refactors into classes
- ImageMeta struct saves image metadata - DataHandler takes care of loading dataset and parsing the label information - YoloProcessing takes care of image preprocessing and yolo postprocessing - HailoHandler connects to Hailo device, pushes the desired network hef file and runs the dataset on the hailo chip
This commit is contained in:
raphael
parent
c64b8a27dd
commit
be89e739b4
278
inference.py
278
inference.py
@ -1,24 +1,89 @@
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import os
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from multiprocessing import Process
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import json
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import os
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import time
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import numpy as np
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t
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from PIL import Image
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from detection_tools.utils.visualization_utils import \
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visualize_boxes_and_labels_on_image_array
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from hailo_platform import (ConfigureParams, FormatType, HEF,
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HailoStreamInterface, InferVStreams,
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InputVStreamParams, OutputVStreamParams,
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PcieDevice)
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import numpy as np
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import tensorflow as tf
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from tensorflow.image import combined_non_max_suppression
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from detection_tools.utils.visualization_utils import visualize_boxes_and_labels_on_image_array
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# Collect images from data files
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from hailo_platform import (HEF, PcieDevice, HailoStreamInterface, InferVStreams, ConfigureParams, \
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InputVStreamParams, OutputVStreamParams, InputVStreams, OutputVStreams, FormatType)
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# preprocess dataset for yolov5 size
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# yolov5 640x640
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# resnet18 320x320
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class ImageMeta:
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def __init__(self, image_height, image_width, channels):
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self.image_height = image_height
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self.image_width = image_width
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self.channels = channels
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def preproc(image, output_height=640, output_width=640, resize_side=256):
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class DataHandler:
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def __init__(self, path, image_meta):
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self.images_path = path
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self.image_meta = image_meta
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def load_data(self, preprocess_fn):
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names = []
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images_list = [img_name for img_name in os.listdir(self.images_path)
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if os.path.splitext(os.path.join(self.images_path, img_name))[1] == '.jpg']
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dataset = np.zeros((1, self.image_meta.image_height,
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self.image_meta.image_width,
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self.image_meta.channels),
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dtype=np.float32)
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for idx, img_name in enumerate(images_list):
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img = Image.open(os.path.join(self.images_path, img_name))
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img_preproc = preprocess_fn(img)
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dataset[idx, :, :, :] = np.array(img_preproc)
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names.append(img_name)
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break
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self.dataset = dataset
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self.names = names
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def _get_coco_labels(self):
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coco_names = json.load(open(os.path.join(os.path.dirname(__file__), 'coco_names.json')))
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coco_names = {int(k): {'id': int(k), 'name': str(v)} for (k, v) in coco_names.items()}
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return coco_names
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def _get_labels(self, label_name):
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filename = os.path.join(os.path.dirname(__file__), label_name + '.json')
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names = json.load(open(filename))
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names = {int(k): {'id': int(k), 'name': str(v)} for (k, v) in names.items()}
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return names
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COCO_17_14 = {0: 0, 1: 1, 2: 2, 3: 3, 4: 4, 5: 5, 6: 6, 7: 7, 8: 8, 9: 9,
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10: 10, 11: 11, 12: 13, 13: 14, 14: 15, 15: 16, 16: 17, 17: 18,
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18: 19, 19: 20, 20: 21, 21: 22, 22: 23, 23: 24, 24: 25, 25: 27,
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26: 28, 27: 31, 28: 32, 29: 33, 30: 34, 31: 35, 32: 36, 33: 37,
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34: 38, 35: 39, 36: 40, 37: 41, 38: 42, 39: 43, 40: 44, 41: 46,
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42: 47, 43: 48, 44: 49, 45: 50, 46: 51, 47: 52, 48: 53, 49: 54,
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50: 55, 51: 56, 52: 57, 53: 58, 54: 59, 55: 60, 56: 61, 57: 62,
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58: 63, 59: 64, 60: 65, 61: 67, 62: 70, 63: 72, 64: 73, 65: 74,
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66: 75, 67: 76, 68: 77, 69: 78, 70: 79, 71: 80, 72: 81, 73: 82,
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74: 84, 75: 85, 76: 86, 77: 87, 78: 88, 79: 89, 80: 90}
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class YoloProcessing:
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def __init__(self, imageMeta, classes):
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self.output_height = imageMeta.image_height
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self.output_width = imageMeta.image_width
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self.classes = classes
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def preproc(self, image, resize_side=256):
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'''
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imagenet-standard: aspect-preserving resize to 256px smaller-side,
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@ -26,100 +91,18 @@ def preproc(image, output_height=640, output_width=640, resize_side=256):
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'''
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new_width = int(image.width/image.height*resize_side)
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new_height = resize_side
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x , y = (new_width-output_width)/2, 0
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x, y = (new_width-self.output_width)/2, 0
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# Select area to crop
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area = (x, y, x+output_width, y+output_height)
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area = (x, y, x+self.output_width, y+self.output_height)
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# Crop, show, and save image
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cropped_img = image.resize((new_width, new_height)).crop(area)
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return cropped_img
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# Collect images from data files
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def dataset_read(hef):
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images_path = './minimal_data'
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names = []
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images_list = [img_name for img_name in os.listdir(images_path) if
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os.path.splitext(os.path.join(images_path, img_name))[1] == '.jpg']
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# Define dataset params
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input_vstream_info = hef.get_input_vstream_infos()[0]
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output_vstream_infos = hef.get_output_vstream_infos()
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image_height, image_width, channels = input_vstream_info.shape
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# dataset = np.zeros((len(images_list), image_height, image_width, channels),
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# dtype=np.float32)
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dataset = np.zeros((1, image_height, image_width, channels),
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dtype=np.float32)
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for idx, img_name in enumerate(images_list):
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img = Image.open(os.path.join(images_path, img_name))
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img_preproc = preproc(img)
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dataset[idx,:,:,:] = np.array(img_preproc)
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names.append(img_name)
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break
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return dataset, names
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# Generate random dataset
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def dataset_random(image_height, image_width, channels):
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num_of_images = 10
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low, high = 2, 20
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dataset = np.random.randint(low, high, (num_of_images, image_height,
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image_width, channels)).astype(np.float32)
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return dataset
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def init_hailo(model_name='yolov5m'):
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target = PcieDevice()
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hef_path = f'hef/{model_name}.hef'
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hef = HEF(hef_path)
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# Configure network groups
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configure_params = ConfigureParams.create_from_hef(hef=hef, interface=HailoStreamInterface.PCIe)
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network_groups = target.configure(hef, configure_params)
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network_group = network_groups[0]
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return hef, network_group
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'''
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The target can be used as a context manager ("with" statement) to ensure it's released on time.
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Here it's avoided for the sake of simplicity
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'''
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def run_hailo(dataset, names, hef, network_group):
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# Create input and output virtual streams params
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# Quantized argument signifies whether or not the incoming data is already quantized.
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# Data is quantized by HailoRT if and only if quantized == False .
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input_vstreams_params = InputVStreamParams.make(network_group,
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quantized=False,
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format_type=FormatType.FLOAT32)
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# TODO: change to FLOAT32
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output_vstreams_params = OutputVStreamParams.make(network_group, quantized=False, format_type=FormatType.FLOAT32)
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# output_vstreams_params = OutputVStreamParams.make(network_group,
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# quantized=True,
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# format_type=FormatType.INT8)
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input_vstream_info = hef.get_input_vstream_infos()[0]
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output_vstream_infos = hef.get_output_vstream_infos()
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input_data = {input_vstream_info.name: dataset}
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network_group_params = network_group.create_params()
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with InferVStreams(network_group, input_vstreams_params, output_vstreams_params) as infer_pipeline:
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with network_group.activate(network_group_params):
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infer_results = infer_pipeline.infer(input_data)
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out = [infer_results[i.name] for i in output_vstream_infos]
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return out, names, dataset, names
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# 20 x 20 -> 32
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# stride = 32
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def yolo_postprocess_numpy(net_out, anchors_for_stride, stride):
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# 20 x 20 -> 32
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# stride = 32
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def yolo_postprocess_numpy(self, net_out, anchors_for_stride, stride):
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"""
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net_out is shape: [N, 19, 19, 255] or [N, 38, 38, 255] or [N, 76, 76, 255]
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first we reshape it to be as in gluon and then follow gluon's shapes.
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@ -156,7 +139,7 @@ def yolo_postprocess_numpy(net_out, anchors_for_stride, stride):
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class_pred = pred[:, :, :, 5:] # dim [N, HxW, 3, 80]
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offsets = offsets[:, :, :H, :W, :] # dim [1, 1, H, W, 2]
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offsets = np.reshape(offsets, (1, -1, 1, 2)) # dim [1, HxW, 1, 2]
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box_centers, box_scales, confidence, class_pred = _yolo5_decode(
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box_centers, box_scales, confidence, class_pred = self._yolo5_decode(
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raw_box_centers=raw_box_centers,
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raw_box_scales=raw_box_scales,
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objness=objness,
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@ -183,12 +166,13 @@ def yolo_postprocess_numpy(net_out, anchors_for_stride, stride):
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detection_boxes = detection_boxes_tmp # now scheme is: ymin, xmin, ymax, xmax
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return detection_boxes.astype(np.float32), detection_scores.astype(np.float32)
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def _yolo5_decode(raw_box_centers, raw_box_scales, objness, class_pred, anchors_for_stride, offsets, stride):
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def _yolo5_decode(self, raw_box_centers, raw_box_scales, objness, class_pred, anchors_for_stride, offsets, stride):
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box_centers = (raw_box_centers * 2. - 0.5 + offsets) * stride
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box_scales = (raw_box_scales * 2) ** 2 * anchors_for_stride # dim [N, HxW, 3, 2]
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return box_centers, box_scales, objness, class_pred
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def postprocessing(endnodes):
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def postprocessing(self, endnodes):
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"""
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endnodes is a list of 3 output tensors:
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endnodes[0] - stride 32 of input
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@ -207,14 +191,13 @@ def postprocessing(endnodes):
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anchors_list = [[10, 13, 16, 30, 33, 23], [30, 61, 62, 45, 59, 119], [116, 90, 156, 198, 373, 326]]
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# TODO make prettier
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strides = [8, 16, 32]
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num_classes = 80
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for output_ind, output_branch in enumerate(endnodes): # iterating over the output layers:
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stride = strides[::-1][output_ind]
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anchors_for_stride = np.array(anchors_list[::-1][output_ind])
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anchors_for_stride = np.reshape(anchors_for_stride, (1, 1, -1, 2)) # dim [1, 1, 3, 2]
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detection_boxes, detection_scores = yolo_postprocess_numpy(output_branch,
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detection_boxes, detection_scores = self.yolo_postprocess_numpy(output_branch,
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anchors_for_stride,
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stride)
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@ -251,7 +234,7 @@ def postprocessing(endnodes):
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# adding offset to the class prediction and cast to integer
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def translate_coco_2017_to_2014(nmsed_classes):
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return np.vectorize(COCO_2017_TO_2014_TRANSLATION.get)(nmsed_classes).astype(np.int32)
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return np.vectorize(COCO_17_14.get)(nmsed_classes).astype(np.int32)
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nmsed_classes = tf.cast(tf.add(nmsed_classes, labels_offset), tf.int16)
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nmsed_classes = translate_coco_2017_to_2014(nmsed_classes)
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@ -262,56 +245,58 @@ def postprocessing(endnodes):
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'num_detections': num_detections}
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def _get_face_detection_visualization_data(logits):
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boxes = logits['detection_boxes'][0]
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face_landmarks = logits.get('face_landmarks')
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if face_landmarks is not None:
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face_landmarks = face_landmarks[0].reshape((-1, 5, 2))[:, :, (1, 0)]
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boxes = boxes[:, (1, 0, 3, 2)]
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# No name to prevent clobbering the visualization
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labels = {1: {'id': 1, 'name': ''}}
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return boxes, labels, face_landmarks
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class HailoHandler:
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def __init__(self, hef_path='hef/yolov5m.hef'):
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target = PcieDevice()
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self.hef = HEF(hef_path)
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def _get_coco_labels():
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coco_names = json.load(open(os.path.join(os.path.dirname(__file__), 'coco_names.json')))
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coco_names = {int(k): {'id': int(k), 'name': str(v)} for (k, v) in coco_names.items()}
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return coco_names
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# Configure network groups
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configure_params = ConfigureParams.create_from_hef(hef=self.hef,
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interface=HailoStreamInterface.PCIe)
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network_groups = target.configure(self.hef, configure_params)
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self.network_group = network_groups[0]
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self.input_vstreams_params = InputVStreamParams.make(self.network_group,
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quantized=False,
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format_type=FormatType.FLOAT32)
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self.output_vstreams_params = OutputVStreamParams.make(self.network_group, quantized=False, format_type=FormatType.FLOAT32)
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self.input_vstream_info = self.hef.get_input_vstream_infos()[0]
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self.output_vstream_infos = self.hef.get_output_vstream_infos()
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self.network_group_params = self.network_group.create_params()
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def run_hailo(self, dataset):
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input_data = {self.input_vstream_info.name: dataset}
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with InferVStreams(self.network_group, self.input_vstreams_params, self.output_vstreams_params) as infer_pipeline:
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with self.network_group.activate(self.network_group_params):
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infer_results = infer_pipeline.infer(input_data)
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out = [infer_results[i.name] for i in self.output_vstream_infos]
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return out
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def _get_labels(label_name):
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filename = os.path.join(os.path.dirname(__file__), label_name + '.json')
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names = json.load(open(filename))
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names = {int(k): {'id': int(k), 'name': str(v)} for (k, v) in names.items()}
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return names
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def process_yolo5():
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hef, network_group = init_hailo("yolov5m_22_2")
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imageMeta = ImageMeta(640, 640, 3)
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processor = YoloProcessing(imageMeta, classes=3)
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data = DataHandler('./minimal_data', imageMeta)
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data.load_data(processor.preproc)
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dataset, names = dataset_read(hef)
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hailo = HailoHandler('hef/yolov5m_22_2.hef')
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out = hailo.run_hailo(data.dataset)
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samples = 1000
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start_time = time.time()
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fps = 0
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while samples > 0:
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if start_time + 1 < time.time():
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print("fps: " + str(fps))
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start_time = time.time()
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fps = 0
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out, names, dataset, names = run_hailo(dataset, names, hef, network_group)
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logits = postprocessing(out)
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fps += 1
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samples -= 1
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logits = processor.postprocessing(out)
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labels = _get_labels("daria_names")
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labels = data._get_labels("daria_names")
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image = visualize_boxes_and_labels_on_image_array(
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dataset[0],
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data.dataset[0],
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logits['detection_boxes'].numpy()[0],
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logits['detection_classes'][0],
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logits['detection_scores'].numpy()[0],
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@ -324,16 +309,5 @@ def process_yolo5():
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Image.fromarray(np.uint8(image)).save('/home/maintenance/test.png')
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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,
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11: 11, 12: 13, 13: 14, 14: 15, 15: 16, 16: 17, 17: 18, 18: 19,
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19: 20, 20: 21, 21: 22, 22: 23, 23: 24, 24: 25, 25: 27, 26: 28,
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27: 31, 28: 32, 29: 33, 30: 34, 31: 35, 32: 36, 33: 37, 34: 38,
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35: 39, 36: 40, 37: 41, 38: 42, 39: 43, 40: 44, 41: 46, 42: 47,
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43: 48, 44: 49, 45: 50, 46: 51, 47: 52, 48: 53, 49: 54, 50: 55,
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51: 56, 52: 57, 53: 58, 54: 59, 55: 60, 56: 61, 57: 62, 58: 63,
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59: 64, 60: 65, 61: 67, 62: 70, 63: 72, 64: 73, 65: 74, 66: 75,
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67: 76, 68: 77, 69: 78, 70: 79, 71: 80, 72: 81, 73: 82, 74: 84,
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75: 85, 76: 86, 77: 87, 78: 88, 79: 89, 80: 90}
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if __name__ == "__main__":
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process_yolo5()
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