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#!/usr/bin/env python3
# -----------------------------------------------------------------------------
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# -----------------------------------------------------------------------------
import math
from typing import Optional
import torch.nn as nn
from torch import Tensor
from transformers import PreTrainedModel
from transformers.activations import ACT2FN
from transformers.modeling_outputs import BaseModelOutputWithNoAttention, BaseModelOutputWithPoolingAndNoAttention
from .configuration_resnet import ResNet10Config
class MaxPool2dJax(nn.Module):
"""Mimics JAX's MaxPool with padding='SAME' for exact parity."""
def __init__(self, kernel_size, stride=2):
super().__init__()
# Ensure kernel_size and stride are tuples
self.kernel_size = kernel_size if isinstance(kernel_size, tuple) else (kernel_size, kernel_size)
self.stride = stride if isinstance(stride, tuple) else (stride, stride)
self.maxpool = nn.MaxPool2d(
kernel_size=self.kernel_size,
stride=self.stride,
padding=0, # No padding
)
def _compute_padding(self, input_height, input_width):
"""Calculate asymmetric padding to match JAX's 'SAME' behavior."""
# Compute padding needed for height and width
pad_h = max(
0, (math.ceil(input_height / self.stride[0]) - 1) * self.stride[0] + self.kernel_size[0] - input_height
)
pad_w = max(
0, (math.ceil(input_width / self.stride[1]) - 1) * self.stride[1] + self.kernel_size[1] - input_width
)
# Asymmetric padding (JAX-style: more padding on the bottom/right if needed)
pad_top = pad_h // 2
pad_bottom = pad_h - pad_top
pad_left = pad_w // 2
pad_right = pad_w - pad_left
return (pad_left, pad_right, pad_top, pad_bottom)
def forward(self, x):
"""Apply asymmetric padding before convolution."""
_, _, h, w = x.shape
# Compute asymmetric padding
pad_left, pad_right, pad_top, pad_bottom = self._compute_padding(h, w)
x = nn.functional.pad(
x, (pad_left, pad_right, pad_top, pad_bottom), value=-float("inf")
) # Pad right/bottom by 1 to match JAX's maxpooling padding="SAME"
return nn.MaxPool2d(kernel_size=3, stride=2, padding=0)(x)
class Conv2dJax(nn.Module):
"""Mimics JAX's Conv2D with padding='SAME' for exact parity."""
def __init__(self, in_channels, out_channels, kernel_size, stride=1, bias=False):
super().__init__()
# Ensure kernel_size and stride are tuples
self.kernel_size = kernel_size if isinstance(kernel_size, tuple) else (kernel_size, kernel_size)
self.stride = stride if isinstance(stride, tuple) else (stride, stride)
self.conv = nn.Conv2d(
in_channels,
out_channels,
kernel_size=self.kernel_size,
stride=self.stride,
padding=0, # No padding
bias=bias,
)
def _compute_padding(self, input_height, input_width):
"""Calculate asym
metric padding to match JAX's 'SAME' behavior."""
# Compute padding needed for height and width
pad_h = max(
0, (math.ceil(input_height / self.stride[0]) - 1) * self.stride[0] + self.kernel_size[0] - input_height
)
pad_w = max(
0, (math.ceil(input_width / self.stride[1]) - 1) * self.stride[1] + self.kernel_size[1] - input_width
)
# Asymmetric padding (JAX-style: more padding on the bottom/right if needed)
pad_top = pad_h // 2
pad_bottom = pad_h - pad_top
pad_left = pad_w // 2
pad_right = pad_w - pad_left
return (pad_left, pad_right, pad_top, pad_bottom)
def forward(self, x):
"""Apply asymmetric padding before convolution."""
_, _, h, w = x.shape
# Compute asymmetric padding
pad_left, pad_right, pad_top, pad_bottom = self._compute_padding(h, w)
x = nn.functional.pad(x, (pad_left, pad_right, pad_top, pad_bottom))
return self.conv(x)
class BasicBlock(nn.Module):
def __init__(self, in_channels, out_channels, activation, stride=1, norm_groups=4):
super().__init__()
self.conv1 = Conv2dJax(
in_channels,
out_channels,
kernel_size=3,
stride=stride,
bias=False,
)
self.norm1 = nn.GroupNorm(num_groups=norm_groups, num_channels=out_channels)
self.act1 = ACT2FN[activation]
self.act2 = ACT2FN[activation]
self.conv2 = Conv2dJax(out_channels, out_channels, kernel_size=3, stride=1, bias=False)
self.norm2 = nn.GroupNorm(num_groups=norm_groups, num_channels=out_channels)
self.shortcut = None
if in_channels != out_channels:
self.shortcut = nn.Sequential(
Conv2dJax(in_channels, out_channels, kernel_size=1, stride=stride, bias=False),
nn.GroupNorm(num_groups=norm_groups, num_channels=out_channels),
)
def forward(self, x):
identity = x
out = self.conv1(x)
out = self.norm1(out)
out = self.act1(out)
out = self.conv2(out)
out = self.norm2(out)
if self.shortcut is not None:
identity = self.shortcut(identity)
out += identity
return self.act2(out)
class Encoder(nn.Module):
def __init__(self, config: ResNet10Config):
super().__init__()
self.config = config
self.stages = nn.ModuleList([])
for i, size in enumerate(self.config.hidden_sizes):
if i == 0:
self.stages.append(
BasicBlock(
self.config.embedding_size,
size,
activation=self.config.hidden_act,
)
)
else:
self.stages.append(
BasicBlock(
self.config.hidden_sizes[i - 1],
size,
activation=self.config.hidden_act,
stride=2,
)
)
def forward(self, hidden_state: Tensor, output_hidden_states: bool = False) -> BaseModelOutputWithNoAttention:
hidden_states = () if output_hidden_states else None
for stage in self.stages:
if output_hidden_states:
hidden_states = hidden_states + (hidden_state,)
hidden_state = stage(hidden_state)
if output_hidden_states:
hidden_states = hidden_states + (hidden_state,)
return BaseModelOutputWithNoAttention(
last_hidden_state=hidden_state,
hidden_states=hidden_states,
)
class ResNet10(PreTrainedModel):
config_class = ResNet10Config
def __init__(self, config):
super().__init__(config)
self.embedder = nn.Sequential(
nn.Conv2d(
self.config.num_channels,
self.config.embedding_size,
kernel_size=7,
stride=2,
padding=3,
bias=False,
),
# The original code has a small trick -
# https://github.com/rail-berkeley/hil-serl/blob/main/serl_launcher/serl_launcher/vision/resnet_v1.py#L119
# class MyGroupNorm(nn.GroupNorm):
# def __call__(self, x):
# if x.ndim == 3:
# x = x[jnp.newaxis]
# x = super().__call__(x)
# return x[0]
# else:
# return super().__call__(x)
nn.GroupNorm(num_groups=4, eps=1e-5, num_channels=self.config.embedding_size),
ACT2FN[self.config.hidden_act],
MaxPool2dJax(kernel_size=3, stride=2),
)
self.encoder = Encoder(self.config)
self.pooler = nn.AdaptiveAvgPool2d(output_size=1)
def _init_pooler(self):
if self.config.pooler == "avg":
self.pooler = nn.AdaptiveAvgPool2d(output_size=1)
elif self.config.pooler == "max":
self.pooler = nn.MaxPool2d(kernel_size=3, stride=2)
elif self.config.pooler == "spatial_learned_embeddings":
raise ValueError("Invalid pooler, it exist in the hil serl version, but weights are missing")
# In the original HIl-SERL code is used SpatialLearnedEmbeddings as pooliing method
# Check https://github.com/rail-berkeley/hil-serl/blob/7d17d13560d85abffbd45facec17c4f9189c29c0/serl_launcher/serl_launcher/agents/continuous/sac.py#L490
# But weights for this custom layer are missing
# Probably it means that pretrained weights used other way of pooling - probably it's AvgPool2d
# self.pooler = nn.Sequential(
# SpatialLearnedEmbeddings(
# height=height,
# width=width,
# channel=channel,
# num_features=self.num_spatial_blocks,
# ),
# nn.Dropout(0.1, deterministic=not train),
# )
else:
raise ValueError(f"Invalid pooler: {self.config.pooler}")
def forward(self, x: Tensor, output_hidden_states: Optional[bool] = None) -> BaseModelOutputWithNoAttention:
output_hidden_states = (
output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
)
embedding_output = self.embedder(x)
encoder_outputs = self.encoder(embedding_output, output_hidden_states=output_hidden_states)
pooler_output = self.pooler(encoder_outputs.last_hidden_state)
return BaseModelOutputWithPoolingAndNoAttention(
last_hidden_state=encoder_outputs.last_hidden_state,
hidden_states=encoder_outputs.hidden_states,
pooler_output=pooler_output,
)
def print_model_hash(self):
print("Model parameters hashes:")
for name, param in self.named_parameters():
print(name, param.sum())
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