Closed di0002ya closed 2 years ago
aws-taylor
commented
2 years ago Hello @di0002ya,
The issue you are running into is related to the empty --io-config '{"inputs": {}, "outputs": []}'. Are you able to share the commands necessary to reproduce this issue?
-Taylor
di0002ya
commented
2 years ago Hi @aws-taylor , thanks for your help! I tried to re-organize my model wrapper yesterday.
import logging
import warnings
from ts.torch_handler.base_handler import BaseHandler
import os
import torch
import numpy as np
from collections import OrderedDict
import base64
import io
from PIL import Image
import math
import cv2
import json
from mmocr.apis import init_detector, model_inference
from mmocr.utils.model import revert_sync_batchnorm
from mmocr.datasets.pipelines.crop import crop_img
from mmcv import imfrombytes
import torch.nn as nn
def iminvert(img):
"""Invert (negate) an image.
Args:
img (ndarray): Image to be inverted.
Returns:
ndarray: The inverted image.
"""
return np.full_like(img, 255) - img
class WrapDet(nn.Module):
def __init__(self):
super(WrapDet, self).__init__()
model_dir = 'mmocr'
# det_ckpt = "checkpoints/textdet/dbnetpp/dbnetpp_r50dcnv2_fpnc_1200e_icdar2015-20220502-d7a76fff.pth"
det_ckpt = "checkpoints/dbnet_r18_fpnc_sbn_1200e_icdar2015_20210329-ba3ab597.pth"
recog_ckpt = "checkpoints/textrec/abinet_academic-f718abf6.pth"
det_config = "dbr18config.py"
recog_config = "abinet_recog_config.py"
det_ckpt = os.path.join(model_dir, det_ckpt)
det_config = os.path.join(model_dir, det_config)
recog_model = init_detector(recog_config, recog_ckpt, device="cpu")
self.recog_model = revert_sync_batchnorm(recog_model)
detect_model = init_detector(det_config, det_ckpt, device="cpu")
self.detect_model = revert_sync_batchnorm(detect_model)
self.batch_mode = False
self.recog_batch_size = 1
def forward(self, inputs):
image = transforms.ToPILImage()(inputs)
arr = iminvert(image)
det_result = self.single_inference(self.detect_model, [arr], batch_mode = False, batch_size = 0)
# text recognition
bboxes_list = [res['boundary_result'] for res in det_result]
print(f"total bboxes list:{len(bboxes_list)}")
bboxes = bboxes_list[0]
end2end_res = []
img_e2e_res = {}
img_e2e_res['result'] = []
box_imgs = []
for bbox in bboxes:
box_res = {}
box_res['box'] = [round(x) for x in bbox[:-1]]
box_res['box_score'] = float(bbox[-1])
box = bbox[:8]
if len(bbox) > 9:
min_x = min(bbox[0:-1:2])
min_y = min(bbox[1:-1:2])
max_x = max(bbox[0:-1:2])
max_y = max(bbox[1:-1:2])
box = [
min_x, min_y, max_x, min_y, max_x, max_y, min_x, max_y
]
box_img = crop_img(arr, box)
if self.batch_mode:
box_imgs.append(box_img)
else:
recog_result = model_inference(self.recog_model, box_img)
text = recog_result['text']
text_score = recog_result['score']
if isinstance(text_score, list):
text_score = sum(text_score) / max(1, len(text))
box_res['text'] = text
box_res['text_score'] = text_score
img_e2e_res['result'].append(box_res)
if self.batch_mode:
recog_results = self.single_inference(
self.recog_model, box_imgs, batch_mode=self.batch_mode, batch_size=self.recog_batch_size)
for i, recog_result in enumerate(recog_results):
text = recog_result['text']
text_score = recog_result['score']
if isinstance(text_score, (list, tuple)):
text_score = sum(text_score) / max(1, len(text))
img_e2e_res['result'][i]['text'] = text
img_e2e_res['result'][i]['text_score'] = text_score
end2end_res.append(img_e2e_res)
# return tuple(end2end_res[0]['result'])
return (torch.FloatTensor([end2end_res[0]['result'][0]['box_score']]))
def single_inference(self, model, arrays, batch_mode, batch_size):
result = []
if batch_mode:
if batch_size == 0:
result = model_inference(model, arrays, batch_mode=True)
else:
n = batch_size
arr_chunks = [
arrays[i:i + n] for i in range(0, len(arrays), n)
]
for chunk in arr_chunks:
result.extend(model_inference(model, chunk, batch_mode=True))
else:
for arr in arrays:
result.append(model_inference(model, arr, batch_mode=False))
return resultBefore conversion, I test det_model(inputs)without any error.
Then I proceed to model conversion:
model_neuron = torch.neuron.trace(det_model, example_inputs=[inputs], strict = False)
inference_check.html.zip
The log are shown in attached file.
di0002ya
commented
2 years ago After running model_neuron = torch.neuron.trace(det_model, example_inputs=[inputs], strict = False). it shows WARNING:Neuron:torch.neuron.trace was unable to compile > 50% of the operators in the compiled model! WARNING:Neuron:Please review the torch.neuron.analyze_model output and if you believe you are seeing a failure WARNING:Neuron:Lodge an issue on https://github.com/aws/aws-neuron-sdk/issues if you believe the model is not compiling as expected without breaking the process.
However, when I tried to run inference on neuron model using following command:
model_neuron.eval()
model_neuron(inputs)Log was shown below:
2022-Aug-18 08:22:45.0374 5279:5279 ERROR NRT:nrt_init Unable to read compatible Driver version.
2022-Aug-18 08:22:45.0374 5279:5279 ERROR NRT:nrt_init Please check /dev/neuron# is accessible. If you're using containers, please ensure Neuron Devices are passed to the container by specifying `--device /dev/neuron#`.Hello @di0002ya ,
Are you able to share the associated model configuration and checkpoint files? We have a candidate fix, but without a reproduction it's difficult to test. Specifically these files:
det_ckpt = "checkpoints/dbnet_r18_fpnc_sbn_1200e_icdar2015_20210329-ba3ab597.pth"
recog_ckpt = "checkpoints/textrec/abinet_academic-f718abf6.pth"
det_config = "dbr18config.py"
recog_config = "abinet_recog_config.py"
Hi team, I tried to compiled model by using pytorch neuron and deployed to inferentia.
I firstly run:
torch.neuron.analyze_model(det_model, inputs)Output shows all operations are supported.
However, when I tried
model_neuron = torch.neuron.trace(det_model, inputs )I got runtime error. May I know how to solve it? Thanks in advance!