Sourcery refactored main branch #1

sourcery-ai · 2023-07-17T10:11:38Z

Branch main refactored by Sourcery.

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sourcery-ai

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sourcery-ai · 2023-07-17T10:11:41Z

XTI_hijack.py

-            return (sample,)
-
-        return UNet2DConditionOutput(sample=sample)
+        return (sample, ) if not return_dict else UNet2DConditionOutput(sample=sample)


Function unet_forward_XTI refactored with the following changes:

Lift code into else after jump in control flow (reintroduce-else)

Replace if statement with if expression (assign-if-exp)

sourcery-ai · 2023-07-17T10:11:41Z

fine_tune.py

-        save_stable_diffusion_format = args.save_model_as.lower() == "ckpt" or args.save_model_as.lower() == "safetensors"
+        save_stable_diffusion_format = args.save_model_as.lower() in [
+            "ckpt",
+            "safetensors",
+        ]


Function train refactored with the following changes:

Replace multiple comparisons of same variable with in operator (merge-comparisons)

sourcery-ai · 2023-07-17T10:11:42Z

bitsandbytes_windows/cextension.py

-            # self.lib = ct.cdll.LoadLibrary(binary_path)
-            self.lib = ct.cdll.LoadLibrary(str(binary_path))            # $$$
        else:
            print(f"CUDA SETUP: Loading binary {binary_path}...")
-            # self.lib = ct.cdll.LoadLibrary(binary_path)
-            self.lib = ct.cdll.LoadLibrary(str(binary_path))            # $$$
+
+        # self.lib = ct.cdll.LoadLibrary(binary_path)
+        self.lib = ct.cdll.LoadLibrary(str(binary_path))            # $$$


Function CUDALibrary_Singleton.initialize refactored with the following changes:

Hoist repeated code outside conditional statement (hoist-statement-from-if)

sourcery-ai · 2023-07-17T10:11:42Z

bitsandbytes_windows/main.py

-        # TODO: handle different compute capabilities; for now, take the max
-        return ccs[-1]
-    return None
+    return ccs[-1] if ccs is not None else None


Function get_compute_capability refactored with the following changes:

Lift code into else after jump in control flow (reintroduce-else)

Replace if statement with if expression (assign-if-exp)

This removes the following comments ( why? ):

# TODO: handle different compute capabilities; for now, take the max

sourcery-ai · 2023-07-17T10:11:42Z

bitsandbytes_windows/main.py

-
-    binary_name = "libbitsandbytes_cpu.so"
-    #if not torch.cuda.is_available():
-        #print('No GPU detected. Loading CPU library...')
-        #return binary_name
-
-    cudart_path = determine_cuda_runtime_lib_path()
-    if cudart_path is None:
-        print(
-            "WARNING: No libcudart.so found! Install CUDA or the cudatoolkit package (anaconda)!"
-        )
-        return binary_name
-
-    print(f"CUDA SETUP: CUDA runtime path found: {cudart_path}")
-    cuda = get_cuda_lib_handle()
-    cc = get_compute_capability(cuda)
-    print(f"CUDA SETUP: Highest compute capability among GPUs detected: {cc}")
-    cuda_version_string = get_cuda_version(cuda, cudart_path)
-
-
-    if cc == '':
-        print(
-            "WARNING: No GPU detected! Check your CUDA paths. Processing to load CPU-only library..."
-        )
-        return binary_name
-
-    # 7.5 is the minimum CC vor cublaslt
-    has_cublaslt = cc in ["7.5", "8.0", "8.6"]
-
-    # TODO:
-    # (1) CUDA missing cases (no CUDA installed by CUDA driver (nvidia-smi accessible)
-    # (2) Multiple CUDA versions installed
-
-    # we use ls -l instead of nvcc to determine the cuda version
-    # since most installations will have the libcudart.so installed, but not the compiler
-    print(f'CUDA SETUP: Detected CUDA version {cuda_version_string}')
-
-    def get_binary_name():
-        "if not has_cublaslt (CC < 7.5), then we have to choose  _nocublaslt.so"
-        bin_base_name = "libbitsandbytes_cuda"
-        if has_cublaslt:
-            return f"{bin_base_name}{cuda_version_string}.so"
-        else:
-            return f"{bin_base_name}{cuda_version_string}_nocublaslt.so"
-
-    binary_name = get_binary_name()
-
-    return binary_name


Function evaluate_cuda_setup refactored with the following changes:

Remove unreachable code (remove-unreachable-code)

This removes the following comments ( why? ):

# TODO: #if not torch.cuda.is_available(): # we use ls -l instead of nvcc to determine the cuda version # (2) Multiple CUDA versions installed # 7.5 is the minimum CC vor cublaslt #return binary_name # (1) CUDA missing cases (no CUDA installed by CUDA driver (nvidia-smi accessible) #print('No GPU detected. Loading CPU library...') # since most installations will have the libcudart.so installed, but not the compiler

sourcery-ai · 2023-07-17T10:11:45Z

finetune/blip/blip.py

-        image_embeds = self.visual_encoder(image) 
+        image_embeds = self.visual_encoder(image)
        image_atts = torch.ones(image_embeds.size()[:-1],dtype=torch.long).to(image.device)
-        
+
        text = self.tokenizer(caption, padding='longest', truncation=True, max_length=40, return_tensors="pt").to(image.device) 
-        
+
        text.input_ids[:,0] = self.tokenizer.bos_token_id
-        
-        decoder_targets = text.input_ids.masked_fill(text.input_ids == self.tokenizer.pad_token_id, -100)         
+
+        decoder_targets = text.input_ids.masked_fill(text.input_ids == self.tokenizer.pad_token_id, -100)
        decoder_targets[:,:self.prompt_length] = -100
-     
+
        decoder_output = self.text_decoder(text.input_ids, 
                                           attention_mask = text.attention_mask, 
                                           encoder_hidden_states = image_embeds,
                                           encoder_attention_mask = image_atts,                  
                                           labels = decoder_targets,
                                           return_dict = True,   
-                                          )   
-        loss_lm = decoder_output.loss
-
-        return loss_lm
+                                          )
+        return decoder_output.loss


Function BLIP_Decoder.forward refactored with the following changes:

Inline variable that is immediately returned (inline-immediately-returned-variable)

sourcery-ai · 2023-07-17T10:11:45Z

finetune/blip/blip.py

-        
+
    state_dict = checkpoint['model']
-    
-    state_dict['visual_encoder.pos_embed'] = interpolate_pos_embed(state_dict['visual_encoder.pos_embed'],model.visual_encoder) 
+
+    state_dict['visual_encoder.pos_embed'] = interpolate_pos_embed(state_dict['visual_encoder.pos_embed'],model.visual_encoder)
    if 'visual_encoder_m.pos_embed' in model.state_dict().keys():
        state_dict['visual_encoder_m.pos_embed'] = interpolate_pos_embed(state_dict['visual_encoder_m.pos_embed'],
-                                                                         model.visual_encoder_m)    
+                                                                         model.visual_encoder_m)
    for key in model.state_dict().keys():
        if key in state_dict.keys():
            if state_dict[key].shape!=model.state_dict()[key].shape:
                del state_dict[key]
-    
+
    msg = model.load_state_dict(state_dict,strict=False)
-    print('load checkpoint from %s'%url_or_filename)  
+    print(f'load checkpoint from {url_or_filename}')


Function load_checkpoint refactored with the following changes:

Replace interpolated string formatting with f-string (replace-interpolation-with-fstring)

sourcery-ai · 2023-07-17T10:11:46Z

finetune/blip/med.py

                "heads (%d)" % (config.hidden_size, config.num_attention_heads)
            )
-        
+


Function BertSelfAttention.__init__ refactored with the following changes:

Replace multiple comparisons of same variable with in operator (merge-comparisons)

sourcery-ai · 2023-07-17T10:11:46Z

finetune/blip/med.py

        attention_scores = torch.matmul(query_layer, key_layer.transpose(-1, -2))

-        if self.position_embedding_type == "relative_key" or self.position_embedding_type == "relative_key_query":
+        if self.position_embedding_type in ["relative_key", "relative_key_query"]:


Function BertSelfAttention.forward refactored with the following changes:

Split conditional into multiple branches (split-or-ifs)

Merge duplicate blocks in conditional (merge-duplicate-blocks)

Remove redundant conditional (remove-redundant-if)

Replace multiple comparisons of same variable with in operator (merge-comparisons)

sourcery-ai · 2023-07-17T10:11:46Z

finetune/blip/med.py

        attention_output = self.output(self_outputs[0], hidden_states)
-        outputs = (attention_output,) + self_outputs[1:]  # add attentions if we output them
-        return outputs
+        return (attention_output,) + self_outputs[1:]


Function BertAttention.forward refactored with the following changes:

Inline variable that is immediately returned (inline-immediately-returned-variable)

This removes the following comments ( why? ):

# add attentions if we output them

sourcery-ai · 2023-07-17T10:11:51Z

finetune/blip/med.py

-        layer_output = self.output(intermediate_output, attention_output)
-        return layer_output
+        return self.output(intermediate_output, attention_output)


Function BertLayer.feed_forward_chunk refactored with the following changes:

Inline variable that is immediately returned (inline-immediately-returned-variable)

sourcery-ai · 2023-07-17T10:11:51Z

finetune/blip/med.py

-        prediction_scores = self.predictions(sequence_output)
-        return prediction_scores
+        return self.predictions(sequence_output)


Function BertOnlyMLMHead.forward refactored with the following changes:

Inline variable that is immediately returned (inline-immediately-returned-variable)

sourcery-ai · 2023-07-17T10:11:51Z

finetune/blip/med.py

-   
+


Function BertModel.get_extended_attention_mask refactored with the following changes:

Replace call to format with f-string (use-fstring-for-formatting)

sourcery-ai · 2023-07-17T10:11:52Z

finetune/blip/vit.py

    for i, block in enumerate(model.blocks.children()):
        block_prefix = f'{prefix}Transformer/encoderblock_{i}/'
-        mha_prefix = block_prefix + 'MultiHeadDotProductAttention_1/'
+        mha_prefix = f'{block_prefix}MultiHeadDotProductAttention_1/'


Function _load_weights refactored with the following changes:

Use f-string instead of string concatenation (use-fstring-for-concatenation)

sourcery-ai · 2023-07-17T10:11:52Z

finetune/blip/vit.py

-    # interpolate position embedding
-    embedding_size = pos_embed_checkpoint.shape[-1]


Function interpolate_pos_embed refactored with the following changes:

Move assignments closer to their usage (move-assign)

Swap if/else branches (swap-if-else-branches)

Remove unnecessary else after guard condition (remove-unnecessary-else)

sourcery-ai · 2023-07-17T10:11:54Z

library/lpw_stable_diffusion.py

-        for module in self.unet.modules():
-            if (
-                hasattr(module, "_hf_hook")
-                and hasattr(module._hf_hook, "execution_device")
-                and module._hf_hook.execution_device is not None
-            ):
-                return torch.device(module._hf_hook.execution_device)
-        return self.device
+        return next(
+            (
+                torch.device(module._hf_hook.execution_device)
+                for module in self.unet.modules()
+                if (
+                    hasattr(module, "_hf_hook")
+                    and hasattr(module._hf_hook, "execution_device")
+                    and module._hf_hook.execution_device is not None
+                )
+            ),
+            self.device,
+        )


Function StableDiffusionLongPromptWeightingPipeline._execution_device refactored with the following changes:

Use the built-in function next instead of a for-loop (use-next)

sourcery-ai · 2023-07-17T10:11:54Z

library/lpw_stable_diffusion.py

-        if (callback_steps is None) or (
-            callback_steps is not None and (not isinstance(callback_steps, int) or callback_steps <= 0)
+        if (
+            callback_steps is None
+            or not isinstance(callback_steps, int)
+            or callback_steps <= 0


Function StableDiffusionLongPromptWeightingPipeline.check_inputs refactored with the following changes:

Remove redundant conditional (remove-redundant-if)

sourcery-ai · 2023-07-17T10:11:54Z

library/lpw_stable_diffusion.py

-        else:
-            # get the original timestep using init_timestep
-            offset = self.scheduler.config.get("steps_offset", 0)
-            init_timestep = int(num_inference_steps * strength) + offset
-            init_timestep = min(init_timestep, num_inference_steps)
+        # get the original timestep using init_timestep
+        offset = self.scheduler.config.get("steps_offset", 0)
+        init_timestep = int(num_inference_steps * strength) + offset
+        init_timestep = min(init_timestep, num_inference_steps)

-            t_start = max(num_inference_steps - init_timestep + offset, 0)
-            timesteps = self.scheduler.timesteps[t_start:].to(device)
-            return timesteps, num_inference_steps - t_start
+        t_start = max(num_inference_steps - init_timestep + offset, 0)
+        timesteps = self.scheduler.timesteps[t_start:].to(device)
+        return timesteps, num_inference_steps - t_start


Function StableDiffusionLongPromptWeightingPipeline.get_timesteps refactored with the following changes:

Remove unnecessary else after guard condition (remove-unnecessary-else)

sourcery-ai · 2023-07-17T10:11:54Z

library/lpw_stable_diffusion.py

-            else:
-                if latents.shape != shape:
-                    raise ValueError(f"Unexpected latents shape, got {latents.shape}, expected {shape}")
+            elif latents.shape == shape:
                latents = latents.to(device)

+            else:
+                raise ValueError(f"Unexpected latents shape, got {latents.shape}, expected {shape}")


Function StableDiffusionLongPromptWeightingPipeline.prepare_latents refactored with the following changes:

Merge else clause's nested if statement into elif (merge-else-if-into-elif)

Lift code into else after jump in control flow (reintroduce-else)

Swap if/else branches (swap-if-else-branches)

sourcery-ai · 2023-07-17T10:11:54Z

library/model_util.py

-
-        mapping.append({"old": old_item, "new": new_item})
-
+        mapping.append({"old": new_item, "new": new_item})


Function renew_attention_paths refactored with the following changes:

Use previously assigned local variable (use-assigned-variable)

This removes the following comments ( why? ):

# new_item = new_item.replace('norm.bias', 'group_norm.bias') # new_item = shave_segments(new_item, n_shave_prefix_segments=n_shave_prefix_segments) # new_item = new_item.replace('proj_out.weight', 'proj_attn.weight') # new_item = new_item.replace('norm.weight', 'group_norm.weight') # new_item = new_item.replace('proj_out.bias', 'proj_attn.bias')

sourcery-ai · 2023-07-17T10:11:58Z

library/model_util.py

-    # extract state_dict for UNet
-    unet_state_dict = {}
    unet_key = "model.diffusion_model."
    keys = list(checkpoint.keys())
-    for key in keys:
-        if key.startswith(unet_key):
-            unet_state_dict[key.replace(unet_key, "")] = checkpoint.pop(key)
-
-    new_checkpoint = {}
-
-    new_checkpoint["time_embedding.linear_1.weight"] = unet_state_dict["time_embed.0.weight"]
-    new_checkpoint["time_embedding.linear_1.bias"] = unet_state_dict["time_embed.0.bias"]
-    new_checkpoint["time_embedding.linear_2.weight"] = unet_state_dict["time_embed.2.weight"]
-    new_checkpoint["time_embedding.linear_2.bias"] = unet_state_dict["time_embed.2.bias"]
-
-    new_checkpoint["conv_in.weight"] = unet_state_dict["input_blocks.0.0.weight"]
-    new_checkpoint["conv_in.bias"] = unet_state_dict["input_blocks.0.0.bias"]
-
-    new_checkpoint["conv_norm_out.weight"] = unet_state_dict["out.0.weight"]
-    new_checkpoint["conv_norm_out.bias"] = unet_state_dict["out.0.bias"]
-    new_checkpoint["conv_out.weight"] = unet_state_dict["out.2.weight"]
-    new_checkpoint["conv_out.bias"] = unet_state_dict["out.2.bias"]
+    unet_state_dict = {
+        key.replace(unet_key, ""): checkpoint.pop(key)
+        for key in keys
+        if key.startswith(unet_key)
+    }
+    new_checkpoint = {
+        "time_embedding.linear_1.weight": unet_state_dict[
+            "time_embed.0.weight"
+        ],
+        "time_embedding.linear_1.bias": unet_state_dict["time_embed.0.bias"],
+        "time_embedding.linear_2.weight": unet_state_dict[
+            "time_embed.2.weight"
+        ],
+        "time_embedding.linear_2.bias": unet_state_dict["time_embed.2.bias"],
+        "conv_in.weight": unet_state_dict["input_blocks.0.0.weight"],
+        "conv_in.bias": unet_state_dict["input_blocks.0.0.bias"],
+        "conv_norm_out.weight": unet_state_dict["out.0.weight"],
+        "conv_norm_out.bias": unet_state_dict["out.0.bias"],
+        "conv_out.weight": unet_state_dict["out.2.weight"],
+        "conv_out.bias": unet_state_dict["out.2.bias"],
+    }


Function convert_ldm_unet_checkpoint refactored with the following changes:

Move assignment closer to its usage within a block (move-assign-in-block)

Merge dictionary assignment with declaration [×10] (merge-dict-assign)

Convert for loop into dictionary comprehension (dict-comprehension)

This removes the following comments ( why? ):

# extract state_dict for UNet

sourcery-ai · 2023-07-17T10:11:58Z

library/model_util.py

-    # extract state dict for VAE
-    vae_state_dict = {}
    vae_key = "first_stage_model."
    keys = list(checkpoint.keys())
-    for key in keys:
-        if key.startswith(vae_key):
-            vae_state_dict[key.replace(vae_key, "")] = checkpoint.get(key)
+    vae_state_dict = {
+        key.replace(vae_key, ""): checkpoint.get(key)
+        for key in keys
+        if key.startswith(vae_key)
+    }
    # if len(vae_state_dict) == 0:
    #   # 渡されたcheckpointは.ckptから読み込んだcheckpointではなくvaeのstate_dict
    #   vae_state_dict = checkpoint

-    new_checkpoint = {}
-
-    new_checkpoint["encoder.conv_in.weight"] = vae_state_dict["encoder.conv_in.weight"]
-    new_checkpoint["encoder.conv_in.bias"] = vae_state_dict["encoder.conv_in.bias"]
-    new_checkpoint["encoder.conv_out.weight"] = vae_state_dict["encoder.conv_out.weight"]
-    new_checkpoint["encoder.conv_out.bias"] = vae_state_dict["encoder.conv_out.bias"]
-    new_checkpoint["encoder.conv_norm_out.weight"] = vae_state_dict["encoder.norm_out.weight"]
-    new_checkpoint["encoder.conv_norm_out.bias"] = vae_state_dict["encoder.norm_out.bias"]
-
-    new_checkpoint["decoder.conv_in.weight"] = vae_state_dict["decoder.conv_in.weight"]
-    new_checkpoint["decoder.conv_in.bias"] = vae_state_dict["decoder.conv_in.bias"]
-    new_checkpoint["decoder.conv_out.weight"] = vae_state_dict["decoder.conv_out.weight"]
-    new_checkpoint["decoder.conv_out.bias"] = vae_state_dict["decoder.conv_out.bias"]
-    new_checkpoint["decoder.conv_norm_out.weight"] = vae_state_dict["decoder.norm_out.weight"]
-    new_checkpoint["decoder.conv_norm_out.bias"] = vae_state_dict["decoder.norm_out.bias"]
-
-    new_checkpoint["quant_conv.weight"] = vae_state_dict["quant_conv.weight"]
-    new_checkpoint["quant_conv.bias"] = vae_state_dict["quant_conv.bias"]
-    new_checkpoint["post_quant_conv.weight"] = vae_state_dict["post_quant_conv.weight"]
-    new_checkpoint["post_quant_conv.bias"] = vae_state_dict["post_quant_conv.bias"]
+    new_checkpoint = {
+        "encoder.conv_in.weight": vae_state_dict["encoder.conv_in.weight"],
+        "encoder.conv_in.bias": vae_state_dict["encoder.conv_in.bias"],
+        "encoder.conv_out.weight": vae_state_dict["encoder.conv_out.weight"],
+        "encoder.conv_out.bias": vae_state_dict["encoder.conv_out.bias"],
+        "encoder.conv_norm_out.weight": vae_state_dict[
+            "encoder.norm_out.weight"
+        ],
+        "encoder.conv_norm_out.bias": vae_state_dict["encoder.norm_out.bias"],
+        "decoder.conv_in.weight": vae_state_dict["decoder.conv_in.weight"],
+        "decoder.conv_in.bias": vae_state_dict["decoder.conv_in.bias"],
+        "decoder.conv_out.weight": vae_state_dict["decoder.conv_out.weight"],
+        "decoder.conv_out.bias": vae_state_dict["decoder.conv_out.bias"],
+        "decoder.conv_norm_out.weight": vae_state_dict[
+            "decoder.norm_out.weight"
+        ],
+        "decoder.conv_norm_out.bias": vae_state_dict["decoder.norm_out.bias"],
+        "quant_conv.weight": vae_state_dict["quant_conv.weight"],
+        "quant_conv.bias": vae_state_dict["quant_conv.bias"],
+        "post_quant_conv.weight": vae_state_dict["post_quant_conv.weight"],
+        "post_quant_conv.bias": vae_state_dict["post_quant_conv.bias"],
+    }


Function convert_ldm_vae_checkpoint refactored with the following changes:

Move assignment closer to its usage within a block (move-assign-in-block)

Merge dictionary assignment with declaration [×16] (merge-dict-assign)

Convert for loop into dictionary comprehension (dict-comprehension)

This removes the following comments ( why? ):

# extract state dict for VAE

sourcery-ai · 2023-07-17T10:11:59Z

library/model_util.py

-    for i in range(len(block_out_channels)):
+    for _ in block_out_channels:


Function create_unet_diffusers_config refactored with the following changes:

Replace unused for index with underscore [×2] (for-index-underscore)

Replace index in for loop with direct reference (for-index-replacement)

sourcery-ai · 2023-07-17T10:11:59Z

library/model_util.py

-    config = dict(
+    return dict(


Function create_vae_diffusers_config refactored with the following changes:

Inline variable that is immediately returned (inline-immediately-returned-variable)

sourcery-ai · 2023-07-17T10:11:59Z

library/model_util.py

-    text_model_dict = {}
-    for key in keys:
-        if key.startswith("cond_stage_model.transformer"):
-            text_model_dict[key[len("cond_stage_model.transformer.") :]] = checkpoint[key]
-    return text_model_dict
+    return {
+        key[len("cond_stage_model.transformer.") :]: checkpoint[key]
+        for key in keys
+        if key.startswith("cond_stage_model.transformer")
+    }


Function convert_ldm_clip_checkpoint_v1 refactored with the following changes:

Convert for loop into dictionary comprehension (dict-comprehension)

Inline variable that is immediately returned (inline-immediately-returned-variable)

sourcery-ai · 2023-07-17T10:12:01Z

library/train_util.py

-            if reso in self.predefined_resos_set:
-                pass
-            else:
+            if reso not in self.predefined_resos_set:


Function BucketManager.select_bucket refactored with the following changes:

Swap if/else to remove empty if body (remove-pass-body)

sourcery-ai · 2023-07-17T10:12:01Z

library/train_util.py

-            self.caption_extension = "." + self.caption_extension
+            self.caption_extension = f".{self.caption_extension}"


Function DreamBoothSubset.__init__ refactored with the following changes:

Use f-string instead of string concatenation (use-fstring-for-concatenation)

sourcery-ai · 2023-07-17T10:12:01Z

library/train_util.py

-        if not self.current_epoch == epoch:  # epochが切り替わったらバケツをシャッフルする
+        if self.current_epoch != epoch:  # epochが切り替わったらバケツをシャッフルする


Function BaseDataset.set_current_epoch refactored with the following changes:

Simplify logical expression using De Morgan identities (de-morgan)

sourcery-ai · 2023-07-17T10:12:01Z

library/train_util.py

-                tag = tag.strip()
-                if tag:
+                if tag := tag.strip():


Function BaseDataset.set_tag_frequency refactored with the following changes:

Use named expression to simplify assignment and conditional (use-named-expression)

sourcery-ai · 2023-07-17T10:12:01Z

library/train_util.py

-                    if type(str_to) == list:
-                        caption = random.choice(str_to)
-                    else:
-                        caption = str_to
+                    caption = random.choice(str_to) if type(str_to) == list else str_to


Function BaseDataset.process_caption refactored with the following changes:

Replace if statement with if expression (assign-if-exp)

'Refactored by Sourcery'

02bbce3

sourcery-ai bot requested a review from admariner July 17, 2023 10:11

sourcery-ai bot commented Jul 17, 2023

View reviewed changes

		"heads (%d)" % (config.hidden_size, config.num_attention_heads)
		)

		# interpolate position embedding
		embedding_size = pos_embed_checkpoint.shape[-1]


		mapping.append({"old": old_item, "new": new_item})

		mapping.append({"old": new_item, "new": new_item})

		for i in range(len(block_out_channels)):
		for _ in block_out_channels:

		self.caption_extension = "." + self.caption_extension
		self.caption_extension = f".{self.caption_extension}"

		if not self.current_epoch == epoch: # epochが切り替わったらバケツをシャッフルする
		if self.current_epoch != epoch: # epochが切り替わったらバケツをシャッフルする

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