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networks/vit_seg_configs.py

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+import ml_collections
+
+def get_b16_config():
+    """Returns the ViT-B/16 configuration."""
+    config = ml_collections.ConfigDict()
+    config.patches = ml_collections.ConfigDict({'size': (16, 16)})
+    config.hidden_size = 768
+    config.transformer = ml_collections.ConfigDict()
+    config.transformer.mlp_dim = 3072
+    config.transformer.num_heads = 12
+    config.transformer.num_layers = 12
+    config.transformer.attention_dropout_rate = 0.0
+    config.transformer.dropout_rate = 0.1
+
+    config.classifier = 'seg'
+    config.representation_size = None
+    config.resnet_pretrained_path = None
+    config.pretrained_path = '../model/vit_checkpoint/imagenet21k/ViT-B_16.npz'
+    config.patch_size = 16
+
+    config.decoder_channels = (256, 128, 64, 16)
+    config.n_classes = 2
+    config.activation = 'softmax'
+    return config
+
+
+def get_testing():
+    """Returns a minimal configuration for testing."""
+    config = ml_collections.ConfigDict()
+    config.patches = ml_collections.ConfigDict({'size': (16, 16)})
+    config.hidden_size = 1
+    config.transformer = ml_collections.ConfigDict()
+    config.transformer.mlp_dim = 1
+    config.transformer.num_heads = 1
+    config.transformer.num_layers = 1
+    config.transformer.attention_dropout_rate = 0.0
+    config.transformer.dropout_rate = 0.1
+    config.classifier = 'token'
+    config.representation_size = None
+    return config
+
+def get_r50_b16_config():
+    """Returns the Resnet50 + ViT-B/16 configuration."""
+    config = get_b16_config()
+    config.patches.grid = (16, 16)
+    config.resnet = ml_collections.ConfigDict()
+    config.resnet.num_layers = (3, 4, 9)
+    config.resnet.width_factor = 1
+
+    config.classifier = 'seg'
+    config.pretrained_path = '../model/vit_checkpoint/imagenet21k/R50+ViT-B_16.npz'
+    config.decoder_channels = (256, 128, 64, 16)
+    config.skip_channels = [512, 256, 64, 16]
+    config.n_classes = 2
+    config.n_skip = 3
+    config.activation = 'softmax'
+
+    return config
+
+
+def get_b32_config():
+    """Returns the ViT-B/32 configuration."""
+    config = get_b16_config()
+    config.patches.size = (32, 32)
+    config.pretrained_path = '../model/vit_checkpoint/imagenet21k/ViT-B_32.npz'
+    return config
+
+
+def get_l16_config():
+    """Returns the ViT-L/16 configuration."""
+    config = ml_collections.ConfigDict()
+    config.patches = ml_collections.ConfigDict({'size': (16, 16)})
+    config.hidden_size = 1024
+    config.transformer = ml_collections.ConfigDict()
+    config.transformer.mlp_dim = 4096
+    config.transformer.num_heads = 16
+    config.transformer.num_layers = 24
+    config.transformer.attention_dropout_rate = 0.0
+    config.transformer.dropout_rate = 0.1
+    config.representation_size = None
+
+    # custom
+    config.classifier = 'seg'
+    config.resnet_pretrained_path = None
+    config.pretrained_path = '../model/vit_checkpoint/imagenet21k/ViT-L_16.npz'
+    config.decoder_channels = (256, 128, 64, 16)
+    config.n_classes = 2
+    config.activation = 'softmax'
+    return config
+
+
+def get_r50_l16_config():
+    """Returns the Resnet50 + ViT-L/16 configuration. customized """
+    config = get_l16_config()
+    config.patches.grid = (16, 16)
+    config.resnet = ml_collections.ConfigDict()
+    config.resnet.num_layers = (3, 4, 9)
+    config.resnet.width_factor = 1
+
+    config.classifier = 'seg'
+    config.resnet_pretrained_path = '../model/vit_checkpoint/imagenet21k/R50+ViT-B_16.npz'
+    config.decoder_channels = (256, 128, 64, 16)
+    config.skip_channels = [512, 256, 64, 16]
+    config.n_classes = 2
+    config.activation = 'softmax'
+    return config
+
+
+def get_l32_config():
+    """Returns the ViT-L/32 configuration."""
+    config = get_l16_config()
+    config.patches.size = (32, 32)
+    return config
+
+
+def get_h14_config():
+    """Returns the ViT-L/16 configuration."""
+    config = ml_collections.ConfigDict()
+    config.patches = ml_collections.ConfigDict({'size': (14, 14)})
+    config.hidden_size = 1280
+    config.transformer = ml_collections.ConfigDict()
+    config.transformer.mlp_dim = 5120
+    config.transformer.num_heads = 16
+    config.transformer.num_layers = 32
+    config.transformer.attention_dropout_rate = 0.0
+    config.transformer.dropout_rate = 0.1
+    config.classifier = 'token'
+    config.representation_size = None
+
+    return config

networks/vit_seg_modeling.py

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+# coding=utf-8
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+import copy
+import logging
+import math
+
+from os.path import join as pjoin
+
+import torch
+import torch.nn as nn
+import numpy as np
+
+from torch.nn import CrossEntropyLoss, Dropout, Softmax, Linear, Conv2d, LayerNorm
+from torch.nn.modules.utils import _pair
+from scipy import ndimage
+from . import vit_seg_configs as configs
+from .vit_seg_modeling_resnet_skip import ResNetV2
+
+
+logger = logging.getLogger(__name__)
+
+
+ATTENTION_Q = "MultiHeadDotProductAttention_1/query"
+ATTENTION_K = "MultiHeadDotProductAttention_1/key"
+ATTENTION_V = "MultiHeadDotProductAttention_1/value"
+ATTENTION_OUT = "MultiHeadDotProductAttention_1/out"
+FC_0 = "MlpBlock_3/Dense_0"
+FC_1 = "MlpBlock_3/Dense_1"
+ATTENTION_NORM = "LayerNorm_0"
+MLP_NORM = "LayerNorm_2"
+
+
+def np2th(weights, conv=False):
+    """Possibly convert HWIO to OIHW."""
+    if conv:
+        weights = weights.transpose([3, 2, 0, 1])
+    return torch.from_numpy(weights)
+
+
+def swish(x):
+    return x * torch.sigmoid(x)
+
+
+ACT2FN = {"gelu": torch.nn.functional.gelu, "relu": torch.nn.functional.relu, "swish": swish}
+
+
+class Attention(nn.Module):
+    def __init__(self, config, vis):
+        super(Attention, self).__init__()
+        self.vis = vis
+        self.num_attention_heads = config.transformer["num_heads"]
+        self.attention_head_size = int(config.hidden_size / self.num_attention_heads)
+        self.all_head_size = self.num_attention_heads * self.attention_head_size
+
+        self.query = Linear(config.hidden_size, self.all_head_size)
+        self.key = Linear(config.hidden_size, self.all_head_size)
+        self.value = Linear(config.hidden_size, self.all_head_size)
+
+        self.out = Linear(config.hidden_size, config.hidden_size)
+        self.attn_dropout = Dropout(config.transformer["attention_dropout_rate"])
+        self.proj_dropout = Dropout(config.transformer["attention_dropout_rate"])
+
+        self.softmax = Softmax(dim=-1)
+
+    def transpose_for_scores(self, x):
+        new_x_shape = x.size()[:-1] + (self.num_attention_heads, self.attention_head_size)
+        x = x.view(*new_x_shape)
+        return x.permute(0, 2, 1, 3)
+
+    def forward(self, hidden_states):
+        mixed_query_layer = self.query(hidden_states)
+        mixed_key_layer = self.key(hidden_states)
+        mixed_value_layer = self.value(hidden_states)
+
+        query_layer = self.transpose_for_scores(mixed_query_layer)
+        key_layer = self.transpose_for_scores(mixed_key_layer)
+        value_layer = self.transpose_for_scores(mixed_value_layer)
+
+        attention_scores = torch.matmul(query_layer, key_layer.transpose(-1, -2))
+        attention_scores = attention_scores / math.sqrt(self.attention_head_size)
+        attention_probs = self.softmax(attention_scores)
+        weights = attention_probs if self.vis else None
+        attention_probs = self.attn_dropout(attention_probs)
+
+        context_layer = torch.matmul(attention_probs, value_layer)
+        context_layer = context_layer.permute(0, 2, 1, 3).contiguous()
+        new_context_layer_shape = context_layer.size()[:-2] + (self.all_head_size,)
+        context_layer = context_layer.view(*new_context_layer_shape)
+        attention_output = self.out(context_layer)
+        attention_output = self.proj_dropout(attention_output)
+        return attention_output, weights
+
+
+class Mlp(nn.Module):
+    def __init__(self, config):
+        super(Mlp, self).__init__()
+        self.fc1 = Linear(config.hidden_size, config.transformer["mlp_dim"])
+        self.fc2 = Linear(config.transformer["mlp_dim"], config.hidden_size)
+        self.act_fn = ACT2FN["gelu"]
+        self.dropout = Dropout(config.transformer["dropout_rate"])
+
+        self._init_weights()
+
+    def _init_weights(self):
+        nn.init.xavier_uniform_(self.fc1.weight)
+        nn.init.xavier_uniform_(self.fc2.weight)
+        nn.init.normal_(self.fc1.bias, std=1e-6)
+        nn.init.normal_(self.fc2.bias, std=1e-6)
+
+    def forward(self, x):
+        x = self.fc1(x)
+        x = self.act_fn(x)
+        x = self.dropout(x)
+        x = self.fc2(x)
+        x = self.dropout(x)
+        return x
+
+
+class Embeddings(nn.Module):
+    """Construct the embeddings from patch, position embeddings.
+    """
+    def __init__(self, config, img_size, in_channels=3):
+        super(Embeddings, self).__init__()
+        self.hybrid = None
+        self.config = config
+        img_size = _pair(img_size)
+
+        if config.patches.get("grid") is not None:   # ResNet
+            grid_size = config.patches["grid"]
+            patch_size = (img_size[0] // 16 // grid_size[0], img_size[1] // 16 // grid_size[1])
+            patch_size_real = (patch_size[0] * 16, patch_size[1] * 16)
+            n_patches = (img_size[0] // patch_size_real[0]) * (img_size[1] // patch_size_real[1])  
+            self.hybrid = True
+        else:
+            patch_size = _pair(config.patches["size"])
+            n_patches = (img_size[0] // patch_size[0]) * (img_size[1] // patch_size[1])
+            self.hybrid = False
+
+        if self.hybrid:
+            self.hybrid_model = ResNetV2(block_units=config.resnet.num_layers, width_factor=config.resnet.width_factor)
+            in_channels = self.hybrid_model.width * 16
+        self.patch_embeddings = Conv2d(in_channels=in_channels,
+                                       out_channels=config.hidden_size,
+                                       kernel_size=patch_size,
+                                       stride=patch_size)
+        self.position_embeddings = nn.Parameter(torch.zeros(1, n_patches, config.hidden_size))
+
+        self.dropout = Dropout(config.transformer["dropout_rate"])
+
+
+    def forward(self, x):
+        if self.hybrid:
+            x, features = self.hybrid_model(x)
+        else:
+            features = None
+        x = self.patch_embeddings(x)  # (B, hidden. n_patches^(1/2), n_patches^(1/2))
+        x = x.flatten(2)
+        x = x.transpose(-1, -2)  # (B, n_patches, hidden)
+
+        embeddings = x + self.position_embeddings
+        embeddings = self.dropout(embeddings)
+        return embeddings, features
+
+
+class Block(nn.Module):
+    def __init__(self, config, vis):
+        super(Block, self).__init__()
+        self.hidden_size = config.hidden_size
+        self.attention_norm = LayerNorm(config.hidden_size, eps=1e-6)
+        self.ffn_norm = LayerNorm(config.hidden_size, eps=1e-6)
+        self.ffn = Mlp(config)
+        self.attn = Attention(config, vis)
+
+    def forward(self, x):
+        h = x
+        x = self.attention_norm(x)
+        x, weights = self.attn(x)
+        x = x + h
+
+        h = x
+        x = self.ffn_norm(x)
+        x = self.ffn(x)
+        x = x + h
+        return x, weights
+
+    def load_from(self, weights, n_block):
+        ROOT = f"Transformer/encoderblock_{n_block}"
+        with torch.no_grad():
+            query_weight = np2th(weights[pjoin(ROOT, ATTENTION_Q, "kernel")]).view(self.hidden_size, self.hidden_size).t()
+            key_weight = np2th(weights[pjoin(ROOT, ATTENTION_K, "kernel")]).view(self.hidden_size, self.hidden_size).t()
+            value_weight = np2th(weights[pjoin(ROOT, ATTENTION_V, "kernel")]).view(self.hidden_size, self.hidden_size).t()
+            out_weight = np2th(weights[pjoin(ROOT, ATTENTION_OUT, "kernel")]).view(self.hidden_size, self.hidden_size).t()
+
+            query_bias = np2th(weights[pjoin(ROOT, ATTENTION_Q, "bias")]).view(-1)
+            key_bias = np2th(weights[pjoin(ROOT, ATTENTION_K, "bias")]).view(-1)
+            value_bias = np2th(weights[pjoin(ROOT, ATTENTION_V, "bias")]).view(-1)
+            out_bias = np2th(weights[pjoin(ROOT, ATTENTION_OUT, "bias")]).view(-1)
+
+            self.attn.query.weight.copy_(query_weight)
+            self.attn.key.weight.copy_(key_weight)
+            self.attn.value.weight.copy_(value_weight)
+            self.attn.out.weight.copy_(out_weight)
+            self.attn.query.bias.copy_(query_bias)
+            self.attn.key.bias.copy_(key_bias)
+            self.attn.value.bias.copy_(value_bias)
+            self.attn.out.bias.copy_(out_bias)
+
+            mlp_weight_0 = np2th(weights[pjoin(ROOT, FC_0, "kernel")]).t()
+            mlp_weight_1 = np2th(weights[pjoin(ROOT, FC_1, "kernel")]).t()
+            mlp_bias_0 = np2th(weights[pjoin(ROOT, FC_0, "bias")]).t()
+            mlp_bias_1 = np2th(weights[pjoin(ROOT, FC_1, "bias")]).t()
+
+            self.ffn.fc1.weight.copy_(mlp_weight_0)
+            self.ffn.fc2.weight.copy_(mlp_weight_1)
+            self.ffn.fc1.bias.copy_(mlp_bias_0)
+            self.ffn.fc2.bias.copy_(mlp_bias_1)
+
+            self.attention_norm.weight.copy_(np2th(weights[pjoin(ROOT, ATTENTION_NORM, "scale")]))
+            self.attention_norm.bias.copy_(np2th(weights[pjoin(ROOT, ATTENTION_NORM, "bias")]))
+            self.ffn_norm.weight.copy_(np2th(weights[pjoin(ROOT, MLP_NORM, "scale")]))
+            self.ffn_norm.bias.copy_(np2th(weights[pjoin(ROOT, MLP_NORM, "bias")]))
+
+
+class Encoder(nn.Module):
+    def __init__(self, config, vis):
+        super(Encoder, self).__init__()
+        self.vis = vis
+        self.layer = nn.ModuleList()
+        self.encoder_norm = LayerNorm(config.hidden_size, eps=1e-6)
+        for _ in range(config.transformer["num_layers"]):
+            layer = Block(config, vis)
+            self.layer.append(copy.deepcopy(layer))
+
+    def forward(self, hidden_states):
+        attn_weights = []
+        for layer_block in self.layer:
+            hidden_states, weights = layer_block(hidden_states)
+            if self.vis:
+                attn_weights.append(weights)
+        encoded = self.encoder_norm(hidden_states)
+        return encoded, attn_weights
+
+
+class Transformer(nn.Module):
+    def __init__(self, config, img_size, vis):
+        super(Transformer, self).__init__()
+        self.embeddings = Embeddings(config, img_size=img_size)
+        self.encoder = Encoder(config, vis)
+
+    def forward(self, input_ids):
+        embedding_output, features = self.embeddings(input_ids)
+        encoded, attn_weights = self.encoder(embedding_output)  # (B, n_patch, hidden)
+        return encoded, attn_weights, features
+
+
+class Conv2dReLU(nn.Sequential):
+    def __init__(
+            self,
+            in_channels,
+            out_channels,
+            kernel_size,
+            padding=0,
+            stride=1,
+            use_batchnorm=True,
+    ):
+        conv = nn.Conv2d(
+            in_channels,
+            out_channels,
+            kernel_size,
+            stride=stride,
+            padding=padding,
+            bias=not (use_batchnorm),
+        )
+        relu = nn.ReLU(inplace=True)
+
+        bn = nn.BatchNorm2d(out_channels)
+
+        super(Conv2dReLU, self).__init__(conv, bn, relu)
+
+
+class DecoderBlock(nn.Module):
+    def __init__(
+            self,
+            in_channels,
+            out_channels,
+            skip_channels=0,
+            use_batchnorm=True,
+    ):
+        super().__init__()
+        self.conv1 = Conv2dReLU(
+            in_channels + skip_channels,
+            out_channels,
+            kernel_size=3,
+            padding=1,
+            use_batchnorm=use_batchnorm,
+        )
+        self.conv2 = Conv2dReLU(
+            out_channels,
+            out_channels,
+            kernel_size=3,
+            padding=1,
+            use_batchnorm=use_batchnorm,
+        )
+        self.up = nn.UpsamplingBilinear2d(scale_factor=2)
+
+    def forward(self, x, skip=None):
+        x = self.up(x)
+        if skip is not None:
+            x = torch.cat([x, skip], dim=1)
+        x = self.conv1(x)
+        x = self.conv2(x)
+        return x
+
+
+class SegmentationHead(nn.Sequential):
+
+    def __init__(self, in_channels, out_channels, kernel_size=3, upsampling=1):
+        conv2d = nn.Conv2d(in_channels, out_channels, kernel_size=kernel_size, padding=kernel_size // 2)
+        upsampling = nn.UpsamplingBilinear2d(scale_factor=upsampling) if upsampling > 1 else nn.Identity()
+        super().__init__(conv2d, upsampling)
+
+
+class DecoderCup(nn.Module):
+    def __init__(self, config):
+        super().__init__()
+        self.config = config
+        head_channels = 512
+        self.conv_more = Conv2dReLU(
+            config.hidden_size,
+            head_channels,
+            kernel_size=3,
+            padding=1,
+            use_batchnorm=True,
+        )
+        decoder_channels = config.decoder_channels
+        in_channels = [head_channels] + list(decoder_channels[:-1])
+        out_channels = decoder_channels
+
+        if self.config.n_skip != 0:
+            skip_channels = self.config.skip_channels
+            for i in range(4-self.config.n_skip):  # re-select the skip channels according to n_skip
+                skip_channels[3-i]=0
+
+        else:
+            skip_channels=[0,0,0,0]
+
+        blocks = [
+            DecoderBlock(in_ch, out_ch, sk_ch) for in_ch, out_ch, sk_ch in zip(in_channels, out_channels, skip_channels)
+        ]
+        self.blocks = nn.ModuleList(blocks)
+
+    def forward(self, hidden_states, features=None):
+        B, n_patch, hidden = hidden_states.size()  # reshape from (B, n_patch, hidden) to (B, h, w, hidden)
+        h, w = int(np.sqrt(n_patch)), int(np.sqrt(n_patch))
+        x = hidden_states.permute(0, 2, 1)
+        x = x.contiguous().view(B, hidden, h, w)
+        x = self.conv_more(x)
+        for i, decoder_block in enumerate(self.blocks):
+            if features is not None:
+                skip = features[i] if (i < self.config.n_skip) else None
+            else:
+                skip = None
+            x = decoder_block(x, skip=skip)
+        return x
+
+
+class VisionTransformer(nn.Module):
+    def __init__(self, config, img_size=224, num_classes=21843, zero_head=False, vis=False):
+        super(VisionTransformer, self).__init__()
+        self.num_classes = num_classes
+        self.zero_head = zero_head
+        self.classifier = config.classifier
+        self.transformer = Transformer(config, img_size, vis)
+        self.decoder = DecoderCup(config)
+        self.segmentation_head = SegmentationHead(
+            in_channels=config['decoder_channels'][-1],
+            out_channels=config['n_classes'],
+            kernel_size=3,
+        )
+        self.config = config
+
+    def forward(self, x):
+        if x.size()[1] == 1:
+            x = x.repeat(1,3,1,1)
+        x, attn_weights, features = self.transformer(x)  # (B, n_patch, hidden)
+        x = self.decoder(x, features)
+        logits = self.segmentation_head(x)
+        return logits
+
+    def load_from(self, weights):
+        with torch.no_grad():
+
+            res_weight = weights
+            self.transformer.embeddings.patch_embeddings.weight.copy_(np2th(weights["embedding/kernel"], conv=True))
+            self.transformer.embeddings.patch_embeddings.bias.copy_(np2th(weights["embedding/bias"]))
+
+            self.transformer.encoder.encoder_norm.weight.copy_(np2th(weights["Transformer/encoder_norm/scale"]))
+            self.transformer.encoder.encoder_norm.bias.copy_(np2th(weights["Transformer/encoder_norm/bias"]))
+
+            posemb = np2th(weights["Transformer/posembed_input/pos_embedding"])
+
+            posemb_new = self.transformer.embeddings.position_embeddings
+            if posemb.size() == posemb_new.size():
+                self.transformer.embeddings.position_embeddings.copy_(posemb)
+            elif posemb.size()[1]-1 == posemb_new.size()[1]:
+                posemb = posemb[:, 1:]
+                self.transformer.embeddings.position_embeddings.copy_(posemb)
+            else:
+                logger.info("load_pretrained: resized variant: %s to %s" % (posemb.size(), posemb_new.size()))
+                ntok_new = posemb_new.size(1)
+                if self.classifier == "seg":
+                    _, posemb_grid = posemb[:, :1], posemb[0, 1:]
+                gs_old = int(np.sqrt(len(posemb_grid)))
+                gs_new = int(np.sqrt(ntok_new))
+                print('load_pretrained: grid-size from %s to %s' % (gs_old, gs_new))
+                posemb_grid = posemb_grid.reshape(gs_old, gs_old, -1)
+                zoom = (gs_new / gs_old, gs_new / gs_old, 1)
+                posemb_grid = ndimage.zoom(posemb_grid, zoom, order=1)  # th2np
+                posemb_grid = posemb_grid.reshape(1, gs_new * gs_new, -1)
+                posemb = posemb_grid
+                self.transformer.embeddings.position_embeddings.copy_(np2th(posemb))
+
+            # Encoder whole
+            for bname, block in self.transformer.encoder.named_children():
+                for uname, unit in block.named_children():
+                    unit.load_from(weights, n_block=uname)
+
+            if self.transformer.embeddings.hybrid:
+                self.transformer.embeddings.hybrid_model.root.conv.weight.copy_(np2th(res_weight["conv_root/kernel"], conv=True))
+                gn_weight = np2th(res_weight["gn_root/scale"]).view(-1)
+                gn_bias = np2th(res_weight["gn_root/bias"]).view(-1)
+                self.transformer.embeddings.hybrid_model.root.gn.weight.copy_(gn_weight)
+                self.transformer.embeddings.hybrid_model.root.gn.bias.copy_(gn_bias)
+
+                for bname, block in self.transformer.embeddings.hybrid_model.body.named_children():
+                    for uname, unit in block.named_children():
+                        unit.load_from(res_weight, n_block=bname, n_unit=uname)
+
+CONFIGS = {
+    'ViT-B_16': configs.get_b16_config(),
+    'ViT-B_32': configs.get_b32_config(),
+    'ViT-L_16': configs.get_l16_config(),
+    'ViT-L_32': configs.get_l32_config(),
+    'ViT-H_14': configs.get_h14_config(),
+    'R50-ViT-B_16': configs.get_r50_b16_config(),
+    'R50-ViT-L_16': configs.get_r50_l16_config(),
+    'testing': configs.get_testing(),
+}
+
+

networks/vit_seg_modeling_resnet_skip.py

@@ -0,0 +1,160 @@
+import math
+
+from os.path import join as pjoin
+from collections import OrderedDict
+
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+
+
+def np2th(weights, conv=False):
+    """Possibly convert HWIO to OIHW."""
+    if conv:
+        weights = weights.transpose([3, 2, 0, 1])
+    return torch.from_numpy(weights)
+
+
+class StdConv2d(nn.Conv2d):
+
+    def forward(self, x):
+        w = self.weight
+        v, m = torch.var_mean(w, dim=[1, 2, 3], keepdim=True, unbiased=False)
+        w = (w - m) / torch.sqrt(v + 1e-5)
+        return F.conv2d(x, w, self.bias, self.stride, self.padding,
+                        self.dilation, self.groups)
+
+
+def conv3x3(cin, cout, stride=1, groups=1, bias=False):
+    return StdConv2d(cin, cout, kernel_size=3, stride=stride,
+                     padding=1, bias=bias, groups=groups)
+
+
+def conv1x1(cin, cout, stride=1, bias=False):
+    return StdConv2d(cin, cout, kernel_size=1, stride=stride,
+                     padding=0, bias=bias)
+
+
+class PreActBottleneck(nn.Module):
+    """Pre-activation (v2) bottleneck block.
+    """
+
+    def __init__(self, cin, cout=None, cmid=None, stride=1):
+        super().__init__()
+        cout = cout or cin
+        cmid = cmid or cout//4
+
+        self.gn1 = nn.GroupNorm(32, cmid, eps=1e-6)
+        self.conv1 = conv1x1(cin, cmid, bias=False)
+        self.gn2 = nn.GroupNorm(32, cmid, eps=1e-6)
+        self.conv2 = conv3x3(cmid, cmid, stride, bias=False)  # Original code has it on conv1!!
+        self.gn3 = nn.GroupNorm(32, cout, eps=1e-6)
+        self.conv3 = conv1x1(cmid, cout, bias=False)
+        self.relu = nn.ReLU(inplace=True)
+
+        if (stride != 1 or cin != cout):
+            # Projection also with pre-activation according to paper.
+            self.downsample = conv1x1(cin, cout, stride, bias=False)
+            self.gn_proj = nn.GroupNorm(cout, cout)
+
+    def forward(self, x):
+
+        # Residual branch
+        residual = x
+        if hasattr(self, 'downsample'):
+            residual = self.downsample(x)
+            residual = self.gn_proj(residual)
+
+        # Unit's branch
+        y = self.relu(self.gn1(self.conv1(x)))
+        y = self.relu(self.gn2(self.conv2(y)))
+        y = self.gn3(self.conv3(y))
+
+        y = self.relu(residual + y)
+        return y
+
+    def load_from(self, weights, n_block, n_unit):
+        conv1_weight = np2th(weights[pjoin(n_block, n_unit, "conv1/kernel")], conv=True)
+        conv2_weight = np2th(weights[pjoin(n_block, n_unit, "conv2/kernel")], conv=True)
+        conv3_weight = np2th(weights[pjoin(n_block, n_unit, "conv3/kernel")], conv=True)
+
+        gn1_weight = np2th(weights[pjoin(n_block, n_unit, "gn1/scale")])
+        gn1_bias = np2th(weights[pjoin(n_block, n_unit, "gn1/bias")])
+
+        gn2_weight = np2th(weights[pjoin(n_block, n_unit, "gn2/scale")])
+        gn2_bias = np2th(weights[pjoin(n_block, n_unit, "gn2/bias")])
+
+        gn3_weight = np2th(weights[pjoin(n_block, n_unit, "gn3/scale")])
+        gn3_bias = np2th(weights[pjoin(n_block, n_unit, "gn3/bias")])
+
+        self.conv1.weight.copy_(conv1_weight)
+        self.conv2.weight.copy_(conv2_weight)
+        self.conv3.weight.copy_(conv3_weight)
+
+        self.gn1.weight.copy_(gn1_weight.view(-1))
+        self.gn1.bias.copy_(gn1_bias.view(-1))
+
+        self.gn2.weight.copy_(gn2_weight.view(-1))
+        self.gn2.bias.copy_(gn2_bias.view(-1))
+
+        self.gn3.weight.copy_(gn3_weight.view(-1))
+        self.gn3.bias.copy_(gn3_bias.view(-1))
+
+        if hasattr(self, 'downsample'):
+            proj_conv_weight = np2th(weights[pjoin(n_block, n_unit, "conv_proj/kernel")], conv=True)
+            proj_gn_weight = np2th(weights[pjoin(n_block, n_unit, "gn_proj/scale")])
+            proj_gn_bias = np2th(weights[pjoin(n_block, n_unit, "gn_proj/bias")])
+
+            self.downsample.weight.copy_(proj_conv_weight)
+            self.gn_proj.weight.copy_(proj_gn_weight.view(-1))
+            self.gn_proj.bias.copy_(proj_gn_bias.view(-1))
+
+class ResNetV2(nn.Module):
+    """Implementation of Pre-activation (v2) ResNet mode."""
+
+    def __init__(self, block_units, width_factor):
+        super().__init__()
+        width = int(64 * width_factor)
+        self.width = width
+
+        self.root = nn.Sequential(OrderedDict([
+            ('conv', StdConv2d(3, width, kernel_size=7, stride=2, bias=False, padding=3)),
+            ('gn', nn.GroupNorm(32, width, eps=1e-6)),
+            ('relu', nn.ReLU(inplace=True)),
+            # ('pool', nn.MaxPool2d(kernel_size=3, stride=2, padding=0))
+        ]))
+
+        self.body = nn.Sequential(OrderedDict([
+            ('block1', nn.Sequential(OrderedDict(
+                [('unit1', PreActBottleneck(cin=width, cout=width*4, cmid=width))] +
+                [(f'unit{i:d}', PreActBottleneck(cin=width*4, cout=width*4, cmid=width)) for i in range(2, block_units[0] + 1)],
+                ))),
+            ('block2', nn.Sequential(OrderedDict(
+                [('unit1', PreActBottleneck(cin=width*4, cout=width*8, cmid=width*2, stride=2))] +
+                [(f'unit{i:d}', PreActBottleneck(cin=width*8, cout=width*8, cmid=width*2)) for i in range(2, block_units[1] + 1)],
+                ))),
+            ('block3', nn.Sequential(OrderedDict(
+                [('unit1', PreActBottleneck(cin=width*8, cout=width*16, cmid=width*4, stride=2))] +
+                [(f'unit{i:d}', PreActBottleneck(cin=width*16, cout=width*16, cmid=width*4)) for i in range(2, block_units[2] + 1)],
+                ))),
+        ]))
+
+    def forward(self, x):
+        features = []
+        b, c, in_size, _ = x.size()
+        x = self.root(x)
+        features.append(x)
+        x = nn.MaxPool2d(kernel_size=3, stride=2, padding=0)(x)
+        for i in range(len(self.body)-1):
+            x = self.body[i](x)
+            right_size = int(in_size / 4 / (i+1))
+            if x.size()[2] != right_size:
+                pad = right_size - x.size()[2]
+                assert pad < 3 and pad > 0, "x {} should {}".format(x.size(), right_size)
+                feat = torch.zeros((b, x.size()[1], right_size, right_size), device=x.device)
+                feat[:, :, 0:x.size()[2], 0:x.size()[3]] = x[:]
+            else:
+                feat = x
+            features.append(feat)
+        x = self.body[-1](x)
+        return x, features[::-1]