Merge branch 'alexander-soare-fx-feature-extract-new'

tests/test_models.py

@@ -4,9 +4,16 @@ import platform
 import os
 import fnmatch
 
+try:
+    from torchvision.models.feature_extraction import create_feature_extractor, get_graph_node_names, NodePathTracer
+    has_fx_feature_extraction = True
+except ImportError:
+    has_fx_feature_extraction = False
+
 import timm
 from timm import list_models, create_model, set_scriptable, has_model_default_key, is_model_default_key, \
     get_model_default_value
+from timm.models.fx_features import _leaf_modules, _autowrap_functions    
 
 if hasattr(torch._C, '_jit_set_profiling_executor'):
     # legacy executor is too slow to compile large models for unit tests
@@ -297,3 +304,144 @@ def test_model_forward_features(model_name, batch_size):
         assert e == o.shape[1]
         assert o.shape[0] == batch_size
         assert not torch.isnan(o).any()
+
+
+@pytest.mark.timeout(120)
+@pytest.mark.parametrize('model_name', list_models(exclude_filters=EXCLUDE_FILTERS))
+@pytest.mark.parametrize('batch_size', [1])
+def test_model_forward_fx(model_name, batch_size):
+    """
+    Symbolically trace each model and run single forward pass through the resulting GraphModule
+    Also check that the output of a forward pass through the GraphModule is the same as that from the original Module
+    """
+    if not has_fx_feature_extraction:
+        pytest.skip("Can't test FX. Torch >= 1.10 and Torchvision >= 0.11 are required.")
+
+    model = create_model(model_name, pretrained=False)
+    model.eval()
+
+    input_size = _get_input_size(model=model, target=TARGET_FWD_SIZE)
+    if max(input_size) > MAX_FWD_SIZE:
+        pytest.skip("Fixed input size model > limit.")
+
+    # This block of code does a bit of juggling to handle any case where there are multiple outputs in train mode
+    # So we trace once and look at the graph, and get the indices of the nodes that lead into the original fx output
+    # node. Then we use those indices to select from train_nodes returned by torchvision get_graph_node_names
+    tracer = NodePathTracer(leaf_modules=list(_leaf_modules), autowrap_functions=list(_autowrap_functions))
+    graph = tracer.trace(model)
+    graph_nodes = list(reversed(graph.nodes))
+    output_node_names = [n.name for n in graph_nodes[0]._input_nodes.keys()]
+    graph_node_names = [n.name for n in graph_nodes]
+    output_node_indices = [-graph_node_names.index(node_name) for node_name in output_node_names]
+    train_nodes, eval_nodes = get_graph_node_names(
+        model, tracer_kwargs={'leaf_modules': list(_leaf_modules), 'autowrap_functions': list(_autowrap_functions)})
+    eval_return_nodes = [eval_nodes[ix] for ix in output_node_indices]
+
+    fx_model = create_feature_extractor(
+        model, train_return_nodes=[train_nodes[-1]], eval_return_nodes=eval_return_nodes,
+        tracer_kwargs={'leaf_modules': list(_leaf_modules), 'autowrap_functions': list(_autowrap_functions)})
+
+    inputs = torch.randn((batch_size, *input_size))
+    outputs = model(inputs)
+    if isinstance(outputs, tuple):
+        outputs = torch.cat(outputs)
+    fx_outputs = tuple(fx_model(inputs).values())
+    if isinstance(fx_outputs, tuple):
+        fx_outputs = torch.cat(fx_outputs)
+
+    assert torch.all(fx_outputs == outputs)
+    assert outputs.shape[0] == batch_size
+    assert not torch.isnan(outputs).any(), 'Output included NaNs'
+    
+
+@pytest.mark.timeout(120)
+@pytest.mark.parametrize('model_name', list_models(exclude_filters=EXCLUDE_FILTERS, name_matches_cfg=True))
+@pytest.mark.parametrize('batch_size', [2])
+def test_model_backward_fx(model_name, batch_size):
+    """Symbolically trace each model and run single backward pass through the resulting GraphModule"""
+    if not has_fx_feature_extraction:
+        pytest.skip("Can't test FX. Torch >= 1.10 and Torchvision >= 0.11 are required.")
+
+    input_size = _get_input_size(model_name=model_name, target=TARGET_BWD_SIZE)
+    if max(input_size) > MAX_BWD_SIZE:
+        pytest.skip("Fixed input size model > limit.")
+
+    model = create_model(model_name, pretrained=False, num_classes=42)
+    model.train()
+
+    num_params = sum([x.numel() for x in model.parameters()])
+
+    input_size = _get_input_size(model=model, target=TARGET_FWD_SIZE)
+    if max(input_size) > MAX_FWD_SIZE:
+        pytest.skip("Fixed input size model > limit.")
+
+    # This block of code does a bit of juggling to handle any case where there are multiple outputs in train mode
+    # So we trace once and look at the graph, and get the indices of the nodes that lead into the original fx output
+    # node. Then we use those indices to select from train_nodes returned by torchvision get_graph_node_names
+    tracer = NodePathTracer(leaf_modules=list(_leaf_modules), autowrap_functions=list(_autowrap_functions))
+    graph = tracer.trace(model)
+    graph_nodes = list(reversed(graph.nodes))
+    output_node_names = [n.name for n in graph_nodes[0]._input_nodes.keys()]
+    graph_node_names = [n.name for n in graph_nodes]
+    output_node_indices = [-graph_node_names.index(node_name) for node_name in output_node_names]
+    train_nodes, eval_nodes = get_graph_node_names(
+        model, tracer_kwargs={'leaf_modules': list(_leaf_modules), 'autowrap_functions': list(_autowrap_functions)})
+    train_return_nodes = [train_nodes[ix] for ix in output_node_indices]
+    
+    model = create_feature_extractor(
+        model, train_return_nodes=train_return_nodes, eval_return_nodes=[eval_nodes[-1]],
+        tracer_kwargs={'leaf_modules': list(_leaf_modules), 'autowrap_functions': list(_autowrap_functions)})
+
+    inputs = torch.randn((batch_size, *input_size))
+    outputs = tuple(model(inputs).values())
+    if isinstance(outputs, tuple):
+        outputs = torch.cat(outputs)
+    outputs.mean().backward()
+    for n, x in model.named_parameters():
+        assert x.grad is not None, f'No gradient for {n}'
+    num_grad = sum([x.grad.numel() for x in model.parameters() if x.grad is not None])
+
+    assert outputs.shape[-1] == 42
+    assert num_params == num_grad, 'Some parameters are missing gradients'
+    assert not torch.isnan(outputs).any(), 'Output included NaNs'
+
+# reason: model is scripted after fx tracing, but beit has torch.jit.is_scripting() control flow
+EXCLUDE_FX_JIT_FILTERS = [
+    'deit_*_distilled_patch16_224',
+    'levit*',
+    'pit_*_distilled_224',
+]
+
+@pytest.mark.timeout(120)
+@pytest.mark.parametrize(
+    'model_name', list_models(
+        exclude_filters=EXCLUDE_FILTERS + EXCLUDE_JIT_FILTERS + EXCLUDE_FX_JIT_FILTERS, name_matches_cfg=True))
+@pytest.mark.parametrize('batch_size', [1])
+def test_model_forward_fx_torchscript(model_name, batch_size):
+    """Symbolically trace each model, script it, and run single forward pass"""
+    if not has_fx_feature_extraction:
+        pytest.skip("Can't test FX. Torch >= 1.10 and Torchvision >= 0.11 are required.")
+
+    input_size = _get_input_size(model_name=model_name, target=TARGET_JIT_SIZE)
+    if max(input_size) > MAX_JIT_SIZE:
+        pytest.skip("Fixed input size model > limit.")
+
+    with set_scriptable(True):
+        model = create_model(model_name, pretrained=False)
+    model.eval()
+
+    input_size = _get_input_size(model=model, target=TARGET_FWD_SIZE)
+    if max(input_size) > MAX_FWD_SIZE:
+        pytest.skip("Fixed input size model > limit.")
+
+    train_nodes, eval_nodes = get_graph_node_names(
+        model, tracer_kwargs={'leaf_modules': list(_leaf_modules), 'autowrap_functions': list(_autowrap_functions)})
+    model = create_feature_extractor(
+        model, train_return_nodes=[train_nodes[-1]], eval_return_nodes=[eval_nodes[-1]],
+        tracer_kwargs={'leaf_modules': list(_leaf_modules), 'autowrap_functions': list(_autowrap_functions)})
+
+    model = torch.jit.script(model)
+    outputs = model(torch.randn((batch_size, *input_size)))[train_nodes[-1]]
+
+    assert outputs.shape[0] == batch_size
+    assert not torch.isnan(outputs).any(), 'Output included NaNs'

timm/models/beit.py

@@ -86,9 +86,11 @@ class Attention(nn.Module):
         self.qkv = nn.Linear(dim, all_head_dim * 3, bias=False)
         if qkv_bias:
             self.q_bias = nn.Parameter(torch.zeros(all_head_dim))
+            self.register_buffer('k_bias', torch.zeros(all_head_dim), persistent=False)
             self.v_bias = nn.Parameter(torch.zeros(all_head_dim))
         else:
             self.q_bias = None
+            self.k_bias = None
             self.v_bias = None
 
         if window_size:
@@ -127,13 +129,7 @@ class Attention(nn.Module):
 
     def forward(self, x, rel_pos_bias: Optional[torch.Tensor] = None):
         B, N, C = x.shape
-        qkv_bias = None
-        if self.q_bias is not None:
-            if torch.jit.is_scripting():
-                # FIXME requires_grad breaks w/ torchscript
-                qkv_bias = torch.cat((self.q_bias, torch.zeros_like(self.v_bias), self.v_bias))
-            else:
-                qkv_bias = torch.cat((self.q_bias, torch.zeros_like(self.v_bias, requires_grad=False), self.v_bias))
+        qkv_bias = torch.cat((self.q_bias, self.k_bias, self.v_bias)) if self.q_bias is not None else None
         qkv = F.linear(input=x, weight=self.qkv.weight, bias=qkv_bias)
         qkv = qkv.reshape(B, N, 3, self.num_heads, -1).permute(2, 0, 3, 1, 4)
         q, k, v = qkv.unbind(0)  # make torchscript happy (cannot use tensor as tuple)

timm/models/coat.py

@@ -19,6 +19,7 @@ from timm.data import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD
 from .helpers import build_model_with_cfg, overlay_external_default_cfg
 from .layers import PatchEmbed, Mlp, DropPath, to_2tuple, trunc_normal_
 from .registry import register_model
+from .layers import _assert
 
 
 __all__ = [
@@ -105,7 +106,7 @@ class ConvRelPosEnc(nn.Module):
     def forward(self, q, v, size: Tuple[int, int]):
         B, h, N, Ch = q.shape
         H, W = size
-        assert N == 1 + H * W
+        _assert(N == 1 + H * W, '')
 
         # Convolutional relative position encoding.
         q_img = q[:, :, 1:, :]  # [B, h, H*W, Ch]
@@ -177,7 +178,7 @@ class ConvPosEnc(nn.Module):
     def forward(self, x, size: Tuple[int, int]):
         B, N, C = x.shape
         H, W = size
-        assert N == 1 + H * W
+        _assert(N == 1 + H * W, '')
 
         # Extract CLS token and image tokens.
         cls_token, img_tokens = x[:, :1], x[:, 1:]  # [B, 1, C], [B, H*W, C]
@@ -275,7 +276,7 @@ class ParallelBlock(nn.Module):
         """ Feature map interpolation. """
         B, N, C = x.shape
         H, W = size
-        assert N == 1 + H * W
+        _assert(N == 1 + H * W, '')
 
         cls_token = x[:, :1, :]
         img_tokens = x[:, 1:, :]

timm/models/convit.py

@@ -30,6 +30,7 @@ from .helpers import build_model_with_cfg
 from .layers import DropPath, to_2tuple, trunc_normal_, PatchEmbed, Mlp
 from .registry import register_model
 from .vision_transformer_hybrid import HybridEmbed
+from .fx_features import register_notrace_module
 
 import torch
 import torch.nn as nn
@@ -56,6 +57,7 @@ default_cfgs = {
 }
 
 
+@register_notrace_module  # reason: FX can't symbolically trace control flow in forward method
 class GPSA(nn.Module):
     def __init__(self, dim, num_heads=8, qkv_bias=False, attn_drop=0., proj_drop=0.,
                  locality_strength=1.):

timm/models/crossvit.py

@@ -22,6 +22,7 @@ NOTE: model names have been renamed from originals to represent actual input res
 Modifed from Timm. https://github.com/rwightman/pytorch-image-models/blob/master/timm/models/vision_transformer.py
 
 """
+from typing import Tuple
 
 import torch
 import torch.nn as nn
@@ -31,8 +32,9 @@ from functools import partial
 from typing import List
 
 from timm.data import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD
+from .fx_features import register_notrace_function
 from .helpers import build_model_with_cfg
-from .layers import DropPath, to_2tuple, trunc_normal_
+from .layers import DropPath, to_2tuple, trunc_normal_, _assert
 from .registry import register_model
 from .vision_transformer import Mlp, Block
 
@@ -116,8 +118,10 @@ class PatchEmbed(nn.Module):
     def forward(self, x):
         B, C, H, W = x.shape
         # FIXME look at relaxing size constraints
-        assert H == self.img_size[0] and W == self.img_size[1], \
-            f"Input image size ({H}*{W}) doesn't match model ({self.img_size[0]}*{self.img_size[1]})."
+        _assert(H == self.img_size[0],
+                f"Input image size ({H}*{W}) doesn't match model ({self.img_size[0]}*{self.img_size[1]}).")
+        _assert(W == self.img_size[1],
+                f"Input image size ({H}*{W}) doesn't match model ({self.img_size[0]}*{self.img_size[1]}).")
         x = self.proj(x).flatten(2).transpose(1, 2)
         return x
 
@@ -255,6 +259,27 @@ def _compute_num_patches(img_size, patches):
     return [i[0] // p * i[1] // p for i, p in zip(img_size, patches)]
 
 
+@register_notrace_function
+def scale_image(x, ss: Tuple[int, int], crop_scale: bool = False):  # annotations for torchscript
+    """
+    Pulled out of CrossViT.forward_features to bury conditional logic in a leaf node for FX tracing.
+    Args:
+        x (Tensor): input image
+        ss (tuple[int, int]): height and width to scale to
+        crop_scale (bool): whether to crop instead of interpolate to achieve the desired scale. Defaults to False
+    Returns:
+        Tensor: the "scaled" image batch tensor
+    """
+    H, W = x.shape[-2:]
+    if H != ss[0] or W != ss[1]:
+        if crop_scale and ss[0] <= H and ss[1] <= W:
+            cu, cl = int(round((H - ss[0]) / 2.)), int(round((W - ss[1]) / 2.))
+            x = x[:, :, cu:cu + ss[0], cl:cl + ss[1]]
+        else:
+            x = torch.nn.functional.interpolate(x, size=ss, mode='bicubic', align_corners=False)
+    return x
+
+
 class CrossViT(nn.Module):
     """ Vision Transformer with support for patch or hybrid CNN input stage
     """
@@ -342,17 +367,12 @@ class CrossViT(nn.Module):
              range(self.num_branches)])
 
     def forward_features(self, x):
-        B, C, H, W = x.shape
+        B = x.shape[0]
         xs = []
         for i, patch_embed in enumerate(self.patch_embed):
             x_ = x
             ss = self.img_size_scaled[i]
-            if H != ss[0] or W != ss[1]:
-                if self.crop_scale and ss[0] <= H and ss[1] <= W:
-                    cu, cl = int(round((H - ss[0]) / 2.)), int(round((W - ss[1]) / 2.))
-                    x_ = x_[:, :, cu:cu + ss[0], cl:cl + ss[1]]
-                else:
-                    x_ = torch.nn.functional.interpolate(x_, size=ss, mode='bicubic', align_corners=False)
+            x_ = scale_image(x_, ss, self.crop_scale)
             x_ = patch_embed(x_)
             cls_tokens = self.cls_token_0 if i == 0 else self.cls_token_1  # hard-coded for torch jit script
             cls_tokens = cls_tokens.expand(B, -1, -1)

timm/models/fx_features.py

@@ -0,0 +1,73 @@
+""" PyTorch FX Based Feature Extraction Helpers
+Using https://pytorch.org/vision/stable/feature_extraction.html
+"""
+from typing import Callable
+from torch import nn
+
+from .features import _get_feature_info
+
+try:
+    from torchvision.models.feature_extraction import create_feature_extractor
+    has_fx_feature_extraction = True
+except ImportError:
+    has_fx_feature_extraction = False
+
+# Layers we went to treat as leaf modules
+from .layers import Conv2dSame, ScaledStdConv2dSame, BatchNormAct2d, BlurPool2d, CondConv2d, StdConv2dSame, DropPath
+from .layers.non_local_attn import BilinearAttnTransform
+from .layers.pool2d_same import MaxPool2dSame, AvgPool2dSame
+
+# NOTE: By default, any modules from timm.models.layers that we want to treat as leaf modules go here
+# BUT modules from timm.models should use the registration mechanism below
+_leaf_modules = {
+    BatchNormAct2d,  # reason: flow control for jit scripting
+    BilinearAttnTransform,  # reason: flow control t <= 1
+    BlurPool2d,  # reason: TypeError: F.conv2d received Proxy in groups=x.shape[1]
+    # Reason: get_same_padding has a max which raises a control flow error
+    Conv2dSame, MaxPool2dSame,  ScaledStdConv2dSame, StdConv2dSame, AvgPool2dSame,
+    CondConv2d,  # reason: TypeError: F.conv2d received Proxy in groups=self.groups * B (because B = x.shape[0])
+    DropPath,  # reason: TypeError: rand recieved Proxy in `size` argument
+}
+
+try:
+    from .layers import InplaceAbn
+    _leaf_modules.add(InplaceAbn)
+except ImportError:
+    pass
+
+
+def register_notrace_module(module: nn.Module):
+    """
+    Any module not under timm.models.layers should get this decorator if we don't want to trace through it.
+    """
+    _leaf_modules.add(module)
+    return module
+
+
+# Functions we want to autowrap (treat them as leaves)
+_autowrap_functions = set()
+
+
+def register_notrace_function(func: Callable):
+    """
+    Decorator for functions which ought not to be traced through
+    """
+    _autowrap_functions.add(func)
+    return func
+
+
+class FeatureGraphNet(nn.Module):
+    def __init__(self, model, out_indices, out_map=None):
+        super().__init__()
+        assert has_fx_feature_extraction, 'Please update to PyTorch 1.10+, torchvision 0.11+ for FX feature extraction'
+        self.feature_info = _get_feature_info(model, out_indices)
+        if out_map is not None:
+            assert len(out_map) == len(out_indices)
+        return_nodes = {info['module']: out_map[i] if out_map is not None else info['module']
+                        for i, info in enumerate(self.feature_info) if i in out_indices}
+        self.graph_module = create_feature_extractor(
+            model, return_nodes,
+            tracer_kwargs={'leaf_modules': list(_leaf_modules), 'autowrap_functions': list(_autowrap_functions)})
+
+    def forward(self, x):
+        return list(self.graph_module(x).values())

timm/models/helpers.py

@@ -14,6 +14,7 @@ import torch.nn as nn
 
 
 from .features import FeatureListNet, FeatureDictNet, FeatureHookNet
+from .fx_features import FeatureGraphNet
 from .hub import has_hf_hub, download_cached_file, load_state_dict_from_hf, load_state_dict_from_url
 from .layers import Conv2dSame, Linear
 
@@ -477,6 +478,8 @@ def build_model_with_cfg(
                 feature_cls = feature_cls.lower()
                 if 'hook' in feature_cls:
                     feature_cls = FeatureHookNet
+                elif feature_cls == 'fx':
+                    feature_cls = FeatureGraphNet
                 else:
                     assert False, f'Unknown feature class {feature_cls}'
         model = feature_cls(model, **feature_cfg)

timm/models/layers/bottleneck_attn.py

@@ -22,6 +22,7 @@ import torch.nn.functional as F
 
 from .helpers import to_2tuple, make_divisible
 from .weight_init import trunc_normal_
+from .trace_utils import _assert
 
 
 def rel_logits_1d(q, rel_k, permute_mask: List[int]):
@@ -133,8 +134,8 @@ class BottleneckAttn(nn.Module):
 
     def forward(self, x):
         B, C, H, W = x.shape
-        assert H == self.pos_embed.height
-        assert W == self.pos_embed.width
+        _assert(H == self.pos_embed.height, '')
+        _assert(W == self.pos_embed.width, '')
 
         x = self.qkv(x)  # B, (2 * dim_head_qk + dim_head_v) * num_heads, H, W
 
@@ -154,5 +155,3 @@ class BottleneckAttn(nn.Module):
         out = (attn @ v).transpose(-1, -2).reshape(B, self.dim_out_v, H, W)  # B, dim_out, H, W
         out = self.pool(out)
         return out
-
-

timm/models/layers/evo_norm.py

@@ -12,6 +12,8 @@ Hacked together by / Copyright 2020 Ross Wightman
 import torch
 import torch.nn as nn
 
+from .trace_utils import _assert
+
 
 class EvoNormBatch2d(nn.Module):
     def __init__(self, num_features, apply_act=True, momentum=0.1, eps=1e-5, drop_block=None):
@@ -72,9 +74,9 @@ class EvoNormSample2d(nn.Module):
             nn.init.ones_(self.v)
 
     def forward(self, x):
-        assert x.dim() == 4, 'expected 4D input'
+        _assert(x.dim() == 4, 'expected 4D input')
         B, C, H, W = x.shape
-        assert C % self.groups == 0
+        _assert(C % self.groups == 0, '')
         if self.apply_act:
             n = x * (x * self.v).sigmoid()
             x = x.reshape(B, self.groups, -1)

timm/models/layers/halo_attn.py

@@ -16,7 +16,7 @@ The attention mechanism works but it's slow as implemented.
 
 Hacked together by / Copyright 2021 Ross Wightman
 """
-from typing import Tuple, List
+from typing import List
 
 import torch
 from torch import nn
@@ -24,6 +24,7 @@ import torch.nn.functional as F
 
 from .helpers import make_divisible
 from .weight_init import trunc_normal_
+from .trace_utils import _assert
 
 
 def rel_logits_1d(q, rel_k, permute_mask: List[int]):
@@ -167,8 +168,8 @@ class HaloAttn(nn.Module):
 
     def forward(self, x):
         B, C, H, W = x.shape
-        assert H % self.block_size == 0
-        assert W % self.block_size == 0
+        _assert(H % self.block_size == 0, '')
+        _assert(W % self.block_size == 0, '')
         num_h_blocks = H // self.block_size
         num_w_blocks = W // self.block_size
         num_blocks = num_h_blocks * num_w_blocks

timm/models/layers/non_local_attn.py

@@ -10,6 +10,7 @@ from torch.nn import functional as F
 
 from .conv_bn_act import ConvBnAct
 from .helpers import make_divisible
+from .trace_utils import _assert
 
 
 class NonLocalAttn(nn.Module):
@@ -83,7 +84,7 @@ class BilinearAttnTransform(nn.Module):
 
     def resize_mat(self, x, t: int):
         B, C, block_size, block_size1 = x.shape
-        assert block_size == block_size1
+        _assert(block_size == block_size1, '')
         if t <= 1:
             return x
         x = x.view(B * C, -1, 1, 1)
@@ -95,7 +96,8 @@ class BilinearAttnTransform(nn.Module):
         return x
 
     def forward(self, x):
-        assert x.shape[-1] % self.block_size == 0 and x.shape[-2] % self.block_size == 0
+        _assert(x.shape[-1] % self.block_size == 0, '')
+        _assert(x.shape[-2] % self.block_size == 0, '')
         B, C, H, W = x.shape
         out = self.conv1(x)
         rp = F.adaptive_max_pool2d(out, (self.block_size, 1))

timm/models/layers/selective_kernel.py

@@ -9,6 +9,7 @@ from torch import nn as nn
 
 from .conv_bn_act import ConvBnAct
 from .helpers import make_divisible
+from .trace_utils import _assert
 
 
 def _kernel_valid(k):
@@ -34,7 +35,7 @@ class SelectiveKernelAttn(nn.Module):
         self.fc_select = nn.Conv2d(attn_channels, channels * num_paths, kernel_size=1, bias=False)
 
     def forward(self, x):
-        assert x.shape[1] == self.num_paths
+        _assert(x.shape[1] == self.num_paths, '')
         x = x.sum(1).mean((2, 3), keepdim=True)
         x = self.fc_reduce(x)
         x = self.bn(x)

timm/models/nest.py

@@ -25,8 +25,10 @@ import torch.nn.functional as F
 from torch import nn
 
 from timm.data import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD
+from .fx_features import register_notrace_function
 from .helpers import build_model_with_cfg, named_apply
 from .layers import PatchEmbed, Mlp, DropPath, create_classifier, trunc_normal_
+from .layers import _assert
 from .layers import create_conv2d, create_pool2d, to_ntuple
 from .registry import register_model
 
@@ -128,8 +130,8 @@ class ConvPool(nn.Module):
         """
         x is expected to have shape (B, C, H, W)
         """
-        assert x.shape[-2] % 2 == 0, 'BlockAggregation requires even input spatial dims'
-        assert x.shape[-1] % 2 == 0, 'BlockAggregation requires even input spatial dims'
+        _assert(x.shape[-2] % 2 == 0, 'BlockAggregation requires even input spatial dims')
+        _assert(x.shape[-1] % 2 == 0, 'BlockAggregation requires even input spatial dims')
         x = self.conv(x)
         # Layer norm done over channel dim only
         x = self.norm(x.permute(0, 2, 3, 1)).permute(0, 3, 1, 2)
@@ -144,8 +146,8 @@ def blockify(x, block_size: int):
         block_size (int): edge length of a single square block in units of H, W
     """
     B, H, W, C  = x.shape
-    assert H % block_size == 0, '`block_size` must divide input height evenly'
-    assert W % block_size == 0, '`block_size` must divide input width evenly'
+    _assert(H % block_size == 0, '`block_size` must divide input height evenly')
+    _assert(W % block_size == 0, '`block_size` must divide input width evenly')
     grid_height = H // block_size
     grid_width = W // block_size
     x = x.reshape(B, grid_height, block_size, grid_width, block_size, C)
@@ -153,6 +155,7 @@ def blockify(x, block_size: int):
     return x  # (B, T, N, C)
 
 
+@register_notrace_function  # reason: int receives Proxy
 def deblockify(x, block_size: int):
     """blocks to image
     Args:

timm/models/nfnet.py

@@ -26,6 +26,7 @@ import torch
 import torch.nn as nn
 
 from timm.data import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD
+from .fx_features import register_notrace_module
 from .helpers import build_model_with_cfg
 from .registry import register_model
 from .layers import ClassifierHead, DropPath, AvgPool2dSame, ScaledStdConv2d, ScaledStdConv2dSame,\
@@ -318,6 +319,7 @@ class DownsampleAvg(nn.Module):
         return self.conv(self.pool(x))
 
 
+@register_notrace_module  # reason: mul_ causes FX to drop a relevant node. https://github.com/pytorch/pytorch/issues/68301
 class NormFreeBlock(nn.Module):
     """Normalization-Free pre-activation block.
     """

timm/models/rexnet.py

@@ -10,6 +10,7 @@ Changes for timm, feature extraction, and rounded channel variant hacked togethe
 Copyright 2020 Ross Wightman
 """
 
+import torch
 import torch.nn as nn
 from functools import partial
 from math import ceil
@@ -92,7 +93,7 @@ class LinearBottleneck(nn.Module):
         if self.use_shortcut:
             if self.drop_path is not None:
                 x = self.drop_path(x)
-            x[:, 0:self.in_channels] += shortcut
+            x = torch.cat([x[:, 0:self.in_channels] + shortcut, x[:, self.in_channels:]], dim=1)
         return x
 
 

timm/models/swin_transformer.py

@@ -21,11 +21,14 @@ import torch.nn as nn
 import torch.utils.checkpoint as checkpoint
 
 from timm.data import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD
+from .fx_features import register_notrace_function
 from .helpers import build_model_with_cfg, overlay_external_default_cfg
 from .layers import PatchEmbed, Mlp, DropPath, to_2tuple, trunc_normal_
+from .layers import _assert
 from .registry import register_model
 from .vision_transformer import checkpoint_filter_fn, _init_vit_weights
 
+
 _logger = logging.getLogger(__name__)
 
 
@@ -100,6 +103,7 @@ def window_partition(x, window_size: int):
     return windows
 
 
+@register_notrace_function  # reason: int argument is a Proxy
 def window_reverse(windows, window_size: int, H: int, W: int):
     """
     Args:
@@ -270,7 +274,7 @@ class SwinTransformerBlock(nn.Module):
     def forward(self, x):
         H, W = self.input_resolution
         B, L, C = x.shape
-        assert L == H * W, "input feature has wrong size"
+        _assert(L == H * W, "input feature has wrong size")
 
         shortcut = x
         x = self.norm1(x)
@@ -329,8 +333,8 @@ class PatchMerging(nn.Module):
         """
         H, W = self.input_resolution
         B, L, C = x.shape
-        assert L == H * W, "input feature has wrong size"
-        assert H % 2 == 0 and W % 2 == 0, f"x size ({H}*{W}) are not even."
+        _assert(L == H * W, "input feature has wrong size")
+        _assert(H % 2 == 0 and W % 2 == 0, f"x size ({H}*{W}) are not even.")
 
         x = x.view(B, H, W, C)
 

timm/models/tnt.py

@@ -9,12 +9,12 @@ https://gitee.com/mindspore/mindspore/tree/master/model_zoo/research/cv/TNT
 import math
 import torch
 import torch.nn as nn
-from functools import partial
 
 from timm.data import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD
 from timm.models.helpers import build_model_with_cfg
 from timm.models.layers import Mlp, DropPath, trunc_normal_
 from timm.models.layers.helpers import to_2tuple
+from timm.models.layers import _assert
 from timm.models.registry import register_model
 from timm.models.vision_transformer import resize_pos_embed
 
@@ -109,7 +109,9 @@ class Block(nn.Module):
         pixel_embed = pixel_embed + self.drop_path(self.mlp_in(self.norm_mlp_in(pixel_embed)))
         # outer
         B, N, C = patch_embed.size()
-        patch_embed[:, 1:] = patch_embed[:, 1:] + self.proj(self.norm1_proj(pixel_embed).reshape(B, N - 1, -1))
+        patch_embed = torch.cat(
+            [patch_embed[:, 0:1], patch_embed[:, 1:] + self.proj(self.norm1_proj(pixel_embed).reshape(B, N - 1, -1))],
+            dim=1)
         patch_embed = patch_embed + self.drop_path(self.attn_out(self.norm_out(patch_embed)))
         patch_embed = patch_embed + self.drop_path(self.mlp(self.norm_mlp(patch_embed)))
         return pixel_embed, patch_embed
@@ -136,8 +138,10 @@ class PixelEmbed(nn.Module):
 
     def forward(self, x, pixel_pos):
         B, C, H, W = x.shape
-        assert H == self.img_size[0] and W == self.img_size[1], \
-            f"Input image size ({H}*{W}) doesn't match model ({self.img_size[0]}*{self.img_size[1]})."
+        _assert(H == self.img_size[0],
+            f"Input image size ({H}*{W}) doesn't match model ({self.img_size[0]}*{self.img_size[1]}).")
+        _assert(W == self.img_size[1],
+            f"Input image size ({H}*{W}) doesn't match model ({self.img_size[0]}*{self.img_size[1]}).")
         x = self.proj(x)
         x = self.unfold(x)
         x = x.transpose(1, 2).reshape(B * self.num_patches, self.in_dim, self.new_patch_size[0], self.new_patch_size[1])

timm/models/twins.py

@@ -22,9 +22,10 @@ from functools import partial
 
 from timm.data import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD
 from .layers import Mlp, DropPath, to_2tuple, trunc_normal_
+from .fx_features import register_notrace_module
 from .registry import register_model
 from .vision_transformer import Attention
-from .helpers import build_model_with_cfg, overlay_external_default_cfg
+from .helpers import build_model_with_cfg
 
 
 def _cfg(url='', **kwargs):
@@ -62,6 +63,7 @@ default_cfgs = {
 Size_ = Tuple[int, int]
 
 
+@register_notrace_module  # reason: FX can't symbolically trace control flow in forward method
 class LocallyGroupedAttn(nn.Module):
     """ LSA: self attention within a group
     """

timm/models/vgg.py

@@ -12,7 +12,8 @@ from typing import Union, List, Dict, Any, cast
 
 from timm.data import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD
 from .helpers import build_model_with_cfg
-from .layers import ClassifierHead, ConvBnAct
+from .fx_features import register_notrace_module
+from .layers import ClassifierHead
 from .registry import register_model
 
 __all__ = [
@@ -52,6 +53,7 @@ cfgs: Dict[str, List[Union[str, int]]] = {
 }
 
 
+@register_notrace_module  # reason: FX can't symbolically trace control flow in forward method
 class ConvMlp(nn.Module):
 
     def __init__(self, in_features=512, out_features=4096, kernel_size=7, mlp_ratio=1.0,

timm/models/xcit.py

@@ -21,6 +21,7 @@ from .vision_transformer import _cfg, Mlp
 from .registry import register_model
 from .layers import DropPath, trunc_normal_, to_2tuple
 from .cait import ClassAttn
+from .fx_features import register_notrace_module
 
 
 def _cfg(url='', **kwargs):
@@ -97,6 +98,7 @@ default_cfgs = {
 }
 
 
+@register_notrace_module  # reason: FX can't symbolically trace torch.arange in forward method
 class PositionalEncodingFourier(nn.Module):
     """
     Positional encoding relying on a fourier kernel matching the one used in the "Attention is all of Need" paper.