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Please use the 'ee.Image.gd' accessor instead.\n", " img = gd.download.BaseImage(image)\n" ] }, { "output_type": "display_data", "data": { "text/plain": [ "...Masks/binary_Punjab_Wheat_2024_Sieved: 0%| |0/585 tiles [00:00 0, 1.0, 0.0).astype(np.float32)\n", " target_h, target_w = mask.shape\n", " small_roi = ee.Geometry.BBox(cx-offset, cy-offset, cx+offset, cy+offset)\n", "\n", " # 2. Imagery\n", " stack = []\n", " for i, (start, end) in enumerate(TIME_WINDOWS):\n", " fname = f'satmae_time_{i}.tif'\n", " attempts = 0\n", " while not os.path.exists(fname) and attempts < 3:\n", " try:\n", " print(f\"Downloading T{i+1}: {start} to {end}...\")\n", " img = ee.ImageCollection('COPERNICUS/S2_SR_HARMONIZED').filterBounds(small_roi).filterDate(start, end).median().select(['B2','B3','B4','B8','B11','B12'])\n", " geemap.download_ee_image(img, fname, region=small_roi, scale=10, crs='EPSG:4326', overwrite=True)\n", " except:\n", " attempts += 1\n", " time.sleep(2)\n", "\n", " if not os.path.exists(fname):\n", " with rasterio.open(mask_file) as src:\n", " profile = src.profile\n", " profile.update(count=6, dtype=rasterio.float32)\n", " with rasterio.open(fname, 'w', **profile) as dst:\n", " dst.write(np.zeros((6, target_h, target_w), dtype=np.float32))\n", "\n", " with rasterio.open(fname) as src:\n", " arr = src.read()\n", " arr = np.transpose(arr, (1, 2, 0))\n", " if arr.shape[:2] != (target_h, target_w):\n", " arr = cv2.resize(arr, (target_w, target_h), interpolation=cv2.INTER_LINEAR)\n", " arr = np.clip(arr / S2_SCALE, 0, 1).astype(np.float32)\n", " stack.append(arr)\n", "\n", " # 3. Tiling\n", " full_cube = np.stack(stack, axis=2)\n", " x_out, y_out = [], []\n", " stride = PATCH_SIZE\n", "\n", " for y in range(0, target_h, stride):\n", " for x in range(0, target_w, stride):\n", " img_p = full_cube[y:y+stride, x:x+stride]\n", " mask_p = mask[y:y+stride, x:x+stride]\n", " if img_p.shape[0] != PATCH_SIZE or img_p.shape[1] != PATCH_SIZE: continue\n", " if np.mean(mask_p) < 0.01 or np.isnan(img_p).any(): continue\n", " x_out.append(img_p)\n", " y_out.append(mask_p)\n", "\n", " X = np.array(x_out, dtype=np.float32).transpose(0, 4, 3, 1, 2)\n", " y = np.array(y_out, dtype=np.float32)[:, None, :, :]\n", "\n", " print(f\"Dataset Generated. Shape: {X.shape}\")\n", " X_train, X_val, y_train, y_val = train_test_split(X, y, test_size=0.2, random_state=42)\n", "\n", " np.save(os.path.join(SAVE_DIR, 'train_x.npy'), X_train)\n", " np.save(os.path.join(SAVE_DIR, 'train_y.npy'), y_train)\n", " np.save(os.path.join(SAVE_DIR, 'val_x.npy'), X_val)\n", " np.save(os.path.join(SAVE_DIR, 'val_y.npy'), y_val)\n", " print(\"Done.\")\n", "\n", "generate_satmae_partial_data()" ] }, { "cell_type": "code", "source": [ "import torch\n", "import torch.nn as nn\n", "from huggingface_hub import hf_hub_download\n", "\n", "class SatMAEPatchEmbed(nn.Module):\n", " def __init__(self, in_chans=6, embed_dim=768, patch_size=16):\n", " super().__init__()\n", " self.proj = nn.Conv2d(in_chans, embed_dim, kernel_size=patch_size, stride=patch_size)\n", "\n", " def forward(self, x):\n", " B, C, T, H, W = x.shape\n", " x = x.permute(0, 2, 1, 3, 4).reshape(B * T, C, H, W)\n", " x = self.proj(x).flatten(2).transpose(1, 2)\n", " x = x.reshape(B, T, -1, x.shape[-1])\n", " return x\n", "\n", "class SatMAEBackbone(nn.Module):\n", " def __init__(self, num_frames=3, in_chans=6, embed_dim=768, depth=12, num_heads=12):\n", " super().__init__()\n", " self.patch_embed = SatMAEPatchEmbed(in_chans=in_chans, embed_dim=embed_dim)\n", " num_patches = (224 // 16) ** 2\n", "\n", " self.pos_embed = nn.Parameter(torch.zeros(1, 1, num_patches + 1, embed_dim))\n", " self.time_embed = nn.Parameter(torch.zeros(1, num_frames, 1, embed_dim))\n", " self.cls_token = nn.Parameter(torch.zeros(1, 1, 1, embed_dim))\n", "\n", " encoder_layer = nn.TransformerEncoderLayer(d_model=embed_dim, nhead=num_heads, dim_feedforward=embed_dim*4, activation=\"gelu\", batch_first=True, norm_first=True)\n", " self.blocks = nn.TransformerEncoder(encoder_layer, num_layers=depth)\n", " self.norm = nn.LayerNorm(embed_dim)\n", "\n", " def forward(self, x):\n", " x = self.patch_embed(x)\n", " B, T, N, D = x.shape\n", "\n", " x = x + self.time_embed\n", " x = x.reshape(B, T*N, D)\n", "\n", " spatial_pos = self.pos_embed[:, :, 1:, :].expand(B, T, -1, -1).reshape(B, T*N, D)\n", " x = x + spatial_pos\n", "\n", " cls_token = self.cls_token.expand(B, -1, -1, -1).reshape(B, 1, D) + self.pos_embed[:, :, 0, :].expand(B, 1, D)\n", " x = torch.cat((cls_token, x), dim=1)\n", "\n", " x = self.blocks(x)\n", " x = self.norm(x)\n", " return x\n", "\n", "class SatMAESegmentation(nn.Module):\n", " def __init__(self, num_frames=3, in_chans=6, embed_dim=768):\n", " super().__init__()\n", " print(\"Initializing SatMAE Partial FT Model...\")\n", " self.num_frames = num_frames\n", " self.backbone = SatMAEBackbone(num_frames=num_frames, in_chans=in_chans, embed_dim=embed_dim)\n", "\n", " # --- AUTOMATIC WEIGHT LOADING ---\n", " print(\"Downloading Pretrained MAE Weights (facebook/vit-mae-base)...\")\n", " try:\n", " p = hf_hub_download(\"facebook/vit-mae-base\", \"pytorch_model.bin\")\n", " sd = torch.load(p, map_location='cpu')\n", "\n", " new_sd = {}\n", " for k, v in sd.items():\n", "\n", " # Adapt Patch Embeddings (3 Ch -> 6 Ch)\n", " if 'patch_embed.proj.weight' in k:\n", " print(f\" Adapting Weights: {v.shape} -> 6 Channels\")\n", " new_w = torch.zeros(768, in_chans, 16, 16)\n", " new_w[:, :3, :, :] = v # Copy RGB\n", " # Initialize Bands 4-6 with average of RGB\n", " new_w[:, 3:, :, :] = v.mean(dim=1, keepdim=True).repeat(1, in_chans-3, 1, 1)\n", " new_sd['backbone.patch_embed.proj.weight'] = new_w\n", " continue\n", "\n", " if 'patch_embed.proj.bias' in k:\n", " new_sd['backbone.patch_embed.proj.bias'] = v\n", " continue\n", "\n", " # Backbone Block Mapping\n", " if 'blocks.' in k:\n", " new_k = f\"backbone.{k.replace('blocks.', 'blocks.layers.')}\"\n", " new_k = new_k.replace('mlp.fc1', 'linear1')\n", " new_k = new_k.replace('mlp.fc2', 'linear2')\n", " new_sd[new_k] = v\n", " elif 'norm.' in k:\n", " new_sd[f\"backbone.{k}\"] = v\n", " elif 'pos_embed' in k:\n", " if v.shape == self.backbone.pos_embed.shape:\n", " new_sd['backbone.pos_embed'] = v\n", " elif 'cls_token' in k:\n", " new_sd['backbone.cls_token'] = v\n", "\n", " missing, unexpected = self.backbone.load_state_dict(new_sd, strict=False)\n", " print(f\"Weights Loaded! (Matched {len(new_sd)} keys)\")\n", "\n", " except Exception as e:\n", " print(f\"Weight Download Failed: {e}. Using Random Initialization.\")\n", "\n", " # --- PARTIAL FREEZE STRATEGY (10 Frozen / 2 Unfrozen) ---\n", " print(\"Applying Partial Freeze Strategy...\")\n", "\n", " # 1. Freeze ALL Blocks initially\n", " for param in self.backbone.blocks.parameters():\n", " param.requires_grad = False\n", "\n", " # 2. Unfreeze Last 2 Blocks (Layers 10 and 11)\n", " print(\"Unfreezing Last 2 Encoder Blocks...\")\n", " for layer in self.backbone.blocks.layers[-2:]:\n", " for param in layer.parameters():\n", " param.requires_grad = True\n", "\n", " # 3. Unfreeze Embeddings & Norms\n", " self.backbone.patch_embed.proj.weight.requires_grad = True\n", " self.backbone.pos_embed.requires_grad = True\n", " self.backbone.time_embed.requires_grad = True\n", " self.backbone.cls_token.requires_grad = True\n", " self.backbone.norm.weight.requires_grad = True\n", " self.backbone.norm.bias.requires_grad = True\n", "\n", " # 4. Decoder\n", " self.temporal_agg = nn.Conv2d(embed_dim * num_frames, embed_dim, kernel_size=1)\n", " self.decoder = nn.Sequential(\n", " nn.Upsample(scale_factor=2), nn.Conv2d(embed_dim, 256, 3, 1, 1), nn.BatchNorm2d(256), nn.GELU(),\n", " nn.Upsample(scale_factor=2), nn.Conv2d(256, 128, 3, 1, 1), nn.BatchNorm2d(128), nn.GELU(),\n", " nn.Upsample(scale_factor=2), nn.Conv2d(128, 64, 3, 1, 1), nn.BatchNorm2d(64), nn.GELU(),\n", " nn.Upsample(scale_factor=2), nn.Conv2d(64, 32, 3, 1, 1), nn.BatchNorm2d(32), nn.GELU(),\n", " nn.Conv2d(32, 1, 1)\n", " )\n", "\n", " def forward(self, x):\n", " features = self.backbone(x)[:, 1:, :]\n", " B, L, D = features.shape\n", " H_p = 14\n", " features = features.view(B, self.num_frames, H_p, H_p, D)\n", " features = features.permute(0, 1, 4, 2, 3).reshape(B, self.num_frames * D, H_p, H_p)\n", " features = self.temporal_agg(features)\n", " return self.decoder(features)" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 17 }, "id": "gkW0KoORjKer", "executionInfo": { "status": "ok", "timestamp": 1768764001735, "user_tz": -330, "elapsed": 6565, "user": { "displayName": "Enigma Cypher", "userId": "06934947732805796536" } }, "outputId": "4af0fe11-e5a8-4c5e-c5c4-dc29e26d3f87" }, "execution_count": null, "outputs": [ { "output_type": "display_data", "data": { "text/plain": [ "" ], "text/html": [ "\n", " \n", " " ] }, "metadata": {} } ] }, { "cell_type": "code", "source": [ "import torch.optim as optim\n", "import torch.optim.swa_utils as swa_utils\n", "from torch.utils.data import Dataset, DataLoader\n", "from scipy.ndimage import distance_transform_edt as distance\n", "import numpy as np\n", "import glob\n", "\n", "def apply_augmentation(x, y):\n", " if np.random.rand() > 0.5: x = torch.flip(x, [4]); y = torch.flip(y, [3])\n", " if np.random.rand() > 0.5: x = torch.flip(x, [3]); y = torch.flip(y, [2])\n", " return x, y\n", "\n", "def manage_rolling_checkpoints(save_dir, keep_k=5):\n", " files = sorted(glob.glob(os.path.join(save_dir, \"epoch_*.pth\")), key=os.path.getmtime)\n", " if len(files) > keep_k:\n", " for f in files[:-keep_k]: os.remove(f)\n", "\n", "class DiceLoss(nn.Module):\n", " def __init__(self, smooth=1e-6): super().__init__(); self.smooth = smooth\n", " def forward(self, inputs, targets):\n", " inputs = torch.sigmoid(inputs).reshape(-1); targets = targets.reshape(-1)\n", " inter = (inputs * targets).sum()\n", " return 1 - (2. * inter + self.smooth) / (inputs.sum() + targets.sum() + self.smooth)\n", "\n", "class HausdorffDTLoss(nn.Module):\n", " def __init__(self, alpha=2.0): super().__init__(); self.alpha = alpha\n", " def forward(self, pred, gt):\n", " with torch.no_grad():\n", " gt_np = gt.cpu().numpy(); dist_map = np.zeros_like(gt_np)\n", " for i in range(len(gt_np)):\n", " mask = (gt_np[i, 0] > 0.5).astype(np.uint8)\n", " if mask.sum() == 0: continue\n", " d_in = distance(mask); d_out = distance(1 - mask)\n", " dist_map[i, 0] = (d_out - d_in)\n", " dist_map = torch.tensor(dist_map, device=pred.device, dtype=torch.float32)\n", " return torch.mean((torch.sigmoid(pred) - gt) ** 2 * (1 + self.alpha * torch.abs(dist_map)))\n", "\n", "class CompoundLoss(nn.Module):\n", " def __init__(self): super().__init__(); self.dice = DiceLoss(); self.boundary = HausdorffDTLoss()\n", " def forward(self, p, t): return 0.7*self.dice(p, t) + 0.3*self.boundary(p, t)" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 17 }, "id": "EZS8vE5njRLP", "executionInfo": { "status": "ok", "timestamp": 1768764036770, "user_tz": -330, "elapsed": 507, "user": { "displayName": "Enigma Cypher", "userId": "06934947732805796536" } }, "outputId": "75e9d4a8-5c41-47a8-8d43-a1cc42ea540b" }, "execution_count": null, "outputs": [ { "output_type": "display_data", "data": { "text/plain": [ "" ], "text/html": [ "\n", " \n", " " ] }, "metadata": {} } ] }, { "cell_type": "code", "source": [ "import torch\n", "import torch.nn as nn\n", "import torch.optim as optim\n", "import torch.optim.swa_utils as swa_utils\n", "from torch.utils.data import Dataset, DataLoader\n", "import numpy as np\n", "import os\n", "import glob\n", "import re\n", "from google.colab import auth\n", "from googleapiclient.discovery import build\n", "from googleapiclient.errors import HttpError\n", "from huggingface_hub import hf_hub_download\n", "\n", "# ==========================================\n", "# 1. TRASH CLEANER SETUP\n", "# ==========================================\n", "try:\n", " print(\"Authenticating for Trash Cleaner...\")\n", " auth.authenticate_user()\n", "except:\n", " print(\" Authentication skipped/failed. Trash cleaning might not work.\")\n", "\n", "def empty_trash_specific(keyword=\"epoch\"):\n", " \"\"\"Permanently deletes files from Drive Trash matching the keyword.\"\"\"\n", " try:\n", " service = build('drive', 'v3')\n", " query = f\"trashed = true and name contains '{keyword}'\"\n", " results = service.files().list(q=query, fields=\"files(id, name)\").execute()\n", " items = results.get('files', [])\n", "\n", " if not items:\n", " print(f\" Trash is clean.\")\n", " return\n", "\n", " print(f\" Deleting {len(items)} '{keyword}' files from Trash...\")\n", " for item in items:\n", " try:\n", " service.files().delete(fileId=item['id']).execute()\n", " except:\n", " pass\n", " print(\" Trash Purged.\")\n", " except Exception as e:\n", " print(f\" Trash Error: {e}\")\n", "\n", "# ==========================================\n", "# 2. MODEL DEFINITION (PARTIAL FINE-TUNE)\n", "# ==========================================\n", "\n", "class SatMAEPatchEmbed(nn.Module):\n", " def __init__(self, in_chans=6, embed_dim=768, patch_size=16):\n", " super().__init__()\n", " self.proj = nn.Conv2d(in_chans, embed_dim, kernel_size=patch_size, stride=patch_size)\n", "\n", " def forward(self, x):\n", " B, C, T, H, W = x.shape\n", " x = x.permute(0, 2, 1, 3, 4).reshape(B * T, C, H, W)\n", " x = self.proj(x).flatten(2).transpose(1, 2)\n", " x = x.reshape(B, T, -1, x.shape[-1])\n", " return x\n", "\n", "class SatMAEBackbone(nn.Module):\n", " def __init__(self, num_frames=3, in_chans=6, embed_dim=768, depth=12, num_heads=12):\n", " super().__init__()\n", " self.patch_embed = SatMAEPatchEmbed(in_chans=in_chans, embed_dim=embed_dim)\n", " num_patches = (224 // 16) ** 2\n", "\n", " self.pos_embed = nn.Parameter(torch.zeros(1, 1, num_patches + 1, embed_dim))\n", " self.time_embed = nn.Parameter(torch.zeros(1, num_frames, 1, embed_dim))\n", " self.cls_token = nn.Parameter(torch.zeros(1, 1, 1, embed_dim))\n", "\n", " encoder_layer = nn.TransformerEncoderLayer(d_model=embed_dim, nhead=num_heads, dim_feedforward=embed_dim*4, activation=\"gelu\", batch_first=True, norm_first=True)\n", " self.blocks = nn.TransformerEncoder(encoder_layer, num_layers=depth)\n", " self.norm = nn.LayerNorm(embed_dim)\n", "\n", " def forward(self, x):\n", " x = self.patch_embed(x)\n", " B, T, N, D = x.shape\n", " x = x + self.time_embed\n", " x = x.reshape(B, T*N, D)\n", " spatial_pos = self.pos_embed[:, :, 1:, :].expand(B, T, -1, -1).reshape(B, T*N, D)\n", " x = x + spatial_pos\n", " cls_token = self.cls_token.expand(B, -1, -1, -1).reshape(B, 1, D) + self.pos_embed[:, :, 0, :].expand(B, 1, D)\n", " x = torch.cat((cls_token, x), dim=1)\n", " x = self.blocks(x)\n", " x = self.norm(x)\n", " return x\n", "\n", "class SatMAESegmentation(nn.Module):\n", " def __init__(self, num_frames=3, in_chans=6, embed_dim=768):\n", " super().__init__()\n", " print(\" Initializing SatMAE Partial FT Model...\")\n", " self.num_frames = num_frames\n", " self.backbone = SatMAEBackbone(num_frames=num_frames, in_chans=in_chans, embed_dim=embed_dim)\n", "\n", " # --- AUTOMATIC WEIGHT LOADING ---\n", " print(\" Downloading Pretrained MAE Weights (facebook/vit-mae-base)...\")\n", " try:\n", " p = hf_hub_download(\"facebook/vit-mae-base\", \"pytorch_model.bin\")\n", " sd = torch.load(p, map_location='cpu')\n", "\n", " new_sd = {}\n", " for k, v in sd.items():\n", " if 'patch_embed.proj.weight' in k:\n", " new_w = torch.zeros(768, in_chans, 16, 16)\n", " new_w[:, :3, :, :] = v # Copy RGB\n", " new_w[:, 3:, :, :] = v.mean(dim=1, keepdim=True).repeat(1, in_chans-3, 1, 1)\n", " new_sd['backbone.patch_embed.proj.weight'] = new_w\n", " elif 'patch_embed.proj.bias' in k:\n", " new_sd['backbone.patch_embed.proj.bias'] = v\n", " elif 'blocks.' in k:\n", " new_k = f\"backbone.{k.replace('blocks.', 'blocks.layers.')}\"\n", " new_k = new_k.replace('mlp.fc1', 'linear1').replace('mlp.fc2', 'linear2')\n", " new_sd[new_k] = v\n", " elif 'norm.' in k: new_sd[f\"backbone.{k}\"] = v\n", " elif 'pos_embed' in k:\n", " if v.shape == self.backbone.pos_embed.shape: new_sd['backbone.pos_embed'] = v\n", " elif 'cls_token' in k: new_sd['backbone.cls_token'] = v\n", "\n", " self.backbone.load_state_dict(new_sd, strict=False)\n", " print(f\" Weights Loaded!\")\n", " except Exception as e:\n", " print(f\" Weight Download Failed: {e}. Random Init.\")\n", "\n", " # --- PARTIAL FREEZE STRATEGY (10 Frozen / 2 Unfrozen) ---\n", " print(\"Applying Partial Freeze Strategy...\")\n", "\n", " # 1. Freeze ALL Blocks initially\n", " for param in self.backbone.blocks.parameters():\n", " param.requires_grad = False\n", "\n", " # 2. Unfreeze Last 2 Blocks (Layers 10 and 11)\n", " print(\" Unfreezing Last 2 Encoder Blocks...\")\n", " for layer in self.backbone.blocks.layers[-2:]:\n", " for param in layer.parameters():\n", " param.requires_grad = True\n", "\n", " # 3. Unfreeze Embeddings & Norms\n", " self.backbone.patch_embed.proj.weight.requires_grad = True\n", " self.backbone.pos_embed.requires_grad = True\n", " self.backbone.time_embed.requires_grad = True\n", " self.backbone.cls_token.requires_grad = True\n", " self.backbone.norm.weight.requires_grad = True\n", " self.backbone.norm.bias.requires_grad = True\n", "\n", " # 4. Decoder\n", " self.temporal_agg = nn.Conv2d(embed_dim * num_frames, embed_dim, kernel_size=1)\n", " self.decoder = nn.Sequential(\n", " nn.Upsample(scale_factor=2), nn.Conv2d(embed_dim, 256, 3, 1, 1), nn.BatchNorm2d(256), nn.GELU(),\n", " nn.Upsample(scale_factor=2), nn.Conv2d(256, 128, 3, 1, 1), nn.BatchNorm2d(128), nn.GELU(),\n", " nn.Upsample(scale_factor=2), nn.Conv2d(128, 64, 3, 1, 1), nn.BatchNorm2d(64), nn.GELU(),\n", " nn.Upsample(scale_factor=2), nn.Conv2d(64, 32, 3, 1, 1), nn.BatchNorm2d(32), nn.GELU(),\n", " nn.Conv2d(32, 1, 1)\n", " )\n", "\n", " def forward(self, x):\n", " features = self.backbone(x)[:, 1:, :]\n", " B, L, D = features.shape\n", " features = features.view(B, self.num_frames, 14, 14, D)\n", " features = features.permute(0, 1, 4, 2, 3).reshape(B, self.num_frames * D, 14, 14)\n", " features = self.temporal_agg(features)\n", " return self.decoder(features)\n", "\n", "# ==========================================\n", "# 3. CONFIGURATION & SETUP\n", "# ==========================================\n", "SAVE_DIR = '/content/drive/MyDrive/SatMAE_PartialFT_Results_1/'\n", "CHECKPOINT_PATH = os.path.join(SAVE_DIR, \"checkpoint_satmae_partial.pth\")\n", "BATCH_SIZE = 8\n", "EPOCHS = 5000\n", "PATIENCE_TRIGGER = 150\n", "SWA_DURATION = 50\n", "\n", "device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')\n", "print(f\"Device: {device}\")\n", "\n", "model = SatMAESegmentation(num_frames=3, in_chans=6).to(device)\n", "criterion = CompoundLoss()\n", "optimizer = optim.AdamW(filter(lambda p: p.requires_grad, model.parameters()), lr=1e-4)\n", "scheduler = optim.lr_scheduler.CosineAnnealingWarmRestarts(optimizer, T_0=50, T_mult=2)\n", "\n", "swa_model = swa_utils.AveragedModel(model)\n", "swa_scheduler = swa_utils.SWALR(optimizer, swa_lr=5e-5)\n", "\n", "class MmapDataset(Dataset):\n", " def __init__(self, x_path, y_path):\n", " self.data = np.load(x_path, mmap_mode='r')\n", " self.target = np.load(y_path, mmap_mode='r')\n", " def __len__(self): return len(self.data)\n", " def __getitem__(self, index):\n", " return torch.from_numpy(self.data[index].copy()), torch.from_numpy(self.target[index].copy())\n", "\n", "if not os.path.exists(SAVE_DIR): os.makedirs(SAVE_DIR)\n", "train_ds = MmapDataset(os.path.join(SAVE_DIR, 'train_x.npy'), os.path.join(SAVE_DIR, 'train_y.npy'))\n", "val_ds = MmapDataset(os.path.join(SAVE_DIR, 'val_x.npy'), os.path.join(SAVE_DIR, 'val_y.npy'))\n", "train_loader = DataLoader(train_ds, BATCH_SIZE, shuffle=True)\n", "val_loader = DataLoader(val_ds, BATCH_SIZE, shuffle=False)\n", "\n", "# ==========================================\n", "# 4. ROBUST RESUME LOGIC\n", "# ==========================================\n", "start_epoch = 0; best_loss = float('inf'); patience_counter = 0; swa_active = False; swa_epoch_counter = 0\n", "\n", "if os.path.exists(CHECKPOINT_PATH):\n", " try:\n", " print(f\"Attempting to load main checkpoint: {CHECKPOINT_PATH}\")\n", " ckpt = torch.load(CHECKPOINT_PATH, map_location=device)\n", " model.load_state_dict(ckpt['model_state_dict'])\n", " optimizer.load_state_dict(ckpt['optimizer_state_dict'])\n", " start_epoch = ckpt['epoch'] + 1\n", " best_loss = ckpt['best_loss']\n", " patience_counter = ckpt.get('patience_counter', 0)\n", " swa_active = ckpt.get('swa_active', False)\n", " swa_epoch_counter = ckpt.get('swa_epoch_counter', 0)\n", " print(\" Main checkpoint loaded successfully.\")\n", " except Exception as e:\n", " print(f\" Main checkpoint corrupted. Searching for backups...\")\n", " epoch_files = glob.glob(os.path.join(SAVE_DIR, \"epoch_*.pth\"))\n", " if epoch_files:\n", " latest_file = max(epoch_files, key=os.path.getmtime)\n", " print(f\" Rescuing from backup: {latest_file}\")\n", " try:\n", " ckpt = torch.load(latest_file, map_location=device)\n", " model.load_state_dict(ckpt)\n", " match = re.search(r'epoch_(\\d+).pth', latest_file)\n", " start_epoch = int(match.group(1)) if match else 0\n", " print(f\"Rescue successful. Resuming from Epoch {start_epoch}.\")\n", " except:\n", " print(\"Backup failed. Starting from Scratch.\")\n", " else:\n", " print(\" No backups found. Starting from Scratch.\")\n", "\n", "# ==========================================\n", "# 5. TRAINING LOOP\n", "# ==========================================\n", "print(f\" Starting Training. Max Epochs: {EPOCHS}\")\n", "\n", "for ep in range(start_epoch, EPOCHS):\n", " model.train(); train_loss = 0\n", " for x, y in train_loader:\n", " x, y = x.to(device), y.to(device)\n", " x, y = apply_augmentation(x, y)\n", " optimizer.zero_grad()\n", " loss = criterion(model(x), y)\n", " loss.backward(); optimizer.step()\n", " train_loss += loss.item()\n", "\n", " model.eval(); val_loss = 0\n", " with torch.no_grad():\n", " for x, y in val_loader:\n", " x, y = x.to(device), y.to(device)\n", " val_loss += criterion(model(x), y).item()\n", "\n", " avg_t = train_loss / len(train_loader); avg_v = val_loss / len(val_loader)\n", " status_msg = \"\"\n", "\n", " if swa_active:\n", " swa_model.update_parameters(model); swa_scheduler.step()\n", " swa_epoch_counter += 1; status_msg = f\"SWA Phase ({swa_epoch_counter}/{SWA_DURATION})\"\n", " if swa_epoch_counter >= SWA_DURATION:\n", " print(\" SWA Complete. Saving & Stopping.\")\n", " swa_utils.update_bn(train_loader, swa_model, device=device)\n", " torch.save(swa_model.state_dict(), os.path.join(SAVE_DIR, \"satmae_partial_swa_final.pth\"))\n", " break\n", " else:\n", " scheduler.step()\n", " if avg_v < best_loss:\n", " best_loss = avg_v; patience_counter = 0; status_msg = \"New Best Model!\"\n", " torch.save(model.state_dict(), os.path.join(SAVE_DIR, \"best_model.pth\"))\n", " else:\n", " patience_counter += 1; status_msg = f\"No Improvement ({patience_counter}/{PATIENCE_TRIGGER})\"\n", "\n", " if patience_counter >= PATIENCE_TRIGGER:\n", " print(f\" Patience limit reached. Triggering SWA.\")\n", " swa_active = True; swa_epoch_counter = 0\n", "\n", " print(f\"Epoch {ep+1} | Train: {avg_t:.4f} | Val: {avg_v:.4f} | {status_msg}\")\n", "\n", " # Save Epoch Checkpoint\n", " epoch_ckpt = os.path.join(SAVE_DIR, f\"epoch_{ep+1}.pth\")\n", " torch.save(model.state_dict(), epoch_ckpt)\n", "\n", " # Maintenance\n", " manage_rolling_checkpoints(SAVE_DIR, keep_k=5)\n", " if (ep + 1) % 10 == 0:\n", " print(\" Maintenance: Emptying 'epoch' files from Drive Trash...\")\n", " empty_trash_specific(keyword=\"epoch\")\n", "\n", " # Save Resume State\n", " torch.save({\n", " 'epoch': ep,\n", " 'model_state_dict': model.state_dict(),\n", " 'optimizer_state_dict': optimizer.state_dict(),\n", " 'best_loss': best_loss,\n", " 'patience_counter': patience_counter,\n", " 'swa_active': swa_active,\n", " 'swa_epoch_counter': swa_epoch_counter\n", " }, CHECKPOINT_PATH)" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 1000 }, "id": "DIGgKnwYjXL1", "executionInfo": { "status": "error", "timestamp": 1768772473840, "user_tz": -330, "elapsed": 1407165, "user": { "displayName": "Enigma Cypher", "userId": "06934947732805796536" } }, "outputId": "cd013a46-dfb0-4ce4-812b-4839df06adf8" }, "execution_count": null, "outputs": [ { "data": { "text/html": [ "\n", " \n", " " ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "metadata": { "tags": null }, "name": "stdout", "output_type": "stream", "text": [ "Authenticating for Trash Cleaner...\n", "Device: cuda\n", " Initializing SatMAE Partial FT Model...\n", " Downloading Pretrained MAE Weights (facebook/vit-mae-base)...\n", " Weights Loaded!\n", "Applying Partial Freeze Strategy...\n", " Unfreezing Last 2 Encoder Blocks...\n", "Attempting to load main checkpoint: /content/drive/MyDrive/SatMAE_PartialFT_Results_1/checkpoint_satmae_partial.pth\n", " Main checkpoint corrupted. Searching for backups...\n", " Rescuing from backup: /content/drive/MyDrive/SatMAE_PartialFT_Results_1/epoch_121.pth\n", "Rescue successful. Resuming from Epoch 121.\n", " Starting Training. Max Epochs: 5000\n", "Epoch 122 | Train: 0.3627 | Val: 0.6601 | New Best Model!\n", "Epoch 123 | Train: 0.4789 | Val: 4.2347 | No Improvement (1/150)\n", "Epoch 124 | Train: 0.3872 | Val: 4.2443 | No Improvement (2/150)\n", "Epoch 125 | Train: 0.3794 | Val: 3.4719 | No Improvement (3/150)\n", "Epoch 126 | Train: 0.3666 | Val: 0.4990 | New Best Model!\n", "Epoch 127 | Train: 0.3661 | Val: 0.2869 | New Best Model!\n", "Epoch 128 | Train: 0.3667 | Val: 0.3365 | No Improvement (1/150)\n", "Epoch 129 | Train: 0.3614 | Val: 0.3680 | No Improvement (2/150)\n", "Epoch 130 | Train: 0.3578 | Val: 0.3907 | No Improvement (3/150)\n" ] }, { "metadata": { "tags": null }, "name": "stderr", "output_type": "stream", "text": [ "WARNING:google_auth_httplib2:httplib2 transport does not support per-request timeout. 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Set the timeout when constructing the httplib2.Http instance.\n" ] }, { "metadata": { "tags": null }, "name": "stdout", "output_type": "stream", "text": [ " Maintenance: Emptying 'epoch' files from Drive Trash...\n", " Deleting 8 'epoch' files from Trash...\n", " Trash Purged.\n", "Epoch 131 | Train: 0.3573 | Val: 0.4085 | No Improvement (4/150)\n", "Epoch 132 | Train: 0.3565 | Val: 0.4167 | No Improvement (5/150)\n", "Epoch 133 | Train: 0.3562 | Val: 0.4043 | No Improvement (6/150)\n", "Epoch 134 | Train: 0.3529 | Val: 0.3796 | No Improvement (7/150)\n", "Epoch 135 | Train: 0.3549 | Val: 0.3592 | No Improvement (8/150)\n", "Epoch 136 | Train: 0.3533 | Val: 0.3494 | No Improvement (9/150)\n", "Epoch 137 | Train: 0.3483 | Val: 0.3384 | No Improvement (10/150)\n", "Epoch 138 | Train: 0.3512 | Val: 0.3212 | No Improvement (11/150)\n", "Epoch 139 | Train: 0.3499 | Val: 0.3071 | No Improvement (12/150)\n", "Epoch 140 | Train: 0.3487 | Val: 0.2961 | No Improvement (13/150)\n" ] }, { "metadata": { "tags": null }, "name": "stderr", "output_type": "stream", "text": [ "WARNING:google_auth_httplib2:httplib2 transport does not support per-request timeout. 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Set the timeout when constructing the httplib2.Http instance.\n" ] }, { "metadata": { "tags": null }, "name": "stdout", "output_type": "stream", "text": [ " Deleting 10 'epoch' files from Trash...\n", " Trash Purged.\n", "Epoch 221 | Train: 0.2983 | Val: 0.3020 | No Improvement (43/150)\n", "Epoch 222 | Train: 0.3024 | Val: 0.3065 | No Improvement (44/150)\n", "Epoch 223 | Train: 0.2990 | Val: 0.3028 | No Improvement (45/150)\n", "Epoch 224 | Train: 0.2978 | Val: 0.3001 | No Improvement (46/150)\n", "Epoch 225 | Train: 0.3007 | Val: 0.3138 | No Improvement (47/150)\n", "Epoch 226 | Train: 0.3000 | Val: 0.3319 | No Improvement (48/150)\n", "Epoch 227 | Train: 0.2989 | Val: 0.3324 | No Improvement (49/150)\n", "Epoch 228 | Train: 0.2970 | Val: 0.3178 | No Improvement (50/150)\n", "Epoch 229 | Train: 0.2993 | Val: 0.2946 | No Improvement (51/150)\n", "Epoch 230 | Train: 0.2982 | Val: 0.2876 | No Improvement (52/150)\n" ] }, { "metadata": { "tags": null }, "name": "stderr", "output_type": "stream", "text": [ "WARNING:google_auth_httplib2:httplib2 transport does not support per-request timeout. 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Set the timeout when constructing the httplib2.Http instance.\n" ] }, { "metadata": { "tags": null }, "name": "stdout", "output_type": "stream", "text": [ " Deleting 10 'epoch' files from Trash...\n", " Trash Purged.\n", "Epoch 231 | Train: 0.3011 | Val: 0.2763 | No Improvement (53/150)\n", "Epoch 232 | Train: 0.2997 | Val: 0.2723 | No Improvement (54/150)\n", "Epoch 233 | Train: 0.2976 | Val: 0.2758 | No Improvement (55/150)\n", "Epoch 234 | Train: 0.2961 | Val: 0.2863 | No Improvement (56/150)\n", "Epoch 235 | Train: 0.2965 | Val: 0.3041 | No Improvement (57/150)\n", "Epoch 236 | Train: 0.2962 | Val: 0.3236 | No Improvement (58/150)\n", "Epoch 237 | Train: 0.2947 | Val: 0.3282 | No Improvement (59/150)\n", "Epoch 238 | Train: 0.2972 | Val: 0.3138 | No Improvement (60/150)\n", "Epoch 239 | Train: 0.2945 | Val: 0.3051 | No Improvement (61/150)\n", "Epoch 240 | Train: 0.2941 | Val: 0.2987 | No Improvement (62/150)\n" ] }, { "metadata": { "tags": null }, "name": "stderr", "output_type": "stream", "text": [ "WARNING:google_auth_httplib2:httplib2 transport does not support per-request timeout. Set the timeout when constructing the httplib2.Http instance.\n", "WARNING:google_auth_httplib2:httplib2 transport does not support per-request timeout. Set the timeout when constructing the httplib2.Http instance.\n" ] }, { "metadata": { "tags": null }, "name": "stdout", "output_type": "stream", "text": [ " Maintenance: Emptying 'epoch' files from Drive Trash...\n", " Deleting 10 'epoch' files from Trash...\n", " Trash Purged.\n", "Epoch 241 | Train: 0.2945 | Val: 0.3005 | No Improvement (63/150)\n", "Epoch 242 | Train: 0.2930 | Val: 0.2985 | No Improvement (64/150)\n", "Epoch 243 | Train: 0.2930 | Val: 0.2930 | No Improvement (65/150)\n", "Epoch 244 | Train: 0.2938 | Val: 0.2929 | No Improvement (66/150)\n", "Epoch 245 | Train: 0.2929 | Val: 0.2905 | No Improvement (67/150)\n", "Epoch 246 | Train: 0.2934 | Val: 0.2920 | No Improvement (68/150)\n", "Epoch 247 | Train: 0.2931 | Val: 0.2964 | No Improvement (69/150)\n", "Epoch 248 | Train: 0.2960 | Val: 0.2956 | No Improvement (70/150)\n", "Epoch 249 | Train: 0.2969 | Val: 0.2965 | No Improvement (71/150)\n", "Epoch 250 | Train: 0.2957 | Val: 0.3000 | No Improvement (72/150)\n" ] }, { "metadata": { "tags": null }, "name": "stderr", "output_type": "stream", "text": [ "WARNING:google_auth_httplib2:httplib2 transport does not support per-request timeout. Set the timeout when constructing the httplib2.Http instance.\n" ] }, { "metadata": { "tags": null }, "name": "stdout", "output_type": "stream", "text": [ " Maintenance: Emptying 'epoch' files from Drive Trash...\n" ] }, { "metadata": { "tags": null }, "name": "stderr", "output_type": "stream", "text": [ "WARNING:google_auth_httplib2:httplib2 transport does not support per-request timeout. Set the timeout when constructing the httplib2.Http instance.\n" ] }, { "metadata": { "tags": null }, "name": "stdout", "output_type": "stream", "text": [ " Deleting 10 'epoch' files from Trash...\n", " Trash Purged.\n", "Epoch 251 | Train: 0.2947 | Val: 0.3030 | No Improvement (73/150)\n", "Epoch 252 | Train: 0.2946 | Val: 0.3016 | No Improvement (74/150)\n", "Epoch 253 | Train: 0.2944 | Val: 0.2999 | No Improvement (75/150)\n", "Epoch 254 | Train: 0.2941 | Val: 0.2979 | No Improvement (76/150)\n", "Epoch 255 | Train: 0.2917 | Val: 0.2967 | No Improvement (77/150)\n", "Epoch 256 | Train: 0.2921 | Val: 0.2944 | No Improvement (78/150)\n", "Epoch 257 | Train: 0.2943 | Val: 0.2916 | No Improvement (79/150)\n", "Epoch 258 | Train: 0.2941 | Val: 0.2900 | No Improvement (80/150)\n", "Epoch 259 | Train: 0.2919 | Val: 0.2876 | No Improvement (81/150)\n", "Epoch 260 | Train: 0.2923 | Val: 0.2853 | No Improvement (82/150)\n" ] }, { "metadata": { "tags": null }, "name": "stderr", "output_type": "stream", "text": [ "WARNING:google_auth_httplib2:httplib2 transport does not support per-request timeout. Set the timeout when constructing the httplib2.Http instance.\n" ] }, { "metadata": { "tags": null }, "name": "stdout", "output_type": "stream", "text": [ " Maintenance: Emptying 'epoch' files from Drive Trash...\n" ] }, { "metadata": { "tags": null }, "name": "stderr", "output_type": "stream", "text": [ "WARNING:google_auth_httplib2:httplib2 transport does not support per-request timeout. Set the timeout when constructing the httplib2.Http instance.\n" ] }, { "metadata": { "tags": null }, "name": "stdout", "output_type": "stream", "text": [ " Deleting 10 'epoch' files from Trash...\n", " Trash Purged.\n", "Epoch 261 | Train: 0.2953 | Val: 0.2837 | No Improvement (83/150)\n", "Epoch 262 | Train: 0.2918 | Val: 0.2827 | No Improvement (84/150)\n", "Epoch 263 | Train: 0.2949 | Val: 0.2820 | No Improvement (85/150)\n", "Epoch 264 | Train: 0.2926 | Val: 0.2817 | No Improvement (86/150)\n", "Epoch 265 | Train: 0.2949 | Val: 0.2820 | No Improvement (87/150)\n", "Epoch 266 | Train: 0.2910 | Val: 0.2823 | No Improvement (88/150)\n", "Epoch 267 | Train: 0.2912 | Val: 0.2828 | No Improvement (89/150)\n", "Epoch 268 | Train: 0.2933 | Val: 0.2834 | No Improvement (90/150)\n", "Epoch 269 | Train: 0.2913 | Val: 0.2842 | No Improvement (91/150)\n", "Epoch 270 | Train: 0.2960 | Val: 0.2847 | No Improvement (92/150)\n" ] }, { "metadata": { "tags": null }, "name": "stderr", "output_type": "stream", "text": [ "WARNING:google_auth_httplib2:httplib2 transport does not support per-request timeout. Set the timeout when constructing the httplib2.Http instance.\n", "WARNING:google_auth_httplib2:httplib2 transport does not support per-request timeout. Set the timeout when constructing the httplib2.Http instance.\n" ] }, { "metadata": { "tags": null }, "name": "stdout", "output_type": "stream", "text": [ " Maintenance: Emptying 'epoch' files from Drive Trash...\n", " Deleting 9 'epoch' files from Trash...\n", " Trash Purged.\n", "Epoch 271 | Train: 0.2921 | Val: 0.2853 | No Improvement (93/150)\n", "Epoch 272 | Train: 0.2910 | Val: 0.3177 | No Improvement (94/150)\n", "Epoch 273 | Train: 0.2985 | Val: 0.3767 | No Improvement (95/150)\n", "Epoch 274 | Train: 0.2933 | Val: 0.3584 | No Improvement (96/150)\n", "Epoch 275 | Train: 0.2948 | Val: 0.3221 | No Improvement (97/150)\n", "Epoch 276 | Train: 0.2918 | Val: 0.2974 | No Improvement (98/150)\n", "Epoch 277 | Train: 0.2910 | Val: 0.3031 | No Improvement (99/150)\n", "Epoch 278 | Train: 0.2895 | Val: 0.2779 | No Improvement (100/150)\n", "Epoch 279 | Train: 0.2946 | Val: 0.2408 | No Improvement (101/150)\n", "Epoch 280 | Train: 0.2917 | Val: 0.2504 | No Improvement (102/150)\n" ] }, { "metadata": { "tags": null }, "name": "stderr", "output_type": "stream", "text": [ "WARNING:google_auth_httplib2:httplib2 transport does not support per-request timeout. Set the timeout when constructing the httplib2.Http instance.\n" ] }, { "metadata": { "tags": null }, "name": "stdout", "output_type": "stream", "text": [ " Maintenance: Emptying 'epoch' files from Drive Trash...\n" ] }, { "metadata": { "tags": null }, "name": "stderr", "output_type": "stream", "text": [ "WARNING:google_auth_httplib2:httplib2 transport does not support per-request timeout. Set the timeout when constructing the httplib2.Http instance.\n" ] }, { "metadata": { "tags": null }, "name": "stdout", "output_type": "stream", "text": [ " Deleting 11 'epoch' files from Trash...\n", " Trash Purged.\n", "Epoch 281 | Train: 0.2912 | Val: 0.2899 | No Improvement (103/150)\n", "Epoch 282 | Train: 0.2891 | Val: 0.2961 | No Improvement (104/150)\n", "Epoch 283 | Train: 0.2869 | Val: 0.3130 | No Improvement (105/150)\n", "Epoch 284 | Train: 0.2869 | Val: 0.3129 | No Improvement (106/150)\n", "Epoch 285 | Train: 0.2861 | Val: 0.3344 | No Improvement (107/150)\n", "Epoch 286 | Train: 0.2900 | Val: 0.3838 | No Improvement (108/150)\n", "Epoch 287 | Train: 0.2866 | Val: 0.3436 | No Improvement (109/150)\n", "Epoch 288 | Train: 0.2883 | Val: 0.3023 | No Improvement (110/150)\n", "Epoch 289 | Train: 0.2858 | Val: 0.3589 | No Improvement (111/150)\n", "Epoch 290 | Train: 0.2862 | Val: 0.4738 | No Improvement (112/150)\n" ] }, { "metadata": { "tags": null }, "name": "stderr", "output_type": "stream", "text": [ "WARNING:google_auth_httplib2:httplib2 transport does not support per-request timeout. Set the timeout when constructing the httplib2.Http instance.\n", "WARNING:google_auth_httplib2:httplib2 transport does not support per-request timeout. Set the timeout when constructing the httplib2.Http instance.\n" ] }, { "metadata": { "tags": null }, "name": "stdout", "output_type": "stream", "text": [ " Maintenance: Emptying 'epoch' files from Drive Trash...\n", " Deleting 9 'epoch' files from Trash...\n", " Trash Purged.\n", "Epoch 291 | Train: 0.2851 | Val: 0.3493 | No Improvement (113/150)\n", "Epoch 292 | Train: 0.2872 | Val: 0.3624 | No Improvement (114/150)\n", "Epoch 293 | Train: 0.2827 | Val: 0.3507 | No Improvement (115/150)\n", "Epoch 294 | Train: 0.2827 | Val: 0.2824 | No Improvement (116/150)\n", "Epoch 295 | Train: 0.2786 | Val: 0.3416 | No Improvement (117/150)\n", "Epoch 296 | Train: 0.2830 | Val: 0.3233 | No Improvement (118/150)\n", "Epoch 297 | Train: 0.2815 | Val: 0.2765 | No Improvement (119/150)\n", "Epoch 298 | Train: 0.2807 | Val: 0.2658 | No Improvement (120/150)\n", "Epoch 299 | Train: 0.2786 | Val: 0.2585 | No Improvement (121/150)\n", "Epoch 300 | Train: 0.2781 | Val: 0.2699 | No Improvement (122/150)\n" ] }, { "metadata": { "tags": null }, "name": "stderr", "output_type": "stream", "text": [ "WARNING:google_auth_httplib2:httplib2 transport does not support per-request timeout. Set the timeout when constructing the httplib2.Http instance.\n" ] }, { "metadata": { "tags": null }, "name": "stdout", "output_type": "stream", "text": [ " Maintenance: Emptying 'epoch' files from Drive Trash...\n" ] }, { "metadata": { "tags": null }, "name": "stderr", "output_type": "stream", "text": [ "WARNING:google_auth_httplib2:httplib2 transport does not support per-request timeout. Set the timeout when constructing the httplib2.Http instance.\n" ] }, { "metadata": { "tags": null }, "name": "stdout", "output_type": "stream", "text": [ " Deleting 11 'epoch' files from Trash...\n", " Trash Purged.\n", "Epoch 301 | Train: 0.2817 | Val: 0.3098 | No Improvement (123/150)\n", "Epoch 302 | Train: 0.2762 | Val: 0.3389 | No Improvement (124/150)\n", "Epoch 303 | Train: 0.2755 | Val: 0.3261 | No Improvement (125/150)\n", "Epoch 304 | Train: 0.2756 | Val: 0.3018 | No Improvement (126/150)\n", "Epoch 305 | Train: 0.2765 | Val: 0.2689 | No Improvement (127/150)\n", "Epoch 306 | Train: 0.2722 | Val: 0.2576 | No Improvement (128/150)\n", "Epoch 307 | Train: 0.2781 | Val: 0.2461 | No Improvement (129/150)\n", "Epoch 308 | Train: 0.2715 | Val: 0.2330 | New Best Model!\n", "Epoch 309 | Train: 0.2742 | Val: 0.2479 | No Improvement (1/150)\n", "Epoch 310 | Train: 0.2694 | Val: 0.2941 | No Improvement (2/150)\n" ] }, { "metadata": { "tags": null }, "name": "stderr", "output_type": "stream", "text": [ "WARNING:google_auth_httplib2:httplib2 transport does not support per-request timeout. Set the timeout when constructing the httplib2.Http instance.\n" ] }, { "metadata": { "tags": null }, "name": "stdout", "output_type": "stream", "text": [ " Maintenance: Emptying 'epoch' files from Drive Trash...\n" ] }, { "metadata": { "tags": null }, "name": "stderr", "output_type": "stream", "text": [ "WARNING:google_auth_httplib2:httplib2 transport does not support per-request timeout. Set the timeout when constructing the httplib2.Http instance.\n" ] }, { "metadata": { "tags": null }, "name": "stdout", "output_type": "stream", "text": [ " Deleting 10 'epoch' files from Trash...\n", " Trash Purged.\n", "Epoch 311 | Train: 0.2704 | Val: 0.3626 | No Improvement (3/150)\n", "Epoch 312 | Train: 0.2678 | Val: 0.3726 | No Improvement (4/150)\n", "Epoch 313 | Train: 0.2665 | Val: 0.3473 | No Improvement (5/150)\n", "Epoch 314 | Train: 0.2654 | Val: 0.3142 | No Improvement (6/150)\n", "Epoch 315 | Train: 0.2643 | Val: 0.2922 | No Improvement (7/150)\n", "Epoch 316 | Train: 0.2736 | Val: 0.2659 | No Improvement (8/150)\n", "Epoch 317 | Train: 0.2695 | Val: 0.2643 | No Improvement (9/150)\n", "Epoch 318 | Train: 0.2694 | Val: 0.2713 | No Improvement (10/150)\n", "Epoch 319 | Train: 0.2697 | Val: 0.2718 | No Improvement (11/150)\n", "Epoch 320 | Train: 0.2624 | Val: 0.2781 | No Improvement (12/150)\n" ] }, { "metadata": { "tags": null }, "name": "stderr", "output_type": "stream", "text": [ "WARNING:google_auth_httplib2:httplib2 transport does not support per-request timeout. Set the timeout when constructing the httplib2.Http instance.\n", "WARNING:google_auth_httplib2:httplib2 transport does not support per-request timeout. Set the timeout when constructing the httplib2.Http instance.\n" ] }, { "metadata": { "tags": null }, "name": "stdout", "output_type": "stream", "text": [ " Maintenance: Emptying 'epoch' files from Drive Trash...\n", " Deleting 9 'epoch' files from Trash...\n", " Trash Purged.\n", "Epoch 321 | Train: 0.2599 | Val: 0.2905 | No Improvement (13/150)\n", "Epoch 322 | Train: 0.2648 | Val: 0.2819 | No Improvement (14/150)\n", "Epoch 323 | Train: 0.2597 | Val: 0.2708 | No Improvement (15/150)\n", "Epoch 324 | Train: 0.2593 | Val: 0.2667 | No Improvement (16/150)\n", "Epoch 325 | Train: 0.2677 | Val: 0.2467 | No Improvement (17/150)\n", "Epoch 326 | Train: 0.2622 | Val: 0.2615 | No Improvement (18/150)\n", "Epoch 327 | Train: 0.2654 | Val: 0.2992 | No Improvement (19/150)\n", "Epoch 328 | Train: 0.2635 | Val: 0.3147 | No Improvement (20/150)\n", "Epoch 329 | Train: 0.2591 | Val: 0.3235 | No Improvement (21/150)\n", "Epoch 330 | Train: 0.2612 | Val: 0.3283 | No Improvement (22/150)\n" ] }, { "metadata": { "tags": null }, "name": "stderr", "output_type": "stream", "text": [ "WARNING:google_auth_httplib2:httplib2 transport does not support per-request timeout. Set the timeout when constructing the httplib2.Http instance.\n", "WARNING:google_auth_httplib2:httplib2 transport does not support per-request timeout. Set the timeout when constructing the httplib2.Http instance.\n" ] }, { "metadata": { "tags": null }, "name": "stdout", "output_type": "stream", "text": [ " Maintenance: Emptying 'epoch' files from Drive Trash...\n", " Deleting 11 'epoch' files from Trash...\n", " Trash Purged.\n", "Epoch 331 | Train: 0.2633 | Val: 0.2806 | No Improvement (23/150)\n", "Epoch 332 | Train: 0.2617 | Val: 0.2450 | No Improvement (24/150)\n", "Epoch 333 | Train: 0.2596 | Val: 0.2334 | No Improvement (25/150)\n", "Epoch 334 | Train: 0.2570 | Val: 0.2416 | No Improvement (26/150)\n", "Epoch 335 | Train: 0.2617 | Val: 0.2592 | No Improvement (27/150)\n", "Epoch 336 | Train: 0.2562 | Val: 0.2931 | No Improvement (28/150)\n", "Epoch 337 | Train: 0.2572 | Val: 0.3014 | No Improvement (29/150)\n", "Epoch 338 | Train: 0.2590 | Val: 0.2854 | No Improvement (30/150)\n", "Epoch 339 | Train: 0.2554 | Val: 0.2624 | No Improvement (31/150)\n", "Epoch 340 | Train: 0.2591 | Val: 0.2469 | No Improvement (32/150)\n" ] }, { "metadata": { "tags": null }, "name": "stderr", "output_type": "stream", "text": [ "WARNING:google_auth_httplib2:httplib2 transport does not support per-request timeout. Set the timeout when constructing the httplib2.Http instance.\n", "WARNING:google_auth_httplib2:httplib2 transport does not support per-request timeout. Set the timeout when constructing the httplib2.Http instance.\n" ] }, { "metadata": { "tags": null }, "name": "stdout", "output_type": "stream", "text": [ " Maintenance: Emptying 'epoch' files from Drive Trash...\n", " Deleting 10 'epoch' files from Trash...\n", " Trash Purged.\n", "Epoch 341 | Train: 0.2587 | Val: 0.2430 | No Improvement (33/150)\n", "Epoch 342 | Train: 0.2581 | Val: 0.2452 | No Improvement (34/150)\n", "Epoch 343 | Train: 0.2573 | Val: 0.2698 | No Improvement (35/150)\n", "Epoch 344 | Train: 0.2541 | Val: 0.3199 | No Improvement (36/150)\n", "Epoch 345 | Train: 0.2584 | Val: 0.3247 | No Improvement (37/150)\n", "Epoch 346 | Train: 0.2582 | Val: 0.2988 | No Improvement (38/150)\n", "Epoch 347 | Train: 0.2545 | Val: 0.2754 | No Improvement (39/150)\n", "Epoch 348 | Train: 0.2548 | Val: 0.2637 | No Improvement (40/150)\n", "Epoch 349 | Train: 0.2520 | Val: 0.2601 | No Improvement (41/150)\n", "Epoch 350 | Train: 0.2532 | Val: 0.2751 | No Improvement (42/150)\n" ] }, { "metadata": { "tags": null }, "name": "stderr", "output_type": "stream", "text": [ "WARNING:google_auth_httplib2:httplib2 transport does not support per-request timeout. Set the timeout when constructing the httplib2.Http instance.\n", "WARNING:google_auth_httplib2:httplib2 transport does not support per-request timeout. Set the timeout when constructing the httplib2.Http instance.\n" ] }, { "metadata": { "tags": null }, "name": "stdout", "output_type": "stream", "text": [ " Maintenance: Emptying 'epoch' files from Drive Trash...\n", " Deleting 10 'epoch' files from Trash...\n", " Trash Purged.\n", "Epoch 351 | Train: 0.2502 | Val: 0.2878 | No Improvement (43/150)\n", "Epoch 352 | Train: 0.2505 | Val: 0.2833 | No Improvement (44/150)\n", "Epoch 353 | Train: 0.2501 | Val: 0.2761 | No Improvement (45/150)\n", "Epoch 354 | Train: 0.2497 | Val: 0.2635 | No Improvement (46/150)\n", "Epoch 355 | Train: 0.2481 | Val: 0.2528 | No Improvement (47/150)\n", "Epoch 356 | Train: 0.2476 | Val: 0.2586 | No Improvement (48/150)\n", "Epoch 357 | Train: 0.2471 | Val: 0.2708 | No Improvement (49/150)\n", "Epoch 358 | Train: 0.2449 | Val: 0.2722 | No Improvement (50/150)\n", "Epoch 359 | Train: 0.2531 | Val: 0.2486 | No Improvement (51/150)\n", "Epoch 360 | Train: 0.2442 | Val: 0.2434 | No Improvement (52/150)\n" ] }, { "metadata": { "tags": null }, "name": "stderr", "output_type": "stream", "text": [ "WARNING:google_auth_httplib2:httplib2 transport does not support per-request timeout. Set the timeout when constructing the httplib2.Http instance.\n", "WARNING:google_auth_httplib2:httplib2 transport does not support per-request timeout. Set the timeout when constructing the httplib2.Http instance.\n" ] }, { "metadata": { "tags": null }, "name": "stdout", "output_type": "stream", "text": [ " Maintenance: Emptying 'epoch' files from Drive Trash...\n", " Deleting 10 'epoch' files from Trash...\n", " Trash Purged.\n", "Epoch 361 | Train: 0.2427 | Val: 0.2500 | No Improvement (53/150)\n", "Epoch 362 | Train: 0.2465 | Val: 0.2455 | No Improvement (54/150)\n", "Epoch 363 | Train: 0.2464 | Val: 0.2367 | No Improvement (55/150)\n", "Epoch 364 | Train: 0.2452 | Val: 0.2401 | No Improvement (56/150)\n", "Epoch 365 | Train: 0.2526 | Val: 0.2467 | No Improvement (57/150)\n", "Epoch 366 | Train: 0.2432 | Val: 0.2544 | No Improvement (58/150)\n", "Epoch 367 | Train: 0.2431 | Val: 0.2486 | No Improvement (59/150)\n", "Epoch 368 | Train: 0.2504 | Val: 0.2366 | No Improvement (60/150)\n", "Epoch 369 | Train: 0.2496 | Val: 0.2343 | No Improvement (61/150)\n", "Epoch 370 | Train: 0.2412 | Val: 0.2475 | No Improvement (62/150)\n" ] }, { "metadata": { "tags": null }, "name": "stderr", "output_type": "stream", "text": [ "WARNING:google_auth_httplib2:httplib2 transport does not support per-request timeout. Set the timeout when constructing the httplib2.Http instance.\n" ] }, { "metadata": { "tags": null }, "name": "stdout", "output_type": "stream", "text": [ " Maintenance: Emptying 'epoch' files from Drive Trash...\n" ] }, { "metadata": { "tags": null }, "name": "stderr", "output_type": "stream", "text": [ "WARNING:google_auth_httplib2:httplib2 transport does not support per-request timeout. Set the timeout when constructing the httplib2.Http instance.\n" ] }, { "metadata": { "tags": null }, "name": "stdout", "output_type": "stream", "text": [ " Deleting 10 'epoch' files from Trash...\n", " Trash Purged.\n", "Epoch 371 | Train: 0.2462 | Val: 0.2561 | No Improvement (63/150)\n", "Epoch 372 | Train: 0.2462 | Val: 0.2436 | No Improvement (64/150)\n", "Epoch 373 | Train: 0.2402 | Val: 0.2345 | No Improvement (65/150)\n", "Epoch 374 | Train: 0.2448 | Val: 0.2425 | No Improvement (66/150)\n", "Epoch 375 | Train: 0.2444 | Val: 0.2611 | No Improvement (67/150)\n", "Epoch 376 | Train: 0.2430 | Val: 0.2700 | No Improvement (68/150)\n", "Epoch 377 | Train: 0.2420 | Val: 0.2643 | No Improvement (69/150)\n", "Epoch 378 | Train: 0.2414 | Val: 0.2544 | No Improvement (70/150)\n", "Epoch 379 | Train: 0.2417 | Val: 0.2484 | No Improvement (71/150)\n", "Epoch 380 | Train: 0.2418 | Val: 0.2501 | No Improvement (72/150)\n" ] }, { "metadata": { "tags": null }, "name": "stderr", "output_type": "stream", "text": [ "WARNING:google_auth_httplib2:httplib2 transport does not support per-request timeout. Set the timeout when constructing the httplib2.Http instance.\n", "WARNING:google_auth_httplib2:httplib2 transport does not support per-request timeout. Set the timeout when constructing the httplib2.Http instance.\n" ] }, { "metadata": { "tags": null }, "name": "stdout", "output_type": "stream", "text": [ " Maintenance: Emptying 'epoch' files from Drive Trash...\n", " Deleting 9 'epoch' files from Trash...\n", " Trash Purged.\n", "Epoch 381 | Train: 0.2421 | Val: 0.2505 | No Improvement (73/150)\n", "Epoch 382 | Train: 0.2411 | Val: 0.2505 | No Improvement (74/150)\n", "Epoch 383 | Train: 0.2402 | Val: 0.2499 | No Improvement (75/150)\n", "Epoch 384 | Train: 0.2393 | Val: 0.2532 | No Improvement (76/150)\n", "Epoch 385 | Train: 0.2426 | Val: 0.2522 | No Improvement (77/150)\n", "Epoch 386 | Train: 0.2380 | Val: 0.2526 | No Improvement (78/150)\n", "Epoch 387 | Train: 0.2394 | Val: 0.2476 | No Improvement (79/150)\n", "Epoch 388 | Train: 0.2400 | Val: 0.2489 | No Improvement (80/150)\n", "Epoch 389 | Train: 0.2393 | Val: 0.2509 | No Improvement (81/150)\n", "Epoch 390 | Train: 0.2372 | Val: 0.2548 | No Improvement (82/150)\n" ] }, { "metadata": { "tags": null }, "name": "stderr", "output_type": "stream", "text": [ "WARNING:google_auth_httplib2:httplib2 transport does not support per-request timeout. 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Set the timeout when constructing the httplib2.Http instance.\n" ] }, { "metadata": { "tags": null }, "name": "stdout", "output_type": "stream", "text": [ " Deleting 10 'epoch' files from Trash...\n", " Trash Purged.\n", "Epoch 391 | Train: 0.2385 | Val: 0.2639 | No Improvement (83/150)\n", "Epoch 392 | Train: 0.2382 | Val: 0.2716 | No Improvement (84/150)\n", "Epoch 393 | Train: 0.2376 | Val: 0.2722 | No Improvement (85/150)\n", "Epoch 394 | Train: 0.2374 | Val: 0.2593 | No Improvement (86/150)\n", "Epoch 395 | Train: 0.2382 | Val: 0.2457 | No Improvement (87/150)\n", "Epoch 396 | Train: 0.2379 | Val: 0.2367 | No Improvement (88/150)\n", "Epoch 397 | Train: 0.2374 | Val: 0.2362 | No Improvement (89/150)\n", "Epoch 398 | Train: 0.2365 | Val: 0.2411 | No Improvement (90/150)\n", "Epoch 399 | Train: 0.2357 | Val: 0.2479 | No Improvement (91/150)\n", "Epoch 400 | Train: 0.2371 | Val: 0.2529 | No Improvement (92/150)\n" ] }, { "metadata": { "tags": null }, "name": "stderr", "output_type": "stream", "text": [ "WARNING:google_auth_httplib2:httplib2 transport does not support per-request timeout. 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Set the timeout when constructing the httplib2.Http instance.\n" ] }, { "metadata": { "tags": null }, "name": "stdout", "output_type": "stream", "text": [ " Deleting 11 'epoch' files from Trash...\n", " Trash Purged.\n", "Epoch 401 | Train: 0.2340 | Val: 0.2524 | No Improvement (93/150)\n", "Epoch 402 | Train: 0.2335 | Val: 0.2486 | No Improvement (94/150)\n", "Epoch 403 | Train: 0.2370 | Val: 0.2414 | No Improvement (95/150)\n", "Epoch 404 | Train: 0.2330 | Val: 0.2424 | No Improvement (96/150)\n", "Epoch 405 | Train: 0.2360 | Val: 0.2467 | No Improvement (97/150)\n", "Epoch 406 | Train: 0.2326 | Val: 0.2532 | No Improvement (98/150)\n", "Epoch 407 | Train: 0.2366 | Val: 0.2542 | No Improvement (99/150)\n", "Epoch 408 | Train: 0.2338 | Val: 0.2495 | No Improvement (100/150)\n", "Epoch 409 | Train: 0.2334 | Val: 0.2421 | No Improvement (101/150)\n", "Epoch 410 | Train: 0.2350 | Val: 0.2366 | No Improvement (102/150)\n" ] }, { "metadata": { "tags": null }, "name": "stderr", "output_type": "stream", "text": [ "WARNING:google_auth_httplib2:httplib2 transport does not support per-request timeout. 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Set the timeout when constructing the httplib2.Http instance.\n" ] }, { "metadata": { "tags": null }, "name": "stdout", "output_type": "stream", "text": [ " Deleting 9 'epoch' files from Trash...\n", " Trash Purged.\n", "Epoch 411 | Train: 0.2339 | Val: 0.2349 | No Improvement (103/150)\n", "Epoch 412 | Train: 0.2329 | Val: 0.2377 | No Improvement (104/150)\n", "Epoch 413 | Train: 0.2319 | Val: 0.2444 | No Improvement (105/150)\n", "Epoch 414 | Train: 0.2346 | Val: 0.2472 | No Improvement (106/150)\n", "Epoch 415 | Train: 0.2349 | Val: 0.2471 | No Improvement (107/150)\n", "Epoch 416 | Train: 0.2302 | Val: 0.2467 | No Improvement (108/150)\n", "Epoch 417 | Train: 0.2342 | Val: 0.2451 | No Improvement (109/150)\n", "Epoch 418 | Train: 0.2354 | Val: 0.2447 | No Improvement (110/150)\n", "Epoch 419 | Train: 0.2346 | Val: 0.2478 | No Improvement (111/150)\n", "Epoch 420 | Train: 0.2328 | Val: 0.2539 | No Improvement (112/150)\n" ] }, { "metadata": { "tags": null }, "name": "stderr", "output_type": "stream", "text": [ "WARNING:google_auth_httplib2:httplib2 transport does not support per-request timeout. 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Set the timeout when constructing the httplib2.Http instance.\n" ] }, { "metadata": { "tags": null }, "name": "stdout", "output_type": "stream", "text": [ " Deleting 11 'epoch' files from Trash...\n", " Trash Purged.\n", "Epoch 421 | Train: 0.2324 | Val: 0.2590 | No Improvement (113/150)\n", "Epoch 422 | Train: 0.2342 | Val: 0.2603 | No Improvement (114/150)\n", "Epoch 423 | Train: 0.2334 | Val: 0.2566 | No Improvement (115/150)\n", "Epoch 424 | Train: 0.2334 | Val: 0.2480 | No Improvement (116/150)\n", "Epoch 425 | Train: 0.2320 | Val: 0.2401 | No Improvement (117/150)\n", "Epoch 426 | Train: 0.2325 | Val: 0.2335 | No Improvement (118/150)\n", "Epoch 427 | Train: 0.2320 | Val: 0.2287 | New Best Model!\n", "Epoch 428 | Train: 0.2321 | Val: 0.2265 | New Best Model!\n", "Epoch 429 | Train: 0.2314 | Val: 0.2270 | No Improvement (1/150)\n", "Epoch 430 | Train: 0.2304 | Val: 0.2313 | No Improvement (2/150)\n" ] }, { "metadata": { "tags": null }, "name": "stderr", "output_type": "stream", "text": [ "WARNING:google_auth_httplib2:httplib2 transport does not support per-request timeout. Set the timeout when constructing the httplib2.Http instance.\n", "WARNING:google_auth_httplib2:httplib2 transport does not support per-request timeout. Set the timeout when constructing the httplib2.Http instance.\n" ] }, { "metadata": { "tags": null }, "name": "stdout", "output_type": "stream", "text": [ " Maintenance: Emptying 'epoch' files from Drive Trash...\n", " Deleting 10 'epoch' files from Trash...\n", " Trash Purged.\n", "Epoch 431 | Train: 0.2321 | Val: 0.2385 | No Improvement (3/150)\n", "Epoch 432 | Train: 0.2319 | Val: 0.2467 | No Improvement (4/150)\n", "Epoch 433 | Train: 0.2304 | Val: 0.2532 | No Improvement (5/150)\n", "Epoch 434 | Train: 0.2303 | Val: 0.2571 | No Improvement (6/150)\n", "Epoch 435 | Train: 0.2297 | Val: 0.2582 | No Improvement (7/150)\n", "Epoch 436 | Train: 0.2318 | Val: 0.2564 | No Improvement (8/150)\n", "Epoch 437 | Train: 0.2294 | Val: 0.2539 | No Improvement (9/150)\n", "Epoch 438 | Train: 0.2323 | Val: 0.2516 | No Improvement (10/150)\n", "Epoch 439 | Train: 0.2317 | Val: 0.2497 | No Improvement (11/150)\n", "Epoch 440 | Train: 0.2311 | Val: 0.2486 | No Improvement (12/150)\n" ] }, { "metadata": { "tags": null }, "name": "stderr", "output_type": "stream", "text": [ "WARNING:google_auth_httplib2:httplib2 transport does not support per-request timeout. Set the timeout when constructing the httplib2.Http instance.\n", "WARNING:google_auth_httplib2:httplib2 transport does not support per-request timeout. Set the timeout when constructing the httplib2.Http instance.\n" ] }, { "metadata": { "tags": null }, "name": "stdout", "output_type": "stream", "text": [ " Maintenance: Emptying 'epoch' files from Drive Trash...\n", " Deleting 10 'epoch' files from Trash...\n", " Trash Purged.\n", "Epoch 441 | Train: 0.2319 | Val: 0.2481 | No Improvement (13/150)\n", "Epoch 442 | Train: 0.2316 | Val: 0.2484 | No Improvement (14/150)\n", "Epoch 443 | Train: 0.2308 | Val: 0.2485 | No Improvement (15/150)\n", "Epoch 444 | Train: 0.2312 | Val: 0.2488 | No Improvement (16/150)\n", "Epoch 445 | Train: 0.2290 | Val: 0.2491 | No Improvement (17/150)\n", "Epoch 446 | Train: 0.2306 | Val: 0.2488 | No Improvement (18/150)\n", "Epoch 447 | Train: 0.2310 | Val: 0.2480 | No Improvement (19/150)\n", "Epoch 448 | Train: 0.2316 | Val: 0.2467 | No Improvement (20/150)\n", "Epoch 449 | Train: 0.2308 | Val: 0.2454 | No Improvement (21/150)\n", "Epoch 450 | Train: 0.2285 | Val: 0.2437 | No Improvement (22/150)\n" ] }, { "metadata": { "tags": null }, "name": "stderr", "output_type": "stream", "text": [ "WARNING:google_auth_httplib2:httplib2 transport does not support per-request timeout. Set the timeout when constructing the httplib2.Http instance.\n" ] }, { "metadata": { "tags": null }, "name": "stdout", "output_type": "stream", "text": [ " Maintenance: Emptying 'epoch' files from Drive Trash...\n" ] }, { "metadata": { "tags": null }, "name": "stderr", "output_type": "stream", "text": [ "WARNING:google_auth_httplib2:httplib2 transport does not support per-request timeout. Set the timeout when constructing the httplib2.Http instance.\n" ] }, { "metadata": { "tags": null }, "name": "stdout", "output_type": "stream", "text": [ " Deleting 10 'epoch' files from Trash...\n", " Trash Purged.\n", "Epoch 451 | Train: 0.2314 | Val: 0.2423 | No Improvement (23/150)\n", "Epoch 452 | Train: 0.2287 | Val: 0.2414 | No Improvement (24/150)\n", "Epoch 453 | Train: 0.2284 | Val: 0.2408 | No Improvement (25/150)\n", "Epoch 454 | Train: 0.2307 | Val: 0.2406 | No Improvement (26/150)\n", "Epoch 455 | Train: 0.2309 | Val: 0.2403 | No Improvement (27/150)\n", "Epoch 456 | Train: 0.2305 | Val: 0.2402 | No Improvement (28/150)\n", "Epoch 457 | Train: 0.2311 | Val: 0.2401 | No Improvement (29/150)\n", "Epoch 458 | Train: 0.2301 | Val: 0.2401 | No Improvement (30/150)\n", "Epoch 459 | Train: 0.2307 | Val: 0.2403 | No Improvement (31/150)\n", "Epoch 460 | Train: 0.2305 | Val: 0.2403 | No Improvement (32/150)\n" ] }, { "metadata": { "tags": null }, "name": "stderr", "output_type": "stream", "text": [ "WARNING:google_auth_httplib2:httplib2 transport does not support per-request timeout. Set the timeout when constructing the httplib2.Http instance.\n" ] }, { "metadata": { "tags": null }, "name": "stdout", "output_type": "stream", "text": [ " Maintenance: Emptying 'epoch' files from Drive Trash...\n" ] }, { "metadata": { "tags": null }, "name": "stderr", "output_type": "stream", "text": [ "WARNING:google_auth_httplib2:httplib2 transport does not support per-request timeout. Set the timeout when constructing the httplib2.Http instance.\n" ] }, { "metadata": { "tags": null }, "name": "stdout", "output_type": "stream", "text": [ " Deleting 10 'epoch' files from Trash...\n", " Trash Purged.\n", "Epoch 461 | Train: 0.2306 | Val: 0.2406 | No Improvement (33/150)\n", "Epoch 462 | Train: 0.2313 | Val: 0.2410 | No Improvement (34/150)\n", "Epoch 463 | Train: 0.2306 | Val: 0.2414 | No Improvement (35/150)\n", "Epoch 464 | Train: 0.2302 | Val: 0.2417 | No Improvement (36/150)\n", "Epoch 465 | Train: 0.2308 | Val: 0.2420 | No Improvement (37/150)\n", "Epoch 466 | Train: 0.2307 | Val: 0.2422 | No Improvement (38/150)\n", "Epoch 467 | Train: 0.2283 | Val: 0.2424 | No Improvement (39/150)\n", "Epoch 468 | Train: 0.2304 | Val: 0.2425 | No Improvement (40/150)\n", "Epoch 469 | Train: 0.2305 | Val: 0.2428 | No Improvement (41/150)\n", "Epoch 470 | Train: 0.2301 | Val: 0.2429 | No Improvement (42/150)\n" ] }, { "metadata": { "tags": null }, "name": "stderr", "output_type": "stream", "text": [ "WARNING:google_auth_httplib2:httplib2 transport does not support per-request timeout. Set the timeout when constructing the httplib2.Http instance.\n", "WARNING:google_auth_httplib2:httplib2 transport does not support per-request timeout. Set the timeout when constructing the httplib2.Http instance.\n" ] }, { "metadata": { "tags": null }, "name": "stdout", "output_type": "stream", "text": [ " Maintenance: Emptying 'epoch' files from Drive Trash...\n", " Deleting 8 'epoch' files from Trash...\n", " Trash Purged.\n", "Epoch 471 | Train: 0.2305 | Val: 0.2431 | No Improvement (43/150)\n", "Epoch 472 | Train: 0.2303 | Val: 0.4298 | No Improvement (44/150)\n", "Epoch 473 | Train: 0.2347 | Val: 0.3535 | No Improvement (45/150)\n", "Epoch 474 | Train: 0.2325 | Val: 0.2685 | No Improvement (46/150)\n", "Epoch 475 | Train: 0.2347 | Val: 0.2876 | No Improvement (47/150)\n", "Epoch 476 | Train: 0.2336 | Val: 0.2328 | No Improvement (48/150)\n", "Epoch 477 | Train: 0.2329 | Val: 0.2413 | No Improvement (49/150)\n", "Epoch 478 | Train: 0.2301 | Val: 0.2961 | No Improvement (50/150)\n", "Epoch 479 | Train: 0.2284 | Val: 0.2866 | No Improvement (51/150)\n", "Epoch 480 | Train: 0.2269 | Val: 0.2378 | No Improvement (52/150)\n" ] }, { "metadata": { "tags": null }, "name": "stderr", "output_type": "stream", "text": [ "WARNING:google_auth_httplib2:httplib2 transport does not support per-request timeout. Set the timeout when constructing the httplib2.Http instance.\n" ] }, { "metadata": { "tags": null }, "name": "stdout", "output_type": "stream", "text": [ " Maintenance: Emptying 'epoch' files from Drive Trash...\n" ] }, { "metadata": { "tags": null }, "name": "stderr", "output_type": "stream", "text": [ "WARNING:google_auth_httplib2:httplib2 transport does not support per-request timeout. Set the timeout when constructing the httplib2.Http instance.\n" ] }, { "metadata": { "tags": null }, "name": "stdout", "output_type": "stream", "text": [ " Deleting 11 'epoch' files from Trash...\n", " Trash Purged.\n", "Epoch 481 | Train: 0.2252 | Val: 0.2627 | No Improvement (53/150)\n", "Epoch 482 | Train: 0.2287 | Val: 0.2488 | No Improvement (54/150)\n", "Epoch 483 | Train: 0.2279 | Val: 0.2370 | No Improvement (55/150)\n", "Epoch 484 | Train: 0.2251 | Val: 0.2169 | New Best Model!\n", "Epoch 485 | Train: 0.2346 | Val: 0.2316 | No Improvement (1/150)\n", "Epoch 486 | Train: 0.2251 | Val: 0.2509 | No Improvement (2/150)\n", "Epoch 487 | Train: 0.2311 | Val: 0.2859 | No Improvement (3/150)\n", "Epoch 488 | Train: 0.2292 | Val: 0.2495 | No Improvement (4/150)\n", "Epoch 489 | Train: 0.2314 | Val: 0.2588 | No Improvement (5/150)\n", "Epoch 490 | Train: 0.2306 | Val: 0.2920 | No Improvement (6/150)\n" ] }, { "metadata": { "tags": null }, "name": "stderr", "output_type": "stream", "text": [ "WARNING:google_auth_httplib2:httplib2 transport does not support per-request timeout. Set the timeout when constructing the httplib2.Http instance.\n" ] }, { "metadata": { "tags": null }, "name": "stdout", "output_type": "stream", "text": [ " Maintenance: Emptying 'epoch' files from Drive Trash...\n" ] }, { "metadata": { "tags": null }, "name": "stderr", "output_type": "stream", "text": [ "WARNING:google_auth_httplib2:httplib2 transport does not support per-request timeout. Set the timeout when constructing the httplib2.Http instance.\n" ] }, { "metadata": { "tags": null }, "name": "stdout", "output_type": "stream", "text": [ " Deleting 11 'epoch' files from Trash...\n", " Trash Purged.\n", "Epoch 491 | Train: 0.2266 | Val: 0.2745 | No Improvement (7/150)\n", "Epoch 492 | Train: 0.2235 | Val: 0.2874 | No Improvement (8/150)\n", "Epoch 493 | Train: 0.2196 | Val: 0.2748 | No Improvement (9/150)\n", "Epoch 494 | Train: 0.2273 | Val: 0.2980 | No Improvement (10/150)\n", "Epoch 495 | Train: 0.2296 | Val: 0.3445 | No Improvement (11/150)\n", "Epoch 496 | Train: 0.2231 | Val: 0.2936 | No Improvement (12/150)\n", "Epoch 497 | Train: 0.2263 | Val: 0.2717 | No Improvement (13/150)\n", "Epoch 498 | Train: 0.2211 | Val: 0.2624 | No Improvement (14/150)\n", "Epoch 499 | Train: 0.2233 | Val: 0.2607 | No Improvement (15/150)\n", "Epoch 500 | Train: 0.2223 | Val: 0.2738 | No Improvement (16/150)\n" ] }, { "metadata": { "tags": null }, "name": "stderr", "output_type": "stream", "text": [ "WARNING:google_auth_httplib2:httplib2 transport does not support per-request timeout. 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Set the timeout when constructing the httplib2.Http instance.\n" ] }, { "metadata": { "tags": null }, "name": "stdout", "output_type": "stream", "text": [ " Deleting 10 'epoch' files from Trash...\n", " Trash Purged.\n", "Epoch 501 | Train: 0.2228 | Val: 0.2575 | No Improvement (17/150)\n", "Epoch 502 | Train: 0.2199 | Val: 0.2409 | No Improvement (18/150)\n", "Epoch 503 | Train: 0.2180 | Val: 0.2458 | No Improvement (19/150)\n", "Epoch 504 | Train: 0.2175 | Val: 0.2508 | No Improvement (20/150)\n", "Epoch 505 | Train: 0.2167 | Val: 0.2566 | No Improvement (21/150)\n", "Epoch 506 | Train: 0.2240 | Val: 0.2423 | No Improvement (22/150)\n", "Epoch 507 | Train: 0.2148 | Val: 0.2458 | No Improvement (23/150)\n", "Epoch 508 | Train: 0.2205 | Val: 0.2703 | No Improvement (24/150)\n", "Epoch 509 | Train: 0.2206 | Val: 0.2255 | No Improvement (25/150)\n", "Epoch 510 | Train: 0.2138 | Val: 0.2389 | No Improvement (26/150)\n" ] }, { "metadata": { "tags": null }, "name": "stderr", "output_type": "stream", "text": [ "WARNING:google_auth_httplib2:httplib2 transport does not support per-request timeout. Set the timeout when constructing the httplib2.Http instance.\n", "WARNING:google_auth_httplib2:httplib2 transport does not support per-request timeout. Set the timeout when constructing the httplib2.Http instance.\n" ] }, { "metadata": { "tags": null }, "name": "stdout", "output_type": "stream", "text": [ " Maintenance: Emptying 'epoch' files from Drive Trash...\n", " Deleting 10 'epoch' files from Trash...\n", " Trash Purged.\n", "Epoch 511 | Train: 0.2190 | Val: 0.2793 | No Improvement (27/150)\n", "Epoch 512 | Train: 0.2215 | Val: 0.2425 | No Improvement (28/150)\n", "Epoch 513 | Train: 0.2110 | Val: 0.2228 | No Improvement (29/150)\n", "Epoch 514 | Train: 0.2172 | Val: 0.2427 | No Improvement (30/150)\n", "Epoch 515 | Train: 0.2098 | Val: 0.2524 | No Improvement (31/150)\n", "Epoch 516 | Train: 0.2162 | Val: 0.2377 | No Improvement (32/150)\n", "Epoch 517 | Train: 0.2145 | Val: 0.2271 | No Improvement (33/150)\n", "Epoch 518 | Train: 0.2136 | Val: 0.2317 | No Improvement (34/150)\n", "Epoch 519 | Train: 0.2126 | Val: 0.2309 | No Improvement (35/150)\n", "Epoch 520 | Train: 0.2103 | Val: 0.2177 | No Improvement (36/150)\n" ] }, { "metadata": { "tags": null }, "name": "stderr", "output_type": "stream", "text": [ "WARNING:google_auth_httplib2:httplib2 transport does not support per-request timeout. Set the timeout when constructing the httplib2.Http instance.\n" ] }, { "metadata": { "tags": null }, "name": "stdout", "output_type": "stream", "text": [ " Maintenance: Emptying 'epoch' files from Drive Trash...\n" ] }, { "metadata": { "tags": null }, "name": "stderr", "output_type": "stream", "text": [ "WARNING:google_auth_httplib2:httplib2 transport does not support per-request timeout. Set the timeout when constructing the httplib2.Http instance.\n" ] }, { "metadata": { "tags": null }, "name": "stdout", "output_type": "stream", "text": [ " Deleting 10 'epoch' files from Trash...\n", " Trash Purged.\n", "Epoch 521 | Train: 0.2118 | Val: 0.2246 | No Improvement (37/150)\n", "Epoch 522 | Train: 0.2082 | Val: 0.2379 | No Improvement (38/150)\n", "Epoch 523 | Train: 0.2073 | Val: 0.2471 | No Improvement (39/150)\n", "Epoch 524 | Train: 0.2151 | Val: 0.2624 | No Improvement (40/150)\n", "Epoch 525 | Train: 0.2120 | Val: 0.2599 | No Improvement (41/150)\n", "Epoch 526 | Train: 0.2122 | Val: 0.2416 | No Improvement (42/150)\n", "Epoch 527 | Train: 0.2170 | Val: 0.2349 | No Improvement (43/150)\n", "Epoch 528 | Train: 0.2067 | Val: 0.2646 | No Improvement (44/150)\n", "Epoch 529 | Train: 0.2121 | Val: 0.2697 | No Improvement (45/150)\n", "Epoch 530 | Train: 0.2105 | Val: 0.2549 | No Improvement (46/150)\n" ] }, { "metadata": { "tags": null }, "name": "stderr", "output_type": "stream", "text": [ "WARNING:google_auth_httplib2:httplib2 transport does not support per-request timeout. Set the timeout when constructing the httplib2.Http instance.\n" ] }, { "metadata": { "tags": null }, "name": "stdout", "output_type": "stream", "text": [ " Maintenance: Emptying 'epoch' files from Drive Trash...\n" ] }, { "metadata": { "tags": null }, "name": "stderr", "output_type": "stream", "text": [ "WARNING:google_auth_httplib2:httplib2 transport does not support per-request timeout. Set the timeout when constructing the httplib2.Http instance.\n" ] }, { "metadata": { "tags": null }, "name": "stdout", "output_type": "stream", "text": [ " Deleting 8 'epoch' files from Trash...\n", " Trash Purged.\n", "Epoch 531 | Train: 0.2087 | Val: 0.2428 | No Improvement (47/150)\n", "Epoch 532 | Train: 0.2110 | Val: 0.2655 | No Improvement (48/150)\n", "Epoch 533 | Train: 0.2105 | Val: 0.2502 | No Improvement (49/150)\n", "Epoch 534 | Train: 0.2098 | Val: 0.2236 | No Improvement (50/150)\n", "Epoch 535 | Train: 0.2056 | Val: 0.2356 | No Improvement (51/150)\n", "Epoch 536 | Train: 0.2097 | Val: 0.2369 | No Improvement (52/150)\n", "Epoch 537 | Train: 0.2078 | Val: 0.2203 | No Improvement (53/150)\n", "Epoch 538 | Train: 0.2086 | Val: 0.2080 | New Best Model!\n", "Epoch 539 | Train: 0.2052 | Val: 0.2262 | No Improvement (1/150)\n", "Epoch 540 | Train: 0.2047 | Val: 0.2486 | No Improvement (2/150)\n" ] }, { "metadata": { "tags": null }, "name": "stderr", "output_type": "stream", "text": [ "WARNING:google_auth_httplib2:httplib2 transport does not support per-request timeout. Set the timeout when constructing the httplib2.Http instance.\n", "WARNING:google_auth_httplib2:httplib2 transport does not support per-request timeout. Set the timeout when constructing the httplib2.Http instance.\n" ] }, { "metadata": { "tags": null }, "name": "stdout", "output_type": "stream", "text": [ " Maintenance: Emptying 'epoch' files from Drive Trash...\n", " Deleting 12 'epoch' files from Trash...\n", " Trash Purged.\n", "Epoch 541 | Train: 0.2056 | Val: 0.2398 | No Improvement (3/150)\n", "Epoch 542 | Train: 0.2053 | Val: 0.2418 | No Improvement (4/150)\n", "Epoch 543 | Train: 0.2038 | Val: 0.2537 | No Improvement (5/150)\n", "Epoch 544 | Train: 0.2043 | Val: 0.2697 | No Improvement (6/150)\n", "Epoch 545 | Train: 0.2018 | Val: 0.2799 | No Improvement (7/150)\n", "Epoch 546 | Train: 0.2017 | Val: 0.2553 | No Improvement (8/150)\n", "Epoch 547 | Train: 0.2013 | Val: 0.2358 | No Improvement (9/150)\n", "Epoch 548 | Train: 0.2001 | Val: 0.2332 | No Improvement (10/150)\n", "Epoch 549 | Train: 0.2012 | Val: 0.2265 | No Improvement (11/150)\n", "Epoch 550 | Train: 0.1988 | Val: 0.2332 | No Improvement (12/150)\n" ] }, { "metadata": { "tags": null }, "name": "stderr", "output_type": "stream", "text": [ "WARNING:google_auth_httplib2:httplib2 transport does not support per-request timeout. 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Set the timeout when constructing the httplib2.Http instance.\n" ] }, { "metadata": { "tags": null }, "name": "stdout", "output_type": "stream", "text": [ " Deleting 10 'epoch' files from Trash...\n", " Trash Purged.\n", "Epoch 551 | Train: 0.2013 | Val: 0.2356 | No Improvement (13/150)\n", "Epoch 552 | Train: 0.1982 | Val: 0.2311 | No Improvement (14/150)\n", "Epoch 553 | Train: 0.1983 | Val: 0.2350 | No Improvement (15/150)\n", "Epoch 554 | Train: 0.1964 | Val: 0.2464 | No Improvement (16/150)\n", "Epoch 555 | Train: 0.2012 | Val: 0.2560 | No Improvement (17/150)\n", "Epoch 556 | Train: 0.1971 | Val: 0.2409 | No Improvement (18/150)\n", "Epoch 557 | Train: 0.1952 | Val: 0.2339 | No Improvement (19/150)\n", "Epoch 558 | Train: 0.1994 | Val: 0.2290 | No Improvement (20/150)\n", "Epoch 559 | Train: 0.1959 | Val: 0.2533 | No Improvement (21/150)\n", "Epoch 560 | Train: 0.1973 | Val: 0.2483 | No Improvement (22/150)\n" ] }, { "metadata": { "tags": null }, "name": "stderr", "output_type": "stream", "text": [ "WARNING:google_auth_httplib2:httplib2 transport does not support per-request timeout. 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Set the timeout when constructing the httplib2.Http instance.\n" ] }, { "metadata": { "tags": null }, "name": "stdout", "output_type": "stream", "text": [ " Deleting 8 'epoch' files from Trash...\n", " Trash Purged.\n", "Epoch 561 | Train: 0.1962 | Val: 0.2303 | No Improvement (23/150)\n", "Epoch 562 | Train: 0.1935 | Val: 0.2239 | No Improvement (24/150)\n", "Epoch 563 | Train: 0.1945 | Val: 0.2241 | No Improvement (25/150)\n", "Epoch 564 | Train: 0.1946 | Val: 0.2194 | No Improvement (26/150)\n", "Epoch 565 | Train: 0.1929 | Val: 0.2195 | No Improvement (27/150)\n", "Epoch 566 | Train: 0.1931 | Val: 0.2297 | No Improvement (28/150)\n", "Epoch 567 | Train: 0.1977 | Val: 0.2414 | No Improvement (29/150)\n", "Epoch 568 | Train: 0.1909 | Val: 0.2318 | No Improvement (30/150)\n", "Epoch 569 | Train: 0.1949 | Val: 0.2151 | No Improvement (31/150)\n", "Epoch 570 | Train: 0.1954 | Val: 0.2220 | No Improvement (32/150)\n" ] }, { "metadata": { "tags": null }, "name": "stderr", "output_type": "stream", "text": [ "WARNING:google_auth_httplib2:httplib2 transport does not support per-request timeout. 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Set the timeout when constructing the httplib2.Http instance.\n" ] }, { "metadata": { "tags": null }, "name": "stdout", "output_type": "stream", "text": [ " Deleting 12 'epoch' files from Trash...\n", " Trash Purged.\n", "Epoch 571 | Train: 0.1932 | Val: 0.2426 | No Improvement (33/150)\n", "Epoch 572 | Train: 0.1955 | Val: 0.2358 | No Improvement (34/150)\n", "Epoch 573 | Train: 0.1922 | Val: 0.2169 | No Improvement (35/150)\n", "Epoch 574 | Train: 0.1888 | Val: 0.2121 | No Improvement (36/150)\n", "Epoch 575 | Train: 0.1897 | Val: 0.2162 | No Improvement (37/150)\n", "Epoch 576 | Train: 0.1907 | Val: 0.2310 | No Improvement (38/150)\n", "Epoch 577 | Train: 0.1887 | Val: 0.2238 | No Improvement (39/150)\n", "Epoch 578 | Train: 0.1892 | Val: 0.2123 | No Improvement (40/150)\n", "Epoch 579 | Train: 0.1884 | Val: 0.2148 | No Improvement (41/150)\n", "Epoch 580 | Train: 0.1877 | Val: 0.2379 | No Improvement (42/150)\n" ] }, { "metadata": { "tags": null }, "name": "stderr", "output_type": "stream", "text": [ "WARNING:google_auth_httplib2:httplib2 transport does not support per-request timeout. 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Set the timeout when constructing the httplib2.Http instance.\n" ] }, { "metadata": { "tags": null }, "name": "stdout", "output_type": "stream", "text": [ " Maintenance: Emptying 'epoch' files from Drive Trash...\n", " Deleting 10 'epoch' files from Trash...\n", " Trash Purged.\n", "Epoch 581 | Train: 0.1878 | Val: 0.2466 | No Improvement (43/150)\n", "Epoch 582 | Train: 0.1924 | Val: 0.2216 | No Improvement (44/150)\n", "Epoch 583 | Train: 0.1867 | Val: 0.2325 | No Improvement (45/150)\n", "Epoch 584 | Train: 0.1873 | Val: 0.2409 | No Improvement (46/150)\n", "Epoch 585 | Train: 0.1863 | Val: 0.2324 | No Improvement (47/150)\n", "Epoch 586 | Train: 0.1915 | Val: 0.2263 | No Improvement (48/150)\n", "Epoch 587 | Train: 0.1870 | Val: 0.2199 | No Improvement (49/150)\n", "Epoch 588 | Train: 0.1863 | Val: 0.2251 | No Improvement (50/150)\n", "Epoch 589 | Train: 0.1886 | Val: 0.2200 | No Improvement (51/150)\n", "Epoch 590 | Train: 0.1883 | Val: 0.2169 | No Improvement (52/150)\n" ] }, { "metadata": { "tags": null }, "name": "stderr", "output_type": "stream", "text": [ "WARNING:google_auth_httplib2:httplib2 transport does not support per-request timeout. Set the timeout when constructing the httplib2.Http instance.\n", "WARNING:google_auth_httplib2:httplib2 transport does not support per-request timeout. Set the timeout when constructing the httplib2.Http instance.\n" ] }, { "metadata": { "tags": null }, "name": "stdout", "output_type": "stream", "text": [ " Maintenance: Emptying 'epoch' files from Drive Trash...\n", " Deleting 10 'epoch' files from Trash...\n", " Trash Purged.\n", "Epoch 591 | Train: 0.1843 | Val: 0.2148 | No Improvement (53/150)\n", "Epoch 592 | Train: 0.1840 | Val: 0.2206 | No Improvement (54/150)\n", "Epoch 593 | Train: 0.1869 | Val: 0.2233 | No Improvement (55/150)\n", "Epoch 594 | Train: 0.1842 | Val: 0.2203 | No Improvement (56/150)\n", "Epoch 595 | Train: 0.1833 | Val: 0.2129 | No Improvement (57/150)\n", "Epoch 596 | Train: 0.1852 | Val: 0.2167 | No Improvement (58/150)\n", "Epoch 597 | Train: 0.1844 | Val: 0.2175 | No Improvement (59/150)\n", "Epoch 598 | Train: 0.1828 | Val: 0.2055 | New Best Model!\n", "Epoch 599 | Train: 0.1814 | Val: 0.2085 | No Improvement (1/150)\n", "Epoch 600 | Train: 0.1830 | Val: 0.2295 | No Improvement (2/150)\n" ] }, { "metadata": { "tags": null }, "name": "stderr", "output_type": "stream", "text": [ "WARNING:google_auth_httplib2:httplib2 transport does not support per-request timeout. Set the timeout when constructing the httplib2.Http instance.\n", "WARNING:google_auth_httplib2:httplib2 transport does not support per-request timeout. Set the timeout when constructing the httplib2.Http instance.\n" ] }, { "metadata": { "tags": null }, "name": "stdout", "output_type": "stream", "text": [ " Maintenance: Emptying 'epoch' files from Drive Trash...\n", " Deleting 9 'epoch' files from Trash...\n", " Trash Purged.\n", "Epoch 601 | Train: 0.1831 | Val: 0.2368 | No Improvement (3/150)\n", "Epoch 602 | Train: 0.1847 | Val: 0.2253 | No Improvement (4/150)\n", "Epoch 603 | Train: 0.1822 | Val: 0.2245 | No Improvement (5/150)\n", "Epoch 604 | Train: 0.1835 | Val: 0.2340 | No Improvement (6/150)\n", "Epoch 605 | Train: 0.1821 | Val: 0.2265 | No Improvement (7/150)\n", "Epoch 606 | Train: 0.1812 | Val: 0.2236 | No Improvement (8/150)\n", "Epoch 607 | Train: 0.1809 | Val: 0.2177 | No Improvement (9/150)\n", "Epoch 608 | Train: 0.1802 | Val: 0.2103 | No Improvement (10/150)\n", "Epoch 609 | Train: 0.1803 | Val: 0.2109 | No Improvement (11/150)\n", "Epoch 610 | Train: 0.1792 | Val: 0.2154 | No Improvement (12/150)\n" ] }, { "metadata": { "tags": null }, "name": "stderr", "output_type": "stream", "text": [ "WARNING:google_auth_httplib2:httplib2 transport does not support per-request timeout. 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Set the timeout when constructing the httplib2.Http instance.\n" ] }, { "metadata": { "tags": null }, "name": "stdout", "output_type": "stream", "text": [ " Deleting 11 'epoch' files from Trash...\n", " Trash Purged.\n", "Epoch 611 | Train: 0.1819 | Val: 0.2276 | No Improvement (13/150)\n", "Epoch 612 | Train: 0.1797 | Val: 0.2305 | No Improvement (14/150)\n", "Epoch 613 | Train: 0.1790 | Val: 0.2273 | No Improvement (15/150)\n", "Epoch 614 | Train: 0.1774 | Val: 0.2289 | No Improvement (16/150)\n", "Epoch 615 | Train: 0.1784 | Val: 0.2226 | No Improvement (17/150)\n", "Epoch 616 | Train: 0.1804 | Val: 0.2158 | No Improvement (18/150)\n", "Epoch 617 | Train: 0.1778 | Val: 0.2082 | No Improvement (19/150)\n", "Epoch 618 | Train: 0.1766 | Val: 0.2213 | No Improvement (20/150)\n", "Epoch 619 | Train: 0.1787 | Val: 0.2314 | No Improvement (21/150)\n", "Epoch 620 | Train: 0.1766 | Val: 0.2175 | No Improvement (22/150)\n", " Maintenance: Emptying 'epoch' files from Drive Trash...\n" ] }, { "metadata": { "tags": null }, "name": "stderr", "output_type": "stream", "text": [ "WARNING:google_auth_httplib2:httplib2 transport does not support per-request timeout. Set the timeout when constructing the httplib2.Http instance.\n", "WARNING:google_auth_httplib2:httplib2 transport does not support per-request timeout. Set the timeout when constructing the httplib2.Http instance.\n" ] }, { "metadata": { "tags": null }, "name": "stdout", "output_type": "stream", "text": [ " Deleting 9 'epoch' files from Trash...\n", " Trash Purged.\n", "Epoch 621 | Train: 0.1756 | Val: 0.2241 | No Improvement (23/150)\n", "Epoch 622 | Train: 0.1764 | Val: 0.2274 | No Improvement (24/150)\n", "Epoch 623 | Train: 0.1746 | Val: 0.2293 | No Improvement (25/150)\n", "Epoch 624 | Train: 0.1774 | Val: 0.2292 | No Improvement (26/150)\n", "Epoch 625 | Train: 0.1752 | Val: 0.2101 | No Improvement (27/150)\n", "Epoch 626 | Train: 0.1741 | Val: 0.2117 | No Improvement (28/150)\n", "Epoch 627 | Train: 0.1765 | Val: 0.2163 | No Improvement (29/150)\n", "Epoch 628 | Train: 0.1732 | Val: 0.2150 | No Improvement (30/150)\n", "Epoch 629 | Train: 0.1790 | Val: 0.2101 | No Improvement (31/150)\n", "Epoch 630 | Train: 0.1778 | Val: 0.2126 | No Improvement (32/150)\n", " Maintenance: Emptying 'epoch' files from Drive Trash...\n" ] }, { "metadata": { "tags": null }, "name": "stderr", "output_type": "stream", "text": [ "WARNING:google_auth_httplib2:httplib2 transport does not support per-request timeout. Set the timeout when constructing the httplib2.Http instance.\n", "WARNING:google_auth_httplib2:httplib2 transport does not support per-request timeout. Set the timeout when constructing the httplib2.Http instance.\n" ] }, { "metadata": { "tags": null }, "name": "stdout", "output_type": "stream", "text": [ " Deleting 11 'epoch' files from Trash...\n", " Trash Purged.\n", "Epoch 631 | Train: 0.1776 | Val: 0.2127 | No Improvement (33/150)\n", "Epoch 632 | Train: 0.1725 | Val: 0.2146 | No Improvement (34/150)\n", "Epoch 633 | Train: 0.1722 | Val: 0.2165 | No Improvement (35/150)\n", "Epoch 634 | Train: 0.1749 | Val: 0.2232 | No Improvement (36/150)\n", "Epoch 635 | Train: 0.1763 | Val: 0.2326 | No Improvement (37/150)\n", "Epoch 636 | Train: 0.1723 | Val: 0.2254 | No Improvement (38/150)\n", "Epoch 637 | Train: 0.1752 | Val: 0.2079 | No Improvement (39/150)\n", "Epoch 638 | Train: 0.1744 | Val: 0.2033 | New Best Model!\n", "Epoch 639 | Train: 0.1735 | Val: 0.2168 | No Improvement (1/150)\n", "Epoch 640 | Train: 0.1712 | Val: 0.2325 | No Improvement (2/150)\n" ] }, { "metadata": { "tags": null }, "name": "stderr", "output_type": "stream", "text": [ "WARNING:google_auth_httplib2:httplib2 transport does not support per-request timeout. Set the timeout when constructing the httplib2.Http instance.\n" ] }, { "metadata": { "tags": null }, "name": "stdout", "output_type": "stream", "text": [ " Maintenance: Emptying 'epoch' files from Drive Trash...\n" ] }, { "metadata": { "tags": null }, "name": "stderr", "output_type": "stream", "text": [ "WARNING:google_auth_httplib2:httplib2 transport does not support per-request timeout. Set the timeout when constructing the httplib2.Http instance.\n" ] }, { "output_type": "stream", "name": "stdout", "text": [ " Deleting 10 'epoch' files from Trash...\n", " Trash Purged.\n", "Epoch 641 | Train: 0.1753 | Val: 0.2287 | No Improvement (3/150)\n", "Epoch 642 | Train: 0.1745 | Val: 0.2204 | No Improvement (4/150)\n", "Epoch 643 | Train: 0.1737 | Val: 0.2244 | No Improvement (5/150)\n", "Epoch 644 | Train: 0.1730 | Val: 0.2299 | No Improvement (6/150)\n", "Epoch 645 | Train: 0.1748 | Val: 0.2252 | No Improvement (7/150)\n", "Epoch 646 | Train: 0.1742 | Val: 0.2177 | No Improvement (8/150)\n", "Epoch 647 | Train: 0.1741 | Val: 0.2123 | No Improvement (9/150)\n", "Epoch 648 | Train: 0.1724 | Val: 0.2157 | No Improvement (10/150)\n", "Epoch 649 | Train: 0.1708 | Val: 0.2284 | No Improvement (11/150)\n", "Epoch 650 | Train: 0.1735 | Val: 0.2236 | No Improvement (12/150)\n" ] }, { "output_type": "stream", "name": "stderr", "text": [ "WARNING:google_auth_httplib2:httplib2 transport does not support per-request timeout. 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Set the timeout when constructing the httplib2.Http instance.\n" ] }, { "output_type": "stream", "name": "stdout", "text": [ " Deleting 10 'epoch' files from Trash...\n", " Trash Purged.\n", "Epoch 651 | Train: 0.1702 | Val: 0.2098 | No Improvement (13/150)\n", "Epoch 652 | Train: 0.1714 | Val: 0.2131 | No Improvement (14/150)\n", "Epoch 653 | Train: 0.1723 | Val: 0.2121 | No Improvement (15/150)\n", "Epoch 654 | Train: 0.1715 | Val: 0.2155 | No Improvement (16/150)\n", "Epoch 655 | Train: 0.1721 | Val: 0.2227 | No Improvement (17/150)\n", "Epoch 656 | Train: 0.1712 | Val: 0.2215 | No Improvement (18/150)\n", "Epoch 657 | Train: 0.1708 | Val: 0.2232 | No Improvement (19/150)\n", "Epoch 658 | Train: 0.1708 | Val: 0.2273 | No Improvement (20/150)\n", "Epoch 659 | Train: 0.1697 | Val: 0.2126 | No Improvement (21/150)\n", "Epoch 660 | Train: 0.1696 | Val: 0.2042 | No Improvement (22/150)\n" ] }, { "output_type": "stream", "name": "stderr", "text": [ "WARNING:google_auth_httplib2:httplib2 transport does not support per-request timeout. Set the timeout when constructing the httplib2.Http instance.\n", "WARNING:google_auth_httplib2:httplib2 transport does not support per-request timeout. Set the timeout when constructing the httplib2.Http instance.\n" ] }, { "output_type": "stream", "name": "stdout", "text": [ " Maintenance: Emptying 'epoch' files from Drive Trash...\n", " Deleting 10 'epoch' files from Trash...\n", " Trash Purged.\n", "Epoch 661 | Train: 0.1698 | Val: 0.2140 | No Improvement (23/150)\n", "Epoch 662 | Train: 0.1687 | Val: 0.2221 | No Improvement (24/150)\n", "Epoch 663 | Train: 0.1679 | Val: 0.2227 | No Improvement (25/150)\n", "Epoch 664 | Train: 0.1675 | Val: 0.2160 | No Improvement (26/150)\n", "Epoch 665 | Train: 0.1698 | Val: 0.2149 | No Improvement (27/150)\n", "Epoch 666 | Train: 0.1684 | Val: 0.2167 | No Improvement (28/150)\n", "Epoch 667 | Train: 0.1702 | Val: 0.2150 | No Improvement (29/150)\n", "Epoch 668 | Train: 0.1686 | Val: 0.2158 | No Improvement (30/150)\n", "Epoch 669 | Train: 0.1695 | Val: 0.2224 | No Improvement (31/150)\n", "Epoch 670 | Train: 0.1662 | Val: 0.2236 | No Improvement (32/150)\n" ] }, { "output_type": "stream", "name": "stderr", "text": [ "WARNING:google_auth_httplib2:httplib2 transport does not support per-request timeout. Set the timeout when constructing the httplib2.Http instance.\n", "WARNING:google_auth_httplib2:httplib2 transport does not support per-request timeout. Set the timeout when constructing the httplib2.Http instance.\n" ] }, { "output_type": "stream", "name": "stdout", "text": [ " Maintenance: Emptying 'epoch' files from Drive Trash...\n", " Deleting 9 'epoch' files from Trash...\n", " Trash Purged.\n", "Epoch 671 | Train: 0.1677 | Val: 0.2177 | No Improvement (33/150)\n", "Epoch 672 | Train: 0.1686 | Val: 0.2092 | No Improvement (34/150)\n", "Epoch 673 | Train: 0.1656 | Val: 0.2036 | No Improvement (35/150)\n", "Epoch 674 | Train: 0.1658 | Val: 0.2116 | No Improvement (36/150)\n", "Epoch 675 | Train: 0.1680 | Val: 0.2260 | No Improvement (37/150)\n", "Epoch 676 | Train: 0.1674 | Val: 0.2313 | No Improvement (38/150)\n", "Epoch 677 | Train: 0.1670 | Val: 0.2206 | No Improvement (39/150)\n", "Epoch 678 | Train: 0.1666 | Val: 0.2087 | No Improvement (40/150)\n", "Epoch 679 | Train: 0.1673 | Val: 0.2041 | No Improvement (41/150)\n", "Epoch 680 | Train: 0.1665 | Val: 0.2030 | New Best Model!\n" ] }, { "output_type": "stream", "name": "stderr", "text": [ "WARNING:google_auth_httplib2:httplib2 transport does not support per-request timeout. Set the timeout when constructing the httplib2.Http instance.\n", "WARNING:google_auth_httplib2:httplib2 transport does not support per-request timeout. Set the timeout when constructing the httplib2.Http instance.\n" ] }, { "output_type": "stream", "name": "stdout", "text": [ " Maintenance: Emptying 'epoch' files from Drive Trash...\n", " Deleting 11 'epoch' files from Trash...\n", " Trash Purged.\n", "Epoch 681 | Train: 0.1665 | Val: 0.2064 | No Improvement (1/150)\n", "Epoch 682 | Train: 0.1662 | Val: 0.2092 | No Improvement (2/150)\n", "Epoch 683 | Train: 0.1656 | Val: 0.2096 | No Improvement (3/150)\n", "Epoch 684 | Train: 0.1640 | Val: 0.2137 | No Improvement (4/150)\n", "Epoch 685 | Train: 0.1641 | Val: 0.2133 | No Improvement (5/150)\n", "Epoch 686 | Train: 0.1657 | Val: 0.2097 | No Improvement (6/150)\n", "Epoch 687 | Train: 0.1632 | Val: 0.2129 | No Improvement (7/150)\n", "Epoch 688 | Train: 0.1649 | Val: 0.2162 | No Improvement (8/150)\n", "Epoch 689 | Train: 0.1649 | Val: 0.2155 | No Improvement (9/150)\n", "Epoch 690 | Train: 0.1646 | Val: 0.2146 | No Improvement (10/150)\n" ] }, { "output_type": "stream", "name": "stderr", "text": [ "WARNING:google_auth_httplib2:httplib2 transport does not support per-request timeout. Set the timeout when constructing the httplib2.Http instance.\n" ] }, { "output_type": "stream", "name": "stdout", "text": [ " Maintenance: Emptying 'epoch' files from Drive Trash...\n" ] }, { "output_type": "stream", "name": "stderr", "text": [ "WARNING:google_auth_httplib2:httplib2 transport does not support per-request timeout. Set the timeout when constructing the httplib2.Http instance.\n" ] }, { "output_type": "stream", "name": "stdout", "text": [ " Deleting 10 'epoch' files from Trash...\n", " Trash Purged.\n", "Epoch 691 | Train: 0.1644 | Val: 0.2159 | No Improvement (11/150)\n", "Epoch 692 | Train: 0.1642 | Val: 0.2198 | No Improvement (12/150)\n", "Epoch 693 | Train: 0.1644 | Val: 0.2237 | No Improvement (13/150)\n", "Epoch 694 | Train: 0.1642 | Val: 0.2186 | No Improvement (14/150)\n", "Epoch 695 | Train: 0.1626 | Val: 0.2102 | No Improvement (15/150)\n", "Epoch 696 | Train: 0.1636 | Val: 0.2101 | No Improvement (16/150)\n", "Epoch 697 | Train: 0.1646 | Val: 0.2134 | No Improvement (17/150)\n", "Epoch 698 | Train: 0.1635 | Val: 0.2144 | No Improvement (18/150)\n", "Epoch 699 | Train: 0.1630 | Val: 0.2162 | No Improvement (19/150)\n", "Epoch 700 | Train: 0.1639 | Val: 0.2112 | No Improvement (20/150)\n" ] }, { "output_type": "stream", "name": "stderr", "text": [ "WARNING:google_auth_httplib2:httplib2 transport does not support per-request timeout. 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Set the timeout when constructing the httplib2.Http instance.\n" ] }, { "output_type": "stream", "name": "stdout", "text": [ " Maintenance: Emptying 'epoch' files from Drive Trash...\n", " Deleting 10 'epoch' files from Trash...\n", " Trash Purged.\n", "Epoch 701 | Train: 0.1624 | Val: 0.2062 | No Improvement (21/150)\n", "Epoch 702 | Train: 0.1624 | Val: 0.2033 | No Improvement (22/150)\n", "Epoch 703 | Train: 0.1622 | Val: 0.2059 | No Improvement (23/150)\n", "Epoch 704 | Train: 0.1630 | Val: 0.2100 | No Improvement (24/150)\n", "Epoch 705 | Train: 0.1626 | Val: 0.2148 | No Improvement (25/150)\n", "Epoch 706 | Train: 0.1623 | Val: 0.2154 | No Improvement (26/150)\n", "Epoch 707 | Train: 0.1619 | Val: 0.2082 | No Improvement (27/150)\n", "Epoch 708 | Train: 0.1613 | Val: 0.2065 | No Improvement (28/150)\n", "Epoch 709 | Train: 0.1608 | Val: 0.2095 | No Improvement (29/150)\n", "Epoch 710 | Train: 0.1622 | Val: 0.2125 | No Improvement (30/150)\n" ] }, { "output_type": "stream", "name": "stderr", "text": [ "WARNING:google_auth_httplib2:httplib2 transport does not support per-request timeout. Set the timeout when constructing the httplib2.Http instance.\n" ] }, { "output_type": "stream", "name": "stdout", "text": [ " Maintenance: Emptying 'epoch' files from Drive Trash...\n" ] }, { "output_type": "stream", "name": "stderr", "text": [ "WARNING:google_auth_httplib2:httplib2 transport does not support per-request timeout. Set the timeout when constructing the httplib2.Http instance.\n" ] }, { "output_type": "stream", "name": "stdout", "text": [ " Deleting 8 'epoch' files from Trash...\n", " Trash Purged.\n", "Epoch 711 | Train: 0.1627 | Val: 0.2159 | No Improvement (31/150)\n", "Epoch 712 | Train: 0.1622 | Val: 0.2157 | No Improvement (32/150)\n", "Epoch 713 | Train: 0.1606 | Val: 0.2111 | No Improvement (33/150)\n", "Epoch 714 | Train: 0.1602 | Val: 0.2114 | No Improvement (34/150)\n", "Epoch 715 | Train: 0.1617 | Val: 0.2147 | No Improvement (35/150)\n", "Epoch 716 | Train: 0.1615 | Val: 0.2131 | No Improvement (36/150)\n", "Epoch 717 | Train: 0.1612 | Val: 0.2111 | No Improvement (37/150)\n", "Epoch 718 | Train: 0.1596 | Val: 0.2069 | No Improvement (38/150)\n", "Epoch 719 | Train: 0.1595 | Val: 0.2034 | No Improvement (39/150)\n", "Epoch 720 | Train: 0.1587 | Val: 0.2062 | No Improvement (40/150)\n" ] }, { "output_type": "stream", "name": "stderr", "text": [ "WARNING:google_auth_httplib2:httplib2 transport does not support per-request timeout. 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Set the timeout when constructing the httplib2.Http instance.\n" ] }, { "output_type": "stream", "name": "stdout", "text": [ " Maintenance: Emptying 'epoch' files from Drive Trash...\n", " Deleting 10 'epoch' files from Trash...\n", " Trash Purged.\n", "Epoch 721 | Train: 0.1609 | Val: 0.2137 | No Improvement (41/150)\n", "Epoch 722 | Train: 0.1617 | Val: 0.2175 | No Improvement (42/150)\n", "Epoch 723 | Train: 0.1613 | Val: 0.2176 | No Improvement (43/150)\n", "Epoch 724 | Train: 0.1600 | Val: 0.2160 | No Improvement (44/150)\n", "Epoch 725 | Train: 0.1611 | Val: 0.2149 | No Improvement (45/150)\n", "Epoch 726 | Train: 0.1615 | Val: 0.2094 | No Improvement (46/150)\n", "Epoch 727 | Train: 0.1585 | Val: 0.2065 | No Improvement (47/150)\n", "Epoch 728 | Train: 0.1608 | Val: 0.2082 | No Improvement (48/150)\n", "Epoch 729 | Train: 0.1579 | Val: 0.2103 | No Improvement (49/150)\n", "Epoch 730 | Train: 0.1595 | Val: 0.2141 | No Improvement (50/150)\n" ] }, { "output_type": "stream", "name": "stderr", "text": [ "WARNING:google_auth_httplib2:httplib2 transport does not support per-request timeout. Set the timeout when constructing the httplib2.Http instance.\n" ] }, { "output_type": "stream", "name": "stdout", "text": [ " Maintenance: Emptying 'epoch' files from Drive Trash...\n" ] }, { "output_type": "stream", "name": "stderr", "text": [ "WARNING:google_auth_httplib2:httplib2 transport does not support per-request timeout. Set the timeout when constructing the httplib2.Http instance.\n" ] }, { "output_type": "stream", "name": "stdout", "text": [ " Deleting 12 'epoch' files from Trash...\n", " Trash Purged.\n", "Epoch 731 | Train: 0.1607 | Val: 0.2141 | No Improvement (51/150)\n", "Epoch 732 | Train: 0.1576 | Val: 0.2118 | No Improvement (52/150)\n", "Epoch 733 | Train: 0.1606 | Val: 0.2085 | No Improvement (53/150)\n", "Epoch 734 | Train: 0.1592 | Val: 0.2060 | No Improvement (54/150)\n", "Epoch 735 | Train: 0.1573 | Val: 0.2059 | No Improvement (55/150)\n", "Epoch 736 | Train: 0.1571 | Val: 0.2092 | No Improvement (56/150)\n", "Epoch 737 | Train: 0.1602 | Val: 0.2119 | No Improvement (57/150)\n", "Epoch 738 | Train: 0.1587 | Val: 0.2114 | No Improvement (58/150)\n", "Epoch 739 | Train: 0.1598 | Val: 0.2113 | No Improvement (59/150)\n", "Epoch 740 | Train: 0.1586 | Val: 0.2105 | No Improvement (60/150)\n" ] }, { "output_type": "stream", "name": "stderr", "text": [ "WARNING:google_auth_httplib2:httplib2 transport does not support per-request timeout. 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Set the timeout when constructing the httplib2.Http instance.\n" ] }, { "output_type": "stream", "name": "stdout", "text": [ " Deleting 10 'epoch' files from Trash...\n", " Trash Purged.\n", "Epoch 741 | Train: 0.1567 | Val: 0.2075 | No Improvement (61/150)\n", "Epoch 742 | Train: 0.1584 | Val: 0.2048 | No Improvement (62/150)\n", "Epoch 743 | Train: 0.1595 | Val: 0.2031 | No Improvement (63/150)\n", "Epoch 744 | Train: 0.1596 | Val: 0.2047 | No Improvement (64/150)\n", "Epoch 745 | Train: 0.1578 | Val: 0.2076 | No Improvement (65/150)\n", "Epoch 746 | Train: 0.1591 | Val: 0.2096 | No Improvement (66/150)\n", "Epoch 747 | Train: 0.1592 | Val: 0.2091 | No Improvement (67/150)\n", "Epoch 748 | Train: 0.1592 | Val: 0.2080 | No Improvement (68/150)\n", "Epoch 749 | Train: 0.1584 | Val: 0.2085 | No Improvement (69/150)\n", "Epoch 750 | Train: 0.1565 | Val: 0.2098 | No Improvement (70/150)\n" ] }, { "output_type": "stream", "name": "stderr", "text": [ "WARNING:google_auth_httplib2:httplib2 transport does not support per-request timeout. 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Set the timeout when constructing the httplib2.Http instance.\n" ] }, { "output_type": "stream", "name": "stdout", "text": [ " Maintenance: Emptying 'epoch' files from Drive Trash...\n", " Deleting 9 'epoch' files from Trash...\n", " Trash Purged.\n", "Epoch 751 | Train: 0.1591 | Val: 0.2116 | No Improvement (71/150)\n", "Epoch 752 | Train: 0.1564 | Val: 0.2123 | No Improvement (72/150)\n", "Epoch 753 | Train: 0.1576 | Val: 0.2122 | No Improvement (73/150)\n", "Epoch 754 | Train: 0.1580 | Val: 0.2102 | No Improvement (74/150)\n", "Epoch 755 | Train: 0.1574 | Val: 0.2072 | No Improvement (75/150)\n", "Epoch 756 | Train: 0.1581 | Val: 0.2039 | No Improvement (76/150)\n", "Epoch 757 | Train: 0.1572 | Val: 0.2035 | No Improvement (77/150)\n", "Epoch 758 | Train: 0.1588 | Val: 0.2045 | No Improvement (78/150)\n", "Epoch 759 | Train: 0.1564 | Val: 0.2056 | No Improvement (79/150)\n", "Epoch 760 | Train: 0.1574 | Val: 0.2048 | No Improvement (80/150)\n" ] }, { "output_type": "stream", "name": "stderr", "text": [ "WARNING:google_auth_httplib2:httplib2 transport does not support per-request timeout. Set the timeout when constructing the httplib2.Http instance.\n" ] }, { "output_type": "stream", "name": "stdout", "text": [ " Maintenance: Emptying 'epoch' files from Drive Trash...\n" ] }, { "output_type": "stream", "name": "stderr", "text": [ "WARNING:google_auth_httplib2:httplib2 transport does not support per-request timeout. Set the timeout when constructing the httplib2.Http instance.\n" ] }, { "output_type": "stream", "name": "stdout", "text": [ " Deleting 11 'epoch' files from Trash...\n", " Trash Purged.\n", "Epoch 761 | Train: 0.1582 | Val: 0.2044 | No Improvement (81/150)\n", "Epoch 762 | Train: 0.1568 | Val: 0.2046 | No Improvement (82/150)\n", "Epoch 763 | Train: 0.1554 | Val: 0.2045 | No Improvement (83/150)\n", "Epoch 764 | Train: 0.1579 | Val: 0.2050 | No Improvement (84/150)\n", "Epoch 765 | Train: 0.1581 | Val: 0.2069 | No Improvement (85/150)\n", "Epoch 766 | Train: 0.1560 | Val: 0.2062 | No Improvement (86/150)\n", "Epoch 767 | Train: 0.1578 | Val: 0.2050 | No Improvement (87/150)\n", "Epoch 768 | Train: 0.1577 | Val: 0.2048 | No Improvement (88/150)\n", "Epoch 769 | Train: 0.1556 | Val: 0.2052 | No Improvement (89/150)\n", "Epoch 770 | Train: 0.1567 | Val: 0.2055 | No Improvement (90/150)\n" ] }, { "output_type": "stream", "name": "stderr", "text": [ "WARNING:google_auth_httplib2:httplib2 transport does not support per-request timeout. Set the timeout when constructing the httplib2.Http instance.\n", "WARNING:google_auth_httplib2:httplib2 transport does not support per-request timeout. Set the timeout when constructing the httplib2.Http instance.\n" ] }, { "output_type": "stream", "name": "stdout", "text": [ " Maintenance: Emptying 'epoch' files from Drive Trash...\n", " Deleting 9 'epoch' files from Trash...\n", " Trash Purged.\n", "Epoch 771 | Train: 0.1572 | Val: 0.2052 | No Improvement (91/150)\n", "Epoch 772 | Train: 0.1552 | Val: 0.2044 | No Improvement (92/150)\n", "Epoch 773 | Train: 0.1573 | Val: 0.2036 | No Improvement (93/150)\n", "Epoch 774 | Train: 0.1564 | Val: 0.2040 | No Improvement (94/150)\n", "Epoch 775 | Train: 0.1564 | Val: 0.2047 | No Improvement (95/150)\n", "Epoch 776 | Train: 0.1551 | Val: 0.2054 | No Improvement (96/150)\n", "Epoch 777 | Train: 0.1551 | Val: 0.2061 | No Improvement (97/150)\n", "Epoch 778 | Train: 0.1568 | Val: 0.2066 | No Improvement (98/150)\n", "Epoch 779 | Train: 0.1551 | Val: 0.2070 | No Improvement (99/150)\n", "Epoch 780 | Train: 0.1549 | Val: 0.2070 | No Improvement (100/150)\n" ] }, { "output_type": "stream", "name": "stderr", "text": [ "WARNING:google_auth_httplib2:httplib2 transport does not support per-request timeout. Set the timeout when constructing the httplib2.Http instance.\n" ] }, { "output_type": "stream", "name": "stdout", "text": [ " Maintenance: Emptying 'epoch' files from Drive Trash...\n" ] }, { "output_type": "stream", "name": "stderr", "text": [ "WARNING:google_auth_httplib2:httplib2 transport does not support per-request timeout. Set the timeout when constructing the httplib2.Http instance.\n" ] }, { "output_type": "stream", "name": "stdout", "text": [ " Deleting 11 'epoch' files from Trash...\n", " Trash Purged.\n", "Epoch 781 | Train: 0.1569 | Val: 0.2070 | No Improvement (101/150)\n", "Epoch 782 | Train: 0.1560 | Val: 0.2064 | No Improvement (102/150)\n", "Epoch 783 | Train: 0.1543 | Val: 0.2060 | No Improvement (103/150)\n", "Epoch 784 | Train: 0.1569 | Val: 0.2056 | No Improvement (104/150)\n", "Epoch 785 | Train: 0.1571 | Val: 0.2058 | No Improvement (105/150)\n", "Epoch 786 | Train: 0.1543 | Val: 0.2055 | No Improvement (106/150)\n", "Epoch 787 | Train: 0.1546 | Val: 0.2047 | No Improvement (107/150)\n", "Epoch 788 | Train: 0.1544 | Val: 0.2045 | No Improvement (108/150)\n", "Epoch 789 | Train: 0.1568 | Val: 0.2047 | No Improvement (109/150)\n", "Epoch 790 | Train: 0.1564 | Val: 0.2056 | No Improvement (110/150)\n" ] }, { "output_type": "stream", "name": "stderr", "text": [ "WARNING:google_auth_httplib2:httplib2 transport does not support per-request timeout. 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Set the timeout when constructing the httplib2.Http instance.\n" ] }, { "output_type": "stream", "name": "stdout", "text": [ " Deleting 10 'epoch' files from Trash...\n", " Trash Purged.\n", "Epoch 791 | Train: 0.1561 | Val: 0.2068 | No Improvement (111/150)\n", "Epoch 792 | Train: 0.1566 | Val: 0.2076 | No Improvement (112/150)\n", "Epoch 793 | Train: 0.1567 | Val: 0.2078 | No Improvement (113/150)\n", "Epoch 794 | Train: 0.1564 | Val: 0.2078 | No Improvement (114/150)\n", "Epoch 795 | Train: 0.1565 | Val: 0.2078 | No Improvement (115/150)\n", "Epoch 796 | Train: 0.1564 | Val: 0.2070 | No Improvement (116/150)\n", "Epoch 797 | Train: 0.1561 | Val: 0.2061 | No Improvement (117/150)\n", "Epoch 798 | Train: 0.1557 | Val: 0.2057 | No Improvement (118/150)\n", "Epoch 799 | Train: 0.1540 | Val: 0.2057 | No Improvement (119/150)\n", "Epoch 800 | Train: 0.1542 | Val: 0.2053 | No Improvement (120/150)\n" ] }, { "output_type": "stream", "name": "stderr", "text": [ "WARNING:google_auth_httplib2:httplib2 transport does not support per-request timeout. 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Set the timeout when constructing the httplib2.Http instance.\n" ] }, { "output_type": "stream", "name": "stdout", "text": [ " Deleting 10 'epoch' files from Trash...\n", " Trash Purged.\n", "Epoch 801 | Train: 0.1563 | Val: 0.2057 | No Improvement (121/150)\n", "Epoch 802 | Train: 0.1560 | Val: 0.2062 | No Improvement (122/150)\n", "Epoch 803 | Train: 0.1557 | Val: 0.2062 | No Improvement (123/150)\n", "Epoch 804 | Train: 0.1538 | Val: 0.2063 | No Improvement (124/150)\n", "Epoch 805 | Train: 0.1539 | Val: 0.2057 | No Improvement (125/150)\n", "Epoch 806 | Train: 0.1558 | Val: 0.2052 | No Improvement (126/150)\n", "Epoch 807 | Train: 0.1569 | Val: 0.2047 | No Improvement (127/150)\n", "Epoch 808 | Train: 0.1556 | Val: 0.2047 | No Improvement (128/150)\n", "Epoch 809 | Train: 0.1557 | Val: 0.2047 | No Improvement (129/150)\n", "Epoch 810 | Train: 0.1551 | Val: 0.2047 | No Improvement (130/150)\n" ] }, { "output_type": "stream", "name": "stderr", "text": [ "WARNING:google_auth_httplib2:httplib2 transport does not support per-request timeout. Set the timeout when constructing the httplib2.Http instance.\n" ] }, { "output_type": "stream", "name": "stdout", "text": [ " Maintenance: Emptying 'epoch' files from Drive Trash...\n" ] }, { "output_type": "stream", "name": "stderr", "text": [ "WARNING:google_auth_httplib2:httplib2 transport does not support per-request timeout. Set the timeout when constructing the httplib2.Http instance.\n" ] }, { "output_type": "stream", "name": "stdout", "text": [ " Deleting 9 'epoch' files from Trash...\n", " Trash Purged.\n", "Epoch 811 | Train: 0.1559 | Val: 0.2049 | No Improvement (131/150)\n", "Epoch 812 | Train: 0.1554 | Val: 0.2051 | No Improvement (132/150)\n", "Epoch 813 | Train: 0.1550 | Val: 0.2055 | No Improvement (133/150)\n", "Epoch 814 | Train: 0.1558 | Val: 0.2056 | No Improvement (134/150)\n", "Epoch 815 | Train: 0.1537 | Val: 0.2055 | No Improvement (135/150)\n", "Epoch 816 | Train: 0.1557 | Val: 0.2056 | No Improvement (136/150)\n", "Epoch 817 | Train: 0.1549 | Val: 0.2058 | No Improvement (137/150)\n", "Epoch 818 | Train: 0.1535 | Val: 0.2058 | No Improvement (138/150)\n" ] }, { "output_type": "error", "ename": "KeyboardInterrupt", "evalue": "", "traceback": [ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[0;31mKeyboardInterrupt\u001b[0m Traceback (most recent call last)", "\u001b[0;32m/tmp/ipython-input-144100968.py\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[1;32m 288\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 289\u001b[0m \u001b[0;31m# Save Resume State\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 290\u001b[0;31m torch.save({\n\u001b[0m\u001b[1;32m 291\u001b[0m \u001b[0;34m'epoch'\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mep\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 292\u001b[0m \u001b[0;34m'model_state_dict'\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mmodel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstate_dict\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;32m/usr/local/lib/python3.12/dist-packages/torch/serialization.py\u001b[0m in \u001b[0;36msave\u001b[0;34m(obj, f, pickle_module, pickle_protocol, _use_new_zipfile_serialization, _disable_byteorder_record)\u001b[0m\n\u001b[1;32m 965\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0m_use_new_zipfile_serialization\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 966\u001b[0m \u001b[0;32mwith\u001b[0m \u001b[0m_open_zipfile_writer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mf\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mopened_zipfile\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 967\u001b[0;31m _save(\n\u001b[0m\u001b[1;32m 968\u001b[0m \u001b[0mobj\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 969\u001b[0m \u001b[0mopened_zipfile\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;32m/usr/local/lib/python3.12/dist-packages/torch/serialization.py\u001b[0m in \u001b[0;36m_save\u001b[0;34m(obj, zip_file, pickle_module, pickle_protocol, _disable_byteorder_record)\u001b[0m\n\u001b[1;32m 1266\u001b[0m \u001b[0mstorage\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mstorage\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcpu\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1267\u001b[0m \u001b[0;31m# Now that it is on the CPU we can directly copy it into the zip file\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1268\u001b[0;31m \u001b[0mzip_file\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mwrite_record\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mname\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mstorage\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnum_bytes\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 1269\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1270\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;31mKeyboardInterrupt\u001b[0m: " ] } ] }, { "cell_type": "code", "source": [ "import torch\n", "import torch.nn as nn\n", "import numpy as np\n", "import matplotlib.pyplot as plt\n", "import os\n", "import time\n", "import cv2\n", "from scipy.spatial.distance import directed_hausdorff\n", "from sklearn.metrics import precision_score, recall_score, f1_score, accuracy_score, jaccard_score\n", "from torch.utils.data import DataLoader, Dataset\n", "from google.colab import drive\n", "\n", "# 1. PATHS AND DEVICE SETUP\n", "if not os.path.exists('/content/drive'):\n", " drive.mount('/content/drive', force_remount=True)\n", "\n", "# TARGET DIRECTORY FOR PARTIAL FINETUNING\n", "SAVE_DIR = '/content/drive/MyDrive/SatMAE_PartialFT_Results_1/'\n", "CHECKPOINT_PATH = os.path.join(SAVE_DIR, \"checkpoint_satmae_partial.pth\")\n", "DEVICE = torch.device('cuda' if torch.cuda.is_available() else 'cpu')\n", "\n", "# 2. DATA LOADER (MMAP for memory efficiency)\n", "class MmapDataset(Dataset):\n", " def __init__(self, x_path, y_path):\n", " self.data = np.load(x_path, mmap_mode='r')\n", " self.target = np.load(y_path, mmap_mode='r')\n", " def __len__(self): return len(self.data)\n", " def __getitem__(self, index):\n", " return torch.from_numpy(self.data[index].copy()), torch.from_numpy(self.target[index].copy())\n", "\n", "# 3. ADVANCED METRIC HELPERS\n", "def calculate_boundary_iou(gt_mask, pred_mask):\n", " # Detect edges to calculate IoU specifically on the boundaries\n", " gt_edges = cv2.Canny(gt_mask.astype(np.uint8)*255, 100, 200) > 0\n", " pred_edges = cv2.Canny(pred_mask.astype(np.uint8)*255, 100, 200) > 0\n", " intersection = np.logical_and(gt_edges, pred_edges).sum()\n", " union = np.logical_or(gt_edges, pred_edges).sum()\n", " return intersection / union if union > 0 else 0.0\n", "\n", "def calculate_hausdorff(gt_mask, pred_mask):\n", " # Measure the maximum distance between the predicted boundary and the ground truth boundary\n", " gt_coords = np.argwhere(gt_mask)\n", " pred_coords = np.argwhere(pred_mask)\n", " if len(gt_coords) == 0 or len(pred_coords) == 0: return 0.0\n", " return max(directed_hausdorff(gt_coords, pred_coords)[0], directed_hausdorff(pred_coords, gt_coords)[0])\n", "\n", "# 4. EXECUTION FUNCTION\n", "def evaluate_partial_ft(model_dir, checkpoint_path, num_samples=3):\n", " # Load Validation Data\n", " val_x = os.path.join(model_dir, 'val_x.npy')\n", " val_y = os.path.join(model_dir, 'val_y.npy')\n", " val_ds = MmapDataset(val_x, val_y)\n", " val_loader = DataLoader(val_ds, batch_size=8, shuffle=False)\n", "\n", " # Initialize Model\n", " model = SatMAESegmentation(num_frames=3, in_chans=6).to(DEVICE)\n", "\n", " # Load Weights\n", " if os.path.exists(checkpoint_path):\n", " print(f\" Loading Checkpoint: {checkpoint_path}\")\n", " ckpt = torch.load(checkpoint_path, map_location=DEVICE)\n", " model.load_state_dict(ckpt['model_state_dict'])\n", " else:\n", " print(\" Error: Checkpoint file not found.\")\n", " return\n", "\n", " model.eval()\n", " all_preds, all_targets = [], []\n", " boundary_ious, hausdorff_dists = [], []\n", " total_frames = 0\n", " start_time = time.time()\n", "\n", " print(\" Calculating metrics...\")\n", " with torch.no_grad():\n", " for x, y in val_loader:\n", " x = x.to(DEVICE)\n", " probs = torch.sigmoid(model(x))\n", " preds_bin = (probs > 0.5).cpu().numpy().astype(np.uint8)\n", " targets_bin = y.numpy().astype(np.uint8)\n", " total_frames += x.size(0)\n", "\n", " for i in range(preds_bin.shape[0]):\n", " p, t = preds_bin[i, 0], targets_bin[i, 0]\n", " boundary_ious.append(calculate_boundary_iou(t, p))\n", " hausdorff_dists.append(calculate_hausdorff(t, p))\n", " all_preds.append(p.flatten())\n", " all_targets.append(t.flatten())\n", "\n", " # Final Report Calculation\n", " fps = total_frames / (time.time() - start_time)\n", " y_p, y_t = np.concatenate(all_preds), np.concatenate(all_targets)\n", "\n", " print(\"\\n\" + \"=\"*50)\n", " print(\" SATMAE PARTIAL FT - PERFORMANCE \")\n", " print(\"=\"*50)\n", " print(f\"Pixel Accuracy: {accuracy_score(y_t, y_p):.4f}\")\n", " print(f\"IoU (Jaccard): {jaccard_score(y_t, y_p):.4f}\")\n", " print(f\"F1-Score (Dice): {f1_score(y_t, y_p):.4f}\")\n", " print(f\"Precision: {precision_score(y_t, y_p):.4f}\")\n", " print(f\"Recall: {recall_score(y_t, y_p):.4f}\")\n", " print(\"-\" * 30)\n", " print(f\"Boundary IoU: {np.mean(boundary_ious):.4f}\")\n", " print(f\"Hausdorff Dist (px): {np.mean(hausdorff_dists):.2f}\")\n", " print(f\"Inference Speed: {fps:.2f} FPS\")\n", " print(\"=\"*50 + \"\\n\")\n", "\n", " # VISUAL SAMPLES\n", " print(\" Plotting Samples...\")\n", " x_batch, y_batch = next(iter(val_loader))\n", " with torch.no_grad():\n", " vis_preds = (torch.sigmoid(model(x_batch.to(DEVICE))) > 0.5).cpu().numpy()\n", "\n", " x_np = x_batch.numpy()\n", " y_np = y_batch.numpy()\n", "\n", " fig, axs = plt.subplots(num_samples, 3, figsize=(15, 5 * num_samples))\n", " for i in range(num_samples):\n", " # Extract RGB at Time Step 0 to avoid IndexErrors\n", " # x_np shape: (Batch, Channels, Time, H, W)\n", " try:\n", " r, g, b = x_np[i, 2, 0], x_np[i, 1, 0], x_np[i, 0, 0]\n", " img = np.stack([r, g, b], axis=2)\n", " except:\n", " img = x_np[i, 0, 0] # Fallback to single band\n", " img = np.stack([img, img, img], axis=2)\n", "\n", " # Normalize 0-1\n", " img = (img - img.min()) / (img.max() - img.min() + 1e-6)\n", "\n", " axs[i, 0].imshow(img)\n", " axs[i, 0].set_title(f\"Input Sample {i+1}\")\n", " axs[i, 1].imshow(y_np[i, 0], cmap='gray')\n", " axs[i, 1].set_title(\"Ground Truth\")\n", " axs[i, 2].imshow(vis_preds[i, 0], cmap='gray')\n", "\n", " iou = jaccard_score(y_np[i,0].flatten(), vis_preds[i,0].flatten())\n", " axs[i, 2].set_title(f\"Prediction (IoU: {iou:.2f})\")\n", "\n", " for ax in axs[i]: ax.axis('off')\n", "\n", " plt.tight_layout()\n", " plt.show()\n", "\n", "# RUN EVALUATION\n", "evaluate_partial_ft(SAVE_DIR, CHECKPOINT_PATH)" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 440 }, "id": "rvl3sDOo476-", "executionInfo": { "status": "error", "timestamp": 1768772481807, "user_tz": -330, "elapsed": 1867, "user": { "displayName": "Enigma Cypher", "userId": "06934947732805796536" } }, "outputId": "5593573e-4e57-4a9c-eb11-ba8485f85002" }, "execution_count": null, "outputs": [ { "output_type": "display_data", "data": { "text/plain": [ "" ], "text/html": [ "\n", " \n", " " ] }, "metadata": {} }, { "output_type": "stream", "name": "stdout", "text": [ " Initializing SatMAE Partial FT Model...\n", " Downloading Pretrained MAE Weights (facebook/vit-mae-base)...\n", " Weights Loaded!\n", "Applying Partial Freeze Strategy...\n", " Unfreezing Last 2 Encoder Blocks...\n", " Loading Checkpoint: /content/drive/MyDrive/SatMAE_PartialFT_Results_1/checkpoint_satmae_partial.pth\n" ] }, { "output_type": "error", "ename": "RuntimeError", "evalue": "PytorchStreamReader failed locating file data/44: file not found", "traceback": [ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[0;31mRuntimeError\u001b[0m Traceback (most recent call last)", "\u001b[0;32m/tmp/ipython-input-2482868117.py\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[1;32m 143\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 144\u001b[0m \u001b[0;31m# RUN EVALUATION\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 145\u001b[0;31m \u001b[0mevaluate_partial_ft\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mSAVE_DIR\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mCHECKPOINT_PATH\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", "\u001b[0;32m/tmp/ipython-input-2482868117.py\u001b[0m in \u001b[0;36mevaluate_partial_ft\u001b[0;34m(model_dir, checkpoint_path, num_samples)\u001b[0m\n\u001b[1;32m 59\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mos\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpath\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mexists\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcheckpoint_path\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 60\u001b[0m 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"\u001b[0;32m/usr/local/lib/python3.12/dist-packages/torch/serialization.py\u001b[0m in \u001b[0;36mload\u001b[0;34m(f, map_location, pickle_module, weights_only, mmap, **pickle_load_args)\u001b[0m\n\u001b[1;32m 1519\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mweights_only\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1520\u001b[0m \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1521\u001b[0;31m return _load(\n\u001b[0m\u001b[1;32m 1522\u001b[0m \u001b[0mopened_zipfile\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1523\u001b[0m \u001b[0mmap_location\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;32m/usr/local/lib/python3.12/dist-packages/torch/serialization.py\u001b[0m in \u001b[0;36m_load\u001b[0;34m(zip_file, map_location, pickle_module, pickle_file, overall_storage, 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SETUP\n", "if not os.path.exists('/content/drive'):\n", " drive.mount('/content/drive', force_remount=True)\n", "\n", "SAVE_DIR = '/content/drive/MyDrive/SatMAE_PartialFT_Results_1/'\n", "DEVICE = torch.device('cuda' if torch.cuda.is_available() else 'cpu')\n", "\n", "# 2. DATA LOADER\n", "class MmapDataset(Dataset):\n", " def __init__(self, x_path, y_path):\n", " self.data = np.load(x_path, mmap_mode='r')\n", " self.target = np.load(y_path, mmap_mode='r')\n", " def __len__(self): return len(self.data)\n", " def __getitem__(self, index):\n", " return torch.from_numpy(self.data[index].copy()), torch.from_numpy(self.target[index].copy())\n", "\n", "# 3. METRIC HELPERS\n", "def calculate_boundary_iou(gt_mask, pred_mask):\n", " gt_edges = cv2.Canny(gt_mask.astype(np.uint8)*255, 100, 200) > 0\n", " pred_edges = cv2.Canny(pred_mask.astype(np.uint8)*255, 100, 200) > 0\n", " inter = np.logical_and(gt_edges, pred_edges).sum()\n", " union = np.logical_or(gt_edges, pred_edges).sum()\n", " return inter / union if union > 0 else 0.0\n", "\n", "def calculate_hausdorff(gt_mask, pred_mask):\n", " gt_coords = np.argwhere(gt_mask)\n", " pred_coords = np.argwhere(pred_mask)\n", " if len(gt_coords) == 0 or len(pred_coords) == 0: return 0.0\n", " return max(directed_hausdorff(gt_coords, pred_coords)[0], directed_hausdorff(pred_coords, gt_coords)[0])\n", "\n", "# 4. ROBUST LOAD FUNCTION\n", "def get_best_available_weights(model_dir):\n", " # Order of preference: 1. Best Model, 2. Latest Epoch backup\n", " best_path = os.path.join(model_dir, \"best_model.pth\")\n", " if os.path.exists(best_path):\n", " return best_path\n", "\n", " epoch_files = glob.glob(os.path.join(model_dir, \"epoch_*.pth\"))\n", " if epoch_files:\n", " return max(epoch_files, key=os.path.getmtime)\n", "\n", " return None\n", "\n", "# 5. EXECUTION\n", "def run_evaluation(model_dir):\n", " weights_path = get_best_available_weights(model_dir)\n", " if not weights_path:\n", " print(\" No weights found in directory.\")\n", " return\n", "\n", " print(f\" Loading weights from: {weights_path}\")\n", "\n", " # Init Model\n", " model = SatMAESegmentation(num_frames=3, in_chans=6).to(DEVICE)\n", " model.load_state_dict(torch.load(weights_path, map_location=DEVICE))\n", " model.eval()\n", "\n", " # Data\n", " val_ds = MmapDataset(os.path.join(model_dir, 'val_x.npy'), os.path.join(model_dir, 'val_y.npy'))\n", " val_loader = DataLoader(val_ds, batch_size=8, shuffle=False)\n", "\n", " all_preds, all_targets = [], []\n", " boundary_ious, hausdorff_dists = [], []\n", " total_frames = 0\n", " start_time = time.time()\n", "\n", " print(\" Evaluating...\")\n", " with torch.no_grad():\n", " for x, y in val_loader:\n", " x = x.to(DEVICE)\n", " preds = torch.sigmoid(model(x))\n", " preds_bin = (preds > 0.5).cpu().numpy().astype(np.uint8)\n", " targets_bin = y.numpy().astype(np.uint8)\n", " total_frames += x.size(0)\n", "\n", " for i in range(preds_bin.shape[0]):\n", " p, t = preds_bin[i, 0], targets_bin[i, 0]\n", " boundary_ious.append(calculate_boundary_iou(t, p))\n", " hausdorff_dists.append(calculate_hausdorff(t, p))\n", " all_preds.append(p.flatten()); all_targets.append(t.flatten())\n", "\n", " # Report\n", " fps = total_frames / (time.time() - start_time)\n", " y_p, y_t = np.concatenate(all_preds), np.concatenate(all_targets)\n", "\n", " print(\"\\n\" + \"=\"*50)\n", " print(f\"Pixel Accuracy: {accuracy_score(y_t, y_p):.4f}\")\n", " print(f\"IoU (Jaccard): {jaccard_score(y_t, y_p):.4f}\")\n", " print(f\"F1-Score (Dice): {f1_score(y_t, y_p):.4f}\")\n", " print(f\"Boundary IoU: {np.mean(boundary_ious):.4f}\")\n", " print(f\"Hausdorff Dist: {np.mean(hausdorff_dists):.2f}\")\n", " print(f\"Speed: {fps:.2f} FPS\")\n", " print(\"=\"*50)\n", "\n", " # Visualization\n", " x_batch, y_batch = next(iter(val_loader))\n", " with torch.no_grad():\n", " vis_preds = (torch.sigmoid(model(x_batch.to(DEVICE))) > 0.5).cpu().numpy()\n", "\n", " fig, axs = plt.subplots(3, 3, figsize=(15, 15))\n", " for i in range(3):\n", " # RGB extraction from (B, C, T, H, W) -> Using T=0\n", " img = x_batch[i, [2, 1, 0], 0].permute(1, 2, 0).numpy()\n", " img = (img - img.min()) / (img.max() - img.min() + 1e-6)\n", "\n", " axs[i, 0].imshow(img)\n", " axs[i, 0].set_title(\"Input RGB\")\n", " axs[i, 1].imshow(y_batch[i, 0], cmap='gray')\n", " axs[i, 1].set_title(\"Ground Truth\")\n", " axs[i, 2].imshow(vis_preds[i, 0], cmap='gray')\n", " axs[i, 2].set_title(\"Prediction\")\n", " for ax in axs[i]: ax.axis('off')\n", " plt.show()\n", "\n", "run_evaluation(SAVE_DIR)" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 1000 }, "id": "KEtJDcNUDtC7", "executionInfo": { "status": "error", "timestamp": 1768772534126, "user_tz": -330, "elapsed": 9438, "user": { "displayName": "Enigma Cypher", "userId": "06934947732805796536" } }, "outputId": "69d94bcb-9de1-4309-cd04-d9ba3a3ea5d4" }, "execution_count": null, "outputs": [ { "output_type": "display_data", "data": { "text/plain": [ "" ], "text/html": [ "\n", " \n", " " ] }, "metadata": {} }, { "output_type": "stream", "name": "stdout", "text": [ "🚀 Loading weights from: /content/drive/MyDrive/SatMAE_PartialFT_Results_1/best_model.pth\n", " Initializing SatMAE Partial FT Model...\n", " Downloading Pretrained MAE Weights (facebook/vit-mae-base)...\n", " Weights Loaded!\n", "Applying Partial Freeze Strategy...\n", " Unfreezing Last 2 Encoder Blocks...\n", "📊 Evaluating...\n", "\n", "==================================================\n", "Pixel Accuracy: 0.9143\n", "IoU (Jaccard): 0.8858\n", "F1-Score (Dice): 0.9394\n", "Boundary IoU: 0.1070\n", "Hausdorff Dist: 12.86\n", "Speed: 1.49 FPS\n", "==================================================\n" ] }, { "output_type": "error", "ename": "IndexError", "evalue": "index 2 is out of bounds for dimension 0 with size 2", "traceback": [ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[0;31mIndexError\u001b[0m Traceback (most recent call last)", "\u001b[0;32m/tmp/ipython-input-458025177.py\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[1;32m 127\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshow\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 128\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 129\u001b[0;31m \u001b[0mrun_evaluation\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mSAVE_DIR\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", "\u001b[0;32m/tmp/ipython-input-458025177.py\u001b[0m in \u001b[0;36mrun_evaluation\u001b[0;34m(model_dir)\u001b[0m\n\u001b[1;32m 115\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mi\u001b[0m \u001b[0;32min\u001b[0m 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dimension 0 with size 2" ] }, { "output_type": "display_data", "data": { "text/plain": [ "
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\n" }, "metadata": {} } ] }, { "cell_type": "code", "source": [ "import torch\n", "import torch.nn as nn\n", "import numpy as np\n", "import matplotlib.pyplot as plt\n", "import os\n", "import time\n", "import cv2\n", "from scipy.spatial.distance import directed_hausdorff\n", "from sklearn.metrics import (precision_score, recall_score, f1_score, accuracy_score,\n", " jaccard_score, matthews_corrcoef, confusion_matrix,\n", " roc_auc_score, precision_recall_curve, auc, fbeta_score)\n", "from torch.utils.data import DataLoader, Dataset\n", "from google.colab import drive\n", "\n", "# 1. SETUP & PATHS\n", "SAVE_DIR = '/content/drive/MyDrive/SatMAE_PartialFT_Results_1/'\n", "DEVICE = torch.device('cuda' if torch.cuda.is_available() else 'cpu')\n", "\n", "# 2. METRIC HELPERS\n", "def calculate_boundary_iou(gt_mask, pred_mask):\n", " gt_edges = cv2.Canny(gt_mask.astype(np.uint8)*255, 100, 200) > 0\n", " pred_edges = cv2.Canny(pred_mask.astype(np.uint8)*255, 100, 200) > 0\n", " inter = np.logical_and(gt_edges, pred_edges).sum()\n", " union = np.logical_or(gt_edges, pred_edges).sum()\n", " return inter / union if union > 0 else 0.0\n", "\n", "def calculate_hausdorff(gt_mask, pred_mask):\n", " gt_coords = np.argwhere(gt_mask)\n", " pred_coords = np.argwhere(pred_mask)\n", " if len(gt_coords) == 0 or len(pred_coords) == 0: return 0.0\n", " return max(directed_hausdorff(gt_coords, pred_coords)[0], directed_hausdorff(pred_coords, gt_coords)[0])\n", "\n", "# 3. COMPREHENSIVE EVALUATION FUNCTION\n", "def run_final_metrics_report(model_dir):\n", " # Load Model Weights\n", " weights_path = os.path.join(model_dir, \"best_model.pth\")\n", " if not os.path.exists(weights_path):\n", " weights_path = os.path.join(model_dir, \"satmae_finetune_swa_final.pth\")\n", "\n", " print(f\" Loading weights: {weights_path}\")\n", " model = SatMAESegmentation(num_frames=3, in_chans=6).to(DEVICE)\n", " model.load_state_dict(torch.load(weights_path, map_location=DEVICE))\n", " model.eval()\n", "\n", " # Load Data\n", " val_ds = MmapDataset(os.path.join(model_dir, 'val_x.npy'), os.path.join(model_dir, 'val_y.npy'))\n", " val_loader = DataLoader(val_ds, batch_size=8, shuffle=False)\n", "\n", " all_preds, all_targets, all_probs = [], [], []\n", " boundary_ious, hausdorff_dists = [], []\n", " total_frames = 0\n", " start_time = time.time()\n", "\n", " print(\" Evaluating Validation Set...\")\n", " with torch.no_grad():\n", " for x, y in val_loader:\n", " x = x.to(DEVICE)\n", " outputs = model(x)\n", " probs = torch.sigmoid(outputs).cpu().numpy()\n", " preds_bin = (probs > 0.5).astype(np.uint8)\n", " targets_bin = y.numpy().astype(np.uint8)\n", "\n", " total_frames += x.size(0)\n", "\n", " for i in range(preds_bin.shape[0]):\n", " p, t = preds_bin[i, 0], targets_bin[i, 0]\n", " boundary_ious.append(calculate_boundary_iou(t, p))\n", " hausdorff_dists.append(calculate_hausdorff(t, p))\n", " all_preds.append(p.flatten())\n", " all_targets.append(t.flatten())\n", " all_probs.append(probs[i, 0].flatten())\n", "\n", " # Consolidate Data\n", " y_p = np.concatenate(all_preds)\n", " y_t = np.concatenate(all_targets)\n", " y_prob = np.concatenate(all_probs)\n", "\n", " # Advanced Statistical Calculations\n", " tn, fp, fn, tp = confusion_matrix(y_t, y_p).ravel()\n", " prec_pts, rec_pts, _ = precision_recall_curve(y_t, y_prob)\n", " pr_auc_val = auc(rec_pts, prec_pts)\n", " fps_val = total_frames / (time.time() - start_time)\n", "\n", " # PRINT DETAILED REPORT\n", " print(\"\\n\" + \"=\"*60)\n", " print(\" SATMAE FULL FINETUNE - IN-DEPTH METRICS REPORT \")\n", " print(\"=\"*60)\n", " print(f\"{'Metric':<28} | {'Value':<10}\")\n", " print(\"-\" * 45)\n", " print(f\"{'Pixel Accuracy':<28} | {accuracy_score(y_t, y_p):.4f}\")\n", " print(f\"{'IoU (Jaccard Index)':<28} | {jaccard_score(y_t, y_p):.4f}\")\n", " print(f\"{'F1-Score (Dice)':<28} | {f1_score(y_t, y_p):.4f}\")\n", " print(f\"{'F2-Score (Recall Focus)':<28} | {fbeta_score(y_t, y_p, beta=2):.4f}\")\n", " print(f\"{'MCC (Matthews Corr)':<28} | {matthews_corrcoef(y_t, y_p):.4f}\")\n", " print(f\"{'ROC-AUC':<28} | {roc_auc_score(y_t, y_prob):.4f}\")\n", " print(f\"{'PR-AUC':<28} | {pr_auc_val:.4f}\")\n", " print(\"-\" * 45)\n", " print(f\"{'Precision':<28} | {precision_score(y_t, y_p):.4f}\")\n", " print(f\"{'Recall (Sensitivity)':<28} | {recall_score(y_t, y_p):.4f}\")\n", " print(f\"{'Specificity':<28} | {tn / (tn + fp):.4f}\")\n", " print(f\"{'False Positive Rate':<28} | {fp / (fp + tn):.4f}\")\n", " print(\"-\" * 45)\n", " print(f\"{'Boundary IoU':<28} | {np.mean(boundary_ious):.4f}\")\n", " print(f\"{'Hausdorff Distance (px)':<28} | {np.mean(hausdorff_dists):.2f}\")\n", " print(f\"{'Inference Speed':<28} | {fps_val:.2f} FPS\")\n", " print(\"=\"*60 + \"\\n\")\n", "\n", " # 4. PLOTTING BLOCK\n", " fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(14, 5))\n", "\n", " # Confusion Matrix\n", " cm = confusion_matrix(y_t, y_p)\n", " im = ax1.imshow(cm, interpolation='nearest', cmap=plt.cm.Blues)\n", " ax1.set_title(\"Confusion Matrix\")\n", " fig.colorbar(im, ax=ax1)\n", " ax1.set_xticks([0, 1])\n", " ax1.set_yticks([0, 1])\n", " ax1.set_xticklabels(['Background', 'Target'])\n", " ax1.set_yticklabels(['Background', 'Target'])\n", " ax1.set_xlabel('Predicted Label')\n", " ax1.set_ylabel('True Label')\n", "\n", " # PR Curve\n", " ax2.plot(rec_pts, prec_pts, color='darkgreen', lw=2, label=f'PR Curve (AUC = {pr_auc_val:.2f})')\n", " ax2.set_xlabel('Recall')\n", " ax2.set_ylabel('Precision')\n", " ax2.set_title('Precision-Recall Curve')\n", " ax2.legend(loc=\"lower left\")\n", " ax2.grid(True, linestyle='--', alpha=0.5)\n", "\n", " plt.tight_layout()\n", " plt.show()\n", "\n", "# Run the complete analysis\n", "run_final_metrics_report(SAVE_DIR)" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 1000 }, "id": "1bWrjsl5EGbF", "executionInfo": { "status": "ok", "timestamp": 1768772879873, "user_tz": -330, "elapsed": 6189, "user": { "displayName": "Enigma Cypher", "userId": "06934947732805796536" } }, "outputId": "ae8e6179-a72c-48f1-9e99-12f486047bd9" }, "execution_count": null, "outputs": [ { "output_type": "display_data", "data": { "text/plain": [ "" ], "text/html": [ "\n", " \n", " " ] }, "metadata": {} }, { "output_type": "stream", "name": "stdout", "text": [ "📥 Loading weights: /content/drive/MyDrive/SatMAE_PartialFT_Results_1/best_model.pth\n", " Initializing SatMAE Partial FT Model...\n", " Downloading Pretrained MAE Weights (facebook/vit-mae-base)...\n", " Weights Loaded!\n", "Applying Partial Freeze Strategy...\n", " Unfreezing Last 2 Encoder Blocks...\n", "📊 Evaluating Validation Set...\n", "\n", "============================================================\n", " SATMAE FULL FINETUNE - IN-DEPTH METRICS REPORT \n", "============================================================\n", "Metric | Value \n", "---------------------------------------------\n", "Pixel Accuracy | 0.9143\n", "IoU (Jaccard Index) | 0.8858\n", "F1-Score (Dice) | 0.9394\n", "F2-Score (Recall Focus) | 0.9387\n", "MCC (Matthews Corr) | 0.7927\n", "ROC-AUC | 0.9637\n", "PR-AUC | 0.9830\n", "---------------------------------------------\n", "Precision | 0.9406\n", "Recall (Sensitivity) | 0.9383\n", "Specificity | 0.8558\n", "False Positive Rate | 0.1442\n", "---------------------------------------------\n", "Boundary IoU | 0.1070\n", "Hausdorff Distance (px) | 12.86\n", "Inference Speed | 2.69 FPS\n", "============================================================\n", "\n" ] }, { "output_type": "display_data", "data": { "text/plain": [ "
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\n" 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