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# 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.
import math
import einops
import torch
import apex
import torch.nn.functional as F
from megatron import get_args
from megatron.model.module import MegatronModule
from megatron.model.vision.vit_backbone import VitBackbone, VitMlpHead
from megatron.model.vision.mit_backbone import mit_b3, mit_b5
from tasks.vision.segmentation.seg_heads import SetrSegmentationHead, SegformerSegmentationHead
class SetrSegmentationModel(MegatronModule):
def __init__(self,
num_classes,
pre_process=True,
post_process=True):
super(SetrSegmentationModel, self).__init__()
args = get_args()
assert post_process & pre_process
self.hidden_size = args.hidden_size
self.num_classes = num_classes
self.backbone = VitBackbone(
pre_process=pre_process,
post_process=post_process,
class_token=False,
post_layer_norm=False,
drop_path_rate=0.1
)
self.head = SetrSegmentationHead(
self.hidden_size,
self.num_classes
)
def set_input_tensor(self, input_tensor):
"""See megatron.model.transformer.set_input_tensor()"""
pass
def forward(self, input):
# [b hw c]
hidden_states = self.backbone(input)
result_final = self.head(hidden_states)
return result_final
class SegformerSegmentationModel(MegatronModule):
def __init__(self,
num_classes,
pre_process=True,
post_process=True):
super(SegformerSegmentationModel, self).__init__()
args = get_args()
self.hidden_size = args.hidden_size
self.num_classes = num_classes
self.pre_process = pre_process
self.post_process = post_process
self.backbone = mit_b5()
self.head = SegformerSegmentationHead(
feature_strides=[4, 8, 16, 32],
in_channels=[64, 128, 320, 512],
embedding_dim=768,
dropout_ratio=0.1
)
def set_input_tensor(self, input_tensor):
"""See megatron.model.transformer.set_input_tensor()"""
pass
def forward(self, input):
# [b hw c]
hidden_states = self.backbone(input)
hidden_states = self.head(hidden_states)
return hidden_states
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