vision_model.py

from typing import Tuple, Sequence, Dict, Union, Optional, Callable
import torch
import torch.nn as nn
import torch.nn.functional as F

""" 
Two vision encoder here, VisionEncoder: simple and ResNetFe: resnet style
Note: batch norm will be replaced by group norm before using with the noise predictor 
as group norm works better with unet for action noise prediction.
"""

#This is a simple visionencoder (without resnet)
class VisionEncoder(nn.Module):
    def __init__(self ):
        super(VisionEncoder, self).__init__() 
        self.conv1 = nn.Conv2d(3, 64, kernel_size=7, stride=2, padding=3, bias=False) # (x+2*3-7)/2+1=(x-1)/2+1=48 : 64*48*48
        self.gn1 = nn.GroupNorm(32, 64) #  num_groups, num_channels
        self.relu = nn.ReLU(inplace=True)
        self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1)       #(x+2*1-3)/2+1=(x-1)/2+1=24  : 64*24*24

        self.conv2 = nn.Conv2d(64, 128, kernel_size=3, stride=2, padding=1, bias=False)
        self.conv3 = nn.Conv2d(128, 256, kernel_size=3, stride=2, padding=1, bias=False)
        self.avgpool = nn.AdaptiveAvgPool2d((1, 1))
        self.fc = nn.Linear(256, 512)   
        
    def forward(self, x):
        x = self.conv1(x)
        x = self.gn1(x)
        x = self.relu(x)
        x = self.maxpool(x)

        x = self.conv2(x)
        x = self.conv3(x)
        
        x = self.avgpool(x)
        x = torch.flatten(x, 1)
        x = self.fc(x)
        return x
 

class ResidualBlock(nn.Module):
    def __init__(self, in_channels, out_channels, stride=1, downsample=None):
        super(ResidualBlock, self).__init__()
        self.conv1 = nn.Conv2d(in_channels, out_channels, kernel_size=3, stride=stride, padding=1, bias=False)
        self.bn1 = nn.BatchNorm2d(out_channels)
        self.conv2 = nn.Conv2d(out_channels, out_channels, kernel_size=3, stride=1, padding=1, bias=False)
        self.bn2 = nn.BatchNorm2d(out_channels)
        self.downsample = downsample

    def forward(self, x):
        identity = x
        if self.downsample:
            identity = self.downsample(x)

        out = self.conv1(x)
        out = self.bn1(out)
        out = F.relu(out)

        out = self.conv2(out)
        out = self.bn2(out)

        out += identity
        out = F.relu(out)
        return out


class ResNetFe(nn.Module):
    def __init__(self, block, layers):
        super(ResNetFe, self).__init__()
        self.in_channels = 64
        self.conv1 = nn.Conv2d(3, 64, kernel_size=7, stride=2, padding=3, bias=False) # (x+2*3-7)/2+1=(x-1)/2+1=48 : 64*48*48
        self.bn1 = nn.BatchNorm2d(64)
        self.relu = nn.ReLU(inplace=True)
        self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1)       #(x+2*1-3)/2+1=(x-1)/2+1=24  : 64*24*24

        # Two ResNet layers
        self.layer1 = self._make_layer(block, 64, layers[0])
        self.layer2 = self._make_layer(block, 128, layers[1], stride=2)

        self.avgpool = nn.AdaptiveAvgPool2d((1, 1))
        self.fc = nn.Linear(128, 512)  # Output 512-dimensional features

    def _make_layer(self, block, out_channels, blocks, stride=1):
        downsample = None
        if stride != 1 or self.in_channels != out_channels:
            downsample = nn.Sequential(
                nn.Conv2d(self.in_channels, out_channels, kernel_size=1, stride=stride, bias=False),
                nn.BatchNorm2d(out_channels),
            )

        layers = []
        layers.append(block(self.in_channels, out_channels, stride, downsample))
        self.in_channels = out_channels
        for _ in range(1, blocks):
            layers.append(block(out_channels, out_channels))

        return nn.Sequential(*layers)

    def forward(self, x):
        if len(x.shape) == 4:
            x = x.unsqueeze(1)
        batch_size, seq_len, channels, height, width = x.shape
        x = x.view(batch_size * seq_len, channels, height, width)
        
        x = self.conv1(x)
        x = self.bn1(x)
        x = self.relu(x)
        x = self.maxpool(x)

        x = self.layer1(x)
        x = self.layer2(x)

        x = self.avgpool(x)
        x = torch.flatten(x, 1)
        x = self.fc(x)
        return x

 
 
 ### ----------------- utility functions from the original diffusion policy codebase ---------------------- ###
def replace_submodules(
        root_module: nn.Module,
        predicate: Callable[[nn.Module], bool],
        func: Callable[[nn.Module], nn.Module]) -> nn.Module:
    """
    Replace all submodules selected by the predicate with
    the output of func.

    predicate: Return true if the module is to be replaced.
    func: Return new module to use.
    """
    if predicate(root_module):
        return func(root_module)

    bn_list = [k.split('.') for k, m
        in root_module.named_modules(remove_duplicate=True)
        if predicate(m)]
    for *parent, k in bn_list:
        parent_module = root_module
        if len(parent) > 0:
            parent_module = root_module.get_submodule('.'.join(parent))
        if isinstance(parent_module, nn.Sequential):
            src_module = parent_module[int(k)]
        else:
            src_module = getattr(parent_module, k)
        tgt_module = func(src_module)
        if isinstance(parent_module, nn.Sequential):
            parent_module[int(k)] = tgt_module
        else:
            setattr(parent_module, k, tgt_module)
    # verify that all modules are replaced
    bn_list = [k.split('.') for k, m
        in root_module.named_modules(remove_duplicate=True)
        if predicate(m)]
    assert len(bn_list) == 0
    return root_module

def replace_bn_with_gn(
    root_module: nn.Module,
    features_per_group: int=16) -> nn.Module:
    """
    Relace all BatchNorm layers with GroupNorm.
    """
    replace_submodules(
        root_module=root_module,
        predicate=lambda x: isinstance(x, nn.BatchNorm2d),
        func=lambda x: nn.GroupNorm(
            num_groups=x.num_features//features_per_group,
            num_channels=x.num_features)
    )
    return root_module