(config=config, encoder=encoder, decoder=decoder)
|
||||
|
||||
output = enc_dec_model(pixel_values, decoder_input_ids=decoder_input_ids, labels=decoder_input_ids)
|
||||
|
||||
loss_init = output.loss
|
||||
|
||||
max_diff = np.max(np.abs(loss_pretrained - loss_init))
|
||||
expected_diff = 0.0
|
||||
|
||||
self.assertAlmostEqual(max_diff, expected_diff, places=4)
|
||||
|
||||
|
||||
@require_vision
|
||||
@require_tf
|
||||
class TFViT2GPT2ModelIntegrationTest(unittest.TestCase):
|
||||
@slow
|
||||
def test_inference_coco_en(self):
|
||||
|
||||
loc = "ydshieh/vit-gpt2-coco-en"
|
||||
|
||||
feature_extractor = ViTFeatureExtractor.from_pretrained(loc)
|
||||
tokenizer = AutoTokenizer.from_pretrained(loc)
|
||||
model = TFVisionEncoderDecoderModel.from_pretrained(loc)
|
||||
|
||||
# We will verify our results on an image of cute cats
|
||||
img = Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png")
|
||||
pixel_values = feature_extractor(images=img, return_tensors="tf").pixel_values
|
||||
|
||||
decoder_input_ids = tf.constant([[model.config.decoder_start_token_id]])
|
||||
|
||||
logits = model(pixel_values, decoder_input_ids)[0].numpy()
|
||||
|
||||
# verify the logits
|
||||
expected_shape = (1, 1, model.config.decoder.vocab_size)
|
||||
self.assertEqual(logits.shape, expected_shape)
|
||||
|
||||
EXPECTED_LOGIT_SLICE = np.array(
|
||||
[
|
||||
-38.705807,
|
||||
-30.639929,
|
||||
-31.41903,
|
||||
-39.012012,
|
||||
-38.38696,
|
||||
-34.887207,
|
||||
-33.290855,
|
||||
-35.68447,
|
||||
-38.508484,
|
||||
-36.124645,
|
||||
]
|
||||
)
|
||||
max_diff = np.amax(np.abs(logits[0, 0, :10] - EXPECTED_LOGIT_SLICE))
|
||||
self.assertLessEqual(max_diff, 1e-4)
|
||||
|
||||
def generate_step(pixel_values):
|
||||
outputs = model.generate(
|
||||
pixel_values, max_length=16, num_beams=4, return_dict_in_generate=True, output_scores=True
|
||||
)
|
||||
output_ids = outputs.sequences
|
||||
preds = tokenizer.batch_decode(output_ids, skip_special_tokens=True)
|
||||
preds = [pred.strip() for pred in preds]
|
||||
|
||||
return preds, outputs.scores.numpy()
|
||||
|
||||
preds, scores = generate_step(pixel_values)
|
||||
|
||||
# should produce
|
||||
# ["a cat laying on top of a couch next to another cat"]
|
||||
self.assertEqual(preds, ["a cat laying on top of a couch next to another cat"])
|
||||
@@ -0,0 +1,777 @@
|
||||
# coding=utf-8
|
||||
# Copyright 2021 HuggingFace Inc. team.
|
||||
#
|
||||
# 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 tempfile
|
||||
import unittest
|
||||
|
||||
from datasets import load_dataset
|
||||
from packaging import version
|
||||
|
||||
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
|
||||
from transformers.utils import cached_property, is_torch_available, is_vision_available
|
||||
|
||||
from ...test_modeling_common import floats_tensor, ids_tensor, random_attention_mask
|
||||
from ..bart.test_modeling_bart import BartModelTester
|
||||
from ..bert.test_modeling_bert import BertModelTester
|
||||
from ..deit.test_modeling_deit import DeiTModelTester
|
||||
from ..swin.test_modeling_swin import SwinModelTester
|
||||
from ..trocr.test_modeling_trocr import TrOCRStandaloneDecoderModelTester
|
||||
from ..vit.test_modeling_vit import ViTModelTester
|
||||
|
||||
|
||||
if is_torch_available():
|
||||
import numpy as np
|
||||
import torch
|
||||
|
||||
from transformers import (
|
||||
AutoTokenizer,
|
||||
BartForCausalLM,
|
||||
BertLMHeadModel,
|
||||
DeiTModel,
|
||||
SwinModel,
|
||||
TrOCRForCausalLM,
|
||||
VisionEncoderDecoderConfig,
|
||||
VisionEncoderDecoderModel,
|
||||
ViTModel,
|
||||
)
|
||||
from transformers.modeling_outputs import BaseModelOutput
|
||||
from transformers.models.vit.modeling_vit import to_2tuple
|
||||
|
||||
|
||||
if is_vision_available():
|
||||
import PIL
|
||||
from PIL import Image
|
||||
|
||||
from transformers import TrOCRProcessor, ViTFeatureExtractor
|
||||
|
||||
|
||||
@require_torch
|
||||
class EncoderDecoderMixin:
|
||||
def get_encoder_decoder_model(self, config, decoder_config):
|
||||
pass
|
||||
|
||||
def prepare_config_and_inputs(self):
|
||||
pass
|
||||
|
||||
def get_pretrained_model_and_inputs(self):
|
||||
pass
|
||||
|
||||
def check_encoder_decoder_model_from_pretrained_configs(
|
||||
self, config, decoder_config, decoder_input_ids, decoder_attention_mask, pixel_values=None, **kwargs
|
||||
):
|
||||
encoder_decoder_config = VisionEncoderDecoderConfig.from_encoder_decoder_configs(config, decoder_config)
|
||||
self.assertTrue(encoder_decoder_config.decoder.is_decoder)
|
||||
|
||||
enc_dec_model = VisionEncoderDecoderModel(encoder_decoder_config)
|
||||
enc_dec_model.to(torch_device)
|
||||
enc_dec_model.eval()
|
||||
|
||||
self.assertTrue(enc_dec_model.config.is_encoder_decoder)
|
||||
|
||||
outputs_encoder_decoder = enc_dec_model(
|
||||
pixel_values=pixel_values,
|
||||
decoder_input_ids=decoder_input_ids,
|
||||
decoder_attention_mask=decoder_attention_mask,
|
||||
)
|
||||
|
||||
self.assertEqual(
|
||||
outputs_encoder_decoder["logits"].shape, (decoder_input_ids.shape + (decoder_config.vocab_size,))
|
||||
)
|
||||
|
||||
def check_encoder_decoder_model(
|
||||
self, config, decoder_config, decoder_input_ids, decoder_attention_mask, pixel_values=None, **kwargs
|
||||
):
|
||||
encoder_model, decoder_model = self.get_encoder_decoder_model(config, decoder_config)
|
||||
enc_dec_model = VisionEncoderDecoderModel(encoder=encoder_model, decoder=decoder_model)
|
||||
self.assertTrue(enc_dec_model.config.decoder.is_decoder)
|
||||
self.assertTrue(enc_dec_model.config.decoder.add_cross_attention)
|
||||
self.assertTrue(enc_dec_model.config.is_encoder_decoder)
|
||||
enc_dec_model.to(torch_device)
|
||||
outputs_encoder_decoder = enc_dec_model(
|
||||
pixel_values=pixel_values,
|
||||
decoder_input_ids=decoder_input_ids,
|
||||
decoder_attention_mask=decoder_attention_mask,
|
||||
output_hidden_states=True,
|
||||
)
|
||||
self.assertEqual(
|
||||
outputs_encoder_decoder["logits"].shape, (decoder_input_ids.shape + (decoder_config.vocab_size,))
|
||||
)
|
||||
encoder_outputs = BaseModelOutput(last_hidden_state=outputs_encoder_decoder.encoder_hidden_states[-1])
|
||||
outputs_encoder_decoder = enc_dec_model(
|
||||
encoder_outputs=encoder_outputs,
|
||||
decoder_input_ids=decoder_input_ids,
|
||||
decoder_attention_mask=decoder_attention_mask,
|
||||
)
|
||||
|
||||
self.assertEqual(
|
||||
outputs_encoder_decoder["logits"].shape, (decoder_input_ids.shape + (decoder_config.vocab_size,))
|
||||
)
|
||||
|
||||
def check_encoder_decoder_model_from_pretrained(
|
||||
self,
|
||||
config,
|
||||
decoder_config,
|
||||
decoder_input_ids,
|
||||
decoder_attention_mask,
|
||||
return_dict,
|
||||
pixel_values=None,
|
||||
**kwargs
|
||||
):
|
||||
encoder_model, decoder_model = self.get_encoder_decoder_model(config, decoder_config)
|
||||
kwargs = {"encoder_model": encoder_model, "decoder_model": decoder_model, "return_dict": return_dict}
|
||||
enc_dec_model = VisionEncoderDecoderModel.from_encoder_decoder_pretrained(**kwargs)
|
||||
enc_dec_model.to(torch_device)
|
||||
outputs_encoder_decoder = enc_dec_model(
|
||||
pixel_values=pixel_values,
|
||||
decoder_input_ids=decoder_input_ids,
|
||||
decoder_attention_mask=decoder_attention_mask,
|
||||
output_hidden_states=True,
|
||||
return_dict=True,
|
||||
)
|
||||
|
||||
self.assertEqual(
|
||||
outputs_encoder_decoder["logits"].shape, (decoder_input_ids.shape + (decoder_config.vocab_size,))
|
||||
)
|
||||
|
||||
def check_save_and_load(
|
||||
self, config, decoder_config, decoder_input_ids, decoder_attention_mask, pixel_values=None, **kwargs
|
||||
):
|
||||
encoder_model, decoder_model = self.get_encoder_decoder_model(config, decoder_config)
|
||||
enc_dec_model = VisionEncoderDecoderModel(encoder=encoder_model, decoder=decoder_model)
|
||||
enc_dec_model.to(torch_device)
|
||||
enc_dec_model.eval()
|
||||
with torch.no_grad():
|
||||
outputs = enc_dec_model(
|
||||
pixel_values=pixel_values,
|
||||
decoder_input_ids=decoder_input_ids,
|
||||
decoder_attention_mask=decoder_attention_mask,
|
||||
)
|
||||
out_2 = outputs[0].cpu().numpy()
|
||||
out_2[np.isnan(out_2)] = 0
|
||||
|
||||
with tempfile.TemporaryDirectory() as tmpdirname:
|
||||
enc_dec_model.save_pretrained(tmpdirname)
|
||||
enc_dec_model = VisionEncoderDecoderModel.from_pretrained(tmpdirname)
|
||||
enc_dec_model.to(torch_device)
|
||||
|
||||
after_outputs = enc_dec_model(
|
||||
pixel_values=pixel_values,
|
||||
decoder_input_ids=decoder_input_ids,
|
||||
decoder_attention_mask=decoder_attention_mask,
|
||||
)
|
||||
out_1 = after_outputs[0].cpu().numpy()
|
||||
out_1[np.isnan(out_1)] = 0
|
||||
max_diff = np.amax(np.abs(out_1 - out_2))
|
||||
self.assertLessEqual(max_diff, 1e-5)
|
||||
|
||||
def check_save_and_load_encoder_decoder_model(
|
||||
self, config, decoder_config, decoder_input_ids, decoder_attention_mask, pixel_values=None, **kwargs
|
||||
):
|
||||
encoder_model, decoder_model = self.get_encoder_decoder_model(config, decoder_config)
|
||||
enc_dec_model = VisionEncoderDecoderModel(encoder=encoder_model, decoder=decoder_model)
|
||||
enc_dec_model.to(torch_device)
|
||||
enc_dec_model.eval()
|
||||
with torch.no_grad():
|
||||
outputs = enc_dec_model(
|
||||
pixel_values=pixel_values,
|
||||
decoder_input_ids=decoder_input_ids,
|
||||
decoder_attention_mask=decoder_attention_mask,
|
||||
)
|
||||
out_2 = outputs[0].cpu().numpy()
|
||||
out_2[np.isnan(out_2)] = 0
|
||||
|
||||
with tempfile.TemporaryDirectory() as encoder_tmp_dirname, tempfile.TemporaryDirectory() as decoder_tmp_dirname:
|
||||
enc_dec_model.encoder.save_pretrained(encoder_tmp_dirname)
|
||||
enc_dec_model.decoder.save_pretrained(decoder_tmp_dirname)
|
||||
VisionEncoderDecoderModel.from_encoder_decoder_pretrained(
|
||||
encoder_pretrained_model_name_or_path=encoder_tmp_dirname,
|
||||
decoder_pretrained_model_name_or_path=decoder_tmp_dirname,
|
||||
)
|
||||
|
||||
after_outputs = enc_dec_model(
|
||||
pixel_values=pixel_values,
|
||||
decoder_input_ids=decoder_input_ids,
|
||||
decoder_attention_mask=decoder_attention_mask,
|
||||
)
|
||||
out_1 = after_outputs[0].cpu().numpy()
|
||||
out_1[np.isnan(out_1)] = 0
|
||||
max_diff = np.amax(np.abs(out_1 - out_2))
|
||||
self.assertLessEqual(max_diff, 1e-5)
|
||||
|
||||
def check_encoder_decoder_model_output_attentions(
|
||||
self,
|
||||
config,
|
||||
decoder_config,
|
||||
decoder_input_ids,
|
||||
decoder_attention_mask,
|
||||
labels=None,
|
||||
pixel_values=None,
|
||||
**kwargs
|
||||
):
|
||||
# make the decoder inputs a different shape from the encoder inputs to harden the test
|
||||
decoder_input_ids = decoder_input_ids[:, :-1]
|
||||
decoder_attention_mask = decoder_attention_mask[:, :-1]
|
||||
encoder_model, decoder_model = self.get_encoder_decoder_model(config, decoder_config)
|
||||
enc_dec_model = VisionEncoderDecoderModel(encoder=encoder_model, decoder=decoder_model)
|
||||
enc_dec_model.to(torch_device)
|
||||
outputs_encoder_decoder = enc_dec_model(
|
||||
pixel_values=pixel_values,
|
||||
decoder_input_ids=decoder_input_ids,
|
||||
decoder_attention_mask=decoder_attention_mask,
|
||||
output_attentions=True,
|
||||
)
|
||||
|
||||
encoder_attentions = outputs_encoder_decoder["encoder_attentions"]
|
||||
self.assertEqual(len(encoder_attentions), config.num_hidden_layers)
|
||||
|
||||
# in ViT, the seq_len equals the number of patches + 1 (we add 1 for the [CLS] token)
|
||||
image_size = to_2tuple(encoder_model.config.image_size)
|
||||
patch_size = to_2tuple(encoder_model.config.patch_size)
|
||||
num_patches = (image_size[1] // patch_size[1]) * (image_size[0] // patch_size[0])
|
||||
seq_len = num_patches + 1
|
||||
self.assertEqual(encoder_attentions[0].shape[-3:], (config.num_attention_heads, seq_len, seq_len))
|
||||
|
||||
decoder_attentions = outputs_encoder_decoder["decoder_attentions"]
|
||||
num_decoder_layers = (
|
||||
decoder_config.num_decoder_layers
|
||||
if hasattr(decoder_config, "num_decoder_layers")
|
||||
else decoder_config.num_hidden_layers
|
||||
)
|
||||
self.assertEqual(len(decoder_attentions), num_decoder_layers)
|
||||
|
||||
self.assertEqual(
|
||||
decoder_attentions[0].shape[-3:],
|
||||
(decoder_config.num_attention_heads, decoder_input_ids.shape[-1], decoder_input_ids.shape[-1]),
|
||||
)
|
||||
|
||||
cross_attentions = outputs_encoder_decoder["cross_attentions"]
|
||||
self.assertEqual(len(cross_attentions), num_decoder_layers)
|
||||
|
||||
cross_attention_input_seq_len = decoder_input_ids.shape[-1]
|
||||
self.assertEqual(
|
||||
cross_attentions[0].shape[-3:],
|
||||
(decoder_config.num_attention_heads, cross_attention_input_seq_len, seq_len),
|
||||
)
|
||||
|
||||
def check_encoder_decoder_model_generate(self, config, decoder_config, pixel_values=None, **kwargs):
|
||||
encoder_model, decoder_model = self.get_encoder_decoder_model(config, decoder_config)
|
||||
enc_dec_model = VisionEncoderDecoderModel(encoder=encoder_model, decoder=decoder_model)
|
||||
|
||||
# Generate until max length
|
||||
if hasattr(enc_dec_model.config, "eos_token_id"):
|
||||
enc_dec_model.config.eos_token_id = None
|
||||
if hasattr(enc_dec_model.config, "decoder") and hasattr(enc_dec_model.config.decoder, "eos_token_id"):
|
||||
enc_dec_model.config.decoder.eos_token_id = None
|
||||
enc_dec_model.to(torch_device)
|
||||
|
||||
inputs = pixel_values
|
||||
|
||||
# Bert does not have a bos token id, so use pad_token_id instead
|
||||
generated_output = enc_dec_model.generate(
|
||||
inputs, decoder_start_token_id=enc_dec_model.config.decoder.pad_token_id
|
||||
)
|
||||
self.assertEqual(generated_output.shape, (inputs.shape[0],) + (decoder_config.max_length,))
|
||||
|
||||
def test_encoder_decoder_model(self):
|
||||
input_ids_dict = self.prepare_config_and_inputs()
|
||||
self.check_encoder_decoder_model(**input_ids_dict)
|
||||
|
||||
def test_encoder_decoder_model_from_pretrained_configs(self):
|
||||
input_ids_dict = self.prepare_config_and_inputs()
|
||||
self.check_encoder_decoder_model_from_pretrained_configs(**input_ids_dict)
|
||||
|
||||
def test_encoder_decoder_model_from_pretrained(self):
|
||||
input_ids_dict = self.prepare_config_and_inputs()
|
||||
self.check_encoder_decoder_model_from_pretrained(**input_ids_dict, return_dict=False)
|
||||
|
||||
def test_encoder_decoder_model_from_pretrained_return_dict(self):
|
||||
input_ids_dict = self.prepare_config_and_inputs()
|
||||
self.check_encoder_decoder_model_from_pretrained(**input_ids_dict, return_dict=True)
|
||||
|
||||
def test_save_and_load_from_pretrained(self):
|
||||
input_ids_dict = self.prepare_config_and_inputs()
|
||||
self.check_save_and_load(**input_ids_dict)
|
||||
|
||||
def test_save_and_load_from_encoder_decoder_pretrained(self):
|
||||
input_ids_dict = self.prepare_config_and_inputs()
|
||||
self.check_save_and_load_encoder_decoder_model(**input_ids_dict)
|
||||
|
||||
def test_encoder_decoder_model_output_attentions(self):
|
||||
input_ids_dict = self.prepare_config_and_inputs()
|
||||
self.check_encoder_decoder_model_output_attentions(**input_ids_dict)
|
||||
|
||||
def test_encoder_decoder_model_generate(self):
|
||||
input_ids_dict = self.prepare_config_and_inputs()
|
||||
self.check_encoder_decoder_model_generate(**input_ids_dict)
|
||||
|
||||
@slow
|
||||
def test_real_model_save_load_from_pretrained(self):
|
||||
model_2, inputs = self.get_pretrained_model_and_inputs()
|
||||
model_2.to(torch_device)
|
||||
|
||||
with torch.no_grad():
|
||||
outputs = model_2(**inputs)
|
||||
out_2 = outputs[0].cpu().numpy()
|
||||
out_2[np.isnan(out_2)] = 0
|
||||
|
||||
with tempfile.TemporaryDirectory() as tmp_dirname:
|
||||
model_2.save_pretrained(tmp_dirname)
|
||||
model_1 = VisionEncoderDecoderModel.from_pretrained(tmp_dirname)
|
||||
model_1.to(torch_device)
|
||||
|
||||
after_outputs = model_1(**inputs)
|
||||
out_1 = after_outputs[0].cpu().numpy()
|
||||
out_1[np.isnan(out_1)] = 0
|
||||
max_diff = np.amax(np.abs(out_1 - out_2))
|
||||
self.assertLessEqual(max_diff, 1e-5)
|
||||
|
||||
|
||||
@require_torch
|
||||
class DeiT2RobertaModelTest(EncoderDecoderMixin, unittest.TestCase):
|
||||
def get_pretrained_model_and_inputs(self):
|
||||
model = VisionEncoderDecoderModel.from_encoder_decoder_pretrained(
|
||||
"hf-internal-testing/tiny-random-deit", "hf-internal-testing/tiny-random-roberta"
|
||||
)
|
||||
batch_size = 13
|
||||
pixel_values = floats_tensor(
|
||||
[
|
||||
batch_size,
|
||||
model.encoder.config.num_channels,
|
||||
model.encoder.config.image_size,
|
||||
model.encoder.config.image_size,
|
||||
]
|
||||
)
|
||||
# for DEiT, the sequence length is equal to the number of patches + 2 (for the [CLS] and distillation tokens)
|
||||
decoder_input_ids = ids_tensor([batch_size, 4], model.decoder.config.vocab_size)
|
||||
decoder_attention_mask = random_attention_mask([batch_size, 4])
|
||||
inputs = {
|
||||
"pixel_values": pixel_values,
|
||||
"decoder_input_ids": decoder_input_ids,
|
||||
"decoder_attention_mask": decoder_attention_mask,
|
||||
}
|
||||
|
||||
return model, inputs
|
||||
|
||||
def check_encoder_decoder_model_output_attentions(
|
||||
self,
|
||||
config,
|
||||
decoder_config,
|
||||
decoder_input_ids,
|
||||
decoder_attention_mask,
|
||||
labels=None,
|
||||
pixel_values=None,
|
||||
**kwargs
|
||||
):
|
||||
# make the decoder inputs a different shape from the encoder inputs to harden the test
|
||||
decoder_input_ids = decoder_input_ids[:, :-1]
|
||||
decoder_attention_mask = decoder_attention_mask[:, :-1]
|
||||
encoder_model, decoder_model = self.get_encoder_decoder_model(config, decoder_config)
|
||||
enc_dec_model = VisionEncoderDecoderModel(encoder=encoder_model, decoder=decoder_model)
|
||||
enc_dec_model.to(torch_device)
|
||||
outputs_encoder_decoder = enc_dec_model(
|
||||
pixel_values=pixel_values,
|
||||
decoder_input_ids=decoder_input_ids,
|
||||
decoder_attention_mask=decoder_attention_mask,
|
||||
output_attentions=True,
|
||||
)
|
||||
|
||||
encoder_attentions = outputs_encoder_decoder["encoder_attentions"]
|
||||
self.assertEqual(len(encoder_attentions), config.num_hidden_layers)
|
||||
|
||||
# in DEiT, the seq_len equals the number of patches + 2 (we add 2 for the [CLS] and distillation tokens)
|
||||
image_size = to_2tuple(encoder_model.config.image_size)
|
||||
patch_size = to_2tuple(encoder_model.config.patch_size)
|
||||
num_patches = (image_size[1] // patch_size[1]) * (image_size[0] // patch_size[0])
|
||||
seq_len = num_patches + 2
|
||||
self.assertEqual(encoder_attentions[0].shape[-3:], (config.num_attention_heads, seq_len, seq_len))
|
||||
|
||||
decoder_attentions = outputs_encoder_decoder["decoder_attentions"]
|
||||
num_decoder_layers = (
|
||||
decoder_config.num_decoder_layers
|
||||
if hasattr(decoder_config, "num_decoder_layers")
|
||||
else decoder_config.num_hidden_layers
|
||||
)
|
||||
self.assertEqual(len(decoder_attentions), num_decoder_layers)
|
||||
|
||||
self.assertEqual(
|
||||
decoder_attentions[0].shape[-3:],
|
||||
(decoder_config.num_attention_heads, decoder_input_ids.shape[-1], decoder_input_ids.shape[-1]),
|
||||
)
|
||||
|
||||
cross_attentions = outputs_encoder_decoder["cross_attentions"]
|
||||
self.assertEqual(len(cross_attentions), num_decoder_layers)
|
||||
|
||||
cross_attention_input_seq_len = decoder_input_ids.shape[-1]
|
||||
self.assertEqual(
|
||||
cross_attentions[0].shape[-3:],
|
||||
(decoder_config.num_attention_heads, cross_attention_input_seq_len, seq_len),
|
||||
)
|
||||
|
||||
def get_encoder_decoder_model(self, config, decoder_config):
|
||||
encoder_model = DeiTModel(config).eval()
|
||||
decoder_model = BertLMHeadModel(decoder_config).eval()
|
||||
return encoder_model, decoder_model
|
||||
|
||||
def prepare_config_and_inputs(self):
|
||||
bert_model_tester = BertModelTester(self)
|
||||
deit_model_tester = DeiTModelTester(self)
|
||||
encoder_config_and_inputs = deit_model_tester.prepare_config_and_inputs()
|
||||
decoder_config_and_inputs = bert_model_tester.prepare_config_and_inputs_for_decoder()
|
||||
config, pixel_values, _ = encoder_config_and_inputs
|
||||
(
|
||||
decoder_config,
|
||||
decoder_input_ids,
|
||||
decoder_token_type_ids,
|
||||
decoder_input_mask,
|
||||
decoder_sequence_labels,
|
||||
decoder_token_labels,
|
||||
decoder_choice_labels,
|
||||
encoder_attention_mask,
|
||||
_,
|
||||
) = decoder_config_and_inputs
|
||||
|
||||
# make sure that cross attention layers are added
|
||||
decoder_config.add_cross_attention = True
|
||||
return {
|
||||
"config": config,
|
||||
"pixel_values": pixel_values,
|
||||
"decoder_config": decoder_config,
|
||||
"decoder_input_ids": decoder_input_ids,
|
||||
"decoder_token_type_ids": decoder_token_type_ids,
|
||||
"decoder_attention_mask": decoder_input_mask,
|
||||
"decoder_sequence_labels": decoder_sequence_labels,
|
||||
"decoder_token_labels": decoder_token_labels,
|
||||
"decoder_choice_labels": decoder_choice_labels,
|
||||
"labels": decoder_token_labels,
|
||||
}
|
||||
|
||||
|
||||
@require_torch
|
||||
class ViT2BertModelTest(EncoderDecoderMixin, unittest.TestCase):
|
||||
def get_pretrained_model_and_inputs(self):
|
||||
model = VisionEncoderDecoderModel.from_encoder_decoder_pretrained(
|
||||
"hf-internal-testing/tiny-random-vit", "hf-internal-testing/tiny-bert"
|
||||
)
|
||||
batch_size = 13
|
||||
pixel_values = floats_tensor(
|
||||
[
|
||||
batch_size,
|
||||
model.encoder.config.num_channels,
|
||||
model.encoder.config.image_size,
|
||||
model.encoder.config.image_size,
|
||||
]
|
||||
)
|
||||
# for ViT, the sequence length is equal to the number of patches + 1 (for the [CLS] token)
|
||||
decoder_input_ids = ids_tensor([batch_size, 4], model.decoder.config.vocab_size)
|
||||
decoder_attention_mask = random_attention_mask([batch_size, 4])
|
||||
inputs = {
|
||||
"pixel_values": pixel_values,
|
||||
"decoder_input_ids": decoder_input_ids,
|
||||
"decoder_attention_mask": decoder_attention_mask,
|
||||
}
|
||||
|
||||
return model, inputs
|
||||
|
||||
def get_encoder_decoder_model(self, config, decoder_config):
|
||||
encoder_model = ViTModel(config).eval()
|
||||
decoder_model = BertLMHeadModel(decoder_config).eval()
|
||||
return encoder_model, decoder_model
|
||||
|
||||
def prepare_config_and_inputs(self):
|
||||
vit_model_tester = ViTModelTester(self)
|
||||
bert_model_tester = BertModelTester(self)
|
||||
encoder_config_and_inputs = vit_model_tester.prepare_config_and_inputs()
|
||||
decoder_config_and_inputs = bert_model_tester.prepare_config_and_inputs_for_decoder()
|
||||
|
||||
config, pixel_values, _ = encoder_config_and_inputs
|
||||
|
||||
(
|
||||
decoder_config,
|
||||
decoder_input_ids,
|
||||
decoder_token_type_ids,
|
||||
decoder_input_mask,
|
||||
decoder_sequence_labels,
|
||||
decoder_token_labels,
|
||||
decoder_choice_labels,
|
||||
encoder_attention_mask,
|
||||
_,
|
||||
) = decoder_config_and_inputs
|
||||
|
||||
# make sure that cross attention layers are added
|
||||
decoder_config.add_cross_attention = True
|
||||
return {
|
||||
"config": config,
|
||||
"pixel_values": pixel_values,
|
||||
"decoder_config": decoder_config,
|
||||
"decoder_input_ids": decoder_input_ids,
|
||||
"decoder_token_type_ids": decoder_token_type_ids,
|
||||
"decoder_attention_mask": decoder_input_mask,
|
||||
"decoder_sequence_labels": decoder_sequence_labels,
|
||||
"decoder_token_labels": decoder_token_labels,
|
||||
"decoder_choice_labels": decoder_choice_labels,
|
||||
"labels": decoder_token_labels,
|
||||
}
|
||||
|
||||
|
||||
@require_torch
|
||||
class Swin2BartModelTest(EncoderDecoderMixin, unittest.TestCase):
|
||||
def get_encoder_decoder_model(self, config, decoder_config):
|
||||
encoder_model = SwinModel(config).eval()
|
||||
decoder_model = BartForCausalLM(decoder_config).eval()
|
||||
return encoder_model, decoder_model
|
||||
|
||||
def prepare_config_and_inputs(self):
|
||||
model_tester_encoder = SwinModelTester(self, batch_size=13, embed_dim=32)
|
||||
model_tester_decoder = BartModelTester(self, batch_size=13, hidden_size=32, max_position_embeddings=512)
|
||||
encoder_config_and_inputs = model_tester_encoder.prepare_config_and_inputs()
|
||||
decoder_config_and_inputs = model_tester_decoder.prepare_config_and_inputs()
|
||||
config, pixel_values, _ = encoder_config_and_inputs
|
||||
decoder_config, decoder_inputs_dict = decoder_config_and_inputs
|
||||
|
||||
# make sure that cross attention layers are added
|
||||
decoder_config.add_cross_attention = True
|
||||
# disable cache for now
|
||||
decoder_config.use_cache = False
|
||||
return {
|
||||
"config": config,
|
||||
"pixel_values": pixel_values,
|
||||
"decoder_config": decoder_config,
|
||||
**decoder_inputs_dict,
|
||||
}
|
||||
|
||||
def check_encoder_decoder_model_output_attentions(
|
||||
self,
|
||||
config,
|
||||
decoder_config,
|
||||
decoder_input_ids,
|
||||
decoder_attention_mask,
|
||||
labels=None,
|
||||
pixel_values=None,
|
||||
**kwargs
|
||||
):
|
||||
# make the decoder inputs a different shape from the encoder inputs to harden the test
|
||||
decoder_input_ids = decoder_input_ids[:, :-1]
|
||||
decoder_attention_mask = decoder_attention_mask[:, :-1]
|
||||
encoder_model, decoder_model = self.get_encoder_decoder_model(config, decoder_config)
|
||||
enc_dec_model = VisionEncoderDecoderModel(encoder=encoder_model, decoder=decoder_model)
|
||||
enc_dec_model.to(torch_device)
|
||||
outputs_encoder_decoder = enc_dec_model(
|
||||
pixel_values=pixel_values,
|
||||
decoder_input_ids=decoder_input_ids,
|
||||
decoder_attention_mask=decoder_attention_mask,
|
||||
output_attentions=True,
|
||||
)
|
||||
|
||||
encoder_attentions = outputs_encoder_decoder["encoder_attentions"]
|
||||
self.assertEqual(len(encoder_attentions), config.num_hidden_layers)
|
||||
|
||||
# in Swin, the seq_len equals:
|
||||
seq_len = encoder_model.config.window_size**2
|
||||
self.assertEqual(encoder_attentions[0].shape[-3:], (config.num_attention_heads[0], seq_len, seq_len))
|
||||
|
||||
decoder_attentions = outputs_encoder_decoder["decoder_attentions"]
|
||||
num_decoder_layers = (
|
||||
decoder_config.num_decoder_layers
|
||||
if hasattr(decoder_config, "num_decoder_layers")
|
||||
else decoder_config.num_hidden_layers
|
||||
)
|
||||
self.assertEqual(len(decoder_attentions), num_decoder_layers)
|
||||
|
||||
self.assertEqual(
|
||||
decoder_attentions[0].shape[-3:],
|
||||
(decoder_config.num_attention_heads, decoder_input_ids.shape[-1], decoder_input_ids.shape[-1]),
|
||||
)
|
||||
|
||||
cross_attentions = outputs_encoder_decoder["cross_attentions"]
|
||||
self.assertEqual(len(cross_attentions), num_decoder_layers)
|
||||
|
||||
encoder_seq_len = ((config.image_size // config.patch_size) ** 2) // (4 ** (len(config.depths) - 1))
|
||||
cross_attention_input_seq_len = decoder_input_ids.shape[-1]
|
||||
self.assertEqual(
|
||||
cross_attentions[0].shape[-3:],
|
||||
(decoder_config.num_attention_heads, cross_attention_input_seq_len, encoder_seq_len),
|
||||
)
|
||||
|
||||
# there are no published pretrained BART-causal checkpoints for now
|
||||
def test_real_model_save_load_from_pretrained(self):
|
||||
pass
|
||||
|
||||
|
||||
@require_torch
|
||||
class ViT2TrOCR(EncoderDecoderMixin, unittest.TestCase):
|
||||
def get_encoder_decoder_model(self, config, decoder_config):
|
||||
encoder_model = ViTModel(config).eval()
|
||||
decoder_model = TrOCRForCausalLM(decoder_config).eval()
|
||||
return encoder_model, decoder_model
|
||||
|
||||
def prepare_config_and_inputs(self):
|
||||
model_tester_encoder = ViTModelTester(self, batch_size=13)
|
||||
model_tester_decoder = TrOCRStandaloneDecoderModelTester(
|
||||
self, batch_size=13, d_model=32, max_position_embeddings=512
|
||||
)
|
||||
encoder_config_and_inputs = model_tester_encoder.prepare_config_and_inputs()
|
||||
decoder_config_and_inputs = model_tester_decoder.prepare_config_and_inputs()
|
||||
config, pixel_values, _ = encoder_config_and_inputs
|
||||
(decoder_config, decoder_input_ids, decoder_attention_mask, _) = decoder_config_and_inputs
|
||||
|
||||
# make sure that cross attention layers are added
|
||||
decoder_config.add_cross_attention = True
|
||||
# disable cache for now
|
||||
decoder_config.use_cache = False
|
||||
return {
|
||||
"config": config,
|
||||
"pixel_values": pixel_values,
|
||||
"decoder_config": decoder_config,
|
||||
"decoder_input_ids": decoder_input_ids,
|
||||
"decoder_attention_mask": decoder_attention_mask,
|
||||
}
|
||||
|
||||
# there are no published pretrained TrOCR checkpoints for now
|
||||
def test_real_model_save_load_from_pretrained(self):
|
||||
pass
|
||||
|
||||
|
||||
@require_vision
|
||||
@require_torch
|
||||
class TrOCRModelIntegrationTest(unittest.TestCase):
|
||||
@cached_property
|
||||
def default_processor(self):
|
||||
return TrOCRProcessor.from_pretrained("microsoft/trocr-base-handwritten") if is_vision_available() else None
|
||||
|
||||
@slow
|
||||
def test_inference_handwritten(self):
|
||||
model = VisionEncoderDecoderModel.from_pretrained("microsoft/trocr-base-handwritten").to(torch_device)
|
||||
|
||||
ds = load_dataset("hf-internal-testing/fixtures_ocr", split="test")
|
||||
image = Image.open(ds[0]["file"]).convert("RGB")
|
||||
|
||||
processor = self.default_processor
|
||||
pixel_values = processor(images=image, return_tensors="pt").pixel_values.to(torch_device)
|
||||
|
||||
# forward pass
|
||||
decoder_input_ids = torch.tensor([[model.config.decoder.decoder_start_token_id]]).to(torch_device)
|
||||
outputs = model(pixel_values=pixel_values, decoder_input_ids=decoder_input_ids)
|
||||
logits = outputs.logits
|
||||
|
||||
# verify the logits
|
||||
expected_shape = torch.Size((1, 1, model.decoder.config.vocab_size))
|
||||
self.assertEqual(outputs.logits.shape, expected_shape)
|
||||
|
||||
expected_slice = torch.tensor(
|
||||
[-1.4502, -4.6683, -0.5347, -2.9291, 9.1435, -3.0571, 8.9764, 1.7560, 8.7358, -1.5311]
|
||||
).to(torch_device)
|
||||
|
||||
self.assertTrue(torch.allclose(logits[0, 0, :10], expected_slice, atol=1e-4))
|
||||
|
||||
@slow
|
||||
def test_inference_printed(self):
|
||||
model = VisionEncoderDecoderModel.from_pretrained("microsoft/trocr-base-printed").to(torch_device)
|
||||
|
||||
ds = load_dataset("hf-internal-testing/fixtures_ocr", split="test")
|
||||
image = Image.open(ds[1]["file"]).convert("RGB")
|
||||
|
||||
processor = self.default_processor
|
||||
pixel_values = processor(images=image, return_tensors="pt").pixel_values.to(torch_device)
|
||||
|
||||
# forward pass
|
||||
decoder_input_ids = torch.tensor([[model.config.decoder.decoder_start_token_id]]).to(torch_device)
|
||||
outputs = model(pixel_values=pixel_values, decoder_input_ids=decoder_input_ids)
|
||||
logits = outputs.logits
|
||||
|
||||
# verify the logits
|
||||
expected_shape = torch.Size((1, 1, model.decoder.config.vocab_size))
|
||||
self.assertEqual(outputs.logits.shape, expected_shape)
|
||||
|
||||
is_pillow_less_than_9 = version.parse(PIL.__version__) < version.parse("9.0.0")
|
||||
|
||||
if is_pillow_less_than_9:
|
||||
expected_slice = torch.tensor(
|
||||
[-5.6816, -5.8388, 1.1398, -6.9034, 6.8505, -2.4393, 1.2284, -1.0232, -1.9661, -3.9210],
|
||||
device=torch_device,
|
||||
)
|
||||
else:
|
||||
expected_slice = torch.tensor(
|
||||
[-5.6844, -5.8372, 1.1518, -6.8984, 6.8587, -2.4453, 1.2347, -1.0241, -1.9649, -3.9109],
|
||||
device=torch_device,
|
||||
)
|
||||
|
||||
self.assertTrue(torch.allclose(logits[0, 0, :10], expected_slice, atol=1e-4))
|
||||
|
||||
|
||||
@require_vision
|
||||
@require_torch
|
||||
class ViT2GPT2ModelIntegrationTest(unittest.TestCase):
|
||||
@slow
|
||||
def test_inference_coco_en(self):
|
||||
|
||||
loc = "ydshieh/vit-gpt2-coco-en"
|
||||
|
||||
feature_extractor = ViTFeatureExtractor.from_pretrained(loc)
|
||||
tokenizer = AutoTokenizer.from_pretrained(loc)
|
||||
model = VisionEncoderDecoderModel.from_pretrained(loc)
|
||||
model.to(torch_device)
|
||||
model.eval()
|
||||
|
||||
# We will verify our results on an image of cute cats
|
||||
img = Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png")
|
||||
pixel_values = feature_extractor(images=img, return_tensors="pt").pixel_values.to(torch_device)
|
||||
|
||||
decoder_input_ids = torch.tensor([[model.config.decoder_start_token_id]]).to(torch_device)
|
||||
|
||||
with torch.no_grad():
|
||||
logits = model(pixel_values, decoder_input_ids)[0].detach().cpu().numpy()
|
||||
|
||||
# verify the logits
|
||||
expected_shape = (1, 1, model.config.decoder.vocab_size)
|
||||
self.assertEqual(logits.shape, expected_shape)
|
||||
|
||||
EXPECTED_LOGIT_SLICE = np.array(
|
||||
[
|
||||
-38.705807,
|
||||
-30.639929,
|
||||
-31.41903,
|
||||
-39.012012,
|
||||
-38.38696,
|
||||
-34.887207,
|
||||
-33.290855,
|
||||
-35.68447,
|
||||
-38.508484,
|
||||
-36.124645,
|
||||
]
|
||||
)
|
||||
max_diff = np.amax(np.abs(logits[0, 0, :10] - EXPECTED_LOGIT_SLICE))
|
||||
self.assertLessEqual(max_diff, 1e-4)
|
||||
|
||||
def generate_step(pixel_values):
|
||||
|
||||
outputs = model.generate(
|
||||
pixel_values, max_length=16, num_beams=4, return_dict_in_generate=True, output_scores=True
|
||||
)
|
||||
output_ids = outputs.sequences
|
||||
preds = tokenizer.batch_decode(output_ids, skip_special_tokens=True)
|
||||
preds = [pred.strip() for pred in preds]
|
||||
|
||||
return preds, outputs.sequences_scores.detach().cpu().numpy()
|
||||
|
||||
preds, scores = generate_step(pixel_values)
|
||||
|
||||
EXPECTED_SCORES = np.array([-0.59562886])
|
||||
max_diff = np.amax(np.abs(scores - EXPECTED_SCORES))
|
||||
self.assertLessEqual(max_diff, 1e-4)
|
||||
|
||||
# should produce
|
||||
# ["a cat laying on top of a couch next to another cat"]
|
||||
self.assertEqual(preds, ["a cat laying on top of a couch next to another cat"])
|
||||
Reference in New Issue
Block a user