update
This commit is contained in:
li chen
76
models/configs.py
Executable file
76
models/configs.py
Executable file
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import ml_collections
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def get_testing():
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"""Returns a minimal configuration for testing."""
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config = ml_collections.ConfigDict()
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config.patches = ml_collections.ConfigDict({'size': (16, 16)})
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config.hidden_size = 1
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config.transformer = ml_collections.ConfigDict()
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config.transformer.mlp_dim = 1
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config.transformer.num_heads = 1
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config.transformer.num_layers = 1
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config.transformer.attention_dropout_rate = 0.0
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config.transformer.dropout_rate = 0.1
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config.classifier = 'token'
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config.representation_size = None
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return config
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def get_b16_config():
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"""Returns the ViT-B/16 configuration."""
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config = ml_collections.ConfigDict()
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config.patches = ml_collections.ConfigDict({'size': (16, 16)})
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config.split = 'non-overlap'
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config.slide_step = 12
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config.hidden_size = 768
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config.transformer = ml_collections.ConfigDict()
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config.transformer.mlp_dim = 3072
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config.transformer.num_heads = 12
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config.transformer.num_layers = 12
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config.transformer.attention_dropout_rate = 0.0
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config.transformer.dropout_rate = 0.1
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config.classifier = 'token'
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config.representation_size = None
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return config
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def get_b32_config():
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"""Returns the ViT-B/32 configuration."""
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config = get_b16_config()
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config.patches.size = (32, 32)
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return config
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def get_l16_config():
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"""Returns the ViT-L/16 configuration."""
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config = ml_collections.ConfigDict()
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config.patches = ml_collections.ConfigDict({'size': (16, 16)})
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config.hidden_size = 1024
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config.transformer = ml_collections.ConfigDict()
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config.transformer.mlp_dim = 4096
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config.transformer.num_heads = 16
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config.transformer.num_layers = 24
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config.transformer.attention_dropout_rate = 0.0
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config.transformer.dropout_rate = 0.1
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config.classifier = 'token'
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config.representation_size = None
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return config
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def get_l32_config():
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"""Returns the ViT-L/32 configuration."""
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config = get_l16_config()
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config.patches.size = (32, 32)
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return config
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def get_h14_config():
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"""Returns the ViT-L/16 configuration."""
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config = ml_collections.ConfigDict()
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config.patches = ml_collections.ConfigDict({'size': (14, 14)})
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config.hidden_size = 1280
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config.transformer = ml_collections.ConfigDict()
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config.transformer.mlp_dim = 5120
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config.transformer.num_heads = 16
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config.transformer.num_layers = 32
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config.transformer.attention_dropout_rate = 0.0
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config.transformer.dropout_rate = 0.1
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config.classifier = 'token'
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config.representation_size = None
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return config
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390
models/modeling.py
Executable file
390
models/modeling.py
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# coding=utf-8
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from __future__ import absolute_import
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from __future__ import division
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from __future__ import print_function
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import copy
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import logging
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import math
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from os.path import join as pjoin
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import torch
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import torch.nn as nn
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import torch.nn.functional as F
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import numpy as np
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from torch.nn import CrossEntropyLoss, Dropout, Softmax, Linear, Conv2d, LayerNorm
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from torch.nn.modules.utils import _pair
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from scipy import ndimage
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import models.configs as configs
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logger = logging.getLogger(__name__)
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ATTENTION_Q = "MultiHeadDotProductAttention_1/query"
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ATTENTION_K = "MultiHeadDotProductAttention_1/key"
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ATTENTION_V = "MultiHeadDotProductAttention_1/value"
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ATTENTION_OUT = "MultiHeadDotProductAttention_1/out"
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FC_0 = "MlpBlock_3/Dense_0"
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FC_1 = "MlpBlock_3/Dense_1"
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ATTENTION_NORM = "LayerNorm_0"
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MLP_NORM = "LayerNorm_2"
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def np2th(weights, conv=False):
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"""Possibly convert HWIO to OIHW."""
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if conv:
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weights = weights.transpose([3, 2, 0, 1])
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return torch.from_numpy(weights)
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def swish(x):
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return x * torch.sigmoid(x)
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ACT2FN = {"gelu": torch.nn.functional.gelu, "relu": torch.nn.functional.relu, "swish": swish}
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class LabelSmoothing(nn.Module):
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"""
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NLL loss with label smoothing.
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"""
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def __init__(self, smoothing=0.0):
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"""
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Constructor for the LabelSmoothing module.
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:param smoothing: label smoothing factor
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"""
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super(LabelSmoothing, self).__init__()
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self.confidence = 1.0 - smoothing
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self.smoothing = smoothing
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def forward(self, x, target):
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logprobs = torch.nn.functional.log_softmax(x, dim=-1)
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nll_loss = -logprobs.gather(dim=-1, index=target.unsqueeze(1))
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nll_loss = nll_loss.squeeze(1)
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smooth_loss = -logprobs.mean(dim=-1)
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loss = self.confidence * nll_loss + self.smoothing * smooth_loss
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return loss.mean()
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class Attention(nn.Module):
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def __init__(self, config):
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super(Attention, self).__init__()
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self.num_attention_heads = config.transformer["num_heads"]
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self.attention_head_size = int(config.hidden_size / self.num_attention_heads)
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self.all_head_size = self.num_attention_heads * self.attention_head_size
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self.query = Linear(config.hidden_size, self.all_head_size)
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self.key = Linear(config.hidden_size, self.all_head_size)
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self.value = Linear(config.hidden_size, self.all_head_size)
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self.out = Linear(config.hidden_size, config.hidden_size)
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self.attn_dropout = Dropout(config.transformer["attention_dropout_rate"])
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self.proj_dropout = Dropout(config.transformer["attention_dropout_rate"])
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self.softmax = Softmax(dim=-1)
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def transpose_for_scores(self, x):
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new_x_shape = x.size()[:-1] + (self.num_attention_heads, self.attention_head_size)
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x = x.view(*new_x_shape)
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return x.permute(0, 2, 1, 3)
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def forward(self, hidden_states):
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mixed_query_layer = self.query(hidden_states)
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mixed_key_layer = self.key(hidden_states)
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mixed_value_layer = self.value(hidden_states)
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query_layer = self.transpose_for_scores(mixed_query_layer)
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key_layer = self.transpose_for_scores(mixed_key_layer)
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value_layer = self.transpose_for_scores(mixed_value_layer)
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attention_scores = torch.matmul(query_layer, key_layer.transpose(-1, -2))
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attention_scores = attention_scores / math.sqrt(self.attention_head_size)
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attention_probs = self.softmax(attention_scores)
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weights = attention_probs
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attention_probs = self.attn_dropout(attention_probs)
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context_layer = torch.matmul(attention_probs, value_layer)
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context_layer = context_layer.permute(0, 2, 1, 3).contiguous()
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new_context_layer_shape = context_layer.size()[:-2] + (self.all_head_size,)
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context_layer = context_layer.view(*new_context_layer_shape)
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attention_output = self.out(context_layer)
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attention_output = self.proj_dropout(attention_output)
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return attention_output, weights
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class Mlp(nn.Module):
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def __init__(self, config):
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super(Mlp, self).__init__()
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self.fc1 = Linear(config.hidden_size, config.transformer["mlp_dim"])
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self.fc2 = Linear(config.transformer["mlp_dim"], config.hidden_size)
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self.act_fn = ACT2FN["gelu"]
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self.dropout = Dropout(config.transformer["dropout_rate"])
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self._init_weights()
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def _init_weights(self):
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nn.init.xavier_uniform_(self.fc1.weight)
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nn.init.xavier_uniform_(self.fc2.weight)
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nn.init.normal_(self.fc1.bias, std=1e-6)
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nn.init.normal_(self.fc2.bias, std=1e-6)
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def forward(self, x):
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x = self.fc1(x)
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x = self.act_fn(x)
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x = self.dropout(x)
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x = self.fc2(x)
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x = self.dropout(x)
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return x
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class Embeddings(nn.Module):
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"""Construct the embeddings from patch, position embeddings.
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"""
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def __init__(self, config, img_size, in_channels=3):
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super(Embeddings, self).__init__()
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self.hybrid = None
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img_size = _pair(img_size)
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patch_size = _pair(config.patches["size"])
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if config.split == 'non-overlap':
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n_patches = (img_size[0] // patch_size[0]) * (img_size[1] // patch_size[1])
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self.patch_embeddings = Conv2d(in_channels=in_channels,
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out_channels=config.hidden_size,
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kernel_size=patch_size,
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stride=patch_size)
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elif config.split == 'overlap':
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n_patches = ((img_size[0] - patch_size[0]) // config.slide_step + 1) * ((img_size[1] - patch_size[1]) // config.slide_step + 1)
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self.patch_embeddings = Conv2d(in_channels=in_channels,
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out_channels=config.hidden_size,
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kernel_size=patch_size,
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stride=(config.slide_step, config.slide_step))
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self.position_embeddings = nn.Parameter(torch.zeros(1, n_patches+1, config.hidden_size))
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self.cls_token = nn.Parameter(torch.zeros(1, 1, config.hidden_size))
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self.dropout = Dropout(config.transformer["dropout_rate"])
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def forward(self, x):
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B = x.shape[0]
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cls_tokens = self.cls_token.expand(B, -1, -1)
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if self.hybrid:
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x = self.hybrid_model(x)
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x = self.patch_embeddings(x)
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x = x.flatten(2)
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x = x.transpose(-1, -2)
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x = torch.cat((cls_tokens, x), dim=1)
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embeddings = x + self.position_embeddings
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embeddings = self.dropout(embeddings)
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return embeddings
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class Block(nn.Module):
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def __init__(self, config):
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super(Block, self).__init__()
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self.hidden_size = config.hidden_size
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self.attention_norm = LayerNorm(config.hidden_size, eps=1e-6)
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self.ffn_norm = LayerNorm(config.hidden_size, eps=1e-6)
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self.ffn = Mlp(config)
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self.attn = Attention(config)
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def forward(self, x):
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h = x
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x = self.attention_norm(x)
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x, weights = self.attn(x)
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x = x + h
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h = x
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x = self.ffn_norm(x)
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x = self.ffn(x)
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x = x + h
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return x, weights
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def load_from(self, weights, n_block):
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ROOT = f"Transformer/encoderblock_{n_block}"
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with torch.no_grad():
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query_weight = np2th(weights[pjoin(ROOT, ATTENTION_Q, "kernel")]).view(self.hidden_size, self.hidden_size).t()
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key_weight = np2th(weights[pjoin(ROOT, ATTENTION_K, "kernel")]).view(self.hidden_size, self.hidden_size).t()
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value_weight = np2th(weights[pjoin(ROOT, ATTENTION_V, "kernel")]).view(self.hidden_size, self.hidden_size).t()
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out_weight = np2th(weights[pjoin(ROOT, ATTENTION_OUT, "kernel")]).view(self.hidden_size, self.hidden_size).t()
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query_bias = np2th(weights[pjoin(ROOT, ATTENTION_Q, "bias")]).view(-1)
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key_bias = np2th(weights[pjoin(ROOT, ATTENTION_K, "bias")]).view(-1)
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value_bias = np2th(weights[pjoin(ROOT, ATTENTION_V, "bias")]).view(-1)
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out_bias = np2th(weights[pjoin(ROOT, ATTENTION_OUT, "bias")]).view(-1)
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self.attn.query.weight.copy_(query_weight)
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self.attn.key.weight.copy_(key_weight)
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self.attn.value.weight.copy_(value_weight)
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self.attn.out.weight.copy_(out_weight)
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self.attn.query.bias.copy_(query_bias)
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self.attn.key.bias.copy_(key_bias)
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self.attn.value.bias.copy_(value_bias)
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self.attn.out.bias.copy_(out_bias)
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mlp_weight_0 = np2th(weights[pjoin(ROOT, FC_0, "kernel")]).t()
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mlp_weight_1 = np2th(weights[pjoin(ROOT, FC_1, "kernel")]).t()
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mlp_bias_0 = np2th(weights[pjoin(ROOT, FC_0, "bias")]).t()
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mlp_bias_1 = np2th(weights[pjoin(ROOT, FC_1, "bias")]).t()
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self.ffn.fc1.weight.copy_(mlp_weight_0)
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self.ffn.fc2.weight.copy_(mlp_weight_1)
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self.ffn.fc1.bias.copy_(mlp_bias_0)
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self.ffn.fc2.bias.copy_(mlp_bias_1)
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self.attention_norm.weight.copy_(np2th(weights[pjoin(ROOT, ATTENTION_NORM, "scale")]))
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self.attention_norm.bias.copy_(np2th(weights[pjoin(ROOT, ATTENTION_NORM, "bias")]))
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self.ffn_norm.weight.copy_(np2th(weights[pjoin(ROOT, MLP_NORM, "scale")]))
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self.ffn_norm.bias.copy_(np2th(weights[pjoin(ROOT, MLP_NORM, "bias")]))
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class Part_Attention(nn.Module):
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def __init__(self):
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super(Part_Attention, self).__init__()
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def forward(self, x):
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length = len(x)
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last_map = x[0]
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for i in range(1, length):
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last_map = torch.matmul(x[i], last_map)
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last_map = last_map[:,:,0,1:]
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_, max_inx = last_map.max(2)
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return _, max_inx
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class Encoder(nn.Module):
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def __init__(self, config):
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super(Encoder, self).__init__()
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self.layer = nn.ModuleList()
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for _ in range(config.transformer["num_layers"] - 1):
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layer = Block(config)
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self.layer.append(copy.deepcopy(layer))
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self.part_select = Part_Attention()
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self.part_layer = Block(config)
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self.part_norm = LayerNorm(config.hidden_size, eps=1e-6)
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def forward(self, hidden_states):
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attn_weights = []
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for layer in self.layer:
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hidden_states, weights = layer(hidden_states)
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attn_weights.append(weights)
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part_num, part_inx = self.part_select(attn_weights)
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part_inx = part_inx + 1
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parts = []
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B, num = part_inx.shape
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for i in range(B):
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parts.append(hidden_states[i, part_inx[i,:]])
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parts = torch.stack(parts).squeeze(1)
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concat = torch.cat((hidden_states[:,0].unsqueeze(1), parts), dim=1)
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part_states, part_weights = self.part_layer(concat)
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part_encoded = self.part_norm(part_states)
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return part_encoded
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class Transformer(nn.Module):
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def __init__(self, config, img_size):
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super(Transformer, self).__init__()
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self.embeddings = Embeddings(config, img_size=img_size)
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self.encoder = Encoder(config)
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def forward(self, input_ids):
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embedding_output = self.embeddings(input_ids)
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part_encoded = self.encoder(embedding_output)
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return part_encoded
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class VisionTransformer(nn.Module):
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def __init__(self, config, img_size=224, num_classes=21843, smoothing_value=0, zero_head=False):
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super(VisionTransformer, self).__init__()
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self.num_classes = num_classes
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self.smoothing_value = smoothing_value
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self.zero_head = zero_head
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self.classifier = config.classifier
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self.transformer = Transformer(config, img_size)
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self.part_head = Linear(config.hidden_size, num_classes)
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def forward(self, x, labels=None):
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part_tokens = self.transformer(x)
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part_logits = self.part_head(part_tokens[:, 0])
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if labels is not None:
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if self.smoothing_value == 0:
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loss_fct = CrossEntropyLoss()
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else:
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loss_fct = LabelSmoothing(self.smoothing_value)
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part_loss = loss_fct(part_logits.view(-1, self.num_classes), labels.view(-1))
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contrast_loss = con_loss(part_tokens[:, 0], labels.view(-1))
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loss = part_loss + contrast_loss
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return loss, part_logits
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else:
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return part_logits
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def load_from(self, weights):
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with torch.no_grad():
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self.transformer.embeddings.patch_embeddings.weight.copy_(np2th(weights["embedding/kernel"], conv=True))
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self.transformer.embeddings.patch_embeddings.bias.copy_(np2th(weights["embedding/bias"]))
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self.transformer.embeddings.cls_token.copy_(np2th(weights["cls"]))
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self.transformer.encoder.part_norm.weight.copy_(np2th(weights["Transformer/encoder_norm/scale"]))
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self.transformer.encoder.part_norm.bias.copy_(np2th(weights["Transformer/encoder_norm/bias"]))
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posemb = np2th(weights["Transformer/posembed_input/pos_embedding"])
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posemb_new = self.transformer.embeddings.position_embeddings
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if posemb.size() == posemb_new.size():
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self.transformer.embeddings.position_embeddings.copy_(posemb)
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else:
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logger.info("load_pretrained: resized variant: %s to %s" % (posemb.size(), posemb_new.size()))
|
||||
ntok_new = posemb_new.size(1)
|
||||
|
||||
if self.classifier == "token":
|
||||
posemb_tok, posemb_grid = posemb[:, :1], posemb[0, 1:]
|
||||
ntok_new -= 1
|
||||
else:
|
||||
posemb_tok, posemb_grid = posemb[:, :0], posemb[0]
|
||||
|
||||
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)
|
||||
posemb_grid = posemb_grid.reshape(1, gs_new * gs_new, -1)
|
||||
posemb = np.concatenate([posemb_tok, posemb_grid], axis=1)
|
||||
self.transformer.embeddings.position_embeddings.copy_(np2th(posemb))
|
||||
|
||||
for bname, block in self.transformer.encoder.named_children():
|
||||
if bname.startswith('part') == False:
|
||||
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(weights["conv_root/kernel"], conv=True))
|
||||
gn_weight = np2th(weights["gn_root/scale"]).view(-1)
|
||||
gn_bias = np2th(weights["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(weights, n_block=bname, n_unit=uname)
|
||||
|
||||
|
||||
def con_loss(features, labels):
|
||||
B, _ = features.shape
|
||||
features = F.normalize(features)
|
||||
cos_matrix = features.mm(features.t())
|
||||
pos_label_matrix = torch.stack([labels == labels[i] for i in range(B)]).float()
|
||||
neg_label_matrix = 1 - pos_label_matrix
|
||||
pos_cos_matrix = 1 - cos_matrix
|
||||
neg_cos_matrix = cos_matrix - 0.4
|
||||
neg_cos_matrix[neg_cos_matrix < 0] = 0
|
||||
loss = (pos_cos_matrix * pos_label_matrix).sum() + (neg_cos_matrix * neg_label_matrix).sum()
|
||||
loss /= (B * B)
|
||||
return loss
|
||||
|
||||
|
||||
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(),
|
||||
'testing': configs.get_testing(),
|
||||
}
|
||||
Reference in New Issue
Block a user