diff --git a/.gitattributes b/.gitattributes
index 957b2579c6ef20995a09efd9a17f8fd90606f5ed..8b62e1fdfcbc62e7bc30cc2651e9e4c82ac1a5f8 100644
--- a/.gitattributes
+++ b/.gitattributes
@@ -25,3 +25,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zstandard filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+moonlit-flower-278.ckpt filter=lfs diff=lfs merge=lfs -text
diff --git a/.gitignore b/.gitignore
new file mode 100644
index 0000000000000000000000000000000000000000..eae9231543b2fe3f29794be89c4b3599111c7eff
--- /dev/null
+++ b/.gitignore
@@ -0,0 +1,4 @@
+*.DS_Store
+__pycache__/
+*.py[cod]
+*$py.class
diff --git a/README.md b/README.md
index 3ed69629806402a9dc40d33a6029a78f1c885fe8..00bdbcc960594531a7c6661f3088d676029dc903 100644
--- a/README.md
+++ b/README.md
@@ -1,8 +1,8 @@
 ---
 title: Interpretable Vertebral Fracture Diagnosis
-emoji: 🐠
-colorFrom: red
-colorTo: red
+emoji: 🦴
+colorFrom: indigo
+colorTo: yellow
 sdk: streamlit
 sdk_version: 1.2.0
 app_file: app.py
diff --git a/app.py b/app.py
new file mode 100644
index 0000000000000000000000000000000000000000..d1a1e2e0aca9ab05a44e7bb73ab06c415d6ebd35
--- /dev/null
+++ b/app.py
@@ -0,0 +1,288 @@
+import streamlit as st
+import numpy as np
+import PIL.Image
+from st_clickable_images import clickable_images
+import os
+from monai.transforms import CenterSpatialCrop, ScaleIntensityRange, Orientation
+import base64
+from io import BytesIO
+import torch
+from glob import glob
+from model import VerseFxClassifier
+from netdissect import nethook, imgviz
+import tempfile
+import nibabel as nib
+import pathlib
+
+import warnings
+warnings.filterwarnings("ignore")
+
+# inlined Network Dissection results
+unit_levels = torch.tensor([1.8715330362319946, 1.5618106126785278, 1.2870054244995117, 2.801919937133789, 1.1172661781311035, 2.2070984840393066, 2.3209457397460938, 2.022796392440796, 2.0127036571502686, 2.782788038253784, 1.013718843460083, 2.491750955581665, 1.5298184156417847, 1.7949274778366089, 2.1840720176696777, 2.73867130279541, 1.9927071332931519, 1.4070216417312622, 1.8516860008239746, 1.4621922969818115, 1.7988444566726685, 2.0956199169158936, 2.890246629714966, 0.9635668992996216, 1.8309086561203003, 1.8866947889328003, 1.8208155632019043, 1.3282618522644043, 2.787090301513672, 1.6975336074829102, 2.388171434402466, 3.1032965183258057, 1.996658444404602, 1.8226428031921387, 2.557448148727417, 1.8223134279251099, 1.2595659494400024, 1.8109630346298218, 2.6617250442504883, 1.9107582569122314, 2.254500389099121, 1.218552827835083, 3.087602376937866, 3.3800148963928223, 3.153672218322754, 2.919377326965332, 2.0350027084350586, 3.0219407081604004, 2.4654042720794678, 1.4958505630493164, 2.2895171642303467, 1.3284631967544556, 3.229510545730591, 1.9460035562515259, 1.855022668838501, 3.15183424949646, 2.582113742828369, 1.8321630954742432, 2.7707386016845703, 2.824443817138672, 2.662318468093872, 2.466081380844116, 1.0707639455795288, 1.856846570968628, 1.9820237159729004, 2.5840156078338623, 1.603718638420105, 2.741654396057129, 1.7408792972564697, 1.5616865158081055, 2.621121406555176, 2.187910318374634, 2.029402494430542, 2.3087165355682373, 2.3417551517486572, 2.4370405673980713, 2.363990545272827, 1.7908833026885986, 2.29636287689209, 2.5254483222961426, 3.2696034908294678, 1.4013628959655762, 1.645676851272583, 2.7126476764678955, 2.717543125152588, 1.0994248390197754, 1.9232852458953857, 1.985698938369751, 2.004666328430176, 2.385585069656372, 2.5118658542633057, 3.444154977798462, 2.0752625465393066, 2.9441027641296387, 1.6907892227172852, 2.695660352706909, 3.08571457862854, 1.8869487047195435, 1.5935581922531128, 2.224071502685547, 2.877380609512329, 3.0157597064971924, 2.1446480751037598, 2.4394376277923584, 3.298722267150879, 2.208728313446045, 1.9590588808059692, 1.789717197418213, 2.6814987659454346, 2.2261674404144287, 3.002722978591919, 3.0650651454925537, 1.9212583303451538, 1.6315948963165283, 1.6328997611999512, 2.4739739894866943, 0.9252153635025024, 3.089088201522827, 2.7511496543884277, 1.997342586517334, 2.5561487674713135, 1.6858017444610596, 2.7134108543395996, 2.513460159301758, 1.8604570627212524, 2.7962076663970947, 1.111690878868103, 2.1877119541168213, 2.1126585006713867, 2.9239501953125, 1.4319941997528076, 3.041599988937378, 2.2168679237365723, 1.792368769645691, 2.1387674808502197, 1.3679250478744507, 1.347702145576477, 3.0506792068481445, 1.5423274040222168, 1.8090440034866333, 1.869529366493225, 2.8993425369262695, 1.5416679382324219, 3.003296375274658, 3.1893210411071777, 2.3816075325012207, 2.281187057495117, 2.7733864784240723, 1.3033744096755981, 1.4627212285995483, 1.942519187927246, 1.4943166971206665, 2.48635196685791, 1.9112900495529175, 2.908750534057617, 1.9310427904129028, 1.8946770429611206, 1.2220033407211304, 2.0171048641204834, 1.197824478149414, 2.093484878540039, 2.240743398666382, 1.4367271661758423, 1.5200153589248657, 2.6623482704162598, 2.34277606010437, 2.378328323364258, 3.4981000423431396, 1.8303442001342773, 2.1322264671325684, 1.8304965496063232, 2.0963211059570312, 1.932998776435852, 0.9879118800163269, 1.989233136177063, 2.0391933917999268, 3.078193187713623, 2.9010426998138428, 1.451486587524414, 1.4458937644958496, 3.3362858295440674, 0.8172016143798828, 2.8464856147766113, 2.3619463443756104, 2.0269312858581543, 1.87027108669281, 2.5867714881896973, 1.0947588682174683, 2.485373020172119, 1.4596120119094849, 2.9054574966430664, 2.267271041870117, 1.9901957511901855, 1.708791971206665, 1.5335347652435303, 3.0039384365081787, 1.581254482269287, 1.6688708066940308, 2.138035535812378, 1.8489503860473633, 1.463232398033142, 2.745103597640991, 1.7890992164611816, 3.209639310836792, 2.186699628829956, 1.384399175643921, 2.347090482711792, 2.911564350128174, 2.7910614013671875, 3.0139355659484863, 2.8508076667785645, 3.1651434898376465, 2.020735263824463, 1.3879002332687378, 1.347353458404541, 1.3600330352783203, 1.563052773475647, 2.427166223526001, 2.3583383560180664, 2.0502967834472656, 1.0467418432235718, 1.5168964862823486, 2.550285816192627, 2.2569706439971924, 1.280961275100708, 2.153566360473633, 0.8621286749839783, 1.5903816223144531, 1.6175390481948853, 1.2808561325073242, 2.129512310028076, 1.923080563545227, 2.4000368118286133, 2.7758114337921143, 2.756497859954834, 2.8936665058135986, 1.9632121324539185, 1.4698351621627808, 2.9193220138549805, 2.2707347869873047, 2.1808905601501465, 2.915626049041748, 2.199504852294922, 2.225417375564575, 1.8788528442382812, 1.6902912855148315, 2.703303098678589, 1.6111797094345093, 1.4749184846878052, 2.7335896492004395, 1.1770113706588745, 1.5911366939544678, 2.5799360275268555, 2.450134515762329, 1.584707498550415, 2.0303263664245605, 1.5416966676712036, 1.6474940776824951, 3.166107654571533, 1.8914194107055664, 2.731400489807129, 3.456698179244995, 3.1407928466796875, 2.657524585723877, 1.8312366008758545, 1.3835384845733643, 1.3457938432693481, 1.1902421712875366, 1.739147663116455, 2.8404054641723633, 1.5782982110977173, 1.4647060632705688, 1.3077998161315918, 1.8057410717010498, 1.1732816696166992, 1.4494800567626953, 2.1183741092681885, 3.5306854248046875, 2.348907470703125, 1.5650557279586792, 1.6930912733078003, 2.298933267593384, 1.1758023500442505, 1.6107817888259888, 1.3251513242721558, 2.080108404159546, 1.862548589706421, 3.099520206451416, 2.8438494205474854, 1.6832661628723145, 2.074307680130005, 2.0457262992858887, 2.8403425216674805, 3.117814540863037, 2.058823823928833, 2.234037160873413, 1.2487999200820923, 1.7322322130203247, 2.6813132762908936, 2.924269199371338, 1.7503197193145752, 3.2688212394714355, 1.8045146465301514, 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+corr_rank = {299: 98, 194: 401, 281: 441, 1: 419, 289: 227, 65: 268, 23: 101, 453: 418, 321: 362, 259: 431, 257: 446, 477: 17, 92: 497, 17: 234, 314: 60, 331: 354, 315: 123, 318: 192, 445: 233, 238: 240, 311: 489, 265: 7, 126: 22, 431: 254, 223: 10, 179: 14, 362: 230, 448: 78, 478: 199, 418: 197, 139: 249, 403: 111, 262: 206, 316: 282, 150: 34, 142: 12, 444: 288, 261: 147, 180: 205, 413: 455, 322: 11, 5: 400, 474: 198, 167: 204, 226: 257, 230: 304, 225: 406, 482: 222, 373: 77, 352: 164, 2: 299, 329: 136, 152: 74, 271: 420, 386: 369, 377: 196, 165: 13, 229: 106, 457: 170, 192: 466, 99: 440, 214: 316, 461: 511, 19: 430, 82: 337, 505: 344, 199: 457, 416: 329, 484: 390, 104: 83, 496: 210, 465: 301, 462: 484, 361: 91, 231: 317, 100: 32, 467: 477, 63: 310, 421: 320, 273: 387, 368: 307, 449: 220, 385: 328, 8: 336, 432: 168, 217: 182, 255: 436, 366: 461, 151: 487, 67: 159, 14: 503, 36: 27, 131: 131, 112: 300, 37: 470, 29: 391, 163: 449, 510: 212, 21: 303, 202: 402, 409: 277, 158: 315, 16: 464, 44: 173, 197: 479, 410: 35, 495: 366, 341: 163, 425: 124, 77: 296, 38: 67, 175: 498, 181: 252, 248: 396, 15: 65, 301: 149, 18: 172, 81: 372, 154: 237, 306: 504, 123: 176, 105: 408, 185: 456, 145: 338, 398: 99, 40: 331, 451: 184, 108: 57, 94: 425, 213: 6, 286: 30, 203: 120, 39: 404, 64: 463, 10: 126, 348: 241, 28: 295, 278: 207, 216: 216, 224: 263, 330: 421, 303: 52, 206: 395, 417: 166, 354: 291, 228: 264, 446: 154, 509: 346, 440: 385, 85: 201, 363: 313, 121: 393, 423: 232, 277: 162, 86: 188, 483: 411, 364: 505, 374: 287, 176: 251, 78: 415, 351: 374, 227: 414, 51: 39, 250: 368, 488: 363, 288: 253, 434: 501, 68: 323, 276: 469, 469: 115, 130: 248, 168: 333, 397: 428, 365: 25, 128: 214, 3: 185, 26: 447, 307: 183, 419: 417, 222: 133, 493: 157, 382: 4, 319: 193, 60: 40, 327: 416, 433: 424, 430: 62, 345: 460, 486: 155, 35: 18, 45: 267, 407: 112, 507: 375, 141: 23, 260: 96, 338: 492, 387: 454, 189: 85, 182: 58, 282: 191, 350: 153, 323: 427, 143: 179, 472: 26, 302: 361, 0: 378, 244: 379, 426: 355, 215: 334, 140: 281, 253: 318, 489: 494, 267: 305, 111: 33, 346: 152, 390: 139, 210: 105, 212: 273, 188: 413, 239: 258, 439: 208, 391: 308, 378: 422, 312: 382, 97: 224, 491: 63, 162: 359, 389: 265, 272: 97, 443: 386, 75: 118, 245: 297, 263: 148, 284: 107, 137: 178, 415: 399, 209: 161, 201: 42, 335: 90, 135: 73, 310: 432, 122: 29, 172: 405, 328: 478, 173: 215, 308: 459, 480: 491, 412: 388, 494: 36, 476: 246, 479: 9, 9: 64, 344: 383, 119: 332, 55: 174, 103: 202, 395: 506, 56: 28, 73: 512, 353: 326, 120: 94, 339: 218, 12: 266, 193: 16, 295: 458, 106: 495, 287: 217, 304: 228, 124: 499, 148: 250, 422: 483, 264: 46, 113: 3, 166: 103, 369: 327, 156: 2, 498: 321, 334: 151, 169: 442, 506: 48, 4: 352, 43: 158, 249: 135, 31: 127, 233: 134, 427: 356, 375: 510, 107: 409, 183: 144, 320: 89, 138: 465, 211: 189, 41: 358, 292: 51, 79: 235, 456: 389, 116: 130, 280: 306, 343: 342, 473: 438, 357: 351, 511: 93, 450: 209, 279: 451, 144: 236, 293: 108, 187: 256, 11: 171, 127: 5, 471: 302, 313: 319, 13: 330, 468: 380, 291: 340, 243: 480, 475: 261, 487: 294, 160: 41, 254: 481, 429: 8, 59: 213, 326: 150, 57: 142, 125: 247, 359: 298, 87: 70, 258: 95, 70: 69, 383: 486, 347: 325, 497: 493, 69: 223, 129: 271, 492: 156, 384: 219, 91: 160, 360: 137, 74: 117, 285: 398, 340: 473, 240: 121, 424: 467, 266: 397, 508: 140, 178: 50, 400: 474, 388: 259, 235: 59, 420: 433, 376: 243, 294: 452, 232: 353, 402: 231, 49: 371, 317: 341, 408: 423, 372: 276, 48: 102, 102: 269, 256: 472, 428: 167, 171: 439, 275: 349, 435: 245, 84: 175, 134: 203, 71: 116, 161: 410, 324: 194, 342: 496, 207: 43, 242: 360, 25: 284, 218: 239, 499: 37, 195: 145, 83: 187, 186: 238, 283: 286, 247: 75, 490: 407, 464: 345, 436: 384, 164: 488, 219: 365, 118: 49, 251: 445, 503: 211, 394: 448, 190: 226, 153: 272, 62: 79, 170: 290, 96: 82, 80: 143, 305: 53, 157: 412, 191: 364, 332: 350, 7: 507, 101: 72, 399: 335, 437: 169, 333: 502, 447: 475, 401: 462, 290: 482, 252: 24, 370: 109, 438: 55, 452: 125, 241: 312, 297: 429, 184: 15, 93: 81, 337: 476, 463: 221, 349: 229, 208: 44, 61: 289, 136: 177, 355: 242, 20: 500, 356: 76, 381: 47, 33: 370, 296: 275, 274: 426, 117: 84, 442: 490, 458: 260, 34: 186, 236: 86, 481: 468, 309: 373, 269: 88, 455: 113, 500: 255, 66: 471, 58: 434, 174: 119, 396: 110, 268: 87, 504: 322, 155: 435, 298: 122, 200: 181, 6: 485, 72: 129, 76: 508, 109: 392, 204: 339, 46: 347, 237: 274, 196: 367, 454: 293, 110: 21, 90: 19, 502: 38, 325: 453, 52: 128, 95: 71, 159: 225, 441: 278, 459: 92, 42: 56, 405: 403, 485: 200, 54: 444, 205: 285, 32: 66, 30: 357, 336: 61, 234: 190, 379: 244, 393: 309, 146: 394, 404: 292, 221: 348, 147: 180, 115: 270, 53: 314, 24: 443, 371: 165, 246: 146, 411: 31, 88: 1, 133: 324, 380: 138, 22: 280, 98: 20, 460: 114, 114: 262, 27: 141, 466: 343, 501: 279, 358: 195, 470: 381, 50: 376, 132: 311, 270: 437, 220: 45, 47: 68, 89: 80, 149: 132, 367: 283, 406: 100, 392: 450, 177: 104, 198: 509, 414: 54, 300: 377}
+
+# inlined and adapted pytorch_grad_cam/activations_and_gradients.py
+class ActivationsAndGradients:
+    """ Class for extracting activations and
+    registering gradients from targetted intermediate layers """
+
+    def __init__(self, model, target_layer, reshape_transform):
+        self.model = model
+        self.gradients = []
+        self.activations = []
+        self.reshape_transform = reshape_transform
+
+        target_layer.register_forward_hook(self.save_activation)
+
+        #Backward compitability with older pytorch versions:
+        if hasattr(target_layer, 'register_full_backward_hook'):
+            target_layer.register_full_backward_hook(self.save_gradient)
+        else:
+            target_layer.register_backward_hook(self.save_gradient)
+
+    def save_activation(self, module, input, output):
+        activation = output[0]
+        if self.reshape_transform is not None:
+            activation = self.reshape_transform(activation)
+        self.activations.append(activation.cpu().detach())
+
+    def save_gradient(self, module, grad_input, grad_output):
+        # Gradients are computed in reverse order
+        grad = grad_output[0]
+        if self.reshape_transform is not None:
+            grad = self.reshape_transform(grad)
+        self.gradients = [grad.cpu().detach()] + self.gradients
+
+    def __call__(self, x):
+        self.gradients = []
+        self.activations = []
+        return self.model(x)
+
+# inlined and adapted pytorch_grad_cam/grad_cam.py
+class DetectorGradCAM:
+    def __init__(self, model, target_layer, use_cuda=False, reshape_transform=None):
+        self.model = model.eval()
+        self.target_layer = target_layer
+        self.cuda = use_cuda
+        if self.cuda:
+            self.model = model.cuda()
+        self.reshape_transform = reshape_transform
+        self.activations_and_grads = ActivationsAndGradients(self.model,
+            target_layer, reshape_transform)
+
+    def forward(self, input_img):
+        return self.model(input_img)
+
+    def get_cam_weights(self, input_tensor, target_category, activations, grads, k=5):
+        a = torch.tensor(activations)
+        return torch.topk((a * (a > unit_levels.view(unit_levels.shape[0], 1, 1, 1).repeat(1, 8, 8, 8)))[0].sum(dim=(1,2,3)), k=k).indices
+
+    def get_loss(self, output, target_category):
+        loss = 0
+        for i in range(len(target_category)):
+            loss = loss + output[i, target_category[i]]
+        return loss
+
+    def get_cam_image(self, input_tensor, target_category, activations, grads, eigen_smooth=False):
+        weights = self.get_cam_weights(input_tensor, target_category, activations, grads)
+        weighted_activations = weights[:, :, None, None] * activations
+        cam = weighted_activations.sum(axis=1)
+        return cam
+
+    def forward(self, input_tensor, target_category=None, k=5):
+
+        if self.cuda:
+            input_tensor = input_tensor.cuda()
+
+        output = self.activations_and_grads(input_tensor)
+
+        if type(target_category) is int:
+            target_category = [target_category] * input_tensor.size(0)
+
+        if target_category is None:
+            target_category = np.argmax(output.cpu().data.numpy(), axis=-1)
+        else:
+            assert(len(target_category) == input_tensor.size(0))
+
+        self.model.zero_grad()
+        loss = self.get_loss(output, target_category)
+        loss.backward(retain_graph=True)
+
+        activations = self.activations_and_grads.activations[-1].cpu().data.numpy()
+        grads = self.activations_and_grads.gradients[-1].cpu().data.numpy()
+
+        return self.get_cam_weights(input_tensor, target_category, activations, grads, k=k).tolist()
+
+class AttrDict(dict):
+    def __init__(self, *args, **kwargs):
+        super(AttrDict, self).__init__(*args, **kwargs)
+        self.__dict__ = self
+
+# hide header bar for print
+hide_streamlit_style = """
+<style>
+header {visibility:hidden;}
+</style>
+"""
+
+st.markdown(hide_streamlit_style, unsafe_allow_html=True)
+
+received_input = None
+
+scale = ScaleIntensityRange(a_min=-1000, a_max=1000, b_min=0, b_max=1, clip=True)
+crop = CenterSpatialCrop(roi_size=(64,64,64))
+
+preprocess = lambda arr: scale(crop(arr[None, ...].clip(-1000, 1000)))
+to_image = lambda v: PIL.Image.fromarray((255*v[0,:,:,v.shape[-1]//2]).astype('uint8')).convert('RGB')
+
+def to_base64(image: PIL.Image):
+    buffered = BytesIO()
+    image.save(buffered, format="PNG")
+    return "data:image/png;base64," + base64.b64encode(buffered.getvalue()).decode("utf-8")
+
+def base64_slice(path: str):
+    return to_base64(to_image(preprocess(np.load(path))))
+
+def bundle_builder(path: str, local=True, desc=""):
+    if local: path = os.path.join(".", path)
+
+    vertebra = np.float32(preprocess(np.load(path)))
+    slice = to_base64(to_image(vertebra))
+
+    return (slice, desc, vertebra)
+
+examples = [
+        bundle_builder("examples/l5.npy", desc="L5 - no fracture"),
+]
+
+with st.empty():
+    with st.container():
+        upload = st.file_uploader("Upload vertebra to classify (nii, nii.gz, npy)")
+
+        if upload is not None:
+            suffix = ''.join(pathlib.Path(upload.name).suffixes)
+            with tempfile.NamedTemporaryFile(suffix=suffix) as fp:
+                fp.write(upload.getvalue())
+                fp.seek(0)
+
+                if 'nii' in suffix:
+                    try:
+                        nii = nib.load(fp.name)
+                    except:
+                        raise Exception("Unable to load uploaded NIfTI file. Please ensure that it has the correct file extensions.")
+
+                    nifti_data = nii.get_fdata()
+                    data = Orientation(axcodes='IPL')(nifti_data[None, ...], affine=nii.affine)[0][0]
+                elif 'npy' in suffix:
+                    try:
+                        data = np.load(fp)
+                    except:
+                        raise Exception("Unable to load provided NumPy file.")
+                else:
+                    raise Exception("Invalid input data format. Please provide a NIfTI or NumPy array file.")
+
+                assert len(data.shape) == 3, "Invalid number of dimensions. Expects three-dimensional input."
+                assert all([a >= 64 for a in data.shape]), "Invalid shape. Shape must not be smaller than 64x64x64."
+
+                fp.close()
+
+                vertebra = np.float32(preprocess(data))
+                slice = to_base64(to_image(vertebra))
+
+                received_input = (slice, upload.name, vertebra)
+
+        with st.container():
+            st.caption("Or pick one of these examples:")
+
+            clicked = clickable_images(
+                [ex[0] for ex in examples],
+                titles=[ex[1] for ex in examples],
+                div_style={"display": "flex", "justify-content": "left", "flex-wrap": "wrap"},
+                img_style={"margin": "0 5px 5px 0", "height": "135px"},
+            )
+
+            if clicked > -1:
+                received_input = examples[clicked]
+
+    if received_input is not None:
+        with st.container():
+            col1, col2 = st.columns([1,3])
+            with col1:
+                st.image(received_input[0], width=140)
+            with col2:
+                top_container = st.container()
+                top_container.write("**Concept Visualization**")
+                top_container.write(f"Input: {received_input[1]}")
+            with st.spinner('Running inference'):
+                saved_checkpoint = "moonlit-flower-278.ckpt"
+
+                # TODO inline config
+                checkpoint = torch.load(saved_checkpoint, map_location="cpu")
+
+                checkpoint['hyper_parameters']['dataset_path'] = '.'
+                checkpoint['hyper_parameters']['batch_size'] = 1
+
+                module = VerseFxClassifier.load_from_checkpoint(saved_checkpoint, hparams=checkpoint['hyper_parameters'], map_location="cpu")
+                model = module.backbone
+
+                model.eval()
+
+                sample = torch.tensor(received_input[2][None, ...])
+
+                cam = DetectorGradCAM(model, model.down_tr512, use_cuda=False)
+
+                detectors = cam.forward(input_tensor=sample, target_category=0, k=5)
+                ranks = [corr_rank[unit] for unit in detectors]
+
+                model = nethook.InstrumentedModel(model)
+                model.retain_layer("down_tr512")
+
+                pred = (torch.sigmoid(model(sample)) > 0.5).long().item()
+
+                acts = model.retained_layer("down_tr512")[0]
+
+                ld_res = acts.shape[-1]
+                img_slices = torch.linspace(int(64/ld_res/2), 64-int(64/ld_res/2), ld_res, dtype=torch.long)
+
+                iv = imgviz.ImageVisualizer(224, image_size=64, source="zc", percent_level=0.99)
+
+            top_container.write(f"Prediction: {'fracture' if pred==1 else 'no fracture'}")
+
+            image_margin = """
+            <style>
+            img{margin-right:5px}*/
+            </style>
+            """
+            st.markdown(image_margin, unsafe_allow_html=True)
+
+            for i, detector in enumerate(detectors):
+                def paper_typo_fix(d):
+                    # in the paper, unit 424 is mistakenly referred to as unit 22.
+                    # to ensure consistency, we simply swap the label of both
+                    if d != 424 and d!= 22: return str(d)
+                    if d == 424: return "22"
+                    else: return "424"
+
+                st.markdown(f"Detector unit #{paper_typo_fix(detector)} (relevance rank {i+1}, positive correlation rank {ranks[i]})")
+                concepts = glob(f"concepts/{detector}_*.png")
+                if len(concepts) == 0:
+                    st.caption("No statistically significant activations, unable to show general concept")
+                else:
+                    st.caption("General concept")
+                    sorted_concepts = sorted(concepts, key=lambda x: int(x.replace('.png', '').split('/')[-1].split('_')[1]))
+                    st.image([to_base64(PIL.Image.open(c)) for c in sorted_concepts], width=75)
+                activations = [to_base64(PIL.Image.fromarray(iv.pytorch_masked_image(
+                                (sample[0, ..., img_slices[slice]]).repeat(3, 1, 1),
+                                acts[..., slice],
+                                detector,
+                                level=unit_levels[detector]).permute(1,2,0).cpu().numpy())) for slice in range(0, ld_res)]
+                st.caption("Image-specific activation")
+                st.image(activations, width=75)
+
+                st.markdown('<div style="margin-top:20px;border-top: 1px solid rgba(49, 51, 63, 0.2);margin-bottom:40px"></div>', unsafe_allow_html=True)
+
+            def on_click(*args, **kwargs):
+                # force reload of the page to reset internal state
+                st.markdown('<meta http-equiv="refresh" content="0">', unsafe_allow_html=True)
+
+            st.button("Reset", on_click=on_click)
\ No newline at end of file
diff --git a/backbones/__init__.py b/backbones/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..f8aab7393ffd41cdf050814b071266cac9ac01f0
--- /dev/null
+++ b/backbones/__init__.py
@@ -0,0 +1,93 @@
+import os
+import torch
+
+from monai.networks.nets import DenseNet121, DenseNet169, DenseNet201, DenseNet264
+from backbones.unet3d import UNet3D
+
+import utils.config
+
+def _freeze_layers_if_any(model, hparams):
+    if len(hparams.frozen_layers) == 0:
+        return model
+
+    for (name, param) in model.named_parameters():
+        if any([name.startswith(to_freeze_name) for to_freeze_name in hparams.frozen_layers]):
+            param.requires_grad = False
+
+    return model
+
+def _replace_inplace_operations(model):
+    # Grad-CAM compatibility
+    for module in model.modules():
+        if hasattr(module, "inplace"):
+            setattr(module, "inplace", False)
+    return model
+
+def get_backbone(hparams):
+    backbone = None
+
+    in_channels = 1 + (hparams.mask == 'channel') + hparams.input_dim * hparams.coordinates
+
+    if hparams.model_name.startswith('DenseNet'):
+        if hparams.model_name == "DenseNet121":
+            net_selection = DenseNet121
+        elif hparams.model_name == "DenseNet169":
+            net_selection = DenseNet169
+        elif hparams.model_name == "DenseNet201":
+            net_selection = DenseNet201
+        elif hparams.model_name == "DenseNet264":
+            net_selection = DenseNet264
+        else:
+            raise ValueError(f"Unknown DenseNet: {hparams.model_name}")
+
+        backbone = net_selection(
+            spatial_dims = hparams.input_dim, 
+            in_channels = in_channels, 
+            out_channels = hparams.num_classes - (hparams.loss == 'ordinal_regression'), 
+            dropout_prob = hparams.dropout,
+            act = ("relu", {"inplace": False}) # inplace has to be set to False to enable use of Grad-CAM
+        )
+
+        # ensure activation maps are not shrunk too much
+        backbone.features.transition2.pool = torch.nn.Identity()
+        backbone.features.transition3.pool = torch.nn.Identity()
+
+    elif hparams.model_name.lower().startswith("resne"):
+        # if you use pre-trained models, please add "pretrained_resnet" to the transforms hyperparameter
+        backbone = torch.hub.load('pytorch/vision:v0.10.0', hparams.model_name, pretrained=hparams.model_name.lower().endswith('-pretrained'))
+
+        # reset final fully connected layer to expected number of classes
+        backbone.fc.out_features = hparams.num_classes - (hparams.loss == 'ordinal_regression')
+
+    elif hparams.model_name == 'ModelsGenesis':
+        backbone = UNet3D(
+            in_channels=in_channels,
+            input_size=hparams.input_size,
+            n_class=hparams.num_classes - (hparams.loss == 'ordinal_regression')
+        )
+
+        weight_dir = os.path.join('data_sl', utils.config.globals["MODELS_GENESIS_PATH"])
+
+        checkpoint = torch.load(weight_dir,map_location=torch.device('cpu')) 
+        state_dict = checkpoint['state_dict']
+        unparalled_state_dict = {} 
+
+        for key in state_dict.keys():
+            unparalled_state_dict[key.replace("module.", "")] = state_dict[key]
+
+        backbone.load_state_dict(unparalled_state_dict, strict=False) 
+
+    elif hparams.model_name == 'UNet3D':
+        # this is the architecture of Models Genesis minus the pretraining
+        backbone = UNet3D(
+            in_channels=in_channels,
+            input_size=hparams.input_size,
+            n_class=hparams.num_classes - (hparams.loss == 'ordinal_regression')
+        )
+    else:
+          raise NotImplementedError
+
+    backbone = _replace_inplace_operations(backbone)
+    backbone = _freeze_layers_if_any(backbone, hparams)
+
+    return backbone
\ No newline at end of file
diff --git a/backbones/classifier.py b/backbones/classifier.py
new file mode 100644
index 0000000000000000000000000000000000000000..bcd59e07cc8fe1249e542d325b9b59e258b5d718
--- /dev/null
+++ b/backbones/classifier.py
@@ -0,0 +1,80 @@
+import torch.nn as nn
+
+class LinearBlock(nn.Module):
+    def __init__(self, in_channels, out_channels):
+        super(LinearBlock, self).__init__()
+        self.linear_block = nn.Sequential(
+            nn.Linear(in_channels, out_channels),
+            nn.BatchNorm1d(out_channels),
+            nn.LeakyReLU()
+        )
+
+    def forward(self, x):
+        x = self.linear_block(x)
+        return x
+
+class FracClassifier(nn.Module):
+
+    def __init__(
+            self,
+            encoder_channels,
+            classifier_channels=(256, 128, 64),
+            final_channels=2,
+            linear_kernel=4096,
+            p = 0.
+    ):
+        super(FracClassifier, self).__init__()
+        
+        # resnet gives 
+        self.linear_kernel = linear_kernel
+        self.initial_conv = nn.Conv3d(encoder_channels, 256, kernel_size=3,stride=1, padding=1)
+        self.bn_init = nn.InstanceNorm3d(256, affine=True)
+        self.drop_1 = nn.Dropout(p=p)
+        
+        self.initial_conv1 = nn.Conv3d(256, 128, kernel_size=3,stride=1, padding=1)
+        self.bn_init1 = nn.InstanceNorm3d(128, affine=True)
+        self.drop_2 = nn.Dropout(p=p)
+        
+        self.initial_conv2 = nn.Conv3d(128, 64, kernel_size=3,stride=1, padding=1)
+        self.bn_init2 = nn.InstanceNorm3d(64, affine=True)
+        self.drop_3 = nn.Dropout(p=p)
+        
+        self.initial_conv3 = nn.Conv3d(64, 8, kernel_size=3,stride=1, padding=1)
+        self.bn_init3 = nn.InstanceNorm3d(8, affine=True)
+        self.drop_4 = nn.Dropout(p=p)
+        
+        self.vector_shape = encoder_channels
+        self.layer1 = LinearBlock(self.linear_kernel, classifier_channels[0])
+        self.drop_5 = nn.Dropout(p=p)
+        
+        self.layer2 = LinearBlock(classifier_channels[0], classifier_channels[1])
+        self.drop_6 = nn.Dropout(p=p)
+        
+        self.layer3 = LinearBlock(classifier_channels[1], classifier_channels[2])
+        self.drop_7 = nn.Dropout(p=p)
+        
+        self.final_dense = nn.Linear(classifier_channels[2], final_channels)
+        # initialization of weights dropped as pytorch already uses them (now)
+
+
+    def forward(self, x):
+        x = self.initial_conv(x)
+        x = self.drop_1(self.bn_init(x))
+        
+        x = self.initial_conv1(x)
+        x = self.drop_2(self.bn_init1(x))
+        
+        x = self.initial_conv2(x)
+        x = self.drop_3(self.bn_init2(x))
+        
+        x = self.initial_conv3(x)
+        x = self.drop_4(self.bn_init3(x))     
+
+        x = x.view(x.shape[0], -1)
+
+        x = self.drop_5(self.layer1(x))
+        x = self.drop_6(self.layer2(x))
+        x = self.drop_7(self.layer3(x))
+        x = self.final_dense(x)
+
+        return x
\ No newline at end of file
diff --git a/backbones/unet3d.py b/backbones/unet3d.py
new file mode 100644
index 0000000000000000000000000000000000000000..8ab23b67bead4dc106dfe80d3d6feee3a4844bbd
--- /dev/null
+++ b/backbones/unet3d.py
@@ -0,0 +1,116 @@
+# Used for Models Genesis
+import math
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+
+from backbones.classifier import FracClassifier
+
+
+class ContBatchNorm3d(nn.modules.batchnorm._BatchNorm):
+    def _check_input_dim(self, input):
+
+        if input.dim() != 5:
+            raise ValueError('expected 5D input (got {}D input)'.format(input.dim()))
+
+    def forward(self, input):
+        self._check_input_dim(input)
+        return F.batch_norm(
+            input, self.running_mean, self.running_var, self.weight, self.bias,
+            True, self.momentum, self.eps)
+
+
+class LUConv(nn.Module):
+    def __init__(self, in_chan, out_chan, act):
+        super(LUConv, self).__init__()
+        self.conv1 = nn.Conv3d(in_chan, out_chan, kernel_size=3, padding=1)
+        self.bn1 = ContBatchNorm3d(out_chan)
+
+        if act == 'relu':
+            self.activation = nn.ReLU(out_chan)
+        elif act == 'prelu':
+            self.activation = nn.PReLU(out_chan)
+        elif act == 'elu':
+            self.activation = nn.ELU(inplace=True)
+        else:
+            raise
+
+    def forward(self, x):
+        out = self.activation(self.bn1(self.conv1(x)))
+        return out
+
+
+def _make_nConv(in_channel, depth, act, double_chnnel=False):
+    if double_chnnel:
+        layer1 = LUConv(in_channel, 32 * (2 ** (depth+1)),act)
+        layer2 = LUConv(32 * (2 ** (depth+1)), 32 * (2 ** (depth+1)),act)
+    else:
+        layer1 = LUConv(in_channel, 32*(2**depth),act)
+        layer2 = LUConv(32*(2**depth), 32*(2**depth)*2,act)
+
+    return nn.Sequential(layer1,layer2)
+
+class DownTransition(nn.Module):
+    def __init__(self, in_channel,depth, act):
+        super(DownTransition, self).__init__()
+        self.ops = _make_nConv(in_channel, depth,act)
+        self.maxpool = nn.MaxPool3d(2)
+        self.current_depth = depth
+
+    def forward(self, x):
+        if self.current_depth == 3:
+            out = self.ops(x)
+            out_before_pool = out
+        else:
+            out_before_pool = self.ops(x)
+            out = self.maxpool(out_before_pool)
+        return out, out_before_pool
+
+class UpTransition(nn.Module):
+    def __init__(self, inChans, outChans, depth,act):
+        super(UpTransition, self).__init__()
+        self.depth = depth
+        self.up_conv = nn.ConvTranspose3d(inChans, outChans, kernel_size=2, stride=2)
+        self.ops = _make_nConv(inChans+ outChans//2,depth, act, double_chnnel=True)
+
+    def forward(self, x, skip_x):
+        out_up_conv = self.up_conv(x)
+        concat = torch.cat((out_up_conv,skip_x),1)
+        out = self.ops(concat)
+        return out
+
+
+class OutputTransition(nn.Module):
+    def __init__(self, inChans, n_labels):
+
+        super(OutputTransition, self).__init__()
+        self.final_conv = nn.Conv3d(inChans, n_labels, kernel_size=1)
+        #self.sigmoid = nn.Sigmoid()
+
+    def forward(self, x):
+        out = torch.sigmoid(self.final_conv(x))
+        return out
+
+class UNet3D(nn.Module):
+    # the number of convolutions in each layer corresponds
+    # to what is in the actual prototxt, not the intent
+    def __init__(self, input_size, n_class=1, act='relu', in_channels=1):
+        super(UNet3D, self).__init__()
+
+        self.down_tr64 = DownTransition(in_channels,0,act)
+        self.down_tr128 = DownTransition(64,1,act)
+        self.down_tr256 = DownTransition(128,2,act)
+        self.down_tr512 = DownTransition(256,3,act)
+
+        # Classification
+        self.classifier = FracClassifier(encoder_channels=512, final_channels=n_class, linear_kernel=int(math.pow(input_size / 32, 3) * 512))
+
+    def forward(self, x):
+        self.out64, _ = self.down_tr64(x)
+        self.out128, _ = self.down_tr128(self.out64)
+        self.out256, _ = self.down_tr256(self.out128)
+        self.out512, _ = self.down_tr512(self.out256)
+
+        self.out = self.classifier(self.out512)
+
+        return self.out
\ No newline at end of file
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diff --git a/config.yaml b/config.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..2f61775c21e0f64d425aa9cd75dc2a1eca314a9c
--- /dev/null
+++ b/config.yaml
@@ -0,0 +1,48 @@
+# Weights & Biases
+USE_WANDB: True #@param {type:"boolean"}
+WANDB_API_KEY: 'REPLACE_ME' #@param {type:"string"}
+
+# Data
+DATASET_PATH: 'REPLACE_ME' #@param {type:"string"}
+MIN_VERTEBRAE_LEVEL: 8 #@param {type:"number"}
+INPUT_DIM: 3 #@param [2, 3] {allow-input: true}
+INPUT_SIZE: 64 #@param {type:"slider", min:32, max:112, step:4}
+
+OVERSAMPLING: True
+FOLD: 1
+
+# Mask
+# one of 'none', 'channel' (mask is additional input channel), 'apply' (single vertebra mask is applied to input),
+# 'apply_all' (visible vertebra mask applied to input), 'crop' (mask is used to crop input)
+MASK: 'none'
+
+# Whether coordinates are provided in additional channels
+# See Liu, Rosanne, et al. "An intriguing failing of convolutional neural networks and the CoordConv solution" (https://arxiv.org/pdf/1807.03247.pdf)
+COORDINATES: False
+
+# Whether to save the best-performing model (wrt validation F1)
+SAVE_MODEL: False
+
+# Data Augmentation
+TRANSFORMS: ['modelsgenesis', 'intensity', 'spatial3d-simple']
+
+# Task
+TASK: "detection" # "detection", "grading", "simple_grading"
+LOSS: 'binary_cross_entropy' # "ordinal_regression", "cross_entropy", "focal"
+
+# Model
+BATCH_SIZE: 16 #@param {type:"slider", min:32, max:512, step:32}
+LEARNING_RATE: 0.001 #@param {type:"number"}
+# AUTO_LR_FIND = True #@param {type: 'boolean'}
+DROPOUT: 0.3 #@param {type:"number"}
+WEIGHTED_LOSS: True #@param {type:"boolean"}
+EARLY_STOPPING_PATIENCE: 40 #@param {type:"number"}
+
+# Available backbones: 
+#   - DenseNet121
+#   - ModelsGenesis (3D)
+#   - UNet3D
+MODEL_NAME: 'UNet3D' #@param ["DenseNet121"] {allow-input: true}
+
+# Identify all modules to freeze by name, e.g. ['down_tr64', 'down_tr128', 'down_tr256', 'down_tr512'] for Models Genesis/UNet3D
+FROZEN_LAYERS: []
\ No newline at end of file
diff --git a/data.py b/data.py
new file mode 100644
index 0000000000000000000000000000000000000000..bb4b55c6eaaf4b97f804b5c9e22fb9dfcad1098c
--- /dev/null
+++ b/data.py
@@ -0,0 +1,115 @@
+import pandas as pd
+import os
+from pathlib import Path
+from monai.data import Dataset, DataLoader
+import numpy as np
+from torch.utils.data import Subset
+import pytorch_lightning as pl
+from torchsampler import ImbalancedDatasetSampler
+from torch.utils.data.dataloader import default_collate
+
+from transforms import get_training_transforms, get_base_transforms
+
+class VerseDataModule(pl.LightningDataModule):
+  def __init__(self, hparams):
+    super().__init__()
+    self.save_hyperparameters(dict(hparams), logger=False)
+    self.data_dir = Path(self.hparams.dataset_path)
+    self.csv_path = self.data_dir / 'fxall_labels.csv'
+
+    if "modelsgenesis" in hparams.transforms:
+      self.image_dir = self.data_dir / 'raw'
+    else:
+      self.image_dir = self.data_dir / 'ct'
+
+    if not os.path.exists(self.image_dir):
+      # legacy support
+      self.image_dir = self.data_dir
+      self.csv_path = self.data_dir / 'slice_labels.csv'
+
+    self.mask_dir = self.data_dir / 'seg'
+    if hparams.mask != 'none' and not os.path.exists(self.mask_dir):
+      raise RuntimeError("Configured to use masks, but 'seg' folder missing in dataset path")
+
+    self.df = pd.read_csv(self.csv_path, index_col=0)
+
+    # TODO temporary fix to check for non-existing files
+    if "path" in self.df.columns:
+      self.df = self.df[self.df["path"].apply(lambda p: os.path.exists(self.image_dir / p))]
+
+    # FIXME slice_labels.csv provides the path in 'image', fxall_labels.csv in 'path'
+    if "image" not in self.df.columns:
+      self.df['image'] = self.df['path']
+
+    self.transforms = {
+      'training': get_training_transforms(hparams, self.image_dir, self.mask_dir),
+      'validation': get_base_transforms(hparams, self.image_dir, self.mask_dir),
+      'test': get_base_transforms(hparams, self.image_dir, self.mask_dir)
+    }
+
+    self.datasets = {}
+    self.idxs = {}
+
+  def setup(self, stage=None):
+    # dropping samples without fracture grading
+    # graded_idxs = ~self.df.fx.isna()
+    # vertebrae_level_idxs = self.df.level_idx >= self.hparams.min_vertebrae_level
+    # included_idxs = graded_idxs & vertebrae_level_idxs
+    
+    if stage == 'fit' or stage is None:
+      phases = ['training', 'validation']
+    else:
+      phases = ['test']
+
+    for split in phases:
+      # get official verse partitions
+      idxs = self.df[f'split_{self.hparams.fold}'] == split
+      # idxs = np.where(included_idxs & idxs)[0]
+      idxs = np.where(idxs)[0]
+      self.idxs[split] = idxs
+      self.datasets[split] = Dataset(
+        self.df.iloc[idxs].to_dict('records'), 
+        transform=self.transforms[split]
+      )
+
+  def get_label(self, data):
+    train_df = self.df.iloc[self.idxs['training']]
+    grading = train_df.fx_grading
+    if self.hparams.task == 'detection':
+        return train_df.fx
+    elif self.hparams.task == 'grading':
+        return grading
+    elif self.hparams.task == 'simple_grading':
+        if grading in [2,3]:
+          return 1
+        if grading>3:
+          return grading-2
+        else:
+          return grading
+
+  def train_dataloader(self):
+    return DataLoader(
+      self.datasets['training'], 
+      batch_size=self.hparams.batch_size,
+      sampler=ImbalancedDatasetSampler(
+        num_samples=self.df.iloc[self.idxs['training']].fx.sum() * 2,
+        dataset=self.datasets['training'],
+        callback_get_label=self.get_label,
+        ) if self.hparams.oversampling else None, 
+      num_workers=2,
+      shuffle=not self.hparams.oversampling
+    )
+
+  def val_dataloader(self):
+    return DataLoader(
+      self.datasets['validation'], 
+      batch_size=self.hparams.batch_size, 
+      num_workers=8,
+    )
+
+  def test_dataloader(self):
+    return DataLoader(
+      self.datasets['test'], 
+      batch_size=self.hparams.batch_size, 
+      num_workers=8,
+    )
diff --git a/examples/.gitattributes b/examples/.gitattributes
new file mode 100644
index 0000000000000000000000000000000000000000..aaae91778657a3d4625f3980929a5769270433ff
--- /dev/null
+++ b/examples/.gitattributes
@@ -0,0 +1,8 @@
+l3.npy filter=lfs diff=lfs merge=lfs -text
+l5.npy filter=lfs diff=lfs merge=lfs -text
+t11.npy filter=lfs diff=lfs merge=lfs -text
+t6.npy filter=lfs diff=lfs merge=lfs -text
+l1.npy filter=lfs diff=lfs merge=lfs -text
+l1_osteoporotic.npy filter=lfs diff=lfs merge=lfs -text
+l2.npy filter=lfs diff=lfs merge=lfs -text
+l2_osteoporotic.npy filter=lfs diff=lfs merge=lfs -text
diff --git a/examples/l5.npy b/examples/l5.npy
new file mode 100644
index 0000000000000000000000000000000000000000..de02639306223326a1680f975a9f7a5391f695f4
--- /dev/null
+++ b/examples/l5.npy
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:fa71021670b0c7c247d203717f54e359340e2c58c2b551404cc04e566d26eb0c
+size 1769600
diff --git a/globals.yaml b/globals.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..e077592f5b002b6fa51809afb76f365b7b60daa6
--- /dev/null
+++ b/globals.yaml
@@ -0,0 +1,2 @@
+# Miscellaneous
+MODELS_GENESIS_PATH: '.'
\ No newline at end of file
diff --git a/loss.py b/loss.py
new file mode 100644
index 0000000000000000000000000000000000000000..2bd776dbf485060a753adffbba21e04ed6003b78
--- /dev/null
+++ b/loss.py
@@ -0,0 +1,82 @@
+from typing import Optional
+import torch
+import torch.nn.functional as F
+
+def get_breadstick_probabilities(logits):
+    # logits to linear projectsion to probabilities 
+    projections = torch.sigmoid(logits)
+    batch_size, num_projections = projections.shape
+    num_classes = num_projections + 1
+    probs = torch.zeros(batch_size, num_classes, dtype=projections.dtype, device=projections.device)
+    prob = 0
+    for i in range(projections.shape[-1]):
+        prob = projections[:,i] * (1 - prob)
+        probs[:,i] = prob
+    probs[:, -1] = (1 - projections.select(-1, -1)) * prob
+    return probs
+
+def ordinal_regression_loss(logits, targets, tao=1, eta=0.15, class_weights=None):
+    '''
+    ordinal regression loss based on this paper:
+    Liu, Xiaofeng, et al. "Unimodal regularized neuron stick-breaking for ordinal classification." Neurocomputing 388 (2020): 34-44.
+
+    it is important to have N-1 logits with N classes
+
+    '''
+    probs = get_breadstick_probabilities(logits)
+    batch_size, num_classes = probs.shape
+    class_weights = class_weights if class_weights is not None else torch.ones(num_classes, device=logits.device)
+
+    # cross entropy loss with unimodal regularization
+    # distribution of normalized exponential function
+
+    q = torch.softmax(
+        torch.exp(
+            -torch.abs(
+                torch.arange(num_classes, device=targets.device).repeat(batch_size, 1) - targets.reshape(batch_size,1)
+            ) / tao
+        ), dim=-1
+    )
+    # smooth distribution with eta
+    
+    q = (1 - eta) * q + eta / num_classes
+    loss = torch.sum(class_weights * q * -torch.log(probs), dim=-1).mean()
+
+    return loss
+
+# Adapted from https://github.com/kornia/kornia/blob/c2273bbfe152c86a48923e473a37c05e28f7fe43/kornia/losses/focal.py
+def focal_loss(input: torch.Tensor, target: torch.Tensor, alpha: float = 0.25, gamma: Optional[float] = 2.0) -> torch.Tensor:
+    """Criterion that computes Focal loss.
+    According to Lin, Tsung-Yi, et al. "Focal Loss for Dense Object Detection". Proceedings of the IEEE international conference on computer vision (2017): 2980-2988,
+    the Focal loss is computed as follows:
+    .. math::
+        \text{FL}(p_t) = -\alpha_t (1 - p_t)^{\gamma} \, \text{log}(p_t)
+    """
+    assert input.size(0) == target.size(0)
+
+    if len(input.shape) == 1:
+        # binary focal loss
+        probs_pos = torch.sigmoid(input)
+        probs_neg = torch.sigmoid(-input)
+        loss_tmp = -alpha * torch.pow(probs_neg, gamma) * target * F.logsigmoid(input) - (1 - alpha) * torch.pow(probs_pos, gamma) * (1.0 - target) * F.logsigmoid(-input)
+
+    else:
+        assert len(input.shape) >= 2
+
+        assert target.size()[1:] == input.size()[2:]
+
+        # compute softmax over the classes axis
+        input_soft: torch.Tensor = F.softmax(input, dim=1)
+        log_input_soft: torch.Tensor = F.log_softmax(input, dim=1)
+
+        # create the labels one hot tensor
+        one_hot = torch.zeros((target.shape[0], input.shape[1]) + target.shape[1:], device=input.device, dtype=input.dtype)
+        target_one_hot: torch.Tensor = one_hot.scatter_(1, target.unsqueeze(1), 1.0) + 1e-6
+
+        # compute the actual focal loss
+        weight = torch.pow(-input_soft + 1.0, gamma)
+
+        focal = -alpha * weight * log_input_soft
+        loss_tmp = torch.einsum('bc...,bc...->b...', (target_one_hot, focal))
+
+    return loss_tmp.mean()
\ No newline at end of file
diff --git a/model.py b/model.py
new file mode 100644
index 0000000000000000000000000000000000000000..b603fe10adc7ff57611eb29ff3a8814cd1c56c40
--- /dev/null
+++ b/model.py
@@ -0,0 +1,181 @@
+from typing import List
+import torch
+import torch.nn.functional as F
+import torchmetrics
+import pytorch_lightning as pl
+import matplotlib.pyplot as plt
+import wandb
+from tqdm import tqdm
+
+from backbones import get_backbone
+from utils.confusion_viz import ConfusionVisualizer
+
+from utils.plots import get_confusion_matrix_figure
+from utils.grad_cam import GradCAMBuilder
+
+from loss import ordinal_regression_loss, get_breadstick_probabilities, focal_loss
+from utils.val_loop_hook import ValidationLoopHook
+
+class VerseFxClassifier(pl.LightningModule):
+    def __init__(self, hparams):
+        super().__init__()
+        self.save_hyperparameters(dict(hparams))
+        self.backbone = get_backbone(self.hparams)
+        metric_args = dict(average='macro', num_classes=self.hparams.num_classes)
+        metrics = torchmetrics.MetricCollection([
+            torchmetrics.Accuracy(**metric_args), 
+            torchmetrics.F1(**metric_args), 
+            torchmetrics.Precision(**metric_args), 
+            torchmetrics.Recall(**metric_args)
+        ])
+        self.train_metrics = metrics.clone(prefix='train/')
+        self.val_metrics = metrics.clone(prefix='val/')
+        
+        self.class_weights = None
+
+        image_shape = (1 + (self.hparams.mask =='channel') + self.hparams.input_dim * self.hparams.coordinates,) + (self.hparams.input_size,) * self.hparams.input_dim
+        grad_cam_builder = GradCAMBuilder(image_shape, target_category=0 if self.hparams.task == 'detection' else None)
+
+        confusion_visualizer = ConfusionVisualizer(image_shape, 2 if self.hparams.task == 'detection' else self.hparams.num_classes)
+
+        self.validation_hooks: List[ValidationLoopHook] = [grad_cam_builder, confusion_visualizer]
+        
+    def get_class_weights(self, dm: pl.LightningDataModule):
+        targets = []
+        for batch in tqdm(dm.train_dataloader(), desc="Determining class weights"):
+            targets.append(self.batch_to_targets(batch))
+        targets = torch.cat(targets)
+        classes, counts = torch.unique(targets, return_counts=True)
+    
+        return (1 / counts) * torch.sum(counts) / classes.shape[0]
+    
+    def on_pretrain_routine_start(self):
+        # FIXME This is slightly inefficient if multiple GPUs are used as this routine
+        # is called once per device. There might be a better hook available.
+        super().on_pretrain_routine_start()
+        
+        if self.hparams.weighted_loss:
+            self.class_weights = self.get_class_weights(self.trainer.datamodule).to(self.device)
+            
+            if self.hparams.loss == 'binary_cross_entropy':
+                # Only keep the positive class weight
+                self.class_weights = self.class_weights[-1]
+
+    def forward(self, x):
+        return self.backbone(x)
+
+    def loss(self, logits, targets):
+        if self.hparams.loss == 'cross_entropy':
+            return F.cross_entropy(logits, targets, weight=self.class_weights)
+        elif self.hparams.loss == 'binary_cross_entropy':
+            return F.binary_cross_entropy_with_logits(logits.squeeze(-1), targets.float(),
+                                                      pos_weight=self.class_weights)
+        elif self.hparams.loss == 'ordinal_regression':
+            return ordinal_regression_loss(logits, targets, class_weights=self.class_weights)
+        elif self.hparams.loss == 'focal':
+            return focal_loss(logits.squeeze(-1), targets.float())
+        else:
+            raise ValueError
+
+    def logits_to_predictions(self, logits):
+        if self.hparams.loss == 'binary_cross_entropy' or (self.hparams.loss == 'focal' and self.hparams.task == 'detection'):
+            probs = torch.sigmoid(logits.squeeze(-1))
+            preds = probs.gt(0.5).long()
+        elif self.hparams.loss == 'cross_entropy' or self.hparams.loss == 'focal':
+            probs = torch.softmax(logits)
+            preds = probs.argmax(-1)
+        elif self.hparams.loss == 'ordinal_regression':
+            probs = get_breadstick_probabilities(logits)
+            preds = probs.argmax(-1)
+        else:
+            raise ValueError
+    
+        return probs, preds
+
+    def batch_to_targets(self, batch):
+        if self.hparams.task == 'detection':
+            return batch['fx'].long()
+        elif self.hparams.task == 'grading':
+            return batch['fx_grading'].long()
+        elif self.hparams.task == 'simple_grading':
+            targets = batch['fx_grading'].long()
+            targets[torch.bitwise_or(targets==2, targets==3)] = 1
+            targets[targets>3] -= 2
+            return targets
+
+    def training_step(self, batch, batch_idx):
+        logits = self(batch['image'])
+        targets = self.batch_to_targets(batch)
+        loss = self.loss(logits, targets)
+        probs, preds = self.logits_to_predictions(logits)
+        self.log("train/loss", loss, on_step=True, on_epoch=True, prog_bar=True, logger=True, batch_size=self.hparams.batch_size)
+        return {'loss': loss,  'probs': probs.detach(), 'preds': preds.detach(), 'targets': targets.detach()}
+
+    def training_epoch_end(self, outputs):
+        outputs = {k: torch.cat([d[k] for d in outputs]) for k in outputs[0] if k != 'loss'}
+        metrics = self.train_metrics(outputs['probs'], outputs['targets'])
+        self.log_dict(metrics)
+
+        targets_flat = outputs['targets'].cpu().numpy()
+        preds_flat = outputs['preds'].cpu().numpy()
+
+        # sklearn confusion matrix
+        self.logger.experiment.log({
+            "train/confusion_matrix": get_confusion_matrix_figure(
+                targets_flat,
+                preds_flat,
+                title="Training Confusion Matrix"
+            )
+        })
+        plt.close('all')
+
+    def validation_step(self, batch, batch_idx):
+        logits = self(batch['image'])
+        targets = self.batch_to_targets(batch)
+        loss = self.loss(logits, targets)
+        probs, preds = self.logits_to_predictions(logits)
+        self.log("val/loss", loss, on_step=True, on_epoch=True, prog_bar=True, logger=True, batch_size=self.hparams.batch_size)
+        for val_hook in self.validation_hooks:
+            val_hook.process(batch, targets, logits, preds)
+        metrics = self.val_metrics(probs, targets)
+        self.log_dict(metrics)
+        return {'loss': loss, 'probs': probs.detach(), 'preds': preds.detach(), 'targets': targets.detach()}
+
+    def validation_epoch_end(self, outputs):
+        outputs = {k: torch.cat([d[k] for d in outputs]) for k in outputs[0] if k != 'loss'}
+        metrics = self.val_metrics(outputs['probs'], outputs['targets'])
+        self.log_dict(metrics)
+
+        targets_flat = outputs['targets'].cpu().numpy()
+        preds_flat = outputs['preds'].cpu().numpy()
+
+        # sklearn confusion matrix
+        self.logger.experiment.log({
+            "val/confusion_matrix": get_confusion_matrix_figure(
+                targets_flat,
+                preds_flat,
+                title="Validation Confusion Matrix",
+            )
+        })
+        plt.close('all')
+        # wandb confusion matrix
+        # print(targets_flat, targets_flat.squeeze(-1).shape, type(preds_flat[0]))
+        self.logger.experiment.log({
+            "full_fx_grading": wandb.plot.confusion_matrix(
+                # probs=outputs['y_pred'],
+                preds=list(preds_flat), 
+                y_true=list(targets_flat),
+                class_names=None,
+            ),
+            "epoch": self.current_epoch
+        })
+
+    def on_train_epoch_end(self):
+        # Trigger all validation hooks and reset them afterwards
+        for val_hook in self.validation_hooks:
+            val_hook.trigger(self)
+            val_hook.reset()
+
+    def configure_optimizers(self):
+        optimizer = torch.optim.Adam(self.parameters(), lr=self.hparams.learning_rate)
+        return optimizer
\ No newline at end of file
diff --git a/moonlit-flower-278.ckpt b/moonlit-flower-278.ckpt
new file mode 100644
index 0000000000000000000000000000000000000000..d848e369f13a5fdeb55df22ef4c4dc3574618007
--- /dev/null
+++ b/moonlit-flower-278.ckpt
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:84d9c8edfebb6f8ff3c8e84105cb12d20b458ca3be45076565d8604abe46e982
+size 153434311
diff --git a/netdissect/LBFGS.py b/netdissect/LBFGS.py
new file mode 100644
index 0000000000000000000000000000000000000000..c321f390d199b71ae30422cf3f1a0a9fb262a0a7
--- /dev/null
+++ b/netdissect/LBFGS.py
@@ -0,0 +1,1107 @@
+import torch
+import numpy as np
+from functools import reduce
+from copy import deepcopy
+from torch.optim import Optimizer
+
+#%% Helper Functions for L-BFGS
+
+def is_legal(v):
+    """
+    Checks that tensor is not NaN or Inf.
+
+    Inputs:
+        v (tensor): tensor to be checked
+
+    """
+    legal = not torch.isnan(v).any() and not torch.isinf(v)
+
+    return legal
+
+def polyinterp(points, x_min_bound=None, x_max_bound=None, plot=False):
+    """
+    Gives the minimizer and minimum of the interpolating polynomial over given points
+    based on function and derivative information. Defaults to bisection if no critical
+    points are valid.
+
+    Based on polyinterp.m Matlab function in minFunc by Mark Schmidt with some slight
+    modifications.
+
+    Implemented by: Hao-Jun Michael Shi and Dheevatsa Mudigere
+    Last edited 12/6/18.
+
+    Inputs:
+        points (nparray): two-dimensional array with each point of form [x f g]
+        x_min_bound (float): minimum value that brackets minimum (default: minimum of points)
+        x_max_bound (float): maximum value that brackets minimum (default: maximum of points)
+        plot (bool): plot interpolating polynomial
+
+    Outputs:
+        x_sol (float): minimizer of interpolating polynomial
+        F_min (float): minimum of interpolating polynomial
+
+    Note:
+      . Set f or g to np.nan if they are unknown
+
+    """
+    no_points = points.shape[0]
+    order = np.sum(1 - np.isnan(points[:,1:3]).astype('int')) - 1
+
+    x_min = np.min(points[:, 0])
+    x_max = np.max(points[:, 0])
+
+    # compute bounds of interpolation area
+    if(x_min_bound is None):
+        x_min_bound = x_min
+    if(x_max_bound is None):
+        x_max_bound = x_max
+
+    # explicit formula for quadratic interpolation
+    if no_points == 2 and order == 2 and plot is False:
+        # Solution to quadratic interpolation is given by:
+        # a = -(f1 - f2 - g1(x1 - x2))/(x1 - x2)^2
+        # x_min = x1 - g1/(2a)
+        # if x1 = 0, then is given by:
+        # x_min = - (g1*x2^2)/(2(f2 - f1 - g1*x2))
+
+        if(points[0, 0] == 0):
+            x_sol = -points[0, 2]*points[1, 0]**2/(2*(points[1, 1] - points[0, 1] - points[0, 2]*points[1, 0]))
+        else:
+            a = -(points[0, 1] - points[1, 1] - points[0, 2]*(points[0, 0] - points[1, 0]))/(points[0, 0] - points[1, 0])**2
+            x_sol = points[0, 0] - points[0, 2]/(2*a)
+
+        x_sol = np.minimum(np.maximum(x_min_bound, x_sol), x_max_bound)
+
+    # explicit formula for cubic interpolation
+    elif no_points == 2 and order == 3 and plot is False:
+        # Solution to cubic interpolation is given by:
+        # d1 = g1 + g2 - 3((f1 - f2)/(x1 - x2))
+        # d2 = sqrt(d1^2 - g1*g2)
+        # x_min = x2 - (x2 - x1)*((g2 + d2 - d1)/(g2 - g1 + 2*d2))
+        d1 = points[0, 2] + points[1, 2] - 3*((points[0, 1] - points[1, 1])/(points[0, 0] - points[1, 0]))
+        d2 = np.sqrt(d1**2 - points[0, 2]*points[1, 2])
+        if np.isreal(d2):
+            x_sol = points[1, 0] - (points[1, 0] - points[0, 0])*((points[1, 2] + d2 - d1)/(points[1, 2] - points[0, 2] + 2*d2))
+            x_sol = np.minimum(np.maximum(x_min_bound, x_sol), x_max_bound)
+        else:
+            x_sol = (x_max_bound + x_min_bound)/2
+
+    # solve linear system
+    else:
+        # define linear constraints
+        A = np.zeros((0, order+1))
+        b = np.zeros((0, 1))
+
+        # add linear constraints on function values
+        for i in range(no_points):
+            if not np.isnan(points[i, 1]):
+                constraint = np.zeros((1, order+1))
+                for j in range(order, -1, -1):
+                    constraint[0, order - j] = points[i, 0]**j
+                A = np.append(A, constraint, 0)
+                b = np.append(b, points[i, 1])
+
+        # add linear constraints on gradient values
+        for i in range(no_points):
+            if not np.isnan(points[i, 2]):
+                constraint = np.zeros((1, order+1))
+                for j in range(order):
+                    constraint[0, j] = (order-j)*points[i,0]**(order-j-1)
+                A = np.append(A, constraint, 0)
+                b = np.append(b, points[i, 2])
+
+        # check if system is solvable
+        if(A.shape[0] != A.shape[1] or np.linalg.matrix_rank(A) != A.shape[0]):
+            x_sol = (x_min_bound + x_max_bound)/2
+            f_min = np.Inf
+        else:
+            # solve linear system for interpolating polynomial
+            coeff = np.linalg.solve(A, b)
+
+            # compute critical points
+            dcoeff = np.zeros(order)
+            for i in range(len(coeff) - 1):
+                dcoeff[i] = coeff[i]*(order-i)
+
+            crit_pts = np.array([x_min_bound, x_max_bound])
+            crit_pts = np.append(crit_pts, points[:, 0])
+
+            if not np.isinf(dcoeff).any():
+                roots = np.roots(dcoeff)
+                crit_pts = np.append(crit_pts, roots)
+
+            # test critical points
+            f_min = np.Inf
+            x_sol = (x_min_bound + x_max_bound)/2 # defaults to bisection
+            for crit_pt in crit_pts:
+                if np.isreal(crit_pt) and crit_pt >= x_min_bound and crit_pt <= x_max_bound:
+                    F_cp = np.polyval(coeff, crit_pt)
+                    if np.isreal(F_cp) and F_cp < f_min:
+                        x_sol = np.real(crit_pt)
+                        f_min = np.real(F_cp)
+
+            if (plot):
+                import matplotlib.pyplot as plt
+                plt.figure()
+                x = np.arange(x_min_bound, x_max_bound, (x_max_bound - x_min_bound)/10000)
+                f = np.polyval(coeff, x)
+                plt.plot(x, f)
+                plt.plot(x_sol, f_min, 'x')
+
+    return x_sol
+
+#%% L-BFGS Optimizer
+
+class LBFGS(Optimizer):
+    """
+    Implements the L-BFGS algorithm. Compatible with multi-batch and full-overlap
+    L-BFGS implementations and (stochastic) Powell damping. Partly based on the 
+    original L-BFGS implementation in PyTorch, Mark Schmidt's minFunc MATLAB code, 
+    and Michael Overton's weak Wolfe line search MATLAB code.
+
+    Implemented by: Hao-Jun Michael Shi and Dheevatsa Mudigere
+    Last edited 12/6/18.
+
+    Warnings:
+      . Does not support per-parameter options and parameter groups.
+      . All parameters have to be on a single device.
+
+    Inputs:
+        lr (float): steplength or learning rate (default: 1)
+        history_size (int): update history size (default: 10)
+        line_search (str): designates line search to use (default: 'Wolfe')
+            Options:
+                'None': uses steplength designated in algorithm
+                'Armijo': uses Armijo backtracking line search
+                'Wolfe': uses Armijo-Wolfe bracketing line search
+        dtype: data type (default: torch.float)
+        debug (bool): debugging mode
+
+    References:
+    [1] Berahas, Albert S., Jorge Nocedal, and Martin Takác. "A Multi-Batch L-BFGS 
+        Method for Machine Learning." Advances in Neural Information Processing 
+        Systems. 2016.
+    [2] Bollapragada, Raghu, et al. "A Progressive Batching L-BFGS Method for Machine 
+        Learning." International Conference on Machine Learning. 2018.
+    [3] Lewis, Adrian S., and Michael L. Overton. "Nonsmooth Optimization via Quasi-Newton
+        Methods." Mathematical Programming 141.1-2 (2013): 135-163.
+    [4] Liu, Dong C., and Jorge Nocedal. "On the Limited Memory BFGS Method for 
+        Large Scale Optimization." Mathematical Programming 45.1-3 (1989): 503-528.
+    [5] Nocedal, Jorge. "Updating Quasi-Newton Matrices With Limited Storage." 
+        Mathematics of Computation 35.151 (1980): 773-782.
+    [6] Nocedal, Jorge, and Stephen J. Wright. "Numerical Optimization." Springer New York,
+        2006.
+    [7] Schmidt, Mark. "minFunc: Unconstrained Differentiable Multivariate Optimization 
+        in Matlab." Software available at http://www.cs.ubc.ca/~schmidtm/Software/minFunc.html 
+        (2005).
+    [8] Schraudolph, Nicol N., Jin Yu, and Simon Günter. "A Stochastic Quasi-Newton 
+        Method for Online Convex Optimization." Artificial Intelligence and Statistics. 
+        2007.
+    [9] Wang, Xiao, et al. "Stochastic Quasi-Newton Methods for Nonconvex Stochastic 
+        Optimization." SIAM Journal on Optimization 27.2 (2017): 927-956.
+
+    """
+
+    def __init__(self, params, lr=1, history_size=10, line_search='Wolfe', 
+                 dtype=torch.float, debug=False):
+
+        # ensure inputs are valid
+        if not 0.0 <= lr:
+            raise ValueError("Invalid learning rate: {}".format(lr))
+        if not 0 <= history_size:
+            raise ValueError("Invalid history size: {}".format(history_size))
+        if line_search not in ['Armijo', 'Wolfe', 'None']:
+            raise ValueError("Invalid line search: {}".format(line_search))
+
+        defaults = dict(lr=lr, history_size=history_size, line_search=line_search, 
+                        dtype=dtype, debug=debug)
+        super(LBFGS, self).__init__(params, defaults)
+
+        if len(self.param_groups) != 1:
+            raise ValueError("L-BFGS doesn't support per-parameter options "
+                             "(parameter groups)")
+
+        self._params = self.param_groups[0]['params']
+        self._numel_cache = None
+
+        state = self.state['global_state']
+        state.setdefault('n_iter', 0)
+        state.setdefault('curv_skips', 0)
+        state.setdefault('fail_skips', 0)
+        state.setdefault('H_diag',1)
+        state.setdefault('fail', True)
+
+        state['old_dirs'] = []
+        state['old_stps'] = []
+
+    def _numel(self):
+        if self._numel_cache is None:
+            self._numel_cache = reduce(lambda total, p: total + p.numel(), self._params, 0)
+        return self._numel_cache
+
+    def _gather_flat_grad(self):
+        views = []
+        for p in self._params:
+            if p.grad is None:
+                view = p.data.new(p.data.numel()).zero_()
+            elif p.grad.data.is_sparse:
+                view = p.grad.data.to_dense().view(-1)
+            else:
+                view = p.grad.data.view(-1)
+            views.append(view)
+        return torch.cat(views, 0)
+
+    def _add_update(self, step_size, update):
+        offset = 0
+        for p in self._params:
+            numel = p.numel()
+            # view as to avoid deprecated pointwise semantics
+            p.data.add_(step_size, update[offset:offset + numel].view_as(p.data))
+            offset += numel
+        assert offset == self._numel()
+
+    def _copy_params(self):
+        current_params = []
+        for param in self._params:
+            current_params.append(deepcopy(param.data))
+        return current_params
+
+    def _load_params(self, current_params):
+        i = 0
+        for param in self._params:
+            param.data[:] = current_params[i]
+            i += 1
+
+    def line_search(self, line_search):
+        """
+        Switches line search option.
+        
+        Inputs:
+            line_search (str): designates line search to use
+                Options:
+                    'None': uses steplength designated in algorithm
+                    'Armijo': uses Armijo backtracking line search
+                    'Wolfe': uses Armijo-Wolfe bracketing line search
+        
+        """
+        
+        group = self.param_groups[0]
+        group['line_search'] = line_search
+        
+        return
+
+    def two_loop_recursion(self, vec):
+        """
+        Performs two-loop recursion on given vector to obtain Hv.
+
+        Inputs:
+            vec (tensor): 1-D tensor to apply two-loop recursion to
+
+        Output:
+            r (tensor): matrix-vector product Hv
+
+        """
+
+        group = self.param_groups[0]
+        history_size = group['history_size']
+
+        state = self.state['global_state']
+        old_dirs = state.get('old_dirs')    # change in gradients
+        old_stps = state.get('old_stps')    # change in iterates
+        H_diag = state.get('H_diag')
+
+        # compute the product of the inverse Hessian approximation and the gradient
+        num_old = len(old_dirs)
+
+        if 'rho' not in state:
+            state['rho'] = [None] * history_size
+            state['alpha'] = [None] * history_size
+        rho = state['rho']
+        alpha = state['alpha']
+
+        for i in range(num_old):
+            rho[i] = 1. / old_stps[i].dot(old_dirs[i])
+
+        q = vec
+        for i in range(num_old - 1, -1, -1):
+            alpha[i] = old_dirs[i].dot(q) * rho[i]
+            q.add_(-alpha[i], old_stps[i])
+
+        # multiply by initial Hessian 
+        # r/d is the final direction
+        r = torch.mul(q, H_diag)
+        for i in range(num_old):
+            beta = old_stps[i].dot(r) * rho[i]
+            r.add_(alpha[i] - beta, old_dirs[i])
+
+        return r
+
+    def curvature_update(self, flat_grad, eps=1e-2, damping=False):
+        """
+        Performs curvature update.
+
+        Inputs:
+            flat_grad (tensor): 1-D tensor of flattened gradient for computing 
+                gradient difference with previously stored gradient
+            eps (float): constant for curvature pair rejection or damping (default: 1e-2)
+            damping (bool): flag for using Powell damping (default: False)
+        """
+
+        assert len(self.param_groups) == 1
+
+        # load parameters
+        if(eps <= 0):
+            raise(ValueError('Invalid eps; must be positive.'))
+
+        group = self.param_groups[0]
+        history_size = group['history_size']
+        debug = group['debug']
+
+        # variables cached in state (for tracing)
+        state = self.state['global_state']
+        fail = state.get('fail')
+        
+        # check if line search failed
+        if not fail:
+            
+            d = state.get('d')
+            t = state.get('t')
+            old_dirs = state.get('old_dirs')
+            old_stps = state.get('old_stps')
+            H_diag = state.get('H_diag')
+            prev_flat_grad = state.get('prev_flat_grad')
+            Bs = state.get('Bs')
+    
+            # compute y's
+            y = flat_grad.sub(prev_flat_grad)
+            s = d.mul(t)
+            sBs = s.dot(Bs)
+            ys = y.dot(s)  # y*s
+
+            # update L-BFGS matrix
+            if ys > eps*sBs or damping == True:
+    
+                # perform Powell damping
+                if damping == True and ys < eps*sBs:
+                    if debug:
+                        print('Applying Powell damping...')
+                    theta = ((1-eps)*sBs)/(sBs - ys)
+                    y = theta*y + (1-theta)*Bs
+    
+                # updating memory
+                if len(old_dirs) == history_size:
+                    # shift history by one (limited-memory)
+                    old_dirs.pop(0)
+                    old_stps.pop(0)
+    
+                # store new direction/step
+                old_dirs.append(s)
+                old_stps.append(y)
+    
+                # update scale of initial Hessian approximation
+                H_diag = ys / y.dot(y)  # (y*y)
+                
+                state['old_dirs'] = old_dirs
+                state['old_stps'] = old_stps
+                state['H_diag'] = H_diag
+
+            else:
+                # save skip
+                state['curv_skips'] += 1
+                if debug:
+                    print('Curvature pair skipped due to failed criterion')
+
+        else:
+            # save skip
+            state['fail_skips'] += 1
+            if debug:
+                print('Line search failed; curvature pair update skipped')
+
+        return
+
+    def _step(self, p_k, g_Ok, g_Sk=None, options={}):
+        """
+        Performs a single optimization step.
+
+        Inputs:
+            p_k (tensor): 1-D tensor specifying search direction
+            g_Ok (tensor): 1-D tensor of flattened gradient over overlap O_k used
+                            for gradient differencing in curvature pair update
+            g_Sk (tensor): 1-D tensor of flattened gradient over full sample S_k
+                            used for curvature pair damping or rejection criterion,
+                            if None, will use g_Ok (default: None)
+            options (dict): contains options for performing line search
+
+        Options for Armijo backtracking line search:
+            'closure' (callable): reevaluates model and returns function value
+            'current_loss' (tensor): objective value at current iterate (default: F(x_k))
+            'gtd' (tensor): inner product g_Ok'd in line search (default: g_Ok'd)
+            'eta' (tensor): factor for decreasing steplength > 0 (default: 2)
+            'c1' (tensor): sufficient decrease constant in (0, 1) (default: 1e-4)
+            'max_ls' (int): maximum number of line search steps permitted (default: 10)
+            'interpolate' (bool): flag for using interpolation (default: True)
+            'inplace' (bool): flag for inplace operations (default: True)
+            'ls_debug' (bool): debugging mode for line search
+
+        Options for Wolfe line search:
+            'closure' (callable): reevaluates model and returns function value
+            'current_loss' (tensor): objective value at current iterate (default: F(x_k))
+            'gtd' (tensor): inner product g_Ok'd in line search (default: g_Ok'd)
+            'eta' (float): factor for extrapolation (default: 2)
+            'c1' (float): sufficient decrease constant in (0, 1) (default: 1e-4)
+            'c2' (float): curvature condition constant in (0, 1) (default: 0.9)
+            'max_ls' (int): maximum number of line search steps permitted (default: 10)
+            'interpolate' (bool): flag for using interpolation (default: True)
+            'inplace' (bool): flag for inplace operations (default: True)
+            'ls_debug' (bool): debugging mode for line search
+
+        Outputs (depends on line search):
+          . No line search:
+                t (float): steplength
+          . Armijo backtracking line search:
+                F_new (tensor): loss function at new iterate
+                t (tensor): final steplength
+                ls_step (int): number of backtracks
+                closure_eval (int): number of closure evaluations
+                desc_dir (bool): descent direction flag
+                    True: p_k is descent direction with respect to the line search
+                    function
+                    False: p_k is not a descent direction with respect to the line
+                    search function
+                fail (bool): failure flag
+                    True: line search reached maximum number of iterations, failed
+                    False: line search succeeded
+          . Wolfe line search:
+                F_new (tensor): loss function at new iterate
+                g_new (tensor): gradient at new iterate
+                t (float): final steplength
+                ls_step (int): number of backtracks
+                closure_eval (int): number of closure evaluations
+                grad_eval (int): number of gradient evaluations
+                desc_dir (bool): descent direction flag
+                    True: p_k is descent direction with respect to the line search
+                    function
+                    False: p_k is not a descent direction with respect to the line
+                    search function
+                fail (bool): failure flag
+                    True: line search reached maximum number of iterations, failed
+                    False: line search succeeded
+
+        Notes:
+          . If encountering line search failure in the deterministic setting, one
+            should try increasing the maximum number of line search steps max_ls.
+
+        """
+
+        assert len(self.param_groups) == 1
+
+        # load parameter options
+        group = self.param_groups[0]
+        lr = group['lr']
+        line_search = group['line_search']
+        dtype = group['dtype']
+        debug = group['debug']
+
+        # variables cached in state (for tracing)
+        state = self.state['global_state']
+        d = state.get('d')
+        t = state.get('t')
+        prev_flat_grad = state.get('prev_flat_grad')
+        Bs = state.get('Bs')
+
+        # keep track of nb of iterations
+        state['n_iter'] += 1
+
+        # set search direction
+        d = p_k
+
+        # modify previous gradient
+        if prev_flat_grad is None:
+            prev_flat_grad = g_Ok.clone()
+        else:
+            prev_flat_grad.copy_(g_Ok)
+
+        # set initial step size
+        t = lr
+
+        # closure evaluation counter
+        closure_eval = 0
+
+        if g_Sk is None:
+            g_Sk = g_Ok.clone()
+
+        # perform Armijo backtracking line search
+        if(line_search == 'Armijo'):
+
+            # load options
+            if(options):
+                if('closure' not in options.keys()):
+                    raise(ValueError('closure option not specified.'))
+                else:
+                    closure = options['closure']
+
+                if('gtd' not in options.keys()):
+                    gtd = g_Ok.dot(d)
+                else:
+                    gtd = options['gtd']
+
+                if('current_loss' not in options.keys()):
+                    F_k = closure()
+                    closure_eval += 1
+                else:
+                    F_k = options['current_loss']
+
+                if('eta' not in options.keys()):
+                    eta = 2
+                elif(options['eta'] <= 0):
+                    raise(ValueError('Invalid eta; must be positive.'))
+                else:
+                    eta = options['eta']
+
+                if('c1' not in options.keys()):
+                    c1 = 1e-4
+                elif(options['c1'] >= 1 or options['c1'] <= 0):
+                    raise(ValueError('Invalid c1; must be strictly between 0 and 1.'))
+                else:
+                    c1 = options['c1']
+
+                if('max_ls' not in options.keys()):
+                    max_ls = 10
+                elif(options['max_ls'] <= 0):
+                    raise(ValueError('Invalid max_ls; must be positive.'))
+                else:
+                    max_ls = options['max_ls']
+
+                if('interpolate' not in options.keys()):
+                    interpolate = True
+                else:
+                    interpolate = options['interpolate']
+
+                if('inplace' not in options.keys()):
+                    inplace = True
+                else:
+                    inplace = options['inplace']
+                    
+                if('ls_debug' not in options.keys()):
+                    ls_debug = False
+                else:
+                    ls_debug = options['ls_debug']
+
+            else:
+                raise(ValueError('Options are not specified; need closure evaluating function.'))
+
+            # initialize values
+            if(interpolate):
+                if(torch.cuda.is_available()):
+                    F_prev = torch.tensor(np.nan, dtype=dtype).cuda()
+                else:
+                    F_prev = torch.tensor(np.nan, dtype=dtype)
+
+            ls_step = 0
+            t_prev = 0 # old steplength
+            fail = False # failure flag
+
+            # begin print for debug mode
+            if ls_debug:
+                print('==================================== Begin Armijo line search ===================================')
+                print('F(x): %.8e  g*d: %.8e' %(F_k, gtd))
+
+            # check if search direction is descent direction
+            if gtd >= 0:
+                desc_dir = False
+                if debug:
+                    print('Not a descent direction!')
+            else:
+                desc_dir = True
+
+            # store values if not in-place
+            if not inplace:
+                current_params = self._copy_params()
+
+            # update and evaluate at new point
+            self._add_update(t, d)
+            F_new = closure()
+            closure_eval += 1
+
+            # print info if debugging
+            if(ls_debug):
+                print('LS Step: %d  t: %.8e  F(x+td): %.8e  F-c1*t*g*d: %.8e  F(x): %.8e'
+                      %(ls_step, t, F_new, F_k + c1*t*gtd, F_k))
+
+            # check Armijo condition
+            while F_new > F_k + c1*t*gtd or not is_legal(F_new):
+
+                # check if maximum number of iterations reached
+                if(ls_step >= max_ls):
+                    if inplace:
+                        self._add_update(-t, d)
+                    else:
+                        self._load_params(current_params)
+
+                    t = 0
+                    F_new = closure()
+                    closure_eval += 1
+                    fail = True
+                    break
+
+                else:
+                    # store current steplength
+                    t_new = t
+
+                    # compute new steplength
+
+                    # if first step or not interpolating, then multiply by factor
+                    if(ls_step == 0 or not interpolate or not is_legal(F_new)):
+                        t = t/eta
+
+                    # if second step, use function value at new point along with 
+                    # gradient and function at current iterate
+                    elif(ls_step == 1 or not is_legal(F_prev)):
+                        t = polyinterp(np.array([[0, F_k.item(), gtd.item()], [t_new, F_new.item(), np.nan]]))
+
+                    # otherwise, use function values at new point, previous point,
+                    # and gradient and function at current iterate
+                    else:
+                        t = polyinterp(np.array([[0, F_k.item(), gtd.item()], [t_new, F_new.item(), np.nan], 
+                                                [t_prev, F_prev.item(), np.nan]]))
+
+                    # if values are too extreme, adjust t
+                    if(interpolate):
+                        if(t < 1e-3*t_new):
+                            t = 1e-3*t_new
+                        elif(t > 0.6*t_new):
+                            t = 0.6*t_new
+
+                        # store old point
+                        F_prev = F_new
+                        t_prev = t_new
+
+                    # update iterate and reevaluate
+                    if inplace:
+                        self._add_update(t-t_new, d)
+                    else:
+                        self._load_params(current_params)
+                        self._add_update(t, d)
+
+                    F_new = closure()
+                    closure_eval += 1
+                    ls_step += 1 # iterate
+                    
+                    # print info if debugging
+                    if(ls_debug):
+                        print('LS Step: %d  t: %.8e  F(x+td):   %.8e  F-c1*t*g*d: %.8e  F(x): %.8e'
+                              %(ls_step, t, F_new, F_k + c1*t*gtd, F_k))
+
+            # store Bs
+            if Bs is None:
+                Bs = (g_Sk.mul(-t)).clone()
+            else:
+                Bs.copy_(g_Sk.mul(-t))
+                
+            # print final steplength
+            if ls_debug:
+                print('Final Steplength:', t)
+                print('===================================== End Armijo line search ====================================')
+
+            state['d'] = d
+            state['prev_flat_grad'] = prev_flat_grad
+            state['t'] = t
+            state['Bs'] = Bs
+            state['fail'] = fail
+
+            return F_new, t, ls_step, closure_eval, desc_dir, fail
+
+        # perform weak Wolfe line search
+        elif(line_search == 'Wolfe'):
+
+            # load options
+            if(options):
+                if('closure' not in options.keys()):
+                    raise(ValueError('closure option not specified.'))
+                else:
+                    closure = options['closure']
+
+                if('current_loss' not in options.keys()):
+                    F_k = closure()
+                    closure_eval += 1
+                else:
+                    F_k = options['current_loss']
+
+                if('gtd' not in options.keys()):
+                    gtd = g_Ok.dot(d)
+                else:
+                    gtd = options['gtd']
+
+                if('eta' not in options.keys()):
+                    eta = 2
+                elif(options['eta'] <= 1):
+                    raise(ValueError('Invalid eta; must be greater than 1.'))
+                else:
+                    eta = options['eta']
+
+                if('c1' not in options.keys()):
+                    c1 = 1e-4
+                elif(options['c1'] >= 1 or options['c1'] <= 0):
+                    raise(ValueError('Invalid c1; must be strictly between 0 and 1.'))
+                else:
+                    c1 = options['c1']
+
+                if('c2' not in options.keys()):
+                    c2 = 0.9
+                elif(options['c2'] >= 1 or options['c2'] <= 0):
+                    raise(ValueError('Invalid c2; must be strictly between 0 and 1.'))
+                elif(options['c2'] <= c1):
+                    raise(ValueError('Invalid c2; must be strictly larger than c1.'))
+                else:
+                    c2 = options['c2']
+
+                if('max_ls' not in options.keys()):
+                    max_ls = 10
+                elif(options['max_ls'] <= 0):
+                    raise(ValueError('Invalid max_ls; must be positive.'))
+                else:
+                    max_ls = options['max_ls']
+
+                if('interpolate' not in options.keys()):
+                    interpolate = True
+                else:
+                    interpolate = options['interpolate']
+
+                if('inplace' not in options.keys()):
+                    inplace = True
+                else:
+                    inplace = options['inplace']
+                    
+                if('ls_debug' not in options.keys()):
+                    ls_debug = False
+                else:
+                    ls_debug = options['ls_debug']
+
+            else:
+                raise(ValueError('Options are not specified; need closure evaluating function.'))
+
+            # initialize counters
+            ls_step = 0
+            grad_eval = 0 # tracks gradient evaluations
+            t_prev = 0 # old steplength
+
+            # initialize bracketing variables and flag
+            alpha = 0
+            beta = float('Inf')
+            fail = False
+
+            # initialize values for line search
+            if(interpolate):
+                F_a = F_k
+                g_a = gtd
+
+                if(torch.cuda.is_available()):
+                    F_b = torch.tensor(np.nan, dtype=dtype).cuda()
+                    g_b = torch.tensor(np.nan, dtype=dtype).cuda()
+                else:
+                    F_b = torch.tensor(np.nan, dtype=dtype)
+                    g_b = torch.tensor(np.nan, dtype=dtype)
+
+            # begin print for debug mode
+            if ls_debug:
+                print('==================================== Begin Wolfe line search ====================================')
+                print('F(x): %.8e  g*d: %.8e' %(F_k, gtd))
+
+            # check if search direction is descent direction
+            if gtd >= 0:
+                desc_dir = False
+                if debug:
+                    print('Not a descent direction!')
+            else:
+                desc_dir = True
+
+            # store values if not in-place
+            if not inplace:
+                current_params = self._copy_params()
+
+            # update and evaluate at new point
+            self._add_update(t, d)
+            F_new = closure()
+            closure_eval += 1
+
+            # main loop
+            while True:
+
+                # check if maximum number of line search steps have been reached
+                if(ls_step >= max_ls):
+                    if inplace:
+                        self._add_update(-t, d)
+                    else:
+                        self._load_params(current_params)
+
+                    t = 0
+                    F_new = closure()
+                    F_new.backward()
+                    g_new = self._gather_flat_grad()
+                    closure_eval += 1
+                    grad_eval += 1
+                    fail = True
+                    break
+
+                # print info if debugging
+                if(ls_debug):
+                    print('LS Step: %d  t: %.8e  alpha: %.8e  beta: %.8e' 
+                          %(ls_step, t, alpha, beta))
+                    print('Armijo:  F(x+td): %.8e  F-c1*t*g*d: %.8e  F(x): %.8e'
+                          %(F_new, F_k + c1*t*gtd, F_k))
+
+                # check Armijo condition
+                if(F_new > F_k + c1*t*gtd):
+
+                    # set upper bound
+                    beta = t
+                    t_prev = t
+
+                    # update interpolation quantities
+                    if(interpolate):
+                        F_b = F_new
+                        if(torch.cuda.is_available()):
+                            g_b = torch.tensor(np.nan, dtype=dtype).cuda()
+                        else:
+                            g_b = torch.tensor(np.nan, dtype=dtype)
+
+                else:
+
+                    # compute gradient
+                    F_new.backward()
+                    g_new = self._gather_flat_grad()
+                    grad_eval += 1
+                    gtd_new = g_new.dot(d)
+                    
+                    # print info if debugging
+                    if(ls_debug):
+                        print('Wolfe: g(x+td)*d: %.8e  c2*g*d: %.8e  gtd: %.8e'
+                              %(gtd_new, c2*gtd, gtd))
+
+                    # check curvature condition
+                    if(gtd_new < c2*gtd):
+
+                        # set lower bound
+                        alpha = t
+                        t_prev = t
+
+                        # update interpolation quantities
+                        if(interpolate):
+                            F_a = F_new
+                            g_a = gtd_new
+
+                    else:
+                        break
+
+                # compute new steplength
+
+                # if first step or not interpolating, then bisect or multiply by factor
+                if(not interpolate or not is_legal(F_b)):
+                    if(beta == float('Inf')):
+                        t = eta*t
+                    else:
+                        t = (alpha + beta)/2.0
+
+                # otherwise interpolate between a and b
+                else:
+                    t = polyinterp(np.array([[alpha, F_a.item(), g_a.item()],[beta, F_b.item(), g_b.item()]]))
+
+                    # if values are too extreme, adjust t
+                    if(beta == float('Inf')):
+                        if(t > 2*eta*t_prev):
+                            t = 2*eta*t_prev
+                        elif(t < eta*t_prev):
+                            t = eta*t_prev
+                    else:
+                        if(t < alpha + 0.2*(beta - alpha)):
+                            t = alpha + 0.2*(beta - alpha)
+                        elif(t > (beta - alpha)/2.0):
+                            t = (beta - alpha)/2.0
+
+                    # if we obtain nonsensical value from interpolation
+                    if(t <= 0):
+                        t = (beta - alpha)/2.0
+
+                # update parameters
+                if inplace:
+                    self._add_update(t - t_prev, d)
+                else:
+                    self._load_params(current_params)
+                    self._add_update(t, d)
+
+                # evaluate closure
+                F_new = closure()
+                closure_eval += 1
+                ls_step += 1
+
+            # store Bs
+            if Bs is None:
+                Bs = (g_Sk.mul(-t)).clone()
+            else:
+                Bs.copy_(g_Sk.mul(-t))
+                
+            # print final steplength
+            if ls_debug:
+                print('Final Steplength:', t)
+                print('===================================== End Wolfe line search =====================================')
+
+            state['d'] = d
+            state['prev_flat_grad'] = prev_flat_grad
+            state['t'] = t
+            state['Bs'] = Bs
+            state['fail'] = fail
+
+            return F_new, g_new, t, ls_step, closure_eval, grad_eval, desc_dir, fail
+
+        else:
+
+            # perform update
+            self._add_update(t, d)
+
+            # store Bs
+            if Bs is None:
+                Bs = (g_Sk.mul(-t)).clone()
+            else:
+                Bs.copy_(g_Sk.mul(-t))
+
+            state['d'] = d
+            state['prev_flat_grad'] = prev_flat_grad
+            state['t'] = t
+            state['Bs'] = Bs
+            state['fail'] = False
+
+            return t
+        
+    def step(self, p_k, g_Ok, g_Sk=None, options={}):
+        return self._step(p_k, g_Ok, g_Sk, options)
+
+#%% Full-Batch (Deterministic) L-BFGS Optimizer (Wrapper)
+
+class FullBatchLBFGS(LBFGS):
+    """
+    Implements full-batch or deterministic L-BFGS algorithm. Compatible with
+    Powell damping. Can be used when evaluating a deterministic function and
+    gradient. Wraps the LBFGS optimizer. Performs the two-loop recursion,
+    updating, and curvature updating in a single step.
+
+    Implemented by: Hao-Jun Michael Shi and Dheevatsa Mudigere
+    Last edited 11/15/18.
+
+    Warnings:
+      . Does not support per-parameter options and parameter groups.
+      . All parameters have to be on a single device.
+
+    Inputs:
+        lr (float): steplength or learning rate (default: 1)
+        history_size (int): update history size (default: 10)
+        line_search (str): designates line search to use (default: 'Wolfe')
+            Options:
+                'None': uses steplength designated in algorithm
+                'Armijo': uses Armijo backtracking line search
+                'Wolfe': uses Armijo-Wolfe bracketing line search
+        dtype: data type (default: torch.float)
+        debug (bool): debugging mode
+
+    """
+
+    def __init__(self, params, lr=1, history_size=10, line_search='Wolfe', 
+                 dtype=torch.float, debug=False):
+        super(FullBatchLBFGS, self).__init__(params, lr, history_size, line_search, 
+             dtype, debug)
+
+    def step(self, options={}):
+        """
+        Performs a single optimization step.
+
+        Inputs:
+            options (dict): contains options for performing line search
+            
+        General Options:
+            'eps' (float): constant for curvature pair rejection or damping (default: 1e-2)
+            'damping' (bool): flag for using Powell damping (default: False)
+
+        Options for Armijo backtracking line search:
+            'closure' (callable): reevaluates model and returns function value
+            'current_loss' (tensor): objective value at current iterate (default: F(x_k))
+            'gtd' (tensor): inner product g_Ok'd in line search (default: g_Ok'd)
+            'eta' (tensor): factor for decreasing steplength > 0 (default: 2)
+            'c1' (tensor): sufficient decrease constant in (0, 1) (default: 1e-4)
+            'max_ls' (int): maximum number of line search steps permitted (default: 10)
+            'interpolate' (bool): flag for using interpolation (default: True)
+            'inplace' (bool): flag for inplace operations (default: True)
+            'ls_debug' (bool): debugging mode for line search
+
+        Options for Wolfe line search:
+            'closure' (callable): reevaluates model and returns function value
+            'current_loss' (tensor): objective value at current iterate (default: F(x_k))
+            'gtd' (tensor): inner product g_Ok'd in line search (default: g_Ok'd)
+            'eta' (float): factor for extrapolation (default: 2)
+            'c1' (float): sufficient decrease constant in (0, 1) (default: 1e-4)
+            'c2' (float): curvature condition constant in (0, 1) (default: 0.9)
+            'max_ls' (int): maximum number of line search steps permitted (default: 10)
+            'interpolate' (bool): flag for using interpolation (default: True)
+            'inplace' (bool): flag for inplace operations (default: True)
+            'ls_debug' (bool): debugging mode for line search
+
+        Outputs (depends on line search):
+          . No line search:
+                t (float): steplength
+          . Armijo backtracking line search:
+                F_new (tensor): loss function at new iterate
+                t (tensor): final steplength
+                ls_step (int): number of backtracks
+                closure_eval (int): number of closure evaluations
+                desc_dir (bool): descent direction flag
+                    True: p_k is descent direction with respect to the line search
+                    function
+                    False: p_k is not a descent direction with respect to the line
+                    search function
+                fail (bool): failure flag
+                    True: line search reached maximum number of iterations, failed
+                    False: line search succeeded
+          . Wolfe line search:
+                F_new (tensor): loss function at new iterate
+                g_new (tensor): gradient at new iterate
+                t (float): final steplength
+                ls_step (int): number of backtracks
+                closure_eval (int): number of closure evaluations
+                grad_eval (int): number of gradient evaluations
+                desc_dir (bool): descent direction flag
+                    True: p_k is descent direction with respect to the line search
+                    function
+                    False: p_k is not a descent direction with respect to the line
+                    search function
+                fail (bool): failure flag
+                    True: line search reached maximum number of iterations, failed
+                    False: line search succeeded
+
+        Notes:
+          . If encountering line search failure in the deterministic setting, one
+            should try increasing the maximum number of line search steps max_ls.
+
+        """
+        
+        # load options for damping and eps
+        if('damping' not in options.keys()):
+            damping = False
+        else:
+            damping = options['damping']
+            
+        if('eps' not in options.keys()):
+            eps = 1e-2
+        else:
+            eps = options['eps']
+        
+        # gather gradient
+        grad = self._gather_flat_grad()
+        
+        # update curvature if after 1st iteration
+        state = self.state['global_state']
+        if(state['n_iter'] > 0):
+            self.curvature_update(grad, eps, damping)
+
+        # compute search direction
+        p = self.two_loop_recursion(-grad)
+
+        # take step
+        return self._step(p, grad, options=options)
diff --git a/netdissect/README.md b/netdissect/README.md
new file mode 100644
index 0000000000000000000000000000000000000000..f49a7f77264cb1ebed19cb96eaf376b55fa2bf78
--- /dev/null
+++ b/netdissect/README.md
@@ -0,0 +1 @@
+The code for network dissection in this folder is taken from the original implementation here: https://github.com/davidbau/dissect
\ No newline at end of file
diff --git a/netdissect/__init__.py b/netdissect/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..89e9b6988092e8fa287491afabe704a6d8a8222b
--- /dev/null
+++ b/netdissect/__init__.py
@@ -0,0 +1,82 @@
+'''
+Netdissect package.
+
+To run dissection:
+
+1. Load up the convolutional model you wish to dissect, and wrap it
+   in an InstrumentedModel.  Call imodel.retain_layers([layernames,..])
+   to analyze a specified set of layers.
+2. Load the segmentation dataset using the BrodenDataset class;
+   use the transform_image argument to normalize images to be
+   suitable for the model, or the size argument to truncate the dataset.
+3. Write a function to recover the original image (with RGB scaled to
+   [0...1]) given a normalized dataset image; ReverseNormalize in this
+   package inverts transforms.Normalize for this purpose.
+4. Choose a directory in which to write the output, and call
+   dissect(outdir, model, dataset).
+
+Example:
+
+    from netdissect import InstrumentedModel, dissect
+    from netdissect import BrodenDataset, ReverseNormalize
+
+    model = InstrumentedModel(load_my_model())
+    model.eval()
+    model.cuda()
+    model.retain_layers(['conv1', 'conv2', 'conv3', 'conv4', 'conv5'])
+    bds = BrodenDataset('datasets/broden1_227',
+            transform_image=transforms.Compose([
+                transforms.ToTensor(),
+                transforms.Normalize(IMAGE_MEAN, IMAGE_STDEV)]),
+            size=1000)
+    dissect('result/dissect', model, bds,
+            recover_image=ReverseNormalize(IMAGE_MEAN, IMAGE_STDEV),
+            examples_per_unit=10)
+'''
+
+__all__ = [
+	'actviz',
+	'autoeval',
+	'bargraph',
+	'broden',
+	'customnet',
+	'easydict',
+	'encoder_loss',
+	'encoder_net',
+	'evalablate',
+	'frechet_distance',
+	'fsd',
+	'fullablate',
+	'imgsave',
+	'imgviz',
+	'invert',
+	'LBFGS',
+	'make_z_dataset',
+	'modelconfig',
+	'multilayer_graph',
+	'nethook',
+	'oldalexnet',
+	'oldresnet152',
+	'oldvgg16',
+	'optimize_residuals',
+	'optimize_z_lbfgs',
+	'parallelfolder',
+	'pbar',
+	'pidfile',
+	'plotutil',
+	'proggan',
+	'renormalize',
+	'runningstats',
+	'samplegan',
+	'sampler',
+	'segdata',
+	'segmenter',
+	'segviz',
+	'setting',
+	'show',
+	'statedict',
+	'tally',
+	'upsample',
+	'workerpool',
+	'zdataset',
+]
diff --git a/netdissect/__main__.py b/netdissect/__main__.py
new file mode 100644
index 0000000000000000000000000000000000000000..12e34d4b10e9c93e4fe0607a6b2f614a257738ec
--- /dev/null
+++ b/netdissect/__main__.py
@@ -0,0 +1,431 @@
+import torch, sys, os, argparse, textwrap, numbers, numpy, json, PIL
+from torchvision import transforms
+from torch.utils.data import TensorDataset
+from netdissect import pbar
+from netdissect import InstrumentedModel, BrodenDataset, dissect
+from netdissect import MultiSegmentDataset, GeneratorSegRunner
+from netdissect import ImageOnlySegRunner
+from netdissect.parallelfolder import ParallelImageFolders
+from netdissect.zdataset import z_dataset_for_model
+from netdissect.autoeval import autoimport_eval
+from netdissect.modelconfig import create_instrumented_model
+from netdissect.pidfile import exit_if_job_done, mark_job_done
+
+help_epilog = '''\
+Example: to dissect three layers of the pretrained alexnet in torchvision:
+
+python -m netdissect \\
+        --model "torchvision.models.alexnet(pretrained=True)" \\
+        --layers features.6:conv3 features.8:conv4 features.10:conv5 \\
+        --imgsize 227 \\
+        --outdir dissect/alexnet-imagenet
+
+To dissect a progressive GAN model:
+
+python -m netdissect \\
+        --model "proggan.from_pth_file('model/churchoutdoor.pth')" \\
+        --gan
+'''
+
+def main():
+    # Training settings
+    def strpair(arg):
+        p = tuple(arg.split(':'))
+        if len(p) == 1:
+            p = p + p
+        return p
+    def intpair(arg):
+        p = arg.split(',')
+        if len(p) == 1:
+            p = p + p
+        return tuple(int(v) for v in p)
+
+    parser = argparse.ArgumentParser(description='Net dissect utility',
+            prog='python -m netdissect',
+            epilog=textwrap.dedent(help_epilog),
+            formatter_class=argparse.RawDescriptionHelpFormatter)
+    parser.add_argument('--model', type=str, default=None,
+                        help='constructor for the model to test')
+    parser.add_argument('--pthfile', type=str, default=None,
+                        help='filename of .pth file for the model')
+    parser.add_argument('--unstrict', action='store_true', default=False,
+                        help='ignore unexpected pth parameters')
+    parser.add_argument('--modelkey', type=str, default=None,
+                        help='key within pthfile containing state_dict')
+    parser.add_argument('--submodule', type=str, default=None,
+                        help='submodule to load from pthfile state dict')
+    parser.add_argument('--outdir', type=str, default='dissect',
+                        help='directory for dissection output')
+    parser.add_argument('--layers', type=strpair, nargs='+',
+                        help='space-separated list of layer names to dissect' +
+                        ', in the form layername[:reportedname]')
+    parser.add_argument('--segments', type=str, default='datasets/broden',
+                        help='directory containing segmentation dataset')
+    parser.add_argument('--segmenter', type=str, default=None,
+                        help='constructor for asegmenter class')
+    parser.add_argument('--normalizer', type=str, default=None,
+                        help='Normalize rgb with imagenet, zc, or pt ranges')
+    parser.add_argument('--download', action='store_true', default=False,
+                        help='downloads Broden dataset if needed')
+    parser.add_argument('--imagedir', type=str, default=None,
+                        help='directory containing image-only dataset')
+    parser.add_argument('--imgsize', type=intpair, default=(227, 227),
+                        help='input image size to use')
+    parser.add_argument('--netname', type=str, default=None,
+                        help='name for network in generated reports')
+    parser.add_argument('--meta', type=str, nargs='+',
+                        help='json files of metadata to add to report')
+    parser.add_argument('--merge', type=str,
+                        help='json file of unit data to merge in report')
+    parser.add_argument('--examples', type=int, default=20,
+                        help='number of image examples per unit')
+    parser.add_argument('--size', type=int, default=10000,
+                        help='dataset subset size to use')
+    parser.add_argument('--batch_size', type=int, default=100,
+                        help='batch size for forward pass')
+    parser.add_argument('--num_workers', type=int, default=24,
+                        help='number of DataLoader workers')
+    parser.add_argument('--quantile_threshold', type=strfloat, default=None,
+                        choices=[FloatRange(0.0, 1.0), 'iqr'],
+                        help='quantile to use for masks')
+    parser.add_argument('--whiten', default=None,
+                        help='set to pca to whiten units')
+    parser.add_argument('--no-labels', action='store_true', default=False,
+                        help='disables labeling of units')
+    parser.add_argument('--maxiou', action='store_true', default=False,
+                        help='enables maxiou calculation')
+    parser.add_argument('--covariance', action='store_true', default=False,
+                        help='enables covariance calculation')
+    parser.add_argument('--rank_all_labels', action='store_true', default=False,
+                        help='include low-information labels in rankings')
+    parser.add_argument('--no-images', action='store_true', default=False,
+                        help='disables generation of unit images')
+    parser.add_argument('--no-report', action='store_true', default=False,
+                        help='disables generation report summary')
+    parser.add_argument('--no-cuda', action='store_true', default=False,
+                        help='disables CUDA usage')
+    parser.add_argument('--gen', action='store_true', default=False,
+                        help='test a generator model (e.g., a GAN)')
+    parser.add_argument('--gan', action='store_true', default=False,
+                        help='synonym for --gen')
+    parser.add_argument('--perturbation', default=None,
+                        help='filename of perturbation attack to apply')
+    parser.add_argument('--add_scale_offset', action='store_true', default=None,
+                        help='offsets masks according to stride and padding')
+    parser.add_argument('--quiet', action='store_true', default=False,
+                        help='silences console output')
+    if len(sys.argv) == 1:
+        parser.print_usage(sys.stderr)
+        sys.exit(1)
+    args = parser.parse_args()
+    args.images = not args.no_images
+    args.report = not args.no_report
+    args.labels = not args.no_labels
+    if args.gan:
+        args.gen = args.gan
+
+    # Set up console output
+    pbar.verbose(not args.quiet)
+
+    # Exit right away if job is already done or being done.
+    if args.outdir is not None:
+        exit_if_job_done(args.outdir)
+
+    # Speed up pytorch
+    torch.backends.cudnn.benchmark = True
+
+    # Special case: download flag without model to test.
+    if args.model is None and args.download:
+        from netdissect.broden import ensure_broden_downloaded
+        for resolution in [224, 227, 384]:
+            ensure_broden_downloaded(args.segments, resolution, 1)
+        from netdissect.segmenter import ensure_upp_segmenter_downloaded
+        ensure_upp_segmenter_downloaded('datasets/segmodel')
+        sys.exit(0)
+
+    # Help if broden is not present
+    if not args.gen and not args.imagedir and not os.path.isdir(args.segments):
+        pbar.print('Segmentation dataset not found at %s.' % args.segments)
+        pbar.print('Specify dataset directory using --segments [DIR]')
+        pbar.print('To download Broden, run: netdissect --download')
+        sys.exit(1)
+
+    # Default segmenter class
+    if args.gen and args.segmenter is None:
+        args.segmenter = ("netdissect.segmenter.UnifiedParsingSegmenter(" +
+                "segsizes=[256], segdiv='quad')")
+
+    # Default threshold
+    if args.quantile_threshold is None:
+        if args.gen:
+            args.quantile_threshold = 'iqr'
+        else:
+            args.quantile_threshold = 0.005
+
+    # Set up CUDA
+    args.cuda = not args.no_cuda and torch.cuda.is_available()
+    if args.cuda:
+        torch.backends.cudnn.benchmark = True
+
+    # Construct the network with specified layers instrumented
+    if args.model is None:
+        pbar.print('No model specified')
+        sys.exit(1)
+    model = create_instrumented_model(args)
+
+    # Update any metadata from files, if any
+    meta = getattr(model, 'meta', {})
+    if args.meta:
+        for mfilename in args.meta:
+            with open(mfilename) as f:
+                meta.update(json.load(f))
+
+    # Load any merge data from files
+    mergedata = None
+    if args.merge:
+        with open(args.merge) as f:
+            mergedata = json.load(f)
+
+    # Set up the output directory, verify write access
+    if args.outdir is None:
+        args.outdir = os.path.join('dissect', type(model).__name__)
+        exit_if_job_done(args.outdir)
+        pbar.print('Writing output into %s.' % args.outdir)
+    os.makedirs(args.outdir, exist_ok=True)
+    train_dataset = None
+
+    if not args.gen:
+        # Load dataset for classifier case.
+        # Load perturbation
+        perturbation = numpy.load(args.perturbation
+                ) if args.perturbation else None
+        segrunner = None
+
+        # Load broden dataset
+        if args.imagedir is not None:
+            dataset = try_to_load_images(args.imagedir, args.imgsize,
+                    perturbation, args.size)
+            segrunner = ImageOnlySegRunner(dataset)
+        else:
+            dataset = try_to_load_broden(args.segments, args.imgsize, 1,
+                perturbation, args.download, args.size,
+                normalizer_named(args.normalizer))
+        if dataset is None:
+            dataset = try_to_load_multiseg(args.segments, args.imgsize,
+                    perturbation, args.size)
+        if dataset is None:
+            pbar.print('No segmentation dataset found in %s',
+                    args.segments)
+            pbar.print('use --download to download Broden.')
+            sys.exit(1)
+    else:
+        # For segmenter case the dataset is just a random z
+        dataset = z_dataset_for_model(model, args.size)
+        train_dataset = z_dataset_for_model(model, args.size, seed=2)
+        segrunner = GeneratorSegRunner(autoimport_eval(args.segmenter))
+
+    # Run dissect
+    dissect(args.outdir, model, dataset,
+            train_dataset=train_dataset,
+            segrunner=segrunner,
+            examples_per_unit=args.examples,
+            netname=args.netname,
+            quantile_threshold=args.quantile_threshold,
+            meta=meta,
+            merge=mergedata,
+            pca_units=(args.whiten == 'pca'),
+            make_images=args.images,
+            make_labels=args.labels,
+            make_maxiou=args.maxiou,
+            make_covariance=args.covariance,
+            make_report=args.report,
+            make_row_images=args.images,
+            make_single_images=True,
+            rank_all_labels=args.rank_all_labels,
+            batch_size=args.batch_size,
+            num_workers=args.num_workers,
+            settings=vars(args))
+
+    # Mark the directory so that it's not done again.
+    mark_job_done(args.outdir)
+
+class AddPerturbation(object):
+    def __init__(self, perturbation):
+        self.perturbation = perturbation
+
+    def __call__(self, pic):
+        if self.perturbation is None:
+            return pic
+        # Convert to a numpy float32 array
+        npyimg = numpy.array(pic, numpy.uint8, copy=False
+                ).astype(numpy.float32)
+        # Center the perturbation
+        oy, ox = ((self.perturbation.shape[d] - npyimg.shape[d]) // 2
+                for d in [0, 1])
+        npyimg += self.perturbation[
+                oy:oy+npyimg.shape[0], ox:ox+npyimg.shape[1]]
+        # Pytorch conventions: as a float it should be [0..1]
+        npyimg.clip(0, 255, npyimg)
+        return npyimg / 255.0
+
+def test_dissection():
+    pbar.verbose(True)
+    from torchvision.models import alexnet
+    from torchvision import transforms
+    model = InstrumentedModel(alexnet(pretrained=True))
+    model.eval()
+    # Load an alexnet
+    model.retain_layers([
+        ('features.0', 'conv1'),
+        ('features.3', 'conv2'),
+        ('features.6', 'conv3'),
+        ('features.8', 'conv4'),
+        ('features.10', 'conv5') ])
+    # load broden dataset
+    bds = BrodenDataset('datasets/broden',
+            transform=transforms.Compose([
+                transforms.ToTensor(),
+                transforms.Normalize(IMAGE_MEAN, IMAGE_STDEV)]),
+            size=100)
+    # run dissect
+    dissect('dissect/test', model, bds,
+            examples_per_unit=10)
+
+def try_to_load_images(directory, imgsize, perturbation, size):
+    # Load plain image dataset
+    # TODO: allow other normalizations.
+    return ParallelImageFolders(
+            [directory],
+            transform=transforms.Compose([
+                transforms.Resize(imgsize),
+                AddPerturbation(perturbation),
+                transforms.ToTensor(),
+                transforms.Normalize(IMAGE_MEAN, IMAGE_STDEV)]),
+            size=size)
+
+def try_to_load_broden(directory, imgsize, broden_version, perturbation,
+        download, size, normalizer=None):
+    # Load broden dataset
+    ds_resolution = (224 if max(imgsize) <= 224 else
+                     227 if max(imgsize) <= 227 else 384)
+    if not os.path.isfile(os.path.join(directory,
+           'broden%d_%d' % (broden_version, ds_resolution), 'index.csv')):
+        return None
+    # normalizers are "imagenet", "pm1", "pt"
+    if normalizer is None:
+        normalizer = transforms.Normalize(IMAGE_MEAN, IMAGE_STDEV)
+    return BrodenDataset(directory,
+            resolution=ds_resolution,
+            download=download,
+            broden_version=broden_version,
+            transform=transforms.Compose([
+                transforms.Resize(imgsize),
+                AddPerturbation(perturbation),
+                transforms.ToTensor(),
+                normalizer]),
+            size=size)
+
+def try_to_load_multiseg(directory, imgsize, perturbation, size):
+    if not os.path.isfile(os.path.join(directory, 'labelnames.json')):
+        return None
+    minsize = min(imgsize) if hasattr(imgsize, '__iter__') else imgsize
+    return MultiSegmentDataset(directory,
+            transform=(transforms.Compose([
+                transforms.Resize(minsize),
+                transforms.CenterCrop(imgsize),
+                AddPerturbation(perturbation),
+                transforms.ToTensor(),
+                transforms.Normalize(IMAGE_MEAN, IMAGE_STDEV)]),
+            transforms.Compose([
+                transforms.Resize(minsize, interpolation=PIL.Image.NEAREST),
+                transforms.CenterCrop(imgsize)])),
+            size=size)
+
+def add_scale_offset_info(model, layer_names):
+    '''
+    Creates a 'scale_offset' property on the model which guesses
+    how to offset the featuremap, in cases where the convolutional
+    padding does not exacly correspond to keeping featuremap pixels
+    centered on the downsampled regions of the input.  This mainly
+    shows up in AlexNet: ResNet and VGG pad convolutions to keep
+    them centered and do not need this.
+    '''
+    model.scale_offset = {}
+    seen = set()
+    sequence = []
+    aka_map = {}
+    for name in layer_names:
+        aka = name
+        if not isinstance(aka, str):
+            name, aka = name
+        aka_map[name] = aka
+    for name, layer in model.named_modules():
+        sequence.append(layer)
+        if name in aka_map:
+            seen.add(name)
+            aka = aka_map[name]
+            model.scale_offset[aka] = sequence_scale_offset(sequence)
+    for name in aka_map:
+        assert name in seen, ('Layer %s not found' % name)
+
+def dilation_scale_offset(dilations):
+    '''Composes a list of (k, s, p) into a single total scale and offset.'''
+    if len(dilations) == 0:
+        return (1, 0)
+    scale, offset = dilation_scale_offset(dilations[1:])
+    kernel, stride, padding = dilations[0]
+    scale *= stride
+    offset *= stride
+    offset += (kernel - 1) / 2.0 - padding
+    return scale, offset
+
+def dilations(modulelist):
+    '''Converts a list of modules to (kernel_size, stride, padding)'''
+    result = []
+    for module in modulelist:
+        settings = tuple(getattr(module, n, d)
+            for n, d in (('kernel_size', 1), ('stride', 1), ('padding', 0)))
+        settings = (((s, s) if not isinstance(s, tuple) else s)
+            for s in settings)
+        if settings != ((1, 1), (1, 1), (0, 0)):
+            result.append(zip(*settings))
+    return zip(*result)
+
+def sequence_scale_offset(modulelist):
+    '''Returns (yscale, yoffset), (xscale, xoffset) given a list of modules'''
+    return tuple(dilation_scale_offset(d) for d in dilations(modulelist))
+
+
+def strfloat(s):
+    try:
+        return float(s)
+    except:
+        return s
+
+class FloatRange(object):
+    def __init__(self, start, end):
+        self.start = start
+        self.end = end
+    def __eq__(self, other):
+        return isinstance(other, float) and self.start <= other <= self.end
+    def __repr__(self):
+        return '[%g-%g]' % (self.start, self.end)
+
+# Many models use this normalization.
+IMAGE_MEAN = [0.485, 0.456, 0.406]
+IMAGE_STDEV = [0.229, 0.224, 0.225]
+def normalizer_named(n):
+    if n is None:
+        return None
+    if n == 'imagenet':
+        return transforms.Normalize(IMAGE_MEAN, IMAGE_STDEV)
+    if n == 'zc':
+        # Transform pytorch [0..1] to [-1..1]
+        return transforms.Normalize([0.5, 0.5, 0.5], [0.5, 0.5, 0.5])
+    if n == 'pt':
+        # Leave pytorch [0..1] unchanged.
+        return transforms.Normalize([0.0, 0.0, 0.0], [1.0, 1.0, 1.0])
+    assert False, 'unknown normalizer %s' % n
+
+if __name__ == '__main__':
+    main()
diff --git a/netdissect/actviz.py b/netdissect/actviz.py
new file mode 100644
index 0000000000000000000000000000000000000000..5989013931ff2c2b241715db5eea1f391c768ef9
--- /dev/null
+++ b/netdissect/actviz.py
@@ -0,0 +1,170 @@
+import os
+import numpy
+from scipy.interpolate import RectBivariateSpline
+
+def activation_visualization(image, data, level, alpha=0.5, source_shape=None,
+        target_shape=None, crop=False, zoom=None, border=2,
+        negate=False, return_mask=False, **kwargs):
+    """
+    Makes a visualiztion image of activation data overlaid on the image.
+    Params:
+        image The original image.
+        data The single channel feature map.
+        alpha The darkening to apply in inactive regions of the image.
+        level The threshold of activation levels to highlight.
+    """
+    if len(image.shape) == 2:
+        # Puff up grayscale image to RGB.
+        image = image[:,:,None] * numpy.array([[[1, 1, 1]]])
+    surface = activation_surface(data, target_shape=image.shape[:2],
+            source_shape=source_shape, **kwargs)
+    if negate:
+        surface = -surface
+        level = -level
+    if crop:
+        # crop to source_shape
+        if source_shape is not None:
+            ch, cw = ((t - s) // 2 for s, t in zip(
+                source_shape, image.shape[:2]))
+            image = image[ch:ch+source_shape[0], cw:cw+source_shape[1]]
+            surface = surface[ch:ch+source_shape[0], cw:cw+source_shape[1]]
+        if crop is True:
+            crop = surface.shape
+        elif not hasattr(crop, '__len__'):
+            crop = (crop, crop)
+        if zoom is not None:
+            source_rect = best_sub_rect(surface >= level, crop, zoom,
+                    pad=border)
+        else:
+            source_rect = (0, surface.shape[0], 0, surface.shape[1])
+        image = zoom_image(image, source_rect, crop)
+        surface = zoom_image(surface, source_rect, crop)
+    # Rescale visualization to target_shape if specified.
+    if target_shape is not None and (image.shape[0] != target_shape[0]
+            or image.shape[1] != target_shape[1]):
+        image = zoom_image(image, target_shape=target_shape)
+        surface = zoom_image(surface, target_shape=target_shape)
+    mask = (surface >= level)
+    # Add a yellow border at the edge of the mask for contrast
+    result = (mask[:, :, None] * (1 - alpha) + alpha) * image
+    if border:
+        edge = mask_border(mask)[:,:,None]
+        result = numpy.maximum(edge * numpy.array([[[200, 200, 0]]]), result)
+    if not return_mask:
+        return result
+    mask_image = (1 - mask[:, :, None]) * numpy.array(
+            [[[0, 0, 0, 255 * (1 - alpha)]]], dtype=numpy.uint8)
+    if border:
+        mask_image = numpy.maximum(edge * numpy.array([[[200, 200, 0, 255]]]),
+                mask_image)
+    return result, mask_image
+
+def activation_surface(data, target_shape=None, source_shape=None,
+        scale_offset=None, deg=1, pad=True):
+    """
+    Generates an upsampled activation sample.
+    Params:
+        target_shape Shape of the output array.
+        source_shape The centered shape of the output to match with data
+            when upscaling.  Defaults to the whole target_shape.
+        scale_offset The amount by which to scale, then offset data
+            dimensions to end up with target dimensions.  A pair of pairs.
+        deg Degree of interpolation to apply (1 = linear, etc).
+        pad True to zero-pad the edge instead of doing a funny edge interp.
+    """
+    # Default is that nothing is resized.
+    if target_shape is None:
+        target_shape = data.shape
+    # Make a default scale_offset to fill the image if there isn't one
+    if scale_offset is None:
+        scale = tuple(float(ts) / ds
+                for ts, ds in zip(target_shape, data.shape))
+        offset = tuple(0.5 * s - 0.5 for s in scale)
+    else:
+        scale, offset = (v for v in zip(*scale_offset))
+    # Now we adjust offsets to take into account cropping and so on
+    if source_shape is not None:
+        offset = tuple(o + (ts - ss) / 2.0
+                for o, ss, ts in zip(offset, source_shape, target_shape))
+    # Pad the edge with zeros for sensible edge behavior
+    if pad:
+        zeropad = numpy.zeros(
+                (data.shape[0] + 2, data.shape[1] + 2), dtype=data.dtype)
+        zeropad[1:-1, 1:-1] = data
+        data = zeropad
+        offset = tuple((o - s) for o, s in zip(offset, scale))
+    # Upsample linearly
+    ty, tx = (numpy.arange(ts) for ts in target_shape)
+    sy, sx = (numpy.arange(ss) * s + o
+            for ss, s, o in zip(data.shape, scale, offset))
+    levels = RectBivariateSpline(
+            sy, sx, data, kx=deg, ky=deg)(ty, tx, grid=True)
+    # Return the mask.
+    return levels
+
+def mask_border(mask, border=2):
+    """Given a mask computes a border mask"""
+    from scipy import ndimage
+    struct = ndimage.generate_binary_structure(2, 2)
+    erosion = numpy.ones((mask.shape[0] + 10, mask.shape[1] + 10), dtype='int')
+    erosion[5:5+mask.shape[0], 5:5+mask.shape[1]] = ~mask
+    for _ in range(border):
+        erosion = ndimage.binary_erosion(erosion, struct)
+    return ~mask ^ erosion[5:5+mask.shape[0], 5:5+mask.shape[1]]
+
+def bounding_rect(mask, pad=0):
+    """Returns (r, b, l, r) boundaries so that all nonzero pixels in mask
+    have locations (i, j) with  t <= i < b, and l <= j < r."""
+    nz = mask.nonzero()
+    if len(nz[0]) == 0:
+        # print('no pixels')
+        return (0, mask.shape[0], 0, mask.shape[1])
+    (t, b), (l, r) = [(max(0, p.min() - pad), min(s, p.max() + 1 + pad))
+            for p, s in zip(nz, mask.shape)]
+    return (t, b, l, r)
+
+def best_sub_rect(mask, shape, max_zoom=None, pad=2):
+    """Finds the smallest subrectangle containing all the nonzeros of mask,
+    matching the aspect ratio of shape, and where the zoom-up ratio is no
+    more than max_zoom"""
+    t, b, l, r = bounding_rect(mask, pad=pad)
+    height = max(b - t, int(round(float(shape[0]) * (r - l) / shape[1])))
+    if max_zoom is not None:
+        height = int(max(round(float(shape[0]) / max_zoom), height))
+    width = int(round(float(shape[1]) * height / shape[0]))
+    nt = min(mask.shape[0] - height, max(0, (b + t - height) // 2))
+    nb = nt + height
+    nl = min(mask.shape[1] - width, max(0, (r + l - width) // 2))
+    nr = nl + width
+    return (nt, nb, nl, nr)
+
+def zoom_image(img, source_rect=None, target_shape=None):
+    """Zooms pixels from the source_rect of img to target_shape."""
+    import warnings
+    from scipy.ndimage import zoom
+    if target_shape is None:
+        target_shape = img.shape
+    if source_rect is None:
+        st, sb, sl, sr = (0, img.shape[0], 0, img.shape[1])
+    else:
+        st, sb, sl, sr = source_rect
+    source = img[st:sb, sl:sr]
+    if source.shape == target_shape:
+        return source
+    zoom_tuple = tuple(float(t) / s
+            for t, s in zip(target_shape, source.shape[:2])
+            ) + (1,) * (img.ndim - 2)
+    with warnings.catch_warnings():
+        warnings.simplefilter('ignore', UserWarning) # "output shape of zoom"
+        target = zoom(source, zoom_tuple)
+    assert target.shape[:2] == target_shape, (target.shape, target_shape)
+    return target
+
+def choose_level(feature_map, percentile=0.8):
+    '''
+    Chooses the top 80% level (or whatever the level chosen).
+    '''
+    data_range = numpy.sort(feature_map.flatten())
+    return numpy.interp(
+            percentile, numpy.linspace(0, 1, len(data_range)), data_range)
+
diff --git a/netdissect/autoeval.py b/netdissect/autoeval.py
new file mode 100644
index 0000000000000000000000000000000000000000..ecc86a1f7b403f57821dde2a2b4f0619c0d6cae3
--- /dev/null
+++ b/netdissect/autoeval.py
@@ -0,0 +1,37 @@
+from collections import defaultdict
+from importlib import import_module
+
+def autoimport_eval(term):
+    '''
+    Used to evaluate an arbitrary command-line constructor specifying
+    a class, with automatic import of global module names.
+    '''
+
+    class DictNamespace(object):
+        def __init__(self, d):
+            self.__d__ = d
+        def __getattr__(self, key):
+            return self.__d__[key]
+
+    class AutoImportDict(defaultdict):
+        def __init__(self, wrapped=None, parent=None):
+            super().__init__()
+            self.wrapped = wrapped
+            self.parent = parent
+        def __missing__(self, key):
+            if self.wrapped is not None:
+                if key in self.wrapped:
+                    return self.wrapped[key]
+            if self.parent is not None:
+                key = self.parent + '.' + key
+            if key in __builtins__:
+                return __builtins__[key]
+            mdl = import_module(key)
+            # Return an AutoImportDict for any namespace packages
+            if hasattr(mdl, '__path__'): # and not hasattr(mdl, '__file__'):
+                return DictNamespace(
+                        AutoImportDict(wrapped=mdl.__dict__, parent=key))
+            return mdl
+
+    return eval(term, {}, AutoImportDict())
+
diff --git a/netdissect/bargraph.py b/netdissect/bargraph.py
new file mode 100644
index 0000000000000000000000000000000000000000..b139175894d739cb2a6edc2ba1403f89a1488005
--- /dev/null
+++ b/netdissect/bargraph.py
@@ -0,0 +1,109 @@
+from xml.etree import ElementTree as et
+
+def make_svg_bargraph(labels, heights, categories=None, palette=None,
+        barheight=100, barwidth=12, show_labels=True, file_header=False,
+        data_url=False):
+    if palette is None:
+        palette = default_bargraph_palette
+    if categories is None:
+        categories = [('', len(labels))]
+    unitheight = float(barheight) / max(max(heights, default=1), 1)
+    textheight = barheight if show_labels else 0
+    labelsize = float(barwidth)
+    gap = float(barwidth) / 4
+    # textsize = barwidth + gap
+    textsize = barwidth + gap / 2
+    rollup = max(heights, default=1)
+    textmargin = float(labelsize) * 2 / 3
+    leftmargin = 32
+    rightmargin = 8
+    svgwidth = len(heights) * (barwidth + gap) + 2 * leftmargin + rightmargin
+    svgheight = barheight + textheight
+
+    # create an SVG XML element
+    svg = et.Element('svg', width=str(svgwidth), height=str(svgheight),
+            version='1.1', xmlns='http://www.w3.org/2000/svg')
+
+    # Draw the bar graph
+    basey = svgheight - textheight
+    x = leftmargin
+    # Add units scale on left
+    if len(heights):
+        for h in [1, (max(heights) + 1) // 2, max(heights)]:
+            et.SubElement(svg, 'text', x='0', y='0',
+                style=('font-family:sans-serif;font-size:%dpx;' +
+                'text-anchor:end;alignment-baseline:hanging;' +
+                'transform:translate(%dpx, %dpx);') %
+                (textsize, x - gap, basey - h * unitheight)).text = str(h)
+        et.SubElement(svg, 'text', x='0', y='0',
+                style=('font-family:sans-serif;font-size:%dpx;' +
+                'text-anchor:middle;' +
+                'transform:translate(%dpx, %dpx) rotate(-90deg)') %
+                (textsize, x - gap - textsize, basey - h * unitheight / 2)
+                ).text = 'units'
+    # Draw big category background rectangles
+    for catindex, (cat, catcount) in enumerate(categories):
+        if not catcount:
+            continue
+        et.SubElement(svg, 'rect', x=str(x), y=str(basey - rollup * unitheight),
+                width=(str((barwidth + gap) * catcount - gap)),
+                height = str(rollup*unitheight),
+                fill=palette[catindex % len(palette)][1])
+        x += (barwidth + gap) * catcount
+    # Draw small bars as well as 45degree text labels
+    x = leftmargin
+    catindex = -1
+    catcount = 0
+    for label, height in zip(labels, heights):
+        while not catcount and catindex <= len(categories):
+            catindex += 1
+            catcount = categories[catindex][1]
+            color = palette[catindex % len(palette)][0]
+        et.SubElement(svg, 'rect', x=str(x), y=str(basey-(height * unitheight)),
+                width=str(barwidth), height=str(height * unitheight),
+                fill=color)
+        x += barwidth
+        if show_labels:
+            et.SubElement(svg, 'text', x='0', y='0',
+                style=('font-family:sans-serif;font-size:%dpx;text-anchor:end;'+
+                'transform:translate(%dpx, %dpx) rotate(-45deg);') %
+                (labelsize, x, basey + textmargin)).text = label
+        x += gap
+        catcount -= 1
+    # Text labels for each category
+    x = leftmargin
+    for cat, catcount in categories:
+        if not catcount:
+            continue
+        et.SubElement(svg, 'text', x='0', y='0',
+            style=('font-family:sans-serif;font-size:%dpx;text-anchor:end;'+
+            'transform:translate(%dpx, %dpx) rotate(-90deg);') %
+            (textsize, x + (barwidth + gap) * catcount - gap,
+                basey - rollup * unitheight + gap)).text = '%d %s' % (
+                    catcount, cat + ('s' if catcount != 1 else ''))
+        x += (barwidth + gap) * catcount
+    # Output - this is the bare svg.
+    result = et.tostring(svg).decode('utf-8')
+    if file_header or data_url:
+        result = ''.join([
+            '<?xml version=\"1.0\" standalone=\"no\"?>\n',
+            '<!DOCTYPE svg PUBLIC \"-//W3C//DTD SVG 1.1//EN\"\n',
+            '\"http://www.w3.org/Graphics/SVG/1.1/DTD/svg11.dtd\">\n',
+            result])
+    if data_url:
+        import base64
+        result = 'data:image/svg+xml;base64,' + base64.b64encode(
+                result.encode('utf-8')).decode('utf-8')
+    return result
+
+default_bargraph_palette = [
+    ('#4B4CBF', '#B6B6F2'),
+    ('#55B05B', '#B6F2BA'),
+    ('#50BDAC', '#A5E5DB'),
+    ('#81C679', '#C0FF9B'),
+    ('#F0883B', '#F2CFB6'),
+    ('#D4CF24', '#F2F1B6'),
+    ('#D92E2B', '#F2B6B6'),
+    ('#AB6BC6', '#CFAAFF'),
+]
+
diff --git a/netdissect/broden.py b/netdissect/broden.py
new file mode 100644
index 0000000000000000000000000000000000000000..4e51181a7c70877e0c0991c60467ac3cec071447
--- /dev/null
+++ b/netdissect/broden.py
@@ -0,0 +1,272 @@
+import os, errno, numpy, torch, csv, re, shutil, os, zipfile
+from collections import OrderedDict
+from torchvision.datasets.folder import default_loader
+from torchvision import transforms
+from scipy import ndimage
+from urllib.request import urlopen
+
+class BrodenDataset(torch.utils.data.Dataset):
+    '''
+    A multicategory segmentation data set.
+
+    Returns three streams:
+    (1) The image (3, h, w).
+    (2) The multicategory segmentation (labelcount, h, w).
+    (3) A bincount of pixels in the segmentation (labelcount).
+
+    Net dissect also assumes that the dataset object has three properties
+    with human-readable labels:
+
+    ds.labels = ['red', 'black', 'car', 'tree', 'grid', ...]
+    ds.categories = ['color', 'part', 'object', 'texture']
+    ds.label_category = [0, 0, 2, 2, 3, ...] # The category for each label
+    '''
+    def __init__(self, directory='datasets/broden', resolution=384,
+            split='train', categories=None,
+            transform=None, transform_segment=None,
+            download=False, size=None, include_bincount=True,
+            broden_version=1, max_segment_depth=6):
+        assert resolution in [224, 227, 384]
+        if download:
+            ensure_broden_downloaded(directory, resolution, broden_version)
+        self.directory = directory
+        self.resolution = resolution
+        self.resdir = os.path.join(directory, 'broden%d_%d' %
+                (broden_version, resolution))
+        self.loader = default_loader
+        self.transform = transform
+        self.transform_segment = transform_segment
+        self.include_bincount = include_bincount
+        # The maximum number of multilabel layers that coexist at an image.
+        self.max_segment_depth = max_segment_depth
+        with open(os.path.join(self.resdir, 'category.csv'),
+                encoding='utf-8') as f:
+            self.category_info = OrderedDict()
+            for row in csv.DictReader(f):
+                self.category_info[row['name']] = row
+        if categories is not None:
+            # Filter out unused categories
+            categories = set([c for c in categories if c in self.category_info])
+            for cat in list(self.category_info.keys()):
+                if cat not in categories:
+                    del self.category_info[cat]
+        categories = list(self.category_info.keys())
+        self.categories = categories
+
+        # Filter out unneeded images.
+        with open(os.path.join(self.resdir, 'index.csv'),
+                encoding='utf-8') as f:
+            all_images = [decode_index_dict(r) for r in csv.DictReader(f)]
+        self.image = [row for row in all_images
+            if index_has_any_data(row, categories)
+            and (split is None or row['split'] == split)]
+        if size is not None:
+            self.image = self.image[:size]
+        with open(os.path.join(self.resdir, 'label.csv'),
+                encoding='utf-8') as f:
+            self.label_info = build_dense_label_array([
+                decode_label_dict(r) for r in csv.DictReader(f)])
+            self.labels = [l['name'] for l in self.label_info]
+        # Build dense remapping arrays for labels, so that you can
+        # get dense ranges of labels for each category.
+        self.category_map = {}
+        self.category_unmap = {}
+        self.category_label = {}
+        for cat in self.categories:
+            with open(os.path.join(self.resdir, 'c_%s.csv' % cat),
+                    encoding='utf-8') as f:
+                c_data = [decode_label_dict(r) for r in csv.DictReader(f)]
+            self.category_unmap[cat], self.category_map[cat] = (
+                    build_numpy_category_map(c_data))
+            self.category_label[cat] = build_dense_label_array(
+                    c_data, key='code')
+        self.num_labels = len(self.labels)
+        # Primary categories for each label is the category in which it
+        # appears with the maximum coverage.
+        self.label_category = numpy.zeros(self.num_labels, dtype=int)
+        for i in range(self.num_labels):
+            maxcoverage, self.label_category[i] = max(
+               (self.category_label[cat][self.category_map[cat][i]]['coverage']
+                    if i < len(self.category_map[cat])
+                       and self.category_map[cat][i] else 0, ic)
+                for ic, cat in enumerate(categories))
+
+    def __len__(self):
+        return len(self.image)
+
+    def __getitem__(self, idx):
+        record = self.image[idx]
+        # example record: {
+        #    'image': 'opensurfaces/25605.jpg', 'split': 'train',
+        #    'ih': 384, 'iw': 384, 'sh': 192, 'sw': 192,
+        #    'color': ['opensurfaces/25605_color.png'],
+        #    'object': [], 'part': [],
+        #    'material': ['opensurfaces/25605_material.png'],
+        #    'scene': [], 'texture': []}
+        image = self.loader(os.path.join(self.resdir, 'images',
+            record['image']))
+        segment = numpy.zeros(shape=(self.max_segment_depth,
+            record['sh'], record['sw']), dtype=int)
+        if self.include_bincount:
+            bincount = numpy.zeros(shape=(self.num_labels,), dtype=int)
+        depth = 0
+        for cat in self.categories:
+            for layer in record[cat]:
+                if isinstance(layer, int):
+                    segment[depth,:,:] = layer
+                    if self.include_bincount:
+                        bincount[layer] += segment.shape[1] * segment.shape[2]
+                else:
+                    png = numpy.asarray(self.loader(os.path.join(
+                        self.resdir, 'images', layer)))
+                    segment[depth,:,:] = png[:,:,0] + png[:,:,1] * 256
+                    if self.include_bincount:
+                        bincount += numpy.bincount(segment[depth,:,:].flatten(),
+                            minlength=self.num_labels)
+                depth += 1
+        if self.transform:
+            image = self.transform(image)
+        if self.transform_segment:
+            segment = self.transform_segment(segment)
+        if self.include_bincount:    
+            bincount[0] = 0
+            return (image, segment, bincount)
+        else:
+            return (image, segment)
+
+def build_dense_label_array(label_data, key='number', allow_none=False):
+    '''
+    Input: set of rows with 'number' fields (or another field name key).
+    Output: array such that a[number] = the row with the given number.
+    '''
+    result = [None] * (max([d[key] for d in label_data]) + 1)
+    for d in label_data:
+        result[d[key]] = d
+    # Fill in none
+    if not allow_none:
+        example = label_data[0]
+        def make_empty(k):
+            return dict((c, k if c is key else type(v)())
+                    for c, v in example.items())
+        for i, d in enumerate(result):
+            if d is None:
+                result[i] = dict(make_empty(i))
+    return result
+
+def build_numpy_category_map(map_data, key1='code', key2='number'):
+    '''
+    Input: set of rows with 'number' fields (or another field name key).
+    Output: array such that a[number] = the row with the given number.
+    '''
+    results = list(numpy.zeros((max([d[key] for d in map_data]) + 1),
+            dtype=numpy.int16) for key in (key1, key2))
+    for d in map_data:
+        results[0][d[key1]] = d[key2]
+        results[1][d[key2]] = d[key1]
+    return results
+
+def index_has_any_data(row, categories):
+    for c in categories:
+        for data in row[c]:
+            if data: return True
+    return False
+
+def decode_label_dict(row):
+    result = {}
+    for key, val in row.items():
+        if key == 'category':
+            result[key] = dict((c, int(n))
+                for c, n in [re.match('^([^(]*)\(([^)]*)\)$', f).groups()
+                    for f in val.split(';')])
+        elif key == 'name':
+            result[key] = val
+        elif key == 'syns':
+            result[key] = val.split(';')
+        elif re.match('^\d+$', val):
+            result[key] = int(val)
+        elif re.match('^\d+\.\d*$', val):
+            result[key] = float(val)
+        else:
+            result[key] = val
+    return result
+
+def decode_index_dict(row):
+    result = {}
+    for key, val in row.items():
+        if key in ['image', 'split']:
+            result[key] = val
+        elif key in ['sw', 'sh', 'iw', 'ih']:
+            result[key] = int(val)
+        else:
+            item = [s for s in val.split(';') if s]
+            for i, v in enumerate(item):
+                if re.match('^\d+$', v):
+                    item[i] = int(v)
+            result[key] = item
+    return result
+
+class ScaleSegmentation:
+    '''
+    Utility for scaling segmentations, using nearest-neighbor zooming.
+    '''
+    def __init__(self, target_height, target_width):
+        self.target_height = target_height
+        self.target_width = target_width
+    def __call__(self, seg):
+        ratio = (1, self.target_height / float(seg.shape[1]),
+                self.target_width / float(seg.shape[2]))
+        return ndimage.zoom(seg, ratio, order=0)
+
+def scatter_batch(seg, num_labels, omit_zero=True, dtype=torch.uint8):
+    '''
+    Utility for scattering semgentations into a one-hot representation.
+    '''
+    result = torch.zeros(*((seg.shape[0], num_labels,) + seg.shape[2:]),
+            dtype=dtype, device=seg.device)
+    result.scatter_(1, seg, 1)
+    if omit_zero:
+        result[:,0] = 0
+    return result
+
+def ensure_broden_downloaded(directory, resolution, broden_version=1):
+    assert resolution in [224, 227, 384]
+    baseurl = 'http://netdissect.csail.mit.edu/data/'
+    dirname = 'broden%d_%d' % (broden_version, resolution)
+    if os.path.isfile(os.path.join(directory, dirname, 'index.csv')):
+        return # Already downloaded
+    zipfilename = 'broden1_%d.zip' % resolution
+    download_dir = os.path.join(directory, 'download')
+    os.makedirs(download_dir, exist_ok=True)
+    full_zipfilename = os.path.join(download_dir, zipfilename)
+    if not os.path.exists(full_zipfilename):
+        url = '%s/%s' % (baseurl, zipfilename)
+        print('Downloading %s' % url)
+        data = urlopen(url)
+        with open(full_zipfilename, 'wb') as f:
+            f.write(data.read())
+    print('Unzipping %s' % zipfilename)
+    with zipfile.ZipFile(full_zipfilename, 'r') as zip_ref:
+        zip_ref.extractall(directory)
+    assert os.path.isfile(os.path.join(directory, dirname, 'index.csv'))
+
+def test_broden_dataset():
+    '''
+    Testing code.
+    '''
+    bds = BrodenDataset('datasets/broden', resolution=384,
+            transform=transforms.Compose([
+                        transforms.Resize(224),
+                        transforms.ToTensor()]),
+            transform_segment=transforms.Compose([
+                        ScaleSegmentation(224, 224)
+                        ]),
+            include_bincount=True)
+    loader = torch.utils.data.DataLoader(bds, batch_size=100, num_workers=24)
+    for i in range(1,20):
+        print(bds.label[i]['name'],
+                list(bds.category.keys())[bds.primary_category[i]])
+    for i, (im, seg, bc) in enumerate(loader):
+        print(i, im.shape, seg.shape, seg.max(), bc.shape)
+
+if __name__ == '__main__':
+    test_broden_dataset()
diff --git a/netdissect/customnet.py b/netdissect/customnet.py
new file mode 100644
index 0000000000000000000000000000000000000000..e450ca395015d76af44da20693ad0ab5bf431b80
--- /dev/null
+++ b/netdissect/customnet.py
@@ -0,0 +1,194 @@
+'''
+Customized version of pytorch resnet, alexnets.
+'''
+
+import numpy, torch, math, os
+from torch import nn
+from collections import OrderedDict
+from torchvision.models import resnet
+from torchvision.models.alexnet import model_urls as alexnet_model_urls
+
+class CustomResNet(nn.Module):
+    '''
+    Customizable ResNet, compatible with pytorch's resnet, but:
+     * The top-level sequence of modules can be modified to add
+       or remove or alter layers.
+     * Extra outputs can be produced, to allow backprop and access
+       to internal features.
+     * Pooling is replaced by resizable GlobalAveragePooling so that
+       any size can be input (e.g., any multiple of 32 pixels).
+     * halfsize=True halves striding on the first pooling to
+       set the default size to 112x112 instead of 224x224.
+    '''
+    def __init__(self, size=None, block=None, layers=None, num_classes=1000,
+            extra_output=None, modify_sequence=None, halfsize=False):
+        standard_sizes = {
+            18: (resnet.BasicBlock, [2, 2, 2, 2]),
+            34: (resnet.BasicBlock, [3, 4, 6, 3]),
+            50: (resnet.Bottleneck, [3, 4, 6, 3]),
+            101: (resnet.Bottleneck, [3, 4, 23, 3]),
+            152: (resnet.Bottleneck, [3, 8, 36, 3])
+        }
+        assert (size in standard_sizes) == (block is None) == (layers is None)
+        if size in standard_sizes:
+            block, layers = standard_sizes[size]
+        if modify_sequence is None:
+            modify_sequence = lambda x: x
+        self.inplanes = 64
+        norm_layer = nn.BatchNorm2d
+        self._norm_layer = norm_layer # for recent resnet
+        self.dilation = 1
+        self.groups = 1
+        self.base_width = 64
+        sequence = modify_sequence([
+            ('conv1', nn.Conv2d(3, 64, kernel_size=7, stride=2,
+                padding=3, bias=False)),
+            ('bn1', norm_layer(64)),
+            ('relu', nn.ReLU(inplace=True)),
+            ('maxpool', nn.MaxPool2d(3, stride=1 if halfsize else 2,
+                padding=1)),
+            ('layer1', self._make_layer(block, 64, layers[0])),
+            ('layer2', self._make_layer(block, 128, layers[1], stride=2)),
+            ('layer3', self._make_layer(block, 256, layers[2], stride=2)),
+            ('layer4', self._make_layer(block, 512, layers[3], stride=2)),
+            ('avgpool', GlobalAveragePool2d()),
+            ('fc', nn.Linear(512 * block.expansion, num_classes))
+        ])
+        super(CustomResNet, self).__init__()
+        for name, layer in sequence:
+            setattr(self, name, layer)
+        self.extra_output = extra_output
+
+    def _make_layer(self, block, channels, depth, stride=1):
+        return resnet.ResNet._make_layer(self, block, channels, depth, stride)
+
+    def forward(self, x):
+        extra = []
+        for name, module in self._modules.items():
+            x = module(x)
+            if self.extra_output and name in self.extra_output:
+                extra.append(x)
+        if self.extra_output:
+            return (x,) + tuple(extra)
+        return x
+
+class CustomAlexNet(nn.Module):
+    '''
+    Customizable AlexNet, compatible with pytorch's alexnet, but:
+     * The top-level sequence of modules can be modified to add
+       or remove or alter layers.
+     * Extra outputs can be produced, to allow backprop and access
+       to internal features.
+     * halfsize=True halves striding on the first convolution to
+       allow 119x119 images to be processed rather than 227x227 only.
+    '''
+    def __init__(self, channels=None, num_classes=1000,
+            extra_output=None, modify_sequence=None, halfsize=False):
+        if channels is None:
+            channels = [3, 64, 192, 384, 256, 256, 4096, 4096]
+        if modify_sequence is None:
+            modify_sequence = lambda x: x
+        sequence = modify_sequence([
+            ('conv1', nn.Conv2d(channels[0], channels[1],
+                kernel_size=11, stride=4, padding=2)),
+            ('relu1', nn.ReLU(inplace=True)),
+            ('pool1', nn.MaxPool2d(kernel_size=3, stride=1 if halfsize else 2)),
+            ('conv2', nn.Conv2d(channels[1], channels[2],
+                kernel_size=5, padding=2)),
+            ('relu2', nn.ReLU(inplace=True)),
+            ('pool2', nn.MaxPool2d(kernel_size=3, stride=2)),
+            ('conv3', nn.Conv2d(channels[2], channels[3],
+                kernel_size=3, padding=1)),
+            ('relu3', nn.ReLU(inplace=True)),
+            ('conv4', nn.Conv2d(channels[3], channels[4],
+                kernel_size=3, padding=1)),
+            ('relu4', nn.ReLU(inplace=True)),
+            ('conv5', nn.Conv2d(channels[4], channels[5],
+                kernel_size=3, padding=1)),
+            ('relu5', nn.ReLU(inplace=True)),
+            ('pool5', nn.MaxPool2d(kernel_size=3, stride=2)),
+            ('flatten', Vectorize()),
+            ('dropout6', nn.Dropout()),
+            ('fc6', nn.Linear(channels[5] * 6 * 6, channels[6])),
+            ('relu6', nn.ReLU(inplace=True)),
+            ('dropout7', nn.Dropout()),
+            ('fc7', nn.Linear(channels[6], channels[7])),
+            ('relu7', nn.ReLU(inplace=True)),
+            ('fc8', nn.Linear(channels[7], num_classes))
+        ])
+        super(CustomAlexNet, self).__init__()
+        for name, layer in sequence:
+            setattr(self, name, layer)
+        self.extra_output = extra_output
+
+    def forward(self, x):
+        extra = []
+        for name, module in self._modules.items():
+            x = module(x)
+            if self.extra_output and name in self.extra_output:
+                extra.append(x)
+        if self.extra_output:
+            return (x,) + tuple(extra)
+        return x
+
+    def load_state_dict(self, state_dict, **kwargs):
+        '''
+        Translates from pytorch's AlexNet parameter names
+        into the custom parameter names.
+        '''
+        custom_names = [
+            ('features.0.', 'conv1.'),
+            ('features.3.', 'conv2.'),
+            ('features.6.', 'conv3.'),
+            ('features.8.', 'conv4.'),
+            ('features.10.', 'conv5.'),
+            ('classifier.1.', 'fc6.'),
+            ('classifier.4.', 'fc7.'),
+            ('classifier.6.', 'fc8.')
+        ]
+        custom_state_dict = {}
+        for k, v in state_dict.items():
+            for op, np in custom_names:
+                if k.startswith(op):
+                    k = np + k[len(op):]
+                    break
+            custom_state_dict[k] = v
+        super(CustomAlexNet, self).load_state_dict(custom_state_dict, **kwargs)
+
+class Vectorize(nn.Module):
+    def __init__(self):
+        super(Vectorize, self).__init__()
+    def forward(self, x):
+        x = x.view(x.size(0), int(numpy.prod(x.size()[1:])))
+        return x
+
+class GlobalAveragePool2d(nn.Module):
+    def __init__(self):
+        super(GlobalAveragePool2d, self).__init__()
+    def forward(self, x):
+        x = torch.mean(x.view(x.size(0), x.size(1), -1), dim=2)
+        return x
+
+if __name__ == '__main__':
+    import torch.utils.model_zoo as model_zoo
+    # Verify that at the default settings, pytorch standard pretrained
+    # models can be loaded into each of the custom nets.
+    print('Loading alexnet')
+    model = CustomAlexNet()
+    model.load_state_dict(model_zoo.load_url(alexnet_model_urls['alexnet']))
+    print('Loading resnet18')
+    model = CustomResNet(18)
+    model.load_state_dict(model_zoo.load_url(resnet.model_urls['resnet18']))
+    print('Loading resnet34')
+    model = CustomResNet(34)
+    model.load_state_dict(model_zoo.load_url(resnet.model_urls['resnet34']))
+    print('Loading resnet50')
+    model = CustomResNet(50)
+    model.load_state_dict(model_zoo.load_url(resnet.model_urls['resnet50']))
+    print('Loading resnet101')
+    model = CustomResNet(101)
+    model.load_state_dict(model_zoo.load_url(resnet.model_urls['resnet101']))
+    print('Loading resnet152')
+    model = CustomResNet(152)
+    model.load_state_dict(model_zoo.load_url(resnet.model_urls['resnet152']))
+
diff --git a/netdissect/easydict.py b/netdissect/easydict.py
new file mode 100644
index 0000000000000000000000000000000000000000..0188f524b87eef75c175772ff262b93b47919ba7
--- /dev/null
+++ b/netdissect/easydict.py
@@ -0,0 +1,126 @@
+'''
+From https://github.com/makinacorpus/easydict.
+'''
+
+class EasyDict(dict):
+    """
+    Get attributes
+
+    >>> d = EasyDict({'foo':3})
+    >>> d['foo']
+    3
+    >>> d.foo
+    3
+    >>> d.bar
+    Traceback (most recent call last):
+    ...
+    AttributeError: 'EasyDict' object has no attribute 'bar'
+
+    Works recursively
+
+    >>> d = EasyDict({'foo':3, 'bar':{'x':1, 'y':2}})
+    >>> isinstance(d.bar, dict)
+    True
+    >>> d.bar.x
+    1
+
+    Bullet-proof
+
+    >>> EasyDict({})
+    {}
+    >>> EasyDict(d={})
+    {}
+    >>> EasyDict(None)
+    {}
+    >>> d = {'a': 1}
+    >>> EasyDict(**d)
+    {'a': 1}
+
+    Set attributes
+
+    >>> d = EasyDict()
+    >>> d.foo = 3
+    >>> d.foo
+    3
+    >>> d.bar = {'prop': 'value'}
+    >>> d.bar.prop
+    'value'
+    >>> d
+    {'foo': 3, 'bar': {'prop': 'value'}}
+    >>> d.bar.prop = 'newer'
+    >>> d.bar.prop
+    'newer'
+
+
+    Values extraction
+
+    >>> d = EasyDict({'foo':0, 'bar':[{'x':1, 'y':2}, {'x':3, 'y':4}]})
+    >>> isinstance(d.bar, list)
+    True
+    >>> from operator import attrgetter
+    >>> map(attrgetter('x'), d.bar)
+    [1, 3]
+    >>> map(attrgetter('y'), d.bar)
+    [2, 4]
+    >>> d = EasyDict()
+    >>> d.keys()
+    []
+    >>> d = EasyDict(foo=3, bar=dict(x=1, y=2))
+    >>> d.foo
+    3
+    >>> d.bar.x
+    1
+
+    Still like a dict though
+
+    >>> o = EasyDict({'clean':True})
+    >>> o.items()
+    [('clean', True)]
+
+    And like a class
+
+    >>> class Flower(EasyDict):
+    ...     power = 1
+    ...
+    >>> f = Flower()
+    >>> f.power
+    1
+    >>> f = Flower({'height': 12})
+    >>> f.height
+    12
+    >>> f['power']
+    1
+    >>> sorted(f.keys())
+    ['height', 'power']
+    """
+    def __init__(self, d=None, **kwargs):
+        if d is None:
+            d = {}
+        if kwargs:
+            d.update(**kwargs)
+        for k, v in d.items():
+            setattr(self, k, v)
+        # Class attributes
+        for k in self.__class__.__dict__.keys():
+            if not (k.startswith('__') and k.endswith('__')):
+                setattr(self, k, getattr(self, k))
+
+    def __setattr__(self, name, value):
+        if isinstance(value, (list, tuple)):
+            value = [self.__class__(x)
+                     if isinstance(x, dict) else x for x in value]
+        elif isinstance(value, dict) and not isinstance(value, self.__class__):
+            value = self.__class__(value)
+        super(EasyDict, self).__setattr__(name, value)
+        super(EasyDict, self).__setitem__(name, value)
+
+    __setitem__ = __setattr__
+
+def load_json(filename):
+    import json
+    with open(filename) as f:
+        return EasyDict(json.load(f))
+
+if __name__ == "__main__":
+    import doctest
+    doctest.testmod()
diff --git a/netdissect/encoder_loss.py b/netdissect/encoder_loss.py
new file mode 100644
index 0000000000000000000000000000000000000000..27e449b4ac2f701dabd6784fb1ad49f8ff5f8ec1
--- /dev/null
+++ b/netdissect/encoder_loss.py
@@ -0,0 +1,9 @@
+from torch.nn.functional import cosine_similarity
+
+def cor_distance(x, y, eps=1e-12):
+    # Analogous to L1 distance, but in terms of Pearson's correlation
+    return (1.0 - cosine_similarity(x, y, eps=eps)).sqrt().mean()
+
+def cor_square_error(x, y, eps=1e-12):
+    # Analogous to MSE, but in terms of Pearson's correlation
+    return (1.0 - cosine_similarity(x, y, eps=eps)).mean()
diff --git a/netdissect/encoder_net.py b/netdissect/encoder_net.py
new file mode 100644
index 0000000000000000000000000000000000000000..af96213f1b3835bb6ae5dd24c00c2219a53c29aa
--- /dev/null
+++ b/netdissect/encoder_net.py
@@ -0,0 +1,296 @@
+import numpy, math
+from . import customnet, nethook
+from collections import OrderedDict
+import torch.nn
+import re
+
+def make_over5_resnet(halfsize=False):
+    # A resnet with the global pooling layer replaced by a single 1x1
+    # conv layer, to produce a 512x8x8 featuremap.  Also adds a leaky
+    # ReLU to better resemble the distribution of r produced by the GAN.
+    resnet_depth = 18
+    # Make an encoder model.
+    def change_out(layers):
+        numch = 512 if resnet_depth < 50 else 2048
+        ind = [i for i, (n, l) in enumerate(layers) if n == 'layer4'][0] + 1
+        layers[ind:] = [('layer5',
+            torch.nn.Sequential(OrderedDict([
+                ('conv5', torch.nn.Conv2d(numch, 512, kernel_size=1)),
+                ('relu5', torch.nn.LeakyReLU(
+                    inplace=True, negative_slope=0.2))
+            ])))]
+        return layers
+    encoder = customnet.CustomResNet(
+            resnet_depth, modify_sequence=change_out, halfsize=halfsize)
+    return encoder
+
+class HybridLayerNormEncoder(torch.nn.Sequential):
+    def __init__(self, halfsize=False):
+        sequence = [
+            ('resnet', make_over5_resnet(halfsize=halfsize)),
+            ('inv4', LayerNormEncoder(512, 512)),
+            ('inv3', LayerNormEncoder(512, 512, stride=2)),
+            ('inv2', LayerNormEncoder(512, 512, skip_conv3=True)),
+            ('inv1', Layer1toZNormEncoder())
+        ]
+        super().__init__(OrderedDict(sequence))
+
+class LayerNormEncoder(torch.nn.Sequential):
+    def __init__(self, chan_in, chan_out=None, stride=1,
+            skip_conv3=False, skip_pnorm=False):
+        if chan_out is None:
+            chan_out = chan_in
+        sequence = []
+        if not skip_pnorm:
+            sequence.append(('pnorm', PixelNormLayer()))
+        sequence.extend([
+            ('conv1', torch.nn.Conv2d(chan_in, chan_out,
+                kernel_size=3, padding=1)),
+            ('bn1',   torch.nn.BatchNorm2d(chan_out)),
+            ('relu1', torch.nn.LeakyReLU(inplace=True, negative_slope=0.2)),
+            ('conv2', torch.nn.Conv2d(chan_out, chan_out,
+                kernel_size=3, padding=1)),
+            ('bn2',   torch.nn.BatchNorm2d(chan_out)),
+            ('relu2', torch.nn.LeakyReLU(inplace=True, negative_slope=0.2)),
+            ])
+        if not skip_conv3:
+            sequence.append(
+                ('conv3', torch.nn.Conv2d(chan_out, chan_out,
+                    kernel_size=1, padding=0, stride=stride)))
+        super().__init__(OrderedDict(sequence))
+        with torch.no_grad():
+            for n, p in self.named_parameters():
+                if n.endswith('.bias'):
+                    p.zero_()
+                elif not n.startswith('bn'):
+                    torch.nn.init.kaiming_normal_(p)
+
+class Layer1toZNormEncoder(torch.nn.Sequential):
+    def __init__(self):
+        super().__init__(OrderedDict([
+            ('pnorm', PixelNormLayer()),
+            ('conv1', torch.nn.Conv2d(512, 512, kernel_size=4, padding=0)),
+            ('bn1',   torch.nn.BatchNorm2d(512)),
+            ('relu1', torch.nn.LeakyReLU(inplace=True, negative_slope=0.2)),
+            ('conv2', torch.nn.Conv2d(512, 512, kernel_size=1, padding=0)),
+            ('pnormout', PixelNormLayer())
+            ]))
+        with torch.no_grad():
+            for n, p in self.named_parameters():
+                if n.endswith('.bias'):
+                    p.zero_()
+                elif not n.startswith('bn'):
+                    torch.nn.init.kaiming_normal_(p)
+
+class ResidualGenerator(nethook.InstrumentedModel):
+    '''
+    '''
+    def __init__(self, generator, z, residual_layers):
+        '''
+        ResidualGenerator(generator, z, ['z', 'layer1', 'layer2'])
+        Returns a model that computes generator(z), but which has
+        additional internal parameters dz, d1, d2, etc, that
+        adjust the computation so that the output of layerN is
+        adjusted by dN, for example, if a network normally computes
+
+        x = layer4(layer3(layer2(layer1(z)))), then specifying
+
+        the innermost three layers will cause this to compute:
+
+        x = layer4(d3 + layer3(d2 + layer2(d1 + layer1(dz + z))))
+        '''
+        # First temporarily hook the layers of the generator to
+        # collect initial values (and output shapes) of each layer.
+        with torch.no_grad(), nethook.InstrumentedModel(generator) as g:
+            g.retain_layers([n for n in residual_layers if n != 'z'])
+            init_out = g(z)
+            init_layers = g.retained_features()
+            init_layers['z'] = z
+        # Then, permanently hook the layers of the generator to add
+        # residual adjustments dz, d1, d2, etc at each layer.
+        super().__init__(generator)
+        for k, v in init_layers.items():
+            # layer3.conv1 -> 3_conv1, shortened name.
+            name = k.replace('layer', '', 1).replace('.', '_')
+            # 3_conv1 -> self.init_3_conv1, buffer with unperturbed value
+            self.register_buffer('init_%s' % name, v.clone())
+            # Add parameter 'dz', etc for any variable listed in residuals
+            if k in residual_layers:
+                # 3_conv1 -> self.d3_conv1, parameter initialized to 0
+                dname = 'd' + name
+                setattr(self, dname, torch.nn.Parameter(torch.zeros_like(v)))
+                # Change model to add self.d[name] after computing layer k.
+                if k != 'z':
+                    self.edit_layer(k, add_adjustment, attr=dname)
+
+    def forward(self):
+        return super().forward(self.init_z + getattr(self, 'dz', 0))
+
+class FixedGANPriorGenerator(nethook.InstrumentedModel):
+    '''
+    Combines the ideas of ResidualGenerator and GANPriorRUNetGenerator.
+    '''
+    def __init__(self, generator, z, additive=False):
+        self.additive = additive
+        # To begin with, we want to glue skip connections into our
+        # generator.  Modify its 'forward' method to accept skip args.
+        generator = SkipAdjustedSequence(generator)
+        skip_layers = ['layer8', 'layer10', 'layer12', 'layer14']
+        # Gather some initial values programmatically with a temporary hook.
+        with torch.no_grad(), nethook.InstrumentedModel(generator) as g:
+            g.retain_layers([n for n in (
+                skip_layers)
+                if n != 'z'])
+            init_out = g(z)
+            init_layers = g.retained_features()
+            init_layers['z'] = z
+        # Then, permanently hook the layers of the generator to add
+        # residual adjustments dz, d1, d2, etc at each layer.
+        super().__init__(generator)
+        for k, v in init_layers.items():
+            # Record all the init_N values for reporting and reference.
+            name = k.replace('layer', '', 1).replace('.', '_')
+            self.register_buffer('init_%s' % name, v.clone())
+        # Now the deep image prior u-net side, melding with pixels.
+        # Start with a fixed random featuremap
+        seed = 1
+        rng = numpy.random.RandomState(seed)
+        self.register_buffer('noise', torch.from_numpy(
+            rng.randn(1, 32, 256, 256)).float())
+        # put through 8 conv layers
+        self.down14 = UnetDownsample(32, 32, self.init_14.size(1), 4, stride=1,
+                rng=rng)
+        self.down12 = UnetDownsample(32, 32, self.init_12.size(1), 4, rng=rng)
+        self.down10 = UnetDownsample(32, 32, self.init_10.size(1), 4, rng=rng)
+        self.down8  = UnetDownsample(32, 0, self.init_8.size(1), 4, rng=rng)
+        # Finally, also retain the editing layer, layer4
+        self.train(True)
+
+    def forward(self, z=None):
+        # First run the deep-image-prior noise maker.
+        z14, s14 = self.down14(self.noise)
+        z12, s12 = self.down12(z14)
+        z10, s10 = self.down10(z12)
+        _, s8   = self.down8(z10)
+        # _, s6   = self.down6(z8)
+        # _, s4   = self.down4(z6)
+        if z is None:
+            z = self.init_z
+        # Collect together adjustments before running the generator.
+        if self.additive:
+            x = super().forward(z,
+                    add_layer8=(s8),
+                    add_layer10=(s10),
+                    add_layer12=(s12),
+                    add_layer14=(s14),
+                    )
+        else:
+            x = super().forward(z,
+                    mult_layer8=(1 + s8),
+                    mult_layer10=(1 + s10),
+                    mult_layer12=(1 + s12),
+                    mult_layer14=(1 + s14),
+                    )
+        return dict(
+                x=x,
+                # Note: this is retained before it is multiplied
+                s8=s8,
+                s10=s10,
+                s12=s12,
+                s14=s14)
+
+class BaselineTunedDirectGenerator(nethook.InstrumentedModel):
+    '''
+    Combines the ideas of ResidualGenerator and GANPriorRUNetGenerator.
+    '''
+    def __init__(self, generator, z, tune_layers=None):
+        # To begin with, we want to glue skip connections into our
+        # generator.  Modify its 'forward' method to accept skip args.
+        generator = SkipAdjustedSequence(generator)
+        if tune_layers is None:
+            tune_layers = ['layer8', 'layer10', 'layer12', 'layer14']
+        # Gather some initial values programmatically with a temporary hook.
+        with torch.no_grad(), nethook.InstrumentedModel(generator) as g:
+            g.retain_layers([n for n in (
+                tune_layers)
+                if n != 'z'])
+            init_out = g(z)
+            init_layers = g.retained_features()
+            init_layers['z'] = z
+        # Then, permanently hook the layers of the generator to add
+        # residual adjustments dz, d1, d2, etc at each layer.
+        super().__init__(generator)
+        self.adjustments = []
+        for k, v in init_layers.items():
+            # Record all the init_N values for reporting and reference.
+            name = k.replace('layer', '', 1).replace('.', '_')
+            dname = 'd%s' % name
+            self.register_buffer('init_%s' % name, v.clone())
+            if 'layer' in k:
+                self.adjustments.append(('mult_%s' % k, dname))
+            setattr(self, dname, torch.nn.Parameter(torch.zeros_like(v)))
+        self.train(True)
+
+    def forward(self, z=None, **kwargs):
+        # Collect together adjustments before running the generator.
+        kwadj = {k: 1 +
+            kwargs.get(dname, getattr(self, dname)) # Allow kwargs to override
+            for k, dname in self.adjustments}
+        if z is None:
+            z = self.init_z
+        x = super().forward(z, **kwadj)
+        kwout = {dname: getattr(self, dname) for k, dname in self.adjustments}
+        return dict(x=x, **kwout)
+
+class SkipAdjustedSequence(torch.nn.Sequential):
+    def __init__(self, sequential, share_weights=False):
+        '''
+        Creates a subsequence of a pytorch Sequential model, copying over
+        modules together with parameters for the subsequence.  Only
+        modules from first_layer to last_layer (inclusive) are included.
+
+        If share_weights is True, then references the original modules
+        and their parameters without copying them.  Otherwise, by default,
+        makes a separate brand-new copy.
+        '''
+        included_children = OrderedDict()
+        for name, layer in sequential._modules.items():
+            included_children[name] = layer if share_weights else (
+                        copy.deepcopy(layer))
+        if not len(included_children):
+            raise ValueError('Empty subsequence')
+        super().__init__(OrderedDict(included_children))
+
+    def forward(this, x, **kwargs):
+        '''
+        Runs the sequence, except after each step 'layer', adds
+        any 'add_layer' value from kwargs to the output; similarly
+        multiplies any 'mult_layer' value from kwargs if present.
+        '''
+        seen = set()
+        for name, layer in this._modules.items():
+            x = layer(x)
+            add = kwargs.get('add_' + name, None)
+            if add is not None:
+                x = x + add
+                seen.add('add_' + name)
+            mult = kwargs.get('mult_' + name, None)
+            if mult is not None:
+                x = x * mult
+                seen.add('mult_' + name)
+        for name in kwargs.keys():
+            assert name in seen, '%s not applied' % name
+        return x
+
+class PixelNormLayer(torch.nn.Module):
+    def __init__(self):
+        super(PixelNormLayer, self).__init__()
+
+    def forward(self, x):
+        return x / torch.sqrt(torch.mean(x**2, dim=1, keepdim=True) + 1e-8)
+
+def add_adjustment(x, idecoder, attr):
+    adjustment = getattr(idecoder, attr)
+    x = x + adjustment
+    return x
+
diff --git a/netdissect/evalablate.py b/netdissect/evalablate.py
new file mode 100644
index 0000000000000000000000000000000000000000..cc0422638699e4b7fe15dc7ed8193ecdd5a5a44a
--- /dev/null
+++ b/netdissect/evalablate.py
@@ -0,0 +1,231 @@
+import torch, sys, os, argparse, textwrap, numbers, numpy, json, PIL
+from torchvision import transforms
+from torch.utils.data import TensorDataset
+from netdissect import pbar
+from netdissect.nethook import edit_layers
+from netdissect.zdataset import standard_z_sample
+from netdissect.autoeval import autoimport_eval
+from netdissect.easydict import EasyDict
+from netdissect.modelconfig import create_instrumented_model
+
+help_epilog = '''\
+Example:
+
+python -m netdissect.evalablate \
+      --segmenter "netdissect.segmenter.UnifiedParsingSegmenter(segsizes=[256], segdiv='quad')" \
+      --model "proggan.from_pth_file('models/lsun_models/${SCENE}_lsun.pth')" \
+      --outdir dissect/dissectdir \
+      --classes mirror coffeetable tree \
+      --layers layer4 \
+      --size 1000
+
+Output layout:
+dissectdir/layer5/ablation/mirror-iqr.json
+{ class: "mirror",
+  classnum: 43,
+  pixel_total: 41342300,
+  class_pixels: 1234531,
+  layer: "layer5",
+  ranking: "mirror-iqr",
+  ablation_units: [341, 23, 12, 142, 83, ...]
+  ablation_pixels: [143242, 132344, 429931, ...]
+}
+
+'''
+
+def main():
+    # Training settings
+    def strpair(arg):
+        p = tuple(arg.split(':'))
+        if len(p) == 1:
+            p = p + p
+        return p
+
+    parser = argparse.ArgumentParser(description='Ablation eval',
+            epilog=textwrap.dedent(help_epilog),
+            formatter_class=argparse.RawDescriptionHelpFormatter)
+    parser.add_argument('--model', type=str, default=None,
+                        help='constructor for the model to test')
+    parser.add_argument('--pthfile', type=str, default=None,
+                        help='filename of .pth file for the model')
+    parser.add_argument('--outdir', type=str, default='dissect', required=True,
+                        help='directory for dissection output')
+    parser.add_argument('--layers', type=strpair, nargs='+',
+                        help='space-separated list of layer names to edit' + 
+                        ', in the form layername[:reportedname]')
+    parser.add_argument('--classes', type=str, nargs='+',
+                        help='space-separated list of class names to ablate')
+    parser.add_argument('--metric', type=str, default='iou',
+                        help='ordering metric for selecting units')
+    parser.add_argument('--unitcount', type=int, default=30,
+                        help='number of units to ablate')
+    parser.add_argument('--segmenter', type=str,
+                        help='directory containing segmentation dataset')
+    parser.add_argument('--netname', type=str, default=None,
+                        help='name for network in generated reports')
+    parser.add_argument('--batch_size', type=int, default=5,
+                        help='batch size for forward pass')
+    parser.add_argument('--size', type=int, default=200,
+                        help='number of images to test')
+    parser.add_argument('--no-cuda', action='store_true', default=False,
+                        help='disables CUDA usage')
+    parser.add_argument('--quiet', action='store_true', default=False,
+                        help='silences console output')
+    if len(sys.argv) == 1:
+        parser.print_usage(sys.stderr)
+        sys.exit(1)
+    args = parser.parse_args()
+
+    # Set up console output
+    pbar.verbose(not args.quiet)
+
+    # Speed up pytorch
+    torch.backends.cudnn.benchmark = True
+
+    # Set up CUDA
+    args.cuda = not args.no_cuda and torch.cuda.is_available()
+    if args.cuda:
+        torch.backends.cudnn.benchmark = True
+
+    # Take defaults for model constructor etc from dissect.json settings.
+    with open(os.path.join(args.outdir, 'dissect.json')) as f:
+        dissection = EasyDict(json.load(f))
+    if args.model is None:
+        args.model = dissection.settings.model
+    if args.pthfile is None:
+        args.pthfile = dissection.settings.pthfile
+    if args.segmenter is None:
+        args.segmenter = dissection.settings.segmenter
+
+    # Instantiate generator
+    model = create_instrumented_model(args, gen=True, edit=True)
+    if model is None:
+        print('No model specified')
+        sys.exit(1)
+
+    # Instantiate model
+    device = next(model.parameters()).device
+    input_shape = model.input_shape
+
+    # 4d input if convolutional, 2d input if first layer is linear.
+    raw_sample = standard_z_sample(args.size, input_shape[1], seed=2).view(
+            (args.size,) + input_shape[1:])
+    dataset = TensorDataset(raw_sample)
+
+    # Create the segmenter
+    segmenter = autoimport_eval(args.segmenter)
+
+    # Now do the actual work.
+    labelnames, catnames = (
+                segmenter.get_label_and_category_names(dataset))
+    label_category = [catnames.index(c) if c in catnames else 0
+            for l, c in labelnames]
+    labelnum_from_name = {n[0]: i for i, n in enumerate(labelnames)}
+
+    segloader = torch.utils.data.DataLoader(dataset,
+                batch_size=args.batch_size, num_workers=10,
+                pin_memory=(device.type == 'cuda'))
+
+    # Index the dissection layers by layer name.
+    dissect_layer = {lrec.layer: lrec for lrec in dissection.layers}
+
+    # First, collect a baseline
+    for l in model.ablation:
+        model.ablation[l] = None
+
+    # For each sort-order, do an ablation
+    for classname in pbar(args.classes):
+        pbar.post(c=classname)
+        for layername in pbar(model.ablation):
+            pbar.post(l=layername)
+            rankname = '%s-%s' % (classname, args.metric)
+            classnum = labelnum_from_name[classname]
+            try:
+                ranking = next(r for r in dissect_layer[layername].rankings
+                        if r.name == rankname)
+            except:
+                print('%s not found' % rankname)
+                sys.exit(1)
+            ordering = numpy.argsort(ranking.score)
+            # Check if already done
+            ablationdir = os.path.join(args.outdir, layername, 'pixablation')
+            if os.path.isfile(os.path.join(ablationdir, '%s.json'%rankname)):
+                with open(os.path.join(ablationdir, '%s.json'%rankname)) as f:
+                    data = EasyDict(json.load(f))
+                # If the unit ordering is not the same, something is wrong
+                if not all(a == o
+                        for a, o in zip(data.ablation_units, ordering)):
+                    continue
+                if len(data.ablation_effects) >= args.unitcount:
+                    continue # file already done.
+                measurements = data.ablation_effects
+            measurements = measure_ablation(segmenter, segloader,
+                    model, classnum, layername, ordering[:args.unitcount])
+            measurements = measurements.cpu().numpy().tolist()
+            os.makedirs(ablationdir, exist_ok=True)
+            with open(os.path.join(ablationdir, '%s.json'%rankname), 'w') as f:
+                json.dump(dict(
+                    classname=classname,
+                    classnum=classnum,
+                    baseline=measurements[0],
+                    layer=layername,
+                    metric=args.metric,
+                    ablation_units=ordering.tolist(),
+                    ablation_effects=measurements[1:]), f)
+
+def measure_ablation(segmenter, loader, model, classnum, layer, ordering):
+    total_bincount = 0
+    data_size = 0
+    device = next(model.parameters()).device
+    for l in model.ablation:
+        model.ablation[l] = None
+    feature_units = model.feature_shape[layer][1]
+    feature_shape = model.feature_shape[layer][2:]
+    repeats = len(ordering)
+    total_scores = torch.zeros(repeats + 1)
+    for i, batch in enumerate(pbar(loader)):
+        z_batch = batch[0]
+        model.ablation[layer] = None
+        tensor_images = model(z_batch.to(device))
+        seg = segmenter.segment_batch(tensor_images, downsample=2)
+        mask = (seg == classnum).max(1)[0]
+        downsampled_seg = torch.nn.functional.adaptive_avg_pool2d(
+                mask.float()[:,None,:,:], feature_shape)[:,0,:,:]
+        total_scores[0] += downsampled_seg.sum().cpu()
+        # Now we need to do an intervention for every location
+        # that had a nonzero downsampled_seg, if any.
+        interventions_needed = downsampled_seg.nonzero()
+        location_count = len(interventions_needed)
+        if location_count == 0:
+            continue
+        interventions_needed = interventions_needed.repeat(repeats, 1)
+        inter_z = batch[0][interventions_needed[:,0]].to(device)
+        inter_chan = torch.zeros(repeats, location_count, feature_units,
+                device=device)
+        for j, u in enumerate(ordering):
+            inter_chan[j:, :, u] = 1
+        inter_chan = inter_chan.view(len(inter_z), feature_units)
+        inter_loc = interventions_needed[:,1:]
+        scores = torch.zeros(len(inter_z))
+        batch_size = len(batch[0])
+        for j in range(0, len(inter_z), batch_size):
+            ibz = inter_z[j:j+batch_size]
+            ibl = inter_loc[j:j+batch_size].t()
+            imask = torch.zeros((len(ibz),) + feature_shape, device=ibz.device)
+            imask[(torch.arange(len(ibz)),) + tuple(ibl)] = 1
+            ibc = inter_chan[j:j+batch_size]
+            model.ablation[layer] = (
+                    imask.float()[:,None,:,:] * ibc[:,:,None,None])
+            tensor_images = model(ibz)
+            seg = segmenter.segment_batch(tensor_images, downsample=2)
+            mask = (seg == classnum).max(1)[0]
+            downsampled_iseg = torch.nn.functional.adaptive_avg_pool2d(
+                    mask.float()[:,None,:,:], feature_shape)[:,0,:,:]
+            scores[j:j+batch_size] = downsampled_iseg[
+                    (torch.arange(len(ibz)),) + tuple(ibl)]
+        scores = scores.view(repeats, location_count).sum(1)
+        total_scores[1:] += scores
+    return total_scores
+
+if __name__ == '__main__':
+    main()
diff --git a/netdissect/frechet_distance.py b/netdissect/frechet_distance.py
new file mode 100644
index 0000000000000000000000000000000000000000..8ce1435aa7eff4d18489193acdba13df391f8a9b
--- /dev/null
+++ b/netdissect/frechet_distance.py
@@ -0,0 +1,118 @@
+#!/usr/bin/env python3
+"""Calculates the Frechet Distance (FD) between two samples.
+
+Code apapted from https://github.com/bioinf-jku/TTUR to use PyTorch instead
+of Tensorflow
+
+Copyright 2018 Institute of Bioinformatics, JKU Linz
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+   http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+"""
+import numpy as np
+import torch
+from scipy import linalg
+
+def sample_frechet_distance(sample1, sample2, eps=1e-6,
+        return_components=False):
+    '''
+    Both samples should be numpy arrays.
+    Returns the Frechet distance.
+    '''
+    (mu1, sigma1), (mu2, sigma2) = [calculate_activation_statistics(s)
+            for s in [sample1, sample2]]
+    return calculate_frechet_distance(mu1, sigma1, mu2, sigma2, eps=eps,
+            return_components=return_components)
+
+def calculate_frechet_distance(mu1, sigma1, mu2, sigma2, eps=1e-6,
+        return_components=False):
+    """Numpy implementation of the Frechet Distance.
+    The Frechet distance between two multivariate Gaussians X_1 ~ N(mu_1, C_1)
+    and X_2 ~ N(mu_2, C_2) is
+            d^2 = ||mu_1 - mu_2||^2 + Tr(C_1 + C_2 - 2*sqrt(C_1*C_2)).
+
+    Stable version by Dougal J. Sutherland.
+
+    Params:
+    -- mu1   : Numpy array containing the activations of a layer of the
+               inception net (like returned by the function 'get_predictions')
+               for generated samples.
+    -- mu2   : The sample mean over activations, precalculated on an
+               representative data set.
+    -- sigma1: The covariance matrix over activations for generated samples.
+    -- sigma2: The covariance matrix over activations, precalculated on an
+               representative data set.
+
+    Returns:
+    --   : The Frechet Distance.
+    """
+
+    mu1 = np.atleast_1d(mu1)
+    mu2 = np.atleast_1d(mu2)
+
+    sigma1 = np.atleast_2d(sigma1)
+    sigma2 = np.atleast_2d(sigma2)
+
+    assert mu1.shape == mu2.shape, \
+        'Training and test mean vectors have different lengths'
+    assert sigma1.shape == sigma2.shape, \
+        'Training and test covariances have different dimensions'
+
+    diff = mu1 - mu2
+
+    # Product might be almost singular
+    covmean, _ = linalg.sqrtm(sigma1.dot(sigma2), disp=False)
+    if not np.isfinite(covmean).all():
+        msg = ('fid calculation produces singular product; '
+               'adding %s to diagonal of cov estimates') % eps
+        print(msg)
+        offset = np.eye(sigma1.shape[0]) * eps
+        covmean = linalg.sqrtm((sigma1 + offset).dot(sigma2 + offset))
+
+    # Numerical error might give slight imaginary component
+    if np.iscomplexobj(covmean):
+        if not np.allclose(np.diagonal(covmean).imag, 0, atol=1e-3):
+            m = np.max(np.abs(covmean.imag))
+            raise ValueError('Imaginary component {}'.format(m))
+        covmean = covmean.real
+
+    tr_covmean = np.trace(covmean)
+
+    meandiff = diff.dot(diff)
+    covdiff = np.trace(sigma1) + np.trace(sigma2) - 2 * tr_covmean
+    if return_components:
+        return (meandiff + covdiff, meandiff, covdiff)
+    else:
+        return meandiff + covdiff
+
+
+def calculate_activation_statistics(act):
+    """Calculation of the statistics used by the FID.
+    Params:
+    -- files       : List of image files paths
+    -- model       : Instance of inception model
+    -- batch_size  : The images numpy array is split into batches with
+                     batch size batch_size. A reasonable batch size
+                     depends on the hardware.
+    -- dims        : Dimensionality of features returned by Inception
+    -- cuda        : If set to True, use GPU
+    -- verbose     : If set to True and parameter out_step is given, the
+                     number of calculated batches is reported.
+    Returns:
+    -- mu    : The mean over samples of the activations of the pool_3 layer of
+               the inception model.
+    -- sigma : The covariance matrix of the activations of the pool_3 layer of
+               the inception model.
+    """
+    mu = np.mean(act, axis=0)
+    sigma = np.cov(act, rowvar=False)
+    return mu, sigma
diff --git a/netdissect/fsd.py b/netdissect/fsd.py
new file mode 100644
index 0000000000000000000000000000000000000000..86f70663516a7113962f02ad6d959b0a75fe2f3c
--- /dev/null
+++ b/netdissect/fsd.py
@@ -0,0 +1,221 @@
+import torch, argparse, sys, os, numpy
+from .sampler import FixedRandomSubsetSampler, FixedSubsetSampler
+from torch.utils.data import DataLoader
+from torchvision import transforms
+from . import pbar
+from . import zdataset
+from . import segmenter
+from . import frechet_distance
+from . import parallelfolder
+
+
+NUM_OBJECTS=336
+
+def main():
+    parser = argparse.ArgumentParser(description='Net dissect utility',
+            prog='python -m %s.fsd' % __package__)
+    parser.add_argument('true_dir')
+    parser.add_argument('gen_dir')
+    parser.add_argument('--size', type=int, default=10000)
+    parser.add_argument('--cachedir', default=None)
+    parser.add_argument('--histout', default=None)
+    parser.add_argument('--maxscale', type=float, default=50)
+    parser.add_argument('--labelcount', type=int, default=30)
+    parser.add_argument('--dpi', type=float, default=100)
+    if len(sys.argv) == 1:
+        parser.print_usage(sys.stderr)
+        sys.exit(1)
+    args = parser.parse_args()
+    true_dir, gen_dir = args.true_dir, args.gen_dir
+    seed1, seed2 = [1, 1 if true_dir != gen_dir else 2]
+    true_tally, gen_tally = [
+            cached_tally_directory(d, size=args.size, cachedir=args.cachedir,
+                seed=seed)
+            for d, seed in [(true_dir, seed1), (gen_dir, seed2)]]
+    fsd, meandiff, covdiff = frechet_distance.sample_frechet_distance(
+            true_tally * 100, gen_tally * 100, return_components=True)
+    print('fsd: %f; meandiff: %f; covdiff: %f' % (fsd, meandiff, covdiff))
+    if args.histout is not None:
+        diff_figure(true_tally * 100, gen_tally * 100,
+                labelcount=args.labelcount,
+                maxscale=args.maxscale,
+                dpi=args.dpi
+                ).savefig(args.histout)
+
+def cached_tally_directory(directory, size=10000, cachedir=None, seed=1,
+        download_from=None):
+    basename = ('%s_segtally_%d.npy' % (directory, size)).replace('/', '_')
+    if seed != 1:
+        basename = '%d_%s' % (seed, basename)
+    if cachedir is not None:
+        filename = os.path.join(cachedir, basename.replace('/', '_'))
+    else:
+        filename = basename
+    if not os.path.isfile(filename) and download_from:
+        from urllib.request import urlretrieve
+        from urllib.parse import urljoin
+        with pbar.reporthook() as hook:
+            urlretrieve(urljoin(download_from, basename), filename,
+                    reporthook=hook)
+    if os.path.isfile(filename):
+        return numpy.load(filename)
+    os.makedirs(cachedir, exist_ok=True)
+    result = tally_directory(directory, size, seed=seed)
+    numpy.save(filename, result)
+    return result
+
+def tally_directory(directory, size=10000, seed=1):
+    dataset = parallelfolder.ParallelImageFolders(
+                [directory],
+                transform=transforms.Compose([
+                    transforms.Resize(256),
+                    transforms.CenterCrop(256),
+                    transforms.ToTensor(),
+                    transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))
+                    ]))
+    loader = DataLoader(dataset,
+                        sampler=FixedRandomSubsetSampler(dataset, end=size,
+                            seed=seed),
+                        batch_size=10, pin_memory=True)
+    upp = segmenter.UnifiedParsingSegmenter()
+    labelnames, catnames = upp.get_label_and_category_names()
+    result = numpy.zeros((size, NUM_OBJECTS), dtype=numpy.float)
+    batch_result = torch.zeros(loader.batch_size, NUM_OBJECTS,
+            dtype=torch.float).cuda()
+    with torch.no_grad():
+        batch_index = 0
+        for [batch] in pbar(loader):
+            seg_result = upp.segment_batch(batch.cuda())
+            for i in range(len(batch)):
+                batch_result[i] = (
+                    seg_result[i,0].view(-1).bincount(
+                        minlength=NUM_OBJECTS).float()
+                    / (seg_result.shape[2] * seg_result.shape[3])
+                )
+            result[batch_index:batch_index+len(batch)] = (
+                    batch_result.cpu().numpy())
+            batch_index += len(batch)
+    return result
+
+def tally_dataset_objects(dataset, size=10000):
+    loader = DataLoader(dataset,
+                        sampler=FixedRandomSubsetSampler(dataset, end=size),
+                        batch_size=10, pin_memory=True)
+    upp = segmenter.UnifiedParsingSegmenter()
+    labelnames, catnames = upp.get_label_and_category_names()
+    result = numpy.zeros((size, NUM_OBJECTS), dtype=numpy.float)
+    batch_result = torch.zeros(loader.batch_size, NUM_OBJECTS,
+            dtype=torch.float).cuda()
+    with torch.no_grad():
+        batch_index = 0
+        for [batch] in pbar(loader):
+            seg_result = upp.segment_batch(batch.cuda())
+            for i in range(len(batch)):
+                batch_result[i] = (
+                    seg_result[i,0].view(-1).bincount(
+                        minlength=NUM_OBJECTS).float()
+                    / (seg_result.shape[2] * seg_result.shape[3])
+                )
+            result[batch_index:batch_index+len(batch)] = (
+                    batch_result.cpu().numpy())
+            batch_index += len(batch)
+    return result
+
+def tally_generated_objects(model, size=10000):
+    zds = zdataset.z_dataset_for_model(model, size)
+    loader = DataLoader(zds, batch_size=10, pin_memory=True)
+    upp = segmenter.UnifiedParsingSegmenter()
+    labelnames, catnames = upp.get_label_and_category_names()
+    result = numpy.zeros((size, NUM_OBJECTS), dtype=numpy.float)
+    batch_result = torch.zeros(loader.batch_size, NUM_OBJECTS,
+            dtype=torch.float).cuda()
+    with torch.no_grad():
+        batch_index = 0
+        for [zbatch] in pbar(loader):
+            img = model(zbatch.cuda())
+            seg_result = upp.segment_batch(img)
+            for i in range(len(zbatch)):
+                batch_result[i] = (
+                    seg_result[i,0].view(-1).bincount(
+                        minlength=NUM_OBJECTS).float()
+                    / (seg_result.shape[2] * seg_result.shape[3])
+                    )
+            result[batch_index:batch_index+len(zbatch)] = (
+                    batch_result.cpu().numpy())
+            batch_index += len(zbatch)
+    return result
+
+def diff_figure(ttally, gtally,
+              labelcount=30, labelleft=True, dpi=100,
+              maxscale=50.0, legend=False):
+    from matplotlib.backends.backend_agg import FigureCanvasAgg as FigureCanvas
+    from matplotlib.figure import Figure
+    tresult, gresult = [t.mean(0) for t in [ttally, gtally]]
+    upp = segmenter.UnifiedParsingSegmenter()
+    labelnames, catnames = upp.get_label_and_category_names()
+    x = []
+    labels = []
+    gen_amount = []
+    change_frac = []
+    true_amount = []
+    for label in numpy.argsort(-tresult):
+        if label == 0 or labelnames[label][1] == 'material':
+            continue
+        if tresult[label] == 0:
+            break
+        x.append(len(x))
+        labels.append(labelnames[label][0].split()[0])
+        true_amount.append(tresult[label].item())
+        gen_amount.append(gresult[label].item())
+        change_frac.append((float(gresult[label] - tresult[label])
+                            / tresult[label]))
+        if len(x) >= labelcount:
+            break
+    fig = Figure(dpi=dpi, figsize=(1.4 + 5.0 * labelcount / 30, 4.0))
+    FigureCanvas(fig)
+    a1, a0 = fig.subplots(2, 1, gridspec_kw = {'height_ratios':[1, 2]})
+    a0.bar(x, change_frac, label='relative delta')
+    a0.set_xticks(x)
+    a0.set_xticklabels(labels, rotation='vertical')
+    if labelleft:
+        a0.set_ylabel('relative delta\n(gen - train) / train')
+    a0.set_xlim(-1.0, len(x))
+    a0.set_ylim([-1, 1.1])
+    a0.grid(axis='y', antialiased=False, alpha=0.25)
+    if legend:
+        a0.legend(loc=2)
+    prev_high = None
+    for ix, cf in enumerate(change_frac):
+        if cf > 1.15:
+            if prev_high == (ix - 1):
+                offset = 0.1
+            else:
+                offset = 0.0
+                prev_high = ix
+            a0.text(ix, 1.15 + offset,
+                    '%.1f' % cf, horizontalalignment='center', size=6)
+
+    a1.bar(x, true_amount, label='training')
+    a1.plot(x, gen_amount, linewidth=3, color='red', label='generated')
+    a1.set_yscale('log')
+    a1.set_xlim(-1.0, len(x))
+    a1.set_ylim(maxscale / 5000, maxscale)
+    from matplotlib.ticker import LogLocator
+    # a1.yaxis.set_major_locator(LogLocator(subs=(1,)))
+    # a1.yaxis.set_minor_locator(LogLocator(subs=(1,), numdecs=10))
+    # a1.yaxis.set_minor_locator(LogLocator(subs=(1,2,3,4,5,6,7,8,9)))
+    # a1.yaxis.set_minor_locator(yminor_locator)
+    if labelleft:
+        a1.set_ylabel('mean area\nlog scale')
+    if legend:
+        a1.legend()
+    a1.set_yticks([1e-2, 1e-1, 1.0, 1e+1])
+    a1.set_yticks([a * b for a in [1e-2, 1e-1, 1.0, 1e+1] for b in range(1,10)
+        if maxscale / 5000 <= a * b <= maxscale],
+            True) # minor ticks.
+    a1.set_xticks([])
+    fig.tight_layout()
+    return fig
+
+if __name__ == '__main__':
+    main()
diff --git a/netdissect/fullablate.py b/netdissect/fullablate.py
new file mode 100644
index 0000000000000000000000000000000000000000..4f89381c6ec3119bc4fa3d1de3a5b92c14b62cc5
--- /dev/null
+++ b/netdissect/fullablate.py
@@ -0,0 +1,218 @@
+import torch, sys, os, argparse, textwrap, numbers, numpy, json, PIL
+from torchvision import transforms
+from torch.utils.data import TensorDataset
+from netdissect import pbar
+from netdissect.nethook import edit_layers
+from netdissect.zdataset import standard_z_sample
+from netdissect.autoeval import autoimport_eval
+from netdissect.easydict import EasyDict
+from netdissect.modelconfig import create_instrumented_model
+
+help_epilog = '''\
+Example:
+
+python -m netdissect.evalablate \
+      --segmenter "netdissect.GanImageSegmenter(segvocab='lowres', segsizes=[160,288], segdiv='quad')" \
+      --model "proggan.from_pth_file('models/lsun_models/${SCENE}_lsun.pth')" \
+      --outdir dissect/dissectdir \
+      --classname tree \
+      --layer layer4 \
+      --size 1000
+
+Output layout:
+dissectdir/layer5/ablation/mirror-iqr.json
+{ class: "mirror",
+  classnum: 43,
+  pixel_total: 41342300,
+  class_pixels: 1234531,
+  layer: "layer5",
+  ranking: "mirror-iqr",
+  ablation_units: [341, 23, 12, 142, 83, ...]
+  ablation_pixels: [143242, 132344, 429931, ...]
+}
+
+'''
+
+def main():
+    # Training settings
+    def strpair(arg):
+        p = tuple(arg.split(':'))
+        if len(p) == 1:
+            p = p + p
+        return p
+
+    parser = argparse.ArgumentParser(description='Ablation eval',
+            epilog=textwrap.dedent(help_epilog),
+            formatter_class=argparse.RawDescriptionHelpFormatter)
+    parser.add_argument('--model', type=str, default=None,
+                        help='constructor for the model to test')
+    parser.add_argument('--pthfile', type=str, default=None,
+                        help='filename of .pth file for the model')
+    parser.add_argument('--outdir', type=str, default='dissect', required=True,
+                        help='directory for dissection output')
+    parser.add_argument('--layer', type=strpair,
+                        help='space-separated list of layer names to edit' + 
+                        ', in the form layername[:reportedname]')
+    parser.add_argument('--classname', type=str,
+                        help='class name to ablate')
+    parser.add_argument('--metric', type=str, default='iou',
+                        help='ordering metric for selecting units')
+    parser.add_argument('--unitcount', type=int, default=30,
+                        help='number of units to ablate')
+    parser.add_argument('--segmenter', type=str,
+                        help='directory containing segmentation dataset')
+    parser.add_argument('--netname', type=str, default=None,
+                        help='name for network in generated reports')
+    parser.add_argument('--batch_size', type=int, default=25,
+                        help='batch size for forward pass')
+    parser.add_argument('--mixed_units', action='store_true', default=False,
+                        help='true to keep alpha for non-zeroed units')
+    parser.add_argument('--size', type=int, default=200,
+                        help='number of images to test')
+    parser.add_argument('--no-cuda', action='store_true', default=False,
+                        help='disables CUDA usage')
+    parser.add_argument('--quiet', action='store_true', default=False,
+                        help='silences console output')
+    if len(sys.argv) == 1:
+        parser.print_usage(sys.stderr)
+        sys.exit(1)
+    args = parser.parse_args()
+
+    # Set up console output
+    pbar.verbose(not args.quiet)
+
+    # Speed up pytorch
+    torch.backends.cudnn.benchmark = True
+
+    # Set up CUDA
+    args.cuda = not args.no_cuda and torch.cuda.is_available()
+    if args.cuda:
+        torch.backends.cudnn.benchmark = True
+
+    # Take defaults for model constructor etc from dissect.json settings.
+    with open(os.path.join(args.outdir, 'dissect.json')) as f:
+        dissection = EasyDict(json.load(f))
+    if args.model is None:
+        args.model = dissection.settings.model
+    if args.pthfile is None:
+        args.pthfile = dissection.settings.pthfile
+    if args.segmenter is None:
+        args.segmenter = dissection.settings.segmenter
+    if args.layer is None:
+        args.layer = dissection.settings.layers[0]
+    args.layers = [args.layer]
+
+    # Also load specific analysis
+    layername = args.layer[1]
+    if args.metric == 'iou':
+        summary = dissection
+    else:
+        with open(os.path.join(args.outdir, layername, args.metric,
+                args.classname, 'summary.json')) as f:
+            summary = EasyDict(json.load(f))
+
+    # Instantiate generator
+    model = create_instrumented_model(args, gen=True, edit=True)
+    if model is None:
+        print('No model specified')
+        sys.exit(1)
+
+    # Instantiate model
+    device = next(model.parameters()).device
+    input_shape = model.input_shape
+
+    # 4d input if convolutional, 2d input if first layer is linear.
+    raw_sample = standard_z_sample(args.size, input_shape[1], seed=3).view(
+            (args.size,) + input_shape[1:])
+    dataset = TensorDataset(raw_sample)
+
+    # Create the segmenter
+    segmenter = autoimport_eval(args.segmenter)
+
+    # Now do the actual work.
+    labelnames, catnames = (
+                segmenter.get_label_and_category_names(dataset))
+    label_category = [catnames.index(c) if c in catnames else 0
+            for l, c in labelnames]
+    labelnum_from_name = {n[0]: i for i, n in enumerate(labelnames)}
+
+    segloader = torch.utils.data.DataLoader(dataset,
+                batch_size=args.batch_size, num_workers=10,
+                pin_memory=(device.type == 'cuda'))
+
+    # Index the dissection layers by layer name.
+
+    # First, collect a baseline
+    for l in model.ablation:
+        model.ablation[l] = None
+
+    # For each sort-order, do an ablation
+    classname = args.classname
+    classnum = labelnum_from_name[classname]
+
+    # Get iou ranking from dissect.json
+    iou_rankname = '%s-%s' % (classname, 'iou')
+    dissect_layer = {lrec.layer: lrec for lrec in dissection.layers}
+    iou_ranking = next(r for r in dissect_layer[layername].rankings
+                if r.name == iou_rankname)
+
+    # Get trained ranking from summary.json
+    rankname = '%s-%s' % (classname, args.metric)
+    summary_layer = {lrec.layer: lrec for lrec in summary.layers}
+    ranking = next(r for r in summary_layer[layername].rankings
+                if r.name == rankname)
+
+    # Get ordering, first by ranking, then break ties by iou.
+    ordering = [t[2] for t in sorted([(s1, s2, i)
+        for i, (s1, s2) in enumerate(zip(ranking.score, iou_ranking.score))])]
+    values = (-numpy.array(ranking.score))[ordering]
+    if not args.mixed_units:
+        values[...] = 1
+
+    ablationdir = os.path.join(args.outdir, layername, 'fullablation')
+    measurements = measure_full_ablation(segmenter, segloader,
+            model, classnum, layername,
+            ordering[:args.unitcount], values[:args.unitcount])
+    measurements = measurements.cpu().numpy().tolist()
+    os.makedirs(ablationdir, exist_ok=True)
+    with open(os.path.join(ablationdir, '%s.json'%rankname), 'w') as f:
+        json.dump(dict(
+            classname=classname,
+            classnum=classnum,
+            baseline=measurements[0],
+            layer=layername,
+            metric=args.metric,
+            ablation_units=ordering,
+            ablation_values=values.tolist(),
+            ablation_effects=measurements[1:]), f)
+
+def measure_full_ablation(segmenter, loader, model, classnum, layer,
+        ordering, values):
+    '''
+    Quick and easy counting of segmented pixels reduced by ablating units.
+    '''
+    device = next(model.parameters()).device
+    feature_units = model.feature_shape[layer][1]
+    feature_shape = model.feature_shape[layer][2:]
+    repeats = len(ordering)
+    total_scores = torch.zeros(repeats + 1)
+    print(ordering)
+    print(values.tolist())
+    with torch.no_grad():
+        for l in model.ablation:
+            model.ablation[l] = None
+        for i, [ibz] in enumerate(pbar(loader)):
+            ibz = ibz.cuda()
+            for num_units in pbar(range(len(ordering) + 1)):
+                ablation = torch.zeros(feature_units, device=device)
+                ablation[ordering[:num_units]] = torch.tensor(
+                        values[:num_units]).to(ablation.device, ablation.dtype)
+                model.ablation[layer] = ablation
+                tensor_images = model(ibz)
+                seg = segmenter.segment_batch(tensor_images, downsample=2)
+                mask = (seg == classnum).max(1)[0]
+                total_scores[num_units] += mask.sum().float().cpu()
+    return total_scores
+
+if __name__ == '__main__':
+    main()
diff --git a/netdissect/imgsave.py b/netdissect/imgsave.py
new file mode 100644
index 0000000000000000000000000000000000000000..66915e15b4785b4819f00e677ed58e59f9756f85
--- /dev/null
+++ b/netdissect/imgsave.py
@@ -0,0 +1,62 @@
+# A utility for saving a large number of images quickly without
+# blocking a single thread to wait for each individual image to save.
+
+import os
+import PIL
+from .workerpool import WorkerBase, WorkerPool
+from . import pbar
+
+
+def all_items_and_filenames(img_array, filename_pattern, index=()):
+    for i, data in enumerate(img_array):
+        inner_index = index + (i,)
+        if PIL.Image.isImageType(data):
+            yield data, (filename_pattern % inner_index)
+        else:
+            for img, name in all_items_and_filenames(data, filename_pattern,
+                    inner_index):
+                yield img, name
+
+def expand_last_filename(img_array, filename_pattern):
+    index, data = (), img_array
+    while not PIL.Image.isImageType(data):
+        index += (len(data) - 1,)
+        data = data[len(data) - 1]
+    return filename_pattern % index
+
+def num_items(img_array):
+    num = 1
+    while not PIL.Image.isImageType(img_array):
+       num *= len(img_array)
+       img_array = img_array[-1]
+    return num
+
+def save_image_set(img_array, filename_pattern, sourcefile=None):
+    '''
+    Saves all the (PIL) images in the given array, using the
+    given filename pattern (which should contain a `%d` to get
+    the index number of the image).
+    '''
+    if sourcefile is not None:
+        last_filename = expand_last_filename(img_array, filename_pattern)
+        # Do nothing if the last file exists and is newer than the sourcefile
+        if os.path.isfile(last_filename) and (os.path.getmtime(last_filename)
+                >= os.path.getmtime(sourcefile)):
+            pbar.descnext(None)
+            return
+    # Use multiple threads to write all the image files faster.
+    pool = WorkerPool(worker=SaveImageWorker)
+    for img, filename in pbar(
+            all_items_and_filenames(img_array, filename_pattern),
+            total=num_items(img_array)):
+        pool.add(img, filename)
+    pool.join()
+
+class SaveImageWorker(WorkerBase):
+    def work(self, img, filename, quality=99):
+        os.makedirs(os.path.dirname(filename), exist_ok=True)
+        img.save(filename, optimize=True, quality=quality)
+
+class SaveImagePool(WorkerPool):
+    def __init__(self, *args, **kwargs):
+        super().__init__(*args, worker=SaveImageWorker, **kwargs)
diff --git a/netdissect/imgviz.py b/netdissect/imgviz.py
new file mode 100644
index 0000000000000000000000000000000000000000..69ae33d14c72eadb0ab1c5c009bebdd8d292725d
--- /dev/null
+++ b/netdissect/imgviz.py
@@ -0,0 +1,454 @@
+import PIL, torch, os
+from monai.transforms import CenterSpatialCrop
+import numpy as np
+from . import upsample, renormalize, segviz, tally
+from matplotlib import cm
+
+class ImageVisualizer:
+    def __init__(self, size, image_size=None, data_size=None,
+            renormalizer=None, scale_offset=None, level=None, actrange=None,
+            source=None, convolutions=None, quantiles=None,
+            percent_level=None):
+        '''
+        An ImageVisualizer produces visualizations of unit activations
+        as heatmaps or overlays on top of the original image.  The output
+        visualization size is given by `size`.  This may be scaled
+        from the original image size `image_size` (inferred from source
+        if not specified) and activation `data_size` (inferred from
+        convolutions if not specified).  Imagedata is renormalized as
+        rendered RGB using renormalizer (inferred from source if not
+        specified) and upsampling can be offset using scale_offset
+        (inferred from convolutions if not specified).  Per-unit
+        `quantiles` can be provided for computing heatmap ranges
+        (default to 1% to 99% quantiles), and threshold levels
+        (default to `percent_level`=0.95).
+        '''
+        if image_size is None and source is not None:
+            image_size = upsample.image_size_from_source(source)
+        if renormalizer is None and source is not None:
+            renormalizer = renormalize.renormalizer(
+                    source=source, target='byte')
+        if scale_offset is None and convolutions is not None:
+            scale_offset = upsample.sequence_scale_offset(convolutions)
+        if data_size is None and convolutions is not None:
+            data_size = upsample.sequence_data_size(convolutions, image_size)
+        if level is None and quantiles is not None:
+            level = quantiles.quantiles([percent_level or 0.95])[:,0]
+        if actrange is None and quantiles is not None:
+            actrange = quantiles.quantiles([0.01, 0.99])
+        if isinstance(size, int):
+            size = (size, size)
+        self.size = size
+        self.image_size = image_size
+        self.data_size = data_size
+        self.renormalizer = renormalizer
+        self.scale_offset = scale_offset
+        self.percent_level = percent_level
+        self.level = level
+        self.actrange = actrange
+        self.quantiles = quantiles
+        self.upsampler = None
+        if self.data_size is not None:
+            self.upsampler = upsample.upsampler(size, data_size,
+                    image_size=self.image_size,
+                    scale_offset=scale_offset)
+
+    def heatmap(self, activations, unit=None, mode='bilinear',
+            amax=None, amin=None):
+        '''
+        Produces a heatmap from the given activations.  The unit,
+        if specified, is an index into the activations tensor,
+        and is also used to dereference quantile ranges.
+        '''
+        if amax is None or amin is None:
+            amin, amax = self.range_for(activations, unit)
+        if unit is None:
+            a = activations
+        else:
+            a = activations[unit]
+        upsampler = self.upsampler_for(a)
+        a = upsampler(a[None,None,...], mode=mode)[0,0].cpu()
+        return PIL.Image.fromarray(
+                (cm.hot(((a - amin) / (1e-10 + amax - amin)).numpy()) * 255
+                    ).astype('uint8'))
+
+    def segmentation(self, segmentations, label=None):
+        '''
+        Visualizes the given segmentation tensor.  If label is specified,
+        zeros all labels other than the given one.
+        '''
+        if label is not None:
+            segmentations = segmentations.clone()
+            segmentations[segmentations != label] = 0
+        return segviz.seg_as_image(segmentations, size=self.size)
+
+    def segment_key(self, segmentations, segmodel, num=None, label=None):
+        '''
+        Obtains a list of the top segment labels present in the given
+        segmetation tensor, for use in a legend.
+        '''
+        if label is not None:
+            segmentations = segmentations.clone()
+            segmentations[segmentations != label] = 0
+        if num is None:
+            num = self.size[0] // 17
+        return segviz.segment_key(segmentations, segmodel, num)
+
+    def segment_key_with_lbls(self, segmentations, seglbs):
+        '''
+        Obtains a list of the top segment labels present in the given
+        segmetation tensor, for use in a legend.
+        '''
+        return segviz.segment_key_with_lbls(segmentations, seglbs)
+
+    def image(self, imagedata):
+        '''
+        Converts the given tensor imagedata to a PIL image, scaling
+        and renormalizing as needed.
+        '''
+        return PIL.Image.fromarray(self.pytorch_image(imagedata)
+                .permute(1, 2, 0).byte().cpu().numpy())
+
+    def masked_image(self, imagedata, activations=None, unit=None,
+            level=None, percent_level=None, **kwargs):
+        '''
+        Visualizes the given activations, thresholded at a specified level,
+        overlaid on the given image, as a PIL image.
+        '''
+        result_image = self.pytorch_masked_image(imagedata,
+                activations=activations,
+                unit=unit, level=level, percent_level=percent_level,
+                **kwargs)
+        return PIL.Image.fromarray(
+            result_image.permute(1, 2, 0).cpu().numpy())
+
+    def pytorch_masked_image(self, imagedata, activations=None, unit=None,
+            level=None, percent_level=None, thickness=1, mask=None,
+            border_color=None, outside_bright=0.5, inside_color=None):
+        '''
+        Visualizes the given activations, thresholded at a specified level,
+        overlaid on the given image, as a pytorch byte tensor (channel first).
+        '''
+        scaled_image = self.pytorch_image(imagedata).float().cpu()
+        if mask is None:
+            mask = self.pytorch_mask(activations, unit, level=level,
+                    percent_level=percent_level).cpu()
+        border = border_from_mask(mask, thickness)
+        inside = (mask & (~border))
+        outside = (~mask & (~border))
+        inside, outside, border = [d.float() for d in [inside, outside, border]]
+        if border_color is None:
+            border_color = [255.0, 255.0, 0] # yellow
+        border_color = torch.tensor(border_color,
+                dtype=border.dtype, device=border.device)[:,None,None]
+        if inside_color is not None:
+            inside_color = torch.tensor(inside_color,
+                dtype=border.dtype, device=border.device)[:,None,None]
+        result_image = (
+                (scaled_image if inside_color is None
+                    else inside_color) * inside +
+                border_color * border +
+                outside_bright * scaled_image * outside).clamp(0, 255).byte()
+        return result_image
+
+    def masked_delta(self, imagedata, activations, unit=None,
+            above=None, below=None):
+        '''
+        Visualizes the given activations, thresholded at a specified level,
+        overlaid on the given image, as a PIL image.
+        '''
+        result_image = self.pytorch_masked_delta(imagedata, activations,
+                unit=unit, above=above, below=below)
+        return PIL.Image.fromarray(
+            result_image.permute(1, 2, 0).cpu().numpy())
+
+    def pytorch_masked_delta(self, imagedata, delta, unit=None,
+            above=None, below=None):
+        '''
+        Visualizes the given activations, thresholded at a specified level,
+        with green for high numbrers and red for low numbers.
+        '''
+        scaled_image = self.pytorch_image(imagedata).float().cpu()
+        amask, bmask, aborder, bborder = [torch.tensor(0) for _ in range(4)]
+        if above is not None:
+            amask = self.pytorch_mask(delta, unit, level=above).cpu()
+            aborder = border_from_mask(amask)
+        if below is not None:
+            bmask = ~self.pytorch_mask(delta, unit, level=below).cpu()
+            bborder = border_from_mask(bmask)
+        inside = ((amask | bmask) & ~(aborder | bborder))
+        outside = (~(amask | bmask) & ~(aborder | bborder))
+        inside, outside, aborder, bborder = [d.float()
+                for d in [inside, outside, aborder, bborder]]
+        red, green = [torch.tensor(c, dtype=torch.float, device=aborder.device
+            )[:,None,None] for c in [[255, 0, 0], [0, 255, 0]]]
+        result_image = (
+                scaled_image * inside +
+                green * aborder +
+                red * bborder +
+                0.5 * scaled_image * outside).clamp(0, 255).byte()
+        return result_image
+
+    def pytorch_mask(self, activations, unit, level=None, percent_level=None):
+        '''
+        Computes a pytorch mask of the (upsampled) activations above a
+        specified level.
+        '''
+        if unit is None:
+            a = activations
+        else:
+            a = activations[unit]
+        if level is None:
+            level = self.level_for(activations, unit,
+                    percent_level=percent_level)
+        upsampler = self.upsampler_for(a)
+        return (upsampler(a[None, None,...])[0,0] > level)
+
+    def pytorch_image(self, imagedata):
+        '''
+        Scales the given image to the visualized size.  returns as a
+        pytorch byte tensor, in (rgb, y, x) channel order.
+        '''
+        if len(imagedata.shape) == 4:
+            imagedata = imagedata[0]
+        renormalizer = self.renormalizer_for(imagedata)
+        return torch.nn.functional.interpolate(
+                renormalizer(imagedata).float()[None,...],
+                size=self.size)[0]
+
+    def upsampler_for(self, a):
+        '''
+        Returns an upsampler instance, defaulting to simple upscaling
+        if a specific upsampler is not specified.
+        '''
+        if self.upsampler is not None:
+            return self.upsampler
+        return upsample.upsampler(self.size, a.shape,
+                    image_size=self.image_size,
+                    scale_offset=self.scale_offset,
+                    dtype=a.dtype, device=a.device)
+
+    def range_for(self, activations, unit):
+        '''
+        Returns a range of activations, using quantiles (1% to 99%)
+        if given, or just using the min and max of the data if
+        quantiles are not given.
+        '''
+        if unit is not None and self.actrange is not None:
+            if hasattr(unit, '__len__'):
+                unit = unit[1]
+            return tuple(i.item() for i in self.actrange[unit])
+        return activations.min().item(), activations.max().item()
+
+    def level_for(self, activations, unit, percent_level=None):
+        '''
+        Returns the cutoff level for a unit, using quantiles if given
+        or just using stats over the specific instance if not.
+        '''
+        if unit is not None:
+            if hasattr(unit, '__len__'):
+                unit = unit[1]
+            if percent_level is not None and self.quantiles is not None:
+                return self.quantiles.quantiles(percent_level)[unit].item()
+            if self.level is not None:
+                return self.level[unit].item()
+        s, _ = activations.view(-1).sort()
+        if percent_level is None:
+            percent_level = self.percent_level or 0.95
+        return s[int(len(s) * percent_level)]
+
+    def renormalizer_for(self, image):
+        '''
+        Returns the renormalizer to use for visualizing the tensor
+        image data as RGB.
+        '''
+        if self.renormalizer is not None:
+            return self.renormalizer
+        return renormalize.renormalizer('zc', 'byte')
+
+    def masked_image_grid_for_topk(
+            self, compute, dataset, topk, k=None, **kwargs):
+        def compute_viz(gather_indices, *data_batch):
+            acts_batch = compute(*data_batch)
+            if isinstance(acts_batch, tuple):
+                acts_batch, image_batch = acts_batch
+            else:
+                image_batch = data_batch[0]
+            for gather_for, acts, imgt in (zip(gather_indices, acts_batch, image_batch["image"])):
+                # TODO This is only a single direction
+                # Visualize all low-dimensional activations in that direction
+                ld_res = acts.shape[-1]
+                img_slices = torch.linspace(int(64/ld_res/2), 64-int(64/ld_res/2), ld_res, dtype=torch.long)
+                for unit, rank in gather_for:
+                    yield((unit, rank), torch.stack([
+                        self.pytorch_masked_image(
+                            # This will break when used for any other tasks
+                            (imgt[..., img_depth]).repeat(3, 1, 1),
+                            acts[..., ld_depth],
+                            unit).permute(1,2,0).cpu()
+                            for ld_depth, img_depth in enumerate(img_slices)]))
+                #for unit, rank in gather_for:
+                #    three_channel_image = imgt[..., 0][0].repeat(3, 1, 1)
+                #    yield((unit, rank), self.pytorch_masked_image(three_channel_image, acts[..., 0], unit).permute(1,2,0).cpu())
+        gt = tally.gather_topk(compute_viz, dataset, topk=topk, k=k, **kwargs)
+        return gt.result()
+
+    def masked_single_image_for_topk(
+            self, compute, dataset, topk, k=None, **kwargs):
+        def compute_viz(gather_indices, *data_batch):
+            acts_batch = compute(*data_batch)
+            if isinstance(acts_batch, tuple):
+                acts_batch, image_batch = acts_batch
+            else:
+                image_batch = data_batch[0]
+            for gather_for, acts, imgt in (zip(gather_indices, acts_batch, image_batch["image"])):
+                # TODO This is only a single direction
+                # Visualize all low-dimensional activations in that direction
+                ld_res = acts.shape[-1]
+                img_slices = torch.linspace(int(64/ld_res/2), 64-int(64/ld_res/2), ld_res, dtype=torch.long)
+                for unit, rank in gather_for:
+                    #masked_acts = torch.zeros_like(acts[unit])
+                    #mask = (acts[unit] > self.level_for(acts[unit], unit))
+                    #masked_acts[mask] = acts[unit][mask]
+                    masked_acts = torch.zeros_like(acts)
+                    mask = (acts > self.level_for(acts, unit))
+                    masked_acts[mask] = acts[mask]
+                    selected_dim = torch.argmax(torch.sum(masked_acts.view(-1, ld_res), dim=0)).item()
+
+                    yield((unit, rank), 
+                        self.pytorch_masked_image(
+                            (imgt[..., img_slices[selected_dim]]).repeat(3, 1, 1),
+                            acts[..., selected_dim],
+                            unit).permute(1,2,0).cpu()[None, ...])
+                #for unit, rank in gather_for:
+                #    three_channel_image = imgt[..., 0][0].repeat(3, 1, 1)
+                #    yield((unit, rank), self.pytorch_masked_image(three_channel_image, acts[..., 0], unit).permute(1,2,0).cpu())
+        gt = tally.gather_topk(compute_viz, dataset, topk=topk, k=k, **kwargs)
+        return gt.result()
+
+    def individual_masked_images_for_topk(
+            self, compute, dataset, topk, k=None, **kwargs):
+        # Example compute function:
+        # def compute(image_batch):
+        #   image_batch = image_batch.cuda()
+        #   acts_batch = model.retained_layer(layername)
+        gt = self.masked_image_grid_for_topk(
+                compute, dataset, topk, k=k, **kwargs)
+        return [[PIL.Image.fromarray(d.cpu().numpy()) for d in row]
+                for row in gt]
+
+    def masked_images_for_topk(
+            self, compute, dataset, topk, k=None, gap=5, **kwargs):
+        # Example compute function:
+        # def compute(image_batch):
+        #   image_batch = image_batch.cuda()
+        #   acts_batch = model.retained_layer(layername)
+        gt = self.masked_image_grid_for_topk(
+                compute, dataset, topk, k=k, **kwargs)
+        return [strip_image_from_grid_row(row, gap=gap) for row in gt]
+
+    def masked_single_images_for_topk(
+            self, compute, dataset, topk, k=None, gap=5, per_row=5, **kwargs):
+        # Example compute function:
+        # def compute(image_batch):
+        #   image_batch = image_batch.cuda()
+        #   acts_batch = model.retained_layer(layername)
+        gt = self.masked_single_image_for_topk(
+                compute, dataset, topk, k=k, **kwargs)
+        return [strip_image_from_grid_row(torch.vstack([x.unsqueeze(0) for x in row.squeeze(1).split(per_row, dim=0) if x.shape[0] == per_row]), gap=gap) for row in gt]
+        #return [strip_image_from_grid_row(torch.stack([gt[i+j] for j in range(per_row)]), gap=gap) for i in range(len(gt)//per_row)]
+
+    def image_activation_tuples_for_topk(
+        self, compute, dataset, data_path, topk, k=None, **kwargs):
+
+        def compute_viz(gather_indices, *data_batch):
+            acts_batch = compute(*data_batch)
+            if isinstance(acts_batch, tuple):
+                acts_batch, image_batch = acts_batch
+            else:
+                image_batch = data_batch[0]
+
+            for gather_for, acts, path, level_idx in zip(gather_indices, acts_batch, image_batch["path"], image_batch["level_idx"]):
+                raw_ct = torch.tensor(np.load(os.path.join(data_path, 'raw', path)))[None, ...]
+                seg = torch.tensor(np.load(os.path.join(data_path, 'seg', path)))
+
+                mask = torch.zeros_like(seg)
+                mask[seg == level_idx] = 1
+
+                crop = CenterSpatialCrop(64)
+                raw_ct_crop = crop(raw_ct)
+                mask_ct_crop = crop(raw_ct * mask[None, ...])
+
+                upsampled_acts = torch.nn.Upsample(size=(64,)*3, mode="trilinear", align_corners=True)(acts[None, ...])
+
+                # normalize activations to [0, 1] per unit
+                normalized_upsampled_acts = torch.clone(upsampled_acts)
+                normalized_upsampled_acts -= torch.amin(normalized_upsampled_acts, dim=(-3, -2, -1), keepdim=True)
+                normalized_upsampled_acts /= torch.amax(normalized_upsampled_acts, dim=(-3, -2, -1), keepdim=True)
+                
+                for unit, rank in gather_for:
+                    mask = upsampled_acts[0, unit] > self.level_for(upsampled_acts, unit)
+                    normalized_upsampled_acts[0, unit][~mask] = 0
+
+                    yield ((unit, rank), torch.cat((raw_ct_crop.cpu(), normalized_upsampled_acts[0, unit][None, ...].detach().cpu(), mask_ct_crop.cpu()), dim=0))
+
+        gt = tally.gather_topk(compute_viz, dataset, topk=topk, k=k, **kwargs)
+        return gt.result()
+
+    def masked_image_grid_for_row(self, compute, dataset, unit, indexes):
+        results = []
+        for rank in indexes:
+            img_batch = dataset[rank][0][None,...]
+            acts_batch = compute(img_batch)
+            results.append(self.pytorch_masked_image(
+                img_batch[0], acts_batch[0], unit)
+                .permute(1,2,0).cpu()[None,...])
+        return torch.cat(results)
+
+    def masked_image_row(self, compute, dataset, unit, indexes, gap=5):
+        row = self.masked_image_grid_for_row(
+                compute, dataset, unit, indexes)
+        return strip_image_from_grid_row(row, gap=gap)
+
+    def masked_image_for_conditional_topk(self, compute, dataset,
+            ctk, classnum, unit, k=10, gap=5):
+        row = self.masked_image_grid_for_row(
+                compute, dataset, unit,
+                ctk.conditional(classnum).result()[1][unit][:k])
+        return strip_image_from_grid_row(row, gap=gap)
+
+def strip_image_from_grid_row(row, gap=5, bg=255):
+    strip = torch.full(
+            (row.shape[0] * (row.shape[3] + gap) - gap,
+             row.shape[1] * (row.shape[3] + gap) - gap,
+             row.shape[4]), bg, dtype=row.dtype)
+    for i in range(0, row.shape[0] * row.shape[1]):
+        strip[(i // row.shape[1]) * (row.shape[2] + gap) : ((i // row.shape[1])+1) * (row.shape[2] + gap) - gap,
+              (i % row.shape[1]) * (row.shape[3] + gap) : ((i % row.shape[1])+1) * (row.shape[3] + gap) - gap,
+              :] = row[i // row.shape[1]][i % row.shape[1]]
+    return PIL.Image.fromarray(strip.numpy())
+
+def border_from_mask(mask, thickness=1, outside=True):
+    a = mask
+    out = torch.zeros_like(a)
+    for it in range(thickness):
+        h = (a[:-1,:] != a[1:,:])
+        v = (a[:,:-1] != a[:,1:])
+        d = (a[:-1,:-1] != a[1:,1:])
+        u = (a[1:,:-1] != a[:-1,1:])
+        out[:-1,:-1] |= d
+        out[1:,1:] |= d
+        out[1:,:-1] |= u
+        out[:-1,1:] |= u
+        out[:-1,:] |= h
+        out[1:,:] |= h
+        out[:,:-1] |= v
+        out[:,1:] |= v
+        if it > 0:
+            out |= a
+        a = out
+    if outside:
+        out &= ~mask
+    return out
+
+
diff --git a/netdissect/invert.py b/netdissect/invert.py
new file mode 100644
index 0000000000000000000000000000000000000000..7f50f32cd0d869db0ffa1114d03e6fcdf90a0aba
--- /dev/null
+++ b/netdissect/invert.py
@@ -0,0 +1,293 @@
+import torch, itertools, os
+from collections import defaultdict, OrderedDict
+from gan_training import checkpoints
+from . import nethook, pbar, LBFGS
+from seeing.nethook import InstrumentedModel
+from torch.nn.functional import mse_loss
+
+def refine_z_lbfgs(init_z, G, target_x, F=None, lambda_f=0,
+        R=None, lambda_r=0, num_steps=1000, quiet=False,
+        show=None, show_every=100):
+    '''
+    Starting at init_z, uss LBFGS to find a z for which G(z) -> target_x.
+    By default uses l1_loss, but can also mse(F(G(z)), F(target_x))
+    '''
+    z = init_z.clone()
+    parameters = [z]
+    nethook.set_requires_grad(False, G)
+    nethook.set_requires_grad(True, *parameters)
+    if lambda_f:
+        with torch.no_grad():
+            target_f = F(target_x)
+            nethook.set_requires_grad(False, F)
+
+    optimizer = LBFGS.FullBatchLBFGS(parameters)
+
+    def closure():
+        optimizer.zero_grad()
+        current_x = G(z)
+        loss = torch.nn.functional.l1_loss(target_x, current_x)
+        if lambda_f:
+            loss += torch.nn.functional.mse_loss(target_f, F(current_x)
+                    ) * lambda_f
+        if lambda_r:
+            loss += R(z) * lambda_r
+        return loss
+
+    pb = (lambda x: x) if quiet else pbar
+
+    with torch.enable_grad():
+        for step_num in pb(range(num_steps + 1)):
+            if step_num == 0:
+                loss = closure()
+                loss.backward()
+                lr, F_eval, G_eval = 0, 0, 0
+            else:
+                options = {'closure': closure, 'current_loss': loss,
+                        'max_ls': 10}
+                loss, _, lr, _, _, _, _, _ = optimizer.step(options)
+            if show and (step_num % show_every == 0):
+                with torch.no_grad():
+                    show(x=G(z), z=z, loss=loss, it=step_num)
+
+    return z
+
+def split_gen_layers(enc, gen, layername):
+    '''
+    Given an inverter layername, splits the generator sequence into three:
+    (1) generator sequence before the layers to be inverted
+    (2) sequence of generator layers to be inverted by enc[layername]
+    (3) generator sequence after the layers to be inverted
+    '''
+    info = list(enc.inverse_info().items())
+    index = [i for i, (n, v) in enumerate(info) if n == layername][0]
+    upto_layer = info[index - 1][1]['first_layer'] if index > 0 else None
+    args = info[index][1]
+    first_layer = args['first_layer']
+    layers = nethook.subsequence(gen,
+         first_layer=first_layer, upto_layer=upto_layer)
+    prev_layers = nethook.subsequence(gen, upto_layer=first_layer)
+    next_layers = (nethook.subsequence(gen, first_layer=upto_layer) if
+            upto_layer else torch.nn.Sequential())
+    return prev_layers, layers, next_layers
+
+def last_gen_layername(enc, gen, layername):
+    _, layers, _ = split_gen_layers(enc, gen, layername)
+    return [n for n, c in layers.named_children()][-1]
+
+def layers_after(enc, layername):
+    layernames = [n for n, c in enc.named_children()]
+    index = layernames.index(layername)
+    if index + 1 < len(layernames):
+        return nethook.subsequence(enc, layernames[index + 1])
+    else:
+        return torch.nn.Sequential()
+
+def train_inv_layer(enc, gen, dists, layername, combine_z=None,
+        batch_size=100, test_batches=10, checkpoint_dir='ckpts',
+        resume_from=None, logfile=None, **kwargs):
+    '''
+    Inverts a single layer of a multilayer inverter.
+    Both enc and should be a nn.Sequential subclasses, and
+    layername specifies the layer of enc to train.  That layer
+    of enc will be trained to invert a set of gen layers.
+    Which layers specifically are determined by split_gen_layers,
+    which depends on enc.inverse_info(), to specify how each inverter
+    layer relates to layers of gen.
+    '''
+    if logfile is None:
+        logfile = os.path.join(checkpoint_dir, 'log.txt')
+    prev_layers, layers, next_layers = split_gen_layers(enc, gen, layername)
+    inv = getattr(enc, layername)
+    device = next(gen.parameters()).device
+    args = enc.inverse_info()[layername]
+    kwargs_out = {k: v for k, v in args.items() if k != 'first_layer'}
+    kwargs_out.update(kwargs)
+    if 'x_weight' not in kwargs_out:
+        kwargs_out['x_weight'] = 0
+    nethook.set_requires_grad(False, layers, prev_layers, next_layers)
+    if combine_z is not None:
+        nethook.set_requires_grad(False, combine_z)
+        prev_layers_old = prev_layers
+        prev_layers = lambda *a: prev_layers_old(combine_z(*a))
+    zsampler = infinite_sampler(dists, prev_layers, batch_size, device)
+    tsample = test_sampler(dists, prev_layers, batch_size, test_batches, device)
+    train_inverse(inv, layers, zsampler, test_sampler=tsample,
+            resume_from=resume_from,
+            checkpoint_dir=checkpoint_dir, logfile=logfile,
+            **kwargs_out)
+
+def train_inv_joint(enc, gen, dists, combine_z=None,
+        inv_layer=None, gen_layer=None,
+        batch_size=50, test_batches=10,
+        checkpoint_dir='ckpts',
+        logfile=None, **kwargs):
+    if logfile is None:
+        logfile = os.path.join(checkpoint_dir, 'log.txt')
+    device = next(gen.parameters()).device
+    zsampler = infinite_sampler(dists, combine_z, batch_size, device)
+    tsample = test_sampler(dists, combine_z, batch_size, test_batches, device)
+
+    with InstrumentedModel(gen) as G, InstrumentedModel(enc) as E:
+        G.retain_layer(gen_layer, detach=False)
+        nethook.set_requires_grad(False, G)
+        E.retain_layer(inv_layer, detach=False)
+        train_inverse(E, G, zsampler, inv_layer, gen_layer,
+            r_weight=1.0, ir_weight=1.0, test_sampler=tsample,
+            checkpoint_dir=checkpoint_dir,
+            checkpoint_selector=lambda x: x.model,
+            logfile=logfile, **kwargs)
+
+def train_inverse(inv, gen, sampler, inv_layer=None, gen_layer=None,
+        z_weight=1.0, x_weight=1.0, r_weight=0.0, ir_weight=0.0, reg_weight=0.0,
+        adjust_z=None, regularize_z=None,
+        test_sampler=None, lr=0.01, lr_milestones=None,
+        epoch_batches=100, num_epochs=100, save_every=50,
+        logfile=None,
+        checkpoint_dir=None, checkpoint_selector=None, resume_from=None):
+    '''
+    To set this up:
+    inv and gen should both be instrumented models,
+    and inv layer and gen layer should be retained on both of them
+    without detach.
+    '''
+    if lr_milestones is None:
+        lr_milestones = []
+    optimizer = torch.optim.Adam(inv.parameters(), lr=lr)
+    scheduler = torch.optim.lr_scheduler.MultiStepLR(
+            optimizer, milestones=lr_milestones, gamma=0.1)
+
+    ckpts = checkpoints.CheckpointIO(checkpoint_dir)
+    ckpts.register_modules(optimizer=optimizer, scheduler=scheduler,
+            inv=inv if not checkpoint_selector else checkpoint_selector(inv))
+    if resume_from is not None:
+        ckpts.load('ckpt_%d.pt' % resume_from)
+        start_epoch = resume_from + 1
+    else:
+        start_epoch = 0
+
+    def inv_loss(z):
+        return sum(loss * weight
+                for loss, weight in component_loss(z).values())
+
+    def component_loss(true_z):
+        observed_x = gen(true_z)
+        if gen_layer:
+            true_r = gen.retained_layer(gen_layer, clear=True)
+        estimated_z = inv(observed_x)
+        if inv_layer:
+            inverted_r = inv.retained_layer(inv_layer, clear=True)
+        if adjust_z:
+            fixed_z = adjust_z(estimated_z, true_z)
+        else:
+            fixed_z = estimated_z
+        if x_weight or r_weight:
+            reconstructed_x = gen(fixed_z)
+        if gen_layer:
+            reconstructed_r = gen.retained_layer(gen_layer, clear=True)
+        losses = OrderedDict()
+        if reg_weight:
+            losses['reg'] = (regularize_z(estimated_z, true_z), reg_weight)
+        if z_weight:
+            losses['z'] = (mse_loss(true_z, estimated_z), z_weight)
+        if ir_weight:
+            losses['ir'] = (cor_square_error(true_r, inverted_r), ir_weight)
+        if x_weight:
+            losses['x'] = (mse_loss(observed_x, reconstructed_x), x_weight)
+        if r_weight:
+            losses['r'] = (cor_square_error(true_r, reconstructed_r), r_weight)
+        return losses
+
+    with torch.no_grad():
+        for epoch, epoch_loader in pbar(
+                epoch_grouper(sampler, epoch_batches, num_epochs=1+num_epochs,
+                    start_epoch=start_epoch),
+                total=(1+num_epochs-start_epoch)):
+            if epoch > 0:
+                for (z_batch,) in epoch_loader:
+                    with torch.enable_grad():
+                        optimizer.zero_grad()
+                        loss = inv_loss(z_batch)
+                        loss.backward()
+                        pbar.post(l=loss.item())
+                        optimizer.step()
+                scheduler.step()
+            if test_sampler is not None:
+                stats = MeanStats()
+                for (z_batch,) in test_sampler:
+                    stats.add(component_loss(z_batch), len(z_batch))
+                logline = stats.logline(epoch)
+                pbar.print(logline)
+                if logfile is not None:
+                    with open(logfile, 'a') as f:
+                        f.write(logline + '\n')
+            elif epoch > 0:
+                pbar.print('%d: loss=%4g' % (epoch, loss.item()))
+            if epoch % save_every == 0 or epoch == num_epochs:
+                ckpts.save(epoch, 'ckpt_%d.pt' % epoch)
+
+def infinite_sampler(dists, f, batch_size, device):
+    '''
+    Returns an infinite generator that runs through randomized z
+    batches, forever.
+    '''
+    while True:
+        zs = [dist.sample([batch_size]).to(device) for dist in dists]
+        r = zs[0] if f is None else f(*zs)
+        yield (r,)
+
+def test_sampler(dists, f, batch_size, test_batches, device):
+    class TestSampler():
+        def __init__(self):
+            self.num_batches = test_batches
+            self.zs_batches = [
+                    dist.sample([test_batches, batch_size]).to(device)
+                    for dist in dists]
+        
+        def __iter__(self):
+            for i in range(self.num_batches):
+                zs = [uncombined[i] for uncombined in self.zs_batches]
+                r = zs[0] if f is None else f(*zs)
+                yield (r,)
+    return TestSampler()
+
+def epoch_grouper(loader, epoch_size, num_epochs=None, start_epoch=0):
+    '''
+    To use with an infinite training loader: groups the training data
+    batches into epochs of the given size.
+    '''
+    it = iter(loader)
+    epoch = start_epoch
+    while True:
+        chunk_it = itertools.islice(it, epoch_size)
+        try:
+            first_el = next(chunk_it)
+        except StopIteration:
+            return
+        yield epoch, itertools.chain((first_el,), chunk_it)
+        epoch += 1
+        if num_epochs is not None and epoch >= num_epochs:
+            return
+
+def cor_square_error(x, y, eps=1e-12):
+    # Analogous to MSE, but in terms of Pearson's correlation
+    return (1.0 - torch.nn.functional.cosine_similarity(x, y, eps=eps)).mean()
+
+
+class MeanStats:
+    def __init__(self):
+        self.tally = defaultdict(float)
+        self.count = 0
+
+    def add(self, c, size):
+        for n, (loss, weight) in c.items():
+            self.tally[n] += loss.item() * size
+        self.count += size
+
+    def summary(self):
+        return {n: v / self.count for n, v in self.tally.items()}
+
+    def logline(self, i=None):
+        prefix = '' if i is None else '%d: ' % i
+        return prefix + ' '.join('%s=%4g' % (n, v)
+                for n, v in self.summary().items())
diff --git a/netdissect/labwidget.py b/netdissect/labwidget.py
new file mode 100644
index 0000000000000000000000000000000000000000..def8a1dcfcae8947d9ce5d901607919fdc68705e
--- /dev/null
+++ b/netdissect/labwidget.py
@@ -0,0 +1,965 @@
+"""
+labwidget by David Bau.
+
+Base class for a lightweight javascript notebook widget framework
+that is portable across Google colab and Jupyter notebooks.
+No use of requirejs: the design uses all inline javascript.
+
+Defines Model, Widget, Trigger, and Property, which set up data binding
+using the communication channels available in either google colab
+environment or jupyter notebook.
+
+This module also defines Label, Textbox, Range, Choice, and Div
+widgets; the code for these are good examples of usage of Widget,
+Trigger, and Property objects.
+
+Within HTML widgets, user interaction should update the javascript
+model using model.set('propname', value); this will propagate to
+the python model and notify any registered python listeners; similarly
+model.on('propname', callback) will listen for property changes
+that come from python.
+
+TODO: Support jupyterlab also.
+"""
+
+import json, html, re
+from inspect import signature
+
+class Model(object):
+    '''
+    Abstract base class that supports data binding.  Within __init__,
+    a model subclass defines databound events and properties using:
+
+       self.evtname = Trigger()
+       self.propname = Property(initval)
+
+    Any Trigger or Property member can be watched by registering a
+    listener with `model.on('propname', callback)`.
+
+    An event can be triggered by `model.evtname.trigger(value)`.
+    A property can be read with `model.propname`, and can be set by
+    `model.propname = value`; this also triggers notifications.
+    In both these cases, any registered listeners will be called
+    with the given value.
+    '''
+    def on(self, name, cb):
+        '''
+        Registers a listener for named events and properties.
+        A space-separated list of names can be provided as `name`.
+        '''
+        for n in name.split():
+            self.prop(n).on(cb)
+        return self
+
+    def off(self, name, cb=None):
+        '''
+        Unregisters a listener for named events and properties.
+        A space-separated list of names can be provided as `name`.
+        '''
+        for n in name.split():
+            self.prop(n).off(cb)
+        return self
+
+    def prop(self, name):
+        '''
+        Returns the underlying Trigger or Property object for a
+        property, rather than its held value.
+        '''
+        curvalue = super().__getattribute__(name)
+        if not isinstance(curvalue, Trigger):
+            raise AttributeError('%s not a property or trigger but %s'
+                    % (name, str(type(curvalue))))
+        return curvalue
+
+    def _initprop_(self, name, value):
+        '''
+        To be overridden in base classes.  Handles initialization of
+        a new Trigger or Property member.
+        '''
+        value.name = name
+        value.target = self
+        return
+
+    def __setattr__(self, name, value):
+        '''
+        When a member is an Trigger or Property, then assignment notation
+        is delegated to the Trigger or Property so that notifications
+        and reparenting can be handled.  That is, `model.name = value`
+        turns into `prop(name).set(value)`.
+        '''
+        if hasattr(self, name):
+            curvalue = super().__getattribute__(name)
+            if isinstance(curvalue, Trigger):
+                # Delegte "set" to the underlying Property.
+                curvalue.set(value)
+            else:
+                super().__setattr__(name, value)
+        else:
+            super().__setattr__(name, value)
+            if isinstance(value, Trigger):
+                self._initprop_(name, value)
+
+    def __getattribute__(self, name):
+        '''
+        When a member is a Property, then property getter
+        notation is delegated to the peoperty object.
+        '''
+        curvalue = super().__getattribute__(name)
+        if isinstance(curvalue, Property):
+            return curvalue.value
+        return curvalue
+
+class Widget(Model):
+    '''
+    Base class for an HTML widget that uses a Javascript model object
+    to syncrhonize HTML view state with the backend Python model state.
+    Each widget subclass overrides widget_js to provide Javascript code
+    that defines the widget's behavior.  This javascript will be wrapped
+    in an immediately-invoked function and included in the widget's HTML
+    representation (_repr_html_) when the widget is viewed.
+
+    A widget's javascript is provided with two local variables:
+
+       element - the widget's root HTML element.  By default this is
+                 a <div> but can be overridden in widget_html.
+       model   - the object representing the data model for the widget.
+                 within javascript.
+
+    The model object provides the following javascript API:
+
+       model.get('propname') obtains a current property value.
+       model.set('propname', 'value') requests a change in value.
+       model.on('propname', callback) listens for property changes.
+       model.trigger('evtname', value) triggers an event.
+
+    Note that model.set just requests a change but does not change the
+    value immediately: model.get will not reflect the change until the
+    python backend has handled it and notified the javascript of the new
+    value, which will trigger any callbacks previously registered using
+    .on('propname', callback).  Thus Widget impelements a V-shaped
+    notification protocol:
+
+    User entry ->                 |              -> User-visible feedback
+        js model.set ->           |        -> js.model.on  callback
+          python prop.trigger ->  |   -> python prop.notify
+                         python prop.handle
+
+    Finally, all widgets provide standard databinding for style and data
+    properties, which are write-only (python-to-js) properties that
+    let python directly control CSS styles and HTML dataset attributes
+    for the top-level widget element.
+    '''
+
+    def __init__(self, style=None, data=None):
+        # In the jupyter case, there can be some delay between js injection
+        # and comm creation, so we need to queue some initial messages.
+        if WIDGET_ENV == 'jupyter':
+            self._comms = []
+            self._queue = []
+        # Each call to _repr_html_ creates a unique view instance.
+        self._viewcount = 0
+        # Python notification is handled by Property objects.
+        def handle_remote_set(name, value):
+            with capture_output(self): # make errors visible.
+                self.prop(name).trigger(value)
+        self._recv_from_js_(handle_remote_set)
+        # The style and data properties come standard, and are used to
+        # control the style and data attributes on the toplevel element.
+        self.style = Property(style)
+        self.data = Property(data)
+        # Each widget has a "write" event that is used to insert
+        # html before the widget.
+        self.write = Trigger()
+
+    def widget_js(self):
+        '''
+        Override to define the javascript logic for the widget.  Should
+        render the initial view based on the current model state (if not
+        already rendered using widget_html) and set up listeners to keep
+        the model and the view synchornized.
+        '''
+        return ''
+
+    def widget_html(self):
+        '''
+        Override to define the initial HTML view of the widget.  Should
+        define an element with id given by view_id().
+        '''
+        return f'<div {self.std_attrs()}></div>'
+
+    def view_id(self):
+        '''
+        Returns an HTML element id for the view currently being rendered.
+        Note that each time _repr_html_ is called, this id will change.
+        '''
+        return f"_{id(self)}_{self._viewcount}"
+
+    def std_attrs(self):
+        '''
+        Returns id and (if applicable) style attributes, escaped and
+        formatted for use within the top-level element of widget HTML.
+        '''
+        return (f'id="{self.view_id()}"' +
+                style_attr(self.style) +
+                data_attrs(self.data))
+
+
+    def _repr_html_(self):
+        '''
+        Returns the HTML code for the widget.
+        '''
+        self._viewcount += 1
+        json_data = json.dumps({
+                k: v.value for k, v in vars(self).items()
+                if isinstance(v, Property)})
+        json_data = re.sub('</', '<\\/', json_data)
+
+        std_widget_js = minify(f'''
+          var model = new Model("{id(self)}", {json_data});
+          var element = document.getElementById("{self.view_id()}");
+          model.on('write', (ev) => {{
+            var dummy = document.createElement('div');
+            dummy.innerHTML = ev.value.trim();
+            dummy.childNodes.forEach((item) => {{
+              element.parentNode.insertBefore(item, element);
+            }});
+          }});
+          function upd(a) {{ return (e) => {{ for (k in e.value) {{
+            element[a][k] = e.value[k];
+          }}}}}}
+          model.on('style', upd('style'));
+          model.on('data', upd('dataset'));
+        ''')
+
+        return ''.join([
+          self.widget_html(),
+          '<script>(function() {',
+          WIDGET_MODEL_JS,
+          std_widget_js,
+          self.widget_js(),
+          '})();</script>'
+        ]);
+
+    def _initprop_(self, name, value):
+        if not hasattr(self, '_viewcount'):
+            raise ValueError('base Model __init__ must be called')
+        super()._initprop_(name, value)
+        def notify_js(event):
+            self._send_to_js_(id(self), name, event.value)
+        if isinstance(value, Trigger):
+            value.on(notify_js, internal=True)
+
+    def _send_to_js_(self, *args):
+        if self._viewcount > 0:
+            if WIDGET_ENV == 'colab':
+                colab_output.eval_js(minify(f"""
+                (window.send_{id(self)} = window.send_{id(self)} ||
+                new BroadcastChannel("channel_{id(self)}")
+                ).postMessage({json.dumps(args)});
+                """), ignore_result=True)
+            elif WIDGET_ENV == 'jupyter':
+                if not self._comms:
+                    self._queue.append(args)
+                    return
+                for comm in self._comms:
+                    comm.send(args)
+
+    def _recv_from_js_(self, fn):
+        if WIDGET_ENV == 'colab':
+            colab_output.register_callback(f"invoke_{id(self)}", fn)
+        elif WIDGET_ENV == 'jupyter':
+            def handle_comm(msg):
+                fn(*(msg['content']['data']))
+                # TODO: handle closing also.
+            def handle_close(close_msg):
+                comm_id = close_msg['content']['comm_id']
+                self._comms = [c for c in self._comms if c.comm_id != comm_id]
+            def open_comm(comm, open_msg):
+                self._comms.append(comm)
+                comm.on_msg(handle_comm)
+                comm.on_close(handle_close)
+                comm.send('ok')
+                if self._queue:
+                    for args in self._queue:
+                        comm.send(args)
+                    self._queue.clear()
+                if open_msg['content']['data']:
+                    handle_comm(open_msg)
+            cname = "comm_" + str(id(self))
+            COMM_MANAGER.register_target(cname, open_comm)
+
+    def display(self):
+        from IPython.core.display import display
+        display(self)
+        return self
+
+class Trigger(object):
+    """
+    Trigger is the base class for Property and other data-bound
+    field objects.  Trigger holds a list of listeners that need to
+    be notified about the event.
+
+    Multple Trigger objects can be tied (typically a parent Model can
+    have Triggers that are triggered by children models).  To support
+    this, each Trigger can have a parent.
+
+    Trigger objects provide a notification protocol where view
+    interactions trigger events at a leaf that are sent up to the
+    root Trigger to be handled.  By default, the root handler accepts
+    events by notifying all listeners and children in the tree.
+    """
+    def __init__(self):
+        self._listeners = []
+        self.parent = None
+        # name and target are set in Model._initprop_.
+        self.name = None
+        self.target = None
+    def handle(self, value):
+        '''
+        Method to override; called at the root when an event has been
+        triggered, and on a child when the parent has notified.  By
+        default notifies all listeners.
+        '''
+        self.notify(value)
+    def trigger(self, value=None):
+        '''
+        Triggers an event to be handled by the root.  By default, the root
+        handler will accept the event so all the listeners will be notified.
+        '''
+        if self.parent is not None:
+            self.parent.trigger(value)
+        else:
+            self.handle(value)
+    def set(self, value):
+        '''
+        Sets the parent Trigger.  Child Triggers trigger events by
+        triggering parents, and in turn they handle notifications
+        that come from parents.
+        '''
+        if self.parent is not None:
+            self.parent.off(self.handle)
+            self.parent = None
+        if isinstance(value, Trigger):
+            ancestor = value.parent
+            while ancestor is not None:
+                if ancestor == self:
+                    raise ValueError('bound properties should not form a loop')
+                ancestor = ancestor.parent
+            self.parent = value
+            self.parent.on(self.handle, internal=True)
+        elif not isinstance(self, Property):
+            raise ValueError('only properties can be set to a value')
+    def notify(self, value=None):
+        '''
+        Notifies listeners and children.  If a listener accepts an argument,
+        the value will be passed as a single argument.
+        '''
+        for cb, internal in self._listeners:
+            with enter_handler(self.name, internal) as ctx:
+                if ctx.silence:
+                    # do not notify recursively...
+                    # print(f'silenced recursive {self.name} {cb.__name__}')
+                    pass
+                elif len(signature(cb).parameters) == 0:
+                    cb() # no-parameter callback.
+                else:
+                    cb(Event(value, self.name, self.target))
+    def on(self, cb, internal=False):
+        '''
+        Registers a listener.  Calling multiple times registers
+        multiple listeners.
+        '''
+        self._listeners.append((cb, internal))
+    def off(self, cb=None):
+        '''
+        Unregisters a listener.
+        '''
+        self._listeners = [(c, i) for c, i in self._listeners
+                if c != cb and cb is not None]
+
+class Property(Trigger):
+    """
+    A Property is just an Trigger that remembers its last value.
+    """
+    def __init__(self, value=None):
+        '''
+        Can be initialized with a starting value.
+        '''
+        super().__init__()
+        self.set(value)
+    def handle(self, value):
+        '''
+        The default handling for a Property is to store the value,
+        then notify listeners.  This method can be overridden,
+        for example to validate values.
+        '''
+        self.value = value
+        self.notify(value)
+    def set(self, value):
+        '''
+        When a Property value is set to an ordinary value, it
+        triggers an event which causes a notification to be
+        sent to update all linked Properties.  A Property set
+        to another Property becomes a child of the value.
+        '''
+        # Handle setting a parent Property
+        if isinstance(value, Property):
+            super().set(value)
+            self.handle(value.value)
+        elif isinstance(value, Trigger):
+            raise ValueError('Cannot set a Property to an Trigger')
+        else:
+            self.trigger(value)
+
+class Event(object):
+    def __init__(self, value, name, target, **kwargs):
+        for k, v in kwargs.items():
+            setattr(self, k, v)
+        self.value = value
+        self.name = name
+        self.target = target
+
+entered_handler_stack = []
+class enter_handler(object):
+    def __init__(self, name, internal):
+        global entered_handler_stack
+        self.internal = internal
+        self.name = name
+        self.silence = (not internal) and (len(entered_handler_stack) > 0)
+    def __enter__(self):
+        global entered_handler_stack
+        if not self.internal:
+            entered_handler_stack.append(self)
+        return self
+    def __exit__(self, exc_type, exc_value, exc_tb):
+        global entered_handler_stack
+        if not self.internal:
+            entered_handler_stack.pop()
+
+class capture_output(object):
+    """Context manager for capturing stdout/stderr.  This is used,
+    by default, to wrap handler code that is invoked by a triggering
+    event coming from javascript.  Any stdout/stderr or exceptions
+    that are thrown are formatted and written above the relevant widget."""
+    def __init__(self, widget):
+        from io import StringIO
+        self.widget = widget
+        self.buffer = StringIO()
+    def __enter__(self):
+        import sys
+        self.saved = dict(stdout=sys.stdout, stderr=sys.stderr)
+        sys.stdout = self.buffer
+        sys.stderr = self.buffer
+    def __exit__(self, exc_type, exc_value, exc_tb):
+        import sys, traceback
+        captured = self.buffer.getvalue()
+        if len(captured):
+            self.widget.write.trigger(f'<pre>{html.escape(captured)}</pre>')
+        if exc_type:
+            import traceback
+            tbtxt = ''.join(
+                    traceback.format_exception(exc_type, exc_value, exc_tb))
+            self.widget.write.trigger(
+                    f'<pre style="color:red;text-align:left">{tbtxt}</pre>')
+        sys.stdout = self.saved['stdout']
+        sys.stderr = self.saved['stderr']
+
+
+##########################################################################
+## Specific widgets
+##########################################################################
+
+class Button(Widget):
+    def __init__(self, label='button', style=None, **kwargs):
+        super().__init__(style=defaulted(style, display='block'), **kwargs)
+        self.click = Trigger()
+        self.label = Property(label)
+    def widget_js(self):
+        return minify('''
+          element.addEventListener('click', (e) => {
+            model.trigger('click');
+          })
+          model.on('label', (ev) => {
+            element.value = ev.value;
+          })
+        ''')
+    def widget_html(self):
+        return f'''<input {self.std_attrs()} type="button" value="{
+            html.escape(str(self.label))}">'''
+
+class Label(Widget):
+    def __init__(self, value='', **kwargs):
+        super().__init__(**kwargs)
+        # databinding is defined using Property objects.
+        self.value = Property(value)
+
+    def widget_js(self):
+        # Both "model" and "element" objects are defined within the scope
+        # where the js is run.    "element" looks for the element with id
+        # self.view_id(); if widget_html is overridden, this id should be used.
+        return minify('''
+            model.on('value', (ev) => {
+                element.innerText = model.get('value');
+            });
+        ''')
+    def widget_html(self):
+        return f'''<label {self.std_attrs()}>{
+            html.escape(str(self.value))}</label>'''
+
+class Textbox(Widget):
+    def __init__(self, value='', size=20, style=None, desc=None, **kwargs):
+        super().__init__(style=defaulted(style, display='inline-block'), **kwargs)
+        # databinding is defined using Property objects.
+        self.value = Property(value)
+        self.size = Property(size)
+        self.desc = Property(desc)
+
+    def widget_js(self):
+        # Both "model" and "element" objects are defined within the scope
+        # where the js is run.    "element" looks for the element with id
+        # self.view_id(); if widget_html is overridden, this id should be used.
+        return minify('''
+          element.value = model.get('value');
+          element.size = model.get('size');
+          element.addEventListener('keydown', (e) => {
+            if (e.code == 'Enter') {
+              model.set('value', element.value);
+            }
+          });
+          element.addEventListener('blur', (e) => {
+            model.set('value', element.value);
+          });
+          model.on('value', (ev) => {
+            element.value = model.get('value');
+          });
+          model.on('size', (ev) => {
+            element.size = model.get('size');
+          });
+        ''')
+    def widget_html(self):
+
+        html_str = f'''<input {self.std_attrs()} value="{
+            html.escape(str(self.value))}" size="{self.size}">'''
+        if self.desc is not None:
+            html_str = f"""<span>{self.desc}</span>{html_str}"""
+        return html_str
+
+class Range(Widget):
+    def __init__(self, value=50, min=0, max=100, **kwargs):
+        super().__init__(**kwargs)
+        # databinding is defined using Property objects.
+        self.value = Property(value)
+        self.min = Property(min)
+        self.max = Property(max)
+
+    def widget_js(self):
+        # Note that the 'input' event would enable during-drag feedback,
+        # but this is pretty slow on google colab.
+        return minify('''
+          element.addEventListener('change', (e) => {
+            model.set('value', element.value);
+          });
+          model.on('value', (e) => {
+            if (!element.matches(':active')) {
+              element.value = e.value;
+            }
+          })
+        ''')
+    def widget_html(self):
+        return f'''<input {self.std_attrs()} type="range" value="{
+            self.value}" min="{self.min}" max="{self.max}">'''
+
+class Choice(Widget):
+    """
+    A set of radio button choices.
+    """
+    def __init__(self, choices=None, selection=None, horizontal=False,
+            **kwargs):
+        super().__init__(**kwargs)
+        if choices is None:
+            choices = []
+        self.choices = Property(choices)
+        self.horizontal = Property(horizontal)
+        self.selection = Property(selection)
+    def widget_js(self):
+        # Note that the 'input' event would enable during-drag feedback,
+        # but this is pretty slow on google colab.
+        return minify('''
+          function esc(unsafe) {
+            return unsafe.replace(/&/g, "&amp;").replace(/</g, "&lt;")
+                   .replace(/>/g, "&gt;").replace(/"/g, "&quot;");
+          }
+          function render() {
+            var lines = model.get('choices').map((c) => {
+              return '<label><input type="radio" name="choice" value="' +
+                 esc(c) + '">' + esc(c) + '</label>'
+            });
+            element.innerHTML = lines.join(model.get('horizontal')?' ':'<br>');
+          }
+          model.on('choices horizontal', render);
+          model.on('selection', (ev) => {
+            [...element.querySelectorAll('input')].forEach((e) => {
+              e.checked = (e.value == ev.value);
+            })
+          });
+          element.addEventListener('change', (e) => {
+            model.set('selection', element.choice.value);
+          });
+        ''')
+    def widget_html(self):
+        radios = [
+            f"""<label><input name="choice" type="radio" {
+            'checked' if value == self.selection else ''
+            } value="{html.escape(value)}">{html.escape(value)}</label>"""
+            for value in self.choices ]
+        sep = " " if self.horizontal else "<br>"
+        return f'<form {self.std_attrs()}>{sep.join(radios)}</form>'
+
+class Menu(Widget):
+    """
+    A dropdown choice.
+    """
+    def __init__(self, choices=None, selection=None, **kwargs):
+        super().__init__(**kwargs)
+        if choices is None:
+            choices = []
+        self.choices = Property(choices)
+        self.selection = Property(selection)
+    def widget_js(self):
+        return minify('''
+          function esc(unsafe) {
+            return unsafe.replace(/&/g, "&amp;").replace(/</g, "&lt;")
+                   .replace(/>/g, "&gt;").replace(/"/g, "&quot;");
+          }
+          function render() {
+            var selection = model.get('selection');
+            var lines = model.get('choices').map((c) => {
+              return '<option value="' + esc(''+c) + '"' +
+                     (c == selection ? ' selected' : '') +
+                     '>' + esc(''+c) + '</option>';
+            });
+            element.menu.innerHTML = lines.join('\\n');
+          }
+          model.on('choices horizontal', render);
+          model.on('selection', (ev) => {
+            [...element.querySelectorAll('option')].forEach((e) => {
+              e.selected = (e.value == ev.value);
+            })
+          });
+          element.addEventListener('change', (e) => {
+            model.set('selection', element.menu.value);
+          });
+        ''')
+    def widget_html(self):
+        options = [
+            f"""<option value="{html.escape(str(value))}" {
+            'selected' if value == self.selection else ''
+            }>{html.escape(str(value))}</option>"""
+            for value in self.choices ]
+        sep = "\n"
+        return f'''<form {self.std_attrs()}"><select name="menu">{
+             sep.join(options)}</select></form>'''
+
+class Datalist(Widget):
+    """
+    An input with a dropdown choice.
+    """
+    def __init__(self, choices=None, value=None, **kwargs):
+        super().__init__(**kwargs)
+        if choices is None:
+            choices = []
+        self.choices = Property(choices)
+        self.value = Property(value)
+    def datalist_id(self):
+        return self.view_id() + '-dl'
+    def widget_js(self):
+        # The mousedown/mouseleave dance defeats the prefix-matching behavior
+        # of the built-in datalist by erasing value momentarily on mousedown.
+        return minify('''
+          function esc(unsafe) {
+            return unsafe.replace(/&/g, "&amp;").replace(/</g, "&lt;")
+                   .replace(/>/g, "&gt;").replace(/"/g, "&quot;");
+          }
+          function render() {
+            var lines = model.get('choices').map((c) => {
+              return '<option value="' + esc(''+c) + '">';
+            });
+            element.inp.list.innerHTML = lines.join('\\n');
+          }
+          model.on('choices', render);
+          model.on('value', (ev) => {
+            element.inp.value = ev.value;
+          });
+          function restoreValue() {
+            var inp = element.inp;
+            if (inp.value == '') {
+              inp.value = inp.placeholder;
+              inp.placeholder = '';
+            }
+          }
+          element.inp.addEventListener('mousedown', (e) => {
+            var inp = element.inp;
+            if (inp.value != '') {
+              inp.placeholder = inp.value;
+              inp.value = '';
+              if (e.clientX < inp.getBoundingClientRect().right - 25) {
+                setTimeout(restoreValue, 0);
+              }
+            }
+          });
+          element.inp.addEventListener('mouseleave', restoreValue)
+          element.inp.addEventListener('change', (e) => {
+            model.set('value', element.inp.value);
+          });
+        ''')
+    def widget_html(self):
+        options = [
+            f"""<option value="{html.escape(str(value))}">"""
+            for value in self.choices ]
+        return ''.join([
+          f'<form {self.std_attrs()} onsubmit="return false;">',
+          f'<input name="inp" list="{self.datalist_id()}" autocomplete="off">',
+          f'<datalist id="{self.datalist_id()}">',
+          ''.join(options),
+          f'</datalist></form>'])
+
+class Div(Widget):
+    """
+    Just an empty DIV element.  Use the innerHTML property to
+    change its contents, or use the clear() and print() method.
+    """
+    def __init__(self, innerHTML='', **kwargs):
+        super().__init__(**kwargs)
+        # TODO: unify more closely with the show() library.
+        self.innerHTML = Property(innerHTML)
+
+    def clear(self):
+        """Clears the contents of the div."""
+        self.innerHTML = ''
+
+    def show(self, *args):
+        from . import show
+        self.innerHTML = show.html(args)
+
+    def print(self, *args, replace=False):
+        """Appends plain text (as a pre) into the div."""
+        newHTML = '<pre>%s</pre>' % ' '.join(
+                html.escape(str(text)) for text in args)
+        if replace:
+            self.innerHTML = newHTML
+        else:
+            self.innerHTML += newHTML
+
+    def widget_js(self):
+        # Note that if we want innerHTML to support script execution,
+        # we need to do it explicitly, like this.
+        return minify('''
+          model.on('innerHTML', (ev) => {
+            element.innerHTML = ev.value;
+            Array.from(element.querySelectorAll("script")).forEach(old=>{
+              const newScript = document.createElement("script");
+              Array.from(old.attributes).forEach(attr =>
+                 newScript.setAttribute(attr.name, attr.value));
+              newScript.appendChild(document.createTextNode(old.innerHTML));
+              old.parentNode.replaceChild(newScript, old);
+            });
+          });
+        ''')
+    def widget_html(self):
+        return f'''<div {self.std_attrs()}>{self.innerHTML}</div>'''
+
+class ClickDiv(Div):
+    '''
+    A Div that triggers click events when anything inside them is clicked.
+    If a clicked element contains a data-click value, then that value is
+    sent as the click event value.
+    '''
+    def __init__(self, innerHTML='', **kwargs):
+        super().__init__(innertHTML, **kwargs)
+        self.click = Trigger()
+
+    def widget_js(self):
+        return super().widget_js() + minify('''
+          element.addEventListener('click', (ev) => {
+            var target = ev.target;
+            while (target && target != element && !target.dataset.click) {
+              target = target.parentElement;
+            }
+            var value = target.dataset.click;
+            model.trigger('click', value);
+          });
+        ''')
+
+class Image(Widget):
+    """
+    Just a IMG element.  Use the src property to change its contents by url,
+    or use the clear() and render(imgdata) methods to convert PIL or
+    tensor image data to a url to display.
+    """
+    def __init__(self, src='', style=None, **kwargs):
+        super().__init__(style=defaulted(style, margin=0), **kwargs)
+        self.src = Property(src)
+        self.click = Trigger()
+
+    def clear(self):
+        """Clears the image."""
+        self.src = ''
+
+    def render(self, imgdata, source=None):
+        """Converts a pil image or some tensor to a url to show inline."""
+        from . import renormalize
+        self.src = renormalize.as_url(imgdata, source=source)
+
+    def widget_js(self):
+        return minify('''
+          model.on('src', (ev) => { element.src = ev.value; });
+          element.addEventListener('click', (ev) => {
+            model.trigger('click');
+          });
+        ''')
+
+    def widget_html(self):
+        return f'''<img {self.std_attrs()} src="{html.escape(self.src)}">'''
+
+##########################################################################
+## Utils
+##########################################################################
+
+def minify(t):
+    # TODO: plug in some more real minification.
+    return re.sub(r'\n\s*', '\n', t)
+
+def style_attr(d):
+    if not d:
+        return ''
+    return ' style="%s"' % html.escape(css_style_from_dict(d))
+
+def data_attrs(d):
+    if not d:
+        return ''
+    return ''.join([
+        ' data-%s="%s"' % (k, html.escape(str(v))) for k, v in d.items()])
+
+def css_style_from_dict(d):
+    return ';'.join(
+            re.sub('([A-Z]+)', r'-\1',k).lower() + ':' +
+            re.sub('([][\\!"#$%&\'()*+,./:;<=>?@^`{|}~])', r'\\\1', str(v))
+        for k, v in d.items())
+
+def defaulted(d, **kwargs):
+    if d is None:
+        return kwargs
+    result = dict(kwargs)
+    result.update(d)
+    return result
+
+##########################################################################
+## Implementation Details
+##########################################################################
+
+WIDGET_ENV = None
+if WIDGET_ENV is None:
+    try:
+        from google.colab import output as colab_output
+        WIDGET_ENV = 'colab'
+    except:
+        pass
+if WIDGET_ENV is None:
+    try:
+        from ipykernel.comm import Comm as jupyter_comm
+        COMM_MANAGER = get_ipython().kernel.comm_manager
+        WIDGET_ENV = 'jupyter'
+    except:
+        pass
+
+SEND_RECV_JS = """
+function recvFromPython(obj_id, fn) {
+  var recvname = "recv_" + obj_id;
+  if (window[recvname] === undefined) {
+    window[recvname] = new BroadcastChannel("channel_" + obj_id);
+  }
+  window[recvname].addEventListener("message", (ev) => {
+    if (ev.data == 'ok') {
+      window[recvname].ok = true;
+      return;
+    }
+    fn.apply(null, ev.data.slice(1));
+  });
+}
+function sendToPython(obj_id, ...args) {
+  google.colab.kernel.invokeFunction('invoke_' + obj_id, args, {})
+}
+""" if WIDGET_ENV == 'colab' else """
+function getChan(obj_id) {
+  var cname = "comm_" + obj_id;
+  if (!window[cname]) { window[cname] = []; }
+  var chan = window[cname];
+  if (!chan.comm && Jupyter.notebook.kernel) {
+    chan.comm = Jupyter.notebook.kernel.comm_manager.new_comm(cname, {});
+    chan.comm.on_msg((ev) => {
+      if (chan.retry) { clearInterval(chan.retry); chan.retry = null; }
+      if (ev.content.data == 'ok') { return; }
+      var args = ev.content.data.slice(1);
+      for (fn of chan) { fn.apply(null, args); }
+    });
+    chan.retries = 5;
+    chan.retry = setInterval(() => {
+      if (chan.retries) { chan.retries -= 1; chan.comm.open(); }
+      else { clearInterval(chan.retry); chan.retry = null; }
+    }, 2000);
+  }
+  return chan;
+}
+function recvFromPython(obj_id, fn) {
+  getChan(obj_id).push(fn);
+}
+function sendToPython(obj_id, ...args) {
+  var comm = getChan(obj_id).comm;
+  if (comm) { comm.send(args); }
+}
+"""
+
+
+WIDGET_MODEL_JS = minify(SEND_RECV_JS + """
+class Model {
+  constructor(obj_id, init) {
+    this._id = obj_id;
+    this._listeners = {};
+    this._data = Object.assign({}, init)
+    recvFromPython(this._id, (name, value) => {
+      this._data[name] = value;
+      var e = new Event(name); e.value = value;
+      if (this._listeners.hasOwnProperty(name)) {
+        this._listeners[name].forEach((fn) => { fn(e); });
+      }
+    })
+  }
+  trigger(name, value) {
+    sendToPython(this._id, name, value);
+  }
+  get(name) {
+    return this._data[name];
+  }
+  set(name, value) {
+    this.trigger(name, value);
+  }
+  on(name, fn) {
+    name.split(/\s+/).forEach((n) => {
+      if (!this._listeners.hasOwnProperty(n)) {
+        this._listeners[n] = [];
+      }
+      this._listeners[n].push(fn);
+    });
+  }
+  off(name, fn) {
+    name.split(/\s+/).forEach((n) => {
+      if (!fn) {
+        delete this._listeners[n];
+      } else if (this._listeners.hasOwnProperty(n)) {
+        this._listeners[n] = this._listeners[n].filter(
+            (e) => { return e !== fn; });
+      }
+    });
+  }
+}
+""")
diff --git a/netdissect/lightbox.html b/netdissect/lightbox.html
new file mode 100644
index 0000000000000000000000000000000000000000..fbb6fd3ff294df8e5a2c0e486430659afc62032f
--- /dev/null
+++ b/netdissect/lightbox.html
@@ -0,0 +1,75 @@
+<!DOCTYPE html>
+<html>
+<!--
+  +lightbox.html, a page for automatically showing all images in a
+  directory on an Apache server. Just copy it into the directory.
+  Works by scraping the default directory HTML at "./" - David Bau.
+-->
+<head>
+<script src="https://cdn.jsdelivr.net/npm/vue@2.5.16/dist/vue.js"
+  integrity="sha256-CMMTrj5gGwOAXBeFi7kNokqowkzbeL8ydAJy39ewjkQ="
+  crossorigin="anonymous"></script>
+<script src="https://cdn.jsdelivr.net/npm/lodash@4.17.10/lodash.js"
+  integrity="sha256-qwbDmNVLiCqkqRBpF46q5bjYH11j5cd+K+Y6D3/ja28="
+  crossorigin="anonymous"></script>
+<script
+  src="https://code.jquery.com/jquery-3.3.1.js"
+  integrity="sha256-2Kok7MbOyxpgUVvAk/HJ2jigOSYS2auK4Pfzbm7uH60="
+  crossorigin="anonymous"></script>
+<script src="https://cdnjs.cloudflare.com/ajax/libs/lity/2.3.1/lity.js"
+  integrity="sha256-28JiZvE/RethQIYCwkMdtSMHgI//KoTLeB2tSm10trs="
+  crossorigin="anonymous"></script>
+<link rel="stylesheet"
+  href="https://cdnjs.cloudflare.com/ajax/libs/lity/2.3.1/lity.css"
+  integrity="sha256-76wKiAXVBs5Kyj7j0T43nlBCbvR6pqdeeZmXI4ATnY0="
+  crossorigin="anonymous" />
+<style>
+h3 { font-family: sans-serif; font-size: 18px; }
+.thumb, .filter { font-family: sans-serif; font-size: 12px; }
+.filter { padding-bottom: 10px; }
+.thumb { display: inline-block; margin: 1px; text-align: center; }
+.thumb img, .thumb div { max-width: 150px; word-break: break-all; }
+</style>
+</head>
+<body>
+<div id="app" v-if="images">
+<h3>Images in <a :href="directory">{{ directory }}</a></h3>
+<div class="filter">
+Filter: <input v-model="pattern" placeholder="regexp">
+</div>
+<div v-for="r in images" class="thumb" v-if="patternRe.test(r)">
+<div>{{ r }}</div>
+<a :href="r" data-lity><img :src="r"></a>
+</div>
+</div><!--app-->
+</body>
+<script>
+var theapp = new Vue({
+  el: '#app',
+  data: {
+    directory: window.location.pathname.replace(/[^\/]*$/, ''),
+    images: null,
+    pattern: '',
+  },
+  created: function() {
+    var self = this;
+    $.get('./?' + Math.random(), function(d) {
+      var imgurls = $.map($(d).find('a'),
+                      x => x.href).filter(
+                      x => x.match(/\.(jpg|jpeg|png|gif|svg)$/i)).map(
+                      x => x.replace(/.*\//, ''));
+      self.images = imgurls;
+    }, 'html');
+  },
+  computed: {
+    patternRe: function() {
+      try {
+        return RegExp(this.pattern);
+      } catch (e) {
+        return /.*/;
+      }
+    }
+  },
+})
+</script>
+</html>
diff --git a/netdissect/make_z_dataset.py b/netdissect/make_z_dataset.py
new file mode 100644
index 0000000000000000000000000000000000000000..5db1aa7d273c6a435f79577319492250376906fb
--- /dev/null
+++ b/netdissect/make_z_dataset.py
@@ -0,0 +1,93 @@
+import torch, copy, argparse, re, os, numpy
+from seeing import nethook, setting, renormalize, zdataset, pbar
+from seeing import encoder_net, nethook
+from seeing import workerpool
+from seeing.encoder_loss import cor_square_error
+from torch.nn.functional import mse_loss, l1_loss
+from seeing.LBFGS import FullBatchLBFGS
+
+
+torch.backends.cudnn.benchmark = True
+
+parser = argparse.ArgumentParser()
+parser.add_argument('--model', default='church')
+parser.add_argument('--dataset', default='church_outdoor_train')
+parser.add_argument('--iterations', type=int, default=0)
+args = parser.parse_args()
+
+batch_size = 32
+expdir = 'results/z_dataset/%s/it_%d' % (args.dataset, args.iterations)
+
+G = setting.load_proggan('church').eval().cuda()
+
+E = encoder_net.HybridLayerNormEncoder()
+filename = 'results/church/invert_hybrid_bottom_b5/snapshots/epoch_1000.pth.tar'
+E.load_state_dict(torch.load(filename)['state_dict'])
+E.eval().cuda()
+
+dataset = setting.load_dataset(args.dataset, full=True)
+loader = torch.utils.data.DataLoader(dataset, shuffle=False,
+        batch_size=batch_size, num_workers=10, pin_memory=True)
+
+torch.set_grad_enabled(False)
+
+class SaveNpyWorker(workerpool.WorkerBase):
+    def work(self, filename, data):
+        dirname = os.path.dirname(filename)
+        os.makedirs(dirname, exist_ok=True)
+        numpy.save(filename, data)
+
+saver_pool = workerpool.WorkerPool(worker=SaveNpyWorker)
+
+def target_filename_from_source(filename):
+    patharr = filename.split('/')
+    patharr = patharr[patharr.index(args.dataset)+1:]
+    patharr[-1] = os.path.splitext(patharr[-1])[0] + '.npy'
+    return os.path.join(expdir, *patharr)
+
+def refine_z_lbfgs(init_z, target_x, lambda_f=1.0, num_steps=100):
+
+    z = init_z.clone()
+    parameters = [z]
+    F = E
+    target_f = F(target_x)
+    nethook.set_requires_grad(False, G, E, target_x, target_f)
+    nethook.set_requires_grad(True, *parameters)
+    optimizer = FullBatchLBFGS(parameters)
+
+    def closure():
+        optimizer.zero_grad()
+        current_x = G(z)
+        loss = l1_loss(target_x, current_x)
+        if lambda_f:
+            loss += mse_loss(target_f, F(current_x)) * lambda_f
+        return loss
+
+    with torch.enable_grad():
+        for step_num in pbar(range(num_steps + 1)):
+            if step_num == 0:
+                loss = closure()
+                loss.backward()
+            else:
+                options = dict(closure=closure, current_loss=loss, max_ls=10)
+                loss, _, _, _, _, _, _, _ = optimizer.step(options)
+    return z
+
+index = 0
+for [im] in pbar(loader):
+    im = im.cuda()
+    z = E(im)
+    if args.iterations > 0:
+        #for i in range(len(im)):
+        #    z[i:i+1] = refine_z_lbfgs(z[i:i+1], im[i:i+1],
+        #            num_steps=args.iterations)
+        z = refine_z_lbfgs(z, im, num_steps=args.iterations)
+
+    cpu_z = z.cpu().numpy()
+    for i in range(len(im)):
+        filename = target_filename_from_source(dataset.images[index + i][0])
+        data = cpu_z[i].copy()[None]
+        pbar.print(filename, data.shape)
+        saver_pool.add(filename, data)
+    index += len(im)
+saver_pool.join()
diff --git a/netdissect/modelconfig.py b/netdissect/modelconfig.py
new file mode 100644
index 0000000000000000000000000000000000000000..2bc62e0e9dacc8eec3d2d08ec0d53798cbfe0a87
--- /dev/null
+++ b/netdissect/modelconfig.py
@@ -0,0 +1,142 @@
+import numbers
+import torch
+from netdissect.autoeval import autoimport_eval
+from netdissect import pbar
+from netdissect.nethook import InstrumentedModel
+from netdissect.easydict import EasyDict
+
+def create_instrumented_model(args, **kwargs):
+    '''
+    Creates an instrumented model out of a namespace of arguments that
+    correspond to ArgumentParser command-line args:
+      model: a string to evaluate as a constructor for the model.
+      pthfile: (optional) filename of .pth file for the model.
+      layers: a list of layers to instrument, defaulted if not provided.
+      edit: True to instrument the layers for editing.
+      gen: True for a generator model.  One-pixel input assumed.
+      imgsize: For non-generator models, (y, x) dimensions for RGB input.
+      cuda: True to use CUDA.
+  
+    The constructed model will be decorated with the following attributes:
+      input_shape: (usually 4d) tensor shape for single-image input.
+      output_shape: 4d tensor shape for output.
+      feature_shape: map of layer names to 4d tensor shape for featuremaps.
+      retained: map of layernames to tensors, filled after every evaluation.
+      ablation: if editing, map of layernames to [0..1] alpha values to fill.
+      replacement: if editing, map of layernames to values to fill.
+
+    When editing, the feature value x will be replaced by:
+        `x = (replacement * ablation) + (x * (1 - ablation))`
+    '''
+
+    args = EasyDict(vars(args), **kwargs)
+
+    # Construct the network
+    if args.model is None:
+        pbar.print('No model specified')
+        return None
+    if isinstance(args.model, torch.nn.Module):
+        model = args.model
+    else:
+        model = autoimport_eval(args.model)
+    # Unwrap any DataParallel-wrapped model
+    if isinstance(model, torch.nn.DataParallel):
+        model = next(model.children())
+
+    # Load its state dict
+    meta = {}
+    if getattr(args, 'pthfile', None) is not None:
+        data = torch.load(args.pthfile)
+        modelkey = getattr(args, 'modelkey', 'state_dict')
+        if modelkey in data:
+            meta = {}
+            for key in data:
+                if isinstance(data[key], numbers.Number):
+                    meta[key] = data[key]
+            data = data[modelkey]
+        submodule = getattr(args, 'submodule', None)
+        if submodule is not None and len(submodule):
+            remove_prefix = submodule + '.'
+            data = { k[len(remove_prefix):]: v for k, v in data.items()
+                    if k.startswith(remove_prefix)}
+            if not len(data):
+                pbar.print('No submodule %s found in %s' %
+                        (submodule, args.pthfile))
+                return None
+        model.load_state_dict(data, strict=not getattr(args, 'unstrict', False))
+
+    # Decide which layers to instrument.
+    if getattr(args, 'layer', None) is not None:
+        args.layers = [args.layer]
+    # If the layer '?' is the only specified, just print out all layers.
+    if getattr(args, 'layers', None) is not None:
+        if len(args.layers) == 1 and args.layers[0] == ('?', '?'):
+            for name, layer in model.named_modules():
+                pbar.print(name)
+            import sys
+            sys.exit(0)
+    if getattr(args, 'layers', None) is None:
+        # Skip wrappers with only one named model
+        container = model
+        prefix = ''
+        while len(list(container.named_children())) == 1:
+            name, container = next(container.named_children())
+            prefix += name + '.'
+        # Default to all nontrivial top-level layers except last.
+        args.layers = [prefix + name
+                for name, module in container.named_children()
+                if type(module).__module__ not in [
+                    # Skip ReLU and other activations.
+                    'torch.nn.modules.activation',
+                    # Skip pooling layers.
+                    'torch.nn.modules.pooling']
+                ][:-1]
+        pbar.print('Defaulting to layers: %s' % ' '.join(args.layers))
+
+    # Now wrap the model for instrumentation.
+    model = InstrumentedModel(model)
+    model.meta = meta
+
+    # Instrument the layers.
+    model.retain_layers(args.layers)
+    model.eval()
+    if args.cuda:
+        model.cuda()
+
+    # Annotate input, output, and feature shapes
+    annotate_model_shapes(model,
+            gen=getattr(args, 'gen', False),
+            imgsize=getattr(args, 'imgsize', None))
+    return model
+
+def annotate_model_shapes(model, gen=False, imgsize=None):
+    assert (imgsize is not None) or gen
+
+    # Figure the input shape.
+    if gen:
+        # We can guess a generator's input shape by looking at the model.
+        # Examine first conv in model to determine input feature size.
+        first_layer = [c for c in model.modules()
+                if isinstance(c, (torch.nn.Conv2d, torch.nn.ConvTranspose2d,
+                    torch.nn.Linear))][0]
+        # 4d input if convolutional, 2d input if first layer is linear.
+        if isinstance(first_layer, (torch.nn.Conv2d, torch.nn.ConvTranspose2d)):
+            input_shape = (1, first_layer.in_channels, 1, 1)
+        else:
+            input_shape = (1, first_layer.in_features)
+    else:
+        # For a classifier, the input image shape is given as an argument.
+        input_shape = (1, 3) + tuple(imgsize)
+
+    # Run the model once to observe feature shapes.
+    device = next(model.parameters()).device
+    dry_run = torch.zeros(input_shape).to(device)
+    with torch.no_grad():
+        output = model(dry_run)
+
+    # Annotate shapes.
+    model.input_shape = input_shape
+    model.feature_shape = { layer: feature.shape
+            for layer, feature in model.retained_features().items() }
+    model.output_shape = output.shape
+    return model
diff --git a/netdissect/multilayer_graph.py b/netdissect/multilayer_graph.py
new file mode 100644
index 0000000000000000000000000000000000000000..b13d080d74375d298d707f79b01415347b0ed422
--- /dev/null
+++ b/netdissect/multilayer_graph.py
@@ -0,0 +1,132 @@
+import argparse, os, shutil, json
+from netdissect.easydict import EasyDict
+from xml.etree import ElementTree as et
+from collections import defaultdict
+
+def parseargs():
+    parser = argparse.ArgumentParser()
+    def aa(*args, **kwargs):
+        parser.add_argument(*args, **kwargs)
+    aa('--model', choices=['resnet18-bn', 'resnet18-hn', 'resnet18-ha'],
+            default='resnet18-bn')
+    aa('--iteration', type=int, default=0)
+    aa('--dataset', choices=['places'],
+            default='places')
+    aa('--seg', choices=['net', 'netp', 'netq', 'netpq',
+            'netpqc', 'netpqxc', 'human'],
+            default='net')
+    aa('--layers', nargs='+')
+    aa('--quantile', type=float, default=0.01)
+    aa('--miniou', type=float, default=0.025)
+    args = parser.parse_args()
+    return args
+
+def main():
+    args = parseargs()
+    threshold_iou = args.miniou
+    layer_report = {}
+    qdir = '-%d' % (args.quantile * 1000) if args.quantile != 0.01 else ''
+    for layer in args.layers:
+        input_filename = 'results/%s-%d-%s-%s-%s%s/report.json' % (
+                args.model, args.iteration, args.dataset, args.seg, layer, qdir)
+        with open(input_filename) as f:
+            layer_report[layer] = EasyDict(json.load(f))
+    # Now assemble the data needed for the graph
+    # (Layername, [(catname, [unitcount, unitcount, unitcount]), (catname..)
+    cat_order = ['object', 'part', 'material', 'color']
+    graph_data = []
+    for layer in args.layers:
+        layer_data = []
+        catmap = defaultdict(lambda: defaultdict(int))
+        units = layer_report[layer].get('units',
+                layer_report[layer].get('images', None)) # old format
+        for unitrec in units:
+            if unitrec.iou is None or unitrec.iou < threshold_iou:
+                continue
+            catmap[unitrec.cat][unitrec.label] += 1
+        for cat in cat_order:
+            if cat not in catmap:
+                continue
+            # For this graph we do not need labels
+            cat_data = list(catmap[cat].values())
+            cat_data.sort(key=lambda x: -x)
+            layer_data.append((cat, cat_data))
+        graph_data.append((layer, layer_data))
+    # Now make the actual graph
+    largest_layer = max(sum(len(cat_data)
+            for cat, cat_data in layer_data)
+            for layer, layer_data in graph_data)
+    layer_height = 14
+    layer_gap = 2
+    barwidth = 3
+    bargap = 0
+    leftmargin = 48
+    margin = 8
+    svgwidth = largest_layer * (barwidth + bargap) + margin + leftmargin
+    svgheight = ((layer_height + layer_gap) * len(args.layers) - layer_gap +
+            2 * margin)
+    textsize = 10
+
+    # create an SVG XML element
+    svg = et.Element('svg', width=str(svgwidth), height=str(svgheight),
+            version='1.1', xmlns='http://www.w3.org/2000/svg')
+
+    # Draw big category background rectangles
+    y = margin
+    for layer, layer_data in graph_data:
+        et.SubElement(svg, 'text', x='0', y='0',
+            style=('font-family:sans-serif;font-size:%dpx;' +
+                'text-anchor:end;alignment-baseline:hanging;' +
+                'transform:translate(%dpx, %dpx);') %
+                (textsize, leftmargin - 4, y + (layer_height - textsize) / 2)
+            ).text = str(layer)
+        barmax = max(max(cat_data) if len(cat_data) else 1
+                for cat, cat_data in layer_data) if len(layer_data) else 1
+        barscale = float(layer_height) / barmax
+        x = leftmargin
+        for cat, cat_data in layer_data:
+            catwidth = len(cat_data) * (barwidth + bargap)
+            et.SubElement(svg, 'rect',
+                    x=str(x), y=str(y),
+                    width=str(catwidth),
+                    height=str(layer_height),
+                    fill=cat_palette[cat][1])
+            for bar in cat_data:
+                barheight = barscale * bar
+                et.SubElement(svg, 'rect',
+                        x=str(x), y=str(y + layer_height - barheight),
+                        width=str(barwidth),
+                        height=str(barheight),
+                        fill=cat_palette[cat][0])
+                x += barwidth + bargap
+        y += layer_height + layer_gap
+
+    # Output - this is the bare svg.
+    result = et.tostring(svg).decode('utf-8')
+    # Now add the file header.
+    result = ''.join([
+            '<?xml version=\"1.0\" standalone=\"no\"?>\n',
+            '<!DOCTYPE svg PUBLIC \"-//W3C//DTD SVG 1.1//EN\"\n',
+            '\"http://www.w3.org/Graphics/SVG/1.1/DTD/svg11.dtd\">\n',
+            result])
+    output_filename = 'results/%s-%s-%s-%s%s/multilayer-%d.svg' % (
+                args.model, args.iteration, args.dataset, args.seg, qdir,
+                args.miniou * 1000)
+    os.makedirs(os.path.dirname(output_filename), exist_ok=True)
+    print('writing to %s' % output_filename)
+    with open(output_filename, 'w') as f:
+        f.write(result)
+
+cat_palette = {
+    'object':   ('#4B4CBF', '#B6B6F2'),
+    'part':     ('#55B05B', '#B6F2BA'),
+    'material': ('#50BDAC', '#A5E5DB'),
+    'texture':  ('#81C679', '#C0FF9B'),
+    'color':    ('#F0883B', '#F2CFB6'),
+    'other1':   ('#D4CF24', '#F2F1B6'),
+    'other2':   ('#D92E2B', '#F2B6B6'),
+    'other3':   ('#AB6BC6', '#CFAAFF')
+}
+
+if __name__ == '__main__':
+    main()
diff --git a/netdissect/netdissect b/netdissect/netdissect
new file mode 120000
index 0000000000000000000000000000000000000000..ed983f65a970a7444e3411836004ce2a369166c2
--- /dev/null
+++ b/netdissect/netdissect
@@ -0,0 +1 @@
+netdissect
\ No newline at end of file
diff --git a/netdissect/nethook.py b/netdissect/nethook.py
new file mode 100644
index 0000000000000000000000000000000000000000..8328058b2a525ede5cdeebfd7284ac03e419b5c6
--- /dev/null
+++ b/netdissect/nethook.py
@@ -0,0 +1,421 @@
+'''
+Utilities for instrumenting a torch model.
+
+InstrumentedModel will wrap a pytorch model and allow hooking
+arbitrary layers to monitor or modify their output directly.
+'''
+
+import torch, numpy, types, copy, inspect
+from collections import OrderedDict, defaultdict
+
+class InstrumentedModel(torch.nn.Module):
+    '''
+    A wrapper for hooking, probing and intervening in pytorch Modules.
+    Example usage:
+
+    ```
+    model = load_my_model()
+    with inst as InstrumentedModel(model):
+        inst.retain_layer(layername)
+        inst.edit_layer(layername, ablation=0.5, replacement=target_features)
+        inst(inputs)
+        original_features = inst.retained_layer(layername)
+    ```
+    '''
+
+    def __init__(self, model):
+        super().__init__()
+        self.model = model
+        self._retained = OrderedDict()
+        self._detach_retained = {}
+        self._editargs = defaultdict(dict)
+        self._editrule = {}
+        self._hooked_layer = {}
+        self._old_forward = {}
+        if isinstance(model, torch.nn.Sequential):
+            self._hook_sequential()
+
+    def __enter__(self):
+        return self
+
+    def __exit__(self, type, value, traceback):
+        self.close()
+
+    def forward(self, *inputs, **kwargs):
+        return self.model(*inputs, **kwargs)
+
+    def layer_names(self):
+        '''
+        Returns a list of layer names.
+        '''
+        return [name for name, _ in self.model.named_modules()]
+
+    def retain_layer(self, layername, detach=True):
+        '''
+        Pass a fully-qualified layer name (E.g., module.submodule.conv3)
+        to hook that layer and retain its output each time the model is run.
+        A pair (layername, aka) can be provided, and the aka will be used
+        as the key for the retained value instead of the layername.
+        '''
+        self.retain_layers([layername], detach=detach)
+
+    def retain_layers(self, layernames, detach=True):
+        '''
+        Retains a list of a layers at once.
+        '''
+        self.add_hooks(layernames)
+        for layername in layernames:
+            aka = layername
+            if not isinstance(aka, str):
+                layername, aka = layername
+            if aka not in self._retained:
+                self._retained[aka] = None
+                self._detach_retained[aka] = detach
+
+    def stop_retaining_layers(self, layernames):
+        '''
+        Removes a list of layers from the set retained.
+        '''
+        self.add_hooks(layernames)
+        for layername in layernames:
+            aka = layername
+            if not isinstance(aka, str):
+                layername, aka = layername
+            if aka in self._retained:
+                del self._retained[aka]
+                del self._detach_retained[aka]
+
+    def retained_features(self, clear=False):
+        '''
+        Returns a dict of all currently retained features.
+        '''
+        result = OrderedDict(self._retained)
+        if clear:
+            for k in result:
+                self._retained[k] = None
+        return result
+
+    def retained_layer(self, aka=None, clear=False):
+        '''
+        Retrieve retained data that was previously hooked by retain_layer.
+        Call this after the model is run.  If clear is set, then the
+        retained value will return and also cleared.
+        '''
+        if aka is None:
+            # Default to the first retained layer.
+            aka = next(self._retained.keys().__iter__())
+        result = self._retained[aka]
+        if clear:
+            self._retained[aka] = None
+        return result
+
+    def edit_layer(self, layername, rule=None, **kwargs):
+        '''
+        Pass a fully-qualified layer name (E.g., module.submodule.conv3)
+        to hook that layer and modify its output each time the model is run.
+        The output of the layer will be modified to be a convex combination
+        of the replacement and x interpolated according to the ablation, i.e.:
+        `output = x * (1 - a) + (r * a)`.
+        '''
+        if not isinstance(layername, str):
+            layername, aka = layername
+        else:
+            aka = layername
+
+        # The default editing rule is apply_ablation_replacement
+        if rule is None:
+            rule = apply_ablation_replacement
+
+        self.add_hooks([(layername, aka)])
+        self._editargs[aka].update(kwargs)
+        self._editrule[aka] = rule
+
+    def remove_edits(self, layername=None):
+        '''
+        Removes edits at the specified layer, or removes edits at all layers
+        if no layer name is specified.
+        '''
+        if layername is None:
+            self._editargs.clear()
+            self._editrule.clear()
+            return
+
+        if not isinstance(layername, str):
+            layername, aka = layername
+        else:
+            aka = layername
+        if aka in self._editargs:
+            del self._editargs[aka]
+        if aka in self._editrule:
+            del self._editrule[aka]
+
+    def add_hooks(self, layernames):
+        '''
+        Sets up a set of layers to be hooked.
+
+        Usually not called directly: use edit_layer or retain_layer instead.
+        '''
+        needed = set()
+        aka_map = {}
+        for name in layernames:
+            aka = name
+            if not isinstance(aka, str):
+                name, aka = name
+            if self._hooked_layer.get(aka, None) != name:
+                aka_map[name] = aka
+                needed.add(name)
+        if not needed:
+            return
+        for name, layer in self.model.named_modules():
+            if name in aka_map:
+                needed.remove(name)
+                aka = aka_map[name]
+                self._hook_layer(layer, name, aka)
+        for name in needed:
+            raise ValueError('Layer %s not found in model' % name)
+
+    def _hook_layer(self, layer, layername, aka):
+        '''
+        Internal method to replace a forward method with a closure that
+        intercepts the call, and tracks the hook so that it can be reverted.
+        '''
+        if aka in self._hooked_layer:
+            raise ValueError('Layer %s already hooked' % aka)
+        if layername in self._old_forward:
+            raise ValueError('Layer %s already hooked' % layername)
+        self._hooked_layer[aka] = layername
+        self._old_forward[layername] = (layer, aka,
+                layer.__dict__.get('forward', None))
+        editor = self
+        original_forward = layer.forward
+        def new_forward(self, *inputs, **kwargs):
+            original_x = original_forward(*inputs, **kwargs)
+            x = editor._postprocess_forward(original_x, aka)
+            return x
+        layer.forward = types.MethodType(new_forward, layer)
+
+    def _unhook_layer(self, aka):
+        '''
+        Internal method to remove a hook, restoring the original forward method.
+        '''
+        if aka not in self._hooked_layer:
+            return
+        layername = self._hooked_layer[aka]
+        # Remove any retained data and any edit rules
+        if aka in self._retained:
+            del self._retained[aka]
+            del self._detach_retained[aka]
+        self.remove_edits(aka)
+        # Restore the unhooked method for the layer
+        layer, check, old_forward = self._old_forward[layername]
+        assert check == aka
+        if old_forward is None:
+            if 'forward' in layer.__dict__:
+                del layer.__dict__['forward']
+        else:
+            layer.forward = old_forward
+        del self._old_forward[layername]
+        del self._hooked_layer[aka]
+
+    def _postprocess_forward(self, x, aka):
+        '''
+        The internal method called by the hooked layers after they are run.
+        '''
+        # Retain output before edits, if desired.
+        if aka in self._retained:
+            if self._detach_retained[aka]:
+                # U-Net3D implementation fix
+                if not isinstance(x, tuple):
+                    self._retained[aka] = x.detach()
+                else:
+                    self._retained[aka] = x[0].detach()
+            else:
+                if not isinstance(x, tuple):
+                    self._retained[aka] = x
+                else:
+                    self._retained[aka] = x[0]
+        # Apply any edits requested.
+        rule = self._editrule.get(aka, None)
+        if rule is not None:
+            x = invoke_with_optional_args(
+                rule, x, self, name=aka, **(self._editargs[aka]))
+        return x
+
+    def _hook_sequential(self):
+        '''
+        Replaces 'forward' of sequential with a version that takes
+        additional keyword arguments: layer allows a single layer to be run;
+        first_layer and last_layer allow a subsequence of layers to be run.
+        '''
+        model = self.model
+        self._hooked_layer['.'] = '.'
+        self._old_forward['.'] = (model, '.',
+                model.__dict__.get('forward', None))
+        def new_forward(this, x, layer=None, first_layer=None, last_layer=None):
+            # TODO: decide whether to support hierarchical names here.
+            assert layer is None or (first_layer is None and last_layer is None)
+            first_layer, last_layer = [str(layer) if layer is not None
+                    else str(d) if d is not None else None
+                    for d in [first_layer, last_layer]]
+            including_children = (first_layer is None)
+            for name, layer in this._modules.items():
+                if name == first_layer:
+                    first_layer = None
+                    including_children = True
+                if including_children:
+                    x = layer(x)
+                if name == last_layer:
+                    last_layer = None
+                    including_children = False
+            assert first_layer is None, '%s not found' % first_layer
+            assert last_layer is None, '%s not found' % last_layer
+            return x
+        model.forward = types.MethodType(new_forward, model)
+
+    def close(self):
+        '''
+        Unhooks all hooked layers in the model.
+        '''
+        for aka in list(self._old_forward.keys()):
+            self._unhook_layer(aka)
+        assert len(self._old_forward) == 0
+
+def apply_ablation_replacement(x, imodel, **buffers):
+    if buffers is not None:
+        # Apply any edits requested.
+        a = make_matching_tensor(buffers, 'ablation', x)
+        if a is not None:
+            x = x * (1 - a)
+            v = make_matching_tensor(buffers, 'replacement', x)
+            if v is not None:
+                x += (v * a)
+    return x
+
+def make_matching_tensor(valuedict, name, data):
+    '''
+    Converts `valuedict[name]` to be a tensor with the same dtype, device,
+    and dimension count as `data`, and caches the converted tensor.
+    '''
+    v = valuedict.get(name, None)
+    if v is None:
+        return None
+    if not isinstance(v, torch.Tensor):
+        # Accept non-torch data.
+        v = torch.from_numpy(numpy.array(v))
+        valuedict[name] = v
+    if not v.device == data.device or not v.dtype == data.dtype:
+        # Ensure device and type matches.
+        assert not v.requires_grad, '%s wrong device or type' % (name)
+        v = v.to(device=data.device, dtype=data.dtype)
+        valuedict[name] = v
+    if len(v.shape) < len(data.shape):
+        # Ensure dimensions are unsqueezed as needed.
+        assert not v.requires_grad, '%s wrong dimensions' % (name)
+        v = v.view((1,) + tuple(v.shape) +
+                (1,) * (len(data.shape) - len(v.shape) - 1))
+        valuedict[name] = v
+    return v
+
+def subsequence(sequential, first_layer=None, last_layer=None,
+        after_layer=None, upto_layer=None, single_layer=None,
+        share_weights=False):
+    '''
+    Creates a subsequence of a pytorch Sequential model, copying over
+    modules together with parameters for the subsequence.  Only
+    modules from first_layer to last_layer (inclusive) are included,
+    or modules between after_layer and upto_layer (exclusive).
+    Handles descent into dotted layer names as long as all references
+    are within nested Sequential models.
+
+    If share_weights is True, then references the original modules
+    and their parameters without copying them.  Otherwise, by default,
+    makes a separate brand-new copy.
+    '''
+    assert ((single_layer is None) or
+            (first_layer is last_layer is after_layer is upto_layer is None))
+    if single_layer is not None:
+        first_layer = single_layer
+        last_layer = single_layer
+    first, last, after, upto = [None if d is None else d.split('.')
+            for d in [first_layer, last_layer, after_layer, upto_layer]]
+    return hierarchical_subsequence(sequential, first=first, last=last,
+            after=after, upto=upto, share_weights=share_weights)
+
+def hierarchical_subsequence(sequential, first, last, after, upto,
+        share_weights=False, depth=0):
+    '''
+    Recursive helper for subsequence() to support descent into dotted
+    layer names.  In this helper, first, last, after, and upto are
+    arrays of names resulting from splitting on dots.  Can only
+    descend into nested Sequentials.
+    '''
+    assert (last is None) or (upto is None)
+    assert (first is None) or (after is None)
+    if first is last is after is upto is None:
+        return sequential if share_weights else copy.deepcopy(sequential)
+    assert isinstance(sequential, torch.nn.Sequential), ('.'.join(
+        (first or last or after or upto)[:depth] or 'arg') + ' not Sequential')
+    including_children = (first is None) and (after is None)
+    included_children = OrderedDict()
+    (F, FN), (L, LN), (A, AN), (U, UN) = [
+            (d[depth], (None if len(d) == depth+1 else d))
+            if d is not None else (None, None)
+            for d in [first, last, after, upto]]
+    for name, layer in sequential._modules.items():
+        if name == F:
+            first = None
+            including_children = True
+        if name == A and AN is not None:
+            after = None
+            including_children = True
+        if name == U and UN is None:
+            upto = None
+            including_children = False
+        if including_children:
+            FR, LR, AR, UR = [n if n is None or n[depth] == name else None
+                    for n in [FN, LN, AN, UN]]
+            chosen = hierarchical_subsequence(layer,
+                    first=FR, last=LR, after=AR, upto=UR,
+                    share_weights=share_weights, depth=depth+1)
+            if chosen is not None:
+                included_children[name] = chosen
+        if name == L:
+            last = None
+            including_children = False
+        if name == U and UN is not None:
+            upto = None
+            including_children = False
+        if name == A and AN is None:
+            after = None
+            including_children = True
+    for name in [first, last, after, upto]:
+        if name is not None:
+            raise ValueError('Layer %s not found' % '.'.join(name))
+    # Omit empty subsequences except at the outermost level,
+    # where we should not return None.
+    if not len(included_children) and depth > 0:
+        return None
+    return torch.nn.Sequential(included_children)
+
+def set_requires_grad(requires_grad, *models):
+    for model in models:
+        if isinstance(model, torch.nn.Module):
+            for param in model.parameters():
+                param.requires_grad = requires_grad
+        elif isinstance(model, (torch.nn.Parameter, torch.Tensor)):
+            model.requires_grad = requires_grad
+        else:
+            assert False, 'unknown type %r' % type(model)
+
+def invoke_with_optional_args(fn, *args, **kwargs):
+    argspec = inspect.getfullargspec(fn)
+    kwtaken = 0
+    if argspec.varkw is None:
+        kwtaken = len([k for k in kwargs if k in argspec.args])
+        kwargs = {k: v for k, v in kwargs.items()
+                if k in argspec.args or
+                argspec.kwonlyargs and k in argspec.kwonlyargs}
+    if argspec.varargs is None:
+        args = args[:len(argspec.args) - kwtaken]
+    return fn(*args, **kwargs)
+
diff --git a/netdissect/oldalexnet.py b/netdissect/oldalexnet.py
new file mode 100644
index 0000000000000000000000000000000000000000..18cb793e8093b6a98e959af7da105f44671c1ead
--- /dev/null
+++ b/netdissect/oldalexnet.py
@@ -0,0 +1,105 @@
+from __future__ import print_function
+# based on https://github.com/jiecaoyu/pytorch_imagenet
+
+import os
+import torch
+import sys
+import torch.nn as nn
+import torch.nn.functional as F
+from collections import OrderedDict
+import torchvision.transforms
+import numpy
+
+def load_places_alexnet(weight_file):
+    model = AlexNet()
+    state_dict = torch.load(weight_file)
+    model.load_state_dict(state_dict)
+    return model
+
+
+class AlexNet(nn.Sequential):
+
+    def __init__(self, num_classes=None,
+            include_lrn=True, split_groups=True,
+            include_dropout=True):
+        w = [3, 96, 256, 384, 384, 256, 4096, 4096, 365]
+        if num_classes is not None:
+            w[-1] = num_classes
+        if split_groups is True:
+            groups = [1, 2, 1, 2, 2]
+        else:
+            groups = [1, 1, 1, 1, 1]
+        sequence = OrderedDict()
+        for name, module in [
+                ('conv1', nn.Conv2d(w[0], w[1], kernel_size=11,
+                    stride=4,
+                    groups=groups[0], bias=True)),
+                ('relu1', nn.ReLU(inplace=True)),
+                ('pool1', nn.MaxPool2d(kernel_size=3, stride=2)),
+                ('lrn1', LRN(local_size=5, alpha=0.0001, beta=0.75)),
+                ('conv2', nn.Conv2d(w[1], w[2], kernel_size=5, padding=2,
+                    groups=groups[1], bias=True)),
+                ('relu2', nn.ReLU(inplace=True)),
+                ('pool2', nn.MaxPool2d(kernel_size=3, stride=2)),
+                ('lrn2', LRN(local_size=5, alpha=0.0001, beta=0.75)),
+                ('conv3', nn.Conv2d(w[2], w[3], kernel_size=3, padding=1,
+                    groups=groups[2], bias=True)),
+                ('relu3', nn.ReLU(inplace=True)),
+                ('conv4', nn.Conv2d(w[3], w[4], kernel_size=3, padding=1,
+                    groups=groups[3], bias=True)),
+                ('relu4', nn.ReLU(inplace=True)),
+                ('conv5', nn.Conv2d(w[4], w[5], kernel_size=3, padding=1,
+                    groups=groups[4], bias=True)),
+                ('relu5', nn.ReLU(inplace=True)),
+                ('pool5', nn.MaxPool2d(kernel_size=3, stride=2)),
+                ('flatten', Vectorize()),
+                ('fc6', nn.Linear(w[5] * 6 * 6, w[6], bias=True)),
+                ('relu6', nn.ReLU(inplace=True)),
+                ('dropout6', nn.Dropout()),
+                ('fc7', nn.Linear(w[6], w[7], bias=True)),
+                ('relu7', nn.ReLU(inplace=True)),
+                ('dropout7', nn.Dropout()),
+                ('fc8', nn.Linear(w[7], w[8])) ]:
+            if not include_lrn and name.startswith('lrn'):
+                continue
+            if not include_dropout and name.startswith('drop'):
+                continue
+            sequence[name] = module
+        super(AlexNet, self).__init__(sequence)
+
+class LRN(nn.Module):
+    def __init__(self, local_size=1, alpha=1.0, beta=0.75,
+            ACROSS_CHANNELS=True):
+        super(LRN, self).__init__()
+        self.ACROSS_CHANNELS = ACROSS_CHANNELS
+        if ACROSS_CHANNELS:
+            self.average=nn.AvgPool3d(kernel_size=(local_size, 1, 1),
+                    stride=1,
+                    padding=(int((local_size-1.0)/2), 0, 0))
+        else:
+            self.average=nn.AvgPool2d(kernel_size=local_size,
+                    stride=1,
+                    padding=int((local_size-1.0)/2))
+        self.alpha = alpha
+        self.beta = beta
+
+    def forward(self, x):
+        if self.ACROSS_CHANNELS:
+            div = x.pow(2).unsqueeze(1)
+            div = self.average(div).squeeze(1)
+            div = div.mul(self.alpha).add(1.0).pow(self.beta)
+        else:
+            div = x.pow(2)
+            div = self.average(div)
+            div = div.mul(self.alpha).add(1.0).pow(self.beta)
+        x = x.div(div)
+        return x
+
+class Vectorize(nn.Module):
+    def __init__(self):
+        super(Vectorize, self).__init__()
+
+    def forward(self, x):
+        x = x.view(x.size(0), int(numpy.prod(x.size()[1:])))
+        return x
+
diff --git a/netdissect/oldresnet152.py b/netdissect/oldresnet152.py
new file mode 100644
index 0000000000000000000000000000000000000000..f783c2c0c0b11a9bde635dac3090c38af448ab88
--- /dev/null
+++ b/netdissect/oldresnet152.py
@@ -0,0 +1,922 @@
+
+import torch
+import torch.nn as nn
+
+from functools import reduce
+from torch.autograd import Variable
+
+def load_places_resnet152(weight_file):
+    model = OldResNet152()
+    state_dict = torch.load(weight_file)
+    model.load_state_dict(state_dict)
+    return model
+
+class LambdaBase(nn.Sequential):
+    def __init__(self, fn, *args):
+        super(LambdaBase, self).__init__(*args)
+        self.lambda_func = fn
+
+    def forward_prepare(self, input):
+        output = []
+        for module in self._modules.values():
+            output.append(module(input))
+        return output if output else input
+
+class Lambda(LambdaBase):
+    def forward(self, input):
+        return self.lambda_func(self.forward_prepare(input))
+
+class LambdaMap(LambdaBase):
+    def forward(self, input):
+        return list(map(self.lambda_func,self.forward_prepare(input)))
+
+class LambdaReduce(LambdaBase):
+    def forward(self, input):
+        return reduce(self.lambda_func,self.forward_prepare(input))
+
+
+class OldResNet152(nn.Sequential):
+    def __init__(self):
+        children = [
+# resnet152_places365 = nn.Sequential( # Sequential,
+            nn.Conv2d(3,64,(7, 7),(2, 2),(3, 3),1,1,bias=False),
+            nn.BatchNorm2d(64),
+            nn.ReLU(),
+            nn.MaxPool2d((3, 3),(2, 2),(1, 1)),
+            nn.Sequential( # Sequential,
+                nn.Sequential( # Sequential,
+                    LambdaMap(lambda x: x, # ConcatTable,
+                        nn.Sequential( # Sequential,
+                            nn.Conv2d(64,64,(1, 1),(1, 1),(0, 0),1,1,bias=False),
+                            nn.BatchNorm2d(64),
+                            nn.ReLU(),
+                            nn.Conv2d(64,64,(3, 3),(1, 1),(1, 1),1,1,bias=False),
+                            nn.BatchNorm2d(64),
+                            nn.ReLU(),
+                            nn.Conv2d(64,256,(1, 1),(1, 1),(0, 0),1,1,bias=False),
+                            nn.BatchNorm2d(256),
+                        ),
+                        nn.Sequential( # Sequential,
+                            nn.Conv2d(64,256,(1, 1),(1, 1),(0, 0),1,1,bias=False),
+                            nn.BatchNorm2d(256),
+                        ),
+                    ),
+                    LambdaReduce(lambda x,y: x+y), # CAddTable,
+                    nn.ReLU(),
+                ),
+                nn.Sequential( # Sequential,
+                    LambdaMap(lambda x: x, # ConcatTable,
+                        nn.Sequential( # Sequential,
+                            nn.Conv2d(256,64,(1, 1),(1, 1),(0, 0),1,1,bias=False),
+                            nn.BatchNorm2d(64),
+                            nn.ReLU(),
+                            nn.Conv2d(64,64,(3, 3),(1, 1),(1, 1),1,1,bias=False),
+                            nn.BatchNorm2d(64),
+                            nn.ReLU(),
+                            nn.Conv2d(64,256,(1, 1),(1, 1),(0, 0),1,1,bias=False),
+                            nn.BatchNorm2d(256),
+                        ),
+                        Lambda(lambda x: x), # Identity,
+                    ),
+                    LambdaReduce(lambda x,y: x+y), # CAddTable,
+                    nn.ReLU(),
+                ),
+                nn.Sequential( # Sequential,
+                    LambdaMap(lambda x: x, # ConcatTable,
+                        nn.Sequential( # Sequential,
+                            nn.Conv2d(256,64,(1, 1),(1, 1),(0, 0),1,1,bias=False),
+                            nn.BatchNorm2d(64),
+                            nn.ReLU(),
+                            nn.Conv2d(64,64,(3, 3),(1, 1),(1, 1),1,1,bias=False),
+                            nn.BatchNorm2d(64),
+                            nn.ReLU(),
+                            nn.Conv2d(64,256,(1, 1),(1, 1),(0, 0),1,1,bias=False),
+                            nn.BatchNorm2d(256),
+                        ),
+                        Lambda(lambda x: x), # Identity,
+                    ),
+                    LambdaReduce(lambda x,y: x+y), # CAddTable,
+                    nn.ReLU(),
+                ),
+            ),
+            nn.Sequential( # Sequential,
+                nn.Sequential( # Sequential,
+                    LambdaMap(lambda x: x, # ConcatTable,
+                        nn.Sequential( # Sequential,
+                            nn.Conv2d(256,128,(1, 1),(1, 1),(0, 0),1,1,bias=False),
+                            nn.BatchNorm2d(128),
+                            nn.ReLU(),
+                            nn.Conv2d(128,128,(3, 3),(2, 2),(1, 1),1,1,bias=False),
+                            nn.BatchNorm2d(128),
+                            nn.ReLU(),
+                            nn.Conv2d(128,512,(1, 1),(1, 1),(0, 0),1,1,bias=False),
+                            nn.BatchNorm2d(512),
+                        ),
+                        nn.Sequential( # Sequential,
+                            nn.Conv2d(256,512,(1, 1),(2, 2),(0, 0),1,1,bias=False),
+                            nn.BatchNorm2d(512),
+                        ),
+                    ),
+                    LambdaReduce(lambda x,y: x+y), # CAddTable,
+                    nn.ReLU(),
+                ),
+                nn.Sequential( # Sequential,
+                    LambdaMap(lambda x: x, # ConcatTable,
+                        nn.Sequential( # Sequential,
+                            nn.Conv2d(512,128,(1, 1),(1, 1),(0, 0),1,1,bias=False),
+                            nn.BatchNorm2d(128),
+                            nn.ReLU(),
+                            nn.Conv2d(128,128,(3, 3),(1, 1),(1, 1),1,1,bias=False),
+                            nn.BatchNorm2d(128),
+                            nn.ReLU(),
+                            nn.Conv2d(128,512,(1, 1),(1, 1),(0, 0),1,1,bias=False),
+                            nn.BatchNorm2d(512),
+                        ),
+                        Lambda(lambda x: x), # Identity,
+                    ),
+                    LambdaReduce(lambda x,y: x+y), # CAddTable,
+                    nn.ReLU(),
+                ),
+                nn.Sequential( # Sequential,
+                    LambdaMap(lambda x: x, # ConcatTable,
+                        nn.Sequential( # Sequential,
+                            nn.Conv2d(512,128,(1, 1),(1, 1),(0, 0),1,1,bias=False),
+                            nn.BatchNorm2d(128),
+                            nn.ReLU(),
+                            nn.Conv2d(128,128,(3, 3),(1, 1),(1, 1),1,1,bias=False),
+                            nn.BatchNorm2d(128),
+                            nn.ReLU(),
+                            nn.Conv2d(128,512,(1, 1),(1, 1),(0, 0),1,1,bias=False),
+                            nn.BatchNorm2d(512),
+                        ),
+                        Lambda(lambda x: x), # Identity,
+                    ),
+                    LambdaReduce(lambda x,y: x+y), # CAddTable,
+                    nn.ReLU(),
+                ),
+                nn.Sequential( # Sequential,
+                    LambdaMap(lambda x: x, # ConcatTable,
+                        nn.Sequential( # Sequential,
+                            nn.Conv2d(512,128,(1, 1),(1, 1),(0, 0),1,1,bias=False),
+                            nn.BatchNorm2d(128),
+                            nn.ReLU(),
+                            nn.Conv2d(128,128,(3, 3),(1, 1),(1, 1),1,1,bias=False),
+                            nn.BatchNorm2d(128),
+                            nn.ReLU(),
+                            nn.Conv2d(128,512,(1, 1),(1, 1),(0, 0),1,1,bias=False),
+                            nn.BatchNorm2d(512),
+                        ),
+                        Lambda(lambda x: x), # Identity,
+                    ),
+                    LambdaReduce(lambda x,y: x+y), # CAddTable,
+                    nn.ReLU(),
+                ),
+                nn.Sequential( # Sequential,
+                    LambdaMap(lambda x: x, # ConcatTable,
+                        nn.Sequential( # Sequential,
+                            nn.Conv2d(512,128,(1, 1),(1, 1),(0, 0),1,1,bias=False),
+                            nn.BatchNorm2d(128),
+                            nn.ReLU(),
+                            nn.Conv2d(128,128,(3, 3),(1, 1),(1, 1),1,1,bias=False),
+                            nn.BatchNorm2d(128),
+                            nn.ReLU(),
+                            nn.Conv2d(128,512,(1, 1),(1, 1),(0, 0),1,1,bias=False),
+                            nn.BatchNorm2d(512),
+                        ),
+                        Lambda(lambda x: x), # Identity,
+                    ),
+                    LambdaReduce(lambda x,y: x+y), # CAddTable,
+                    nn.ReLU(),
+                ),
+                nn.Sequential( # Sequential,
+                    LambdaMap(lambda x: x, # ConcatTable,
+                        nn.Sequential( # Sequential,
+                            nn.Conv2d(512,128,(1, 1),(1, 1),(0, 0),1,1,bias=False),
+                            nn.BatchNorm2d(128),
+                            nn.ReLU(),
+                            nn.Conv2d(128,128,(3, 3),(1, 1),(1, 1),1,1,bias=False),
+                            nn.BatchNorm2d(128),
+                            nn.ReLU(),
+                            nn.Conv2d(128,512,(1, 1),(1, 1),(0, 0),1,1,bias=False),
+                            nn.BatchNorm2d(512),
+                        ),
+                        Lambda(lambda x: x), # Identity,
+                    ),
+                    LambdaReduce(lambda x,y: x+y), # CAddTable,
+                    nn.ReLU(),
+                ),
+                nn.Sequential( # Sequential,
+                    LambdaMap(lambda x: x, # ConcatTable,
+                        nn.Sequential( # Sequential,
+                            nn.Conv2d(512,128,(1, 1),(1, 1),(0, 0),1,1,bias=False),
+                            nn.BatchNorm2d(128),
+                            nn.ReLU(),
+                            nn.Conv2d(128,128,(3, 3),(1, 1),(1, 1),1,1,bias=False),
+                            nn.BatchNorm2d(128),
+                            nn.ReLU(),
+                            nn.Conv2d(128,512,(1, 1),(1, 1),(0, 0),1,1,bias=False),
+                            nn.BatchNorm2d(512),
+                        ),
+                        Lambda(lambda x: x), # Identity,
+                    ),
+                    LambdaReduce(lambda x,y: x+y), # CAddTable,
+                    nn.ReLU(),
+                ),
+                nn.Sequential( # Sequential,
+                    LambdaMap(lambda x: x, # ConcatTable,
+                        nn.Sequential( # Sequential,
+                            nn.Conv2d(512,128,(1, 1),(1, 1),(0, 0),1,1,bias=False),
+                            nn.BatchNorm2d(128),
+                            nn.ReLU(),
+                            nn.Conv2d(128,128,(3, 3),(1, 1),(1, 1),1,1,bias=False),
+                            nn.BatchNorm2d(128),
+                            nn.ReLU(),
+                            nn.Conv2d(128,512,(1, 1),(1, 1),(0, 0),1,1,bias=False),
+                            nn.BatchNorm2d(512),
+                        ),
+                        Lambda(lambda x: x), # Identity,
+                    ),
+                    LambdaReduce(lambda x,y: x+y), # CAddTable,
+                    nn.ReLU(),
+                ),
+            ),
+            nn.Sequential( # Sequential,
+                nn.Sequential( # Sequential,
+                    LambdaMap(lambda x: x, # ConcatTable,
+                        nn.Sequential( # Sequential,
+                            nn.Conv2d(512,256,(1, 1),(1, 1),(0, 0),1,1,bias=False),
+                            nn.BatchNorm2d(256),
+                            nn.ReLU(),
+                            nn.Conv2d(256,256,(3, 3),(2, 2),(1, 1),1,1,bias=False),
+                            nn.BatchNorm2d(256),
+                            nn.ReLU(),
+                            nn.Conv2d(256,1024,(1, 1),(1, 1),(0, 0),1,1,bias=False),
+                            nn.BatchNorm2d(1024),
+                        ),
+                        nn.Sequential( # Sequential,
+                            nn.Conv2d(512,1024,(1, 1),(2, 2),(0, 0),1,1,bias=False),
+                            nn.BatchNorm2d(1024),
+                        ),
+                    ),
+                    LambdaReduce(lambda x,y: x+y), # CAddTable,
+                    nn.ReLU(),
+                ),
+                nn.Sequential( # Sequential,
+                    LambdaMap(lambda x: x, # ConcatTable,
+                        nn.Sequential( # Sequential,
+                            nn.Conv2d(1024,256,(1, 1),(1, 1),(0, 0),1,1,bias=False),
+                            nn.BatchNorm2d(256),
+                            nn.ReLU(),
+                            nn.Conv2d(256,256,(3, 3),(1, 1),(1, 1),1,1,bias=False),
+                            nn.BatchNorm2d(256),
+                            nn.ReLU(),
+                            nn.Conv2d(256,1024,(1, 1),(1, 1),(0, 0),1,1,bias=False),
+                            nn.BatchNorm2d(1024),
+                        ),
+                        Lambda(lambda x: x), # Identity,
+                    ),
+                    LambdaReduce(lambda x,y: x+y), # CAddTable,
+                    nn.ReLU(),
+                ),
+                nn.Sequential( # Sequential,
+                    LambdaMap(lambda x: x, # ConcatTable,
+                        nn.Sequential( # Sequential,
+                            nn.Conv2d(1024,256,(1, 1),(1, 1),(0, 0),1,1,bias=False),
+                            nn.BatchNorm2d(256),
+                            nn.ReLU(),
+                            nn.Conv2d(256,256,(3, 3),(1, 1),(1, 1),1,1,bias=False),
+                            nn.BatchNorm2d(256),
+                            nn.ReLU(),
+                            nn.Conv2d(256,1024,(1, 1),(1, 1),(0, 0),1,1,bias=False),
+                            nn.BatchNorm2d(1024),
+                        ),
+                        Lambda(lambda x: x), # Identity,
+                    ),
+                    LambdaReduce(lambda x,y: x+y), # CAddTable,
+                    nn.ReLU(),
+                ),
+                nn.Sequential( # Sequential,
+                    LambdaMap(lambda x: x, # ConcatTable,
+                        nn.Sequential( # Sequential,
+                            nn.Conv2d(1024,256,(1, 1),(1, 1),(0, 0),1,1,bias=False),
+                            nn.BatchNorm2d(256),
+                            nn.ReLU(),
+                            nn.Conv2d(256,256,(3, 3),(1, 1),(1, 1),1,1,bias=False),
+                            nn.BatchNorm2d(256),
+                            nn.ReLU(),
+                            nn.Conv2d(256,1024,(1, 1),(1, 1),(0, 0),1,1,bias=False),
+                            nn.BatchNorm2d(1024),
+                        ),
+                        Lambda(lambda x: x), # Identity,
+                    ),
+                    LambdaReduce(lambda x,y: x+y), # CAddTable,
+                    nn.ReLU(),
+                ),
+                nn.Sequential( # Sequential,
+                    LambdaMap(lambda x: x, # ConcatTable,
+                        nn.Sequential( # Sequential,
+                            nn.Conv2d(1024,256,(1, 1),(1, 1),(0, 0),1,1,bias=False),
+                            nn.BatchNorm2d(256),
+                            nn.ReLU(),
+                            nn.Conv2d(256,256,(3, 3),(1, 1),(1, 1),1,1,bias=False),
+                            nn.BatchNorm2d(256),
+                            nn.ReLU(),
+                            nn.Conv2d(256,1024,(1, 1),(1, 1),(0, 0),1,1,bias=False),
+                            nn.BatchNorm2d(1024),
+                        ),
+                        Lambda(lambda x: x), # Identity,
+                    ),
+                    LambdaReduce(lambda x,y: x+y), # CAddTable,
+                    nn.ReLU(),
+                ),
+                nn.Sequential( # Sequential,
+                    LambdaMap(lambda x: x, # ConcatTable,
+                        nn.Sequential( # Sequential,
+                            nn.Conv2d(1024,256,(1, 1),(1, 1),(0, 0),1,1,bias=False),
+                            nn.BatchNorm2d(256),
+                            nn.ReLU(),
+                            nn.Conv2d(256,256,(3, 3),(1, 1),(1, 1),1,1,bias=False),
+                            nn.BatchNorm2d(256),
+                            nn.ReLU(),
+                            nn.Conv2d(256,1024,(1, 1),(1, 1),(0, 0),1,1,bias=False),
+                            nn.BatchNorm2d(1024),
+                        ),
+                        Lambda(lambda x: x), # Identity,
+                    ),
+                    LambdaReduce(lambda x,y: x+y), # CAddTable,
+                    nn.ReLU(),
+                ),
+                nn.Sequential( # Sequential,
+                    LambdaMap(lambda x: x, # ConcatTable,
+                        nn.Sequential( # Sequential,
+                            nn.Conv2d(1024,256,(1, 1),(1, 1),(0, 0),1,1,bias=False),
+                            nn.BatchNorm2d(256),
+                            nn.ReLU(),
+                            nn.Conv2d(256,256,(3, 3),(1, 1),(1, 1),1,1,bias=False),
+                            nn.BatchNorm2d(256),
+                            nn.ReLU(),
+                            nn.Conv2d(256,1024,(1, 1),(1, 1),(0, 0),1,1,bias=False),
+                            nn.BatchNorm2d(1024),
+                        ),
+                        Lambda(lambda x: x), # Identity,
+                    ),
+                    LambdaReduce(lambda x,y: x+y), # CAddTable,
+                    nn.ReLU(),
+                ),
+                nn.Sequential( # Sequential,
+                    LambdaMap(lambda x: x, # ConcatTable,
+                        nn.Sequential( # Sequential,
+                            nn.Conv2d(1024,256,(1, 1),(1, 1),(0, 0),1,1,bias=False),
+                            nn.BatchNorm2d(256),
+                            nn.ReLU(),
+                            nn.Conv2d(256,256,(3, 3),(1, 1),(1, 1),1,1,bias=False),
+                            nn.BatchNorm2d(256),
+                            nn.ReLU(),
+                            nn.Conv2d(256,1024,(1, 1),(1, 1),(0, 0),1,1,bias=False),
+                            nn.BatchNorm2d(1024),
+                        ),
+                        Lambda(lambda x: x), # Identity,
+                    ),
+                    LambdaReduce(lambda x,y: x+y), # CAddTable,
+                    nn.ReLU(),
+                ),
+                nn.Sequential( # Sequential,
+                    LambdaMap(lambda x: x, # ConcatTable,
+                        nn.Sequential( # Sequential,
+                            nn.Conv2d(1024,256,(1, 1),(1, 1),(0, 0),1,1,bias=False),
+                            nn.BatchNorm2d(256),
+                            nn.ReLU(),
+                            nn.Conv2d(256,256,(3, 3),(1, 1),(1, 1),1,1,bias=False),
+                            nn.BatchNorm2d(256),
+                            nn.ReLU(),
+                            nn.Conv2d(256,1024,(1, 1),(1, 1),(0, 0),1,1,bias=False),
+                            nn.BatchNorm2d(1024),
+                        ),
+                        Lambda(lambda x: x), # Identity,
+                    ),
+                    LambdaReduce(lambda x,y: x+y), # CAddTable,
+                    nn.ReLU(),
+                ),
+                nn.Sequential( # Sequential,
+                    LambdaMap(lambda x: x, # ConcatTable,
+                        nn.Sequential( # Sequential,
+                            nn.Conv2d(1024,256,(1, 1),(1, 1),(0, 0),1,1,bias=False),
+                            nn.BatchNorm2d(256),
+                            nn.ReLU(),
+                            nn.Conv2d(256,256,(3, 3),(1, 1),(1, 1),1,1,bias=False),
+                            nn.BatchNorm2d(256),
+                            nn.ReLU(),
+                            nn.Conv2d(256,1024,(1, 1),(1, 1),(0, 0),1,1,bias=False),
+                            nn.BatchNorm2d(1024),
+                        ),
+                        Lambda(lambda x: x), # Identity,
+                    ),
+                    LambdaReduce(lambda x,y: x+y), # CAddTable,
+                    nn.ReLU(),
+                ),
+                nn.Sequential( # Sequential,
+                    LambdaMap(lambda x: x, # ConcatTable,
+                        nn.Sequential( # Sequential,
+                            nn.Conv2d(1024,256,(1, 1),(1, 1),(0, 0),1,1,bias=False),
+                            nn.BatchNorm2d(256),
+                            nn.ReLU(),
+                            nn.Conv2d(256,256,(3, 3),(1, 1),(1, 1),1,1,bias=False),
+                            nn.BatchNorm2d(256),
+                            nn.ReLU(),
+                            nn.Conv2d(256,1024,(1, 1),(1, 1),(0, 0),1,1,bias=False),
+                            nn.BatchNorm2d(1024),
+                        ),
+                        Lambda(lambda x: x), # Identity,
+                    ),
+                    LambdaReduce(lambda x,y: x+y), # CAddTable,
+                    nn.ReLU(),
+                ),
+                nn.Sequential( # Sequential,
+                    LambdaMap(lambda x: x, # ConcatTable,
+                        nn.Sequential( # Sequential,
+                            nn.Conv2d(1024,256,(1, 1),(1, 1),(0, 0),1,1,bias=False),
+                            nn.BatchNorm2d(256),
+                            nn.ReLU(),
+                            nn.Conv2d(256,256,(3, 3),(1, 1),(1, 1),1,1,bias=False),
+                            nn.BatchNorm2d(256),
+                            nn.ReLU(),
+                            nn.Conv2d(256,1024,(1, 1),(1, 1),(0, 0),1,1,bias=False),
+                            nn.BatchNorm2d(1024),
+                        ),
+                        Lambda(lambda x: x), # Identity,
+                    ),
+                    LambdaReduce(lambda x,y: x+y), # CAddTable,
+                    nn.ReLU(),
+                ),
+                nn.Sequential( # Sequential,
+                    LambdaMap(lambda x: x, # ConcatTable,
+                        nn.Sequential( # Sequential,
+                            nn.Conv2d(1024,256,(1, 1),(1, 1),(0, 0),1,1,bias=False),
+                            nn.BatchNorm2d(256),
+                            nn.ReLU(),
+                            nn.Conv2d(256,256,(3, 3),(1, 1),(1, 1),1,1,bias=False),
+                            nn.BatchNorm2d(256),
+                            nn.ReLU(),
+                            nn.Conv2d(256,1024,(1, 1),(1, 1),(0, 0),1,1,bias=False),
+                            nn.BatchNorm2d(1024),
+                        ),
+                        Lambda(lambda x: x), # Identity,
+                    ),
+                    LambdaReduce(lambda x,y: x+y), # CAddTable,
+                    nn.ReLU(),
+                ),
+                nn.Sequential( # Sequential,
+                    LambdaMap(lambda x: x, # ConcatTable,
+                        nn.Sequential( # Sequential,
+                            nn.Conv2d(1024,256,(1, 1),(1, 1),(0, 0),1,1,bias=False),
+                            nn.BatchNorm2d(256),
+                            nn.ReLU(),
+                            nn.Conv2d(256,256,(3, 3),(1, 1),(1, 1),1,1,bias=False),
+                            nn.BatchNorm2d(256),
+                            nn.ReLU(),
+                            nn.Conv2d(256,1024,(1, 1),(1, 1),(0, 0),1,1,bias=False),
+                            nn.BatchNorm2d(1024),
+                        ),
+                        Lambda(lambda x: x), # Identity,
+                    ),
+                    LambdaReduce(lambda x,y: x+y), # CAddTable,
+                    nn.ReLU(),
+                ),
+                nn.Sequential( # Sequential,
+                    LambdaMap(lambda x: x, # ConcatTable,
+                        nn.Sequential( # Sequential,
+                            nn.Conv2d(1024,256,(1, 1),(1, 1),(0, 0),1,1,bias=False),
+                            nn.BatchNorm2d(256),
+                            nn.ReLU(),
+                            nn.Conv2d(256,256,(3, 3),(1, 1),(1, 1),1,1,bias=False),
+                            nn.BatchNorm2d(256),
+                            nn.ReLU(),
+                            nn.Conv2d(256,1024,(1, 1),(1, 1),(0, 0),1,1,bias=False),
+                            nn.BatchNorm2d(1024),
+                        ),
+                        Lambda(lambda x: x), # Identity,
+                    ),
+                    LambdaReduce(lambda x,y: x+y), # CAddTable,
+                    nn.ReLU(),
+                ),
+                nn.Sequential( # Sequential,
+                    LambdaMap(lambda x: x, # ConcatTable,
+                        nn.Sequential( # Sequential,
+                            nn.Conv2d(1024,256,(1, 1),(1, 1),(0, 0),1,1,bias=False),
+                            nn.BatchNorm2d(256),
+                            nn.ReLU(),
+                            nn.Conv2d(256,256,(3, 3),(1, 1),(1, 1),1,1,bias=False),
+                            nn.BatchNorm2d(256),
+                            nn.ReLU(),
+                            nn.Conv2d(256,1024,(1, 1),(1, 1),(0, 0),1,1,bias=False),
+                            nn.BatchNorm2d(1024),
+                        ),
+                        Lambda(lambda x: x), # Identity,
+                    ),
+                    LambdaReduce(lambda x,y: x+y), # CAddTable,
+                    nn.ReLU(),
+                ),
+                nn.Sequential( # Sequential,
+                    LambdaMap(lambda x: x, # ConcatTable,
+                        nn.Sequential( # Sequential,
+                            nn.Conv2d(1024,256,(1, 1),(1, 1),(0, 0),1,1,bias=False),
+                            nn.BatchNorm2d(256),
+                            nn.ReLU(),
+                            nn.Conv2d(256,256,(3, 3),(1, 1),(1, 1),1,1,bias=False),
+                            nn.BatchNorm2d(256),
+                            nn.ReLU(),
+                            nn.Conv2d(256,1024,(1, 1),(1, 1),(0, 0),1,1,bias=False),
+                            nn.BatchNorm2d(1024),
+                        ),
+                        Lambda(lambda x: x), # Identity,
+                    ),
+                    LambdaReduce(lambda x,y: x+y), # CAddTable,
+                    nn.ReLU(),
+                ),
+                nn.Sequential( # Sequential,
+                    LambdaMap(lambda x: x, # ConcatTable,
+                        nn.Sequential( # Sequential,
+                            nn.Conv2d(1024,256,(1, 1),(1, 1),(0, 0),1,1,bias=False),
+                            nn.BatchNorm2d(256),
+                            nn.ReLU(),
+                            nn.Conv2d(256,256,(3, 3),(1, 1),(1, 1),1,1,bias=False),
+                            nn.BatchNorm2d(256),
+                            nn.ReLU(),
+                            nn.Conv2d(256,1024,(1, 1),(1, 1),(0, 0),1,1,bias=False),
+                            nn.BatchNorm2d(1024),
+                        ),
+                        Lambda(lambda x: x), # Identity,
+                    ),
+                    LambdaReduce(lambda x,y: x+y), # CAddTable,
+                    nn.ReLU(),
+                ),
+                nn.Sequential( # Sequential,
+                    LambdaMap(lambda x: x, # ConcatTable,
+                        nn.Sequential( # Sequential,
+                            nn.Conv2d(1024,256,(1, 1),(1, 1),(0, 0),1,1,bias=False),
+                            nn.BatchNorm2d(256),
+                            nn.ReLU(),
+                            nn.Conv2d(256,256,(3, 3),(1, 1),(1, 1),1,1,bias=False),
+                            nn.BatchNorm2d(256),
+                            nn.ReLU(),
+                            nn.Conv2d(256,1024,(1, 1),(1, 1),(0, 0),1,1,bias=False),
+                            nn.BatchNorm2d(1024),
+                        ),
+                        Lambda(lambda x: x), # Identity,
+                    ),
+                    LambdaReduce(lambda x,y: x+y), # CAddTable,
+                    nn.ReLU(),
+                ),
+                nn.Sequential( # Sequential,
+                    LambdaMap(lambda x: x, # ConcatTable,
+                        nn.Sequential( # Sequential,
+                            nn.Conv2d(1024,256,(1, 1),(1, 1),(0, 0),1,1,bias=False),
+                            nn.BatchNorm2d(256),
+                            nn.ReLU(),
+                            nn.Conv2d(256,256,(3, 3),(1, 1),(1, 1),1,1,bias=False),
+                            nn.BatchNorm2d(256),
+                            nn.ReLU(),
+                            nn.Conv2d(256,1024,(1, 1),(1, 1),(0, 0),1,1,bias=False),
+                            nn.BatchNorm2d(1024),
+                        ),
+                        Lambda(lambda x: x), # Identity,
+                    ),
+                    LambdaReduce(lambda x,y: x+y), # CAddTable,
+                    nn.ReLU(),
+                ),
+                nn.Sequential( # Sequential,
+                    LambdaMap(lambda x: x, # ConcatTable,
+                        nn.Sequential( # Sequential,
+                            nn.Conv2d(1024,256,(1, 1),(1, 1),(0, 0),1,1,bias=False),
+                            nn.BatchNorm2d(256),
+                            nn.ReLU(),
+                            nn.Conv2d(256,256,(3, 3),(1, 1),(1, 1),1,1,bias=False),
+                            nn.BatchNorm2d(256),
+                            nn.ReLU(),
+                            nn.Conv2d(256,1024,(1, 1),(1, 1),(0, 0),1,1,bias=False),
+                            nn.BatchNorm2d(1024),
+                        ),
+                        Lambda(lambda x: x), # Identity,
+                    ),
+                    LambdaReduce(lambda x,y: x+y), # CAddTable,
+                    nn.ReLU(),
+                ),
+                nn.Sequential( # Sequential,
+                    LambdaMap(lambda x: x, # ConcatTable,
+                        nn.Sequential( # Sequential,
+                            nn.Conv2d(1024,256,(1, 1),(1, 1),(0, 0),1,1,bias=False),
+                            nn.BatchNorm2d(256),
+                            nn.ReLU(),
+                            nn.Conv2d(256,256,(3, 3),(1, 1),(1, 1),1,1,bias=False),
+                            nn.BatchNorm2d(256),
+                            nn.ReLU(),
+                            nn.Conv2d(256,1024,(1, 1),(1, 1),(0, 0),1,1,bias=False),
+                            nn.BatchNorm2d(1024),
+                        ),
+                        Lambda(lambda x: x), # Identity,
+                    ),
+                    LambdaReduce(lambda x,y: x+y), # CAddTable,
+                    nn.ReLU(),
+                ),
+                nn.Sequential( # Sequential,
+                    LambdaMap(lambda x: x, # ConcatTable,
+                        nn.Sequential( # Sequential,
+                            nn.Conv2d(1024,256,(1, 1),(1, 1),(0, 0),1,1,bias=False),
+                            nn.BatchNorm2d(256),
+                            nn.ReLU(),
+                            nn.Conv2d(256,256,(3, 3),(1, 1),(1, 1),1,1,bias=False),
+                            nn.BatchNorm2d(256),
+                            nn.ReLU(),
+                            nn.Conv2d(256,1024,(1, 1),(1, 1),(0, 0),1,1,bias=False),
+                            nn.BatchNorm2d(1024),
+                        ),
+                        Lambda(lambda x: x), # Identity,
+                    ),
+                    LambdaReduce(lambda x,y: x+y), # CAddTable,
+                    nn.ReLU(),
+                ),
+                nn.Sequential( # Sequential,
+                    LambdaMap(lambda x: x, # ConcatTable,
+                        nn.Sequential( # Sequential,
+                            nn.Conv2d(1024,256,(1, 1),(1, 1),(0, 0),1,1,bias=False),
+                            nn.BatchNorm2d(256),
+                            nn.ReLU(),
+                            nn.Conv2d(256,256,(3, 3),(1, 1),(1, 1),1,1,bias=False),
+                            nn.BatchNorm2d(256),
+                            nn.ReLU(),
+                            nn.Conv2d(256,1024,(1, 1),(1, 1),(0, 0),1,1,bias=False),
+                            nn.BatchNorm2d(1024),
+                        ),
+                        Lambda(lambda x: x), # Identity,
+                    ),
+                    LambdaReduce(lambda x,y: x+y), # CAddTable,
+                    nn.ReLU(),
+                ),
+                nn.Sequential( # Sequential,
+                    LambdaMap(lambda x: x, # ConcatTable,
+                        nn.Sequential( # Sequential,
+                            nn.Conv2d(1024,256,(1, 1),(1, 1),(0, 0),1,1,bias=False),
+                            nn.BatchNorm2d(256),
+                            nn.ReLU(),
+                            nn.Conv2d(256,256,(3, 3),(1, 1),(1, 1),1,1,bias=False),
+                            nn.BatchNorm2d(256),
+                            nn.ReLU(),
+                            nn.Conv2d(256,1024,(1, 1),(1, 1),(0, 0),1,1,bias=False),
+                            nn.BatchNorm2d(1024),
+                        ),
+                        Lambda(lambda x: x), # Identity,
+                    ),
+                    LambdaReduce(lambda x,y: x+y), # CAddTable,
+                    nn.ReLU(),
+                ),
+                nn.Sequential( # Sequential,
+                    LambdaMap(lambda x: x, # ConcatTable,
+                        nn.Sequential( # Sequential,
+                            nn.Conv2d(1024,256,(1, 1),(1, 1),(0, 0),1,1,bias=False),
+                            nn.BatchNorm2d(256),
+                            nn.ReLU(),
+                            nn.Conv2d(256,256,(3, 3),(1, 1),(1, 1),1,1,bias=False),
+                            nn.BatchNorm2d(256),
+                            nn.ReLU(),
+                            nn.Conv2d(256,1024,(1, 1),(1, 1),(0, 0),1,1,bias=False),
+                            nn.BatchNorm2d(1024),
+                        ),
+                        Lambda(lambda x: x), # Identity,
+                    ),
+                    LambdaReduce(lambda x,y: x+y), # CAddTable,
+                    nn.ReLU(),
+                ),
+                nn.Sequential( # Sequential,
+                    LambdaMap(lambda x: x, # ConcatTable,
+                        nn.Sequential( # Sequential,
+                            nn.Conv2d(1024,256,(1, 1),(1, 1),(0, 0),1,1,bias=False),
+                            nn.BatchNorm2d(256),
+                            nn.ReLU(),
+                            nn.Conv2d(256,256,(3, 3),(1, 1),(1, 1),1,1,bias=False),
+                            nn.BatchNorm2d(256),
+                            nn.ReLU(),
+                            nn.Conv2d(256,1024,(1, 1),(1, 1),(0, 0),1,1,bias=False),
+                            nn.BatchNorm2d(1024),
+                        ),
+                        Lambda(lambda x: x), # Identity,
+                    ),
+                    LambdaReduce(lambda x,y: x+y), # CAddTable,
+                    nn.ReLU(),
+                ),
+                nn.Sequential( # Sequential,
+                    LambdaMap(lambda x: x, # ConcatTable,
+                        nn.Sequential( # Sequential,
+                            nn.Conv2d(1024,256,(1, 1),(1, 1),(0, 0),1,1,bias=False),
+                            nn.BatchNorm2d(256),
+                            nn.ReLU(),
+                            nn.Conv2d(256,256,(3, 3),(1, 1),(1, 1),1,1,bias=False),
+                            nn.BatchNorm2d(256),
+                            nn.ReLU(),
+                            nn.Conv2d(256,1024,(1, 1),(1, 1),(0, 0),1,1,bias=False),
+                            nn.BatchNorm2d(1024),
+                        ),
+                        Lambda(lambda x: x), # Identity,
+                    ),
+                    LambdaReduce(lambda x,y: x+y), # CAddTable,
+                    nn.ReLU(),
+                ),
+                nn.Sequential( # Sequential,
+                    LambdaMap(lambda x: x, # ConcatTable,
+                        nn.Sequential( # Sequential,
+                            nn.Conv2d(1024,256,(1, 1),(1, 1),(0, 0),1,1,bias=False),
+                            nn.BatchNorm2d(256),
+                            nn.ReLU(),
+                            nn.Conv2d(256,256,(3, 3),(1, 1),(1, 1),1,1,bias=False),
+                            nn.BatchNorm2d(256),
+                            nn.ReLU(),
+                            nn.Conv2d(256,1024,(1, 1),(1, 1),(0, 0),1,1,bias=False),
+                            nn.BatchNorm2d(1024),
+                        ),
+                        Lambda(lambda x: x), # Identity,
+                    ),
+                    LambdaReduce(lambda x,y: x+y), # CAddTable,
+                    nn.ReLU(),
+                ),
+                nn.Sequential( # Sequential,
+                    LambdaMap(lambda x: x, # ConcatTable,
+                        nn.Sequential( # Sequential,
+                            nn.Conv2d(1024,256,(1, 1),(1, 1),(0, 0),1,1,bias=False),
+                            nn.BatchNorm2d(256),
+                            nn.ReLU(),
+                            nn.Conv2d(256,256,(3, 3),(1, 1),(1, 1),1,1,bias=False),
+                            nn.BatchNorm2d(256),
+                            nn.ReLU(),
+                            nn.Conv2d(256,1024,(1, 1),(1, 1),(0, 0),1,1,bias=False),
+                            nn.BatchNorm2d(1024),
+                        ),
+                        Lambda(lambda x: x), # Identity,
+                    ),
+                    LambdaReduce(lambda x,y: x+y), # CAddTable,
+                    nn.ReLU(),
+                ),
+                nn.Sequential( # Sequential,
+                    LambdaMap(lambda x: x, # ConcatTable,
+                        nn.Sequential( # Sequential,
+                            nn.Conv2d(1024,256,(1, 1),(1, 1),(0, 0),1,1,bias=False),
+                            nn.BatchNorm2d(256),
+                            nn.ReLU(),
+                            nn.Conv2d(256,256,(3, 3),(1, 1),(1, 1),1,1,bias=False),
+                            nn.BatchNorm2d(256),
+                            nn.ReLU(),
+                            nn.Conv2d(256,1024,(1, 1),(1, 1),(0, 0),1,1,bias=False),
+                            nn.BatchNorm2d(1024),
+                        ),
+                        Lambda(lambda x: x), # Identity,
+                    ),
+                    LambdaReduce(lambda x,y: x+y), # CAddTable,
+                    nn.ReLU(),
+                ),
+                nn.Sequential( # Sequential,
+                    LambdaMap(lambda x: x, # ConcatTable,
+                        nn.Sequential( # Sequential,
+                            nn.Conv2d(1024,256,(1, 1),(1, 1),(0, 0),1,1,bias=False),
+                            nn.BatchNorm2d(256),
+                            nn.ReLU(),
+                            nn.Conv2d(256,256,(3, 3),(1, 1),(1, 1),1,1,bias=False),
+                            nn.BatchNorm2d(256),
+                            nn.ReLU(),
+                            nn.Conv2d(256,1024,(1, 1),(1, 1),(0, 0),1,1,bias=False),
+                            nn.BatchNorm2d(1024),
+                        ),
+                        Lambda(lambda x: x), # Identity,
+                    ),
+                    LambdaReduce(lambda x,y: x+y), # CAddTable,
+                    nn.ReLU(),
+                ),
+                nn.Sequential( # Sequential,
+                    LambdaMap(lambda x: x, # ConcatTable,
+                        nn.Sequential( # Sequential,
+                            nn.Conv2d(1024,256,(1, 1),(1, 1),(0, 0),1,1,bias=False),
+                            nn.BatchNorm2d(256),
+                            nn.ReLU(),
+                            nn.Conv2d(256,256,(3, 3),(1, 1),(1, 1),1,1,bias=False),
+                            nn.BatchNorm2d(256),
+                            nn.ReLU(),
+                            nn.Conv2d(256,1024,(1, 1),(1, 1),(0, 0),1,1,bias=False),
+                            nn.BatchNorm2d(1024),
+                        ),
+                        Lambda(lambda x: x), # Identity,
+                    ),
+                    LambdaReduce(lambda x,y: x+y), # CAddTable,
+                    nn.ReLU(),
+                ),
+                nn.Sequential( # Sequential,
+                    LambdaMap(lambda x: x, # ConcatTable,
+                        nn.Sequential( # Sequential,
+                            nn.Conv2d(1024,256,(1, 1),(1, 1),(0, 0),1,1,bias=False),
+                            nn.BatchNorm2d(256),
+                            nn.ReLU(),
+                            nn.Conv2d(256,256,(3, 3),(1, 1),(1, 1),1,1,bias=False),
+                            nn.BatchNorm2d(256),
+                            nn.ReLU(),
+                            nn.Conv2d(256,1024,(1, 1),(1, 1),(0, 0),1,1,bias=False),
+                            nn.BatchNorm2d(1024),
+                        ),
+                        Lambda(lambda x: x), # Identity,
+                    ),
+                    LambdaReduce(lambda x,y: x+y), # CAddTable,
+                    nn.ReLU(),
+                ),
+                nn.Sequential( # Sequential,
+                    LambdaMap(lambda x: x, # ConcatTable,
+                        nn.Sequential( # Sequential,
+                            nn.Conv2d(1024,256,(1, 1),(1, 1),(0, 0),1,1,bias=False),
+                            nn.BatchNorm2d(256),
+                            nn.ReLU(),
+                            nn.Conv2d(256,256,(3, 3),(1, 1),(1, 1),1,1,bias=False),
+                            nn.BatchNorm2d(256),
+                            nn.ReLU(),
+                            nn.Conv2d(256,1024,(1, 1),(1, 1),(0, 0),1,1,bias=False),
+                            nn.BatchNorm2d(1024),
+                        ),
+                        Lambda(lambda x: x), # Identity,
+                    ),
+                    LambdaReduce(lambda x,y: x+y), # CAddTable,
+                    nn.ReLU(),
+                ),
+                nn.Sequential( # Sequential,
+                    LambdaMap(lambda x: x, # ConcatTable,
+                        nn.Sequential( # Sequential,
+                            nn.Conv2d(1024,256,(1, 1),(1, 1),(0, 0),1,1,bias=False),
+                            nn.BatchNorm2d(256),
+                            nn.ReLU(),
+                            nn.Conv2d(256,256,(3, 3),(1, 1),(1, 1),1,1,bias=False),
+                            nn.BatchNorm2d(256),
+                            nn.ReLU(),
+                            nn.Conv2d(256,1024,(1, 1),(1, 1),(0, 0),1,1,bias=False),
+                            nn.BatchNorm2d(1024),
+                        ),
+                        Lambda(lambda x: x), # Identity,
+                    ),
+                    LambdaReduce(lambda x,y: x+y), # CAddTable,
+                    nn.ReLU(),
+                ),
+            ),
+            nn.Sequential( # Sequential,
+                nn.Sequential( # Sequential,
+                    LambdaMap(lambda x: x, # ConcatTable,
+                        nn.Sequential( # Sequential,
+                            nn.Conv2d(1024,512,(1, 1),(1, 1),(0, 0),1,1,bias=False),
+                            nn.BatchNorm2d(512),
+                            nn.ReLU(),
+                            nn.Conv2d(512,512,(3, 3),(2, 2),(1, 1),1,1,bias=False),
+                            nn.BatchNorm2d(512),
+                            nn.ReLU(),
+                            nn.Conv2d(512,2048,(1, 1),(1, 1),(0, 0),1,1,bias=False),
+                            nn.BatchNorm2d(2048),
+                        ),
+                        nn.Sequential( # Sequential,
+                            nn.Conv2d(1024,2048,(1, 1),(2, 2),(0, 0),1,1,bias=False),
+                            nn.BatchNorm2d(2048),
+                        ),
+                    ),
+                    LambdaReduce(lambda x,y: x+y), # CAddTable,
+                    nn.ReLU(),
+                ),
+                nn.Sequential( # Sequential,
+                    LambdaMap(lambda x: x, # ConcatTable,
+                        nn.Sequential( # Sequential,
+                            nn.Conv2d(2048,512,(1, 1),(1, 1),(0, 0),1,1,bias=False),
+                            nn.BatchNorm2d(512),
+                            nn.ReLU(),
+                            nn.Conv2d(512,512,(3, 3),(1, 1),(1, 1),1,1,bias=False),
+                            nn.BatchNorm2d(512),
+                            nn.ReLU(),
+                            nn.Conv2d(512,2048,(1, 1),(1, 1),(0, 0),1,1,bias=False),
+                            nn.BatchNorm2d(2048),
+                        ),
+                        Lambda(lambda x: x), # Identity,
+                    ),
+                    LambdaReduce(lambda x,y: x+y), # CAddTable,
+                    nn.ReLU(),
+                ),
+                nn.Sequential( # Sequential,
+                    LambdaMap(lambda x: x, # ConcatTable,
+                        nn.Sequential( # Sequential,
+                            nn.Conv2d(2048,512,(1, 1),(1, 1),(0, 0),1,1,bias=False),
+                            nn.BatchNorm2d(512),
+                            nn.ReLU(),
+                            nn.Conv2d(512,512,(3, 3),(1, 1),(1, 1),1,1,bias=False),
+                            nn.BatchNorm2d(512),
+                            nn.ReLU(),
+                            nn.Conv2d(512,2048,(1, 1),(1, 1),(0, 0),1,1,bias=False),
+                            nn.BatchNorm2d(2048),
+                        ),
+                        Lambda(lambda x: x), # Identity,
+                    ),
+                    LambdaReduce(lambda x,y: x+y), # CAddTable,
+                    nn.ReLU(),
+                ),
+            ),
+            nn.AvgPool2d((7, 7),(1, 1)),
+            Lambda(lambda x: x.view(x.size(0),-1)), # View,
+            nn.Sequential(Lambda(lambda x: x.view(1,-1) if 1==len(x.size()) else x )
+                    ,nn.Linear(2048,365)), # Linear,
+        ]
+
+        super(OldResNet152, self).__init__(*children)
diff --git a/netdissect/oldvgg16.py b/netdissect/oldvgg16.py
new file mode 100644
index 0000000000000000000000000000000000000000..0b285cb1ceec77febf7213b03c61bc9069d55f75
--- /dev/null
+++ b/netdissect/oldvgg16.py
@@ -0,0 +1,43 @@
+import collections, torch, torchvision, numpy
+
+def load_places_vgg16(weight_file):
+    model = torchvision.models.vgg16(num_classes=365)
+    model.features = torch.nn.Sequential(collections.OrderedDict(zip([
+        'conv1_1', 'relu1_1',
+        'conv1_2', 'relu1_2',
+        'pool1',
+        'conv2_1', 'relu2_1',
+        'conv2_2', 'relu2_2',
+        'pool2',
+        'conv3_1', 'relu3_1',
+        'conv3_2', 'relu3_2',
+        'conv3_3', 'relu3_3',
+        'pool3',
+        'conv4_1', 'relu4_1',
+        'conv4_2', 'relu4_2',
+        'conv4_3', 'relu4_3',
+        'pool4',
+        'conv5_1', 'relu5_1',
+        'conv5_2', 'relu5_2',
+        'conv5_3', 'relu5_3',
+        'pool5'],
+        model.features)))
+
+    model.classifier = torch.nn.Sequential(collections.OrderedDict(zip([
+        'fc6', 'relu6',
+        'drop6',
+        'fc7', 'relu7',
+        'drop7',
+        'fc8a'],
+        model.classifier)))
+
+    state_dict = torch.load(weight_file)
+    converted_state_dict = ({
+        l: torch.from_numpy(numpy.array(v)).view_as(p)
+        for k, v in state_dict.items()
+        for l, p in model.named_parameters() if k in l})
+    model.load_state_dict(converted_state_dict)
+
+    # TODO: figure out normalizations etc.
+
+    return model
diff --git a/netdissect/optimize_residuals.py b/netdissect/optimize_residuals.py
new file mode 100644
index 0000000000000000000000000000000000000000..aed25bdf17afc84b55a5dc71c7bcdc015f0d14ea
--- /dev/null
+++ b/netdissect/optimize_residuals.py
@@ -0,0 +1,270 @@
+import torch, multiprocessing, itertools, os, shutil, PIL, argparse, numpy
+from collections import OrderedDict
+from numbers import Number
+from torch.nn.functional import mse_loss, l1_loss
+from . import pbar
+from . import zdataset
+from . import proggan, customnet, parallelfolder
+from . import encoder_net, encoder_loss, setting
+from torchvision import transforms, models
+from torchvision.models.vgg import model_urls
+from .pidfile import exit_if_job_done, mark_job_done
+from . import nethook
+from .pidfile import exit_if_job_done, mark_job_done
+from .encoder_loss import cor_square_error
+from .nethook import InstrumentedModel
+
+torch.backends.cudnn.benchmark = True
+
+parser = argparse.ArgumentParser()
+parser.add_argument('--lr', type=float, help='Learning rate', default=0.01)
+parser.add_argument('--image_number', type=int, help='Image number',
+        default=95)
+parser.add_argument('--image_source', #choices=['val', 'train', 'gan', 'test'],
+        default='test')
+parser.add_argument('--redo', type=int, help='Nonzero to delete done.txt',
+        default=0)
+parser.add_argument('--residuals', nargs='*', help='Residuals to adjust',
+        default=None)
+parser.add_argument('--model', type=str, help='Dataset being modeled',
+        default='church')
+parser.add_argument('--halfsize', type=int,
+        help='Set to 1 for half size enoder',
+        default=0)
+parser.add_argument('--snapshot_every', type=int,
+        help='only generate snapshots every n iterations',
+        default=1000)
+args = parser.parse_args()
+
+num_steps = 3000
+lr_milestones = [800, 1200, 1800]
+residuals = (args.residuals if args.residuals is not None
+        else ['layer1', 'layer2', 'layer3'])
+global_seed = 1
+learning_rate = args.lr
+image_number = args.image_number
+expgroup = 'optimize_residuals'
+# Use an explicit directory name for a different selection of residuals.
+if args.residuals is not None:
+    expgroup += '_' + '_'.join(residuals)
+imagetypecode = (dict(val='i', train='n', gan='z', test='t')
+        .get(args.image_source, args.image_source[0]))
+expname = 'opt_%s_%d' % (imagetypecode, image_number)
+expdir = os.path.join('results', args.model, expgroup, 'cases', expname)
+sumdir = os.path.join('results', args.model, expgroup,
+        'summary_%s' % imagetypecode)
+os.makedirs(expdir, exist_ok=True)
+os.makedirs(sumdir, exist_ok=True)
+
+# First load single image optimize (load via test ParallelFolder dataset).
+
+def main():
+    pbar.print('Running %s' % expdir)
+    delete_log()
+
+    # Grab a target image
+    dirname = os.path.join(expdir, 'images')
+    os.makedirs(dirname, exist_ok=True)
+    loaded_image, loaded_z = setting.load_test_image(image_number,
+            args.image_source, model=args.model)
+    visualize_results((image_number, 'target'),
+            loaded_image[0], summarize=True)
+
+    # Load the pretrained generator model.
+    gan_generator = setting.load_proggan(args.model)
+    # We will wrap this model
+    unwrapped_H = nethook.subsequence(gan_generator, last_layer='layer4')
+    # Edit the output of this layer
+    F = nethook.subsequence(gan_generator, first_layer='layer5')
+
+    # Load a pretrained gan inverter
+    encoder = nethook.InstrumentedModel(
+            encoder_net.HybridLayerNormEncoder(halfsize=args.halfsize))
+    encoder.load_state_dict(torch.load(os.path.join('results', args.model,
+       'invert_hybrid_cse/snapshots/epoch_1000.pth.tar'))['state_dict'])
+    encoder.eval()
+    E = nethook.subsequence(encoder.model, last_layer='resnet')
+    D = nethook.subsequence(encoder.model, first_layer='inv4')
+
+    # Also make a conv features model from pretrained VGG
+    vgg = models.vgg16(pretrained=True)
+    VF = nethook.subsequence(vgg.features, last_layer='20')
+
+    # Move models and data to GPU
+    for m in [F, unwrapped_H, E, D, VF]:
+        m.cuda()
+
+    # Some constants for the GPU
+    with torch.no_grad():
+        # Our true image is constant
+        true_p = loaded_image.cuda()
+        # Invert our image once!
+        init_r = E(true_p)
+        init_z = D(init_r)
+        # Compute our features once!
+        true_v = VF(true_p)
+        # For GAN-generated images we have ground truth.
+        if loaded_z is None:
+            true_z = None
+            true_r = None
+            true_r1, true_r2, true_r3 = None, None, None
+        else:
+            true_z = loaded_z.cuda()
+            with InstrumentedModel(unwrapped_H) as inst_H:
+                inst_H.retain_layers(['layer1', 'layer2', 'layer3'])
+                true_r = inst_H(true_z)
+                true_r1, true_r2, true_r3 = [inst_H.retained_layer(n)
+                        for n in ['layer1', 'layer2', 'layer3']]
+
+    # The model we learn are the top-level parameters of this wrapped model.
+    H = encoder_net.ResidualGenerator(
+            unwrapped_H, init_z, residuals)
+    H.eval()
+    H.cuda()
+
+    # Set up optimizer
+    set_requires_grad(False, F, H, E, D, VF)
+    parameters = OrderedDict(H.named_parameters(recurse=False))
+    for n, p in parameters.items():
+        p.requires_grad = True
+    optimizer = torch.optim.Adam(parameters.values(), lr=learning_rate)
+    scheduler = torch.optim.lr_scheduler.MultiStepLR(
+            optimizer, milestones=lr_milestones, gamma=0.5)
+
+    # Phase 1: find a better r4 by seeking d1, d2, d3, etc.
+    for step_num in pbar(range(num_steps + 1)):
+        current_r = H()
+        current_p = F(current_r)
+        current_v = VF(current_p)
+
+        loss_p = l1_loss(true_p, current_p)
+        loss_v = l1_loss(true_v, current_v)
+        loss_z = H.dz.pow(2).mean() if hasattr(H, 'dz') else 0
+        loss_1 = H.d1.pow(2).mean() if hasattr(H, 'd1') else 0
+        loss_2 = H.d2.pow(2).mean() if hasattr(H, 'd2') else 0
+        loss_3 = H.d3.pow(2).mean() if hasattr(H, 'd3') else 0
+        loss_4 = H.d4.pow(2).mean() if hasattr(H, 'd4') else 0
+        loss_r = mse_loss(init_r, current_r)
+        loss = (loss_p + loss_v + loss_z + loss_1 + loss_2 + loss_3 + loss_4)
+
+        all_loss = dict(loss=loss, loss_v=loss_v, loss_p=loss_p,
+                loss_r=loss_r,
+                loss_z=loss_z,
+                loss_1=loss_1,
+                loss_2=loss_2,
+                loss_3=loss_3,
+                loss_4=loss_4
+                )
+        all_loss = { k: v.item() for k, v in all_loss.items()
+                if v is not 0 }
+
+        if (step_num % args.snapshot_every == 0) or (step_num == num_steps):
+            with torch.no_grad():
+                if true_r is not None:
+                    all_loss['err_r'] = cor_square_error(current_r, true_r
+                            ) * 100
+                all_loss['err_p'] = (current_p - true_p).pow(2).mean() * 100
+                log_progress('%d ' % step_num + ' '.join(
+                    '%s=%.3f' % (k, all_loss[k])
+                    for k in sorted(all_loss.keys())), phase='a')
+                visualize_results((image_number, 'a', step_num), current_p,
+                        summarize=(step_num in [0, num_steps]))
+                checkpoint_dict = OrderedDict(all_loss)
+                for s in residuals:
+                    s = s.replace('layer', '')
+                    checkpoint_dict['init_%s' % s] = getattr(H, 'init_' + s)
+                    checkpoint_dict['d_%s' % s] = getattr(H, 'd' + s)
+                    checkpoint_dict['current_%s' % s] = (
+                            getattr(H, 'init_' + s) + getattr(H, 'd' + s))
+            save_checkpoint(
+                phase='a',
+                step=step_num,
+                current_r=current_r,
+                current_p=current_p,
+                true_z=true_z,
+                true_r=true_r,
+                true_p=true_p,
+                lr=learning_rate,
+                optimizer=optimizer.state_dict(),
+                **checkpoint_dict)
+
+        optimizer.zero_grad()
+        loss.backward()
+        if step_num < num_steps:
+            optimizer.step()
+            scheduler.step()
+
+def delete_log():
+    try:
+        os.remove(os.path.join(expdir, 'log.txt'))
+    except:
+        pass
+
+def log_progress(s, phase='a'):
+    with open(os.path.join(expdir, 'log.txt'), 'a') as f:
+        f.write(phase + ' ' + s + '\n')
+    pbar.print(s)
+
+def save_checkpoint(**kwargs):
+    dirname = os.path.join(expdir, 'snapshots')
+    os.makedirs(dirname, exist_ok=True)
+    filename = 'step_%s_%d.pth.tar' % (kwargs['phase'], kwargs['step'])
+    torch.save(kwargs, os.path.join(dirname, filename))
+    # Also save as .mat file for analysis.
+    numeric_data = {
+            k: v.detach().cpu().numpy() if isinstance(v, torch.Tensor) else v
+            for k, v in kwargs.items()
+            if isinstance(v, (Number, numpy.ndarray, torch.Tensor))}
+    filename = 'step_%s_%d.npz' % (kwargs['phase'], kwargs['step'])
+    numpy.savez(os.path.join(dirname, filename), **numeric_data)
+
+def visualize_results(step, img, summarize=False):
+    if isinstance(step, tuple):
+        filename = '%s.png' % ('_'.join(str(i) for i in step))
+    else:
+        filename = '%s.png' % str(step)
+    dirname = os.path.join(expdir, 'images')
+    os.makedirs(dirname, exist_ok=True)
+    save_tensor_image(img, os.path.join(dirname, filename))
+    lbname = os.path.join(dirname, '+lightbox.html')
+    if not os.path.exists(lbname):
+        shutil.copy(os.path.join(os.path.dirname(__file__),
+            'lightbox.html'), lbname)
+    if summarize:
+        save_tensor_image(img, os.path.join(sumdir, filename))
+        lbname = os.path.join(sumdir, '+lightbox.html')
+        if not os.path.exists(lbname):
+            shutil.copy(os.path.join(os.path.dirname(__file__),
+                'lightbox.html'), lbname)
+
+def save_tensor_image(img, filename):
+    if len(img.shape) == 4:
+        img = img[0]
+    np_data = ((img.permute(1, 2, 0) / 2 + 0.5) * 255
+            ).clamp(0, 255).byte().cpu().numpy()
+    PIL.Image.fromarray(np_data).save(filename)
+
+def set_requires_grad(requires_grad, *models):
+    for model in models:
+        if isinstance(model, torch.nn.Module):
+            for param in model.parameters():
+                param.requires_grad = requires_grad
+        elif isintance(model, torch.nn.Parameter):
+            model.requires_grad = requires_grad
+        else:
+            assert False, 'unknown type %r' % type(model)
+
+def edit(x):
+    x = x.clone()
+    x[:,EDIT_UNITS] = 0
+    return x
+
+#unit_level99 = {}
+#for cls in ablation_units:
+#    corpus = numpy.load('reltest/churchoutdoor/layer4/ace/%s/corpus.npz' % cls)
+
+
+if __name__ == '__main__':
+    exit_if_job_done(expdir, redo=args.redo)
+    main()
+    mark_job_done(expdir)
diff --git a/netdissect/optimize_z_lbfgs.py b/netdissect/optimize_z_lbfgs.py
new file mode 100644
index 0000000000000000000000000000000000000000..0e02d225d48f3f21aca51ea429b25f4c76419214
--- /dev/null
+++ b/netdissect/optimize_z_lbfgs.py
@@ -0,0 +1,206 @@
+import torch, multiprocessing, itertools, os, shutil, PIL, argparse, numpy
+from collections import OrderedDict
+from numbers import Number
+from torch.nn.functional import mse_loss, l1_loss
+from seeing import pbar
+from seeing import zdataset, seededsampler
+from seeing import proggan, customnet, parallelfolder
+from seeing import encoder_net, encoder_loss, setting
+from torchvision import transforms, models
+from torchvision.models.vgg import model_urls
+from seeing.pidfile import exit_if_job_done, mark_job_done
+from seeing import nethook, LBFGS
+from seeing.encoder_loss import cor_square_error
+from seeing.nethook import InstrumentedModel
+
+torch.backends.cudnn.benchmark = True
+
+parser = argparse.ArgumentParser()
+parser.add_argument('--image_number', type=int, help='Image number',
+        default=95)
+parser.add_argument('--image_source', #choices=['val', 'train', 'gan', 'test'],
+        default='test')
+parser.add_argument('--redo', type=int, help='Nonzero to delete done.txt',
+        default=0)
+parser.add_argument('--model', type=str, help='Dataset being modeled',
+        default='church')
+parser.add_argument('--halfsize', type=int,
+        help='Set to 1 for half size enoder',
+        default=0)
+parser.add_argument('--lambda_f', type=float, help='Feature regularizer',
+        default=0.25)
+parser.add_argument('--num_steps', type=int,
+        help='run for n steps',
+        default=3000)
+parser.add_argument('--snapshot_every', type=int,
+        help='only generate snapshots every n iterations',
+        default=1000)
+args = parser.parse_args()
+
+
+num_steps = args.num_steps
+global_seed = 1
+image_number = args.image_number
+expgroup = 'optimize_lbfgs'
+imagetypecode = (dict(val='i', train='n', gan='z', test='t')
+        .get(args.image_source, args.image_source[0]))
+expname = 'opt_%s_%d' % (imagetypecode, image_number)
+expdir = os.path.join('results', args.model, expgroup, 'cases', expname)
+sumdir = os.path.join('results', args.model, expgroup,
+        'summary_%s' % imagetypecode)
+os.makedirs(expdir, exist_ok=True)
+os.makedirs(sumdir, exist_ok=True)
+
+# First load single image optimize (load via test ParallelFolder dataset).
+
+def main():
+    pbar.print('Running %s' % expdir)
+    delete_log()
+
+    # Grab a target image
+    dirname = os.path.join(expdir, 'images')
+    os.makedirs(dirname, exist_ok=True)
+    loaded_x, loaded_z = setting.load_test_image(image_number,
+            args.image_source, model=args.model)
+    visualize_results((image_number, 'target'),
+            loaded_x[0], summarize=True)
+
+    # Load the pretrained generator model.
+    G = setting.load_proggan(args.model)
+
+    # Load a pretrained gan inverter
+    E = nethook.InstrumentedModel(
+            encoder_net.HybridLayerNormEncoder(halfsize=args.halfsize))
+    E.load_state_dict(torch.load(os.path.join('results', args.model,
+       'invert_hybrid_cse/snapshots/epoch_1000.pth.tar'))['state_dict'])
+    E.eval()
+
+    G.cuda()
+    E.cuda()
+    F = E
+
+    torch.set_grad_enabled(False)
+    # Some constants for the GPU
+    # Our true image is constant
+    true_x = loaded_x.cuda()
+    # Invert our image once!
+    init_z = E(true_x)
+    # For GAN-generated images we have ground truth.
+    if loaded_z is None:
+        true_z = None
+    else:
+        true_z = loaded_z.cuda()
+
+    current_z = init_z.clone()
+    target_x = loaded_x.clone().cuda()
+    target_f = F(loaded_x.cuda())
+    parameters = [current_z]
+    show_every = args.snapshot_every
+
+    nethook.set_requires_grad(False, G, E)
+    nethook.set_requires_grad(True, *parameters)
+    optimizer = LBFGS.FullBatchLBFGS(parameters)
+
+    def compute_all_loss():
+        current_x = G(current_z)
+        all_loss = {}
+        all_loss['x'] = l1_loss(target_x, current_x)
+        all_loss['z'] = 0.0 if not args.lambda_f else (
+            mse_loss(target_f, F(current_x)) * args.lambda_f)
+        return current_x, all_loss
+
+    def closure():
+        optimizer.zero_grad()
+        _, all_loss = compute_all_loss()
+        return sum(all_loss.values())
+
+    with torch.enable_grad():
+        for step_num in pbar(range(num_steps + 1)):
+            if step_num == 0:
+                loss = closure()
+                loss.backward()
+            else:
+                options = {'closure': closure, 'current_loss': loss,
+                        'max_ls': 10}
+                loss, _, lr, _, _, _, _, _ = optimizer.step(options)
+            if step_num % show_every == 0:
+                with torch.no_grad():
+                    current_x, all_loss = compute_all_loss()
+                    log_progress('%d ' % step_num + ' '.join(
+                        '%s=%.3f' % (k, all_loss[k])
+                        for k in sorted(all_loss.keys())), phase='a')
+                    visualize_results((image_number, 'a', step_num), current_x,
+                        summarize=(step_num in [0, num_steps]))
+                checkpoint_dict = OrderedDict(all_loss)
+                checkpoint_dict['init_z'] = init_z
+                checkpoint_dict['target_x'] = target_x
+                checkpoint_dict['current_z'] = target_x
+                save_checkpoint(
+                    phase='a',
+                    step=step_num,
+                    optimizer=optimizer.state_dict(),
+                    **checkpoint_dict)
+
+def delete_log():
+    try:
+        os.remove(os.path.join(expdir, 'log.txt'))
+    except:
+        pass
+
+def log_progress(s, phase='a'):
+    with open(os.path.join(expdir, 'log.txt'), 'a') as f:
+        f.write(phase + ' ' + s + '\n')
+    pbar.print(s)
+
+def save_checkpoint(**kwargs):
+    dirname = os.path.join(expdir, 'snapshots')
+    os.makedirs(dirname, exist_ok=True)
+    filename = 'step_%s_%d.pth.tar' % (kwargs['phase'], kwargs['step'])
+    torch.save(kwargs, os.path.join(dirname, filename))
+    # Also save as .mat file for analysis.
+    numeric_data = {
+            k: v.detach().cpu().numpy() if isinstance(v, torch.Tensor) else v
+            for k, v in kwargs.items()
+            if isinstance(v, (Number, numpy.ndarray, torch.Tensor))}
+    filename = 'step_%s_%d.npz' % (kwargs['phase'], kwargs['step'])
+    numpy.savez(os.path.join(dirname, filename), **numeric_data)
+
+def visualize_results(step, img, summarize=False):
+    # TODO: add editing etc.
+    if isinstance(step, tuple):
+        filename = '%s.png' % ('_'.join(str(i) for i in step))
+    else:
+        filename = '%s.png' % str(step)
+    dirname = os.path.join(expdir, 'images')
+    os.makedirs(dirname, exist_ok=True)
+    save_tensor_image(img, os.path.join(dirname, filename))
+    lbname = os.path.join(dirname, '+lightbox.html')
+    if not os.path.exists(lbname):
+        shutil.copy('seeing/lightbox.html', lbname)
+    if summarize:
+        save_tensor_image(img, os.path.join(sumdir, filename))
+        lbname = os.path.join(sumdir, '+lightbox.html')
+        if not os.path.exists(lbname):
+            shutil.copy('seeing/lightbox.html', lbname)
+
+def save_tensor_image(img, filename):
+    if len(img.shape) == 4:
+        img = img[0]
+    np_data = ((img.permute(1, 2, 0) / 2 + 0.5) * 255
+            ).clamp(0, 255).byte().cpu().numpy()
+    PIL.Image.fromarray(np_data).save(filename)
+
+def set_requires_grad(requires_grad, *models):
+    for model in models:
+        if isinstance(model, torch.nn.Module):
+            for param in model.parameters():
+                param.requires_grad = requires_grad
+        elif isintance(model, torch.nn.Parameter):
+            model.requires_grad = requires_grad
+        else:
+            assert False, 'unknown type %r' % type(model)
+
+if __name__ == '__main__':
+    exit_if_job_done(expdir, redo=args.redo)
+    main()
+    mark_job_done(expdir)
diff --git a/netdissect/paintwidget.py b/netdissect/paintwidget.py
new file mode 100644
index 0000000000000000000000000000000000000000..100f9dec6ad345a236e33d3dca88a15faf3fb22b
--- /dev/null
+++ b/netdissect/paintwidget.py
@@ -0,0 +1,175 @@
+from .labwidget import Widget, Property, minify
+import html
+
+class PaintWidget(Widget):
+  def __init__(self,
+          width=256, height=256,
+          image='', mask='', brushsize=10.0, oneshot=False, disabled=False,
+          vanishing=True, opacity=0.7,
+          **kwargs):
+    super().__init__(**kwargs)
+    self.mask = Property(mask)
+    self.image = Property(image)
+    self.vanishing = Property(vanishing)
+    self.brushsize = Property(brushsize)
+    self.erase = Property(False)
+    self.oneshot = Property(oneshot)
+    self.disabled = Property(disabled)
+    self.width = Property(width)
+    self.height = Property(height)
+    self.opacity = Property(opacity)
+    self.startpos = Property(None)
+    self.dragpos = Property(None)
+    self.dragging = Property(False)
+
+  def widget_js(self):
+    return minify(f'''
+      {PAINT_WIDGET_JS}
+      var pw = new PaintWidget(element, model);
+    ''')
+  def widget_html(self):
+    v = self.view_id()
+    return minify(f'''
+    <style>
+    #{v} {{ position: relative; display: inline-block; }}
+    #{v} .paintmask {{
+      position: absolute; top:0; left: 0; z-index: 1;
+      opacity: { self.opacity } }}
+    #{v} .paintmask.vanishing {{
+      opacity: 0; transition: opacity .1s ease-in-out; }}
+    #{v} .paintmask.vanishing:hover {{ opacity: { self.opacity }; }}
+    </style>
+    <div id="{v}"></div>
+    ''')
+
+PAINT_WIDGET_JS = """
+class PaintWidget {
+  constructor(el, model) {
+    this.el = el;
+    this.model = model;
+    this.size_changed();
+    this.model.on('mask', this.mask_changed.bind(this));
+    this.model.on('image', this.image_changed.bind(this));
+    this.model.on('vanishing', this.mask_changed.bind(this));
+    this.model.on('width', this.size_changed.bind(this));
+    this.model.on('height', this.size_changed.bind(this));
+  }
+  mouse_stroke(first_event) {
+    var self = this;
+    if (first_event.which === 3 || first_event.button === 2) {
+        first_event.preventDefault();
+        self.mask_canvas.style.pointerEvents = 'none';
+        setTimeout(() => {
+            self.mask_canvas.style.pointerEvents = 'all';
+        }, 3000);
+        return;
+    }
+    if (self.model.get('disabled')) { return; }
+    if (self.model.get('oneshot')) {
+        var canvas = self.mask_canvas;
+        var ctx = canvas.getContext('2d');
+        ctx.clearRect(0, 0, canvas.width, canvas.height);
+    }
+    function track_mouse(evt) {
+      if (evt.type == 'keydown' || self.model.get('disabled')) {
+        if (self.model.get('disabled') || evt.key === "Escape") {
+          window.removeEventListener('mousemove', track_mouse);
+          window.removeEventListener('mouseup', track_mouse);
+          window.removeEventListener('keydown', track_mouse, true);
+          if (self.model.get('dragging')) {
+            self.model.set('dragging', false);
+          }
+          self.mask_changed();
+        }
+        return;
+      }
+      if (evt.type == 'mouseup' ||
+        (typeof evt.buttons != 'undefined' && evt.buttons == 0)) {
+        window.removeEventListener('mousemove', track_mouse);
+        window.removeEventListener('mouseup', track_mouse);
+        window.removeEventListener('keydown', track_mouse, true);
+        self.model.set('dragging', false);
+        self.model.set('mask', self.mask_canvas.toDataURL());
+        return;
+      }
+      var p = self.cursor_position(evt);
+      var d = self.model.get('dragging');
+      var e = self.model.get('erase') ^ (evt.ctrlKey);
+      if (!d) { self.model.set('startpos', [p.x, p.y]); }
+      self.model.set('dragpos', [p.x, p.y]);
+      if (!d) { self.model.set('dragging', true); }
+      self.fill_circle(p.x, p.y,
+          self.model.get('brushsize'),
+          e);
+    }
+    this.mask_canvas.focus();
+    window.addEventListener('mousemove', track_mouse);
+    window.addEventListener('mouseup', track_mouse);
+    window.addEventListener('keydown', track_mouse, true);
+    track_mouse(first_event);
+  }
+  mask_changed() {
+    this.mask_canvas.classList.toggle("vanishing", this.model.get('vanishing'));
+    this.draw_data_url(this.mask_canvas, this.model.get('mask'));
+  }
+  image_changed() {
+    this.image.src = this.model.get('image');
+  }
+  size_changed() {
+    this.mask_canvas = document.createElement('canvas');
+    this.image = document.createElement('img');
+    this.mask_canvas.className = "paintmask";
+    this.image.className = "paintimage";
+    for (var attr of ['width', 'height']) {
+      this.mask_canvas[attr] = this.model.get(attr);
+      this.image[attr] = this.model.get(attr);
+    }
+
+    this.el.innerHTML = '';
+    this.el.appendChild(this.image);
+    this.el.appendChild(this.mask_canvas);
+    this.mask_canvas.addEventListener('mousedown',
+        this.mouse_stroke.bind(this));
+    this.mask_changed();
+    this.image_changed();
+  }
+
+  cursor_position(evt) {
+    const rect = this.mask_canvas.getBoundingClientRect();
+    const x = event.clientX - rect.left;
+    const y = event.clientY - rect.top;
+    return {x: x, y: y};
+  }
+
+  fill_circle(x, y, r, erase, blur) {
+    var ctx = this.mask_canvas.getContext('2d');
+    ctx.save();
+    if (blur) {
+        ctx.filter = 'blur(' + blur + 'px)';
+    }
+    ctx.globalCompositeOperation = (
+        erase ? "destination-out" : 'source-over');
+    ctx.fillStyle = '#fff';
+    ctx.beginPath();
+    ctx.arc(x, y, r, 0, 2 * Math.PI);
+    ctx.fill();
+    ctx.restore()
+  }
+
+  draw_data_url(canvas, durl) {
+    var ctx = canvas.getContext('2d');
+    var img = new Image;
+    canvas.pendingImg = img;
+    function imgdone() {
+      if (canvas.pendingImg == img) {
+        ctx.clearRect(0, 0, canvas.width, canvas.height);
+        ctx.drawImage(img, 0, 0);
+        canvas.pendingImg = null;
+      }
+    }
+    img.addEventListener('load', imgdone);
+    img.addEventListener('error', imgdone);
+    img.src = durl;
+  }
+}
+"""
diff --git a/netdissect/parallelfolder.py b/netdissect/parallelfolder.py
new file mode 100644
index 0000000000000000000000000000000000000000..7658b4bc805672b61c7d292dda4caad98935f540
--- /dev/null
+++ b/netdissect/parallelfolder.py
@@ -0,0 +1,191 @@
+'''
+Variants of pytorch's ImageFolder for loading image datasets with more
+information, such as parallel feature channels in separate files,
+cached files with lists of filenames, etc.
+'''
+
+import os, torch, re, random, numpy, itertools
+import torch.utils.data as data
+from torchvision.datasets.folder import default_loader as tv_default_loader
+from PIL import Image
+from collections import OrderedDict
+from . import pbar
+
+def grayscale_loader(path):
+    with open(path, 'rb') as f:
+        return Image.open(f).convert('L')
+
+class ndarray(numpy.ndarray):
+    '''
+    Wrapper to make ndarrays into heap objects so that shared_state can
+    be attached as an attribute.
+    '''
+    pass
+
+def default_loader(filename):
+    '''
+    Handles both numpy files and image formats.
+    '''
+    if filename.endswith('.npy'):
+        return numpy.load(filename).view(ndarray)
+    elif filename.endswith('.npz'):
+        return numpy.load(filename)
+    else:
+        return tv_default_loader(filename)
+
+class ParallelImageFolders(data.Dataset):
+    """
+    A data loader that looks for parallel image filenames, for example
+
+    photo1/park/004234.jpg
+    photo1/park/004236.jpg
+    photo1/park/004237.jpg
+
+    photo2/park/004234.png
+    photo2/park/004236.png
+    photo2/park/004237.png
+    """
+    def __init__(self, image_roots,
+            transform=None,
+            loader=default_loader,
+            stacker=None,
+            classification=False,
+            intersection=False,
+            filter_tuples=None,
+            normalize_filename=None,
+            verbose=None,
+            size=None,
+            shuffle=None,
+            lazy_init=True):
+        self.image_roots = image_roots
+        if transform is not None and not hasattr(transform, '__iter__'):
+            transform = [transform for _ in image_roots]
+        self.transforms = transform
+        self.stacker = stacker
+        self.loader = loader
+        def do_lazy_init():
+            self.images, self.classes, self.class_to_idx = (
+                    make_parallel_dataset(image_roots,
+                        classification=classification,
+                        intersection=intersection,
+                        filter_tuples=filter_tuples,
+                        normalize_fn=normalize_filename,
+                        verbose=verbose))
+            if len(self.images) == 0:
+                raise RuntimeError("Found 0 images within: %s" % image_roots)
+            if shuffle is not None:
+                random.Random(shuffle).shuffle(self.images)
+            if size is not None:
+                self.images = self.images[:size]
+            self._do_lazy_init = None
+        # Do slow initialization lazily.
+        if lazy_init:
+            self._do_lazy_init = do_lazy_init
+        else:
+            do_lazy_init()
+
+    def __getattr__(self, attr):
+        if self._do_lazy_init is not None:
+            self._do_lazy_init()
+            return getattr(self, attr)
+        raise AttributeError()
+
+    def __getitem__(self, index):
+        if self._do_lazy_init is not None:
+            self._do_lazy_init()
+        paths = self.images[index]
+        if self.classes is not None:
+            classidx = paths[-1]
+            paths = paths[:-1]
+        sources = [self.loader(path) for path in paths]
+        # Add a common shared state dict to allow random crops/flips to be
+        # coordinated.
+        shared_state = {}
+        for s in sources:
+            try:
+                s.shared_state = shared_state
+            except:
+                pass
+        if self.transforms is not None:
+            sources = [transform(source) if transform is not None else source
+                    for source, transform
+                    in itertools.zip_longest(sources, self.transforms)]
+        if self.stacker is not None:
+            sources = self.stacker(sources)
+            if self.classes is not None:
+                sources = (sources, classidx)
+        else:
+            if self.classes is not None:
+                sources.append(classidx)
+            sources = tuple(sources)
+        return sources
+
+    def __len__(self):
+        if self._do_lazy_init is not None:
+            self._do_lazy_init()
+        return len(self.images)
+
+def is_npy_file(path):
+    return path.endswith('.npy') or path.endswith('.NPY')
+
+def is_image_file(path):
+    return None != re.search(r'\.(jpe?g|png)$', path, re.IGNORECASE)
+
+def walk_image_files(rootdir, verbose=None):
+    indexfile = '%s.txt' % rootdir
+    if os.path.isfile(indexfile):
+        basedir = os.path.dirname(rootdir)
+        with open(indexfile) as f:
+            result = sorted([os.path.join(basedir, line.strip())
+                for line in f.readlines()])
+            return result
+    result = []
+    for dirname, _, fnames in sorted(pbar(os.walk(rootdir),
+            desc='Walking %s' % os.path.basename(rootdir))):
+        for fname in sorted(fnames):
+            if is_image_file(fname) or is_npy_file(fname):
+                result.append(os.path.join(dirname, fname))
+    return result
+
+def make_parallel_dataset(image_roots, classification=False,
+        intersection=False, filter_tuples=None, normalize_fn=None,
+        verbose=None):
+    """
+    Returns ([(img1, img2, clsid), (img1, img2, clsid)..],
+             classes, class_to_idx)
+    """
+    image_roots = [os.path.expanduser(d) for d in image_roots]
+    image_sets = OrderedDict()
+    if normalize_fn is None:
+        normalize_fn = lambda x: os.path.splitext(x)[0]
+    for j, root in enumerate(image_roots):
+        for path in walk_image_files(root, verbose=verbose):
+            key = normalize_fn(os.path.relpath(path, root))
+            if key not in image_sets:
+                image_sets[key] = []
+            if not intersection and len(image_sets[key]) != j:
+                raise RuntimeError(
+                    'Images not parallel: %s missing from one dir' % (key))
+            image_sets[key].append(path)
+    if classification:
+        classes = sorted(set([os.path.basename(os.path.dirname(k))
+            for k in image_sets.keys()]))
+        class_to_idx = dict({k: v for v, k in enumerate(classes)})
+        for k, v in image_sets.items():
+            v.append(class_to_idx[os.path.basename(os.path.dirname(k))])
+    else:
+        classes, class_to_idx = None, None
+    tuples = []
+    for key, value in image_sets.items():
+        if len(value) != len(image_roots) + (1 if classification else 0):
+            if intersection:
+                continue
+            else:
+                raise RuntimeError(
+                    'Images not parallel: %s missing from one dir' % (key))
+        value = tuple(value)
+        if filter_tuples and not filter_tuples(value):
+            continue
+        tuples.append(value)
+    return tuples, classes, class_to_idx
+
diff --git a/netdissect/pbar.py b/netdissect/pbar.py
new file mode 100644
index 0000000000000000000000000000000000000000..a39185935d6771e253cb699958cb1f5068e0148e
--- /dev/null
+++ b/netdissect/pbar.py
@@ -0,0 +1,190 @@
+'''
+Utilities for showing progress bars, controlling default verbosity, etc.
+'''
+
+# If the tqdm package is not available, then do not show progress bars;
+# just connect print_progress to print.
+import sys, types, builtins
+try:
+    from tqdm import tqdm
+    try:
+        from tqdm.notebook import tqdm as tqdm_nb
+    except:
+        from tqdm import tqdm_notebook as tqdm_nb
+except:
+    tqdm = None
+
+default_verbosity = True
+next_description = None
+python_print = builtins.print
+
+def post(**kwargs):
+    '''
+    When within a progress loop, pbar.post(k=str) will display
+    the given k=str status on the right-hand-side of the progress
+    status bar.  If not within a visible progress bar, does nothing.
+    '''
+    innermost = innermost_tqdm()
+    if innermost is not None:
+        innermost.set_postfix(**kwargs)
+
+def desc(desc):
+    '''
+    When within a progress loop, pbar.desc(str) changes the
+    left-hand-side description of the loop toe the given description.
+    '''
+    innermost = innermost_tqdm()
+    if innermost is not None:
+        innermost.set_description(str(desc))
+
+def descnext(desc):
+    '''
+    Called before starting a progress loop, pbar.descnext(str)
+    sets the description text that will be used in the following loop.
+    '''
+    global next_description
+    if not default_verbosity or tqdm is None:
+        return
+    next_description = desc
+
+def print(*args):
+    '''
+    When within a progress loop, will print above the progress loop.
+    '''
+    global next_description
+    next_description = None
+    if default_verbosity:
+        msg = ' '.join(str(s) for s in args)
+        if tqdm is None:
+            python_print(msg)
+        else:
+            tqdm.write(msg)
+
+def tqdm_terminal(it, *args, **kwargs):
+    '''
+    Some settings for tqdm that make it run better in resizable terminals.
+    '''
+    return tqdm(it, *args, dynamic_ncols=True, ascii=True,
+            leave=(innermost_tqdm() is not None), **kwargs)
+
+def in_notebook():
+    '''
+    True if running inside a Jupyter notebook.
+    '''
+    # From https://stackoverflow.com/a/39662359/265298
+    try:
+        shell = get_ipython().__class__.__name__
+        if shell == 'ZMQInteractiveShell':
+            return True   # Jupyter notebook or qtconsole
+        elif shell == 'TerminalInteractiveShell':
+            return False  # Terminal running IPython
+        else:
+            return False  # Other type (?)
+    except NameError:
+        return False      # Probably standard Python interpreter
+
+def innermost_tqdm():
+    '''
+    Returns the innermost active tqdm progress loop on the stack.
+    '''
+    if hasattr(tqdm, '_instances') and len(tqdm._instances) > 0:
+        return max(tqdm._instances, key=lambda x: x.pos)
+    else:
+        return None
+
+def reporthook(*args, **kwargs):
+    '''
+    For use with urllib.request.urlretrieve.
+
+    with pbar.reporthook() as hook:
+        urllib.request.urlretrieve(url, filename, reporthook=hook)
+    '''
+    kwargs2 = dict(unit_scale=True, miniters=1)
+    kwargs2.update(kwargs)
+    bar = __call__(None, *args, **kwargs2)
+    class ReportHook(object):
+        def __init__(self, t):
+            self.t = t
+        def __call__(self, b=1, bsize=1, tsize=None):
+            if hasattr(self.t, 'total'):
+                if tsize is not None:
+                    self.t.total = tsize
+            if hasattr(self.t, 'update'):
+                self.t.update(b * bsize - self.t.n)
+        def __enter__(self):
+            return self
+        def __exit__(self, *exc):
+            if hasattr(self.t, '__exit__'):
+                self.t.__exit__(*exc)
+    return ReportHook(bar)
+
+def __call__(x, *args, **kwargs):
+    '''
+    Invokes a progress function that can wrap iterators to print
+    progress messages, if verbose is True.
+
+    If verbose is False or tqdm is unavailable, then a quiet
+    non-printing identity function is used.
+
+    verbose can also be set to a spefific progress function rather
+    than True, and that function will be used.
+    '''
+    global default_verbosity, next_description
+    if not default_verbosity or tqdm is None:
+        return x
+    if default_verbosity == True:
+        fn = tqdm_nb if in_notebook() else tqdm_terminal
+    else:
+        fn = default_verbosity
+    if next_description is not None:
+        kwargs = dict(kwargs)
+        kwargs['desc'] = next_description
+        next_description = None
+    return fn(x, *args, **kwargs)
+
+class VerboseContextManager():
+    def __init__(self, v, entered=False):
+        self.v, self.entered, self.saved = v, False, []
+        if entered:
+            self.__enter__()
+            self.entered = True
+    def __enter__(self):
+        global default_verbosity
+        if self.entered:
+            self.entered = False
+        else:
+            self.saved.append(default_verbosity)
+            default_verbosity = self.v
+        return self
+    def __exit__(self, exc_type, exc_value, exc_traceback):
+        global default_verbosity
+        default_verbosity = self.saved.pop()
+    def __call__(self, v=True):
+        '''
+        Calling the context manager makes a new context that is
+        pre-entered, so it works as both a plain function and as a
+        factory for a context manager.
+        '''
+        new_v = v if self.v else not v
+        cm = VerboseContextManager(new_v, entered=True)
+        default_verbosity = new_v
+        return cm
+
+# Use as either "with pbar.verbose:" or "pbar.verbose(False)", or also
+# "with pbar.verbose(False):"
+verbose = VerboseContextManager(True)
+
+# Use as either "with @pbar.quiet" or "pbar.quiet(True)". or also
+# "with pbar.quiet(True):"
+quiet = VerboseContextManager(False)
+
+class CallableModule(types.ModuleType):
+    def __init__(self):
+        # or super().__init__(__name__) for Python 3
+        types.ModuleType.__init__(self, __name__)
+        self.__dict__.update(sys.modules[__name__].__dict__)
+    def __call__(self, x, *args, **kwargs):
+        return __call__(x, *args, **kwargs)
+
+sys.modules[__name__] = CallableModule()
+
diff --git a/netdissect/pidfile.py b/netdissect/pidfile.py
new file mode 100644
index 0000000000000000000000000000000000000000..c5bf70b1e28abeeb37dff3a536ca44a9d8df9fb5
--- /dev/null
+++ b/netdissect/pidfile.py
@@ -0,0 +1,113 @@
+'''
+Utility for simple distribution of work on multiple processes, by
+making sure only one process is working on a job at once.
+'''
+
+import os, errno, socket, atexit, time, sys
+
+def exclusive_dirfn(*args):
+    '''
+    Convenience function to get exclusive access to an unfinished
+    experiment directory.  Exits the program if the directory is
+    already done or busy (using exit_of_job_done).  Otherwise,
+    returns a function creates filenames within that directory.
+    '''
+    directory = os.path.join(*[str(a) for a in args])
+    exit_if_job_done(directory)
+    def dirfn(*fn):
+        return os.path.join(directory, *fn)
+    dirfn.dir = directory
+    def done():
+        mark_job_done(directory)
+    dirfn.done = done
+    print('Working in %s' % directory)
+    return dirfn
+
+def exit_if_job_done(directory, redo=False, force=False, verbose=True):
+    if pidfile_taken(os.path.join(directory, 'lockfile.pid'),
+            force=force, verbose=verbose):
+        sys.exit(0)
+    donefile = os.path.join(directory, 'done.txt')
+    if os.path.isfile(donefile):
+        with open(donefile) as f:
+            msg = f.read()
+        if redo or force:
+            if verbose:
+                print('Removing %s %s' % (donefile, msg))
+            os.remove(donefile)
+        else:
+            if verbose:
+                print('%s %s' % (donefile, msg))
+            sys.exit(0)
+
+def mark_job_done(directory):
+    with open(os.path.join(directory, 'done.txt'), 'w') as f:
+        f.write('done by %d@%s %s at %s' %
+                (os.getpid(), socket.gethostname(),
+                 os.getenv('STY', ''),
+                 time.strftime('%c')))
+
+def pidfile_taken(path, verbose=False, force=False):
+    '''
+    Usage.  To grab an exclusive lock for the remaining duration of the
+    current process (and exit if another process already has the lock),
+    do this:
+
+    if pidfile_taken('job_423/lockfile.pid', verbose=True):
+        sys.exit(0)
+
+    To do a batch of jobs, just run a script that does them all on
+    each available machine, sharing a network filesystem.  When each
+    job grabs a lock, then this will automatically distribute the
+    jobs so that each one is done just once on one machine.
+    '''
+
+    # Try to create the file exclusively and write my pid into it.
+    try:
+        os.makedirs(os.path.dirname(path), exist_ok=True)
+        fd = os.open(path, os.O_CREAT | os.O_EXCL | os.O_RDWR)
+    except OSError as e:
+        if e.errno == errno.EEXIST:
+            # If we cannot because there was a race, yield the conflicter.
+            conflicter = 'race'
+            try:
+                with open(path, 'r') as lockfile:
+                    conflicter = lockfile.read().strip() or 'empty'
+            except:
+                pass
+            # Force is for manual one-time use, for deleting stale lockfiles.
+            if force:
+                if verbose:
+                    print('Removing %s from %s' % (path, conflicter))
+                os.remove(path)
+                return pidfile_taken(path, verbose=verbose, force=False)
+            if verbose:
+                print('%s held by %s' % (path, conflicter))
+            return conflicter
+        else:
+            # Other problems get an exception.
+            raise
+    # Register to delete this file on exit.
+    lockfile = os.fdopen(fd, 'r+')
+    atexit.register(delete_pidfile, lockfile, path)
+    # Write my pid into the open file.
+    lockfile.write('%d@%s %s\n' % (os.getpid(), socket.gethostname(),
+        os.getenv('STY', '')))
+    lockfile.flush()
+    os.fsync(lockfile)
+    # Return 'None' to say there was not a conflict.
+    return None
+
+def delete_pidfile(lockfile, path):
+    '''
+    Runs at exit after pidfile_taken succeeds.
+    '''
+    if lockfile is not None:
+        try:
+            lockfile.close()
+        except:
+            pass
+    try:
+        os.unlink(path)
+    except:
+        pass
diff --git a/netdissect/plotutil.py b/netdissect/plotutil.py
new file mode 100644
index 0000000000000000000000000000000000000000..187bcb9d5615c8ec51a43148b011c06b8ed6aff7
--- /dev/null
+++ b/netdissect/plotutil.py
@@ -0,0 +1,61 @@
+import matplotlib.pyplot as plt
+import numpy
+
+def plot_tensor_images(data, **kwargs):
+    data = ((data + 1) / 2 * 255).permute(0, 2, 3, 1).byte().cpu().numpy()
+    width = int(numpy.ceil(numpy.sqrt(data.shape[0])))
+    height = int(numpy.ceil(data.shape[0] / float(width)))
+    kwargs = dict(kwargs)
+    margin = 0.01
+    if 'figsize' not in kwargs:
+        # Size figure to one display pixel per data pixel
+        dpi = plt.rcParams['figure.dpi']
+        kwargs['figsize'] = (
+                (1 + margin) * (width * data.shape[2] / dpi),
+                (1 + margin) * (height * data.shape[1] / dpi))
+    f, axarr = plt.subplots(height, width, **kwargs)
+    if len(numpy.shape(axarr)) == 0:
+        axarr = numpy.array([[axarr]])
+    if len(numpy.shape(axarr)) == 1:
+        axarr = axarr[None,:]
+    for i, im in enumerate(data):
+        ax = axarr[i // width, i % width]
+        ax.imshow(data[i])
+        ax.axis('off')
+    for i in range(i, width * height):
+        ax = axarr[i // width, i % width]
+        ax.axis('off')
+    plt.subplots_adjust(wspace=margin, hspace=margin,
+            left=0, right=1, bottom=0, top=1)
+    plt.show()
+
+def plot_max_heatmap(data, shape=None, **kwargs):
+    if shape is None:
+        shape = data.shape[2:]
+    data = data.max(1)[0].cpu().numpy()
+    vmin = data.min()
+    vmax = data.max()
+    width = int(numpy.ceil(numpy.sqrt(data.shape[0])))
+    height = int(numpy.ceil(data.shape[0] / float(width)))
+    kwargs = dict(kwargs)
+    margin = 0.01
+    if 'figsize' not in kwargs:
+        # Size figure to one display pixel per data pixel
+        dpi = plt.rcParams['figure.dpi']
+        kwargs['figsize'] = (
+                width * shape[1] / dpi, height * shape[0] / dpi)
+    f, axarr = plt.subplots(height, width, **kwargs)
+    if len(numpy.shape(axarr)) == 0:
+        axarr = numpy.array([[axarr]])
+    if len(numpy.shape(axarr)) == 1:
+        axarr = axarr[None,:]
+    for i, im in enumerate(data):
+        ax = axarr[i // width, i % width]
+        img = ax.imshow(data[i], vmin=vmin, vmax=vmax, cmap='hot')
+        ax.axis('off')
+    for i in range(i, width * height):
+        ax = axarr[i // width, i % width]
+        ax.axis('off')
+    plt.subplots_adjust(wspace=margin, hspace=margin,
+            left=0, right=1, bottom=0, top=1)
+    plt.show()
diff --git a/netdissect/proggan.py b/netdissect/proggan.py
new file mode 100644
index 0000000000000000000000000000000000000000..a82f6fecac2cf7b6d9016e201265691236997490
--- /dev/null
+++ b/netdissect/proggan.py
@@ -0,0 +1,302 @@
+import torch, numpy, itertools
+import torch.nn as nn
+from collections import OrderedDict
+
+
+def print_network(net, verbose=False):
+    num_params = 0
+    for param in net.parameters():
+        num_params += param.numel()
+    if verbose:
+        print(net)
+    print('Total number of parameters: {:3.3f} M'.format(num_params / 1e6))
+
+
+def from_pth_file(filename):
+    '''
+    Instantiate from a pth file.
+    '''
+    return from_state_dict(torch.load(filename))
+
+def from_state_dict(state_dict):
+    if 'state_dict' in state_dict:
+        state_dict = state_dict['state_dict']
+    # Convert old version of parameter names
+    if 'features.0.conv.weight' in state_dict:
+        state_dict = state_dict_from_old_pt_dict(state_dict)
+    sizes = sizes_from_state_dict(state_dict)
+    result = ProgressiveGenerator(sizes=sizes)
+    result.load_state_dict(state_dict)
+    return result
+
+###############################################################################
+# Modules
+###############################################################################
+
+class ProgressiveGenerator(nn.Sequential):
+    def __init__(self, resolution=None, sizes=None, modify_sequence=None,
+            output_tanh=True):
+        '''
+        A pytorch progessive GAN generator that can be converted directly
+        from either a tensorflow model or a theano model.  It consists of
+        a sequence of convolutional layers, organized in pairs, with an
+        upsampling and reduction of channels at every other layer; and
+        then finally followed by an output layer that reduces it to an
+        RGB [-1..1] image.
+
+        The network can be given more layers to increase the output
+        resolution.  The sizes argument indicates the fieature depth at
+        each upsampling, starting with the input z: [input-dim, 4x4-depth,
+        8x8-depth, 16x16-depth...].  The output dimension is 2 * 2**len(sizes)
+
+        Some default architectures can be selected by supplying the
+        resolution argument instead.
+
+        The optional modify_sequence function can be used to transform the
+        sequence of layers before the network is constructed.
+
+        If output_tanh is set to False, the network leaves the output
+        unclamped; otherwise, it applies a tanh to clamp the output to
+        [-1,1] before output.
+        '''
+        assert (resolution is None) != (sizes is None)
+        if sizes is None:
+            sizes = {
+                    8: [512, 512, 512],
+                    16: [512, 512, 512, 512],
+                    32: [512, 512, 512, 512, 256],
+                    64: [512, 512, 512, 512, 256, 128],
+                    128: [512, 512, 512, 512, 256, 128, 64],
+                    256: [512, 512, 512, 512, 256, 128, 64, 32],
+                    1024: [512, 512, 512, 512, 512, 256, 128, 64, 32, 16]
+                }[resolution]
+        # Follow the schedule of upsampling given by sizes.
+        # layers are called: layer1, layer2, etc; then output_128x128
+        sequence = []
+        def add_d(layer, name=None):
+            if name is None:
+                name = 'layer%d' % (len(sequence) + 1)
+            sequence.append((name, layer))
+        add_d(NormConvBlock(sizes[0], sizes[1], kernel_size=4, padding=3))
+        add_d(NormConvBlock(sizes[1], sizes[1], kernel_size=3, padding=1))
+        for i, (si, so) in enumerate(zip(sizes[1:-1], sizes[2:])):
+            add_d(NormUpscaleConvBlock(si, so, kernel_size=3, padding=1))
+            add_d(NormConvBlock(so, so, kernel_size=3, padding=1))
+        # Create an output layer.  During training, the progressive GAN
+        # learns several such output layers for various resolutions; we
+        # just include the last (highest resolution) one.
+        dim = 4 * (2 ** (len(sequence) // 2 - 1))
+        add_d(OutputConvBlock(sizes[-1], tanh=output_tanh),
+                name='output_%dx%d' % (dim, dim))
+        # Allow the sequence to be modified
+        if modify_sequence is not None:
+            sequence = modify_sequence(sequence)
+        super().__init__(OrderedDict(sequence))
+
+    def forward(self, x):
+        # Convert vector input to 1x1 featuremap.
+        x = x.view(x.shape[0], x.shape[1], 1, 1)
+        return super().forward(x)
+
+class PixelNormLayer(nn.Module):
+    def __init__(self):
+        super(PixelNormLayer, self).__init__()
+
+    def forward(self, x):
+        return x / torch.sqrt(torch.mean(x**2, dim=1, keepdim=True) + 1e-8)
+
+class DoubleResolutionLayer(nn.Module):
+    def forward(self, x):
+        x = nn.functional.interpolate(x, scale_factor=2, mode='nearest')
+        return x
+
+class WScaleLayer(nn.Module):
+    def __init__(self, size, fan_in, gain=numpy.sqrt(2)):
+        super(WScaleLayer, self).__init__()
+        self.scale = gain / numpy.sqrt(fan_in) # No longer a parameter
+        self.b = nn.Parameter(torch.randn(size))
+        self.size = size
+
+    def forward(self, x):
+        x_size = x.size()
+        x = x * self.scale + self.b.view(1, -1, 1, 1).expand(
+            x_size[0], self.size, x_size[2], x_size[3])
+        return x
+
+class NormConvBlock(nn.Module):
+    def __init__(self, in_channels, out_channels, kernel_size, padding):
+        super(NormConvBlock, self).__init__()
+        self.norm = PixelNormLayer()
+        self.conv = nn.Conv2d(
+            in_channels, out_channels, kernel_size, 1, padding, bias=False)
+        self.wscale = WScaleLayer(out_channels, in_channels,
+                gain=numpy.sqrt(2) / kernel_size)
+        self.relu = nn.LeakyReLU(inplace=True, negative_slope=0.2)
+
+    def forward(self, x):
+        x = self.norm(x)
+        x = self.conv(x)
+        x = self.relu(self.wscale(x))
+        return x
+
+class NormUpscaleConvBlock(nn.Module):
+    def __init__(self, in_channels, out_channels, kernel_size, padding):
+        super(NormUpscaleConvBlock, self).__init__()
+        self.norm = PixelNormLayer()
+        self.up = DoubleResolutionLayer()
+        self.conv = nn.Conv2d(
+            in_channels, out_channels, kernel_size, 1, padding, bias=False)
+        self.wscale = WScaleLayer(out_channels, in_channels,
+                gain=numpy.sqrt(2) / kernel_size)
+        self.relu = nn.LeakyReLU(inplace=True, negative_slope=0.2)
+
+    def forward(self, x):
+        x = self.norm(x)
+        x = self.up(x)
+        x = self.conv(x)
+        x = self.relu(self.wscale(x))
+        return x
+
+class OutputConvBlock(nn.Module):
+    def __init__(self, in_channels, tanh=False):
+        super().__init__()
+        self.norm = PixelNormLayer()
+        self.conv = nn.Conv2d(
+                in_channels, 3, kernel_size=1, padding=0, bias=False)
+        self.wscale = WScaleLayer(3, in_channels, gain=1)
+        self.clamp = nn.Hardtanh() if tanh else (lambda x: x)
+
+    def forward(self, x):
+        x = self.norm(x)
+        x = self.conv(x)
+        x = self.wscale(x)
+        x = self.clamp(x)
+        return x
+
+###############################################################################
+# Conversion
+###############################################################################
+
+def from_tf_parameters(parameters):
+    '''
+    Instantiate from tensorflow variables.
+    '''
+    state_dict = state_dict_from_tf_parameters(parameters)
+    sizes = sizes_from_state_dict(state_dict)
+    result = ProgressiveGenerator(sizes=sizes)
+    result.load_state_dict(state_dict)
+    return result
+
+def from_old_pt_dict(parameters):
+    '''
+    Instantiate from old pytorch state dict.
+    '''
+    state_dict = state_dict_from_old_pt_dict(parameters)
+    sizes = sizes_from_state_dict(state_dict)
+    result = ProgressiveGenerator(sizes=sizes)
+    result.load_state_dict(state_dict)
+    return result
+
+def sizes_from_state_dict(params):
+    '''
+    In a progressive GAN, the number of channels can change after each
+    upsampling.  This function reads the state dict to figure the
+    number of upsamplings and the channel depth of each filter.
+    '''
+    sizes = []
+    for i in itertools.count():
+        pt_layername = 'layer%d' % (i + 1)
+        try:
+            weight = params['%s.conv.weight' % pt_layername]
+        except KeyError:
+            break
+        if i == 0:
+            sizes.append(weight.shape[1])
+        if i % 2 == 0:
+            sizes.append(weight.shape[0])
+    return sizes
+
+def state_dict_from_tf_parameters(parameters):
+    '''
+    Conversion from tensorflow parameters
+    '''
+    def torch_from_tf(data):
+        return torch.from_numpy(data.eval())
+
+    params = dict(parameters)
+    result = {}
+    sizes = []
+    for i in itertools.count():
+        resolution = 4 * (2 ** (i // 2))
+        # Translate parameter names.  For example:
+        # 4x4/Dense/weight -> layer1.conv.weight
+        # 32x32/Conv0_up/weight -> layer7.conv.weight
+        # 32x32/Conv1/weight -> layer8.conv.weight
+        tf_layername = '%dx%d/%s' % (resolution, resolution,
+                'Dense' if i == 0 else 'Conv' if i == 1 else
+                'Conv0_up' if i % 2 == 0 else 'Conv1')
+        pt_layername = 'layer%d' % (i + 1)
+        # Stop looping when we run out of parameters.
+        try:
+            weight = torch_from_tf(params['%s/weight' % tf_layername])
+        except KeyError:
+            break
+        # Transpose convolution weights into pytorch format.
+        if i == 0:
+            # Convert dense layer to 4x4 convolution
+            weight = weight.view(weight.shape[0], weight.shape[1] // 16,
+                   4, 4).permute(1, 0, 2, 3).flip(2, 3)
+            sizes.append(weight.shape[0])
+        elif i % 2 == 0:
+            # Convert inverse convolution to convolution
+            weight = weight.permute(2, 3, 0, 1).flip(2, 3)
+        else:
+            # Ordinary Conv2d conversion.
+            weight = weight.permute(3, 2, 0, 1)
+            sizes.append(weight.shape[1])
+        result['%s.conv.weight' % (pt_layername)] = weight
+        # Copy bias vector.
+        bias = torch_from_tf(params['%s/bias' % tf_layername])
+        result['%s.wscale.b' % (pt_layername)] = bias
+    # Copy just finest-grained ToRGB output layers.  For example:
+    # ToRGB_lod0/weight -> output.conv.weight
+    i -= 1
+    resolution = 4 * (2 ** (i // 2))
+    tf_layername = 'ToRGB_lod0'
+    pt_layername = 'output_%dx%d' % (resolution, resolution)
+    result['%s.conv.weight' % pt_layername] = torch_from_tf(
+            params['%s/weight' % tf_layername]).permute(3, 2, 0, 1)
+    result['%s.wscale.b' % pt_layername] = torch_from_tf(
+            params['%s/bias' % tf_layername])
+    # Return parameters
+    return result
+
+def state_dict_from_old_pt_dict(params):
+    '''
+    Conversion from the old pytorch model layer names.
+    '''
+    result = {}
+    sizes = []
+    for i in itertools.count():
+        old_layername = 'features.%d' % i
+        pt_layername = 'layer%d' % (i + 1)
+        try:
+            weight = params['%s.conv.weight' % (old_layername)]
+        except KeyError:
+            break
+        if i == 0:
+            sizes.append(weight.shape[0])
+        if i % 2 == 0:
+            sizes.append(weight.shape[1])
+        result['%s.conv.weight' % (pt_layername)] = weight
+        result['%s.wscale.b' % (pt_layername)] = params[
+                '%s.wscale.b' % (old_layername)]
+    # Copy the output layers.
+    i -= 1
+    resolution = 4 * (2 ** (i // 2))
+    pt_layername = 'output_%dx%d' % (resolution, resolution)
+    result['%s.conv.weight' % pt_layername] = params['output.conv.weight']
+    result['%s.wscale.b' % pt_layername] = params['output.wscale.b']
+    # Return parameters and also network architecture sizes.
+    return result
+
diff --git a/netdissect/renormalize.py b/netdissect/renormalize.py
new file mode 100644
index 0000000000000000000000000000000000000000..feedc8e4bcba3b68e03acc7b8d72d89401a1b20f
--- /dev/null
+++ b/netdissect/renormalize.py
@@ -0,0 +1,125 @@
+import numpy, torch, PIL, io, base64, re
+from torchvision import transforms
+
+def as_tensor(data, source='zc', target='zc'):
+    renorm = renormalizer(source=source, target=target)
+    return renorm(data)
+
+def as_image(data, source='zc', target='byte'):
+    assert len(data.shape) == 3
+    renorm = renormalizer(source=source, target=target)
+    return PIL.Image.fromarray(renorm(data).
+            permute(1,2,0).cpu().numpy())
+
+def as_url(data, source='zc', size=None):
+    if isinstance(data, PIL.Image.Image):
+        img = data
+    else:
+        img = as_image(data, source)
+    if size is not None:
+        img = img.resize(size, resample=PIL.Image.BILINEAR)
+    buffered = io.BytesIO()
+    img.save(buffered, format='png')
+    b64 = base64.b64encode(buffered.getvalue()).decode('utf-8')
+    return 'data:image/png;base64,%s' % (b64)
+
+def from_image(im, target='zc', size=None):
+    if im.format != 'RGB':
+        im = im.convert('RGB')
+    if size is not None:
+        im = im.resize(size, resample=PIL.Image.BILINEAR)
+    pt = transforms.functional.to_tensor(im)
+    renorm = renormalizer(source='pt', target=target)
+    return renorm(pt)
+
+def from_url(url, target='zc', size=None):
+    image_data = re.sub('^data:image/.+;base64,', '', url)
+    im = PIL.Image.open(io.BytesIO(base64.b64decode(image_data)))
+    if target == 'image' and size is None:
+        return im
+    return from_image(im, target, size=size)
+
+def renormalizer(source='zc', target='zc'):
+    '''
+    Returns a function that imposes a standard normalization on
+    the image data.  The returned renormalizer operates on either
+    3d tensor (single image) or 4d tensor (image batch) data.
+    The normalization target choices are:
+
+        zc (default) - zero centered [-1..1]
+        pt - pytorch [0..1]
+        imagenet - zero mean, unit stdev imagenet stats (approx [-2.1...2.6])
+        byte - as from an image file, [0..255]
+
+    If a source is provided (a dataset or transform), then, the renormalizer
+    first reverses any normalization found in the data source before
+    imposing the specified normalization.  When no source is provided,
+    the input data is assumed to be pytorch-normalized (range [0..1]).
+    '''
+    if isinstance(source, str):
+        oldoffset, oldscale = OFFSET_SCALE[source]
+    else:
+        normalizer = find_normalizer(source)
+        oldoffset, oldscale = (
+                (normalizer.mean, normalizer.std) if normalizer is not None
+                else OFFSET_SCALE['pt'])
+    newoffset, newscale = (target if isinstance(target, tuple)
+            else OFFSET_SCALE[target])
+    return Renormalizer(oldoffset, oldscale, newoffset, newscale,
+            tobyte=(target == 'byte'))
+
+# The three commonly-seen image normalization schemes.
+OFFSET_SCALE=dict(
+            pt=([0.0, 0.0, 0.0], [1.0, 1.0, 1.0]),
+            zc=([0.5, 0.5, 0.5], [0.5, 0.5, 0.5]),
+            imagenet=([0.485, 0.456, 0.406], [0.229, 0.224, 0.225]),
+            imagenet_meanonly=([0.485, 0.456, 0.406],
+                [1.0/255, 1.0/255, 1.0/255]),
+            places_meanonly=([0.475, 0.441, 0.408],
+                [1.0/255, 1.0/255, 1.0/255]),
+            byte=([0.0, 0.0, 0.0], [1.0/255, 1.0/255, 1.0/255]))
+
+NORMALIZER={k: transforms.Normalize(*OFFSET_SCALE[k]) for k in OFFSET_SCALE}
+
+def find_normalizer(source=None):
+    '''
+    Crawl around the transforms attached to a dataset looking for a
+    Normalize transform to return.
+    '''
+    if source is None:
+        return None
+    if isinstance(source, (transforms.Normalize, Renormalizer)):
+        return source
+    t = getattr(source, 'transform', None)
+    if t is not None:
+        return find_normalizer(t)
+    ts = getattr(source, 'transforms', None)
+    if ts is not None:
+        for t in reversed(ts):
+            result = find_normalizer(t)
+            if result is not None:
+                return result
+    return None
+
+class Renormalizer:
+    def __init__(self, oldoffset, oldscale, newoffset, newscale, tobyte=False):
+        self.mul = torch.from_numpy(
+                numpy.array(oldscale) / numpy.array(newscale))
+        self.add = torch.from_numpy(
+                (numpy.array(oldoffset) - numpy.array(newoffset))
+                / numpy.array(newscale))
+        self.tobyte = tobyte
+        # Store these away to allow the data to be renormalized again
+        self.mean = newoffset
+        self.std = newscale
+
+    def __call__(self, data):
+        mul, add = [d.to(data.device, data.dtype) for d in [self.mul, self.add]]
+        if data.ndimension() == 3:
+            mul, add = [d[:, None, None] for d in [mul, add]]
+        elif data.ndimension() == 4:
+            mul, add = [d[None, :, None, None] for d in [mul, add]]
+        result = data.mul(mul).add_(add)
+        if self.tobyte:
+            result = result.clamp(0, 255).byte()
+        return result
diff --git a/netdissect/report.html b/netdissect/report.html
new file mode 100644
index 0000000000000000000000000000000000000000..d902a992e37846c7de7794bc4a824eb48f0103bc
--- /dev/null
+++ b/netdissect/report.html
@@ -0,0 +1,80 @@
+<!DOCTYPE html>
+<html>
+<head>
+<script src="https://cdn.jsdelivr.net/npm/vue@2.5.16/dist/vue.js"
+  integrity="sha256-CMMTrj5gGwOAXBeFi7kNokqowkzbeL8ydAJy39ewjkQ="
+  crossorigin="anonymous"></script>
+<script src="https://cdn.jsdelivr.net/npm/lodash@4.17.10/lodash.js"
+  integrity="sha256-qwbDmNVLiCqkqRBpF46q5bjYH11j5cd+K+Y6D3/ja28="
+  crossorigin="anonymous"></script>
+<script
+  src="https://code.jquery.com/jquery-3.3.1.js"
+  integrity="sha256-2Kok7MbOyxpgUVvAk/HJ2jigOSYS2auK4Pfzbm7uH60="
+  crossorigin="anonymous"></script>
+<script src="https://cdn.jsdelivr.net/npm/csv-js@1.0.0/csv.js"
+  integrity="sha256-+aP5dQ9d0B/YIqKRETp8wA0YYl4sqXdUG9QM7M9A42E="
+  crossorigin="anonymous"></script>
+<script src="https://cdnjs.cloudflare.com/ajax/libs/lity/2.3.1/lity.js"
+  integrity="sha256-28JiZvE/RethQIYCwkMdtSMHgI//KoTLeB2tSm10trs="
+  crossorigin="anonymous"></script>
+<link rel="stylesheet"
+  href="https://cdnjs.cloudflare.com/ajax/libs/lity/2.3.1/lity.css"
+  integrity="sha256-76wKiAXVBs5Kyj7j0T43nlBCbvR6pqdeeZmXI4ATnY0="
+  crossorigin="anonymous" />
+<style>
+.header { display: block; margin: 1px; text-align: center; }
+.header img {max-width: 90pw}
+.thumb { display: block; margin: 1px; text-align: center; }
+.thumb img, .thumb div { max-width: 90pw; word-break: break-all; }
+.header a { text-decoration: underline; cursor: pointer; }
+</style>
+</head>
+<body>
+<div id="report" v-if="header && units">
+<div class="header">
+  <h2>{{ header.name }}</h2>
+  <img :src="header.image">
+  <div>sort by:
+    <a @click="sort_order = ['iou']; sort_dir = ['desc']"
+      >iou</a>
+    <a @click="sort_order = ['label', 'iou']; sort_dir = ['asc', 'desc']" 
+      >label</a>
+    <a @click="sort_order = ['unit']; sort_dir = ['asc']"
+      >unit</a>
+  </div>
+</div>
+<div v-for="r in _.orderBy(units, sort_order, sort_dir)"
+  class="thumb">
+  <div style="display: inline-block">
+  <div style="text-align:left;">unit {{ r.unit }} ({{ r.label }}, iou {{ r.iou | fixed(4) }}) </div>
+<a :href="r.image" data-lity><img :src="r.image"></a>
+  </div>
+</div>
+</div><!--app-->
+</body>
+<script>
+var theapp = new Vue({
+  el: '#report',
+  data: {
+    header: null,
+    units: null,
+    sort_order: ['unit'],
+    sort_dir: ['desc']
+  },
+  created: function() {
+    var self = this;
+    $.getJSON('./report.json?' + Math.random(), function(d) {
+      self.header = d.header;
+      self.units= d.units;
+    });
+  },
+  filters: {
+    fixed: function(value, digits, truncate) {
+       if (typeof value != 'number') return value;
+       var fixed = value.toFixed(digits);
+       return truncate ? +fixed : fixed;
+    }
+  }
+})
+</script>
+</html>
diff --git a/netdissect/runningstats.py b/netdissect/runningstats.py
new file mode 100644
index 0000000000000000000000000000000000000000..c3499118442a007727cbf5d9ed835e2dbd992634
--- /dev/null
+++ b/netdissect/runningstats.py
@@ -0,0 +1,1240 @@
+'''
+Running statistics on the GPU using pytorch.
+
+RunningTopK maintains top-k statistics for a set of channels in parallel.
+RunningQuantile maintains (sampled) quantile statistics for a set of channels.
+'''
+
+import torch, math, numpy
+from collections import defaultdict
+
+class RunningTopK:
+    '''
+    A class to keep a running tally of the the top k values (and indexes)
+    of any number of torch feature components.  Will work on the GPU if
+    the data is on the GPU.
+
+    This version flattens all arrays to avoid crashes.
+    '''
+    def __init__(self, k=100, state=None):
+        if state is not None:
+            self.set_state_dict(state)
+            return
+        self.k = k
+        self.count = 0
+        # This version flattens all data internally to 2-d tensors,
+        # to avoid crashes with the current pytorch topk implementation.
+        # The data is puffed back out to arbitrary tensor shapes on ouput.
+        self.data_shape = None
+        self.top_data = None
+        self.top_index = None
+        self.next = 0
+        self.linear_index = 0
+        self.perm = None
+
+    def add(self, data, index=None):
+        '''
+        Adds a batch of data to be considered for the running top k.
+        The zeroth dimension enumerates the observations.  All other
+        dimensions enumerate different features.
+        '''
+        if self.top_data is None:
+            # Allocation: allocate a buffer of size 5*k, at least 10, for each.
+            self.data_shape = data.shape[1:]
+            feature_size = int(numpy.prod(self.data_shape))
+            self.top_data = torch.zeros(
+                    feature_size, max(10, self.k * 5), out=data.new())
+            self.top_index = self.top_data.clone().long()
+            self.linear_index = 0 if len(data.shape) == 1 else torch.arange(
+                feature_size, out=self.top_index.new()).mul_(
+                        self.top_data.shape[-1])[:,None]
+        size = data.shape[0]
+        sk = min(size, self.k)
+        if self.top_data.shape[-1] < self.next + sk:
+            # Compression: if full, keep topk only.
+            self.top_data[:,:self.k], self.top_index[:,:self.k] = (
+                    self.result(sorted=False, flat=True))
+            self.next = self.k
+            free = self.top_data.shape[-1] - self.next
+        # Pick: copy the top sk of the next batch into the buffer.
+        # Currently strided topk is slow.  So we clone after transpose.
+        # TODO: remove the clone() if it becomes faster.
+        cdata = data.contiguous().view(size, -1).t().clone()
+        td, ti = cdata.topk(sk, sorted=False)
+        self.top_data[:,self.next:self.next+sk] = td
+        if index is not None:
+            ti = index[ti]
+        else:
+            ti = ti + self.count
+        self.top_index[:,self.next:self.next+sk] = ti
+        self.next += sk
+        self.count += size
+
+    def size(self):
+        return self.count
+
+    def result(self, sorted=True, flat=False):
+        '''
+        Returns top k data items and indexes in each dimension,
+        with channels in the first dimension and k in the last dimension.
+        '''
+        k = min(self.k, self.next)
+        # bti are top indexes relative to buffer array.
+        td, bti = self.top_data[:,:self.next].topk(k, sorted=sorted)
+        # we want to report top indexes globally, which is ti.
+        ti = self.top_index.view(-1)[
+                (bti + self.linear_index).view(-1)
+                ].view(*bti.shape)
+        if flat:
+            return td, ti
+        else:
+            return (td.view(*(self.data_shape + (-1,))),
+                    ti.view(*(self.data_shape + (-1,))))
+
+    def to_(self, device):
+        self.top_data = self.top_data.to(device)
+        self.top_index = self.top_index.to(device)
+        if isinstance(self.linear_index, torch.Tensor):
+            self.linear_index = self.linear_index.to(device)
+
+    def state_dict(self):
+        return dict(
+                constructor=self.__module__ + '.' +
+                    self.__class__.__name__ + '()',
+                k=self.k,
+                count=self.count,
+                data_shape=tuple(self.data_shape),
+                top_data=self.top_data.cpu().numpy(),
+                top_index=self.top_index.cpu().numpy(),
+                next=self.next,
+                linear_index=(self.linear_index.cpu().numpy()
+                    if isinstance(self.linear_index, torch.Tensor)
+                    else self.linear_index),
+                perm=self.perm)
+
+    def set_state_dict(self, dic):
+        self.k = dic['k'].item()
+        self.count = dic['count'].item()
+        self.data_shape = tuple(dic['data_shape'])
+        self.top_data = torch.from_numpy(dic['top_data'])
+        self.top_index = torch.from_numpy(dic['top_index'])
+        self.next = dic['next'].item()
+        self.linear_index = (torch.from_numpy(dic['linear_index'])
+                if len(dic['linear_index'].shape) > 0
+                else dic['linear_index'].item())
+
+class RunningConditionalTopK:
+    def __init__(self, k=None, state=None):
+        self.running_topk = {}
+        if state is not None:
+            self.set_state_dict(state)
+            return
+        self.k = k
+        self.count = 0
+
+    def add(self, condition, data, index):
+        if condition not in self.running_topk:
+            self.running_topk[condition] = RunningTopK()
+        rv = self.running_topk[condition]
+        rv.add(data, index)
+        self.count += len(data)
+
+    def keys(self):
+        return self.running_topk.keys()
+
+    def conditional(self, c):
+        return self.running_topk[c]
+
+    def has_conditional(self, c):
+        return c in self.running_topk
+
+    def to_(self, device, conditions=None):
+        if conditions is None:
+            conditions = self.keys()
+        for cond in conditions:
+            if cond in self.running_topk:
+                self.running_topk[cond].to_(device)
+
+    def state_dict(self):
+        conditions = sorted(self.running_topk.keys())
+        result = dict(
+                constructor=self.__module__ + '.' +
+                    self.__class__.__name__ + '()',
+                conditions=conditions)
+        for i, c in enumerate(conditions):
+            result.update({
+                '%d.%s' % (i, k): v
+                for k, v in self.running_topk[c].state_dict().items()})
+        return result
+
+    def set_state_dict(self, dic):
+        conditions = list(dic['conditions'])
+        subdicts = defaultdict(dict)
+        for k, v in dic.items():
+            if '.' in k:
+                p, s = k.split('.', 1)
+                subdicts[p][s] = v
+        self.running_topk = {
+                c: RunningTopK(state=subdicts[str(i)])
+                for i, c in enumerate(conditions)}
+
+class GatherTensor:
+    """
+    A tensor for gathering results, allocated and shaped on first insert.
+    Creaed by tally.gather_topk for gathering topk visualizations.
+    """
+    def __init__(self, topk=None, data_shape=None, k=None, state=None):
+        if state is not None:
+            self.set_state_dict(state)
+            return
+        if k is None and topk is not None:
+            k = topk.k
+        if data_shape is None and topk is not None:
+            data_shape = topk.data_shape
+        assert k is not None
+        assert data_shape is not None
+        self.k = k
+        self.data_shape = data_shape
+        self._grid = None
+        self._queue = defaultdict(list)
+
+    def add(self, index, rank, data):
+        if self._grid is None:
+            # Allocation: pick up data shape from add.
+            shape = self.data_shape
+            if isinstance(shape, int):
+                shape = (shape,)
+            shape = shape + (self.k,) + data.shape
+            self._grid = torch.zeros(shape, dtype=data.dtype)
+        self._queue[index].append((rank, data))
+        if len(self._queue) > len(self._grid) // 2:
+            self._flush_queue()
+
+    def _flush_queue(self):
+        if len(self._queue):
+            for index in sorted(self._queue.keys()):
+                for rank, data in self._queue[index]:
+                    self._grid[index][rank] = data
+            self._queue.clear()
+
+    def to_(self, device):
+        self._flush_queue()
+        if self._grid is not None:
+            self._grid = self._grid.to(device)
+
+    def state_dict(self):
+        self._flush_queue()
+        return dict(
+                constructor=self.__module__ + '.' +
+                    self.__class__.__name__ + '()',
+                k=self.k,
+                data_shape=tuple(self.data_shape),
+                grid=self._grid.cpu().numpy())
+
+    def result(self):
+        self._flush_queue()
+        return self._grid
+
+    def set_state_dict(self, dic):
+        self.k = dic['k'].item()
+        self.data_shape = tuple(dic['data_shape'])
+        self._grid = torch.from_numpy(dic['grid'])
+        self._queue = defaultdict(list)
+
+class RunningQuantile:
+    """
+    Streaming randomized quantile computation for torch.
+
+    Add any amount of data repeatedly via add(data).  At any time,
+    quantile estimates (or old-style percentiles) can be read out using
+    quantiles(q) or percentiles(p).
+
+    Implemented as a sorted sample that retains at least r samples
+    (by default r = 3072); the number of retained samples will grow to
+    a finite ceiling as the data is accumulated.  Accuracy scales according
+    to r: the default is to set resolution to be accurate to better than about
+    0.1%, while limiting storage to about 50,000 samples.
+
+    Good for computing quantiles of huge data without using much memory.
+    Works well on arbitrary data with probability near 1.
+
+    Based on the optimal KLL quantile algorithm by Karnin, Lang, and Liberty
+    from FOCS 2016.  http://ieee-focs.org/FOCS-2016-Papers/3933a071.pdf
+    """
+
+    def __init__(self, r=3 * 1024, buffersize=None, seed=None,
+            state=None):
+        if state is not None:
+            self.set_state_dict(state)
+            return
+        self.depth = None
+        self.dtype = None
+        self.device = None
+        resolution = r * 2 # sample array is at least half full before discard
+        self.resolution = resolution
+        # Default buffersize: 128 samples (and smaller than resolution).
+        if buffersize is None:
+            buffersize = min(128, (resolution + 7) // 8)
+        self.buffersize = buffersize
+        self.samplerate = 1.0
+        self.data = None
+        self.firstfree = [0]
+        self.randbits = torch.ByteTensor(resolution)
+        self.currentbit = len(self.randbits) - 1
+        self.extremes = None
+        self.count = 0
+        self.batchcount = 0
+
+    def size(self):
+        return self.count
+
+    def _lazy_init(self, incoming):
+        self.depth = incoming.shape[1]
+        self.dtype = incoming.dtype
+        self.device = incoming.device
+        self.data = [torch.zeros(self.depth, self.resolution,
+            dtype=self.dtype, device=self.device)]
+        self.extremes = torch.zeros(self.depth, 2,
+                dtype=self.dtype, device=self.device)
+        self.extremes[:,0] = float('inf')
+        self.extremes[:,-1] = -float('inf')
+
+    def to_(self, device):
+        """Switches internal storage to specified device."""
+        if device != self.device:
+            old_data = self.data
+            old_extremes = self.extremes
+            self.data = [d.to(device) for d in self.data]
+            self.extremes = self.extremes.to(device)
+            self.device = self.extremes.device
+            del old_data
+            del old_extremes
+
+    def add(self, incoming):
+        if self.depth is None:
+            self._lazy_init(incoming)
+        assert len(incoming.shape) == 2
+        assert incoming.shape[1] == self.depth, (incoming.shape[1], self.depth)
+        self.count += incoming.shape[0]
+        self.batchcount += 1
+        # Convert to a flat torch array.
+        if self.samplerate >= 1.0:
+            self._add_every(incoming)
+            return
+        # If we are sampling, then subsample a large chunk at a time.
+        self._scan_extremes(incoming)
+        chunksize = int(math.ceil(self.buffersize / self.samplerate))
+        for index in range(0, len(incoming), chunksize):
+            batch = incoming[index:index+chunksize]
+            sample = sample_portion(batch, self.samplerate)
+            if len(sample):
+                self._add_every(sample)
+
+    def _add_every(self, incoming):
+        supplied = len(incoming)
+        index = 0
+        while index < supplied:
+            ff = self.firstfree[0]
+            available = self.data[0].shape[1] - ff
+            if available == 0:
+                if not self._shift():
+                    # If we shifted by subsampling, then subsample.
+                    incoming = incoming[index:]
+                    if self.samplerate >= 0.5:
+                        # First time sampling - the data source is very large.
+                        self._scan_extremes(incoming)
+                    incoming = sample_portion(incoming, self.samplerate)
+                    index = 0
+                    supplied = len(incoming)
+                ff = self.firstfree[0]
+                available = self.data[0].shape[1] - ff
+            copycount = min(available, supplied - index)
+            self.data[0][:,ff:ff + copycount] = torch.t(
+                    incoming[index:index + copycount,:])
+            self.firstfree[0] += copycount
+            index += copycount
+
+    def _shift(self):
+        index = 0
+        # If remaining space at the current layer is less than half prev
+        # buffer size (rounding up), then we need to shift it up to ensure
+        # enough space for future shifting.
+        while self.data[index].shape[1] - self.firstfree[index] < (
+                -(-self.data[index-1].shape[1] // 2) if index else 1):
+            if index + 1 >= len(self.data):
+                return self._expand()
+            data = self.data[index][:,0:self.firstfree[index]]
+            data = data.sort()[0]
+            if index == 0 and self.samplerate >= 1.0:
+                self._update_extremes(data[:,0], data[:,-1])
+            offset = self._randbit()
+            position = self.firstfree[index + 1]
+            subset = data[:,offset::2]
+            self.data[index + 1][:,position:position + subset.shape[1]] = subset
+            self.firstfree[index] = 0
+            self.firstfree[index + 1] += subset.shape[1]
+            index += 1
+        return True
+
+    def _scan_extremes(self, incoming):
+        # When sampling, we need to scan every item still to get extremes
+        self._update_extremes(
+                torch.min(incoming, dim=0)[0],
+                torch.max(incoming, dim=0)[0])
+
+    def _update_extremes(self, minr, maxr):
+        self.extremes[:,0] = torch.min(
+                torch.stack([self.extremes[:,0], minr]), dim=0)[0]
+        self.extremes[:,-1] = torch.max(
+                torch.stack([self.extremes[:,-1], maxr]), dim=0)[0]
+
+    def _randbit(self):
+        self.currentbit += 1
+        if self.currentbit >= len(self.randbits):
+            self.randbits.random_(to=2)
+            self.currentbit = 0
+        return self.randbits[self.currentbit]
+
+    def state_dict(self):
+        return dict(
+                constructor=self.__module__ + '.' +
+                    self.__class__.__name__ + '()',
+                resolution=self.resolution,
+                depth=self.depth,
+                buffersize=self.buffersize,
+                samplerate=self.samplerate,
+                data=[d.cpu().numpy()[:,:f].T
+                    for d, f in zip(self.data, self.firstfree)],
+                sizes=[d.shape[1] for d in self.data],
+                extremes=self.extremes.cpu().numpy(),
+                size=self.count,
+                batchcount=self.batchcount)
+
+    def set_state_dict(self, dic):
+        self.resolution = int(dic['resolution'])
+        self.randbits = torch.ByteTensor(self.resolution)
+        self.currentbit = len(self.randbits) - 1
+        self.depth = int(dic['depth'])
+        self.buffersize = int(dic['buffersize'])
+        self.samplerate = float(dic['samplerate'])
+        firstfree = []
+        buffers = []
+        for d, s in zip(dic['data'], dic['sizes']):
+            firstfree.append(d.shape[0])
+            buf = numpy.zeros((d.shape[1], s), dtype=d.dtype)
+            buf[:,:d.shape[0]] = d.T
+            buffers.append(torch.from_numpy(buf))
+        self.firstfree = firstfree
+        self.data = buffers
+        self.extremes = torch.from_numpy((dic['extremes']))
+        self.count = int(dic['size'])
+        self.batchcount = int(dic.get('batchcount', 0))
+        self.dtype = self.extremes.dtype
+        self.device = self.extremes.device
+
+    def minmax(self):
+        if self.firstfree[0]:
+            self._scan_extremes(self.data[0][:,:self.firstfree[0]].t())
+        return self.extremes.clone()
+
+    def median(self):
+        return self.quantiles([0.5])[:,0]
+
+    def mean(self):
+        return self.integrate(lambda x: x) / self.count
+
+    def variance(self):
+        mean = self.mean()[:,None]
+        return self.integrate(lambda x: (x - mean).pow(2)) / (self.count - 1)
+
+    def stdev(self):
+        return self.variance().sqrt()
+
+    def _expand(self):
+        cap = self._next_capacity()
+        if cap > 0:
+            # First, make a new layer of the proper capacity.
+            self.data.insert(0, torch.zeros(self.depth, cap,
+                dtype=self.dtype, device=self.device))
+            self.firstfree.insert(0, 0)
+        else:
+            # Unless we're so big we are just subsampling.
+            assert self.firstfree[0] == 0
+            self.samplerate *= 0.5
+        for index in range(1, len(self.data)):
+            # Scan for existing data that needs to be moved down a level.
+            amount = self.firstfree[index]
+            if amount == 0:
+                continue
+            position = self.firstfree[index-1]
+            # Move data down if it would leave enough empty space there
+            # This is the key invariant: enough empty space to fit half
+            # of the previous level's buffer size (rounding up)
+            if self.data[index-1].shape[1] - (amount + position) >= (
+                    -(-self.data[index-2].shape[1] // 2) if (index-1) else 1):
+                self.data[index-1][:,position:position + amount] = (
+                        self.data[index][:,:amount])
+                self.firstfree[index-1] += amount
+                self.firstfree[index] = 0
+            else:
+                # Scrunch the data if it would not.
+                data = self.data[index][:,:amount]
+                data = data.sort()[0]
+                if index == 1:
+                    self._update_extremes(data[:,0], data[:,-1])
+                offset = self._randbit()
+                scrunched = data[:,offset::2]
+                self.data[index][:,:scrunched.shape[1]] = scrunched
+                self.firstfree[index] = scrunched.shape[1]
+        return cap > 0
+
+    def _next_capacity(self):
+        cap = int(math.ceil(self.resolution * (0.67 ** len(self.data))))
+        if cap < 2:
+            return 0
+        # Round up to the nearest multiple of 8 for better GPU alignment.
+        cap = -8 * (-cap // 8)
+        return max(self.buffersize, cap)
+
+    def _weighted_summary(self, sort=True):
+        if self.firstfree[0]:
+            self._scan_extremes(self.data[0][:,:self.firstfree[0]].t())
+        size = sum(self.firstfree)
+        weights = torch.FloatTensor(size) # Floating point
+        summary = torch.zeros(self.depth, size,
+                dtype=self.dtype, device=self.device)
+        index = 0
+        for level, ff in enumerate(self.firstfree):
+            if ff == 0:
+                continue
+            summary[:,index:index + ff] = self.data[level][:,:ff]
+            weights[index:index + ff] = 2.0 ** level
+            index += ff
+        assert index == summary.shape[1]
+        if sort:
+            summary, order = torch.sort(summary, dim=-1)
+            weights = weights[order.view(-1).cpu()].view(order.shape)
+            summary = torch.cat(
+                    [self.extremes[:,:1], summary,
+                        self.extremes[:,1:]], dim=-1)
+            weights = torch.cat(
+                    [torch.zeros(weights.shape[0], 1), weights,
+                        torch.zeros(weights.shape[0], 1)], dim=-1)
+        return (summary, weights)
+
+    def quantiles(self, quantiles, old_style=False):
+        if not hasattr(quantiles, 'cpu'):
+            quantiles = torch.tensor(quantiles)
+        qshape = quantiles.shape
+        if self.count == 0:
+            return torch.full((self.depth,) + qshape, torch.nan)
+        summary, weights = self._weighted_summary()
+        cumweights = torch.cumsum(weights, dim=-1) - weights / 2
+        if old_style:
+            # To be convenient with torch.percentile
+            cumweights -= cumweights[:,0:1].clone()
+            cumweights /= cumweights[:,-1:].clone()
+        else:
+            cumweights /= torch.sum(weights, dim=-1, keepdim=True)
+        result = torch.zeros(self.depth, quantiles.numel(),
+                dtype=self.dtype, device=self.device)
+        # numpy is needed for interpolation
+        nq = quantiles.view(-1).cpu().numpy()
+        ncw = cumweights.cpu().numpy()
+        nsm = summary.cpu().numpy()
+        for d in range(self.depth):
+            result[d] = torch.tensor(numpy.interp(nq, ncw[d], nsm[d]),
+                    dtype=self.dtype, device=self.device)
+        return result.view((self.depth,) + qshape)
+
+    def integrate(self, fun):
+        result = None
+        for level, ff in enumerate(self.firstfree):
+            if ff == 0:
+                continue
+            term = torch.sum(
+                    fun(self.data[level][:,:ff]) * (2.0 ** level),
+                    dim=-1)
+            if result is None:
+                result = term
+            else:
+                result += term
+        if result is not None:
+            result /= self.samplerate
+        return result
+
+    def percentiles(self, percentiles):
+        return self.quantiles(percentiles, old_style=True)
+
+    def readout(self, count=1001, old_style=True):
+        return self.quantiles(
+                torch.linspace(0.0, 1.0, count), old_style=old_style)
+
+    def normalize(self, data):
+        '''
+        Given input data as taken from the training distirbution,
+        normalizes every channel to reflect quantile values,
+        uniformly distributed, within [0, 1].
+        '''
+        assert self.count > 0
+        assert data.shape[0] == self.depth
+        summary, weights = self._weighted_summary()
+        cumweights = torch.cumsum(weights, dim=-1) - weights / 2
+        cumweights /= torch.sum(weights, dim=-1, keepdim=True)
+        result = torch.zeros_like(data).float()
+        # numpy is needed for interpolation
+        ndata = data.cpu().numpy().reshape((data.shape[0], -1))
+        ncw = cumweights.cpu().numpy()
+        nsm = summary.cpu().numpy()
+        for d in range(self.depth):
+            normed = torch.tensor(numpy.interp(ndata[d], nsm[d], ncw[d]),
+                dtype=torch.float, device=data.device).clamp_(0.0, 1.0)
+            if len(data.shape) > 1:
+                normed = normed.view(*(data.shape[1:]))
+            result[d] = normed
+        return result
+
+
+class RunningConditionalQuantile:
+    '''
+    Equivalent to a map from conditions (any python hashable type)
+    to RunningQuantiles.  The reason for the type is to allow limited
+    GPU memory to be exploited while counting quantile stats on many
+    different conditions, a few of which are common and which benefit
+    from GPU, but most of which are rare and would not all fit into
+    GPU RAM.
+
+    To move a set of conditions to a device, use rcq.to_(device, conds).
+    Then in the future, move the tallied data to the device before
+    calling rcq.add, that is, rcq.add(cond, data.to(device)).
+
+    To allow the caller to decide which conditions to allow to use GPU,
+    rcq.most_common_conditions(n) returns a list of the n most commonly
+    added conditions so far.
+    '''
+    def __init__(self, r=3 * 1024, buffersize=None, seed=None,
+            state=None):
+        self.first_rq = None
+        self.call_stats = defaultdict(int)
+        self.running_quantiles = {}
+        if state is not None:
+            self.set_state_dict(state)
+            return
+        self.rq_args = dict(r=r, buffersize=buffersize,
+                seed=seed)
+
+    def add(self, condition, incoming):
+        if condition not in self.running_quantiles:
+            self.running_quantiles[condition] = RunningQuantile(**self.rq_args)
+            if self.first_rq is None:
+                self.first_rq = self.running_quantiles[condition]
+        self.call_stats[condition] += 1
+        rq = self.running_quantiles[condition]
+        # For performance reasons, the caller can move some conditions to
+        # the CPU if they are not among the most common conditions.
+        if rq.device is not None and (rq.device != incoming.device):
+            rq.to_(incoming.device)
+        self.running_quantiles[condition].add(incoming)
+
+    def most_common_conditions(self, n):
+        return sorted(self.call_stats.keys(),
+                key=lambda c: -self.call_stats[c])[:n]
+
+    def collected_add(self, conditions, incoming):
+        for c in conditions:
+            self.add(c, incoming)
+
+    def keys(self):
+        return self.running_quantiles.keys()
+
+    def sizes(self):
+        return {k: self.running_quantiles[k].size() for k in self.keys()}
+
+    def conditional(self, c):
+        return self.running_quantiles[c]
+
+    def has_conditional(self, c):
+        return c in self.running_quantiles
+
+    def collected_quantiles(self, conditions, quantiles, old_style=False):
+        result = torch.zeros(
+                size=(len(conditions), self.first_rq.depth, len(quantiles)),
+                dtype=self.first_rq.dtype,
+                device=self.first_rq.device)
+        for i, c in enumerate(conditions):
+            if c in self.running_quantiles:
+                result[i] = self.running_quantiles[c].quantiles(
+                        quantiles, old_style)
+        return result
+
+    def collected_normalize(self, conditions, values):
+        result = torch.zeros(
+                size=(len(conditions), values.shape[0], values.shape[1]),
+                dtype=torch.float,
+                device=self.first_rq.device)
+        for i, c in enumerate(conditions):
+            if c in self.running_quantiles:
+                result[i] = self.running_quantiles[c].normalize(values)
+        return result
+
+    def to_(self, device, conditions=None):
+        if conditions is None:
+            conditions = self.keys()
+        for cond in conditions:
+            if cond in self.running_quantiles:
+                self.running_quantiles[cond].to_(device)
+
+    def state_dict(self):
+        conditions = sorted(self.running_quantiles.keys())
+        result = dict(
+                constructor=self.__module__ + '.' +
+                    self.__class__.__name__ + '()',
+                rq_args=self.rq_args,
+                conditions=conditions)
+        for i, c in enumerate(conditions):
+            result.update({
+                '%d.%s' % (i, k): v
+                for k, v in self.running_quantiles[c].state_dict().items()})
+        return result
+
+    def set_state_dict(self, dic):
+        self.rq_args = dic['rq_args'].item()
+        conditions = list(dic['conditions'])
+        subdicts = defaultdict(dict)
+        for k, v in dic.items():
+            if '.' in k:
+                p, s = k.split('.', 1)
+                subdicts[p][s] = v
+        self.running_quantiles = {
+                c: RunningQuantile(state=subdicts[str(i)])
+                for i, c in enumerate(conditions)}
+        if conditions:
+            self.first_rq = self.running_quantiles[conditions[0]]
+
+    # example usage:
+    # levels = rqc.conditional(()).quantiles(1 - fracs)
+    # denoms = 1 - rqc.collected_normalize(cats, levels)
+    # isects = 1 - rqc.collected_normalize(labels, levels)
+    # unions = fracs + denoms[cats] - isects
+    # iou = isects / unions
+
+
+class RunningVariance:
+    '''
+    Running computation of mean and variance. Use this when you just need
+    basic stats without covariance.
+    '''
+    def __init__(self, state=None):
+        if state is not None:
+            self.set_state_dict(state)
+            return
+        self.count = 0
+        self.batchcount = 0
+        self._mean = None
+        self.v_cmom2 = None
+
+    def add(self, a):
+        if len(a.shape) == 1:
+            a = a[None, :]
+        if len(a.shape) > 2:
+            a = (a.view(a.shape[0], a.shape[1], -1).permute(0, 2, 1)
+                    .contiguous().view(-1, a.shape[1]))
+        batch_count = a.shape[0]
+        batch_mean = a.sum(0) / batch_count
+        centered = a - batch_mean
+        self.batchcount += 1
+        # Initial batch.
+        if self._mean is None:
+            self.count = batch_count
+            self._mean = batch_mean
+            self.v_cmom2 = centered.pow(2).sum(0)
+            return
+        # Update a batch using Chan-style update for numerical stability.
+        oldcount = self.count
+        self.count += batch_count
+        new_frac = float(batch_count) / self.count
+        # Update the mean according to the batch deviation from the old mean.
+        delta = batch_mean.sub_(self._mean).mul_(new_frac)
+        self._mean.add_(delta)
+        # Update the variance using the batch deviation
+        self.v_cmom2.add_(centered.pow(2).sum(0))
+        self.v_cmom2.add_(delta.pow_(2).mul_(new_frac * oldcount))
+
+    def size(self):
+        return self.count
+
+    def mean(self):
+        return self._mean
+
+    def variance(self):
+        return self.v_cmom2 / (self.count - 1)
+
+    def stdev(self):
+        return self.variance().sqrt()
+
+    def to_(self, device):
+        self._mean = self._mean.to(device)
+        self.v_cmom2 = self.v_cmom2.to(device)
+
+    def state_dict(self):
+        return dict(
+                constructor=self.__module__ + '.' +
+                    self.__class__.__name__ + '()',
+                count=self.count,
+                batchcount=self.batchcount,
+                mean=self._mean.cpu().numpy(),
+                cmom2=self.v_cmom2.cpu().numpy())
+
+    def set_state_dict(self, dic):
+        self.count = dic['count'].item()
+        self.batchcount = dic['batchcount'].item()
+        self._mean = torch.from_numpy(dic['mean'])
+        self.v_cmom2 = torch.from_numpy(dic['cmom2'])
+
+
+class RunningConditionalVariance:
+    def __init__(self, state=None):
+        self.running_var = {}
+        if state is not None:
+            self.set_state_dict(state)
+            return
+
+    def add(self, condition, incoming):
+        if condition not in self.running_var:
+            self.running_var[condition] = RunningVariance()
+        rv = self.running_var[condition]
+        rv.add(incoming)
+
+    def collected_add(self, conditions, incoming):
+        for c in conditions:
+            self.add(c, incoming)
+
+    def keys(self):
+        return self.running_var.keys()
+
+    def conditional(self, c):
+        return self.running_var[c]
+
+    def has_conditional(self, c):
+        return c in self.running_var
+
+    def to_(self, device, conditions=None):
+        if conditions is None:
+            conditions = self.keys()
+        for cond in conditions:
+            if cond in self.running_var:
+                self.running_var[cond].to_(device)
+
+    def state_dict(self):
+        conditions = sorted(self.running_var.keys())
+        result = dict(
+                constructor=self.__module__ + '.' +
+                    self.__class__.__name__ + '()',
+                conditions=conditions)
+        for i, c in enumerate(conditions):
+            result.update({
+                '%d.%s' % (i, k): v
+                for k, v in self.running_var[c].state_dict().items()})
+        return result
+
+    def set_state_dict(self, dic):
+        conditions = list(dic['conditions'])
+        subdicts = defaultdict(dict)
+        for k, v in dic.items():
+            if '.' in k:
+                p, s = k.split('.', 1)
+                subdicts[p][s] = v
+        self.running_var = {
+                c: RunningVariance(state=subdicts[str(i)])
+                for i, c in enumerate(conditions)}
+
+class RunningCrossCovariance:
+    '''
+    Running computation. Use this when an off-diagonal block of the
+    covariance matrix is needed (e.g., when the whole covariance matrix
+    does not fit in the GPU).
+
+    Chan-style numerically stable update of mean and full covariance matrix.
+    Chan, Golub. LeVeque. 1983. http://www.jstor.org/stable/2683386
+    '''
+    def __init__(self, state=None):
+        if state is not None:
+            self.set_state_dict(state)
+            return
+        self.count = 0
+        self._mean = None
+        self.cmom2 = None
+        self.v_cmom2 = None
+
+    def add(self, a, b):
+        if len(a.shape) == 1:
+            a = a[None, :]
+            b = b[None, :]
+        assert(a.shape[0] == b.shape[0])
+        if len(a.shape) > 2:
+            a, b = [d.view(d.shape[0], d.shape[1], -1).permute(0, 2, 1
+                ).contiguous().view(-1, d.shape[1]) for d in [a, b]]
+        batch_count = a.shape[0]
+        batch_mean = [d.sum(0) / batch_count for d in [a, b]]
+        centered = [d - bm for d, bm in zip([a, b], batch_mean)]
+        # If more than 10 billion operations, divide into batches.
+        sub_batch = -(-(10 << 30) // (a.shape[1] * b.shape[1]))
+        # Initial batch.
+        if self._mean is None:
+            self.count = batch_count
+            self._mean = batch_mean
+            self.v_cmom2 = [c.pow(2).sum(0) for c in centered]
+            self.cmom2 = a.new(a.shape[1], b.shape[1]).zero_()
+            progress_addbmm(self.cmom2, centered[0][:,:,None],
+                    centered[1][:,None,:], sub_batch)
+            return
+        # Update a batch using Chan-style update for numerical stability.
+        oldcount = self.count
+        self.count += batch_count
+        new_frac = float(batch_count) / self.count
+        # Update the mean according to the batch deviation from the old mean.
+        delta = [bm.sub_(m).mul_(new_frac)
+                for bm, m in zip(batch_mean, self._mean)]
+        for m, d in zip(self._mean, delta):
+            m.add_(d)
+        # Update the cross-covariance using the batch deviation
+        progress_addbmm(self.cmom2, centered[0][:,:,None],
+                centered[1][:,None,:], sub_batch)
+        self.cmom2.addmm_(alpha=new_frac * oldcount,
+                mat1=delta[0][:,None], mat2=delta[1][None,:])
+        # Update the variance using the batch deviation
+        for c, vc2, d in zip(centered, self.v_cmom2, delta):
+            vc2.add_(c.pow(2).sum(0))
+            vc2.add_(d.pow_(2).mul_(new_frac * oldcount))
+
+    def mean(self):
+        return self._mean
+
+    def variance(self):
+        return [vc2 / (self.count - 1) for vc2 in self.v_cmom2]
+
+    def stdev(self):
+        return [v.sqrt() for v in self.variance()]
+
+    def covariance(self):
+        return self.cmom2 / (self.count - 1)
+
+    def correlation(self):
+        covariance = self.covariance()
+        rstdev = [s.reciprocal() for s in self.stdev()]
+        cor = rstdev[0][:,None] * covariance * rstdev[1][None,:]
+        # Remove NaNs
+        cor[torch.isnan(cor)] = 0
+        return cor
+
+    def to_(self, device):
+        self._mean = [m.to(device) for m in self._mean]
+        self.v_cmom2 = [vcs.to(device) for vcs in self.v_cmom2]
+        self.cmom2 = self.cmom2.to(device)
+
+    def state_dict(self):
+        return dict(
+                constructor=self.__module__ + '.' +
+                    self.__class__.__name__ + '()',
+                count=self.count,
+                mean_a=self._mean[0].cpu().numpy(),
+                mean_b=self._mean[1].cpu().numpy(),
+                cmom2_a=self.v_cmom2[0].cpu().numpy(),
+                cmom2_b=self.v_cmom2[1].cpu().numpy(),
+                cmom2=self.cmom2.cpu().numpy())
+
+    def set_state_dict(self, dic):
+        self.count = dic['count'].item()
+        self._mean = [torch.from_numpy(dic[k]) for k in ['mean_a', 'mean_b']]
+        self.v_cmom2 = [torch.from_numpy(dic[k])
+                for k in ['cmom2_a', 'cmom2_b']]
+        self.cmom2 = torch.from_numpy(dic['cmom2'])
+
+class RunningCovariance:
+    '''
+    Running computation. Use this when the entire covariance matrix is needed,
+    and when the whole covariance matrix fits in the GPU.
+
+    Chan-style numerically stable update of mean and full covariance matrix.
+    Chan, Golub. LeVeque. 1983. http://www.jstor.org/stable/2683386
+    '''
+    def __init__(self, state=None):
+        if state is not None:
+            self.set_state_dict(state)
+            return
+        self.count = 0
+        self._mean = None
+        self.cmom2 = None
+
+    def add(self, a):
+        if len(a.shape) == 1:
+            a = a[None, :]
+        batch_count = a.shape[0]
+        batch_mean = a.sum(0) / batch_count
+        centered = a - batch_mean
+        # If more than 10 billion operations, divide into batches.
+        sub_batch = -(-(10 << 30) // (a.shape[1] * a.shape[1]))
+        # Initial batch.
+        if self._mean is None:
+            self.count = batch_count
+            self._mean = batch_mean
+            self.cmom2 = a.new(a.shape[1], a.shape[1]).zero_()
+            progress_addbmm(self.cmom2, centered[:,:,None], centered[:,None,:],
+                    sub_batch)
+            return
+        # Update a batch using Chan-style update for numerical stability.
+        oldcount = self.count
+        self.count += batch_count
+        new_frac = float(batch_count) / self.count
+        # Update the mean according to the batch deviation from the old mean.
+        delta = batch_mean.sub_(self._mean).mul_(new_frac)
+        self._mean.add_(delta)
+        # Update the variance using the batch deviation
+        progress_addbmm(self.cmom2, centered[:,:,None], centered[:,None,:],
+                    sub_batch)
+        self.cmom2.addmm_(
+            alpha=new_frac * oldcount, mat1=delta[:,None], mat2=delta[None,:])
+
+    def cpu_(self):
+        self._mean = self._mean.cpu()
+        self.cmom2 = self.cmom2.cpu()
+
+    def cuda_(self):
+        self._mean = self._mean.cuda()
+        self.cmom2 = self.cmom2.cuda()
+
+    def to_(self, device):
+        self._mean, self.cmom2 = [m.to(device)
+                for m in [self._mean, self.cmom2]]
+
+    def mean(self):
+        return self._mean
+
+    def covariance(self):
+        return self.cmom2 / self.count
+
+    def correlation(self):
+        covariance = self.covariance()
+        rstdev = covariance.diag().sqrt().reciprocal()
+        return rstdev[:,None] * covariance * rstdev[None,:]
+
+    def variance(self):
+        return self.covariance().diag()
+
+    def stdev(self):
+        return self.variance().sqrt()
+
+    def state_dict(self):
+        return dict(
+                constructor=self.__module__ + '.' +
+                    self.__class__.__name__ + '()',
+                count=self.count,
+                mean=self._mean.cpu().numpy(),
+                cmom2=self.cmom2.cpu().numpy())
+
+    def set_state_dict(self, dic):
+        self.count = dic['count'].item()
+        self._mean = torch.from_numpy(dic['mean'])
+        self.cmom2 = torch.from_numpy(dic['cmom2'])
+
+class RunningSecondMoment:
+    '''
+    Running computation. Use this when the entire non-centered 2nd-moment
+    "covariance-like" matrix is needed, and when the whole matrix fits
+    in the GPU.
+    '''
+    def __init__(self, state=None):
+        if state is not None:
+            self.set_state_dict(state)
+            return
+        self.count = 0
+        self.mom2 = None
+
+    def add(self, a):
+        if len(a.shape) == 1:
+            a = a[None, :]
+        # Initial batch reveals the shape of the data.
+        if self.count == 0:
+            self.mom2 = a.new(a.shape[1], a.shape[1]).zero_()
+        batch_count = a.shape[0]
+        # If more than 10 billion operations, divide into batches.
+        sub_batch = -(-(10 << 30) // (a.shape[1] * a.shape[1]))
+        # Update the covariance using the batch deviation
+        self.count += batch_count
+        progress_addbmm(self.mom2, a[:,:,None], a[:,None,:], sub_batch)
+
+    def cpu_(self):
+        self.mom2 = self.mom2.cpu()
+
+    def cuda_(self):
+        self.mom2 = self.mom2.cuda()
+
+    def to_(self, device):
+        self.mom2 = self.mom2.to(device)
+
+    def moment(self):
+        return self.mom2 / self.count
+
+    def state_dict(self):
+        return dict(
+                constructor=self.__module__ + '.' +
+                    self.__class__.__name__ + '()',
+                count=self.count,
+                mom2=self.mom2.cpu().numpy())
+
+    def set_state_dict(self, dic):
+        self.count = dic['count'].item()
+        self.mom2 = torch.from_numpy(dic['mom2'])
+
+class RunningBincount:
+    '''
+    Running bincount.  The counted array should be an integer type with
+    non-negative integers.  Also
+    '''
+    def __init__(self, state=None):
+        if state is not None:
+            self.set_state_dict(state)
+            return
+        self.count = 0
+        self._bincount = None
+
+    def add(self, a, size=None):
+        a = a.view(-1)
+        bincount = a.bincount()
+        if self._bincount is None:
+            self._bincount = bincount
+        elif len(self._bincount) < len(bincount):
+            bincount[:len(self._bincount)] += self._bincount
+            self._bincount = bincount
+        else:
+            self._bincount[:len(bincount)] += bincount
+        if size is None:
+            self.count += len(a)
+        else:
+            self.count += size
+
+    def cpu_(self):
+        self._bincount = self._bincount.cpu()
+
+    def cuda_(self):
+        self._bincount = self._bincount.cuda()
+
+    def to_(self, device):
+        self._bincount = self._bincount.to(device)
+
+    def size(self):
+        return self.count
+
+    def mean(self):
+        return (self._bincount).float() / self.count
+
+    def bincount(self):
+        return self._bincount
+
+    def state_dict(self):
+        return dict(
+                constructor=self.__module__ + '.' +
+                    self.__class__.__name__ + '()',
+                count=self.count,
+                bincount=self._bincount.cpu().numpy())
+
+    def set_state_dict(self, dic):
+        self.count = dic['count'].item()
+        self._bincount = torch.from_numpy(dic['bincount'])
+
+def progress_addbmm(accum, x, y, batch_size):
+    '''
+    Break up very large adbmm operations into batches so progress can be seen.
+    '''
+    from . import pbar
+    if x.shape[0] <= batch_size:
+        return accum.addbmm_(x, y)
+    for i in pbar(range(0, x.shape[0], batch_size), desc='bmm'):
+        accum.addbmm_(x[i:i+batch_size], y[i:i+batch_size])
+    return accum
+
+
+def sample_portion(vec, p=0.5):
+    bits = torch.bernoulli(torch.zeros(vec.shape[0], dtype=torch.uint8,
+        device=vec.device), p)
+    return vec[bits]
+
+if __name__ == '__main__':
+    import warnings
+    warnings.filterwarnings("error")
+    import time
+    import argparse
+    parser = argparse.ArgumentParser(
+        description='Test things out')
+    parser.add_argument('--mode', default='cpu', help='cpu or cuda')
+    parser.add_argument('--test_size', type=int, default=1000000)
+    args = parser.parse_args()
+
+    # An adverarial case: we keep finding more numbers in the middle
+    # as the stream goes on.
+    amount = args.test_size
+    quantiles = 1000
+    data = numpy.arange(float(amount))
+    data[1::2] = data[-1::-2] + (len(data) - 1)
+    data /= 2
+    depth = 50
+    test_cuda = torch.cuda.is_available()
+    alldata = data[:,None] + (numpy.arange(depth) * amount)[None, :]
+    actual_sum = torch.FloatTensor(numpy.sum(alldata * alldata, axis=0))
+    amt = amount // depth
+    for r in range(depth):
+        numpy.random.shuffle(alldata[r*amt:r*amt+amt,r])
+    if args.mode == 'cuda':
+        alldata = torch.cuda.FloatTensor(alldata)
+        dtype = torch.float
+        device = torch.device('cuda')
+    else:
+        alldata = torch.FloatTensor(alldata)
+        dtype = torch.float
+        device = None
+    starttime = time.time()
+    qc = RunningQuantile(r=3 * 1024)
+    qc.add(alldata)
+    # Test state dict
+    saved = qc.state_dict()
+    # numpy.savez('foo.npz', **saved)
+    # saved = numpy.load('foo.npz')
+    qc = RunningQuantile(state=saved)
+    assert not qc.device.type == 'cuda'
+    qc.add(alldata)
+    actual_sum *= 2
+    ro = qc.readout(1001).cpu()
+    endtime = time.time()
+    gt = torch.linspace(0, amount, quantiles+1)[None,:] + (
+            torch.arange(qc.depth, dtype=torch.float) * amount)[:,None]
+    maxreldev = torch.max(torch.abs(ro - gt) / amount) * quantiles
+    print("Maximum relative deviation among %d perentiles: %f" % (
+        quantiles, maxreldev))
+    minerr = torch.max(torch.abs(qc.minmax().cpu()[:,0] -
+            torch.arange(qc.depth, dtype=torch.float) * amount))
+    maxerr = torch.max(torch.abs((qc.minmax().cpu()[:, -1] + 1) -
+            (torch.arange(qc.depth, dtype=torch.float) + 1) * amount))
+    print("Minmax error %f, %f" % (minerr, maxerr))
+    interr = torch.max(torch.abs(qc.integrate(lambda x: x * x).cpu()
+            - actual_sum) / actual_sum)
+    print("Integral error: %f" % interr)
+    medianerr = torch.max(torch.abs(qc.median() -
+        alldata.median(0)[0]) / alldata.median(0)[0]).cpu()
+    print("Median error: %f" % interr)
+    meanerr = torch.max(
+            torch.abs(qc.mean() - alldata.mean(0)) / alldata.mean(0)).cpu()
+    print("Mean error: %f" % meanerr)
+    varerr = torch.max(
+            torch.abs(qc.variance() - alldata.var(0)) / alldata.var(0)).cpu()
+    print("Variance error: %f" % varerr)
+    counterr = ((qc.integrate(lambda x: torch.ones(x.shape[-1]).cpu())
+                - qc.size()) / (0.0 + qc.size())).item()
+    print("Count error: %f" % counterr)
+    print("Time %f" % (endtime - starttime))
+    # Algorithm is randomized, so some of these will fail with low probability.
+    assert maxreldev < 1.0
+    assert minerr == 0.0
+    assert maxerr == 0.0
+    assert interr < 0.01
+    assert abs(counterr) < 0.001
+    print("OK")
diff --git a/netdissect/samplegan.py b/netdissect/samplegan.py
new file mode 100644
index 0000000000000000000000000000000000000000..e7c82b5fea7158718f62bf7805a2940b69ae48ff
--- /dev/null
+++ b/netdissect/samplegan.py
@@ -0,0 +1,100 @@
+'''
+A simple tool to generate a sample of output of a GAN.
+'''
+
+import torch, numpy, os, argparse, numbers, sys, shutil
+from PIL import Image
+from torch.utils.data import TensorDataset
+from .zdataset import standard_z_sample
+from . import pbar
+from .autoeval import autoimport_eval
+from .workerpool import WorkerBase, WorkerPool
+
+def main():
+    parser = argparse.ArgumentParser(description='GAN sample making utility')
+    parser.add_argument('--model', type=str, default=None,
+            help='constructor for the model to test')
+    parser.add_argument('--pthfile', type=str, default=None,
+            help='filename of .pth file for the model')
+    parser.add_argument('--outdir', type=str, default='images',
+            help='directory for image output')
+    parser.add_argument('--size', type=int, default=100,
+            help='number of images to output')
+    parser.add_argument('--seed', type=int, default=1,
+            help='seed')
+    parser.add_argument('--quiet', action='store_true', default=False,
+            help='silences console output')
+    if len(sys.argv) == 1:
+        parser.print_usage(sys.stderr)
+        sys.exit(1)
+    args = parser.parse_args()
+    pbar.quiet(args.quiet)
+
+    # Instantiate the model
+    model = autoimport_eval(args.model)
+    if args.pthfile is not None:
+        data = torch.load(args.pthfile)
+        if 'state_dict' in data:
+            data = data['state_dict']
+        model.load_state_dict(data)
+    # Unwrap any DataParallel-wrapped model
+    if isinstance(model, torch.nn.DataParallel):
+        model = next(model.children())
+    # Examine first conv in model to determine input feature size.
+    first_layer = [c for c in model.modules()
+            if isinstance(c, (torch.nn.Conv2d, torch.nn.ConvTranspose2d,
+                torch.nn.Linear))][0]
+    # 4d input if convolutional, 2d input if first layer is linear.
+    if isinstance(first_layer, (torch.nn.Conv2d, torch.nn.ConvTranspose2d)):
+        z_channels = first_layer.in_channels
+        spatialdims = (1, 1)
+    else:
+        z_channels = first_layer.in_features
+        spatialdims = ()
+    model.cuda()
+
+    # Get the sample of z vectors
+    indexes = torch.arange(args.size)
+    z_sample = standard_z_sample(args.size, z_channels, seed=args.seed)
+    z_sample = z_sample.view(tuple(z_sample.shape) + spatialdims)
+
+    save_znum_images(args.outdir, model, z_sample, indexes)
+    copy_lightbox_to(args.outdir)
+
+
+
+def save_znum_images(dirname, model, z_sample, indexes,
+        name_template="image_{}.png", lightbox=False, batch_size=100):
+    os.makedirs(dirname, exist_ok=True)
+    with torch.no_grad():
+        # Now generate images
+        z_loader = torch.utils.data.DataLoader(TensorDataset(z_sample),
+                    batch_size=batch_size, num_workers=2,
+                    pin_memory=True)
+        saver = WorkerPool(SaveImageWorker)
+        for batch_num, [z] in enumerate(pbar(z_loader,
+                desc='Saving images')):
+            z = z.cuda()
+            start_index = batch_num * batch_size
+            im = ((model(z) + 1) / 2 * 255).clamp(0, 255).byte().permute(
+                    0, 2, 3, 1).cpu()
+            for i in range(len(im)):
+                index = i + start_index
+                if indexes is not None:
+                    index = indexes[index].item()
+                filename = os.path.join(dirname, name_template.format(index))
+                saver.add(im[i].numpy(), filename)
+    saver.join()
+
+def copy_lightbox_to(dirname):
+   srcdir = os.path.realpath(
+       os.path.join(os.getcwd(), os.path.dirname(__file__)))
+   shutil.copy(os.path.join(srcdir, 'lightbox.html'),
+           os.path.join(dirname, '+lightbox.html'))
+
+class SaveImageWorker(WorkerBase):
+    def work(self, data, filename):
+        Image.fromarray(data).save(filename, optimize=True, quality=100)
+
+if __name__ == '__main__':
+    main()
diff --git a/netdissect/sampler.py b/netdissect/sampler.py
new file mode 100644
index 0000000000000000000000000000000000000000..ab74f8c6f86b6beaac10643dfa0a29582dd4d567
--- /dev/null
+++ b/netdissect/sampler.py
@@ -0,0 +1,176 @@
+'''
+A sampler is just a list of integer listing the indexes of the
+inputs in a data set to sample.  For reproducibility, the
+FixedRandomSubsetSampler uses a seeded prng to produce the same
+sequence always.  FixedSubsetSampler is just a wrapper for an
+explicit list of integers.
+
+coordinate_sample solves another sampling problem: when testing
+convolutional outputs, we can reduce data explosing by sampling
+random points of the feature map rather than the entire feature map.
+coordinate_sample does this in a deterministic way that is also
+resolution-independent.
+'''
+
+import numpy
+import random
+from torch.utils.data.sampler import Sampler
+
+class FixedSubsetSampler(Sampler):
+    """Represents a fixed sequence of data set indices.
+    Subsets can be created by specifying a subset of output indexes.
+    """
+    def __init__(self, samples):
+        self.samples = samples
+
+    def __iter__(self):
+        return iter(self.samples)
+
+    def __len__(self):
+        return len(self.samples)
+
+    def __getitem__(self, key):
+        return self.samples[key]
+
+    def subset(self, new_subset):
+        return FixedSubsetSampler(self.dereference(new_subset))
+
+    def dereference(self, indices):
+        '''
+        Translate output sample indices (small numbers indexing the sample)
+        to input sample indices (larger number indexing the original full set)
+        '''
+        return [self.samples[i] for i in indices]
+
+
+class FixedRandomSubsetSampler(FixedSubsetSampler):
+    """Samples a fixed number of samples from the dataset, deterministically.
+    Arguments:
+        data_source,
+        sample_size,
+        seed (optional)
+    """
+    def __init__(self, data_source, start=None, end=None, seed=1):
+        rng = random.Random(seed)
+        shuffled = list(range(len(data_source)))
+        rng.shuffle(shuffled)
+        self.data_source = data_source
+        super(FixedRandomSubsetSampler, self).__init__(shuffled[start:end])
+
+    def class_subset(self, class_filter):
+        '''
+        Returns only the subset matching the given rule.
+        '''
+        if isinstance(class_filter, int):
+            rule = lambda d: d[1] == class_filter
+        else:
+            rule = class_filter
+        return self.subset([i for i, j in enumerate(self.samples)
+                if rule(self.data_source[j])])
+
+def coordinate_sample(shape, sample_size, seeds, grid=13, seed=1, flat=False):
+    '''
+    Returns a (end-start) sets of sample_size grid points within
+    the shape given.  If the shape dimensions are a multiple of 'grid',
+    then sampled points within the same row will never be duplicated.
+    '''
+    if flat:
+        sampind = numpy.zeros((len(seeds), sample_size), dtype=int)
+    else:
+        sampind = numpy.zeros((len(seeds), 2, sample_size), dtype=int)
+    assert sample_size <= grid
+    for j, seed in enumerate(seeds):
+        rng = numpy.random.RandomState(seed)
+        # Shuffle the 169 random grid squares, and pick :sample_size.
+        square_count = grid ** len(shape)
+        square = numpy.stack(numpy.unravel_index(
+            rng.choice(square_count, square_count)[:sample_size],
+            (grid,) * len(shape)))
+        # Then add a random offset to each x, y and put in the range [0...1)
+        # Notice this selects the same locations regardless of resolution.
+        uniform = (square + rng.uniform(size=square.shape)) / grid
+        # TODO: support affine scaling so that we can align receptive field
+        # centers exactly when sampling neurons in different layers.
+        coords = (uniform * numpy.array(shape)[:,None]).astype(int)
+        # Now take sample_size without replacement.  We do this in a way
+        # such that if sample_size is decreased or increased up to 'grid',
+        # the selected points become a subset, not totally different points.
+        if flat:
+            sampind[j] = numpy.ravel_multi_index(coords, dims=shape)
+        else:
+            sampind[j] = coords
+    return sampind
+
+def main():
+    from . import parallelfolder
+    import argparse, os, shutil
+
+    parser = argparse.ArgumentParser(description='Net dissect utility',
+            prog='python -m %s.sampler' % __package__)
+    parser.add_argument('indir')
+    parser.add_argument('outdir')
+    parser.add_argument('--size', type=int, default=100)
+    parser.add_argument('--test', action='store_true', default=False)
+    args = parser.parse_args()
+    if os.path.exists(args.outdir):
+        print('%s already exists' % args.outdir)
+        sys.exit(1)
+    os.makedirs(args.outdir)
+    dataset = parallelfolder.ParallelImageFolders([args.indir])
+    sampler = FixedRandomSubsetSampler(dataset, end=args.size)
+    seen_filenames = set()
+    def number_filename(filename, number):
+        if '.' in filename:
+            a, b = filename.rsplit('.', 1)
+            return a + '_%d.' % number + b
+        return filename + '_%d' % number
+    for i in sampler.dereference(range(args.size)):
+        sourcefile = dataset.images[i][0]
+        filename = os.path.basename(sourcefile)
+        template = filename
+        num = 0
+        while filename in seen_filenames:
+            num += 1
+            filename = number_filename(template, num)
+        seen_filenames.add(filename)
+        shutil.copy(os.path.join(args.indir, sourcefile),
+                os.path.join(args.outdir, filename))
+
+def test():
+    from numpy.testing import assert_almost_equal
+    # Test that coordinate_sample is deterministic, in-range, and scalable.
+    assert_almost_equal(coordinate_sample((26, 26), 10, range(101, 102)),
+            [[[14,  0, 12, 11,  8, 13, 11, 20,  7, 20],
+              [ 9, 22,  7, 11, 23, 18, 21, 15,  2,  5]]])
+    assert_almost_equal(coordinate_sample((13, 13), 10, range(101, 102)),
+            [[[ 7,  0,  6,  5,  4,  6,  5, 10,  3, 20 // 2],
+              [ 4, 11,  3,  5, 11,  9, 10,  7,  1,  5 // 2]]])
+    assert_almost_equal(coordinate_sample((13, 13), 10, range(100, 102),
+        flat=True),
+            [[  8,  24,  67, 103,  87,  79, 138,  94,  98,  53],
+             [ 95,  11,  81,  70,  63,  87,  75, 137,  40, 2+10*13]])
+    assert_almost_equal(coordinate_sample((13, 13), 10, range(101, 103),
+        flat=True),
+            [[ 95,  11,  81,  70,  63,  87,  75, 137,  40, 132],
+             [  0,  78, 114, 111,  66,  45,  72,  73,  79, 135]])
+    assert_almost_equal(coordinate_sample((26, 26), 10, range(101, 102),
+        flat=True),
+            [[373,  22, 319, 297, 231, 356, 307, 535, 184, 5+20*26]])
+    # Test FixedRandomSubsetSampler
+    fss = FixedRandomSubsetSampler(range(10))
+    assert len(fss) == 10
+    assert_almost_equal(list(fss), [6, 8, 9, 7, 5, 3, 0, 4, 1, 2])
+    fss = FixedRandomSubsetSampler(range(10), 3, 8)
+    assert len(fss) == 5
+    assert_almost_equal(list(fss), [7, 5, 3, 0, 4])
+    fss = FixedRandomSubsetSampler([(i, i % 3) for i in range(10)]
+            ).class_subset(class_filter=1)
+    assert len(fss) == 3
+    assert_almost_equal(list(fss), [7, 4, 1])
+
+if __name__ == '__main__':
+    import sys
+    if '--test' in sys.argv[1:]:
+        test()
+    else:
+        main()
diff --git a/netdissect/segdata.py b/netdissect/segdata.py
new file mode 100644
index 0000000000000000000000000000000000000000..a08de11e54ab69313a207426d3f9f23e7254afb5
--- /dev/null
+++ b/netdissect/segdata.py
@@ -0,0 +1,74 @@
+import os, numpy, torch, json
+from .parallelfolder import ParallelImageFolders
+from torchvision import transforms
+from torchvision.transforms.functional import to_tensor, normalize
+
+class FieldDef(object):
+    def __init__(self, field, index, bitshift, bitmask, labels):
+        self.field = field
+        self.index = index
+        self.bitshift = bitshift
+        self.bitmask = bitmask
+        self.labels = labels
+
+class MultiSegmentDataset(object):
+    '''
+    Just like ClevrMulticlassDataset, but the second stream is a one-hot
+    segmentation tensor rather than a flat one-hot presence vector.
+
+    MultiSegmentDataset('datasets/clevrseg',
+        imgdir='images/train/positive',
+        segdir='images/train/segmentation')
+    '''
+    def __init__(self, directory, transform=None,
+            imgdir='img', segdir='seg', val=False, size=None):
+        self.segdataset = ParallelImageFolders(
+                [os.path.join(directory, imgdir),
+                 os.path.join(directory, segdir)],
+                transform=transform)
+        self.fields = []
+        with open(os.path.join(directory, 'labelnames.json'), 'r') as f:
+            for defn in json.load(f):
+                self.fields.append(FieldDef(
+                    defn['field'], defn['index'], defn['bitshift'],
+                    defn['bitmask'], defn['label']))
+        self.labels = ['-'] # Reserve label 0 to mean "no label"
+        self.categories = []
+        self.label_category = [0]
+        for fieldnum, f in enumerate(self.fields):
+            self.categories.append(f.field)
+            f.firstchannel = len(self.labels)
+            f.channels = len(f.labels) - 1
+            for lab in f.labels[1:]:
+                self.labels.append(lab)
+                self.label_category.append(fieldnum)
+        # Reserve 25% of the dataset for validation.
+        first_val = int(len(self.segdataset) * 0.75)
+        self.val = val
+        self.first = first_val if val else 0
+        self.length = len(self.segdataset) - first_val if val else first_val
+        # Truncate the dataset if requested.
+        if size:
+            self.length = min(size, self.length)
+
+    def __len__(self):
+        return self.length
+
+    def __getitem__(self, index):
+        img, segimg = self.segdataset[index + self.first]
+        segin = numpy.array(segimg, numpy.uint8, copy=False)
+        segout = torch.zeros(len(self.categories),
+                segin.shape[0], segin.shape[1], dtype=torch.int64)
+        for i, field in enumerate(self.fields):
+            fielddata = ((torch.from_numpy(segin[:, :, field.index])
+                    >> field.bitshift) & field.bitmask)
+            segout[i] = field.firstchannel + fielddata - 1
+        bincount = numpy.bincount(segout.flatten(),
+                minlength=len(self.labels))
+        return img, segout, bincount
+
+if __name__ == '__main__':
+    ds = MultiSegmentDataset('datasets/clevrseg')
+    print(ds[0])
+    import pdb; pdb.set_trace()
+
diff --git a/netdissect/segmenter.py b/netdissect/segmenter.py
new file mode 100644
index 0000000000000000000000000000000000000000..e33009bc611357865c20bd826a1edc06a6287844
--- /dev/null
+++ b/netdissect/segmenter.py
@@ -0,0 +1,660 @@
+# Usage as a simple differentiable segmenter base class
+
+import os, torch, numpy, json, glob
+import skimage.morphology
+from collections import OrderedDict
+from . import upsegmodel
+from . import segmodel as segmodel_module
+from .easydict import EasyDict
+from urllib.request import urlretrieve
+
+class BaseSegmenter:
+    def get_label_and_category_names(self):
+        '''
+        Returns two lists: first, a list of tuples [(label, category), ...]
+        where the label and category are human-readable strings indicating
+        the meaning of a segmentation class.  The 0th segmentation class
+        should be reserved for a label ('-') that means "no prediction."
+        The second list should just be a list of [category,...] listing
+        all categories in a canonical order.
+        '''
+        raise NotImplemented()
+
+    def segment_batch(self, tensor_images, downsample=1):
+        '''
+        Returns a multilabel segmentation for the given batch of (RGB [-1...1])
+        images.  Each pixel of the result is a torch.long indicating a
+        predicted class number.  Multiple classes can be predicted for
+        the same pixel: output shape is (n, multipred, y, x), where
+        multipred is 3, 5, or 6, for how many different predicted labels can
+        be given for each pixel (depending on whether subdivision is being
+        used).  If downsample is specified, then the output y and x dimensions
+        are downsampled from the original image.
+        '''
+        raise NotImplemented()
+
+    def predict_single_class(self, tensor_images, classnum, downsample=1):
+        '''
+        Given a batch of images (RGB, normalized to [-1...1]) and
+        a specific segmentation class number, returns a tuple with
+           (1) a differentiable ([0..1]) prediction score for the class
+               at every pixel of the input image.
+           (2) a binary mask showing where in the input image the
+               specified class is the best-predicted label for the pixel.
+        Does not work on subdivided labels.
+        '''
+        raise NotImplemented()
+
+class MergedSegmenter:
+    def __init__(self, component_segmenters):
+        self.component_segmenters = component_segmenters
+        self.labels = []
+        self.cats = []
+        self.offsets = []
+        for sgm in self.component_segmenters:
+            self.offsets.append(len(self.labels))
+            slabels, scats = sgm.get_label_and_category_names()
+            self.labels.extend(slabels)
+            for cat in scats:
+                if cat not in self.cats:
+                    self.cats.append(cat)
+
+    def get_label_and_category_names(self):
+        return (self.labels, self.cats)
+
+    def segment_batch(self, tensor_images, downsample=1):
+        segresult = []
+        for i, sgm in enumerate(self.component_segmenters):
+            segresult.append(
+                    sgm.segment_batch(tensor_images, downsample=downsample)
+                    + self.offsets[i])
+        return torch.cat(segresult, dim=1)
+
+    def predict_single_class(self, tensor_images, classnum, downsample=1):
+        for i, sgm in enumerate(self.component_segmenters):
+            if i + 1 == len(self.offsets) or classnum < self.offsets[i + 1]:
+                classnum -= self.offsets[i]
+                break
+        return sgm.predict_single_class(tensor_images, classnum,
+                downsample=downsamples)
+
+
+class NoSegmenter(BaseSegmenter):
+    def get_label_and_category_names(self):
+        return [('-', '-')], ['-']
+
+    def segment_batch(self, tensor_images, downsample=1):
+        '''
+        Returns the all-zero segmentation.
+        '''
+        return torch.zeros(
+                (tensor_images.shape[0], 1,
+                    tensor_images.shape[2] // downsample,
+                    tensor_images.shape[3] // downsample),
+                dtype=torch.long,
+                device=tensor_images.device)
+
+    def predict_single_class(self, tensor_images, classnum, downsample=1):
+        '''
+        Returns the all-zero segmentation.
+        '''
+        pred = torch.zeros(
+                (tensor_images.shape[0], 1,
+                    tensor_images.shape[2] // downsample,
+                    tensor_images.shape[3] // downsample),
+                dtype=torch.float32,
+                device=tensor_images.device)
+        mask = torch.zeros(
+                (tensor_images.shape[0], 1,
+                    tensor_images.shape[2] // downsample,
+                    tensor_images.shape[3] // downsample),
+                dtype=torch.uint8,
+                device=tensor_images.device)
+        return pred, mask
+
+class UnifiedParsingSegmenter(BaseSegmenter):
+    '''
+    This is a wrapper for a more complicated multi-class segmenter,
+    as described in https://arxiv.org/pdf/1807.10221.pdf, and as
+    released in https://github.com/CSAILVision/unifiedparsing.
+    For our purposes and to simplify processing, we do not use
+    whole-scene predictions, and we only consume part segmentations
+    for the three largest object classes (sky, building, person).
+    '''
+
+    def __init__(self, segsizes=None, segdiv=None, all_parts=False):
+        # Create a segmentation model
+        if segsizes is None:
+            segsizes = [256]
+        if segdiv == None:
+            segdiv = 'undivided'
+        segvocab = 'upp'
+        segarch = ('resnet50', 'upernet')
+        epoch = 40
+        ensure_segmenter_downloaded('datasets/segmodel', 'upp')
+        segmodel = load_unified_parsing_segmentation_model(
+                segarch, segvocab, epoch)
+        segmodel.cuda()
+        self.segmodel = segmodel
+        self.segsizes = segsizes
+        self.segdiv = segdiv
+        mult = 1
+        if self.segdiv == 'quad':
+            mult = 5
+        self.divmult = mult
+        # Assign class numbers for parts.
+        first_partnumber = (
+                (len(segmodel.labeldata['object']) - 1) * mult + 1 +
+                (len(segmodel.labeldata['material']) - 1))
+        if all_parts:
+            partobjects = segmodel.labeldata['object_part'].keys()
+        else:
+            # Use only parts for these three types of objects, for efficiency.
+            partobjects = ['sky', 'building', 'person']
+        partnumbers = {}
+        partnames = []
+        objectnumbers = {k: v
+                for v, k in enumerate(segmodel.labeldata['object'])}
+        part_index_translation = []
+        # We merge some classes.  For example "door" is both an object
+        # and a part of a building.  To avoid confusion, we just count
+        # such classes as objects, and add part scores to the same index.
+        for owner in partobjects:
+            part_list = segmodel.labeldata['object_part'][owner]
+            numeric_part_list = []
+            for part in part_list:
+                if part in objectnumbers:
+                    numeric_part_list.append(objectnumbers[part])
+                elif part in partnumbers:
+                    numeric_part_list.append(partnumbers[part])
+                else:
+                    partnumbers[part] = len(partnames) + first_partnumber
+                    partnames.append(part)
+                    numeric_part_list.append(partnumbers[part])
+            part_index_translation.append(torch.tensor(numeric_part_list))
+        self.objects_with_parts = [objectnumbers[obj] for obj in partobjects]
+        self.part_index = part_index_translation
+        self.part_names = partnames
+        # For now we'll just do object and material labels.
+        self.num_classes = 1 + (
+                len(segmodel.labeldata['object']) - 1) * mult + (
+                len(segmodel.labeldata['material']) - 1) + len(partnames)
+        self.num_object_classes = len(self.segmodel.labeldata['object']) - 1
+
+    def get_label_and_category_names(self, dataset=None):
+        '''
+        Lists label and category names.
+        '''
+        # Labels are ordered as follows:
+        # 0, [object labels] [divided object labels] [materials] [parts]
+        # The zero label is reserved to mean 'no prediction'.
+        if self.segdiv == 'quad':
+            suffixes = ['t', 'l', 'b', 'r']
+        else:
+            suffixes = []
+        divided_labels = []
+        for suffix in suffixes:
+            divided_labels.extend([('%s-%s' % (label, suffix), 'part')
+                for label in self.segmodel.labeldata['object'][1:]])
+        # Create the whole list of labels
+        labelcats = (
+                [(label, 'object')
+                    for label in self.segmodel.labeldata['object']] +
+                divided_labels +
+                [(label, 'material')
+                    for label in self.segmodel.labeldata['material'][1:]] +
+                [(label, 'part') for label in self.part_names])
+        return labelcats, ['object', 'part', 'material']
+
+    def raw_seg_prediction(self, tensor_images, downsample=1):
+        '''
+        Generates a segmentation by applying multiresolution voting on
+        the segmentation model, using (rounded to 32 pixels) a set of
+        resolutions in the example benchmark code.
+        '''
+        y, x = tensor_images.shape[2:]
+        b = len(tensor_images)
+        tensor_images = (tensor_images + 1) / 2 * 255
+        tensor_images = torch.flip(tensor_images, (1,)) # BGR!!!?
+        tensor_images -= torch.tensor([102.9801, 115.9465, 122.7717]).to(
+                   dtype=tensor_images.dtype, device=tensor_images.device
+                   )[None,:,None,None]
+        seg_shape = (y // downsample, x // downsample)
+        # We want these to be multiples of 32 for the model.
+        sizes = [(s, s) for s in self.segsizes]
+        pred = {category: torch.zeros(
+            len(tensor_images), len(self.segmodel.labeldata[category]),
+            seg_shape[0], seg_shape[1]).cuda()
+            for category in ['object', 'material']}
+        part_pred = {partobj_index: torch.zeros(
+            len(tensor_images), len(partindex),
+            seg_shape[0], seg_shape[1]).cuda()
+            for partobj_index, partindex in enumerate(self.part_index)}
+        for size in sizes:
+            if size == tensor_images.shape[2:]:
+                resized = tensor_images
+            else:
+                resized = torch.nn.AdaptiveAvgPool2d(size)(tensor_images)
+            r_pred = self.segmodel(
+                dict(img=resized), seg_size=seg_shape)
+            for k in pred:
+                pred[k] += r_pred[k]
+            for k in part_pred:
+                part_pred[k] += r_pred['part'][k]
+        return pred, part_pred
+
+    def segment_batch(self, tensor_images, downsample=1):
+        '''
+        Returns a multilabel segmentation for the given batch of (RGB [-1...1])
+        images.  Each pixel of the result is a torch.long indicating a
+        predicted class number.  Multiple classes can be predicted for
+        the same pixel: output shape is (n, multipred, y, x), where
+        multipred is 3, 5, or 6, for how many different predicted labels can
+        be given for each pixel (depending on whether subdivision is being
+        used).  If downsample is specified, then the output y and x dimensions
+        are downsampled from the original image.
+        '''
+        pred, part_pred = self.raw_seg_prediction(tensor_images,
+                downsample=downsample)
+        piece_channels = 2 if self.segdiv == 'quad' else 0
+        y, x = tensor_images.shape[2:]
+        seg_shape = (y // downsample, x // downsample)
+        segs = torch.zeros(len(tensor_images), 3 + piece_channels,
+                seg_shape[0], seg_shape[1],
+                dtype=torch.long, device=tensor_images.device)
+        _, segs[:,0] = torch.max(pred['object'], dim=1)
+        # Get materials and translate to shared numbering scheme
+        _, segs[:,1] = torch.max(pred['material'], dim=1)
+        maskout = (segs[:,1] == 0)
+        segs[:,1] += (len(self.segmodel.labeldata['object']) - 1) * self.divmult
+        segs[:,1][maskout] = 0
+        # Now deal with subparts of sky, buildings, people
+        for i, object_index in enumerate(self.objects_with_parts):
+            trans = self.part_index[i].to(segs.device)
+            # Get the argmax, and then translate to shared numbering scheme
+            seg = trans[torch.max(part_pred[i], dim=1)[1]]
+            # Only trust the parts where the prediction also predicts the
+            # owning object.
+            mask = (segs[:,0] == object_index)
+            segs[:,2][mask] = seg[mask]
+
+        if self.segdiv == 'quad':
+            segs = self.expand_segment_quad(segs, self.segdiv)
+        return segs
+
+    def predict_single_class(self, tensor_images, classnum, downsample=1):
+        '''
+        Given a batch of images (RGB, normalized to [-1...1]) and
+        a specific segmentation class number, returns a tuple with
+           (1) a differentiable ([0..1]) prediction score for the class
+               at every pixel of the input image.
+           (2) a binary mask showing where in the input image the
+               specified class is the best-predicted label for the pixel.
+        Does not work on subdivided labels.
+        '''
+        result = 0
+        pred, part_pred = self.raw_seg_prediction(tensor_images,
+                downsample=downsample)
+        material_offset = (len(self.segmodel.labeldata['object']) - 1
+            ) * self.divmult
+        if material_offset < classnum < material_offset + len(
+                self.segmodel.labeldata['material']):
+            return (
+                pred['material'][:, classnum - material_offset],
+                pred['material'].max(dim=1)[1] == classnum - material_offset)
+        mask = None
+        if classnum < len(self.segmodel.labeldata['object']):
+            result = pred['object'][:, classnum]
+            mask = (pred['object'].max(dim=1)[1] == classnum)
+        # Some objects, like 'door', are also a part of other objects,
+        # so add the part prediction also.
+        for i, object_index in enumerate(self.objects_with_parts):
+            local_index = (self.part_index[i] == classnum).nonzero()
+            if len(local_index) == 0:
+                continue
+            local_index = local_index.item()
+            # Ignore part predictions outside the mask. (We could pay
+            # atttention to and penalize such predictions.)
+            mask2 = (pred['object'].max(dim=1)[1] == object_index) * (
+                    part_pred[i].max(dim=1)[1] == local_index)
+            if mask is None:
+                mask = mask2
+            else:
+                mask = torch.max(mask, mask2)
+            result = result + (part_pred[i][:, local_index])
+        assert result is not 0, 'unrecognized class %d' % classnum
+        return result, mask
+
+    def expand_segment_quad(self, segs, segdiv='quad'):
+        shape = segs.shape
+        segs[:,3:] = segs[:,0:1] # start by copying the object channel
+        num_seg_labels = self.num_object_classes
+        # For every connected component present (using generator)
+        for i, mask in component_masks(segs[:,0:1]):
+            # Figure the bounding box of the label
+            top, bottom = mask.any(dim=1).nonzero()[[0, -1], 0]
+            left, right = mask.any(dim=0).nonzero()[[0, -1], 0]
+            # Chop the bounding box into four parts
+            vmid = (top + bottom + 1) // 2
+            hmid = (left + right + 1) // 2
+            # Construct top, bottom, right, left masks
+            quad_mask = mask[None,:,:].repeat(4, 1, 1)
+            quad_mask[0, vmid:, :] = 0   # top
+            quad_mask[1, :, hmid:] = 0   # right
+            quad_mask[2, :vmid, :] = 0   # bottom
+            quad_mask[3, :, :hmid] = 0   # left
+            quad_mask = quad_mask.long()
+            # Modify extra segmentation labels by offsetting
+            segs[i,3,:,:] += quad_mask[0] * num_seg_labels
+            segs[i,4,:,:] += quad_mask[1] * (2 * num_seg_labels)
+            segs[i,3,:,:] += quad_mask[2] * (3 * num_seg_labels)
+            segs[i,4,:,:] += quad_mask[3] * (4 * num_seg_labels)
+        # remove any components that were too small to subdivide
+        mask = segs[:,3:] <= self.num_object_classes
+        segs[:,3:][mask] = 0
+        return segs
+
+class SemanticSegmenter(BaseSegmenter):
+    def __init__(self, modeldir=None, segarch=None, segvocab=None,
+            segsizes=None, segdiv=None, epoch=None):
+        # Create a segmentation model
+        if modeldir == None:
+            modeldir = 'datasets/segmodel'
+        if segvocab == None:
+            segvocab = 'baseline'
+        if segarch == None:
+            segarch = ('resnet50_dilated8', 'ppm_bilinear_deepsup')
+        if segdiv == None:
+            segdiv = 'undivided'
+        elif isinstance(segarch, str):
+            segarch = segarch.split(',')
+        segmodel = load_segmentation_model(modeldir, segarch, segvocab, epoch)
+        if segsizes is None:
+            segsizes = getattr(segmodel.meta, 'segsizes', [256])
+        self.segsizes = segsizes
+        # Verify segmentation model to has every out_channel labeled.
+        assert len(segmodel.meta.labels) == list(c for c in segmodel.modules()
+            if isinstance(c, torch.nn.Conv2d))[-1].out_channels
+        segmodel.cuda()
+        self.segmodel = segmodel
+        self.segdiv = segdiv
+        # Image normalization
+        self.bgr = (segmodel.meta.imageformat.byteorder == 'BGR')
+        self.imagemean = torch.tensor(segmodel.meta.imageformat.mean)
+        self.imagestd = torch.tensor(segmodel.meta.imageformat.stdev)
+        # Map from labels to external indexes, and labels to channel sets.
+        self.labelmap = {'-': 0}
+        self.channelmap = {'-': []}
+        self.labels = [('-', '-')]
+        num_labels = 1
+        self.num_underlying_classes = len(segmodel.meta.labels)
+        # labelmap maps names to external indexes.
+        for i, label in enumerate(segmodel.meta.labels):
+            if label.name not in self.channelmap:
+                self.channelmap[label.name] = []
+            self.channelmap[label.name].append(i)
+            if getattr(label, 'internal', None) or label.name in self.labelmap:
+                continue
+            self.labelmap[label.name] = num_labels
+            num_labels += 1
+            self.labels.append((label.name, label.category))
+        # Each category gets its own independent softmax.
+        self.category_indexes = { category.name:
+                [i for i, label in enumerate(segmodel.meta.labels)
+                   if label.category == category.name]
+                for category in segmodel.meta.categories }
+        # catindexmap maps names to category internal indexes
+        self.catindexmap = {}
+        for catname, indexlist in self.category_indexes.items():
+            for index, i in enumerate(indexlist):
+                self.catindexmap[segmodel.meta.labels[i].name] = (
+                        (catname, index))
+        # After the softmax, each category is mapped to external indexes.
+        self.category_map = { catname:
+                torch.tensor([
+                    self.labelmap.get(segmodel.meta.labels[ind].name, 0)
+                    for ind in catindex])
+                for catname, catindex in self.category_indexes.items()}
+        self.category_rules = segmodel.meta.categories
+        # Finally, naive subdivision can be applied.
+        mult = 1
+        if self.segdiv == 'quad':
+            mult = 5
+            suffixes = ['t', 'l', 'b', 'r']
+            divided_labels = []
+            for suffix in suffixes:
+                divided_labels.extend([('%s-%s' % (label, suffix), cat)
+                    for label, cat in self.labels[1:]])
+                self.channelmap.update({
+                    '%s-%s' % (label, suffix): self.channelmap[label]
+                    for label, cat in self.labels[1:] })
+            self.labels.extend(divided_labels)
+        # For examining a single class
+        self.channellist = [self.channelmap[name] for name, _ in self.labels]
+
+    def get_label_and_category_names(self, dataset=None):
+        return self.labels, self.segmodel.categories
+
+    def segment_batch(self, tensor_images, downsample=1):
+        return self.raw_segment_batch(tensor_images, downsample)[0]
+
+    def raw_segment_batch(self, tensor_images, downsample=1):
+        pred = self.raw_seg_prediction(tensor_images, downsample)
+        catsegs = {}
+        for catkey, catindex in self.category_indexes.items():
+            _, segs = torch.max(pred[:, catindex], dim=1)
+            catsegs[catkey] = segs
+        masks = {}
+        segs = torch.zeros(len(tensor_images), len(self.category_rules),
+                pred.shape[2], pred.shape[2], device=pred.device,
+                dtype=torch.long)
+        for i, cat in enumerate(self.category_rules):
+            catmap = self.category_map[cat.name].to(pred.device)
+            translated = catmap[catsegs[cat.name]]
+            if getattr(cat, 'mask', None) is not None:
+                if cat.mask not in masks:
+                    maskcat, maskind = self.catindexmap[cat.mask]
+                    masks[cat.mask] = (catsegs[maskcat] == maskind)
+                translated *= masks[cat.mask].long()
+            segs[:,i] = translated
+        if self.segdiv == 'quad':
+            segs = self.expand_segment_quad(segs,
+                    self.num_underlying_classes, self.segdiv)
+        return segs, pred
+
+    def raw_seg_prediction(self, tensor_images, downsample=1):
+        '''
+        Generates a segmentation by applying multiresolution voting on
+        the segmentation model, using (rounded to 32 pixels) a set of
+        resolutions in the example benchmark code.
+        '''
+        y, x = tensor_images.shape[2:]
+        b = len(tensor_images)
+        # Flip the RGB order if specified.
+        if self.bgr:
+           tensor_images = torch.flip(tensor_images, (1,))
+        # Transform from our [-1..1] range to torch standard [0..1] range
+        # and then apply normalization.
+        tensor_images = ((tensor_images + 1) / 2
+                ).sub_(self.imagemean[None,:,None,None].to(tensor_images.device)
+                ).div_(self.imagestd[None,:,None,None].to(tensor_images.device))
+        # Output shape can be downsampled.
+        seg_shape = (y // downsample, x // downsample)
+        # We want these to be multiples of 32 for the model.
+        sizes = [(s, s) for s in self.segsizes]
+        pred = torch.zeros(
+            len(tensor_images), (self.num_underlying_classes),
+            seg_shape[0], seg_shape[1]).cuda()
+        for size in sizes:
+            if size == tensor_images.shape[2:]:
+                resized = tensor_images
+            else:
+                resized = torch.nn.AdaptiveAvgPool2d(size)(tensor_images)
+            raw_pred = self.segmodel(
+                dict(img_data=resized), segSize=seg_shape)
+            softmax_pred = torch.empty_like(raw_pred)
+            for catindex in self.category_indexes.values():
+                softmax_pred[:, catindex] = torch.nn.functional.softmax(
+                        raw_pred[:, catindex], dim=1)
+            pred += softmax_pred
+        return pred
+
+    def expand_segment_quad(self, segs, num_seg_labels, segdiv='quad'):
+        shape = segs.shape
+        output = segs.repeat(1, 3, 1, 1)
+        # For every connected component present (using generator)
+        for i, mask in component_masks(segs):
+            # Figure the bounding box of the label
+            top, bottom = mask.any(dim=1).nonzero()[[0, -1], 0]
+            left, right = mask.any(dim=0).nonzero()[[0, -1], 0]
+            # Chop the bounding box into four parts
+            vmid = (top + bottom + 1) // 2
+            hmid = (left + right + 1) // 2
+            # Construct top, bottom, right, left masks
+            quad_mask = mask[None,:,:].repeat(4, 1, 1)
+            quad_mask[0, vmid:, :] = 0   # top
+            quad_mask[1, :, hmid:] = 0   # right
+            quad_mask[2, :vmid, :] = 0   # bottom
+            quad_mask[3, :, :hmid] = 0   # left
+            quad_mask = quad_mask.long()
+            # Modify extra segmentation labels by offsetting
+            output[i,1,:,:] += quad_mask[0] * num_seg_labels
+            output[i,2,:,:] += quad_mask[1] * (2 * num_seg_labels)
+            output[i,1,:,:] += quad_mask[2] * (3 * num_seg_labels)
+            output[i,2,:,:] += quad_mask[3] * (4 * num_seg_labels)
+        return output
+
+    def predict_single_class(self, tensor_images, classnum, downsample=1):
+        '''
+        Given a batch of images (RGB, normalized to [-1...1]) and
+        a specific segmentation class number, returns a tuple with
+           (1) a differentiable ([0..1]) prediction score for the class
+               at every pixel of the input image.
+           (2) a binary mask showing where in the input image the
+               specified class is the best-predicted label for the pixel.
+        Does not work on subdivided labels.
+        '''
+        seg, pred = self.raw_segment_batch(tensor_images,
+                downsample=downsample)
+        result = pred[:,self.channellist[classnum]].sum(dim=1)
+        mask = (seg == classnum).max(1)[0]
+        return result, mask
+
+def component_masks(segmentation_batch):
+    '''
+    Splits connected components into regions (slower, requires cpu).
+    '''
+    npbatch = segmentation_batch.cpu().numpy()
+    for i in range(segmentation_batch.shape[0]):
+        labeled, num = skimage.morphology.label(npbatch[i][0], return_num=True)
+        labeled = torch.from_numpy(labeled).to(segmentation_batch.device)
+        for label in range(1, num):
+            yield i, (labeled == label)
+
+def load_unified_parsing_segmentation_model(segmodel_arch, segvocab, epoch):
+    segmodel_dir = 'datasets/segmodel/%s-%s-%s' % ((segvocab,) + segmodel_arch)
+    # Load json of class names and part/object structure
+    with open(os.path.join(segmodel_dir, 'labels.json')) as f:
+        labeldata = json.load(f)
+    nr_classes={k: len(labeldata[k])
+                for k in ['object', 'scene', 'material']}
+    nr_classes['part'] = sum(len(p) for p in labeldata['object_part'].values())
+    # Create a segmentation model
+    segbuilder = upsegmodel.ModelBuilder()
+    # example segmodel_arch = ('resnet101', 'upernet')
+    seg_encoder = segbuilder.build_encoder(
+            arch=segmodel_arch[0],
+            fc_dim=2048,
+            weights=os.path.join(segmodel_dir, 'encoder_epoch_%d.pth' % epoch))
+    seg_decoder = segbuilder.build_decoder(
+            arch=segmodel_arch[1],
+            fc_dim=2048, use_softmax=True,
+            nr_classes=nr_classes,
+            weights=os.path.join(segmodel_dir, 'decoder_epoch_%d.pth' % epoch))
+    segmodel = upsegmodel.SegmentationModule(
+            seg_encoder, seg_decoder, labeldata)
+    segmodel.categories = ['object', 'part', 'material']
+    segmodel.eval()
+    return segmodel
+
+def load_segmentation_model(modeldir, segmodel_arch, segvocab, epoch=None):
+    # Load csv of class names
+    segmodel_dir = 'datasets/segmodel/%s-%s-%s' % ((segvocab,) + segmodel_arch)
+    with open(os.path.join(segmodel_dir, 'labels.json')) as f:
+        labeldata = EasyDict(json.load(f))
+    # Automatically pick the last epoch available.
+    if epoch is None:
+        choices = [os.path.basename(n)[14:-4] for n in
+                glob.glob(os.path.join(segmodel_dir, 'encoder_epoch_*.pth'))]
+        epoch = max([int(c) for c in choices if c.isdigit()])
+    # Create a segmentation model
+    segbuilder = segmodel_module.ModelBuilder()
+    # example segmodel_arch = ('resnet101', 'upernet')
+    seg_encoder = segbuilder.build_encoder(
+            arch=segmodel_arch[0],
+            # fc_dim=2048,
+            weights=os.path.join(segmodel_dir, 'encoder_epoch_%d.pth' % epoch))
+    seg_decoder = segbuilder.build_decoder(
+            arch=segmodel_arch[1],
+            # fc_dim=2048, inference=True,
+            use_softmax=True,
+            num_class=len(labeldata.labels),
+            weights=os.path.join(segmodel_dir, 'decoder_epoch_%d.pth' % epoch))
+    segmodel = segmodel_module.SegmentationModule(seg_encoder, seg_decoder,
+                                  torch.nn.NLLLoss(ignore_index=-1))
+    segmodel.categories = [cat.name for cat in labeldata.categories]
+    segmodel.labels = [label.name for label in labeldata.labels]
+    categories = OrderedDict()
+    label_category = numpy.zeros(len(segmodel.labels), dtype=int)
+    for i, label in enumerate(labeldata.labels):
+        label_category[i] = segmodel.categories.index(label.category)
+    segmodel.meta = labeldata
+    segmodel.eval()
+    return segmodel
+
+def ensure_segmenter_downloaded(directory, segvocab):
+    baseurl = 'https://dissect.csail.mit.edu/models/segmodel'
+    if segvocab == 'upp':
+        dirname = 'upp-resnet50-upernet'
+        files = ['decoder_epoch_40.pth', 'encoder_epoch_40.pth', 'labels.json']
+    elif segvocab == 'color':
+        dirname = 'color-resnet18dilated-ppm_deepsup'
+        files = ['decoder_epoch_20.pth', 'encoder_epoch_20.pth', 'labels.json']
+    else:
+        assert False, segvocab
+    download_dir = os.path.join(directory, dirname)
+    os.makedirs(download_dir, exist_ok=True)
+    for fn in files:
+        if os.path.isfile(os.path.join(download_dir, fn)):
+            continue # Skip files already downloaded
+        url = '%s/%s/%s' % (baseurl, dirname, fn)
+        print('Downloading %s' % url)
+        urlretrieve(url, os.path.join(download_dir, fn))
+    assert os.path.isfile(os.path.join(directory, dirname, 'labels.json'))
+
+def test_main():
+    '''
+    Test the unified segmenter.
+    '''
+    from PIL import Image
+    testim = Image.open('script/testdata/test_church_242.jpg')
+    tensor_im = (torch.from_numpy(numpy.asarray(testim)).permute(2, 0, 1)
+            .float() / 255 * 2 - 1)[None, :, :, :].cuda()
+    segmenter = UnifiedParsingSegmenter()
+    seg = segmenter.segment_batch(tensor_im)
+    bc = torch.bincount(seg.view(-1))
+    labels, cats = segmenter.get_label_and_category_names()
+    for label in bc.nonzero()[:,0]:
+        if label.item():
+            # What is the prediction for this class?
+            pred, mask = segmenter.predict_single_class(tensor_im, label.item())
+            assert mask.sum().item() == bc[label].item()
+            assert len(((seg == label).max(1)[0] - mask).nonzero()) == 0
+            inside_pred = pred[mask].mean().item()
+            outside_pred = pred[~mask].mean().item()
+            print('%s (%s, #%d): %d pixels, pred %.2g inside %.2g outside' %
+                (labels[label.item()] + (label.item(), bc[label].item(),
+                    inside_pred, outside_pred)))
+
+if __name__ == '__main__':
+    test_main()
diff --git a/netdissect/segmodel/__init__.py b/netdissect/segmodel/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..76b40a0a36bc2976f185dbdc344c5a7c09b65920
--- /dev/null
+++ b/netdissect/segmodel/__init__.py
@@ -0,0 +1 @@
+from .models import ModelBuilder, SegmentationModule
diff --git a/netdissect/segmodel/colors150.npy b/netdissect/segmodel/colors150.npy
new file mode 100644
index 0000000000000000000000000000000000000000..0348540b3afb0b4907be18375c28e90d58ba91fa
Binary files /dev/null and b/netdissect/segmodel/colors150.npy differ
diff --git a/netdissect/segmodel/mobilenet.py b/netdissect/segmodel/mobilenet.py
new file mode 100644
index 0000000000000000000000000000000000000000..1bcb53ce6f4b37788fc2890c2686ae7c50b9ea97
--- /dev/null
+++ b/netdissect/segmodel/mobilenet.py
@@ -0,0 +1,174 @@
+"""
+This MobileNetV2 implementation is modified from the following repository:
+https://github.com/tonylins/pytorch-mobilenet-v2
+"""
+
+import os
+import sys
+import torch
+import torch.nn as nn
+import math
+try:
+  from lib.nn import SynchronizedBatchNorm2d
+except ImportError:
+  from torch.nn import BatchNorm2d as SynchronizedBatchNorm2d
+
+try:
+    from urllib import urlretrieve
+except ImportError:
+    from urllib.request import urlretrieve
+
+
+__all__ = ['mobilenetv2']
+
+
+model_urls = {
+    'mobilenetv2': 'http://sceneparsing.csail.mit.edu/model/pretrained_resnet/mobilenet_v2.pth.tar',
+}
+
+
+def conv_bn(inp, oup, stride):
+    return nn.Sequential(
+        nn.Conv2d(inp, oup, 3, stride, 1, bias=False),
+        SynchronizedBatchNorm2d(oup),
+        nn.ReLU6(inplace=True)
+    )
+
+
+def conv_1x1_bn(inp, oup):
+    return nn.Sequential(
+        nn.Conv2d(inp, oup, 1, 1, 0, bias=False),
+        SynchronizedBatchNorm2d(oup),
+        nn.ReLU6(inplace=True)
+    )
+
+
+class InvertedResidual(nn.Module):
+    def __init__(self, inp, oup, stride, expand_ratio):
+        super(InvertedResidual, self).__init__()
+        self.stride = stride
+        assert stride in [1, 2]
+
+        hidden_dim = round(inp * expand_ratio)
+        self.use_res_connect = self.stride == 1 and inp == oup
+
+        if expand_ratio == 1:
+            self.conv = nn.Sequential(
+                # dw
+                nn.Conv2d(hidden_dim, hidden_dim, 3, stride, 1, groups=hidden_dim, bias=False),
+                SynchronizedBatchNorm2d(hidden_dim),
+                nn.ReLU6(inplace=True),
+                # pw-linear
+                nn.Conv2d(hidden_dim, oup, 1, 1, 0, bias=False),
+                SynchronizedBatchNorm2d(oup),
+            )
+        else:
+            self.conv = nn.Sequential(
+                # pw
+                nn.Conv2d(inp, hidden_dim, 1, 1, 0, bias=False),
+                SynchronizedBatchNorm2d(hidden_dim),
+                nn.ReLU6(inplace=True),
+                # dw
+                nn.Conv2d(hidden_dim, hidden_dim, 3, stride, 1, groups=hidden_dim, bias=False),
+                SynchronizedBatchNorm2d(hidden_dim),
+                nn.ReLU6(inplace=True),
+                # pw-linear
+                nn.Conv2d(hidden_dim, oup, 1, 1, 0, bias=False),
+                SynchronizedBatchNorm2d(oup),
+            )
+
+    def forward(self, x):
+        if self.use_res_connect:
+            return x + self.conv(x)
+        else:
+            return self.conv(x)
+
+
+class MobileNetV2(nn.Module):
+    def __init__(self, n_class=1000, input_size=224, width_mult=1.):
+        super(MobileNetV2, self).__init__()
+        block = InvertedResidual
+        input_channel = 32
+        last_channel = 1280
+        interverted_residual_setting = [
+            # t, c, n, s
+            [1, 16, 1, 1],
+            [6, 24, 2, 2],
+            [6, 32, 3, 2],
+            [6, 64, 4, 2],
+            [6, 96, 3, 1],
+            [6, 160, 3, 2],
+            [6, 320, 1, 1],
+        ]
+
+        # building first layer
+        assert input_size % 32 == 0
+        input_channel = int(input_channel * width_mult)
+        self.last_channel = int(last_channel * width_mult) if width_mult > 1.0 else last_channel
+        self.features = [conv_bn(3, input_channel, 2)]
+        # building inverted residual blocks
+        for t, c, n, s in interverted_residual_setting:
+            output_channel = int(c * width_mult)
+            for i in range(n):
+                if i == 0:
+                    self.features.append(block(input_channel, output_channel, s, expand_ratio=t))
+                else:
+                    self.features.append(block(input_channel, output_channel, 1, expand_ratio=t))
+                input_channel = output_channel
+        # building last several layers
+        self.features.append(conv_1x1_bn(input_channel, self.last_channel))
+        # make it nn.Sequential
+        self.features = nn.Sequential(*self.features)
+
+        # building classifier
+        self.classifier = nn.Sequential(
+            nn.Dropout(0.2),
+            nn.Linear(self.last_channel, n_class),
+        )
+
+        self._initialize_weights()
+
+    def forward(self, x):
+        x = self.features(x)
+        x = x.mean(3).mean(2)
+        x = self.classifier(x)
+        return x
+
+    def _initialize_weights(self):
+        for m in self.modules():
+            if isinstance(m, nn.Conv2d):
+                n = m.kernel_size[0] * m.kernel_size[1] * m.out_channels
+                m.weight.data.normal_(0, math.sqrt(2. / n))
+                if m.bias is not None:
+                    m.bias.data.zero_()
+            elif isinstance(m, SynchronizedBatchNorm2d):
+                m.weight.data.fill_(1)
+                m.bias.data.zero_()
+            elif isinstance(m, nn.Linear):
+                n = m.weight.size(1)
+                m.weight.data.normal_(0, 0.01)
+                m.bias.data.zero_()
+
+
+def mobilenetv2(pretrained=False, **kwargs):
+    """Constructs a MobileNet_V2 model.
+
+    Args:
+        pretrained (bool): If True, returns a model pre-trained on ImageNet
+    """
+    model = MobileNetV2(n_class=1000, **kwargs)
+    if pretrained:
+        model.load_state_dict(load_url(model_urls['mobilenetv2']), strict=False)
+    return model
+
+
+def load_url(url, model_dir='./pretrained', map_location=None):
+    if not os.path.exists(model_dir):
+        os.makedirs(model_dir)
+    filename = url.split('/')[-1]
+    cached_file = os.path.join(model_dir, filename)
+    if not os.path.exists(cached_file):
+        sys.stderr.write('Downloading: "{}" to {}\n'.format(url, cached_file))
+        urlretrieve(url, cached_file)
+    return torch.load(cached_file, map_location=map_location)
+
diff --git a/netdissect/segmodel/models.py b/netdissect/segmodel/models.py
new file mode 100644
index 0000000000000000000000000000000000000000..c3cd781e1e403d2d341cbf9ac612ebf2112ac17e
--- /dev/null
+++ b/netdissect/segmodel/models.py
@@ -0,0 +1,587 @@
+import torch
+import torch.nn as nn
+import torchvision
+from . import resnet, resnext, mobilenet
+try:
+  from lib.nn import SynchronizedBatchNorm2d
+except ImportError:
+  from torch.nn import BatchNorm2d as SynchronizedBatchNorm2d
+
+
+class SegmentationModuleBase(nn.Module):
+    def __init__(self):
+        super(SegmentationModuleBase, self).__init__()
+
+    def pixel_acc(self, pred, label):
+        _, preds = torch.max(pred, dim=1)
+        valid = (label >= 0).long()
+        acc_sum = torch.sum(valid * (preds == label).long())
+        pixel_sum = torch.sum(valid)
+        acc = acc_sum.float() / (pixel_sum.float() + 1e-10)
+        return acc
+
+
+class SegmentationModule(SegmentationModuleBase):
+    def __init__(self, net_enc, net_dec, crit, deep_sup_scale=None):
+        super(SegmentationModule, self).__init__()
+        self.encoder = net_enc
+        self.decoder = net_dec
+        self.crit = crit
+        self.deep_sup_scale = deep_sup_scale
+
+    def forward(self, feed_dict, *, segSize=None):
+        # training
+        if segSize is None:
+            if self.deep_sup_scale is not None: # use deep supervision technique
+                (pred, pred_deepsup) = self.decoder(self.encoder(feed_dict['img_data'], return_feature_maps=True))
+            else:
+                pred = self.decoder(self.encoder(feed_dict['img_data'], return_feature_maps=True))
+
+            loss = self.crit(pred, feed_dict['seg_label'])
+            if self.deep_sup_scale is not None:
+                loss_deepsup = self.crit(pred_deepsup, feed_dict['seg_label'])
+                loss = loss + loss_deepsup * self.deep_sup_scale
+
+            acc = self.pixel_acc(pred, feed_dict['seg_label'])
+            return loss, acc
+        # inference
+        else:
+            pred = self.decoder(self.encoder(feed_dict['img_data'], return_feature_maps=True), segSize=segSize)
+            return pred
+
+
+def conv3x3(in_planes, out_planes, stride=1, has_bias=False):
+    "3x3 convolution with padding"
+    return nn.Conv2d(in_planes, out_planes, kernel_size=3, stride=stride,
+                     padding=1, bias=has_bias)
+
+
+def conv3x3_bn_relu(in_planes, out_planes, stride=1):
+    return nn.Sequential(
+            conv3x3(in_planes, out_planes, stride),
+            SynchronizedBatchNorm2d(out_planes),
+            nn.ReLU(inplace=True),
+            )
+
+
+class ModelBuilder():
+    # custom weights initialization
+    def weights_init(self, m):
+        classname = m.__class__.__name__
+        if classname.find('Conv') != -1:
+            nn.init.kaiming_normal_(m.weight.data)
+        elif classname.find('BatchNorm') != -1:
+            m.weight.data.fill_(1.)
+            m.bias.data.fill_(1e-4)
+        #elif classname.find('Linear') != -1:
+        #    m.weight.data.normal_(0.0, 0.0001)
+
+    def build_encoder(self, arch='resnet50dilated', fc_dim=512, weights=''):
+        pretrained = True if len(weights) == 0 else False
+        arch = arch.lower()
+        if arch == 'mobilenetv2dilated':
+            orig_mobilenet = mobilenet.__dict__['mobilenetv2'](pretrained=pretrained)
+            net_encoder = MobileNetV2Dilated(orig_mobilenet, dilate_scale=8)
+        elif arch == 'resnet18':
+            orig_resnet = resnet.__dict__['resnet18'](pretrained=pretrained)
+            net_encoder = Resnet(orig_resnet)
+        elif arch == 'resnet18dilated':
+            orig_resnet = resnet.__dict__['resnet18'](pretrained=pretrained)
+            net_encoder = ResnetDilated(orig_resnet, dilate_scale=8)
+        elif arch == 'resnet34':
+            raise NotImplementedError
+            orig_resnet = resnet.__dict__['resnet34'](pretrained=pretrained)
+            net_encoder = Resnet(orig_resnet)
+        elif arch == 'resnet34dilated':
+            raise NotImplementedError
+            orig_resnet = resnet.__dict__['resnet34'](pretrained=pretrained)
+            net_encoder = ResnetDilated(orig_resnet, dilate_scale=8)
+        elif arch == 'resnet50':
+            orig_resnet = resnet.__dict__['resnet50'](pretrained=pretrained)
+            net_encoder = Resnet(orig_resnet)
+        elif arch == 'resnet50dilated':
+            orig_resnet = resnet.__dict__['resnet50'](pretrained=pretrained)
+            net_encoder = ResnetDilated(orig_resnet, dilate_scale=8)
+        elif arch == 'resnet101':
+            orig_resnet = resnet.__dict__['resnet101'](pretrained=pretrained)
+            net_encoder = Resnet(orig_resnet)
+        elif arch == 'resnet101dilated':
+            orig_resnet = resnet.__dict__['resnet101'](pretrained=pretrained)
+            net_encoder = ResnetDilated(orig_resnet, dilate_scale=8)
+        elif arch == 'resnext101':
+            orig_resnext = resnext.__dict__['resnext101'](pretrained=pretrained)
+            net_encoder = Resnet(orig_resnext) # we can still use class Resnet
+        else:
+            raise Exception('Architecture undefined!')
+
+        # net_encoder.apply(self.weights_init)
+        if len(weights) > 0:
+            print('Loading weights for net_encoder')
+            net_encoder.load_state_dict(
+                torch.load(weights, map_location=lambda storage, loc: storage), strict=False)
+        return net_encoder
+
+    def build_decoder(self, arch='ppm_deepsup',
+                      fc_dim=512, num_class=150,
+                      weights='', use_softmax=False):
+        arch = arch.lower()
+        if arch == 'c1_deepsup':
+            net_decoder = C1DeepSup(
+                num_class=num_class,
+                fc_dim=fc_dim,
+                use_softmax=use_softmax)
+        elif arch == 'c1':
+            net_decoder = C1(
+                num_class=num_class,
+                fc_dim=fc_dim,
+                use_softmax=use_softmax)
+        elif arch == 'ppm':
+            net_decoder = PPM(
+                num_class=num_class,
+                fc_dim=fc_dim,
+                use_softmax=use_softmax)
+        elif arch == 'ppm_deepsup':
+            net_decoder = PPMDeepsup(
+                num_class=num_class,
+                fc_dim=fc_dim,
+                use_softmax=use_softmax)
+        elif arch == 'upernet_lite':
+            net_decoder = UPerNet(
+                num_class=num_class,
+                fc_dim=fc_dim,
+                use_softmax=use_softmax,
+                fpn_dim=256)
+        elif arch == 'upernet':
+            net_decoder = UPerNet(
+                num_class=num_class,
+                fc_dim=fc_dim,
+                use_softmax=use_softmax,
+                fpn_dim=512)
+        else:
+            raise Exception('Architecture undefined!')
+
+        net_decoder.apply(self.weights_init)
+        if len(weights) > 0:
+            print('Loading weights for net_decoder')
+            net_decoder.load_state_dict(
+                torch.load(weights, map_location=lambda storage, loc: storage), strict=False)
+        return net_decoder
+
+
+class Resnet(nn.Module):
+    def __init__(self, orig_resnet):
+        super(Resnet, self).__init__()
+
+        # take pretrained resnet, except AvgPool and FC
+        self.conv1 = orig_resnet.conv1
+        self.bn1 = orig_resnet.bn1
+        self.relu1 = orig_resnet.relu1
+        self.conv2 = orig_resnet.conv2
+        self.bn2 = orig_resnet.bn2
+        self.relu2 = orig_resnet.relu2
+        self.conv3 = orig_resnet.conv3
+        self.bn3 = orig_resnet.bn3
+        self.relu3 = orig_resnet.relu3
+        self.maxpool = orig_resnet.maxpool
+        self.layer1 = orig_resnet.layer1
+        self.layer2 = orig_resnet.layer2
+        self.layer3 = orig_resnet.layer3
+        self.layer4 = orig_resnet.layer4
+
+    def forward(self, x, return_feature_maps=False):
+        conv_out = []
+
+        x = self.relu1(self.bn1(self.conv1(x)))
+        x = self.relu2(self.bn2(self.conv2(x)))
+        x = self.relu3(self.bn3(self.conv3(x)))
+        x = self.maxpool(x)
+
+        x = self.layer1(x); conv_out.append(x);
+        x = self.layer2(x); conv_out.append(x);
+        x = self.layer3(x); conv_out.append(x);
+        x = self.layer4(x); conv_out.append(x);
+
+        if return_feature_maps:
+            return conv_out
+        return [x]
+
+
+class ResnetDilated(nn.Module):
+    def __init__(self, orig_resnet, dilate_scale=8):
+        super(ResnetDilated, self).__init__()
+        from functools import partial
+
+        if dilate_scale == 8:
+            orig_resnet.layer3.apply(
+                partial(self._nostride_dilate, dilate=2))
+            orig_resnet.layer4.apply(
+                partial(self._nostride_dilate, dilate=4))
+        elif dilate_scale == 16:
+            orig_resnet.layer4.apply(
+                partial(self._nostride_dilate, dilate=2))
+
+        # take pretrained resnet, except AvgPool and FC
+        self.conv1 = orig_resnet.conv1
+        self.bn1 = orig_resnet.bn1
+        self.relu1 = orig_resnet.relu1
+        self.conv2 = orig_resnet.conv2
+        self.bn2 = orig_resnet.bn2
+        self.relu2 = orig_resnet.relu2
+        self.conv3 = orig_resnet.conv3
+        self.bn3 = orig_resnet.bn3
+        self.relu3 = orig_resnet.relu3
+        self.maxpool = orig_resnet.maxpool
+        self.layer1 = orig_resnet.layer1
+        self.layer2 = orig_resnet.layer2
+        self.layer3 = orig_resnet.layer3
+        self.layer4 = orig_resnet.layer4
+
+    def _nostride_dilate(self, m, dilate):
+        classname = m.__class__.__name__
+        if classname.find('Conv') != -1:
+            # the convolution with stride
+            if m.stride == (2, 2):
+                m.stride = (1, 1)
+                if m.kernel_size == (3, 3):
+                    m.dilation = (dilate//2, dilate//2)
+                    m.padding = (dilate//2, dilate//2)
+            # other convoluions
+            else:
+                if m.kernel_size == (3, 3):
+                    m.dilation = (dilate, dilate)
+                    m.padding = (dilate, dilate)
+
+    def forward(self, x, return_feature_maps=False):
+        conv_out = []
+
+        x = self.relu1(self.bn1(self.conv1(x)))
+        x = self.relu2(self.bn2(self.conv2(x)))
+        x = self.relu3(self.bn3(self.conv3(x)))
+        x = self.maxpool(x)
+
+        x = self.layer1(x); conv_out.append(x);
+        x = self.layer2(x); conv_out.append(x);
+        x = self.layer3(x); conv_out.append(x);
+        x = self.layer4(x); conv_out.append(x);
+
+        if return_feature_maps:
+            return conv_out
+        return [x]
+
+
+class MobileNetV2Dilated(nn.Module):
+    def __init__(self, orig_net, dilate_scale=8):
+        super(MobileNetV2Dilated, self).__init__()
+        from functools import partial
+
+        # take pretrained mobilenet features
+        self.features = orig_net.features[:-1]
+
+        self.total_idx = len(self.features)
+        self.down_idx = [2, 4, 7, 14]
+
+        if dilate_scale == 8:
+            for i in range(self.down_idx[-2], self.down_idx[-1]):
+                self.features[i].apply(
+                    partial(self._nostride_dilate, dilate=2)
+                )
+            for i in range(self.down_idx[-1], self.total_idx):
+                self.features[i].apply(
+                    partial(self._nostride_dilate, dilate=4)
+                )
+        elif dilate_scale == 16:
+            for i in range(self.down_idx[-1], self.total_idx):
+                self.features[i].apply(
+                    partial(self._nostride_dilate, dilate=2)
+                )
+
+    def _nostride_dilate(self, m, dilate):
+        classname = m.__class__.__name__
+        if classname.find('Conv') != -1:
+            # the convolution with stride
+            if m.stride == (2, 2):
+                m.stride = (1, 1)
+                if m.kernel_size == (3, 3):
+                    m.dilation = (dilate//2, dilate//2)
+                    m.padding = (dilate//2, dilate//2)
+            # other convoluions
+            else:
+                if m.kernel_size == (3, 3):
+                    m.dilation = (dilate, dilate)
+                    m.padding = (dilate, dilate)
+
+    def forward(self, x, return_feature_maps=False):
+        if return_feature_maps:
+            conv_out = []
+            for i in range(self.total_idx):
+                x = self.features[i](x)
+                if i in self.down_idx:
+                    conv_out.append(x)
+            conv_out.append(x)
+            return conv_out
+
+        else:
+            return [self.features(x)]
+
+
+# last conv, deep supervision
+class C1DeepSup(nn.Module):
+    def __init__(self, num_class=150, fc_dim=2048, use_softmax=False):
+        super(C1DeepSup, self).__init__()
+        self.use_softmax = use_softmax
+
+        self.cbr = conv3x3_bn_relu(fc_dim, fc_dim // 4, 1)
+        self.cbr_deepsup = conv3x3_bn_relu(fc_dim // 2, fc_dim // 4, 1)
+
+        # last conv
+        self.conv_last = nn.Conv2d(fc_dim // 4, num_class, 1, 1, 0)
+        self.conv_last_deepsup = nn.Conv2d(fc_dim // 4, num_class, 1, 1, 0)
+
+    def forward(self, conv_out, segSize=None):
+        conv5 = conv_out[-1]
+
+        x = self.cbr(conv5)
+        x = self.conv_last(x)
+
+        if self.use_softmax:  # is True during inference
+            x = nn.functional.interpolate(
+                x, size=segSize, mode='bilinear', align_corners=False)
+            x = nn.functional.softmax(x, dim=1)
+            return x
+
+        # deep sup
+        conv4 = conv_out[-2]
+        _ = self.cbr_deepsup(conv4)
+        _ = self.conv_last_deepsup(_)
+
+        x = nn.functional.log_softmax(x, dim=1)
+        _ = nn.functional.log_softmax(_, dim=1)
+
+        return (x, _)
+
+
+# last conv
+class C1(nn.Module):
+    def __init__(self, num_class=150, fc_dim=2048, use_softmax=False):
+        super(C1, self).__init__()
+        self.use_softmax = use_softmax
+
+        self.cbr = conv3x3_bn_relu(fc_dim, fc_dim // 4, 1)
+
+        # last conv
+        self.conv_last = nn.Conv2d(fc_dim // 4, num_class, 1, 1, 0)
+
+    def forward(self, conv_out, segSize=None):
+        conv5 = conv_out[-1]
+        x = self.cbr(conv5)
+        x = self.conv_last(x)
+
+        if self.use_softmax: # is True during inference
+            x = nn.functional.interpolate(
+                x, size=segSize, mode='bilinear', align_corners=False)
+            x = nn.functional.softmax(x, dim=1)
+        else:
+            x = nn.functional.log_softmax(x, dim=1)
+
+        return x
+
+
+# pyramid pooling
+class PPM(nn.Module):
+    def __init__(self, num_class=150, fc_dim=4096,
+                 use_softmax=False, pool_scales=(1, 2, 3, 6)):
+        super(PPM, self).__init__()
+        self.use_softmax = use_softmax
+
+        self.ppm = []
+        for scale in pool_scales:
+            self.ppm.append(nn.Sequential(
+                nn.AdaptiveAvgPool2d(scale),
+                nn.Conv2d(fc_dim, 512, kernel_size=1, bias=False),
+                SynchronizedBatchNorm2d(512),
+                nn.ReLU(inplace=True)
+            ))
+        self.ppm = nn.ModuleList(self.ppm)
+
+        self.conv_last = nn.Sequential(
+            nn.Conv2d(fc_dim+len(pool_scales)*512, 512,
+                      kernel_size=3, padding=1, bias=False),
+            SynchronizedBatchNorm2d(512),
+            nn.ReLU(inplace=True),
+            nn.Dropout2d(0.1),
+            nn.Conv2d(512, num_class, kernel_size=1)
+        )
+
+    def forward(self, conv_out, segSize=None):
+        conv5 = conv_out[-1]
+
+        input_size = conv5.size()
+        ppm_out = [conv5]
+        for pool_scale in self.ppm:
+            ppm_out.append(nn.functional.interpolate(
+                pool_scale(conv5),
+                (input_size[2], input_size[3]),
+                mode='bilinear', align_corners=False))
+        ppm_out = torch.cat(ppm_out, 1)
+
+        x = self.conv_last(ppm_out)
+
+        if self.use_softmax:  # is True during inference
+            x = nn.functional.interpolate(
+                x, size=segSize, mode='bilinear', align_corners=False)
+            x = nn.functional.softmax(x, dim=1)
+        else:
+            x = nn.functional.log_softmax(x, dim=1)
+        return x
+
+
+# pyramid pooling, deep supervision
+class PPMDeepsup(nn.Module):
+    def __init__(self, num_class=150, fc_dim=4096,
+                 use_softmax=False, pool_scales=(1, 2, 3, 6)):
+        super(PPMDeepsup, self).__init__()
+        self.use_softmax = use_softmax
+
+        self.ppm = []
+        for scale in pool_scales:
+            self.ppm.append(nn.Sequential(
+                nn.AdaptiveAvgPool2d(scale),
+                nn.Conv2d(fc_dim, 512, kernel_size=1, bias=False),
+                SynchronizedBatchNorm2d(512),
+                nn.ReLU(inplace=True)
+            ))
+        self.ppm = nn.ModuleList(self.ppm)
+        self.cbr_deepsup = conv3x3_bn_relu(fc_dim // 2, fc_dim // 4, 1)
+
+        self.conv_last = nn.Sequential(
+            nn.Conv2d(fc_dim+len(pool_scales)*512, 512,
+                      kernel_size=3, padding=1, bias=False),
+            SynchronizedBatchNorm2d(512),
+            nn.ReLU(inplace=True),
+            nn.Dropout2d(0.1),
+            nn.Conv2d(512, num_class, kernel_size=1)
+        )
+        self.conv_last_deepsup = nn.Conv2d(fc_dim // 4, num_class, 1, 1, 0)
+        self.dropout_deepsup = nn.Dropout2d(0.1)
+
+    def forward(self, conv_out, segSize=None):
+        conv5 = conv_out[-1]
+
+        input_size = conv5.size()
+        ppm_out = [conv5]
+        for pool_scale in self.ppm:
+            ppm_out.append(nn.functional.interpolate(
+                pool_scale(conv5),
+                (input_size[2], input_size[3]),
+                mode='bilinear', align_corners=False))
+        ppm_out = torch.cat(ppm_out, 1)
+
+        x = self.conv_last(ppm_out)
+
+        if self.use_softmax:  # is True during inference
+            x = nn.functional.interpolate(
+                x, size=segSize, mode='bilinear', align_corners=False)
+            x = nn.functional.softmax(x, dim=1)
+            return x
+
+        # deep sup
+        conv4 = conv_out[-2]
+        _ = self.cbr_deepsup(conv4)
+        _ = self.dropout_deepsup(_)
+        _ = self.conv_last_deepsup(_)
+
+        x = nn.functional.log_softmax(x, dim=1)
+        _ = nn.functional.log_softmax(_, dim=1)
+
+        return (x, _)
+
+
+# upernet
+class UPerNet(nn.Module):
+    def __init__(self, num_class=150, fc_dim=4096,
+                 use_softmax=False, pool_scales=(1, 2, 3, 6),
+                 fpn_inplanes=(256, 512, 1024, 2048), fpn_dim=256):
+        super(UPerNet, self).__init__()
+        self.use_softmax = use_softmax
+
+        # PPM Module
+        self.ppm_pooling = []
+        self.ppm_conv = []
+
+        for scale in pool_scales:
+            self.ppm_pooling.append(nn.AdaptiveAvgPool2d(scale))
+            self.ppm_conv.append(nn.Sequential(
+                nn.Conv2d(fc_dim, 512, kernel_size=1, bias=False),
+                SynchronizedBatchNorm2d(512),
+                nn.ReLU(inplace=True)
+            ))
+        self.ppm_pooling = nn.ModuleList(self.ppm_pooling)
+        self.ppm_conv = nn.ModuleList(self.ppm_conv)
+        self.ppm_last_conv = conv3x3_bn_relu(fc_dim + len(pool_scales)*512, fpn_dim, 1)
+
+        # FPN Module
+        self.fpn_in = []
+        for fpn_inplane in fpn_inplanes[:-1]: # skip the top layer
+            self.fpn_in.append(nn.Sequential(
+                nn.Conv2d(fpn_inplane, fpn_dim, kernel_size=1, bias=False),
+                SynchronizedBatchNorm2d(fpn_dim),
+                nn.ReLU(inplace=True)
+            ))
+        self.fpn_in = nn.ModuleList(self.fpn_in)
+
+        self.fpn_out = []
+        for i in range(len(fpn_inplanes) - 1): # skip the top layer
+            self.fpn_out.append(nn.Sequential(
+                conv3x3_bn_relu(fpn_dim, fpn_dim, 1),
+            ))
+        self.fpn_out = nn.ModuleList(self.fpn_out)
+
+        self.conv_last = nn.Sequential(
+            conv3x3_bn_relu(len(fpn_inplanes) * fpn_dim, fpn_dim, 1),
+            nn.Conv2d(fpn_dim, num_class, kernel_size=1)
+        )
+
+    def forward(self, conv_out, segSize=None):
+        conv5 = conv_out[-1]
+
+        input_size = conv5.size()
+        ppm_out = [conv5]
+        for pool_scale, pool_conv in zip(self.ppm_pooling, self.ppm_conv):
+            ppm_out.append(pool_conv(nn.functional.interpolate(
+                pool_scale(conv5),
+                (input_size[2], input_size[3]),
+                mode='bilinear', align_corners=False)))
+        ppm_out = torch.cat(ppm_out, 1)
+        f = self.ppm_last_conv(ppm_out)
+
+        fpn_feature_list = [f]
+        for i in reversed(range(len(conv_out) - 1)):
+            conv_x = conv_out[i]
+            conv_x = self.fpn_in[i](conv_x) # lateral branch
+
+            f = nn.functional.interpolate(
+                f, size=conv_x.size()[2:], mode='bilinear', align_corners=False) # top-down branch
+            f = conv_x + f
+
+            fpn_feature_list.append(self.fpn_out[i](f))
+
+        fpn_feature_list.reverse() # [P2 - P5]
+        output_size = fpn_feature_list[0].size()[2:]
+        fusion_list = [fpn_feature_list[0]]
+        for i in range(1, len(fpn_feature_list)):
+            fusion_list.append(nn.functional.interpolate(
+                fpn_feature_list[i],
+                output_size,
+                mode='bilinear', align_corners=False))
+        fusion_out = torch.cat(fusion_list, 1)
+        x = self.conv_last(fusion_out)
+
+        if self.use_softmax:  # is True during inference
+            x = nn.functional.interpolate(
+                x, size=segSize, mode='bilinear', align_corners=False)
+            x = nn.functional.softmax(x, dim=1)
+            return x
+
+        x = nn.functional.log_softmax(x, dim=1)
+
+        return x
diff --git a/netdissect/segmodel/object150_info.csv b/netdissect/segmodel/object150_info.csv
new file mode 100644
index 0000000000000000000000000000000000000000..8b34d8f3874a38b96894863c5458a7c3c2b0e2e6
--- /dev/null
+++ b/netdissect/segmodel/object150_info.csv
@@ -0,0 +1,151 @@
+Idx,Ratio,Train,Val,Stuff,Name
+1,0.1576,11664,1172,1,wall
+2,0.1072,6046,612,1,building;edifice
+3,0.0878,8265,796,1,sky
+4,0.0621,9336,917,1,floor;flooring
+5,0.0480,6678,641,0,tree
+6,0.0450,6604,643,1,ceiling
+7,0.0398,4023,408,1,road;route
+8,0.0231,1906,199,0,bed
+9,0.0198,4688,460,0,windowpane;window
+10,0.0183,2423,225,1,grass
+11,0.0181,2874,294,0,cabinet
+12,0.0166,3068,310,1,sidewalk;pavement
+13,0.0160,5075,526,0,person;individual;someone;somebody;mortal;soul
+14,0.0151,1804,190,1,earth;ground
+15,0.0118,6666,796,0,door;double;door
+16,0.0110,4269,411,0,table
+17,0.0109,1691,160,1,mountain;mount
+18,0.0104,3999,441,0,plant;flora;plant;life
+19,0.0104,2149,217,0,curtain;drape;drapery;mantle;pall
+20,0.0103,3261,318,0,chair
+21,0.0098,3164,306,0,car;auto;automobile;machine;motorcar
+22,0.0074,709,75,1,water
+23,0.0067,3296,315,0,painting;picture
+24,0.0065,1191,106,0,sofa;couch;lounge
+25,0.0061,1516,162,0,shelf
+26,0.0060,667,69,1,house
+27,0.0053,651,57,1,sea
+28,0.0052,1847,224,0,mirror
+29,0.0046,1158,128,1,rug;carpet;carpeting
+30,0.0044,480,44,1,field
+31,0.0044,1172,98,0,armchair
+32,0.0044,1292,184,0,seat
+33,0.0033,1386,138,0,fence;fencing
+34,0.0031,698,61,0,desk
+35,0.0030,781,73,0,rock;stone
+36,0.0027,380,43,0,wardrobe;closet;press
+37,0.0026,3089,302,0,lamp
+38,0.0024,404,37,0,bathtub;bathing;tub;bath;tub
+39,0.0024,804,99,0,railing;rail
+40,0.0023,1453,153,0,cushion
+41,0.0023,411,37,0,base;pedestal;stand
+42,0.0022,1440,162,0,box
+43,0.0022,800,77,0,column;pillar
+44,0.0020,2650,298,0,signboard;sign
+45,0.0019,549,46,0,chest;of;drawers;chest;bureau;dresser
+46,0.0019,367,36,0,counter
+47,0.0018,311,30,1,sand
+48,0.0018,1181,122,0,sink
+49,0.0018,287,23,1,skyscraper
+50,0.0018,468,38,0,fireplace;hearth;open;fireplace
+51,0.0018,402,43,0,refrigerator;icebox
+52,0.0018,130,12,1,grandstand;covered;stand
+53,0.0018,561,64,1,path
+54,0.0017,880,102,0,stairs;steps
+55,0.0017,86,12,1,runway
+56,0.0017,172,11,0,case;display;case;showcase;vitrine
+57,0.0017,198,18,0,pool;table;billiard;table;snooker;table
+58,0.0017,930,109,0,pillow
+59,0.0015,139,18,0,screen;door;screen
+60,0.0015,564,52,1,stairway;staircase
+61,0.0015,320,26,1,river
+62,0.0015,261,29,1,bridge;span
+63,0.0014,275,22,0,bookcase
+64,0.0014,335,60,0,blind;screen
+65,0.0014,792,75,0,coffee;table;cocktail;table
+66,0.0014,395,49,0,toilet;can;commode;crapper;pot;potty;stool;throne
+67,0.0014,1309,138,0,flower
+68,0.0013,1112,113,0,book
+69,0.0013,266,27,1,hill
+70,0.0013,659,66,0,bench
+71,0.0012,331,31,0,countertop
+72,0.0012,531,56,0,stove;kitchen;stove;range;kitchen;range;cooking;stove
+73,0.0012,369,36,0,palm;palm;tree
+74,0.0012,144,9,0,kitchen;island
+75,0.0011,265,29,0,computer;computing;machine;computing;device;data;processor;electronic;computer;information;processing;system
+76,0.0010,324,33,0,swivel;chair
+77,0.0009,304,27,0,boat
+78,0.0009,170,20,0,bar
+79,0.0009,68,6,0,arcade;machine
+80,0.0009,65,8,1,hovel;hut;hutch;shack;shanty
+81,0.0009,248,25,0,bus;autobus;coach;charabanc;double-decker;jitney;motorbus;motorcoach;omnibus;passenger;vehicle
+82,0.0008,492,49,0,towel
+83,0.0008,2510,269,0,light;light;source
+84,0.0008,440,39,0,truck;motortruck
+85,0.0008,147,18,1,tower
+86,0.0008,583,56,0,chandelier;pendant;pendent
+87,0.0007,533,61,0,awning;sunshade;sunblind
+88,0.0007,1989,239,0,streetlight;street;lamp
+89,0.0007,71,5,0,booth;cubicle;stall;kiosk
+90,0.0007,618,53,0,television;television;receiver;television;set;tv;tv;set;idiot;box;boob;tube;telly;goggle;box
+91,0.0007,135,12,0,airplane;aeroplane;plane
+92,0.0007,83,5,1,dirt;track
+93,0.0007,178,17,0,apparel;wearing;apparel;dress;clothes
+94,0.0006,1003,104,0,pole
+95,0.0006,182,12,1,land;ground;soil
+96,0.0006,452,50,0,bannister;banister;balustrade;balusters;handrail
+97,0.0006,42,6,1,escalator;moving;staircase;moving;stairway
+98,0.0006,307,31,0,ottoman;pouf;pouffe;puff;hassock
+99,0.0006,965,114,0,bottle
+100,0.0006,117,13,0,buffet;counter;sideboard
+101,0.0006,354,35,0,poster;posting;placard;notice;bill;card
+102,0.0006,108,9,1,stage
+103,0.0006,557,55,0,van
+104,0.0006,52,4,0,ship
+105,0.0005,99,5,0,fountain
+106,0.0005,57,4,1,conveyer;belt;conveyor;belt;conveyer;conveyor;transporter
+107,0.0005,292,31,0,canopy
+108,0.0005,77,9,0,washer;automatic;washer;washing;machine
+109,0.0005,340,38,0,plaything;toy
+110,0.0005,66,3,1,swimming;pool;swimming;bath;natatorium
+111,0.0005,465,49,0,stool
+112,0.0005,50,4,0,barrel;cask
+113,0.0005,622,75,0,basket;handbasket
+114,0.0005,80,9,1,waterfall;falls
+115,0.0005,59,3,0,tent;collapsible;shelter
+116,0.0005,531,72,0,bag
+117,0.0005,282,30,0,minibike;motorbike
+118,0.0005,73,7,0,cradle
+119,0.0005,435,44,0,oven
+120,0.0005,136,25,0,ball
+121,0.0005,116,24,0,food;solid;food
+122,0.0004,266,31,0,step;stair
+123,0.0004,58,12,0,tank;storage;tank
+124,0.0004,418,83,0,trade;name;brand;name;brand;marque
+125,0.0004,319,43,0,microwave;microwave;oven
+126,0.0004,1193,139,0,pot;flowerpot
+127,0.0004,97,23,0,animal;animate;being;beast;brute;creature;fauna
+128,0.0004,347,36,0,bicycle;bike;wheel;cycle
+129,0.0004,52,5,1,lake
+130,0.0004,246,22,0,dishwasher;dish;washer;dishwashing;machine
+131,0.0004,108,13,0,screen;silver;screen;projection;screen
+132,0.0004,201,30,0,blanket;cover
+133,0.0004,285,21,0,sculpture
+134,0.0004,268,27,0,hood;exhaust;hood
+135,0.0003,1020,108,0,sconce
+136,0.0003,1282,122,0,vase
+137,0.0003,528,65,0,traffic;light;traffic;signal;stoplight
+138,0.0003,453,57,0,tray
+139,0.0003,671,100,0,ashcan;trash;can;garbage;can;wastebin;ash;bin;ash-bin;ashbin;dustbin;trash;barrel;trash;bin
+140,0.0003,397,44,0,fan
+141,0.0003,92,8,1,pier;wharf;wharfage;dock
+142,0.0003,228,18,0,crt;screen
+143,0.0003,570,59,0,plate
+144,0.0003,217,22,0,monitor;monitoring;device
+145,0.0003,206,19,0,bulletin;board;notice;board
+146,0.0003,130,14,0,shower
+147,0.0003,178,28,0,radiator
+148,0.0002,504,57,0,glass;drinking;glass
+149,0.0002,775,96,0,clock
+150,0.0002,421,56,0,flag
diff --git a/netdissect/segmodel/resnet.py b/netdissect/segmodel/resnet.py
new file mode 100644
index 0000000000000000000000000000000000000000..e86da6e62ed81a2c15a1cd6cce35c6da1f437917
--- /dev/null
+++ b/netdissect/segmodel/resnet.py
@@ -0,0 +1,235 @@
+import os
+import sys
+import torch
+import torch.nn as nn
+import math
+try:
+  from lib.nn import SynchronizedBatchNorm2d
+except ImportError:
+  from torch.nn import BatchNorm2d as SynchronizedBatchNorm2d
+
+try:
+    from urllib import urlretrieve
+except ImportError:
+    from urllib.request import urlretrieve
+
+
+__all__ = ['ResNet', 'resnet18', 'resnet50', 'resnet101'] # resnet101 is coming soon!
+
+
+model_urls = {
+    'resnet18': 'http://sceneparsing.csail.mit.edu/model/pretrained_resnet/resnet18-imagenet.pth',
+    'resnet50': 'http://sceneparsing.csail.mit.edu/model/pretrained_resnet/resnet50-imagenet.pth',
+    'resnet101': 'http://sceneparsing.csail.mit.edu/model/pretrained_resnet/resnet101-imagenet.pth'
+}
+
+
+def conv3x3(in_planes, out_planes, stride=1):
+    "3x3 convolution with padding"
+    return nn.Conv2d(in_planes, out_planes, kernel_size=3, stride=stride,
+                     padding=1, bias=False)
+
+
+class BasicBlock(nn.Module):
+    expansion = 1
+
+    def __init__(self, inplanes, planes, stride=1, downsample=None):
+        super(BasicBlock, self).__init__()
+        self.conv1 = conv3x3(inplanes, planes, stride)
+        self.bn1 = SynchronizedBatchNorm2d(planes)
+        self.relu = nn.ReLU(inplace=True)
+        self.conv2 = conv3x3(planes, planes)
+        self.bn2 = SynchronizedBatchNorm2d(planes)
+        self.downsample = downsample
+        self.stride = stride
+
+    def forward(self, x):
+        residual = x
+
+        out = self.conv1(x)
+        out = self.bn1(out)
+        out = self.relu(out)
+
+        out = self.conv2(out)
+        out = self.bn2(out)
+
+        if self.downsample is not None:
+            residual = self.downsample(x)
+
+        out += residual
+        out = self.relu(out)
+
+        return out
+
+
+class Bottleneck(nn.Module):
+    expansion = 4
+
+    def __init__(self, inplanes, planes, stride=1, downsample=None):
+        super(Bottleneck, self).__init__()
+        self.conv1 = nn.Conv2d(inplanes, planes, kernel_size=1, bias=False)
+        self.bn1 = SynchronizedBatchNorm2d(planes)
+        self.conv2 = nn.Conv2d(planes, planes, kernel_size=3, stride=stride,
+                               padding=1, bias=False)
+        self.bn2 = SynchronizedBatchNorm2d(planes)
+        self.conv3 = nn.Conv2d(planes, planes * 4, kernel_size=1, bias=False)
+        self.bn3 = SynchronizedBatchNorm2d(planes * 4)
+        self.relu = nn.ReLU(inplace=True)
+        self.downsample = downsample
+        self.stride = stride
+
+    def forward(self, x):
+        residual = x
+
+        out = self.conv1(x)
+        out = self.bn1(out)
+        out = self.relu(out)
+
+        out = self.conv2(out)
+        out = self.bn2(out)
+        out = self.relu(out)
+
+        out = self.conv3(out)
+        out = self.bn3(out)
+
+        if self.downsample is not None:
+            residual = self.downsample(x)
+
+        out += residual
+        out = self.relu(out)
+
+        return out
+
+
+class ResNet(nn.Module):
+
+    def __init__(self, block, layers, num_classes=1000):
+        self.inplanes = 128
+        super(ResNet, self).__init__()
+        self.conv1 = conv3x3(3, 64, stride=2)
+        self.bn1 = SynchronizedBatchNorm2d(64)
+        self.relu1 = nn.ReLU(inplace=True)
+        self.conv2 = conv3x3(64, 64)
+        self.bn2 = SynchronizedBatchNorm2d(64)
+        self.relu2 = nn.ReLU(inplace=True)
+        self.conv3 = conv3x3(64, 128)
+        self.bn3 = SynchronizedBatchNorm2d(128)
+        self.relu3 = nn.ReLU(inplace=True)
+        self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1)
+
+        self.layer1 = self._make_layer(block, 64, layers[0])
+        self.layer2 = self._make_layer(block, 128, layers[1], stride=2)
+        self.layer3 = self._make_layer(block, 256, layers[2], stride=2)
+        self.layer4 = self._make_layer(block, 512, layers[3], stride=2)
+        self.avgpool = nn.AvgPool2d(7, stride=1)
+        self.fc = nn.Linear(512 * block.expansion, num_classes)
+
+        for m in self.modules():
+            if isinstance(m, nn.Conv2d):
+                n = m.kernel_size[0] * m.kernel_size[1] * m.out_channels
+                m.weight.data.normal_(0, math.sqrt(2. / n))
+            elif isinstance(m, SynchronizedBatchNorm2d):
+                m.weight.data.fill_(1)
+                m.bias.data.zero_()
+
+    def _make_layer(self, block, planes, blocks, stride=1):
+        downsample = None
+        if stride != 1 or self.inplanes != planes * block.expansion:
+            downsample = nn.Sequential(
+                nn.Conv2d(self.inplanes, planes * block.expansion,
+                          kernel_size=1, stride=stride, bias=False),
+                SynchronizedBatchNorm2d(planes * block.expansion),
+            )
+
+        layers = []
+        layers.append(block(self.inplanes, planes, stride, downsample))
+        self.inplanes = planes * block.expansion
+        for i in range(1, blocks):
+            layers.append(block(self.inplanes, planes))
+
+        return nn.Sequential(*layers)
+
+    def forward(self, x):
+        x = self.relu1(self.bn1(self.conv1(x)))
+        x = self.relu2(self.bn2(self.conv2(x)))
+        x = self.relu3(self.bn3(self.conv3(x)))
+        x = self.maxpool(x)
+
+        x = self.layer1(x)
+        x = self.layer2(x)
+        x = self.layer3(x)
+        x = self.layer4(x)
+
+        x = self.avgpool(x)
+        x = x.view(x.size(0), -1)
+        x = self.fc(x)
+
+        return x
+
+def resnet18(pretrained=False, **kwargs):
+    """Constructs a ResNet-18 model.
+
+    Args:
+        pretrained (bool): If True, returns a model pre-trained on ImageNet
+    """
+    model = ResNet(BasicBlock, [2, 2, 2, 2], **kwargs)
+    if pretrained:
+        model.load_state_dict(load_url(model_urls['resnet18']))
+    return model
+
+'''
+def resnet34(pretrained=False, **kwargs):
+    """Constructs a ResNet-34 model.
+
+    Args:
+        pretrained (bool): If True, returns a model pre-trained on ImageNet
+    """
+    model = ResNet(BasicBlock, [3, 4, 6, 3], **kwargs)
+    if pretrained:
+        model.load_state_dict(load_url(model_urls['resnet34']))
+    return model
+'''
+
+def resnet50(pretrained=False, **kwargs):
+    """Constructs a ResNet-50 model.
+
+    Args:
+        pretrained (bool): If True, returns a model pre-trained on ImageNet
+    """
+    model = ResNet(Bottleneck, [3, 4, 6, 3], **kwargs)
+    if pretrained:
+        model.load_state_dict(load_url(model_urls['resnet50']), strict=False)
+    return model
+
+
+def resnet101(pretrained=False, **kwargs):
+    """Constructs a ResNet-101 model.
+
+    Args:
+        pretrained (bool): If True, returns a model pre-trained on ImageNet
+    """
+    model = ResNet(Bottleneck, [3, 4, 23, 3], **kwargs)
+    if pretrained:
+        model.load_state_dict(load_url(model_urls['resnet101']), strict=False)
+    return model
+
+# def resnet152(pretrained=False, **kwargs):
+#     """Constructs a ResNet-152 model.
+#
+#     Args:
+#         pretrained (bool): If True, returns a model pre-trained on ImageNet
+#     """
+#     model = ResNet(Bottleneck, [3, 8, 36, 3], **kwargs)
+#     if pretrained:
+#         model.load_state_dict(load_url(model_urls['resnet152']))
+#     return model
+
+def load_url(url, model_dir='./pretrained', map_location=None):
+    if not os.path.exists(model_dir):
+        os.makedirs(model_dir)
+    filename = url.split('/')[-1]
+    cached_file = os.path.join(model_dir, filename)
+    if not os.path.exists(cached_file):
+        sys.stderr.write('Downloading: "{}" to {}\n'.format(url, cached_file))
+        urlretrieve(url, cached_file)
+    return torch.load(cached_file, map_location=map_location)
diff --git a/netdissect/segmodel/resnext.py b/netdissect/segmodel/resnext.py
new file mode 100644
index 0000000000000000000000000000000000000000..d2c2ad7b295a171feadd7fd82a1567ebd44f7a2c
--- /dev/null
+++ b/netdissect/segmodel/resnext.py
@@ -0,0 +1,183 @@
+import os
+import sys
+import torch
+import torch.nn as nn
+import math
+try:
+  from lib.nn import SynchronizedBatchNorm2d
+except ImportError:
+  from torch.nn import BatchNorm2d as SynchronizedBatchNorm2d
+
+try:
+    from urllib import urlretrieve
+except ImportError:
+    from urllib.request import urlretrieve
+
+
+__all__ = ['ResNeXt', 'resnext101'] # support resnext 101
+
+
+model_urls = {
+    #'resnext50': 'http://sceneparsing.csail.mit.edu/model/pretrained_resnet/resnext50-imagenet.pth',
+    'resnext101': 'http://sceneparsing.csail.mit.edu/model/pretrained_resnet/resnext101-imagenet.pth'
+}
+
+
+def conv3x3(in_planes, out_planes, stride=1):
+    "3x3 convolution with padding"
+    return nn.Conv2d(in_planes, out_planes, kernel_size=3, stride=stride,
+                     padding=1, bias=False)
+
+
+class GroupBottleneck(nn.Module):
+    expansion = 2
+
+    def __init__(self, inplanes, planes, stride=1, groups=1, downsample=None):
+        super(GroupBottleneck, self).__init__()
+        self.conv1 = nn.Conv2d(inplanes, planes, kernel_size=1, bias=False)
+        self.bn1 = SynchronizedBatchNorm2d(planes)
+        self.conv2 = nn.Conv2d(planes, planes, kernel_size=3, stride=stride,
+                               padding=1, groups=groups, bias=False)
+        self.bn2 = SynchronizedBatchNorm2d(planes)
+        self.conv3 = nn.Conv2d(planes, planes * 2, kernel_size=1, bias=False)
+        self.bn3 = SynchronizedBatchNorm2d(planes * 2)
+        self.relu = nn.ReLU(inplace=True)
+        self.downsample = downsample
+        self.stride = stride
+
+    def forward(self, x):
+        residual = x
+
+        out = self.conv1(x)
+        out = self.bn1(out)
+        out = self.relu(out)
+
+        out = self.conv2(out)
+        out = self.bn2(out)
+        out = self.relu(out)
+
+        out = self.conv3(out)
+        out = self.bn3(out)
+
+        if self.downsample is not None:
+            residual = self.downsample(x)
+
+        out += residual
+        out = self.relu(out)
+
+        return out
+
+
+class ResNeXt(nn.Module):
+
+    def __init__(self, block, layers, groups=32, num_classes=1000):
+        self.inplanes = 128
+        super(ResNeXt, self).__init__()
+        self.conv1 = conv3x3(3, 64, stride=2)
+        self.bn1 = SynchronizedBatchNorm2d(64)
+        self.relu1 = nn.ReLU(inplace=True)
+        self.conv2 = conv3x3(64, 64)
+        self.bn2 = SynchronizedBatchNorm2d(64)
+        self.relu2 = nn.ReLU(inplace=True)
+        self.conv3 = conv3x3(64, 128)
+        self.bn3 = SynchronizedBatchNorm2d(128)
+        self.relu3 = nn.ReLU(inplace=True)
+        self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1)
+
+        self.layer1 = self._make_layer(block, 128, layers[0], groups=groups)
+        self.layer2 = self._make_layer(block, 256, layers[1], stride=2, groups=groups)
+        self.layer3 = self._make_layer(block, 512, layers[2], stride=2, groups=groups)
+        self.layer4 = self._make_layer(block, 1024, layers[3], stride=2, groups=groups)
+        self.avgpool = nn.AvgPool2d(7, stride=1)
+        self.fc = nn.Linear(1024 * block.expansion, num_classes)
+
+        for m in self.modules():
+            if isinstance(m, nn.Conv2d):
+                n = m.kernel_size[0] * m.kernel_size[1] * m.out_channels // m.groups
+                m.weight.data.normal_(0, math.sqrt(2. / n))
+            elif isinstance(m, SynchronizedBatchNorm2d):
+                m.weight.data.fill_(1)
+                m.bias.data.zero_()
+
+    def _make_layer(self, block, planes, blocks, stride=1, groups=1):
+        downsample = None
+        if stride != 1 or self.inplanes != planes * block.expansion:
+            downsample = nn.Sequential(
+                nn.Conv2d(self.inplanes, planes * block.expansion,
+                          kernel_size=1, stride=stride, bias=False),
+                SynchronizedBatchNorm2d(planes * block.expansion),
+            )
+
+        layers = []
+        layers.append(block(self.inplanes, planes, stride, groups, downsample))
+        self.inplanes = planes * block.expansion
+        for i in range(1, blocks):
+            layers.append(block(self.inplanes, planes, groups=groups))
+
+        return nn.Sequential(*layers)
+
+    def forward(self, x):
+        x = self.relu1(self.bn1(self.conv1(x)))
+        x = self.relu2(self.bn2(self.conv2(x)))
+        x = self.relu3(self.bn3(self.conv3(x)))
+        x = self.maxpool(x)
+
+        x = self.layer1(x)
+        x = self.layer2(x)
+        x = self.layer3(x)
+        x = self.layer4(x)
+
+        x = self.avgpool(x)
+        x = x.view(x.size(0), -1)
+        x = self.fc(x)
+
+        return x
+
+
+'''
+def resnext50(pretrained=False, **kwargs):
+    """Constructs a ResNet-50 model.
+
+    Args:
+        pretrained (bool): If True, returns a model pre-trained on Places
+    """
+    model = ResNeXt(GroupBottleneck, [3, 4, 6, 3], **kwargs)
+    if pretrained:
+        model.load_state_dict(load_url(model_urls['resnext50']), strict=False)
+    return model
+'''
+
+
+def resnext101(pretrained=False, **kwargs):
+    """Constructs a ResNet-101 model.
+
+    Args:
+        pretrained (bool): If True, returns a model pre-trained on Places
+    """
+    model = ResNeXt(GroupBottleneck, [3, 4, 23, 3], **kwargs)
+    if pretrained:
+        model.load_state_dict(load_url(model_urls['resnext101']), strict=False)
+    return model
+
+
+# def resnext152(pretrained=False, **kwargs):
+#     """Constructs a ResNeXt-152 model.
+#
+#     Args:
+#         pretrained (bool): If True, returns a model pre-trained on Places
+#     """
+#     model = ResNeXt(GroupBottleneck, [3, 8, 36, 3], **kwargs)
+#     if pretrained:
+#         model.load_state_dict(load_url(model_urls['resnext152']))
+#     return model
+
+
+def load_url(url, model_dir='./pretrained', map_location=None):
+    if not os.path.exists(model_dir):
+        os.makedirs(model_dir)
+    filename = url.split('/')[-1]
+    cached_file = os.path.join(model_dir, filename)
+    if not os.path.exists(cached_file):
+        sys.stderr.write('Downloading: "{}" to {}\n'.format(url, cached_file))
+        urlretrieve(url, cached_file)
+    return torch.load(cached_file, map_location=map_location)
diff --git a/netdissect/segviz.py b/netdissect/segviz.py
new file mode 100644
index 0000000000000000000000000000000000000000..db7f5dfc5ced056f0db84520ba0284ff11183956
--- /dev/null
+++ b/netdissect/segviz.py
@@ -0,0 +1,320 @@
+import numpy, scipy, PIL, torch
+
+def seg_as_image(seg, size=None):
+    return PIL.Image.fromarray(
+            segment_visualization(seg.cpu().numpy(), size=size))
+
+def swatch_image(label, size=15):
+    return PIL.Image.new("RGB", (size, size), tuple(high_contrast[
+        label % len(high_contrast)]))
+
+def segment_key(seg, segmodel, max_labels=6):
+    seglabels, _ = segmodel.get_label_and_category_names()
+    bc = torch.bincount(seg.view(-1)).cpu()
+    result = []
+    for ind in bc.sort()[1].flip(0):
+        if len(result) >= max_labels or bc[ind].item() == 0:
+            break
+        result.append((swatch_image(ind), seglabels[ind][0]))
+    return result
+
+def segment_key_with_lbls(seg, seglabels, max_labels=6):
+    bc = torch.bincount(seg.view(-1)).cpu()
+    result = []
+    for ind in bc.sort()[1].flip(0):
+        if len(result) >= max_labels or bc[ind].item() == 0:
+            break
+        result.append((swatch_image(ind), seglabels[ind]))
+    return result
+
+def segment_visualization(seg, size=None):
+    # Handle both 2d tensor (single label segmentation) and 3d tensor
+    # (multilabel segmentation)
+    if len(seg.shape) == 2:
+        seg = seg[None,:,:]
+    result = numpy.zeros((seg.shape[1] * seg.shape[2], 3), dtype=numpy.uint8)
+    flatseg = seg.reshape(seg.shape[0], seg.shape[1] * seg.shape[2])
+    bc = numpy.bincount(flatseg.flatten())
+    top = numpy.argsort(-bc)
+    # In a multilabel segmentation, we can't draw everything.
+    # Draw the fewest-pixel labels last.  (We could pick the opposite order.)
+    for label in top:
+        if label == 0:
+            continue
+        if bc[label] == 0:
+            break
+        bitmap = ((flatseg == label).sum(axis=0) > 0)
+        result[bitmap] = high_contrast_arr[label % len(high_contrast_arr)]
+    result = result.reshape((seg.shape[1], seg.shape[2], 3))
+    if size is not None:
+        if not hasattr(size, '__len__'):
+            size = (size, size)
+        zoom_ratio = [t / s for t, s in zip(size, result.shape[:2])] + [1]
+        result = scipy.ndimage.zoom(result, zoom_ratio, order=0)
+    return result
+
+# A palette that maximizes perceptual contrast between entries.
+# https://stackoverflow.com/questions/33295120
+high_contrast = [
+    [0, 0, 0], [255, 255, 0], [28, 230, 255], [255, 52, 255],
+    [0, 137, 65],   # tree - it green
+    [183, 151, 98], # building - make it brownish
+    [0, 111, 166], [163, 0, 89],
+    [255, 219, 229], [122, 73, 0], [0, 0, 166], [99, 255, 172],
+    [255, 74, 70],  # chair - make it orange red
+    [0, 77, 67], [143, 176, 255], [153, 125, 135],
+    [90, 0, 7], [128, 150, 147], [254, 255, 230], [27, 68, 0],
+    [79, 198, 1], [59, 93, 255], [74, 59, 83], [255, 47, 128],
+    [97, 97, 90], [186, 9, 0], [107, 121, 0], [0, 194, 160],
+    [255, 170, 146], [255, 144, 201], [185, 3, 170], [209, 97, 0],
+    [221, 239, 255], [0, 0, 53], [123, 79, 75], [161, 194, 153],
+    [48, 0, 24], [10, 166, 216], [1, 51, 73], [0, 132, 111],
+    [55, 33, 1], [255, 181, 0], [194, 255, 237], [160, 121, 191],
+    [204, 7, 68], [192, 185, 178], [194, 255, 153], [0, 30, 9],
+    [0, 72, 156], [111, 0, 98], [12, 189, 102], [238, 195, 255],
+    [69, 109, 117], [183, 123, 104], [122, 135, 161], [120, 141, 102],
+    [136, 85, 120], [250, 208, 159], [255, 138, 154], [209, 87, 160],
+    [190, 196, 89], [69, 102, 72], [0, 134, 237], [136, 111, 76],
+    [52, 54, 45], [180, 168, 189], [0, 166, 170], [69, 44, 44],
+    [99, 99, 117], [163, 200, 201], [255, 145, 63], [147, 138, 129],
+    [87, 83, 41], [0, 254, 207], [176, 91, 111], [140, 208, 255],
+    [59, 151, 0], [4, 247, 87], [200, 161, 161], [30, 110, 0],
+    [121, 0, 215], [167, 117, 0], [99, 103, 169], [160, 88, 55],
+    [107, 0, 44], [119, 38, 0], [215, 144, 255], [155, 151, 0],
+    [84, 158, 121], [255, 246, 159], [32, 22, 37], [114, 65, 143],
+    [188, 35, 255], [153, 173, 192], [58, 36, 101], [146, 35, 41],
+    [91, 69, 52], [253, 232, 220], [64, 78, 85], [0, 137, 163],
+    [203, 126, 152], [164, 232, 4], [50, 78, 114], [106, 58, 76],
+    [131, 171, 88], [0, 28, 30], [209, 247, 206], [0, 75, 40],
+    [200, 208, 246], [163, 164, 137], [128, 108, 102], [34, 40, 0],
+    [191, 86, 80], [232, 48, 0], [102, 121, 109], [218, 0, 124],
+    [255, 26, 89], [138, 219, 180], [30, 2, 0], [91, 78, 81],
+    [200, 149, 197], [50, 0, 51], [255, 104, 50], [102, 225, 211],
+    [207, 205, 172], [208, 172, 148], [126, 211, 121], [1, 44, 88],
+    [122, 123, 255], [214, 142, 1], [53, 51, 57], [120, 175, 161],
+    [254, 178, 198], [117, 121, 124], [131, 115, 147], [148, 58, 77],
+    [181, 244, 255], [210, 220, 213], [149, 86, 189], [106, 113, 74],
+    [0, 19, 37], [2, 82, 95], [10, 163, 247], [233, 129, 118],
+    [219, 213, 221], [94, 188, 209], [61, 79, 68], [126, 100, 5],
+    [2, 104, 78], [150, 43, 117], [141, 133, 70], [150, 149, 197],
+    [231, 115, 206], [216, 106, 120], [62, 137, 190], [202, 131, 78],
+    [81, 138, 135], [91, 17, 60], [85, 129, 59], [231, 4, 196],
+    [0, 0, 95], [169, 115, 153], [75, 129, 96], [89, 115, 138],
+    [255, 93, 167], [247, 201, 191], [100, 49, 39], [81, 58, 1],
+    [107, 148, 170], [81, 160, 88], [164, 91, 2], [29, 23, 2],
+    [226, 0, 39], [231, 171, 99], [76, 96, 1], [156, 105, 102],
+    [100, 84, 123], [151, 151, 158], [0, 106, 102], [57, 20, 6],
+    [244, 215, 73], [0, 69, 210], [0, 108, 49], [221, 182, 208],
+    [124, 101, 113], [159, 178, 164], [0, 216, 145], [21, 160, 138],
+    [188, 101, 233], [255, 255, 254], [198, 220, 153], [32, 59, 60],
+    [103, 17, 144], [107, 58, 100], [245, 225, 255], [255, 160, 242],
+    [204, 170, 53], [55, 69, 39], [139, 180, 0], [121, 120, 104],
+    [198, 0, 90], [59, 0, 10], [200, 98, 64], [41, 96, 124],
+    [64, 35, 52], [125, 90, 68], [204, 184, 124], [184, 129, 131],
+    [170, 81, 153], [181, 214, 195], [163, 132, 105], [159, 148, 240],
+    [167, 69, 113], [184, 148, 166], [113, 187, 140], [0, 180, 51],
+    [120, 158, 201], [109, 128, 186], [149, 63, 0], [94, 255, 3],
+    [228, 255, 252], [27, 225, 119], [188, 177, 229], [118, 145, 47],
+    [0, 49, 9], [0, 96, 205], [210, 0, 150], [137, 85, 99],
+    [41, 32, 29], [91, 50, 19], [167, 111, 66], [137, 65, 46],
+    [26, 58, 42], [73, 75, 90], [168, 140, 133], [244, 171, 170],
+    [163, 243, 171], [0, 198, 200], [234, 139, 102], [149, 138, 159],
+    [189, 201, 210], [159, 160, 100], [190, 71, 0], [101, 129, 136],
+    [131, 164, 133], [69, 60, 35], [71, 103, 93], [58, 63, 0],
+    [6, 18, 3], [223, 251, 113], [134, 142, 126], [152, 208, 88],
+    [108, 143, 125], [215, 191, 194], [60, 62, 110], [216, 61, 102],
+    [47, 93, 155], [108, 94, 70], [210, 91, 136], [91, 101, 108],
+    [0, 181, 127], [84, 92, 70], [134, 96, 151], [54, 93, 37],
+    [37, 47, 153], [0, 204, 255], [103, 78, 96], [252, 0, 156],
+    [146, 137, 107], [30, 35, 36], [222, 201, 178], [157, 73, 72],
+    [133, 171, 180], [52, 33, 66], [208, 150, 133], [164, 172, 172],
+    [0, 255, 255], [174, 156, 134], [116, 42, 51], [14, 114, 197],
+    [175, 216, 236], [192, 100, 185], [145, 2, 140], [254, 237, 191],
+    [255, 183, 137], [156, 184, 228], [175, 255, 209], [42, 54, 76],
+    [79, 74, 67], [100, 112, 149], [52, 187, 255], [128, 119, 129],
+    [146, 0, 3], [179, 165, 167], [1, 134, 21], [241, 255, 200],
+    [151, 111, 92], [255, 59, 193], [255, 95, 107], [7, 125, 132],
+    [245, 109, 147], [87, 113, 218], [78, 30, 42], [131, 0, 85],
+    [2, 211, 70], [190, 69, 45], [0, 144, 94], [190, 0, 40],
+    [110, 150, 227], [0, 118, 153], [254, 201, 109], [156, 106, 125],
+    [63, 161, 184], [137, 61, 227], [121, 180, 214], [127, 212, 217],
+    [103, 81, 187], [178, 141, 45], [226, 122, 5], [221, 156, 184],
+    [170, 188, 122], [152, 0, 52], [86, 26, 2], [143, 127, 0],
+    [99, 80, 0], [205, 125, 174], [138, 94, 45], [255, 179, 225],
+    [107, 100, 102], [198, 211, 0], [1, 0, 226], [136, 236, 105],
+    [143, 204, 190], [33, 0, 28], [81, 31, 77], [227, 246, 227],
+    [255, 142, 177], [107, 79, 41], [163, 127, 70], [106, 89, 80],
+    [31, 42, 26], [4, 120, 77], [16, 24, 53], [230, 224, 208],
+    [255, 116, 254], [0, 164, 95], [143, 93, 248], [75, 0, 89],
+    [65, 47, 35], [216, 147, 158], [219, 157, 114], [96, 65, 67],
+    [181, 186, 206], [152, 158, 183], [210, 196, 219], [165, 135, 175],
+    [119, 215, 150], [127, 140, 148], [255, 155, 3], [85, 81, 150],
+    [49, 221, 174], [116, 182, 113], [128, 38, 71], [42, 55, 63],
+    [1, 74, 104], [105, 102, 40], [76, 123, 109], [0, 44, 39],
+    [122, 69, 34], [59, 88, 89], [229, 211, 129], [255, 243, 255],
+    [103, 159, 160], [38, 19, 0], [44, 87, 66], [145, 49, 175],
+    [175, 93, 136], [199, 112, 106], [97, 171, 31], [140, 242, 212],
+    [197, 217, 184], [159, 255, 251], [191, 69, 204], [73, 57, 65],
+    [134, 59, 96], [185, 0, 118], [0, 49, 119], [197, 130, 210],
+    [193, 179, 148], [96, 43, 112], [136, 120, 104], [186, 191, 176],
+    [3, 0, 18], [209, 172, 254], [127, 222, 254], [75, 92, 113],
+    [163, 160, 151], [230, 109, 83], [99, 123, 93], [146, 190, 165],
+    [0, 248, 179], [190, 221, 255], [61, 181, 167], [221, 50, 72],
+    [182, 228, 222], [66, 119, 69], [89, 140, 90], [185, 76, 89],
+    [129, 129, 213], [148, 136, 139], [254, 214, 189], [83, 109, 49],
+    [110, 255, 146], [228, 232, 255], [32, 226, 0], [255, 208, 242],
+    [76, 131, 161], [189, 115, 34], [145, 92, 78], [140, 71, 135],
+    [2, 81, 23], [162, 170, 69], [45, 27, 33], [169, 221, 176],
+    [255, 79, 120], [82, 133, 0], [0, 154, 46], [23, 252, 228],
+    [113, 85, 90], [82, 93, 130], [0, 25, 90], [150, 120, 116],
+    [85, 85, 88], [11, 33, 44], [30, 32, 43], [239, 191, 196],
+    [111, 151, 85], [111, 117, 134], [80, 29, 29], [55, 45, 0],
+    [116, 29, 22], [94, 179, 147], [181, 180, 0], [221, 74, 56],
+    [54, 61, 255], [173, 101, 82], [102, 53, 175], [131, 107, 186],
+    [152, 170, 127], [70, 72, 54], [50, 44, 62], [124, 185, 186],
+    [91, 105, 101], [112, 125, 61], [122, 0, 29], [110, 70, 54],
+    [68, 58, 56], [174, 129, 255], [72, 144, 121], [137, 115, 52],
+    [0, 144, 135], [218, 113, 60], [54, 22, 24], [255, 111, 1],
+    [0, 102, 121], [55, 14, 119], [75, 58, 131], [201, 226, 230],
+    [196, 65, 112], [255, 69, 38], [115, 190, 84], [196, 223, 114],
+    [173, 255, 96], [0, 68, 125], [220, 206, 201], [189, 148, 121],
+    [101, 110, 91], [236, 82, 0], [255, 110, 194], [122, 97, 126],
+    [221, 174, 162], [119, 131, 127], [165, 51, 39], [96, 142, 255],
+    [181, 153, 215], [165, 1, 73], [78, 0, 37], [201, 177, 169],
+    [3, 145, 154], [27, 42, 37], [229, 0, 241], [152, 46, 11],
+    [182, 113, 128], [224, 88, 89], [0, 96, 57], [87, 143, 155],
+    [48, 82, 48], [206, 147, 76], [179, 194, 190], [192, 186, 192],
+    [181, 6, 211], [23, 12, 16], [76, 83, 79], [34, 68, 81],
+    [62, 65, 65], [120, 114, 109], [182, 96, 43], [32, 4, 65],
+    [221, 181, 136], [73, 114, 0], [197, 170, 182], [3, 60, 97],
+    [113, 178, 245], [169, 224, 136], [73, 121, 176], [162, 195, 223],
+    [120, 65, 73], [45, 43, 23], [62, 14, 47], [87, 52, 76],
+    [0, 145, 190], [228, 81, 209], [75, 75, 106], [92, 1, 26],
+    [124, 128, 96], [255, 148, 145], [76, 50, 93], [0, 92, 139],
+    [229, 253, 164], [104, 209, 182], [3, 38, 65], [20, 0, 35],
+    [134, 131, 169], [207, 255, 0], [167, 44, 62], [52, 71, 90],
+    [177, 187, 154], [180, 160, 79], [141, 145, 142], [161, 104, 166],
+    [129, 61, 58], [66, 82, 24], [218, 131, 134], [119, 97, 51],
+    [86, 57, 48], [132, 152, 174], [144, 193, 211], [181, 102, 107],
+    [155, 88, 94], [133, 100, 101], [173, 124, 144], [226, 188, 0],
+    [227, 170, 224], [178, 194, 254], [253, 0, 57], [0, 155, 117],
+    [255, 244, 109], [232, 126, 172], [223, 227, 230], [132, 133, 144],
+    [170, 146, 151], [131, 161, 147], [87, 121, 119], [62, 113, 88],
+    [198, 66, 137], [234, 0, 114], [196, 168, 203], [85, 200, 153],
+    [231, 143, 207], [0, 69, 71], [246, 226, 227], [150, 103, 22],
+    [55, 143, 219], [67, 94, 106], [218, 0, 4], [27, 0, 15],
+    [91, 156, 143], [110, 43, 82], [1, 17, 21], [227, 232, 196],
+    [174, 59, 133], [234, 28, 169], [255, 158, 107], [69, 125, 139],
+    [146, 103, 139], [0, 205, 187], [156, 204, 4], [0, 46, 56],
+    [150, 197, 127], [207, 246, 180], [73, 40, 24], [118, 110, 82],
+    [32, 55, 14], [227, 209, 159], [46, 60, 48], [178, 234, 206],
+    [243, 189, 164], [162, 78, 61], [151, 111, 217], [140, 159, 168],
+    [124, 43, 115], [78, 95, 55], [93, 84, 98], [144, 149, 111],
+    [106, 167, 118], [219, 203, 246], [218, 113, 255], [152, 124, 149],
+    [82, 50, 60], [187, 60, 66], [88, 77, 57], [79, 193, 95],
+    [162, 185, 193], [121, 219, 33], [29, 89, 88], [189, 116, 78],
+    [22, 11, 0], [32, 34, 26], [107, 130, 149], [0, 224, 228],
+    [16, 36, 1], [27, 120, 42], [218, 169, 181], [176, 65, 93],
+    [133, 146, 83], [151, 160, 148], [6, 227, 196], [71, 104, 140],
+    [124, 103, 85], [7, 92, 0], [117, 96, 213], [125, 159, 0],
+    [195, 109, 150], [77, 145, 62], [95, 66, 118], [252, 228, 200],
+    [48, 48, 82], [79, 56, 27], [229, 165, 50], [112, 102, 144],
+    [170, 154, 146], [35, 115, 99], [115, 1, 62], [255, 144, 121],
+    [167, 154, 116], [2, 155, 219], [255, 1, 105], [199, 210, 231],
+    [202, 136, 105], [128, 255, 205], [187, 31, 105], [144, 176, 171],
+    [125, 116, 169], [252, 199, 219], [153, 55, 91], [0, 171, 77],
+    [171, 174, 209], [190, 157, 145], [230, 229, 167], [51, 44, 34],
+    [221, 88, 123], [245, 255, 247], [93, 48, 51], [109, 56, 0],
+    [255, 0, 32], [181, 123, 179], [215, 255, 230], [197, 53, 169],
+    [38, 0, 9], [106, 135, 129], [168, 171, 180], [212, 82, 98],
+    [121, 75, 97], [70, 33, 178], [141, 164, 219], [199, 200, 144],
+    [111, 233, 173], [162, 67, 167], [178, 176, 129], [24, 27, 0],
+    [40, 97, 84], [76, 164, 59], [106, 149, 115], [168, 68, 29],
+    [92, 114, 123], [115, 134, 113], [208, 207, 203], [137, 123, 119],
+    [31, 63, 34], [65, 69, 167], [218, 152, 148], [161, 117, 122],
+    [99, 36, 60], [173, 170, 255], [0, 205, 226], [221, 188, 98],
+    [105, 142, 177], [32, 132, 98], [0, 183, 224], [97, 74, 68],
+    [155, 187, 87], [122, 92, 84], [133, 122, 80], [118, 107, 126],
+    [1, 72, 51], [255, 131, 71], [122, 142, 186], [39, 71, 64],
+    [148, 100, 68], [235, 216, 230], [100, 98, 65], [55, 57, 23],
+    [106, 212, 80], [129, 129, 123], [212, 153, 227], [151, 148, 64],
+    [1, 26, 18], [82, 101, 84], [181, 136, 92], [164, 153, 165],
+    [3, 173, 137], [179, 0, 139], [227, 196, 181], [150, 83, 31],
+    [134, 113, 117], [116, 86, 158], [97, 125, 159], [231, 4, 82],
+    [6, 126, 175], [166, 151, 182], [183, 135, 168], [156, 255, 147],
+    [49, 29, 25], [58, 148, 89], [110, 116, 110], [176, 197, 174],
+    [132, 237, 247], [237, 52, 136], [117, 76, 120], [56, 70, 68],
+    [199, 132, 123], [0, 182, 197], [127, 166, 112], [193, 175, 158],
+    [42, 127, 255], [114, 165, 140], [255, 192, 127], [157, 235, 221],
+    [217, 124, 142], [126, 124, 147], [98, 230, 116], [181, 99, 158],
+    [255, 168, 97], [194, 165, 128], [141, 156, 131], [183, 5, 70],
+    [55, 43, 46], [0, 152, 255], [152, 89, 117], [32, 32, 76],
+    [255, 108, 96], [68, 80, 131], [133, 2, 170], [114, 54, 31],
+    [150, 118, 163], [72, 68, 73], [206, 214, 194], [59, 22, 74],
+    [204, 167, 99], [44, 127, 119], [2, 34, 123], [163, 126, 111],
+    [205, 230, 220], [205, 255, 251], [190, 129, 26], [247, 113, 131],
+    [237, 230, 226], [205, 198, 180], [255, 224, 158], [58, 114, 113],
+    [255, 123, 89], [78, 78, 1], [74, 198, 132], [139, 200, 145],
+    [188, 138, 150], [207, 99, 83], [220, 222, 92], [94, 170, 221],
+    [246, 160, 173], [226, 105, 170], [163, 218, 228], [67, 110, 131],
+    [0, 46, 23], [236, 251, 255], [161, 194, 182], [80, 0, 63],
+    [113, 105, 91], [103, 196, 187], [83, 110, 255], [93, 90, 72],
+    [137, 0, 57], [150, 147, 129], [55, 21, 33], [94, 70, 101],
+    [170, 98, 195], [141, 111, 129], [44, 97, 53], [65, 6, 1],
+    [86, 70, 32], [230, 144, 52], [109, 166, 189], [229, 142, 86],
+    [227, 166, 139], [72, 177, 118], [210, 125, 103], [181, 178, 104],
+    [127, 132, 39], [255, 132, 230], [67, 87, 64], [234, 228, 8],
+    [244, 245, 255], [50, 88, 0], [75, 107, 165], [173, 206, 255],
+    [155, 138, 204], [136, 81, 56], [88, 117, 193], [126, 115, 17],
+    [254, 165, 202], [159, 139, 91], [165, 91, 84], [137, 0, 106],
+    [175, 117, 111], [42, 32, 0], [116, 153, 161], [255, 181, 80],
+    [0, 1, 30], [209, 81, 28], [104, 129, 81], [188, 144, 138],
+    [120, 200, 235], [133, 2, 255], [72, 61, 48], [196, 34, 33],
+    [94, 167, 255], [120, 87, 21], [12, 234, 145], [255, 250, 237],
+    [179, 175, 157], [62, 61, 82], [90, 155, 194], [156, 47, 144],
+    [141, 87, 0], [173, 215, 156], [0, 118, 139], [51, 125, 0],
+    [197, 151, 0], [49, 86, 220], [148, 69, 117], [236, 255, 220],
+    [210, 76, 178], [151, 112, 60], [76, 37, 127], [158, 3, 102],
+    [136, 255, 236], [181, 100, 129], [57, 109, 43], [86, 115, 95],
+    [152, 131, 118], [155, 177, 149], [169, 121, 92], [228, 197, 211],
+    [159, 79, 103], [30, 43, 57], [102, 67, 39], [175, 206, 120],
+    [50, 46, 223], [134, 180, 135], [194, 48, 0], [171, 232, 107],
+    [150, 101, 109], [37, 14, 53], [166, 0, 25], [0, 128, 207],
+    [202, 239, 255], [50, 63, 97], [164, 73, 220], [106, 157, 59],
+    [255, 90, 228], [99, 106, 1], [209, 108, 218], [115, 96, 96],
+    [255, 186, 173], [211, 105, 180], [255, 222, 214], [108, 109, 116],
+    [146, 125, 94], [132, 93, 112], [91, 98, 193], [47, 74, 54],
+    [228, 95, 53], [255, 59, 83], [172, 132, 221], [118, 41, 136],
+    [112, 236, 152], [64, 133, 67], [44, 53, 51], [46, 24, 45],
+    [50, 57, 37], [25, 24, 27], [47, 46, 44], [2, 60, 50],
+    [155, 158, 226], [88, 175, 173], [92, 66, 77], [122, 197, 166],
+    [104, 93, 117], [185, 188, 189], [131, 67, 87], [26, 123, 66],
+    [46, 87, 170], [229, 81, 153], [49, 110, 71], [205, 0, 197],
+    [106, 0, 77], [127, 187, 236], [243, 86, 145], [215, 197, 74],
+    [98, 172, 183], [203, 161, 188], [162, 138, 154], [108, 63, 59],
+    [255, 228, 125], [220, 186, 227], [95, 129, 109], [58, 64, 74],
+    [125, 191, 50], [230, 236, 220], [133, 44, 25], [40, 83, 102],
+    [184, 203, 156], [14, 13, 0], [75, 93, 86], [107, 84, 63],
+    [226, 113, 114], [5, 104, 236], [46, 181, 0], [210, 22, 86],
+    [239, 175, 255], [104, 32, 33], [45, 32, 17], [218, 76, 255],
+    [112, 150, 142], [255, 123, 125], [74, 25, 48], [232, 194, 130],
+    [231, 219, 188], [166, 132, 134], [31, 38, 60], [54, 87, 78],
+    [82, 206, 121], [173, 170, 169], [138, 159, 69], [101, 66, 210],
+    [0, 251, 140], [93, 105, 123], [204, 210, 127], [148, 165, 161],
+    [121, 2, 41], [227, 131, 230], [126, 164, 193], [78, 68, 82],
+    [75, 44, 0], [98, 11, 112], [49, 76, 30], [135, 74, 166],
+    [227, 0, 145], [102, 70, 10], [235, 154, 139], [234, 195, 163],
+    [152, 234, 179], [171, 145, 128], [184, 85, 47], [26, 43, 47],
+    [148, 221, 197], [157, 140, 118], [156, 131, 51], [148, 169, 201],
+    [57, 41, 53], [140, 103, 94], [204, 233, 58], [145, 113, 0],
+    [1, 64, 11], [68, 152, 150], [28, 163, 112], [224, 141, 167],
+    [139, 74, 78], [102, 119, 118], [70, 146, 173], [103, 189, 168],
+    [105, 37, 92], [211, 191, 255], [74, 81, 50], [126, 146, 133],
+    [119, 115, 60], [231, 160, 204], [81, 162, 136], [44, 101, 106],
+    [77, 92, 94], [201, 64, 58], [221, 215, 243], [0, 88, 68],
+    [180, 162, 0], [72, 143, 105], [133, 129, 130], [212, 233, 185],
+    [61, 115, 151], [202, 232, 206], [214, 0, 52], [170, 103, 70],
+    [158, 85, 133], [186, 98, 0]
+]
+
+high_contrast_arr = numpy.array(high_contrast, dtype=numpy.uint8)
diff --git a/netdissect/setting.py b/netdissect/setting.py
new file mode 100644
index 0000000000000000000000000000000000000000..e706015de3ac8bc4309b00a2fe2144c613260356
--- /dev/null
+++ b/netdissect/setting.py
@@ -0,0 +1,250 @@
+import torch, torchvision, os, collections
+from . import parallelfolder, zdataset, renormalize, encoder_net, segmenter
+from . import bargraph
+
+def load_proggan(domain):
+    # Automatically download and cache progressive GAN model
+    # (From Karras, converted from Tensorflow to Pytorch.)
+    from . import proggan
+    weights_filename = dict(
+        bedroom='proggan_bedroom-d8a89ff1.pth',
+        church='proggan_churchoutdoor-7e701dd5.pth',
+        conferenceroom='proggan_conferenceroom-21e85882.pth',
+        diningroom='proggan_diningroom-3aa0ab80.pth',
+        kitchen='proggan_kitchen-67f1e16c.pth',
+        livingroom='proggan_livingroom-5ef336dd.pth',
+        restaurant='proggan_restaurant-b8578299.pth',
+        celebhq='proggan_celebhq-620d161c.pth')[domain]
+    # Posted here.
+    url = 'http://gandissect.csail.mit.edu/models/' + weights_filename
+    try:
+        sd = torch.hub.load_state_dict_from_url(url) # pytorch 1.1
+    except:
+        sd = torch.hub.model_zoo.load_url(url) # pytorch 1.0
+    model = proggan.from_state_dict(sd)
+    return model
+
+def load_vgg16(domain='places'):
+    assert domain == 'places'
+    model = torchvision.models.vgg16(num_classes=365)
+    model.features = torch.nn.Sequential(collections.OrderedDict(zip([
+        'conv1_1', 'relu1_1',
+        'conv1_2', 'relu1_2',
+        'pool1',
+        'conv2_1', 'relu2_1',
+        'conv2_2', 'relu2_2',
+        'pool2',
+        'conv3_1', 'relu3_1',
+        'conv3_2', 'relu3_2',
+        'conv3_3', 'relu3_3',
+        'pool3',
+        'conv4_1', 'relu4_1',
+        'conv4_2', 'relu4_2',
+        'conv4_3', 'relu4_3',
+        'pool4',
+        'conv5_1', 'relu5_1',
+        'conv5_2', 'relu5_2',
+        'conv5_3', 'relu5_3',
+        'pool5'],
+        model.features)))
+    model.classifier = torch.nn.Sequential(collections.OrderedDict(zip([
+        'fc6', 'relu6',
+        'drop6',
+        'fc7', 'relu7',
+        'drop7',
+        'fc8a'],
+        model.classifier)))
+    baseurl = 'http://gandissect.csail.mit.edu/models/'
+    url = baseurl + 'vgg16_places365-6e38b568.pth'
+    try:
+        sd = torch.hub.load_state_dict_from_url(url) # pytorch 1.1
+    except:
+        sd = torch.hub.model_zoo.load_url(url) # pytorch 1.0
+
+    model.load_state_dict(sd)
+    model.eval()
+    return model
+
+
+def load_proggan_ablation(modelname):
+    # Automatically download and cache progressive GAN model
+    # (From Karras, converted from Tensorflow to Pytorch.)
+
+    from . import proggan_ablation
+    model_classname, weights_filename = {
+		"equalized-learning-rate": (proggan_ablation.G128_equallr,
+            "equalized-learning-rate-88ed833d.pth"),
+        "minibatch-discrimination": (proggan_ablation.G128_minibatch_disc,
+            "minibatch-discrimination-604c5731.pth"),
+        "minibatch-stddev": (proggan_ablation.G128_minibatch_disc,
+            "minibatch-stddev-068bc667.pth"),
+        "pixelwise-normalization": (proggan_ablation.G128_pixelwisenorm,
+            "pixelwise-normalization-4da7e9ce.pth"),
+        "progressive-training": (proggan_ablation.G128_simple,
+            "progressive-training-70bd90ac.pth"),
+        # "revised-training-parameters": (_,
+        #     "revised-training-parameters-902f5486.pth")
+        "small-minibatch": (proggan_ablation.G128_simple,
+            "small-minibatch-04143d18.pth"),
+        "wgangp": (proggan_ablation.G128_simple,
+            "wgangp-beaa509a.pth")
+        }[modelname]
+    # Posted here.
+    url = 'http://gandissect.csail.mit.edu/models/ablations/' + weights_filename
+    try:
+        sd = torch.hub.load_state_dict_from_url(url) # pytorch 1.1
+    except:
+        sd = torch.hub.model_zoo.load_url(url) # pytorch 1.0
+    model = model_classname()
+    model.load_state_dict(sd)
+    return model
+
+def load_proggan_inversion(modelname):
+    # A couple inversion models pretrained using the code in this repo.
+
+    from . import proggan_ablation
+    model_classname, weights_filename = {
+		"church": (encoder_net.HybridLayerNormEncoder,
+            "church_invert_hybrid_cse-43e52428.pth"),
+		"bedroom": (encoder_net.HybridLayerNormEncoder,
+            "bedroom_invert_hybrid_cse-b943528e.pth"),
+        }[modelname]
+    # Posted here.
+    url = 'http://gandissect.csail.mit.edu/models/encoders/' + weights_filename
+    try:
+        sd = torch.hub.load_state_dict_from_url(url) # pytorch 1.1
+    except:
+        sd = torch.hub.model_zoo.load_url(url) # pytorch 1.0
+    if 'state_dict' in sd:
+        sd = sd['state_dict']
+    sd = {k.replace('model.', ''): v for k, v in sd.items()}
+    model = model_classname()
+    model.load_state_dict(sd)
+    model.eval()
+    return model
+
+
+g_datasets = {}
+
+def load_dataset(domain, split=None, full=False, download=True):
+    if domain in g_datasets:
+        return g_datasets[domain]
+    if domain == 'places':
+        if split is None:
+            split = 'val'
+        dirname = 'datasets/microimagenet'
+        if download and not os.path.exists(dirname):
+            os.makedirs('datasets', exist_ok=True)
+            torchvision.datasets.utils.download_and_extract_archive(
+                'http://gandissect.csail.mit.edu/datasets/' +
+                'microimagenet.zip',
+                'datasets')
+        return parallelfolder.ParallelImageFolders([dirname],
+                classification=True,
+                shuffle=True,
+                transform=g_places_transform)
+    else:
+        # Assume lsun dataset
+        if split is None:
+            split = 'train'
+        dirname = os.path.join(
+                'datasets', 'lsun' if full else 'minilsun', domain)
+        dirname += '_' + split
+        if download and not full and not os.path.exists('datasets/minilsun'):
+            os.makedirs('datasets', exist_ok=True)
+            torchvision.datasets.utils.download_and_extract_archive(
+                    'http://gandissect.csail.mit.edu/datasets/minilsun.zip',
+                    'datasets',
+                    md5='a67a898673a559db95601314b9b51cd5')
+        return parallelfolder.ParallelImageFolders([dirname],
+                shuffle=True,
+                transform=g_transform)
+
+g_transform = torchvision.transforms.Compose([
+    torchvision.transforms.Resize(256),
+    torchvision.transforms.CenterCrop(256),
+    torchvision.transforms.ToTensor(),
+    torchvision.transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))])
+
+g_places_transform = torchvision.transforms.Compose([
+    torchvision.transforms.Resize(256),
+    torchvision.transforms.CenterCrop(224),
+    torchvision.transforms.ToTensor(),
+    renormalize.NORMALIZER['imagenet']])
+
+def load_segmenter(segmenter_name='netpqc'):
+    '''Loads the segementer.'''
+    all_parts = ('p' in segmenter_name)
+    quad_seg = ('q' in segmenter_name)
+    textures = ('x' in segmenter_name)
+    colors = ('c' in segmenter_name)
+
+    segmodels = []
+    segmodels.append(segmenter.UnifiedParsingSegmenter(segsizes=[256],
+            all_parts=all_parts,
+            segdiv=('quad' if quad_seg else None)))
+    if textures:
+        segmenter.ensure_segmenter_downloaded('datasets/segmodel', 'texture')
+        segmodels.append(segmenter.SemanticSegmenter(
+            segvocab="texture", segarch=("resnet18dilated", "ppm_deepsup")))
+    if colors:
+        segmenter.ensure_segmenter_downloaded('datasets/segmodel', 'color')
+        segmodels.append(segmenter.SemanticSegmenter(
+            segvocab="color", segarch=("resnet18dilated", "ppm_deepsup")))
+    if len(segmodels) == 1:
+        segmodel = segmodels[0]
+    else:
+        segmodel = segmenter.MergedSegmenter(segmodels)
+    seglabels = [l for l, c in segmodel.get_label_and_category_names()[0]]
+    segcatlabels = segmodel.get_label_and_category_names()[0]
+    return segmodel, seglabels, segcatlabels
+
+def graph_conceptcatlist(conceptcatlist,  cats = None, print_nums = False, **kwargs):
+    count = collections.defaultdict(int)
+    catcount = collections.defaultdict(int)
+    for c in conceptcatlist:
+        count[c] += 1
+    for c in count.keys():
+        catcount[c[1]] += 1
+    if cats is None:
+        cats = ['object', 'part', 'material', 'texture', 'color']
+    catorder = dict((c, i) for i, c in enumerate(cats))
+    sorted_labels = sorted(count.keys(),
+        key=lambda x: (catorder[x[1]], -count[x]))
+    sorted_labels
+    tot_num = 0
+    if print_nums:
+        for k in sorted_labels:
+            print(count[k])
+            tot_num += count[k]
+        print("Total unique concepts: {}".format(tot_num))
+    return bargraph.make_svg_bargraph(
+        [label for label, cat in sorted_labels],
+        [count[k] for k in sorted_labels],
+        [(c, catcount[c]) for c in cats], **kwargs)
+
+def save_concept_graph(filename, conceptlist):
+    svg = graph_conceptlist(conceptlist, file_header=True)
+    with open(filename, 'w') as f:
+        f.write(svg)
+
+def save_conceptcat_graph(filename, conceptcatlist):
+    svg = graph_conceptcatlist(conceptcatlist, barheight=80, file_header=True)
+    with open(filename, 'w') as f:
+        f.write(svg)
+
+def load_test_image(imgnum, split, model, full=False):
+    if split == 'gan':
+        with torch.no_grad():
+            generator = load_proggan(model)
+            z = zdataset.z_sample_for_model(generator, size=(imgnum + 1)
+                    )[imgnum]
+            z = z[None]
+            return generator(z), z
+    assert split in ['train', 'val']
+    ds = load_dataset(model, split, full=full)
+    return ds[imgnum][0][None], None
+
+if __name__ == '__main__':
+    main()
+
diff --git a/netdissect/show.py b/netdissect/show.py
new file mode 100644
index 0000000000000000000000000000000000000000..6cd8190a4cd69b6aa9164048593d6a9f47a7e82f
--- /dev/null
+++ b/netdissect/show.py
@@ -0,0 +1,144 @@
+# show.py
+#
+# An abbreviated way to output simple HTML layout of text and images
+# into a python notebook.
+#
+# - show a PIL image to show an inline HTML <img>.
+# - show an array of items to vertically stack them, centered in a block.
+# - show an array of arrays to horizontally lay them out as inline blocks.
+# - show an array of tuples to create a table.
+
+import PIL.Image, base64, io, IPython, types, sys
+import html as html_module
+from IPython.display import display
+
+g_buffer = None
+
+def blocks(obj, space=''):
+    return IPython.display.HTML(space.join(blocks_tags(obj)))
+
+def rows(obj, space=''):
+    return IPython.display.HTML(space.join(rows_tags(obj)))
+
+def rows_tags(obj):
+    if isinstance(obj, dict):
+        obj = obj.items()
+    results = []
+    results.append('<table style="display:inline-table">')
+    for row in obj:
+        results.append('<tr style="padding:0">')
+        for item in row:
+            results.append('<td style="text-align:left; vertical-align:top;' +
+                'padding:1px">')
+            results.extend(blocks_tags(item))
+            results.append('</td>')
+        results.append('</tr>')
+    results.append('</table>')
+    return results
+
+def blocks_tags(obj):
+    results = []
+    if hasattr(obj, '_repr_html_'):
+        results.append(obj._repr_html_())
+    elif isinstance(obj, PIL.Image.Image):
+        results.append(pil_to_html(obj))
+    elif isinstance(obj, (str, int, float)):
+        results.append('<div>')
+        results.append(html_module.escape(str(obj)))
+        results.append('</div>')
+    elif isinstance(obj, dict):
+        results.extend(blocks_tags([(k, v) for k, v in obj.items()]))
+    elif hasattr(obj, '__iter__'):
+        if hasattr(obj, 'tolist'):
+            # Handle numpy/pytorch tensors as lists.
+            try:
+                obj = obj.tolist()
+            except:
+                pass
+        blockstart, blockend, tstart, tend, rstart, rend, cstart, cend = [
+          '<div style="display:inline-block;text-align:center;line-height:1;' +
+              'vertical-align:top;padding:1px">',
+          '</div>',
+          '<table style="display:inline-table">',
+          '</table>',
+          '<tr style="padding:0">',
+          '</tr>',
+          '<td style="text-align:left; vertical-align:top; padding:1px">',
+          '</td>',
+          ]
+        needs_end = False
+        table_mode = False
+        for i, line in enumerate(obj):
+            if i == 0:
+                needs_end = True
+                if isinstance(line, tuple):
+                    table_mode = True
+                    results.append(tstart)
+                else:
+                    results.append(blockstart)
+            if table_mode:
+                results.append(rstart)
+                if not isinstance(line, str) and hasattr(line, '__iter__'):
+                    for cell in line:
+                        results.append(cstart)
+                        results.extend(blocks_tags(cell))
+                        results.append(cend)
+                else:
+                    results.append(cstart)
+                    results.extend(blocks_tags(line))
+                    results.append(cend)
+                results.append(rend)
+            else:
+                results.extend(blocks_tags(line))
+        if needs_end:
+            results.append(table_mode and tend or blockend)
+    return results
+
+def pil_to_b64(img, format='png'):
+    buffered = io.BytesIO()
+    img.save(buffered, format=format)
+    return base64.b64encode(buffered.getvalue()).decode('utf-8')
+
+def pil_to_url(img, format='png'):
+    return 'data:image/%s;base64,%s' % (format, pil_to_b64(img, format))
+
+def pil_to_html(img, margin=1):
+    mattr = ' style="margin:%dpx"' % margin
+    return '<img src="%s"%s>' % (pil_to_url(img), mattr)
+
+def a(x, cols=None):
+    global g_buffer
+    if g_buffer is None:
+        g_buffer = []
+    g_buffer.append(x)
+    if cols is not None and len(g_buffer) >= cols:
+        flush()
+
+def reset():
+    global g_buffer
+    g_buffer = None
+
+def flush(*args, **kwargs):
+    global g_buffer
+    if g_buffer is not None:
+        x = g_buffer
+        g_buffer = None
+        display(blocks(x, *args, **kwargs))
+
+def show(x=None, *args, **kwargs):
+    flush(*args, **kwargs)
+    if x is not None:
+        display(blocks(x, *args, **kwargs))
+
+def html(obj, space=''):
+    return blocks(obj, space)._repr_html_()
+
+class CallableModule(types.ModuleType):
+    def __init__(self):
+        # or super().__init__(__name__) for Python 3
+        types.ModuleType.__init__(self, __name__)
+        self.__dict__.update(sys.modules[__name__].__dict__)
+    def __call__(self, x=None, *args, **kwargs):
+        show(x, *args, **kwargs)
+
+sys.modules[__name__] = CallableModule()
diff --git a/netdissect/statedict.py b/netdissect/statedict.py
new file mode 100644
index 0000000000000000000000000000000000000000..858a903b57724d9e3a17b8150beea30bdc206b97
--- /dev/null
+++ b/netdissect/statedict.py
@@ -0,0 +1,100 @@
+'''
+Utilities for dealing with simple state dicts as npz files instead of pth files.
+'''
+
+import torch
+from collections.abc import MutableMapping, Mapping
+
+def load_from_numpy_dict(model, numpy_dict, prefix='', examples=None):
+    '''
+    Loads a model from numpy_dict using load_state_dict.
+    Converts numpy types to torch types using the current state_dict
+    of the model to determine types and devices for the tensors.
+    Supports loading a subdict by prepending the given prefix to all keys.
+    '''
+    if prefix:
+        if not prefix.endswith('.'):
+            prefix = prefix + '.'
+        numpy_dict = PrefixSubDict(numpy_dict, prefix)
+    if examples is None:
+        exampels = model.state_dict()
+    torch_state_dict = TorchTypeMatchingDict(numpy_dict, examples)
+    model.load_state_dict(torch_state_dict)
+
+def save_to_numpy_dict(model, numpy_dict, prefix=''):
+    '''
+    Saves a model by copying tensors to numpy_dict.
+    Converts torch types to numpy types using `t.detach().cpu().numpy()`.
+    Supports saving a subdict by prepending the given prefix to all keys.
+    '''
+    if prefix:
+        if not prefix.endswith('.'):
+            prefix = prefix + '.'
+    for k, v in model.numpy_dict().items():
+        if isinstance(v, torch.Tensor):
+            v = v.detach().cpu().numpy()
+        numpy_dict[prefix + k] = v
+
+class TorchTypeMatchingDict(Mapping):
+    '''
+    Provides a view of a dict of numpy values as torch tensors, where the
+    types are converted to match the types and devices in the given
+    dict of examples.
+    '''
+    def __init__(self, data, examples):
+        self.data = data
+        self.examples = examples
+        self.cached_data = {}
+    def __getitem__(self, key):
+        if key in self.cached_data:
+            return self.cached_data[key]
+        val = self.data[key]
+        if key not in self.examples:
+            return val
+        example = self.examples.get(key, None)
+        example_type = type(example)
+        if example is not None and type(val) != example_type:
+            if isinstance(example, torch.Tensor):
+                val = torch.from_numpy(val)
+            else:
+                val = example_type(val)
+        if isinstance(example, torch.Tensor):
+            val = val.to(dtype=example.dtype, device=example.device)
+        self.cached_data[key] = val
+        return val
+    def __iter__(self):
+        return self.data.keys()
+    def __len__(self):
+        return len(self.data)
+
+class PrefixSubDict(MutableMapping):
+    '''
+    Provides a view of the subset of a dict where string keys begin with
+    the given prefix.  The prefix is stripped from all keys of the view.
+    '''
+    def __init__(self, data, prefix=''):
+        self.data = data
+        self.prefix = prefix
+        self._cached_keys = None
+    def __getitem__(self, key):
+        return self.data[self.prefix + key]
+    def __setitem__(self, key, value):
+        pkey = self.prefix + key
+        if self._cached_keys is not None and pkey not in self.data:
+            self._cached_keys = None
+        self.data[pkey] = value
+    def __delitem__(self, key):
+        pkey = self.prefix + key
+        if self._cached_keys is not None and pkey in self.data:
+            self._cached_keys = None
+        del self.data[pkey]
+    def __cached_keys(self):
+        if self._cached_keys is None:
+            plen = len(self.prefix)
+            self._cached_keys = list(k[plen:] for k in self.data
+                    if k.startswith(self.prefix))
+        return self._cached_keys
+    def __iter__(self):
+        return iter(self.__cached_keys())
+    def __len__(self):
+        return len(self.__cached_keys())
diff --git a/netdissect/tally.py b/netdissect/tally.py
new file mode 100644
index 0000000000000000000000000000000000000000..ddce680216771fe6a95417512bd6e03d2164188a
--- /dev/null
+++ b/netdissect/tally.py
@@ -0,0 +1,654 @@
+'''
+Batchwise tally functions, analogous to tensor.topk, mean+variance,
+bincount, covaraince, and sort (for quantiles), implemented in a way
+that permits fast computation of statistics over large data sets that
+do not fit in memory at once.
+
+These functions are useful because, while many statistics are much
+cheaper to compute on the GPU than on the CPU, they may require too
+much memory to compute all at once.  Instead the statistics need
+to be computed in a running fashion, one batch at a time, and
+accumulated in a way that economizes GPU memory.
+
+Use the tally functions by passing a batch computation function and
+an underlying dataset.  A DataLoader will be created, and then the
+function will be called to compute samples of data to tally.
+
+Underlying running statistics algorithms are implemented in the
+runningstats package.
+'''
+import torch, numpy, os
+from . import runningstats, pbar
+from .sampler import FixedSubsetSampler
+from collections import defaultdict
+import warnings
+
+def tally_topk(compute, dataset, sample_size=None, batch_size=10, k=100,
+        cachefile=None, **kwargs):
+    '''
+    Computes the topk statistics for a large data sample that can be
+    computed from a dataset.  The compute function should return one
+    batch of samples as a (sample, unit)-dimension tensor.
+
+    k specifies the number of top samples to retain.
+    Results are returned as a RunningTopK object.
+    '''
+    with torch.no_grad():
+        args = dict(sample_size=sample_size, k=k)
+        cached_state = load_cached_state(cachefile, args)
+        if cached_state is not None:
+            return runningstats.RunningTopK(state=cached_state)
+        rtk = runningstats.RunningTopK(k=k)
+        loader = make_loader(dataset, sample_size, batch_size, **kwargs)
+        for batch in pbar(loader):
+            sample = call_compute(compute, batch)
+            rtk.add(sample)
+        rtk.to_('cpu')
+        save_cached_state(cachefile, rtk, args)
+        return rtk
+
+def gather_topk(compute, dataset, topk, k=None,
+        cachefile=None, **kwargs):
+    '''
+    Gathers data from topk examples computed over a dataset.
+    The compute function will be provided a batch of data together
+    with a list of units [[(unit, rank), ...], [(unit, rank),...]]
+    with one list of relevant units for each batch item.  It should
+    return a generator that yields [(unit, rank), data]
+    '''
+    with torch.no_grad():
+        if k is None:
+            k = topk.k
+        args = dict(k=k, count=topk.count)
+        cached_state = load_cached_state(cachefile, args)
+        if cached_state is not None:
+            return runningstats.GatherTensor(state=cached_state)
+        gt = runningstats.GatherTensor(topk=topk, k=k)
+        needed_images = defaultdict(list)
+        for unit, imgnums in enumerate(topk.result()[1][:,:k]):
+            for rank, imgnum in enumerate(imgnums.numpy()):
+                needed_images[imgnum].append((unit, rank))
+        needed_sample = FixedSubsetSampler(sorted(needed_images.keys()))
+        loader = make_loader(dataset, sampler=needed_sample, **kwargs)
+        for batchnum, batch in enumerate(pbar(loader)):
+            # Verse data fix
+            if isinstance(batch, dict):
+                batch = [batch]
+            assert isinstance(batch, list)
+            if isinstance(batch[0], dict):
+                batch.insert(0, [needed_images[
+                    needed_sample[index + batchnum * loader.batch_size]]
+                        for index in range(len(batch[0]["image"]))])
+            else:
+                batch.insert(0, [needed_images[
+                    needed_sample[index + batchnum * loader.batch_size]]
+                        for index in range(len(batch[0]))])
+            sample = call_compute(compute, batch)
+            for (unit, rank), data in sample:
+                gt.add(unit, rank, data)
+        save_cached_state(cachefile, gt, args)
+        return gt
+
+def tally_conditional_topk(compute, dataset, k=100,
+        batch_size=50, sample_size=None, cachefile=None, **kwargs):
+    '''
+    Computes conditional topk, e.g., top k examples for each class.
+    '''
+    with torch.no_grad():
+        args = dict(sample_size=sample_size)
+        cached_state = load_cached_state(cachefile, args)
+        if cached_state is not None:
+            return runningstats.RunningConditionalTopK(state=cached_state)
+        loader = make_loader(dataset, sample_size, batch_size, **kwargs)
+        ctk = runningstats.RunningConditionalTopK(k=k)
+        for i, batch in enumerate(pbar(loader)):
+            # Add the index to the batch
+            first_index = i * loader.batch_size
+            index_batch = torch.arange(first_index, first_index + len(batch[0]))
+            batch.append(index_batch)
+            sample_set = call_compute(compute, batch)
+            for cond, data, index in sample_set:
+                ctk.add(cond, data, index)
+        # At the end, move all to the CPU
+        ctk.to_('cpu')
+        save_cached_state(cachefile, ctk, args)
+        return ctk
+
+def tally_quantile(compute, dataset, sample_size=None, batch_size=10,
+        r=4096, cachefile=None, **kwargs):
+    '''
+    Computes quantile sketch statistics for a large data sample that can
+    be computed from a dataset.  The compute function should return one
+    batch of samples as a (sample, unit)-dimension tensor.
+
+    The underlying quantile sketch is an optimal KLL sorted sampler that
+    retains at least r samples (where r is the specified resolution).
+    '''
+    with torch.no_grad():
+        args = dict(sample_size=sample_size, r=r)
+        cached_state = load_cached_state(cachefile, args)
+        if cached_state is not None:
+            return runningstats.RunningQuantile(state=cached_state)
+        loader = make_loader(dataset, sample_size, batch_size, **kwargs)
+        rq = runningstats.RunningQuantile()
+        for batch in pbar(loader):
+            sample = call_compute(compute, batch)
+            rq.add(sample)
+        rq.to_('cpu')
+        save_cached_state(cachefile, rq, args)
+        return rq
+
+def tally_topk_and_quantile(compute, dataset, sample_size=None,
+        batch_size=10, k=100, r=4096, cachefile=None, **kwargs):
+    '''
+    Computes both topk and quantile statistics in one pass over the
+    data.  The compute function should return a pair (first for topk
+    and second for quantile stats).
+    '''
+    with torch.no_grad():
+        args = dict(sample_size=sample_size, k=k, r=r)
+        cached_state = load_cached_state(cachefile, args)
+        cs = CombinedState(state=cached_state,
+                rtk=runningstats.RunningTopK(k=k),
+                rq=runningstats.RunningQuantile(r=r))
+        if cached_state is not None:
+            return cs.rtx, cs.rq
+        loader = make_loader(dataset, sample_size, batch_size, **kwargs)
+        for batch in pbar(loader):
+            sample_tk, sample_q = call_compute(compute, batch)
+            cs.rtk.add(sample_tk)
+            cs.rq.add(sample_q)
+        cs.rtk.to_('cpu')
+        cs.rq.to_('cpu')
+        save_cached_state(cachefile, cs, args)
+        return cs.rtk, cs.rq
+
+def tally_conditional_quantile(compute, dataset,
+        sample_size=None, batch_size=1, gpu_cache=64, r=1024,
+        cachefile=None, **kwargs):
+    '''
+    Computes conditional quantile sketches for a large data sample that
+    can be computed from a dataset.  The compute function should return a
+    sequence of sample batch tuples (condition, (sample, unit)-tensor),
+    one for each condition relevant to the batch.
+    '''
+    with torch.no_grad():
+        args = dict(sample_size=sample_size, r=r)
+        cached_state = load_cached_state(cachefile, args)
+        if cached_state is not None:
+            return runningstats.RunningConditionalQuantile(state=cached_state)
+        loader = make_loader(dataset, sample_size, batch_size, **kwargs)
+        cq = runningstats.RunningConditionalQuantile(r=r)
+        most_common_conditions = set()
+        for i, batch in enumerate(pbar(loader)):
+            sample_set = call_compute(compute, batch)
+            for cond, sample in sample_set:
+                # Move uncommon conditional data to the cpu before collating.
+                if cond not in most_common_conditions:
+                    sample = sample.cpu()
+                cq.add(cond, sample)
+            # Move uncommon conditions off the GPU.
+            if i and not i & (i - 1):  # if i is a power of 2:
+                common_conditions = set(cq.most_common_conditions(gpu_cache))
+                cq.to_('cpu', [k for k in cq.keys()
+                        if k not in common_conditions])
+        # At the end, move all to the CPU
+        cq.to_('cpu')
+        save_cached_state(cachefile, cq, args)
+        return cq
+
+def conditional_samples(activations, segments):
+    '''
+    Helper function when defining generators for *_conditional tallies.
+    Transforms a batch of activations and segmentations into a
+    sequence of conditional statistics, i.e., activations that
+    are at the same location as the segmentation label.
+    Both activations nad segments should be 4d tensors with
+    the same sample, y, and x dimensions.  Segments can be
+    a multilabel segmentation.  The zero segmentation value is
+    assumed to be unused.
+
+    Returns a generator for a sequence of (condition, (sample, unit)-tensor)
+    listing every condition present in the segments, along with the
+    set of activations overlapping that condition.  The activation tensor
+    is 2d in (sample, unit) order, where sample is the number of samples
+    with for the condition.
+    '''
+    channels = activations.shape[1]
+    if len(activations.shape) == 5:
+        activations_by_channel = activations.permute(0, 2, 3, 4, 1).contiguous()
+    else:
+        activations_by_channel = activations.permute(0, 2, 3, 1).contiguous()
+    try:
+        segcounts = segments.view(-1).bincount()
+    except Exception:
+        segcounts = segments.reshape(-1).bincount()
+    conditions = (segcounts[1:].nonzero() + 1)[:, 0]
+    def sample_generator():
+        # First yield the full set of activations, unconditioned
+        yield (0, activations_by_channel.view(-1, channels))
+        # Then a set of activations for each condition present in the image
+        for condition in conditions:
+            mask = (segments == condition).max(1)[0][...,None]
+            mask = mask.expand(activations_by_channel.shape)
+            yield (condition.item(),
+                    activations_by_channel[mask].view(-1, channels))
+    return sample_generator()
+
+def tally_mean(compute, dataset, sample_size=None, batch_size=10,
+        cachefile=None, **kwargs):
+    '''
+    Computes unitwise mean and variance stats for a large data sample that
+    can be computed from a dataset.  The compute function should return one
+    batch of samples as a (sample, unit)-dimension tensor.
+    '''
+    with torch.no_grad():
+        args = dict(sample_size=sample_size)
+        cached_state = load_cached_state(cachefile, args)
+        if cached_state is not None:
+            return runningstats.RunningVariance(state=cached_state)
+        loader = make_loader(dataset, sample_size, batch_size, **kwargs)
+        rv = runningstats.RunningVariance()
+        for batch in pbar(loader):
+            sample = call_compute(compute, batch)
+            rv.add(sample)
+        rv.to_('cpu')
+        save_cached_state(cachefile, rv, args)
+        return rv
+
+def tally_conditional_mean(compute, dataset,
+        sample_size=None, batch_size=1, cachefile=None, loader = None, pass_with_lbl = False, **kwargs):
+    '''
+    Computes conditional mean and variance for a large data sample that
+    can be computed from a dataset.  The compute function should return a
+    sequence of sample batch tuples (condition, (sample, unit)-tensor),
+    one for each condition relevant to the batch.
+    '''
+    with torch.no_grad():
+        args = dict(sample_size=sample_size)
+        cached_state = load_cached_state(cachefile, args)
+        if cached_state is not None:
+            return runningstats.RunningConditionalVariance(state=cached_state)
+        if not loader:
+            loader = make_loader(dataset, sample_size, batch_size, **kwargs)
+        cv = runningstats.RunningConditionalVariance()
+        for i, batch in enumerate(pbar(loader)):
+            sample_set = call_compute(compute, batch, pass_with_lbl)
+            for cond, sample in sample_set:
+                # Move uncommon conditional data to the cpu before collating.
+                cv.add(cond, sample)
+        # At the end, move all to the CPU
+        # cv.to_('cpu')
+        # save_cached_state(cachefile, cv, args)
+        return cv
+
+def tally_bincount(compute, dataset, sample_size=None, batch_size=10,
+        multi_label_axis=None, cachefile=None, **kwargs):
+    '''
+    Computes bincount totals for a large data sample that can be
+    computed from a dataset.  The compute function should return one
+    batch of samples as a (sample, unit)-dimension tensor.
+    '''
+    with torch.no_grad():
+        args = dict(sample_size=sample_size)
+        cached_state = load_cached_state(cachefile, args)
+        if cached_state is not None:
+            return runningstats.RunningBincount(state=cached_state)
+        loader = make_loader(dataset, sample_size, batch_size, **kwargs)
+        rbc = runningstats.RunningBincount()
+        for batch in pbar(loader):
+            sample = call_compute(compute, batch)
+            if multi_label_axis:
+                multilabel = sample.shape[multi_label_axis]
+                size = sample.numel() // multilabel
+            else:
+                size = None
+            rbc.add(sample, size=size)
+        rbc.to_('cpu')
+        save_cached_state(cachefile, rbc, args)
+        return rbc
+
+def tally_cat(compute, dataset, sample_size=None, batch_size=10,
+        cachefile=None, **kwargs):
+    '''
+    Computes a concatenated tensor for data batches that can be
+    computed from a dataset.  The compute function should return
+    a tensor that should be concatenated to the others along its
+    first dimension.
+    '''
+    with torch.no_grad():
+        args = dict(sample_size=sample_size)
+        cached_state = load_cached_state(cachefile, args)
+        if cached_state is not None:
+            return torch.from_numpy(cached_state['data'])
+        loader = make_loader(dataset, sample_size, batch_size, **kwargs)
+        result = []
+        for batch in pbar(loader):
+            result.append(call_compute(compute, batch).cpu())
+        data = torch.cat(result)
+        class SavedTensor:
+            def __init__(self, data):
+                self.data = data
+            def state_dict(self):
+                return dict(data=self.data.numpy())
+        save_cached_state(cachefile, SavedTensor(data), args)
+        return data
+
+def tally_cat_dict(compute, dataset, sample_size=None, batch_size=10,
+        cachefile=None, **kwargs):
+    '''
+    Computes a dict of concatenated tensors for data batches that can
+    be computed from a dataset.  The compute function should return a
+    dict of tensors tensor that can be concatenated to previous results
+    with the same key along their first dimensions.
+    '''
+    with torch.no_grad():
+        args = dict(sample_size=sample_size)
+        cached_state = load_cached_state(cachefile, args)
+        if cached_state is not None:
+            return {k: torch.from_numpy(v) for k, v in cached_state.items()}
+        loader = make_loader(dataset, sample_size, batch_size, **kwargs)
+        result = defaultdict(list)
+        for batch in pbar(loader):
+            for k, v in call_compute(compute, batch).items():
+                result[k].append(v.cpu())
+        data = {k: torch.cat(v) for k, v in result.items()}
+        data.update(args)
+        class SavedDict:
+            def __init__(self, data):
+                self.data = data
+            def state_dict(self):
+                return data
+        save_cached_state(cachefile, SavedDict(data), args)
+        return data
+
+def tally_covariance(compute, dataset, sample_size=None, batch_size=10,
+        cachefile=None, **kwargs):
+    '''
+    Computes covariance statistics for a large data sample that can
+    be computed from a dataset.  The compute function should return one
+    batch of samples as a (sample, unit)-dimension tensor.
+    '''
+    with torch.no_grad():
+        args = dict(sample_size=sample_size)
+        cached_state = load_cached_state(cachefile, args)
+        if cached_state is not None:
+            return runningstats.RunningCovariance(state=cached_state)
+        loader = make_loader(dataset, sample_size, batch_size, **kwargs)
+        rcov = runningstats.RunningCovariance()
+        for batch in pbar(loader):
+            sample = call_compute(compute, batch)
+            rcov.add(sample)
+        rcov.to_('cpu')
+        save_cached_state(cachefile, rcov, args)
+        return rcov
+
+def tally_cross_covariance(compute, dataset, sample_size=None, batch_size=10,
+        cachefile=None, **kwargs):
+    '''
+    Computes cross covariance statistics for a large data sample that can
+    be computed from a dataset.  The compute function should return one
+    batch of samples as pair of [(sample, unitA), (sample, unitB)] tensors.
+    '''
+    with torch.no_grad():
+        args = dict(sample_size=sample_size)
+        cached_state = load_cached_state(cachefile, args)
+        if cached_state is not None:
+            return runningstats.RunningCrossCovariance(state=cached_state)
+        loader = make_loader(dataset, sample_size, batch_size, **kwargs)
+        rcov = runningstats.RunningCrossCovariance()
+        for batch in pbar(loader):
+            sampleA, sampleB = call_compute(compute, batch)
+            rcov.add(sampleA, sampleB)
+        rcov.to_('cpu')
+        save_cached_state(cachefile, rcov, args)
+        return rcov
+
+def tally_second_moment(compute, dataset, sample_size=None, batch_size=10,
+        cachefile=None, **kwargs):
+    '''
+    Computes second_moment statistics for a large data sample that can
+    be computed from a dataset.  The compute function should return one
+    batch of samples as a (sample, unit)-dimension tensor.
+    '''
+    with torch.no_grad():
+        args = dict(sample_size=sample_size)
+        cached_state = load_cached_state(cachefile, args)
+        if cached_state is not None:
+            return runningstats.RunningSecondMoment(state=cached_state)
+        loader = make_loader(dataset, sample_size, batch_size, **kwargs)
+        r2mom = runningstats.RunningSecondMoment()
+        for batch in pbar(loader):
+            sample = call_compute(compute, batch)
+            r2mom.add(sample)
+        r2mom.to_('cpu')
+        save_cached_state(cachefile, r2mom, args)
+        return r2mom
+
+def batch_bincount(data, num_labels):
+    '''
+    Computes elementwise bincount on a batch of data.  The input tensor is
+    size (batch_size, ...), and the output is (batch_size, num_labels),
+    where each row of the output is the bincount of the corresponding
+    entry of the input.
+    '''
+    data = data.view(len(data), -1)
+    strided = data + torch.arange(len(data), dtype=data.dtype,
+         device=data.device)[:,None] * num_labels
+    counts = torch.bincount(strided.view(-1), minlength=num_labels * len(data))
+    return counts.view(len(data), num_labels)
+
+def iou_from_conditional_indicator_mean(condmv):
+    '''
+    Given a RunningConditionalVariance containing mean values of
+    indictors, estimates all-pairs IoU statistics for all units
+    between the conditions and the indicators.
+    The result is a tensor of dimension (units, conditions)
+    containing IoU estimates for each combination.
+    '''
+    # Old code using python loops.
+    # gt = {k: condi99.conditional(k).size() /
+    #      condi99.conditional(0).size() for k in condi99.keys()}
+    # act = condi99.conditional(0).mean()
+    # isect = {k: condi99.conditional(k).mean() * gt[k] for k in condi99.keys()}
+    # union = {k: act + gt[k] - isect[k] for k in condi99.keys()}
+    # iou = {k: isect[k] / union[k] for k in condi99.keys()}
+
+    # New code arranging with pytorch tensors
+    uncond_size = condmv.conditional(0).size()
+    units = condmv.conditional(0).mean().shape[0]
+    conditions = max(condmv.keys()) + 1
+    act = condmv.conditional(0).mean()
+    gt = torch.zeros(conditions)
+    isect = torch.zeros(conditions, units)
+    for k in condmv.keys():
+        gt[k] = condmv.conditional(k).size() / condmv.conditional(0).size()
+        isect[k] = condmv.conditional(k).mean() * gt[k]
+    union = act[None,:].cpu() + gt[:,None].cpu() - isect.cpu()
+    iou = isect / union
+    return iou
+
+def iou_from_conditional_quantile(condq, cutoff=0.95, min_batches=2):
+    '''
+    Given a RunningConditionalQuantile, estimates all-pairs
+    IoU statistics for all units and conditions at the specified
+    quantile cutoff.  Note that cutoff can be a list of cutoffs.
+    The result is a tensor of dimension (units, conditions, cutoffs)
+    containing IoU estimates for each combination.
+
+    Conditions that are sampled in fewer than min_batches are given IoU 0.
+    '''
+    return intersection_from_conditional_quantile(condq,
+            statistic=intersection_over_union,
+            cutoff=cutoff, min_batches=min_batches)
+
+def iqr_from_conditional_quantile(condq, cutoff=0.95, min_batches=2):
+    '''
+    Given a RunningConditionalQuantile, estimates all-pairs
+    IQR statistics for all units and conditions at the specified
+    quantile cutoff.  Similar to iou_from_conditional_quantile.
+    '''
+    return intersection_from_conditional_quantile(condq,
+            statistic=information_quality_ratio,
+            cutoff=cutoff, min_batches=min_batches)
+
+def mi_from_conditional_quantile(condq, cutoff=0.95, min_batches=2):
+    '''
+    Given a RunningConditionalQuantile, estimates all-pairs
+    mutual information for all units and conditions at the specified
+    quantile cutoff.  Similar to iou_from_conditional_quantile.
+    '''
+    return intersection_from_conditional_quantile(condq,
+            statistic=mutual_information,
+            cutoff=cutoff, min_batches=min_batches)
+
+def intersection_from_conditional_quantile(
+        condq, statistic=lambda x: x[0,0], cutoff=0.95, min_batches=2):
+    '''
+    There are a variety of ways of scoring the intersection between a
+    prediction (a) and a true variable (b) that are all expressions of
+    [[p(a&b), p(a&!b)], [p(!a&b), p(!a&!b)]].  This computes any of
+    them by passing the above array to a 'statistic' function.
+    By default it returns p(a&b).
+    '''
+    with warnings.catch_warnings():
+        warnings.simplefilter('ignore', UserWarning)
+        cutoff = torch.tensor(cutoff)
+    uncond_size = condq.conditional(0).size()
+    units = condq.conditional(0).depth
+    scores = torch.zeros((units, max(condq.keys()) + 1) + cutoff.shape)
+    # math: actlevel = level such that p(x > level) = cutoff
+    actlevel = condq.conditional(0).quantiles(cutoff)
+    # use a progress bar if it's going to be more than a few seconds.
+    prog = pbar if cutoff.numel() * units > 1e5 else lambda x: x
+    for c in prog(sorted(condq.keys())):
+        rq = condq.conditional(c)
+        if c == 0 or rq.batchcount < min_batches:
+            continue
+        # math: condp = p(x > actlevel | cond)
+        condp = rq.normalize(actlevel)
+        truth = float(rq.size()) / uncond_size
+        isect = truth * (1 - condp)
+        pred = (1 - cutoff)
+        union = pred + truth - isect
+        # Compute relative mutual information directly.
+        arr = torch.stack([
+            isect,         pred - isect,
+            truth - isect, 1 - union]).view((2, 2) + isect.shape)
+        scores[:,c,...] = statistic(arr)
+    return scores
+
+def intersection_over_union(arr):
+    return arr[0,0] / (1 - arr[1,1])
+
+def mutual_information(arr):
+    total = 0
+    for j in range(arr.shape[0]):
+        for k in range(arr.shape[1]):
+            joint = arr[j,k]
+            ind = arr[j,:].sum(dim=0) * arr[:,k].sum(dim=0)
+            term = joint * (joint / ind).log()
+            term[torch.isnan(term)] = 0
+            total += term
+    return total.clamp_(0)
+
+def joint_entropy(arr):
+    total = 0
+    for j in range(arr.shape[0]):
+        for k in range(arr.shape[1]):
+            joint = arr[j,k]
+            term = joint * joint.log()
+            term[torch.isnan(term)] = 0
+            total += term
+    return (-total).clamp_(0)
+
+def information_quality_ratio(arr):
+    iqr = mutual_information(arr) / joint_entropy(arr)
+    iqr[torch.isnan(iqr)] = 0
+    return iqr
+
+def call_compute(compute, batch, pass_with_lbl = False):
+    '''Utility for passing a dataloader batch to a compute function.'''
+    if isinstance(batch, list):
+        if pass_with_lbl:
+            return compute(batch)
+        return compute(*batch)
+    elif isinstance(batch, dict):
+        return compute(**batch)
+    else:
+        return compute(batch)
+
+def make_loader(dataset, sample_size=None, batch_size=10, sampler=None,
+        **kwargs):
+    '''Utility for creating a dataloader on fixed sample subset.'''
+    if isinstance(dataset, torch.Tensor):
+        dataset = torch.utils.data.TensorDataset(dataset)
+    if sampler is None:
+        if sample_size is not None:
+            if sample_size > len(dataset):
+                pbar.print("Warning: sample size %d > dataset size %d" %
+                        (sample_size, len(dataset)))
+                sample_size = len(dataset)
+            sampler = FixedSubsetSampler(list(range(sample_size)))
+    return torch.utils.data.DataLoader(
+            dataset,
+            sampler=sampler,
+            batch_size=batch_size,
+            **kwargs)
+
+def push_key_prefix(prefix, d):
+    return {prefix + '.' + k: v for k, v in d.items()}
+
+def pull_key_prefix(prefix, d):
+    pd = prefix + '.'
+    lpd = len(pd)
+    return {k[lpd:]: v for k, v in d.items() if k.startswith(pd)}
+
+class CombinedState(object):
+    '''
+    An object that can load and save state_dict made up of a
+    hierarchy of objects that each have a state_dict.
+    '''
+    def __init__(self, state=None, **kwargs):
+        self._objs = kwargs
+        if state is not None:
+            for prefix, obj in self._objs.items():
+                objs.load_state_dict(pull_key_prefix(prefix, state))
+    def __getattr__(self, k):
+        if k in self._objs:
+            return self._objs[k]
+        raise AttributeError()
+    def state_dict(self):
+        result = {}
+        for prefix, obj in self._objs.items():
+            result.update(push_key_prefix(prefix, obj.state_dict()))
+        return result
+
+def load_cached_state(cachefile, args):
+    if cachefile is None:
+        return None
+    try:
+        dat = numpy.load(cachefile, allow_pickle=True)
+        for a, v in args.items():
+            if a not in dat or dat[a] != v:
+                pbar.print('%s %s changed from %s to %s' % (
+                    cachefile, a, dat[a], v))
+                return None
+    except:
+        return None
+    else:
+        pbar.descnext(None)
+        pbar.print('Loading cached %s' % cachefile)
+        return dat
+
+def save_cached_state(cachefile, obj, args):
+    if cachefile is None:
+        return
+    os.makedirs(os.path.dirname(cachefile), exist_ok=True)
+    dat = obj.state_dict()
+    for a, v in args.items():
+        if a in dat:
+            assert(dat[a] == v)
+        dat[a] = v
+    numpy.savez(cachefile, **dat)
+
diff --git a/netdissect/tool/allunitsample.py b/netdissect/tool/allunitsample.py
new file mode 100644
index 0000000000000000000000000000000000000000..9f86e196ce63ebfcad1fcee8bd2b7358463ff3d1
--- /dev/null
+++ b/netdissect/tool/allunitsample.py
@@ -0,0 +1,199 @@
+'''
+A simple tool to generate sample of output of a GAN,
+subject to filtering, sorting, or intervention.
+'''
+
+import torch, numpy, os, argparse, sys, shutil, errno, numbers
+from PIL import Image
+from torch.utils.data import TensorDataset
+from netdissect.zdataset import standard_z_sample
+from netdissect.progress import default_progress, verbose_progress
+from netdissect.autoeval import autoimport_eval
+from netdissect.workerpool import WorkerBase, WorkerPool
+from netdissect.nethook import retain_layers
+from netdissect.runningstats import RunningTopK
+
+def main():
+    parser = argparse.ArgumentParser(description='GAN sample making utility')
+    parser.add_argument('--model', type=str, default=None,
+            help='constructor for the model to test')
+    parser.add_argument('--pthfile', type=str, default=None,
+            help='filename of .pth file for the model')
+    parser.add_argument('--outdir', type=str, default='images',
+            help='directory for image output')
+    parser.add_argument('--size', type=int, default=100,
+            help='number of images to output')
+    parser.add_argument('--test_size', type=int, default=None,
+            help='number of images to test')
+    parser.add_argument('--layer', type=str, default=None,
+            help='layer to inspect')
+    parser.add_argument('--seed', type=int, default=1,
+            help='seed')
+    parser.add_argument('--quiet', action='store_true', default=False,
+            help='silences console output')
+    if len(sys.argv) == 1:
+        parser.print_usage(sys.stderr)
+        sys.exit(1)
+    args = parser.parse_args()
+    verbose_progress(not args.quiet)
+
+    # Instantiate the model
+    model = autoimport_eval(args.model)
+    if args.pthfile is not None:
+        data = torch.load(args.pthfile)
+        if 'state_dict' in data:
+            meta = {}
+            for key in data:
+                if isinstance(data[key], numbers.Number):
+                    meta[key] = data[key]
+            data = data['state_dict']
+        model.load_state_dict(data)
+    # Unwrap any DataParallel-wrapped model
+    if isinstance(model, torch.nn.DataParallel):
+        model = next(model.children())
+    # Examine first conv in model to determine input feature size.
+    first_layer = [c for c in model.modules()
+            if isinstance(c, (torch.nn.Conv2d, torch.nn.ConvTranspose2d,
+                torch.nn.Linear))][0]
+    # 4d input if convolutional, 2d input if first layer is linear.
+    if isinstance(first_layer, (torch.nn.Conv2d, torch.nn.ConvTranspose2d)):
+        z_channels = first_layer.in_channels
+        spatialdims = (1, 1)
+    else:
+        z_channels = first_layer.in_features
+        spatialdims = ()
+    # Instrument the model
+    retain_layers(model, [args.layer])
+    model.cuda()
+
+    if args.test_size is None:
+        args.test_size = args.size * 20
+    z_universe = standard_z_sample(args.test_size, z_channels,
+            seed=args.seed)
+    z_universe = z_universe.view(tuple(z_universe.shape) + spatialdims)
+    indexes = get_all_highest_znums(
+            model, z_universe, args.size, seed=args.seed)
+    save_chosen_unit_images(args.outdir, model, z_universe, indexes,
+            lightbox=True)
+
+
+def get_all_highest_znums(model, z_universe, size,
+        batch_size=10, seed=1):
+    # The model should have been instrumented already
+    retained_items = list(model.retained.items())
+    assert len(retained_items) == 1
+    layer = retained_items[0][0]
+    # By default, a 10% sample
+    progress = default_progress()
+    num_units = None
+    with torch.no_grad():
+        # Pass 1: collect max activation stats
+        z_loader = torch.utils.data.DataLoader(TensorDataset(z_universe),
+                    batch_size=batch_size, num_workers=2,
+                    pin_memory=True)
+        rtk = RunningTopK(k=size)
+        for [z] in progress(z_loader, desc='Finding max activations'):
+            z = z.cuda()
+            model(z)
+            feature = model.retained[layer]
+            num_units = feature.shape[1]
+            max_feature = feature.view(
+                    feature.shape[0], num_units, -1).max(2)[0]
+            rtk.add(max_feature)
+        td, ti = rtk.result()
+        highest = ti.sort(1)[0]
+    return highest
+
+def save_chosen_unit_images(dirname, model, z_universe, indices,
+        shared_dir="shared_images",
+        unitdir_template="unit_{}",
+        name_template="image_{}.jpg",
+        lightbox=False, batch_size=50, seed=1):
+    all_indices = torch.unique(indices.view(-1), sorted=True)
+    z_sample = z_universe[all_indices]
+    progress = default_progress()
+    sdir = os.path.join(dirname, shared_dir)
+    created_hashdirs = set()
+    for index in range(len(z_universe)):
+        hd = hashdir(index)
+        if hd not in created_hashdirs:
+            created_hashdirs.add(hd)
+            os.makedirs(os.path.join(sdir, hd), exist_ok=True)
+    with torch.no_grad():
+        # Pass 2: now generate images
+        z_loader = torch.utils.data.DataLoader(TensorDataset(z_sample),
+                    batch_size=batch_size, num_workers=2,
+                    pin_memory=True)
+        saver = WorkerPool(SaveImageWorker)
+        for batch_num, [z] in enumerate(progress(z_loader,
+                desc='Saving images')):
+            z = z.cuda()
+            start_index = batch_num * batch_size
+            im = ((model(z) + 1) / 2 * 255).clamp(0, 255).byte().permute(
+                    0, 2, 3, 1).cpu()
+            for i in range(len(im)):
+                index = all_indices[i + start_index].item()
+                filename = os.path.join(sdir, hashdir(index),
+                        name_template.format(index))
+                saver.add(im[i].numpy(), filename)
+        saver.join()
+    linker = WorkerPool(MakeLinkWorker)
+    for u in progress(range(len(indices)), desc='Making links'):
+        udir = os.path.join(dirname, unitdir_template.format(u))
+        os.makedirs(udir, exist_ok=True)
+        for r in range(indices.shape[1]):
+            index = indices[u,r].item()
+            fn = name_template.format(index)
+            # sourcename = os.path.join('..', shared_dir, fn)
+            sourcename = os.path.join(sdir, hashdir(index), fn)
+            targname = os.path.join(udir, fn)
+            linker.add(sourcename, targname)
+        if lightbox:
+            copy_lightbox_to(udir)
+    linker.join()
+
+def copy_lightbox_to(dirname):
+   srcdir = os.path.realpath(
+       os.path.join(os.getcwd(), os.path.dirname(__file__)))
+   shutil.copy(os.path.join(srcdir, 'lightbox.html'),
+           os.path.join(dirname, '+lightbox.html'))
+
+def hashdir(index):
+    # To keep the number of files the shared directory lower, split it
+    # into 100 subdirectories named as follows.
+    return '%02d' % (index % 100)
+
+class SaveImageWorker(WorkerBase):
+    # Saving images can be sped up by sending jpeg encoding and
+    # file-writing work to a pool.
+    def work(self, data, filename):
+        Image.fromarray(data).save(filename, optimize=True, quality=100)
+
+class MakeLinkWorker(WorkerBase):
+    # Creating symbolic links is a bit slow and can be done faster
+    # in parallel rather than waiting for each to be created.
+    def work(self, sourcename, targname):
+        try:
+            os.link(sourcename, targname)
+        except OSError as e:
+            if e.errno == errno.EEXIST:
+                os.remove(targname)
+                os.link(sourcename, targname)
+            else:
+                raise
+
+class MakeSyminkWorker(WorkerBase):
+    # Creating symbolic links is a bit slow and can be done faster
+    # in parallel rather than waiting for each to be created.
+    def work(self, sourcename, targname):
+        try:
+            os.symlink(sourcename, targname)
+        except OSError as e:
+            if e.errno == errno.EEXIST:
+                os.remove(targname)
+                os.symlink(sourcename, targname)
+            else:
+                raise
+
+if __name__ == '__main__':
+    main()
diff --git a/netdissect/tool/ganseg.py b/netdissect/tool/ganseg.py
new file mode 100644
index 0000000000000000000000000000000000000000..e6225736d336cf75aedb8a7d7aec1229b497f6a9
--- /dev/null
+++ b/netdissect/tool/ganseg.py
@@ -0,0 +1,89 @@
+'''
+A simple tool to generate sample of output of a GAN,
+and apply semantic segmentation on the output.
+'''
+
+import torch, numpy, os, argparse, sys, shutil
+from PIL import Image
+from torch.utils.data import TensorDataset
+from netdissect.zdataset import standard_z_sample, z_dataset_for_model
+from netdissect.progress import default_progress, verbose_progress
+from netdissect.autoeval import autoimport_eval
+from netdissect.workerpool import WorkerBase, WorkerPool
+from netdissect.nethook import edit_layers, retain_layers
+from netdissect.segviz import segment_visualization
+from netdissect.segmenter import UnifiedParsingSegmenter
+from scipy.io import savemat
+
+def main():
+    parser = argparse.ArgumentParser(description='GAN output segmentation util')
+    parser.add_argument('--model', type=str, default=
+            'netdissect.proggan.from_pth_file("' + 
+            'models/karras/churchoutdoor_lsun.pth")',
+            help='constructor for the model to test')
+    parser.add_argument('--outdir', type=str, default='images',
+            help='directory for image output')
+    parser.add_argument('--size', type=int, default=100,
+            help='number of images to output')
+    parser.add_argument('--seed', type=int, default=1,
+            help='seed')
+    parser.add_argument('--quiet', action='store_true', default=False,
+            help='silences console output')
+    #if len(sys.argv) == 1:
+    #    parser.print_usage(sys.stderr)
+    #    sys.exit(1)
+    args = parser.parse_args()
+    verbose_progress(not args.quiet)
+
+    # Instantiate the model
+    model = autoimport_eval(args.model)
+
+    # Make the standard z
+    z_dataset = z_dataset_for_model(model, size=args.size)
+
+    # Make the segmenter
+    segmenter = UnifiedParsingSegmenter()
+
+    # Write out text labels
+    labels, cats = segmenter.get_label_and_category_names()
+    with open(os.path.join(args.outdir, 'labels.txt'), 'w') as f:
+        for i, (label, cat) in enumerate(labels):
+            f.write('%s %s\n' % (label, cat))
+
+    # Move models to cuda
+    model.cuda()
+
+    batch_size = 10
+    progress = default_progress()
+    dirname = args.outdir
+
+    with torch.no_grad():
+        # Pass 2: now generate images
+        z_loader = torch.utils.data.DataLoader(z_dataset,
+                    batch_size=batch_size, num_workers=2,
+                    pin_memory=True)
+        for batch_num, [z] in enumerate(progress(z_loader,
+                desc='Saving images')):
+            z = z.cuda()
+            start_index = batch_num * batch_size
+            tensor_im = model(z)
+            byte_im = ((tensor_im + 1) / 2 * 255).clamp(0, 255).byte().permute(
+                    0, 2, 3, 1).cpu()
+            seg = segmenter.segment_batch(tensor_im)
+            for i in range(len(tensor_im)):
+                index = i + start_index
+                filename = os.path.join(dirname, '%d_img.jpg' % index)
+                Image.fromarray(byte_im[i].numpy()).save(
+                        filename, optimize=True, quality=100)
+                filename = os.path.join(dirname, '%d_seg.mat' % index)
+                savemat(filename, dict(seg=seg[i].cpu().numpy()))
+                filename = os.path.join(dirname, '%d_seg.png' % index)
+                Image.fromarray(segment_visualization(seg[i].cpu().numpy(),
+                    tensor_im.shape[2:])).save(filename)
+    srcdir = os.path.realpath(
+       os.path.join(os.getcwd(), os.path.dirname(__file__)))
+    shutil.copy(os.path.join(srcdir, 'lightbox.html'),
+           os.path.join(dirname, '+lightbox.html'))
+
+if __name__ == '__main__':
+    main()
diff --git a/netdissect/tool/lightbox.html b/netdissect/tool/lightbox.html
new file mode 100644
index 0000000000000000000000000000000000000000..fb0ebdf64766a43c9353428853be77deb5c52665
--- /dev/null
+++ b/netdissect/tool/lightbox.html
@@ -0,0 +1,59 @@
+<!DOCTYPE html>
+<html>
+<!--
+  +lightbox.html, a page for automatically showing all images in a
+  directory on an Apache server. Just copy it into the directory.
+  Works by scraping the default directory HTML at "./" - David Bau.
+-->
+<head>
+<script src="https://cdn.jsdelivr.net/npm/vue@2.5.16/dist/vue.js"
+  integrity="sha256-CMMTrj5gGwOAXBeFi7kNokqowkzbeL8ydAJy39ewjkQ="
+  crossorigin="anonymous"></script>
+<script src="https://cdn.jsdelivr.net/npm/lodash@4.17.10/lodash.js"
+  integrity="sha256-qwbDmNVLiCqkqRBpF46q5bjYH11j5cd+K+Y6D3/ja28="
+  crossorigin="anonymous"></script>
+<script
+  src="https://code.jquery.com/jquery-3.3.1.js"
+  integrity="sha256-2Kok7MbOyxpgUVvAk/HJ2jigOSYS2auK4Pfzbm7uH60="
+  crossorigin="anonymous"></script>
+<script src="https://cdnjs.cloudflare.com/ajax/libs/lity/2.3.1/lity.js"
+  integrity="sha256-28JiZvE/RethQIYCwkMdtSMHgI//KoTLeB2tSm10trs="
+  crossorigin="anonymous"></script>
+<link rel="stylesheet"
+  href="https://cdnjs.cloudflare.com/ajax/libs/lity/2.3.1/lity.css"
+  integrity="sha256-76wKiAXVBs5Kyj7j0T43nlBCbvR6pqdeeZmXI4ATnY0="
+  crossorigin="anonymous" />
+<style>
+.thumb { display: inline-block; margin: 1px; text-align: center; }
+.thumb img { max-width: 150px; }
+</style>
+</head>
+<body>
+<div id="app" v-if="images">
+<h3>Images in <a :href="directory">{{ directory }}</a></h3>
+<div v-for="r in images" class="thumb">
+<div>{{ r }}</div>
+<a :href="r" data-lity><img :src="r"></a>
+</div>
+</div><!--app-->
+</body>
+<script>
+var theapp = new Vue({
+  el: '#app',
+  data: {
+    directory: window.location.pathname.replace(/[^\/]*$/, ''),
+    images: null
+  },
+  created: function() {
+    var self = this;
+    $.get('./?' + Math.random(), function(d) {
+      var imgurls = $.map($(d).find('a'),
+                      x => x.href).filter(
+                      x => x.match(/\.(jpg|jpeg|png|gif)$/i)).map(
+                      x => x.replace(/.*\//, ''));
+      self.images = imgurls;
+    }, 'html');
+  }
+})
+</script>
+</html>
diff --git a/netdissect/tool/makesample.py b/netdissect/tool/makesample.py
new file mode 100644
index 0000000000000000000000000000000000000000..36276267677360d8238a8dbf71e9753dcc327681
--- /dev/null
+++ b/netdissect/tool/makesample.py
@@ -0,0 +1,169 @@
+'''
+A simple tool to generate sample of output of a GAN,
+subject to filtering, sorting, or intervention.
+'''
+
+import torch, numpy, os, argparse, numbers, sys, shutil
+from PIL import Image
+from torch.utils.data import TensorDataset
+from netdissect.zdataset import standard_z_sample
+from netdissect.progress import default_progress, verbose_progress
+from netdissect.autoeval import autoimport_eval
+from netdissect.workerpool import WorkerBase, WorkerPool
+from netdissect.nethook import edit_layers, retain_layers
+
+def main():
+    parser = argparse.ArgumentParser(description='GAN sample making utility')
+    parser.add_argument('--model', type=str, default=None,
+            help='constructor for the model to test')
+    parser.add_argument('--pthfile', type=str, default=None,
+            help='filename of .pth file for the model')
+    parser.add_argument('--outdir', type=str, default='images',
+            help='directory for image output')
+    parser.add_argument('--size', type=int, default=100,
+            help='number of images to output')
+    parser.add_argument('--test_size', type=int, default=None,
+            help='number of images to test')
+    parser.add_argument('--layer', type=str, default=None,
+            help='layer to inspect')
+    parser.add_argument('--seed', type=int, default=1,
+            help='seed')
+    parser.add_argument('--maximize_units', type=int, nargs='+', default=None,
+            help='units to maximize')
+    parser.add_argument('--ablate_units', type=int, nargs='+', default=None,
+            help='units to ablate')
+    parser.add_argument('--quiet', action='store_true', default=False,
+            help='silences console output')
+    if len(sys.argv) == 1:
+        parser.print_usage(sys.stderr)
+        sys.exit(1)
+    args = parser.parse_args()
+    verbose_progress(not args.quiet)
+
+    # Instantiate the model
+    model = autoimport_eval(args.model)
+    if args.pthfile is not None:
+        data = torch.load(args.pthfile)
+        if 'state_dict' in data:
+            meta = {}
+            for key in data:
+                if isinstance(data[key], numbers.Number):
+                    meta[key] = data[key]
+            data = data['state_dict']
+        model.load_state_dict(data)
+    # Unwrap any DataParallel-wrapped model
+    if isinstance(model, torch.nn.DataParallel):
+        model = next(model.children())
+    # Examine first conv in model to determine input feature size.
+    first_layer = [c for c in model.modules()
+            if isinstance(c, (torch.nn.Conv2d, torch.nn.ConvTranspose2d,
+                torch.nn.Linear))][0]
+    # 4d input if convolutional, 2d input if first layer is linear.
+    if isinstance(first_layer, (torch.nn.Conv2d, torch.nn.ConvTranspose2d)):
+        z_channels = first_layer.in_channels
+        spatialdims = (1, 1)
+    else:
+        z_channels = first_layer.in_features
+        spatialdims = ()
+    # Instrument the model if needed
+    if args.maximize_units is not None:
+        retain_layers(model, [args.layer])
+    model.cuda()
+
+    # Get the sample of z vectors
+    if args.maximize_units is None:
+        indexes = torch.arange(args.size)
+        z_sample = standard_z_sample(args.size, z_channels, seed=args.seed)
+        z_sample = z_sample.view(tuple(z_sample.shape) + spatialdims)
+    else:
+        # By default, if maximizing units, get a 'top 5%' sample.
+        if args.test_size is None:
+            args.test_size = args.size * 20
+        z_universe = standard_z_sample(args.test_size, z_channels,
+                seed=args.seed)
+        z_universe = z_universe.view(tuple(z_universe.shape) + spatialdims)
+        indexes = get_highest_znums(model, z_universe, args.maximize_units,
+                args.size, seed=args.seed)
+        z_sample = z_universe[indexes]
+
+    if args.ablate_units:
+        edit_layers(model, [args.layer])
+        dims = max(2, max(args.ablate_units) + 1) # >=2 to avoid broadcast
+        model.ablation[args.layer] = torch.zeros(dims)
+        model.ablation[args.layer][args.ablate_units] = 1
+
+    save_znum_images(args.outdir, model, z_sample, indexes,
+            args.layer, args.ablate_units)
+    copy_lightbox_to(args.outdir)
+
+
+def get_highest_znums(model, z_universe, max_units, size,
+        batch_size=100, seed=1):
+    # The model should have been instrumented already
+    retained_items = list(model.retained.items())
+    assert len(retained_items) == 1
+    layer = retained_items[0][0]
+    # By default, a 10% sample
+    progress = default_progress()
+    num_units = None
+    with torch.no_grad():
+        # Pass 1: collect max activation stats
+        z_loader = torch.utils.data.DataLoader(TensorDataset(z_universe),
+                    batch_size=batch_size, num_workers=2,
+                    pin_memory=True)
+        scores = []
+        for [z] in progress(z_loader, desc='Finding max activations'):
+            z = z.cuda()
+            model(z)
+            feature = model.retained[layer]
+            num_units = feature.shape[1]
+            max_feature = feature[:, max_units, ...].view(
+                    feature.shape[0], len(max_units), -1).max(2)[0]
+            total_feature = max_feature.sum(1)
+            scores.append(total_feature.cpu())
+        scores = torch.cat(scores, 0)
+        highest = (-scores).sort(0)[1][:size].sort(0)[0]
+    return highest
+
+
+def save_znum_images(dirname, model, z_sample, indexes, layer, ablated_units,
+        name_template="image_{}.png", lightbox=False, batch_size=100, seed=1):
+    progress = default_progress()
+    os.makedirs(dirname, exist_ok=True)
+    with torch.no_grad():
+        # Pass 2: now generate images
+        z_loader = torch.utils.data.DataLoader(TensorDataset(z_sample),
+                    batch_size=batch_size, num_workers=2,
+                    pin_memory=True)
+        saver = WorkerPool(SaveImageWorker)
+        if ablated_units is not None:
+            dims = max(2, max(ablated_units) + 1) # >=2 to avoid broadcast
+            mask = torch.zeros(dims)
+            mask[ablated_units] = 1
+            model.ablation[layer] = mask[None,:,None,None].cuda()
+        for batch_num, [z] in enumerate(progress(z_loader,
+                desc='Saving images')):
+            z = z.cuda()
+            start_index = batch_num * batch_size
+            im = ((model(z) + 1) / 2 * 255).clamp(0, 255).byte().permute(
+                    0, 2, 3, 1).cpu()
+            for i in range(len(im)):
+                index = i + start_index
+                if indexes is not None:
+                    index = indexes[index].item()
+                filename = os.path.join(dirname, name_template.format(index))
+                saver.add(im[i].numpy(), filename)
+    saver.join()
+
+def copy_lightbox_to(dirname):
+   srcdir = os.path.realpath(
+       os.path.join(os.getcwd(), os.path.dirname(__file__)))
+   shutil.copy(os.path.join(srcdir, 'lightbox.html'),
+           os.path.join(dirname, '+lightbox.html'))
+
+class SaveImageWorker(WorkerBase):
+    def work(self, data, filename):
+        Image.fromarray(data).save(filename, optimize=True, quality=100)
+
+if __name__ == '__main__':
+    main()
diff --git a/netdissect/train_hybrid_bottom.py b/netdissect/train_hybrid_bottom.py
new file mode 100644
index 0000000000000000000000000000000000000000..ec32c3208760275d7ff9b93872ede55a7f6d75e0
--- /dev/null
+++ b/netdissect/train_hybrid_bottom.py
@@ -0,0 +1,306 @@
+import torch, multiprocessing, itertools, os, shutil, PIL, argparse, numpy
+from torch.nn.functional import mse_loss
+from collections import defaultdict, OrderedDict
+from seeing import encoder_net, setting
+from seeing.encoder_loss import cor_square_error
+from seeing import zdataset, pbar, nethook
+from seeing import proggan, customnet, parallelfolder
+from torchvision import transforms, models
+from seeing.pidfile import exit_if_job_done, mark_job_done
+
+torch.backends.cudnn.benchmark = True
+
+parser = argparse.ArgumentParser()
+parser.add_argument('--lr', type=float, help='Learning rate', default=None)
+parser.add_argument('--model', type=str, help='Dataset being modeled',
+        default='church')
+parser.add_argument('--bottom', type=int, help='Number of bottom layers',
+        default=4)
+parser.add_argument('--recover', nargs='+', default=None,
+        help='recovey losses: subset of layer1, layer2, layer3, layer4, x')
+args = parser.parse_args()
+
+global_seed = 1
+variant = None
+if args.lr is None:
+    args.lr = {1: 1.5e-4, 2: 1.5e-4, 3: 1.5e-4, 4: 1.5e-4}.get(
+            args.bottom, 1.5e-4)
+if args.recover == None:
+    variant = ['b%d' % args.bottom]
+    args.recover = ['z'] + ['layer%d' % i
+            for i in range(1, min(4, args.bottom) + 1)] + (
+                    [] if args.bottom < 5 else ['x'])
+if variant is None:
+    variant = sum([
+        [prefix + str(k).replace('layer', '') for k in lst]
+        for prefix, lst in [
+            ('b', args.bottom),
+            ('r', args.recover)]
+        ], [])
+
+# make a directory name like 'invert_hybrid_s1_rz_r2_r4_rx'
+# default '_cse' corresponds to '_i4_rz_r4_rx'
+expname = 'invert_hybrid_bottom_' + '_'.join(variant)
+expdir = os.path.join('results', args.model, expname)
+os.makedirs(expdir, exist_ok=True)
+
+num_epochs = 1000
+# lr_milestones = [50, 150, 350]  # Reduce learning rate after these epochs
+lr_milestones = [500]
+lr_gamma = 0.2
+
+def main():
+    torch.manual_seed(global_seed)
+    pbar.print('Training %s' % expname)
+
+    # Log the configuration
+    logline = '; '.join('%s: %r' % (k, v) for k, v in vars(args).items())
+    with open(os.path.join(expdir, 'log.txt'), 'a') as f:
+        f.write(logline + '\n')
+    pbar.print(logline)
+
+    # Load a progressive GAN
+    whole_generator = setting.load_proggan(args.model)
+    if args.bottom < 5:
+        # Truncate it to the specified depth of layers.
+        generator = nethook.subsequence(whole_generator,
+                last_layer='layer%d' % args.bottom)
+        renderer = nethook.subsequence(whole_generator,
+                first_layer='layer%d' % (args.bottom + 1))
+    else:
+        generator = whole_generator
+        renderer = torch.nn.Sequential()
+    # Make a stacked encoder, and initialize with pretrained layers
+    encoder = encoder_net.HybridLayerNormEncoder()
+    if args.bottom < 5:
+        # Truncate the encoder to the matching set of layers
+        encoder = nethook.subsequence(encoder,
+                first_layer='inv%d' % args.bottom)
+    if args.bottom > 1:
+        # Load a pretrained stack of encoder layers (all but the last one)
+        prev_filename = (os.path.join('results', args.model,
+            'invert_hybrid_bottom_b%d/snapshots/epoch_1000.pth.tar'
+            % (args.bottom - 1)))
+        encoder.load_state_dict(torch.load(prev_filename)['state_dict'],
+                strict=False)
+    # Load a pretrained single layer (just the last one)
+    if args.bottom < 5:
+        layer = getattr(encoder, 'inv%d' % args.bottom)
+        layer_filename = os.path.join('results', args.model,
+                'invert_layer_%d_cse/snapshots/epoch_100.pth.tar'
+                % args.bottom)
+    else:
+        layer = encoder.resnet
+        layer_filename = os.path.join('results', args.model,
+                'invert_over5_resnet/snapshots/epoch_100.pth.tar')
+    pbar.print('Loading %s' % layer_filename)
+    layer.load_state_dict(torch.load(layer_filename)['state_dict'])
+
+    # Instrument the generator model so that we can add # extra
+    # loss terms based on reconstruction of intermediate layers.
+    generator = nethook.InstrumentedModel(generator)
+    retained_layer_list = [n for n in args.recover if n not in ['x', 'z']]
+    generator.retain_layers(retained_layer_list, detach=False)
+
+    # Move models to GPU
+    for m in [generator, encoder, renderer]:
+        m.cuda()
+
+    # Set up a training data loader: unending batches of random z.
+    batch_size = 32
+    train_loader = training_loader(generator, batch_size)
+
+    # Test data loader is finite, fixed set of z.
+    test_loader = testing_loader(generator, batch_size)
+
+    # Set up optimizer
+    set_requires_grad(False, generator)
+    optimize_conv3 = False
+    if optimize_conv3:
+        target_params = encoder.parameters()
+    else:
+        target_params = []
+        # The conv3 filters of each layer are redundant with
+        # the conv1 in the following layer, so freeze the conv3 layers.
+        for n, p in encoder.named_parameters():
+            if (n.startswith('inv') and not n.startswith('inv1.')
+                    and 'conv3' in n):
+                p.requires_grad = False
+            else:
+                target_params.append(p)
+    learning_rate = args.lr
+    optimizer = torch.optim.Adam(target_params, lr=learning_rate)
+    scheduler = torch.optim.lr_scheduler.MultiStepLR(
+            optimizer, milestones=lr_milestones, gamma=lr_gamma)
+
+    epoch_batches = 100
+    for epoch, epoch_loader in enumerate(pbar(
+            epoch_grouper(train_loader, epoch_batches), total=(1+num_epochs))):
+        # Training loop (for 0th epoch, do no training, just testing)
+        if epoch > 0:
+            for (z_batch,) in pbar(epoch_loader, total=epoch_batches):
+                (z_batch,) = [d.cuda() for d in [z_batch]]
+                loss = encoder_loss(z_batch, generator, encoder)
+                loss.backward()
+                pbar.post(l=loss.item())
+                optimizer.step()
+            scheduler.step()
+        # Testing loop
+        with torch.no_grad():
+            losses = defaultdict(float)
+            count = 0
+            for i, (z_batch,) in enumerate(pbar(test_loader)):
+                (z_batch,) = [d.cuda() for d in [z_batch]]
+                nb = len(z_batch)
+                # Some other debugging losses
+                count += nb
+                losses['loss'] += nb * (
+                        encoder_loss(z_batch, generator, encoder).item())
+                for name, mloss in monitor_losses(
+                        z_batch, generator, encoder).items():
+                    losses[name] += nb * mloss.item()
+                if epoch % 10 == 0 and i == 0:
+                    visualize_results(epoch, z_batch, generator, encoder,
+                            renderer)
+            losses = { name: loss / count for name, loss in losses.items() }
+            logline = '%d ' % epoch + ' '.join("%s=%4g" % (name, losses[name])
+                    for name in sorted(losses.keys()))
+            pbar.print(logline)
+            with open(os.path.join(expdir, 'log.txt'), 'a') as f:
+                f.write(logline + '\n')
+        if epoch % 10 == 0:
+            save_checkpoint(
+                    epoch=epoch,
+                    state_dict=encoder.state_dict(),
+                    lr=learning_rate,
+                    optimizer=optimizer.state_dict(),
+                    **losses)
+        if epoch == num_epochs:
+            break
+
+def save_checkpoint(**kwargs):
+    dirname = os.path.join(expdir, 'snapshots')
+    os.makedirs(dirname, exist_ok=True)
+    filename = 'epoch_%d.pth.tar' % kwargs['epoch']
+    torch.save(kwargs, os.path.join(dirname, filename))
+
+def visualize_results(epoch, true_z, generator, encoder, renderer):
+    dirname = os.path.join(expdir, 'images')
+    os.makedirs(dirname, exist_ok=True)
+    true_r, recovered_r = (
+            generate_and_recover_features(true_z, generator, encoder))
+    true_im, recovered_im = [renderer(d['x']) for d in [true_r, recovered_r]]
+    num_images = 6
+    for i in range(min(len(true_z), num_images)):
+        for name, im in [
+                ('epoch_%d_%d_g.png', true_im),
+                ('epoch_%d_%d_r.png', recovered_im),
+                ]:
+            save_tensor_image(im[i], os.path.join(dirname, name % (epoch, i)))
+        rawdat = OrderedDict(sum([
+            [(template % k.replace('layer', ''), v[i].cpu().numpy())
+                for k, v in feats.items()]
+            for template, feats in [
+                ('t_%s', true_r),
+                ('r_%s', recovered_r)]], []))
+        numpy.savez(os.path.join(dirname, 'epoch_%d_%d.npz' % (epoch, i)),
+                **rawdat)
+    shutil.copy('seeing/lightbox.html',
+            os.path.join(dirname, '+lightbox.html'))
+
+def save_tensor_image(img, filename):
+    np_data = ((img.permute(1, 2, 0) / 2 + 0.5) * 255).byte().cpu().numpy()
+    PIL.Image.fromarray(np_data).save(filename)
+    
+def generate_and_recover_features(true_z, generator, encoder):
+    global args
+    true_x = generator(true_z)
+    true_r = generator.retained_features(clear=True)
+    true_r['z'] = true_z
+    true_r['x'] = true_x
+    recovered_z = encoder(true_x)
+    recovered_x = generator(recovered_z)
+    recovered_r = generator.retained_features(clear=True)
+    recovered_r['z'] = recovered_z
+    recovered_r['x'] = recovered_x
+    return true_r, recovered_r
+
+def monitor_losses(true_z, generator, encoder, all_losses=True):
+    global args
+    true_r, recovered_r = (
+            generate_and_recover_features(true_z, generator, encoder))
+    losses = {}
+    for layer in recovered_r.keys() if all_losses else args.recover:
+        if layer == 'x':
+            losses['rx'] = (
+                    mse_loss(true_r[layer], recovered_r[layer]))
+        else:
+            losses['r' + layer.replace('layer', '')] = (
+                    cor_square_error(true_r[layer], recovered_r[layer]))
+    return losses
+
+def encoder_loss(true_z, generator, encoder):
+    return sum(monitor_losses(true_z, generator, encoder,
+        all_losses=False).values())
+
+def training_loader(z_generator, batch_size):
+    '''
+    Returns an infinite generator that runs through randomized z
+    batches, forever.
+    '''
+    g_epoch = 1
+    while True:
+        z_data = zdataset.z_dataset_for_model(
+                z_generator, size=10000, seed=g_epoch + global_seed)
+        dataloader = torch.utils.data.DataLoader(
+                z_data,
+                shuffle=False,
+                batch_size=batch_size,
+                num_workers=10,
+                pin_memory=True)
+        for batch in dataloader:
+            yield batch
+        g_epoch += 1
+
+def testing_loader(z_generator, batch_size):
+    '''
+    Returns an a short iterator that returns a small set of test data.
+    '''
+    z_data = zdataset.z_dataset_for_model(
+        z_generator, size=1000, seed=global_seed)
+    dataloader = torch.utils.data.DataLoader(
+                z_data,
+                shuffle=False,
+                batch_size=batch_size,
+                num_workers=10,
+                pin_memory=True)
+    return dataloader
+
+def epoch_grouper(loader, epoch_size):
+    '''
+    To use with the infinite training loader: groups the training data
+    batches into epochs of the given size.
+    '''
+    it = iter(loader)
+    while True:
+        chunk_it = itertools.islice(it, epoch_size)
+        try:
+            first_el = next(chunk_it)
+        except StopIteration:
+            return
+        yield itertools.chain((first_el,), chunk_it)
+
+def set_requires_grad(requires_grad, *models):
+    for model in models:
+        if model is not None:
+            for param in model.parameters():
+                param.requires_grad = requires_grad
+
+class IdentityLayer(torch.nn.Module):
+    def forward(self, x):
+        return x
+
+if __name__ == '__main__':
+    exit_if_job_done(expdir)
+    main()
+    mark_job_done(expdir)
diff --git a/netdissect/train_hybrid_inv.py b/netdissect/train_hybrid_inv.py
new file mode 100644
index 0000000000000000000000000000000000000000..d8cc673804ea1267d09f98cc995854244fd30ab1
--- /dev/null
+++ b/netdissect/train_hybrid_inv.py
@@ -0,0 +1,317 @@
+import torch, multiprocessing, itertools, os, shutil, PIL, argparse, numpy
+from torch.nn.functional import mse_loss
+from collections import defaultdict, OrderedDict
+from . import encoder_net, setting
+from .encoder_loss import cor_square_error
+from . import zdataset, pbar, nethook
+from . import proggan, customnet, parallelfolder
+from torchvision import transforms, models
+from .pidfile import exit_if_job_done, mark_job_done
+
+torch.backends.cudnn.benchmark = True
+
+parser = argparse.ArgumentParser()
+parser.add_argument('--lr', type=float, help='Learning rate', default=1e-5)
+parser.add_argument('--model', type=str, help='Dataset being modeled',
+        default='church')
+parser.add_argument('--estimate', nargs='+', default=None,
+        help='estimation losses: subset of z, layer1, layer2, layer3, layer4')
+parser.add_argument('--recover', nargs='+', default=None,
+        help='recovey losses: subset of layer1, layer2, layer3, layer4, x')
+args = parser.parse_args()
+
+global_seed = 1
+variant = None
+if args.estimate == args.recover == None:
+    variant = ['cse']
+    args.estimate = ['layer4']
+    args.recover = ['z', 'layer4', 'x']
+if args.estimate is None:
+    args.estimate = []
+if args.recover is None:
+    args.recover = []
+if variant is None:
+    variant = sum([
+        [prefix + k.replace('layer', '') for k in lst]
+        for prefix, lst in [
+            ('e', args.estimate),
+            ('r', args.recover)]
+        ], [])
+# make a directory name like 'invert_hybrid_s1_rz_r2_r4_rx'
+# default '_cse' corresponds to '_i4_rz_r4_rx'
+expname = 'invert_hybrid_' + '_'.join(variant)
+expdir = os.path.join('results', args.model, expname)
+os.makedirs(expdir, exist_ok=True)
+
+num_epochs = 1000
+lr_milestones = [50, 150, 350]  # Reduce learning rate after these epochs
+
+def main():
+    torch.manual_seed(global_seed)
+    pbar.print('Training %s' % expname)
+
+    # Load a progressive GAN
+    generator = setting.load_proggan(args.model)
+    # Make a stacked encoder, and load each of its pretrained layers
+    encoder = encoder_net.HybridLayerNormEncoder()
+    for layernum in range(1, 4+1):
+        layer = getattr(encoder, 'inv%d' % layernum)
+        layer_filename = (os.path.join('results', args.model,
+                'invert_layer_%d_cse/snapshots/epoch_100.pth.tar' % layernum))
+        pbar.print('Loading %s' % layer_filename)
+        layer.load_state_dict(torch.load(layer_filename)['state_dict'])
+    resnet_filename = (os.path.join('results', args.model,
+            'invert_over5_resnet/snapshots/epoch_100.pth.tar'))
+    pbar.print('Loading %s' % resnet_filename)
+    encoder.resnet.load_state_dict(torch.load(resnet_filename)['state_dict'])
+
+    # Instrument both the encoder and generator model so that we can add
+    # extra loss terms based on reconstruction of intermediate layers.
+    generator = nethook.InstrumentedModel(generator)
+    generator_layer_list = ['layer1', 'layer2', 'layer3', 'layer4']
+    for n in args.recover:
+        if n not in generator_layer_list and n not in ['x', 'z']:
+            generator_layer_list.append(n)
+    generator.retain_layers(generator_layer_list, detach=False)
+    encoder = nethook.InstrumentedModel(encoder)
+    encoder.retain_layers([
+            ('resnet', 'layer4'),
+            ('inv4', 'layer3'),
+            ('inv3', 'layer2'),
+            ('inv2', 'layer1')], detach=False)
+
+    # Move models to GPU
+    for m in [generator, encoder]:
+        m.cuda()
+
+    # Set up a training data loader: unending batches of random z.
+    batch_size = 32
+    train_loader = training_loader(generator, batch_size)
+
+    # Test data loader is finite, fixed set of z.
+    test_loader = testing_loader(generator, batch_size)
+
+    # Set up optimizer
+    set_requires_grad(False, generator)
+    optimize_conv3 = False
+    if optimize_conv3:
+        target_params = encoder.parameters()
+    else:
+        target_params = []
+        # The conv3 filters of each layer are redundant with
+        # the conv1 in the following layer, so freeze the conv3 layers.
+        for n, p in encoder.named_parameters():
+            if (n.startswith('inv') and not n.startswith('inv1.')
+                    and 'conv3' in n):
+                p.requires_grad = False
+            else:
+                target_params.append(p)
+    learning_rate = args.lr
+    optimizer = torch.optim.Adam(target_params, lr=learning_rate)
+    scheduler = torch.optim.lr_scheduler.MultiStepLR(
+            optimizer, milestones=lr_milestones, gamma=0.5)
+
+    epoch_batches = 100
+    for epoch, epoch_loader in enumerate(pbar(
+            epoch_grouper(train_loader, epoch_batches), total=(1+num_epochs))):
+        # Training loop (for 0th epoch, do no training, just testing)
+        if epoch > 0:
+            for (z_batch,) in pbar(epoch_loader, total=epoch_batches):
+                (z_batch,) = [d.cuda() for d in [z_batch]]
+                loss = encoder_loss(z_batch, generator, encoder)
+                loss.backward()
+                pbar.post(l=loss.item())
+                optimizer.step()
+            scheduler.step()
+        # Testing loop
+        with torch.no_grad():
+            losses = defaultdict(float)
+            count = 0
+            for i, (z_batch,) in enumerate(pbar(test_loader)):
+                (z_batch,) = [d.cuda() for d in [z_batch]]
+                nb = len(z_batch)
+                # Some other debugging losses
+                count += nb
+                losses['loss'] += nb * (
+                        encoder_loss(z_batch, generator, encoder).item())
+                for name, mloss in monitor_losses(
+                        z_batch, generator, encoder).items():
+                    losses[name] += nb * mloss.item()
+                if epoch % 10 == 0 and i == 0:
+                    visualize_results(epoch, z_batch, generator, encoder)
+            losses = { name: loss / count for name, loss in losses.items() }
+            logline = '%d ' % epoch + ' '.join("%s=%4g" % (name, losses[name])
+                    for name in sorted(losses.keys()))
+            pbar.print(logline)
+            with open(os.path.join(expdir, 'log.txt'), 'a') as f:
+                f.write(logline + '\n')
+        if epoch % 10 == 0:
+            save_checkpoint(
+                    epoch=epoch,
+                    state_dict=encoder.state_dict(),
+                    lr=learning_rate,
+                    optimizer=optimizer.state_dict(),
+                    **losses)
+        if epoch == num_epochs:
+            break
+
+def save_checkpoint(**kwargs):
+    dirname = os.path.join(expdir, 'snapshots')
+    os.makedirs(dirname, exist_ok=True)
+    filename = 'epoch_%d.pth.tar' % kwargs['epoch']
+    torch.save(kwargs, os.path.join(dirname, filename))
+
+def visualize_results(epoch, true_z, generator, encoder):
+    dirname = os.path.join(expdir, 'images')
+    os.makedirs(dirname, exist_ok=True)
+    true_r, estimated_r, recovered_r = (
+            generate_and_recover_features(true_z, generator, encoder))
+    num_images = 6
+    for i in range(min(len(true_z), num_images)):
+        for name, im in [
+                ('epoch_%d_%d_g.png', true_r['x']),
+                ('epoch_%d_%d_r.png', recovered_r['x']),
+                ]:
+            save_tensor_image(im[i], os.path.join(dirname, name % (epoch, i)))
+        rawdat = OrderedDict(sum([
+            [(template % k.replace('layer', ''), v[i].cpu().numpy())
+                for k, v in feats.items()]
+            for template, feats in [
+                ('t_%s', true_r),
+                ('e_%s', estimated_r),
+                ('r_%s', recovered_r)]], []))
+        numpy.savez(os.path.join(dirname, 'epoch_%d_%d.npz' % (epoch, i)),
+                **rawdat)
+    shutil.copy(os.path.join(os.dirname(__file__), 'lightbox.html'),
+            os.path.join(dirname, '+lightbox.html'))
+
+def save_tensor_image(img, filename):
+    np_data = ((img.permute(1, 2, 0) / 2 + 0.5) * 255).byte().cpu().numpy()
+    PIL.Image.fromarray(np_data).save(filename)
+    
+def generate_recovered(generator, estimated_r):
+    return generator(estimated_r['z'])
+
+def generate_and_recover_features(true_z, generator, encoder):
+    global args
+    true_x = generator(true_z)
+    true_r = generator.retained_features(clear=True)
+    true_r['z'] = true_z
+    true_r['x'] = true_x
+    recovered_z = encoder(true_x)
+    estimated_r = encoder.retained_features(clear=True)
+    estimated_r['z'] = recovered_z
+    recovered_x = generate_recovered(generator, estimated_r)
+    recovered_r = generator.retained_features(clear=True)
+    recovered_r['z'] = recovered_z
+    recovered_r['x'] = recovered_x
+    return true_r, estimated_r, recovered_r
+
+def monitor_losses(true_z, generator, encoder, all_losses=True):
+    global args
+    true_r, estimated_r, recovered_r = (
+            generate_and_recover_features(true_z, generator, encoder))
+    losses = {}
+    for layer in estimated_r.keys() if all_losses else args.estimate:
+        losses['i' + layer.replace('layer', '')] = (
+                cor_square_error(true_r[layer], estimated_r[layer]))
+    for layer in recovered_r.keys() if all_losses else args.recover:
+        if layer == 'x':
+            losses['rx'] = (
+                    mse_loss(true_r[layer], recovered_r[layer]))
+        else:
+            losses['r' + layer.replace('layer', '')] = (
+                    cor_square_error(true_r[layer], recovered_r[layer]))
+    return losses
+
+def encoder_loss(true_z, generator, encoder):
+    return sum(monitor_losses(true_z, generator, encoder,
+        all_losses=False).values())
+
+def old_encoder_loss(true_z, generator, encoder):
+    true_p = generator(true_z)
+    true_r = generator.retained_features(clear=True)['layer4']
+    recovered_z = encoder(true_p)
+    estimated_r = encoder.retained_features(clear=True)['layer4']
+    recovered_p = generator(recovered_z)
+    recovered_r = generator.retained_features(clear=True)['layer4']
+    loss_z = cor_square_error(true_z, recovered_z)
+    loss_ir = cor_square_error(true_r, estimated_r)
+    loss_r = cor_square_error(true_r, recovered_r)
+    loss_p = mse_loss(true_p, recovered_p)
+    return loss_z + loss_ir + loss_r + loss_p
+
+def old_monitor_losses(true_z, generator, encoder):
+    true_p = generator(true_z)
+    true_r = generator.retained_features(clear=True)['layer4']
+    recovered_z = encoder(true_p)
+    estimated_r = encoder.retained_features(clear=True)['layer4']
+    recovered_p = generator(recovered_z)
+    recovered_r = generator.retained_features(clear=True)['layer4']
+    return dict(
+            loss_z=cor_square_error(true_z, recovered_z),
+            loss_ir=cor_square_error(true_r, estimated_r),
+            loss_r=cor_square_error(true_r, recovered_r),
+            loss_p=mse_loss(true_p, recovered_p))
+
+def training_loader(z_generator, batch_size):
+    '''
+    Returns an infinite generator that runs through randomized z
+    batches, forever.
+    '''
+    g_epoch = 1
+    while True:
+        z_data = zdataset.z_dataset_for_model(
+                z_generator, size=10000, seed=g_epoch + global_seed)
+        dataloader = torch.utils.data.DataLoader(
+                z_data,
+                shuffle=False,
+                batch_size=batch_size,
+                num_workers=10,
+                pin_memory=True)
+        for batch in dataloader:
+            yield batch
+        g_epoch += 1
+
+def testing_loader(z_generator, batch_size):
+    '''
+    Returns an a short iterator that returns a small set of test data.
+    '''
+    z_data = zdataset.z_dataset_for_model(
+        z_generator, size=1000, seed=global_seed)
+    dataloader = torch.utils.data.DataLoader(
+                z_data,
+                shuffle=False,
+                batch_size=batch_size,
+                num_workers=10,
+                pin_memory=True)
+    return dataloader
+
+def epoch_grouper(loader, epoch_size):
+    '''
+    To use with the infinite training loader: groups the training data
+    batches into epochs of the given size.
+    '''
+    it = iter(loader)
+    while True:
+        chunk_it = itertools.islice(it, epoch_size)
+        try:
+            first_el = next(chunk_it)
+        except StopIteration:
+            return
+        yield itertools.chain((first_el,), chunk_it)
+
+def set_requires_grad(requires_grad, *models):
+    for model in models:
+        if model is not None:
+            for param in model.parameters():
+                param.requires_grad = requires_grad
+
+class IdentityLayer(torch.nn.Module):
+    def forward(self, x):
+        return x
+
+if __name__ == '__main__':
+    exit_if_job_done(expdir)
+    main()
+    mark_job_done(expdir)
diff --git a/netdissect/train_multilayer_inv.py b/netdissect/train_multilayer_inv.py
new file mode 100644
index 0000000000000000000000000000000000000000..f139cab131dae4797b5fcbb6924f4144104efe00
--- /dev/null
+++ b/netdissect/train_multilayer_inv.py
@@ -0,0 +1,177 @@
+import torch, multiprocessing, itertools, os, shutil, PIL, argparse, numpy
+from torch.nn.functional import mse_loss
+from . import pbar, setting
+from . import encoder_net
+from . import nethook, zdataset
+from . import proggan, customnet, parallelfolder
+from torchvision import transforms, models
+from torchvision.models.vgg import model_urls
+from .pidfile import exit_if_job_done, mark_job_done
+
+parser = argparse.ArgumentParser()
+parser.add_argument('--lr', type=float, help='Learning rate', default=0.01)
+parser.add_argument('--model', type=str, help='Dataset being modeled',
+        default='church')
+args = parser.parse_args()
+
+global_seed = 1
+expname = 'invert_over5_resnet'
+expdir = os.path.join('results', args.model, expname)
+os.makedirs(expdir, exist_ok=True)
+
+def main():
+    torch.manual_seed(global_seed)
+    pbar.print('Training %s' % expdir)
+
+    # Load a progressive GAN
+    full_generator = setting.load_proggan(args.model)
+    # Make a subset model with only some layers.
+    decoder = nethook.subsequence(full_generator, first_layer='layer5')
+    generator = nethook.subsequence(full_generator, last_layer='layer4')
+
+    # Make an encoder model.
+    encoder = encoder_net.make_over5_resnet()
+
+    # Also make a conv features model from pretrained VGG
+    vgg = models.vgg16(pretrained=True)
+    features = nethook.subsequence(vgg.features, last_layer='20')
+
+    # Move models to GPU
+    for m in [generator, decoder, encoder, features]:
+        m.cuda()
+
+    # Set up adata loaders that just feed random z
+    batch_size = 32
+    train_loader = training_loader(generator, batch_size)
+    test_loader = testing_loader(generator, batch_size)
+
+    # Set up optimizer
+    set_requires_grad(False, decoder, generator, features)
+    learning_rate = args.lr
+    optimizer = torch.optim.Adam(encoder.parameters(), lr=learning_rate)
+
+    epoch_batches = 100
+    num_epochs = 100
+    # img_elems = 256*256*3
+    # rep_elems = 8*8*512
+    # alpha = float(rep_elems) / (rep_elems + img_elems)
+    for epoch, epoch_loader in enumerate(pbar(
+            epoch_grouper(train_loader, epoch_batches), total=num_epochs)):
+        if epoch > num_epochs:
+            break
+        # Training loop
+        if epoch > 0:
+            for (z_batch,) in pbar(epoch_loader, total=epoch_batches):
+                z_batch = z_batch.cuda()
+                r_batch = generator(z_batch)
+                optimizer.zero_grad()
+                loss = encoder_decoder_loss(encoder, decoder, r_batch)
+                loss.backward()
+                pbar.post(l=loss.item())
+                optimizer.step()
+        # Testing loop
+        with torch.no_grad():
+            loss = 0.0
+            count = 0
+            for i, (z_batch, ) in enumerate(pbar(test_loader)):
+                z_batch = z_batch.cuda()
+                r_batch = generator(z_batch)
+                count += len(z_batch)
+                loss += (encoder_decoder_loss(encoder, decoder, r_batch) *
+                        len(z_batch))
+                if i == 0 and epoch % 10 == 0:
+                    visualize_results(epoch, r_batch, encoder, decoder)
+            loss /= count
+            pbar.print("Epoch", epoch, "Loss", loss.item())
+            with open(os.path.join(expdir, 'log.txt'), 'a') as f:
+                f.write('{} {}\n'.format(epoch, loss.item()))
+        if epoch % 10 == 0:
+            save_checkpoint(
+                    epoch=epoch,
+                    state_dict=encoder.state_dict(),
+                    loss=loss.item(),
+                    lr=learning_rate,
+                    optimizer=optimizer.state_dict())
+
+def save_checkpoint(**kwargs):
+    dirname = os.path.join(expdir, 'snapshots')
+    os.makedirs(dirname, exist_ok=True)
+    filename = 'epoch_%d.pth.tar' % kwargs['epoch']
+    torch.save(kwargs, os.path.join(dirname, filename))
+
+def visualize_results(epoch, z_batch, encoder, decoder):
+    dirname = os.path.join(expdir, 'images')
+    os.makedirs(dirname, exist_ok=True)
+    generated = decoder(z_batch)
+    encoded = encoder(generated)
+    recovered = decoder(encoded)
+    for i in range(min(len(z_batch), 6)):
+        save_tensor_image(generated[i], os.path.join(dirname,
+            'epoch_%d_%d_g.png' % (epoch, i)))
+        save_tensor_image(recovered[i], os.path.join(dirname,
+            'epoch_%d_%d_r.png' % (epoch, i)))
+    shutil.copy(os.path.join(os.path.dirname(__file__), 'lightbox.html'),
+            os.path.join(dirname, '+lightbox.html'))
+
+def save_tensor_image(img, filename):
+    np_data = ((img.permute(1, 2, 0) / 2 + 0.5) * 255).byte().cpu().numpy()
+    PIL.Image.fromarray(np_data).save(filename)
+    
+def encoder_decoder_loss(encoder, decoder, encoded_batch):
+    reencoded_batch = encoder(decoder(encoded_batch))
+    encoder_loss = mse_loss(encoded_batch, reencoded_batch)
+    return encoder_loss
+
+def training_loader(z_generator, batch_size):
+    '''
+    Returns an infinite generator that runs through randomized
+    training data repeatedly in shuffled order, forever.
+    '''
+    epoch = 0
+    while True:
+        z_dataset = zdataset.z_dataset_for_model(
+                z_generator, size=batch_size * 50, seed=epoch + global_seed)
+        dataloader = torch.utils.data.DataLoader(
+                z_dataset,
+                batch_size=batch_size, num_workers=2,
+                pin_memory=True)
+        for batch in dataloader:
+            yield batch
+        epoch += 1
+
+def testing_loader(z_generator, batch_size):
+    '''
+    Returns an a short iterator that returns a small set of test data.
+    '''
+    z_dataset = zdataset.z_dataset_for_model(
+        z_generator, size=1000, seed=global_seed - 1)
+    dataloader = torch.utils.data.DataLoader(
+            z_dataset,
+            batch_size=32, num_workers=2,
+            pin_memory=True)
+    return dataloader
+
+def epoch_grouper(loader, epoch_size):
+    '''
+    To use with the infinite training loader: groups the training data
+    batches into epochs of the given size.
+    '''
+    it = iter(loader)
+    while True:
+        chunk_it = itertools.islice(it, epoch_size)
+        try:
+            first_el = next(chunk_it)
+        except StopIteration:
+            return
+        yield itertools.chain((first_el,), chunk_it)
+
+def set_requires_grad(requires_grad, *models):
+    for model in models:
+        if model is not None:
+            for param in model.parameters():
+                param.requires_grad = requires_grad
+
+if __name__ == '__main__':
+    exit_if_job_done(expdir)
+    main()
+    mark_job_done(expdir)
diff --git a/netdissect/train_onelayer_inv.py b/netdissect/train_onelayer_inv.py
new file mode 100644
index 0000000000000000000000000000000000000000..95a0649cfc5e86225ae54fe1c650d48d4910cc6f
--- /dev/null
+++ b/netdissect/train_onelayer_inv.py
@@ -0,0 +1,247 @@
+import torch, multiprocessing, itertools, os, shutil, PIL, argparse, numpy
+from torch.nn.functional import mse_loss, cosine_similarity
+from collections import defaultdict
+from . import encoder_net, setting
+from . import nethook, zdataset, pbar
+from . import proggan, customnet, parallelfolder
+from .encoder_loss import cor_square_error
+from torchvision import transforms, models
+from .pidfile import exit_if_job_done, mark_job_done
+
+parser = argparse.ArgumentParser()
+parser.add_argument('--lr', type=float, help='Learning rate', default=1e-4)
+parser.add_argument('--invert_layer', type=int, help='Layer to invert',
+        default=1)
+parser.add_argument('--model', type=str, help='Dataset being modeled',
+        default='church')
+args = parser.parse_args()
+
+global_seed = 1
+invert_layer = args.invert_layer
+expname = 'invert_layer_%d_cse' % invert_layer
+expdir = os.path.join('results', args.model, expname)
+os.makedirs(expdir, exist_ok=True)
+
+lr_milestones = [20, 60]  # Reduce learning rate after 20 and 60 epochs
+if invert_layer == 15 or invert_layer == 2:
+    lr_milestones = [60, 80]
+
+def main():
+    torch.manual_seed(global_seed)
+    pbar.print('Training %s' % expdir)
+
+    # Load a progressive GAN
+    generator = setting.load_proggan(args.model)
+    # Make a subset model with only some layers.
+    if invert_layer == 1:
+        s_maker = IdentityLayer()
+    else:
+        s_maker = nethook.subsequence(generator,
+                last_layer='layer%d' % (invert_layer - 1))
+    r_maker = nethook.subsequence(generator,
+            first_layer='layer%d' % invert_layer,
+            last_layer='layer%d' % invert_layer)
+    r_decoder = nethook.subsequence(generator,
+            first_layer='layer%d' % (invert_layer + 1))
+
+    # Make an encoder model.
+    if invert_layer == 1:
+        encoder = encoder_net.Layer1toZNormEncoder()
+    else:
+        channels = [512, # "layer0" is z
+            512, 512, 512, 512, 512, 512, 256, 256, 128, 128, 64, 64, 32, 32, 3]
+        encoder = encoder_net.LayerNormEncoder(
+            channels[invert_layer],
+            channels[invert_layer - 1],
+            stride=(2 if (invert_layer % 2 == 1 and invert_layer < 15) else 1),
+            skip_conv3=(invert_layer == 2),
+            skip_pnorm=(invert_layer == 15))
+
+    # Move models to GPU
+    for m in [generator, encoder, s_maker, r_maker, r_decoder]:
+        m.cuda()
+
+    # Set up a training data loader: unending batches of random z.
+    batch_size = 32
+    train_loader = training_loader(generator, batch_size)
+
+    # Test data loader is finite, fixed set of z.
+    test_loader = testing_loader(generator, batch_size)
+
+    # Set up optimizer
+    set_requires_grad(False, generator, s_maker, r_maker, r_decoder)
+    learning_rate = args.lr
+    optimizer = torch.optim.Adam(encoder.parameters(), lr=learning_rate)
+    scheduler = torch.optim.lr_scheduler.MultiStepLR(
+            optimizer, milestones=lr_milestones, gamma=0.1)
+
+    epoch_batches = 100
+    num_epochs = 100
+    # img_elems = 256*256*3
+    # rep_elems = 8*8*512
+    # alpha = float(rep_elems) / (rep_elems + img_elems)
+    for epoch, epoch_loader in enumerate(pbar(
+            epoch_grouper(train_loader, epoch_batches, num_epochs=1+num_epochs),
+            total=(1+num_epochs))):
+        # Training loop (for 0th epoch, do no training, just testing)
+        if epoch > 0:
+            for (z_batch,) in pbar(epoch_loader, total=epoch_batches):
+                (z_batch,) = [d.cuda() for d in [z_batch]]
+                loss = encoder_loss(z_batch, s_maker, r_maker, encoder)
+                loss.backward()
+                pbar.post(l=loss.item())
+                optimizer.step()
+            scheduler.step()
+        # Testing loop
+        with torch.no_grad():
+            losses = defaultdict(float)
+            count = 0
+            for i, (z_batch,) in enumerate(pbar(test_loader)):
+                (z_batch,) = [d.cuda() for d in [z_batch]]
+                nb = len(z_batch)
+                # Some other debugging losses
+                count += nb
+                losses['loss'] += nb * (
+                        encoder_loss(z_batch, s_maker, r_maker, encoder).item())
+                for name, mloss in monitor_losses(
+                        z_batch, encoder, s_maker, r_maker, r_decoder).items():
+                    losses[name] += nb * mloss.item()
+                if epoch % 10 == 0 and i == 0:
+                    visualize_results(epoch, z_batch,
+                            encoder, s_maker, r_maker, r_decoder)
+            losses = { name: loss / count for name, loss in losses.items() }
+            logline = '%d ' % epoch + ' '.join("%s=%4g" % (name, losses[name])
+                    for name in sorted(losses.keys()))
+            pbar.print(logline)
+            with open(os.path.join(expdir, 'log.txt'), 'a') as f:
+                f.write(logline + '\n')
+        if epoch % 10 == 0:
+            save_checkpoint(
+                    epoch=epoch,
+                    state_dict=encoder.state_dict(),
+                    lr=learning_rate,
+                    optimizer=optimizer.state_dict(),
+                    **losses)
+        if epoch == num_epochs:
+            break
+
+def save_checkpoint(**kwargs):
+    dirname = os.path.join(expdir, 'snapshots')
+    os.makedirs(dirname, exist_ok=True)
+    filename = 'epoch_%d.pth.tar' % kwargs['epoch']
+    torch.save(kwargs, os.path.join(dirname, filename))
+
+def visualize_results(epoch, z_batch, encoder, s_maker, r_maker, r_decoder):
+    dirname = os.path.join(expdir, 'images')
+    os.makedirs(dirname, exist_ok=True)
+    true_s = s_maker(z_batch)
+    true_r = r_maker(true_s)
+    image_batch = r_decoder(true_r)
+    estimated_s = encoder(true_r)
+    estimated_r = r_maker(estimated_s)
+    estimated_image = r_decoder(estimated_r)
+    # For 6 images of the batch, save four images, plus an .npz file.
+    for i in range(min(len(z_batch), 6)):
+        for name, im in [
+                ('epoch_%d_%d_g.png', image_batch),
+                ('epoch_%d_%d_r.png', estimated_image),
+                ]:
+            save_tensor_image(im[i], os.path.join(dirname, name % (epoch, i)))
+        numpy.savez(os.path.join(dirname, 'epoch_%d_%d.npz' % (epoch, i)),
+            true_z=z_batch[i].cpu().numpy(),
+            true_s=true_s[i].cpu().numpy(),
+            true_r=true_r[i].cpu().numpy(),
+            estimated_s=estimated_s[i].cpu().numpy(),
+            estimated_r=estimated_r[i].cpu().numpy())
+    shutil.copy(os.path.join(os.path.dirname(__file__), 'lightbox.html'),
+            os.path.join(dirname, '+lightbox.html'))
+
+def save_tensor_image(img, filename):
+    np_data = ((img.permute(1, 2, 0) / 2 + 0.5) * 255).byte().cpu().numpy()
+    PIL.Image.fromarray(np_data).save(filename)
+
+def encoder_loss(z_batch, s_maker, r_maker, encoder):
+    true_s = s_maker(z_batch)
+    true_r = r_maker(true_s)
+    recovered_s = encoder(true_r)
+    recovered_r = r_maker(recovered_s)
+    return (cor_square_error(true_s, recovered_s)
+            + 0.01 * cor_square_error(true_r, recovered_r))
+
+def monitor_losses(z_batch, encoder, s_maker, r_maker, r_decoder):
+    true_s = s_maker(z_batch)
+    true_r = r_maker(true_s)
+    true_image = r_decoder(true_r)
+    recovered_s = encoder(true_r)
+    recovered_r = r_maker(recovered_s)
+    recovered_image = r_decoder(recovered_r)
+    return dict(
+            loss_cs=cor_square_error(true_s, recovered_s),
+            loss_cr=cor_square_error(true_r, recovered_r),
+            loss_p=mse_loss(true_image, recovered_image))
+
+def training_loader(z_generator, batch_size):
+    '''
+    Returns an infinite generator that runs through randomized z
+    batches, forever.
+    '''
+    g_epoch = 1
+    while True:
+        z_data = zdataset.z_dataset_for_model(
+                z_generator, size=10000, seed=g_epoch + global_seed)
+        dataloader = torch.utils.data.DataLoader(
+                z_data,
+                shuffle=False,
+                batch_size=batch_size,
+                num_workers=10,
+                pin_memory=True)
+        for batch in dataloader:
+            yield batch
+        g_epoch += 1
+
+def testing_loader(z_generator, batch_size):
+    '''
+    Returns an a short iterator that returns a small set of test data.
+    '''
+    z_data = zdataset.z_dataset_for_model(
+        z_generator, size=1000, seed=global_seed)
+    dataloader = torch.utils.data.DataLoader(
+                z_data,
+                shuffle=False,
+                batch_size=batch_size,
+                num_workers=10,
+                pin_memory=True)
+    return dataloader
+
+def epoch_grouper(loader, epoch_size, num_epochs=None):
+    '''
+    To use with the infinite training loader: groups the training data
+    batches into epochs of the given size.
+    '''
+    it = iter(loader)
+    epoch = 0
+    while True:
+        chunk_it = itertools.islice(it, epoch_size)
+        try:
+            first_el = next(chunk_it)
+        except StopIteration:
+            return
+        yield itertools.chain((first_el,), chunk_it)
+        epoch += 1
+        if num_epochs is not None and epoch >= num_epochs:
+            return
+
+def set_requires_grad(requires_grad, *models):
+    for model in models:
+        if model is not None:
+            for param in model.parameters():
+                param.requires_grad = requires_grad
+
+class IdentityLayer(torch.nn.Module):
+    def forward(self, x):
+        return x
+
+if __name__ == '__main__':
+    exit_if_job_done(expdir)
+    main()
+    mark_job_done(expdir)
diff --git a/netdissect/upsample.py b/netdissect/upsample.py
new file mode 100644
index 0000000000000000000000000000000000000000..eaa4369a685218991bd816269989dd3219ec59c5
--- /dev/null
+++ b/netdissect/upsample.py
@@ -0,0 +1,179 @@
+import torch
+from torchvision import transforms
+
+def upsampler(target_shape, data_shape=None,
+        image_size=None, scale_offset=None,
+        source=None, convolutions=None, dtype=torch.float, device=None):
+    '''
+    Returns a function that will upsample a batch of torch data from the
+    expected data_shape to the specified target_shape. Can use scale_offset
+    and image_size to center the grid in a nondefault way: scale_offset
+    maps feature pixels to image_size pixels, and it is assumed that
+    the target_shape is a uniform downsampling of image_size.
+    '''
+    if source is not None:
+        assert image_size is None
+        image_size = image_size_from_source(source)
+    if convolutions is not None:
+        assert scale_offset is None
+        scale_offset = sequence_scale_offset(convolutions)
+        if image_size is not None and data_shape is None:
+            data_shape = sequence_data_size(convolutions, image_size)
+    assert data_shape is not None
+    assert len(data_shape) == 2
+    grid = upsample_grid(data_shape, target_shape, image_size, scale_offset,
+            dtype, device)
+    batch_grid = grid
+    # padding mode could be 'border'
+    def upsample_func(data, mode='bilinear', padding_mode='zeros'):
+        nonlocal grid, batch_grid
+        # Use the same grid over the whole batch
+        if batch_grid.shape[0] != data.shape[0]:
+            batch_grid = grid.expand((data.shape[0],) + grid.shape[1:])
+        if batch_grid.device != data.device:
+            batch_grid = batch_grid.to(data.device)
+        try:
+            return torch.nn.functional.grid_sample(data, batch_grid, mode=mode,
+                    padding_mode=padding_mode, align_corners=True)
+        except:
+            return torch.nn.functional.grid_sample(data, batch_grid, mode=mode,
+                    padding_mode=padding_mode) # older pytorch version
+    return upsample_func
+
+def sequence_scale_offset(modulelist):
+    '''Returns (yscale, yoffset), (xscale, xoffset) given a list of modules.
+    To convert output coordinates back to input coordinates while preserving
+    centers of receptive fields, the affine transformation is:
+        inpx = outx * xscale + xoffset
+        inpy = outy * yscale + yoffset
+    In both coordinate systems, (0, 0) refers to the upper-left corner
+    of the first pixel, (0.5, 0.5) refers to the center of that pixel,
+    and (1, 1) refers to the lower-right corner of that same pixel.
+
+    Modern convnets tend to add padding to keep receptive fields centered
+    while scaling, which will result in zero offsets.  For example, after resnet
+    does five stride-2 reductions, the scale_offset is just ((32, 0), (32, 0)).
+    However, AlexNet does not pad every layer, and after five stride-2
+    reductions, the scale_offset is ((32, 31), (32, 31)).
+    '''
+    return tuple(convconfig_scale_offset(d) for d in convconfigs(modulelist))
+
+def sequence_data_size(modulelist, input_size):
+    '''Returns (yscale, yoffset), (xscale, xoffset) given a list of modules.
+    To convert output coordinates back to input coordinates while preserving
+    centers of receptive fields, the affine transformation is:
+        inpx = outx * xscale + xoffset
+        inpy = outy * yscale + yoffset
+    In both coordinate systems, (0, 0) refers to the upper-left corner
+    of the first pixel, (0.5, 0.5) refers to the center of that pixel,
+    and (1, 1) refers to the lower-right corner of that same pixel.
+
+    Modern convnets tend to add padding to keep receptive fields centered
+    while scaling, which will result in zero offsets.  For example, after resnet
+    does five stride-2 reductions, the scale_offset is just ((32, 0), (32, 0)).
+    However, AlexNet does not pad every layer, and after five stride-2
+    reductions, the scale_offset is ((32, 31), (32, 31)).
+    '''
+    return tuple(convconfig_data_size(d, s)
+            for d, s in zip(convconfigs(modulelist), input_size))
+
+def convconfig_scale_offset(convconfigs):
+    '''Composes a lists of [(k, d, s, p)...] into a single total scale and
+    offset that returns to the input coordinates.
+    '''
+    if len(convconfigs) == 0:
+        return (1, 0)
+    scale, offset = convconfig_scale_offset(convconfigs[1:])
+    kernel, dilation, stride, padding = convconfigs[0]
+    scale *= stride
+    offset *= stride
+    offset += (kernel - 1) * dilation / 2.0 - padding
+    return scale, offset
+
+def convconfig_data_size(convconfigs, data_size):
+    '''Applies a list of [(k, d, s, p)...] to the given input size to obtain
+    an output size.
+    '''
+    for kernel, dilation, stride, padding in convconfigs:
+        data_size = (1 + (data_size + 2 * padding
+            - dilation * (kernel - 1) - 1) // stride)
+    return data_size
+
+def convconfigs(modulelist):
+    '''Converts a list of modules to a pair of lists of
+    [(kernel_size, dilation, stride, padding)...]: one for x, and one for y.'''
+    result = []
+    for module in modulelist:
+        settings = tuple(getattr(module, n, d)
+            for n, d in (('kernel_size', 1),
+                ('dilation', 1), ('stride', 1), ('padding', 0)))
+        settings = tuple((s if isinstance(s, tuple) else (s, s))
+            for s in settings)
+        if settings != ((1, 1), (1, 1), (1, 1), (0, 0)):
+            result.append(zip(*settings))
+    return list(zip(*result))
+
+def upsample_grid(data_shape, target_shape, image_size=None,
+        scale_offset=None, dtype=torch.float, device=None):
+    '''Prepares a grid to use with grid_sample to upsample a batch of
+    features in data_shape to the target_shape. Can use scale_offset
+    and image_size to center the grid in a nondefault way: scale_offset
+    maps feature pixels to image_size pixels, and it is assumed that
+    the target_shape is a uniform downsampling of image_size.'''
+    # Default is that nothing is resized.
+    if target_shape is None:
+        target_shape = data_shape
+    # Make a default scale_offset to fill the image if there isn't one
+    if scale_offset is None:
+        scale = tuple(float(ts) / ds
+                for ts, ds in zip(target_shape, data_shape))
+        offset = tuple(0.5 * s - 0.5 for s in scale)
+    else:
+        scale, offset = (v for v in zip(*scale_offset))
+        # Handle downsampling for different input vs target shape.
+        if image_size is not None:
+            scale = tuple(s * (ts - 1) / (ns - 1)
+                    for s, ns, ts in zip(scale, image_size, target_shape))
+            offset = tuple(o * (ts - 1) / (ns - 1)
+                    for o, ns, ts in zip(offset, image_size, target_shape))
+    # Pytorch needs target coordinates in terms of source coordinates [-1..1]
+    ty, tx = (((torch.arange(ts, dtype=dtype, device=device) - o)
+                  * (2 / (s * max(1, (ss - 1)))) - 1)
+        for ts, ss, s, o, in zip(target_shape, data_shape, scale, offset))
+    # Whoa, note that grid_sample reverses the order y, x -> x, y.
+    grid = torch.stack(
+        (tx[None,:].expand(target_shape), ty[:,None].expand(target_shape)),2
+       )[None,:,:,:].expand((1, target_shape[0], target_shape[1], 2))
+    return grid
+
+def image_size_from_source(source):
+    sizer = find_sizer(source)
+    if sizer is not None:
+        size = sizer.size
+    elif hasattr(source, 'resolution'):
+        size = source.resolution
+    if hasattr(size, '__len__'):
+        return size
+    return (size, size)
+
+def find_sizer(source):
+    '''
+    Crawl around the transforms attached to a dataset looking for
+    the last crop or resize transform to return.
+    '''
+    if source is None:
+        return None
+    if isinstance(source, (transforms.Resize, transforms.RandomCrop,
+        transforms.RandomResizedCrop, transforms.CenterCrop)):
+        return source
+    t = getattr(source, 'transform', None)
+    if t is not None:
+        return find_sizer(t)
+    ts = getattr(source, 'transforms', None)
+    if ts is not None:
+        for t in reversed(ts):
+            result = find_sizer(t)
+            if result is not None:
+                return result
+    return None
+
diff --git a/netdissect/upsegmodel/__init__.py b/netdissect/upsegmodel/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..76b40a0a36bc2976f185dbdc344c5a7c09b65920
--- /dev/null
+++ b/netdissect/upsegmodel/__init__.py
@@ -0,0 +1 @@
+from .models import ModelBuilder, SegmentationModule
diff --git a/netdissect/upsegmodel/models.py b/netdissect/upsegmodel/models.py
new file mode 100644
index 0000000000000000000000000000000000000000..68a773ad100f2df6e4edfdc61229adf5060d0f0a
--- /dev/null
+++ b/netdissect/upsegmodel/models.py
@@ -0,0 +1,427 @@
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+import torchvision
+from . import resnet, resnext
+try:
+    from lib.nn import SynchronizedBatchNorm2d
+except ImportError:
+    from torch.nn import BatchNorm2d as SynchronizedBatchNorm2d
+
+
+class SegmentationModuleBase(nn.Module):
+    def __init__(self):
+        super(SegmentationModuleBase, self).__init__()
+
+    @staticmethod
+    def pixel_acc(pred, label, ignore_index=-1):
+        _, preds = torch.max(pred, dim=1)
+        valid = (label != ignore_index).long()
+        acc_sum = torch.sum(valid * (preds == label).long())
+        pixel_sum = torch.sum(valid)
+        acc = acc_sum.float() / (pixel_sum.float() + 1e-10)
+        return acc
+
+    @staticmethod
+    def part_pixel_acc(pred_part, gt_seg_part, gt_seg_object, object_label, valid):
+        mask_object = (gt_seg_object == object_label)
+        _, pred = torch.max(pred_part, dim=1)
+        acc_sum = mask_object * (pred == gt_seg_part)
+        acc_sum = torch.sum(acc_sum.view(acc_sum.size(0), -1), dim=1)
+        acc_sum = torch.sum(acc_sum * valid)
+        pixel_sum = torch.sum(mask_object.view(mask_object.size(0), -1), dim=1)
+        pixel_sum = torch.sum(pixel_sum * valid)
+        return acc_sum, pixel_sum 
+
+    @staticmethod
+    def part_loss(pred_part, gt_seg_part, gt_seg_object, object_label, valid):
+        mask_object = (gt_seg_object == object_label)
+        loss = F.nll_loss(pred_part, gt_seg_part * mask_object.long(), reduction='none')
+        loss = loss * mask_object.float()
+        loss = torch.sum(loss.view(loss.size(0), -1), dim=1)
+        nr_pixel = torch.sum(mask_object.view(mask_object.shape[0], -1), dim=1)
+        sum_pixel = (nr_pixel * valid).sum()
+        loss = (loss * valid.float()).sum() / torch.clamp(sum_pixel, 1).float()
+        return loss
+
+
+class SegmentationModule(SegmentationModuleBase):
+    def __init__(self, net_enc, net_dec, labeldata, loss_scale=None):
+        super(SegmentationModule, self).__init__()
+        self.encoder = net_enc
+        self.decoder = net_dec
+        self.crit_dict = nn.ModuleDict()
+        if loss_scale is None:
+            self.loss_scale = {"object": 1, "part": 0.5, "scene": 0.25, "material": 1}
+        else:
+            self.loss_scale = loss_scale
+
+        # criterion
+        self.crit_dict["object"] = nn.NLLLoss(ignore_index=0)  # ignore background 0
+        self.crit_dict["material"] = nn.NLLLoss(ignore_index=0)  # ignore background 0
+        self.crit_dict["scene"] = nn.NLLLoss(ignore_index=-1)  # ignore unlabelled -1
+
+        # Label data - read from json
+        self.labeldata = labeldata
+        object_to_num = {k: v for v, k in enumerate(labeldata['object'])}
+        part_to_num = {k: v for v, k in enumerate(labeldata['part'])}
+        self.object_part = {object_to_num[k]:
+                [part_to_num[p] for p in v]
+                for k, v in labeldata['object_part'].items()}
+        self.object_with_part = sorted(self.object_part.keys())
+        self.decoder.object_part = self.object_part
+        self.decoder.object_with_part = self.object_with_part
+
+    def forward(self, feed_dict, *, seg_size=None):
+        if seg_size is None: # training
+
+            if feed_dict['source_idx'] == 0:
+                output_switch = {"object": True, "part": True, "scene": True, "material": False}
+            elif feed_dict['source_idx'] == 1:
+                output_switch = {"object": False, "part": False, "scene": False, "material": True}
+            else:
+                raise ValueError
+
+            pred = self.decoder(
+                self.encoder(feed_dict['img'], return_feature_maps=True),
+                output_switch=output_switch
+            )
+
+            # loss
+            loss_dict = {}
+            if pred['object'] is not None:  # object
+                loss_dict['object'] = self.crit_dict['object'](pred['object'], feed_dict['seg_object'])
+            if pred['part'] is not None:  # part
+                part_loss = 0
+                for idx_part, object_label in enumerate(self.object_with_part):
+                    part_loss += self.part_loss(
+                        pred['part'][idx_part], feed_dict['seg_part'],
+                        feed_dict['seg_object'], object_label, feed_dict['valid_part'][:, idx_part])
+                loss_dict['part'] = part_loss
+            if pred['scene'] is not None:  # scene
+                loss_dict['scene'] = self.crit_dict['scene'](pred['scene'], feed_dict['scene_label'])
+            if pred['material'] is not None:  # material
+                loss_dict['material'] = self.crit_dict['material'](pred['material'], feed_dict['seg_material'])
+            loss_dict['total'] = sum([loss_dict[k] * self.loss_scale[k] for k in loss_dict.keys()])
+
+            # metric 
+            metric_dict= {}
+            if pred['object'] is not None:
+                metric_dict['object'] = self.pixel_acc(
+                    pred['object'], feed_dict['seg_object'], ignore_index=0)
+            if pred['material'] is not None:
+                metric_dict['material'] = self.pixel_acc(
+                    pred['material'], feed_dict['seg_material'], ignore_index=0)
+            if pred['part'] is not None:
+                acc_sum, pixel_sum = 0, 0
+                for idx_part, object_label in enumerate(self.object_with_part):
+                    acc, pixel = self.part_pixel_acc(
+                        pred['part'][idx_part], feed_dict['seg_part'], feed_dict['seg_object'],
+                        object_label, feed_dict['valid_part'][:, idx_part])
+                    acc_sum += acc
+                    pixel_sum += pixel
+                metric_dict['part'] = acc_sum.float() / (pixel_sum.float() + 1e-10)
+            if pred['scene'] is not None:
+                metric_dict['scene'] = self.pixel_acc(
+                    pred['scene'], feed_dict['scene_label'], ignore_index=-1)
+
+            return {'metric': metric_dict, 'loss': loss_dict}
+        else: # inference
+            output_switch = {"object": True, "part": True, "scene": True, "material": True}
+            pred = self.decoder(self.encoder(feed_dict['img'], return_feature_maps=True),
+                                output_switch=output_switch, seg_size=seg_size)
+            return pred
+
+
+def conv3x3(in_planes, out_planes, stride=1, has_bias=False):
+    "3x3 convolution with padding"
+    return nn.Conv2d(in_planes, out_planes, kernel_size=3, stride=stride,
+                     padding=1, bias=has_bias)
+
+
+def conv3x3_bn_relu(in_planes, out_planes, stride=1):
+    return nn.Sequential(
+            conv3x3(in_planes, out_planes, stride),
+            SynchronizedBatchNorm2d(out_planes),
+            nn.ReLU(inplace=True),
+            )
+
+
+class ModelBuilder:
+    def __init__(self):
+        pass
+
+    # custom weights initialization
+    @staticmethod
+    def weights_init(m):
+        classname = m.__class__.__name__
+        if classname.find('Conv') != -1:
+            nn.init.kaiming_normal_(m.weight.data, nonlinearity='relu')
+        elif classname.find('BatchNorm') != -1:
+            m.weight.data.fill_(1.)
+            m.bias.data.fill_(1e-4)
+        #elif classname.find('Linear') != -1:
+        #    m.weight.data.normal_(0.0, 0.0001)
+
+    def build_encoder(self, arch='resnet50_dilated8', fc_dim=512, weights=''):
+        pretrained = True if len(weights) == 0 else False
+        if arch == 'resnet50':
+            orig_resnet = resnet.__dict__['resnet50'](pretrained=pretrained)
+            net_encoder = Resnet(orig_resnet)
+        elif arch == 'resnet101':
+            orig_resnet = resnet.__dict__['resnet101'](pretrained=pretrained)
+            net_encoder = Resnet(orig_resnet)
+        elif arch == 'resnext101':
+            orig_resnext = resnext.__dict__['resnext101'](pretrained=pretrained)
+            net_encoder = Resnet(orig_resnext) # we can still use class Resnet
+        else:
+            raise Exception('Architecture undefined!')
+
+        # net_encoder.apply(self.weights_init)
+        if len(weights) > 0:
+            # print('Loading weights for net_encoder')
+            net_encoder.load_state_dict(
+                torch.load(weights, map_location=lambda storage, loc: storage), strict=False)
+        return net_encoder
+
+    def build_decoder(self, nr_classes,
+                      arch='ppm_bilinear_deepsup', fc_dim=512,
+                      weights='', use_softmax=False):
+        if arch == 'upernet_lite':
+            net_decoder = UPerNet(
+                nr_classes=nr_classes,
+                fc_dim=fc_dim,
+                use_softmax=use_softmax,
+                fpn_dim=256)
+        elif arch == 'upernet':
+            net_decoder = UPerNet(
+                nr_classes=nr_classes,
+                fc_dim=fc_dim,
+                use_softmax=use_softmax,
+                fpn_dim=512)
+        else:
+            raise Exception('Architecture undefined!')
+
+        net_decoder.apply(self.weights_init)
+        if len(weights) > 0:
+            # print('Loading weights for net_decoder')
+            net_decoder.load_state_dict(
+                torch.load(weights, map_location=lambda storage, loc: storage), strict=False)
+        return net_decoder
+
+
+class Resnet(nn.Module):
+    def __init__(self, orig_resnet):
+        super(Resnet, self).__init__()
+
+        # take pretrained resnet, except AvgPool and FC
+        self.conv1 = orig_resnet.conv1
+        self.bn1 = orig_resnet.bn1
+        self.relu1 = orig_resnet.relu1
+        self.conv2 = orig_resnet.conv2
+        self.bn2 = orig_resnet.bn2
+        self.relu2 = orig_resnet.relu2
+        self.conv3 = orig_resnet.conv3
+        self.bn3 = orig_resnet.bn3
+        self.relu3 = orig_resnet.relu3
+        self.maxpool = orig_resnet.maxpool
+        self.layer1 = orig_resnet.layer1
+        self.layer2 = orig_resnet.layer2
+        self.layer3 = orig_resnet.layer3
+        self.layer4 = orig_resnet.layer4
+
+    def forward(self, x, return_feature_maps=False):
+        conv_out = []
+
+        x = self.relu1(self.bn1(self.conv1(x)))
+        x = self.relu2(self.bn2(self.conv2(x)))
+        x = self.relu3(self.bn3(self.conv3(x)))
+        x = self.maxpool(x)
+
+        x = self.layer1(x); conv_out.append(x);
+        x = self.layer2(x); conv_out.append(x);
+        x = self.layer3(x); conv_out.append(x);
+        x = self.layer4(x); conv_out.append(x);
+
+        if return_feature_maps:
+            return conv_out
+        return [x]
+
+
+# upernet
+class UPerNet(nn.Module):
+    def __init__(self, nr_classes, fc_dim=4096,
+                 use_softmax=False, pool_scales=(1, 2, 3, 6),
+                 fpn_inplanes=(256,512,1024,2048), fpn_dim=256):
+        # Lazy import so that compilation isn't needed if not being used.
+        from .prroi_pool import PrRoIPool2D
+        super(UPerNet, self).__init__()
+        self.use_softmax = use_softmax
+
+        # PPM Module
+        self.ppm_pooling = []
+        self.ppm_conv = []
+
+        for scale in pool_scales:
+            # we use the feature map size instead of input image size, so down_scale = 1.0
+            self.ppm_pooling.append(PrRoIPool2D(scale, scale, 1.))
+            self.ppm_conv.append(nn.Sequential(
+                nn.Conv2d(fc_dim, 512, kernel_size=1, bias=False),
+                SynchronizedBatchNorm2d(512),
+                nn.ReLU(inplace=True)
+            ))
+        self.ppm_pooling = nn.ModuleList(self.ppm_pooling)
+        self.ppm_conv = nn.ModuleList(self.ppm_conv)
+        self.ppm_last_conv = conv3x3_bn_relu(fc_dim + len(pool_scales)*512, fpn_dim, 1)
+
+        # FPN Module
+        self.fpn_in = []
+        for fpn_inplane in fpn_inplanes[:-1]: # skip the top layer
+            self.fpn_in.append(nn.Sequential(
+                nn.Conv2d(fpn_inplane, fpn_dim, kernel_size=1, bias=False),
+                SynchronizedBatchNorm2d(fpn_dim),
+                nn.ReLU(inplace=True)
+            ))
+        self.fpn_in = nn.ModuleList(self.fpn_in)
+
+        self.fpn_out = []
+        for i in range(len(fpn_inplanes) - 1): # skip the top layer
+            self.fpn_out.append(nn.Sequential(
+                conv3x3_bn_relu(fpn_dim, fpn_dim, 1),
+            ))
+        self.fpn_out = nn.ModuleList(self.fpn_out)
+
+        self.conv_fusion = conv3x3_bn_relu(len(fpn_inplanes) * fpn_dim, fpn_dim, 1)
+
+        # background included. if ignore in loss, output channel 0 will not be trained.
+        self.nr_scene_class, self.nr_object_class, self.nr_part_class, self.nr_material_class = \
+            nr_classes['scene'], nr_classes['object'], nr_classes['part'], nr_classes['material']
+
+        # input: PPM out, input_dim: fpn_dim
+        self.scene_head = nn.Sequential(
+            conv3x3_bn_relu(fpn_dim, fpn_dim, 1),
+            nn.AdaptiveAvgPool2d(1),
+            nn.Conv2d(fpn_dim, self.nr_scene_class, kernel_size=1, bias=True)
+        )
+
+        # input: Fusion out, input_dim: fpn_dim
+        self.object_head = nn.Sequential(
+            conv3x3_bn_relu(fpn_dim, fpn_dim, 1),
+            nn.Conv2d(fpn_dim, self.nr_object_class, kernel_size=1, bias=True)
+        )
+
+        # input: Fusion out, input_dim: fpn_dim
+        self.part_head = nn.Sequential(
+            conv3x3_bn_relu(fpn_dim, fpn_dim, 1),
+            nn.Conv2d(fpn_dim, self.nr_part_class, kernel_size=1, bias=True)
+        )
+
+        # input: FPN_2 (P2), input_dim: fpn_dim
+        self.material_head = nn.Sequential(
+            conv3x3_bn_relu(fpn_dim, fpn_dim, 1),
+            nn.Conv2d(fpn_dim, self.nr_material_class, kernel_size=1, bias=True)
+        )
+
+    def forward(self, conv_out, output_switch=None, seg_size=None):
+
+        output_dict = {k: None for k in output_switch.keys()}
+
+        conv5 = conv_out[-1]
+        input_size = conv5.size()
+        ppm_out = [conv5]
+        roi = [] # fake rois, just used for pooling
+        for i in range(input_size[0]): # batch size
+            roi.append(torch.Tensor([i, 0, 0, input_size[3], input_size[2]]).view(1, -1)) # b, x0, y0, x1, y1
+        roi = torch.cat(roi, dim=0).type_as(conv5)
+        ppm_out = [conv5]
+        for pool_scale, pool_conv in zip(self.ppm_pooling, self.ppm_conv):
+            ppm_out.append(pool_conv(F.interpolate(
+                pool_scale(conv5, roi.detach()),
+                (input_size[2], input_size[3]),
+                mode='bilinear', align_corners=False)))
+        ppm_out = torch.cat(ppm_out, 1)
+        f = self.ppm_last_conv(ppm_out)
+
+        if output_switch['scene']: # scene
+            output_dict['scene'] = self.scene_head(f)
+
+        if output_switch['object'] or output_switch['part'] or output_switch['material']:
+            fpn_feature_list = [f]
+            for i in reversed(range(len(conv_out) - 1)):
+                conv_x = conv_out[i]
+                conv_x = self.fpn_in[i](conv_x) # lateral branch
+
+                f = F.interpolate(
+                    f, size=conv_x.size()[2:], mode='bilinear', align_corners=False) # top-down branch
+                f = conv_x + f
+
+                fpn_feature_list.append(self.fpn_out[i](f))
+            fpn_feature_list.reverse() # [P2 - P5]
+
+            # material
+            if output_switch['material']:
+                output_dict['material'] = self.material_head(fpn_feature_list[0])
+
+            if output_switch['object'] or output_switch['part']:
+                output_size = fpn_feature_list[0].size()[2:]
+                fusion_list = [fpn_feature_list[0]]
+                for i in range(1, len(fpn_feature_list)):
+                    fusion_list.append(F.interpolate(
+                        fpn_feature_list[i],
+                        output_size,
+                        mode='bilinear', align_corners=False))
+                fusion_out = torch.cat(fusion_list, 1)
+                x = self.conv_fusion(fusion_out)
+
+                if output_switch['object']: # object
+                    output_dict['object'] = self.object_head(x)
+                if output_switch['part']:
+                    output_dict['part'] = self.part_head(x)
+
+        if self.use_softmax:  # is True during inference
+            # inference scene
+            x = output_dict['scene']
+            x = x.squeeze(3).squeeze(2)
+            x = F.softmax(x, dim=1)
+            output_dict['scene'] = x
+
+            # inference object, material
+            for k in ['object', 'material']:
+                x = output_dict[k]
+                x = F.interpolate(x, size=seg_size, mode='bilinear', align_corners=False)
+                x = F.softmax(x, dim=1)
+                output_dict[k] = x
+
+            # inference part
+            x = output_dict['part']
+            x = F.interpolate(x, size=seg_size, mode='bilinear', align_corners=False)
+            part_pred_list, head = [], 0
+            for idx_part, object_label in enumerate(self.object_with_part):
+                n_part = len(self.object_part[object_label])
+                _x = F.interpolate(x[:, head: head + n_part], size=seg_size, mode='bilinear', align_corners=False)
+                _x = F.softmax(_x, dim=1)
+                part_pred_list.append(_x)
+                head += n_part
+            output_dict['part'] = part_pred_list
+
+        else:   # Training
+            # object, scene, material
+            for k in ['object', 'scene', 'material']:
+                if output_dict[k] is None:
+                    continue
+                x = output_dict[k]
+                x = F.log_softmax(x, dim=1)
+                if k == "scene":  # for scene
+                    x = x.squeeze(3).squeeze(2)
+                output_dict[k] = x
+            if output_dict['part'] is not None:
+                part_pred_list, head = [], 0
+                for idx_part, object_label in enumerate(self.object_with_part):
+                    n_part = len(self.object_part[object_label])
+                    x = output_dict['part'][:, head: head + n_part]
+                    x = F.log_softmax(x, dim=1)
+                    part_pred_list.append(x)
+                    head += n_part
+                output_dict['part'] = part_pred_list
+
+        return output_dict
diff --git a/netdissect/upsegmodel/prroi_pool/.gitignore b/netdissect/upsegmodel/prroi_pool/.gitignore
new file mode 100644
index 0000000000000000000000000000000000000000..18495eade007bad45f5ca772771d99f91e441e50
--- /dev/null
+++ b/netdissect/upsegmodel/prroi_pool/.gitignore
@@ -0,0 +1,2 @@
+*.o
+/_prroi_pooling
diff --git a/netdissect/upsegmodel/prroi_pool/README.md b/netdissect/upsegmodel/prroi_pool/README.md
new file mode 100644
index 0000000000000000000000000000000000000000..bb98946d3b48a2069a58f179eb6da63e009c3849
--- /dev/null
+++ b/netdissect/upsegmodel/prroi_pool/README.md
@@ -0,0 +1,66 @@
+# PreciseRoIPooling
+This repo implements the **Precise RoI Pooling** (PrRoI Pooling), proposed in the paper **Acquisition of Localization Confidence for Accurate Object Detection** published at ECCV 2018 (Oral Presentation).
+
+**Acquisition of Localization Confidence for Accurate Object Detection**
+
+_Borui Jiang*, Ruixuan Luo*, Jiayuan Mao*, Tete Xiao, Yuning Jiang_ (* indicates equal contribution.)
+
+https://arxiv.org/abs/1807.11590
+
+## Brief
+
+In short, Precise RoI Pooling is an integration-based (bilinear interpolation) average pooling method for RoI Pooling. It avoids any quantization and has a continuous gradient on bounding box coordinates. It is:
+
+- different from the original RoI Pooling proposed in [Fast R-CNN](https://arxiv.org/abs/1504.08083). PrRoI Pooling uses average pooling instead of max pooling for each bin and has a continuous gradient on bounding box coordinates. That is, one can take the derivatives of some loss function w.r.t the coordinates of each RoI and optimize the RoI coordinates.
+- different from the RoI Align proposed in [Mask R-CNN](https://arxiv.org/abs/1703.06870). PrRoI Pooling uses a full integration-based average pooling instead of sampling a constant number of points. This makes the gradient w.r.t. the coordinates continuous.
+
+For a better illustration, we illustrate RoI Pooling, RoI Align and PrRoI Pooing in the following figure. More details including the gradient computation can be found in our paper.
+
+<center><img src="./_assets/prroi_visualization.png" width="80%"></center>
+
+## Implementation
+
+PrRoI Pooling was originally implemented by [Tete Xiao](http://tetexiao.com/) based on MegBrain, an (internal) deep learning framework built by Megvii Inc. It was later adapted into open-source deep learning frameworks. Currently, we only support PyTorch. Unfortunately, we don't have any specific plan for the adaptation into other frameworks such as TensorFlow, but any contributions (pull requests) will be more than welcome.
+
+## Usage (PyTorch 1.0)
+
+In the directory `pytorch/`, we provide a PyTorch-based implementation of PrRoI Pooling. It requires PyTorch 1.0+ and only supports CUDA (CPU mode is not implemented).
+Since we use PyTorch JIT for cxx/cuda code compilation, to use the module in your code, simply do:
+
+```
+from prroi_pool import PrRoIPool2D
+
+avg_pool = PrRoIPool2D(window_height, window_width, spatial_scale)
+roi_features = avg_pool(features, rois)
+
+# for those who want to use the "functional"
+
+from prroi_pool.functional import prroi_pool2d
+roi_features = prroi_pool2d(features, rois, window_height, window_width, spatial_scale)
+```
+
+
+## Usage (PyTorch 0.4)
+
+**!!! Please first checkout to the branch pytorch0.4.**
+
+In the directory `pytorch/`, we provide a PyTorch-based implementation of PrRoI Pooling. It requires PyTorch 0.4 and only supports CUDA (CPU mode is not implemented).
+To use the PrRoI Pooling module, first goto `pytorch/prroi_pool` and execute `./travis.sh` to compile the essential components (you may need `nvcc` for this step). To use the module in your code, simply do:
+
+```
+from prroi_pool import PrRoIPool2D
+
+avg_pool = PrRoIPool2D(window_height, window_width, spatial_scale)
+roi_features = avg_pool(features, rois)
+
+# for those who want to use the "functional"
+
+from prroi_pool.functional import prroi_pool2d
+roi_features = prroi_pool2d(features, rois, window_height, window_width, spatial_scale)
+```
+
+Here,
+
+- RoI is an `m * 5` float tensor of format `(batch_index, x0, y0, x1, y1)`, following the convention in the original Caffe implementation of RoI Pooling, although in some frameworks the batch indices are provided by an integer tensor.
+- `spatial_scale` is multiplied to the RoIs. For example, if your feature maps are down-sampled by a factor of 16 (w.r.t. the input image), you should use a spatial scale of `1/16`.
+- The coordinates for RoI follows the [L, R) convension. That is, `(0, 0, 4, 4)` denotes a box of size `4x4`.
diff --git a/netdissect/upsegmodel/prroi_pool/__init__.py b/netdissect/upsegmodel/prroi_pool/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..0c40b7a7e2bca8a0dbd28e13815f2f2ad6c4728b
--- /dev/null
+++ b/netdissect/upsegmodel/prroi_pool/__init__.py
@@ -0,0 +1,13 @@
+#! /usr/bin/env python3
+# -*- coding: utf-8 -*-
+# File   : __init__.py
+# Author : Jiayuan Mao, Tete Xiao
+# Email  : maojiayuan@gmail.com, jasonhsiao97@gmail.com
+# Date   : 07/13/2018
+# 
+# This file is part of PreciseRoIPooling.
+# Distributed under terms of the MIT license.
+# Copyright (c) 2017 Megvii Technology Limited.
+
+from .prroi_pool import *
+
diff --git a/netdissect/upsegmodel/prroi_pool/build.py b/netdissect/upsegmodel/prroi_pool/build.py
new file mode 100644
index 0000000000000000000000000000000000000000..b198790817a2d11d65d6211b011f9408d9d34270
--- /dev/null
+++ b/netdissect/upsegmodel/prroi_pool/build.py
@@ -0,0 +1,50 @@
+#! /usr/bin/env python3
+# -*- coding: utf-8 -*-
+# File   : build.py
+# Author : Jiayuan Mao, Tete Xiao
+# Email  : maojiayuan@gmail.com, jasonhsiao97@gmail.com
+# Date   : 07/13/2018
+# 
+# This file is part of PreciseRoIPooling.
+# Distributed under terms of the MIT license.
+# Copyright (c) 2017 Megvii Technology Limited.
+
+import os
+import torch
+
+from torch.utils.ffi import create_extension
+
+headers = []
+sources = []
+defines = []
+extra_objects = []
+with_cuda = False
+
+if torch.cuda.is_available():
+    with_cuda = True
+
+    headers+= ['src/prroi_pooling_gpu.h']
+    sources += ['src/prroi_pooling_gpu.c']
+    defines += [('WITH_CUDA', None)]
+
+    this_file = os.path.dirname(os.path.realpath(__file__))
+    extra_objects_cuda = ['src/prroi_pooling_gpu_impl.cu.o']
+    extra_objects_cuda = [os.path.join(this_file, fname) for fname in extra_objects_cuda]
+    extra_objects.extend(extra_objects_cuda)
+else:
+    # TODO(Jiayuan Mao @ 07/13): remove this restriction after we support the cpu implementation.
+    raise NotImplementedError('Precise RoI Pooling only supports GPU (cuda) implememtations.')
+
+ffi = create_extension(
+    '_prroi_pooling',
+    headers=headers,
+    sources=sources,
+    define_macros=defines,
+    relative_to=__file__,
+    with_cuda=with_cuda,
+    extra_objects=extra_objects
+)
+
+if __name__ == '__main__':
+    ffi.build()
+
diff --git a/netdissect/upsegmodel/prroi_pool/functional.py b/netdissect/upsegmodel/prroi_pool/functional.py
new file mode 100644
index 0000000000000000000000000000000000000000..7dc7a8c282e846bd633c4fdc4190c4dca3da5a6f
--- /dev/null
+++ b/netdissect/upsegmodel/prroi_pool/functional.py
@@ -0,0 +1,70 @@
+#! /usr/bin/env python3
+# -*- coding: utf-8 -*-
+# File   : functional.py
+# Author : Jiayuan Mao, Tete Xiao
+# Email  : maojiayuan@gmail.com, jasonhsiao97@gmail.com
+# Date   : 07/13/2018
+#
+# This file is part of PreciseRoIPooling.
+# Distributed under terms of the MIT license.
+# Copyright (c) 2017 Megvii Technology Limited.
+
+import torch
+import torch.autograd as ag
+
+try:
+    from os.path import join as pjoin, dirname
+    from torch.utils.cpp_extension import load as load_extension
+    root_dir = pjoin(dirname(__file__), 'src')
+    _prroi_pooling = load_extension(
+        '_prroi_pooling',
+        [pjoin(root_dir, 'prroi_pooling_gpu.c'), pjoin(root_dir, 'prroi_pooling_gpu_impl.cu')],
+        verbose=False
+    )
+except ImportError:
+    raise ImportError('Can not compile Precise RoI Pooling library.')
+
+__all__ = ['prroi_pool2d']
+
+
+class PrRoIPool2DFunction(ag.Function):
+    @staticmethod
+    def forward(ctx, features, rois, pooled_height, pooled_width, spatial_scale):
+        assert 'FloatTensor' in features.type() and 'FloatTensor' in rois.type(), \
+                'Precise RoI Pooling only takes float input, got {} for features and {} for rois.'.format(features.type(), rois.type())
+
+        pooled_height = int(pooled_height)
+        pooled_width = int(pooled_width)
+        spatial_scale = float(spatial_scale)
+
+        features = features.contiguous()
+        rois = rois.contiguous()
+        params = (pooled_height, pooled_width, spatial_scale)
+
+        if features.is_cuda:
+            output = _prroi_pooling.prroi_pooling_forward_cuda(features, rois, *params)
+            ctx.params = params
+            # everything here is contiguous.
+            ctx.save_for_backward(features, rois, output)
+        else:
+            raise NotImplementedError('Precise RoI Pooling only supports GPU (cuda) implememtations.')
+
+        return output
+
+    @staticmethod
+    def backward(ctx, grad_output):
+        features, rois, output = ctx.saved_tensors
+        grad_input = grad_coor = None
+
+        if features.requires_grad:
+            grad_output = grad_output.contiguous()
+            grad_input = _prroi_pooling.prroi_pooling_backward_cuda(features, rois, output, grad_output, *ctx.params)
+        if rois.requires_grad:
+            grad_output = grad_output.contiguous()
+            grad_coor = _prroi_pooling.prroi_pooling_coor_backward_cuda(features, rois, output, grad_output, *ctx.params)
+
+        return grad_input, grad_coor, None, None, None
+
+
+prroi_pool2d = PrRoIPool2DFunction.apply
+
diff --git a/netdissect/upsegmodel/prroi_pool/prroi_pool.py b/netdissect/upsegmodel/prroi_pool/prroi_pool.py
new file mode 100644
index 0000000000000000000000000000000000000000..998b2b80531058fa91ac138e79ae39c5c0174601
--- /dev/null
+++ b/netdissect/upsegmodel/prroi_pool/prroi_pool.py
@@ -0,0 +1,28 @@
+#! /usr/bin/env python3
+# -*- coding: utf-8 -*-
+# File   : prroi_pool.py
+# Author : Jiayuan Mao, Tete Xiao
+# Email  : maojiayuan@gmail.com, jasonhsiao97@gmail.com
+# Date   : 07/13/2018
+# 
+# This file is part of PreciseRoIPooling.
+# Distributed under terms of the MIT license.
+# Copyright (c) 2017 Megvii Technology Limited.
+
+import torch.nn as nn
+
+from .functional import prroi_pool2d
+
+__all__ = ['PrRoIPool2D']
+
+
+class PrRoIPool2D(nn.Module):
+    def __init__(self, pooled_height, pooled_width, spatial_scale):
+        super().__init__()
+
+        self.pooled_height = int(pooled_height)
+        self.pooled_width = int(pooled_width)
+        self.spatial_scale = float(spatial_scale)
+
+    def forward(self, features, rois):
+        return prroi_pool2d(features, rois, self.pooled_height, self.pooled_width, self.spatial_scale)
diff --git a/netdissect/upsegmodel/prroi_pool/src/prroi_pooling_gpu.c b/netdissect/upsegmodel/prroi_pool/src/prroi_pooling_gpu.c
new file mode 100644
index 0000000000000000000000000000000000000000..1e652963cdb76fe628d0a33bc270d2c25a0f3770
--- /dev/null
+++ b/netdissect/upsegmodel/prroi_pool/src/prroi_pooling_gpu.c
@@ -0,0 +1,113 @@
+/*
+ * File   : prroi_pooling_gpu.c
+ * Author : Jiayuan Mao, Tete Xiao
+ * Email  : maojiayuan@gmail.com, jasonhsiao97@gmail.com
+ * Date   : 07/13/2018
+ *
+ * Distributed under terms of the MIT license.
+ * Copyright (c) 2017 Megvii Technology Limited.
+ */
+
+#include <math.h>
+#include <torch/extension.h>
+
+#include <ATen/ATen.h>
+#include <ATen/cuda/CUDAContext.h>
+
+#include <THC/THC.h>
+
+#include "prroi_pooling_gpu_impl.cuh"
+
+
+at::Tensor prroi_pooling_forward_cuda(const at::Tensor &features, const at::Tensor &rois, int pooled_height, int pooled_width, float spatial_scale) {
+    int nr_rois = rois.size(0);
+    int nr_channels = features.size(1);
+    int height = features.size(2);
+    int width = features.size(3);
+    int top_count = nr_rois * nr_channels * pooled_height * pooled_width;
+    auto output = at::zeros({nr_rois, nr_channels, pooled_height, pooled_width}, features.options());
+
+    if (output.numel() == 0) {
+        THCudaCheck(cudaGetLastError());
+        return output;
+    }
+
+    cudaStream_t stream = at::cuda::getCurrentCUDAStream();
+    PrRoIPoolingForwardGpu(
+        stream, features.data<float>(), rois.data<float>(), output.data<float>(),
+        nr_channels, height, width, pooled_height, pooled_width, spatial_scale,
+        top_count
+    );
+
+    THCudaCheck(cudaGetLastError());
+    return output;
+}
+
+at::Tensor prroi_pooling_backward_cuda(
+    const at::Tensor &features, const at::Tensor &rois, const at::Tensor &output, const at::Tensor &output_diff,
+    int pooled_height, int pooled_width, float spatial_scale) {
+
+    auto features_diff = at::zeros_like(features);
+
+    int nr_rois = rois.size(0);
+    int batch_size = features.size(0);
+    int nr_channels = features.size(1);
+    int height = features.size(2);
+    int width = features.size(3);
+    int top_count = nr_rois * nr_channels * pooled_height * pooled_width;
+    int bottom_count = batch_size * nr_channels * height * width;
+
+    if (output.numel() == 0) {
+        THCudaCheck(cudaGetLastError());
+        return features_diff;
+    }
+
+    cudaStream_t stream = at::cuda::getCurrentCUDAStream();
+    PrRoIPoolingBackwardGpu(
+        stream,
+        features.data<float>(), rois.data<float>(), output.data<float>(), output_diff.data<float>(),
+        features_diff.data<float>(),
+        nr_channels, height, width, pooled_height, pooled_width, spatial_scale,
+        top_count, bottom_count
+    );
+
+    THCudaCheck(cudaGetLastError());
+    return features_diff;
+}
+
+at::Tensor prroi_pooling_coor_backward_cuda(
+    const at::Tensor &features, const at::Tensor &rois, const at::Tensor &output, const at::Tensor &output_diff,
+    int pooled_height, int pooled_width, float spatial_scale) {
+
+    auto coor_diff = at::zeros_like(rois);
+
+    int nr_rois = rois.size(0);
+    int nr_channels = features.size(1);
+    int height = features.size(2);
+    int width = features.size(3);
+    int top_count = nr_rois * nr_channels * pooled_height * pooled_width;
+    int bottom_count = nr_rois * 5;
+
+    if (output.numel() == 0) {
+        THCudaCheck(cudaGetLastError());
+        return coor_diff;
+    }
+
+    cudaStream_t stream = at::cuda::getCurrentCUDAStream();
+    PrRoIPoolingCoorBackwardGpu(
+        stream,
+        features.data<float>(), rois.data<float>(), output.data<float>(), output_diff.data<float>(),
+        coor_diff.data<float>(),
+        nr_channels, height, width, pooled_height, pooled_width, spatial_scale,
+        top_count, bottom_count
+    );
+
+    THCudaCheck(cudaGetLastError());
+    return coor_diff;
+}
+
+PYBIND11_MODULE(TORCH_EXTENSION_NAME, m) {
+    m.def("prroi_pooling_forward_cuda", &prroi_pooling_forward_cuda, "PRRoIPooling_forward");
+    m.def("prroi_pooling_backward_cuda", &prroi_pooling_backward_cuda, "PRRoIPooling_backward");
+    m.def("prroi_pooling_coor_backward_cuda", &prroi_pooling_coor_backward_cuda, "PRRoIPooling_backward_coor");
+}
diff --git a/netdissect/upsegmodel/prroi_pool/src/prroi_pooling_gpu.h b/netdissect/upsegmodel/prroi_pool/src/prroi_pooling_gpu.h
new file mode 100644
index 0000000000000000000000000000000000000000..bc9d35181dd97c355fb6a5b17bc9e82e24ef1566
--- /dev/null
+++ b/netdissect/upsegmodel/prroi_pool/src/prroi_pooling_gpu.h
@@ -0,0 +1,22 @@
+/*
+ * File   : prroi_pooling_gpu.h
+ * Author : Jiayuan Mao, Tete Xiao
+ * Email  : maojiayuan@gmail.com, jasonhsiao97@gmail.com 
+ * Date   : 07/13/2018
+ * 
+ * Distributed under terms of the MIT license.
+ * Copyright (c) 2017 Megvii Technology Limited.
+ */
+
+int prroi_pooling_forward_cuda(THCudaTensor *features, THCudaTensor *rois, THCudaTensor *output, int pooled_height, int pooled_width, float spatial_scale);
+
+int prroi_pooling_backward_cuda(
+    THCudaTensor *features, THCudaTensor *rois, THCudaTensor *output, THCudaTensor *output_diff, THCudaTensor *features_diff,
+    int pooled_height, int pooled_width, float spatial_scale
+);
+
+int prroi_pooling_coor_backward_cuda(
+    THCudaTensor *features, THCudaTensor *rois, THCudaTensor *output, THCudaTensor *output_diff, THCudaTensor *features_diff,
+    int pooled_height, int pooled_width, float spatial_scal
+);
+
diff --git a/netdissect/upsegmodel/prroi_pool/src/prroi_pooling_gpu_impl.cu b/netdissect/upsegmodel/prroi_pool/src/prroi_pooling_gpu_impl.cu
new file mode 100644
index 0000000000000000000000000000000000000000..452b02055495ad721ba41b2708bccecc9b1aa2f3
--- /dev/null
+++ b/netdissect/upsegmodel/prroi_pool/src/prroi_pooling_gpu_impl.cu
@@ -0,0 +1,443 @@
+/*
+ * File   : prroi_pooling_gpu_impl.cu
+ * Author : Tete Xiao, Jiayuan Mao
+ * Email  : jasonhsiao97@gmail.com
+ *
+ * Distributed under terms of the MIT license.
+ * Copyright (c) 2017 Megvii Technology Limited.
+ */
+
+#include "prroi_pooling_gpu_impl.cuh"
+
+#include <cstdio>
+#include <cfloat>
+
+#define CUDA_KERNEL_LOOP(i, n) \
+    for (int i = blockIdx.x * blockDim.x + threadIdx.x; \
+        i < (n); \
+        i += blockDim.x * gridDim.x)
+
+#define CUDA_POST_KERNEL_CHECK \
+    do { \
+        cudaError_t err = cudaGetLastError(); \
+        if (cudaSuccess != err) { \
+            fprintf(stderr, "cudaCheckError() failed : %s\n", cudaGetErrorString(err)); \
+            exit(-1); \
+        } \
+    } while(0)
+
+#define CUDA_NUM_THREADS 512
+
+namespace {
+
+static int CUDA_NUM_BLOCKS(const int N) {
+  return (N + CUDA_NUM_THREADS - 1) / CUDA_NUM_THREADS;
+}
+
+__device__ static float PrRoIPoolingGetData(F_DEVPTR_IN data, const int h, const int w, const int height, const int width)
+{
+    bool overflow = (h < 0) || (w < 0) || (h >= height) || (w >= width);
+    float retVal = overflow ? 0.0f : data[h * width + w];
+    return retVal;
+}
+
+__device__ static float PrRoIPoolingGetCoeff(float dh, float dw){
+    dw = dw > 0 ? dw : -dw;
+    dh = dh > 0 ? dh : -dh;
+    return (1.0f - dh) * (1.0f - dw);
+}
+
+__device__ static float PrRoIPoolingSingleCoorIntegral(float s, float t, float c1, float c2) {
+    return 0.5 * (t * t - s * s) * c2 + (t - 0.5 * t * t - s + 0.5 * s * s) * c1;
+}
+
+__device__ static float PrRoIPoolingInterpolation(F_DEVPTR_IN data, const float h, const float w, const int height, const int width){
+    float retVal = 0.0f;
+    int h1 = floorf(h);
+    int w1 = floorf(w);
+    retVal += PrRoIPoolingGetData(data, h1, w1, height, width) * PrRoIPoolingGetCoeff(h - float(h1), w - float(w1));
+    h1 = floorf(h)+1;
+    w1 = floorf(w);
+    retVal += PrRoIPoolingGetData(data, h1, w1, height, width) * PrRoIPoolingGetCoeff(h - float(h1), w - float(w1));
+    h1 = floorf(h);
+    w1 = floorf(w)+1;
+    retVal += PrRoIPoolingGetData(data, h1, w1, height, width) * PrRoIPoolingGetCoeff(h - float(h1), w - float(w1));
+    h1 = floorf(h)+1;
+    w1 = floorf(w)+1;
+    retVal += PrRoIPoolingGetData(data, h1, w1, height, width) * PrRoIPoolingGetCoeff(h - float(h1), w - float(w1));
+    return retVal;
+}
+
+__device__ static float PrRoIPoolingMatCalculation(F_DEVPTR_IN this_data, const int s_h, const int s_w, const int e_h, const int e_w,
+        const float y0, const float x0, const float y1, const float x1, const int h0, const int w0)
+{
+    float alpha, beta, lim_alpha, lim_beta, tmp;
+    float sum_out = 0;
+
+    alpha = x0 - float(s_w);
+    beta = y0 - float(s_h);
+    lim_alpha = x1 - float(s_w);
+    lim_beta = y1 - float(s_h);
+    tmp = (lim_alpha - 0.5f * lim_alpha * lim_alpha - alpha + 0.5f * alpha * alpha)
+        * (lim_beta - 0.5f * lim_beta * lim_beta - beta + 0.5f * beta * beta);
+    sum_out += PrRoIPoolingGetData(this_data, s_h, s_w, h0, w0) * tmp;
+
+    alpha = float(e_w) - x1;
+    lim_alpha = float(e_w) - x0;
+    tmp = (lim_alpha - 0.5f * lim_alpha * lim_alpha - alpha + 0.5f * alpha * alpha)
+        * (lim_beta - 0.5f * lim_beta * lim_beta - beta + 0.5f * beta * beta);
+    sum_out += PrRoIPoolingGetData(this_data, s_h, e_w, h0, w0) * tmp;
+
+    alpha = x0 - float(s_w);
+    beta = float(e_h) - y1;
+    lim_alpha = x1 - float(s_w);
+    lim_beta = float(e_h) - y0;
+    tmp = (lim_alpha - 0.5f * lim_alpha * lim_alpha - alpha + 0.5f * alpha * alpha)
+        * (lim_beta - 0.5f * lim_beta * lim_beta - beta + 0.5f * beta * beta);
+    sum_out += PrRoIPoolingGetData(this_data, e_h, s_w, h0, w0) * tmp;
+
+    alpha = float(e_w) - x1;
+    lim_alpha = float(e_w) - x0;
+    tmp = (lim_alpha - 0.5f * lim_alpha * lim_alpha - alpha + 0.5f * alpha * alpha)
+        * (lim_beta - 0.5f * lim_beta * lim_beta - beta + 0.5f * beta * beta);
+    sum_out += PrRoIPoolingGetData(this_data, e_h, e_w, h0, w0) * tmp;
+
+    return sum_out;
+}
+
+__device__ static void PrRoIPoolingDistributeDiff(F_DEVPTR_OUT diff, const float top_diff, const int h, const int w, const int height, const int width, const float coeff)
+{
+    bool overflow = (h < 0) || (w < 0) || (h >= height) || (w >= width);
+    if (!overflow)
+        atomicAdd(diff + h * width + w, top_diff * coeff);
+}
+
+__device__ static void PrRoIPoolingMatDistributeDiff(F_DEVPTR_OUT diff, const float top_diff, const int s_h, const int s_w, const int e_h, const int e_w,
+        const float y0, const float x0, const float y1, const float x1, const int h0, const int w0)
+{
+    float alpha, beta, lim_alpha, lim_beta, tmp;
+
+    alpha = x0 - float(s_w);
+    beta = y0 - float(s_h);
+    lim_alpha = x1 - float(s_w);
+    lim_beta = y1 - float(s_h);
+    tmp = (lim_alpha - 0.5f * lim_alpha * lim_alpha - alpha + 0.5f * alpha * alpha)
+        * (lim_beta - 0.5f * lim_beta * lim_beta - beta + 0.5f * beta * beta);
+    PrRoIPoolingDistributeDiff(diff, top_diff, s_h, s_w, h0, w0, tmp);
+
+    alpha = float(e_w) - x1;
+    lim_alpha = float(e_w) - x0;
+    tmp = (lim_alpha - 0.5f * lim_alpha * lim_alpha - alpha + 0.5f * alpha * alpha)
+        * (lim_beta - 0.5f * lim_beta * lim_beta - beta + 0.5f * beta * beta);
+    PrRoIPoolingDistributeDiff(diff, top_diff, s_h, e_w, h0, w0, tmp);
+
+    alpha = x0 - float(s_w);
+    beta = float(e_h) - y1;
+    lim_alpha = x1 - float(s_w);
+    lim_beta = float(e_h) - y0;
+    tmp = (lim_alpha - 0.5f * lim_alpha * lim_alpha - alpha + 0.5f * alpha * alpha)
+        * (lim_beta - 0.5f * lim_beta * lim_beta - beta + 0.5f * beta * beta);
+    PrRoIPoolingDistributeDiff(diff, top_diff, e_h, s_w, h0, w0, tmp);
+
+    alpha = float(e_w) - x1;
+    lim_alpha = float(e_w) - x0;
+    tmp = (lim_alpha - 0.5f * lim_alpha * lim_alpha - alpha + 0.5f * alpha * alpha)
+        * (lim_beta - 0.5f * lim_beta * lim_beta - beta + 0.5f * beta * beta);
+    PrRoIPoolingDistributeDiff(diff, top_diff, e_h, e_w, h0, w0, tmp);
+}
+
+__global__ void PrRoIPoolingForward(
+        const int nthreads,
+        F_DEVPTR_IN bottom_data,
+        F_DEVPTR_IN bottom_rois,
+        F_DEVPTR_OUT top_data,
+        const int channels,
+        const int height,
+        const int width,
+        const int pooled_height,
+        const int pooled_width,
+        const float spatial_scale) {
+
+  CUDA_KERNEL_LOOP(index, nthreads) {
+    // (n, c, ph, pw) is an element in the pooled output
+    int pw = index % pooled_width;
+    int ph = (index / pooled_width) % pooled_height;
+    int c = (index / pooled_width / pooled_height) % channels;
+    int n = index / pooled_width / pooled_height / channels;
+
+    bottom_rois += n * 5;
+    int roi_batch_ind = bottom_rois[0];
+
+    float roi_start_w = bottom_rois[1] * spatial_scale;
+    float roi_start_h = bottom_rois[2] * spatial_scale;
+    float roi_end_w = bottom_rois[3] * spatial_scale;
+    float roi_end_h = bottom_rois[4] * spatial_scale;
+
+    float roi_width = max(roi_end_w - roi_start_w, ((float)0.0));
+    float roi_height = max(roi_end_h - roi_start_h, ((float)0.0));
+    float bin_size_h = roi_height / static_cast<float>(pooled_height);
+    float bin_size_w = roi_width / static_cast<float>(pooled_width);
+
+    const float *this_data = bottom_data + (roi_batch_ind * channels + c) * height * width;
+    float *this_out = top_data + index;
+
+    float win_start_w = roi_start_w + bin_size_w * pw;
+    float win_start_h = roi_start_h + bin_size_h * ph;
+    float win_end_w = win_start_w + bin_size_w;
+    float win_end_h = win_start_h + bin_size_h;
+
+    float win_size = max(float(0.0), bin_size_w * bin_size_h);
+    if (win_size == 0) {
+        *this_out = 0;
+        return;
+    }
+
+    float sum_out = 0;
+
+    int s_w, s_h, e_w, e_h;
+
+    s_w = floorf(win_start_w);
+    e_w = ceilf(win_end_w);
+    s_h = floorf(win_start_h);
+    e_h = ceilf(win_end_h);
+
+    for (int w_iter = s_w; w_iter < e_w; ++w_iter)
+        for (int h_iter = s_h; h_iter < e_h; ++h_iter)
+            sum_out += PrRoIPoolingMatCalculation(this_data, h_iter, w_iter, h_iter + 1, w_iter + 1,
+                max(win_start_h, float(h_iter)), max(win_start_w, float(w_iter)),
+                min(win_end_h, float(h_iter) + 1.0), min(win_end_w, float(w_iter + 1.0)),
+                height, width);
+    *this_out = sum_out / win_size;
+  }
+}
+
+__global__ void PrRoIPoolingBackward(
+        const int nthreads,
+        F_DEVPTR_IN bottom_rois,
+        F_DEVPTR_IN top_diff,
+        F_DEVPTR_OUT bottom_diff,
+        const int channels,
+        const int height,
+        const int width,
+        const int pooled_height,
+        const int pooled_width,
+        const float spatial_scale) {
+
+  CUDA_KERNEL_LOOP(index, nthreads) {
+    // (n, c, ph, pw) is an element in the pooled output
+    int pw = index % pooled_width;
+    int ph = (index / pooled_width) % pooled_height;
+    int c = (index / pooled_width / pooled_height) % channels;
+    int n = index / pooled_width / pooled_height / channels;
+    bottom_rois += n * 5;
+
+    int roi_batch_ind = bottom_rois[0];
+    float roi_start_w = bottom_rois[1] * spatial_scale;
+    float roi_start_h = bottom_rois[2] * spatial_scale;
+    float roi_end_w = bottom_rois[3] * spatial_scale;
+    float roi_end_h = bottom_rois[4] * spatial_scale;
+
+    float roi_width = max(roi_end_w - roi_start_w, (float)0);
+    float roi_height = max(roi_end_h - roi_start_h, (float)0);
+    float bin_size_h = roi_height / static_cast<float>(pooled_height);
+    float bin_size_w = roi_width / static_cast<float>(pooled_width);
+
+    const float *this_out_grad = top_diff + index;
+    float *this_data_grad = bottom_diff + (roi_batch_ind * channels + c) * height * width;
+
+    float win_start_w = roi_start_w + bin_size_w * pw;
+    float win_start_h = roi_start_h + bin_size_h * ph;
+    float win_end_w = win_start_w + bin_size_w;
+    float win_end_h = win_start_h + bin_size_h;
+
+    float win_size = max(float(0.0), bin_size_w * bin_size_h);
+
+    float sum_out = win_size == float(0) ? float(0) : *this_out_grad / win_size;
+
+    int s_w, s_h, e_w, e_h;
+
+    s_w = floorf(win_start_w);
+    e_w = ceilf(win_end_w);
+    s_h = floorf(win_start_h);
+    e_h = ceilf(win_end_h);
+
+    for (int w_iter = s_w; w_iter < e_w; ++w_iter)
+        for (int h_iter = s_h; h_iter < e_h; ++h_iter)
+            PrRoIPoolingMatDistributeDiff(this_data_grad, sum_out, h_iter, w_iter, h_iter + 1, w_iter + 1,
+                max(win_start_h, float(h_iter)), max(win_start_w, float(w_iter)),
+                min(win_end_h, float(h_iter) + 1.0), min(win_end_w, float(w_iter + 1.0)),
+                height, width);
+
+  }
+}
+
+__global__ void PrRoIPoolingCoorBackward(
+        const int nthreads,
+        F_DEVPTR_IN bottom_data,
+        F_DEVPTR_IN bottom_rois,
+        F_DEVPTR_IN top_data,
+        F_DEVPTR_IN top_diff,
+        F_DEVPTR_OUT bottom_diff,
+        const int channels,
+        const int height,
+        const int width,
+        const int pooled_height,
+        const int pooled_width,
+        const float spatial_scale) {
+
+  CUDA_KERNEL_LOOP(index, nthreads) {
+    // (n, c, ph, pw) is an element in the pooled output
+    int pw = index % pooled_width;
+    int ph = (index / pooled_width) % pooled_height;
+    int c = (index / pooled_width / pooled_height) % channels;
+    int n = index / pooled_width / pooled_height / channels;
+    bottom_rois += n * 5;
+
+    int roi_batch_ind = bottom_rois[0];
+    float roi_start_w = bottom_rois[1] * spatial_scale;
+    float roi_start_h = bottom_rois[2] * spatial_scale;
+    float roi_end_w = bottom_rois[3] * spatial_scale;
+    float roi_end_h = bottom_rois[4] * spatial_scale;
+
+    float roi_width = max(roi_end_w - roi_start_w, (float)0);
+    float roi_height = max(roi_end_h - roi_start_h, (float)0);
+    float bin_size_h = roi_height / static_cast<float>(pooled_height);
+    float bin_size_w = roi_width / static_cast<float>(pooled_width);
+
+    const float *this_out_grad = top_diff + index;
+    const float *this_bottom_data = bottom_data + (roi_batch_ind * channels + c) * height * width;
+    const float *this_top_data = top_data + index;
+    float *this_data_grad = bottom_diff + n * 5;
+
+    float win_start_w = roi_start_w + bin_size_w * pw;
+    float win_start_h = roi_start_h + bin_size_h * ph;
+    float win_end_w = win_start_w + bin_size_w;
+    float win_end_h = win_start_h + bin_size_h;
+
+    float win_size = max(float(0.0), bin_size_w * bin_size_h);
+
+    float sum_out = win_size == float(0) ? float(0) : *this_out_grad / win_size;
+
+    // WARNING: to be discussed
+    if (sum_out == 0)
+        return;
+
+    int s_w, s_h, e_w, e_h;
+
+    s_w = floorf(win_start_w);
+    e_w = ceilf(win_end_w);
+    s_h = floorf(win_start_h);
+    e_h = ceilf(win_end_h);
+
+    float g_x1_y = 0, g_x2_y = 0, g_x_y1 = 0, g_x_y2 = 0;
+    for (int h_iter = s_h; h_iter < e_h; ++h_iter) {
+        g_x1_y += PrRoIPoolingSingleCoorIntegral(max(win_start_h, float(h_iter)) - h_iter,
+                min(win_end_h, float(h_iter + 1)) - h_iter,
+                PrRoIPoolingInterpolation(this_bottom_data, h_iter, win_start_w, height, width),
+                PrRoIPoolingInterpolation(this_bottom_data, h_iter + 1, win_start_w, height, width));
+
+        g_x2_y += PrRoIPoolingSingleCoorIntegral(max(win_start_h, float(h_iter)) - h_iter,
+                min(win_end_h, float(h_iter + 1)) - h_iter,
+                PrRoIPoolingInterpolation(this_bottom_data, h_iter, win_end_w, height, width),
+                PrRoIPoolingInterpolation(this_bottom_data, h_iter + 1, win_end_w, height, width));
+    }
+
+    for (int w_iter = s_w; w_iter < e_w; ++w_iter) {
+        g_x_y1 += PrRoIPoolingSingleCoorIntegral(max(win_start_w, float(w_iter)) - w_iter,
+                min(win_end_w, float(w_iter + 1)) - w_iter,
+                PrRoIPoolingInterpolation(this_bottom_data, win_start_h, w_iter, height, width),
+                PrRoIPoolingInterpolation(this_bottom_data, win_start_h, w_iter + 1, height, width));
+
+        g_x_y2 += PrRoIPoolingSingleCoorIntegral(max(win_start_w, float(w_iter)) - w_iter,
+                min(win_end_w, float(w_iter + 1)) - w_iter,
+                PrRoIPoolingInterpolation(this_bottom_data, win_end_h, w_iter, height, width),
+                PrRoIPoolingInterpolation(this_bottom_data, win_end_h, w_iter + 1, height, width));
+    }
+
+    float partial_x1 = -g_x1_y + (win_end_h - win_start_h) * (*this_top_data);
+    float partial_y1 = -g_x_y1 + (win_end_w - win_start_w) * (*this_top_data);
+    float partial_x2 = g_x2_y - (win_end_h - win_start_h) * (*this_top_data);
+    float partial_y2 = g_x_y2 - (win_end_w - win_start_w) * (*this_top_data);
+
+    partial_x1 = partial_x1 / win_size * spatial_scale;
+    partial_x2 = partial_x2 / win_size * spatial_scale;
+    partial_y1 = partial_y1 / win_size * spatial_scale;
+    partial_y2 = partial_y2 / win_size * spatial_scale;
+
+    // (b, x1, y1, x2, y2)
+
+    this_data_grad[0] = 0;
+    atomicAdd(this_data_grad + 1, (partial_x1 * (1.0 - float(pw) / pooled_width) + partial_x2 * (1.0 - float(pw + 1) / pooled_width))
+            * (*this_out_grad));
+    atomicAdd(this_data_grad + 2, (partial_y1 * (1.0 - float(ph) / pooled_height) + partial_y2 * (1.0 - float(ph + 1) / pooled_height))
+            * (*this_out_grad));
+    atomicAdd(this_data_grad + 3, (partial_x2 * float(pw + 1) / pooled_width + partial_x1 * float(pw) / pooled_width)
+            * (*this_out_grad));
+    atomicAdd(this_data_grad + 4, (partial_y2 * float(ph + 1) / pooled_height + partial_y1 * float(ph) / pooled_height)
+            * (*this_out_grad));
+  }
+}
+
+} /* !anonymous namespace */
+
+#ifdef __cplusplus
+extern "C" {
+#endif
+
+void PrRoIPoolingForwardGpu(
+    cudaStream_t stream,
+    F_DEVPTR_IN bottom_data,
+    F_DEVPTR_IN bottom_rois,
+    F_DEVPTR_OUT top_data,
+    const int channels_, const int height_, const int width_,
+    const int pooled_height_, const int pooled_width_,
+    const float spatial_scale_,
+    const int top_count) {
+
+    PrRoIPoolingForward<<<CUDA_NUM_BLOCKS(top_count), CUDA_NUM_THREADS, 0, stream>>>(
+        top_count, bottom_data, bottom_rois, top_data,
+        channels_, height_, width_, pooled_height_, pooled_width_, spatial_scale_);
+
+    CUDA_POST_KERNEL_CHECK;
+}
+
+void PrRoIPoolingBackwardGpu(
+    cudaStream_t stream,
+    F_DEVPTR_IN bottom_data,
+    F_DEVPTR_IN bottom_rois,
+    F_DEVPTR_IN top_data,
+    F_DEVPTR_IN top_diff,
+    F_DEVPTR_OUT bottom_diff,
+    const int channels_, const int height_, const int width_,
+    const int pooled_height_, const int pooled_width_,
+    const float spatial_scale_,
+    const int top_count, const int bottom_count) {
+
+    cudaMemsetAsync(bottom_diff, 0, sizeof(float) * bottom_count, stream);
+    PrRoIPoolingBackward<<<CUDA_NUM_BLOCKS(top_count), CUDA_NUM_THREADS, 0, stream>>>(
+        top_count, bottom_rois, top_diff, bottom_diff,
+        channels_, height_, width_, pooled_height_, pooled_width_, spatial_scale_);
+    CUDA_POST_KERNEL_CHECK;
+}
+
+void PrRoIPoolingCoorBackwardGpu(
+    cudaStream_t stream,
+    F_DEVPTR_IN bottom_data,
+    F_DEVPTR_IN bottom_rois,
+    F_DEVPTR_IN top_data,
+    F_DEVPTR_IN top_diff,
+    F_DEVPTR_OUT bottom_diff,
+    const int channels_, const int height_, const int width_,
+    const int pooled_height_, const int pooled_width_,
+    const float spatial_scale_,
+    const int top_count, const int bottom_count) {
+
+    cudaMemsetAsync(bottom_diff, 0, sizeof(float) * bottom_count, stream);
+    PrRoIPoolingCoorBackward<<<CUDA_NUM_BLOCKS(top_count), CUDA_NUM_THREADS, 0, stream>>>(
+        top_count, bottom_data, bottom_rois, top_data, top_diff, bottom_diff,
+        channels_, height_, width_, pooled_height_, pooled_width_, spatial_scale_);
+    CUDA_POST_KERNEL_CHECK;
+}
+
+} /* !extern "C" */
+
diff --git a/netdissect/upsegmodel/prroi_pool/src/prroi_pooling_gpu_impl.cuh b/netdissect/upsegmodel/prroi_pool/src/prroi_pooling_gpu_impl.cuh
new file mode 100644
index 0000000000000000000000000000000000000000..95ad56797ca6299ededf63718d742343f8dab8e7
--- /dev/null
+++ b/netdissect/upsegmodel/prroi_pool/src/prroi_pooling_gpu_impl.cuh
@@ -0,0 +1,59 @@
+/*
+ * File   : prroi_pooling_gpu_impl.cuh
+ * Author : Tete Xiao, Jiayuan Mao
+ * Email  : jasonhsiao97@gmail.com
+ *
+ * Distributed under terms of the MIT license.
+ * Copyright (c) 2017 Megvii Technology Limited.
+ */
+
+#ifndef PRROI_POOLING_GPU_IMPL_CUH
+#define PRROI_POOLING_GPU_IMPL_CUH
+
+#ifdef __cplusplus
+extern "C" {
+#endif
+
+#define F_DEVPTR_IN const float *
+#define F_DEVPTR_OUT float *
+
+void PrRoIPoolingForwardGpu(
+    cudaStream_t stream,
+    F_DEVPTR_IN bottom_data,
+    F_DEVPTR_IN bottom_rois,
+    F_DEVPTR_OUT top_data,
+    const int channels_, const int height_, const int width_,
+    const int pooled_height_, const int pooled_width_,
+    const float spatial_scale_,
+    const int top_count);
+
+void PrRoIPoolingBackwardGpu(
+    cudaStream_t stream,
+    F_DEVPTR_IN bottom_data,
+    F_DEVPTR_IN bottom_rois,
+    F_DEVPTR_IN top_data,
+    F_DEVPTR_IN top_diff,
+    F_DEVPTR_OUT bottom_diff,
+    const int channels_, const int height_, const int width_,
+    const int pooled_height_, const int pooled_width_,
+    const float spatial_scale_,
+    const int top_count, const int bottom_count);
+
+void PrRoIPoolingCoorBackwardGpu(
+    cudaStream_t stream,
+    F_DEVPTR_IN bottom_data,
+    F_DEVPTR_IN bottom_rois,
+    F_DEVPTR_IN top_data,
+    F_DEVPTR_IN top_diff,
+    F_DEVPTR_OUT bottom_diff,
+    const int channels_, const int height_, const int width_,
+    const int pooled_height_, const int pooled_width_,
+    const float spatial_scale_,
+    const int top_count, const int bottom_count);
+
+#ifdef __cplusplus
+} /* !extern "C" */
+#endif
+
+#endif /* !PRROI_POOLING_GPU_IMPL_CUH */
+
diff --git a/netdissect/upsegmodel/prroi_pool/test_prroi_pooling2d.py b/netdissect/upsegmodel/prroi_pool/test_prroi_pooling2d.py
new file mode 100644
index 0000000000000000000000000000000000000000..a29d92c80538f5550808dc51f92dcaf65cbd9fb0
--- /dev/null
+++ b/netdissect/upsegmodel/prroi_pool/test_prroi_pooling2d.py
@@ -0,0 +1,56 @@
+# -*- coding: utf-8 -*-
+# File   : test_prroi_pooling2d.py
+# Author : Jiayuan Mao
+# Email  : maojiayuan@gmail.com
+# Date   : 18/02/2018
+#
+# This file is part of Jacinle.
+
+import unittest
+
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+
+from jactorch.utils.unittest import TorchTestCase
+
+from prroi_pool import PrRoIPool2D
+
+
+class TestPrRoIPool2D(TorchTestCase):
+    def test_forward(self):
+        pool = PrRoIPool2D(7, 7, spatial_scale=0.5)
+        features = torch.rand((4, 16, 24, 32)).cuda()
+        rois = torch.tensor([
+            [0, 0, 0, 14, 14],
+            [1, 14, 14, 28, 28],
+        ]).float().cuda()
+
+        out = pool(features, rois)
+        out_gold = F.avg_pool2d(features, kernel_size=2, stride=1)
+
+        self.assertTensorClose(out, torch.stack((
+            out_gold[0, :, :7, :7],
+            out_gold[1, :, 7:14, 7:14],
+        ), dim=0))
+
+    def test_backward_shapeonly(self):
+        pool = PrRoIPool2D(2, 2, spatial_scale=0.5)
+
+        features = torch.rand((4, 2, 24, 32)).cuda()
+        rois = torch.tensor([
+            [0, 0, 0, 4, 4],
+            [1, 14, 14, 18, 18],
+        ]).float().cuda()
+        features.requires_grad = rois.requires_grad = True
+        out = pool(features, rois)
+
+        loss = out.sum()
+        loss.backward()
+
+        self.assertTupleEqual(features.size(), features.grad.size())
+        self.assertTupleEqual(rois.size(), rois.grad.size())
+
+
+if __name__ == '__main__':
+    unittest.main()
diff --git a/netdissect/upsegmodel/resnet.py b/netdissect/upsegmodel/resnet.py
new file mode 100644
index 0000000000000000000000000000000000000000..ea5fdf82fafa3058c5f00074d55fbb1e584d5865
--- /dev/null
+++ b/netdissect/upsegmodel/resnet.py
@@ -0,0 +1,235 @@
+import os
+import sys
+import torch
+import torch.nn as nn
+import math
+try:
+    from lib.nn import SynchronizedBatchNorm2d
+except ImportError:
+    from torch.nn import BatchNorm2d as SynchronizedBatchNorm2d
+
+try:
+    from urllib import urlretrieve
+except ImportError:
+    from urllib.request import urlretrieve
+
+
+__all__ = ['ResNet', 'resnet50', 'resnet101'] # resnet101 is coming soon!
+
+
+model_urls = {
+    'resnet50': 'http://sceneparsing.csail.mit.edu/model/pretrained_resnet/resnet50-imagenet.pth',
+    'resnet101': 'http://sceneparsing.csail.mit.edu/model/pretrained_resnet/resnet101-imagenet.pth'
+}
+
+
+def conv3x3(in_planes, out_planes, stride=1):
+    "3x3 convolution with padding"
+    return nn.Conv2d(in_planes, out_planes, kernel_size=3, stride=stride,
+                     padding=1, bias=False)
+
+
+class BasicBlock(nn.Module):
+    expansion = 1
+
+    def __init__(self, inplanes, planes, stride=1, downsample=None):
+        super(BasicBlock, self).__init__()
+        self.conv1 = conv3x3(inplanes, planes, stride)
+        self.bn1 = SynchronizedBatchNorm2d(planes)
+        self.relu = nn.ReLU(inplace=True)
+        self.conv2 = conv3x3(planes, planes)
+        self.bn2 = SynchronizedBatchNorm2d(planes)
+        self.downsample = downsample
+        self.stride = stride
+
+    def forward(self, x):
+        residual = x
+
+        out = self.conv1(x)
+        out = self.bn1(out)
+        out = self.relu(out)
+
+        out = self.conv2(out)
+        out = self.bn2(out)
+
+        if self.downsample is not None:
+            residual = self.downsample(x)
+
+        out += residual
+        out = self.relu(out)
+
+        return out
+
+
+class Bottleneck(nn.Module):
+    expansion = 4
+
+    def __init__(self, inplanes, planes, stride=1, downsample=None):
+        super(Bottleneck, self).__init__()
+        self.conv1 = nn.Conv2d(inplanes, planes, kernel_size=1, bias=False)
+        self.bn1 = SynchronizedBatchNorm2d(planes)
+        self.conv2 = nn.Conv2d(planes, planes, kernel_size=3, stride=stride,
+                               padding=1, bias=False)
+        self.bn2 = SynchronizedBatchNorm2d(planes)
+        self.conv3 = nn.Conv2d(planes, planes * 4, kernel_size=1, bias=False)
+        self.bn3 = SynchronizedBatchNorm2d(planes * 4)
+        self.relu = nn.ReLU(inplace=True)
+        self.downsample = downsample
+        self.stride = stride
+
+    def forward(self, x):
+        residual = x
+
+        out = self.conv1(x)
+        out = self.bn1(out)
+        out = self.relu(out)
+
+        out = self.conv2(out)
+        out = self.bn2(out)
+        out = self.relu(out)
+
+        out = self.conv3(out)
+        out = self.bn3(out)
+
+        if self.downsample is not None:
+            residual = self.downsample(x)
+
+        out += residual
+        out = self.relu(out)
+
+        return out
+
+
+class ResNet(nn.Module):
+
+    def __init__(self, block, layers, num_classes=1000):
+        self.inplanes = 128
+        super(ResNet, self).__init__()
+        self.conv1 = conv3x3(3, 64, stride=2)
+        self.bn1 = SynchronizedBatchNorm2d(64)
+        self.relu1 = nn.ReLU(inplace=True)
+        self.conv2 = conv3x3(64, 64)
+        self.bn2 = SynchronizedBatchNorm2d(64)
+        self.relu2 = nn.ReLU(inplace=True)
+        self.conv3 = conv3x3(64, 128)
+        self.bn3 = SynchronizedBatchNorm2d(128)
+        self.relu3 = nn.ReLU(inplace=True)
+        self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1)
+
+        self.layer1 = self._make_layer(block, 64, layers[0])
+        self.layer2 = self._make_layer(block, 128, layers[1], stride=2)
+        self.layer3 = self._make_layer(block, 256, layers[2], stride=2)
+        self.layer4 = self._make_layer(block, 512, layers[3], stride=2)
+        self.avgpool = nn.AvgPool2d(7, stride=1)
+        self.fc = nn.Linear(512 * block.expansion, num_classes)
+
+        for m in self.modules():
+            if isinstance(m, nn.Conv2d):
+                n = m.kernel_size[0] * m.kernel_size[1] * m.out_channels
+                m.weight.data.normal_(0, math.sqrt(2. / n))
+            elif isinstance(m, SynchronizedBatchNorm2d):
+                m.weight.data.fill_(1)
+                m.bias.data.zero_()
+
+    def _make_layer(self, block, planes, blocks, stride=1):
+        downsample = None
+        if stride != 1 or self.inplanes != planes * block.expansion:
+            downsample = nn.Sequential(
+                nn.Conv2d(self.inplanes, planes * block.expansion,
+                          kernel_size=1, stride=stride, bias=False),
+                SynchronizedBatchNorm2d(planes * block.expansion),
+            )
+
+        layers = []
+        layers.append(block(self.inplanes, planes, stride, downsample))
+        self.inplanes = planes * block.expansion
+        for i in range(1, blocks):
+            layers.append(block(self.inplanes, planes))
+
+        return nn.Sequential(*layers)
+
+    def forward(self, x):
+        x = self.relu1(self.bn1(self.conv1(x)))
+        x = self.relu2(self.bn2(self.conv2(x)))
+        x = self.relu3(self.bn3(self.conv3(x)))
+        x = self.maxpool(x)
+
+        x = self.layer1(x)
+        x = self.layer2(x)
+        x = self.layer3(x)
+        x = self.layer4(x)
+
+        x = self.avgpool(x)
+        x = x.view(x.size(0), -1)
+        x = self.fc(x)
+
+        return x
+
+'''
+def resnet18(pretrained=False, **kwargs):
+    """Constructs a ResNet-18 model.
+
+    Args:
+        pretrained (bool): If True, returns a model pre-trained on Places
+    """
+    model = ResNet(BasicBlock, [2, 2, 2, 2], **kwargs)
+    if pretrained:
+        model.load_state_dict(load_url(model_urls['resnet18']))
+    return model
+
+
+def resnet34(pretrained=False, **kwargs):
+    """Constructs a ResNet-34 model.
+
+    Args:
+        pretrained (bool): If True, returns a model pre-trained on Places
+    """
+    model = ResNet(BasicBlock, [3, 4, 6, 3], **kwargs)
+    if pretrained:
+        model.load_state_dict(load_url(model_urls['resnet34']))
+    return model
+'''
+
+def resnet50(pretrained=False, **kwargs):
+    """Constructs a ResNet-50 model.
+
+    Args:
+        pretrained (bool): If True, returns a model pre-trained on Places
+    """
+    model = ResNet(Bottleneck, [3, 4, 6, 3], **kwargs)
+    if pretrained:
+        model.load_state_dict(load_url(model_urls['resnet50']), strict=False)
+    return model
+
+
+def resnet101(pretrained=False, **kwargs):
+    """Constructs a ResNet-101 model.
+
+    Args:
+        pretrained (bool): If True, returns a model pre-trained on Places
+    """
+    model = ResNet(Bottleneck, [3, 4, 23, 3], **kwargs)
+    if pretrained:
+        model.load_state_dict(load_url(model_urls['resnet101']), strict=False)
+    return model
+
+# def resnet152(pretrained=False, **kwargs):
+#     """Constructs a ResNet-152 model.
+#
+#     Args:
+#         pretrained (bool): If True, returns a model pre-trained on Places
+#     """
+#     model = ResNet(Bottleneck, [3, 8, 36, 3], **kwargs)
+#     if pretrained:
+#         model.load_state_dict(load_url(model_urls['resnet152']))
+#     return model
+
+def load_url(url, model_dir='./pretrained', map_location=None):
+    if not os.path.exists(model_dir):
+        os.makedirs(model_dir)
+    filename = url.split('/')[-1]
+    cached_file = os.path.join(model_dir, filename)
+    if not os.path.exists(cached_file):
+        sys.stderr.write('Downloading: "{}" to {}\n'.format(url, cached_file))
+        urlretrieve(url, cached_file)
+    return torch.load(cached_file, map_location=map_location)
diff --git a/netdissect/upsegmodel/resnext.py b/netdissect/upsegmodel/resnext.py
new file mode 100644
index 0000000000000000000000000000000000000000..4c618c9da5be17feb975833532e19474fca82dba
--- /dev/null
+++ b/netdissect/upsegmodel/resnext.py
@@ -0,0 +1,183 @@
+import os
+import sys
+import torch
+import torch.nn as nn
+import math
+try:
+    from lib.nn import SynchronizedBatchNorm2d
+except ImportError:
+    from torch.nn import BatchNorm2d as SynchronizedBatchNorm2d
+
+try:
+    from urllib import urlretrieve
+except ImportError:
+    from urllib.request import urlretrieve
+
+
+__all__ = ['ResNeXt', 'resnext101'] # support resnext 101
+
+
+model_urls = {
+    #'resnext50': 'http://sceneparsing.csail.mit.edu/model/pretrained_resnet/resnext50-imagenet.pth',
+    'resnext101': 'http://sceneparsing.csail.mit.edu/model/pretrained_resnet/resnext101-imagenet.pth'
+}
+
+
+def conv3x3(in_planes, out_planes, stride=1):
+    "3x3 convolution with padding"
+    return nn.Conv2d(in_planes, out_planes, kernel_size=3, stride=stride,
+                     padding=1, bias=False)
+
+
+class GroupBottleneck(nn.Module):
+    expansion = 2
+
+    def __init__(self, inplanes, planes, stride=1, groups=1, downsample=None):
+        super(GroupBottleneck, self).__init__()
+        self.conv1 = nn.Conv2d(inplanes, planes, kernel_size=1, bias=False)
+        self.bn1 = SynchronizedBatchNorm2d(planes)
+        self.conv2 = nn.Conv2d(planes, planes, kernel_size=3, stride=stride,
+                               padding=1, groups=groups, bias=False)
+        self.bn2 = SynchronizedBatchNorm2d(planes)
+        self.conv3 = nn.Conv2d(planes, planes * 2, kernel_size=1, bias=False)
+        self.bn3 = SynchronizedBatchNorm2d(planes * 2)
+        self.relu = nn.ReLU(inplace=True)
+        self.downsample = downsample
+        self.stride = stride
+
+    def forward(self, x):
+        residual = x
+
+        out = self.conv1(x)
+        out = self.bn1(out)
+        out = self.relu(out)
+
+        out = self.conv2(out)
+        out = self.bn2(out)
+        out = self.relu(out)
+
+        out = self.conv3(out)
+        out = self.bn3(out)
+
+        if self.downsample is not None:
+            residual = self.downsample(x)
+
+        out += residual
+        out = self.relu(out)
+
+        return out
+
+
+class ResNeXt(nn.Module):
+
+    def __init__(self, block, layers, groups=32, num_classes=1000):
+        self.inplanes = 128
+        super(ResNeXt, self).__init__()
+        self.conv1 = conv3x3(3, 64, stride=2)
+        self.bn1 = SynchronizedBatchNorm2d(64)
+        self.relu1 = nn.ReLU(inplace=True)
+        self.conv2 = conv3x3(64, 64)
+        self.bn2 = SynchronizedBatchNorm2d(64)
+        self.relu2 = nn.ReLU(inplace=True)
+        self.conv3 = conv3x3(64, 128)
+        self.bn3 = SynchronizedBatchNorm2d(128)
+        self.relu3 = nn.ReLU(inplace=True)
+        self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1)
+
+        self.layer1 = self._make_layer(block, 128, layers[0], groups=groups)
+        self.layer2 = self._make_layer(block, 256, layers[1], stride=2, groups=groups)
+        self.layer3 = self._make_layer(block, 512, layers[2], stride=2, groups=groups)
+        self.layer4 = self._make_layer(block, 1024, layers[3], stride=2, groups=groups)
+        self.avgpool = nn.AvgPool2d(7, stride=1)
+        self.fc = nn.Linear(1024 * block.expansion, num_classes)
+
+        for m in self.modules():
+            if isinstance(m, nn.Conv2d):
+                n = m.kernel_size[0] * m.kernel_size[1] * m.out_channels // m.groups
+                m.weight.data.normal_(0, math.sqrt(2. / n))
+            elif isinstance(m, SynchronizedBatchNorm2d):
+                m.weight.data.fill_(1)
+                m.bias.data.zero_()
+
+    def _make_layer(self, block, planes, blocks, stride=1, groups=1):
+        downsample = None
+        if stride != 1 or self.inplanes != planes * block.expansion:
+            downsample = nn.Sequential(
+                nn.Conv2d(self.inplanes, planes * block.expansion,
+                          kernel_size=1, stride=stride, bias=False),
+                SynchronizedBatchNorm2d(planes * block.expansion),
+            )
+
+        layers = []
+        layers.append(block(self.inplanes, planes, stride, groups, downsample))
+        self.inplanes = planes * block.expansion
+        for i in range(1, blocks):
+            layers.append(block(self.inplanes, planes, groups=groups))
+
+        return nn.Sequential(*layers)
+
+    def forward(self, x):
+        x = self.relu1(self.bn1(self.conv1(x)))
+        x = self.relu2(self.bn2(self.conv2(x)))
+        x = self.relu3(self.bn3(self.conv3(x)))
+        x = self.maxpool(x)
+
+        x = self.layer1(x)
+        x = self.layer2(x)
+        x = self.layer3(x)
+        x = self.layer4(x)
+
+        x = self.avgpool(x)
+        x = x.view(x.size(0), -1)
+        x = self.fc(x)
+
+        return x
+
+
+'''
+def resnext50(pretrained=False, **kwargs):
+    """Constructs a ResNet-50 model.
+
+    Args:
+        pretrained (bool): If True, returns a model pre-trained on Places
+    """
+    model = ResNeXt(GroupBottleneck, [3, 4, 6, 3], **kwargs)
+    if pretrained:
+        model.load_state_dict(load_url(model_urls['resnext50']), strict=False)
+    return model
+'''
+
+
+def resnext101(pretrained=False, **kwargs):
+    """Constructs a ResNet-101 model.
+
+    Args:
+        pretrained (bool): If True, returns a model pre-trained on Places
+    """
+    model = ResNeXt(GroupBottleneck, [3, 4, 23, 3], **kwargs)
+    if pretrained:
+        model.load_state_dict(load_url(model_urls['resnext101']), strict=False)
+    return model
+
+
+# def resnext152(pretrained=False, **kwargs):
+#     """Constructs a ResNeXt-152 model.
+#
+#     Args:
+#         pretrained (bool): If True, returns a model pre-trained on Places
+#     """
+#     model = ResNeXt(GroupBottleneck, [3, 8, 36, 3], **kwargs)
+#     if pretrained:
+#         model.load_state_dict(load_url(model_urls['resnext152']))
+#     return model
+
+
+def load_url(url, model_dir='./pretrained', map_location=None):
+    if not os.path.exists(model_dir):
+        os.makedirs(model_dir)
+    filename = url.split('/')[-1]
+    cached_file = os.path.join(model_dir, filename)
+    if not os.path.exists(cached_file):
+        sys.stderr.write('Downloading: "{}" to {}\n'.format(url, cached_file))
+        urlretrieve(url, cached_file)
+    return torch.load(cached_file, map_location=map_location)
diff --git a/netdissect/workerpool.py b/netdissect/workerpool.py
new file mode 100644
index 0000000000000000000000000000000000000000..fe79124ddc86d0e7251d9e1a5d1012e7165249e3
--- /dev/null
+++ b/netdissect/workerpool.py
@@ -0,0 +1,158 @@
+'''
+WorkerPool and WorkerBase for handling the common problems in managing
+a multiprocess pool of workers that aren't done by multiprocessing.Pool,
+including setup with per-process state, debugging by putting the worker
+on the main thread, and correct handling of unexpected errors, and ctrl-C.
+
+To use it,
+1. Put the per-process setup and the per-task work in the
+   setup() and work() methods of your own WorkerBase subclass.
+2. To prepare the process pool, instantiate a WorkerPool, passing your
+   subclass type as the first (worker) argument, as well as any setup keyword
+   arguments.  The WorkerPool will instantiate one of your workers in each
+   worker process (passing in the setup arguments in those processes).
+   If debugging, the pool can have process_count=0 to force all the work
+   to be done immediately on the main thread; otherwise all the work
+   will be passed to other processes.
+3. Whenever there is a new piece of work to distribute, call pool.add(*args).
+   The arguments will be queued and passed as worker.work(*args) to the
+   next available worker.
+4. When all the work has been distributed, call pool.join() to wait for all
+   the work to complete and to finish and terminate all the worker processes.
+   When pool.join() returns, all the work will have been done.
+
+No arrangement is made to collect the results of the work: for example,
+the return value of work() is ignored.  If you need to collect the
+results, use your own mechanism (filesystem, shared memory object, queue)
+which can be distributed using setup arguments.
+'''
+
+from multiprocessing import Process, Queue, cpu_count
+import signal
+import atexit
+import sys
+
+class WorkerBase(Process):
+    '''
+    Subclass this class and override its work() method (and optionally,
+    setup() as well) to define the units of work to be done in a process
+    worker in a woker pool.
+    '''
+    def __init__(self, i, process_count, queue, initargs):
+        if process_count > 0:
+            # Make sure we ignore ctrl-C if we are not on main process.
+            signal.signal(signal.SIGINT, signal.SIG_IGN)
+        self.process_id = i
+        self.process_count = process_count
+        self.queue = queue
+        super(WorkerBase, self).__init__()
+        self.setup(**initargs)
+    def run(self):
+        # Do the work until None is dequeued
+        while True:
+            try:
+                work_batch = self.queue.get()
+            except (KeyboardInterrupt, SystemExit):
+                print('Exiting...')
+                break
+            if work_batch is None:
+                self.queue.put(None) # for another worker
+                return
+            self.work(*work_batch)
+    def setup(self, **initargs):
+        '''
+        Override this method for any per-process initialization.
+        Keywoard args are passed from WorkerPool constructor.
+        '''
+        pass
+    def work(self, *args):
+        '''
+        Override this method for one-time initialization.
+        Args are passed from WorkerPool.add() arguments.
+        '''
+        raise NotImplementedError('worker subclass needed')
+
+class WorkerPool(object):
+    '''
+    Instantiate this object (passing a WorkerBase subclass type
+    as its first argument) to create a worker pool.  Then call
+    pool.add(*args) to queue args to distribute to worker.work(*args),
+    and call pool.join() to wait for all the workers to complete.
+    '''
+    def __init__(self, worker=WorkerBase, process_count=None, **initargs):
+        global active_pools
+        if process_count is None:
+            process_count = cpu_count()
+        if process_count == 0:
+            # zero process_count uses only main process, for debugging.
+            self.queue = None
+            self.processes = None
+            self.worker = worker(None, 0, None, initargs)
+            return
+        # Ctrl-C strategy: worker processes should ignore ctrl-C.  Set
+        # this up to be inherited by child processes before forking.
+        original_sigint_handler = signal.signal(signal.SIGINT, signal.SIG_IGN)
+        active_pools[id(self)] = self
+        self.queue = Queue(maxsize=(process_count * 3))
+        self.processes = None   # Initialize before trying to construct workers
+        self.processes = [worker(i, process_count, self.queue, initargs)
+                for i in range(process_count)]
+        for p in self.processes:
+            p.start()
+        # The main process should handle ctrl-C.  Restore this now.
+        signal.signal(signal.SIGINT, original_sigint_handler)
+    def add(self, *work_batch):
+        if self.queue is None:
+            if hasattr(self, 'worker'):
+                self.worker.work(*work_batch)
+            else:
+                print('WorkerPool shutting down.', file=sys.stderr)
+        else:
+            try:
+                # The queue can block if the work is so slow it gets full.
+                self.queue.put(work_batch)
+            except (KeyboardInterrupt, SystemExit):
+                # Handle ctrl-C if done while waiting for the queue.
+                self.early_terminate()
+    def join(self):
+        # End the queue, and wait for all worker processes to complete nicely.
+        if self.queue is not None:
+            self.queue.put(None)
+            for p in self.processes:
+                p.join()
+            self.queue = None
+        # Remove myself from the set of pools that need cleanup on shutdown.
+        try:
+            del active_pools[id(self)]
+        except:
+            pass
+    def early_terminate(self):
+        # When shutting down unexpectedly, first end the queue.
+        if self.queue is not None:
+            try:
+                self.queue.put_nowait(None)  # Nonblocking put throws if full.
+                self.queue = None
+            except:
+                pass
+        # But then don't wait: just forcibly terminate workers.
+        if self.processes is not None:
+            for p in self.processes:
+                p.terminate()
+            self.processes = None
+        try:
+            del active_pools[id(self)]
+        except:
+            pass
+    def __del__(self):
+        if self.queue is not None:
+            print('ERROR: workerpool.join() not called!', file=sys.stderr)
+            self.join()
+
+# Error and ctrl-C handling: kill worker processes if the main process ends.
+active_pools = {}
+def early_terminate_pools():
+    for _, pool in list(active_pools.items()):
+        pool.early_terminate()
+
+atexit.register(early_terminate_pools)
+
diff --git a/netdissect/zdataset.py b/netdissect/zdataset.py
new file mode 100644
index 0000000000000000000000000000000000000000..5cda5b6e912f08653951094296f571b074f006a7
--- /dev/null
+++ b/netdissect/zdataset.py
@@ -0,0 +1,109 @@
+import os, torch, numpy, itertools
+from torch.utils.data import TensorDataset
+
+def z_dataset_for_model(model, size=100, seed=1, indices=None):
+    if indices is not None:
+        indices = torch.as_tensor(indices, dtype=torch.int64, device='cpu')
+        zs = z_sample_for_model(model, indices.max().item() + 1, seed)
+        zs = zs[indices]
+    else:
+        zs = z_sample_for_model(model, size, seed)
+    return TensorDataset(zs)
+
+def z_sample_for_model(model, size=100, seed=1):
+    # If the model is marked with an input shape, use it.
+    if hasattr(model, 'input_shape'):
+        sample = standard_z_sample(size, model.input_shape[1], seed=seed).view(
+                (size,) + model.input_shape[1:])
+        return sample
+    # Examine first conv in model to determine input feature size.
+    first_layer = [c for c in model.modules()
+            if isinstance(c, (torch.nn.Conv2d, torch.nn.ConvTranspose2d,
+                torch.nn.Linear))][0]
+    # 4d input if convolutional, 2d input if first layer is linear.
+    if isinstance(first_layer, (torch.nn.Conv2d, torch.nn.ConvTranspose2d)):
+        sample = standard_z_sample(
+                size, first_layer.in_channels, seed=seed)[:,:,None,None]
+    else:
+        sample = standard_z_sample(
+                size, first_layer.in_features, seed=seed)
+    return sample
+
+def standard_z_sample(size, depth, seed=1, device=None):
+	'''
+	Generate a standard set of random Z as a (size, z_dimension) tensor.
+	With the same random seed, it always returns the same z (e.g.,
+	the first one is always the same regardless of the size.)
+	'''
+	# Use numpy RandomState since it can be done deterministically
+	# without affecting global state
+	rng = numpy.random.RandomState(seed)
+	result = torch.from_numpy(
+			rng.standard_normal(size * depth)
+			.reshape(size, depth)).float()
+	if device is not None:
+		result = result.to(device)
+	return result
+
+def standard_y_sample(size, num_classes, seed=1, device=None):
+	'''
+	Generate a standard set of random categorical as a (size,) tensor
+    of integers up to (num_classes-1).
+	With the same random seed, it always returns the same y (e.g.,
+	the first one is always the same regardless of the size.)
+	'''
+	# Use numpy RandomState since it can be done deterministically
+	# without affecting global state
+	rng = numpy.random.RandomState(seed)
+	result = torch.from_numpy(
+			rng.randint(num_classes, size=size)).long()
+	if device is not None:
+		result = result.to(device)
+	return result
+
+def training_loader(z_generator, batch_size, loader_size=10000):
+    '''
+    Returns an infinite generator that runs through randomized z
+    batches, forever.
+    '''
+    g_epoch = 1
+    while True:
+        z_data = z_dataset_for_model(
+                z_generator, size=epoch_size, seed=g_epoch + 1)
+        dataloader = torch.utils.data.DataLoader(
+                z_data,
+                shuffle=False,
+                batch_size=batch_size,
+                num_workers=10,
+                pin_memory=True)
+        for batch in dataloader:
+            yield batch
+        g_epoch += 1
+
+def testing_loader(z_generator, batch_size, test_size=1000):
+    '''
+    Returns an a short iterator that returns a small set of test data.
+    '''
+    z_data = z_dataset_for_model(
+        z_generator, size=test_size, seed=1)
+    dataloader = torch.utils.data.DataLoader(
+                z_data,
+                shuffle=False,
+                batch_size=batch_size,
+                num_workers=10,
+                pin_memory=True)
+    return dataloader
+
+def epoch_grouper(loader, epoch_size):
+    '''
+    To use with the infinite training loader: groups the training data
+    batches into epochs of the given size.
+    '''
+    it = iter(loader)
+    while True:
+        chunk_it = itertools.islice(it, epoch_size)
+        try:
+            first_el = next(chunk_it)
+        except StopIteration:
+            return
+        yield itertools.chain((first_el,), chunk_it)
diff --git a/packages.txt b/packages.txt
new file mode 100644
index 0000000000000000000000000000000000000000..d0f1245c6fdf5825392593ac2622dae14f0b8c57
--- /dev/null
+++ b/packages.txt
@@ -0,0 +1 @@
+python3-opencv
diff --git a/requirements.txt b/requirements.txt
new file mode 100644
index 0000000000000000000000000000000000000000..d473b3c4e2d2ad00026c04eeacc8ec7d69ef2afa
--- /dev/null
+++ b/requirements.txt
@@ -0,0 +1,19 @@
+matplotlib
+nibabel
+numpy
+Pillow
+scipy
+tqdm
+pandas
+monai
+pytorch-lightning 
+wandb
+sklearn
+grad-cam==1.3.5
+ttach==0.0.3
+pyyaml
+torchmetrics
+https://github.com/ufoym/imbalanced-dataset-sampler/archive/master.zip
+opencv-python-headless
+streamlit
+st-clickable-images
\ No newline at end of file
diff --git a/train.py b/train.py
new file mode 100644
index 0000000000000000000000000000000000000000..f5bf3114a1fa0a74101b2885353b6636bbcb743d
--- /dev/null
+++ b/train.py
@@ -0,0 +1,53 @@
+import matplotlib
+matplotlib.use('Agg')
+
+import os
+import wandb
+import pytorch_lightning as pl
+
+from data import VerseDataModule
+from model import VerseFxClassifier
+from utils.config import get_config
+
+if __name__ == '__main__':
+    config = get_config("config.yaml")
+
+    USE_WANDB = 'online' if config.pop('USE_WANDB', False) else 'disabled'
+    WANDB_API_KEY = config.pop('WANDB_API_KEY')
+    SAVE_MODEL = config.pop('SAVE_MODEL')
+
+    wandb.login(key=WANDB_API_KEY)
+
+    run = wandb.init(
+        project=f'fx-{config["task"]}-baseline-3d', 
+        entity='ifl-diva',
+        config=config,
+        mode=USE_WANDB
+    )
+
+    hparams = wandb.config
+
+    wandb_logger = pl.loggers.WandbLogger()
+
+    model = VerseFxClassifier(hparams)
+    data = VerseDataModule(hparams)
+
+    callbacks = [pl.callbacks.EarlyStopping(monitor="val/F1", mode="max", patience=hparams.early_stopping_patience)]
+
+    if bool(SAVE_MODEL):
+        callbacks.append(pl.callbacks.model_checkpoint.ModelCheckpoint(monitor='val/F1', mode="max",
+                                                                       dirpath='saved_models',
+                                                                       filename=f"{wandb.run.name}-epoch{{epoch}}-val_F1={{val/F1:.3f}}",
+                                                                       auto_insert_metric_name=False))
+
+    trainer = pl.Trainer(
+        gpus=1,
+        logger=wandb_logger,
+        log_every_n_steps=2,
+        #max_epochs=2,
+        callbacks=callbacks,
+        # auto_lr_find=hparams.auto_lr_find,
+    )
+
+    with run:
+        trainer.fit(model, data)
\ No newline at end of file
diff --git a/transforms/__init__.py b/transforms/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..c97a726db1b1f102fa6fe222bde48eef07fa7043
--- /dev/null
+++ b/transforms/__init__.py
@@ -0,0 +1,76 @@
+from pathlib import Path
+from typing import Callable, List, Optional, Tuple
+
+from monai.transforms import Compose
+
+from transforms.base import get_image_loading_transform, get_apply_crop_transform, get_stacking_transform
+from transforms.mask import get_mask_transform
+from transforms.coordinates import get_normalized_coordinates_transform
+from transforms.augmentation import *
+from transforms.backbone import *
+
+
+def _build_transforms_composition(hparams, transform_getters: List[Callable], *initial_args) -> Tuple[Compose, List[str]]:
+    """
+    Builds a transforms composition from the given functions, which take the hparams and loaded keys as arguments, and
+    produce a Compose containing the desired transforms. The initialization function receives the provided initial arguments.
+    """
+    transforms = []
+    keys = []
+
+    for i in range(0, len(transform_getters)):
+        if len(keys) == 0:
+            assert i == 0, f"Function {transform_getters[i]} did not yield any loaded keys."
+            # initialize
+            transform, keys = transform_getters[0](hparams, *initial_args)
+        else:
+            transform, keys = transform_getters[i](hparams, keys)
+        transforms.append(transform)
+
+    return Compose(transforms), keys
+
+def _get_config_transform_by_name(transform_name: str) -> Callable:
+    if transform_name == "intensity":
+        return intensity_transform
+    elif transform_name.startswith("spatial3d"):
+        if "simple" in transform_name:
+            return lambda hparams, loaded_keys: spatial_transform(hparams, loaded_keys, mode='simple')
+        else:
+            return lambda hparams, loaded_keys: spatial_transform(hparams, loaded_keys, mode='default')
+    elif transform_name == "modelsgenesis":
+        return models_genesis_transform
+    elif transform_name == "pretrained_resnet":
+        return pretrained_resnet_transform
+    elif transform_name == "robustness":
+        return robustness_transform
+    else:
+        raise ValueError(f"Unknown transform: {transform_name}")
+
+def get_training_transforms(hparams, image_dir: Path, mask_dir: Optional[Path] = None) -> Compose:
+    transforms_base = [get_image_loading_transform, get_mask_transform]
+
+    # robustness has to run early as we may need to operate on the whole volume for affine transformation and padding, 
+    # which must occur prior to any cropping or normalization
+    if "robustness" in hparams.transforms: transforms_base.append(_get_config_transform_by_name("robustness"))
+
+    transforms_base.extend([get_apply_crop_transform, get_normalized_coordinates_transform])
+
+    # preprocessing transforms must be run first
+    preprocessing_transforms = ["modelsgenesis", "pretrained_resnet"]
+    config_transforms = [_get_config_transform_by_name(transform_name) for transform_name in hparams.transforms if transform_name in preprocessing_transforms]
+    
+    # then append the rest minus the robustness transform that is run earlier
+    exclusion_criterion = lambda transform_name: transform_name in preprocessing_transforms or transform_name == "robustness"
+    config_transforms.extend([_get_config_transform_by_name(transform_name) for transform_name in hparams.transforms if not exclusion_criterion])
+
+    # the stacking transform must not occur before config transforms are run to avoid any interference
+    return _build_transforms_composition(hparams, transforms_base + config_transforms + [get_stacking_transform], image_dir, mask_dir)[0]
+
+def get_base_transforms(hparams, image_dir: Path, mask_dir: Optional[Path] = None) -> Compose:
+    transforms_base = [get_image_loading_transform, get_mask_transform, get_apply_crop_transform, get_normalized_coordinates_transform]
+
+    # apply preprocessing transforms
+    preprocessing_transforms = ["modelsgenesis", "pretrained_resnet"]
+    config_transforms = [_get_config_transform_by_name(transform_name) for transform_name in hparams.transforms if transform_name in preprocessing_transforms]
+
+    return _build_transforms_composition(hparams, transforms_base + config_transforms + [get_stacking_transform], image_dir, mask_dir)[0]
\ No newline at end of file
diff --git a/transforms/augmentation.py b/transforms/augmentation.py
new file mode 100644
index 0000000000000000000000000000000000000000..2fdf0f3e7721eee5bc245b1628926c9904ac8118
--- /dev/null
+++ b/transforms/augmentation.py
@@ -0,0 +1,82 @@
+import math
+from typing import List, Tuple
+
+try:
+    from typing import Literal
+except ImportError:
+    from typing_extensions import Literal
+
+from monai.transforms import Compose, OneOf
+from monai.transforms import RandGaussianNoiseD, RandAdjustContrastD, RandGaussianSmoothD
+from monai.transforms import RandFlipD, Rand3DElasticD, RandRotateD, RandZoomD, RandAffineD
+
+def intensity_transform(hparams, loaded_keys) -> Tuple[Compose, List[str]]:
+    assert len(loaded_keys) > 0
+    image_key = loaded_keys[0]
+
+    return OneOf([
+            RandGaussianNoiseD(keys=image_key),
+            RandAdjustContrastD(
+                keys=image_key,
+                gamma=1.5
+            ),
+            RandGaussianSmoothD(
+                # gaussian smoothing is also applied to mask
+                keys=loaded_keys,
+                sigma_x=(0.25, 0.75),
+                sigma_y=(0.25, 0.75),
+                sigma_z=(0.25, 0.75)
+            ),
+    ]), loaded_keys
+
+def robustness_transform(hparams, loaded_keys) -> Tuple[Compose, List[str]]:
+    keys_to_transform = [key for key in loaded_keys if key != "coordinates"]
+    return Compose([
+            RandAffineD(
+                keys=keys_to_transform,
+                translate_range=(((-20, 10), 5, 5)), # permits keypoint misplacing as well as minor centering deviations
+                padding_mode="zeros",
+                prob=1 # elevated probability
+            ),
+            RandRotateD(
+                keys=keys_to_transform,
+                range_x=math.radians(7), # spine roll, patient is not lying perfectly flat on their back
+                range_y=math.radians(5), # spine yaw, patient position is slightly skewed
+                range_z=math.radians(20), # spine pitch, strong deviation permitted
+                prob=1 # elevated probability
+            ),
+    ]), loaded_keys
+
+def spatial_transform(hparams, loaded_keys, mode: Literal['simple', 'default'] = "default") -> Tuple[Compose, List[str]]:
+    keys_to_transform = [key for key in loaded_keys if key != "coordinates"]
+    transforms = [
+            RandFlipD(
+                keys=keys_to_transform,
+                spatial_axis=2 # only flip the vertebrae
+            ),
+            RandRotateD(
+                keys=keys_to_transform,
+                range_x=math.radians(7), # spine roll, patient is not lying perfectly flat on their back
+                range_y=math.radians(5), # spine yaw, patient position is slightly skewed
+                range_z=math.radians(2), # spine pitch, patient's spine has a slight slope
+                                         # (may simulate kyphosis but resulting images are questionable
+                                         #  for larger rotation angles)
+                prob=0.1 if not "robustness" in hparams.transforms else 0 # prevent attempts to re-rotate
+            ),
+            RandZoomD(
+                keys=keys_to_transform,
+                mode='nearest',#['area', 'nearest'],
+                min_zoom=0.9, max_zoom=1.1,
+            ),
+    ]
+
+    if mode == 'simple':
+        transforms.append( 
+            Rand3DElasticD(
+                sigma_range=(1, 2),
+                magnitude_range=(2, 8),
+                keys=keys_to_transform
+            )
+        )
+
+    return Compose(transforms), loaded_keys
\ No newline at end of file
diff --git a/transforms/backbone.py b/transforms/backbone.py
new file mode 100644
index 0000000000000000000000000000000000000000..73e7b8da125c40a46193d4f945b8060fc25d4015
--- /dev/null
+++ b/transforms/backbone.py
@@ -0,0 +1,46 @@
+from typing import List, Tuple
+
+from monai.transforms import Compose, CenterSpatialCropD, ResizeD, NormalizeIntensityD, ScaleIntensityRangeD, adaptor
+from monai.transforms.utils_pytorch_numpy_unification import clip
+
+
+class HUClipTransform:
+    """Clips image to the desired HU range."""
+    def __init__(self, range: Tuple[int, int]):
+        self.range = range
+
+    def __call__(self, image):
+        return clip(image, *self.range)
+
+def models_genesis_transform(hparams, loaded_keys) -> Tuple[Compose, List[str]]:
+    """
+    Transforms image to match the normalization used in Models Genesis pre-training:
+        - Clip HU to [-1000, 1000]
+        - Scale to [0, 1]
+
+    See: https://github.com/MrGiovanni/ModelsGenesis/tree/master/pytorch#3-fine-tune-models-genesis-on-your-own-target-task
+    """
+
+    assert len(loaded_keys) > 0
+    image_key = loaded_keys[0]
+
+    return Compose([
+            adaptor(HUClipTransform((-1000, 1000)), image_key),
+            ScaleIntensityRangeD(
+                keys=image_key,
+                a_min=-1000,
+                a_max=1000,
+                b_min=0,
+                b_max=1,
+                clip=True
+            )
+        ]), loaded_keys
+
+
+def pretrained_resnet_transform(hparams, loaded_keys) -> Tuple[Compose, List[str]]:
+    # TODO this is untested
+    return Compose([
+        ResizeD(keys=loaded_keys, spatial_size=256 * hparams.input_dim, mode='trilinear'),
+        CenterSpatialCropD(keys=loaded_keys, roi_size=224 * hparams.input_dim),
+        NormalizeIntensityD(keys=loaded_keys, subtrahend=[0.485, 0.456, 0.406], divisor=[0.229, 0.224, 0.225])
+    ]), loaded_keys
\ No newline at end of file
diff --git a/transforms/base.py b/transforms/base.py
new file mode 100644
index 0000000000000000000000000000000000000000..c883c92c93d3c7d7b9768821a8ac11683f78dab7
--- /dev/null
+++ b/transforms/base.py
@@ -0,0 +1,92 @@
+import torch
+import numpy as np
+
+from pathlib import Path
+from typing import List, Optional, Tuple
+from monai.transforms import Compose, LoadImageD, LambdaD, AddChannelD, CenterSpatialCropD
+
+class DuplicateKeyD:
+  """
+  Duplicates a key in the given data dictionary.
+  """
+  def __init__(self, from_key: str, to_key: str):
+    self.from_key = from_key
+    self.to_key = to_key
+
+  def __call__(self, data):
+    d = dict(data)
+    return {**d, self.to_key: d[self.from_key]}
+
+
+class MergeKeysD:
+  """
+  Stacks images from multiple keys into a single one.
+  """
+  def __init__(self, keys: List[str], to_key: str):
+    self.keys = keys
+    self.to_key = to_key
+
+  def __call__(self, data):
+    d = dict(data)
+    
+    if len(self.keys) == 1 and self.keys[0] == self.to_key:
+        # nothing to stack or move
+        return d
+
+    arrays_to_stack = [d.pop(key) for key in self.keys]
+
+    if isinstance(arrays_to_stack[0], torch.Tensor):
+        d[self.to_key] = torch.cat(arrays_to_stack, dim=0)
+    else:
+        d[self.to_key] = np.concatenate(arrays_to_stack, axis=0)
+
+    return d
+
+
+def get_image_loading_transform(hparams, image_dir: Path, mask_dir: Optional[Path] = None) -> Tuple[Compose, List[str]]:
+    """
+    Loads an image and, depending on the configuration, its corresponding mask.
+    """
+
+    if hparams.mask == 'none':
+        return Compose([
+            LambdaD(keys='image', func=lambda p: image_dir / p),
+            LoadImageD(keys='image'),
+        ]), ['image']
+
+    else:
+        assert mask_dir is not None
+        return Compose([
+            DuplicateKeyD(from_key='image', to_key='mask'),
+            # load image and mask
+            LambdaD(keys='image', func=lambda p: image_dir / p),
+            LoadImageD(keys='image'),
+            LambdaD(keys='mask', func=lambda p: mask_dir / p),
+            LoadImageD(keys='mask'),
+        ]), ['image', 'mask']
+
+def get_apply_crop_transform(hparams, loaded_keys: List[str]) -> Tuple[Compose, List[str]]:
+    """
+    Applies a crop to the loaded keys to achieve the desired size, if appropriate for the given configuration.
+    """
+
+    if hparams.mask == 'crop':
+        return Compose([]), loaded_keys
+    else:
+        return Compose([
+            AddChannelD(keys=loaded_keys),
+            CenterSpatialCropD(keys=loaded_keys, roi_size=[hparams.input_size] * hparams.input_dim),
+        ]), loaded_keys
+
+def get_stacking_transform(hparams, loaded_keys: List[str]) -> Tuple[Compose, List[str]]:
+    """
+    Stacks multiple loaded keys (i.e. image and mask) as channels into the single 'image' key. If a single key
+    is loaded, do nothing.
+    """
+
+    if len(loaded_keys) > 1:
+        return Compose([
+            MergeKeysD(loaded_keys, 'image')
+        ]), ['image']
+    else:
+        return Compose([]), loaded_keys
\ No newline at end of file
diff --git a/transforms/coordinates.py b/transforms/coordinates.py
new file mode 100644
index 0000000000000000000000000000000000000000..28028c89f0dd97f6f2767cbf3287a7a08b39e429
--- /dev/null
+++ b/transforms/coordinates.py
@@ -0,0 +1,114 @@
+import torch
+import torch.nn as nn
+import numpy as np
+from monai.transforms import Compose
+from typing import List, Tuple
+
+def __define_coord_channels__(x_dim, y_dim, z_dim=None):
+    """
+    Returns coord x and y channels from 0 to x_dim-1 and from 0 to y_dim -1
+    """
+
+    if z_dim is None:
+        # original implementation (https://github.com/jmfacil/camconvs/blob/894add0858343e00da52143231bd30cda0f9f385/python/CAM/blocks/camconvs.py)
+        xx_ones = torch.ones([y_dim], dtype=torch.long)
+        xx_ones = torch.unsqueeze(xx_ones, -1)
+        xx_range = torch.tile(torch.unsqueeze(torch.arange(x_dim), 0), [1])
+        xx_range = torch.unsqueeze(xx_range, 1)
+        xx_channel = torch.matmul(xx_ones, xx_range)
+
+        yy_ones = torch.ones([x_dim], dtype=torch.long)
+        yy_ones = torch.unsqueeze(yy_ones, 1)
+        yy_range = torch.tile(torch.unsqueeze(torch.arange(y_dim), 0), [1])
+        yy_range = torch.unsqueeze(yy_range, -1)
+        yy_channel = torch.matmul(yy_range, yy_ones)
+
+        return xx_channel.float(), yy_channel.float()
+    
+    else:
+        # simplified 3d version
+        x = torch.unsqueeze(torch.arange(y_dim)[None, :, None].repeat(x_dim, 1, z_dim), 0).float()
+        y = torch.unsqueeze(torch.arange(x_dim)[:, None, None].repeat(1, y_dim, z_dim), 0).float()
+        z = torch.unsqueeze(torch.arange(z_dim)[None, None, :].repeat(x_dim, y_dim, 1), 0).float()
+
+        return x, y, z
+
+class PositionalEncoding3D(nn.Module):
+    def __init__(self, channels):
+        """
+        :param channels: The last dimension of the tensor you want to apply pos emb to.
+        """
+        super(PositionalEncoding3D, self).__init__()
+        channels = int(np.ceil(channels/6)*2)
+        if channels % 2:
+            channels += 1
+        self.channels = channels
+        inv_freq = 1. / (10000 ** (torch.arange(0, channels, 2).float() / channels))
+        self.register_buffer('inv_freq', inv_freq)
+
+    def forward(self, tensor):
+        """
+        :param tensor: A 5d tensor of size (batch_size, x, y, z, ch)
+        :return: Positional Encoding Matrix of size (batch_size, x, y, z, ch)
+        """
+        if len(tensor.shape) != 5:
+            raise RuntimeError("The input tensor has to be 5d!")
+        batch_size, x, y, z, orig_ch = tensor.shape
+        pos_x = torch.arange(x, device=tensor.device).type(self.inv_freq.type())
+        pos_y = torch.arange(y, device=tensor.device).type(self.inv_freq.type())
+        pos_z = torch.arange(z, device=tensor.device).type(self.inv_freq.type())
+        sin_inp_x = torch.einsum("i,j->ij", pos_x, self.inv_freq)
+        sin_inp_y = torch.einsum("i,j->ij", pos_y, self.inv_freq)
+        sin_inp_z = torch.einsum("i,j->ij", pos_z, self.inv_freq)
+        emb_x = torch.cat((sin_inp_x.sin(), sin_inp_x.cos()), dim=-1).unsqueeze(1).unsqueeze(1)
+        emb_y = torch.cat((sin_inp_y.sin(), sin_inp_y.cos()), dim=-1).unsqueeze(1)
+        emb_z = torch.cat((sin_inp_z.sin(), sin_inp_z.cos()), dim=-1)
+        emb = torch.zeros((x,y,z,self.channels*3),device=tensor.device).type(tensor.type())
+        emb[:,:,:,:self.channels] = emb_x
+        emb[:,:,:,self.channels:2*self.channels] = emb_y
+        emb[:,:,:,2*self.channels:] = emb_z
+
+        return emb[None,:,:,:,:orig_ch].repeat(batch_size, 1, 1, 1, 1)
+
+class PositionalEncodingPermute3D(nn.Module):
+    def __init__(self, channels):
+        """
+        Accepts (batchsize, ch, x, y, z) instead of (batchsize, x, y, z, ch)        
+        """
+        super(PositionalEncodingPermute3D, self).__init__()
+        self.penc = PositionalEncoding3D(channels)
+
+    def forward(self, tensor):
+        tensor = tensor.permute(0,2,3,4,1)
+        enc = self.penc(tensor)
+        return enc.permute(0,4,1,2,3)
+
+class AddCoordinateChannels:
+    def __init__(self, to_key: str, input_size: int, input_dim: int, sinusodal: bool = False):
+        self.to_key = to_key
+        self.input_size = input_size
+        self.input_dim = input_dim
+        self.sinusodal = sinusodal
+
+    def get_normalized_coordinate_channels(self) -> np.ndarray:
+        if not self.sinusodal:
+            channels = __define_coord_channels__(*((self.input_size,)*self.input_dim))
+            normalize = lambda channel: (channel/(self.input_size-1))*2.0-1.0
+            channels = [normalize(c) for c in channels]
+            return torch.cat(channels).numpy()
+        else:
+            pep = PositionalEncodingPermute3D(self.input_dim)
+            channels = pep(torch.zeros(1, self.input_dim, *(((self.input_size,)*self.input_dim))))
+            return channels.squeeze(0).numpy()
+
+    def __call__(self, data):
+        d = dict(data)
+        return {**d, self.to_key: self.get_normalized_coordinate_channels()}
+
+def get_normalized_coordinates_transform(hparams, loaded_keys) -> Tuple[Compose, List[str]]:
+    if hparams.coordinates:
+        return Compose([
+            AddCoordinateChannels("coordinates", hparams.input_size, hparams.input_dim, sinusodal=True)
+        ]), loaded_keys + ["coordinates"]
+    else:
+        return Compose([]), loaded_keys
\ No newline at end of file
diff --git a/transforms/mask.py b/transforms/mask.py
new file mode 100644
index 0000000000000000000000000000000000000000..16fbb2098d92ee6f7758897327b7b156756b50bf
--- /dev/null
+++ b/transforms/mask.py
@@ -0,0 +1,69 @@
+import numpy as np
+from typing import List, Tuple
+from monai.transforms import Compose, AddChannelD, MaskIntensityD, DeleteItemsD, CropForegroundD, ResizeD
+
+class SelectMaskByLevelD:
+  """
+  Selects a mask segment from a mask image based on a given level index. May also
+  be applied to a single channel.
+  """
+  def __init__(self, mask_key: str, level_idx_key: str):
+    self.mask_key = mask_key
+    self.level_idx_key = level_idx_key
+
+  def __call__(self, data):
+    d = dict(data)
+    mask = np.zeros_like(d[self.mask_key])
+    mask[d[self.mask_key] == d[self.level_idx_key]] = 1
+    d[self.mask_key] = mask
+    return d
+
+def get_mask_transform(hparams, loaded_keys: List[str], level_idx_key='level_idx') -> Tuple[Compose, List[str]]:
+    """
+    Depending on the configuration values for 'MASK', the transform returned by this method does one of the following:
+        - nothing ('none')
+        - applies the mask of the critical vertebra to the image ('apply')
+        - applies the mask of all visible vertebrae to the image ('apply_all')
+        - loads the mask into the 'mask' key s.t. it will later be stacked with the image ('channel')
+        - crop the image to the critical vertebra and upsample it ('crop')
+    """
+
+    if hparams.mask == 'none':
+        return Compose([]), loaded_keys
+
+    assert len(loaded_keys) == 2
+    image_key, mask_key = loaded_keys
+
+    if hparams.mask == 'apply':
+        return Compose([
+            # only select relevant vertebra
+            SelectMaskByLevelD(mask_key=mask_key, level_idx_key=level_idx_key),
+            # apply mask
+            MaskIntensityD(keys=image_key, mask_key=mask_key),
+            # once the mask is applied, release it
+            DeleteItemsD(keys=mask_key),
+        ]), [image_key]
+
+    elif hparams.mask == 'apply_all':
+        return Compose([
+            # keeps all vertebra in the mask
+            # apply mask
+            MaskIntensityD(keys=image_key, mask_key=mask_key),
+            # once the mask is applied, release it
+            DeleteItemsD(keys=mask_key),
+        ]), [image_key]
+
+    elif hparams.mask == 'channel':
+        return Compose([
+            SelectMaskByLevelD(mask_key=mask_key, level_idx_key=level_idx_key),
+        ]), loaded_keys
+
+    elif hparams.mask == 'crop':
+        # TODO CropForegroundD ignores one spatial dimension, thus not truly cropping
+        return Compose([
+            SelectMaskByLevelD(mask_key=mask_key, level_idx_key=level_idx_key),
+            CropForegroundD(keys=image_key, source_key=mask_key, margin=2),
+            DeleteItemsD(keys=mask_key),
+            AddChannelD(keys=image_key),
+            ResizeD(keys=image_key, spatial_size=[hparams.input_size] * hparams.input_dim, mode='trilinear'),
+        ]), [image_key]
\ No newline at end of file
diff --git a/utils/__init__.py b/utils/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/utils/config.py b/utils/config.py
new file mode 100644
index 0000000000000000000000000000000000000000..a432d7778c01c64b54fa86e2a979bcc5b6bdba9b
--- /dev/null
+++ b/utils/config.py
@@ -0,0 +1,88 @@
+from typing import Dict, Any, Iterable
+import numbers
+import yaml
+import os
+
+with open('globals.yaml', 'r') as stream:
+        globals = yaml.load(stream, Loader=yaml.FullLoader)
+
+def _prepare_config(config) -> Dict[str, Any]:
+    """
+    Returns the final configuration used by the model. May be used to clean user input,
+    sort items, etc.
+    """
+
+    classes_per_task = {'detection': 1, 'grading': 6, 'simple_grading': 4}
+    NUM_CLASSES = classes_per_task[config["TASK"]]
+
+    return {
+        "learning_rate": config["LEARNING_RATE"],
+        "model_name": config["MODEL_NAME"],
+        "dataset": "VerSe",
+        "batch_size": config["BATCH_SIZE"], 
+        "input_size": config["INPUT_SIZE"],
+        "input_dim": config["INPUT_DIM"],
+        "mask": config["MASK"],
+        "coordinates": config["COORDINATES"],
+        "oversampling": config["OVERSAMPLING"],
+        "fold": config["FOLD"],
+        "dropout": config["DROPOUT"],
+        "frozen_layers": config["FROZEN_LAYERS"],
+        "num_classes": NUM_CLASSES,
+        "early_stopping_patience": config["EARLY_STOPPING_PATIENCE"],
+        "min_vertebrae_level": config["MIN_VERTEBRAE_LEVEL"],
+        "dataset_path": config["DATASET_PATH"],
+        "loss": config["LOSS"], 
+        "weighted_loss": config["WEIGHTED_LOSS"],
+        "transforms": sorted(config["TRANSFORMS"]),
+        "task": config["TASK"],
+
+        # passed through, will not be part of final hyperparameters
+        "USE_WANDB": config["USE_WANDB"],
+        "WANDB_API_KEY": config["WANDB_API_KEY"],
+        "SAVE_MODEL": config["SAVE_MODEL"]
+    }
+
+def _sanity_check_config(config):
+    """
+    Runs simple assertions to test that the config file is actually valid.
+    """
+    # option validity assertions
+    assert any([config['mask'] == o for o in ['none', 'channel', 'apply', 'apply_all', 'crop']])
+    assert os.path.exists(config["dataset_path"])
+
+    # datatype assertions
+    for numeric_key in ["batch_size", "input_size", "input_dim", "dropout", "early_stopping_patience", "min_vertebrae_level", "fold"]:
+        assert isinstance(config[numeric_key], numbers.Number)
+
+    for list_key in ["transforms", "frozen_layers"]:
+        assert isinstance(config[list_key], Iterable)
+
+    # logic assertions
+    assert not (config['task'] == 'detection' and config['loss'] != 'binary_cross_entropy' and config['loss'] != 'focal')
+
+    # ensure models fit the data
+    nets_3d = ["UNet3D", "ModelsGenesis"]
+    for net_3d in nets_3d:
+        assert not (net_3d in config['model_name']) or config['input_dim'] == 3
+
+    if config['oversampling'] and config['weighted_loss']:
+        print("Oversampling as well as weighted loss are enabled, you may want to disable one")
+
+    if config['loss'] == 'focal' and config['weighted_loss']:
+        print("Focal loss does not support manual class weighting")
+
+    if config['loss'] == 'focal' and config['oversampling']:
+        print("Focal loss and oversampling are enabled, you may want to disable oversampling")
+
+def get_config(config_file_path: str):
+    """
+    Retrieves the configuration from the given file path after running a sanity check and
+    pre-processing steps.
+    """
+
+    with open(config_file_path, 'r') as stream:
+        config_stream = yaml.load(stream, Loader=yaml.FullLoader)
+        config = _prepare_config(config_stream)
+        _sanity_check_config(config)
+        return config
\ No newline at end of file
diff --git a/utils/confusion_viz.py b/utils/confusion_viz.py
new file mode 100644
index 0000000000000000000000000000000000000000..e7250cd3c4ce887aa336be303998099e19c7644a
--- /dev/null
+++ b/utils/confusion_viz.py
@@ -0,0 +1,99 @@
+from threading import local
+import torch
+import wandb
+import numpy as np
+import PIL.Image
+from typing import Iterable
+
+from utils.val_loop_hook import ValidationLoopHook
+
+def _strip_image_from_grid_row(row, gap=5, bg=255):
+    strip = torch.full(
+            (row.shape[0] * (row.shape[3] + gap) - gap,
+            row.shape[1] * (row.shape[3] + gap) - gap), bg, dtype=row.dtype)
+    for i in range(0, row.shape[0] * row.shape[1]):
+        strip[(i // row.shape[1]) * (row.shape[2] + gap) : ((i // row.shape[1])+1) * (row.shape[2] + gap) - gap,
+            (i % row.shape[1]) * (row.shape[3] + gap) : ((i % row.shape[1])+1) * (row.shape[3] + gap) - gap] = row[i // row.shape[1]][i % row.shape[1]]
+    return PIL.Image.fromarray(strip.numpy())
+
+class ConfusionVisualizer(ValidationLoopHook):
+    def __init__(self, image_shape: Iterable[int], num_classes: int, num_images: int = 5, num_slices: int = 8):
+        self.image_shape = image_shape
+        self.num_images = num_images
+        self.num_classes = num_classes
+        self.num_slices = num_slices
+
+        self.activations = -99 * torch.ones(self.num_classes, self.num_images)
+        self.images = torch.zeros(torch.Size([self.num_classes, self.num_images]) + torch.Size(self.image_shape))
+
+    def process(self, batch, target_batch, logits_batch, prediction_batch):
+        image_batch = batch["image"]
+
+        with torch.no_grad():
+            local_activations = torch.amax(logits_batch, dim=-1)
+
+        # filter samples where the prediction does not line up with the target
+        confused_samples = (prediction_batch != target_batch)
+
+        # filter public dataset samples
+        public = torch.tensor(["verse" in id for id in batch["verse_id"]]).type_as(confused_samples)
+
+        mask = confused_samples & public
+
+        for current_idx in torch.nonzero(mask).squeeze(1):
+            target_class = target_batch[current_idx]
+            # next item in local batch has a higher activation than the previous confusions for this class, replace it
+            if local_activations[current_idx] > torch.min(self.activations[target_class]):
+                idx_to_replace = torch.argsort(self.activations[target_class])[0]
+                self.activations[target_class, idx_to_replace] = local_activations[current_idx]
+                self.images[target_class, idx_to_replace] = image_batch[current_idx].cpu()
+
+    def trigger(self, module):
+        for class_idx in range(self.num_classes):
+            # determine final order such that the highest activations are placed on top
+            sorted_idx = torch.argsort(self.activations[class_idx], descending=True)
+
+            self.images[class_idx] = self.images[class_idx, sorted_idx]
+
+            normalize = lambda x: (x - np.min(x))/np.ptp(x)
+
+            if len(self.images.shape) == 6:
+                # 3D, visualize slices
+                img_res = self.images[class_idx].shape[-1]
+                img_slices = torch.linspace(0, img_res-1, self.num_slices+2, dtype=torch.long)[1:-1]
+                
+                # Show all images slices in a larger combined image
+                top_confusing_samples = _strip_image_from_grid_row(
+                    torch.stack([
+                        torch.stack([
+                            torch.tensor(
+                                np.uint8(255 * normalize((self.images[class_idx, i, 0, ..., img_slices[s]]).numpy()))
+                            )
+                        for s in range(self.num_slices)])
+                    for i in range(self.num_images if self.num_images < self.images[class_idx].shape[0] else self.images[class_idx].shape[0])])
+                )
+
+            elif len(self.images.shape) == 5:
+                # 2D
+                top_confusing_samples = _strip_image_from_grid_row(
+                    torch.stack([
+                        torch.stack([
+                            torch.tensor(
+                                np.uint8(255 * normalize((self.images[class_idx, i, 0, ...]).numpy()))
+                            )
+                        ])
+                    for i in range(self.num_images if self.num_images < self.images[class_idx].shape[0] else self.images[class_idx].shape[0])])
+                )
+
+            else:
+                raise RuntimeError("Unknown image shape found for confusion visualization")
+
+            module.logger.experiment.log({
+                # class_idx represents the ground truth, i.e. these were samples to be classified as class_idx
+                # but they were predicted to belong to a different class
+                f"val/top_confusing_of_class_{class_idx}": wandb.Image(top_confusing_samples)
+            })
+
+    def reset(self):
+        self.activations = -99 * torch.ones(self.num_classes, self.num_images)
+        self.images = torch.zeros(torch.Size([self.num_classes, self.num_images]) + torch.Size(self.image_shape))
\ No newline at end of file
diff --git a/utils/grad_cam.py b/utils/grad_cam.py
new file mode 100644
index 0000000000000000000000000000000000000000..5c4ca9eb9774e062115f829badda5b0c9718f508
--- /dev/null
+++ b/utils/grad_cam.py
@@ -0,0 +1,183 @@
+from typing import Callable, Iterable, Tuple
+import torch
+import numpy as np
+import PIL.Image
+import cv2
+import wandb
+
+from tqdm import tqdm
+from pytorch_grad_cam import GradCAM
+
+from utils.val_loop_hook import ValidationLoopHook
+
+def _get_grad_cam_target(model):
+    """
+    Determines the appropriate GradCAM target.
+    """
+
+    # very naive check
+    if hasattr(model, "features"):
+        return getattr(model, "features")
+
+    pooling = [torch.nn.AdaptiveAvgPool1d, torch.nn.AvgPool1d, torch.nn.MaxPool1d, torch.nn.AdaptiveMaxPool1d,
+               torch.nn.AdaptiveAvgPool2d, torch.nn.AvgPool2d, torch.nn.MaxPool2d, torch.nn.AdaptiveMaxPool2d,
+               torch.nn.AdaptiveAvgPool3d, torch.nn.AvgPool3d, torch.nn.MaxPool3d, torch.nn.AdaptiveMaxPool3d]
+    convolutions = [torch.nn.Conv1d, torch.nn.Conv2d, torch.nn.Conv3d]
+
+    # reverse search starting from the final module
+    inverted_modules = list(model.modules())[::-1]
+    for i, module in enumerate(inverted_modules):
+        if any([isinstance(module, po) for po in pooling]):
+            # if a pooling layer was hit, pick the module directly before it
+            return inverted_modules[i+1]
+        elif any([isinstance(module, co) for co in convolutions]):
+            # if a convolution was hit (but no pooling layer), pick that one instead
+            return module
+        elif isinstance(module, torch.nn.Sequential):
+            # if a sequential module is hit, explore it
+            for child in list(module.children())[::-1]:
+                sequential_result = _get_grad_cam_target(child)
+                if sequential_result is not None:
+                    return sequential_result
+
+def _show_cam_on_image(img: np.ndarray, mask: np.ndarray, use_rgb: bool = False, colormap: int = cv2.COLORMAP_JET) -> np.ndarray:
+        """ This function overlays the cam mask on the image as an heatmap.
+        By default the heatmap is in BGR format.
+        :param img: The base image in RGB or BGR format.
+        :param mask: The cam mask.
+        :param use_rgb: Whether to use an RGB or BGR heatmap, this should be set to True if 'img' is in RGB format.
+        :param colormap: The OpenCV colormap to be used.
+        :returns: The default image with the cam overlay.
+        """
+        heatmap = cv2.applyColorMap(np.uint8(255 * mask), colormap)
+        if use_rgb:
+            heatmap = cv2.cvtColor(heatmap, cv2.COLOR_BGR2RGB)
+        heatmap = np.float32(heatmap) / 255
+
+        normalize = lambda x: (x - np.min(x))/np.ptp(x)
+
+        cam = 0.6 * heatmap + normalize(img)
+        cam = cam / np.max(cam)
+        return np.uint8(255 * cam)
+
+def _strip_image_from_grid_row(row, gap=5, bg=255):
+    strip = torch.full(
+            (row.shape[0] * (row.shape[3] + gap) - gap,
+             row.shape[1] * (row.shape[3] + gap) - gap,
+             row.shape[4]), bg, dtype=row.dtype)
+    for i in range(0, row.shape[0] * row.shape[1]):
+        strip[(i // row.shape[1]) * (row.shape[2] + gap) : ((i // row.shape[1])+1) * (row.shape[2] + gap) - gap,
+              (i % row.shape[1]) * (row.shape[3] + gap) : ((i % row.shape[1])+1) * (row.shape[3] + gap) - gap,
+              :] = row[i // row.shape[1]][i % row.shape[1]]
+    return PIL.Image.fromarray(strip.numpy())
+
+
+class GradCAMBuilder(ValidationLoopHook):
+    def __init__(self, image_shape: Iterable[int], target_category: int = None, num_images: int = 5):
+        self.image_shape = image_shape
+        self.target_category = target_category
+        self.num_images = num_images
+
+        self.targets = torch.zeros(self.num_images)
+        self.activations = torch.zeros(self.num_images)
+        self.images = torch.zeros(torch.Size([self.num_images]) + torch.Size(self.image_shape))
+
+    def process(self, batch, target_batch, logits_batch, prediction_batch):
+        image_batch = batch["image"]
+
+        with torch.no_grad():
+            if self.target_category is not None:
+                local_activations = logits_batch[:, self.target_category]
+            else:
+                local_activations = torch.amax(logits_batch, dim=-1)
+
+        # filter samples where the prediction lines up with the target
+        target_match = (prediction_batch == target_batch)
+
+        # filter public dataset samples
+        public = torch.tensor(["verse" in id for id in batch["verse_id"]]).type_as(target_match)
+
+        mask = target_match & public
+
+        if torch.max(mask) == False:
+            # no samples match criteria in this batch, skip
+            return
+
+        # identify better activations and replace them accordingly
+        local_top_idx = torch.argsort(local_activations, descending=True)
+        # filter samples
+        local_top_idx = local_top_idx[mask[local_top_idx]]
+        current_idx = 0
+
+        while current_idx < self.num_images and local_activations[local_top_idx[current_idx]] > torch.min(self.activations):
+            # next item in local batch matches criteria and has a higher activation than one in the global batch, replace it
+            idx_to_replace = torch.argsort(self.activations)[0]
+
+            self.activations[idx_to_replace] = local_activations[local_top_idx[current_idx]]
+            self.images[idx_to_replace] = image_batch[local_top_idx[current_idx]]
+            self.targets[idx_to_replace] = target_batch[local_top_idx[current_idx]]
+
+            current_idx += 1
+
+    def trigger(self, module):
+        model = module.backbone
+        module.eval()
+
+        # determine the Grad-CAM target module/layer
+        grad_cam_target = _get_grad_cam_target(model)
+
+        cam = GradCAM(model, [grad_cam_target], use_cuda=torch.cuda.is_available())
+
+        # determine final order such that the highest activations are placed on top
+        sorted_idx = torch.argsort(self.activations, descending=True)
+
+        self.activations = self.activations[sorted_idx]
+        self.images = self.images[sorted_idx]
+        self.targets = self.targets[sorted_idx]
+
+        # if a polyaxon experiment crashes here, remove the GradCAMBuilder instance from the
+        # model.validation_hooks list
+        grad_cams = cam(input_tensor=self.images, target_category=self.target_category)
+
+        module.train()
+
+        if len(self.images.shape) == 5:
+            # 3D, visualize slices
+            ld_res = grad_cams.shape[-1]
+            img_res = self.images.shape[-1]
+            img_slices = torch.linspace(int(img_res/ld_res/2), img_res-int(img_res/ld_res/2), ld_res, dtype=torch.long)
+            
+            # Show all images slices in a larger combined image
+            grad_cams_image = _strip_image_from_grid_row(
+                torch.stack([
+                    torch.stack([
+                        torch.tensor(
+                            _show_cam_on_image((self.images[i, 0, ..., img_slices[s]]).unsqueeze(-1).repeat(1, 1, 3).numpy(), grad_cams[i, ..., s], use_rgb=True)
+                        )
+                    for s in range(grad_cams.shape[-1])])
+                for i in range(self.num_images if self.num_images < grad_cams.shape[0] else grad_cams.shape[0])])
+            )
+
+        elif len(self.images.shape) == 4:
+            # 2D
+            grad_cams_image = _strip_image_from_grid_row(
+                torch.stack([
+                    torch.stack([
+                        torch.tensor(
+                            _show_cam_on_image((self.images[i, 0, ...]).unsqueeze(-1).repeat(1, 1, 3).numpy(), grad_cams[i, ...], use_rgb=True)
+                        )
+                    ])
+                for i in range(self.num_images if self.num_images < grad_cams.shape[0] else grad_cams.shape[0])])
+            )
+
+        else:
+            raise RuntimeError("Attempting to build Grad-CAMs for data that is neither 2D nor 3D")
+
+        module.logger.experiment.log({
+            "val/grad_cam": wandb.Image(grad_cams_image)
+        })
+
+    def reset(self):
+        self.targets = torch.zeros(self.num_images)
+        self.activations = torch.zeros(self.num_images)
+        self.images = torch.zeros(torch.Size([self.num_images]) + torch.Size(self.image_shape))
\ No newline at end of file
diff --git a/utils/plots.py b/utils/plots.py
new file mode 100644
index 0000000000000000000000000000000000000000..c91223ab2dabf0a3552878f4945a4da2e69afbc7
--- /dev/null
+++ b/utils/plots.py
@@ -0,0 +1,25 @@
+from sklearn.metrics import confusion_matrix, ConfusionMatrixDisplay
+import matplotlib.pyplot as plt
+import numpy as np
+import numpy as np
+
+def get_confusion_matrix_figure(y_true, y_pred, labels=None, title="Confusion matrix"):
+    """
+    Returns a confusion matrix plot.
+    """
+
+    plt.figure(dpi=600)
+    label_codes = np.arange(len(labels)) if labels is not None else None
+    cm = confusion_matrix(y_true, y_pred, labels=label_codes)
+    disp = ConfusionMatrixDisplay(confusion_matrix=cm, display_labels=labels)
+    values_format = None # Format specification for values in confusion matrix. If `None`, the format specification is 'd' or '.2g' whichever is shorter.
+    disp.plot(
+        include_values=True,
+        cmap=plt.cm.Blues, # 'viridis'
+        ax=None, 
+        xticks_rotation='horizontal',
+        values_format=values_format
+    )
+    fig = disp.figure_
+    fig.suptitle(title)
+    return fig
\ No newline at end of file
diff --git a/utils/val_loop_hook.py b/utils/val_loop_hook.py
new file mode 100644
index 0000000000000000000000000000000000000000..dd1937b42da1f1d23747fa3262426e13db9b723e
--- /dev/null
+++ b/utils/val_loop_hook.py
@@ -0,0 +1,25 @@
+from abc import ABC, abstractmethod
+import torch
+import pytorch_lightning as pl
+
+class ValidationLoopHook(ABC):
+    @abstractmethod
+    def process(self, batch: torch.Tensor, target_batch: torch.Tensor, logits_batch: torch.Tensor, prediction_batch: torch.Tensor) -> None:
+        """
+        Called for every validation batch to process results.
+        """
+        pass
+
+    @abstractmethod
+    def trigger(self, module: pl.LightningModule):
+        """
+        Called after the validation epoch has concluced to further interact with the module and/or log data.
+        """
+        pass
+
+    @abstractmethod
+    def reset(self):
+        """
+        Called right after build() to clean up before the next validation epoch starts.
+        """
+        pass
\ No newline at end of file