# Copyright 2021 The HuggingFace Inc. team. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """Testing suite for the PyTorch VisionTextDualEncoder model.""" import collections import tempfile import unittest import numpy as np from transformers.testing_utils import require_torch, require_vision, slow, torch_device from transformers.utils import is_torch_available, is_vision_available from ...test_modeling_common import floats_tensor, ids_tensor, random_attention_mask 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 ..vit.test_modeling_vit import ViTModelTester if is_torch_available(): import torch from transformers import ( BertModel, CLIPVisionModel, DeiTModel, RobertaModel, VisionTextDualEncoderConfig, VisionTextDualEncoderModel, ViTModel, ) 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 ): # The backbones don't support dynamic attention setting, so we manually change it. FIXME; when bert is refactored text_config._attn_implementation = "eager" vision_config._attn_implementation = "eager" 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 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) @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, "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 ): # The backbones don't support dynamic attention setting, so we manually change it. FIXME; when bert is refactored text_config._attn_implementation = "eager" vision_config._attn_implementation = "eager" 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, "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 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, "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=1.0) 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]]) torch.testing.assert_close(outputs.logits_per_image, expected_logits, rtol=1e-3, atol=1e-3)