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- build/torch210-cu126-x86_64-linux/{_deformable_detr_cuda_fe2bb66.abi3.so → _deformable_detr_cuda_3a0c7d5.abi3.so} +1 -1
- build/torch210-cu126-x86_64-linux/_ops.py +3 -3
- build/torch210-cu126-x86_64-linux/metadata.json +5 -5
- build/torch210-cu126-x86_64-linux/metadata.json.sigstore +1 -1
- build/torch210-cu126-x86_64-linux/symbols.json +4 -4
- build/torch210-cu128-x86_64-linux/{_deformable_detr_cuda_fe2bb66.abi3.so → _deformable_detr_cuda_3a0c7d5.abi3.so} +1 -1
- build/torch210-cu128-x86_64-linux/_ops.py +3 -3
- build/torch210-cu128-x86_64-linux/metadata.json +5 -5
- build/torch210-cu128-x86_64-linux/metadata.json.sigstore +1 -1
- build/torch210-cu128-x86_64-linux/symbols.json +4 -4
- build/torch210-cu130-x86_64-linux/{_deformable_detr_cuda_fe2bb66.abi3.so → _deformable_detr_cuda_3a0c7d5.abi3.so} +1 -1
- build/torch210-cu130-x86_64-linux/_ops.py +3 -3
- build/torch210-cu130-x86_64-linux/metadata.json +5 -5
- build/torch210-cu130-x86_64-linux/metadata.json.sigstore +1 -1
- build/torch210-cu130-x86_64-linux/symbols.json +4 -4
- build/torch211-cu126-x86_64-linux/{_deformable_detr_cuda_fe2bb66.abi3.so → _deformable_detr_cuda_3a0c7d5.abi3.so} +1 -1
- build/torch211-cu126-x86_64-linux/_ops.py +3 -3
- build/torch211-cu126-x86_64-linux/metadata.json +5 -5
- build/torch211-cu126-x86_64-linux/metadata.json.sigstore +1 -1
- build/torch211-cu126-x86_64-linux/symbols.json +4 -4
- build/torch211-cu128-x86_64-linux/{_deformable_detr_cuda_fe2bb66.abi3.so → _deformable_detr_cuda_3a0c7d5.abi3.so} +1 -1
- build/torch211-cu128-x86_64-linux/_ops.py +3 -3
- build/torch211-cu128-x86_64-linux/metadata.json +5 -5
- build/torch211-cu128-x86_64-linux/metadata.json.sigstore +1 -1
- build/torch211-cu128-x86_64-linux/symbols.json +4 -4
- build/torch211-cu130-x86_64-linux/{_deformable_detr_cuda_fe2bb66.abi3.so → _deformable_detr_cuda_3a0c7d5.abi3.so} +1 -1
- build/torch211-cu130-x86_64-linux/_ops.py +3 -3
- build/torch211-cu130-x86_64-linux/metadata.json +5 -5
- build/torch211-cu130-x86_64-linux/metadata.json.sigstore +1 -1
- build/torch211-cu130-x86_64-linux/symbols.json +4 -4
- build/torch212-cu126-x86_64-linux/{_deformable_detr_cuda_fe2bb66.abi3.so → _deformable_detr_cuda_3a0c7d5.abi3.so} +1 -1
- build/torch212-cu126-x86_64-linux/_ops.py +3 -3
- build/torch212-cu126-x86_64-linux/metadata.json +5 -5
- build/torch212-cu126-x86_64-linux/metadata.json.sigstore +1 -1
- build/torch212-cu126-x86_64-linux/symbols.json +4 -4
- build/torch212-cu130-x86_64-linux/{_deformable_detr_cuda_fe2bb66.abi3.so → _deformable_detr_cuda_3a0c7d5.abi3.so} +1 -1
- build/torch212-cu130-x86_64-linux/_ops.py +3 -3
- build/torch212-cu130-x86_64-linux/metadata.json +5 -5
- build/torch212-cu130-x86_64-linux/metadata.json.sigstore +1 -1
- build/torch212-cu130-x86_64-linux/symbols.json +4 -4
- build/torch212-cu132-x86_64-linux/{_deformable_detr_cuda_fe2bb66.abi3.so → _deformable_detr_cuda_3a0c7d5.abi3.so} +1 -1
- build/torch212-cu132-x86_64-linux/_ops.py +3 -3
- build/torch212-cu132-x86_64-linux/metadata.json +5 -5
- build/torch212-cu132-x86_64-linux/metadata.json.sigstore +1 -1
- build/torch212-cu132-x86_64-linux/symbols.json +4 -4
- build/torch213-cu126-x86_64-linux/{_deformable_detr_cuda_fe2bb66.abi3.so → _deformable_detr_cuda_3a0c7d5.abi3.so} +1 -1
- build/torch213-cu126-x86_64-linux/_ops.py +3 -3
- build/torch213-cu126-x86_64-linux/metadata.json +5 -5
- build/torch213-cu126-x86_64-linux/metadata.json.sigstore +1 -1
- build/torch213-cu126-x86_64-linux/symbols.json +4 -4
build/torch210-cu126-x86_64-linux/{_deformable_detr_cuda_fe2bb66.abi3.so → _deformable_detr_cuda_3a0c7d5.abi3.so}
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build/torch210-cu126-x86_64-linux/_ops.py
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@@ -1,9 +1,9 @@
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import torch
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from . import
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ops = torch.ops.
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def add_op_namespace_prefix(op_name: str):
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"""
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Prefix op by namespace.
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"""
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-
return f"
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import torch
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+
from . import _deformable_detr_cuda_3a0c7d5
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+
ops = torch.ops._deformable_detr_cuda_3a0c7d5
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def add_op_namespace_prefix(op_name: str):
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"""
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Prefix op by namespace.
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"""
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+
return f"_deformable_detr_cuda_3a0c7d5::{op_name}"
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build/torch210-cu126-x86_64-linux/metadata.json
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build/torch210-cu126-x86_64-linux/metadata.json.sigstore
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|
build/torch210-cu126-x86_64-linux/symbols.json
CHANGED
|
@@ -1,11 +1,11 @@
|
|
| 1 |
{
|
| 2 |
"schema_version": 1,
|
| 3 |
-
"module": "
|
| 4 |
"functions": [
|
| 5 |
{
|
| 6 |
"name": "ms_deform_attn_forward",
|
| 7 |
"qualname": "ms_deform_attn_forward",
|
| 8 |
-
"module": "
|
| 9 |
"kind": "function",
|
| 10 |
"doc": null,
|
| 11 |
"signature": {
|
|
@@ -53,7 +53,7 @@
|
|
| 53 |
{
|
| 54 |
"name": "ms_deform_attn_backward",
|
| 55 |
"qualname": "ms_deform_attn_backward",
|
| 56 |
-
"module": "
|
| 57 |
"kind": "function",
|
| 58 |
"doc": null,
|
| 59 |
"signature": {
|
|
@@ -109,7 +109,7 @@
|
|
| 109 |
{
|
| 110 |
"name": "MultiScaleDeformableAttention",
|
| 111 |
"qualname": "MultiScaleDeformableAttention",
|
| 112 |
-
"module": "
|
| 113 |
"kind": "class",
|
| 114 |
"doc": "Base class for all neural network modules.\n\nYour models should also subclass this class.\n\nModules can also contain other Modules, allowing them to be nested in\na tree structure. You can assign the submodules as regular attributes::\n\n import torch.nn as nn\n import torch.nn.functional as F\n\n\n class Model(nn.Module):\n def __init__(self) -> None:\n super().__init__()\n self.conv1 = nn.Conv2d(1, 20, 5)\n self.conv2 = nn.Conv2d(20, 20, 5)\n\n def forward(self, x):\n x = F.relu(self.conv1(x))\n return F.relu(self.conv2(x))\n\nSubmodules assigned in this way will be registered, and will also have their\nparameters converted when you call :meth:`to`, etc.\n\n.. note::\n As per the example above, an ``__init__()`` call to the parent class\n must be made before assignment on the child.\n\n:ivar training: Boolean represents whether this module is in training or\n evaluation mode.\n:vartype training: bool",
|
| 115 |
"signature": {
|
|
|
|
| 1 |
{
|
| 2 |
"schema_version": 1,
|
| 3 |
+
"module": "_deformable_detr_cuda_3a0c7d5",
|
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| 53 |
{
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| 54 |
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| 55 |
"qualname": "ms_deform_attn_backward",
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{
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| 110 |
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|
| 113 |
"kind": "class",
|
| 114 |
"doc": "Base class for all neural network modules.\n\nYour models should also subclass this class.\n\nModules can also contain other Modules, allowing them to be nested in\na tree structure. You can assign the submodules as regular attributes::\n\n import torch.nn as nn\n import torch.nn.functional as F\n\n\n class Model(nn.Module):\n def __init__(self) -> None:\n super().__init__()\n self.conv1 = nn.Conv2d(1, 20, 5)\n self.conv2 = nn.Conv2d(20, 20, 5)\n\n def forward(self, x):\n x = F.relu(self.conv1(x))\n return F.relu(self.conv2(x))\n\nSubmodules assigned in this way will be registered, and will also have their\nparameters converted when you call :meth:`to`, etc.\n\n.. note::\n As per the example above, an ``__init__()`` call to the parent class\n must be made before assignment on the child.\n\n:ivar training: Boolean represents whether this module is in training or\n evaluation mode.\n:vartype training: bool",
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build/torch210-cu128-x86_64-linux/{_deformable_detr_cuda_fe2bb66.abi3.so → _deformable_detr_cuda_3a0c7d5.abi3.so}
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build/torch210-cu128-x86_64-linux/_ops.py
CHANGED
|
@@ -1,9 +1,9 @@
|
|
| 1 |
import torch
|
| 2 |
-
from . import
|
| 3 |
-
ops = torch.ops.
|
| 4 |
|
| 5 |
def add_op_namespace_prefix(op_name: str):
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| 6 |
"""
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| 7 |
Prefix op by namespace.
|
| 8 |
"""
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| 9 |
-
return f"
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|
|
|
| 1 |
import torch
|
| 2 |
+
from . import _deformable_detr_cuda_3a0c7d5
|
| 3 |
+
ops = torch.ops._deformable_detr_cuda_3a0c7d5
|
| 4 |
|
| 5 |
def add_op_namespace_prefix(op_name: str):
|
| 6 |
"""
|
| 7 |
Prefix op by namespace.
|
| 8 |
"""
|
| 9 |
+
return f"_deformable_detr_cuda_3a0c7d5::{op_name}"
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build/torch210-cu128-x86_64-linux/metadata.json
CHANGED
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@@ -1,6 +1,6 @@
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{
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"kernel": {
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"commit": "
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|
build/torch210-cu128-x86_64-linux/symbols.json
CHANGED
|
@@ -1,11 +1,11 @@
|
|
| 1 |
{
|
| 2 |
"schema_version": 1,
|
| 3 |
-
"module": "
|
| 4 |
"functions": [
|
| 5 |
{
|
| 6 |
"name": "ms_deform_attn_forward",
|
| 7 |
"qualname": "ms_deform_attn_forward",
|
| 8 |
-
"module": "
|
| 9 |
"kind": "function",
|
| 10 |
"doc": null,
|
| 11 |
"signature": {
|
|
@@ -53,7 +53,7 @@
|
|
| 53 |
{
|
| 54 |
"name": "ms_deform_attn_backward",
|
| 55 |
"qualname": "ms_deform_attn_backward",
|
| 56 |
-
"module": "
|
| 57 |
"kind": "function",
|
| 58 |
"doc": null,
|
| 59 |
"signature": {
|
|
@@ -109,7 +109,7 @@
|
|
| 109 |
{
|
| 110 |
"name": "MultiScaleDeformableAttention",
|
| 111 |
"qualname": "MultiScaleDeformableAttention",
|
| 112 |
-
"module": "
|
| 113 |
"kind": "class",
|
| 114 |
"doc": "Base class for all neural network modules.\n\nYour models should also subclass this class.\n\nModules can also contain other Modules, allowing them to be nested in\na tree structure. You can assign the submodules as regular attributes::\n\n import torch.nn as nn\n import torch.nn.functional as F\n\n\n class Model(nn.Module):\n def __init__(self) -> None:\n super().__init__()\n self.conv1 = nn.Conv2d(1, 20, 5)\n self.conv2 = nn.Conv2d(20, 20, 5)\n\n def forward(self, x):\n x = F.relu(self.conv1(x))\n return F.relu(self.conv2(x))\n\nSubmodules assigned in this way will be registered, and will also have their\nparameters converted when you call :meth:`to`, etc.\n\n.. note::\n As per the example above, an ``__init__()`` call to the parent class\n must be made before assignment on the child.\n\n:ivar training: Boolean represents whether this module is in training or\n evaluation mode.\n:vartype training: bool",
|
| 115 |
"signature": {
|
|
|
|
| 1 |
{
|
| 2 |
"schema_version": 1,
|
| 3 |
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{
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|
| 114 |
"doc": "Base class for all neural network modules.\n\nYour models should also subclass this class.\n\nModules can also contain other Modules, allowing them to be nested in\na tree structure. You can assign the submodules as regular attributes::\n\n import torch.nn as nn\n import torch.nn.functional as F\n\n\n class Model(nn.Module):\n def __init__(self) -> None:\n super().__init__()\n self.conv1 = nn.Conv2d(1, 20, 5)\n self.conv2 = nn.Conv2d(20, 20, 5)\n\n def forward(self, x):\n x = F.relu(self.conv1(x))\n return F.relu(self.conv2(x))\n\nSubmodules assigned in this way will be registered, and will also have their\nparameters converted when you call :meth:`to`, etc.\n\n.. note::\n As per the example above, an ``__init__()`` call to the parent class\n must be made before assignment on the child.\n\n:ivar training: Boolean represents whether this module is in training or\n evaluation mode.\n:vartype training: bool",
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build/torch210-cu130-x86_64-linux/{_deformable_detr_cuda_fe2bb66.abi3.so → _deformable_detr_cuda_3a0c7d5.abi3.so}
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build/torch210-cu130-x86_64-linux/_ops.py
CHANGED
|
@@ -1,9 +1,9 @@
|
|
| 1 |
import torch
|
| 2 |
-
from . import
|
| 3 |
-
ops = torch.ops.
|
| 4 |
|
| 5 |
def add_op_namespace_prefix(op_name: str):
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| 6 |
"""
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Prefix op by namespace.
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| 8 |
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| 9 |
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return f"
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| 1 |
import torch
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| 2 |
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from . import _deformable_detr_cuda_3a0c7d5
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| 3 |
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ops = torch.ops._deformable_detr_cuda_3a0c7d5
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| 4 |
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| 5 |
def add_op_namespace_prefix(op_name: str):
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| 6 |
"""
|
| 7 |
Prefix op by namespace.
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| 8 |
"""
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return f"_deformable_detr_cuda_3a0c7d5::{op_name}"
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build/torch210-cu130-x86_64-linux/metadata.json
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|
build/torch210-cu130-x86_64-linux/symbols.json
CHANGED
|
@@ -1,11 +1,11 @@
|
|
| 1 |
{
|
| 2 |
"schema_version": 1,
|
| 3 |
-
"module": "
|
| 4 |
"functions": [
|
| 5 |
{
|
| 6 |
"name": "ms_deform_attn_forward",
|
| 7 |
"qualname": "ms_deform_attn_forward",
|
| 8 |
-
"module": "
|
| 9 |
"kind": "function",
|
| 10 |
"doc": null,
|
| 11 |
"signature": {
|
|
@@ -53,7 +53,7 @@
|
|
| 53 |
{
|
| 54 |
"name": "ms_deform_attn_backward",
|
| 55 |
"qualname": "ms_deform_attn_backward",
|
| 56 |
-
"module": "
|
| 57 |
"kind": "function",
|
| 58 |
"doc": null,
|
| 59 |
"signature": {
|
|
@@ -109,7 +109,7 @@
|
|
| 109 |
{
|
| 110 |
"name": "MultiScaleDeformableAttention",
|
| 111 |
"qualname": "MultiScaleDeformableAttention",
|
| 112 |
-
"module": "
|
| 113 |
"kind": "class",
|
| 114 |
"doc": "Base class for all neural network modules.\n\nYour models should also subclass this class.\n\nModules can also contain other Modules, allowing them to be nested in\na tree structure. You can assign the submodules as regular attributes::\n\n import torch.nn as nn\n import torch.nn.functional as F\n\n\n class Model(nn.Module):\n def __init__(self) -> None:\n super().__init__()\n self.conv1 = nn.Conv2d(1, 20, 5)\n self.conv2 = nn.Conv2d(20, 20, 5)\n\n def forward(self, x):\n x = F.relu(self.conv1(x))\n return F.relu(self.conv2(x))\n\nSubmodules assigned in this way will be registered, and will also have their\nparameters converted when you call :meth:`to`, etc.\n\n.. note::\n As per the example above, an ``__init__()`` call to the parent class\n must be made before assignment on the child.\n\n:ivar training: Boolean represents whether this module is in training or\n evaluation mode.\n:vartype training: bool",
|
| 115 |
"signature": {
|
|
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|
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| 53 |
{
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"module": "_deformable_detr_cuda_3a0c7d5.layers",
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| 114 |
"doc": "Base class for all neural network modules.\n\nYour models should also subclass this class.\n\nModules can also contain other Modules, allowing them to be nested in\na tree structure. You can assign the submodules as regular attributes::\n\n import torch.nn as nn\n import torch.nn.functional as F\n\n\n class Model(nn.Module):\n def __init__(self) -> None:\n super().__init__()\n self.conv1 = nn.Conv2d(1, 20, 5)\n self.conv2 = nn.Conv2d(20, 20, 5)\n\n def forward(self, x):\n x = F.relu(self.conv1(x))\n return F.relu(self.conv2(x))\n\nSubmodules assigned in this way will be registered, and will also have their\nparameters converted when you call :meth:`to`, etc.\n\n.. note::\n As per the example above, an ``__init__()`` call to the parent class\n must be made before assignment on the child.\n\n:ivar training: Boolean represents whether this module is in training or\n evaluation mode.\n:vartype training: bool",
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build/torch211-cu126-x86_64-linux/{_deformable_detr_cuda_fe2bb66.abi3.so → _deformable_detr_cuda_3a0c7d5.abi3.so}
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build/torch211-cu126-x86_64-linux/_ops.py
CHANGED
|
@@ -1,9 +1,9 @@
|
|
| 1 |
import torch
|
| 2 |
-
from . import
|
| 3 |
-
ops = torch.ops.
|
| 4 |
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| 5 |
def add_op_namespace_prefix(op_name: str):
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| 6 |
"""
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Prefix op by namespace.
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| 8 |
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| 9 |
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return f"
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| 1 |
import torch
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| 2 |
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from . import _deformable_detr_cuda_3a0c7d5
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| 3 |
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ops = torch.ops._deformable_detr_cuda_3a0c7d5
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| 4 |
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| 5 |
def add_op_namespace_prefix(op_name: str):
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| 6 |
"""
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| 7 |
Prefix op by namespace.
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| 8 |
"""
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return f"_deformable_detr_cuda_3a0c7d5::{op_name}"
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build/torch211-cu126-x86_64-linux/metadata.json
CHANGED
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"id": "
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|
build/torch211-cu126-x86_64-linux/symbols.json
CHANGED
|
@@ -1,11 +1,11 @@
|
|
| 1 |
{
|
| 2 |
"schema_version": 1,
|
| 3 |
-
"module": "
|
| 4 |
"functions": [
|
| 5 |
{
|
| 6 |
"name": "ms_deform_attn_forward",
|
| 7 |
"qualname": "ms_deform_attn_forward",
|
| 8 |
-
"module": "
|
| 9 |
"kind": "function",
|
| 10 |
"doc": null,
|
| 11 |
"signature": {
|
|
@@ -53,7 +53,7 @@
|
|
| 53 |
{
|
| 54 |
"name": "ms_deform_attn_backward",
|
| 55 |
"qualname": "ms_deform_attn_backward",
|
| 56 |
-
"module": "
|
| 57 |
"kind": "function",
|
| 58 |
"doc": null,
|
| 59 |
"signature": {
|
|
@@ -109,7 +109,7 @@
|
|
| 109 |
{
|
| 110 |
"name": "MultiScaleDeformableAttention",
|
| 111 |
"qualname": "MultiScaleDeformableAttention",
|
| 112 |
-
"module": "
|
| 113 |
"kind": "class",
|
| 114 |
"doc": "Base class for all neural network modules.\n\nYour models should also subclass this class.\n\nModules can also contain other Modules, allowing them to be nested in\na tree structure. You can assign the submodules as regular attributes::\n\n import torch.nn as nn\n import torch.nn.functional as F\n\n\n class Model(nn.Module):\n def __init__(self) -> None:\n super().__init__()\n self.conv1 = nn.Conv2d(1, 20, 5)\n self.conv2 = nn.Conv2d(20, 20, 5)\n\n def forward(self, x):\n x = F.relu(self.conv1(x))\n return F.relu(self.conv2(x))\n\nSubmodules assigned in this way will be registered, and will also have their\nparameters converted when you call :meth:`to`, etc.\n\n.. note::\n As per the example above, an ``__init__()`` call to the parent class\n must be made before assignment on the child.\n\n:ivar training: Boolean represents whether this module is in training or\n evaluation mode.\n:vartype training: bool",
|
| 115 |
"signature": {
|
|
|
|
| 1 |
{
|
| 2 |
"schema_version": 1,
|
| 3 |
+
"module": "_deformable_detr_cuda_3a0c7d5",
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{
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| 114 |
"doc": "Base class for all neural network modules.\n\nYour models should also subclass this class.\n\nModules can also contain other Modules, allowing them to be nested in\na tree structure. You can assign the submodules as regular attributes::\n\n import torch.nn as nn\n import torch.nn.functional as F\n\n\n class Model(nn.Module):\n def __init__(self) -> None:\n super().__init__()\n self.conv1 = nn.Conv2d(1, 20, 5)\n self.conv2 = nn.Conv2d(20, 20, 5)\n\n def forward(self, x):\n x = F.relu(self.conv1(x))\n return F.relu(self.conv2(x))\n\nSubmodules assigned in this way will be registered, and will also have their\nparameters converted when you call :meth:`to`, etc.\n\n.. note::\n As per the example above, an ``__init__()`` call to the parent class\n must be made before assignment on the child.\n\n:ivar training: Boolean represents whether this module is in training or\n evaluation mode.\n:vartype training: bool",
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build/torch211-cu128-x86_64-linux/{_deformable_detr_cuda_fe2bb66.abi3.so → _deformable_detr_cuda_3a0c7d5.abi3.so}
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build/torch211-cu128-x86_64-linux/_ops.py
CHANGED
|
@@ -1,9 +1,9 @@
|
|
| 1 |
import torch
|
| 2 |
-
from . import
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| 3 |
-
ops = torch.ops.
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| 4 |
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| 5 |
def add_op_namespace_prefix(op_name: str):
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"""
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Prefix op by namespace.
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return f"
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import torch
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from . import _deformable_detr_cuda_3a0c7d5
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ops = torch.ops._deformable_detr_cuda_3a0c7d5
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| 4 |
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def add_op_namespace_prefix(op_name: str):
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| 6 |
"""
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| 7 |
Prefix op by namespace.
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| 8 |
"""
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return f"_deformable_detr_cuda_3a0c7d5::{op_name}"
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build/torch211-cu128-x86_64-linux/metadata.json
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|
build/torch211-cu128-x86_64-linux/symbols.json
CHANGED
|
@@ -1,11 +1,11 @@
|
|
| 1 |
{
|
| 2 |
"schema_version": 1,
|
| 3 |
-
"module": "
|
| 4 |
"functions": [
|
| 5 |
{
|
| 6 |
"name": "ms_deform_attn_forward",
|
| 7 |
"qualname": "ms_deform_attn_forward",
|
| 8 |
-
"module": "
|
| 9 |
"kind": "function",
|
| 10 |
"doc": null,
|
| 11 |
"signature": {
|
|
@@ -53,7 +53,7 @@
|
|
| 53 |
{
|
| 54 |
"name": "ms_deform_attn_backward",
|
| 55 |
"qualname": "ms_deform_attn_backward",
|
| 56 |
-
"module": "
|
| 57 |
"kind": "function",
|
| 58 |
"doc": null,
|
| 59 |
"signature": {
|
|
@@ -109,7 +109,7 @@
|
|
| 109 |
{
|
| 110 |
"name": "MultiScaleDeformableAttention",
|
| 111 |
"qualname": "MultiScaleDeformableAttention",
|
| 112 |
-
"module": "
|
| 113 |
"kind": "class",
|
| 114 |
"doc": "Base class for all neural network modules.\n\nYour models should also subclass this class.\n\nModules can also contain other Modules, allowing them to be nested in\na tree structure. You can assign the submodules as regular attributes::\n\n import torch.nn as nn\n import torch.nn.functional as F\n\n\n class Model(nn.Module):\n def __init__(self) -> None:\n super().__init__()\n self.conv1 = nn.Conv2d(1, 20, 5)\n self.conv2 = nn.Conv2d(20, 20, 5)\n\n def forward(self, x):\n x = F.relu(self.conv1(x))\n return F.relu(self.conv2(x))\n\nSubmodules assigned in this way will be registered, and will also have their\nparameters converted when you call :meth:`to`, etc.\n\n.. note::\n As per the example above, an ``__init__()`` call to the parent class\n must be made before assignment on the child.\n\n:ivar training: Boolean represents whether this module is in training or\n evaluation mode.\n:vartype training: bool",
|
| 115 |
"signature": {
|
|
|
|
| 1 |
{
|
| 2 |
"schema_version": 1,
|
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| 114 |
"doc": "Base class for all neural network modules.\n\nYour models should also subclass this class.\n\nModules can also contain other Modules, allowing them to be nested in\na tree structure. You can assign the submodules as regular attributes::\n\n import torch.nn as nn\n import torch.nn.functional as F\n\n\n class Model(nn.Module):\n def __init__(self) -> None:\n super().__init__()\n self.conv1 = nn.Conv2d(1, 20, 5)\n self.conv2 = nn.Conv2d(20, 20, 5)\n\n def forward(self, x):\n x = F.relu(self.conv1(x))\n return F.relu(self.conv2(x))\n\nSubmodules assigned in this way will be registered, and will also have their\nparameters converted when you call :meth:`to`, etc.\n\n.. note::\n As per the example above, an ``__init__()`` call to the parent class\n must be made before assignment on the child.\n\n:ivar training: Boolean represents whether this module is in training or\n evaluation mode.\n:vartype training: bool",
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build/torch211-cu130-x86_64-linux/{_deformable_detr_cuda_fe2bb66.abi3.so → _deformable_detr_cuda_3a0c7d5.abi3.so}
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build/torch211-cu130-x86_64-linux/_ops.py
CHANGED
|
@@ -1,9 +1,9 @@
|
|
| 1 |
import torch
|
| 2 |
-
from . import
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| 3 |
-
ops = torch.ops.
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| 4 |
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| 5 |
def add_op_namespace_prefix(op_name: str):
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| 6 |
"""
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Prefix op by namespace.
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| 9 |
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return f"
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import torch
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from . import _deformable_detr_cuda_3a0c7d5
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ops = torch.ops._deformable_detr_cuda_3a0c7d5
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| 4 |
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def add_op_namespace_prefix(op_name: str):
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| 6 |
"""
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| 7 |
Prefix op by namespace.
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| 8 |
"""
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return f"_deformable_detr_cuda_3a0c7d5::{op_name}"
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|
build/torch211-cu130-x86_64-linux/symbols.json
CHANGED
|
@@ -1,11 +1,11 @@
|
|
| 1 |
{
|
| 2 |
"schema_version": 1,
|
| 3 |
-
"module": "
|
| 4 |
"functions": [
|
| 5 |
{
|
| 6 |
"name": "ms_deform_attn_forward",
|
| 7 |
"qualname": "ms_deform_attn_forward",
|
| 8 |
-
"module": "
|
| 9 |
"kind": "function",
|
| 10 |
"doc": null,
|
| 11 |
"signature": {
|
|
@@ -53,7 +53,7 @@
|
|
| 53 |
{
|
| 54 |
"name": "ms_deform_attn_backward",
|
| 55 |
"qualname": "ms_deform_attn_backward",
|
| 56 |
-
"module": "
|
| 57 |
"kind": "function",
|
| 58 |
"doc": null,
|
| 59 |
"signature": {
|
|
@@ -109,7 +109,7 @@
|
|
| 109 |
{
|
| 110 |
"name": "MultiScaleDeformableAttention",
|
| 111 |
"qualname": "MultiScaleDeformableAttention",
|
| 112 |
-
"module": "
|
| 113 |
"kind": "class",
|
| 114 |
"doc": "Base class for all neural network modules.\n\nYour models should also subclass this class.\n\nModules can also contain other Modules, allowing them to be nested in\na tree structure. You can assign the submodules as regular attributes::\n\n import torch.nn as nn\n import torch.nn.functional as F\n\n\n class Model(nn.Module):\n def __init__(self) -> None:\n super().__init__()\n self.conv1 = nn.Conv2d(1, 20, 5)\n self.conv2 = nn.Conv2d(20, 20, 5)\n\n def forward(self, x):\n x = F.relu(self.conv1(x))\n return F.relu(self.conv2(x))\n\nSubmodules assigned in this way will be registered, and will also have their\nparameters converted when you call :meth:`to`, etc.\n\n.. note::\n As per the example above, an ``__init__()`` call to the parent class\n must be made before assignment on the child.\n\n:ivar training: Boolean represents whether this module is in training or\n evaluation mode.\n:vartype training: bool",
|
| 115 |
"signature": {
|
|
|
|
| 1 |
{
|
| 2 |
"schema_version": 1,
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| 114 |
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build/torch212-cu126-x86_64-linux/{_deformable_detr_cuda_fe2bb66.abi3.so → _deformable_detr_cuda_3a0c7d5.abi3.so}
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build/torch212-cu126-x86_64-linux/_ops.py
CHANGED
|
@@ -1,9 +1,9 @@
|
|
| 1 |
import torch
|
| 2 |
-
from . import
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| 3 |
-
ops = torch.ops.
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| 4 |
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| 5 |
def add_op_namespace_prefix(op_name: str):
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Prefix op by namespace.
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return f"
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import torch
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from . import _deformable_detr_cuda_3a0c7d5
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ops = torch.ops._deformable_detr_cuda_3a0c7d5
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def add_op_namespace_prefix(op_name: str):
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"""
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Prefix op by namespace.
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"""
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return f"_deformable_detr_cuda_3a0c7d5::{op_name}"
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build/torch212-cu126-x86_64-linux/metadata.json
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|
build/torch212-cu126-x86_64-linux/symbols.json
CHANGED
|
@@ -1,11 +1,11 @@
|
|
| 1 |
{
|
| 2 |
"schema_version": 1,
|
| 3 |
-
"module": "
|
| 4 |
"functions": [
|
| 5 |
{
|
| 6 |
"name": "ms_deform_attn_forward",
|
| 7 |
"qualname": "ms_deform_attn_forward",
|
| 8 |
-
"module": "
|
| 9 |
"kind": "function",
|
| 10 |
"doc": null,
|
| 11 |
"signature": {
|
|
@@ -53,7 +53,7 @@
|
|
| 53 |
{
|
| 54 |
"name": "ms_deform_attn_backward",
|
| 55 |
"qualname": "ms_deform_attn_backward",
|
| 56 |
-
"module": "
|
| 57 |
"kind": "function",
|
| 58 |
"doc": null,
|
| 59 |
"signature": {
|
|
@@ -109,7 +109,7 @@
|
|
| 109 |
{
|
| 110 |
"name": "MultiScaleDeformableAttention",
|
| 111 |
"qualname": "MultiScaleDeformableAttention",
|
| 112 |
-
"module": "
|
| 113 |
"kind": "class",
|
| 114 |
"doc": "Base class for all neural network modules.\n\nYour models should also subclass this class.\n\nModules can also contain other Modules, allowing them to be nested in\na tree structure. You can assign the submodules as regular attributes::\n\n import torch.nn as nn\n import torch.nn.functional as F\n\n\n class Model(nn.Module):\n def __init__(self) -> None:\n super().__init__()\n self.conv1 = nn.Conv2d(1, 20, 5)\n self.conv2 = nn.Conv2d(20, 20, 5)\n\n def forward(self, x):\n x = F.relu(self.conv1(x))\n return F.relu(self.conv2(x))\n\nSubmodules assigned in this way will be registered, and will also have their\nparameters converted when you call :meth:`to`, etc.\n\n.. note::\n As per the example above, an ``__init__()`` call to the parent class\n must be made before assignment on the child.\n\n:ivar training: Boolean represents whether this module is in training or\n evaluation mode.\n:vartype training: bool",
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| 10 |
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|
| 53 |
{
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| 54 |
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| 55 |
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{
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| 110 |
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| 111 |
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|
| 113 |
"kind": "class",
|
| 114 |
"doc": "Base class for all neural network modules.\n\nYour models should also subclass this class.\n\nModules can also contain other Modules, allowing them to be nested in\na tree structure. You can assign the submodules as regular attributes::\n\n import torch.nn as nn\n import torch.nn.functional as F\n\n\n class Model(nn.Module):\n def __init__(self) -> None:\n super().__init__()\n self.conv1 = nn.Conv2d(1, 20, 5)\n self.conv2 = nn.Conv2d(20, 20, 5)\n\n def forward(self, x):\n x = F.relu(self.conv1(x))\n return F.relu(self.conv2(x))\n\nSubmodules assigned in this way will be registered, and will also have their\nparameters converted when you call :meth:`to`, etc.\n\n.. note::\n As per the example above, an ``__init__()`` call to the parent class\n must be made before assignment on the child.\n\n:ivar training: Boolean represents whether this module is in training or\n evaluation mode.\n:vartype training: bool",
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build/torch212-cu130-x86_64-linux/{_deformable_detr_cuda_fe2bb66.abi3.so → _deformable_detr_cuda_3a0c7d5.abi3.so}
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size 9846232
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build/torch212-cu130-x86_64-linux/_ops.py
CHANGED
|
@@ -1,9 +1,9 @@
|
|
| 1 |
import torch
|
| 2 |
-
from . import
|
| 3 |
-
ops = torch.ops.
|
| 4 |
|
| 5 |
def add_op_namespace_prefix(op_name: str):
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| 6 |
"""
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| 7 |
Prefix op by namespace.
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| 8 |
"""
|
| 9 |
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return f"
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|
|
|
| 1 |
import torch
|
| 2 |
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from . import _deformable_detr_cuda_3a0c7d5
|
| 3 |
+
ops = torch.ops._deformable_detr_cuda_3a0c7d5
|
| 4 |
|
| 5 |
def add_op_namespace_prefix(op_name: str):
|
| 6 |
"""
|
| 7 |
Prefix op by namespace.
|
| 8 |
"""
|
| 9 |
+
return f"_deformable_detr_cuda_3a0c7d5::{op_name}"
|
build/torch212-cu130-x86_64-linux/metadata.json
CHANGED
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@@ -1,6 +1,6 @@
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{
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"name": "deformable-detr",
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"id": "
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"version": 1,
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"kernel": {
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"commit": "
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|
build/torch212-cu130-x86_64-linux/symbols.json
CHANGED
|
@@ -1,11 +1,11 @@
|
|
| 1 |
{
|
| 2 |
"schema_version": 1,
|
| 3 |
-
"module": "
|
| 4 |
"functions": [
|
| 5 |
{
|
| 6 |
"name": "ms_deform_attn_forward",
|
| 7 |
"qualname": "ms_deform_attn_forward",
|
| 8 |
-
"module": "
|
| 9 |
"kind": "function",
|
| 10 |
"doc": null,
|
| 11 |
"signature": {
|
|
@@ -53,7 +53,7 @@
|
|
| 53 |
{
|
| 54 |
"name": "ms_deform_attn_backward",
|
| 55 |
"qualname": "ms_deform_attn_backward",
|
| 56 |
-
"module": "
|
| 57 |
"kind": "function",
|
| 58 |
"doc": null,
|
| 59 |
"signature": {
|
|
@@ -109,7 +109,7 @@
|
|
| 109 |
{
|
| 110 |
"name": "MultiScaleDeformableAttention",
|
| 111 |
"qualname": "MultiScaleDeformableAttention",
|
| 112 |
-
"module": "
|
| 113 |
"kind": "class",
|
| 114 |
"doc": "Base class for all neural network modules.\n\nYour models should also subclass this class.\n\nModules can also contain other Modules, allowing them to be nested in\na tree structure. You can assign the submodules as regular attributes::\n\n import torch.nn as nn\n import torch.nn.functional as F\n\n\n class Model(nn.Module):\n def __init__(self) -> None:\n super().__init__()\n self.conv1 = nn.Conv2d(1, 20, 5)\n self.conv2 = nn.Conv2d(20, 20, 5)\n\n def forward(self, x):\n x = F.relu(self.conv1(x))\n return F.relu(self.conv2(x))\n\nSubmodules assigned in this way will be registered, and will also have their\nparameters converted when you call :meth:`to`, etc.\n\n.. note::\n As per the example above, an ``__init__()`` call to the parent class\n must be made before assignment on the child.\n\n:ivar training: Boolean represents whether this module is in training or\n evaluation mode.\n:vartype training: bool",
|
| 115 |
"signature": {
|
|
|
|
| 1 |
{
|
| 2 |
"schema_version": 1,
|
| 3 |
+
"module": "_deformable_detr_cuda_3a0c7d5",
|
| 4 |
"functions": [
|
| 5 |
{
|
| 6 |
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| 53 |
{
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| 54 |
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| 55 |
"qualname": "ms_deform_attn_backward",
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| 109 |
{
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| 110 |
"name": "MultiScaleDeformableAttention",
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| 111 |
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| 112 |
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"module": "_deformable_detr_cuda_3a0c7d5.layers",
|
| 113 |
"kind": "class",
|
| 114 |
"doc": "Base class for all neural network modules.\n\nYour models should also subclass this class.\n\nModules can also contain other Modules, allowing them to be nested in\na tree structure. You can assign the submodules as regular attributes::\n\n import torch.nn as nn\n import torch.nn.functional as F\n\n\n class Model(nn.Module):\n def __init__(self) -> None:\n super().__init__()\n self.conv1 = nn.Conv2d(1, 20, 5)\n self.conv2 = nn.Conv2d(20, 20, 5)\n\n def forward(self, x):\n x = F.relu(self.conv1(x))\n return F.relu(self.conv2(x))\n\nSubmodules assigned in this way will be registered, and will also have their\nparameters converted when you call :meth:`to`, etc.\n\n.. note::\n As per the example above, an ``__init__()`` call to the parent class\n must be made before assignment on the child.\n\n:ivar training: Boolean represents whether this module is in training or\n evaluation mode.\n:vartype training: bool",
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| 115 |
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build/torch212-cu132-x86_64-linux/{_deformable_detr_cuda_fe2bb66.abi3.so → _deformable_detr_cuda_3a0c7d5.abi3.so}
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build/torch212-cu132-x86_64-linux/_ops.py
CHANGED
|
@@ -1,9 +1,9 @@
|
|
| 1 |
import torch
|
| 2 |
-
from . import
|
| 3 |
-
ops = torch.ops.
|
| 4 |
|
| 5 |
def add_op_namespace_prefix(op_name: str):
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| 6 |
"""
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| 7 |
Prefix op by namespace.
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| 8 |
"""
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| 9 |
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return f"
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|
|
|
| 1 |
import torch
|
| 2 |
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from . import _deformable_detr_cuda_3a0c7d5
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| 3 |
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ops = torch.ops._deformable_detr_cuda_3a0c7d5
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| 4 |
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| 5 |
def add_op_namespace_prefix(op_name: str):
|
| 6 |
"""
|
| 7 |
Prefix op by namespace.
|
| 8 |
"""
|
| 9 |
+
return f"_deformable_detr_cuda_3a0c7d5::{op_name}"
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build/torch212-cu132-x86_64-linux/metadata.json
CHANGED
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|
build/torch212-cu132-x86_64-linux/symbols.json
CHANGED
|
@@ -1,11 +1,11 @@
|
|
| 1 |
{
|
| 2 |
"schema_version": 1,
|
| 3 |
-
"module": "
|
| 4 |
"functions": [
|
| 5 |
{
|
| 6 |
"name": "ms_deform_attn_forward",
|
| 7 |
"qualname": "ms_deform_attn_forward",
|
| 8 |
-
"module": "
|
| 9 |
"kind": "function",
|
| 10 |
"doc": null,
|
| 11 |
"signature": {
|
|
@@ -53,7 +53,7 @@
|
|
| 53 |
{
|
| 54 |
"name": "ms_deform_attn_backward",
|
| 55 |
"qualname": "ms_deform_attn_backward",
|
| 56 |
-
"module": "
|
| 57 |
"kind": "function",
|
| 58 |
"doc": null,
|
| 59 |
"signature": {
|
|
@@ -109,7 +109,7 @@
|
|
| 109 |
{
|
| 110 |
"name": "MultiScaleDeformableAttention",
|
| 111 |
"qualname": "MultiScaleDeformableAttention",
|
| 112 |
-
"module": "
|
| 113 |
"kind": "class",
|
| 114 |
"doc": "Base class for all neural network modules.\n\nYour models should also subclass this class.\n\nModules can also contain other Modules, allowing them to be nested in\na tree structure. You can assign the submodules as regular attributes::\n\n import torch.nn as nn\n import torch.nn.functional as F\n\n\n class Model(nn.Module):\n def __init__(self) -> None:\n super().__init__()\n self.conv1 = nn.Conv2d(1, 20, 5)\n self.conv2 = nn.Conv2d(20, 20, 5)\n\n def forward(self, x):\n x = F.relu(self.conv1(x))\n return F.relu(self.conv2(x))\n\nSubmodules assigned in this way will be registered, and will also have their\nparameters converted when you call :meth:`to`, etc.\n\n.. note::\n As per the example above, an ``__init__()`` call to the parent class\n must be made before assignment on the child.\n\n:ivar training: Boolean represents whether this module is in training or\n evaluation mode.\n:vartype training: bool",
|
| 115 |
"signature": {
|
|
|
|
| 1 |
{
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| 53 |
{
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| 54 |
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| 55 |
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{
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| 113 |
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|
| 114 |
"doc": "Base class for all neural network modules.\n\nYour models should also subclass this class.\n\nModules can also contain other Modules, allowing them to be nested in\na tree structure. You can assign the submodules as regular attributes::\n\n import torch.nn as nn\n import torch.nn.functional as F\n\n\n class Model(nn.Module):\n def __init__(self) -> None:\n super().__init__()\n self.conv1 = nn.Conv2d(1, 20, 5)\n self.conv2 = nn.Conv2d(20, 20, 5)\n\n def forward(self, x):\n x = F.relu(self.conv1(x))\n return F.relu(self.conv2(x))\n\nSubmodules assigned in this way will be registered, and will also have their\nparameters converted when you call :meth:`to`, etc.\n\n.. note::\n As per the example above, an ``__init__()`` call to the parent class\n must be made before assignment on the child.\n\n:ivar training: Boolean represents whether this module is in training or\n evaluation mode.\n:vartype training: bool",
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build/torch213-cu126-x86_64-linux/{_deformable_detr_cuda_fe2bb66.abi3.so → _deformable_detr_cuda_3a0c7d5.abi3.so}
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build/torch213-cu126-x86_64-linux/_ops.py
CHANGED
|
@@ -1,9 +1,9 @@
|
|
| 1 |
import torch
|
| 2 |
-
from . import
|
| 3 |
-
ops = torch.ops.
|
| 4 |
|
| 5 |
def add_op_namespace_prefix(op_name: str):
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| 6 |
"""
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| 7 |
Prefix op by namespace.
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| 8 |
"""
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| 9 |
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return f"
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|
| 1 |
import torch
|
| 2 |
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from . import _deformable_detr_cuda_3a0c7d5
|
| 3 |
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ops = torch.ops._deformable_detr_cuda_3a0c7d5
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| 4 |
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| 5 |
def add_op_namespace_prefix(op_name: str):
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| 6 |
"""
|
| 7 |
Prefix op by namespace.
|
| 8 |
"""
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| 9 |
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return f"_deformable_detr_cuda_3a0c7d5::{op_name}"
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build/torch213-cu126-x86_64-linux/metadata.json
CHANGED
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{
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"kernel": {
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"commit": "
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|
build/torch213-cu126-x86_64-linux/symbols.json
CHANGED
|
@@ -1,11 +1,11 @@
|
|
| 1 |
{
|
| 2 |
"schema_version": 1,
|
| 3 |
-
"module": "
|
| 4 |
"functions": [
|
| 5 |
{
|
| 6 |
"name": "ms_deform_attn_forward",
|
| 7 |
"qualname": "ms_deform_attn_forward",
|
| 8 |
-
"module": "
|
| 9 |
"kind": "function",
|
| 10 |
"doc": null,
|
| 11 |
"signature": {
|
|
@@ -53,7 +53,7 @@
|
|
| 53 |
{
|
| 54 |
"name": "ms_deform_attn_backward",
|
| 55 |
"qualname": "ms_deform_attn_backward",
|
| 56 |
-
"module": "
|
| 57 |
"kind": "function",
|
| 58 |
"doc": null,
|
| 59 |
"signature": {
|
|
@@ -109,7 +109,7 @@
|
|
| 109 |
{
|
| 110 |
"name": "MultiScaleDeformableAttention",
|
| 111 |
"qualname": "MultiScaleDeformableAttention",
|
| 112 |
-
"module": "
|
| 113 |
"kind": "class",
|
| 114 |
"doc": "Base class for all neural network modules.\n\nYour models should also subclass this class.\n\nModules can also contain other Modules, allowing them to be nested in\na tree structure. You can assign the submodules as regular attributes::\n\n import torch.nn as nn\n import torch.nn.functional as F\n\n\n class Model(nn.Module):\n def __init__(self) -> None:\n super().__init__()\n self.conv1 = nn.Conv2d(1, 20, 5)\n self.conv2 = nn.Conv2d(20, 20, 5)\n\n def forward(self, x):\n x = F.relu(self.conv1(x))\n return F.relu(self.conv2(x))\n\nSubmodules assigned in this way will be registered, and will also have their\nparameters converted when you call :meth:`to`, etc.\n\n.. note::\n As per the example above, an ``__init__()`` call to the parent class\n must be made before assignment on the child.\n\n:ivar training: Boolean represents whether this module is in training or\n evaluation mode.\n:vartype training: bool",
|
| 115 |
"signature": {
|
|
|
|
| 1 |
{
|
| 2 |
"schema_version": 1,
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| 3 |
+
"module": "_deformable_detr_cuda_3a0c7d5",
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| 4 |
"functions": [
|
| 5 |
{
|
| 6 |
"name": "ms_deform_attn_forward",
|
| 7 |
"qualname": "ms_deform_attn_forward",
|
| 8 |
+
"module": "_deformable_detr_cuda_3a0c7d5",
|
| 9 |
"kind": "function",
|
| 10 |
"doc": null,
|
| 11 |
"signature": {
|
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|
|
| 53 |
{
|
| 54 |
"name": "ms_deform_attn_backward",
|
| 55 |
"qualname": "ms_deform_attn_backward",
|
| 56 |
+
"module": "_deformable_detr_cuda_3a0c7d5",
|
| 57 |
"kind": "function",
|
| 58 |
"doc": null,
|
| 59 |
"signature": {
|
|
|
|
| 109 |
{
|
| 110 |
"name": "MultiScaleDeformableAttention",
|
| 111 |
"qualname": "MultiScaleDeformableAttention",
|
| 112 |
+
"module": "_deformable_detr_cuda_3a0c7d5.layers",
|
| 113 |
"kind": "class",
|
| 114 |
"doc": "Base class for all neural network modules.\n\nYour models should also subclass this class.\n\nModules can also contain other Modules, allowing them to be nested in\na tree structure. You can assign the submodules as regular attributes::\n\n import torch.nn as nn\n import torch.nn.functional as F\n\n\n class Model(nn.Module):\n def __init__(self) -> None:\n super().__init__()\n self.conv1 = nn.Conv2d(1, 20, 5)\n self.conv2 = nn.Conv2d(20, 20, 5)\n\n def forward(self, x):\n x = F.relu(self.conv1(x))\n return F.relu(self.conv2(x))\n\nSubmodules assigned in this way will be registered, and will also have their\nparameters converted when you call :meth:`to`, etc.\n\n.. note::\n As per the example above, an ``__init__()`` call to the parent class\n must be made before assignment on the child.\n\n:ivar training: Boolean represents whether this module is in training or\n evaluation mode.\n:vartype training: bool",
|
| 115 |
"signature": {
|