From d8f80b8427c62dfa0244207e49a12583e5b741a5 Mon Sep 17 00:00:00 2001 From: shrinuKushagra Date: Fri, 3 Dec 2021 15:49:20 -0800 Subject: [PATCH] RCN example (#96) * notebook for running rcn using pgmax * notebook for running rcn using pgmax * addressing PR comments. making it a single notebook * minor edits * fixing type hints * adding docustrings to the public functions * visualization changes * addressing all comments. adding support for training with 100 templates * Updates * More tweaks * Edits * addressed comments around styling * added more explanations * adding joblib to dependency and other comments * comment * Fix MAP inference * Update examples/rcn.py Co-authored-by: Swaroop Guntupalli Co-authored-by: Shrinu Kushagra Co-authored-by: stannis Co-authored-by: Shrinu Kushagra Co-authored-by: Guangyao Zhou Co-authored-by: Swaroop Guntupalli --- examples/example_data/rcn.npz | Bin 0 -> 4401402 bytes examples/rcn.py | 451 ++++++++++++++++++++++++++++++++++ poetry.lock | 73 +++++- 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zn=>hTVMV?Deu-UR-SCR_8ZNBPqnISto4W>m>gg|uzI$@VN;Yif1{oQvypf~Nn}R-M zAMDjb`m)RztQw2JLMNBno{P^CD+hZ%KXbFvl4Qc74&ayL*KxQXlh-(mPkZ2Oyk;o_ zCUqcwQjx80;dwP(qg{!92R5!0M*W(>e4N3$v$*NC#M(B(So_B-yVaz4QM*58KKn`M zkiMKl-sW~z8-8H4Il^_Ll+fJ0Wu&{%e?-=z1yzp*mi7$}kJg1asDM5D=(qas7E-eC zEz>?3+AuaYI#Tt>TAYRon?4^ZRsnI{Q=xUkgwE!4Z*;$7@b_H!afXtacgZ@(z`~}| zPq7ajwNSf9^oPasf1+gK7EwEJJFOrt_@;}oBJ2Ejq5mw{bE3TGT6BEi#e-W{FUDja z2q@#|Sh;14j^BIeVYf+1jQgj3$>`689~M#|o5(~|w;y#d->4+U&w~2(<{y^-{0Zz8 zXJ4YQPHUC42o~O{{n`2-KY@3KsTf$fxY(Ex_X2wNB>u1%eu4xb0Z0H6fCL}`NB|Om z1Rw!O01|)%AOT1K5`Y9C0Z0H6fCL}`NB|Om1Rw!O01|)%AOT1K5`Y9Cf&cFb80u5t zNM9`}x@diNTuvbJtsLMV%OZ|N$DQ^bw6{94*V^`ov#h=2X)@$2xCRpGcx%fJ14C1? zMJ|h6#rN4d+c=4S(A>M$SR;qmB=mu?#jnBwqynk!%K8<~~#4?VN08YWlBF znLA(XyV63w2>c^iHv1IcBl1PI`}W(;` tuple[np.ndarray, np.ndarray]: + """Returns test images sampled randomly from the set of MNIST images. + + Args: + test_size: Desired number of test images. + seed: An integer used for repeatable tests. + + Returns: + test_set: A list of length test_size containing images from the MNIST test dataset. + test_labels: Corresponding labels for the test images. + """ + + mnist_train_size = 60000 + num_per_class = test_size // 10 + + print("Fetching the MNIST dataset") + dataset = fetch_openml_cached("mnist_784", as_frame=False, cache=True) + print("Successfully downloaded the MNIST dataset") + + mnist_images = dataset["data"] + mnist_labels = dataset["target"].astype("int") + full_mnist_test_images = mnist_images[mnist_train_size:] + full_mnist_test_labels = mnist_labels[mnist_train_size:] + + np.random.seed(seed) + + test_set = [] + test_labels = [] + for i in range(10): + idxs = np.random.choice( + np.argwhere(full_mnist_test_labels == i)[:, 0], num_per_class + ) + for idx in idxs: + img = full_mnist_test_images[idx].reshape(28, 28) + img_arr = jax.image.resize(image=img, shape=(112, 112), method="bicubic") + img = jnp.pad( + img_arr, + pad_width=tuple([(p, p) for p in (44, 44)]), + mode="constant", + constant_values=0, + ) + + test_set.append(img) + test_labels.append(i) + + return np.array(test_set), np.array(test_labels) + + +# %% +test_set, test_labels = fetch_mnist_dataset(test_size) + +# %% [markdown] +# # 2. Load the model + +# %% [markdown] +# We load a pre-trained rcn model that has been trained using the code [here](https://github.com/vicariousinc/science_rcn/tree/master/science_rcn). The details of the variables are. +# - train_set and train_labels - A sample of MNIST train dataset containing 100 train images and their labels. +# - frcs and edges - Used to represent the learned rcn graphical models. +# - suppression_masks and filters - Saved numpy arrays that are used to detect the presence or absence of an oriented/directed edge in an image. Please refer to the function get_bu_msg to see how they are used. + +# %% +data = np.load("example_data/rcn.npz", allow_pickle=True, encoding="latin1") +idxs = range(0, 100, 100 // train_size) + +train_set, train_labels, frcs, edges, suppression_masks, filters = ( + data["train_set"][idxs, :, :], + data["train_labels"][idxs], + data["frcs"][idxs], + data["edges"][idxs], + data["suppression_masks"], + data["filters"], +) + +# %% [markdown] +# We initialize the following hyper-parameters. +# - hps and vps - Horizontal and vertical pool sizes respectively for RCN models. This represents the radius of the window around a pool vertex. Thus, a pool vertex will be activated by an input pixel in a rectangle of size [2*hps+1, 2*vps+1]. +# - num_orients - The number of different orientations at which edges are detected. +# - brightness_diff_threshold - The brightness level at a pixel at which we declare the presence of an edge. + +# %% +hps, vps = 12, 12 # +num_orients = filters.shape[0] +brightness_diff_threshold = 40.0 + +# %% [markdown] +# # 3. Visualize loaded model + +# %% [markdown] +# In RCN, a learned model is a weighted graph. The weights (or the 'perturb_radius') constraints how the two vertices are allowed to vary during inference. + +# %% +img_size = 200 +pad = 44 +img_idx = 4 + +model_img = np.ones((200, 200)) +fig, ax = plt.subplots(1, 2, figsize=(20, 10)) + + +frc, edge, train_img = frcs[img_idx], edges[img_idx], train_set[img_idx] +ax[0].imshow(train_img[pad : 200 - pad, pad : 200 - pad], cmap="gray") +ax[0].set_title("Sample training image", fontsize=40) + +for e in edge: + i1, i2, w = e # The vertices for this edge along with the perturn radius. + f1, r1, c1 = frc[i1] + f2, r2, c2 = frc[i2] + + model_img[r1, c1] = 0 + model_img[r2, c2] = 0 + ax[1].text( + (c1 + c2) // 2 - pad, (r1 + r2) // 2 - pad, str(w), color="green", fontsize=25 + ) + ax[1].plot([c1 - pad, c2 - pad], [r1 - pad, r2 - pad], color="green", linewidth=0.5) + +ax[1].axis("off") +ax[1].imshow(model_img[pad : 200 - pad, pad : 200 - pad], cmap="gray") +ax[1].set_title("Corresponding learned rcn model", fontsize=40) + +fig.tight_layout() + + +# %% [markdown] +# ## 3.1 Visualize the filters + +# %% [markdown] +# The filters are used to detect the oriented edges on a given image. They are pre-computed using Gabor filters. + +# %% +fig, ax = plt.subplots(4, 4, figsize=(10, 10)) +for i in range(filters.shape[0]): + idx = np.unravel_index(i, (4, 4)) + ax[idx].imshow(filters[i], cmap="gray") + ax[idx].axis("off") + +fig.tight_layout() + +# %% [markdown] +# # 4. Make pgmax graph + +# %% [markdown] +# Converting the pre-learned rcn model to PGMax factor graph so as to run inference. + +# %% [markdown] +# ## 4.1 Make variables + +# %% +start = time.time() +assert frcs.shape[0] == edges.shape[0] + +M = (2 * hps + 1) * ( + 2 * vps + 1 +) # The number of pool choices for the different variables of the PGM. + +variables_all_models = {} +for idx in range(frcs.shape[0]): + frc = frcs[idx] + variables_all_models[idx] = groups.NDVariableArray( + variable_size=M, shape=(frc.shape[0],) + ) + +end = time.time() +print(f"Creating variables took {end-start:.3f} seconds.") + + +# %% [markdown] +# ## 4.2 Make factors + +# %% [markdown] +# ### 4.2.1 Pre-compute the valid configs for different perturb radius. + +# %% +def valid_configs(r: int, hps: int, vps: int) -> np.ndarray: + """Returns the valid configurations for the potential matrix given the perturb radius. + + Args: + r: Peturb radius + hps: The horizontal pool size. + vps: The vertical pool size. + + Returns: + A configuration matrix (shape n X 2) where n is the number of valid configurations. + """ + + configs = [] + for i, (r1, c1) in enumerate( + np.array(np.unravel_index(np.arange(M), (2 * hps + 1, 2 * vps + 1))).T + ): + r2_min = max(r1 - r, 0) + r2_max = min(r1 + r, 2 * hps) + c2_min = max(c1 - r, 0) + c2_max = min(c1 + r, 2 * vps) + j = np.ravel_multi_index( + tuple(np.mgrid[r2_min : r2_max + 1, c2_min : c2_max + 1]), + (2 * hps + 1, 2 * vps + 1), + ).ravel() + configs.append(np.stack([np.full(j.shape, fill_value=i), j], axis=1)) + + return np.concatenate(configs) + + +max_perturb_radius = ( + 22 # The maximum perturb radius for which to pre-compute the valid configs. +) +valid_configs_list = [valid_configs(r, hps, vps) for r in range(max_perturb_radius)] + +# %% [markdown] +# ### 4.2.2 Make the factor graph + +# %% +start = time.time() +fg = graph.FactorGraph(variables=variables_all_models) + +# Adding rcn model edges to the pgmax factor graph. +for idx in range(edges.shape[0]): + edge = edges[idx] + + for e in edge: + i1, i2, r = e + fg.add_factor( + [(idx, i1), (idx, i2)], + valid_configs_list[r], + ) + +end = time.time() +print(f"Creating factors took {end-start:.3f} seconds.") + + +# %% [markdown] +# # 5. Run inference + +# %% [markdown] +# ## 5.1 Helper functions to initialize the evidence for a given image + +# %% +def get_bu_msg(img: np.ndarray) -> np.ndarray: + """Computes the bottom-up messages given a test image. + + Args: + img: The rgb image to compute bottom up messages on [H, W, 3]. + + Returns: + An array of shape [16, H, W] denoting the presence or absence of an oriented and directional + line-segments at a particular location. The elements of this array belong to the set {+1, -1}. + """ + + # Convolving the image with different gabor filters. + filtered = np.zeros((filters.shape[0],) + img.shape, dtype=np.float32) + for i in range(filters.shape[0]): + kern = filters[i, :, :] + filtered[i] = fftconvolve(img, kern, mode="same") + + # Applying non-max suppression to all the filtered images. + localized = np.zeros_like(filtered) + cross_orient_max = filtered.max(0) + filtered[filtered < 0] = 0 + for i, (layer, suppress_mask) in enumerate(zip(filtered, suppression_masks)): + competitor_maxs = maximum_filter(layer, footprint=suppress_mask, mode="nearest") + localized[i] = competitor_maxs <= layer + localized[cross_orient_max > filtered] = 0 + + # Threshold and binarize + localized *= (filtered / brightness_diff_threshold).clip(0, 1) + localized[localized < 1] = 0 + + # Apply cross-channel pooling. + pooled_channels = [-np.ones_like(sf) for sf in localized] + for i, pc in enumerate(pooled_channels): + for channel_offset in [0, -1, 1]: + ch = (i + channel_offset) % num_orients + pos_chan = localized[ch] + np.maximum(pc, pos_chan, pc) + + # Remapping the elements to set {+1, -1}. + bu_msg = np.array(pooled_channels) + bu_msg[bu_msg == 0] = -1 + return bu_msg + + +# %% [markdown] +# ## 5.1.1 Visualizing bu_msg for a sample image + +# %% [markdown] +# bu_msg has shape (16, H, W) where each 1 <= f <= 16 denotes the present or absense of a oriented edge + +# %% +r_test_img = test_set[4] +r_bu_msg = get_bu_msg(r_test_img) +img = np.ones((200, 200)) +fig, ax = plt.subplots(1, 2, figsize=(20, 10)) +ax[0].imshow(r_test_img, cmap="gray") +ax[0].axis("off") +ax[0].set_title("Input image", fontsize=40) +for i in range(r_bu_msg.shape[0]): + img[r_bu_msg[i] > 0] = 0 + +ax[1].imshow(img, cmap="gray") +ax[1].axis("off") +ax[1].set_title("Max filter response across 16 channels", fontsize=40) +fig.tight_layout() + + +# %% [markdown] +# ## 5.2 Run MAP inference on all test images + +# %% +def get_evidence(bu_msg: np.ndarray, frc: np.ndarray) -> np.ndarray: + """Returns the evidence (shape (n_frcs, M)). + + Args: + bu_msg: Array of shape (n_features, 200, 200). Contains BU messages + frc: Array of shape (n_frcs, 3). + """ + evidence = np.zeros((frc.shape[0], M)) + for v, (f, r, c) in enumerate(frc): + evidence[v] = bu_msg[f, r - hps : r + hps + 1, c - vps : c + vps + 1].ravel() + + return evidence + + +# %% +frcs_dict = {model_idx: frcs[model_idx] for model_idx in range(frcs.shape[0])} +run_bp, _, get_beliefs = graph.BP(fg.bp_state, 30) +scores = np.zeros((len(test_set), frcs.shape[0])) +map_states_dict = {} + +for test_idx in range(len(test_set)): + img = test_set[test_idx] + + start = time.time() + bu_msg = get_bu_msg(img) + evidence_updates = jax.tree_util.tree_map( + lambda frc: get_evidence(bu_msg, frc), frcs_dict + ) + end = time.time() + print(f"Initializing evidences took {end-start:.3f} seconds for image {test_idx}.") + + start = end + map_states = graph.decode_map_states( + get_beliefs(run_bp(evidence_updates=evidence_updates)) + ) + end = time.time() + print(f"Max product inference took {end-start:.3f} seconds for image {test_idx}.") + + map_states_dict[test_idx] = map_states + start = end + score = tree_util.tree_multimap( + lambda evidence, map: jnp.sum(evidence[jnp.arange(map.shape[0]), map]), + evidence_updates, + map_states, + ) + for ii in score: + scores[test_idx, ii] = score[ii] + end = time.time() + print(f"Computing scores took {end-start:.3f} seconds for image {test_idx}.") + + +# %% [markdown] +# # 6. Compute metrics (accuracy) + +# %% +best_model_idx = np.argmax(scores, axis=1) +test_preds = train_labels[best_model_idx] +accuracy = (test_preds == test_labels).sum() / test_labels.shape[0] + +print(f"accuracy = {accuracy}") + + +# %% [markdown] +# # 7. Visualize predictions - backtrace for the top model + +# %% +fig, ax = plt.subplots(5, 4, figsize=(16, 20)) +for test_idx in range(20): + idx = np.unravel_index(test_idx, (5, 4)) + map_state = map_states_dict[test_idx][best_model_idx[test_idx]] + offsets = np.array( + np.unravel_index(map_state, (2 * hps + 1, 2 * vps + 1)) + ).T - np.array([hps, vps]) + activations = frcs[best_model_idx[test_idx]][:, 1:] + offsets + for rd, cd in activations: + ax[idx].plot(cd, rd, "r.") + + ax[idx].imshow(test_set[test_idx], cmap="gray") + ax[idx].set_title( + f"Ground Truth: {test_labels[test_idx]}, Pred: {test_preds[test_idx]}", + fontsize=20, + ) + ax[idx].axis("off") + +fig.tight_layout() + +# %% diff --git a/poetry.lock b/poetry.lock index b5922a7c..9c8c0d95 100644 --- a/poetry.lock +++ b/poetry.lock @@ -485,6 +485,14 @@ MarkupSafe = ">=2.0" [package.extras] i18n = ["Babel (>=2.7)"] +[[package]] +name = "joblib" +version = "1.1.0" +description = "Lightweight pipelining with Python functions" 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