timm
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4 changed files with 179 additions and 10 deletions
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import towhee |
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from towhee.dc2 import pipe, ops |
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from towhee import triton_client |
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import onnxruntime |
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import numpy |
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import torch |
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from statistics import mean |
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import time |
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import argparse |
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import os |
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import re |
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import warnings |
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import logging |
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from transformers import logging as t_logging |
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os.environ['TF_CPP_MIN_LOG_LEVEL'] = '3' |
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warnings.filterwarnings("ignore") |
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t_logging.set_verbosity_error() |
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parser = argparse.ArgumentParser() |
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parser.add_argument('--model', required=True, type=str) |
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parser.add_argument('--pipe', action='store_true') |
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parser.add_argument('--triton', action='store_true') |
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parser.add_argument('--onnx', action='store_true') |
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parser.add_argument('--atol', type=float, default=1e-3) |
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parser.add_argument('--num', type=int, default=100) |
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parser.add_argument('--device', type=str, default='cpu') |
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args = parser.parse_args() |
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model_name = args.model |
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# model_name = 'resnet50' |
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# model_name = 'vgg16' |
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# model_name = 'deit3_base_patch16_224' |
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# model_name = 'deit_tiny_patch16_224' |
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# model_name = 'deit_base_distilled_patch16_224' |
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# model_name = 'convnext_base' |
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# model_name = 'vit_base_patch16_224' |
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# model_name = 'tf_efficientnet_b5' |
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p = ( |
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pipe.input('url') |
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.map('url', 'img', ops.image_decode.cv2_rgb()) |
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.map('img', 'vec', ops.image_embedding.timm(model_name=model_name, device=args.device)) |
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.output('vec') |
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) |
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data = '../towhee.jpeg' |
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out1 = p(data).get()[0] |
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print('Pipe: OK') |
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if args.num and args.pipe: |
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qps = [] |
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for _ in range(10): |
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start = time.time() |
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p.batch([data] * args.num) |
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# for _ in range(args.num): |
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# p(data) |
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end = time.time() |
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q = args.num / (end - start) |
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qps.append(q) |
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print('Pipe qps:', mean(qps)) |
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if args.triton: |
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client = triton_client.Client(url='localhost:8000') |
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out2 = client(data)[0][0][0] |
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print('Triton: OK') |
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if numpy.allclose(out1, out2, atol=args.atol): |
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print('Check accuracy: OK') |
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else: |
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max_diff = numpy.abs(out1 - out2).max() |
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min_diff = numpy.abs(out1 - out2).min() |
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mean_diff = numpy.abs(out1 - out2).mean() |
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print(f'Check accuracy: atol is larger than {args.atol}.') |
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print(f'Maximum absolute difference is {max_diff}.') |
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print(f'Minimum absolute difference is {min_diff}.') |
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print(f'Mean difference is {mean_diff}.') |
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if args.num: |
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qps = [] |
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for _ in range(10): |
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start = time.time() |
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client.batch([data] * args.num) |
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end = time.time() |
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q = args.num / (end - start) |
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qps.append(q) |
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print('Triton qps:', mean(qps)) |
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if args.onnx: |
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op = ops.image_embedding.timm(model_name=model_name, device='cpu').get_op() |
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decoder = ops.image_decode.cv2_rgb().get_op() |
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# if not os.path.exists('test.onnx'): |
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op.save_model('onnx', 'test.onnx') |
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sess = onnxruntime.InferenceSession('test.onnx', |
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providers=['CUDAExecutionProvider']) |
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inputs = decoder(data) |
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inputs = op.convert_img(inputs) |
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inputs = op.tfms(inputs).unsqueeze(0) |
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out3 = sess.run(None, input_feed={'input_0': inputs.cpu().detach().numpy()})[0] |
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op.device = 'cuda' if args.device != 'cpu' else 'cpu' |
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out3 = op.post_proc(torch.from_numpy(out3)).cpu().detach().numpy() |
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print('Onnx: OK') |
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if numpy.allclose(out1, out3, atol=args.atol): |
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print('Check accuracy: OK') |
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else: |
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max_diff = numpy.abs(out1 - out3).max() |
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min_diff = numpy.abs(out1 - out3).min() |
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mean_diff = numpy.abs(out1 - out3).mean() |
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print(f'Check accuracy: atol is larger than {args.atol}.') |
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print(f'Maximum absolute difference is {max_diff}.') |
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print(f'Minimum absolute difference is {min_diff}.') |
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print(f'Mean difference is {mean_diff}.') |
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if args.num: |
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qps = [] |
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for _ in range(10): |
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start = time.time() |
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for _ in range(args.num): |
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inputs = decoder(data) |
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inputs = op.convert_img(inputs) |
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inputs = op.tfms(inputs).unsqueeze(0) |
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outs = sess.run(None, input_feed={'input_0': inputs.cpu().detach().numpy()})[0] |
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outs = op.post_proc(torch.from_numpy(outs)) |
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end = time.time() |
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q = args.num / (end - start) |
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qps.append(q) |
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print('Onnx qps:', mean(qps)) |
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