Lyken17

import numpy as np

from collections import Counter

import tvm
from tvm import relay
# from tvm.relay import ExprFunctor, ExprMutator, ExprVisitor
from tvm.relay.expr_functor import ExprMutator, Call

Lyken17

import numpy as np

from collections import Counter

import tvm
from tvm import relay
from tvm.relay import ExprFunctor, ExprMutator, ExprVisitor
from tvm.relay.expr_functor import ExprMutator, Call

Lyken17

import os, sys
import os.path as osp
import math

import numpy as np
import torch
import torch.nn as nn
from torchvision import transforms, datasets

from ofa.model_zoo import ofa_net

Lyken17

import torch
import torch.nn as nn
import torchvision

from torchvision import models


batch = 1 
dim = 3
res = 224

Lyken17

# dense update
# forward 
input: 1, 48, 8, 8
weight: 240, 48, 1, 1
output: 1, 240, 8, 8

# input 
# (n, c, h, w) => (1, n * c, h, w)
input_1 = 1, 48, 8, 8

Lyken17

import os, os.path as osp
import json
from copy import deepcopy


import numpy as np
from copy import deepcopy

import torch
import torch.nn as nn

Lyken17

from tokenize import group
import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.nn.modules.utils import _single, _pair, _triple
import warnings

from torch.nn.grad import _grad_input_padding

@torch.no_grad()

Lyken17

import torch
import torch.nn as nn
import torch.nn.functional as F


def serialize(raw_idx):
    raw_idx = raw_idx.clone()
    # put 3 int10 into one int32
    d = raw_idx.view(-1, 3)
    d[:, 0] = d[:, 0] << 20

Lyken17

import os
import numpy as np

import torch
import torch.nn as nn
import torch.nn.functional as F

import tvm
from tvm import relay, autotvm, auto_scheduler
import tvm.relay.testing

Lyken17

import torch
import torch.nn as nn
import torch.nn.functional as F

# pytorch way
input = torch.randn(3, 5)
target = torch.randint(5, (3,), dtype=torch.int64)
loss1 = F.cross_entropy(input, target)

## equal with cross_entropy_with_logits