File size: 3,312 Bytes
23bd7af |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 |
# coding=utf-8
# Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""Vision Transformer(VIT) model."""
import torch
from torch.nn.init import trunc_normal_
from megatron import get_args
from megatron.model.utils import get_linear_layer
from megatron.model.vision.vit_backbone import VitBackbone, VitMlpHead
from megatron.model.vision.mit_backbone import mit_b3_avg
from megatron.model.module import MegatronModule
class VitClassificationModel(MegatronModule):
"""Vision Transformer Model."""
def __init__(self, num_classes, finetune=False,
pre_process=True, post_process=True):
super(VitClassificationModel, self).__init__()
args = get_args()
self.hidden_size = args.hidden_size
self.num_classes = num_classes
self.finetune = finetune
self.pre_process = pre_process
self.post_process = post_process
self.backbone = VitBackbone(
pre_process=self.pre_process,
post_process=self.post_process,
single_token_output=True
)
if self.post_process:
if not self.finetune:
self.head = VitMlpHead(self.hidden_size, self.num_classes)
else:
self.head = get_linear_layer(
self.hidden_size,
self.num_classes,
torch.nn.init.zeros_
)
def set_input_tensor(self, input_tensor):
"""See megatron.model.transformer.set_input_tensor()"""
self.backbone.set_input_tensor(input_tensor)
def forward(self, input):
hidden_states = self.backbone(input)
if self.post_process:
hidden_states = self.head(hidden_states)
return hidden_states
class MitClassificationModel(MegatronModule):
"""Mix vision Transformer Model."""
def __init__(self, num_classes,
pre_process=True, post_process=True):
super(MitClassificationModel, self).__init__()
args = get_args()
self.hidden_size = args.hidden_size
self.num_classes = num_classes
self.backbone = mit_b3_avg()
self.head = torch.nn.Linear(512, num_classes)
self.apply(self._init_weights)
def _init_weights(self, m):
if isinstance(m, torch.nn.Linear):
trunc_normal_(m.weight, std=.02)
if isinstance(m, torch.nn.Linear) and m.bias is not None:
torch.nn.init.constant_(m.bias, 0)
def set_input_tensor(self, input_tensor):
"""See megatron.model.transformer.set_input_tensor()"""
pass
def forward(self, input):
hidden_states = self.backbone(input)
hidden_states = self.head(hidden_states)
return hidden_states
|