132 lines
5.1 KiB
Python
132 lines
5.1 KiB
Python
import sys
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import requests
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import cv2
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import random
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import time
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import numpy as np
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import cupy as cp
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import tensorrt as trt
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from PIL import Image
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from collections import OrderedDict, namedtuple
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from pathlib import Path
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def letterbox(im, new_shape=(640, 640), color=(114, 114, 114), auto=True, scaleup=True, stride=32):
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# Resize and pad image while meeting stride-multiple constraints
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shape = im.shape[:2] # current shape [height, width]
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if isinstance(new_shape, int):
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new_shape = (new_shape, new_shape)
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# Scale ratio (new / old)
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r = min(new_shape[0] / shape[0], new_shape[1] / shape[1])
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if not scaleup: # only scale down, do not scale up (for better val mAP)
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r = min(r, 1.0)
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# Compute padding
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new_unpad = int(round(shape[1] * r)), int(round(shape[0] * r))
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dw, dh = new_shape[1] - new_unpad[0], new_shape[0] - new_unpad[1] # wh padding
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if auto: # minimum rectangle
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dw, dh = np.mod(dw, stride), np.mod(dh, stride) # wh padding
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dw /= 2 # divide padding into 2 sides
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dh /= 2
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if shape[::-1] != new_unpad: # resize
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im = cv2.resize(im, new_unpad, interpolation=cv2.INTER_LINEAR)
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top, bottom = int(round(dh - 0.1)), int(round(dh + 0.1))
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left, right = int(round(dw - 0.1)), int(round(dw + 0.1))
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im = cv2.copyMakeBorder(im, top, bottom, left, right, cv2.BORDER_CONSTANT, value=color) # add border
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return im, r, (dw, dh)
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names = ['person', 'bicycle', 'car', 'motorcycle', 'airplane', 'bus', 'train', 'truck', 'boat', 'traffic light',
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'fire hydrant', 'stop sign', 'parking meter', 'bench', 'bird', 'cat', 'dog', 'horse', 'sheep', 'cow',
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'elephant', 'bear', 'zebra', 'giraffe', 'backpack', 'umbrella', 'handbag', 'tie', 'suitcase', 'frisbee',
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'skis', 'snowboard', 'sports ball', 'kite', 'baseball bat', 'baseball glove', 'skateboard', 'surfboard',
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'tennis racket', 'bottle', 'wine glass', 'cup', 'fork', 'knife', 'spoon', 'bowl', 'banana', 'apple',
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'sandwich', 'orange', 'broccoli', 'carrot', 'hot dog', 'pizza', 'donut', 'cake', 'chair', 'couch',
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'potted plant', 'bed', 'dining table', 'toilet', 'tv', 'laptop', 'mouse', 'remote', 'keyboard', 'cell phone',
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'microwave', 'oven', 'toaster', 'sink', 'refrigerator', 'book', 'clock', 'vase', 'scissors', 'teddy bear',
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'hair drier', 'toothbrush']
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colors = {name: [random.randint(0, 255) for _ in range(3)] for i, name in enumerate(names)}
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url = 'https://oneflow-static.oss-cn-beijing.aliyuncs.com/tripleMu/image1.jpg'
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file = requests.get(url)
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img = cv2.imdecode(np.frombuffer(file.content, np.uint8), 1)
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w = Path(sys.argv[1])
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assert w.exists() and w.suffix in ('.engine', '.plan'), 'Wrong engine path'
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mean = np.array([0.485, 0.456, 0.406], dtype=np.float32).reshape(1, 3, 1, 1)
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std = np.array([0.229, 0.224, 0.225], dtype=np.float32).reshape(1, 3, 1, 1)
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mean = cp.asarray(mean)
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std = cp.asarray(std)
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# Infer TensorRT Engine
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Binding = namedtuple('Binding', ('name', 'dtype', 'shape', 'data', 'ptr'))
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logger = trt.Logger(trt.Logger.INFO)
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trt.init_libnvinfer_plugins(logger, namespace="")
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with open(w, 'rb') as f, trt.Runtime(logger) as runtime:
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model = runtime.deserialize_cuda_engine(f.read())
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bindings = OrderedDict()
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fp16 = False # default updated below
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for index in range(model.num_bindings):
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name = model.get_binding_name(index)
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dtype = trt.nptype(model.get_binding_dtype(index))
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shape = tuple(model.get_binding_shape(index))
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data = cp.empty(shape, dtype=cp.dtype(dtype))
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bindings[name] = Binding(name, dtype, shape, data, int(data.data.ptr))
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if model.binding_is_input(index) and dtype == np.float16:
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fp16 = True
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binding_addrs = OrderedDict((n, d.ptr) for n, d in bindings.items())
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context = model.create_execution_context()
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image = img.copy()
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image, ratio, dwdh = letterbox(image, auto=False)
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image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
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image_copy = image.copy()
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image = image.transpose((2, 0, 1))
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image = np.expand_dims(image, 0)
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image = np.ascontiguousarray(image)
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im = cp.asarray(image)
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im = im.astype(cp.float32)
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im /= 255
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im -= mean
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im /= std
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# warmup for 10 times
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for _ in range(10):
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tmp = cp.random.randn(1, 3, 640, 640).astype(cp.float32)
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binding_addrs['image'] = int(tmp.data.ptr)
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context.execute_v2(list(binding_addrs.values()))
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start = time.perf_counter()
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binding_addrs['image'] = int(im.data.ptr)
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context.execute_v2(list(binding_addrs.values()))
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print(f'Cost {(time.perf_counter() - start) * 1000}ms')
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nums = bindings['num_dets'].data
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boxes = bindings['det_boxes'].data
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scores = bindings['det_scores'].data
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classes = bindings['det_classes'].data
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num = int(nums[0][0])
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box_img = boxes[0, :num].round().astype(cp.int32)
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score_img = scores[0, :num]
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clss_img = classes[0, :num]
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for i, (box, score, clss) in enumerate(zip(box_img, score_img, clss_img)):
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name = names[int(clss)]
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color = colors[name]
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cv2.rectangle(image_copy, box[:2].tolist(), box[2:].tolist(), color, 2)
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cv2.putText(image_copy, name, (int(box[0]), int(box[1]) - 2), cv2.FONT_HERSHEY_SIMPLEX,
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0.75, [225, 255, 255], thickness=2)
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cv2.imshow('Result', cv2.cvtColor(image_copy, cv2.COLOR_RGB2BGR))
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cv2.waitKey(0)
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