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test_engine.py
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test_engine.py
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# Copyright (c) 2021, Zhiqiang Wang. All Rights Reserved.
from pathlib import Path
import unittest
import torch
from torch import Tensor
import torch.utils.data
from torchvision.io import read_image
import pytorch_lightning as pl
from yolort.data import DetectionDataModule
from yolort.data.coco import CocoDetection
from yolort.data.transforms import collate_fn, default_train_transforms
from yolort.models.yolo import yolov5_darknet_pan_s_r31
from yolort.models.transform import nested_tensor_from_tensor_list
from yolort.models import yolov5s
from yolort.utils import prepare_coco128
from .dataset_utils import DummyCOCODetectionDataset
from typing import Dict
def default_loader(img_name, is_half=False):
"""
Read Image using TorchVision.io Here
"""
img = read_image(img_name)
img = img.half() if is_half else img.float() # uint8 to fp16/32
img /= 255. # 0 - 255 to 0.0 - 1.0
return img
class EngineTester(unittest.TestCase):
def test_train_with_vanilla_model(self):
# Do forward over image
img_name = "test/assets/zidane.jpg"
img_tensor = default_loader(img_name)
self.assertEqual(img_tensor.ndim, 3)
# Add a dummy image to train
img_dummy = torch.rand((3, 416, 360), dtype=torch.float32)
images = nested_tensor_from_tensor_list([img_tensor, img_dummy])
targets = torch.tensor([[0, 7, 0.3790, 0.5487, 0.3220, 0.2047],
[0, 2, 0.2680, 0.5386, 0.2200, 0.1779],
[0, 3, 0.1720, 0.5403, 0.1960, 0.1409],
[0, 4, 0.2240, 0.4547, 0.1520, 0.0705]], dtype=torch.float)
model = yolov5_darknet_pan_s_r31(num_classes=12)
model.train()
out = model(images, targets)
self.assertIsInstance(out, Dict)
self.assertIsInstance(out["cls_logits"], Tensor)
self.assertIsInstance(out["bbox_regression"], Tensor)
self.assertIsInstance(out["objectness"], Tensor)
def test_train_with_vanilla_module(self):
"""
For issue #86: <https://github.com/zhiqwang/yolov5-rt-stack/issues/86>
"""
# Define the device
device = torch.device('cpu')
# Prepare the datasets for training
# Acquire the images and labels from the coco128 dataset
data_path = Path('data-bin')
coco128_dirname = 'coco128'
coco128_path = data_path / coco128_dirname
image_root = coco128_path / 'images' / 'train2017'
annotation_file = coco128_path / 'annotations' / 'instances_train2017.json'
if not annotation_file.is_file():
prepare_coco128(data_path, dirname=coco128_dirname)
batch_size = 4
dataset = CocoDetection(image_root, annotation_file, default_train_transforms())
sampler = torch.utils.data.RandomSampler(dataset)
batch_sampler = torch.utils.data.BatchSampler(sampler, batch_size, drop_last=True)
data_loader = torch.utils.data.DataLoader(
dataset, batch_sampler=batch_sampler, collate_fn=collate_fn, num_workers=0)
# Sample a pair of images/targets
images, targets = next(iter(data_loader))
images = [img.to(device) for img in images]
targets = [{k: v.to(device) for k, v in t.items()} for t in targets]
# Define the model
model = yolov5s(num_classes=80)
model.train()
out = model(images, targets)
self.assertIsInstance(out, Dict)
self.assertIsInstance(out["cls_logits"], Tensor)
self.assertIsInstance(out["bbox_regression"], Tensor)
self.assertIsInstance(out["objectness"], Tensor)
def test_train_one_epoch(self):
# Setup the DataModule
train_dataset = DummyCOCODetectionDataset(num_samples=128)
data_module = DetectionDataModule(train_dataset, batch_size=16)
# Load model
model = yolov5s()
model.train()
# Trainer
trainer = pl.Trainer(max_epochs=1)
trainer.fit(model, data_module)
def test_predict_with_vanilla_model(self):
# Set image inputs
img_name = "test/assets/zidane.jpg"
img_input = default_loader(img_name)
self.assertEqual(img_input.ndim, 3)
# Load model
model = yolov5s(pretrained=True)
model.eval()
# Perform inference on a list of tensors
out = model([img_input])
self.assertIsInstance(out, list)
self.assertEqual(len(out), 1)
self.assertIsInstance(out[0], Dict)
self.assertIsInstance(out[0]["boxes"], Tensor)
self.assertIsInstance(out[0]["labels"], Tensor)
self.assertIsInstance(out[0]["scores"], Tensor)
def test_predict_with_tensor(self):
# Set image inputs
img_name = "test/assets/zidane.jpg"
img_tensor = default_loader(img_name)
self.assertEqual(img_tensor.ndim, 3)
# Load model
model = yolov5s(pretrained=True)
model.eval()
# Perform inference on a list of image files
predictions = model.predict(img_tensor)
self.assertIsInstance(predictions, list)
self.assertEqual(len(predictions), 1)
self.assertIsInstance(predictions[0], Dict)
self.assertIsInstance(predictions[0]["boxes"], Tensor)
self.assertIsInstance(predictions[0]["labels"], Tensor)
self.assertIsInstance(predictions[0]["scores"], Tensor)
def test_predict_with_tensors(self):
# Set image inputs
img_tensor1 = default_loader("test/assets/zidane.jpg")
self.assertEqual(img_tensor1.ndim, 3)
img_tensor2 = default_loader("test/assets/bus.jpg")
self.assertEqual(img_tensor2.ndim, 3)
img_tensors = [img_tensor1, img_tensor2]
# Load model
model = yolov5s(pretrained=True)
model.eval()
# Perform inference on a list of image files
predictions = model.predict(img_tensors)
self.assertIsInstance(predictions, list)
self.assertEqual(len(predictions), 2)
self.assertIsInstance(predictions[0], Dict)
self.assertIsInstance(predictions[0]["boxes"], Tensor)
self.assertIsInstance(predictions[0]["labels"], Tensor)
self.assertIsInstance(predictions[0]["scores"], Tensor)
def test_predict_with_image_file(self):
# Set image inputs
img_name = "test/assets/zidane.jpg"
# Load model
model = yolov5s(pretrained=True)
model.eval()
# Perform inference on an image file
predictions = model.predict(img_name)
self.assertIsInstance(predictions, list)
self.assertEqual(len(predictions), 1)
self.assertIsInstance(predictions[0], Dict)
self.assertIsInstance(predictions[0]["boxes"], Tensor)
self.assertIsInstance(predictions[0]["labels"], Tensor)
self.assertIsInstance(predictions[0]["scores"], Tensor)
def test_predict_with_image_files(self):
# Set image inputs
img_name1 = "test/assets/zidane.jpg"
img_name2 = "test/assets/bus.jpg"
img_names = [img_name1, img_name2]
# Load model
model = yolov5s(pretrained=True)
model.eval()
# Perform inference on a list of image files
predictions = model.predict(img_names)
self.assertIsInstance(predictions, list)
self.assertEqual(len(predictions), 2)
self.assertIsInstance(predictions[0], Dict)
self.assertIsInstance(predictions[0]["boxes"], Tensor)
self.assertIsInstance(predictions[0]["labels"], Tensor)
self.assertIsInstance(predictions[0]["scores"], Tensor)