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# Copyright 2022 The HuggingFace Team. 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 paddle | |
from paddle import nn | |
from paddlenlp.transformers import ( | |
CLIPPretrainedModel, | |
CLIPVisionConfig, | |
CLIPVisionModel, | |
) | |
from ...models.attention import BasicTransformerBlock | |
from ...utils import logging | |
logger = logging.get_logger(__name__) # pylint: disable=invalid-name | |
class PaintByExampleImageEncoder(CLIPPretrainedModel): | |
config_class = CLIPVisionConfig | |
def __init__(self, config: CLIPVisionConfig): | |
super().__init__(config) | |
self.projection_dim = config.projection_dim | |
self.model = CLIPVisionModel(config) | |
self.mapper = PaintByExampleMapper(config) | |
self.final_layer_norm = nn.LayerNorm(config.hidden_size) | |
self.proj_out = nn.Linear(config.hidden_size, self.projection_dim) | |
# uncondition for scaling | |
self.uncond_vector = self.create_parameter( | |
[1, 1, self.projection_dim], | |
dtype=paddle.get_default_dtype(), | |
default_initializer=nn.initializer.Assign(paddle.rand((1, 1, self.projection_dim))), | |
) | |
def forward(self, pixel_values): | |
clip_output = self.model(pixel_values=pixel_values) | |
latent_states = clip_output.pooler_output | |
latent_states = self.mapper(latent_states[:, None]) | |
latent_states = self.final_layer_norm(latent_states) | |
latent_states = self.proj_out(latent_states) | |
return latent_states | |
class PaintByExampleMapper(nn.Layer): | |
def __init__(self, config): | |
super().__init__() | |
num_layers = (config.num_hidden_layers + 1) // 5 | |
hid_size = config.hidden_size | |
num_heads = 1 | |
self.blocks = nn.LayerList( | |
[ | |
BasicTransformerBlock(hid_size, num_heads, hid_size, activation_fn="gelu", attention_bias=True) | |
for _ in range(num_layers) | |
] | |
) | |
def forward(self, hidden_states): | |
for block in self.blocks: | |
hidden_states = block(hidden_states) | |
return hidden_states | |