[Broken] Evaluate YOLO v3 on Inferentia#
Note: this tutorial runs on tensorflow-neuron 1.x only#
Introduction#
This tutorial walks through compiling and evaluating YOLO v3 model on Inferentia using the AWS Neuron SDK.
In this tutorial we provide two main sections:
Download Dataset and Generate Pretrained SavedModel
Compile the YOLO v3 model.
Deploy the same compiled model.
Verify that this Jupyter notebook is running the Python kernel environment that was set up according to the Tensorflow Installation Guide. You can select the Kernel from the “Kernel -> Change Kernel” option on the top of this Jupyter notebook page.
Instructions of how to setup Neuron Tensorflow environment and run the tutorial as a Jupyter notebook are available in the Tutorial main page Tensorflow-YOLO_v3 Tutorial
Prerequisites#
This demo requires the following pip packages:
pillow matplotlib pycocotools
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%pip install tensorflow_neuron==1.15.5.2.8.9.0 neuron_cc==1.13.5.0 requests pillow matplotlib pycocotools==2.0.1 numpy==1.18.2 torch~=1.5.0 --force \
--extra-index-url=https://pip.repos.neuron.amazonaws.com
Part 1: Download Dataset and Generate Pretrained SavedModel#
Download COCO 2017 validation dataset#
We start by downloading the COCO validation dataset, which we will use to validate our model. The COCO 2017 dataset is widely used for object-detection, segmentation and image captioning.
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!curl -LO http://images.cocodataset.org/zips/val2017.zip
!curl -LO http://images.cocodataset.org/annotations/annotations_trainval2017.zip
!unzip -q val2017.zip
!unzip annotations_trainval2017.zip
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!ls
Generate YOLO v3 tensorflow SavedModel (pretrained on COCO 2017 dataset)#
Script yolo_v3_coco_saved_model.py will generate a tensorflow SavedModel using pretrained weights from YunYang1994/tensorflow-yolov3.
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%run yolo_v3_coco_saved_model.py ./yolo_v3_coco_saved_model
This tensorflow SavedModel can be loaded as a tensorflow predictor. When a JPEG format image is provided as input, the output result of the tensorflow predictor contains information for drawing bounding boxes and classification results.
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import json
import tensorflow as tf
from PIL import Image
import matplotlib.pyplot as plt
import matplotlib.patches as patches
# launch predictor and run inference on an arbitrary image in the validation dataset
yolo_pred_cpu = tf.contrib.predictor.from_saved_model('./yolo_v3_coco_saved_model')
image_path = './val2017/000000581781.jpg'
with open(image_path, 'rb') as f:
feeds = {'image': [f.read()]}
results = yolo_pred_cpu(feeds)
# load annotations to decode classification result
with open('./annotations/instances_val2017.json') as f:
annotate_json = json.load(f)
label_info = {idx+1: cat['name'] for idx, cat in enumerate(annotate_json['categories'])}
# draw picture and bounding boxes
fig, ax = plt.subplots(figsize=(10, 10))
ax.imshow(Image.open(image_path).convert('RGB'))
wanted = results['scores'][0] > 0.1
for xyxy, label_no_bg in zip(results['boxes'][0][wanted], results['classes'][0][wanted]):
xywh = xyxy[0], xyxy[1], xyxy[2] - xyxy[0], xyxy[3] - xyxy[1]
rect = patches.Rectangle((xywh[0], xywh[1]), xywh[2], xywh[3], linewidth=1, edgecolor='g', facecolor='none')
ax.add_patch(rect)
rx, ry = rect.get_xy()
rx = rx + rect.get_width() / 2.0
ax.annotate(label_info[label_no_bg + 1], (rx, ry), color='w', backgroundcolor='g', fontsize=10,
ha='center', va='center', bbox=dict(boxstyle='square,pad=0.01', fc='g', ec='none', alpha=0.5))
plt.show()
Part 2: Compile the Pretrained SavedModel for Neuron#
We make use of the Python compilation API tfn.saved_model.compile that is available in tensorflow-neuron<2. For the purpose of reducing Neuron runtime overhead, it is necessary to make use of arguments no_fuse_ops and minimum_segment_size. Compiled model is saved in ./yolo_v3_coco_saved_model_neuron.
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import shutil
import tensorflow as tf
import tensorflow.neuron as tfn
def no_fuse_condition(op):
return op.name.startswith('Preprocessor') or op.name.startswith('Postprocessor')
with tf.Session(graph=tf.Graph()) as sess:
tf.saved_model.loader.load(sess, ['serve'], './yolo_v3_coco_saved_model')
no_fuse_ops = [op.name for op in sess.graph.get_operations() if no_fuse_condition(op)]
shutil.rmtree('./yolo_v3_coco_saved_model_neuron', ignore_errors=True)
result = tfn.saved_model.compile(
'./yolo_v3_coco_saved_model', './yolo_v3_coco_saved_model_neuron',
# to enforce trivial compilable subgraphs to run on CPU
no_fuse_ops=no_fuse_ops,
minimum_segment_size=100,
batch_size=2,
dynamic_batch_size=True,
)
print(result)
Deploy the model on Inferentia#
Part 3:Evaluate Model Quality after Compilation#
Define evaluation functions#
We first define some handy helper functions for running evaluation on the COCO 2017 dataset.
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import os
import json
import time
import numpy as np
import tensorflow as tf
from pycocotools.coco import COCO
from pycocotools.cocoeval import COCOeval
def cocoapi_eval(jsonfile,
style,
coco_gt=None,
anno_file=None,
max_dets=(100, 300, 1000)):
"""
Args:
jsonfile: Evaluation json file, eg: bbox.json, mask.json.
style: COCOeval style, can be `bbox` , `segm` and `proposal`.
coco_gt: Whether to load COCOAPI through anno_file,
eg: coco_gt = COCO(anno_file)
anno_file: COCO annotations file.
max_dets: COCO evaluation maxDets.
"""
assert coco_gt is not None or anno_file is not None
if coco_gt is None:
coco_gt = COCO(anno_file)
print("Start evaluate...")
coco_dt = coco_gt.loadRes(jsonfile)
if style == 'proposal':
coco_eval = COCOeval(coco_gt, coco_dt, 'bbox')
coco_eval.params.useCats = 0
coco_eval.params.maxDets = list(max_dets)
else:
coco_eval = COCOeval(coco_gt, coco_dt, style)
coco_eval.evaluate()
coco_eval.accumulate()
coco_eval.summarize()
return coco_eval.stats
def bbox_eval(anno_file, bbox_list):
coco_gt = COCO(anno_file)
outfile = 'bbox_detections.json'
print('Generating json file...')
with open(outfile, 'w') as f:
json.dump(bbox_list, f)
map_stats = cocoapi_eval(outfile, 'bbox', coco_gt=coco_gt)
return map_stats
def get_image_as_bytes(images, eval_pre_path):
batch_im_id_list = []
batch_im_name_list = []
batch_img_bytes_list = []
n = len(images)
batch_im_id = []
batch_im_name = []
batch_img_bytes = []
for i, im in enumerate(images):
im_id = im['id']
file_name = im['file_name']
if i % eval_batch_size == 0 and i != 0:
batch_im_id_list.append(batch_im_id)
batch_im_name_list.append(batch_im_name)
batch_img_bytes_list.append(batch_img_bytes)
batch_im_id = []
batch_im_name = []
batch_img_bytes = []
batch_im_id.append(im_id)
batch_im_name.append(file_name)
with open(os.path.join(eval_pre_path, file_name), 'rb') as f:
batch_img_bytes.append(f.read())
return batch_im_id_list, batch_im_name_list, batch_img_bytes_list
def analyze_bbox(results, batch_im_id, _clsid2catid):
bbox_list = []
k = 0
for boxes, scores, classes in zip(results['boxes'], results['scores'], results['classes']):
if boxes is not None:
im_id = batch_im_id[k]
n = len(boxes)
for p in range(n):
clsid = classes[p]
score = scores[p]
xmin, ymin, xmax, ymax = boxes[p]
catid = (_clsid2catid[int(clsid)])
w = xmax - xmin + 1
h = ymax - ymin + 1
bbox = [xmin, ymin, w, h]
# Round to the nearest 10th to avoid huge file sizes, as COCO suggests
bbox = [round(float(x) * 10) / 10 for x in bbox]
bbox_res = {
'image_id': im_id,
'category_id': catid,
'bbox': bbox,
'score': float(score),
}
bbox_list.append(bbox_res)
k += 1
return bbox_list
Here is the actual evaluation loop. To fully utilize all four cores on one Inferentia, the optimal setup is to run multi-threaded inference using a ThreadPoolExecutor. The following cell is a multi-threaded adaptation of the evaluation routine at miemie2013/Keras-YOLOv4.
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from concurrent import futures
def evaluate(yolo_predictor, images, eval_pre_path, anno_file, eval_batch_size, _clsid2catid):
batch_im_id_list, batch_im_name_list, batch_img_bytes_list = get_image_as_bytes(images, eval_pre_path)
# warm up
yolo_predictor({'image': np.array(batch_img_bytes_list[0], dtype=object)})
with futures.ThreadPoolExecutor(4) as exe:
fut_im_list = []
fut_list = []
start_time = time.time()
for batch_im_id, batch_im_name, batch_img_bytes in zip(batch_im_id_list, batch_im_name_list, batch_img_bytes_list):
if len(batch_img_bytes) != eval_batch_size:
continue
fut = exe.submit(yolo_predictor, {'image': np.array(batch_img_bytes, dtype=object)})
fut_im_list.append((batch_im_id, batch_im_name))
fut_list.append(fut)
bbox_list = []
count = 0
for (batch_im_id, batch_im_name), fut in zip(fut_im_list, fut_list):
results = fut.result()
bbox_list.extend(analyze_bbox(results, batch_im_id, _clsid2catid))
for _ in batch_im_id:
count += 1
if count % 100 == 0:
print('Test iter {}'.format(count))
print('==================== Performance Measurement ====================')
print('Finished inference on {} images in {} seconds'.format(len(images), time.time() - start_time))
print('=================================================================')
# start evaluation
box_ap_stats = bbox_eval(anno_file, bbox_list)
return box_ap_stats
Evaluate mean average precision (mAP) score#
Here is the code to calculate mAP scores of the YOLO v3 model. The expected mAP score is around 0.328 if we use the pretrained weights.
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yolo_pred = tf.contrib.predictor.from_saved_model('./yolo_v3_coco_saved_model_neuron')
val_coco_root = './val2017'
val_annotate = './annotations/instances_val2017.json'
clsid2catid = {0: 1, 1: 2, 2: 3, 3: 4, 4: 5, 5: 6, 6: 7, 7: 8, 8: 9, 9: 10, 10: 11, 11: 13, 12: 14, 13: 15, 14: 16,
15: 17, 16: 18, 17: 19, 18: 20, 19: 21, 20: 22, 21: 23, 22: 24, 23: 25, 24: 27, 25: 28, 26: 31,
27: 32, 28: 33, 29: 34, 30: 35, 31: 36, 32: 37, 33: 38, 34: 39, 35: 40, 36: 41, 37: 42, 38: 43,
39: 44, 40: 46, 41: 47, 42: 48, 43: 49, 44: 50, 45: 51, 46: 52, 47: 53, 48: 54, 49: 55, 50: 56,
51: 57, 52: 58, 53: 59, 54: 60, 55: 61, 56: 62, 57: 63, 58: 64, 59: 65, 60: 67, 61: 70, 62: 72,
63: 73, 64: 74, 65: 75, 66: 76, 67: 77, 68: 78, 69: 79, 70: 80, 71: 81, 72: 82, 73: 84, 74: 85,
75: 86, 76: 87, 77: 88, 78: 89, 79: 90}
eval_batch_size = 8
with open(val_annotate, 'r', encoding='utf-8') as f2:
for line in f2:
line = line.strip()
dataset = json.loads(line)
images = dataset['images']
box_ap = evaluate(yolo_pred, images, val_coco_root, val_annotate, eval_batch_size, clsid2catid)