Merge pull request #26 from zwkkk/master

添加FLIP训练功能

cn_clip/clip/model.py

@@ -240,12 +240,30 @@ def __init__(self, input_resolution: int, patch_size: int, width: int, layers: i
     def set_grad_checkpointing(self, enable=True):
         self.transformer.grad_checkpointing = enable
 
-    def forward(self, x: torch.Tensor):
+    def random_masking(self, x, mask_ratio):
+        N, L, D = x.shape  # batch, length, dim
+        len_keep = int((L - 1) * (1 - mask_ratio))
+
+        noise = torch.rand(N, L - 1, device=x.device)
+        ids_shuffle = torch.argsort(noise, dim=1) + torch.ones(N, L - 1, device=x.device,
+                                                               dtype=int)
+        ids_keep = ids_shuffle[:, :len_keep]
+
+        x_masked = torch.gather(x, dim=1, index=ids_keep.unsqueeze(-1).repeat(1, 1, D))
+
+        x0 = x[:, 0, :]
+        x0 = x0.reshape(N, 1, D)
+        x_masked_add = torch.cat([x0, x_masked], axis=1)
+        return x_masked_add
+
+    def forward(self, x: torch.Tensor, mask_ratio: float = 0.0):
         x = self.conv1(x)  # shape = [*, width, grid, grid]
         x = x.reshape(x.shape[0], x.shape[1], -1)  # shape = [*, width, grid ** 2]
         x = x.permute(0, 2, 1)  # shape = [*, grid ** 2, width]
         x = torch.cat([self.class_embedding.to(x.dtype) + torch.zeros(x.shape[0], 1, x.shape[-1], dtype=x.dtype, device=x.device), x], dim=1)  # shape = [*, grid ** 2 + 1, width]
         x = x + self.positional_embedding.to(x.dtype)
+        if mask_ratio != 0:
+            x = self.random_masking(x, mask_ratio)
         x = self.ln_pre(x)
 
         x = x.permute(1, 0, 2)  # NLD -> LND
@@ -282,7 +300,7 @@ def __init__(self,
                  text_type_vocab_size: int,
                  tokenizer = _tokenizer,
                  # vision head width, added this param for ViT-H
-                 vision_head_width: int = 64,                 
+                 vision_head_width: int = 64,
                  ):
         super().__init__()
 
@@ -357,23 +375,23 @@ def set_grad_checkpointing(self, enable=True):
     def dtype(self):
         return self.visual.conv1.weight.dtype
 
-    def encode_image(self, image):
-        return self.visual(image.type(self.dtype))
+    def encode_image(self, image, mask_ratio=0):
+        return self.visual(image.type(self.dtype), mask_ratio)
 
     def encode_text(self, text):
         pad_index = self.tokenizer.vocab['[PAD]']
         attn_mask = text.ne(pad_index).type(self.dtype)
         x = self.bert(text, attention_mask=attn_mask)[0].type(self.dtype) # [batch_size, seq_length, hidden_size]
         return x[:, 0, :] @ self.text_projection
 
-    def forward(self, image, text):
+    def forward(self, image, text, mask_ratio=0):
         assert image is not None or text is not None, "text and image cannot both be None!"
 
         if image is None:
             return self.encode_text(text)
         elif text is None:
             return self.encode_image(image)
-        image_features = self.encode_image(image)
+        image_features = self.encode_image(image, mask_ratio)
         text_features = self.encode_text(text)
 
         image_features = image_features / image_features.norm(dim=-1, keepdim=True)
@@ -500,4 +518,4 @@ def parse(x):
 to_2tuple = _ntuple(2)
 to_3tuple = _ntuple(3)
 to_4tuple = _ntuple(4)
-to_ntuple = lambda n, x: _ntuple(n)(x)
+to_ntuple = lambda n, x: _ntuple(n)(x)

cn_clip/training/main.py

@@ -88,7 +88,7 @@ def main():
             model_info['vision_layers'] = eval(model_info['vision_layers'])         
         for k, v in json.load(ft).items():
             model_info[k] = v
-    
+
     model = CLIP(**model_info)
     if args.clip_weight_path is not None:
         assert os.path.exists(args.clip_weight_path), "Pretrained CLIP weight not exists!"

cn_clip/training/params.py

@@ -129,6 +129,12 @@ def parse_args():
         default="ViT-B-16",
         help="Name of the vision backbone to use.",
     )
+    parser.add_argument(
+        "--mask_ratio",
+        default=0,
+        type=float,
+        help="mask ratio of patches.",
+    )
     parser.add_argument(
         "--clip-weight-path",
         default=None,

cn_clip/training/train.py

@@ -16,7 +16,7 @@ def is_master(args):
     return args.rank == 0
 
 def get_loss(model, images, texts, loss_img, loss_txt, args):
-    image_features, text_features, logit_scale = model(images, texts)
+    image_features, text_features, logit_scale = model(images, texts, args.mask_ratio)
     logit_scale = logit_scale.mean()
     if args.aggregate:
         world_size = dist.get_world_size()

run_scripts/flickr30k_finetune_vit-b-16_rbt-base_flip.sh

@@ -0,0 +1,85 @@
+#!/usr/bin/env
+
+# Guide:
+# This script supports distributed training on multi-gpu workers (as well as single-worker training). 
+# Please set the options below according to the comments. 
+# For multi-gpu workers training, these options should be manually set for each worker. 
+# After setting the options, please run the script on each worker.
+# Command: bash run_scripts/muge_finetune_vit-b-16_rbt-base.sh ${DATAPATH}
+
+# Number of GPUs per GPU worker
+GPUS_PER_NODE=8 
+# Number of GPU workers, for single-worker training, please set to 1
+WORKER_CNT=1
+# The ip address of the rank-0 worker, for single-worker training, please set to localhost
+export MASTER_ADDR=XX.XX.XX.XX
+# The port for communication
+export MASTER_PORT=8514
+# The rank of this worker, should be in {0, ..., WORKER_CNT-1}, for single-worker training, please set to 0
+export RANK=0 
+
+export PYTHONPATH=${PYTHONPATH}:`pwd`/cn_clip/
+
+DATAPATH=${1}
+
+# data options
+train_data=${DATAPATH}/datasets/Flickr30k-CN/lmdb/train
+val_data=${DATAPATH}/datasets/Flickr30k-CN/lmdb/valid # if val_data is not specified, the validation will be automatically disabled
+
+# restore options
+resume=${DATAPATH}/pretrained_weights/clip_cn_vit-b-16.pt # or specify your customed ckpt path to resume
+reset_data_offset="--reset-data-offset"
+reset_optimizer="--reset-optimizer"
+# reset_optimizer=""
+
+# output options
+output_base_dir=${DATAPATH}/experiments/
+name=flickr30k_finetune_vit-b-16_roberta-base_bs128_8gpu
+save_step_frequency=999999 # disable it
+save_epoch_frequency=1
+log_interval=1
+report_training_batch_acc="--report-training-batch-acc"
+# report_training_batch_acc=""
+
+# training hyper-params
+context_length=52
+warmup=100
+batch_size=128
+valid_batch_size=128
+lr=5e-5
+wd=0.001
+max_epochs=3 # or specify your customed ckpt path to resume
+valid_step_interval=150
+valid_epoch_interval=1
+vision_model=ViT-B-16
+text_model=RoBERTa-wwm-ext-base-chinese
+mask_ratio=0.5 # use flip: set mask ratio
+use_augment="--use-augment"
+# use_augment=""
+
+python3 -m torch.distributed.launch --nproc_per_node=${GPUS_PER_NODE} --nnodes=${WORKER_CNT} --node_rank=${RANK} \
+          --master_addr=${MASTER_ADDR} --master_port=${MASTER_PORT} cn_clip/training/main.py \
+          --train-data=${train_data} \
+          --val-data=${val_data} \
+          --resume=${resume} \
+          ${reset_data_offset} \
+          ${reset_optimizer} \
+          --logs=${output_base_dir} \
+          --name=${name} \
+          --save-step-frequency=${save_step_frequency} \
+          --save-epoch-frequency=${save_epoch_frequency} \
+          --log-interval=${log_interval} \
+          ${report_training_batch_acc} \
+          --context-length=${context_length} \
+          --warmup=${warmup} \
+          --batch-size=${batch_size} \
+          --valid-batch-size=${valid_batch_size} \
+          --valid-step-interval=${valid_step_interval} \
+          --valid-epoch-interval=${valid_epoch_interval} \
+          --lr=${lr} \
+          --wd=${wd} \
+          --max-epochs=${max_epochs} \
+          --vision-model=${vision_model} \
+          --mask_ratio=${mask_ratio} \
+          ${use_augment} \
+          --text-model=${text_model}

run_scripts/muge_finetune_vit-b-16_rbt-base_flip.sh

@@ -0,0 +1,85 @@
+#!/usr/bin/env
+
+# Guide:
+# This script supports distributed training on multi-gpu workers (as well as single-worker training). 
+# Please set the options below according to the comments. 
+# For multi-gpu workers training, these options should be manually set for each worker. 
+# After setting the options, please run the script on each worker.
+# Command: bash run_scripts/muge_finetune_vit-b-16_rbt-base.sh ${DATAPATH}
+
+# Number of GPUs per GPU worker
+GPUS_PER_NODE=8 
+# Number of GPU workers, for single-worker training, please set to 1
+WORKER_CNT=1
+# The ip address of the rank-0 worker, for single-worker training, please set to localhost
+export MASTER_ADDR=XX.XX.XX.XX
+# The port for communication
+export MASTER_PORT=8514
+# The rank of this worker, should be in {0, ..., WORKER_CNT-1}, for single-worker training, please set to 0
+export RANK=0 
+
+export PYTHONPATH=${PYTHONPATH}:`pwd`/cn_clip/
+
+DATAPATH=${1}
+
+# data options
+train_data=${DATAPATH}/datasets/MUGE/lmdb/train
+val_data=${DATAPATH}/datasets/MUGE/lmdb/valid # if val_data is not specified, the validation will be automatically disabled
+
+# restore options
+resume=${DATAPATH}/pretrained_weights/clip_cn_vit-b-16.pt # or specify your customed ckpt path to resume
+reset_data_offset="--reset-data-offset"
+reset_optimizer="--reset-optimizer"
+# reset_optimizer=""
+
+# output options
+output_base_dir=${DATAPATH}/experiments/
+name=muge_finetune_vit-b-16_roberta-base_bs128_8gpu
+save_step_frequency=999999 # disable it
+save_epoch_frequency=1
+log_interval=1
+report_training_batch_acc="--report-training-batch-acc"
+# report_training_batch_acc=""
+
+# training hyper-params
+context_length=52
+warmup=100
+batch_size=128
+valid_batch_size=128
+lr=5e-5
+wd=0.001
+max_epochs=3 # or specify your customed ckpt path to resume
+valid_step_interval=150
+valid_epoch_interval=1
+vision_model=ViT-B-16
+text_model=RoBERTa-wwm-ext-base-chinese
+mask_ratio=0.5 # use flip: set mask ratio
+use_augment="--use-augment"
+# use_augment=""
+
+python3 -m torch.distributed.launch --nproc_per_node=${GPUS_PER_NODE} --nnodes=${WORKER_CNT} --node_rank=${RANK} \
+          --master_addr=${MASTER_ADDR} --master_port=${MASTER_PORT} cn_clip/training/main.py \
+          --train-data=${train_data} \
+          --val-data=${val_data} \
+          --resume=${resume} \
+          ${reset_data_offset} \
+          ${reset_optimizer} \
+          --logs=${output_base_dir} \
+          --name=${name} \
+          --save-step-frequency=${save_step_frequency} \
+          --save-epoch-frequency=${save_epoch_frequency} \
+          --log-interval=${log_interval} \
+          ${report_training_batch_acc} \
+          --context-length=${context_length} \
+          --warmup=${warmup} \
+          --batch-size=${batch_size} \
+          --valid-batch-size=${valid_batch_size} \
+          --valid-step-interval=${valid_step_interval} \
+          --valid-epoch-interval=${valid_epoch_interval} \
+          --lr=${lr} \
+          --wd=${wd} \
+          --max-epochs=${max_epochs} \
+          --vision-model=${vision_model} \
+          --mask_ratio=${mask_ratio} \
+          ${use_augment} \
+          --text-model=${text_model}