Upload ResNet10

config.json

@@ -1,6 +1,11 @@
 {
+  "_name_or_path": "lilkm/resnet10",
+  "architectures": [
+    "ResNet10"
+  ],
   "auto_map": {
-    "AutoConfig": "configuration_resnet.ResNet10Config"
+    "AutoConfig": "lilkm/resnet10--configuration_resnet.ResNet10Config",
+    "AutoModel": "modeling_resnet.ResNet10"
   },
   "depths": [
     1,
@@ -19,5 +24,6 @@
   "model_type": "resnet10",
   "num_channels": 3,
   "pooler": "avg",
+  "torch_dtype": "float32",
   "transformers_version": "4.48.1"
 }

model.safetensors

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:1be729cd30827ec597a7601d7b10ce1d6a50729b79119142761fbb9bba090d5f
+size 19627000

modeling_resnet.py

@@ -0,0 +1,262 @@
+#!/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
+
+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 MyGroupNorm(nn.Module):
+    def __init__(self, num_groups, num_channels, eps=1e-5, affine=True):
+        super().__init__()
+        self.group_norm = nn.GroupNorm(num_groups, num_channels, eps, affine)
+
+    def forward(self, x):
+        if x.ndim == 3:
+            x = x.unsqueeze(0)
+            x = self.group_norm(x)
+            x = x.squeeze(0)
+        else:
+            x = self.group_norm(x)
+        return 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 = MyGroupNorm(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 = MyGroupNorm(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),
+                MyGroupNorm(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,
+            ),
+            MyGroupNorm(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)
+
+    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)
+
+        return BaseModelOutputWithNoAttention(
+            last_hidden_state=encoder_outputs.last_hidden_state,
+            hidden_states=encoder_outputs.hidden_states,
+        )
+
+    def print_model_hash(self):
+        print("Model parameters hashes:")
+        for name, param in self.named_parameters():
+            print(name, param.sum())