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tests for yi, stable diffusion, timm models, etc
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# Import necessary modules and define fixtures if needed | ||
import os | ||
import pytest | ||
import torch | ||
from PIL import Image | ||
from swarms.models.bioclip import BioClip | ||
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# Define fixtures if needed | ||
@pytest.fixture | ||
def sample_image_path(): | ||
return "path_to_sample_image.jpg" | ||
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@pytest.fixture | ||
def clip_instance(): | ||
return BioClip("microsoft/BiomedCLIP-PubMedBERT_256-vit_base_patch16_224") | ||
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# Basic tests for the BioClip class | ||
def test_clip_initialization(clip_instance): | ||
assert isinstance(clip_instance.model, torch.nn.Module) | ||
assert hasattr(clip_instance, "model_path") | ||
assert hasattr(clip_instance, "preprocess_train") | ||
assert hasattr(clip_instance, "preprocess_val") | ||
assert hasattr(clip_instance, "tokenizer") | ||
assert hasattr(clip_instance, "device") | ||
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def test_clip_call_method(clip_instance, sample_image_path): | ||
labels = [ | ||
"adenocarcinoma histopathology", | ||
"brain MRI", | ||
"covid line chart", | ||
"squamous cell carcinoma histopathology", | ||
"immunohistochemistry histopathology", | ||
"bone X-ray", | ||
"chest X-ray", | ||
"pie chart", | ||
"hematoxylin and eosin histopathology", | ||
] | ||
result = clip_instance(sample_image_path, labels) | ||
assert isinstance(result, dict) | ||
assert len(result) == len(labels) | ||
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def test_clip_plot_image_with_metadata(clip_instance, sample_image_path): | ||
metadata = { | ||
"filename": "sample_image.jpg", | ||
"top_probs": {"label1": 0.75, "label2": 0.65}, | ||
} | ||
clip_instance.plot_image_with_metadata(sample_image_path, metadata) | ||
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# More test cases can be added to cover additional functionality and edge cases | ||
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# Parameterized tests for different image and label combinations | ||
@pytest.mark.parametrize( | ||
"image_path, labels", | ||
[ | ||
("image1.jpg", ["label1", "label2"]), | ||
("image2.jpg", ["label3", "label4"]), | ||
# Add more image and label combinations | ||
], | ||
) | ||
def test_clip_parameterized_calls(clip_instance, image_path, labels): | ||
result = clip_instance(image_path, labels) | ||
assert isinstance(result, dict) | ||
assert len(result) == len(labels) | ||
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# Test image preprocessing | ||
def test_clip_image_preprocessing(clip_instance, sample_image_path): | ||
image = Image.open(sample_image_path) | ||
processed_image = clip_instance.preprocess_val(image) | ||
assert isinstance(processed_image, torch.Tensor) | ||
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# Test label tokenization | ||
def test_clip_label_tokenization(clip_instance): | ||
labels = ["label1", "label2"] | ||
tokenized_labels = clip_instance.tokenizer(labels) | ||
assert isinstance(tokenized_labels, torch.Tensor) | ||
assert tokenized_labels.shape[0] == len(labels) | ||
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# More tests can be added to cover other methods and edge cases | ||
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# End-to-end tests with actual images and labels | ||
def test_clip_end_to_end(clip_instance, sample_image_path): | ||
labels = [ | ||
"adenocarcinoma histopathology", | ||
"brain MRI", | ||
"covid line chart", | ||
"squamous cell carcinoma histopathology", | ||
"immunohistochemistry histopathology", | ||
"bone X-ray", | ||
"chest X-ray", | ||
"pie chart", | ||
"hematoxylin and eosin histopathology", | ||
] | ||
result = clip_instance(sample_image_path, labels) | ||
assert isinstance(result, dict) | ||
assert len(result) == len(labels) | ||
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# Test label tokenization with long labels | ||
def test_clip_long_labels(clip_instance): | ||
labels = ["label" + str(i) for i in range(100)] | ||
tokenized_labels = clip_instance.tokenizer(labels) | ||
assert isinstance(tokenized_labels, torch.Tensor) | ||
assert tokenized_labels.shape[0] == len(labels) | ||
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# Test handling of multiple image files | ||
def test_clip_multiple_images(clip_instance, sample_image_path): | ||
labels = ["label1", "label2"] | ||
image_paths = [sample_image_path, "image2.jpg"] | ||
results = clip_instance(image_paths, labels) | ||
assert isinstance(results, list) | ||
assert len(results) == len(image_paths) | ||
for result in results: | ||
assert isinstance(result, dict) | ||
assert len(result) == len(labels) | ||
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# Test model inference performance | ||
def test_clip_inference_performance(clip_instance, sample_image_path, benchmark): | ||
labels = [ | ||
"adenocarcinoma histopathology", | ||
"brain MRI", | ||
"covid line chart", | ||
"squamous cell carcinoma histopathology", | ||
"immunohistochemistry histopathology", | ||
"bone X-ray", | ||
"chest X-ray", | ||
"pie chart", | ||
"hematoxylin and eosin histopathology", | ||
] | ||
result = benchmark(clip_instance, sample_image_path, labels) | ||
assert isinstance(result, dict) | ||
assert len(result) == len(labels) | ||
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# Test different preprocessing pipelines | ||
def test_clip_preprocessing_pipelines(clip_instance, sample_image_path): | ||
labels = ["label1", "label2"] | ||
image = Image.open(sample_image_path) | ||
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# Test preprocessing for training | ||
processed_image_train = clip_instance.preprocess_train(image) | ||
assert isinstance(processed_image_train, torch.Tensor) | ||
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# Test preprocessing for validation | ||
processed_image_val = clip_instance.preprocess_val(image) | ||
assert isinstance(processed_image_val, torch.Tensor) | ||
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# ... |
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