[Test refactor 1/5] Per-folder tests reorganization (#15725)
* Per-folder tests reorganization Co-authored-by: sgugger <sylvain.gugger@gmail.com> Co-authored-by: Stas Bekman <stas@stason.org>
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tests/vision_text_dual_encoder/__init__.py
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tests/vision_text_dual_encoder/__init__.py
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# coding=utf-8
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# Copyright 2021 The HuggingFace Inc. team. All rights reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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""" Testing suite for the PyTorch VisionTextDualEncoder model. """
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import collections
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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.file_utils import is_flax_available, is_torch_available, is_vision_available
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from transformers.testing_utils import (
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is_pt_flax_cross_test,
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require_flax,
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require_torch,
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require_vision,
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slow,
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torch_device,
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)
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from ..bert.test_modeling_flax_bert import FlaxBertModelTester
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from ..clip.test_modeling_flax_clip import FlaxCLIPVisionModelTester
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from ..test_modeling_flax_common import floats_tensor, ids_tensor, random_attention_mask
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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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FlaxBertModel,
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FlaxCLIPVisionModel,
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FlaxVisionTextDualEncoderModel,
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FlaxViTModel,
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VisionTextDualEncoderConfig,
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VisionTextDualEncoderProcessor,
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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 VisionTextDualEncoderModel
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if is_vision_available():
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from PIL import Image
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# Inspired by
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# https://github.com/rwightman/pytorch-image-models/blob/b9bd960a032c75ca6b808ddeed76bee5f3ed4972/timm/models/layers/helpers.py
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# From PyTorch internals
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def to_2tuple(x):
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if isinstance(x, collections.abc.Iterable):
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return x
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return (x, x)
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@require_flax
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class VisionTextDualEncoderMixin:
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def get_vision_text_model(self, config, text_config):
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pass
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def prepare_config_and_inputs(self):
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pass
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def get_pretrained_model_and_inputs(self):
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pass
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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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def check_model_from_pretrained_configs(
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self, text_config, input_ids, attention_mask, vision_config, pixel_values=None, **kwargs
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):
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config = VisionTextDualEncoderConfig.from_vision_text_configs(vision_config, text_config)
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model = FlaxVisionTextDualEncoderModel(config)
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output = model(input_ids=input_ids, pixel_values=pixel_values, attention_mask=attention_mask)
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self.assertEqual(output["text_embeds"].shape, (input_ids.shape[0], config.projection_dim))
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self.assertEqual(output["image_embeds"].shape, (pixel_values.shape[0], config.projection_dim))
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def check_vision_text_dual_encoder_from_pretrained(
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self, text_config, input_ids, attention_mask, vision_config, pixel_values=None, **kwargs
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):
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vision_model, text_model = self.get_vision_text_model(vision_config, text_config)
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kwargs = {"vision_model": vision_model, "text_model": text_model}
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model = FlaxVisionTextDualEncoderModel.from_vision_text_pretrained(**kwargs)
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output = model(input_ids=input_ids, pixel_values=pixel_values, attention_mask=attention_mask)
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self.assertEqual(output["text_embeds"].shape, (input_ids.shape[0], model.config.projection_dim))
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self.assertEqual(output["image_embeds"].shape, (pixel_values.shape[0], model.config.projection_dim))
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def check_save_load(self, text_config, input_ids, attention_mask, vision_config, pixel_values=None, **kwargs):
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vision_model, text_model = self.get_vision_text_model(vision_config, text_config)
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kwargs = {"vision_model": vision_model, "text_model": text_model}
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model = FlaxVisionTextDualEncoderModel.from_vision_text_pretrained(**kwargs)
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output = model(input_ids=input_ids, pixel_values=pixel_values, attention_mask=attention_mask)
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out_1 = output[0]
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with tempfile.TemporaryDirectory() as tmpdirname:
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model.save_pretrained(tmpdirname)
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model = FlaxVisionTextDualEncoderModel.from_pretrained(tmpdirname)
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after_output = model(input_ids=input_ids, pixel_values=pixel_values, attention_mask=attention_mask)
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out_2 = after_output[0]
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max_diff = np.amax(np.abs(out_2 - out_1))
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self.assertLessEqual(max_diff, 1e-3)
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def check_vision_text_output_attention(
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self, text_config, input_ids, attention_mask, vision_config, pixel_values=None, **kwargs
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):
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vision_model, text_model = self.get_vision_text_model(vision_config, text_config)
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kwargs = {"vision_model": vision_model, "text_model": text_model}
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model = FlaxVisionTextDualEncoderModel.from_vision_text_pretrained(**kwargs)
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output = model(
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input_ids=input_ids, pixel_values=pixel_values, attention_mask=attention_mask, output_attentions=True
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)
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vision_attentions = output.vision_model_output.attentions
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self.assertEqual(len(vision_attentions), vision_config.num_hidden_layers)
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# in ViT, the seq_len equals the number of patches + 1 (we add 1 for the [CLS] token)
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image_size = to_2tuple(vision_model.config.image_size)
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patch_size = to_2tuple(vision_model.config.patch_size)
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num_patches = (image_size[1] // patch_size[1]) * (image_size[0] // patch_size[0])
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seq_len = num_patches + 1
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self.assertEqual(vision_attentions[0].shape[-3:], (vision_config.num_attention_heads, seq_len, seq_len))
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text_attentions = output.text_model_output.attentions
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self.assertEqual(len(text_attentions), text_config.num_hidden_layers)
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self.assertEqual(
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text_attentions[0].shape[-3:],
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(text_config.num_attention_heads, input_ids.shape[-1], input_ids.shape[-1]),
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)
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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[:4], pt_outputs[:4]):
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self.assert_almost_equals(fx_output, pt_output.numpy(), 4e-2)
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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 = FlaxVisionTextDualEncoderModel.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[:4], pt_outputs[:4]):
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self.assert_almost_equals(fx_output_loaded, pt_output.numpy(), 4e-2)
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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 = VisionTextDualEncoderModel.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[:4], pt_outputs_loaded[:4]):
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self.assert_almost_equals(fx_output, pt_output_loaded.numpy(), 4e-2)
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def check_equivalence_pt_to_flax(self, vision_config, text_config, inputs_dict):
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config = VisionTextDualEncoderConfig.from_vision_text_configs(vision_config, text_config)
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pt_model = VisionTextDualEncoderModel(config)
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fx_model = FlaxVisionTextDualEncoderModel(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, vision_config, text_config, inputs_dict):
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config = VisionTextDualEncoderConfig.from_vision_text_configs(vision_config, text_config)
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pt_model = VisionTextDualEncoderModel(config)
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fx_model = FlaxVisionTextDualEncoderModel(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_model_from_pretrained_configs(self):
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inputs_dict = self.prepare_config_and_inputs()
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self.check_model_from_pretrained_configs(**inputs_dict)
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def test_vision_text_dual_encoder_from_pretrained(self):
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inputs_dict = self.prepare_config_and_inputs()
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self.check_vision_text_dual_encoder_from_pretrained(**inputs_dict)
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def test_save_load(self):
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inputs_dict = self.prepare_config_and_inputs()
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self.check_save_load(**inputs_dict)
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def test_vision_text_output_attention(self):
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inputs_dict = self.prepare_config_and_inputs()
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self.check_vision_text_output_attention(**inputs_dict)
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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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vision_config = config_inputs_dict.pop("vision_config")
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text_config = config_inputs_dict.pop("text_config")
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inputs_dict = config_inputs_dict
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self.check_equivalence_pt_to_flax(vision_config, text_config, inputs_dict)
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self.check_equivalence_flax_to_pt(vision_config, text_config, inputs_dict)
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@slow
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def test_real_model_save_load_from_pretrained(self):
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model_2, inputs = self.get_pretrained_model_and_inputs()
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outputs = model_2(**inputs)
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out_2 = outputs[0]
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with tempfile.TemporaryDirectory() as tmp_dirname:
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model_2.save_pretrained(tmp_dirname)
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model_1 = FlaxVisionTextDualEncoderModel.from_pretrained(tmp_dirname)
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after_outputs = model_1(**inputs)
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out_1 = after_outputs[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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@require_flax
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class FlaxViTBertModelTest(VisionTextDualEncoderMixin, unittest.TestCase):
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def get_pretrained_model_and_inputs(self):
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model = FlaxVisionTextDualEncoderModel.from_vision_text_pretrained(
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"hf-internal-testing/tiny-random-vit",
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"hf-internal-testing/tiny-bert",
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vision_from_pt=True,
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text_from_pt=True,
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)
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batch_size = 13
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pixel_values = floats_tensor(
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[
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batch_size,
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model.config.vision_config.num_channels,
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model.config.vision_config.image_size,
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model.config.vision_config.image_size,
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]
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)
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input_ids = ids_tensor([batch_size, 4], model.config.text_config.vocab_size)
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attention_mask = random_attention_mask([batch_size, 4])
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inputs = {"pixel_values": pixel_values, "input_ids": input_ids, "attention_mask": attention_mask}
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return model, inputs
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def get_vision_text_model(self, vision_config, text_config):
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vision_model = FlaxViTModel(vision_config)
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text_model = FlaxBertModel(text_config)
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return vision_model, text_model
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def prepare_config_and_inputs(self):
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vit_model_tester = FlaxViTModelTester(self)
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bert_model_tester = FlaxBertModelTester(self)
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vision_config_and_inputs = vit_model_tester.prepare_config_and_inputs()
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text_config_and_inputs = bert_model_tester.prepare_config_and_inputs()
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vision_config, pixel_values = vision_config_and_inputs
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text_config, input_ids, token_type_ids, attention_mask = text_config_and_inputs
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# make sure that cross attention layers are added
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return {
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"text_config": text_config,
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"vision_config": vision_config,
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"pixel_values": pixel_values,
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"attention_mask": attention_mask,
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"text_config": text_config,
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"input_ids": input_ids,
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"token_type_ids": token_type_ids,
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}
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@require_torch
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class FlaxCLIPVisionBertModelTest(VisionTextDualEncoderMixin, unittest.TestCase):
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def get_pretrained_model_and_inputs(self):
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model = FlaxVisionTextDualEncoderModel.from_vision_text_pretrained(
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"hf-internal-testing/tiny-random-clip",
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"hf-internal-testing/tiny-bert",
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vision_from_pt=True,
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text_from_pt=True,
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)
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batch_size = 13
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pixel_values = floats_tensor(
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[
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batch_size,
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model.config.vision_config.num_channels,
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model.config.vision_config.image_size,
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model.config.vision_config.image_size,
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]
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)
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input_ids = ids_tensor([batch_size, 4], model.config.text_config.vocab_size)
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attention_mask = random_attention_mask([batch_size, 4])
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inputs = {"pixel_values": pixel_values, "input_ids": input_ids, "attention_mask": attention_mask}
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return model, inputs
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def get_vision_text_model(self, vision_config, text_config):
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vision_model = FlaxCLIPVisionModel(vision_config)
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text_model = FlaxBertModel(text_config)
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return vision_model, text_model
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def prepare_config_and_inputs(self):
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clip_model_tester = FlaxCLIPVisionModelTester(self)
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bert_model_tester = FlaxBertModelTester(self)
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vision_config_and_inputs = clip_model_tester.prepare_config_and_inputs()
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text_config_and_inputs = bert_model_tester.prepare_config_and_inputs()
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vision_config, pixel_values = vision_config_and_inputs
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text_config, input_ids, token_type_ids, attention_mask = text_config_and_inputs
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# make sure that cross attention layers are added
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return {
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"text_config": text_config,
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"vision_config": vision_config,
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"pixel_values": pixel_values,
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"attention_mask": attention_mask,
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"text_config": text_config,
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"input_ids": input_ids,
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"token_type_ids": token_type_ids,
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}
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@require_flax
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@require_vision
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class FlaxVisionTextDualEncoderIntegrationTest(unittest.TestCase):
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@slow
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def test_inference(self):
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model = FlaxVisionTextDualEncoderModel.from_pretrained("clip-italian/clip-italian", logit_scale_init_value=1)
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processor = VisionTextDualEncoderProcessor.from_pretrained("clip-italian/clip-italian")
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image = Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png")
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inputs = processor(
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text=["una foto di un gatto", "una foto di un cane"], images=image, padding=True, return_tensors="np"
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)
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outputs = model(**inputs)
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# verify the logits
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self.assertEqual(outputs.logits_per_image.shape, (inputs.pixel_values.shape[0], inputs.input_ids.shape[0]))
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self.assertEqual(
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outputs.logits_per_text.shape,
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(inputs.input_ids.shape[0], inputs.pixel_values.shape[0]),
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)
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expected_logits = np.array([[1.2284727, 0.3104122]])
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self.assertTrue(np.allclose(outputs.logits_per_image, expected_logits, atol=1e-3))
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@@ -0,0 +1,527 @@
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# coding=utf-8
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# Copyright 2021 The HuggingFace Inc. team. All rights reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
|
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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||||
#
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# Unless required by applicable law or agreed to in writing, software
|
||||
# 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
|
||||
# limitations under the License.
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""" Testing suite for the PyTorch VisionTextDualEncoder model. """
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|
||||
import collections
|
||||
import tempfile
|
||||
import unittest
|
||||
|
||||
import numpy as np
|
||||
|
||||
from transformers.file_utils import is_flax_available, is_torch_available, is_vision_available
|
||||
from transformers.testing_utils import is_pt_flax_cross_test, require_torch, require_vision, slow, torch_device
|
||||
|
||||
from ..bert.test_modeling_bert import BertModelTester
|
||||
from ..clip.test_modeling_clip import CLIPVisionModelTester
|
||||
from ..deit.test_modeling_deit import DeiTModelTester
|
||||
from ..roberta.test_modeling_roberta import RobertaModelTester
|
||||
from ..test_modeling_common import floats_tensor, ids_tensor, random_attention_mask
|
||||
from ..vit.test_modeling_vit import ViTModelTester
|
||||
|
||||
|
||||
if is_torch_available():
|
||||
import torch
|
||||
|
||||
from transformers import (
|
||||
BertModel,
|
||||
CLIPVisionModel,
|
||||
DeiTModel,
|
||||
RobertaModel,
|
||||
VisionTextDualEncoderConfig,
|
||||
VisionTextDualEncoderModel,
|
||||
ViTModel,
|
||||
)
|
||||
|
||||
if is_flax_available():
|
||||
from transformers import FlaxVisionTextDualEncoderModel
|
||||
from transformers.modeling_flax_pytorch_utils import (
|
||||
convert_pytorch_state_dict_to_flax,
|
||||
load_flax_weights_in_pytorch_model,
|
||||
)
|
||||
|
||||
if is_vision_available():
|
||||
from PIL import Image
|
||||
|
||||
from transformers import VisionTextDualEncoderProcessor
|
||||
|
||||
|
||||
# Inspired by
|
||||
# https://github.com/rwightman/pytorch-image-models/blob/b9bd960a032c75ca6b808ddeed76bee5f3ed4972/timm/models/layers/helpers.py
|
||||
# From PyTorch internals
|
||||
def to_2tuple(x):
|
||||
if isinstance(x, collections.abc.Iterable):
|
||||
return x
|
||||
return (x, x)
|
||||
|
||||
|
||||
@require_torch
|
||||
class VisionTextDualEncoderMixin:
|
||||
def get_vision_text_model(self, config, text_config):
|
||||
pass
|
||||
|
||||
def prepare_config_and_inputs(self):
|
||||
pass
|
||||
|
||||
def get_pretrained_model_and_inputs(self):
|
||||
pass
|
||||
|
||||
def check_model_from_pretrained_configs(
|
||||
self, text_config, input_ids, attention_mask, vision_config, pixel_values=None, **kwargs
|
||||
):
|
||||
config = VisionTextDualEncoderConfig.from_vision_text_configs(vision_config, text_config)
|
||||
|
||||
model = VisionTextDualEncoderModel(config)
|
||||
model.to(torch_device)
|
||||
model.eval()
|
||||
|
||||
output = model(input_ids=input_ids, pixel_values=pixel_values, attention_mask=attention_mask)
|
||||
|
||||
self.assertEqual(output["text_embeds"].shape, (input_ids.shape[0], config.projection_dim))
|
||||
self.assertEqual(output["image_embeds"].shape, (pixel_values.shape[0], config.projection_dim))
|
||||
|
||||
def check_vision_text_dual_encoder_model(
|
||||
self, text_config, input_ids, attention_mask, vision_config, pixel_values=None, **kwargs
|
||||
):
|
||||
vision_model, text_model = self.get_vision_text_model(vision_config, text_config)
|
||||
model = VisionTextDualEncoderModel(vision_model=vision_model, text_model=text_model)
|
||||
model.to(torch_device)
|
||||
model.eval()
|
||||
|
||||
output = model(input_ids=input_ids, pixel_values=pixel_values, attention_mask=attention_mask)
|
||||
|
||||
self.assertEqual(output["text_embeds"].shape, (input_ids.shape[0], model.config.projection_dim))
|
||||
self.assertEqual(output["image_embeds"].shape, (pixel_values.shape[0], model.config.projection_dim))
|
||||
|
||||
def check_vision_text_dual_encoder_from_pretrained(
|
||||
self, text_config, input_ids, attention_mask, vision_config, pixel_values=None, **kwargs
|
||||
):
|
||||
|
||||
vision_model, text_model = self.get_vision_text_model(vision_config, text_config)
|
||||
kwargs = {"vision_model": vision_model, "text_model": text_model}
|
||||
model = VisionTextDualEncoderModel.from_vision_text_pretrained(**kwargs)
|
||||
model.to(torch_device)
|
||||
model.eval()
|
||||
|
||||
output = model(input_ids=input_ids, pixel_values=pixel_values, attention_mask=attention_mask)
|
||||
|
||||
self.assertEqual(output["text_embeds"].shape, (input_ids.shape[0], model.config.projection_dim))
|
||||
self.assertEqual(output["image_embeds"].shape, (pixel_values.shape[0], model.config.projection_dim))
|
||||
|
||||
def check_save_load(self, text_config, input_ids, attention_mask, vision_config, pixel_values=None, **kwargs):
|
||||
vision_model, text_model = self.get_vision_text_model(vision_config, text_config)
|
||||
model = VisionTextDualEncoderModel(vision_model=vision_model, text_model=text_model)
|
||||
model.to(torch_device)
|
||||
model.eval()
|
||||
|
||||
with torch.no_grad():
|
||||
output = model(input_ids=input_ids, pixel_values=pixel_values, attention_mask=attention_mask)
|
||||
out_1 = output[0].cpu().numpy()
|
||||
|
||||
with tempfile.TemporaryDirectory() as tmpdirname:
|
||||
model.save_pretrained(tmpdirname)
|
||||
model = VisionTextDualEncoderModel.from_pretrained(tmpdirname).eval()
|
||||
model.to(torch_device)
|
||||
|
||||
after_output = model(input_ids=input_ids, pixel_values=pixel_values, attention_mask=attention_mask)
|
||||
out_2 = after_output[0].cpu().numpy()
|
||||
max_diff = np.amax(np.abs(out_2 - out_1))
|
||||
self.assertLessEqual(max_diff, 1e-5)
|
||||
|
||||
def check_vision_text_output_attention(
|
||||
self, text_config, input_ids, attention_mask, vision_config, pixel_values=None, **kwargs
|
||||
):
|
||||
vision_model, text_model = self.get_vision_text_model(vision_config, text_config)
|
||||
model = VisionTextDualEncoderModel(vision_model=vision_model, text_model=text_model)
|
||||
model.to(torch_device)
|
||||
model.eval()
|
||||
|
||||
output = model(
|
||||
input_ids=input_ids, pixel_values=pixel_values, attention_mask=attention_mask, output_attentions=True
|
||||
)
|
||||
|
||||
vision_attentions = output.vision_model_output.attentions
|
||||
self.assertEqual(len(vision_attentions), vision_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(vision_model.config.image_size)
|
||||
patch_size = to_2tuple(vision_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(vision_attentions[0].shape[-3:], (vision_config.num_attention_heads, seq_len, seq_len))
|
||||
|
||||
text_attentions = output.text_model_output.attentions
|
||||
self.assertEqual(len(text_attentions), text_config.num_hidden_layers)
|
||||
|
||||
self.assertEqual(
|
||||
text_attentions[0].shape[-3:],
|
||||
(text_config.num_attention_heads, input_ids.shape[-1], input_ids.shape[-1]),
|
||||
)
|
||||
|
||||
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 flax is {diff} (>= {tol}).")
|
||||
|
||||
def check_pt_flax_equivalence(self, pt_model, fx_model, input_ids, attention_mask, pixel_values, **kwargs):
|
||||
|
||||
pt_model.to(torch_device)
|
||||
pt_model.eval()
|
||||
|
||||
# prepare inputs
|
||||
inputs_dict = {"input_ids": input_ids, "attention_mask": attention_mask, "pixel_values": pixel_values}
|
||||
pt_inputs = inputs_dict
|
||||
flax_inputs = {k: v.numpy() for k, v in pt_inputs.items()}
|
||||
|
||||
with torch.no_grad():
|
||||
pt_outputs = pt_model(**pt_inputs).to_tuple()
|
||||
|
||||
fx_outputs = fx_model(**flax_inputs).to_tuple()
|
||||
self.assertEqual(len(fx_outputs), len(pt_outputs), "Output lengths differ between Flax and PyTorch")
|
||||
for fx_output, pt_output in zip(fx_outputs[:4], pt_outputs[:4]):
|
||||
self.assert_almost_equals(fx_output, pt_output.numpy(), 4e-2)
|
||||
|
||||
# PT -> Flax
|
||||
with tempfile.TemporaryDirectory() as tmpdirname:
|
||||
pt_model.save_pretrained(tmpdirname)
|
||||
fx_model_loaded = FlaxVisionTextDualEncoderModel.from_pretrained(tmpdirname, from_pt=True)
|
||||
|
||||
fx_outputs_loaded = fx_model_loaded(**flax_inputs).to_tuple()
|
||||
self.assertEqual(len(fx_outputs_loaded), len(pt_outputs), "Output lengths differ between Flax and PyTorch")
|
||||
for fx_output_loaded, pt_output in zip(fx_outputs_loaded[:4], pt_outputs[:4]):
|
||||
self.assert_almost_equals(fx_output_loaded, pt_output.numpy(), 4e-2)
|
||||
|
||||
# Flax -> PT
|
||||
with tempfile.TemporaryDirectory() as tmpdirname:
|
||||
fx_model.save_pretrained(tmpdirname)
|
||||
pt_model_loaded = VisionTextDualEncoderModel.from_pretrained(tmpdirname, from_flax=True)
|
||||
|
||||
pt_model_loaded.to(torch_device)
|
||||
pt_model_loaded.eval()
|
||||
|
||||
with torch.no_grad():
|
||||
pt_outputs_loaded = pt_model_loaded(**pt_inputs).to_tuple()
|
||||
|
||||
self.assertEqual(len(fx_outputs), len(pt_outputs_loaded), "Output lengths differ between Flax and PyTorch")
|
||||
for fx_output, pt_output_loaded in zip(fx_outputs[:4], pt_outputs_loaded[:4]):
|
||||
self.assert_almost_equals(fx_output, pt_output_loaded.numpy(), 4e-2)
|
||||
|
||||
def check_equivalence_pt_to_flax(self, vision_config, text_config, inputs_dict):
|
||||
|
||||
config = VisionTextDualEncoderConfig.from_vision_text_configs(vision_config, text_config)
|
||||
|
||||
pt_model = VisionTextDualEncoderModel(config)
|
||||
fx_model = FlaxVisionTextDualEncoderModel(config)
|
||||
|
||||
fx_state = convert_pytorch_state_dict_to_flax(pt_model.state_dict(), fx_model)
|
||||
fx_model.params = fx_state
|
||||
|
||||
self.check_pt_flax_equivalence(pt_model, fx_model, **inputs_dict)
|
||||
|
||||
def check_equivalence_flax_to_pt(self, vision_config, text_config, inputs_dict):
|
||||
|
||||
config = VisionTextDualEncoderConfig.from_vision_text_configs(vision_config, text_config)
|
||||
|
||||
pt_model = VisionTextDualEncoderModel(config)
|
||||
fx_model = FlaxVisionTextDualEncoderModel(config)
|
||||
|
||||
pt_model = load_flax_weights_in_pytorch_model(pt_model, fx_model.params)
|
||||
|
||||
self.check_pt_flax_equivalence(pt_model, fx_model, **inputs_dict)
|
||||
|
||||
def test_vision_text_dual_encoder_model(self):
|
||||
inputs_dict = self.prepare_config_and_inputs()
|
||||
self.check_vision_text_dual_encoder_model(**inputs_dict)
|
||||
|
||||
def test_model_from_pretrained_configs(self):
|
||||
inputs_dict = self.prepare_config_and_inputs()
|
||||
self.check_model_from_pretrained_configs(**inputs_dict)
|
||||
|
||||
def test_vision_text_dual_encoder_from_pretrained(self):
|
||||
inputs_dict = self.prepare_config_and_inputs()
|
||||
self.check_vision_text_dual_encoder_from_pretrained(**inputs_dict)
|
||||
|
||||
def test_save_load(self):
|
||||
inputs_dict = self.prepare_config_and_inputs()
|
||||
self.check_save_load(**inputs_dict)
|
||||
|
||||
def test_vision_text_output_attention(self):
|
||||
inputs_dict = self.prepare_config_and_inputs()
|
||||
self.check_vision_text_output_attention(**inputs_dict)
|
||||
|
||||
@is_pt_flax_cross_test
|
||||
def test_pt_flax_equivalence(self):
|
||||
|
||||
config_inputs_dict = self.prepare_config_and_inputs()
|
||||
vision_config = config_inputs_dict.pop("vision_config")
|
||||
text_config = config_inputs_dict.pop("text_config")
|
||||
|
||||
inputs_dict = config_inputs_dict
|
||||
|
||||
self.check_equivalence_pt_to_flax(vision_config, text_config, inputs_dict)
|
||||
self.check_equivalence_flax_to_pt(vision_config, text_config, inputs_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()
|
||||
|
||||
with tempfile.TemporaryDirectory() as tmp_dirname:
|
||||
model_2.save_pretrained(tmp_dirname)
|
||||
model_1 = VisionTextDualEncoderModel.from_pretrained(tmp_dirname)
|
||||
model_1.to(torch_device)
|
||||
|
||||
after_outputs = model_1(**inputs)
|
||||
out_1 = after_outputs[0].cpu().numpy()
|
||||
max_diff = np.amax(np.abs(out_1 - out_2))
|
||||
self.assertLessEqual(max_diff, 1e-5)
|
||||
|
||||
|
||||
@require_torch
|
||||
class ViTBertModelTest(VisionTextDualEncoderMixin, unittest.TestCase):
|
||||
def get_pretrained_model_and_inputs(self):
|
||||
model = VisionTextDualEncoderModel.from_vision_text_pretrained(
|
||||
"hf-internal-testing/tiny-random-vit", "hf-internal-testing/tiny-bert"
|
||||
)
|
||||
batch_size = 13
|
||||
pixel_values = floats_tensor(
|
||||
[
|
||||
batch_size,
|
||||
model.vision_model.config.num_channels,
|
||||
model.vision_model.config.image_size,
|
||||
model.vision_model.config.image_size,
|
||||
]
|
||||
)
|
||||
input_ids = ids_tensor([batch_size, 4], model.text_model.config.vocab_size)
|
||||
attention_mask = random_attention_mask([batch_size, 4])
|
||||
inputs = {"pixel_values": pixel_values, "input_ids": input_ids, "attention_mask": attention_mask}
|
||||
|
||||
return model, inputs
|
||||
|
||||
def get_vision_text_model(self, vision_config, text_config):
|
||||
vision_model = ViTModel(vision_config).eval()
|
||||
text_model = BertModel(text_config).eval()
|
||||
return vision_model, text_model
|
||||
|
||||
def prepare_config_and_inputs(self):
|
||||
vit_model_tester = ViTModelTester(self)
|
||||
bert_model_tester = BertModelTester(self)
|
||||
vision_config_and_inputs = vit_model_tester.prepare_config_and_inputs()
|
||||
text_config_and_inputs = bert_model_tester.prepare_config_and_inputs()
|
||||
|
||||
vision_config, pixel_values, _ = vision_config_and_inputs
|
||||
|
||||
(
|
||||
text_config,
|
||||
input_ids,
|
||||
token_type_ids,
|
||||
input_mask,
|
||||
sequence_labels,
|
||||
token_labels,
|
||||
choice_labels,
|
||||
) = text_config_and_inputs
|
||||
|
||||
return {
|
||||
"text_config": text_config,
|
||||
"vision_config": vision_config,
|
||||
"pixel_values": pixel_values,
|
||||
"attention_mask": input_mask,
|
||||
"text_config": text_config,
|
||||
"input_ids": input_ids,
|
||||
"text_token_type_ids": token_type_ids,
|
||||
"text_sequence_labels": sequence_labels,
|
||||
"text_token_labels": token_labels,
|
||||
"text_choice_labels": choice_labels,
|
||||
}
|
||||
|
||||
|
||||
@require_torch
|
||||
class DeiTRobertaModelTest(VisionTextDualEncoderMixin, unittest.TestCase):
|
||||
def get_pretrained_model_and_inputs(self):
|
||||
model = VisionTextDualEncoderModel.from_vision_text_pretrained(
|
||||
"hf-internal-testing/tiny-random-deit", "hf-internal-testing/tiny-random-roberta"
|
||||
)
|
||||
batch_size = 13
|
||||
pixel_values = floats_tensor(
|
||||
[
|
||||
batch_size,
|
||||
model.vision_model.config.num_channels,
|
||||
model.vision_model.config.image_size,
|
||||
model.vision_model.config.image_size,
|
||||
]
|
||||
)
|
||||
input_ids = ids_tensor([batch_size, 4], model.text_model.config.vocab_size)
|
||||
attention_mask = random_attention_mask([batch_size, 4])
|
||||
inputs = {"pixel_values": pixel_values, "input_ids": input_ids, "attention_mask": attention_mask}
|
||||
|
||||
return model, inputs
|
||||
|
||||
def check_vision_text_output_attention(
|
||||
self, text_config, input_ids, attention_mask, vision_config, pixel_values=None, **kwargs
|
||||
):
|
||||
vision_model, text_model = self.get_vision_text_model(vision_config, text_config)
|
||||
model = VisionTextDualEncoderModel(vision_model=vision_model, text_model=text_model)
|
||||
model.to(torch_device)
|
||||
model.eval()
|
||||
|
||||
output = model(
|
||||
input_ids=input_ids, pixel_values=pixel_values, attention_mask=attention_mask, output_attentions=True
|
||||
)
|
||||
|
||||
vision_attentions = output.vision_model_output.attentions
|
||||
self.assertEqual(len(vision_attentions), vision_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(vision_model.config.image_size)
|
||||
patch_size = to_2tuple(vision_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(vision_attentions[0].shape[-3:], (vision_config.num_attention_heads, seq_len, seq_len))
|
||||
|
||||
text_attentions = output.text_model_output.attentions
|
||||
self.assertEqual(len(text_attentions), text_config.num_hidden_layers)
|
||||
|
||||
self.assertEqual(
|
||||
text_attentions[0].shape[-3:],
|
||||
(text_config.num_attention_heads, input_ids.shape[-1], input_ids.shape[-1]),
|
||||
)
|
||||
|
||||
def get_vision_text_model(self, vision_config, text_config):
|
||||
vision_model = DeiTModel(vision_config).eval()
|
||||
text_model = RobertaModel(text_config).eval()
|
||||
return vision_model, text_model
|
||||
|
||||
def prepare_config_and_inputs(self):
|
||||
vit_model_tester = DeiTModelTester(self)
|
||||
bert_model_tester = RobertaModelTester(self)
|
||||
vision_config_and_inputs = vit_model_tester.prepare_config_and_inputs()
|
||||
text_config_and_inputs = bert_model_tester.prepare_config_and_inputs()
|
||||
|
||||
vision_config, pixel_values, _ = vision_config_and_inputs
|
||||
|
||||
(
|
||||
text_config,
|
||||
input_ids,
|
||||
token_type_ids,
|
||||
input_mask,
|
||||
sequence_labels,
|
||||
token_labels,
|
||||
choice_labels,
|
||||
) = text_config_and_inputs
|
||||
|
||||
return {
|
||||
"text_config": text_config,
|
||||
"vision_config": vision_config,
|
||||
"pixel_values": pixel_values,
|
||||
"attention_mask": input_mask,
|
||||
"text_config": text_config,
|
||||
"input_ids": input_ids,
|
||||
"text_token_type_ids": token_type_ids,
|
||||
"text_sequence_labels": sequence_labels,
|
||||
"text_token_labels": token_labels,
|
||||
"text_choice_labels": choice_labels,
|
||||
}
|
||||
|
||||
# skip as DeiT is not available in Flax
|
||||
def test_pt_flax_equivalence(self):
|
||||
pass
|
||||
|
||||
|
||||
@require_torch
|
||||
class CLIPVisionBertModelTest(VisionTextDualEncoderMixin, unittest.TestCase):
|
||||
def get_pretrained_model_and_inputs(self):
|
||||
model = VisionTextDualEncoderModel.from_vision_text_pretrained(
|
||||
"hf-internal-testing/tiny-random-clip", "hf-internal-testing/tiny-bert"
|
||||
)
|
||||
batch_size = 13
|
||||
pixel_values = floats_tensor(
|
||||
[
|
||||
batch_size,
|
||||
model.vision_model.config.num_channels,
|
||||
model.vision_model.config.image_size,
|
||||
model.vision_model.config.image_size,
|
||||
]
|
||||
)
|
||||
input_ids = ids_tensor([batch_size, 4], model.text_model.config.vocab_size)
|
||||
attention_mask = random_attention_mask([batch_size, 4])
|
||||
inputs = {"pixel_values": pixel_values, "input_ids": input_ids, "attention_mask": attention_mask}
|
||||
|
||||
return model, inputs
|
||||
|
||||
def get_vision_text_model(self, vision_config, text_config):
|
||||
vision_model = CLIPVisionModel(vision_config).eval()
|
||||
text_model = BertModel(text_config).eval()
|
||||
return vision_model, text_model
|
||||
|
||||
def prepare_config_and_inputs(self):
|
||||
clip_model_tester = CLIPVisionModelTester(self)
|
||||
bert_model_tester = BertModelTester(self)
|
||||
vision_config_and_inputs = clip_model_tester.prepare_config_and_inputs()
|
||||
text_config_and_inputs = bert_model_tester.prepare_config_and_inputs()
|
||||
|
||||
vision_config, pixel_values = vision_config_and_inputs
|
||||
|
||||
(
|
||||
text_config,
|
||||
input_ids,
|
||||
token_type_ids,
|
||||
input_mask,
|
||||
sequence_labels,
|
||||
token_labels,
|
||||
choice_labels,
|
||||
) = text_config_and_inputs
|
||||
|
||||
return {
|
||||
"text_config": text_config,
|
||||
"vision_config": vision_config,
|
||||
"pixel_values": pixel_values,
|
||||
"attention_mask": input_mask,
|
||||
"text_config": text_config,
|
||||
"input_ids": input_ids,
|
||||
"text_token_type_ids": token_type_ids,
|
||||
"text_sequence_labels": sequence_labels,
|
||||
"text_token_labels": token_labels,
|
||||
"text_choice_labels": choice_labels,
|
||||
}
|
||||
|
||||
|
||||
@require_vision
|
||||
@require_torch
|
||||
class VisionTextDualEncoderIntegrationTest(unittest.TestCase):
|
||||
@slow
|
||||
def test_inference(self):
|
||||
model = VisionTextDualEncoderModel.from_pretrained("clip-italian/clip-italian", logit_scale_init_value=1)
|
||||
processor = VisionTextDualEncoderProcessor.from_pretrained("clip-italian/clip-italian")
|
||||
|
||||
image = Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png")
|
||||
inputs = processor(
|
||||
text=["una foto di un gatto", "una foto di un cane"], images=image, padding=True, return_tensors="pt"
|
||||
)
|
||||
|
||||
outputs = model(**inputs)
|
||||
|
||||
# verify the logits
|
||||
self.assertEqual(outputs.logits_per_image.shape, (inputs.pixel_values.shape[0], inputs.input_ids.shape[0]))
|
||||
self.assertEqual(
|
||||
outputs.logits_per_text.shape,
|
||||
(inputs.input_ids.shape[0], inputs.pixel_values.shape[0]),
|
||||
)
|
||||
|
||||
expected_logits = torch.tensor([[1.2284727, 0.3104122]])
|
||||
|
||||
self.assertTrue(torch.allclose(outputs.logits_per_image, expected_logits, atol=1e-3))
|
||||
@@ -0,0 +1,170 @@
|
||||
# Copyright 2021 The HuggingFace Team. All rights reserved.
|
||||
#
|
||||
# Licensed under the Apache License, Version 2.0 (the "License");
|
||||
# you may not use this file except in compliance with the License.
|
||||
# You may obtain a copy of the License at
|
||||
#
|
||||
# http://www.apache.org/licenses/LICENSE-2.0
|
||||
#
|
||||
# Unless required by applicable law or agreed to in writing, software
|
||||
# distributed under the License is distributed on an "AS IS" BASIS,
|
||||
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
||||
# See the License for the specific language governing permissions and
|
||||
# limitations under the License.
|
||||
|
||||
import json
|
||||
import os
|
||||
import shutil
|
||||
import tempfile
|
||||
import unittest
|
||||
|
||||
import numpy as np
|
||||
|
||||
from transformers import BertTokenizerFast
|
||||
from transformers.file_utils import FEATURE_EXTRACTOR_NAME, is_vision_available
|
||||
from transformers.models.bert.tokenization_bert import VOCAB_FILES_NAMES, BertTokenizer
|
||||
from transformers.testing_utils import require_tokenizers, require_vision
|
||||
|
||||
|
||||
if is_vision_available():
|
||||
from PIL import Image
|
||||
|
||||
from transformers import VisionTextDualEncoderProcessor, ViTFeatureExtractor
|
||||
|
||||
|
||||
@require_tokenizers
|
||||
@require_vision
|
||||
class VisionTextDualEncoderProcessorTest(unittest.TestCase):
|
||||
def setUp(self):
|
||||
self.tmpdirname = tempfile.mkdtemp()
|
||||
|
||||
# fmt: off
|
||||
vocab_tokens = ["[UNK]", "[CLS]", "[SEP]", "[PAD]", "[MASK]", "want", "##want", "##ed", "wa", "un", "runn", "##ing", ",", "low", "lowest"]
|
||||
# fmt: on
|
||||
self.vocab_file = os.path.join(self.tmpdirname, VOCAB_FILES_NAMES["vocab_file"])
|
||||
with open(self.vocab_file, "w", encoding="utf-8") as vocab_writer:
|
||||
vocab_writer.write("".join([x + "\n" for x in vocab_tokens]))
|
||||
|
||||
feature_extractor_map = {
|
||||
"do_resize": True,
|
||||
"size": 18,
|
||||
"do_normalize": True,
|
||||
"image_mean": [0.5, 0.5, 0.5],
|
||||
"image_std": [0.5, 0.5, 0.5],
|
||||
}
|
||||
self.feature_extractor_file = os.path.join(self.tmpdirname, FEATURE_EXTRACTOR_NAME)
|
||||
with open(self.feature_extractor_file, "w", encoding="utf-8") as fp:
|
||||
json.dump(feature_extractor_map, fp)
|
||||
|
||||
def get_tokenizer(self, **kwargs):
|
||||
return BertTokenizer.from_pretrained(self.tmpdirname, **kwargs)
|
||||
|
||||
def get_feature_extractor(self, **kwargs):
|
||||
return ViTFeatureExtractor.from_pretrained(self.tmpdirname, **kwargs)
|
||||
|
||||
def tearDown(self):
|
||||
shutil.rmtree(self.tmpdirname)
|
||||
|
||||
def prepare_image_inputs(self):
|
||||
"""This function prepares a list of PIL images, or a list of numpy arrays if one specifies numpify=True,
|
||||
or a list of PyTorch tensors if one specifies torchify=True.
|
||||
"""
|
||||
|
||||
image_inputs = [np.random.randint(255, size=(3, 30, 400), dtype=np.uint8)]
|
||||
|
||||
image_inputs = [Image.fromarray(np.moveaxis(x, 0, -1)) for x in image_inputs]
|
||||
|
||||
return image_inputs
|
||||
|
||||
def test_save_load_pretrained_default(self):
|
||||
tokenizer = self.get_tokenizer()
|
||||
feature_extractor = self.get_feature_extractor()
|
||||
|
||||
processor = VisionTextDualEncoderProcessor(tokenizer=tokenizer, feature_extractor=feature_extractor)
|
||||
|
||||
processor.save_pretrained(self.tmpdirname)
|
||||
processor = VisionTextDualEncoderProcessor.from_pretrained(self.tmpdirname)
|
||||
|
||||
self.assertEqual(processor.tokenizer.get_vocab(), tokenizer.get_vocab())
|
||||
self.assertIsInstance(processor.tokenizer, (BertTokenizer, BertTokenizerFast))
|
||||
|
||||
self.assertEqual(processor.feature_extractor.to_json_string(), feature_extractor.to_json_string())
|
||||
self.assertIsInstance(processor.feature_extractor, ViTFeatureExtractor)
|
||||
|
||||
def test_save_load_pretrained_additional_features(self):
|
||||
processor = VisionTextDualEncoderProcessor(
|
||||
tokenizer=self.get_tokenizer(), feature_extractor=self.get_feature_extractor()
|
||||
)
|
||||
processor.save_pretrained(self.tmpdirname)
|
||||
|
||||
tokenizer_add_kwargs = self.get_tokenizer(bos_token="(BOS)", eos_token="(EOS)")
|
||||
feature_extractor_add_kwargs = self.get_feature_extractor(do_normalize=False, padding_value=1.0)
|
||||
|
||||
processor = VisionTextDualEncoderProcessor.from_pretrained(
|
||||
self.tmpdirname, bos_token="(BOS)", eos_token="(EOS)", do_normalize=False, padding_value=1.0
|
||||
)
|
||||
|
||||
self.assertEqual(processor.tokenizer.get_vocab(), tokenizer_add_kwargs.get_vocab())
|
||||
self.assertIsInstance(processor.tokenizer, (BertTokenizer, BertTokenizerFast))
|
||||
|
||||
self.assertEqual(processor.feature_extractor.to_json_string(), feature_extractor_add_kwargs.to_json_string())
|
||||
self.assertIsInstance(processor.feature_extractor, ViTFeatureExtractor)
|
||||
|
||||
def test_feature_extractor(self):
|
||||
feature_extractor = self.get_feature_extractor()
|
||||
tokenizer = self.get_tokenizer()
|
||||
|
||||
processor = VisionTextDualEncoderProcessor(tokenizer=tokenizer, feature_extractor=feature_extractor)
|
||||
|
||||
image_input = self.prepare_image_inputs()
|
||||
|
||||
input_feat_extract = feature_extractor(image_input, return_tensors="np")
|
||||
input_processor = processor(images=image_input, return_tensors="np")
|
||||
|
||||
for key in input_feat_extract.keys():
|
||||
self.assertAlmostEqual(input_feat_extract[key].sum(), input_processor[key].sum(), delta=1e-2)
|
||||
|
||||
def test_tokenizer(self):
|
||||
feature_extractor = self.get_feature_extractor()
|
||||
tokenizer = self.get_tokenizer()
|
||||
|
||||
processor = VisionTextDualEncoderProcessor(tokenizer=tokenizer, feature_extractor=feature_extractor)
|
||||
|
||||
input_str = "lower newer"
|
||||
|
||||
encoded_processor = processor(text=input_str)
|
||||
|
||||
encoded_tok = tokenizer(input_str)
|
||||
|
||||
for key in encoded_tok.keys():
|
||||
self.assertListEqual(encoded_tok[key], encoded_processor[key])
|
||||
|
||||
def test_processor(self):
|
||||
feature_extractor = self.get_feature_extractor()
|
||||
tokenizer = self.get_tokenizer()
|
||||
|
||||
processor = VisionTextDualEncoderProcessor(tokenizer=tokenizer, feature_extractor=feature_extractor)
|
||||
|
||||
input_str = "lower newer"
|
||||
image_input = self.prepare_image_inputs()
|
||||
|
||||
inputs = processor(text=input_str, images=image_input)
|
||||
|
||||
self.assertListEqual(list(inputs.keys()), ["input_ids", "token_type_ids", "attention_mask", "pixel_values"])
|
||||
|
||||
# test if it raises when no input is passed
|
||||
with self.assertRaises(ValueError):
|
||||
processor()
|
||||
|
||||
def test_tokenizer_decode(self):
|
||||
feature_extractor = self.get_feature_extractor()
|
||||
tokenizer = self.get_tokenizer()
|
||||
|
||||
processor = VisionTextDualEncoderProcessor(tokenizer=tokenizer, feature_extractor=feature_extractor)
|
||||
|
||||
predicted_ids = [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]
|
||||
|
||||
decoded_processor = processor.batch_decode(predicted_ids)
|
||||
decoded_tok = tokenizer.batch_decode(predicted_ids)
|
||||
|
||||
self.assertListEqual(decoded_tok, decoded_processor)
|
||||
Reference in New Issue
Block a user