ivanpanshin

def swa(paths):
    state_dicts = []
    for path in paths:
        state_dicts.append(torch.load(path)["model_state_dict"])

    average_dict = OrderedDict()
    for k in state_dicts[0].keys():
        average_dict[k] = sum([state_dict[k] for state_dict in state_dicts]) / len(state_dicts)

    return average_dict

ivanpanshin

class ArcModule(nn.Module):
    def __init__(self, in_features, out_features, s=10, m=0.5):
        super().__init__()
        self.in_features = in_features
        self.out_features = out_features
        self.s = s
        self.m = m
        self.weight = nn.Parameter(torch.FloatTensor(out_features, in_features))
        nn.init.xavier_normal_(self.weight)

ivanpanshin

import os
import onnx
import torch
import torch.nn as nn
import onnxruntime as ort
from onnxsim import simplify
from PIL import Image
from ssd.config import cfg
from ssd.data.datasets import COCODataset, VOCDataset
import argparse

ivanpanshin

import onnx
import torch
import torchvision
import onnxruntime as ort
from onnxsim import simplify
import numpy as np

if __name__ == '__main__':
    model = torchvision.models.resnet18(pretrained=True).cuda().eval()
    dummy_input = torch.ones(1, 3, 224, 224, device="cuda")

ivanpanshin

import utils.inference as inference_utils # TRT/TF inference wrappers
import utils.model as model_utils # UFF conversion
import tensorrt as trt
import argparse

if __name__ == '__main__':
    parser = argparse.ArgumentParser(description='TRT params')
    parser.add_argument('--FP', default="32", type=str)
    args = parser.parse_args()

ivanpanshin

import onnx
import torch
import numpy as np
import onnxruntime as ort
import torchvision as tv
from onnxsim import simplify
dummy_input = torch.ones(1, 3, 300, 300, device="cuda")
pt_model_det = tv.models.resnet50().cuda().eval().requires_grad_(False)
pre_det_res = pt_model_det(dummy_input.cuda())
print(f"Torch output shape: {pre_det_res.shape}")

ivanpanshin

import onnx
import torch
import torch.nn as nn
import torchvision
import torchvision.transforms as transforms
#import cv2
from PIL import Image
import os
import matplotlib.pyplot as plt
import time

ivanpanshin

from caffe2.proto import caffe2_pb2
from caffe2.python import core, workspace
from detectron2.export.caffe2_inference import ProtobufDetectionModel
from detectron2.export.api import Caffe2Model
import numpy as np
import os
import torch
import cv2
import time
import shutil

ivanpanshin

from caffe2.proto import caffe2_pb2
from caffe2.python import core, workspace
from detectron2.export.caffe2_inference import ProtobufDetectionModel
from detectron2.export.api import Caffe2Model
import numpy as np
import os
import torch
import cv2
print("Required modules imported.")

ivanpanshin

from flask import Flask, request, jsonify

server = Flask(__name__)

def run_request():
    index = int(request.json['index'])
    list = ['red', 'green', 'blue', 'yellow', 'black']
    return list[index]

@server.route('/', methods=['GET', 'POST'])