Update demo_support/generation.py

demo_support/generation.py

@@ -1,204 +1,204 @@
-import torch
-import torch_redstone as rst
-import transformers
-import numpy as np
-from torch import nn
-from typing import Tuple, List, Union, Optional
-from transformers import GPT2Tokenizer, GPT2LMHeadModel, GPT2Config
-from huggingface_hub import hf_hub_download
-from diffusers import StableUnCLIPImg2ImgPipeline
-
-
-class Wrapper(transformers.modeling_utils.PreTrainedModel):
-    def __init__(self) -> None:
-        super().__init__(transformers.configuration_utils.PretrainedConfig())
-        self.param = torch.nn.Parameter(torch.tensor(0.))
-
-    def forward(self, x):
-        return rst.ObjectProxy(image_embeds=x)
-
-class MLP(nn.Module):
-
-    def forward(self, x: T) -> T:
-        return self.model(x)
-
-    def __init__(self, sizes: Tuple[int, ...], bias=True, act=nn.Tanh):
-        super(MLP, self).__init__()
-        layers = []
-        for i in range(len(sizes) -1):
-            layers.append(nn.Linear(sizes[i], sizes[i + 1], bias=bias))
-            if i < len(sizes) - 2:
-                layers.append(act())
-        self.model = nn.Sequential(*layers)
-
-class ClipCaptionModel(nn.Module):
-
-    #@functools.lru_cache #FIXME
-    def get_dummy_token(self, batch_size: int, device: D) -> T:
-        return torch.zeros(batch_size, self.prefix_length, dtype=torch.int64, device=device)
-
-    def forward(self, tokens: T, prefix: T, mask: Optional[T] = None, labels: Optional[T] = None):
-        embedding_text = self.gpt.transformer.wte(tokens)
-        prefix_projections = self.clip_project(prefix).view(-1, self.prefix_length, self.gpt_embedding_size)
-        #print(embedding_text.size()) #torch.Size([5, 67, 768])
-        #print(prefix_projections.size()) #torch.Size([5, 1, 768])
-        embedding_cat = torch.cat((prefix_projections, embedding_text), dim=1)
-        if labels is not None:
-            dummy_token = self.get_dummy_token(tokens.shape[0], tokens.device)
-            labels = torch.cat((dummy_token, tokens), dim=1)
-        out = self.gpt(inputs_embeds=embedding_cat, labels=labels, attention_mask=mask)
-        return out
-
-    def __init__(self, prefix_length: int, prefix_size: int = 512):
-        super(ClipCaptionModel, self).__init__()
-        self.prefix_length = prefix_length
-        self.gpt = GPT2LMHeadModel(GPT2Config.from_pretrained('gpt2'))
-        self.gpt_embedding_size = self.gpt.transformer.wte.weight.shape[1]
-        if prefix_length > 10:  # not enough memory
-            self.clip_project = nn.Linear(prefix_size, self.gpt_embedding_size * prefix_length)
-        else:
-            self.clip_project = MLP((prefix_size, (self.gpt_embedding_size * prefix_length) // 2, self.gpt_embedding_size * prefix_length))
-
-class ClipCaptionPrefix(ClipCaptionModel):
-
-    def parameters(self, recurse: bool = True):
-        return self.clip_project.parameters()
-
-    def train(self, mode: bool = True):
-        super(ClipCaptionPrefix, self).train(mode)
-        self.gpt.eval()
-        return self
-
-def generate2(
-    model,
-    tokenizer,
-    tokens=None,
-    prompt=None,
-    embed=None,
-    entry_count=1,
-    entry_length=67,  # maximum number of words
-    top_p=0.8,
-    temperature=1.,
-    stop_token: str = '.',
-):
-    model.eval()
-    generated_num = 0
-    generated_list = []
-    stop_token_index = tokenizer.encode(stop_token)[0]
-    filter_value = -float("Inf")
-    device = next(model.parameters()).device
-    score_col = []
-    with torch.no_grad():
-
-        for entry_idx in range(entry_count):
-            if embed is not None:
-                generated = embed
-            else:
-                if tokens is None:
-                    tokens = torch.tensor(tokenizer.encode(prompt))
-                    tokens = tokens.unsqueeze(0).to(device)
-
-                generated = model.gpt.transformer.wte(tokens)
-
-            for i in range(entry_length):
-
-                outputs = model.gpt(inputs_embeds=generated)
-                logits = outputs.logits
-                logits = logits[:, -1, :] / (temperature if temperature > 0 else 1.0)
-                sorted_logits, sorted_indices = torch.sort(logits, descending=True)
-                cumulative_probs = torch.cumsum(torch.softmax(sorted_logits, dim=-1), dim=-1)
-                sorted_indices_to_remove = cumulative_probs > top_p
-                sorted_indices_to_remove[..., 1:] = sorted_indices_to_remove[
-                                                    ..., :-1
-                                                    ].clone()
-                sorted_indices_to_remove[..., 0] = 0
-
-                indices_to_remove = sorted_indices[sorted_indices_to_remove]
-                logits[:, indices_to_remove] = filter_value
-                next_token = torch.argmax(torch.softmax(logits, dim=-1), -1).reshape(1, 1)
-                score = torch.softmax(logits, dim=-1).reshape(-1)[next_token.item()].item()
-                score_col.append(score)
-                next_token_embed = model.gpt.transformer.wte(next_token)
-                if tokens is None:
-                    tokens = next_token
-                else:
-                    tokens = torch.cat((tokens, next_token), dim=1)
-                generated = torch.cat((generated, next_token_embed), dim=1)
-                if stop_token_index == next_token.item():
-                    break
-
-            output_list = list(tokens.squeeze(0).cpu().numpy())
-            output_text = tokenizer.decode(output_list)
-            generated_list.append(output_text)
-    return generated_list[0]
-
-
-@torch.no_grad()
-def pc_to_text(pc_encoder: torch.nn.Module, pc, cond_scale):
-    ref_dev = next(pc_encoder.parameters()).device
-    prefix = pc_encoder(torch.tensor(pc.T[None], device=ref_dev))
-    prefix = prefix.float() * cond_scale
-    prefix = prefix.to(next(model.parameters()).device)
-    prefix_embed = model.clip_project(prefix).reshape(1, prefix_length, -1)
-    text = generate2(model, tokenizer, embed=prefix_embed)
-    return text
-
-@torch.no_grad()
-def pc_to_image(pc_encoder: torch.nn.Module, pc, prompt, noise_level, width, height, cfg_scale, num_steps, callback):
-    ref_dev = next(pc_encoder.parameters()).device
-    enc = pc_encoder(torch.tensor(pc.T[None], device=ref_dev))
-    enc = torch.nn.functional.normalize(enc, dim=-1) * (768 ** 0.5) / 2
-    if torch.cuda.is_available():
-        enc = enc.to('cuda:' + str(torch.cuda.current_device()))
-    # enc = enc.type(half)
-    # with torch.autocast("cuda"):
-    return pipe(
-        prompt=', '.join(["best quality"] + ([prompt] if prompt else [])),
-        negative_prompt="cropped, worst quality, low quality, normal quality, jpeg artifacts, signature, watermark, username, blurry",
-        image=enc,
-        width=width, height=height,
-        guidance_scale=cfg_scale,
-        noise_level=noise_level,
-        callback=callback,
-        num_inference_steps=num_steps
-    ).images[0]
-
-
-N = type(None)
-V = np.array
-ARRAY = np.ndarray
-ARRAYS = Union[Tuple[ARRAY, ...], List[ARRAY]]
-VS = Union[Tuple[V, ...], List[V]]
-VN = Union[V, N]
-VNS = Union[VS, N]
-T = torch.Tensor
-TS = Union[Tuple[T, ...], List[T]]
-TN = Optional[T]
-TNS = Union[Tuple[TN, ...], List[TN]]
-TSN = Optional[TS]
-TA = Union[T, ARRAY]
-
-
-D = torch.device
-
-pipe = StableUnCLIPImg2ImgPipeline.from_pretrained(
-    "diffusers/stable-diffusion-2-1-unclip-i2i-l",
-    # variant="fp16",
-    image_encoder = Wrapper()
-)
-# pe = pipe.text_encoder.text_model.embeddings
-# pe.position_ids = torch.arange(pe.position_ids.shape[-1]).expand((1, -1)).to(pe.position_ids)  # workaround
-if torch.cuda.is_available():
-    pipe = pipe.to('cuda:' + str(torch.cuda.current_device()))
-    pipe.enable_model_cpu_offload(torch.cuda.current_device())
-pipe.enable_attention_slicing()
-pipe.enable_vae_slicing()
-
-tokenizer = GPT2Tokenizer.from_pretrained("gpt2")
-prefix_length = 10
-model = ClipCaptionModel(prefix_length)
-# print(model.gpt_embedding_size)
-model.load_state_dict(torch.load(hf_hub_download('OpenShape/clipcap-cc', 'conceptual_weights.pt'), map_location='cpu'))
-model.eval()
-if torch.cuda.is_available():
+import torch
+import torch_redstone as rst
+import transformers
+import numpy as np
+from torch import nn
+from typing import Tuple, List, Union, Optional
+from transformers import GPT2Tokenizer, GPT2LMHeadModel, GPT2Config
+from huggingface_hub import hf_hub_download
+from diffusers import StableUnCLIPImg2ImgPipeline
+
+N = type(None)
+V = np.array
+ARRAY = np.ndarray
+ARRAYS = Union[Tuple[ARRAY, ...], List[ARRAY]]
+VS = Union[Tuple[V, ...], List[V]]
+VN = Union[V, N]
+VNS = Union[VS, N]
+T = torch.Tensor
+TS = Union[Tuple[T, ...], List[T]]
+TN = Optional[T]
+TNS = Union[Tuple[TN, ...], List[TN]]
+TSN = Optional[TS]
+TA = Union[T, ARRAY]
+
+
+D = torch.device
+
+class Wrapper(transformers.modeling_utils.PreTrainedModel):
+    def __init__(self) -> None:
+        super().__init__(transformers.configuration_utils.PretrainedConfig())
+        self.param = torch.nn.Parameter(torch.tensor(0.))
+
+    def forward(self, x):
+        return rst.ObjectProxy(image_embeds=x)
+
+class MLP(nn.Module):
+
+    def forward(self, x: T) -> T:
+        return self.model(x)
+
+    def __init__(self, sizes: Tuple[int, ...], bias=True, act=nn.Tanh):
+        super(MLP, self).__init__()
+        layers = []
+        for i in range(len(sizes) -1):
+            layers.append(nn.Linear(sizes[i], sizes[i + 1], bias=bias))
+            if i < len(sizes) - 2:
+                layers.append(act())
+        self.model = nn.Sequential(*layers)
+
+class ClipCaptionModel(nn.Module):
+
+    #@functools.lru_cache #FIXME
+    def get_dummy_token(self, batch_size: int, device: D) -> T:
+        return torch.zeros(batch_size, self.prefix_length, dtype=torch.int64, device=device)
+
+    def forward(self, tokens: T, prefix: T, mask: Optional[T] = None, labels: Optional[T] = None):
+        embedding_text = self.gpt.transformer.wte(tokens)
+        prefix_projections = self.clip_project(prefix).view(-1, self.prefix_length, self.gpt_embedding_size)
+        #print(embedding_text.size()) #torch.Size([5, 67, 768])
+        #print(prefix_projections.size()) #torch.Size([5, 1, 768])
+        embedding_cat = torch.cat((prefix_projections, embedding_text), dim=1)
+        if labels is not None:
+            dummy_token = self.get_dummy_token(tokens.shape[0], tokens.device)
+            labels = torch.cat((dummy_token, tokens), dim=1)
+        out = self.gpt(inputs_embeds=embedding_cat, labels=labels, attention_mask=mask)
+        return out
+
+    def __init__(self, prefix_length: int, prefix_size: int = 512):
+        super(ClipCaptionModel, self).__init__()
+        self.prefix_length = prefix_length
+        self.gpt = GPT2LMHeadModel(GPT2Config.from_pretrained('gpt2'))
+        self.gpt_embedding_size = self.gpt.transformer.wte.weight.shape[1]
+        if prefix_length > 10:  # not enough memory
+            self.clip_project = nn.Linear(prefix_size, self.gpt_embedding_size * prefix_length)
+        else:
+            self.clip_project = MLP((prefix_size, (self.gpt_embedding_size * prefix_length) // 2, self.gpt_embedding_size * prefix_length))
+
+class ClipCaptionPrefix(ClipCaptionModel):
+
+    def parameters(self, recurse: bool = True):
+        return self.clip_project.parameters()
+
+    def train(self, mode: bool = True):
+        super(ClipCaptionPrefix, self).train(mode)
+        self.gpt.eval()
+        return self
+
+def generate2(
+    model,
+    tokenizer,
+    tokens=None,
+    prompt=None,
+    embed=None,
+    entry_count=1,
+    entry_length=67,  # maximum number of words
+    top_p=0.8,
+    temperature=1.,
+    stop_token: str = '.',
+):
+    model.eval()
+    generated_num = 0
+    generated_list = []
+    stop_token_index = tokenizer.encode(stop_token)[0]
+    filter_value = -float("Inf")
+    device = next(model.parameters()).device
+    score_col = []
+    with torch.no_grad():
+
+        for entry_idx in range(entry_count):
+            if embed is not None:
+                generated = embed
+            else:
+                if tokens is None:
+                    tokens = torch.tensor(tokenizer.encode(prompt))
+                    tokens = tokens.unsqueeze(0).to(device)
+
+                generated = model.gpt.transformer.wte(tokens)
+
+            for i in range(entry_length):
+
+                outputs = model.gpt(inputs_embeds=generated)
+                logits = outputs.logits
+                logits = logits[:, -1, :] / (temperature if temperature > 0 else 1.0)
+                sorted_logits, sorted_indices = torch.sort(logits, descending=True)
+                cumulative_probs = torch.cumsum(torch.softmax(sorted_logits, dim=-1), dim=-1)
+                sorted_indices_to_remove = cumulative_probs > top_p
+                sorted_indices_to_remove[..., 1:] = sorted_indices_to_remove[
+                                                    ..., :-1
+                                                    ].clone()
+                sorted_indices_to_remove[..., 0] = 0
+
+                indices_to_remove = sorted_indices[sorted_indices_to_remove]
+                logits[:, indices_to_remove] = filter_value
+                next_token = torch.argmax(torch.softmax(logits, dim=-1), -1).reshape(1, 1)
+                score = torch.softmax(logits, dim=-1).reshape(-1)[next_token.item()].item()
+                score_col.append(score)
+                next_token_embed = model.gpt.transformer.wte(next_token)
+                if tokens is None:
+                    tokens = next_token
+                else:
+                    tokens = torch.cat((tokens, next_token), dim=1)
+                generated = torch.cat((generated, next_token_embed), dim=1)
+                if stop_token_index == next_token.item():
+                    break
+
+            output_list = list(tokens.squeeze(0).cpu().numpy())
+            output_text = tokenizer.decode(output_list)
+            generated_list.append(output_text)
+    return generated_list[0]
+
+
+@torch.no_grad()
+def pc_to_text(pc_encoder: torch.nn.Module, pc, cond_scale):
+    ref_dev = next(pc_encoder.parameters()).device
+    prefix = pc_encoder(torch.tensor(pc.T[None], device=ref_dev))
+    prefix = prefix.float() * cond_scale
+    prefix = prefix.to(next(model.parameters()).device)
+    prefix_embed = model.clip_project(prefix).reshape(1, prefix_length, -1)
+    text = generate2(model, tokenizer, embed=prefix_embed)
+    return text
+
+@torch.no_grad()
+def pc_to_image(pc_encoder: torch.nn.Module, pc, prompt, noise_level, width, height, cfg_scale, num_steps, callback):
+    ref_dev = next(pc_encoder.parameters()).device
+    enc = pc_encoder(torch.tensor(pc.T[None], device=ref_dev))
+    enc = torch.nn.functional.normalize(enc, dim=-1) * (768 ** 0.5) / 2
+    if torch.cuda.is_available():
+        enc = enc.to('cuda:' + str(torch.cuda.current_device()))
+    # enc = enc.type(half)
+    # with torch.autocast("cuda"):
+    return pipe(
+        prompt=', '.join(["best quality"] + ([prompt] if prompt else [])),
+        negative_prompt="cropped, worst quality, low quality, normal quality, jpeg artifacts, signature, watermark, username, blurry",
+        image=enc,
+        width=width, height=height,
+        guidance_scale=cfg_scale,
+        noise_level=noise_level,
+        callback=callback,
+        num_inference_steps=num_steps
+    ).images[0]
+
+
+
+pipe = StableUnCLIPImg2ImgPipeline.from_pretrained(
+    "diffusers/stable-diffusion-2-1-unclip-i2i-l",
+    # variant="fp16",
+    image_encoder = Wrapper()
+)
+# pe = pipe.text_encoder.text_model.embeddings
+# pe.position_ids = torch.arange(pe.position_ids.shape[-1]).expand((1, -1)).to(pe.position_ids)  # workaround
+if torch.cuda.is_available():
+    pipe = pipe.to('cuda:' + str(torch.cuda.current_device()))
+    pipe.enable_model_cpu_offload(torch.cuda.current_device())
+pipe.enable_attention_slicing()
+pipe.enable_vae_slicing()
+
+tokenizer = GPT2Tokenizer.from_pretrained("gpt2")
+prefix_length = 10
+model = ClipCaptionModel(prefix_length)
+# print(model.gpt_embedding_size)
+model.load_state_dict(torch.load(hf_hub_download('OpenShape/clipcap-cc', 'conceptual_weights.pt'), map_location='cpu'))
+model.eval()
+if torch.cuda.is_available():
     model = model.cuda()