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adds inference tutorial setup for custom yolov5

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- postprocessing for yolov5 included
- loading hef file of custom yolov5 onto hailo chip
- running inference
- saving image and printing fps for inference
  and postprocessing
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raphael 2022-02-24 16:49:23 +01:00
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import os
from multiprocessing import Process
import json
import time
import numpy as np
from PIL import Image
import tensorflow as tf
from tensorflow.image import combined_non_max_suppression
from detection_tools.utils.visualization_utils import visualize_boxes_and_labels_on_image_array
from hailo_platform import (HEF, PcieDevice, HailoStreamInterface, InferVStreams, ConfigureParams, \
InputVStreamParams, OutputVStreamParams, InputVStreams, OutputVStreams, FormatType)
# preprocess dataset for yolov5 size
# yolov5 640x640
# resnet18 320x320
def preproc(image, output_height=640, output_width=640, resize_side=256):
'''
imagenet-standard: aspect-preserving resize to 256px smaller-side,
then central-crop to 224px
'''
new_width = int(image.width/image.height*resize_side)
new_height = resize_side
x , y = (new_width-output_width)/2, 0
# Select area to crop
area = (x, y, x+output_width, y+output_height)
# Crop, show, and save image
cropped_img = image.resize((new_width, new_height)).crop(area)
return cropped_img
# Collect images from data files
def dataset_read(hef):
images_path = './minimal_data'
names = []
images_list = [img_name for img_name in os.listdir(images_path) if
os.path.splitext(os.path.join(images_path, img_name))[1] == '.jpg']
# Define dataset params
input_vstream_info = hef.get_input_vstream_infos()[0]
output_vstream_infos = hef.get_output_vstream_infos()
image_height, image_width, channels = input_vstream_info.shape
# dataset = np.zeros((len(images_list), image_height, image_width, channels),
# dtype=np.float32)
dataset = np.zeros((1, image_height, image_width, channels),
dtype=np.float32)
for idx, img_name in enumerate(images_list):
img = Image.open(os.path.join(images_path, img_name))
img_preproc = preproc(img)
dataset[idx,:,:,:] = np.array(img_preproc)
names.append(img_name)
break
return dataset, names
# Generate random dataset
def dataset_random(image_height, image_width, channels):
num_of_images = 10
low, high = 2, 20
dataset = np.random.randint(low, high, (num_of_images, image_height,
image_width, channels)).astype(np.float32)
return dataset
def init_hailo(model_name='yolov5m'):
target = PcieDevice()
hef_path = f'hef/{model_name}.hef'
hef = HEF(hef_path)
# Configure network groups
configure_params = ConfigureParams.create_from_hef(hef=hef, interface=HailoStreamInterface.PCIe)
network_groups = target.configure(hef, configure_params)
network_group = network_groups[0]
return hef, network_group
'''
The target can be used as a context manager ("with" statement) to ensure it's released on time.
Here it's avoided for the sake of simplicity
'''
def run_hailo(dataset, names, hef, network_group):
# Create input and output virtual streams params
# Quantized argument signifies whether or not the incoming data is already quantized.
# Data is quantized by HailoRT if and only if quantized == False .
input_vstreams_params = InputVStreamParams.make(network_group,
quantized=False,
format_type=FormatType.FLOAT32)
# TODO: change to FLOAT32
output_vstreams_params = OutputVStreamParams.make(network_group, quantized=False, format_type=FormatType.FLOAT32)
# output_vstreams_params = OutputVStreamParams.make(network_group,
# quantized=True,
# format_type=FormatType.INT8)
input_vstream_info = hef.get_input_vstream_infos()[0]
output_vstream_infos = hef.get_output_vstream_infos()
input_data = {input_vstream_info.name: dataset}
network_group_params = network_group.create_params()
with InferVStreams(network_group, input_vstreams_params, output_vstreams_params) as infer_pipeline:
with network_group.activate(network_group_params):
infer_results = infer_pipeline.infer(input_data)
out = [infer_results[i.name] for i in output_vstream_infos]
return out, names, dataset, names
# 20 x 20 -> 32
# stride = 32
def yolo_postprocess_numpy(net_out, anchors_for_stride, stride):
"""
net_out is shape: [N, 19, 19, 255] or [N, 38, 38, 255] or [N, 76, 76, 255]
first we reshape it to be as in gluon and then follow gluon's shapes.
output_ind = 0 for stride 32, 1 for stride 16, 2 for stride 8.
"""
# net_out = net_out.astype(np.float32) / 256
num_classes = 4
BS = net_out.shape[0] # batch size
H = net_out.shape[1]
W = net_out.shape[2]
num_anchors = anchors_for_stride.size // 2 # 2 params for each anchor.
num_pred = 1 + 4 + num_classes # 2 box centers, 2 box scales, 1 objness, num_classes class scores
alloc_size = (128, 128)
grid_x = np.arange(alloc_size[1])
grid_y = np.arange(alloc_size[0])
grid_x, grid_y = np.meshgrid(grid_x, grid_y) # dims [128,128], [128,128]
offsets = np.concatenate((grid_x[:, :, np.newaxis], grid_y[:, :, np.newaxis]), axis=-1) # dim [128,128,2]
offsets = np.expand_dims(np.expand_dims(offsets, 0), 0) # dim [1,1,128,128,2]
pred = net_out.transpose((0, 3, 1, 2)) # now dims are: [N,C,H,W] as in Gluon.
pred = np.reshape(pred, (BS, num_anchors * num_pred, -1)) # dim [N, 255, HxW]
# dim [N, 361, 255], we did it so that the 255 be the last dim and can be reshaped.
pred = pred.transpose((0, 2, 1))
pred = np.reshape(pred, (BS, -1, num_anchors, num_pred)) # dim [N, HxW, 3, 85]]
raw_box_centers = pred[:, :, :, 0:2] # dim [N, HxW, 3, 2]
raw_box_scales = pred[:, :, :, 2:4] # dim [N,HxW, 3, 2]
objness = pred[:, :, :, 4:5] # dim [N, HxW, 3, 1]
class_pred = pred[:, :, :, 5:] # dim [N, HxW, 3, 80]
offsets = offsets[:, :, :H, :W, :] # dim [1, 1, H, W, 2]
offsets = np.reshape(offsets, (1, -1, 1, 2)) # dim [1, HxW, 1, 2]
box_centers, box_scales, confidence, class_pred = _yolo5_decode(
raw_box_centers=raw_box_centers,
raw_box_scales=raw_box_scales,
objness=objness,
class_pred=class_pred,
anchors_for_stride=anchors_for_stride,
offsets=offsets,
stride=stride)
class_score = class_pred * confidence # dim [N, HxW, 3, 80]
wh = box_scales / 2.0
# dim [N, HxW, 3, 4]. scheme xmin, ymin, xmax, ymax
bbox = np.concatenate((box_centers - wh, box_centers + wh), axis=-1)
detection_boxes = np.reshape(bbox, (BS, -1, 1, 4)) # dim [N, num_detections, 1, 4]
detection_scores = np.reshape(class_score, (BS, -1, num_classes)) # dim [N, num_detections, 80]
# switching scheme from xmin, ymin, xmanx, ymax to ymin, xmin, ymax, xmax:
detection_boxes_tmp = np.zeros(detection_boxes.shape)
detection_boxes_tmp[:, :, :, 0] = detection_boxes[:, :, :, 1]
detection_boxes_tmp[:, :, :, 1] = detection_boxes[:, :, :, 0]
detection_boxes_tmp[:, :, :, 2] = detection_boxes[:, :, :, 3]
detection_boxes_tmp[:, :, :, 3] = detection_boxes[:, :, :, 2]
detection_boxes = detection_boxes_tmp # now scheme is: ymin, xmin, ymax, xmax
return detection_boxes.astype(np.float32), detection_scores.astype(np.float32)
def _yolo5_decode(raw_box_centers, raw_box_scales, objness, class_pred, anchors_for_stride, offsets, stride):
box_centers = (raw_box_centers * 2. - 0.5 + offsets) * stride
box_scales = (raw_box_scales * 2) ** 2 * anchors_for_stride # dim [N, HxW, 3, 2]
return box_centers, box_scales, objness, class_pred
def postprocessing(endnodes):
"""
endnodes is a list of 3 output tensors:
endnodes[0] - stride 32 of input
endnodes[1] - stride 16 of input
endnodes[2] - stride 8 of input
Returns:
a tensor with dims: [BS, Total_num_of_detections_in_image, 6]
where:
total_num_of_detections_in_image = H*W*((1/32^2) + (1/16^2) + (1/8^2))*num_anchors*num_classes,
with H, W as input dims.
If H=W=608, num_anchors=3, num_classes=80 (coco 2017), we get:
total_num_of_detections = 1819440 ~ 1.8M detections per image for the NMS
"""
H_input = 640
W_input = 640
anchors_list = [[10, 13, 16, 30, 33, 23], [30, 61, 62, 45, 59, 119], [116, 90, 156, 198, 373, 326]]
# TODO make prettier
strides = [8, 16, 32]
num_classes = 80
for output_ind, output_branch in enumerate(endnodes): # iterating over the output layers:
stride = strides[::-1][output_ind]
anchors_for_stride = np.array(anchors_list[::-1][output_ind])
anchors_for_stride = np.reshape(anchors_for_stride, (1, 1, -1, 2)) # dim [1, 1, 3, 2]
detection_boxes, detection_scores = yolo_postprocess_numpy(output_branch,
anchors_for_stride,
stride)
# detection_boxes is a [BS, num_detections, 1, 4] tensor, detection_scores is a
# [BS, num_detections, num_classes] tensor
detection_boxes = detection_boxes / H_input # normalization of box coordinates to 1
BS = endnodes[0].shape[0]
H = H_input // stride
W = W_input // stride
num_anchors = anchors_for_stride.size // 2
num_detections = H * W * num_anchors
# detection_boxes.set_shape((BS, num_detections, 1, 4))
# detection_scores.set_shape((BS, num_detections, num_classes))
# concatenating the detections from the different output layers:
if output_ind == 0:
detection_boxes_full = detection_boxes
detection_scores_full = detection_scores
else:
detection_boxes_full = tf.concat([detection_boxes_full, detection_boxes], axis=1)
detection_scores_full = tf.concat([detection_scores_full, detection_scores], axis=1)
score_threshold = 0.5
nms_iou_threshold = 0.5
labels_offset = 1
(nmsed_boxes, nmsed_scores, nmsed_classes, num_detections) = \
combined_non_max_suppression(boxes=detection_boxes_full,
scores=detection_scores_full,
score_threshold=score_threshold,
iou_threshold=nms_iou_threshold,
max_output_size_per_class=100,
max_total_size=100)
# adding offset to the class prediction and cast to integer
def translate_coco_2017_to_2014(nmsed_classes):
return np.vectorize(COCO_2017_TO_2014_TRANSLATION.get)(nmsed_classes).astype(np.int32)
nmsed_classes = tf.cast(tf.add(nmsed_classes, labels_offset), tf.int16)
nmsed_classes = translate_coco_2017_to_2014(nmsed_classes)
return {'detection_boxes': nmsed_boxes,
'detection_scores': nmsed_scores,
'detection_classes': nmsed_classes,
'num_detections': num_detections}
def _get_face_detection_visualization_data(logits):
boxes = logits['detection_boxes'][0]
face_landmarks = logits.get('face_landmarks')
if face_landmarks is not None:
face_landmarks = face_landmarks[0].reshape((-1, 5, 2))[:, :, (1, 0)]
boxes = boxes[:, (1, 0, 3, 2)]
# No name to prevent clobbering the visualization
labels = {1: {'id': 1, 'name': ''}}
return boxes, labels, face_landmarks
def _get_coco_labels():
coco_names = json.load(open(os.path.join(os.path.dirname(__file__), 'coco_names.json')))
coco_names = {int(k): {'id': int(k), 'name': str(v)} for (k, v) in coco_names.items()}
return coco_names
def _get_labels(label_name):
filename = os.path.join(os.path.dirname(__file__), label_name + '.json')
names = json.load(open(filename))
names = {int(k): {'id': int(k), 'name': str(v)} for (k, v) in names.items()}
return names
def process_yolo5():
hef, network_group = init_hailo("yolov5m_22_2")
dataset, names = dataset_read(hef)
samples = 1000
start_time = time.time()
fps = 0
while samples > 0:
if start_time + 1 < time.time():
print("fps: " + str(fps))
start_time = time.time()
fps = 0
out, names, dataset, names = run_hailo(dataset, names, hef, network_group)
logits = postprocessing(out)
fps += 1
samples -= 1
labels = _get_labels("daria_names")
image = visualize_boxes_and_labels_on_image_array(
dataset[0],
logits['detection_boxes'].numpy()[0],
logits['detection_classes'][0],
logits['detection_scores'].numpy()[0],
labels,
use_normalized_coordinates=True,
max_boxes_to_draw=100,
min_score_thresh=.5,
agnostic_mode=False,
line_thickness=4)
Image.fromarray(np.uint8(image)).save('/home/maintenance/test.png')
COCO_2017_TO_2014_TRANSLATION = {0: 0, 1: 1, 2: 2, 3: 3, 4: 4, 5: 5, 6: 6, 7: 7, 8: 8, 9: 9, 10: 10,
11: 11, 12: 13, 13: 14, 14: 15, 15: 16, 16: 17, 17: 18, 18: 19,
19: 20, 20: 21, 21: 22, 22: 23, 23: 24, 24: 25, 25: 27, 26: 28,
27: 31, 28: 32, 29: 33, 30: 34, 31: 35, 32: 36, 33: 37, 34: 38,
35: 39, 36: 40, 37: 41, 38: 42, 39: 43, 40: 44, 41: 46, 42: 47,
43: 48, 44: 49, 45: 50, 46: 51, 47: 52, 48: 53, 49: 54, 50: 55,
51: 56, 52: 57, 53: 58, 54: 59, 55: 60, 56: 61, 57: 62, 58: 63,
59: 64, 60: 65, 61: 67, 62: 70, 63: 72, 64: 73, 65: 74, 66: 75,
67: 76, 68: 77, 69: 78, 70: 79, 71: 80, 72: 81, 73: 82, 74: 84,
75: 85, 76: 86, 77: 87, 78: 88, 79: 89, 80: 90}
if __name__ == "__main__":
process_yolo5()