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import torch
import torch.nn.functional as F
from typing import List, Optional
from torch import Tensor, nn
import copy
from cliport.models.resnet import IdentityBlock, ConvBlock
from cliport.models.core.unet import Up
from cliport.models.core import fusion
from cliport.models.core.fusion import FusionConvLat
from cliport.models.backbone_full import Backbone
from cliport.models.misc import NestedTensor
from cliport.models.position_encoding import build_position_encoding
from transformers import RobertaModel, RobertaTokenizerFast
class FeatureResizer(nn.Module):
"""
This class takes as input a set of embeddings of dimension C1 and outputs a set of
embedding of dimension C2, after a linear transformation, dropout and normalization (LN).
"""
def __init__(self, input_feat_size, output_feat_size, dropout, do_ln=True):
super().__init__()
self.do_ln = do_ln
# Object feature encoding
self.fc = nn.Linear(input_feat_size, output_feat_size, bias=True)
self.layer_norm = nn.LayerNorm(output_feat_size, eps=1e-12)
self.dropout = nn.Dropout(dropout)
def forward(self, encoder_features):
x = self.fc(encoder_features)
if self.do_ln:
x = self.layer_norm(x)
output = self.dropout(x)
return output
class MDETRLingUNetLat_fuse(nn.Module):
""" CLIP RN50 with U-Net skip connections and lateral connections """
def __init__(self, input_shape, output_dim, cfg, device, preprocess):
super(MDETRLingUNetLat_fuse, self).__init__()
self.input_shape = input_shape
self.output_dim = output_dim
self.input_dim = 2048 # penultimate layer channel-size of mdetr
self.cfg = cfg
self.device = device
self.batchnorm = self.cfg['train']['batchnorm']
self.lang_fusion_type = self.cfg['train']['lang_fusion_type']
self.bilinear = True
self.up_factor = 2 if self.bilinear else 1
self.preprocess = preprocess
self.backbone = Backbone('resnet101', True, True, False)
self.position_embedding = build_position_encoding()
self.input_proj = nn.Conv2d(2048, 256, kernel_size=1)
self.tokenizer = RobertaTokenizerFast.from_pretrained('roberta-base')
self.text_encoder = RobertaModel.from_pretrained('roberta-base')
self.resizer = FeatureResizer(
input_feat_size=768,
output_feat_size=256,
dropout=0.1,
)
encoder_layer = TransformerEncoderLayer(d_model=256, nhead=8, dim_feedforward=2048, dropout=0.1, activation='relu', normalize_before=False)
self.encoder = TransformerEncoder(encoder_layer, 6, None)
mdter_checkpoint = torch.load('/home/yzc/shared/project/GPT-CLIPort/ckpts/mdetr_pretrained_resnet101_checkpoint.pth', map_location="cpu")['model']
checkpoint_new = {}
for param in mdter_checkpoint:
if 'transformer.text_encoder' in param or 'transformer.encoder.' in param or 'input_proj' in param or 'resizer' in param:
param_new = param.replace('transformer.','')
checkpoint_new[param_new] = mdter_checkpoint[param]
elif 'backbone.0.body' in param:
param_new = param.replace('backbone.0.body', 'backbone.body')
checkpoint_new[param_new] = mdter_checkpoint[param]
self.load_state_dict(checkpoint_new, True)
self._build_decoder()
def _build_decoder(self):
# language
self.up_fuse1 = nn.UpsamplingBilinear2d(scale_factor=2)
self.up_fuse2 = nn.UpsamplingBilinear2d(scale_factor=4)
self.up_fuse3 = nn.UpsamplingBilinear2d(scale_factor=8)
self.lang_fuser1 = fusion.names[self.lang_fusion_type](input_dim=self.input_dim // 2)
self.lang_fuser2 = fusion.names[self.lang_fusion_type](input_dim=self.input_dim // 4)
self.lang_fuser3 = fusion.names[self.lang_fusion_type](input_dim=self.input_dim // 8)
self.proj_input_dim = 768
self.lang_proj1 = nn.Linear(self.proj_input_dim, 1024)
self.lang_proj2 = nn.Linear(self.proj_input_dim, 512)
self.lang_proj3 = nn.Linear(self.proj_input_dim, 256)
# vision
self.conv1 = nn.Sequential(
nn.Conv2d(self.input_dim+256, 1024, kernel_size=3, stride=1, padding=1, bias=False),
nn.ReLU(True)
)
self.up1 = Up(2048+256, 1024 // self.up_factor, self.bilinear)
self.lat_fusion1 = FusionConvLat(input_dim=1024+512, output_dim=512)
self.up2 = Up(1024+256, 512 // self.up_factor, self.bilinear)
self.lat_fusion2 = FusionConvLat(input_dim=512+256, output_dim=256)
self.up3 = Up(512+256, 256 // self.up_factor, self.bilinear)
self.lat_fusion3 = FusionConvLat(input_dim=256+128, output_dim=128)
self.layer1 = nn.Sequential(
ConvBlock(128, [64, 64, 64], kernel_size=3, stride=1, batchnorm=self.batchnorm),
IdentityBlock(64, [64, 64, 64], kernel_size=3, stride=1, batchnorm=self.batchnorm),
nn.UpsamplingBilinear2d(scale_factor=2),
)
self.lat_fusion4 = FusionConvLat(input_dim=128+64, output_dim=64)
self.layer2 = nn.Sequential(
ConvBlock(64, [32, 32, 32], kernel_size=3, stride=1, batchnorm=self.batchnorm),
IdentityBlock(32, [32, 32, 32], kernel_size=3, stride=1, batchnorm=self.batchnorm),
nn.UpsamplingBilinear2d(scale_factor=2),
)
self.lat_fusion5 = FusionConvLat(input_dim=64+32, output_dim=32)
self.layer3 = nn.Sequential(
ConvBlock(32, [16, 16, 16], kernel_size=3, stride=1, batchnorm=self.batchnorm),
IdentityBlock(16, [16, 16, 16], kernel_size=3, stride=1, batchnorm=self.batchnorm),
nn.UpsamplingBilinear2d(scale_factor=2),
)
self.lat_fusion6 = FusionConvLat(input_dim=32+16, output_dim=16)
self.conv2 = nn.Sequential(
nn.Conv2d(16, self.output_dim, kernel_size=1)
)
def encode_image(self, img):
img = NestedTensor.from_tensor_list(img)
with torch.no_grad():
xs = self.backbone(img)
out = []
pos = []
for name, x in xs.items():
out.append(x)
# position encoding
pos.append(self.position_embedding(x).to(x.tensors.dtype))
return out, pos
def encode_text(self, x):
with torch.no_grad():
tokenized = self.tokenizer.batch_encode_plus(x, padding="longest", return_tensors="pt").to(self.device)
encoded_text = self.text_encoder(**tokenized)
# Transpose memory because pytorch's attention expects sequence first
text_memory = encoded_text.last_hidden_state.transpose(0, 1)
text_memory_mean = torch.mean(text_memory, 0)
# Invert attention mask that we get from huggingface because its the opposite in pytorch transformer
text_attention_mask = tokenized.attention_mask.ne(1).bool()
# Resize the encoder hidden states to be of the same d_model as the decoder
text_memory_resized = self.resizer(text_memory)
return text_memory_resized, text_attention_mask, text_memory_mean
def forward(self, x, lat, l):
x = self.preprocess(x, dist='mdetr')
in_type = x.dtype
in_shape = x.shape
x = x[:,:3] # select RGB
x = x.permute(0, 1, 3, 2)
with torch.no_grad():
features, pos = self.encode_image(x)
x1, mask = features[-1].decompose()
x2, _ = features[-2].decompose()
x3, _ = features[-3].decompose()
x4, _ = features[-4].decompose()
#print(x1.shape, x2.shape, x3.shape, x4.shape)
src = self.input_proj(x1)
pos_embed = pos[-1]
bs, c, h, w = src.shape
src = src.flatten(2).permute(2, 0, 1)
device = self.device
pos_embed = pos_embed.flatten(2).permute(2, 0, 1)
mask = mask.flatten(1)
text_memory_resized, text_attention_mask, l_input = self.encode_text(l)
# l_input = l_input.view(1, -1)
# text_memory_resized = text_memory_resized.repeat(1, src.shape[1], 1)
# text_attention_mask = text_attention_mask.repeat(src.shape[1], 1)
#print(src.shape, text_memory_resized.shape, mask.shape, text_attention_mask.shape)
if src.shape[1] == int(36*8):
text_memory_resized = text_memory_resized.repeat_interleave(36, dim=1)
l_input = l_input.repeat_interleave(36, dim=0)
text_attention_mask = text_attention_mask.repeat_interleave(36, dim=0)
src = torch.cat([src, text_memory_resized], dim=0)
# For mask, sequence dimension is second
mask = torch.cat([mask, text_attention_mask], dim=1)
# Pad the pos_embed with 0 so that the addition will be a no-op for the text tokens
pos_embed = torch.cat([pos_embed, torch.zeros_like(text_memory_resized)], dim=0)
img_memory, img_memory_all = self.encoder(src, src_key_padding_mask=mask, pos=pos_embed)
dim = img_memory.shape[-1]
fuse1 = img_memory_all[-1][:h*w].permute(1,2,0).reshape(bs, dim, h, w)
fuse2 = self.up_fuse1(img_memory_all[-2][:h*w].permute(1,2,0).reshape(bs, dim, h, w))
fuse3 = self.up_fuse2(img_memory_all[-3][:h*w].permute(1,2,0).reshape(bs, dim, h, w))
fuse4 = self.up_fuse3(img_memory_all[-4][:h*w].permute(1,2,0).reshape(bs, dim, h, w))
assert x1.shape[1] == self.input_dim
x1 = torch.cat((x1, fuse1), 1)
x2 = torch.cat((x2, fuse2), 1)
x3 = torch.cat((x3, fuse3), 1)
x4 = torch.cat((x4, fuse4), 1)
x = self.conv1(x1)
x = self.lang_fuser1(x, l_input, x2_mask=None, x2_proj=self.lang_proj1)
x = self.up1(x, x2)
x = self.lat_fusion1(x, lat[-6].permute(0, 1, 3, 2))
x = self.lang_fuser2(x, l_input, x2_mask=None, x2_proj=self.lang_proj2)
x = self.up2(x, x3)
x = self.lat_fusion2(x, lat[-5].permute(0, 1, 3, 2))
x = self.lang_fuser3(x, l_input, x2_mask=None, x2_proj=self.lang_proj3)
x = self.up3(x, x4)
x = self.lat_fusion3(x, lat[-4].permute(0, 1, 3, 2))
x = self.layer1(x)
x = self.lat_fusion4(x, lat[-3].permute(0, 1, 3, 2))
x = self.layer2(x)
x = self.lat_fusion5(x, lat[-2].permute(0, 1, 3, 2))
x = self.layer3(x)
x = self.lat_fusion6(x, lat[-1].permute(0, 1, 3, 2))
x = self.conv2(x)
x = F.interpolate(x, size=(in_shape[-1], in_shape[-2]), mode='bilinear')
x = x.permute(0, 1, 3, 2)
return x
class TransformerEncoder(nn.Module):
def __init__(self, encoder_layer, num_layers, norm=None):
super().__init__()
self.layers = _get_clones(encoder_layer, num_layers)
self.num_layers = num_layers
self.norm = norm
def forward(
self,
src,
mask: Optional[Tensor] = None,
src_key_padding_mask: Optional[Tensor] = None,
pos: Optional[Tensor] = None,
):
output = src
output_all = []
for layer in self.layers:
output = layer(output, src_mask=mask, src_key_padding_mask=src_key_padding_mask, pos=pos)
output_all.append(output)
if self.norm is not None:
output = self.norm(output)
return output, output_all
class TransformerEncoderLayer(nn.Module):
def __init__(self, d_model, nhead, dim_feedforward=2048, dropout=0.1, activation="relu", normalize_before=False):
super().__init__()
self.self_attn = nn.MultiheadAttention(d_model, nhead, dropout=dropout)
# Implementation of Feedforward model
self.linear1 = nn.Linear(d_model, dim_feedforward)
self.dropout = nn.Dropout(dropout)
self.linear2 = nn.Linear(dim_feedforward, d_model)
self.norm1 = nn.LayerNorm(d_model)
self.norm2 = nn.LayerNorm(d_model)
self.dropout1 = nn.Dropout(dropout)
self.dropout2 = nn.Dropout(dropout)
self.activation = _get_activation_fn(activation)
self.normalize_before = normalize_before
print(self.normalize_before)
def with_pos_embed(self, tensor, pos: Optional[Tensor]):
return tensor if pos is None else tensor + pos
def forward_post(
self,
src,
src_mask: Optional[Tensor] = None,
src_key_padding_mask: Optional[Tensor] = None,
pos: Optional[Tensor] = None,
):
q = k = self.with_pos_embed(src, pos)
src2 = self.self_attn(q, k, value=src, attn_mask=src_mask, key_padding_mask=src_key_padding_mask)[0]
src = src + self.dropout1(src2)
src = self.norm1(src)
src2 = self.linear2(self.dropout(self.activation(self.linear1(src))))
src = src + self.dropout2(src2)
src = self.norm2(src)
return src
def forward_pre(
self,
src,
src_mask: Optional[Tensor] = None,
src_key_padding_mask: Optional[Tensor] = None,
pos: Optional[Tensor] = None,
):
src2 = self.norm1(src)
q = k = self.with_pos_embed(src2, pos)
src2 = self.self_attn(q, k, value=src2, attn_mask=src_mask, key_padding_mask=src_key_padding_mask)[0]
src = src + self.dropout1(src2)
src2 = self.norm2(src)
src2 = self.linear2(self.dropout(self.activation(self.linear1(src2))))
src = src + self.dropout2(src2)
return src
def forward(
self,
src,
src_mask: Optional[Tensor] = None,
src_key_padding_mask: Optional[Tensor] = None,
pos: Optional[Tensor] = None,
):
if self.normalize_before:
return self.forward_pre(src, src_mask, src_key_padding_mask, pos)
return self.forward_post(src, src_mask, src_key_padding_mask, pos)
def _get_clones(module, N):
return nn.ModuleList([copy.deepcopy(module) for i in range(N)])
def _get_activation_fn(activation):
"""Return an activation function given a string"""
if activation == "relu":
return F.relu
if activation == "gelu":
return F.gelu
if activation == "glu":
return F.glu
raise RuntimeError(f"activation should be relu/gelu, not {activation}.")
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