From c64b8a27ddb5403ac38a22227c646763b269c25d Mon Sep 17 00:00:00 2001 From: raphael Date: Thu, 24 Feb 2022 16:49:23 +0100 Subject: [PATCH] adds inference tutorial setup for custom yolov5 - postprocessing for yolov5 included - loading hef file of custom yolov5 onto hailo chip - running inference - saving image and printing fps for inference and postprocessing --- inference.py | 339 +++++++++++++++++++++++++++++++++++++++++++++++++++ 1 file changed, 339 insertions(+) create mode 100644 inference.py diff --git a/inference.py b/inference.py new file mode 100644 index 0000000..70c5a5a --- /dev/null +++ b/inference.py @@ -0,0 +1,339 @@ +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()