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38e5a7e
1
Parent(s):
801bd57
final CAM layers fixed
Browse files- app.py +30 -47
- list_modules.py +61 -0
- modules_SuSy_pt.txt +225 -0
- modules_ai-image-detector-deploy.txt +579 -0
app.py
CHANGED
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@@ -1,12 +1,12 @@
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# app.py ββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ
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"""
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Twoβstage local AIβimage detector
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1. haywoodsloan/ai-image-detector-deploy β Real vs
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2. SuSy.pt β Likely generator (
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β’
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β’
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"""
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import gradio as gr
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@@ -22,33 +22,30 @@ BIN_ID = "haywoodsloan/ai-image-detector-deploy"
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bin_proc = AutoImageProcessor.from_pretrained(BIN_ID)
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bin_model = AutoModelForImageClassification.from_pretrained(BIN_ID)
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bin_model.eval()
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-
CAM_LAYER_BIN = "swinv2.layers.3.blocks.1.norm2" # <-- was *.stages.7.*
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# ββββββββββββ Stageβ2 model (SuSy) ββββββββββββββββββββββββββββββββββ
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susy_model
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GEN_CLASSES = [
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"Stable Diffusion 1.x", "DALLΒ·E 3", "MJ V5/V6",
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"Stable Diffusion XL", "MJ V1/V2",
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]
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CAM_LAYER_SUSY = "blocks.11"
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PATCH, TOP = 224, 5
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# ββββββββββββ Heatβmap helper βββββββββββββββββββββββββββββββββββββββ
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def grad_cam_overlay(model, inputs, target_layer, class_idx, orig_pil):
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-
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for k, v in (inputs.items() if isinstance(inputs, dict) else {"x": inputs}).items():
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inputs[k] = v.to(device)
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-
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cam_ex = GradCAM(model, target_layer=target_layer, input_shape=next(iter(inputs.values())).shape)
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scores = model(**inputs).logits if isinstance(inputs, dict) else model(inputs)
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-
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score.backward()
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mask = cam_ex(class_idx)[0].cpu().numpy()
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mask = (mask - mask.min()) / (mask.max() - mask.min() + 1e-6)
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mask = Image.fromarray(np.uint8(plt.cm.jet(mask)[:, :, :3] * 255)).resize(orig_pil.size, Image.BICUBIC)
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@@ -61,13 +58,12 @@ to_gray_pil = transforms.Compose([transforms.PILToTensor(), transforms.Graysca
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def susy_predict(img: Image.Image) -> dict:
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w, h = img.size
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npx, npy = max(1, w // PATCH), max(1, h // PATCH)
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-
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patches = np.zeros((npx * npy, PATCH, PATCH, 3), dtype=np.uint8)
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for i in range(npx):
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for j in range(npy):
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x, y = i * PATCH, j * PATCH
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-
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patches[i * npy + j] = np.array(crop)
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contrasts = []
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for p in patches:
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@@ -75,7 +71,7 @@ def susy_predict(img: Image.Image) -> dict:
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glcm = graycomatrix(g, [5], [0], 256, symmetric=True, normed=True)
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contrasts.append(graycoprops(glcm, "contrast")[0, 0])
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idx
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tensor = torch.from_numpy(patches[idx].transpose(0, 3, 1, 2)).float() / 255.0
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with torch.no_grad():
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@@ -93,11 +89,9 @@ def pipeline(img_arr):
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logits = bin_model(**inp_bin).logits
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probs = torch.softmax(logits, -1)[0].tolist() # [artificial, real]
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-
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probs_d = dict(zip(labels, probs))
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ai_conf, real_conf = probs_d.get("artificial", 0.0), probs_d.get("real", 0.0)
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# GradβCAM for
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class_idx = 0 if ai_conf >= real_conf else 1
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heatmaps.append(
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grad_cam_overlay(
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@@ -108,23 +102,22 @@ def pipeline(img_arr):
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)
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)
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#
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msg
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bar_df = None
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bar_vis = False
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# Stageβ2
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if ai_conf > real_conf:
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msg = f"AIβgenerated ({ai_conf*100:.1f} %)"
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gen_probs = susy_predict(img)
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bar_df = pd.DataFrame({"class": gen_probs.keys(), "prob": gen_probs.values()})
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bar_vis = True
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# SuSy heatβmap
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with torch.no_grad():
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t_inp = to_tensor(img.resize((224, 224))).unsqueeze(0)
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-
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susy_class =
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heatmaps.append(
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grad_cam_overlay(
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susy_model, t_inp,
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@@ -134,12 +127,7 @@ def pipeline(img_arr):
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)
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)
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-
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return (
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msg,
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gr.update(value=bar_df, visible=bar_vis),
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heatmaps
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)
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# ββββββββββββ Gradio UI βββββββββββββββββββββββββββββββββββββββββββββ
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with gr.Blocks(theme=gr.themes.Soft()) as demo:
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@@ -163,11 +151,6 @@ with gr.Blocks(theme=gr.themes.Soft()) as demo:
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columns=2, height=300, visible=True
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)
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btn.click(
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pipeline,
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inputs=img_in,
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outputs=[txt_bin, bar_gen, gal_cam]
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)
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demo.launch()
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-
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# app.py ββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ
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"""
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Twoβstage local AIβimage detector
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1. haywoodsloan/ai-image-detector-deploy β Real vsΒ AI (SwinβV2)
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2. SuSy.pt β Likely generator (ResNetβbased)
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Includes GradβCAM overlays:
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β’ always show heatβmap for binary decision
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β’ if image is flagged AI, also show heatβmap for SuSy
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"""
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import gradio as gr
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bin_proc = AutoImageProcessor.from_pretrained(BIN_ID)
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bin_model = AutoModelForImageClassification.from_pretrained(BIN_ID)
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bin_model.eval()
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CAM_LAYER_BIN = "encoder.layers.3.blocks.1.layernorm_after" # <- from dump
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# ββββββββββββ Stageβ2 model (SuSy) ββββββββββββββββββββββββββββββββββ
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susy_model = torch.jit.load("SuSy.pt").eval()
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CAM_LAYER_SUSY = "feature_extractor.resnet_model.layer4.1.relu" # <- from dump
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+
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GEN_CLASSES = [
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"Stable Diffusion 1.x", "DALLΒ·E 3", "MJ V5/V6",
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"Stable Diffusion XL", "MJ V1/V2",
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]
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PATCH, TOP = 224, 5
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# ββββββββββββ Heatβmap helper βββββββββββββββββββββββββββββββββββββββ
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def grad_cam_overlay(model, inputs, target_layer, class_idx, orig_pil):
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# prepare GradβCAM extractor
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cam_ex = GradCAM(model, target_layer=target_layer,
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input_shape=next(iter(inputs.values()) if isinstance(inputs, dict) else [inputs]).shape)
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# forward & backward
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scores = model(**inputs).logits if isinstance(inputs, dict) else model(inputs)
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scores[0, class_idx].backward()
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# normalise cam
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mask = cam_ex(class_idx)[0].cpu().numpy()
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mask = (mask - mask.min()) / (mask.max() - mask.min() + 1e-6)
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mask = Image.fromarray(np.uint8(plt.cm.jet(mask)[:, :, :3] * 255)).resize(orig_pil.size, Image.BICUBIC)
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def susy_predict(img: Image.Image) -> dict:
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w, h = img.size
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npx, npy = max(1, w // PATCH), max(1, h // PATCH)
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patches = np.zeros((npx * npy, PATCH, PATCH, 3), dtype=np.uint8)
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+
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for i in range(npx):
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for j in range(npy):
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x, y = i * PATCH, j * PATCH
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patches[i * npy + j] = np.array(img.crop((x, y, x + PATCH, y + PATCH)).resize((PATCH, PATCH)))
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contrasts = []
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for p in patches:
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glcm = graycomatrix(g, [5], [0], 256, symmetric=True, normed=True)
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contrasts.append(graycoprops(glcm, "contrast")[0, 0])
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idx = np.argsort(contrasts)[::-1][:TOP]
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tensor = torch.from_numpy(patches[idx].transpose(0, 3, 1, 2)).float() / 255.0
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with torch.no_grad():
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logits = bin_model(**inp_bin).logits
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probs = torch.softmax(logits, -1)[0].tolist() # [artificial, real]
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ai_conf, real_conf = probs[0], probs[1]
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# GradβCAM for winning class
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class_idx = 0 if ai_conf >= real_conf else 1
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heatmaps.append(
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grad_cam_overlay(
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)
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)
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# defaults
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msg, bar_df, bar_vis = f"Authentic ({real_conf*100:.1f} %)", None, False
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# Stageβ2 if AI
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if ai_conf > real_conf:
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msg = f"AIβgenerated ({ai_conf*100:.1f} %)"
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gen_probs = susy_predict(img)
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bar_df = pd.DataFrame({"class": gen_probs.keys(), "prob": gen_probs.values()})
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bar_vis = True
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# SuSy heatβmap: choose mostβprobable generator class
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with torch.no_grad():
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t_inp = to_tensor(img.resize((224, 224))).unsqueeze(0)
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logits_susy = susy_model(t_inp)
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susy_class = logits_susy[0, 1:].argmax().item() + 1 # skip 'real'
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heatmaps.append(
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grad_cam_overlay(
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susy_model, t_inp,
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)
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)
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return msg, gr.update(value=bar_df, visible=bar_vis), heatmaps
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# ββββββββββββ Gradio UI βββββββββββββββββββββββββββββββββββββββββββββ
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with gr.Blocks(theme=gr.themes.Soft()) as demo:
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columns=2, height=300, visible=True
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)
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btn.click(pipeline, inputs=img_in, outputs=[txt_bin, bar_gen, gal_cam])
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demo.launch()
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list_modules.py
ADDED
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#!/usr/bin/env python
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"""
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list_modules.py
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ββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ
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Print (and optionally save) the dotted names of **all** subβmodules
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inside a PyTorch model. Handy for locating the correct layer name
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for GradβCAM, feature hooks, etc.
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USAGE
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-----
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edit MODEL_SOURCE and MODEL_TYPE below, then:
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python list_modules.py
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Outputs:
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β’ console β first `LIMIT` names (to keep logs short)
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β’ file β full list written to `modules_<model>.txt`
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"""
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from __future__ import annotations
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import torch, argparse, pathlib, sys
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from transformers import AutoModel
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# ββββββββββββββ CONFIG βββββββββββββββββββββββββββββββββββββββββββββββ
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MODEL_SOURCE = "haywoodsloan/ai-image-detector-deploy"
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MODEL_TYPE = "huggingface"
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LIMIT = 2000 # how many lines to print to stdout (None = all)
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# βββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ
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def load_model(src: str, src_type: str):
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if src_type == "huggingface":
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model = AutoModel.from_pretrained(src)
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elif src_type == "torchscript":
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model = torch.jit.load(src)
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else:
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raise ValueError("MODEL_TYPE must be 'huggingface' or 'torchscript'")
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model.eval()
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return model
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def dump_module_names(model: torch.nn.Module,
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out_file: pathlib.Path,
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limit: int | None = None):
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names = [n for n, _ in model.named_modules()] # includes root '' at idx 0
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total = len(names)
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print(f"\nβΆ total {total} subβmodules found\n")
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for idx, name in enumerate(names):
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if limit is None or idx < limit:
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print(f"{idx:4d}: {name}")
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out_file.write_text("\n".join(names), encoding="utfβ8")
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print(f"\nβΆ wrote full list to {out_file}")
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def main():
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model = load_model(MODEL_SOURCE, MODEL_TYPE)
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txt_path = pathlib.Path(f"modules_{MODEL_SOURCE.split('/')[-1].replace('.','_')}.txt")
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dump_module_names(model, txt_path, LIMIT)
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if __name__ == "__main__":
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main()
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modules_SuSy_pt.txt
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|
|
| 1 |
+
|
| 2 |
+
feature_extractor
|
| 3 |
+
feature_extractor.resnet_model
|
| 4 |
+
feature_extractor.resnet_model.conv1
|
| 5 |
+
feature_extractor.resnet_model.bn1
|
| 6 |
+
feature_extractor.resnet_model.relu
|
| 7 |
+
feature_extractor.resnet_model.maxpool
|
| 8 |
+
feature_extractor.resnet_model.layer1
|
| 9 |
+
feature_extractor.resnet_model.layer1.0
|
| 10 |
+
feature_extractor.resnet_model.layer1.0.conv1
|
| 11 |
+
feature_extractor.resnet_model.layer1.0.bn1
|
| 12 |
+
feature_extractor.resnet_model.layer1.0.relu
|
| 13 |
+
feature_extractor.resnet_model.layer1.0.conv2
|
| 14 |
+
feature_extractor.resnet_model.layer1.0.bn2
|
| 15 |
+
feature_extractor.resnet_model.layer1.1
|
| 16 |
+
feature_extractor.resnet_model.layer1.1.conv1
|
| 17 |
+
feature_extractor.resnet_model.layer1.1.bn1
|
| 18 |
+
feature_extractor.resnet_model.layer1.1.relu
|
| 19 |
+
feature_extractor.resnet_model.layer1.1.conv2
|
| 20 |
+
feature_extractor.resnet_model.layer1.1.bn2
|
| 21 |
+
feature_extractor.resnet_model.layer2
|
| 22 |
+
feature_extractor.resnet_model.layer2.0
|
| 23 |
+
feature_extractor.resnet_model.layer2.0.conv1
|
| 24 |
+
feature_extractor.resnet_model.layer2.0.bn1
|
| 25 |
+
feature_extractor.resnet_model.layer2.0.relu
|
| 26 |
+
feature_extractor.resnet_model.layer2.0.conv2
|
| 27 |
+
feature_extractor.resnet_model.layer2.0.bn2
|
| 28 |
+
feature_extractor.resnet_model.layer2.0.downsample
|
| 29 |
+
feature_extractor.resnet_model.layer2.0.downsample.0
|
| 30 |
+
feature_extractor.resnet_model.layer2.0.downsample.1
|
| 31 |
+
feature_extractor.resnet_model.layer2.1
|
| 32 |
+
feature_extractor.resnet_model.layer2.1.conv1
|
| 33 |
+
feature_extractor.resnet_model.layer2.1.bn1
|
| 34 |
+
feature_extractor.resnet_model.layer2.1.relu
|
| 35 |
+
feature_extractor.resnet_model.layer2.1.conv2
|
| 36 |
+
feature_extractor.resnet_model.layer2.1.bn2
|
| 37 |
+
feature_extractor.resnet_model.layer3
|
| 38 |
+
feature_extractor.resnet_model.layer3.0
|
| 39 |
+
feature_extractor.resnet_model.layer3.0.conv1
|
| 40 |
+
feature_extractor.resnet_model.layer3.0.bn1
|
| 41 |
+
feature_extractor.resnet_model.layer3.0.relu
|
| 42 |
+
feature_extractor.resnet_model.layer3.0.conv2
|
| 43 |
+
feature_extractor.resnet_model.layer3.0.bn2
|
| 44 |
+
feature_extractor.resnet_model.layer3.0.downsample
|
| 45 |
+
feature_extractor.resnet_model.layer3.0.downsample.0
|
| 46 |
+
feature_extractor.resnet_model.layer3.0.downsample.1
|
| 47 |
+
feature_extractor.resnet_model.layer3.1
|
| 48 |
+
feature_extractor.resnet_model.layer3.1.conv1
|
| 49 |
+
feature_extractor.resnet_model.layer3.1.bn1
|
| 50 |
+
feature_extractor.resnet_model.layer3.1.relu
|
| 51 |
+
feature_extractor.resnet_model.layer3.1.conv2
|
| 52 |
+
feature_extractor.resnet_model.layer3.1.bn2
|
| 53 |
+
feature_extractor.resnet_model.layer4
|
| 54 |
+
feature_extractor.resnet_model.layer4.0
|
| 55 |
+
feature_extractor.resnet_model.layer4.0.conv1
|
| 56 |
+
feature_extractor.resnet_model.layer4.0.bn1
|
| 57 |
+
feature_extractor.resnet_model.layer4.0.relu
|
| 58 |
+
feature_extractor.resnet_model.layer4.0.conv2
|
| 59 |
+
feature_extractor.resnet_model.layer4.0.bn2
|
| 60 |
+
feature_extractor.resnet_model.layer4.0.downsample
|
| 61 |
+
feature_extractor.resnet_model.layer4.0.downsample.0
|
| 62 |
+
feature_extractor.resnet_model.layer4.0.downsample.1
|
| 63 |
+
feature_extractor.resnet_model.layer4.1
|
| 64 |
+
feature_extractor.resnet_model.layer4.1.conv1
|
| 65 |
+
feature_extractor.resnet_model.layer4.1.bn1
|
| 66 |
+
feature_extractor.resnet_model.layer4.1.relu
|
| 67 |
+
feature_extractor.resnet_model.layer4.1.conv2
|
| 68 |
+
feature_extractor.resnet_model.layer4.1.bn2
|
| 69 |
+
feature_extractor.resnet_model.avgpool
|
| 70 |
+
feature_extractor.resnet_model.fc
|
| 71 |
+
feature_extractor.stages
|
| 72 |
+
feature_extractor.stages.0
|
| 73 |
+
feature_extractor.stages.0.0
|
| 74 |
+
feature_extractor.stages.0.1
|
| 75 |
+
feature_extractor.stages.0.2
|
| 76 |
+
feature_extractor.stages.0.3
|
| 77 |
+
feature_extractor.stages.1
|
| 78 |
+
feature_extractor.stages.1.0
|
| 79 |
+
feature_extractor.stages.1.0.conv1
|
| 80 |
+
feature_extractor.stages.1.0.bn1
|
| 81 |
+
feature_extractor.stages.1.0.relu
|
| 82 |
+
feature_extractor.stages.1.0.conv2
|
| 83 |
+
feature_extractor.stages.1.0.bn2
|
| 84 |
+
feature_extractor.stages.1.1
|
| 85 |
+
feature_extractor.stages.1.1.conv1
|
| 86 |
+
feature_extractor.stages.1.1.bn1
|
| 87 |
+
feature_extractor.stages.1.1.relu
|
| 88 |
+
feature_extractor.stages.1.1.conv2
|
| 89 |
+
feature_extractor.stages.1.1.bn2
|
| 90 |
+
feature_extractor.stages.2
|
| 91 |
+
feature_extractor.stages.2.0
|
| 92 |
+
feature_extractor.stages.2.0.conv1
|
| 93 |
+
feature_extractor.stages.2.0.bn1
|
| 94 |
+
feature_extractor.stages.2.0.relu
|
| 95 |
+
feature_extractor.stages.2.0.conv2
|
| 96 |
+
feature_extractor.stages.2.0.bn2
|
| 97 |
+
feature_extractor.stages.2.0.downsample
|
| 98 |
+
feature_extractor.stages.2.0.downsample.0
|
| 99 |
+
feature_extractor.stages.2.0.downsample.1
|
| 100 |
+
feature_extractor.stages.2.1
|
| 101 |
+
feature_extractor.stages.2.1.conv1
|
| 102 |
+
feature_extractor.stages.2.1.bn1
|
| 103 |
+
feature_extractor.stages.2.1.relu
|
| 104 |
+
feature_extractor.stages.2.1.conv2
|
| 105 |
+
feature_extractor.stages.2.1.bn2
|
| 106 |
+
feature_extractor.stages.3
|
| 107 |
+
feature_extractor.stages.3.0
|
| 108 |
+
feature_extractor.stages.3.0.conv1
|
| 109 |
+
feature_extractor.stages.3.0.bn1
|
| 110 |
+
feature_extractor.stages.3.0.relu
|
| 111 |
+
feature_extractor.stages.3.0.conv2
|
| 112 |
+
feature_extractor.stages.3.0.bn2
|
| 113 |
+
feature_extractor.stages.3.0.downsample
|
| 114 |
+
feature_extractor.stages.3.0.downsample.0
|
| 115 |
+
feature_extractor.stages.3.0.downsample.1
|
| 116 |
+
feature_extractor.stages.3.1
|
| 117 |
+
feature_extractor.stages.3.1.conv1
|
| 118 |
+
feature_extractor.stages.3.1.bn1
|
| 119 |
+
feature_extractor.stages.3.1.relu
|
| 120 |
+
feature_extractor.stages.3.1.conv2
|
| 121 |
+
feature_extractor.stages.3.1.bn2
|
| 122 |
+
feature_extractor.stages.4
|
| 123 |
+
feature_extractor.stages.4.0
|
| 124 |
+
feature_extractor.stages.4.0.conv1
|
| 125 |
+
feature_extractor.stages.4.0.bn1
|
| 126 |
+
feature_extractor.stages.4.0.relu
|
| 127 |
+
feature_extractor.stages.4.0.conv2
|
| 128 |
+
feature_extractor.stages.4.0.bn2
|
| 129 |
+
feature_extractor.stages.4.0.downsample
|
| 130 |
+
feature_extractor.stages.4.0.downsample.0
|
| 131 |
+
feature_extractor.stages.4.0.downsample.1
|
| 132 |
+
feature_extractor.stages.4.1
|
| 133 |
+
feature_extractor.stages.4.1.conv1
|
| 134 |
+
feature_extractor.stages.4.1.bn1
|
| 135 |
+
feature_extractor.stages.4.1.relu
|
| 136 |
+
feature_extractor.stages.4.1.conv2
|
| 137 |
+
feature_extractor.stages.4.1.bn2
|
| 138 |
+
feature_extractor.f11
|
| 139 |
+
feature_extractor.f11.0
|
| 140 |
+
feature_extractor.f11.1
|
| 141 |
+
feature_extractor.f11.2
|
| 142 |
+
feature_extractor.f12
|
| 143 |
+
feature_extractor.f12.0
|
| 144 |
+
feature_extractor.f12.1
|
| 145 |
+
feature_extractor.f12.2
|
| 146 |
+
feature_extractor.f22
|
| 147 |
+
feature_extractor.f22.0
|
| 148 |
+
feature_extractor.f22.1
|
| 149 |
+
feature_extractor.f22.2
|
| 150 |
+
feature_extractor.f13
|
| 151 |
+
feature_extractor.f13.0
|
| 152 |
+
feature_extractor.f13.1
|
| 153 |
+
feature_extractor.f13.2
|
| 154 |
+
feature_extractor.f23
|
| 155 |
+
feature_extractor.f23.0
|
| 156 |
+
feature_extractor.f23.1
|
| 157 |
+
feature_extractor.f23.2
|
| 158 |
+
feature_extractor.f33
|
| 159 |
+
feature_extractor.f33.0
|
| 160 |
+
feature_extractor.f33.1
|
| 161 |
+
feature_extractor.f33.2
|
| 162 |
+
feature_extractor.f14
|
| 163 |
+
feature_extractor.f14.0
|
| 164 |
+
feature_extractor.f14.1
|
| 165 |
+
feature_extractor.f14.2
|
| 166 |
+
feature_extractor.f24
|
| 167 |
+
feature_extractor.f24.0
|
| 168 |
+
feature_extractor.f24.1
|
| 169 |
+
feature_extractor.f24.2
|
| 170 |
+
feature_extractor.f34
|
| 171 |
+
feature_extractor.f34.0
|
| 172 |
+
feature_extractor.f34.1
|
| 173 |
+
feature_extractor.f34.2
|
| 174 |
+
feature_extractor.f44
|
| 175 |
+
feature_extractor.f44.0
|
| 176 |
+
feature_extractor.f44.1
|
| 177 |
+
feature_extractor.f44.2
|
| 178 |
+
feature_extractor.bottlenecks
|
| 179 |
+
feature_extractor.bottlenecks.0
|
| 180 |
+
feature_extractor.bottlenecks.0.0
|
| 181 |
+
feature_extractor.bottlenecks.0.1
|
| 182 |
+
feature_extractor.bottlenecks.0.2
|
| 183 |
+
feature_extractor.bottlenecks.1
|
| 184 |
+
feature_extractor.bottlenecks.1.0
|
| 185 |
+
feature_extractor.bottlenecks.1.1
|
| 186 |
+
feature_extractor.bottlenecks.1.2
|
| 187 |
+
feature_extractor.bottlenecks.2
|
| 188 |
+
feature_extractor.bottlenecks.2.0
|
| 189 |
+
feature_extractor.bottlenecks.2.1
|
| 190 |
+
feature_extractor.bottlenecks.2.2
|
| 191 |
+
feature_extractor.bottlenecks.3
|
| 192 |
+
feature_extractor.bottlenecks.3.0
|
| 193 |
+
feature_extractor.bottlenecks.3.1
|
| 194 |
+
feature_extractor.bottlenecks.3.2
|
| 195 |
+
feature_extractor.bottlenecks.4
|
| 196 |
+
feature_extractor.bottlenecks.4.0
|
| 197 |
+
feature_extractor.bottlenecks.4.1
|
| 198 |
+
feature_extractor.bottlenecks.4.2
|
| 199 |
+
feature_extractor.bottlenecks.5
|
| 200 |
+
feature_extractor.bottlenecks.5.0
|
| 201 |
+
feature_extractor.bottlenecks.5.1
|
| 202 |
+
feature_extractor.bottlenecks.5.2
|
| 203 |
+
feature_extractor.bottlenecks.6
|
| 204 |
+
feature_extractor.bottlenecks.6.0
|
| 205 |
+
feature_extractor.bottlenecks.6.1
|
| 206 |
+
feature_extractor.bottlenecks.6.2
|
| 207 |
+
feature_extractor.bottlenecks.7
|
| 208 |
+
feature_extractor.bottlenecks.7.0
|
| 209 |
+
feature_extractor.bottlenecks.7.1
|
| 210 |
+
feature_extractor.bottlenecks.7.2
|
| 211 |
+
feature_extractor.bottlenecks.8
|
| 212 |
+
feature_extractor.bottlenecks.8.0
|
| 213 |
+
feature_extractor.bottlenecks.8.1
|
| 214 |
+
feature_extractor.bottlenecks.8.2
|
| 215 |
+
feature_extractor.bottlenecks.9
|
| 216 |
+
feature_extractor.bottlenecks.9.0
|
| 217 |
+
feature_extractor.bottlenecks.9.1
|
| 218 |
+
feature_extractor.bottlenecks.9.2
|
| 219 |
+
feature_extractor.gap
|
| 220 |
+
mlp
|
| 221 |
+
mlp.fc1
|
| 222 |
+
mlp.fc2
|
| 223 |
+
mlp.fc3
|
| 224 |
+
mlp.dropout
|
| 225 |
+
softmax
|
modules_ai-image-detector-deploy.txt
ADDED
|
@@ -0,0 +1,579 @@
|
|
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|
|
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|
|
|
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|
|
|
|
|
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|
|
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|
|
|
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|
|
|
|
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|
|
|
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|
|
|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
|
|
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|
|
|
|
|
|
|
|
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|
|
|
|
|
|
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|
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|
|
|
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|
|
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|
|
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|
|
|
|
|
|
|
|
|
|
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|
|
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|
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|
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|
| 1 |
+
|
| 2 |
+
embeddings
|
| 3 |
+
embeddings.patch_embeddings
|
| 4 |
+
embeddings.patch_embeddings.projection
|
| 5 |
+
embeddings.norm
|
| 6 |
+
embeddings.dropout
|
| 7 |
+
encoder
|
| 8 |
+
encoder.layers
|
| 9 |
+
encoder.layers.0
|
| 10 |
+
encoder.layers.0.blocks
|
| 11 |
+
encoder.layers.0.blocks.0
|
| 12 |
+
encoder.layers.0.blocks.0.attention
|
| 13 |
+
encoder.layers.0.blocks.0.attention.self
|
| 14 |
+
encoder.layers.0.blocks.0.attention.self.continuous_position_bias_mlp
|
| 15 |
+
encoder.layers.0.blocks.0.attention.self.continuous_position_bias_mlp.0
|
| 16 |
+
encoder.layers.0.blocks.0.attention.self.continuous_position_bias_mlp.1
|
| 17 |
+
encoder.layers.0.blocks.0.attention.self.continuous_position_bias_mlp.2
|
| 18 |
+
encoder.layers.0.blocks.0.attention.self.query
|
| 19 |
+
encoder.layers.0.blocks.0.attention.self.key
|
| 20 |
+
encoder.layers.0.blocks.0.attention.self.value
|
| 21 |
+
encoder.layers.0.blocks.0.attention.self.dropout
|
| 22 |
+
encoder.layers.0.blocks.0.attention.output
|
| 23 |
+
encoder.layers.0.blocks.0.attention.output.dense
|
| 24 |
+
encoder.layers.0.blocks.0.attention.output.dropout
|
| 25 |
+
encoder.layers.0.blocks.0.layernorm_before
|
| 26 |
+
encoder.layers.0.blocks.0.drop_path
|
| 27 |
+
encoder.layers.0.blocks.0.intermediate
|
| 28 |
+
encoder.layers.0.blocks.0.intermediate.dense
|
| 29 |
+
encoder.layers.0.blocks.0.intermediate.intermediate_act_fn
|
| 30 |
+
encoder.layers.0.blocks.0.output
|
| 31 |
+
encoder.layers.0.blocks.0.output.dense
|
| 32 |
+
encoder.layers.0.blocks.0.output.dropout
|
| 33 |
+
encoder.layers.0.blocks.0.layernorm_after
|
| 34 |
+
encoder.layers.0.blocks.1
|
| 35 |
+
encoder.layers.0.blocks.1.attention
|
| 36 |
+
encoder.layers.0.blocks.1.attention.self
|
| 37 |
+
encoder.layers.0.blocks.1.attention.self.continuous_position_bias_mlp
|
| 38 |
+
encoder.layers.0.blocks.1.attention.self.continuous_position_bias_mlp.0
|
| 39 |
+
encoder.layers.0.blocks.1.attention.self.continuous_position_bias_mlp.1
|
| 40 |
+
encoder.layers.0.blocks.1.attention.self.continuous_position_bias_mlp.2
|
| 41 |
+
encoder.layers.0.blocks.1.attention.self.query
|
| 42 |
+
encoder.layers.0.blocks.1.attention.self.key
|
| 43 |
+
encoder.layers.0.blocks.1.attention.self.value
|
| 44 |
+
encoder.layers.0.blocks.1.attention.self.dropout
|
| 45 |
+
encoder.layers.0.blocks.1.attention.output
|
| 46 |
+
encoder.layers.0.blocks.1.attention.output.dense
|
| 47 |
+
encoder.layers.0.blocks.1.attention.output.dropout
|
| 48 |
+
encoder.layers.0.blocks.1.layernorm_before
|
| 49 |
+
encoder.layers.0.blocks.1.drop_path
|
| 50 |
+
encoder.layers.0.blocks.1.intermediate
|
| 51 |
+
encoder.layers.0.blocks.1.intermediate.dense
|
| 52 |
+
encoder.layers.0.blocks.1.intermediate.intermediate_act_fn
|
| 53 |
+
encoder.layers.0.blocks.1.output
|
| 54 |
+
encoder.layers.0.blocks.1.output.dense
|
| 55 |
+
encoder.layers.0.blocks.1.output.dropout
|
| 56 |
+
encoder.layers.0.blocks.1.layernorm_after
|
| 57 |
+
encoder.layers.0.downsample
|
| 58 |
+
encoder.layers.0.downsample.reduction
|
| 59 |
+
encoder.layers.0.downsample.norm
|
| 60 |
+
encoder.layers.1
|
| 61 |
+
encoder.layers.1.blocks
|
| 62 |
+
encoder.layers.1.blocks.0
|
| 63 |
+
encoder.layers.1.blocks.0.attention
|
| 64 |
+
encoder.layers.1.blocks.0.attention.self
|
| 65 |
+
encoder.layers.1.blocks.0.attention.self.continuous_position_bias_mlp
|
| 66 |
+
encoder.layers.1.blocks.0.attention.self.continuous_position_bias_mlp.0
|
| 67 |
+
encoder.layers.1.blocks.0.attention.self.continuous_position_bias_mlp.1
|
| 68 |
+
encoder.layers.1.blocks.0.attention.self.continuous_position_bias_mlp.2
|
| 69 |
+
encoder.layers.1.blocks.0.attention.self.query
|
| 70 |
+
encoder.layers.1.blocks.0.attention.self.key
|
| 71 |
+
encoder.layers.1.blocks.0.attention.self.value
|
| 72 |
+
encoder.layers.1.blocks.0.attention.self.dropout
|
| 73 |
+
encoder.layers.1.blocks.0.attention.output
|
| 74 |
+
encoder.layers.1.blocks.0.attention.output.dense
|
| 75 |
+
encoder.layers.1.blocks.0.attention.output.dropout
|
| 76 |
+
encoder.layers.1.blocks.0.layernorm_before
|
| 77 |
+
encoder.layers.1.blocks.0.drop_path
|
| 78 |
+
encoder.layers.1.blocks.0.intermediate
|
| 79 |
+
encoder.layers.1.blocks.0.intermediate.dense
|
| 80 |
+
encoder.layers.1.blocks.0.intermediate.intermediate_act_fn
|
| 81 |
+
encoder.layers.1.blocks.0.output
|
| 82 |
+
encoder.layers.1.blocks.0.output.dense
|
| 83 |
+
encoder.layers.1.blocks.0.output.dropout
|
| 84 |
+
encoder.layers.1.blocks.0.layernorm_after
|
| 85 |
+
encoder.layers.1.blocks.1
|
| 86 |
+
encoder.layers.1.blocks.1.attention
|
| 87 |
+
encoder.layers.1.blocks.1.attention.self
|
| 88 |
+
encoder.layers.1.blocks.1.attention.self.continuous_position_bias_mlp
|
| 89 |
+
encoder.layers.1.blocks.1.attention.self.continuous_position_bias_mlp.0
|
| 90 |
+
encoder.layers.1.blocks.1.attention.self.continuous_position_bias_mlp.1
|
| 91 |
+
encoder.layers.1.blocks.1.attention.self.continuous_position_bias_mlp.2
|
| 92 |
+
encoder.layers.1.blocks.1.attention.self.query
|
| 93 |
+
encoder.layers.1.blocks.1.attention.self.key
|
| 94 |
+
encoder.layers.1.blocks.1.attention.self.value
|
| 95 |
+
encoder.layers.1.blocks.1.attention.self.dropout
|
| 96 |
+
encoder.layers.1.blocks.1.attention.output
|
| 97 |
+
encoder.layers.1.blocks.1.attention.output.dense
|
| 98 |
+
encoder.layers.1.blocks.1.attention.output.dropout
|
| 99 |
+
encoder.layers.1.blocks.1.layernorm_before
|
| 100 |
+
encoder.layers.1.blocks.1.drop_path
|
| 101 |
+
encoder.layers.1.blocks.1.intermediate
|
| 102 |
+
encoder.layers.1.blocks.1.intermediate.dense
|
| 103 |
+
encoder.layers.1.blocks.1.intermediate.intermediate_act_fn
|
| 104 |
+
encoder.layers.1.blocks.1.output
|
| 105 |
+
encoder.layers.1.blocks.1.output.dense
|
| 106 |
+
encoder.layers.1.blocks.1.output.dropout
|
| 107 |
+
encoder.layers.1.blocks.1.layernorm_after
|
| 108 |
+
encoder.layers.1.downsample
|
| 109 |
+
encoder.layers.1.downsample.reduction
|
| 110 |
+
encoder.layers.1.downsample.norm
|
| 111 |
+
encoder.layers.2
|
| 112 |
+
encoder.layers.2.blocks
|
| 113 |
+
encoder.layers.2.blocks.0
|
| 114 |
+
encoder.layers.2.blocks.0.attention
|
| 115 |
+
encoder.layers.2.blocks.0.attention.self
|
| 116 |
+
encoder.layers.2.blocks.0.attention.self.continuous_position_bias_mlp
|
| 117 |
+
encoder.layers.2.blocks.0.attention.self.continuous_position_bias_mlp.0
|
| 118 |
+
encoder.layers.2.blocks.0.attention.self.continuous_position_bias_mlp.1
|
| 119 |
+
encoder.layers.2.blocks.0.attention.self.continuous_position_bias_mlp.2
|
| 120 |
+
encoder.layers.2.blocks.0.attention.self.query
|
| 121 |
+
encoder.layers.2.blocks.0.attention.self.key
|
| 122 |
+
encoder.layers.2.blocks.0.attention.self.value
|
| 123 |
+
encoder.layers.2.blocks.0.attention.self.dropout
|
| 124 |
+
encoder.layers.2.blocks.0.attention.output
|
| 125 |
+
encoder.layers.2.blocks.0.attention.output.dense
|
| 126 |
+
encoder.layers.2.blocks.0.attention.output.dropout
|
| 127 |
+
encoder.layers.2.blocks.0.layernorm_before
|
| 128 |
+
encoder.layers.2.blocks.0.drop_path
|
| 129 |
+
encoder.layers.2.blocks.0.intermediate
|
| 130 |
+
encoder.layers.2.blocks.0.intermediate.dense
|
| 131 |
+
encoder.layers.2.blocks.0.intermediate.intermediate_act_fn
|
| 132 |
+
encoder.layers.2.blocks.0.output
|
| 133 |
+
encoder.layers.2.blocks.0.output.dense
|
| 134 |
+
encoder.layers.2.blocks.0.output.dropout
|
| 135 |
+
encoder.layers.2.blocks.0.layernorm_after
|
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encoder.layers.2.blocks.13.attention.self.value
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| 422 |
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encoder.layers.2.blocks.13.attention.self.dropout
|
| 423 |
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encoder.layers.2.blocks.13.attention.output
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| 424 |
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encoder.layers.2.blocks.13.attention.output.dense
|
| 425 |
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encoder.layers.2.blocks.13.attention.output.dropout
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| 426 |
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encoder.layers.2.blocks.13.layernorm_before
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| 427 |
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encoder.layers.2.blocks.13.drop_path
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| 428 |
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encoder.layers.2.blocks.13.intermediate
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| 429 |
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encoder.layers.2.blocks.13.intermediate.dense
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| 430 |
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encoder.layers.2.blocks.13.intermediate.intermediate_act_fn
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| 431 |
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encoder.layers.2.blocks.13.output
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encoder.layers.2.blocks.13.output.dense
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encoder.layers.2.blocks.13.output.dropout
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encoder.layers.2.blocks.13.layernorm_after
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| 435 |
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encoder.layers.2.blocks.14
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encoder.layers.2.blocks.14.attention
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encoder.layers.2.blocks.14.attention.self
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encoder.layers.2.blocks.14.attention.self.continuous_position_bias_mlp
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encoder.layers.2.blocks.14.attention.self.continuous_position_bias_mlp.0
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encoder.layers.2.blocks.14.attention.self.continuous_position_bias_mlp.1
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encoder.layers.2.blocks.14.attention.self.continuous_position_bias_mlp.2
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encoder.layers.2.blocks.14.attention.self.query
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| 443 |
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encoder.layers.2.blocks.14.attention.self.key
|
| 444 |
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encoder.layers.2.blocks.14.attention.self.value
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| 445 |
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encoder.layers.2.blocks.14.attention.self.dropout
|
| 446 |
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encoder.layers.2.blocks.14.attention.output
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| 447 |
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encoder.layers.2.blocks.14.attention.output.dense
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| 448 |
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encoder.layers.2.blocks.14.attention.output.dropout
|
| 449 |
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encoder.layers.2.blocks.14.layernorm_before
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| 450 |
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encoder.layers.2.blocks.14.drop_path
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| 451 |
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encoder.layers.2.blocks.14.intermediate
|
| 452 |
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encoder.layers.2.blocks.14.intermediate.dense
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| 453 |
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encoder.layers.2.blocks.14.intermediate.intermediate_act_fn
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encoder.layers.2.blocks.14.output
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encoder.layers.2.blocks.14.output.dense
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encoder.layers.2.blocks.14.output.dropout
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encoder.layers.2.blocks.14.layernorm_after
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encoder.layers.2.blocks.15
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encoder.layers.2.blocks.15.attention
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encoder.layers.2.blocks.15.attention.self
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encoder.layers.2.blocks.15.attention.self.continuous_position_bias_mlp
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encoder.layers.2.blocks.15.attention.self.continuous_position_bias_mlp.0
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encoder.layers.2.blocks.15.attention.self.continuous_position_bias_mlp.1
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encoder.layers.2.blocks.15.attention.self.continuous_position_bias_mlp.2
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encoder.layers.2.blocks.15.attention.self.query
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| 466 |
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encoder.layers.2.blocks.15.attention.self.key
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| 467 |
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encoder.layers.2.blocks.15.attention.self.value
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| 468 |
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encoder.layers.2.blocks.15.attention.self.dropout
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| 469 |
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encoder.layers.2.blocks.15.attention.output
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| 470 |
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encoder.layers.2.blocks.15.attention.output.dense
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| 471 |
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encoder.layers.2.blocks.15.attention.output.dropout
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| 472 |
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encoder.layers.2.blocks.15.layernorm_before
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| 473 |
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encoder.layers.2.blocks.15.drop_path
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| 474 |
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encoder.layers.2.blocks.15.intermediate
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| 475 |
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encoder.layers.2.blocks.15.intermediate.dense
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| 476 |
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encoder.layers.2.blocks.15.intermediate.intermediate_act_fn
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| 477 |
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encoder.layers.2.blocks.15.output
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| 478 |
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encoder.layers.2.blocks.15.output.dense
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| 479 |
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encoder.layers.2.blocks.15.output.dropout
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| 480 |
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encoder.layers.2.blocks.15.layernorm_after
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| 481 |
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encoder.layers.2.blocks.16
|
| 482 |
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encoder.layers.2.blocks.16.attention
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| 483 |
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encoder.layers.2.blocks.16.attention.self
|
| 484 |
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encoder.layers.2.blocks.16.attention.self.continuous_position_bias_mlp
|
| 485 |
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encoder.layers.2.blocks.16.attention.self.continuous_position_bias_mlp.0
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encoder.layers.2.blocks.16.attention.self.continuous_position_bias_mlp.1
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encoder.layers.2.blocks.16.attention.self.continuous_position_bias_mlp.2
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| 488 |
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encoder.layers.2.blocks.16.attention.self.query
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| 489 |
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encoder.layers.2.blocks.16.attention.self.key
|
| 490 |
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encoder.layers.2.blocks.16.attention.self.value
|
| 491 |
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encoder.layers.2.blocks.16.attention.self.dropout
|
| 492 |
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encoder.layers.2.blocks.16.attention.output
|
| 493 |
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encoder.layers.2.blocks.16.attention.output.dense
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| 494 |
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encoder.layers.2.blocks.16.attention.output.dropout
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| 495 |
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encoder.layers.2.blocks.16.layernorm_before
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| 496 |
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encoder.layers.2.blocks.16.drop_path
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| 497 |
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encoder.layers.2.blocks.16.intermediate
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| 498 |
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encoder.layers.2.blocks.16.intermediate.dense
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| 499 |
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encoder.layers.2.blocks.16.intermediate.intermediate_act_fn
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| 500 |
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encoder.layers.2.blocks.16.output
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| 501 |
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encoder.layers.2.blocks.16.output.dense
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| 502 |
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encoder.layers.2.blocks.16.output.dropout
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| 503 |
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encoder.layers.2.blocks.16.layernorm_after
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| 504 |
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encoder.layers.2.blocks.17
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| 505 |
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encoder.layers.2.blocks.17.attention
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| 506 |
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encoder.layers.2.blocks.17.attention.self
|
| 507 |
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encoder.layers.2.blocks.17.attention.self.continuous_position_bias_mlp
|
| 508 |
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encoder.layers.2.blocks.17.attention.self.continuous_position_bias_mlp.0
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encoder.layers.2.blocks.17.attention.self.continuous_position_bias_mlp.1
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encoder.layers.2.blocks.17.attention.self.continuous_position_bias_mlp.2
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encoder.layers.2.blocks.17.attention.self.query
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| 512 |
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encoder.layers.2.blocks.17.attention.self.key
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| 513 |
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encoder.layers.2.blocks.17.attention.self.value
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| 514 |
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encoder.layers.2.blocks.17.attention.self.dropout
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| 515 |
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encoder.layers.2.blocks.17.attention.output
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| 516 |
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encoder.layers.2.blocks.17.attention.output.dense
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| 517 |
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encoder.layers.2.blocks.17.attention.output.dropout
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| 518 |
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encoder.layers.2.blocks.17.layernorm_before
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| 519 |
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encoder.layers.2.blocks.17.drop_path
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| 520 |
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encoder.layers.2.blocks.17.intermediate
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| 521 |
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encoder.layers.2.blocks.17.intermediate.dense
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| 522 |
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encoder.layers.2.blocks.17.intermediate.intermediate_act_fn
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| 523 |
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encoder.layers.2.blocks.17.output
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| 524 |
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encoder.layers.2.blocks.17.output.dense
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| 525 |
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encoder.layers.2.blocks.17.output.dropout
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| 526 |
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encoder.layers.2.blocks.17.layernorm_after
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| 527 |
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encoder.layers.2.downsample
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| 528 |
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encoder.layers.2.downsample.reduction
|
| 529 |
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encoder.layers.2.downsample.norm
|
| 530 |
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encoder.layers.3
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| 531 |
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encoder.layers.3.blocks
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| 532 |
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encoder.layers.3.blocks.0
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| 533 |
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encoder.layers.3.blocks.0.attention
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| 534 |
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encoder.layers.3.blocks.0.attention.self
|
| 535 |
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encoder.layers.3.blocks.0.attention.self.continuous_position_bias_mlp
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| 536 |
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encoder.layers.3.blocks.0.attention.self.continuous_position_bias_mlp.0
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| 537 |
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encoder.layers.3.blocks.0.attention.self.continuous_position_bias_mlp.1
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| 538 |
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encoder.layers.3.blocks.0.attention.self.continuous_position_bias_mlp.2
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| 539 |
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encoder.layers.3.blocks.0.attention.self.query
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| 540 |
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encoder.layers.3.blocks.0.attention.self.key
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| 541 |
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encoder.layers.3.blocks.0.attention.self.value
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| 542 |
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encoder.layers.3.blocks.0.attention.self.dropout
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encoder.layers.3.blocks.0.attention.output
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| 544 |
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encoder.layers.3.blocks.0.attention.output.dense
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| 545 |
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encoder.layers.3.blocks.0.attention.output.dropout
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| 546 |
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encoder.layers.3.blocks.0.layernorm_before
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| 547 |
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encoder.layers.3.blocks.0.drop_path
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| 548 |
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encoder.layers.3.blocks.0.intermediate
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| 549 |
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encoder.layers.3.blocks.0.intermediate.dense
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| 550 |
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encoder.layers.3.blocks.0.intermediate.intermediate_act_fn
|
| 551 |
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encoder.layers.3.blocks.0.output
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| 552 |
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encoder.layers.3.blocks.0.output.dense
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| 553 |
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encoder.layers.3.blocks.0.output.dropout
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| 554 |
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encoder.layers.3.blocks.0.layernorm_after
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encoder.layers.3.blocks.1
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| 556 |
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encoder.layers.3.blocks.1.attention
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| 557 |
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encoder.layers.3.blocks.1.attention.self
|
| 558 |
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encoder.layers.3.blocks.1.attention.self.continuous_position_bias_mlp
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| 559 |
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encoder.layers.3.blocks.1.attention.self.continuous_position_bias_mlp.0
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| 560 |
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encoder.layers.3.blocks.1.attention.self.continuous_position_bias_mlp.1
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| 561 |
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encoder.layers.3.blocks.1.attention.self.continuous_position_bias_mlp.2
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| 562 |
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encoder.layers.3.blocks.1.attention.self.query
|
| 563 |
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encoder.layers.3.blocks.1.attention.self.key
|
| 564 |
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encoder.layers.3.blocks.1.attention.self.value
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| 565 |
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encoder.layers.3.blocks.1.attention.self.dropout
|
| 566 |
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encoder.layers.3.blocks.1.attention.output
|
| 567 |
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encoder.layers.3.blocks.1.attention.output.dense
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| 568 |
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encoder.layers.3.blocks.1.attention.output.dropout
|
| 569 |
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encoder.layers.3.blocks.1.layernorm_before
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| 570 |
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encoder.layers.3.blocks.1.drop_path
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| 571 |
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encoder.layers.3.blocks.1.intermediate
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| 572 |
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encoder.layers.3.blocks.1.intermediate.dense
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| 573 |
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encoder.layers.3.blocks.1.intermediate.intermediate_act_fn
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| 574 |
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encoder.layers.3.blocks.1.output
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| 575 |
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encoder.layers.3.blocks.1.output.dense
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| 576 |
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encoder.layers.3.blocks.1.output.dropout
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| 577 |
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encoder.layers.3.blocks.1.layernorm_after
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| 578 |
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layernorm
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| 579 |
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pooler
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