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81
models/experimental/functional_yolov4/reference/downsample1.py
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# SPDX-FileCopyrightText: © 2023 Tenstorrent Inc. | ||
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# SPDX-License-Identifier: Apache-2.0 | ||
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import torch | ||
import torch.nn as nn | ||
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class Mish(torch.nn.Module): | ||
def __init__(self): | ||
super().__init__() | ||
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def forward(self, x): | ||
x = x * (torch.tanh(torch.nn.functional.softplus(x))) | ||
return x | ||
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class DownSample1(nn.Module): | ||
def __init__(self): | ||
super().__init__() | ||
self.c1 = nn.Conv2d(3, 32, 3, 1, 1, bias=False) | ||
self.b1 = nn.BatchNorm2d(32) | ||
# self.relu = nn.ReLU(inplace=True) | ||
self.relu = Mish() | ||
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self.c2 = nn.Conv2d(32, 64, 3, 2, 1, bias=False) | ||
self.b2 = nn.BatchNorm2d(64) | ||
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self.c3 = nn.Conv2d(64, 64, 1, 1, 0, bias=False) | ||
self.b3 = nn.BatchNorm2d(64) | ||
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self.c4 = nn.Conv2d(64, 64, 1, 1, 0, bias=False) | ||
self.b4 = nn.BatchNorm2d(64) | ||
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self.c5 = nn.Conv2d(64, 32, 1, 1, 0, bias=False) | ||
self.b5 = nn.BatchNorm2d(32) | ||
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self.c6 = nn.Conv2d(32, 64, 3, 1, 1, bias=False) | ||
self.b6 = nn.BatchNorm2d(64) | ||
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self.c7 = nn.Conv2d(64, 64, 1, 1, 0, bias=False) | ||
self.b7 = nn.BatchNorm2d(64) | ||
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self.c8 = nn.Conv2d(128, 64, 1, 1, 0, bias=False) | ||
self.b8 = nn.BatchNorm2d(64) | ||
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def forward(self, input: torch.Tensor): | ||
x1 = self.c1(input) | ||
x1_b = self.b1(x1) | ||
x1_m = self.relu(x1_b) | ||
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x2 = self.c2(x1_m) | ||
x2_b = self.b2(x2) | ||
x2_m = self.relu(x2_b) | ||
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x3 = self.c3(x2_m) | ||
x3_b = self.b3(x3) | ||
x3_m = self.relu(x3_b) | ||
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x4 = self.c4(x2_m) | ||
x4_b = self.b4(x4) | ||
x4_m = self.relu(x4_b) | ||
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x5 = self.c5(x4_m) | ||
x5_b = self.b5(x5) | ||
x5_m = self.relu(x5_b) | ||
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x6 = self.c6(x5_m) | ||
x6_b = self.b6(x6) | ||
x6_m = self.relu(x6_b) | ||
x6_m = x6_m + x4_m | ||
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x7 = self.c7(x6_m) | ||
x7_b = self.b7(x7) | ||
x7_m = self.relu(x7_b) | ||
x7_m = torch.cat([x7_m, x3_m], dim=1) | ||
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x8 = self.c8(x7_m) | ||
x8_b = self.b8(x8) | ||
x8_m = self.relu(x8_b) | ||
return x8_m |
63 changes: 63 additions & 0 deletions
63
models/experimental/functional_yolov4/reference/downsample2.py
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# SPDX-FileCopyrightText: © 2023 Tenstorrent Inc. | ||
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# SPDX-License-Identifier: Apache-2.0 | ||
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import torch | ||
import torch.nn as nn | ||
from models.experimental.functional_yolov4.reference.resblock import ResBlock | ||
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class Mish(torch.nn.Module): | ||
def __init__(self): | ||
super().__init__() | ||
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def forward(self, x): | ||
x = x * (torch.tanh(torch.nn.functional.softplus(x))) | ||
return x | ||
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class DownSample2(nn.Module): | ||
def __init__(self): | ||
super().__init__() | ||
self.c1 = nn.Conv2d(64, 128, 3, 2, 1, bias=False) | ||
self.b1 = nn.BatchNorm2d(128) | ||
self.relu = Mish() | ||
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self.c2 = nn.Conv2d(128, 64, 1, 1, 0, bias=False) | ||
self.b2 = nn.BatchNorm2d(64) | ||
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self.c3 = nn.Conv2d(128, 64, 1, 1, 0, bias=False) | ||
self.b3 = nn.BatchNorm2d(64) | ||
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self.res = ResBlock(ch = 64, nblocks=2) | ||
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self.c4 = nn.Conv2d(64, 64, 1, 1, 0, bias=False) | ||
self.b4 = nn.BatchNorm2d(64) | ||
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self.c5 = nn.Conv2d(128, 128, 1, 1, 0, bias=False) | ||
self.b5 = nn.BatchNorm2d(128) | ||
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def forward(self, input: torch.Tensor): | ||
x1 = self.c1(input) | ||
x1_b = self.b1(x1) | ||
x1_m = self.relu(x1_b) | ||
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x2 = self.c2(x1_m) | ||
x2_b = self.b2(x2) | ||
x2_m = self.relu(x2_b) | ||
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x3 = self.c3(x1_m) | ||
x3_b = self.b3(x3) | ||
x3_m = self.relu(x3_b) | ||
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r1 = self.res(x3_m) | ||
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x4 = self.c4(r1) | ||
x4_b = self.b4(x4) | ||
x4_m = self.relu(x4_b) | ||
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x4_m = torch.cat([x4_m, x2_m], dim=1) | ||
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x5 = self.c5(x4_m) | ||
x5_b = self.b5(x5) | ||
x5_m = self.relu(x5_b) | ||
return x5_m |
64 changes: 64 additions & 0 deletions
64
models/experimental/functional_yolov4/reference/downsample3.py
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# SPDX-FileCopyrightText: © 2023 Tenstorrent Inc. | ||
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# SPDX-License-Identifier: Apache-2.0 | ||
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import torch | ||
import torch.nn as nn | ||
from models.experimental.functional_yolov4.reference.resblock import ResBlock | ||
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class Mish(torch.nn.Module): | ||
def __init__(self): | ||
super().__init__() | ||
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def forward(self, x): | ||
x = x * (torch.tanh(torch.nn.functional.softplus(x))) | ||
return x | ||
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class DownSample3(nn.Module): | ||
def __init__(self): | ||
super().__init__() | ||
self.c1 = nn.Conv2d(128, 256, 3, 2, 1, bias=False) | ||
self.b1 = nn.BatchNorm2d(256) | ||
self.relu = Mish() | ||
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self.c2 = nn.Conv2d(256, 128, 1, 1, 0, bias=False) | ||
self.b2 = nn.BatchNorm2d(128) | ||
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self.c3 = nn.Conv2d(256, 128, 1, 1, 0, bias=False) | ||
self.b3 = nn.BatchNorm2d(128) | ||
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self.res = ResBlock(ch=128, nblocks=8) | ||
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self.c4 = nn.Conv2d(128, 128, 1, 1, 0, bias=False) | ||
self.b4 = nn.BatchNorm2d(128) | ||
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self.c5 = nn.Conv2d(256, 256, 1, 1, 0, bias=False) | ||
self.b5 = nn.BatchNorm2d(256) | ||
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def forward(self, input: torch.Tensor): | ||
x1 = self.c1(input) | ||
x1_b = self.b1(x1) | ||
x1_m = self.relu(x1_b) | ||
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x2 = self.c2(x1_m) | ||
x2_b = self.b2(x2) | ||
x2_m = self.relu(x2_b) | ||
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x3 = self.c3(x1_m) | ||
x3_b = self.b3(x3) | ||
x3_m = self.relu(x3_b) | ||
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r1 = self.res(x3_m) | ||
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x4 = self.c4(r1) | ||
x4_b = self.b4(x4) | ||
x4_m = self.relu(x4_b) | ||
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x4_m = torch.cat([x4_m, x2_m], dim=1) | ||
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x5 = self.c5(x4_m) | ||
x5_b = self.b5(x5) | ||
x5_m = self.relu(x5_b) | ||
return x5_m |
64 changes: 64 additions & 0 deletions
64
models/experimental/functional_yolov4/reference/downsample4.py
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@@ -0,0 +1,64 @@ | ||
# SPDX-FileCopyrightText: © 2023 Tenstorrent Inc. | ||
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# SPDX-License-Identifier: Apache-2.0 | ||
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import torch | ||
import torch.nn as nn | ||
from models.experimental.functional_yolov4.reference.resblock import ResBlock | ||
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class Mish(torch.nn.Module): | ||
def __init__(self): | ||
super().__init__() | ||
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def forward(self, x): | ||
x = x * (torch.tanh(torch.nn.functional.softplus(x))) | ||
return x | ||
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class DownSample4(nn.Module): | ||
def __init__(self): | ||
super().__init__() | ||
self.c1 = nn.Conv2d(256, 512, 3, 2, 1, bias=False) | ||
self.b1 = nn.BatchNorm2d(512) | ||
self.relu = Mish() | ||
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self.c2 = nn.Conv2d(512, 256, 1, 1, 0, bias=False) | ||
self.b2 = nn.BatchNorm2d(256) | ||
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self.c3 = nn.Conv2d(512, 256, 1, 1, 0, bias=False) | ||
self.b3 = nn.BatchNorm2d(256) | ||
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self.res = ResBlock(ch=256, nblocks=8) | ||
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self.c4 = nn.Conv2d(256, 256, 1, 1, 0, bias=False) | ||
self.b4 = nn.BatchNorm2d(256) | ||
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self.c5 = nn.Conv2d(512, 512, 1, 1, 0, bias=False) | ||
self.b5 = nn.BatchNorm2d(512) | ||
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def forward(self, input: torch.Tensor): | ||
x1 = self.c1(input) | ||
x1_b = self.b1(x1) | ||
x1_m = self.relu(x1_b) | ||
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x2 = self.c2(x1_m) | ||
x2_b = self.b2(x2) | ||
x2_m = self.relu(x2_b) | ||
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x3 = self.c3(x1_m) | ||
x3_b = self.b3(x3) | ||
x3_m = self.relu(x3_b) | ||
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# resblock | ||
r = self.res(x3_m) | ||
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x4 = self.c4(r) | ||
x4_b = self.b4(x4) | ||
x4_m = self.relu(x4_b) | ||
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x4_m = torch.cat([x4_m, x2_m], dim=1) | ||
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x5 = self.c5(x4_m) | ||
x5_b = self.b5(x5) | ||
x5_m = self.relu(x5_b) | ||
return x5_m |
64 changes: 64 additions & 0 deletions
64
models/experimental/functional_yolov4/reference/downsample5.py
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Original file line number | Diff line number | Diff line change |
---|---|---|
@@ -0,0 +1,64 @@ | ||
# SPDX-FileCopyrightText: © 2023 Tenstorrent Inc. | ||
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# SPDX-License-Identifier: Apache-2.0 | ||
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import torch | ||
import torch.nn as nn | ||
from models.experimental.functional_yolov4.reference.resblock import ResBlock | ||
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class Mish(torch.nn.Module): | ||
def __init__(self): | ||
super().__init__() | ||
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def forward(self, x): | ||
x = x * (torch.tanh(torch.nn.functional.softplus(x))) | ||
return x | ||
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class DownSample5(nn.Module): | ||
def __init__(self): | ||
super().__init__() | ||
self.c1 = nn.Conv2d(512, 1024, 3, 2, 1, bias=False) | ||
self.b1 = nn.BatchNorm2d(1024) | ||
self.relu = Mish() | ||
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self.c2 = nn.Conv2d(1024, 512, 1, 1, 0, bias=False) | ||
self.b2 = nn.BatchNorm2d(512) | ||
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self.c3 = nn.Conv2d(1024, 512, 1, 1, 0, bias=False) | ||
self.b3 = nn.BatchNorm2d(512) | ||
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self.res = ResBlock(512, 4) | ||
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self.c4 = nn.Conv2d(512, 512, 1, 1, 0, bias=False) | ||
self.b4 = nn.BatchNorm2d(512) | ||
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self.c5 = nn.Conv2d(1024, 1024, 1, 1, 0, bias=False) | ||
self.b5 = nn.BatchNorm2d(1024) | ||
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def forward(self, input: torch.Tensor): | ||
x1 = self.c1(input) | ||
x1_b = self.b1(x1) | ||
x1_m = self.relu(x1_b) | ||
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x2 = self.c2(x1_m) | ||
x2_b = self.b2(x2) | ||
x2_m = self.relu(x2_b) | ||
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x3 = self.c3(x1_m) | ||
x3_b = self.b3(x3) | ||
x3_m = self.relu(x3_b) | ||
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# resblock | ||
r = self.res(x3_m) | ||
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x4 = self.c4(r) | ||
x4_b = self.b4(x4) | ||
x4_m = self.relu(x4_b) | ||
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x4_m = torch.cat([x4_m, x2_m], dim=1) | ||
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x5 = self.c5(x4_m) | ||
x5_b = self.b5(x5) | ||
x5_m = self.relu(x5_b) | ||
return x5_m |
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