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#
# Copyright (c) 2022 salesforce.com, inc.
# All rights reserved.
# SPDX-License-Identifier: BSD-3-Clause
# For full license text, see the LICENSE file in the repo root or https://opensource.org/licenses/BSD-3-Clause
#
Integration tests for BLIP models.
"""
import pytest
import torch
from lavis.models import load_model, load_model_and_preprocess
from PIL import Image
# setup device to use
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
# load sample image
raw_image = Image.open("docs/_static/merlion.png").convert("RGB")
precision = 1e-1
class TestBlip:
def test_caption(self):
# loads BLIP caption base model, with finetuned checkpoints on MSCOCO captioning dataset.
# this also loads the associated image processors
model, vis_processors, _ = load_model_and_preprocess(
name="blip_caption", model_type="base_coco", is_eval=True, device=device
)
# preprocess the image
# vis_processors stores image transforms for "train" and "eval" (validation / testing / inference)
image = vis_processors["eval"](raw_image).unsqueeze(0).to(device)
# generate caption
caption = model.generate({"image": image})
assert caption == ["a large fountain spewing water into the air"]
# generate multiple captions
captions = model.generate({"image": image}, num_captions=3)
assert len(captions) == 3
def test_caption_large(self):
# loads BLIP caption base model, with finetuned checkpoints on MSCOCO captioning dataset.
# this also loads the associated image processors
model, vis_processors, _ = load_model_and_preprocess(
name="blip_caption", model_type="large_coco", is_eval=True, device=device
)
# preprocess the image
# vis_processors stores image transforms for "train" and "eval" (validation / testing / inference)
image = vis_processors["eval"](raw_image).unsqueeze(0).to(device)
# generate caption
caption = model.generate({"image": image})
assert caption == ["a large statue of a person spraying water from a fountain"]
# generate multiple captions
captions = model.generate({"image": image}, num_captions=3)
assert len(captions) == 3
def test_caption_forward(self):
model, vis_processors, _ = load_model_and_preprocess(
name="blip_caption", model_type="base_coco", is_eval=True, device=device
)
# preprocess the image
# vis_processors stores image transforms for "train" and "eval" (validation / testing / inference)
image = vis_processors["eval"](raw_image).unsqueeze(0).to(device)
text_input = ["a large statue of a person spraying water from a fountain"]
samples = {"image": image, "text_input": text_input}
output = model(samples)
assert output.intermediate_output.image_embeds.shape == torch.Size(
[1, 577, 768]
)
assert output.intermediate_output.decoder_labels.shape == torch.Size([1, 13])
assert pytest.approx(2.7152, precision) == output.loss.item()
assert (
pytest.approx(-0.0200, precision)
== torch.mean(output.intermediate_output.image_embeds).item()
)
assert all(
output.intermediate_output.decoder_labels[0]
== torch.LongTensor(
[
-100,
-100,
-100,
-100,
1997,
1037,
2711,
29035,
2300,
2013,
1037,
9545,
102,
]
).to(device)
)
def test_vqa(self):
model, vis_processors, txt_processors = load_model_and_preprocess(
name="blip_vqa", model_type="vqav2", is_eval=True, device=device
)
# ask a random question.
question = "Which city is this photo taken?"
image = vis_processors["eval"](raw_image).unsqueeze(0).to(device)
question = txt_processors["eval"](question)
samples = {"image": image, "text_input": question}
answer = model.predict_answers(
samples=samples,
inference_method="generate",
)
assert answer == ["singapore"]
answer_list = ["Singapore", "London", "Palo Alto", "Tokyo"]
answers = model.predict_answers(samples, answer_list=answer_list)
assert answers == ["Singapore"]
def test_retrieval(self):
model = load_model("blip_retrieval", "coco", is_eval=True, device=device)
images = torch.randn(4, 3, 384, 384).to(device)
text_input = [
"caption of image 1",
"another caption of image 1",
"caption of image 2",
"caption of image 3",
]
image_id = torch.tensor([1, 1, 2, 3]).to(device)
samples = {
"image": images,
"text_input": text_input,
"image_id": image_id,
"epoch": 0,
"iters": 0,
"num_iters_per_epoch": 100,
}
output = model(samples)
assert output.intermediate_output.image_embeds.shape == torch.Size(
[4, 577, 768]
)
assert output.intermediate_output.text_embeds.shape == torch.Size([4, 35, 768])
assert output.intermediate_output.image_embeds_m.shape == torch.Size(
[4, 577, 768]
)
assert output.intermediate_output.text_embeds_m.shape == torch.Size(
[4, 35, 768]
)
assert (
output.intermediate_output.encoder_output.last_hidden_state.shape
== torch.Size([4, 35, 768])
)
assert output.intermediate_output.itm_logits.shape == torch.Size([12, 2])
assert all(
output.intermediate_output.itm_labels
== torch.LongTensor([1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0]).to(device)
)
def test_pretrain(self):
model = load_model("blip_pretrain", "base", is_eval=True, device=device)
images = torch.randn(4, 3, 224, 224).to(device)
text_input = [
"caption of image 1",
"another caption of image 1",
"caption of image 2",
"caption of image 3",
]
samples = {
"image": images,
"text_input": text_input,
"epoch": 0,
"iters": 0,
"num_iters_per_epoch": 100,
}
output = model(samples)
assert output.intermediate_output.image_embeds.shape == torch.Size(
[4, 197, 768]
)
assert output.intermediate_output.text_embeds.shape == torch.Size([4, 30, 768])
assert output.intermediate_output.itm_logits.shape == torch.Size([12, 2])
assert all(
output.intermediate_output.itm_labels
== torch.LongTensor([1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0]).to(device)
)
assert output.intermediate_output.decoder_labels.shape == torch.Size([4, 30])
assert output.intermediate_output.decoder_output.logits.shape == torch.Size(
[4, 30, 30524]
)
def test_feature_extractor(self):
from PIL import Image
from lavis.models import load_model_and_preprocess
raw_image = Image.open("docs/_static/merlion.png").convert("RGB")
caption = "a large fountain spewing water into the air"
model, vis_processors, txt_processors = load_model_and_preprocess(
"blip_feature_extractor", model_type="base", is_eval=True, device=device
)
image = vis_processors["eval"](raw_image).unsqueeze(0).to(device)
text_input = txt_processors["eval"](caption)
sample = {"image": image, "text_input": [text_input]}
features_multimodal = model.extract_features(sample)
features_text = model.extract_features(sample, mode="text")
features_image = model.extract_features(sample, mode="image")
assert features_multimodal.image_embeds.shape == torch.Size([1, 197, 768])
assert features_multimodal.multimodal_embeds.shape == torch.Size([1, 12, 768])
assert features_text.text_embeds.shape == torch.Size([1, 12, 768])
assert features_text.text_embeds_proj.shape == torch.Size([1, 12, 256])
assert features_image.image_embeds.shape == torch.Size([1, 197, 768])
assert features_image.image_embeds_proj.shape == torch.Size([1, 197, 256])
assert torch.mean(features_multimodal.image_embeds).item() == pytest.approx(
-0.02032, precision
)
assert torch.mean(
features_multimodal.multimodal_embeds
).item() == pytest.approx(-0.00095, precision)
assert torch.mean(features_text.text_embeds).item() == pytest.approx(
-6.6098e-5, precision
)
assert torch.mean(features_text.text_embeds_proj).item() == pytest.approx(
-0.002149, precision
)
assert torch.mean(features_image.image_embeds).item() == pytest.approx(
-0.02032, precision
)
assert torch.mean(features_image.image_embeds_proj).item() == pytest.approx(
-0.0023, precision
)
def test_itm(self):
from PIL import Image
from lavis.models import load_model_and_preprocess
def compute_itm():
img = vis_processors["eval"](raw_image).unsqueeze(0).to(device)
txt = txt_processors["eval"](caption)
itm_output = model({"image": img, "text_input": [txt]}, match_head="itm")
itm_scores = torch.nn.functional.softmax(itm_output, dim=1)
return itm_scores
def compute_itc():
img = vis_processors["eval"](raw_image).unsqueeze(0).to(device)
txt = txt_processors["eval"](caption)
itc_score = model({"image": img, "text_input": [txt]}, match_head="itc")
return itc_score
raw_image = Image.open("docs/_static/merlion.png").convert("RGB")
model, vis_processors, txt_processors = load_model_and_preprocess(
"blip_image_text_matching", model_type="base", is_eval=True, device=device
)
caption = "merlion in Singapore"
itm_scores = compute_itm()
itc_score = compute_itc()
assert itm_scores[:, 1].item() == pytest.approx(0.98613, abs=1e-5)
assert itc_score.item() == pytest.approx(0.4633, abs=1e-4)
caption = "a random irrelevant caption"
itm_scores = compute_itm()
itc_score = compute_itc()
assert itm_scores[:, 1].item() == pytest.approx(0.05704, abs=1e-5)
assert itc_score.item() == pytest.approx(0.23282, abs=1e-5)
# test BLIP ITM large
model, vis_processors, txt_processors = load_model_and_preprocess(
"blip_image_text_matching", model_type="large", is_eval=True, device=device
)
caption = "merlion in Singapore"
itm_scores = compute_itm()
itc_score = compute_itc()
assert itm_scores[:, 1].item() == pytest.approx(0.99466, abs=1e-5)
assert itc_score.item() == pytest.approx(0.4474, abs=1e-4)
caption = "a random irrelevant caption"
itm_scores = compute_itm()
itc_score = compute_itc()
assert itm_scores[:, 1].item() == pytest.approx(0.04744, abs=1e-5)
assert itc_score.item() == pytest.approx(0.12821, abs=1e-5)
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