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import attr |
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import numpy as np |
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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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from collections import OrderedDict |
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from functools import partial |
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from dall_e.utils import Conv2d |
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@attr.s(eq=False, repr=False) |
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class EncoderBlock(nn.Module): |
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n_in: int = attr.ib(validator=lambda i, a, x: x >= 1) |
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n_out: int = attr.ib(validator=lambda i, a, x: x >= 1 and x % 4 ==0) |
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n_layers: int = attr.ib(validator=lambda i, a, x: x >= 1) |
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device: torch.device = attr.ib(default=None) |
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requires_grad: bool = attr.ib(default=False) |
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def __attrs_post_init__(self) -> None: |
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super().__init__() |
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self.n_hid = self.n_out // 4 |
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self.post_gain = 1 / (self.n_layers ** 2) |
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make_conv = partial(Conv2d, device=self.device, requires_grad=self.requires_grad) |
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self.id_path = make_conv(self.n_in, self.n_out, 1) if self.n_in != self.n_out else nn.Identity() |
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self.res_path = nn.Sequential(OrderedDict([ |
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('relu_1', nn.ReLU()), |
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('conv_1', make_conv(self.n_in, self.n_hid, 3)), |
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('relu_2', nn.ReLU()), |
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('conv_2', make_conv(self.n_hid, self.n_hid, 3)), |
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('relu_3', nn.ReLU()), |
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('conv_3', make_conv(self.n_hid, self.n_hid, 3)), |
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('relu_4', nn.ReLU()), |
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('conv_4', make_conv(self.n_hid, self.n_out, 1)),])) |
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def forward(self, x: torch.Tensor) -> torch.Tensor: |
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return self.id_path(x) + self.post_gain * self.res_path(x) |
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@attr.s(eq=False, repr=False) |
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class Encoder(nn.Module): |
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group_count: int = 4 |
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n_hid: int = attr.ib(default=256, validator=lambda i, a, x: x >= 64) |
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n_blk_per_group: int = attr.ib(default=2, validator=lambda i, a, x: x >= 1) |
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input_channels: int = attr.ib(default=3, validator=lambda i, a, x: x >= 1) |
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vocab_size: int = attr.ib(default=8192, validator=lambda i, a, x: x >= 512) |
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device: torch.device = attr.ib(default=torch.device('cpu')) |
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requires_grad: bool = attr.ib(default=False) |
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use_mixed_precision: bool = attr.ib(default=True) |
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def __attrs_post_init__(self) -> None: |
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super().__init__() |
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blk_range = range(self.n_blk_per_group) |
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n_layers = self.group_count * self.n_blk_per_group |
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make_conv = partial(Conv2d, device=self.device, requires_grad=self.requires_grad) |
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make_blk = partial(EncoderBlock, n_layers=n_layers, device=self.device, |
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requires_grad=self.requires_grad) |
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self.blocks = nn.Sequential(OrderedDict([ |
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('input', make_conv(self.input_channels, 1 * self.n_hid, 7)), |
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('group_1', nn.Sequential(OrderedDict([ |
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*[(f'block_{i + 1}', make_blk(1 * self.n_hid, 1 * self.n_hid)) for i in blk_range], |
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('pool', nn.MaxPool2d(kernel_size=2)), |
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]))), |
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('group_2', nn.Sequential(OrderedDict([ |
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*[(f'block_{i + 1}', make_blk(1 * self.n_hid if i == 0 else 2 * self.n_hid, 2 * self.n_hid)) for i in blk_range], |
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('pool', nn.MaxPool2d(kernel_size=2)), |
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]))), |
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('group_3', nn.Sequential(OrderedDict([ |
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*[(f'block_{i + 1}', make_blk(2 * self.n_hid if i == 0 else 4 * self.n_hid, 4 * self.n_hid)) for i in blk_range], |
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('pool', nn.MaxPool2d(kernel_size=2)), |
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]))), |
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('group_4', nn.Sequential(OrderedDict([ |
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*[(f'block_{i + 1}', make_blk(4 * self.n_hid if i == 0 else 8 * self.n_hid, 8 * self.n_hid)) for i in blk_range], |
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]))), |
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('output', nn.Sequential(OrderedDict([ |
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('relu', nn.ReLU()), |
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('conv', make_conv(8 * self.n_hid, self.vocab_size, 1, use_float16=False)), |
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]))), |
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])) |
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def forward(self, x: torch.Tensor) -> torch.Tensor: |
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if len(x.shape) != 4: |
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raise ValueError(f'input shape {x.shape} is not 4d') |
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if x.shape[1] != self.input_channels: |
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raise ValueError(f'input has {x.shape[1]} channels but model built for {self.input_channels}') |
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if x.dtype != torch.float32: |
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raise ValueError('input must have dtype torch.float32') |
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return self.blocks(x) |
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