from torch_receptive_field import receptive_field
receptive_field(model, input_size=(channels, H, W))

from torch_receptive_field import receptive_field
dict = receptive_field(model, input_size=(channels, H, W))
receptive_field_for_unit(receptive_field_dict, "2", (2,2))

import torch
import torch.nn as nn
import torch.nn.functional as F
from torch_receptive_field import receptive_field

class Net(nn.Module):
    def __init__(self):
        super(Net, self).__init__()
        self.conv = nn.Conv2d(3, 64, kernel_size=7, stride=2, padding=3, bias=False)
        self.bn = nn.BatchNorm2d(64)
        self.relu = nn.ReLU(inplace=True)
        self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1)

    def forward(self, x):
        y = self.conv(x)
        y = self.bn(y)
        y = self.relu(y)
        y = self.maxpool(y)
        return y


device = torch.device("cuda" if torch.cuda.is_available() else "cpu") # PyTorch v0.4.0
model = Net().to(device)

receptive_field_dict = receptive_field(model, (3, 256, 256))
receptive_field_for_unit(receptive_field_dict, "2", (2,2))