Add working Gluon Xception-65 model. Some cleanup still needed.

convert/convert_from_mxnet.py

@@ -5,7 +5,7 @@ import os
 import mxnet as mx
 import gluoncv
 import torch
-from models.model_factory import create_model
+from timm import create_model
 
 parser = argparse.ArgumentParser(description='Convert from MXNet')
 parser.add_argument('--model', default='all', type=str, metavar='MODEL',

timm/models/__init__.py

@@ -10,6 +10,7 @@ from .pnasnet import *
 from .gen_efficientnet import *
 from .inception_v3 import *
 from .gluon_resnet import *
+from .gluon_xception import *
 
 from .registry import *
 from .factory import create_model

timm/models/gluon_xception.py

@@ -0,0 +1,438 @@
+"""Pytorch impl of Gluon Xception
+This is a port of the Gluon Xception code and weights, itself ported from a PyTorch DeepLab impl.
+
+Gluon model: (https://gluon-cv.mxnet.io/_modules/gluoncv/model_zoo/xception.html)
+Original PyTorch DeepLab impl: https://github.com/jfzhang95/pytorch-deeplab-xception
+
+Hacked together by Ross Wightman
+"""
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+from collections import OrderedDict
+
+from .registry import register_model
+from .helpers import load_pretrained
+from .adaptive_avgmax_pool import select_adaptive_pool2d
+from timm.data import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD
+
+__all__ = ['Xception65', 'Xception71']
+
+default_cfgs = {
+    'gluon_xception65': {
+        'url': 'https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/gluon_xception-7015a15c.pth',
+        'input_size': (3, 299, 299),
+        'crop_pct': 0.875,
+        'interpolation': 'bicubic',
+        'mean': IMAGENET_DEFAULT_MEAN,
+        'std': IMAGENET_DEFAULT_STD,
+        'num_classes': 1000,
+        'first_conv': 'conv1',
+        'classifier': 'fc'
+        # The resize parameter of the validation transform should be 333, and make sure to center crop at 299x299
+    },
+    'gluon_xception71': {
+        'url': '',
+        'input_size': (3, 299, 299),
+        'crop_pct': 0.875,
+        'interpolation': 'bicubic',
+        'mean': IMAGENET_DEFAULT_MEAN,
+        'std': IMAGENET_DEFAULT_STD,
+        'num_classes': 1000,
+        'first_conv': 'conv1',
+        'classifier': 'fc'
+        # The resize parameter of the validation transform should be 333, and make sure to center crop at 299x299
+    }
+}
+
+
+""" PADDING NOTES
+The original PyTorch and Gluon impl of these models dutifully reproduced the 
+aligned padding added to Tensorflow models for Deeplab. This padding was compensating
+for  Tensorflow 'SAME' padding. PyTorch symmetric padding behaves the way we'd want it to. 
+
+So, I'm phasing out the 'fixed_padding' ported from TF and replacing with normal 
+PyTorch padding, some asserts to validate the equivalence for any scenario we'd 
+care about before removing altogether.
+"""
+_USE_FIXED_PAD = False
+
+
+def _pytorch_padding(kernel_size, stride=1, dilation=1, **_):
+    if _USE_FIXED_PAD:
+        return 0  # FIXME remove once verified
+    else:
+        padding = ((stride - 1) + dilation * (kernel_size - 1)) // 2
+
+        # FIXME remove once verified
+        fp = _fixed_padding(kernel_size, dilation)
+        assert all(padding == p for p in fp)
+
+        return padding
+
+
+def _fixed_padding(kernel_size, dilation):
+    kernel_size_effective = kernel_size + (kernel_size - 1) * (dilation - 1)
+    pad_total = kernel_size_effective - 1
+    pad_beg = pad_total // 2
+    pad_end = pad_total - pad_beg
+    return [pad_beg, pad_end, pad_beg, pad_end]
+
+
+class SeparableConv2d(nn.Module):
+    def __init__(self, inplanes, planes, kernel_size=3, stride=1,
+                 dilation=1, bias=False, norm_layer=None, norm_kwargs=None):
+        super(SeparableConv2d, self).__init__()
+        norm_kwargs = norm_kwargs if norm_kwargs is not None else {}
+        self.kernel_size = kernel_size
+        self.dilation = dilation
+
+        padding = _fixed_padding(self.kernel_size, self.dilation)
+        if _USE_FIXED_PAD and any(p > 0 for p in padding):
+            self.fixed_padding = nn.ZeroPad2d(padding)
+        else:
+            self.fixed_padding = None
+
+        # depthwise convolution
+        self.conv_dw = nn.Conv2d(
+            inplanes, inplanes, kernel_size, stride=stride,
+            padding=_pytorch_padding(kernel_size, stride, dilation), dilation=dilation, groups=inplanes, bias=bias)
+        self.bn = norm_layer(num_features=inplanes, **norm_kwargs)
+        # pointwise convolution
+        self.conv_pw = nn.Conv2d(inplanes, planes, kernel_size=1, bias=bias)
+
+    def forward(self, x):
+        if self.fixed_padding is not None:
+            # FIXME remove once verified
+            x = self.fixed_padding(x)
+        x = self.conv_dw(x)
+        x = self.bn(x)
+        x = self.conv_pw(x)
+        return x
+
+
+class Block(nn.Module):
+    def __init__(self, inplanes, planes, num_reps, stride=1, dilation=1, norm_layer=None,
+                 norm_kwargs=None, start_with_relu=True, grow_first=True, is_last=False):
+        super(Block, self).__init__()
+        norm_kwargs = norm_kwargs if norm_kwargs is not None else {}
+        if planes != inplanes or stride != 1:
+            self.skip = nn.Sequential()
+            self.skip.add_module('conv1', nn.Conv2d(
+                inplanes, planes, 1, stride=stride, bias=False)),
+            self.skip.add_module('bn1', norm_layer(num_features=planes, **norm_kwargs))
+        else:
+            self.skip = None
+
+        rep = OrderedDict()
+        l = 1
+        filters = inplanes
+        if grow_first:
+            if start_with_relu:
+                rep['act%d' % l] = nn.ReLU(inplace=False)  # NOTE: silent failure if inplace=True here
+            rep['conv%d' % l] = SeparableConv2d(
+                inplanes, planes, 3, 1, dilation, norm_layer=norm_layer, norm_kwargs=norm_kwargs)
+            rep['bn%d' % l] = norm_layer(num_features=planes, **norm_kwargs)
+            filters = planes
+            l += 1
+
+        for _ in range(num_reps - 1):
+            if grow_first or start_with_relu:
+                # FIXME being conservative with inplace here, think it's fine to leave True?
+                rep['act%d' % l] = nn.ReLU(inplace=grow_first or not start_with_relu)
+            rep['conv%d' % l] = SeparableConv2d(
+                filters, filters, 3, 1, dilation, norm_layer=norm_layer, norm_kwargs=norm_kwargs)
+            rep['bn%d' % l] = norm_layer(num_features=filters, **norm_kwargs)
+            l += 1
+
+        if not grow_first:
+            rep['act%d' % l] = nn.ReLU(inplace=True)
+            rep['conv%d' % l] = SeparableConv2d(
+                inplanes, planes, 3, 1, dilation, norm_layer=norm_layer, norm_kwargs=norm_kwargs)
+            rep['bn%d' % l] = norm_layer(num_features=planes, **norm_kwargs)
+            l += 1
+
+        if stride != 1:
+            rep['act%d' % l] = nn.ReLU(inplace=True)
+            rep['conv%d' % l] = SeparableConv2d(
+                planes, planes, 3, stride, norm_layer=norm_layer, norm_kwargs=norm_kwargs)
+            rep['bn%d' % l] = norm_layer(num_features=planes, **norm_kwargs)
+            l += 1
+        elif is_last:
+            rep['act%d' % l] = nn.ReLU(inplace=True)
+            rep['conv%d' % l] = SeparableConv2d(
+                planes, planes, 3, 1, dilation, norm_layer=norm_layer, norm_kwargs=norm_kwargs)
+            rep['bn%d' % l] = norm_layer(num_features=planes, **norm_kwargs)
+            l += 1
+        self.rep = nn.Sequential(rep)
+
+    def forward(self, x):
+        skip = x
+        if self.skip is not None:
+            skip = self.skip(skip)
+        x = self.rep(x) + skip
+        return x
+
+
+class Xception65(nn.Module):
+    """Modified Aligned Xception
+    """
+
+    def __init__(self, num_classes=1000, in_chans=3, output_stride=32, norm_layer=nn.BatchNorm2d,
+                 norm_kwargs=None, drop_rate=0., global_pool='avg'):
+        super(Xception65, self).__init__()
+        self.drop_rate = drop_rate
+        norm_kwargs = norm_kwargs if norm_kwargs is not None else {}
+        if output_stride == 32:
+            entry_block3_stride = 2
+            exit_block20_stride = 2
+            middle_block_dilation = 1
+            exit_block_dilations = (1, 1)
+        elif output_stride == 16:
+            entry_block3_stride = 2
+            exit_block20_stride = 1
+            middle_block_dilation = 1
+            exit_block_dilations = (1, 2)
+        elif output_stride == 8:
+            entry_block3_stride = 1
+            exit_block20_stride = 1
+            middle_block_dilation = 2
+            exit_block_dilations = (2, 4)
+        else:
+            raise NotImplementedError
+
+        # Entry flow
+        self.conv1 = nn.Conv2d(in_chans, 32, kernel_size=3, stride=2, padding=1, bias=False)
+        self.bn1 = norm_layer(num_features=32, **norm_kwargs)
+        self.relu = nn.ReLU(inplace=True)
+
+        self.conv2 = nn.Conv2d(32, 64, kernel_size=3, stride=1, padding=1, bias=False)
+        self.bn2 = norm_layer(num_features=64)
+
+        self.block1 = Block(
+            64, 128, num_reps=2, stride=2,
+            norm_layer=norm_layer, norm_kwargs=norm_kwargs, start_with_relu=False)
+        self.block2 = Block(
+            128, 256, num_reps=2, stride=2,
+            norm_layer=norm_layer, norm_kwargs=norm_kwargs, start_with_relu=False, grow_first=True)
+        self.block3 = Block(
+            256, 728, num_reps=2, stride=entry_block3_stride,
+            norm_layer=norm_layer, norm_kwargs=norm_kwargs, start_with_relu=True, grow_first=True, is_last=True)
+
+        # Middle flow
+        self.mid = nn.Sequential(OrderedDict([('block%d' % i,  Block(
+            728, 728, num_reps=3, stride=1, dilation=middle_block_dilation,
+            norm_layer=norm_layer, norm_kwargs=norm_kwargs, start_with_relu=True, grow_first=True))
+                                              for i in range(4, 20)]))
+
+        # Exit flow
+        self.block20 = Block(
+            728, 1024, num_reps=2, stride=exit_block20_stride, dilation=exit_block_dilations[0],
+            norm_layer=norm_layer, norm_kwargs=norm_kwargs, start_with_relu=True, grow_first=False, is_last=True)
+
+        self.conv3 = SeparableConv2d(
+            1024, 1536, 3, stride=1, dilation=exit_block_dilations[1],
+            norm_layer=norm_layer, norm_kwargs=norm_kwargs)
+        self.bn3 = norm_layer(num_features=1536, **norm_kwargs)
+
+        self.conv4 = SeparableConv2d(
+            1536, 1536, 3, stride=1, dilation=exit_block_dilations[1],
+            norm_layer=norm_layer, norm_kwargs=norm_kwargs)
+        self.bn4 = norm_layer(num_features=1536, **norm_kwargs)
+
+        self.conv5 = SeparableConv2d(
+            1536, 2048, 3, stride=1, dilation=exit_block_dilations[1],
+            norm_layer=norm_layer, norm_kwargs=norm_kwargs)
+        self.bn5 = norm_layer(num_features=2048, **norm_kwargs)
+        self.avgpool = nn.AdaptiveAvgPool2d(1)
+        self.fc = nn.Linear(in_features=2048, out_features=num_classes)
+
+    def forward(self, x):
+        # Entry flow
+        x = self.conv1(x)
+        x = self.bn1(x)
+        x = self.relu(x)
+
+        x = self.conv2(x)
+        x = self.bn2(x)
+        x = self.relu(x)
+
+        x = self.block1(x)
+        # add relu here
+        x = self.relu(x)
+        # c1 = x
+        x = self.block2(x)
+        # c2 = x
+        x = self.block3(x)
+
+        # Middle flow
+        x = self.mid(x)
+        # c3 = x
+
+        # Exit flow
+        x = self.block20(x)
+        x = self.relu(x)
+        x = self.conv3(x)
+        x = self.bn3(x)
+        x = self.relu(x)
+
+        x = self.conv4(x)
+        x = self.bn4(x)
+        x = self.relu(x)
+
+        x = self.conv5(x)
+        x = self.bn5(x)
+        x = self.relu(x)
+
+        x = self.avgpool(x)
+        x = x.view(x.size(0), -1)
+        if self.drop_rate > 0.:
+            x = F.dropout(x, p=self.drop_rate, training=self.training)
+        x = self.fc(x)
+        return x
+
+
+class Xception71(nn.Module):
+    """Modified Aligned Xception
+    """
+
+    def __init__(self, num_classes=1000, in_chans=3, output_stride=32, norm_layer=nn.BatchNorm2d,
+                 norm_kwargs=None, drop_rate=0., global_pool='avg'):
+        super(Xception71, self).__init__()
+        self.drop_rate = drop_rate
+        norm_kwargs = norm_kwargs if norm_kwargs is not None else {}
+        if output_stride == 32:
+            entry_block3_stride = 2
+            exit_block20_stride = 2
+            middle_block_dilation = 1
+            exit_block_dilations = (1, 1)
+        elif output_stride == 16:
+            entry_block3_stride = 2
+            exit_block20_stride = 1
+            middle_block_dilation = 1
+            exit_block_dilations = (1, 2)
+        elif output_stride == 8:
+            entry_block3_stride = 1
+            exit_block20_stride = 1
+            middle_block_dilation = 2
+            exit_block_dilations = (2, 4)
+        else:
+            raise NotImplementedError
+        
+        # Entry flow
+        self.conv1 = nn.Conv2d(in_chans, 32, kernel_size=3, stride=2, padding=1, bias=False)
+        self.bn1 = norm_layer(num_features=32, **norm_kwargs)
+        self.relu = nn.ReLU(inplace=True)
+
+        self.conv2 = nn.Conv2d(32, 64, kernel_size=3, stride=1, padding=1, bias=False)
+        self.bn2 = norm_layer(num_features=64)
+
+        self.block1 = Block(
+            64, 128, num_reps=2, stride=2, norm_layer=norm_layer,
+            norm_kwargs=norm_kwargs, start_with_relu=False)
+        self.block2 = nn.Sequential(*[
+            Block(
+                128, 256, num_reps=2, stride=1, norm_layer=norm_layer,
+                norm_kwargs=norm_kwargs, start_with_relu=False, grow_first=True),
+            Block(
+                256, 256, num_reps=2, stride=2, norm_layer=norm_layer,
+                norm_kwargs=norm_kwargs, start_with_relu=False, grow_first=True),
+            Block(
+                256, 728, num_reps=2, stride=2, norm_layer=norm_layer,
+                norm_kwargs=norm_kwargs, start_with_relu=False, grow_first=True)])
+        self.block3 = Block(
+            728, 728, num_reps=2, stride=entry_block3_stride, norm_layer=norm_layer,
+            norm_kwargs=norm_kwargs, start_with_relu=True, grow_first=True, is_last=True)
+
+        # Middle flow
+        self.mid = nn.Sequential(OrderedDict([('block%d' % i, Block(
+            728, 728, num_reps=3, stride=1, dilation=middle_block_dilation,
+            norm_layer=norm_layer, norm_kwargs=norm_kwargs, start_with_relu=True, grow_first=True))
+                                              for i in range(4, 20)]))
+
+        # Exit flow
+        self.block20 = Block(
+            728, 1024, num_reps=2, stride=exit_block20_stride, dilation=exit_block_dilations[0],
+            norm_layer=norm_layer, norm_kwargs=norm_kwargs, start_with_relu=True, grow_first=False, is_last=True)
+
+        self.conv3 = SeparableConv2d(
+            1024, 1536, 3, stride=1, dilation=exit_block_dilations[1],
+            norm_layer=norm_layer, norm_kwargs=norm_kwargs)
+        self.bn3 = norm_layer(num_features=1536, **norm_kwargs)
+
+        self.conv4 = SeparableConv2d(
+            1536, 1536, 3, stride=1, dilation=exit_block_dilations[1],
+            norm_layer=norm_layer, norm_kwargs=norm_kwargs)
+        self.bn4 = norm_layer(num_features=1536, **norm_kwargs)
+
+        self.conv5 = SeparableConv2d(
+            1536, 2048, 3, stride=1, dilation=exit_block_dilations[1],
+            norm_layer=norm_layer, norm_kwargs=norm_kwargs)
+        self.bn5 = norm_layer(num_features=2048, **norm_kwargs)
+        self.avgpool = nn.AdaptiveAvgPool2d(1)
+        self.fc = nn.Linear(in_features=2048, out_features=num_classes)
+
+    def forward(self, x):
+        # Entry flow
+        x = self.conv1(x)
+        x = self.bn1(x)
+        x = self.relu(x)
+
+        x = self.conv2(x)
+        x = self.bn2(x)
+        x = self.relu(x)
+
+        x = self.block1(x)
+        # add relu here
+        x = self.relu(x)
+        # low_level_feat = x
+        x = self.block2(x)
+        # c2 = x
+        x = self.block3(x)
+
+        # Middle flow
+        x = self.mid(x)
+        # c3 = x
+
+        # Exit flow
+        x = self.block20(x)
+        x = self.relu(x)
+        x = self.conv3(x)
+        x = self.bn3(x)
+        x = self.relu(x)
+
+        x = self.conv4(x)
+        x = self.bn4(x)
+        x = self.relu(x)
+
+        x = self.conv5(x)
+        x = self.bn5(x)
+        x = self.relu(x)
+
+        x = self.avgpool(x)
+        x = x.view(x.size(0), -1)
+        if self.drop_rate > 0.:
+            x = F.dropout(x, p=self.drop_rate, training=self.training)
+        x = self.fc(x)
+        return x
+
+
+@register_model
+def gluon_xception65(pretrained=False, num_classes=1000, in_chans=3, **kwargs):
+    default_cfg = default_cfgs['gluon_xception65']
+    model = Xception65(num_classes=num_classes, in_chans=in_chans, **kwargs)
+    model.default_cfg = default_cfg
+    if pretrained:
+        load_pretrained(model, default_cfg, num_classes, in_chans)
+    return model
+
+
+@register_model
+def gluon_xception71(pretrained=False, num_classes=1000, in_chans=3, **kwargs):
+    default_cfg = default_cfgs['gluon_xception71']
+    model = Xception71(num_classes=num_classes, in_chans=in_chans, **kwargs)
+    model.default_cfg = default_cfg
+    if pretrained:
+        load_pretrained(model, default_cfg, num_classes, in_chans)
+    return model
+