diff --git a/deploy/configs/inference_ram.yaml b/deploy/configs/inference_ram.yaml new file mode 100644 index 0000000000..0c513047f5 --- /dev/null +++ b/deploy/configs/inference_ram.yaml @@ -0,0 +1,36 @@ +Global: + infer_imgs: "docs/images/inference_deployment/whl_demo.jpg" + inference_model_dir: "./inference" + batch_size: 1 + use_gpu: False + enable_mkldnn: False + cpu_num_threads: 10 + enable_benchmark: True + use_fp16: False + ir_optim: False # do not set it as True since there is a bug which leads the invaild initilize for predictor + use_tensorrt: False + gpu_mem: 8000 + enable_profile: False + +PreProcess: + transform_ops: + - ResizeImage: + resize_short: 384 + - CropImage: + size: 384 + - NormalizeImage: + scale: 1.0/255.0 + mean: [0.485, 0.456, 0.406] + std: [0.229, 0.224, 0.225] + order: "" + channel_num: 3 + - ToCHWImage: +PostProcess: + main_indicator: RamOutPut + RamOutPut: + language: "en" + tag_list: "ppcls/utils/ram/ram_tag_list.txt" + tag_list_chinese: "ppcls/utils/ram/ram_tag_list_chinese.txt" + ram_class_threshold_path: "ppcls/utils/ram/ram_tag_list_threshold.txt" + + diff --git a/deploy/python/postprocess.py b/deploy/python/postprocess.py index e6709dcef9..ca3d1facca 100644 --- a/deploy/python/postprocess.py +++ b/deploy/python/postprocess.py @@ -71,7 +71,7 @@ def __init__(self, func_list, main_indicator="Topk"): def __call__(self, x, image_file=None): rtn = None for func in self.func_list: - tmp = func(x, image_file) + tmp = func(*x, image_file) if type(func).__name__ in self.main_indicator: rtn = tmp return rtn @@ -496,3 +496,74 @@ def __call__(self, batch_preds, file_names=None): ).astype(np.int8).tolist() batch_res.append({"attributes": label_res, "output": pred_res}) return batch_res + + +class RamOutPut(object): + def __init__(self, + language="cn", + tag_list="", + tag_list_chinese="", + threshold=0.68, + delete_tag_index=[], + ram_class_threshold_path=""): + self.language = language + assert tag_list, tag_list_chinese + self.tag_list = self.load_tag_list(tag_list) + self.delete_tag_index = delete_tag_index + self.tag_list_chinese = self.load_tag_list(tag_list_chinese) + self.num_class = len(self.tag_list) + self.class_threshold = paddle.ones([self.num_class]) * threshold + with open(ram_class_threshold_path, "r", encoding="utf-8") as f: + ram_class_threshold = [float(s.strip()) for s in f] + for key, value in enumerate(ram_class_threshold): + self.class_threshold[key] = value + + def load_tag_list(self, tag_list_file): + with open(tag_list_file, "r", encoding="utf-8") as f: + tag_list = f.read().splitlines() + tag_list = np.array(tag_list) + return tag_list + + def __call__(self, logits, bs, file_names=None): + batch_res = [] + if bs is None: + if len(logits.shape) < 2: + bs = 1 + else: + bs = logits.shape[0] + logits = paddle.to_tensor(logits).reshape([bs,-1]) + targets = paddle.where( + F.sigmoid(logits) > self.class_threshold, + paddle.to_tensor([1.0]), paddle.zeros(self.num_class)) + targets = targets.reshape([bs, -1]) + res = {} + tag = targets.cpu().numpy() + tag[:, self.delete_tag_index] = 0 + tag_output = [] + tag_output_chinese = [] + for b in range(bs): + index = np.argwhere(tag[b] == 1) + token = self.tag_list[index].squeeze(axis=1) + tag_output.append(" | ".join(token)) + token_chinese = self.tag_list_chinese[index].squeeze(axis=1) + tag_output_chinese.append(" | ".join(token_chinese)) + res["cn"] = tag_output_chinese + res["en"] = tag_output + res["all"] = f"en : {tag_output}, cn: {tag_output_chinese}" + + scores = F.sigmoid(logits).numpy() + class_ids_list = [] + scores_list = [] + + for b in range(bs): + index = np.argwhere(tag[b] == 1) + class_ids_list.append(index.tolist()) + scores_list.append(scores[b][index].tolist()) + + outputformat = { + "class_ids": class_ids_list, + "scores": scores_list, + "label_names": res[self.language] + } + batch_res.append(outputformat) + return outputformat diff --git a/deploy/python/predict_multimodal.py b/deploy/python/predict_multimodal.py new file mode 100644 index 0000000000..6397e734b8 --- /dev/null +++ b/deploy/python/predict_multimodal.py @@ -0,0 +1,68 @@ +# copyright (c) 2023 PaddlePaddle Authors. All Rights Reserve. +# +# 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 os + +import cv2 +import numpy as np + +from paddleclas.deploy.utils import logger, config +from paddleclas.deploy.utils.get_image_list import get_image_list +from paddleclas.deploy.python.predict_cls import ClsPredictor + + +def main(config): + cls_predictor = ClsPredictor(config) + image_list = get_image_list(config["Global"]["infer_imgs"]) + + batch_imgs = [] + batch_names = [] + cnt = 0 + for idx, img_path in enumerate(image_list): + img = cv2.imread(img_path) + if img is None: + logger.warning( + "Image file failed to read and has been skipped. The path: {}". + format(img_path)) + else: + img = img[:, :, ::-1] + print(img.shape) + batch_imgs.append(img) + img_name = os.path.basename(img_path) + batch_names.append(img_name) + cnt += 1 + + if cnt % config["Global"]["batch_size"] == 0 or (idx + 1 + ) == len(image_list): + if len(batch_imgs) == 0: + continue + batch_results = cls_predictor.predict(batch_imgs) + for number, result_dict in enumerate(batch_results): + if len(batch_imgs) == 0: + continue + for number, result_key in enumerate(batch_results.keys()): + print( + f"{img_name}-{result_key}:{batch_results[result_key]}") + batch_imgs = [] + batch_names = [] + if cls_predictor.benchmark: + cls_predictor.auto_logger.report() + return + + + +if __name__ == "__main__": + args = config.parse_args() + config = config.get_config(args.config, overrides=args.override, show=True) + main(config) diff --git a/ppcls/arch/__init__.py b/ppcls/arch/__init__.py index 7800da33ba..c1151732fb 100644 --- a/ppcls/arch/__init__.py +++ b/ppcls/arch/__init__.py @@ -20,6 +20,7 @@ from . import backbone, gears from .backbone import * +from .ram import * from .gears import build_gear, add_ml_decoder_head from .utils import * from .backbone.base.theseus_layer import TheseusLayer diff --git a/ppcls/arch/backbone/clip/__init__.py b/ppcls/arch/backbone/clip/__init__.py new file mode 100644 index 0000000000..e69de29bb2 diff --git a/ppcls/arch/clip/__init__.py b/ppcls/arch/clip/__init__.py new file mode 100644 index 0000000000..014e71984b --- /dev/null +++ b/ppcls/arch/clip/__init__.py @@ -0,0 +1,2 @@ +from .clip import CLIP_vit_base_patch16_224_with_TextEncoder, CLIP_vit_base_patch32_224_with_TextEncoder, CLIP_vit_large_patch14_224_with_TextEncoder, CLIP_vit_large_patch16_224_with_TextEncoder +from .tokenizer import Tokenizer \ No newline at end of file diff --git a/ppcls/arch/clip/clip.py b/ppcls/arch/clip/clip.py new file mode 100644 index 0000000000..fa2352aae4 --- /dev/null +++ b/ppcls/arch/clip/clip.py @@ -0,0 +1,596 @@ +# copyright (c) 2023 PaddlePaddle Authors. All Rights Reserve. +# +# 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. +# Code was based on https://github.com/AgentMaker/Paddle-CLIP, https://github.com/openai/CLIP/ +# reference: https://arxiv.org/abs/2103.00020 + +import math + +import paddle +import paddle.nn as nn +from paddle.nn import functional as F +from paddle.nn.initializer import Assign, Normal, Constant + + +class Identity(nn.Layer): + def __init__(self): + super(Identity, self).__init__() + + def forward(self, inputs): + return inputs + + +class QuickGELU(nn.Layer): + def forward(self, x): + return x * nn.functional.sigmoid(1.702 * x) + + + + +class Attention(nn.Layer): + def __init__( + self, + embed_dim, + num_heads=8, + output_dim=None, + qkv_bias=True, + scaled_cosine=False, + scale_heads=False, + logit_scale_max=math.log(1. / 0.01), + attn_drop=0., + proj_drop=0. + ): + super().__init__() + self.scaled_cosine = scaled_cosine + self.scale_heads = scale_heads + assert embed_dim % num_heads == 0, 'dim should be divisible by num_heads' + self.num_heads = num_heads + self.head_dim = embed_dim // num_heads + self.scale = self.head_dim ** -0.5 + self.logit_scale_max = logit_scale_max + + # keeping in_proj in this form (instead of nn.Linear) to match weight scheme of original + self.in_proj = nn.Linear(embed_dim, embed_dim * 3, bias_attr=qkv_bias) + + if self.scaled_cosine: + self.logit_scale = paddle.log(10 * paddle.ones((num_heads, 1, 1))) + else: + self.logit_scale = None + self.attn_drop = nn.Dropout(attn_drop) + if self.scale_heads: + self.head_scale = paddle.ones((num_heads, 1, 1)) + else: + self.head_scale = None + self.out_proj = nn.Linear(embed_dim, output_dim) if output_dim else nn.Linear(embed_dim, embed_dim) + self.out_drop = nn.Dropout(proj_drop) + + def forward(self, x, attn_mask=None): + L, N, C = paddle.shape(x) + q, k, v = self.in_proj(x).chunk(3, axis=-1) + + q = q.reshape([L, N * self.num_heads, -1]).transpose([1, 0, 2]) * self.scale + k = k.reshape([L, N * self.num_heads, -1]).transpose([1, 0, 2]) * self.scale + v = v.reshape([L, N * self.num_heads, -1]).transpose([1, 0, 2]) * self.scale + + if self.logit_scale is not None: + attn = paddle.bmm(F.normalize(q, dim=-1), F.normalize(k, axis=-1).transpose([0, 2, 1])) + logit_scale = paddle.clip(self.logit_scale, max=self.logit_scale_max).exp() + attn = attn.reshape([N, self.num_heads, L, L]) * logit_scale + else: + q = q * self.scale + attn = paddle.bmm(q, k.transpose([0, 2, 1])) + + if attn_mask is not None: + if attn_mask.dtype == paddle.bool: + new_attn_mask = paddle.zeros_like(attn_mask, dtype=q.dtype) + new_attn_mask.masked_fill_(attn_mask, float("-inf")) + attn_mask = new_attn_mask + attn += attn_mask + + attn = F.softmax(attn, axis=-1) + attn = self.attn_drop(attn) + attn = attn.reshape([-1, L, L]) + x = paddle.bmm(attn, v) + if self.head_scale is not None: + x = x.reshape([N, self.num_heads, L, C]) * self.head_scale + x = x.reshape([-1, L, C]) + x = x.transpose([1, 0, 2]).reshape([L, N, C]) + x = self.out_proj(x) + x = self.out_drop(x) + return x + + +class Bottleneck(nn.Layer): + expansion = 4 + + def __init__(self, inplanes, planes, stride=1): + super().__init__() + self.conv1 = nn.Conv2D(inplanes, planes, 1, bias_attr=False) + self.bn1 = nn.BatchNorm2D(planes) + + self.conv2 = nn.Conv2D(planes, planes, 3, padding=1, bias_attr=False) + self.bn2 = nn.BatchNorm2D(planes) + + self.avgpool = nn.AvgPool2D(stride) if stride > 1 else Identity() + + self.conv3 = nn.Conv2D( + planes, planes * self.expansion, 1, bias_attr=False) + self.bn3 = nn.BatchNorm2D(planes * self.expansion) + + self.relu = nn.ReLU() + self.downsample = None + self.stride = stride + + if stride > 1 or inplanes != planes * Bottleneck.expansion: + self.downsample = nn.Sequential( + ("-1", nn.AvgPool2D(stride)), ("0", nn.Conv2D( + inplanes, + planes * self.expansion, + 1, + stride=1, + bias_attr=False)), + ("1", nn.BatchNorm2D(planes * self.expansion))) + + def forward(self, x): + identity = x + + out = self.relu(self.bn1(self.conv1(x))) + out = self.relu(self.bn2(self.conv2(out))) + out = self.avgpool(out) + out = self.bn3(self.conv3(out)) + + if self.downsample is not None: + identity = self.downsample(x) + + out += identity + out = self.relu(out) + return out + + +class AttentionPool2D(nn.Layer): + def __init__(self, spacial_dim, embed_dim, num_heads, output_dim=None): + super().__init__() + positional_embedding = self.create_parameter( + shape=(spacial_dim**2 + 1, embed_dim), + default_initializer=Assign( + paddle.randn((spacial_dim**2 + 1, embed_dim)) / embed_dim + **0.5)) + self.add_parameter("positional_embedding", positional_embedding) + + self.attn = Attention(embed_dim, num_heads, output_dim) + + def forward(self, x): + x = x.reshape((x.shape[0], x.shape[1], + x.shape[2] * x.shape[3])).transpose((2, 0, 1)) + x = paddle.concat([x.mean(axis=0, keepdim=True), x], axis=0) + x = x + self.positional_embedding.unsqueeze(1) + x = x.transpose((1, 0, 2)) + x = self.attn(query=x, key=x, value=x) + x = x.transpose((1, 0, 2)) + return x[0] + + +class ModifiedResNet(nn.Layer): + def __init__(self, + layers, + output_dim, + heads, + input_resolution=224, + width=64): + super().__init__() + self.output_dim = output_dim + self.input_resolution = input_resolution + + self.conv1 = nn.Conv2D( + 3, width // 2, kernel_size=3, stride=2, padding=1, bias_attr=False) + self.bn1 = nn.BatchNorm2D(width // 2) + + self.conv2 = nn.Conv2D( + width // 2, width // 2, kernel_size=3, padding=1, bias_attr=False) + self.bn2 = nn.BatchNorm2D(width // 2) + + self.conv3 = nn.Conv2D( + width // 2, width, kernel_size=3, padding=1, bias_attr=False) + self.bn3 = nn.BatchNorm2D(width) + + self.avgpool = nn.AvgPool2D(2) + self.relu = nn.ReLU() + + # residual layers + self._inplanes = width + self.layer1 = self._make_layer(width, layers[0]) + self.layer2 = self._make_layer(width * 2, layers[1], stride=2) + self.layer3 = self._make_layer(width * 4, layers[2], stride=2) + self.layer4 = self._make_layer(width * 8, layers[3], stride=2) + + embed_dim = width * 32 + self.attnpool = AttentionPool2D(input_resolution // 32, embed_dim, + heads, output_dim) + + def _make_layer(self, planes, blocks, stride=1): + layers = [Bottleneck(self._inplanes, planes, stride)] + + self._inplanes = planes * Bottleneck.expansion + for _ in range(1, blocks): + layers.append(Bottleneck(self._inplanes, planes)) + + return nn.Sequential(*layers) + + def stem(self, x): + for conv, bn in [(self.conv1, self.bn1), (self.conv2, self.bn2), + (self.conv3, self.bn3)]: + x = self.relu(bn(conv(x))) + + x = self.avgpool(x) + return x + + def forward(self, x): + x = self.stem(x) + x = self.layer1(x) + x = self.layer2(x) + x = self.layer3(x) + x = self.layer4(x) + x = self.attnpool(x) + return x + + +class ResidualAttentionBlock(nn.Layer): + def __init__(self, d_model, n_head, attn_mask=None): + super().__init__() + self.attn = Attention(d_model, n_head) + self.ln_1 = nn.LayerNorm(d_model) + self.mlp = nn.Sequential(("c_fc", nn.Linear(d_model, d_model * 4)), + ("gelu", QuickGELU()), + ("c_proj", nn.Linear(d_model * 4, d_model))) + self.ln_2 = nn.LayerNorm(d_model) + self.attn_mask = attn_mask + + def attention(self, x): + return self.attn(x, attn_mask=self.attn_mask) + + def forward(self, x): + x = x + self.attention(self.ln_1(x)) + x = x + self.mlp(self.ln_2(x)) + return x + + +class Transformer(nn.Layer): + def __init__(self, width, layers, heads, attn_mask=None): + super().__init__() + self.width = width + self.layers = layers + self.resblocks = nn.Sequential(*[ + ResidualAttentionBlock(width, heads, attn_mask) + for _ in range(layers) + ]) + + def forward(self, x): + return self.resblocks(x) + + +class VisualTransformer(nn.Layer): + def __init__(self, input_resolution, patch_size, width, layers, heads, + output_dim): + super().__init__() + self.input_resolution = input_resolution + self.output_dim = output_dim + self.conv1 = nn.Conv2D( + in_channels=3, + out_channels=width, + kernel_size=patch_size, + stride=patch_size, + bias_attr=False) + + scale = width**-0.5 + + class_embedding = self.create_parameter( + shape=(width, ), + default_initializer=Assign(scale * paddle.randn((width, )))) + self.add_parameter("class_embedding", class_embedding) + + positional_embedding = self.create_parameter( + shape=(width, ), + default_initializer=Assign(scale * paddle.randn(( + (input_resolution // patch_size)**2 + 1, width)))) + self.add_parameter("positional_embedding", positional_embedding) + + self.ln_pre = nn.LayerNorm(width) + + self.transformer = Transformer(width, layers, heads) + + self.ln_post = nn.LayerNorm(width) + + proj = self.create_parameter( + shape=(width, ), + default_initializer=Assign(scale * paddle.randn(( + (width, output_dim))))) + self.add_parameter("proj", proj) + + def forward(self, x): + x = self.conv1(x) + x = x.reshape((x.shape[0], x.shape[1], -1)) + x = x.transpose((0, 2, 1)) + zeros = paddle.zeros((x.shape[0], 1, x.shape[-1]), dtype='float32') + x = paddle.concat([self.class_embedding + zeros, x], axis=1) + x = x + self.positional_embedding + x = self.ln_pre(x) + x = self.transformer(x) + x = self.ln_post(x[:, 0, :]) + + if self.proj is not None: + x = x @ self.proj + + return x + + +class CLIP(nn.Layer): + def __init__( + self, + embed_dim, + # vision + image_resolution, + vision_layers, + vision_width, + vision_patch_size, + # text + context_length, + vocab_size, + transformer_width, + transformer_heads, + transformer_layers): + super().__init__() + self.context_length = context_length + self.embed_dim = embed_dim + + if isinstance(vision_layers, (tuple, list)): + vision_heads = vision_width * 32 // 64 + self.visual = ModifiedResNet( + layers=vision_layers, + output_dim=embed_dim, + heads=vision_heads, + input_resolution=image_resolution, + width=vision_width) + else: + vision_heads = vision_width // 64 + self.visual = VisualTransformer( + input_resolution=image_resolution, + patch_size=vision_patch_size, + width=vision_width, + layers=vision_layers, + heads=vision_heads, + output_dim=embed_dim) + + self.transformer = Transformer( + width=transformer_width, + layers=transformer_layers, + heads=transformer_heads, + attn_mask=self.build_attention_mask()) + + self.vocab_size = vocab_size + self.token_embedding = nn.Embedding(vocab_size, transformer_width) + + positional_embedding = self.create_parameter( + shape=(self.context_length, transformer_width), + default_initializer=Assign( + paddle.empty((self.context_length, transformer_width)))) + self.add_parameter("positional_embedding", positional_embedding) + + self.ln_final = nn.LayerNorm(transformer_width) + + text_projection = self.create_parameter( + shape=(transformer_width, embed_dim), + default_initializer=Assign( + paddle.empty((transformer_width, embed_dim)))) + self.add_parameter("text_projection", text_projection) + + logit_scale = self.create_parameter( + shape=(1, ), default_initializer=Assign(paddle.ones([1]))) + self.add_parameter("logit_scale", logit_scale) + + self.initialize_parameters() + + def initialize_parameters(self): + Normal(std=0.02)(self.token_embedding.weight) + Normal(std=0.01)(self.positional_embedding) + + if isinstance(self.visual, ModifiedResNet): + if self.visual.attnpool is not None: + std = self.embed_dim**-0.5 + normal_ = Normal(std=std) + normal_(self.visual.attnpool.attn.q_proj.weight) + normal_(self.visual.attnpool.attn.k_proj.weight) + normal_(self.visual.attnpool.attn.v_proj.weight) + normal_(self.visual.attnpool.attn.out_proj.weight) + + for resnet_block in [ + self.visual.layer1, self.visual.layer2, self.visual.layer3, + self.visual.layer4 + ]: + for name, param in resnet_block.named_parameters(): + if name.endswith("bn3.weight"): + Constant(value=0.0)(param) + + proj_std = (self.transformer.width ** -0.5) * \ + ((2 * self.transformer.layers) ** -0.5) + attn_std = self.transformer.width**-0.5 + fc_std = (2 * self.transformer.width)**-0.5 + + for resblock in self.transformer.resblocks: + normal_ = Normal(std=attn_std) + normal_(resblock.attn.in_proj.weight) + Normal(std=proj_std)(resblock.attn.out_proj.weight) + Normal(std=fc_std)(resblock.mlp.c_fc.weight) + Normal(std=proj_std)(resblock.mlp.c_proj.weight) + + if self.text_projection is not None: + Normal(std=self.transformer.width**-0.5)(self.text_projection) + + def build_attention_mask(self): + mask = paddle.full((self.context_length, self.context_length), + float("-inf")) + mask = paddle.triu(mask, diagonal=1) + return mask + + def text_global_pool(self, x, text=None, pool_type='first'): + if pool_type == 'first': + pooled, tokens = x[:, 0], x[:, 1:] + elif pool_type == 'last': + pooled, tokens = x[:, -1], x[:, :-1] + elif pool_type == 'argmax': + # take features from the eot embedding (eot_token is the highest number in each sequence) + assert text is not None + index = paddle.to_tensor( [paddle.arange(x.shape[0]), text.argmax(axis=-1)]) + pooled, tokens = paddle.index_select(x, index), x + else: + pooled = tokens = x + + return pooled, tokens + + def encode_image(self, image): + return self.visual(image) + + def encode_text(self, text): + x = self.token_embedding(text) + x = x + self.positional_embedding + x = x.transpose([1, 0, 2]) + x = self.transformer(x) + x = x.transpose([1, 0, 2]) + x = self.ln_final(x) + + x, _ = self.text_global_pool(x , text) + if self.text_projection is not None: + if isinstance(self.text_projection, paddle.nn.Linear): + x = self.text_projection(x) + else: + x = x @ self.text_projection + return x + + def forward(self, image, text): + image_features = self.encode_image(image) + text_features = self.encode_text(text) + + # normalized features + image_features = image_features / \ + image_features.norm(axis=-1, keepdim=True) + text_features = text_features / \ + text_features.norm(axis=-1, keepdim=True) + + # cosine similarity as logits + logit_scale = self.logit_scale.exp() + logits_per_image = logit_scale * image_features @text_features.t() + logits_per_text = logit_scale * text_features @image_features.t() + + # unify the format for paddle loss + results = {"image": logits_per_image, "text":logits_per_text} + return results + + + +def tokenize(texts, tokenizer, context_length=77): + """ + Returns the tokenized representation of given input string(s) + Parameters + ---------- + texts : Union[str, List[str]] + An input string or a list of input strings to tokenize + context_length : int + The context length to use; all CLIP models use 77 as the context length + Returns + ------- + A two-dimensional tensor containing the resulting tokens, shape = [number of input strings, context_length] + """ + if isinstance(texts, str): + texts = [texts] + + sot_token = tokenizer.encoder["<|startoftext|>"] + eot_token = tokenizer.encoder["<|endoftext|>"] + all_tokens = [[sot_token] + tokenizer.encode(text) + [eot_token] + for text in texts] + result = paddle.zeros((len(all_tokens), context_length), dtype='int64') + + for i, tokens in enumerate(all_tokens): + if len(tokens) > context_length: + raise RuntimeError( + f"Input {texts[i]} is too long for context length {context_length}" + ) + result[i, :len(tokens)] = paddle.to_tensor(tokens) + + return result + + +def CLIP_vit_base_patch32_224_with_TextEncoder(**kwargs): + model = CLIP( + embed_dim=512, + image_resolution=224, + vision_layers=12, + vision_width=768, + vision_patch_size=32, + context_length=77, + vocab_size=49408, + transformer_width=512, + transformer_heads=8, + transformer_layers=12) + return model + + +def CLIP_vit_base_patch16_224_with_TextEncoder(**kwargs): + model = CLIP( + embed_dim=512, + image_resolution=224, + vision_layers=12, + vision_width=768, + vision_patch_size=16, + context_length=77, + vocab_size=49408, + transformer_width=512, + transformer_heads=8, + transformer_layers=12) + return model + + +def CLIP_vit_large_patch14_224_with_TextEncoder(**kwargs): + model = CLIP( + embed_dim=768, + image_resolution=224, + vision_layers=24, + vision_width=1024, + vision_patch_size=14, + context_length=77, + vocab_size=49408, + transformer_width=768, + transformer_heads=12, + transformer_layers=12) + return model + + +def CLIP_vit_large_patch16_224_with_TextEncoder(**kwargs): + model = CLIP( + embed_dim=768, + image_resolution=224, + vision_layers=24, + vision_width=1024, + vision_patch_size=16, + context_length=77, + vocab_size=49408, + transformer_width=768, + transformer_heads=12, + transformer_layers=12) + return model + + +CLIP_DICT = { + "vit-b-32-224": CLIP_vit_base_patch32_224_with_TextEncoder(), + "vit-b-16-224": CLIP_vit_base_patch16_224_with_TextEncoder(), + "vit-l-16-224": CLIP_vit_base_patch16_224_with_TextEncoder(), + "vit-l-14-224": CLIP_vit_large_patch14_224_with_TextEncoder(), +} diff --git a/ppcls/arch/clip/tokenizer.py b/ppcls/arch/clip/tokenizer.py new file mode 100644 index 0000000000..084d8148c0 --- /dev/null +++ b/ppcls/arch/clip/tokenizer.py @@ -0,0 +1,160 @@ +# copyright (c) 2023 PaddlePaddle Authors. All Rights Reserve. +# +# 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. +# Code was based on https://github.com/AgentMaker/Paddle-CLIP, https://github.com/openai/CLIP/ +# reference: https://arxiv.org/abs/2103.00020 + +import gzip +import html +import os +from functools import lru_cache + +import ftfy +import regex as re + + +@lru_cache() +def default_bpe(): + return "ppcls/utils/ram/bpe_simple_vocab_16e6.txt.gz" + + +@lru_cache() +def bytes_to_unicode(): + """ + Returns list of utf-8 byte and a corresponding list of unicode strings. + The reversible bpe codes work on unicode strings. + This means you need a large # of unicode characters in your vocab if you want to avoid UNKs. + When you're at something like a 10B token dataset you end up needing around 5K for decent coverage. + This is a signficant percentage of your normal, say, 32K bpe vocab. + To avoid that, we want lookup tables between utf-8 bytes and unicode strings. + And avoids mapping to whitespace/control characters the bpe code barfs on. + """ + bs = list(range(ord("!"), ord("~")+1)) + \ + list(range(ord("¡"), ord("¬")+1)) + \ + list(range(ord("®"), ord("ÿ")+1)) + cs = bs[:] + n = 0 + for b in range(2**8): + if b not in bs: + bs.append(b) + cs.append(2**8 + n) + n += 1 + cs = [chr(n) for n in cs] + return dict(zip(bs, cs)) + + +def get_pairs(word): + """Return set of symbol pairs in a word. + Word is represented as tuple of symbols (symbols being variable-length strings). + """ + pairs = set() + prev_char = word[0] + for char in word[1:]: + pairs.add((prev_char, char)) + prev_char = char + return pairs + + +def basic_clean(text): + text = ftfy.fix_text(text) + text = html.unescape(html.unescape(text)) + return text.strip() + + +def whitespace_clean(text): + text = re.sub(r'\s+', ' ', text) + text = text.strip() + return text + + +class Tokenizer(object): + def __init__(self, bpe_path: str=default_bpe()): + self.byte_encoder = bytes_to_unicode() + self.byte_decoder = {v: k for k, v in self.byte_encoder.items()} + merges = gzip.open(bpe_path).read().decode("utf-8").split('\n') + merges = merges[1:49152 - 256 - 2 + 1] + merges = [tuple(merge.split()) for merge in merges] + vocab = list(bytes_to_unicode().values()) + vocab = vocab + [v + '' for v in vocab] + for merge in merges: + vocab.append(''.join(merge)) + vocab.extend(['<|startoftext|>', '<|endoftext|>']) + self.encoder = dict(zip(vocab, range(len(vocab)))) + self.decoder = {v: k for k, v in self.encoder.items()} + self.bpe_ranks = dict(zip(merges, range(len(merges)))) + self.cache = { + '<|startoftext|>': '<|startoftext|>', + '<|endoftext|>': '<|endoftext|>' + } + self.pat = re.compile( + r"""<\|startoftext\|>|<\|endoftext\|>|'s|'t|'re|'ve|'m|'ll|'d|[\p{L}]+|[\p{N}]|[^\s\p{L}\p{N}]+""", + re.IGNORECASE) + + def bpe(self, token): + if token in self.cache: + return self.cache[token] + word = tuple(token[:-1]) + (token[-1] + '', ) + pairs = get_pairs(word) + + if not pairs: + return token + '' + + while True: + bigram = min( + pairs, key=lambda pair: self.bpe_ranks.get(pair, float('inf'))) + if bigram not in self.bpe_ranks: + break + first, second = bigram + new_word = [] + i = 0 + while i < len(word): + try: + j = word.index(first, i) + new_word.extend(word[i:j]) + i = j + except: + new_word.extend(word[i:]) + break + + if word[i] == first and i < len(word) - 1 and word[ + i + 1] == second: + new_word.append(first + second) + i += 2 + else: + new_word.append(word[i]) + i += 1 + new_word = tuple(new_word) + word = new_word + if len(word) == 1: + break + else: + pairs = get_pairs(word) + word = ' '.join(word) + self.cache[token] = word + return word + + def encode(self, text): + bpe_tokens = [] + text = whitespace_clean(basic_clean(text)).lower() + for token in re.findall(self.pat, text): + token = ''.join(self.byte_encoder[b] + for b in token.encode('utf-8')) + bpe_tokens.extend(self.encoder[bpe_token] + for bpe_token in self.bpe(token).split(' ')) + return bpe_tokens + + def decode(self, tokens): + text = ''.join([self.decoder[token] for token in tokens]) + text = bytearray([self.byte_decoder[c] for c in text]).decode( + 'utf-8', errors="replace").replace('', ' ') + return text diff --git a/ppcls/arch/ram/__init__.py b/ppcls/arch/ram/__init__.py new file mode 100644 index 0000000000..c2bff3a624 --- /dev/null +++ b/ppcls/arch/ram/__init__.py @@ -0,0 +1,2 @@ +from .ram import ram +from .ram_plus import ram_plus \ No newline at end of file diff --git a/ppcls/arch/ram/bert.py b/ppcls/arch/ram/bert.py new file mode 100644 index 0000000000..33f882767f --- /dev/null +++ b/ppcls/arch/ram/bert.py @@ -0,0 +1,1397 @@ +# copyright (c) 2023 PaddlePaddle Authors. All Rights Reserve. +# +# 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. +# Code was based on https://github.com/PaddlePaddle/PaddleNLP/ + +import warnings +from typing import Optional, Tuple +import math +from dataclasses import dataclass + +import paddle +import paddle.nn as nn +import paddle.nn.functional as F +from paddle import Tensor +from paddle.nn import Layer + +try: + from paddle.incubate.nn import FusedTransformerEncoderLayer +except ImportError: + FusedTransformerEncoderLayer = None +from paddlenlp.transformers.model_utils import PretrainedModel, register_base_model, prune_linear_layer, find_pruneable_heads_and_indices, apply_chunking_to_forward +from paddlenlp.layers import Linear as TransposedLinear +from paddlenlp.utils.converter import StateDictNameMapping, init_name_mappings +from paddlenlp.utils.env import CONFIG_NAME +from paddlenlp.transformers.activations import ACT2FN +from paddlenlp.transformers.model_outputs import ( + BaseModelOutputWithPoolingAndCrossAttentions, + BaseModelOutputWithPastAndCrossAttentions, + CausalLMOutputWithCrossAttentions, + MaskedLMOutput,) +from paddlenlp.transformers.bert.configuration import ( + BERT_PRETRAINED_INIT_CONFIGURATION, + BERT_PRETRAINED_RESOURCE_FILES_MAP, + BertConfig, ) + +__all__ = [ + "BertModel", + "BertPretrainedModel", + "BertForPretraining", + "BertPretrainingCriterion", + "BertPretrainingHeads", + "BertLMHeadModel", + "BertForMaskedLM", +] + + +class BertSelfAttention(nn.Layer): + def __init__(self, config, is_cross_attention): + super().__init__() + self.config = config + if config.hidden_size % config.num_attention_heads != 0 and not hasattr( + config, "embedding_size"): + raise ValueError( + "The hidden size (%d) is not a multiple of the number of attention " + "heads (%d)" % + (config.hidden_size, config.num_attention_heads)) + + self.num_attention_heads = config.num_attention_heads + self.attention_head_size = int(config.hidden_size / + config.num_attention_heads) + self.all_head_size = self.num_attention_heads * self.attention_head_size + + self.query = nn.Linear(config.hidden_size, self.all_head_size) + if is_cross_attention: + self.key = nn.Linear(config.encoder_width, self.all_head_size) + self.value = nn.Linear(config.encoder_width, self.all_head_size) + else: + self.key = nn.Linear(config.hidden_size, self.all_head_size) + self.value = nn.Linear(config.hidden_size, self.all_head_size) + + self.dropout = nn.Dropout(config.attention_probs_dropout_prob) + self.position_embedding_type = getattr( + config, "position_embedding_type", "absolute") + if self.position_embedding_type == "relative_key" or self.position_embedding_type == "relative_key_query": + self.max_position_embeddings = config.max_position_embeddings + self.distance_embedding = nn.Embedding( + 2 * config.max_position_embeddings - 1, + self.attention_head_size) + self.save_attention = False + + def save_attn_gradients(self, attn_gradients): + self.attn_gradients = attn_gradients + + def get_attn_gradients(self): + return self.attn_gradients + + def save_attention_map(self, attention_map): + self.attention_map = attention_map + + def get_attention_map(self): + return self.attention_map + + def transpose_for_scores(self, x): + new_x_shape = x.shape[:-1] + [ + self.num_attention_heads, self.attention_head_size + ] + x = x.reshape(new_x_shape) + return x.transpose([0, 2, 1, 3]) + + def forward( + self, + hidden_states, + attention_mask=None, + head_mask=None, + encoder_hidden_states=None, + encoder_attention_mask=None, + past_key_value=None, + output_attentions=False, ): + mixed_query_layer = self.query(hidden_states) + + # If this is instantiated as a cross-attention module, the keys + # and values come from an encoder; the attention mask needs to be + # such that the encoder's padding tokens are not attended to. + is_cross_attention = encoder_hidden_states is not None + + if is_cross_attention: + # print(self.key.weight.shape) + key_layer = self.transpose_for_scores( + self.key(encoder_hidden_states)) + value_layer = self.transpose_for_scores( + self.value(encoder_hidden_states)) + attention_mask = encoder_attention_mask + elif past_key_value is not None: + key_layer = self.transpose_for_scores(self.key(hidden_states)) + value_layer = self.transpose_for_scores(self.value(hidden_states)) + key_layer = paddle.concat([past_key_value[0], key_layer], axis=2) + value_layer = paddle.concat( + [past_key_value[1], value_layer], axis=2) + else: + key_layer = self.transpose_for_scores(self.key(hidden_states)) + value_layer = self.transpose_for_scores(self.value(hidden_states)) + + query_layer = self.transpose_for_scores(mixed_query_layer) + + past_key_value = (key_layer, value_layer) + + # compatible with higher versions of transformers + if key_layer.shape[0] > query_layer.shape[0]: + key_layer = key_layer[:query_layer.shape[0], :, :, :] + attention_mask = attention_mask[:query_layer.shape[0], :, :] + value_layer = value_layer[:query_layer.shape[0], :, :, :] + + # Take the dot product between "query" and "key" to get the raw attention scores. + key = key_layer.transpose([0, 1, 3, 2]) + attention_scores = paddle.matmul(query_layer, key) + + if self.position_embedding_type == "relative_key" or self.position_embedding_type == "relative_key_query": + seq_length = hidden_states.shape[1] + position_ids_l = paddle.arange( + seq_length, dtype=paddle.int32).reshape([-1, 1]) + position_ids_r = paddle.arange( + seq_length, dtype=paddle.int32).reshape([-1, 1]) + distance = position_ids_l - position_ids_r + positional_embedding = self.distance_embedding( + distance + self.max_position_embeddings - 1) + positional_embedding = positional_embedding # fp16 compatibility + + if self.position_embedding_type == "relative_key": + relative_position_scores = paddle.einsum( + "bhld,lrd->bhlr", query_layer, positional_embedding) + attention_scores = attention_scores + relative_position_scores + elif self.position_embedding_type == "relative_key_query": + relative_position_scores_query = paddle.einsum( + "bhld,lrd->bhlr", query_layer, positional_embedding) + relative_position_scores_key = paddle.einsum( + "bhrd,lrd->bhlr", key_layer, positional_embedding) + attention_scores = attention_scores + relative_position_scores_query + relative_position_scores_key + + attention_scores = attention_scores / math.sqrt( + self.attention_head_size) + if attention_mask is not None: + # Apply the attention mask is (precomputed for all layers in BertModel forward() function) + attention_scores = attention_scores + attention_mask + + # Normalize the attention scores to probabilities. + attention_probs = nn.Softmax(axis=-1)(attention_scores) + + if is_cross_attention and self.save_attention: + self.save_attention_map(attention_probs) + + # This is actually dropping out entire tokens to attend to, which might + # seem a bit unusual, but is taken from the original Transformer paper. + attention_probs_dropped = self.dropout(attention_probs) + + # Mask heads if we want to + if head_mask is not None: + attention_probs_dropped = attention_probs_dropped * head_mask + + context_layer = paddle.matmul(attention_probs_dropped, value_layer) + + context_layer = context_layer.transpose([0, 2, 1, 3]) + new_context_layer_shape = context_layer.shape[:-2] + [ + self.all_head_size, + ] + context_layer = context_layer.reshape(new_context_layer_shape) + + outputs = (context_layer, attention_probs) if output_attentions else ( + context_layer, ) + + outputs = outputs + (past_key_value, ) + return outputs + + +class BertSelfOutput(nn.Layer): + def __init__(self, config): + super().__init__() + self.dense = nn.Linear(config.hidden_size, config.hidden_size) + self.LayerNorm = nn.LayerNorm( + config.hidden_size, epsilon=config.layer_norm_eps) + self.dropout = nn.Dropout(config.hidden_dropout_prob) + + def forward(self, hidden_states, input_tensor): + hidden_states = self.dense(hidden_states) + hidden_states = self.dropout(hidden_states) + hidden_states = self.LayerNorm(hidden_states + input_tensor) + return hidden_states + + +class BertAttention(nn.Layer): + def __init__(self, config, is_cross_attention=False): + super().__init__() + self.self = BertSelfAttention(config, is_cross_attention) + self.output = BertSelfOutput(config) + self.pruned_heads = set() + + def prune_heads(self, heads): + if len(heads) == 0: + return + heads, index = find_pruneable_heads_and_indices( + heads, self.self.num_attention_heads, + self.self.attention_head_size, self.pruned_heads) + + # Prune linear layers + self.self.query = prune_linear_layer(self.self.query, index) + self.self.key = prune_linear_layer(self.self.key, index) + self.self.value = prune_linear_layer(self.self.value, index) + self.output.dense = prune_linear_layer(self.output.dense, index, dim=1) + + # Update hyper params and store pruned heads + self.self.num_attention_heads = self.self.num_attention_heads - len( + heads) + self.self.all_head_size = self.self.attention_head_size * self.self.num_attention_heads + self.pruned_heads = self.pruned_heads.union(heads) + + def forward( + self, + hidden_states, + attention_mask=None, + head_mask=None, + encoder_hidden_states=None, + encoder_attention_mask=None, + past_key_value=None, + output_attentions=False, ): + self_outputs = self.self( + hidden_states, + attention_mask, + head_mask, + encoder_hidden_states, + encoder_attention_mask, + past_key_value, + output_attentions, ) + attention_output = self.output(self_outputs[0], hidden_states) + outputs = (attention_output, + ) + self_outputs[1:] # add attentions if we output them + return outputs + + +class BertIntermediate(nn.Layer): + def __init__(self, config): + super().__init__() + self.dense = nn.Linear(config.hidden_size, config.intermediate_size) + if isinstance(config.hidden_act, str): + self.intermediate_act_fn = ACT2FN[config.hidden_act] + else: + self.intermediate_act_fn = config.hidden_act + + def forward(self, hidden_states): + hidden_states = self.dense(hidden_states) + hidden_states = self.intermediate_act_fn(hidden_states) + return hidden_states + + +class BertOutput(nn.Layer): + def __init__(self, config): + super().__init__() + self.dense = nn.Linear(config.intermediate_size, config.hidden_size) + self.LayerNorm = nn.LayerNorm( + config.hidden_size, epsilon=config.layer_norm_eps) + self.dropout = nn.Dropout(config.hidden_dropout_prob) + + def forward(self, hidden_states, input_tensor): + hidden_states = self.dense(hidden_states) + hidden_states = self.dropout(hidden_states) + hidden_states = self.LayerNorm(hidden_states + input_tensor) + return hidden_states + + +class BertLayer(nn.Layer): + def __init__(self, config, layer_num): + super().__init__() + self.config = config + self.chunk_size_feed_forward = config.chunk_size_feed_forward + self.seq_len_dim = 1 + self.attention = BertAttention(config) + self.layer_num = layer_num + if self.config.add_cross_attention: + self.crossattention = BertAttention( + config, is_cross_attention=self.config.add_cross_attention) + self.intermediate = BertIntermediate(config) + self.output = BertOutput(config) + + def forward( + self, + hidden_states, + attention_mask=None, + head_mask=None, + encoder_hidden_states=None, + encoder_attention_mask=None, + past_key_value=None, + output_attentions=False, + mode=None, ): + + if mode == 'tagging': + + assert encoder_hidden_states is not None, "encoder_hidden_states must be given for cross-attention layers" + + cross_attention_outputs = self.crossattention( + hidden_states, + attention_mask, + head_mask, + encoder_hidden_states, + encoder_attention_mask, + output_attentions=output_attentions, ) + attention_output = cross_attention_outputs[0] + outputs = cross_attention_outputs[ + 1:-1] # add cross attentions if we output attention weights + + present_key_value = cross_attention_outputs[-1] + + else: + # decoder uni-directional self-attention cached key/values tuple is at positions 1,2 + self_attn_past_key_value = past_key_value[: + 2] if past_key_value is not None else None + self_attention_outputs = self.attention( + hidden_states, + attention_mask, + head_mask, + output_attentions=output_attentions, + past_key_value=self_attn_past_key_value, ) + attention_output = self_attention_outputs[0] + + outputs = self_attention_outputs[1:-1] + present_key_value = self_attention_outputs[-1] + + if mode == 'multimodal': + assert encoder_hidden_states is not None, "encoder_hidden_states must be given for cross-attention layers" + + cross_attention_outputs = self.crossattention( + attention_output, + attention_mask, + head_mask, + encoder_hidden_states, + encoder_attention_mask, + output_attentions=output_attentions, ) + attention_output = cross_attention_outputs[0] + outputs = outputs + cross_attention_outputs[ + 1: + -1] # add cross attentions if we output attention weights + layer_output = apply_chunking_to_forward( + self.feed_forward_chunk, self.chunk_size_feed_forward, + self.seq_len_dim, attention_output) + outputs = (layer_output, ) + outputs + + outputs = outputs + (present_key_value, ) + + return outputs + + def feed_forward_chunk(self, attention_output): + intermediate_output = self.intermediate(attention_output) + layer_output = self.output(intermediate_output, attention_output) + return layer_output + + +class BertEncoder(nn.Layer): + def __init__(self, config): + super().__init__() + self.config = config + self.layer = nn.LayerList( + [BertLayer(config, i) for i in range(config.num_hidden_layers)]) + self.gradient_checkpointing = False + + def forward( + self, + hidden_states, + attention_mask=None, + head_mask=None, + encoder_hidden_states=None, + encoder_attention_mask=None, + past_key_values=None, + use_cache=None, + output_attentions=False, + output_hidden_states=False, + return_dict=True, + mode='multimodal', ): + all_hidden_states = () if output_hidden_states else None + all_self_attentions = () if output_attentions else None + all_cross_attentions = ( + ) if output_attentions and self.config.add_cross_attention else None + + next_decoder_cache = () if use_cache else None + + for i in range(self.config.num_hidden_layers): + layer_module = self.layer[i] + if output_hidden_states: + all_hidden_states = all_hidden_states + (hidden_states, ) + + layer_head_mask = head_mask[i] if head_mask is not None else None + past_key_value = past_key_values[ + i] if past_key_values is not None else None + + if self.gradient_checkpointing and self.training: + + if use_cache: + use_cache = False + + def create_custom_forward(module): + def custom_forward(*inputs): + return module(*inputs, past_key_value, + output_attentions) + + return custom_forward + + layer_outputs = paddle.utils.checkpoint.checkpoint( + create_custom_forward(layer_module), + hidden_states, + attention_mask, + layer_head_mask, + encoder_hidden_states, + encoder_attention_mask, + mode=mode, ) + else: + layer_outputs = layer_module( + hidden_states, + attention_mask, + layer_head_mask, + encoder_hidden_states, + encoder_attention_mask, + past_key_value, + output_attentions, + mode=mode, ) + + hidden_states = layer_outputs[0] + if use_cache: + next_decoder_cache += (layer_outputs[-1], ) + if output_attentions: + all_self_attentions = all_self_attentions + (layer_outputs[1], + ) + + if output_hidden_states: + all_hidden_states = all_hidden_states + (hidden_states, ) + + if not return_dict: + return tuple(v + for v in [ + hidden_states, + next_decoder_cache, + all_hidden_states, + all_self_attentions, + all_cross_attentions, + ] if v is not None) + return BaseModelOutputWithPastAndCrossAttentions( + last_hidden_state=hidden_states, + past_key_values=next_decoder_cache, + hidden_states=all_hidden_states, + attentions=all_self_attentions, + cross_attentions=all_cross_attentions, ) + + +class BertEmbeddings(Layer): + """ + Include embeddings from word, position and token_type embeddings + """ + + def __init__(self, config: BertConfig): + super(BertEmbeddings, self).__init__() + + self.word_embeddings = nn.Embedding(config.vocab_size, + config.hidden_size) + self.position_embeddings = nn.Embedding(config.max_position_embeddings, + config.hidden_size) + + self.layer_norm = nn.LayerNorm(config.hidden_size) + self.dropout = nn.Dropout(config.hidden_dropout_prob) + + self.register_buffer( + "position_ids", + paddle.arange(config.max_position_embeddings).expand((1, -1))) + self.position_embedding_type = getattr( + config, "position_embedding_type", "absolute") + + def forward( + self, + input_ids: Tensor, + token_type_ids: Optional[Tensor]=None, + position_ids: Optional[Tensor]=None, + inputs_embeds=None, + past_key_values_length: Optional[int]=None, ): + if input_ids is not None: + input_shape = input_ids.shape + else: + input_shape = inputs_embeds.shape[:-1] + + seq_length = input_shape[1] + + if position_ids is None: + position_ids = self.position_ids[:, past_key_values_length: + seq_length + + past_key_values_length] + + if inputs_embeds is None: + inputs_embeds = self.word_embeddings(input_ids) + + if token_type_ids is None: + token_type_ids = paddle.zeros_like(input_ids, dtype="int64") + + input_embedings = self.word_embeddings(input_ids) + position_embeddings = self.position_embeddings(position_ids) + + embeddings = input_embedings + position_embeddings + embeddings = self.layer_norm(embeddings) + embeddings = self.dropout(embeddings) + return embeddings + + +class BertPooler(Layer): + """ + Pool the result of BertEncoder. + """ + + def __init__(self, config: BertConfig): + """init the bert pooler with config & args/kwargs + + Args: + config (BertConfig): BertConfig instance. Defaults to None. + """ + super(BertPooler, self).__init__() + + self.dense = nn.Linear(config.hidden_size, config.hidden_size) + self.activation = nn.Tanh() + self.pool_act = config.pool_act + + def forward(self, hidden_states): + # We "pool" the model by simply taking the hidden state corresponding + # to the first token. + first_token_tensor = hidden_states[:, 0] + pooled_output = self.dense(first_token_tensor) + if self.pool_act == "tanh": + pooled_output = self.activation(pooled_output) + return pooled_output + + +class BertPretrainedModel(PretrainedModel): + """ + An abstract class for pretrained BERT models. It provides BERT related + `model_config_file`, `resource_files_names`, `pretrained_resource_files_map`, + `pretrained_init_configuration`, `base_model_prefix` for downloading and + loading pretrained models. + See :class:`~paddlenlp.transformers.model_utils.PretrainedModel` for more details. + """ + + model_config_file = CONFIG_NAME + config_class = BertConfig + resource_files_names = {"model_state": "model_state.pdparams"} + base_model_prefix = "bert" + + pretrained_init_configuration = BERT_PRETRAINED_INIT_CONFIGURATION + pretrained_resource_files_map = BERT_PRETRAINED_RESOURCE_FILES_MAP + + @classmethod + def _get_name_mappings(cls, + config: BertConfig) -> list[StateDictNameMapping]: + mappings: list[StateDictNameMapping] = [] + model_mappings = [ + "embeddings.word_embeddings.weight", + "embeddings.position_embeddings.weight", + "embeddings.token_type_embeddings.weight", + ["embeddings.LayerNorm.weight", "embeddings.layer_norm.weight"], + ["embeddings.LayerNorm.bias", "embeddings.layer_norm.bias"], + ["pooler.dense.weight", None, "transpose"], + "pooler.dense.bias", + # for TokenClassification + ] + for layer_index in range(config.num_hidden_layers): + layer_mappings = [ + [ + f"encoder.layer.{layer_index}.attention.self.query.weight", + f"encoder.layers.{layer_index}.self_attn.q_proj.weight", + "transpose", + ], + [ + f"encoder.layer.{layer_index}.attention.self.query.bias", + f"encoder.layers.{layer_index}.self_attn.q_proj.bias", + ], + [ + f"encoder.layer.{layer_index}.attention.self.key.weight", + f"encoder.layers.{layer_index}.self_attn.k_proj.weight", + "transpose", + ], + [ + f"encoder.layer.{layer_index}.attention.self.key.bias", + f"encoder.layers.{layer_index}.self_attn.k_proj.bias", + ], + [ + f"encoder.layer.{layer_index}.attention.self.value.weight", + f"encoder.layers.{layer_index}.self_attn.v_proj.weight", + "transpose", + ], + [ + f"encoder.layer.{layer_index}.attention.self.value.bias", + f"encoder.layers.{layer_index}.self_attn.v_proj.bias", + ], + [ + f"encoder.layer.{layer_index}.attention.output.dense.weight", + f"encoder.layers.{layer_index}.self_attn.out_proj.weight", + "transpose", + ], + [ + f"encoder.layer.{layer_index}.attention.output.dense.bias", + f"encoder.layers.{layer_index}.self_attn.out_proj.bias", + ], + [ + f"encoder.layer.{layer_index}.intermediate.dense.weight", + f"encoder.layers.{layer_index}.linear1.weight", + "transpose", + ], + [ + f"encoder.layer.{layer_index}.intermediate.dense.bias", + f"encoder.layers.{layer_index}.linear1.bias" + ], + [ + f"encoder.layer.{layer_index}.attention.output.LayerNorm.weight", + f"encoder.layers.{layer_index}.norm1.weight", + ], + [ + f"encoder.layer.{layer_index}.attention.output.LayerNorm.bias", + f"encoder.layers.{layer_index}.norm1.bias", + ], + [ + f"encoder.layer.{layer_index}.output.dense.weight", + f"encoder.layers.{layer_index}.linear2.weight", + "transpose", + ], + [ + f"encoder.layer.{layer_index}.output.dense.bias", + f"encoder.layers.{layer_index}.linear2.bias" + ], + [ + f"encoder.layer.{layer_index}.output.LayerNorm.weight", + f"encoder.layers.{layer_index}.norm2.weight" + ], + [ + f"encoder.layer.{layer_index}.output.LayerNorm.bias", + f"encoder.layers.{layer_index}.norm2.bias" + ], + ] + model_mappings.extend(layer_mappings) + + init_name_mappings(model_mappings) + + # base-model prefix "BertModel" + if "BertModel" not in config.architectures: + for mapping in model_mappings: + mapping[0] = "bert." + mapping[0] + mapping[1] = "bert." + mapping[1] + + # downstream mappings + if "BertForQuestionAnswering" in config.architectures: + model_mappings.extend( + [["qa_outputs.weight", "classifier.weight", "transpose"], + ["qa_outputs.bias", "classifier.bias"]]) + if ("BertForMultipleChoice" in config.architectures or + "BertForSequenceClassification" in config.architectures or + "BertForTokenClassification" in config.architectures): + model_mappings.extend( + [["classifier.weight", "classifier.weight", "transpose"]]) + + mappings = [ + StateDictNameMapping( + *mapping, index=index) + for index, mapping in enumerate(model_mappings) + ] + return mappings + + def _init_weights(self, layer): + """Initialization hook""" + if isinstance(layer, (nn.Linear, nn.Embedding)): + # In the dygraph mode, use the `set_value` to reset the parameter directly, + # and reset the `state_dict` to update parameter in static mode. + if isinstance(layer.weight, paddle.Tensor): + layer.weight.set_value( + paddle.tensor.normal( + mean=0.0, + std=self.config.initializer_range, + shape=layer.weight.shape, )) + + elif isinstance(layer, nn.LayerNorm): + layer._epsilon = self.config.layer_norm_eps + + +@register_base_model +class BertModel(BertPretrainedModel): + """ + The bare BERT Model transformer outputting raw hidden-states. + + This model inherits from :class:`~paddlenlp.transformers.model_utils.PretrainedModel`. + Refer to the superclass documentation for the generic methods. + + This model is also a Paddle `paddle.nn.Layer `__ subclass. Use it as a regular Paddle Layer + and refer to the Paddle documentation for all matter related to general usage and behavior. + + Args: + config (:class:`BertConfig`): + An instance of BertConfig used to construct BertModel. + """ + + def __init__(self, config: BertConfig, add_pooling_layer=True): + super(BertModel, self).__init__(config) + + self.pad_token_id = config.pad_token_id + self.initializer_range = config.initializer_range + self.embeddings = BertEmbeddings(config) + self.encoder = BertEncoder(config) + if config.fuse and FusedTransformerEncoderLayer is None: + warnings.warn( + "FusedTransformerEncoderLayer is not supported by the running Paddle. " + "The flag fuse_transformer will be ignored. Try Paddle >= 2.3.0" + ) + + self.pooler = BertPooler(config) if add_pooling_layer else None + + def get_input_embeddings(self): + return self.embeddings.word_embeddings + + def get_extended_attention_mask(self, + attention_mask: Tensor, + input_shape: Tuple[int], + is_decoder: bool) -> Tensor: + """ + Makes broadcastable attention and causal masks so that future and masked tokens are ignored. + + Arguments: + attention_mask (:obj:`paddle.Tensor`): + Mask with ones indicating tokens to attend to, zeros for tokens to ignore. + input_shape (:obj:`Tuple[int]`): + The shape of the input to the model. + device: (:obj:`paddle.device`): + The device of the input to the model. + + Returns: + :obj:`paddle.Tensor` The extended attention mask, with a the same dtype as :obj:`attention_mask.dtype`. + """ + # We can provide a self-attention mask of dimensions [batch_size, from_seq_length, to_seq_length] + # ourselves in which case we just need to make it broadcastable to all heads. + if attention_mask.dim() == 3: + extended_attention_mask = attention_mask[:, None, :, :] + elif attention_mask.dim() == 2: + # Provided a padding mask of dimensions [batch_size, seq_length] + # - if the model is a decoder, apply a causal mask in addition to the padding mask + # - if the model is an encoder, make the mask broadcastable to [batch_size, num_heads, seq_length, seq_length] + if is_decoder: + batch_size, seq_length = input_shape + + seq_ids = paddle.arange(seq_length) + causal_mask = seq_ids[None, None, :].tile( + [batch_size, seq_length, 1]) <= seq_ids[None, :, None] + # in case past_key_values are used we need to add a prefix ones mask to the causal mask + # causal and attention masks must have same type with pytorch version < 1.3 + causal_mask = causal_mask.astype(attention_mask.dtype) + + if causal_mask.shape[1] < attention_mask.shape[1]: + prefix_seq_len = attention_mask.shape[ + 1] - causal_mask.shape[1] + causal_mask = paddle.concat( + [ + paddle.ones( + (batch_size, seq_length, prefix_seq_len), + dtype=causal_mask.dtype), + causal_mask, + ], + axis=-1, ) + + extended_attention_mask = causal_mask[:, + None, :, :] * attention_mask[:, + None, + None, :] + else: + extended_attention_mask = attention_mask[:, None, None, :] + else: + raise ValueError( + "Wrong shape for input_ids (shape {}) or attention_mask (shape {})". + format(input_shape, attention_mask.shape)) + + # Since attention_mask is 1.0 for positions we want to attend and 0.0 for + # masked positions, this operation will create a tensor which is 0.0 for + # positions we want to attend and -10000.0 for masked positions. + # Since we are adding it to the raw scores before the softmax, this is + # effectively the same as removing these entirely. + extended_attention_mask = extended_attention_mask.astype( + dtype=paddle.float16) # fp16 compatibility + extended_attention_mask = (1.0 - extended_attention_mask) * -10000.0 + return extended_attention_mask + + def set_input_embeddings(self, value): + self.embeddings.word_embeddings = value + + def get_head_mask(self, + head_mask: Optional[Tensor], + num_hidden_layers: int, + is_attention_chunked: bool=False) -> Tensor: + """ + Prepare the head mask if needed. + + Args: + head_mask (`paddle.Tensor` with shape `[num_heads]` or `[num_hidden_layers x num_heads]`, *optional*): + The mask indicating if we should keep the heads or not (1.0 for keep, 0.0 for discard). + num_hidden_layers (`int`): + The number of hidden layers in the model. + is_attention_chunked: (`bool`, *optional*, defaults to `False`): + Whether or not the attentions scores are computed by chunks or not. + + Returns: + `paddle.Tensor` with shape `[num_hidden_layers x batch x num_heads x seq_length x seq_length]` or list with + `[None]` for each layer. + """ + if head_mask is not None: + head_mask = self._convert_head_mask_to_5d(head_mask, + num_hidden_layers) + if is_attention_chunked is True: + head_mask = head_mask.unsqueeze(-1) + else: + head_mask = [None] * num_hidden_layers + + return head_mask + + def _convert_head_mask_to_5d(self, head_mask, num_hidden_layers): + """-> [num_hidden_layers x batch x num_heads x seq_length x seq_length]""" + if len(head_mask.shape) == 1: + head_mask = head_mask.unsqueeze(0).unsqueeze(0).unsqueeze( + -1).unsqueeze(-1) + head_mask = head_mask.expand([num_hidden_layers, -1, -1, -1, -1]) + elif len(head_mask.shape) == 2: + head_mask = head_mask.unsqueeze(1).unsqueeze(-1).unsqueeze( + -1) # We can specify head_mask for each layer + assert len( + head_mask. + shape) == 5, f"head_mask.dim != 5, instead {len(head_mask.shape)}" + head_mask = head_mask.astype( + dtype=self.dtype) # switch to float if need + fp16 compatibility + return head_mask + + def invert_attention_mask(self, encoder_attention_mask: Tensor) -> Tensor: + """ + Invert an attention mask (e.g., switches 0. and 1.). + + Args: + encoder_attention_mask (`paddle.Tensor`): An attention mask. + + Returns: + `paddle.Tensor`: The inverted attention mask. + """ + if len(encoder_attention_mask.shape) == 3: + encoder_extended_attention_mask = encoder_attention_mask[:, + None, :, :] + if len(encoder_attention_mask.shape) == 2: + encoder_extended_attention_mask = encoder_attention_mask[:, None, + None, :] + # T5 has a mask that can compare sequence ids, we can simulate this here with this transposition + # Cf. https://github.com/tensorflow/mesh/blob/8d2465e9bc93129b913b5ccc6a59aa97abd96ec6/mesh_tensorflow + # /transformer/transformer_layers.py#L270 + # encoder_extended_attention_mask = (encoder_extended_attention_mask == + # encoder_extended_attention_mask.transpose(-1, -2)) + + encoder_extended_attention_mask = ( + 1.0 - encoder_extended_attention_mask + ) * paddle.finfo(paddle.float16).min + + return encoder_extended_attention_mask + + def forward( + self, + input_ids: Tensor=None, + token_type_ids: Optional[Tensor]=None, + position_ids: Optional[Tensor]=None, + attention_mask: Optional[Tensor]=None, + past_key_values: Optional[Tuple[Tuple[Tensor]]]=None, + encoder_embeds=None, + head_mask=None, + inputs_embeds=None, + encoder_hidden_states=None, + encoder_attention_mask=None, + use_cache: Optional[bool]=None, + output_hidden_states: Optional[bool]=None, + output_attentions: Optional[bool]=None, + return_dict: Optional[bool]=None, + is_decoder=False, + mode='multimodal', ): + r""" + The BertModel forward method, overrides the `__call__()` special method. + + Args: + input_ids (Tensor): + Indices of input sequence tokens in the vocabulary. They are + numerical representations of tokens that build the input sequence. + Its data type should be `int64` and it has a shape of [batch_size, sequence_length]. + token_type_ids (Tensor, optional): + Segment token indices to indicate different portions of the inputs. + Selected in the range ``[0, type_vocab_size - 1]``. + If `type_vocab_size` is 2, which means the inputs have two portions. + Indices can either be 0 or 1: + + - 0 corresponds to a *sentence A* token, + - 1 corresponds to a *sentence B* token. + + Its data type should be `int64` and it has a shape of [batch_size, sequence_length]. + Defaults to `None`, which means we don't add segment embeddings. + position_ids(Tensor, optional): + Indices of positions of each input sequence tokens in the position embeddings. Selected in the range ``[0, + max_position_embeddings - 1]``. + Shape as `(batch_size, num_tokens)` and dtype as int64. Defaults to `None`. + attention_mask (Tensor, optional): + Mask used in multi-head attention to avoid performing attention on to some unwanted positions, + usually the paddings or the subsequent positions. + Its data type can be int, float and bool. + When the data type is bool, the `masked` tokens have `False` values and the others have `True` values. + When the data type is int, the `masked` tokens have `0` values and the others have `1` values. + When the data type is float, the `masked` tokens have `-INF` values and the others have `0` values. + It is a tensor with shape broadcasted to `[batch_size, num_attention_heads, sequence_length, sequence_length]`. + Defaults to `None`, which means nothing needed to be prevented attention to. + past_key_values (tuple(tuple(Tensor)), optional): + The length of tuple equals to the number of layers, and each inner + tuple haves 4 tensors of shape `(batch_size, num_heads, sequence_length - 1, embed_size_per_head)`) + which contains precomputed key and value hidden states of the attention blocks. + If `past_key_values` are used, the user can optionally input only the last `input_ids` (those that + don't have their past key value states given to this model) of shape `(batch_size, 1)` instead of all + `input_ids` of shape `(batch_size, sequence_length)`. + use_cache (`bool`, optional): + If set to `True`, `past_key_values` key value states are returned. + Defaults to `None`. + output_hidden_states (bool, optional): + Whether to return the hidden states of all layers. + Defaults to `None`. + output_attentions (bool, optional): + Whether to return the attentions tensors of all attention layers. + Defaults to `None`. + return_dict (bool, optional): + Whether to return a :class:`~paddlenlp.transformers.model_outputs.ModelOutput` object. If `False`, the output + will be a tuple of tensors. Defaults to `None`. + + Returns: + An instance of :class:`~paddlenlp.transformers.model_outputs.BaseModelOutputWithPoolingAndCrossAttentions` if + `return_dict=True`. Otherwise it returns a tuple of tensors corresponding + to ordered and not None (depending on the input arguments) fields of + :class:`~paddlenlp.transformers.model_outputs.BaseModelOutputWithPoolingAndCrossAttentions`. + + Example: + .. code-block:: + + import paddle + from paddlenlp.transformers import BertModel, BertTokenizer + + tokenizer = BertTokenizer.from_pretrained('bert-wwm-chinese') + model = BertModel.from_pretrained('bert-wwm-chinese') + + inputs = tokenizer("欢迎使用百度飞桨!") + inputs = {k:paddle.to_tensor([v]) for (k, v) in inputs.items()} + output = model(**inputs) + """ + if is_decoder: + use_cache = use_cache if use_cache is not None else self.config.use_cache + else: + use_cache = False + + if input_ids is not None and inputs_embeds is not None: + raise ValueError( + "You cannot specify both input_ids and inputs_embeds at the same time" + ) + elif input_ids is not None: + input_shape = input_ids.shape + batch_size, seq_length = input_shape + elif inputs_embeds is not None: + input_shape = inputs_embeds.shape[:-1] + batch_size, seq_length = input_shape + elif encoder_embeds is not None: + input_shape = encoder_embeds.shape[:-1] + batch_size, seq_length = input_shape + else: + raise ValueError( + "You have to specify either input_ids or inputs_embeds or encoder_embeds" + ) + return_dict = return_dict if return_dict is not None else self.config.use_return_dict + output_hidden_states = (output_hidden_states + if output_hidden_states is not None else + self.config.output_hidden_states) + output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions + use_cache = use_cache if use_cache is not None else self.config.use_cache + + # If a 2D or 3D attention mask is provided for the cross-attention + # we need to make broadcastable to [batch_size, num_heads, seq_length, seq_length] + if encoder_hidden_states is not None: + if type(encoder_hidden_states) == list: + encoder_batch_size, encoder_sequence_length, _ = encoder_hidden_states[ + 0].shape + else: + encoder_batch_size, encoder_sequence_length, _ = encoder_hidden_states.shape + encoder_hidden_shape = (encoder_batch_size, + encoder_sequence_length) + + if type(encoder_attention_mask) == list: + encoder_extended_attention_mask = [ + self.invert_attention_mask(mask) + for mask in encoder_attention_mask + ] + elif encoder_attention_mask is None: + encoder_attention_mask = paddle.ones(encoder_hidden_shape) + encoder_extended_attention_mask = self.invert_attention_mask( + encoder_attention_mask) + else: + encoder_extended_attention_mask = self.invert_attention_mask( + encoder_attention_mask) + else: + encoder_extended_attention_mask = None + + past_key_values_length = past_key_values[0][0].shape[ + 2] if past_key_values is not None else 0 + if attention_mask is None: + attention_mask = paddle.ones( + (batch_size, seq_length + past_key_values_length)) + + if encoder_embeds is None: + embedding_output = self.embeddings( + input_ids=input_ids, + position_ids=position_ids, + inputs_embeds=inputs_embeds, + past_key_values_length=past_key_values_length, ) + else: + embedding_output = encoder_embeds + + head_mask = self.get_head_mask(head_mask, + self.config.num_hidden_layers) + extended_attention_mask = self.get_extended_attention_mask( + attention_mask, input_shape, is_decoder) + encoder_outputs = self.encoder( + embedding_output, + attention_mask=extended_attention_mask, + head_mask=head_mask, + encoder_hidden_states=encoder_hidden_states, + encoder_attention_mask=encoder_extended_attention_mask, + past_key_values=past_key_values, + use_cache=use_cache, + output_attentions=output_attentions, + output_hidden_states=output_hidden_states, + return_dict=return_dict, + mode=mode, ) + sequence_output = encoder_outputs[0] + pooled_output = self.pooler( + sequence_output) if self.pooler is not None else None + if not return_dict: + return (sequence_output, pooled_output) + encoder_outputs[1:] + return BaseModelOutputWithPoolingAndCrossAttentions( + last_hidden_state=sequence_output, + pooler_output=pooled_output, + past_key_values=encoder_outputs.past_key_values, + hidden_states=encoder_outputs.hidden_states, + attentions=encoder_outputs.attentions, ) + + + + + +class BertLMPredictionHead(Layer): + """ + Bert Model with a `language modeling` head on top for CLM fine-tuning. + """ + + def __init__(self, config: BertConfig): + super(BertLMPredictionHead, self).__init__() + + self.transform = nn.Linear(config.hidden_size, config.hidden_size) + self.activation = getattr(nn.functional, config.hidden_act) + self.layer_norm = nn.LayerNorm(config.hidden_size) + self.decoder = TransposedLinear(config.hidden_size, config.vocab_size) + # link bias to load pretrained weights + self.decoder_bias = self.decoder.bias + + def forward(self, hidden_states, masked_positions=None): + if masked_positions is not None: + hidden_states = paddle.reshape(hidden_states, + [-1, hidden_states.shape[-1]]) + hidden_states = paddle.tensor.gather(hidden_states, + masked_positions) + # gather masked tokens might be more quick + hidden_states = self.transform(hidden_states) + hidden_states = self.activation(hidden_states) + hidden_states = self.layer_norm(hidden_states) + hidden_states = self.decoder(hidden_states) + return hidden_states + + + + + +class BertOnlyMLMHead(nn.Layer): + def __init__(self, config: BertConfig): + super().__init__() + self.predictions = BertLMPredictionHead(config=config) + + def forward(self, sequence_output, masked_positions=None): + prediction_scores = self.predictions(sequence_output, masked_positions) + return prediction_scores + + +class BertForMaskedLM(BertPretrainedModel): + """ + Bert Model with a `masked language modeling` head on top. + + Args: + config (:class:`BertConfig`): + An instance of BertConfig used to construct BertForMaskedLM. + + """ + + def __init__(self, config: BertConfig): + super(BertForMaskedLM, self).__init__(config) + self.bert = BertModel(config) + + self.cls = BertOnlyMLMHead(config=config) + self.tie_weights() + + def get_output_embeddings(self): + return self.cls.predictions.decoder + + def forward( + self, + input_ids: Tensor, + token_type_ids: Optional[Tensor]=None, + position_ids: Optional[Tensor]=None, + attention_mask: Optional[Tensor]=None, + masked_positions: Optional[Tensor]=None, + labels: Optional[Tensor]=None, + output_hidden_states: Optional[bool]=None, + output_attentions: Optional[bool]=None, + return_dict: Optional[bool]=None, ): + r""" + + Args: + input_ids (Tensor): + See :class:`BertModel`. + token_type_ids (Tensor, optional): + See :class:`BertModel`. + position_ids (Tensor, optional): + See :class:`BertModel`. + attention_mask (Tensor, optional): + See :class:`BertModel`. + labels (Tensor of shape `(batch_size, sequence_length)`, optional): + Labels for computing the masked language modeling loss. Indices should be in `[-100, 0, ..., + vocab_size]` (see `input_ids` docstring) Tokens with indices set to `-100` are ignored (masked), the + loss is only computed for the tokens with labels in `[0, ..., vocab_size]` + output_hidden_states (bool, optional): + Whether to return the hidden states of all layers. + Defaults to `None`. + output_attentions (bool, optional): + Whether to return the attentions tensors of all attention layers. + Defaults to `None`. + return_dict (bool, optional): + Whether to return a :class:`~paddlenlp.transformers.model_outputs.MaskedLMOutput` object. If + `False`, the output will be a tuple of tensors. Defaults to `None`. + + Returns: + An instance of :class:`~paddlenlp.transformers.model_outputs.MaskedLMOutput` if `return_dict=True`. + Otherwise it returns a tuple of tensors corresponding to ordered and + not None (depending on the input arguments) fields of :class:`~paddlenlp.transformers.model_outputs.MaskedLMOutput`. + + Example: + .. code-block:: + + import paddle + from paddlenlp.transformers import BertForMaskedLM, BertTokenizer + + tokenizer = BertTokenizer.from_pretrained('bert-base-uncased') + model = BertForMaskedLM.from_pretrained('bert-base-uncased') + + inputs = tokenizer("Welcome to use PaddlePaddle and PaddleNLP!") + inputs = {k:paddle.to_tensor([v]) for (k, v) in inputs.items()} + + logits = model(**inputs) + print(logits.shape) + # [1, 13, 30522] + + """ + return_dict = return_dict if return_dict is not None else self.config.use_return_dict + outputs = self.bert( + input_ids, + token_type_ids=token_type_ids, + position_ids=position_ids, + attention_mask=attention_mask, + output_attentions=output_attentions, + output_hidden_states=output_hidden_states, + return_dict=return_dict, ) + sequence_output = outputs[0] + prediction_scores = self.cls(sequence_output, + masked_positions=masked_positions) + + masked_lm_loss = None + if labels is not None: + loss_fct = paddle.nn.CrossEntropyLoss( + ) # -100 index = padding token + masked_lm_loss = loss_fct( + prediction_scores.reshape((-1, prediction_scores.shape[-1])), + labels.reshape((-1, ))) + if not return_dict: + output = (prediction_scores, ) + outputs[2:] + return (((masked_lm_loss, ) + output) if masked_lm_loss is not None + else (output[0] if len(output) == 1 else output)) + + return MaskedLMOutput( + loss=masked_lm_loss, + logits=prediction_scores, + hidden_states=outputs.hidden_states, + attentions=outputs.attentions, ) + + +class BertLMHeadModel(BertPretrainedModel): + + _keys_to_ignore_on_load_unexpected = [r"pooler"] + _keys_to_ignore_on_load_missing = [ + r"position_ids", r"predictions.decoder.bias" + ] + + def __init__(self, config): + super().__init__(config) + + self.bert = BertModel(config, add_pooling_layer=False) + self.cls = BertOnlyMLMHead(config) + + self.init_weights() + + def get_output_embeddings(self): + return self.cls.predictions.decoder + + def set_output_embeddings(self, new_embeddings): + self.cls.predictions.decoder = new_embeddings + + def forward( + self, + input_ids=None, + attention_mask=None, + position_ids=None, + head_mask=None, + inputs_embeds=None, + encoder_hidden_states=None, + encoder_attention_mask=None, + labels=None, + past_key_values=None, + use_cache=None, + output_attentions=None, + output_hidden_states=None, + return_dict=None, + return_logits=False, + is_decoder=True, + reduction='mean', + mode='multimodal', ): + r""" + encoder_hidden_states (:obj:`paddle.FloatTensor` of shape :obj:`(batch_size, sequence_length, hidden_size)`, `optional`): + Sequence of hidden-states at the output of the last layer of the encoder. Used in the cross-attention if + the model is configured as a decoder. + encoder_attention_mask (:obj:`paddle.FloatTensor` of shape :obj:`(batch_size, sequence_length)`, `optional`): + Mask to avoid performing attention on the padding token indices of the encoder input. This mask is used in + the cross-attention if the model is configured as a decoder. Mask values selected in ``[0, 1]``: + - 1 for tokens that are **not masked**, + - 0 for tokens that are **masked**. + labels (:obj:`paddle.LongTensor` of shape :obj:`(batch_size, sequence_length)`, `optional`): + Labels for computing the left-to-right language modeling loss (next word prediction). Indices should be in + ``[-100, 0, ..., config.vocab_size]`` (see ``input_ids`` docstring) Tokens with indices set to ``-100`` are + ignored (masked), the loss is only computed for the tokens with labels n ``[0, ..., config.vocab_size]`` + past_key_values (:obj:`tuple(tuple(paddle.FloatTensor))` of length :obj:`config.n_layers` with each tuple having 4 tensors of shape :obj:`(batch_size, num_heads, sequence_length - 1, embed_size_per_head)`): + Contains precomputed key and value hidden states of the attention blocks. Can be used to speed up decoding. + If :obj:`past_key_values` are used, the user can optionally input only the last :obj:`decoder_input_ids` + (those that don't have their past key value states given to this model) of shape :obj:`(batch_size, 1)` + instead of all :obj:`decoder_input_ids` of shape :obj:`(batch_size, sequence_length)`. + use_cache (:obj:`bool`, `optional`): + If set to :obj:`True`, :obj:`past_key_values` key value states are returned and can be used to speed up + decoding (see :obj:`past_key_values`). + Returns: + Example:: + >>> from transformers import BertTokenizer, BertLMHeadModel, BertConfig + >>> import paddle + >>> tokenizer = BertTokenizer.from_pretrained('bert-base-cased') + >>> config = BertConfig.from_pretrained("bert-base-cased") + >>> model = BertLMHeadModel.from_pretrained('bert-base-cased', config=config) + >>> inputs = tokenizer("Hello, my dog is cute", return_tensors="pt") + >>> outputs = model(**inputs) + >>> prediction_logits = outputs.logits + """ + return_dict = return_dict if return_dict is not None else self.config.use_return_dict + if labels is not None: + use_cache = False + + outputs = self.bert( + input_ids, + attention_mask=attention_mask, + position_ids=position_ids, + head_mask=head_mask, + inputs_embeds=inputs_embeds, + encoder_hidden_states=encoder_hidden_states, + encoder_attention_mask=encoder_attention_mask, + past_key_values=past_key_values, + use_cache=use_cache, + output_attentions=output_attentions, + output_hidden_states=output_hidden_states, + return_dict=return_dict, + is_decoder=is_decoder, + mode=mode, ) + + sequence_output = outputs[0] + prediction_scores = self.cls(sequence_output) + + if return_logits: + return prediction_scores[:, :-1, :] + + lm_loss = None + if labels is not None: + # we are doing next-token prediction; shift prediction scores and input ids by one + shifted_prediction_scores = prediction_scores[:, :-1, :] + labels = labels[:, 1:] + loss_fct = nn.CrossEntropyLoss(reduction=reduction) + lm_loss = loss_fct( + shifted_prediction_scores.reshape( + [-1, self.config.vocab_size]), labels.reshape([-1])) + if reduction == 'none': + lm_loss = lm_loss.reshape(prediction_scores.shape[0], + -1).sum(1) + + if not return_dict: + output = (prediction_scores, ) + outputs[2:] + return ((lm_loss, ) + output) if lm_loss is not None else output + + return CausalLMOutputWithCrossAttentions( + loss=lm_loss, + logits=prediction_scores, + past_key_values=outputs.past_key_values, + hidden_states=outputs.hidden_states, + attentions=outputs.attentions, + cross_attentions=outputs.cross_attentions, ) + + def prepare_inputs_for_generation(self, + input_ids, + past=None, + attention_mask=None, + **model_kwargs): + input_shape = input_ids.shape + # if model is used as a decoder in encoder-decoder model, the decoder attention mask is created on the fly + if attention_mask is None: + attention_mask = input_ids.new_ones(input_shape) + + # cut decoder_input_ids if past is used + if past is not None: + input_ids = input_ids[:, -1:] + + return { + "input_ids": input_ids, + "attention_mask": attention_mask, + "past_key_values": past, + "encoder_hidden_states": + model_kwargs.get("encoder_hidden_states", None), + "encoder_attention_mask": + model_kwargs.get("encoder_attention_mask", None), + "is_decoder": True, + } + + def _reorder_cache(self, past, beam_idx): + reordered_past = () + for layer_past in past: + reordered_past += (tuple( + past_state.index_select(0, beam_idx) + for past_state in layer_past), ) + return reordered_past diff --git a/ppcls/arch/ram/ram.py b/ppcls/arch/ram/ram.py new file mode 100644 index 0000000000..d24a98b91d --- /dev/null +++ b/ppcls/arch/ram/ram.py @@ -0,0 +1,511 @@ +# copyright (c) 2023 PaddlePaddle Authors. All Rights Reserve. +# +# 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. +# Code was based on https://github.com/xinyu1205/recognize-anything/tree/main +# reference: https://arxiv.org/abs/2306.03514 + +import yaml +import numpy as np + +from paddle import nn +import paddle +from paddlenlp.transformers import BertTokenizer +from paddle.nn import functional as F +from paddle.nn.initializer import Constant +from paddlenlp.transformers.bert.configuration import BertConfig + +from ..backbone.model_zoo.vision_transformer import VisionTransformer +from .bert import BertModel, BertLMHeadModel +from ..clip.clip import CLIP_DICT, tokenize +from ..clip.tokenizer import Tokenizer +from ..backbone.legendary_models.swin_transformer import SwinTransformer + + +class RamVis(VisionTransformer): + def forward_features(self, x): + return x + + +class RamSwin(SwinTransformer): + def forward_features(self, x): + x, output_dimensions = self.patch_embed(x) + if self.ape: + x = x + self.absolute_pos_embed + x = self.pos_drop(x) + + for layer in self.layers: + x, output_dimensions = layer(x, output_dimensions) + + x = self.norm(x) # B L C + x_cls = self.avgpool(x.transpose([0, 2, 1])) # B C 1 + return paddle.concat([x_cls.transpose([0, 2, 1]), x], axis=1) + + def forward(self, x): + x = self.forward_features(x) + return x + + +def RamSwin_large_patch4_window12_384(): + return RamSwin( + img_size=384, + patch_size=4, + in_chans=3, + embed_dim=192, + depths=[2, 2, 18, 2], + num_heads=[6, 12, 24, 48], + window_size=12, + mlp_ratio=4., + qkv_bias=True, + drop_rate=0.0, + drop_path_rate=0.1, + ape=False, + patch_norm=True, + use_checkpoint=False) + + +def RamSwin_large_patch4_window7_224(): + return RamSwin( + img_size=224, + patch_size=4, + in_chans=3, + embed_dim=192, + depths=[2, 2, 18, 2], + num_heads=[6, 12, 24, 48], + window_size=7, + mlp_ratio=4., + qkv_bias=True, + drop_rate=0.0, + drop_path_rate=0.1, + ape=False, + patch_norm=True, + use_checkpoint=False) + + +def RamSwin_base_patch4_window12_384(): + return RamSwin( + img_size=384, + patch_size=4, + in_chans=3, + embed_dim=128, + depths=[2, 2, 18, 2], + num_heads=[4, 8, 16, 32], + window_size=12, + mlp_ratio=4., + qkv_bias=True, + drop_rate=0.0, + drop_path_rate=0.1, + ape=False, + patch_norm=True, + use_checkpoint=False) + + +def RamSwin_base_patch4_window7_224(): + return RamSwin( + img_size=224, + patch_size=4, + in_chans=3, + embed_dim=128, + depths=[2, 2, 18, 2], + num_heads=[4, 8, 16, 32], + window_size=7, + mlp_ratio=4., + qkv_bias=True, + drop_rate=0.0, + drop_path_rate=0.1, + ape=False, + patch_norm=True, + use_checkpoint=False) + + +CONFIG_PATH = 'ppcls' + + +def masked_fill(x, mask, value): + y = paddle.full(x.shape, value, x.dtype) + return paddle.where(mask, y, x) + + +def build_text_embed(model_clip, tokenizer, caption): + with paddle.no_grad(): + texts = tokenize(caption, tokenizer) + text_embeddings = model_clip.encode_text(texts) + text_embeddings /= text_embeddings.norm(axis=-1, keepdim=True) + return text_embeddings + + +class AsymmetricLoss(nn.Layer): + def __init__(self, + gamma_neg=4, + gamma_pos=1, + clip=0.05, + eps=1e-8, + disable_torch_grad_focal_loss=True): + super(AsymmetricLoss, self).__init__() + + self.gamma_neg = gamma_neg + self.gamma_pos = gamma_pos + self.clip = clip + self.disable_torch_grad_focal_loss = disable_torch_grad_focal_loss + self.eps = eps + + def forward(self, x, y): + """" + Parameters + ---------- + x: input logits + y: targets (multi-label binarized vector) + """ + + # Calculating Probabilities + x_sigmoid = nn.functional.sigmoid(x) + xs_pos = x_sigmoid + xs_neg = 1 - x_sigmoid + + # Asymmetric Clipping + if self.clip is not None and self.clip > 0: + xs_neg = (xs_neg + self.clip).clip(max=1) + + # Basic CE calculation + los_pos = y * paddle.log(xs_pos.clip(min=self.eps)) + los_neg = (1 - y) * paddle.log(xs_neg.clip(min=self.eps)) + loss = los_pos + los_neg + + # Asymmetric Focusing + if self.gamma_neg > 0 or self.gamma_pos > 0: + if self.disable_torch_grad_focal_loss: + paddle.set_grad_enabled(False) + pt0 = xs_pos * y + pt1 = xs_neg * (1 - y) # pt = p if t > 0 else 1-p + pt = pt0 + pt1 + one_sided_gamma = self.gamma_pos * y + self.gamma_neg * (1 - y) + one_sided_w = paddle.pow(1 - pt, one_sided_gamma.astype(pt.dtype)) + if self.disable_torch_grad_focal_loss: + paddle.set_grad_enabled(True) + loss *= one_sided_w + + return -loss.sum() + + +def init_tokenizer(tokenizer_name="bert-base-uncased"): + tokenizer = BertTokenizer.from_pretrained(tokenizer_name) + tokenizer.add_special_tokens({'bos_token': '[DEC]'}) + tokenizer.add_special_tokens({'additional_special_tokens': ['[ENC]']}) + tokenizer.enc_token_id = tokenizer.additional_special_tokens_ids[0] + return tokenizer + + +def read_yaml(rpath): + with open(rpath, 'r') as f: + return yaml.safe_load(f) + + +def create_vit(vit, + image_size, + use_grad_checkpointing=False, + ckpt_layer=0, + drop_path_rate=0): + + assert vit in ['base', 'large'], "vit parameter must be base or large" + if vit == 'base': + vision_width = 768 + visual_encoder = RamVis( + img_size=image_size, + patch_size=16, + embed_dim=vision_width, + depth=12, + class_num=0, + num_heads=12, + use_grad_checkpointing=use_grad_checkpointing, + ckpt_layer=ckpt_layer, + drop_path_rate=0 or drop_path_rate) + elif vit == 'large': + vision_width = 1024 + visual_encoder = RamVis( + img_size=image_size, + patch_size=16, + class_num=0, + embed_dim=vision_width, + depth=24, + num_heads=16, + use_grad_checkpointing=use_grad_checkpointing, + ckpt_layer=ckpt_layer, + drop_path_rate=0.1 or drop_path_rate) + return visual_encoder, vision_width + + +class RAM(nn.Layer): + def __init__(self, + med_config='', + image_size=384, + vit='base', + vit_grad_ckpt=False, + vit_ckpt_layer=0, + prompt='', + clip_pretraind=None, + threshold=0.68, + delete_tag_index=[], + tag_list='', + tag_list_chinese='', + clip_version='', + q2l_config='', + ram_class_threshold_path='', + pretrained='', + stage='eval'): + + super().__init__() + + # create image encoder + self.stage = stage + if self.stage == 'train': + assert clip_pretraind + self.clip_tokenizer = Tokenizer() + assert clip_version in CLIP_DICT.keys( + ), 'please check the clip structure' + self.CLIP = CLIP_DICT[clip_version] + params = paddle.load(clip_pretraind) + self.CLIP.set_state_dict(params) + self.CLIP.eval() + + if vit == 'swin_b': + vision_width = 1024 + if image_size == 224: + self.visual_encoder = RamSwin_base_patch4_window7_224() + elif image_size == 384: + self.visual_encoder = RamSwin_base_patch4_window12_384() + + elif vit == 'swin_l': + vision_width = 1536 + if image_size == 224: + self.visual_encoder = RamSwin_large_patch4_window7_224() + elif image_size == 384: + self.visual_encoder = RamSwin_large_patch4_window12_384() + + else: + self.visual_encoder, vision_width = create_vit( + vit, image_size, vit_grad_ckpt, vit_ckpt_layer) + + # create tokenzier + self.tokenizer = init_tokenizer() + + # Tag2Text employ encoder-decoder architecture for image-tag-text generation: image-tag interaction encoder and image-tag-text decoder + # create image-tag interaction encoder + encoder_config = BertConfig.from_dict(read_yaml(med_config)) + encoder_config.encoder_width = 512 + self.tag_encoder = BertModel( + config=encoder_config, add_pooling_layer=False) + + # create image-tag-text decoder + decoder_config = BertConfig.from_dict(read_yaml(med_config)) + self.text_decoder = BertLMHeadModel(config=decoder_config) + + self.delete_tag_index = delete_tag_index + self.prompt = prompt + self.prompt_length = len(self.tokenizer(self.prompt).input_ids) - 1 + + # load tag list + self.tag_list = self.load_tag_list(tag_list) + self.tag_list_chinese = self.load_tag_list(tag_list_chinese) + + # create image-tag recognition decoder + self.threshold = threshold + self.num_class = len(self.tag_list) + q2l_config = BertConfig.from_dict(read_yaml(q2l_config)) + q2l_config.encoder_width = 512 + self.tagging_head = BertModel( + config=q2l_config, add_pooling_layer=False) + self.tagging_head.resize_token_embeddings(len(self.tokenizer)) + self.label_embed = self.create_parameter( + shape=(self.num_class, q2l_config.encoder_width), + default_initializer=Constant()) + + if q2l_config.hidden_size != 512: + self.wordvec_proj = nn.Linear(512, q2l_config.hidden_size) + else: + self.wordvec_proj = nn.Identity() + + self.fc = nn.Linear(q2l_config.hidden_size, 1) + + self.del_selfattention() + + self.tagging_loss_function = AsymmetricLoss( + gamma_neg=7, gamma_pos=0, clip=0.05) + + self.image_proj = nn.Linear(vision_width, 512) + + # adjust thresholds for some tags + self.class_threshold = paddle.ones([self.num_class]) * self.threshold + ram_class_threshold_path = ram_class_threshold_path + with open(ram_class_threshold_path, 'r', encoding='utf-8') as f: + ram_class_threshold = [float(s.strip()) for s in f] + for key, value in enumerate(ram_class_threshold): + self.class_threshold[key] = value + + def load_tag_list(self, tag_list_file): + with open(tag_list_file, 'r', encoding='utf-8') as f: + tag_list = f.read().splitlines() + tag_list = np.array(tag_list) + return tag_list + + # delete self-attention layer of image-tag recognition decoder to reduce computation, follower Query2Label + def del_selfattention(self): + del self.tagging_head.embeddings + for layer in self.tagging_head.encoder.layer: + del layer.attention + + def forward(self, + image_ram, + text=None, + image_tag=None, + tag_input_tokenzier=None, + image_clip=None): + """ + image-》 image_ram + image224 -> image_clip + call function as forward + + Args: + image: type: paddle.Tensor shape: batch_size * 3 * 384 * 384 + caption: type: paddle.Tensor len: batch_size * embedding_size + tag: type: paddle.Tensor shape: batch * class_num (e.g. 3429) value: positive sample is 1.0, negative sample is 0.0 + + Returns: + loss: type: paddle.Tensor + """ + assert self.stage == 'train' + label_embed = nn.functional.relu(self.wordvec_proj(self.label_embed)) + clip_feature = self.CLIP.encode_image(image_clip) + + image_embeds = self.image_proj(self.visual_encoder(image_ram)) + image_atts = paddle.ones( + paddle.shape(image_embeds)[:-1], dtype=paddle.int32) + + ##================= Distillation from CLIP ================## + image_cls_embeds = image_embeds[:, 0, :] + image_spatial_embeds = image_embeds[:, 1:, :] + + loss_dis = 0. + if isinstance(clip_feature, paddle.Tensor): + loss_dis = F.l1_loss(image_cls_embeds, clip_feature) + +##================= Image Tagging ================## + bs = paddle.shape(image_embeds)[0] + #label_embed = paddle.repeat_interleave(label_embed.unsqueeze(0),[bs, 1, 1]) + label_embed = label_embed.unsqueeze(0).tile([bs, 1, 1]).squeeze(1) + + tagging_embed = self.tagging_head( + encoder_embeds=label_embed, + encoder_hidden_states=image_embeds, + encoder_attention_mask=image_atts, + return_dict=False, + mode='tagging', ) + + logits = self.fc(tagging_embed[0]).squeeze(-1) + loss_tag = 0. + if isinstance(image_tag, paddle.Tensor): + loss_tag = self.tagging_loss_function(logits, image_tag) + + ##================= Image-Tag-Text Generation ================## + encoder_input_ids = tag_input_tokenzier.get("input_ids") + encoder_input_ids[:, 0] = self.tokenizer.enc_token_id + + # put input tag into image-tag interaction encoder to interact with image embeddings + output_tagembedding = self.tag_encoder( + encoder_input_ids, + attention_mask=tag_input_tokenzier.get("attention_mask"), + encoder_hidden_states=image_embeds, + encoder_attention_mask=image_atts, + return_dict=True, ) + + decoder_input_ids = text.get("input_ids") + decoder_input_ids[:, 0] = self.tokenizer.enc_token_id + + decoder_targets = masked_fill( + decoder_input_ids, + decoder_input_ids == self.tokenizer.pad_token_id, -100) + decoder_targets[:, :self.prompt_length] = -100 + + decoder_output = self.text_decoder( + decoder_input_ids, + attention_mask=text.get("attention_mask"), + encoder_hidden_states=output_tagembedding.last_hidden_state, + encoder_attention_mask=None, + labels=decoder_targets, + return_dict=True, ) + + loss_t2t = decoder_output.loss + + return loss_t2t, loss_tag, loss_dis + + # to support paddle framework + def inference( + self, + image, + threshold=0.4, + tag_input=None, ): + + label_embed = F.relu(self.wordvec_proj(self.label_embed)) + + image_embeds = self.image_proj(self.visual_encoder(image)) + image_atts = paddle.ones(image_embeds.shape[:-1], dtype=paddle.int32) + + # recognized image tags using image-tag recogntiion decoder + image_cls_embeds = image_embeds[:, 0, :] + image_spatial_embeds = image_embeds[:, 1:, :] + + bs = image_spatial_embeds.shape[0] + label_embed = label_embed.unsqueeze(0).tile([bs, 1, 1]).squeeze(1) + tagging_embed = self.tagging_head( + encoder_embeds=label_embed, + encoder_hidden_states=image_embeds, + encoder_attention_mask=image_atts, + return_dict=False, + mode='tagging', ) + + logits = self.fc(tagging_embed[0]).squeeze(-1) + + return logits + + def generate_tag_openset( + self, + image, + threshold=0.68, + tag_input=None, ): + + label_embed = F.relu(self.wordvec_proj(self.label_embed)) + + image_embeds = self.image_proj(self.visual_encoder(image)) + image_atts = paddle.ones( + [image_embeds.size()[:-1]], dtype=paddle.int32) + + # recognized image tags using image-tag recogntiion decoder + image_cls_embeds = image_embeds[:, 0, :] + image_spatial_embeds = image_embeds[:, 1:, :] + + bs = image_spatial_embeds.shape[0] + label_embed = label_embed.unsqueeze(0).repeat([bs, 1, 1]) + tagging_embed = self.tagging_head( + encoder_embeds=label_embed, + encoder_hidden_states=image_embeds, + encoder_attention_mask=image_atts, + return_dict=False, + mode='tagging', ) + + logits = self.fc(tagging_embed[0]).squeeze(-1) + + return logits + + +# load RAM pretrained model parameters +def ram(pretrained='', **kwargs): + model = RAM(pretrained='', **kwargs) + return model diff --git a/ppcls/arch/ram/ram_plus.py b/ppcls/arch/ram/ram_plus.py new file mode 100644 index 0000000000..695e529720 --- /dev/null +++ b/ppcls/arch/ram/ram_plus.py @@ -0,0 +1,249 @@ +# copyright (c) 2023 PaddlePaddle Authors. All Rights Reserve. +# +# 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. +# Code was based on https://github.com/xinyu1205/recognize-anything/tree/main +# reference: https://arxiv.org/abs/2310.15200 + +import paddle +import numpy as np +from paddle import nn +from paddle.nn import functional as F +from paddle.nn.initializer import Constant + +from .bert import BertModel, BertLMHeadModel +from ..clip.clip import tokenize +from .ram import RAM, AsymmetricLoss + + +def build_text_embed(model_clip, texts): + with paddle.no_grad(): + text_embeddings = model_clip.encode_text(texts) + text_embeddings /= text_embeddings.norm(axis=-1, keepdim=True) + return text_embeddings + + +class RAM_plus(RAM): + def __init__(self, + med_config='', + image_size=None, + vit='', + vit_grad_ckpt=False, + vit_ckpt_layer=0, + prompt='', + threshold=0.68, + delete_tag_index=[], + clip_pretraind='', + tag_list='', + tag_list_chinese='', + clip_version='', + q2l_config='', + ram_class_threshold_path='', + pretrained='', + stage='eval'): + + super().__init__( + med_config=med_config, + image_size=image_size, + vit=vit, + vit_grad_ckpt=vit_grad_ckpt, + vit_ckpt_layer=vit_ckpt_layer, + prompt=prompt, + threshold=threshold, + delete_tag_index=delete_tag_index, + clip_pretraind=clip_pretraind, + tag_list=tag_list, + clip_version=clip_version, + tag_list_chinese=tag_list_chinese, + q2l_config=q2l_config, + ram_class_threshold_path=ram_class_threshold_path, + stage=stage) + + self.label_embed = self.create_parameter( + shape=(self.num_class * 51, 512), default_initializer=Constant()) + self.reweight_scale = self.create_parameter( + shape=(1, ), default_initializer=Constant(1. * np.log(1 / 0.07))) + self.text_alignment_loss_function = AsymmetricLoss( + gamma_neg=4, gamma_pos=0, clip=0.05) + + def forward(self, image_ram, caption, image_tag, parse_tag, + imageclip=None): + """ + call function as forward + + Args: + image_ram: type: paddle.Tensor shape: batch_size * 3 * 384 * 384 + caption: type: list[string] len: batch_size + image_tag: type: paddle.Tensor shape: batch * class_num (e.g. 3429) value: positive sample is 1.0, negative sample is 0.0 + parse_tag: text for image_tag + imageclip = image for clip encoder + + Returns: + loss: type: paddle.Tensor + """ + assert self.stage == 'train' + clip_feature = self.CLIP.encode_image(imageclip) + batch_text_embed = build_text_embed(self.CLIP, caption) + image_embeds = self.image_proj(self.visual_encoder(image_ram)) + image_atts = paddle.ones(image_embeds.shape[:-1], dtype=paddle.int32) + ##================= Distillation from CLIP ================## + image_cls_embeds = image_embeds[:, 0, :] + image_spatial_embeds = image_embeds[:, 1:, :] + + loss_dis = F.l1_loss(image_cls_embeds, clip_feature) + + ##================= Image Tagging ================## + bs = paddle.shape(image_embeds)[0] + des_per_class = int(self.label_embed.shape[0] / self.num_class) + image_cls_embeds = image_cls_embeds / image_cls_embeds.norm( + axis=-1, keepdim=True) + reweight_scale = self.reweight_scale.exp() + logits_per_image = (reweight_scale * image_cls_embeds + @self.label_embed.t()) + logits_per_image = logits_per_image.reshape([bs, -1, des_per_class]) + weight_normalized = nn.functional.softmax(logits_per_image, axis=2) + label_embed_reweight = paddle.empty([bs, self.num_class, 512]) + for i in range(bs): + reshaped_value = self.label_embed.reshape([-1, des_per_class, 512]) + product = weight_normalized[i].unsqueeze(-1) * reshaped_value + label_embed_reweight[i] = product.sum(axis=1) + + label_embed = nn.functional.relu( + self.wordvec_proj(label_embed_reweight)) + + tagging_embed = self.tagging_head( + encoder_embeds=label_embed, + encoder_hidden_states=image_embeds, + encoder_attention_mask=image_atts, + return_dict=False, + mode='tagging', ) + + logits = self.fc(tagging_embed[0]).squeeze(-1) + loss_tag = self.tagging_loss_function(logits, image_tag) + + ##================= Image-text Alignment ================## + + batch_text_embed = F.relu( + self.wordvec_proj( + batch_text_embed.astype(self.label_embed.dtype))) + batch_text_embed = batch_text_embed.unsqueeze(0).tile([bs, 1, 1]) + alignment_embedding = self.tagging_head( + encoder_embeds=batch_text_embed, + encoder_hidden_states=image_embeds, + encoder_attention_mask=image_atts, + return_dict=False, + mode='tagging', ) + alignment_logits = self.fc(alignment_embedding[0]).squeeze(-1) + + with paddle.no_grad(): + alignment_targets = paddle.zeros(alignment_logits.shape) + alignment_targets.fill_diagonal_(1) + + loss_alignment = self.text_alignment_loss_function(alignment_logits, + alignment_targets) + + return loss_tag, loss_dis, loss_alignment + + # to support paddle framework + def inference(self, image): + + image_embeds = self.image_proj(self.visual_encoder(image)) + image_atts = paddle.ones(image_embeds.shape[:-1], dtype=paddle.int32) + + image_cls_embeds = image_embeds[:, 0, :] + image_spatial_embeds = image_embeds[:, 1:, :] + + bs = image_spatial_embeds.shape[0] + + des_per_class = int(self.label_embed.shape[0] / self.num_class) + + image_cls_embeds = image_cls_embeds / image_cls_embeds.norm( + axis=-1, keepdim=True) + reweight_scale = self.reweight_scale.exp() + logits_per_image = (reweight_scale * image_cls_embeds + @self.label_embed.t()) + logits_per_image = logits_per_image.reshape([bs, -1, des_per_class]) + + weight_normalized = F.softmax(logits_per_image, axis=2) + label_embed_reweight = paddle.empty([bs, self.num_class, 512]) + + for i in range(bs): + # boardingcast + reshaped_value = self.label_embed.reshape([-1, des_per_class, 512]) + product = weight_normalized[i].unsqueeze(-1) * reshaped_value + label_embed_reweight[i] = product.sum(axis=1) + + label_embed = F.relu(self.wordvec_proj(label_embed_reweight)) + + # recognized image tags using alignment decoder + tagging_embed = self.tagging_head( + encoder_embeds=label_embed, + encoder_hidden_states=image_embeds, + encoder_attention_mask=image_atts, + return_dict=False, + mode='tagging', ) + + logits = self.fc(tagging_embed[0]).squeeze(-1) + + return logits + + def generate_tag_openset( + self, + image, + threshold=0.68, + tag_input=None, ): + + image_embeds = self.image_proj(self.visual_encoder(image)) + image_atts = paddle.ones(image_embeds.shape[:-1], dtype=paddle.int32) + + image_cls_embeds = image_embeds[:, 0, :] + image_spatial_embeds = image_embeds[:, 1:, :] + + bs = image_spatial_embeds.shape[0] + + des_per_class = int(self.label_embed.shape[0] / self.num_class) + + image_cls_embeds = image_cls_embeds / image_cls_embeds.norm( + axis=-1, keepdim=True) + reweight_scale = self.reweight_scale.exp() + logits_per_image = (reweight_scale * image_cls_embeds + @self.label_embed.t()) + logits_per_image = logits_per_image.reshape([bs, -1, des_per_class]) + + weight_normalized = F.softmax(logits_per_image, axis=2) + label_embed_reweight = paddle.empty([bs, self.num_class, 512]) + + for i in range(bs): + # boardingcast + reshaped_value = self.label_embed.reshape([-1, des_per_class, 512]) + product = weight_normalized[i].unsqueeze(-1) * reshaped_value + label_embed_reweight[i] = product.sum(axis=1) + + label_embed = F.relu(self.wordvec_proj(label_embed_reweight)) + + # recognized image tags using alignment decoder + tagging_embed = self.tagging_head( + encoder_embeds=label_embed, + encoder_hidden_states=image_embeds, + encoder_attention_mask=image_atts, + return_dict=False, + mode='tagging', ) + + logits = self.fc(tagging_embed[0]).squeeze(-1) + + return logits + + +# load RAM pretrained model parameters +def ram_plus(pretrained='', **kwargs): + model = RAM_plus(pretrained='', **kwargs) + return model diff --git a/ppcls/configs/RAM/CLIP.yaml b/ppcls/configs/RAM/CLIP.yaml new file mode 100644 index 0000000000..6cadf224ae --- /dev/null +++ b/ppcls/configs/RAM/CLIP.yaml @@ -0,0 +1,124 @@ +# global configs +Global: + checkpoints: null + pretrained_model: "ViT-B-32.pdparams" # pretrain model for ram and ram plus, default random initilize + output_dir: ./output/ + device: cpu + save_interval: 1 + eval_during_train: True + eval_interval: 1 + epochs: 120 + print_batch_step: 1 + use_visualdl: False + # used for static mode and model export + image_shape: [3, 224, 224] + save_inference_dir: ./inference_text + + + +# model architecture +Arch: + name: CLIP_vit_base_patch32_224_with_TextEncoder + clip: "text" + +# data loader for train and eval +DataLoader: + Train: + dataset: + name: RAMPretrainDataset + ann_file: [./visual-genome/vg_ram.json] + tag_list: 'ppcls/utils/ram/ram_tag_list.txt' + model_name: "RAM" + transform_ops_ram: + - ResizeImage: + size: 384 + interpolation: bicubic + backend: pil + - NormalizeImage: + scale: 1.0/255.0 + mean: [0.485, 0.456, 0.406] + std: [0.229, 0.224, 0.225] + order: '' + - ToCHWImage: + transform_ops_clip: + - ResizeImage: + size: 224 + interpolation: bicubic + backend: pil + - NormalizeImage: + scale: 1.0/255.0 + mean: [0.48145466, 0.4578275, 0.40821073] + std: [0.26862954, 0.26130258, 0.27577711] + order: '' + - ToCHWImage: + + sampler: + name: DistributedBatchSampler + batch_size: 52 + drop_last: False + shuffle: True + loader: + num_workers: 4 + use_shared_memory: True + + Eval: + dataset: + name: RAMPretrainDataset + ann_file: [./visual-genome/vg_ram.json] + tag_list: 'ppcls/utils/ram/ram_tag_list.txt' + model_name: "RAM" + transform_ops_ram: + - ResizeImage: + size: 384 + interpolation: bicubic + backend: pil + - NormalizeImage: + scale: 1.0/255.0 + mean: [0.485, 0.456, 0.406] + std: [0.229, 0.224, 0.225] + order: '' + - ToCHWImage: + transform_ops_clip: + - ResizeImage: + size: 224 + interpolation: bicubic + backend: pil + - NormalizeImage: + scale: 1.0/255.0 + mean: [0.48145466, 0.4578275, 0.40821073] + std: [0.26862954, 0.26130258, 0.27577711] + order: '' + - ToCHWImage: + + sampler: + name: DistributedBatchSampler + batch_size: 52 + drop_last: False + shuffle: True + loader: + num_workers: 4 + use_shared_memory: True + +Infer: + infer_imgs: docs/images/inference_deployment/whl_demo.jpg + batch_size: 1 + transforms: + - DecodeImage: + to_rgb: True + channel_first: False + - ResizeImage: + resize_short: 224 + - CropImage: + size: 224 + - NormalizeImage: + scale: 1.0/255.0 + mean: [0.485, 0.456, 0.406] + std: [0.229, 0.224, 0.225] + order: '' + - ToCHWImage: + PostProcess: + name: RamOutPut + language: "cn" + tag_list: "ppcls/utils/ram/ram_tag_list.txt" + tag_list_chinese: "ppcls/utils/ram/ram_tag_list_chinese.txt" + ram_class_threshold_path: "ppcls/utils/ram/ram_tag_list_threshold.txt" diff --git a/ppcls/configs/ram/RAM.yaml b/ppcls/configs/ram/RAM.yaml new file mode 100644 index 0000000000..a33581140f --- /dev/null +++ b/ppcls/configs/ram/RAM.yaml @@ -0,0 +1,172 @@ +# global configs +Global: + checkpoints: null + pretrained_model: "ram.pdparams" # pretrain model for ram and ram plus, default random initilize + output_dir: ./output/ + device: cpu + save_interval: 1 + eval_during_train: True + eval_interval: 1 + epochs: 120 + print_batch_step: 1 + use_visualdl: False + # used for static mode and model export + image_shape: [3, 384, 384] + save_inference_dir: ./inference + + +# mixed precision +AMP: + use_amp: True + use_fp16_test: False + scale_loss: 128.0 + use_dynamic_loss_scaling: True + use_promote: False + # O1: mixed fp16, O2: pure fp16 + level: O1 + + +# model architecture +Arch: + name: ram + vit: swin_l + vit_grad_ckpt: False + vit_ckpt_layer: 0 + image_size: 384 + prompt: 'a picture of ' + med_config: 'ppcls/configs/ram/RAM_bert.yaml' + delete_tag_index: [] + tag_list: 'ppcls/utils/ram/ram_tag_list.txt' + tag_list_chinese: 'ppcls/utils/ram/ram_tag_list_chinese.txt' + clip_pretraind: ./ViT-B-32.pdparams #for CLIP a necessary part for training ram + clip_version: 'vit-b-32-224' + q2l_config: 'ppcls/configs/ram/RAM_q2l.yaml' + ram_class_threshold_path: 'ppcls/utils/ram/ram_tag_list_threshold.txt' + stage: train + threshold: 0.68 + +# loss function config for traing/eval process +Loss: + Train: + - RAMLoss: + weight: 1.0 + Eval: + - RAMLoss: + weight: 1.0 + +Optimizer: + name: AdamW + beta1: 0.9 + beta2: 0.999 + epsilon: 1e-8 + weight_decay: 0.05 + layerwise_decay: 0.6 + filter_bias_and_bn: True + lr: + name: Cosine + learning_rate: 0.0004 + eta_min: 1e-6 + warmup_epoch: 10 + warmup_start_lr: 5e-7 + +# data loader for train and eval +DataLoader: + Train: + dataset: + name: RAMPretrainDataset + ann_file: [./visual-genome/vg_ram.json] + tag_list: 'ppcls/utils/ram/ram_tag_list.txt' + model_name: "RAM" + transform_ops_ram: + - ResizeImage: + size: 384 + interpolation: bicubic + backend: pil + - NormalizeImage: + scale: 1.0/255.0 + mean: [0.485, 0.456, 0.406] + std: [0.229, 0.224, 0.225] + order: '' + - ToCHWImage: + transform_ops_clip: + - ResizeImage: + size: 224 + interpolation: bicubic + backend: pil + - NormalizeImage: + scale: 1.0/255.0 + mean: [0.48145466, 0.4578275, 0.40821073] + std: [0.26862954, 0.26130258, 0.27577711] + order: '' + - ToCHWImage: + + sampler: + name: DistributedBatchSampler + batch_size: 52 + drop_last: False + shuffle: True + loader: + num_workers: 4 + use_shared_memory: True + + Eval: + dataset: + name: RAMPretrainDataset + ann_file: [./visual-genome/vg_ram.json] + tag_list: 'ppcls/utils/ram/ram_tag_list.txt' + model_name: "RAM" + transform_ops_ram: + - ResizeImage: + size: 384 + interpolation: bicubic + backend: pil + - NormalizeImage: + scale: 1.0/255.0 + mean: [0.485, 0.456, 0.406] + std: [0.229, 0.224, 0.225] + order: '' + - ToCHWImage: + transform_ops_clip: + - ResizeImage: + size: 224 + interpolation: bicubic + backend: pil + - NormalizeImage: + scale: 1.0/255.0 + mean: [0.48145466, 0.4578275, 0.40821073] + std: [0.26862954, 0.26130258, 0.27577711] + order: '' + - ToCHWImage: + + sampler: + name: DistributedBatchSampler + batch_size: 52 + drop_last: False + shuffle: True + loader: + num_workers: 4 + use_shared_memory: True + +Infer: + infer_imgs: docs/images/inference_deployment/whl_demo.jpg + batch_size: 1 + transforms: + - DecodeImage: + to_rgb: True + channel_first: False + - ResizeImage: + resize_short: 384 + - CropImage: + size: 384 + - NormalizeImage: + scale: 1.0/255.0 + mean: [0.485, 0.456, 0.406] + std: [0.229, 0.224, 0.225] + order: '' + - ToCHWImage: + PostProcess: + name: RamOutPut + language: "cn" + tag_list: "ppcls/utils/ram/ram_tag_list.txt" + tag_list_chinese: "ppcls/utils/ram/ram_tag_list_chinese.txt" + ram_class_threshold_path: "ppcls/utils/ram/ram_tag_list_threshold.txt" diff --git a/ppcls/configs/ram/RAM_bert.yaml b/ppcls/configs/ram/RAM_bert.yaml new file mode 100644 index 0000000000..18287a458e --- /dev/null +++ b/ppcls/configs/ram/RAM_bert.yaml @@ -0,0 +1,20 @@ +architectures: [ + 'BertModel' + ] #default uncased + +attention_probs_dropout_prob: 0.1 # attention dropout rata +hidden_act: gelu # acitvation +hidden_dropout_prob: 0.1 +hidden_size: 768 # embedding size +initializer_range: 0.02 +intermediate_size: 3072 +layer_norm_eps: 1.0e-12 +max_position_embeddings: 512 +model_type: bert +num_attention_heads: 12 +num_hidden_layers: 12 +pad_token_id: 0 +type_vocab_size: 2 +vocab_size: 30524 # text embedding size +encoder_width: 768 +add_cross_attention: true \ No newline at end of file diff --git a/ppcls/configs/ram/RAM_plus.yaml b/ppcls/configs/ram/RAM_plus.yaml new file mode 100644 index 0000000000..12fcbd1ff4 --- /dev/null +++ b/ppcls/configs/ram/RAM_plus.yaml @@ -0,0 +1,176 @@ +# global configs +Global: + checkpoints: null + pretrained_model: "ram_plus.pdparams" # pretrain model for ram and ram plus, default random initilize + output_dir: ./output/ + device: cpu + save_interval: 1 + eval_during_train: True + eval_interval: 1 + epochs: 120 + print_batch_step: 1 + use_visualdl: False + # used for static mode and model export + image_shape: [3, 384, 384] + save_inference_dir: ./inference + + +# mixed precision +AMP: + use_amp: True + use_fp16_test: False + scale_loss: 128.0 + use_dynamic_loss_scaling: True + use_promote: False + # O1: mixed fp16, O2: pure fp16 + level: O1 + + +# model architecture +Arch: + name: ram_plus + vit: swin_l + vit_grad_ckpt: False + vit_ckpt_layer: 0 + image_size: 384 + prompt: 'a picture of ' + med_config: 'ppcls/configs/ram/RAM_bert.yaml' + delete_tag_index: [] + tag_list: 'ppcls/utils/ram/ram_tag_list.txt' + tag_list_chinese: 'ppcls/utils/ram/ram_tag_list_chinese.txt' + clip_pretraind: ./ViT-B-32.pdparams #for CLIP a necessary part for training ram + clip_version: 'vit-b-32-224' + q2l_config: 'ppcls/configs/ram/RAM_q2l.yaml' + ram_class_threshold_path: 'ppcls/utils/ram/ram_tag_list_threshold.txt' + stage: train + threshold: 0.68 + + +# loss function config for traing/eval process +Loss: + Train: + - RAMLoss: + weight: 1.0 + Eval: + - RAMLoss: + weight: 1.0 + + +Optimizer: + name: AdamW + beta1: 0.9 + beta2: 0.999 + epsilon: 1e-8 + weight_decay: 0.05 + layerwise_decay: 0.6 + filter_bias_and_bn: True + lr: + name: Cosine + learning_rate: 0.0004 + eta_min: 1e-6 + warmup_epoch: 10 + warmup_start_lr: 5e-7 + + +# data loader for train and eval +DataLoader: + Train: + dataset: + name: RAMPretrainDataset + ann_file: [./visual-genome/vg_ram.json] + tag_list: 'ppcls/utils/ram/ram_tag_list.txt' + model_name: "RAM_plus" + transform_ops_ram: + - ResizeImage: + size: 384 + interpolation: bicubic + backend: pil + - NormalizeImage: + scale: 1.0/255.0 + mean: [0.485, 0.456, 0.406] + std: [0.229, 0.224, 0.225] + order: '' + - ToCHWImage: + transform_ops_clip: + - ResizeImage: + size: 224 + interpolation: bicubic + backend: pil + - NormalizeImage: + scale: 1.0/255.0 + mean: [0.48145466, 0.4578275, 0.40821073] + std: [0.26862954, 0.26130258, 0.27577711] + order: '' + - ToCHWImage: + + sampler: + name: DistributedBatchSampler + batch_size: 52 + drop_last: False + shuffle: True + loader: + num_workers: 4 + use_shared_memory: True + + Eval: + dataset: + name: RAMPretrainDataset + ann_file: [./visual-genome/vg_ram.json] + tag_list: 'ppcls/utils/ram/ram_tag_list.txt' + model_name: "RAM_plus" + transform_ops_ram: + - ResizeImage: + size: 384 + interpolation: bicubic + backend: pil + - NormalizeImage: + scale: 1.0/255.0 + mean: [0.485, 0.456, 0.406] + std: [0.229, 0.224, 0.225] + order: '' + - ToCHWImage: + transform_ops_clip: + - ResizeImage: + size: 224 + interpolation: bicubic + backend: pil + - NormalizeImage: + scale: 1.0/255.0 + mean: [0.48145466, 0.4578275, 0.40821073] + std: [0.26862954, 0.26130258, 0.27577711] + order: '' + - ToCHWImage: + + sampler: + name: DistributedBatchSampler + batch_size: 52 + drop_last: False + shuffle: True + loader: + num_workers: 4 + use_shared_memory: True + +Infer: + infer_imgs: docs/images/inference_deployment/whl_demo.jpg + batch_size: 1 + transforms: + - DecodeImage: + to_rgb: True + channel_first: False + - ResizeImage: + resize_short: 384 + - CropImage: + size: 384 + - NormalizeImage: + scale: 1.0/255.0 + mean: [0.485, 0.456, 0.406] + std: [0.229, 0.224, 0.225] + order: '' + - ToCHWImage: + PostProcess: + name: RamOutPut + language: "cn" + tag_list: "ppcls/utils/ram/ram_tag_list.txt" + tag_list_chinese: "ppcls/utils/ram/ram_tag_list_chinese.txt" + ram_class_threshold_path: "ppcls/utils/ram/ram_tag_list_threshold.txt" + diff --git a/ppcls/configs/ram/RAM_q2l.yaml b/ppcls/configs/ram/RAM_q2l.yaml new file mode 100644 index 0000000000..d1e4598230 --- /dev/null +++ b/ppcls/configs/ram/RAM_q2l.yaml @@ -0,0 +1,21 @@ +architectures: [ + 'BertModel' + ] #default uncased + +attention_probs_dropout_prob: 0.1 # attention dropout rata +hidden_act: gelu # acitvation +hidden_dropout_prob: 0.1 +hidden_size: 768 # embedding size +initializer_range: 0.02 +intermediate_size: 3072 +layer_norm_eps: 1.0e-12 +max_position_embeddings: 512 +model_type: bert +num_attention_heads: 4 +num_hidden_layers: 2 +pad_token_id: 0 +type_vocab_size: 2 +vocab_size: 30524 # text embedding size +encoder_width: 768 +add_cross_attention: true # whether using cross-attention default False +add_tag_cross_attention : false \ No newline at end of file diff --git a/ppcls/configs/ram/README.md b/ppcls/configs/ram/README.md new file mode 100644 index 0000000000..d3900086e6 --- /dev/null +++ b/ppcls/configs/ram/README.md @@ -0,0 +1,390 @@ +# RAM, RAM++ 图文标签模型 + +## 目录 + +* [1. 模型介绍](#1) +* [2. 数据和模型准备](#2) +* [3. 模型训练](#3) +* [4. 模型评估](#4) +* [5. 模型预测](#5) +* [6. 基于预测引擎预测](#6) + * [6.1 导出 inference model](#6.1) + * [6.2 基于 Python 预测引擎推理](#6.2) +* [7. 引用](#7) + + +## 1. 模型介绍 + +RAM以及RAM++（下文简称RAM类模型）主要用于标注类任务，其中两个模型的主要贡献为提出了集训练-推理-tag一体化的框架。其通过堆叠vision encoder以及text encoder，实现多种下游任务。核心方法包括： + +1. 结合CLIP架构，提出 Image-Tag Recognition Decoder，Image-Text Alignment Encoder，Image-Tag Interaction Encoder，Image-Tag-Text Generation Decoder以及Generation Encoder 5个组件分别实现text-image对齐，text-tag对齐。 +2. RAM++进一步使用大语言模型（large language model，LLM）的语义信息，提升text-image对齐的能力。 + +使用RAM类模型时，作者在多个分类任务上取得了最先进的结果： + +| Model | BackBone | Store Size | Inference Prompt | CLIP | OpenImages-MAP | +|-------|------------|--------|------------------|------|----------------| +| RAM | Swin-large | 5.63GB | LLM Tag Dec | VIT-base-patch16-224 | 82.2 | +| RAM++ | Swin-base | 3.01GB | LLM Tag Dec | VIT-base-patch16-224 | 86.6 | +注：LLM Tag Dec表示基于LLM改写的文本tag。例如给定prompt："A photo of a cat" 对应LLM tag Dec为："Cat is a small general with sofa". + +`PaddleClas` paddleclas分别实现了基于不同backbone的RAM类模型: +```yaml +# model architecture +Arch: + name: ram_plus + vit: swin_l + vit_grad_ckpt: False + vit_ckpt_layer: 0 + image_size: 384 + prompt: 'a picture of ' + med_config: 'ppcls/configs/ram/ram_bert.yaml' + delete_tag_index: [] + tag_list: 'ppcls/utils/ram/ram_tag_list.txt' + tag_list_chinese: 'ppcls/utils/ram/ram_tag_list_chinese.txt' + clip_pretraind: ./ViT-B-32.pdparams #for CLIP a necessary part for training ram + clip_version: 'vit-b-32-224' + q2l_config: 'ppcls/configs/ram/ram_q2l.yaml' + ram_class_threshold_path: 'ppcls/utils/RAM/ram_tag_list_threshold.txt' + stage: train + threshold: 0.68 +``` +参数注释： + - name 模型参数，使用RAM模型可以指定为ram，使用RAM++模型可以指定为ram_plus，默认为ram + - vit 视觉主干网络参数，包括 vit：vision transformer，swin_b： swin base模型，swin_l, swin large模型 + - image_size 图片分辨率 + - prompt RAM训练时所使用的文本提示前缀 + - med_config RAM类模型所使用的Bert模型配置文件，默认配置路径：'ppcls/configs/ram/config_bert.yaml' + - delete_tag_index 屏蔽tag所用参数，例如传递[1,3,2]则表示屏蔽index为1，2，3的tag标签 + - tag_list 英文tag标签文件路劲，默认ppcls/utils/RAM/ram_tag_list.txt + - tag_list_chinese 中文tag标签文件路劲，默认ppcls/utils/RAM/ram_tag_list.txt + - clip_version 所使用的CLIP结构，默认 vit-b-32-224 + - clip_pretraind 训练所使用的CLIP预训练参数路径，当需要训练RAM类模型时，不能为None + - q2l_config 基于bert 的text-tag alignment encoder模型配置文件默认 ppcls/configs/ram/config_q2l.yaml + - ram_class_threshold_path tag生成阈值文件默认ppcls/utils/RAM/ram_tag_list_threshold.txt + - stage 指定RAM，RAM++模型是否进行训练，stage = train表示需要训练，训练时clip_pretraind不能为None，stage = eval表示无需训练 + - threshold 输出TAG所需的阈值数值，表示当该tag对应概率大于该值，则认为属于该tag +注意，RAM类模型的推理和训练，需要使用tools/train_multimodal.py, tools/infer_multimodal.py 以及predict_multimodal.py接口，支持多模态输入的动态图训练，推理以及静态图推理。 + + +## 2. 数据和模型准备 + +* 前往官方[repo](https://github.com/xinyu1205/recognize-anything/tree/main)下载对应数据集json文件。同时按照json文件目录格式，准备相应的数据。目录格式为： +```json +{ + { + "image_path": "visual-genome/VG_100K/1.jpg", + "caption": ["trees line the sidewalk"], + "union_label_id": [4480], + "parse_label_id": [[4253, 2461, 2966]] + } +} +``` +参数注释： + - image_path 数据集路径 + - caption 对应图片标注 + - union_label_id 标注对应id + - parse_label_id 将标注仅需名词化后，结果对应的id。例如将"trees line the sidewalk"名词化得到 "trees" "line"以及"sidewalk"，其对应的id分别是4253, 2461, 2966 +其中务必保证数据集文件路径符合image_path +* 本文档中，为RAM类模型提供了统一的动态训练以及推理配置文件，结构如下： +```yaml +# global configs +Global: + checkpoints: null + pretrained_model: "ram.pdparams" # pretrain model for ram and ram plus, default random initilize + output_dir: ./output/ + device: gpu + save_interval: 1 + eval_during_train: True + eval_interval: 1 + epochs: 120 + print_batch_step: 10 + use_visualdl: False + # used for static mode and model export + image_shape: [3, 384, 384] + save_inference_dir: ./inference + + +# mixed precision +AMP: + use_amp: True + use_fp16_test: False + scale_loss: 128.0 + use_dynamic_loss_scaling: True + use_promote: False + # O1: mixed fp16, O2: pure fp16 + level: O1 + + +# model architecture +Arch: + name: ram + vit: swin_l + vit_grad_ckpt: False + vit_ckpt_layer: 0 + image_size: 384 + prompt: 'a picture of ' + med_config: 'ppcls/configs/ram/ram_bert.yaml' + delete_tag_index: [] + tag_list: 'ppcls/utils/ram/ram_tag_list.txt' + tag_list_chinese: 'ppcls/utils/ram/ram_tag_list_chinese.txt' + clip_pretraind: ./ViT-B-32.pdparams #for CLIP a necessary part for training ram + clip_version: 'vit-b-32-224' + q2l_config: 'ppcls/configs/ram/ram_q2l.yaml' + ram_class_threshold_path: 'ppcls/utils/RAM/ram_tag_list_threshold.txt' + stage: train + threshold: 0.68 + +# loss function config for traing/eval process +Loss: + Train: + - RAMLoss: + weight: 1.0 + Eval: + - RAMLoss: + weight: 1.0 + +Optimizer: + name: AdamW + beta1: 0.9 + beta2: 0.999 + epsilon: 1e-8 + weight_decay: 0.05 + layerwise_decay: 0.6 + filter_bias_and_bn: True + lr: + name: Cosine + learning_rate: 0.0004 + eta_min: 1e-6 + warmup_epoch: 10 + warmup_start_lr: 5e-7 + +# data loader for train and eval +DataLoader: + Train: + dataset: + name: RAMPretrainDataset + ann_file: [./visual-genome/vg_ram.json] + transform_ops_ram: + - ResizeImage: + size: 384 + interpolation: bicubic + backend: pil + - NormalizeImage: + scale: 1.0/255.0 + mean: [0.485, 0.456, 0.406] + std: [0.229, 0.224, 0.225] + order: '' + - ToCHWImage: + transform_ops_clip: + - ResizeImage: + resize_short: 224 + interpolation: bicubic + backend: pil + - NormalizeImage: + scale: 1.0/255.0 + mean: [0.48145466, 0.4578275, 0.40821073] + std: [0.26862954, 0.26130258, 0.27577711] + order: '' + - ToCHWImage: + + sampler: + name: DistributedBatchSampler + batch_size: 52 + drop_last: False + shuffle: True + loader: + num_workers: 4 + use_shared_memory: True + + Eval: + dataset: + name: RAMPretrainDataset + ann_file: [./visual-genome/vg_ram.json] + transform_ops_ram: + - ResizeImage: + size: 384 + interpolation: bicubic + backend: pil + - NormalizeImage: + scale: 1.0/255.0 + mean: [0.485, 0.456, 0.406] + std: [0.229, 0.224, 0.225] + order: '' + - ToCHWImage: + transform_ops_clip: + - ResizeImage: + resize_short: 224 + interpolation: bicubic + backend: pil + - NormalizeImage: + scale: 1.0/255.0 + mean: [0.48145466, 0.4578275, 0.40821073] + std: [0.26862954, 0.26130258, 0.27577711] + order: '' + - ToCHWImage: + + sampler: + name: DistributedBatchSampler + batch_size: 52 + drop_last: False + shuffle: True + loader: + num_workers: 4 + use_shared_memory: True + +Infer: + infer_imgs: docs/images/inference_deployment/whl_demo.jpg + batch_size: 1 + transforms: + - DecodeImage: + to_rgb: True + channel_first: False + - ResizeImage: + resize_short: 384 + - CropImage: + size: 384 + - NormalizeImage: + scale: 1.0/255.0 + mean: [0.485, 0.456, 0.406] + std: [0.229, 0.224, 0.225] + order: '' + - ToCHWImage: + PostProcess: + name: RamOutPut + language: "cn" + tag_list: "ppcls/utils/RAM/ram_tag_list.txt" + tag_list_chinese: "ppcls/utils/RAM/ram_tag_list_chinese.txt" + ram_class_threshold_path: "ppcls/utils/RAM/ram_tag_list_threshold.txt" + + +``` +用户可以根据自身需求，更改相应配置。注意arch参数请参照本文档。 + +## 3. 模型训练 +以RAM为例： +```shell +# 多卡 +export CUDA_VISIBLE_DEVICES=0,1,2,3 +python3 -m paddle.distributed.launch \ + --gpus="0,1,2,3" \ + tools/train_multimodal.py \ + -c ./ppcls/configs/ram/RAM.yaml +# 单卡 +python3 tools/train_multimodal.py \ + -c ./ppcls/configs//ram/RAM.yaml +``` +以RAM++为例： +```shell +# 多卡 +export CUDA_VISIBLE_DEVICES=0,1,2,3 +python3 -m paddle.distributed.launch \ + --gpus="0,1,2,3" \ + tools/train_multimodal.py \ + -c ./ppcls/configs/ram/RAM_plus.yaml +# 单卡 +python3 tools/train_multimodal.py \ + -c ./ppcls/configs//ram/RAM_plus.yaml +``` + + + +## 4. 模型预测 + +```bash +python3 tools/infer_multimodal.py \ + -c ./ppcls/configs/ram/RAM.yaml \ + -o Global.pretrained_model="./output/ram/best_model" +``` + +得到类似下面的输出： +``` +{'class_ids': [[[593], [871], [998], [2071], [3336], [3862], [4389]]], 'scores': [[[0.9708361625671387], [0.9998403787612915], [0.9122695922851562], +[0.8888279795646667], [0.8671568036079407], [0.8900104761123657], [0.811939001083374]]], 'label_names': ['棕色 | 鸡 | 公鸡 | 母鸡 | 红色 | 站/矗立/摊位 | 走 ']} +``` + + +## 5. 基于预测引擎预测 + + +### 5.1 导出 inference model + +```bash +python3 tools/export_model.py \ + -c ./ppcls/configs/ram/RAM.yaml \ + -o Global.pretrained_model="./output/ram/" +``` +inference model 的路径默认在当前路径下 `./inference` +`./inference` 文件夹下应有如下文件结构： + +``` +├── inference +│ ├── inference.pdiparams +│ ├── inference.pdiparams.info +│ └── inference.pdmodel +``` + + + +### 5.2 基于 Python 预测引擎推理 + +切换到depoly目录下，并且使用deploy中的脚本进行推理前需要确认paddleclas为非本地安装, 如不是请进行切换，不然会出现包的导入错误。 + +```shell +# 本地安装 +pip install -e . +# 非本地安装 +python setup.py install + +# 进入deploy目录下 +cd deploy +``` + + + +#### 5.2.1 预测单张图像 + +运行下面的命令，对图像 `docs/images/inference_deployment/whl_demo.jpg` 进行分类。 + +```shell +# linux使用`python3`，windows使用`python (-m)`来执行脚本 +# 使用下面的命令使用 GPU 进行预测 +python3 python/predict_multimodal.py \ + -c deploy/configs/inference_ram.yaml \ + -o Global.inference_model_dir=../inference/ \ + -o Global.infer_imgs=docs/images/inference_deployment/whl_demo.jpg +# 使用下面的命令使用 CPU 进行预测 +#更改 `config_infer.yaml` 配置文件后 +python3 python/predict_multimodal.py \ + -c deploy/configs/inference_ram.yaml \ + -o Global.inference_model_dir=../inference/ \ + -o Global.infer_imgs=docs/images/inference_deployment/whl_demo.jpg +``` + +输出结果如下： + +``` +whl_demo.jpg-class_ids: [[[593], [871], [998], [2071], [3336], [3862], [4389]]], +whl_demo.jpg-scores: [[[0.9708361625671387], [0.9998403787612915], [0.9122695922851562], +[0.8888279795646667], [0.8671568036079407], [0.8900104761123657], [0.811939001083374]]], +whl_demo.jpg-label_names: ['棕色 | 鸡 | 公鸡 | 母鸡 | 红色 | 站/矗立/摊位 | 走 '] +``` + + + +## 6. 引用 +``` +@article{huang2023inject, + title={Inject Semantic Concepts into Image Tagging for Open-Set Recognition}, + author={Huang, Xinyu and Huang, Yi-Jie and Zhang, Youcai and Tian, Weiwei and Feng, Rui and Zhang, Yuejie and Xie, Yanchun and Li, Yaqian and Zhang, Lei}, + journal={arXiv preprint arXiv:2310.15200}, + year={2023} +} + +@article{zhang2023recognize, + title={Recognize Anything: A Strong Image Tagging Model}, + author={Zhang, Youcai and Huang, Xinyu and Ma, Jinyu and Li, Zhaoyang and Luo, Zhaochuan and Xie, Yanchun and Qin, Yuzhuo and Luo, Tong and Li, Yaqian and Liu, Shilong and others}, + journal={arXiv preprint arXiv:2306.03514}, + year={2023} +} +``` \ No newline at end of file diff --git a/ppcls/data/__init__.py b/ppcls/data/__init__.py index baeed665d0..492b40f959 100644 --- a/ppcls/data/__init__.py +++ b/ppcls/data/__init__.py @@ -37,6 +37,7 @@ from ppcls.data.dataloader.custom_label_dataset import CustomLabelDataset from ppcls.data.dataloader.cifar import Cifar10, Cifar100 from ppcls.data.dataloader.metabin_sampler import DomainShuffleBatchSampler, NaiveIdentityBatchSampler +from ppcls.data.dataloader.ram_dataset import RAMPretrainDataset # sampler from ppcls.data.dataloader.DistributedRandomIdentitySampler import DistributedRandomIdentitySampler diff --git a/ppcls/data/dataloader/__init__.py b/ppcls/data/dataloader/__init__.py index 391dcef65b..ff62485025 100644 --- a/ppcls/data/dataloader/__init__.py +++ b/ppcls/data/dataloader/__init__.py @@ -14,3 +14,4 @@ from ppcls.data.dataloader.custom_label_dataset import CustomLabelDataset from ppcls.data.dataloader.cifar import Cifar10, Cifar100 from ppcls.data.dataloader.metabin_sampler import DomainShuffleBatchSampler, NaiveIdentityBatchSampler +from ppcls.data.dataloader.ram_dataset import RAMPretrainDataset diff --git a/ppcls/data/dataloader/ram_dataset.py b/ppcls/data/dataloader/ram_dataset.py new file mode 100644 index 0000000000..782949fb97 --- /dev/null +++ b/ppcls/data/dataloader/ram_dataset.py @@ -0,0 +1,163 @@ +# copyright (c) 2023 PaddlePaddle Authors. All Rights Reserve. +# +# 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 json +import os +import re +import numpy as np + +from paddle.io import Dataset +import paddle +from .common_dataset import create_operators +from ppcls.data.preprocess import transform +from ppcls.arch.clip.tokenizer import Tokenizer +from ppcls.arch.clip.clip import tokenize +from ppcls.arch.ram.ram import init_tokenizer + +from PIL import Image +from PIL import ImageFile + +ImageFile.LOAD_TRUNCATED_IMAGES = True +Image.MAX_IMAGE_PIXELS = None + + +def pre_caption(caption, max_words=50): + caption = re.sub( + r"([.!\"()*#:;~])", + ' ', + caption.lower(), ) + caption = re.sub( + r"\s{2,}", + ' ', + caption, ) + caption = caption.rstrip('\n') + caption = caption.strip(' ') + + #truncate caption + caption_words = caption.split(' ') + if len(caption_words) > max_words: + caption = ' '.join(caption_words[:max_words]) + + return caption + + +class RAMPretrainDataset(Dataset): + def __init__(self, + ann_file, + width=384, + class_num=4585, + root='', + tag_list='', + model_name='', + transform_ops_ram=None, + transform_ops_clip=None): + + self.ann = [] + for f in ann_file: + print('loading ' + f) + ann = json.load(open(f, 'r')) + self.ann += ann + self.width = width + self.name = model_name.lower() + self.transform_clip = create_operators(transform_ops_clip) + self.transform = create_operators(transform_ops_ram) + self.class_num = class_num + self.root = root + self.tag_list = self.load_tag_list(tag_list) + self.bert_tokenizer = init_tokenizer() + self.clip_tokenizer = Tokenizer() + + + def __len__(self): + return len(self.ann) + + def load_tag_list(self, tag_list_file): + with open(tag_list_file, 'r', encoding='utf-8') as f: + tag_list = f.read().splitlines() + tag_list = np.array(tag_list) + return tag_list + + def collect_fn_list(self, data): + image_ram_list = [] + text_list = [] + image_tag_list = [] + image_parse_tag_list = [] + image_clip_list = [] + for item in data: + i1,i2,i3,i4,i5 = item + image_ram_list.append(i1) + text_list.append(i2) + image_tag_list.append(i3) + image_parse_tag_list.append(i4) + image_clip_list.append(i5) + image_rams = paddle.stack(image_ram_list) + if self.name == "ram": + text_list = self.bert_tokenizer( + text_list, + padding='longest', + truncation=True, + max_length=40, + return_attention_mask=True, + return_tensors='pd') + else: + text_list = tokenize(text_list, self.clip_tokenizer) + image_tags = paddle.stack(image_tag_list) + image_parse_tag_list = np.stack(image_parse_tag_list) + tag_input = [] + for b in range(len(image_ram_list)): + index = np.argwhere(image_parse_tag_list[b] == 1) + token = self.tag_list[index].squeeze(axis=1) + tag_input.append(' | '.join(token)) + image_parse_tags = self.bert_tokenizer( + tag_input, + padding='max_length', + truncation=True, + max_length=40, + return_attention_mask=True, + return_tensors='pd') + image_clips = paddle.stack(image_clip_list) + return [image_rams, text_list , image_tags, image_parse_tags, image_clips] + + def __getitem__(self, index): + + ann = self.ann[index] + + image_path_use = os.path.join(self.root, ann['image_path']) + + try: + image = Image.open(image_path_use).convert('RGB') + image_ram = transform(image, self.transform) + image_ram = paddle.to_tensor(image_ram) + image224 = transform(image, self.transform_clip) + image224 = paddle.to_tensor(image224) + except: + image224 = paddle.ones([3, 224, 224]) + image_ram = paddle.ones([3, self.width, self.width]) + + num = ann['union_label_id'] + image_tag = np.zeros([self.class_num]) + image_tag[num] = 1 + image_tag = paddle.to_tensor(image_tag, dtype=paddle.int32) + + caption_index = np.random.randint(0, len(ann['caption'])) + + caption = pre_caption(ann['caption'][caption_index], 30) + + num = ann['parse_label_id'][caption_index] + parse_tag = np.zeros([self.class_num]) + parse_tag[num] = 1 + + return [image_ram, caption, image_tag, parse_tag, image224] + + diff --git a/ppcls/data/postprocess/__init__.py b/ppcls/data/postprocess/__init__.py index 202f5be8ba..a86c3bbfe8 100644 --- a/ppcls/data/postprocess/__init__.py +++ b/ppcls/data/postprocess/__init__.py @@ -20,6 +20,7 @@ from .threshoutput import ThreshOutput, MultiLabelThreshOutput from .attr_rec import VehicleAttribute, PersonAttribute, TableAttribute from .scoreoutput import ScoreOutput +from .ramoutput import RamOutPut def build_postprocess(config): diff --git a/ppcls/data/postprocess/ramoutput.py b/ppcls/data/postprocess/ramoutput.py new file mode 100644 index 0000000000..9297d39fa5 --- /dev/null +++ b/ppcls/data/postprocess/ramoutput.py @@ -0,0 +1,94 @@ +# copyright (c) 2023 PaddlePaddle Authors. All Rights Reserve. +# +# 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 os +import numpy as np + +import paddle +import paddle.nn.functional as F + + +class RamOutPut(object): + def __init__(self, + language="cn", + tag_list="", + tag_list_chinese="", + threshold=0.68, + ram_class_threshold_path="", + delete_tag_index=[]): + """ + """ + self.language = language + assert tag_list, tag_list_chinese + self.tag_list = self.load_tag_list(tag_list) + self.delete_tag_index = delete_tag_index + self.tag_list_chinese = self.load_tag_list(tag_list_chinese) + self.num_class = len(self.tag_list) + self.class_threshold = paddle.ones([self.num_class]) * threshold + ram_class_threshold_path = ram_class_threshold_path + with open(ram_class_threshold_path, "r", encoding="utf-8") as f: + ram_class_threshold = [float(s.strip()) for s in f] + for key, value in enumerate(ram_class_threshold): + self.class_threshold[key] = value + + def load_tag_list(self, tag_list_file): + with open(tag_list_file, "r", encoding="utf-8") as f: + tag_list = f.read().splitlines() + tag_list = np.array(tag_list) + return tag_list + + def __call__(self, logits, file_names=None): + """ + logits is the result from model + bs is the batch size from model + file_names is useless but need in order to fit support framework of ppcls + """ + bs = len(file_names) + targets = paddle.where( + F.sigmoid(logits) > self.class_threshold, + paddle.to_tensor([1.0]), paddle.zeros(self.num_class)) + targets = targets.reshape([bs, -1]) + res = {} + tag = targets.cpu().numpy() + tag[:, self.delete_tag_index] = 0 + tag_output = [] + tag_output_chinese = [] + for b in range(bs): + index = np.argwhere(tag[b] == 1) + token = self.tag_list[index].squeeze(axis=1) + tag_output.append(" | ".join(token)) + token_chinese = self.tag_list_chinese[index].squeeze(axis=1) + tag_output_chinese.append(" | ".join(token_chinese)) + res["cn"] = tag_output_chinese + res["en"] = tag_output + res["all"] = f"en : {tag_output}, cn: {tag_output_chinese}" + + scores = F.sigmoid(logits).numpy().reshape([bs, -1]) + class_ids_list = [] + scores_list = [] + + for b in range(bs): + index = np.argwhere(tag[b] == 1) + class_ids_list.append(index.tolist()) + scores_list.append(scores[b][index].tolist()) + + + + outputformat = { + "class_ids": class_ids_list, + "scores": scores_list, + "label_names": res[self.language] + } + + return outputformat diff --git a/ppcls/engine/engine_multimodal.py b/ppcls/engine/engine_multimodal.py new file mode 100644 index 0000000000..0b8a9197f9 --- /dev/null +++ b/ppcls/engine/engine_multimodal.py @@ -0,0 +1,145 @@ +# copyright (c) 2023 PaddlePaddle Authors. All Rights Reserve. +# +# 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 os +import shutil +import platform +import paddle +import paddle.distributed as dist +from visualdl import LogWriter +from paddle import nn +import numpy as np +import random + +from ppcls.utils import logger +from ppcls.utils import save_predict_result + +from ppcls.data.utils.get_image_list import get_image_list +from ppcls.engine.train.utils import type_name +from ppcls.engine import engine +from ppcls.engine import train as train_method +from ppcls.engine.engine import ExportModel, load_dygraph_pretrain + + +class EngineMultimodal(engine.Engine): + def __init__(self, config, mode="train"): + super().__init__(config, mode) + self.train_epoch_func = train_method.train_epoch_multimodal + if mode == "train": + self.train_dataloader.collate_fn = self.train_dataloader.dataset.collect_fn_list + self.eval_dataloader.collate_fn = self.train_dataloader.dataset.collect_fn_list + + @paddle.no_grad() + def eval(self, epoch_id=0): + assert self.mode in ["train", "eval"] + self.model.eval() + eval_result = self.eval_func(self, epoch_id) + self.model.train() + return eval_result + + @paddle.no_grad() + def infer(self): + assert self.mode == "infer" and self.eval_mode == "classification" + results = [] + total_trainer = dist.get_world_size() + local_rank = dist.get_rank() + infer_imgs = self.config["Infer"]["infer_imgs"] + infer_list = self.config["Infer"].get("infer_list", None) + image_list = get_image_list(infer_imgs, infer_list=infer_list) + # data split + image_list = image_list[local_rank::total_trainer] + + batch_size = self.config["Infer"]["batch_size"] + self.model.eval() + batch_data = [] + image_file_list = [] + save_path = self.config["Infer"].get("save_dir", None) + for idx, image_file in enumerate(image_list): + with open(image_file, 'rb') as f: + x = f.read() + for process in self.preprocess_func: + x = process(x) + batch_data.append(x) + image_file_list.append(image_file) + if len(batch_data) >= batch_size or idx == len(image_list) - 1: + batch_tensor = paddle.to_tensor(batch_data) + + with self.auto_cast(is_eval=True): + tag_output = self.model.inference(batch_tensor) + + result = self.postprocess_func(*tag_output, image_file_list) + if not save_path: + logger.info(result) + results.extend(result) + batch_data.clear() + image_file_list.clear() + if save_path: + save_predict_result(save_path, results) + return results + + def export(self): + assert self.mode == "export" + use_multilabel = self.config["Global"].get("use_multilabel", False) + model = ExportModelMultiModal(self.config["Arch"], self.model, + use_multilabel) + if self.config["Global"]["pretrained_model"] is not None: + load_dygraph_pretrain(model.base_model, + self.config["Global"]["pretrained_model"]) + + model.eval() + + # for re-parameterization nets + for layer in self.model.sublayers(): + if hasattr(layer, "re_parameterize") and not getattr(layer, + "is_repped"): + layer.re_parameterize() + + save_path = os.path.join(self.config["Global"]["save_inference_dir"], + "inference") + + model = paddle.jit.to_static( + model, + input_spec=[ + paddle.static.InputSpec( + shape=[None] + self.config["Global"]["image_shape"], + dtype='float32') + ]) + if hasattr(model.base_model, + "quanter") and model.base_model.quanter is not None: + model.base_model.quanter.save_quantized_model(model, + save_path + "_int8") + else: + paddle.jit.save(model, save_path) + if self.config["Global"].get("export_for_fd", False): + src_path = self.config["Global"]["infer_config_path"] + dst_path = os.path.join( + self.config["Global"]["save_inference_dir"], 'inference.yml') + shutil.copy(src_path, dst_path) + logger.info( + f"Export succeeded! The inference model exported has been saved in \"{self.config['Global']['save_inference_dir']}\"." + ) + + +class ExportModelMultiModal(ExportModel): + def __init__(self, config, model, use_multilabel): + super().__init__(config, model, use_multilabel) + self.CLIP_model = config.get("clip","") + def forward(self, x): + if self.CLIP_model == "image": + return self.base_model.encode_image(x) + elif self.CLIP_model == "text": + return self.base_model.encode_text(x) + else: + x = self.base_model.inference(x) + return x diff --git a/ppcls/engine/train/__init__.py b/ppcls/engine/train/__init__.py index 50bf9037f4..ddd35f0e5f 100644 --- a/ppcls/engine/train/__init__.py +++ b/ppcls/engine/train/__init__.py @@ -16,3 +16,4 @@ from ppcls.engine.train.train_fixmatch_ccssl import train_epoch_fixmatch_ccssl from ppcls.engine.train.train_progressive import train_epoch_progressive from ppcls.engine.train.train_metabin import train_epoch_metabin +from ppcls.engine.train.train_multimodal import train_epoch_multimodal diff --git a/ppcls/engine/train/train_multimodal.py b/ppcls/engine/train/train_multimodal.py new file mode 100644 index 0000000000..7447c5dbe7 --- /dev/null +++ b/ppcls/engine/train/train_multimodal.py @@ -0,0 +1,95 @@ +# copyright (c) 2023 PaddlePaddle Authors. All Rights Reserve. +# +# 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 time +import paddle +from ppcls.engine.train.utils import update_loss, update_metric, log_info, type_name +from ppcls.utils import profiler + + +def train_epoch_multimodal(engine, epoch_id, print_batch_step): + tic = time.time() + + if not hasattr(engine, "train_dataloader_iter"): + engine.train_dataloader_iter = iter(engine.train_dataloader) + + for iter_id in range(engine.iter_per_epoch): + # fetch data batch from dataloader + try: + batch = next(engine.train_dataloader_iter) + except Exception: + # NOTE: reset DALI dataloader manually + if engine.use_dali: + engine.train_dataloader.reset() + engine.train_dataloader_iter = iter(engine.train_dataloader) + batch = next(engine.train_dataloader_iter) + assert isinstance(batch, tuple) or isinstance(batch, list) + profiler.add_profiler_step(engine.config["profiler_options"]) + if iter_id == 5: + for key in engine.time_info: + engine.time_info[key].reset() + engine.time_info["reader_cost"].update(time.time() - tic) + + batch_size = batch[0][0].shape[0] + + engine.global_step += 1 + + # image input + with engine.auto_cast(is_eval=False): + out = forward(engine, batch) + loss_dict = engine.train_loss_func(out, batch[1]) + + # loss + loss = loss_dict["loss"] / engine.update_freq + + # backward & step opt + scaled = engine.scaler.scale(loss) + scaled.backward() + if (iter_id + 1) % engine.update_freq == 0: + for i in range(len(engine.optimizer)): + # optimizer.step() with auto amp + engine.scaler.step(engine.optimizer[i]) + engine.scaler.update() + + if (iter_id + 1) % engine.update_freq == 0: + # clear grad + for i in range(len(engine.optimizer)): + engine.optimizer[i].clear_grad() + # step lr(by step) + for i in range(len(engine.lr_sch)): + if not getattr(engine.lr_sch[i], "by_epoch", False): + engine.lr_sch[i].step() + # update ema + if engine.ema: + engine.model_ema.update(engine.model) + + # below code just for logging + # update metric_for_logger + update_metric(engine, out, batch, batch_size) + # update_loss_for_logger + update_loss(engine, loss_dict, batch_size) + engine.time_info["batch_cost"].update(time.time() - tic) + if iter_id % print_batch_step == 0: + log_info(engine, batch_size, epoch_id, iter_id) + tic = time.time() + + # step lr(by epoch) + for i in range(len(engine.lr_sch)): + if getattr(engine.lr_sch[i], "by_epoch", False) and \ + type_name(engine.lr_sch[i]) != "ReduceOnPlateau": + engine.lr_sch[i].step() + + +def forward(engine, batch): + return engine.model(*batch) diff --git a/ppcls/loss/__init__.py b/ppcls/loss/__init__.py index 7ab8be4fab..ee9dfbcc00 100644 --- a/ppcls/loss/__init__.py +++ b/ppcls/loss/__init__.py @@ -21,6 +21,7 @@ from .pairwisecosface import PairwiseCosface from .dmlloss import DMLLoss from .distanceloss import DistanceLoss +from .ramloss import RAMLoss from .softtargetceloss import SoftTargetCrossEntropy from .distillationloss import DistillationCELoss from .distillationloss import DistillationGTCELoss @@ -70,7 +71,7 @@ def __call__(self, input, batch): loss_dict = {} # just for accelerate classification traing speed if len(self.loss_func) == 1: - loss = self.loss_func[0](input, batch) + loss = self.loss_func[0](*input, batch) loss_dict.update(loss) loss_dict["loss"] = list(loss.values())[0] else: diff --git a/ppcls/loss/contrasiveloss.py b/ppcls/loss/contrasiveloss.py index d27dbe22e3..e9fe03a629 100644 --- a/ppcls/loss/contrasiveloss.py +++ b/ppcls/loss/contrasiveloss.py @@ -20,6 +20,7 @@ import paddle import paddle.nn as nn +from paddle.nn import functional as F from ppcls.loss.xbm import CrossBatchMemory @@ -39,6 +40,7 @@ def __init__(self, embedding_size: int, normalize_feature=True, epsilon: float=1e-5, + is_text_image_pairs=False, feature_from: str="features"): super(ContrastiveLoss, self).__init__() self.margin = margin @@ -46,9 +48,22 @@ def __init__(self, self.normalize_feature = normalize_feature self.epsilon = epsilon self.feature_from = feature_from + self.is_text_image_pairs = is_text_image_pairs + + def text_image_pairs_constrative_loss(self, logits_per_image, + logits_per_text, labels): + total_loss = (F.cross_entropy(logits_per_image, labels) + + F.cross_entropy(logits_per_text, labels)) / 2 + + return {"Contrastive_loss": total_loss} def forward(self, input: Dict[str, paddle.Tensor], target: paddle.Tensor) -> Dict[str, paddle.Tensor]: + + if self.is_text_image_pairs: + return self.text_image_pairs_constrative_loss( + input["image"], input["text"], target) + feats = input[self.feature_from] labels = target diff --git a/ppcls/loss/ramloss.py b/ppcls/loss/ramloss.py new file mode 100644 index 0000000000..9e63fcc016 --- /dev/null +++ b/ppcls/loss/ramloss.py @@ -0,0 +1,27 @@ +# copyright (c) 2023 PaddlePaddle Authors. All Rights Reserve. +# +# 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.nn as nn + + +# loss for RAM and RAM++ +class RAMLoss(nn.Layer): + def __init__(self): + super().__init__() + + def forward(self, loss_tag, loss_dis, loss_alignment, *kwargs): + + ## **kwargs is useless but to be compatiable with the framework + loss = loss_tag + loss_dis + loss_alignment + return {"RAMLoss": loss} diff --git a/ppcls/utils/ram/bpe_simple_vocab_16e6.txt.gz b/ppcls/utils/ram/bpe_simple_vocab_16e6.txt.gz new file mode 100644 index 0000000000..7b5088a527 Binary files /dev/null and b/ppcls/utils/ram/bpe_simple_vocab_16e6.txt.gz differ diff --git a/ppcls/utils/ram/ram_tag_list.txt b/ppcls/utils/ram/ram_tag_list.txt new file mode 100644 index 0000000000..49c840b719 --- /dev/null +++ b/ppcls/utils/ram/ram_tag_list.txt @@ -0,0 +1,4585 @@ +3D CG rendering +3D glasses +abacus +abalone +monastery +belly +academy +accessory +accident +accordion +acorn +acrylic paint +act +action +action film +activity +actor +adaptation +add +adhesive tape +adjust +adult +adventure +advertisement +antenna +aerobics +spray can +afro +agriculture +aid +air conditioner +air conditioning +air sock +aircraft cabin +aircraft model +air field +air line +airliner +airman +plane +airplane window +airport +airport runway +airport terminal +airship +airshow +aisle +alarm +alarm clock +mollymawk +album +album cover +alcohol +alcove +algae +alley +almond +aloe vera +alp +alpaca +alphabet +german shepherd +altar +amber +ambulance +bald eagle +American shorthair +amethyst +amphitheater +amplifier +amusement park +amusement ride +anchor +ancient +anemone +angel +angle +animal +animal sculpture +animal shelter +animation +animation film +animator +anime +ankle +anklet +anniversary +trench coat +ant +antelope +antique +antler +anvil +apartment +ape +app +app icon +appear +appearance +appetizer +applause +apple +apple juice +apple pie +apple tree +applesauce +appliance +appointment +approach +apricot +apron +aqua +aquarium +aquarium fish +aqueduct +arcade +arcade machine +arch +arch bridge +archaelogical excavation +archery +archipelago +architect +architecture +archive +archway +area +arena +argument +arm +armadillo +armband +armchair +armoire +armor +army +army base +army tank +array +arrest +arrow +art +art exhibition +art gallery +art print +art school +art studio +art vector illustration +artichoke +article +artifact +artist +artists loft +ash +ashtray +asia 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+little +broken +romantic +wide +royal +rich +open +cute +ancient +cold +political +elderly +gold +full +rustic +metallic +floral +sad +wet +fancy +senior +tiny +stylish +large +frosty +orange +transparent +electronic +shallow +scared +armed +dirty +historic +black +few +windy +some +square +ornamental +sandy +thin \ No newline at end of file diff --git a/tools/__init__.py b/tools/__init__.py index cdefb80509..634230de88 100644 --- a/tools/__init__.py +++ b/tools/__init__.py @@ -12,4 +12,4 @@ # See the License for the specific language governing permissions and # limitations under the License. -from . import infer \ No newline at end of file +from . import infer diff --git a/tools/export_model_multimodal.py b/tools/export_model_multimodal.py new file mode 100644 index 0000000000..a786fcf748 --- /dev/null +++ b/tools/export_model_multimodal.py @@ -0,0 +1,33 @@ +# copyright (c) 2023 PaddlePaddle Authors. All Rights Reserve. +# +# 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 os +import sys +__dir__ = os.path.dirname(os.path.abspath(__file__)) +sys.path.append(os.path.abspath(os.path.join(__dir__, '../'))) + +import paddle +import paddle.nn as nn + +from ppcls.utils import config +from ppcls.engine.engine_multimodal import EngineMultimodal + +if __name__ == "__main__": + args = config.parse_args() + config = config.get_config( + args.config, overrides=args.override, show=False) + if config["Arch"].get("use_sync_bn", False): + config["Arch"]["use_sync_bn"] = False + engine = EngineMultimodal(config, mode="export") + engine.export() diff --git a/tools/infer_multimodal.py b/tools/infer_multimodal.py new file mode 100644 index 0000000000..861a01b160 --- /dev/null +++ b/tools/infer_multimodal.py @@ -0,0 +1,29 @@ +# copyright (c) 2023 PaddlePaddle Authors. All Rights Reserve. +# +# 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 os +import sys + +__dir__ = os.path.dirname(os.path.abspath(__file__)) +sys.path.append(os.path.abspath(os.path.join(__dir__, '../'))) + +from ppcls.utils import config +from ppcls.engine.engine_multimodal import EngineMultimodal + +if __name__ == "__main__": + args = config.parse_args() + config = config.get_config( + args.config, overrides=args.override, show=False) + engine = EngineMultimodal(config, mode="infer") + engine.infer() diff --git a/tools/train_multimodal.py b/tools/train_multimodal.py new file mode 100644 index 0000000000..c3a9818bb7 --- /dev/null +++ b/tools/train_multimodal.py @@ -0,0 +1,30 @@ +# copyright (c) 2023 PaddlePaddle Authors. All Rights Reserve. +# +# 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 os +import sys +__dir__ = os.path.dirname(os.path.abspath(__file__)) +sys.path.append(os.path.abspath(os.path.join(__dir__, '../'))) + +from ppcls.utils import config +from ppcls.engine.engine_multimodal import EngineMultimodal + +if __name__ == "__main__": + args = config.parse_args() + config = config.get_config( + args.config, overrides=args.override, show=False) + config.profiler_options = args.profiler_options + + engine = EngineMultimodal(config, mode="train") + engine.train()